Lineplot from a wide-form dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lineplot from a wide-form dataset\n",
    "* https://seaborn.pydata.org/examples/wide_data_lineplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomState(MT19937) at 0x120476EB8"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs = np.random.RandomState(365)\n",
    "rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -2.14159895,  -1.23856596,   1.39294679,   0.53950231],\n",
       "       [ -1.76274504,  -1.45289681,   0.74345461,  -2.07158943],\n",
       "       [  0.72688886,  -1.24306808,   0.32987875,  -3.03094917],\n",
       "       ...,\n",
       "       [ 40.78279333,  14.96154129, -12.91381291,  10.50360701],\n",
       "       [ 41.07376835,  13.95902742, -12.0379894 ,  10.90847636],\n",
       "       [ 40.18591798,  13.869666  , -13.44694313,  10.71704547]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = rs.randn(365, 4).cumsum(axis=0)\n",
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(values).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04',\n",
       "               '2016-01-05', '2016-01-06', '2016-01-07', '2016-01-08',\n",
       "               '2016-01-09', '2016-01-10',\n",
       "               ...\n",
       "               '2016-12-21', '2016-12-22', '2016-12-23', '2016-12-24',\n",
       "               '2016-12-25', '2016-12-26', '2016-12-27', '2016-12-28',\n",
       "               '2016-12-29', '2016-12-30'],\n",
       "              dtype='datetime64[ns]', length=365, freq='D')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range(\"1 1 2016\", periods=365, freq=\"D\")\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>-2.141599</td>\n",
       "      <td>-1.238566</td>\n",
       "      <td>1.392947</td>\n",
       "      <td>0.539502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2016-01-01 -2.141599 -1.238566  1.392947  0.539502"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(values, dates, columns=[\"A\", \"B\", \"C\", \"D\"])\n",
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>40.185918</td>\n",
       "      <td>13.869666</td>\n",
       "      <td>-13.446943</td>\n",
       "      <td>10.717045</td>\n",
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      ],
      "text/plain": [
       "                    A          B          C          D\n",
       "2016-12-30  40.185918  13.869666 -13.446943  10.717045"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-0.458300</td>\n",
       "      <td>-0.677791</td>\n",
       "      <td>0.851888</td>\n",
       "      <td>-1.946814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>-0.239556</td>\n",
       "      <td>-0.403172</td>\n",
       "      <td>0.745531</td>\n",
       "      <td>-2.504348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-09</th>\n",
       "      <td>-0.261204</td>\n",
       "      <td>-0.254974</td>\n",
       "      <td>0.798896</td>\n",
       "      <td>-2.610642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-10</th>\n",
       "      <td>-0.602155</td>\n",
       "      <td>-0.126817</td>\n",
       "      <td>0.942284</td>\n",
       "      <td>-2.381506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>-0.762539</td>\n",
       "      <td>-0.038982</td>\n",
       "      <td>1.077379</td>\n",
       "      <td>-2.300033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>-0.725814</td>\n",
       "      <td>0.399516</td>\n",
       "      <td>1.567837</td>\n",
       "      <td>-2.081189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>-0.880924</td>\n",
       "      <td>0.723558</td>\n",
       "      <td>1.854528</td>\n",
       "      <td>-2.269472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-14</th>\n",
       "      <td>-0.892647</td>\n",
       "      <td>0.909081</td>\n",
       "      <td>2.373457</td>\n",
       "      <td>-2.299502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2016-01-01       NaN       NaN       NaN       NaN\n",
       "2016-01-02       NaN       NaN       NaN       NaN\n",
       "2016-01-03       NaN       NaN       NaN       NaN\n",
       "2016-01-04       NaN       NaN       NaN       NaN\n",
       "2016-01-05       NaN       NaN       NaN       NaN\n",
       "2016-01-06       NaN       NaN       NaN       NaN\n",
       "2016-01-07 -0.458300 -0.677791  0.851888 -1.946814\n",
       "2016-01-08 -0.239556 -0.403172  0.745531 -2.504348\n",
       "2016-01-09 -0.261204 -0.254974  0.798896 -2.610642\n",
       "2016-01-10 -0.602155 -0.126817  0.942284 -2.381506\n",
       "2016-01-11 -0.762539 -0.038982  1.077379 -2.300033\n",
       "2016-01-12 -0.725814  0.399516  1.567837 -2.081189\n",
       "2016-01-13 -0.880924  0.723558  1.854528 -2.269472\n",
       "2016-01-14 -0.892647  0.909081  2.373457 -2.299502"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.rolling(7).mean()\n",
    "data.head(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-17</th>\n",
       "      <td>40.372286</td>\n",
       "      <td>14.756488</td>\n",
       "      <td>-14.727881</td>\n",
       "      <td>11.329401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-18</th>\n",
       "      <td>40.019334</td>\n",
       "      <td>14.994212</td>\n",
       "      <td>-14.693699</td>\n",
       "      <td>11.184410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-19</th>\n",
       "      <td>39.375075</td>\n",
       "      <td>15.016319</td>\n",
       "      <td>-14.379424</td>\n",
       "      <td>11.338507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-20</th>\n",
       "      <td>38.743908</td>\n",
       "      <td>15.251274</td>\n",
       "      <td>-14.043725</td>\n",
       "      <td>11.526092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-21</th>\n",
       "      <td>38.453403</td>\n",
       "      <td>15.438575</td>\n",
       "      <td>-13.651803</td>\n",
       "      <td>11.471229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-22</th>\n",
       "      <td>38.335901</td>\n",
       "      <td>15.672412</td>\n",
       "      <td>-13.417281</td>\n",
       "      <td>11.407427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-23</th>\n",
       "      <td>38.219439</td>\n",
       "      <td>15.965895</td>\n",
       "      <td>-13.057782</td>\n",
       "      <td>11.060059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-24</th>\n",
       "      <td>38.191991</td>\n",
       "      <td>16.228454</td>\n",
       "      <td>-12.743142</td>\n",
       "      <td>10.481270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-25</th>\n",
       "      <td>38.257600</td>\n",
       "      <td>16.220786</td>\n",
       "      <td>-12.660004</td>\n",
       "      <td>9.825187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26</th>\n",
       "      <td>38.698745</td>\n",
       "      <td>16.181793</td>\n",
       "      <td>-12.785039</td>\n",
       "      <td>9.346538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>39.459673</td>\n",
       "      <td>16.207105</td>\n",
       "      <td>-12.889378</td>\n",
       "      <td>8.956668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>39.886961</td>\n",
       "      <td>16.112783</td>\n",
       "      <td>-13.009065</td>\n",
       "      <td>8.975601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>40.232831</td>\n",
       "      <td>15.907644</td>\n",
       "      <td>-12.845443</td>\n",
       "      <td>9.060119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>40.508511</td>\n",
       "      <td>15.527725</td>\n",
       "      <td>-13.059966</td>\n",
       "      <td>9.235144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    A          B          C          D\n",
       "2016-12-17  40.372286  14.756488 -14.727881  11.329401\n",
       "2016-12-18  40.019334  14.994212 -14.693699  11.184410\n",
       "2016-12-19  39.375075  15.016319 -14.379424  11.338507\n",
       "2016-12-20  38.743908  15.251274 -14.043725  11.526092\n",
       "2016-12-21  38.453403  15.438575 -13.651803  11.471229\n",
       "2016-12-22  38.335901  15.672412 -13.417281  11.407427\n",
       "2016-12-23  38.219439  15.965895 -13.057782  11.060059\n",
       "2016-12-24  38.191991  16.228454 -12.743142  10.481270\n",
       "2016-12-25  38.257600  16.220786 -12.660004   9.825187\n",
       "2016-12-26  38.698745  16.181793 -12.785039   9.346538\n",
       "2016-12-27  39.459673  16.207105 -12.889378   8.956668\n",
       "2016-12-28  39.886961  16.112783 -13.009065   8.975601\n",
       "2016-12-29  40.232831  15.907644 -12.845443   9.060119\n",
       "2016-12-30  40.508511  15.527725 -13.059966   9.235144"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(14)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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     "execution_count": 22,
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Mzz5OVVoWXudNwH3EJYTe9g4qZw9Urt4EXvs87sNnUJ+xi6If32ju3K0/tIPG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\nbnBqMbhrAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122905e80>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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338alfj3cq8A2lsIACKoVU1fHO7ZvHN+zQZVZ+LXl55P8zFisKSlkTp9O0Lhx\n6GqGl2nj3bsXbq1aog1WRpZ2oxHJ3R3JiYlAAgXjlq1k//QT2T/9ROi9Ifh67XGcC/aVMIVFUHS6\n7EKvytsbV+1JANRNehD48DhAWf+xWxSlbzocR8BTT+H36BBMcXGkPP8CstlM6sSJ1Fm4EEnn3CTF\nqvHtEQiugbjUPGZtTgAgprYfj7arOqP/tA8/xJqSAoDk6nbZdueUf/6aNZyZ9A4hr/8X7z59KkXG\nikSWZczx8VgSTuLepjWagABni+TAeuYM1tOnsSQmoYusjXvLlgCY4uLQ1KhxydlX9k8/ASCp7Xi5\n7FUOqnXg5o+q4AyRXZOQ7VAYNgJ7gwFo/P1wKT6IatlYpW3jgY57STod6hLFHvLqK47j2pAQAseO\nJWPqVGw5uZhPnsS1QYOb8RFcM8IACKoFsizzwcoj2GVQqySm3N8UVRXZwEW2WCg+rUQkeXTsQMCo\nJ6/Y3lZgJPXNt7CdPcuZ997HvX37KuMSshUUoHJ3R1JdOUJcNplIuO9+sNuRXFwIGjcO/5EjnDOb\nsVjIX7uOwl27KNq3j6LYWMcpvyFDcG/ZEvOJEySNGInk7kbIK6/iXdsKO7+D+j0pdGmPceNGAHwa\nu6J2UUGrYdD1FfAIghXjYfdPSCrwODMLBj4JwY1h+lPKQzxDoFG/axI1YMRwZLMZ/8eGlVlQdhbC\nAFQTUt98k8Lde1C5u4MkUWPiW7g1aXLd98lbsYLcpcuUUYqfL5JOh1xUhFfv3nh26nQTJL92UnKK\nWLwnhcV7UsgzWelUL5C9p3II8XKlyGpj76kcQKnq2SDEq2IeKsuw7RtIiVUqNHoEQZOB4F/nmm8h\n6XREfP8dOQsW4Nmp01WVoNrTg5DXX+f0f/6DLSuLjKmfEfruOzfakxsm46tpZH79NZK7O55duxI6\n6e3LKimVmxsudesqPm6zmawfZ+F97z1oa9SoZKnB5ZNPSD50+JLnLCeVGWPObwuw5eRATg4pL7yA\npWkegU0KsPvW58zMlYDydwz8dBHUqFF2c5b+X0KHsTDvUej1PtSIVmr+ZBqU8x2eBe21BSJIWi1B\nzz0LnFt3+BBbfgEafz+Cx48v5ydQfoQBqKLkr1uHKS6OgOHDUbm7U5x9Fsvx4wBIrq7oal7//rWZ\n335LxudfXPJczh8LqTltGl53dr8huctDQqaRD1bGsepwGrJcenzhbsWlcqKkzg8o9fxfuruCp80p\nsXDw99L36yZD99eh84tXvEyWZbJ/+AGf++9H4+eH36BB1/xI7773krtsKZaTJ/Ef/vjln2GzIRcX\no3JxwW4ykfP7H+QuXYI9L+i4bi4AACAASURBVJ+a07/GJSrKIcuNjL6LDhwg8+uvlXsVFuLapDEq\nb++L2mXPmYN3z55o/P0IGd4ba0IdcvekEjrl45uu/GVZJm/FCvKWLUdydaHmZ58px4OCAcUAaAJ9\n8e5/P+5t26CLiEAXEQGyTPCLz6Ir2kfG4ljsxeBdqwjUOhK/3YX5xBkAAkaNQlvrMoY/SA9jtyuZ\nvgBHVym/XX2h9Yjr74vFwunX3yBv2TJAMT5+wx5zRB5VFsIAVEFkq5X0Dz7EkphI/sq/iPpzMR5t\n2yAXW7FlZePRvp1jZJa3ciW6OnVw1euv4cal2lUTGordaMSen698QV5+qdJj02VZZsaGE3y6Oh7L\neds0erloypR1DvJywd9dR8d6ATzbvR4Bni4VJ4QkQb8vIP5vKC4CezHYrfDPJLAYwbPnZWXPmDqV\nrJnfcXbefCJm/M+hjK/tsRKh77yLykWH2tcXgOLMTDSBgZgTEjBu3ETR3r3kr1mDa5MmRP46l4KN\nG0l77z3HPczHjuESFUVxdjaJQ4fh0bkTASNGoA0NvWY5bLm5qH18cG3cGI+OHTFu2ULQyy8ROGqU\ncj4vT3EJaTTk//MPae++R+aXXxDeQ8JDq5Th8K0vwZEGyOETMW7eoswOGjXGcvwYRfv2kb96DWEf\nfoA2LAy7yUT61Kl4dOiAR4cOqFxLR86W5GTM8UfR+mhxkY8hRXVx7JAlyzLJzz1HwZp/HO0LH3tM\n8e+3a0qYZjOe3kmom0TDo/8p7eD6D+HkRqSCNPykeLx6qzCd1aILDYRBs5EOfg6cwb1DewLHjL7y\nh3VO+QP0fB8St0LLR8GlHLNRSUI2m5SXWi3urVtjz88DYQAEZ3/7DUtiIgA+D9yPpFLh//jj+D9e\nOlKUZZm0d9/l7Nxf0dWpQ9SC31B5eFzxvoFjxqCrWxeXevVwqVPHcR+sVkc0gik+HpWrK7patW5S\n70qZuyOJKSW1fAAeaFWTEZ0iaRLmjcVmJz3PTJiv2w1t1l6clUXOgt8xHTqE76CH8OzSRTlht5V+\noV084fVk5XVKLPL8x8jecZac5bPxbLIH2i0EIGfxYsxHDOjqRJH/198Yt2wpuZe9XP7cc6M9WZbJ\n+vZbMr6ahi4qyjHTO0fQ80p0ybkRtjY8HNfGjZDNFgDSP/oYy4kTWE6cIG/pMgKfexbfBx90hJ9e\nDtPhw5x6Zix1Fi9C7etL2CcfY0lIcIQnmgzxJI8di+TqgluTJuSv/1eR12JGZ00Cx746MvLmL0n+\nbgsF+08DILm4IJvNjmedevppas2axelXX8O4eTNnZ/8MQMT33+HauDGnX3sN478bSj8bz2K0HjZU\nQZF4DxuHz4CBeHToQMGaf1D7+ODesYMyeLFZqZP7Ix6ByveFMweg2AIaHWz7FtZPLtNnTd0WeLYf\nAw16g6s3Hl274BYTQ9ALzyNprkMdunrDiJXgUb69pCWtlvAvv8RuNKJydb2+Z1cgwgBUMWwFBWR+\nrWSQamvWxG/IkEu2kyQJdaDyz2c5cYKkkU9Qc9pXaIKCyrQzHz1K+tTPCH75JVzq1cO7Z8+L7kOJ\norAkJjqSVuosWog2LKyiu+fgYEouk5Yo0/ZgLxe+GRpDTO3ShVAXjZoI/xvbJDtv9WpSX30Ne2Eh\n6qBAAsc+o8yCtn8L+36F+2ZAcMMy19iDmpIS15aCvdsAkHYdxnbmJKrgWmR9+z8sJ0+Waa+NiKDW\nD9+j8S9/ATDT/v1kfvMt2O1llb9Gg9edd+LWrBkAuogIwqd+ilfPnmUUhkenTlhTUyncvh3b2bOk\nvfMuZ3/+hYiZMy8KRT2HbLdzesIEis+cIW3KB4ROfh+Nv7+jH7Ldzpl33sGarBhGy7FSuWq88x5a\n21/K3rZ174RlLyFlGnBXxVGAYgjPV/6aoCA87+iOpNUiuZTO3lReXriHqpE2TsK6b1MZ+awFGqwF\nGkhLxfWvr6FfP+W7IIPPgP6ovbzAmAV/jsUjp8QX3+JR6PclqEs+mwa9YOvXkJukRPTc8V/oOK70\nPBA0duw1/Y0uiWfQ1dtcAUmSUHt6Xr3hTUQYgMokdT+cPQlhLcD30iPsrJnfYcvOBiD45ZdQyRaw\n2kB7cWhh4NNPK2nmW7ZStG8fp54ZS62ZMxwuBXNCAicffgS70Yhx0yYif19wRVdR/pp/sGUqG6dn\nTPuasMnv32CHL01uoZUxc2Kx2OyoVRLThrQqo/wrCrMhHnthIQDBL7+Ma91IrN/0JfGHeHRexQQn\nDsDl7b1Yks9gSTyJLjISXWQkKj/li61xL8a3TiHyghHYBv+KJjjYYQAkd3e8+/Qm5JVXbjiaw615\ncyIXLODs3LlYT51CFxmJ3yMPo6tXr4xfX+3ri/c991x0vU+/vvj060vO4sVkfPklxadTsSQkkPTE\nSKJ+/11RlhdQ8O+/mA8rCXWasFAktbrMeUmlotb335H9449k/zIHW2YmkqsrNSa8gU+/fiD3Ld3I\n5JFf4dvO+OuNaMPDybd3RtK5KHV0oqJwb9PaEVUU8URbbO9NxLhtF0gSqpTNsHsW/vXcMeVo8a5Z\nhMkeiTEnEHvGKexFJjT2VCjMQvIMxn/oo2AugCXjFCNuU2ZBhERD38/KKHf8o2DMZsg6Cn5R1apK\nZ2UhDEBlcmABbPlSeV2rI0S0wdfkB/XDwDsUa2oq2T/+CIBr82Z49bwbPo9Wtprr8yFEP1DGDymp\n1dT8+mtOv/oa+atWYTpwgGN39SDs44/xurM7OfN/w25UFlD9R4zA5SoxxwFPjKRw924K/vmH3MWL\nCXru2evyJ18r7yw7zKlsJZnrP730FbpBu3HHDtxbtkTSatHVikBydydsyhTls1z+Enkb9mAt8MFa\noCFhIaj/6Y4tNxeAgKeeIvilFwmdNAmNvz+B4YdRH/8TGnaB4BrUnv0TllOnKD5zBtemTRX/ddZx\n2L0EdB6KW0FXvlmLq74BoZPevqG++w4ciM+995L+ySdk/zQba2ISecuX4/fwwxe1zfruewBUnp4E\njBx5yfupivMJ1Gfj93I7CnfF4jZ0EppGJYXszl9wDqgLnV5AWj8Zb694vPs/B60eu/iGx9bA0nGo\nQ5ri/civ4BsBWQ1g3WT8eraFGk0hoj0e+nsIUKmUKpvLXoTeH5TuoXtsDfz1emkEDpDv3xSvoX+A\n5hJrQ67eEO78jNuqijAAlcnZk6Wvk7ZA0hbqAsROguj7yV6b6Zg6h7zyCpJaA00fUozGwlFwaDE8\nPKfMl0/l5kbo5MlYk5MxHT6M3WjEelrxw/oM6E/2jz/i/8RIgl+6ckTLOYKeH4e90EjQuHE3Rfkn\nZBpZtEdxK9yhD+KpLtcebnk1Cvfs4dQTT+LRtSvhn03F8447qL+2qzIjStmNvPMH8hLL+mzPKX9A\nCbFF+UxDXnsNii2cWhROxF1vO9roTq9El7gJ4osgPQ5yT5XezC9KWVC+3NZ/lYCk1RL82msUZ2bh\ndXcPvHv3vqiNymBwxMr7PfLwxTOEvNPw74dw4A+w5KMGvLTAH4Pg4V+gXo+LH9zxOdg9G/KSIW7Z\nxQbAYoQVJYuzZxOUXbNAMR6vnFB2z7oQd38Y9FPZY6n7SpV/7c7Q/XXiM12I8Qq58gcjuCTCANxM\nTqwH2a74SaEknvhZOLQQEjdD2mGQbYAMB/8gOAC0bXwx13q0tE5IZGfYNl2JTjEsh71zlciD81B7\nehA5fx55f68i/aOP0Pgr7hSXhg0J/+ILvO6+xBf2Mrg2aEDtWbMqoPMXY7PLvLn4IPaSYKQ3ugWg\n+vcDcPOD9leJwLgM1tOnObtgAdbEJPLXr0e2WhX3xtGjpXkS2Sfg95FIEkT2zoXRm7EUaChYvx5T\n3BFcG+pxaxWDNvyCNQ+NjvQ6DxJxflJUSiwc/vPSwpxNgNn9oeUwJV78UkqtEpAkifBPP3G8txcW\nIrm5IeUmY927Ftdp3yjttFr8hg0re7HFCN/3UvzmFxJQFwIv40LUuUPvyXA2Edo/U/acLMOKV5S/\nAyi+eO/zBhfX8zmFtVTadxwHnV5QXD5ZsVe/TnBJhAG4mWz8FBI2QNun4Z6PFEVXq53yA2AxYlg3\nD33WauSja5A0dvzr5sLwe0vv0aAXvBQH33QCYzose0FZP4jqUuZRklaLT9978b73HijZxUiSJLx7\nXTqM8WrYCoyc/Xk2xi1bCXj6abThYWjDwpAkqVz1SwotxTz9cyybjilrDEObe1N/cT/IT4XHl5VL\nRktSEicfGlRmFA8Q+vbEUuVvyoM5gxTlDEgdxkCIHpcQcLlanfdL4VNTUYJaV/AOh1rtIbKLshfs\n+ilgLVRixHu+W3pNbgq4+SpuokpEttvJXfwnGVM/Ibi7Hz6azRiPu0C2MkAIGD7UUZrCwbbppcq/\ndidoPwaiuirRNTXbXNrNco7GAy59fPu3sPeXsvcsL1Hd4JWTcJVMZcG1IQzAzSLzqKL8oXS6eyE6\nDwoCW2D0iSZtXiHB48fj2bkTSBf8c3sGw33fwtzByqLX8pdhzJayC14lSJIEFyzolYd9H0/Abf5f\nABTu3Fl6QqXCpX59on5fgKTVXubqUvJMVpbvT2XR7hR2nFQWtxuHejNR9wvkpUDNtuB37TV9ijMy\nHJFOGV9Ncyh/lbc3LnXqEDDqSbzuuktpXJQDcwcpi4CgjEx7vH3Nz7okd72p/FxIeCto3B+WvgCt\nRyrG/hx/v67MBu+cADHDQX31z+2GkWXshYWkf/IJtuxszvyZiWsPFb51ilC72CnK1BEYuFkZnZ9z\nKRZmw+aSNaqQaMUwn1O0keXIEck/oxjGv19X3nuFwYM/lI2nv15u5FrBRQgzerOI/bH09ZUyBS0W\n0j/+GHP8UZLHPY8tv6DsAts56t1VqngyDbBvboWKeyHyqIfZ1PgSctjtmA0G7CYlicVkiCd79s9K\nPsF5FFls/Lwtkd6fbeC/Cw84lH+negH8/nA42oO/KQ3dfJWR9DkO/K4sep+HNSWF7LlzSX1rIsfu\n7onJEA+A36CHlN+PDUO/YzuR834tVf5KL8Ccr7zU36sk79xMBeIXCcMWKYbgHKY8iP8LTDlKTZn3\naygzw4qi6Kzie986HVa9CT/1hw+jlMVWT09qTJwIgL1YjS2iBwxdiL1lC4Kb5yMlrFcS4M5xbA2Y\n85TXd00s/yhblhVX5ddt4Y8nFDeoxg0emQtelV8qQnB5xAzgZmA1wd45yuvILhBY/7JNdT/NxnTo\nEAC+Dz545ZDCtk/D9hnKQtu6yUpUUAW6FWRZJsuURaBbIM3DYhg5UMvfd3vxdb3XkQ4YsBcVIWm1\nmOIOg92ObLOROmECpgMHyF+9mqAXXwCUmj6PzNhGUnah495eLhrubhzCxH5NcN/4tqIUAO58s9Tg\nbf4CVr+lLCD2/8pxbd5ff5H+calPO3XCBCJ/m49b69ZEzJyJR+fL1DBy84OhC2HTZ9DzvcpxG1xo\nvLVuMPAbWDMRcpKUtZx1k5XqkQHlcEGdw2aFNW/Dzu+VDOYLyU8DwLtXT+zvv0f6J59yelkWdcZ2\nIaGFRIt/h4M5F1ZNUBZ11RpoNgi8QuHAb1D/7vLLBnB0NZjOc80N/Frx3wuqFMIA3AwO/6mMzEBx\nB1xAwabNuEU3Qe3ri2XQQ7geOoQmKIjAZ67iG9W6wp1vwOIxiu9836/Q5sqVJ6+Hf5P/ZeKWiUzv\nMZ0mAU3YNmQbiXmJhPg3hLsvjj+3pqRgy8oCFDfRqdFjsH74ES/N3+tQ/l6uGkZ3q8vobnWVjF5z\nAexWskCp3RlClSQnbFY4vER5vXs25oC7selCcW/TBv+RIynYtInCrdtwb9uWGhPfcsTHX1S+YvsM\naHgv+JTMKrxDlfUXZ6HWQvT9oO+jRNZs+kwxAstegKGLLunGuyqyDAufUoIJzkelVYrYRbRV/PUl\n+D7wAD79+2M3mVDpdNhcfKDry4qxzToKf70K95bMSqK6XLS+dN1IEjzwvdLntINQ7+4bv6fgpiAM\nwM1g1w/Kb48gaNjXcdiakkLGV9PIXbwY97ZtqfXdTPDxofavc9GGXpyMc0maDYb9vykx562fqDCR\nZVlm5v6ZZJuyGbd2HKsfXI2bxo2G/g1JyE1gTtwcIrwieLxJaTkKbXg4kb8vIOPzL8j9808C3pvM\nU9tMJOYW4FJspldMFJ8NblG2lMO+X5WRJ5RdDFRr4f4ZyNPakX1ER/r8V1EHBlJ35V+oPT2o+dU0\nKLY6ktwuwpQH/7wDO2dC3BJ47M+q5S/WuinrDwUZyoJowgb4eaBiwJsMvNrVZTm6ulT5hzSFuydB\naHOlMNllDIqk1aI+f82m7dOw5xfIjFd89RWNSqXMKLj2AnmCyue2MQCLji7i75N/82HXD/FxKXGz\nWIyQdUwZkeWfUabojfopkR7lJe0QnFLKCNByKHarjbwli8j5448ydcqLDhxw+LKvq7KnSq24NSrY\nnWGxW2gc0JiUghQG1B2A6ryF6Fc3vEpcdhyBboEMaTgE7XmLmBp/f0LfmUTQ8+N4Y10KibmnuOPU\nbv4T+yuarf4kzgvEs1s3/EcMR+Pjo0SEAPjWVkaI5yH71+HMiVbk7FFi622ZWeSvWY3vwIGoPa/g\n6sowwM/3K64xUOLzC9LLhhpWFe75CFL3KiPjkxvh9B7Q36PUrgGljk36oSu7S8x5gAQu3sqaQ3lK\nEmhdYeTfEDvLETUmuP24pQ1Asb0YTUkETlJ+EptPb+bDHR8yuflzyMtfQjKsuPiijVOVrNvGA8o3\ngtx1LoZeIivOg8z/dlOKVp2H5113EfKf8egiIyG2HDHMFyr/9DhFoZYzCxXARe3CG+3f4LW2r2G1\nW8uce7TRo0zYPIGG/g05az5LsHvJZtk2Oza7jKtWzZ58ifm7ThFszOalvQtQIWPPysKclYXZYCB3\n0SIiJozANeuYctN2T1/0+eYuWkzOdkX5a92LCe8Bbh0uv34CKFEmC0YoIbKguJX6f1k1lT8oazbD\nFsPvIxQDYCmApK1K8pgsw5Jn4dAieHypEmJ6KZo+CMUmJSLsRurRuPtDl5fLf72g2nPLGoA0Yxoj\n/h7BmOZj6FunL/FnldH2nvQ9ZM/uj39m/KUvNKYrX84aTeHpjZeOyLkcdhsYlM0lqHcX7i3vgVnz\nAFAHBuIzoD8+996La+PGN9K1UmQZ9vysZFg2fwT6fV6u29hlO0fPHkXvr0etUqO+QDH3iepDs6Bm\nRPmU1p6ftvYY/9twAgno3yKMvw4qboQcTz8833gLfp2N5O4GNjumgwcpzsgg6fUviOqhQevjBi2H\nlnmG+dgxzpSUOlb7eBDZ/TgaVzt831MJHWzQ6+K+H1oEi54urQdzx+vQ7ZXr+5s5A88gGL4MLIXK\naN6tpBTGmf1w8A9lRrpgBIxYfvmNaS74/ASC8nBLGoBCayEj/h7BqfxTvL7pdWqajLzZ/DlONRlO\nLfdoPv3uO96SJ7HG3pKtus54aYuJy3MhWjrJaO0KPDEq27ydUyS2YiXS4lzd74J02DdP2RYuuKES\nbx6kB7UW+1ObsK7/EZfoNrhFNiH8K6Xkq1f37tcUN39dyHbYN18ZDcbOUiI3Gt579esuYMvpLYxZ\nM4ZWwa14u+PbDkV/Dp1a5zhWVFzErC0GPl2d4jg/Z3tp1ujgpt7UGdIXhjyoiCjLZM38joypU1H5\nBKB5eT5kxJXJ/pTtdk6NHoNcUrgt7NMv0LBDiXKxFCj5D70/KM0WTolV6sGcc7WptHDPx+XamMOp\n6NzLztpCmyv9XDEe8k/D1+2VIII731DCY90DyoaYCgQ3yC1jAOyyHQmJwuJCPLQeTOo4idc2vkY3\nt3BaLn4RQqIJHLqIh2ftYVd6FFul96ldvwXTHo3BVavmpd/2Mm3vab6z3cOznusZ1rALDhW1+TMl\ndO/OCUqCzLKXSv3N5whugjx6E+lffkPOvPkEveCL/+Mdbu42iyo1DJwO0zuA1QgLhkPfzy8qFXE1\nfj3yKwBx2XEEuF16c29Zlnl/+/v8eWwp+ZnNAGXhMsBDR5bRQrivG+Puqkc9VUaZ6yRJImDUk6g8\nPZBUKqRgPQTrKdy5E7dWrZDUaiSVCt8H7ifjiy8JHPeckgxHJwiop0S7WAvLJladWF+q/F28lTDL\nRn25JWjzpJIgt+kzsJlh+zfKzzm6T4Bu/7n89QLBdXBTDIBerx8CTAB0wGcGg+Hrm/Gc89mWuo3n\n1z5Pk8Am/Nj7R9qYLPx5OhOPs7sAkFN28dmP37MvPwX3OrsI8RjMzIfboFEr/vTPBrUg1MeNb/89\nzicFvVi93Zd5zWy46dRK6r9sV6JMLoO9UT8yPvzIsdFF7rKl+A8bWiFZuVfErzY8+D3MH6a4Qv58\nRskkbvHINd9iYoeJ/HL4F1SSCm/dxdsAgqLIk/NTMNkK0XjvRZfVjwVPdaV5hC/mYhs6tQpJkoiN\nzbj42txT+Jfsa2ArKCDt3ffI/VOppxO18A9cGzfGb9gwXBo2xKv7eVtSNuoHI/9SImaaDy497uan\nJBa1e0qpB3MrlfmVJCVaqO6dyqAjaWvpObUO6tzhHLkEtyQVbgD0en048D4QA5iBLXq9fp3BYLj0\nrs0VxIbkDZhsJmLTYjl58DciFz2Lp03JKJXdAnhPPYbvE2vjWf8XJLUJ76A9DuUPoFJJvNpbT7bR\nzG+7ktl3KocX5+9l+qOtUHmWrZci6zzZHvY4/6Rq0RacphBX8tYHMuovpcSuJjSUmp9/XvEun8uh\n7wOPLVY2rTblwOLRSjJPWCslg7h2xyteHuwezEutX7rqY6Jc7uLf/Eysua15+a6GNI9QQjJdNFcw\ncimx8ENvxYffazJo/DHu3OE4nfzsc0T9uRi1l1dZ5X+O0ObKT5n+3guN+pd7N6ZqQVRXqNUBlj6v\nhM561oABX0FEm6tfKxBcIzdjBtADWGswGLIB9Hr978CDwOWHzxWAnyqK1prmNDWl4bnwaSW0TaWB\nuybySXYXvt+slEgOkjrg7pVAx/AOF22kLUkS79/XlJScIjYfy+KvQ2d46udddKlfg5aDthCdvxm7\n1p3x+0JZfMRU+nBZBpvEtu7/Yab2MJFjnripu2ldksjOMPhnpfBZcREcX6v87P8Nnv73kqPkhNwE\nZu6fyfDo4TTwu/pG6wePRlCUPBw/dy2Pd7yGMs7xq+D3kcrM5MgK6Pg86oha1Prf/zgz6R1MR47g\n9+gQVNe7K9LtUvpXrVVcfH0/U9Y5RAE0QQUjXVjD5UbR6/X/BTwMBsOEkvdPAm0NBsNTV7ouNjY2\nEkgozzN3nMyi5b63aK+KK3vPhq+wz7s77204ix3QB2iZ0NUDN7VLGcV/IUaLndfXZlF8Jp2I/DSy\nXX047htOlK8GWYaTucqG5TU81PTWpNNs6ypebjQYWVIR4Kbiq95BuGicE4niYkyh1v7P8c6Mpcgl\niEOdpiF5KKGCNtlGkimJKDdlQffn0z/zT/Y/SEh81OAjgnSXDynMt9h5Ykk6NhnurqumTq09NPBo\nQKRb5CXbe2QdoMG28ahKQkpPNRlLep0HyjY6vxCZQCC4UaJiYmJOXs8FN2MGcKlv9DVnmkRHR+Pi\ncoWSs0BsbCwxMaW7/CxK2k9tWYnQMcta1thb8oetK2v3tgCUkgw6jYovhrWnYQ3Fx22z25hnmEf3\niO6EeZYdrct2Oz/9OxXz6h+QkNkZ0pC3OjxJQk6xo01MqAffNLSSN3UO1sQkvmnUmNFyM7KK7Owq\n8GHcXVeJX79MX26UzIJofrI2YvG2wyTn2tH+I/FWv2D6twjiheXD2JN/krWD1uGh8yTevpF/suGR\nBoPo3aG3Uj/GnA+B9ZSbpR2CI8uh7p0sSA3BJqeDZOWwx4dsO3OWPpF9eCCmrFKPjY0lpmkj+GYk\n2K3KyPXBH4ho3J+ICutl5VHRf5+qwK3Wp9u9P2azmYMHD5brWTfDAKQA5xf+CAVO34TnOHilTyNW\nBn1ORsYvfFTQiwWHjGXOSxK8PzDaofzNNjNP/P0E+zL2cSjzEJO7TObCC+yrViChzI46tNXzbPd6\nfLfhGE/tWkC0NYvahVlk5Zx1XNIqwodGNh/iUvOYtu4YvZrUQF/j4r1Yr4ftJ7L44K8jnMoupE2k\nP091rUPzmr6oVBfb2AJzMT9vTeSLf+IxWe2c+9OazcW88vt+ViWeZHvBEQBWzOnDQ261aJKwijd1\nOh5KnQnHYiFxC9z9bqkBKDbDuvdh3fu00TVggKoHG1270zm8AytOruBQ1iGsdita1QVrHesnl+5+\ndvckEbooEFRRboYBWAO8rdfrgwAj8ABwRffPjeLtqmVw5ybAFD4GXsotIvFUBrFpRVjsEn2a1nAo\nf1CyXs9ls25N3UqhtRB3rbtSLMvVFUmS8OzSlYINGwh59RVcGzbk5YgIBq3+DmPidqDslMbvsWEE\njRnNu6dyePSPd7HbXBg8Q82TnaOINU2lrn84TzZ9khoe114Kd8vxTB77fgfFJdtnrTx4hpUHzxDs\n5ULneoG80KMBtQKUGPK0PBMPfrvFsc8uKDX3uzYI4rddp8g2WkjaJdEzopBclYo+aQdA3k8zoJm5\nJInq+FpAKqusQ1uAxhWKTURa4vlCF89xt20U15pA37p96RjW8aKkMfecI7C1JOgrvDW0K99OXwKB\n4OZT4QbAYDCk6PX6N4B1KGGg3xkMhh1XueyGMR0+TNasH/F94AFYvgyfhYvo6e+P5x3dCGj0NFA2\nvHF089E0D2rOQw0ewl3rTtGhQ5waPZqwyVPw7NKZoOfHEfL6f1GVuKNkWcanWzdUZhPGHTvRhofh\nP3QY7u3aogtXKk/OSXgPXeA/WLI6k1No5ZM1+/FssIVdGXbW7nWhT1Q/BrWOQKXN5t/kf+kdpezX\nWmgtJM+SR6BbIBqVhh82JTB5RRzFdhmNSqJVLT92JmYjy5Ceb2bhnhSW7j9Np3qBaFQS205kUWC2\nAVAnyIN3B0TTsW4APuNyPQAAIABJREFUkiTRp7k7Q2ccIt5Um0DzZH5schBd9g9KglXjAUq56hPr\nlQ1sfMKhIK20ZrtKBeP2sHfxF4Qf/5UgKZe6xt3w8yD0tTtCl/EU1WqLqdiEn6sfmHKpvW+qEjKr\n0sKAaVWrIJtAICjDTckDMBgMc4Gbu2PJeRi3bCFp1FNgs5G3dKnjeHFGBvlr1ykbfF9AA78GjsiX\nv48swf+Z9/DMyCd57Fjqrl6FNqRspIkkSfj064tPPyXhyC7bmbZnGiu2/Y8V969AJakIclcWUVvU\nkcmW3DiWlYOtsDYajwSSsv7f3p3Hx3jtDxz/TCaTVRIkJEIkYnkQFLHUvl67oq2dluLettpqe9FL\ne2/bS1vdFK2f3tLbS3VBW3u56KWorVJbLcceJEQkZI9MMvP7Y2IqBFkmmUnm+369vF6TZ57nPOeb\njPOd5znnOSeH+efP8OnuHXjVmodep+fRuo8C8OfNf+ZQ/CECvQL5i/Y2M9bHW/tH33m0MYNbhBCX\nnMmmY3H8dDyObSoeY46ZbSoeQ8V96Cufh8uDeTwyhAndvIhNPYJO14UcUw5v/zaFSnVTST/bnV1x\n9djb7wU6dJwCGYmWBUwAWk245+/W6B3ExNieJN1swYwK3zMweyM6Uzac286muF/5IDiURjXaMbvz\nbHD3xXyrwe/wV6jaoCh/TiFEKSkXTwJ7tWiBe5063FTKssHFBb1/ZfQ+vlQaNRIXb8tMkinbtuHd\nti0ut61pazKZSHhrFjXjLBO2BTz33F2N/y23DxvdGbOThUcWAnA+6TzhFcMZ2WAkJrOJ55s9j5+7\nH+evpfFdVCSHrh4j3teTEykmXP0sD6YZTIHozJZ75yE+IbkJIJj3cxt/D4MLi8e2onW45cncQF8P\nRj8cyuiHQ/ntwnVW7L/E/87uIz1gFehM9KvfjNc6dmDkhhFcSLlA3/C+tKnWhmMJlscv3L0vkp5W\njy9+OU+HMS3BI/8Hvu605mAsMTcyAC/S//QuuhpTYf/n8PsPbPH25HJOOpejN3P02lEiAiLINvhB\n01HQUZ5WFcLRlYsEoHNzI/TLJVxftoychEQqPzE6zzh8s8lE/Jy5JHz2GX4DBlBt1jvodDrMOTlc\nmj6NZr9aOnOT6wZR/6mxmM1m3tn3DtUrVGdEgxEYXAxsid7CB/s/4MXIF+kV1otWQa3wdPWkskdl\n67inUN9QXnv4Net5wwK8mdyzPlAfgOOXk3lhzRnOx1UiLa02r685xpAwM4/WfZSqnlU5fDSSq0kp\ngJnX+0dYG/87Na9ZieY1K/HzxUTe2VeNuPQ4RjXrzOmkU1zLsCy6HuYbRr/wflxNv8qP536kns9w\nll29yv9OXOVsfCrhVfIfe38lKZPFu8+TacyhXqAPn/zPMntnFR93HmteAwyhlvVvWz/DS8uGc1zn\nzvCWL1vnCjrfbCoPtS3malJCiFJRLhIAgN7Xl4AJ+d/KMGdnWxc2T1q9Go8Iy9QDV2fPJm215ZaR\nsXoVqsybjc7VlW0Xt/HNiW8I9g7m8XqPY3AxsPfyXmJSY3hl+yv4uvnSNrgtM9vNpHW11n+sL/AA\nDar5smbsREYs2sOBxBss338JT6MP/2jegUOn/Nl6/Di43KRG/W+oHvw8UPO+5XUK6UTb6m05ef0k\nEf4RAKwdtJYfz/7IkxFPotPpmNBkAmMixhCdkMmyfZYpkxfvOs+bAxrdVd6hizcY/flekjOz73rv\nLx3D8TDcdj8/qBHVnvuNNenX0fn88aR0tns5mpZBiHLOKR4tdHFzo8YnH+MaZOnczDh4kOzLl7n+\ntWUSNLfatWm4fCXhtSyLcFxMuUiITwixabF8cuATyz56NyL8I3iq0VO0CGwBQI+wHgVu/G/xdNPz\nr9GRBPt5ALDkUAoN/rGRmeuPA2Yqhn1FkvkEk3+ebJ3C+k5ms5lfYn7BZDZhcDFYG3+wTOswptGY\nPA+6GfQG6lT1oWM9Sx/FiqhLJGXknfN/56lrjFqUf+M/OLIGY9vVums7egM6n6qYzCa2X9rOdye/\nK9TvQghhX+XmCuBBXP39qTFvLldmvkXQP/+JvkIFqv3zn1z98ENC/m8+rv5/3G4Z3XA0oxqM4syN\nM1T1tny7ndLSdve0q/p4sPDJFjy+YDcZxhyysi2DSt1d9TzdbDT/d+wfNA9sjqsu/z/PjpgdTPxp\nIvUq1eO9ju9Ru2LBFhcf2y6M7SfjSc/KYemeaCZ2sYz3P3ElmQlL9pNhtIwkmjmwEX0bV2PP2QRq\nVPKicY37J7k3dr3BytMr8TZ4836d9wv6axBC2JnTJAAAzyZNCFv2rfXbsV//fvh064qL190rael0\nOupUqlNidYkI9uPzMS34eMNBGoYFU6dqBdrXCSCkshd9tObU9L337Z8VJ1cAcCnlknXkUUF0qluF\nOlUrcPpqKp/87zT9mwRTraIHL3xzgAxjDjodzB3WjEcesvSf9G5csFW1uod2Z+XplQBEZ0TTAVkA\nXIiywKkSAHDXHED5Nf6lpW3tANzbVCIyMu8KYbca/+SsZJar5TzZ8Mk86/DOaDuDVadXkWXKuuf0\nzflxcdExY0Ajhi/cQ4Yxh7/9cJhO9apwMi4VgBe61rU2/oXRvnp7/v7w3+ldqzcnj9xjpTUhhMNx\nugRQVpxIPMH4TeNJupmEl6sXIxqMwJhjJDYtllDfUMY0GlOkctvU9mdE65p8vfcCu84ksOtMAmB5\ngOzZLgW7lXQnF50LQ7QhgOX5iFtDQoUQjs0pOoHLotp+tfExWOYS2nN5D3sv72XQmkE8seEJLqZc\nLFbZ03rXJ9Q/75XPjAGN7j+vfwFcy7jGR9EfMerHURy9drRYZQkhSp4kAAdl0BuY3no6H3X+iLld\n5uLn7kd0cjSJmYksOLjgwQXch4+HgSVPtaJRdV9qVvZiSk+NdnWKv7hKXFocx1KPkW3OZt6BeQAk\nZiZyJP4IR+KPFLt8IYRtyS0gB9ahxh+dqfUr16dveF8aVG7AUG3ofY4qmFB/b9Y9b9vO2oiACB4N\nfJQ0rzTrA3GfHvqUb058Q40KNdjw2Aabnk8IUTySAMqQWR1m2bsKD9QnoA8tWrSw/rw71rKmbXjF\nP1YQe3Xnq+y4tIOXIl9iUN1BpV5HIYSFJABhU7ePsjKbzbwY+SIGFwPB3pbRRUuOLmHNmTXo8l03\nSAhRmiQBiBKj0+noVrNbnm1hfmEEeQfRoXoH67f/W+sxCCFKl3QCi1LVsUZHNj++mX+0+QcAKlHR\nd2VfNp7faOeaCeF8JAEIu8nKyWLiTxO5lnGNaduncSH5gr2rJIRTkQQg7MZN78Zb7d/CQ+/Byy1e\nvu/0F0II25M+AGFXrau1Zu2gtdb1ks1mM2bMuOgK/93kStoV67KaQogHkysAYXdB3kFkm7JZf3Y9\nQ9cNZeO5gvUHmMwmkm4mWX9+aetL9FvZj/Vn15dUVYUoVyQBCIeQbcrmvV/f43jicRYcWoAxx3jP\nfRMyEhi/aTxdl3fltV8sD5ylGdM4nnicmNQYjiccL61qC1GmSQIQDsHD1YOnGj2Fj5sPw+oPu2vW\nVoCouCiuZ17H39OfZlWbkZCZwPZL24lNjcVF58KUllMI8g6if+3+ABhzjJjMptIORZQzOaYcDl49\nSFZOlr2rYnOSAITDGNFgBOsGrWNkg5G4urhiNput7/1w6gfGbhzLviv7ABgbMZa+4X2Z0mIKvm6+\neLp6MrLBSP772H/RKmsYTUambJ/CcrXcXuGIcuBowlEeXfMoozeMZn/cfuv2g1cP5vl8llWSAITD\nMLgYqOxhWVN464WtjNk4hsTMRGJTY5mxZwZmzOy5vAcAL4MXszrMYlTDUVRw+2OB+1udx+/te4+f\nLvzEp4c+Jd2YXvrBiHLB1+BrnX13x6UdpBvTeX3X64zeMJqlx5fauXbFJwlAOJy9l/cyaeskfrv6\nG/MPzCe4QjDvtH8HP3c/BtYZWKAymlZtCkCgdyDXMq6VZHVFOZSQYVknI8Q3hGebPsuk5pMY13gc\n6dnp/HzxZwCWHltq3a+g4tLimPzzZHp814PXd70OwK9XfmXtmbVkm+5ej7ukyXg54XAiAyPpG96X\ndWfX0TmkMwC9avWiXfV2+Lj5FKiM3rV6U8FQgQ41OhRpSKlwXkuPLWXegXm83/F9OoV0Ynzj8Xne\nn9t1LouOLOKNNm/g72lZS9xkNhXocxabFsu2i9u4mXPTut5HmjGN6Tun4+HqwZ9C/2T7gO5D/mcI\nh+Pq4srb7d9mTuc5tK7W2rq9oI0/WG4FdQrphIvOhTRjGrOjZkuHsLhL0s0ktl7YyudHPudQ/CFi\nU2OZ+9tcMrIzmL5zOilZKXcd81CVh/i468f4e/pjNpuZHTWbN3a9gdF075FrKlEB0KxqM+Z0mUPj\ngMb0Du8NQNMqTekV1ovuNbuXTJD3IQlAOCSdTke30G646d2KVU5cWhyjfhzFF79/wZ7YPTaqnSjL\n0oxpXM+8DsCq06t4YesLzPltDgfiDhBcIZhZHWYR6BXIwh4LH/il47PDn/HF71+w8vRK/rb9b/nu\ns+3iNoauG2q9ddS+enu+7vs1Ef6WZVM9DZ5MbjE535FvJU0SgCjXPA2exKTGALDu7Do710bY24Gr\nB+i/sj+bozcDMLDOQDz0Huh1euu61t1Cu7F20Foa+jd8YHmP1XuM+pXrU6diHV5u8TIAF1MuWgce\nJN1MYtqOaeSYc3jv1/fyHTnkrncn0DvQViEWivQBiHLN182X55o+RxWvKna5xBaOI+lmEi9tfYmE\nzATOJZ0DwM/dj6V9lhLkHZRnSnJPV88ClRngGcDSPkvJNmXjbfDGZDbxzJZniEmJYX73+bQNbss7\nHd5h6vapTG051S7f8u9HrgBEufdExBP0rtUbg97AlbQr5WL8tri3HFOO9fXMPTN5bM1jrDu7Di9X\nL4ZoQ3DVuVLDp4b1c6BV1vBz9yvy+dz17ngbvAHYGbOT6ORoss3ZnL1xFoDOIZ3Z+NhGOoV0KkZU\nJUMSgHAaOy7tYMCqAXx36jt7V0WUALPZzKIjixi+fri1ca9gqMDJ6ydZeHghBr2BZ5s+y5qBaxhR\nf0SJfBtvEdiCeV3mMarBKCp5VLJuv/V8i6ORBCCcQlZOFjP3zCQ9O906ykOULytOrmDub3M5nnic\nYwnHAGhVrRUANXxqWPcL8Q0psVsxXgYvutTswiutXqFveN8SOYctSQIQTsFN78YrrV7B2+DNjLYz\nCnyPV5QdWmWNUN9QKntUxqA3ANA2uC2/DP+FyS0m27l2jkk6gYXT6BLShdUDVltHXOy5vIcAjwDq\nVKpj55oJW3ioykMs77ecmNQY6laqa93u6+aLr5uvHWvmuOQKQDgNnU5nbfyXq+U8vflpxm0ax6H4\nQ3aumSiOTdc2sfjoYnJMOXgZvPI0/uL+JAEIp5SclUyOOYd0YzrX0m03V1DSzSRe3/U63VZ04+vj\nX9us3PLImGPk1yu/kpyVXOQyUrNSWXl1JR/s/4A3d79pw9o5B7kFJJzS+Mbj8ffwp37l+jTwb2Cz\ncj899Ck/nPoBHTraV28PwJozazCZTTxS+xGZlyhXZnYmj655lIspF2lWtRlLei8pUjnRydF4uHiQ\nYcqwzhslCk4SgHBag+oOsr6OTo7G2+BNgGdAscqc1HwSxxKO4eHqQYhPCAA/X/yZTdGbOJF4gr+1\nyn+6AGfj4ephHao5JmIMYFkV7odTP/BQlYeoV6legUbqRARE8H6997kecF0SQBEUOQFomtYOmAMY\ngATgKaVUtKZpFYGvgHAgHhiilLpii8oKYWsZ2Rm8vO1ldsbsZFyjcbwY+WKhy9h2cRtrzqzh3Q7v\n4uHqwYLuC/Bw9bA2YFfSLB//A1cPkG5Mz/PEqTMxm818f+p7+oX3w8PVg/61+3Py+kk6VO8AwOrT\nq5mxZwYAExpP4IXmL9y3vGsZ1/D38MfVxZVetXqVeP3Lo+Jcj34FjFNKNc19PS93+0xgh1KqAbAQ\nmFu8KgpRcjxdPUkzpgGWVcfutxZxfuLS4pi2Yxqbozfztx2Wb/deBq88t3o+7Pwh4xuP58veXzpt\n4w9w5NoR3tz9JgNXD0QlKp5t+ixzuszBoDdgNptZdXoVAO2C2zGhyQSAe/YPmMwmxm4cy7D1wziY\ncrDUYihvinQFoGmaO/CaUupw7qbDwPO5r/sCHXNffwPM1zTNoJQq3P8sIUrJkxFPolXSGN5guHX8\neEFlZGcQ6hvK0YSj9K7VO999gryDmNR8EmCZpuDk9ZM27XcoK9afXQ/A5bTLdz0Zq9PpWNx7MWdv\nnCXQOxBPV0/SjemMWD+CRgGNmN56ep6hnFsvbOV88nkAGhsal1oM5U2REoBS6iawFEDTNBfgDWBV\n7tvBwOXc/bI1TUsGqgCxxa2sECWhW81udKvZrUjHhvmF8VWfr9gZs/OBc71ExUUxc89MLqZcZP2g\n9XabAdJeJjWfRKOARpxLOkcVryp3ve+ic8nzTMaCQwuITo4mOjkak8nEe53es77XsUZHJjadyPen\nvqdzpc6lUf1ySfegibE0TRsMfHTH5hNKqe6aprkBi4FKQH+llFHTtCzASymVnXv8JaDFg/oBoqKi\nwoBzRQtDiOKLuxnHT4k/0dKvJXW97j+W/HzGefYn7+eRKo/g5lKwNQtOpp3k7XNvA9DTvyfDqw0v\ndp3LsxvGG/w75t9UdavKsKBhmDBxMOUgwe7B1PCwTO2QZcoq8O/fCdSKjIw8X5gDHngFoJRaAay4\nc7umaRWANVg6gAfcdosnBggCLmma5gr45u5TII0aNcLd3f2++0RFRREZGVnQIh1aeYoFym48qVmp\nPL38aTJzMjFXMDMschiQfzyH4w/z9sa3yTJlccJ4gqV9llpng7yfSCI5pDtEnYp1GFF/hN36A+zx\nN1pwaAGhPqF0rdkVD1ePAh/X1dwVM2ZcdC4kZyXz5to3yTHn8FWfr6jqVRUou5+5eylsPDdv3uT3\n338v0rmK0wm8FDiNZZTPzdu2/wg8kft6KJYOYbn/LxxaBbcK9AjrAUC6MT3PlMJ3CvUNpWW1lrjo\nXOgX3q9Ajf8tszrMYnzj8U7VGZyQkcBnhz7jlR2v8MH+Dwp1rE6nw0XnQkpWCs/99BwxqTGkZaVx\n8vrJEqqtcylqJ3AzYABwDDigaRpArFKqD/B34D+aph0FbgAjbVRXIUrU002eZkzEmAdOJeDn7sf8\nrvM5mnCUJlWaFOlcv8X9xscHPmZ66+nlfuqCMzfO4GXwIjkrmT61+hSpDFcXVwbVGcRQbSjtq7cv\n1vz94g9F7QQ+AOT7lIZSKhF4pDiVEsIeQnxDrK/Tjem4uuT972E2m1l5eiXdanbDz92vyI1/XFoc\nY/87FpPZxHK1nFcffrVY9XZ0raq1YuuQreyO3U2zqs2KVIanq2eeB/eEbchz6ULcJjUrlQ9+/YDu\n33W3Dlu8JSouitd3vU73Fd3ZcWlHkc8R6B1Im2ptLGvRuuiLW2WHdjzhOEaTETe9G51COjnckojO\nTqaCEOI27q7ubDi3gZSsFJYcW8L06tOt7x1PPA5AjjmHiICIYp3n1YdfpZJ7JSq4VShWOY7sUsol\nxmwcQ7hfOG+1f4vwiuH2rpK4gyQAIW5jcDEwosEItl3cxoQmE9Bd+eMb6+iGownxCeHA1QPFXuLv\n1jxBYFmXwNfNl4b+DYtVpr1lZmeSakzlUsolmlZtyqIji0jPTuf3hN+5nHZZEoADkgQgxB3GRIzh\nqUZPodPp2HRxE09seIIXm79I88DmdA7pbNNJx2btm8VXx7+iTsU6LOu3DDd92RzTnnQziYGrB3It\nwzK19vah25nWeho+bj5kZmfSrno7O9dQ5Ef6AIS4g95Fb71XvSVxCweuHuDZn54lKi7K5ueq6F4R\ngJjUGOstprLIz92PL3t/SZMAS8f4ofhDuOvd+WuLvzK99fQHHC3sRa4AhLgHs9lMdEY0NX1q0rRq\n07tGBdnCuMbjuJZxjTERY/IsXF6W3Mi8QUWPigR6BzKs/jDGGcblGe0jHb+OSxKAEPeg0+mYWmtq\niT5lanAx8NrDr1l/Xnd2HQYXA11CuuCmd+NK2hW2X9rOqeunmNJyCm56N7ZEb7Gse1uxLm2C29i1\ngf0l5hde3Poi/Wv3Z2LTifSv3d9udRGFJ7eAhHAAZrOZeb/NY9qOaUz+eTJnbpwBLKuJzdgzg2/V\nt+hyH705ef0kH+z/gL9s+Qvfqm/tVuesnCym75xOZk4mK0+v5HLaZbvVRRSNXAEI4SDc9G54G7xJ\nM6ZZbwcFellmDPV09bTegso2ZQOWUUmP13vcPpXFUt8lvZcw/8B8OoZ0pFFAI7vVRRSNJAAhHIBO\np+PphyxTUeyO3Y2Pmw8A7aq346s+X9EooJH1Vs/EphPpGdaTcL9wDC4GbuZYpuJy199/EkVbMpvN\n6HQ6Qn1D80zTLMoWuQUkhAPxcPWgS80u1p8DPANoUqVJnhXG9C56tMoaBr2BC8kXGPXjKN7Z+06p\n1nPWvlm8tect67BPUTbJFYAQZdisfbM4kXiCE4knGF5/OFplrcTPGZ0czTK1jBxzDvEZ8czpMqfE\nzylKhlwBCFGGTWs1DR+DD6MajLI+U1DS0oxptAhqgQ7LbStRdskVgBBlWIhvCFuHbi3V+/8N/Ruy\nqMcirqRdIcg7qNTOK2xPrgCEKONuNf5nb5xlc/TmEj3XuaRzZGZnAkjjXw5IAhCiHPgo6iMGrB7A\n5J8nszt2t3V7SlaKzc5hNpt5edvLdF3elYWHF9qsXGE/kgCEKAd6hfWyrC+g03Mp9RJfH/+awWsH\nM3jtYMxms03O8fu13zl94zQpRtslFWFfkgCEKAca+Dfg3Y7vsrzfcgbXG8yNmzc4kXiCmNQYjiUe\nA+BI/BFuZN4o8jnqV67Ph50+pH319jxSWxb9Kw+kE1iIcqJrza7W109GPMm+K/toFdSKSu6VmL1/\nNouPLWZUg1F0del6n1LuzaA30COsBz3CetiqysLOJAEIUQ55G7z5T6//AJZ794evHbauQdyybstC\nl7czZifx6fH0DOuJl8HLxrUV9iIJQIhyTqfTMbHpRFaeWskQbQjZF7ILdbzZbGbBoQUcjj/MoiOL\nWDdonUzxXE5IAhDCCbQMaknLIMs3/6iLhVvY5vrN66Qb0wH4U+ifpPEvRyQBCOFE4tPjWXN1DTmX\nc2hVrVWBjqnsUZkV/Vfww6kf6BnWs4RrKEqTJAAhnERWThYDVw8kOSuZhGMJBUoAu2J30TKwJQa9\ngSHakFKopShNMgxUCCfhpnezfoOPz4gnKyfrvvsfvHqQZ7Y8w+C1gzmWcKw0qihKmVwBCOFExkaM\npX52fQa3H3zXvfyY1BhWnlpJQ/+GdK3Zle9PfY/JbCI6JbpU5xoSpUcSgBBOJMQ3hKteV9HpdJxL\nOke6MZ2IgAiuZVxj4KqBZOZk0je8L11rduWNNm/g4+ZDqE8otSvWtnfVRQmQBCCEE9pxaQevbH+F\nFGMKUaOiCPAMoGdYT1afWW2dP0jvomdqy6l2rqkoSZIAhHBCcelx1jl94tLiCPENYVLzSTxe73Ga\nVm1q59qJ0iIJQAgn9Hi9x6ngVoEdl3ZYt1XxqkIVryp2rJUobZIAhHBSvcJ60Susl72rIexIhoEK\nIYSTkgQghBBOShKAEEI4KUkAQgjhpCQBCCGEk5IEIIQQTsqRhoHqAbKy7j9B1S03b94s0cqUpvIU\nC0g8ZUF5i8mZ47mtzdQX9jw6s9lc2GNKRFRUVHtgxwN3FEIIkZ8OkZGROwtzgCNdAfwKdAAuAzl2\nrosQQpQVeqAalja0UBzmCkAIIUTpkk5gIYRwUpIAhBDCSUkCEEIIJyUJQAghnJQkACGEcFKSAIQQ\nwklJAhBCCCdVqg+CaZr2OjAk98f1SqmpmqZ1B2YDnsAypdRrdxyzGNiqlPpP7s/VgEVAMJAOjFRK\nnc/nXE2BhYAfsB14WimVfdv7/wRMSqk3ymosmqZ1AOYAbsA54Eml1PUyGssTwLtA3G31eLWwsThC\nPEBlYNNtu/kBVZRSFYoSjyPElPs3agXMB9yBC8B4pdQVR4/nXsfftr1YbYEt4tE0rSoF/MzYsm0r\ntSuA3F9GD6AZ0BSI1DRtOPBvYADQAGipaVrv3P2DNU1bCwy+o6gvgbVKqWa5r9+9xymXAs8rpeoB\nOmBCbrl+mqZ9Dkwu67EAXwCjlVKNgWPAlDIcS0vgZaVU09x/RW387R6PUurqrTiA5sB54M9FicdR\nYtI0TQd8B0xVSjUBlgCflYV47nW8LdoCW8VTyM+Mzdq20rwFdBn4q1IqSyllBI4D9YBTSqlzuRls\nKX/8UkYCq4HltwrQNC0AeAj4V+6mL4A8WTV3v1DAUym1J3fTf24rdwBwCviwHMTSQCl1TNM0A1Ad\nKPS3fweKpSXwhKZphzRNW6ppWqUixOJI8dwyFkhXSn1dxHgcJaaA3O1bc7evA3ppmubuyPHc6/hc\ntmgLbBLPHe75mbF121Zqt4CUUkdvvdY0rS4wFJiH5Zd3y2WgRu7+7+fu2/6292tjufT8SNO0Lrmv\nn8vndMH3KXdJbrlvlINYjJqmNQa2AEZgelmNJff1LGAf8DbwCZb/KGU1HjRN02NplB4pbBy3c5CY\nrgFpmqb1UEptAoYBBsAfiHXgeO51vE3aAhvGc+v4B31mbNq2lXonsKZpEcBmLJcpZ/LZxXSfw12x\nXGZtUUo9hCWLLs5nP10hyy0SR4hFKXVEKRUIzACWFbDqd7F3LEqpQUqpvUopM/Ae0KcQ1b+LvePJ\n1Qs4qZQ6UqBKP4A9Y8r9uzwGTNc07QBQEUgACjZ/ez5KKZ5SU8x4bnnQZ8ambVtpdwK3A74HXlRK\nfatpWicg6LZdqnH/bxNXgBSl1Lrcn78G5mmaFgz8mLstFnimkOUWmr1j0TTNA+illFqVu30pRbyU\ndYBY/ICnlFI2z8nxAAABhklEQVQf5W7XYbmiKRJ7x3PbzwOBb4sax+0cJCajUqpzbn0qA38HEh05\nHqVUsb5IFJQN4rklz2empNu2UksAmqaFAKuAoUqp/+Vu3mt5S6uDZRTLCCwdJ/lSSp3RNC1G07Te\nSqkNQH8gSikVi6Xz5fbzZWqa1k4p9QvwBLChnMViBOZrmnZRKRWFZQRCoeYCd6BYUoGpmqbtUkrt\nxXIpv7KwsThQPLe04d4drWUxpi80TXtaKfUrlm+5K5RShf72WdrxlDRbxHObPJ+Zkm7bSvMKYDLg\nAczWNO3Wtk+BMVgypweWTPfdA8oZBPxL07T3gWTgyXvsNxJYqGmaD3AAyz05W7F7LEqpHE3ThgKf\n5d43jAHGl+FYhgALNE3zBE5i+WAXhd3jue29cOBS4UO4i6PE9Ezu8V7AYWBcUYKh9OMpabaKBwr2\nmbFZ2ybrAQghhJOSJ4GFEMJJSQIQQggnJQlACCGclCQAIYRwUpIAhBDCSUkCEEIIJyUJQAghnJQk\nACGEcFL/D2afU857I9C8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=data, palette=\"tab10\", linewidth=2.5)"
   ]
  },
  {
   "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": "3.6.1"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}