{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas  as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "from datetime import datetime\n",
    "import math\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "font = {'family' : 'bold',\n",
    "        'weight' : 'bold',\n",
    "        'size'   : 25}\n",
    "\n",
    "plt.rc('font', **font)\n",
    "\n",
    "# plt.rcParams.update({'font.size': 20})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the .Run files and Extract the main information\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Dori\\Output\\05-01-Run.out', header=None, skiprows=3, skipfooter=40, engine='python', delim_whitespace=True)\n",
    "a = A.loc[1,0:29].copy()\n",
    "\n",
    "A.loc[1,] = ''\n",
    "\n",
    "for i in range(len(a)):\n",
    "    A.loc[1,i+4] = a[i]\n",
    "    \n",
    "for i in range(37):\n",
    "    A.loc[1,i] = str(A.loc[0,i])+' '+str(A.loc[1,i])\n",
    "    \n",
    "    \n",
    "A = A.rename(columns=A.iloc[1])\n",
    "\n",
    "A = A[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>RunNr</th>\n",
       "      <th>Day1</th>\n",
       "      <th>Month1</th>\n",
       "      <th>Year1</th>\n",
       "      <th>Rain mm</th>\n",
       "      <th>ETo mm</th>\n",
       "      <th>GD °C.day</th>\n",
       "      <th>CO2 ppm</th>\n",
       "      <th>Irri mm</th>\n",
       "      <th>Infilt mm</th>\n",
       "      <th>...</th>\n",
       "      <th>ExpStr %</th>\n",
       "      <th>StoStr %</th>\n",
       "      <th>BioMass ton/ha</th>\n",
       "      <th>Brelative %</th>\n",
       "      <th>HI %</th>\n",
       "      <th>Yield ton/ha</th>\n",
       "      <th>WPet kg/m3</th>\n",
       "      <th>DayN</th>\n",
       "      <th>MonthN</th>\n",
       "      <th>YearN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>2017</td>\n",
       "      <td>797</td>\n",
       "      <td>1185</td>\n",
       "      <td>5630</td>\n",
       "      <td>407.21</td>\n",
       "      <td>0</td>\n",
       "      <td>724</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>2018</td>\n",
       "      <td>1281</td>\n",
       "      <td>1187</td>\n",
       "      <td>5637</td>\n",
       "      <td>409.21</td>\n",
       "      <td>0</td>\n",
       "      <td>1051</td>\n",
       "      <td>...</td>\n",
       "      <td>33</td>\n",
       "      <td>37</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>2019</td>\n",
       "      <td>1206</td>\n",
       "      <td>1219</td>\n",
       "      <td>5675</td>\n",
       "      <td>411.21</td>\n",
       "      <td>0</td>\n",
       "      <td>1019</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  RunNr  Day1  Month1  Year1  Rain mm ETo mm GD °C.day CO2 ppm Irri mm  \\\n",
       "2      1    30      11   2017     797   1185      5630  407.21       0   \n",
       "3      2    30      11   2018    1281   1187      5637  409.21       0   \n",
       "4      3    30      11   2019    1206   1219      5675  411.21       0   \n",
       "\n",
       "  Infilt mm  ... ExpStr % StoStr % BioMass ton/ha Brelative %  HI %  \\\n",
       "2       724  ...        0        0          0.000          -9  -9.9   \n",
       "3      1051  ...       33       37          0.005           0  -9.9   \n",
       "4      1019  ...        0        0          0.000          -9  -9.9   \n",
       "\n",
       "  Yield ton/ha WPet kg/m3 DayN  MonthN  YearN   \n",
       "2        0.000       0.00    15       2   2018  \n",
       "3        0.000       0.00    16       2   2019  \n",
       "4        0.000       0.00    19       2   2020  \n",
       "\n",
       "[3 rows x 37 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = A[0:0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "05-01-Run\n",
      "05-03-Run\n",
      "05-06-Run\n",
      "05-09-Run\n",
      "05-13-Run\n",
      "05-17-Run\n",
      "05-20-Run\n",
      "05-25-Run\n",
      "05-27-Run\n",
      "05-31-Run\n",
      "06-03-Run\n",
      "06-06-Run\n",
      "06-10-Run\n",
      "06-13-Run\n",
      "06-17-Run\n",
      "06-20-Run\n",
      "06-25-Run\n",
      "06-27-Run\n",
      "07-01-Run\n",
      "07-05-Run\n",
      "07-07-Run\n",
      "07-10-Run\n",
      "07-13-Run\n",
      "07-17-Run\n",
      "07-20-Run\n",
      "07-24-Run\n",
      "07-27-Run\n",
      "07-31-Run\n",
      "08-03-Run\n",
      "08-07-Run\n",
      "08-10-Run\n",
      "08-13-Run\n",
      "08-17-Run\n",
      "08-20-Run\n",
      "08-24-Run\n",
      "08-27-Run\n",
      "08-31-Run\n",
      "09-01-Run\n",
      "09-05-Run\n",
      "09-10-Run\n",
      "09-15-Run\n",
      "09-20-Run\n",
      "09-25-Run\n",
      "09-30-Run\n",
      "10-01-Run\n",
      "10-05-Run\n",
      "10-15-Run\n",
      "10-20-Run\n",
      "10-25-Run\n",
      "10-30-Run\n",
      "10-31-Run\n"
     ]
    }
   ],
   "source": [
    "# Read AquaCrop output files and extract the main information\n",
    "\n",
    "loc = r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Bogande\\Output GIS 2'\n",
    "\n",
    "j = 1\n",
    "for file in os.listdir(loc):\n",
    "        #print(file)\n",
    "        fName, fExt = os.path.splitext(file) \n",
    "        if fName[6:] == 'Run':\n",
    "            print(fName)\n",
    "#             print(file)\n",
    "            A = pd.read_csv(loc+ '\\\\' +file, header=None, skiprows=3, skipfooter=40, engine='python', delim_whitespace=True)\n",
    "            a = A.loc[1,0:29].copy()\n",
    "\n",
    "            A.loc[1,] = ''\n",
    "\n",
    "            for i in range(len(a)):\n",
    "                A.loc[1,i+4] = a[i]\n",
    "\n",
    "            for i in range(37):\n",
    "                A.loc[1,i] = str(A.loc[0,i])+' '+str(A.loc[1,i])\n",
    "\n",
    "            A = A.rename(columns=A.iloc[1])\n",
    "\n",
    "            A = A[2:]\n",
    "            \n",
    "            A[['Month1 ']] = int(fName[0:2]) \n",
    "            A[['Day1 ']] = int(fName[3:5]) \n",
    "\n",
    "            B = B.append(A)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loc+ '\\\\' +\n",
    "# file\n",
    "# fName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RunNr ', 'Day1 ', 'Month1 ', 'Year1 ', 'Rain mm', 'ETo mm',\n",
       "       'GD °C.day', 'CO2 ppm', 'Irri mm', 'Infilt mm', 'Runoff mm', 'Drain mm',\n",
       "       'Upflow mm', 'E mm', 'E/Ex %', 'Tr mm', 'TrW mm', 'Tr/Trx %',\n",
       "       'SaltIn ton/ha', 'SaltOut ton/ha', 'SaltUp ton/ha', 'SaltProf ton/ha',\n",
       "       'Cycle days', 'SaltStr %', 'FertStr %', 'WeedStr %', 'TempStr %',\n",
       "       'ExpStr %', 'StoStr %', 'BioMass ton/ha', 'Brelative %', 'HI %',\n",
       "       'Yield ton/ha', 'WPet kg/m3', 'DayN ', 'MonthN ', 'YearN '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>RunNr</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rain mm</th>\n",
       "      <th>ETo mm</th>\n",
       "      <th>GD °C.day</th>\n",
       "      <th>CO2 ppm</th>\n",
       "      <th>Irri mm</th>\n",
       "      <th>...</th>\n",
       "      <th>StoStr %</th>\n",
       "      <th>BioMass ton/ha</th>\n",
       "      <th>Brelative %</th>\n",
       "      <th>HI %</th>\n",
       "      <th>Yield ton/ha</th>\n",
       "      <th>WPet kg/m3</th>\n",
       "      <th>DayN</th>\n",
       "      <th>MonthN</th>\n",
       "      <th>YearN</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>123</td>\n",
       "      <td>368</td>\n",
       "      <td>1844</td>\n",
       "      <td>406.21</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.163</td>\n",
       "      <td>58</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>123</td>\n",
       "      <td>376</td>\n",
       "      <td>1884</td>\n",
       "      <td>406.21</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.209</td>\n",
       "      <td>46</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-05-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>145</td>\n",
       "      <td>386</td>\n",
       "      <td>1942</td>\n",
       "      <td>406.21</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>0.267</td>\n",
       "      <td>30</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-05-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>157</td>\n",
       "      <td>400</td>\n",
       "      <td>2016</td>\n",
       "      <td>406.21</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.442</td>\n",
       "      <td>25</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-05-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>194</td>\n",
       "      <td>416</td>\n",
       "      <td>2105</td>\n",
       "      <td>406.21</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1.031</td>\n",
       "      <td>34</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.01</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-05-13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index RunNr  Day Month  Year Rain mm ETo mm GD °C.day CO2 ppm Irri mm  ...  \\\n",
       "0      2      1   1     5  2017     123    368      1844  406.21       0  ...   \n",
       "1      2      1   3     5  2017     123    376      1884  406.21       0  ...   \n",
       "2      2      1   6     5  2017     145    386      1942  406.21       0  ...   \n",
       "3      2      1   9     5  2017     157    400      2016  406.21       0  ...   \n",
       "4      2      1  13     5  2017     194    416      2105  406.21       0  ...   \n",
       "\n",
       "  StoStr % BioMass ton/ha Brelative %  HI % Yield ton/ha WPet kg/m3 DayN   \\\n",
       "0        3          0.163          58  -9.9        0.000       0.00    29   \n",
       "1        4          0.209          46  -9.9        0.000       0.00     1   \n",
       "2        8          0.267          30  -9.9        0.000       0.00     4   \n",
       "3        7          0.442          25  -9.9        0.000       0.00     8   \n",
       "4        6          1.031          34   1.5        0.016       0.01    13   \n",
       "\n",
       "  MonthN  YearN        Time  \n",
       "0       6   2017 2017-05-01  \n",
       "1       7   2017 2017-05-03  \n",
       "2       7   2017 2017-05-06  \n",
       "3       7   2017 2017-05-09  \n",
       "4       7   2017 2017-05-13  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B = B.rename(columns={\"Year1 \": \"Year\", \"Month1 \": \"Month\", \"Day1 \": \"Day\"})\n",
    "\n",
    "\n",
    "# # For mixmath yeas\n",
    "# B = B.reset_index()\n",
    "# B = B.iloc[:, 1:39]\n",
    "# for k in range(len(B)):\n",
    "#     if B.at[k, \"Year\"]== '2018':\n",
    "#         B.at[k, \"Year\"] = '2020'\n",
    "\n",
    "B['Time'] = pd.to_datetime(B[['Year', 'Month', 'Day']]) \n",
    "B = B.sort_values(by=['Time'])\n",
    "B = B.reset_index()\n",
    "B.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "153"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = B.iloc[:, 2:39]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "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>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rain mm</th>\n",
       "      <th>ETo mm</th>\n",
       "      <th>GD °C.day</th>\n",
       "      <th>CO2 ppm</th>\n",
       "      <th>Irri mm</th>\n",
       "      <th>Infilt mm</th>\n",
       "      <th>Runoff mm</th>\n",
       "      <th>...</th>\n",
       "      <th>StoStr %</th>\n",
       "      <th>BioMass ton/ha</th>\n",
       "      <th>Brelative %</th>\n",
       "      <th>HI %</th>\n",
       "      <th>Yield ton/ha</th>\n",
       "      <th>WPet kg/m3</th>\n",
       "      <th>DayN</th>\n",
       "      <th>MonthN</th>\n",
       "      <th>YearN</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "      <td>541</td>\n",
       "      <td>960</td>\n",
       "      <td>5049</td>\n",
       "      <td>410.21</td>\n",
       "      <td>0</td>\n",
       "      <td>514</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2.404</td>\n",
       "      <td>26</td>\n",
       "      <td>40.4</td>\n",
       "      <td>0.970</td>\n",
       "      <td>2.09</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "      <td>541</td>\n",
       "      <td>982</td>\n",
       "      <td>5153</td>\n",
       "      <td>410.21</td>\n",
       "      <td>0</td>\n",
       "      <td>514</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.753</td>\n",
       "      <td>18</td>\n",
       "      <td>40.4</td>\n",
       "      <td>0.708</td>\n",
       "      <td>1.95</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "      <td>541</td>\n",
       "      <td>1000</td>\n",
       "      <td>5245</td>\n",
       "      <td>411.21</td>\n",
       "      <td>0</td>\n",
       "      <td>514</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.389</td>\n",
       "      <td>14</td>\n",
       "      <td>40.3</td>\n",
       "      <td>0.561</td>\n",
       "      <td>1.83</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>2019-10-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "      <td>541</td>\n",
       "      <td>1022</td>\n",
       "      <td>5349</td>\n",
       "      <td>411.21</td>\n",
       "      <td>0</td>\n",
       "      <td>514</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.719</td>\n",
       "      <td>7</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>2019-10-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "      <td>541</td>\n",
       "      <td>1025</td>\n",
       "      <td>5365</td>\n",
       "      <td>411.21</td>\n",
       "      <td>0</td>\n",
       "      <td>514</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.601</td>\n",
       "      <td>6</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>2019-10-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Day Month  Year Rain mm ETo mm GD °C.day CO2 ppm Irri mm Infilt mm  \\\n",
       "148  15    10  2019     541    960      5049  410.21       0       514   \n",
       "149  20    10  2019     541    982      5153  410.21       0       514   \n",
       "150  25    10  2019     541   1000      5245  411.21       0       514   \n",
       "151  30    10  2019     541   1022      5349  411.21       0       514   \n",
       "152  31    10  2019     541   1025      5365  411.21       0       514   \n",
       "\n",
       "    Runoff mm  ... StoStr % BioMass ton/ha Brelative %  HI % Yield ton/ha  \\\n",
       "148        27  ...        0          2.404          26  40.4        0.970   \n",
       "149        27  ...        0          1.753          18  40.4        0.708   \n",
       "150        27  ...        0          1.389          14  40.3        0.561   \n",
       "151        27  ...        2          0.719           7  -9.9        0.000   \n",
       "152        27  ...        2          0.601           6  -9.9        0.000   \n",
       "\n",
       "    WPet kg/m3 DayN  MonthN  YearN        Time  \n",
       "148       2.09    21      12   2019 2019-10-15  \n",
       "149       1.95    28      12   2019 2019-10-20  \n",
       "150       1.83     3       1   2020 2019-10-25  \n",
       "151       0.00    10       1   2020 2019-10-30  \n",
       "152       0.00    11       1   2020 2019-10-31  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Yield reduction calculation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "B.iloc[:,:36] = B.iloc[:,:36].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "B['Yield red (%)'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(B)):\n",
    "    B.at[i,'Yield red (%)'] = ((B['Yield ton/ha'].max() - B.at[i,'Yield ton/ha'])*100)/B['Yield ton/ha'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B.to_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Bogande\\Results GIS 2\\Yiel_Bogande.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Gaoua\\Results\\Yiel_variation_Gaoua.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = B.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rain mm</th>\n",
       "      <th>ETo mm</th>\n",
       "      <th>GD °C.day</th>\n",
       "      <th>CO2 ppm</th>\n",
       "      <th>Irri mm</th>\n",
       "      <th>Infilt mm</th>\n",
       "      <th>Runoff mm</th>\n",
       "      <th>...</th>\n",
       "      <th>BioMass ton/ha</th>\n",
       "      <th>Brelative %</th>\n",
       "      <th>HI %</th>\n",
       "      <th>Yield ton/ha</th>\n",
       "      <th>WPet kg/m3</th>\n",
       "      <th>DayN</th>\n",
       "      <th>MonthN</th>\n",
       "      <th>YearN</th>\n",
       "      <th>Time</th>\n",
       "      <th>Yield red (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>27.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>1079.0</td>\n",
       "      <td>5780.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.389</td>\n",
       "      <td>28.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>1.399</td>\n",
       "      <td>2.66</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>28.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>5798.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.380</td>\n",
       "      <td>28.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>1.396</td>\n",
       "      <td>2.66</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-28</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>29.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>5815.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.368</td>\n",
       "      <td>28.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>1.391</td>\n",
       "      <td>2.66</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-29</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>30.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>5832.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.353</td>\n",
       "      <td>28.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>1.385</td>\n",
       "      <td>2.66</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>1093.0</td>\n",
       "      <td>5848.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.334</td>\n",
       "      <td>28.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>1.377</td>\n",
       "      <td>2.67</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Day  Month    Year  Rain mm  ETo mm  GD °C.day  CO2 ppm  Irri mm  \\\n",
       "640  27.0   11.0  2020.0    507.0  1079.0     5780.0   413.21      0.0   \n",
       "641  28.0   11.0  2020.0    507.0  1082.0     5798.0   413.21      0.0   \n",
       "642  29.0   11.0  2020.0    507.0  1086.0     5815.0   413.21      0.0   \n",
       "643  30.0   11.0  2020.0    507.0  1090.0     5832.0   413.21      0.0   \n",
       "644   1.0   12.0  2020.0    507.0  1093.0     5848.0   413.21      0.0   \n",
       "\n",
       "     Infilt mm  Runoff mm  ...  BioMass ton/ha  Brelative %  HI %  \\\n",
       "640      429.0       78.0  ...           3.389         28.0  41.3   \n",
       "641      429.0       78.0  ...           3.380         28.0  41.3   \n",
       "642      429.0       78.0  ...           3.368         28.0  41.3   \n",
       "643      429.0       78.0  ...           3.353         28.0  41.3   \n",
       "644      429.0       78.0  ...           3.334         28.0  41.3   \n",
       "\n",
       "     Yield ton/ha  WPet kg/m3  DayN   MonthN   YearN        Time  \\\n",
       "640         1.399        2.66   24.0      2.0  2021.0 2020-11-27   \n",
       "641         1.396        2.66   25.0      2.0  2021.0 2020-11-28   \n",
       "642         1.391        2.66   26.0      2.0  2021.0 2020-11-29   \n",
       "643         1.385        2.66   27.0      2.0  2021.0 2020-11-30   \n",
       "644         1.377        2.67   28.0      2.0  2021.0 2020-12-01   \n",
       "\n",
       "     Yield red (%)  \n",
       "640             12  \n",
       "641             12  \n",
       "642             13  \n",
       "643             13  \n",
       "644             14  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "B['Month'] = B['Month'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "C.iloc[:,:36] = C.iloc[:,:36].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Day', 'Month', 'Year', 'Rain mm', 'ETo mm', 'GD °C.day', 'CO2 ppm',\n",
       "       'Irri mm', 'Infilt mm', 'Runoff mm', 'Drain mm', 'Upflow mm', 'E mm',\n",
       "       'E/Ex %', 'Tr mm', 'TrW mm', 'Tr/Trx %', 'SaltIn ton/ha',\n",
       "       'SaltOut ton/ha', 'SaltUp ton/ha', 'SaltProf ton/ha', 'Cycle days',\n",
       "       'SaltStr %', 'FertStr %', 'WeedStr %', 'TempStr %', 'ExpStr %',\n",
       "       'StoStr %', 'BioMass ton/ha', 'Brelative %', 'HI %', 'Yield ton/ha',\n",
       "       'WPet kg/m3', 'DayN ', 'MonthN ', 'YearN ', 'Time', 'Yield red (%)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run the analysis below for (Seasonal and average): \n",
    "    - Yield variation: 'Yield ton/ha'\n",
    "    - Evaporative Water Productivity: 'WPet kg/m3'\n",
    "    - Leaf expansion stress: 'ExpStr %'\n",
    "    - Stomatal closure stress: 'StoStr %'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Arrange column Time before converting it into Mon-Day\n",
    "\n",
    "# for i in range(len(C)):\n",
    "#     C.at[i,'Time'] = datetime.strptime(C.at[i,'Time'], '%m/%d/%Y')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "C1 = C.loc[C['Month']==5]\n",
    "C2 = C.loc[C['Month']==6]\n",
    "C3 = C.loc[C['Month']==7]\n",
    "C4 = C.loc[C['Month']==8]\n",
    "C5 = C.loc[C['Month']==9]\n",
    "C6 = C.loc[C['Month']==10]\n",
    "C7 = C.loc[C['Month']==11]\n",
    "# C8 = C.loc[C['Month']==12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(C.at[1,'Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "C['Mon-Day'] = C['Time'].dt.strftime('%b-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(C['Mon-Day'][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "C2016 = C.loc[C['Year']==2016]\n",
    "C2017 = C.loc[C['Year']==2017]\n",
    "C2018 = C.loc[C['Year']==2018]\n",
    "C2019 = C.loc[C['Year']==2019]\n",
    "C2020 = C.loc[C['Year']==2020]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": false
   },
   "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>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rain mm</th>\n",
       "      <th>ETo mm</th>\n",
       "      <th>GD °C.day</th>\n",
       "      <th>CO2 ppm</th>\n",
       "      <th>Irri mm</th>\n",
       "      <th>Infilt mm</th>\n",
       "      <th>Runoff mm</th>\n",
       "      <th>...</th>\n",
       "      <th>Brelative %</th>\n",
       "      <th>HI %</th>\n",
       "      <th>Yield ton/ha</th>\n",
       "      <th>WPet kg/m3</th>\n",
       "      <th>DayN</th>\n",
       "      <th>MonthN</th>\n",
       "      <th>YearN</th>\n",
       "      <th>Time</th>\n",
       "      <th>Yield red (%)</th>\n",
       "      <th>Mon-Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>2378.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.04</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>96</td>\n",
       "      <td>May-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>2397.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.06</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-02</td>\n",
       "      <td>95</td>\n",
       "      <td>May-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>2416.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.08</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>93</td>\n",
       "      <td>May-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>2433.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>91</td>\n",
       "      <td>May-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>2453.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-05</td>\n",
       "      <td>88</td>\n",
       "      <td>May-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Day  Month    Year  Rain mm  ETo mm  GD °C.day  CO2 ppm  Irri mm  \\\n",
       "430  1.0    5.0  2020.0    304.0   440.0     2378.0   412.21      0.0   \n",
       "431  2.0    5.0  2020.0    322.0   443.0     2397.0   412.21      0.0   \n",
       "432  3.0    5.0  2020.0    322.0   447.0     2416.0   412.21      0.0   \n",
       "433  4.0    5.0  2020.0    323.0   451.0     2433.0   412.21      0.0   \n",
       "434  5.0    5.0  2020.0    323.0   453.0     2453.0   412.21      0.0   \n",
       "\n",
       "     Infilt mm  Runoff mm  ...  Brelative %  HI %  Yield ton/ha  WPet kg/m3  \\\n",
       "430      255.0       48.0  ...         43.0   2.4         0.051        0.04   \n",
       "431      270.0       52.0  ...         42.0   3.2         0.071        0.06   \n",
       "432      270.0       52.0  ...         42.0   4.2         0.098        0.08   \n",
       "433      271.0       52.0  ...         42.0   5.5         0.133        0.10   \n",
       "434      271.0       52.0  ...         42.0   7.0         0.179        0.13   \n",
       "\n",
       "     DayN   MonthN   YearN        Time  Yield red (%)  Mon-Day  \n",
       "430   29.0      7.0  2020.0 2020-05-01             96   May-01  \n",
       "431   30.0      7.0  2020.0 2020-05-02             95   May-02  \n",
       "432   31.0      7.0  2020.0 2020-05-03             93   May-03  \n",
       "433    1.0      8.0  2020.0 2020-05-04             91   May-04  \n",
       "434    2.0      8.0  2020.0 2020-05-05             88   May-05  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C2020.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Single simulation (Yield, WP_ET)\n",
    "\n",
    "fig1, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "#Yield ton/ha\n",
    "sctr = ax.scatter( C1['Time'], C1['Yield ton/ha'], label='May')# c=C['Month'],cmap='RdYlGn')\n",
    "sctr = ax.scatter( C2['Time'], C2['Yield ton/ha'], label='June')\n",
    "sctr = ax.scatter( C3['Time'], C3['Yield ton/ha'], label='July')\n",
    "sctr = ax.scatter( C4['Time'], C4['Yield ton/ha'], label='August')\n",
    "sctr = ax.scatter( C5['Time'], C5['Yield ton/ha'], label='September')\n",
    "sctr = ax.scatter( C6['Time'], C6['Yield ton/ha'], label='October')\n",
    "sctr = ax.scatter( C7['Time'], C7['Yield ton/ha'], label='November')\n",
    "# sctr = ax.scatter( C8['Time'], C8['Yield ton/ha'], label='December')\n",
    "\n",
    "\n",
    "\n",
    "ax.set_title('90-Maize Yield variation - Oussoubidiagna')\n",
    "ax.set_ylabel('Yied (Ton/ha)')\n",
    "ax.set_xlabel('Sowing date ')\n",
    "\n",
    "# ax.set_ylim([1.2,1.75])\n",
    "\n",
    "\n",
    "# plt.legend()\n",
    "\n",
    "#plt.colorbar(sctr, ax=ax, )\n",
    "\n",
    "# plt.savefig(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Oussoubidiagna\\Results\\Yield_seasonal_variation.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['May-01', 'May-27', 'Jun-23', 'Jul-20', 'Aug-16', 'Sep-12', 'Oct-09',\n",
       "       'Nov-05', 'Dec-02'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.date_range(start='5/1/2019', end='12/2/2019', periods=9) \n",
    "p = p.strftime('%b-%d') \n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only for Unimaid \n",
    "\n",
    "# C2020 = C2018.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Single simulation (Yield, WP_ET)\n",
    "\n",
    "fig1, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "#Yield ton/ha\n",
    "# sctr = ax.scatter( C2016['Mon-Day'], C2016['Yield ton/ha'], label='2016', marker = 's', color = 'blue', s=80, facecolors='none',)\n",
    "# sctr = ax.scatter( C2017['Mon-Day'], C2017['Yield ton/ha'], label='2017',  color = 'black', s=80)\n",
    "sctr = ax.scatter( C2018['Mon-Day'], C2018['Yield ton/ha'], label='2018', marker = 'x', color = 'dimgray', s=80)\n",
    "sctr = ax.scatter( C2019['Mon-Day'], C2019['Yield ton/ha'], label='2019', marker = 'v' , color = 'black', s=80,  facecolors='none',)\n",
    "sctr = ax.scatter( C2020['Mon-Day'], C2020['Yield ton/ha'], label='2020',  marker = '*',  color = 'blue', s=80)\n",
    "\n",
    "# Change year\n",
    "# sctr = ax.scatter( C2018['Mon-Day'], C2018['Yield ton/ha'], label='2020', marker = '*',  color = 'blue', s=80)\n",
    "# sctr = ax.scatter( C2018['Mon-Day'], C2018['Yield ton/ha'], label='2017', marker = 'x', color = 'dimgray', s=80)\n",
    "\n",
    "ax.set_xticks(p,)\n",
    "\n",
    "ax.set_title('90-Maize Yield variation - Oussoubidiagna', fontsize = 30, fontweight='bold')\n",
    "ax.set_ylabel('Yied (t/ha)', fontsize = 30, fontweight='bold')\n",
    "ax.set_xlabel('Sowing date ', fontsize = 30, fontweight='bold')\n",
    "\n",
    "ax.set_ylim([0.0,1.8])\n",
    "\n",
    "\n",
    "plt.legend(loc='lower right', fontsize = 30)\n",
    "\n",
    "ax.text(217, 1.7, r'a', fontsize=30)\n",
    "\n",
    "#plt.colorbar(sctr, ax=ax, )\n",
    "# \n",
    "# plt.savefig(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Oussoubidiagna\\Results\\Yield_variation4.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar plot of yield response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Tamale\\Results\\Yiel_variation_Tamale.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = B.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {
    "scrolled": false
   },
   "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>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rain mm</th>\n",
       "      <th>ETo mm</th>\n",
       "      <th>GD °C.day</th>\n",
       "      <th>CO2 ppm</th>\n",
       "      <th>Irri mm</th>\n",
       "      <th>Infilt mm</th>\n",
       "      <th>Runoff mm</th>\n",
       "      <th>...</th>\n",
       "      <th>BioMass ton/ha</th>\n",
       "      <th>Brelative %</th>\n",
       "      <th>HI %</th>\n",
       "      <th>Yield ton/ha</th>\n",
       "      <th>WPet kg/m3</th>\n",
       "      <th>DayN</th>\n",
       "      <th>MonthN</th>\n",
       "      <th>YearN</th>\n",
       "      <th>Time</th>\n",
       "      <th>Yield red (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>2020</td>\n",
       "      <td>297</td>\n",
       "      <td>1198</td>\n",
       "      <td>5931</td>\n",
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       "      <td>0</td>\n",
       "      <td>266</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>3.540</td>\n",
       "      <td>29</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.515</td>\n",
       "      <td>2.17</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>2021</td>\n",
       "      <td>11/26/2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>2020</td>\n",
       "      <td>297</td>\n",
       "      <td>1202</td>\n",
       "      <td>5952</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0</td>\n",
       "      <td>266</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>3.546</td>\n",
       "      <td>29</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.518</td>\n",
       "      <td>2.17</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2021</td>\n",
       "      <td>11/27/2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>2020</td>\n",
       "      <td>297</td>\n",
       "      <td>1207</td>\n",
       "      <td>5971</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0</td>\n",
       "      <td>266</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>3.539</td>\n",
       "      <td>29</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.515</td>\n",
       "      <td>2.17</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>2021</td>\n",
       "      <td>11/28/2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>29</td>\n",
       "      <td>11</td>\n",
       "      <td>2020</td>\n",
       "      <td>297</td>\n",
       "      <td>1211</td>\n",
       "      <td>5989</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0</td>\n",
       "      <td>266</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>3.532</td>\n",
       "      <td>29</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.512</td>\n",
       "      <td>2.17</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>2021</td>\n",
       "      <td>11/29/2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>2020</td>\n",
       "      <td>297</td>\n",
       "      <td>1216</td>\n",
       "      <td>6008</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0</td>\n",
       "      <td>266</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>3.524</td>\n",
       "      <td>29</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.508</td>\n",
       "      <td>2.17</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>2021</td>\n",
       "      <td>11/30/2020</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Day  Month  Year  Rain mm  ETo mm  GD °C.day  CO2 ppm  Irri mm  \\\n",
       "406   26     11  2020      297    1198       5931   413.21        0   \n",
       "407   27     11  2020      297    1202       5952   413.21        0   \n",
       "408   28     11  2020      297    1207       5971   413.21        0   \n",
       "409   29     11  2020      297    1211       5989   413.21        0   \n",
       "410   30     11  2020      297    1216       6008   413.21        0   \n",
       "\n",
       "     Infilt mm  Runoff mm  ...  BioMass ton/ha  Brelative %  HI %  \\\n",
       "406        266         30  ...           3.540           29  42.8   \n",
       "407        266         30  ...           3.546           29  42.8   \n",
       "408        266         30  ...           3.539           29  42.8   \n",
       "409        266         30  ...           3.532           29  42.8   \n",
       "410        266         30  ...           3.524           29  42.8   \n",
       "\n",
       "     Yield ton/ha  WPet kg/m3  DayN   MonthN   YearN         Time  \\\n",
       "406         1.515        2.17     23        2    2021  11/26/2020   \n",
       "407         1.518        2.17     24        2    2021  11/27/2020   \n",
       "408         1.515        2.17     25        2    2021  11/28/2020   \n",
       "409         1.512        2.17     26        2    2021  11/29/2020   \n",
       "410         1.508        2.17     27        2    2021  11/30/2020   \n",
       "\n",
       "     Yield red (%)  \n",
       "406              7  \n",
       "407              7  \n",
       "408              7  \n",
       "409              7  \n",
       "410              8  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [],
   "source": [
    "B['Month'] = B['Month'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [],
   "source": [
    "C.iloc[:,:36] = C.iloc[:,:36].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arrange column Time before converting it into Mon-Day\n",
    "\n",
    "for i in range(len(C)):\n",
    "    C.at[i,'Time'] = datetime.strptime(C.at[i,'Time'], '%m/%d/%Y')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "C['Station'] = 'Tam'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rain mm</th>\n",
       "      <th>ETo mm</th>\n",
       "      <th>GD °C.day</th>\n",
       "      <th>CO2 ppm</th>\n",
       "      <th>Irri mm</th>\n",
       "      <th>Infilt mm</th>\n",
       "      <th>Runoff mm</th>\n",
       "      <th>...</th>\n",
       "      <th>Brelative %</th>\n",
       "      <th>HI %</th>\n",
       "      <th>Yield ton/ha</th>\n",
       "      <th>WPet kg/m3</th>\n",
       "      <th>DayN</th>\n",
       "      <th>MonthN</th>\n",
       "      <th>YearN</th>\n",
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       "      <th>Station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>2264.0</td>\n",
       "      <td>410.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>1.512</td>\n",
       "      <td>0.58</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019-05-01 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>Tam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>2282.0</td>\n",
       "      <td>410.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>1.511</td>\n",
       "      <td>0.57</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019-05-02 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>Tam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>410.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>1.511</td>\n",
       "      <td>0.57</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019-05-03 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>Tam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>2318.0</td>\n",
       "      <td>410.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>1.513</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019-05-04 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>Tam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>2334.0</td>\n",
       "      <td>410.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>1.520</td>\n",
       "      <td>0.58</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019-05-05 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>Tam</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Day  Month    Year  Rain mm  ETo mm  GD °C.day  CO2 ppm  Irri mm  \\\n",
       "0  1.0    5.0  2019.0    416.0   480.0     2264.0   410.21      0.0   \n",
       "1  2.0    5.0  2019.0    416.0   483.0     2282.0   410.21      0.0   \n",
       "2  3.0    5.0  2019.0    416.0   486.0     2300.0   410.21      0.0   \n",
       "3  4.0    5.0  2019.0    418.0   491.0     2318.0   410.21      0.0   \n",
       "4  5.0    5.0  2019.0    437.0   493.0     2334.0   410.21      0.0   \n",
       "\n",
       "   Infilt mm  Runoff mm  ...  Brelative %  HI %  Yield ton/ha  WPet kg/m3  \\\n",
       "0      400.0       16.0  ...         31.0  40.1         1.512        0.58   \n",
       "1      400.0       16.0  ...         31.0  40.1         1.511        0.57   \n",
       "2      400.0       16.0  ...         31.0  40.1         1.511        0.57   \n",
       "3      402.0       16.0  ...         31.0  40.1         1.513        0.58   \n",
       "4      420.0       18.0  ...         32.0  40.1         1.520        0.58   \n",
       "\n",
       "   DayN   MonthN   YearN                  Time  Yield red (%)  Station  \n",
       "0   29.0      7.0  2019.0  2019-05-01 00:00:00              7      Tam  \n",
       "1   30.0      7.0  2019.0  2019-05-02 00:00:00              7      Tam  \n",
       "2   31.0      7.0  2019.0  2019-05-03 00:00:00              7      Tam  \n",
       "3    1.0      8.0  2019.0  2019-05-04 00:00:00              7      Tam  \n",
       "4    2.0      8.0  2019.0  2019-05-05 00:00:00              7      Tam  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [],
   "source": [
    "C['Mon-Day'] = C['Time'].dt.strftime('%b-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "C  = C.loc[C['Year']==2020]\n",
    "Tam = C.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [],
   "source": [
    "# C1 = C.loc[C['Time']<=datetime(2019, 6, 15, 0, 0)]\n",
    "# C2 = C.loc[(C['Time']>=datetime(2019, 6, 15, 0, 0)) & (C['Time']<=datetime(2019, 9, 30, 0, 0))]\n",
    "# C3 = C.loc[C['Time']>=datetime(2019, 9, 30, 0, 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [],
   "source": [
    "C1 = C.loc[C['Time']<=datetime(2020, 7, 29, 0, 0)]\n",
    "C2 = C.loc[(C['Time']>=datetime(2020, 7, 29, 0, 0)) & (C['Time']<=datetime(2020, 11, 30, 0, 0))]\n",
    "C3 = C.loc[C['Time']>=datetime(2020, 11, 30, 0, 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Far1 , Far2, Far3  = Far.loc[Far['Time']<=datetime(2019, 5, 21, 0, 0)] , Far.loc[(Far['Time']>=datetime(2019, 5, 21, 0, 0)) &\n",
    "#                             (Far['Time']<=datetime(2019, 9, 19, 0, 0))] , Far.loc[Far['Time']>=datetime(2019, 11, 30, 0, 0)] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "Tam1 , Tam2, Tam3  = C1.copy() , C2.copy(), C3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['May-01', 'May-27', 'Jun-23', 'Jul-20', 'Aug-16', 'Sep-12', 'Oct-09',\n",
       "       'Nov-05', 'Dec-02'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = pd.date_range(start='5/1/2019', end='12/02/2019', periods=9) \n",
    "q = q.strftime('%b-%d') \n",
    "\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {
    "scrolled": true
   },
   "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>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rain mm</th>\n",
       "      <th>ETo mm</th>\n",
       "      <th>GD °C.day</th>\n",
       "      <th>CO2 ppm</th>\n",
       "      <th>Irri mm</th>\n",
       "      <th>Infilt mm</th>\n",
       "      <th>Runoff mm</th>\n",
       "      <th>...</th>\n",
       "      <th>HI %</th>\n",
       "      <th>Yield ton/ha</th>\n",
       "      <th>WPet kg/m3</th>\n",
       "      <th>DayN</th>\n",
       "      <th>MonthN</th>\n",
       "      <th>YearN</th>\n",
       "      <th>Time</th>\n",
       "      <th>Yield red (%)</th>\n",
       "      <th>Station</th>\n",
       "      <th>Mon-Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>452.0</td>\n",
       "      <td>2243.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-01 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>2261.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-02 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-03 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>2294.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-04 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2312.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-05 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>2329.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-06 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>2347.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-07 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>2365.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-08 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-09 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>2399.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-10 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>2416.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-11 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>2434.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-12 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>2452.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-13 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>2469.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-14 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2487.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-15 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>2505.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-16 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>502.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-17 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>503.0</td>\n",
       "      <td>2536.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-18 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-19 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>2573.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-20 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>513.0</td>\n",
       "      <td>2589.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-21 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>22.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>2606.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-22 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>521.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-23 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>524.0</td>\n",
       "      <td>2643.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-24 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>2660.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-25 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>26.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>2679.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-26 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>27.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>2696.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-27 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>28.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>2714.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-28 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>29.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>2733.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-29 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>542.0</td>\n",
       "      <td>2749.0</td>\n",
       "      <td>412.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-05-30 00:00:00</td>\n",
       "      <td>100</td>\n",
       "      <td>Tam</td>\n",
       "      <td>May-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>31.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>5474.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.568</td>\n",
       "      <td>2.10</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-10-31 00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Oct-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1093.0</td>\n",
       "      <td>5491.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.566</td>\n",
       "      <td>2.11</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-01 00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1097.0</td>\n",
       "      <td>5508.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.565</td>\n",
       "      <td>2.12</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-02 00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>5544.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.564</td>\n",
       "      <td>2.14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-04 00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>5560.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.563</td>\n",
       "      <td>2.14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-05 00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>5576.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.561</td>\n",
       "      <td>2.14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-06 00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>5592.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.562</td>\n",
       "      <td>2.15</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-07 00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>5608.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.557</td>\n",
       "      <td>2.15</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-08 00:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1128.0</td>\n",
       "      <td>5625.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.555</td>\n",
       "      <td>2.16</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-09 00:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1132.0</td>\n",
       "      <td>5641.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.556</td>\n",
       "      <td>2.17</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-10 00:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1137.0</td>\n",
       "      <td>5657.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.552</td>\n",
       "      <td>2.17</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-11 00:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1141.0</td>\n",
       "      <td>5674.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.554</td>\n",
       "      <td>2.18</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-12 00:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1144.0</td>\n",
       "      <td>5692.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.549</td>\n",
       "      <td>2.18</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-13 00:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>5713.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.550</td>\n",
       "      <td>2.18</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-14 00:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1151.0</td>\n",
       "      <td>5732.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.549</td>\n",
       "      <td>2.18</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-15 00:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1155.0</td>\n",
       "      <td>5749.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.543</td>\n",
       "      <td>2.17</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-16 00:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1159.0</td>\n",
       "      <td>5766.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.541</td>\n",
       "      <td>2.17</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-17 00:00:00</td>\n",
       "      <td>6</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1164.0</td>\n",
       "      <td>5782.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.536</td>\n",
       "      <td>2.17</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-18 00:00:00</td>\n",
       "      <td>6</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1169.0</td>\n",
       "      <td>5799.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.531</td>\n",
       "      <td>2.17</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-19 00:00:00</td>\n",
       "      <td>6</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1173.0</td>\n",
       "      <td>5817.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.529</td>\n",
       "      <td>2.17</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-20 00:00:00</td>\n",
       "      <td>6</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>21.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1177.0</td>\n",
       "      <td>5835.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.526</td>\n",
       "      <td>2.17</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-21 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1181.0</td>\n",
       "      <td>5855.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.522</td>\n",
       "      <td>2.17</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-22 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>5874.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.519</td>\n",
       "      <td>2.17</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-23 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>24.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>5893.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.517</td>\n",
       "      <td>2.16</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-24 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>25.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1194.0</td>\n",
       "      <td>5912.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.518</td>\n",
       "      <td>2.17</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-25 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>26.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1198.0</td>\n",
       "      <td>5931.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.515</td>\n",
       "      <td>2.17</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-26 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>27.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>5952.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.518</td>\n",
       "      <td>2.17</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-27 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>28.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1207.0</td>\n",
       "      <td>5971.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.515</td>\n",
       "      <td>2.17</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-28 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>29.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1211.0</td>\n",
       "      <td>5989.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.512</td>\n",
       "      <td>2.17</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-29 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>30.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>6008.0</td>\n",
       "      <td>413.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.508</td>\n",
       "      <td>2.17</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2020-11-30 00:00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>Tam</td>\n",
       "      <td>Nov-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Day  Month    Year  Rain mm  ETo mm  GD °C.day  CO2 ppm  Irri mm  \\\n",
       "206   1.0    5.0  2020.0     51.0   452.0     2243.0   412.21      0.0   \n",
       "207   2.0    5.0  2020.0     51.0   456.0     2261.0   412.21      0.0   \n",
       "208   3.0    5.0  2020.0     51.0   458.0     2277.0   412.21      0.0   \n",
       "209   4.0    5.0  2020.0     51.0   460.0     2294.0   412.21      0.0   \n",
       "210   5.0    5.0  2020.0     51.0   462.0     2312.0   412.21      0.0   \n",
       "211   6.0    5.0  2020.0     51.0   466.0     2329.0   412.21      0.0   \n",
       "212   7.0    5.0  2020.0     51.0   470.0     2347.0   412.21      0.0   \n",
       "213   8.0    5.0  2020.0     51.0   475.0     2365.0   412.21      0.0   \n",
       "214   9.0    5.0  2020.0     51.0   479.0     2382.0   412.21      0.0   \n",
       "215  10.0    5.0  2020.0     51.0   482.0     2399.0   412.21      0.0   \n",
       "216  11.0    5.0  2020.0     51.0   484.0     2416.0   412.21      0.0   \n",
       "217  12.0    5.0  2020.0     51.0   488.0     2434.0   412.21      0.0   \n",
       "218  13.0    5.0  2020.0     51.0   491.0     2452.0   412.21      0.0   \n",
       "219  14.0    5.0  2020.0     52.0   493.0     2469.0   412.21      0.0   \n",
       "220  15.0    5.0  2020.0     52.0   496.0     2487.0   412.21      0.0   \n",
       "221  16.0    5.0  2020.0     52.0   501.0     2505.0   412.21      0.0   \n",
       "222  17.0    5.0  2020.0    125.0   502.0     2520.0   412.21      0.0   \n",
       "223  18.0    5.0  2020.0    163.0   503.0     2536.0   412.21      0.0   \n",
       "224  19.0    5.0  2020.0    163.0   507.0     2555.0   412.21      0.0   \n",
       "225  20.0    5.0  2020.0    163.0   512.0     2573.0   412.21      0.0   \n",
       "226  21.0    5.0  2020.0    169.0   513.0     2589.0   412.21      0.0   \n",
       "227  22.0    5.0  2020.0    169.0   516.0     2606.0   412.21      0.0   \n",
       "228  23.0    5.0  2020.0    169.0   521.0     2625.0   412.21      0.0   \n",
       "229  24.0    5.0  2020.0    169.0   524.0     2643.0   412.21      0.0   \n",
       "230  25.0    5.0  2020.0    169.0   528.0     2660.0   412.21      0.0   \n",
       "231  26.0    5.0  2020.0    169.0   532.0     2679.0   412.21      0.0   \n",
       "232  27.0    5.0  2020.0    169.0   534.0     2696.0   412.21      0.0   \n",
       "233  28.0    5.0  2020.0    169.0   537.0     2714.0   412.21      0.0   \n",
       "234  29.0    5.0  2020.0    169.0   540.0     2733.0   412.21      0.0   \n",
       "235  30.0    5.0  2020.0    170.0   542.0     2749.0   412.21      0.0   \n",
       "..    ...    ...     ...      ...     ...        ...      ...      ...   \n",
       "381  31.0   10.0  2020.0    297.0  1089.0     5474.0   413.21      0.0   \n",
       "382   1.0   11.0  2020.0    297.0  1093.0     5491.0   413.21      0.0   \n",
       "383   2.0   11.0  2020.0    297.0  1097.0     5508.0   413.21      0.0   \n",
       "384   4.0   11.0  2020.0    297.0  1106.0     5544.0   413.21      0.0   \n",
       "385   5.0   11.0  2020.0    297.0  1110.0     5560.0   413.21      0.0   \n",
       "386   6.0   11.0  2020.0    297.0  1115.0     5576.0   413.21      0.0   \n",
       "387   7.0   11.0  2020.0    297.0  1120.0     5592.0   413.21      0.0   \n",
       "388   8.0   11.0  2020.0    297.0  1124.0     5608.0   413.21      0.0   \n",
       "389   9.0   11.0  2020.0    297.0  1128.0     5625.0   413.21      0.0   \n",
       "390  10.0   11.0  2020.0    297.0  1132.0     5641.0   413.21      0.0   \n",
       "391  11.0   11.0  2020.0    297.0  1137.0     5657.0   413.21      0.0   \n",
       "392  12.0   11.0  2020.0    297.0  1141.0     5674.0   413.21      0.0   \n",
       "393  13.0   11.0  2020.0    297.0  1144.0     5692.0   413.21      0.0   \n",
       "394  14.0   11.0  2020.0    297.0  1148.0     5713.0   413.21      0.0   \n",
       "395  15.0   11.0  2020.0    297.0  1151.0     5732.0   413.21      0.0   \n",
       "396  16.0   11.0  2020.0    297.0  1155.0     5749.0   413.21      0.0   \n",
       "397  17.0   11.0  2020.0    297.0  1159.0     5766.0   413.21      0.0   \n",
       "398  18.0   11.0  2020.0    297.0  1164.0     5782.0   413.21      0.0   \n",
       "399  19.0   11.0  2020.0    297.0  1169.0     5799.0   413.21      0.0   \n",
       "400  20.0   11.0  2020.0    297.0  1173.0     5817.0   413.21      0.0   \n",
       "401  21.0   11.0  2020.0    297.0  1177.0     5835.0   413.21      0.0   \n",
       "402  22.0   11.0  2020.0    297.0  1181.0     5855.0   413.21      0.0   \n",
       "403  23.0   11.0  2020.0    297.0  1185.0     5874.0   413.21      0.0   \n",
       "404  24.0   11.0  2020.0    297.0  1190.0     5893.0   413.21      0.0   \n",
       "405  25.0   11.0  2020.0    297.0  1194.0     5912.0   413.21      0.0   \n",
       "406  26.0   11.0  2020.0    297.0  1198.0     5931.0   413.21      0.0   \n",
       "407  27.0   11.0  2020.0    297.0  1202.0     5952.0   413.21      0.0   \n",
       "408  28.0   11.0  2020.0    297.0  1207.0     5971.0   413.21      0.0   \n",
       "409  29.0   11.0  2020.0    297.0  1211.0     5989.0   413.21      0.0   \n",
       "410  30.0   11.0  2020.0    297.0  1216.0     6008.0   413.21      0.0   \n",
       "\n",
       "     Infilt mm  Runoff mm  ...  HI %  Yield ton/ha  WPet kg/m3  DayN   \\\n",
       "206       51.0        0.0  ...  -9.9         0.000        0.00   29.0   \n",
       "207       51.0        0.0  ...  -9.9         0.000        0.00   30.0   \n",
       "208       51.0        0.0  ...  -9.9         0.000        0.00   31.0   \n",
       "209       51.0        0.0  ...  -9.9         0.000        0.00    1.0   \n",
       "210       51.0        0.0  ...  -9.9         0.000        0.00    2.0   \n",
       "211       51.0        0.0  ...  -9.9         0.000        0.00    3.0   \n",
       "212       51.0        0.0  ...  -9.9         0.000        0.00    4.0   \n",
       "213       51.0        0.0  ...  -9.9         0.000        0.00    5.0   \n",
       "214       51.0        0.0  ...  -9.9         0.000        0.00    6.0   \n",
       "215       51.0        0.0  ...  -9.9         0.000        0.00    7.0   \n",
       "216       51.0        0.0  ...  -9.9         0.000        0.00    8.0   \n",
       "217       51.0        0.0  ...  -9.9         0.000        0.00    9.0   \n",
       "218       51.0        0.0  ...  -9.9         0.000        0.00   10.0   \n",
       "219       52.0        0.0  ...  -9.9         0.000        0.00   11.0   \n",
       "220       52.0        0.0  ...  -9.9         0.000        0.00   12.0   \n",
       "221       52.0        0.0  ...  -9.9         0.000        0.00   13.0   \n",
       "222      120.0        6.0  ...  -9.9         0.000        0.00   14.0   \n",
       "223      144.0       19.0  ...  -9.9         0.000        0.00   15.0   \n",
       "224      144.0       19.0  ...  -9.9         0.000        0.00   16.0   \n",
       "225      144.0       19.0  ...  -9.9         0.000        0.00   17.0   \n",
       "226      149.0       19.0  ...  -9.9         0.000        0.00   18.0   \n",
       "227      149.0       19.0  ...  -9.9         0.000        0.00   19.0   \n",
       "228      149.0       19.0  ...  -9.9         0.000        0.00   20.0   \n",
       "229      149.0       19.0  ...  -9.9         0.000        0.00   21.0   \n",
       "230      149.0       19.0  ...  -9.9         0.000        0.00   22.0   \n",
       "231      149.0       19.0  ...  -9.9         0.000        0.00   23.0   \n",
       "232      149.0       19.0  ...  -9.9         0.000        0.00   24.0   \n",
       "233      149.0       19.0  ...  -9.9         0.000        0.00   25.0   \n",
       "234      149.0       19.0  ...  -9.9         0.000        0.00   26.0   \n",
       "235      150.0       19.0  ...  -9.9         0.000        0.00   27.0   \n",
       "..         ...        ...  ...   ...           ...         ...    ...   \n",
       "381      266.0       30.0  ...  42.8         1.568        2.10   28.0   \n",
       "382      266.0       30.0  ...  42.8         1.566        2.11   29.0   \n",
       "383      266.0       30.0  ...  42.8         1.565        2.12   30.0   \n",
       "384      266.0       30.0  ...  42.8         1.564        2.14    1.0   \n",
       "385      266.0       30.0  ...  42.8         1.563        2.14    2.0   \n",
       "386      266.0       30.0  ...  42.8         1.561        2.14    3.0   \n",
       "387      266.0       30.0  ...  42.8         1.562        2.15    4.0   \n",
       "388      266.0       30.0  ...  42.8         1.557        2.15    5.0   \n",
       "389      266.0       30.0  ...  42.8         1.555        2.16    6.0   \n",
       "390      266.0       30.0  ...  42.8         1.556        2.17    7.0   \n",
       "391      266.0       30.0  ...  42.8         1.552        2.17    8.0   \n",
       "392      266.0       30.0  ...  42.8         1.554        2.18    9.0   \n",
       "393      266.0       30.0  ...  42.8         1.549        2.18   10.0   \n",
       "394      266.0       30.0  ...  42.8         1.550        2.18   11.0   \n",
       "395      266.0       30.0  ...  42.8         1.549        2.18   12.0   \n",
       "396      266.0       30.0  ...  42.8         1.543        2.17   13.0   \n",
       "397      266.0       30.0  ...  42.8         1.541        2.17   14.0   \n",
       "398      266.0       30.0  ...  42.8         1.536        2.17   15.0   \n",
       "399      266.0       30.0  ...  42.8         1.531        2.17   16.0   \n",
       "400      266.0       30.0  ...  42.8         1.529        2.17   17.0   \n",
       "401      266.0       30.0  ...  42.8         1.526        2.17   18.0   \n",
       "402      266.0       30.0  ...  42.8         1.522        2.17   19.0   \n",
       "403      266.0       30.0  ...  42.8         1.519        2.17   20.0   \n",
       "404      266.0       30.0  ...  42.8         1.517        2.16   21.0   \n",
       "405      266.0       30.0  ...  42.8         1.518        2.17   22.0   \n",
       "406      266.0       30.0  ...  42.8         1.515        2.17   23.0   \n",
       "407      266.0       30.0  ...  42.8         1.518        2.17   24.0   \n",
       "408      266.0       30.0  ...  42.8         1.515        2.17   25.0   \n",
       "409      266.0       30.0  ...  42.8         1.512        2.17   26.0   \n",
       "410      266.0       30.0  ...  42.8         1.508        2.17   27.0   \n",
       "\n",
       "     MonthN   YearN                  Time  Yield red (%)  Station  Mon-Day  \n",
       "206      7.0  2020.0  2020-05-01 00:00:00            100      Tam   May-01  \n",
       "207      7.0  2020.0  2020-05-02 00:00:00            100      Tam   May-02  \n",
       "208      7.0  2020.0  2020-05-03 00:00:00            100      Tam   May-03  \n",
       "209      8.0  2020.0  2020-05-04 00:00:00            100      Tam   May-04  \n",
       "210      8.0  2020.0  2020-05-05 00:00:00            100      Tam   May-05  \n",
       "211      8.0  2020.0  2020-05-06 00:00:00            100      Tam   May-06  \n",
       "212      8.0  2020.0  2020-05-07 00:00:00            100      Tam   May-07  \n",
       "213      8.0  2020.0  2020-05-08 00:00:00            100      Tam   May-08  \n",
       "214      8.0  2020.0  2020-05-09 00:00:00            100      Tam   May-09  \n",
       "215      8.0  2020.0  2020-05-10 00:00:00            100      Tam   May-10  \n",
       "216      8.0  2020.0  2020-05-11 00:00:00            100      Tam   May-11  \n",
       "217      8.0  2020.0  2020-05-12 00:00:00            100      Tam   May-12  \n",
       "218      8.0  2020.0  2020-05-13 00:00:00            100      Tam   May-13  \n",
       "219      8.0  2020.0  2020-05-14 00:00:00            100      Tam   May-14  \n",
       "220      8.0  2020.0  2020-05-15 00:00:00            100      Tam   May-15  \n",
       "221      8.0  2020.0  2020-05-16 00:00:00            100      Tam   May-16  \n",
       "222      8.0  2020.0  2020-05-17 00:00:00            100      Tam   May-17  \n",
       "223      8.0  2020.0  2020-05-18 00:00:00            100      Tam   May-18  \n",
       "224      8.0  2020.0  2020-05-19 00:00:00            100      Tam   May-19  \n",
       "225      8.0  2020.0  2020-05-20 00:00:00            100      Tam   May-20  \n",
       "226      8.0  2020.0  2020-05-21 00:00:00            100      Tam   May-21  \n",
       "227      8.0  2020.0  2020-05-22 00:00:00            100      Tam   May-22  \n",
       "228      8.0  2020.0  2020-05-23 00:00:00            100      Tam   May-23  \n",
       "229      8.0  2020.0  2020-05-24 00:00:00            100      Tam   May-24  \n",
       "230      8.0  2020.0  2020-05-25 00:00:00            100      Tam   May-25  \n",
       "231      8.0  2020.0  2020-05-26 00:00:00            100      Tam   May-26  \n",
       "232      8.0  2020.0  2020-05-27 00:00:00            100      Tam   May-27  \n",
       "233      8.0  2020.0  2020-05-28 00:00:00            100      Tam   May-28  \n",
       "234      8.0  2020.0  2020-05-29 00:00:00            100      Tam   May-29  \n",
       "235      8.0  2020.0  2020-05-30 00:00:00            100      Tam   May-30  \n",
       "..       ...     ...                  ...            ...      ...      ...  \n",
       "381      1.0  2021.0  2020-10-31 00:00:00              4      Tam   Oct-31  \n",
       "382      1.0  2021.0  2020-11-01 00:00:00              4      Tam   Nov-01  \n",
       "383      1.0  2021.0  2020-11-02 00:00:00              4      Tam   Nov-02  \n",
       "384      2.0  2021.0  2020-11-04 00:00:00              4      Tam   Nov-04  \n",
       "385      2.0  2021.0  2020-11-05 00:00:00              4      Tam   Nov-05  \n",
       "386      2.0  2021.0  2020-11-06 00:00:00              4      Tam   Nov-06  \n",
       "387      2.0  2021.0  2020-11-07 00:00:00              4      Tam   Nov-07  \n",
       "388      2.0  2021.0  2020-11-08 00:00:00              5      Tam   Nov-08  \n",
       "389      2.0  2021.0  2020-11-09 00:00:00              5      Tam   Nov-09  \n",
       "390      2.0  2021.0  2020-11-10 00:00:00              5      Tam   Nov-10  \n",
       "391      2.0  2021.0  2020-11-11 00:00:00              5      Tam   Nov-11  \n",
       "392      2.0  2021.0  2020-11-12 00:00:00              5      Tam   Nov-12  \n",
       "393      2.0  2021.0  2020-11-13 00:00:00              5      Tam   Nov-13  \n",
       "394      2.0  2021.0  2020-11-14 00:00:00              5      Tam   Nov-14  \n",
       "395      2.0  2021.0  2020-11-15 00:00:00              5      Tam   Nov-15  \n",
       "396      2.0  2021.0  2020-11-16 00:00:00              5      Tam   Nov-16  \n",
       "397      2.0  2021.0  2020-11-17 00:00:00              6      Tam   Nov-17  \n",
       "398      2.0  2021.0  2020-11-18 00:00:00              6      Tam   Nov-18  \n",
       "399      2.0  2021.0  2020-11-19 00:00:00              6      Tam   Nov-19  \n",
       "400      2.0  2021.0  2020-11-20 00:00:00              6      Tam   Nov-20  \n",
       "401      2.0  2021.0  2020-11-21 00:00:00              7      Tam   Nov-21  \n",
       "402      2.0  2021.0  2020-11-22 00:00:00              7      Tam   Nov-22  \n",
       "403      2.0  2021.0  2020-11-23 00:00:00              7      Tam   Nov-23  \n",
       "404      2.0  2021.0  2020-11-24 00:00:00              7      Tam   Nov-24  \n",
       "405      2.0  2021.0  2020-11-25 00:00:00              7      Tam   Nov-25  \n",
       "406      2.0  2021.0  2020-11-26 00:00:00              7      Tam   Nov-26  \n",
       "407      2.0  2021.0  2020-11-27 00:00:00              7      Tam   Nov-27  \n",
       "408      2.0  2021.0  2020-11-28 00:00:00              7      Tam   Nov-28  \n",
       "409      2.0  2021.0  2020-11-29 00:00:00              7      Tam   Nov-29  \n",
       "410      2.0  2021.0  2020-11-30 00:00:00              8      Tam   Nov-30  \n",
       "\n",
       "[205 rows x 40 columns]"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tam#['Mon-Day']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Single simulation (Yield, WP_ET)\n",
    "\n",
    "fig1, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "#Yield ton/ha\n",
    "ax.bar( Tam1['Mon-Day'], Tam1['Yield ton/ha'],  color = 'gray')\n",
    "ax.bar( Tam2['Mon-Day'], Tam2['Yield ton/ha'], color = 'black')\n",
    "ax.bar( Tam3['Mon-Day'], Tam3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax.set_xticks(q,)\n",
    "\n",
    "ax.set_title('90-Maize Yield variation - Finkoloni', fontsize = 30, fontweight='bold')\n",
    "ax.set_ylabel('Yied (Ton/ha)', fontsize = 25, fontweight='bold')\n",
    "ax.set_xlabel('Sowing date ', fontsize = 25, fontweight='bold')\n",
    "\n",
    "ax.set_ylim([0.0,1.8])\n",
    "\n",
    "# plt.axhline(y=0.6, color='k', linestyle='--')\n",
    "ax.vlines('May-14',0,5, 'r')\n",
    "ax.vlines('May-20',0,5, 'b')\n",
    "\n",
    "# plt.legend(fontsize = 25)\n",
    "\n",
    "#plt.colorbar(sctr, ax=ax, )\n",
    "\n",
    "# plt.savefig(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Finkoloni\\Results\\Yield_var_bar.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Bog1 , Bog2, Bog3  = Bog.loc[Bog['Time']<=datetime(2018, 6, 30, 0, 0)] , Bog.loc[(Bog['Time']>=datetime(2018, 6, 30, 0, 0)) & \n",
    "                            (Bog['Time']<=datetime(2018, 9, 12, 0, 0))] , Bog.loc[Bog['Time']>=datetime(2018, 9, 12, 0, 0)] \n",
    "\n",
    "Bor1 , Bor2, Bor3  = Bor.loc[Bor['Time']<=datetime(2018, 6, 3, 0, 0)] , Bor.loc[(Bor['Time']>=datetime(2018, 6, 3, 0, 0)) & \n",
    "                            (Bor['Time']<=datetime(2018, 10, 24, 0, 0))] , Bor.loc[Bor['Time']>=datetime(2018, 10, 24, 0, 0)]  \n",
    "\n",
    "Daf1 , Daf2, Daf3  = Daf.loc[Daf['Time']<=datetime(2018, 5, 1, 0, 0)] , Daf.loc[(Daf['Time']>=datetime(2018, 5, 1, 0, 0)) &\n",
    "                            (Daf['Time']<=datetime(2018, 11, 30, 0, 0))] , Daf.loc[Daf['Time']>=datetime(2018, 11, 30, 0, 0)] \n",
    "\n",
    "Ded1 , Ded2, Ded3  = Ded.loc[Ded['Time']<=datetime(2018, 5, 18, 0, 0)] , Ded.loc[(Ded['Time']>=datetime(2018, 5, 18, 0, 0)) &\n",
    "                            (Ded['Time']<=datetime(2018, 10, 24, 0, 0))] , Ded.loc[Ded['Time']>=datetime(2018, 10, 24, 0, 0)] \n",
    "\n",
    "Dor1 , Dor2, Dor3  = Dor.loc[Dor['Time']<=datetime(2018, 6, 18, 0, 0)] , Dor.loc[(Dor['Time']>=datetime(2018, 6, 18, 0, 0)) &\n",
    "                            (Dor['Time']<=datetime(2018, 7, 11, 0, 0))] , Dor.loc[Dor['Time']>=datetime(2018, 7, 11, 0, 0)]  \n",
    "\n",
    "Far1 , Far2, Far3  = Far.loc[Far['Time']<=datetime(2018, 5, 5, 0, 0)] , Far.loc[(Far['Time']>=datetime(2018, 5, 5, 0, 0)) &\n",
    "                            (Far['Time']<=datetime(2018, 11, 23, 0, 0))] , Far.loc[Far['Time']>=datetime(2018, 11, 23, 0, 0)] \n",
    "\n",
    "Fin1 , Fin2, Fin3  = Fin.loc[Fin['Time']<=datetime(2018, 6, 13, 0, 0)] , Fin.loc[(Fin['Time']>=datetime(2018, 6, 13, 0, 0)) &\n",
    "                            (Fin['Time']<=datetime(2018, 8, 21, 0, 0))] , Fin.loc[Fin['Time']>=datetime(2018, 8, 21, 0, 0)] \n",
    "\n",
    "Gao1 , Gao2, Gao3  = Gao.loc[Gao['Time']<=datetime(2018, 5, 9, 0, 0)] , Gao.loc[(Gao['Time']>=datetime(2018, 5, 9, 0, 0)) & \n",
    "                            (Gao['Time']<=datetime(2018, 10, 16, 0, 0))] , Gao.loc[Gao['Time']>=datetime(2018, 10, 16, 0, 0)] \n",
    "\n",
    "Kou1 , Kou2, Kou3  = Kou.loc[Kou['Time']<=datetime(2018, 6, 1, 0, 0)] , Kou.loc[(Kou['Time']>=datetime(2018, 6, 1, 0, 0)) &\n",
    "                            (Kou['Time']<=datetime(2018, 11, 18, 0, 0))] , Kou.loc[Kou['Time']>=datetime(2018, 11, 18, 0, 0)] \n",
    "\n",
    "Man1 , Man2, Man3  = Man.loc[Man['Time']<=datetime(2018, 5, 1, 0, 0)] , Man.loc[(Man['Time']>=datetime(2018, 5, 1, 0, 0)) & \n",
    "                            (Man['Time']<=datetime(2018, 9, 10, 0, 0))] , Man.loc[Man['Time']>=datetime(2018, 9, 10, 0, 0)] \n",
    "\n",
    "Oua1 , Oua2, Oua3  = Oua.loc[Oua['Time']<=datetime(2018, 5, 10, 0, 0)] , Oua.loc[(Oua['Time']>=datetime(2018, 5, 10, 0, 0)) & \n",
    "                            (Oua['Time']<=datetime(2018, 10, 30, 0, 0))] , Oua.loc[Oua['Time']>=datetime(2018, 10, 30, 0, 0)] \n",
    "\n",
    "Ous1 , Ous2, Ous3  = Ous.loc[Ous['Time']<=datetime(2018, 6, 15, 0, 0)] , Ous.loc[(Ous['Time']>=datetime(2018, 6, 15, 0, 0)) & \n",
    "                            (Ous['Time']<=datetime(2018, 11, 30, 0, 0))] , Ous.loc[Ous['Time']>=datetime(2018, 11, 30, 0, 0)] \n",
    "\n",
    "Po1 , Po2, Po3  = Po.loc[Po['Time']<=datetime(2018, 5, 1, 0, 0)] , Po.loc[(Po['Time']>=datetime(2018, 5, 1, 0, 0)) & \n",
    "                            (Po['Time']<=datetime(2018, 10, 25, 0, 0))] , Po.loc[Po['Time']>=datetime(2018, 10, 25, 0, 0)] \n",
    "\n",
    "Pus1 , Pus2, Pus3  = Pus.loc[Pus['Time']<=datetime(2018, 5, 5, 0, 0)] , Pus.loc[(Pus['Time']>=datetime(2018, 5, 5, 0, 0)) & \n",
    "                            (Pus['Time']<=datetime(2018, 10, 4, 0, 0))] , Pus.loc[Pus['Time']>=datetime(2018, 10, 4, 0, 0)] \n",
    "\n",
    "# Sel1 , Sel2, Sel3  = Sel.loc[Sel['Time']<=datetime(2018, 5, 6, 0, 0)] , Sel.loc[(Sel['Time']>=datetime(2018, 5, 6, 0, 0)) &\n",
    "#                             (Sel['Time']<=datetime(2018, 5, 29, 0, 0))] , Sel.loc[Sel['Time']>=datetime(2018, 5, 29, 0, 0)] \n",
    "Sel1 , Sel2, Sel3  = Sel.loc[Sel['Time']<=datetime(2018, 5, 6, 0, 0)] , Sel.loc[(Sel['Time']>=datetime(2018, 5, 6, 0, 0)) &\n",
    "                            (Sel['Time']<=datetime(2018, 10, 15, 0, 0))] , Sel.loc[Sel['Time']>=datetime(2018, 10, 15, 0, 0)] \n",
    "\n",
    "Sir1 , Sir2, Sir3  = Sir.loc[Sir['Time']<=datetime(2018, 5, 18, 0, 0)] , Sir.loc[(Sir['Time']>=datetime(2018, 5, 18, 0, 0)) &\n",
    "                            (Sir['Time']<=datetime(2018, 11, 12, 0, 0))] , Sir.loc[Sir['Time']>=datetime(2018, 11, 12, 0, 0)] \n",
    "\n",
    "Som1 , Som2, Som3  = Som.loc[Som['Time']<=datetime(2018, 6, 25, 0, 0)] , Som.loc[(Som['Time']>=datetime(2018, 6, 25, 0, 0)) &\n",
    "                            (Som['Time']<=datetime(2018, 8, 7, 0, 0))] , Som.loc[Som['Time']>=datetime(2018, 8, 7, 0, 0)] \n",
    "\n",
    "Tam1 , Tam2, Tam3  = Tam.loc[Tam['Time']<=datetime(2018, 7, 29, 0, 0)] , Tam.loc[(Tam['Time']>=datetime(2018, 7, 29, 0, 0)) & \n",
    "                            (Tam['Time']<=datetime(2018, 11, 19, 0, 0))] , Tam.loc[Tam['Time']>=datetime(2018, 11, 19, 0, 0)] \n",
    "\n",
    "Tan1 , Tan2, Tan3  = Tan.loc[Tan['Time']<=datetime(2018, 5, 1, 0, 0)] , Tan.loc[(Tan['Time']>=datetime(2018, 5, 1, 0, 0)) & \n",
    "                            (Tan['Time']<=datetime(2018, 10, 14, 0, 0))] , Tan.loc[Tan['Time']>=datetime(2018, 10, 14, 0, 0)] \n",
    "\n",
    "Uni1 , Uni2, Uni3  = Uni.loc[Uni['Time']<=datetime(2018, 6, 14, 0, 0)] , Uni.loc[(Uni['Time']>=datetime(2018, 6, 14, 0, 0)) &\n",
    "                            (Uni['Time']<=datetime(2018, 7, 23, 0, 0))] , Uni.loc[Uni['Time']>=datetime(2018, 7, 23, 0, 0)] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x2952 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Single simulation (Yield, WP_ET)\n",
    "\n",
    "fig1, ((ax5, ax10) , (ax, ax7) , (ax14, ax9),  (ax18, ax4), (ax2, ax13), (ax11, ax12), (ax8, ax17),\n",
    "       (ax3, ax6),  ) = plt.subplots(nrows=8, ncols=2, figsize=(30, 41), )#sharex=True,) figsize=(20, 10)) (ax20, ax16),\n",
    "\n",
    "# Bog\n",
    "ax.bar( Bog1['Mon-Day'], Bog1['Yield ton/ha'],  color = 'gray')\n",
    "ax.bar( Bog2['Mon-Day'], Bog2['Yield ton/ha'], color = 'black')\n",
    "ax.bar( Bog3['Mon-Day'],Bog3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax.set_xticks(p,)\n",
    "\n",
    "ax.set_title('Bogande', fontsize = 20, fontweight='bold')\n",
    "ax.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax.set_ylim([0.0,1.8])\n",
    "\n",
    "ax.vlines('Jun-23',0,5, 'r',  linewidth=3)\n",
    "ax.vlines('Jun-23',0,5, 'b', ls='--', linewidth=3)\n",
    "ax.vlines('Jun-23',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Bor\n",
    "ax2.bar( Bor1['Mon-Day'], Bor1['Yield ton/ha'],  color = 'gray')\n",
    "ax2.bar( Bor2['Mon-Day'], Bor2['Yield ton/ha'], color = 'black')\n",
    "ax2.bar( Bor3['Mon-Day'],Bor3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax2.set_xticks(p,)\n",
    "\n",
    "ax2.set_title('Boromo', fontsize = 20, fontweight='bold')\n",
    "ax2.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax2.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax2.set_ylim([0.0,1.8])\n",
    "\n",
    "ax2.vlines('Jun-06',0,5, 'r',  linewidth=3)\n",
    "ax2.vlines('Jun-06',0,5, 'b', ls='--', linewidth=3)\n",
    "ax2.vlines('Jun-23',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Daf\n",
    "ax3.bar( Daf1['Mon-Day'], Daf1['Yield ton/ha'],  color = 'gray')\n",
    "ax3.bar( Daf2['Mon-Day'], Daf2['Yield ton/ha'], color = 'black')\n",
    "ax3.bar( Daf3['Mon-Day'],Daf3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax3.set_xticks(p,)\n",
    "\n",
    "ax3.set_title('Daffiama', fontsize = 20, fontweight='bold')\n",
    "ax3.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "ax3.set_xlabel('Sowing date ', fontsize = 20, fontweight='bold')\n",
    "\n",
    "ax3.set_ylim([0.0,1.8])\n",
    "\n",
    "ax3.vlines('May-10',0,5, 'r',  linewidth=3)\n",
    "ax3.vlines('May-10',0,5, 'b', ls='--', linewidth=3)\n",
    "ax3.vlines('May-10',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Ded\n",
    "ax4.bar( Ded1['Mon-Day'], Ded1['Yield ton/ha'],  color = 'gray')\n",
    "ax4.bar( Ded2['Mon-Day'], Ded2['Yield ton/ha'], color = 'black')\n",
    "ax4.bar( Ded3['Mon-Day'],Ded3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax4.set_xticks(p,)\n",
    "\n",
    "ax4.set_title('Dedougou', fontsize = 20, fontweight='bold')\n",
    "ax4.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax4.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax4.set_ylim([0.0,1.8])\n",
    "\n",
    "ax4.vlines('Jun-01',0,5, 'r',  linewidth=3)\n",
    "ax4.vlines('Jun-01',0,5, 'b', ls='--', linewidth=3)\n",
    "ax4.vlines('Jun-05',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Dor\n",
    "ax5.bar( Dor1['Mon-Day'], Dor1['Yield ton/ha'],  color = 'gray')\n",
    "ax5.bar( Dor2['Mon-Day'], Dor2['Yield ton/ha'], color = 'black')\n",
    "ax5.bar( Dor3['Mon-Day'],Dor3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax5.set_xticks(p,)\n",
    "\n",
    "ax5.set_title('Dori', fontsize = 20, fontweight='bold')\n",
    "ax5.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax5.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax5.set_ylim([0.0,1.8])\n",
    "\n",
    "ax5.vlines('May-28',0,5, 'r',  linewidth=3)\n",
    "ax5.vlines('May-28',0,5, 'b', ls='--', linewidth=3)\n",
    "ax5.vlines('Aug-03',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Gao\n",
    "ax6.bar( Gao1['Mon-Day'], Gao1['Yield ton/ha'],  color = 'gray')\n",
    "ax6.bar( Gao2['Mon-Day'], Gao2['Yield ton/ha'], color = 'black')\n",
    "ax6.bar( Gao3['Mon-Day'],Gao3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax6.set_xticks(p,)\n",
    "\n",
    "ax6.set_title('Gaoua', fontsize = 20, fontweight='bold')\n",
    "ax6.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "ax6.set_xlabel('Sowing date ', fontsize = 20, fontweight='bold')\n",
    "\n",
    "ax6.set_ylim([0.0,1.8])\n",
    "\n",
    "ax6.vlines('May-23',0,5, 'r',  linewidth=3)\n",
    "ax6.vlines('May-23',0,5, 'b', ls='--', linewidth=3)\n",
    "ax6.vlines('Jun-16',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Kou\n",
    "ax7.bar( Kou1['Mon-Day'], Kou1['Yield ton/ha'],  color = 'gray')\n",
    "ax7.bar( Kou2['Mon-Day'], Kou2['Yield ton/ha'], color = 'black')\n",
    "ax7.bar( Kou3['Mon-Day'],Kou3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax7.set_xticks(p,)\n",
    "\n",
    "ax7.set_title('Kourounikoto', fontsize = 20, fontweight='bold')\n",
    "ax7.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax7.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax7.set_ylim([0.0,1.8])\n",
    "\n",
    "ax7.vlines('Jun-12',0,5, 'r',  linewidth=3)\n",
    "ax7.vlines('Jun-30',0,5, 'b', ls='--', linewidth=3)\n",
    "ax7.vlines('Jul-06',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Man\n",
    "ax8.bar( Man1['Mon-Day'], Man1['Yield ton/ha'],  color = 'gray')\n",
    "ax8.bar( Man2['Mon-Day'], Man2['Yield ton/ha'], color = 'black')\n",
    "ax8.bar( Man3['Mon-Day'],Man3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax8.set_xticks(p,)\n",
    "\n",
    "ax8.set_title('Mandouri', fontsize = 20, fontweight='bold')\n",
    "ax8.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax8.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax8.set_ylim([0.0,1.8])\n",
    "\n",
    "ax8.vlines('May-17',0,5, 'r',  linewidth=3)\n",
    "ax8.vlines('Jun-23',0,5, 'b', ls='--', linewidth=3)\n",
    "ax8.vlines('May-24',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Oua\n",
    "ax9.bar( Oua1['Mon-Day'], Oua1['Yield ton/ha'],  color = 'gray')\n",
    "ax9.bar( Oua2['Mon-Day'], Oua2['Yield ton/ha'], color = 'black')\n",
    "ax9.bar( Oua3['Mon-Day'],Oua3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax9.set_xticks(p,)\n",
    "\n",
    "ax9.set_title('Ouahigouya', fontsize = 20, fontweight='bold')\n",
    "ax9.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax9.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax9.set_ylim([0.0,1.8])\n",
    "\n",
    "ax9.vlines('May-24',0,5, 'r',  linewidth=3)\n",
    "ax9.vlines('Jun-23',0,5, 'b', ls='--', linewidth=3)\n",
    "ax9.vlines('May-24',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Ous\n",
    "ax10.bar( Ous1['Mon-Day'], Ous1['Yield ton/ha'],  color = 'gray')\n",
    "ax10.bar( Ous2['Mon-Day'], Ous2['Yield ton/ha'], color = 'black')\n",
    "ax10.bar( Ous3['Mon-Day'],Ous3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax10.set_xticks(p,)\n",
    "\n",
    "ax10.set_title('Oussoubidiagna', fontsize = 20, fontweight='bold')\n",
    "ax10.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax10.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax10.set_ylim([0.0,1.8])\n",
    "\n",
    "ax10.vlines('Jul-01',0,5, 'r',  linewidth=3)\n",
    "ax10.vlines('Jul-01',0,5, 'b', ls='--', linewidth=3)\n",
    "ax10.vlines('Jul-01',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Po\n",
    "ax11.bar( Po1['Mon-Day'], Po1['Yield ton/ha'],  color = 'gray')\n",
    "ax11.bar( Po2['Mon-Day'], Po2['Yield ton/ha'], color = 'black')\n",
    "ax11.bar( Po3['Mon-Day'],Po3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax11.set_xticks(p,)\n",
    "\n",
    "ax11.set_title('Po', fontsize = 20, fontweight='bold')\n",
    "ax11.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax11.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax11.set_ylim([0.0,1.8])\n",
    "\n",
    "ax11.vlines('May-05',0,5, 'r',  linewidth=3)\n",
    "ax11.vlines('May-05',0,5, 'b', ls='--', linewidth=3)\n",
    "ax11.vlines('May-10',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Pus\n",
    "ax12.bar( Pus1['Mon-Day'], Pus1['Yield ton/ha'],  color = 'gray')\n",
    "ax12.bar( Pus2['Mon-Day'], Pus2['Yield ton/ha'], color = 'black')\n",
    "ax12.bar( Pus3['Mon-Day'],Pus3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax12.set_xticks(p,)\n",
    "\n",
    "ax12.set_title('Pusiga', fontsize = 20, fontweight='bold')\n",
    "ax12.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax12.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax12.set_ylim([0.0,1.8])\n",
    "\n",
    "ax12.vlines('May-28',0,5, 'r',  linewidth=3)\n",
    "ax12.vlines('Jul-24',0,5, 'b', ls='--', linewidth=3)\n",
    "ax12.vlines('Jun-01',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Sel\n",
    "ax13.bar( Sel1['Mon-Day'], Sel1['Yield ton/ha'],  color = 'gray')\n",
    "ax13.bar( Sel2['Mon-Day'], Sel2['Yield ton/ha'], color = 'black')\n",
    "ax13.bar( Sel3['Mon-Day'],Sel3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax13.set_xticks(p,)\n",
    "\n",
    "ax13.set_title('Selingue', fontsize = 20, fontweight='bold')\n",
    "ax13.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax13.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax13.set_ylim([0.0,1.8])\n",
    "\n",
    "ax13.vlines('May-20',0,5, 'r',  linewidth=3)\n",
    "ax13.vlines('May-20',0,5, 'b', ls='--', linewidth=3)\n",
    "ax13.vlines('May-25',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Sir\n",
    "ax14.bar( Sir1['Mon-Day'], Sir1['Yield ton/ha'],  color = 'gray')\n",
    "ax14.bar( Sir2['Mon-Day'], Sir2['Yield ton/ha'], color = 'black')\n",
    "ax14.bar( Sir3['Mon-Day'],Sir3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax14.set_xticks(p,)\n",
    "\n",
    "ax14.set_title('Sirakoro', fontsize = 20, fontweight='bold')\n",
    "ax14.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax14.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax14.set_ylim([0.0,1.8])\n",
    "\n",
    "ax14.vlines('May-28',0,5, 'r',  linewidth=3)\n",
    "ax14.vlines('May-28',0,5, 'b', ls='--', linewidth=3)\n",
    "ax14.vlines('Jun-27',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Tan\n",
    "ax17.bar( Tan1['Mon-Day'], Tan1['Yield ton/ha'],  color = 'gray')\n",
    "ax17.bar( Tan2['Mon-Day'], Tan2['Yield ton/ha'], color = 'black')\n",
    "ax17.bar( Tan3['Mon-Day'],Tan3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax17.set_xticks(p,)\n",
    "\n",
    "ax17.set_title('Tanguieta', fontsize = 20, fontweight='bold')\n",
    "ax17.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax17.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax17.set_ylim([0.0,1.8])\n",
    "\n",
    "ax17.vlines('May-14',0,5, 'r',  linewidth=3)\n",
    "ax17.vlines('May-14',0,5, 'b', ls='--', linewidth=3)\n",
    "ax17.vlines('May-14',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Far\n",
    "ax18.bar( Far1['Mon-Day'], Far1['Yield ton/ha'],  color = 'gray')\n",
    "ax18.bar( Far2['Mon-Day'], Far2['Yield ton/ha'], color = 'black')\n",
    "ax18.bar( Far3['Mon-Day'],Far3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax18.set_xticks(p,)\n",
    "\n",
    "ax18.set_title('Fari', fontsize = 20, fontweight='bold')\n",
    "ax18.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax18.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax18.set_ylim([0.0,1.8])\n",
    "\n",
    "ax18.vlines('May-18',0,5, 'r',  linewidth=3)\n",
    "ax18.vlines('May-18',0,5, 'b', ls='--', linewidth=3)\n",
    "ax18.vlines('May-15',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# plt.legend(fontsize = 12)\n",
    "\n",
    "#plt.colorbar(sctr, ax=ax, )\n",
    "\n",
    "\n",
    "plt.subplots_adjust(hspace=0.3)\n",
    "\n",
    "# plt.savefig(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\All_Yield_response184.png')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "Bog1 , Bog2, Bog3  = Bog.loc[Bog['Time']<=datetime(2019, 6, 7, 0, 0)] , Bog.loc[(Bog['Time']>=datetime(2019, 6, 7, 0, 0)) & \n",
    "                            (Bog['Time']<=datetime(2019, 10, 2, 0, 0))] , Bog.loc[Bog['Time']>=datetime(2019, 10, 2, 0, 0)] \n",
    "\n",
    "Bor1 , Bor2, Bor3  = Bor.loc[Bor['Time']<=datetime(2019, 5, 1, 0, 0)] , Bor.loc[(Bor['Time']>=datetime(2019, 5, 1, 0, 0)) & \n",
    "                            (Bor['Time']<=datetime(2019, 11, 3, 0, 0))] , Bor.loc[Bor['Time']>=datetime(2019, 11, 3, 0, 0)]  \n",
    "\n",
    "Daf1 , Daf2, Daf3  = Daf.loc[Daf['Time']<=datetime(2019, 8, 19, 0, 0)] , Daf.loc[(Daf['Time']>=datetime(2019, 8, 19, 0, 0)) &\n",
    "                            (Daf['Time']<=datetime(2019, 9, 20, 0, 0))] , Daf.loc[Daf['Time']>=datetime(2019, 9, 20, 0, 0)] \n",
    "\n",
    "Ded1 , Ded2, Ded3  = Ded.loc[Ded['Time']<=datetime(2019, 5, 18, 0, 0)] , Ded.loc[(Ded['Time']>=datetime(2019, 5, 18, 0, 0)) &\n",
    "                            (Ded['Time']<=datetime(2019, 10, 24, 0, 0))] , Ded.loc[Ded['Time']>=datetime(2019, 10, 24, 0, 0)] \n",
    "\n",
    "Dor1 , Dor2, Dor3  = Dor.loc[Dor['Time']<=datetime(2019, 6, 21, 0, 0)] , Dor.loc[(Dor['Time']>=datetime(2019, 6, 21, 0, 0)) &\n",
    "                            (Dor['Time']<=datetime(2019, 7, 11, 0, 0))] , Dor.loc[Dor['Time']>=datetime(2019, 7, 11, 0, 0)]  \n",
    "\n",
    "Far1 , Far2, Far3  = Far.loc[Far['Time']<=datetime(2019, 5, 21, 0, 0)] , Far.loc[(Far['Time']>=datetime(2019, 5, 21, 0, 0)) &\n",
    "                            (Far['Time']<=datetime(2019, 9, 19, 0, 0))] , Far.loc[Far['Time']>=datetime(2019, 9, 19, 0, 0)] \n",
    "\n",
    "Fin1 , Fin2, Fin3  = Fin.loc[Fin['Time']<=datetime(2019, 6, 13, 0, 0)] , Fin.loc[(Fin['Time']>=datetime(2019, 6, 13, 0, 0)) &\n",
    "                            (Fin['Time']<=datetime(2019, 9, 11, 0, 0))] , Fin.loc[Fin['Time']>=datetime(2019, 9, 11, 0, 0)] \n",
    "\n",
    "Gao1 , Gao2, Gao3  = Gao.loc[Gao['Time']<=datetime(2019, 5, 4, 0, 0)] , Gao.loc[(Gao['Time']>=datetime(2019, 5, 4, 0, 0)) & \n",
    "                            (Gao['Time']<=datetime(2019, 9, 26, 0, 0))] , Gao.loc[Gao['Time']>=datetime(2019, 9, 26, 0, 0)] \n",
    "\n",
    "Kou1 , Kou2, Kou3  = Kou.loc[Kou['Time']<=datetime(2019, 5, 31, 0, 0)] , Kou.loc[(Kou['Time']>=datetime(2019, 5, 31, 0, 0)) &\n",
    "                            (Kou['Time']<=datetime(2019, 11, 1, 0, 0))] , Kou.loc[Kou['Time']>=datetime(2019, 11, 1, 0, 0)] \n",
    "\n",
    "Man1 , Man2, Man3  = Man.loc[Man['Time']<=datetime(2019, 5, 1, 0, 0)] , Man.loc[(Man['Time']>=datetime(2019, 5, 1, 0, 0)) & \n",
    "                            (Man['Time']<=datetime(2019, 6, 18, 0, 0))] , Man.loc[Man['Time']>=datetime(2019, 6, 18, 0, 0)] \n",
    "\n",
    "Oua1 , Oua2, Oua3  = Oua.loc[Oua['Time']<=datetime(2019, 5, 8, 0, 0)] , Oua.loc[(Oua['Time']>=datetime(2019, 5, 8, 0, 0)) & \n",
    "                            (Oua['Time']<=datetime(2019, 10, 28, 0, 0))] , Oua.loc[Oua['Time']>=datetime(2019, 10, 28, 0, 0)] \n",
    "\n",
    "Ous1 , Ous2, Ous3  = Ous.loc[Ous['Time']<=datetime(2019, 6, 15, 0, 0)] , Ous.loc[(Ous['Time']>=datetime(2019, 6, 15, 0, 0)) & \n",
    "                            (Ous['Time']<=datetime(2019, 11, 19, 0, 0))] , Ous.loc[Ous['Time']>=datetime(2019, 11, 19, 0, 0)] \n",
    "\n",
    "Po1 , Po2, Po3  = Po.loc[Po['Time']<=datetime(2019, 5, 1, 0, 0)] , Po.loc[(Po['Time']>=datetime(2019, 5, 1, 0, 0)) & \n",
    "                            (Po['Time']<=datetime(2019, 10, 31, 0, 0))] , Po.loc[Po['Time']>=datetime(2019, 10, 31, 0, 0)] \n",
    "\n",
    "Pus1 , Pus2, Pus3  = Pus.loc[Pus['Time']<=datetime(2019, 5, 1, 0, 0)] , Pus.loc[(Pus['Time']>=datetime(2019, 5, 1, 0, 0)) & \n",
    "                            (Pus['Time']<=datetime(2019, 10, 26, 0, 0))] , Pus.loc[Pus['Time']>=datetime(2019, 10, 26, 0, 0)] \n",
    "\n",
    "Sel1 , Sel2, Sel3  = Sel.loc[Sel['Time']<=datetime(2019, 5, 6, 0, 0)] , Sel.loc[(Sel['Time']>=datetime(2019, 5, 6, 0, 0)) &\n",
    "                            (Sel['Time']<=datetime(2019, 5, 29, 0, 0))] , Sel.loc[Sel['Time']>=datetime(2019, 5, 29, 0, 0)] \n",
    "# Sel1 , Sel2, Sel3  = Sel.loc[Sel['Time']<=datetime(2019, 5, 6, 0, 0)] , Sel.loc[(Sel['Time']>=datetime(2019, 5, 6, 0, 0)) &\n",
    "#                             (Sel['Time']<=datetime(2019, 10, 15, 0, 0))] , Sel.loc[Sel['Time']>=datetime(2019, 10, 15, 0, 0)] \n",
    "\n",
    "Sir1 , Sir2, Sir3  = Sir.loc[Sir['Time']<=datetime(2019, 6, 12, 0, 0)] , Sir.loc[(Sir['Time']>=datetime(2019, 6, 12, 0, 0)) &\n",
    "                            (Sir['Time']<=datetime(2019, 11, 15, 0, 0))] , Sir.loc[Sir['Time']>=datetime(2019, 11, 15, 0, 0)] \n",
    "\n",
    "Som1 , Som2, Som3  = Som.loc[Som['Time']<=datetime(2019, 5, 30, 0, 0)] , Som.loc[(Som['Time']>=datetime(2019, 5, 30, 0, 0)) &\n",
    "                            (Som['Time']<=datetime(2019, 8, 7, 0, 0))] , Som.loc[Som['Time']>=datetime(2019, 8, 7, 0, 0)] \n",
    "\n",
    "Tam1 , Tam2, Tam3  = Tam.loc[Tam['Time']<=datetime(2019, 5, 1, 0, 0)] , Tam.loc[(Tam['Time']>=datetime(2019, 5, 1, 0, 0)) & \n",
    "                            (Tam['Time']<=datetime(2019, 11, 19, 0, 0))] , Tam.loc[Tam['Time']>=datetime(2019, 11, 19, 0, 0)] \n",
    "\n",
    "Tan1 , Tan2, Tan3  = Tan.loc[Tan['Time']<=datetime(2019, 5, 1, 0, 0)] , Tan.loc[(Tan['Time']>=datetime(2019, 5, 1, 0, 0)) & \n",
    "                            (Tan['Time']<=datetime(2019, 11, 6, 0, 0))] , Tan.loc[Tan['Time']>=datetime(2019, 11, 6, 0, 0)] \n",
    "\n",
    "Uni1 , Uni2, Uni3  = Uni.loc[Uni['Time']<=datetime(2019, 6, 14, 0, 0)] , Uni.loc[(Uni['Time']>=datetime(2019, 6, 14, 0, 0)) &\n",
    "                            (Uni['Time']<=datetime(2019, 7, 23, 0, 0))] , Uni.loc[Uni['Time']>=datetime(2019, 7, 23, 0, 0)] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2952 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Single simulation (Yield, WP_ET)\n",
    "\n",
    "fig1, ((ax5, ax10) , (ax, ax7) , (ax15, ax9),  (ax14, ax4), (ax18, ax2), (ax13, ax11), (ax12, ax8),\n",
    "       (ax17, ax3), (ax6, ax16),) = plt.subplots(nrows=9, ncols=2, figsize=(30, 41), )#sharex=True,) figsize=(20, 10))\n",
    "\n",
    "# Bog\n",
    "ax.bar( Bog1['Mon-Day'], Bog1['Yield ton/ha'],  color = 'gray')\n",
    "ax.bar( Bog2['Mon-Day'], Bog2['Yield ton/ha'], color = 'black')\n",
    "ax.bar( Bog3['Mon-Day'],Bog3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax.set_xticks(p,)\n",
    "\n",
    "ax.set_title('Bogande', fontsize = 20, fontweight='bold')\n",
    "ax.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax.set_ylim([0.0,1.8])\n",
    "\n",
    "ax.vlines('May-22',0,5, 'r', linewidth=3)\n",
    "ax.vlines('Jul-04',0,5, 'b', ls='--', linewidth=3)\n",
    "ax.vlines('Jun-01',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Bor\n",
    "ax2.bar( Bor1['Mon-Day'], Bor1['Yield ton/ha'],  color = 'gray')\n",
    "ax2.bar( Bor2['Mon-Day'], Bor2['Yield ton/ha'], color = 'black')\n",
    "ax2.bar( Bor3['Mon-Day'],Bor3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax2.set_xticks(p,)\n",
    "\n",
    "ax2.set_title('Boromo', fontsize = 20, fontweight='bold')\n",
    "ax2.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax2.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax2.set_ylim([0.0,1.8])\n",
    "\n",
    "ax2.vlines('May-04',0,5, 'r',  linewidth=3)\n",
    "ax2.vlines('May-04',0,5, 'b', ls='--', linewidth=3)\n",
    "ax2.vlines('May-10',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Daf\n",
    "ax3.bar( Daf1['Mon-Day'], Daf1['Yield ton/ha'],  color = 'gray')\n",
    "ax3.bar( Daf2['Mon-Day'], Daf2['Yield ton/ha'], color = 'black')\n",
    "ax3.bar( Daf3['Mon-Day'],Daf3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax3.set_xticks(p,)\n",
    "\n",
    "ax3.set_title('Daffiama', fontsize = 20, fontweight='bold')\n",
    "ax3.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax3.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax3.set_ylim([0.0,1.8])\n",
    "\n",
    "ax3.vlines('Sep-04',0,5, 'r',  linewidth=3)\n",
    "ax3.vlines('Sep-04',0,5, 'b', ls='--', linewidth=3)\n",
    "ax3.vlines('Sep-12',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Ded\n",
    "ax4.bar( Ded1['Mon-Day'], Ded1['Yield ton/ha'],  color = 'gray')\n",
    "ax4.bar( Ded2['Mon-Day'], Ded2['Yield ton/ha'], color = 'black')\n",
    "ax4.bar( Ded3['Mon-Day'],Ded3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax4.set_xticks(p,)\n",
    "\n",
    "ax4.set_title('Dedougou', fontsize = 20, fontweight='bold')\n",
    "ax4.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax4.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax4.set_ylim([0.0,1.8])\n",
    "\n",
    "ax4.vlines('May-13',0,5, 'r',  linewidth=3)\n",
    "ax4.vlines('May-30',0,5, 'b', ls='--', linewidth=3)\n",
    "ax4.vlines('May-30',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Dor\n",
    "ax5.bar( Dor1['Mon-Day'], Dor1['Yield ton/ha'],  color = 'gray')\n",
    "ax5.bar( Dor2['Mon-Day'], Dor2['Yield ton/ha'], color = 'black')\n",
    "ax5.bar( Dor3['Mon-Day'],Dor3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax5.set_xticks(p,)\n",
    "\n",
    "ax5.set_title('Dori', fontsize = 20, fontweight='bold')\n",
    "ax5.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax5.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax5.set_ylim([0.0,1.8])\n",
    "\n",
    "ax5.vlines('May-21',0,5, 'r',  linewidth=3)\n",
    "ax5.vlines('Jul-31',0,5, 'b', ls='--', linewidth=3)\n",
    "ax5.vlines('Jul-31',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Gao\n",
    "ax6.bar( Gao1['Mon-Day'], Gao1['Yield ton/ha'],  color = 'gray')\n",
    "ax6.bar( Gao2['Mon-Day'], Gao2['Yield ton/ha'], color = 'black')\n",
    "ax6.bar( Gao3['Mon-Day'],Gao3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax6.set_xticks(p,)\n",
    "\n",
    "ax6.set_title('Gaoua', fontsize = 20, fontweight='bold')\n",
    "ax6.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "ax6.set_xlabel('Sowing date ', fontsize = 20, fontweight='bold')\n",
    "\n",
    "ax6.set_ylim([0.0,1.8])\n",
    "\n",
    "ax6.vlines('May-20',0,5, 'r',  linewidth=3)\n",
    "ax6.vlines('May-20',0,5, 'b', ls='--', linewidth=3)\n",
    "ax6.vlines('May-27',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Kou\n",
    "ax7.bar( Kou1['Mon-Day'], Kou1['Yield ton/ha'],  color = 'gray')\n",
    "ax7.bar( Kou2['Mon-Day'], Kou2['Yield ton/ha'], color = 'black')\n",
    "ax7.bar( Kou3['Mon-Day'],Kou3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax7.set_xticks(p,)\n",
    "\n",
    "ax7.set_title('Kourounikoto', fontsize = 20, fontweight='bold')\n",
    "ax7.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax7.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax7.set_ylim([0.0,1.8])\n",
    "\n",
    "ax7.vlines('May-23',0,5, 'r',  linewidth=3)\n",
    "ax7.vlines('Jun-10',0,5, 'b', ls='--', linewidth=3)\n",
    "ax7.vlines('Jul-04',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Man\n",
    "ax8.bar( Man1['Mon-Day'], Man1['Yield ton/ha'],  color = 'gray')\n",
    "ax8.bar( Man2['Mon-Day'], Man2['Yield ton/ha'], color = 'black')\n",
    "ax8.bar( Man3['Mon-Day'],Man3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax8.set_xticks(p,)\n",
    "\n",
    "ax8.set_title('Mandouri', fontsize = 20, fontweight='bold')\n",
    "ax8.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax8.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax8.set_ylim([0.0,1.8])\n",
    "\n",
    "ax8.vlines('May-07',0,5, 'r',  linewidth=3)\n",
    "ax8.vlines('May-07',0,5, 'b', ls='--', linewidth=3)\n",
    "ax8.vlines('Jun-01',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Oua\n",
    "ax9.bar( Oua1['Mon-Day'], Oua1['Yield ton/ha'],  color = 'gray')\n",
    "ax9.bar( Oua2['Mon-Day'], Oua2['Yield ton/ha'], color = 'black')\n",
    "ax9.bar( Oua3['Mon-Day'],Oua3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax9.set_xticks(p,)\n",
    "\n",
    "ax9.set_title('Ouahigouya', fontsize = 20, fontweight='bold')\n",
    "ax9.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax9.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax9.set_ylim([0.0,1.8])\n",
    "\n",
    "ax9.vlines('May-20',0,5, 'r',  linewidth=3)\n",
    "ax9.vlines('Jun-13',0,5, 'b', ls='--', linewidth=3)\n",
    "ax9.vlines('May-19',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Ous\n",
    "ax10.bar( Ous1['Mon-Day'], Ous1['Yield ton/ha'],  color = 'gray')\n",
    "ax10.bar( Ous2['Mon-Day'], Ous2['Yield ton/ha'], color = 'black')\n",
    "ax10.bar( Ous3['Mon-Day'],Ous3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax10.set_xticks(p,)\n",
    "\n",
    "ax10.set_title('Oussoubidiagna', fontsize = 20, fontweight='bold')\n",
    "ax10.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax10.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax10.set_ylim([0.0,1.8])\n",
    "\n",
    "ax10.vlines('Aug-27',0,5, 'r',  linewidth=3)\n",
    "ax10.vlines('Aug-27',0,5, 'b', ls='--', linewidth=3)\n",
    "ax10.vlines('Aug-27',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Po\n",
    "ax11.bar( Po1['Mon-Day'], Po1['Yield ton/ha'],  color = 'gray')\n",
    "ax11.bar( Po2['Mon-Day'], Po2['Yield ton/ha'], color = 'black')\n",
    "ax11.bar( Po3['Mon-Day'],Po3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax11.set_xticks(p,)\n",
    "\n",
    "ax11.set_title('Po', fontsize = 20, fontweight='bold')\n",
    "ax11.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax11.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax11.set_ylim([0.0,1.8])\n",
    "\n",
    "ax11.vlines('May-03',0,5, 'r',  linewidth=3)\n",
    "ax11.vlines('May-03',0,5, 'b', ls='--', linewidth=3)\n",
    "ax11.vlines('May-07',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Pus\n",
    "ax12.bar( Pus1['Mon-Day'], Pus1['Yield ton/ha'],  color = 'gray')\n",
    "ax12.bar( Pus2['Mon-Day'], Pus2['Yield ton/ha'], color = 'black')\n",
    "ax12.bar( Pus3['Mon-Day'],Pus3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax12.set_xticks(p,)\n",
    "\n",
    "ax12.set_title('Pusiga', fontsize = 20, fontweight='bold')\n",
    "ax12.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax12.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax12.set_ylim([0.0,1.8])\n",
    "\n",
    "ax12.vlines('May-13',0,5, 'r',  linewidth=3)\n",
    "ax12.vlines('May-28',0,5, 'b', ls='--', linewidth=3)\n",
    "ax12.vlines('May-29',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Sel\n",
    "ax13.bar( Sel1['Mon-Day'], Sel1['Yield ton/ha'],  color = 'gray')\n",
    "ax13.bar( Sel2['Mon-Day'], Sel2['Yield ton/ha'], color = 'black')\n",
    "ax13.bar( Sel3['Mon-Day'],Sel3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax13.set_xticks(p,)\n",
    "\n",
    "ax13.set_title('Selingue', fontsize = 20, fontweight='bold')\n",
    "ax13.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax13.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax13.set_ylim([0.0,1.8])\n",
    "\n",
    "ax13.vlines('May-22',0,5, 'r',  linewidth=3)\n",
    "ax13.vlines('May-22',0,5, 'b', ls='--', linewidth=3)\n",
    "ax13.vlines('May-31',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Sir\n",
    "ax14.bar( Sir1['Mon-Day'], Sir1['Yield ton/ha'],  color = 'gray')\n",
    "ax14.bar( Sir2['Mon-Day'], Sir2['Yield ton/ha'], color = 'black')\n",
    "ax14.bar( Sir3['Mon-Day'],Sir3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax14.set_xticks(p,)\n",
    "\n",
    "ax14.set_title('Sirakoro', fontsize = 20, fontweight='bold')\n",
    "ax14.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax14.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax14.set_ylim([0.0,1.8])\n",
    "\n",
    "ax14.vlines('Jul-02',0,5, 'r',  linewidth=3)\n",
    "ax14.vlines('Jul-02',0,5, 'b', ls='--', linewidth=3)\n",
    "ax14.vlines('Jun-30',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Som\n",
    "ax15.bar( Som1['Mon-Day'], Som1['Yield ton/ha'],  color = 'gray')\n",
    "ax15.bar( Som2['Mon-Day'], Som2['Yield ton/ha'], color = 'black')\n",
    "ax15.bar( Som3['Mon-Day'],Som3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax15.set_xticks(p,)\n",
    "\n",
    "ax15.set_title('Somo', fontsize = 20, fontweight='bold')\n",
    "ax15.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax15.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax15.set_ylim([0.0,1.8])\n",
    "\n",
    "ax15.vlines('May-16',0,5, 'r',  linewidth=3)\n",
    "ax15.vlines('Jun-15',0,5, 'b', ls='--', linewidth=3)\n",
    "ax15.vlines('Jun-15',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Tam\n",
    "ax16.bar( Tam1['Mon-Day'], Tam1['Yield ton/ha'],  color = 'gray')\n",
    "ax16.bar( Tam2['Mon-Day'], Tam2['Yield ton/ha'], color = 'black')\n",
    "ax16.bar( Tam3['Mon-Day'],Tam3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax16.set_xticks(p,)\n",
    "\n",
    "ax16.set_title('Tamale', fontsize = 20, fontweight='bold')\n",
    "ax16.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "ax16.set_xlabel('Sowing date ', fontsize = 20, fontweight='bold')\n",
    "\n",
    "ax16.set_ylim([0.0,1.8])\n",
    "\n",
    "ax16.vlines('May-03',0,5, 'r',  linewidth=3)\n",
    "ax16.vlines('May-03',0,5, 'b', ls='--', linewidth=3)\n",
    "ax16.vlines('May-17',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Tan\n",
    "ax17.bar( Tan1['Mon-Day'], Tan1['Yield ton/ha'],  color = 'gray')\n",
    "ax17.bar( Tan2['Mon-Day'], Tan2['Yield ton/ha'], color = 'black')\n",
    "ax17.bar( Tan3['Mon-Day'],Tan3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax17.set_xticks(p,)\n",
    "\n",
    "ax17.set_title('Tanguieta', fontsize = 20, fontweight='bold')\n",
    "ax17.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax17.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax17.set_ylim([0.0,1.8])\n",
    "\n",
    "ax17.vlines('May-15',0,5, 'r',  linewidth=3)\n",
    "ax17.vlines('May-15',0,5, 'b', ls='--', linewidth=3)\n",
    "ax17.vlines('May-22',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Far\n",
    "ax18.bar( Far1['Mon-Day'], Far1['Yield ton/ha'],  color = 'gray')\n",
    "ax18.bar( Far2['Mon-Day'], Far2['Yield ton/ha'], color = 'black')\n",
    "ax18.bar( Far3['Mon-Day'],Far3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax18.set_xticks(p,)\n",
    "\n",
    "ax18.set_title('Fari', fontsize = 20, fontweight='bold')\n",
    "ax18.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax18.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax18.set_ylim([0.0,1.8])\n",
    "\n",
    "ax18.vlines('Jun-11',0,5, 'r',  linewidth=3)\n",
    "ax18.vlines('Jun-11',0,5, 'b', ls='--', linewidth=3)\n",
    "ax18.vlines('Jul-13',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "\n",
    "# # Fin\n",
    "# ax19.bar( Fin1['Mon-Day'], Fin1['Yield ton/ha'],  color = 'gray')\n",
    "# ax19.bar( Fin2['Mon-Day'], Fin2['Yield ton/ha'], color = 'black')\n",
    "# ax19.bar( Fin3['Mon-Day'],Fin3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "# ax19.set_xticks(p,)\n",
    "\n",
    "# ax19.set_title('90-Maize Yield variation - Finkoloni', fontsize = 12, fontweight='bold')\n",
    "# ax19.set_ylabel('Yied (Ton/ha)', fontsize = 12, fontweight='bold')\n",
    "# ax19.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "# ax19.set_ylim([0.0,1.8])\n",
    "\n",
    "# ax19.vlines('May-14',0,5, 'r')\n",
    "# ax19.vlines('Jun-05',0,5, 'b')\n",
    "# ax19.vlines('Jun-02',0,5, 'c')\n",
    "\n",
    "# # Uni\n",
    "# ax20.bar( Uni1['Mon-Day'], Uni1['Yield ton/ha'],  color = 'gray')\n",
    "# ax20.bar( Uni2['Mon-Day'], Uni2['Yield ton/ha'], color = 'black')\n",
    "# ax20.bar( Uni3['Mon-Day'], Uni3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "# ax20.set_xticks(p,)\n",
    "\n",
    "# ax20.set_title('90-Maize Yield variation - Unimaid', fontsize = 12, fontweight='bold')\n",
    "# ax20.set_ylabel('Yied (Ton/ha)', fontsize = 12, fontweight='bold')\n",
    "# ax20.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "# ax20.set_ylim([0.0,1.8])\n",
    "\n",
    "# ax20.vlines('Jun-14',0,5, 'r')\n",
    "# ax20.vlines('Jun-14',0,5, 'b')\n",
    "# ax20.vlines('Jul-09',0,5, 'c')\n",
    "\n",
    "\n",
    "# plt.legend(fontsize = 12)\n",
    "\n",
    "#plt.colorbar(sctr, ax=ax, )\n",
    "\n",
    "\n",
    "plt.subplots_adjust(hspace=0.3)\n",
    "\n",
    "# plt.savefig(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\All_Yield_response194.png')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "Bog1 , Bog2, Bog3  = Bog.loc[Bog['Time']<=datetime(2020, 6, 30, 0, 0)] , Bog.loc[(Bog['Time']>=datetime(2020, 6, 30, 0, 0)) & \n",
    "                            (Bog['Time']<=datetime(2020, 8, 15, 0, 0))] , Bog.loc[Bog['Time']>=datetime(2020, 8, 15, 0, 0)] \n",
    "\n",
    "Bor1 , Bor2, Bor3  = Bor.loc[Bor['Time']<=datetime(2020, 5, 14, 0, 0)] , Bor.loc[(Bor['Time']>=datetime(2020, 5, 14, 0, 0)) & \n",
    "                            (Bor['Time']<=datetime(2020, 10, 2, 0, 0))] , Bor.loc[Bor['Time']>=datetime(2020, 10, 2, 0, 0)]  \n",
    "\n",
    "Daf1 , Daf2, Daf3  = Daf.loc[Daf['Time']<=datetime(2020, 5, 1, 0, 0)] , Daf.loc[(Daf['Time']>=datetime(2020, 5, 1, 0, 0)) &\n",
    "                            (Daf['Time']<=datetime(2020, 10, 24, 0, 0))] , Daf.loc[Daf['Time']>=datetime(2020, 10, 24, 0, 0)] \n",
    "\n",
    "Ded1 , Ded2, Ded3  = Ded.loc[Ded['Time']<=datetime(2020, 5, 28, 0, 0)] , Ded.loc[(Ded['Time']>=datetime(2020, 5, 28, 0, 0)) &\n",
    "                            (Ded['Time']<=datetime(2020, 11, 14, 0, 0))] , Ded.loc[Ded['Time']>=datetime(2020, 11, 14, 0, 0)] \n",
    "\n",
    "Dor1 , Dor2, Dor3  = Dor.loc[Dor['Time']<=datetime(2020, 5, 14, 0, 0)] , Dor.loc[(Dor['Time']>=datetime(2020, 5, 14, 0, 0)) &\n",
    "                            (Dor['Time']<=datetime(2020, 7, 27, 0, 0))] , Dor.loc[Dor['Time']>=datetime(2020, 7, 27, 0, 0)]  \n",
    "\n",
    "Far1 , Far2, Far3  = Far.loc[Far['Time']<=datetime(2020, 5, 1, 0, 0)] , Far.loc[(Far['Time']>=datetime(2020, 5, 1, 0, 0)) &\n",
    "                            (Far['Time']<=datetime(2020, 9, 29, 0, 0))] , Far.loc[Far['Time']>=datetime(2020, 9, 29, 0, 0)] \n",
    "\n",
    "Fin1 , Fin2, Fin3  = Fin.loc[Fin['Time']<=datetime(2020, 5, 1, 0, 0)] , Fin.loc[(Fin['Time']>=datetime(2020, 5, 1, 0, 0)) &\n",
    "                            (Fin['Time']<=datetime(2020, 9, 10, 0, 0))] , Fin.loc[Fin['Time']>=datetime(2020, 9, 10, 0, 0)] \n",
    "\n",
    "Gao1 , Gao2, Gao3  = Gao.loc[Gao['Time']<=datetime(2020, 6, 14, 0, 0)] , Gao.loc[(Gao['Time']>=datetime(2020, 6, 14, 0, 0)) & \n",
    "                            (Gao['Time']<=datetime(2020, 9, 30, 0, 0))] , Gao.loc[Gao['Time']>=datetime(2020, 9, 30, 0, 0)] \n",
    "\n",
    "Kou1 , Kou2, Kou3  = Kou.loc[Kou['Time']<=datetime(2020, 6, 3, 0, 0)] , Kou.loc[(Kou['Time']>=datetime(2020, 6, 3, 0, 0)) &\n",
    "                            (Kou['Time']<=datetime(2020, 11, 12, 0, 0))] , Kou.loc[Kou['Time']>=datetime(2020, 11, 12, 0, 0)] \n",
    "\n",
    "Man1 , Man2, Man3  = Man.loc[Man['Time']<=datetime(2020, 7, 7, 0, 0)] , Man.loc[(Man['Time']>=datetime(2020, 7, 7, 0, 0)) & \n",
    "                            (Man['Time']<=datetime(2020, 10, 20, 0, 0))] , Man.loc[Man['Time']>=datetime(2020, 10, 20, 0, 0)] \n",
    "\n",
    "Oua1 , Oua2, Oua3  = Oua.loc[Oua['Time']<=datetime(2020, 5, 14, 0, 0)] , Oua.loc[(Oua['Time']>=datetime(2020, 5, 14, 0, 0)) & \n",
    "                            (Oua['Time']<=datetime(2020, 10, 9, 0, 0))] , Oua.loc[Oua['Time']>=datetime(2020, 10, 9, 0, 0)] \n",
    "\n",
    "Ous1 , Ous2, Ous3  = Ous.loc[Ous['Time']<=datetime(2020, 5, 23, 0, 0)] , Ous.loc[(Ous['Time']>=datetime(2020, 5, 23, 0, 0)) & \n",
    "                            (Ous['Time']<=datetime(2020, 11, 19, 0, 0))] , Ous.loc[Ous['Time']>=datetime(2020, 11, 19, 0, 0)] \n",
    "\n",
    "Po1 , Po2, Po3  = Po.loc[Po['Time']<=datetime(2020, 5, 1, 0, 0)] , Po.loc[(Po['Time']>=datetime(2020, 5, 1, 0, 0)) & \n",
    "                            (Po['Time']<=datetime(2020, 10, 18, 0, 0))] , Po.loc[Po['Time']>=datetime(2020, 10, 18, 0, 0)] \n",
    "\n",
    "Pus1 , Pus2, Pus3  = Pus.loc[Pus['Time']<=datetime(2020, 5, 1, 0, 0)] , Pus.loc[(Pus['Time']>=datetime(2020, 5, 1, 0, 0)) & \n",
    "                            (Pus['Time']<=datetime(2020, 10, 15, 0, 0))] , Pus.loc[Pus['Time']>=datetime(2020, 10, 15, 0, 0)] \n",
    "\n",
    "Sel1 , Sel2, Sel3  = Sel.loc[Sel['Time']<=datetime(2020, 5, 6, 0, 0)] , Sel.loc[(Sel['Time']>=datetime(2020, 5, 6, 0, 0)) &\n",
    "                            (Sel['Time']<=datetime(2020, 5, 29, 0, 0))] , Sel.loc[Sel['Time']>=datetime(2020, 5, 29, 0, 0)] \n",
    "# Sel1 , Sel2, Sel3  = Sel.loc[Sel['Time']<=datetime(2020, 5, 6, 0, 0)] , Sel.loc[(Sel['Time']>=datetime(2020, 5, 6, 0, 0)) &\n",
    "#                             (Sel['Time']<=datetime(2020, 10, 15, 0, 0))] , Sel.loc[Sel['Time']>=datetime(2020, 10, 15, 0, 0)] \n",
    "\n",
    "Sir1 , Sir2, Sir3  = Sir.loc[Sir['Time']<=datetime(2020, 7, 3, 0, 0)] , Sir.loc[(Sir['Time']>=datetime(2020, 7, 3, 0, 0)) &\n",
    "                            (Sir['Time']<=datetime(2020, 11, 18, 0, 0))] , Sir.loc[Sir['Time']>=datetime(2020, 11, 18, 0, 0)] \n",
    "\n",
    "Som1 , Som2, Som3  = Som.loc[Som['Time']<=datetime(2020, 6, 19, 0, 0)] , Som.loc[(Som['Time']>=datetime(2020, 6, 19, 0, 0)) &\n",
    "                            (Som['Time']<=datetime(2020, 8, 16, 0, 0))] , Som.loc[Som['Time']>=datetime(2020, 8, 16, 0, 0)] \n",
    "\n",
    "Tam1 , Tam2, Tam3  = Tam.loc[Tam['Time']<=datetime(2020, 7, 29, 0, 0)] , Tam.loc[(Tam['Time']>=datetime(2020, 7, 29, 0, 0)) & \n",
    "                            (Tam['Time']<=datetime(2020, 11, 30, 0, 0))] , Tam.loc[Tam['Time']>=datetime(2020, 11, 30, 0, 0)] \n",
    "\n",
    "Tan1 , Tan2, Tan3  = Tan.loc[Tan['Time']<=datetime(2020, 5, 1, 0, 0)] , Tan.loc[(Tan['Time']>=datetime(2020, 5, 1, 0, 0)) & \n",
    "                            (Tan['Time']<=datetime(2020, 10, 31, 0, 0))] , Tan.loc[Tan['Time']>=datetime(2020, 10, 31, 0, 0)] \n",
    "\n",
    "Uni1 , Uni2, Uni3  = Uni.loc[Uni['Time']<=datetime(2020, 6, 7, 0, 0)] , Uni.loc[(Uni['Time']>=datetime(2020, 6, 7, 0, 0)) &\n",
    "                            (Uni['Time']<=datetime(2020, 10, 9, 0, 0))] , Uni.loc[Uni['Time']>=datetime(2020, 10, 9, 0, 0)] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mMc9KUm96+Vyo1wa3Xi/EljS+bGSTpIaxwU2SJEmSJGl4eCecNL5sZJOkBrPBTZIkSZLG11yHKRjU0Ah15NN+vuf5+jq179Pp/dT1mt20n+dh+drayCWpLpUa2SJiXWB3YGtgAyCAPwG/B87JzJv6XqEklXr5Z0ySJEnNYYaUNG7m2kDRqsmNBVUbWKYz16/TMKmjIajba47a17ZJ6m6QljS/Zmxki4gHAy8GXgUspAhFnWREXAQcD3wlM+/stYiIeD6wB7AD8FjgIcBJmfnSXo9RHmcpsHmX1X/MzI2qHE+jZ+cXnLJ8+vwa69D0vPqoO782kiSp6cyQ0mCYZ5tllBuCJPVPv+/AlNQ8XRvZImJl4I3A4cDak4uB5IEhKYEFFAFqIXBURBwJfCwz7+mhjiMogtHtwLXAdhXeQ7tbgY90WH77HI6pEXHBazeouwRVZFiRJEkaDmZIabDMs/XzokdJdZrNnbK9dFPq7zRpbqa7k+1SYItyujUQ3UvRvcdN5fJ1gQ2BlVu2Wxs4Gngd8Mge6ngzRTD6PcXViGf1VH1nt2TmkjnsL0lDwX+GJElSw5ghNfZ6+QB0OtN9aNrtdXrd3+7Luhv2rhslqZtef/f7e1Caveka2R7RMn0BcApwLvCLzLy7dcOIWIWim45dgRcCj+9wjK4yc3kg8q4VDdQpm0xN71lbFVLfGZglSVIDmCE1lubte7A1z85Rv8cNm02XZ016TUlSb+b6u7fKa1TZR6rTdI1sy4DPAB/JzCunO0gZmH5aPo6JiEcAbwFe3q9CK1g1Il4KbAbcAfyKYkDt+2qoRU3zyZaLYj9RXxl6IAOOJEnS0DNDamzU0rj7yV5u8pQkqblmM56lF0io6aZrZNs8M2+czUHLQPWGiJiYXVlzshHwxbZlV0bEQZl5dg31SOrAq1IkSZJGjhlSI80P9iRJao7p7p6T5tOCbitmG476fYyKTgD2pghJawCPAY6jGBfguxHx2HmuR3MUER0frevap7vtr3r49Z9/ft9LkqQ6mCElSZLUBL18biz1y3R3sg2dzGy/6vFi4NURcTtwKLAEeE6nfSPiYOBggM0222yAVaqT2dwqPNOx+v2a/e4zfhz5B60e4/59J0mS1I0ZUjPxf2lJkkbHXD8Dljqp3MgWEesBewKbAKt22iYzj5pbWX33KYqAtHu3DTLzeOB4gIULF/oTMw9q+QX1spahIT4/fy/bSbfuEqcbgLpJ+jmgqSRJkkaXGVLDpNHd2jcoz0qSNI7solKdVGpki4g3Ae8HVpth06YFpD+Vz2vUWsWYa0RYOfCqqekGhZL5vvtuNvv04y5DSZIkjRczpNRHDc2zkiSp8+e2Nr6Nh54b2SJiEXBMh1UJRNt80+xSPv+h1irGkI0yUrP4R16SJM0XM6SGhblVkiQNine/jb4qd7K9pm1+8rsgWuYH/p9pRKwMbAXck5lXtCz/e+D6zLy5bfvNgY+Vs18adH1q9of4e5y4dPn02fWVIUmSJI0DM6TUR+ZZSZJGx3RDBjX583U9UJVGtp0oQtAvgG8Bi8v59YAXAx8FTgBeX7WIiNgP2K+c3ah83iUiTiynb8zMw8rpjYFLgKuALVoO8wLg7RFxFnAlcBtFkHo6Rdck3wGOrlqbRssPP3/Q8mmvVdS4m5iYWD69ePHiGiuRJEkjygypxhrGu9fMs5IkjZ/p7oSzMa4ZqjSybVg+nwHcPrkwM/8CfCIidgUOAn4JHFuxjh2Al7Ut27J8QBGGDmN6ZwHbAv9I0bXHGsAtwI+BLwJfTL/bBsYfaEmSJEltzJCSJEnSPPDz+fpUaWSbbDK9Gbh7+cKINTPzr8Bvy20OoWJAyswlwJIet11Kh4u2MvNs7DFBkiRJkprCDKlG8cMnSZI0Drrd/bZkyZLl0/Zq1T9VGtlupuiGYw3ghpbl+0fE54CnlvNbtu+o0WM3c9Jo8WdakiQNgBlSkiRJaqjJzwMXL168wrSqqdLIdgNFQFoXOL1l+UeB/2TqysDb+lOa1H9bveb7UzOffEp9hUiSJEmjzwwp9ZF5VpIkzYfWi/G7sTFuSpVGtp9T9FX/aOAi4BpgE4pgFBQDWCcrhieNkF5+uJruDy9cZWrmk/XVIUmSJI0BM6TUR+ZZSZLUJJ3uhJucHydVGtm+AiwDbs/MjIhDgG8Aq5Xrg6JP/bf1t0RJ0nwa5z+KkiSpr8yQkiRJ0pjpdrPOqHZL2XMjW2aeCZzZMv+9iNgOeDawPnAZ8LXMvLvLIaT6fWKruiuQJEmSxoIZUuoz86wkSRoho9LgVuVOtgfIzKuBY/tUixpoFLqIXMGpm9ZdgSRJkjS2zJDSHJhnJUnSiBrmnrUW1F2AJKnZJiYmRq/BXZIkSZIkSZLmqNKdbBGxM/BWYCdgHTo30mVmrtGH2iRJkiRJQ8wMKUmSJGmU9dzIFhFPBr4NrEQxQHU3OdeiJEmSJEnDzQypJhiVsT4kSZLUTFXuZJsotzcAaXgdetnU9IfqK0MaVn5IIUmSKjBDSv1knpUkSWqcKo1sO1CEowC+B1wG3IWBScPkGddPTRtKJEmSpEEyQ0r9ZJ6VJElqnCqNbH8DVgW+mpkvHlA9kiRJkqTRYIaUJEmSNNKqNLL9GHg68IcB1SIN3Es+dMHy6ZNqrEMaBZNdR4LdR0qSpI7MkFIfmWclSZKap0oj27uAJwMvjogPZ+ZNA6pJGpgvnf725dOGEkmSJGmgzJBSH5lnJUmSmqdrI1tEvK3D4vOARcDvIuLrwFLgnvaNMvOofhWo+Td5d4p3pkiSJEnqlRlSkiRJ0riZ7k62D9J9QOq1gZdPs68BSZLGiF1HSpIkzJCSJEmSxsyCWeyTdA9OMYdaJEmSJEmjxwwpSZIkaSTNNCabgUcjZd0jfjQ1c+Ru9RUijTi7nZUkaWyZIVWrUe5hwTwrSZLUPNM1sq0+b1VI8+Qve987NXNkfXVIkiRJI8gMKQ2QeVaSJKl5ujayZeZdEfHEzPzxfBYkSZIkSRo+ZkhJkiRJ42am7iLPiYgbgNOArwNnZeZ9gy9LGpAjt6+7Amns2HWkJEljxQwpDYp5VpIkqXFmamS7EdgIOAQ4GLglIk4Dvgl8PzPvHnB9Un+d+dC6K5AkSZJGmRlSGhTzrCRJUuMsmGH9RsAi4GPAtcA6wIHAfwE3RsRXIuIFEbHGQKuUJEmSJA0DM6QkSZKksTFtI1tm3p+ZZ2fmGzNzc2Bn4D+A3wN/B/wzcDLw54j4ZkQcEBFrD7xq9d3ExMTyLt0kSZIkaTbMkJIkSZLGyUx3sq0gM3+amf+WmdsC/wC8B7gYWA14NnAC8MeI+H5EHNz3aiVJkiRJQ8MMKUmSJGmUzTQmW1eZeTFFOFoSEVsCzweeCzwOeDLwJOD4fhQp9c37fj01/c76ypAkSZLGjRlSmiPzrCRJUuPMupGtVWb+ATgKOCoiHg48D3hOP44t9dUTbqq7AkmSJGnsmSHVT2Mz9IF5VpIkqXEqdRfZi8y8LjOPzcy9+n1sSZIkSdJoMUNKkiRJGlaV72SLiMcAOwHr0KWRLjOPmmNd0kAccfg3l08fWWMdkiRJ0rgwQ0r9YZ6VJElqnp4b2SJiDeAUYN8eNjcgqZHe+5OPLp82lEiSJEmDY4aU+ss8K0mS1DxV7mT7IPDUcjq7bBPTrJMkjbnW8TIWL15cYyWSJGkemCElSZIkjbQqjWzPZSr8xABqkSRJkiSNDjOkJEmSpJFWpZFtvfL5GuCFwGXAXXjV4VDybhJJkiRJA2aGlCRJkjTSqjSyLQW2Br6QmT8dTDnSYK16zPlTM2/eub5CJEmSpNG3FDOk1DfmWUmSpOZZUGHbkyi6+Nh6QLVIA3f3DsuWPyRJkiQNlBlS6iPzrCRJUvNUaWQ7CrgAeGFEHBkRGw+oJkmSJEnS8DNDamAmJiZWGAZBkiRJqkOV7iL/QtEoF8A7gHdExL3AfW3bZWau0af6pP7618fWXYEkSZI0LsyQUj+ZZyVJkhqnSiPbahQDVCdFSAJYuXy0chBrNdcv16m7AkmSJGlcmCGlfjLPSpIkNU6VRjaYCkaSJEmSJM3EDClJkiRpZFVpZPv3gVUhSZIkSRo1ZkhJkiRJI63nRrbMfMegioiI5wN7ADsAjwUeApyUmS+dxbE2Ad4D7AusB1wP/BcwkZl/6VvRQ8hBoSVJkiTNFzOkJEmSpFFXtbvIQTmCIhjdDlwLbDebg0TEVsB5wIbAacClwE7Am4B9I2LXzLypLxVrOB134dT0IfWVIUmSJGlOzJAaP+ZZSZKkxllQdYeI2DEivhIRV0fE7eXzVyLicXOo483ANsCawGvmcJxPUISjN2bmfpn59szcCzgG2BZ43xyOrVGwze1TD0mSJEkDZ4aU+sQ8K0mS1DiVGtki4iDgJ8ALgU2AB5fPLwTOi4hXzKaIzDwrM3+XmTmb/cvatgT2AZYCH29bvRi4A9g/ItaY7WtIkiRJknpnhlQ/TUxMOAyCJEmSGqXn7iIj4lHAp8p9OgWZlYBPRMQFmXlxn+qrYq/y+YzMvL91RWbeFhHnUgSonYEz57s4NcMXDzlm+fT+NdYhSZIkjTozpNRf5llJkqTmqTIm25uAlSnCUQIXADcAG1H0WR/l8d5APb2Db1s+X95l/e8oAtI2GJDG1ksv/9byaUOJVK/Wq5AXL15cYyWSJGlAzJBSH5lnJUmSmqdKI9ui8vlGYFFm/nZyRUT8PXAWsF7LdvNtrfL51i7rJ5ev3WllRBwMHAyw2Wab9bcySZIkSRo/ZkhJkiRJI63KmGwbU1x9+IXWcASQmb8BPk9xJeLG/Suvr6J87thnf2Yen5kLM3PhBhtsMI9lSZImx9dwjA1JkkaKGVKSJEnSSKvSyDaTWQ843SeTVxmu1WX9mm3bSZIkSZLqY4aUJEmSNNSqdBd5HbAV8JKI+FhmXjW5IiK2YKpL8Ov6Vl01l5XP23RZv3X53K2//ZE0eVeI4x0V4gvnT80csHN9hUiSJEmjzwwp9ZF5VpIkqXmqNLKdTRGQHgpcFhFnMzVo9R7AKhRXIp7T7yJ7dFb5vE9ELMjM+ydXRMRDgF2BZcD5nXbWmNh0Wd0VSJIkSePCDCn1k3lWkiSpcao0sn0UeBlFF5OrAE9qWTfZV/19wLH9Ka2ziFiZIqjdk5lXTC7PzCsi4gxgH+B1bXVMAGsAx2XmHYOsT5IkSZIEmCE1R63j9do7iyRJkpqo50a2zPxVRLwF+Ajd+84/LDN/UbWIiNgP2K+c3ah83iUiTiynb8zMw8rpjYFLgKuALdoO9VrgPOCjEbF3ud3jgUUUXXy8s2ptGjH771R3BZIkSdJYMENKfWaelSRJapwqd7KRmcdGxMXAocATKQaC/itwLnB0Zp413f7T2IHiCsdWW5YPKMLQYcygvBJxIfAeYF/gacD1FFdQTmTmzbOsT6Pi2gfXXYEkSZI0NsyQUh+ZZyVJkhqnUiMbQBmCzgJo77d+tjJzCbCkx22XMtW1SKf11wAHzbUmSZIkSdLcmSElSZIkjaoF062MiPsj4t6yi48H6Ec4kiSp3cTExApjcIzb60uSNKzMkJIkSZLGybSNbKWuV/xJkiRJktTGDClJkiRpLFTuLlLNN3n3xeLFi2uupIFOPW9q+gX1lSFpeLTe0ebvVUmSJNXGPCtJktQ4NrJpvKx/d90VSKqoSRcO2OAmSZKk2phnJUmSGqfXRrZ1ImKzXg+amVfPsh5JkmpRtTGvvcGtSY2BkiQ1gBlSs+K4uJIkSRomvTayHV4+epEVjivNq4ue/9rl0zvWWIek2RnGO8mGsWZJkvrADCn1mXlWkiSpeXoNMg5crZHwTzddUncJksZAt7vaprv7rdN0p2NIkjQkzJBSn5lnJUmSmserBSVJQ6nXhqhu2w1LQ9aw1ClJkiRJkiSNm14b2S4EfjPIQiRJmq3ZNLhJkqSBMkNKkiRJGnm9NrKdnJkfHmgl0jy4joe1zF1fWx2SNBve1SZJGiJmSKnPzLOSJEnNY3eRI8APXXu38be/OjXz9N3rK0SSJEmSpArMs5IkSc1jI5vGy4Pvq7sCSfNg8uKDUb/wYFzepyRJkjDPSpIkNZCNbJIkjQAb3CRJ0rBy3IHf5owAACAASURBVFxJkiQNq5ka2Sb/0z1v0IVI8+JpT6y7AkmSJGmUmSGlQTHPSpIkNc60jWyZ6eVkGi3LvHlT0uhzrE5JUl3MkNIAmWclSZIaZ0G3FRExERHrzPbAEbFORCyZ7f6SJEmSpOFhhpQkSZI0bro2sgHvAq6OiE9ExG69HjAidomIjwFXlceQJEmSJI0+M6QkSZKksTJTXwMPBg4BDomIG4CfAD8FrgZuLrdZF9gUWAjsAmxcLg8g+12wJEmSJKmxzJCSJEmSxsZ0jWxvAY6gCEAADwOeUz66iZbpm4H3zKk6ddU63o4qOOuHU9OLaqtCkuaN47NJkuaRGVIaJPOsJElS43TtLjIzPwJsBXwA+FO5OFgxBEWH5X8G3g9slZkf7Wu1kiRJkqRGMkNKkiRJGjfTdheZmbcC7ywHn94P2BfYHXgEUw10CVwJnAN8D/hmZt4zqIIlSZIkSc1khlSvJu+29057SZIkDbOZxmQDoAw8p5YPIiKA9SiuPLwpM+8fWIVSH+WiqT41YprtJEmSJM2eGVLqP/OsJElS8/TUyNYuMxO4sc+1SJIkSZJGkBlSkiRJ0ijqOiabJEmSJEmSJEmSpM5sZJMkaYxMTEwsHwNFkiRJkiRJ0uzNqrtIaVids/ouUzPLflJfIZIkSZIkVWCelSRJah4b2YaIdx7M3R7fef/UTMug0ZIkSZIkNZl5VpIkqXnsLlKSJEmSJEmSJEmqyDvZNF7uXKnuCiSpMSbvkF68eHHNlUiSpHHg/x5zZJ6VJElqHBvZNF6evlvdFUiSJEmSVJ15VpIkqXHsLlKSJEmSJEmSJEmqqOudbBHx7tkeNDPfM9t9JUmSJEnDxwwpSZIkadxM113kEiBneVwDkiRJkiSNlyWYISVJkiSNkapjskXLdPawXHPkwNB9tt5dU9M31VeGJEmSNCbMkFK/mGclSZIaZ6ZGtuiwLMvl7euywzKpWb72k6npRfWVIUmSJI0oM6Q0KOZZSZKkxlnQbUVmLmh9AKsA3ypXvxvYHFitfJ68zeoH5TJJkiRJ0hgxQ6rdxMTE8ockSZI0iro2snXwZuCZwJcz88jMvCYz7y6f3wt8heJaqrcNolCpHxbcuNLyhyRJkqSBMkNKfWSelSRJap4qY7K9vHy+osv6Kyi6+jgAeN9cipIG5b4X7L582n5pJEmSpIEyQ0p9ZJ6VJElqnip3sm1RPj8zIlZuXRERq1BcoQiwWR/qkiRJkiQNty3KZzOkJEmSpJFUpZHtz+XzDsCvImJJRLwmIpYAvwQeW66/cTaFRMQmEfG5iLguIu6KiKUR8ZGIWKfCMX4YETnNw77+JUmSJGl+mCElSZIkjbQq3UV+DfhXIIFtgXe1rJvsqSCBU6sWERFbAecBGwKnAZcCOwFvAvaNiF0z86YKh+w2qvK9VWuTJEmSJM2KGVKSJEnSSKvSyLYE2Bt4DEUQapUUIenicruqPkERjt6YmcdOLoyID1MMlv0+4NW9HiwzZ1ODxsCnNtl/aubaL9ZXiCRJkjT6lmCGlPrGPCtJktQ8PTeyZeZfI2JX4L0UA1g/pGX1bcAJwLsy87YqBUTElsA+wFLg422rFwMHA/tHxKGZeUeVY0vtXvPFl0/NLDKUSJIkSYNihhxPExPdbgrUXJlnJUmSmqfKnWxk5u3AmyPiMGA7YG3gFuDSzLxvljXsVT6fkZn3t73ebRFxLkWA2hk4s5cDRsQ/A48A7gYuAX6QmXfNsj5JkiRJ0iyYISVJkiSNskqNbJPKMPSbPtWwbfl8eZf1v6MISNvQY0ACTm6b/1NEvC4zvzaL+jRKrlm97gokSZKksWOGlPrAPCtJktQ4lRvZImIL4I3AE4ENgPcDFwA7lJucVPGKxLXK51u7rJ9cvnYPxzoNOBr4OXATsDnwMuBQ4KsR8YzM/G6nHSPiYIpuRdhss816q1zD54DH112BJEmSNFbMkFKfmGclSZIap1IjW0Q8B/gSsBrFINVJ0a/+6sCJ5fw1wFl9rDHK5/aBsh8gM49pW3QZcHhEXAccSxHmOgakzDweOB5g4cKFM76WJEmSJGl6ZkhJkiRJo2xBrxtGxDbASRRhaAWZeQFwbTn7zIo1TF5luFaX9Wu2bTcbnwHuBXaIiIfMtLEkSZIkaW7MkJIkSZJGXc+NbMBhFFcfJlNhqNXZFFcM7lKxhsvK5226rN+6fO7W3/6MMvNvwG3l7BqzPY4kSZIkqWdmSEmSJEkjrUp3kXtThKNfUISgv7Wt/335vHHFGia7BdknIhZk5v2TK8orBncFlgHnVzzuchGxLbAORUi6cbbH0QjY5rap6VlHbkmSJEk9MEOOiYmJCQAWL15ccyUjzjwrSZLUOFUa2SaDz2mZeXdEtK+/p3zesEoBmXlFRJwB7AO8jqLf+0kTFFcNHpeZd0wujIjtyn0vbVm2JXBXZv5f6/EjYn3ghHL25My8t0p9GjHHXTQ1vai+MiRJkqQxYIaU+sk8K0mS1DhVGtnuBlYGVu2yfqvyuf3qxF68FjgP+GhE7A1cAjye4t/Gy4F3tm1/SfncmtJ2Bz4TEWcDVwA3A5sBT6Poq/9C4G2zqE2SJEmSVJ0ZUpIkSdJIq9LIdiXwGOCFEfGB1hVlVxrPp+gK5HdViyivRFwIvAfYlyLUXA98FJjIzJt7OMxFwJeAHYEdKAa7vg34NXAKxZWMd1etTaNl9cunhiFcVmMdkiRJ0hgwQ0p9ZJ6VJElqniqNbGdQBKQtmRpoGuD1wHuB1SkC0hmzKSQzrwEO6nHbB/Qzkpm/Bg6czWtrfNx5yB7Lpx/wTSRJkiSpn8yQUh+ZZyVJkppnwcybLPefFFf1ATyMIgwBbE4RjijXf7w/pUmSJEmShpgZUpIkSdJI67mRLTOvBV7MVK8E0fa8DHhxZl7Xv/IkSZIkScPIDDnaJiYmmJiYqLsMSZIkqVZVuoskM78TEY8C3gg8EViXYnDoHwPHZuZV/S9RkiRJkjSMzJCSJEmSRlmlRjaAzLwaOGwAtUgDd+hj3zk188v31VeIJEmSNCbMkFJ/mGclSZKap3IjmzTMPvyRJ03NLDKUSJIkSZKGg3lWkiSpebo2skXE58rJkzPzjJb5mWRmvmLupUmSJEmShoUZUpIkSdK4me5OtgOBBC4GzmiZn06U2xiQ1Ey/WKvuCiRJkqRRdSBmSGlwzLOSJEmNM5vuIqPL8pnCk1S/N/9j3RVIkiRJ48YMKfWDeVaSJKlxpmtku5oi9NzaNi9JkiRJUjsz5AibmJhYPr148eIaK5EkSZKao2sjW2ZuEREvyMxTJ+fnrSpJkiRJ0lAxQ0qSJEkaNwtmWH9yRJwUEWvPSzWSJEmSpGFmhpQkSZI0NmZqZAvgRcDFEfGUeahHGqxdbpx6SJIkSeo3M6Q0KOZZSZKkxpluTLZWDwe+ExHHAYdl5p0DrEkanPdfPDW9qL4yJEmSpBFnhpT6zTwrSZLUODPdyXYuxZWIWT4fAvwiIp4w6MIkSZIkSUPHDClJkiRpbMx0J9vuwJuAI4HVy2WPBM6OiKOBT1GEpxVk5tX9LFLqlw3Pu3f59J9qrEOSJEkaUWbIETIxMVF3CWphnpUkSWqeaRvZMjOBj0TEfwOfA3ajCEQrAW8rHw/YbabjSnX54zufvHw6aqxDkiRJGkVmSGlwzLOSJEnNM1N3kQBk5hWZuQfwTlbs+qPbQ5IkSZI0psyQkiRJksZBT41sABGxCDiYqa49HtDFhyRJkiRJYIaUJEmSNPpm7JIjItYAjqYIR8sXA7cA38KgJEmSJEkqmSElSZIkjYtpG9kiYm/gM8BmrNjFxxnAKzLz/wZeodRHz9v741MzZ76uvkIkSZKkEWSGlAbHPCtJktQ8M93J9j+seJXhMuBtmfmJwZUkDc43jnjU1MyZ9dUhSZIkjSgz5JCbmJiouwR1YZ6VJElqnhm7iywF8BPggMy8YoD1SJIkSZKGnxlSkiRJ0sjrpZHtHmAJcFRm3j/YcqQBO3PDuiuQJEmSRp0ZUhoE86wkSVLjzNTI9iuKKw9/NR/FSAN35KNm3kaSJEnSbJkhpUExz0qSJDXOTI1sj8vMe+alEkmSJEnSsDNDSpIkSRobC6ZbaTiSJEmSJPXKDClJkiRpnEzbyCZJkiRJkkbbxMQEExMTdZchSZIkDZ2ZuouURsszrpuaPr2+MiRJkiRJqsQ8K0mS1Dg2smm8HHr51LShRJIkSZI0LMyzkiRJjWN3kZIkSZIkSZIkSVJF3smmsbLd6bcsn760xjokSZIkSarCPCtJktQ8NrJprFzyoecsn44a65AkSZKkOk1MTACwePHimitRr8yzkiRJzWN3kZIkSZIkSZIkSVJFNrJJkiRJkiRJkiRJFdnIJkmSJEmSJEmSJFXkmGwaKzu/4JSpmVNfWF8hkiRJkjSPJsdgA8dhG1bmWUmSpOaxkU1j5YLXbjA1c2p9dUiSJEmSVIV5VpIkqXnsLlKSJEmSJEmSJEmqyDvZNF5O2aTuCiRJkiRpXrR2EakRYJ6VJElqnMbcyRYRm0TE5yLiuoi4KyKWRsRHImKdisdZt9xvaXmc68rj+t+o4JOPnHpIkiRJGlpmSI0d86wkSVLjNOJOtojYCjgP2BA4DbgU2Al4E7BvROyamTf1cJz1yuNsA/wAOBnYDjgIeHpE7JKZfxjMu5AkSZIkzQczZHeTd68tXry45kokSZKk0deUO9k+QRGO3piZ+2Xm2zNzL+AYYFvgfT0e5/0U4eiYzNy7PM5+FEFrw/J1JEmSJEnDzQwpSZIkqXa1N7JFxJbAPsBS4ONtqxcDdwD7R8QaMxxnDWD/cvv2S/Y+Vh7/KeXrSZIkSZKGkBlSkiRJUlPU3sgG7FU+n5GZ97euyMzbgHOBBwM7z3CcXYDVgXPL/VqPcz9wRjm7aM4Va3i97MqphyRJkqRhZIbUeDLPSpIkNU4TxmTbtny+vMv631FcpbgNcOYcj0N5HI2rA6+amv58fWVIkiRJmjUzpMaTeVaSJKlxmnAn21rl861d1k8uX3uejiNJkiRJai4zpCRJkqRGiMyst4CI44FXAa/KzM90WP9+4B3AOzLzg9Mc53CKwa3fl5lHdFh/MHAccFxmvrrL+oPL2W2By2bxdgZlfeDGuovQnHgOR4vnc7h5/kaL53O0eD5HS9PO5+aZuUHdRWjuzJA9adrPn6rzHI4Wz+do8XyOFs/naPF8jpa6z2dPGbIJ3UVOXh24Vpf1a7ZtN5DjZObxwPEzvEYtIuLCzFxYdx2aPc/haPF8DjfP32jxfI4Wz+do8XxqgMyQM/Dnb/h5DkeL53O0eD5Hi+dztHg+R8uwnM8mdBc5ebVft37uty6fu/WT3+/jSJIkSZKaywwpSZIkqRGa0Mh2Vvm8T0SsUE9EPATYFVgGnD/Dcc4vt9u13K/1OAsoBr5ufT1JkiRJ0vAxQ0qSJElqhNob2TLzCuAMYAvgdW2rJ4A1gC9k5h2TCyNiu4jYru04twNfLLdf0nac15fH/35m/qGP5c+XRnZBoko8h6PF8zncPH+jxfM5Wjyfo8XzqYEwQ/bEn7/h5zkcLZ7P0eL5HC2ez9Hi+RwtQ3E+IzPrroGI2Ao4D9gQOA24BHg8sIiia44nZOZNLdsnQGZG23HWK4+zDfAD4KfA9sCzgT+Vx7li0O9HkiRJkjQ4ZkhJkiRJTdCIRjaAiNgUeA+wL7AecD3wX8BEZt7ctm3HgFSuWxdYDOwHPAy4Cfgu8O7MvHaQ70GSJEmSND/MkJIkSZLq1phGNklqgog4EDgBOCgzT6y3GkmSJElSk5khJUkab7WPydZUEZHl4/6yK5Ju253Vsu2B81hiTyLiURFxSkT8KSL+FhGXRcRERKzeYduVI+JNEXFCRPwiIu4u39cr66h9ECbPVd11dBMRT4iI70TEzRFxZ0T8KiL+NSJW6rDtni3fe50eH6zjPdSlznMbEZtExDsj4tSI+H35eyMj4pHT7LNTRHwgIr4bETeU24/VldLl12zy+3XbuuvpJiK2L39vnhYRV7fU/KAe9n1MRHwhIq6JiLvK38VnR8QB81H7oEXEShHxqvI93RwR95Tv8VcR8ZmIeFbdNU6KiDUi4iUR8eWIuDQi7oiI2yLiwog4NCJW6bLfKyLiuIi4oPy9nBFx5HzXX4eIWFj+T/CHiFgWEX+NiF9HxH9ExMZzPPaBs/3fKSI2jog3lL8/l5Y/WzdFxP9ExHO77PPDGf5mZkR8di7vqUk6vLe7IuLPEfG/5c/mUzv9b9EE5d/Uz0XEdWXdSyPiIxGxTodtK38vSIPU8jNnhjRDzpswQ85anec2zJBzFubJkRBmypEV5smh1uH9mSmnMeMv9DF3L8XX6BXA4e0rI2JrYI+W7RolIh5PMa7AysDXgGuAvYB3A3tHxN6ZeVfLLmsAHymn/wjcAGw6fxWPt4h4NvB14G/AV4GbgWcCxwC7Ai/osuvZwA87LP9x/6tUFwuBI4EErgRuBdaeYZ9/Ad4E3EMxhshDB1lg00REUPxuTSCAVwGH1VpUd0+h+L15H/A7ip/R1Wbaqfxn7zPAncDpwFKK74tHA08DvjCQaudJ+c/U6RRddN0CfBu4FlgX2Irie3w74Ft11dhmN+BLFL9bz6LoTmxdit+zRwPPLf8u/q1tvw8BawF/Aa6jeG8jrfz5/CDwNor/cf4HOBVYBXgCxc/qayPiZZn5tRpKfAPwbxS/b8+i+H9lc+C5wJMi4pjMfEvbPifS+W/l5PHWpegabtRMlM8rUfz++Xtgf4rfvxdGxEsy8/K6imsXDxxj61JgJ4q/l/tGxK6tY2wxu+8FadDMkGbIeWOGHGpmyDkwTw5/ngQz5agyT44cM2UvmTIzfXR4UPyhvhb4GUXf/g/qsM2/l9t9s3w+sO66W2pbCfhtWdezWpYvoAhLCby9bZ9VgKcCDyvnl5TbvbLu99Pn85p119GhrjUpBla/C1jYsny18hdDAi9q22fPcvmSuutvwqNf5xY4sOrPM7AJxT9ba5bzPyyP8chp9tkB+EdglZb6r6376ziP5+sp5Xs+ofwD9ufJr0XTHsC2wOOB1cv5pWXtD/i70LLPzhT/TP4C2KjD+pXrfl99+Lq8tPw6/AJYq8P6BwOL6q6zpZ4dgJe0f58BDwEuKt/LoR322xfYvJye/P1wZN3vZ8Bfq3cz9YHP33dY/zxgWfk9PqtzPJvftS37PhfYo8Py7Sk+oEpgxx6PtW25/Q2j8HPZ8r66/k2k+EDulHKbq4EN6663pbbvl3W9oW35h8vlnxrU94IPH/14YIYEM+R81mWGbMi5nc3/NZgh5/o1N0824L314WtjphzBB+bJkXhM9zcSM+UDHnYXObNPAxsBz2hdGBErAy+j+Of1N512jIgdI+I/I+KX5S3Pf4uI30XEh9pvT4yIV5e3Xr67y7E2Km+Z/nWPde9B8c1wTmYuv+IjM++nuJIA4NXl1QWT6+7OzO9m5vU9vsZIiKkuM5Z0Wb80Ipa2LVt+W3JELCpvG76tvPX52xGxfcUyng9sAJycmRdOLszi6pcjytnXVDzm2JvNuZ2NzLw2M3+UmX+tsM8vMvPnmXn3XF9/SL2qfP40cBKwPvCc9o0i4sTyHG7RYV3X8xsRj4uIM1p+Lv9fROwSEUvKffbstdDMvCwzL8jMZb3uAxxF8UHVSzPzhg7HvKfCsZrqCeXziZl5a/vKzLwzM89qXx4RL46im6y/lH8XL4mIIyJi1Q7bZvn79eER8cUoug1ZFhEXRcS/VCm2/Jk7qf1nLjNvo7iyEIoPntr3+15mXlXltYZZ+bP2LoorpJ+VmQ/4Hyczvw68meJ7/JMRsaDtGP8cEWe2/O+zNCK+EhELy/U/pPhABOCEWLELii1mqjEzv5GZZ3dYfgnFVfzQ4Vx2cfBkHSPyczmjzPwj8CKKD/M2pfOdNutG0R3VJeXP3K3lOd2n23FnOu8ziYgtgX0oPnj6eNvqxcAdwP4RsUbLe+nn94LUT2bIEWeGHF1myKFhnhwNZsoRY54cD2bKB7KRbWZfoTgB7X3KP4ui1fbT0+z7KopvuMsofvg/RXFF41uAcyPiIS3bfgn4K/DK6Nyf6cspuhM5rse69yqfv9e+IjP/AFxOcevjlj0eT509AziD4tx9CvgRxW37Z0fE+hWO0/V8AedQdA/whE7/MACPjIjXR8ThEfHyKLqgkRorIh5K8Tv08sw8j6l/jg7uvlel4+9G8XOzF/Ad4GMUV0mdRXGL+EBFxORVqRcCvyk/RDksij7a927/B3KITd5ev02vO0TRR/mXgUcC36D4p+dm4L3A96LzuATrUHwY+RiK75UvUPztOiki3jrr6lc0+c/wvX063jA7iOL/jW9m5nQfyn6GoquTbSk+lCUKJwInA/9AcY6PofjbuBtTHzafSNFtA+XzRMvjljnW3/O5jGLMhAMorkyb7v+5kVN+YD45DsSLWz8wj4jNKa7EfTvFVeGfoggX21P8nL6q9VgVzvtMJv8XOqOsr7Xe24BzKa5m3rnH4/lzrTqZITUdM6Q0B+bJkcmTYKYcRebJMWGmbDOI2/JG4UHLbfcUP/j3Apu0rP8exS2DD2aqH+0D246xObBSh2NP9hv9b23LP1Yuf0bb8gD+QBHUHnD7dJf6Ty2P9bwu608v1z91mmMsYQy6+mCGLjMoWr+Xti07sNznXmDvtnUfKNe9rUJdP2Oa20+Bi8v123eou9Pja8A6dX+9h/zcHjiHWn7IDF19dKl/LLr6oPgjm/x/9u48TLKqPPz49x1EWX7IjiiKLCpEg2CcgAiBGVGDO6ioRFlVcIsbxKhgZtqFRIOKoEQNKgZR3MUd1MAYBTEQUVFARQdEFGWVTYbl/f1xbk3fqanqruqunltV/f08z31u3bXeqttVdd4+554Db6qtuwi4t/09oxSeEtimw3lWu76UxiO/7PT9Brys9hlZNIv4lzNF9x6UFpRJKfif0+Hz+ZN+/jaGdaJ0VbOium6nUW6xf+gU+7c+W1+g6iqltq31e/OatvWt9+wzwILa+m0pidQKYLsBvJZvVM9z5DT7tV7DOHft8Z3qNb60h31Pr/Y9tlo+olr+IW3lFUorxQd2eC8PHWDs96d003Evtd/LKfY/sIrh7Kbf9zm4jqv9JnbY536UpCGBbWvrz63ew/YuxjaidOVzB/CA2vqer/s08fw7XbrYqba3yskvH/TfgpPToCbMIcEcsrV9OeaQQz/NwbU9dBaxnIs5ZK+v23xyDPLJ6rWaU47ZhPnk2Ex0+I3ssI85ZTWNU+uHufSflAt6OKysjX0ScHpm3t7toMy8MjPv6bDpo5RWa3/ftv4/qvmRbeufTPny/3R2uH26iw2rebf9W+unG1hXUzsjM7/Ttu7D1byfFk4zuV5/ohQud6L0/7w5ZTyEH1H6N/7KmLVw0hioWra8hPIjVR+o+VTKP4PaW3z36/GUFm3nZGb7oLMfprTAnmtbVPPnUVrpPJvyGX8YJXHYCfha1eppZGXmjyh96F9bzT8PLI+I6yPiixHxjLZDXkP5p9LhuXpXKW+jtGJ8YYenuofyD8WVrZAy8zfAicDalAF3ZywiXkXpI/9iyu/zfPfAav7bHvZt7fOgav6P1fzI9vJKZt6Tc9iVWPXdcgrlDpH/yNK1w3RarZ0/POVeYyoz72Sy9fDmABGxM6Ul6ecz84y2/W+idLGxDqWc0TKo6z6QsusM/xakuWAOqW7MIaUZMp8cn3wSzCnHlPnkPGJOOanTLbRqk5kXROnH/vCIeDvlR3sB09wKGqXP/SMp3X08knKh6wXWrdqe52cR8V3gKRHxkMxsfdm0PrQfrJ17F2C/tqe8KTNP6PFltW7hzB73V2cXdljXum4rx0yI0l/3orb9lmfmqT0+z2rXK0u/xvW+jW+l3HJ7HuWHfQ/gGUzeQq0ZiohDgW3aVp+bmeeu8WBG3xOA7YGzMvN3tfWfBI4HDo2It+TM+7J+TDX/XvuGzLy3+nys0hVFRLyW1X9gv5SZF88whrVq85dk5ler5T9HxCGURGkhpUDxqRk+x1DIzM9ExBeBxcCelPd/T8rv034R8V+UFmbrAjsD1wGvrfUiUHcn5b1pd1WVALU7l1I4ewxARGwEvLbDfidUBbnVRMSzgRMorZOeM4u/u3HST/lg5b5Vn+Z/DVxbJcszD2BmZZx3AwdQupN4fQ/P8XBKwf9a5vfvZPv13r2abxidx6LZvJr/FUA/1z3K+AiHtq/PzE7P00us3fT1tyDNFXNITcEcch4wh5wz5pNjlE+COeUYMp+cf8wpsZKtH/9JaeGwL6V/2Yt6+NB/mnKb968pH7g/UL7woXxpd+ob/WRgL0oStiQitqT0NX1xZv6wtt8ulB+CuispX+wwWTO7IZ3dv20/zcxqP7KZeXf1Y18fF2ERq1+vZZTWVjDA65WZf46ITwLHUP6W5vuX/SAcStVHdJtz12wYY6H1D59T6ysz8/qI+AolUXgWpbuamWh9hq7tsr3T+tdSumaqW075R8NM3FjN76T04b9SZmZEnElJinZlPJKiuyjjipwNUI0J8xxKC76DgS9SujMKSmGq/btwOt2uZWsA8NY136jLuU+lw3d1ROxH6e/7j8DiLGPNqIz7syOwdQ/7Prh2TOsfC7/rsm8/pivjrCIi/p0ycPZ3gadVremmcwTlb/Jj8zURjoh1gE2qxT9V802r+ZOqqZv/V837ue7b0PkzurSaz7osNMO/BWkumUOqE3PI+eFQzCHngvnkmOWTYE45Zswn5xFzykl2A9C70yj9hX6I0npwyltBI2IhJTn6NrBjZh6WmW+qalbfCnS7rfsLlC//F1c/Kh0Hq87MUzMz2qZtartcXs27DR7aGth4TdzqPuxat4t3q3Tu9sHsWWYu7XC9FtV26Xq9qkFbt6XcEt/rD3bri239mcY8PoA1dwAAIABJREFUJgZybTNzUYfrt3QgEc4jEbE5k62JPhURWZ+YvFW8PmD1VNew0+3df67mD+gSxmrrM3ObDtf31ClfzNRan+dbsm2g1UoraVp3Fs8xtKpb+T9DGaQWSmvTVgHmRx3e61WmDqfsdi23rOY3V8+7vMs5l7cfGBEHUMaduRbYOzMvb99nHmu12n3iVDtVZZRF1eL3mUw6t+p4QB96KOPU43gvcDRlvIqnZOat052/6lrnEJifA1TX7En5br229jlpfVZfM81n9bBqv56ve2aeO81nflZl15n8LUhrgDnk+DKHHF/mkEPKfHKlsc4nwZxyxJlPzi/mlBUr2XqU5bbgz1Fq2W9j+tYiD6vmX87Va7R3pcuPYbXvKZQ/rGdQWiPeShkMsh//Xc33bd8QEdtR/tiupPcC9zhrFVAe0r4hIh7GmhlzoOv1orQkXA84L3tvkf24aj7fr+8wXFtNOoTyz6GLgI90mf4EPDEitq2O6XoNKa332rVah+/ZvqEaX+LxMw2+Dz+hdGGxWUR0Ksz/dTVfvgZiadIt1TyqgsnPgEdFxCZTHNPJ1lWXAO0WVfO+upKIiH+g/IZfQ0mGftlnPOPuVMqYBftHxKOm2O9wSt/5lwPLMvM24BLgARHxmCmOa2mNN7TWlHt1EcUHKC2Hv0VpYdZ1jKM2+1NawH4752lr0+r78Jhq8ZO1TT+o5n/Xy3lmcN2nck41f3L7eEARsQGlC7M7ajG2ts3mb0GaU+aQY20Y8gxzyLkxDNdWnZlPFvMlnwRzylF0KuaT84I5ZZvMdOowUWqjr25btzWl1cwebevfXu1/aG3d46p1n2/bdwtKgSAp/al3eu6tKS3Orq72+9AM4l8L+Hl1/DNr6xdQWlok8MZpzrG02u8lTV+PAV/XbFu3NqWW/SZgi9r6dSm35q92rShdP6xyzTs8z7l9xHV/SmHwTmBhbf06wHnV+V7QdswewIIO53oRpbXWncA2Tb/n43Zte4zl3OocD+sz/qtn+pyjMAGXVa9z1yn2eVu1zzuq5edXy59s228nSoE7gaW19QuAX1Xrn9J2zMtafyfAolm8juXVOe4zxT6t34WP1z+nVdx3AHf18/cxjBNwIOXW/07fQ1sCv6zegwOqdYdXy18CNupwzMbA37Sta12vT7e9j9sCN1Tv4/Z9xHwIpTD+a+ChM3jNre+Htzf9/s/xtX1r9TqvAB7ZYft+wO2UssoTautfWh33Q2DDtmMWAA+sLT+12ndiBvEFpcVgVt/l6/R5/HeqY5/T9Hs9h9dwtd/E2rYtgM9U+1wJbNa2/bvV5+TwLsfvxKq/qT1f9x7iPqs61z+2rX9Ptf6Dg/xbcHIa9IQ5JJhDmkOO0LSmrm2PsZyLOeR0r9d8cozyyer1mFOO4YT55FhMdPiNrG0zp2ybHJOtD5l5FXBVj7v/L+V212dXA6N+j3J78lMotfTXTPU8EfE1Sj/60NbNR4+x3hMRh1Fat30uIj5Xxb4PpbXO95m87XqliHgjpe9cKH3YAhwWEa1WPN/LzFP6jWeYZeZdEfE+4C3Aj6oBV+9D+aG/himu1QBj+HNEvJTS0vXciDiD8mP/TGCHav2n2w47HVhQ/X1dTUmm/pbSyvVu4MjscEv7fLImr21EnFpbbH2G3hkRrZZXp2Tm92r77wi8se00G7ed5+jMvG5QMTYpysDtOwA/zVXHBmn3EUpLmMMiYgllPIhfAgdGxIOBCyj/RHpWte159YOzDEb9EuCbwJcj4vOUgt2jKdf9G5Tv4U7dbnSLfTPKINotm7VirbolAfi3zLysts9xlO/bg4GdIuJcSkun51A+q0dl5q96jWFI7Qa8BvhDRHwP+E21flvgaZR/RJxJNR5CZn40Ih4LvAK4IiLOovwubVIdsxfwMUryWveT6rkuioizKV30PJ/SivgNmXlFL8FGxGJKn/4LKK2bDovVB8tebTDk6u+p9RvYusPgGdXfI8BlmflvvcQwQpZSuop6PfDj6lr9jPJPp8dTrscdwIGZ+d+1406hvFcHA7+sxov4E6WF4hMo7//Sat/zKYnVa6uWqK1xEk7KzOnGjvkXyl0ad1DGunhjh2t5cWZ+qX1l1QJ9cfV8X57meUZeTA40vYDymXkU5Rrdl5LAvLDD78w/UMqPH4mIV1O+d2+i3InzaErr6d0pY09Af9d9Oq+g/GP4xIjYB7iU8ve2mNKlxzFt+8/4b0FaU8whAXPIuYrBHHIOmEMOJ/PJscwnwZxyXHPKpZhPjg1zSqCXnLLpWtFhneijRRAdWiFW6zehDEK9HPgL5Yf5OEq3Dcvp0gqxOvZZ1Tn/d5av45GUVofXUVql/QKYANbtsv+5TLby6DSd2vS1mcV7sVb1GlZ02BaUwuoVwArKj/S7ul0rBtwKsXbcHpRa8xurD/dPKYMtrtVh33+m3ML622rf1t/Yx4Cdm36/x/XaThHDVJ+bTt8Pi3o4Zpum39sBXqPTq9f06h72Pbvad/9q+SGUfxDcUP2t/y/w7Np7uLTDOXarPh+3VNO3KT/g76+O2aWP2Lfp4Vot6nDcepRCwGWU79+bqzie0utzD/NUXZdXUgahvpwyfsEKyqDFX6e0iO7UIvHpwFcphakVlMGmf0j5Ld2xbd+k/C49CPhEdcxfgP8D/qHPeFuf7amm5R2OO3WaY85t+lrM4TXeldJ69jfVZ+9WSjcOxwMPnuK4FwLLqr/5v1THn87qrUr3pSRHt/bzvdfDNelaXgHeWW3/16bf3zm+du3vx52UsuBFlFZ6+3b6fNaO3wB4c7X/rdX1/w3wNco4J+vP9Lr3EPtDKGWZ31ffEVcC7wM2GeTfgpPTXEyYQ47d5xBzyLGd1uS1nSIGc8je3yvzyTHLJ2vXxpxyTHNKzCdHeurwnphTTjFFdTINmaqWeAmlm42PNBzOWIiIB1Janf0uMx883f4aHV5b9Soivk9JmDbM0u+zhljVsnNZZi5qOhZJkoadOeTgmWeML6+tZsJ8cvSYU0paExZMv4vWtGogvpdRWtlMNzi2erd/NT+/0Sg0F7y2Wiki1ouI1QYkj4hDKV0TnG1CJEmSxok55JwxzxhfXlt1ZD4pSeqXY7INkYh4GvA3wDMofe8fnZm3NxvV6IuItwKPAA6g9DP/7mYj0qB4bdXF1pTxE75FGbT6PsBjKP073wQc1WBskiRJA2MOOTfMM8aX11Y9MJ+UJPXF7iKHSDVY7SGUwRM/ChybmT0PpqrOqlvDbwEuBN6Wmec0HJIGxGurTiJiY+Dfgb2BLYH7Ufpo/zbwjuxxUGM1z649JEmamjnk3DDPGF9eW03HfHK8mFNKWhOsZJMkSZIkSZIkSZL65JhskiRJkiRJkiRJUp+sZJMkSZIkSZIkSZL6ZCWbJEmSJEmSJEmS1Ccr2SRJkiRJkiRJkqQ+WckmSZIkSZIkSZIk9clKNkmSJEmSJEmSJKlPVrJJkiRJkiRJkiRJfbKSTZIkSZIkSZIkSeqTlWySJEmSJEmSJElSn6xkkyRJkiRJkiRJkvpkJZskSZIkSZIkNSAisjYtra1f1LZtUXNRSpK6sZJNkjRUIuLZEfHViPh9RKyIiFsi4qqI+EFE/GdEHNl0jJIkSZI0TiJim7YKnVM77HNM2z4XRsSmDYSrORARy6e6/pKkzu7TdACSJLVExCnAi9tWrw38P+AhwG7AAcCH1nBokiRJkjRvRcS7gH+qrfof4OmZ+eeGQhon9ff1vMaikCTNiJVskqShEBFPZtUKth8BZwE3A5sAjwb2biA0SZIkSZqXImIBcDJQ71HkLGD/zLyjmai6i4h1gHsy866mY+lVZh7fdAySpJmzu0hJ0rDYt/b4CmDXzHxTZv5bZr4hM/cFNgcOaj8wIraIiLdFxEURcXPVzeQ1EXFmRDyjw/7tfdsvjohXRcSlEfGXiPhFRLyq2netiHhjRPwqIu6s5m+IiOj0IiLiqRHxhYj4XRXHnyPi4og4LiIeMJi3SpIkSZLmVkTcBziNVSvYPg88s72CLSI2qPKk8yLixoi4KyL+GBFnR8RBVWVdff8pxxuLiHNr285t27bKGGYRsVdEfDsibgLuALaq7btNRLw3Ii6JiFurfO/KiDgjIv6uw2teWj9/h+09j58WEc+LiPMj4rYqT/1KROzY6zmnEhFPqM7bOu67EXH/2vaec+SIOLV6rQ+trT6k2/WJ4sCI+EZEXFud+6aIuCAi3lSPQ5LmA+9kkyQNi7VqjzcCtgV+Wd8hM28FvlJfFxG7Ves2bzvfA4FnAs+MiNOAQzPz3i7P/S5gYW354cBJEbEZ5Q66/WvbtgfeCawLTNTiWAB8BDi07dxrAztX00sj4pmZeX6XOCRJkiRpGKwDfAGoV8h8HHhxZt5T3zEiHka5u227tnNsDjypmg6ucqFB3/32ZOBYVs0nW3E9A/gkZfiBuq2r6fkR8a+Z+eYBxwTwVqC9Eu/pwG4R8cjMvG6mJ46IJ1By4PWqVd8Ent16bweUI3d77nWBLwJ/37ZpQ2DXanppRPx9Zv6y/XhJGkdWskmShsX/1R5vClweET8FLqR0Hfk/mfnj+gFVC7kvM5k83AP8F3A1JXnYuVp/EHAZcFyX515ISUz+F3gJJfkAWFLNvw5cBLys9lyvj4jjat2Q/BOrVrBdApwJPAA4hFLZthlwZkQ8PDNv7vZGSJIkSVLDngfUe+94P/DqzFzl7q6IWAv4EqtWsH0W+DmwD7Bnte6JwPuAIwYc5+7A7ZTKtKuAnYC7ImJb4NOUxpFU+5xKGY7g+bV43xQRl2TmJwcc199R8suzgMXAHtX6zSnDJLxzJiftUMH2OeCFmbmi2j6THPkMSv76ZmDjavuFlPev5Ypq/h5WrWA7H/gW8AjgBdW6bSl576Mz8+6ZvE5JGiVWskmShsXpwMuB3arloNxF9ujWDhFxOfDGzPxSteoQYIvaOV6WmadU+74D+AmlsA9wdES8s73VZeVbwFMzMyPiauBDtW3fzMynVef8PWU8AoD7AzsCP63uYju6dswVwN9m5l+q484DPlpt2xw4HHjvNO+HJEmSJDWlXsH2ucz8xy77PRV4VG35HZl5LEBEvBX4NqWSCeDwiHjzbO7i6uAeYK/MvKi+MiLezWQFG8CzMvPb1bbjgV8xWaH0z5RKukH6IbBnZt4VEWtTKrlaueuuMzznE4CjmKxgOxV4SVuOO5Mc+ZvAN6MMmdB6T37WPlZcRGxCaZTa8j/A4tbzR8QvgH+ptv0V5S7IL87wtUrSyHBMNknSUKhauO1D6YLx6i677QB8odaH/J61ba0Weq3z3cmqidLGlIJ+J5+stchc3rbtU7XH7d1dtBKQHSh3qa08plXBVjkNqLfg2wNJkiRJGg3Piojndtm2Z9vyx1oPqq4IP17bthbwuAHH9o32CrYOcV3dqmCr4rqB0utIy6MjYoMBx3VKq9eTav6b2raNOx8yrbcwWcF2EnB4h0akg8qRO9mNVW/Y+Hjb83+0bX/zXknzgpVskqShkZm3ZebSzHwIpeLqYMpdZX+s7RbA66vHm9TW39jqIqPmD23Lm9DZ72qP289xTe1xe1cXrd/R9vOu8rxVBWK9tWa3OCRJkiRpGCynVNJA6fr+jIg4uMN+U+ZCHZa75ULRtny/6QKsXNZlff152mPotK5jxVdERO1xrzHB6o0376w9HsT/Y3/d3nVnZVA5cieDutaSNFasZJMkDaXM/EVmnpaZLwMexqqVXQ+t5jfU1m0cEfdtO82Wbcs30NldXdbD6hVrnbSfd5XnjYj7sOqdbt3ikCRJkqRhsIwyxlYrV1oLODUiXtG235S5UIfl1v73tq1f2bVj1R3/9j3GeVuX9fW42mPotO7G6eICHt5jTLB6jtmpQqxfl9Yevzcijuywz6By5E5meq0laaxZySZJGgoRcUhEvCIiNuqw+U5WvcPs+mr+/dq6tSh3vrXOdz/gH2rbb2TVpGSQLmfVO9VeEBHr1JYPYtVuNepxS5IkSdLQyczPAfsBra7wA/hARLyhtlt7bnNY60FVWXZIbds9wA+qxze1HVfvRvKllLGsZ6Me14Mj4om1uDYBnlXb/pPMvGWquKrX8qZZxjRbrwG+UVv+j4g4pG2f2eTI9YrB9VjdBazaCPXg6n1pOXyKWCRpbN1n+l0kSVojtgWWACdExPeBHwF/AjYAngZsU9v369X848CxTCZg/xERj6eM6fZMJgd0BnhPh/7qByIz742I9wDHVaseBvxvRHyJ0pqvnvhcR22cAkmSJEkaVpn59Yh4KvBl4P9Vq98ZERtk5luArwE/Bx5ZbTsmIh5RrduHVccIOzUzW40TLwP+DNy/dtxjKHeOPWEAob8feDnQavx4ZkR8rHrO57Nq95D/Xnt8Qdt5vhARZ1OGM3j0AOKajbuA5wJnU8Y7C+CjEXFnZp5R7TObHPlqSi4L8LSIeCclJ1+RmSdm5g0R8VHgiGqfvYDvRcS3KHf5vaB2rsuBr8z6FUvSCLCSTZI0bNYGFlVTJxcCxwNk5s0R8SxK4X1Tyu/aYR2O+RTwr4MOtM07KYNGH1Qt/3U11d0A7JeZ7a0jJUmSJGkoZeY5EfEkyl1UrZ5Hjo2I9TPz9RGxP3AWkw0jD+hwmnOAV9fOuSIi3ktpaAmlt62nV4+voFQo7TiLmH8dEQcCp1PuyloPeGWHXf89Mz9RO+6CiDiXyXx0w9rr+RqlAWhjMvP2iHg6cC6wM+V9Oy0i/pKZX5pljvxZJl/3ekDrjsXbgBOrx68FtgNadwbuXk11VwHPqsYml6SxZ3eRkqRhcQLwbErh/XzgN5TC/F3AHylJ2auAPWpdeZCZ5wOPotxFdjFwK6ULiz9QEov9M/Mf5uoutloc92bmwZTWgWcCv69ivw34CaUS7q8z0y4zJEmSJI2UzPwBsJhyZ1PL6yLiw8CvKBU+b6LcCXYzJSe7Dvg2cCjwpMy8ve20E8A/MVmpdg1wErArcO0AYv4SsBMlx7wUuJ0yDMHVwGeAxZn5hg6H7gd8sIphBeWuu6Oq9Y2rGm3+PeV9h1KR9unqjsPZ5MgfpFzDy1l1uIb6c99RPfdBlIrVP1Xn/jOlQeyxwM6ZeflAXqwkjYDIHMS4m5IkSZIkSZIkSdL84Z1skiRJkiRJkiRJUp+sZJMkSZIkSZIkSZL6ZCWbJEmSJEmSJEmS1Ccr2SRJkiRJkiRJkqQ+WckmSZIkSZIkSZIk9ek+TQcwjDbbbLPcZpttmg5D4+6iiyYfP/axzcWhnnnJJEkaXRdddNF1mbl503FoPJlDjg7L9JIkSepFrzlkZOaaiGekLFy4MC+88MKmw9C4i5h87OdwJHjJJEkaXRFxUWYubDoOjSdzyNFhmV6SJEm96DWH9E42SerV7tfVFjZrLAxJkiRJM2SZXpIkSQNkJZsk9eq4S2oLi5qKQpIkSdJMWaaXJEnSAC1oOgBJkiRJkiRJkiRp1HgnmyT1aIsrNp1cWNRYGJIkSZJmaIvz7p5cWNRYGJIkSRoTVrJJUo+uffFOTYcgSZIkaRauPeZJkwtvzuYCkSRJ0liwu0hJkiRJkiRJkiSpT1aySZIkSZIkSZIkSX2ykk2SJEmSJEmSJEnqk2OySVKPnnPytSsff/4VD2gwEkmSJEkz8Zx9PrDy8ecbjEOSJEnjwUo2SerRFx55aW3JSjZJkiRp1Hzh2Ec2HYIkSZLGiN1FSpIkSZIkSZIkSX3yTjZJ6tV3tph8vKixKCRJkiTNlGV6SZIkDZCVbJLUq7fXupZ5W3NhSJIkSZohy/SSJEkaILuLlCRJkiRJkiRJkvrknWySNA9ExMrHmblyudvj1rIkSZIkSZIkqTMr2SRpTNUrzGZ7vBVukiRJkiRJkrQqK9kkqVdPv6a28KDGwpjKbCvWejmvFW6SJEkaWSNQppckSdLosJJNknp11C9qC8OTkNe7e5QkSZI0hSEt00uSJGk0WckmSSOo6Yq1pp9fkiRJkiQ1o5dx36fS6zGOKS9pFFjJJkk92vFXD5xcWDT3zzdVIXOYWOEmSZKkUbHjV2+aXFjUWBiSNDR6qTDrtfKsKVO9hnFg5aI03Kxkk6QeXfqSHeb8OYa50NoLC3uSJEkaZpe+e//JheMtr0qaP0alwmzQer1jrgm93sEnabhZySZJDejWMmycWOEmSZIkSdLcsqJmMHq5W2wm3WROdXfgoGIe1PkkzYyVbJK0BljwsVtJSZIkSZLazXY8M82t+XTXn6SZ6auSLSI2AfYCHg5sDgTwR+BXwHcz8/qBRyhJI8TCV29MCCRJmh/MISVJ42y245mZE2uQ/JuTmjFtJVtErAccCLwUWEhJijrJiLgI+DDwqcy8vdcgIuK5wN7ALsDOwAbA6Zn5ol7PUZ1nOfDQLpuvzcwt+zmfpPExkIF8D/gMALs97nFccNTWHY+XJEma78whNcweV5XpAX7QYBzjarZ34QyiwmJQx/TzGgZ5jK9hOF73jP9vIA25qb5nJc1M10q2iFgbeDXwZmCj1mogWT1JSmABJYFaCLwrIt4OvD8z7+ohjmMpidGtwNXAjn28hnY3Ayd0WH/rLM4paUTMacH3FZsDcAFXDPa885wFOkmSxoM5pEbBBVWZXsPDCgtJGg6DrqiW5oup7mS7DNimelz/tNxN6d7j+mr9JsAWwNq1/TYCjgdeCTyshzheR0mMfkVpjXhOT9F3dlNmLp3F8ZKGkGOazQ9eZ0mSRpo5pDREmriTSJKkFn8fNF9MVcm2be3xBcBngO8DF2fmivqOEXFfSjcdewDPA3brcI6uMnNlQmQLJkn9WmM/2p958NyeX6uwwk2SpJFjDqnhVy/TL2osijlh+VmSNKzmsuGGlXlq2lSVbHcApwAnZOZvpjpJlTD9sJreGxHbAq8HDh9UoH24X0S8CNgauA34CWVA7XsaiEXSHGnkB/Q/emlUrblioUmSpKFnDqnhVy/Tn9xcGINiJbMkaVz12n1lL8f4vyTNpakq2R6amdfN5KRVQvWPETExs7BmZUvgtLZ1v4mIwzJzWQPxSGOvW5cigzqv1M4KN0mShpI5pLQGWBaWJKk/7Xd7+1uqQVrQbcNMk6NBn6NPHwP2oSRJ6wM7AR+ijAvwjYjYeQ3HI81rETHtVN+v/bHUi/a/J0mS1AxzSGluWN6VJGnuTPU/S6kXU93JNnIys73V4yXAyyLiVuAoYCmwf6djI+II4AiArbfeeg6jlEafff1rWPm3KUmS+mEOKUmSpG669d416HHlNNr6rmSLiE0pwwM/GLhfp30y812zC2vgPkhJkPbqtkNmfhj4MMDChQv9BEhtbMEBHFIbWuTjzYWh3tgVgCRJw8EcUkOlXqZn28bCmI75lyRJo8//Rc0PfVWyRcRrgOOAdabZddgSpD9W8/UbjUIaAb20ypi3Dr1y8rGVbCNtYqI0Wl+yZEnDkayqFRcMX2ySJM2EOaSGTr1MP2SVbOZfkiSNr6nuftNo67mSLSIWA+/tsCmBaFseNrtX8183GoUkaejUK7a6WbJkySoVc50eD/r49gq3Xo63Yk6SNEzMISVJkqTpdbvpwQq40dDPnWwvb1tuXeGoLc95s6uIWBvYHrgrM6+orX8U8PvMvKFt/4cC768WPzHX8UmjaFjv6hk6py4HYO9Fe7Os2UikjrpV2PnZliQ1xBxSQ2fvqkwPlE5MG2Y3UpIkqRsbVo+GfirZdqUkQRcDXwaWVMubAgcCJwIfA17VbxARsR+wX7W4ZTXfPSJOrR5fl5lHV4+3Ai4FrgS2qZ3mAOCNEXEO8BvgFkoi9TRK1yRfB47vNzZplPlFPGAfPwyAc09N4rCGY5H6ZGW6JKkB5pAaOud+vFaQP/XQxuKQJEnqRy89HrWWtWb1U8m2RTU/G7i1tTIzbwROjog9gMOAHwMn9RnHLsAhbeu2qyYoydDRTO0cYAfgMZSuPdYHbgK+B5wGnJY2DZMAK9+k+c4KN0nSGmIOKbUxF5MkSXPJssaa108lW6sbjxuAFStXRtw/M/8M/Lza50j6TJAycymwtMd9l9OhS5HMXAb24CZJUj8sfEmS5pA5pCRJktSgTne8+f+fweqnku0GSjcc6wN/qK0/KCI+CjylWt6u/UBJkjT8rHCTJA2YOaQkSZI0hOr/A6qzMq5//VSy/YGSIG0CfLW2/kTgfUy2DLxlMKFJGqRuX5zqw8vPAmD743/VcCDS3LPCTZI0AOaQGjrbV2V6gCsajEOSJGkUOPbb9PqpZPsRpa/6vwYuAn4LPJiSGAVlAOtk1eRJ0hzwC60hz7svAL/m6oYDkSRJGgnmkBo6v67K9JIkSZq59v9Pz+e73/qpZPsUcAdwa2ZmRBwJfAFYp9oelD713zDYECVJUtPmc2FJkjRj5pCSJEnSPDNVV5TjqOdKtsz8DvCd2vI3I2JH4FnAZsDlwOcyc0WXU0iaBbt7HAInb990BJIkSSPDHFJDqV6mX9RYFJIkSfPOuPbO1s+dbKvJzKuAkwYUiyQNt88+pOkIpKHgXW2SpJkyh1TjLNNLkiQNhXH5/9KsKtkkSZIkSZIkSZKkmRrlu9z6qmSLiMcB/wTsCmwMLOiwW2bm+gOITZIkSZI0wswhJbv+lyRJGmc9V7JFxJOArwFrUQao7iZnG5QkSRoNo9zSSJI0t8whJUmSJI27fu5km6j2NwGS1pCZtHgcl75sh9JRl08+fndzYUiSJI0Ic0gNn3qZnh0aC0OSJEnjoZ9Ktl0oyVEA3wQuB+7EhEnSfPH0308+tpJNkiRpOuaQGj71Mr2VbJIkSZqlfirZ/gLcD/h0Zh44R/FIkiRJksaDOaQkSZKksdZPJdv3gKcBv56jWCRpuL37AgBe+KKDOL3hUKRh5PhskqQ25pAaOi+syvQALFrUWBySJEkaD/1Usr0FeBJwYES8JzOvn6OYpHnNMdWG2FffCMAnvvJvNv+tAAAgAElEQVTPnP6ChmORJEkafuaQGjqfqMr0xT83FockSZLGQ9dKtoh4Q4fV5wGLgV9GxOeB5cBd7Ttl5rsGFaAkSZIkafiZQ0qSJEmab6a6k+3f6D4g9UbA4VMca4IkSdI85525kjTvmENKkiRJmlcWzOCYpHviFLOIRZIkSZI0fswhNe9MTEysMl6tJEmSxtN0Y7KZ8EhSy7H/A8Am7/l5w4FIkiQNLXNIDbVNqjI9wA0NxiFJkqTxMFUl27prLApJGgX73A3Ajfyx4UCk0WPXkZI0L5hDaujdWJXpJUmSpEHoWsmWmXdGxJ6Z+b01GZAkSZIkafSYQ0qSJEmab6brLvK7EfEH4Ezg88A5mXnP3IclSUPo7X/VdATSWPCuNkkaa+aQGm71Mv2ixqKQJEnSmJiuku06YEvgSOAI4KaIOBP4InBWZq6Y4/gkaXh85wFNRyBJkjTszCE13CzTS5IkaYAWTLN9S2Ax8H7gamBj4FDgS8B1EfGpiDggItaf0yglSZIkSaPAHFKSJEnSvDHlnWyZeS+wrJpeHRG7As8B9gMeDjwfeB5wZ0ScRWmd+OXMvGlOo5YkSWOh1XUk2H2kJI0Dc0hJkiRJ88l0d7KtIjN/mJn/nJk7AI8G3gpcAqwDPAv4GHBtRJwVEUcMPFpJkiRJ0sgwh5QkSZI0zqYbk62rzLyEkhwtjYjtgOcCzwb+FngS8ETgw4MIUpKGwjt+Ovn4mObCkMaVd7VJ0ngzh9RQqJfp2Wmgp26VZSzHSJIkzR8zrmSry8xfA+8C3hURD6J0B7L/IM4tSUPj8dc3HYEkSdJYMIdUYyzTS5IkaYD66i6yF5l5TWaelJlPGPS5JUnS/DExMbHK3W2SpPFkDilJkiRpVPV9J1tE7ATsCmxMl0q6zHzXLOOSpOHz5i8CcOyxb+HtDYciSZI0KswhNUyOrcr0AJy3qLE4JEmSNB56rmSLiPWBzwD79rC7CZKk8XP+iQC87anvs5JNWsMc40SSRo85pIbR26oyffG+xuKQJEnSeOinu8h/A54CxBT7TLVNkiRJkjR/mENKkiRJGmv9dBf5bCCrxyZCkiSpEfVx2ryzTZKGmjmkJEmSpLHWz51sm1bz3wKPo/Snvx6wbtu03iADlCRJkiSNJHNISZIkSWOtnzvZlgMPB/4rM384N+FI0hB77w8AuN/JP2o4EEkt3tUmSUNtOeaQGjL3q8r0AHc2GIckSZLGQz+VbKcDE5QkSZLmn13uAGAFdzQciCRJ0kgwh9TQWbHL4MryNvaRJElSP91Fvgu4AHheRLw9Iraao5gkSZIkSaPPHFKSJEnSWOvnTrYbKZVyAbwJeFNE3A3c07ZfZub6A4pPkobHa3duOgJJkqRRYg6p4VMv01/cXBiSJEkaD/1Usq0DZDVFtW7taqrLAcQlScPnxxs3HYEkSdIoMYfU8LFML0mSpAHqp5INJhMjSZIkSZKmYw4pSZIkaWz1U8n2zjmLQpIkSZI0bswhJUmSJI21nivZMvNNcxVERDwX2BvYBdgZ2AA4PTNfNINzPRh4K7AvsCnwe+BLwERm3jiwoCVJkiRJXZlDSpIkSRp3/XYXOVeOpSRGtwJXAzvO5CQRsT1wHrAFcCZwGbAr8Bpg34jYIzOvH0jEkuafD104+fjI5sKQJEmSOaRmqF6mZ2FjYUiSJGk8LOj3gIh4bER8KiKuiohbq/mnIuJvZxHH64BHAPcHXj6L85xMSY5enZn7ZeYbM/MJwHuBHYB3zOLckua7R9w6OUmSJKkn5pAaKpbpJUmSNEB93ckWEYcBHwLWYnIA6/WA5wHPjYiXZeZH+g0iM8+pPUe/h7eO2w54MrAc+EDb5iXAEcBBEXFUZt42oyeRJEmSJPXMHFLjZmJioukQJEmSNER6rmSLiEcCH6yOyQ67rAWcHBEXZOYlA4qvH0+o5mdn5r31DZl5S0R8n5JAPQ74zpoOTtIYOPK9AJx22ic4qOFQJEmShp05pIbRaVWZHoDLFzUWhyRJksZDP3eyvQZYm5IcJXAB8AdgS0qf9VGd7x9pZrSiHar5L7ps/yUlQXoEJkiSZuIXXwbgRbtuYCWbJEnS9MwhNXReVJXpJUmSpEHop5JtcTW/DlicmT9vbYiIRwHnAJvW9lvTNqzmN3fZ3lq/UaeNEXEEpTsQtt5668FGJkmSJEnzjzmkJEmSpLG2oI99t6K0PvyvenIEkJk/Az5OaYm41eDCG6hWR/2duikhMz+cmQszc+Hmm2++BsOSJEmSpLFkDilJkiRprPVTyTadjonHGtRqZbhhl+33b9tPkiRJktQcc0hJkiRJI62f7iKvAbYHXhgR78/MK1sbImIbWDlE0TUDi64/l1fzR3TZ/vBq3q2/fUma2n/9AIA47YKGA5EkSRoJ5pAaOlGV6aH5Wl5JkiSNvn4q2ZZREqQHAJdHxDImB63eG7gvpYz63UEH2aNzqvmTI2JBZt7b2hARGwB7AHcAP+h0sCRN6yF3NB2BJEnSKDGH1PCxTC9JkqQB6qe7yBOBe6rH9wWeCLyomt+3Wn8PcNLAousgItaOiB0jYvv6+sy8Ajgb2AZ4ZdthE8D6lLEAbpvL+CRJkiRJgDmkxsTExAQTExNNhyFJkqQh1POdbJn5k4h4PXAC3XtVODozL+43iIjYD9ivWtyymu8eEadWj6/LzKOrx1sBlwJXUpKhulcA5wEnRsQ+1X67AYspXXwc029skrTSQbs2HYEkSdLIMIfUUKqX6X/bXBiSJEkaD/10F0lmnhQRlwBHAXtSBoL+M/B94PjMPGeq46ewC3BI27rtqglKMnQ008jMKyJiIfBWYF/gqcDvKS0oJzLzhhnGJ0lw9XpNRyBJkjRSzCE1dCzTS5IkaYD6qmQDqJKgcwDa+62fqcxcCiztcd/lQEyx/bfAYbONSZIkSZI0e+aQkiRJksbVlGOyRcS9EXF31cXHagaRHEmSJEmSxoM5pCRJkqT5ZMpKtkrXFn+SJEmSJLUxh5QkSZI0L/TdXaQkzVufPW/y8QHNhSFJkiRphupleh7fWBiSJEkaD1aySVKvNlvRdASSJEmSZsMyvSRJkgao10q2jSNi615PmplXzTAeSZIkSdLoM4fUSJuYmABgyZIlDUciSZKkYdZrJdubq6kX2cd5JWl0PPcVAFz0fz/isQ2HIkmSNOTMITWULqrK9ABc9/PmApEkSdJY6DWRceBqSbr+UgD+Zuv7NRyIJEnS0DOH1FD6m6pML0mSJA3CgqYDkCRJkiRJkiRJkkZNr3eyXQj8bC4DkSRJkiSNDXNISZIkSWOv10q2MzLzPXMaiSQNvQcCcM01DYchSZI0/MwhNZSuqcr0AA9qMA5JkiSNBweXlqRefe3TAGx18f80HIgkSZKkmdiqKtMDZINxSJIkaTxYySZJvVrvnqYjkCRJkjQbluklSZI0QFaySZIkSZKkeW1iYmLl4yVLljQYiSRJkkbJdJVsrVLmeXMdiCQNvafu2XQEkiRJw84cUsOtXqa/vbkwJEmSNB6mrGTLzImptkvSvHKHN/9KkiRNxRxSQ88yvSRJkgZoQbcNETERERvP9MQRsXFELJ3p8ZIkSZKk0WEOKUmSJGm+6VrJBrwFuCoiTo6Iv+v1hBGxe0S8H7iyOockSZIkafyZQ0qSJEmaV6brJ2E94EjgyIj4A3A+8EPgKuCGap9NgIcAC4Hdga2q9QHkoAOWJEmSJA0tc0hJkiRJ88ZUlWyvB46lJEAADwT2r6Zuovb4BuCts4pOkobJOedOPl7cWBSSJEnDyhxSw69epmdRQ0FIkiRpXHTtLjIzTwC2B/4V+GO1Olg1CYoO6/8EHAdsn5knDjRaSZIkSdJQMoeUJEmSNN9M2V1kZt4MHFMNPr0fsC+wF7AtkxV0CfwG+C7wTeCLmXnXXAUsSZIkSRpO5pAaJRMTE02HIEmSpBE33ZhsAFQJz2eriYgIYFNKy8PrM/PeOYtQkobF4tJHZGau0hxbkiRJqzKH1LDKxZP9vk8sXdpcIJIkSRoLPVWytcvMBK4bcCySJEmSpDFkDilJkiRpHHUdk02SJEmSJEmSJElSZ1aySZIkSZIkSZIkSX2aUXeRkjQvrbs7AN+98O6GA5EkSZI0E9+tyvSSJEnSIFjJJkm9+vpxAOx96/caDkSSJEnSTOxdlekBli5b1mAkkiRJGgd2FylJkiRJkiRJkiT1yTvZJKlXt6/VdASSJEmSZsMyvSRJkgbISjZJ6tXT/q7pCCRJkiTNRr1Mv/S/m4tDkiRJY8HuIiVJkiRJkiRJkqQ+db2TLSL+ZaYnzcy3zvRYSZIkSdLoMYeUJEmSNN9M1V3kUiBneF4TJEmSJEmaX5ZiDilJkiRpHul3TLaoPc4e1kvS+Nj0zsnH1zcXhiRJ0ggxh9RwqZfpJUmSpFmarpItOqzLan37tuywTpLGx+fOn3y8uLkwJEmShpg5pIZbvUy/rLkwJEmSNB4WdNuQmQvqE3Bf4MvV5n8BHgqsU82XVOv/u1onSZIkSZpHzCE1jCYmJlZOkiRJ0qB1rWTr4HXAM4BPZubbM/O3mbmimr8N+BTl3o43zEWgktS469aC69ZiwY33bToSSZKkUWAOqaGz4Lq1Vk6SJEnSbPVTyXZ4Nb+iy/YrKF19HDyriCRpWB2wFxywF/fs//imI5EkSRoF5pAaOvccsNfKSZIkSZqtfirZtqnmz4iItesbIuK+lBaKAFsPIC5JkiRJ0mjbppqbQ0qSJEkaS/1Usv2pmu8C/CQilkbEyyNiKfBjYOdq+3UzCSQiHhwRH42IayLizohYHhEnRMTGfZzj3IjIKSb7+pckSZKkNcMcUpIkSdJYu08f+34OeC2QwA7AW2rbopon8Nl+g4iI7YHzgC2AM4HLgF2B1wD7RsQemXl9H6fsNqLx3f3GJkmSJEmaEXNISZIkSWOtn0q2pcA+wE6URKguKUnSJdV+/TqZkhy9OjNPaq2MiPdQBst+B/CyXk+WmTOJQZKm9uCDAPjgV29vOBBJkqSRsBRzSA2ZD1ZlekmSJGkQeq5ky8w/R8QewNsoA1hvUNt8C/Ax4C2ZeUs/AUTEdsCTgeXAB9o2LwGOAA6KiKMy87Z+zi1JA3Xa4QC8nB82HIgkSdLwM4dUUyYmut2YCC+vyvQAS5ctWxPhSJIkaYz1cycbmXkr8LqIOBrYEdgIuAm4LDPvmWEMT6jmZ2fmvW3Pd0tEfJ+SQD0O+E4vJ4yI5wPbAiuAS4H/zsw7ZxifJEmSJGkGzCElSZIkjbO+KtlaqmToZwOKYYdq/osu239JSZAeQY8JEnBG2/IfI+KVmfm5GcQnScVv1206AkmSpJFkDqmhYZlekiRJA9R3JVtEbAO8GtgT2Bw4DrgA2KXa5fQ+WyRuWM1v7rK9tX6jHs51JnA88CPgeuChwCHAUcCnI+LpmfmNTgdGxBGUbkXYeuute4tc0vxy8G5NRyBJkjRyzCE1VOpl+qXfbC4OSZIkjYW+KtkiYn/gE8A6lEGqk9Kv/rrAqdXyb4FzBhhjVPP2gbJXk5nvbVt1OfDmiLgGOImSzHVMkDLzw8CHARYuXDjtc0mSJEmSpmYOKUmSJGmcLeh1x4h4BHA6JRlaRWZeAFxdLT6jzxharQw37LL9/m37zcQpwN3ALhGxwXQ7S5IkSZJmxxxSkiRJ0rjruZINOJrS+jCZTIbqllFaDO7eZwyXV/NHdNn+8Grerb/9aWXmX4BbqsX1Z3oeSZIkSVLPzCElSZIkjbV+uovch5IcXUxJgv7Stv1X1XyrPmNodQvy5IhYkJn3tjZULQb3AO4AftDneVeKiB2AjSlJ0nUzPY+kee4Rt0w+nvG/bCRJkuYNc0itMRMTE73tWC/TS5IkSbPUTyVbK/E5MzNXRET79ruq+Rb9BJCZV0TE2cCTgVdS+r1vmaC0GvxQZt7WWhkRO1bHXlZbtx1wZ2b+rn7+iNgM+Fi1eEZm3t1PfJK00ocumny8uLkwJEmSRoQ5pIZPvUy/rLkwJEmSNB76qWRbAawN3K/L9u2reXvrxF68AjgPODEi9gEuBXaj/Bv7F8AxbftfWs3rWdpewCkRsQy4ArgB2Bp4KqWv/guBN8wgNkmSJElS/8whJUmSJI21firZfgPsBDwvIv61vqHqSuO5lK5AftlvEFVLxIXAW4F9KUnN74ETgYnMvKGH01wEfAJ4LLALZbDrW4CfAp+htGRc0W9skrTSL8owluuuux53NByKJEnSCDCH1NBZ9xf9DE0vSZIkTa2fSrazKQnSdkwONA3wKuBtwLqUBOnsmQSSmb8FDutx39X6GcnMnwKHzuS5JaknR+4NwO2ZxMENxyJJkjT8zCE1dG6vyvQAE0uXNheIJEmSxkI/TbjeR2nVB/BASjIE8FBKckS1/QODCU2SJEmSNMLMISVJkiSNtZ4r2TLzauBAWNlLWrTN7wAOzMxrBheeJEmSJGkUmUNqrk1MTDAxMdF0GJIkSZrH+ukuksz8ekQ8Eng1sCewCWVw6O8BJ2XmlYMPUZIkSZI0iswhJUmSJI2zvirZADLzKuDoOYhFkobbzscAcNRpNzYciCRJ0ugwh9QwOaoq0wPcv8E4JEmSNB76rmSTpHnrhCcC8B5+3HAgkiRJkmbiPVWZHmDpsmUNRiJJkqRx0LWSLSI+Wj08IzPPri1PJzPzxbMPTZIkSZI0KswhJUmSJM03U93JdiiQwCXA2bXlqUS1jwmSpPFz8YZNRyBJkjTMDsUcUsPOMr0kSZIGaCbdRUaX9dMlT5I02l73mKYjkCRJGkXmkBoe9TL90i83F4ckSZLGwlSVbFdRkp6b25YlSZIkSWpnDqk5NTExsfLxkiVLGoxEkiRJKrpWsmXmNhFxQGZ+trW8xqKSJEmSJI0Uc0hJkiRJ882CabafERGnR8RGayQaSZIkSdIoM4eUJEmSNG9MNyZbAC8A9o6IF2fmWWsgJkkaTrtfN/n4/ObCkCRJGmLmkBpu9TK9JEmSNEvTVbK1PAj4ekR8CDg6M2+fw5gkaTgdd8nk48XNhSFJkjQCzCE1nOpl+mXNhSFJkqTxMF13kd+ntETMan4kcHFEPH6uA5MkSZIkjRxzSEmSJEnzxnR3su0FvAZ4O7Bute5hwLKIOB74ICV5WkVmXjXIICVpKJx3NwBbPOAB/LHhUCRJkoaUOaQGamJiYqDn26Iq00uSJEmDMGUlW2YmcEJEfAX4KPB3lIRoLeAN1bTaYdOdV5JG0jFPAuDaTOIlDcciSZI0hMwhNeyurcr0ABNLlzYXiCRJksbCdN1FApCZV2Tm3sAxrNr1R7dJkiRJkjRPmUNKkiRJmg96qmQDiIjFwBFMdu2xWhcfkiRJkiSBOaQkSZKk8TdtlxwRsT5wPCU5WrkauAn4MiZKkiRJkqSKOaQkSZKk+WLKSraI2Ac4BdiaVbv4OBt4cWb+bs4jlKRhsc8HAHjOydc2HIgkSdJwMofUsHtOVaYHeDR/ajASSZIkjYPp7mT7Fqu2MrwDeENmnjx3IUnSkDr2kQB8gUsbDkSSJGlomUNq1iYmJgBYsmTJwM/9hapMD/DoZcsGfn5JkiTNL9N2F1kJ4Hzg4My8Yg7jkSRJkiSNPnNISZIkSWOvl0q2u4ClwLsy8/+zd+fhklXVwcbf1QgKfMg8g6Ai4EDEgIhitAE1EAFxQnGiUQE1cYiQRA2xuxWNURGnoCBqOyCKIwSikpAGIyjYqFEUEcFGGUQGkUFkXN8f+9zu09VV91bdW/eeGt7f85yn6oy1qnYNZ9XeZ+8HZjccSRpg527WdASSJEnDwBxSg6t+Tt9ts2NJkiSpg6lOKX9CaXn4k7kIRpIG2nGPmXobSZKk8WYOqcFWP6df1FgUkiRJGhFTVbI9MTPvnZNIJEmSJEnDzhxSkiRJ0tiYN9lKkyNJkiRJUrfMISVJkiSNk0kr2SRJkiRJkmbb4sWLWbx4cdNhSJIkST1xmF9J6tYB1628f1ZzYUiSJEmapvo5vSRJkjRDVrJJUreO/uXK+1aySZIkScOnfk5/fnNhSJIkaTTYXaQkSZIkSZIkSZLUI69kk6RunXUrADvv/Gh+0XAokiRJknq3c3VOD8B6zcUhSZKk0WAlmyR16/jnAnBZJnFEw7FIkiRJQ2zx4sUr7i9cuHDOHvey6pweYPGiRXP2uJIkSRpNdhcpSZIkSZIkSZIk9chKNkmSJEmSJEmSJKlHVrJJkiRJkiRJkiRJPXJMNknq1gtPB2DPD/y24UAkSZKk4VMfh60pe1bn9AD78/MGI5EkSdIosJJNkrr1uk0BuIgrGw5EkiRJ0nRcVJ3TA+x/foOBSJIkaSTYXaQkSZIkSZIkSZLUI69kk6Runb5N0xFIkiRJQ2Wii8iFCxc2HEmlfk6/eXNhSJIkaTQMzJVsEbFNRHwqIq6LiLsjYnlEfDAiNuzxOBtV+y2vjnNddVz/HZc0Mx/bYeUkSZKkRplDalo8p5ckSVIfDcSVbBHxSOBCYDPgDOAXwB7AG4H9ImKvzLy5i+NsXB1nR+B/gC8COwOHA8+OiCdn5lWz8ywkSZIkSXPBHHKwDdzVa5IkSdIsGZQr2U6kJEdvyMyDM/MtmbkPcAKwE/CuLo/zbkpydEJm7lsd52BKorVZ9TiSJEmSpOFmDilJkiSpcY1XskXEI4BnAcuBf29ZvRC4E3h5RKw7xXHWBV5ebd/aXO6j1fH/uno8SZIkSdIQMoccPIsXL14xSZIkSeOk8Uo2YJ/q9pzMfKC+IjNvBy4A1gH2nOI4TwbWBi6o9qsf5wHgnGp27xlHLGk8HfbrlZMkSZKaYg6p6fOcXpIkSX00CGOy7VTd/rLD+isorRR3BM6d4XGojiNJvVtw9cr7n2kuDEmSpDFnDqnpq5/Tn99cGJIkSRoNg3Al2/rV7R87rJ9YvsEcHUeSJEmSNLjMISVJkiQNhMjMZgOIOBk4AjgiM09ps/7dwFuBt2bmeyY5ztsog1u/KzOPbbP+SOAk4KTMfE2H9UdWszsBl0/j6cyWTYCbmg5C02b5jRbLc3RYlqPDshwtlufoGLSy3C4zN206CM2cOWRXBu3zp+mxHEeT5TqaLNfRYDmOBstxdDRdll3lkIPQXeRE68D1O6x/aMt2s3KczDwZOHmKx2hERCzLzN2bjkPTY/mNFstzdFiWo8OyHC2W5+iwLDWLzCGn4OdvNFiOo8lyHU2W62iwHEeD5Tg6hqUsB6G7yInWfp36uX9Uddupn/x+H0eSJEmSNLjMISVJkiQNhEGoZFta3T4rIlaJJyLWA/YC7gK+P8Vxvl9tt1e1X/048ygDX9cfT5IkSZI0fMwhJUmSJA2ExivZMvNK4Bxge+BvW1YvBtYFPpuZd04sjIidI2LnluPcAXyu2n5Ry3H+rjr+tzPzqj6GP1cGsgsSdc3yGy2W5+iwLEeHZTlaLM/RYVlqVphDdsXP32iwHEeT5TqaLNfRYDmOBstxdAxFWUZmNh0DEfFI4EJgM+AM4DLgScDelK45npKZN9e2T4DMjJbjbFwdZ0fgf4CLgUcDzwF+Xx3nytl+PpIkSZKk2WMOKUmSJGkQDEQlG0BEbAu8A9gP2Bi4HvgGsDgzb2nZtm2CVK3bCFgIHAxsCdwMfBN4e2ZeM5vPQZIkSZI0N8whJUmSJDVtYCrZJKlpEbEA+DRweGYuaTYaSZIkSdKwMa+UJGm8ND4m26CKiKymB6quSDptt7S27YI5DLErEfGYiDg9In4fEX+OiMsjYnFErN1m2zUj4o0R8emI+HFE3FM9r1c3EXu/TZRT03F0EhFPiYj/jIhbIuJPEfGTiHhTRKzRZtv5tfddu+k9TTyHpjRZthGxTUT8c0R8OSJ+VX1nZETsMMk+e0TEv0bENyPid9X2Y9lKunrtJt63OzUdTycR8ejqu/OMiPhNLeYHdbHvLhHx2Yj4bUTcXX0fnx8Rr5iL2OdCRKwREUdUz+uWiLi3ep4/iYhTIuKgpmOcEBHrRsRLI+ILEfGLiLgzIm6PiGURcXRErNVhv1dFxEkRcVH1HZ0Rcdxcxz+XImL36pzgqoi4KyJui4ifRsT7ImLrGR57wXTPnSJi64h4ffUdurz6XN0cEf8VEc/rsM95U/xuZkR8cibPaZC0eW53R8SNEfHD6jO5f7vzi0FQ/a5+KiKuq+JeHhEfjIgN22zb83tBmk21z5w55IjkkBOiwXyjG2Eu2RdNlnOYV/ZFmF8OtTCvHElhXjmU2jwvc8pJTPnlPebuo7xGrwLe1royIh4FPL223UCJiCdRxhVYE/gK8FtgH+DtwL4RsW9m3l3bZV3gg9X9G4DfAdvOXcTjKyKeA3wV+DPwJeAW4EDgBGAv4IUddj0fOK/N8u/2P0p1sDtwHJDAr4E/AhtMsc9LgDcC91LGD9l8NgMcVBERlO/XBAI4Ajim0aA6+2vKd+f9wBWUz+pDptqpOtE7BfgTcBawnPL+eBzwN8BnZyXaOVSdVJ1F6arrVuBs4BpgI+CRlPf7zsCZTcXY4q+Az1O+Z5dSuhXbiPKd+37gedXv459b9jseWB/4A3Ad5bmNpOqz+R7gHynnOP8FfBlYC3gK5XP6uog4LDO/0kCIrwf+ifKdu5RyvrId8DzgGRFxQma+uWWfJbT/vZw43kaUruFGzeLqdg3Kd89jgZdTvnuXRcRLM/OXTQXXKlYfY+sXwB6U38z9ImKv+hhbTO+9IM02c0hzyDllLjkyzCtnyPxyuPNL88rRY145Mswpu8kpM9OpzUT5Ub4G+AGlb/8Htdnm36rtvl7dLmg67lpsawA/r+I6qLZ8HiVZSuAtLfusBewPbFnNL6q2e3XTz6ePZZpNx9EmrodSBlW/G9i9tvwh1ZdCAi9u2Wd+tXxR0/EPwtSvsgUW9PpZBrahnFw9tJo/rzrGDpPssyvwBGCtWvzXNP06NlBuf109909XP8mf+LwAACAASURBVGI3TrwmgzYBOwFPAtau5pdXsa/221DbZ0/KieSPgS3arF+z6efVp9fmZdVr8WNg/Tbr1wH2bjrOWjy7Ai9tfa8B6wGXVM/l6Db77QdsV92f+K44runnM0uv0dtZ+QfPY9usfz5wV/X+nlbZTuf7trbv84Cnt1n+aMofUgns1uWxdqq2/92ofCar59Xxd5HyB9zp1Ta/ATZrOt5abN+u4np9y/IPVMs/PlvvBSenfkyYQ8KI5ZAtZZtNx9EmLnPJASzn6ZznYF7Zj/IzvxyA5zaD18S8csQmzCuHeprsNxFzytUmu4uc2ieALYAD6gsjYk3gMMqJ68/a7RgRu0XEhyLi/6rLnP8cEVdExPGtlydGxGuqSy/f3uFYW1SXSf+0y7ifTnkzfCczV7TyyMwHKC0IAF5TtSqYWHdPZn4zM6/v8jGGXqzsKmNRh/XLI2J5y7IVlyJHxN7VpcK3V5c7nx0Rj+4xjBcAmwJfzMxlEwuztHY5tpp9bY/HHHvTKdvpyMxrMvN/M/O2Hvb5cWb+KDPvmenjD7kjqttPAKcCmwDPbd0oIpZUZbl9m3UdyzkinhgR59Q+n/8dEU+OiEXVPvO7DTQzL8/MizLzrm73Ad5L+bPqZZn5uzbHvLeHYw2yp1S3SzLzj60rM/NPmbm0dXlEHBqlu6w/VL+Pl0XEsRHx4DbbZvVdu1VEfC5KlyF3RcQlEfGSXoKtPn+ntn7+MvN2SqtCKH8+te73rcy8upfHGkbV5+xfKC2iD8rM1c5xMvOrwN9T3t8fi4h5Lcd4UUScWzv3WR4Rp0XE7tX68yh/fgB8OlbtgmL7qWLMzK9l5vltll9GacEPbcqwgyMn4hihz+SkMvMG4MWUP++2pf2VNhtF6X7qsuqz9seqTJ/V6bhTlftUIuIRwLMofzL9e8vqhcCdwMsjYt3ac+nne0HqJ3PIMWAuOR7MK4eK+eVwM68cIeaVo82ccnVWsk3tNEoBtPYpfxCl1vYTk+x7BOUNdznlQ/9xSovGNwMXRMR6tW0/D9wGvDra92f6Skp3Iid1Gfc+1e23Wldk5lXALymXPj6iy+NpdQcA51DK7ePA/1Iuzz8/Ijbp4Tgdywr4DqUbgKe0O0EAdoiIv4uIt0XEK6N0PyMNvIjYnPI9+svMvJCVJ0ZHdt6rp+P/FeXzsw/wn8BHKS2kllIuE59VETHREnUZ8LPqT5RjovTNvm/ryeOQm7jMfsdud4jSR/kXgB2Ar1FOfm4B3gl8K9qPRbAh5U/JXSjvl89SfsNOjYh/mHb0q5o4Gb6vT8cbRodTzje+npmT/Sl7CqV7k50of8oSxRLgi8BfUMr2BMrv41+x8s/mJZRuG6huF9emW2cYf9dlGGWchFdQWqZNdj43cqo/zCfGfji0/od5RGxHaX37FkoL8I9TkotHUz6fR9SP1UO5T2XifOicKr56vLcDF1BaMO/Z5fH8PKtJ5pCairmk1EfmlyORX5pXjhbzyhFnTtliNi7LG4WJ2mX2lA/8fcA2tfXfolwyuA4r+81e0HKM7YA12hx7oo/of2pZ/tFq+QEtywO4ipKorXbJdIf4v1wd6/kd1p9Vrd9/kmMsYoS6+qDNZa5M0VUGpeZ7ecuyBdU+9wH7tqz712rdP/YQ1w+Y5NJT4NJq/aPbxN1u+gqwYdOv95CX7YIZxHIeU3Tr0SH+serWg/JDm8Bba8suAR5ofe0oJ04JbN/mOKuVM6UByRXtvuOA19Q+K/NnEP9yJunOg9JiMikn/EvbfE5/0st7ZJAnShc191Rl9znKpfbbTbL9xOfsa1Tdo9TWTfzuvLFl+cTrdjowr7b84ZQk6h7gEX14Lt+sHueoKbabeA4j160HcG713I7oYttTq22PreaPrOYvpuV8hdI6ccs2r+GCPsb+UEr3HA9Q+82cZPtDqxjOafp1n4VyXO13sc02D6YkDQk8vLb8vOo1bO1ebANK9z13AZvXlndd7lPE8z46dKtTrZ84T35tv98LTk79mjCHhBHLIVvKNluWzcdccqSmWSjnBTOI5TzMK3t5vcwvhzy/xLyy8TLoc3maVw75RJvfxDbbmFNW0yi0dJgLn6AU6CthRW3sM4FTM/NPnXbKzKsz8/42qz5FabH21y3LP1bdHtWy/FmUL/wvZZtLpjtYv7rttP3E8qkG0lVnX8zMc1uWnVzd9tKSaTpldSPlJHIXSn/Pm1LGQvgRpU/j/xiRlkwaQVXrlldTfqjqAzMvofwh1Nrqu1dPobRkW5qZrQPOnkxphT3bNqtuD6G01Hke5bO+AyVh2AU4u2rxNNQy80eU/vNvqG6/CiyPiJsj4usRcWDLLm+k/LH0yly9e5R3UlowvrTNQ91P+WNxRWukzPw18GFgTcrAu9MWEX9H6R//x5Tf6XG1ZXX72y62ndhmq+r29dXtUa3nK5l5f85iV2LV98oplCtEPpala4epTLRsPnnSrUZUZt7NyhbDmwJExOMpLUi/mplfbNn+VkoXGw+hnGtM6Fe59+XcdZrvBWk2mENqMuaSUp+YX45GfmleOXLMK8eAOeVK7S6bVYvMvChKP/avjIjjKD/Q85jiEtAofe4fRenu4zGUgq6frG7d8jg/i4jvAPtHxLaZOfElM/Fh/Xjt2LsCB7c85K2Z+cEun9bEJZzZ5fZa3bI2yybKbMV4CVH65Z7fst3yzFzS5eOsVlZZ+jKu92d8B+Vy2wspP+R7AQey8rJpTVNELAC2b1l8XmaeN+fBjI59gEcC387Ma2vLvwC8H1gQEf+S0+/H+gnV7XdbV2TmA9XnZJUuKCLiTaz+I/uNzPzxNGNYo3b76sw8q5q/LSIOoyRGu1NOKk6b5mMMjMw8PSK+DuwNPJVSBk+l/E4dHBGfpbQwWxt4PHAT8KZabwJ1d1Nen1a/qZKfVudRTtKeABARGwBvarPdB6sTutVExPOAD1JaKT1/Bu+9UdDL+cGKbas+zR8H3FAlyNMPYHrnOMcDL6R0J/HmLh7jUZQT/xsY79/K1vJ+cnW7frQfe2bT6vbRAL2Ue5RxERa0Ls/Mdo/TTayd9PRekGaLOaSmYC45ZswrZ5X55Yjkl+aVI8W8cnyYU2IlWy8+QWnVsB+lX9lLuviwf4lySfdVlA/a7yhf8lC+qNv1i34i8DRKErYwIrag9Cv948y8uLbdrpQv/7qrKV/msLJmdn3ae2jLdurdaj+qmXlf9eNeHxNhPquX1fmUVlXQx7LKzNsi4gvAP1PeR+P6Bd9PC6j6hW5x3tyGMVIm/vRZUl+YmTdHxH9QEoPnULqrmY6Jz9INHda3W/4mSvdMdcspfzRMxx+q27spffavkJkZEWdQkqA9GOIkqK5KIM6pJqqxYZ5Pab33CuDrlC6NgnJS1fq9OJVO5Tkx6PdEuW/Q4dhLaPO9HREHU/r9/j2wd5YxZ8bZ9cDOwMO62Hab2j4TfyJc22HbXkx1jrOKiHgfZcDs7wDPrlrTTeVIynvx0+Oa/EbEQ4CNqtkbq9uNq9tnVlMn/6+67aXct6f9Z3NRdTvj86Fpvhek2WQOqU7MJcfPAswrZ4v55Qjll+aVI8O8cgyYU65kFwDd+xylv9CTKK0HJ70ENCJ2pyRH/w3snJmHZ+Zbq5rVdwCdLuH+GuUL/1XVD0nbwaozc0lmRsu0fW2Ty6vbTgOGTgxqPBeXtQ+yicvDO1U4d/pQdi0zF7Upq/m1TTqWVTVI68Mpl8B3+wM98aW27nRjHhF9KdvMnN+m/Bb1JcIxFBGbsrIl0WkRkfWJlZeL1weonqws213ifVt1u3mHMFZbnpnbtynnJZM+mclNfK5vz5bBVisTSdLaM3iMgVZd0n86ZbBaKC1MJ05kftTm9V5lanPITuW5RXX7x+pxl3c45vLWHSPihZTxZ24Anp6Zl7duM4YmWug+Y7KNqnOU+dXsBaxMNLduu0MPujjHqcdxAnAMZWyK/TPzjqmOX3WjcxiM18DUbTyV8r16Q+3zMfEZfeMUn9HDq+26LvfMPG+Kz/qMzl2n816Q5oA55GgzlxwP5pUDzPxyhZHNL80rh5Z55Xgwp6xYydalLJcCf4VSu34nU7cM2aG6PTNXr8negw4/fNW2p1DeWAdSWiPeQRkEshf/U93u17oiIh5BebNdTfcn26Nq4kRk29YVEbEDczPeQMeyorQgXAe4MLtvjb1ndWvZFk2WrVZ3GOUPokuAT3aYbgSeEREPr/bpWJaU1nqtJlqIP7V1RTW+xFOmG3wPfkLpumKTiGh3Ev+46nb5HMTStNur26hOUH4GPDYiNppkn3YeVnUN0Gp+ddtTVxIR8RLKb/l1lEToih7jGVVLKOMUPDciHjvJdq+k9Jl/OXB+Zt4JXApsHhFPmGS/CRPjDa0x6VYdRPHvlFbC/0VpYdZxjKMWz6W0ev3vHNMWptV34T9Xs1+orfp+dftX3RxnGuU+maXV7bNaxwKKiPUo3ZfdVYtxYt1M3gvSrDKHHHmDkG+YS86+QShndWZ+WYxDfmleOVyWYF450swpW2SmU5uJUgt9Tcuyh1FayOzVsvy4avsFtWV7Vsu+2rLtZpQf/6T0pd7usR9GaW12TbXdSdOIfw3g59X+B9WWz6O0rkjgLVMcY1G13aubLo8+lmm2LFuTUsN+K7BZbfnalEvwVysnSjcPq5R3m8c5r4e4Hko56bsb2L22/CHAhdXxXtyyz17AvDbHehmlVdbdwPZNv+ajVrZdxnJedYwdeoz/muk+5jBNwC+q57vHJNu8s9rmXdX8i6r5L7RstwvlRDuBRbXl84BfVcv3b9nnNRPvF2D+DJ7H8uoYD5pkm4nfhs/UP69V3HcB9/byPhnUCTiU0gVAu++kLYArqtfhhdWyV1bz3wA2aLPPhsBftiybKLMvtbyWDwduqV7LR/YQ82GUk/GrgO2m8ZwnviuOa/r1n6UyfUf1/K4EHtNm/cHAnyjnKvvUlh9R7XcxsH7LPvOALWvzf1Ntu3ga8QWlpWBW3+cP6XH/c6t9n9/0az2LZbja72Jt3WbA6dU2VwObtKz/TvX5eGWH/Xdh1d/Vrsu9i7i/XR3r9S3LP1At/3g/3wtOTv2eMIeEEcshW8o2W5aZS47YNFfl3GUs52Fe2c1zNr8ckfwS88rGy2AWytS8cogn2vwm1taZU7ZMjsnWg8z8DfCbLjf/AeUy1+dVg6B+l3JJ8v6U2vnrJnuciDib0o8+tHTz0WWs90fE4ZSWbV+JiK9Use9LaZlzASsvtV4hIt5C6TMXSt+1AIdHxESLne9m5im9xjOoMvPeiPgQ8C/Aj6oBVh9E+WG/jknKqY8x3BYRR1BauZ4XEV+k/LgfBOxULf9Sy26nAvOq99Y1lCTqiZQWrvcBR2WbS9jHyVyWbUQsqc1OfH7+LSImWlqdkpnfrW2/M/CWlsNs2HKcYzLzpn7FOAiiDNy+E/DTXHV8kFafpLSGOTwiFlLGg7gCODQitgEuovyR9Jxq3SH1nbMMPv1q4FvAmRHxVcpJ3V9Qyv+blO/idt1sdIp9E8qg2RM2mYi16oYE4D2Z+YvaNu+mfOe+AtglIs6jtHJ6PuUze3Rm/qrbGAbYk4A3Ar+LiO8Cv66WPxx4NuUPiDOoxkDIzE9FxG7A64ArI+LblN+njap9ngZ8mpKw1v2keqxLIuIcStc8L6K0Hv7HzLyym2AjYm9Kf/7zKK2cDo/VB8pebTDk6j018Vs4caXBgdV7EuAXmfmebmIYAoso3US9Gfi/qox+RvmT6SmUcrgLODQz/6e23ymU1+gVwBXV2BA3Ulom7kN53RdV236PklC9qWp9OjE2wkcyc6pxY95OuUrjLsq4Fm9pU4Y/zsxvtC6sWpzvXT3emVM8ztCLlQNNz6N8Vh5LKaO1KAnMS9v81ryEcv74yYh4A+U791bKlTh/QWkp/WTKeBPQW7lP5XWUP4U/HBH7ApdR3m97U7r0+OeW7af9XpDmijkkMGI55ARzyfFgXjm4zC9HLr80ryzMK80rB4o5JdBNTtl0reigTvTQAog2rRCr5RtRBqFeDvyZ8iP8bkqXDcvp0Aqx2vc51TF/MMPn8RhKq8ObKC3SfgksBtbusP15rGzZ0W5a0nTZTPN1WKOK/54264JyYnolcA/lR/m9ncqJPrc+rO23F6XG/A/VB/unlIEW12iz7T9RLl/9bbXtxPvr08Djm369R7VsJ4lhss9Mu++G+V3ss33Tr+0slNWp1XN7QxfbnlNt+9xqflvKHwS3VO/5HwDPq72Wi9oc40nV5+T2avpvyo/4R6t9du0h9u27KLP5bfZbh3Ii8AvKd/Afqzj27/axB32qyuZvKQNQX04Zs+AeyqDF/0lpFd2uNeIBwFmUk6p7KANNX0z5Td25Zduk/D5tBXy+2ufPwA+Bl/QY78TnfLJpeZv9lkyxz3lNl8UslO0elJayv64+d3dQunF4P7DNJPu9FDi/er//udr/VFZvSbofJSm6o5fvvi7KouP5CvBv1fp/bfr1neWya3097qacC15CaaW3X7vPZW3/9YC3VdvfUZX/r4GzKWOarDvdcu8i9m0p5zPXV98NVwMfAjbq53vByWk2JswhR/JziLnkWExzWc6TxGBe2dvrZX45Qvkl5pXmlavvZ17ZbLmZU/ZwLhvVwTRgqlrihZRuNj7ZcDhDLyK2pLQwuzYzt5lqew0Py1a9iogLKAnS+ln6ftaAq1pznp+Z85uORZKkQWUOOTvMN8aD5azpMr8cHuaVkmbLvKk30VyrBuJ7DaVFzVSDY6s7z61uv9doFJoNlq1WExHrRMRqg5BHxAJKtwTnmABJkqRRYQ45q8w3xoPlrI7MLyVJk3FMtgESEc8G/hI4kNL3/jGZ+admoxpuEfEOYEfghZT+5Y9vNiL1i2WrKTyMMmbCf1EGqX4Q8ARKH8+3Akc3GJskSVJfmEPOHvON8WA5q0vml5KkjuwucoBUg9MeRhk08VPAsZnZ9cCpWl11KfjtwDLgnZm5tOGQ1CeWrSYTERsC7wOeDmwBPJjSN/t/A+/KLgcz1mCwWw9Jktozh5w95hvjwXJWN8wvR4N5paTZYiWbJEmSJEmSJEmS1CPHZJMkSZIkSZIkSZJ6ZCWbJEmSJEmSJEmS1CMr2SRJkiRJkiRJkqQeWckmSZIkSZIkSZIk9chKNkmSJEmSJEmSJKlHVrJJkiRJkiRJkiRJPbKSTZIkSZIkSZIkSeqRlWySJEmSJEmSJElSj6xkkyRJkiRJkiRJknpkJZskSZIkSZIkSZLUIyvZJEmSJEmSJEmSpB5ZySZJGnoRcV5EZDWd13Q87Uw3xohYVNsvZzFESZIkSRop5mGSpNlmJZskqW8iYvt6IhIRS9psM79lm0VzH6kkSZIkaboi4lER8f6IWBYRN0fEvdXtsog4PiIe1XSMkiTNhQc1HYAkSX3wMeCs6v5vmwxkEsMQoyRJkiR1FBHzgMXA21i98f5G1bQb8KaIeBewKDMfmNso++Ic4I6mg5AkDT4r2SRJQy8zv9R0DFMZhhglSZIkaQrvBY6uzd8GfBG4GtgOeDHwUEoF3L8AawP/MMcxzlhmXghc2HQckqTBZ3eRkqSB0dqNZET8ZUScERF/iIi7IuLiiHh2m/069rPf5pjPjIjvRsSfIuL6iPhQRKxbbfu8iPhB9Vi/i4iTImL9luM9KCLeGRFnR8SvIuKWqmuUW6uuUd4ZERv2EmO1/q8j4n8j4s7q+Z4ZEX8x09dUkiRJkvohInZn1Qq264C/yMyjMvPdmXkU8BfV8gnHRMRutWNMOtZZp6EFppuHtTn+xhHxkYi4JiLujogrIuIfIiJatpsqzh0i4vQqjjsj4vsR8fw2wyPMb7Pv30TE1yLi2oi4JyJui4gfR8S7I2LzNtsv7zQkQ0QsaHm87avl368t+3qbY7bG+eRq+a4RcWJEfC8ifls9t7sj4rqI+M+IeNFUr7EkjRuvZJMkDar9gLcCa9WWPRE4MyKekZlLp3HMAymtKScamawNvAF4XEScBXygtu1DgCOBRwH7tCw/ts2x16d0i7Ib8PKI2DMzf9dNUBGxAPgUMJHYrVPF+gzggm6OIUmSJEmz7LUt8+/IzKvrCzLz6og4Djixtvh1wKtm+Nj9yMO2AH4APLy2bAfK1XnrULrBnFJEPA74DlCv1HsS8BXgjEn2mwd8EljQsmpN4PHVdEREHJSZ3+smlkl8CPhCdf+AiNgqM+uVny+u3f957fGeyurlDLBlNe0fEXtn5mtmGJ8kjQwr2SRJg+pJwDXAqcC2wEuq5fOAfwSmU8n2l8DPgK9RKvGeWC3fp5p+CnwDOAB4QrVu74h4UmZeVM0n8Gvg+8C1wB+ANSiJ2osoydl2lATw76YKKCK2BP6dlRVs9wJLgBuBQygVbZIkSZLUtKe3zHfqEv80Vq1ke1ofHrsfedhOwJ8p42XfRalMWrta9+aIeHdm3ttFLEtYtYLtW8DFwL7AcybZ7x9YtYLtUkql3ObAYZTKtk2AMyLiUZn5xy5i6eQrwPuArSn//74KeCeUqwKB59e2/WTt/t3Vc/kRcBNlXLr/B+wFzK+2OSoiTsnMZTOIT5JGhpVskqRBdSewZ2ZeCxAR6wAHV+v2mOYxbwaekpm3RcTngctr624E9srM2yPiNODntXV7ABcBZOadwCMiYhNKReC2lITu58AyViaQ+3cZ0yuq/ScclZmfBoiI9wFXUgYPlyRJkqQmbVW7f2tm3tpuo8y8NSL+SLnKrHW/aeljHvbizDwDICJ+A3ywWv5QYGdKw8uOImIPylVzE76YmYdW6xYD/8PqlZETV7EdU1t0JfDEzPxztf5CSu8mAJsCrwROmOK5dJSZ90bEicC7qkWvjoh3ZeYDlMrATarl9wCfre33CeATEfFYStefm1Aagp5NyYsnctf9Ka+7JI09K9kkSYPqjIkKtkq9QmzKvvY7OCszb6vuL2+z7vbq/hUt61Y8XkQ8BPgopQXiGpM81tZdxvTE2v37gc9PzFTJ6RnA4V0eS5IkSZJGTp/ysOsmKtgql7es7ybPfGLL/IqrwDLzgWrMtNUq2ShX0W1Smz9tooKt8jngZFb+V7sXM6hkq5xMGS7hIcDDKBVjZ7NqV5FnZOZNEzMRsSvwGUoF22S2mWFskjQy5k29iSRJXWvtWmPtNtu0Lrunw7GWt8zfXbsfTM+KSrvMbH3c62rr7mtZV/+9fDelq43JEjuAB3cZ0wa1+39o0z3JDV0eR5IkSZJmU31Mrw0iYv12G0XEBqy8iq11v9Zto3Z/shyqH3nY8pb5u1vmu/mfdIOW+etb5juNB9faO8kq21U56E21RZ16M2nNhTs+36ry7NTaoqMiYi1W9hADcMqKA0esTamEm6qCbdLHlaRxYyWbJKmfbgYeqM0/vM02j2iZ/32HY7VWNuV0g5rkmHWtFWud1Fv9XUpJQNbKzKD0ed+rehcrG0bEmi3rN5/GMSVJkiSp385vmX9Rh+1al3+ndv+BlnX1RpiPmuSx+5GH9SPHbO0ic7OW+S067HfLZNtV46TVr3Srb19/zVobrU72msHK7jAB/gY4gpUVhVcD/11b/1es2rXnCZR8dF71Ot84xWNJ0liykk2S1DdVdxc/qi16YkSs6BM/IjYF/rZlt+/PRWx9VE98lmbmT6v+7tcGDprG8X5Qu78G8LKJmaoF6GQDZ0uSJEnSXPlYy/zbI2Lb+oJq/l8m2a+1kmrPar95wFsneex+52HTdXHL/Esn7lTPYUGH/S5n1SvVXlx1gTnh5aw6rM8Ftfv11+wJ1dVoRMTWwGGTBZuZl1LGiYOSb76/tvrT1RhtE+qvMcDnM/P3mZkRsQ9lrDhJUgvHZJMk9dtHgCW1+bMj4lLgdmAXYL3auvOqk/5hcjnwuOr+ERGRwG3ACyn97Pfqc8BCVrZIPCkinkxpJXgInbsJkSRJkqQ5k5nLIuIDwJurRVsDP42IL1KuitqOcsVZvavI4zNzWW3+opbDfi0izqHkUpN1U9jvPGxaMvMHEXEJsFu16FVVY9IfAc+gjKXWbr8Hqtfu3dWiHYAfRMQ3KFe11SvLbgI+XZu/CHhCbb8fRsRlwN7Axl2E/SFgn+r+RMXeA8CnWrZrHaPu1Ig4DdiSzpWHkjT2rGSTJPVVZn4mIp7IyivWglK51upyaldtDZF3AKdX9x8CvKG6fzvwVeD5vRwsM6+LiNcDn6C8VmtSuvCAMl7dBXRI1CRJkiRpjv0DcBflqrN5lAq1o9ps9wDwr8Db6wsz86KIOA+YXy1an1JRBmU8sGd3eNy+5mEztIDSBeaG1fxBrLyarvU51K8U+zfg0ZSr1qBUGj6OVd0CHJyZ9avXPlw95kQF2WOr6X7gW8B+U8R7FnAl8MjasnMy87f1jTLzkoj4T0q3kgA7A4ur+/9Vxb7NFI8lSWPH7iIlSX2XmX8H7At8AbiKkoRNDOS8FHgj8JeZeW1jQU5TZn4ZeB5wCaUS7A/AmZRuTqZ1VV5mfpKSiF1Aea1uA74JPJVV+8iXJEmSpMZk5gOZeSylwuUDwA8p3RneX93+sFr+6Mw8tqU7wgkHAx8HbqDkVL8Ajq6Wd3rcvudh01X1xvIk4CuU53wXsIxyFd+pLZv/obbfA5n5CkqF3BnA9ZRx4u4EfkKphHtcZta7iiQzL6NciXYe8CfgDkql19OAL3UR7wOUHmfqTumw+QsoXUpeW8W2nFJZehCljCVJLSJzOmN8SpIkSZIkSdJ4iYg1gczM+9qs+wYrx9W+DdgkM++dy/jaiYhnAudUszcCWw9CXJI0CuwuUpIkSZIkSZK680hgaUR8AfgppdJqS+BFlHHZJny0yYqsiNgA2BXYBFhUW3WiFWyS1D9eySZJkiRJkiRJXYiInYHLptjsi8DL213tNlciYj5luIa6q4BdM/P2uY9IkkaTY7JJkiRJkiRJUneup4xbdjHlGtZiCAAAIABJREFUKrZ7KeOyXQWcBuyXmYc2WcHWxg2U8eLmW8EmSf3llWySJEmSJEmSJElSjxyTrY1NNtkkt99++6bDkGbdJZesvL/bbs3FIUmSNNsuueSSmzJz06bj0Ggyh2wxQonGCD0VSZIk9aDbHNIr2drYfffdc9myZU2HIc26iJX3/SqQJEmjLCIuyczdm45Do8kcssUIJRoj9FQkSZLUg25zSK9kk6Q++4+bblpx/8BNNmkwEkmSJEkz8uSbajOe20uSJGlVVrJJUp8ddOmlK+7n/PnNBSJJkiRpZt59aW1mflNRSJIkaUDNazoASZIkSZIkSZIkadh4JZs0xg7gP2pzBzYWx6g5YOONmw5BkiRJUh9sdmXt3H5+Y2FIkiRpQFnJJo2x/+Cg2pyjePfLf+yyS9MhSJIkSeqDG17lub0kSZI6s7tISZIkSZIkSZIkqUdWskmSJEmSJEmSJEk9spJNkiRJkiRJkiRJ6pFjskljbBELa/fVL1+44YYV91+y+eYNRiJJkiRpJp5/4spz+6++znN7SZIkrcpKNmmMLa5VrS3quJV69dLLLltx30o2SZIkaXh97TGX1eY8t5ckSdKq7C5SkiRJkiRJkiRJ6pFXsklSnx262WZNhyBJkiSpH86tndvPbywKSZIkDSgr2SSpz77wmMc0HYIkSZKkfjiudm7/zubCkCRJ0mCykk2SJEmSJEmS5kBEtF2emSvWZeZchiRJmgEr2SRJalg9kaonXK1JVus6SZIkSdLgaJe/teZyvR6rlbmgJA0WK9kkSZoFnSrLWs0kQbICTpIkSZLmVtOVX5M1zJQkzT0r2SSpz06+7roV94/caqsGI9FcmGnrxNkwWQWfiZckSVIPDriuNuO5vTSuhqUiq5v8dNCfgyQNGyvZJKnPjvrlL1fct5Jt9Ax7hZWtHiVJknpw9C9rM57bS6NsnHKlTt1aSpJ6ZyWbJEktxim5qhvX5y1JkiRpPA1KbySDYtgblUpSE6xkk8bYEZxcmzuysThGzRFbbtl0COqCFUq9MdmSJEnjaOdf1c7t5zcWhqQZ8sqt3pkzS1J3rGSTxtjJHFWbs5KtX07eaaemQ1AHtlLsn059/ZtwSZKkUXLZqz23l4aFY5DNHSvcJGklK9kkSUOtU+u6Vp78zw0r3CRJkiT102RXVNmQsnnm4JLGnZVskqSBZCI1GiZLuGz9KEmSJI0387zRNdOcvtM+5o+SBk1PlWwRsRHwNOBRwKZAAL8HfgV8JzNv7nuEkqSh003FymTrPGkeP90mYL43JGm4mENK0mjp5rzdvE+zrdv313Tek+aikno1ZSVbRKwDHAocAexOSYrayYi4BDgZOC0z/9S3KCXNiiM5acX9kxuMY9itdqL2whcC8P7jj+eYhz2s7T7TOcGbat10jj9bjy3Npn58Bro9viSpd+aQGiV7fuC3K+5//83bNhiJZttMr6xp4k//XuPs93OTxon/gUjqpGMlW0SsCbwBeBuwwcRiIFk9SUpgHiWB2h14b0QcB3w0M++dKoiIeAHwdGBX4PHAesCpmfmyXp5MRCwHtuuw+obM3KKX40mj7hMcueK+lWy9XUkz6Z/wr3sdAMdceeUsRitpJrptnTibldwmY5JGjTmkmjKbV9Zc9Jf1c3or2UaN52WS+sGr36TxNtmVbL8Atq/u189E76N073FztXwjYDNgzdp2GwDvB/4W2KGLOI6lJEZ3ANcAO3cVfXt/BD7YZvkdMzimJElS3023ct9WlJIGlDmkZtVcXVnTqeJlOr1BdNJtw5tuGgPZDVr3xvE5S2qOjS+l8TBZJdvDa/cvAk4HLgB+nJn31DeMiLUoLQj3Ag4BntTmGJP5e0pi9CtKa8SlXe7Xzq2ZuWgG+0tq0FwnPbOSnJ9+OgBvfvOb+UD/jy5Jk/LPI0kNModU3zX+J+Tp28zJw3RbSTdbr8dsViDONObp/Elt94aShsm4N4CQht1klWx3AacAH8zMX092kCphuriaToiIhwNvBl7ZTRCZuSIh8kRIGg2drsBo1UsSORvdts2Kj30MgONPPNFKNkmN6uW7WJL6wBxSMzZwfy5+rJsLK9Wt6Vxx1+uxB+J9I0l9MHC/iZLamqySbbvMvGk6B60SqtdHxOLphTUjD46IlwEPA+4EfgJ8JzPvbyAWaaz4B4ckDS9bT0rqA3NISZKkWWJjAmkwdaxkm25y1O9jTMMWwOdalv06Ig7PzPMbiEcaaf7AS5IkCcwhNX021pMkqTeO2S0NjnlNB9Bnnwb2pSRJ6wK7ACdRBt/+ZkQ8vtOOEXFkRCyLiGU33njjXMQqDY2IWPGDPXHfRFiSxoff+5JGmDlkQ8wrJEmaff7WSrNvsu4i24qIjYH5wDbAg9ttk5nvnVlY05OZrV2LXAq8JiLuAI4GFgHP7bDvycDJALvvvrvV/BprdhM2Q4cdBsCiX086FIkkDSV/IyT1yhxSQ+2w2jn9Z5oLQ5KkmVq8uJz2LFy4sOFIpNHSUyVbRLwReDfwkCk2bSRBmsTHKQnS05oOROpW65+Yk42V0+0l4quvW/lfgK1a+mjBAgAWX311s3FI0iybSNLARE1Se+aQGnoLauf0VrJJkkaEuZzUP11XskXE3sAJbVYlEC3zg+b31e26jUYhTaH+AydJ0rDp9Du2cOFCkzhpDJlDqpUt6CVJGjzmatLM9HIl22tb5icSoajND+qlME+ubq9qNAqpRdM/YgtZtDKWOX/0EbZkCVD9qdxsJJI0kJr+/ZM0Z8whNfyWLG86AkmS5kxrrmYDGWlqvVSy7UFJgn4MnAksrOY3Bg4FPkwZNPrv+hzjKiJiTeCRwL2ZeWVt+WOB6zPzlpbttwM+Ws1+fjZjk7oxSFerLapVAQ1OVCPgM6UfmUVLlvi6SlIXTNykkWUOqeH3mcObjkCSpIEwWc8l0jjrpZJts+r2HOCOiYWZ+QfgxIjYCzgc+D/gI70EEREHAwdXs1tUt0+OiCXV/Zsy85jq/tbAZcDVwPa1w7wQeEtELAV+DdxOSaSeTen//z+B9/cSl9QPttaXJKl7/m5KI8UcUpIkacR59ZvGXS+VbBPdeNwC3LNiYcRDM/M24OfVNkfRY4IE7Aoc1rLsEdUEJRk6hsktBXYCnkDp2mNd4Fbgu8DngM9l5iD29S9JkiRJo8gcUpIkaYzVK9xsUKlR1Usl2y2UFoLrAr+rLX95RHwK2L+af0TrjlPJzEVQGxxq8m2X06bf/sw8Hzi/18eWZsMgdQkpSdIwsxWkNNTMISVJkrSaya5+MwfUsOmlku13lARpI+Cs2vIPAx9iZdJye39CkzTbDuTM2txBjcUxcl77WgCO/tWvGg5EkkaLLR+loWMOqeH32m+vvP+xv24uDkmSxtBk48BZMadB0Usl248o3Wg8DrgE+C2wDSUxCsoA1smqyZM0Nobxy/wsDmw6hNF0yCEAfOCaaxoORJJGlxVu0lAwh9TwO2Stlfc/1lwYkiSpO5NVzEmzoZdKttOAu4A7MjMj4ijga5QBoaEkST8H/rG/IUqSJEmTs9JNGkjmkJIkSRoI3XRROTEv9aLrSrbMPBc4tzb/rYjYGXgOsAlwOfCVzLynwyGkkeKXrzo68UQA3n/88RzTcCiSNI78jZYGgzmkRsKJj2w6AkmSNIe8Ek696uVKttVk5m+Aj/QpFkkaDV/+MgBHn366lWySJEk15pAaOl/etukIJEnSgOg0DpwNPcfbjCrZpHEzjOOuSZI07kx4JEmSJElzwfxz/PRUyRYRewL/AOwBbAjMa7NZZua6fYhNkiRJ6isTHmlumUNKkiRJGmVdV7JFxDOBs4E1KANUd5IzDUqSJEmaC16lLs0ec0hJkiRJo66XK9kWV9ubAEnSZI4+GoAjL7+84UAkSZIaZQ6p4Xd07Zz++ObCkCRJw8mGnaOvl0q2XSnJUQDfAi4H7saESZJWdcABAHzi+usbDkSS1Au7kpT6zhxSw++A2jm9lWySJGkG6hVu5p+jo5dKtj8DDwa+lJmHzlI8kiRJ0kCwxaE0Y+aQkiRJ0hSscBtuvVSyfRd4NnDVLMUiaY6dxJEr7h/VYBwj5/jSxPWkk07ydZWkEWHSI02LOaSG3/EXNR2BJEmSBlgvlWz/AjwTODQiPpCZN89STJLmyJF8YsV9K4P66KyzADhyq618XSVpBFnhJnXNHFLD76y3NB2BJEkaM/aqMlw6VrJFxD+2WXwhsDdwRUR8FVgO3Nu6UWa+t18BSpIkSYPMSjepMIeUJEmS+ssKt8E32ZVs76HzgNQbAK+cZF8TJI0E/zSTJEm98NxBY84cUpIkSZol5puDqZfuIid0SpoAYor1kiRJ0tgwCZIAc0hJkiSpr+q5Zp1559ybqpIt5iQKSY3YjWW1ud0bi2PkHHssAC/5+c8bDkSSNEiscNOYMIfUaDn2f1feP+6vmotDkiSpS3YxObcmq2Rbe86ikNSIH7Jb0yGMpn33BeC03/++4UAkSYPMxEcjyBxSo2ff+1beP665MCRJkqZjOo09zVV707GSLTPvjoinZuZ35zIgSZIkadx4lZtGgTmkJEmSNLjMO2fHVN1FficifgecAXwVWJqZ989+WJI0xI4rTVxPPfVUXtpwKJKk4WTLQQ0xc0iNluMe3XQEkiRJjbFibmpTVbLdBGwBHAUcCdwaEWcAXwe+nZn3zHJ8kjR8zj0XgJdsvrmVbJKkGTOp0ZAxh9RoOXfzpiOQJEnqu5l0I9nLPuNg3hTrtwD2Bj4KXANsCCwAvgHcFBGnRcQLI2LdWY1SkiRJkjQMzCElSZKkIbN48eJVKtHUvUmvZMvMB4Dzq+kNEbEH8HzgYOBRwIuAQ4C7I+LblNaJZ2bmrbMatTSL/DKRJEmDypaDGnTmkJIkSdJ4cJiDYqruIleRmRcDFwP/FBGPA14APBfYBXgOcBBwX0ScB3w1M0/ub7iSJEmSJljppkFnDilJkiSNvnGucOupkq0uMy8FLgUWRcQjKMnS84AnAs8EngGYIEkaP+96FwAH/vSnDQciSRonVrhp0JlDaii9q3ZO/8/NhSFJkqTBNO1KtrrMvAp4L/DeiNiK0h3Ic/txbEkaOk95CgBn3Xxzw4FIksaZlW4aZOaQGhpP8ZxekiSpF+OWi87r9wEz87rM/Ehm7tPvY0uSJEmSRos5pCRJkqRh1fOVbBGxC7AHsCEdKuky870zjEvSHDiTA1fcP6jBOEbO294GwJlnnunrKkkaCOPWklCDxRxSQ+1tX286AkmSJA2wrivZImJd4HRgvy42N0GShsCBnNV0CKPpe98D4MBNNmk4EEmS2hvnQak1d8whNRK+9+GmI5AkSdIA66W7yPcA+wMxyTaTrZMkSZIkjQ9zSEmSJEkjrZfuIp8HZHXfREiSJEkaAXYlqVlkDilJkiRppPVSybZxdftb4BDgcuBuViZN0lDyjyVJkqSV7EpSfWQOKUmSJGmk9VLJthx4FPDZzLx4dsKRNJe24tra3NaNxTFyTjgBgPk/+lHDgUiSNDM2RtIMLcccUsPuhO+vvP/3ezYXhyRJkgZSL5VspwKLKUmSpBFwPVs1HcJo2nVXAM7/4x8bDkSSJKlR5pAafrve1XQEkiRJGmDzetj2vcBFwCERcVxEeNmLJEmSJKkTc0hJkiRJI62XK9n+QKmUC+CtwFsj4j7g/pbtMjPX7VN8kjR83vQmAJYuXcreDYciSZLUIHNIDb83Pb7pCCRJkjTAeqlkewhlgOqkJEkAa1ZTnYNYSxpv//d/AMzfcMOGA5EkSWqUOaSG3/95Ti9JkqTOeqlkg5WJkSRJkiRJUzGHlCRJkjSyeqlk+7dZi0KSJEmSNGrMISVJkiSNtK4r2TLzrbMVRES8AHg6sCvweGA94NTMfNk0jrUN8A5gP2Bj4HrgG8DizPxD34KWJEmSJHVkDilJkiRp1PXaXeRsOZaSGN0BXAPsPJ2DRMQjgQuBzYAzgF8AewBvBPaLiL0y8+a+RKyhtnjx4qZD0Cg76SQAdlu2rOFAJEmSRpY5pObGSbVz+qOaC0OSJEmDaV6vO0TEbhFxWkT8JiLuqG5Pi4gnziCOvwd2BB4KvHYGxzmRkhy9ITMPzsy3ZOY+wAnATsC7ZnBsSerOjjvCjjvywzvuaDoSSZKkxplDaqjteMfKSZIkSWrRUyVbRBwOfA84BNgGWKe6PQS4MCJeNZ0gMnNpZl6RmTmd/avYHgE8C1gO/HvL6oXAncDLI2Ld6T6GJEmSJKl75pCSJEmSRlnX3UVGxGOAj1f7tEtk1gBOjIiLMvPSPsXXi32q23My84H6isy8PSIuoCRQewLnznVw0iBaxm4r7u/eYBwj56jSj8yyZct8XSVJ0tgyh9RIOOqEpiOQJEnSAOtlTLY3AmtSkqMELgJ+B2xB6bM+quO9nmZ6Kt+puv1lh/VXUBKkHTFBkgDYjR82HcJo+mX5GtptvfUaDkSSJKlR5pAafr88s+kIJEmSNMB6qWTbu7q9Cdg7M38+sSIiHgssBTaubTfX1q9u/9hh/cTyDdqtjIgjgSMBHvawh/U3MkmSJEkaP+aQkiRJkkZaL2OybU1pffjZenIEkJk/Az5DaYm4df/C66uobtv22Z+ZJ2fm7pm5+6abbjqHYUmSJEnSSDKHlCRJkjTSeqlkm8q0B5zuk4lWhut3WP/Qlu0kSZIkSc0xh5QkSZI01HrpLvI64JHASyPio5l59cSKiNgeeHltuyZcXt3u2GH9o6rbTv3ta4QtXrx4xf2FCxc2GMlgiVX+14iO26lHn/0sADtedFHDgUiSJDXKHFLD77PfX3n/FXs2F4ckSZIGUi+VbOdTEqTNgcsj4nxWDlr9dGAtSkvE7/Q7yC4trW6fFRHzMvOBiRURsR6wF3AX8P12O0tS32y7LQBX3HVXw4FIkiQ1yhxSw29bz+klSZLUWS/dRX4YuL+6vxbwDOBl1e1a1fL7gY/0Lbo2ImLNiNg5Ih5ZX56ZVwLnANsDf9uy22JgXcpYAHfOZnySJEmSJMAcUpIkSdKI6/pKtsz8SUS8GfggnfvOPyYzf9xrEBFxMHBwNbtFdfvkiFhS3b8pM4+p7m8NXAZcTUmG6l4HXAh8OCL2rbZ7ErA3pYuPf+41Nknq2ctLz0eXX345OzUciiRJUlPMITUSXr5H0xFIkiRpgPXSXSSZ+ZGIuBQ4GngqZSDo24ALgPdn5tLJ9p/ErsBhLcseUU1QkqFjmEJmXhkRuwPvAPYD/ga4ntKCcnFm3jLN+CSpe9dcA8CO66zTcCCSJEnNMofU0LvGc3pJkiR11lMlG0CVBC0FaO23froycxGwqMttlwMxyfrfAofPNCZJkiRJ0syZQ0qSJEkaVZOOyRYRD0TEfVUXH6vpR3IkSZIkSRoN5pCSJEmSxsmklWyVji3+JEmSJElqYQ4pSZIkaSz03F2kNCwWL17cdAgaV1/+MgBbXXhhw4FIkiRJmpEv187pX9hcGJIkSRpMVrJJUr9tsgkA199zT8OBSJIkSZqRTTynlyRJUmfdVrJtGBEP6/agmfmbacYjSZIkSRp+5pCSJEmSRl63lWxvq6ZuZA/HldSga9lqxf2tG4xj5LzgBQBce+21vq6SJGlcmUNqNLzgdU1HIEmSpAHWbSLjwNXSCNqK65sOYTTdfDMAWz34wQ0HIkmS1BhzSI2Gmy9rOgJJkiQNsHlNByBJkiRJkiRJkiQNm26vZFsG/Gw2A5EkSZIkjQxzSEmSJEkjr9tKti9m5gdmNRJJc+46tqzN2XWkJEmS+sYcUiPCnEmSJEmdObi0NMa25rranMNm9M3ZZwOw3v/+b8OBSJIkSZqRs7+08v6zn9ZcHJIkSRpIVrJppCxevBiAhQsXNhyJxto66wBwx/33NxyIJEmSpBlZx3N6SZIk/X/27jtesrI+/PjnuwiKBJCqKAoogiWIZYMiiCCKqEDUhEQssKALxliB+DNK3L22FBuaWCgiiljQxIKxYGExiI0NqBgpEpYSFOldinx/fzzP7D07O3PvzG0zd+7n/Xqd15k59TvnTDnfeZ7zPN0tGnQAkiRJkiRJkiRJ0nwz2Z1sY3V8zmwHIkkj4/nPB+CWW25howGHIkmSNMfMITVanr/7oCOQJEnSEJuwkC0zxyaaL0nq4M47AdjwfrbIK0mSFhZzSI2cO72mlyRJUnddm4uMiLGI2GSqG46ITSJi+VTXlyRJkiTNH+aQkiRJkhaaifpk+wfgioj4aEQ8o9cNRsSuEfFvwOV1G5IkSZKk0WcOKUmSJGlBmazdgwcCRwBHRMTvgB8BPwWuAG6oy2wKPBxYDOwKPKxODyBnOmBJkiRJ0tAyh5QkSZK0YExUyHYkcAwlAQLYCnhRHbqJxuMbgHdMKzpJmo/OPBOAWLFisHFIkiTNLXNIjZ4zV4w/3mtgUUiSJGlIdS1ky8xjI+KTwJuBVwJbMp4AtWoXtmoaNhOja4ETgPdm5s0zHrHUMDY23q/6smXLBhiJJEmStLCZQ0qSJElaaCZsLrImOG+rnU+/ENgX2APYjvH+3BK4DPgB8C3gy5l5z2wFLEmSJEkaTuaQkiRJkhaSyfpkA6AmPF+sAxERwGaU2ofXZ+Z9sxahpFmTjQrEMcFy6tNepR2ZzPS4SpKkBckcUiNjL9uIlCRJUnc9FbK1y8wErpvhWCRJkiRJI8gcUpIkSdIoWjT5IpIkSZIkSZIkSZKaLGSTJEmSJEmSJEmS+jSl5iIljYaVPLnx7L8HFsfIWX99AG69994BByJJkiRpWtbfdfzxnT8aXBySJEkaShaySQvYYlY2nsXA4hg53/gGABudffaAA5EkSZI0Ld94z/jjvfYaXBySJEkaSjYXqXlnbGyMsbGxQYchSZIkSZIkSZIWMO9kk6SZdscdAPzJhhty24BDkSRJkjQNd6wz6AgkSZI0xCxkk6SZ9oIXAHBrpo1wSpIkSfPZC54x6AgkSZI0xGwuUpIkSZIkSZIkSepT1zvZIuLtU91oZr5jqutKkiRJkuYfc0hJkiRJC81EzUUuB3KK2zVBkiRJkqSFZTnmkJIkSZIWkH77ZGt2L5Q9TJekhWezzQC4+q67BhyIJEnSwJlDan7brHFNf/3gwpAkSdJwmqyQLTpMyzq9fV52mCZJC8+XvgTAw370owEHIkmSNOfMITVavtS4pt9rcGFIkiRpOC3qNiMzFzUHYD3ga3X224FtgAfU8bI6/ft1mjRjxsbGVg+SJEmShpM5pCRJkqSFpmshWwdvAvYHPpuZ78rMKzPz7jp+J/A5Sr2uN89GoJJm3lZcvXrQDLruOrjuOrZab71BRyJJkjRI5pCa/65bZ3yQJEmS2vTTJ9thdXxpl/mXUpr6OBh493SCkjQ3ruZhqx/bTs8MOvBAAK7O9LhKkqSFzBxS89+Beww6AkmSJA2xfu5k27aO94+IdZszImI9Sg1FgEfMQFySJEmSpPlt2zo2h5QkSZI0kvopZLu2jp8I/CIilkfE30TEcuDnwM51/nVTCSQito6IkyLi6oi4KyJWRcSxEbFJH9tYERE5wWBb/5IkSZI0N8whJUmSJI20fpqL/BLwRiCBHYF/aMxrtYiWwBf7DSIiHgWcA2wJfBW4ENgFeAOwb0TslpnX97HJsS7T7+03NkmSJEnSlJhDSpIkSRpp/RSyLQf2BnaiJEJNSUmSLqjL9eujlOTo9Zn5r62JEfEBSmfZ7wZe3evGMnMqMUgLzuns13j29YHFMXK23hqAi++4Y8CBSJIkDdRyzCE13239ivHHV50yuDgkSZI0lHpuLjIzbwF2Az4E3EZJiFrDbcCHgd3rcj2LiEcC+wCrgI+0zV4G3A68IiI26Ge7kiZ3AKevHjSDTjkFTjmFHX/600FHIkmSNDDmkBoJpxw2PkiSJElt+rmTjcy8DXhTRBwNPAZ4EHATcGFm/nGKMTyrjs/IzPva9ndrRPyQkkA9DfheLxuMiL8GtgPuBn4NfD8z75pifBqAsbFurbVIkiRJmi/MISVJkiSNsr4K2VpqMvSrGYphxzq+uMv8SygJ0g70mCABn297/vuI+NvM/NIU4pOk/lx5JQCP3mEHLhlwKJIkScPAHFLz1pXrDzoCSZIkDbG+C9kiYlvg9cDuwBbAe4CfAE+si5zaZ43Ejev45i7zW9Mf1MO2vgq8DzgPuB7YBjgEOAr4QkTsl5nf7CM2SerfwQcDcHEmMeBQJEmSBs0cUvPawU8ddASSJEkaYn0VskXEi4DPAA+gtKOfwIbA+sDJ9fmVwJkzGGPrP+r2jrLXkpkfbJt0EfDWiLga+FdKMtcxQYqIw4HDAR7xiEdMOVhJkiRJUmEOKUmSJGmULep1wYjYATiVkgytITN/AlxVn+7fZwytWoYbd5m/UdtyU3EicC/wxIjYsNMCmXl8Zi7OzMVbbLHFNHYlSZIkSTKHlCRJkjTqei5kA46m1D5MxpOhprMoNQZ37TOGi+p4hy7zH13H3drbn1Rm/gG4tT7dYKrbkSRJkiT1zBxSkiRJ0kjrp7nIvSnJ0fmUJOgPbfN/U8cP6zOGVrMg+0TEosy8rzWj1hjcDbgT+HGf210tInYENqEkSddNdTuS1JMdyv89K2+9dZIFJUmSRpo5pOa/HRrX9FMutpUkSdKo6qeQrZX4fDUz746I9vn31PGW/QSQmZdGxBnAPsDfUtq9bxmj1Bo8LjNvb02MiMfUdS9sTHskcFdm/l9z+xGxOfDJ+vTzmXlvP/FpboyNja1+vGzZsgFGIs2A444DYPHKlQMORJIkaaDMITX/Hde4pt9rcGFIkiRpOPVTyHY3sC5w/y7zH1XH7bUTe/Ea4BzgwxGxN/Br4KmUS9iLgbe1Lf/rOm5maXsAJ0bEWcClwA3AI4DnU9rqPxd48xRikyRJkiT1zxxSkiRJ0kjrp5DtMmAn4K8i4h+bM2pTGn9JaQrkkn6teETiAAAgAElEQVSDqDURFwPvAPalJDW/BT4MjGXmDT1sZiXwGeApwBMpnV3fCvwSOI1Sk/HufmOTRtmTGa+V+d8DjGPkXFzakXnyk5/scZUkSQuZOaTmv4v76cpekiRJC00/hWxnUBKkRzLe0TTAa4F3AutTEqQzphJIZl4JHNrjsmu1M5KZvwSWTGXf0kK1ksWrH6/1odLUHXEEACszPa6SJGkhM4fU/HfEMwcdgSRJkoZYP1WyPkSp1QewFSUZAtiGkhxR539kZkKTJEmSJM1j5pCSJEmSRlrPhWyZeRVwEHBnnRRt4zuBgzLz6pkLT5IkSZI0H5lDSpIkSRp1/TQXSWZ+IyIeB7we2B3YlNI59NnAv2bm5TMfoiRJkiRpPjKHlCRJkjTK+ipkA8jMK4CjZyEWLTBjY2ODDmHBO56ljWcnDCyOkbPzzgCsuPHGAQciSZI0eOaQmtd2ftv445+/e3BxSJIkaSj1XcgmaXQcwfGNZxayzZhjjwVgr5//fMCBSJIkSZqWY589/ngvC9kkSZK0pq6FbBFxUn34+cw8o/F8MpmZr5x+aJIkSZKk+cIcUpIkSdJCM9GdbEuABC4Azmg8n0jUZUyQJC1c558PwDOf+UzOGnAokiRJc2gJ5pAaNedvPOgIJEmSNMSm0lxkdJk+WfIkSQvDm94EwIrMrl+YkiRJC4g5pOavNz1p0BFIkiRpiE1UyHYFJem5ue25JEmSJEntzCElSZIkLShdC9kyc9uIODAzv9h6PmdRSZIkSZLmFXNISZIkSQvNZM1Ffj4iXgj8bWbeNBcBaXSNjY2tfrxs2bIBRiJJkiRplphDSpIkSVowFk0yP4CXABdExHPnIB5Jmv923RV23ZXTr7tu0JFIkiTNNXNIjZZdrxsfJEmSpDaT3cnW8lDgGxFxHHB0Zt4xizFJ0vz2nvcAcMAFFww4EEmSpIExh9RoeE/jmn6vwYUhSZKk4TTZnWw/pNREzDo+Ajg/Ip4+24FJkiRJkuYdc0hJkiRJC8Zkd7LtAbwBeBewfp22PXBWRLwP+DgleVpDZl4xk0FKmh37cfrqx18fYBwj55xzANhv//09rpIkaaExh9RoOefeQUcgSZKkITZhIVtmJnBsRJwOnAQ8g5IQrQO8uQ5rrTbZdiUNh9M5YPXjGGAcI+dtbwPg9Le+1eMqSZIWFHNIjZy3PWfQEUiSJGmI9ZTIZOalwDMj4u+BdzPe9Ic0obGxsUGHIEmSJGmOmUNKkiRJWggm65NttYjYCzic8aY91mriQ5IkSZIkMIeUJEmSNPomvZMtIjYA3kdJjlZPBm4CvoaJkiRJkiSpMoeUJEmStFBMWMgWEXsDJwKPoCRFrSY+zgBemZn/N+sRSpo1y1nWeGbTnjNm770B+Ow11ww4EEmSpLllDqmRs/dHxh9/728HF4ckSZKG0mR3sn2HNWsZ3gm8OTM/OnshSZorYyxf45lmyDHHAPCyX/96wIFIkiTNOXNIjZZjHjf++HuDC0OSJEnDadLmIqsAfgQcXDuwliRJkiSpG3NISZIkSSOvl0K2e4DlwL9k5n2zG45GwdhYuSNq2bJlkywpjajvlSquBx10EJ8bcCiSJEkDYA6p0fG9LQcdgSRJkobYZIVsv6DUPPzFXAQjSSPhXe8C4LPvfKeFbJIkaaExh9RoedfjJl9GkiRJC9ZkhWx/lpn3zEkkkiRJkqT5zhxSkiRJ0oKxaKKZJkeSJEmSpF6ZQ0qSJElaSCYsZJMkSZIkSZIkSZK0tsmai5Qk9Wu//QA4/uqrBxyIJEmSpGnZr3FN//XBhSFJkqThZCGbpm1sbGz142XLlg0wEmlIHHUUAEdcfPGAA5EkSZI0LUc1ruktZJMkSVIbm4uUJEmSJEmSJEmS+uSdbNICtpTjVz8+YYBxjJyvlyquS5cu9bhKkiRJ89nXbxp0BJIkSRpiFrJJC9jxHLH6sYVBM+j97wfg+Pe9z+MqSZIkzWfvf9GgI5AkSdIQs7lITcnY2NgafbFJkiRJkiRJkiQtJBaySZIkSZIkSZIkSX2ykE2SJEmSJEmSJEnqk32ySQvY4RzXeHZE1+XUpwMPBOD9V1454EAkSZIkTcuBp40//uJfDS4OSZIkDSUL2dSTZv9ry5YtG2AkmkkncHjjmYVsM+Y1rwHg6EsvHXAgkiRJkqblNVuMP/7i4MKQJEnScLK5SEmSJEmSJEmSJKlP3skmSTPttNKkzJFHHskHBhyKJEmSpGk4betBRyBJkqQhZiGbumo2ESmpDx/7GADv/+hHLWSTJEmS5rOPbT/oCCRJkjTEhqa5yIjYOiJOioirI+KuiFgVEcdGxCZ9bmfTut6qup2r63atfiZJkiRJI8IcUpIkSdKgDcWdbBHxKOAcYEvgq8CFwC7AG4B9I2K3zLy+h+1sVrezA/B94PPAY4BDgRdExK6Z+b+z8yrmv+ada8uWLRtgJJIkSZLUnTmkJEmSpGEwLHeyfZSSHL0+M1+YmW/JzGcBHwR2BN7d43beQ0mOPpiZe9ftvJCSaG1Z9yNJkiRJmt/MISVJkiQN3MDvZIuIRwL7AKuAj7TNXgYcDrwiIo7KzNsn2M4GwCuA2+t6Tf8GvAl4bkQ80pqI4+x3TZoFhxwCwPLLLhtwIJIkSaPHHFJz6pDGNf2nBheGJEmShtPAC9mAZ9XxGZl5X3NGZt4aET+kJFBPA743wXZ2Bdav27m1bTv3RcQZlGRrL2DBJkg2CSnNgSVLABi7/PLBxiFJkjSazCE1d5Y0ruktZJMkSVKbYWgucsc6vrjL/EvqeIc52o4kSZIkaXiZQ0qSJEkaCpGZgw0g4nhgKbA0M0/sMP/dwFuBt2bmP06wnbdS2t1/d2Ye02H+UuB44PjMPKLD/MMptRShJFsXTeHlzJbNgesGHYRmled4YfF8Lyye74XF872weL77M+jjtU1mbjHA/WuGmEP2ZNCfN02f53D0eE5Hi+dz9HhOR4vnczQM+jz2lEMOQ3ORk4k6nm5p4ITbyczjKQnU0ImIczNz8aDj0OzxHC8snu+FxfO9sHi+FxbPd388XppD5pB+3uY9z+Ho8ZyOFs/n6PGcjhbP52iYL+dxGJqLvLmON+4yf6O25WZ7O5IkSZKk4WUOKUmSJGkoDEMhW6tJjW7t3D+6jru1kz/T25EkSZIkDS9zSEmSJElDYRgK2c6s430iYo14ImJDYDfgTuDHk2znx3W53ep6ze0sAvZp2998MpRNkGhGeY4XFs/3wuL5Xlg83wuL57s/Hi/NFHPIyfl5m/88h6PHczpaPJ+jx3M6Wjyfo2FenMfInG4z9TMQRMS3KQnM6zPzXxvTPwC8CTguM1/dmP4YgMy8sG07x1E6nv5AZh7VmP564EPAtzNz39l8LZIkSZKk2WUOKUmSJGkYDEsh26OAc4Atga8CvwaeCuxFaZrj6Zl5fWP5BMjMaNvOZnU7OwDfB34KPBb4c+D3dTuXzvbrkSRJkiTNHnNISZIkScNgGJqLpCYti4GTKYnRUcCjgA8DuzaTo0m2cz2wa11v+7qdpwKfBJ5iciRptkXEkojIiFgy6FgkSZJGlTmkpGFiHihJ0sI1FIVsAJl5ZWYemplbZeZ6mblNZr4hM2/osGy010BszLuhrrdN3c5WmXlYZl7VTzz14igj4r5aS7Lbcmc2ll3Szz7mQkQ8LiJOi4jfR8QfIuKiiBiLiPU7LLtuRLwhIj4ZEedHxN31db1qELEPQutcDjqObiLi6RHxjYi4ISLuiIhfRMQbI2KdDsvu2Xhvdhr+aRCvYVgN8txHxNYR8baI+GJE/KZ+72REbD/BOrtExD9GxDcj4nd1+b6+5xaSenxb7/0dBx1PNxHx2Pod/dWIuKIR8/16WHeniPh0RFwZEXfV7/2zIuLguYh9GETEOhGxtL7uGyLinnocfhERJ0bEAYOOsSUiNoiIl0XEZyPiwoi4PSJujYhzI+KoiFivy3qvjIjjIuIn9XcgI+Jdcx3/sImIxfX65X8j4s6IuCUifhkR742Ih01z21P+0ywiHhYRr6vf1avqZ/P6iPhORLy4yzorJvn9zoj4RJ9xtK9/V0RcGxH/XT8bz+t0LTEM6m/kSRFxdY17VUQcGxGbdFi27+Ot+c0ccnaEOeQaYoB5Qi/CHHFaBnl+wzxwRoS53kgIc7mRFuZr806H12QOOYFJv8gXuHspx+iVwFvbZ0bEo4FnNpYbKhHxVEqTJ+sCXwKuBJ4FvB3YOyL2zsy7GqtsABxbH18D/A54+NxFrIlExJ8D/w78AfgCcAOwP/BBSufuB3ZZ9SxgRYfpZ898lJqixcC7gAQuA24GHjTJOi8F3gDcQ2ke6cGzGeB8FhFB+R5PIIClwNEDDaq751K+o/8IXEL5vD9gspXqxeSJwB3A14FVlPfQnwLPBz49K9EOkXpx93VgX+Am4D+Bq4BNKXc2vBR4DPC1QcXY5hnAZyjf5WcCX6HEuj/wPuDF9Xf6D23rvR/YGLgRuJry2has+vn+J+DNlOux7wBfBNYDnk75rL8mIg7JzC8NIMTXAf+P8t1+JuXaahvgxcCzI+KDmXlk2zon0/l3u7W9TYFvTjGesTpeh/Id8XjgFZTvyHMj4mWZefEUtz3jYu3mAC8EdqH8/u0bEbu13a00leMtzTRzSHPIOWOOOO+ZB06Tud5o5HrmcqPLfG0kmEP2kkNmpkOHgfIDfRXwM+C3wP06LPPPdbkv1/GSQcfdiG0d4H9qXAc0pi+iJEsJvKVtnfWA5wFb1efL63KvGvTrmePznoOOo0NcG1H6hLgLWNyY/oD6xZHAS9rW2bNOXz7o+OfDMFPnHljS7/cBsDXlIm2j+nxF3cb2E6zzROBJwHqN+K8a9HEcxoGSzCSl2affAde2jtuwDcCOlCaq1q/PV9XY1/oNaqzzNMrF6vnAQzrMX3fQr2uOjt3L67E6H9i4w/wHAnsNOs5GPE8EXtb+XgQ2BFbW13JUh/X2Bbapj1vfN+8a9OsZ4HF8O+N/TD2+w/y/AO6sn5Epnf+pfK831n0x8MwO0x9L+SMtKc3R9bKtHevyv+v3cz3Rbxzlz7nT6jJXAFsO+rw2Yvt2jet1bdM/UKd/fLaOt4PDVAbMIWEEc8iJvkMHHJc54hCd36lcL2AeOBPnz1xvCF7bDBwbc7kRHTBfm7fDRL+PmEOuNQxNc5FD7ATgIcB+zYkRsS5wCOXi9VedVoyIp0TEhyLi5/VW5z9ExCUR8f722xMj4tX11su3d9nWQ+qt0r/sMe5nUt4MP8jM1TU9MvM+Su0BgFfXGgWteXdn5jcz87c97mNBiPEmNZZ3mb8qIla1TVt9q3JE7FVvJb613g79nxHx2D7D+EtgC+DzmXlua2KWWjHH1Kd/0+c2NYmpnPupyMyrMvO/MvOWPtY5PzPPy8y7p7v/BWBpHZ8AnApsDryofaGIOLme7207zOv6XoiIP4uIMxqf8e9GxK4Rsbyus2evgWbmRZn5k8y8s9d1gH+h/Cn28sz8XYdt3tPHtuazp9fxyZl5c/vMzLwjM89snx4RB0VptuvG+jv964g4JiLu32HZrN/nD42IU6I0X3JnRKyMiJf2E2z9DJ/a/hnOzFspNRyh/BHWvt63MvPyfvY1qupn9R8oNbkPyMy1rscy89+BN1E+Ix+LiEVt2/jriPhe4zptVUR8LiIW1/krKH/aAHwy1mwuY9vJYszM/8jMszpM/zXljgPocJ67OLwVx0x+rjPzGuAllD/2Hk7nO282jdI01a/re/7metz26bbdyY7tZCLikcA+lD+gPtI2exlwO/CKiNig8Vpm8nhL02EOOeLMEUebeeC8Yq43GszlRpD52ugyh1ybhWyT+xzlBLS3KX8ApdT2hAnWXUp5w11E+cB/nFKj8UjghxGxYWPZzwC3AK+Kzu2ZHkZpTuS4HuN+Vh1/q31GZv4vcDHl1sdH9rg9Tc1+wBmUc/tx4L8ot/OfFRGb97GdrucT+AGl2YCnd7qQALaPiNdGxFsj4rAoTdRIC0JEPJjyfX1xZp7D+MXX4d3X6mv7z6B8Bp8FfAP4N0otrDMpt6PPqoho1X49F/hV/cPm6CjtwO/dfoE64lq3++/Q6wpR2kn/LLA98B+Ui7AbgHcC34rO/SNsQvlzdCfK++nTlN/SUyPi76Yc/ZpaF+T3ztD2RtWhlGujL2fmRH8gn0hpjmVHyh/IRHEy8HngCZTz/0HK7/QzGP9j/GRKExPU8VhjuGma8fd8nqP063AwpRbdRNeeU1L/QG/1B3FQ8w/0iNiGUiP3LZTa4R+nJBuPpXxOlja31cexnUzr2ueMGl8z3luBH1JqNT+tx+35udJcMofURMwRpRlgrjdSuZ653GgyXxth5pBtZuO2vFEYaNxyT/mw3wts3Zj/Lcotgw9kvA3tJW3b2AZYp8O2W+1F/7+26f9Wp+/XNj2A/6UkamvdNt0l/i/Wbf1Fl/lfr/OfN8E2ljNiTX30eN6zbdqeTNCkBqV0fFXbtCV1nXuBvdvm/WOd9+Y+4voZE9yeClxQ5z+2Q9ydhi8Bmwz6eA/TMAvnfsk0YlnBJM2EdIl/wTYTMsFxeUs9Nn/fmLYSuK/9+FIuzhLYtsN21novUCqqXNLpuxR4dePztuc04l/FBE2IUGppJiW5OLPDZ/0X/byP5vNAaTbn7npuT6Hc8r/NBMu3Pqv/QW2ypTGv9fv3hrbpreN6GrCoMX07SkJ3N/DIGXgt36z7OWKS5VqvYaSbGJng9X+vvv6lPSx7al32mPr88Pr8p7RdW1FqUW7V4TgvmcHYN6I0I3Ifjd/uCZY/qMZwxhT3t9ZvXIdl7k9JIhLYrjF9RY2zvcmxB1Ga9LkTeHBjes/HdpJ43kuXpnbq/NZ189/M9PF2cJjqgDkkjGAO2ek7FHPEkRlm4fwumUYsKzAP7Od4meuNSK6HudzAz8EsnVfztXk80OH3scMy5pB1GKVaD7PpBMoJPQxWl8Y+Bzg1M+/otlJmXp6Zf+ww6yRKrbXntk3/WB0f0TZ9H8qX/heyw23TXWxcx92Wb02frFNdTc/nM/N7bdOOr+N+aj5N5XxeS7no3InSLvQWlP4SzqO0eXz6iNV8ktZQa9G8ivKD2OwM+mTKH0/ttcv79XRKrbkzM7O9U9vjKbW9Z9uWdfxXlBpBL6Z8X2xPSU52Av6z1qoaaZl5HqUt/2vq+N+BVRFxfUR8OSL2b1vlDZQ/uQ7LtZtseSelNuXLOuzqj5Q/OFfXisrMy4APA+tSOgCesoh4LaWt/vMp1wvqbqs6vrKHZVvLPLSOX1fHR7RfW2XmH3MWmz2r300nUu5m+ViWZigm06qRffyES01DZt7FeC3iLQAiYmdKbdJ/z8zPty1/E6XJjQdQritaZurYzsi17BSPtzQTzCHVjTmiNE3meqOV65nLjSzztRFnDjmu062zapOZP4nSjv1hEfEuyo/1Iia5/TNKm/tHUJr7eBzlRDcvWB/Wtp9fRcQPgOdFxMMzs/UF0/qgfryx7ScCL2zb5U2ZeWyPL6t1C2f2uLym5twO01rndXWfClHa8d6zbblVmXlyj/tZ63xmaeu42d7xbZRbcs+h/ODvBuzP+G3VmiURsQTYtm3yisxcMefBLCzPAh4FfDsz/68x/bPA+4AlEfEPOfW2sp9Ux2e3z8jM++pnbY3mLiLijaz9Y/6VzDx/ijGs0xi/KjO/Xp/fEhGHUJKxxZSLl89NcR/zRmaeFhFfBvYCdqeco90pv5cvjIhPU2q5rQ/sDFwHvLHRqkHTXZTj1+6Kmoi1W0G5WHwSQEQ8CHhjh+WOrReWa4mIFwPHUmpL/cU03psLRT/XMquXre2v/ylwTU3opx7A1K7H3g8cSGn64sge9vFoSpJyDbP/m91+THet442jc780W9TxYwH6ObZR+khY0j49Mzvtp5dYu+nreEszxRxSEzBHXCDMA2eVud6I5XrmciPJfG1hMIfEQrZ+nECp2bAvpU3ZlT180L9Aub37fykfst9RvuihfFl3ahv9o8AelCRsWUQ8hNLG9PmZ+dPGck+k/AA0XU75QofxktmN6WyjtuU0O9b68c3Me+tFQLPfhD1Z+3yeRamFBTN4PjPzloj4LPA2ynttIf4AzLUl1Hal26yY2zAWnNafSyc3J2bm9RFxOiUZ+XNK0zhT0fo8XtNlfqfpb6Q0A9W0ivKnxlTcWMd3UfoJWC0zMyK+Skm8dmEEEq9e1GTmjDpQ+6j5C0pNwoOBL1OaVwrKxV37d+9kup3vVkfkrffFg7ps+2Q6/DZExAsp7Y//HtgrS983mthvgccAj+hh2a0b67T+/Pi/Lsv2Y7LrsTVExHspHXv/AHhBrfk3mcMp79dPzmayHhEPADatT6+t483q+Dl16OZP6rifY7stnT8jy+t42tc+Uzze0kwyh1Qn5ogLxxLMA2eLud4I5nrmciPHfG3EmUOOsxmA3p1CaS/0OErtwQlv/4yIxZTk6LvAYzLz0Mz8+1qy+g6g2+3c/0H50n9l/THp2Fl1Zp6cmdE2bNtY5KI67tZpaKtj47m4xX2+a91G3q1QutsHt2eZubzD+dyzsUjX81k7c92Ocqt8rz/krS++DaYa8wIxI+c+M/fscH6Xz0iE6igitmC8ttLnIiKbA+O3pTc7xZ7ofHe6lfyWOn5wlzDWmp6Z23Z4L5w84YuZWOu74dZs69S1aiVm609jH/NabVrgNEqnuVBqvbYuqM7rcD7WGDpsstv5fkgd31z3u6rLNle1rxgRB1L6wbkGeGZmXtS+jDpq1Sx+9kQL1eupPevTHzKeGD+s4wp96OF6rBnHB4GjKX1qPC8zb5ts+7X5n0NgTjrQ3p3y/XdN433a+qy8YZLPyqF1uZ6PbWaumOQzN61r2akcb2kWmEOOLnPE0WYeOMTM9VYb+VzPXG7eM18bfeaQlYVsPcpyO/CXKCXrtzN5LZHt6/hruXYp9i50+RGsy55IeWPtT6mNeBulA8h+fL+O922fERGPpLzZLqf3C+6FrHXh8vD2GRGxPXPTJ0HX80mpafhA4JzsvYb20+rY8z+xYTj3mppDKH9ErQQ+0WW4Fnh2RGxX1+l6vik1BNu1aqLv3j6j9mXx9KkG34dfUJrJ2DwiOiUMf1rHq+YglmF3ax1HvVD6FfD4iNh0gnU6eURtoqDdnnXcV3MWEfFSyjXF1ZSk7JI+41nITqb0q/CiiHj8BMsdRmnb/yLgrMy8HbgAeHBEPGmC9VpafSOtM+FSXUTxEUrt5u9QasN17Y+pzYsotXS/m7NYI7Z+Z72tPv1sY9aP6/gZvWxnCsd2ImfW8T7t/QNFxIaUJs3ubMTYmjed4y3NKHPIkTYMeYI54uwZhvOr7sz1ioWU65nLzU8nY742sswh22SmQ4eBUgJ9Vdu0R1Bqy+zWNv1ddfkljWlPq9P+vW3ZLSkXAklpT73Tvh9BqXF2VV3uuCnEvw7wP3X9AxrTF1FqWCTwlkm2sbwu96pBn485Pu/ZNm1dSin8TcCWjenrU27ZX+tcUpqFWOM90WE/K/qIayPKReJdwOLG9AcA59TtvaRtnd2ARR229XJKLa67gG0HfcyHZZirc99jLCvqNrbvM/6rprrPURuAC+sx2WWCZd5Zl3l3ff7X9fln25bbiXJRn8DyxvRFwG/q9Oe1rfPq1nsK2HMar2NV3cb9Jlim9Rv0qeZnvsZ9J3BPP++l+ToAB1GaIuj0vfcQ4JJ6nA6s0w6rz78CPKjDOpsAT26b1jqnX2g71tsBN9Rj/ag+Yj6EkhD8L7DNFF5z6/vmXYM+/gM87++ox+BS4HEd5r8QuINyXfWsxvSldb2fAhu3rbMI2Krx/Pl12bEpxBeUGo1Zfzce0Of636vr/sU0j9Nav3GNeVsCp9VlLgc2b5v/g/o+PazL+jux5m9kz8e2h7i/Xbf1urbpH6jTPz6Tx9vBYboD5pAwgjlkp+9QzBFHZpir89tjLCswD+zlNZvrjViuh7ncwM/BLJ5b87V5OtDh97ExzxyybbBPtj5k5hXAFT0u/jPKLa4vrh2ink25Lfl5lJL5qyfaT0T8J6UdfWhr5qPHWP8YEYdSard9KSK+VGPfm1JL54eM3269WkS8hdJeLpR2awEOjYhW7Z2zM/PEfuOZzzLznoj4EPAPwHm1I9b7US4ArmaCczmDMdwSEUspNWFXRMTnKRcBBwA71ulfaFvtVGBRff9dRUm2/oxSC/Ze4IjscKu7xs3luY+IkxtPW5/Bf46IVo2tEzPz7MbyjwHe0raZTdq2c3RmXjdTMc4XUTqJ3xH4Za7ZD0m7T1Bq3RwaEcsofU9cAhwUEVsDP6H8YfXndd5fNVfO0uH1q4BvAV+LiH+nXDg+gfIe+SblO79T0x7dYt+c0lF3y+atWGvTJwD/lJkXNpZ5D+W7/WBgp4hYQalJ9ReUz/1RmfmbXmOYx54KvAH4XUScDVxWp28HvIDyp8hXqf0yZOZJEfEU4DXApRHxbcrv5KZ1nT2AT1KS6KZf1H2tjIgzKM0F/TWlRvObM/PSXoKNiL0ofQssotS2OjTW7rR7rQ6Z63uu9ZvcuuNh//qeBbgwM/+plxhGxHJKs1ZHAj+v5/FXlD/Hnk45V3cCB2Xm9xvrnUg5jgcDl9Q+La6l1KB8FuXcLK/L/oiS+L2x1pZt9eXwr5k5WT83b6fcUXInpT+Ot3Q4z+dn5lfaJ9aa8nvV/X1tkv30JMY7nl5Eec8+nnIc1qMkNC/r8LvxUsr15Cci4vWU78abKHfmPIFSi3pXSh8U0N+xncxrKH8Ufzgi9gZ+TTmne1Ga+Hhb2/JTPt7SbDGHBEYwhzRHHG3mgcPLXG9kcz1zuWIUc7nlmK/Na+aQQC855KBLRYd1oI/aQHSohVinb0rphHoV8AfKD/J7KM02rKJLLcS67p/Xbf5smq/jcZRah9dRaqVdDIwB63dZfr+QV3AAACAASURBVAXjtTs6DScP+tzM4jlfp77GuzvMC8qF7KXA3ZQf73/pdi6Z4VqKjfV2o5Sq31g//L+kdMa4Todl/x/lFtcr67Kt9+AngZ0HfbyHaZjLcz9BDBN97jp9v+zZwzrbDvrYDuh8nlpf/+t7WPaMuuyL6vOHU/6MuKF+bn4GvLhxvJd32MZT62ft1jp8l3Kx8G91nSf2Efu2PZzXPTus90DKBceFlO/6m2scz+t13/N9qOfubymdYV9E6UfhbkrHyd+g1NDuVDNyP+DrlIu7uymdXv+U8tv+mLZlk/I7+VDgM3WdPwD/Dby0z3hb3xUTDas6rHfyJOusGPS5GND534VSw/ey+tm9jdLkxPuArSdY72XAWfUz84e6/qmsXfN1X0rydls/37E9nK+u11bAP9f5/zgDx6d9n3dRrg1XUmrt7dvp89FYf0PgrXX52+oxvgz4T0p/JxtM9dj2EPvDKdcuv62f0cuBDwGbzuTxdnCYiQFzyJH73GGOONLDXJ7fCWIwD+zveJnrjWCuh7ncyOdymK/Nu6HDcTCHnGCIujENmVpKvIzSzMYnBhzOghARW1FqpP1fZm492fIaHZ57zYaI+CElKds4SxvTmudqDdOzMnPPQcciSVI7c8iZZ54w2jy/mipzvfnHXE7SbFo0+SKaa7UjvldTatdM1jm2Zs6L6vhHA41Cg+C515RExAMjYq2OzyNiCaXpgzNMuiRJ0mwzh5w15gmjzfOrrsz1JEm9sk+2IRIRLwCeDOxPaXv/6My8Y7BRjb6IeAewA3AgpR369w82Is0Vz71mwCMo/TR8h9Ix9v2AJ1Hakr4JOGqAsUmSpBFnDjk7zBNGm+dXPTLXkyT1xOYih0jtqPYQSoeJJwHHZGbPnahqauot47cC5wLvzMwzBxyS5ojnXtMVEZsA7wWeCTwEuD+lHfjvAu/OHjtO1vxgEyOSpGFjDjk7zBNGm+dXvTDXGy3mcpJmk4VskiRJkiRJkiRJUp/sk02SJEmSJEmSJEnqk4VskiRJkiRJkiRJUp8sZJMkSZIkSZIkSZL6ZCGbJEmSJEmSJEmS1CcL2SRJkiRJkiRJkqQ+WcgmSZIkSZIkSZIk9clCNkmSJEmSJEmSJKlPFrJJkiRJkiRJkiRJfbKQTZIkSZIkSZIkSeqThWySJEmSJEmSJElSnyxkkyRJkiRJkiRJkvpkIZskaWAiIhvD8jnc76rGfk+eq/1KkiRJkuZeRJzcyAFXDToeSdLouN+gA5AkjY6IeDFwGPAUYDPgLuBG4Grgl8C5mXnc4CKUJEmSJM2miNgWuKxt8h8p+eFNwFXAL4DPA9/PzJzL+CRJmkkWskmSZkREnAi8sm3yusCfAA8HngocCDQL2f6u8ficWQ1QkiRJkjQo6wAPrMNDgV2AVwE/joiDMnPVAGOTJGnKLGSTJE1bROzDmgVs5wHfBm4GNgWeADyzfb3MfN8U97dRZt4ylXWHSUTcD1g3M+8cdCySJEmSNEvOBb4ArA88EtgP2LzOexrwo4h4ema23/0mSdLQs082SdJM2Lfx+FJgl8z8+8z8p8x8c2buC2wBvKK5Urc+2SJiz7Z5e0bE0og4LyLuoDQtQkRsGhH/HBHfiYjLIuLmiLgnIq6PiHMi4u8iYv1eX0REbBwRP27s9+aI2KMxPyLioIj4ZkRcExF3R8RNEfGTiPj7iNiowzbX6P8tInaKiK9GxPXAPZQ7/FrLbhkR74yIlXXfd0fE1XX5/Xt9HZIkSZI0RH6Vme/LzHdm5qHANsBnGvMfApzcXCEi1o2IV0XEdyPi2pobXRcRZ0TEgd12FBEvi4hzI+LOut6ptfnKCUXEhhHx5ppH3ljzyt/X/b0iIha1Lb9Wzto2f0Vj3ooO+3tORPwgIm6v+zs9InaOiOXN7XZYb72IeHVEnFmPRyv/PSsiXhsR929bftu2OJe0zbevOkmaJu9kkyTNhHUajx8EbAdc0lwgM28DTp/i9t8BPKPD9IcCb+4wfVNg1zq8JCL2yMzbJ9pBRGxMufuuVeh1PbBvZp5b568PfBl4btuqG1OaOtkFWBoRz83MS+jsCcCPgA067P+plOOzRdusrYADgAMi4hRgSWbeN9FrkSRJkqRhlZl3RMShwM7ATnXyHhGxS2b+NCI2Bb4F/FnbqpsBzwGeExGfA17ezI0i4u3AWGP5BwAvBfahLT9tiojtKbngI9tmbdHaH3BwRBwwE62QRMTLgU8DUSc9kHJ3397A2ROst3mN88ltszYF9qjDKyNin8y8drpxSpJ6YyGbJGkm/Hfj8WbARRHxS0qzIOcB/5WZP5/G9p8BXAH8B3ALpRAP4D7gQuCnwO+AG4H1gMcCf0n5nXsy8DdA16YpOxSw/RZ4Tmb+qrHYB1izgO1HwHeAHYCX1GnbAV+NiCdk5r0ddvUkSoffpwIXAdsDt9c74L7GeAHbHylJ11WUArad6/RX1Nf7nm6vRZIkSZKGXWbeGxEnAR9sTN6bktt9mvECtj8Anwd+Azwe+GtKy1wHARdQc6OIeBKwrLGt24CTgLsoedSuneKIiHWAr7BmAdsXgf+p8exepz0b+BBweN8vds39PQT4OOMFbPcCnwJ+T8krnzPB6qewZgHbt4EfU47V8+u0J1LyzX2mE6ckqXcWskmSZsKplIKsViFVUO7aekJrgYi4CHhLZn5lCtu/DHhKZt7YnJiZ/wM8NiIeRkksHkpp538l8Kd1AHge3QvZ2gvYVgHPzsxLG7FvSumUu+W/gL0y8491/sXA2+u8xwL7U+566+Qv249BRLwO2LIx6dWZeWKd925K85g71HlHR8Q/t/YtSZIkSfPURW3Pt46IPwVe0Jh2SGae1noSEVcy3prJUY3c6HDW7BbngMw8s65zHPBrYN0OMTyfUnjX8u7MPKau9w7gu8Bedd5hEfHWzLyunxfZ5mDWbNnktZl5XN3fByh33D2ofaWI2Ik1u2k4NTNf3pj/qbptKHf6PSkzz5tGnJKkHtknmyRp2updW3tTmua4qstiOwL/McW+xT7SXsAGEBGbRMRXgCsphVofoRSmvZfxAjaArSfY9gsZL2C7EHhGs4CteiprVkz5VFsh10lty+/WZV8XdClk3L3xuHUXGwCZeRfw2cb8TSgFeZIkSZI0n0Xb82TtbgK+0NZHWbO7gE0Zz42aTUte2SpgA6j5XbdmGHdve/7Jxnr3Ue4ya1kHeFqX7fSqGed9bfu7Dvhql/W6xln1mpNKkmaYhWySpBmRmbdn5vLMfDilQO1g4DhKsxctARw5hc1f2GX6J4A/Z+3krN39J5nfcj2lycl2m7Y9/90kz9uXb+n2OprL35iZd09x+5IkSZI0X+zY9vwq+s91Wk3uN+/+uqbDcp2m0WF/U8312nPSbjloM84besj9uu13tuOUJPXI5iIlSTMuMy8GLgZOiYi/oxQuPbTO3mYKm7y9fUJEPJDSX1nLmZQmQi7LzD9GxGnAgT1s+xJKX2r3o9T2+1pEvCAz/9BY5oa2dR4yyfP25VvWeh0dlt8kItZrS7Z63b4kSZIkDb2IuB9waNvk7wGL26b9MzBR84ytVkhuakx7cIflOk2DzrnepW3POy1/X9v09VsPImIR8Kgu+2vGuUlE3K+tP+/2/U0U56/anvcVZ/XoLvuTJPXIO9kkSdMWEYdExGsiYq224ykdTTcLjK6fod0+iNJcR8vXM/M3tYBtS8bbzZ/MOcBhlKZJAJ4FfDki1mss8xNKh9QtB9fEqeWwtm3+sMd9d1p+Hcbb0ici7g+8tDH/Rkp/ApIkSZI070TE+pTmDXdqTD4rM3/G2s063pWZ72sfgM8Al2bmFXW5nzXWeXhErM4HI+JRrN3cYkt77ra64K/mfIc05v0R+HF93CwsgzWbkVzK+B127X7aeLwO8JLG/jantNTSV5xVt5y0a5wR8VzgKV32J0nqkXeySZJmwnbAMuDYiPghcB5wLbAhpdPqbRvLfmOG9vl7SsLQKtg7JiIeTCksewWwea8bysxTImIT4EN10r7AaRHxl5l5b2beEBEnUe6UA9gDODsivkOp+feSxuYuAk7v87V8CjiG8UTsYxHxdEpzKQcAOzSW/UBbf3CSJEmSNMweHxFHAw+g3OG1H2vma9cASwAy85cR8U3geXXe2yNid0rlyDspLaQspvRt9l+UvrkBTgSOYLw5xK/VHO4uSn64bpfY/hP4H+Bx9fnbImKHOm1v1iycO7n2mwaltZZbgI0a6z2JcqfYsyY4FqdQcucN6vNP1Nd3PXAQazYnuVpm/iIizgD2qZNeVgvlfkw5Hi9oLP69zDyvrndLRFwIPKbOe0VEPIxyLPdBkjRtkZmTLyVJ0gQiYjklUZjMucCzMvPWul7zR2gsM5fX6XtSmn9s2SszV3TY798B/9JhP/9HSYqeU59fnpnbNtZbxXizlZ/KzCV1+hjw9sZ2TgNeWu+OWx/4GvDsCV7fFcA+mXnRZPvq8Fp2pRTObTbB9j8HvMJCNkmSJEnDKiK2BS7rcfEfAi/PzFWN9TcDvkkpSJvIWZm5Z2O9dwD/0GG5W4DfAE+uz9vzwx2Ab7Nm5dB2ZwL7ZeYdjfWW0zkPvhS4h/GCrfY4Xw58mrX7R/sD5XjsXZ9nZi5qrLcFcAbwxAni/CXwnMxc3QddRCwBPtlh2Wsp52mX+nyN4yJJ6o3NRUqSZsKxwIuBDwM/olyo305JLH5PSUheC+zWKmCbCZn5XkptxV/XfV0LnAo8Fbh6CttbBvxrY9JfASdFxKLMvBN4LqUW5Lfrvu6lJGznUu5E27lZwNbnvn8EPB54D3A+cFvd/u8ohW8vysyXWsAmSZIkaR66j3L31G8pTTueQKmAuXuzgA0gM68Hnk5pAvHblDvd7qE0nf8r4EuU5hIPbFvv7ZSm98+j3MF2A/BFSmHdL7sFVvsU3xn4e0pXATdTcrHrgO9S7rJ7TrOArRoD/o7xQrWrKfnkLjXmbvv7DKX1lLPrMbmFUqi4G2v2B3dj23rXUpp7fC1wVn1999blzgbeAOzSLGCr651cX8MFlK4crqUU8j0FuyKQpGnzTjZJkiRJkiRJmgMRsX6txNk+fSNKQdjD66RvZebz2peTJA0X+2STJEmSJEmSpLlxSES8hnKX3SXAHcAjgVczXsAG8IEBxCZJ6pOFbJIkSZIkSZI0d3aqQyf3Am/OzO/MYTySpCmykE2SJEmSJEmS5sbZwImUfue2Ajai9Gm+CvgB8PHM/NXAopMk9cU+2SRJkiRJkiRJkqQ+LRp0AJIkSZIkSZIkSdJ8Y3ORHWy++ea57bbbDjoMSQvcypXjj5/ylMHFIUnSKFi5cuV1mbnFoOPQaBqqHNKLyGnzEEqSJKnXHNLmIjtYvHhxnnvuuYMOQ9ICFzH+2K9q9er0665b/Xj/zTcfYCSSNFwiYmVmLh50HBpNQ5VDehE5bR5CeU0tSZJ6zSG9k02SpBFywAUXrH6ce+45uEAkSZKkecprakmS1Cv7ZJMkSZIkSZIkSZL65J1skjSk9uP0xrP9BxaH5pf9Ntts0CFIkiTNa/vtN+gINGheU0uSpF5ZyCZJQ+p0Dmg8szMI9eb0nXYadAiSJEnz2umnT76MRpvX1JIkqVc2FylJkiRJkiRJkiT1yUI2SZIkSZIkSZIkqU8WskmSJEmSJEmSJEl9sk82SRpSy1nWeCz15rPXXLP68Usf/OABRiJJkjQ/LV/e+bEWDq+pJUlSryIzBx3D0Fm8eHGee+65gw5D0gIXMf7Yr2r1KlasWP0499xzYHFI0rCJiJWZuXjQcWg0DVUO6UXktHkI5TW1JEnqNYe0uUhJkiRJkiRJkiSpTzYXKUnSCDloyy0HHYIkSZI0r3lNLUmSemUhmyRJI+Szj3vcoEOQJEmS5jWvqSVJUq8sZJMkSZIkSZIkDVQ0OsXMzNXP2x9L0jCxkE2SJEmSJEmSNOeaBWvTXccCOEmDYCGbJEmSJEmSJGnWdLorba72Y+GbpNlkIZskSSPk+KuvXv348Ic+dICRrK1bsjNRLcRe1jFhkiRJ0kwa5mtqab4YtpzNu98kzRYL2SRJGiFHXHzx6sez+YfAVArMZjuWTvu0YE6SJEn9mqtramkUzOZdaXPFu98kTYeFbJIkabVeEqRRSDZ6LZiTJEmSJK1poeRMCyU/ljQ9FrJJ0pBayvGNZ4cPLA7NL0u32mrSZdrv6FooCdJUeCecJEkLz9Klg45Ag9bLNbW00Jg3djfo1l0kDZaFbJI0pI7niMYzC9nUm+N33HH141FotmNYzWZBpbUlJUkarOOPn3wZjbbmNbW0kFhANHsmyiEtwJTmNwvZJEmaZ7w4H25TqcXoeZMkSZI0V8whh5cFndL8YyGbJElDpFuy411po2e6yazJsCRJkqQmmy0cbb38XzCVCp3T7bPcLim00PVVyBYRmwJ7AI8GtgAC+D3wG+AHmXn9jEcoSdIQ6/Vi1GRHs8kmJiUNK3NISZJ6129FSws01K7XArepVOSdzjqT/Rfi/yeazyYtZIuIBwIHAUuBxZSkqJOMiJXA8cDnMvOOGYtSkobIdO406medpRy3+vEJU7jYmMoFyly9trmOcxCvbWAOPBCA91955YAD0TAycZE0F8whNd8d3ugO2f7ZZta8ucav19QAfPGL045zJpmbjfZrk9S7qdzZJ82GroVsEbEu8HrgrcCDWpOBZO0kKYFFlARqMfAvEfEu4N8y857JgoiIvwSeCTwR2BnYEDg1M1/ez4uJiFXANl1mX5OZD+lne5IEE9cCmk0n0MjuOWLO9qt57jWvAeDoSy8dcCAaFb3012A/gZLAHFKj44QTxh9byDY18/5aoF5TA2sUsk3VTBbWSJJ61+/3bOu51I+J7mS7ENi2Pm6+G++lNO9xfZ2+KbAlsG5juQcB7wP+Fti+hziOoSRGtwFXAY/pKfrObgaO7TD9tmlsU9ICM9R3KknSEJjJ5kVMYqSRYQ4pDch0/xycyp+Q3fi7Lkmaz8xb1a+JCtm2azz+CXAa8EPg/My8u7lgRKxHqUG4G/BXwFM7bGMib6IkRr+h1EY8s8f1OrkpM5dPY31JC5S1AzUSTjsNgCOPPJIPDDgUqVe93AnXei5pqJlDSnPI/GUW1WtqSZKavMtYnUxUyHYncCJwbGZeNtFGasL00zp8MCK2A44EDusliMxcnRB594ikueQPokbOxz4GwPs/+lEL2TRy7ChbGnrmkNIQ6LUZZz87E6jX1JIk9cK8c2GbqJBtm8y8biobrQnV6yJibGphTcv9I+LlwCOA24FfAD/IzD8OIBZJkiQNKTvKlmacOaQ0y6wkKEnS/GKF0NHXtZBtqsnRTG9jCh4CnNI27bKIODQzzxpAPJKGiH+cSpL6MdHdAJLWZA4pTZ1/ukmSpBbzzvll0aADmGGfBPamJEkbADsBx1E63/5mROzcbcWIODwizo2Ic6+99tq5iFWSJEnzWESsTn5ajycaJA0lc0jNqol+E/x9kCRJk/F6YfhN1FxkRxGxGbAnsDVw/07LZOa/TC+sqcnM9qZFLgBeHRG3AUcBy4EXdVn3eOB4gMWLF1tELI0Qf4i0oBxyCADLL5uwKxxJA2KzlFqIzCG1ENjX2Yip19QAfOpTg4tDkqQGc8jh1FchW0S8AXgP8IBJFh1IgjSBj1MSpD0GHYikueFt1VqwliwBYOzyywcbh6S+2DSYRpU5pOaLsbFS3rps2bKO0wGWL1/ecV2/q0dQvaYGLGSTJA2l5jVK+/WL5lbPhWwRsRfwwQ6zEoi258Pm93W8wUCjkCRJkvpkxRHNV+aQmk3NP5aali1btkaBWafHndaRJEmar9oL3Lpd/3jNMzv6uZPtb9qetxKhaDwf1jYRdq3j/8/enYdJVpWHH/++gxsgKAgoi4Cg4BIjykRFNDCSGFQGV0xwY1Bh1MQNiDFuPa24xADuC0uUH4ogaFTAjURnMIobKO4soiMguAAqoAgi7++Pc3qmpqaqq6qnu29V9ffzPPe5VXert+rUct46957z00ajkDSnxu1PyAlWrLnd+e8AqYOTTwZqRarZSCTNgdbfOpMljQBzSG2wbleYzaV99lkFwL777jtvj6khU+vUkiSNE084mhuDNLI9nJIEXQScBUzU+/cADgbeRRk0+l9mOcZ1RMQdgV2BP2fm5S3LHwRck5nXt22/E/CeevcjcxmbJM2mFS1NJDaWqG+1O5sVJ5/s+0ZaQKZrcGviD2qpMofUwIbh+2zJkvPq4+xLl/+iNO7sIlKStMB06wlAvQ3SyLZNnZ8L3DS1MDN/C7wvIvYGDgW+C7x7kCAi4snAk+vde9X5XhFxcr19bWYeVW9vD/wY+Dmwc8thDgJeFRErgZ8BN1ISqSdS+v//LHDMIHFJkiRJo6ifsYVamUhpjphDqi/dvpskSZLULK9+622QRrapbjyuB25dszBi88y8AfhR3WY5AyZIwB7AIW3LdqkTlGToKKa3EtgdeCila49Ngd8BXwE+DHw4x6UPOUmSJGkOOYaRZok5pCRJkjSmvPqtGKSR7XrKGYKbAr9sWf6ciPgg8Ph6f5f2HXvJzBXQMvjQ9NuupkO//Zl5HnDeoI8tSZIkaXAOrq0+mEMucP1+T0iSJGl8LLQTNQdpZPslJUHaEjinZfm7gHeyNmm5cXZCk6SFbSlntdw7sLE4NGJe9CIAjvzJTxoORJIkc0iNpo9+9J8AuOACWLy44WDUjFqnBuD9728uDkmSxsw4Xv02SCPbdyjdaPwVcCFwJbADJTEKygDWybrJkyRphs5hadMhaBQ94xkAHHfVVQ0HIkmSOaRG06WX7l7nNrItWLVODdjIJknSPBjl3lAGaWQ7DbgZuCkzMyKWA/9NGRAaSpL0I+CVsxuiJEmSJGkEmUNKkiRJGmt9N7Jl5heBL7bc/3xE3B94ErAVcAnw8cy8tcshJEnSXHvf+wA45thjOarhUCRJC5s55MLkWGsaC7VOLUmS1MsgV7KtJzOvAN49S7FIkqQNdeaZABx5xhk2skmSho45pKSRUOvUkiRJvWxQI5skNWmU++qVJEmSRk23geq9ek2SJEkL1UCNbBHxSOBfgYcDWwCLOmyWmbnpLMQmSZIkSRph5pCjzZPaJEmSpOn13cgWEX8PfAbYiDJAdTe5oUFJkiRJkkabOaQkSZKkcTfIlWyTdXsTIEmShtWRRwJw+CWXNByIJEnmkJJGVK1TA3Dssc3FIUmSht4gjWx7UJKjAD4PXALcggmTJEnD44ADADjxmmsaDkSSJHPIUeT4ahJr6tSAjWySJGlagzSy/Qm4M/CxzDx4juKRJEmSJI0Hc0hJkiRJY22QRravAE8EfjpHsUiSWhzP4WtuL28wDo2Yeqbt8ccf7/tGktQ0c0iNpAMOOBuApUuXYucAC5RXr0mSpD4N0sj2OuDvgYMj4rjMvG6OYpIkAYdz4prbNpaob+ecA8Dh223n+0aS1DRzSI2kxYu/DcDhhy/F3jMXqFqnliRJ6qVrI1tEvLLD4vOBJcBlEfEJYDXw5/aNMvNtsxWgJEmSJGn4mUOOrqlx2CYmJhqORJIkSRot013J9la6D0h9d+B50+xrgiRpTjgQuyRJ0tAyh5QkSZK0oCyawT5J98QpNiAWSZIkSdL4MYeUJEmSNJZ6jclmwiNJDdmTC1ruLW4sDo2Y174WgGf+6EcNByJJWqDMITXyjj/+MADOOgsOPLDhYNSMWqcG4Oijm4tDkiQNveka2TaetygkSev5Nns2HYJG0X77AXDar3/dcCCSpAXIHFJj4ZprtqtzG9kWrFqnBmxkkyRJ0+rayJaZt0TEozPzK/MZkCRJkiRp9JhDSpIkSVpoenUX+eWI+CXwaeATwMrM/MvchyVJkmaknml76qmn8qyGQ5EkLUjmkJJGn1evSZKkPvVqZLsWuBewHDgc+F1EfBr4JPCFzLx1juOTJEmD+OIXAXjmPe9pI5skqQnmkJJGX61TS5Ik9bKox/p7AUuA9wBXAVsAy4BPAddGxGkRcVBEbDqnUUqSJEmSRoE5pCRJkqQFY9pGtsy8PTPPy8yXZuZOwCOB/wR+AtwV+EfgdOA3EfHJiHhuRNx9zqOWJEmSJA0dc0hJkiRJC0mv7iLXkZnfBL4J/FtE/BXwdOApwIOBJwEHArdFxCrgE5l5wuyGK2khmpycbDoESZIkzYA5pCRJkqRxNlAjW6vM/AHwA2BFROxCSZaeCvwN8PfA3wEmSJIkzac3vQmApd//fsOBSJK0LnNISSOj1qkBeM1rmotDkiQNvRk3srXKzJ8CbwPeFhHbAU+jnJ0oSZLm06MeBcA5113XcCCSJHVnDilpqNU6tSRJUi/Tjsk2E5l5dWa+OzMfO9vHliRJkiSNF3NISZIkSaNq4CvZIuLBwMOBLejSSJeZb9vAuCRpwTuLpWtuH9hgHBoxr341AGeddZbvG0nSUDCH1Kg5+ODT6vxgvv3thoNRM2qdWpIkqZe+G9kiYlPgDGD/PjY3QZKkDbSUc5oOQaPoa18DYOlWWzUciCRpoTOH1KjaffdLAVi6FBvZFqpap5YkSeplkO4i3wo8HohptplunSRJkiRp4TCHlCRJkjTWBuku8qlA1tsmQpIkSZKk6ZhDSpIkSRprg1zJdo86vxJ4JKU//U2AjdumTWYzQEmSJEnSSDKHlCRJkjTWBrmSbTVwP+CUzPzm3IQjScXk5CQAExMTDUfSnO34Rcu97RuLQyPm7W8HYN/vfKfhQCRJMofUaDrmmCMAOP54WL684WDUjFqnBuAVr2guDkmSNPQGaWQ7FZikJEmSpDl2Dds1HYJG0R57AHDe73/fcCCSJJlDajTddNNmdd5wIGpOrVNLkiT1Mkh3kW8DvgE8IyKOjggvq5AkSZIkdWMOKUmSJGmsDXIl228pjXIB/Dvw7xFxG/CXtu0yMzedpfgkQyDeDAAAIABJREFUSdIgXv5yAFauXMmShkORJC145pCSRlOtU0uSJPUySCPbXYCsU9Rld6xTq5yFuCRJ0kx897sA7LvFFg0HIkmSOaSkEVXr1JIkSb0M0sgGaxMjSZIkSZJ6MYeUJEmSNLYGaWT7jzmLQpIkSZI0bswhJUmSJI21vhvZMvPf5yqIiHg6sA+wB/AQYDPg1Mx89gyOtQPwBmB/4B7ANcCngMnM/O2sBS1JkiRJ6socUpIkSdK4G7S7yLnyWkpidBNwFXD/mRwkInYFzge2AT4NXAw8HHgZsH9E7J2Z181KxJJm1eTk5JrbExMTDUYijbjjjwdgzwsuaDgQSZLmlDmkpLlT69QALF/eXBySJGnoLRp0h4jYMyJOi4grIuKmOj8tIv5mA+J4BbAbsDnwog04zvsoydFLM/PJmfmqzHws8HZgd+BNG3BsSZKG3267wW678e2bbmo6EkmSAHNISSOo1qnZbbemI5EkSUNuoEa2iDgU+BrwDGAHYJM6fwZwfkQ8fyZBZObKzLwsM3Mm+9fYdgEeB6wG3tu2egL4A/CciNh0po8hSZIkSeqfOaQkSZKkcdZ3d5ER8UDgA3WfTonMRsD7IuIbmfmDWYpvEI+t83Mz8/bWFZl5Y0R8lZJAPRL44nwHJ0mDuoA919xe3GAcGjG1O5sLLrjA940kqVHmkBpVhx9+Qp0fzjnnNByMmmEXkZIkqU+DXMn2MuCOlOQoga9TBoP+OmsTpjsAL5nNAAewe51f2mX9ZXXutf6SRsKefHvNJPXt0kvh0kvZc7PNmo5EkiRzSI2k7ba7hu22u4Y99+y9rcZUrVNzabevB0mSpKLvK9mAJXV+LbAkM380tSIiHgSsBO7Rst18u1ud/77L+qnld++0MiIOBw4H2HHHHWc3MkmSJElaeMwhJUmSJI21Qa5k255ytuEprckRQGb+EPh/QNTthlHUecc++zPzhMxcnJmLt95663kMS5IkSZLGkjmkJEmSpLE2SCNbLzMecHqWTJ1leLcu6zdv206SJEmS1BxzSEmSJEkjbZBGtqspZ/I9KyJ2al0RETsDz2nZrgmX1Hm3/vLvV+d2qC1pJAS5ZpL6dsopcMop7PaNbzQdiSRJ5pAaSStWTLBixQQRvbfVmKp1ak45pelIJEnSkBtkTLbzgF2BewKXRMR5wC+BewH7AHeinIn45dkOsk8r6/xxEbEoM2+fWhERmwF7AzdTBtmWNAQmJyebDkEaP/e+NwCX3Xxzw4FIkmQOKWlE1Tq1JElSL4NcyfYu4C/19p2AvwOeXed3qsv/Arx71qLrICLuGBH3j4hdW5dn5uXAucDOwD+37TYJbEoZC+APcxmfJEmSJAkwh5QkSZI05vq+ki0zvxcRRwDvoHvf+Udl5kWDBhERTwaeXO/eq873ioiT6+1rM/Ooent74MfAzynJUKsXA+cD74qI/ep2jwCWULr4eM2gsUmSNFKeU3reuuSSS9i94VAkSQubOaSkkfWc5/TeRpIkicG6iyQz3x0RPwCOBB5NGQj6BuCrwDGZuXK6/aexB3BI27Jd6gQlGTqKHjLz8ohYDLwB2B94AnAN5QzKycy8fobxSZI0Gq66CoDdNtmk4UAkSTKHlDSiap1akiSpl4Ea2QBqErQSoL3f+pnKzBXAij63XU0ZPLvb+iuBQzc0JkmSJEnShjOHlCRJkjSuph2TLSJuj4jbahcf65mN5EiSJEmSNB7MISVJkiQtJNM2slVdz/iTJEmSJKmNOaQkSZKkBWHg7iIlSdIQO/NMALY7//yGA5EkSZJGVK1TA3DQQc3FIUmShp6NbJLm1eTkZNMhSONtq60AuObWWxsORJIkSRpRtU4tSZLUS7+NbFtExI79HjQzr5hhPJIkSZKk0WcOKUmSJGns9dvI9uo69SMHOK4kqYtfsN2a29s3GIdGzNOfDsAvfvEL3zeSpCaZQ2pkHXHEsQAceeSRnHhiw8GoGbVOLUmS1Eu/iYwDV0vSPNuOa5oOQaPouusA2O7Od244EEnSAmcOqZG1+eY3AbDddj021PiqdWpJkqReFjUdgCRJkiRJkiRJkjRq+r2S7QLgh3MZiCRJkiRpbJhDSpIkSRp7/TaynZ6Zx81pJJKkdVzNti337DpSkiSNFHNIjawbbrgrAFdf3XAgkiRJGnoOLi1JQ2p7WrN6hzVRnz7zGQA2+7//azgQSZKk0XTccUfWOaxY0WwsakitUwPwxCc2F4ckSRp6NrJJmnOTk5MATExMNByJtABssgkAN/3lLw0HIkmSJI2oWqeWJEnqZVHTAUiSJEmSJEmSJEmjpteVbJN1fv5cByJJkmbBE54AwA033MDmDYciSVqQzCEljb5ap5YkSepl2ka2zJycbr0kSRoyN98MwGZ3sEdoSdL8M4eUNBZqnVqSJKmXrt1FRsRkRGwx0wNHxBYRsWKm+0uSJEmSRoc5pCRJkqSFZrox2V4HXBER74uIx/R7wIjYKyLeA/y8HkOSJEmSNP7MISVJkiQtKL36ktoEWA4sj4hfAl8DvglcAVxft9kSuDewGNgL2L4uDyBnO2BJkiRJ0tAyh5QkSZK0YEzXyHYE8FpKAgSwLfCUOnUTLbevB96wQdFJkqTBrFwJQKxa1WwckqSFyBxS0niodWoAlixpLg5JkjT0unYXmZnvAHYF3gL8ui4O1k2CosPy3wBvBnbNzHfNarSSRsbk5CSTk457L0mStFCYQ0qSJElaaKbtLjIzfw+8pg4+/WRgf+BvgfuwtoEugZ8BXwY+D3wyM/88VwFLkiRJkoaTOaQkSZKkhaTXmGwA1ITnzDoREQHcg3Lm4XWZefucRShJC1S2nPQd02wnraN2Z5OZvm8kSY0xh9QoW7Gi9MgxMTGBnXMsUHYRKUmS+tRXI1u7zEzg2lmORZIkSZI0hswhJUmSJI2jrmOySZIkSZIkSZIkSerMRjZJkiRJkiRJkiRpQDPqLlKSNPcu5GEt977dWBwaMRtvDMCNt93WcCCSJEmj6eqrtwXgwgsbDkTNqXVqAG6+ubk4JEnS0LORTdKsmGwZEXxiYqLBSMbHYlqz+mgsDo2Yz34WgM2/8pWGA5EkSRpNJ5xweJ3DihXNxqKG1Do1AEuWNBeHJEkaenYXKUmSJEmSJEmSJA3IK9kkSRonf/wjAHfdbDNuajgUSZIkaSTVOrUkSVIvNrJJkjROnvhEAG7MtJNRSZIkaSZqnVqSJKkXu4uUJEmSJEmSJEmSBtT1SraIeP1MD5qZb5jpvpIkSZKk0WMOKUmSJGmhma67yBVAzvC4JkiSJEmStLCswBxSkiRJ0gIy6JhsrcO7ZB/LJUnSfLrHPQC4+pZbGg5EkiTAHFLSKKp1agCuu665OCRJ0tDr1cgWHZZlXd6+LjsskzTGJicnmw5BUruPfxyA7b/2tYYDkSQtUOaQkkZfrVMDsGRJc3FIkqSht6jbisxc1DoBdwLOqqtfD+wE3KXOJ+ryL9VlkiRJkqQFxBxSkiRJ0kIzSHeRrwCWAh/NzKNbll8JvDEidgcOBl4JvGn2QpSkhWlbrl5z+5oG49CIufZaALbdbjvfN5KkpplDaiTd9a43ArDZZps1HIkaU+vUkiRJvQzSyPa8Or+8y/rLKV19PBcTJEnaYFez/Zrb9qOkvh10EABXZ/q+kSQ1zRxSI+moo44DYGJiAnvIX6BqnVqSJKmXrt1FdrBznS+NiDu2roiIO1HOUATYcRbikiRJkiSNtp3r3BxSkiRJ0lgapJHtN3W+B/C9iFgRES+KiBXAd4GH1PUzuqY+InaIiA9GxNURcUtErI6Id0TEFgMcY1VE5DSTff1LkiRJ0vwwh5QkSZI01gbpLvLjwMuBBHYHXteybqpHqgTOHDSIiNgVOB/YBvg0cDHwcOBlwP4RsXdmXjfAIbt16HDboLFJkiRJkmbEHFKSJEnSWBukkW0FsB/wYEoi1CopSdIP6naDeh8lOXppZr57amFEHEcZLPtNwAv7PVhmziQGSX2YrIMSTExMNBzJ+DubA1rundNYHBoxO+wAwKV//GPDgUiSZA6p0XTJJbsBcPbZDQei5tQ6NQBXXdVcHJIkaej13V1kZt4A7A28E7iJkhBNTTcB7wIeXbfrW0TsAjwOWA28t231BPAH4DkRsekgx5WkUXcgZ6+ZpL59+MPw4Q+z+ze/2XQkkqQFzhxSo+q00w7mtNMO5sADm45Ejal1aj784aYjkSRJQ26QK9nIzJuAV0TEUcD9gbsDvwMuzsy/zDCGx9b5uZl5e9vj3RgRX6UkUI8EvtjPASPiH4H7ALcCPwa+lJm3zDA+SZIkSdIMmENKkiRJGmcDNbJNqcnQD2cpht3r/NIu6y+jJEi70WeCBJzedv/XEfHPmfnxGcQnSdLouPJKAO63225c1nAokiRNMYeUNFJqnVqSJKmXgRvZImJn4KXAo4GtgTcD3wD2qJucOuAZiXer8993WT+1/O59HOvTwDHAd4DrgJ2AQ4AjgY9FxAGZ+bkBYpMkabQ897kAXJpJNByKJElgDilpBNU6tSRJUi8DNbJFxFOAjwB3ofSjn8BmwMbAyfX+lcDKWYxx6j/C9oGy15OZb29bdAnw6oi4Gng3JZnrmCBFxOHA4QA77rjjjIOVJEmSJBXmkJIkSZLG2aJ+N4yI3YBTKcnQOjLzG8BV9e7SAWOYOsvwbl3Wb9623UycBNwG7BERm3XaIDNPyMzFmbl466233oCHkiRJkiSZQ0qSJEkad303sgFHUc4+TNYmQ63Oo5wxuNeAMVxS57t1WX+/Ou/W335Pmfkn4MZ6d9OZHkdaqCYnJ5mcnGw6DEmSJI0Wc0hJkiRJY22Q7iL3oyRHF1GSoD+1rf9JnW8/YAxT3YI8LiIWZebtUyvqGYN7AzcDXx/wuGtExO7AFpQk6dqZHkeSpKG3W/m/8cIbb+yxoSRJc84cUtJo2q2lDf/SGbfXS5KkBWCQRrapxOfTmXlrRLSv/3OdbzNIAJl5eUScCzwO+GdKv/dTJilnDR6fmX+YWhgR96/7XtyybBfglsz8RevxI2Ir4EP17umZedsg8UmSNFKOPx6AxRde2HAgkiSZQ0oaUbVODcCSJc3FIUmSht4gjWy3AncE7txl/a513n52Yj9eDJwPvCsi9gN+DDwCWELp4uM1bdv/uM5bs7S/BU6KiPOAy4HrgR2BJ1D66r8AeOUMYpMkSZIkDc4cUpIkSdJYG6SR7WfAg4FnRMRbWlfUrjSeTukK5LJBg6hnIi4G3gDsT0lqrgHeBUxm5vV9HOZC4CPAnsAelMGubwS+D5xBOZPx1kFjk6SmPIy1VyJ9u8E4NGJqdzYPe9jDfN9IkppmDqmRtO22V9f5dg1HosbYRaQkSerTII1s51ISpF1YO9A0wL8AbwQ2piRI584kkMy8Eji0z23X62ckM78PLJvJY0vSMLqQxWtur/elJ3WzfDkAF2b6vpEkNc0cUiNp+fITAZiYmGBysuFg1Ixap5YkSepl0QDbvpNyVh/AtpRkCGAnSnJEXf/e2QlNkiRJkjTCzCElSZIkjbW+r2TLzKsi4mBKtxmbUC6sSNZeYHEzcHBmXj3rUUqaV5Mtp2tOTEw0GIkkSZJGlTmkJEmSpHE3SHeRZOZnI+KBwEuBRwNbUgaH/grw7sz8+eyHKEmSJEkaReaQkiRJksbZQI1sAJl5BXDUHMQiSWpxAoe13DuxsTg0Yh7yEABW/fa3DQciSVJhDqlRc8EFDwPghBMaDkTNqXVqAL773ebikCRJQ2/gRjZJ0vxYTmtWbyOb+vSOdwCwxD8DJEmSZuScc5bWOaxY0WwsakitUwOwZElzcUiSpKHXtZEtIj5Yb56emee23O8lM/P5Gx6aJEmSJGlUmENKkiRJWmimu5JtGWVQ6h8A57bcn87UQNYmSJIkNeGiiwDYZ599OK/hUCRJC84yzCEljYNap5YkSeplJt1FRpflvZInSUNscnKy6RAkzYZXvAKAVZldf7AlSZpn5pCSRkutU0uSJPUyXSPbFZSk5/dt9yVJkiRJamcOKUmSJGlB6drIlpk7R8RBmXnm1P15i0qSJEmSNFLMISVJkiQtNIt6rD89Ik6NiLvPSzSSJEmSpFFmDilJkiRpwejVyBbAPwE/iIh/mId4JEnShthrL9hrL86+9tqmI5EkLUzmkJJGX61Ts9deTUciSZKG3HRjsrXaDvhsRBwPHJWZf5zDmCRJ0ky9+c0AHPiDHzQciCRpgTOHlDS6ap0agCVLmotDkiQNvV5Xsn2VciZi1vly4KKIeNRcByZJkiRJGjnmkJIkSZIWjF5Xsv0t8DLgaGDjuuy+wHkRcQzwAUrytI7MvGI2g5Q0NyYnJwGYmJhoOBJ1cgBnr7l9ToNxaMScfz4AByxd6vtGktQEc0iNvN12u6TOd284EjWm1qklSZJ6mbaRLTMTeEdEnA18EHgMJSHaCHhlndbbrddxJUm9nc2Ba25Hg3FoxLzmNQCc/epX+76RJM07c0iNg2c+83SgnIxYz0vUQlPr1JIkSb306i4SgMy8PDP3AV7Dul1/dJskSZIkSQuUOaQkSZKkhaCvRjaAiFgCHM7arj3W6+JDkiRJkiQwh5QkSZI0/np2yRERmwLHUJKjNYuB3wFnYaIkSZIkSarMISVJkiQtFNM2skXEfsBJwI6s28XHucDzM/MXcx6hJC1QK5houedgEOrTfvsB8NFf/arhQCRJC5E5pMbBypX7AJAJYWemC1OtUwPwxS82F4ckSRp6va5k+x/WPcvwZuCVmfm+uQtJ0lyarCN3T0xM9NhSTZtkxTr3pL689rUAPOvHP244EEnSAmUOqZF33nn71jmsWNFoKGpKrVMDNrJJkqRp9ewusgrga8BzM/PyOYxHkiRJkjT6zCElSZIkjb1+Gtn+DKwA3paZt89tOJIkaYPUM20PPvhgTms4FEnSgmUOKWm0efWaJEnqU69Gtu9Rzjz83nwEI0mSNtDRRwPw0Te+0UY2SVITzCEljb5ap5YkSeqlVyPb32Tmn+clEkmSJEnSqDOHlCRJkrRgLJpupcmRJEmSJKlf5pCSJEmSFpJ+xmSTNMImJyfX3J6YmGgwEkmSJEmSJEmSxoeNbJIkjZMDDgDghKuvbjgQSZIkaUTVOjUA55zTXBySJGno2cgmSdI4OfJIAJZfemnDgUiSJEkjqtapARvZJEnStKYdk02SJEmSJEmSJEnS+rySTZKG1GGcsOb2iQ3GoRFTz7Q97LDDfN9IkiTNwMMediEAe+65Z8ORqDFevSZJkvpkI5s0hiYnJ5sOQbPgBJavuW1jifp27LEAnHDMMb5vJEmSZuDAA0sDy8TEnphaLVC1Ti1JktSL3UVKkiRJkiRJkiRJA7KRTZIkSZIkSZIkSRqQjWySJEmSJEmSJEnSgByTTRoTU+OwTUxMNByJZsvhHN9yb3nX7aR1HHQQAMdeeWXDgUiSJI2ms846AIBf/AK2377hYNSMWqcG4Mwzm4tDkiQNPRvZJGlIncjhLfdsZFOfXvxiAI66/PKGA5EkSRpN3/72nnUOK1Y0G4saUuvUgI1skiRpWnYXKUmSJEmSJEmSJA3IK9mkEWYXkZLWc8YZABxxxBEc13AokiRJ0kiqdWpJkqRebGSTJGmcvP/9ABz7vvfZyCZJkiTNRK1TS5Ik9TI03UVGxA4R8cGIuDoibomI1RHxjojYYsDjbFn3W12Pc3U97g5zFbs0XyYnJ9dMkiRJ0kJmDilJkiSpaUNxJVtE7AqcD2wDfBq4GHg48DJg/4jYOzOv6+M496jH2Q34EnA6cH/gUOCJEbFXZv50bp6FJEmSJGk+mENKkiRJGgZD0cgGvI+SHL00M989tTAijgNeAbwJeGEfx3kzJTl6e2Ye0XKclwLvrI+z/yzGLc05r1qTJEmS1mMOKUmSJKlxjXcXGRG7AI8DVgPvbVs9AfwBeE5EbNrjOJsCz6nbT7Stfk89/j/Ux5MkaTwdcggccggrfvazpiORJGlOmENKmnO1Ts0hhzQdiSRJGnKNN7IBj63zczPz9tYVmXkj8FVgE+CRPY6zF7Ax8NW6X+txbgfOrXeXbHDEkiQNq2XLYNkyJn/+86YjkSRprphDSppbtU7NsmUNByJJkobdMDSy7V7nl3ZZf1md7zZPx5EkSZIkDS9zSEmSJElDITKz2QAiTgAOAw7LzJM6rH8T8Grg1Zn5lmmO82pKv/tvyszXdlh/GHACcEJmLu+w/nDg8Hp3d+CSGTydubIVcG3TQWiDWIbjxzIdL5bn+LFMx4vlOfqGoQx3ysytG45Bs8Acsi/D8JnThrEMx49lOl4sz/FjmY4Xy3M8NF2OfeWQd5iPSDZQ1PmGtgZOe5zMPIGSQA2diLggMxc3HYdmzjIcP5bpeLE8x49lOl4sz9FnGWqemUP6mRt5luH4sUzHi+U5fizT8WJ5jodRKcdh6C7y93V+ty7rN2/bbq6PI0mSJEkaXuaQkiRJkobCMDSyTXWp0a2f+/vVebd+8mf7OJIkSZKk4WUOKUmSJGkoDEMj28o6f1xErBNPRGwG7A3cDHy9x3G+Xrfbu+7XepxFwOPaHm+UDGUXJBqIZTh+LNPxYnmOH8t0vFieo88y1Gwyh+zNz9zoswzHj2U6XizP8WOZjhfLczyMRDlG5oZ2Uz8LQUR8gZLAvDQz392y/DjgFcDxmfnCluX3B8jMi9uOczxl4OnjMvPIluUvBd4JfCEz95/L5yJJkiRJmlvmkJIkSZKGwbA0su0KnA9sA3wa+DHwCGAJpWuOR2XmdS3bJ0BmRttx7lGPsxvwJeCbwAOAJwG/rse5fK6fjyRJkiRp7phDSpIkSRoGw9BdJDVpWQycTEmMjgR2Bd4F7NWaHPU4znXAXnW/+9bjPAL4ELCnyZGkfkTEsojIiFjWdCySJElanzmkpGFiDilJ0sI1FI1sAJl5ZWYempnbZuadMnOnzHxZZl7fYdtoPwOxZd31db+d6nG2zcznZeZVg8RTK0cZEbfXsyS7bbeyZdtlgzzGfIiIB0bEGRHx64j4U0RcEhGTEbFxh23vGBEvi4gPRcRFEXFrfV4vaCL2uTBVVk3H0U1EPCoiPhsR10fEHyPiexHx8ojYqMO2+7a89zpNb23iOTSpyfKNiB0i4jURcWZE/KR+d2RE3HeafR4eEW+JiM9FxC/r9gN9V42T+vpNvX93bzqebiLiAfV79NMRcUVLzHfoY98HR8QpEXFlRNxSv5vPi4jnzkfs8yUiNoqIw+pzuz4i/lyf6/ci4qSIOLDpGKdExKYR8ayI+GhEXBwRf4iIGyPigog4MiLu1GW/50fE8RHxjfp9nRFx9HzH34SIWFzrCj+NiJsj4oaI+H5E/GdEbL+Bx57xH1QRsX1EvKR+p66un7HrIuJ/IuKpXfZZ1eO3NCPivzbkOQ2DDs/ploj4TUR8u34mH9+prjEM6u/rByPi6hr36oh4R0Rs0WHbgd8DGm3mkHMjzCHXEQ3mGP0Ic8gN0mT5hjnkrAjzyLEQ5pBjLcwhR06H52QOOY2eX+QL3G2U1+j5wKvbV0bE/YB9WrYbKhHxCEqXJ3cEPg5cCTwWeD2wX0Tsl5m3tOyyKfCOevtXwC+Be89fxAtbRDwJ+ATwJ+BjwPXAUuDtlMHbD+qy63nAqg7LvzL7UWoai4GjgQR+BvweuHuPfZ4JvAz4M6WLo3vOZYDDLCKC8l2bQACHAUc1GlR3/0D5Hv0LcBnlM3uXXjvVCt9JwB+Bc4DVlPfIXwFPAE6Zk2jnWa1knQPsD/wO+AxwFbAl5QqDZwL3B85qKsY2jwE+QvnOXQl8ihLrUuAY4Kn19/JPbfsdC9wN+C1wNeW5jbX6OX0r8EpK3ed/gDOBOwGPonxmXxwRh2TmxxsI8SXAv1G+g1dS6jE7AU8F/i4i3p6ZR7TtczKdf0Onjrcl8Lm5CLYhk3W+EeX750HAcyjfvxdExLMy89KmgmsX63cHeDHwcMpv5/4RsXfb1UozeQ9Is80c0hxy3phDjjxzyA1kHjkeeaQ55PgyhxwL5pD95JCZ6dRhovxAXwV8C7gGuEOHbf6jbvfJOl/WdNwtsW0E/KjGdWDL8kWUZCmBV7Xtcyfg8cC29f6Kut0Lmn4+s1yu2XQcHeLanDLmwy3A4pbld6lfDAn8U9s++9blK5qOf1im2SpfYNmgn2lgB0pFa/N6f1U9xn2n2WcP4KHAnVriv6rp17GhsvuH+vw/VH/QfjP1ugzbBOxO6UZq43p/dY19vd+Jln0eSalQXgTcq8P6Ozb9vGbx9Xl2fT0uAu7WYf0mwJKm42yJZw/gWe3vN2Az4ML6XI7ssN/+wE719tR3xtFNP585fq1ez9o/gR7UYf3TgJvre31GZTyT79+WfZ8K7NNh+QMof1olpeu3fo61e93+l+Pw+Zzu95Hy59wZdZsrgG2ajrclti/UuF7Stvy4uvwDc/UecHKayYQ5JJhDzmdc5pBDVL4zqcNgDjkb5WceOQTPbRZeG3PIMZ0whxzZabrfR8wh15uGprvIIXYicC/ggNaFEXFH4BBK5fWHnXaMiD0j4p0R8d16qfOfIuKyiDi2/fLEiHhhvfTy9V2Oda96qfT3+4x7H8qb4cuZueZMj8y8nXL2AMAL6xkFU+tuzczPZeY1fT7GWIi1XWas6LJ+dUSsblu25lLkiFhSLxW+sV7u/JmIeMCAYTwd2Bo4PTMvmFqY5ayX19a7LxrwmGJm5TsTmXlVZv5fZt4wwD4XZeZ3MvPWDX38MXBYnZ8InApsBTylfaOIOLmW584d1nUt64j4m4g4t+Vz+r8RsVdErKj77NtvoJl5SWZ+IzNv7ncf4G2UP66enZm/7HDMPw9wrGH3qDo/OTN/374yM/+YmSvbl0fEwVG6z/pt/b38cUS8NiLu3GHbrN+720XEh6N0I3JzRFwYEc8cJNhRkhGLAAAgAElEQVT6OTy1/XOYmTdSzjSE8odU+36fz8yfD/JYo6x+5l5HOWv6wMxcr+6TmZ8AXkF5r78/Iha1HeMfI+KLLXWi1RFxWkQsrutXUf4gAfhQrNs1xc69YszM/87M8zos/zHl7H7oUJZdHD4Vx5h9PteTmb8C/onyx9696XzlzZZRuqb6cf2s/b6W5eO6HbdXefcSEbsAj6P8AfXettUTwB+A50TEpi3PZTbfA9KGMIccc+aQ480ccqSYR44Hc8gxZA45vswh12cjW2+nUQqgvU/5AymttidOs+9hlDfcJZQP/AcoZzQeAXw1IjZr2fYjwA3AC6Jzf6bPo3QncnyfcT+2zj/fviIzfwpcSrn0cZc+j6fODgDOpZTdB4D/o1yuf15EbDXAcbqWF/BlSrcAj+pUUQDuGxH/EhGvjojnRemCRhoZEXFPynfqpZl5PmsrSId332ug4z+G8jl6LPBZ4D2UM6VWUi4Zn1MRMXWG6gXAD+ufKkdF6at9v/ZK5BiYuux+t353iNJf+UeB+wL/TakMXQ+8Efh8dB6nYAvKn5QPprxnTqH8pp0aEf864+jXNVUxvm2WjjfKDqXUQz6ZmdP9WXsSpeuT3Sl/1hLFycDpwF9TyvjtlN/Mx7D2T+iTKd05UOeTLdPvNjD+vssyyhgKz6WcsTZdPW9s1D/Qp8aDOLj1D/SI2IlyRu6rKGeHf4CSbDyA8vk8rPVYA5R3L1N1o3NrfK3x3gh8lXJW8yP7PJ6fZ80nc0hNxxxSmgXmkWOVR5pDjidzyDFmDtlmLi7LG4eJlkvuKR/224AdWtZ/nnLJ4Cas7UN7WdsxdgI26nDsqf6i/61t+Xvq8gPalgfwU0qitt5l013iP7Me62ld1p9T1z9+mmOsYAF09UGPLjMord+r25Ytq/vcBuzXtu4tdd0rB4jrW0xz+Snwg7r+AR3i7jR9HNii6dd7DMp32QbEsooeXX10iX/BdfVB+dFN4N9bll0I3N7++lEqUAns3OE465U15WSSyzp93wEvbPnM7LsB8a9mmm4+KGdSJiUBWNnh8/q9Qd4nwz5Ruq+5tZbfhymX3u80zfZTn7f/pnad0rJu6nfoZW3Lp167M4BFLcvvQ0msbgV2mYXn8rn6OMt7bDf1HMa2qw/gi/U5HtbHtqfWbV9b7x9e73+TtnoM5YzFbTu8lstmMfbNKV123E7L7+g02x9cYzi36dd9Fl+D9X4fO2xzZ0oSkcB9Wpavqq9de5djd6d06XMzcM+W5X2Xd494/pMuXe3U9VP15hfN9nvAyWmmE+aQYA45tX415pAjMc1B+S7bgFhWYQ45yOtlHjkmeSTmkI2XwRyVqznkCE90+H3ssI05ZJ3G6ayHuXQipUCfB2taY/8eODUz/9htp8z8eWb+pcOqD1LOWvuHtuXvr/PlbcsfR/nS/1h2uGy6i7vVebftp5b3GlRX0zs9M7/YtuyEOh/kzKaZlNdvKJXKB1P6fd6aMh7Cdyh9Gp89Zmc2aQzVM11eQPnRah2w+WTKn0PtZ4AP6lGUM9tWZmb7wLMnUM7Inmvb1PkzKGftPJXymb8vJYF4MPCZeubTyMvM71D61P9VnX8CWB0R10XEJyNiadsuL6P82fS8XL/rlDdSzmp8VoeH+gvlj8Y1Zydl5s+AdwF3pAzEO2MR8S+UPvMvovxuL3Tb1vmVfWw7tc12df6SOl/eXo/JzL/kHHYxVr9jTqJcOfL+LF0+9DJ19vMJ0241ZjLzFtaeRbw1QEQ8hHI26Scy8/S27X9H6XLjLpR6x5TZKu9ZqcvO8D0gzQZzSHVjDiltIPPI8cojzSHHljnkmDOHXKvTpbNqk5nfiNKP/fMi4mjKj/Uielz+GaXP/eWU7j4eSCno1grr9m2P88OI+DLw+Ii4d2ZOfcFMfVA/0HLsPYAntz3k7zLzHX0+ralLOLPP7dXZBR2WTZXbmjETovTTvW/bdqsz8+Q+H2e98srSl3Frf8Y3US65PZ/yg743sJS1l01rA0TEMmDntsWrMnPVvAczXh4L7Ap8ITN/0bL8o8AxwLKIeF3OvD/rh9b5V9pXZObt9fOyTpcUEfFy1v/B/VRmXjTDGDZqmb8gM8+p92+IiEMoCdNiSgXjtBk+xlDJzDMi4pPAEuDRlHJ4NOV368kRcQrlbLONgYcA1wIvb+ldoNUtlNeo3RU1IWq3ilJpeyhARNwdeHmH7d5RK3jriYinAu+gnLX0tA14/42TQeoNa7atfZ3/FfCrmjzPPICZ1X2OBQ6idDNxRB+PcT9KQvArFubvZ3s571Xnd4vO49JsXecPABikvKOMkbCsfXlmdnqcfmLtZqD3gDRbzCE1DXPIBcIcck6ZR45ZHmkOOZbMIRcGc0hsZBvEiZQzG/an9Cl7YR8f9I9RLu/+KeVD9kvKFz2UL+tOfaO/D/hbShI2ERH3ovQxfVFmfrNluz0oPwCtfk75Qoe1LbN3o7PN27bTzKz345qZt9Uf+dZxEfZl/fI6j3KWFcxieWXmDRHxUeA1lPfSQvyCnwvLqH1Dt1k1v2GMnak/gE5uXZiZ10XE2ZSE4UmU7mtmYuoz9asu6zstfzmlq6ZWqyl/PMzEb+v8Fkpf/mtkZkbEpynJ0cMZg+RoSk0qzq0TdayYp1HO6Hsu8ElKN0dBqWS1f0f20q1MpwYEnyr7u3c59sl0+A6PiCdT+gH/NbAkyxg0KuMB3R/YsY9td2jZZ+qPhl902XYQveo+64iI/6QMov1l4In1LLteDqe8Jz+00BLjiLgLsGW9+5s6v0ed/32durlrnQ9S3jvT+bO5os43uG40w/eANJvMIdWJOeTCsQxzyLliHjmGeaQ55Ngxhxxz5pBr2Q1A/z5M6S/0eMrZg9Ne/hkRiynJ0f8C98/MQzPz32vL6huAbpdz/zflS//59cek42DVmXlyZkbbtHPLJpfUebdBQ6cGNp6PS9yH3dRl4t0anbt9MPuWmSs6lNe+LZt0La86WOt9KJfC9/tDPfXFtulMYx4js1K+mblvhzJcMSsRLlARsTVrzyg6LSKydWLtpeOtA1dPV56dLve+oc7v2SWM9ZZn5s4dyvrkaZ/M9KY+3zdm28Cr1VTytPEGPMbQq5f4n0EZvBbK2adTFZvvdHjN15k6HLJbmd6rzn9fH3d1l2Oubt8xIg6ijEfzK2CfzLykfZsFbOos3r+bbqNad9m33v0qa5PQ7TvuMIA+6j6tcbwdOIoyfsXjM/OmXsevXe0cAgtnsOo2j6Z8t/6q5fMx9Rl9WY/P6KF1u77LOzNX9fisb1BddibvAWkOmEOOL3PI8WYOOcTMI9cY+zzSHHLkmUOOP3PIyka2PmW5HPjjlJb1P9D7LJH71vlZuX4r9sPp8iNYtz2J8sZaSjkb8SbKAJCD+FKd79++IiJ2obzZfk7/Fe5xNlUxuXf7ioi4L/Mz5kDX8qKcSbgJcH72fwb2I+vc8h2O8lVnh1D+LLoQ+K8u02+Av4uI+9R9upYn5Sy+dlNniz+6fUUdb+JRMw1+AN+jdGWxVUR0qtT/VZ2vnodYhsGNdR61wvJD4EERseU0+3SyY+0qoN2+dT5QtxIR8UzKb/vVlOTosgHjGXcnU8YweEpEPGia7Z5H6Uf/EuC8zPwD8APgnhHx0Gn2mzI1DtFG027VRRTvpZxJ/D+UM8+6jn3U5imUM2L/NxfY2af1+/A19e5HW1Z9vc4f089xZlDe01lZ549rHx8oIjajdGl2c0uMU+s25D0gzSpzyLE2DDmGOeTcGYbyVXfmkcVCyiPNIUfTyZhDji1zyDaZ6dRhorRAX9W2bEfK2TJ7ty0/um6/rGXZI+uyT7Rtuw2lIpCU/tQ7PfaOlDPOrqrbHT+D+DcCflT3P7Bl+SLKGRYJvKrHMVbU7V7QdHnMcrlm27I7UlrZfwds07J8Y8ol+euVFaXbh3XKvMPjrBogrs0plcBbgMUty+8CnF+P909t++wNLOpwrGdTztK6Bdi56dd8HMu3z1hW1WPcd8D4r5rpY47aBFxcn/PDp9nmjXWbN9X7/1jvf7RtuwdTKt4JrGhZvgj4SV3++LZ9Xjj1ngH23YDnsboe4w7TbDP1O/H/Wj+3Ne6bgT8P8l4Z5gk4mNIlQKfvp3sBl9XX4qC67Hn1/qeAu3fYZwvgYW3LpsrtY22v532A6+vruesAMR9CqZj/FNhpBs956jvj6KZf/zku2zfU53k58MAO658M/JFSh3lsy/LD6n7fBO7Wts8iYNuW+0+o207OIL6gnD2Y9fv9LgPu/8W679Oafq3noOzW+31sWbcNcEbd5ufAVm3rv1w/H8/rsv+DWff3te/y7iPuL9RjvaRt+XF1+Qdm8z3g5LShE+aQYA5pDjli03yVb5+xrMIcsp/nbB45Znkk5pCNl8Eclq055IhOdPh9bFlnDtk2OSbbADLzCuCKPjf/FuUS16fWAVG/Qrks+fGUlvmrp3uciPgMpR99aOvmo89Y/xIRh1LObvt4RHy8xr4f5Sydr7L2cus1IuJVlP5yofRbC3BoREydvfOVzDxp0HiGWWb+OSLeCbwO+E4daPUOlB/4q5mmrGYxhhsi4jDKma6rIuJ0yo/8gcDudfnH2nY7FVhU319XUZKpv6Gc5XobsDw7XMq+0Mxn+UbEyS13pz5H/xERU2ddnZSZX2nZ/v7Aq9oOs0XbcY7KzGtnK8ZhEWUg992B7+e6Y4W0+y/KmTGHRsQEZXyIy4CDI2IH4BuUP5WeVNc9o3XnLINSvwD4PHBWRHyCUrn7a8p74HOU7+VO3W90i30rymDaU7aairV2TwLw1sy8uGWbN1O+f58LPDgiVlHOdnoa5bN7ZGb+pN8YhtwjgJcBv4yIrwA/q8vvAzyR8ufEp6njI2TmByNiT+DFwOUR8QXK79WWdZ+/BT5ESWZbfa8+1oURcS6l255/pJxZ/MrMvLyfYCNiCaWP/0WUs54OjfUHz15vYOT6vpr6bZy68mBpfV8CXJyZb+0nhhGygtKF1BHAd2tZ/ZDyR9SjKOVxM3BwZn6pZb+TKK/Vc4HL6vgRv6GcrfhYyuu/om77NUqS9fJ6ZurUuAnvzsxeY8q8nnL1xs2UsS9e1aEsL8rMT7UvrGelL6mPd1aPxxlZsXbg6UWUz8qDKGVzJ0pC86wOvznPpNQn/ysiXkr53v0d5cqcv6acRb0XZQwKGKy8e3kx5Y/id0XEfsCPKe+zJZQuPl7Ttv2M3wPSXDGHBMwh5yoGc8g5Yg45vMwjxzaPNIcszCHXMoccEuaQQD85ZNOtosM6McDZQHQ4C7Eu35IyCPVq4E+UH+Q3U7ptWE2XsxDrvk+qx/zWBj6PB1LOOryWclbapcAksHGX7Vex9uyOTtPJTZfNBrwWG9XncGuHdUGpqF4O3Er5cX5bt7Jils9CbNlvb0qr+W/rh/v7lMEWN+qw7b9RLmG9sm479R77EPCQpl/vcS7faWKY7rPT6Tti3z722bnp13aOyuvU+vxe2se259Ztn1Lv35vyh8H19b3/LeCpLa/nig7HeET9vNxYp/+l/KC/p+6zxwCx79xHue3bYb9NKJWCiynfx7+vcTy+38cehamWzz9TBqW+hDKewa2UAYw/SzlTutMZigcA51AqWbdSBp/+JuU39v5t2ybl92o74CN1nz8B3waeOWC8U5/36abVHfY7ucc+q5ouizks44dTzqb9Wf0M3kTp3uEYYIdp9nsWcF597/+p7n8q659luj8lUbppkO/CPsqkaz0G+I+6/i1Nv75zVGbtr8MtlLrhhZSz9vbv9Lls2X8z4NV1+5tquf8M+AxlvJNNZ1refcR+b0rd5pr63fBz4J3AlrP5HnBymo0Jc8ix+9xhDjnW03yW7zQxmEMO9nqZR45hHok5pDlk9/3MIZsrM3PIAeqyUQ+mIVNbiSco3Wz8V8PhjIWI2JZyxtkvMnOHXttrtFi+momI+Colcbpbln6gNQLqmZ7nZea+TcciSdKwMIecfeYY483y1UyZR44ec0hJc2lR70003+pAfC+knF3Ta3Bs9e8pdf61RqPQXLF81VFEbBIR6w1OHhHLKN0TnGtiJEmSRpk55Jwxxxhvlq+6Mo+UJPXLMdmGSEQ8EXgYsJTS9/5RmfnHZqMafRHxBmA34CBKP/PHNhuRZpPlqz7sSBlL4X8og1ffAXgopb/n3wFHNhibJEnSjJlDzg1zjPFm+apP5pGSpL7YXeQQqQPVHkIZMPGDwGszs+9BVNVZvST8RuAC4I2ZubLhkDSLLF/1EhFbAP8J7APcC7gzpa/2/wXelH0ObqzhYVcfkiQV5pBzwxxjvFm+6od55Hgxh5Q0l2xkkyRJkiRJkiRJkgbkmGySJEmSJEmSJEnSgGxkkyRJkiRJkiRJkgZkI5skSZIkSZIkSZI0IBvZJEmSJEmSJEmSpAHZyCZJkiRJkiRJkiQNyEY2SZIkSZIkSZIkaUA2skmSJEmSJEmSJEkDspFNkiRJkiRJkiRJGpCNbJIkSZIkSZIkSdKAbGSTJEmSJEmSJEmSBmQjmyRJkiRJkiRJkjQgG9kkSfMmIlZFRNZp1bAecxgeq49YsmVa0WQskiRJkqT+RMQL2vK5HZqOSZI0c3doOgBJ0miKiJ2Bn/WzbWbGnAYjSZIkSVIH5q6SpLlkI5skaT69Hzin3r6yyUDGwL+23D6/sSgkSZIkSYP4Buvmc79rKhBJ0oazkU2SNFsuAD423QaZOe169S8zj2k6BkmSJEkaQT1z17kQEZtn5g2Z+X3g+/P9+JKkuWEjmyRptvywV8NPHcdsn3r3vMzct2Vdtmw6CZwFTAB/C9yFkoRMZuZn+gkmIu4EnAkcWBf9GXh2Zp7Rss0TgBcAjwC2Bv4E/BT4LPDOzPxVP4/VcrxtgJcATwDuC2wMXAt8CzgpM89u235fYGXLoiXANsArgL8GbgO+DPxrZl7ctu86r1dmrhgkVun/s3fn4ZJU5eHHv+8gIBD2RVZBQHDjJ5FxQURZokEDiGuCiowLM2qiKBCTIGZmFDUxIkQNyEAQMQjiFgSNkpABoxjMjKKiCIgMiywyoOyyvr8/qu5MT0933+6+fW91V38/z9NPVVedOv32rdvd9Z5TdUqSJEkaU93krvsBbwR2B7YCNilX3U6R3302M/+raZs/Af6zYdGLgWcA7wR2AW4Bdo6ItwOnNZTbLjNv7v/tSJKqZCebJGkYHQD8HbBWw7LnAt+IiD/JzMWtNyu06GB7EHhtZn6rXD8L+FdgTtOmawLPLh9HRMTBmfmDbgKOiOcDF1B01jXaqozj4Ij4AjAnMx9vU82HgL2blh0IPD8inpGZy7uJRZIkSZI0JQcDb22x/Mnl4zUR8f7M/KcOdXyE1fM7SVLN2MkmSRqUZ0bEMS2WX5mZ3+6xrucDNwNnA9sBbyiXzwLez6pXf62iRQfbvcBBmXlpQ7G/ZtUOtiuB84EnAYdTdLZtBpwfEU/NzLs7BRsRG1BceTfRwfYYcFb5Hg6m6LQDOAz4JfDRNlXtTXFW5Hcormrbq1y+OfA24B87xSFJkiRJmlQ3uev9FKOK/Ay4C3gA2Ah4KfCcssyHI+KsDiOg7A3cAHwNuI+ic06SVDN2skmSBmV2+Wj2eaDXTrb7gRdk5m8AImJd4JBy3fM6bNfcwXYXcEBm/t9EgfIqtsaE6jrguZn5h3L9ZcAZ5brNKc5ePHGSeA+nGOZxwjsy8/Syvo8AP6UYHgTgmIj4x8x8rEU9PwRelJmPRMSaFJ10E/V2et+SJEmSpO5Mmrtm5gfK3PE5wNOBjSmG8z+flZ1sawP7Aee0eZ3rgNmZ+fvBhS5JGjZ2skmShtH5Ex1spasb5jfusN2eDfO3AS/NzCubyuxKcZXahHMmOthKXwAWsfI3ci8m72R7UcP8xFVsAGTmQxHxRWBBQ/xPp7h6rtnpmflIud0jEXE9KzvZOr1vSZIkSdKARMRLKfLCHSYpum2HdZ+2g02S6s9ONknSoHw+M+cMqK5lTc8fapiPLuu4B7ijxfJNmp7f1vgkMx+NiOXAlm3Kt9JY5neZ+XCn1+hQ57Km543ve1YXcUiSJEmSOuuYu0bEthRXrK3TRV1rd1j3yx7jkiSNIBvsJEnD6JGm59nldjdRjHUPxfCM/xURmzaVuavp+ZaNTyLiCax6pVtz+VYay2xc3heu7Wt0qLPf9y1JkiRJGoyDWbWD7Shg48wMYMMe6rl/oFFJkoaSnWySpDr5NcW92yauAHsWcFFEbNRQ5mpgecPzv4iIJzY8P4xVr/T+fhev21hmDeDNE08iYm3gDQ3rfwdc1UWdkiRJkqSZt1nT839tGPbxL2Y6GEnScLOTTZJUK5l5MUWn1mPloucA346I9cv1jwOfbNhkZ+D/IuLDEXEacGrDuuXA57p42c+z6tCUp0TEGRHxIeByiqvqJnwyMx9DkiRJkjSMrm56/u2IOC4i/g04pYqAJEnDy3uySZJqJzO/FhFzgX8tFz0f+GZEHJCZDwD/CDyd4qo1KK54e1ZTNXcBh3Rzo+rMvDsiXglcAGxK8fv6lhZFzwE+1uv7kSRJkiTNmK8DPwN2K5/vWT6gOAmzVa4nSRpTXskmSaqlzDwDOKZh0d7A+RHxxMx8PDPfTDHW/vnArRT3Q7sf+ClFJ9yzMrOboSInXu8HwDOBjwJXUNwb7lHgNorOt1dl5hu8ik2SJEmShldmPgzsB5xBMbrJQ8AvgaOBd1QYmiRpCEVmVh2DJEmSJEmSJEmSNFK8kk2SJEmSJEmSJEnqkZ1skiRJkiRJkiRJUo/sZJMkSZIkSZIkSZJ6ZCebJEmSJEmSJEmS1CM72SRJkiRJkiRJkqQePaHqAIbRZpttljvssEPVYYyWpUtXzu+xR3VxDBn/LJIkScNh6dKlyzNz86rjUD2ZQ0rDxVxckiRNVbc5ZGTmTMQzUmbPnp1LliypOozRErFyfkz+py5YvnzF/EGbbdayzBj+WSRJkoZSRCzNzNlVx6F6MoeUhou5eDW6aSeRJGlUdJtDeiWb1KeDr7xyxXzus091gUiSJEmSJFXMdhJJ0jjynmySJEmSJEmSJElSj7ySTerTgZtuOnmZA2cgEEmSJEmStMKBXNDw7KDK4hg33bSTSJJUN3aySX26YLfdJi9zwaRFJEmSJEnSAF3AwQ3PvCnbTOmmnUSSpLpxuEhJkiRJkiRJkiSpR3aySZIkSZIkSZIkST2yk02SJEmSJEmSJEnqkfdkk/r0xdtvXzH/hic9qWWZBQtaz0uSJEmSpOmxgPkN85op3bSTSJJUN5HpDWCbzZ49O5csWVJ1GKMlYuX8mPxPxSWXrJjPffZpXWb8/iySJElDKSKWZubsquNQPZlDSsPFXLwa3bSTSJI0KrrNIR0uUpIkSZIkSZIkSeqRw0VKfTp0iy2qDkGSJEmSJGko2E4iSRpHdrJJffriM55RdQiSJEmSJElDwXYSSdI4crhISZIkSZIkSZIkqUd2skmSJEmSJEmSJEk9spNNkiRJkiRJkiRJ6pH3ZJP6tOiWW1bMz9166wojkSRJkiRJqpbtJJKkcWQnm9Sneddcs2Leg0dJgxYRK+Yzc8Xz5nlJkiRJGga2k0iSxpGdbJIkDYnGjrWpbmMHnCRJkqRmnfKHdif2dbtNP68jSdKos5NN6tMRW201eZkjZiAQSSOnm4RVkiRJUn+OYFHDs7mVxTHdeu0wm3YXXgjAEUccwWldbtIuzlHpgHMEEklS+EW/utmzZ+eSJUuqDmO0NB6w+T8lSavp9azQRv2cVdq8vSSNu4hYmpmzq45D9WQOKQ2ZEWyj6LazZir5Qz/b9HIl23Rt02n7Kgz67yFJGk7d5pBeySZJ0jQYxbMwJUmSJE3dIDvM1NlUOxCrZt4oSaPPTjZJkqZgVBLjYUokJUmSpFFhh1m99XNV2kzEM6rDZ0rSOOqpky0iNgFeDDwV2BwI4LfAr4DvZuadA49QkqQZMmwJliRJo84cUlLVhu6+ZVIfqhiy03xXkrozaSdbRKwLHAocAcymSIpayYhYCiwCzsnMBwYWpTSETrjpphXzR2+3Xcsycxvur7xoUcsiGoCqx4B3+8H8zYd5n9WNnYSSpOlkDimpnZnqKJjLqQ0l5w0oek3qda8DVm0v0ejqdBWnJGmltp1sEbEm8B7gWGCjicVAsnqSlMAsigRqNvDxiDge+ExmPjJZEBHxWuAlwO7As4H1gbMz8029vJmIWAZs32b17Zm5ZS/1aWaM6o/2Mdddt2K+XSfbaaetnLeTrXfdJlOSJEmqnjmkpkM/HS8aLlXkbqfRcMarnWwz513vAlZtL1E99XPiqyTVVacr2X4J7FDON35bPkoxvMed5fJNgC2ANRvKbQR8AvhLYOcu4jiOIjG6D7gZeFpX0bd2N3BSi+X3TaFODdhUrxzx8vXhNcgrjCRNP79PJUkDZA6pKRvEsYlDp828UT15VtLM8btVUl116mR7SsP85cB5wPeBKzLz4caCEbEWxRmEewGvB57foo5O3keRGP2K4mzExV1u18rvM3PBFLYfKsM0DNxUDbJjrd26meywOWrbbQdeZ7N+zs6sIpkxgZIkSRLmkEOhn06lqo3K8N393Nur8e87qo3K4z7Uunpw3nkAHHXUUXyy4lA0OrwyWdKo69TJ9iBwOnBSZl7fqZIyYfph+TgxIp4CHAW8tZsgMnNFQuQB2/DqtG+yRblBJG4z0ck3Ux2T/bx+N++ln9fs9vW95F8aL6Pa8CNJGhrmkCOqynvk1t1UThZtVtW9jaWunXIKACecfLKdbBoIrzKWNAo6dbJtn5nL+6m0TKjeHREL+wtrStaOiDcBTwbuB34KfDczH6sgFkmSJEkaF+aQkiRpRjlcraSqte1k6zc5GnQdfdgS+ELTsusj4i2ZeWkF8UiSNHI8M1CS1CtzSEmSNEy6veJXkqZiVtUBDNjngFMFgJAAACAASURBVP0pkqT1gN2AUyluvv0fEfHsdhtGxNyIWBIRS+64446ZiFWSJEmSVC1zSEmSxlxEjM0wxpIGr9NwkS1FxKbAPsC2wNqtymTmx6cWVn8ys3lokSuBd0TEfcDRwALgVW22XQQsApg9e7anMGhyhx8OwILrO95uQpJqwSE3JEn9MoeUpDFhO4lqwHtXSupVT51sEXEk8FHgiZMUrSRB6uCzFAnSi6sORDUyZw4AC2+4odo4JEmSpCFlDilJY8R2Eo0pT0qVxlvXnWwRsS9wYotVCUTT82Hz23K6XqVRSJIkSdKYMIeUJEnjxvubS+OnlyvZ3tn0fOJbIhqeD+vgtXuW019XGoXq5cwzAZg/fz7NY8ystGBmYpGkGbRwYfGtN3/+/IojkSQNOXNISZWY35CLt8/XNXBdtZNI46Oxw23BggUr5ufPn79KXm2OLY22XjrZnkeRBF0BfAOYXz7fFDgU+BTFTaP/asAxriIi1gR2Ah7JzOsalj8TuDUz72oqvz3wmfLpv01nbBozn/88AAvOPLPDwaOHlZIkSRpb5pCSKrGgIRc3K59BXbWTSOpkosMN7HSTRkUvnWxblNOLgPsmFmbm74CTI2Iv4C3AT4BP9xJERBwCHFI+3bKc7hkRZ5bzyzPzmHJ+G+Aq4AZgh4ZqXgf8bUQsBq4H7qVIpP6MYvz/bwGf6CUuSZIkSVLfzCElSZL61Nzh5tVv0nDqpZNt4vrWu4CHVyyM2CAz7wF+UZaZR48JErA7cHjTsh3LBxTJ0DF0thjYFfhjiqE91gN+D3wP+ALwhXQgXEmSBsqz7CRJHZhDSpIkTbPGvLxRp465TttI6k0vnWx3UZwhuB5wW8PywyLiDODl5fMdmzecTGYuoMubV2XmMlqM25+ZlwKX9vrakiRJkqRpYQ4pSZI0Qrq5eq4TO+k0jnrpZLuNIkHaBLiwYfmngH9mZdJy72BCk4bcO4v7uB/9q191KPSNhvmDpzUcSaqaV7VJkpqYQ0qqxEHm4tXoqp1EkqR66aWT7ccUw2g8C1gK3ARsS5EYBcUNrJNVkyepvl7/egA+efPNHQodNDOxSNIQcox4SRp75pCSKnGhuXg1umonkVR3tgVo3PTSyXYO8CBwX2ZmRMwDvkZxQ2gokqRfAO8fbIiSJEnTz5tKS9LAmUNKkiSNMXNpjYOuO9ky82Lg4obn346IpwGvBDYDrga+kpkPt6lCqpeTTwbgEyecMOkd1SVp3FVxYN3rDZ57rbcTO+kkyRxSksaO7SSSOmiXS5sza9T1ciXbajLzRuDTA4pFGi1f/jIAR593ngePkjQERuWecN4sWtI4M4eUpBqznUTSALQ7SdY8WcNqSp1skiRJvZrqAfNUr0QbFZ7JJ0mSJElSYTpv8WBnnqaip062iHgB8NfA84CNgVktimVmrjeA2CRJ0hjxnmjtjfv7lzS6zCElSZI0k6baYVbnDrc6v7cqdd3JFhEvBb4JrEFxg+p2cqpBSZIkqTUPiiWNCnNISZIkVanTfeB63d78W+30ciXbwrK8CZAEcPTRAMy9+uqKA5EkSZKGkjmkJI0T20kkjZB+RoxxxB210ksn2+4UyVEA3wauBh7ChEnj6sADATjt1lsrDkSSNK48q07SkDOHlKRxYjuJpDFlbj7eeulk+wOwNvClzDx0muKRJElSHzyolzSEzCElSZI0Vjrdb1711Esn2/eAPwN+PU2xSKPlhBMAOPXUU5nXttDcmYpGkiRJGjbmkJIqcWpDLt4+X9fAddVOIknjyw63euqlk+2DwEuBQyPik5l55zTFJI2GCy8EYO7WW3c4eDxtpqKRJGkVHrxLGgLmkJIqMbchF7ezZwZ11U4iSVK9tO1ki4j3t1h8GbAvcG1EfBVYBjzSXCgzPz6oACVJkiRJw88cUpIkSepO47CSjTxRdvR0upLtH2h/Q+qNgLd22NYESZIkaUh4VZukGWIOKUmSJGmszOpjm6R94hRTiEWSJEmSVD/mkJIkSVKXFi5c2PZKNw2fye7JZsIjtXPccQC84Re/6FBoScP87GkNR5IkSRoC5pCSKreHuXg1umonkST1wpFphl+nTrZ1ZiwKaRTtvz8A5/z2tx0K7TEzsUiS1KXGs+E8SJc0YOaQkobCj8zFq9FVO4kkqV/m88OpbSdbZj4UES/KzO/NZECSJEmSpNFjDilJkiRp3Ew2XOR3I+I24Hzgq8DizHxs+sOSRsDxxwNw9tln88aKQ5EkSZKGhDmkJI0r20kkacZ4VdvwmKyTbTmwJTAPmAv8PiLOB74OfCczH57m+KThdfHFALzhSU/y4FGSJEkqmENK0riynUSSKmGHW7Um62TbEtgbeA3wSmA7YA5wOHB/RHwT+Brwrcy8fxrjlCRJ0oB5IC5pGphDSpIkSRobszqtzMzHM/PSzHxPZm4PvAD4J+BXwB8Bfw6cC9wREV+PiDdHxEbTHrUkSZIkaeiYQ0qSJEkaJx072Zpl5g8z828yc1fg/wEfAq4EnkhxluLngNsj4jsRMXfg0UqSJEmSRoY5pCRJkqQ6m2y4yLYy80qK5GhBROwIvBZ4NfBc4KXAnwCLBhGkNJQ+8hEADvrZzyoORJKkwZgYPtKhIyVNB3NISao520kkSWOo7062Rpn5a+DjwMcjYmuK8fdfNYi6paH1whcCcOGdd1YciCRJkjRazCElqYZsJ5EkjaGehovsRmbekpmfzsz9Bl23JEmSJKlezCElSZIkjaqer2SLiN2A5wEb06aTLjM/PsW4pOF37LEAfOMb3+DgtoUOmqloJEmSpKFkDilppn2jIRdvn69r4LpqJ5EkqV667mSLiPWA84ADuihugqT6+8EPADhos806FLpwZmKRJGnAvD+bpKkyh5RUlYPMxavRVTuJJEn10stwkf8AvByIDmU6rZMkSZIkjQ9zSEmSJEm11stwka8Gspw3EZIkSZIkdWIOKUmSJKnWermSbdNyehPwAorx9NcF1ml6rDvIACVJklSthQsXrhg+UpJ6YA4pSZIkqdZ6uZJtGfBU4KzM/OH0hCONkBNPBGCfH/+4Q6HfNMxvM63hSJIkSUNmGeaQkiqwtbl4NbpqJ5EkqV566WQ7G1hIkSRJ2n13AC69++4OhbaemVgkSZKk4WMOKakSt5qLV6OrdhJJkuqll062jwOvAF4fEdcBp2TmbybZRpIkSTXSOGzk/PnzK4xE0ggwh5QkSZJUa710sv2O4h5uAfwd8HcR8SjwWFO5zMz1BhSfNLze+14AFi9ezL4VhyJJkiQNIXNISRontpNIksZQL51sTwSyfES5bM3y0SgHEJc0/H7yEwD22XjjigORJEmShpI5pCSNE9tJJEljqJdONliZGEmSJEmSNBlzSEmSJEm11Usn2z9OWxSSJEmSpLoxh5QkSZJUa113smXm301XEBHxWuAlwO7As4H1gbMz80191LUt8CHgAGBT4Fbg34GFmfm7gQUtSZIkSWrLHFKSJElS3fU6XOR0OY4iMboPuBl4Wj+VRMROwGXAFsD5wC+B5wFHAgdExF6ZeedAIpZOPRWAPZYsqTgQSZIkaeyYQ0rSsLGdRJI0hmb1ukFE7BER50TEjRFxXzk9JyKeO4U43gfsAmwAvHMK9ZxMkRy9JzMPycy/zcz9gBOBXYGPTKFuaVW77AK77MKP7ruv6kgkSZKkoWUOKUljwnYSSdIY6qmTLSLeAvwAeD2wLbBuOX09cFlEvK2fIDJzcWZem5nZz/ZlbDsCLwOWAf/StHo+cD9wWESs1+9rSJIkSZK6Zw4pSZIkqc66Hi4yIp4BfLbcplUiswZwckRcnplXDii+XuxXTi/KzMcbV2TmvRHxfYoE6gXAxTMdnGpo3jwAlixZwuy2hfaYqWgkSZKkoWIOKakqSxpy8fb5ugauq3YSSZLqpZd7sh0JrEmRHCVwOXAbsCXFmPVR1vduYN5gw+zKruX0mjbrr6VIkHbBBEmDcE3xr7bH+ut3KPSjmYlFkiRJGj7mkJIqsYe5eDW6aieRJKleeulk27ecLgf2zcxfTKyIiGcCi4FNG8rNtA3L6d1t1k8s32gGYpEkSZKkcWcOKUmSJKnWerkn2zYUZx+e1ZgcAWTmz4HPU5yJuM3gwhuoKKctx+yPiLkRsSQiltxxxx0zGJYkSZIk1ZI5pCRJkqRa66WTbTJ933B6QCbOMtywzfoNmsqtIjMXZebszJy9+eabDzw4SZIkSdIqzCElSZIkjbRehou8BdgJeGNEfCYzb5hYERE7AIc1lKvC1eV0lzbrn1pO2423L/XmrLMA2OXyyzsUamw3iLalJEmSpBoyh5RUiTAXr0ZX7SSSJNVLL51sl1IkSE8Cro6IS1l50+qXAGtR9Ch8d9BBdmlxOX1ZRMzKzMcnVkTE+sBewIPA/1YRnGpou+0AuPbBBysORJIkSRpK5pCSNE5sJ5EkjaFehov8FPBYOb8W8CfAm8rpWuXyx4BPDyy6FiJizYh4WkTs1Lg8M68DLgJ2AP6yabOFwHoU9wK4fzrjkyRJkiQB5pCSJEmSaq7rK9ky86cRcRRwEu3Hzj8mM6/oNYiIOAQ4pHy6ZTndMyLOLOeXZ+Yx5fw2wFXADRTJUKN3AZcBn4qI/ctyzwf2pRji4wO9xia1dVgxus3VV1/NrhWHIkmSJA0bc0hJGjO2k0iSxlAvw0WSmZ+OiCuBo4EXUdwI+h7g+8AnMnNxp+072B04vGnZjuUDimToGCaRmddFxGzgQ8ABwCuAWynOoFyYmXf1GZ+0uptvBmCXddetOBBJkiRpOJlDStIYsZ1EkjSGeupkAyiToMUAzePW9yszFwALuiy7jA53rc3Mm4C3TDUmSZIkSdLUmUNKkiRJqquO92SLiMcj4tFyiI/VDCI5kiRJkiTVgzmkJEmSpHHSsZOt1PaMP0mSJEmSmphDSpIkSRoLPQ8XKan05S8DsPVll1UciCRJkiRJUsVsJ5EkjSE72aR+bbYZALc+/HDFgUiSJEmSJFXMdhJJ0hjqtpNt44h4creVZuaNfcYjSZIkSRp95pCSJEmSaq/bTrZjy0c3sod6pdH12tcC8Jvf/IZt2hbaeqaikSRJkoaJOaSkyvymIRdvn69r4LpqJ5EkqV66TWS8cbXU7M47Adh67bU7FLp1ZmKRJEmShos5pKTKbG0uXo2u2kkkSaqXWVUHIEmSJEmSJEmSJI2abq9kWwL8fDoDkSRJkiTVhjmkJEmSpNrrtpPt3Mz85LRGItXSVg3zDlchSZKksWEOKakyt5iLS5KkGeLNpaV+ffObAKz/P//TodAtDfPelkKSJEmSpOm2jbl4NbpqJ5EkqV7sZJP6te66ANz32GMVByJJkiRJklQx20kkSWNoVtUBSJIkSZIkSZIkSaNmsivZFpbTy6Y7EGnkvOIVANxzzz1sUHEokiRJ0pAwh5SkcWU7iSRpDHXsZMvMhZ3WS2PtwQcBWP8JjroqSZIkgTmkJI0120kkSWOo7XCREbEwIjbut+KI2DgiFvS7vSRJkiRpdJhDSpIkSRo3ne7J9kHgxog4OSL27rbCiNgzIj4D3FDWIUmSJEmqP3NISZIkSWNlsuu31wXmAfMi4jbgB8APgRuBu8oymwDbAbOBPYFtyuUB5KADliRJkiQNLXNISZIkSWOjUyfbUcBxFAkQwFbAq8pHO9EwfxfwoSlFJw2zxYsBiEsuqTYOSZIkaTiYQ0rSOLOdRJI0htoOF5mZJwE7AR8DflsuDlZNgqLF8juAjwI7ZeanBhqtJEmSJGkomUNKkiRJGjcdh4vMzLuBD5Q3nz4EOAB4MfAUVnbQJXA98F3g28DXM/OR6QpYkiRJkjSczCElSZIkjZPJ7skGQJnwfLl8EBEBbEpx5uGdmfn4tEUoDat99wUgM1c5NXdV7ddIkiRJdWUOKalKjVm6WfkM6qqdRJKkeumqk61ZZiawfMCxSJIkSZJqyBxSkiRJUh21vSebJEmSJEmSJEmSpNbsZJMkSZIkSZIkSZJ61NdwkZKAddYB4N5HH+1Q6DkN8z+a1nAkSZIkSRIsNRevRlftJJIk1YudbFK/vvUtADb43vc6FFraMO9tfyVJkiRJmm6zzcWr0VU7iSRJ9eJwkZIkSZIkSZIkSVKP7GST+vXAA/DAA/zRGmtUHYkkSZIkSVK1bCeRJI0hh4uU+vVnfwbAvZkOPiFJkiRJksab7SSSpDHklWySJEmSJEmSJElSj9peyRYRf99vpZn5oX63lSRJkiSNHnNISZIkSeOm03CRC4Dss14TJEmSJEkaLwswh5QkSZI0Rnq9J1vjkMrZxXKpvjbdFIBbHnqo4kAkSZKkoWUOKUnjwnYSSdIYmqyTrdV9SrNc3rwuWyyT6usrXwFgmx/8oOJAJEmSpKFhDilJ48p2EknSGJrVbkVmzmp8AGsB3yhX/z2wPfDEcjq/XP7f5TJJkiRJ0hgxh5QkSZI0btp2srXwPuAg4IuZeXxm3pSZD5fTDwPnAPsC75+OQKWhs3w5LF/OVmut1aHQLQ0PSZIkaayYQ0qqxFbcsuKhGdRVO4kkSfXSyz3Z3lpOr2uz/jqKoT7eDHxkKkFJI+F1rwPglswOY9xsM1PRSJIkScPGHFJSJW5pyMUdk3YGddVOIklSvfRyJdsO5fSgiFizcUVErEVxhiLAkwcQlyRJkiRptO1QTs0hJUmSJNVSL1ey3UFxWc7uwE8j4kvA7cCTgD8HdinLLR9ohJIkSZKkUWQOKUmSJKnWermS7SusvMp+V+CDwGfK6a7lugS+3E8gEbFtRJwREbdExEMRsSwiToqIjXuo45KIyA4Pb6gtSZIkSTPDHFKSJElSrfVyJdsCYH9gN4pEqFFSJEhXluV6EhE7AZcBWwDnA78EngccCRwQEXtl5p09VLmwzfJHe41NamvbbQG45oEHOhQ6sGH+wmkNR5IkSRoyCzCHlFSBC8zFq9FVO4kkSfXSdSdbZt4TEXsBH6a4gfX6DavvBT4HfDAz7+0jjpMpkqP3ZOanJxZGxCeB91HcBPsdPcS6oI8YpN584QsA7PrDH3YodEHDvLf9lSRJ0vgwh5RUlYPNxavRVTuJJEn10stwkWTmfZn5PmATirMR9y6nm2Tme/tJjiJiR+BlwDLgX5pWzwfuBw6LiPV6rVuSJEmSVB1zSEmSJEl11stwkStk5mPAzwcUw37l9KLMfLzpde6NiO9TJFAvAC7upsKI+HPgKcDDwFXAf2fmQwOKVyrcdBMAT91lF66tOBRJkiRpmJlDStIYsJ1EkjSGeu5ki4gdgPcALwI2Bz4KXA7sXhY5u0ygurVrOb2mzfprKRKkXegyQQLObXr+24j4y8z8Sg9xSZ29+c0AXJPp4BOSJElSG+aQkjQmbCeRJI2hnoaLjIhXUZx9eCQwG3gyxbj66wBnUoyp/+IeY9iwnN7dZv3E8o26qOt84CBg2zKmpwEfK7f9UkS8vN2GETE3IpZExJI77rijq8AlSZIkSe2ZQ0qSJEmqs6472SJiF+BsisRjFZl5OXBz+fSgwYS28qUnXmaygpl5YmZemJm/ycw/ZObVmXkscDTFe/1oh20XZebszJy9+eabDyZySZIkSRpT5pCSJEmS6q6XK9mOAZ5Ikajc3GL9pRTJzJ49xjBxluGGbdZv0FSuH6cDjwK7R8T6U6hHkiRJktQdc0hJkiRJtdbLPdn2p0iOrqBIgv7QtP5X5XSbHmO4upzu0mb9U8tpu/H2J5WZf4iIe4GNgfWAe/utS1phl+Jfdum9/jtJkiRJLZhDStI4sZ1EkjSGeulkm0h8zs/MhyNWu4XpI+V0ix5jWFxOXxYRszLz8YkV5RmDewEPAv/bY70rRMSuFMnRvcDyfuuRVnHqqQDMXrq04kAkSZKkoWQOKUnjxHYSSdIY6mW4yIfL6dpt1u9UTpvPTuwoM68DLgJ2AP6yafVCirMGz8rM+ycWRsTTIuJpjQUjYseIWO0MyIjYjOJm2gDnZuajvcQnSZIkSeqLOaQkSZKkWuvlSrbrgd2A10fExxpXlGf5vZZiKJBr+4jjXcBlwKciYn/gKuD5wL4UQ3x8oKn8VRMv3bDsxcDpEXEpcB1wF/Bk4BUUY/UvAd7fR2xSa9cUo8885znP4UdtC3n2liRJksaWOaSkSjynIRdvn69r4LpqJ5EkqV566WS7iCJB2pGVY+AD/BXwYWAdigTpol6DyMzrImI28CHgAIqk5lbgU8DCzLyri2qWAv8G7AHsTnGz63uBnwHnAadm5sPtN5d6NG8eAEszWW3gmxVmz1Q0kiRJ0rAxh5RUiaUNuXj7fF0D11U7iSRJ9dJLJ9s/A3OBPwK2okiGALZn5THLvcC/9BNIZt4EvKXLsqv9Vmfmz4A5/by2JEmSJGngzCElSZIk1VrX92TLzJuBQyluIA0rk6KJ6YPAoZl5y+DCkyRJkiSNInNISZIkSXXXy5VsZOa3IuIZwHuAFwGbUIxb/z3g05l5w+BDlCRJkiSNInNISZIkSXXWUycbQGbeCBwzDbFIo+XZzwbgkt/9rkOhIxrmT5vWcCRJkqRhZA4paaYtMhevRlftJJIk1UvPnWySSiedBMC+P/lJh0KLGuY9sJckSZIkabrNMxevRlftJJIk1UvbTraIOKOcPTczL2p4PpnMzLdNPTRJkiRJ0qgwh5QkSZI0bjpdyTYHSOBK4KKG551EWcYESfV3xRUAvOQlL+HSikORJEmShsAczCElaXzZTiJJGkP9DBcZbZZPljxJ9fK+9wFwSWbbD4UkSZIkc0hJGgu2k0iSxlCnTrYbKZKeu5ueS5IkSZLUzBxSkiRJ0lhp28mWmTtExOsy88sTz2csKkmSJEnSSDGHlCRJkjRuZk2y/tyIODsiNpqRaCRJkiRJo8wcUpIkSdLYmKyTLYC/AK6MiD+dgXik0bHnnrDnnlywfHnVkUiSJEnDwhxSksaV7SSSpDHU6Z5sjbYGvhURpwLHZOYD0xiTNBo++lEADr7yyooDkSRJkoaOOaQkjRvbSSRJY2iyK9m+T3EmYpbTecAVEfHC6Q5MkiRJkjRyzCElSZIkjY3JrmR7MXAkcDywTrlsZ+DSiPgE8FmK5GkVmXnjIIOUhtJllwFw4EEHcWHbQhfMVDSSJEnSMDCHlFS5Axty8fb5ugauq3YSSZLqpWMnW2YmcFJEXACcAexNkRCtAby/fKy22WT1SrXwgQ8AcMGxxxJtCx08U9FIkiRJlTOHlDQMLmjIxdvn6xq4rtpJJEmql8mGiwQgM6/LzJcAH2DVoT/aPSRJkiRJY8ocUpIkSdI46KqTDSAi9gXmsnJoj9WG+JAkSZIkCcwhJUmSJNXfpENyRMR6wCcokqMVi4HfA9/AREmSJEmSVDKHlCRJkjQuOnayRcT+wOnAk1l1iI+LgLdl5m+mPUJpWO2/PwBfvP32DoXmN8wvnNZwJEmSpKqZQ0oaBgvMxavRVTuJJEn1MtmVbP/JqmcZPgi8PzNPnr6QpBFx3HEAvPGqqzoUWtAw74G9JEmSas8cUlLlFpqLV6OrdhJJkupl0uEiSwH8AHhzZl43jfFIkiRJkkafOaQkSZKk2pvVRZlHgGOBvU2OpAYXXwwXX8yhW2xRdSSSJEnSMDGHlKRxZDuJJGkMTXYl208pzjz86UwEI42U448H4Isf/jDnVByKJEmSNCTMISVpXNlOIkkaQ5N1sj03Mx+ZkUgkSZIkSaPOHFKSJEnS2Og4XKTJkSRJkiSpW+aQkiRJksZJN/dkkyRJkiRJkiRJktRgsuEiJbVz4IEALLrllooDkSRJkiRJqpjtJJKkMWQnm9Svo48GYN4111QciCRJkiRJUsVsJ5EkjSGHi5QkSZIkSZIkSZJ65JVsUr8uvBCAI444gtPaFlo0U9FIkiRJkiTgiIZcvH2+roHrqp1EkqR6sZNN6tcJJwCw6BOf6HDwOG+mopEkSZIkScCihlzczp4Z1FU7iSRJ9eJwkZIkSZIkSZIkSVKP7GSTJEmSJEmSJEmSemQnmyRJkiRJkiRJktQj78km9et1rwPghJtu6lDo1IZ5788mSZIkSdJ0m2suXo2u2kkkSaoXO9mkfr3rXQAcc911HQrNbZj3wF6SJEmSpOl2mrl4NbpqJ5EkqV4cLlKSJEmSJEmSJEnqkZ1sUr/OOw/OO4+jtt226kgkSZIkSZKqZTuJJGkM2ckm9euUU+CUUzhh552rjkSSJEmSJKlatpNIksbQ0HSyRcS2EXFGRNwSEQ9FxLKIOCkiNu6xnk3K7ZaV9dxS1utpNJIkSZJUE+aQkiRJkqr2hKoDAIiInYDLgC2A84FfAs8DjgQOiIi9MvPOLurZtKxnF+C/gXOBpwFvAf4sIvbMzF9Pz7uQJEmSJM0Ec0hJkiRJw2BYrmQ7mSI5ek9mHpKZf5uZ+wEnArsCH+myno9SJEcnZub+ZT2HUCRaW5SvI0mSJEkabeaQkiRJkipXeSdbROwIvAxYBvxL0+r5wP3AYRGx3iT1rAccVpaf37T6M2X9f1q+njR1hx8Ohx/OguuvrzoSSZIkaWyYQ0rSkLKdRJI0hirvZAP2K6cXZebjjSsy817g+8C6wAsmqWdPYB3g++V2jfU8DlxUPt13yhFLAHPmwJw5LLzhhqojkSRJksaJOaQkDSPbSSRJY2gYOtl2LafXtFl/bTndZYbqkSRJkiQNL3NISZIkSUMhMrPaACIWAUcAR2Tm6S3WfwQ4Fjg2Mz/WoZ5jKcbd/0hmHtdi/RHAImBRZs5rsX4uMLd8uitwdR9vZ7psBiyvOgj1zf1XL+7P+nBf1of7sl7cn/UxbPty+8zcvOogNHXmkF0Zts+feuc+rB/3ab24P+vHfVov7s96qHo/dpVDPmEmIpmiKKdT7Q3sWE9mLqJIoIZORCzJzNlVx6H+uP/qxf1ZH+7L+nBf1ov7sz7cl6qQOaSfvNuzDgAAIABJREFUv5HnPqwf92m9uD/rx31aL+7PehiV/TgMw0XeXU43bLN+g6Zy012PJEmSJGl4mUNKkiRJGgrD0Mk2MaRGu3Hun1pO242TP+h6JEmSJEnDyxxSkiRJ0lAYhk62xeX0ZRGxSjwRsT6wF/Ag8L+T1PO/Zbm9yu0a65kFvKzp9UbJUA5Boq65/+rF/Vkf7sv6cF/Wi/uzPtyXmi7mkJPz8zf63If14z6tF/dn/bhP68X9WQ8jsR8jc6rD1A8giIjvUCQw78nMTzcs/yTwPuDUzHxHw/KnAWTmL5vqOZXixtOfzMyjG5a/B/hn4DuZecB0vhdJkiRJ0vQyh5QkSZI0DIalk20n4DJgC+B84Crg+cC+FENzvDAz72wonwCZGU31bFrWswvw38APgacDrwR+W9Zz3XS/H0mSJEnS9DGHlCRJkjQMhmG4SMqkZTZwJkVidDSwE/ApYM/G5GiSeu4E9iy327ms5/nA54A9TI4kdRIRcyIiI2JO1bFIkiSpPXNIScPEXFKSpPE1FJ1sAJl5U2a+JTO3ysy1MnP7zDwyM+9qUTaaz0BsWHdXud32ZT1bZeZbM/PmXuIpD44yIh4vz5JsV25xQ9k5vbzGTIiIZ0TEeRHx24j4Q0RcHRELI2KdFmXXjIgjI+JzEXFFRDxcvq+3VxH7oE3sp6rjaCciXhgR34qIuyLigYj4aUS8NyLWaFF2n4b/u1aPf6jiPVSlyn0bEdtGxAci4ssR8avyOyMjYucO2zwvIj4WEf8REbeV5Xv6jqqL8m838X+7a9XxtBMRTy+/O8+PiBsbYn5CF9vuFhFnRcRNEfFQ+X18aUS8eSZinwkRsUZEHFG+r7si4pHyff40Ik6PiIOrjnFCRKwXEW+MiC9GxC8j4v6IuDcilkTE0RGxVpvt3hYRp0bE5eV3dEbE8TMd/0yKiNnlMcGvI+LBiLgnIn4WEf8UEdtMse6+G6IiYpuIeHf5Hbqs/FzdGRH/GRGvbrPNJZP8bmZE/OtU3tMwafHeHoqIOyLiR+Vn8uWtji+GQfm7ekZE3FLGvSwiToqIjVuU7fl/QfViDjk9whxyFVFhrtGNMI+ckir3b5hLDkSYU9ZCmFPWWphbjpwW78mcsoNJv8jH3KMUf6O3Acc2r4yIpwIvaSg3VCLi+RRDnqwJfAW4CdgP+Htg/4jYPzMfathkPeCkcv524DZgu5mLeHxFxCuBrwJ/AL4E3AUcBJxIceP217XZ9FLgkhbLvzf4KNXGbOB4IIHrgbuBjSbZ5g3AkcAjFEMbPWk6AxxWEREU368JBHAEcEylQbX3pxTfnY8B11J8Vp842UblQd7pwAPAhcAyiv+PZwGvAM6almhnUHlQdSFwAPB74JvAzcAmFFcUvAF4GvCNqmJssjfwbxTfs4uBf6eI9SDgE8Cry9/HPzRtdwKwIfA74BaK91ZL5WfzH4D3Uxzj/CfwZWAt4IUUn9N3RcThmfmVCkJ8N/A3FN+5iymOV7YHXg38SUScmJlHNW1zJq1/Lyfq2wT4j+kItmILy+kaFN89zwQOo/juXRIRb8zMa6oKrlmsPvzfL4HnUfxmHhARezVdndTP/4I03cwhzSFnjHnkyDOXnCJzSnPKiphTdsncshbMKbvJKTPTR4sHxQ/0zcD/AbcCT2hR5h/Lcl8vp3OqjrshtjWAX5RxHdywfBZFspTA3zZtsxbwcmCr8vmCstzbq34/A9ynWXUcLeLagOJ+Dw8BsxuWP7H8UkjgL5q22adcvqDq+IfhMah9C8zp9bMMbEtxgLVB+fySso6dO2yzO/DHwFoN8d9c9d+xgv32p+V7/1z5I3bHxN9k2B7ArhRDR61TPl9Wxr7ab0PDNi+gOIi8Atiyxfo1q35fA/rbvKn8W1wBbNhi/brAvlXH2RDP7sAbm//XgPWBpeV7ObrFdgcA25fzE98Vx1f9fqbpb/T3rGzseWaL9a8BHiz/v/vat/183zZs+2rgJS2WP52icSophnjrpq5dy/K31eUzWb6vtr+LFI1x55VlbgS2qDrehti+U8b17qblnyyXf3a6/hd8+BjEA3NIqFkO2bBfs+o4WsRlHjlE+7efYxvMJQex/8wph+C9DeBvY05Z0wfmliP76PT7iDnlao+hGS5yiJ0GbAkc2LgwItYEDqc4eP15qw0jYo+I+OeI+El5qfMfIuLaiDih+fLEiHhHeenl37epa8vyUumfdRn3Syj+Gb6bmSvO9MjMxynOHgB4R3lGwcS6hzPzPzLz1i5fY+TFyuEyFrRZvywiljUtW3EZckTsW14mfG95qfM3I+LpPYbxWmBz4NzMXDKxMIszXo4rn76zxzrHXj/7th+ZeXNm/k9m3tPDNldk5o8z8+Gpvv6IO6KcngacDWwGvKq5UEScWe7LHVqsa7ufI+K5EXFRw+fzvyJiz4hYUG6zT7eBZubVmXl5Zj7Y7TbAxykaq96Umbe1qPORHuoaZi8sp2dm5t3NKzPzgcxc3Lw8Ig6NYris35W/j1dFxHERsXaLsll+124dEV+IYtiQByNiaUS8oZdgy8/f2c2fv8y8l+LMQigaoJq3+3Zm3tDLa42i8nP2QYqzow/OzNWOcTLzq8D7KP6/T4mIWU11/HlEXNxw7LMsIs6JiNnl+ksoGkIAPherDkGxw2QxZubXMvPSFsuvojiLH1rswzbmTsRRo89kR5l5O/AXFA1529H6SptNohiK6qrys3Z3uU9f1q7eyfb7ZCJiR+BlFA1O/9K0ej5wP3BYRKzX8F4G+b8gDZI5ZM2ZR9abueRIMaesB3PKGjK3rC9zytXZyTa5cyh2QPOY8gdT9Nqe1mHbIyj+4a6m+MB/luKMxqOA70fE+g1l/w24B3h7tB7P9K0Uw4mc2mXc+5XTbzevyMxfA9dQXPq4Y5f1aXUHAhdR7LfPAv9Dcan+pRGxWQ/1tN1XwHcphgR4YauDBGDniPiriDg2It4axfAz0tCLiCdRfI9ek5mXsfKgaG77rXqqf2+Kz89+wLeAz1CcHbWY4jLxaRURE2elLgF+XjakHBPF+Oz7Nx84jriJy+x36XaDKMYn/yKwM/A1ioOfu4APA9+O1vcl2JiiUXI3iv+Xsyh+w86OiL/uO/pVTRwIPzqg+kbRWyiON76emZ0aZU+nGOJkV4pGWaJwJnAu8P8o9u2JFL+Pe7OysflMimEbKKcLGx6/n2L8Xe/DKO6V8GaKM9M6Hc/VTtlgPnH/h0MbG8wjYnuKM3D/luJs8M9SJBdPp/h8HtFYVw/7fTITx0MXlfE1xnsv8H2Ks5hf0GV9fp5VJXNIdWIeKQ2AOaU5JeaUw87cssbMKZtMx2V5dXjQcMk9xYf9UWDbhvXfprhkcF1WjqE9p6mO7YE1WtQ9MV703zQt/0y5/MCm5QH8miJRW+2y6Tbxf7ms6zVt1l9Yrn95hzoWUKOhPmhxmSuTDJdB0fO9rGnZnHKbR4H9m9Z9rFz3/h7i+j86XHoKXFmuf3qLuFs9vgJsXPXfe8T37ZwpxHIJkwzx0Sb+sRrig+KHNoG/a1i2FHi8+W9HcdCUwA4t6lltP1OcQHJtq+844B0Nn5V9phD/MjoM7UFx9mRSHPQvbvE5/Wkv/yPD/KAYrubhct99geJS++07lJ/4nH2NcqiUhnUTvztHNi2f+LudB8xqWP4UikTqYWDHAbyX/yhfZ94k5SbeQ+2G9gAuLt/bEV2UPbsse1z5fG75/Ic0Ha9QnJm4VYu/4ZwBxr4BxdAcj9Pwm9mh/KFlDBdV/Xefhv242u9iizJrUyQNCTylYfkl5d+weYixjSiG8HkQeFLD8q73+yTx/BNthtYp108cJ79z0P8LPnwM6oE5JNQsh2zYr9m0bB/MI2vxmIb9O2cKsVyCuWQvfy9zSnNKc8ohfmBuOdIPWvw+tihjTlk+6nTWw3Q6jWKHvhVW9Ma+FDg7Mx9ot1Fm3pCZj7VYdQbFWWt/2rT8lHI6r2n5yyi+9L+ULS6bbmPDctqu/MTyyW6qq/bOzcyLm5YtKqe9nNXUz766g+KAcjeKMZ83p7gXwo8pxjO+oGZnNalGyrNb3k7xQ9V4k+YzKRqEms/67tULKc5mW5yZzTebXURxFvZ026Kcvp7iTJ1XU3zWd6ZIGnYDvlme7TTSMvPHFGPo315Ovwosi4g7I+LrEXFQ0yZHUjQuvTVXHyrlwxRnMb6xxUs9RtGwuOJspMy8HvgUsCbFjXf7FhF/RTFG/hUUv9PjaqtyelMXZSfKbF1O311O5zUfr2TmYzmNQ4mV3yunU1whckoWQztMZuIs50UdS9VUZj7EyrOGNweIiGdTnD361cw8t6n87ymG2HgixbHGhEHt94Ecu/b5vyBNB3NItWMeKU2ROaU5JeaUo8DcsubMKVdqdemsmmTm5VGMY//WiDie4sd6FpNc/hnFmPvzKIb7eAbFjm48YN2m6XV+HhHfBV4eEdtl5sQXzMQH9bMNde8OHNL0kr/PzJO6fFsTl3Bml+W1uiUtlk3ssxX3S4hijO59msoty8wzu3yd1fZVFuMYN45lfB/F5baXUfyY7wUcxMpLptWniJgD7NC0+JLMvGTGg6mP/YCdgO9k5m8aln8R+AQwJyI+mP2PYf3H5fR7zSsy8/Hyc7LKMBQR8V5W/5H998y8os8Y1miYvj0zLyyf3xMRh1MkSbMpDirO6fM1hkZmnhcRXwf2BV5EsQ9eRPE7dUhEnEVxdtk6wLOB5cB7G0YTaPQQxd+n2Y1lAtTsEoqDtD8GiIiNgPe2KHdSeUC3moh4NXASxVlKr5nC/14d9HJ8sKJsOab5s4DbyyS5/wD6O8Y5AXgdxXASR3XxGk+lOPC/nfH+rWze33uW0w2j9X1oNi+nTwfoZb9HcU+EOc3LM7PV63QTazs9/S9I08UcUh2YR44Jc8lpZU5pTmlOOfzMLceDOSV2svXiNIozGw6gGFN2aRcf9C9RXN79a4oP2W0UX/RQfFm3Ghv9ZODFFEnY/IjYkmKM6Ssy84cN5Xan+AFodAPFFzqs7JndkNY2aCqn3q32w5qZj5Y/8I33RNiH1ffVpRRnWMEA91Vm3hMRXwQ+QPF/NI5f7oM2h3JM6CaXzGwYtTLR6HNm48LMvDMiLqBIEl5JMWRNPyY+S7e3Wd9q+XsphmdqtIyisaEfvyunD1GM379CZmZEnE+RED2PGiREAGUScVH5oLw3zGsozuB7M/B1imGN4v+zd+fhkpTl3ce/97gCAWWVTRxABqIxEhlRRIWRxKACYiIaMGwahkQTjUB83RJmBDEqi1GDghtqAEWSyKKJJAQwMIiZcUUREAGBQWVAYEBkvd8/qvpMTc/pc7p7TndV9/l+rquu6q6t79N1lv6dp+p5KD5Utf9enE6n89kaALx13p/a4dhnMMnv7YjYn6Lf718BC7IYc2Y2ux3YCdimi223ruzT+ofCbR227cV0n3FWExEfphgs+5vAq8qr6aazkOJ78XOzNQBHxJOBjcqnd5Tzjcv5H5VTJ79Tzns573OZ/GdzUTlf689DfX4vSINkhtRkzJGzx2GYJQfFTGmmNFM2n9lyzJkpV7EbgO59kaK/0NMorh6c8vbPiJhPEY7+G9gpMw/PzHeVLavvAzrdzv1vFL/031T+MZl0sOrMPCMzo22aW9nk2nLeadDQ1sDGw7jFvclat4h3anDu9EPZtcxcNMm52rOyScdzVQ7Uui3FbfDd/pFu/VJbr9+ax8SMnNvM3HOS87doRiqchSJiU1ZdRXR2RGR1YtXt4tXBqqc6l5Pd4n1vOX9ahzLWWJ6Zcyc5z2dM+cVMrfVzvTLbBlsttQLTOmvxGo1W3tJ/DsVgtVBcbdr6IPPdSd7v1aZJDtnpfG5ezu8pX/emDse8qX3HiDiAYvyZXwJ7ZOa17dvMQq2rdf9wqo3Kzyh7lk+vYFXY3GrSHXrQxWecah2nAMdQjFPxisy8b7rjl13qHAqzZ1DqDl5M8Xv1l5Wfj9bP6Num+Rk9vNyu6/OemZdO87O+Vp9d+/lekIbADDm+zJHjzSzZYGbKCWZKM2XTmS3Hn5myZCNbl7K4Hfhcipb1+5n+KpFnlvPzc81W7F3p8Eew3PbTFN9Y+1JcjXgfxQCQvfifcr53+4qI2I7im+1muv/APa5aH0qe3r4iIp7JcMYb6HiuKK4iXBdYkt1fjf3Ccu65LdR5brWmQyn+QbQM+EyH6Q7gDyNi23KfjueS4sq9dq0rxF/cvqIcY+JF/Rbfgx9QdF+xSURM9kH+98r5TUOopW4ry3mUH1B+BDw7IjaaYp/JbFN2DdBuz3LeUzcSEXEQxd/y5RRh6Poe6xlXZ1CMVfCaiHj2FNu9kaK//GuByzLzfuBq4GkR8QdT7NfSGm/ocVNu1UEU/pniiuH/orjCrOMYR21eQ3Hl63/nLL3KtPxd+J7y6VmVVd8q5y/p5jh9nPepXFLOX94+HlBErE/RhdkDlRpb69bme0EaKDPkWGtC1jBHDk4Tzq86M1MWzJRmyqY7A7Pl2DJTtslMp0kmihboW9uWbUNxtczubcuPL7c/rLLsheWyf23bdjOKDwJJ0Z/6ZK+9DcUVZ7eW253WR/2PA35c7r9fZfkciissEnjnNMdYVG73F3Wfjxk8p9m27AkULex3A5tVlq9DcTv+GueJosuH1c73JK9zaQ91bUDxAfBBYH5l+ZOBJeXx/qxtn92BOZMc688prtB6EJhb93s+bue2y1ouLY/xzB7rv7Xf1xylCfhJ+fXuOsU2x5XbvL98/vry+Vlt2z2H4sN2Aosqy+cAPy2Xv6Jtn79sfb8Ae67F13FTeYzHT7FN62/D56s/r2XdDwAP9/J90tQJOJCiC4DJfidtDlxfvg8HlMveWD7/KvDUSfbZEHhe27LWOfty23u5LXBX+V5u30PNh1J8EP8Z8Iw+vubW74rj637/B3RO31d+fTcAz5pk/f7Abyg+q7yssvyIcr9vA09p22cOsEXl+SvLbRf3UV9QXCWY5e/zJ/e4/8Xlvn9a93s9wHO4xt/FyrrNgHPKbW4GNmlb/83y5+ONHfZ/Dqv/Xe36vHdR9zfKY/1N2/KTy+WfnMnvBSenmZ4wQ8KYZcjKec22ZebIMZmGdX67rOVSzJLdfM1mSjOlmXJEJsyWIzsxyd/HyjozZdvkmGw9yMyfAz/vcvP/o7jF9U/KAVEvp7gt+RUULfPLp3qdiPgaRT/60NbNR5e1PhoRh1Nc3XZuRJxb1r4XxVU6V7DqdusJEfFOiv5yoei3FuDwiGhdvXN5Zn6613qaKjMfjoh/Av4e+G45yOrjKf64L2eK8zSDNdwbEUdQXOV6aUR8ieIP/H7AjuXyL7ftdiYwp/zeupUiSD2f4grXR4Ajc5Lb2GeTYZ7biDij8rT18/PBiGhdbfXpzLy8sv1OwDvbDrNh23GOycwVM1VjE0QxePuOwA9z9fFB2n2G4mqYwyPiWIoxIa4HDoyIrYGrKP6R9Opy3euqO2cxEPVfAP8JnB8R/0rxge73Kc7/f1D8Lp6sy41OtW9CMYB2yyatWssuSQD+MTN/UtnmBIrfuYcAz4mISymucPpTip/ZozPzp93W0GAvAN4G/CIiLgduLJdvC7yK4p8R51GOh5CZn42IXYA3AzdExDco/j5tVO7zUuBzFOG16gflay2LiIsouul5PcWVxO/IzBu6KTYiFlD06T+H4iqnw2PNwbLXGAi5/J5q/S1s3Wmwb/k9CfCTzPzHbmoYAYsouoo6Cvh+eY5+RPEPpxdRnIcHgAMz838q+32a4j06BLi+HCfiDoqrEl9G8b4vKre9kiJM/W15BWprfISPZeZ0Y8f8A8VdGg9QjHHxzknO4fcy86vtC8urzxeUr3f+NK8z8mLVQNNzKH5Wnk1xjp5IEWDeMMnfmoMoPj9+JiLeSvE7926KO3F+n+Kq6d0oxpyA3s77dN5M8Y/hj0bEXsA1FN9vCyi69HhP2/Z9fy9Iw2KGBMYsQ4I5ctyZJZvLTGmmBDPliFmE2XKkmSmBbjJl3a2iTZ3o4WogJrkKsVy+EcUg1DcBv6X4g3wCRbcNN9HhKsRy31eXx/y/tfw6nkVx1eEKiqvSrgMWA+t02P5SVl3dMdl0Rt3nps/34XFl/Q9Nsi4oPqTeADxE8Yf5Q53OEzN8BWJlv90pWsx/Xf5g/5BioMXHTbLt/6O4ffWWctvW99fngOfW/X6P67mdooapfmYm+92wZxf7zK37vR3AuTqz/Nre2sW2F5XbvqZ8/nSKfxLcVX7P/x/wJ5X3ctEkx3hB+XOyspz+m+KP+MfLfXbuofa5XZyzPSfZb12KDwI/ofgdfE9Zxyu6fe2mT+W5eQvFINTXUoxf8BDFgMVfp7gyerIrEvcBLqT4UPUQxWDT36b4m7pT27ZJ8fdpS+Bfyn1+C3wHOKjHels/51NNN02y3xnT7HNp3ediAOd2V4qrZm8sf+7uo+jG4URg6yn2ewNwWfn9/tty/zNZ82rSvSkC0X29/O7r4lx0/LwCfLBc/4G6398Bn7v29+NBis+Cyyiu0tt7sp/Lyv7rA+8ut7+vPP83Al+jGN9kvX7Pexe1P53i88zt5e+Gm4F/Ajaaye8FJ6dBTJghx+7nEHPkWE/DPL9T1GCW7O39MlOaKav7mSlHZMJsOXLTJO+DmXKKKcqDqWHKVuJjKbrZ+EzN5Yy8iNiC4mqz2zJz6+m21+jw3KpXEXEFRVh6ShZ9P6vhyis7L8vMPeuuRZKkpjJDzjyzxnjz/KpfZsrRY6aUNEhzpt9Ew1YOxPeXFFfXTDc4trrzmnJ+Za1VaBA8t1pDRKwbEWsMSB4Rh1F0SXCRYUiSJI0LM+TAmDXGm+dXHZkpJUndcky2BomIVwHPA/al6Hv/mMz8Tb1VjbaIeB8wDziAoo/5k+qtSDPFc6tpbEMxfsJ/UQxY/XjgDyj6eL4bOLrG2iRJkmaEGXIwzBrjzfOrLpkpJUldsbvIBikHqj2UYsDEzwLvzcyuB1HVmsrbwVcCS4HjMvOSmkvSDPHcaioRsSHwYWAPYHPgSRT9s/838P7sckBjNYNde0iSNDkz5GCYNcab51fdMFOOFzOlpEGykU2SJEmSJEmSJEnqkWOySZIkSZIkSZIkST2ykU2SJEmSJEmSJEnqkY1skiRJkiRJkiRJUo9sZJMkSZIkSZIkSZJ6ZCObJEmSJEmSJEmS1CMb2SRJkiRJkiRJkqQe2cgmSZIkSZIkSZIk9chGNkmSJEmSJEmSJKlHNrJJkiRJkiRJkiRJPbKRTZIkSZIkSZIkSeqRjWySJEmSJEmSJElSj2xkkyRJkiRJkiRJknpkI5skaSAiYm5E5CTTYxFxf0T8NCLOjYhX112rJEmSJGn2mSK3ZkQ8FBG3RcQFEXFA3bVKkpopMrPuGiRJYygi5gI3drn5BzLz3YOrRpIkSZKk1fWYW/8NeH1mPjKwgiRJI8dGNknSQEwSVpYCXwYCmAu8CXhSue5R4GmZeecA69kgM+8d1PElSZIkSaOli9x6MLB+Zf3fZObHh1SeJGkE2F2kJGlYfpSZJ2bmhzPzLcCnKuseB+xQ3bjstuOUiLg6Iu6LiN9GxM0R8aWIeEn7wSPisLauPZ4ZEe+IiGsi4kHg/LbtXxkR/1Z2//FQRNwbEd+LiBMi4mmTHP/SyrEvLY9/bkT8OiLujoh/j4jty22fGxFfK495b/n4dyd7UyJis4g4LiKWRcQ9ZS3LI+K8iNi397dZkiRJktSn9tz62rb1E91GRuHAiPiPiPhlmeXujoirIuJdEbHBcEuXJNXh8XUXIEmaXSIigG2A3dpW3V7ZZl/gLOB32rbZppxeHxHTdTH5WWCyxrg5wGeAw9pWPQF4bjkdERH7ZeaVHY69DXAVsFFl2f7AiyLiCOBLwDqVda8Enh8Rz8rMFZVaXgBcAGzadvwtgP2A/SLii8BhmfnYFF+rJEmSJGnmXdH2fAuAiFgH+Hfgj9vWPwXYtZyOiIg/zszrB16lJKk23skmSRqWQyMigceAm4BdKuvOzMybASJiW4ruOVoNbL8BTgU+APysss+7IuKgKV7vJcA1wAfLfb9bLv87Vm9guxp4P/Bp4OFy2SbAeRHxlA7H3hZI4EPAuZXlmwHnAfeV675aWbcpRReZlF/nBhR317Ua2B4FPgccB3y/st/BwDun+DolSZIkSYOxe9vz1sWhJ7N6A9uVwPsoLrhs2ZYiV3qTgySNMX/JS5LqdgXwV5Xnf83qd4G9OjP/GyAiTgR+CmxYrvt/FHe8TeZbwILM/G1rQXkX2zGVbW4Ant/aJiKWUNwBB0Xj1xuBUzoc/9WZeUW5323AlpV1+2Xmt8rXu5XyakeKqxlbDqVolGv5y8z8dHm89wM/AOaV646JiA9m5qMdapEkSZIkrb1nR8QxFGOyPQM4pG39VyJiI+AvKsv+lyJ7PgoQEdcB/1Cu+11gX4q73iRJY8g72SRJw7KU4i6yvwNOZNUVgLsDV0TExuXzF1f2ubXVwAaQmXdR3CnW8vsRUR2EuurEagNbaUeKu9Razm7b5ovAI5Xn7VctttzcamBrPa88vjEzv1XW+xir3323YeVx9et8FPhC60lmPsjqjYcbUoQzSZIkSdLgzAc+TNEzyVuAat48D/gk8AJWv3Hh820XRH6W1XXKlZKkMeCdbJKkYflRZp7YehIRnwSup7hC8DnAu4GjWX2cs19Mcpz2ZRsCKyfZ7ieTLNuo7flqx8rMRyJiBbB5h+1bbmt7/lDl8fK2ddVGu+rFLdVj/zozq8dYo7YpapEkSZIkzbyHgTuB71BckPnlzMzyTraq9uxmlpOkWcRGNklSLTLzhrJBqzUm2YJyfldls81ZU/uyX3d4ifsnWXZX2/PVjlX2lV+90619+5aHOyyH1RvVplI99oYR8cS2hrb2r7NTLZIkSZKkmfH5zDxsmm2mzJWTPDfLSdI3A691AAAgAElEQVQYs7tISVItImI7Vm/QekI5r3bDuHVE/GFln42AV1fW/yAzJ7uLrZNrgRWV538WEU+uPD+Y1S9AqdYy06rHfhyVvv4j4knAQZX1vwauGWAtkiRJkqTuXMXqF1ceUo7H3fLGtu0HmSslSTXzTjZJ0rC0BpAG2IyiESkq6y8v5x8H/gpoNX6dFxGfA+4FXs/q45p9uJcCMvOxiDgZOKFc9Ezg/yLiqxRXGx5a2XwF8Llejt+jzwPvZdWdfJ+IiBcBtwL7AfMq257c1se/JEmSJKkGmXlXRHwWWFgueilweUT8F7AD8GeVza8FLhhyiZKkIbKRTZI0LPPLaTI3AccBZObPIuJA4Exg3XJ6yyT7fDgz/6WPOj4I/C7FXWsAv1dOVXcB+2fm3X0cvyuZeU9EvJoicG1M8Tf58Ek2PRv4wKDqkCRJkiT17G+B7YBWzyu7lVPVz4FXZ2a3QwpIkkaQ3UVKkurwCMUA0kso7ub6g8xc3lqZmV8FngN8lKKbxN8AD1Hc5XUOsCAz39HPC2fmY5l5CMXdYucBt1OMsXY/8AOKRrjfy8yBd+mRmVcCz6a4s+57wH0U780vKBrfXpOZB3kXmyRJkiQ1R2Y+APwxxcWb3wDuoMhy9wJLKXLuczPz2tqKlCQNRWRm3TVIkiRJkiRJkiRJI8U72SRJkiRJkiRJkqQe2cgmSZIkSZIkSZIk9chGNkmSJEmSJEmSJKlHNrJJkiRJkiRJkiRJPbKRTZIkSZIkSZIkSerR4+suoIk22WSTnDt3bt1lFJYtW/V4l13qq2PE+LZJkiSpatmyZSsyc9O669B4alSG7NaIhKYRKVOSJEljptsMGZk5jHpGyvz583Pp0qV1l1GIWPXYc9W1pr1tF6xYMfF43002qbESSZKk2SkilmXm/Lrr0HhqVIbs0gUvetHE432XLKmxkqk1LdtJkiRpdug2Q3onmzQE+1199cTj3HPP+gqRJEmSJGC/E06YeGzblSRJktQfx2STJEmSJEmSJEmSeuSdbBpL++xTdwWr22fjjesuQZIkSZIm7FPtIrLBvW00LdtJkiRJVY7JNolG9advB/SSJEnSWnNMNg1SozJkt8yakiRJUkfdZki7i5QkSZIkSZIkSZJ6ZCObJEmSJEmSJEmS1CMb2SRJkiRJkiRJkqQePb7uAqRBWLRo8sd1OeuXv5x4fNDTnlZjJZIkSZIEZ+2118Tjg2qsYzpNy3aSJElSVaQDHK+hUYNWOxh1X5r2tsWll048zj33rK0OSZKk2arbQaulfjQqQ3ZpVDJK07KdJEmSZoduM6TdRUqSJEmSJEmSJEk9srtIaQgO3GyzukuQJEmSpAkHXnzxqicNvpNNkiRJajIb2aQhOOtZz6q7BEmSJEmacNbxx696ctxx9RUiSZIkjTAb2SRJkiRJkiRJRGUwzMyceN7+WJJUsJFNkiRJkiRJkmapasPa2u5jA5yk2cZGNkmSJEmSJEmaRfppWOv12Da4SZoNbGSThuD05csnHi/ccssaK5EkSZIkOH2ffSYeL6yxDknS8NTR+DVZd5PDrkGSBslGNmkIjrzuuonHNrJJzdcpBFRN1Td9t/tIkiTV5cijj554bCObJI2vpmZQG9wkjQsb2SRJs9JUgzkPuwYDhSRJkiRpJoxi49VU+dzcLKnpbGTTWDriiLorWN0RW2xRdwnSrNXNHWZN4KDRkiRpmI648MJVT/bcs7Y6ptO0bCdJTTEqWXcmjWIDoqTxF/5CWtP8+fNz6dKldZdRqP6R9FxJmuWG1Y1jk/eRJPUnIpZl5vy669B4alSG7JZZU5JGzjAy6NruX9drStJM6zZDeiebJGlguu3ywQYmSZIkSZLs5aRfdjEpqS42skmShn5lmvpnWJAkSZKk0WSOHj4bLSUNWk+NbBGxEfBSYAdgUyCAXwE/Bb6ZmXfOeIWSpCnNxN1iGk32Ry9JajozpCRpNjB7j56p/pciSb2YtpEtItYFDgSOAOZThKLJZEQsA04Hzs7M38xYlVKPFi5c9fj00+uro+WkW26ZeHz0058+lNds8phS7uPdYpp5NrhJkprCDDkaTjrggInHR9dYx3SalO06fR73s9foG7exqYb9mt3sX8drDvN90njx972kXnRsZIuIJwBvBd4NPLW1GEjWDEkJzKEIUPOBD0XE8cDHM/Ph6YqIiNcCewA7A88F1gfOzMw/7+WLiYibgGd0WP3LzNy8l+NpdH3qU6se1x3EAI654YaJx8NqZJMkSZKGyQw5Wo5585snHvfTyDa8f2iv+ofmpz41+H9o93NnQ69f81TqamyowzAaKLrt9UOSujEqv+8lDddUd7L9BJhbPq7+ZD9C0b3HneXyjYDNgCdUtnsqcCLwFuCZXdTxXopgdB9wK7BTV9VP7h7gI5Msv28tjqkRNpuuOPEPqqQWP2xLkmpghhwzTWuUqbJniJlVd8PesP85LUnjrJ+/i5L6N1Uj27aVx1cB5wBXAN/LzIeqG0bEEymuINwdeB3wgkmOMZW3UwSjn1JcjXhJl/tN5u7MXLQW+2tIBtmtXjevOcw/IEdtvXXP+3T6epocdCVJkjSrmSFHyFHnnDPxOBYsAGygkCRptppNNylIM22qRrYHgE8DH8nMG6c6SBmYvl1Op0TEtsBRwBu7KSIzJwKRH+g1TMMaV+vkHXboeZ9OdUrS2rChXpI0QGbIBug2b5z0iU9MPD95KJVJkqRRsLY3OUizzVSNbM/IzBX9HLQMVH8TEYv7K2utPCki/hzYBrgf+AHwzcx8tIZaJEkaGX5YliStJTOkJEmSpFmlYyNbv+Fopo/Rh82BL7YtuzEiDs/My2qoRxX+A1eSRsNMdtUrSZodzJCSJEmzh/8PkApz6i5ghn0O2IsiJK0HPAc4jWLw7f+IiOd22jEiFkbE0ohYescddwyjVkmSxkpETHzIbj1unzptN90+073mdK8jSVIHZkhJkqS11G2Gl8bRVN1FTioiNgb2BLYGnjTZNpn5obUrqz+Z2d61yNXAX0bEfcDRwCLgNR32PR04HWD+/Pk2v6+F9oEyvaoBOPRQABbdOOXQFJKkDjqNh7k2+7frd9xNSdLUzJDNtKjMKAB8/vP1FSJJksbK4sWrPl4de+yxNVYiDUdPjWwR8TbgBODJ02xaS0CawicpAtJL6y5kXHmVwjQOOwyAxTffXG8dkqQZ103D3KJFiyYeGzIkzSZmyOZaXGYUwEY2SZI0MK1GN7OwxlXXjWwRsQA4ZZJVCUTb86b5VTlfr9YqBshfVpIkjY7q323/hksaV2ZISZIkVU2WhVvPpVHVy51sf9X2vBWEovK8qbcz7VbOf1ZrFTNoql9CnX5ZVbX/U6+fX3DN/qfgIgCOPXYRHd6C4TrjDKB8f+utRJIkSRoWM2SDHVtmFKDhGWUR0KBsJ0mSZpwNbhplvTSy7UoRgr4HnA8cWz7fGDgQ+CjFoNF/PcM1riYingBsDzycmTdUlj8buD0z72rb/hnAx8un/zLI2tQkxS/mRYsaEsTK7lcWnXFGwwOsJKlOBgtJY8YM2WCLKl1ENjujNCzbSZKkgTIXa9T00si2WTm/CLivtTAzfw2cGhG7A4cD3wc+1ksREbE/sH/5dPNyvltEnFE+XpGZx5SPtwKuAW4G5lYOcwDwzoi4BLgRWEkRpF5F0f//14ETe6lLkiSpLgYLSWPADClJkqS10uze1KTeGtla3XjcBTw0sTBig8y8F/hxuc2R9BiQgJ2BQ9uWbVdOUIShY5jaJcCOwB9QdO2xHnA3cDnwReCLmdnEvv671qnrxzpq8JeaJEnD5d9gSSPIDClJkiRprPXSyHYXxRWC6wG/qCw/OCI+C7yifL5d+47TycxFtDpan37bm5ik3/7MvAy4rNfXliRJkiQNhBlSkiRJM8aLT9VEvTSy/YIiIG0EXFhZ/lHgn1gVWlbOTGnS2jgfgH33rbmMlr8qxnw/+qc/rbkQSdI4MFhIGhFmyAY7uswoAHziE/UVMq2GZTtJktQIDrGgpuilke27FN1o/B6wDLgF2JoiGAXFANbJ6uFJI250f1kVCezCpnw3vu51AJx86601FyJJkiQNjRmywU4uMwrQ8Ea2hmU7SZLUOKP7P2yNg14a2c4GHgDuy8yMiCOBf6MYEBqKkPRj4B0zW6IkSZIkaQSZISVJkjR09v6iYeq6kS0zLwYurjz/z4jYCXg1sAlwLXBuZj7U4RDS7HXqqQCceNJJ046+LklSL7xiT1JTmSGb7cQyowBmFEmSNLZscNOg9XIn2xoy8+fAx2aoFml8feUrABx9zjkGWEmSJM1aZsjmOLrMKGAjmyRJmh28SFWDsFaNbJIkSZIkSZIkSaPEBjfNlJ4a2SLihcDfAbsCGwJzJtksM3O9GahNkiRJPTAkSGoaM6QkSZKarj1L28WketF1I1tE/BHwNeBxFANUd5JrW5QkSZIkabSZISVJkjTqbHDTdHq5k21xub0BSOrV0UcDsPDaa2suRJI0mxgGJNXMDNlgC8uMAsBJJ9VXiCRJ0oio3vFWZeae3XppZNuZIhwF8J/AtcCDGJik6e2zDwCfuv32mguRJEmShsYM2WCfKjMKYCObJEnSWvIi19mrl0a23wJPAr6cmQcOqB5JkiRJ0ngwQ0qSJGnWqTa4OXb6+Oulke1y4FXAzwZUizSDFgJw2mmnc+SRNZcCE1eGnnbaaTShHEmSJGkIzJANdlrl7rVmZ5SGZTtJkqQ+2eA2nnppZPt74I+AAyPi5My8c0A1STPgUwAsXNiQIHbhhQAs3HLLhgdYSdK4susKSTUwQzbYwjKjQNMb2RqW7SRJkmaADW7jo2MjW0S8Y5LFS4AFwPUR8a/ATcDD7Rtl5odmqkBJkiRJUvOZISVJkqT+eGHs6JrqTrZ/pPOA1E8F3jjFvgYkSZIkSZpdzJCSJEnSWrLBbbT00l1kS6fQBBDTrJckSVLN7JZC0pCZISVJkqQ+mN+bb7pGthhKFdKMWwrALrvUXEbLe98LwEE//nHNhUiSJEkDZYYcEQeVGQWA44+vr5BpNSzbSZIk1cQGt2aaqpFtnaFVIc24IoF95zs1l9Gy114AnP2rX9VciCRJkjQwZsgRcnaZUYCGN7I1LNtJkiRJFR0b2TLzwYh4cWZePsyCJEmSJEmjxwwpSZIkabaZrrvIb0bEL4DzgH8FLsnMRwdfljRmyitDzzzzTN5QcymSJFXZ3YSkGWaGHBFnVu5eM6NIkiRJ/ZmukW0FsDlwJLAQuDsizgP+HfhGZj404Pqk8XDxxQAc9LSnGWAlSZI0zsyQI+KgMqOAjWySJEmjqHXRrBfM1mvONOs3BxYAHwduBTYEDgO+CqyIiLMj4oCIWG+gVUqSJEmSRoEZUpIkSRqyxYsXr9ZTjYZnyjvZMvMx4LJyemtE7Ar8KbA/sAPweuB1wIMR8Q2KqxPPz8y7B1q1JEmSBsIr4SStDTOkJEmSVC+HhRiu6bqLXE1mfhv4NvD/IuL3gNcCrwGeA7wa2A94JCIuBf41M0+f2XIlSZIkSaPCDClJkiTVxwa3weupka0qM68GrgYWRcR2FGHpT4DnA38E/CFgQJIA3v9+APb94Q9rLkSSJEmqhxmyWfYtMwoA73lPfYVIkiRJI6zvRraqzPwZ8CHgQxGxJUV3IK+ZiWNLY+FFLwLgwjvvrLkQSZK6Z9eRkgbFDFm/C8uMIkmSpNnBu9oGY85MHzAzl2fmxzLzZTN9bEmSJEnSeDFDSpIkScO3ePHi1Rre1J+e72SLiOcAuwIb0qGRLjM/tJZ1SWtpXwDOP/8C9tuv5lIA3v1uAM4//3yaUI4kSb3yijdJ/TJDNtP5ZUYBGp5RGpbtJEmSpIquG9kiYj3gHGDvLjY3IKlmFwKw7741l9Fy5ZUA7LvJJjUXIkmSJA2HGbLZ9i0zSvM1LNtJkiRJFb3cyfaPwCvKx9lhm5hinSRJksaAd7VJ6pIZUpIkSdJY66WR7U9YFX5iALVIkiRJksaHGVKSJEnSWJu0P/wONi7ntwAvpOhPf11gnbZp3ZksUJIkSc3mYMmSOjBDSpIkSRprvdzJdhOwA/CFzPz2YMqRZsptAGy5Zc1ltJxyCgB7fve7NRciSZIkDc1NmCEba88yowDw9rfXV8i0GpbtJEmSpIpeGtnOBBZThCSp4YoEdvvtNZfRsvPOAFx2zz01FyJJkiQNjRmywS4rM0rzNSzbSZIkSRW9dBf5IeAq4HURcXxEbDWgmiRJkiRJo88MKUmSJGms9XIn268pGuUCeBfwroh4BHi0bbvMzPVmqD5pPPzt3wJwySWXsKDmUiRJGqTW2GzHHntszZVIagAzZINdUmYUwIwiSZIk9amXRrYnA1lOUS57QjlV5QzUJY2X738fgD033LDmQiRJkqShMUM22J5lRpEkSZLUv14a2WBVMJIkSZIkaTpmSEmSJEljq5dGtg8OrApJkiRJ0rgxQ0qSJEkaa103smXmuwZVRES8FtgD2Bl4LrA+cGZm/nkfx9oaeB+wN7AxcDvwVWBxZv56xoqWJEmSJHVkhpQkSZI07nrtLnJQ3ksRjO4DbgV26ucgEbE9sATYDDgP+AmwK/A2YO+I2D0z75yRiqVenHYaALssXVpzIZIkSdJYMEOupV3KjALAkUfWV4gkSZI0wub0ukNE7BIRZ0fEzyPivnJ+dkQ8fy3qeDswD9gA+Ku1OM6pFOHorZm5f2a+MzNfBpwC7Ai8fy2OLfVv3jyYN4/v3Hdf3ZVIkiRJQ2WGbKbvzJs3MUmSJEnqT0+NbBFxOHAl8Dpga2Ddcv46YElEvKmfIjLzksy8PjOzn/3L2rYDXg7cBPxz2+pjgfuBgyNivX5fQ5IkSZLUPTOkJEmSpHHWdXeREfEs4JPlPpMFmccBp0bEVZl59QzV14uXlfOLMvOx6orMXBkRV1AEqBcCFw+7OA3bLgAsXbqM+fNrLgUmul9ZunQpTShHkiRJGjQzZLMtrXQR2eyM0rBsJ0mSJFX0Mibb24AnUISjBK4CfgFsTtFnfZTH+xugjg7ddyzn13VYfz1FQJrHGAYktfsOALvsUnMZLdcV35a7rL9+zYVIkiRJQ2OGbLBdruv0ZTdNw7KdJEmSVNFLI9uCcr4CWJCZP26tiIhnA5cAG1e2G7anlPN7OqxvLX/qEGqRJEmSpNnODClJkiRprPUyJttWFFcffqEajgAy80fA5ymuRNxq5sqbUVHOJ+2zPyIWRsTSiFh6xx13DLEsSZIkSRpLZkhJkiRJY62XRrbp9D3g9AxpXWX4lA7rN2jbbjWZeXpmzs/M+ZtuuumMFydJkiRJWo0ZUpIkSdJI66W7yOXA9sAbIuLjmXlza0VEzAUOrmxXh2vL+bwO63co56PS8bzWSpHXI6bZbFi+8AUA5l11Vc2FSJIkSUNjhmyweWVGAeCQQ+orZFoNy3aSJElSRS+NbJdRBKSnAddGxGWsGrR6D+CJFJ9+vznTRXbpknL+8oiYk5mPtVZExPrA7sADwLfqKE6z3NOfDsD1DzxQcyGSJEnS0JghG+z6MqNIkiRJ6l8v3UV+FHi0fPxE4A+BPy/nTyyXPwp8bMaqm0REPCEidoqI7avLM/MG4CJgLvCWtt0WA+tRjAVw/yDrkyRJkiQBZkhJkiRJY67rO9ky8wcRcRTwETr3nX9MZn6v1yIiYn9g//Lp5uV8t4g4o3y8IjOPKR9vBVwD3EwRhqreDCwBPhoRe5XbvQBYQNHFx3t6rU2aEQcXPeFce+217FhzKZIkSdIwmCGb7dqDD554bEaRJEmS+tNLd5Fk5sci4mrgaODFFANB3wtcAZyYmZdMtf8UdgYObVu2XTlBEYaOYRqZeUNEzAfeB+wNvBK4neIKysWZeVef9Ulr59ZbAZi37ro1FyJJkiQNjxmyueaVGUWSJElS/3pqZAMoQ9AlAO391vcrMxcBi7rc9iag45DHmXkLcPja1iRJkiRJWntmSEmSJEnjasox2SLisYh4pOziYw0zEY4kSZIkSePBDClJkiRpNpmyka3U8Yo/SZIkSZLamCElSZIkzQo9dxcpqQ9f+QoAWy5ZUnMhkiRJkgRblhkFgAMOqK8QSZIkaYTZyCYNwyabAHD7Qw/VXIgkSZIkwe1lRpEkSZLUv24b2TaMiG26PWhm/rzPeiRJkiRJo88MKUmSJGnsddvI9u5y6kb2cFxpQLYE4LbblrPVVjWXAvDa1wJw22230YRyJEmSpAEzQzbcbWVGARqeURqW7SRJkqSKboOMA1drxNwOwJZb1lxGy513ArDlk55UcyGSJEnSUJghG27LMqM0X8OynSRJklQxp+4CJEmSJEmSJEmSpFHT7Z1sS4EfDbIQSZIkSdLYMENKkiRJGnvdNrJ9KTNPHmgl0ozaAoDly2suQ5IkSZqdzJCaIWY7SZIkNZeDS2tMFQmsMQNjf+1rAKz/v/9bcyGSJEmSBOuXGQWAV72qvkKm1bBsJ0mSJFXYyCYNw7rrAnDfo4/WXIgkSZIkwX1lRpEkSZLUvzl1FyBJkiRJkiRJkiSNmunuZFtczpcMuhBprL3ylQDce++9bFBzKZIkSdIAmSFHxL1lRgHMKJIkSVKfpmxky8zFU62X1KUHHgBg/cfbQ6skSZLGlxlydKxfZhRJkiRJ/evYXWRELI6IDfs9cERsGBGL+t1fkiRJkjQ6zJCSJEmSZpupxmT7e+DnEXFqRLyk2wNGxG4R8XHg5vIYkiRJkqTxZ4aUJEmSNKtM13fdusCRwJER8QvgSuDbwM+Bu8ptNgKeDswHdgO2KpcHkDNdsCRJkiSpscyQkiRJkmaNqRrZjgLeSxGAALYAXlNOnUTl8V3A+9aqOmlcXHIJAHHppfXWIUmSJA2OGXKERJlRAFiwoL5CJEmSpBHWsbvIzPwIsD3wAeBX5eJg9RAUkyy/AzgB2D4zPzqj1UqSJEmSGskMKUmSJGm2mbK7yMy8B3hPOfj0/sDewEuBbVnVQJfAjcA3gf8E/j0zHx5UwZIkSZKkZjJDSpIkSZpNphuTDYAy8HylnIiIADamuPLwzsx8bGAVSn0pLorNTCKm2XQYyu5XMpMmlCNJkiQNkhmy+bLSRWSzM0rDsp0kSZJU0VUjW7vMTGDFDNciSZIkSRpDZkhJkiRJ46jjmGySJEmSJEmSJEmSJmcjmyRJkiRJkiRJktSjvrqLlJrveQAsW1ZzGS3rrAPAykceqbkQSZIkSYKVZUYB4IEH6itkWg3LdpIkSVKFjWwaU0UCmz+/5jJavv51ADa4/PKaC5EkSZIk2KDMKAAsWFBfIdNqWLaTJEmSKuwuUpIkSZIkSZIkSeqRd7JJw/Cb3wDwO+uvz301lyJJkiRJv1NmFMCMIkmSJPXJRjZpGF71KgBWZhI1lyJJkiRJK8uMAphRJEmSpD7ZXaQkSZIkSZIkSZLUo453skXEP/R70Mx8X7/7SpIkSZJGjxlSkiRJ0mwzVXeRi4Ds87gGJEmSJEmaXRZhhpQkSZI0i/Q6Jlu1q/bsYrkkgI03BmD5gw/WXIgkSZI0VGbIhlpeZhQA7ryzvkIkSZKkETZdI9tk4x9nubx9XU6yTBLAuecCsNWVV9ZciCRJkjRQZsgRsVWZUQBYsKC+QiRJkqQRNqfTisycU52AJwLnl6v/AXgG8ORyfmy5/H/KZZIkSZKkWcQMKUmSJGm26djINom3A/sCZ2Xm8Zl5S2Y+VM6PA84GFgDvGEShUm+WA8vZYou66yitWAErVrDFE59YdyWSJEnSsJghG2yLFSsmpmZrWLaTJEmSKnoZk+2N5fyGDutvoOjq4xDg/WtTlLT2tgJg+fIkmtABzQEHALA80/5wJEmSNFuYIRtseZlRoOl9djYs20mSJEkVvdzJNrec7xsRT6iuiIgnUlyhCLDNDNQlSZIkSRptc8u5GVKSJEnSWOrlTrY7KC4h2xn4QUR8Gfgl8DTg9cC8crum9zUhSZIkSRo8M6QkSZKksdbLnWznsqoXiR2Bvwc+Xs53LNcl8JV+ComIrSPisxGxPCIejIibIuIjEbFhD8e4NCJyiskBtSVJkiRpOMyQkiRJksZaL3eyLQL2Ap5DEYSqkiIgXV1u15OI2B5YAmwGnAf8BNgVeBuwd0Tsnpl39nDIxR2WP9JrbRpV+wBwwQU1l9Gy9dYAXPeb39RciCRJkjQ0izBDNtZ1ZUYB4NZb6ytkWg3LdpIkSVJF141smXlvROwOHEcxgPX6ldUrgc8Bf5+ZK/uo41SKcPTWzPxYa2FEnAy8nWIQ7L/sodZFfdSgsVIksP32q7mMli9+EYAdv/3tmguRJEmShsMM2Ww7lhkFgAUL6itkWg3LdpIkSVJFL91Fkpn3ZebbgY0orkZ8STnfKDP/tp9wFBHbAS8HbgL+uW31scD9wMERsV6vx5YkSZIk1ccMKUmSJGmc9dJd5ITMfBT40QzV8LJyflFmPtb2Oisj4gqKAPVC4OJuDhgRrwe2BR4CrgH+JzMfnKF6pd7dcgsAO8ybx/U1lyJJkiQNmxmyeXYoMwpgRpEkSZL61HMjW0TMBd4KvBjYFDgBuArYudzkzDJAdWvHcn5dh/XXUwSkeXQZkIAvtT3/VUS8JTPP7aEuaeYccggA12VOjPwuSZIkzQZmyGa6rswogBlFkiRJ6lNP3UVGxGsorj58GzAf2IaiX/11gDMo+tR/aY81PKWc39NhfWv5U7s41nnAvsDWZU07AR8o9/1yRLyi044RsTAilkbE0jvuuKOrwiVJkiRJnZkhJUmSJI2zrhvZImIecCZF8FhNZl4F3Fo+3XdmSlv10q2XmW7DzDwlMy/MzNsy87eZeW1mvhs4muJrPWGKfU/PzPmZOX/TTTedmcolSZIkaZYyQ0qSJEkad73cyXYM8GSKoHLrJOsvowgzu/VYQ+sqw6d0WL9B23b9+DTwCFa0T1AAACAASURBVLBzRKy/FseRJEmSJHXHDClJkiRprPUyJtteFOHoexQh6Ldt639azrfqsYZry/m8Dut3KOed+tufVmb+NiJWAhsC6wEr+z2W1Jd5xbf3spV+60mSJGnWMEM22LJ5lbfvur7fKkmSJGlW66WRrRV8zsvMhyLWGBr54XK+WY81XFLOXx4RczLzsdaK8orB3YEHgG/1eNwJEbEjRThaCazo9zhS3047DYD5y5bVXIgkSZI0NGbIBptfZhQAFiyorxBJkiRphPXSXeRD5fxJHdZvX87br06cUmbeAFwEzAXe0rZ6McVVg1/IzPtbCyNip4jYqbphRGwXEWtcARkRm1AMpg3wpcx8pJf6JEmSJEl9MUNKkiRJGmu93Ml2I/Ac4HUR8YHqivIqv9dSdAVyfR91vBlYAnw0IvYCrgFeACyg6OLjPW3bX9N66cqylwKfjojLgBuAu4BtgFdS9NW/FHhHH7VpJBV3jD3vebvwne/UXApMdL/yvOc9jyaUI0mSJA2BGbLBnlfpIrLZGaVh2U6SJEmq6KWR7SKKgLQdq/rAB/hr4DhgHYqAdFGvRWTmDRExH3gfsDdFqLkd+CiwODPv6uIwy4B/AXYBdqYY7Hol8EPgHOC0zHyo8+4aL/MBWLYsWbNXmhoceSQAyzJpQjmSJEnSEJghG2xZmVGAhmeUhmU7SZIkqaKXRrZ/AhYCvwNsQRGGAJ7Bqs/kK4F/7qeQzLwFOLzLbdf4aJ2ZPwQO6+e1JUmSJEkzzgwpSZIkaax1PSZbZt4KHEgxgDSsCkWt+QPAgZm5fObKkyRJkiSNIjOkJEmSpHHXy51sZObXI+JZwFuBFwMbUfRbfznwscy8eeZLlCRJkiSNIjOkJEmSpHHWUyMbQGb+HDhmALVIM+gIAE4/veYyWp77XAAu/fWvay5EkiRJGi4zZDNdWmYUAL7//foKmVbDsp0kSZJU0XMjmzQaigRWGcu7Xh/5CAALGh1eJUmSJM0WC8qMUjxZUF8h02pYtpMkSZIqOjayRcRny4dfysyLKs+nk5n5prUvTZIkSZI0KsyQkiRJkmabqe5kOwxI4GrgosrzqUS5jQFJqvre9wDYY489uKzmUiRJkqQBOQwz5MjYo8wogBlFkiRJ6lM/3UVGh+XThSdp9nr72wG4NLPjD5AkSZI0psyQDXRpmVGg8wmSJEmSNLWpGtl+ThF67ml7LkmSJElSOzOkJEmSpFmlYyNbZs6NiAMy8yut50OrSpIkSZI0UsyQkiRJkmabOdOs/1JEnBkRTx1KNZIkSZKkUWaGlCRJkjRrTDcmWwB/BuwREW/KzG8MoSZp/Oy2GwAXrFhRcyGSJEnSQJkhR8QFZUYB4Mor6ytEkiRJGmHTNbK1bAl8PSJOA47JzN8MsCZp/JxwAgD7XX11zYVIkiRJQ2GGbLj9yowCwIIF9RUiSZIkjbDpuou8guJKxCznRwLfi4gXDbowSZIkSdLIMUNKkiRJmjWma2R7KXAU8ABFSAJ4JnBZRHwgIp4REdu0T4MsWOrOBcAF7LNP3XWUliyBJUvYZ+ON665EkiRJGiQz5IjYZ8mSianZGpbtJEmSpIopu4vMzAQ+EhEXAJ8FXkIRlB4HvKOc1thtuuNKg7cfABdckETUXArAe94DwAXvfjdNKEeSJEkaBDPk6LigzChAwzNKw7KdJEmSVDHdnWwAZOYNmbkH8B5W7/qj0yRJkiRJmqXMkJIkSZJmg64a2QAiYgGwkFVdfuQUm0uSJEmSZjEzpCRJkqRxN22XHBGxHnAiRTiaWAzcDZyPQUmSJEmSVDJDSpIkSZotpmxki4i9gE8D27B6Fx8XAW/KzNsGXqHUl2MBWLSo3iom7LUXAGf98pc1FyJJkiQNjhlydJxVZhQALr64vkKm1bBsJ0mSJFVMdyfbf7H6VYYPAO/IzFMHV5I0ExYBsHhxvVVMeO97AXjDNdfUXIgkSZI0UGbIEfGGMqMADW9kWwQ0KNtJkiRJFdN2F1kK4ErgkMy8YYD1SJIkSZJGnxlSkiRJ0tjrppHtYYpLxz6UmY8NthxpTJVXhh544IGcXXMpkiRJ0oCZIUfAgZW718wokiRJUn+ma2T7AcWVhz8YRjHS2Dr+eADOOu44A6wkSZLGmRlyRJxVZhSwkU2SJEnq13SNbM/PzIeHUokkSZIkadSZISVJkiTNGnOmWmk4kiRJkiR1ywwpSZIkaTaZspFNkiRJkiRJkiRJ0pqm6y5S0kzYZx8ATl++vOZCJEmSJAlOLzMKABdeWF8hkiRJ0gizkU0ahqOPBuDI666ruRBJkiRJgiPLjALYyCZJkiT1ye4iJUmSJEmSJEmSpB7ZyKYxdTpwOkccUXcdpQsvhAsv5Igttqi7EkmSJEniiAsvnJiarWHZTpIkSaqwu0iNqSMBOP30hXzqUzWXAnDSSQCcfuKJNKEcSZIkSbPb6WVGARqeURqW7SRJkqQK72STJEmSJEmSJEmSemQjmyRJkiRJkiRJktQjG9kkSZIkSZIkSZKkHjkmm8bUaQAsXFhzGS0HHADASbfcUnMhkiRJkgQnlRkFgK98pb5CptWwbCdJkiRV2MimMVUksMYMjP3mNwNwzA031FyIJEmSJMExZUYBGt7I1rBsJ0mSJFXYXaQkSZIkSZIkSZLUI+9kk4bhnHMAOOqoozi55lIkSZIk6agyowBmFEmSJKlPNrJJw/CJTwBw0qmnGmAlSZIk1e6kMqOAjWySJElSvxrTXWREbB0Rn42I5RHxYETcFBEfiYgNezzORuV+N5XHWV4ed+tB1S5JkiRJGi4zpCRJkqS6NeJOtojYHlgCbAacB/wE2BV4G7B3ROyemXd2cZyNy+PMA/4H+BKwE3A48KqI2C0zfzaYr0KSJEmSNAxmSEmSJElN0JQ72U6lCEdvzcz9M/Odmfky4BRgR+D9XR7nBIpwdEpm7lUeZ3+KoLVZ+TqSJEmSpNFmhpQkSZJUu9ob2SJiO+DlwE3AP7etPha4Hzg4Itab5jjrAQeX2x/btvrj5fH/uHw9abgOPRQOPZRFN95YdyWSJEnSSDNDzoxFhx46MUmSJEnqTxO6i3xZOb8oMx+rrsjMlRFxBUWAeiFw8RTH2Q1YpzzOyrbjPBYRFwH/n737DpesqhI2/q5GUGDIObcRDChKiwqGRtQBFYyoiEqDAuqMosDnGNDuNsyYMI8KplYHQcSEmHC0G0ZREQQVFVSkESRIDorE9f2xT3VXV1fdW3Vv1a26p97f85ynqk6qVXffCmvvffY+DNgTcLgPzawFCwBYfOmlw41DkiRJmv3MIftgcZWjAPD5zw8tDkmSJGk2G/qVbJShPAD+0GH7H6vbB83QeSRJkiRJo8scUpIkSdJIiMwcbgARxwOHAodm5qfbbH8X8GbgzZn5XxOc582UcffflZnHtNl+KHA8cHxmHt5m+2GUXopQkq2LpvByBmVT4NphB6FpsxzrxzKtH8u0fizT+rFMZ6dRKLcdMnOzIcegPjCH7MoovOc0PZZhvVie9WJ51ovlWS+WZ70Muzy7yiFHYbjIyUR1O93WwAnPk5nHUxKokRMR52TmvGHHoemxHOvHMq0fy7R+LNP6sUxnJ8tNM8wc0vfcrGcZ1ovlWS+WZ71YnvViedbLbCnPURgu8qbqdoMO29dv2W/Q55EkSZIkjS5zSEmSJEkjYRQa2RpDanQa5/6B1W2ncfL7fR5JkiRJ0ugyh5QkSZI0EkahkW1pdfu0iFglnohYD9gDuA342STn+Vm13x7Vcc3nmQM8reX5ZpORHIJEPbMc68cyrR/LtH4s0/qxTGcny039ZA45Od9zs59lWC+WZ71YnvViedaL5Vkvs6I8I3O6w9T3IYiI71MSmNdm5keb1n8AeD1wXGa+smn9TgCZeWHLeY6jTDz9gcw8qmn9a4EPA9/PzL0H+VokSZIkSYNlDilJkiRpFIxKI9v9gbOAzYFvAr8HHgPsSRmaY/fMvK5p/wTIzGg5zybVeR4E/Ag4G3gw8Czgb9V5Lh7065EkSZIkDY45pCRJkqRRMArDRVIlLfOAJZTE6Cjg/sBHgMc1J0eTnOc64HHVcQ+ozvMY4HPAriZHkroVEQsiIiNiwbBjkSRJ0qrMISWNCnNHSZLG20g0sgFk5mWZeXBmbpWZa2XmDpl5RGZe32bfaO2B2LTt+uq4HarzbJWZh2Tm5b3EU/1Ayoi4p+ol2Wm/pU37LujlOWZCRDwkIk6OiL9FxD8j4qKIWBwRa7fZd82IOCIiPhcR50fEHdXresUwYh+URnkNO45OImL3iPhORFwfEf+IiF9HxOsiYo02+85v+v9rt7x7GK9hmIZZvhGxbUS8JSK+EhF/qj4/MiIeMMExu0XEf0XEdyPiqmr/nj6v6qb6Gzb+h3ccdjydRMSDq8/Tb0bEX5pivlcXx+4cEV+IiMsi4vbqM/qMiHjZTMQ+UyJijYg4tHpt10fEndVr/XVEfDoi9ht2jA0RsW5EHBgRX4qICyPi7xFxS0ScExFHRcRaHY57eUQcFxE/rz6zMyLeOdPxD0NEzKt+M/w5Im6LiJsj4jcR8b6I2Gaa555yZVVEbBMRr6k+V5dX77HrIuIHEfHcDscsm+T7NCPiM9N5TcPU5rXcHhHXRMQvq/fiPu1+Z4yC6rv1sxFxRRX38oj4UERs1Gbfnstes5s55GCEOeQqYoj5RTfC/HHKhlm2Ye44bWHeWAthzlhbYb44q7V5feaQE5j0A33M3UX5G70ceHPrxoh4IPCkpv1GSkQ8hjLkyZrAKcBlwJOBtwF7RcRemXl70yHrAh+q7l8NXAVsN3MRKyKeBXwV+CfwZeB6YF/gg5QJ3PfvcOgZwLI263/c/yg1gXnAO4EELgFuAjac5JgXA0cAd1KGOdpikAGOuogIymduAgEcChw91KA6+1fK5+ndwB8p79v7THZQ9SPw08A/gNOA5ZT/k4cBTwe+MJBoZ1j1Y+s0YG/gRuDbwOXAxpQrDV4M7AScOqwYWzwB+B/K5+5S4BuUWPcF3g88t/re/GfLcccCGwA3AFdQXlutVe/TdwNvoPwG+gHwFWAtYHfKe/bVEXFQZp4yhBBfA/wH5XN4KeX3zA7Ac4GnRMQHM/PIlmOW0P57tHG+jYHvDiLYGba4ul2D8rnzUOCllM/dcyLiwMz8w7CCaxWrDwd4IbAb5Xtz74jYo+VqpamUvdRv5pDmkDPG/HFWM3ecBvPGeuSN5oz1ZL5YO+aQ3eSQmenSZqF8UV8O/AK4ErhXm33eU+339ep2wbDjboptDeB3VVz7Na2fQ0mWEnhjyzFrAfsAW1WPF1X7vWLYr2cAZZvDjqNNXOtT5n24HZjXtP4+1YdDAi9qOWZ+tX7RsOMflaVf5Qss6PV9DWxL+dG1fvV4WXWOB0xwzC7AI4G1muK/fNh/xyGW379Wf4PPVV9s1zT+NqO2ADtShpNau3q8vIp9te+LpmMeS/mReT6wZZvtaw77dfXx7/OS6u9xPrBBm+3rAHsOO86meHYBDmz9fwPWA86tXstRbY7bG9ihut/43HjnsF/PgP9Wb2NlhdBD22x/HnBb9b8+pTKeymdw07HPBZ7UZv2DKRVYSRkCrptz7Vjtf9Vsfn9O9N1IqaA7udrnL8Dmw463KbbvV3G9pmX9B6r1nxxU2bu4TGXBHBJqmENO9Bk65LjMH0ekbKfyuwVzx+n+zc0bR+C19eFvY85YwwXzxVosE31HYg652jIyw0WOsE8BWwLPbF4ZEWsCB1F+vP623YERsWtEfDgiflVd8vzPiPhjRBzbenliRLyyuvTybR3OtWV1yfRvuoz7SZR/hjMzc0WPj8y8h9KTAOCVVe+CxrY7MvO7mXlll89RG7Fy2IxFHbYvj4jlLetWXJocEXtWlw7fUl3+/O2IeHCPYTwf2Aw4KTPPaazM0gPmmOrhq3o8p5ha+U5FZl6emf+XmTf3cMz5mXleZt4x3eeviUOr208BJwCbAs9p3SkillRlOrfNto7lHRGPjojTm96r/xsRj4uIRdUx87sNNDMvysyfZ+Zt3R4DvJdSgfWSzLyqzTnv7OFco2736nZJZt7UujEz/5GZS1vXR8QBUYbRuqH63vx9RBwTEfdus29Wn71bR8QXowwrcltEnBsRL+4l2Oq9eELrezEzb6H0PIRSMdV63Pcy89Jenms2q95zb6X0oN4vM1f7DZSZXwVeT/lf/0REzGk5xwsj4odNv42WR8SJETGv2r6MUmEC8LlYdYiKuZPFmJlfy8wz2qz/PaWXP7Qpyw4Oa8RRs/fnCpl5NfAiSuXedrS/8mbjKMNT/b56j91UleHTOp13snKeTETcD3gapSLqv1s2LwT+Drw0ItZtei39LHtpOswha878sb7MHWcN88Z6MGesGfPF8WAOuTob2SZ3IqUAWseU34/SavupCY49lPIPdxHlzf9JSo/GI4GfRMR6Tfv+D3Az8IpoP57pIZThRI7rMu4nV7ffa92QmX8G/kC59PF+XZ5PnT0TOJ1Sfp8E/o9y6f4ZEbFpD+fpWGbAmZQhAnZv96MBeEBE/HtEvDkiDokyDI00q0TEFpTP1j9k5lms/NF0WOejejr/EyjvpScD3wE+Ruk9tZRy6fhARUSjt+o5wG+rypWjo4zdvlfrD8saaFx+/6BuD4gyhvmXgAcAX6P8KLoeeAfwvWg/b8FGlMrKnSn/M1+gfLedEBH/b8rRr6rxY/muPp1vNjuY8nvk65k5UaXtpylDoexIqbQliiXAScDDKWX8Qcr35hNYWRm9hDKsA9Xt4qblxmnG33VZRplT4WWUnmsT/d6b9aoK9Ma8EAc0V6BHxA6UnrlvpPQS/yQl2Xgw5X15aPO5eijnyTR+F51exdcc7y3ATyi9mx/b5fl8H2smmUNqIuaP0jSYN9YqbzRnrB/zxTFhDtliEJfl1WGh6bJ7yhv/LmDbpu3fo1wyuA4rx9Fe0HKOHYA12py7MW70f7Ss/1i1/pkt6wP4MyVRW+3y6Q7xf6U61/M6bD+t2r7PBOdYRM2G+mgq22xZN58Jhs2gtIAvb1m3oDrmLmCvlm3/VW17Qw9x/YIJLkEFLqi2P7hN3O2WU4CNhv33rkH5LphGLMuYZMiPDvGP65Afb6xe/5ua1p0L3NP6N6T8qEpgbpvzrFbelE4lf2z3uQe8sul9M38a8S9ngmE/KD0rk5IQLG3znv11L/8ro75QhrK5oyq/L1Iuwd9hgv0b77mvUQ2l0rSt8X10RMv6xt/uZGBO0/r7UhKtO4D79eG1fLd6nsMn2a/xGuo89McPq9d4aBf7nlDte0z1+LDq8dm0/J6h9GLcqs3fckEfY1+fMozHPTR9l06w/wFVDKcP++/eh9e+2ndjm33uTUkiErhv0/pl1d+sdcixDSlD+9wGbNG0vutyniSe99FhyJ1qe+N386v6XfYuLlNdMIeEGuaQ7T5DMX+sxTKAsl0wjViWYe7Y7es2b6xJ3og549DLYABlar5Yk4U235Ft9jGHrJY69X4YpE9RCvQQWNEa+1TghMz8R6eDMvPSzLy7zabPUnqt/WvL+k9Ut4e3rH8a5cP/y9nm8ukONqhuO+3fWD/ZxLqa3EmZ+cOWdcdXt730cppKmV1D+YG5M2UM6M0ocyKcRxnj+Fs16+Wkmqp6vLyC8uXVPIHzEkolUWtP8F7tTunptjQzWyejPZ7SM3vQNq9uX0DpvfNcyvv+AZSEYmfg21VvqFkvM8+jjLF/dXX7VWB5RFwXEV+PiH1bDjmCUul0SK4+lMo7KL0cD2zzVHdTKhxX9FLKzEuAjwBrUibknbKI+HfKGPrnU76/x91W1e1lXezb2Gfr6vY11e3hrb9nMvPuHOBQY9VnzKcpV5B8IsvQD5Np9IY+fsK9aiIzb2dlb+LNACLiEZSepV/NzJNa9r+RMuTGfSi/ORr6Vc59+S07xbKX+sEcUp2YP0pTZN5Yr7zRnLGWzBfHiDnkSu0uoVWLzPx5lHHsD4mId1K+tOcwyaWgUcbcP5wy3MdDKAXd/IN1m5bn+W1EnAnsExHbZWbjw6bxpv1k07l3AZ7d8pQ3ZuaHunxZjUs4s8v91dk5bdY1ym7FvAlRxuye37Lf8sxc0uXzrFZmWcY2bh7f+FbKZbdnUb7c9wD2ZeVl1JqGiFgAzG1ZvSwzl814MPXzZOD+wPcz869N678EvB9YEBFvzamPcf3I6vbHrRsy857qPbPKEBUR8TpW/+L9RmaeP8UY1mi6fUVmnlY9vjkiDqIkUPMoPzROnOJzjJTMPDkivg7sCTyeUg6Pp3x/PTsivkDpgbY28AjgWuB1TaMMNLud8jdq9ZcqQWq1jPLj7ZEAEbEh8Lo2+32o+qG3moh4LvAhSu+l503j/69Oevn9sGLfaszzhwFXV8n01AOY2m+gY4H9KcNNHNnFczyQkhhczXh9h7aW7+Oq2w2i/dw0m1W3DwbopZyjzJewoHV9ZrZ7nm5i7aSnspf6xRxSEzB/HAPmjgNj3lizvNGcsXbMF8ePOSQ2svXiU5QeDntTxpc9t4s3/Zcpl3n/mfKGu4rygQ/lQ7vd2OgfB55IScIWRsSWlLGmz8/Ms5v224XyRdDsUsoHO6xsmd2A9tZv2U9Tt9oXbWbeVX3hN8+NMJ/Vy+wMSo8r6GOZZebNEfEl4C2U/6dx/8DvlwVUY0W3WDazYdRSoyJoSfPKzLwuIr5FSSCeRRnGZioa76urO2xvt/51lCGbmi2nVEBMxQ3V7e2Usf1XyMyMiG9SkqXdqEGy1FAlGadXC9WcMc+j9PB7GfB1ynBHQfmx1fo5OZlOZdqYILxR9ht2OPcS2nyOR8SzKeOB/w3YM8tcNCrzAu0EbN/Fvts2HdOoePhrh317MdlvoFVExPsoE2ufCTyj6m03mcMo/5OfG5dEOSLuA2xcPbymut2kun1qtXTyL9VtL+U8l/bvyUXV7bR/F02x7KV+ModUO+aP42EB5o6DYN5Yw7zRnLFWzBfHiDnkSg4D0L0vUsYLPY7Se3DCS0EjYh4lOfpfYKfMPDgz31S1rL4d6HRZ99coH/4vr75U2k5WnZlLMjNalrlNu1xU3XaaPLQxsfFMXOo+GzQuGe/U8Nzpzdm1zFzUpszmN+3SscyqiVvvS7ksvtsv7caH27pTjblG+lK+mTm/TRku6kuEYywiNmNlL6MTIyKbF1ZeQt48kfVEZdrusu+bq9stOoSx2vrMnNumvJdM+GIm1niP35ItE7BWGsnU2tN4jpFXXep/MmUSWyi9URs/cM5r8zdfZWlzyk5lumV1e1P1vMs7nHN564ERsT9lXpqrgSdl5kWt+4yxRq/ep0y0U/UbZn718CesTEq3aXtAD7r4DdQcxweBoynzWeyTmbdOdv5q6J2DYOwmsH485TP16qb3ReO9ecQk782Dq/26LufMXDbJe3xav2WnUvbSAJhD1pf5Y32ZO44o88YVap83mjPOauaL48UcsmIjW5eyXBZ8CqWV/e9M3lvkAdXtqbl6i/ZudPgyrPb9NOUfa19Kb8RbKZNB9uJH1e3erRsi4n6Uf7ZL6f4Hd901fqRs17ohIh7AzMw70LHMKL0J1wHOyu57YT+2urWMR6N81dlBlEqjc4HPdFiuAZ4SEfetjulYppRefa0avcYf37qhmndi96kG34NfU4a22DQi2v3If1h1u3wGYhkFt1S3Uf1w+S3w0IjYeIJj2tm+GjKg1fzqtqehJiLixZTv+CsoydIfe4yn7pZQ5jR4TkQ8dIL9DqGMrX8RcEZm/h24ANgiIh45wXENjfmI1phwrw6i+G9Kz+IfUHqgdZwDqcVzKD1k/zfHpDdq9Tn4lurhl5o2/ay6fUI355lCOU9kaXX7tNb5gSJiPcqQZrc1xdjYNp2yl/rKHLLWRiG/MH8cjFEoW7Vn3liMU95ozjj7LMF8cSyYQ7bITJc2C6U1+vKWddtTes3s0bL+ndX+C5rWPbZa99WWfTen/CBIynjq7Z57e0qPs8ur/Y6bQvxrAL+rjt+vaf0cSk+LBN44yTkWVfu9YtjlMYCyzZZ1a1Ja2m8ENm9avzbl8vzVyosy/MMq5d7meZb1ENf6lB+EtwPzmtbfBzirOt+LWo7ZA5jT5lwvofTYuh2YO+y/eR3Lt8tYllXneECP8V8+1eecjQtwYfW6d5tgn3dU+7yrevzC6vGXWvbbmfJDPIFFTevnAH+q1u/TcswrG/83wPxpvI7l1TnuNcE+je+Lzze/d6u4bwPu7OX/ZZQX4ADK0ADtPqO2BP5Y/S32r9YdUj3+BrBhm2M2Ah7Vsq5Rbl9u+XveF7i++nvev4eYD6L8WP8zsMMUXnPjc+Odw/77D7hs3169zouBh7TZ/mzgH5TfMk9uWn9oddzZwAYtx8wBtmp6/PRq38VTiC8oPQqz+oy/T4/H/7A69nnD/lv3scxW+25s2rY5cHK1z6XApi3bz6zeF4d0OH5nVv1u7bqcu4j7+9W5XtOy/gPV+k/2s+xdXKa7YA4JNcwh232GYv5Yi2WmyrbLWJZh7jjZ6zVvrFneiDnj0MtgQOVqvliDhTbfkU3bzCFbFudk60Fm/gX4S5e7/4Jyuetzq4lRf0y5PHkfSiv9FRM9T0R8mzKOPrQM89FlrHdHxMGU3m2nRMQpVex7UXrr/ISVl12vEBFvpIydC2UMW4CDI6LRi+fHmfnpXuMZdZl5Z0R8GHgrcF416eq9KF/2VzBBefUxhpsj4lBKb9dlEXES5Qt/P2DHav2XWw47AZhT/Y9dTkmoHk3p6XoXcHi2uax93Mxk+UbEkqaHjffSeyKi0QPr05n546b9dwLe2HKajVrOc3RmXtuvGEdJlAnddwR+k6vOGdLqM5QeMgdHxELKPBF/BA6IiG2Bn1Mql55VbXtB88FZJql+BfA94NSI+CrlB9/DKf8H36V8PrcbjqNT7JtSJtduY0oeGgAAIABJREFU2LQRazVcCcC7M/PCpn3+k/I5/DJg54hYRukB9TzK+/eozPxTtzGMuMcARwBXRcSPgUuq9fcFnkGpqPgm1XwJmfnZiNgVeDVwcUR8n/K9tXF1zBOBz1GS22a/rp7r3Ig4nTKEzwspvYzfkJkXdxNsROxJGfN/DqX308Gx+mTaq02WXP1fNb4jG1cg7Fv9XwJcmJnv7iaGWWQRZSipI4FfVWX1W0ql1O6U8rgNOCAzf9R03Kcpf6uXAX+s5pO4htKD8cmUv/+iat+fUhKv11U9VRvzKHw0MyebW+ZtlKs4bqPMhfHGNmV5fmZ+o3Vl1UN9z+r5Tp3keWadWDnx9BzKe+ShlDJZi5LQHNjm++bFlN+Tn4mI11I+b2+kXJnzcEpv6sdR5qKA3sp5Mq+mVBR/JCL2An5P+f/akzLEx1ta9p9y2UuDYg4J1DCHNH+sL3PH0WTeWNu80ZyxqFvOuAjzxdowhwS6ySGH3So6qgs99AiiTS/Eav3GlEmolwP/pHwx/ydl2IbldOiFWB37rOqcv5jm63gIpdfhtZReaX8AFgNrd9h/GSt7ebRblgy7bKb591ijeh13tNkWlB+sFwN3UL6o39upvOhzT8Sm4/agtJzfUL3Bf0OZcHGNNvv+B+Uy1suqfRv/Z58DHjHsv3edy3eCGCZ6/7T7nJjfxTFzh/23HWCZnVC9xtd2se/p1b7PqR5vR6k4uL76//8F8Nymv+miNud4TPWeuaVa/pfyxf6x6phdeoh9bhdlN7/NcetQfhxcSPlcvqmKY59un3s2LFX5/BtlkuqLKPMb3EGZ1Pg7lB7T7XosPhM4jfJj6w7KZNRnU75rd2rZNynfW1sD/1Md80/gl8CLe4y38Z6faFne5rglkxyzbNhlMcAy3o3Su/aS6j14K2WYh/cD205w3IHAGdX//j+r409g9V6ne1OSp1t7+Tzsokw6/p4B3lNt/69h/337XFatr/92ym/Dcym99vZu935sOn494M3V/rdW5X0J8G3KvCfrTrWcu4h9O8rvmiurz4RLgQ8DG/ez7F1c+rFgDlm79x3mj7VdZrJsJ4jB3LH7v5V5Yw3zRswZa50zYr44q5c2fxNzyAmWqE6mEVO1Ei+kDLPxmSGHUxsRsRWl59lfM3PbyfbX7GL5aqoi4ieURGqDLONBaxaoen6ekZnzhx2LJEnDZg7Zf+YX9WXZairMG2cfc0ZJM2HO5LtoplUT8b2S0stmssmx1ZvnVLc/HWoUGhTLVx1FxDoRsdpE5RGxgDJkwekmSpIkaTYyhxwY84v6smzVlnmjJKlXzsk2QiLiGcCjgH0pY+8fnZn/GG5U9RARbwceBOxPGWv+2OFGpH6yfNWl7SnzKvyAMpn1vYBHUsZ9vhE4aoixSZIk9cwccjDML+rLslUXzBslST1xuMgRUk1WexBl8sTPAsdkZteTqaqz6vLwW4BzgHdk5tIhh6Q+snzVjYjYCHgf8CRgS+DelLHb/xd4V3Y52bFGh0N/SJLGnTnkYJhf1Jdlq8mYN9aLOaOkmWAjmyRJkiRJkiRJktQj52STJEmSJEmSJEmSemQjmyRJkiRJkiRJktQjG9kkSZIkSZIkSZKkHtnIJkmSJEmSJEmSJPXIRjZJkiRJkiRJkiSpRzaySZIkSZIkSZIkST2ykU2SJEmSJEmSJEnqkY1skiRJkiRJkiRJUo9sZJMkSZIkSZIkSZJ6ZCObJEmSJEmSJEmS1CMb2SRJkiRJkiRJkqQe2cgmSVKTiFgUEdlYhh2PJEmSJGn0RMTc5twxIhYMO6bJRMSSpniXDzseSaoDG9kkSTOqTSLSvNwREX+NiG9FxP7DjlWSJEmSND0dcsCrIuLeHfb/VZv9585s1JIkdedeww5AkqQmawJbV8szI+JrwAsz864ZjOF04NYZfD5JkiRJGjdbAAcCn21eGRFPAR4+lIjGw0nABdX9m4YZiCTVhY1skqRhOwf4MhDAXOClwHrVtucCrwQ+NlPBZOZZwFkz9XySJEmSNKZeT0sjG3DUMAKpu4hYPzNvzszvAd8bdjySVCcOFylJGrbfZub7M/N9mflvwPNbtu8PEBHLmoYKWda8Q0TMbxlKZH7TtjkR8aqIODMiro2IuyLipoj4U0ScGhHHRMS6TftPOCdbRDwgIk6OiOsj4u8R8bOIeN4kMcyNiA9WMVwaEbdUQ2P+LSJ+FBGHRcQafflrSpIkSdJou7u6fVhEPLWxMiIeAvxryz6riIiNI+I9EfGDiLikyu3ujIjrIuKsiPh/EbF2m+OWN+VqS6q87oQqJ7s9Ii6IiIM6POdWEfGZiLg6Im6LiPMj4lBKR9EJRcTu1fNcEhH/rHLI30XERyLifm32n2reu6hl27oR8V8R8eeIuBP4SLWfc7JJUp95JZskadT8pOXxVtM83yeBQ1vWrV8t9wf2Bf4H+PtkJ4qIhwFnAhs1rX4McArwzQkOfRjwujbrNwP2rJZnR8QzM/OeyeKQJEmSpFnsm5RRS6BczfaD6v6RrGy4+gbwvDbHbg28oc36jYHHVcuLIuKJmdkpx3skcC4lJ2x4KLAkIsjMzzdWRsS2lJFOtmva9xHA8cC3Opy/cezbgWNYvTHuwdXy8og4MDO/MdF5pui7wBMGcF5JUgsb2SRJo2aPlsdXTvVEEfEvwCFNq34ELAXuDWwLPJqSTHVrCas2sH0POBvYC3jWBMfdBfyKMjTmNZSx79emJHfPpCRd+1ASzVN6iEeSJEmSZpvfUKYIeCqwd0Q8GLgOeEm1/SLg27RvZLsHuJCSh10F3ACsRWm0ej6lrvNRwKuA93d4/odXx32QkpcdCjRGFnkj8PmmfT/Kqg1s/0fJKR9F6bDZVkS8EHhr06rllGkS1gEOBv6lun9iRDwsMy/udK4pegLlb/QDSv57Q5/PL0mq2MgmSRq2h0bE0ZSGph2Al7Vs/8o0zn0vViZLAAdm5lXNO1Q9E6+d7EQRsRuwa9OqkzLzgGrbYkoD3pPaHdsY9z4i7k9JxjYH7qRcFfcoYJtq132wkU2SJElS/R1LaWQLytVsV1EagwA+BKw2dD9AZv4OeHBEbEPpNLk1paHsXMoIIg+rdt2Hzo1sCeyVmecBRMQ/WTnyyE4RsV5m3hIRW7JqZ8pl1XH3VMd9hlU7dTZrvtruJuDRmXltddzXKfkjwH2A1wJHdDjPVH0N2N+RUiRp8GxkkyQN27xqaeeblOEepyQzb4yI3wA7V6t+GxFnAxdTej/+pJFYdeHRLY8/0/Q890TEEjo0skXEDsAXmXy4jm27jEWSJEmSZq3M/H5E/JYysshLWTl8/3WUK8le2O64iNgI+BywHxPPiTZRbvXTljzwopbtGwG3UPLU5uf4Ykuj1edp08gWEetQRi1p+FajgQ0gM5dW86HNrVa1jubSD/9pA5skzQwb2SRJo+ROSlL1S0qj1Jczs10PxtZk6t5t9ml4EXACsAtlnP69VzlRxC+BvTPzmkli27DlceswllfR2ddZNcnqZKLXIUmSJEl18gFK58X7VAvAJzPztoiO7WefYeKh+hsmyq2Wtzy+veXxnOq2NQe8epLHDRuxas7aLle8ipWNbBt3OE8veW+rC3vYV5I0DXMm30WSpIH6fGZGtayVmVtl5jMy86SWBrbmXnhrt5zjgZ1Onpm/y8xHAjsBLwbeBpwE3Fbt8ijgPV3EeWPL481bHm/Z7qCIeBCrNrCdRBnTf43MDOAXXTy3JEmSJNXNCazaUHUH8LFOO1dXiO3XtGopJRe8V5VbdTvVwJ0tj9sOTcnqOeAWkzxuuKHlnO1yxeZ11zfdn1Le2yoz/z75XpKkfrCRTZI0WzQnODtGxIYAEbEB8G+dDoqIR0VEZOZFmXliZr6jmkvt0027tQ4F2c7ZLY8PbHqOOcCCDsdt2vL4K5l5eTXE5IOBR3Tx3JIkSZJUK5l5O/DfTatObJ1Du8WGrDrn9mmZ+afMvDsiNgf27HOI57JqY9lLq9yv4aB2B2XmP4Dzm1btGxEr8sKImM/Kq9gAftJ0f0p5ryRpeBwuUpI0W/wceE51f33gvGp+tT2AbSY47kzghog4A7iC0qtwW1ZtFLu+zXGryMxfRMS5wK7VqpdHxGbAecBT6DyO/p8ovREbydiHI+KRwL9UMaw12XNLkiRJUk19BPhNdb+1Y2Orv1EaoRrDOB4TEVtQGsJeyuodHKclM6+MiG+x8uq5+cCyiPgRZUSUfSc4/H3Al6r7GwC/iIiTgHVYdR6324GPNj2eat4rSRoSG9kkSbPFZ4GjgM2qx3NZ2fvv28AzJjh2W5quPGtxN/DeLmNYQGm026h6vB8rE67WGO4ByMy/RcQngVc3xXJMdf/XwMWsbLiTJEmSpLGRmTcB3+hy37si4j9Zmb9tBLyhuv9X4AfAU/sc4r9T8rVGA9cTqgXgR8CTO8R6YkTsDLypWjUXeGPLbrcBL8nMPzWtm07eK0kaAoeLlCTNCpl5DfBE4DTgFuAfwFmU3oPvn+DQV1OGhjyPMrn0ncA/gT9T5gDYPTO/3WUMFwCPAU6h9KC8DTgHeFF1rmY3NN1/LfBm4JLq+a8APgE8Cbi1m+eWJEmSpHGXme8DDgd+T8mtrqHkYo+h5Fn9fr7LqnMvqZ7rduC3lBzv0EmOfTMlhz0R+AtlzrnbgIsow2Q+PDO/1nLMVPNeSdKQRGanuT0lSVKziFgTyMy8q822bwDPqh7eDGyama0TakuSJEmSJEmqCYeLlCSpe/cHlkbElyjzBlwDbAW8kDIvW8PHbGCTJEmSJEmS6s0r2SRJ6lJE7EQZlmQiJwEvbXe1myRJkiRJkqT68Eo2SZK6dyVlHPwnAvcFNgTuqtb/HPh8Zn5/eOFJkiRJkiRJmileySZJkiRJkiRJkiT1aM6wA5AkSZIkSZIkSZJmG4eLbGPTTTfNuXPnDjuMKTv33JX3d911eHFIkiRJo+Lcc8+9NjM3G3YcqqfZnkNKGj/WHUmSNLFuc0gb2dqYO3cu55xzzrDDmLKIlfdn8cvoybeuvXbF/X033XSIkUiSJGkURcSlw45B9TXbc0hJ42cc644GxTopSaqnbnNIG9lUC/tdcMGK+zl//vACkSRJkiRJ0tiwTkqSxptzskmSJEmSJEmSJEk98kq2Gnom32p6tO/Q4phJz9xkk2GHIEmSJEmSNCuMY93RoFgnJUnjzUa2GvoW+zU9yqHFMZO+tfPOww5BkiRJkiRpVhjHuqNBsU5Kksabw0VKkiRJkiRJkiRJPbKRTZIkSZIkSZIkSeqRjWySJEmSJEmSJElSj5yTrYYWsbDp/nj40tVXr7j/4i22GGIkkiRJkiRJo20c644GxTopSRpvkenkpq3mzZuX55xzzrDDmLKIlffHpXhj2bIV93P+/KHFIUmSpNEUEedm5rxhx6F6mu05pKTxM451R4NinZQk1VO3OaTDRUqSJEmSJEmSJEk9crhI1cIBm28+7BAkSZIkSZI0ZqyTkqTxZiObauFLD3nIsEOQJEmSJEnSmLFOSpLGm41skiRJkiRJkqQpi6aJ/tKJ/iSNERvZJEnSjGokX5m5SiLWzKRMkiRJkkZbc27Xbn1jW6f9JKkObGSTJElTNlEjWbvGtF6Sqk7H20NSkiRJkmZeP3IxO1pKqhsb2VQLx19xxYr7h2299RAjkaT6G6VeiCZokiRJkoap7nVSnXKuQT6X+Zyk2cRGNtXC4X/4w4r7dfxBI0kzrQ7De0z3SjpJkiRJmkwd66RGIX+yQ6Wk2cJGNkmSBMxsD8VhcrhJSZIkSVpptuVIo9AIKEkNNrLV0KEc3/TosKHFMZMO3WqrYYcgSbNGp/nNxtFsSyYlSZKkfhjHuqNBma11UnXJBc3pJA2bjWw1dDyHNz0ajx9Kx++447BDkKSRZk+/yZmcSZIkaVyMY93RoIxynVQdpgHoxbi9XkmjwUY2SZJqwjHr+8uETJIkSdJsU5cr1PrNfFnSoNjIJknSLONwjzPPBjdJkiRJo8IrtvqrU47t31NSN2xkkyRpBJk0ja5uGjZby8zykyRJktQrO1gO16jk5TYCSqOtp0a2iNgYeCLwQGAzIIC/AX8CzszM6/oeoXp2GMetuH/8BPvVybGXXbbi/lHbbTfESCRpciZK420qjXSSNFuZQ0rSaKp73VE3OVenjnGNx10/z/77A/D+Y4/l6O23n9bztu6n0dWP8p3u/4ENbtJomLSRLSLWAQ4ADgXmUZKidjIizqV8N5+Ymf/oNoiIeD7wJGAX4BHAesAJmfmSbs9RnWc5sEOHzVdn5pa9nG+2+lTThLV1/KHUztEXX7zivo1skqS6scFN0mxiDilJ/TXR1TRTr8Bf+bvyU9M4X78aEWaqUaJbPf3NX/1qYNW6KWkYbLyVhqNjI1tErAm8FngzsGFjNeVbuN038xxKAjUPeG9EvBP4WGbe2UUcx1ASo1uBy4GdengNrW4CPtRm/a3TOKckSZPqNhmUpsPeipJGlTmkJE2PI15ImgndTG1g3il1b6Ir2S4E5lb3m7/Z76IM73FdtX5jYHNgzab9NgTeD/wb8IAu4ng9JTH6E6U34tKuom/vxsxcNI3jR4o9ELpz5LbbDjuEtiwzafbqpueiya+GzcRH0ogxh5SkHvhbriZOPhmAI488kg8MORRpOiaq37DjsNTZRI1s9226/3PgZOAnwPmZeUfzjhGxFmWYjj2AFwCPaXOOjjJzRUJkZWXvpvujbFQm8ZyODzzwgUB/hy/otvJ8ouM76bbyXlJvBvU+lyRJXTGHlKRJ+JlVQ5/4BADHfvzjNrJpLDiKj7SqiRrZbgM+DXwoMy+Z6CRVwnR2tXwwIu4LHAkc0q9Ae3DviHgJsD3wd+DXlAm17x5CLEPXzSW/vZ5rIoNssBrHq8LG8TVLDV5JJvXGntCSRoA5pCRJkgBzVI2PiRrZdsjMa6dy0iqhek1ELJ5aWNOyJfDFlnWXRMTBmXnGEOIZGdNpWPODcLR4JZxmo8kayWwwk/rL7wNJQ2AOKUlt+LtM0rizwU11NqfThqkmR/0+R48+B+xFSZLWBXYGjqPMC/DdiHjEDMcjDVVEtF2kQfF/ThpNvhclzQRzSEkqzIUkaWJ+RqpOJrqSbdbJzNZejxcAr4yIW4GjgEXAc9odGxGHAYcBbL/99gOMUgNx0EEALLpkwlFpVLH3iKZ79aNXT0qSpDowh5QkTZt1UtK0OMebZrueG9kiYhNgPrAtcO92+2Tme6cXVt99kpIgPbHTDpl5PHA8wLx583zXzjYLFgCw+NJLhxvHLOUXliZjw6xUH4sXr6xPXrhw4RAjkTQuzCEljQOvyBhj1klJM8JcVqOqp0a2iDgC+E/gPpPsOmoJ0t+q23WHGoU0C9jg1t5E8+DNxHM2m8pzdttIZmIoSZL6yRxSkiRJ/WaDm0ZJ141sEbEn8ME2mxKIlsej5nHV7Z+HGsUMWciiFfeHMWv4UCxZApQP1bF5zTNgXK5eajf0YT+On25jWLfPKUlTZWIiaZDMISXVXeO31Gz8HTWWdUeDYp2UNHSz+fNYs18vV7K9quVxIxGKpscDvwQiItYE7g/cmZkXN61/KHBlZl7fsv8OwMeqh/8z6PhGwaKmr/Sx+XL//OcBWLRkyfi85hlWtwa3UboSbbqxTLeRUJIaTEwk9Zk5pCSNqLGsOxoU66SkkdLcmbTZwoULzXk1EL00su1GSYLOB04FFlaPNwEOAD4CfA74916DiIhnA8+uHm5Z3T4uIpZU96/NzKOr+9sAvwcuBeY2nWZ/4I0RsRS4BLiFkkg9gzI0yXeA9/cam6TVzcYGt1GL2SvRJI06kw9JfWAOKalWHAVAkurDz3T1Sy+NbJtXt6cDtzZWZuYNwMcjYg/gYOBXwEd7jGMX4KCWdferFijJ0NFMbCmwI/BIytAe6wI3Aj8Gvgh8Ma3NlgZilBqMvKpLkiRpZJhDSpIkaeS1NrjZ6VS96KWRrVFbfT1wx4qVEetn5s3A76p9DqfHBCkzF0HTYNAT77ucNkOKZOYZwBm9PK+k/uumkWu6c5U1n7v1viSp/0wwJE2ROaQkSZJmNYef1GR6aWS7njIMx7rAVU3rXxoRnwX2qR7fr/VAzax9ObXp0X5Di2NGvapM93DUn/405EDUjW4bzLwSTZJGj0NqSOqBOaQkjaixrDsaFOukJGGuPM56aWS7ipIgbQyc1rT+I8CHWdkz8Jb+hKapOo19hx3CzHvBCwD4wOWXDzkQSZLGh0mEpEmYQ0rSiBrLuqNBsU5KUgtz5fHSSyPbeZSx6h8GnAtcBmxLSYyCMoF1smryJEmSpDHhUBmSWphDSpIkaew158o2wNVPL41sJwK3AbdmZkbE4cDXgPtU24Mypv4b+hui1IWPfxyA9x977KSzm0uSJEmaEeaQkqT6s05K0hTZ4FYPXTeyZeYPgR82Pf5eROwEPAvYFLgIOCUz7+hwCmlwvvIVAI46+WR/0EiSNAI69dRrZhIh1Zs5pCRpLFgnJalPvOJtdurlSrbVZOZfgI/2KRZJkiSNkdakweEmpfozh5QkSZJ6Y4PbaJtWI5skSZI0CDa4SZKkUdDpinxJkobFfHm09NTIFhGPBf4fsBuwETCnzW6Zmev2ITZJkiRJ0ixmDilJkiQNjle5DV/XjWwR8VTg28AalAmqO8npBiX17KijADjsoouGHIgkSZopJhPSaDOHlCSNBeukJGms9XIl2+JqfxMgjZ5nPhOAT1155ZADkSRJklQxh5Qk1Z91UpJGhB1Rh6OXRrZdKMlRAN8DLgJux4RJkiRJA2SiIM1a5pCSJEnSkDh328zopZHtn8C9gS9n5gEDikd9cByHrbh/+BDjmFHHHgvAcccdNz6vWZIkrcIEQho55pCSNKLGsu5oUKyTkqSx1ksj24+BZwB/HlAs6pPD+NSK+2Pz5X7aaQActvXW4/OaJUkaQ81XtUkaeeaQkjSixrLuaFCsk5I0CzhCzOD00sj2VuCpwAER8YHMvG5AMUmSJEmSZj9zSEmSJGnE2ODWXx0b2SLiDW1WnwXsCfwxIr4KLAfubN0pM9/brwAlSZIkSaPPHFKSJEmafZx6YXomupLt3XSekHpD4JAJjjVBkiRJkqTxYg4pqRasbJQkSd3qZbjIhk5JE0BMsl0zYFfOaXo0b2hxzKhjjgHgxb/73ZADkSRJktTCHFKSRsxY1h0NinVSkmrKYSW7M1kjW8xIFOqrX7LrsEOYeXvtBcCJf/vbkAORJEmSxpo5pCTNAmNZdzQo1klJ0libqJFt7RmLQpIkSZI025lDSpIkSTXlcMrtdWxky8zbI+LxmfnjmQxImpJ3vhOAE044gQOHHIokSZI0jswhJUljyTopSTXSPESkujNnku1nRsQVEfGJiHhKRKwxI1FJvfrhD+GHP+TFW2wx7EgkSZKkcWYOKUkaL9ZJSdJYm2xOtmuBLYHDgcOAGyPim8DXge9n5h0Djk+SJEmSNHuYQ0qSJEljpPnqt3EcSnKyK9m2BPYEPgZcDmwELAC+AVwbESdGxP4Rse5Ao5QkSZIkzQbmkJIkSZLGxoRXsmXmPcAZ1fLaiNgNeB7wbOCBwAuBFwC3R8T3Kb0TT83MGwcatSRJkiRp5JhDSpIkSfU30dxtjW3jclXbZMNFriIzzwbOBv4jIh4GPB94DrAz8CxgP+CuiFgGfDUzj+9vuFIH73oXAPv+5jdDDkSSJElSgzmkpNlg3Ie50jRZJyVJY62nRrZmmXkBcAGwKCLuR0mWngs8Gngq8BTABEkzY/fdATjtuuuGHIgkSZKkdswhJUm1ZJ2UJI21yeZk60pm/jkz35uZjwW2A46gDA8iSZIkSdIqzCElSZIk1cGUr2TrJDOvAD5aLRqCU9l3xf39hhjHjHrzmwE49dRTx+c1S5IkSTVgDilJM28s644GxTopSRprPTeyRcTOwG7ARnS4Ei4z3zvNuDQN+3LasEOYeT/9KQD7brrpkAORJEmS1MwcUpJGz1jWHQ2KdVKSNNa6bmSLiHWBk4G9u9jdBEmSJEmSxpg5pCRJkqS662VOtncD+wAxwT4TbZMkSZIkjQ9zSEmSJEm11stwkc8FsrpvIiRJkiRJmog5pCRJkjTGFi9evOL+woULhxjJ4PTSyLZJdXsZ8ALgIuB2ViZNGhFb89emR9sMLY4Z9cEPAjD/vPOGHIgkSZKkijmkJI2osaw7GhTrpCRprPXSyLYceCDwhcw8ezDhqB+uZOthhzDzdtkFgDNuumnIgUiSJEmqLMccUtKIau5ZP47Gsu5oUKyTkqSx1sucbCdQhvh44IBikSRJkiTVhzmkJEmSpFrr5Uq29wJPB14QERcDn8jMv05yjDQzXvc6AJYuXcqeQw5FkiRJEmAOKUkaB9ZJSdJY66WR7QbKlW8BvAl4U0TcBdzdsl9m5rp9ik/qzq9+BcD8jTYaciCSJEmSKuaQkqT6s05KksZaL41s96FMUJ2UJAlgzWpp5iTWkiRJkiRzSEmSJEm11ksjG6xMjCRJkiRJmow5pCRJkqTa6qWR7T0Di0KSJEmSVDfmkJIkSZJqretGtsx806CCiIjnA08CdgEeAawHnJCZL5nCubYF3g7sDWwCXAl8A1icmTf0LWiNluOOA2DXc84ZciCSJEmSwBxSkjQmrJOSpLHW63CRg3IMJTG6Fbgc2GkqJ4mI+wNnAZsD3wQuBHYDjgD2jog9MvO6vkSs0fKgBwHwy1tvHXIgkiRJkmaAOaSkni1evBiAhQsXDjkS1Yp1UpI01ub0ekBE7BoRJ0bEXyLi1ur2xIh49DTieD3wIGB94FXTOM/HKcnRazPz2Zn5xsx8MvBBYEfgXdM4tyRJkiSpR+aQkiRJkuqqpyvZIuJg4DhgDVZOYL0O8ALg+RHxysz8TK9BZObSpufo9fDGcfcDngYsB/67ZfNC4DDgpRGrwG/2AAAgAElEQVRxVGb+fUpPMkucw64r7s8bYhwz6vDDATjnnHPG5zVLkiRJI84cUpJG01jWHQ2KdVKSNNa6bmSLiIcAn6yOyTa7rAF8PCJ+npkX9Cm+Xjy5uj09M+9p3pCZt0TETygJ1GOBH850cDNpV3457BBm3h/+AMCu66035EAkSZIkgTmkJI2ysaw7GhTrpCRprPUyXOQRwJqU5CiBn1Emg/4ZKxOmewGv6WeAPdixuv1Dh+1/rG4fNAOxSJIkSdK4M4eUJEmSVGu9DBe5Z3V7LbBnZv6usSEiHgosBTZp2m+mbVDd3tRhe2P9hu02RsRhlOFA2H777fsbmSRJkiSNH3NISZIkSbXWy5Vs21B6G36hOTkCyMzfAp+njLG/Tf/C66vGQP3thikhM4/PzHmZOW+zzTabwbAkSZIkqZbMISVJkiTVWi9Xsk2mbeIxgxq9DDfosH39lv1qK1YpiqlNAj7rfOELADzo5z8fciCSJEmSumQOKUlDMpZ1R4NinZQkjbVermS7gvKte2BE7NC8ISLmAi9t2m8YLqpuO42X/8DqttN4+5rNttsOttuOP95227AjkSRJklSYQ0qS6s86KUkaa71cyXYGcH9gC+CiiDgDuArYEngSsBalJ+KZ/Q6yS0ur26dFxJzMvKexISLWA/YAbqNMsi1JkiRJGixzSElDtXjx4hX3Fy5cOMRIJElSXfXSyPYR4CDK1W9rAU9p2ta4rvxu4KP9Ca29iFiTkqjdmZkXN9Zn5sURcTrwNODfWuJYDKwLHJeZfx9kfBqSl5ZOsBdddBE7DjkUSZIkSYA5pCRpHFgnJUljretGtsz8dUQcCXyIzmPnH52Z5/caREQ8G3h29XDL6vZxEbGkun9tZh5d3d8G+D1wKTC35VSvBs4CPhIRe1X7PQbYkzLEx1t6jU2zxOWXA/CgddYZciCSJEmSwBxSkjQmrJOSpLHWy5VsZOZHI+IC4Cjg8ZSJoG8GfgK8PzOXTnT8BHah9HBsdr9qgZIMHc0kqp6I84C3A3sDTweupPSgXJyZ108xPkmSJElSj8whJUmSJNVZT41sAFUStBSgddz6qcrMRcCiLvddzsqhRdptvww4eLoxSZIkSZKmzxxSkiRJUl3NmWhjRNwTEXdVQ3ysph/JkSRJkiSpHswhJUmSJI2Tbq5k69jjTxoZX/kKAFufddaQA5EkSZLGnjmkJGl8WCclSWOt5+EipZG06aYAXHnHHUMORJIkSZIkSWPDOilJGms2skmSJEmSJKkWFi9ePOwQJEnSGOm2kW2jiNi+25Nm5l+mGI/64K9sveL+NkOMY0Y9//kA/PWvfx2f1yxJkiSNLnNISRphY1l3NCjWSUnSWOu2ke3N1dKN7OG8GoCtuXLYIcy8664DYOt733vIgUiSJEnCHFKSRtpY1h0NinVSkjTWuk1knLhakiRJktQtc0hJkiRJtTdn2AFIkiRJkiRJkiRJs023V7KdA/x2kIGof65gq6ZHXv4vSZIkacaZQ0rSCLPuSJKk/ui2ke2kzPzAQCNR32zDFU2PxmSUlm9/G4D1/u//hhyIJEmSJMwhJWmkjWXd0aBYJyVJY83JpVUP66wDwK133z3kQCRJkiRJkjQ2rJOSpLHmnGySJEmSJEmatRYvXszixYuHHYYkSRpDXsmmenj60wG4+eabWX/IoUiSJEmSJGlMWCclSWNtska2RjegswYdiDQtt90GwHr3st1YkiRJGiJzSEnSeLFOSpLG2oSf/pnptfaSJEmSpK6YQ0qSJEkaJx3nZIuIxRGx0VRPHBEbRcSiqR4vSZIkSZo9zCElSZIkjZuOjWzAW4G/RMTHI+IJ3Z4wIh4XER8DLq3OIUmSJEmqP3NISZIkSWNlssGC1wEOBw6PiKuAnwJnA38Brq/22RjYDpgHPA7YplofQPY7YKmtpUsBiGXLhhuHJEmSNN7MISVJ48U6KUkaaxM1sh0JHENJgAC2Ap5TLZ1E0/3rgbdPKzpJkiRJ0mxhDilJkiRprHRsZMvMD0XE54A3AC8HNmdlAtToXdjoadicGF0DfAp4X2be1PeIJUmSJEkjxxxS0kxZvHjxivsLFy4cYiSSJGncTThcZJXgvKWafPrZwN7AE4H7snI+twQuAc4Evgd8PTPvHFTAmlw25asxwX61sueeAGTm+LxmSZIkacSYQ0rS7DCWdUeDYp2UJI21yeZkA6BKeL5SLUREAJtQvoevy8x7BhahJEmSJGlWMYeUJEmSNA66amRrlZkJXNvnWCRJkiRJNWQOKUmSJKmO5ky+iyRJkiRJkiRJkqRmU7qSTaPtXB7V9OiXQ4tjRq29NgC33HXXkAORJEmSJEkabWNZdzQo1klJ0lizka2G5nFu06MxmXL1O98BYP0f/3jIgUiSJEmSJI22saw7GhTrpCRprDlcpCRJkiRJkiRJktQjr2RTPfzjHwD8y3rrceuQQ5EkSZIkSf21ePHiYYcgtWedlCSNNRvZVA/PeAYAt2Q6yIEkSZIkSZJmhnVSkjTWHC5SkiRJkiRJkiRJ6pGNbJIkSZIkSZIkSVKPOg4XGRFvm+pJM/PtUz1WkiRJkjT7mENKkiRJGjcTzcm2CMgpntcESTNrk00AuOL224cciCRJkjS2FmEOKUkaN9ZJSdJYm6iRrZ3m+Tuzi/XSzDjlFAC2+elPhxyIJEmSpCbmkJKkerNOSpLG2mSNbNFmXVbrW7dlm3WSJEmSpPFhDimpbxYvXgzAwoULhxyJJElSe3M6bcjMOc0LsBZwarX5bcAOwH2q28avnR9V6zREW3HFimVsXHstXHstW6211rAjkSRJksaSOaQkzR5jWXc0KNZJSdJY62W4yNcD+wJfysx3Nq2/DHhHROwIHAC8AXhX/0JUr65gmxX3x6Zb6P77A3BF5vi8ZkmSJGm0mUNK0ogay7qjQbFOSpLGWscr2do4pLq9uMP2iynfyy+bVkSSJEmSpDowh5QkSZJUa700ss2tbveNiDWbN0TEWpQeigDb9yEuSZIkSdLsNre6NYeUJEmSVEu9NLJdU93uAvw6IhZFxKsiYhHwK+AR1fZrpxJIRGwbEZ+NiCsi4vaIWB4RH4qIjXo4x7KIyAkWx/qXJEmSpJlhDilJkiSp1nqZk+0U4HVAAjsCb23a1hhyOIGv9BpERNwfOAvYHPgmcCGw2/9n787DJauqw/2/qxEF+SkKiEiYHMEBxxYVUBkcIAJO0YgTjQoYE0WFGL9I7L6KQwwgDkFpUNoBRRwSERVRQmMEFSESxSgg0AhBkUGUedD1+2Of6q6urqpbdbvqnhrez/Ocp6rOVKtq13DW2fvsDRwM7BERO2XmDX3scqbD/Hv6jW0cfYO9mh6dVlsc82qLLQC45Lbbag5EkiRJUsUcUpJG1FSeOxoWz0lJ0lTrp5JtCbA7sD0lEWqWlCTpomq9fh1LSY7ekpkfa8yMiKMpg2W/D3hjrzvLzLnEMDH24RtNj6ZkyNXPfQ6Abc87r+ZAJEmSJFWWYA4pqQ8zM6vquxcvXlxjJJNvKs8dDYvnpCRpqvXcXWRm/gnYCfgIcAvlH7gx3QJ8FNi5Wq9nEfEw4HnACuDfWhYvBm4FXhMRG/SzX0mSJElSfcwhJUmSJE26fq5kIzNvAd4WEYcC2wEPAG4CfpWZf55jDLtVt2dk5l9anu/miDiHkkA9HTizlx1GxN8CDwXuAn4J/Gdm3jnH+DQOrroKgEc+6lFcWnMokiRJkgpzSEnSxPOclCRNtb4q2RqqZOgXA4ph2+r2kg7LL6UkSI+ixwQJOLnl8e8j4u8z8ytziE/j4LWvBeCSTDs5kCRJkkaMOaQkaWJ5TkqSplrP3UU2RMQ2EXF0RJwXEVdExAER8fiIeG01rdPnLjesbv/YYXlj/gN62NfXgb2BLYD1KS0lP1Bt+6WI2LPThhFxYEScHxHnX3fddT0FLkmSJEnqzhxSkiRJ0qTq60q2iHgx8HlgPUo/+gncj5KMLKseXwWcNcAYG41AWgfKXkNmfrhl1sXAYRFxDfAx4P3AtztsuxRYCrBw4cJZn0uSJEmS1J05pCRJkqRJ1vOVbBHxKOAkSjK0msz8MXB19XDvPmNotDLcsMPy+7esNxcnAPcAT4yI+63FfiRJkiRJPTCHlCRJkjTp+rmS7VBK68OkJENbtiw/G3gV8Iw+Y7i4un1Uh+WPrG479bc/q8y8IyJuBh4IbADcPNd9aUQ9qnx8LrjZopUkSZJGhDmkpFnNzMzUHYK0djwnJUlTrZ9Ktt0pydGFlCTojpblv65u/6rPGBrdgjwvIhZk5l8aC6oWgzsBtwM/6nO/K0XEtpTk6Gbg+rnuRyPsuOMAWHjBBTUHIkmSJKliDilJmnyek5KkqdZzd5GsSny+npl3tVl+d3W7aT8BZOZlwBnANsDftyyeobQa/Gxm3tqYGRHbRcR2zStGxMMiYo3kLCI2AU6sHp6cmff0E58kSZIkaU7MISVJkiRNtH6uZLsLWBe4T4flD69uW1sn9uJNwLnARyNid+CXwNOAXSldfLyrZf1fVrfRNO9ZwAkRcTZwGXAjsBXw15S++s8H3jGH2MbOk1nVcua/a4xjXl1SeoJ58pOfPD2vWZIkSRpt5pCSNKKm8tzRsHhOSpKmWj+VbFcA2wMvj4gPNC+outL4G0pXIJf2G0RmXhYRC4H3AHtQkprfAh8FZjLzxh52cwHweeApwBMpg13fDPwcOAU4rkPryYlzAQtX3o8u602Ugw4C4ILM6XnNkiRJ0mgzh5SkETWV546GxXNSkjTV+qlkO4OSID2MVQNNA/wD8F5gfUqCdMZcAsnMq4D9e1x3jf+szPw5sGguzy1JkiRJGjhzSEmSJEkTrZ8x2T5CadUH8BBKMgSwNSU5olr+b4MJTZIkSZI0xswhJUmSJE20nivZMvNqYF/g9mpWtNzeDuybmdcMLjxJkiRJ0jgyh5TUyczMDDMzM3WHIUmStNb66S6SzPxWRDwGeAuwM7ARZXDoHwAfy8wrBx+i+rWUA5oeHV9bHPPqCU8AYPkf/lBzIJIkSZIazCElaTRN5bmjYfGclCRNtb4q2QAy8zfAoUOIRQNyEEubHk3JgdIxxwCw6//8T82BSJIkSWpmDilJo2cqzx0Ni+ekJGmq9TMmmyRJkiRJkiRJkiS6XMkWEZ+u7p6cmWc0PZ5NZubr1z40qQ8XXgjAs5/9bM6uORRJkiRpGplDSpKmkuekJGmqdesuchGQwEXAGU2Pu4lqHRMkza+3vQ2A5ZkrR1GXJEmSNK8WYQ4pSZo2npOSpKnW95hs0PH/YrbkSZIkSZI0fcwhpSk3MzOz8v7ixYtrjESSJGmwulWy/YaS9Pyx5bEkSZIkSa3MISVJkiRNlY6VbJm5TUS8LDO/3Hg8b1FJkiRJksaKOaQkSZKkaTNbd5EnR8SLgL/PzJvmIyBpTp7xDAC+cf31NQciSZIkTTVzSEnSdPGclCRNtdkq2QJ4BfDsiHh9Zn5nHmKS+vf+9wOwz0UX1RyIJEmSNNXMISVJ08VzUpI01Rb0uN7mwLci4tiIuO8wA5IkSZIkjT1zSEmSJEkTb7ZKtnMoLRGzuj0IuDAidhx2YJq7vfjGymlqnHsunHsue228cd2RSJIkSdPMHFKSxsBUnjsaFs9JSdJUm627yGcBBwNHAOtX8x4BnB0RRwKfpCRPq8nM3wwySPXnG+yz8n7UGMe8ete7APjGYYdNz2uWJEmSRo85pCQAZmZm6g5BXUzluaNh8ZyUJE21rleyZXEM8ATgB6xqkbgO8A7gcuCKlunyYQYsSZIkSRpN5pCSJEmSpklPY7Jl5mWZ+WzgXaze9UenSZIkSZI0pcwhJUmSJE2DnirZACJiV+BAVnXtsUYXH5IkSZIkgTmkJEmSpMk325hsRMQGwJGU5GjlbOAm4FRMlEbOEhY3PZqSPtB33x2AL1x7bc2BSJIkSdPNHFKSRt9UnjsaFs9JSdJU61rJFhG7AycAW7F6Fx9nAK/PzP8beoTq2wxLVns0FQ4/HIBX/fKXNQciSZIkTS9zSEkaD1N57mhYPCclSVNttivZvsvqrQxvB96RmccOLyRJkiRJ0pgyh5Sm2MxMqaxZvHjxLGtKkiRNhl7HZAvgh8ATTI40ks48E848k3033bTuSCRJkiSZQ0qSpoXnpCRpqvVSyXY3cBjwzMy8bMjxSHNzxBFwxBF84TGPqTsSSZIkadqZQ0qSpofnpCRpqs3WXeTPgNdm5s/mIxhJkiRJ0lgzh5QkSZI0NWarZHtqZt49L5FIkiRJksadOaQkSZKkqdG1u0iTI0mSJElSr8whJUmSJE2T2a5kk8bDXnsBsPSaa2oORJIkSZKk6TAzM7Py/uLFi2uMRKqR56QkaapZyabJcMghABx0ySU1ByJJkiRJkqSp4TkpSZpqXbuLlCRJkiRJkiRJkrQmr2SbQAewdOX942uMY16ddhoABxxwwPS8ZkmSJEmSpDmYynNHw+I5KUmaalayTaClHLTy/tT8uR91FABLjzxyel6zJEmSJEnSHEzluaNh8ZyUJE01K9kkSZIkSZLUk5mZmbpDkCRJGhmOySZJkiRJkiRJkiT1yUo2SZIkSZIkSZIkqU92FzmBDuS4pkcHdVxvorzsZQAcddVVNQciSZIkSZI02qby3NGweE5KkqaalWwT6HgObHo0JQdKb3oTAIdedlnNgUiSJEmSNFka47AtXry45kg0KFN57mhYPCclSVPN7iIlSZIkSZIkSZKkPlnJpslwyilwyim8fYst6o5EkiRJkiRJ08JzUpI01ewuUpPhE58A4Khjj+XomkORJEmSJGmcNbqHBLuIlGblOSlJmmojcyVbRGwREZ+OiGsi4s6IWBERx0TEA/vcz0bVdiuq/VxT7dfmJJIkSZI0IcwhJUmSJNVtJK5ki4iHA+cCmwJfB34F7AAcDOwRETtl5g097Gfjaj+PAv4TOBnYDtgfeEFEPCMzLx/Oq5AkSZIkzQdzSGnwmq9ekyRJUm9G5Uq2YynJ0Vsy80WZ+c7M3A34MLAt8L4e9/N+SnL04czcvdrPiyiJ1qbV80iSJEmSxps5pDQAMzMzVq5JkiSthdor2SLiYcDzgBXAv7UsXgzcCrwmIjaYZT8bAK+p1m/tMPzj1f6fXz2fJs1++8F++7HkiivqjkSSJEnSEJlDSpJGiuekJGmq1V7JBuxW3Z6RmX9pXpCZNwPnAPcFnj7Lfp4BrA+cU23XvJ+/AGdUD3dd64g1ehYtgkWLmLnyyrojkSRJkjRc5pCSpNHhOSlJmmqjUMm2bXV7SYfll1a3j5qn/UiSJEmSRpc5pCRJkqSREJlZbwARS4EDgAMy84Q2y98HHAYclpkf6LKfwyj97r8vMw9vs/wAYCmwNDMParP8QODA6uG2wMVzeDnDsglwfd1BaM4sv8lieU4Oy3JyWJaTxfKcHKNWlltn5oPqDkJrzxyyJ6P2/dPas0wnm+U7uSzbyWOZTjbLd7TUXR495ZD3mo9I1lJUt2tbG9h1P5m5lJJAjZyIOD8zF9Ydh+bG8psslufksCwnh2U5WSzPyWFZqkbmkH7/Jo5lOtks38ll2U4ey3SyWb6jZVzKYxS6i/xjdbthh+X3b1lv2PuRJEmSJI0uc0hJkiRJI2EUKtkaXWp06uf+kdVtp37yB70fSZIkSdLoMoeUJEmSNBJGoZLtrOr2eRGxWjwRcT9gJ+B24Eez7OdH1Xo7Vds172cB8LyW5xsnI9kFiXpm+U0Wy3NyWJaTw7KcLJbn5LAsNSzmkLPz+zd5LNPJZvlOLst28limk83yHS1jUR6Rubbd1A8giIjvUBKYt2Tmx5rmHw28DTguM9/YNH87gMz8Vct+jqMMPH10Zh7SNP8twEeA72TmHsN8LZIkSZKk4TKHlCRJkjQKRqWS7eHAucCmwNeBXwJPA3aldM2xY2be0LR+AmRmtOxn42o/jwL+EzgPeDTwQuD31X4uG/brkSRJkiQNjzmkJEmSpFEwEpVsABGxJfAeYA9gY+C3wH8AM5l5Y8u6bROkatlGwGLgRcBDgBuAbwPvzsyrh/kaJEmSJEnzwxxSkiRJUt1GppJNkuoWEYuAE4H9M3NZvdFIkiRJksadeaYkSZNtweyrTKeIyGr6S9UVSaf1zmpad9E8htiTiHhMRJwSEb+PiDsi4uKImImI9dusu25EHBwRJ0bEhRFxV/W63lBH7IPWKKe64+gkInaMiG9FxI0RcVtE/Cwi3hoR67RZd5emz1276YN1vIa61Fm2EbFFRLwrIr4cEb+ufjMyIh7RZZsdIuIDEfHtiPhdtf5UtpKu3rvG53bbuuPpJCIeXf12fj0iftMU87162Hb7iPhsRFwVEXdWv8dnR8Rr5yP2+RAR60TEAdXrujEi7q5e588i4oSI2KfuGBsiYoOIeFVEfCEifhURt0bEzRFxfkQcEhH37rDd6yPiuIj4cfUbnRFxxHzHP58iYmF1THB5RNweEX+KiJ9HxL9GxF+t5b4XzfXYKSL+KiLeXP2Grqi+VzdExHcj4iUdtlk+y/9mRsSn1uY1jZI2r+3OiLguIv67+k7u2e74YhRU/6ufjohrqrhXRMQxEfHANuv2/VmQhqnpO2cOOSE5ZDtRY+7RizCvHJg6yzrMMwcuzD0nQph7To0wH61Vm/jMKbuY9Qd6yt1DeY9eDxzWujAiHgk8u2m9kRIRT6OMK7Au8BXgKmA34N3A7hGxe2be2bTJBsAx1f1rgd8BW85fxNMrIl4IfBW4A/gScCOwN/BhYCfgZR02PRtY3mb+DwYfpTpYCBwBJHAF8EfgAbNs80rgYOBuyvghDx5mgKMqIoLy+5pAAAcAh9YaVGfPp/x2/hm4lPJdXW+2jaoDthOA24DTgBWUz8fjgL8GPjuUaOdRdVB1GqWrrpuAbwJXAxsBD6d83rcDTq0rxhbPBD5P+Z09i9Kt2EaU39wjgZdU/493tGx3FLAh8AfgGsprm0jVd/ODwDsoxzjfBb4M3BvYkfI9fVNE7JeZX6khxDcD/0T5zT2LcryyNfAS4DkR8eHMfHvLNsto/3/Z2N9GlK7hJs1MdbsO5bfnscBrKL+950fEqzLzkrqCaxVrjrH1K2AHyn/mHhGxU/MYW8ztsyANmzmkOWRtzCsninnmAJl7mnvWxNxzDsxHR445ZS85ZWY6tZkof7xXAz+h9O1/rzbr/Eu13r9Xt4vqjrsptnWA/63i2qdp/gJKspTAO1u2uTewJ/CQ6vGSar031P16BlimWXccbeK6P2VQ9TuBhU3z16t+FBJ4Rcs2u1Tzl9Qd/yhMgypbYFG/32VgC8qB0/2rx8urfTyiyzZPBJ4E3Lsp/qvrfh9rKLfnV6/9xOpP7LrGezJqE7At8DRg/erxiir2Nf4bmrZ5OuWA8EJgszbL1637dQ3ovXl19V5cCGzYZvl9gV3rjrMpnicCr2r9rAH3Ay6oXsshbbbbA9i6ut/4rTii7tczpPfo3aw6ofPYNstfCtxefb7nVLZz+b1t2vYlwLPbzH805QRUAk/pcV/bVuv/blK+k9Xr6vi/SDnhdkq1zm+ATeuOtym271Rxvbll/tHV/E8O67Pg5DSICXNImLAcsks5Z91xtInLvHJEy3ouxz2YZw66LM09R+C1DeC9Mfecggnz0ZGYuv0HYk65xmR3kbM7HtgM2Kt5ZkSsC+xHOVj9RbsNI+IpEfGRiPif6hLmOyLi0og4qvXyxIh4Y3Xp5bs77Guz6hLon/cY97MpH4bvZ+bKFhyZ+RdKSwCAN1atAxrL7srMb2fmb3t8jrEXq7rHWNJh+YqIWNEyb+UlxRGxa3XJ783VZcvfjIhH9xnG3wAPAk7OzPMbM7O0ZDm8evh3fe5z6s2lbOciM6/OzP/KzD/1sc2FmfnTzLxrbZ9/zB1Q3R4PnARsAry4daWIWFaV5TZtlnUs54h4akSc0fT9/F5EPCMillTb7NJroJl5cWb+ODNv73Ub4EOUk1Wvzszftdnn3X3sa5TtWN0uy8w/ti7MzNsy86zW+RGxb5Tusv5Q/T/+MiIOj4j7tFk3q9/azSPic1G6A7k9Ii6IiFf2E2z1/Tup9fuXmTdTWgxCOeHUut3pmXllP881jqrv2T9TWkDvk5lrHONk5leBt1E+35+IiAUt+/jbiDiz6dhnRUR8MSIWVsuXU05wAJwYq3dBsc1sMWbm1zLz7Dbzf0lptQ9tyrCDAxtxTNB3sqvMvBZ4BeVk3Za0v9JmoyjdTf2y+q79sSrT53Xa72zlPpuIeBjwPMqJpH9rWbwYuBV4TURs0PRaBvlZkAbJHHLKmFdOD/PMsWXuORnMPSec+eh4MKdck5Vss/sipQBa+5Tfh1Jre3yXbQ+gfOAupnx5P0lp0fh24JyIuF/Tup8H/gS8Idr3Z/o6Sncix/UY927V7emtCzLzcuASyqWPD+txf1rTXsAZlHL7JPBflEvwz46ITfrYT8eyAr5PudR/x3Z//sAjIuIfIuKwiHhdlO5npJEXEQ+m/I5ekpnnsuoA58DOW/W1/2dSvj+7Ad8CPk5p6XQW5TLxoYqIRsvT84FfVCdODo3S7/rurQeBY65xmf2jet0gSl/jXwAeAXyNcvBzI/Be4PRoP97AAyknJbenfF4+S/kPOyki/nHO0a+ucVB7z4D2N472pxxv/HtmdjspewKl65JtKSdliWIZcDLweErZfpjy//hMVp1sXkbptoHqdqZpumkt4++5DKOMgfBaSsu0bsdzE6c6Yd4Y12Hf5hPmEbE1pWXtOymtvD9JSS4eTfl+HtC8rz7KfTaN46Ezqvia470ZOIfSOvnpPe7P77PqZA6pfphXSkNk7mnuibnnODEfHRPmlC2GcVneJEw0XVZP+eLeA2zRtPx0yiWD92VVP9mLWvaxNbBOm303+oH+p5b5H6/m79UyP4DLKYnaGpdDd0DoQZ0AACAASURBVIj/y9W+Xtph+WnV8j277GMJE9TVB20uc2WW7jEoNd8rWuYtqra5B9i9ZdkHqmXv6COun9Dl0lPgomr5o9vE3W76CvDAut/vMS/bRWsRy3Jm6cajQ/xT1Y0H5Y82gf/XNO8C4C+t7x3lACiBbdrsZ41ypjQgubTdbxzwxqbvyi5rEf8KunTZQWkVmZSD+bPafE9/1s9nZJQnSpc0d1Vl9znKpfZbd1m/8T37GlUXKE3LGv87B7fMb7xvpwALmuY/lJIg3QU8bACv5dvV8xw0y3qN1zBxXXYAZ1av7YAe1j2pWvfw6vGB1ePzaDleobQyfEib93DRAGO/P6Wbjb/Q9J/ZZf19qxjOqPt9H0I5rvG/2Gad+1CShgQe2jR/efUetnYp9gBK1zy3Aw9umt9zuc8Sz7/SocucannjOPnvBv1ZcHIa1IQ5JExYDtmlnLNl3i6YV07cNISyXrQWsSzHPHOu7525p7mnueeYTJiPjsxEm//ANuuYU1bTJLVmGKbjKQX6OlhZG/tc4KTMvK3TRpl5ZWb+uc2iT1NaqT2/Zf4nqtuDWuY/j/Jj/qVsczl0BxtWt53Wb8yfbeBcdXZyZp7ZMm9pddtPa6W5lNV1lAPF7Sl9OT+IMhbCTyl9E39jwloraYJUrVveQPmjah58eRnlhFBrq+9+7UhppXZWZrYOHLuU0gp72Datbl9OaanzEsp3/RGUZGB74JtVy6Wxlpk/pfSNf211+1VgRUTcEBH/HhF7t2xyMOVk0utyzS5Q3ktpnfiqNk/1Z8qJxZWtkTLzCuCjwLqUgXfnLCL+gdL3/YWU/+lp9ZDq9qoe1m2ss3l1++bq9qDW45XM/HMOsSux6nflBMoVIp/I0rXDbBqtl5d2XWtCZeadrGoN/CCAiHgCpSXoVzPz5Jb1b6J0sbEe5VijYVDlPpBj1zl+FqRhMIdUr8wrpSEx9zT3xNxz3JiPjhFzylXaXRKrFpn54yj92L8uIo6g/AkvYJZLOaP0uX8QpbuPx1AKuvkA9a9anucXEfF9YM+I2DIzGz8WjS/dJ5v2/UTgRS1PeVNmHtPjy2pcwpk9rq81nd9mXqPMVo6XEKXv7V1a1luRmct6fJ41yipLn8TN/RLfQrnc9lzKn/ROwN6suvxZcxQRi4BtWmYvz8zl8x7M5NgNeDjwncz8v6b5XwCOBBZFxD/n3PujflJ1+4PWBZn5l+p7slr3EhHxVtb8k/2PzLxwjjGs03T7hsw8rXr8p4jYj5L8LKQcVHxxjs8xMjLzlIj4d2BXYGdKGexM+Z96UUR8ltJSbH3gCcD1wFubehNodifl/Wn1myqxabWccpD2JICIeADw1jbrHVMd0K0hIl4CHENppfTStfjsTYJ+jg9Wrlv1af444Noq+Z17AHM7xjkKeBmlO4m39/Acj6Qc+F/LdP9Xtpb3M6rbDaP9WDMPqm4fDdBPuUcZ32BR6/zMbPc8vcTaSV+fBWlYzCHVB/PKKWSeOW/MPc09zT3Hi/no+DGnxEq2fhxPabGwB6V/2At6+NJ+iXLZ9uWUL8zvKD/gUH6E2/WFfizwLEoStjgiNqP0HX1hZp7XtN4TKT/sza6k/FDDqprZDWnv/i3rqX9r/GFm5j3VH3fzmAi7sGZZnU1pOQUDLKvM/FNEfAF4F+VzNO4/1KNgEVX/zi2Wz28YE6Vx0mdZ88zMvCEivkE5+H8hpYuauWh8l67tsLzd/LdSumdqtoJycmEu/lDd3knpl3+lzMyI+Dol0dmBCUh0AKrk4Ixqohob5qWUlnmvBf6d0o1RUA6qWn8XZ9OpPBsDezfK/QEd9r2MNr/bEfEiSr/fvwd2zTLmzDT7LbAdsFUP627RtE3jRMH/dVi3H7Md46wmIv6VMvD194EXVK3pZnMg5bN44rQmthGxHrBR9fC66nbj6va51dTJ/1fd9lPu29D+u7mkul3r46E5fhakYTKHVC/MK6fTIswz54O5p7mnued4MR8dI+aUq3jZf+8+R+kv9DhK68Gul3JGxEJKcvQ9YLvM3D8z/19Vs/oeoNNl2l+j/Ji/vvqTaDtYdWYuy8xombZpWuXi6rbTYKCNgYzn49L1Uda49LtThXOnL2XPMnNJm7LapWmVjmVVDcD6UMrl7b3++TZ+1DaYa8wTYiBlm5m7tCm/JQOJcApFxINY1SLoixGRzROrLhdvHoS6W1m2u8T7T9XtgzuEscb8zNymTTkv6/piumt8r2/OlsFWK41EaP21eI6RVl3SfwplsFoorUgbBzI/bfN+rza12WWn8tysuv1j9bwrOuxzReuGEfEyyvgz1wLPzsyLW9eZQo1WuM/ptlJ1jLJL9fAcViWRf9V2gz70cIzTHMeHgUMp40/smZm3zLb/qquc/WA8B5geoJ0pv6vXNn0/Gt/Rg2f5ju5frddzuWfm8lm+62t17DqXz4I0D8whp4d55fQwzxwT5p4rmXuae44T89HxYk5ZsZKtR1ku8/0KpZb8VmZv/fGI6vbUXLNGegc6/LlV655A+WDtTWmNeAtlMMd+/Gd1u0frgoh4GOXDdiW9H2BPqsbBxpatCyLiEczPeAMdy4rSavC+wLnZe2vsp1e3lm1RZ9lqTftRThBdAHyqw3Qd8JyIeGi1TceypLTIa9VoIb5z64JqTIkd5xp8H35G6ZZik4hod4D+uOp2xTzEUrebq9uoDlB+ATw2Ijbqsk07W1VdA7Tapbrtq0uIiHgl5b/8GkqSc2mf8UyqZZQxCF4cEY/tst7rKH3fXwycnZm3AhcBD46IJ3XZrqEx3tA6XdfqIIp/o7QE/i6lhVnHMY5avJjSovV7OaWtR6vfwndVD7/QtOhH1e0ze9nPHMq9m7Oq2+e1jv8TEfejdFl2e1OMjWVr81mQhsoccqqMQu5hXjk/RqGs1Rtzz8Lc09xznCzDfHQsmFO2yEynNhOlNvnqlnlbUVrB7NQy/4hq/UVN855ezftqy7qbUv7gk9J/ervn3orSwuzqar3j5hD/OsD/Vtvv0zR/AaXlRALvnGUfS6r13lB3eQywTLNl3rqUGvabgE2b5q9Pucx+jXKidOuwWnm3eZ7lfcR1f8qB3Z3Awqb56wHnVvt7Rcs2OwEL2uzr1ZSWV3cC29T9nk9a2fYYy/JqH4/oM/6r5/qc4zQBv6pe7w5d1nlvtc77qsd/Wz3+Qst621MOohNY0jR/AfDrav6eLdu8sfF5AXZZi9exotrHvbqs0/hv+Ezz97WK+3bg7n4+J6M6AftSugBo95u0GXBp9T68rJr3uurxfwAPaLPNA4Ent8xrlNmXWt7LhwI3Vu/lw/uIeT/KQfXlwNZzeM2N34oj6n7/h1Sm76le32XAY9osfxFwG+VYZbem+QdU250HbNiyzQLgIU2P/7pad2YO8QWlxV9Wv+fr9bn9mdW2L637vR5iGa7xv9i0bFPglGqdK4FNWpZ/v/p+vK7D9tuz+v9qz+XeQ9zfqfb15pb5R1fzPznIz4KT06AnzCFhwnLILuWcLfPMKydwmq+y7jGW5Zhn9vuemXuae5p7juGE+ehITLT5D2xaZk7ZMjkmWx8y8zfAb3pc/SeUy1VfUg10+gPK5cZ7UmrZr+n2PBHxTUo/+tDSzUePsf45IvantGb7SkR8pYp9d0rrm3NYdRn1ShHxTkrft1D6oAXYPyIarXJ+kJkn9BvPqMrMuyPiI8A/Az+tBk+9F+VP+xq6lNMAY/hTRBxAaeW6PCJOpvxx7wNsW83/UstmJwELqs/W1ZTE6amUFq73AAdlm8vTp8l8lm1ELGt62Pj+/EtENFpRnZCZP2hafzvgnS27eWDLfg7NzOsHFeMoiDJY+7bAz3P18UFafYrSGmb/iFhMGQPiUmDfiNgC+DHlRNILq2Uvb944ywDTbwBOB06NiK9SDs4eTyn/b1N+i9t1pdEp9k0oA2M3bNKItepqBOCDmfmrpnXeT/nNfS2wfUQsp7RWeinlO3tIZv661xhG2NOAg4HfRcQPgCuq+Q8FXkA54fB1qnEOMvPTEfEU4E3AZRHxHcr/00bVNs8CTqQkpc1+Vj3XBRFxBqUrnr+ltBZ+R2Ze1kuwEbErpa/+BZRWTvvHmoNgrzGocfWZavwXNq402Lv6TAL8KjM/2EsMY2AJpWuotwP/U5XRLygnlXaklMPtwL6Z+Z9N251AeY9eC1xajf9wHaWF4W6U931Jte4PKYnRW6uWpY1xDz6WmbONFfNuylUat1PGrnhnmzK8MDP/o3Vm1cJ81+r5Tp3lecZerBpoegHlu/JYShndm5LAvKrNf80rKcePn4qIt1B+c2+iXInzeEpr6GdQxpKA/sp9Nm+inAj+aETsDvyS8nnbldKlx7ta1p/zZ0GaL+aQwITlkO2YV04P88zxYO5p7gnmnmNsCeajI8OcEuglp6y7VnRUJ/po8UObVojV/I0og1CvAO6g/NG+n9JNwwo6tEKstn1htc+frOXreAyl1eH1lFZolwAzwPod1l/OqlYb7aZldZfNHN+Hdar472qzLCgHopcBd1H+cD/UqZwYcIvDpu12otSY/6H6Yv+cMtDiOm3W/SfK5atXVes2Pl8nAk+o+/2e1LLtEkO370y734Zdethmm7rf2yGU1UnVa3tLD+ueUa374urxlpSTAjdWn/mfAC9pei+XtNnH06rvyc3V9D3Kn/jHq22e2Efs2/RQZru02e6+lAOBX1F+g/9YxbFnr8896lNVNn9PGVz6Ysq4BHdRBh/+FqUldLuWhnsBp1EOqu6iDCJ9HuU/dbuWdZPy/7Q58PlqmzuA/wZe2We8je95t2lFm+2WzbLN8rrLYghluwOlNewV1ffuFko3DkcCW3TZ7lXA2dXn/Y5q+5NYs5XoHpTk5pZ+fvt6KIuOxyvAv1TLP1D3+zvksmt9P+6kHAteQGmlt0e772XT9vcDDqvWv6Uq/yuAb1LGLdlgruXeQ+xbUo5nflv9NlwJfATYaJCfBSenYUyYQ0789xDzyqmZ5rOsu8Rgnjn38jP3NPds3s7ccwwnzEfrfv/NKfs4lo1qZxoxVS3xYko3G5+qOZyxFxEPobQo+7/M3GK29TU+LFv1KyLOoSRBG2bp+1kjrmqxeXZm7lJ3LJIkjSpzyOEz95gelrUGwdxz/Jh7SpqLBbOvovlWDcT3RkqrmdkGx1ZvXlzd/rDWKDQMlq3WEBH3jYg1Bh2PiEWU7gXOMMmRJEmTwhxy3ph7TA/LWj0x95QkOSbbCImIFwBPBvam9L1/aGbeVm9U4y0i3gM8CngZpU/5o+qNSINi2WoWW1HGSPguZSDqewFPovTxfBNwSI2xSZIkDYQ55Pww95gelrXmwNxTkqac3UWOkGow2v0ogx9+Gjg8M3seHFVrqi7zvhk4H3hvZp5Vc0gaEMtW3UTEA4F/BZ4NbAbch9Lv+veA92WPAxVrNNhlhyRJ7ZlDzg9zj+lhWatf5p6TxdxT0lxYySZJkiRJkiRJkiT1yTHZJEmSJEmSJEmSpD5ZySZJkiRJkiRJkiT1yUo2SZIkSZIkSZIkqU9WskmSJEmSJEmSJEl9spJNkiRJkiRJkiRJ6pOVbJIkSZIkSZIkSVKfrGSTJEmSJEmSJEmS+mQlmyRJkiRJkiRJktQnK9kkSZIkSZIkSZKkPlnJJkmSJEmSJEmSJPXJSjZJkvoUER+MiKymO+qOR5IkSZLUXUS8LCLOiYg/NeVz2bT8XhHxroj4RUTc3rTO8mr58tZ5kiRZySZJ6igiVjQnHz1Oy+qOexxFxI+a3sPT645HkiRJkgYtIrZpk0PeExG3RsT/RcSPI+L4iNg9ImKAz7s78CVgR+B+HVb7Z+AI4DHAeoN6bknSZLtX3QFIkjSGvglcX92/p85AJEmSJGnMrQPct5o2B3YA3gD8KCL2zcwVA3iOVwGNSrs7gWNYldM1vKbp/m+AE4DbgauqeZ8ATqvuX4UkSVjJJknq7n3Ahi3z/rXp/uWURKPZRUONaARk5n8B/1V3HJIkSZI05s6nXGG2PvAwYC9gk2rZ04EfRsSOmXnFWj7PNk33f5KZ75xlnc9m5nubF2bml9YyBknSBLK7SElSR5l5fGYe2Ty1rHJV6/LMPD0iFkbEJ6ouEK+OiNsi4s6q+4/TIuKlrc8VEdu1dBnyioh4ftXv/c0RcUtEfC8iFraLNSKeHhFnVOv+qVp354h4Y8t+N2vapmMXjRGxR8t2T29a1nVMtoi4d/W8/xkR10fE3dXt6RHx4pZ1P1iNA/C0ptnPb30vqnUfEREfiYj/iojfVK/1roi4NiLOjIjXR4T/7ZIkSZLGxS+qPPK9mbk/sDXw+ablmwHLGg8iYpeIOCEizo+Ia6qx0+6o8qOvRcTzmnceEUuqfGvXptk7N4+t1hhrjVVXugEc3rTOkmpfHcdki4jXRcTJUcZz+32Vp90SEb+MiOMi4tGtL7wRW9N0/4g4usqbb4uIH0bEc6p1N672c231es+PiL3a7LOv90eStPa8kk2SNAy7AG9sM3/zanpBRHwkM9/aZR9/D+zcMm93YHlEPD4zL2/MjIg9gK8D925ZdxdgXsc3i4hNq+d8UsuijYHnUyrQPgPsn5nZuv0sngi8pc38TYHdqmmviHjJHPYtSZIkSbXKzNsiYn/gCcD21exnRcQOmXke5Uq317fZdMtqenFEHJaZH5ifiFd6E/CUlnnrAttV034RsUdmLu+yjzOB5kalTwdOj4h9gQ8AD29a9hTg6xHxnMw8q2n+qL4/kjSxrGSTJA3DHcCPgAuBG4BbKINLP7OaAA6OiE9l5s877GNn4BfAfwBPBRot7jYA3gy8DSAiNgA+w6oKtgS+SOnK8sXACwb2qnrzBVZVsN1exXIFJUF8GaV15H7Az4CjWTW+25uBrartLqb0/9/w0+r27ur+BcB1wJ8o3aosBP66WudFlNd8GpIkSZI0ZjLznoj4NPDhptm7A+cBt1K67v8ZcCNwG2WIg+ewqoJqJiI+k5nXAGdQ8tG/o3RHCasPe9AYW+00Vh8a4bvVtgDn9hD2ddU+fg38gZK7bUbJSbcE7gN8HHhcl308GTi+ivcfKJV06wCnAH8GllJy7TdRzukuAN4BNFey9fv+SJLWkpVskqSBy8yPAx+PiO0plUsbU5KMb1C6RWxUiO0BdKpkuwx4WmbeGhFBGevtMdWyHZrWeynlSq6GmcycAYiIDwD/S+lyZOiidGW5e9OsV2TmqU3Lf8eqK9H+MSI+3BjfLSL+hlWVbCvadM1JZn6d0lrxkZSKvE0p7+tZlISp8T7siZVskiRJksbXxS2PtwDIzMVV941PpuSHDwTuoTTObFQirUvJyz6XmecC51ZdKzYq2a5ql29FRHMl27nt1ukkM/eMiPUoV589nNLI9Crge8D+1WqPjYgtM/OqDrt5d2a+r4plM2DfpmWHZ+YHm5a9vJrfnBv3/f70+vokSZ1ZySZJGriqsmkZ8NhZVt2iy7LPZOatAJmZEXEpqyrZHti03lNbtvtU407V1cjJwD/1EvcAPLPl8ddL/WBbm1GSr1/3uvOIeDglEXrGLKt2e18lSZIkadS1JlIJEBG7U672eugs289rThQRBwPvAe4/y6pbsOrquVYnNd1f0bLsC033L22635wbj+z7I0mTzEo2SdJARcT9KF0gbjrbupQuMzpZ0fL4zqb7C5ruP6Blvd+2PP5dD3HAmklct9g62ajP9R9Ej5Vs1dV8p7KqorGbucQuSZIkSaNi25bHV0fE5pSxuDfoYft5y4kiYh/gmB5X7xbX/zXdv6tlWXPXjvc0P31THCP5/kjSpLOSTZI0aLuyegXbvwBHZeZ1ABHxR2Zv3QelG8Rm2WG9m1oeb8rqFW2bdXmOvzTdX79l2SO7bNfJjS2P38ea8TX7TR/73p7VK9g+CxwGXFNd6fczVg0MLkmSJEljKSLuxaouFhvOBPZi9QqkfwQ+lZl/iIj7UsYjq8Mrmu7fCvwNcHZm3h4Rf01phDqrzGzNgZuX3dNpWZNRfX8kaaJZySZJGrRNWh5/tqmC7a/prYKtH+e1PH4VcGT1fPdl9YSnVXMF2GMi4n6ZeXNEbAS8cQ6x/KDl8a0d+vrfHHhqZja3VGxOqO7bZt+t7+uXGttHxOOBR88hXkmSJEkaGRGxPnAcqzcgPDszfxIRz21Z/dOZ+Yfqfre8b9iac7XLM/P0psfzGVdrzjgq748kTTQr2SRJg9Y6QPWXIuLLlD7fXzuE5/sacBSl60WAf4mIx1H6uX8xsHWXbX8M7Fnd3xj4aUT8N7Az8JB+A6kSv7MoV/MBvC8idgV+ROnucnPKGHILgTMoXXk0XN10/xkR8WFWdRfyEeASytV8je5Ajo2IzwAbUlp5+p8uSZIkadw8NiIOBdajjFm9F6tXFl0LLKrut+aa34qIb1J6IXnlkOPs5mKgUQG4fUR8CbgI2AXYbZ7jaDYq748kTTRPyEmSBiozz4mIM4Hdq1mPqyaA04CnsapCbBDPd2tE7EepsFqXMl7bftXiPwOnA3s0bdLcReRS4GBWDRb98GqC0qXHC+YQ0iuq53wSpULsuaxKuLr5MqtaF94LeGvTsk9m5tURcSLwumre1sC7q/sXUF774+cQryRJkiTVZWE1tXMO8OrMXFE9PhW4EHhi9fhp1QRwImt2MTlfjqE0KG302vLyaoL5jWtU3x9JmmgL6g5AkjSRXkhJNH5L6QbxcuC9wEtZvZJrIDLz28CzgO8Bt1TTckpFX2t3kjc1bffbartvV9vcBvwXpVLu43OM5feUROYA4LvA7ynvwY2U1oynUCoBX92y3deAN1Xr3Nlh9wcBi4Erq31eDXyM0jry9rnEK0mSJEk1+wsln/kt8BPgeGC3zNy5qYKtMWbZ7sAJwHXAXZSrt/4ReMM8x7xSZl4GPJOSV95KySt/BOxDGUt7vuIYyfdHkiZdZGbdMUiStFYiYr3MvKPN/HsD57OqP/8LM/NJ8xqcJEmSJEmSpIlkd5GSpEmwR0T8K3Ay8Cvgj5TuFF/P6gNmH11DbJIkSZIkSZImkJVskqRJ8Qjg8A7LEvhgZn5uHuORJEmSJEmSNMGsZJMkTYL/AY4Fdga2ADYE7qCMXXYOcHxm/qS+8CRJkiRJkiRNGsdkkyRJkiRJkiRJkvq0oO4AJEmSJEmSJEmSpHFjd5FtbLLJJrnNNtvUHYbm6IILVt1/ylPqi0OSJEmj44ILLrg+Mx9UdxyaTOOYQ5o3SZIkSZ31mkNaydbGNttsw/nnn193GJqjiFX3LUZJkiQBRMSVdcegyTWOOaR5kyRJktRZrzmklWzSgH3j+utX3t97k01qjESSJEmSJpe5lyRJkupmJZs0YPtcdNHK+7nLLvUFIkmSJEkTzNxLkiRJdVtQdwCSJEmSJEmSJEnSuPFKNk2cvfhG06O95//5N9543p9TkiRJkvpRd940COZekiRJqpuVbJo432Cfpkc5/8+//fbz/pySJEmS1I+686ZBMPeSJElS3ewuUpIkSZIkSZIkSeqTlWySJEmSJEmSJElSn6xkkyRJkiRJkiRJkvrkmGyaOEtY3HR//n3h2mtX3n/lgx9cQwSSJEmS1F3dedMgmHtJkiSpbpE5ngMcD9PChQvz/PPPrzsMzVHEqvt1fLxj+fJVz7/LLvMfgCRJktYQERdk5sK649BkGsccsu68aRDMvSRJkjQsveaQdhcpSZIkSZIkSZIk9cnuIqUB23fTTesOQZIkSZImnrmXJEmS6mYlmzRgX3jMY+oOQZIkSZImnrmXJEmS6mYlmyRJkiRJkiRpXkXzAKFNclwHC5U0laxkkyRJY6tbUtZY1ul+47EkSZIkaTha86/m3GyQ20hSXaxkkyRJtVnbSrJhPr8kSZIkqX+DzNl63bc5nKS6WMkmDdjSa65Zef/AzTevMRJJGk3j0gqxUyWfJEkaDeZeklSvYTWGXJtYwBxO0vyykk0asIMuuWTlfRM9SdNsUiup7G5SkqTRYO4lSfNrXHMhr36TNExWskmSpDmb9mRl2l+/JEmSpMlW9xVqwzSulYaSRouVbJo4B7C06dGB8//8D3nIvD+nJM2nSboqbZgm9Uo+SdJkqDtvGgRzL0kanFHq+rEund4D8zhJ3VjJpomzlIOaHs1/srh0223n/TkladDsz354vPpNkjQK6s6bBsHcS5L6Yy4yN1a4SerGSjZJkqaMLRRHU7eKzV7KzGRPkiRJEpjzDZMNUiW1spJNkqQJ0a3yxQP/yTeXSjo/D5IkSdL46KXCzGP8+tggUppOfVWyRcRGwLOARwIPAgL4PfBr4PuZecPAI5QkacL026pwLlc1Sb3otWJOkubKHFKSNM16Od7uxuPyydDr56C5jOeyTafn7efzJql/s1ayRcR9gX2BA4CFlKSonYyIC4ClwBcz87Zeg4iIvwGeDTwReAJwP+CkzHx1r/uo9rMC2LrD4mszc7N+9jfJer3aYW27p5qPE8mt6x3IcSuXHT+H7dc65pe/HIAjjzqKQ7faqv/t5/nk+zhu38s281rmbj/r9paZiZHGm1fCSeqHOeR4aM6bltYYx9o46qqrVt4/ZMst5+U55+M4dpDHq27jNmuzzagd643L+ybN1Vw+R3M5X9vL/kb990AaJR0r2SJiXeAtwGHAAxqzgWTNJCmBBZQEaiHwoYg4Avh4Zt7dQxyHUxKjW4Crge36eA2t/ggc02b+LWuxz7E1H3/43VpW1OH41QbtPqjjekPzpjcBcOhll83/c0uSNI+6HQPUfTwgaf6ZQ46X5rxpkJVsvf43DKRS6qyzVt6dr0o2aZqMWkWwpPpY4SZ11u1Ktl8B21T3m//57qF073FDNX8jYFNg3ab1HgAcCfw98Ige4ngbJTH6NaU14lndV+/qpsxcshbbjz0PQCRJ0igxIZOmhjmkajOshh7+h0mStLpRu+BCqlu3SraHNt3/A353ngAAIABJREFUMXAKcA5wYWbe1bxiRNyb0k3HTsDLgae12UdHmbkyIfLy6v550D9iTjkFgLe//e0cXXMokiSNGo9bpIlmDqn51UPu1ev/jt2+SZK09sz3NI26VbLdDpwAHJOZV3TbSZUwnVdNH46IhwJvB143qED7cJ+IeDWwFXAr8DPKgNp/riEWTaNPfAKAo4491ko2SZK6MAGTJo455IQbuW6B+8y9bHkvSdL8Md/TtOhWybZ1Zl4/l51WCdWbI2JmbmGtlc2Az7XMuyIi9s/Ms2uIZyhsWSdJkibNyJ28ldQvc8gJ0+9VYJIkSe30MmZr47E0bhZ0WjDX5GjQ++jTicDulCRpA2B74DjKuADfjognzHM8AxURKydJkqRp4TGQNB7MISeDv7mSJKkuHodoHHW7km3sZGZrq8eLgDdGxC3AIcAS4MXtto2IA4EDAbbaaqshRilJkqS5spWjpEGaxByyl7HFWn8/vXpYkiSNGnM/jYu+K9kiYmNgF2AL4D7t1snMD61dWAP3SUqC9KxOK2TmUmApwMKFC0fmW2uyM4b22w+AJVd0HYZCkiStpZmZVefGFy9eXGMkkroxh9TQmHtJkjQ1PE+uUdVXJVtEHAy8H1hvllVHLUH6fXW7Qa1RaDosWgTAzJVX1huHJElTplHpZoWbNDrMITVU5l6SJE0lG1xqlPRcyRYRuwIfbrMogWh5PGqeUd1eXmsUkiRJmhfNFW4mYFI9zCElSZI0bOZ7qls/V7L9XcvjRiIUTY+HPiJhRKwLPBy4OzMva5r/WOC3mXljy/pbAx+vHn5+2PGpfotZsvJ+6wAL82LZshLH4sX1PL8kSerIBEyaV+aQI6z2vGkQzL0kSVIT8z3VoZ9Kth0oSdCFwKnA4urxxsC+wEeBE4F/6DeIiHgR8KLq4WbV7TMiYll1//rMPLS6/1fAL4ErgW2advMy4J0RcRZwBXAzJZF6AaVrkm8BR/Ybm8bPkqb0qpZE6zOfKXEsW2aiJ0nSCDMBk4bOHHKE1Z43DYK5lyRJ6sIhBTQf+qlk27S6PQO4pTEzM/8AHBsROwH7A/8DfKzPOJ4I7Ncy72HVBCUZOpTuzgK2BZ5E6dpjA+Am4AfA54DPpaMiSpIkqQMTMGngzCElSZI0EmxkqWHpp5Kt0Y3HjcBdK2dG3D8z/wT8b7XOQfSZIGXmEmjqq6L7uito06VIZp4NnN3P80qSJEntWOEmDYQ5pCRJkkaOFW4apH4q2W6kdMOxAfC7pvmviYhPA3tWjx/WuqEkSZI0rkzApDkzh5QkSdJIM9/T2uqnku13lARpI+C0pvkfBT7CqpaBNw8mNGlu9ubUpkf7zH8Af1fGdz/k17+e/+eWJElDZQIm9cUccoTVnjcNgrmXJEkaMHs1Ub/6qWT7KaWv+scBFwBXAVtQEqOgDGCdrJ48SfPuNPauN4CXvxyAo6++ut44JEmSpHqZQ46w2vOmQTD3kiRJQ2QjS/Win0q2LwK3A7dkZkbEQcDXgPWq5UHpU/8dgw1RkiRJGj0mXNKszCElSZI0Ecz/1EnPlWyZeSZwZtPj0yNiO+CFwCbAxcBXMvOuDruQpsOxxwJw5FFHcWjNoUiSpPljtyLS6swhNXTmXpIkSapZP1eyrSEzfwN8bECxSJPhy18G4JBTTjHRkyRJkpqYQ2qgzL0kSVINvKpNzRbUHYAkSZI0aWZmZlZLvCRJkiRJk8n8b7r1dSVbRDwd+EdgB+CBtK+ky8zcYACxSZIkSWPPVo6aZuaQkiRJmibmf9On50q2iHgu8E1gHcoA1Z3k2gYlSZIkTSITLk0Tc0hJkiRNM/O/6dDPlWwz1fomQFI3hxwCwIEXX1xzIJIkSVKtzCE1XOZekiRpTFjhNrn6qWR7IiU5CuB04GLgTkyYpNXttRcAx//2tzUHIkmSRplJlqaAOaSGy9xLkiSNqUY+aC44/vqpZLsDuA/wpczcd0jxSJIkSZImgzmkJEmSNAsbYI63firZfgC8ALh8SLFIA3EcB668f1AdARx1VInjuOPqeX5JkiRpNJhDjrDa86ZBMPeSJElSzfqpZPtn4LnAvhFxdGbeMKSYpLVyIMevvF9LonXaaSWOzTc30ZMkST2zuxBNIHPIEVZ73jQI5l6SJGnCeFXb+OlYyRYR72gz+1xgV+DSiPgqsAK4u3WlzPzQoAKUJEmSJI0+c0hJkiRpsGyMOfq6Xcn2QToPSP0A4HVdtjVBkiRJkubIREpjyhxSkiRJ0lTpp7vIhk5JE0DMslySJElSH+wuRBPAHFKSJElaSzbGHE2zVbLFvEQhDdBTOL/p0cL5D+DwwwF45f/+7/w/tyRJklQvc8gxUXveNAjmXpIkaUrZGHN0dKtkW3/eopAG6L95Sr0B7L47AF/8/e/rjUOSJE0cEymNOHPIMVJ73jQI5l6SJEnmiTXrWMmWmXdGxM6Z+YP5DEiSJEmSNH7MISVJkiRNm9m6i/x+RPwO+DrwVeCszPzz8MOSxtgRRwBw0kkn8aqaQ5EkSZPL1ooaUeaQmj/mXpIkSWtw7Lb5tWCW5dcDmwEHAd8Bfh8Rn46IvSPi3kOPThpHZ54JZ57JKx/84LojkSRJkuabOaTmj7mXJElSVzMzM6s10NTgzXYl22bAM4GXAi8EtgQWAfsBt0bEN4GvAd/KzFuHGKckSZIkafSZQ0qSJEkjyN5QhqNrJVtm/gU4u5reEhE7UJKlFwGPBP4WeDlwZ0R8B/h34NTMvGmoUUuSJEmSRo45pCRJkjT6rHAbnNm6i1xNZp6Xmf+UmdsCjwfeA1wErEdppXgicG1EfCciDhx4tJIkSZKksWEOKUmSJGmSzdZdZEeZeRElOVoSEQ8D/gZ4CfBU4LnAc4ClgwhSGivvex8Ae//85zUHIkmSpomDW2vUmUNq4My9JEmSBsJ8cu7mXMnWLDMvBz4EfCgiNqd0B/LiQexbGjs77gjAaTfcUHMgkiRJ0mgyh9RAmHtJkiQNnBVu/emru8heZOY1mfmxzNxt0PuWJEmSJE0Wc0hJkiRJ46rvK9kiYntgB+CBdKiky8wPrWVc0pydyt4r7+9TRwCHHVbiOPXUep5fkiRJGiHmkKOp9rxpEMy9JEmShqpxVRt4ZVsnPVeyRcQGwCnAHj2sboKk2uzNafUG8MMfljg22aTeOCRJ0tSyew+NAnPI0VZ73jQI5l6SJEnzxgq39vrpLvKDwJ5AdFmn2zJJkiRJ0vQwh5QkSZI00frpLvIlQFb3TYQkSZIkSd2YQ0qSJEmaaP1Usm1c3V4FvJz/n737DpesqhI2/q5GUGDIQZHUigIGRpQ2IDo0MMOgAqKOAROgAmYMfI6ZBjGNOGIWRGVQgohjABOK3aigaKOoOCTRJoNkmpzW98c+1X26uureqtt176lb9/09z3lO1YmraldatffZGy4C7mFp0iRJkiRpyNilhxpkDilJkiRppPVTybYIeCxwXGb+dnLCkVbcI7mqdm/jqQ/gU58CYO4f/jD155YkSZKGxyLMIYdW43nTIJh7SZIkqWH9VLIdDxxKSZKkoXUNj2w2gG23BeDMW29tNg5JkqQ2XtWmKWYOOcQaz5sGwdxLkiRJDZvVx7b/BZwDvCQiDo+IadrUTZIkSZI0BcwhJUmSJI20fq5ku5lSKRfAe4D3RMT9wANt22Vmrj6g+KTp521vA2D+/Pns1HAokiRJY2ld2eZVbZok5pCaXOZekiRJalg/lWwPowxQnZQkCWDlaqpzEGvNbH/8IwBz11mn4UAkSZKkRplDanKZe0mSJKlh/VSywdLESJIkSZKk8ZhDSpIkSRpZ/VSyfXzSopAkSZIkjRpzSEmSJEkjredKtsx8z2QFERH/AewIbAs8CVgDOD4zXzmBY20CHAbsBqwHXAN8Fzg0M28eWNCSJEmSpK7MISVJkiSNun67i5ws76ckRrcDVwJbT+QgEbEFcDawIfA94ELgacBBwG4RsUNm3jiQiKVujjoKgO0WLmw4EEmSJGlkmUPK3EuSJEmNm9XvDhGxXUScGBGXR8Tt1fzEiHjqCsTxdmBLYE3gDStwnC9QkqO3ZuZemfnuzNwZ+BSwFfDhFTi21Jstt4Qtt+T3t9/edCSSJElS48whNWnMvSRJktSwvirZImI/4NfAS4BNgNWq+UuAsyPitRMJIjPnZ+YlmZkT2b+K7dHArsAi4PNtqw8B7gBeFRGrT/QckiRJkqTemUNKkiRJGmU9dxcZEY8HvlTt0ymRWQn4QkSck5nnDyi+fuxczU/PzAfrKzJzcUScRUmgngGcMdXBaeosZLslt+c0EcCBB5Y4Fi5s5vySJEnSEDCHHG6N502DYO4lSZKkhvUzJttBwMqU5CiBc4BrgUdQ+qyP6nhvAQ4cbJg92aqaX9xl/SWUBGlLRjBB0lLb8ftmA7i4vAS3W2ONZuOQJEmSmmUOOcQaz5sGwdxLkiRJDeunkm2nan4DsFNm/l9rRUQ8AZgPrFfbbqqtVc1v7bK+tXztTisj4gDgAIDNNttssJFJkiRJ0sxjDilJkiRppPUzJtvGlNaHx9WTI4DM/AvwP5SWiBsPLryBimresc/+zDw6M+dk5pwNNthgCsOSJEmSpJFkDilJkiRppPVTyTaeCQ84PSCtVoZrdVm/Ztt2kiRJkqTmmENKkiRJmtb66S7yamAL4BUR8bnMvKy1IiJmA6+qbdeEi6r5ll3WP7aad+tvXyMilsnVo+t2k+a44wDY8pxzpv7ckiRJ0vAwhxxijedNg2DuJUmSpIb1U8l2JiVBejhwUUScydJBq3cEVqG0RPzFoIPs0fxqvmtEzMrMB1srImINYAfgLuA3TQSnGWTTTQG45K67Gg5EkiRJapQ5pCaXuZckSZIa1k93kZ8BHqhurwL8K/DKar5KtfwB4LMDi66DiFg5IraOiC3qyzPzUuB0YDbwprbdDgVWp4wFcMdkxidJkiRJAswhJUmSJI24nq9ky8w/RcQ7gCPp3nf+wZl5Xr9BRMRewF7V3UdU8+0j4tjq9g2ZeXB1e2PgAuAySjJU90bgbOAzEbFLtd3TgZ0oXXy8r9/YpL69qvR6c9FFF7FVw6FIkiRJTTGH1KQz95IkSVLD+ukuksz8bEScD7wTeBZlIOjbgLOAIzJz/lj7j2FbYJ+2ZY+uJijJ0MGMIzMvjYg5wGHAbsBzgWsoLSgPzcybJhif1LsrrwRgy9VWazgQSZIkqVnmkJpU5l6SJElqWF+VbABVEjQfoL3f+onKzHnAvB63XcQYozJn5hXAfisakyRJkiRpxZlDSpIkSRpVY47JFhEPRsT9VRcfyxlEciRJkiRJGg3mkJIkSZJmkjEr2SpdW/xJkiRJktTGHFKSJEnSjNB3d5GSxvGtbwHwyLPPbjgQSZIkSRph5l6SJElqmJVs0qCtvz4A19x7b8OBSJIkSdIIM/eSJElSw3qtZFsnIjbr9aCZefkE45EkSZIkTX/mkJIkSZJGXq+VbO+tpl5kH8eVBu4qHrnk9sZNBPAf/1HiuOqqZs4vSZIkNc8ccsg1njcNgrmXJEmSGtZrIuPA1Zo2Hsk1zQZw440ljoc+tNk4JEmSpOaYQw65xvOmQTD3kiRJUsNmNR2AJEmSJEmSJEmSNN30eiXbQuAvkxmIJEmSJGlkmENKkiRJGnm9VrKdlJn/PamRSANyNRvV7o1AFyiSJEnS9GMOOeTMmyRJkqQV5+DSGjkbc3XtXgNDQfzgBwCs8ctfTv25JUmSJKkHjedNg2DuJUmSpIZZySYN2mqrAXD7Aw80HIgkSZIkjTBzL0mSJDVsVtMBSJIkSZIkSZIkSdPNeFeyHVrNz57sQKSR8dznAnDbbbexZsOhSJIkSVPMHFJTx9xLkiRJDRuzki0zDx1rvaQO7roLgDUeYm+skiRJmlnMITWlzL0kSZLUsK7dRUbEoRGxzkQPHBHrRMS8ie4vSZIkSZo+zCElSZIkzTRjjcn2AeDyiPhCRDy71wNGxPYR8TngsuoYkiRJkqTRZw4pSZIkaUYZr0+F1YADgQMj4lrg18BvgcuBm6pt1gU2BeYA2wMbV8sDyEEHLEmSJEkaWuaQkiRJkmaMsSrZ3gG8n5IAAWwEvKCauona7ZuAw1YoOmk6mj8fgFiwoNk4JEmSpKllDqmpZe4lSZKkhnXtLjIzjwS2AD4K/KNaHCybBEWH5dcDHwG2yMzPDDRaSZIkSdJQMoeUJEmSNNOM2V1kZt4KvK8afHovYDfgX4BHsbSCLoG/A78Afgx8JzPvm6yAJUmSJEnDyRxSkiRJ0kwy3phsAFQJz7eqiYgIYD1Ky8MbM/PBSYtQ6lPWGsrGGNtNmp12KnFkNnN+SZIkqWHmkMOv8bxpEMy9JEmS1LCeKtnaZWYCNww4FkmSJEnSCDKHlCRJkjSKuo7JJkmSJEmSJEmSJKkzK9kkSZIkSZIkSZKkPk2ou0hpmJ3LU2r3fj/1Aay6KgCL779/6s8tSZIkST1oPG8aBHMvSZIkNcxKNo2cOZxbu9fA8Nc//CEAa/7qV1N/bkmSJEnqQeN50yCYe0mSJKlhdhcpSZIkSZIkSZIk9clKNmnQ7rwT7ryTf1pppaYjkSRJkqTRZe4lSZKkhtldpDRoz3seAIszp2unK5IkSZI0/My9JEmS1DCvZJMkSZIkSZIkSZL61PVKtoj44EQPmpmHTXRfSZIkSdL0Yw4pSZIkaaYZq7vIeUBO8LgmSJIkSZI0s8zDHFKSJEnSDNLvmGz1bs6zh+XSzLPeegBcfc89DQciSZIkNc4cUpPH3EuSJEkNG6+SrdPYwVktb1+XHZZJM88ppwCw8a9/3XAgkiRJ0pQzh9TUMfeSJElSw2Z1W5GZs+oTsArw/Wr1B4HNgYdV80Oq5T+vlkmSJEmSZhBzSEmSJEkzTddKtg7eDuwBnJCZh2fmFZl5bzX/EHAisBPwrskIVOrVRly9ZGrEDTfADTew0SqrNHN+SZIkaTiYQw6xxvOmQTD3kiRJUsP6GZPtNdX80i7rL6V09fFq4MMrEpS0Iq5m4yW3G+l75sUvLnFk2veNJEmSZjJzyCHWeN40COZekiRJalg/V7LNruZ7RMTK9RURsQqlhSLAZgOIS5IkSZI0vc2u5uaQkiRJkkZSP5Vs11fzbYE/RcS8iHhDRMwD/gg8qVp/w0QCiYhNIuKrEXF1RNwTEYsi4siIWKePYyyIiBxjsq9/SZIkSZoa5pCSJEmSRlo/3UWeArwNSGAr4AO1da2eGRL4Vr9BRMQWwNnAhsD3gAuBpwEHAbtFxA6ZeWMfhzy0y/L7+41NkiRJkjQh5pCSJEmSRlo/lWzzgF2AbSiJUF1SkqTzq+369QVKcvTWzPxsa2FE/DdlsOwPA6/v9WCZOZEYNCJOZffavdOmPoBNNgHg4jvvnPpzS5IkScNjHuaQQ6vxvGkQzL0kSZLUsJ4r2TLztojYAfgQZQDrNWqrFwNfAz6QmYv7CSAiHg3sCiwCPt+2+hDgAOBVEfHOzLyjn2NrZtqTU2v3Ghj++utfB2Cr3/526s8tSZIkDQlzyOHWeN40COZekiRJalg/V7KRmbcDb4+Ig4GtgbWBW4ALM/OBCcawczU/PTMfbDvf4og4i5JAPQM4o5cDRsRLgUcB9wIXAD/PzHsmGJ8kSZIkaQLMISVJkiSNsr4q2VqqZOgvA4phq2p+cZf1l1ASpC3pMUECTmq7/4+IeFNmnjKB+KT+XHEFAI/dcksuaTgUSZIkaRiYQ2pSmHtJkiSpYX1XskXEbOCtwLOADYCPAOcA21abHN9ni8S1qvmtXda3lq/dw7G+BxwB/AG4Edgc2Ad4J/DNiNg9M3/UaceIOIDSrQibbbZZb5FLnbz61QBcnDldO12RJEmSBsYcUpPG3EuSJEkN66uSLSJeAHwDeBil0/ak9Ku/KnBsdf8KYP4AY2z9Vm4fKHs5mfmptkUXAe+NiKuBz1KSuY4JUmYeDRwNMGfOnHHPJUmSJEkamzmkJEmSpFE2q9cNI2JL4HhKMrSMzDwHuLK6u0efMbRaGa7VZf2abdtNxDHA/cC2EbHGeBtLkiRJklaMOaQkSZKkUddzJRtwMKX1YbI0Gao7k9JicPs+Y7iomm/ZZf1jq3m3/vbHlZl3A4uru6tP9DiSJEmSpJ6ZQ0qSJEkaaf10F7kLJTk6j5IE3d22/q/VfOM+Y2h1C7JrRMzKzAdbK6oWgzsAdwG/6fO4S0TEVsA6lCTphokeR+rJliXXP3fx4nE2lCRJkkaaOaQml7mXJEmSGtZPJVsr8fleZt4bsdywwvdV8w37CSAzL42I04FdgTdR+r1vOZTSavCozLyjtTAitq72vbC27NHAPZl5Vf34EbE+8LXq7kmZeX8/8Ul9O+ooAOace27DgUiSJEmNMofU5DL3kiRJUsP6qWS7F1gZeGiX9VtU8/bWib14I3A28JmI2AW4AHg6sBOli4/3tW1/QTWvZ2n/AhwTEWcClwI3AZsBz6X01b8QeNcEYpMkSZIk9c8cUpIkSdJI66eS7e/ANsBLIuKj9RVVVxr/QekK5JJ+g6haIs4BDgN2oyQ11wCfAQ7NzJt6OMy5wDeA7YBtKYNdLwb+DJxMacl4b7+xafp5CktbMf6+iQAuLkM/POUpT2nm/JIkSdJwMIccYo3nTYNg7iVJkqSG9VPJdjolQXo0SweaBngz8CFgVUqCdPpEAsnMK4D9etx2uX5GMvPPwL4TObdGy7nMWXJ7uRfKVDjwwBJHZjPnlyRJkoaDOeQQazxvGgRzL0mSJDVsVh/bfprSqg9gI0oyBLA5JTmiWv/5wYQmSZIkSZrGzCElSZIkjbSeK9ky80pgb+CualG0ze8C9s7MqwcXniRJkiRpOjKHlCRJkjTq+ukuksz8YUQ8Hngr8CxgXcrg0L8CPpuZlw0+REmSJEnSdGQOKUmSJGmU9VXJBpCZlwMHT0Is0kAczf61e1+e+gCe9CQAFtx889SfW5IkSRoy5pDDqfG8aRDMvSRJktSwvivZpGF3IEfX7jWQLB55JAA7/fGPU39uSZIkSepB43nTIJh7SZIkqWFdK9ki4qvVzZMy8/Ta/fFkZr52xUOTJEmSJE0X5pCSJEmSZpqxrmTbF0jgfOD02v2xRLWNCZJmrvPOA2DHHXfkzIZDkSRJkqbQvphDaiqZe0mSJKlhE+kuMrosHy95kmaGt78dgAWZXd8skiRJ0gxiDqnJYe4lSZKkho1VyXY5Jem5te2+JEmSJEntzCElSZIkzShdK9kyc3ZEvDgzv9W6P2VRSZIkSZKmFXNISZIkSTPNrHHWnxQRx0fE2lMSjSRJkiRpOjOHlCRJkjRjjFfJFsDLgPMj4t+nIB5p+tt+e9h+e0694YamI5EkSZKmmjmkpo65lyRJkho21phsdY8EfhgRRwEHZ+adkxiTNL195CMA7Hn++Q0HIkmSJDXGHFKTz9xLkiRJDRvvSrazKC0Rs5ofCJwXEc+c7MAkSZIkSdOOOaQkSZKkGWO8K9n+BTgIOBxYtVr2GODMiDgC+BIleVpGZl4+yCClfuzOqUtun9ZEAGefXeLYY49mzi9JkiQ1xxxymmg8bxoEcy9JkiQ1bMxKtsxM4MiIOBX4KvBsSkK0EvCualput/GOK02mU9lzye1oIoD3va/E8d73NnN+SZIkqSHmkNNH43nTIJh7SZIkqWHjdRcJQGZempk7Au9j2a4/uk2SJEmSpBnKHFKSJEnSTNBTJRtAROwEHMDSrj2W6+JDkiRJkiQwh5QkSZI0+sbtkiMiVgeOoCRHSxYDtwDfx0RJkiRJklQxh5QkSZI0U4xZyRYRuwDHAJuxbBcfpwOvzcyrJj1CqU/zOKR279CpD2CXXQA44brrpv7ckiRJUoPMIaePxvOmQTD3kiRJUsPGu5LtpyzbyvAu4F2Z+YXJC0laMYcyb5l7U+797wfgFRdcMPXnliRJkpplDjlNNJ43DYK5lyRJkho2bneRlQB+Dbw6My+dxHgkSZIkSdOfOaQkSZKkkTerh23uA94LPNvkSOrBGWfAGWew94YbNh2JJEmS1ARzSE0Ncy9JkiQ1bLwr2f5EaXn4p6kIRhoJhx8OwAkf+hAnNhyKJEmSNMXMITV1zL0kSZLUsPEq2Z6amfdNSSSSJEmSpOnOHFKSJEnSjDFmd5EmR5IkSZKkXplDSpIkSZpJehmTTZIkSZIkSZIkSVLNeN1FSurX7rsDcPTVVzcciCRJkiSNMHMvSZIkNcxKNmnQ3vlOAA68+OKGA5EkSZKkEWbuJUmSpIbZXaQkSZIkSZIkSZLUJ69k08jZn6OX3P5yEwGcdlqJY//9mzm/JEmSJI2j8bxpEMy9JEmS1DAr2TRyjubAJbcbSbQ++ckSxxFHmOhJkiRJGkqN502DYO4lSZKkhtldpCRJkiRJkiRJktQnK9kkSZIkSZIkSZKkPlnJJkmSJEmSJEmSJPXJMdk0cg7gqNq9A7tuN2le/GIAPnnFFVN/bkmSJEnqQeN50yCYe0mSJKlhVrJp5HyZA2r3GkgW3/hGAA6+9NKpP7ckSZIk9aDxvGkQzL0kSZLUMLuLlCRJkiRJkiRJkvpkJZs0aCefDCefzDs22aTpSCRJkiRpdJl7SZIkqWFDU8kWEZtExFcj4uqIuCciFkXEkRGxTp/HWbfab1F1nKur4/qrW1Pji1+EL36RTz7mMU1HIkmSJI0sc0iZe0mSJKlpQzEmW0RsAZwNbAh8D7gQeBpwELBbROyQmTf2cJz1quNsCfwcOAnYGtgPeF5EbJ+Zf5ucRyFJkiRJmgrmkJIkSZKGwbBcyfYFSnL01szcKzPfnZk7A58CtgI+3ONxPkJJjj6VmbtUx9mLkmhtWJ1HkiRJkjS9mUNKkiRJalzjlWwR8WhgV2AR8Pm21YcAdwCviojVxznO6sCrqu0PaVv9uer4/16dT5IkSZI0DZk6ZZvNAAAgAElEQVRDSpIkSRoWjVeyATtX89Mz88H6isxcDJwFrAY8Y5zjbA+sCpxV7Vc/zoPA6dXdnVY4Ymks++wD++zDvL//velIJEmSpFFkDqnC3EuSJEkNG4Yx2baq5hd3WX8JpZXilsAZK3gcquNIk2fffQE49LLLmo1DkiRJGk3mkCrMvSRJktSwYbiSba1qfmuX9a3la0/RcSRJkiRJw8scUpIkSdJQiMxsNoCIo4H9gf0z85gO6z8CvAd4T2Z+bIzjvJcyuPWHM/P9HdYfABwFHJWZr++y/oDq7lbARRN4OJNlfeCGpoPQhFl+o8XyHB2W5eiwLEeL5Tk6hq0sN8/MDZoOQivOHLInw/b+08RYjqPHMh09lunosUxHg+U4Gpoux55yyGHoLrLVOnCtLuvXbNtuUo6TmUcDR49zjkZExMLMnNN0HJoYy2+0WJ6jw7IcHZblaLE8R4dlqUlkDjkO33+jwXIcPZbp6LFMR49lOhosx9EwXcpxGLqLbLX269bP/WOrebd+8gd9HEmSJEnS8DKHlCRJkjQUhqGSbX413zUiloknItYAdgDuAn4zznF+U223Q7Vf/TizKANf188nSZIkSZp+zCElSZIkDYXGK9ky81LgdGA28Ka21YcCqwPHZeYdrYURsXVEbN12nNuBr1fbz2s7zpur4/8kM/82wPCnylB2QaKeWX6jxfIcHZbl6LAsR4vlOTosS00Kc8ie+P4bDZbj6LFMR49lOnos09FgOY6GaVGOkZlNx0BEbAGcDWwIfA+4AHg6sBOla45nZuaNte0TIDOj7TjrVcfZEvg58FvgccDzgX9Ux7l0sh+PJEmSJGnymENKkiRJGgZDUckGEBGbAocBuwHrAdcA3wUOzcyb2rbtmCBV69YFDgH2AjYCbgR+BHwwM6+czMcgSZIkSZoa5pCSJEmSmjY0lWyS1LSI2Bf4GrBfZh7bbDSSJEmSpOnCfFKSpJmp8THZhlVEZDU9WHVF0m27+bVt953CEHsSEY+PiJMj4h8RcXdEXBQRh0bEqh22XTkiDoqIr0XEeRFxb/W4XtdE7IPWKqem4+gmIp4ZET+MiJsi4s6I+FNEvC0iVuqw7dza667T9LEmHkNTmizbiNgkIt4XEd+KiL9WnxkZEY8ZY5+nRcRHI+JHEXFttf2MbCVdPXet1+1WTcfTTUQ8rvrs/F5EXF6L+SE97LtNRBwXEVdExD3V5/GZEfHqqYh9KkTEShGxf/W4boqI+6rH+aeIOCYi9mw6xpaIWD0iXhERJ0TEhRFxR0QsjoiFEfHOiFily36vjYijIuKc6jM6I+LwqY5/KkXEnOo3wd8i4q6IuC0i/hwRn4iIjVfw2PtO9LdTRGwcEW+pPkMXVe+rGyPipxHxwi77LBjnezMj4isr8piGSYfHdk9EXB8Rv6/ek8/p9PtiGFTfq1+NiKuruBdFxJERsU6Hbft+LUiTqfaeM4cckRyyJRrMN3oR5pIrpMnyDfPJgQjzypER5pYjJ8wrp6UOj8uccgzjfojPcPdTnqPXAu9tXxkRjwV2rG03VCLi6ZRxBVYGTgGuAHYGPgjsEhG7ZOY9tV1WB46sbl8HXAtsOnURz1wR8Xzg28DdwDeBm4A9gE8BOwAv7rLrmcCCDst/Nfgo1cUc4HAggb8DtwJrj7PPy4GDgPso44c8fDIDHFYREZTP1wQC2B84uNGguvt3ymfnA8AllPfqw8bbqfqhdwxwJ3AasIjy+ngi8FzguEmJdgpVP6pOo3TVdQvwA+BKYF1gC8rrfWvg+03F2ObZwDcon7PzKd2KrUv5zD0CeGH1/Xh3236fBNYCbgaupjy2kVS9Nz8GvIvyG+enwLeAVYBnUt6nb4yIfTLzlAZCfAvwn5TP3PmU3yubAy8E/jUiPpWZ72jb51g6f1+2jrcupWu4UXNoNV+J8tnzBOBVlM/ehRHxisy8uKng2sXyY2xdCDyN8p25W0TsUB9ji4m9FqTJZg5pDjmlzCWnPfPJFWReORp5JZhbjhrzypFhTtlLTpmZTh0mypfzlcDvKH37P6TDNh+vtvtONd+36bhrsa0E/F8V15615bMoyVIC727bZxXgOcBG1f151Xava/rxDLBMs+k4OsS1JmVQ9XuAObXlD6s+FBJ4Wds+c6vl85qOfximQZUtsG+/72VgE8oPqzWr+wuqYzxmjH22BZ4MrFKL/8qmn8cGyu3fq8f+tepL7PrWczJsE7AV8HRg1er+oir25b4bavs8g/JD8jzgER3Wr9z04xrQc/PK6rk4D1irw/rVgJ2ajrMWz7bAK9pfa8AawLnVY3lnh/12Azavbrc+Kw5v+vFM0nP0QZb+0fOEDutfBNxVvb4nVLYT+byt7ftCYMcOyx9H+WMqge16PNZW1fbXjsp7snpcXb8XKX/EnVxtczmwYdPx1mL7SRXXW9qW/3e1/EuT9VpwchrEhDkkjFgO2Va22XQcHeIylxyi8p3I7xvMJwdRfuaVQ/DYBvT8mFuO0IR55bSexvpuxJxyucnuIsf3ZeARwO71hRGxMrAP5YfrXzrtGBHbRcSnI+KP1SXOd0fEJRHxyfbLEyPi9dWllx/scqxHVJdI/7nHuHekvBh+kZlLWnhk5oOUFgQAr69aFbTW3ZuZP8rMa3o8x7QXS7vKmNdl/aKIWNS2bMmlyBGxU3Wp8OLqcucfRMTj+gzjP4ANgJMyc2FrYZaWLu+v7r6hz2POeBMp24nIzCsz85eZeVsf+5yXmX/IzHtX9PzT3P7V/MvA8cD6wAvaN4qIY6uynN1hXddyjoinRsTptffnzyJi+4iYV+0zt9dAM/OizDwnM+/qdR/gvyh/Vr0yM6/tcMz7+jjWMHtmNT82M29tX5mZd2bm/PblEbF3lO6ybq6+Hy+IiPdHxEM7bJvVZ+0jI+LrUboLuSsizo2Il/cTbPX+O779/ZeZiyktCqH8+dS+348z87J+zjUdVe+zD1BaRu+Zmcv9xsnMbwNvp7y+vxgRs9qO8dKIOKP222dRRJwYEXOq9Qsof4IAfC2W7YJi9ngxZub/ZuaZHZZfQGnBDx3KsIsDWnGM0HtyTJl5HfAyyp94m9L5Spt1o3RDdUH1Xru1KtNdux13vHIfT0Q8GtiV8mfT59tWHwLcAbwqIlavPZZBvhakQTKHnAHMJUeb+eS0Yl45OswtR4R55Wgzp1yelWzjO5FSAO19yu9JqbX98hj77k95wV1EedN/idKi8R3AWRGxRm3bbwC3Aa+Lzv2ZvobSnchRPca9czX/cfuKzPwbcDHl0sdH93g8LW934HRKuX0J+CXlMv0zI2L9Po7TtayAX1C6A3hmpx8HwGMi4s0R8d6IeE2U7mekoRcRD6d8jl6cmWez9IfRAd336uv4z6a8f3YGfgh8jtJCaj7lMvFJFRGtFqkLgb9Uf6IcHKVf9l3afzxOc63L7LfsdYcofZSfADwG+F/Kj5+bgA8BP47OYxKsQ/lTchvK6+U4ynfY8RHx/yYc/bJaP4bvH9DxpqP9KL83vpOZY/0pewyla5OtKH/KEsWxwEnAP1PK9lOU78dns/TP5mMp3TZQzQ+tTbesYPw9l2GUMRJeTWmZNtbvuZFT/WHeGvdh7/of5hGxOaXl7bspLcG/REkuHkd5f+5fP1Yf5T6e1u+h06v46vEuBs6itF5+Ro/H8/2sJplDajzmktIAmFeOVF4J5pajxLxyxJlTtpmMy/JGYaJ2uT3lDX8/sElt/Y8plwyuxtL+s/dtO8bmwEodjt3qK/o/25Z/rlq+e9vyAP5GSdSWu1y6S/zfqo71oi7rT6vWP2eMY8xjhLr6oMNlrozTVQal5ntR27J9q33uB3ZpW/fRat27+ojrd4xx6SlwfrX+cR3i7jSdAqzT9PM9zct23xWIZQHjdO/RJf4Z1b0H5Ys2gffUlp0LPNj+3FF+OCUwu8NxlitnSgOSSzp9xgGvr71X5q5A/IsYo1sPSsvJpPzYn9/hffqnfl4jwzxRuqq5tyq7r1Mutd98jO1b77P/peompbau9b1zUNvy1vN2MjCrtvxRlATqXuDRA3gsP6rOc+A427Uewyh26XFG9dj272Hb46tt31/dP6C6/1vafq9QWidu1OE53HeAsa9J6Z7jQWrfmWNsv3cVw+lNP++TUI7LfS922OahlKQhgUfVli+onsP27sXWpnTdcxfw8Nrynst9nHg+QZcudar1rd/Jbxj0a8HJaVAT5pAwYjlkW9lm27K5mEuOxDQJ5bvvCsSyAPPJfp4v88oRySurx2tuOSIT5pXTfqLDd2OHbcwpq2nUWjxMli9TCvQ1sKQ29t+A4zPzzm47ZeZlmflAh1VfpbRY+/e25V+s5ge2Ld+V8mH/zexwuXQXa1Xzbtu3lo83oK66Oykzz2hbdnQ176dF00TK6nrKj8ltKH09b0AZC+EPlD6NTx3BFk0aEVXrltdRvqjqAzQfS/lDqL3Vd7+eSWnFNj8z2wecPZrSCnuybVjNX0JpqfNCynv9MZRkYRvgB1WLp2ktM/9A6Tv/umr+bWBRRNwYEd+JiD3adjmI8sfSa3L5blI+RGm9+IoOp3qA8sfiktZImfl34DPAypSBdycsIt5M6Rv/PMr39Ey1UTW/oodtW9s8spq/pZof2P57JTMfyEnsSqz6XDmGcoXIF7N07TCeVgvno8fcakRl5j0sbS28AUBEPInSgvTbmXlS2/a3ULrYeBjlt0bLoMp9IL9dJ/hakCaDOaTGYi4prSDzytHKK8HccsSYV84A5pRLdbpkVm0y85wo/di/JiIOp3xRz2KcS0Cj9Ll/IKW7j8dTCrr+Y3XjtvP8JSJ+ATwnIjbNzNaHTOvN+qXasbcF9mo75S2ZeWSPD6t1CWf2uL2Wt7DDslaZLRkvIUr/3HPbtluUmcf2eJ7lyipLX8b1/oxvp1xuezblS3wHYA+WXjatCYqIfYHZbYsXZOaCKQ9mdOwMbAH8JDOvqi0/ATgC2DciPpAT78f6ydX8V+0rMvPB6n2yTPcTEfE2lv+S/W5mnjfBGFaqzV+XmadV92+LiH0oCdIcyo+KEyd4jqGRmSdHxHeAnYBnUcrgWZTvqb0i4jhKC7NVgScBNwBvq/UmUHcP5flpd3mV+LRbQPmR9mSAiFgbeFuH7Y6sftAtJyJeCBxJaaX0ohV47Y2Cfn4fLNm26tP8icB1VXI88QAm9hvnk8CLKd1JvKOHczyW8sP/Omb2d2V7eW9fzdeKzmPQbFDNHwfQT7lHGRdh3/blmdnpPL3E2k1frwVpsphDahzmkjOE+eSkMq8csbwSzC1HiHnlzGFOiZVs/fgypUXDbpR+Zc/t4c3+Tcql3X+jvNGupXzAQ/mQ7tQv+heAf6EkYYdExCMo/Uufl5m/rW23LeWDv+4yygc5LK2ZXYvO1mzbTv1b7gs1M++vvtjrYyLMZfmyOpPSugoGWFaZeVtEnAC8j/I6mqkf8IO0L1W/0G0WTG0YI6X1p8+x9YWZeWNEnEpJEJ5P6a5mIlrvpeu6rO+0/G2U7pnqFlH+aJiIm6v5PZS++5fIzIyI71GSoacxOsnQfZSxRU4HqMaGeRGl5d6rge9QujQKyo+q9s/F8XQrz9bg361yX7vLsY+lw+d2ROxF6ff7H8BOWcacmcmuAbYGNuth201q+7T+TLiqy7b9GO83zjIi4hOUAbN/ATyvak03ngMor8WvzdTENyIeBqxb3b2+mq9Xzf+tmrr5p2reT7nPpvN7c141X+HfQxN8LUiTyRxS3ZhLzhz7Yj45WcwrRzCvBHPLEWFeOQOYUy5lFwC9+zqlv9CjKK0Hx7wENCLmUJKjnwFbZ+Z+mfmeqmb1MKDbpdz/S/mwf231JdJxsOrMPDYzo22aXdvkomrebbDQ1qDGU3F5+zBrXRrercK525uyZ5k5r0NZza1t0rWsqgFaH0W5/L3XL+fWh9rqE415RAykbDNzbofymzeQCGegiNiApS2JToyIrE8svVy8PlD1WGXZ6RLv26r5w7uEsdzyzJzdoZyPHfPBjK31vl6cbYOtVlrJ0qorcI6hVl3SfzJlsFooLU1bP2T+0OH5XmbqcMhu5fmIan5rdd5FXY65qH3HiHgxZfyZ64AdM/Oi9m1moFZL3X8da6PqN8rc6u5ZLE0yN+64Qx96+I1Tj+NTwMGUMSqek5m3j3f8qjudfWBmDUzdwbMon6vX1d4frffoQeO8R/ertuu53DNzwTjv9RX67TqR14I0BcwhR5u55Ggznxxi5pVLjHxeCeaW05R55cxgTlmxkq1HWS4DPoVSu34H47cQeUw1/34uX5P9NLp8AVbbHkN5Ye1BaY14O2UQyH78vJrv1r4iIh5NebFdRu8/tkdV6wfJpu0rIuIxTM14A13LitKCcDXg7Oy9NfYzqrllWzRZtlrePpQ/iM4FvtJluh7414h4VLVP17KktNpr12oh/qz2FdX4Es+caPB9+BOl24r1I6LTD/gnVvNFUxBL0xZX86h+oPwFeEJErDvGPp1sVnUN0G5uNe+rK4mIeDnlu/xqShJ0SZ/xjKpjKWMUvCAinjDGdq+h9Jl/EXBmZt4BnA88PCKePMZ+La3xhlYac6suovg8pbXwTyktzLqOcdTmBZQWrz/LGdq6tPosfF9194Taqt9U82f3cpwJlPtY5lfzXdvHAoqINSjdl91Vi7G1bkVeC9KkMoccecOQb5hLTp5hKF91Z15ZzKS8Eswtp5NjMa8caeaUbTLTqcNEqYW+sm3ZZpSWMju0LT+82n7f2rJnVMu+3bbthpQfAUnpS73TuTejtDa7struqAnEvxLwf9X+e9aWz6K0rEjg3eMcY1613euaLo8Blmm2LVuZUsN+C7BhbfmqlEvxlysnSncPy5R3h/Ms6COuNSk//u4B5tSWPww4uzrey9r22QGY1eFYr6S0zroHmN30cz5qZdtjLAuqYzymz/ivnOg5p9MEXFg93qeNsc2Hqm0+XN1/aXX/hLbttqH8yE5gXm35LOCv1fLntO3z+tbrBZi7Ao9jUXWMh4yxTeu74X/q79cq7ruA+/p5nQzrBOxN6QKg02fSI4BLqufhxdWy11T3vwus3WGfdYCntC1rldk3257LRwE3Vc/lFn3EvA/lx/jfgM0n8JhbnxWHN/38T1KZHlY9vkuBx3dYvxdwJ+W3ys615ftX+/0WWKttn1nARrX7z622PXQC8QWlpWBWn+cP63P/M6p9X9T0cz2JZbjc92Jt3YbAydU2lwHrt63/RfX+eE2X/bdh2e/Vnsu9h7h/Uh3rLW3L/7ta/qVBvhacnAY9YQ4JI5ZDtpVtti0zlxyRaarKt8dYFmA+2ctjNq8csbyyekzmliM0YV45rSc6fDfW1plTtk2OydaHzLwcuLzHzX9Hucz1hdVgqL+iXI78HErt/NVjnScifkDpRx/auvnoMdYHImI/Ssu2UyLilCr2XSgtdM5i6WXWS0TEuyl95kLpuxZgv4hotdz5VWYe0288wyoz74uITwMfAP5QDa76EMqX+tWMUU4DjOG2iNif0sp1QUScRPli3xPYqlr+zbbdjgdmVa+tKylJ1FMpLVzvBw7MDpevzyRTWbYRcWztbuv98/GIaLWyOiYzf1Xbfmvg3W2HWaftOAdn5g2DinEYRBm4fSvgz7ns+CDtvkJpDbNfRBxCGQ/iEmDviNgEOIfyR9Lzq3Uvqe+cZRDq1wE/Br4fEd+m/Kj7Z0r5/4jyWdypu41usa9PGTy7Zf1WrFV3JAAfy8wLa9t8hPKZ+2pgm4hYQGnl9CLKe/admfnXXmMYYk8HDgKujYhfAX+vlj8KeB7lj4jvUY2FkJlfjYjtgDcCl0bETyjfT+tW+/wL8DVK4lr3p+pc50bE6ZQuel5KaUX8rsy8tJdgI2InSl/+syitnPaL5QfJXm4w5Oo11foubF1psEf1mgS4MDM/1ksM08A8SjdR7wD+WJXRXyh/Nj2TUg53AXtn5s9r+x1DeY5eDVxSjRFxPaVl4s6U531ete2vKQnV26qWp61xET6bmeONG/NBylUad1HGt3h3hzI8LzO/276wanm+U3W+749znmkvlg40PYvyXnkCpYxWoSQwr+jwXfNyyu/Hr0TEWymfubdQrsT5Z0qL6e0pY01Af+U+njdS/hT+TETsAlxAeb3tROnS431t20/4tSBNFXNIYMRyyBZzydFmPjm8zCtHNq8Ec8tRyy3nYV457ZlTAr3klE3Xig7rRB8tgejQCrFavi5lEOpFwN2UL+OPULpsWESXVojVvs+vjvm7FXwcj6e0OryB0iLtYuBQYNUu2y9gaauOTtOxTZfNBJ+Hlar47+2wLig/UC8F7qV8If9Xt3JiwK0Pa/vtQKkxv7l6Y/+ZMtDiSh22/U/K5atXVNu2Xl9fA57U9PM9qmU7RgxjvWc6fTbM7WGf2U0/t5NQVsdXj+2tPWx7erXtC6r7m1L+ILipes3/Dnhh7bmc1+EYT6/eJ4ur6WeUL/HPVfts20fss3sos7kd9luN8kPgQspn8K1VHM/p9dzDPlVl8ybK4NMXUcYuuJcyaPEPKa2iO7VE3B04jfKj6l7KINO/pXynbt22bVK+nx4JfKPa527g98DL+4y39T4fa1rUYb9jx9lnQdNlMQll+zRKi9m/V++72yndOBwBbDLGfq8Azqxe73dX+x/P8q1Id6MkRbf389nXQ1l0/b0CfLxa/9Gmn99JLrv25+Meym/Bcymt9Hbr9L6s7b8G8N5q+9ur8v878APK2CarT7Tce4h9U8rvmWuqz4bLgE8D6w7yteDkNBkT5pAj+T7EXHKkp6ks3zFiMJ/s7/kyrxzBvLJWPuaWI5ZbYl45LacOz4U55RhTVAfTkKlqiQ+hdLPxlYbDmfYiYiNKS7OrMnOT8bbX9GHZql8RcRYlUVorS9/PGnJVq84zM3Nu07FIkjSszCEnh/nGaLN8NVHmldOTuaWkyTBr/E001aqB+F5PaVkz3uDY6s0LqvmvG41Ck8Gy1XIiYrWIWG4w8ojYl9ItwekmQpIkaVSYQ04q843RZvmqK/NKSVIvHJNtiETE84CnAHtQ+t4/ODPvbDaq6S0iDgO2BF5M6V/+k81GpEGxbDWOzShjJ/yUMlj1Q4AnU/p4vgV4Z4OxSZIkDYQ55OQx3xhtlq96ZF4pSRqX3UUOkWqQ2n0ogyZ+FXh/ZvY8gKqWV10GvhhYCHwoM+c3HJIGxLLVWCJiHeATwI7AI4CHUvpl/xnw4exxIGMNB7v0kCSpM3PIyWO+MdosX/XCvHL0mFtKmgxWskmSJEmSJEmSJEl9ckw2SZIkSZIkSZIkqU9WskmSJEmSJEmSJEl9spJNkiRJkiRJkiRJ6pOVbJIkSZIkSZIkSVKfrGSTJEmSJEmSJEmS+mQlmyRJkiRJkiRJktQnK9kkSZIkSZIkSZKkPlnJJkmSJEmSJEmSJPXJSjZJkiRJkiRJkiSpT1aySZIkSZIkSZIkSX2ykk2SJEmSJEmSGhIR10ZEVtOXmo5HktQ7K9kkSY2KiDUj4k0R8f2IuCwibo+IuyJiUUScExGfj4jdI2K1pmOVJEmSJE2eKg/MPqdjm45bkjRzPaTpACRJM1dEvAz4ArBOh9WbV9PTgDcC/w84YuqikyRJkiRJkqTurGSTJDUiIt5AqWCr+wMwH7geWB14HPBsYMOpjU6SJEmS1IAPA2u1LftE7fbfgC+2rT9/UiOSJGkMVrJJkqZcRDwW+HRt0b3AqzPzmx22nQXsBGTb8r2AFwJPAh5OuRruAeAa4NfAZzPznC7nfybwJuCZwEbVfpcBPwOOzMy/tW2/ANixuntmZs6trZtLqRhs2SkzF1TrZgMHAdtRrspbF3gocAslETwJ+EpmPtApTkmSJEmaSTLzy+3LIqJeyXZFZi7Xw0lEzAFeCzwZ2ISSe60E3EBpzPm1zPx22z5bAxfUFu0NrAK8A9gKuBb4MvBx4EHgDcCbgUcD1wHfAA7JzPtrx9yQ0gvLU4AtKHnqasBt1bm+DXwhM+/p6QlZetzZlNzy3yi55crAVcBPgSMy86/9HE+SNDiRmeNvJUnSAEXE5yiVXC3vycyP9XmMU4AXjbHJg5SKu+Pb9jsMeD8QXfa7E3hFZn63ts8CJlbJtjtw6jgP5UfA7pn54DjbSZIkSdKMExH1Py+Xycdq2xzMsle8dfLpzHxbbZ/2SraFwJwO+32Rkj++vsO6L2XmG2rHnAP8bpw4zgHmZubdtf2upTQeBTgqM19fW7cXpUJv9S7HuxN4aWaeNs55JUmTwCvZJElN+Lfa7aS0DuzXLZQrz/4PuBm4G1gfeB6wNTAL+HREfLuVvETES4EP1I6xCPgmpWXhfsA/VbdPjIgnZualE4ir7n7gj5Rk7XrgVmBVSuvK3SmJ2nMoV+SdsoLnkiRJkqSZ6m7gN8B5wI3A7cAalOEHnl1tc1BEfCUz/9zlGHOAX1IaUb6CciUalCvYAH4BLAD2oVxNBvC6iPhAZt5Q3X+QkqP+jnIl3C2U3kyeQMn7VgKeDrwO+Nx4D6rqBeZE4GHVor9Scsd7KI1On0jJYU+KiMdl5hXjHVOSNFizmg5AkjQjbVK7fX1m3lhfGRE3RES2T/VtMvN1lAqqb1Iqy+6iJDE/rG22HvDU2v131W7fCjw1M9+dmW8F9qytexjw1ok9tGVi/HFmbgt8FPg9sJjSTcgvgKtrmz5nRc8lSZIkSTNVZn4uM7enjPv9f8AdwE2UnkXurW262xiHOQ/YOTMPoXQZWXdubd3ba8sfQmlE2Yrj95n5BErjznMo+d/twG+BS2r79ZoDHsTSCrYrgCdn5nsycx6lsu7aat3qLNtbjCRpinglmySpaRPqtzgiXkYZ123DcTbdpNp+NWrJD3BqrbUhmTk/IhYBs6tFO0wkrrYYNwe+ztKWk2PGKEmSJEnqX9VN47GUK8bGMlbu9c3a+EUjsEwAACAASURBVGqL2tadXBtL+5K2devU4lgf+B/guSsQR109l9wUWBzRbeQDntXjMSVJA+SVbJKkJlxZu71hRKzbtv6DlMGiF3baOSKeDBzP+BVsULrmgJL41LORaztsW1/WHtOS03c5fiffYfwKtvGOIUmSJEnqIiLWAH7A+BVsMHbudVXt9r1t6+o9kdzftq7+/+pxjF/BNl4cdd3y0k426GNbSdKAeCWbJKkJPwW2rG4HsC/w362VmfkFgIh4Ip0Hnn4xSxOZBF5JuTJtcUQ8HvhLh31urrZtVZI9osM29WU31W4/WLu9ats+j+1wHCJiS5a9cu4kSsXh1Zn5YET8lmW7spQkSZIk9W8nlm2A+XHgk5l5PUBE3Aqs2cNx7htjXXvF2nIiYm2W7Y7yJ8Abgcsy84GI+D6wRw9x1N3E0qve/gocNca2N/d5bEnSAFjJJklqwmeAA4CVq/uHR8SFmfnDMfapW792+1bgpMxsVYS9rNMOmXlnRJzH0oqvPSJi/VaXkRExl6VdRQKcVbt9S+32VhGxdmbeEhFr0b3f+/Xb7n8rM6+szvU44Eld9pMkSZIk9a499zquVsH2XHqrYBuEdVm255PvZ+bfqjg2prdeTtr9Cvjn6vZGwPcys727SiLiWSybt0qSpoiVbJKkKZeZF0fEOymVbVCuDvtBRPwK+CVlcOgNgV26HOKi2u21gR9FxC+B7YC9xjj1J4ATqttrAb+LiJOA1YDX1La7B/hs7f45wAuq22sCf6iuRNsB2LjLuf5KuQKudcXdp6tuLv+JcuXeKmPEKUmSJEnqzUVt978ZEd+iXAH26imM4yrgDmD16v6hEbEpJSfch5K79uvTwGsp3UuuDiysHtvfqmWPBf6FkpfuDZy/Ig9AktQ/K9kkSY3IzM9GxGJKRdsa1eJn0X2w5nr3jV8F3sbSbjN2rSaArwH7dTnniRGxDfCeatFs4N1tm90FvDIz/9p2vneytI/72Sy96u0HwPM6nOsfEfElSvcgVLG+v7r9J+BSSqWgJEmSJGmCMvOsiDiDpY00n1hNAKcBT2cKxivLzHsi4uPAYdWi9Vmab14GzKd0bdnPMS+OiL2Br1Mq2dakVLpJkobErPE3kSRpcmTmscDmwMHA6cA1lKvI7gX+Afwa+BzwfErXGK39bqZUxp1M6RLjbuCPlKvRDmMMmfleSku/E4HLq3PdRWn9+HngnzPzf9v2ub7a5zRgMXAncDalP/0jxjjdW4H3An+n9O9/NfBFYEfg9rHilCRJkiT17PnAkZSc8j7KlV4fAl7EsmNsT6rM/BDwZkp+eR8lrz0OeEZ1eyLH/A7weErPLOdRctJ7gSspva58CtgZOGUFw5ckTUBkZtMxSJIkSZIkSZIkSdOKV7JJkiRJkiRJkiRJfbKSTZIkSZIkSZIkSeqTlWySJEmSJEmSJElSn6xkkyRJkiRJkiRJkvpkJZskSZIkSZIkSZLUp4c0HcAwWn/99XP27NlNh9GYc89denu77ZqLQ5IkSRqUc88994bM3KDpODSaZnoO2TRzWEmSJA1arzmklWwdzJ49m4ULFzYdRmMilt6ewU+DJEmSRkhEXNZ0DBpdMz2HbJo5rCRJkgat1xzSSjZJGoBTb7hhye091l+/wUgkSZIkSVKT/I9AkmYOK9kkaQD2PP/8Jbdz7tzmApEkSZIkSY3yPwJJmjlmNR2AJEmSJEmSJEmSNN14JZuWszun1u7t0Vgc0nSy+3rrNR2CJEmSNCOZw0oaNv5HIEkzh5VsWs6p7Fm7l43FIU0np26zTdMhSJIkSTOSOaykYeN/BJI0c9hdpCRJkiRJkiRJktQnK9kkSZIkSZIkSZKkPlnJJkmSJEmSJEmSJPXJMdm0nHkcUrstqRcnXHfdktsvf/jDG4xEkiRJmlnMYSUNG/8jkKSZIzIdFLjdnDlzcuHChU2H0ZiIpbd9eUi9iQULltzOuXMbi0OSJHUWEedm5pym49Bomuk5ZNPMYSUNG/8jkKTpr9cc0u4iJUmSJEmSJEmSpD7ZXaQkDcDeG27YdAiSJEmSJGkI+B+BJM0cVrJJ0gCc8PjHNx2CZrio+kmyG2hJkiRJapb/EUjSzGElmyRJ01S3irWoDUxipZskSZIkSZI0OaxkkyRpmphI5ZkVbpIkSZIkSdLksJJNkqQhVq8kG+TxrHCTJEmSJEmSVoyVbJI0AEdfffWS2wc88pENRqJRMOiKtfHOY4WbJEmSJA2O/xFI0sxhJZskDcCBF1+85LY/oDURTVd42a2kJEmSJA2G/xFI0sxhJZskSQ1pumKtGyvcJEmSJEmSpPFZyabl7M/RtXsHNBaHNJ3sv9FGTYegacDKK0mSpMEzh5U0bPyPQJJmjvBPvuXNmTMnFy5c2HQYzamPBeTrQ5L6Vr9Crdv4avV1/5+9+46brCwPPv67FgGB0BVpAqICmviKuhoRCyUaLCAWNFaKApYoKrxGsTz7KGpiRIkFBXl1LVgQNQo2EgQMYjAQUbGAIksRlKZ0qdf7x31md3Z25nlmZueZM+X3/Xzmc2ZOm2vOmVOuc9/nPp3ez6Wf6QfxnZI0riLi/MxcXHccmkxTn0PWzRxWkiRJA9ZtDumdbJIkDcCoNv04aNPyOyVJkiRJo2GuiqTdjmfLMpIWioVskiTNwTu8OrPATZIkSdK4WJ3WP3oZr3WYOut2+XYzj16W9Vzry8I4Sb3qqZAtIjYBngw8FLg/EMA1wG+BH2Tm9QOPUJKk1bC6TS+qOyYikqR2zCElSXUZlRxlVOIYpm4LKqddt/+NcfsP9XtH4aALwjvF1MswqRvzFrJFxLrAi4CDgcWUpKidjIjzgeOBL2bmbQOLUkN1CMctf3/8HONJ46CfZ1P14+grrlj+/vAHPrCveah/1hocLZ6gStJ0M4fUsJnDSmoYmVxwv/2Ala8VwOTmStPyuwZRuNPpP9rLdY267socxJ2H7X5Hr9P1M/9Ow3pZD90WHrYO0+TrWMgWEWsCrweOBDZq9AaSVZOkBBZREqjFwPsj4ijgo5l513xBRMTzgacAOwOPBNYHTszMl/byYyJiGbBth8F/zMzNe5nftPokhyx/b4KicbS6J9X93Pl0xCWXLB92xDbbdDXNyJz816SfAtBu1o0kSaqHOaTqYg4rTa+RLdx5zWsAVrpW0M64XRcwP++Oy0bNBlHY2W5+7YapHnPdyfZrYLvqffPav5vSvMf1Vf9NgM2ANZvG2wj4APBa4CFdxPF2SmJ0C3AlsFNX0bd3I3BMm/63rMY8JY2YSbtzaVqaNPREczqN439VktQXc0hJkvo0ihfORzEmadr0UuDm8wXrMVch24Oa3p8LnAT8ELggM+9sHjEi1qLUINwVeAHwt23mMZc3UhKj31JqI57R5XTt/Dkzl6zG9NLU6WaH3G0tpX6mn4g7l046CYA3velNfLCGr+/nlv2FXJ/tvscDusD/gyRNOHNISdKCG4vrBKt5jWCYF8c7XROSNDkscFtYcxWy3Q6cAByTmZfONZMqYfpx9fpQRDwIeBNwUDdBZObyhMgduTQcbmsD9vGPA3D0scfWUsjWrWEVcFiQom54kidJE8ccUpK0IMYuxxzwNYJBPzPLY6c03frZp4zN/rcGcxWybZuZ1/Uz0yqhel1EzPYX1mpZOyJeCmwD3Ar8DPhBZt5TQyzSSPEkStKo8iHBkjQRzCElSZKkCTToAv5Jut7TsZCt3+Ro0PPow+bA51r6XRoRB2bmWTXEI0mSVsO4P4tQkqaFOaQkaVAm9UKsJGmF5ms8s7Mr6trNzMzUFVJfFtUdwIB9GtiTkiStBzwCOI7y8O3vRMQjO00YEYdExHkRcd611147jFiloYgI72CTNJEa+7fWV6fx5ppekjS1zCElSZIk9W2u5iLbiohNgd2ArYG1242Tme9fvbD6k5mtTYtcCLwqIm4BDgeWAM/pMO3xwPEAixcvtoqMxpa1vWqy//4ALLl0zsePSBqSTne8dSpQ66bZg9amDZYsWbL8/bjVspKkYTKHlCRNHa8RSNLU6KmQLSIOA94L3HeeUWtJkObwCUqC9OS6A5E0oQ44AIDZyy6rNw5JtWk0bTAzM7PSe0maZuaQkqRuTVQLE14jkKSp0XUhW0TsDnyozaAEouXzqLmm6q5XaxTSAmlus1aSNFo67aMtgJM06cwhJUmSJE26Xu5ke3XL50YiFE2fR7XKyS5V93e1RjEmZljS9GlJh7FUNwvWRszSpUB1B0u9kUgaE+P8UF9J6pI5pIbCHFYaTxP9uAuvEUjS1OilkO1xlCToAuCbwEz1eVPgRcCHKQ+N/scBx7iSiFgTeDBwV2Ze0tT/r4GrM/OGlvG3BT5affz8QsY2KZasdPhfUlcY0nj5zGcAWLJ0qSfQkvpiE5OSJpA5pIbCHFbSyPEagSRNjV4K2TaruqcBtzR6ZuafgGMjYlfgQOCnwEd6CSIi9gX2rT5uXnV3iYil1fvrMvOI6v1WwK+Ay4DtmmazH/CWiDgDuBS4mZJIPZPS/v+3gQ/0Epc0arwAK0mSpDFiDilJkiRpovVSyNa4h/sG4M7lPSM2yMybgF9W4xxKjwkSsDOwf0u/7asXlGToCOZ2BrAj8ChK0x7rAX8GzgY+B3wuJ+7ec0mSNImsVCFpQphDSpIkSZpovRSy3UCpIbge8Iem/i+LiE8BT68+b9864XwycwldtumQmcto025/Zp4FnNXrd0ujzgutkjTdfHabpDFmDilJkiRpovVSyPYHSoK0CXBqU/8PA//GiqTl5sGEprrszTeXvz+lxjiksfLqVwNw+G9/W3MgkiRJI8McUkNhDitp5HiNQJKmRi+FbD+hNKPxN8D5wBXA1pTEKCgPsE5WTp40hk5l77pDkMbPC14AwAevvLLmQCRJkkaGOaSGwhxW0sjxGoEkTY1eCtm+CNwO3JKZGRGHAl+jPBAaSpL0S+DNgw1RkiRJYNORksaOOaQkSZKkidZ1IVtmng6c3vT5uxGxE/Bs4H7ARcDJmXlnh1lI0uQ69lgAPnD00RxRcyiSJEmjwBxSkjS1vEYgSVOjlzvZVpGZlwMfGVAskjS+vvIVAA4/6SRPoCVJkjowh5Sk6dbcMsNE8xqBJE2N1SpkkyRJUj1sOlKSJEmSJKlePRWyRcTjgf8LPA7YGFjUZrTMzPUGEJskSZIkaYyZQ0qSJEmaZF0XskXEU4FvAWtQHlDdSa5uUJIkSZKk8WYOKUmSJGnS9XIn22w1vgmQtIBs/mtMHX44AIdcdFHNgUiSJI0Mc0hJ0nTyGoEkTY1eCtl2piRHAXwXuAi4AxMmSYJnPQuAT159dc2BSJIkjQxzSEnSdPIagSRNjV4K2f4CrA18OTNftEDxSJIkqQ+NO6G9C1rSCDGHlCRJkjTReilkOxt4JvC7BYpFI+I4Dmn6dHxtcUhj5eijATjuuOM4tOZQJEmSRoQ5pIbCHFbSyPEagSRNjV4K2d4BPBV4UUR8MDOvX6CYVLND+GTTJxMUqSunngrAIVtu6Qm0JElSYQ6poTCHlTRyvEYgSVOjYyFbRLy5Te9zgN2B30TEV4FlwF2tI2Xm+wcVoCRJkiRp9JlDSpIkSZo2c93J9s90fiD1RsBBc0xrgiRJkiRJ08UcUpK0Cp8dLEmaZL00F9nQKWkCiHmGS5IkaYE1LmSAFzMkjQRzSEmSJEkTab5CthhKFBopj+G85e/PrzEOaay8/e0AvPiXv6w5EEmSpFqZQ2rozGEljRyvEUjS1JirkG2doUWhkfK/PKbuEKZO8x0HGlN77gnAF6+5puZAJEmSamMOqVqYw0oaOV4jkKSp0bGQLTPviIgnZubZwwxIkiRJkjR+zCElSZIkTZv5mov8QUT8AfgG8FXgjMy8Z+HDkqQxc9RRAJx44om8pOZQJEmSamQOKUmS1wgkaWosmmf4dcDmwKHA94BrIuJTEbF3RKy14NFJ0rg4/XQ4/XRe/IAH1B2JJElSncwhJUnyGoEkTY357mTbHHgS8Dzg2cADgQOA/YFbI+JbwNeAb2fmrQsYpyRJkvrQ/NzPmZmZGiORNCXMISVJkiRNjTnvZMvMezPzrMx8fWZuCzwe+Ffgt8BfAS8EvgRcGxFfj4iXR8RGCx61JEmSJGnkmENKkiRJmibz3cm2ksz8MfBj4J8i4m+A5wPPAR5BqaW4D3B3RJwJfDUzjx9suJIkSZKkcWEOKUnTx5YUJEnTpKdCtmaZeSFwIbAkIranJEvPBR4LPBX4O8AESdJ0eM97ANj75z+vORBJkqTRZA4pSZoaXiOQpKnRdyFbs8z8HfB+4P0RsSWl/f3nDGLekjQWnvAEAE69/vqaA5EkSRp95pCSpInmNQJJmhoDKWRrlplXAR+pXpI6aG4+QZIkSZpW5pCSJEmSxlXPhWwR8QjgccDGwKJ242Tm+1czLtXom+zd9OmU2uKQxsqRRwLwzW9+k31qDkWSOvH5GJLqYA6phWYOK2nkeI1AkqZG14VsEbEecBKwVxejmyCNsb05te4QpPHzox8BsPf97ldzIJIkSaPBHFLDYg4raeR4jUCSpkbbWoQd/DPwdCDmGGeuYZIkSZKk6WEOKUmSJGmi9dJc5HOBrN6bCEmSJEmS5mIOKUmSJGmi9XIn26ZV9wrg8ZT29NcF1ml5rTvIACVJkiRJY8kcUpIkSdJE6+VOtmXAQ4HPZuaPFyYcjYIt+f3y91fVGIc0Vj70IQB2+8lPag5EkiRpZCzDHFJDYA4r1W92drbuEEaL1wgkaWr0Ush2IjBLSZI0wa5my7pDkMbPzjsDcNaNN9YciCR1r3ExZGZmpuZIJE0oc0gNhTmspJHjNQJJmhq9NBf5fuBc4AURcVREbLVAMUmSJEmSxp85pCRJkqSJ1sudbH+iFMoF8FbgrRFxN3BPy3iZmesNKD5ponjHwAR7wxsAOOOMM9i95lAkSZJGhDmkJGk6eY1AkqZGL4Vs9wWyekXVb83q1SwHEJckjZef/hSA3TbeuOZAJEmSRoY5pCRpOnmNQJKmRi+FbLAiMZIkSZIkaT7mkJIkSZImVi+FbP+yYFFIkiRJkiaNOaQkSZKkidZ1IVtmvnWhgoiI5wNPAXYGHgmsD5yYmS/tY15bA+8C9gI2Ba4G/h2Yzcw/DSxoSZIkSVJH5pCSJEmSJl2vzUUulLdTEqNbgCuBnfqZSUQ8GDgH2Az4BvBr4HHAYcBeEbFrZl4/kIglqdlxxwHwmPPOqzkQSZKkqWAOKUkaXV4jkKSpsajXCSLiMRHxxYi4PCJuqbpfjIjHrkYcbwR2ADYAXr0a8zmWkhy9PjP3zcy3ZOYewIeAHYH3rMa8JamzHXaAHXbgf2+5pe5IJEmSRoo5pCRNptnZWWZnZ+sOYzR5jUCSpkZPhWwRcSDwI+AFwNbAulX3BcA5EfGKfoLIzDMy8zeZmf1MX8W2PfA0YBnwsZbBM8CtwMsiYr1+v0OSJEmS1D1zSEmSJEmTrOvmIiPi4cAnqmnaJTJrAMdGxLmZeeGA4uvFHlX3tMy8t3lAZt4cET+kJFCPB04fdnDj5Dwe0/Tp/NrimATNNbpmZmZqjEQL7tBDATjvvPNYXHMokiRJo8AcUsNiDitp5HiNQJKmRi/PZDsMWJOSHCVwLvAHYHNKm/VRze91wKGDDbMrO1bdizsM/w0lQdoBE6Q5PYb/rTsEafxcXHY9j1l//ZoDkSRJGhnmkBoKc1hJI8drBJI0NXopZNu96l4H7J6Zv2wMiIi/Bs4ANm0ab9g2rLo3dhje6L9Ru4ERcQhwCMA222wz2MgkSZIkafqYQ0qSJEmaaL08k20rSu3DzzYnRwCZ+QvgM5SaiFsNLryBiqrbts3+zDw+Mxdn5uL73//+QwxLkiRJkiaSOaQkSZKkidZLIdt8+n7g9IA0ahlu2GH4Bi3jSZIkSZLqYw4pSZIkaaz10lzkVcCDgZdExEcz87LGgIjYDnhZ03h1uKjq7tBh+EOrbqf29lWJply37qxXGhuf/SwAO5x7bs2BSJIkjQxzSA2FOaykkeM1AkmaGr0Usp1FSZAeAFwUEWex4qHVTwHWopzP/mDQQXbpjKr7tIhYlJn3NgZExPrArsDtwH/XEZykCffABwLwm9tvrzkQSZKkkWEOKUkTZHZ2dvn7mZmZGiMZA14jkKSp0UtzkR8G7qnerwX8HfDSqrtW1f8e4CMDi66NiFgzInaKiAc398/MS4DTgO2A17ZMNgusR3kWwK0LGZ8kSZIkCTCHlCRJkjThur6TLTN/FhFvAo6hcwsMR2TmBb0GERH7AvtWHzevurtExNLq/XWZeUT1fivgV8BllGSo2WuAc4APR8Se1Xh/C+xOaeLjbb3GJkldeVlp7eiiiy5ix5pDkSRJGgXmkJKkqeU1AkmaGr00F0lmfiQiLgQOB55IeRD0TcAPgQ9k5hlzTT+HnYH9W/ptX72gJENHMI/MvCQiFgPvAvYCngFcTalBOZuZN/QZn9S15uYTNEWuvBKAHdZdt+ZAJEmSRoc5pCRpKnmNQJKmRk+FbABVEnQGQGu79f3KzCXAki7HXQbEHMOvAA5c3ZgkSZIkSavPHFKSJEnSpJrzmWwRcW9E3F018bGKQSRHkiRJkqTJYA4pSZIkaZrMWchW6VjjT5IkSZKkFuaQkiRJkqZCz81FSpLa+MpXANjynHNqDkSSJEmSJNXKawSSNDUsZJOkQbjf/QC4+s47aw5EkiRJkqTBmJ2drTuE8eQ1AkmaGt0Wsm0cEdt0O9PMvLzPeCRJkiRJ488cUpIkSdLE67aQ7cjq1Y3sYb4aQb9ny6ZPV9UWhzRWnv98AH7/+9+zVc2hSJIkjQBzSA2NOaykkeM1AkmaGt0mMj64eopsydV1hzB2bD5BXH89AFuuvXbNgUiSJI0Ec0gNjTmspJHjNQJJmhqL6g5AkiRJkiRJkiRJGjfd3sl2HvCLhQxEkiRJkjQxzCElSZIkTbxuC9m+lJkfXNBINDKuYovl77ecYzxJkiRJ6sAcUkNjDitJkqS6+HBprWKrpgdFZ41xSGPlW98CYP3/+q+aA5EkSZKmizmspJHjNQJJmhoWsknSIKy7LgC33HNPzYFIkiRJktSf2dnZ5e9nZmZqjGTMeY1AkqbGoroDkCRJkiRJkiRJksbNfHeyNaqvnLPQgUjSWHvGMwC46aab2KDmUCRJkmpkDilJktcIJGlqzFnIlpmzcw2XplmjCQWbTxAAt98OwPr3sRVeSZI0vcwhJUnCawSSNEU6NhcZEbMRsXG/M46IjSNiSb/TS5IkSZLGhzmkJEmSpGkz1zPZ3gFcHhHHRsSTup1hROwSER8FLqvmIUmSJEmafOaQkiRJkqbKfPcsrwscChwaEX8AfgT8GLgcuKEaZxPggcBiYBdgq6p/ADnogCVJkiRJI8scUpIkSdLUmKuQ7U3A2ykJEMAWwHOqVyfR9P4G4F2rFZ0kjYszzgAgzjyz3jgkSZLqYw4pSRJ4jUCSpkjHQrbMPCYiPg28GXgFsBkrEqBG7cJGTcPmxOha4JPAv2bmjQOPWJIkSZI0cswhJWk8zc7O1h2CJElja87mIqsE523Vw6f3BfYCngw8iBXPc0vgUuAHwHeBr2fmXQsVsCRJkiRpNJlDSpIkSZom8z2TDYAq4flK9SIiAtiUUvvw+sy8d8Ei1NDlSpVKfSRCs0btrpmZmZoj0cjZfXcAMlfegiRJkqaROaSGyRxW0sjxGoEkTY2uCtlaZWYC1w04FkmSJEnSBDKHlCRJkjSJFs0/iiRJkiRJkiRJkqRmFrJJkiRJkiRJkiRJPeqruUhNtvN59PL3j6kxDmmsrLMOADfffXfNgUiSJEnTxRxW0sjxGoEkTQ0L2bSKxZy//L2PjJa69O1vA7DB2WfXHIgkSZI0Xcxhpd7Nzs4CMDMzU3MkE8prBJI0NWwuUpIkSZIkSZIkSeqRhWySNAi33Qa33cZfrbFG3ZFIkiRJkqQ6eY1AkqaGzUVKc2g0nwA2oaB5PPOZANycSdQciiRJkiRJqpHXCCRpangnmyRJkiRJkiRJktSjjneyRcQ7+51pZr6r32klSZIkSePHHFKSJEnStJmrucglQPY5XxMkSZIkSZouSzCHlCRJkjRFen0mW3MzwtlFf0maDptuCsBVd9xRcyCSJEkjxRxSkjR9vEYgSVNjvkK2ds/mzKp/67Bs00+SpsPJJwOw1Y9+VHMgkiRJtTKHlKQRNDs7u/z9zMxMjZFMCa8RSNLUWNRpQGYuan4BawHfrAa/E9gWuG/VbRydv1/1kyRJkiRNEXNISZIkSdOml+Yi3wjsDXwhM49q6n8F8O6I2BF4EfBm4D2DC1HDtgVXNX3asrY46tJcu0vq2nXXAbDFlltydc2hSJIkjQhzSA3FtOewkkaQ1wgkaWr0Ush2UNW9pMPwSyhNfbwcE6SxdhVbNX3y8QhSV/bbD4CrMm3zSJIkqTCH1FCYw0oaOV4jkKSp0bG5yDa2q7p7R8SazQMiYi1KDUWAbQYQlyRJkiRpvG1Xdc0hJUmSJE2kXgrZrq26OwM/i4glEfHqiFgC/BR4ZDX8un4CiYitI+JTEXFVRNwREcsi4piI2LiHeZwZETnHy7b+JUmSJGk4zCElSZIkTbRemos8GXgDpe2FHYF3NA1r3PmcwFd6DSIiHgycA2wGfAP4NfA44DBgr4jYNTOv72GWnR6qdXevsUmSJEmS+mIOKUmSJGmi9VLItgTYE3gEqzZynpQk6cJqvF4dS0mOXp+ZH2n0jIgPUh6W/R7gVd3OLDP7iUGVU3jW8vd7zzGepCZbbw3AxbfdVnMgkiRJI2MJ5pAaAnNYSSPHawSSNDW6LmTLzJsiYlfg3ZQHWK/fNPhm4NPAOzLz5l4CiIjtgacBy4CPtQyeAQ4BXhYRh2fmrb3MW/3Zh1OWv/eR0VKXPvc5AHb88Y9rDkSSmeLRrQAAIABJREFUJGk0mENqWMxhpfZmZzvdpKsF5zUCSZoavdzJRmbeArwxIo4AdgI2Av4M/Doz7+kzhj2q7mmZeW/L990cET+kJFCPB07vZoYR8ULgQcCdwK+A72fmHX3GpyngiackSZI0eOaQkiRJkiZZT4VsDVUy9IsBxbBj1b24w/DfUBKkHegyQQK+1PL5moh4bWae3Ed8kjS/K64A4KE77MBvag5FkiRp1JhDSpKmitcIJGlq9FzIFhHbAa8HngjcH3gvcC6wczXKiT3WSNyw6t7YYXij/0ZdzOsbwAeAnwDXA9sC+wOHA1+OiGdl5nfaTRgRh1CaFWGbbbbpLnJJanj5ywG4OJOoORRJkqRRYg4pSZo6XiOQpKnRUyFbRDwH+DxwX8pDqpPSrv46wNLq8xXAGQOMsXEsmrdp9cz8UEuvi4AjI+Iq4COUZK5tgpSZxwPHAyxevNhm3CVJkiRpNZlDSpIkSZpki7odMSJ2AE6kJEMrycxzgSurj3v3GEOjluGGHYZv0DJeP04A7gZ2joj15xtZkiRJkrR6zCElSZIkTbquC9mAIyi1D5MVyVCzsyg1BnfpMYaLqu4OHYY/tOp2am9/Xpn5F+Dm6uN6/c5HkiRJktQ1c0hJkiRJE62X5iL3pCRHF1CSoL+0DP9t1d2qxxgazYI8LSIWZea9jQFVjcFdgduB/+5xvstFxI7AxpQk6bp+56PJMjs7C8DMzEzNkWgi7FCu8Zx/883zjChJkjQ1zCElaci81jEivEYgSVOjl0K2RuLzjcy8M2KVx3beVXU36yWAzLwkIk4Dnga8ltLufcMspdbgcZl5a6NnROxUTfvrpn7bA3dk5u+b5x8R9wM+XX38Umbe3Ut8ktSV444DYPH559cciCRJ0sgwh5QkTSevEUjS1OilkO1OYE1g7Q7DH1x1W2snduM1wDnAhyNiT+BXwN8Cu1Oa+Hhby/i/qrrNWdqTgRMi4izgEuAGYBvgGZS2+s8D3txHbJIkSZKk3plDSpIkSZpovRSyXQo8AnhBRLyveUDVlMbzKU2B/KbXIKqaiIuBdwF7UZKaq4EPA7OZeUMXszkf+DzwGGBnysOubwZ+DpxEqcl4Z6+xTaNH01zL5jG1xSGNlYvLIz8e/ehH8781hyJJkjQizCE1FOawkkaO1wgkaWr0Ush2GiVB2p4VD5oG+Efg3cA6lATptH4CycwrgAO7HHeVdkYy8+fAAf18t1Z2PoubPmVtcUhj5dBDATg/k1V2UJIkSdPJHFJDYQ4raeR4jUCSpsaiHsb9N0qtPoAtWHHmui0lOaIa/rHBhCZJkiRJGmPmkJIkSZImWteFbJl5JfAi4PaqV7R0bwdelJlXDS48SZIkSdI4MoeUJEmSNOl6aS6SzPx2RDwceD3wRGATysOhzwY+kpmXDT5EaXBmZ2cBmJmZqTkSSZIkafKZQ0rSwmpc5wCvdUiSVIeeCtkAMvNy4IgFiEUj4ngOXv7+kBrjkMbKIx8JwJl/+lPNgUiSJI0Wc0gtNHNYSSPHawSSNDV6LmTT5DuU45e/N0GRunTMMQDs/tOf1hyIJEmSNF3MYSWNHK8RSNLU6FjIFhGfqt5+KTNPa/o8n8zMV6x+aJIkSZKkcWEOKUmSJGnazHUn2wFAAhcCpzV9nktU45ggSZouF1wAwFOe8hTOqjkUSZKkmhyAOaQkSV4jkKQp0k9zkdGh/3zJkyRNrje+EYAzMzvuJCVJkqaUOaQkabp4jUCSpsZchWyXU5KeG1s+S5IkSZLUyhxSkiRJ0lTpWMiWmdtFxH6Z+ZXG56FFJQ3I7Ozs8vczMzM1RiJJkiRNNnNISRqO5msdkiSpXovmGf6liDgxIjYaSjSSJEmSpHFmDilJkiRpasxXyBbAPwAXRsTfDyEeSRpPu+wCu+zCKdddV3ckkiRJdTKHlCTJawSSNDXmeiZbsy2Bb0fEccARmXnbAsYkSePnve8FYJ8LL6w5EEmSpJFgDilJml5eI5CkqTHfnWw/pNREzKp7KHBBRDxhoQOTJEmSJI0dc0hJkiRJU2O+O9meDBwGHAWsU/V7CHBWRHwA+AQleVpJZl4+yCA1XM/ilKZPe9cWhzRWzjkHgGftvTen1hyKJElSjcwhNXTmsJJGjtcIJGlqzFnIlpkJHBMRpwCfAp5ESYjWAN5cvVaZbL75arSdwj5Nn1bJfyW187a3AXDKkUcSNYciSZJUF3NI1cEcVpNudnZ2+fuZmZkaI1HXvEYgSVNjvuYiAcjMSzLzKcDbWLnpj04vSZIkSdKUMoeUJEmSNA26KmQDiIjdgUNYUS3M6mGSJEmSpLbMISVJkiRNunmb5IiI9YAPUJKj5b2BPwPfxERJkiRJklQxh5QkSZI0LeYsZIuIPYETgG1YuYmP04BXZObvFzxCDd0SZpreS+rKnnsC8IU//rHmQCRJkupjDqk6mMNKGjleI5CkqTHfnWz/wcq1DG8H3pyZxy5cSKrbbFNasqTjWJJW8va3A/CSX/2q5kAkSZJqZQ6poTOHlTRyvEYgSVNj3uYiKwH8CHh5Zl6ygPFIkiRJksafOaQkrYbZ2dm6Q5AkSV1Y1MU4dwFHAk8yOZKkDk4/HU4/nRdttlndkUiSJNXNHFKSNN28RiBJU2O+O9l+Rql5+LNhBCNJY+uoowD4wrvfzRdrDkWSJKlG5pCSJHmNQJKmxnyFbI/NzLuGEokkSZIkadyZQ0qSJEmaGnM2F2lyJEmSJEnqljmkJEmSpGnSzTPZJEmSJEmSJEmSJDWZr7lISVI3nvUsAI6/6qqaA5EkSZIkjaPZ2VkAZmZmao5Eq81rBJI0NSxkk6RBOPxwAA69+OKaA5EkSZIkSbXyGoEkTQ2bi5QkSZIkSZIkSZJ65J1sWsXBHN/06ZDa4pDGyqmnAnDwwQfzyZpDkSRJkqaJOaykkeM1AkmaGhayaRXHc2jTJxMUqStHHw3A8R/4gCfQkiRJ0hCZw0oaOV4jkKSpYSGbJEmSJEmSNGSzs7PL38/MzNQYiSRJ6pfPZJMkSZIkSZIkSZJ6ZCGbJEmSJEmSJEmS1CObi9QqDuG45e+Pn2M8SU322w+Ao6+4ouZAJEmSpOliDitp5HiNQJKmhoVsWsUnmx4UbYIidek1rwHgiEsuqTkQSZIkabqYw2qcND+HTRPMawSSNDVsLlKSJEmSJEmSJEnqkYVskjQIJ50EJ53Em7beuu5IJEmSJElSnbxGIElTY2QK2SJi64j4VERcFRF3RMSyiDgmIjbucT6bVNMtq+ZzVTVfj2qSFs7HPw4f/zhHP+QhdUciSZI0FcwhJY2L2dlZm4mcNl4jkKSpMRLPZIuIBwPnAJsB3wB+DTwOOAzYKyJ2zczru5jPptV8dgC+D3wJ2Ak4EHhmROySmb9bmF8hSZIkSRoGc0hJkiRJo2AkCtmAYynJ0esz8yONnhHxQeCNwHuAV3Uxn/dSkqMPZeabmubzeuDfqu/Za4BxS5IkSZKGzxxS0shqvmttZmamxkgkSdJCq72QLSK2B54GLAM+1jJ4BjgEeFlEHJ6Zt84xn/WAlwG3VtM1+ygl0fr7iNjemoiSJEmSNJ7MISWNIpuDlCRpOo3CM9n2qLqnZea9zQMy82bgh8C6wOPnmc8uwDrAD6vpmudzL3Ba9XH31Y5Yklrtvz/svz9LLr207kgkSZImnTmkJGm0eY1AkqZG7XeyATtW3Ys7DP8NpZbiDsDpqzkfqvlI0mAdcAAAs5ddVm8ckiRJk88cUpI02rxGIElTYxTuZNuw6t7YYXij/0ZDmo8kSZIkaXSZQ0qSJEkaCZGZ9QYQcTxwMHBwZp7QZvh7gbcCb83Mf55jPkdSHm79nsx8e5vhhwDHAcdl5ioPwK6GH1J93BG4qI+fs1DuB1xXdxBaLa7DyeL6nCyuz8ni+pwsrs/JMQrrctvMvH/NMWgAzCG7Mgrb3CRz+Q6Py3p4XNbD5fIenmlf1tP++xeay3dh1b18u8ohR6G5yEbtwA07DN+gZbwFmU9mHg8cP8931CIizsvMxXXHof65DieL63OyuD4ni+tzsrg+J4frUgNmDjkPt7mF5fIdHpf18Lish8vlPTzTvqyn/fcvNJfvwhqX5TsKzUU2avt1auf+oVW3Uzv5g56PJEmSJGl0mUNKkiRJGgmjUMh2RtV9WkSsFE9ErA/sCtwO/Pc88/nvarxdq+ma57OI8uDr5u+TJEmSJI0fc0hJkiRJI6H2QrbMvAQ4DdgOeG3L4FlgPeCzmXlro2dE7BQRO7XM5xbgc9X4S1rm84/V/L+Xmb8bYPjDMpJNkKgnrsPJ4vqcLK7PyeL6nCyuz8nhutTAmEN2xW1uYbl8h8dlPTwu6+FyeQ/PtC/raf/9C83lu7DGYvlGZtYdAxHxYOAcYDPgG8CvgL8Fdqc0zfGEzLy+afwEyMxomc+m1Xx2AL4P/Bh4GPBs4JpqPpcs9O+RJEmSJC0cc0hJkiRJo2AkCtkAIuKBwLuAvYBNgauBfwdmM/OGlnHbJkjVsE2AGWBfYAvgeuA7wDsz88qF/A2SJEmSpOEwh5QkSZJUt5EpZJOkURARBwCfBg7MzKX1RiNJkiRJ0uoz15UkaWHU/ky2URURWb3urZoi6TTeGU3jHjDEELsSEQ+PiJMi4pqI+EtEXBQRsxGxTptx14yIwyLi0xFxQUTcWf2uV9YR+0JorKu64+gkIp4QEd+OiBsi4raI+FlEvCEi1mgz7m5N/712r3+u4zfUpc51GxFbR8TbIuIrEfHbar+REfGQOaZ5XES8LyK+ExF/qMaf2prS1fJr/Hd3rDueTiLiYdU+9BsRcXlTzPfpYtpHRMRnI+KKiLij2i+fFREvH0bswxARa0TEwdXvuiEi7qp+588i4oSI2KfuGBsiYr2IeElEfCEifh0Rt0bEzRFxXkQcHhFrdZjuFRFxXEScW+2nMyKOGnb8wxYRi6vzg99FxO0RcVNE/Dwi/jUitlrNeR/Q73lURGwVEa+r9qXLqm3r+oj4j4h4bodpzpzn+JkR8f9W5zeNmja/746IuDYi/rfaNp/e7lxjFFTH2E9FxFVV3Msi4piI2LjNuD3/H6RBatrGzCEXIIcc9+U77sesxvcN47t6EREPjYh/iojvRznPvjMi/hjlfH33DtM8OSI+FxEXVuvgLxFxaUR8MyL2HPZvmE+dyz4ido2I90fE/1TnDndUy+qEmDvfXafab1xULd9rqv3Kw4YZ/3zCPHQYsY9NjggQEU+NiKMj4vQq3oyIs+cYv+t9e4xoTtU0j66P/9X4fxUR746IX1Xj/7labs/o+4f0Fm9jO7gsIu7bYZxl3W4rw1RtF2+otoPbq//atyPiCR3GX9rhHKDx2qnddKsZozlkDznkSP3BRtDdlGX0CuDI1oER8VDgKU3jjZSI+FvKcwXWBE4GrgD2AN4J7BkRe2bmHU2TrAccU73/I/AH4IHDi3i6RcSzga8CfwG+DNwA7A18CNgV2K/DpGcBZ7bp3/EkQAO3GDgKSOBS4EZgo3mmeTFwGHAX5RkiD1jIAEdZRARlP5tAAAcDR9QaVGd/T9mH3gP8hrK9tj2Za1ad6J4A3AacCiyj/Ef+BngG8NkFiXaIqpOrUylNdv0Z+BZwJbAJ8GDKf34n4Jt1xdjiScDnKfvaMyjNi21C2e9+AHhudZz8S8t0RwMbAn8CrqL8tolVbZ//DLyZcr7zH8BXgLWAJ1C21ddExP6ZeXINIb4O+CfKvvcMyrnLtsBzgb+LiA9l5ptapllK++NmY36bUJqJm0SzVXcNyj7or4GXUfbB50XESzLz4rqCaxWrPnPr18DjKMfPvSJi1+ZnbtHf/0EaNHPIhc0hx3n5esxaGO8GXgj8Evg25dxuR2AfYJ+IOCwzP9wyzR7V61zK//1WYJtqmr0j4qjMfMeQ4h91XwXuTzken0jZtnahbIP/EBFPzcwfNU8QEWtTzhl3Bc4D/o2yX9gPeGZE7JGZ5w7vJ7RnHrrweegY5ogAr6U8l/UvwG+BVS7Kt5h33w4czmjnVD0f/yNiI+C/KP+lXwDHUc4J9gG+1WHfu1C2Ad5AWcYjr9r3fAl4PnAR8FHKNvFC4AcR8bzM/EaHyf+Nsi21um4hYq2YQ3aTQ2amrzYvykH2SuB/KG3736fNOP9Sjff1qntA3XE3xbYG5SQzgX2a+i+i7CwTeEvLNGsBTwe2qD4vqcZ7Zd2/Z8DrNeuOo01cG1AerH4HsLip/32rHUMC/9AyzW5V/yV1xz8Kr0GtW+CAXrdnYGvKBfsNqs9nVvN4yBzT7Aw8ClirKf4r616ONa27v69+/6erg9m1jeUyai9Kwv63wDrV52VV7KscI5qmeTzlRPoCYPM2w9es+3cNaNm8tFoWFwAbthm+LrB73XE2xbMz8JLW/xqwPnB+9VsObzPdXsC21fvG/uKoun/PAi6nd7KiAsFftxn+POD26j/e1/rtZ7/bNO1zgae06f8wSoWHBB7T5bx2rMb/w6Rsl02/reMxklLJ46RqnMuBzeqOtym271Vxva6l/wer/p9YqP+DL1/9vDCHhAXMISdg+Y71MWuuY0nNy/UA4FFt+j8FuJOSY2/RMuy+Hea1FaWw+J7WaWr+jXXmuv8EbNmm/5HVvH7eZthbq2FfARY19X921f8Xzf1rXK7moQsf91jliFVMu1AKEdYAtqviP3uO8bvZt3+c0c6p+jn+H1P1/2rz/5BSKH9ptf996AKvq6RUrLi+Wtb3azPOvNtKDf+xF1Ux/bD5eAQ8lnLMugZYv2WapdU02w0xTnPIHs7Pav/ho/pixQn8IdX7fVuGr0k5CP+QFXewHNAyzmMoJcw/rTb6v1BqnBwNbNwy7quqebyzQzybU+54WeUEpsP4e1TzO6vNsO2rYcuonsvXYR5LmIJCNuYprKqW07KWfgc01jmwO6VQ5WbgJkrNnIf1GNdB1fw+0+26nC/uaXstxLpdjVjOZJ5Ctg7xT2shW+Ok7QnV/jGBF7YZbykdTirmWteUE5XTmrbR/6ScODf2cbutRuzLmD+5+UE1zt/UvawXeD0eW/3ON/Q43YsotYX+RDlO/gp4O7B2m3Gz2r62BD5HOfm8nVIo9uIB/pYXV991yjzjNfYXE1nIRkkq76IkSI+YY7zGOcyvablgQqmNdzorzoOWAV+kqlDStL9s99puNeM/ng6FpR3Gb+x/3lf3sl+AddkxQaqGL6q2wwSOaTN8E+B91fZ5OyXZOB142hzznHPddxFz43z10jb/q/WBWyh3Hqy3EP8HX776eWEOCcMpZBvL5TvPbxv5Y1a7YwkjkEvPE/Np1Xc9r4dpGgW0uw5r2dax7AcQ0xqUu6MS2LSpfwCXVf0f1Ga6Rm5Ue8EK5qHDWMZjnSPSRSHbPNM39u33MMI5FX0c/ykFK0n7QsN/rIYdvcD/r8Z5wRuq9x9pM07HbQV4QbWd3Fj9Z35OqSSwdtM496XcOXZNp+0N+ET1Hc/sMu6O+0HK3aVJeXZmc/+l3a7PAS/fnGO4OWTTy2eyze+LlIXf2qb8PpRS20/OMe3BwD9Qbv38NGWjuxp4E/DDiFi/adzPUw66r+zQnulBlOYujusy7j2q7ndbB2Tm74CLKbc+bt/l/NTesygnTTdR1u9/UW65Pysi7tfDfDquL8rO9zbgCVWzC60eEhH/GBFHRsRBVRMp0liIiAdQ9qcXZ+Y5lH0llIsng5j/kyjb0B6U5mM+Sjm4n0G5XXxBRUTjLsfzgF9ExO4RcUSUZ37tGRGTdBxu3G6/Q7cTRHmGyBeAhwBfAz5GOZl6N/DdDu2mb0y5w/cRlP/LZynHshMj4v/2Hf3K7qq6dw9ofuPqQMq5x9cz8+dzjHcCpenMHSm1xoliKaUZjP9DWb8fohwnn0Q5fkJJFhpNYXyD0hRF49WuGYxedL0eozyD7+WUE+i5zu0mUmbeS7kgDfCiqgkTACJiW8pFirdQanh/gtKs9cMo2+nBzfPqYd3Pp3FudFoVX3O8N1Muoq9LqaXdDbdrDZM55MIa1+U7l2k8Zg0ql55LT/v+iNiMcrfQHZT/iDpLVizXe5r6P5jSdNvFmXlpm+kazZvu0WbY0JiHDi0PnaQcsR+NfdAiRjun6uf4v3nV/V2b+TX6DesZlx8DLgEOjYiu/msR8V5W5DRfoGyjAbwX+F5ErAmQ5fERX6bcoff0NvNZm1JY90fKHVTzfe/alIL92yjrsdV8+8inR3kO6RERsW9EbDDfdy4Uc8gWwyr9HLcXTXeVUHZ0dwNbNw3/LqUEdl0615LbFlijzbwbbT7/U0v/j1b9n9XSPyg7qFtpc3t1h/i/whw1tihtIifw9DnmsQTvZIO5a4DdDezZMux91bA39xDX/1TTtL39FLiwGv6wNnG3e51MS03MSX8twLo9YDViORPvZOv2d7+l+u1vbep3PnBv6/KjxxqElBPZ37Tb17GilliygDUIgedUw7/Aiho+za+f9fI/GeUXpfnTO6t19znKLffbzjF+Y1v7GlWzJ03DGsefw1r6N5bbSazc9MyDKInXncD2A/gt36m+59B5xmv8hkm9k+306vcd3MW4J1bjvr363LjL4Me0nLtQaj5v0fR5tfe7beLZgHI3w710USOeFU12nFb3cl+gdTlnLcRqnLUpSUTSVPO8Oqbdy6rNVm9EafrnduABTf27XvfzxPOvzFFrkBXnza8e9P/Bl69+X5hDwhDuZBvX5TvH7xqLY1a7YwkjkEvPEe+2lFrwt9IhN6Y8W3tJ9X9ZSjmfvLubY8uYL/sDBhDTC6t5/ail/zOr/m1bhKA8gyiBL9e8TM1Dh5CHMuY5IqtxJxsr9u2N+EY2p6KP4z+lQDCBh7cZv3En218W+P/VfF7Q2Ld8rWWcVbYVyh2lSbkbb/Om/vcBTqmGHdlm/JPbxLBfNayru/YoTZEmHe6CpxyXEji3pf/SNttxUiqqvHYBl2/OM445ZPWapBr0C+mTlBV6ECwvjX0qcGJm3tZposy8LDPvaTPoU5SN4O9b+n+86h7a0v9plIPDlzPzxi5j3rDqdhq/0X+jLuen9r6Umae39Du+6vZSO6mf9XUt5cTwEZRbXhu1Kn5Cac/5lAm7S0YTpqrl8krKAav5gctLKRcuWmsn9+oJlNpvZ2Rm6wPhj6fUxlpom1XdF1Bq7DyXsr0/hJJkPILyUOC1hhDLgsrMn1Da3P9j1f0qsCwiro+Ir0fE3i2THEa5iHFQZt7eMuzdlFqPL2nzVfdQLoAtr5WUpZbshynNRL1sdX5HRPwj5blrF1CO19Nsi6p7RRfjNsbZsuq+ruoe2nrukpn3ZObVA4ivrWrfcgLlboaPZ+avupisUWv5+DnHmmBZHmTeqG18f4CIeCSlJu1XM/NLLeP/GZihNKHyvKZBg1r3AzmX7fP/IA2COeTCGsflu4opP2YNKpdeRXWnwImUi39LMvNPHUZdTDmWvQ3Yn3KB9cDM/HiH8QVExIOAj1DO5Q9vGTzy+xHz0OHloZOSI/aqZd/e2P+Mck7Vz3Z7atVd0ny3d0RsSrk7HGDtiFhnQDHOKTNPBn4EPCcinjjP6AdV3aMy8w9N82js0+6laT+QmT+ibLd7R8QmLfPav+p+pstQ+91H/oBSuWFbYB3KXcNHVMM+GhEDuQu3V+aQK7S7xVYtMvPciPg5cFBEHEXZ0BYxT9MM1a2lh1Kao3g4ZUU3F3ps1fI9v4iIH1Bu/XxgZjZ2ro0N5RNN894Z2LflK/+cmcd0+bMat3Bml+OrvfPa9Gust40bPSJiN0oNp2bLMnNpl9+zyvrKzF9QHhjccAvllttzKBeHdwX2ZsUt4+pTRBxAqcHU7MzMPHPowUyWPSgnBt/LzN839f8C8AHggIh4R2be1Xbq+T2q6p7dOiAz7622lZWaEoiIN7DqwfbfM/OCPmNYo6n7ysxsnIjeFBH7UxKexZSTiy/2+R0jIzNPioivU56v8UTKOngi5Xi1b0R8llLDbh3gkcB1wBuaWhVodgdl+bS6PNs3PXMm5WTtUQARsRGlbfZWx1QndquIiOdSHuD8B0otvn7/e5Oil3OF5eNGxHrA3wB/rBLr/gPo73znaEqNwv9iRYI313c8lJIE/BGPma3rfJequ2FELGkz/v2r7sMAeln3EbEdZX+wksxs9z3dxNpJT/8HaVDMIRfWBC3faT5mLUguXV3s/RwlH/4yJa9oKzM/AXwiIu5LKTR9FfDZiNg1M1/V1a8Yc73mulWTmt+hnAO8NktTiz19ZdWtcz9iHjrEPHTcc8Q+Ne/bN6Ps08Ytp2obV1O/d1IqnOwHPCwiTqfcSf5synMIb6s+t6vYslAOpzQbenREPD6r25HaeHTV/X7rgMy8OCKuBB4UERs1/S8+A7yHcv5wLCxvdvbvgZ9k5s8a81jN7bntPjIzWysA/47yOy+i3H33noj4fx0qEi00c0gsZOvFJyk1IPaiPKPk/C52cl+m3KL9O8oJ8B8oBwQoO/V2z9c6FngyJUmYiYjNKe1EX5CZP24ab2fKgaLZZZSLg7CiZHZD2tugZTz1Z5WDcGbeXZ0MNLfbvxurrq+zKDWlYIDrKzNviogvUGrjPZnxT75GwQFUbWK3OHO4YUycxsWJpc09M/P6iDiFcsL/bErzp/1obE9/7DC8Xf83UGoGNVtGKbjuR6PW2h2UtviXy8yMiG9QkpvHMQGFbABVMnpa9Wpc6HgepYb4yykPk/8fysnN/Vl13zifTuuzUQOtsd436jDvpbTZd0fEvpT2v6+hPIC4Xdvy0+ZqYCfKczXms3XTNI2E4vcdxu3FfOc7K4mIfwXeSKnp98yqZt18DqH8Hz89zQWr1QXGRs3Ma6vuplX3qdWrk7+qur2s++1ov40uqbqrfW7U5/9BGiRzyIU1bst3JR6zBpZLL1edd36ecmHsJOClc1xkbf7evwC/Ag6r7oLJR3EZAAAYIUlEQVQ7NCL+s7ozYtIdQJe5blXA9n3KM6MOy8xj20w3DvsR89Ah56HjmiP2o3XfDnyTss2Mck7V83abmX+IiMcCb6dU8H8N5X93KuWuw98BN2bmnasdfZcy80cRcTKl6cgXUI757TR+Z6e7o66mrK8NWfG/+Czld+1PVchGuavyPqx6F9tc2/NA95GZeWpE/J5SSejhwFzP/Rs4c8gVbEque5+jtBd6HOWPO2fTDBGxmHLy/p/ATpl5YGa+tSpZfRfQ6Zbsr1EODq+oDjptH6acmUszM1pe2zWN0nhIb6cHPj606g7jNvVR17idvFOhc6cNs2uZuaTN+tqtaZSO6yvKQ10fRLllvtuLvo0d23r9xjwhBrJuM3O3NutvyUAinFIRcX9W1KT6YkRk84sVt4033/I+1/psd6v3TVX3AR3CWKV/Zm7XZl0vnfPHzK2xbd+cLQ9drTSSn6E0oVCH6tb+kygPrYVSc7RxQvOTNst7pVebWXZan40HL99Yfe+yDvNc1jphROxHaYf+j8BTMtMH3ReN2rd/N9dI1fnKbtXHH7IiEdmq7QQ96OJ8pzmOD1GazDiD8ryAW+abf9VEzv6Ummxz3v0wBZ5I2b/+sWk7aWyrh82zrR5Yjdf1us/MM+fZ5lfrXLaf/4O0AMwhF9a4Ld/mWCblmDUKuTSwPG/+IuUugy8AL87S9FevGs37rfIdI2aouW5EbEEpeHs45Q62D3eY5UjvR8xDl6s1Dx2HHLEfHfbt45BT9bXdZua1mXlYZm6fmWtl5gMy8xWUa5hBKTgdtrdQnhH2vujcHGrjv7Z5h+FbtIxHZl5JqWTwuIjYqeq9f/VdX2ieeJ7t+beUu/u2r45brfrZR9Z5DdgcsmIhW5ey3B56MqVWwa3MX9PjIVX3m7lqDbPH0eFAVo17AuWPtTelttwtlPbEe9G45XWv1gERsT3lz3YZ3RfaTLLGycUDWwdExEMYTlvhHdcXpdbkusA52X0N7MdX3Wlfv6OwbtXe/pQLGecD/6/D61rg76K0+Q9zrE9KLbxWjZrMq7THHeV5hU/oN/ge/IzS3MX9qqYEWv1N1V02hFjqdnPVjepE5RfAX8eqbZrPZ5uqiYBWu1XdnprSiIgXU47pV1EK2H7TYzyTbCklAXhORPz1HOMdRHluwEXAWZl5K3Ah8ICIeNQc0zU0mrRYY86xOojiY5Qag/9BqW3W8Xk8LZ5DqS37nznFdy9W+8S3VR+bk8T/rrpP6mY+faz7uZxRdZ8WLc+YjYj1Kc2A3d4UY2PY6vwfpIEyh1xYY7h8J/GYNRL5VnUh9WTKHWyfBV6W/TeZ1bjI108B3TANbdlHxNaUuwd3Al6V7e9ga7gEuBzYoSmPa/b0qrtKM21DYh5ajEoeOpI5Yq/m2bcvZfRzqkEf/w+uuj0fJ1dXZl5CudPsQax4zlerxv9ht9YB1f5za+DSXLUJ0aVVd/+qCc7/A3wnM6+lS9V13XMo13nb5Vg97SMjYkPKvjkZ8vZsDtkiM321eVH+nFe29NuGUuNl15b+R1XjH9DU7/FVv6+2jLsZ5WCelHbE2333NpQTuiur8Y7rI/41gF9W0+/T1H8RpbZ+Am+ZZx5LqvFeWff6GPB6zZZ+a1JK2f8MbNbUfx3KbfWrrCtKkworrfM233NmD3FtQDmRuwNY3NT/vpSdbwL/0DLNrsCiNvN6KaWm1R3AdnUv80lbt13GcmY1j4f0GP+V/X7nuL2AX1e/+XFzjPPuapz3VJ9fWH3+Qst4j6CcnCflweaN/osotYSSUgOleZpXNf4zwG6r8TuWVfO4zxzjNI4Rn2neZqu4b6fUfOr6vzKqL+BFlKYA2u2XNgd+Uy2H/ap+B1Wf/x3YqM00GwOPbunXWGdfblmWDwJuqJblg3uIeX9KMvI7YNs+fnNjf3FU3ct/Adfru6rfeAnw8DbD96W0t383sEdT/4Or6X4MbNgyzSJgi6bPz6jGne0jvqDU5s9qv37fHqc/vZr2eXUv6wVej6scI5uGbUZpUispyfP9Wob/oNpODuow/SNY+Rjb9brvIu7vVfN6XUv/D1b9PzHI/4MvX6v7whwSFjCHnIDlO9bHrHbHEkYjl14b+FY13Qm0ORdtM81T2o1HeU7X76t5PbWO5Vznsu/w3dtQzgPvAQ7scpq3Vt/zFVY+Z3921f8X3aynBVqW5qFDzEMZwxyxZd7bVfM+e45x5t23M/o5Vc/H/2rYX7WZ1yur8X8CrLnA/69Vzguq/ptQCsdvoBQ2r7StUAq6E7gUuH/Lcvj3atjb2sx3Hcp+9wpKU5sJPKfP7SIpdyzet6n/YynXcq8BNmjZVlbZVinNLX69mtdpC7R8s8Mwc8iWl89k60FmXk6pkdON/6FsLM+N8lDTsym3Lz+dUivhqrm+JyK+RWnnHVqaoegy1nsi4kBKyffJVZu0lwN7Umra/JAVt2UvFxFvoZSAQ2mzF+DAiGjUwDk7M0/oNZ5Rlpl3RcS/Ae8AfhLlgaz3oZwIXMUc62qAMdwUEQdTat+dGRFfohwM9qG03Xwyq7YlfCKwqPp/XUkpkHsspRbm3cChOaBb3sfVMNdtRCxt+tjYhv4lIho1s07IzLObxt+Jcht7s43/f3t3HmVJVR9w/PsbBlkMsrvFEYgLRg0YjQxBRVCTYBAIeBCBoEPQrMZ4XFDjhhsSXI6IJ8YTEIwHxTWAYtyQAUFEQRA0DuugBkFQhmGRRfCXP3732dVvXi+vZ5rX0/39nFOnq17dW+/WrXrv1a/r1r1923ltZv5yXZVxrogavHxH4PIcP45FvxOpVjGHR8TbqHEzrgIObq0pL6SCvv3auhd1M2cNKv0y4CvAGRHxeeqidifqHPgf6jt5UPcZE5V9G8YPnr5Nr6ytexGAYzJzRSfN0dR370uAP4qI5VQr5BdSn9vXZObV0y3DHLYU+Bfgxog4j7pghQpu9qYuSE+njW2QmR+LiKdRfbdfExFfpX6ntmp5dgdOogLRrsvae10cEV+jusI5iGqte2RWy7UpRcSe1BgAi6jWTofHmoNrrzEYdDuner+JvRbx+7RzEmBFZh4znTKsJ46iup14NfCDdpx+RP1jZzfqWNwFHJyZ3dZ2J1D19BLgqqhxH26mWmc+h6r7o1raC6ig8lWt1WpvTIXjM3OqvujfSgVyd1F93L9hwHG8NDNP63+xtVLcs73fGVO8z7wQYwNPL6I+M0+ijtODqIDm0AG/O4dQ15MnRsQrqe/eW6kWnjtRLaH/lAoGYbhjP5V/pBobfSginkuNl7OUOm5XMtZysmfG54M0W4whgVmMIden+mUe/mbNhVga+A/qn8u/pG6QvXVAvS7PzOWd5dOBWyPiQuqfpYupG2x7tfnjM/Prs1zutfIA1v051I2Gi4HtOtcSXSf3/e/hA8ALqHGRLoyIs6i47UDqmu9vcnAXhrPKOHQkceh6FSMCtN+ul7XF3phRj+v+ryYzl3WyTOe7/RLqczEnY6oZ/v5vCvwiIr5O3VSGenJpF+p83z9HNHZoZt4SEUcDx06w/tsRcSxwJPDDtr93Up/LJ1PXB+8dkO+uiPgscAR1jv6KauQxrFOBA6jvyEuixoLcmjpnNwBenpm3ddI/ATg7Ii6g4qGbqKeu/4y6AXctY+fsOmcMCUwnhlzXdznny8QQT5UwoJVce30r6hHV64C7qS+Zo6kvouuYoJVcy9tr4fO9tdyPJ1KtDn5J3Q2/Eng7sMkE6Zcz1gpk0HTyqI/NWtTFBm0f7h2wLqgbHtcA91I/JsdOdKxYx63vOvmeQd01X9U+3JdTgy1uMCDt66lHWH/W0vbOsZOAnUdd3/P12E5Shsk+N4O+H/aYRp7tR123s3S8Tmn798pppP0andZBVBcdn6ZuQt9F/bPkgE59HjVgG0vbZ+X2Nn2D+jH/cMvzlCHKvv00jtseA/JtSl0QrKC+i1e3cjx/uu8916d2bP6Jakl1BTUWwb3UoMFfpp6yHdSC8QXU4Mg3tfQ3Uhdq76LGS+mmTep36pHUoPY3te++71PjbgxT3t5nfbLpugH5Tp4iz/JRH4tZOr67UK1gV7bP3h1Ulw7vAx41Sb5DqX/OrG7HamX7DuhvgboXFRjeMcx34DSOx4TXLsC/tfXvGXX9PgDHr79O7qGuDS+mWu3tNejz2cm/GfCvLf0d7RxYSQWVfws8eKbHfhplX0Jd29zQviN+AhwHbLUuzwcnp3UxYQw5q5+79b1+1+Y7ihH/ZjGHY+lpnH9rxAjUP/3PpH5Tft3O85+28/4vRlHHc6HuJzkeM4l/NqG+N65q9Xtzq981nuJ5AOvSOPQBjkNZz2LEvs/JhFNf+pOncWxObmnnZEzVyT/t33/qBuGJ7bje2abLqBsWazzhNkvn14TXBdRTzis79bDGU5/UGJ7nUZ/Pu6kbn29ikieZqBtBvW0evxZlX0z9v/fydi6sap+J3Sb4HH20ndM3U09nrm6fiTcBm81i/XYnY8hJpmgb0xzT7hK/jepm48QRF2deiBqo9+fA9Zn5qKnSa/3hsdVMRMT5VOCzeVYf0JrjWivNc3LAYPeSJC10xpCzayHXr/HW6Fj3849x6LpljChpLlg0dRI90NpAfH9PtZCZavBmTd/+7e8FIy2FZoPHVgNFxKYRscag3xGxjOqW4WsGNpIkaX1nDDm7rF/jrRGy7tdDxqGStLA4JtscEhF7A08F9qH6hn9tZv56tKVa/0XEO4DHU/1/3we8f7Ql0rrisdU0PJrq47rXT/hi4I+pR/xvBV4zwrJJkiStFWPI2bXQ69d4a3Ss+/WecagkLSB2FzmHtEE0X0oNTvkx4M05gsFg55v26PjtwEXAOzPz7BEXSeuIx1ZTiYgtqQFrn00NCLsR1Z/7N4B35xADIGv07ApEkqTxjCFn10KvX+Ot0bHu12/GoQ8cY0RJc4E32SRJkiRJkiRJkqQhOSabJEmSJEmSJEmSNCRvskmSJEmSJEmSJElD8iabJEmSJEmSJEmSNCRvskmS5qSIWB4R2abloy7PTHX2ISPiqFGXR5IkSZLmI2NISdIoeJNNkhaAiDggIr4UETdExL0RcXtE/DQivhMR/xkRfzfqMmp6ImJZX9C1/ajLJEmSJGl+MYacP4whJWl2LR51ASRJsysiTgCO6Ht5Q+D3gCXAUuBA4KMPcNGm8hHgS23+Z6MsiCRJkiQtFMaQkiRNnzfZJGkei4g/Z3xwdAnwVWA1sBWwE/DsERRtSpn56VGXQZIkSZIWEmNISZKGY3eRkjS/7dWZvwbYJTPfmJnHZOaRmbkXsC1w2KDMEfHQiHhnRFwcEatbNyE/j4jTI2KfvrQPiYj7Ol1QHNJZt2vn9VsjYlFn3Qmddd/uvD5hf/r9fdRHxFNbmVZFxF0R8d2I2HuCfXpsRHwmIm6JiDtbdycvjIg9+ra7x3QrOSI2j4gPRMTPIuLuiFgREa+PiA0nybO41e2ZEXF1K89vWv1c1NZt2Um/fUQkcFLfplZOUk9LIuK9EXFZ697lnoi4tnXvsuN090+SJEnSgmEMueY+GUMaQ0rShHySTZLmtw0681sAOwBXdRNk5h3AF/szRsTS9vq2faseAewL7BsRnwCWZeZvM/O2iLiI6joEYHfgk535ns2BnakWkf3rzprmfnXtBbwReFDntacDZ0TE8zLz7M4+PRk4F9iyk3Yp8Dng9Bm8NxGxWdvmTp2XdwSOAXabJOvGwJsHvL458LQ2HRYRu2bmjTMo118CpwKb9a3aAXgZ8NcRcWhmfmHYbUuSJEmat4whjSGNISVpCN5kk6T57fud+a2BKyLicuAiKkD5Vmb+oD9TRDwEOIOx4Oh+4L+A/6OCo53b64cBK4Cj2/JZjA+QGDAP1b3IJRHxCOBxnddnEiAtbeU6hRofoNf6cRFwJHB2J+3JjA+OvgJ8F3gusN8M3hvgHYwPji6lAsvHAAdPki+BlcB3gOuBVVRAuwNwELApsB0VRL0CuAV4HfAnbX3P0S0vtHEHImI74LNtG7T3+QxwN7WfT6ECtFMi4kmZee2Q+yxJkiRpfjKGNIYEY0hJmrbIzFGXQZI0SyJiMXAeY0HLIFcAb8jM0zr5/hn4UCfNyzPzhLZuI+Ay4PFt3Spg28y8PyKew/gg56HAr9q0Rfu7NXBaZu4fES8GPtXS3gVsmZn3tPdZzlhf/+dk5h6d8nV/vO4EdszM69u6/wb+qq27JTO3bq/vAlzYyXdqZh7c1i0Cvsn4sQX2zMzlgyqsU47FVODSa+l3JbBTZx/eQgVQPW/PzKP6trENdXyWUAHNImAfxoLKazPzMZ30yxjf3ccOmXld3zbfB7ymLd5I1c9tbd1GVEvUJW39cZn5qsn2U5IkSdLCYAxpDIkxpCQNxTHZJGkey8z7qBZ2b6da6g2yI/CFGN8//jM7870WiL1t3sNYFx5Qrfr+sM2fT7V0625nZyo4Avhg+7t7RATjA5LzeoHFkE7vBUfNFX1l63l6X74TezOZ+VuqheKwnsD4rjQ+3bcPH58oY0RsHBEnUAHMl4CPAO8H3sv4Vpu/P4NyPasz/3Bgda/Pfer4LOmsfyaSJEmShDEkxpBgDClJQ/EmmyTNc5l5Z2YelZlLqGDoJcBHgZs6yQJ4dWd5q878qsy8t2+z/f27b9Xe6x4qSOrZnbGL/Wuo/t176Z/M+ABpJt18AFzXt9wNUKIzv0Vfuhv6lofus37ANn8xxXLX0cARjB/zYJCNhi0U44/fVPrHS5AkSZK0gBlD/o4x5GDGkJLU4ZhskrSAZOaVVHcUn4iI11F94T+yrd6uk/SWzvyWEfGgviDp4X2b7qY/i2r5CBUcPbrNn5uZV0fE9VTLugMZa73YyzcTv+lbnqgf5Fv7lh8K/Kiz3L9P09G/zYdNsdz14s78D6lxAFZk5m8i4liq7/yZ6h6PnwAfniTt7WvxPpIkSZLmMWPIcYwhizGkJHV4k02S5rGIeCnwYOCTmdl/MX8P0A16ftWZPx94UZvfgGq52O1P/5BO2lXAjzvL3UBnZ2oQZoBz299vUcHBKzvpbmX8ANuz4bt9y4fSBrRu/ekvm8E2V1ABRq+7j4Mi4t2d7j5eOknebTrzZ2fm5a0sm1ADg0+kPyDcdECa84Bd2vzDgDMz88f9iSJiV8Z3zSJJkiRpATOGHMcYso8xpCStyZtskjS/7QC8DfhgRJwPXALcTF3Q7w1s30n75c78x4E3M9YNxEciYjeqT/59GRuwGuADmXl/Z/liYDWwORVc9fq07wVI51AB0uadPGe3Pu1nTWZ+LyIuBp7WXjoiIral6uR5wDNmsM37IuIkxoK9xwPfiYgvAn/A+ECy3xVUdycAL2993d9Gtc7ccZJ8/eMi/HtEfAW4D1iemRcBxwP/AGwCbAxcGBGfo7pb2RB4LNVCdAlwOHDpNHZXkiRJ0vxnDNkYQxpDStJ0eJNNkhaGDYE92jTIRcD7eguZuToi9gO+CGxN/V4cPiDfp4D3dF/IzPsjYjmwX+fl6zPz2jZ/LmuaaTcfw1rW3r8XtO3LWIu/M6mgsWe6AdtbgOcwFuw8pU1QrRz3nCDfO4DPtPmNGQuybgc+D7xwgnwXUEHSo9rysxkbl+B1wEWZeV1EHEgdn83aNOj4SZIkSdIgxpBlGcaQkqRJLBp1ASRJs+qDwAHAh6gL65XAnVR3ETdRF/CvAJ6RmeP6Vc/MC4AnUYMrXwrcQbV0u5EKnPbPzEP6WiD29Ac8vwuKMvN/qZaQXd+cyc4NKzN/CCwFPkd1L3IXFRy+GDilL/mqaW7zNuBZwHHAz6nuU66mWn8+f5J8n6WOzcUtzyrgDGBXqn/9ifLdC+xFBXSrmGD8gMw8E3gicAzVjcrt1HG/nur25Diq9WX/fkuSJElauIwhO4whjSElaSqROdHYnpIkzS8RsSGQmXnfgHWnMdZy8jZgm8zs77tekiRJkrRAGENKkqZid5GSpIXkMcDZEfFJ4HKqNeQjgIOoFnk9HzY4kiRJkqQFzxhSkjQpn2STJC0YEfEE4MdTJDsVOGxQS0VJkiRJ0sJhDClJmopPskmSFpIbqMG5dwd2ALagxgi4AbgQ+HhmfnV0xZMkSZIkzSHGkJKkSfkkmyRJkiRJkiRJkjSkRaMugCRJkiRJkiRJkrS+8SabJEmSJEmSJEmSNCRvskmSJEmSJEmSJElD8iabJEmSJEmSJEmSNCRvskmSJEmSJEmSJElD8iabJEmSJEmSJEmSNKT/BxjQahzhX+CsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2160x2952 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Single simulation (Yield, WP_ET)\n",
    "\n",
    "fig1, ((ax5, ax10) , (ax15, ax7) , (ax20, ax9),  (ax14, ax4), (ax19, ax18), (ax2, ax11), (ax12, ax8),\n",
    "       (ax17, ax3), (ax6, ax16), ) = plt.subplots(nrows=9, ncols=2, figsize=(30, 41), )#sharex=True,) figsize=(20, 10)) \n",
    "\n",
    "# # Bog\n",
    "# ax.bar( Bog1['Mon-Day'], Bog1['Yield ton/ha'],  color = 'gray')\n",
    "# ax.bar( Bog2['Mon-Day'], Bog2['Yield ton/ha'], color = 'black')\n",
    "# ax.bar( Bog3['Mon-Day'],Bog3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "# ax.set_xticks(p,)\n",
    "\n",
    "# ax.set_title('90-Maize Yield variation - Bogande', fontsize = 12, fontweight='bold')\n",
    "# ax.set_ylabel('Yied (Ton/ha)', fontsize = 12, fontweight='bold')\n",
    "# # ax.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "# ax.set_ylim([0.0,1.8])\n",
    "\n",
    "\n",
    "# Bor\n",
    "ax2.bar( Bor1['Mon-Day'], Bor1['Yield ton/ha'],  color = 'gray')\n",
    "ax2.bar( Bor2['Mon-Day'], Bor2['Yield ton/ha'], color = 'black')\n",
    "ax2.bar( Bor3['Mon-Day'],Bor3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax2.set_xticks(p,)\n",
    "\n",
    "ax2.set_title('Boromo', fontsize = 20, fontweight='bold')\n",
    "ax2.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax2.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax2.set_ylim([0.0,1.8])\n",
    "\n",
    "ax2.vlines('May-01',0,5, 'r',  linewidth=3)\n",
    "ax2.vlines('May-25',0,5, 'b', ls='--', linewidth=3)\n",
    "ax2.vlines('Jun-05',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Daf\n",
    "ax3.bar( Daf1['Mon-Day'], Daf1['Yield ton/ha'],  color = 'gray')\n",
    "ax3.bar( Daf2['Mon-Day'], Daf2['Yield ton/ha'], color = 'black')\n",
    "ax3.bar( Daf3['Mon-Day'],Daf3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax3.set_xticks(p,)\n",
    "\n",
    "ax3.set_title('Daffiama', fontsize = 20, fontweight='bold')\n",
    "ax3.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax3.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax3.set_ylim([0.0,1.8])\n",
    "\n",
    "ax3.vlines('May-04',0,5, 'r',  linewidth=3)\n",
    "ax3.vlines('May-04',0,5, 'b', ls='--', linewidth=3)\n",
    "ax3.vlines('May-19',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Ded\n",
    "ax4.bar( Ded1['Mon-Day'], Ded1['Yield ton/ha'],  color = 'gray')\n",
    "ax4.bar( Ded2['Mon-Day'], Ded2['Yield ton/ha'], color = 'black')\n",
    "ax4.bar( Ded3['Mon-Day'],Ded3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax4.set_xticks(p,)\n",
    "\n",
    "ax4.set_title('Dedougou', fontsize = 20, fontweight='bold')\n",
    "ax4.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax4.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax4.set_ylim([0.0,1.8])\n",
    "\n",
    "ax4.vlines('May-01',0,5, 'r',  linewidth=3)\n",
    "ax4.vlines('May-26',0,5, 'b', ls='--', linewidth=3)\n",
    "ax4.vlines('Jul-05',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Dor\n",
    "ax5.bar( Dor1['Mon-Day'], Dor1['Yield ton/ha'],  color = 'gray')\n",
    "ax5.bar( Dor2['Mon-Day'], Dor2['Yield ton/ha'], color = 'black')\n",
    "ax5.bar( Dor3['Mon-Day'],Dor3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax5.set_xticks(p,)\n",
    "\n",
    "ax5.set_title('Dori', fontsize = 20, fontweight='bold')\n",
    "ax5.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax5.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax5.set_ylim([0.0,1.8])\n",
    "\n",
    "ax5.vlines('May-27',0,5, 'r',  linewidth=3)\n",
    "ax5.vlines('May-27',0,5, 'b', ls='--', linewidth=3)\n",
    "ax5.vlines('May-27',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Gao\n",
    "ax6.bar( Gao1['Mon-Day'], Gao1['Yield ton/ha'],  color = 'gray')\n",
    "ax6.bar( Gao2['Mon-Day'], Gao2['Yield ton/ha'], color = 'black')\n",
    "ax6.bar( Gao3['Mon-Day'],Gao3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax6.set_xticks(p,)\n",
    "\n",
    "ax6.set_title('Gaoua', fontsize = 20, fontweight='bold')\n",
    "ax6.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "ax6.set_xlabel('Sowing date ', fontsize = 20, fontweight='bold')\n",
    "\n",
    "ax6.set_ylim([0.0,1.8])\n",
    "\n",
    "ax6.vlines('May-10',0,5, 'r',  linewidth=3)\n",
    "ax6.vlines('May-10',0,5, 'b', ls='--', linewidth=3)\n",
    "ax6.vlines('Jul-13',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Kou\n",
    "ax7.bar( Kou1['Mon-Day'], Kou1['Yield ton/ha'],  color = 'gray')\n",
    "ax7.bar( Kou2['Mon-Day'], Kou2['Yield ton/ha'], color = 'black')\n",
    "ax7.bar( Kou3['Mon-Day'],Kou3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax7.set_xticks(p,)\n",
    "\n",
    "ax7.set_title('Kourounikoto', fontsize = 20, fontweight='bold')\n",
    "ax7.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax7.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax7.set_ylim([0.0,1.8])\n",
    "\n",
    "ax7.vlines('May-23',0,5, 'r',  linewidth=3)\n",
    "ax7.vlines('May-23',0,5, 'b', ls='--', linewidth=3)\n",
    "ax7.vlines('May-23',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Man\n",
    "ax8.bar( Man1['Mon-Day'], Man1['Yield ton/ha'],  color = 'gray')\n",
    "ax8.bar( Man2['Mon-Day'], Man2['Yield ton/ha'], color = 'black')\n",
    "ax8.bar( Man3['Mon-Day'],Man3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax8.set_xticks(p,)\n",
    "\n",
    "ax8.set_title('Mandouri', fontsize = 20, fontweight='bold')\n",
    "ax8.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax8.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax8.set_ylim([0.0,1.8])\n",
    "\n",
    "ax8.vlines('Jul-20',0,5, 'r',  linewidth=3)\n",
    "ax8.vlines('Jul-20',0,5, 'b', ls='--', linewidth=3)\n",
    "ax8.vlines('Aug-14',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Oua\n",
    "ax9.bar( Oua1['Mon-Day'], Oua1['Yield ton/ha'],  color = 'gray')\n",
    "ax9.bar( Oua2['Mon-Day'], Oua2['Yield ton/ha'], color = 'black')\n",
    "ax9.bar( Oua3['Mon-Day'],Oua3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax9.set_xticks(p,)\n",
    "\n",
    "ax9.set_title('Ouahigouya', fontsize = 20, fontweight='bold')\n",
    "ax9.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax9.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax9.set_ylim([0.0,1.8])\n",
    "\n",
    "ax9.vlines('May-11',0,5, 'r',  linewidth=3)\n",
    "ax9.vlines('May-27',0,5, 'b', ls='--', linewidth=3)\n",
    "ax9.vlines('May-27',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Ous\n",
    "ax10.bar( Ous1['Mon-Day'], Ous1['Yield ton/ha'],  color = 'gray')\n",
    "ax10.bar( Ous2['Mon-Day'], Ous2['Yield ton/ha'], color = 'black')\n",
    "ax10.bar( Ous3['Mon-Day'],Ous3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax10.set_xticks(p,)\n",
    "\n",
    "ax10.set_title('Oussoubidiagna', fontsize = 20, fontweight='bold')\n",
    "ax10.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax10.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax10.set_ylim([0.0,1.8])\n",
    "\n",
    "ax10.vlines('Jun-07',0,5, 'r',  linewidth=3)\n",
    "ax10.vlines('Jun-07',0,5, 'b', ls='--', linewidth=3)\n",
    "ax10.vlines('Jun-07',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Po\n",
    "ax11.bar( Po1['Mon-Day'], Po1['Yield ton/ha'],  color = 'gray')\n",
    "ax11.bar( Po2['Mon-Day'], Po2['Yield ton/ha'], color = 'black')\n",
    "ax11.bar( Po3['Mon-Day'],Po3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax11.set_xticks(p,)\n",
    "\n",
    "ax11.set_title('Po', fontsize = 20, fontweight='bold')\n",
    "ax11.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax11.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax11.set_ylim([0.0,1.8])\n",
    "\n",
    "ax11.vlines('May-04',0,5, 'r',  linewidth=3)\n",
    "ax11.vlines('May-17',0,5, 'b', ls='--', linewidth=3)\n",
    "ax11.vlines('May-04',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Pus\n",
    "ax12.bar( Pus1['Mon-Day'], Pus1['Yield ton/ha'],  color = 'gray')\n",
    "ax12.bar( Pus2['Mon-Day'], Pus2['Yield ton/ha'], color = 'black')\n",
    "ax12.bar( Pus3['Mon-Day'],Pus3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax12.set_xticks(p,)\n",
    "\n",
    "ax12.set_title('Pusiga', fontsize = 20, fontweight='bold')\n",
    "ax12.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax12.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax12.set_ylim([0.0,1.8])\n",
    "\n",
    "ax12.vlines('May-05',0,5, 'r',  linewidth=3)\n",
    "ax12.vlines('May-05',0,5, 'b', ls='--', linewidth=3)\n",
    "ax12.vlines('May-24',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# # Sel\n",
    "# ax13.bar( Sel1['Mon-Day'], Sel1['Yield ton/ha'],  color = 'gray')\n",
    "# ax13.bar( Sel2['Mon-Day'], Sel2['Yield ton/ha'], color = 'black')\n",
    "# ax13.bar( Sel3['Mon-Day'],Sel3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "# ax13.set_xticks(p,)\n",
    "\n",
    "# ax13.set_title('90-Maize Yield variation - Selingue', fontsize = 12, fontweight='bold')\n",
    "# ax13.set_ylabel('Yied (Ton/ha)', fontsize = 12, fontweight='bold')\n",
    "# # ax13.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "# ax13.set_ylim([0.0,1.8])\n",
    "\n",
    "\n",
    "# Sir\n",
    "ax14.bar( Sir1['Mon-Day'], Sir1['Yield ton/ha'],  color = 'gray')\n",
    "ax14.bar( Sir2['Mon-Day'], Sir2['Yield ton/ha'], color = 'black')\n",
    "ax14.bar( Sir3['Mon-Day'],Sir3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax14.set_xticks(p,)\n",
    "\n",
    "ax14.set_title('Sirakoro', fontsize = 20, fontweight='bold')\n",
    "ax14.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax14.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax14.set_ylim([0.0,1.8])\n",
    "\n",
    "ax14.vlines('Jul-19',0,5, 'r',  linewidth=3)\n",
    "ax14.vlines('Jul-19',0,5, 'b', ls='--', linewidth=3)\n",
    "ax14.vlines('Sep-01',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "# Som\n",
    "ax15.bar( Som1['Mon-Day'], Som1['Yield ton/ha'],  color = 'gray')\n",
    "ax15.bar( Som2['Mon-Day'], Som2['Yield ton/ha'], color = 'black')\n",
    "ax15.bar( Som3['Mon-Day'],Som3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax15.set_xticks(p,)\n",
    "\n",
    "ax15.set_title('Somo', fontsize = 20, fontweight='bold')\n",
    "ax15.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax15.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax15.set_ylim([0.0,1.8])\n",
    "\n",
    "ax15.vlines('Jun-07',0,5, 'r',  linewidth=3)\n",
    "ax15.vlines('Jul-11',0,5, 'b', ls='--', linewidth=3)\n",
    "ax15.vlines('Jul-11',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "\n",
    "# Tan\n",
    "ax17.bar( Tan1['Mon-Day'], Tan1['Yield ton/ha'],  color = 'gray')\n",
    "ax17.bar( Tan2['Mon-Day'], Tan2['Yield ton/ha'], color = 'black')\n",
    "ax17.bar( Tan3['Mon-Day'],Tan3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax17.set_xticks(p,)\n",
    "\n",
    "ax17.set_title('Tanguieta', fontsize = 20, fontweight='bold')\n",
    "ax17.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax17.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax17.set_ylim([0.0,1.8])\n",
    "\n",
    "ax17.vlines('May-25',0,5, 'r',  linewidth=3)\n",
    "ax17.vlines('May-25',0,5, 'b', ls='--', linewidth=3)\n",
    "ax17.vlines('Jun-04',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Far\n",
    "ax18.bar( Far1['Mon-Day'], Far1['Yield ton/ha'],  color = 'gray')\n",
    "ax18.bar( Far2['Mon-Day'], Far2['Yield ton/ha'], color = 'black')\n",
    "ax18.bar( Far3['Mon-Day'],Far3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax18.set_xticks(p,)\n",
    "\n",
    "ax18.set_title('Fari', fontsize = 20, fontweight='bold')\n",
    "ax18.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax18.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax18.set_ylim([0.0,1.8])\n",
    "\n",
    "ax18.vlines('Jun-30',0,5, 'r',  linewidth=3)\n",
    "ax18.vlines('Jun-30',0,5, 'b', ls='--', linewidth=3)\n",
    "ax18.vlines('Jul-29',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Fin\n",
    "ax19.bar( Fin1['Mon-Day'], Fin1['Yield ton/ha'],  color = 'gray')\n",
    "ax19.bar( Fin2['Mon-Day'], Fin2['Yield ton/ha'], color = 'black')\n",
    "ax19.bar( Fin3['Mon-Day'],Fin3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax19.set_xticks(p,)\n",
    "\n",
    "ax19.set_title('Finkoloni', fontsize = 20, fontweight='bold')\n",
    "ax19.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax19.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax19.set_ylim([0.0,1.8])\n",
    "\n",
    "ax19.vlines('May-14',0,5, 'r',  linewidth=3)\n",
    "ax19.vlines('Jun-05',0,5, 'b', ls='--', linewidth=3)\n",
    "ax19.vlines('Jun-02',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Uni\n",
    "ax20.bar( Uni1['Mon-Day'], Uni1['Yield ton/ha'],  color = 'gray')\n",
    "ax20.bar( Uni2['Mon-Day'], Uni2['Yield ton/ha'], color = 'black')\n",
    "ax20.bar( Uni3['Mon-Day'], Uni3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax20.set_xticks(p,)\n",
    "\n",
    "ax20.set_title('Unimaid', fontsize = 20, fontweight='bold')\n",
    "ax20.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "# ax20.set_xlabel('Sowing date ', fontsize = 12, fontweight='bold')\n",
    "\n",
    "ax20.set_ylim([0.0,1.8])\n",
    "\n",
    "ax20.vlines('Jun-14',0,5, 'r',  linewidth=3)\n",
    "ax20.vlines('Jun-14',0,5, 'b', ls='--', linewidth=3)\n",
    "ax20.vlines('Jul-09',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# Tam\n",
    "ax16.bar( Tam1['Mon-Day'], Tam1['Yield ton/ha'],  color = 'gray')\n",
    "ax16.bar( Tam2['Mon-Day'], Tam2['Yield ton/ha'], color = 'black')\n",
    "ax16.bar( Tam3['Mon-Day'],Tam3['Yield ton/ha'], color = 'gray')\n",
    "\n",
    "\n",
    "ax16.set_xticks(q,)\n",
    "\n",
    "ax16.set_title('Tamale', fontsize = 20, fontweight='bold')\n",
    "ax16.set_ylabel('Yied (Ton/ha)', fontsize = 20, fontweight='bold')\n",
    "ax16.set_xlabel('Sowing date ', fontsize = 20, fontweight='bold')\n",
    "\n",
    "ax16.set_ylim([0.0,1.8])\n",
    "\n",
    "ax16.vlines('May-19',0,5, 'r',  linewidth=3)\n",
    "ax16.vlines('May-19',0,5, 'b', ls='--', linewidth=3)\n",
    "ax16.vlines('Aug-14',0,5, 'c', ls=':',  linewidth=3)\n",
    "\n",
    "\n",
    "# plt.legend(fontsize = 12)\n",
    "\n",
    "#plt.colorbar(sctr, ax=ax, )\n",
    "\n",
    "\n",
    "plt.subplots_adjust(hspace=0.3)\n",
    "\n",
    "# plt.savefig(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\All_Yield_response204.png')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "# frames = [Bog, Bor, Daf, Ded, Dor, Far, Fin, Gao, Kou, Man, Oua, Ous, Po, Pus, Sel, Sir, Som, Tam, Tan]\n",
    "# Y_2019 = pd.concat(frames)\n",
    "# Y_2019.to_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Yield_data_19.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 702,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sagoungbome\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# frames = [Bog, Bor, Daf, Ded, Dor, Far, Fin, Gao, Kou, Man, Oua, Ous, Po, Pus, Sel, Sir, Som, Tam, Tan, Uni]\n",
    "# frame = [Y_All, Uni]\n",
    "# Y_All = pd.concat(frame)\n",
    "# Y_All.to_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Yield_data_All.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_All = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Yield_data_All.csv',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "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>BioMass ton/ha</th>\n",
       "      <th>Brelative %</th>\n",
       "      <th>CO2 ppm</th>\n",
       "      <th>Cycle days</th>\n",
       "      <th>Day</th>\n",
       "      <th>DayN</th>\n",
       "      <th>Drain mm</th>\n",
       "      <th>E mm</th>\n",
       "      <th>E/Ex %</th>\n",
       "      <th>ETo mm</th>\n",
       "      <th>...</th>\n",
       "      <th>Tr mm</th>\n",
       "      <th>Tr/Trx %</th>\n",
       "      <th>TrW mm</th>\n",
       "      <th>Upflow mm</th>\n",
       "      <th>WPet kg/m3</th>\n",
       "      <th>WeedStr %</th>\n",
       "      <th>Year</th>\n",
       "      <th>YearN</th>\n",
       "      <th>Yield red (%)</th>\n",
       "      <th>Yield ton/ha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.351</td>\n",
       "      <td>30</td>\n",
       "      <td>406.21</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>179</td>\n",
       "      <td>36</td>\n",
       "      <td>468</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>99</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.482</td>\n",
       "      <td>31</td>\n",
       "      <td>406.21</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>36</td>\n",
       "      <td>472</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>99</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>13</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>98</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.614</td>\n",
       "      <td>32</td>\n",
       "      <td>406.21</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>181</td>\n",
       "      <td>36</td>\n",
       "      <td>476</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>99</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>13</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>97</td>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.744</td>\n",
       "      <td>33</td>\n",
       "      <td>406.21</td>\n",
       "      <td>47</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>182</td>\n",
       "      <td>36</td>\n",
       "      <td>479</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>99</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>13</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>96</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.872</td>\n",
       "      <td>34</td>\n",
       "      <td>406.21</td>\n",
       "      <td>48</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>183</td>\n",
       "      <td>36</td>\n",
       "      <td>483</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>99</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>95</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.996</td>\n",
       "      <td>35</td>\n",
       "      <td>406.21</td>\n",
       "      <td>49</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>183</td>\n",
       "      <td>36</td>\n",
       "      <td>485</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>100</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>93</td>\n",
       "      <td>0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.116</td>\n",
       "      <td>35</td>\n",
       "      <td>406.21</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>184</td>\n",
       "      <td>36</td>\n",
       "      <td>489</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>100</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>90</td>\n",
       "      <td>0.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.234</td>\n",
       "      <td>35</td>\n",
       "      <td>406.21</td>\n",
       "      <td>51</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>185</td>\n",
       "      <td>36</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>100</td>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>87</td>\n",
       "      <td>0.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.342</td>\n",
       "      <td>36</td>\n",
       "      <td>406.21</td>\n",
       "      <td>52</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>185</td>\n",
       "      <td>36</td>\n",
       "      <td>494</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>100</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>83</td>\n",
       "      <td>0.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.445</td>\n",
       "      <td>36</td>\n",
       "      <td>406.21</td>\n",
       "      <td>53</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>36</td>\n",
       "      <td>498</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>100</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>80</td>\n",
       "      <td>0.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.544</td>\n",
       "      <td>36</td>\n",
       "      <td>406.21</td>\n",
       "      <td>54</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>36</td>\n",
       "      <td>499</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>100</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>76</td>\n",
       "      <td>0.391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.635</td>\n",
       "      <td>36</td>\n",
       "      <td>406.21</td>\n",
       "      <td>55</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>36</td>\n",
       "      <td>502</td>\n",
       "      <td>...</td>\n",
       "      <td>33</td>\n",
       "      <td>100</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>72</td>\n",
       "      <td>0.457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.720</td>\n",
       "      <td>36</td>\n",
       "      <td>406.21</td>\n",
       "      <td>56</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>189</td>\n",
       "      <td>36</td>\n",
       "      <td>506</td>\n",
       "      <td>...</td>\n",
       "      <td>35</td>\n",
       "      <td>100</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0.28</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>68</td>\n",
       "      <td>0.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.799</td>\n",
       "      <td>35</td>\n",
       "      <td>406.21</td>\n",
       "      <td>57</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>191</td>\n",
       "      <td>37</td>\n",
       "      <td>511</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>100</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>63</td>\n",
       "      <td>0.595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.875</td>\n",
       "      <td>35</td>\n",
       "      <td>406.21</td>\n",
       "      <td>58</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>193</td>\n",
       "      <td>37</td>\n",
       "      <td>514</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>100</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0.34</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>59</td>\n",
       "      <td>0.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.932</td>\n",
       "      <td>35</td>\n",
       "      <td>406.21</td>\n",
       "      <td>59</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>196</td>\n",
       "      <td>37</td>\n",
       "      <td>519</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>98</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0.37</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>55</td>\n",
       "      <td>0.738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.980</td>\n",
       "      <td>34</td>\n",
       "      <td>406.21</td>\n",
       "      <td>60</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>197</td>\n",
       "      <td>37</td>\n",
       "      <td>522</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>98</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>50</td>\n",
       "      <td>0.809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.016</td>\n",
       "      <td>33</td>\n",
       "      <td>406.21</td>\n",
       "      <td>61</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>197</td>\n",
       "      <td>37</td>\n",
       "      <td>526</td>\n",
       "      <td>...</td>\n",
       "      <td>44</td>\n",
       "      <td>96</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0.43</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>46</td>\n",
       "      <td>0.877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.042</td>\n",
       "      <td>33</td>\n",
       "      <td>406.21</td>\n",
       "      <td>62</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>198</td>\n",
       "      <td>37</td>\n",
       "      <td>531</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>93</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>42</td>\n",
       "      <td>0.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.063</td>\n",
       "      <td>32</td>\n",
       "      <td>406.21</td>\n",
       "      <td>63</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>198</td>\n",
       "      <td>37</td>\n",
       "      <td>535</td>\n",
       "      <td>...</td>\n",
       "      <td>46</td>\n",
       "      <td>91</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0.49</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>38</td>\n",
       "      <td>1.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.079</td>\n",
       "      <td>31</td>\n",
       "      <td>406.21</td>\n",
       "      <td>64</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>198</td>\n",
       "      <td>37</td>\n",
       "      <td>539</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>89</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0.51</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>34</td>\n",
       "      <td>1.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.092</td>\n",
       "      <td>31</td>\n",
       "      <td>406.21</td>\n",
       "      <td>65</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>198</td>\n",
       "      <td>37</td>\n",
       "      <td>543</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>87</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>30</td>\n",
       "      <td>1.142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.121</td>\n",
       "      <td>30</td>\n",
       "      <td>406.21</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>37</td>\n",
       "      <td>546</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>86</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>26</td>\n",
       "      <td>1.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.171</td>\n",
       "      <td>30</td>\n",
       "      <td>406.21</td>\n",
       "      <td>67</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>201</td>\n",
       "      <td>37</td>\n",
       "      <td>548</td>\n",
       "      <td>...</td>\n",
       "      <td>49</td>\n",
       "      <td>87</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>21</td>\n",
       "      <td>1.295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.214</td>\n",
       "      <td>30</td>\n",
       "      <td>406.21</td>\n",
       "      <td>68</td>\n",
       "      <td>25</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>37</td>\n",
       "      <td>551</td>\n",
       "      <td>...</td>\n",
       "      <td>50</td>\n",
       "      <td>87</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.63</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>16</td>\n",
       "      <td>1.376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3.257</td>\n",
       "      <td>30</td>\n",
       "      <td>406.21</td>\n",
       "      <td>69</td>\n",
       "      <td>26</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>204</td>\n",
       "      <td>37</td>\n",
       "      <td>552</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>87</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>15</td>\n",
       "      <td>1.394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3.299</td>\n",
       "      <td>29</td>\n",
       "      <td>406.21</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>206</td>\n",
       "      <td>38</td>\n",
       "      <td>557</td>\n",
       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>87</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0.63</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>14</td>\n",
       "      <td>1.412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.340</td>\n",
       "      <td>29</td>\n",
       "      <td>406.21</td>\n",
       "      <td>71</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>208</td>\n",
       "      <td>38</td>\n",
       "      <td>562</td>\n",
       "      <td>...</td>\n",
       "      <td>54</td>\n",
       "      <td>88</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0.63</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>1.430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3.375</td>\n",
       "      <td>29</td>\n",
       "      <td>406.21</td>\n",
       "      <td>72</td>\n",
       "      <td>29</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>209</td>\n",
       "      <td>38</td>\n",
       "      <td>563</td>\n",
       "      <td>...</td>\n",
       "      <td>55</td>\n",
       "      <td>88</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0.63</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>1.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3.402</td>\n",
       "      <td>29</td>\n",
       "      <td>406.21</td>\n",
       "      <td>73</td>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>210</td>\n",
       "      <td>38</td>\n",
       "      <td>565</td>\n",
       "      <td>...</td>\n",
       "      <td>55</td>\n",
       "      <td>88</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>1.456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>648 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     BioMass ton/ha  Brelative %  CO2 ppm  Cycle days  Day  DayN   Drain mm  \\\n",
       "0             1.351           30   406.21          44    1     29         0   \n",
       "1             1.482           31   406.21          45    2     30         0   \n",
       "2             1.614           32   406.21          46    3     31         0   \n",
       "3             1.744           33   406.21          47    4      1         0   \n",
       "4             1.872           34   406.21          48    5      2         0   \n",
       "5             1.996           35   406.21          49    6      3         0   \n",
       "6             2.116           35   406.21          50    7      4         0   \n",
       "7             2.234           35   406.21          51    8      5         0   \n",
       "8             2.342           36   406.21          52    9      6         0   \n",
       "9             2.445           36   406.21          53   10      7         0   \n",
       "10            2.544           36   406.21          54   11      8         0   \n",
       "11            2.635           36   406.21          55   12      9         0   \n",
       "12            2.720           36   406.21          56   13     10         0   \n",
       "13            2.799           35   406.21          57   14     11         0   \n",
       "14            2.875           35   406.21          58   15     12         0   \n",
       "15            2.932           35   406.21          59   16     13         0   \n",
       "16            2.980           34   406.21          60   17     14         0   \n",
       "17            3.016           33   406.21          61   18     15         0   \n",
       "18            3.042           33   406.21          62   19     16         0   \n",
       "19            3.063           32   406.21          63   20     17         0   \n",
       "20            3.079           31   406.21          64   21     18         0   \n",
       "21            3.092           31   406.21          65   22     19         0   \n",
       "22            3.121           30   406.21          66   23     20         0   \n",
       "23            3.171           30   406.21          67   24     21         0   \n",
       "24            3.214           30   406.21          68   25     22         0   \n",
       "25            3.257           30   406.21          69   26     23         0   \n",
       "26            3.299           29   406.21          70   27     24         0   \n",
       "27            3.340           29   406.21          71   28     25         0   \n",
       "28            3.375           29   406.21          72   29     26         0   \n",
       "29            3.402           29   406.21          73   30     27         0   \n",
       "..              ...          ...      ...         ...  ...    ...       ...   \n",
       "618           0.000            0     0.00           0    3      0         0   \n",
       "619           0.000            0     0.00           0    4      0         0   \n",
       "620           0.000            0     0.00           0    5      0         0   \n",
       "621           0.000            0     0.00           0    6      0         0   \n",
       "622           0.000            0     0.00           0    7      0         0   \n",
       "623           0.000            0     0.00           0    8      0         0   \n",
       "624           0.000            0     0.00           0    9      0         0   \n",
       "625           0.000            0     0.00           0   10      0         0   \n",
       "626           0.000            0     0.00           0   11      0         0   \n",
       "627           0.000            0     0.00           0   12      0         0   \n",
       "628           0.000            0     0.00           0   13      0         0   \n",
       "629           0.000            0     0.00           0   14      0         0   \n",
       "630           0.000            0     0.00           0   15      0         0   \n",
       "631           0.000            0     0.00           0   16      0         0   \n",
       "632           0.000            0     0.00           0   17      0         0   \n",
       "633           0.000            0     0.00           0   18      0         0   \n",
       "634           0.000            0     0.00           0   19      0         0   \n",
       "635           0.000            0     0.00           0   20      0         0   \n",
       "636           0.000            0     0.00           0   21      0         0   \n",
       "637           0.000            0     0.00           0   22      0         0   \n",
       "638           0.000            0     0.00           0   23      0         0   \n",
       "639           0.000            0     0.00           0   24      0         0   \n",
       "640           0.000            0     0.00           0   25      0         0   \n",
       "641           0.000            0     0.00           0   26      0         0   \n",
       "642           0.000            0     0.00           0   27      0         0   \n",
       "643           0.000            0     0.00           0   28      0         0   \n",
       "644           0.000            0     0.00           0   29      0         0   \n",
       "645           0.000            0     0.00           0   30      0         0   \n",
       "646           0.000            0     0.00           0    1      0         0   \n",
       "647           0.000            0     0.00           0    2      0         0   \n",
       "\n",
       "     E mm  E/Ex %  ETo mm  ...  Tr mm  Tr/Trx %  TrW mm  Upflow mm  \\\n",
       "0     179      36     468  ...     16        99      13          0   \n",
       "1     180      36     472  ...     18        99      72          0   \n",
       "2     181      36     476  ...     19        99      81          0   \n",
       "3     182      36     479  ...     21        99      71          0   \n",
       "4     183      36     483  ...     23        99      58          0   \n",
       "5     183      36     485  ...     24       100      89          0   \n",
       "6     184      36     489  ...     26       100      58          0   \n",
       "7     185      36     492  ...     28       100      93          0   \n",
       "8     185      36     494  ...     29       100      66          0   \n",
       "9     186      36     498  ...     31       100      69          0   \n",
       "10    186      36     499  ...     32       100      73          0   \n",
       "11    187      36     502  ...     33       100      59          0   \n",
       "12    189      36     506  ...     35       100      58          0   \n",
       "13    191      37     511  ...     38       100      59          0   \n",
       "14    193      37     514  ...     40       100      55          0   \n",
       "15    196      37     519  ...     42        98     120          0   \n",
       "16    197      37     522  ...     43        98      41          0   \n",
       "17    197      37     526  ...     44        96      39          0   \n",
       "18    198      37     531  ...     45        93      40          0   \n",
       "19    198      37     535  ...     46        91      41          0   \n",
       "20    198      37     539  ...     47        89      42          0   \n",
       "21    198      37     543  ...     47        87      43          0   \n",
       "22    200      37     546  ...     48        86      43          0   \n",
       "23    201      37     548  ...     49        87      44          0   \n",
       "24    203      37     551  ...     50        87      44          0   \n",
       "25    204      37     552  ...     51        87      52          0   \n",
       "26    206      38     557  ...     53        87      49          0   \n",
       "27    208      38     562  ...     54        88      48          0   \n",
       "28    209      38     563  ...     55        88      49          0   \n",
       "29    210      38     565  ...     55        88      49          0   \n",
       "..    ...     ...     ...  ...    ...       ...     ...        ...   \n",
       "618     0       0       0  ...      0         0       0          0   \n",
       "619     0       0       0  ...      0         0       0          0   \n",
       "620     0       0       0  ...      0         0       0          0   \n",
       "621     0       0       0  ...      0         0       0          0   \n",
       "622     0       0       0  ...      0         0       0          0   \n",
       "623     0       0       0  ...      0         0       0          0   \n",
       "624     0       0       0  ...      0         0       0          0   \n",
       "625     0       0       0  ...      0         0       0          0   \n",
       "626     0       0       0  ...      0         0       0          0   \n",
       "627     0       0       0  ...      0         0       0          0   \n",
       "628     0       0       0  ...      0         0       0          0   \n",
       "629     0       0       0  ...      0         0       0          0   \n",
       "630     0       0       0  ...      0         0       0          0   \n",
       "631     0       0       0  ...      0         0       0          0   \n",
       "632     0       0       0  ...      0         0       0          0   \n",
       "633     0       0       0  ...      0         0       0          0   \n",
       "634     0       0       0  ...      0         0       0          0   \n",
       "635     0       0       0  ...      0         0       0          0   \n",
       "636     0       0       0  ...      0         0       0          0   \n",
       "637     0       0       0  ...      0         0       0          0   \n",
       "638     0       0       0  ...      0         0       0          0   \n",
       "639     0       0       0  ...      0         0       0          0   \n",
       "640     0       0       0  ...      0         0       0          0   \n",
       "641     0       0       0  ...      0         0       0          0   \n",
       "642     0       0       0  ...      0         0       0          0   \n",
       "643     0       0       0  ...      0         0       0          0   \n",
       "644     0       0       0  ...      0         0       0          0   \n",
       "645     0       0       0  ...      0         0       0          0   \n",
       "646     0       0       0  ...      0         0       0          0   \n",
       "647     0       0       0  ...      0         0       0          0   \n",
       "\n",
       "     WPet kg/m3  WeedStr %  Year  YearN   Yield red (%)  Yield ton/ha  \n",
       "0          0.00         13  2017    2017             99         0.000  \n",
       "1          0.02         13  2017    2017             98         0.027  \n",
       "2          0.02         13  2017    2017             97         0.040  \n",
       "3          0.03         13  2017    2017             96         0.057  \n",
       "4          0.05         14  2017    2017             95         0.081  \n",
       "5          0.07         14  2017    2017             93         0.113  \n",
       "6          0.09         14  2017    2017             90         0.156  \n",
       "7          0.12         14  2017    2017             87         0.210  \n",
       "8          0.15         14  2017    2017             83         0.267  \n",
       "9          0.18         14  2017    2017             80         0.328  \n",
       "10         0.21         14  2017    2017             76         0.391  \n",
       "11         0.25         14  2017    2017             72         0.457  \n",
       "12         0.28         14  2017    2017             68         0.525  \n",
       "13         0.31         14  2017    2017             63         0.595  \n",
       "14         0.34         14  2017    2017             59         0.667  \n",
       "15         0.37         14  2017    2017             55         0.738  \n",
       "16         0.40         14  2017    2017             50         0.809  \n",
       "17         0.43         14  2017    2017             46         0.877  \n",
       "18         0.46         14  2017    2017             42         0.944  \n",
       "19         0.49         14  2017    2017             38         1.011  \n",
       "20         0.51         14  2017    2017             34         1.077  \n",
       "21         0.54         14  2017    2017             30         1.142  \n",
       "22         0.57         14  2017    2017             26         1.214  \n",
       "23         0.60         14  2017    2017             21         1.295  \n",
       "24         0.63         14  2017    2017             16         1.376  \n",
       "25         0.64         14  2017    2017             15         1.394  \n",
       "26         0.63         14  2017    2017             14         1.412  \n",
       "27         0.63         14  2017    2017             13         1.430  \n",
       "28         0.63         14  2017    2017             12         1.444  \n",
       "29         0.64         14  2017    2017             11         1.456  \n",
       "..          ...        ...   ...     ...            ...           ...  \n",
       "618        0.00          0  2019    2020             99         0.000  \n",
       "619        0.00          0  2019    2020             99         0.000  \n",
       "620        0.00          0  2019    2020             99         0.000  \n",
       "621        0.00          0  2019    2020             99         0.000  \n",
       "622        0.00          0  2019    2020             99         0.000  \n",
       "623        0.00          0  2019    2020             99         0.000  \n",
       "624        0.00          0  2019    2020             99         0.000  \n",
       "625        0.00          0  2019    2020             99         0.000  \n",
       "626        0.00          0  2019    2020             99         0.000  \n",
       "627        0.00          0  2019    2020             99         0.000  \n",
       "628        0.00          0  2019    2020             99         0.000  \n",
       "629        0.00          0  2019    2020             99         0.000  \n",
       "630        0.00          0  2019    2020             99         0.000  \n",
       "631        0.00          0  2019    2020             99         0.000  \n",
       "632        0.00          0  2019    2020             99         0.000  \n",
       "633        0.00          0  2019    2020             99         0.000  \n",
       "634        0.00          0  2019    2020             99         0.000  \n",
       "635        0.00          0  2019    2020             99         0.000  \n",
       "636        0.00          0  2019    2020             99         0.000  \n",
       "637        0.00          0  2019    2020             99         0.000  \n",
       "638        0.00          0  2019    2020             99         0.000  \n",
       "639        0.00          0  2019    2020             99         0.000  \n",
       "640        0.00          0  2019    2020             99         0.000  \n",
       "641        0.00          0  2019    2020             99         0.000  \n",
       "642        0.00          0  2019    2020             99         0.000  \n",
       "643        0.00          0  2019    2020             99         0.000  \n",
       "644        0.00          0  2019    2020             99         0.000  \n",
       "645        0.00          0  2019    2020             99         0.000  \n",
       "646        0.00          0  2019    2020             99         0.000  \n",
       "647        0.00          0  2019    2020             99         0.000  \n",
       "\n",
       "[648 rows x 40 columns]"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_All.loc[Y_All['Station'] == 'Bog']#.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(Y_All)):\n",
    "    Y_All.at[i,'Time'] = datetime.strptime(Y_All.at[i,'Time'], '%m/%d/%Y %H:%M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_All['Mon-Day'] = Y_All['Time'].dt.strftime('%b-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2017-05-01 00:00:00\n",
       "1    2017-05-02 00:00:00\n",
       "2    2017-05-03 00:00:00\n",
       "3    2017-05-04 00:00:00\n",
       "4    2017-05-05 00:00:00\n",
       "Name: Time, dtype: object"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# type(Y_All['Time'])\n",
    "Y_All['Time'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_all = Y_All.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_all = Y_all.loc[Y_all['Year']== 2020]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['May-01', 'Jun-05', 'Jul-11', 'Aug-16', 'Sep-21', 'Oct-27', 'Dec-02'], dtype='object')"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.date_range(start='5/1/2019', end='12/02/2019', periods=7) \n",
    "p = p.strftime('%b-%d') \n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "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>BioMass ton/ha</th>\n",
       "      <th>Brelative %</th>\n",
       "      <th>CO2 ppm</th>\n",
       "      <th>Cycle days</th>\n",
       "      <th>Day</th>\n",
       "      <th>DayN</th>\n",
       "      <th>Drain mm</th>\n",
       "      <th>E mm</th>\n",
       "      <th>E/Ex %</th>\n",
       "      <th>ETo mm</th>\n",
       "      <th>...</th>\n",
       "      <th>Tr mm</th>\n",
       "      <th>Tr/Trx %</th>\n",
       "      <th>TrW mm</th>\n",
       "      <th>Upflow mm</th>\n",
       "      <th>WPet kg/m3</th>\n",
       "      <th>WeedStr %</th>\n",
       "      <th>Year</th>\n",
       "      <th>YearN</th>\n",
       "      <th>Yield red (%)</th>\n",
       "      <th>Yield ton/ha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>3.522</td>\n",
       "      <td>29</td>\n",
       "      <td>412.21</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>50</td>\n",
       "      <td>208</td>\n",
       "      <td>53</td>\n",
       "      <td>410</td>\n",
       "      <td>...</td>\n",
       "      <td>41</td>\n",
       "      <td>99</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0.66</td>\n",
       "      <td>14</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020</td>\n",
       "      <td>14</td>\n",
       "      <td>1.428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076</th>\n",
       "      <td>3.571</td>\n",
       "      <td>30</td>\n",
       "      <td>412.21</td>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>54</td>\n",
       "      <td>208</td>\n",
       "      <td>53</td>\n",
       "      <td>413</td>\n",
       "      <td>...</td>\n",
       "      <td>44</td>\n",
       "      <td>99</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.67</td>\n",
       "      <td>14</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020</td>\n",
       "      <td>13</td>\n",
       "      <td>1.443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>3.633</td>\n",
       "      <td>30</td>\n",
       "      <td>412.21</td>\n",
       "      <td>79</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "      <td>210</td>\n",
       "      <td>53</td>\n",
       "      <td>416</td>\n",
       "      <td>...</td>\n",
       "      <td>46</td>\n",
       "      <td>99</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0.69</td>\n",
       "      <td>14</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>1.463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>3.680</td>\n",
       "      <td>31</td>\n",
       "      <td>412.21</td>\n",
       "      <td>78</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>214</td>\n",
       "      <td>53</td>\n",
       "      <td>420</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>100</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0.67</td>\n",
       "      <td>14</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>1.472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>3.676</td>\n",
       "      <td>31</td>\n",
       "      <td>412.21</td>\n",
       "      <td>79</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>63</td>\n",
       "      <td>217</td>\n",
       "      <td>53</td>\n",
       "      <td>424</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>100</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0.66</td>\n",
       "      <td>14</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>1.471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      BioMass ton/ha  Brelative %  CO2 ppm  Cycle days  Day  DayN   Drain mm  \\\n",
       "1075           3.522           29   412.21          79    1     29        50   \n",
       "1076           3.571           30   412.21          79    2     30        54   \n",
       "1077           3.633           30   412.21          79    3     31        57   \n",
       "1078           3.680           31   412.21          78    4      1        59   \n",
       "1079           3.676           31   412.21          79    5      2        63   \n",
       "\n",
       "      E mm  E/Ex %  ETo mm  ...  Tr mm  Tr/Trx %  TrW mm  Upflow mm  \\\n",
       "1075   208      53     410  ...     41        99      37          0   \n",
       "1076   208      53     413  ...     44        99      40          0   \n",
       "1077   210      53     416  ...     46        99      42          0   \n",
       "1078   214      53     420  ...     48       100      43          0   \n",
       "1079   217      53     424  ...     48       100      43          0   \n",
       "\n",
       "      WPet kg/m3  WeedStr %  Year  YearN   Yield red (%)  Yield ton/ha  \n",
       "1075        0.66         14  2020    2020             14         1.428  \n",
       "1076        0.67         14  2020    2020             13         1.443  \n",
       "1077        0.69         14  2020    2020             11         1.463  \n",
       "1078        0.67         14  2020    2020             11         1.472  \n",
       "1079        0.66         14  2020    2020             11         1.471  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "Bog = Y_all.loc[Y_all['Station'] == 'Bog']\n",
    "Bor = Y_all.loc[Y_all['Station'] == 'Bor']\n",
    "Daf = Y_all.loc[Y_all['Station'] == 'Daf']\n",
    "Ded = Y_all.loc[Y_all['Station'] == 'Ded']\n",
    "Dor = Y_all.loc[Y_all['Station'] == 'Dor']\n",
    "Far = Y_all.loc[Y_all['Station'] == 'Far']\n",
    "Fin = Y_all.loc[Y_all['Station'] == 'Fin']\n",
    "Gao = Y_all.loc[Y_all['Station'] == 'Gao']\n",
    "Kou = Y_all.loc[Y_all['Station'] == 'Kou']\n",
    "Man = Y_all.loc[Y_all['Station'] == 'Man']\n",
    "Oua = Y_all.loc[Y_all['Station'] == 'Oua']\n",
    "Ous = Y_all.loc[Y_all['Station'] == 'Ous']\n",
    "Po = Y_all.loc[Y_all['Station'] == 'Po']\n",
    "Pus = Y_all.loc[Y_all['Station'] == 'Pus']\n",
    "Sel = Y_all.loc[Y_all['Station'] == 'Sel']\n",
    "Sir = Y_all.loc[Y_all['Station'] == 'Sir']\n",
    "Som = Y_all.loc[Y_all['Station'] == 'Som']\n",
    "Tam = Y_all.loc[Y_all['Station'] == 'Tam']\n",
    "Tan = Y_all.loc[Y_all['Station'] == 'Tan']\n",
    "Uni = Y_all.loc[Y_all['Station'] == 'Uni']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = datetime(2020, 6, 15, 0, 0)\n",
    "t2 = datetime(2020, 9, 30, 0, 0)\n",
    "t3 = datetime(2020, 7, 1, 0, 0)\n",
    "t4 = datetime(2020, 7, 31, 0, 0)\n",
    "\n",
    "# C1 = C.loc[C['Time']<=t1]\n",
    "# C2 = C.loc[(C['Time']>=datetime(2019, 7, 1, 0, 0)) & (C['Time']<=datetime(2019, 7, 31, 0, 0))]\n",
    "# C3 = C.loc[C['Time']>=datetime(2019, 8, 1, 0, 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 577,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bog.loc[Bog['Time']<=t1] , Bog.loc[(Bog['Time']>=t1) & (Bog['Time']<=t2)] , Bog.loc[Bog['Time']>=t2] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Bog1 , Bog2, Bog3  = Bog.loc[Bog['Time']<=datetime(2019, 6, 30, 0, 0)] , Bog.loc[(Bog['Time']>=datetime(2019, 6, 30, 0, 0)) & \n",
    "                            (Bog['Time']<=datetime(2019, 8, 15, 0, 0))] , Bog.loc[Bog['Time']>=datetime(2019, 8, 15, 0, 0)] \n",
    "\n",
    "Bor1 , Bor2, Bor3  = Bor.loc[Bor['Time']<=t1] , Bor.loc[(Bor['Time']>=t1) & (Bor['Time']<=t2)] , Bor.loc[Bor['Time']>=t2]  \n",
    "\n",
    "Daf1 , Daf2, Daf3  = Daf.loc[Daf['Time']<=t1] , Daf.loc[(Daf['Time']>=t1) & (Daf['Time']<=t2)] , Daf.loc[Daf['Time']>=t2] \n",
    "\n",
    "Ded1 , Ded2, Ded3  = Ded.loc[Ded['Time']<=t1] , Ded.loc[(Ded['Time']>=t1) & (Ded['Time']<=t2)] , Ded.loc[Ded['Time']>=t2] \n",
    "\n",
    "Dor1 , Dor2, Dor3  = Dor.loc[Dor['Time']<=t3] , Dor.loc[(Dor['Time']>=t3) & (Dor['Time']<=t4)] , Dor.loc[Dor['Time']>=t4]  \n",
    "\n",
    "Far1 , Far2, Far3  = Far.loc[Far['Time']<=t1] , Far.loc[(Far['Time']>=t1) & (Far['Time']<=t2)] , Far.loc[Far['Time']>=t2] \n",
    "\n",
    "Fin1 , Fin2, Fin3  = Fin.loc[Fin['Time']<=t1] , Fin.loc[(Fin['Time']>=t1) & (Fin['Time']<=t2)] , Fin.loc[Fin['Time']>=t2] \n",
    "\n",
    "Gao1 , Gao2, Gao3  = Gao.loc[Gao['Time']<=t1] , Gao.loc[(Gao['Time']>=t1) & (Gao['Time']<=t2)] , Gao.loc[Gao['Time']>=t2] \n",
    "\n",
    "Kou1 , Kou2, Kou3  = Kou.loc[Kou['Time']<=t3] , Kou.loc[(Kou['Time']>=t3) & (Kou['Time']<=t4)] , Kou.loc[Kou['Time']>=t4] \n",
    "\n",
    "Man1 , Man2, Man3  = Man.loc[Man['Time']<=t1] , Man.loc[(Man['Time']>=t1) & (Man['Time']<=t2)] , Man.loc[Man['Time']>=t2] \n",
    "\n",
    "Oua1 , Oua2, Oua3  = Oua.loc[Oua['Time']<=t1] , Oua.loc[(Oua['Time']>=t1) & (Oua['Time']<=t2)] , Oua.loc[Oua['Time']>=t2] \n",
    "\n",
    "Ous1 , Ous2, Ous3  = Ous.loc[Ous['Time']<=t3] , Ous.loc[(Ous['Time']>=t3) & (Ous['Time']<=t4)] , Ous.loc[Ous['Time']>=t4] \n",
    "\n",
    "Po1 , Po2, Po3  = Po.loc[Po['Time']<=t1] , Po.loc[(Po['Time']>=t1) & (Po['Time']<=t2)] , Po.loc[Po['Time']>=t2] \n",
    "\n",
    "Pus1 , Pus2, Pus3  = Pus.loc[Pus['Time']<=t1] , Pus.loc[(Pus['Time']>=t1) & (Pus['Time']<=t2)] , Pus.loc[Pus['Time']>=t2] \n",
    "\n",
    "Sel1 , Sel2, Sel3  = Sel.loc[Sel['Time']<=t1] , Sel.loc[(Sel['Time']>=t1) & (Sel['Time']<=t2)] , Sel.loc[Sel['Time']>=t2] \n",
    "\n",
    "Sir1 , Sir2, Sir3  = Sir.loc[Sir['Time']<=t1] , Sir.loc[(Sir['Time']>=t1) & (Sir['Time']<=t2)] , Sir.loc[Sir['Time']>=t2] \n",
    "\n",
    "Som1 , Som2, Som3  = Som.loc[Som['Time']<=t3] , Som.loc[(Som['Time']>=t3) & (Som['Time']<=t4)] , Som.loc[Som['Time']>=t4] \n",
    "\n",
    "Tam1 , Tam2, Tam3  = Tam.loc[Tam['Time']<=t1] , Tam.loc[(Tam['Time']>=t1) & (Tam['Time']<=t2)] , Tam.loc[Tam['Time']>=t2] \n",
    "\n",
    "Tan1 , Tan2, Tan3  = Tan.loc[Tan['Time']<=t1] , Tan.loc[(Tan['Time']>=t1) & (Tan['Time']<=t2)] , Tan.loc[Tan['Time']>=t2] \n",
    "\n",
    "Uni1 , Uni2, Uni3  = Uni.loc[Uni['Time']<=t1] , Uni.loc[(Uni['Time']>=t1) & (Uni['Time']<=t2)] , Uni.loc[Uni['Time']>=t2] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bog1 , Bog2, Bog3  = Bog.loc[Bog['Time']<=datetime(2020, 6, 30, 0, 0)] , Bog.loc[(Bog['Time']>=datetime(2020, 6, 30, 0, 0)) & \n",
    "#                             (Bog['Time']<=datetime(2020, 8, 15, 0, 0))] , Bog.loc[Bog['Time']>=datetime(2020, 8, 15, 0, 0)] \n",
    "\n",
    "# Bor1 , Bor2, Bor3  = Bor.loc[Bor['Time']<=datetime(2020, 6, 3, 0, 0)] , Bor.loc[(Bor['Time']>=datetime(2020, 6, 3, 0, 0)) & \n",
    "#                             (Bor['Time']<=datetime(2020, 10, 2, 0, 0))] , Bor.loc[Bor['Time']>=datetime(2020, 10, 2, 0, 0)]  \n",
    "\n",
    "# Daf1 , Daf2, Daf3  = Daf.loc[Daf['Time']<=datetime(2020, 8, 19, 0, 0)] , Daf.loc[(Daf['Time']>=datetime(2020, 8, 19, 0, 0)) &\n",
    "#                             (Daf['Time']<=datetime(2020, 9, 20, 0, 0))] , Daf.loc[Daf['Time']>=datetime(2020, 9, 20, 0, 0)] \n",
    "\n",
    "# Ded1 , Ded2, Ded3  = Ded.loc[Ded['Time']<=datetime(2020, 5, 28, 0, 0)] , Ded.loc[(Ded['Time']>=datetime(2020, 5, 28, 0, 0)) &\n",
    "#                             (Ded['Time']<=datetime(2020, 9, 28, 0, 0))] , Ded.loc[Ded['Time']>=datetime(2020, 9, 28, 0, 0)] \n",
    "\n",
    "# Dor1 , Dor2, Dor3  = Dor.loc[Dor['Time']<=datetime(2020, 6, 21, 0, 0)] , Dor.loc[(Dor['Time']>=datetime(2020, 6, 21, 0, 0)) &\n",
    "#                             (Dor['Time']<=datetime(2020, 7, 11, 0, 0))] , Dor.loc[Dor['Time']>=datetime(2020, 7, 11, 0, 0)]  \n",
    "\n",
    "# Far1 , Far2, Far3  = Far.loc[Far['Time']<=datetime(2020, 5, 21, 0, 0)] , Far.loc[(Far['Time']>=datetime(2020, 5, 21, 0, 0)) &\n",
    "#                             (Far['Time']<=datetime(2020, 9, 29, 0, 0))] , Far.loc[Far['Time']>=datetime(2020, 9, 29, 0, 0)] \n",
    "\n",
    "# Fin1 , Fin2, Fin3  = Fin.loc[Fin['Time']<=datetime(2020, 6, 13, 0, 0)] , Fin.loc[(Fin['Time']>=datetime(2020, 6, 13, 0, 0)) &\n",
    "#                             (Fin['Time']<=datetime(2020, 8, 21, 0, 0))] , Fin.loc[Fin['Time']>=datetime(2020, 8, 21, 0, 0)] \n",
    "\n",
    "# Gao1 , Gao2, Gao3  = Gao.loc[Gao['Time']<=datetime(2020, 6, 14, 0, 0)] , Gao.loc[(Gao['Time']>=datetime(2020, 6, 14, 0, 0)) & \n",
    "#                             (Gao['Time']<=datetime(2020, 9, 26, 0, 0))] , Gao.loc[Gao['Time']>=datetime(2020, 9, 26, 0, 0)] \n",
    "\n",
    "# Kou1 , Kou2, Kou3  = Kou.loc[Kou['Time']<=datetime(2020, 6, 3, 0, 0)] , Kou.loc[(Kou['Time']>=datetime(2020, 6, 3, 0, 0)) &\n",
    "#                             (Kou['Time']<=datetime(2020, 11, 1, 0, 0))] , Kou.loc[Kou['Time']>=datetime(2020, 11, 1, 0, 0)] \n",
    "\n",
    "# Man1 , Man2, Man3  = Man.loc[Man['Time']<=datetime(2020, 6, 18, 0, 0)] , Man.loc[(Man['Time']>=datetime(2020, 6, 18, 0, 0)) & \n",
    "#                             (Man['Time']<=datetime(2020, 7, 7, 0, 0))] , Man.loc[Man['Time']>=datetime(2020, 7, 7, 0, 0)] \n",
    "\n",
    "# Oua1 , Oua2, Oua3  = Oua.loc[Oua['Time']<=datetime(2020, 5, 14, 0, 0)] , Oua.loc[(Oua['Time']>=datetime(2020, 5, 14, 0, 0)) & \n",
    "#                             (Oua['Time']<=datetime(2020, 10, 9, 0, 0))] , Oua.loc[Oua['Time']>=datetime(2020, 10, 9, 0, 0)] \n",
    "\n",
    "# Ous1 , Ous2, Ous3  = Ous.loc[Ous['Time']<=datetime(2020, 6, 15, 0, 0)] , Ous.loc[(Ous['Time']>=datetime(2020, 6, 15, 0, 0)) & \n",
    "#                             (Ous['Time']<=datetime(2020, 11, 19, 0, 0))] , Ous.loc[Ous['Time']>=datetime(2020, 11, 19, 0, 0)] \n",
    "\n",
    "# Po1 , Po2, Po3  = Po.loc[Po['Time']<=datetime(2020, 5, 7, 0, 0)] , Po.loc[(Po['Time']>=datetime(2020, 5, 7, 0, 0)) & \n",
    "#                             (Po['Time']<=datetime(2020, 10, 18, 0, 0))] , Po.loc[Po['Time']>=datetime(2020, 10, 18, 0, 0)] \n",
    "\n",
    "# Pus1 , Pus2, Pus3  = Pus.loc[Pus['Time']<=datetime(2020, 5, 5, 0, 0)] , Pus.loc[(Pus['Time']>=datetime(2020, 5, 5, 0, 0)) & \n",
    "#                             (Pus['Time']<=datetime(2020, 10, 4, 0, 0))] , Pus.loc[Pus['Time']>=datetime(2020, 10, 4, 0, 0)] \n",
    "\n",
    "# Sel1 , Sel2, Sel3  = Sel.loc[Sel['Time']<=datetime(2020, 5, 6, 0, 0)] , Sel.loc[(Sel['Time']>=datetime(2020, 5, 6, 0, 0)) &\n",
    "#                             (Sel['Time']<=datetime(2020, 10, 15, 0, 0))] , Sel.loc[Sel['Time']>=datetime(2020, 10, 15, 0, 0)] \n",
    "\n",
    "# Sir1 , Sir2, Sir3  = Sir.loc[Sir['Time']<=datetime(2020, 7, 3, 0, 0)] , Sir.loc[(Sir['Time']>=datetime(2020, 7, 3, 0, 0)) &\n",
    "#                             (Sir['Time']<=datetime(2020, 11, 12, 0, 0))] , Sir.loc[Sir['Time']>=datetime(2020, 11, 12, 0, 0)] \n",
    "\n",
    "# Som1 , Som2, Som3  = Som.loc[Som['Time']<=datetime(2020, 6, 25, 0, 0)] , Som.loc[(Som['Time']>=datetime(2020, 6, 25, 0, 0)) &\n",
    "#                             (Som['Time']<=datetime(2020, 8, 7, 0, 0))] , Som.loc[Som['Time']>=datetime(2020, 8, 7, 0, 0)] \n",
    "\n",
    "# Tam1 , Tam2, Tam3  = Tam.loc[Tam['Time']<=datetime(2020, 7, 29, 0, 0)] , Tam.loc[(Tam['Time']>=datetime(2020, 7, 29, 0, 0)) & \n",
    "#                             (Tam['Time']<=datetime(2020, 11, 19, 0, 0))] , Tam.loc[Tam['Time']>=datetime(2020, 11, 19, 0, 0)] \n",
    "\n",
    "# Tan1 , Tan2, Tan3  = Tan.loc[Tan['Time']<=datetime(2020, 5, 7, 0, 0)] , Tan.loc[(Tan['Time']>=datetime(2020, 5, 7, 0, 0)) & \n",
    "#                             (Tan['Time']<=datetime(2020, 10, 8, 0, 0))] , Tan.loc[Tan['Time']>=datetime(2020, 10, 8, 0, 0)] \n",
    "\n",
    "# Uni1 , Uni2, Uni3  = Uni.loc[Uni['Time']<=datetime(2020, 6, 14, 0, 0)] , Uni.loc[(Uni['Time']>=datetime(2020, 6, 14, 0, 0)) &\n",
    "#                             (Uni['Time']<=datetime(2020, 7, 23, 0, 0))] , Uni.loc[Uni['Time']>=datetime(2020, 7, 23, 0, 0)] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AquaCrop Crop stages\n",
    "- Stage: 0: before/after planting\n",
    "- Stage: 1: emergence or transplant recovery\n",
    "- Stage: 2: vegetative stage\n",
    "- Stage: 3: flowering\n",
    "- Stage: 4: yield formation and ripening"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of Soil Water content in soil profile (WC) and root zone (Wr) (Prof) files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "E = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Gaoua\\Output\\05-10-Prof.out', header=None, skiprows=2, skipfooter=19, engine='python', delim_whitespace=True)# delimiter='\\s+')#, delim_whitespace=True) # \n",
    "E = E.iloc[:,0:15]\n",
    "g = E.loc[2,0:9].copy()\n",
    "\n",
    "E.loc[2,:] = ''\n",
    "\n",
    "for i in range(len(g)):\n",
    "    E.loc[2,i+5] = g[i]\n",
    "    \n",
    "for i in range(15):\n",
    "    E.loc[2,i] = str(E.loc[1,i])+' '+str(E.loc[2,i])\n",
    "    \n",
    "E = E.rename(columns=E.iloc[2])\n",
    "\n",
    "# E = E.iloc[3:,0:15]\n",
    "E = E.iloc[3:,]\n",
    "\n",
    "for i in range(2018,2019):\n",
    "    F = E.loc[E['Year '] == str(i)].copy()\n",
    "    F = F.replace('-9', 0.0)\n",
    "    F.iloc[:,:15] = F.iloc[:,:15].astype(float)\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "H = E.iloc[0:0, ].copy()\n",
    "H['Onset'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# E.head()\n",
    "# F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>DAP</th>\n",
       "      <th>Stage</th>\n",
       "      <th>WCTot mm</th>\n",
       "      <th>Wr(Zx) mm</th>\n",
       "      <th>Z m</th>\n",
       "      <th>Wr mm</th>\n",
       "      <th>Wr(SAT) mm</th>\n",
       "      <th>Wr(FC) mm</th>\n",
       "      <th>Wr(exp) mm</th>\n",
       "      <th>Wr(sto) mm</th>\n",
       "      <th>Wr(sen) mm</th>\n",
       "      <th>Wr(PWP) mm</th>\n",
       "      <th>Onset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Day , Month , Year , DAP , Stage , WCTot mm, Wr(Zx) mm, Z m, Wr mm, Wr(SAT) mm, Wr(FC) mm, Wr(exp) mm, Wr(sto) mm, Wr(sen) mm, Wr(PWP) mm, Onset]\n",
       "Index: []"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E.head()\n",
    "# E[120:]\n",
    "# E.iloc[2,:10]\n",
    "# g\n",
    "# E.iloc[3:,0:15]\n",
    "# Stg_4\n",
    "H.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read AquaCrop output files and extract the main information\n",
    "\n",
    "loc = r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Gaoua\\Output'\n",
    "\n",
    "j = 30\n",
    "for file in os.listdir(loc):\n",
    "        #print(file)\n",
    "        fName, fExt = os.path.splitext(file) \n",
    "        if fName[6:] == 'Prof':\n",
    "            #print(fName)\n",
    "            E = pd.read_csv(loc+ '\\\\' +file, header=None, skiprows=2, skipfooter=19, engine='python', delim_whitespace=True)\n",
    "            E = E.iloc[:,0:15]\n",
    "            g = E.loc[2,0:9].copy()\n",
    "\n",
    "            E.loc[2,:] = ''\n",
    "\n",
    "            for i in range(len(g)):\n",
    "                E.loc[2,i+5] = g[i]\n",
    "\n",
    "            for i in range(15):\n",
    "                E.loc[2,i] = str(E.loc[1,i])+' '+str(E.loc[2,i])\n",
    "\n",
    "            E = E.rename(columns=E.iloc[2])\n",
    "\n",
    "            E = E.iloc[3:,]\n",
    "            \n",
    "            for i in range(2017,2021):\n",
    "                F = E.loc[E['Year '] == str(i)].copy()\n",
    "                F = F.replace('-9', 0.0)\n",
    "                F.iloc[:,:15] = F.iloc[:,:15].astype(float)\n",
    "                F['Onset'] = fName[:5]   \n",
    "                \n",
    "                F =  F.iloc[j:,:]\n",
    "\n",
    "                H = H.append(F,  sort=False)\n",
    "                \n",
    "            j = j +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Day ', 'Month ', 'Year ', 'DAP ', 'Stage ', 'WCTot mm', 'Wr(Zx) mm',\n",
       "       'Z m', 'Wr mm', 'Wr(SAT) mm', 'Wr(FC) mm', 'Wr(exp) mm', 'Wr(sto) mm',\n",
       "       'Wr(sen) mm', 'Wr(PWP) mm', 'Onset'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# F = F.rename(columns={\"Year \": \"Year\", \"Month \": \"Month\", \"Day \": \"Day\"})\n",
    "# F['Time'] = pd.to_datetime(F[['Year', 'Month', 'Day']]) \n",
    "\n",
    "# F['Mon-Day'] = F['Time'].dt.strftime('%b-%d')\n",
    "\n",
    "# F = F.iloc[39:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "H = H.rename(columns={\"Year \": \"Year\", \"Month \": \"Month\", \"Day \": \"Day\"})\n",
    "H['Time'] = pd.to_datetime(H[['Year', 'Month', 'Day']]) \n",
    "\n",
    "H['Mon-Day'] = H['Time'].dt.strftime('%b-%d')\n",
    "\n",
    "H = H.reset_index()\n",
    "\n",
    "H = H.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(H)):\n",
    "    H.at[i,'Onset'] = datetime.strptime(H.at[i,'Onset'], '%m-%d')#.strftime('%b-%d')\n",
    "    H.at[i,'Onset'] = H.at[i,'Onset'].strftime('%b-%d')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# H.to_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Po\\H_new.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>DAP</th>\n",
       "      <th>Stage</th>\n",
       "      <th>WCTot mm</th>\n",
       "      <th>Wr(Zx) mm</th>\n",
       "      <th>Z m</th>\n",
       "      <th>Wr mm</th>\n",
       "      <th>Wr(SAT) mm</th>\n",
       "      <th>Wr(FC) mm</th>\n",
       "      <th>Wr(exp) mm</th>\n",
       "      <th>Wr(sto) mm</th>\n",
       "      <th>Wr(sen) mm</th>\n",
       "      <th>Wr(PWP) mm</th>\n",
       "      <th>Onset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>31.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>471.2</td>\n",
       "      <td>411.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>124.6</td>\n",
       "      <td>156.6</td>\n",
       "      <td>118.3</td>\n",
       "      <td>113.2</td>\n",
       "      <td>90.2</td>\n",
       "      <td>93.2</td>\n",
       "      <td>81.9</td>\n",
       "      <td>07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>468.7</td>\n",
       "      <td>408.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>120.7</td>\n",
       "      <td>156.6</td>\n",
       "      <td>118.3</td>\n",
       "      <td>113.2</td>\n",
       "      <td>91.2</td>\n",
       "      <td>93.2</td>\n",
       "      <td>81.9</td>\n",
       "      <td>07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>466.8</td>\n",
       "      <td>407.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>117.5</td>\n",
       "      <td>156.6</td>\n",
       "      <td>118.3</td>\n",
       "      <td>113.2</td>\n",
       "      <td>90.7</td>\n",
       "      <td>93.2</td>\n",
       "      <td>81.9</td>\n",
       "      <td>07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>465.2</td>\n",
       "      <td>405.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>115.0</td>\n",
       "      <td>156.6</td>\n",
       "      <td>118.3</td>\n",
       "      <td>113.2</td>\n",
       "      <td>90.4</td>\n",
       "      <td>93.2</td>\n",
       "      <td>81.9</td>\n",
       "      <td>07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>462.3</td>\n",
       "      <td>402.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>111.6</td>\n",
       "      <td>156.6</td>\n",
       "      <td>118.3</td>\n",
       "      <td>113.2</td>\n",
       "      <td>92.2</td>\n",
       "      <td>93.2</td>\n",
       "      <td>81.9</td>\n",
       "      <td>07-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Day   Month    Year   DAP   Stage   WCTot mm  Wr(Zx) mm  Z m  Wr mm  \\\n",
       "766  31.0     7.0  2020.0   1.0     1.0     471.2      411.4  0.3  124.6   \n",
       "767   1.0     8.0  2020.0   2.0     1.0     468.7      408.9  0.3  120.7   \n",
       "768   2.0     8.0  2020.0   3.0     1.0     466.8      407.0  0.3  117.5   \n",
       "769   3.0     8.0  2020.0   4.0     1.0     465.2      405.4  0.3  115.0   \n",
       "770   4.0     8.0  2020.0   5.0     1.0     462.3      402.5  0.3  111.6   \n",
       "\n",
       "     Wr(SAT) mm  Wr(FC) mm  Wr(exp) mm  Wr(sto) mm  Wr(sen) mm  Wr(PWP) mm  \\\n",
       "766       156.6      118.3       113.2        90.2        93.2        81.9   \n",
       "767       156.6      118.3       113.2        91.2        93.2        81.9   \n",
       "768       156.6      118.3       113.2        90.7        93.2        81.9   \n",
       "769       156.6      118.3       113.2        90.4        93.2        81.9   \n",
       "770       156.6      118.3       113.2        92.2        93.2        81.9   \n",
       "\n",
       "     Onset  \n",
       "766  07-31  \n",
       "767  07-31  \n",
       "768  07-31  \n",
       "769  07-31  \n",
       "770  07-31  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E[150:]\n",
    "# F.loc = \n",
    "H[85:]\n",
    "F.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>DAP</th>\n",
       "      <th>Stage</th>\n",
       "      <th>WCTot mm</th>\n",
       "      <th>Wr(Zx) mm</th>\n",
       "      <th>Z m</th>\n",
       "      <th>Wr mm</th>\n",
       "      <th>Wr(SAT) mm</th>\n",
       "      <th>Wr(FC) mm</th>\n",
       "      <th>Wr(exp) mm</th>\n",
       "      <th>Wr(sto) mm</th>\n",
       "      <th>Wr(sen) mm</th>\n",
       "      <th>Wr(PWP) mm</th>\n",
       "      <th>Onset</th>\n",
       "      <th>Time</th>\n",
       "      <th>Mon-Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33115</th>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>471.5</td>\n",
       "      <td>387.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>387.1</td>\n",
       "      <td>514.2</td>\n",
       "      <td>408.7</td>\n",
       "      <td>388.7</td>\n",
       "      <td>325.4</td>\n",
       "      <td>325.4</td>\n",
       "      <td>291.2</td>\n",
       "      <td>Jul-31</td>\n",
       "      <td>2020-10-24</td>\n",
       "      <td>Oct-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33116</th>\n",
       "      <td>25.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>470.8</td>\n",
       "      <td>386.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>386.4</td>\n",
       "      <td>514.2</td>\n",
       "      <td>408.7</td>\n",
       "      <td>388.7</td>\n",
       "      <td>325.4</td>\n",
       "      <td>325.4</td>\n",
       "      <td>291.2</td>\n",
       "      <td>Jul-31</td>\n",
       "      <td>2020-10-25</td>\n",
       "      <td>Oct-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33117</th>\n",
       "      <td>26.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>470.2</td>\n",
       "      <td>385.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>385.8</td>\n",
       "      <td>514.2</td>\n",
       "      <td>408.7</td>\n",
       "      <td>388.7</td>\n",
       "      <td>324.9</td>\n",
       "      <td>325.4</td>\n",
       "      <td>291.2</td>\n",
       "      <td>Jul-31</td>\n",
       "      <td>2020-10-26</td>\n",
       "      <td>Oct-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33118</th>\n",
       "      <td>27.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>469.7</td>\n",
       "      <td>385.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>385.3</td>\n",
       "      <td>514.2</td>\n",
       "      <td>408.7</td>\n",
       "      <td>388.7</td>\n",
       "      <td>324.9</td>\n",
       "      <td>325.4</td>\n",
       "      <td>291.2</td>\n",
       "      <td>Jul-31</td>\n",
       "      <td>2020-10-27</td>\n",
       "      <td>Oct-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33119</th>\n",
       "      <td>28.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>469.1</td>\n",
       "      <td>384.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>384.7</td>\n",
       "      <td>514.2</td>\n",
       "      <td>408.7</td>\n",
       "      <td>388.7</td>\n",
       "      <td>325.4</td>\n",
       "      <td>325.4</td>\n",
       "      <td>291.2</td>\n",
       "      <td>Jul-31</td>\n",
       "      <td>2020-10-28</td>\n",
       "      <td>Oct-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Day  Month    Year  DAP   Stage   WCTot mm  Wr(Zx) mm  Z m  Wr mm  \\\n",
       "33115  24.0   10.0  2020.0   0.0     0.0     471.5      387.1  1.0  387.1   \n",
       "33116  25.0   10.0  2020.0   0.0     0.0     470.8      386.4  1.0  386.4   \n",
       "33117  26.0   10.0  2020.0   0.0     0.0     470.2      385.8  1.0  385.8   \n",
       "33118  27.0   10.0  2020.0   0.0     0.0     469.7      385.3  1.0  385.3   \n",
       "33119  28.0   10.0  2020.0   0.0     0.0     469.1      384.7  1.0  384.7   \n",
       "\n",
       "       Wr(SAT) mm  Wr(FC) mm  Wr(exp) mm  Wr(sto) mm  Wr(sen) mm  Wr(PWP) mm  \\\n",
       "33115       514.2      408.7       388.7       325.4       325.4       291.2   \n",
       "33116       514.2      408.7       388.7       325.4       325.4       291.2   \n",
       "33117       514.2      408.7       388.7       324.9       325.4       291.2   \n",
       "33118       514.2      408.7       388.7       324.9       325.4       291.2   \n",
       "33119       514.2      408.7       388.7       325.4       325.4       291.2   \n",
       "\n",
       "        Onset       Time Mon-Day  \n",
       "33115  Jul-31 2020-10-24  Oct-24  \n",
       "33116  Jul-31 2020-10-25  Oct-25  \n",
       "33117  Jul-31 2020-10-26  Oct-26  \n",
       "33118  Jul-31 2020-10-27  Oct-27  \n",
       "33119  Jul-31 2020-10-28  Oct-28  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "H_0 = H.loc[H['Year']==2020.0].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['May-01', 'May-20', 'Jun-09', 'Jun-28', 'Jul-18', 'Aug-06', 'Aug-26',\n",
       "       'Sep-14', 'Oct-04', 'Oct-23', 'Nov-12', 'Dec-02'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = pd.date_range(start='5/1/2019', end='12/02/2019',)\n",
    "q = q.strftime('%b-%d') \n",
    "\n",
    "p = pd.date_range(start='5/1/2019', end='12/02/2019', periods=12) \n",
    "p = p.strftime('%b-%d') \n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['May-01', 'May-02', 'May-03', 'May-04', 'May-05', 'May-06', 'May-07',\n",
       "       'May-08', 'May-09', 'May-10',\n",
       "       ...\n",
       "       'Nov-23', 'Nov-24', 'Nov-25', 'Nov-26', 'Nov-27', 'Nov-28', 'Nov-29',\n",
       "       'Nov-30', 'Dec-01', 'Dec-02'],\n",
       "      dtype='object', length=216)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Day', 'Month', 'Year', 'DAP ', 'Stage ', 'WCTot mm', 'Wr(Zx) mm',\n",
       "       'Z m', 'Wr mm', 'Wr(SAT) mm', 'Wr(FC) mm', 'Wr(exp) mm', 'Wr(sto) mm',\n",
       "       'Wr(sen) mm', 'Wr(PWP) mm', 'Onset', 'Time', 'Mon-Day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "H_FC = []\n",
    "H_PWP = []\n",
    "H_Wr = []\n",
    "H_exp = []\n",
    "H_sen = []\n",
    "H_sto = []\n",
    "H_WCT = []\n",
    "H_WrZ = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in q:\n",
    "#     print(j)\n",
    "    H_FC.append(H_0.loc[H_0['Mon-Day']== j]['Wr(FC) mm'])\n",
    "    H_PWP.append(H_0.loc[H_0['Mon-Day']== j]['Wr(PWP) mm'])\n",
    "    H_Wr.append(H_0.loc[H_0['Mon-Day']== j]['Wr mm'])\n",
    "    H_exp.append(H_0.loc[H_0['Mon-Day']== j]['Wr(exp) mm'])\n",
    "    H_sen.append(H_0.loc[H_0['Mon-Day']== j]['Wr(sen) mm'])\n",
    "    H_sto.append(H_0.loc[H_0['Mon-Day']== j]['Wr(sto) mm'])\n",
    "    H_WCT.append(H_0.loc[H_0['Mon-Day']== j]['WCTot mm'])\n",
    "    H_WrZ.append(H_0.loc[H_0['Mon-Day']== j]['Wr(Zx) mm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# H_PWP  # 'Wr(exp) mm'\n",
    "# H_FC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(216, 216, 216)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(H_FC) , len(H_PWP) , len(H_Wr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = ['1-May', '' , '', '', '', '', '', '', '','10-May', '', '', '', '','' , '','' , '', '', '20-May', '', '', '', '', '', '', '', '', '', '30-May',\n",
    "     '', '', '', '', '','' ,'' , '', '','' , '10-Jun', '', '', '', '', '', '', '', '', '', '20-Jun', '', '', '', '','' , '','' ,'' , '', '30-Jun',\n",
    "     '', '', '', '','' ,'' ,'' ,'' , '','10-Jul','' , '', '', '','' , '', '','' ,'' , '20-Jul','' , '','' ,'' , '','' ,'' , '', '', '','31-Jul', \n",
    "     '','' , '', '', '', '', '', '', '','10-Aug', '', '', '', '','' , '', '', '', '', '20-Aug', '', '', '', '', '', '', '', '','' ,'' ,'30-Aug', \n",
    "     '', '' , '', '', '', '', '', '', '','10-Sep', '', '', '', '','' , '','' , '', '', '20-Sep', '', '', '', '', '', '', '', '', '', '30-Sep',\n",
    "    '', '', '', '','' ,'' ,'' ,'' , '','10-Oct','' , '', '', '','' , '', '','' ,'' , '20-Oct','' , '','' ,'' , '','' ,'' , '', '', '','31-Oct', \n",
    "     '', '' , '', '', '', '', '', '', '','10-Nov', '', '', '', '','' , '','' , '', '', '20-Nov', '', '', '', '', '', '', '', '', '', '30-Nov',]\n",
    "#     '', '', '', '','' ,'' ,'' ,'' , '','10-Dec','' , '', '', '','' , '', '','' ,'' , '20-Dec','' , '','' ,'' , '','' ,'' , '', '', '','31-Dec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(H_FC)\n",
    "# H_FC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['bold'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['bold'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Single simulation (Yield, WP_ET)\n",
    "\n",
    "fig1, (ax2, ax3, ax1, ax) = plt.subplots(4, 1, sharex = True, figsize=(20, 20)) #sharex = True,\n",
    "\n",
    "flierprops = dict(marker='o', markersize=12, markeredgecolor='none', ) #markerfacecolor='green',  markerfacecolor='firebrick'\n",
    "medianprops = dict(linestyle='--', linewidth=2.5, color='blue')\n",
    "\n",
    "\n",
    "colors = ['purple', 'orange', 'tab:green', 'black', 'red']\n",
    "colors_FC = dict(color=colors[0])\n",
    "colors_PWP = dict(color=colors[1])\n",
    "colors_Exp = dict(color=colors[2]) \n",
    "colors_Sen = dict(color=colors[3]) \n",
    "colors_Sto = dict(color=colors[4]) \n",
    "\n",
    "\n",
    "# Yield\n",
    "ax2.scatter( C2020['Mon-Day'], C2020['Yield ton/ha'], label='Yield',) # c=C2020['Yield ton/ha'], cmap='RdYlGn') \n",
    "ax2.set_title('Yield response at different sowing dates - 2020', fontsize=20)\n",
    "ax2.set_ylabel('Yield (ton/ha)')\n",
    "ax2.legend()\n",
    "\n",
    "# Average stress\n",
    "ax3.scatter( C2020['Mon-Day'], C2020['ExpStr %'],  marker = '.' , label='Leaf expansion stress',) #  c=C2020['ExpStr %'], cmap='jet') #color = 'tab:orange',)\n",
    "ax3.scatter( C2020['Mon-Day'], C2020['StoStr %'], marker = '*' , label='Stomatal closure stress',) #  c=C2020['StoStr %'], cmap='RdYlGn_r') #color = 'tab:orange',)\n",
    "ax3.set_title('Average stresses at different sowing dates - 2020', fontsize=20)\n",
    "ax3.set_ylabel('Stress (%)')\n",
    "ax3.legend()\n",
    "\n",
    "\n",
    "# Stresses thresholds \n",
    "ax1.boxplot(H_Wr, showbox=False, showcaps=False, sym='', whis=0, medianprops=medianprops)\n",
    "ax1.boxplot(H_exp, showbox=False, showcaps=False, sym='', whis=0, medianprops=colors_Exp)\n",
    "# ax.boxplot(H_sen, showbox=False, showcaps=False, sym='', whis=0, medianprops=colors_versicolor )\n",
    "ax1.boxplot(H_sto, showbox=False, showcaps=False, sym='', whis=0, medianprops=colors_Sto) \n",
    "ax1.set_xticklabels(s, rotation=40, fontsize=16)\n",
    "\n",
    "ax1.set_title('Actual water content in the soil w/r to water content at different thresholds - 2020', fontsize=20)\n",
    "ax1.set_ylabel('Water Content (mm)')\n",
    "\n",
    "\n",
    "#Soil water content \n",
    "ax.boxplot(H_Wr, flierprops=flierprops, medianprops=medianprops, showbox=False, showcaps=False, sym='', whis=0)\n",
    "ax.boxplot(H_FC, showbox=False, showcaps=False, sym='', whis=0, medianprops=colors_FC) \n",
    "ax.boxplot(H_PWP, showbox=False, showcaps=False, sym='', whis=0, medianprops=colors_Sto) \n",
    "# ax.boxplot(H_WCT, showbox=False, showcaps=False, sym='', whis=0, medianprops=colors_FC) \n",
    "ax.boxplot(H_WrZ, showbox=False, showcaps=False, sym='', whis=0, medianprops=colors_PWP) \n",
    "ax.set_xticklabels(s, rotation=40, fontsize=16)\n",
    "\n",
    "ax.set_title('Water content in the effective root zone (Wr) variation - 2020', fontsize=20)\n",
    "ax.set_ylabel('Water Content (mm)')\n",
    "ax.set_xlabel('Dates ')\n",
    "\n",
    "\n",
    "# ax1.set_ylim([50,175])\n",
    "# ax.set_ylim([40,200])\n",
    "\n",
    "ax.set_xticklabels(s, rotation=40, fontsize=16)\n",
    "\n",
    "# ax.legend(title=\"Before/After planting\", fontsize=10)\n",
    "# ax.legend(lalels = 'Wr', title=\"Before/After planting\", fontsize=10)\n",
    "\n",
    "# Add legend\n",
    "\n",
    "lines = []\n",
    "styles = ['--', '--', '--', '--', '--']\n",
    "colors = ['purple', 'orange', 'blue', 'green', 'red']\n",
    "x = np.linspace(0, 0, 10)\n",
    "\n",
    "for i in range(5):\n",
    "    lines += ax.plot(x, np.sin(x - i * np.pi / 2),\n",
    "                     styles[i], color=colors[i])\n",
    "\n",
    "# Create the second legend and add the artist manually.\n",
    "from matplotlib.legend import Legend\n",
    "leg = Legend(ax, lines[:3], ['Wr at FC', 'Wr at PWP', 'actual Wr'], loc='upper left', frameon=False)\n",
    "ax.add_artist(leg);\n",
    "\n",
    "leg1 = Legend(ax1, lines[2:], ['actual Wr', 'Wr at Leaf exp', 'Wr at Sto clo'], loc='upper left', frameon=False)\n",
    "ax1.add_artist(leg1);\n",
    "\n",
    "# plt.subplots_adjust(wspace=0.003)\n",
    "plt.subplots_adjust(hspace=0.15)\n",
    "\n",
    "# plt.savefig(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Dori\\total_var_2020_-.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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"
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 },
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}