DhakaRainV2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "rain = pd.read_csv(\"rainfallbd.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
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       "      <th>7</th>\n",
       "      <th>...</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bogra</td>\n",
       "      <td>1948</td>\n",
       "      <td>1</td>\n",
       "      <td>***</td>\n",
       "      <td>***</td>\n",
       "      <td>***</td>\n",
       "      <td>***</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bogra</td>\n",
       "      <td>1948</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bogra</td>\n",
       "      <td>1948</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bogra</td>\n",
       "      <td>1948</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bogra</td>\n",
       "      <td>1948</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Station  Year  Month    1    2    3    4    5    6    7  ...   22   23   24  \\\n",
       "0   Bogra  1948      1  ***  ***  ***  ***  ***  ***  ***  ...  ***  ***  ***   \n",
       "1   Bogra  1948      2    0    0    0    4    1    0    0  ...    0    0    0   \n",
       "2   Bogra  1948      3    0    0    0    0    0    0    0  ...    0    0    0   \n",
       "3   Bogra  1948      4    0    0    0    0    7    0    0  ...    0    0    0   \n",
       "4   Bogra  1948      5    0   24    0    0    0    0    0  ...    9    7   24   \n",
       "\n",
       "    25   26   27   28   29   30   31  \n",
       "0  ***  ***  ***  ***  ***  ***  ***  \n",
       "1    0    0    0    0    0  NaN  NaN  \n",
       "2    0    0    0    0    0    0    0  \n",
       "3    0    0    0    0    0   18  NaN  \n",
       "4   31   14    0    1    8    0    0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rain = rain[(rain['Year'] >= 2000) & (rain['Year'] <=2014)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Station</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>1</th>\n",
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       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14748</th>\n",
       "      <td>Ambagan(Ctg)</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>14750</th>\n",
       "      <td>Ambagan(Ctg)</td>\n",
       "      <td>2000</td>\n",
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       "    <tr>\n",
       "      <th>14751</th>\n",
       "      <td>Ambagan(Ctg)</td>\n",
       "      <td>2000</td>\n",
       "      <td>4</td>\n",
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       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>14752</th>\n",
       "      <td>Ambagan(Ctg)</td>\n",
       "      <td>2000</td>\n",
       "      <td>5</td>\n",
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       "      <td>9</td>\n",
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       "      <td>13</td>\n",
       "      <td>99</td>\n",
       "      <td>44</td>\n",
       "      <td>18</td>\n",
       "      <td>87</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
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      ],
      "text/plain": [
       "            Station  Year  Month  1   2   3  4  5  6  7  ...  22 23  24  25  \\\n",
       "14748  Ambagan(Ctg)  2000      1  0   0   0  0  0  0  0  ...  17  0  10   0   \n",
       "14749  Ambagan(Ctg)  2000      2  0   0   0  0  0  9  1  ...   0  0   0   0   \n",
       "14750  Ambagan(Ctg)  2000      3  0   0   0  0  0  0  0  ...   0  0   0   0   \n",
       "14751  Ambagan(Ctg)  2000      4  0   0   0  0  0  0  0  ...  20  0   2  22   \n",
       "14752  Ambagan(Ctg)  2000      5  0  23  11  0  0  0  2  ...   9  0  13  99   \n",
       "\n",
       "       26  27  28  29   30   31  \n",
       "14748   0   0   0   0    7    0  \n",
       "14749   0   0   0   0  NaN  NaN  \n",
       "14750   0   0   0   0    0    0  \n",
       "14751   6  25   1   0   19  NaN  \n",
       "14752  44  18  87  24    9    0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>20776</th>\n",
       "      <td>Teknaf</td>\n",
       "      <td>2014</td>\n",
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       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>146</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Station  Year  Month   1  2  3  4  5  6  7  ...  22   23  24  25  26 27  \\\n",
       "20773  Teknaf  2014      2   0  0  0  0  0  0  0  ...   0    0   0   0   0  0   \n",
       "20774  Teknaf  2014      3   0  0  0  0  0  0  0  ...   0    0   0   0   0  0   \n",
       "20775  Teknaf  2014      4   0  0  0  0  0  0  1  ...   0    0   0   0   0  0   \n",
       "20776  Teknaf  2014      5  10  0  1  0  2  0  0  ...   0    0   0   1   0  0   \n",
       "20777  Teknaf  2014      6   0  0  1  0  6  3  9  ...  60  165  28  58  60  5   \n",
       "\n",
       "      28   29   30   31  \n",
       "20773  0  NaN  NaN  NaN  \n",
       "20774  0    0    0    0  \n",
       "20775  0    0    0  NaN  \n",
       "20776  1   16    0    0  \n",
       "20777  3   53  146  NaN  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rain.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Station</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "      <td>Dhaka</td>\n",
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       "      <th>14858</th>\n",
       "      <td>Dhaka</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14859</th>\n",
       "      <td>Dhaka</td>\n",
       "      <td>2000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>31</td>\n",
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       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Dhaka</td>\n",
       "      <td>2000</td>\n",
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       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>52</td>\n",
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       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Station  Year  Month  1   2  3  4  5   6  7  ... 22  23 24  25  26  27  \\\n",
       "14856   Dhaka  2000      1  0   0  0  0  0   0  0  ...  2   0  7   0   0   0   \n",
       "14857   Dhaka  2000      2  0   0  0  0  1  38  5  ...  0   0  0   0   0   0   \n",
       "14858   Dhaka  2000      3  0   0  0  0  2   0  0  ...  0   0  0   0   0   0   \n",
       "14859   Dhaka  2000      4  0   0  0  0  0   0  0  ...  2   7  0  28  20  31   \n",
       "14860   Dhaka  2000      5  0  58  1  0  0  11  0  ...  2  49  1   6  43  52   \n",
       "\n",
       "       28  29   30   31  \n",
       "14856   0   1    2    0  \n",
       "14857   0   0  NaN  NaN  \n",
       "14858   0   0    0    0  \n",
       "14859   0  14    9  NaN  \n",
       "14860  90  23    0   48  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rain = rain[rain.Station == \"Dhaka\"]\n",
    "rain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Station    object\n",
      "Year        int64\n",
      "Month       int64\n",
      "1          object\n",
      "2          object\n",
      "3          object\n",
      "4          object\n",
      "5          object\n",
      "6          object\n",
      "7          object\n",
      "8          object\n",
      "9          object\n",
      "10         object\n",
      "11         object\n",
      "12         object\n",
      "13         object\n",
      "14         object\n",
      "15         object\n",
      "16         object\n",
      "17         object\n",
      "18         object\n",
      "19         object\n",
      "20         object\n",
      "21         object\n",
      "22         object\n",
      "23         object\n",
      "24         object\n",
      "25         object\n",
      "26         object\n",
      "27         object\n",
      "28         object\n",
      "29         object\n",
      "30         object\n",
      "31         object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(rain.dtypes) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>14859</th>\n",
       "      <td>Dhaka</td>\n",
       "      <td>2000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Dhaka</td>\n",
       "      <td>2000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>52</td>\n",
       "      <td>90</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Station  Year  Month  1   2  3  4  5   6  7  ... 22  23 24  25  26  27  \\\n",
       "14856   Dhaka  2000      1  0   0  0  0  0   0  0  ...  2   0  7   0   0   0   \n",
       "14857   Dhaka  2000      2  0   0  0  0  1  38  5  ...  0   0  0   0   0   0   \n",
       "14858   Dhaka  2000      3  0   0  0  0  2   0  0  ...  0   0  0   0   0   0   \n",
       "14859   Dhaka  2000      4  0   0  0  0  0   0  0  ...  2   7  0  28  20  31   \n",
       "14860   Dhaka  2000      5  0  58  1  0  0  11  0  ...  2  49  1   6  43  52   \n",
       "\n",
       "       28  29 30  31  \n",
       "14856   0   1  2   0  \n",
       "14857   0   0  0   0  \n",
       "14858   0   0  0   0  \n",
       "14859   0  14  9   0  \n",
       "14860  90  23  0  48  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rain.fillna(0, inplace=True) #filling NaN values with zero\n",
    "rain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combining column Month and Year\n",
    "date = rain['Year'].map(str)  + '-' + rain['Month'].map(str)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14859</th>\n",
       "      <td>Dhaka</td>\n",
       "      <td>2000</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2000-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14860</th>\n",
       "      <td>Dhaka</td>\n",
       "      <td>2000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>52</td>\n",
       "      <td>90</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>2000-5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Station  Year  Month  1   2  3  4  5   6  7  ...  23 24  25  26  27  28  \\\n",
       "14856   Dhaka  2000      1  0   0  0  0  0   0  0  ...   0  7   0   0   0   0   \n",
       "14857   Dhaka  2000      2  0   0  0  0  1  38  5  ...   0  0   0   0   0   0   \n",
       "14858   Dhaka  2000      3  0   0  0  0  2   0  0  ...   0  0   0   0   0   0   \n",
       "14859   Dhaka  2000      4  0   0  0  0  0   0  0  ...   7  0  28  20  31   0   \n",
       "14860   Dhaka  2000      5  0  58  1  0  0  11  0  ...  49  1   6  43  52  90   \n",
       "\n",
       "       29 30  31    Date  \n",
       "14856   1  2   0  2000-1  \n",
       "14857   0  0   0  2000-2  \n",
       "14858   0  0   0  2000-3  \n",
       "14859  14  9   0  2000-4  \n",
       "14860  23  0  48  2000-5  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rain['Date'] = date #adding new column\n",
    "rain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "      <th>6</th>\n",
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       "      <th>8</th>\n",
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       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14856</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2000-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14857</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>5</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14858</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14859</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>7</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2000-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14860</th>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>...</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>52</td>\n",
       "      <td>90</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>2000-5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       1   2  3  4  5   6  7  8  9 10  ...  23 24  25  26  27  28  29 30  31  \\\n",
       "14856  0   0  0  0  0   0  0  0  0  0  ...   0  7   0   0   0   0   1  2   0   \n",
       "14857  0   0  0  0  1  38  5  0  0  0  ...   0  0   0   0   0   0   0  0   0   \n",
       "14858  0   0  0  0  2   0  0  0  0  0  ...   0  0   0   0   0   0   0  0   0   \n",
       "14859  0   0  0  0  0   0  0  0  0  0  ...   7  0  28  20  31   0  14  9   0   \n",
       "14860  0  58  1  0  0  11  0  0  0  0  ...  49  1   6  43  52  90  23  0  48   \n",
       "\n",
       "         Date  \n",
       "14856  2000-1  \n",
       "14857  2000-2  \n",
       "14858  2000-3  \n",
       "14859  2000-4  \n",
       "14860  2000-5  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rain.drop(['Station', 'Year', 'Month'], axis=1, inplace=True) #deleting unwanted columns\n",
    "rain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rain.to_csv(\"DhakaRainV2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Unnamed: 0</th>\n",
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       "      <th>6</th>\n",
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       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14856</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2000-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14857</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>5</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14858</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14859</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>7</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2000-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14860</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>52</td>\n",
       "      <td>90</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>2000-5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  1   2  3  4  5   6  7  8  9  ...  23  24  25  26  27  28  29  \\\n",
       "0       14856  0   0  0  0  0   0  0  0  0  ...   0   7   0   0   0   0   1   \n",
       "1       14857  0   0  0  0  1  38  5  0  0  ...   0   0   0   0   0   0   0   \n",
       "2       14858  0   0  0  0  2   0  0  0  0  ...   0   0   0   0   0   0   0   \n",
       "3       14859  0   0  0  0  0   0  0  0  0  ...   7   0  28  20  31   0  14   \n",
       "4       14860  0  58  1  0  0  11  0  0  0  ...  49   1   6  43  52  90  23   \n",
       "\n",
       "   30  31    Date  \n",
       "0   2   0  2000-1  \n",
       "1   0   0  2000-2  \n",
       "2   0   0  2000-3  \n",
       "3   9   0  2000-4  \n",
       "4   0  48  2000-5  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drain = pd.read_csv(\"DhakaRainV2.csv\")\n",
    "drain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0     int64\n",
      "1              int64\n",
      "2              int64\n",
      "3              int64\n",
      "4              int64\n",
      "5              int64\n",
      "6              int64\n",
      "7              int64\n",
      "8              int64\n",
      "9              int64\n",
      "10             int64\n",
      "11             int64\n",
      "12             int64\n",
      "13             int64\n",
      "14             int64\n",
      "15             int64\n",
      "16             int64\n",
      "17             int64\n",
      "18             int64\n",
      "19             int64\n",
      "20             int64\n",
      "21             int64\n",
      "22             int64\n",
      "23             int64\n",
      "24             int64\n",
      "25             int64\n",
      "26             int64\n",
      "27             int64\n",
      "28             int64\n",
      "29             int64\n",
      "30             int64\n",
      "31             int64\n",
      "Date          object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(drain.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0     int64\n",
      "1              int32\n",
      "2              int32\n",
      "3              int32\n",
      "4              int32\n",
      "5              int32\n",
      "6              int32\n",
      "7              int32\n",
      "8              int32\n",
      "9              int32\n",
      "10             int32\n",
      "11             int32\n",
      "12             int32\n",
      "13             int32\n",
      "14             int32\n",
      "15             int32\n",
      "16             int32\n",
      "17             int32\n",
      "18             int32\n",
      "19             int32\n",
      "20             int32\n",
      "21             int32\n",
      "22             int32\n",
      "23             int32\n",
      "24             int32\n",
      "25             int32\n",
      "26             int32\n",
      "27             int32\n",
      "28             int32\n",
      "29             int32\n",
      "30             int32\n",
      "31             int32\n",
      "Date          object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "convert_dict = {'1': int, \n",
    "                '2': int,'3': int, \n",
    "                 '4': int,'5': int, \n",
    "                 '6': int,'7': int, \n",
    "                 '8': int,\n",
    "                '9': int, \n",
    "                 '10': int,'11': int, \n",
    "                 '12': int,'13': int, \n",
    "                 '14': int,'15': int, \n",
    "                '16': int,'17': int, \n",
    "                 '18': int,'19': int, \n",
    "                 '20': int,'21': int, \n",
    "                 '22': int,'23': int, \n",
    "                 '24': int,'25': int, \n",
    "                 '26': int,'27': int, \n",
    "                 '28': int,'29': int, \n",
    "                 '30': int,'31':int\n",
    "               } \n",
    "drain =drain.astype(convert_dict) \n",
    "print(drain.dtypes) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>Date</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14856</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2000-1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14857</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>5</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000-2</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14858</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000-3</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14859</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>28</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2000-4</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14860</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>52</td>\n",
       "      <td>90</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>2000-5</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  1   2  3  4  5   6  7  8  9  ...  24  25  26  27  28  29  30  \\\n",
       "0       14856  0   0  0  0  0   0  0  0  0  ...   7   0   0   0   0   1   2   \n",
       "1       14857  0   0  0  0  1  38  5  0  0  ...   0   0   0   0   0   0   0   \n",
       "2       14858  0   0  0  0  2   0  0  0  0  ...   0   0   0   0   0   0   0   \n",
       "3       14859  0   0  0  0  0   0  0  0  0  ...   0  28  20  31   0  14   9   \n",
       "4       14860  0  58  1  0  0  11  0  0  0  ...   1   6  43  52  90  23   0   \n",
       "\n",
       "   31    Date  Total  \n",
       "0   0  2000-1     13  \n",
       "1   0  2000-2     44  \n",
       "2   0  2000-3    172  \n",
       "3   0  2000-4    189  \n",
       "4  48  2000-5    423  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Working added value\n",
    "drain['Total']=drain.iloc[:,1:31].sum(axis=1)\n",
    "drain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14856</td>\n",
       "      <td>2000-1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14857</td>\n",
       "      <td>2000-2</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14858</td>\n",
       "      <td>2000-3</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14859</td>\n",
       "      <td>2000-4</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14860</td>\n",
       "      <td>2000-5</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Date  Total\n",
       "0       14856  2000-1     13\n",
       "1       14857  2000-2     44\n",
       "2       14858  2000-3    172\n",
       "3       14859  2000-4    189\n",
       "4       14860  2000-5    423"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drain.drop(['1', '2', '3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20','21','22','23','24','25','26','27','28','29','30','31'], axis=1, inplace=True) #deleting unwanted columns\n",
    "drain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "drain.index = pd.to_datetime(drain.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000000</th>\n",
       "      <td>14856</td>\n",
       "      <td>2000-1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000001</th>\n",
       "      <td>14857</td>\n",
       "      <td>2000-2</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000002</th>\n",
       "      <td>14858</td>\n",
       "      <td>2000-3</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000003</th>\n",
       "      <td>14859</td>\n",
       "      <td>2000-4</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000004</th>\n",
       "      <td>14860</td>\n",
       "      <td>2000-5</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Unnamed: 0    Date  Total\n",
       "1970-01-01 00:00:00.000000000       14856  2000-1     13\n",
       "1970-01-01 00:00:00.000000001       14857  2000-2     44\n",
       "1970-01-01 00:00:00.000000002       14858  2000-3    172\n",
       "1970-01-01 00:00:00.000000003       14859  2000-4    189\n",
       "1970-01-01 00:00:00.000000004       14860  2000-5    423"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>Date</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000000</th>\n",
       "      <td>2000-1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000001</th>\n",
       "      <td>2000-2</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000002</th>\n",
       "      <td>2000-3</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000003</th>\n",
       "      <td>2000-4</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000004</th>\n",
       "      <td>2000-5</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Date  Total\n",
       "1970-01-01 00:00:00.000000000  2000-1     13\n",
       "1970-01-01 00:00:00.000000001  2000-2     44\n",
       "1970-01-01 00:00:00.000000002  2000-3    172\n",
       "1970-01-01 00:00:00.000000003  2000-4    189\n",
       "1970-01-01 00:00:00.000000004  2000-5    423"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drain.drop(['Unnamed: 0'], axis=1, inplace=True) #deleting unwanted columns\n",
    "drain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>Date</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000000</th>\n",
       "      <td>2000-1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000001</th>\n",
       "      <td>2000-2</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000002</th>\n",
       "      <td>2000-3</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000003</th>\n",
       "      <td>2000-4</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000004</th>\n",
       "      <td>2000-5</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000005</th>\n",
       "      <td>2000-6</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000006</th>\n",
       "      <td>2000-7</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000007</th>\n",
       "      <td>2000-8</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000008</th>\n",
       "      <td>2000-9</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000009</th>\n",
       "      <td>2000-10</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000010</th>\n",
       "      <td>2000-11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000011</th>\n",
       "      <td>2000-12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000012</th>\n",
       "      <td>2001-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000013</th>\n",
       "      <td>2001-2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000014</th>\n",
       "      <td>2001-3</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000015</th>\n",
       "      <td>2001-4</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000016</th>\n",
       "      <td>2001-5</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000017</th>\n",
       "      <td>2001-6</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000018</th>\n",
       "      <td>2001-7</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000019</th>\n",
       "      <td>2001-8</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000020</th>\n",
       "      <td>2001-9</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000021</th>\n",
       "      <td>2001-10</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000022</th>\n",
       "      <td>2001-11</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000023</th>\n",
       "      <td>2001-12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000024</th>\n",
       "      <td>2002-1</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000025</th>\n",
       "      <td>2002-2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000026</th>\n",
       "      <td>2002-3</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000027</th>\n",
       "      <td>2002-4</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000028</th>\n",
       "      <td>2002-5</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000029</th>\n",
       "      <td>2002-6</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000144</th>\n",
       "      <td>2012-1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000145</th>\n",
       "      <td>2012-2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000146</th>\n",
       "      <td>2012-3</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000147</th>\n",
       "      <td>2012-4</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000148</th>\n",
       "      <td>2012-5</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000149</th>\n",
       "      <td>2012-6</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000150</th>\n",
       "      <td>2012-7</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000151</th>\n",
       "      <td>2012-8</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000152</th>\n",
       "      <td>2012-9</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000153</th>\n",
       "      <td>2012-10</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000154</th>\n",
       "      <td>2012-11</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000155</th>\n",
       "      <td>2012-12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000156</th>\n",
       "      <td>2013-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000157</th>\n",
       "      <td>2013-2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000158</th>\n",
       "      <td>2013-3</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000159</th>\n",
       "      <td>2013-4</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000160</th>\n",
       "      <td>2013-5</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000161</th>\n",
       "      <td>2013-6</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000162</th>\n",
       "      <td>2013-7</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000163</th>\n",
       "      <td>2013-8</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000164</th>\n",
       "      <td>2013-9</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000165</th>\n",
       "      <td>2013-10</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000166</th>\n",
       "      <td>2013-11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000167</th>\n",
       "      <td>2013-12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000168</th>\n",
       "      <td>2014-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000169</th>\n",
       "      <td>2014-2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000170</th>\n",
       "      <td>2014-3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000171</th>\n",
       "      <td>2014-4</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000172</th>\n",
       "      <td>2014-5</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000000173</th>\n",
       "      <td>2014-6</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>174 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Date  Total\n",
       "1970-01-01 00:00:00.000000000   2000-1     13\n",
       "1970-01-01 00:00:00.000000001   2000-2     44\n",
       "1970-01-01 00:00:00.000000002   2000-3    172\n",
       "1970-01-01 00:00:00.000000003   2000-4    189\n",
       "1970-01-01 00:00:00.000000004   2000-5    423\n",
       "1970-01-01 00:00:00.000000005   2000-6    165\n",
       "1970-01-01 00:00:00.000000006   2000-7    197\n",
       "1970-01-01 00:00:00.000000007   2000-8    356\n",
       "1970-01-01 00:00:00.000000008   2000-9    216\n",
       "1970-01-01 00:00:00.000000009  2000-10    278\n",
       "1970-01-01 00:00:00.000000010  2000-11      0\n",
       "1970-01-01 00:00:00.000000011  2000-12      0\n",
       "1970-01-01 00:00:00.000000012   2001-1      0\n",
       "1970-01-01 00:00:00.000000013   2001-2      1\n",
       "1970-01-01 00:00:00.000000014   2001-3     32\n",
       "1970-01-01 00:00:00.000000015   2001-4     46\n",
       "1970-01-01 00:00:00.000000016   2001-5    401\n",
       "1970-01-01 00:00:00.000000017   2001-6    386\n",
       "1970-01-01 00:00:00.000000018   2001-7    200\n",
       "1970-01-01 00:00:00.000000019   2001-8    205\n",
       "1970-01-01 00:00:00.000000020   2001-9    209\n",
       "1970-01-01 00:00:00.000000021  2001-10    166\n",
       "1970-01-01 00:00:00.000000022  2001-11     18\n",
       "1970-01-01 00:00:00.000000023  2001-12      0\n",
       "1970-01-01 00:00:00.000000024   2002-1     22\n",
       "1970-01-01 00:00:00.000000025   2002-2      4\n",
       "1970-01-01 00:00:00.000000026   2002-3     51\n",
       "1970-01-01 00:00:00.000000027   2002-4    111\n",
       "1970-01-01 00:00:00.000000028   2002-5    272\n",
       "1970-01-01 00:00:00.000000029   2002-6    373\n",
       "...                                ...    ...\n",
       "1970-01-01 00:00:00.000000144   2012-1     10\n",
       "1970-01-01 00:00:00.000000145   2012-2      1\n",
       "1970-01-01 00:00:00.000000146   2012-3     37\n",
       "1970-01-01 00:00:00.000000147   2012-4    269\n",
       "1970-01-01 00:00:00.000000148   2012-5    113\n",
       "1970-01-01 00:00:00.000000149   2012-6    175\n",
       "1970-01-01 00:00:00.000000150   2012-7    226\n",
       "1970-01-01 00:00:00.000000151   2012-8    282\n",
       "1970-01-01 00:00:00.000000152   2012-9     81\n",
       "1970-01-01 00:00:00.000000153  2012-10     38\n",
       "1970-01-01 00:00:00.000000154  2012-11     68\n",
       "1970-01-01 00:00:00.000000155  2012-12      5\n",
       "1970-01-01 00:00:00.000000156   2013-1      0\n",
       "1970-01-01 00:00:00.000000157   2013-2      8\n",
       "1970-01-01 00:00:00.000000158   2013-3     26\n",
       "1970-01-01 00:00:00.000000159   2013-4     32\n",
       "1970-01-01 00:00:00.000000160   2013-5    359\n",
       "1970-01-01 00:00:00.000000161   2013-6    325\n",
       "1970-01-01 00:00:00.000000162   2013-7    301\n",
       "1970-01-01 00:00:00.000000163   2013-8    211\n",
       "1970-01-01 00:00:00.000000164   2013-9    172\n",
       "1970-01-01 00:00:00.000000165  2013-10    131\n",
       "1970-01-01 00:00:00.000000166  2013-11      0\n",
       "1970-01-01 00:00:00.000000167  2013-12      4\n",
       "1970-01-01 00:00:00.000000168   2014-1      0\n",
       "1970-01-01 00:00:00.000000169   2014-2     12\n",
       "1970-01-01 00:00:00.000000170   2014-3     10\n",
       "1970-01-01 00:00:00.000000171   2014-4     80\n",
       "1970-01-01 00:00:00.000000172   2014-5    141\n",
       "1970-01-01 00:00:00.000000173   2014-6    342\n",
       "\n",
       "[174 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drain.to_csv(\"DhakaRainV3DateVsTotal.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29b6a42ca90>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drain.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Unnamed: 0</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-1</th>\n",
       "      <td>1970-01-01 00:00:00.000000000</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-2</th>\n",
       "      <td>1970-01-01 00:00:00.000000001</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-3</th>\n",
       "      <td>1970-01-01 00:00:00.000000002</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-4</th>\n",
       "      <td>1970-01-01 00:00:00.000000003</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-5</th>\n",
       "      <td>1970-01-01 00:00:00.000000004</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Unnamed: 0  Total\n",
       "Date                                        \n",
       "2000-1  1970-01-01 00:00:00.000000000     13\n",
       "2000-2  1970-01-01 00:00:00.000000001     44\n",
       "2000-3  1970-01-01 00:00:00.000000002    172\n",
       "2000-4  1970-01-01 00:00:00.000000003    189\n",
       "2000-5  1970-01-01 00:00:00.000000004    423"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drain2 = pd.read_csv(\"DhakaRainV3DateVsTotal.csv\",index_col = \"Date\")\n",
    "drain2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-1</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-2</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-3</th>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-4</th>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-5</th>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Total\n",
       "Date         \n",
       "2000-1     13\n",
       "2000-2     44\n",
       "2000-3    172\n",
       "2000-4    189\n",
       "2000-5    423"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drain2.drop(['Unnamed: 0'], axis=1, inplace=True) #deleting unwanted columns\n",
    "drain2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "drain2.to_csv(\"DhakaRainV4DateVsTotal.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-1</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-2</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-3</th>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-4</th>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-5</th>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Total\n",
       "Date         \n",
       "2000-1     13\n",
       "2000-2     44\n",
       "2000-3    172\n",
       "2000-4    189\n",
       "2000-5    423"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drain3 = pd.read_csv(\"DhakaRainV3DateVsTotal.csv\",index_col = \"Date\")\n",
    "drain2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Unnamed: 0</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>1970-01-01 00:00:00.000000000</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-01</th>\n",
       "      <td>1970-01-01 00:00:00.000000001</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-01</th>\n",
       "      <td>1970-01-01 00:00:00.000000002</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-01</th>\n",
       "      <td>1970-01-01 00:00:00.000000003</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05-01</th>\n",
       "      <td>1970-01-01 00:00:00.000000004</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Unnamed: 0  Total\n",
       "Date                                            \n",
       "2000-01-01  1970-01-01 00:00:00.000000000     13\n",
       "2000-02-01  1970-01-01 00:00:00.000000001     44\n",
       "2000-03-01  1970-01-01 00:00:00.000000002    172\n",
       "2000-04-01  1970-01-01 00:00:00.000000003    189\n",
       "2000-05-01  1970-01-01 00:00:00.000000004    423"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drain3.index = pd.to_datetime(drain3.index)\n",
    "drain3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-01</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-01</th>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-01</th>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05-01</th>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total\n",
       "Date             \n",
       "2000-01-01     13\n",
       "2000-02-01     44\n",
       "2000-03-01    172\n",
       "2000-04-01    189\n",
       "2000-05-01    423"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drain3.drop(['Unnamed: 0'], axis=1, inplace=True) #deleting unwanted columns\n",
    "drain3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29b6ae1a898>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aekmebNWR0RRk+pQQajnu+ed0VUo36wi8McUSEi3N2KT2SlbILgWjT6sZa0elG01NMevGgUIAVXGbvP0oxGxCFiGKlG7WESSjl+BGVKNPOq3JYIze2wHasGy84yPfw8Hji4mO2w5NGn2CG5RpR5qxenrSjRlq9Kap/YdhOa705N6gJKNfL5CFXoIbUUaftBnLCmSl4TL6+VIDj55axkMnlhIdtx2aNPoENyi3UPZHujEt6r+2rpunPxq9qhDkdEXaK9cRZKGX4IZDm5uxSe2VUUZf8bT6xYqR6LjtYKVlr7Qp1Kh0k2KoGZOFcnq/NHpXunE1eindrBfIQi/BjXCzEEhe6P1mrKfRlxsmgD4Vejus0ccvcJYTTJgC0mXeYY9+v5qlltdMzstm7LqCLPQS3GjJukmp0DMmX6q7BX+x3Eh03HZodt3EO4bjUDgULT56IyXmbVg00OhTlITCsBwKVVGk62adQRZ6CW6wAplR04kpZgWyZtqwHYqyp9Uv9YHRh29KVkxGz6YzteyMTTHUTPelm/4UYtvrMeR06bpZT5CFXoIbdsoafditUjNtvym7UO6vRh9XuWHSldrSjE0vpjjM6PvRLGV9gKyuymbsOoIs9BLcsCPDwdNqxgKu84ZJN8tVI/VkzLBGH5fRs5tF2HWTSTHrxrCCCOScnp4/Pwyf0afo/5cYfMhCL8ENX7pJKeumESn0TLqxHYrVmpno2FE0MfqY581uFmHpJqMpaMQV/aPHdxxfFusXo7dC9koZgbB+IAv9EINSiuPz5b4cu2bYeNMHv4OHTwae9pasm4QbpsKSR9UIpBsAWKyk25Bt0uhjnrfP6MPNWFWBYTmJplYxuLJKfxm95clDshm7viAL/RDj/iMLeMOffgdnlqqpH3uh3MALF8t4+uyq/7200yuj0k05VOjT1unT8NGzUX9R6cb9WQqF3qKXpBnrb5gy7VRuUBKDD1nohxhLFQOUupp22mBFrRoaUN2SdZOQcIalm6ph+xo9kL7zxk4h68b2NfrgY8NWN0YK8k1TBEIf7ZVMo3doOjcoicGHLPRDDMaI0/Jxh8EKQK1doWdZNwk2HgHNo+wqhsvot4znAKTvpU8jj569JmHpxmf0KbwHTa4bL+smbcbNYpZzugpATplaL5CFfojBWGQ/Cr3RJlky7aybMAuuNlyNfsdkAUD6u2PTiCm22vjoWaFPg9FbdiDdpD143H8Ox4GqEuQyXqGXOv26gCz0Qwwmr/Rjic8KV5jRt2bdJHuOcHRAxXDtleMFHRMFHYspa/RpDB5p56NnN700braG3RyBAKQ/wDuwVyp9Ob7EYEIW+iEGKy79KPTsJlJpK914O2OTSjde/jrgavTlhoXRrIbpYiZ1100aefSW7zpqlW7SeA/MpkLvjftLWVoxo9KNZPTrArLQXyJ8//giPnPwVKrHZMU4Ddmg07FrIenG8qUbt0gkZ/QOilkNukpQaVioNCwUsxqmR7KpM3rLpsFGr4Q++ugoQSAdRt8s3fSZ0fuFXjL69QBZ6C8RPvHgSXzwG0dSPabhSQn9yC1v57pxIs3YNCZMZTUVhYzm2ytHcho2jGT6otFnE0Y3tGvGpuq6afLR94fRW44bs9yv40sMJmShv0Q4v1prYsdpwHfd9IHRG5Zb1JqkG9osXdgJn9ewHGQ1BcWMiqWqCdOmGMlqmCpmUnfd2A5FVk9W6NvZKwMffQoaveWEBo/0h9G7McsBow/3YCSuXMiZsZcI51brqBg2HIdCCTXzksDso+umnXTj+DtDGaNP9hwNy0ZWU6AoBBfX6gCA0ZwGw8pipWa62elqOlzEchy/eFpxGb2n8zfZK1NsxlpOeMNUfxi3bXsxxZrU6NcTJKO/BGhYNuZLLkNNM1+kn64bvxnbCM6XbbZhrpOkwWMNy0HGY/Ts9SlmXOnG3QiWXt6NZVNfT4973sx1o7dh9GkU+mgEApB+IQ4mTLEbidTo1wMGvtDff2S+L1v8LyUurgYyRCVF+eaSMHqzWbpRFOJHAMRlxgxMuilkNJ/Rj+Q0TBWzANLNu7EcGtrolawZq7aJQEh6s6WUwrSbB48A/ZFumpqxUrpZFxj4Qv+rn30cf/3gict9GolwbrXmf11tpPfBavRxZ6y/YarRLN2ohEAhHqNPYTh4VlNRzGp+03c0q2F6JAMAWErReWM7IUYf13XTxl6ZTWnDlH9sJci6AfrTjNUUgrGcDgBYq6ebEioxmBj4Ql9pWEPfMDofKvTpMnrPddMH54Tv6LEcvwlpOy6bZYw2jTz6jKagmFX97xU9Hz2Q7u5Yy6GBRh87vZJp9CHpxrOaJo1AYCsoXesfo3ccCkoBVVEwmtNACLBWT9cgIDGY4C70hBCVEPI4IeRr3r93EUIOEkKOEEI+TwjJeN/Pev8+6v18Z9yTs2wHlkP7okFfSpxbqftfV1O8aZl9ZPRhFwmTb2zHgaoQf5NTUummEZJuGEZyWrArNMW/y3Yc33UTl9H79sqQdKN7m8eSMnp27OjO2DQZfThmWVEIRrIa1lLO/b8UqJt2U9KpRG+IMPr/AOBw6N9/DOCDlNJrASwDeI/3/fcAWKaU7gHwQe9xsVD3d34ON6M/txJi9CleoEY/N0yFkyXZQBDqRtwS4rL6NJuxDKNZzXe1WCn+XZZNfYdMfI2+TahZSq4bn9FHs25SZPT+isS7UY3n9aEs9L/z5afxnk88fLlPY6jAVegJIdsBvBXAX3n/JgBuB/BF7yGfBHCn9/Ud3r/h/fwN3uOFwRwH/crjmCvVm4pwv3B+te4XhEqKGr3vuunD6xNm9GwVYjvw9XmVkFSGg2c1BYVsM6NnPvU0vOkMVio++tadsWm5bqxLyOiZ9DaW01Of5HUp8MSZFZxfrfd+oIQPXkb/ZwDeB4BdzdMAViiljJ7OAtjmfb0NwBkA8H6+6j1eGH6h75N083v3PIMf/X+/1/cewLmVGnZtKAJIV6P3s276sWEqpGOzc3YcCiZPqwpJZTh4VlN9Rq8QIK+rPqtNMyvdDmn0SXfGtsu6SS7dNCdjMkafZkSBHZGexvP60DVjLdvBycWKHIMoiJ6FnhDyNgBzlNJHw99u81DK8bPwce8ihDxCCHlkfn6+7XOzi7xfmzpWayYurNX77uo5v1rHnk0jAAIZJA30M6a4SaP3boSuY8O9ZNIo9KwZyxh9MauBEOI3O5Pm3YfhzmNNxujbN2PTsVcaEemGEOLOo02R0ZuR8x/La1irDZfWPbtcg2nToTdoXGrwMPpXAfh3hJCTAD4HV7L5MwAThBC25t4O4Jz39SyAHQDg/XwcwBIioJR+jFJ6gFJ6YGZmpu0T95vRs2L2kW8fS33LPUOlYWG1ZmLPjFvoK2k2Yy/BhikgOGeHUjDVQiHJXTesGcsY/ahX8BnjTJPRW7a7USjJebdrxhJCkFGVxDJTVLoBgJympCrLBREOwyvdHPNmJNfkGEQh9Cz0lNL/RCndTindCeAnAXyLUvrTAO4D8E7vYe8GcI/39Ve8f8P7+bdozHeEsZl+NWMNm2LndAE108ZHvn2sL8/BrJW7NhShKgTVNO2VXh6N0Q97pRVm9F4z1vPRAy4rTKXQ64HrZiTn/j+t4eNhMP+4piixewvRQsmgqyS1Zmz42O6UqRQ1+kie/jBKN6zQ2w7tiwnhSkUSH/37AbyXEHIUrgb/ce/7Hwcw7X3/vQDujvsETLrpG6O3HOzZOIqbr5rA0+dWe/9CDDBr5ZbxHIoZNdVmbF8nTLVrxtIgp0dJ2Iy1bNefn1FV30df9Bg9s3CmKd24Q7EVKEqSwSOtO2MBV6dP+h4YER89AG+Ad7oNaSC4kY7ldVQNO9Wmd79xfL7if103hue8LzeEQs0opd8G8G3v6+MAbm3zmDqAH0vh3PruujFtBxmNoJjVsJxyLC4DY/RbJ/LeDtA+NGP7NDN2JKuh3LAC6SbE6FUlWdYNK2xNjD7kvtFUJV3pxnGgqx6jjx1q5oaORU1kaRR6xrYzTdJNuoyeuYbCjB4A1mompkeyqT1PP8EYPeDKN+PQL+PZDA8GemdswOj7I92wiT6FjJqqdh7GuZU6CAE2jeXc5+mDvbIvzVjL8QsBk24sh/pFQlOURBum2M07owY7Y0dzQaHXFZK6j56tFJIw+iibB7xCn5Lrplm66Q+j9zX6vPt6D9Pu2GPzFb+nkyZputIx4IW+381Y6hV6rW9d/LlSA1OFjLfVX0vXXtnnCVNjXqFnNycnVOiVFBl90WP0xUwzo0+685aBUhpo9Al6C5ZDm5IrGTJqckYfjUAAXEafpuMs2PDlPge7kQ9LQ3a5YmCpYuDFW8cApJsEe6VjsAu9x+Trfeqwu4yeoJBR+8YOlioNTHnZLYWMmmqoGdu92g9py7Ad5HQFOV0JIhBoUOiTbphi58xCzYCgGQu4Dc60tGNW11VFgUJIop2x4V2xDBlNTS8CITymUFdSJTktjD4XSDfDgOMLrmxzw9ZxAHJoiggGu9B7xcChyXNV2oFJN/k+SjdLFcMv9MVMuoyeFYd+MXq22qmGXDf+zlglfsEEAMN2X++M5kpnQGCvBDxpKCWN3goNDNESRDdYTvtBKJkUXTcsOwdIn9F30uiHhdEfm3MbsTdu8wr9ADP67x1bGChH04AX+uCNDDMby3bwB199JnF8AZNuihkNRiilMU0sVQw/djccx5sUlNI+b5hys2HCqxA2WBpA4qybus/oFeR0Ff/tzn14xy3b/Z9rKvE3+CRF2BaZ5AZl2rTFWgmk04yN7owF0mf00Zm3TJobpILUDccWysioCq5lmw8HlNHXTRs/+/GH8NE+WbbjYKALfXjodfjrk4tV/M2DJ3Hf83OJjm+EmrFAf5o7TYw+q6YWahZ2pPSjWd0sawWFvslemaQZawWFHgB+9rarcfV00f+5nqLrxgz5x1WFxE6vtJ1O0k0azdhW6SZ9Rt8885ZJN8PC6J87X8I1M0XfnTWoYxBZtPfBEy37RC8bBrrQh8echZkNiygtJXALuBN9HGRUgrxf6FOez+lQrNRMTBWYRp8eo2cMsJBRYdo0cZJkFIYVSDd+1g0Nb5hKVugZA85o7S9BLUXXTXqM3rkEzdjohqn0NXom3eR0BRlVGYoYBEopnj67ihu3jfuf10HV6Nm18OTsysDcjAa70HeQbip+oY/PRGxvCEMzo0/3TVmuGqAUIY1eRcWwUmkss4uJsZu0dXrDdqB7+nktxOhTa8Z6qxAWNBZFmj56ptGrqpJIcurcjE3DR99GutGUlF03rXk6Y3ltKKSbi2sNLFYM7Ns2jrzen89rWmA3bdOmePz0St+e50uPzXI/dogKffB1GozeXyqHBl+kLd0seZuwprzNKIWsBkrTaSKxi4k5VdK2oJq2g6zP6FsLvZIw1Cwq3UShqyS1nbHsPHWFQCXxj2s5DtR2jF5TEzuEjHbSTZ8ZPeDq9MMg3Tx91t25vm/bWMDoB4QtRxG+6T/UR/nmv//L89yPHfBC77T9upJCoTdCDKrQp6UgK/TTIUYPpJNJz87fZ/RpF3qL+q+Nn3VDwxum0pFuOhV6V7pJidFHNPq4NdnydsZGoaskcUG22kk33kohLVkuqtEDrk7fD3vluZVaohV3FE+fWwUhwN4tY8ioChQyuNJN+Kb/0MnFvj2PyI1uwAt9+2ZsOQXphr0ZGa/hCKSbLAmEGH0xcN0A6awcotJN2g1Z03agawTFrNp28Eh6zdj20o2eQiIkQ3iEnlvoYzL6Dq6bbKo7Y5sZPZDeas1/jtDNql9Tpn7mrw7iz755JLXjPX12FbtnRlDIuFHWhYw2sIyevV/bJvJ47NRK37KERKSrwS70PZqxSbZum02M3i2WtZSlm8VIoWfPkwajZ9JTvxg9cyTl9aCBHB08Ete9AvRuxuoJdsbef2Qev/G5x/Ebn3scX33iXMg/7mr0cRcKpt3JR59GqFnrUJOczrLu0ylo7dI3x/J6XyIQ5koNXFxLbwrU02fXsM/bEQu4N8FB1+hftWcaNdP2Zac0YTtU6Job7EJv2r7c0b4Zm6DQW0GSX7+asUtlt9BPFgJ7pfs8yT9YZlS6SZk1uI4kxd817MYIOE2DRxJl3fjN2A7SjRr5lLj/AAAgAElEQVTfdfOpfzuFrz91Af/yzEV87LvHg9RGJSGjd/rno7c8O2s4MI2tdtLKu2mn0Y/n0x8QTilFxbBSsxLPlxq4sFbHPm+jFADkM+k2qtMEI2GvvnYGhADffr79YKUkEP3bB7rQN0zb370XZjWMESeSbpwgWyTfJ+lmuWpgNKf5rNVn9Ck8D7vx+c3YlGMQ/BygrAqHus/nUPg++qQbpho97ZXxXTeluombdkzg9r0bUWlYbTT6+Fk3bRl9StKNFmn0MkafVkHzm9Jqs0a/WjNTjRhxh4Kk93l6xosQZ9EHAFDQ+5dPlRTspr9lPIdbd07ha0+eSz3CRZSUDnShr5sOxj02HC5k6bhuvEKvkL5KN6wRCwSMPg2m009GbzsUtuMV+pCVzR084j4mqb2yVzM2ieumVLcwktMw6sUsN2n0CXoLlu1Ab8foVdV/zeLCvbE2H3vzWA4AcGqpGvu4YbTL0x/P67AcmqrezYhYWoz+mXNrAOCHmQFALqOiOrCMPpCF3/6SrTg2X8FzF0qpPscVxejrlo3xfKt9sOwV+HIjvic9LN30y5cbDjQDgnTGNAt9sQ8afXjzDpvnWmlYTTtj1YSumIZlQ1VIW4YMeOmVsRm9hdGc5qaFNqwmjT7JRq9OPnrmlEnSdGPZQmHs2z4OQoAnzqTjxY6GmgFBDEJSi+Xx+TJOLbpZNEyaTKvQn16sYmY066/uAaCgq6gPKKNntUpXCd68bzNUheCrT5zr8VtiuMIYfSDdhO9glVDIVtziHJ7ooyoEOV1JvdAvlg1MFYOBDoHrJgV7pXcxjebSd90YviMpZD017dasm4SMPtOhyAPuSitu1k2pbgaF3rBhWEGBS+IWMp1WeQVIZ0B4u0I/ltOxe2YktULvN2Mj0g2AxLtj7/7SU/jde54BEDD6ckpJrRfW6v7qhiGfUVE1B3NHL7vhZzUF0yNZvHL3NL725PnU5TERDHihd0Iafat0A8SXb4LllVu4wimNacHNuQmxEL8X0AfpJk1GH9LPi5ng5hT20aexYSqrd7783Gas+PEppSg3LIzmdIx4Uhnr5bgzY+NLTp2ybpj8lOQ9sGza5KFneMn2CTwxu5JKkWg33DytBMuFcgPLVdd8kDajv7hWx6ZoodfVgdfo2Y377S/ZitNLVTw5m577RrRWDXiht33G0dyMDf7IciPeBWqGWCuApvCuNEApxXK1mdFnvdVDGpn0zI5XzKa/M9bfNawGjepqw4ITiilOumGqYTod9XmARSCI/00Ny4FpU5/RA8CKV8Q0lUBJIDm5Pvr2zVggWZ/E6JCjc9OOcSyUDZxbTW5VjMYUA6EpUwkL/VrN8iVV1oRlq8CkcAt986jDfGZwC71fW7zr4hXXTAMAnruwltpzXDEaPaUUDctBIashoyoRe6WNDV6sQFwPcDQWNu2hIKWGBdOmTc1Yd6OHmg6jZ9JNXwp98Np0YvRxm7EvXCxhdrkKw3Y6Om4AT7qJUZBZbstoVvNXO6yIqYri5tHH7et4Fsgo2N9hpizdAMBLdkwASEenb6fR+9bihI3NtbqJkkfAqiEilnSV3LBsLFfNVulGVwd2w5QRqS0zo26tWiinN5f6itHoWeHK6QqymtLkuinVTWwZz3lfx7uQjFAzFgDyGS3VLj7z0IebsYDbkE2H0TfbK9OUboyQrMWOv1Y3YdsBo1cUgjgS+nu/cAjv//sn0bDsjrtiAdaMFX8Cdj2M5nT/JrVSDaQbJYH/PzwzN4yM6v4dSRh9J+nm+s3ulv9UCr03Nzfs1We7b5NYOOumDcNyWhg9kHxz4NxaAwCwaby50BcyA1zoI9bhnK5iNKthvtRI7TlEVzMDW+jZhZfTVG8Ag/tvdzOGjc1+oU8o3XgfrmIo0yUN+LtiR5oLfSGropyiRt9P101GVfwb1VLFgE2DZqymxLM/rtZMPHxyGWs1q2sz1h08Il6Qg0Kv+Tep1ZB0k2jCVAfWzVh+kvfAaOOjB9xi8eKtYziUEqOP3qj8mIUERZOtomqmDct2mlh8OaFOf8HbXRvV6HO6irqZXg4Qg2k7vnso/jHccwpf3xtGs5gvp1jorxTphu0GzOkqslqQ4sdC/beGGD2lFIfOiDWs2ko3KWp+y6zQF5oL/UhWa1raxoXRT+kmtNoZy2nQFOIW+pb0SvFj1wyX/T16arlrM1ZX4jH6cojRM+nGL/R+emWCDVMddsYCyd4Dy5vo1Q437ZjAU2dXE+vdtuO0nD+zFidhx2HHTqVhN7H4pA1ZFqPQznUDBHOl08JnD57GD/3ZdxOtcKLNWACYGclKRt8OPqPXFWT1YLszYwibx/MAXEZ/8MQS7vzwg3hKIFMiWujzKQ4FAVoDzRgKKc2nNfrI6Nk8V11TQAjBZDGD5aoBh4Z89CSe1l0LNeq6N2MJHAphxsZWeO2asSzrJlkefZdmbEKNvp2jBwD2bRtH1bBxMgWmGS30gXQT/9zDefalhtnE6JP2oy6sMkbf3IztV2zJs+fWUDedxJsx2S5shg2jGSykyOivGI2e3amjjJ4xtpnRLBTiMvojc+50eKbF8sCwmzX6gq6maq9k0s30SKtGn8qGKY9153QVqkL84pwGgv6Fe6FOFTJYLHuMPjwcXJBxU0qb+iCZLho9e19EvfTsAzqS1fydyE2MPsmEqTaMGAjZK1PeMMWwZ6M7I/WYd53Hhd0mwkFVCHSVJGT0weeu3LAijD7ZdXlxrY6spjRtlgKCG1Qc581cl7C1EwvuzTQRo2/TtJ8ZyWIhRUZf70GUohjcQu9LN14zlhX6RvBBHslqKNUtnPG2iIssnU0raDgCrnaeJjso1U1oCvGXxgxjeT0RW2AIs4ZoszqNYwNBAZsqZrBYMeDQwJrnbpgSO27DckBpkOHS7UJl74uoFZKxy7GQdLMW0ujjbvRyvIlkbSdMec3YZK4b2rHQXzPjztI9Np+M0XdqJrt6dxKNPqTJ161mRp9Yumlg83iuqYEMoGkjnwjOLFXx8j+6t+NAkOMLbHdvMukmKsNtGMlirW6llltUM23/NeDBABf6UDNWU/xmEbtwRnMaRnM61uomTi+6hV5k6cwaiVGNPq3da5WGhWJWa7lAx/O6v7EkCcKsIRqq9bmHTuMtf35/7GNHZa2pkWDZGS70opoxY1+v3rMBQPdCzxqTooXeZ/Q5DXldhUICRq8mYPSm06q7MqTho+9k3QTcm9amsSyOJmT0nbJ6WGMzLsKMvtSwUDGC1Nk0mrGbRnMt38/HZPQX1+qgFDi70pofVKqb/nWeZIXTzjq8wbNYspV+UlQNu4VEdsPAF/qsrjYNSWaaXzGrYTTnMvrTPqPnf3PMqHST0dyM55TCwUoNy2eUYUwUXEafdPB1mDVE89AfOrGEZ8+vxWYPLYW+kPGXnYkKvRkUel0l3X30XtETlW7KDQvFjOrbCIsZLSTdKLHTK8MJmJ3ONalG34nRA8DumREcm08u3ahtbiY5PVnkb1ijL9ctVBsWNnrN0zSasVFrJRC/icxuaOU2q+qTC0HxTyLjmm0Y/Yy37yct+aZm2n5DmgcDXOg7STfuGzuSVTGW01Gqm750I/JBM6LSTcrjBF1G3/pGTHhaY9JhD2aINbj20+Bvn12uAUiwxyByE5wqZvwGMvPRx9kwxZbDk8UM/vDOffjpl1/V8bFMSxZn9CZGc4GeO5LT/MKuKvHTK6028QEM6TRj28crMOzZOIJjc+VEK07XNdT6kc8nlG5Woxq9YWHDSAaEJCv0lFJcXKtjc6QRCwSuG9HPa2DqaP29E6Fmd2KNvgOjT8t5UzOukELfaGrGBj56didmjP7MUs3fkSek0UcGPaQ9TrDSsH3XRxgTnt1yJaF8Y1gBA4wy+jPL7o1vLe4eA7bhI1ToGVihY1k3IoWHfXgKGQ0/8bKrcMvVUx0fy55HNAaBJVcyhN8DTSFQY6ZXRqW+MHx7ZULpptu+gt0zIyg1rESFwurQTM4l3GW6VrN8hu1q9O61XwwNlo973LrptHjoAcQeEM5MHu2iU06EeiBJNPp2q7Ngd2x6hb6gt9aXThjYQh/YKz3XjcfwGUNghf7sSs3/HRFGFX0z8iln0pc7SDfjBZdtriTMFgkz+kzIlWRYjr/JJG5+STimGGgu9MxeyQqGSM1kHx4ebZG9N6J6OsuiZ2gq9Any6MOZ9lFk2c7YJD56p3MzFgicN0l0erYzNorkzVjTa5h6Gn3DQjHjup6SMPqLpfabpQD4RU60INf9OtKG0S+U/es6ycresFr3RLAolLQKfdW0kbsSGL0v3WgKcqGdsay5U8xoTUt0QFyjD3+wCiln0lc6afQsLVDACtoO4fMPr3jOr9bASHZ86aaZvYbzevzBI94HQqRoMvbFs+RkBVWc0Uekm5B8pnlZN3EyeoLh3V2ybpIwequzjx5wGT2ARDp9p/TNNJqxY3kdIxnNZ/SFjIpiRkvUjGUe+s1tNPpcxn3NxTV6Np2u9bxOLFb9G2ra0k1OVzGaSy8GoW7Yfs3iwQAX+s6MvuA128JLdEBMujEijL6QTb/Qd5Vuasmkm0ZYugnNLD2zFKxw4ko30Z19k+FCz6Qbwhi9QKH3Vks8jJ5pycKFvhGRbjLB1wpxVyQ0xkasQKPvz4Ypo4d0s2ksi5GslojRmx00+pyWtBlrYcyLnCjVTf/aZ4Nf4sKPP+jquhE7fj3i3mOglOLEfNmfYpVIurEcZNu8lzOj2dSCzaqmdWVo9E0RCKFmY8UICihjbhtGMsgKDmh2O+PhFD+2FOyvdDPJpJvEjD7UjA3ZK2eXA+dA3GESzJHE7I9NjN4rFIzZikgrNV+j55Fu4vnoS17RYWDvgea5cNh5i7L6btKNqhAoJLl0043RE0Kwe6aYyEvfLgIBcFdYSQp9iTF6b1+Lz+izaqINU4z9bmzXjPULvdhrHt2Pw7BcNbFWt7B3s1vok9or2wXUbRhJL++mZjj+pjEeDG6ht2zoarAhyLDdAKNSPSigjLntmCo0OXN4YEaWV2luqWbBa+1cN6M5HYS4F1YSuM0792IKb5iaXa6BWfdjN2Mj0s1EIVzo3f8rMaQbX6Pnkm6YRp9MumGkgBXROOcdPo9OOnqSAeFOaEZvN+zeONIfjV5L2IytmxjP6xjJaViqGrAciqK3oTFJBMJSxUAxo7YtaJqqIKMqsaWbaKE/seC+rrs3FpHTlUQafafG+sxoertja4Z15WyYynlb5FmcrWE7TbZFVuivmio0NSR50KLRp1joWfBaO+lGVQjGcjpW03TdRBj91vE8NIUkasYqJJBpMpriv9aBvdJ9rIgEUhMo9LrvuhELqqubTtNKijVmtchKRLjQd/HRA63OJxF024wVxs7pIi6s1WOPjex0M3EZfbxzp5RirWb5O5FZvIDL6JNJN8sVo4lkROEWZLHjs2swWuiPeyulndPFxFn34c9mGDMpMXpK3WHuV8iGKQdZnRV6z75mOq5t0ZNZ2PSpHZOFpoYkD1o0eibdpJBDE45paIeJgp6q68bNAnL/9jPLNeyYymMsr8fX6NvYw5jzxt8wFcMVUxNw3cTx0YcjihnYexDtLYi6eaKjJ6NgRONbz13EPYfOCh67OVuoExhxqAvKFf7zdIhAyCbYMNWwHBi2g7G864K76OXHFzMaChkt0dzY5aqByaLe8eeFjBbbXhm9AR2dL0NXCa6aKnhjRdNtxgKuxFxKIQahYTlwKB9hYhjYQt8w7SATRWcxsLY3D7RZurlqqoCsLqjRR7acpzVpBwhZQDMdCn1eT6zRG6EVSZhNzi5XsWOygLGcFl+jb2MPayn0MZqxVdOV43oxVyDezthwRDED24qvRW2hgoXeH6zdppkJuGTEtB188BtH8KFvHRU6tmnxMXp2g4wbzdtJo895N6k4qZ5s1cgYvd+HyaoYSWivXK6amOzC6PMxosX9nbGR8zo2V8auDUVoquvyS8roOzVjgeQWS3ajSJXRE0J2EELuI4QcJoQ8Qwj5D973pwgh3yCEHPH+P+l9nxBC/oIQcpQQ8iQh5OZYf4xl+9ock27qpuNucfeYzd4tY3j7S7biddfNtIwb7AUrIt1kNQUKSWdnbDnk9W+H8UImMaM3LDuIQPAa0Q3LxsW1BrZPFhIx+mj/AggaskEEgvt9IXulYXM3kPQYjH4tFFHMENXo1RhNZCA0WLsjo1dQaVh4/kJJeDMcr3TDiE/ca9TqsPuWMcM4efp+iFxex0g2fIN1XTdJ5sauVI3uhT6G/z/suglv9js6V/atlYWMllijb/desvGnSZ03IqYGBh5GbwH4TUrpXgC3AfgVQsiLAdwN4F5K6bUA7vX+DQBvBnCt999dAD7CfTYh1E3Hv7BzIUYfti0Wsxo+9K6XYtNYrikPhwfRKFF3nquGSsPGfKmBP/r6Ybz4d/8Zf/PgCeFzL9d7SDf55Bq922MINWMtB2e96IPtk3mM5fREGn1URmAfuCCm2H1PRAs978Wp+a4b/ve0m3TDmDg7b9EES9aM7cTodZXgqbOrMGwHK1VTcAgOn3STS8joO0Ug5LR4nnQAWPVWjWOhiV4A/GYsEN/J5jL6ztJNPsY4QcboHRr8vXXTxumlKvZ4exXyerLB46ZN2+Y4bZ1wZ2gknWAlYmpg6FnoKaXnKaWPeV+XABwGsA3AHQA+6T3skwDu9L6+A8CnqIvvA5gghGzhPiMP7ZqxDcvpaFvMqgoMoQ1TrXfdfEbFt5+fw+v/+334y/uPw6EUDxxZED31UPBa+zcibY2eMXqWcbNjqoCxvBY7T6ddM4mNRFRCDVpAjAWKNJB8H73AjcQfOpJtdd1EVyKijN7iYPTs9bcc6sdy8IBXumGEJ27j1O6g0fvTmmIU+jCjHw19LlkzFoiXSW/ZDlZrZtdm7EhWXJ4M9/EYITuxUIFDXVcT4ElCfWjG7tk4gqym4MlZ/gFJ7SDS62IQ0ugJITsBvBTAQQCbKKXnAfdmAGCj97BtAM6Efm3W+54Q6mZYunFPs9Kw0LCcttp3NNirF9pZoEazGo4vVHDrril8872vww/fsBnPnFsTPfVQ8FoXRl8zE827jLpuLIfiuLdrMimjb7d5h41EZBqvrxcLfCCqhu1HTfSCnhKjL4Z89ECI0QvbKz3W3YHRR1+vlQr/a88bDZF0kHe3rJu4x23S6COvO1u9xdkdy4LSujH66ZEMFgX17vDfyM6LWVav3TgKwJOEEjZj2zF6XVVww9YxPJW00AvsMGfgTsUhhIwA+HsAv0EpXYvmrIcf2uZ7LZ8qQshdcKUdXHVVa4ph3XQwVQw2BAHwc23G862nLWpvM61Wq9kf3HEDLJvi9dfNgBCCG7aO48uHzmGx3MD0SOumjU6o9NDoJwoZUOqyoW6MpRvCzhi24vnwt49h90wRm8dyiTX66IXKmrFKpNCLLJ3rpo18lzmxYcRx3bAPbrjQs6+TM3pPuunC6AE2NJ1iqWrgqukC17GZpt/rWsjFeM3D6KTRs+snznH9Qp/XmohNMaP6/47TkGX7TCaLnV+TDSNZLFQMUEpb5j50Qng1FC70hAQDXlxGH281TCn1IsTbn8/+7RP4wiNnOq6ueMAYfeo+ekKIDrfIf4ZS+iXv2xeZJOP9f877/iyAHaFf3w7gXPSYlNKPUUoPUEoPzMzMtDxn3bIDe6X3//s9GeXmqydbHh+H0Ucbjq+5dgY/cP1G/6K5wdsOLcrqexf65LtjTdvxb4CsyCxVDPzPH78JikIwmtVQN51Ynut2047YSESm0ceJia0alm9j7QXmoxfZhBTMi223YapZoxdtEJq+66azvRIIrk2R4TJBUevMXgH4UmYjNqOn/t8fRiDdxGnGMo2+mdEXslpIuhEvmuzm160Zu2EkA8OTc3lRN21/leAX+vkydkwW/BtpPqMK77hlYASi06yF/dvd+b9JMovYCjDVnbHErXofB3CYUvqnoR99BcC7va/fDeCe0Pd/znPf3AZglUk8vFitmriwWvflAlbQvvPCPEZzGm7YOt7yO1lNFcuj7zLRh+HFMQt9ELzWWaMHkiVYhqUb9vr88ut346YdEwBczRSIF2zWrhm7b9s4XnrVBF60KVjeAmIssGbyb9sOGL2YdJPVlKYP2UgmIt2QuBumGKPvLt289lp3epaI82aZo6gB6Wj07e2V7LjxGH1GU9zQrhCxyesBo48j3fg3vy6vyXTRm9ok4GKpW7bvfmEa/bGQ4wZgzdj4/S2gc79l/3b38/nEmZVYxwea4755wcPoXwXgZwHcTgg55P33FgAfAPCDhJAjAH7Q+zcAfB3AcQBHAfwlgF/mPhsPn/jeSVQNGz/lDabIhhjry3dNt13yuPZKsWZstxApwF1Kb5vI45lzYppapWEhpysdi8J4Plkmve1QODS4mN64dxN+600vwq/dfq3/mDFP3opT6Ns1kzaO5vAPv/wqP0kwTgNPZNu277rhKMhnlqp47+cP4dPfP9UUqQwEDfHwZCxAvNCzKWZjufYfroyXbfLaF7mr02UBjZ7p0dEB2FEkaZoC7jXfzV4ZtxnLNi4yRp/3BtYXfdeN+HGXK0zO6q7RA8BihV+nr5uO/3sVw530dnyhgmtDhb7guXniDHmJxodEcc2GIkayWqKGrEjcN0PPWwKl9AG0190B4A1tHk8B/Ar3GURQblj46wdP4I17N2HvFpdRZ0N/0Ct3T7f9PXHppne2CODKN88KM3q7yVMcBbt4VxPmxTPmunk8h18NFXkg2DUcpyFr2k5H2YmBXWQiH2IR143up1f2/rD97cFT+PKhs/iJl+3AT7/86qafaao7oUxLUOgppfjSY2fximumO/Zq8rqG8byOG7aOQyGCjL5iIK+3z3QJI5dASwe6MHo9gXRTs3xSMeLbnt3jJZkb669yemj0gJgvvW42M/ozyzUYluM7bgD39XCou+pn/QteMEbfSbpRFIJ928bw5Nn4hT5OM3bgdsb+7fdPYbVm4ldv3+N/Lxd60V65p32hF2/Gdp/RyXDD1nGcWKwI6YxuFn3nN4Fl0sfV6Bv+8rCz9DTmjyyMU+h73wTjDGeuCow/E/HRl+sWJgsZ/NGP7se+ba2y3khW848XJ73y4ZPLOL1UxTtv2d7xMb/8A7vx0Z+5BapCvAHw/K97L784Q5KCDHTW6JPcQNZCIXLM1sokBUYWHj65hP/jkw/j0VNL3MddrprIqEpH+RMIMXqBQt8wnaDQN2wc8xw3eyKMHoi3MY31lLqpBfu3T+DwubXY2Ugicd8MA1fov/rEObxs56SvNQMBo58uZvAizwIVhSij59HoAZfRUwocPs/P6jtl0TOMJyz0UUbfDgGj579BrdZMWLbjSTc9Nu/EGPxQFxho7I8S5GDeVcP25wm0QzGr+b78IL2S/1r5+0dnUcyoePONmzs+ZvfMCF7hrTYnCxmhZuxKtXt4F0M2ppZ+/5F5/KcvPdnxfWXvZTzpJoiFZkyeFcpCRgUhwD2HzuGbh+fw3Rf496S4r4ne1U3DZDpei6XtUBi2g8lCBgpxxwme8iS5ndNF/3FxB48DwQq022dz//ZxGLaDFy6WhI/PzktTSNfniGLgCv1yxcBVU8Wm77EL/Lbd0/4HNYqMqsJ2KHfzrtM25Shu2ObKR88KFPpyj0KvqQpGs1rs4SMmB2tgy2leRk8pxRv/9Dv46HeOcb02GdWNjOAtDqbtwLQp91Qclh3P836y0XWdMJrT/AIXpFdynQaqhoV/fOo83nLjFu7m10RBFyv0NbOrFs2geJHdojtjf+3vHsdXnziPN1y/EW/bv7Xl50l89KWQRq+pCvJ6sFGKEIKfOLAD/+frrkFOV4R2yC73iD8AXAPGWE7jzo4J5lArboRyw8aZpSpGslrTioqRkTi9hV7NWADYv81ryM7Ga8hWDbHkSkDAR3+psFa3WppSmkLwUy+/Cne8pPUiZWDBZ4btdGyCMkSbmd3AptuI6IAVw8LGNlNxwhgv6LHHCZoW2zLPw+j5nqNhOZgvNfCdF+Y7bvgIgxAitFU8jq6oqwpXM7bWY6XwX9661/9gBOmVfJX+4PEllBsW7riJf8/fZCGD894YPB4sVw1/4EUv5AQ381BKXSn0B/bgN990XdvHxNn8xhAdxj6S05oa7h94x34AwN8/elZoUPhyhe/mx7z0PKiFbIlsSMpy1cCOqULTyiGOLMnAs9reMZXHZEHHk2dW8dMvF34KoZUxw0AVest2PbFjkQ1RhBD8Pz9yY9ffZazfsBz0WgVHh193g6IQjGQ134rFg0rDRnFD95dWlPWFYdjuBdguCpWBjVvkZfSsB/HE7CqKGbWnIwkQyxoRyaJn0FTCNUqwF6N/5e4NTccEAF7lhr1HO6byfL8A160lIvWtVPmKGuCyURGNvmrYoLTzLm3AvaGqComl/buDXoJjTxb0tu6hYlYVigBfrhr+nNxuENkdW7fY1DoFIzk3K//0UhW7Z5oVBH+PSIwbH0//jBCCG7dPxG7IivS6GAZKumFWwF42s3YQyV7hkT7CGMlqKDf42bc7Bav7G+HquHFnuno6YJfzJ4QIRRWzPBLDcrBcNblWOzmBAQ1x8jl0VeHaGSty4Ysy+l6zBdphssDfjHUc2jOlMYy8rgpJN7027zHkNPFoXjboJbxB7U9//Cb89g+1rhwKGU2M0VfNnhvIANdLz9uMDc+hLmY1lBomzixVcdVU8w7mJM1Y3tqyf9s4XrhYivUctRjSzUAVej8gKSde6P0pVFyFvrf0EcZoThPyo/dimIB4wy6MYHnYfUUiEoNQitzIeF4bkZjYaoxt226cQO/3s2rYXd0Z0WMC/OmV7H0f6eCfb4fJYgY10+Z6bUoNCw7t7hcPIyeYrFhqEwvRDnHmxrbLFtq3bRxXTxdbHlvMqNwaPaX8N7/pkQwWOaUb9vdlNVe6OTFfQcNyWgp9LoZ1mLTqTYcAACAASURBVKGXvZJh//Zx2A4V6v0x9JIq22GgCv2qn5uRhNH3fnN6bWqIYiSncXuBbccd89WLQU0VM1jivECjMDjPXyTYLJowyCNrFQQGP9RCbIoXuqpw+eirhoUCJ+P28+g5M3QqDQu6SoT81CIRF7w5NwxZXfUlCB70isz2jxtjbmy7yIlOKGT5p02VGxYsh3IW+iyWqwZX055JU6wZe87ro+xoYfTeJK9Yrhu+zybbIftkjIbs8DP6WnzpJrCe9X7DDQ4dLQzWuOEBiyju9cGaKrpjxXg06CgYo+tZ6AWiitkS/zov4oBH1hJhl0EQEz8z1lQ+103VsLndPKogo+8Ui90NLLqDZ8UWbPXnZPSa2Ng/Xukmn1H9AfO8aMfoO6GY4dfo2a5ivmasGxDII5U1QmQj/J5GGX2czYAMPM1YwN3kuHE0GyvJsiIw14FhoAr9aigJTxTsheUJweJ9MxhGBRg97weL7fhbjsHq7zl0Djld8XNnOmE0y8/o2RL/9de5W/i5pBuB5X41xiYPTSE9Gb3jUM9HL8joOXfGluuWkGwDBOycr9CLMXq3IItLN71uVrkYc2PbTfTqBJE5rLzZP0CwO5YnBqFuNWv0AEAIsG2yudGeTjO29+dn//bxWBbLcys1P4qEFwNV6NmFk4TR87ASUY1exHUTFPruBY2xviVBnf7MUhVfPnQWP3Xr1S25LlG4jF7MdfM6r9Dz3ATzIs3YmPbKXise9uHlZTiiEQilhtU1zqIdWBORJ+9mVZjRi0ksZU7WLXpcIGD0PD21Ylb1V7u9EMQf8DRj3c/AQqn35ygs3bDXY8tYrkWWC+yVcQIB3esqy/H52b99AscXKr4ExoPVqonVmtmyCumFwSr0tRSasQKMnr8Zq3O/Gb2GjjCwIi2q03/kO8egEoK7XntNz8e6Gr3YDeqGreP4wI/eiDtu6rxngUGo0MdpxqqkJ/NmvQXeZqxoemW5bjWlMvJgso+MXtReGUw7uzTN2E4oZjVUOTX6FY7kSoZpEUbPpBstYPRRfR5wSY6mkFiMnmfDFMM1M0VQCpxb4d9zwcL1hrrQr9ZMaAoR1p+AMKPv/eYEzUx+jb5i8A055pVupnzphv9uPl9q4IuPzOKdB7ZzLd3G8jpqps3VByiFmnY/eetV2DLe2zeeE8jt9hm9kHTTm9EzSYhX+xdl9O4uZ7HrMWjG8mn0hPCvYnOCA7FLQs1YUY2evxlbzKgwvHiNXhCTbjxGz2GxDBh9oNG3K/SAe50m0eh5ast4jDyqoNC3Opu6YaAK/VrdxFi+e75FJ2RFNHqBuy4QMBaepSev75otS5cEIlYfPrkEw3bw4wd29H4wgkhdnkZypWH58bK8iGOvFJNuSE93jKhtU7TQVxoWRgRXmFlNRSGjcjUIV6oGxnI69+suWujLvmuo9zxa0YEmIoye3Yh5mvfLFQOE8LnvxnI6NIVwbZoKfPSK//nsxIxzMVY4AL+9EoiXMOsXes7pZQwDVehXa1bHvO9eyPRZowfApdNXeAs90+gFGP3TZ1ehKQTXb+7ehGXwEyw5LqSK0T2fpx2YdMOT2103bRDCp10yaIrS00fvM3rOcxdNryzFcN0A/PskeJMrGdxCLyDdeLlLvciTyE2boVQ3kdMVrs8RWxXxkKXlqonxPN/NT1EIpooZrk1T4WZsr0JfyIjtV2DgtT4D8RJmTy9VMF3MCF+TA1Xo12pmrEYsEGj0IjtjeaUbtjTlcd6UOaUbXXUbQiKbpp4+t4ZrN41ye9F9xsBxIZUbNhczCyOfUf1EwF5gFkiR1ZobgcCn0fMyekVUuolkufBioqBz++hF5gbndAWG7QidP09RENnlzODm3PB9Xhmj59k0xRNoFsb0SJZTo3ev06ymYM/GEUwWdLwklJIbRlzpxmf0PIXeu65EEmZPL1U7yk3dMFCFfrVmxtosBYSzbvqzYQoAV0P27EoNukp6OmIA1zHA24yllOKZs6vYt5Uv/AoIM3qOG1TdFNai/dRDDp0+zm6+jMrD6MUKvSZQ6C3bQc20+8roRXJugOA1552mxrsicZux4j563pugz+g5GrIrgqsc3siJhmkjqykghGDnhiIe/903YdeG9lq3SI5TGKbtQFNIx5TdMEZjSDenFqu4WlC2AQas0DONPg7Esm56Z0aHwT4oPFr36cUqdkwWuJadkwKF/sJaHYsVo+1gjU4QiSquNOyesQ1RFAT8xrUYQUwal0bP5vPynbsIo/cdPTEKPS+jF2WvosmKFc5Cz7JuRMbnhYeO9AJj9DwDfERfE96bqkjqo0gyaxgmR/IrQ0ZzY515pRvTdnBupSbsuAEGrdDXrFjWSqA5vbIXxO2V/EOOTy1WuRslUwX+Qv/0WTcTY982AUYvwBjKDXGJQmRAQ5xt2xqHj76fjJ7l/4jaKwH+iAtxRu/tAOeMQSg3+DZ8ZXV+6ZOhVOfvqbEbMU+w2XJFTM7ivanWTNufptULhZiMvt285W4Yy/MHD55drsGh4tZKYIAKPaU0kUaveYMweC7UOPZKoHczllKK00tVXM35RkwW+YPNnj67CkLgz9HlgUizp9ewlHbICbDLqmkjL7hi0JXePnrRZmyQXtm70PsOqlgafQZrdbPrDcX0YrlF2KvokBBejZ7dhEViEKIRxd3AJoDxafRi0o1b6A04Pd7Tuun4N8peEA2PYzA4Z1EzjOX4gwfjeuiBASr0DcuBYTux4g8YspoqFoGQMqNfqhgoN6y26X3twDR6nuXyM+dWsXtmRCgrpphRoRA+jb7X+MN2ENkqPl9qCDuqNI6YYiav8K4W/PRKgT0R8TR6HZR2HwDPJiPx9HMYmOmAu9DzSjcxxueV6pY/J7YXfEbfQ6OvmzZqpt11KHgUk4UMHBrEPXQ7Nq+RIQmjF3GWiSTMsrGHvPUljIEp9KsJdsUyZDQ+L7Coj55dpL0CwoI3gp/RNyyHq7v/9Nk1oUYs4GXSc15I5Yb4DlDeyURza3UcPr+G265pP9i9E3SV9Lxx10wbOV3h9qGLZN3EiShm4Nkd+/wFd2botRt7D9hgYDfXtAt9PsbcWJFmLC+jF9kVy8Bknl4b1OqW40tUvZBkwxSvUgBAaGbEmaUqMpqCjaNZ4fMamELPdOS40g3g6vR8jN7z0XPeeXmnTJ1eFCv0ft5NDy13odzAhbW6UCOWYTSn9dToTdtBw3Ji+eiB3tLNt5+fBwD8wHUbhY6vKUrP9Eqe7P8wCCFQCF96JVvBxdHoeXbHskJ/PecYQcBtmgJ8Ka22F/jGc6Ni2jUvizU9RxJ3M1YPXDdrdRN3fPhB/+8PI9gVK+a6cX+3+3VeN23/9euFnJefL9KcBlxGLzK0W4TRn150h6TwOHqiGJhCnySLniGrK1wao6hGD/BNmTq1WAUhwPZJfkYP9M5EOTpXBoCeaZXt4GqA3W9QvLENUTAWWO1RHL713By2jOewd4vY+fO4buK4eVQO7R8IejJxXDc+o++yIe65CyVsGc9hPIa9kod580Zmix4XCF4bXqlVUxV/QPgLF0p44swKHjqx2PI4luYq1ozl+xw1BKSbHZMFmDbF7HKN+zwAxugFNXpOe+XsShXbJ/lHWoYxMIU+SXIlQ0ZVxDZMKfx/Ps/wkVNLFWwey3FfTLzBZicXKgDQ0fPbDTwXUlzmGvjoOxcHw3LwwNEFvP66jcLRFrqqwOzho68YYowecAs9j0afpBnLI90cPr/GvcuZIZBuel/nvENHgMC1xONzB8LxB/yf12JGQ8WwcHHN7U2w/4fh5/NzJFcysNXTak9Gz9+MvcGTSZ85JzYByhCwVwLBzAielcO5lTq2TQx7oa+xyNNkzVjeQq9ybmpg4BknyJZWvOAt9CcWKsioCrbGeJN5oop5d/NGwWOvfPjkEsoNC7dfLybbAN4oQY6sG2FGT/gYPXu/RW8kADBR7D5lyrQdHJsv43oBFxUgJrGI3KhYSN75VT4GK5JFz1DIqqg2bFxcc9Ma2f/DEAk0Y+BNC61b/Iz++s1jUAjw7Dl3MMhnD57GH37t2Z6/J2qvHM/rvsTWDVXDwlLFiFUDgAEq9KspaPQZTeEcJUiFZBuAb8rUqSWxXWu8Gv2JhQqumubbhBXFWE7ved68GfpR8LhuvvXcHDKaglftEWvEAp7rxqFd2U7VsIXPW1UIl4++3LBQzIgFvTGMZjVoCuk4b+D4fAWmTYUZve+jFyj0PDdwVuh5I3NFAs0YGKOfK3mMvtTK6IPRivx1YDyvgxBejZ6zGZtRsXtmxGf0H3/gOD7/8JmezNu0BV033oqomzsLCN6XK4DR80eedkJWU7g2TInedYHeU6aqhoX5UkPI+jSa06AqpCcTObFQiSXbAF6zp6d0Y/vnIwKfXXZhI0/NrmL/tnEhWyiDzuGQqTQs4WPzEoIK52ajdiCEYKKQaWnGskLx3AW3gIg0YoFgYxNXoa/zS3JZTcXMaBbnVvgYPYsDEXHJFTIqKg0bcx6Tn2vL6E0UM6rQjF5VIRjL6b1dNwLSDeDKN8+cW8PZlRqOzVdQalg9C7IhqtFz7nVh78sVwejzuiqkb0XhfoD5pBteDz1DL9dNnM0MikIwWch0TbC0HYpTS9X4hT6no2LYXd0rcZuOikJ6jqA7tVTBzpjnrnnvUTf5phpjfua2iTzOLPUuaKUYewvCmCzoTc1Yx6F40we/iz/6p8M4fL4EXSW4ZkbstckL7GAV7TFsncjjHKd0E4vRZz2NvtRFuhHcFcvAsztWxEcPuEN4LqzVcc+hs/73el03piWmFgS717uvuoNCLzZCkGFgCv1aPf6uWIaspnIxektw9xrgrjS6MfpTgtZKhg0jGb/Z2g7nVmowLCcBo++d0+NLNzFYd7cpU3XTxsW1BvdO4SjYB6ZbQ9Yt9GLnvWtDESe6vOYMcaZLhRHNYHlhroQjc2X8f985ji89NovdMyPC16GuuvZQnl2bZcH3ddtEDmc7MHp3Nm9wDYkMHWEoZJhG70o2y1WzZWW1XDWEGrEMEz3ybiilaAj46IGgIfvXD5z05TtG6DrBbcbyP4efR9VTuqlBIcCmsWEv9DUr0a5YwLNXcizJG5YNXRPX6MsNq6Nbg1kgRQvy2/Zvwb8dX8Tjp5fb/vxEAscN0BxVfHSujMPnW10EvusmhkzRbWNJ3CEJDDoXo7eEGf3ODUWcW631lD94c2I6IcoyDx5fAuCOkJsrNYTiLBgIIdzDR3jnxTJsHc/j3EqtrQ791w+ewGv++D7/BpNEo7+4VvedQHMR540bfyDO6Cd7MHq2AhKRbl7sFfqFcgM/uHcTAODMco9Cb4lumOKTbs6u1LFpLCdMDBgGptDPleqxlmxhZDnslYvlBr713ByuE/Sk+zEIHXb2PXehhO2TeeEew8+/ahemihn86TdeaPvzk4sJC30oqvg/f+kp/MfPH2p5TFwfPeCNE+xQdIJVTlzpxtPoO8hOjkNRM23uebEMuza4szp7sTPenJhOiDL67x9fxLaJPD7x87diw0gGrxDcKcyQ01V/iEY3iLqptk7kUTedtk3NR08tY7Fi4P4j7ua3UsPiHjrCUMiqWCwbKNUtny3PRRqyovn8DL0SLMPzYnkxUcj4zc8337gZkwUdZzgYvWgEAsDH6OPq88CAFHqHUjx1dhU3XzWZ6DhZvXcz9n/86/OoGjbufvP1QsfuFWz2/IU14ZsHO+4vvu4a3H9kAQ+dWGr5+fH5CgoZNda2ZyCwq67UDDx7fg1H5sotbLDcsJDRxD60DHld7eijP+XdpGJLN94+B7PDKqpu2aCUP9CMgd00j893l2/c+ID4cuJE0WWZlLrOoYMnlnDbNdO4arqAg//5jfjxl/GNhIzCjdDtfJ3/ymcfw3/5h6dQaVjICryvrJC0a8g+f9Hdxfovz1wEwALNxF6bYkbzScGN3i7vaEN2uWpiSsBxwzCe17v66Nnzimj0QMDqX71nA3ZMFXqSA9ENU4xA9trUeG71Cij0lYYN06Z49Z4NiY7Ta8PUU7Or+NzDZ/Dzr9yJPRvFivJIl2Azw3JwfL6C6wStcgw/e9tOTBcz+PT3T7X87ORiBTuni7Hm6AIBY3j23BrKDQu2Q32ZiSFOzg1DN43+9FIVozlNyCoXRi9GLzpdioE1h9lqqRNKdRMjgtbNMCYLGRi2g4ph48hcGUsVA7ddMwUAsSybDFld6cjoDcvBN5+9iC89dhbzpYaQtMLYa1Snr5s2Ti5UQAhw73MXUWlY+O4LC8KrzHAv5cbtbqEPN2Qt28FqzYzN6EsNq2Os9flV93lEAuQA4BdeuRO/9aYXYXokix2ThZ47ZQ1LzOihqwoKGbUro3ccivMJNksBA1LoGaM8sDMpo+/ejP34A8cxntfx62+8VvjYjL20a2oeXyjDcmjsQp/PqHjlng145GQroz+xUMEuQWdGGKzQh1cLUZ0+TnIlQ7dJPGwaTtybFHPddPrw1vwserFzH8vp2DCSwYk2jH6h3MC/+18P4Omzq6hw5sR0gp/BUjHw/ePudn/RYLd2yGlqx/C+5y6soWG5OTTfOHxRSHpijo4ooz82X4ZDgbft34qVqolf+7vHcXalht8Q/ByF9ztcv3kMukqavPTMuiiSc8Mw2WOD2mOn3B7YzVe1Hx3YCa/cswG/erv7d+6YKmB2udp2D0alYeE3v/AEqoaNLYIFuVdU8UKlAcN2sC2m4wYYlEJft3Dg6knhZVUU2S7+aEopHji6iNe9aCZWQqYv3bRh9HHCqaK45aoJnF+tN33IDMvB7HINu2Jq3EAg3Tx0cskfzn34fHOYVJwseoZuud1uNn/8c2c++k5zY1mWiyijBzznTRtGf+/hi3hydhX/81+fh+3QZNKNn6po+vp83KySMFxLa/ub36EzKwDclVapLva+ThUzyOlKS6F/wZNt/v1rdiGrKfjWc3P4getm8MrdYivw8LlsHs9h42iuidEH8Qdx7JXdEywfObmMHVN5bIzpWgGAHVN5mDZtsYU6DsW7/vL7+NLjs/j12/fg3a+4Wui4vYaPsM1SQy/d1C0br0oo2wCudOPQ9kv9Fy6WsVBuxH4etgRmtrIXLpZwy3/7Bh4/vYznLpSgKSR2wxQAbrnaXdI/eipw3xw8sQjbodi/XTy1kqGY0UCIuxLZNV3E9ZtHWxh9UummnQPEdihml/mnbbVDLx+96HSpMHZOt7dYfvfIAgDgPi9xMxmjd4vPfLmO7x1bxMuvmYq9ugmj2yrq0OkVzIxmcedLtwIQy9InhLhe+sju2OcvlKGrBHu3jOG1L5qBQoC737xX+LzZ+5TTFYzlNGwcyza5boJdsTEKvbdyXWkjgVBK8ejpZdySsAe4wwsrjDZk731uDk/OruKPf3Q/3vum6/zrlhe9GH3SzVLAgBR6AIn1ecDVLoH2m0keOOp+gOMWerYKuOBpfZ89eBqLFQMfvu8onr9Qwu6ZkUSbvfZuGUVeV5sK/defuoBiRsVrXzQT+7iKQvwivnfLGPZuGcNzF9aaLHSVhniMAEOnAQ3nVmowbRq7EQsEGn0nH70/LzbGTWrXTBHzpUbTCs12KB48uoBX7Zn2NfQkPvopT074yqFzWKmaeOuNW2IfK4yc1tleeejMCm7aMYG3eM8lapndNpFv0ehfuFjyPf+/89a9+Kt3H4glUzI//6axHAgh2NSB0U/F1OiBIP0yjDNLNcyXGrhl55TwccNgmyGjDdmPffcYtk3k8aM3b4t13F5RxQNb6AkhP0wIeZ4QcpQQcnevx6uExMpaj4I1QdoV+gePLuCaDcXYDY1NY1ns2zaGzz50GnXTxj2HziKnK/jm4Tk8fGIptj7PoKkKbtox4Rd6y3bwr89cwO17NyWWtJhOv3fLKPZuGcNy1WxKDkwq3bTz0Sf10AOB66YToxedLhUGk8PCm9WePruKlaqJHz+wAz90g+ubTrIzljHTf3zqPCYLeqIbdhidfPQrVQPHFyq4accEbrtmGlPFjLAnnXnpw3jhYgnXeo6yq6eLuP36TbHOmw0f2TTqyiebxrJNhf6fnj6PrKbEGpUX5P8HBXO54o4XfPS0259Kyui3TuRBCHAm1JB97PQyHj65jPe8epcwk2foNHzkvufm8H99+Wnc9/wcRrJaosDH1As9IUQF8GEAbwbwYgDvIoS8uNvvTI9kErkQGNiut2hD1rQdfP/4Il4ZI1iLgRCCu167G8fnK/idLz+N5aqJ//vOG5HVFJQaVuJCDwAHdk7i2fNrqBoWHjq5hMWKgbfs25z4uGw1cv3mMX+TDpNvlisGFgTdGWHkM+2LTlIPPdDbdVMzkzF6AE3yDfOIv3rPBtz12t0oZFTs2hD/RsXkBNOmeOv+LbE3u0SR7aDRM33+pVdNQFcVfP6u2/DbP3Sd0LG3TuQxV2r4va5yw8Lscg3XbeKfgtUJjNFvHMt6/89hrW6hZtg4OlfGlx8/i597xdVC+fwM0dkOT5xZwSs+cC/u+vQjOHh8CSNZLfFnNKMp2Dqe96Ub26H4828ewVhOw0/EtMoC7Rn97HIVv/53j+NvD57Cg0cXsXNDfFMDACTbitoetwI4Sik9DgCEkM8BuANAx4zPuNt6owgYfXPhOXRmBVXDTiwPvWXfZvzxRB5ffHQWM6NZ3HHTVjx2ehmfOXg6loc+ipuvnoTtUBw6s4J/euoC8rqK1wtOZWoHtuN479Yxv6A/e34NL79mCr/wiYfRsB2885b4nm7Tpr5/2HYojs2X8Z0X3NTKLQneW7bD8Nh8GbddM90SKx3XXgm4Gr1CgE9//xRu2jGBHVMFfPfIAvZtG8P0SBbTI1k8/fs/FGuaD4OmKn689Y+8NN6yvh2ifRHTdlBpWHjohNtw37/ddZZcG+OaZM6bLz9+Fu+8ZQeOeI3YOENvomDyIPu8s//Pler483uPIKer+MXX7Y537IwKTSFYqZm4uFbHXZ9+BFlNxTcPzwEAXnPthlTI5FVTBTxwdAH3H5nHlx47i++8MI/feeveRCs/NjPiL+49gucvlvCul12FD993FA6luO83X49yw4ptUWboR6HfBuBM6N+zAF7eh+dpAdPof+6vH/KLft2ycX6lDoUAr7gmWaHXVAXvefUu/NevPYsfeek2aKqCX719DwzLwW27k9vmbt7hLi1/43OHsFoz8Ya9G4Wz1tthLKdjLKdh67irjW6byONj3z2OT/3bScyXGvjoz9yCW66Ot6xlsskP/9l30bAcXFit+2mT/7u9uw+yqq7jOP7+sC7sLgvLLk8u4rKQICQC0vKQJoM8qWjWWBqW5kw1mzY6aoOTNZXWZGnNMGXqpDM4NY7ZVGSakUCCTqWsgMHCgo/RKMrThIAYoLDf/ji/S9d11132nnO55+73NXNn7z337G8/e+6533vO757zOzNPH5xToRw2oJx+ZSfxnUdb+PnKVz4wFlKmT7c7y6istITvf2o8P1y6hTmLnqaupoJXdx/gq1mFJpfsGdUVvRlQUZrzyYDZykpL2PPfd5m76GkOHI6GFMgc8Tf25H45nc078/QhjBlayTeWbGTRipfoFbYi49hjzRwGmzn5b2jYsr9ycRPb3jrI12Z+hIGV3TsxMDNa6K+bXuO3a17n4HtHWXLt2Sxr2cFP//oyDSNy65/PuPmC07nxN+u5avFzACycN4avnDsqpzb7l59Eq8GiFS9RVV7Kn5u3A3DHpWd2e0DAtpIo9O29Oz7QySqpEWgEqKuri+UPTxs5kEvPOuV9J5P0LulF7YRyPlZX3a1dwraumFrHtrcO8qVzRgJQW1XOTy6bmHO7AFUVpSycN4bN2/cjiWtmdG/rpq2rz65nzrihx3b9bpo7hpUvRGc4XjJxGPPO6H730OxxQ9j4xj6OtLbS56QShg0oY+SgSqbW13BqTW6HEtZWldP0rdksb9nJqhd3tXs8fV1N325/YXrl9BHMGjuEe596hT3vvMu42v4syGEXvD03zB5Ndd/SWI62ybhk4jB27D+EmVFWWsLwAeUMqOiNBFNy/MJxcL8+PHHDDJa17GDpph0cbW3loqryY0ec5KK2qozrZ53GxROjI4Im11VzecNwDhw+wrSRA2nMcX2/ftZpNG39D5L4wtQ6xtX2Z+zJ/fhobX+mjcx9QyyTeflNM1j8960M7NubBVNzr10Xjq9l5/7DfGbycEYN7svv1m1jz4F3c+oOakvHe/HbThuUPg7cZmbnh8ffBDCzH3X0Ow0NDbZ27dpYczjnXLGTtM7MGjqbL4mjbtYAoyWNlNQbWAA8lsDfcc451wWxd92Y2RFJ1wHLgBLgATNrifvvOOec65ok+ugxs6XA0iTads45d3wK5sxY55xzyfBC75xzRc4LvXPOFTkv9M45V+S80DvnXJGL/YSpboWQ9gEvJ9R8HfBaQm0DVAH7Emo7yeyeu31pzZ7W3JDe7IWQe4SZdTosaqEU+vvNrDGhtnd3ZUHk0H4qs3vuDttPZfa05g7tpzJ7mnIXStfNnxJse2+CbUN6s3vu9qU1e1pzQ3qzpyZ3QRR6M0vyxUhylzO12T13h9KaPa25Ib3ZU5O7IAp9wu4/0QFykNbsac0N6c2e1tyQ3uypyV0QffTOOeeS0xO26J1zrkdLXaGXdKqkVZK2SGqRdEOYXiNphaSXw8/qMF2S7goXKm+WNDmrrSck7ZX0eJqySxohaZ2k9aGda9KQOzx3NOReLynx4atjXObnZeVeL+mQpE8Xeu7w3J2SNoXb55LKnEP2sZKelXRY0sI2bT0gaZekTWnJLalM0nOSNoR2vpd09k6ZWapuQC0wOdzvB7xEdBHyHwO3hOm3AHeG+/OBvxBd+Wo60JTV1mzgk8DjacoO9Ab6hPuVwL+BYYWeOzx3IK3rS1abNcAeoKLQcwMXASuIRqrtC6wF+hfYMh8CTAFuBxa2aWsGMBnYVIDrSru5w2tQGe6Xjr0tggAABFhJREFUAk3A9Hyu9x/4307kH4/pxXkUmAu8CNRmvWAvhvv3AVdkzX9svvB4Jnkq9HFnD9MGEp20kVihjzM3eS70CS3zRuChNOQGbga+nTV9MXB5IWXPmu+2toU+TK/PR6GPO3d4rgJ4HpiW7/zZt9R13WSTVA+cRfSJOdTMtgOEn0PCbO1drPyU/KVsX67Zw25mc3j+TjN7Mw25gTJJayWtTrLroz0xri8LgIeTzJotx9wbgAslVUgaBJwHxHth3A/RxewFJ9fckkokrQd2ASvMrCm5tJ1L5MIj+SCpElgC3Ghm+9XxxZe7dLHyfIoju5m9DkyQNAz4o6Tfm9nORAJnwsSzzOvM7E1Jo4CVkjaa2asJxH1/oJjWF0m1wJlEV1BLXK65zWy5pCnAM8Bu4FngSCJh2wbqevaCEkduMzsKTJI0AHhE0ngzS/x7ho6kcoteUinRC/GQmf0hTN4Z3oSZN+OuMH0b79+CGQ7kZeu3PXFnD1vyLcC5acid2fMws38BTxFtNSUq5mV+OfCImb2XbOpYl/ntZjbJzOYSfSAkNa5Ud7MXjLhzm9leovX8gpijHpfUFXpFH6+LgS1mtijrqceAq8P9q4n61zLTvxiOSpgO7MvshuVbXNklDZdUHtqsBs4h6kcs9NzVkvqENgeF3JuTyh1n9qzfu4I8dNvEuMxLJA0MbU4AJgDLCyx7QYgrt6TBYUue8D6dA7wQf+LjcCK/IOjODfgE0a50M7A+3OYTfSn5JNHWypNATZhfwD3Aq8BGoCGrrb8R7c4eJNoiOj8N2Ym+IGom6n9tBhpTkvvs8HhD+PnllK0v9cAbQK+05AbKiD5MNwOrgUkFmP3k8P7bTzR+zDbCkUFEH6rbgffC9MTWmbhyE32Y/jO0swn4btLLvLObnxnrnHNFLnVdN845546PF3rnnCtyXuidc67IeaF3zrki54XeOeeKnBd61yPp/6NotoRRBr8u6UPfD5LqJX0+Xxmdi4sXetdTHbTobNEziM5LmA/c2snv1ANe6F3q+HH0rkeSdMDMKrMejwLWAIOAEcCDRMP6AlxnZs9IWg2MA7YCvwLuAu4gGgG1D3CPmd2Xt3/CuS7yQu96pLaFPkx7CxgLvA20mtkhSaOBh82sQdJMouFoLw7zNwJDzOwHYWiHfwCXmdnWvP4zznUitaNXOpeAzDCFpcDdkiYBR4ExHcw/j2gE0c+Gx1XAaKItfucKhhd65zjWdXOUaGTCW4GdwESi77EOdfRrwPVmlpchi53rLv8y1vV4kgYDvwDutqgvswrYbmatwFVASZj1baJLzGUsA64NQ9siaYykvjhXYHyL3vVU5eEKQKVEF+J4EMgMTXsvsETSZcAq4J0wvRk4ImkD8EvgZ0RH4jwfhrjdDeT1qlnOdYV/Geucc0XOu26cc67IeaF3zrki54XeOeeKnBd655wrcl7onXOuyHmhd865IueF3jnnipwXeuecK3L/A0V6Y2OL9MZkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drain3.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 174 entries, 2000-01-01 to 2014-06-01\n",
      "Data columns (total 1 columns):\n",
      "Total    174 non-null int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 2.7 KB\n"
     ]
    }
   ],
   "source": [
    "drain3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = drain3.head(162)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set = drain3.tail(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scale the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\envs\\tf_gpu\\lib\\site-packages\\sklearn\\preprocessing\\data.py:323: DataConversionWarning: Data with input dtype int64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "train_scaled = scaler.fit_transform(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_scaled = scaler.transform(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def next_batch(training_data,batch_size,steps):\n",
    "    \n",
    "    \n",
    "    # Grab a random starting point for each batch\n",
    "    rand_start = np.random.randint(0,len(training_data)-steps) \n",
    "\n",
    "    # Create Y data for time series in the batches\n",
    "    y_batch = np.array(training_data[rand_start:rand_start+steps+1]).reshape(1,steps+1)\n",
    "\n",
    "    return y_batch[:, :-1].reshape(-1, steps, 1), y_batch[:, 1:].reshape(-1, steps, 1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RNN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just one feature, the time series\n",
    "num_inputs = 1\n",
    "# Num of steps in each batch\n",
    "num_time_steps = 12\n",
    "# 100 neuron layer, play with this\n",
    "num_neurons = 100\n",
    "# Just one output, predicted time series\n",
    "num_outputs = 1\n",
    "\n",
    "## You can also try increasing iterations, but decreasing learning rate\n",
    "# learning rate you can play with this\n",
    "learning_rate = 0.001 \n",
    "# how many iterations to go through (training steps), you can play with this\n",
    "num_train_iterations = 20000\n",
    "# Size of the batch of data\n",
    "batch_size = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = tf.placeholder(tf.float32, [None, num_time_steps, num_inputs])\n",
    "y = tf.placeholder(tf.float32, [None, num_time_steps, num_outputs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-43-efab6ad9b9a9>:4: BasicRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is equivalent as tf.keras.layers.SimpleRNNCell, and will be replaced by that in Tensorflow 2.0.\n"
     ]
    }
   ],
   "source": [
    "# Also play around with GRUCell\n",
    "\n",
    "cell = tf.contrib.rnn.OutputProjectionWrapper(\n",
    "    tf.contrib.rnn.BasicRNNCell(num_units=num_neurons, activation=tf.nn.relu),\n",
    "    output_size=num_outputs) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loss Function and Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = tf.reduce_mean(tf.square(outputs - y)) # MSE\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "train = optimizer.minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 \tMSE: 0.051973257\n",
      "100 \tMSE: 0.031518\n",
      "200 \tMSE: 0.034299683\n",
      "300 \tMSE: 0.009786763\n",
      "400 \tMSE: 0.01516898\n",
      "500 \tMSE: 0.032310642\n",
      "600 \tMSE: 0.027037201\n",
      "700 \tMSE: 0.035580475\n",
      "800 \tMSE: 0.014118784\n",
      "900 \tMSE: 0.0111325355\n",
      "1000 \tMSE: 0.016118923\n",
      "1100 \tMSE: 0.0043939683\n",
      "1200 \tMSE: 0.006896028\n",
      "1300 \tMSE: 0.022781422\n",
      "1400 \tMSE: 0.015144329\n",
      "1500 \tMSE: 0.037710406\n",
      "1600 \tMSE: 0.010607432\n",
      "1700 \tMSE: 0.015257525\n",
      "1800 \tMSE: 0.011115383\n",
      "1900 \tMSE: 0.011481942\n",
      "2000 \tMSE: 0.010179224\n",
      "2100 \tMSE: 0.047941107\n",
      "2200 \tMSE: 0.012441336\n",
      "2300 \tMSE: 0.008621831\n",
      "2400 \tMSE: 0.011459101\n",
      "2500 \tMSE: 0.00506407\n",
      "2600 \tMSE: 0.0037131694\n",
      "2700 \tMSE: 0.011866397\n",
      "2800 \tMSE: 0.047680605\n",
      "2900 \tMSE: 0.0067894007\n",
      "3000 \tMSE: 0.008283041\n",
      "3100 \tMSE: 0.0038034339\n",
      "3200 \tMSE: 0.008794508\n",
      "3300 \tMSE: 0.0063988813\n",
      "3400 \tMSE: 0.0064530387\n",
      "3500 \tMSE: 0.009805302\n",
      "3600 \tMSE: 0.005752258\n",
      "3700 \tMSE: 0.0091271205\n",
      "3800 \tMSE: 0.0068198196\n",
      "3900 \tMSE: 0.013038561\n",
      "4000 \tMSE: 0.0117443325\n",
      "4100 \tMSE: 0.005946122\n",
      "4200 \tMSE: 0.0063841143\n",
      "4300 \tMSE: 0.0076308646\n",
      "4400 \tMSE: 0.002145132\n",
      "4500 \tMSE: 0.0074719354\n",
      "4600 \tMSE: 0.0076872134\n",
      "4700 \tMSE: 0.002398092\n",
      "4800 \tMSE: 0.0044527627\n",
      "4900 \tMSE: 0.010605072\n",
      "5000 \tMSE: 0.0038520324\n",
      "5100 \tMSE: 0.0019267746\n",
      "5200 \tMSE: 0.0061352034\n",
      "5300 \tMSE: 0.024947701\n",
      "5400 \tMSE: 0.009987139\n",
      "5500 \tMSE: 0.0035456026\n",
      "5600 \tMSE: 0.0037885122\n",
      "5700 \tMSE: 0.008268863\n",
      "5800 \tMSE: 0.003241213\n",
      "5900 \tMSE: 0.006802293\n",
      "6000 \tMSE: 0.012904854\n",
      "6100 \tMSE: 0.003344451\n",
      "6200 \tMSE: 0.016911613\n",
      "6300 \tMSE: 0.009093587\n",
      "6400 \tMSE: 0.0092086205\n",
      "6500 \tMSE: 0.0064297044\n",
      "6600 \tMSE: 0.008770646\n",
      "6700 \tMSE: 0.0027640688\n",
      "6800 \tMSE: 0.0014036429\n",
      "6900 \tMSE: 0.003098604\n",
      "7000 \tMSE: 0.0037073346\n",
      "7100 \tMSE: 0.010141471\n",
      "7200 \tMSE: 0.009896977\n",
      "7300 \tMSE: 0.0015295135\n",
      "7400 \tMSE: 0.010653372\n",
      "7500 \tMSE: 0.0148435915\n",
      "7600 \tMSE: 0.0018332396\n",
      "7700 \tMSE: 0.0055243783\n",
      "7800 \tMSE: 0.0048430585\n",
      "7900 \tMSE: 0.01397925\n",
      "8000 \tMSE: 0.005583581\n",
      "8100 \tMSE: 0.00295431\n",
      "8200 \tMSE: 0.0077499445\n",
      "8300 \tMSE: 0.011422332\n",
      "8400 \tMSE: 0.038588222\n",
      "8500 \tMSE: 0.00836365\n",
      "8600 \tMSE: 0.008737905\n",
      "8700 \tMSE: 0.0059771747\n",
      "8800 \tMSE: 0.0041678273\n",
      "8900 \tMSE: 0.004705992\n",
      "9000 \tMSE: 0.0060239322\n",
      "9100 \tMSE: 0.011451569\n",
      "9200 \tMSE: 0.001492082\n",
      "9300 \tMSE: 0.0075759436\n",
      "9400 \tMSE: 0.003956685\n",
      "9500 \tMSE: 0.00675536\n",
      "9600 \tMSE: 0.0047115977\n",
      "9700 \tMSE: 0.0062489063\n",
      "9800 \tMSE: 0.0027333861\n",
      "9900 \tMSE: 0.0076713674\n",
      "10000 \tMSE: 0.007580444\n",
      "10100 \tMSE: 0.013236427\n",
      "10200 \tMSE: 0.0040606256\n",
      "10300 \tMSE: 0.0075840545\n",
      "10400 \tMSE: 0.007674034\n",
      "10500 \tMSE: 0.013684186\n",
      "10600 \tMSE: 0.001961756\n",
      "10700 \tMSE: 0.006303553\n",
      "10800 \tMSE: 0.006232317\n",
      "10900 \tMSE: 0.0019781431\n",
      "11000 \tMSE: 0.0026918093\n",
      "11100 \tMSE: 0.009782891\n",
      "11200 \tMSE: 0.0021935976\n",
      "11300 \tMSE: 0.002644157\n",
      "11400 \tMSE: 0.0042947307\n",
      "11500 \tMSE: 0.019806972\n",
      "11600 \tMSE: 0.004086417\n",
      "11700 \tMSE: 0.009669543\n",
      "11800 \tMSE: 0.0007605465\n",
      "11900 \tMSE: 0.0039018432\n",
      "12000 \tMSE: 0.01001075\n",
      "12100 \tMSE: 0.0045432895\n",
      "12200 \tMSE: 0.01918346\n",
      "12300 \tMSE: 0.009148367\n",
      "12400 \tMSE: 0.00696828\n",
      "12500 \tMSE: 0.03659119\n",
      "12600 \tMSE: 0.0076759085\n",
      "12700 \tMSE: 0.0028172145\n",
      "12800 \tMSE: 0.0023270482\n",
      "12900 \tMSE: 0.0076611503\n",
      "13000 \tMSE: 0.007561577\n",
      "13100 \tMSE: 0.008056379\n",
      "13200 \tMSE: 0.0020095545\n",
      "13300 \tMSE: 0.011038538\n",
      "13400 \tMSE: 0.00739083\n",
      "13500 \tMSE: 0.0009684455\n",
      "13600 \tMSE: 0.004233291\n",
      "13700 \tMSE: 0.0015578987\n",
      "13800 \tMSE: 0.0045515914\n",
      "13900 \tMSE: 0.0024995974\n",
      "14000 \tMSE: 0.0053311805\n",
      "14100 \tMSE: 0.0032298334\n",
      "14200 \tMSE: 0.023246229\n",
      "14300 \tMSE: 0.0010940001\n",
      "14400 \tMSE: 0.05103478\n",
      "14500 \tMSE: 0.008118582\n",
      "14600 \tMSE: 0.003933002\n",
      "14700 \tMSE: 0.0074425004\n",
      "14800 \tMSE: 0.0009842695\n",
      "14900 \tMSE: 0.01707252\n",
      "15000 \tMSE: 0.004537638\n",
      "15100 \tMSE: 0.0055791866\n",
      "15200 \tMSE: 0.0024214732\n",
      "15300 \tMSE: 0.004917824\n",
      "15400 \tMSE: 0.0068613472\n",
      "15500 \tMSE: 0.0011498261\n",
      "15600 \tMSE: 0.007936652\n",
      "15700 \tMSE: 0.0059842705\n",
      "15800 \tMSE: 0.0021544236\n",
      "15900 \tMSE: 0.033440214\n",
      "16000 \tMSE: 0.0071244817\n",
      "16100 \tMSE: 0.0010747792\n",
      "16200 \tMSE: 0.006522261\n",
      "16300 \tMSE: 0.0047405516\n",
      "16400 \tMSE: 0.0010690348\n",
      "16500 \tMSE: 0.0029996156\n",
      "16600 \tMSE: 0.005048159\n",
      "16700 \tMSE: 0.0076966085\n",
      "16800 \tMSE: 0.001375263\n",
      "16900 \tMSE: 0.0023122195\n",
      "17000 \tMSE: 0.0065423534\n",
      "17100 \tMSE: 0.006455844\n",
      "17200 \tMSE: 0.008145517\n",
      "17300 \tMSE: 0.005365294\n",
      "17400 \tMSE: 0.005493768\n",
      "17500 \tMSE: 0.012024969\n",
      "17600 \tMSE: 0.01158271\n",
      "17700 \tMSE: 0.016327964\n",
      "17800 \tMSE: 0.0006157483\n",
      "17900 \tMSE: 0.0050981715\n",
      "18000 \tMSE: 0.0029130338\n",
      "18100 \tMSE: 0.003232578\n",
      "18200 \tMSE: 0.009991885\n",
      "18300 \tMSE: 0.0019577176\n",
      "18400 \tMSE: 0.017066788\n",
      "18500 \tMSE: 0.0048746183\n",
      "18600 \tMSE: 0.0062645175\n",
      "18700 \tMSE: 0.01498524\n",
      "18800 \tMSE: 0.0032561177\n",
      "18900 \tMSE: 0.004740427\n",
      "19000 \tMSE: 0.012759477\n",
      "19100 \tMSE: 0.003086901\n",
      "19200 \tMSE: 0.0016309548\n",
      "19300 \tMSE: 0.0051543186\n",
      "19400 \tMSE: 0.0057959496\n",
      "19500 \tMSE: 0.015032315\n",
      "19600 \tMSE: 0.0014031845\n",
      "19700 \tMSE: 0.0033645786\n",
      "19800 \tMSE: 0.0022114273\n",
      "19900 \tMSE: 0.0024016167\n"
     ]
    }
   ],
   "source": [
    "with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:\n",
    "    sess.run(init)\n",
    "    \n",
    "    for iteration in range(num_train_iterations):\n",
    "        \n",
    "        X_batch, y_batch = next_batch(train_scaled,batch_size,num_time_steps)\n",
    "        sess.run(train, feed_dict={X: X_batch, y: y_batch})\n",
    "        \n",
    "        if iteration % 100 == 0:\n",
    "            \n",
    "            mse = loss.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "            print(iteration, \"\\tMSE:\", mse)\n",
    "    \n",
    "    # Save Model for Later\n",
    "    saver.save(sess, \"./DhakaRainModelV2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-07-01</th>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-01</th>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-01</th>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-01</th>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-01</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-01</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-01</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-01</th>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-01</th>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total\n",
       "Date             \n",
       "2013-07-01    301\n",
       "2013-08-01    211\n",
       "2013-09-01    172\n",
       "2013-10-01    131\n",
       "2013-11-01      0\n",
       "2013-12-01      4\n",
       "2014-01-01      0\n",
       "2014-02-01     12\n",
       "2014-03-01     10\n",
       "2014-04-01     80\n",
       "2014-05-01    141\n",
       "2014-06-01    342"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./DhakaRainModelV2\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    \n",
    "    # Use your Saver instance to restore your saved rnn time series model\n",
    "    saver.restore(sess, \"./DhakaRainModelV2\")\n",
    "\n",
    "    # Create a numpy array for your genreative seed from the last 12 months of the \n",
    "    # training set data. Hint: Just use tail(12) and then pass it to an np.array\n",
    "    train_seed = list(train_scaled[-12:])\n",
    "    \n",
    "    ## Now create a for loop that \n",
    "    for iteration in range(12):\n",
    "        X_batch = np.array(train_seed[-num_time_steps:]).reshape(1, num_time_steps, 1)\n",
    "        y_pred = sess.run(outputs, feed_dict={X: X_batch})\n",
    "        train_seed.append(y_pred[0, -1, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0.2693683]),\n",
       " array([0.33611442]),\n",
       " array([0.0965435]),\n",
       " array([0.04529201]),\n",
       " array([0.08104887]),\n",
       " array([0.00595948]),\n",
       " array([0.]),\n",
       " array([0.00953516]),\n",
       " array([0.03098927]),\n",
       " array([0.03814064]),\n",
       " array([0.42789035]),\n",
       " array([0.38736591]),\n",
       " 0.16752918,\n",
       " 0.2378609,\n",
       " 0.23389223,\n",
       " 0.20294808,\n",
       " -0.005596161,\n",
       " -0.0015969127,\n",
       " 0.0036794692,\n",
       " 0.005679846,\n",
       " 0.06445318,\n",
       " 0.14373751,\n",
       " 0.33531213,\n",
       " 0.40781644]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = scaler.inverse_transform(np.array(train_seed[12:]).reshape(12,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\envs\\tf_gpu\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "test_set['Generated'] = results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Total</th>\n",
       "      <th>Generated</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-07-01</th>\n",
       "      <td>301</td>\n",
       "      <td>140.556976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-01</th>\n",
       "      <td>211</td>\n",
       "      <td>199.565292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-01</th>\n",
       "      <td>172</td>\n",
       "      <td>196.235580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-01</th>\n",
       "      <td>131</td>\n",
       "      <td>170.273438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-01</th>\n",
       "      <td>0</td>\n",
       "      <td>-4.695179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-01</th>\n",
       "      <td>4</td>\n",
       "      <td>-1.339810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>3.087075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-01</th>\n",
       "      <td>12</td>\n",
       "      <td>4.765390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-01</th>\n",
       "      <td>10</td>\n",
       "      <td>54.076214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01</th>\n",
       "      <td>80</td>\n",
       "      <td>120.595772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-01</th>\n",
       "      <td>141</td>\n",
       "      <td>281.326874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-01</th>\n",
       "      <td>342</td>\n",
       "      <td>342.157990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total   Generated\n",
       "Date                         \n",
       "2013-07-01    301  140.556976\n",
       "2013-08-01    211  199.565292\n",
       "2013-09-01    172  196.235580\n",
       "2013-10-01    131  170.273438\n",
       "2013-11-01      0   -4.695179\n",
       "2013-12-01      4   -1.339810\n",
       "2014-01-01      0    3.087075\n",
       "2014-02-01     12    4.765390\n",
       "2014-03-01     10   54.076214\n",
       "2014-04-01     80  120.595772\n",
       "2014-05-01    141  281.326874\n",
       "2014-06-01    342  342.157990"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_set.plot()\n",
    "# plt.savefig('GRUCell_10k.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8LU87PVonyakZdG3ZiMahAU5przoemfwB7r64PbFRwTw1J4UyW7m7w1FKqdOl/gSpP8IVz0PHy53SZEFJGWv2Zbm03g8enPyD/H15YnQXth3J4+Pf9ro7HKWUOl3KbAiKgL63Oq3JFbuPUWozDEnw0uQPMKJrM4YmxPCfH7aTnlfs7nCUUuqkshLY9i0kjgY/55VnlqRmEODnQ782jp00PhuPTv4iwpSru1JUZuPF+VvdHY5SSp20ayEU5dRqGcZzkZyaSVJ8lNOncK7Ko5M/QPsmYdw+pC1frE5j7T5dlEQp5SE2z4bACGh3idOazDhezJZDuS4d4lnB45M/wORhCTRrFMiTc1KwletcP0opNysrga3fQKdRTi35LN1pjW509cleqCfJPyzQj8dHdWbjgRxmrNrv7nCUUt6uouTTxbklnyU7MmgU5Ee3VhFObbc69SL5A1zdsyX92zbmxflbyS4ocXc4Silvtnk2BDaC9s4r+RhjSE7NYFD7GHxdMIVzVfUm+YsIT1/dldyiMl76Ybu7w1FKeauKkk/iKPALdFqzezMLOJBdyGAXD/GsUG+SP0DnFo24eWA805bvZdOBHHeHo5TyRrsXuWiUj2uncK6qXiV/gIcu60hUSABPzU3RhV6UUudfSkXJZ5hTm12SmkGryGDauGgK56rqXfKPCPbn0ZGdWL03i1lrD7g7HKWUN7GV2ks+I51a8rGVG5buzGRwh2hEXF/vh3qY/AFu6BNLr9aRPPfdVvKKSt0djlLKW+xaBEXZTh/lk3Iwh5zC0vMyvr9CvUz+Pj7Wyd/M/GJe/mmHu8NRSnmLzbMgINzpJZ+Ker8rp3Cuql4mf4CerSMZ16817y/dw/Yjee4ORynV0NlKYeu31oVdDqzSVZ0lqRl0ah5Ok3DnlZLO5qzJX0Rai8gvIrJFRFJE5AH79ikickBE1tlvoyq95q8ikioi20RkhKuC//OIToQF+jFFT/4qpVxt9yIozHJ6yaeo1MbKPa6fwrmq2hz5lwF/MsZ0BgYC94pIF/tz/zXG9LLfvgOwPzcO6ApcAbwhIi6ZoahxaACPXN6RpTszmbfpsCu6UEopS8psl5R8Vu3JoqSs/LyN769w1uRvjDlkjFljf5wHbAFa1fCSa4DpxphiY8xuIBXo74xgqzNhQDxdWjTimW82U1BS5qpulFLerPIoHyeXfJJTM/D3Ffq7eArnqs6p5i8ibYDewHL7pvtEZIOIvCciUfZtrYDKE/CkUc2HhYhMEpFVIrIqPT39nAOv4Osj/P2arhzMKeKNX3bWuR2llDqj3Yutko+TL+wCq97fOy6K0EA/p7ddk1onfxEJA2YCDxpjcoGpQHugF3AIeKli12peflpB3hjzljEmyRiT1KRJk3MOvLKkNo25rncr3lq8iz0Z+Q61pZRSp9lcUfIZ7tRms/JL2HQw57zX+6GWyV9E/LES/zRjzFcAxpgjxhibMaYceJuTpZ00oHWll8cCB50XcvUeG9mJAD8f/v7NZld3pZTyJrZS2PINJF7h9JLPsl2ZGMN5Hd9foTajfQR4F9hijPlPpe0tKu12LbDJ/nguME5EAkWkLZAArHBeyNVr2iiIBy9N4OetR1mw5Yiru1NKeYvdi6HwmNNH+YBV7w8L9KNnrOuncK6qNkf+g4GbgWFVhnW+KCIbRWQDcAnwEIAxJgWYAWwG5gP3GmNsrgn/VLcMakOHpmE8/fVmikrPS5dKqYZu82wICIMOzi35gFXvH9guGj/f83/J1VnPMBhjkqm+jv9dDa95FnjWgbjqxN/Xh6ev7srEd5bz9uJdTB6ecL5DUEo1JBUln45XgH+wU5vef6yAvZkF3DaojVPbra16e4XvmQzuEMOo7s15fWEqaVkF7g5HKVWf7fnVKvm4aJQPwJDzPL6/QoNL/gB/G21dg/bst1vcHIlSql5LqSj5XOr0ppNTM2jWKJD2TcKc3nZtNMjk3yoymPsu6cC8TYf5dUfdryFQSnkxW5l1YVfHEU4v+ZSfmMI55rxN4VxVg0z+AH8Y2o746BCmzE2hpKzc3eEopeqbPb9CQaZLRvlsPpTLsfwSt4zvr9Bgk3+Qvy9PXdWFnen5fLh0j7vDUUrVN5tng38oJFzm9KYr6v3uGN9focEmf4BhnZoxvFNT/vfTdo7mFrk7HKVUfWErgy1fu6TkA1a9P6FpGM0aOfeisXPRoJM/wJNXdaHUZvjnvK3uDkUpVV/sTbZKPi4Y5WNN4XzMrUf94AXJPz46lDsvasestQdYsfuYu8NRStUHKbPBPwQ6OL/ks2ZfFkWl5W6t94MXJH+Aey7uQKvIYJ6cs4kym578VUrV4ETJ5woICHF680tSM/D1EQa0O79TOFflFck/OMCXJ0Z3ZuvhPD5dsc/d4SilPNneJVCQ4ZKSD0Byaia9WkcSHuTvkvZryyuSP8AV3ZozuEM0//5+G5nHi90djlLKU212Xcknp6CUjWnZbq/3gxclfxFhylVdKSix8a/vt7k7HKWUJyq3nRzl44KSz7JdmZQb3F7vBy9K/gAJzcK5bXAbPl+1n3X7s90djlLK0+xdAvnpLrmwC6x6f0iAL71aR7qk/XPhVckf4P7hCcSEBfLUnE2Ul5+2wJhSypulzLJKPgmXu6T5JakZDGjbmAA/96de90dwnoUH+fP4qE6sT8vhi9X7z/4CpZR3qCj5JFzukpLPgexCdmXke0S9H7ww+QOM6dWKfm2ieGH+NrLyS9wdjlLKE1SUfFw0ysfdUzhX5ZXJX0SYcnVXcgtLGfPGEjakaf1fKa+XMhv8gl1a8okJCyCxWbhL2j9XXpn8Abq2jOCzSQMpLSvn+qlLeXvxLj0HoJS3OjHK53IICHV688YYlqRmuHUK56q8NvkD9GvTmO8eGMqwTk159rst3PbBSjL0GgClvM/epZB/1GWjfLYdySPjeInH1PvBy5M/QGRIAG/e1Jd/jOnGsl2ZjHz5V5J3ZLg7LKXU+bTZXvLpOMIlzVfklHqV/EWktYj8IiJbRCRFRB6wb28sIj+KyA77fZR9u4jIKyKSKiIbRKSPq9+Eo0SEmwfGM/e+wUQE+3Pze8t5Yf5WSnUeIKUavnIbbJ5rzdvvgpIPWPX+djGhtIp0/vTQdVWbI/8y4E/GmM7AQOBeEekCPAYsMMYkAAvsPwOMBBLst0nAVKdH7SKdmjfi6/uGMK5fa6Yu3MnYN5ex/5guAq9Ug7ZvmVXycdEon5Kycpbvdv8UzlX5nW0HY8wh4JD9cZ6IbAFaAdcAF9t3+xBYCDxq3/6RMcYAv4lIpIi0sLfjObL3w+y7oSQfghpBUAQERRAcFME/YyKYOED4ZH02/3x5MRMu6s6Qbu1P7IN/CHjISRullINSZoNfECS4puSzbn82BSW2+pf8KxORNkBvYDnQrCKhG2MOiUhT+26tgMpXT6XZt52S/EVkEtY3A+Li4uoQugMKjsEn10HeEWjdH4pyIPeQdV+UA2WFdAOeBxBgsf1Wwcfv5AfBGW+RZ35OPzyU8gzlNthiL/kEhrmki+TUDHwELmgX7ZL266rWyV9EwoCZwIPGmNwahitV98RpYyiNMW8BbwEkJSWdvzGWJfkwbSxk7YWbZ0GbwafvU1YMRblQlENZQRZfLU1h8cadtAsrY2KvSJr5F0Fx7skPi6IcyDt88nHpWUpFPn7QtAsMedAaXeDj65r3qpSq2b7f4PgRl43yAave3z02kogQ907hXFWtkr+I+GMl/mnGmK/sm49UlHNEpAVw1L49DWhd6eWxwEFnBewQWyl8cSscXAO/+7j6xA/gFwhhTSCsCX7A7+L60Sopgwc/X8ebS0p5YnRnbh4ef+bxumUllT4csk/9kCjKgcJs2PoNfHk7RP8Thv4Juo8F33P6IqaUctRme8mn4xUuaT6vqJR1+7O566J2LmnfEWfNNmJluHeBLcaY/1R6ai5wC1Z15BZgTqXt94nIdGAAkOMR9X5jYO79sOMHuPJ/0PnKc3r54A4xzHtgKI98sZ4n56SQvCODF2/oQWRIwOk7+wWAXwyE1lDjG/aE9XVz8b9h9l2w8J8w9GHoOcF6vVLKtcrLT47ycVHJZ/muY9jKjcfV+6F2o30GAzcDw0Rknf02CivpXyYiO4DL7D8DfAfsAlKBt4F7nB92Hfw0BdZ/Chc/Dkm31amJmLBA3rulH0+M7swv244y8uVf674usI8vdL0W7kqGcZ9BSGP4+gF4pRcsfwtKC+vWrlKqdvb/BscPu7Tkk5yaQZC/D33jo1zWR12JNSjHvZKSksyqVatc18GyN+D7v0LSHTD6JaecbN2YlsPkz9aw71gB9w9PYPKwBHx9HGjXGNi5ABb9y/pPGdYMBk2Gvre57KhEKa/23V9gzYfw550u+xu77D+LaBEZzEe393dJ+yKy2hiTVJfXNvwrfDd+aSX+zlfDqH85bZRN99gIvrl/KNf0asX/ftrB+Ld/41COA0frItDhUrh9PtzyDTTpBD88Af/rbpWGinKcErdSCnvJZ471N+eixH8kt4gdR48zpINnjfKp0LCTf+oCmHUXxA+B6952+qiasEA//ntjL14a25NNB3IY+fKv/JBy2LFGRaDtULhlLtzxI8Qmwc//sD4EfnnOGqaqlHJMRcmn67Uu66JiCmdPrPdDQ07+B1bD5zdbR9DjPwX/IJd1dX3fWL6ZPIRWkcFM+ng1T83ZRFGpzfGGW/eHiV/ApEXQZigsesH6EPjxKTie7nj7SnmrlNngG+iyuXzAqvc3Dg2gc/NGLuvDEQ0z+WfutMbyh0bDTV9aF1a5WLsmYXx1zyDuGNKWD5ftZczrS0g9muecxlv2gnHT4O5l1n/WJS9bHwLz/wq5njGKVql6o7y80oVdrplbv2IK50Hto/Fx5FygCzW85J93GD62f5W7aRaENz9vXQf6+fL/ruzC+7f242heMVe9uoTPV+7DaSfVm3WBG96D+1ZaX1eX/x+83BO+eRiy9zmnD6Uauv3LIe+QS0f57Ew/zpHcYoZ4aMkHGlryL8qBT26A/AyrXBLTwS1hXNKpKfMeGErvuEgenbmRyZ+tJbeo1HkdxCTAtVPh/jXQawKs+Qhe6Q2z77W+9SilzmyzveST6JoLu8Azp3CuquEk/9IimD4R0rfAjR9Dq75uDadZoyA+vmMAfx6RyLxNhxn9yq+s3Zfl3E6i2sBVL8MD661hrJu+hNeSYOYf4ehW5/alVENwyigf1y2nmJyaSXx0CK0bO38heGdpGMm/3AazJsGeX2HMm9BhuLsjAsDXR7j3kg7MuHMg5eUw9s1lTF240/nLRUa0glEvwgMb4IJ7Yeu38MZA64T3oQ3O7Uup+ixthVXycdH0zQBltnJ+25Xp0Uf90BCSvzEw7y/Wp/mI56DHWHdHdJq+8dZykZd3bcYL87dyy/srOJpX5PyOwpvB5c/Agxvhwkdg10L4v6Hw6Y2Qttr5/SlV35wY5eO6ks/6tByOF5d5dL0fGkLyX/wvWPkODH7AOur1UBHB/rw+oQ/PXdudFbuPMerlZJbtzHRNZ6HR1txBD26ES56wTnC9Mww+GgN7l7mmT6U83YmSz3BrDQ8XWZKagXjgFM5V1e/kv+p9+OVZazK0S592dzRnJSJMGBDH3PuG0CjYj4nv/MYbC1OdXwaqEBwJF/3Z+hC47O9wZBO8f4U1ja1S3iZtJeQddOkoH7DG93drGUFUqGdP0Fh/k/+Wb+DbhyHhcrj6lXq1OEpi83Dm3jeEkd1b8OL8bUz6eBU5BU4cDVRVYLj1zWjyGmv62pRZrutLKU+1eTb4Brh0lE9+cRlr92V5fL0f6mvy37PEmgu/ZR8Y+wH4etYiCbURFujHa+N7M+WqLizans6Vr/3KpgMunr8nqBG0uxi2fWedK1HKW1SUfNoPd+lFnyv2HKPUZjy+3g/1MfkfSYHPxkNUvDWWPyDU3RHVmYhw6+C2fH7nBZTZDNdNXcqny514UVh1EkdaF4Qd3ey6PpTyNAdWQe4Bl47yAViyI4MAPx+S2njeFM5V1a/kn70PPrkeAkLgpq+sOfAbgD5xUXx7/1AGtG3M47M28qcZ6yksccLcQNXpONK63/ada9pXyhOlVJR8Rrq0m+TUDPp1hUiSAAAgAElEQVS1iSLI3/OXZq0/yT8/Ez6+zlof96avILL12V9TjzQODeCD2/rzwPAEZq07wJjXl7Ar/bjzOwpvBq2SYNs857etlCcqL7fq/S4u+RzNK2Lr4bx6Ue+H+pL8S/Lh07GQsx/GT7fmuGmAfH2Ehy7ryAe39edoXhFXv7aE7za6YAXMxJHWrKe57l9dUymXO08ln4qh2/Wh3g/1IfnbSmHGLXBwrTWpWfwgd0fkchd1bMK39w+lQ9Mw7pm2hr9/vZlSW7nzOkgcZd1vn++8NpXyVOer5LMjg4hgf7q2dP0sws7g2cm/vBzm3AepP8KV/4VOo90d0XnTMjKYGXdewK2D2vDekt2Me8vBlcIqa9oZIuO19KMavhOjfIa5tORTeQpnh5ZzPY/OmvxF5D0ROSoimyptmyIiB6os6F7x3F9FJFVEtomIYysl/PQUbJhuXaXa91aHmqqPAvx8mHJ1V14d35uth3IZ/UryidkCHSJiHf3vWmiV1JRqqA6shtw0l1/YtTsjn4M5RfWm3g+1O/L/AKjuqoj/GmN62W/fAYhIF2Ac0NX+mjdEpG6nvZe+BktfgX5/tOap8WJX9WzJnPuGEBMWwM3vLeeVBTscvyo4cSTYimHnL84JUilPtHk2+Pi7vORTsWRjfan3Qy2SvzFmMVDbhWOvAaYbY4qNMbuBVODcl63fMAN++Bt0uQZGvlCvrt51lQ5Nw5h972Cu6dmS//y4nds+WElWfkndG4wfZH0N1tKPaqiMOVnyCY50aVfJqRm0igwmPtpzp3CuypGa/30issFeFqq4oqEVsL/SPmn2bacRkUkiskpEVqWnV1qPNvUnmH23tWatCxZdr89CAqwF458Z041lOzO58tVk1u3Prltjvv7W1Bjb51tTYivV0BxYbY0QdPEoH1u5YenOTIZ0iEHq0YFqXZP/VKA90As4BLxk317dO6+2PmGMecsYk2SMSWrSpIm1MW01fP57aNLZWrPWL7CO4TVcIsJNA+P58u4LABj75lI+WranblcFJ46EggxIW+XcIJXyBCmz7CWfUWff1wEbD+SQV1TG4IT6U/KBOiZ/Y8wRY4zNGFMOvM3J0k4aUPnqq1igdiuMZ6RaY/lDY87bouv1WY/YSL69fwhDE5rw5JwUHpi+jvzisnNrpMOl4OMH2751TZBKuYsxsHkutL/E5SWfinr/oPaePYVzVXVK/iLSotKP1wIVI4HmAuNEJFBE2gIJwIqzNmgrtS+6LnDz+V10vT6LDAngnd8n8ecRiXyz4SDXvL6E1KN5tW8gKALaDNG6v2p4DqyBnH0uH+UD1vj+zi0aERNWvyoVtRnq+RmwDEgUkTQRuQN4UUQ2isgG4BLgIQBjTAowA9gMzAfuNcacvaB8bCcUZFoTtUW3r/u78UI+9qUiP7ljANkFJVz92hLmrDtQ+wYSR0HGduubl1INxWZ7yaeTa0s+hSU2Vu/NYkiH+nXUD7Ub7TPeGNPCGONvjIk1xrxrjLnZGNPdGNPDGHO1MeZQpf2fNca0N8YkGmNqd0hZVgTjPoFWfRx4K95tUIcYvpk8lC4tGvHA9HU8OWcTxWW1OJFbsZzddj36Vw2EMZAyx5q+PNh1s2saY3hi9iZKbOVc2rmZy/pxFc+4wrdxO2s4lnJI84ggPps0kD8ObctHy/byu//7jbSsgppfFBUPzbpp6Uc1HAftJR8Xj/L53087mLkmjQcvTWCAhy/ZWB3PSP6BrltP09v4+/rwt9FdePOmPuw6epwrX01m4bajNb8ocRTsWwYFtb2cQykPljLbGsjgwlE+M1bt5+UFOxjbN5YHhie4rB9X8ozkr5zuim4tmDt5CM0bBXHbByv5zw/bsJ3pquDEkWDKYccP5zdIpZzNGOuq3nYXu2y9j8Xb03n8q40MTYjhueu616ux/ZVp8m/A2saEMuuewVzfJ5ZXfk7llvdWkHm8+PQdW/SC8BawVYd8qnru4Bpr0ScXjfLZfDCXe6atoUPTMN6Y2Ad/3/qbQutv5KpWggN8+ffYnrxwfXdW7DnG6FeST58d1MfHOvGbugBKi9wTqFLOUFHyccEMwAezC7ntgxWEB/nxwW39CQ+qf2uHV6bJ30vc2C+OmXcNIv14MR8t23v6DomjoDQf9iSf/+CUcgYXlnxyi0q57f2VFBTbeP+2fjSPCHJq++6gyd+LdI+NYHinpsxYuf/0YaBtLwT/EF3bV9VfB9e6pORTUlbO3Z+sZmf6cabe1JdOzRvGABVN/l5m4sB4MvNL+D7lyKlP+AdZw223zbOOoJSqbzY7v+RjjOGxrzawJDWT56/vwZB6Nn9PTTT5e5mhHWKIaxzCtN+qKf10Gg15B+HQ+vMfmFKOMMaq97e9yKkln//+tIOv1hzg4cs6ckPfWKe16wk0+XsZHx9hwoA4lu8+xo4jVeYBSrgcxEcv+FL1z6F1kL3XqRd2zVi5n1cW7OB3SbFMHtbBae16Ck3+Xmhs31j8fYVpy/ed+kRoDLQeoLN8qvpn7TQQX+h0pVOaW7Q9nb/OssbyP3tt/R3LXxNN/l4oOiyQkd1aMHNNGoUlVU78Jo6Ewxshe3/1L1bK02yeCyvfhj43O6Xkk3Iwh3s+WU3HZuH1fix/TRrmu1JnddPAePKKyvh6fZXlFiouid8+//wHpdS5OrrFWvmvVV+44gWHmzuYXcjtH6ykUbA/79/ar96P5a+JJn8v1a9NFB2bhTFteZUTvzEJEN1Bh3wqz1eYBdMnWEOUb/zEGrHmgJzCUm59f0WDGstfE03+XkpEmDggnvVpOWxMyzn1ycSRsPtXKMp1T3BKnU25DWb+0SpP3vgxNGrpUHMVY/l3Z+Tz5s0NZyx/TTT5e7Fr+7Qi2N/39KP/xNFQXgo7F7gnMKXO5udnIPVHGPkCxA10qCljDI/N3MDSnZm8cH0PBndoOGP5a6LJ34s1CvLn6p4tmbPuILlFpSefaN0fghvrkE/lmVJmQfJ/oM8tkHS7w83998ftfLX2AH+6rCPX9WlYY/lrosnfy900MJ7CUhuz1lRa+tHH15robfv3YDvHReGVcqUjKTD7HojtD6P+BQ4Owfx85T5e+TmVG5Nac18DHMtfE03+Xq57bAQ9YiOYtnwvpvK0DokjoSjbWuRFKU9QcMw6wRvYCH73Efg5tmD6ou3pPD5rExd2bMIz13ZrkGP5a6LJXzFxQBzbjxxn1d6skxvbDwPfAC39KM9QboOZd0DOAfsJ3hYONVcxlj+xgY/lr8lZ37GIvCciR0VkU6VtjUXkRxHZYb+Psm8XEXlFRFJFZIOI6Irs9cBVPVsSHuTHJ5Xn+wkMs+ZJ2fadTvSm3G/B07DzZxj9knVOygEHsgu57f2VRAT78/5t/QgL9HNSkPVLbT7uPgCuqLLtMWCBMSYBWGD/GWAkkGC/TQKmOidM5UohAX5c3yeWeRsPn7rSV+JIyNoN6dvcF5xSG7+EJS9bJ3f73uJQUzmFpdz2/goKS2y8f1t/mjVq2GP5a3LW5G+MWQxUXdn7GuBD++MPgTGVtn9kLL8BkSLi2PczdV5MGBBHia2cL1enndyYONK61wu+lLsc3ghz7oPWAx2+grekrJy7PrbG8v/fzX1JbB7upCDrp7oWupoZYw4B2O+b2re3AipPCpNm33YaEZkkIqtEZFV6enodw1DO0rFZOP3bNubTFfsor1jovVFLaNlb6/7KPSpO8AZH2U/wBtS5KWMMj87cwLJdmbx4Qw8GeclY/po4+yxHdafLqy0YG2PeMsYkGWOSmjRp4uQwVF1MHBDH3swCklMzTm5MHAVpK+H4UfcFpryPrQy+uBXyDltTN4Q3c6i5//y4nVlrD/DI5R25trf3jOWvSV2T/5GKco79viIzpAGtK+0XC1SZOUx5qiu6NSc6NODUE7+JIwGjE72p8+unp2D3IrjyvxDb16Gmpq/Yx6s/pzKuX2vuvcS7xvLXpK7Jfy5QceblFmBOpe2/t4/6GQjkVJSHlOcL9PNlbFJrFmw9yqGcQmtjs24Q0VpLP+r8Wf85LHsN+k+C3jc51NTCbUf52+xNXNSxCf8Y431j+WtSm6GenwHLgEQRSRORO4DngctEZAdwmf1ngO+AXUAq8DZwj0uiVi4zoX8c5cbw+Ur7qRsR6+h/5y9QUuDe4FTDd3AdfH0/xA+GEc851NSmAzncO20Nic3Ced1Lx/LX5KwDXI0x48/w1PBq9jXAvY4GpdwnLjqECxOaMH3Ffu67pAN+vj5W8l/xlvU1vGIEkFLOlp8Bn98EITEw9kPwrftc+gfs8/J7+1j+muhHoTrNxAFxHM4tYsFW+6mc+CHWJfU65FO5iq3UOsGbnw7jPoGwug8COTGWv9TGB7d791j+mmjyV6cZ1qkpLSKCTq7x6xcAHS6FbfOhvNy9wamG6YcnYM+vcNXL1vDiOious50ylr9jM+8ey18TTf7qNH6+PozrF8fi7enszcy3NiaOgvyjcHCNe4NTDc+6T2H5mzDwHug5rs7NGGN49EtrLP+/bujJoPY6lr8mmvxVtW7s1xpfH+HTFfaj/4RLQXy19KOc68Bq+PpBaDMULvuHQ0299MN2Zq87yJ9HJDKmd7XXlqpKNPmrajWPCOLSzk35YlUaxWU26yrL+EGwVZO/cpLjR+HzmyGsGYz9AHzrflJ2xsr9vPZLKuP7t+aei9s7L8YGTJO/OqObBsZzLL+E+ZsOWxsSR0H6Fji2y72BqfqvrARm3GJN4TDuEwite4lm04Ecnpi9iaEJMfzjGh3LX1ua/NUZDW4fQ3x0CNN+s5d+Eu2Tu27Tq32Vg75/HPYthatfhRY969xMTmEpd09bTXRYAC+P620NTVa1or8pdUY+PsLEAXGs2HOMbYfzoHE7aNJZ6/7KMWs+hpVvwwX3QY+xdW7GGMMjX6znUHYRr03oQ+PQuk/85o00+asa3dC3NQG+Pny63D7fT6dRsHcpFGbV/EKlqpO2Cr59GNpdDJc+7VBTb/+6ix83H+GvozrTNz7KKeF5E03+qkaNQwMY1b05X605QEFJmVX3NzbY8ZO7Q1P1Td4R6wre8BZww/sOneBduecYL8zfxshuzbl9cBvnxehFNPmrs7ppYDx5xWXMXXcQWvaB0KZa+lHnpqwEZtwMRTkwbhqENK5zUxnHi7nv0zW0jgrmhRt66AneOtLkr86qb3wUic3CrSt+fXysE7+pP1l/0ErVxry/wP7lcM3r0Lx7nZuxlRsemL6W7IJS3pjYl0ZBdZ//x9tp8ldnJSJMHBjHxgM5bEjLtko/xbmwN9ndoan6YNX7sPp9GPwgdLvOoaZeXrCDJamZ/OOabnRp2chJAXonTf6qVq7t3YqQAF9roZe2F4FfsM7xr85u33L47s/QfjgMf9KhphZtT+fVn3dwQ99Yftev9dlfoGqkyV/VSniQP9f0asnc9QfJsflD+0us5G+qXaVTKcg9ZNX5I2Lh+nfAx7fOTR3MLuTB6WtJbBbOP67p5sQgvZcmf1VrEwfEU1Razldr0qx5/XP2w5FN7g5LeaKyYivxFx+HcZ86dIK31FbOfZ+uodRmeGNiH4ID6v4hok7S5K9qrVurCHq2jmTa8n2YhBGAaOlHnc4Y+PZPkLYSrp0Kzbo41Nzz87ayZl82L1zfg3ZNwpwUpNLkr87JxAFxpB49zop0P4jtp0M+1elWvQtrP4ahf4Iu1zjU1LyNh3g3eTe3DmrD6B4tnBSgAk3+6hxd1aMljYL8+GT5Pqv0c3At5B50d1jKU+xdCvMehYTL4ZK/OdTUnox8/vLlBnq1juTxUZ2dFKCq4FDyF5E9IrJRRNaJyCr7tsYi8qOI7LDf63XXDUhwgC/X941l/qZDZLW+1NqopR8FcGQzzPg9RMbDdW87dIK3qNTG3dPW4OsrvD6xDwF+epzqbM74jV5ijOlljEmy//wYsMAYkwAssP+sGpCJA+IotRmm7w6BqLaa/BXsXwHvXwE+fjB+OgRHOtTclLkpbDmUy39v7EWryGAnBakqc8XH6TXAh/bHHwJjXNCHcqMOTcMZ2K4xn67ch+k4EnYvskZ1KO+04yf46BoIiYbbv4cmHR1q7svVaUxfuZ97L2nPJYlNnRSkqsrR5G+AH0RktYhMsm9rZow5BGC/r/ZfT0QmicgqEVmVnp7uYBjqfJs4IJ79xwpZHzoIbCWw82d3h6TcYeOX8NmNEN3eSvxR8Q41t/VwLk/M3sgF7aJ56FLHPkRUzRxN/oONMX2AkcC9InJhbV9ojHnLGJNkjElq0qSJg2Go821E1+bEhAUwdXdTCIrU0o83WvkOzPwDtB4At34LYY4dpR8vLuOeT9YQHuTPy+N76cIsLubQb9cYc9B+fxSYBfQHjohICwD7/VFHg1SeJ8DPh98ltebHrRkUtLkUts+Hcpu7w1LngzGw6EVrLH/HK+CmmRAU4WCThkdnbmBPZj6vju9N0/AgJwWrzqTOyV9EQkUkvOIxcDmwCZgL3GLf7RZgjqNBKs80vn8cBlhg+kLhMeukn2rYysth/l/hl2ehxzi48WPwd/yE7EfL9vLthkP8eUQnBraLdkKg6mzqvpoCNANm2efS9gM+NcbMF5GVwAwRuQPYB9R9nTbl0Vo3DuHijk34z04bV/r4I9u+hfgL3B2WchVbKcy5FzZ8DgPvgcuftab4dtC6/dk88+1mhndqyp0XtnNCoKo26pz8jTG7gNNWXjbGZALDHQlK1R8TB8Tzh4/SyYzvT8y2eXD5M+4OSblCaSF8catV3hv2BAx9BJywiEpWfgn3TltD0/AgXvpdT3x8dGGW80XPqCiHXNKpKS0jgvi2uDdkpkLGDneHpJytMBs+vg62fw+j/wMX/tkpib+83PDwjHWk5xUz9aY+RIboAuznkyZ/5RBfH2F8/zj+77B9WJ7O9dOw5B2BD660Jmm74V3od4fTmp66aCe/bEvn/13ZmR6xjl0Ups6dJn/lsBv7teaITxMOh3TUIZ8NSdYeeG8EHNsJE6ZDt+ud1vTSnRm89MM2rurZkpsGOnZtgKobTf7KYU0bBXF5l2bMLuiJ2b8c8jPcHZJy1JHN8O4IKMyC38+FDpc6remjuUXc/9k62sSE8s/ruusC7G6iyV85xU0D4/m6uBdiymHHD+4ORzli/wp4f6T1+LZ50Lqf05ous5Uz+bO15BeX8eZNfQkLdGTAoXKEJn/lFBe0i6agcVcyfaJh67fuDkfVVWrFPD2N4Y7vHV6IpaqXftzO8t3HePbabnRsFu7UttW50eSvnMLHR5gwIJ7vSnpTnroASovcHZI6V5tmwqfjoHHFPD1tnNr8gi1HmLpwJ+P7t+a6PrFObVudO03+ymlu6BvLQknCp6wQdi92dzjqXKx8B768w1qd7TbH5+mpav+xAh6esZ6uLRvx1FVdndq2qhtN/sppokIDaNxlOPkmiNItWvqpF4yBRf+yz9MzAm7+yuF5eqoqLrNx76drKDfWAuxB/roAuyfQ5K+catygBBaV96B083fWPDDKc5WXw/ePwy/P2Ofp+cQp8/RU9ey3W9iQlsO/buhJfHSo09tXdaPJXzlVn7hIUsIHE1J8FHNonbvDUWdiK4XZd8Nvb1jz9IyZCr7+Tu9m7vqDfLRsL38c2pYrujV3evuq7jT5K6cSEeIGXovNCEdWznJ3OKo6pYXw+c2wYTpc8gSMeM4pE7RVlXr0OI/N3EBSfBR/uaKT09tXjtHkr5xu9ICurKET5Vr39zxFOfZ5eubD6JfgIufM01NVQUkZ90xbTZC/L69O6I2/LszicfRfRDldWKAf6S2H0bJ4J7mHdro7HFXh+FH4YHSleXr+4JJujDE8MWsTO44e5+VxvWgRoQuweyJN/solEi78HQApv3zu5kgUAFl7rXl6Mp0/T09V01fu56u1B3hgeAJDE3SJVk+l11Yrl0jo3Is039YE7JyPMX/V+VtcyBhDWlYha/ZlsWZvFqv3ZbE3s4BAP1+CA3zoJGk8XziFQFPMf5r+k7SlkQSvWkuwvy9BJ24+BPv7EhzgS5CfL0EBvgT5+RAc4FvtfhU/+1aZf3/TgRyempvC0IQYJg9LcNNvRNWGJn/lMvltLqNH6oes3LqH/p3bujucBqOo1EbKwRxW781izd5sVu/LIj2vGICQAF96tY7kut6tKLEZmuVu4I/7HqdE/JkS/W92lMdRlFlAUZmNwhIbRaU2ikrLKbHVbVhugJ/PiQ+JIH9fsvJLaBwSwP9u7HXaB4PyLJr8lcu0GTIW/53vsXHxTPp3fsTd4dRbh3OKWLMvy0r2+7LYdCCHUpsBIK5xCEM6xNAnLpI+8VEkNgvHr+LkauoC+PwRiGxO6M2z+HcN0zXYyg1FpTYKSys+EKwPhcLSkx8ShaU2iiu2lVbZVmI9LjeGuy5qT3RY4Hn4zShHaPJXLhMYP4B8vyhi0n4mPW8yTcI1IZxNqa2czQdzTyT7tfuyOZBdCECgnw89YiO4fUhb+sRF0Scu6sy/001fwVeToEkn66rds0zX4OsjhAb6EaqzbHoN/ZdWruPji63D5Vy85Ws+XbGLu4d3dnmXRaU2sgtKySooIaughOyCUvKLy2gU7E/j0ACiQvyJDAkgMtj/5BHy+WIMlJdBWRGUFUNZEcdy89i6/yjbD2Sy61AmaenHEFsxgZTSPATuaexPuzg/4iJ8aBYi+JUXW6/dXQg7iu1tnWzvxP3hjRA3EMZPh2BdJUudzmXJX0SuAF4GfIF3jDHPu6ov5bka9bwatn7O5t++x3ZJp1rXgY0xHC8uI7uglGP5JxO5ldRLyS4o4Vj+yW0V9wUltlrHFh7kR1RIAFH2D4WokAAi7fdRIf727ZW3BRAc4AtlJVCQCQUZkJ8O+RWPM07eVzwuKTiRlE1ZobXeQSWNgUH2G2D9tVRMfVMGHLXfKvj4gV8Q+AVWfx8QYk3H3GaotdB6QEitfx/Ku7gk+YuIL/A6cBmQBqwUkbnGmM2u6E95sPaXYPMJoHfhMmauvp52TUJPSdoViTyroISs/JPbcgpLTtS1qxKBiOCTybpZoyASm4cTFRJA49CTybriPjTAj9wiq+3qPjDy8vOx5Rwk+3AG+UXHOFaaRbTkkSs55JFHvuRSKHkUkUuM5NJICqqNq1x8KQmIpCwoGhMSjUR1JtcEcyTfcOC4Ia2knLwyP4rxxz8wmBbREbSKiSK+WWPimjYmICj4DInd/tg3EHz1y7pyDlf9T+oPpBpjdgGIyHTgGkCTv7cJCEXaXcyI1DUMnrkeOPXI399XThxVR4b4075JGFGhVmmmceWjbvu2qJAAIoL9q/8GYQyUFlhXsRZlW/d5OdZShJWPygsy7Ufs9sfFuSfbECDA3pyPH6WBURQHRJHvF0Web1u2SSOOlTfiaHk4h8rCOFASwv7iUPYUBpFlQjGFPpBzali+PkKn5uH07WPV6fvGRxEbFazDX5VbuSr5twL2V/o5DRhQeQcRmQRMAoiLi3NRGMoT+HQaRavUH3hvVBh+zbuePCoPDSA0wPdkEjTGmnemKMd+Sz/5+Eh2pe2Vb1W2l5fVEIgfhMRAqP0WGW/dV95W6bEERRIgQgAQDtQ0LVl5uSGvqMz6dlFQQrb9m0XziCB6xkbqiVTlcVz1P7K6Q5pTvsMbY94C3gJISkqq/vu9ahgSR8I3DzIs7U0o7niGJG6/2UpqbssvyJpvPigCgiKtZN24faVtVW+R1n1ojHXvoqNtHx8hIsSfiBB/2qDTFivP56rknwa0rvRzLHDQRX0pTxfeHNpeBNvnwc4FJxNyRXKOjD81YQdHnp68gyIgsBH4B7n73SjVILgq+a8EEkSkLXAAGAdMcFFfqj64eZY1h7wmb6U8gkuSvzGmTETuA77HGrj2njEmxRV9qXrCx9e6KaU8gsvOQhljvgO+c1X7Siml6k6ndFZKKS+kyV8ppbyQJn+llPJCmvyVUsoLafJXSikvpMlfKaW8kBjj/pkVRCQH2OGm7iM4bSquBt2vO/v2tn4B4oB9buhX/395R98JxpiIOr3SGOP2G/CWt/Wt77nh92vvO90L37M3/jvXu/fsKWWfr72wb33PDb9fgGw39av/v7yj7zr36xFlH6UaKhFZZYxJcnccSlXlKUf+SjVUb7k7AKWqo0f+SinlhfTIXymlvJBbkr+IHD/L8wtFxCl1UhG5VkSMiHRyRnvn0O/fRCRFRDaIyDoRGXD2Vzmt71gRmSMiO0Rkp4i8LCIBNez/oIiEONinEZGXKv38iIhMcaTNc+jbZv8dp4jIehF5WEQa9IFNpfdccWtTw74Xi8g3TurXiMjHlX72E5F0Z7VfyxjO29+0J7xfe7815sy6aNB/IHbjgWSsBWXOCxG5ALgS6GOM6QFcyqlrGruybwG+AmYbYxKAjkAY8GwNL3sQcCj5A8XAdSIS42A7dVFojOlljOkKXAaMAp46nwG44o/zLCrec8Vtz3nqNx/oJiLB9p8vw1qwqdZExNGp5Ov0Ny0idVlQwuH366nclvyrHo2IyGsicquT+wgDBgN3YP+PUlO/IjJKRLaKSLKIvOLAp3sLIMMYUwxgjMkwxhwUkb4iskhEVovI9yLSwt7vQhH5n4gsFZFNItK/7u+aYUCRMeZ9e9824CHgdhEJFZF/i8hG+zeSySJyP9AS+EVEfnGg3zKsk5sPVX1CROJFZIG9zwUiEiciESKyp+IIXURCRGS/iPg7EAPGmKPAJOA+sfiKyL9EZKW9/zsrxfUX++9ivYg870i/nqCm9wo0EpFZIrJZRN508JvRPGC0/fF44LNKMfS3/z9ea79PtG+/VUS+EJGvgR/q2nENf9OLq3t/InJcRP4uIsuBC+rYbV3e768i0qvSfktEpEcd+69oo6bctUdEnhaRNfb/02f9VtTQj/zHAPONMduBYyLS50w7ikgQ8H/ASGPMEKCJA/3+ALQWke0i8tUIi2wAAAi3SURBVIaIXGRPaq8CNxhj+gLvcerReKgxZhBwj/25uuoKrK68wRiTi3WV6R+AtkBv+zeSacaYV7DWV77EGHOJA/0CvA5MFJGqVxy+BnxU0SfwijEmB1gPXGTf5yrge2NMqYMxYIzZhfV/uylWksgxxvQD+gF/FJG2IjIS6//HAGNMT+BFR/sVkTD7h1vFH+A19u1tRGSLiLwtVmnqh0pHknUVLCdLPrPs26p9r/bn+gN/AroD7YHrHOh7OjDO/jfTA1he6bmtwIXGmN7Ak8BzlZ67ALjFGDPMgb7P9Dd9pvcXCmwyxgwwxiTXsc+6vN93gFsBRKQjEGiM2VDH/msrwxjTB5gKPHK2nRt68h+P9Q+H/X58Dft2AnYZY3bbf/6shn1rZIw5DvTFOgJNBz4H7gS6AT+KyDrgCayF7St8Zn/tYqyjtMg6di9AdUO4BLgQeNMYU2bv61gd+6iW/UPm/7d37zFylWUcx78/S220hY1gRYXaTZMtpTRSozEqjSkaiZpG0tBaLZcqRgNBqMHGKP/gXTRUKGkNaBEKRAJkJTREqbW0CVCUXuyFipFIvSVNqwFDC7Sx28c/3nfc6bAzuzNzZrbb8/v8M2fO9T1n933mnOc95z33ANfVTPog8Is8fC8wJw8/ACzKw5/J34ui/HkRcEU+5r8HzgD6SKm4uyLi1Vz2Io7FYWB+roAXAsslVcrRB6zKqan/AJe0ua3qtM/8PK7evgI8ExEv5CvB+xn8GzQtB7FeUn2qfVtfD/CQpGeBW0gnIxXrCzjO9ep0vf0bAPrb2WCL+/sQMC+f9F0J3N1OGUbol/lzG6m8DXXsNY4jcJTjf3wKfbO3pDNIKZBZkoL0LuEA1tbZrihQ/ifcBGyStBu4BtgTEfUuPWsDdqv34O6hJrBIOg2YArzQxnpH6lZgO3BXg3kqZVgL/EDS6aQfy8eLKICkaaRKf4D0d702ItbVzPNxij8WAr4v6cPAMeAs4Mw8bW9E7MjDI6qcLW5/qH2dS3H/XxVrgZuBuaQfmYrvABsjYr5SI/SmqmmvtLPBBnX6V9Tfv8O5Lrarqf2NiFclrQcuBj4NFHEDy3Ax80j+HGAEsX00z/z/BsyUNCGnCT5a8PoXkFINUyOiNyKmAJWz+qG2+ydgmgbvmlhEiySdI6mvatRs4DlgslJjMJLGS6o+K1qUx88hXbq32knUBuDNkq7I6xsHLCedefwGuEq5wS0HXYCDwKktbu84+czuQVIKomIzg41zl5Ia6ypXSM8AK4BHi6ikkiYDtwMrIz3Esg64utKWIGm6pImkY3Gl8l1OVceiHZeS0oXvjYjZwH4GK+iRqvlGVDlbUG9fAd6f011vIP2vtZoCqfg58O2I2F0zvofBBtHPtbmNWvXq9ByK379arezvauA2YEtBV5aFxsyuB/8ceI5ExD9IQWIXKQ/8h4I39Vng4Zpx/cDiobYbEa+R8u2PSXqSVHFbDcCTgDW58WkXMJOUD1wA/FDSTmAH8KGqZV6StJkUuL5Qu8KRygFvPrBQ0vPAn0npiBtI/4x/B3blMizOi/0U+LXaa/CtthyovuvnOuDz+VhcDiytmvYAcBntpXwq+e89wG9Jgf1bedpq4I/A9nxpfgdwSkQ8Rjqb25rTJMPmSEegBzgQEf+VdCEwtYB1NmPIfc3TngZuAp4lBczautGUiPhnRKwYYtKPSFdzT5HOzIvUqE4Xun+1WtnfiNgGvEzjq+BhdSpmdv0JX0nnAz+LiHbuaOkISZMi4lDO064Cno+IW7qw3U3AsojY2ultWfFy5dwPnEPqaGs86cf9AuATebZHI2JWnn8ZMCkivtn90p58clprWUTMG+2yVJP0TlIaaEZEHGtjPR2JmV3N+Uu6inQW+JVubrcJX5S0BHgj6Vf1jlEuj40N5wF/iYh/U/92wlmVgYi4uSulslGT067fA65vM/B3LGa6bx+zNlRXzoho+f51s25z8DczK6GT/T5/MzMbgoO/WRMkTZG0MT+xu0fS0jz+dEnrlTrTWy/pLXn8DElPSzqSG3pr1zdOqWuArnYUZubgb9aco8BXI+Jc4APANZJmAl8HNuTO9Dbk7wAvktoE6jXyLiU9A2LWVQ7+Zk2IiH0RsT0PHyQF7rNIT3KuybOtIfVBQ0QciIgtwOv6LJJ0NqnDsNVdKLrZcRz8zVqUnwZ/D6kfnTMjYh+kHwhSp3LDuRX4GqkrCLOucvA3a4FS18L9pFs8X25h+Xmkp4G3DTuzWQc4+Js1Kfed00/qErvSk+J+Db6f4R2kTuUauQD4lKS/knqn/Iik+zpUZLPXcfA3a0Lu+uNO4LmI+HHVpLXAkjy8BHik0Xoi4hsRcXZE9JI6vXs8Ii7rQJHNhuSHvMyakHtdfQLYzWCu/gZS3v9B4F2kzvMWRsSLkt4ObAVOy/MfAmZWp4pO1L5p7OTm4G9mVkJO+5iZlZCDv5lZCTn4m5mVkIO/mVkJOfibmZWQg7+VkqSBynt/Je2UdH1++XejZXolLW40j9lY4eBvZfVaRMyOiPOAjwGfBG4cZpleBl96bzam+T5/KyVJhyJiUtX3acAW4K3AVOBeYGKe/OWI2Czpd8C5wF5Sz523ATcBc4EJwKqI8HufbUxw8LdSqg3+edxLwAzgIHAsIg5L6gPuj4j31T6JK+lLwNsi4ruSJgBPkZ7s3dvVnTFrwSmjXQCzE4jy53hgpaTZwAAwvc78FwHvlrQgf+8B+khXBmYnNAd/M/6f9hkg9cZ5I7AfOJ/ULna43mLAtRGxriuFNCuQG3yt9CRNBm4HVkbKg/YA+yLiGHA5MC7PehA4tWrRdcDVuYtnJE2XNBGzMcBn/lZWb5K0g5TiOUpq4K100fwToF/SQmAj8Eoevws4KmkncDewgnQH0Pbc1fO/yK9vNDvRucHXzKyEnPYxMyshB38zsxJy8DczKyEHfzOzEnLwNzMrIQd/M7MScvA3Myuh/wGAQ7E2m/3wowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " test_set.plot()\n",
    " plt.savefig('BasicRNNCell_20k.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_set.plot()\n",
    "# plt.savefig('BasicLSTMCell_10k.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_set.plot()\n",
    "# plt.savefig('MultiRNNCell_10k.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}