{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encantado database - LADS 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:80% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:80% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import sklearn as skl\n",
    "import pylab as pl\n",
    "import itertools\n",
    "import scipy as sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import functions\n",
    "from scipy import stats\n",
    "from plotly.subplots import make_subplots\n",
    "from matplotlib import pyplot as plt\n",
    "from plotly.offline import plot, iplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = pd.read_csv('C:/meujupyter/encantado/Encantado_annual_rainfall.csv', sep= ';', header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {},
   "outputs": [
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       "config": {
        "plotlyServerURL": "https://plotly.com"
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     "metadata": {},
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   ],
   "source": [
    "# Scatter plot of annual rainfall volume\n",
    "fig = px.scatter(df0, x='Year', y='Volume(mm)', title='Annual precipitation (1943-2020)', width=1000, height=500)\n",
    "fig.update_layout(margin=dict(l=20, r=20, b=20, t=40, pad=4),paper_bgcolor='LightSteelBlue')\n",
    "fig.update_xaxes(title_text='Year', type='linear')\n",
    "fig.update_yaxes(title_text='Volume (mm)', type='linear')\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of annual rainfall volume\n",
    "year = df0['Year']\n",
    "volume = df0['Volume(mm)']\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (18, 8)\n",
    "plt.title('Annual precipitation (1943-2020)')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Rainfall (mm)')\n",
    "plt.bar(year, volume, color='b', width=0.65)\n",
    "plt.yticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>decades</th>\n",
       "      <th>events</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1941-1950</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1951-1960</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1961-1970</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1971-1980</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1981-1990</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1991-2000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2001-2010</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011-2020</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     decades  events\n",
       "0  1941-1950       2\n",
       "1  1951-1960       9\n",
       "2  1961-1970       5\n",
       "3  1971-1980       0\n",
       "4  1981-1990       6\n",
       "5  1991-2000       3\n",
       "6  2001-2010       4\n",
       "7  2011-2020      15"
      ]
     },
     "execution_count": 587,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of floods by decades\n",
    "decades = ['1941-1950', '1951-1960', '1961-1970', '1971-1980', '1981-1990', '1991-2000', '2001-2010', '2011-2020']\n",
    "events = [2, 9, 5, 0, 6, 3, 4, 15]\n",
    "\n",
    "df_ = pd.DataFrame({'decades':decades, 'events':events})\n",
    "df_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of floods')"
      ]
     },
     "execution_count": 588,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of floods vs. decades\n",
    "plt.figure(figsize=(12, 6))\n",
    "splot=sns.barplot(x='decades',y='events',data=df_)\n",
    "for p in splot.patches:\n",
    "    splot.annotate(format(p.get_height(), '.1f'), \n",
    "                   (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                   ha = 'center', va = 'center', \n",
    "                   xytext = (0, 9), \n",
    "                   textcoords = 'offset points')\n",
    "plt.xlabel(\"Time Interval\", size=14)\n",
    "plt.ylabel(\"Number of floods\", size=14)\n",
    "#plt.savefig(\"Fig_XX.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dataframe\n",
    "df = pd.read_csv('C:/meujupyter/encantado/Encantado_rain_data.csv', sep= ';', header=0, dtype = {'Station_Code': 'object'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 589,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the dataset type\n",
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Station_Code', 'Data_Type', 'Date', 'Month', 'Measurer',\n",
       "       'Daily_max_vol', 'Day_max_vol', 'Rain_days', 'Monthly_total_vol',\n",
       "       'Annual_vol', 'Year', 'Vol_fortnight1', 'Vol_fortnight2', 'Vol_week1',\n",
       "       'Vol_week2', 'Vol_week3', 'Vol_week4', 'Day01', 'Day02', 'Day03',\n",
       "       'Day04', 'Day05', 'Day06', 'Day07', 'Day08', 'Day09', 'Day10', 'Day11',\n",
       "       'Day12', 'Day13', 'Day14', 'Day15', 'Day16', 'Day17', 'Day18', 'Day19',\n",
       "       'Day20', 'Day21', 'Day22', 'Day23', 'Day24', 'Day25', 'Day26', 'Day27',\n",
       "       'Day28', 'Day29', 'Day30', 'Day31'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 590,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See all the attributes as a list\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(933, 48)"
      ]
     },
     "execution_count": 591,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the number of lines and columns as a tuple\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 933 entries, 0 to 932\n",
      "Data columns (total 48 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Station_Code       933 non-null    object \n",
      " 1   Data_Type          933 non-null    object \n",
      " 2   Date               933 non-null    object \n",
      " 3   Month              933 non-null    object \n",
      " 4   Measurer           933 non-null    object \n",
      " 5   Daily_max_vol      898 non-null    float64\n",
      " 6   Day_max_vol        893 non-null    float64\n",
      " 7   Rain_days          898 non-null    float64\n",
      " 8   Monthly_total_vol  898 non-null    float64\n",
      " 9   Annual_vol         77 non-null     float64\n",
      " 10  Year               78 non-null     float64\n",
      " 11  Vol_fortnight1     898 non-null    float64\n",
      " 12  Vol_fortnight2     898 non-null    float64\n",
      " 13  Vol_week1          898 non-null    float64\n",
      " 14  Vol_week2          898 non-null    float64\n",
      " 15  Vol_week3          898 non-null    float64\n",
      " 16  Vol_week4          898 non-null    float64\n",
      " 17  Day01              898 non-null    float64\n",
      " 18  Day02              898 non-null    float64\n",
      " 19  Day03              898 non-null    float64\n",
      " 20  Day04              898 non-null    float64\n",
      " 21  Day05              898 non-null    float64\n",
      " 22  Day06              898 non-null    float64\n",
      " 23  Day07              898 non-null    float64\n",
      " 24  Day08              898 non-null    float64\n",
      " 25  Day09              898 non-null    float64\n",
      " 26  Day10              898 non-null    float64\n",
      " 27  Day11              898 non-null    float64\n",
      " 28  Day12              898 non-null    float64\n",
      " 29  Day13              898 non-null    float64\n",
      " 30  Day14              898 non-null    float64\n",
      " 31  Day15              898 non-null    float64\n",
      " 32  Day16              898 non-null    float64\n",
      " 33  Day17              898 non-null    float64\n",
      " 34  Day18              898 non-null    float64\n",
      " 35  Day19              898 non-null    float64\n",
      " 36  Day20              898 non-null    float64\n",
      " 37  Day21              898 non-null    float64\n",
      " 38  Day22              898 non-null    float64\n",
      " 39  Day23              898 non-null    float64\n",
      " 40  Day24              898 non-null    float64\n",
      " 41  Day25              898 non-null    float64\n",
      " 42  Day26              898 non-null    float64\n",
      " 43  Day27              898 non-null    float64\n",
      " 44  Day28              898 non-null    float64\n",
      " 45  Day29              842 non-null    float64\n",
      " 46  Day30              824 non-null    float64\n",
      " 47  Day31              526 non-null    float64\n",
      "dtypes: float64(43), object(5)\n",
      "memory usage: 350.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# See the columns, the memory usage, the non-null cells and the type of data in each attribute\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        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>Station_Code</th>\n",
       "      <th>Data_Type</th>\n",
       "      <th>Date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Measurer</th>\n",
       "      <th>Daily_max_vol</th>\n",
       "      <th>Day_max_vol</th>\n",
       "      <th>Rain_days</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Annual_vol</th>\n",
       "      <th>...</th>\n",
       "      <th>Day22</th>\n",
       "      <th>Day23</th>\n",
       "      <th>Day24</th>\n",
       "      <th>Day25</th>\n",
       "      <th>Day26</th>\n",
       "      <th>Day27</th>\n",
       "      <th>Day28</th>\n",
       "      <th>Day29</th>\n",
       "      <th>Day30</th>\n",
       "      <th>Day31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2951010</td>\n",
       "      <td>Raw</td>\n",
       "      <td>01/01/2021</td>\n",
       "      <td>January</td>\n",
       "      <td>Pluviometer</td>\n",
       "      <td>47.7</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>223.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.9</td>\n",
       "      <td>16.7</td>\n",
       "      <td>16.1</td>\n",
       "      <td>17.9</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2951010</td>\n",
       "      <td>Raw</td>\n",
       "      <td>01/12/2020</td>\n",
       "      <td>December</td>\n",
       "      <td>Pluviometer</td>\n",
       "      <td>35.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>91.3</td>\n",
       "      <td>769.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2951010</td>\n",
       "      <td>Raw</td>\n",
       "      <td>01/11/2020</td>\n",
       "      <td>November</td>\n",
       "      <td>Pluviometer</td>\n",
       "      <td>43.5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2951010</td>\n",
       "      <td>Raw</td>\n",
       "      <td>01/10/2020</td>\n",
       "      <td>October</td>\n",
       "      <td>Pluviometer</td>\n",
       "      <td>24.9</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2951010</td>\n",
       "      <td>Raw</td>\n",
       "      <td>01/09/2020</td>\n",
       "      <td>September</td>\n",
       "      <td>Pluviometer</td>\n",
       "      <td>29.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>130.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Station_Code Data_Type        Date      Month     Measurer  Daily_max_vol  \\\n",
       "0      2951010       Raw  01/01/2021    January  Pluviometer           47.7   \n",
       "1      2951010       Raw  01/12/2020   December  Pluviometer           35.8   \n",
       "2      2951010       Raw  01/11/2020   November  Pluviometer           43.5   \n",
       "3      2951010       Raw  01/10/2020    October  Pluviometer           24.9   \n",
       "4      2951010       Raw  01/09/2020  September  Pluviometer           29.7   \n",
       "\n",
       "   Day_max_vol  Rain_days  Monthly_total_vol  Annual_vol  ...  Day22  Day23  \\\n",
       "0         17.0       12.0              223.5         NaN  ...    6.4    0.0   \n",
       "1          3.0        4.0               91.3       769.8  ...    0.0    0.0   \n",
       "2         19.0        5.0               78.2         NaN  ...    0.0    0.0   \n",
       "3         27.0        6.0               62.0         NaN  ...    0.0    0.0   \n",
       "4         18.0        9.0              130.9         NaN  ...    0.0    0.0   \n",
       "\n",
       "   Day24  Day25  Day26  Day27  Day28  Day29  Day30  Day31  \n",
       "0    0.0    1.4    0.0   30.9   16.7   16.1   17.9   29.1  \n",
       "1   15.1    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "2    0.0    0.0    2.6    0.0    0.0    0.0   22.9    NaN  \n",
       "3    0.0    0.0    0.0   24.9    2.4    0.0    0.0    0.0  \n",
       "4    0.0    0.0    0.0    0.0    0.0    0.0    0.0    NaN  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the first 5 rows of the dataframe\n",
    "# Insert a specific number inside () if you want more lines \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Station_Code</th>\n",
       "      <th>Data_Type</th>\n",
       "      <th>Date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Measurer</th>\n",
       "      <th>Daily_max_vol</th>\n",
       "      <th>Day_max_vol</th>\n",
       "      <th>Rain_days</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Annual_vol</th>\n",
       "      <th>...</th>\n",
       "      <th>Day22</th>\n",
       "      <th>Day23</th>\n",
       "      <th>Day24</th>\n",
       "      <th>Day25</th>\n",
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       "      <th>Day27</th>\n",
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       "      <th>Day30</th>\n",
       "      <th>Day31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>2951010</td>\n",
       "      <td>Raw</td>\n",
       "      <td>01/09/1963</td>\n",
       "      <td>September</td>\n",
       "      <td>Pluviometer</td>\n",
       "      <td>35.6</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>180.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>35.6</td>\n",
       "      <td>33.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Station_Code Data_Type        Date      Month     Measurer  Daily_max_vol  \\\n",
       "688      2951010       Raw  01/09/1963  September  Pluviometer           35.6   \n",
       "\n",
       "     Day_max_vol  Rain_days  Monthly_total_vol  Annual_vol  ...  Day22  Day23  \\\n",
       "688         25.0       12.0              180.6         NaN  ...    0.0   22.8   \n",
       "\n",
       "     Day24  Day25  Day26  Day27  Day28  Day29  Day30  Day31  \n",
       "688    7.8   35.6   33.2    0.0    0.0    0.0    2.0    NaN  \n",
       "\n",
       "[1 rows x 48 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See a random sample of the dataframe\n",
    "df.sample() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>930</th>\n",
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       "      <td>Raw</td>\n",
       "      <td>01/07/1943</td>\n",
       "      <td>July</td>\n",
       "      <td>Pluviometer</td>\n",
       "      <td>57.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>167.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>21.2</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>2951010</td>\n",
       "      <td>Raw</td>\n",
       "      <td>01/06/1943</td>\n",
       "      <td>June</td>\n",
       "      <td>Pluviometer</td>\n",
       "      <td>29.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>104.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>2951010</td>\n",
       "      <td>Raw</td>\n",
       "      <td>01/05/1943</td>\n",
       "      <td>May</td>\n",
       "      <td>Pluviometer</td>\n",
       "      <td>93.2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>222.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>56.2</td>\n",
       "      <td>17.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>93.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Station_Code Data_Type        Date      Month     Measurer  Daily_max_vol  \\\n",
       "928      2951010       Raw  01/09/1943  September  Pluviometer           46.5   \n",
       "929      2951010       Raw  01/08/1943     August  Pluviometer           30.0   \n",
       "930      2951010       Raw  01/07/1943       July  Pluviometer           57.2   \n",
       "931      2951010       Raw  01/06/1943       June  Pluviometer           29.4   \n",
       "932      2951010       Raw  01/05/1943        May  Pluviometer           93.2   \n",
       "\n",
       "     Day_max_vol  Rain_days  Monthly_total_vol  Annual_vol  ...  Day22  Day23  \\\n",
       "928         13.0       10.0              111.1         NaN  ...    4.2    0.0   \n",
       "929         11.0        8.0               80.2         NaN  ...    0.0    0.0   \n",
       "930          3.0       10.0              167.1         NaN  ...    0.0    0.0   \n",
       "931          3.0       11.0              104.5         NaN  ...    0.0    0.0   \n",
       "932         30.0        8.0              222.5         NaN  ...   24.0   19.0   \n",
       "\n",
       "     Day24  Day25  Day26  Day27  Day28  Day29  Day30  Day31  \n",
       "928    0.0    0.0    0.0    0.0    0.0    0.0    0.0    NaN  \n",
       "929    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "930    0.0    0.0    0.0    0.0    0.0   12.6   21.2    0.6  \n",
       "931   19.2    0.0    0.0    0.0    0.0    0.0    0.0    NaN  \n",
       "932    0.0   10.1   56.2   17.4    0.0    1.2   93.2    0.0  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 594,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the last 5 lines of the dataframe\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete some attributes\n",
    "df2 = df.drop(['Station_Code','Data_Type','Measurer','Year','Day_max_vol','Day01','Day02','Day03','Day04','Day05','Day06','Day07','Day08','Day09','Day10','Day11','Day12','Day13','Day14','Day15','Day16','Day17','Day18','Day19','Day20','Day21','Day22','Day23','Day24','Day25','Day26','Day27','Day28','Day29','Day30','Day31'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 933 entries, 0 to 932\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Date               933 non-null    object \n",
      " 1   Month              933 non-null    object \n",
      " 2   Daily_max_vol      898 non-null    float64\n",
      " 3   Rain_days          898 non-null    float64\n",
      " 4   Monthly_total_vol  898 non-null    float64\n",
      " 5   Annual_vol         77 non-null     float64\n",
      " 6   Vol_fortnight1     898 non-null    float64\n",
      " 7   Vol_fortnight2     898 non-null    float64\n",
      " 8   Vol_week1          898 non-null    float64\n",
      " 9   Vol_week2          898 non-null    float64\n",
      " 10  Vol_week3          898 non-null    float64\n",
      " 11  Vol_week4          898 non-null    float64\n",
      "dtypes: float64(10), object(2)\n",
      "memory usage: 87.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the last operation\n",
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Daily_max_vol</th>\n",
       "      <th>Rain_days</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Annual_vol</th>\n",
       "      <th>Vol_fortnight1</th>\n",
       "      <th>Vol_fortnight2</th>\n",
       "      <th>Vol_week1</th>\n",
       "      <th>Vol_week2</th>\n",
       "      <th>Vol_week3</th>\n",
       "      <th>Vol_week4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>77.00000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.763808</td>\n",
       "      <td>7.535635</td>\n",
       "      <td>116.941982</td>\n",
       "      <td>1359.47013</td>\n",
       "      <td>56.461024</td>\n",
       "      <td>60.480958</td>\n",
       "      <td>29.053786</td>\n",
       "      <td>27.407238</td>\n",
       "      <td>31.450668</td>\n",
       "      <td>29.030290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.212835</td>\n",
       "      <td>3.378956</td>\n",
       "      <td>69.222102</td>\n",
       "      <td>342.07457</td>\n",
       "      <td>46.210295</td>\n",
       "      <td>46.744517</td>\n",
       "      <td>30.722865</td>\n",
       "      <td>32.575616</td>\n",
       "      <td>33.762517</td>\n",
       "      <td>32.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>644.30000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.525000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>66.950000</td>\n",
       "      <td>1154.90000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>25.525000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>3.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.200000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>106.700000</td>\n",
       "      <td>1350.40000</td>\n",
       "      <td>45.300000</td>\n",
       "      <td>53.450000</td>\n",
       "      <td>21.350000</td>\n",
       "      <td>17.050000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>20.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>50.700000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>155.300000</td>\n",
       "      <td>1553.50000</td>\n",
       "      <td>76.975000</td>\n",
       "      <td>86.375000</td>\n",
       "      <td>40.375000</td>\n",
       "      <td>39.200000</td>\n",
       "      <td>48.075000</td>\n",
       "      <td>41.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>135.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>382.000000</td>\n",
       "      <td>2265.70000</td>\n",
       "      <td>311.500000</td>\n",
       "      <td>301.700000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>220.500000</td>\n",
       "      <td>235.200000</td>\n",
       "      <td>183.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Daily_max_vol   Rain_days  Monthly_total_vol  Annual_vol  \\\n",
       "count     898.000000  898.000000         898.000000    77.00000   \n",
       "mean       38.763808    7.535635         116.941982  1359.47013   \n",
       "std        22.212835    3.378956          69.222102   342.07457   \n",
       "min         0.000000    0.000000           0.000000   644.30000   \n",
       "25%        23.525000    5.000000          66.950000  1154.90000   \n",
       "50%        33.200000    7.000000         106.700000  1350.40000   \n",
       "75%        50.700000   10.000000         155.300000  1553.50000   \n",
       "max       135.000000   20.000000         382.000000  2265.70000   \n",
       "\n",
       "       Vol_fortnight1  Vol_fortnight2   Vol_week1   Vol_week2   Vol_week3  \\\n",
       "count      898.000000      898.000000  898.000000  898.000000  898.000000   \n",
       "mean        56.461024       60.480958   29.053786   27.407238   31.450668   \n",
       "std         46.210295       46.744517   30.722865   32.575616   33.762517   \n",
       "min          0.000000        0.000000    0.000000    0.000000    0.000000   \n",
       "25%         22.500000       25.525000    5.200000    1.000000    4.700000   \n",
       "50%         45.300000       53.450000   21.350000   17.050000   22.000000   \n",
       "75%         76.975000       86.375000   40.375000   39.200000   48.075000   \n",
       "max        311.500000      301.700000  190.000000  220.500000  235.200000   \n",
       "\n",
       "        Vol_week4  \n",
       "count  898.000000  \n",
       "mean    29.030290  \n",
       "std     32.532806  \n",
       "min      0.000000  \n",
       "25%      3.225000  \n",
       "50%     20.400000  \n",
       "75%     41.800000  \n",
       "max    183.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistics of the attributes\n",
    "df2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspect the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                   0\n",
       "Month                  0\n",
       "Daily_max_vol         35\n",
       "Rain_days             35\n",
       "Monthly_total_vol     35\n",
       "Annual_vol           856\n",
       "Vol_fortnight1        35\n",
       "Vol_fortnight2        35\n",
       "Vol_week1             35\n",
       "Vol_week2             35\n",
       "Vol_week3             35\n",
       "Vol_week4             35\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count missing values of each attribute\n",
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1171"
      ]
     },
     "execution_count": 595,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the total missing values\n",
    "df2.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10025"
      ]
     },
     "execution_count": 596,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the non-null cell \n",
    "df2.notnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vmax Monthly total volume = Q3 + (1.5*IQR), Outlier > Vmax\n",
    "# IQR = Q3 -Q1  \n",
    "# See values in df.describe(), above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                 21\n",
       "Month                21\n",
       "Daily_max_vol        21\n",
       "Rain_days            21\n",
       "Monthly_total_vol    21\n",
       "Annual_vol            2\n",
       "Vol_fortnight1       21\n",
       "Vol_fortnight2       21\n",
       "Vol_week1            21\n",
       "Vol_week2            21\n",
       "Vol_week3            21\n",
       "Vol_week4            21\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count how many values are > than the Vmax of the defined attribute.\n",
    "df2[df2['Monthly_total_vol']>287.82].count() # 287.82 is the calculated Vmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Daily_max_vol</th>\n",
       "      <th>Rain_days</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Annual_vol</th>\n",
       "      <th>Vol_fortnight1</th>\n",
       "      <th>Vol_fortnight2</th>\n",
       "      <th>Vol_week1</th>\n",
       "      <th>Vol_week2</th>\n",
       "      <th>Vol_week3</th>\n",
       "      <th>Vol_week4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>01/10/2019</td>\n",
       "      <td>October</td>\n",
       "      <td>53.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>319.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>164.1</td>\n",
       "      <td>155.1</td>\n",
       "      <td>143.7</td>\n",
       "      <td>20.4</td>\n",
       "      <td>66.7</td>\n",
       "      <td>88.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>01/10/2016</td>\n",
       "      <td>October</td>\n",
       "      <td>107.8</td>\n",
       "      <td>11.0</td>\n",
       "      <td>322.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.9</td>\n",
       "      <td>301.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>235.2</td>\n",
       "      <td>66.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>01/10/2015</td>\n",
       "      <td>October</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>293.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196.6</td>\n",
       "      <td>96.9</td>\n",
       "      <td>107.1</td>\n",
       "      <td>89.5</td>\n",
       "      <td>96.6</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>01/08/2013</td>\n",
       "      <td>August</td>\n",
       "      <td>62.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>312.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112.6</td>\n",
       "      <td>200.3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>74.6</td>\n",
       "      <td>33.4</td>\n",
       "      <td>166.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>01/11/2009</td>\n",
       "      <td>November</td>\n",
       "      <td>90.3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>318.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.5</td>\n",
       "      <td>167.8</td>\n",
       "      <td>100.5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>121.1</td>\n",
       "      <td>46.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>01/10/2005</td>\n",
       "      <td>October</td>\n",
       "      <td>56.4</td>\n",
       "      <td>13.0</td>\n",
       "      <td>296.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>197.5</td>\n",
       "      <td>99.0</td>\n",
       "      <td>141.9</td>\n",
       "      <td>55.6</td>\n",
       "      <td>52.4</td>\n",
       "      <td>46.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>01/12/2003</td>\n",
       "      <td>December</td>\n",
       "      <td>100.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>334.3</td>\n",
       "      <td>1802.5</td>\n",
       "      <td>223.8</td>\n",
       "      <td>110.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>218.8</td>\n",
       "      <td>91.1</td>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>01/10/2002</td>\n",
       "      <td>October</td>\n",
       "      <td>103.2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>86.2</td>\n",
       "      <td>30.8</td>\n",
       "      <td>90.4</td>\n",
       "      <td>174.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>01/10/1997</td>\n",
       "      <td>October</td>\n",
       "      <td>72.1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>349.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>254.8</td>\n",
       "      <td>94.4</td>\n",
       "      <td>110.5</td>\n",
       "      <td>144.3</td>\n",
       "      <td>20.4</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>01/02/1996</td>\n",
       "      <td>February</td>\n",
       "      <td>40.8</td>\n",
       "      <td>19.0</td>\n",
       "      <td>314.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147.0</td>\n",
       "      <td>167.4</td>\n",
       "      <td>101.9</td>\n",
       "      <td>45.1</td>\n",
       "      <td>55.4</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>01/10/1994</td>\n",
       "      <td>October</td>\n",
       "      <td>61.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>324.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152.8</td>\n",
       "      <td>171.6</td>\n",
       "      <td>52.4</td>\n",
       "      <td>100.4</td>\n",
       "      <td>139.6</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>01/09/1988</td>\n",
       "      <td>September</td>\n",
       "      <td>83.2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>357.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>127.5</td>\n",
       "      <td>230.3</td>\n",
       "      <td>15.5</td>\n",
       "      <td>112.0</td>\n",
       "      <td>165.7</td>\n",
       "      <td>64.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>01/06/1984</td>\n",
       "      <td>June</td>\n",
       "      <td>64.3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>291.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153.1</td>\n",
       "      <td>138.0</td>\n",
       "      <td>131.6</td>\n",
       "      <td>21.5</td>\n",
       "      <td>120.7</td>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>01/08/1975</td>\n",
       "      <td>August</td>\n",
       "      <td>75.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>298.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>187.7</td>\n",
       "      <td>110.8</td>\n",
       "      <td>46.3</td>\n",
       "      <td>141.4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>86.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>01/06/1972</td>\n",
       "      <td>June</td>\n",
       "      <td>85.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>307.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>212.2</td>\n",
       "      <td>95.0</td>\n",
       "      <td>175.5</td>\n",
       "      <td>36.7</td>\n",
       "      <td>31.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>01/03/1971</td>\n",
       "      <td>March</td>\n",
       "      <td>109.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>330.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.8</td>\n",
       "      <td>175.0</td>\n",
       "      <td>45.2</td>\n",
       "      <td>110.6</td>\n",
       "      <td>162.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>01/09/1967</td>\n",
       "      <td>September</td>\n",
       "      <td>61.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>342.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147.3</td>\n",
       "      <td>195.4</td>\n",
       "      <td>80.5</td>\n",
       "      <td>66.8</td>\n",
       "      <td>195.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>01/12/1966</td>\n",
       "      <td>December</td>\n",
       "      <td>52.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>2147.2</td>\n",
       "      <td>228.7</td>\n",
       "      <td>63.3</td>\n",
       "      <td>138.7</td>\n",
       "      <td>90.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>01/09/1966</td>\n",
       "      <td>September</td>\n",
       "      <td>91.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>311.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>311.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>220.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>01/01/1946</td>\n",
       "      <td>January</td>\n",
       "      <td>69.2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>302.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118.8</td>\n",
       "      <td>184.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>106.3</td>\n",
       "      <td>69.4</td>\n",
       "      <td>114.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>01/06/1944</td>\n",
       "      <td>June</td>\n",
       "      <td>102.1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>294.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>292.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>169.3</td>\n",
       "      <td>123.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date      Month  Daily_max_vol  Rain_days  Monthly_total_vol  \\\n",
       "15   01/10/2019    October           53.4       12.0              319.2   \n",
       "51   01/10/2016    October          107.8       11.0              322.6   \n",
       "63   01/10/2015    October           81.0       14.0              293.5   \n",
       "89   01/08/2013     August           62.2       11.0              312.9   \n",
       "134  01/11/2009   November           90.3       11.0              318.3   \n",
       "183  01/10/2005    October           56.4       13.0              296.5   \n",
       "205  01/12/2003   December          100.2       10.0              334.3   \n",
       "219  01/10/2002    October          103.2       19.0              382.0   \n",
       "279  01/10/1997    October           72.1       18.0              349.2   \n",
       "299  01/02/1996   February           40.8       19.0              314.4   \n",
       "315  01/10/1994    October           61.0       13.0              324.4   \n",
       "388  01/09/1988  September           83.2       14.0              357.8   \n",
       "439  01/06/1984       June           64.3       13.0              291.1   \n",
       "545  01/08/1975     August           75.0       15.0              298.5   \n",
       "583  01/06/1972       June           85.0       15.0              307.2   \n",
       "598  01/03/1971      March          109.0        9.0              330.8   \n",
       "640  01/09/1967  September           61.2       10.0              342.7   \n",
       "649  01/12/1966   December           52.0        9.0              292.0   \n",
       "652  01/09/1966  September           91.0        7.0              311.5   \n",
       "900  01/01/1946    January           69.2       13.0              302.8   \n",
       "919  01/06/1944       June          102.1       12.0              294.8   \n",
       "\n",
       "     Annual_vol  Vol_fortnight1  Vol_fortnight2  Vol_week1  Vol_week2  \\\n",
       "15          NaN           164.1           155.1      143.7       20.4   \n",
       "51          NaN            20.9           301.7        0.0       20.9   \n",
       "63          NaN           196.6            96.9      107.1       89.5   \n",
       "89          NaN           112.6           200.3       38.0       74.6   \n",
       "134         NaN           150.5           167.8      100.5       50.0   \n",
       "183         NaN           197.5            99.0      141.9       55.6   \n",
       "205      1802.5           223.8           110.5        5.0      218.8   \n",
       "219         NaN           117.0           265.0       86.2       30.8   \n",
       "279         NaN           254.8            94.4      110.5      144.3   \n",
       "299         NaN           147.0           167.4      101.9       45.1   \n",
       "315         NaN           152.8           171.6       52.4      100.4   \n",
       "388         NaN           127.5           230.3       15.5      112.0   \n",
       "439         NaN           153.1           138.0      131.6       21.5   \n",
       "545         NaN           187.7           110.8       46.3      141.4   \n",
       "583         NaN           212.2            95.0      175.5       36.7   \n",
       "598         NaN           155.8           175.0       45.2      110.6   \n",
       "640         NaN           147.3           195.4       80.5       66.8   \n",
       "649      2147.2           228.7            63.3      138.7       90.0   \n",
       "652         NaN           311.5             0.0       91.0      220.5   \n",
       "900         NaN           118.8           184.0       12.5      106.3   \n",
       "919         NaN             2.0           292.8        2.0        0.0   \n",
       "\n",
       "     Vol_week3  Vol_week4  \n",
       "15        66.7       88.4  \n",
       "51       235.2       66.5  \n",
       "63        96.6        0.3  \n",
       "89        33.4      166.9  \n",
       "134      121.1       46.7  \n",
       "183       52.4       46.6  \n",
       "205       91.1       19.4  \n",
       "219       90.4      174.6  \n",
       "279       20.4       74.0  \n",
       "299       55.4      112.0  \n",
       "315      139.6       32.0  \n",
       "388      165.7       64.6  \n",
       "439      120.7       17.3  \n",
       "545       24.0       86.8  \n",
       "583       31.0       64.0  \n",
       "598      162.0       13.0  \n",
       "640      195.4        0.0  \n",
       "649       63.3        0.0  \n",
       "652        0.0        0.0  \n",
       "900       69.4      114.6  \n",
       "919      169.3      123.5  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the outliers, i.e, only the lines with values > Vmax \n",
    "df2.loc[df2['Monthly_total_vol']>287.82] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot of the attribute Monthly_total_vol\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = (8, 12)\n",
    "df.boxplot(column='Monthly_total_vol', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identificar os n maiores valores de uma variável\n",
    "df2[['Date','Daily_max_vol','Vol_fortnight1','Vol_fortnight2','Vol_week1','Vol_week2','Vol_week3','Vol_week4']].nlargest(29,'Daily_max_vol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      77.00000\n",
       "mean     1359.47013\n",
       "std       342.07457\n",
       "min       644.30000\n",
       "25%      1154.90000\n",
       "50%      1350.40000\n",
       "75%      1553.50000\n",
       "max      2265.70000\n",
       "Name: Annual_vol, dtype: float64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.Annual_vol.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1359.4701298701295"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Valor médio dos dados por coluna (ex: Coluna X)\n",
    "df2['Annual_vol'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create new columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Daily_max_vol</th>\n",
       "      <th>Rain_days</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Annual_vol</th>\n",
       "      <th>Vol_fortnight1</th>\n",
       "      <th>Vol_fortnight2</th>\n",
       "      <th>Vol_week1</th>\n",
       "      <th>Vol_week2</th>\n",
       "      <th>Vol_week3</th>\n",
       "      <th>Vol_week4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2021</td>\n",
       "      <td>January</td>\n",
       "      <td>47.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>223.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.6</td>\n",
       "      <td>187.9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>75.8</td>\n",
       "      <td>112.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/12/2020</td>\n",
       "      <td>December</td>\n",
       "      <td>35.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>91.3</td>\n",
       "      <td>769.8</td>\n",
       "      <td>76.2</td>\n",
       "      <td>15.1</td>\n",
       "      <td>49.1</td>\n",
       "      <td>27.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/11/2020</td>\n",
       "      <td>November</td>\n",
       "      <td>43.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.2</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>43.5</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/10/2020</td>\n",
       "      <td>October</td>\n",
       "      <td>24.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/09/2020</td>\n",
       "      <td>September</td>\n",
       "      <td>29.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>130.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.2</td>\n",
       "      <td>39.7</td>\n",
       "      <td>32.4</td>\n",
       "      <td>58.8</td>\n",
       "      <td>39.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Month  Daily_max_vol  Rain_days  Monthly_total_vol  \\\n",
       "0  01/01/2021    January           47.7       12.0              223.5   \n",
       "1  01/12/2020   December           35.8        4.0               91.3   \n",
       "2  01/11/2020   November           43.5        5.0               78.2   \n",
       "3  01/10/2020    October           24.9        6.0               62.0   \n",
       "4  01/09/2020  September           29.7        9.0              130.9   \n",
       "\n",
       "   Annual_vol  Vol_fortnight1  Vol_fortnight2  Vol_week1  Vol_week2  \\\n",
       "0         NaN            35.6           187.9       19.2       16.4   \n",
       "1       769.8            76.2            15.1       49.1       27.1   \n",
       "2         NaN             9.2            69.0        6.7        2.5   \n",
       "3         NaN            34.7            27.3       34.7        0.0   \n",
       "4         NaN            91.2            39.7       32.4       58.8   \n",
       "\n",
       "   Vol_week3  Vol_week4  \n",
       "0       75.8      112.1  \n",
       "1        0.0       15.1  \n",
       "2       43.5       25.5  \n",
       "3        0.0       27.3  \n",
       "4       39.7        0.0  "
      ]
     },
     "execution_count": 597,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect columns\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to create cathegories\n",
    "def categorias(Rain):\n",
    "    if Rain > 287.82:\n",
    "        return 'Yes'\n",
    "    else:\n",
    "        return 'No'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column with categoric data\n",
    "df3 = df.drop(['Station_Code','Data_Type','Measurer','Year','Day_max_vol','Day01','Day02','Day03','Day04','Day05','Day06','Day07','Day08','Day09','Day10','Day11','Day12','Day13','Day14','Day15','Day16','Day17','Day18','Day19','Day20','Day21','Day22','Day23','Day24','Day25','Day26','Day27','Day28','Day29','Day30','Day31'], axis=1)\n",
    "df3['Mtv_Outlier?'] = df2['Monthly_total_vol'].apply(categorias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    No\n",
       "1    No\n",
       "2    No\n",
       "3    No\n",
       "4    No\n",
       "Name: Mtv_Outlier?, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the new column\n",
    "df3['Mtv_Outlier?'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Daily_max_vol</th>\n",
       "      <th>Rain_days</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Annual_vol</th>\n",
       "      <th>Vol_fortnight1</th>\n",
       "      <th>Vol_fortnight2</th>\n",
       "      <th>Vol_week1</th>\n",
       "      <th>Vol_week2</th>\n",
       "      <th>Vol_week3</th>\n",
       "      <th>Vol_week4</th>\n",
       "      <th>Mtv_Outlier?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2021</td>\n",
       "      <td>January</td>\n",
       "      <td>47.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>223.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.6</td>\n",
       "      <td>187.9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>75.8</td>\n",
       "      <td>112.1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/12/2020</td>\n",
       "      <td>December</td>\n",
       "      <td>35.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>91.3</td>\n",
       "      <td>769.8</td>\n",
       "      <td>76.2</td>\n",
       "      <td>15.1</td>\n",
       "      <td>49.1</td>\n",
       "      <td>27.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/11/2020</td>\n",
       "      <td>November</td>\n",
       "      <td>43.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.2</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>43.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/10/2020</td>\n",
       "      <td>October</td>\n",
       "      <td>24.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/09/2020</td>\n",
       "      <td>September</td>\n",
       "      <td>29.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>130.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.2</td>\n",
       "      <td>39.7</td>\n",
       "      <td>32.4</td>\n",
       "      <td>58.8</td>\n",
       "      <td>39.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Month  Daily_max_vol  Rain_days  Monthly_total_vol  \\\n",
       "0  01/01/2021    January           47.7       12.0              223.5   \n",
       "1  01/12/2020   December           35.8        4.0               91.3   \n",
       "2  01/11/2020   November           43.5        5.0               78.2   \n",
       "3  01/10/2020    October           24.9        6.0               62.0   \n",
       "4  01/09/2020  September           29.7        9.0              130.9   \n",
       "\n",
       "   Annual_vol  Vol_fortnight1  Vol_fortnight2  Vol_week1  Vol_week2  \\\n",
       "0         NaN            35.6           187.9       19.2       16.4   \n",
       "1       769.8            76.2            15.1       49.1       27.1   \n",
       "2         NaN             9.2            69.0        6.7        2.5   \n",
       "3         NaN            34.7            27.3       34.7        0.0   \n",
       "4         NaN            91.2            39.7       32.4       58.8   \n",
       "\n",
       "   Vol_week3  Vol_week4 Mtv_Outlier?  \n",
       "0       75.8      112.1           No  \n",
       "1        0.0       15.1           No  \n",
       "2       43.5       25.5           No  \n",
       "3        0.0       27.3           No  \n",
       "4       39.7        0.0           No  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the new column in the dataframe\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     912\n",
       "Yes     21\n",
       "Name: Mtv_Outlier?, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the new column\n",
    "pd.value_counts(df3['Mtv_Outlier?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Repeat the process to create another column\n",
    "def categorias(Rain2):\n",
    "    if Rain2 > 158.68:\n",
    "        return 'Yes'\n",
    "    else:\n",
    "        return 'No'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['Vf1_Outlier?'] = df3['Vol_fortnight1'].apply(categorias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     899\n",
       "Yes     34\n",
       "Name: Vf1_Outlier?, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df3['Vf1_Outlier?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Daily_max_vol</th>\n",
       "      <th>Rain_days</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Annual_vol</th>\n",
       "      <th>Vol_fortnight1</th>\n",
       "      <th>Vol_fortnight2</th>\n",
       "      <th>Vol_week1</th>\n",
       "      <th>Vol_week2</th>\n",
       "      <th>Vol_week3</th>\n",
       "      <th>Vol_week4</th>\n",
       "      <th>Mtv_Outlier?</th>\n",
       "      <th>Vf1_Outlier?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2021</td>\n",
       "      <td>January</td>\n",
       "      <td>47.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>223.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.6</td>\n",
       "      <td>187.9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>16.4</td>\n",
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       "      <td>112.1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/12/2020</td>\n",
       "      <td>December</td>\n",
       "      <td>35.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>91.3</td>\n",
       "      <td>769.8</td>\n",
       "      <td>76.2</td>\n",
       "      <td>15.1</td>\n",
       "      <td>49.1</td>\n",
       "      <td>27.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/11/2020</td>\n",
       "      <td>November</td>\n",
       "      <td>43.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.2</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>43.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/10/2020</td>\n",
       "      <td>October</td>\n",
       "      <td>24.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/09/2020</td>\n",
       "      <td>September</td>\n",
       "      <td>29.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>130.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.2</td>\n",
       "      <td>39.7</td>\n",
       "      <td>32.4</td>\n",
       "      <td>58.8</td>\n",
       "      <td>39.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Month  Daily_max_vol  Rain_days  Monthly_total_vol  \\\n",
       "0  01/01/2021    January           47.7       12.0              223.5   \n",
       "1  01/12/2020   December           35.8        4.0               91.3   \n",
       "2  01/11/2020   November           43.5        5.0               78.2   \n",
       "3  01/10/2020    October           24.9        6.0               62.0   \n",
       "4  01/09/2020  September           29.7        9.0              130.9   \n",
       "\n",
       "   Annual_vol  Vol_fortnight1  Vol_fortnight2  Vol_week1  Vol_week2  \\\n",
       "0         NaN            35.6           187.9       19.2       16.4   \n",
       "1       769.8            76.2            15.1       49.1       27.1   \n",
       "2         NaN             9.2            69.0        6.7        2.5   \n",
       "3         NaN            34.7            27.3       34.7        0.0   \n",
       "4         NaN            91.2            39.7       32.4       58.8   \n",
       "\n",
       "   Vol_week3  Vol_week4 Mtv_Outlier? Vf1_Outlier?  \n",
       "0       75.8      112.1           No           No  \n",
       "1        0.0       15.1           No           No  \n",
       "2       43.5       25.5           No           No  \n",
       "3        0.0       27.3           No           No  \n",
       "4       39.7        0.0           No           No  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorias(Rain3):\n",
    "    if Rain3 > 177.65:\n",
    "        return 'Yes'\n",
    "    else:\n",
    "        return 'No'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['Vf2_Outlier?'] = df3['Vol_fortnight2'].apply(categorias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     913\n",
       "Yes     20\n",
       "Name: Vf2_Outlier?, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df3['Vf2_Outlier?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    .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>Month</th>\n",
       "      <th>Daily_max_vol</th>\n",
       "      <th>Rain_days</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Annual_vol</th>\n",
       "      <th>Vol_fortnight1</th>\n",
       "      <th>Vol_fortnight2</th>\n",
       "      <th>Vol_week1</th>\n",
       "      <th>Vol_week2</th>\n",
       "      <th>Vol_week3</th>\n",
       "      <th>Vol_week4</th>\n",
       "      <th>Mtv_Outlier?</th>\n",
       "      <th>Vf1_Outlier?</th>\n",
       "      <th>Vf2_Outlier?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2021</td>\n",
       "      <td>January</td>\n",
       "      <td>47.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>223.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.6</td>\n",
       "      <td>187.9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>75.8</td>\n",
       "      <td>112.1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/12/2020</td>\n",
       "      <td>December</td>\n",
       "      <td>35.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>91.3</td>\n",
       "      <td>769.8</td>\n",
       "      <td>76.2</td>\n",
       "      <td>15.1</td>\n",
       "      <td>49.1</td>\n",
       "      <td>27.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/11/2020</td>\n",
       "      <td>November</td>\n",
       "      <td>43.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.2</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>43.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/10/2020</td>\n",
       "      <td>October</td>\n",
       "      <td>24.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/09/2020</td>\n",
       "      <td>September</td>\n",
       "      <td>29.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>130.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.2</td>\n",
       "      <td>39.7</td>\n",
       "      <td>32.4</td>\n",
       "      <td>58.8</td>\n",
       "      <td>39.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Month  Daily_max_vol  Rain_days  Monthly_total_vol  \\\n",
       "0  01/01/2021    January           47.7       12.0              223.5   \n",
       "1  01/12/2020   December           35.8        4.0               91.3   \n",
       "2  01/11/2020   November           43.5        5.0               78.2   \n",
       "3  01/10/2020    October           24.9        6.0               62.0   \n",
       "4  01/09/2020  September           29.7        9.0              130.9   \n",
       "\n",
       "   Annual_vol  Vol_fortnight1  Vol_fortnight2  Vol_week1  Vol_week2  \\\n",
       "0         NaN            35.6           187.9       19.2       16.4   \n",
       "1       769.8            76.2            15.1       49.1       27.1   \n",
       "2         NaN             9.2            69.0        6.7        2.5   \n",
       "3         NaN            34.7            27.3       34.7        0.0   \n",
       "4         NaN            91.2            39.7       32.4       58.8   \n",
       "\n",
       "   Vol_week3  Vol_week4 Mtv_Outlier? Vf1_Outlier? Vf2_Outlier?  \n",
       "0       75.8      112.1           No           No          Yes  \n",
       "1        0.0       15.1           No           No           No  \n",
       "2       43.5       25.5           No           No           No  \n",
       "3        0.0       27.3           No           No           No  \n",
       "4       39.7        0.0           No           No           No  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorias(Rain4):\n",
    "    if Rain4 > 93.14:\n",
    "        return 'Yes'\n",
    "    else:\n",
    "        return 'No'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['Vw1_Outlier?'] = df3['Vol_week1'].apply(categorias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     888\n",
       "Yes     45\n",
       "Name: Vw1_Outlier?, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df3['Vw1_Outlier?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Daily_max_vol</th>\n",
       "      <th>Rain_days</th>\n",
       "      <th>Monthly_total_vol</th>\n",
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       "      <th>Vol_fortnight2</th>\n",
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       "      <th>Vf1_Outlier?</th>\n",
       "      <th>Vf2_Outlier?</th>\n",
       "      <th>Vw1_Outlier?</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2021</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "      <th>1</th>\n",
       "      <td>01/12/2020</td>\n",
       "      <td>December</td>\n",
       "      <td>35.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>91.3</td>\n",
       "      <td>769.8</td>\n",
       "      <td>76.2</td>\n",
       "      <td>15.1</td>\n",
       "      <td>49.1</td>\n",
       "      <td>27.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/11/2020</td>\n",
       "      <td>November</td>\n",
       "      <td>43.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.2</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>43.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/10/2020</td>\n",
       "      <td>October</td>\n",
       "      <td>24.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/09/2020</td>\n",
       "      <td>September</td>\n",
       "      <td>29.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>130.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.2</td>\n",
       "      <td>39.7</td>\n",
       "      <td>32.4</td>\n",
       "      <td>58.8</td>\n",
       "      <td>39.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Month  Daily_max_vol  Rain_days  Monthly_total_vol  \\\n",
       "0  01/01/2021    January           47.7       12.0              223.5   \n",
       "1  01/12/2020   December           35.8        4.0               91.3   \n",
       "2  01/11/2020   November           43.5        5.0               78.2   \n",
       "3  01/10/2020    October           24.9        6.0               62.0   \n",
       "4  01/09/2020  September           29.7        9.0              130.9   \n",
       "\n",
       "   Annual_vol  Vol_fortnight1  Vol_fortnight2  Vol_week1  Vol_week2  \\\n",
       "0         NaN            35.6           187.9       19.2       16.4   \n",
       "1       769.8            76.2            15.1       49.1       27.1   \n",
       "2         NaN             9.2            69.0        6.7        2.5   \n",
       "3         NaN            34.7            27.3       34.7        0.0   \n",
       "4         NaN            91.2            39.7       32.4       58.8   \n",
       "\n",
       "   Vol_week3  Vol_week4 Mtv_Outlier? Vf1_Outlier? Vf2_Outlier? Vw1_Outlier?  \\\n",
       "0       75.8      112.1           No           No          Yes           No   \n",
       "1        0.0       15.1           No           No           No           No   \n",
       "2       43.5       25.5           No           No           No           No   \n",
       "3        0.0       27.3           No           No           No           No   \n",
       "4       39.7        0.0           No           No           No           No   \n",
       "\n",
       "  Vw2_Outlier? Vw3_Outlier? Vw4_Outlier?  \n",
       "0           No           No           No  \n",
       "1           No           No           No  \n",
       "2           No           No           No  \n",
       "3           No           No           No  \n",
       "4           No           No           No  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorias(Rain5):\n",
    "    if Rain5 > 96.5:\n",
    "        return 'Yes'\n",
    "    else:\n",
    "        return 'No'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['Vw2_Outlier?'] = df3['Vol_week2'].apply(categorias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     890\n",
       "Yes     43\n",
       "Name: Vw2_Outlier?, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df3['Vw2_Outlier?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2021</td>\n",
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       "      <td>12.0</td>\n",
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       "      <td>No</td>\n",
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       "      <th>1</th>\n",
       "      <td>01/12/2020</td>\n",
       "      <td>December</td>\n",
       "      <td>35.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>91.3</td>\n",
       "      <td>769.8</td>\n",
       "      <td>76.2</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/11/2020</td>\n",
       "      <td>November</td>\n",
       "      <td>43.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.2</td>\n",
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       "      <td>43.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/10/2020</td>\n",
       "      <td>October</td>\n",
       "      <td>24.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/09/2020</td>\n",
       "      <td>September</td>\n",
       "      <td>29.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>130.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.2</td>\n",
       "      <td>39.7</td>\n",
       "      <td>32.4</td>\n",
       "      <td>58.8</td>\n",
       "      <td>39.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Month  Daily_max_vol  Rain_days  Monthly_total_vol  \\\n",
       "0  01/01/2021    January           47.7       12.0              223.5   \n",
       "1  01/12/2020   December           35.8        4.0               91.3   \n",
       "2  01/11/2020   November           43.5        5.0               78.2   \n",
       "3  01/10/2020    October           24.9        6.0               62.0   \n",
       "4  01/09/2020  September           29.7        9.0              130.9   \n",
       "\n",
       "   Annual_vol  Vol_fortnight1  Vol_fortnight2  Vol_week1  Vol_week2  \\\n",
       "0         NaN            35.6           187.9       19.2       16.4   \n",
       "1       769.8            76.2            15.1       49.1       27.1   \n",
       "2         NaN             9.2            69.0        6.7        2.5   \n",
       "3         NaN            34.7            27.3       34.7        0.0   \n",
       "4         NaN            91.2            39.7       32.4       58.8   \n",
       "\n",
       "   Vol_week3  Vol_week4 Mtv_Outlier? Vf1_Outlier? Vf2_Outlier? Vw1_Outlier?  \\\n",
       "0       75.8      112.1           No           No          Yes           No   \n",
       "1        0.0       15.1           No           No           No           No   \n",
       "2       43.5       25.5           No           No           No           No   \n",
       "3        0.0       27.3           No           No           No           No   \n",
       "4       39.7        0.0           No           No           No           No   \n",
       "\n",
       "  Vw2_Outlier? Vw3_Outlier? Vw4_Outlier?  \n",
       "0           No           No           No  \n",
       "1           No           No           No  \n",
       "2           No           No           No  \n",
       "3           No           No           No  \n",
       "4           No           No           No  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorias(Rain6):\n",
    "    if Rain6 > 113.13:\n",
    "        return 'Yes'\n",
    "    else:\n",
    "        return 'No'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['Vw3_Outlier?'] = df3['Vol_week3'].apply(categorias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     907\n",
       "Yes     26\n",
       "Name: Vw3_Outlier?, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df3['Vw3_Outlier?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Daily_max_vol</th>\n",
       "      <th>Rain_days</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Annual_vol</th>\n",
       "      <th>Vol_fortnight1</th>\n",
       "      <th>Vol_fortnight2</th>\n",
       "      <th>Vol_week1</th>\n",
       "      <th>Vol_week2</th>\n",
       "      <th>Vol_week3</th>\n",
       "      <th>Vol_week4</th>\n",
       "      <th>Mtv_Outlier?</th>\n",
       "      <th>Vf1_Outlier?</th>\n",
       "      <th>Vf2_Outlier?</th>\n",
       "      <th>Vw1_Outlier?</th>\n",
       "      <th>Vw2_Outlier?</th>\n",
       "      <th>Vw3_Outlier?</th>\n",
       "      <th>Vw4_Outlier?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2021</td>\n",
       "      <td>January</td>\n",
       "      <td>47.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>223.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.6</td>\n",
       "      <td>187.9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>75.8</td>\n",
       "      <td>112.1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/12/2020</td>\n",
       "      <td>December</td>\n",
       "      <td>35.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>91.3</td>\n",
       "      <td>769.8</td>\n",
       "      <td>76.2</td>\n",
       "      <td>15.1</td>\n",
       "      <td>49.1</td>\n",
       "      <td>27.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/11/2020</td>\n",
       "      <td>November</td>\n",
       "      <td>43.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.2</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>43.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/10/2020</td>\n",
       "      <td>October</td>\n",
       "      <td>24.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/09/2020</td>\n",
       "      <td>September</td>\n",
       "      <td>29.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>130.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.2</td>\n",
       "      <td>39.7</td>\n",
       "      <td>32.4</td>\n",
       "      <td>58.8</td>\n",
       "      <td>39.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Month  Daily_max_vol  Rain_days  Monthly_total_vol  \\\n",
       "0  01/01/2021    January           47.7       12.0              223.5   \n",
       "1  01/12/2020   December           35.8        4.0               91.3   \n",
       "2  01/11/2020   November           43.5        5.0               78.2   \n",
       "3  01/10/2020    October           24.9        6.0               62.0   \n",
       "4  01/09/2020  September           29.7        9.0              130.9   \n",
       "\n",
       "   Annual_vol  Vol_fortnight1  Vol_fortnight2  Vol_week1  Vol_week2  \\\n",
       "0         NaN            35.6           187.9       19.2       16.4   \n",
       "1       769.8            76.2            15.1       49.1       27.1   \n",
       "2         NaN             9.2            69.0        6.7        2.5   \n",
       "3         NaN            34.7            27.3       34.7        0.0   \n",
       "4         NaN            91.2            39.7       32.4       58.8   \n",
       "\n",
       "   Vol_week3  Vol_week4 Mtv_Outlier? Vf1_Outlier? Vf2_Outlier? Vw1_Outlier?  \\\n",
       "0       75.8      112.1           No           No          Yes           No   \n",
       "1        0.0       15.1           No           No           No           No   \n",
       "2       43.5       25.5           No           No           No           No   \n",
       "3        0.0       27.3           No           No           No           No   \n",
       "4       39.7        0.0           No           No           No           No   \n",
       "\n",
       "  Vw2_Outlier? Vw3_Outlier? Vw4_Outlier?  \n",
       "0           No           No           No  \n",
       "1           No           No           No  \n",
       "2           No           No           No  \n",
       "3           No           No           No  \n",
       "4           No           No           No  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorias(Rain7):\n",
    "    if Rain7 > 99.66:\n",
    "        return 'Yes'\n",
    "    else:\n",
    "        return 'No'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['Vw4_Outlier?'] = df3['Vol_week4'].apply(categorias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     890\n",
       "Yes     43\n",
       "Name: Vw4_Outlier?, dtype: int64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df3['Vw4_Outlier?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Daily_max_vol</th>\n",
       "      <th>Rain_days</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Annual_vol</th>\n",
       "      <th>Vol_fortnight1</th>\n",
       "      <th>Vol_fortnight2</th>\n",
       "      <th>Vol_week1</th>\n",
       "      <th>Vol_week2</th>\n",
       "      <th>Vol_week3</th>\n",
       "      <th>Vol_week4</th>\n",
       "      <th>Mtv_Outlier?</th>\n",
       "      <th>Vf1_Outlier?</th>\n",
       "      <th>Vf2_Outlier?</th>\n",
       "      <th>Vw1_Outlier?</th>\n",
       "      <th>Vw2_Outlier?</th>\n",
       "      <th>Vw3_Outlier?</th>\n",
       "      <th>Vw4_Outlier?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2021</td>\n",
       "      <td>January</td>\n",
       "      <td>47.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>223.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.6</td>\n",
       "      <td>187.9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>75.8</td>\n",
       "      <td>112.1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/12/2020</td>\n",
       "      <td>December</td>\n",
       "      <td>35.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>91.3</td>\n",
       "      <td>769.8</td>\n",
       "      <td>76.2</td>\n",
       "      <td>15.1</td>\n",
       "      <td>49.1</td>\n",
       "      <td>27.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/11/2020</td>\n",
       "      <td>November</td>\n",
       "      <td>43.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.2</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>43.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/10/2020</td>\n",
       "      <td>October</td>\n",
       "      <td>24.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/09/2020</td>\n",
       "      <td>September</td>\n",
       "      <td>29.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>130.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.2</td>\n",
       "      <td>39.7</td>\n",
       "      <td>32.4</td>\n",
       "      <td>58.8</td>\n",
       "      <td>39.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Month  Daily_max_vol  Rain_days  Monthly_total_vol  \\\n",
       "0  01/01/2021    January           47.7       12.0              223.5   \n",
       "1  01/12/2020   December           35.8        4.0               91.3   \n",
       "2  01/11/2020   November           43.5        5.0               78.2   \n",
       "3  01/10/2020    October           24.9        6.0               62.0   \n",
       "4  01/09/2020  September           29.7        9.0              130.9   \n",
       "\n",
       "   Annual_vol  Vol_fortnight1  Vol_fortnight2  Vol_week1  Vol_week2  \\\n",
       "0         NaN            35.6           187.9       19.2       16.4   \n",
       "1       769.8            76.2            15.1       49.1       27.1   \n",
       "2         NaN             9.2            69.0        6.7        2.5   \n",
       "3         NaN            34.7            27.3       34.7        0.0   \n",
       "4         NaN            91.2            39.7       32.4       58.8   \n",
       "\n",
       "   Vol_week3  Vol_week4 Mtv_Outlier? Vf1_Outlier? Vf2_Outlier? Vw1_Outlier?  \\\n",
       "0       75.8      112.1           No           No          Yes           No   \n",
       "1        0.0       15.1           No           No           No           No   \n",
       "2       43.5       25.5           No           No           No           No   \n",
       "3        0.0       27.3           No           No           No           No   \n",
       "4       39.7        0.0           No           No           No           No   \n",
       "\n",
       "  Vw2_Outlier? Vw3_Outlier? Vw4_Outlier?  \n",
       "0           No           No          Yes  \n",
       "1           No           No           No  \n",
       "2           No           No           No  \n",
       "3           No           No           No  \n",
       "4           No           No           No  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataframe with attributes of interest\n",
    "df3_sub = df2[['Monthly_total_vol','Vol_fortnight1', 'Vol_fortnight2', 'Vol_week1','Vol_week2','Vol_week3','Vol_week4']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot to check high outliers\n",
    "plt.figure(figsize=(24,12))\n",
    "ax = sns.boxplot(data=df3_sub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df2.drop(['Rain'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save figure\n",
    "# plt.savefig('nome.png', dpi=200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "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>Date</th>\n",
       "      <th>River position (m)</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Vol_fortnight1</th>\n",
       "      <th>Vol_fortnight2</th>\n",
       "      <th>Vf_mean</th>\n",
       "      <th>Vol_week1</th>\n",
       "      <th>Vol_week2</th>\n",
       "      <th>Vol_week3</th>\n",
       "      <th>Vol_week4</th>\n",
       "      <th>Vw_mean</th>\n",
       "      <th>Select</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00/10/2018</td>\n",
       "      <td>11.62</td>\n",
       "      <td>182.1</td>\n",
       "      <td>57.4</td>\n",
       "      <td>124.7</td>\n",
       "      <td>91.05</td>\n",
       "      <td>54.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124.7</td>\n",
       "      <td>28.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00/10/2017</td>\n",
       "      <td>11.35</td>\n",
       "      <td>165.0</td>\n",
       "      <td>133.8</td>\n",
       "      <td>31.2</td>\n",
       "      <td>82.50</td>\n",
       "      <td>32.4</td>\n",
       "      <td>101.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.2</td>\n",
       "      <td>31.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00/06/2017</td>\n",
       "      <td>14.43</td>\n",
       "      <td>29.7</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.85</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00/05/2017</td>\n",
       "      <td>11.69</td>\n",
       "      <td>202.7</td>\n",
       "      <td>35.5</td>\n",
       "      <td>167.2</td>\n",
       "      <td>101.35</td>\n",
       "      <td>8.1</td>\n",
       "      <td>27.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>157.8</td>\n",
       "      <td>18.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00/10/2016</td>\n",
       "      <td>17.34</td>\n",
       "      <td>322.6</td>\n",
       "      <td>20.9</td>\n",
       "      <td>301.7</td>\n",
       "      <td>161.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>235.2</td>\n",
       "      <td>66.5</td>\n",
       "      <td>43.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   River position (m)  Monthly_total_vol   Vol_fortnight1  \\\n",
       "0  00/10/2018               11.62               182.1            57.4   \n",
       "1  00/10/2017               11.35               165.0           133.8   \n",
       "2  00/06/2017               14.43                29.7            29.7   \n",
       "3  00/05/2017               11.69               202.7            35.5   \n",
       "4  00/10/2016               17.34               322.6            20.9   \n",
       "\n",
       "   Vol_fortnight2  Vf_mean  Vol_week1  Vol_week2  Vol_week3  Vol_week4  \\\n",
       "0           124.7    91.05       54.1        3.3        0.0      124.7   \n",
       "1            31.2    82.50       32.4      101.4        0.0       31.2   \n",
       "2             0.0    14.85       29.7        0.0        0.0        0.0   \n",
       "3           167.2   101.35        8.1       27.4        9.4      157.8   \n",
       "4           301.7   161.30        0.0       20.9      235.2       66.5   \n",
       "\n",
       "   Vw_mean  Select  \n",
       "0     28.7       1  \n",
       "1     31.8       1  \n",
       "2      0.0       1  \n",
       "3     18.4       1  \n",
       "4     43.7       1  "
      ]
     },
     "execution_count": 526,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4_sub = pd.read_csv('C:/meujupyter/encantado/Encantado_rain_flood.csv', sep= ';', header=0)\n",
    "df4_sub1 = df4_sub\n",
    "df4_sub1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4_sub1.drop(df4_sub1[df4_sub1.Select>=2.0].index, inplace=True)\n",
    "df4_sub1 = df4_sub1.drop(['Vf_mean','Vw_mean'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>River position (m)</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Vol_fortnight1</th>\n",
       "      <th>Vol_fortnight2</th>\n",
       "      <th>Vol_week1</th>\n",
       "      <th>Vol_week2</th>\n",
       "      <th>Vol_week3</th>\n",
       "      <th>Vol_week4</th>\n",
       "      <th>Select</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00/10/2018</td>\n",
       "      <td>11.62</td>\n",
       "      <td>182.1</td>\n",
       "      <td>57.4</td>\n",
       "      <td>124.7</td>\n",
       "      <td>54.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00/10/2017</td>\n",
       "      <td>11.35</td>\n",
       "      <td>165.0</td>\n",
       "      <td>133.8</td>\n",
       "      <td>31.2</td>\n",
       "      <td>32.4</td>\n",
       "      <td>101.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00/06/2017</td>\n",
       "      <td>14.43</td>\n",
       "      <td>29.7</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00/05/2017</td>\n",
       "      <td>11.69</td>\n",
       "      <td>202.7</td>\n",
       "      <td>35.5</td>\n",
       "      <td>167.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>27.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>157.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00/10/2016</td>\n",
       "      <td>17.34</td>\n",
       "      <td>322.6</td>\n",
       "      <td>20.9</td>\n",
       "      <td>301.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>235.2</td>\n",
       "      <td>66.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>00/07/2016</td>\n",
       "      <td>14.98</td>\n",
       "      <td>73.3</td>\n",
       "      <td>11.5</td>\n",
       "      <td>61.8</td>\n",
       "      <td>10.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>36.0</td>\n",
       "      <td>25.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>00/10/2015</td>\n",
       "      <td>15.95</td>\n",
       "      <td>293.5</td>\n",
       "      <td>196.6</td>\n",
       "      <td>96.9</td>\n",
       "      <td>107.1</td>\n",
       "      <td>89.5</td>\n",
       "      <td>96.6</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>00/07/2015</td>\n",
       "      <td>11.27</td>\n",
       "      <td>269.8</td>\n",
       "      <td>189.7</td>\n",
       "      <td>80.1</td>\n",
       "      <td>13.8</td>\n",
       "      <td>175.9</td>\n",
       "      <td>75.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>00/10/2014</td>\n",
       "      <td>11.29</td>\n",
       "      <td>186.3</td>\n",
       "      <td>65.4</td>\n",
       "      <td>120.9</td>\n",
       "      <td>20.0</td>\n",
       "      <td>45.4</td>\n",
       "      <td>100.9</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>00/06/2014</td>\n",
       "      <td>11.07</td>\n",
       "      <td>283.3</td>\n",
       "      <td>161.9</td>\n",
       "      <td>121.4</td>\n",
       "      <td>108.2</td>\n",
       "      <td>53.7</td>\n",
       "      <td>16.9</td>\n",
       "      <td>104.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>00/08/2013</td>\n",
       "      <td>14.65</td>\n",
       "      <td>312.9</td>\n",
       "      <td>112.6</td>\n",
       "      <td>200.3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>74.6</td>\n",
       "      <td>33.4</td>\n",
       "      <td>166.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>00/08/2011</td>\n",
       "      <td>14.95</td>\n",
       "      <td>182.2</td>\n",
       "      <td>113.7</td>\n",
       "      <td>68.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>81.4</td>\n",
       "      <td>11.7</td>\n",
       "      <td>56.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>00/07/2011</td>\n",
       "      <td>19.50</td>\n",
       "      <td>266.8</td>\n",
       "      <td>58.1</td>\n",
       "      <td>208.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>162.7</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>00/03/2011</td>\n",
       "      <td>11.35</td>\n",
       "      <td>173.0</td>\n",
       "      <td>21.7</td>\n",
       "      <td>151.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>151.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>00/09/2010</td>\n",
       "      <td>13.30</td>\n",
       "      <td>245.6</td>\n",
       "      <td>133.5</td>\n",
       "      <td>112.1</td>\n",
       "      <td>111.9</td>\n",
       "      <td>21.6</td>\n",
       "      <td>84.4</td>\n",
       "      <td>27.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>00/09/2009</td>\n",
       "      <td>16.40</td>\n",
       "      <td>136.7</td>\n",
       "      <td>92.0</td>\n",
       "      <td>44.7</td>\n",
       "      <td>42.7</td>\n",
       "      <td>49.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>00/10/2008</td>\n",
       "      <td>17.52</td>\n",
       "      <td>277.8</td>\n",
       "      <td>86.7</td>\n",
       "      <td>191.1</td>\n",
       "      <td>14.5</td>\n",
       "      <td>72.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>183.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>00/07/2007</td>\n",
       "      <td>16.40</td>\n",
       "      <td>223.4</td>\n",
       "      <td>144.0</td>\n",
       "      <td>79.4</td>\n",
       "      <td>60.1</td>\n",
       "      <td>83.9</td>\n",
       "      <td>79.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>00/08/1997</td>\n",
       "      <td>17.53</td>\n",
       "      <td>259.1</td>\n",
       "      <td>156.3</td>\n",
       "      <td>102.8</td>\n",
       "      <td>156.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>00/07/1993</td>\n",
       "      <td>12.25</td>\n",
       "      <td>262.3</td>\n",
       "      <td>136.3</td>\n",
       "      <td>126.0</td>\n",
       "      <td>33.8</td>\n",
       "      <td>102.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>00/05/1992</td>\n",
       "      <td>17.46</td>\n",
       "      <td>213.4</td>\n",
       "      <td>50.5</td>\n",
       "      <td>162.9</td>\n",
       "      <td>10.5</td>\n",
       "      <td>40.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>140.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   River position (m)  Monthly_total_vol   Vol_fortnight1  \\\n",
       "0   00/10/2018               11.62               182.1            57.4   \n",
       "1   00/10/2017               11.35               165.0           133.8   \n",
       "2   00/06/2017               14.43                29.7            29.7   \n",
       "3   00/05/2017               11.69               202.7            35.5   \n",
       "4   00/10/2016               17.34               322.6            20.9   \n",
       "5   00/07/2016               14.98                73.3            11.5   \n",
       "6   00/10/2015               15.95               293.5           196.6   \n",
       "7   00/07/2015               11.27               269.8           189.7   \n",
       "8   00/10/2014               11.29               186.3            65.4   \n",
       "9   00/06/2014               11.07               283.3           161.9   \n",
       "10  00/08/2013               14.65               312.9           112.6   \n",
       "11  00/08/2011               14.95               182.2           113.7   \n",
       "12  00/07/2011               19.50               266.8            58.1   \n",
       "13  00/03/2011               11.35               173.0            21.7   \n",
       "14  00/09/2010               13.30               245.6           133.5   \n",
       "15  00/09/2009               16.40               136.7            92.0   \n",
       "16  00/10/2008               17.52               277.8            86.7   \n",
       "17  00/07/2007               16.40               223.4           144.0   \n",
       "18  00/08/1997               17.53               259.1           156.3   \n",
       "19  00/07/1993               12.25               262.3           136.3   \n",
       "20  00/05/1992               17.46               213.4            50.5   \n",
       "\n",
       "    Vol_fortnight2  Vol_week1  Vol_week2  Vol_week3  Vol_week4  Select  \n",
       "0            124.7       54.1        3.3        0.0      124.7       1  \n",
       "1             31.2       32.4      101.4        0.0       31.2       1  \n",
       "2              0.0       29.7        0.0        0.0        0.0       1  \n",
       "3            167.2        8.1       27.4        9.4      157.8       1  \n",
       "4            301.7        0.0       20.9      235.2       66.5       1  \n",
       "5             61.8       10.2        1.3       36.0       25.8       1  \n",
       "6             96.9      107.1       89.5       96.6        0.3       1  \n",
       "7             80.1       13.8      175.9       75.8        4.3       1  \n",
       "8            120.9       20.0       45.4      100.9       20.0       1  \n",
       "9            121.4      108.2       53.7       16.9      104.5       1  \n",
       "10           200.3       38.0       74.6       33.4      166.9       1  \n",
       "11            68.5       32.3       81.4       11.7       56.8       1  \n",
       "12           208.7        0.1       58.0      162.7       46.0       1  \n",
       "13           151.3        0.0       21.7        0.0      151.3       1  \n",
       "14           112.1      111.9       21.6       84.4       27.7       1  \n",
       "15            44.7       42.7       49.3        1.7       43.0       1  \n",
       "16           191.1       14.5       72.2        8.1      183.0       1  \n",
       "17            79.4       60.1       83.9       79.4        0.0       1  \n",
       "18           102.8      156.3        0.0      102.5        0.3       1  \n",
       "19           126.0       33.8      102.5        0.0      126.0       1  \n",
       "20           162.9       10.5       40.0       22.6      140.3       1  "
      ]
     },
     "execution_count": 528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4_sub1.head(21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Pearson \n",
    "matrix = df4_sub1.corr(method='pearson')\n",
    "sns.set(rc={'axes.facecolor':'white', 'figure.facecolor':'white'})\n",
    "f, ax = plt.subplots(figsize = (14,10))\n",
    "sns.heatmap(matrix, vmax=1.0,vmin=-1.0,annot_kws={'size': 15}, annot=True, fmt='.2f', cmap='Accent')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>River position (m)</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Vol_fortnight1</th>\n",
       "      <th>Vol_fortnight2</th>\n",
       "      <th>Vf_mean</th>\n",
       "      <th>Vol_week1</th>\n",
       "      <th>Vol_week2</th>\n",
       "      <th>Vol_week3</th>\n",
       "      <th>Vol_week4</th>\n",
       "      <th>Vw_mean</th>\n",
       "      <th>Select</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00/10/2018</td>\n",
       "      <td>11.62</td>\n",
       "      <td>182.1</td>\n",
       "      <td>57.4</td>\n",
       "      <td>124.7</td>\n",
       "      <td>91.05</td>\n",
       "      <td>54.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124.7</td>\n",
       "      <td>28.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00/10/2017</td>\n",
       "      <td>11.35</td>\n",
       "      <td>165.0</td>\n",
       "      <td>133.8</td>\n",
       "      <td>31.2</td>\n",
       "      <td>82.50</td>\n",
       "      <td>32.4</td>\n",
       "      <td>101.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.2</td>\n",
       "      <td>31.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00/06/2017</td>\n",
       "      <td>14.43</td>\n",
       "      <td>29.7</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.85</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00/05/2017</td>\n",
       "      <td>11.69</td>\n",
       "      <td>202.7</td>\n",
       "      <td>35.5</td>\n",
       "      <td>167.2</td>\n",
       "      <td>101.35</td>\n",
       "      <td>8.1</td>\n",
       "      <td>27.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>157.8</td>\n",
       "      <td>18.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00/10/2016</td>\n",
       "      <td>17.34</td>\n",
       "      <td>322.6</td>\n",
       "      <td>20.9</td>\n",
       "      <td>301.7</td>\n",
       "      <td>161.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>235.2</td>\n",
       "      <td>66.5</td>\n",
       "      <td>43.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   River position (m)  Monthly_total_vol   Vol_fortnight1  \\\n",
       "0  00/10/2018               11.62               182.1            57.4   \n",
       "1  00/10/2017               11.35               165.0           133.8   \n",
       "2  00/06/2017               14.43                29.7            29.7   \n",
       "3  00/05/2017               11.69               202.7            35.5   \n",
       "4  00/10/2016               17.34               322.6            20.9   \n",
       "\n",
       "   Vol_fortnight2  Vf_mean  Vol_week1  Vol_week2  Vol_week3  Vol_week4  \\\n",
       "0           124.7    91.05       54.1        3.3        0.0      124.7   \n",
       "1            31.2    82.50       32.4      101.4        0.0       31.2   \n",
       "2             0.0    14.85       29.7        0.0        0.0        0.0   \n",
       "3           167.2   101.35        8.1       27.4        9.4      157.8   \n",
       "4           301.7   161.30        0.0       20.9      235.2       66.5   \n",
       "\n",
       "   Vw_mean  Select  \n",
       "0     28.7       1  \n",
       "1     31.8       1  \n",
       "2      0.0       1  \n",
       "3     18.4       1  \n",
       "4     43.7       1  "
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4_sub2 = pd.read_csv('C:/meujupyter/encantado/Encantado_rain_flood.csv', sep= ';', header=0)\n",
    "df4_sub2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4_sub2.drop(df4_sub2[df4_sub2.Select<2.0].index, inplace=True)\n",
    "df4_sub2 = df4_sub2.drop(['Vf_mean','Vw_mean'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "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>Date</th>\n",
       "      <th>River position (m)</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Vol_fortnight1</th>\n",
       "      <th>Vol_fortnight2</th>\n",
       "      <th>Vol_week1</th>\n",
       "      <th>Vol_week2</th>\n",
       "      <th>Vol_week3</th>\n",
       "      <th>Vol_week4</th>\n",
       "      <th>Select</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>00/10/1990</td>\n",
       "      <td>13.02</td>\n",
       "      <td>173.8</td>\n",
       "      <td>125.4</td>\n",
       "      <td>48.4</td>\n",
       "      <td>41.0</td>\n",
       "      <td>84.4</td>\n",
       "      <td>37.8</td>\n",
       "      <td>10.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>00/05/1990</td>\n",
       "      <td>18.45</td>\n",
       "      <td>80.8</td>\n",
       "      <td>17.4</td>\n",
       "      <td>63.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>00/09/1989</td>\n",
       "      <td>17.98</td>\n",
       "      <td>260.4</td>\n",
       "      <td>215.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>24.7</td>\n",
       "      <td>190.6</td>\n",
       "      <td>17.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>00/09/1988</td>\n",
       "      <td>15.56</td>\n",
       "      <td>357.8</td>\n",
       "      <td>127.5</td>\n",
       "      <td>230.3</td>\n",
       "      <td>15.5</td>\n",
       "      <td>112.0</td>\n",
       "      <td>165.7</td>\n",
       "      <td>64.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>00/08/1983</td>\n",
       "      <td>14.50</td>\n",
       "      <td>162.9</td>\n",
       "      <td>55.4</td>\n",
       "      <td>107.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>29.4</td>\n",
       "      <td>101.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>00/07/1983</td>\n",
       "      <td>14.09</td>\n",
       "      <td>224.9</td>\n",
       "      <td>134.0</td>\n",
       "      <td>90.9</td>\n",
       "      <td>67.2</td>\n",
       "      <td>66.8</td>\n",
       "      <td>8.6</td>\n",
       "      <td>82.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>00/09/1967</td>\n",
       "      <td>16.40</td>\n",
       "      <td>342.7</td>\n",
       "      <td>147.3</td>\n",
       "      <td>195.4</td>\n",
       "      <td>80.5</td>\n",
       "      <td>66.8</td>\n",
       "      <td>195.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>00/08/1966</td>\n",
       "      <td>11.91</td>\n",
       "      <td>269.8</td>\n",
       "      <td>194.5</td>\n",
       "      <td>75.3</td>\n",
       "      <td>157.3</td>\n",
       "      <td>37.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>49.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>00/09/1965</td>\n",
       "      <td>14.85</td>\n",
       "      <td>277.6</td>\n",
       "      <td>190.0</td>\n",
       "      <td>87.6</td>\n",
       "      <td>63.8</td>\n",
       "      <td>126.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>74.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>00/08/1965</td>\n",
       "      <td>18.46</td>\n",
       "      <td>276.4</td>\n",
       "      <td>53.6</td>\n",
       "      <td>222.8</td>\n",
       "      <td>16.7</td>\n",
       "      <td>36.9</td>\n",
       "      <td>181.0</td>\n",
       "      <td>41.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>00/10/1963</td>\n",
       "      <td>12.40</td>\n",
       "      <td>271.2</td>\n",
       "      <td>192.9</td>\n",
       "      <td>78.3</td>\n",
       "      <td>9.6</td>\n",
       "      <td>183.3</td>\n",
       "      <td>46.0</td>\n",
       "      <td>32.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>00/09/1960</td>\n",
       "      <td>12.98</td>\n",
       "      <td>144.5</td>\n",
       "      <td>24.3</td>\n",
       "      <td>120.2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>85.6</td>\n",
       "      <td>34.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>00/06/1959</td>\n",
       "      <td>16.57</td>\n",
       "      <td>273.2</td>\n",
       "      <td>26.4</td>\n",
       "      <td>246.8</td>\n",
       "      <td>24.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>162.4</td>\n",
       "      <td>84.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>00/04/1959</td>\n",
       "      <td>11.20</td>\n",
       "      <td>167.8</td>\n",
       "      <td>112.4</td>\n",
       "      <td>55.4</td>\n",
       "      <td>89.6</td>\n",
       "      <td>22.8</td>\n",
       "      <td>17.4</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>00/06/1958</td>\n",
       "      <td>12.55</td>\n",
       "      <td>178.8</td>\n",
       "      <td>149.2</td>\n",
       "      <td>29.6</td>\n",
       "      <td>63.6</td>\n",
       "      <td>85.6</td>\n",
       "      <td>20.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>00/09/1957</td>\n",
       "      <td>13.13</td>\n",
       "      <td>186.0</td>\n",
       "      <td>151.6</td>\n",
       "      <td>34.4</td>\n",
       "      <td>115.0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>34.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>00/04/1956</td>\n",
       "      <td>19.20</td>\n",
       "      <td>89.6</td>\n",
       "      <td>89.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>00/09/1954</td>\n",
       "      <td>18.00</td>\n",
       "      <td>131.9</td>\n",
       "      <td>73.0</td>\n",
       "      <td>58.9</td>\n",
       "      <td>42.1</td>\n",
       "      <td>30.9</td>\n",
       "      <td>51.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>00/07/1954</td>\n",
       "      <td>12.32</td>\n",
       "      <td>100.7</td>\n",
       "      <td>33.0</td>\n",
       "      <td>67.7</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>00/09/1953</td>\n",
       "      <td>13.49</td>\n",
       "      <td>51.6</td>\n",
       "      <td>6.2</td>\n",
       "      <td>45.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>00/10/1950</td>\n",
       "      <td>16.60</td>\n",
       "      <td>98.9</td>\n",
       "      <td>20.7</td>\n",
       "      <td>78.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>17.3</td>\n",
       "      <td>78.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>00/01/1946</td>\n",
       "      <td>17.70</td>\n",
       "      <td>302.8</td>\n",
       "      <td>118.8</td>\n",
       "      <td>184.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>106.3</td>\n",
       "      <td>69.4</td>\n",
       "      <td>114.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   River position (m)  Monthly_total_vol   Vol_fortnight1  \\\n",
       "21  00/10/1990               13.02               173.8           125.4   \n",
       "22  00/05/1990               18.45                80.8            17.4   \n",
       "23  00/09/1989               17.98               260.4           215.3   \n",
       "24  00/09/1988               15.56               357.8           127.5   \n",
       "25  00/08/1983               14.50               162.9            55.4   \n",
       "26  00/07/1983               14.09               224.9           134.0   \n",
       "27  00/09/1967               16.40               342.7           147.3   \n",
       "28  00/08/1966               11.91               269.8           194.5   \n",
       "29  00/09/1965               14.85               277.6           190.0   \n",
       "30  00/08/1965               18.46               276.4            53.6   \n",
       "31  00/10/1963               12.40               271.2           192.9   \n",
       "32  00/09/1960               12.98               144.5            24.3   \n",
       "33  00/06/1959               16.57               273.2            26.4   \n",
       "34  00/04/1959               11.20               167.8           112.4   \n",
       "35  00/06/1958               12.55               178.8           149.2   \n",
       "36  00/09/1957               13.13               186.0           151.6   \n",
       "37  00/04/1956               19.20                89.6            89.6   \n",
       "38  00/09/1954               18.00               131.9            73.0   \n",
       "39  00/07/1954               12.32               100.7            33.0   \n",
       "40  00/09/1953               13.49                51.6             6.2   \n",
       "41  00/10/1950               16.60                98.9            20.7   \n",
       "42  00/01/1946               17.70               302.8           118.8   \n",
       "\n",
       "    Vol_fortnight2  Vol_week1  Vol_week2  Vol_week3  Vol_week4  Select  \n",
       "21            48.4       41.0       84.4       37.8       10.6       2  \n",
       "22            63.4        0.0       17.4        0.0       63.4       2  \n",
       "23            45.1       24.7      190.6       17.0       28.1       2  \n",
       "24           230.3       15.5      112.0      165.7       64.6       2  \n",
       "25           107.5       26.0       29.4      101.0        6.5       2  \n",
       "26            90.9       67.2       66.8        8.6       82.3       2  \n",
       "27           195.4       80.5       66.8      195.4        0.0       2  \n",
       "28            75.3      157.3       37.2       26.0       49.3       2  \n",
       "29            87.6       63.8      126.2       13.4       74.2       2  \n",
       "30           222.8       16.7       36.9      181.0       41.8       2  \n",
       "31            78.3        9.6      183.3       46.0       32.3       2  \n",
       "32           120.2       13.0       11.3       85.6       34.6       2  \n",
       "33           246.8       24.4        2.0      162.4       84.4       2  \n",
       "34            55.4       89.6       22.8       17.4       38.0       2  \n",
       "35            29.6       63.6       85.6       20.8        8.8       2  \n",
       "36            34.4      115.0       36.6       34.4        0.0       2  \n",
       "37             0.0       89.6        0.0        0.0        0.0       2  \n",
       "38            58.9       42.1       30.9       51.6        7.3       2  \n",
       "39            67.7       33.0        0.0       67.7        0.0       2  \n",
       "40            45.4        0.0        6.2        8.4       37.0       2  \n",
       "41            78.2        3.4       17.3       78.2        0.0       2  \n",
       "42           184.0       12.5      106.3       69.4      114.6       2  "
      ]
     },
     "execution_count": 532,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4_sub2.head(22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lgQcesH/WO++8Q1RUFD179mT79u08+eSTdOnShW+//da+Xq9evfjhhx/+zuYvc1qtFpPJeTCw2Vz4BVGj0aDVau3vb86j0WjKpI5/l1btjtVqwWpzbofZakSj0rnmV7ljsjh/+TVbCgem35y/e4PHOZd6kNMpe0q41mVD4a7BZrE6fyEDbEYzCt1d3G9xU+M372EUbmqy5+wopVqWnXyTAqXChlrlnK5R2zCab3/DJN+oYNoPVTCaFDzdq/Bpi9Va2OWuVXge855O5P1HL6NS2njzxzAslWzeBo1WhdVqw3rT5BxmsxWN5vaXnfS0POZ+uItlSw7TvEUVevdr4LRcoYAvP4vhqwWxaDQqHnuqbYnXvzTcy/GjcFdjK7DclL/wvUKnQuHmuvx6HoVW5ZJekRT3XOtIo3LjuU5folG58cvhfwOFQdOjkR+y9OB09PmugXVl8Xe2S1GCvWqhVbuhVmpZtHcKX/zxPBqVlpd7LMdbV743aYWo6Ir1JCklJYXo6GgKCgrIyMigWbNmzJ8/n7CwMC5evAhAw4YNUSqVnD59mvr167NmzRoGDBjAjh07OH78OD/99BMKhYJXXnmFX3/9lVatWnH+/Hm++uorqld37XJw/PhxVq5ciY+PD+PGjeO3335Dp9OxdetWVqxYgVqtZsKECfz44494eHhQrVo1vvjiC+Lj4/npp5/o3r27vaxp06Yxbtw4PvnkExITb9ydfeutt4iMjOSxxx4jISGBkSNHsnLlSqCwO9kPP/xAdnY2PXr0YPTo0fj43LhDFxMTY2+zo9WrVzN//nxmzpzJr7/+Snp6Og8//LA9EGzdujVTpkxh4sSJxfkXlCuTyeQS7KjVhbuQ0WjEaDTa39+cp7LMbmc056NUqlAqVFhtN758qJVaCsyuM1CZLPlolFqnNLVK+1dZN/KrlTrur9GPpQeml07FS4Hn084TLOR+GYtCpQSVAhxmE1No1dgMt59JS+Hnjv8nA1GHB5L+5HKsV4ruO1+Z6NQ2rDYFZgtOgZLJrMBde+snhZm5SqYuqsKFFC2zH7tCmH/hl6HNR7zZfMSbpa9csK9fLTCJUbNrExvnQYdGt54Brbx17RFBtx43utRu23wWpVKBUqlwCpTUaiVGY9Ez/12Xl2ciL89E0hU9Xl46evSuz6b1cVzv5WuzQWJCFgBLFx9mwqSO1Krtz8ULGbcpteyVxPFjyze7BDvX39sMJmwFrsuv57nTMVneinuuvc5T688Lnb6mim8EH20bQ3peYVfD4a3e4GL6UfZd/LXU616a7nW73EpK9nkm/dwcg1Fv78Hw2c5neC96D+3qDGLzqZLpUSPEf6N76m5ntVp57733iIuLIzLS9fFudHQ0a9eupUaNGuzdu5d33nmHjz76iKNHjzJo0CAA8vPzqVq1Kq1atSIwMLDIAAmgW7duBAUVDszt168fMTEx6HQ6HnroIdzcCrt0DB48mJUrV/Lyyy/z4YcfkpycTJcuXXjhhRfuql0xMTHMnFnYl7dGjRrcd999HDlS2Pe9Xbt2aLVaAgMD8fPzIzs72ylIunDhAmFhzrNZderUCYCqVaty33334e7uTrVq1dDrb3wxrFatmj2wrCzS09Od2g7g6+tLfn4+BoOBjIwMvL29USgU9id+SqUSHx8fMjMzy6HGxZeRdwUAX/cQMvKS7Ol+7qEcKWKq1PS8JHzcnWcf83UPJd+Ug8GUbU9rGPYAKqWGQ4kbS6nmJS9v6RHyN8TZ3yt93eDFB1EGe2G9eqNtyhBPrFtziioCAFVVH/y/GorCU0v6uB8xn64cY0juJMS3MLhJz1YT4nfjrm9qtpoHfHKLXCcpQ80r31Qlr0DJ3KcuEx52YzrsPxN01Aw2OgVYVQPM+HpYuJxesZ/Exuy+yNHDV+zvPTy09HkIvH10ZGXe6HLn4+NGVlbRM2rVDQ/AYDCT5BBAX03KRqtV4eGhxdNLi6+vG2dOpzosL8zr4+s6Bqy8lcTxY72ajTLYebYyZYgX1lwjtuyCwuWBHqBU3JhSX6VAGeCBNeXWx2RFUNxzLUCgZ3Ve7LIQN40XH2wZ5jT5TYe6wzCa8/l4SOHPB6iUhcHjx0P+5Id9r7L34spSaknJupftcid5xiyn9yZLPtdyLhHwV9c+IUTR7qm7nVKp5J///CdpaWn85z//cVnev39/Nm7cyO+//07Hjh3R6XRYLBYeeeQRVq1axapVq1i+fLm9G971YKcoKtWNu2RWqxWVSuU05uU6s9lM7dq1Wb9+PVFRUezfv58hQ4bc1WQKN+ex2WxYLIV3cHS6G4+3Hb/8O24LxzoCTk9binqycj39bscwVRTx8fHUq1fPKa1Bgwb2yRni4+NRKpXUrVvXvjwiIgKFQkF8fHxZV/eeJGaexGDKdurbHehZnSCvGpxJ2euSP/7aPuqFOHf3aRDSnvjU/U7jjuoFtyEh4wSGW8w+VBHZsvKxXMq0v0ynrmHNKUDbpoY9j6qqD+rqfhj3JxRZhjLAA/9vh4NSQfqoxf81ARJAeJUCPHRWDl+4cf5KylBzNUND89oGl/wZOSomfV0Nqw0+eSbRKUACCPa1kJiqwejQyyZNr0JvUFItsGI/FTDkmUhLzbO/rlzWk59vom74je48/v7uBAR6cD6+6Fk0u3SLoM9NXetq1PQjO7uA3FwjjZqEMmrs/ajVSoflhWMdU5KzqWhK4vgxHkxE27qGU5qubU1MBy+DDYwHL4NK6TQ7nvb+6qBUFC6rwIp7rvXWBfJStx9RKJT8+7dBLrODTlvdiRnrezFzQ19mbujLyqMfADBzQ1+OXP6tdBtTgoq7Xe7kvmq9+HjICbx0NyZM0ak9CfWuw5Ws0yVSZyH+W93zmCS1Ws0///lPFixYwLVrzl98QkNDqVKlCl988QUDBgwAIDIyklWrVpGbm4vZbOaFF15g48Y731XfsWMH2dnZFBQUsHbtWjp16kRkZCRr164lPz8fs9nMzz//TGRkJIsWLWLevHn07duXN954g/T0dLKzb1w81Wp1kWNmIiMj+emnnwBISEjg4MGDtGjR4q62Q82aNbly5cqdM94kMTGRWrUq9o/cqVQqfHx87EHgrl278Pb2ZvTo0YSFhdG1a1fatm1r/z9mZmZy4MABxo0bR3h4OOHh4YwZM4aYmJhK8yTJbDWy/cxCBrd4lSZVOlPDvylPdphHXPIezqcdQqXU4OMWjEpZGAjvOrcUb10Ao9u8Q5hPBF3rPUrbWtFsPPm5U7k1/JtUvim/b2aykLfkMN6vdEHbsTbqRiH4zo7CuPcSpqN/3fHUKFEGecJf4058XuuB0t+DzJfXYCswowzyLHxVsum+i6JVQ3S7LD5bH0TsaQ9OX9YxY2kY99Ux0KRmASYzpGWrMP11yvlodTBZuSpeH56MVmMjLVtFWraK9JzC46tXCz1mi4J3lodyPlnLqUQdb/wYRkRYAW3rVdyudkWxWKzs+eMi/Qc0on7DYKpV92HUuPuJP5vGpYuZQOE04V7eOlSqwptFO3eco0GjEDp1qUtgkAdt2tWgc7dwfvvracyBfYVdpIeOuI/gEE/q1Q9i6IjmHD54meSrFfupCXBPx4/h52Mo/d3xmd4LVd0APEa3xK1/I3L/U/hl2ZqSQ/6GU/i+1QdNy2po7q+Gz4ze5P/6Z4V/klTcc+3I1m/hpfPnq90TMFny8XELxsctGG+3wp4m13IuOr2u//DqtZyLFJiLfrJbERV3u9zJmZQYDKYcHm8/h2p+Danh35RnOn5KTkEGMed/KeXWCFG5Fau73c06depEixYt+Oijj+zB0HXR0dHMmTOHdu3aAYXd5k6dOsWwYcOwWCw8+OCDDBw4kMuXb3+3KzAwkKeeeoqMjAyio6N58MHCH+k8efIkgwcPxmw28+CDDzJmzBjy8/N56aWXiIqKQq1WM378eKfuYYGBgVStWpWxY8fy7rvv2tOnTp3K66+/zooVKwCYOXMmISF39wOO7du3591338VqtbqMS7qd2NhYp/FSFVF4eDiTJ09m9uzZnD59muzsbObOncvw4cOZNm0aaWlpfPPNN8TF3ehSsnDhQkaMGMGECROwWCwcPHiQZcuWlWMrim/V0Q9QKTU83v4jVAo1J5K2s3j/awCEB7VicvelzN4ynNMpMWTnpzL390cY3mo60/qsJS33Mt/EvERcsvO0177uIS6z4FVGOXN3otAo8fv3Q6BWUbDrPPq3NtuXa1tUI+C7EaQ/8iPGo0noetRDoVIStGysUzk2s5Xk5rPLuvol7okeaVgs8PbyUCwWaFs/jxejCm8aHb/kzqSvqzHnics0rpHPzhOeWG0Knv3M+cmAUmlj61vxBPtamPv0ZRasD2Til9XQqGy0qZfHc31TXSaHqAw2rotDpVQwcnRLlCoFp09d45efj9mX16odwLPj27Ng/h7OxadxJi6VRd8eoEfv+vTu24DMTAOrVhxnX2zhU5ac7AI+/3QPUQ83ZuKkBzEazRw8cJkNa+NuVYUKp1jHz74ErGl5ZDz9E96vdifo50ewXNGTNWUdxthL9nX0r2/Ee2p3/BcMBouV/E2n0VeSGSPv9lx7Pu0QLav3QalU8Wrv1U5lWKxmnl8aXh7VLzXFuQbdSZ5Jz0fbRjO4xf8xudtSlEoVJ6/u4sOtIzFbK8dYYSHKi8J2tz/uUw5WrFjB3r17ee+998q7Krf17rvvEhkZSdeuXe96nZEjRzJ//vy7ngL8mWeeudfq/ffqsqG8a1DhvPmW66/PC7DNmFLeVahw5vyx6s6Z/ge9tPFkeVehwnnjtfnlXQVRiXw+snKNt/5fV5bfLz///PM7Z6pA/tYU4KLQ+PHj+emnn+76x2Q3bNhA7969y/03koQQQgghhBCu/lZ3u9J2/feOKjpvb28++eSTu87fp0+fUqyNEEIIIYQQ4u+QJ0lCCCGEEEII4UCCJCGEEEIIIYRwIEGSEEIIIYQQQjiQIEkIIYQQQgghHEiQJIQQQgghhBAOJEgSQgghhBBCCAcSJAkhhBBCCCGEAwmShBBCCCGEEMKBBElCCCGEEEII4UCCJCGEEEIIIYRwIEGSEEIIIYQQQjiQIEkIIYQQQgghHEiQJIQQQgghhBAOJEgSQgghhBBCCAcSJAkhhBBCCCGEAwmShBBCCCGEEMKBBElCCCGEEEII4UCCJCGEEEIIIYRwIEGSEEIIIYQQQjiQIEkIIYQQQgghHEiQJIQQQgghhBAOJEgSQgghhBBCCAfq8q6AuEtdNpR3DSqe3/uUdw0qnG1R88q7ChXTvmrlXYMKJ6vtC+VdhQppm9ZW3lWocLocLu8aVExd2lwu7yoIIUqRPEkSQgghhCgGCZCE+O8nQZIQQgghhBBCOJAgSQghhBBCCCEcSJAkhBBCCCGEEA5k4gYhhBBCCCFEhbF69Wo+++wzTCYTjz76KKNHj7YvO3nyJFOmTLG/T09Px9fXlzVr1rBy5Uo++OADAgMDAejSpQuTJk26pzpIkCSEEEIIIYSoEJKTk5kzZw4rVqxAq9UyYsQI2rVrR0REBACNGjVi1apVABgMBoYOHcr06dMBOHbsGFOmTKF///5/ux7S3U4IIYQQQghRIezevZvIyEj8/Pzw8PCgd+/ebNhQ9E/hfP7557Rp04bWrVsDhUHSypUrGTBgAC+//DJZWVn3XA8JkoQQQgghhBClSq/Xk5iY6PLS6/VO+VJSUggODra/DwkJITk5ucjyli1bxvjx4+1pwcHBTJgwgVWrVlGlShVmzJhxz/WV7nZCCCGEEEL8D+rit77MPuu775oyf/58l/Tx48czYcIE+3ubzfVHvRUKhUva6tWr6dGjh338EcAnn3xi//vJJ5+kR48e91xfCZKEEEIIIYQQpeqRRx5h4MCBLuk+Pj5O70NDQ9m/f7/9fUpKCiEhIS7rbd68mWeeecb+Pjs7m59//plHH30UKAy21Op7D3Wku50QQgghhBCiVPn4+FC9enWX181BUocOHdizZw/p6ekYDAY2bdpEp06dnPLYbDZOnDhBy5Yt7WkeHh589dVXHDlyBIBFixbRs2fPe66vPEkSQgghhBBCVAihoaFMmjSJcePGYTKZGDJkCM2bN+epp55i4sSJNGvWjPT0dDQaDTqdzr6eSqXio48+Yvr06eTn51O7dm1mzZp1z/WQIEkIIYQQQghRYURFRREVFeWU9uWXX9r/DgwM5I8//nBZr3Xr1vzyyy8lUgfpbieEEEIIIYQQDiRIEkIIIYQQQggHEiQJIYQQQgghhAMJkoQQQgghhBDCgQRJQgghhBBCCOFAgiQhhBBCCCGEcCBBkhBCCCGEEEI4kCBJCCGEEEIIIRxIkCSEEEIIIYQQDiRIEkIIIYQQQggHEiQJIYQQQgghhAN1eVdAlC+FQkl085fpUGcoOrUnJ5K2s+TAa2TnpxaZv1ZAM4bdP52a/k3IMFxl3fG5xFxYAUD7OkN4NHJ2kev9cW4Z38e+UlrNKDWjRo1CpVKxcOHCW+apVasWw4YNo2bNmmRkZLBu3TpiYmLsyzUaDcOHD6dly5YolUoOHDjA8uXLKSgoKIsmlAiFQknz3q9Qp9VQ1DpPkk5v58DKaeTnFL2fuPuGcX/UdKrU74TFlE/CsXUcWjsTiykfAK/AWrR8aBrBtdtgs9lIORfDobVvkZd5pSyb9beV9HbReQbQsv/rVKnfGYVCQXL8bg6umYEh62pZNutvKe455bogr5q81mcDb6ztRqbhRnubVunKhC7fuuT/18p2TvkqupLeVxx1fvw7Ui/s58TWeaXdjBJV3G3ywOhPqdm8v1Pa1TO72PbVKJe8NZr1o+OYBfz6XgdyMxJLpf6lxWKFr38LZMNBb/KMStrWy+MfA64R4GW547pTvq+Cwajk4ycv29Ni4jyY8n1Vl7zL/nmeEN87lynE/yoJkv7HRTWdRPvaQ/gmZhI5BZmMav0Wz3ZcwPubh7jk9dIFMLHLQvZdXMX3sf+kcdiDjGs3i6z8a5y8upP9l1ZzImm70zoP1B1G3ybj2RL3dVk1qcRERUXRuXNndu3adcs8Xl5eTJw4kX379vH999/TuHFjxo0bR1ZWFidPngRgzJgx1KxZk/nz56NSqRg3bhyjR4/mP//5T1k15W9r2nMStVsNIWbZJApyM2g9cCYdx3zO5gWDXfIqVVq6PvkD+foUNn86CK2HP5HDP8Rms3Jg1euoNO50eWIR+uQzbP1iBAqlmpb9p9Hl8e/Z8HE/rBZjObTw3pTkdgHoMHI+KrWO378eg81mo3X0Wzw49ks2zY8q66bds+KcU64L8a7DxM7f4abxdFlWza8Bl9KPM2/7o07pdwq6KpqS3lcK82loPfBtqjboSuqF/WXZnBJRnG0C4BvWgMPr3uX8gZ/saZYizhdu3iG0GfhuqdW7tH27JYCNh7x5dWgyPu5W5qwO5vXFYcx/+vJt1/t1rw8xcZ7cV8fglH4uWUu9Kvm890iSU7q/pwRIQtyOdLf7H6ZSaujW4DFWHp3Fyau7SMg4zle7JxAR3Ia6Qa1c8ncMH4HBlM3SA9NJzo5n25lvib3wC70aPg2AyVKAPv+a/aVVudG3yXh+OjSTy5mnyrp59ywoKIiXXnqJzp07k5aWdtu8HTt2xGAwsHTpUpKTk9m2bRuxsbH06tULAD8/P9q2bcuSJUs4f/48Z8+eZeHChbRp0wY/P78yaM3fp1RpaPDA4xzd8G+untlJxpXj7F48nuA6bQiq5bqf1GoZjbt3CDsXPUvm1VOknNvDsd8+JLBGCwCq1O+Ep19Vdv84kcyrp8i4cpyYpZPwDa1PYM0WZdu4v6Gkt4ta60loeAf+3P4ZGVdOkJn0Jye2zSewxn1o3X3LuHX3prjnFIBu9R/j1d6ryTPpi1xe1bcBl7NOOZ1b9PnXsGErzaaUqJLeVwD8qzal1wurCK3bAWNeVhm2pmQUd5soVVq8A2uTlnCY/Jxr9pfJ4Nr2dkPfJ/PqybJoRokzmeHnPX482SuN1hEG6lcr4PXhVzl+0Z3jF91uuV5imoavNgXSpKbBZdn5ZC11w4wEelucXkr5BijEbd32EElMTKRBgwa8/vrrTuknT56kQYMGrFix4p4+dOnSpaxZswaAKVOmFFnOvHnzmDev+F0HEhISePXVV++Yb+zYsXfM061bNxITS+4x/a3aWl5q+DXGXePN6ZQbXcPSchNJzUmgXnAbl/wRwW05kxLr9OUkLiWG8ODWRZY/qMWrXM48xc6zi0u+8qUoPDyc9PR0ZsyYQWrq7e9WR0REcObMGWw2h20SF0d4eLi9LJvNxtmzZ+3L4+PjsdlsRERElE4DSphflcZo3LxJOXdjP8nNSCQn/RLBtdu65K9SvzNXz+xy+vJyfv9yNs0fAEBawmG2f/Mo5oIc+3KbzQpQaYIBKPntYjEXYDbmUqfVENQ6L9RaD+q0Gkx26nmM+UUHEBVNcc8pAPdV78Wivf/HT4dmFrm8ql8DkrLOFrmssijpfQUgrN6DpJzfy/qP+1Sa/cNRcbeJT0g4SpUGfcrt94WIyLG4e4dwYsvcEq9zWTibpCOvQEkLh6dBVfzNhPmbOHqLIMlihXd/CmFkpwxqBbs+WTufrKNmEelCiNu7Y3c7Pz8/du7cicViQaVSAbBu3ToCAgLu+UMPHTpE27auJ8GScOXKFRISEu6Yb+/evaXy+ZWJv0cVADLynPv1ZxqS8fdw7b/s7xFGQsYJp7QsQzI6tQeeWn9yjRn29Op+jWhVsx+zt4yoVHd8AWJjY4mNjb2rvP7+/i77W1ZWFjqdDk9PT/z9/dHr9VitVvtyq9WKXq/H39+/ROtdWjx8C/eTvJvGxRj0KXj4VXHJ7x1Uh+T43TTrNZnaLQeCDRJOrOfoxg+wmgsw6JMx6JOd1mnc5XlMBblcO195jsuS3i42q5mYZZNpO/g9hkw/jg0b+TmpbFkwBGyV4xgq7jkFYM7WkQDUD4l0WaZQKAnzDqdWQDOm9VmPty6QC+lHWHH4XZKzz5Vw7UtPSe8rACe3f1b6FS9Fxd0mvqENsJgLaNbrJao06ILFlM+lo2s5sXWefZt4B9Xhvt7/ZMvnQ1G7eZd+I0rBNX3h17JgH7NTepC3mZTMor+y/bDdHwUwvGMmH6wMdlpmscKlaxpOX3HjiXk1yMxV0bBaPs/0SaNmsKlU2iDEf4s7Pmz19PSkUaNG7Nu3z572xx9/0KFDB/v7bdu2ER0dTVRUFM8//7z97nu3bt346KOPGDJkCA899BDHjx9n9+7dbN26lblz57Jz504Afv/9d4YMGULXrl1ZunSp0+cvX76cyZMn29/Pnz+fL7744pb1nTlzJsePH+fNN98EYMGCBfTr14+oqCjee+89LBYLM2cW3rEcOnQoAIsWLWLo0KH079+fqKgo4uPj77RZABg/fjwbNmywvx80aBAnTpzg/PnzjB07lqioKIYPH87Ro0fvqryyplW7Y7VasNqcT8ZmqxGNSueaX+WOyeI82YD5r/7gN+fv3uBxzqUe5HTKnhKudcWi1WoxmZwvNGZz4fbUaDRotVr7+5vzaDSaMqnj36XWFu4nNqtzO6zmAlRq1/1Eo/MmvPUIvAJq8cei5zm45k1qNo+i7aD3iiw/InIM9R94jCPr38NYRNeZiqo0totPSDiZV0+x5YvhbPl8GNnXzvHg2C9Ra13H6lRExT2n3EmwVy20ajfUSi2L9k7hiz+eR6PS8nKP5XjrAkuq2qWutI+hyqi428Q3tD6gQJ9ylu3fPMrxzR8R3nYEbQcVjj1SKFVEDv+Ik9sXkHm18nTvvlm+SYFSYUOtck7XqG0YzQqX/HGXdSzb5ceUISlFdp+7kq7BaFZiMit4+eEU3hhxFaNFwcQvq5GRo3JdQQhhd1c9Uvv27cvGjRsBOHr0KA0aNLB/wUtLS+P111/nk08+YfXq1dx///3MmDHDvq6fnx8//fQTI0aM4PPPP6dDhw5069aNiRMn8uCDDwJgNBpZvnw5n3/+OXPmzHH67H79+rFnzx5yc3Ox2WysXr2a6OjoW9Z12rRpNG3alDfeeIPt27ezdetWVqxYwS+//MLFixf58ccfmTZtGlAYgOXk5LB582YWLlzImjVr6NGjB4sX3133sOjoaNatWwfAhQsXKCgooEmTJrzyyiuMHTuW1atX83//93+8+OKLGI0V71G30ZyPUqlCqXA+UaqVWgrMeS75TZZ8NEqtc16V9q+ybuRXK3XcX6Nfpetmdy9MJpNLsKNWF97tMxqNGI1G+/ub81SW2e3MpsL9RKF03k+Uah1mo2v/d6vVRIEhk5il/yD98lEu//kbh9bMoE6rwWg9/JzyNu46njYD3+HE1vmc2fNdaTajxJX0dgmu3ZZmvV5mz5IXuXY+ltQL+9j5/VN4+FelTuuhZdWsv6W455Q7Sck+z6Sfm/PZzqe4kH6E+NT9fLbzGZQKJe3qDCqpape60jyGKqvibpOjm95n5cxWxO36mqyrcVw8vIqDv75JnVZD0Hr40aTbBLBZK/0TNp3ahtWmwHzTnAomswJ3rfMT5QKTgneWh/JEz3SqBxb9VKhGkIlfp55j5ugkGtUooHntfN4adRWrTcGmw5XzaZsQZeWugqSuXbuyY8cOrFYr69evp2/fvvZlR48epXnz5lSvXh2A4cOHO01/fD0QqlevHpmZmUWW3717dxQKBfXq1SMjI8NpmaenJ507d2bTpk0cOHCAGjVqEBoaeleNi4mJ4aGHHsLNzQ21Ws3gwYPZs8f5yYaXlxezZ89m7dq1zJ49m23btpGXd3cX886dO3P48GFycnJYs2YNUVFR5ObmcunSJfvA/RYtWuDr68u5cxWva0hGXuF0y77uIU7pfu6hRU6tm56XhM9NeX3dQ8k35WAwZdvTGoY9gEqp4VDixlKodcWSnp6Oj4+PU5qvry/5+fkYDAYyMjLw9vZGobhxB1CpVOLj43PL46GiuT4tt7u38//e3ScEg951PzFkJaNPOWsfZwSQlXwGAE//GoUJCgWtB77DfX3+yeF173B046xSqn3pKentElizJfnZKRiyb3RFNOXryb52Hu/A2qXQgpJX3HPK3cgzZjl12TVZ8rmWc4kAD9cuWRVVqRxDlVxxtwk2m8uT5utPjDx8q1Kn1RD8qzVl8JsnGDLjJF2fWARAv5c207jrC6XQgtIR4lv4ZC092/nmWmq2mqCbuuCdTNRx8ZqWzzcG0ufNuvR5sy4bD/lw7IIbfd6sS/Jf3fN8PKxOT5nctDaq+ptIyZIJjoW4nbsKkry8vGjYsCEHDhwgJibGqaud41gLAJvN5tS9SKcrfGzu+CXxZtfHOt0qz+DBg1mzZg2rV69m0KC7v3t4c90Al65PSUlJDB8+nOzsbDp16sTAgQOdBuHfjlarpUuXLmzdupUNGzYQFRWFzWZzWd9ms2GxVLypNhMzT2IwZTuNBQj0rE6QVw3OpLiODYm/to96Ic5jyRqEtCc+db/Tl5h6wW1IyDiB4RazVf03iY+Pp169ek5pDRo0sE/OEB8fj1KppG7duvblERERKBSKu+7WWd4yk05iys8mpO6N/cTTvzpeATVJOe86duvahb34V22MQnnjAuwb1gCrxUxuRuH4rdbRbxHeZgQxy17i5PYFpd+IUlDS28WQlYSbVxA6zxvdyFQaN7wCapKder50G1NCintOuZP7qvXi4yEn8NLdGAOrU3sS6l2HK1mnS6TOZaE0jqHKrrjb5IHRn9JxrHNX+4DqzbGY8slJu8CWz4ez7sMebPi4Dxs+7kPs8pcB+P0/j3A2ZlHpNqYEhVcpwENn5fCFG5M0JGWouZqhoXlt5ydsjaoXsOili3w1PsH+erBxDg2qFfDV+AQCvc3s/NOTvm/WJTP3xte9vAIFCWka6oRUvB4uQlQkdz0BZN++fZk9ezZNmzZ16j503333ceTIEfsscEuXLqVdu3a3LUulUhUraGjdujVXr14lNjaWHj163LHs64FQZGQka9euJT8/H7PZzM8//0xkZKRTvmPHjlGrVi0effRR7rvvPnbs2FGsukVHR/PNN9/g6+tLtWrV8PLyokaNGmzatAmAw4cPk5qa6vJFuiIwW41sP7OQwS1epUmVztTwb8qTHeYRl7yH82mHUCk1+LgFo1IWdifbdW4p3roARrd5hzCfCLrWe5S2taLZePJzp3Jr+DepVFN+F4dKpcLHx8ce2O/atQtvb29Gjx5NWFgYXbt2pW3btvbuqZmZmRw4cIBx48YRHh5OeHg4Y8aMISYmptI8SbJajJzZs5AWD02lSv3O+FdtSodR80mO30PapUMoVRrcvIJRqgr3k7Mxi1CpdbQfPgfv4HBCIzrSst+rXDj4M8a8TKo27Ea99uM4sXUeSXHbcfMKtr+URYxFqKhKertcPrmZvMwkHhj9Cf7VmuEb1oD2I+ZiMedz/uDP5dzau1Pcc8qdnEmJwWDK4fH2c6jm15Aa/k15puOn5BRkEHP+l1JuTckp6X3lv0Fxt8mlY+uo3rgXDR58Eq+AWtRo1o+WD03l5I4vMBvzyMu8TE7aRfsr76+nUXmZlyvVWEetGqLbZfHZ+iBiT3tw+rKOGUvDuK+OgSY1CzCZIS1bhckMOo2N6oEmp5eHzor2r3S1ClrUMeCps/L28lDir2o5fVnHG0vC8PWw0LNF9p0rJMT/sLt+1tq1a1emTp3Kiy++6JQeFBTEjBkzGD9+PCaTiapVq/L222/ftqwOHTrw4Ycf4u199/1he/bsSWZmJlqt9rb5wsPDyc7O5pVXXuH999/n5MmTDB48GLPZzIMPPsiYMWOAwi5+0dHRLFu2jCVLltCvXz+0Wi3NmzfnzJkzd12vVq1akZ2dzYgRI+xp77//PtOnT2fevHloNBrmzZt3x3qXl1VHP0Cl1PB4+49QKdScSNrO4v2vARAe1IrJ3Zcye8twTqfEkJ2fytzfH2F4q+lM67OWtNzLfBPzEnHJu53K9HUPcZkF779FeHg4kydPZvbs2Zw+fZrs7Gzmzp3L8OHDmTZtGmlpaXzzzTfExcXZ11m4cCEjRoxgwoQJWCwWDh48yLJly8qxFcV3dNP7KFVq2o/4GIVKTVLcdvavLBzbF1SrFd2fWcaWz4eRci6G/JxUNi8Yyv1Rr9Nn4jrMxlwuHPqFI+v/DUCtFg8D0KznJJr1nOT0OXt+fJELhyrPl9+S3C5mYx5bvhhOy4em0uXx7wAFqRf3s/mzIU7TpVd0xTmn3EmeSc9H20YzuMX/MbnbUpRKFSev7uLDrSMxWyvHmL7rSnJf+W9RnG2ScHQNMWodjTo9Q/Pe/6QgJ5W4Xf/hz98/KedWlLwneqRhscDby0OxWKBt/TxejLoGwPFL7kz6uhpznrhMy7quY7du5u1uZfbjl1mwIYh/fFUNi1VB64g85jxxBZ2mcsyaKUR5Udjutm9ZObHZbJhMJh577DFeffVVmjRpUt5VKhfPLKlV3lWoeH7vU941qHC6+K0v7yqISuL3FrfuAv2/rMvhCn1JFBVElzaXy7sKFVaVIRVveIG4tSX/qllmnzXy35fK7LNKQoUftXft2jUeeughhg4dag+Q1q1bx+eff15k/lWrVpXo5+/fv5+33nqryGVffPHFXU8iIYQQQgghhKgcKnyQFBIS4vQbTVA4LXi/fv3K5PNbt25d4oGXEEIIIYQQouK664kbhBBCCCGEEOJ/gQRJQgghhBBCCOFAgiQhhBBCCCGEcCBBkhBCCCGEEEI4kCBJCCGEEEIIIRxIkCSEEEIIIYQQDiRIEkIIIYQQQggHEiQJIYQQQgghhAMJkoQQQgghhBDCgQRJQgghhBBCCOFAgiQhhBBCCCGEcCBBkhBCCCGEEEI4kCBJCCGEEEIIIRxIkCSEEEIIIYQQDiRIEkIIIYQQQggHEiQJIYQQQgghhAMJkoQQQgghhBDCgQRJQgghhBBCCOFAgiQhhBBCCCGEcCBBkhBCCCGEEEI4kCBJCCGEEEIIIRxIkCSEEEIIIYQQDtTlXQFxd958a3x5V6HC2RY1r7yrUOH8ntm3vKtQIb25M6K8q1AByfFTFDmGXMnx4+qNFvPLuwoV1uflXQEhSog8SRJCCCGEEEIIBxIkCSGEEEIIIYQDCZKEEEIIIYQQwoEESUIIIYQQQgjhQIIkIYQQQgghhHAgQZIQQgghhBBCOJAgSQghhBBCCCEcSJAkhBBCCCGEEA4kSBJCCCGEEEIIBxIkCSGEEEIIIYQDCZKEEEIIIYQQwoEESUIIIYQQQogKY/Xq1fTr14+ePXvyww8/uCyfP38+Xbt2JTo6mujoaHuekydPMnjwYHr37s3UqVMxm833XAf1Pa8phBBCCCGEECUoOTmZOXPmsGLFCrRaLSNGjKBdu3ZERETY8xw/fpwPP/yQli1bOq37yiuvMHPmTFq0aMGrr77KsmXLGDVq1D3VQ54kCSGEEEIIIUqVXq8nMTHR5aXX653y7d69m8jISPz8/PDw8KB3795s2LDBKc/x48f58ssviYqKYsaMGRQUFHD58mXy8/Np0aIFAIMGDXJZrzgkSBJCCCGEEEKUqu+++47u3bu7vL777junfCkpKQQHB9vfh4SEkJycbH+fm5tLo0aN+Ne//sUvv/yCXq/n008/dVkvODjYab3iku52QgghhBBC/A/q0uZymX2WZ69HGDhwoEu6j4+P03ubzeaSR6FQ3CjH05Mvv/zS/v7xxx/n1VdfpXPnzrddr7gkSBJCCCGEEEKUKh8fH5eAqCihoaHs37/f/j4lJYWQkBD7+ytXrrB7926GDBkCFAZVarWa0NBQUlNT7fmuXbvmtF5xSXc7IYQQQgghRIXQoUMH9uzZQ3p6OgaDgU2bNtGpUyf7cjc3N95//30SEhKw2Wz88MMP9OzZk2rVqqHT6Thw4AAAK1eudFqvuORJkhBCCCGEEKJCCA0NZdKkSYwbNw6TycSQIUNo3rw5Tz31FBMnTqRZs2bMmDGD5557DpPJxP33389jjz0GwAcffMC0adPIzc2lcePGjBs37p7rIUGSEEIIIYQQosKIiooiKirKKc1xHFLv3r3p3bu3y3oNGzbkp59+KpE6SHc7IYQQQgghhHAgQZIQQgghhBBCOJAgSQghhBBCCCEcyJgkAUoFXi92xP3hpig8tRh3nkc/czPWtLxbruLWpwGeT0eiqumHNTUXw09Hyf3PPrDeNLe9RkXg0jHkfrOP/NV/lnJDSoZCoaR571eo02ooap0nSae3c2DlNPJzUovM7+4bxv1R06lSvxMWUz4Jx9ZxaO1MLKZ8ALwCa9HyoWkE126DzWYj5VwMh9a+RV7mlbJsVokZNWoUKpWKhQsX3jJPrVq1GDZsGDVr1iQjI4N169YRExNjX67RaBg+fDgtW7ZEqVRy4MABli9fTkFBQVk0oWTdw/FznaqGH4G/PELqQ19jTc6xp2s71SFgwRCX/CldP3PKVxEV9/h5YPSn1Gze3ynt6pldbPtqFAA+IfW4v/9rBNVqhcVsJOH4eo6sfxdTfnapt6UkKRQKoqOj6dChAzqdjhMnTrBkyRKys13b8dJLL9GgQYMiy/nggw84c+YMXl5eDB06lCZNmqBQKDh16hTLly8nMzOzlFtSCkrhGqRuFIL3y13QNAnFlm+mYMc5smdvx5aVX1atumcKhZLo5i/Toc5QdGpPTiRtZ8mB18jOL/oYqhXQjGH3T6emfxMyDFdZd3wuMRdWANC+zhAejZxd5Hp/nFvG97GvlFYzhKj0JEgSeL3QAffopmRNWYc104DP6z3x+yia9LFLisyvfbAOvrP6k/3eVgp2nkfdKATfN3uDWkXugj32fAoPDX4fDkDT8N7nqC8PTXtOonarIcQsm0RBbgatB86k45jP2bxgsEtepUpL1yd/IF+fwuZPB6H18Cdy+IfYbFYOrHodlcadLk8sQp98hq1fjEChVNOy/zS6PP49Gz7uh9ViLIcW3ruoqCg6d+7Mrl27bpnHy8uLiRMnsm/fPr7//nv77DJZWVmcPHkSgDFjxlCzZk3mz5+PSqVi3LhxjB49mv/85z9l1ZQSU9zj5zpVLX/8Px+C0kPrskxTLxjTn8lkPPuzU7o1LbdE614ainP8APiGNeDwunc5f+DGQFvLX8eFWutB16cWkxK/h43zB6B196XdkFm0G/oBuxY+UybtKSlRUVG0b9+eb775hpycHEaNGsWzzz7L+++/75J3wYIFqNU3Ls8KhYLx48djMBiIj48H4Mknn0Sj0fDxxx9js9kYOXIkzz33HO+++26ZtamklPQ1SBnsScB/hpG/6TT6tzej9HUvLPPDAWQ8sayMW1d8UU0n0b72EL6JmUROQSajWr/Fsx0X8P5m1xsnXroAJnZZyL6Lq/g+9p80DnuQce1mkZV/jZNXd7L/0mpOJG13WueBusPo22Q8W+K+LqsmCVEpSXe7/3UaJR5jW5Hz0Q6Mey5iPplC1uTVaFtVR9OiapGreAy/j/zfTpO3+BCWhEwKNp0m97v9uA9sas+jbV+LwBWPogz0KKuWlAilSkODBx7n6IZ/c/XMTjKuHGf34vEE12lDUK1WLvlrtYzG3TuEnYueJfPqKVLO7eHYbx8SWKMFAFXqd8LTryq7f5xI5tVTZFw5TszSSfiG1iewZouybdzfEBQUxEsvvUTnzp1JS0u7bd6OHTtiMBhYunQpycnJbNu2jdjYWHr16gWAn58fbdu2ZcmSJZw/f56zZ8+ycOFC2rRpg5+fXxm0pgTdw/ED4DHmfgKXj8WWXfRdbXW9IMynr2FNzXV64foj5BVKcY8fpUqLd2Bt0hIOk59zzf4yGbIA8PCvTuqFfez9+V9kX4sn7dJBzsYuJjTigbJu2t+iUqno1q0bK1eu5OTJkyQkJPDVV18RERFB3bp1XfLn5eWh1+vtr8jISIKCgvjqq6+wWq3odDoaNGjAxo0bSUhIIDExkfXr11O7dm08PCrXObc0rkFufRtiK7Cgf/M3LOfSMR26jH7mb+ja10JZxbssW1dsKqWGbg0eY+XRWZy8uouEjON8tXsCEcFtqBvkegx1DB+BwZTN0gPTSc6OZ9uZb4m98Au9Gj4NgMlSgD7/mv2lVbnRt8l4fjo0k8uZp8q6eUJUKuUSJI0aNYo1a9Y4peXl5dGuXTvS09Nd8q9YsYIpU6bctswTJ07QrVs3xo4de9f1WLp0qUs9brZkyRKWLLn9HeFu3bqRmJh4V+X/8ccfPPLII3ddx9KmaRiC0kuHcW+CPc1yRY85MRNtq+pFrpOzIIacT3c7J9psKH3c7G91XcIx/HqCtFGLS6XepcWvSmM0bt6knLvRNSw3I5Gc9EsE127rkr9K/c5cPbPL/qUO4Pz+5WyaPwCAtITDbP/mUcwFN7pI2WxWALTuvqXVjBIXHh5Oeno6M2bMcPo166JERERw5swZbLYb3+jj4uIIDw+3l2Wz2Th79qx9eXx8PDabjYiIiNJpQCm5l+MHQNctAv0bm8ie9XuRy9X1gjCfu30wWhEV9/jxCQlHqdKgTznrsgxAn3yaP354HovJAIB3UB1q3z+Iq6d3lk4DSkmNGjVwd3fn9OnT9rS0tDRSU1OpV6/ebdf18fGhX79+rFy5Er1eD4DJZKKgoIDIyEjc3NzQ6XRERkaSnJyMwWAo1baUtNK4BhVsiydz8q/O3b8LT7tO16mKqIZfY9w13pxOuXEMpeUmkpqTQL3gNi75I4LbciYlFpvDHZS4lBjCg1sXWf6gFq9yOfMUO89WrmuzEOWhXLrbDRo0iDVr1tC//41+6Js2baJdu3YEBATcU5nbtm2jf//+vPTSS3e9zqFDh2jb1vXC7WjkyJH3VJ+by7darXz77bd8/vnn1K9f/57LLGnK0MK7apYU53EO1pRclGFF33EzH7/q9F7hqcVjeAsKdp23p2W/u7WEa1o2PHyrAJCX5dxGgz4FD78qLvm9g+qQHL+bZr0mU7vlQLBBwon1HN34AVZzAQZ9MgZ9stM6jbs8j6kgl2vn95ZeQ0pYbGwssbGxd5XX39+fhIQEp7SsrCx0Oh2enp74+/uj1+uxWq325VarFb1ej7+/f4nWu7Tdy/EDkPF4YZcfbZsaRRSqQF0nAE2TMAJXPIIywAPT8SSyP9iO5UJGyVW+FBT3+PENbYDFXECzXi9RpUEXLKZ8Lh1dy4mt87Cancen9XlxPf5Vm5CTnsCu758qvUaUguv7dUaG8/8vMzPzjvt87969yc7OZseOHfY0q9XKd999x5gxY5gzZw4Aer2eDz74wOnmRGVQGtcgS0ImloRMpzyeT7TFcjUb85nb3+Qpb/4ehcdJRp5zGzMNyfh7uD5Z8/cIIyHjhFNaliEZndoDT60/ucYb+1x1v0a0qtmP2VtGOAVVQoiilUuQ1LdvX2bNmkVmZqa9e82vv/7KuHHjmDx5MnFxcSgUCp544gkefvjhO5a3fft2+9MerVbLiBEjmDp1KleuXEGtVjNp0iQ6derEvHnzOHz4MElJSYwYMYKtW7cSExNDcHAwa9euxcvLixMnTpCcnMwLL7zA4MGDmTdvHgATJkxg3bp1zJ07F3d3dxo3bozFYuG9994D4JNPPuHkyZMYDAZmzZpFbm6uU/lhYWHEx8fz1ltv3XbAe1lTuGuwWaxgtjql24xmFLq72D3c1PjNexiFm5rsOTvunL+CU2vdsVot2Kxmp3SruQCVWueSX6PzJrz1CK7EbeOPRc/j7htKq+i3cPMMImbZJJf8EZFjqP/AY+xf+RpGh6dP/020Wi0mk8kpzWwu3J4ajQatVmt/f3MejUZTJnUsKX/7+CmCqoYfCjcNaFTo39gIGhVez7QnYOFI0qK/xZp+5wkhyktxjx/f0PqAAn3KWU7v/ha/sIa07P8ann5ViVnmfMMrdvkrqLUe3NdvCt2e/pH1H/W2T45S0Wm1WqxWq9ONAbjzPq/T6XjggQf4+eefXYKfsLAwLl++zOrVq7FarURHR/Pcc8/x73//u1JNgFIW1yCvSZ3QdQknc8IvrpMLVTBadeExZLU5H0NmqxGNyvUY0qrcMVmc/9/mv8b03Zy/e4PHOZd6kNMpexBC3Fm5BEmenp50796dDRs2MGLECJKTkzl//jx79+7F39+fNWvWkJ6eztChQ2nYsOEdy+vcuTMjRowAYPz48bz44otERkby2GOPkZCQwMiRI1m5ciUARqORdevWAfDnn3/Stm1bHnzwQdauXcvVq1dZvHgxp0+fZty4cQwefGOgcXp6Ou+88w4///wzwcHBTJw4ES8vL/vyiIgI3n33XRYtWsTXX3/N3Llz6datm718gLfffvuu78aXFs+n2+H5dKT9fe6XsShUSlApwHLj4qHQqrEZTEUVcSOPnzv+nwxEHR5I+pPLsV7Rl1q9y4rZlI9SqUKhVGGzWuzpSrUOs9G1G4vVaqLAkEnM0n8UdqO7XDguo+OYBRxc8ybGvEx73sZdx3Nfn39yYut8zuz5riyaUy5MJpPLF7/rg9CNRiNGo9FpULpjnor+5a4kj59bsVzMILn9PGz6fPsYpIwXVxKy5RncBjQm79v9f6sNpam4x8/RTe9zasfn9hsGWVfjsFmtPDD6Ew6umeF0/GRcOQ7AroXPEv1qLNWb9Obi4VWl26ASYjQaUSqVKJVKp0DpTvt8ixYtUCqVLteNiIgIBgwYwJQpU8jKKtx2n332Ge+++y4dOnRg27ZtpdOQElCm1yClAp9pPXAfdh/6Gb9RsC2+RNtSGozmwmNIqVBhtd04htRKLQVm1xskJks+GqXz5C9qlfavsm7kVyt13F+jH0sPTC+digvxX6jcJm4YPHiwfbzO6tWrGTBgADExMQwZUjh7S0BAAN27d2fv3uJ3SXIsp0aNGtx3330cOXIEgObNm99yvQceeACFQkH9+vVdplHdv38/LVu2JDQ0FKVS6fKEq0ePHkDhxevmLhUVSd7SI6QN+s7+Mu6+AIAy2MspnzLE87ZTDauq+hC4eBSq6r6kj/vRpftDZXV9Wm53b+cZ+dx9QjDoXdtoyEpGn3LWPs4IICv5DACe/n91pVIoaD3wHe7r808Or3uHoxtnlVLtK4b09HR8fHyc0nx9fcnPz8dgMJCRkYG3tzcKhcK+XKlU4uPjU+GnLy6p4+dObFn5zpM05JsxJ2Shuk0XvoqguMcPNpvLE9XMq4WDyT18q+LpX51qjXs6Lc/PTsGYl4G7T1gJ1rx0Xb8m+Po6j0P08/O77T5/3333cezYMYxG51kw69atS1ZWlj1AAjAYDCQnJxMcHFxyFS8FZXYN0qrwm/sw7oObkfWvtRiWHSnpppSKjLzCY8jX3fkY8nMPJdPgegyl5yXhc1NeX/dQ8k05GEw3ppdvGPYAKqWGQ4kbS6HWQvx3KrcgqXXr1ly7do2kpCR+/fVXBg8e7NKdwGazYbFYblHCrd2uHDe3Ww/a1OkKH007fnm77uY7gDdTqVS3XLcisWXlY7mUaX+ZTl3DmlPgNDZCVdUHdXU/jPsTiixDGeCB/7fDQakgfdRizKevlVX1S11m0klM+dmE1L1xp9PTvzpeATVJOe/6FPDahb34V22MQnnjyYhvWAOsFjO5GYXbr3X0W4S3GUHMspc4uX1B6TeinMXHx7sMRm/QoIF9cob4+HiUSqXTrF4REREoFAr79MYVVUkcP3ei6x5ByL4XUfi729MUHhrUtf0xn63YkzkU9/h5YPSndBz7hVNaQPXmWEz55KRdIKBGCzqO+Rw3ryCH8mrg5hWEPuVM6TWkhCUmJmIwGJzGowYGBhIUFMSZM7duR0REBKdOuc5AlpGRgY+PD97eN4JmjUZDUFAQKSkpJVv5ElYm1yAF+M0ZgDayJhnPryB/7cnSbFKJSsw8icGUTf2QG8dQoGd1grxqcCbF9aZx/LV91AtxHlvdIKQ98an7ncYd1QtuQ0LGCQymyt/jQ4iyUq5TgA8cOJDPPvsMX19fatasSWRkJD/9VPhbGenp6WzZsuWOEysUxbGchIQEDh48SIsWLVzyqVSquw7C7r//fo4dO0ZKSgo2m41169bdMSAqTvnlxmQhb8lhvF/pgrZj7cLfm5gdhXHvJUxHkwrzaJQogzxBU7i7+LzWA6W/B5kvr8FWYEYZ5Fn4qmTTfRfFajFyZs9CWjw0lSr1O+NftSkdRs0nOX4PaZcOoVRpcPMKRqkq7E52NmYRKrWO9sPn4B0cTmhER1r2e5ULB3/GmJdJ1YbdqNd+HCe2ziMpbjtuXsH2l7KIMRqVkUqlwsfHx36jYNeuXXh7ezN69GjCwsLo2rUrbdu2ZePGwjuYmZmZHDhwgHHjxhEeHk54eDhjxowhJiamwj9JcnEPx8+dGPclYMspwO+9h1DXD0bdKAS/OdFYMw0Yfj1x5wLKUXGPn0vH1lG9cS8aPPgkXgG1qNGsHy0fmsrJHV9gNuZx5eRmctIv0X7EXHzDGhBUqxUdxyzg2sX9XImruF3KbmY2m9m+fTuDBw+mSZMm1KhRgyeffJK4uDjOnz/vcgxB4ax2vr6+XL582aW8o0ePkp6ezlNPPUXNmjWpWrUqTzzxBCaTyelHmyuFUrgGeYxoiVvXCLLf2Yo57tqN5UGeoK7Yv3xithrZfmYhg1u8SpMqnanh35QnO8wjLnkP59MOoVJq8HELRqUsPIZ2nVuKty6A0W3eIcwngq71HqVtrWg2nvzcqdwa/k1kym8hiqlcf0z24Ycfpnv37rz99tsAvPDCC0yfPp2oqCgsFgvPPvssTZo0IS4urljlTp06lddff50VKwp/cXrmzJmEhLj+oGmHDh348MMPne7G3UpAQADTpk3j8ccfR6vVUr16dZcuRbcrv0+fPsVqQ1nKmbsThUaJ378fArWKgl3n0b+12b5c26IaAd+NIP2RHzEeTULXox4KlZKgZc7TrdvMVpKbF/3L3pXJ0U3vo1SpaT/iYxQqNUlx29m/choAQbVa0f2ZZWz5fBgp52LIz0ll84Kh3B/1On0mrsNszOXCoV84sv7fANRq8TAAzXpOollP54kc9vz4IhcO/VKmbSsN4eHhTJ48mdmzZ3P69Gmys7OZO3cuw4cPZ9q0aaSlpfHNN984HccLFy5kxIgRTJgwAYvFwsGDB1m2rOL/yGNRinX87Lvz0yWbvoD0J5bhPbkzAd8NB5US4+6LpD+2FIwV/KYLxTt+Eo6uIUato1GnZ2je+58U5KQSt+s//Pn7JwBYTPn8/vUYWvZ/nR7P/IQNG4knNnBozVtQyWZxW7VqFSqViscffxyVSsWJEydYvLhwGuabjyG40TUvN9f1B4QLCgr48MMPGTx4MBMmTLA/hX3//ffJz68ck1k4KulrkFv/RgD4znS97qaNWYzpoGvgWZGsOvoBKqWGx9t/hEqh5kTSdhbvfw2A8KBWTO6+lNlbhnM6JYbs/FTm/v4Iw1tNZ1qftaTlXuabmJeIS3aeIt3XPcRlFjwhxO0pbJVtvtBykpGRwcKFCxk/fjxKpZKZM2dSq1atYv0u099xtbHrr7L/r9sWNa+8q1Dh/J7Zt7yrUCG9ubNy/f5SWZDjp2hyDLmS48fVG6/NL+8qVFifj7xY3lUQxZD0k+rOmUpIlSEV/0afo3J9klRckydPdvoByuu6devGiy++WKqf7efnh16vp3///qhUKpo0acKwYcNK9TOFEEIIIYQQZa9SBUmzZ5dfVy6FQsG0adPK7fOFEEIIIYQQZaNij2AUQgghhBBCiDImQZIQQgghhBBCOJAgSQghhBBCCCEcSJAkhBBCCCGEEA4kSBJCCCGEEEIIBxIkCSGEEEIIIYQDCZKEEEIIIYQQwoEESUIIIYQQQgjhQIIkIYQQQgghhHAgQZIQQgghhBBCOJAgSQghhBBCCCEcSJAkhBBCCCGEEA4kSBJCCCGEEEIIBxIkCSGEEEIIIYQDCZKEEEIIIYQQwoEESUIIIYQQQgjhQIIkIYQQQgghhHAgQZIQQgghhBBCOJAgSQghhBBCCCEcSJAkhBBCCCGEEA4kSBJCCCGEEEIIBxIkCSGEEEIIIYQDdXlXQNwd24wp5V2FimdftfKuQYXz5s6I8q5ChfTGg2fLuwoVjq/x0/KuQsXU5YXyrkGF8wZ9yrsKFc50TWJ5V0EIUcrkSZIQQgghhBBCOJAgSQghhBBCCCEcSJAkhBBCCCGEEA4kSBJCCCGEEEIIBxIkCSGEEEIIIYQDCZKEEEIIIYQQwoEESUIIIYQQQgjhQIIkIYQQQgghhHAgQZIQQgghhBBCOJAgSQghhBBCCCEcSJAkhBBCCCGEEA4kSBJCCCGEEEIIBxIkCSGEEEIIIYQDCZKEEEIIIYQQwoEESUIIIYQQQgjhQIIkIYQQQgghhHAgQZIQQgghhBBCOJAgSQghhBBCCCEcSJAkhBBCCCGEEA4kSBJCCCGEEEJUGKtXr6Zfv3707NmTH374wWX55s2biY6OZsCAATz//PNkZWUBsHLlSjp27Eh0dDTR0dHMmTPnnuugvuc1hRBCCCGEEKIEJScnM2fOHFasWIFWq2XEiBG0a9eOiIgIAHJycpg+fTo///wzoaGhfPzxx8ybN49p06Zx7NgxpkyZQv/+/f92PeRJkhBCCCGEEKJU6fV6EhMTXV56vd4p3+7du4mMjMTPzw8PDw969+7Nhg0b7MtNJhPTp08nNDQUgAYNGpCUlATAsWPHWLlyJQMGDODll1+2P2G6FxIkCSGEEEIIIUrVd999R/fu3V1e3333nVO+lJQUgoOD7e9DQkJITk62v/f396dHjx4A5Ofn88UXX9jfBwcHM2HCBFatWkWVKlWYMWPGPddXutsJIYQQQgjxP2i6qXqZfdb7jzzCwIEDXdJ9fHyc3ttsNpc8CoXCJS07O5vnn3+ehg0b2sv95JNP7MuffPJJe/B0LyRI+h9nscLXvwWy4aA3eUYlbevl8Y8B1wjwshSZf+tRL37Y7s/lNA0B3mYeaq1nxIOZqP56JpmUrmbe2mCOXHBDp7bRvmEuz/ZJw9vdWoat+nsUCiXNe79CnVZDUes8STq9nQMrp5Gfk1pkfnffMO6Pmk6V+p2wmPJJOLaOQ2tnYjHlA6DzDKBl/9epUr8zCoWC5PjdHFwzA0PW1bJsVslQKvB6sSPuDzdF4anFuPM8+pmbsabl3XFVVQ0/An95hNSHvsaanGNP13aqQ8CCIS75U7p+5pSvohs1ahQqlYqFCxfeMk+tWrUYNmwYNWvWJCMjg3Xr1hETE2NfrtFoGD58OC1btkSpVHLgwAGWL19OQUFBWTShRCkU0LtfQ1q3rY5Opybu1DVW/nSMnBzjLddp3bYGnbvVJSDAg/S0PLZvi2f/3kT7ck8vLQ8Pbkr9BsFYzFb27U1g47o4rFbXC2pFo1AoiW7+Mh3qDEWn9uRE0naWHHiN7Pyizyu1Apox7P7p1PRvQobhKuuOzyXmwooi895fox/PdPyMV399gLTcxCLzVEQKhYLo6Gg6dOiATqfjxIkTLFmyhOzs7CLzN2jQgIEDB1K1alX0ej07duxg06ZN9uVeXl4MHTqUJk2aoFAoOHXqFMuXLyczM7OMWlQyintdnr4klN+Pezul3R+ex4ePXwEg36hg/togdvzphcUKXZrm8EK/VDx0Ff+4Ef9dfHx8XAKiooSGhrJ//377+5SUFEJCQpzypKSk8MQTTxAZGcmrr74KFAZNP//8M48++ihQGGyp1fce6kh3u/9x324JYOMhb14dmszcJy9zTa/m9cVhReaNjfNg5vJQHmqt5+sJl3i6dxpLdvjzw+/+AJgt8K/vq6JU2Pj0mURmjLrKsYvufLAypMjyKqqmPSdRu9UQYpZNYsuCoXj4htFxzOdF5lWqtHR98gd07r5s/nQQf/zwAlUbdadFv1fteTqMnI+Xfw1+/3oMW78chbt3KA+O/bKsmlOivF7ogHt0U7KmrCN97BKUYd74fRR9x/VUtfzx/3IoSg+tyzJNvWBMfyaT0ulTp5c1pfIESFFRUXTu3Pm2eby8vJg4cSIJCQnMnDmTbdu2MW7cOBo1amTPM2bMGMLDw5k/fz6ffPIJ9evXZ/To0aVd/VLRs099WrepztIfDrNg/m58fd0Y+1jrW+Zv2jyMQUOb8vuWeD5473d2/n6OwcOa07hJqD3PuMda4e2tY8H83SxbcoQ2bWvQs0/9smjO3xbVdBLtaw/hm5hJfLBlGP4eYTzbcUGReb10AUzsspCEjOPM3PAQ2+K+ZVy7WTQKe9Alr49bCKPbvFPa1S8VUVFRtG/fnm+++YYPPvgAf39/nn322SLzBgcHM378eI4dO8aMGTNYsWIF/fv3p0uXLvY8Tz75JEFBQXz88cfMmTMHPz8/nnvuuTJqTckpznUZ4Fyyjqd7p/LzlPP215sjb9yEm70qmGMX3Xl37BXeHZvE4fPufLiqcl2Xxf+WDh06sGfPHtLT0zEYDGzatIlOnTrZl1ssFp599ln69u3L1KlT7U+ZPDw8+Oqrrzhy5AgAixYtomfPnvdcDwmS/oeZzPDzHj+e7JVG6wgD9asV8Prwqxy/6M7xi24u+X/d60PnJjkMap9FtUAzXZrmMuyBTNYfLLwrcClVy6VrWh7vkU6tEBNNa+UzKDKTfWc8yrpp90yp0tDggcc5uuHfXD2zk4wrx9m9eDzBddoQVKuVS/5aLaNx9w5h56Jnybx6ipRzezj224cE1mgBgFrrSWh4B/7c/hkZV06QmfQnJ7bNJ7DGfWjdfcu4dX+TRonH2FbkfLQD456LmE+mkDV5NdpW1dG0qHrL1TzG3E/g8rHYsvOLXK6uF4T59DWsqblOLyrBTc6goCBeeuklOnfuTFpa2m3zduzYEYPBwNKlS0lOTmbbtm3ExsbSq1cvAPz8/Gjbti1Llizh/PnznD17loULF9KmTRv8/PzKoDUlR6VS0LFTHdavPcWZ06lcTtSz+PuD1KkbQK3a/kWu4+mpZdOG0xzYl0hGuoG9sQlcTcomon4QADVr+VGnbiBLFx8m6Uo2p06msHb1SR54sDYqVcW+lKmUGro1eIyVR2dx8uouEjKO89XuCUQEt6FukOt5pWP4CAymbJYemE5ydjzbznxL7IVf6NXwaZe8j7R7n8uZp8qiGSVKpVLRrVs3Vq5cycmTJ0lISOCrr74iIiKCunXruuRv0qQJRqORtWvXkpqaysGDBzl27BiNGzcGQKfT0aBBAzZu3EhCQgKJiYmsX7+e2rVr4+FRea5Bxb0uG81wOU1Do+oFBHpb7K/rvTdSslRsOeLNPwZco0nNAprXzueVgSlsOerFtSxVWTdPiLsSGhrKpEmTGDduHA8//DD9+/enefPmPPXUUxw7doytW7fy559/snHjRvtU31OnTkWlUvHRRx8xffp0+vbty4kTJ3jllVfuuR7lcmUZNWoUa9ascUrLy8ujXbt2pKenu+RfsWIFU6ZMuW2ZJ06coFu3bowdO/au67F06VKXetxsyZIlLFmy5LZ5unXrRmKiaxcHx/KvPxaMjo5m4MCB7Nmz567rWVrOJunIK1DSoo7BnlbF30yYv4mjRZyMx3bN4JFuzv8fhQKyDYW7kY+7BaXCxup9PhSYFGTmKtl23JsG1Yr+clwR+VVpjMbNm5RzN7pA5WYkkpN+ieDabV3yV6nfmatndmEy3Jg95fz+5WyaPwAAi7kAszGXOq2GoNZ5odZ6UKfVYLJTz2PM17uUV5FpGoag9NJh3JtgT7Nc0WNOzETb6tZ9mnXdItC/sYnsWb8XuVxdLwjzudsHGBVVeHg46enpzJgxg9TUortNXRcREcGZM2ec+lrHxcURHh5uL8tms3H27Fn78vj4eGw2m33a08qiajVf3Nw0nDt74/+akWEgPS2POnUDilwnds8lft8SD4BSqaDZfVUICfXiTNw1AOqEB5KenkdG+o3zVfzZNNzcNFStdufuG+Wphl9j3DXenE65cV5Jy00kNSeBesFtXPJHBLflTEosNoc7BXEpMYQHOz+J6xwxFl/3YNaemFt6lS8lNWrUwN3dndOnT9vT0tLSSE1NpV69ei75c3Jy8PLyok2bNigUCqpWrUq9evW4ePEiUDjbVUFBAZGRkbi5uaHT6YiMjCQ5ORmDweBSXkVV3OvypWtaLFYFNYOL7sZ64pI7CgU0q3WjvKY181Eq4NhF95JvgBAlJCoqijVr1rBx40aeeuopAL788kuaNWtGz549OXXqFKtWrbK/3n77bQBat27NL7/8wvr16/nss8/w9va+3cfcVrmMSRo0aBBr1qxxmsN806ZNtGvXjoCAoi+gd7Jt2zb69+/PSy+9dNfrHDp0iLZtXb/4Oho5cuQ91efm8mfNmkXXrl0ZM2YM586dY+zYsezYsQOVqvzu5FzTF/77g33MTulB3mZSMl13jYbVncdF5OYrWLXXl7b1C8ejBPlYmNj/Ggs2BrEq1herTUGtYCMfP1V5+sh7+FYBIO+m8UIGfQoeflVc8nsH1SE5fjfNek2mdsuBYIOEE+s5uvEDrOYCbFYzMcsm03bwewyZfhwbNvJzUtmyYAgUMTCxIlOGFp5oLDd1g7Om5KIMu/VJKOPxZQBo29QoolAF6joBaJqEEbjiEZQBHpiOJ5H9wXYsFzJKrvKlJDY2ltjY2LvK6+/vT0JCglNaVlYWOp0OT09P/P390ev1WK03xu9ZrVb0ej3+/kU/famofH0Lv8xlZTnfINHr8/H1u/0Xs+o1fHnhxQdQqZTsjbnEyT9T7GXqby7vr/d+/u4kXMosodqXPH+PwnNHRp7zeSXTkIy/h+tTWH+PMBIyTjilZRmS0ak98NT6k2vMIMS7DtH3vcLsLcNwU3uVXuVLyfV9OiPD+TjPzMwscn8/ePAgu3bt4vHHH+exxx5DpVKxf/9+1q1bBxQeK9999x1jxoyx/3ikXq/ngw8+KHIQeEVV3Ovy+WQtGpWNb7cEEHvaA53GRuemOYztkoFOY+Nalhp/Twtqh68aahX4e1pIyZJh6ULcTrkcIX379mXWrFlkZmbau5H8+uuvjBs3jsmTJxMXF4dCoeCJJ57g4YcfvmN527dvtz/tuf6jU1OnTuXKlSuo1WomTZpEp06dmDdvHocPHyYpKYkRI0awdetWYmJiCA4OZu3atXh5eXHixAmSk5N54YUXGDx4MPPmzQNgwoQJrFu3jrlz5+Lu7k7jxo2xWCy89957QOFsGidPnsRgMDBr1ixyc3Odyu/Vqxft2rUDCgdvFxQUkJeX97ci3L8r36RAqbA5nTwBNGobRrPrLCJO6xoVTPuhCkaTgqd7Fd4ttloLu9y1Cs9jVKcM8gqUfLY+iDd/DOODx65QwXvEAKDWumO1WrBZnS9QVnMBKrXOJb9G50146xFcidvGH4uex903lFbRb+HmGUTMskkA+ISEk3n1FMd+m4PNZqV5r5d5cOyX/PbpQMzG3DJpV0lQuGuwWaxgdp6Ew2Y0o9Dd26lEVcMPhZsGNCr0b2wEjQqvZ9oTsHAkadHfYk2/84QQlYVWq8VkMjmlmc2F+5lGo0Gr1drf35xHo9GUSR1Likarwmq1uUyoYDZb0WhufyJIT8tj7oe7qFbdhwEDm5CdXcDGdXFotSpMJud97/pnqNUV++SiVReeV6w25/+v2WpEo3I9r2hV7pgszjelzJbCJwUalQ6lQsXjkXPYdHIBlzNPER5067FeFZVWq8VqtTrdFIBb7+/u7u4EBgayadMm9u/fT7Vq1Rg2bBj9+/dn9erVAISFhXH58mVWr16N1WolOjqa5557jn//+9+VZvKT4l6XL6RosQE1g40MjMziXLKWT9YFcS1Lzf8NSSHfpECrcQ0S7+Y6L8T/unIJkjw9PenevTsbNmxgxIgRJCcnc/78efbu3Yu/vz9r1qwhPT2doUOH0rBhwzuW17lzZ0aMGAHA+PHjefHFF4mMjOSxxx4jISGBkSNHsnLlSgCMRqP9ztOff/5J27ZtefDBB1m7di1Xr15l8eLFnD59mnHjxjF48GD7Z6Snp/POO+/w888/ExwczMSJE/HyunH3LiIignfffZdFixbx9ddfM3fuXLp162Yv39HXX39No0aNyjVAAtCpbVhtCswWnE7IJrMCd+2t77xl5iqZuqgKF1K0zH7sCmH+hRf+zUe82XzEm6WvXLCvXy0wiVGzaxMb50GHRhX/C6/ZlI9SqUKhVGGz3phJSKnWYTa6dtmwWk0UGDKJWfoPbDYrXC4c19RxzAIOrnkT35D6NOv1Mr++E4khu3CO/53fP8WA/9tNndZDObP727JqWrF5Pt0Oz6cj7e9zv4xFoVKCSgGWG/uHQqvGZjAVVcQdWS5mkNx+HjZ9vn0MUsaLKwnZ8gxuAxqT9+3+2xdQiZhMJpcvf9dn3TEajRiNxiJn4VGr1RX+C17XHhF063GjS+C2zWdRKhUolQqnQEmtVmI0Fj1D13V5eSby8kwkXdHj5aWjR+/6bFofh8lkcQmGrn/Gncosb0Zz4XlFqVBhtd2oq1qppcDsel40WfLRKJ0nOVGrtH+VlUe/JuOxYWPjyaInfqgMjEYjSqUSpVLpFCjdan8fNGgQVquVX375BYCEhASUSiWjR49m69atVKlShQEDBjBlyhT7j0d+9tlnvPvuu3To0IFt27aVTcP+puJel5/okc7wjpn4eBRuw7phRpQKmLE0jOf7paLTFB0MFZZXeWadFaI8lNvtt8GDB9vH66xevZoBAwYQExPDkCGFUwEHBATQvXt39u7dW+yyHcupUaMG9913n32mi+bNm99yvQceeACFQkH9+vVdpgzdv38/LVu2JDQ0FKVS6fKE6/o87BERES7dBxx9++23LF26lFmzZhW7XSUtxLcwuEnPdv5ilpqtJsjH9Y42QFKGmvGfVycpXcPcpy47dcH7M0FHzWCj04m8aoAZXw8Ll9Mrx53wvMzCKVPdvZ1n/nH3CcGgd52y25CVjD7lbGGA9Jes5DMAePrXILBmS/KzU+wBEoApX0/2tfN4B9YuhRaUnLylR0gb9J39Zdx9AQBlsHPXHmWI59+aqtuWle88SUO+GXNCFqrbdOGrjNLT012mPvX19SU/Px+DwUBGRgbe3t5OvwWhVCrx8fGp8FMYx+y+yEcf7LC/zpwuHJ/l7eP8lMTHx82lC951dcMDqFLVeftcTcpGq1Xh4aElMzMfn5vL+6tb383d8CqajLzC84qvu/N5xc89lEyD63klPS8Jn5vy+rqHkm/KwWDKpn2dodTwb8JHg4/z8ZA/+UfXRQC80e83+jZ+oZRaUbKuXyd9fZ0nsPHz8ytyf69bt659/NF158+fR61WExAQQN26dcnKyrIHSAAGg4Hk5GSnH6Ws6Ip7XVYqsQdI19UNK7wup2SqCfE1k5mrwuKQxWyBjFwVQT4V++aCEOWt3IKk1q1bc+3aNZKSkvj1118ZPHiwS79hm82GxVL8g/h25bi5uQ58vE6nK7wAF/WDVTff7brZ9bFFRa173axZs1i+fDk//PADVaq4jm8pa+FVCvDQWTl84cY2ScpQczVDQ/Park9NMnJUTPq6GlYbfPJMIuFhzgNFg30tJKZqMDqcx9P0KvQGJdUC7+1JQ1nLTDqJKT+bkLo3nqB4+lfHK6AmKeddx55cu7AX/6qNUShvXNB8wxpgtZjJzUjAkJWEm1cQOs9A+3KVxg2vgJpkp54v3cb8TbasfCyXMu0v06lrWHMKnMYWqar6oK7uh3F/wm1KujVd9whC9r2Iwv/GOBWFhwZ1bX/MZyvnZA63Eh8f7zIgvUGDBvbJGeLj41EqlU4ze0VERKBQKIiPjy/r6haLIc9EWmqe/XXlsp78fBN1w2/s9/7+7gQEenA+vuj/a5duEfTp18AprUZNP7KzC8jNNXLhXDqBQZ74+t04X4VHBJKfb+LK5aybi6tQEjNPYjBlUz/kxnkl0LM6QV41OJPieiMw/to+6oU4j5dtENKe+NT92LAxe8tw3lzXk5kb+jJzQ1++i30ZgHm/P8r2s4tKtzElJDExEYPBQP36N6ZwDwwMJCgoiDNnzrjkz8jIoFq1ak5p1apVw2q1cu3aNTIyMvDx8XHqoaHRaAgKCiIlJaX0GlLCintdnr4klGmLnKcHj7vshkZtpVqgiaY1DViscOLSjfKOXXTHZoOmNSvPhBZClIdy7cg9cOBAPvvsM3x9falZsyaRkZH89NNPQOFd1y1bttxxYoWiOJaTkJDAwYMHadGihUs+lUp110HY/fffz7Fjx0hJScFms7Fu3brbBkQ3l//tt98SGxvLkiVLCAu79e8dlCWtGqLbZfHZ+iBiT3tw+rKOGUvDuK+OgSY1CzCZIS1bhemvoOej1cFk5ap4fXgyWo2NtGwVadkq0nMKA8ReLfSYLQreWR7K+WQtpxJ1vPFjGBFhBbStV/G72gFYLUbO7FlIi4emUqV+Z/yrNqXDqPkkx+8h7dIhlCoNbl7BKFWFT8bOxixCpdbRfvgcvIPDCY3oSMt+r3Lh4M8Y8zK5fHIzeZlJPDD6E/yrNcM3rAHtR8zFYs7n/MGfy7m1xWSykLfkMN6vdEHbsTbqRiH4zo7CuPcSpqNJhXk0SpRBnnCHcSfXGfclYMspwO+9h1DXD0bdKAS/OdFYMw0Yfj1x5wIqMJVKhY+Pj/0Gyq5du/D29mb06NGEhYXRtWtX2rZty8aNG4HCAesHDhxg3LhxhIeHEx4ezpgxY4iJianwT5JuZrFY2fPHRfoPaET9hsFUq+7DqHH3E382jUsXM4HCacK9vHWoVIXn0Z07ztGgUQidutQlMMiDNu1q0LlbOL9tiAPg4oUMLl7IYPS4+6lW3YcGDYN5KKoRO38/j8VSsQfmm61Gtp9ZyOAWr9KkSmdq+DflyQ7ziEvew/m0Q6iUGnzcglEpC88ru84txVsXwOg27xDmE0HXeo/StlY0G08W/l5bet5lruVctL+uTwiRnneZPGPFDhivM5vNbN++ncGDB9OkSRNq1KjBk08+SVxcHOfPn3c5frZu3UqzZs3o168fQUFBNGvWjCFDhrB9+3by8/M5evQo6enpPPXUU9SsWZOqVavyxBNPYDKZnH6wuaIr7nW5c9Nc/jjlybJdflxOU/P7cU8+Wx/I8I6ZeOhsBPta6NI0h/d/CeHYRTeOXnDjg5XB9GyRTbCvPEkS4nbKdWqThx9+mO7du9un7XvhhReYPn06UVFR9h+KatKkCXFxccUqd+rUqbz++uusWFH46+QzZ850+aVeKPyxqg8//PCuxgYFBAQwbdo0Hn/8cbRaLdWrV7/jrwZfL9/Ly4tPPvkELy8vpynKv/jiC0JDQ29TQul7okcaFgu8vTwUiwXa1s/jxajCKXePX3Jn0tfVmPPEZRrXyGfnCU+sNgXPfuY8S5lSaWPrW/EE+1qY+/RlFqwPZOKX1dCobLSpl8dzfVNdBqFWZEc3vY9Spab9iI9RqNQkxW1n/8ppAATVakX3Z5ax5fNhpJyLIT8nlc0LhnJ/1Ov0mbgOszGXC4d+4cj6fwNgNuax5YvhtHxoKl0e/w5QkHpxP5s/G4K5oPL8WOp1OXN3otAo8fv3Q6BWUbDrPPq3NtuXa1tUI+C7EaQ/8iPGfXd+umTTF5D+xDK8J3cm4LvhoFJi3H2R9MeWQgUfZ3In4eHhTJ48mdmzZ3P69Gmys7OZO3cuw4cPZ9q0aaSlpfHNN984nd8WLlzIiBEjmDBhAhaLhYMHD7Js2bJybMW927guDpVSwcjRLVGqFJw+dY1ffj5mX16rdgDPjm/Pgvl7OBefxpm4VBZ9e4AevevTu28DMjMNrFpxnH2xN/aj7/6zn0FDmvHc+A4UFJjZG3OJzZtOF/XxFc6qox+gUmp4vP1HqBRqTiRtZ/H+1wAID2rF5O5Lmb1lOKdTYsjOT2Xu748wvNV0pvVZS1ruZb6JeYm45N3l3IqStWrVKlQqFY8//jgqlYoTJ06wePFiwPX4OX78OAsWLOChhx6id+/e6PV6du7cyfr16wEoKCjgww8/ZPDgwUyYMMH+BPb9998nP79id8e82d1el1vWNdC1WQ5Gs4Ifd/rx1W8B+HtZGNw+i9Gdb3T7f2VgCnPXBDPluyqolNC5aQ7jH7r9TxYIIUBhq0xzY5ajjIwMFi5cyPjx41EqlcycOZNatWoV63eZ/o6knypRlFFGft9X7c6Z/sd0XT2hvKtQIb3x4Nk7Z/of4+sRVd5VqJCy2laOMT1l6vc+5V2DCmd6z6/KuwoVVpUhlfsG1/+aZ5bUKrPP+nzkxTtnqkAq1ST5kydPdvqhxeu6devGiy++WKqf7efnh16vp3///qhUKpo0acKwYcNK9TOFEEIIIYQQZa9SBUmzZ88ut89WKBRMmzat3D5fCCGEEEIIUTYq9i/wCSGEEEIIIUQZkyBJCCGEEEIIIRxIkCSEEEIIIYQQDiRIEkIIIYQQQggHEiQJIYQQQgghhAMJkoQQQgghhBDCgQRJQgghhBBCCOFAgiQhhBBCCCGEcCBBkhBCCCGEEEI4kCBJCCGEEEIIIRxIkCSEEEIIIYQQDiRIEkIIIYQQQggHEiQJIYQQQgghhAMJkoQQQgghhBDCgQRJQgghhBBCCOFAgiQhhBBCCCGEcCBBkhBCCCGEEEI4kCBJCCGEEEIIIRxIkCSEEEIIIYQQDiRIEkIIIYQQQggHEiQJIYQQQgghhAMJkoQQQgghhBDCgbq8KyDuzpw/VpV3FSqcrLYvlHcVKqB55V2BCsnX+Gl5V6HCycpbXd5VqJDefGt8eVdBVAY73yvvGlRcQ8q7AkKUDHmSJIQQQgghhBAOJEgSQgghhBBCCAcSJAkhhBBCCCGEAwmShBBCCCGEEMKBBElCCCGEEEII4UCCJCGEEEIIIYRwIEGSEEIIIYQQQjiQIEkIIYQQQgghHEiQJIQQQgghhBAOJEgSQgghhBBCCAcSJAkhhBBCCCGEAwmShBBCCCGEEMKBBElCCCGEEEII4UCCJCGEEEIIIYRwIEGSEEIIIYQQQjiQIEkIIYQQQgghHEiQJIQQQgghhBAOJEgSQgghhBBCCAcSJAkhhBBCCCGEAwmShBBCCCGEEMKBBElCCCGEEEII4UCCJCGEEEIIIYRwIEGSEEIIIYQQQjhQl3cFRPlTKKB3v4a0blsdnU5N3KlrrPzpGDk5xluu07ptDTp3q0tAgAfpaXls3xbP/r2J9uW16vjTr38jqlXzwWAwc/BAIpvWx2Gx2MqiSX+LQqEkuvnLdKgzFJ3akxNJ21ly4DWy81Nvu16QV01e67OBN9Z2I9Nw1Z7etEpXJnT51iX/v1a2c8pXkSkUSpr3foU6rYai1nmSdHo7B1ZOIz+n6G3ywOhPqdm8v1Pa1TO72PbVKAB8Qupxf//XCKrVCovZSMLx9RxZ/y6m/OxSb0tJK43jx9NLy8ODm1K/QTAWs5V9exPYuC4Oq7XiHz+ORo0ahUqlYuHChbfMU6tWLYYNG0bNmjXJyMhg3bp1xMTE2JdrNBqGDx9Oy5YtUSqVHDhwgOXLl1NQUFAWTShZSgVeL3bE/eGmKDy1GHeeRz9zM9a0vDuuqqrhR+Avj5D60NdYk3NupNf0w/uVLmjvrwY2MO5LQD9rG9akSnQs3cN2cevTAM+nI1HV9MOamovhp6Pk/mcfFHGMeDzeBp+Xu3C18ful2YqSVQr7ijLIE+8pXdFF1sJmtZG/MY6cD3dgM5hKsyVCVFryJEnQs099WrepztIfDrNg/m58fd0Y+1jrW+Zv2jyMQUOb8vuWeD5473d2/n6OwcOa07hJKAB+/u48+XQ7Ei5l8uH7O1i65DCtWlenb/9GZdWkvyWq6STa1x7CNzGT+GDLMPw9wni244LbrhPiXYd/dFmEm8bTZVk1vwZcSj/OK7+0dnplGZJLqwklrmnPSdRuNYSYZZPYsmAoHr5hdBzz+S3z+4Y14PC6d/nlrVb2164fngNArfWg61OLKcjLZOP8Aez47glC6rSl3dAPyqo5Jaqkjx+AcY+1wttbx4L5u1m25Aht2tagZ5/6ZdGcEhMVFUXnzp1vm8fLy4uJEyeSkJDAzJkz2bZtG+PGjaNRoxvnijFjxhAeHs78+fP55JNPqF+/PqNHjy7t6pcKrxc64B7dlKwp60gfuwRlmDd+H0XfcT1VLX/8vxyK0kPrlK5w1+D/xVBQKkh/bBnpTy1H4eeO/+dDQKMqrWaUuOJuF+2DdfCd1R/DT0dJG/gd2R/uwPOJdng+HemSV10/GO8JHUuz+qWipPcV1Er8vxqKum4gGRNWkvHMT2gaheI3f2AptUCIyk+CpP9xKpWCjp3qsH7tKc6cTuVyop7F3x+kTt0AatX2L3IdT08tmzac5sC+RDLSDeyNTeBqUjYR9YMACAhw59ixJNas+pP0tDzOnk7lyOErRNQLKsum3ROVUkO3Bo+x8ugsTl7dRULGcb7aPYGI4DbUDWpV5Drd6j/Gq71Xk2fSF7m8qm8DLmedQp9/zello3I8FVCqNDR44HGObvg3V8/sJOPKcXYvHk9wnTYE1XLdJkqVFu/A2qQlHCY/55r9ZTJkAeDhX53UC/vY+/O/yL4WT9qlg5yNXUxoxANl3bS/rTSOn5q1/KhTN5Cliw+TdCWbUydTWLv6JA88WBuVquKfsoOCgnjppZfo3LkzaWlpt83bsWNHDAYDS5cuJTk5mW3bthEbG0uvXr0A8PPzo23btixZsoTz589z9uxZFi5cSJs2bfDz8yuD1pQgjRKPsa3I+WgHxj0XMZ9MIWvyarStqqNpUfWWq3mMuZ/A5WOxZee7LNN2qI2qijdZ/1yL+fS1wjL/bx2aiCA0zauUZmtKzj1sF4/h95H/22nyFh/CkpBJwabT5H63H/eBTV3K9n2vH8bDV8qgISWoFPYVXee6aOoHkzlpFaZDlzGfTCFz8q9o29VE07p6abZGiEqr4l9xRamqWs0XNzcN587e+DKTkWEgPS2POnUDilwnds8lft8SD4BSqaDZfVUICfXiTNw1AM7Fp7Ns8RF7/mrVfWjSNMy+vCKr4dcYd403p1NudPdJy00kNSeBesFtilznvuq9WLT3//jp0Mwil1f1a0BS1tlSqW9Z8KvSGI2bNynnbmyT3IxEctIvEVy7rUt+n5BwlCoN+pSi26xPPs0fPzyPxWQAwDuoDrXvH8TV0ztLpwGlqDSOnzrhgaSn55GRbrCvE382DTc3DVWr+ZRia0pGeHg46enpzJgxg9TU23dRjYiI4MyZM9hsN24YxMXFER4ebi/LZrNx9uyNfSk+Ph6bzUZERETpNKCUaBqGoPTSYdybYE+zXNFjTsxE2+rWX1J13SLQv7GJ7Fm/uywzHUsi49mfseU6dO38q7uZ0tetxOpemu5lu+QsiCHn093OiTYbSh/nNntNfBBrSg6GFcdKvN6lqTT2FVUtfyzXcrBczLSnWZNzsGbkoW1ToySrL0SJWL16Nf369aNnz5788MMPLstPnjzJ4MGD6d27N1OnTsVsNgNw5coVRo8eTZ8+fXjuuefIzc295zpUqiBp1KhRrFmzxiktLy+Pdu3akZ6e7pJ/xYoVTJkypayqB8C8efOYN2/eLZf/61//YsWKFWVYo9vz/etCmpXlfOdJr8/H18/9tutWr+HL27P6MvbRVhw6cJmTf6a45Hnznd68OLkTBoOJLb+dKbmKlxJ/j8K7rxl5zmOFMg3J+HsUfQdvztaR7L+0ushlCoWSMO9wagU0Y1qf9fw7ei/PPfglod51S7bipcjDt3Cb5GU5bxODPgUPP9e71b6hDbCYC2jW6yUG/N8eHnp5G816vYxSrXPJ2+fF9fR/ZTs6D38OrZlROg0oRaVx/Pj6uqG/uby/3vv5377MiiA2NpZvv/0Wvb7oJ6uO/P39yczMdErLyspCp9Ph6emJv78/er0eq9VqX261WtHr9fj7F/2krqJShnoDYEnJcUq3puSiDPO+5XoZjy8jf/2pIpdZU3Iw7rnolOb5VDuseUaMBxKLXKeiuZftYj5+FUv8jRsTCk8tHsNbULDrvD1N06o6HgObkjVtQynUunSV1r6i9HVD4a6xpyk8NCh93VEGeJRArYUoOcnJycyZM4fFixezatUqli5d6nSzDOCVV17htddeY+PGjdhsNpYtWwbAm2++yahRo9iwYQNNmzbl008/ved6VKogadCgQS5B0qZNm2jXrh0BAUXfta0okpOTefbZZ9mwoWKdsDVaFVarzWVAuNlsRaO5/e6RnpbH3A93sWzJYZq3qELvfg2clisU8OVnMXy1IBaNRsVjT7k+dahotGp3rFYLVpvZKd1sNaJRuX7Jv5Ngr1po1W6olVoW7Z3CF388j0al5eUey/HWBZZUtUuVWlu4TWxW521iNRegKiLw8Q2tDyjQp5xl+zePcnzzR4S3HUHbQe+65I1d/gqbPxuCITuZbk//iEpTOe5+X1cax49Wq8Jksjrlvf4ZanWlOmXfkVarxWRyHjR+/W6gRqNBq9Xa39+cR6PRuKRXZAp3DTaLFczO/1ub0YxCVzJzKLkPb4Hn6PsLB+NnuXa5qoj+9nZxU+M372EUbmqy5+woLNNTi++7fdG/swVr6r3fRS4vpbGvFOw8jy3HiM+bvVB461B4afGZ3gtsNhSVaPyaqNz0ej2JiYkur5tvqu3evZvIyEj8/Pzw8PCgd+/eTt+fL1++TH5+Pi1atAAK44MNGzZgMpnYt28fvXv3dkq/V5Vqdru+ffsya9YsMjMz7f3Rf/31V8aNG8fkyZOJi4tDoVDwxBNP8PDDD9+xvI0bN7J+/Xo++ugjLly4QO/evfnjjz8ICgriiSee4MUXX8TX15fp06eTmZmJm5sbr732Go0bNyY1NZXXX3+dq1evolAomDx5Mh06dLCXbbFYmDRpEtWrV+ef//wnq1evpnv37uXej75rjwi69bjRTWXb5rMolQqUSoXTFz21WonRaLltWXl5JvLyTCRd0ePlpaNH7/psWh/H9Z4zNhskJhSOQ1m6+DATJnWkVm1/Ll7IKPmGlRCjOR+lUoVSocJqu9F+tVJLgfnOswrdLCX7PJN+bo7BqLePQfps5zO8F72HdnUGsfnUlyVW99JiNhVuE4VShc16Y5so1TrMRoNL/qOb3ufUjs8x/jUGKetqHDarlQdGf8LBNTMw5mXa82ZcOQ7AroXPEv1qLNWb9Obi4VWl26C/oSyOH5PJ4hIMXf+MO5VZ2ZhMJpdgR60uvCwZjUaMRqP9/c15Kvrsdp5PO08kkPtlLAqVElQKcJjlU6FVl8jsYp7PROL94oPkfBFD3uJDf7u80lKS20Xh547/JwNRhweS/uRyrFcKv2h5v9oN84lk8tcV/VSloimLfcWWlU/GC7/g+25fQvZMwJZvIu+HQ5hPpWDLqdjHkvjv8d133zF//nyX9PHjxzNhwgT7+5SUFIKDg+3vQ0JCOHr06C2XBwcHk5ycTEZGBl5eXvbrxvX0e1WpgiRPT0+6d+/Ohg0bGDFiBMnJyZw/f569e/fi7+/PmjVrSE9PZ+jQoTRs2PCO5T3wwAPMnDkTm83Gnj17CAwMZO/evXTr1o3z58/TrFkzRo4cyeuvv07jxo05e/YsL7zwAhs3buTtt99m8ODBdO/enZSUFEaNGsXKlSsBsNlsTJs2jbCwMP75z38C8OSTTwJw4MCBUts+dyNm90WOOgxi9fDQ0uch8PbRkZV5486jj48bWVlF71h1wwMwGMwkXbkR+V9NykarVeHhocXTS4uvrxtnTqc6LC/M61PB+8ln5BVuG1/3EDLykuzpfu6hHLnH6brzjFlO702WfK7lXCLAo3IMrM7LLNwm7t4h5GXd2CbuPiEY9EVsE5vNHiBdl3m18MuKh29VNDov/Ko04vKfv9mX52enYMzLwN0nrBRaUHLK4vjJzMynYaMQp3WuHzc3d8Or7NLT0/HxcR5n5evrS35+PgaDgYyMDLy9vVEoFPZxS0qlEh8fH5duehVN3tIj5G+Is79X+rrBiw+iDPbCevXG9NzKEE+sW3OKKuLuKMDn9Z54DG9B9gfbyf3/9u47vKny/eP4O0kH3YNS9kZZoiCjLGX+kA0tQ7YDJ8uBKCoqKnwBxcFwfh0oKqjIliXIFigIiiCWDWW1QDfdSX5/VENiCgi2Tfrt53VdvS7ynCcn97k5Ocmd85znfBL9b8IudAWVF1OFQEI+6ofBz4uEYfPJPXj5mlffyAZYM3MI3/UYQF7BAYTveoyUiWvIXH6goDfrXymqfSXn1zNc6PoxxlBfLJeyISsX359GkVvMrtmSgvXyq6OK7LV8t99DZKTzjIp//xywv071LwaD4ZrLr/W861WsiiSAPn368PbbbzNgwACWLVtGz5492bx5M//5z38ACA0NpUOHDkRHR+Pv73/Vdfn7+1OjRg1iYmLYvn0799xzDzt37sTPz4+IiAjS09PZt28fzz77rO056enpJCYm8tNPP3H06FFmzpwJ5A3/iI3Nu8hy/vz5pKamsm7dukLKwo3LSM8hI/3yL1FJpkwyM3OoUbM0e34+DUBIiA+hpX05diT/manatq+F1Wrl04922toqVwkmNTWLS5eyaRJRmbbtajL55bXk/jlcoHKVvOsH4uPc+94dp5IOkJGTys3hzdlxfBEApf0qEeZfmUPx1//l47aKnbi/xVs8v+wO0rLyrpvz9vCjbEB1thyZV6CxF5akswfIyUwlvEZzju/Jy4lfSCX8Q6sQf2yHU/9Wg9/FYPRgy9yHbG2hlW7FnJNJ2sXjlK/TnpYDZrLkP81s91nyC6lMKf8wUuLd+7q1onj/HD+aQLcedQkKLmUrvGrWKk1mZg5nTifnu87i6siRI7Ro0cKhrXbt2rbJGY4cOYLRaKRGjRocOZI32UWtWrUwGAy2x+7KmpyJ2a6oNXuasKRl4dW0MpnLfgfyvuh7VAome1fslVZzTYETOuLT51aSn1tBxuL9/zruwlYQeTGG+hIy526wWEkY9BXmv70vznd2PEPv3b4WgU+342LUZ245/K4o9hVT1WCCJnUhcdQiLAl5oyI8G1fCEODtdF2bSGEJDAx0KojyU7ZsWXbt2mV7HB8fT3h4uMNy+4mBzp8/T3h4OKGhoaSlpWE2mzGZTLb2G1XsBrg3adKE8+fPc/bsWZYuXUqfPn2cKker1YrZ/M+GpbRp04atW7dy9OhR+vfvz65du9i0aRPt2rXDYrHg5eXFkiVLbH/ffvstwcHBWCwWPvvsM1v7119/zc03593HpFGjRjzyyCNMmpT/bGfuxGy2sG3rCbr3rMvNdcpQsVIgg4bdzpHDFzn55yw4JpMB/wBvTKa8anzzpqPUrhvOnW1rUDrMl6YRlWnTviY//PlL2M878y4Y7jfgNsqE+3HTzWH0G3Arv+w+Tdy5f/GLaRHItWSz8dBc+jR8jvrl21A55BYeaDmLmLhtHLu4B5PRk8BSZTAZ/9n1EIfit5ORk8b9Ld6iYnAdKofcwsOt3yUtK5HtxxYV8tYUDIs5m0Pb5tKw2/OUv7kNIRVuoeWg2cQd2cbFk3swmjwp5V8GoykvJyd/W0Glep2ofccD+IdWpXKDrjTq9jwHNn1IbnY6Zw6sJS3hJC0GzCSoXG3Cqjam9ZD3OX9iF2di1rt4a69PYbx/ThxP5MTxRAYPu52KlQKpXacM3XrUZfOGY8XiZsxXYzKZCAwMxGTKuwZiy5YtBAQEMHjwYMqVK0e7du1o1qwZq1evBiApKYmff/6ZYcOGUbNmTWrWrMmQIUPYvn27259JcpJjJn3eL3k3fm1dDY+64QS90YPs6JPk7P3zDK2nEWOYH1zjera/eN9ZA9+Bjbj0wTaythzHGOZn+8OrmFxncgN5CXyhI8YQX5KeWo41K/fydpfOm4DAfDLJ4e+vG7CaTyZhTS8GN04thH3FfDoFY1l/Ap/vgKlKMF7NKhP8encyvvsN88mkwtsWkRvQsmVLtm3bRkJCAhkZGaxZs4Y777zTtrxixYp4e3vbRmctXryYO++8E09PT5o0acKKFSsc2m9UsSuSACIjI3nvvfcICgqiSpUqNG/enAULFgB5wzfWrVtHs2b/bJKANm3aMH/+fGrVqkVISAienp6sX7+eVq1aERAQQLVq1ViyJO8aia1bt9puYti8eXO++uorAA4fPkzPnj3JyMi7PqNOnTo8+OCDHDp0iPXr3f9L3+oVMez5+TQDBzfioREtSErMYO6cyxV81WqhvPjK/1G1Wt7kGIdiLvDFnJ+5vUklnhzXhrbta7Jk4T62/3QSgLTULD54dxv+AV6MeeIOBgxuyL7fzvG13bTg7mzJ3ulEn1jC/S3eZmz7eSRcOs0HW/JuhFozrDGvR+6i5hXumfR36TkpvL1+MGZLLmPbf83YDvPJys3gzR8HkmspPuPA9655nRN7FtNiwAzaPzyfS4mn2fLFIwCEVW1M5As/2+6ZFLt3Odu/HUuNxv3p8uQPNOo2gZgtn/DbD28AYM7JZMPHQ8jJSqPjwwu4895PSTy7n42f3AP5nCp3dwX9/gH47JNdpKVm8+iolvQfeBvR20+yds3BIt+2glazZk1ef/112xTfqampzJw5k8qVKzNhwgTatm3Lp59+SkzM5aFHc+fO5ejRo4wePZoRI0YQExNjO/YWN2kzN5O5/HeCp3UjdM4AzGdSSHx8qW25V8OKhG8agVfDiv9ofaX+vEG3/8hWhG8a4fBX6q7a13i2+7iuvHh74N3xJox+XoR9M9Rhm8usf9SFW1GwCnpfIddC0oiFGMP8KL3wHoKmdCVj8T5SXv3h2s8VKWJly5bliSeeYNiwYfTu3Zvu3btz66238uCDD/Lbb3nDQ6dPn86UKVPo0qULGRkZDBs2DICXXnqJb775hq5du7Jr1y4ef/zxG47DYM1vAJ+bO3fuHB06dGDy5Mn07t2btLQ0Jk6cSExMDGazmXvvvZf+/fuzcOFCoqOjmTp16lXX17FjR4YPH87AgQOZOnUqBw8e5JNPPgHyhoP8NXGDp6cnEydO5NZbbyUuLo4XX3yRM2fyrk946qmnaNOmjW3679GjR7Njxw7Gjx/P8uXL8fPzA2D8+PE0a9aMqKio69rmp59Yfu1OJUxys5GuDsHttP2l2L2di8Se7BufAvR/VXJ6/tPWl3Qvby5e918ScTflfh/n6hDkOpyr93qRvVZx2zeKZZFUEqlIcqYiyZmKpPypSHKmIil/KpJE/p3i9kW4pFORdGXFbuKGGzF27Finm1ABtG/fnscee8wFEYmIiIiIiLsqEUXSG2+84eoQRERERESkmCiWEzeIiIiIiIgUFhVJIiIiIiIidlQkiYiIiIiI2FGRJCIiIiIiYkdFkoiIiIiIiB0VSSIiIiIiInZUJImIiIiIiNhRkSQiIiIiImJHRZKIiIiIiIgdFUkiIiIiIiJ2VCSJiIiIiIjYUZEkIiIiIiJiR0WSiIiIiIiIHRVJIiIiIiIidlQkiYiIiIiI2FGRJCIiIiIiYkdFkoiIiIiIiB0VSSIiIiIiInZUJImIiIiIiNhRkSQiIiIiImJHRZKIiIiIiIgdD1cHIP/Mk6sPuDoEt7Pey+rqENzOhqQurg7BPbUd6eoI3M7Lr45ydQhu6aU7Drs6BLfz8uZarg7B7bz0wmxXh+C2PmCcq0MQKRA6kyQiIiIiImJHRZKIiIiIiIgdFUkiIiIiIiJ2VCSJiIiIiIjYUZEkIiIiIiJiR0WSiIiIiIiIHRVJIiIiIiIidlQkiYiIiIiI2FGRJCIiIiIiYkdFkoiIiIiIiB0VSSIiIiIiInZUJImIiIiIiNhRkSQiIiIiImJHRZKIiIiIiIgdFUkiIiIiIiJ2VCSJiIiIiIjYUZEkIiIiIiJiR0WSiIiIiIiIHRVJIiIiIiIidlQkiYiIiIiI2FGRJCIiIiIiYkdFkoiIiIiIiB0VSSIiIiIiInY8XB2AuAGjAf/HWuPT+xYMfl5kbz5GyqS1WC6mX/EpHvXLEvhsBzzrhmOOTyPtvW1kLt1/uUMpDwLHt6fU/90EJiOZq2NInbYea3pOEWzQv2MwGLn1rnFUb9wPD28/zh7cyM+LJ5CZdiHf/j5B5bi9x0TK33wn5pxMYn9bwZ7vJ2HOyXTq2+b+z7hwfBf7f5xV2JtRoAwGA7169aJly5Z4e3uzf/9+5s2bR2pqqlPfJ598ktq1a+e7nunTp3Po0CH8/f3p168f9evXx2Aw8Mcff/Dtt9+SlJRUyFtSsAwGI71ufYqW1fvh7eHH/rMbmffzC6Rm5r+vVA1tQP/bJ1IlpD6JGedYsW8m248vzLfv7ZW78nDr93huaSsuXjpViFtRSG7guPIXU+VgSi+6hwvdPsYSl3a5vUowAePa4nV7RbBC9s5YUl5bj+Ws837ozgYNGoTJZGLu3LlX7FO1alX69+9PlSpVSExMZMWKFWzfvt223NPTk7vvvptGjRphNBr5+eef+fbbb8nKyiqKTShYhfAZZAjxIfCZdni3rg4GyNpxktRp6x32J3d1vceVJlW607neCMIDqpOcEc+WI/NZ88cHWK0WACqH3EKfhs9SNbQB2bmZ7Du7nu9++Q/p2clFuVkixY7OJAn+I1vi0+sWksevIGHoPIzlAgh+u9cV+xtCfAj9bz9yDsRxoe/npH/xM0Gv3oVXy2q2PkETO+HVuCKJjy4kceRCvJpVIXBipyLYmn/vlv97gmqN+7L9mydY934/fIPK0XrIB/n2NZq8aPfAl3j7BLH23Si2fjmSCnU70LDrc3/r50mzvq9RoXa7otiEAtejRw9atGjBp59+yvTp0wkJCeGRRx7Jt+/777/PuHHjbH9PP/00J0+eJCYmhiNHjgDwwAMPEBYWxowZM3jrrbcIDg7m0UcfLcpNKhA9bnmCFtX68un2J5i+rj8hvuV4pPX7+fb19w5lTNu5xCbuY9KqbqyPmcOwiNeoW+4Op76BpcIZ3PQ/hR1+obre48pfTFVDCPlvP4y+Xg7tBh9PQj7sB0YDCfd9Q8KD32II9iHkg77gaSqszShwPXr0oE2bNlft4+/vz5gxY4iNjWXSpEmsX7+eYcOGUbduXVufIUOGULNmTWbPns0777zDzTffzODBgws7/EJRGJ9BwdN7YKoYRMKD35Iw/FtMZfwJmdm78DemAFzPcaV++bbc32IGW458zSsr72LRr9O4q96jdKk3CoAgn3CeaPclF9JimfZDFB9uHUG10rfxUKt3i3KTRIolFUklnacR36GNSXt7E9nbTpB7IJ7kscvwalwJz4YV8n2Kb99bsaRmkfqfdZiPJZD+5R4ylv+O331NATCW9adUt7qkvLKWnL1nyfn5NMkvrKJU17oYw/2Lcuuum9HkSe1W97N31TTOHdpM4pl9/PTVKMpUb0pY1cZO/as26oVPQDibv3iEpHN/EH90G7/98CalKze09QmpcAudRi6hbI2WZKcXv1/uTCYT7du3Z/HixRw4cIDY2Fg++ugjatWqRY0aNZz6p6enk5KSYvtr3rw5YWFhfPTRR1gsFry9valduzarV68mNjaWU6dOsXLlSqpVq4avr68LtvDGmIyetK99H4v3vsaBc1uITdzHRz+NplaZptQIc95XWtccQEZOKl//PJG41COsPzSHHccX0anOQ05974l4ndNJfxTFZhSOGziuAPgOuZ3S3w7Fmup8FtarZTVM5QNIfvp7cg+ez1vnsyvwrBWG563lC3NrCkRYWBhPPvkkbdq04eLFi1ft27p1azIyMvj666+Ji4tj/fr17Nixg06d8n5oCg4OplmzZsybN49jx45x+PBh5s6dS9OmTQkODi6CrSlAhfAZZPD1xCuiCpc+jib3QDy5f8ST9t/teDYojyGoVFFu3XW73uPKnbUGsyd2JRsOfcaFtJPsjl3B2j8+omWNfgA0qdKDHHMWX+56jnMphzlyYRfzdr1A3XKtCfG98ntRRFQklXiedcIx+nuTHR1razOfSSH3VBJejSvl+xyvxpXI2XUKrJfbsqNj8WyUd8D1alQRLFay95y2Lc/ZcxrMlrxhMm4suHw9PEsFEH/08rCWS4mnSEs4SZlqzZz6l7+5DecObSEn43Lxc2zXt6yZ3dP2uNxNdxB/LJqVMzqTnZlSuBtQCCpXroyPjw8HDx60tV28eJELFy5w0003XfW5gYGBdO3alcWLF5OSkrftOTk5ZGVl0bx5c0qVKoW3tzfNmzcnLi6OjIyMQt2WglQ5uB4+ngEcjL+8r1y8dIoLabHcVKapU/9aZZpxKH4HVrs3Tkz8dmqWaeLQr02toQT5lOH7/TMLL/hCdiPHFQDv9rVIeWkNqa9tcFqW89tZEh/5Duul7MuNlrxcGt38iy9AzZo1SUhI4JVXXuHChfyHTf2lVq1aHDp0CKvVbl+JiaFmzZq2dVmtVg4fPmxbfuTIEaxWK7Vq1SqcDSgkhfEZZM02Y03PxqdXfQx+Xhh8PfHpWZ/cE4lYU5wLcHdyvceVFftnsXzfDIc2q9WCr1cQAL+e/oH//jTSNvQub3le4vz+7CMi+StW1yQNGjSIQYMG0b17d1tbeno67dq1Y+XKlYSGhjr0X7hwIdHR0UydOrXIYpw1K+9ak9GjRzu0r127llmzZmG1WqlUqRJTpkwhKMj1Byhj2QAAzPGO47Qt8Zcwlgu44nNyDsT9rX8aRl8vDME+GMsGYElIh9zLB2XMViwJ6RjLBxbsBhQw36C8X6TTk885tGekxOMb7PxrdUBYdeKO/ESDTmOp1igSrBC7fyV7V0/Hkpt3bcCBje8VfuCFKCQkBIDExESH9qSkJNuyK7nrrrtITU1l06ZNtjaLxcJnn33GkCFDeOuttwBISUlh+vTpDl8K3V2Ib97+kJjuuK8kZcTl+wttiG85YhP3O7QlZ8Th7eGLn1cIl7ITCQ+oTq/bxvHGuv6U8nDvs65XcyPHFYDE+78BwKtpZadllvg0sv+2Pr8HI7CkZ5P9s/tfs7Vjxw527Njxj/qGhIQQGxvr0JacnIy3tzd+fn6EhISQkpKCxXL5GGuxWEhJSbnme9LdFMZnkDUpg+TnVhL0cifCd4wBqxXLxXQShs1zKKzc0fUeV04k7HV4XMrDnzY3DWH/2Q0AXEg7yYW0kw597qr7CInpZzmdHFOAkYv87ylWZ5KioqJYvny5Q9uaNWuIiIhwKpDcSVpaGhMnTuTDDz9k6dKl1K5d21ZMuZrBxxOr2eJY0ADW7FwM3vnX0AYfD6xZ5r/1z3ts8DZhKOW8/K8+Bi/3vnbAw8sHi8WM1ZLr0G7JzcLk4e3U39M7gJpNBuAfWpWtX4xg9/KXqXJrD5pFFV1hXti8vLywWCwOX8gAcnNz8fT0vOLzvL29adWqFatXr3YqfsqVK8fp06d58803mT59OnFxcTz66KN4ezvn2F15eeTtKxar476Sa8nG0+S8HV4mH3LMjhfV55rzzop4mrwxGkzc3/wt1hx4v3gPtePGjivXy+fuhvgNvp20NzdhTXbvswPXy8vLi5wcx0lucnPz9jNPT0+8vLxsj//e52rvSXdUGJ9BAB41Qsk5eIGEe+eTcM98zMcTCJ7ZG4Ove+fneo8r9jxNpXj0zv/iaSrFol+m5dsn8rZnaFCxA1/tmuBwdklEnBWrIqlLly7s3r3bYQaspUuXEhUVxdixY+nevTs9evRg8eLF/2h9q1ev5vHHHwfg+PHj1K5d2zYMYvjw4ezdu5cTJ05w3333ERkZycCBA/n9998BuHDhAiNGjCAqKoo+ffrw008/OazbbDYzZswYXnvtNXJycpg4cSJly5YFoHbt2pw9e/bfJeMG+T0UQfiux2x/pgqBGExGMBkc+hm8PLBm5D8TnTUz16nY+euxNSMHa5bz8r/6XGmd7iI3JxOj0YTB6Bi/0cOb3GznoWAWSw5ZGUls//pxEk7v5fTvP7Bn+StUb9wHL9/gIoq6cGVnZ2M0GjEaHQ8XHh4eV51Jq2HDhhiNRqdfz2vVqkXPnj35+OOPOXToEEeOHOG9994jNDSUli1bFso2FIbs3Lx9xWhw3Fc8jF5k5TrPypVjzsTT6DgZgYfJ6891pdO1/iisWFl9IP8LtN1ZQRxXruv1Hm5O0Ev/R9qH20n/as+/Xp+7ycnJcSp2PDzyCobs7Gyys7Ntj//ex91ntyuKzyDPxhXxH92a5KeXk7PrFDm7T5M4ejGm8oH4RN5SOBtWQK73uPIXP68Qnmj3FVVC6jNzwzAS0k87LDcYjAxqMolOdR/hq53Ps/f02kKJX+R/SbEabufn50eHDh1YtWoVAwYMIC4ujmPHjhEdHU1ISAjLly8nISGBfv36UadOnWuur1WrVkyaNAmr1cq2bdsoXbo00dHRtG/fnmPHjtGgQQMGDhzIiy++SL169Th8+DAjR45k9erVTJ48mT59+tChQwfi4+MZNGiQrTizWq1MmDCBcuXK8fTTTwPQsWNHADIzM/nwww8ZOnRooeXpatK//pXMVZdPsRuDSsFjd2As44/l3OVpdI3hflh+zH+qVMu5VIxl/BzajOH+WC5lY03Nylte2heMBts1A5gMGEN9scS79/Sr6UlnAPAJCCc9+XIh6xMYTkbKOaf+GclxmHOzHH6RS447BIBfSGWy05MKN+Ai8Ncwu6CgIIchd8HBwfz6669XfN5tt93Gb7/9RnZ2tkN7jRo1SE5OJjn58nVcGRkZxMXFUaZMmQKOvvAkpuftK0E+4SSmX95Xgn3K8muG876SkH6WQJ9wh7Ygn7Jk5qSRkZNKi+r9CPIJ5+0++wAwGvKK0pe6/sDK/bNZ+fs7hbUp/1pBHFf+EQMEvvh/+N7dkNTpG7n0SfS/CdttJSQkEBjoODQ5KCiIzMxMMjIySExMJCAgAIPBYDtLazQaCQwMdPtp9IviM8jztgpYzl/Ccv6Sbbk1NQvz8QRMVdx7OOL1HlcASvtV4rG2cynl6c/0df2dzkR7GL15qNU71C/fhk+2Pc7OE0sKbwNE/ocUqyIJoE+fPrz99tsMGDCAZcuW0bNnTzZv3sx//pM3XW5oaCgdOnQgOjoaf/+rj+n39/enRo0axMTEsH37du655x527tyJn58fERERpKens2/fPp599lnbc9LT00lMTOSnn37i6NGjzJyZd3F1bm6ubQz5/PnzSU1NZd26dQ6vl5qayogRI6hTpw6RkZEFmZZ/zJqcidluaIrZ04QlLQuvppXJXJZ3lsxUIRCPSsFk74rNdx3Zu0/hE9nAoc27WRVydp/Ou3fJ7tNgMuLZsEJeG+B1eyUwGvKWubGkswfIyUwlvEZzju9ZBIBfSCX8Q6sQf8z5eoLzx6Op2WwgBqOHbYheULnaWMy5XErMP3/FzalTp8jIyODmm2+2nRUqXbo0YWFhHDp06IrPq1WrFsuWLXNqT0xMJDAwkICAANt9ljw9PQkLC2Pbtm2FsxGF4FTSATJyUrk5vDk7juftK6X9KhHmX5lD8c5f3o+c30mLP2ec+kvt8BYcubALK1beWHc3JuPlQ3LV0AY82OodZm24l9PJ7j38riCOK/9E4ISO+PS5leTnVpCxeP+1n1BMHTlyhBYtWji01a5d2zY5w5EjRzAajdSoUcM2rX6tWrUwGAy2x+6qKD6D/vqhzhjqm3d9LEApD0yVgzEvce/95nqPKwHepXmy/XwsVjPTfoji4iXHnBkw8HDrd6ldtiXvbBrO7+c2Oa1DRPJX7IqkJk2acP78ec6ePcvSpUuZPXu2w0XhkHcmx2x2viYmP23atGHr1q0cPXqUiRMnMmzYMIxGI+3atcNiseDl5cWSJZd/dTl37hzBwcG2i8//mm41Li6OsLAw1q5dS6NGjahXrx6TJk2yFVHx8fEMHz6c5s2b89xzz+UXimvkmEmf9wsB49piSUzHcjGdwBf/j+zok+Ts/fNXLE8jxiAfLMkZkGMh47vf8Lu/GYETO3Hp8114t6hKqe51SXxoAZB3AW3mqj8IerUzyRNW5f36+8pdZC793e3PJFnM2RzaNpeG3Z4n61ICmWkXaRI5ibgj27h4cg9GkydePsFkZyRhMedwePsX3NzyXlrc/Ra/rX0b36DyNOr6HMd3f/c/cRYJ8n4A2LhxI3369CEtLY2UlBQGDRpETEwMx44dw2Qy4efnx6VLl2zvu8DAQIKCgjh92rko3rt3LwkJCTz44IMsWLCA3NxcevbsSU5OjsPNMt1driWbjYfm0qfhc6RlJZCSeZFBTV4lJm4bxy7uwWT0xM8rmEvZSZgtOWw5+jWd6j7M4Kb/YV3MJ9Qt25pmVXsxc+M9AE7DYwJLlbG1F7ubPt7AceVavO+sge/ARqS9s5WsLccxhl0+k2BJyYTsf3bMd0d/fw9t2bKFTp06MXjwYNatW0fdunVp1qyZ7fMkKSmJn3/+mWHDhvH5558DefdN2r59u9ufSXJSCJ9BWRuOYD6XStAbPUh9fQPkmPEf3RprZi4Zbl4kXe9xZWCTV/H3DuHNHweSY860HTesWEnNvECbm4Zya8WOfL7jaU4lHbAtB0jLSnS69klELitW1yT9JTIykvfee4+goCCqVKlC8+bNWbAg7+CYkJDAunXraNbMebrm/LRp04b58+dTq1YtQkJC8PT0ZP369bRq1YqAgACqVatmK5K2bt1qu1lf8+bN+eqrrwA4fPgwPXv2tE1fXKdOHR588EEOHTrE+vXrMZvNPPLII3Tp0oXnn38eg8GQfzAukjZzM5nLfyd4WjdC5wzAfCaFxMeX2pZ7NaxI+KYReDXMm77bcjGdxIcW4FEnnLDv7sF30O0kj19B9o7LM+ikvLia7F9OE/J+H0JmR5K94yTJr/xQ5Nt2I/aueZ0TexbTYsAM2j88n0uJp9nyRd6NU8OqNibyhZ9t90zKTLvA2vf74eUbTOcxK2g5cCax+1ayc9HzrtyEArdkyRKio6O5//77GTt2LAkJCXzwQd4NdmvWrMnrr79um54YsM3ceOnSJad1ZWVl8eabb5Kamsro0aN58sknMRgMvP7662RmFq8L8JfsnU70iSXc3+JtxrafR8Kl03ywJe+muDXDGvN65C5q/nlvk9TMC8zccA+VQ+ozofP3tL15GJ9uf5KYuJ+u9hLF1vUeV66lVPe8G6n6j2xF+KYRDn+l7qpdKNtQVP7+HkpNTWXmzJlUrlyZCRMm0LZtWz799FNiYi4PU5s7dy5Hjx5l9OjRjBgxgpiYGNtnUnFT0J9B1vQcEu77GktiOiEf9CH007vBAAlD5zlOIe+m/ulxxdPkTaNKnSnl6c9zdy3j9chdtr9pvfLO+jer2huAYRGvOSx/PXIX1Us3dNEWihQPBmtxmnP3T+fOnaNDhw5MnjyZ3r1722aPi4mJwWw2c++999K/f/9/PAV4x44dGT58OAMHDmTq1KkcPHiQTz75BMgb9jBx4kSSkpLw9PRk4sSJ3HrrrcTFxfHiiy9y5kze+OGnnnqKNm3aOEwBvmPHDsaPH89jjz3G+PHjqV378gf5LbfcwuTJk//5Ntd7/XrT9D9vfQ/3mCHQnWxI6uLqENxT21WujsDtvPzqKFeH4JZeuuPwtTuVMC9vLl73XioKL70w29UhuK0PBp5wdQhyHYry+2W538f9q+efOXOGcePGcfHiRapXr8706dPx83O8PjE+Pp5nn32WCxcuYDQaefrpp2nRogU5OTlERERQufLlW0wsXLgQk+nKsy4XyyKpJFKR5ExFkjMVSVegIsmJiqT8qUhypiLJmYqkK1ORVLwUpyLp4YcfpmfPnnTr1o133nmH9PR0xo1zXOdTTz1Fw4YNGTJkCEePHmXo0KFs2rSJAwcO8NZbb/Hxxx//49crdtck3YixY8c63Jn8L+3bt+exxx5zQUQiIiIiIiVHSkoKKSkpTu2BgYFOM3r+XU5ODjt37uSdd/Jmeo2KimLIkCFORVKnTp2IiIgAoGrVqmRlZZGens5vv/1GQkIC/fv3B/KKqWtdmlMiiqQ33njD1SGIiIiIiJRYn332GbNnO5+FHTVqFKNHj77qcxMTE/H397fdI65MmTLExcU59evUqZPt3x9//DF169a13TKhQ4cOjBw5kgMHDvDggw+ybNkyQkNDr/iaJaJIEhERERERR2/eVbfIXmvCPXfmewucv59FWrlyJVOmTHFoq1atmtPzrjYR2pw5c/j666/54osvABgwYIBtWb169bj11lvZvXu37T6m+VGRJCIiIiIiheqfDKsD6NKlC126OF5j/dfEC2azGZPJxPnz5wkPD8/3+a+99hobN27kyy+/pFy5cgAsXryY22+/nSpVqgB5twvy9PS8ahzFcgpwEREREREpGTw9PWnSpAkrVqwA8oqeO++806nfnDlz2LFjB/PmzbMVSAAxMTG2mauPHj3KgQMHaNy48VVfU2eSRERERETErb300kuMHz+e9957j/Lly/Pmm28CMG/ePOLj4xkzZgzvvPMO/v7+DB061Pa8Dz/8kJEjR/Lcc8/RvXt3DAYD06ZNw9/f/6qvpyJJRERERETcWsWKFZk7d65T+8CBA23/3rlz5xWfP3PmzOt6PQ23ExERERERsaMiSURERERExI6KJBERERERETsqkkREREREROyoSBIREREREbGjIklERERERMSOiiQRERERERE7KpJERERERETsqEgSERERERGxoyJJRERERETEjookEREREREROyqSRERERERE7KhIEhERERERsaMiSURERERExI6KJBERERERETsqkkREREREROx4uDoA+WdeemG2q0NwO21/cXUE7uflzbVcHYJbeonOrg5Bigm9h5y9dMdhV4fgfjZ0hrarXB2FiBQinUkSERERuR4qkET+56lIEhERERERsaMiSURERERExI6KJBERERERETsqkkREREREROyoSBIREREREbGjIklERERERMSOiiQRERERERE7KpJERERERETsqEgSERERERGxoyJJRERERETEjookEREREREROyqSRERERERE7KhIEhERERERsaMiSURERERExI6KJBERERERETsqkkREREREROyoSBIREREREbGjIklERERERMSOiiQRERERERE7KpJERERERETsqEgSERERERGxoyJJRERERETEjookEREREREROx6uDkBcy2Aw0uvWp2hZvR/eHn7sP7uReT+/QGrmhXz7N6nSnc71RhAeUJ3kjHi2HJnPmj8+wGq1OPXtVOdh+jR6jofnVS3szShQBoORW+8aR/XG/fDw9uPswY38vHgCmWn556TV4Hepcmt3h7Zzh7aw/qNBTn0rN+hK6yHvs3RqSy4lniqU+AuV0YD/Y63x6X0LBj8vsjcfI2XSWiwX06/4lFKda+P3UHNMVYKxXLhExoK9XPpkJ1isAHjUDSfgqbZ41i+LNTOXrE1HSX1jI9bkzKLaqhtmMBjo1asXLVu2xNvbm/379zNv3jxSU1Pz7V+7dm0iIyOpUKECKSkpbNq0iTVr1tiW+/v7069fP+rXr4/BYOCPP/7g22+/JSkpqYi2qAAVwr5iz/f+pgQ+1ZZz9V4vzK0oWDeQE4/6ZQl8tgOedcMxx6eR9t42Mpfuty03hPgQ+Ew7vFtXBwNk7ThJ6rT1WOLSimKLCsygQYMwmUzMnTv3in2qVq1K//79qVKlComJiaxYsYLt27fblnt6enL33XfTqFEjjEYjP//8M99++y1ZWVlFsQkF5no/l6uGNqD/7ROpElKfxIxzrNg3k+3HF9qWlw+8iX63T6BGWGNyzdnsjl3Jwl+nkpmT/3FKRPLoTFIJ1+OWJ2hRrS+fbn+C6ev6E+Jbjkdav59v3/rl23J/ixlsOfI1r6y8i0W/TuOueo/Spd4op74Vg+vQ89YnCzv8QnHL/z1BtcZ92f7NE6x7vx++QeVoPeSDK/YPKlebX1ZMYdGrjW1/W7581KlfqYBwmkZOKczQC53/yJb49LqF5PErSBg6D2O5AILf7nXF/l53VCfote5kLNjLxcjPSH1zE37DI/B7qDkAxjJ+hH7SH/OpJC4O+pKkJ5bi2aA8wW/2LKpN+ld69OhBixYt+PTTT5k+fTohISE88sgj+fYtU6YMo0aN4rfffuOVV15h4cKFdO/enbZt29r6PPDAA4SFhTFjxgzeeustgoODefRR532pOCjofcWex81lCBjdujDDLxTXmxNDiA+h/+1HzoE4LvT9nPQvfibo1bvwalnN1id4eg9MFYNIePBbEoZ/i6mMPyEzexf+xhSgHj160KZNm6v28ff3Z8yYMcTGxjJp0iTWr1/PsGHDqFu3rq3PkCFDqFmzJrNnz+add97h5ptvZvDgwYUdfoG7ns9lf+9QxrSdS2ziPiat6sb6mDkMi3iNuuXuAMDbw5fH233JpaxkpqzuxbubHuCm8GbcGzG9KDdJpFhSkVSCmYyetK99H4v3vsaBc1uITdzHRz+NplaZptQIa+zU/85ag9kTu5INhz7jQtpJdseuYO0fH9GyRj+n9d7X/C2OXthTVJtSYIwmT2q3up+9q6Zx7tBmEs/s46evRlGmelPCqjrnxGjyIqB0NS7G/kJm2nnbX05GslPfiH6vk3TuQFFsRuHwNOI7tDFpb28ie9sJcg/Ekzx2GV6NK+HZsEK+T/G9+zYyfzhI+ld7MMcmkbXmIJc+24VP5C0AlOpSB2uWmZSXf8B8NIGcPadJmfQD3i2qYiwfUJRbd91MJhPt27dn8eLFHDhwgNjYWD766CNq1apFjRo1nPrXr1+f7Oxsvv/+ey5cuMDu3bv57bffqFevHgDe3t7Url2b1atXExsby6lTp1i5ciXVqlXD19e3qDfv3ymEfcV+3UFTu5L9y5ki2JACdCM56XsrltQsUv+zDvOxBNK/3EPG8t/xu68pAAZfT7wiqnDp42hyD8ST+0c8af/djmeD8hiCShXl1t2QsLAwnnzySdq0acPFixev2rd169ZkZGTw9ddfExcXx/r169mxYwedOnUCIDg4mGbNmjFv3jyOHTvG4cOHmTt3Lk2bNiU4OLgItqZgXO/ncuuaA8jISeXrnycSl3qE9YfmsOP4IjrVeQiAUN+KHL6wk7nRzxCXeoSjF3ez+fBX1Cnbsqg3TaTYUZFUglUOroePZwAH4y8PV7h46RQX0mK5qUxTp/4r9s9i+b4ZDm1WqwVfryCHtl63PkVSRhxbj35dOIEXouDy9fAsFUD80cs5uZR4irSEk5Sp1sypf2B4TYwmT1LiD191vbWaD8UnIJz962YWeMxFxbNOOEZ/b7KjY21t5jMp5J5KwqtxpXyfk/b+dtLe/cmx0WrFGJj3BS5r/RGSxi51HE7158jNv/q4q8qVK+Pj48PBgwdtbRcvXuTChQvcdNNNTv3T0tLw9/enadOmGAwGKlSowE033cSJEycAyMnJISsri+bNm1OqVCm8vb1p3rw5cXFxZGRkFNl2FYTC2Ff+4j/mDizxaWQs/K3A4y5MN5ITr8aVyNl1CuzeHtnRsXg2yiuqrNlmrOnZ+PSqj8HPC4OvJz4965N7IhFrivsPV61ZsyYJCQm88sorXLiQ/1Cyv9SqVYtDhw5htV5ORkxMDDVr1rSty2q1cvjw5WPxkSNHsFqt1KpVq3A2oBBc7+dyrTLNOBS/A6vdThITv52aZZoAcDblEP/dOpJsc94xJDygOs2rRfH7uc2FvCUixV+xuiZp0KBBDBo0iO7dL1//kZ6eTrt27Vi5ciWhoaEO/RcuXEh0dDRTp04tshhnzZoFwOjRox3af/jhB2bOnInFYqFBgwa88soreHl5FVlc+QnxLQ9AYvo5h/akjDhCfJ1/2TyRsNfhcSkPf9rcNIT9ZzfY2m4q04yW1fvxysrO1C3XquCDLmS+QXk5SU92zElGSjy+weWd+geVrY05N4sGnZ6kfO22mHMyObn3e/b/OAtLbt44+ICw6tx219Os+6AfHqXc++zI1RjL5sVujne81sESfwljufy3K3efYx4Nfl743t2QrC3H8tYVm4Q5Nsmhj9/wZpjPpZJ76OpfmlwtJCQEgMTERIf2pKQk2zJ7u3fvZsuWLdx///3cd999mEwmdu3axYoVKwCwWCx89tlnDBkyhLfeeguAlJQUpk+f7vDFsDgojH0FwLNxJXwjb+FC7zl4tShe1zreSE6MZQPIORD3t/5pGH29MAT7YE3KIPm5lQS93InwHWPAasVyMZ2EYfMcCit3tWPHDnbs2PGP+oaEhBAbG+vQlpycjLe3N35+foSEhJCSkoLFcvn6WIvFQkpKSr7vR3d1vZ/LIb7liE3c79CWnBGHt4cvfl4hXMq+fHya0HkFlUPqcyEtlve2PFQI0Yv8bylWZ5KioqJYvny5Q9uaNWuIiIhwKpDcSXp6Oq+88gqffvop33//PVlZWSxatMjVYeHl4YPFYsZizXVoz7Vk42nyvupzPU2lePTO/+JpKsWiX6YBeUXTvc3f5OvdE0nJjC+0uAuTh1deTqwWx5xYcrMweTjnJKjszYCBlPjDbPz0XvatfZuazQbQLCrv2iOD0UTzu9/mwMb3STr3R1FsQqEx+HhiNVsg13GSDmt2Lgbvf/B7SykPgmf1xlDKg9S3NuXbxf+JO/FuW5OUV3/I92J9d+Ll5YXFYnH4UgaQm5uLp6enU38fHx9Kly7NmjVrmDJlCp9++il169Z1+NGnXLlynD59mjfffJPp06cTFxfHo48+irf31d+P7qYw9hWDnxdBU7qQ8p91WC5cKoywC9WN5MTg44E1y/y3/nmPDd4mADxqhJJz8AIJ984n4Z75mI8nEDyzNwZf532wOPPy8iInJ8ehLTc37zjt6emJl5eX7fHf++T3fnRX1/u57GXyIcfsODFFrjkbwKn/Zzue5vW1fUnOiOeJ9vPwNLn32XoRVytWRVKXLl3YvXu3w0xPS5cuJSoqirFjx9K9e3d69OjB4sWL/9H6Vq9ezeOPPw7A8ePHqV27tu2U//Dhw9m7dy8nTpzgvvvuIzIykoEDB/L7778DcOHCBUaMGEFUVBR9+vThp58ch4mYzWbGjBnDa6+9hq+vLz/++CNhYWGkp6dz8eJFAgMD/3U+/q3s3EyMRhNGg8mh3cPoRVbulWdb8vMK4Yl2X1ElpD4zNwwjIf00AHc3fokTCXvZeWJpocZdmHJz8nJiMDrmxOjhTW6285CnvWteZ/GkxsRs+ZjkczGc+GUJu5e+TPXGffHyDaZ++9FgtXBg43tFtQkFxu+hCMJ3PWb7M1UIxGAygsng0M/g5YE1I+cKa/mzT7APoR/3x7NeWRIeWoDlTIpjB6OBwBf/D7/hzUh55Qey1h8p6M0pcNnZ2RiNRoxGx8Ooh4dHvrNpRUVFYbFYWLRoEbGxsWzfvp0FCxbQuXNn/Pz8qFWrFj179uTjjz/m0KFDHDlyhPfee4/Q0FBatnTv6weKYl8JeK49ufvjyFxRPH5sKIicWDNzMXiZ/tY/77E1IwfPxhXxH92a5KeXk7PrFDm7T5M4ejGm8oHO13IVczk5OU7FjodHXnGZnZ1Ndna27fHf+xSn2e2u93M5x5yJp9FxVIqHyevPdTn2j03cx+HzO3l/yyOU8atCw0p3FXD0Iv9bitVwOz8/Pzp06MCqVasYMGAAcXFxHDt2jOjoaEJCQli+fDkJCQn069ePOnXqXHN9rVq1YtKkSVitVrZt20bp0qWJjo6mffv2HDt2jAYNGjBw4EBefPFF6tWrx+HDhxk5ciSrV69m8uTJ9OnThw4dOhAfH8+gQYNsxZnVamXChAmUK1eOp59+Gsj7pWvjxo08/fTThIeH07q162dmSkzPu/A5yCecxPSztvZgn7L8mnEu3+eU9qvEY23nUsrTn+nr+nM66fIXlpY1+pOdm8mMvnmFpOnPQmNG39/5cudzRJ9YXEhbUnDSk/Jy4hMQTnry5Zz4BIaTkZJPTqxWsv82ScNfZ4x8gypQvXFffALL0uflvOEQBkPeF+quT65l/4+z+H39O4WxGQUi/etfyVwVY3tsDCoFj92BsYw/lnOXp441hvth+fHK0w2bKgQS8lE/DH5eJAybT+7B844dvEwEv9kT7zuqk/zM92R+Xzwmt/hrmF1QUJDDkLvg4GB+/fVXp/41atTgl19+cWg7duwYHh4ehIaGUqNGDZKTk0lOvrw/ZWRkEBcXR5kyZQpnIwpIUewrvpENsGbmEL7rMYC8ggMI3/UYKRPXkLncvfabgsiJ5VwqxjJ+Dm3GcH8sl7KxpmbheVsFLOcvYTl/+cyaNTUL8/EETFWKzxCzfyIhIcHpx8WgoCAyMzPJyMggMTGRgIAADAaDbXiq0WgkMDCwWE2hf72fywnpZwn0CXdoC/IpS2ZOGhk5qZT2q0Sl4Lr8evoH2/KUzHjSshMJ8SlXSFsh8r+hWBVJAH369OHtt99mwIABLFu2jJ49e7J582b+85//ABAaGkqHDh2Ijo7G39//quvy9/enRo0axMTEsH37du655x527tyJn58fERERpKens2/fPp599lnbc9LT00lMTOSnn37i6NGjzJyZdyF+bm6ubbz0/PnzSU1NZd26dQ6v16ZNG3bs2MGbb77JxIkTeeONNwoyNdftVNIBMnJSuTm8OTuO5w3/K+1XiTD/yhyKj3bqH+Bdmifbz8diNTPthyguXnIcHz5h2Z0Oj2+r1Il+jSYwaVUXUq5wfwd3k3T2ADmZqYTXaM7xPXk58QuphH9oFeKPOY+dbzX4XQxGD7bMvTy+O7TSrZhzMkm7eJx1H9yN0XT5bRZa8VZaDX6HDZ/cQ7KbD7+zJmditrtXkdnThCUtC6+mlclc9mchXCEQj0rBZO+KzXcdxlBfQubcDRYrCYO+wnz6b7P+GSD4rZ54RVQhccRCsrceL6zNKXCnTp0iIyODm2++2XZdRenSpQkLC+PQoUNO/RMTE6lYsaJDW8WKFbFYLJw/f55y5coRGBhIQECA7T5Lnp6ehIWFsW3btsLfoH+hKPaV853/6/DYu30tAp9ux8Woz9xy+F1B5CR79yl8Ihs4tHk3q0LO7tNg/bOIKu2LMdQXS8KfZw1KeWCqHIx5yf581lh8HTlyhBYtWji01a5d2zY5w5EjRzAajdSoUYMjR/LORNeqVQuDwWB7XBxc7+fykfM7afG3GWZrh7fgyIVdWLFSLfQ2hrecyTNLImz3WSrtV5nAUmGcSXE+TonIZcWuSGrSpAnnz5/n7NmzLF26lNmzZ7Npk+P1DVarFbPZfIU1OGrTpg1bt27l6NGjTJw4kWHDhmE0GmnXrh0WiwUvLy+WLFli63/u3DmCg4NtF1n/NbVoXFwcYWFhrF27lkaNGlGvXj0mTZrEzJkzSUpKYt++fbazRz169OCJJ54omIT8C7mWbDYemkufhs+RlpVASuZFBjV5lZi4bRy7uAeT0RM/r2AuZSdhtuQwsMmr+HuH8OaPA8kxZxJYKu/XbStWUjMvcD7thMP6/zog/73dnVnM2RzaNpeG3Z4n61ICmWkXaRI5ibgj27h4cg9GkydePsFkZyRhMedw8rcVtBo4m9p3PMDp/T8QUrE+jbo9z4FNH5KbnU5utuNwh1IBeb/4pSeddjoD5fZyzKTP+4WAcW2xJKZjuZhO4Iv/R3b0SXL2/vmLp6cRY5APluQMyLEQ+EJHjCG+JNz3NdasXIxhf/4q/ucF5r4DGlGqXS2SJ6wiN+b85eWAJSnD6foNd5Kbm8vGjRvp06cPaWlppKSkMGjQIGJiYjh27Bgmkwk/Pz8uXbqE2Wzmxx9/ZOTIkXTt2pXo6GjKly9P37592bhxI5mZmezdu5eEhAQefPBBFixYQG5uLj179iQnJ8fhhpnFQiHsK+aTSQ4v8dcNWP/e7rZuICcZ3/2G3/3NCJzYiUuf78K7RVVKda9L4kMLAMjacATzuVSC3uhB6usbIMeM/+jWWDNzySjmRdLf3z9btmyhU6dODB48mHXr1lG3bl2aNWtm+6EyKSmJn3/+mWHDhvH5558DefdN2r59e7E6k3S9n8tbjn5Np7oPM7jpf1gX8wl1y7amWdVezNx4DwB7z6zjQtpJhreYwTe7X6GUpz8DGr/MkfM/s//MehdvrYh7K3ZFEkBkZCTvvfceQUFBVKlShebNm7NgwQImTJhAQkIC69atY9asWcTExFxzXW3atOGRRx7hlltuISQkBE9PT9avX8+TTz6Jn58f1apVY8mSJfTq1YutW7fy4osvsnbtWpo3b85XX33FiBEjOHz4sO3ADVCnTh0efPBBevXqxfr162nYsCHjxo3ju+++o0KFCqxcuZLbb7+9sNP0jyzZOx2T0ZP7W7yNyeDB/rMb+WrXCwDUDGvM2A5f88a6uzl2cQ+NKnXGaDTx3F3LHNZhtuQy4uuargi/UOxd8zpGkwctBszAYPLgbMxGdi2eAEBY1cZ0ePgb1n3Qn/ij24ndu5ztHt7UvfNhbr3rabLSLhCz5RN+3+C+w+j+jbSZmzF4Ggme1g08TGRtOUbKq2tty70aViT0swEk3DOf7L1n8e54EwaTkbBvhjqsx5prIe7WNyjVPe9GkEGTOju91sUhX+X9Yu7GlixZgslk4v7778dkMrF//36++uorIG9K4rFjx/LGG29w8OBB9u3bx/vvv0+3bt246667SElJYfPmzaxcuRKArKws3nzzTfr06cPo0aNtv4C//vrrZGa6/3TOf1fQ+8r/guvKyc5YLBfTSXxoAQHPdSDsu3swn0khefwKsnecBMCankPCfV8T8FQbQj7og8FgIHvPaRKGzsN6KdtVm1kg/v7+SU1NZebMmdx9991MmDCBixcv8umnnzp8zs+dO5cBAwYwevRozGYzu3fv5ptvvnHhVtyYf/q5fDB+O6mZF5i54R7ubjyRCZ2/5+Kl03y6/Uli4vKuk84xZzJjw1D6NXqBpzp+C1Yre06t5ts9rzpMGy4izgzW4ja3LHlnczp06MDkyZPp3bs3aWlpTJw4kZiYGMxmM/feey/9+/f/x1OAd+zYkeHDhzNw4ECmTp3KwYMH+eSTT4C8U/wTJ04kKSkJT09PJk6cyK233kpcXBwvvvgiZ87kjR9+6qmnaNOmjcMU4Dt27GD8+PEsX76cbdu2MWPGDAwGA7Vq1eLll18mIOCfTwf98LziNd1tUWj7S7HbdQtdu2Wjr92pBHrpjqvfx6okenlz8bl3jLiW3j/5aLvK1RG4rQ8GFp/RIwJPP7H82p0KyGtvdb92JzdSLIukkkhFkjMVSc5UJOVPX/KcqUiSf0rvn3yoSLoiFUnFi4qkKyuWw+2u19ixYx3uwv2X9u3b89hjj7kgIhERERERcVclokhy9SxyIiIiIiJSfBSrm8mKiIiIiIgUNhVJIiIiIiIidlQkiYiIiIiI2CkR1ySJiIiIiEjxdebMGcaNG8fFixepXr0606dPx8/Pz6lPt27dqFKlCgBhYWF8/PHHZGdn8/zzz7Nv3z5KlSrF9OnTqVnz6vf41JkkERERERFxay+//DKDBg1i1apV3HLLLbz77rtOfX777Td69OjBkiVLWLJkCR9//DGQd7NpHx8fVq5cyXPPPcf48eOv+XoqkkREREREpFClpKRw6tQpp7+UlJRrPjcnJ4edO3dy1113ARAVFcWqVc73K/vtt984ePAgUVFRDBs2jJiYGAA2bNhAz549AWjatCmJiYmcOXPmqq+p4XYiIiIiIlKoPvvsM2bPnu3UPmrUKEaPHn3V5yYmJuLv74+HR17pUqZMGeLi4pz6eXt707t3bwYMGMDGjRsZOXIkK1asID4+njJlytj6lSlThnPnzlGhQoUrvqaKJBERERGREuiBxF5F9lrl7kkkMjLSqT0wMNDh8cqVK5kyZYpDW7Vq1ZyeZzAYnNrsi602bdrwxhtvcPTo0XzjMRqvPqBORZKIiIiIiBSqwMBAp4IoP126dKFLly4ObTk5OURERGA2mzGZTJw/f57w8HCn586dO5fu3bsTEhICgNVqxcPDg/DwcM6fP0/VqlUBrvh8e7omSURERERE3JanpydNmjRhxYoVACxevJg777zTqd/OnTtZsGABANHR0VgsFmrUqEGbNm1YsmQJALt27cLb2/uqQ+1ARZKIiIiIiLi5l156iW+++YauXbuya9cuHn/8cQDmzZvHjBkzAHj++ef56aef6N69O9OmTeONN97AaDQydOhQsrOz6datG5MnT+a111675utpuJ2IiIiIiLi1ihUrMnfuXKf2gQMH2v5dtmxZPv30U6c+3t7eTJs27bpeT2eSRERERERE7KhIEhERERERsaMiSURERERExI6KJBERERERETsqkkREREREROyoSBIREREREbGjIklERERERMSOiiQRERERERE7KpJERERERETseLg6AJEb1bbpaVeH4HZeajjb1SG4pYmep1wdgvvZPNXVEbill17Qe0j+gQ2dXR2B+xro6gBECobOJImIiIiIiNhRkSQiIiIiImJHRZKIiIiIiIgdFUkiIiIiIiJ2VCSJiIiIiIjYUZEkIiIiIiJiR0WSiIiIiIiIHRVJIiIiIiIidlQkiYiIiIiI2FGRJCIiIiIiYkdFkoiIiIiIiB0VSSIiIiIiInZUJImIiIiIiNhRkSQiIiIiImJHRZKIiIiIiIgdFUkiIiIiIiJ2VCSJiIiIiIjYUZEkIiIiIiJiR0WSiIiIiIiIHRVJIiIiIiIidlQkiYiIiIiI2FGRJCIiIiIiYkdFkoiIiIiIiB0PVwcgrmUwGOl161O0rN4Pbw8/9p/dyLyfXyA188JVnxfmX4UXOq/ipe/bk5RxztZ+S/l2jG47x6n/M4sjHPq5M7MFPv6hNKt2B5CebaTZTek83vM8of7maz53/Oflycg2MuOB07a27TG+jP+8glPfb54+RnjQtdfpDq53P6ka2oD+t0+kSkh9EjPOsWLfTLYfXwhAi+p9ubf5G/k+b+vRb/h8x7jC2owCd737ysR5ZdmwL8Ch7faa6bx5/xkAMrMNzP4+jE2/+2O2QNtb0hjZ9QK+3tZC35YCZzTg/1hrfHrfgsHPi+zNx0iZtBbLxfRrPtVUOZjSi+7hQrePscSlXV5lmB8B49vh3bwqVouVzNUxpL25CWtGTmFuSYG43vdQkyrd6VxvBOEB1UnOiGfLkfms+eMDrFYLAJVDbqFPw2epGtqA7NxM9p1dz3e//If07OSi3Kx/pSCPKwDlA2+i3+0TqBHWmFxzNrtjV7Lw16lk5qQW0RYVvEGDBmEymZg7d+4V+1StWpX+/ftTpUoVEhMTWbFiBdu3b7ct9/T05O6776ZRo0YYjUZ+/vlnvv32W7KysopiE0SKLRVJJVyPW56gRbW+fLr9CdKykhjU5FUeaf0+r6/te8XnhAdUZ0ybzyjl6ee0rGJwbU4m7GPWxnsd2q9VdLmTOetCWb0ngOf6xRHoY+GtZWV48atyzH7o9FWftzQ6kO0xftxWPcOh/WicFzeVz2TqPWcd2kP8ikeBBNe3n/h7hzKm7Vx2nljC5zuepl65OxgW8RrJmec5cG4zu04uY//ZjQ7PaVWjP13qj2JdzMdFtUkF4nr3laNx3jx01wXuanT5S5uXx+UC6I0lZTh4uhRThp7BbDEwbWE4by4JZ0L/uELfloLmP7IlPr1uIXn8CixJGQS++H8Ev92LhKHzrvo8U9UQQj7oi9HXy3GBh5GQj/qBxUri6MVYM3MIfL4jwbMjSRz+TSFuScG4nvdQ/fJtub/FDL7Z/Qr7zq6nSsgtDGk2FZPRkxX7ZxLkE84T7b5kd+xK5v/8En5ewQxqOomHWr3L2+sHu2DrbkxBHle8PXx5vN2XHIzfzpTVvfDzCmJoxDTujZjO+1sedsHW/Xs9evSgTZs2bNmy5Yp9/P39GTNmDDt37uTzzz+nXr16DBs2jOTkZA4cOADAkCFDqFKlCrNnz8ZkMjFs2DAGDx7MJ598UlSbIlIsabhdCWYyetK+9n0s3vsaB85tITZxHx/9NJpaZZpSI6xxvs9pf/N9PHfXMtJzUvJdXiGoNqeT/yAl87zDn5Xi8Ut4Ti58ty2YBzpdpEmtDG6umMWLd59j3wkf9p0odcXnnbroyUdrSlO/SobTsmNxXtQol03pALPDn7GYvPuudz9pXXMAGTmpfP3zROJSj7D+0Bx2HF9EpzoPAZBjznLYN7xMpehSfxQL9kzidNIfRb15N+x695XsXDh90ZO6lbIc9oMAn7wzA/HJJtb9GsDjPc9Tv0oWt1bLZFxkPOv2+nM+2VTUm/fveBrxHdqYtLc3kb3tBLkH4kkeuwyvxpXwbOh8VvUvvkNup/S3Q7GmZjot825TA8+by5D0xBJy9pwm90A8SWOX4hVRBc8mlQpza/61630P3VlrMHtiV7Lh0GdcSDvJ7tgVrP3jI1rW6AdAkyo9yDFn8eWu5ziXcpgjF3Yxb9cL1C3XmhDfK+fXnRT0cSXUtyKHL+xkbvQzxKUe4ejF3Ww+/BV1yrYs6k3718LCwnjyySdp06YNFy9evGrf1q1bk5GRwddff01cXBzr169nx44ddOrUCYDg4GCaNWvGvHnzOHbsGIcPH2bu3Lk0bdqU4ODgItgakeKrmHxNk8JQObgePp4BHIy/fFr+4qVTXEiL5aYyTfN9zm2VOvFF9LMs2DMp3+UVgmtzNvlwocRbFA6f9SY9y0hDu7NB5UNyKReSw94rFElmC0xZEM7AOxOpWibbafmxOG+q5NNeXFzvflKrTDMOxe9wKIxj4rdTs0yTfNcf1fA5Tif9webDXxV88IXoeveVk+e9MFsMV9wX9p/0wWCABlUvr++WKpkYDfDbCZ+C34BC5FknHKO/N9nRsbY285kUck8l4dX4ygWNd/tapLy0htTXNjgtM1UNwXw+DfOJJFubJS4NS2I6Xk0rF2T4Be5630Mr9s9i+b4ZDm1WqwVfryAAfj39A//9aaRt6F3e8rz3m9+ffdxdQR9XzqYc4r9bR5Jtznv/hAdUp3m1KH4/t7mQt6Tg1axZk4SEBF555RUuXLj6KIxatWpx6NAh2/8/QExMDDVr1rSty2q1cvjw5c/lI0eOYLVaqVWrVuFsgMj/iGJVJA0aNIjly5c7tKWnpxMREUFCQoJT/4ULFzJ+/PiiCg+AWbNmMWvWrCsu37BhA+3bty/CiK4sxLc8AInpjtcKJWXEXfHXyLd+HMiuk8vyXWYwGCkXUJOqoQ2Y0Hkl03pF8+gd/6VsQI2CDbwQnU/JG4FaJjDXoT0sIJf4pPxHp365MQQDcHfrJKdlZgucPO/JwTOlGD6rMn2mVuP5ueU4ed6zoEMvNNe7n4T4liMpw3F4WHJGHN4evvh5hTi0VwquS+MqXVn062vF5mzjX653XzkW54WnycqcdaH0f60qQ9+qwkc/hJKVY8hbX7IHIX5mPOxOGnmY8oZlxicXr5HRxrJ5112Z49Mc2i3xlzCWC8jvKQAk3v8NmSvzP5toiU/DGFQKg8/l947B1xNjkA/GUN8CiLrwXO976ETCXs6mHLI9LuXhT5ubhrD/7AYALqSd5PD5nQ7PuavuIySmn+V0ckwBR184CvO4MqHzCl7tvgE/7xC+3fNqAUde+Hbs2MGcOXNIScl/xIa9kJAQkpKSHNqSk5Px9vbGz8+PkJAQUlJSsFguF9QWi4WUlBRCQkIQkSsrVkVSVFSUU5G0Zs0aIiIiCA0NdVFU/9yFCxeYNm2aq8Ow8fLwwWIxY7E6fsnLtWTjafK+7vWV8a+Kl0cpPIxefBE9ng+3jsDT5MVTHb8lwLt0QYVdqDJzDBgNVocvqgCeHlaycw1O/WNOe/PNlmDG943Pd/jcmQRPsnON5OQaeKp3PC8NOEe22cCY/1YkMa14DKG63v3Ey+RDjtnxguBcc97Zk7/371D7fo5e2M3B+G0FHHXhu9595Xi8F1agSplspg47yz3tE/h+VyBvLiljW5+Xp3OheKX1uTODjydWswVyLQ7t1uxcDN43VvBlbT6GNS2bwJc7YQjwxuDvReDETmC1YvB07/fSvznWeppK8eid/8XTVIpFv+T/+RF52zM0qNiBr3ZNcDi75M4K87jy2Y6neX1tX5Iz4nmi/Tw8TVceKl3ceXl5kZPjOHFJbm5eTj09PfHy8rI9/nsfT8/i82OdiCsUqyKpS5cu7N692+FXk6VLlxIVFcXYsWPp3r07PXr0YPHixf9ofatXr+bxxx8H4Pjx49SuXdt2anv48OHs3buXEydOcN999xEZGcnAgQP5/fffgbyCZ8SIEURFRdGnTx9++uknh3WbzWbGjBnDa6+9ZmubMGECo0aNuvEEFLDs3EyMRhNGg+MXDA+jF1m5156B6u/iU4/xxHe38t7mBzme8CtHLuzivc0PYzQYiageVVBhFypvDysWq4Hcv82pkJNrwMfL8QtsVo6B/3xbluH/l0Cl0vnPrlU5LIelzx9l0uCz1K2cd53Jq4POYbEaWPPLlX9RdyfXu5/kmDPxNDpedO9h8vpzXZf7exi9ub1y12I3zO4v17OvAAzvmMDC8cfo1yqZGuWy6XhbGqO7XWD1nkCS0414e+ZfDOWtz72/+Po9FEH4rsdsf6YKgRhMRjA5bo/By+OGZ6KzJmeSOHIRnreUI3zbaMpseBTz2VRy/4jHmubes3Td6LHWzyuEJ9p9RZWQ+szcMIyEdMcJQQwGI4OaTKJT3Uf4aufz7D29tlDiLwyFdVwBiE3cx+HzO3l/yyOU8atCw0p3FXD07iMnJ8ep2PHwyPshIjs7m+zsbNvjv/fR7HYiV1esxnD4+fnRoUMHVq1axYABA4iLi+PYsWNER0cTEhLC8uXLSUhIoF+/ftSpU+ea62vVqhWTJk3CarWybds2SpcuTXR0NO3bt+fYsWM0aNCAgQMH8uKLL1KvXj0OHz7MyJEjWb16NZMnT6ZPnz506NCB+Ph4Bg0aZCvOrFYrEyZMoFy5cjz99NMAtllnbrvttsJM0XVJTM+bdjjIJ5zE9MszrwX7lOXXG5yu++/Tz+aYMzmfdpLQP4dWuLvwoLxf3BJSPQgPvvzr24VUD1oFXnLoe+CUNyfOe/HB6tJ8sDrvTFlOrgGrFTq/XIPPHjtJ2eBcAn0dv+CW8rJSISSn2Ayhut79JCH9LIE+4Q5tQT5lycxJI8NuKt465VphMnqy59TqQoq8cF3PvgJgNOK0L9Qol/clJT7Jg/CgXJIumTBbwPTnz1e5Zki8ZCIs0L1nQkz/+lcyV10e5mUMKgWP3YGxjD+Wc5f/z43hflh+TMtvFf9Izq9nuND1Y4yhvlguZUNWLr4/jSJ34W//Kv7CdiPH2tJ+lXis7VxKefozfV1/p0lNPIzePNTqHeqXb8Mn2x5n54klhbcBhaCgjyul/SpRKbguv57+wbY8JTOetOxEQnzKFdJWuF5CQgKBgYEObUFBQWRmZpKRkUFiYiIBAQEYDAbbdUtGo5HAwECnYXoi4qhYnUkC6NOnj23I3bJly+jZsyfbt2+nb9+8KUNDQ0Pp0KED0dHR11yXv78/NWrUICYmhu3bt3PPPfewc+dOduzYQUREBOnp6ezbt49nn32WXr16MXbsWNLT00lMTOSnn35i5syZ9OrViwcffJDc3FxiY/MuUp4/fz7Lly/ngQceAODgwYOsWbOGESNGFFJWbsyppANk5KRyc3hzW1tpv0qE+VfmUPy18/d3t1XsxIy++/H3vjz00dvDj7IB1TmTfLBAYi5sNctn4ett4Zfjl4dnnE304FyiJ7dWc5y5rm6lLL548gQfjYq1/d1RL43aFbP4aFQspQNy2fy7H11erkHSpctvtfQsA7EXPakeXjwmc7je/eTI+Z3cFN7Moa12eAuOXNjlcN3RTWWaEpu4n4wrzJTo7q5nX4G8eyRN+MLxy1rM6VJ4elioWDqHW6pkYLbA/pOX1/fbCR+sVrgln1kT3Yk1ORPzySTbX84f57GkZTlMqGCqEIhHpWCyd8VeZU1XZqoaTOjcgRiCSmFJSIesXDwbV8IQ4E32thMFtSmF4nrfQwHepXmy/XwMBiPTfohyKpAMGHi49bvUKdeKdzYNL3YFEhT8caVa6G083Pp9AkqF2a2vMoGlwjhjd33X/5ojR45w0003ObTVrl3bNjnDkSNHMBqN1Khx+drgWrVqYTAYOHLkSFGHK1KsFI+fsu00adKE8+fPc/bsWZYuXcrs2bPZtGmTQx+r1YrZ/M9+eW3Tpg1bt27l6NGjTJw4kWHDhmE0GmnXrh0WiwUvLy+WLLn8AXTu3DmCg4OxWCx89tlntik04+LiCAsLY+3atTRq1Ih69eoxadIkZs6cyapVqzh//jx9+vQhJyfHdubpq69cO8wo15LNxkNz6dPwOdKyEkjJvMigJq8SE7eNYxf3YDJ64ucVzKXsJMyWaw+RORS/nYycNO5v8Rbf/TIFo8GDyNvGkZaVyPZji4pgi/49Lw/oFZHMeyvDCPK1EOJn5q1lZbitegb1q2SRkwspGSYCfcx4e1qdhtn5elvwsmtvWD0DP28Lk78tyyOdL2I2G/jvD6EE+Zr5v4bF4waH17ufbDn6NZ3qPszgpv9hXcwn1C3bmmZVezFz4z0O660cUr9YTfn9d9ezr3h6QJtbLvHK12X5ZkswreqmceisN++tLM3drZPw9bbi622m7S1pvL4onKej4rFaYfriMvxfw1TKFJObDtvkmEmf9wsB49piSUzHcjGdwBf/j+zok+Ts/fOsgacRY5APluQMyLn2cELz6RSMZf0JfL4DabO3YioXQNDUbmR89xvmk0mFuz3/0vW+hwY2eRV/7xDe/HEgOeZMAkvlXbdmxUpq5gXa3DSUWyt25PMdT3Mq6YBtOUBaVqLTdT7uqKCPK3vPrONC2kmG/3l/qVKe/gxo/DJHzv/M/jPrXby1BcdkMuHn58elS5cwm81s2bKFTp06MXjwYNatW0fdunVp1qwZM2fOBCApKYmff/6ZYcOG8fnnnwN5903avn27ziSJXEOxO5MEEBkZyXvvvUdQUBBVqlShefPmLFiwAMg79bxu3TqaNWt2jbXkadOmDfPnz6dWrVqEhITg6enJ+vXradWqFQEBAVSrVs1WJG3dupXBg/Nu1Ne8eXNbkXP48GF69uxJRkber7116tThwQcf5NChQ6xfv54xY8awevVqlixZwocffkh4eLjLC6S/LNk7negTS7i/xduMbT+PhEun+WDLowDUDGvM65G7qHmFeyb9XXpOCm+vH4zZksvY9l8ztsN8snIzePPHgeRais/Y5+EdL/J/t6Uy+duyPPFxBcoF5/DywLwvdvtO+tBnanX2nfxnUzIH+Fh44/7TeBjh8Y8q8vjHFfHxsvLW8DN453ORvru6nv0kNfMCMzfcQ+WQ+kzo/D1tbx7Gp9ufJCbO8bq9IJ9wLmUnFfWmFKjr2VfaNUhjfJ94Vu4O4L6ZVXhvZRh9WiRzf4fLM3OOi4ynfpVMxn9WnglflOf2Ghk82eu8S7bt30qbuZnM5b8TPK0boXMGYD6TQuLjS23LvRpWJHzTCLwaVvxnK8y1kDRiIcYwP0ovvIegKV3JWLyPlFd/uPZz3cA/fQ95mrxpVKkzpTz9ee6uZbweucv2N63XDgCaVe0NwLCI1xyWvx65i+qlG7poC69fQR5XcsyZzNgwlMycNJ7q+C2j7vyE2MTfmbnxnmI3c+bV1KxZk9dff902xXdqaiozZ86kcuXKTJgwgbZt2/Lpp58SE3N5+OvcuXM5evQoo0ePZsSIEcTExLjNdxARd2aw2k+uX0ycO3eODh06MHnyZHr37k1aWhoTJ04kJiYGs9nMvffeS//+/Vm4cCHR0dFMnTr1quvr2LEjw4cPZ+DAgUydOpWDBw/a7kR95MgRJk6cSFJSEp6enkycOJFbb72VuLg4XnzxRc6cyRtX/dRTT9GmTRvb9N+jR49mx44djB8/nuXLl+Pn5wfAqVOnGDZsGD/++ON1bfPD86peb5r+5030POXqENzOxBz3vqmmq2hfcWZ48erHxZLqpRdmuzoEKQ42dHZ1BG7rgw8+cHUIch0O3lt0s4PePKd4jYoolkVSSaQiyZm++DpTkZQ/7SvOVCTlT0WS/CMqkq5IRVLxoiLpyordNUk3YuzYsQ53m/5L+/bteeyxx1wQkYiIiIiIuKsSUSS98cYbrg5BRERERESKiWI5cYOIiIiIiEhhUZEkIiIiIiJiR0WSiIiIiIiIHRVJIiIiIiIidlQkiYiIiIiI2FGRJCIiIiIiYkdFkoiIiIiIiB0VSSIiIiIiInZUJImIiIiIiNhRkSQiIiIiImJHRZKIiIiIiIgdD1cHICIiIiIicjVnzpxh3LhxXLx4kerVqzN9+nT8/Pwc+jzyyCOcPXsWAIvFwsGDB1mwYAF16tQhIiKCypUr2/ouXLgQk8l0xddTkSQiIiIiIm7t5ZdfZtCgQXTr1o133nmHd999l3Hjxjn0ef/9923/njFjBg0bNqRBgwbs27ePRo0a8fHHH//j19NwOxERERERKVQpKSmcOnXK6S8lJeWaz83JyWHnzp3cddddAERFRbFq1aor9j9y5AiLFy/mmWeeAeC3334jISGB/v37079/f6Kjo6/5mjqTJCIiIiIiheqzzz5j9uzZTu2jRo1i9OjRV31uYmIi/v7+eHjklS5lypQhLi7uiv3fe+89hg8fjr+/PwAGg4EOHTowcuRIDhw4wIMPPsiyZcsIDQ294jpUJImIiIiIlEDt24y7dqcC8nufe4iMjHRqDwwMdHi8cuVKpkyZ4tBWrVo1p+cZDIZ8Xyc5OZmtW7cyefJkW9uAAQNs/65Xrx633noru3fvpmPHjleMV0WSiIiIiIgUqsDAQKeCKD9dunShS5cuDm05OTlERERgNpsxmUycP3+e8PDwfJ+/ceNG7rzzTry9vW1tixcv5vbbb6dKlSoAWK1WPD09rxqHrkkSERERERG35enpSZMmTVixYgWQV/Tceeed+fb95ZdfaNKkiUNbTEwMn3zyCQBHjx7lwIEDNG7c+KqvqSJJRERERETc2ksvvcQ333xD165d2bVrF48//jgA8+bNY8aMGbZ+sbGxlC1b1uG5I0eOJCEhge7du/PYY48xbdo02/VKV6LhdiIiIiIi4tYqVqzI3LlzndoHDhzo8Pi///2vUx9/f39mzpx5Xa+nM0kiIiIiIiJ2VCSJiIiIiIjYMVitVqurgxARERERkaJV6dPxRfZap+6bWmSvVRB0JklERERERMSOiiQRERERERE7KpJERERERETsqEgSERERERGxoyJJRERERETEjookEREREREROyqSRERERERE7KhIEhERERERsaMiSURERERExI6KJBERERERETsqkkREREREROyoSBIREREREbGjIklERERERMSOiiQRERERERE7KpJERERERETsqEgSERERERGxoyJJRERERETEjookEREREREROyqSRERERERE7KhIEhERERERsaMiSURERERExI6KJBERERERETsqkkREREREROyoSBIREREREbGjIklERERERMSOiiQRERERERE7KpLsrFq1iqioKHr27EmPHj346KOPrtp/6NCh7Nix47pf58cff+TTTz+90TBFRERERKQQebg6AHcRFxfHtGnTWLhwISEhIVy6dImhQ4dSvXp1OnToUKCvtX///gJdn4iIiIiIFBwVSX9KTEwkJyeHzMxMAPz8/Jg6dSre3t7s3buXKVOmkJmZSUhICC+//DKVK1d2eP6HH37IypUrMZvNtG7dmnHjxmEwGJgzZw7z5s3DZDLRrl07IiMjmT9/PgAVKlSgT58+Rb6tIiIiIiJyZSqS/lSnTh06dOhAx44dqVu3LhEREfTo0YPy5cszevRo3n//fSpUqMDmzZt54YUXmDNnju25mzZtYt++fSxYsACDwcC4ceNYunQp1atX56uvvuK7777Dx8eHBx54gC5dujBgwAAAFUgiIiIiIm5IRZKdl19+mREjRrBlyxa2bNlC//79eeihh4iNjeXRRx+19UtLS3N43rZt29i7dy9RUVEAZGZmUqFCBS5cuEC7du0ICAgAsBVW69evL5oNEhERERGR66Yi6U8bNmwgPT2drl270qdPH/r06cM333zDsmXLqFSpEkuWLAHAbDZz4cIFh+eazWbuuece7rvvPgBSUlIwmUwsWLDAoV9cXBw+Pj5Fs0EiIiIiInJDNLvdn0qVKsUbb7zBqVOnALBarRw+fJiGDRuSnJzMrl27APjuu+946qmnHJ7bvHlzlixZwqVLl8jNzWXkyJGsXr2aJk2asGnTJlv72LFj2bdvHyaTidzc3CLfRhERERERuTadSfpT8+bNGTVqFI888gg5OTkA3HHHHYwePZr27dszefJksrKy8Pf3Z9q0aQ7Pbd++PX/88Qf9+/fHbDZzxx13EBkZicFgYMiQIQwYMACLxcL//d//0bJlSzw9PXnmmWcICwtj6NChrthcERERERG5AoPVarW6OggRERERESlalT4dX2Svdeq+qUX2WgVBw+1ERERERETsqEgSERERERGxoyJJRERERETEjookEREREREROyqSRERERERE7KhIEhERERERsaMiSURERERExI6KJBERERERETsqkkREREREROyoSBIREREREbGjIklERERERMSOiiQRERERERE7KpJERERERETsqEgSERERERGxoyJJRERERETEjookEREREREROyqSRERERERE7KhIEhERERERsaMiSURERERExI6KJBERERERETsqkkREREREROyoSBIRERERkWJhxowZzJo1K99l2dnZjBs3ji5duhAZGcmRI0cAsFqtTJs2jc6dO9O1a1d+/vnna76OiiQREREREXFrqampPPfcc3zyySdX7DN37lx8fHxYuXIlzz33HOPHjwdg9erVHDlyhBUrVvDOO+8wfvx4cnNzr/p6HgUavYiIiIiIyN+kpKSQkpLi1B4YGEhgYOA1n79u3TqqVavGfffdd8U+GzZs4LHHHgOgadOmJCYmcubMGTZu3EjXrl0xGo1Ur16dChUqsGfPHpo2bXrFdalIEhEREREpgU7dN7XIXmvWrFnMnj3bqX3UqFGMHj36ms/v3bu3bT1XEh8fT5kyZWyPy5Qpw7lz54iPjyc8PNyp/WpUJImIiIiISKG65557iIyMdGr/+1mklStXMmXKFIe2GjVqMGfOnBt6XaPRiNVqzbf9alQkiYiIiIhIofqnw+q6dOlCly5dbug1wsPDOX/+PFWrVgXg/PnzhIeHU7ZsWc6fP2/r91f71WjiBhERERERKfbatGnDkiVLANi1axfe3t5UqFCBO++8k2XLlmE2mzlx4gTHjx+nQYMGV12XziSJiIiIiEixNG/ePOLj43nssccYOnQoL774It26dcPLy4vXXnsNgM6dO7N371569uwJwOTJkylVqtRV12uw5jdIT0REREREpITScDsRERERERE7KpJERERERETsqEgSERERERGxoyJJRERERETEjookEREREREROyqSRERERERE7KhIkqtKSEhg1qxZREZGcvvtt9OkSROioqJ45513SEhIcHV4Im7NarWSlpbm1G5/128RESkYixYtcmr78ssvXRCJ/C/QfZLkir788kvWrFlDp06daNKkCRUrVsTDw4NTp06xY8cOvv/+ezp37sywYcNcHaqI29m+fTtPPfUU2dnZ1K1bl9dee42yZcsCEBkZme+HuZRcqampzJw5k3PnztGxY0d69eplW/bCCy/w6quvujA618jNzWXRokUEBgbSqlUrXnrpJQ4ePEjjxo156qmn8Pf3d3WIbmPIkCF88cUXrg7DZebMmUNaWhrz589nwIABtvbc3FyWL1/O2rVrXRidFFcerg5A3FfZsmX57LPPnNpr1apFrVq1GDx4MKtXr3ZBZK6zePHiqy7v3bt3kcThTu69914sFssVl3/++edFGI37eO2115g7dy5Vq1blo48+YsiQIXz55ZeEh4dTkn+b0nsof88++yw333wzTZo04cMPP2TXrl22wmjfvn0ujs41XnjhBbKysrh48SLvvvsubdu25dFHH2XlypW89NJLvPHGG64O0SU6dOjg1BYXF2drX7duXVGH5HJVq1Zl//79Tu3e3t5MnTrVBRHJ/wIVSXJFHTt2vGafu+66qwgicR87duy46vKS+AXvwQcf5Mknn2Ty5MkEBga6Ohy3YbFYqF69OgAPPfQQXl5eDB8+nHnz5mEwGFwcnets376d1atX07lz53yXl8T3EMCpU6eYPXs2AG3atOGhhx5i6tSpjB8/vsQW1fv27WPZsmWkp6fTrl07nnjiCQBGjx5dYvcTyCseX3vtNUaNGsVtt92G1Wrl4Ycf5sMPP3R1aC7Trl072rVrR5cuXcjKyqJevXqkpqayb98+mjRp4urwpJhSkSTXNGfOHN59911SU1OBvOssDAYDBw4ccHFkRW/KlCm2f+fk5HDs2DHMZjM33XQTHh4l8+3UqlUrHn74YTZu3FgihwRdSVhYGF9++SU9e/YkICCAe++9l/j4eO677z6Sk5NdHZ7LTJ06laSkJBo3bkzfvn1dHY5bOX/+PGXKlKFUqVK88847DB48mPfff7/EFtUGg4GEhARCQ0N5/fXXbe3nzp276tnr/3Vt27alQYMGPP/88xw9epQRI0bg5eVFxYoVXR2ayy1atIjff/+dTz75hIyMDN5991127drF6NGjXR2aFEO6JkmuqX379nzxxRdUqFDB1aG4jX379jFmzBiCg4OxWCxcuHCBd955h9tuu83VobmE1WrlyJEj1KpVy9WhuI3z58/z2muvcddddzmclf3rR4fo6GgXRuda8fHxLFu2jOHDh7s6FLexdu1aXn75ZSZOnGgbNnX+/Hkefvhh/vjjD37//XcXR1j0fvjhByZNmsSPP/6IyWQCYOvWrYwbN45XX30132FnJc3cuXNZs2YN58+fZ9WqVa4Ox+W6d+/OkiVLbPtLbm4ukZGRLFu2zMWRSXGkIkmu6cEHH+Sdd97By8vL1aG4jQEDBvDss8/aiqJffvmFSZMmsWDBAhdH5jpbt26lVatWDm1/TfxRkuWXl9WrV5e4oar/REZGBj4+Pq4Ow2XS0tIwm80EBQXZ2iwWC8uWLXOYyKEk+fs+kZycjNVqxdvbu0TvK/YOHjzI6tWrGT16dIl/D3Xu3JnvvvsOPz8/IG//6d+/v4okuSElc3yQXJehQ4fSo0cPbrvtNtuvM+A49KykSU9Pdzhr1LBhQ7KyslwYkeusWLGC7OxsZs6cyZgxY2ztOTk5fPjhhyW2SLpSXnJzc/nggw9KfJE0ffp0nnrqKdvjDRs28Morr/Djjz+6MCrX8vf3d8rL5s2bmTFjRoktknx8fBxyEhQUxMaNG3n55ZdL9L4Cl99DN998MzfffLPeQ+T9gBkVFUX79u0B2LRpE4MHD3ZxVFJcqUiSa5o8eTI9evTQeGc7QUFBrF271jaMau3atQQHB7s2KBdJS0tjz549XLp0yWFiC5PJZLvQuiRSXq7u5MmTTJ06lQceeIBXX32Vw4cPaxYqlJf8KCf5U16c3Xvvvdx+++3s2rULDw8PXn/9derVq+fqsKSY0nA7uaYePXroVPXfHD9+nHHjxnHy5EkAKleuzOuvv26bzawk2rZtGy1atHB1GG5HecmfxWJhwoQJfP/99zzyyCM88MADeHp6ujosl1NenCkn+VNe8rds2TIOHz7Mww8/zJo1a0r0TIjy76hIkmuaMmUKBoOBO++80+EA3LRpUxdG5VoHDhygbt26pKenY7FYdFNDYO/evXzyySckJiY6TFlcUu+T9BflxdFf01xD3oQf8+fP5/bbb6d27doAjBo1ylWhuZTy4kw5yZ/ycmXTp0/n3Llz7N+/n2+//ZZHH32U+vXrM378eFeHJsWQhtvJNf01q5L9jdoMBkOJ/ZIHMGHCBLKzs+nRowc9evRQkQQ888wzDBkyhFq1apXYKYvzo7xcmcFgYODAga4Ow+0oL86Uk/wpL462bNnCokWLiIyMxN/fn08//ZSePXuqSJIbojNJIjfo+PHjfP/996xatYrg4GB69uxJv379XB2Wy0RGRrJo0SJXh+F2lJcrS09P5+TJk9x8881kZmbi6+vr6pDcgvLiTDnJn/LiKCoqiu+++46oqCgWLVpEeno6/fv3Z/ny5a4OTYoho6sDEPc1ZswYtm7desXlGzZsKNE3aKtWrRr33XcfDz30EJcuXeK///2vq0NyiTNnznDmzBnq1q3LnDlziI2NtbWdOXPG1eG5jPJyddu2baNXr16MGDGCCxcu0L59e7Zs2eLqsFxOeXGmnORPeXHWuXNnHn/8cZKTk5kzZw5Dhgyhe/furg5LiimdSZIrunTpErNnz2bDhg3UqVOHcuXKYTKZOH36NPv27aNjx46MHDmyRA41W7NmDcuXL2fv3r20bduWnj17cvvtt7s6LJdo3749BoOB/A4lBoOBdevWuSAq11Nerq5fv368++67PPjggyxevJjDhw/z5JNPsnTpUleH5lLKizPlJH/KS/42b97MTz/9hMVioXnz5rRr187VIUkxpWuS5Ir8/Px45plnGDlyJNu3b+fEiRMYjUYaNmzI5MmTS/Rp/b9u7vjGG284zSZ0/vx5ypQp46LIil5JvifH1SgvV2exWBzeJ7Vq1XJhNO5DeXGmnORPebls586dtn+XKlXKdp+kv5aV5Imm5MapSJJr8vf3t90PSPLMmjXrisseeuihEnkNyrPPPuvw2GAwUKpUKWrWrEm/fv3w8vJyUWSupbzkr1y5cqxfvx6DwUBKSgpffvklFSpUcHVYLqe8OFNO8qe8XDZz5swrLivpE03JjdNwO5EC1rt3bxYvXuzqMIrchAkTSE5Ott2TYsWKFeTm5lKmTBkuXbrElClTXBugiygv+bt48SKTJ0/mp59+wmq1EhERwYQJEwgPD3d1aC6lvDhTTvKnvIgULhVJIgWspM5mFhUVxcKFC22PrVYr/fr1Y8GCBfTs2bPEjpNXXq4uKSmJ4OBgV4fhdpQXZ8pJ/pSXy06fPs2ECRM4ffo0X375JWPHjuU///kPlSpVcnVoUgxpdjv5R9LS0jh79qxm55IrysjI4Pz587bHFy9eJCsrCwCz2eyqsFxOecnfgQMH6Ny5M7179yYuLo7/+7//c7gXW0mlvDhTTvKnvDh78cUXGT58OL6+voSFhdG9e3eeeeYZV4clxZSKJLmm999/nzvvvJPBgwczZMgQhgwZwtChQ10dlriZ0aNHExUVxZgxYxg1ahR9+/ZlzJgxzJo1i5YtW7o6PJdRXvI3adIk3nnnHYKDgylbtiwTJ07kpZdecnVYLqe8OFNO8qe8OEtMTKR169ZA3rVI/fv3Jy0tzcVRSXGliRvkmhYsWMDatWsJDQ11dSjFQkkdwdq1a1eaN2/Ozz//jNFo5JVXXiE0NJSmTZuW6KEgykv+MjIyqFmzpu1xq1atmDZtmgsjcg/KizPlJH/Ki7NSpUpx7tw5DAYDALt27Sqxk+PIv6ciSa6pfPnyBAUFuToMt3CtYYYVKlRgwoQJRRSNe/j666+5++67mT17tkP7gQMHABg1apQrwnI55eXqgoOD+eOPP2xfZpYuXarjDMpLfpST/Ckvzp599lkefvhhTp48Sa9evUhOTmbGjBmuDkuKKRVJck3VqlVj0KBBREREOPwiUxK/5A0ZMuSaNwht0qSJCyJznZJ65uxalJermzhxIs888wyHDh2iSZMmVK1alenTp7s6LJdTXpwpJ/lTXhytX7+eWrVqsWDBAj788EN27NhB27ZtqV+/vqtDk2JKs9vJNf39l/C/lMQiSUQKxm+//UaDBg1IT0/HYrHg7+/v6pDcgvLiTDnJn/Jy2ccff8yKFSuYNm0aubm5DBgwgOeff57Dhw9jsVh4/vnnXR2iFEMqkuQfSUhI4Ndff8VsNtOwYUPCwsJcHZJLHT16lK+++or09HSsVisWi4VTp07x5Zdfujo0l1m0aBFTp04lJSUFyDuTYjAYbMPLSirlJX8PPPAAx48fJyIignbt2tGqVSt8fHxcHZbLKS/OlJP8KS+X9ezZk6+//hofHx+mT5/OmTNnePPNN7FarXTt2pWVK1e6OkQphlQkyTVt3ryZ5557joYNG2KxWNizZw+TJ0+mXbt2rg7NZXr16kWHDh1Yv349kZGRbNq0iUqVKjFx4kRXh+YyHTp04L333uPmm292dShuRXm5sqysLLZv387mzZtZv3491atX56OPPnJ1WC6nvDhTTvKnvOTp1asXS5YsAaBfv34MGjSIyMhIALp06aIiSW6IrkmSa3rrrbf46quvqFy5MgCxsbGMGjWqRBdJFouFMWPGkJubS7169RgwYAADBgxwdVguVbZsWRUC+VBe8peQkEB0dDTR0dHs2rWLoKAgbrrpJleH5XLKizPlJH/Ky2Umk4mUlBTS09M5cOAArVq1AvJuLuvhoa+6cmO058g15ebm2gokgMqVK2OxWFwYkev5+PiQnZ1NtWrV2L9/P02aNLHdILSkql+/PmPGjKFVq1Z4e3vb2nv37u26oNyA8pK/li1bEhYWxrBhw5g7d26Jn5XrL8qLM+Ukf8rLZQ899BC9e/cmNzeXvn37Eh4ezooVK3jrrbcYOXKkq8OTYkrD7eSaHnnkEZo3b07fvn2BvPsmbd++nffff9/FkbnOF198wY8//sj06dO5++67qVq1KhaLhU8++cTVobnMs88+m2/7lClTijgS96K85O/48eNs27aNHTt2cOzYMWrVqkVERAT9+/d3dWgupbw4U07yp7w4iouLIzExkTp16gCwceNGSpUqRUREhIsjk+JKRZJc08WLF3n11VfZvn07VquV5s2b8/zzzxMeHu7q0FwqLS0Nf39/zp07x2+//Ubr1q1L7EWzAFu3brUNcfjLmjVr6NSpk4sicg/Ky9X98ccf/PTTT8yfPx/Iy40oL/lRTvKnvIgUDhVJIjfg7rvv5uuvv7Y9NpvN9O7dm2XLlrkwKtdYsWIF2dnZzJw5kzFjxtjac3Nz+eCDD/jhhx9cGJ3rKC9X98QTT7B7925q1KhBmzZtuPPOO6lRo4arw3I55cWZcpI/5UWkcOmaJLmihx9+mA8++ID27dvb7uhtb926dS6IyrWGDRtGdHQ0gO2UPoCHhwft27d3VVgulZaWxp49e7h06RI7duywtZtMJp544gkXRuZaysvVdenShVdffTXfe7vMmjWL0aNHuyAq11NenCkn+VNeRAqXziTJFcXHxxMeHs7p06fzXV6xYsUijsh9TJo0iQkTJrg6DLcyd+5chg4d6uow3I7ycv0iIyNZtGiRq8NwO8qLM+Ukf8qLyL9ndHUA4r7+uuZo6tSpVKxY0eHvueeec3F0rvXcc8/x1VdfMWbMGEaMGMHnn39e4mf8sx9+KJcpL9dPv93lT3lxppzkT3kR+fc03E6uaOTIkfzxxx/ExcXRoUMHW7vZbKZcuXIujMz1Xn/9dU6cOEGfPn2wWq0sXLiQ2NhYnn/+eVeH5jLlypVj2LBh3HbbbQ5TXY8aNcqFUbme8nL98hveK8pLfpST/CkvIv+eiiS5omnTppGUlMTkyZMdhpZ5eHhQunRpF0bmelu3bmXx4sUYjXknY9u2bUuPHj1cHJVrNWzY0NUhuCXlRUREpPhRkSRXdOLECerXr899993HmTNnHJadPHmSpk2buigy1zObzeTm5uLl5WV7bDKZXByVa40aNYqEhAR+/fVXzGYzDRs2JCwszNVhuZzyIiIiUvyoSJIrmjdvHpMmTWLWrFlOywwGA59//rkLonIPPXr0YNiwYXTr1g2A77//3vbvkmrz5s0899xzNGzYEIvFwosvvsjkyZNp166dq0NzKeXl+tWsWdPVIbgl5cWZcpI/5UXk39PsdnLd/rqJakm3ceNGhxvstm3b1tUhuVRUVBQzZsygcuXKAMTGxjJq1CiWLFni4shcS3lx9Oyzz151+ZQpU4ooEveivDhTTvKnvIgUDZ1Jkmtav349u3btYsSIEfTt25eEhATGjBnD4MGDXR2ay7z66qu88MILtGnTxtb2zDPPMG3aNBdG5Vq5ubm2QgCgcuXKJX7GP1Be/q5Zs2auDsEtKS/OlJP8KS8iRUNFklzT7Nmzee2111ixYgW33norL774IkOHDi2RRdLzzz9PbGws+/bt49ChQ7Z2s9lMSkqKCyNzvQoVKjBnzhz69u0LwIIFC0r0vbT+orw4ioyMtP374MGDREdHk5ubS0REBHXr1nVhZK6lvDhTTvKnvIgUDd0nSf6RmjVrsmHDBtq3b4+fnx85OTmuDsklHn30UUaOHEmlSpUYNWqU7e/JJ59k7ty5rg7PpSZPnswvv/xCx44d6dChA3v27OGVV15xdVgup7zkb/HixYwYMYJTp05x5swZRo0axYIFC1wdlsspL86Uk/wpLyKFzCpyDQ899JD1lVdesd5xxx3WS5cuWadMmWJ96KGHXB2W2+rdu7erQyhSQ4cOtVqtVus777zj4kjci/JydT179rQmJCTYHl+8eNHarVs3F0bkHpQXZ8pJ/pQXkcKl4XZyTW+88QZr167lnnvuwdfXl8qVK+tGmFdhLWFzoZw+fZq33nqL7777Lt9rbUrqvqK8XJ3FYiEkJMT2ODQ0VDfARHnJj3KSP+VFpHCpSJJr8vPz49KlS0yfPt027tnX19fVYbmtkvYhNWvWLNavX+/qMNyO8pK/pKQkgoODqV27NpMnT3a4VqtOnToujs51lBdnykn+lBeRoqEpwOWapk2bxokTJ+jTpw9Wq5WFCxdSsWJFnn/+eVeH5pYiIyNZtGiRq8Mochs3bnSY7c/erFmzGD16dBFH5B6UF0cRERE0b96cqKgooqOj2bFjBxaLhYiICEaOHFliby+gvDhTTvKnvIgUDRVJck09e/Zk8eLFGI1583zk5ubSo0cPVq5c6eLI3FNJLZKuRjnJX0nMS0ZGBmvWrGHp0qUcO3aMXr16ERUV5TBNekmkvDhTTvKnvIgUDRVJck3dunVj0aJFeHl5AZCVlUWfPn1Yvny5iyNzT71792bx4sWuDsOtKCf5K+l5iY+PZ9myZSxdupTg4GD69u1Ljx49XB2WyykvzpST/CkvIoVHRZJc0/vvv8+GDRvo1q0bAN9//z1t27blkUcecXFkrvPRRx/Rq1cvypQp47RsxYoVdO3a1QVRua+SeMbkn1Be8pw7d453332XhQsXsm/fPleH4zaUF2fKSf6UF5GCp4kb5JoeeeQR6taty/bt222P27Zt69qgXCwzM5MhQ4ZQtWpVIiMj6dixI56engAqkET+gZSUFFatWsWyZcu4cOECkZGRrFu3ztVhuZzy4kw5yZ/yIlK4VCTJP5KTk0N2djYeHh62YqAk++smsrt27WL58uXMmjWL5s2b069fP93xXOQqVqxYwdKlS9mzZw8dOnTgscceo0mTJq4Oy+WUF2fKSf6UF5GioSJJrmnq1Kn88ssvdOvWDYvFwowZM9i3bx8PP/ywq0NzqYyMDE6dOkVsbCxGo5GgoCAmT55Mo0aNGDt2rKvDcys1a9Z0dQhuqSTm5csvvyQqKoo333xTtxKwo7w4U07yp7yIFA1dkyTXdNddd/H999/j4ZFXU2dlZdG7d+8SPbvd2LFj2b59O23atCEqKsr2K152djatW7cmOjraxREWnWefffaqy6dMmVJEkbgX5UVERKT40pkkuabSpUuTkpJCaGgokDf0zv4u3yVRixYtePXVV51+xfPy8uL77793UVSu0axZM1eH4JaUFxERkeJLZ5Lkmh599FH27dtH+/bt8fDwYNOmTZQuXZrq1asDJesX8dmzZ191+ahRo4ooEvd08OBBoqOjyc3NJSIiQtdn/Ul5ERERKV50JkmuqVOnTnTq1Mn2+JZbbnFhNOKuFi9ezOzZs+nYsSMWi4VRo0bx6KOP0rdvX1eH5lLKi4iISPGjM0kiUiB69erFnDlzbEMxExISGDZsWIm/6bDyIiIiUvzoTJLIDfj222958803SUpKAsBqtWIwGDhw4IBrA3Mhi8XicK1aaGgoBoPBhRG5B+VFRESk+FGRJHID3nvvPT7//HNuuukmV4ficklJSQQHB1O7dm0mT55sG0a2YMEC6tSp4+LoXEd5ERERKb403E6u6f777+eTTz5xdRhupV+/fnz77beuDsMtRERE0Lx5c6KiooiOjmbHjh1YLBYiIiIYOXIk/v7+rg7RJZQXERGR4ktFklzToEGDeOONNyhfvryrQ3G5xYsXA7B+/Xqys7Pp0KGD7f5RAL1793ZNYC6UkZHBmjVrWLp0KceOHaNXr15ERUVRuXJlV4fmUsqLiIhI8aUiSa6pS5cuHD9+nNKlS+Pt7W27/mbdunWuDq3I6QahVxcfH8+yZctYunQpwcHB9O3blx49erg6LJdTXkRERIoXFUlyTadPn863vWLFikUcifvYunUrrVq1cmhbs2aNw1TpJdm5c+d49913WbhwIfv27XN1OG5DeRERESkeNHGDXFPFihVZtmwZhw8f5pFHHmH16tUlclgZwIoVK8jOzmbmzJmMGTPG1p6bm8sHH3xQoouklJQUVq1axbJly7hw4QKRkZEl8mzj3ykvIiIixY+KJLmm6dOnc+7cOfbv38+DDz7Id999xx9//MH48eNdHVqRS0tLY8+ePVy6dIkdO3bY2k0mE0888YQLI3OdFStWsHTpUvbs2UOHDh147LHHaNKkiavDcjnlRUREpPjScDu5pt69e7No0SIiIyNZvHgxubm59OzZkxUrVrg6NJfZtm0bLVq0cHUYbmHw4MFERUXRpUsXfH19XR2O21BeREREii+dSZJrMhqNALYbYGZnZ9vaSqqgoCDGjBlDcnIy9r8zfP755y6MyjW+/PJLV4fglpQXERGR4ktFklxT586defzxx0lOTmbOnDksXbqU7t27uzosl3rmmWe4++67uemmm2zFo4iIiIj8b9BwO/lHNm/ezE8//YTFYqF58+a0a9fO1SG5lG4mKyIiIvK/S0WSXNOIESPo2bMn7du3x8vLy9XhuIUZM2YQGhpK69at8fb2trVXqFDBhVGJiIiISEFQkSTXtGHDBpYvX87OnTtp3bo1PXv2JCIiwtVhuVT79u2d2krqDXZFRERE/teoSJJ/LDMzkw0bNvDhhx+SmJjI+vXrXR2SiIiIiEiB08QN8o8cPnyY77//nlWrVlG+fHmGDRvm6pBcKiEhgVdeeYVt27ZhNptp3rw5EydOJCwszNWhiYiIiMi/pDNJck09evTAZDLRs2dPunfvTnh4uKtDcrlRo0bRqFEj7r77biwWC19//TW7du3igw8+cHVoIiIiIvIvqUiSa4qJiaF27dquDsOt9OrViyVLlji09ejRg2XLlrkoIhEREREpKBpuJ1f0wgsv8OqrrzJp0qR87wVUEm+c+heDwcDZs2cpX748AGfOnMHDQ28nERERkf8F+lYnV3T33XcDMHr0aBdH4n4ee+wx7r77bm677TasViu//vorr776qqvDEhEREZECoOF2clVHjx7Fz8+PsmXL2touXrzI22+/XeKLgoSEBPbu3YvFYuG2226j5anVigAAAf5JREFUdOnSrg5JRERERAqAziTJFc2ePZuPP/4YgHfeeYeIiAg+/vhj3n//fRo1auTi6Fxj8eLF+bZv3rwZgN69exddMCIiIiJSKHQmSa6oQ4cOzJs3j/j4eGbOnElOTg4XLlzg6aef5o477nB1eC5Rp04dSpcuTYsWLfD09HRaPmXKFBdEJSIiIiIFSWeS5Ir8/PwIDw8nPDycvXv30rt3bz766CNMJpOrQ3OZRYsWsWLFCrZu3UqdOnXo2rUrLVu2xGg0ujo0ERERESkgOpMkV9S7d2/b8LIuXbqwcuVK1wbkZn777TdWrFjBjh07uOWWW+jWrRsRERGuDktERERE/iWdSZIrsp/2u1SpUi6MxD01aNCABg0asGvXLqZPn86yZcvYs2ePq8MSERERkX9JZ5Lkim655RbbrHZxcXG2f1utVgwGA+vWrXNleC5jtVrZuXMnq1atYtOmTdStW5fOnTvTrl07fH19XR2eiIiIiPxLKpLkik6fPn3V5RUrViyiSNzHSy+9xObNm6lXrx5dunRRYSQiIiLyP0hFksh1qFOnDsHBwbbCyH5IIlBiz66JiIiI/C9RkSRyHXR2TUREROR/n4okERERERERO7q5i4iIiIiIiB0VSSIiIiIiInZUJImIiIiIiNhRkSQiIiIiImJHRZKIiIiIiIid/weV/0huv8TWvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Pearson \n",
    "matrix = df4_sub2.corr(method='pearson')\n",
    "sns.set(rc={'axes.facecolor':'white', 'figure.facecolor':'white'})\n",
    "f, ax = plt.subplots(figsize = (14,10))\n",
    "sns.heatmap(matrix, vmax=1.0,vmin=-1.0,annot_kws={'size': 15}, annot=True, fmt='.2f', cmap='Dark2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "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>Date</th>\n",
       "      <th>River position (m)</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Vol_fortnight1</th>\n",
       "      <th>Vol_fortnight2</th>\n",
       "      <th>Vf_mean</th>\n",
       "      <th>Vol_week1</th>\n",
       "      <th>Vol_week2</th>\n",
       "      <th>Vol_week3</th>\n",
       "      <th>Vol_week4</th>\n",
       "      <th>Vw_mean</th>\n",
       "      <th>Select</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00/10/2018</td>\n",
       "      <td>11.62</td>\n",
       "      <td>182.1</td>\n",
       "      <td>57.4</td>\n",
       "      <td>124.7</td>\n",
       "      <td>91.05</td>\n",
       "      <td>54.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124.7</td>\n",
       "      <td>28.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00/10/2017</td>\n",
       "      <td>11.35</td>\n",
       "      <td>165.0</td>\n",
       "      <td>133.8</td>\n",
       "      <td>31.2</td>\n",
       "      <td>82.50</td>\n",
       "      <td>32.4</td>\n",
       "      <td>101.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.2</td>\n",
       "      <td>31.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00/06/2017</td>\n",
       "      <td>14.43</td>\n",
       "      <td>29.7</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.85</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00/05/2017</td>\n",
       "      <td>11.69</td>\n",
       "      <td>202.7</td>\n",
       "      <td>35.5</td>\n",
       "      <td>167.2</td>\n",
       "      <td>101.35</td>\n",
       "      <td>8.1</td>\n",
       "      <td>27.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>157.8</td>\n",
       "      <td>18.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00/10/2016</td>\n",
       "      <td>17.34</td>\n",
       "      <td>322.6</td>\n",
       "      <td>20.9</td>\n",
       "      <td>301.7</td>\n",
       "      <td>161.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>235.2</td>\n",
       "      <td>66.5</td>\n",
       "      <td>43.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   River position (m)  Monthly_total_vol   Vol_fortnight1  \\\n",
       "0  00/10/2018               11.62               182.1            57.4   \n",
       "1  00/10/2017               11.35               165.0           133.8   \n",
       "2  00/06/2017               14.43                29.7            29.7   \n",
       "3  00/05/2017               11.69               202.7            35.5   \n",
       "4  00/10/2016               17.34               322.6            20.9   \n",
       "\n",
       "   Vol_fortnight2  Vf_mean  Vol_week1  Vol_week2  Vol_week3  Vol_week4  \\\n",
       "0           124.7    91.05       54.1        3.3        0.0      124.7   \n",
       "1            31.2    82.50       32.4      101.4        0.0       31.2   \n",
       "2             0.0    14.85       29.7        0.0        0.0        0.0   \n",
       "3           167.2   101.35        8.1       27.4        9.4      157.8   \n",
       "4           301.7   161.30        0.0       20.9      235.2       66.5   \n",
       "\n",
       "   Vw_mean  Select  \n",
       "0     28.7       1  \n",
       "1     31.8       1  \n",
       "2      0.0       1  \n",
       "3     18.4       1  \n",
       "4     43.7       1  "
      ]
     },
     "execution_count": 534,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4_sub = pd.read_csv('C:/meujupyter/encantado/Encantado_rain_flood.csv', sep= ';', header=0)\n",
    "df4_sub3 = df4_sub\n",
    "df4_sub3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>River position (m)</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Vf_mean</th>\n",
       "      <th>Vw_mean</th>\n",
       "      <th>Select</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00/10/2018</td>\n",
       "      <td>11.62</td>\n",
       "      <td>182.1</td>\n",
       "      <td>91.05</td>\n",
       "      <td>28.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00/10/2017</td>\n",
       "      <td>11.35</td>\n",
       "      <td>165.0</td>\n",
       "      <td>82.50</td>\n",
       "      <td>31.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00/06/2017</td>\n",
       "      <td>14.43</td>\n",
       "      <td>29.7</td>\n",
       "      <td>14.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00/05/2017</td>\n",
       "      <td>11.69</td>\n",
       "      <td>202.7</td>\n",
       "      <td>101.35</td>\n",
       "      <td>18.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00/10/2016</td>\n",
       "      <td>17.34</td>\n",
       "      <td>322.6</td>\n",
       "      <td>161.30</td>\n",
       "      <td>43.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   River position (m)  Monthly_total_vol   Vf_mean  Vw_mean  \\\n",
       "0  00/10/2018               11.62               182.1    91.05     28.7   \n",
       "1  00/10/2017               11.35               165.0    82.50     31.8   \n",
       "2  00/06/2017               14.43                29.7    14.85      0.0   \n",
       "3  00/05/2017               11.69               202.7   101.35     18.4   \n",
       "4  00/10/2016               17.34               322.6   161.30     43.7   \n",
       "\n",
       "   Select  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4_sub = pd.read_csv('C:/meujupyter/encantado/Encantado_rain_flood.csv', sep= ';', header=0, usecols = lambda column : \n",
    "                      column not in ['Vol_fortnight1','Vol_fortnight2','Vol_week1','Vol_week2','Vol_week3','Vol_week4'])\n",
    "df4_sub4 = df4_sub\n",
    "df4_sub4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>River position (m)</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Vf_mean</th>\n",
       "      <th>Vw_mean</th>\n",
       "      <th>Select</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00/10/2018</td>\n",
       "      <td>11.62</td>\n",
       "      <td>182.1</td>\n",
       "      <td>91.05</td>\n",
       "      <td>28.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00/10/2017</td>\n",
       "      <td>11.35</td>\n",
       "      <td>165.0</td>\n",
       "      <td>82.50</td>\n",
       "      <td>31.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00/06/2017</td>\n",
       "      <td>14.43</td>\n",
       "      <td>29.7</td>\n",
       "      <td>14.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00/05/2017</td>\n",
       "      <td>11.69</td>\n",
       "      <td>202.7</td>\n",
       "      <td>101.35</td>\n",
       "      <td>18.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00/10/2016</td>\n",
       "      <td>17.34</td>\n",
       "      <td>322.6</td>\n",
       "      <td>161.30</td>\n",
       "      <td>43.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   River position (m)  Monthly_total_vol   Vf_mean  Vw_mean  \\\n",
       "0  00/10/2018               11.62               182.1    91.05     28.7   \n",
       "1  00/10/2017               11.35               165.0    82.50     31.8   \n",
       "2  00/06/2017               14.43                29.7    14.85      0.0   \n",
       "3  00/05/2017               11.69               202.7   101.35     18.4   \n",
       "4  00/10/2016               17.34               322.6   161.30     43.7   \n",
       "\n",
       "   Select  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 562,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4_sub4.drop(df4_sub4[df4_sub4.Select==2.0].index, inplace=True) \n",
    "df4_sub4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 577,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Pearson 1991-2020\n",
    "matrix = df4_sub4.corr(method='pearson')\n",
    "sns.set(rc={'axes.facecolor':'white', 'figure.facecolor':'white'})\n",
    "f, ax = plt.subplots(figsize = (14,10))\n",
    "sns.heatmap(matrix, vmax=1.0,vmin=-1.0,annot_kws={'size': 15}, annot=True, fmt='.4f', cmap='Accent')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "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>Date</th>\n",
       "      <th>River position (m)</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Vf_mean</th>\n",
       "      <th>Vw_mean</th>\n",
       "      <th>Select</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00/10/2018</td>\n",
       "      <td>11.62</td>\n",
       "      <td>182.1</td>\n",
       "      <td>91.05</td>\n",
       "      <td>28.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00/10/2017</td>\n",
       "      <td>11.35</td>\n",
       "      <td>165.0</td>\n",
       "      <td>82.50</td>\n",
       "      <td>31.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00/06/2017</td>\n",
       "      <td>14.43</td>\n",
       "      <td>29.7</td>\n",
       "      <td>14.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00/05/2017</td>\n",
       "      <td>11.69</td>\n",
       "      <td>202.7</td>\n",
       "      <td>101.35</td>\n",
       "      <td>18.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00/10/2016</td>\n",
       "      <td>17.34</td>\n",
       "      <td>322.6</td>\n",
       "      <td>161.30</td>\n",
       "      <td>43.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   River position (m)  Monthly_total_vol   Vf_mean  Vw_mean  \\\n",
       "0  00/10/2018               11.62               182.1    91.05     28.7   \n",
       "1  00/10/2017               11.35               165.0    82.50     31.8   \n",
       "2  00/06/2017               14.43                29.7    14.85      0.0   \n",
       "3  00/05/2017               11.69               202.7   101.35     18.4   \n",
       "4  00/10/2016               17.34               322.6   161.30     43.7   \n",
       "\n",
       "   Select  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 554,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4_sub = pd.read_csv('C:/meujupyter/encantado/Encantado_rain_flood.csv', sep= ';', header=0, usecols = lambda column : \n",
    "                      column not in ['Vol_fortnight1','Vol_fortnight2','Vol_week1','Vol_week2','Vol_week3','Vol_week4'])\n",
    "df4_sub5 = df4_sub\n",
    "df4_sub5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>River position (m)</th>\n",
       "      <th>Monthly_total_vol</th>\n",
       "      <th>Vf_mean</th>\n",
       "      <th>Vw_mean</th>\n",
       "      <th>Select</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>00/10/1990</td>\n",
       "      <td>13.02</td>\n",
       "      <td>173.8</td>\n",
       "      <td>86.90</td>\n",
       "      <td>39.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>00/05/1990</td>\n",
       "      <td>18.45</td>\n",
       "      <td>80.8</td>\n",
       "      <td>40.40</td>\n",
       "      <td>8.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>00/09/1989</td>\n",
       "      <td>17.98</td>\n",
       "      <td>260.4</td>\n",
       "      <td>130.20</td>\n",
       "      <td>26.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>00/09/1988</td>\n",
       "      <td>15.56</td>\n",
       "      <td>357.8</td>\n",
       "      <td>178.90</td>\n",
       "      <td>88.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>00/08/1983</td>\n",
       "      <td>14.50</td>\n",
       "      <td>162.9</td>\n",
       "      <td>81.45</td>\n",
       "      <td>27.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   River position (m)  Monthly_total_vol   Vf_mean  Vw_mean  \\\n",
       "21  00/10/1990               13.02               173.8    86.90     39.4   \n",
       "22  00/05/1990               18.45                80.8    40.40      8.7   \n",
       "23  00/09/1989               17.98               260.4   130.20     26.4   \n",
       "24  00/09/1988               15.56               357.8   178.90     88.3   \n",
       "25  00/08/1983               14.50               162.9    81.45     27.7   \n",
       "\n",
       "    Select  \n",
       "21       2  \n",
       "22       2  \n",
       "23       2  \n",
       "24       2  \n",
       "25       2  "
      ]
     },
     "execution_count": 555,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4_sub5.drop(df4_sub5[df4_sub5.Select==1.0].index, inplace=True) \n",
    "df4_sub5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 575,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Pearson 1943-1990\n",
    "matrix = df4_sub5.corr(method='pearson')\n",
    "sns.set(rc={'axes.facecolor':'white', 'figure.facecolor':'white'})\n",
    "f, ax = plt.subplots(figsize = (14,10))\n",
    "sns.heatmap(matrix, vmax=1.0,vmin=-1.0,annot_kws={'size': 15}, annot=True, fmt='.4f', cmap='Dark2')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}