pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Pandas](http://pandas.pydata.org/)\n",
    "* DataFrame\n",
    "* Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row 2    row2 col a\n",
       "row1     row1 col a\n",
       "dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sq = pd.Series({'row1': 'row1 col a', 'row 2': 'row2 col a'})\n",
    "sq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['row 2', 'row1'], dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sq.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column_a</th>\n",
       "      <th>column_b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row 2</th>\n",
       "      <td>row2 col a</td>\n",
       "      <td>row2 col b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>row1 col a</td>\n",
       "      <td>row1 col b</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         column_a    column_b\n",
       "row 2  row2 col a  row2 col b\n",
       "row1   row1 col a  row1 col b"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "     'column_a': {'row1': 'row1 col a', 'row 2': 'row2 col a'}, \n",
    "     'column_b': {'row1': 'row1 col b', 'row 2': 'row2 col b'}, \n",
    "    })\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['row 2', 'row1'], dtype='object')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['column_a', 'column_b'], dtype='object')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>new_column_a</th>\n",
       "      <th>new_column_b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row 2</th>\n",
       "      <td>row2 col a</td>\n",
       "      <td>row2 col b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>row1 col a</td>\n",
       "      <td>row1 col b</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      new_column_a new_column_b\n",
       "row 2   row2 col a   row2 col b\n",
       "row1    row1 col a   row1 col b"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['new_column_a', 'new_column_b']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "row 2    row2 col a\n",
       "row1     row1 col a\n",
       "Name: new_column_a, dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(df.new_column_a))\n",
    "df.new_column_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.new_column_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['row2 col a', 'row1 col a'], dtype=object)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(df.new_column_a.values))\n",
    "df.new_column_a.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data\n",
    "Let's read data from train.csv file into memory (assign to `df` variable in our case)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions:\n",
    "\n",
    "1. How many objects (rows) and features (columns) are there?\n",
    "2. What the name and type of features (columns)?\n",
    "3. Are there missing values?\n",
    "4. How many memory is use for keep this data in RAM?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use `.info()` to find the answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10886 entries, 0 to 10885\n",
      "Data columns (total 12 columns):\n",
      "datetime      10886 non-null object\n",
      "season        10886 non-null int64\n",
      "holiday       10886 non-null int64\n",
      "workingday    10886 non-null int64\n",
      "weather       10886 non-null int64\n",
      "temp          10886 non-null float64\n",
      "atemp         10886 non-null float64\n",
      "humidity      10886 non-null int64\n",
      "windspeed     10886 non-null float64\n",
      "casual        10886 non-null int64\n",
      "registered    10886 non-null int64\n",
      "count         10886 non-null int64\n",
      "dtypes: float64(3), int64(8), object(1)\n",
      "memory usage: 1020.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. `RangeIndex: 10886 entries, 0 to 10885` => there're **10886 rows** (objects)\n",
    "2. `Data columns (total 12 columns):` => there're **12 columns** (features)\n",
    "3. `dtypes: float64(3), int64(8), object(1)` => three types (float, int, object)\n",
    "4. `memory usage: 1020.6+ KB` => use about 1MB\n",
    "5. There are not missing data (because each column has the same number non-missing values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count samples & features:  (10886, 12)\n",
      "Are there missing values:  False\n"
     ]
    }
   ],
   "source": [
    "print(\"count samples & features: \", df.shape)\n",
    "print(\"Are there missing values: \", df.isnull().any().any())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions\n",
    "\n",
    "1. How data looks like?\n",
    "2. Are there categorical variables?\n",
    "3. The categorical variables have high or low cardinality (how many unique values they have)?\n",
    "4. Can we optimize memory usage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-01-01 05:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9.84</td>\n",
       "      <td>12.880</td>\n",
       "      <td>75</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011-01-01 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011-01-01 07:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.20</td>\n",
       "      <td>12.880</td>\n",
       "      <td>86</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2011-01-01 08:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011-01-01 09:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.12</td>\n",
       "      <td>17.425</td>\n",
       "      <td>76</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
       "0  2011-01-01 00:00:00       1        0           0        1   9.84  14.395   \n",
       "1  2011-01-01 01:00:00       1        0           0        1   9.02  13.635   \n",
       "2  2011-01-01 02:00:00       1        0           0        1   9.02  13.635   \n",
       "3  2011-01-01 03:00:00       1        0           0        1   9.84  14.395   \n",
       "4  2011-01-01 04:00:00       1        0           0        1   9.84  14.395   \n",
       "5  2011-01-01 05:00:00       1        0           0        2   9.84  12.880   \n",
       "6  2011-01-01 06:00:00       1        0           0        1   9.02  13.635   \n",
       "7  2011-01-01 07:00:00       1        0           0        1   8.20  12.880   \n",
       "8  2011-01-01 08:00:00       1        0           0        1   9.84  14.395   \n",
       "9  2011-01-01 09:00:00       1        0           0        1  13.12  17.425   \n",
       "\n",
       "   humidity  windspeed  casual  registered  count  \n",
       "0        81     0.0000       3          13     16  \n",
       "1        80     0.0000       8          32     40  \n",
       "2        80     0.0000       5          27     32  \n",
       "3        75     0.0000       3          10     13  \n",
       "4        75     0.0000       0           1      1  \n",
       "5        75     6.0032       0           1      1  \n",
       "6        80     0.0000       2           0      2  \n",
       "7        86     0.0000       1           2      3  \n",
       "8        75     0.0000       1           7      8  \n",
       "9        76     0.0000       8           6     14  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.season.unique()\n",
    "#df[''].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.season.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['datetime', 'season', 'holiday', 'workingday', 'weather', 'temp',\n",
       "       'atemp', 'humidity', 'windspeed', 'casual', 'registered', 'count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datetime 10886\n",
      "season 4\n",
      "holiday 2\n",
      "workingday 2\n",
      "weather 4\n",
      "temp 49\n",
      "atemp 60\n",
      "humidity 89\n",
      "windspeed 28\n",
      "casual 309\n",
      "registered 731\n",
      "count 822\n"
     ]
    }
   ],
   "source": [
    "for column in df.columns:\n",
    "    print(column, df[column].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical variables:\n",
    "* season: 4 unique values\n",
    "* holiday: 2 (binary)\n",
    "* workingday: 2 (binary)\n",
    "* weather: 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.holiday.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10886 entries, 0 to 10885\n",
      "Data columns (total 1 columns):\n",
      "holiday    10886 non-null int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 85.1 KB\n"
     ]
    }
   ],
   "source": [
    "df[ ['holiday'] ].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10886 entries, 0 to 10885\n",
      "Data columns (total 1 columns):\n",
      "holiday    10886 non-null int8\n",
      "dtypes: int8(1)\n",
      "memory usage: 10.7 KB\n"
     ]
    }
   ],
   "source": [
    "df['holiday'] = df['holiday'].astype('int8')\n",
    "df[ ['holiday'] ].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10886 entries, 0 to 10885\n",
      "Data columns (total 12 columns):\n",
      "datetime      10886 non-null object\n",
      "season        10886 non-null int8\n",
      "holiday       10886 non-null int8\n",
      "workingday    10886 non-null int8\n",
      "weather       10886 non-null int8\n",
      "temp          10886 non-null float16\n",
      "atemp         10886 non-null float16\n",
      "humidity      10886 non-null int64\n",
      "windspeed     10886 non-null float16\n",
      "casual        10886 non-null int16\n",
      "registered    10886 non-null int16\n",
      "count         10886 non-null int16\n",
      "dtypes: float16(3), int16(3), int64(1), int8(4), object(1)\n",
      "memory usage: 340.3+ KB\n"
     ]
    }
   ],
   "source": [
    "def optimize_memory(df):\n",
    "    for cat_var in ['holiday', 'weather', 'season', 'workingday']:\n",
    "        df[cat_var] = df[cat_var].astype('int8')\n",
    "\n",
    "    for float_var in ['temp', 'atemp', 'windspeed']:\n",
    "        df[float_var] = df[float_var].astype('float16')\n",
    "\n",
    "\n",
    "    for int_var in ['casual', 'registered', 'count']:\n",
    "        df[int_var] = df[int_var].astype('int16')\n",
    "        \n",
    "    return df\n",
    "\n",
    "df = optimize_memory(df)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10886 entries, 0 to 10885\n",
      "Data columns (total 12 columns):\n",
      "datetime      10886 non-null datetime64[ns]\n",
      "season        10886 non-null int8\n",
      "holiday       10886 non-null int8\n",
      "workingday    10886 non-null int8\n",
      "weather       10886 non-null int8\n",
      "temp          10886 non-null float16\n",
      "atemp         10886 non-null float16\n",
      "humidity      10886 non-null int64\n",
      "windspeed     10886 non-null float16\n",
      "casual        10886 non-null int16\n",
      "registered    10886 non-null int16\n",
      "count         10886 non-null int16\n",
      "dtypes: datetime64[ns](1), float16(3), int16(3), int64(1), int8(4)\n",
      "memory usage: 340.3 KB\n"
     ]
    }
   ],
   "source": [
    "df['datetime'] = pd.to_datetime(df['datetime'])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10886 entries, 0 to 10885\n",
      "Data columns (total 12 columns):\n",
      "datetime      10886 non-null datetime64[ns]\n",
      "season        10886 non-null int8\n",
      "holiday       10886 non-null int8\n",
      "workingday    10886 non-null int8\n",
      "weather       10886 non-null int8\n",
      "temp          10886 non-null float16\n",
      "atemp         10886 non-null float16\n",
      "humidity      10886 non-null int64\n",
      "windspeed     10886 non-null float16\n",
      "casual        10886 non-null int16\n",
      "registered    10886 non-null int16\n",
      "count         10886 non-null int16\n",
      "dtypes: datetime64[ns](1), float16(3), int16(3), int64(1), int8(4)\n",
      "memory usage: 340.3 KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('train.csv', parse_dates=['datetime'])\n",
    "\n",
    "df = optimize_memory(df)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understand Data\n",
    "https://www.kaggle.com/c/bike-sharing-demand/data\n",
    "\n",
    "- **datetime** - hourly date + timestamp  \n",
    "- **season** -  1 = spring, 2 = summer, 3 = fall, 4 = winter \n",
    "- **holiday** - whether the day is considered a holiday\n",
    "- **workingday** - whether the day is neither a weekend nor holiday\n",
    "- **weather** -   \n",
    "    1: Clear, Few clouds, Partly cloudy, Partly cloudy  \n",
    "    2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist  \n",
    "    3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds  \n",
    "    4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog \n",
    "- **temp** - temperature in Celsius\n",
    "- **atemp** - \"feels like\" temperature in Celsius\n",
    "- **humidity** - relative humidity\n",
    "- **windspeed** - wind speed\n",
    "- **casual** - number of non-registered user rentals initiated\n",
    "- **registered** - number of registered user rentals initiated\n",
    "- **count** - number of total rentals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.843750</td>\n",
       "      <td>14.398438</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.023438</td>\n",
       "      <td>13.632812</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.023438</td>\n",
       "      <td>13.632812</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.843750</td>\n",
       "      <td>14.398438</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.843750</td>\n",
       "      <td>14.398438</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  season  holiday  workingday  weather      temp  \\\n",
       "0 2011-01-01 00:00:00       1        0           0        1  9.843750   \n",
       "1 2011-01-01 01:00:00       1        0           0        1  9.023438   \n",
       "2 2011-01-01 02:00:00       1        0           0        1  9.023438   \n",
       "3 2011-01-01 03:00:00       1        0           0        1  9.843750   \n",
       "4 2011-01-01 04:00:00       1        0           0        1  9.843750   \n",
       "\n",
       "       atemp  humidity  windspeed  casual  registered  count  \n",
       "0  14.398438        81        0.0       3          13     16  \n",
       "1  13.632812        80        0.0       8          32     40  \n",
       "2  13.632812        80        0.0       5          27     32  \n",
       "3  14.398438        75        0.0       3          10     13  \n",
       "4  14.398438        75        0.0       0           1      1  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions:\n",
    "1. What is target variable (should be predicted)?\n",
    "2. What the difference between **count**, **registered** and **casual**?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AAFh4bOtx+cZjO2Nfk3aVsCxuW35A/u6ejSplNWrv9qM90j9cUakjtv6Ur6/v\nJG850pO5rri+di3Qkqp0O/V1napObdp9oi+/+h2tgtfIjKlqGdpiLrfx3zyO/Tgwc2/PDI7KnozX\n1PajvROl+hxB88DpgRHZc7K/6GZYmVt0AwAAAADYu/TRHSIictUn/nPmMkz5YaJO9+jYuHzgymXy\njl96VebyXXUvy9JxtWnmnGpirbESz48MQ5G9J/vk/J96sfz0S85Jt62HIZ48kndnlfQ98fs/WCZj\n46EcvOwDjlvUgCfHy6UPXrVMOs6cLboZVhhxBAAAAGQ0C/o8NVHAYt3B06m3TT1VKWGn11WOGe3O\nv82KbJG2tiivVn5XXdJmN9u/rR2To8De/d0l8ofXLI99fZ75jMoSdNRg2tUyByLLouxBIxECRwAA\nAEBqZcuJu/dkv3z02hUykHCaWX0Hcxb1qxMpKtAwpxqcqYzlV3+WqWqmGFLf0NTrbn/ngLGcybxL\nYXW70WQNyCDpSm/JxO9/5lKVr7WLH9yqWl6cVgiWYBKBIwAAAMCxomMvlz2+U9YdOi3L93bFvo5V\nohqLjo3dEukJX2eopa3acxtTCiZolGIaaaQZ6xjNECSLzVNlmlqWuoa68gzBJ58DrXnm2XnHZc/m\nVhfcI8cRAAAAkNDVi/fKy19c3kfoLH3aIkZQNHtV/3BFzgyOxL7ex5w4NuZWI0fjylOLjMcp4Tl3\nNfrKeD4Tj37K9rdWU5ZdLUs7Z7vyfuoBAAAAOfv2k7tEROTCX3hFwS1JpzZaprBemu5Qpo9eu0J2\nHu+TW//0LarlRlwFnWyCcHOqI460chxNTgtr/JosxyF+BTP7tmdqk3Wt8eqPIaP2IDIxVe8V550j\n572oNUIuTFUDAAAAChCGoRzomprrxVVcZ7Ivm70GjbZp7d/O433V8hqXODoWyt/ds1EOnTLn06m1\nKWUgxiZuk3rbKQGJ6qpqDoN/aZN4ayT9Tl7X5M8L1rbLrcsPqJRrcz7Trqr23ad3Z68sJeJXxXrH\nZc/Kn9y8puhmqCFwBAAAADhm6kzetfqw/PZ3npN1B7ud16/Rv9fuYGvWZSp+1f5T8ujmo/L5h3QT\nAmea7qdQb1sOw8ZcJ/7WCrr98wNb5CuP7jC+Lv5a001inXZ/rly0x77ShJgCVrz1h9KvQOkrAkcA\nAABAATa1nxERkf11o458GSWQ53LoKuVql2dM++PL2Wl92tdJbJDIoq6OM4Pp6kppNgR/ZlPeqTIj\ncAQAAACH/0cLAAAgAElEQVRkpNLpybHj1Ky9ZQuNJAnmpDlHiYJDFie92ZbDlTHp6h+ue/3MLVxe\nLppTz7KMXtJNxN6kLoU6rl68L3W9KM6p/mGZ/8AWGRodK7opqfQMjsr/u3+LDI5UCmsDgSMAAAAg\nJY0Obp5BmiggUpY+rUYSZVf5dzK1LGFT/vKO9XLh155pWsST24/LgrXtWVqSSpYgiM/BR9P++Lz6\nns/Hsowue3yn3Lu2XR7ZfDS3OkfHxq3L+MHiPXLfuna5a9VhhRZl0xopvgEAAACfKa9clVbqHEPG\nkS7Z21lkBzhL8CNukyz7cqp/JNHrluzubPqaMBT56x+uFxGRj73ldRla45bOIDyNwKFCQxKqv8a6\nB4YbvzBtuTbbptw4DMNck50XoYgQ4T8s2Gxdhg+j2BhxBAAAABSgiD5a0+k7zhsVSt/QqAxXdKeK\nmHMSRTWm73XFTbPK0od7PkFAyLqSEmi2W7Wpggr7b7wmUq6ClsXtKw/pFpiTXFcLrN+2VS/2qkdz\nHN3kEoEjAAAAwBOuulBJ40GuVtWqr/9Nlz4lH7tuZZN2JC04e5vKSju2ZzzWNivoZd80URDBJndR\n/batPromCEQGhiuJp0q1dvgGtpiqBgAAAMwSSb/dd72q2uYjPUoFJq8zTREa++8qAW8e01YGRuzb\nbjXNyvQ7q2BW4yCRL1PrXLjgkicTv3Y8DGXOtOOkkZ8HrYERRwAAAECModEx+eZjL8hZi850XMdy\nyiiIzDXEqyXHbtCM21cclB1He+NHYTjquGswNbvoASWmTnvaEV1TR8jYtii5v7xjnZNym+1/fIBn\nYttmx4HBahPS5zia+btP3rRapzEp6/XFSGVcRioEz0QYcQQAAADEunnZAbl+yX552bm6j86ugynT\nKot1ySPbRUTktT/zEifVux6REdf5TFPz2ZhRQmn3YWzczT7nObrFbpSPG3b5dEzlKSTiNuytxv7n\nGVQZN1S25kB3fg3w0G9+c5GcHhyRA9/8QNFNKRyBIwAAACBGNF1j1BAI0Oh0hg1+diFp+R4PApjK\nUXQiNhBV0MHJNdCowCZ1kjnZebL9N73K9Wgt8yqE9pgq5kjCk9M9kGw1xNmAqWoAAABAShqd+Kgz\nm0cgQmX0g+l3YSjXP79PTvYONal/ZgvCMJRKg46xxjGp1ah0fMsWuEnKZgSTOUhjcZyqm1bGZ14X\nvuYRcmm4JNOkZuO5mW0IHAEAAAAxNIIYSZcGdx2asBkhFW1a3+5dJ/rkm4/vlL+9e2OyMup+vnrx\nXvmlLzwufUOjmdtkKjcSBTCydGrNI0h0R5e5eH0qzkZr2bf6T29dayg3ZTuSJoJPV2xiGoe3s2/Y\nYuvs+bTyVEi1rRkDdorAEQAAANDi0o4Cqe/8T9+yvoNZGZv4j/7hSpP6Z/7u3rXtIiJyZjB74Cht\n/y8MQ7lp6X451R/fIfc5Ya8awz52nDmbfzuKohg8MF0vs+ESQr6KHNlF4AgAAACI4WihsdJIso82\nuWvM5ekd2fo6t3X0ytcWviD/sGBz6jbdteqwWpuaiWuSdlBLuzyrqWoxomYmbW/SqYWtGyRkWE1D\nLXvO3SFwBAAAAOTEPDLBfS8m6kLarUhlkQvH1bSolK8fqeZU6s0wPe6J7cerdRbT63Sd4NmG8bq2\nyY5tcOeqQ9k3jqqvq9/14fT4dBmRp8h/ReZZI3AEAAAAxIgLtiR9jDcXkV8noJaIO0PncHrQySY3\nkzF3kKP+aq3dGbb1uQvduiNk4t2//oiIJA+g1V9r8aMG3RzQ4k9T8S1IQiMnVmpli+p5gMARAAAA\nkBNTB7a+3+RLVy/tVKks7Y7tzCeejtTccGVMbl9xUMbHk7eykM5siRmDiaZrvcmVkuR8Njs1vozM\nOjM4Kvet1ZveuPtEnzy784RaedNZjUYs29ulbO31wNyiGwAAAAD4zFWOI80Obnv3oEo55s5+OUX7\nsq2jV7Z1bJeXnTtX5r36vGIblXZlsBx65PFBwoQrkyWdqpaDuHqNgStH7fxhdWrdr7/uFSrlveeK\nJSIicvCyD6iU1+rmzV8of/r2eXLpH1xQ+50nMcVSYsQRAAAAEMPYEY3pgQwMV2TBunZjp7vpaIl0\nTav53SueT1SuXY6j7Ey5OfIOLAyMTK781uw4Fx0siw9+KHHUi/ZlxE890/WnmcQ77pwMjY6p1ZNG\n6UYBOXDbioNT/jvukHzm7g3y5n952ml7bLGqGgAAAOC5pN3Mrzy6Xf75/i2y5kB37XeuR44MjY7H\n/j3qJCde1cyQRDjah/r+dtq+t6l+jVFO2sc3NnCTQ98tSwfx969aJn984yoHrWmu/hzaHDuNWE7a\nKXKuT2dZ4jdFtbP+mvjCQ1vlTZc+6b5SwzWycMsxOTUw4r5uS+sOdssLx3pzr5fAEQAAAJBA0o5V\nZ9+wiIj0D1dm/K1ZkMRV5y1pfzxJx31KTqakDVbIZ2Qs1iLSsHr/Kdl7sj975RmkDQilefXWjh5Z\nse+Um8KbFVWW6EidvEZGkS8rubtWH5a+oYrMm79QFqxrVy//RO/QxA8lPSWBBPLR61bK731/ae51\nEzgCAAAAYqRN4hx9a/387s74cg2/e3jT0RQt0+d6pE19EfHHVaNnN7WC6UX+jxtWybu/+7yXy5An\nHZmlU1f2/U+aFLsIU0fNNW6UynWdx9TClHw5D1n94Nm96mUu3dOlXuZsQeAIAAAAiJG2Y7nlSI+I\nTCbHbVpW3S97zo6mqyylFOuKJXpV0s6pqz5s2qBH8pe76e7HBTDc155es3akTkSdA9MotIVbj4mI\nyPpDp/NujvporH++f7OTem0CiL5cr3CHwBEAAACQgKnLHxc4Oadt8lHbmF87zxEB1brqO4d/eM1y\nufjBLanblDSfTdGm78vU3EzxO2qzX5vaz8i8+QvN5SbsYtfyScX8zXd3rtJbij6NuONz6/KDM19f\nsixHC9YdUS3PFyf7hopugvdIjg0AAAB4Lukj+9y2ie7+q172ohl/K2q5+2ikS+9QRR7ccETGxkPZ\nePiM3LMmeR6RqD+uHbcwdYYSp06KCQCZ/rLx8BkRETnek72T2mz/71trHzCpHeu63yUdrZS6Lott\no8O/bG+XXHTLmuZ15djvbRYEzStuO16OOF/ho4Y+/IPludU1XpLga8R0La/Y1yXz5i+UU/3Dqcs7\n1nM29eg7AkcAAABAjLQ5jj71m78gIiL/823z3DQog2gfrlm8V/5hwWZZtT8+iXK0X/esOSwDIxPL\niZsCPGXLoxItz91x5mzs6+yCKSU5KMrNbJbTKwnN4JgvsQFf2uG7o9OCuS5H15QlmBcxXUM3Ltkv\nIiKbj5xJXd67Ll8sH7l2RaptCBwBAAAAMYyJimNe31YNHJwzZ+ariu5ERom7zwyacylN77hf/ODW\nma8xjZpKuGOm1+U1kiYMRcYS9hhtzlOboxXkXNncnr7jGSfubOYyui5l4M71OSlqaqGHl5o3fEyI\nn4TWvXJ0LP3+EzgCAAAAEkj6yB51Suo7sObgU34jU6bXVBkfN77ucPdgwzJM+5C0T+yqMx9Xqulv\nYzl04tsURhz9128vFpF8Ak1feXSHanlFdskPdA3IDdWRGHmKC0S4PB7P7TqZOBjaTFFBzUb1urw/\n+hjA9R2BIwAAACCBpH2NqFOSNJl2EZ2YZt84x/3VvFR89k5eXt/+B4HIeNIRRxZt0uju+jKVJu9r\n0zbmdv/65Dm7Is5TYzus4E9vXSs3LjUHyvKcMBmGIkt2d5YmcXtZ2ukTAkcAAABARnEBhmZBojxT\n4UyvqzJmHnEUJz6YFN8RMwbRcu3aTqgYIjKmpm890tOwjGZdTlc5jtSLddR3jmvmnpN9sdvm2Z8/\n3WC6pjbXgdH2mFGCqVg086GNR+R/3rJGFqxLH7jTNDQ6JusOdjd9nS+B2bSMCwnktC8EjgAAAIAE\nkvbboxV7Ek9ty7G3HK34ZgqgJDVcSR90iiTeVYVDYioi6WpK/3T/lsz1akxVi1P0YIkbLaaCXf7E\nLsWW6FB5/8VGVe2LzyLPao+cnkg2394dn3TepFFgLUvA7fMPbZWPXreyaTCtbCOOfMi3T+AIAAAA\nyMg0aqY2Va0+x5FpRbK4cm2mfsV0iuZEgaMmI47M+Yx0O1uuV2mb3twwNB/z/33ner1KJT45to0i\nOo+mc/T1x15wVp/q+dcryoov7XDJg7iGiIjsONorIiJ9Q5XY15VtxFHcrTev+wKBIwAAACCB+mf3\nuCWQo862KYCQ9CHf1Tfi58yZePzPsqpOKzAF5Bbvsl9Gfmodjf+mcdTVpj4pdDiLmG6oHsB0/FZI\nOsqtaL6tNOby2tI4J7uO98nvfX+p9A3lM+VRxP6Y7DjaK2dHxjJtS+AIAAAASGlodGLEjqmzFfdt\ntnEkj1ajEjhnTvapajbtNAVT4jpBGp1YU4Ahj2/nXU1VKyL+0Kyj+qmbV+fUkvJyfd78CvfMHv/6\n1C554VivLN97quimJNJzdlTef+VS+dx9mzJtT+AIAAAASCB5zqLqv83KKyDAMKdt4vG/6VQ1R0lY\nkwaEVu0/JX9265rkq6AlCMhpHe6micAdR6fyHOWTKYBnsf9Jtkx6fH2ZPlVUYCft+3VfZ799nRn2\ntlE7XY6AKnoU2KObj8o/Lticejub+/Lw6MRIo/WHT6euV4TAEQAAAKBs4kl+StAj5SgkreDDid4h\nOdU/XPtvjeTYWaTdm7+5c4Ms3tUpZ87GTwNJe5jyCCbE5TjKM/hWhK7+YfnSj7c5rSPpVDVfjpLr\nIIXWNf2tx/1LXO5K0bMH/+6ejfLAhiOJX09ybAAAAKDFxI04ypLjaN3Bbvm7ezYmHn1T7ze+sUje\n/LVnZoxSydZvsh+FZE66Pfnz9MOjmc8mDN2veCaSTx2+unPVoaKbkNrASMV6SfvYRdUKilLkeRn6\nEqQzGRqdmdOnbMmx45AcGwAAAPBdTCDE1DnJkuPoz29bK49uPtp0paC48mYk7G7SmW0W4EnLNIIq\nrsMT/c2mf1dcjqPGf7ty0R7r8tXiEAnKKXpkRh4+ddNq+a3LFzsrv6hjmDqoa/Fuc/G20pqS+ff3\nzszp86qXvsi63DxPqw/vQwJHAAAAgKJoaorWSAMP+gwN2QRiYpeYTvAaEZFDpxqPFDFv6iZy9NDG\numknMQfleO+Qk/oxU9K3X1f/iNN2uB7d4sP9IQoMZ7nluW7/0j2d8tVHd8jKfafkLfNeISIir3qZ\nfeCoCEWsYhghcAQAAAA4ptI5sugzFNnhaCZ++fr4I/fFmHw60zuxLkcbfe6+9Ilui7a/a6DoJnjF\nJtAbt21RU9XSsrlHuNjHRu/93qFR6R5IF+y7ZfkB+cSNq1QT1/tyR83r8iJwBAAAAMSIfS43LTNf\n/V2zpLiJ+zBNOgYn+6aOYtHoxGWZUqdZV6AwV820adw0Mt9Fx2lwZGbOllah2rFPWZRNwvi4aaTl\nCBvpJF3PYyrohV97Rv7Lvzyd+PXGqbtNdvXI6UEvA35FJsYncAQAAABkpbFKVrN8Q9V/G3XK3vr1\nRenrTL2FTt6jKYmw43IcRa9PWq4xcXc47b9FPv7WfyciIh+78OcTlmyoq+D+ZMeZs8U2oEVVxrKf\n2EU7Tzb8m+sARKO3URFBhjzeGyOVcaflbzx8Wt75rcVyz5p2p/WkkSQfnGsEjgAAAIAYWZ/Lm3Wi\n4qaGmEZeHD41KP/ykx2ZVlebXpyLDt7DmzrkgfXmJaaTHsPp+63dzvNeNEdERH7mvHLmOMlb2sN/\n31p/Ottpr53RcTcBiVZawasR7ST2rsXVuK9zYgrnukPd+TQmZ1kP91zdZgAAAACwVd+Zin7+6x+u\nl44zZ+V/vOV18suv+amU5U38m3QqUJbRCv+nunrRR97ceDTPgnXxgYVoXydnqul2KvP4cr7Es+Gs\nHeuxS/6d5Ni5CjMcOzMkL/+356iX6zou4kNcyudV1VpB0aMcRRhxBKDEBoYrMjTauvP8AQB+yPrM\nXr+dOY9P420HDHlskn4zn+RVWQIyxulgCcrpGxqVBzd2iIjI6gPpvsW36TD50NlKK69pJ5jpc/fN\nXLZdQ1F5acoeeDk9MBJ7zzubJddXCe8J9UznNO19Lus9hsARgNK64JIn5bcuX1x0MwAAs5irfsi3\nn9ylWp7GVLWsgZirnt2b+LXRiKiog5Q4x5ExmXf5V7oqWt6HqcizMjjSOMG1jaKmqqUNWGmc603t\nZ+wLqeobrsgdKw81/PuvfuXJROWYc7OV8/1ff07rR48ODFec534icASg1Dr7hotuAgCgxRX9vb1m\nFyfpt82a/apMSYfTrohlqCPtKK+kmnXIbeoo+lorO5tRNkOjbjreZQ1SZLFi36nU28Qdned2NU46\nPmqRzLxsTPeU+uvqgkuelI9dv9JpGwgcAQAAAA4k7S9q9yvjyivb1K+kne41B2dOgXN1HIpSxJSn\nIBAZGh2TsZyGzdgGzmyO0chYssDRaMLXRcpyrfk4TdLVoSvJKUkkOm9JR3tlvR4JHAEAAAAx4p6z\nk/a1TB3axNuGU9tRVP/OvA/2rTF1xKNSy9Lprlf23DL1wlDkjV96Qv76h+uKbkpq6adqJXv971+1\nzGk7ilLG9xryQ+AIAODUSGV8Vg3TBjC7xN3dknYYE79O4VY6uVqZDo1O8e9esUShJTMV1WEvS6Ag\njWdeaDxlqFUkHVS183hfunLdpp5pKM9Hr6SrNSIbzdGTJMcGAHhnfDyUX/7i4/KVR3cU3RQAyMxV\nlyhxvqG0IydiXl8LHCklx75m8b70BUVtifmdTTsj5qS42ctDDhTfbGlHfo07ujhcldsqbA5P/3BF\nvv/MHqlMG7XY7J6ZqE5Hp81mf+uv6byDdQSOAADOjFU/He9c1XhVDNhZvrdLnt/dWXQzgJaW9Tk/\n7Upfzco53jskIiKLYxLGTt2ufgWe1NUm8sT2424KrnI1esem1GYdP5upav6N3PAv6BF3/G2Ovav4\njn9HUF9RI8u/8+QuueKZ3fLI5qOF1J+3+vth3secwBEAwLnZ8NBUlE/etFouumVN0c0AZgVTn37h\n1mOJttV8xl+UcNrQu769uOHfVL6RtxSfOyqwbodpUz6PWtuUjrWjHEdl4V0M0oHBkYqIzMyTVh9A\nNF8HCc61o+OX5bzEbZO2PJJjAwC8M5nctLUexgDMTqZb2d2rD6cqwzQiIu0tstnUl+jP7d1nE9Xf\n3j2YqF7tW/mhU83rtaqyoFXVbDrs/vX1/WuRK84WjivoESjPRy+7Y9d4Y42VKWfTI+gXf7y18R8t\n38oEjgAAzvg35L417D3ZJ/PmL5TFO1s/WSngA3c5jpKVPL3fU6nrpWUOzNdt1tbmz7062rfo0Njk\nh2m1JNXFdIBb6xjGme25iGz2fqzu2C3f2yVrD3bbN8gBF/eEu9ek+/JAW/1le+eqmLZY7vpcu80B\nAGhudj+K6Vt/6LSIiDy+LdkUGQB2NO9hpo5L2vLH6gJHP97UkWpbU6zqnDmGUVDGdjZuadqVppqZ\nHLGavQzfk2M/u/OE/NuXv0R+5edeXnRTWtLlT+xK9XqPLo3SqQ9gf/Km1QW2ZMLUKYuGvysmx16y\nu1MOdA3I61/90mQb1Hl+d6f8118+v3lTDG3J+uUsq6oBALzDFDW3OLxAvtLnkojvvCQOjkz7e2Vs\n4hdj46F87r7NKds083dzPB4d6upzxGbkQbMtkx7NP79tnbz/yqXS3j0oD6cMALYq2yvRLjm2f9da\nnmz239U0v+bvtQTnO4fDPz3HUpz6w5wkR+Vbvv6M3LTsQJZmNa0/DUYcAQBQMjYPxgCy0+hXTnn/\nZnwrZ5tSM7WysMHPcfIMVkffplutfmb8nfuduCtl3qtP37VBtnb0yG+/8d84alF2ZfuCwub8Ostx\n5Fijc5Tn7oxZHDwX11iz5NiJqkxxf3b5PunsG25QZ8pKyXEEoN7yvV2y96TucHHAVtkePMuCwwpk\n8/WFO2Te/IXO69G6903v+Njkj4s2bdbp0LxvZ1pFqPqvVY6jmG1dfi4d7x1K9fqOMxNJzIdGxmbF\nSli+mu05jpBdmoCl9ns8r3sGI46AFhPNKz542QcKbgkAZ+hYAFZuXJpt2L/GA3qWERGqQRyL8rM2\nQztPUeJtlctzZTJIVmgzvFDkohquro2irrm0R7KVLr9m91ntc5KmvDODo8XUbbnPjDgCAAAAHJgy\nHSxu9Ityly1JRyJLxynXvHUKq6qVxeS0vNbf12Zsr7HZOJW7Uawt7ZH08cglvR6iV5kCj82KOHJ6\n0HoE6vQ68rhXlio5dhAEPxMEwf1BEOwMguCFIAjeFgTBK4MgeDoIgj3Vf19R9/qLgyDYGwTBriAI\n3mtTNwAAs90s6E8BLae+Y5u1k6vdwdNY6U2bxqpqzhL2Kt98g1qQbHYGPiJd/cPSO1Rp+rqyBdjK\n0tqiRnuprlppeG82K3/NgW77ektzlrPfU21HHH1fRJ4Iw/CNIvJrIvKCiMwXkUVhGL5BRBZV/1uC\nIPgVEfm4iFwgIu8TkWuCIJhjWT8ABz7/0FZ57xVLim4G0PLedOmT8oNn96TebvZ2K4BySfqAnnbZ\ncBvhtH99ZxWjKUl0fTJI5l9782zRpxSWcS9TB16Lh5dN4aYkxzYGkyZ/pzIFuYBzUJrk2EEQ/LSI\nvEtEbhYRCcNwJAzDMyLyIRG5vfqy20Xkw9WfPyQi94ZhOByG4QER2Ssib81aPwB37l59WHad8CvB\n9q7jffLCsd6im4GUeJaJ1zdUke88tTvz9rPxARkouywdlrTvdNO9IbYuxzmOMiXHrm40Hobyxi89\nLnesPJi6jLj2+hSkaYumqvnTpJo8pwoe6BrIrS60hujesvNYr+zO2Hcp0yi/J7efmPG7vAaK2Yw4\ner2IdIrIrUEQbAyC4KYgCF4qIq8Jw/BY9TXHReQ11Z9fKyLtddsfqf4OAJp67/eWyO99f2nRzUCd\n/37Ncrn0ke1FNyPWSGVcntx+vOhmqCsyeSjgiw2HT8u8+QvleE+6FazyYOpr2yWItu+8a/T/s5Zh\nN90slKHRcfnyw/afN2EYehUwigQe53MaJ2O3taJOq4/Xuiu3rzwk76mbLVEfPG+WJL9MI47WHzqd\nve4Ck2PPFZH/IiLXhmH4n0VkQKrT0iLhxNWauolBEPxVEATrgiBY19nZadFEYHZYf2ji4flkn38P\nz2hdGw6fkdtWHCy6GbG+98xu+esfrpele1r0s2T2PBMCM/xw5SEREVmxr8uqnOd3d8rAcPO8KlkY\nR/4ofLtdm26WobcSbVK/6ZYjPYbXFXuDqc/70+qm5HPy7HuBsh3+Mo0e8U1RR0511cgMybE1FDEC\nPO8vEW0CR0dE5EgYhtFk1PtlIpB0IgiCnxURqf57svr3DhF5Xd32P1/93QxhGN4QhuGFYRheeP75\n51s0EZgdblk+sazw6v32yd0w6eiZs/KGLzymNkWue2BE1h/iHOXp6JmzIiJyqn+k4Jbo4rEYmGTT\nKWjvHpSLblkj//dHm/UaJPHfYGfpYKTdYvHOTqmMjRvbZKr/f92xzlFLstPI+zN90yAIdEZe2Rcx\nRf20PN942CRnyjqYV6vdNuW8+mUv0mmEpWb3i/o/X/7ETvnEDatUAjAzV1WzLlJfUTmOwjA8LiLt\nQRD8h+qvfkdEdojIIyJyUfV3F4nIw9WfHxGRjwdBcG4QBK8XkTeIyJqs9QOYqdE96v3fXyq3ez4y\nxEfPvHBCRsdCuWv1IZXyPnrdCvnItStVykIyc9omPuZGp3WgNB3vGZLtR2d+Ww/AnfbuQXlo48T3\njzbP5yPVe8PO48lyY+w52Z+q/Gadh6zP8c06R5+5e4NcvXhffBlZAlgZD7ZNv8xmxFHcPtr26zYc\nPi1nBnW/lDg9OKpanoaiR56lleW6vuDnXi4iIn/xjtdrNycXjU5RniNS/s1PvVhERN702p/Orc44\nzUaehSJyzXP7ZOX+Uyr1FfEuyfu9abuq2t+JyF1BEGwRkV8XkW+IyGUi8rtBEOwRkXdX/1vCMNwu\nIgtkIrj0hIh8JgzDMcv6AUjzB88dx3rlEs9z0fgoUE5Wub+TpI95m9s2cQ7HHM51eNtli+QDVy5z\nVj6AmR7eZBy0ntqL5kw8Co9UkgWXF2451vxFnmg/PRj792afbUWHC6LP4OFK9u6CMddU5tKm+u/X\nrJBP3Gi/CpiIyMDIxFTJT960yrsRpUVfB3mInhXmzHFz9ItayKJsQT9N778yeV5UjbNe5LFuFh98\ncMMRldWy59psHIbhJhG50PCn32nw+q+LyNdt6gTQ2Gz+gHAhug9rxxzCMJw1yY3zvCTbuwflD69Z\nIQ99+u3yuleeJyKTD4EVh4GjIt92vOMBO+fOnQgcNQ1OpHyj1/IIGf4WfRMehqGsSvhtt+Y0iGKW\njU6/TffAxGieO1dlH/U787iFDf+WhdZU9ihwOTQ6XrsmTYo4d2WbPpclx5F/e1gMm/xQ0THM8nhb\nRGCt/l7QsM0pmlXkNdTsLfoPC6pTsQtMjg3AE7MlCJG3ttpx1f04GBsPZXP7GTk7wqBLTQ9u6JCu\n/mFZsG5yAc88RhwVoZarxMMHeiBvGu+DPG8RUSfpJ1uOyYbDZ6zKyPsOkGd9v/RvXiYiIu98Q/Z8\np81WU1It2MJ4kk7sLFD4vju6wGfDR3VRzyP3rm2f8t9Z+kQqCxYUsPtlSo4NwBN0Ht1oi1Z0UU6P\n09k/LB+6ern844826Racws7jvdI75F8uBRvVGSdTHsCj4J+LEUfrD3UXtkxx4Q/XgEds3oXbqyNG\nmr6lUr7pam2K+Xw+cvpsqjLT2mBYtjkN4zSvHHMcveScOSIi8iKL6UOuno/GlMvVbubag91q+57n\nI2bSurQ/A+NGCKJcslz3Da+nVNdZCa6eopJjA0Crm1wKWOfDICqvb2gil4Fp+eO8vO97S+UTN6yS\nu6Um89cAACAASURBVFcfls/dV1wAS1P0zUt9Huy2YHJKiKaV+07JR65dKdc+H5981rUSPKYAXvuz\nW9eKSD7vJc3bUJKy9neZ8+qV5bum6MsbzbUN6r+ht5ke49v0rS8/vE2ur/s8+qPrVsqP1h1RKduv\nPZ0Qd/izrVpYHcHn2XnNnUVgoRZ8U0/voFteEtGKvGnMmBar1JbvPrVLqSR7BI6AFsBUNTdqeSiU\nypszbbndop9Pth/tlc8/tFUe2tghA8MV+dDVy2X3iWQrC/loTlt+yxkf65l4qNhT4uMFIIWMOY5M\nVKZFZNhmeq1ZOsm5dqwdLVGvkU9FvXNsuf0dKw/JNx/fOeV3B06xIEdSri/rsoSjbO5MNu8r7UBg\nWtP3+7tP705dhqtWXvns3sZ1lmxVNQBo6kDXgPQPV4puRnq1PDI6xbXVRsT49wixct8p2dx+Ri6b\n9uBZJrXA3Hgox3rOSmffsLO6ajmGnNUAIDGP34j1Tdtzsq/6O43AhU0nLem2M1+XtdYszZ1coEKv\nQ6qVHFt/VEVJLmLHbL8H3XG0N9MINVejZcpmNu1+/blOkhz7pqX7Y8srKnVBKiTHBuD7eKPf/s5z\n8okbVhXdjNRq05yUPkrbohw8yjmTNMx1tPpYnitlTE4tFHnbN5+Vt3z9GXd11VZFclZFovoB5CR1\njqOpN4feoVH5i9vXqTVnchpu9jJ87+ZMTlWzCByZgl8KO649Cqp+F7Xu71pNzPNz3KbNx3rOyvuv\nXCrXZZhCHk77F+WVbRZG822+tvCFDOW6RXJsAC1pa0dx+Xyyqq2ppjziqIhlR5uZW41qVTSTSeSs\nzTCtwdVnqi+zQ2f7t6OAFlcjPqJih5RW0XzXL0+sMPbBX/0567Ka7fLFD26VxbtOptomcvTM2SkL\nMGS5ZwYKo3Rd3SP1A0fJykvz/HDd8/tURnuX5XOmMmY/Cs/ZvpbkILZZPNv4NIIvti7D7xrutyfP\nempIjg0AbkQjhLQ7FD6OFqmNOLJ48CpalOOovpNRkme11HwJXAE+yCUYn/Zm0uTlJ3qHpH84/cqW\nP/2Sc0Rk8p6dRdI92X2iv5Y8fHLbZFu//bJn5X1XLEnZsqlqK5sq3siDIFAZXaI9KyXRtJkMHtt6\nzLoMHz9GjZ1/m6gHRETkbb/46szb+nKdJH1mr7+XNRq5s68ze54wr6efZjS36AYA0JP3Peqmpfvl\nFee9SD7y5p/Pt+KcRAEe9QdEbz5eJ51Tm6pW5hFHE//mudpN0Wey6PoBH/j8fN7ofv8b31hkV65i\n3h9XjvYMiYhIx5mzMlJJ/9kSfQbbDISN21W7ERLuDqJm0Rp5V/LsANsEzTTeE66ezzy+RYnIxDPg\n6FhoOeLI972ckGYXN7efcdYODVmPeVd/thygBI6AFlDU6INovm/egaPDpwbl373qPOf11CdA7hkc\nlVBC+ZnzXuS83iL4nLg7qTbDqmqu3xtleVACYMmzYX6Zkk3XPtPyu2+NVMblHZc9m2nbQOHLgOn3\n6DAM6w5e9nLL8lE5VrLPqKTNrX839g2Nykhl3DLfl9upakWdhqT1arSvbLP8pozyc1NFKZzsHUr8\nWqaqAS3Ex5Es2h7ZfFTe9e3FsmR3p/O6glry0VB+7atPya9/9WnV8n0KOuSdYM9XYRjK1xfukG1N\ncnLVro263/UOjco/LtgsfUPpp58A8FzK+3UtNKF8m6/l3qv9m7yCGSuMZXlmSLmJzSjWWuDIUZTG\n5tx0D4zoNWSaRh/H4+OhXPLI9lRlqYw4si5BX32b3vmtxfLmrz3jdY4dn5734hTVSs3+S5bn2aIf\ngYdGx+SffrQ59UggjWf3t6YY/UrgCGgBLu93R8+c9eoDb+uRiWGjO4/3Oq9LOzm2dnll4Mu+1rfj\nYNeAXPrIduMD9eDImNy49IB87PqVseXV3nN1Rdzw/H55YMMRuW35Qev2JuXTexMoStp3wY6jMz8/\nQhF5btdJuWXZAZU2uaYyQsB93Mgqp19tqprNiKPMW/pnf1e/bDycbuqMxihi3z9mes5OfFljE3yY\nDPTGl7F8b5fcsCT9qm1o7ljP2Vzrc5VXLIuHN3XIj9YfkW89vrPYhjRB4AhAQ5vaz8jbL3tW7lvb\nHvu6geGKXPzgllxGWkyunOW8KmeroH1/0R7V8jR5/nwYy9RBMT0L/O+7NshtKw7KC4bgY20qR5MD\nYXrIqJ/a2Dc0KoMj9qvZNK6fEWJAVo0Cw39661r56k92zPh9z9lRufLZvanqcBXUtZlu1tk38W12\nnoGAZXu7Mm8bLVBhNWpm2qZTkmN7+oGneXcv23oXNh9tVpeJ4WIIw3DGNJ5P3rRavvFY+s69+9Og\nU4PVe8IiT9SOo73ytm9mm9I6oxkZdkJj0ZpSvNUsG0ngCEBDe0/2i4jImoPdsa+7bcVBuWdNu1z/\n/H73jcoxAfLkMHmt8iYKfHbnxPLGPn3ItFIYotmlEfdQMRmYzPDgUbftmy59St7ytWdSl5GWT9cQ\nUBZpOxZXL04eNPrm4y9MKd/VezRLJ/mpHSem/HeWtqW9NW4/Gj/tN46LEUdTz03r30HHSrzgRVoa\nz4X1JSxY1y5v/cYi2XLEPkGyr0HKiEbzbMrY35V99bK0jO108BCc5nhkDVwNV8YybZcVgSOghTj7\nYErYEc/jISy6uebxIdxKwZTZIOk3lZO5qxq/JnFCyfrlXKv/Rh26gRF3H+hcm8Ak158HaUa8XP/8\nfjncPeg+UaxCMMX3zmx0T7daVc04kmTqv77RHFFqc+yKYHNOTlvknTK9J1buOyUiIvs6+7M3alr5\n7uhcM7MhmBpphT1dvvdUrvUROAJagKtpKz52Tm2WCi1a0qZvOHxabl9x0GVTWprNg2fSVXxMAcza\n29DX3gjQonzu7GjfDsLp/+a8670pp6Tb3Y8nF6hwoSwro9nIY3R2SzBMs4quD5VpTJ6fB4322ZSh\n8UVeJFNy7NRbzE4EjoAW4vnnkgrXq6z44L9fs6LhyikjlXH5xmMv1JJBikx8WBe5klff0Kh8feGO\n3IfMuhC9h5oGjgy5kNoMK60BmJ3cLSEdOi2/mU/ftSG3uqLOnE2CZ9OWtaXXZ8Hdumzp8ArLcRTz\nu7IdQxtWK9PpNSMX9YGupMGmkcq4nHU4mtz1MbQtn8ARUBKjY+PedszzfICtjfTIr0pnshy3H2/q\nkBuW7JdvPzmZnPHmZQfkTZc+JR1n7FekyNKm7z+zR25cekAWrDtiXb8rpill8a+PN7kktmmqWo5X\nZ5OqxsdDpw85AGaqvwWEEspjW4/J87s7dcquKzdzGSX7ALXKcRSz6cItxzKXWxZlO9c2bD57TQFZ\n30cJTWVua1mCo5rBuWbnzaaqD1+9XP7jl5+wKKGBkgQnCRwBJfGOy56V//BFBzerHIVhKEOjdp3Y\nNsNIj3o/WtcuGw+ftqpjOp8+eKNvXkcrk216avtEwtMj3YOZy7X50B6tJlEYKziZgmkX4lY/i5Pl\nebGtLcf8WwlXVvrWEzvlP375CacrvAFFK2pp+tjy6n7+9F0b5J/u36JasF17p094c8emhmhbmxHG\npntkdOyGKyVLAIQa0/VvFTia9m/9z20KUQ2t+8tcR/kaNO4IVqOV/Pm+LdaOY1NX4/Wnd5APAkdA\nSZysLqNr4jpQrXVjvGnpAXnjl56oLQmcSZNVr/7p/i3yh9esyF7+zKq8N5mXZyJwtq0j+yo2raCo\nQF/9eXBeV8J3/QMbJkaBDQwz6gita2w8tJrOpG3KZ4erKWsaZeRxyBRGgbgaceSrkjx6eMdmATlj\nwvQox5HCCdF6LnGedN+mDJtRkAU8t2nXOP0YZjmmWc/D9UtyWNVaCBwBLUX7Jlj/YTmu8GD+8OYO\nERE53jOUuYzaiCOrlqSj9uA57eFD64NyMt9OKP90/xb54FXLVMotm7QPdxpJtKckx64lzM7v6syy\n+hvQCupzUlzyyHZ557eeLbA1M7m6D9Ty85Tk230Ndquq6bXDV+WaUjXV2HiYqf2mz3urAKNhzE30\nO53k2NZFNGFuY+q2F3QtxbUz7fOLqwWDjHU5KKeS4Ya35kC3UkviETgCWoHje2QYhvJbly+WX730\nSXP1OX49lkfnPAyzPcjkwXSo0yZl9nXf8pJ0mleTUqplmMr1iZeNAqxNv48ds/hCQpvLW2xtZITV\nt/tTy3JJY+qL9mIY3t8OUz5T/d8fKU2DLMC///xjDRcDiWO6dscshhwZp75ViwsC++emoiaF5vml\nUekeLT1s79EzZ+WXvvC4LFjb7qR823NE4AjwyIK17XKwayDz9toBgfqAUMeZszLQIMlursmxm+Q4\n0vDhq5fL6y9+bEadvkq6hHwSjExJxryqWvQ7f46hn8EswD/uRggpl6eYniiP+73dKk0TG7OkfLxo\nSnJZ3bHykEo5GiPTpie2FxE5dGpQXn/xY/KTLUctWlcOGoFe9XxxKcvz6RksrWiRm/vWuQkc2SJw\nBHjknx/YIn/wg9afZmTzsNqmMlok3uYjU3ME+fAZ9LHrV8q8+Qtr/z11Na/8kjKXQS7JqQ2/a6vl\n38qh/oTBTM9jnkBLCgKHuUhqS8lblGFYQcpHk6Or3JSL1qKR52zqqmoT/75QTYhsswof11y8or+0\nzPqspN3ql79kroiI9JwdVS5ZB4EjwDO9Q6x+FCfIsXOu3e2eXlqaB4lo/nLcKmHJp6olrzdReTnW\nFSd2jrwxF5FGrTMLyXV1EB5GARWu8mJof/s9+a2+Qq4jhfY0M1zJnphfYwRDmUcfIB2VwNGUHEcT\nfEqO7VpRbxeNPFJp7evsr/3sYrfTnPPo82du20RoppfAEQBXirjhmuTRjjymqk3n+8f95EgX3W/b\nympq3qGZ16TGQ6C53CjvkfuDmHYXWuG8wp158xfKpRnyjBRJO9CjGWAIQ6ndiFy99zQCRnncF2xy\nT02OrrLP52QqdzaYTYEzu9X3mn8J5EPagrTnM/U0L5v3mifXWtLPBo2VyLQviegIulol1PbeR+AI\nKNglD2+bMgXJR358FEyVS+dc+RPB1Tfa9auqJaF95Dx4lppgaIjrB5mpI5lm/s61Zu8DHx50UQ63\nrThYdBNS8aWT0oz6NCvFcksTQLEacaTXjLxw205i5om1So4d/RvO/G1tAZJZdh3mqeh7kcZ7TmUf\nopGk9iXFFZ8ZgSOgYLcrJQUUcREQ8O/xpYg2OfvGWKkcjYBFtO1sCjTYHP/aMa//XZ6rC6YceVf0\nQxkw29RGyzhKFKtRbllWVbMqw7Q1t8OWpJIc2/C7WfVc5PnU1yTK8qVCHF/3gcAR4AnTTeLI6UGZ\nN3+hPL+7s4AW6VMN+vh5T83N1NW89HL2ePpZlZlxSln13yRD0xuXG1eGPweRxOlAsfSDtgrTt6bl\nSXLJpo7JUSD2+1omrkYnt5aZx2jcYnqPKdBby3Gk8Oyq9V7z+XL2/UtWF6Z8eWhxnURb5pO/NTsC\nR4DHNrWfERGRBWv9XJaxCEU8TxX5DPfjjR3SNzSZJM/0wRS1L2mOI5sHmOHKmCxY1z6lDN8+53JZ\nVY3neqBQ6jmOVEvTHRkUV26WAFJ0/86jk2K1/yojjtCaZp7Zik3gyPB+it4nGrk1te4Drh89eL9k\np/ElgcaqmfHl2yFwBHhsTvXTqtJk3rbrTmwZv7HTVNT+b+vokb+/b5Nc/ODW2NdNJmWOt2Btu8yb\nv1DOjmZf5eaKp/fIP9+/RZ7cfjzR6/OcIhX3NsjzQzifb4vdrQD1440dcnYk+zWC2WHPiT6ZN3+h\nbK5+wZG3PEf2tVqgWDNPUtO6rKa+OJrup1jWa3/mJYqlNZP+QpxNz29WybENv4viUG0qq6rp8Pl0\n+jIlPs8Re7HPnRkOh6svHCbLtyuYwBHgCdN7eU710ypxdn3lG43PD8t5fDxp7/7049ns/j1Y7byf\n6J25Kk39pm0x06bqXfnsHhER6eofiS0vTlf/sIiI9J6t1H5nOk4/XHVI5s1faPUNYFb1Dy/Gc5gw\n0BYnbkhynh3aZjXVvilNWN7qA93y9/dtkq/+ZIdNszALPLvzpIiILNx6rOCWuGfXAdC9H2hM36qV\nlcdUNYUpdTYrhrrexTkaUQXI0OiYPLypw6qMMYskR7XrxDBVLeL1M7HSde7DqKrZaPqoNp9SHtSb\nW3QDAEwwPRjNnZMscOTxZ1mL8OgGbjjZURAjcXzR4gNpMil0/FS1yx/fKSJS+MgV44ggzfLrKsjz\nfZj0ATZtm/qHJgKCJw3BSgDp6U+B05vK4PuIo1oZyltrdsryHWXh0bOIsm8/uUtuXnbAqowx5Wst\nuk58WlUtbTn5rvJaXr4EBX0/how4AjxhGmLbVpuqljB3TUG3HN9vdFlpD3fNWlqzD/70K2zZ15WU\np1+a2DOM5CliVxOf85QnolVPG/wzOFKRefMXyk+2HC26KaripllprFykkTsojze6zaDTcMYPGcpw\nvI8uyvelE5uno2fOWpcx1iStQ7zo/TrzhGo8C7p+Pte6ZjRGCGasWE1ho3U0ApeKXwy4QOAI8ITp\n825u28Rb1GaYto9sdifP56mih4omHlUyCx8yTeJWUDMxdui8/bjOJlD4phStbePh04XWH3UYr3h6\n94y/HTk9WFskYjr1PBYx7xGbqrQ/v6dP39LIIeSWTUc06kTp5q7RPCVhKHL41MQKuO5lyHHkoBUu\naLxPNJJjm36ncauZHZ/BrhLzuCnWpRV7u+T04My0EIk5y3Fktz2BI8ATxhFH1XdoRWP8rQWt2jWf\n84sO6vjI7puilCNSfD78iUdN6e4E8btJ77niebnmub1FNwMJ/OE1K6y2f2bHCalY5BaJmN6N7/zW\nYvnw1cvNr/f6JjTJOF3W4mahMVgo2tZqgEZCGiOO7KYIub9OntqRbLEINKaRDnHcJnA07d+Jnyf+\nS+OzvRx3KylRQxvLMzn21Ion/qmMjcsf37RaPnXT6uSbTs9xpNw0LQSOAE+Y8hjFJeCd8roW6LH2\nDY3KBV9+Qpbt6Sq6KTXuP3x0vh1Lep2Ytp3xN5n49nTX8T7j36O6wim/M76w+rr8Pv60V7fIWlce\nJutXDn4p7djuE/1y+RO7dAqD1/Z3Dcj1S/ZblFD0u6k5n1bHCTVGGimM5EnqF1/90szbOjuGmmWF\nYY6fL752J+1pHEO7EUcz31dRYLUMz9h+xNH9OFAvHOt1XocxID0t6LOvcyB9uZ6/xwkcASWQ9Dbi\n6/KNSbxwrE8GRsbke8/MnK5QtDS7v62jp2FiYY1AlE1wJOlIm3d9e7G893tLmpRRV298tbmb0h7H\nzzGm90Yex8N1ULMsoznghyOn7fOT7O8ckPc1uO9oy/cbacM9wpO3Vx6LXr7yZS/KvO3kKBC9qWqe\nHHpMozHq2d2Io9kzV82ulfZBbXOp/h07/WDx1LQCvj6DETgCPJZ85SQ/ovw2z+JJlw3P83k/S1Uf\nvGqZ/PZ3ntNuiqqYL0oS77Onn2mJ1FaGs8m1pZEsU2U57fi/p02cjmKsO9gtA8OVopuRuzAM5T1X\nPF/7750NRjqa2LwH0wZ8s1SlMc0qWQ0WJeRwX7BLmKsxusqi/iTli37HtvG17cezngu+5DiqvzdE\nP7YpjKDmIxhJaExFji2fHEdAi2iBTxWNpNdJO9M+LzE6MDIm2zp65G3fXCQ9g6O135fhkU87cOdj\nwKKw+e+Kku5BC+xqy+seGJGPXrdS/s+9G4tuSiHyGPVSJNM90CrH0bTyshy+0PCTK9qjQGwFovu5\n5ONnXBlp3AdMuUKTigsm63xRZF1ELLVV1WbpBZ065YOjdmhMRXaJwBHgCR+HYrrqYJv2tNU6uFcu\n2iPHeoZk5f5TDV+jthpOypFpGlea6XotOiBT9MonRe+/hhbYhdIZGh0TEZHtR5PlZRgYrsjWIz0u\nm5TakdODcuT0YK51andwGl36f3HbWrlz1eHU5U0mOdVtZ1Sexu7n0TmxSo6tkOMo7jp59cvOzV5w\nVL7o5ziKq61Vqaw+aPVMNbOI6Nop8+diq10xD208Iv/j+pVWZeiM9p5ZxqYjZ+S/fXtxptHDZRkd\nTuAI8ETszSLhjaTo+02aD9dtHT0yOFKOqRl2zwwKH1B1P8cFJ1QCQk0/tRov7+7LN1WJR605bkeR\n9p7sk9MDk0vB+hiYxlRJ3z5/c+d6+f0fLJOzI2NuG5TC0j1d8s5vLS66GYmZ7qONDv+inSfl7Gj2\nYx23zLdNeSplZC8iMZuAQGjszqctI65ce2Ho92eJJx/LTWm00yr45HiOkOvPYK3z7OO00PpyP3ff\nZll9oNtNRSmYdvXyJ3bJwVODsrH9jHX5KoFUg49cZ7eKKoEjwBPGUTgJt3X9bYj27WtguCIfvGqZ\nfPaemVMzmoct8v/qx/fnrjyPSPK8WxO0j91jW4/Jn926xlxnyra5kvTz3uUXrO/+7hJ57/eWeJP/\nDI2lvX9vOjzxUDqax1rqjhU1wi+fIHdY9/+KpSoW6KpzUs/mWKusqhY7tU/3ix33Wvd+rnEtWo1u\nq/07M8cRkrGagqvXjMJMjhryNxdWZ9+w1fZzldoBwANFLTm+5Ui66PpIZaLDs/7Q6Rm1Je50p6ox\nG68DcsrfCkW/S9oZK+pD/tN3bci0nXryUtXSMtSfoAEn+4bl373yPBFJf29ohYc4oChTp7u4qiOU\nLz+8TX72p1+SaVuRfDrGOlOjdbdVCUjVlXWiwUqqWRX9+VIEncCR7gU9vbwnt59QLT8Paa8lq/ea\nzRcBFvUWwZi7TiaOn8bUWl+nRxI4AjxhE6Eu+gbzzAsn021gWEEt6apqRchybkznpOjzFIkLojTb\nV9MueLJbRnEjblzlCGl0nrd19MgFP/dy1VxITc+Xx+8rTMV0Qvf+YcEm+Z03vsb4t35DXoohhSlq\npvNqFwiZDPrcsfKQRUklmKpWC/C4aalOqaHcuvygSkmzmUZybI0Oe30ZmlddUaOX0lY7G0ZZqSQ7\nb5TnMwxVRr75iqlqgCd8v1loMi2HnvY2fqrfbrhlEr4EepJKGkRQqmzyx5iXHT6Vb8Lc6cwf7tW/\nueqMGIpdvf+UfPCqZXLzsgMqdUT7sHhXZ/zram0K5WDXQKq8Yj9cdUj+9aldGVuIpJJOJ7z4wS3y\n+NZjtf9u5Qf8vSf7aiNTI+sPnZb/dfs6GRuPOnjpD8CDGzrkM3dvSNxx2NqhkITcOMLTPphiw3VA\npp5GJ8ouwfbUjYOYv2Ur37qIGTQ/q8sSkE57LtSngNbaUfc7xZFprpXteXW6PA+xq/d92tWhk5br\nEwJHQAkU/sGf7x099s/Rh+OPNx0tuikZTP1kD8NQPnffJlnVYOW1pKN70n57Yt6vZDub9uHkEzeu\nSreBgmZ7EjsKKWVd9cfDdGwGhivS3j0oh7snAmg7jiVbOcuF//ad5+Qv71gX+5r6Y/OlH2+Tq57d\n67pZs17S+/s9a9rlf2ecqukr056f6B2Sd393iVz66PYpv//buzfIMy+cUJ8WFKctY28smrIg4u7j\nU3v6lit2I46qQUKL+uO2LXoaHSZpjDiyYRohWKZzq5Ycu26vh0bH5M9vW6tT8CxicyryyDtng8AR\n4AnzlJeUAQHtPC7K32DEFRftq9+3zPTiPgNGKuPy0MYO+ZObV+fXICU+nidTQEg7OfQjm4/Kd5/e\nXfvvZp/xn7xptfzW5YuNfyviGC7faw5STuf5swtE5CuPbG/+ohLqOTsqIiJrm6yck0dy7Dlt2eqY\nmuPIMFXNk6kMueQ4UhkhpfcNfhjzt2zlz66bZVf/sPyv2/WDCRodZrukxMahgRatmV5+MWyOydqD\n3fLszpSpKBzI49hp9J+iLxo07gm+LmxC4AjwRHzemWZbu73BuBrxVH9zNU1f0zQ6Ni7t3emmTbke\n+lsLljXZZ2PHQ0TODI5MWXI9KZvloeM+zOo7cpp5fNJymWD9s/dslCsX7Ul8bWxSWJbVJOlDRasG\nZFtJ1gfEBzd2KLcE081RyYWR7HdpC9RYaSyPb7fHLYaS6Kw8aQrclT8gUJRrn9uXPq9lAio5jmy2\nNbyvTg+OWrXHVL7vytLOohmPU/Xjwm5qbfZt80DgCPCFYt6CRja1n5GvPLq98G/ITJ3ZySS+btp2\nySPb5bcuX5wt0OKgPRp+/atPy3/+l6dTb6eRrNUcfPL1SE1r7yzKH+Hnd1ZAejNGjlgtKJHsndFm\n8ZRsSrYbsZq+FSXH1lhKPo8RRxplOOqI+ZrjSJN2+ypj481flMHZFPn3GrF7X039V0RqU8xnq6xf\nanj+ljBKu692X/brbFMEAkeAJ/K4Z3z02hVy6/KDUkkZDtceMlkrLaz/ndsu7pLdE0mETSvnNGLT\npvptT/YNyXBlbMYoFY38DRN15cd1YmltRQx8arjahqaUxSU+XUSaCpPHObpz1SF5rC7BdpzjPUMy\n6qiTGNFasdIVmxxHEeOII0++kc5lxFHBQbLpW2rnn3LzWejvjfgnW5LdP9Lq7LNf8ERjFJ6rt4Tv\nXzJpsBvxVezx0Tg/tZkTLXyuCRwBHkv7zJr09Wnvz9PnOB/rOZuugGnilghv1rY8E6NGbD/P3vr1\nRfLXP1w/48Go9vCaogKbwE3cdMDkU9VmipaEnjpVLV3bNJjqjP+2Wbn+mL+Z85zk8HBhitLCK3m+\nV774423y/9l783g7jurA//R7WrzIK17BBrGYsISQZIgnCZBJgCT8Ap8QkhlCJgvDLxPCkMwPkhBi\nmCEQINgsBkKww2LAZvVujC1btpFl2ZZsSZYsWda+Wvt7Wp/09Pb3+vfHvb3dW11dVedU9am+9f18\n7Pt0b/ep01XV1VWnzzn1AYUE26fGp+BXr14E//euZx1oVeSGx3YoHecix5FpETGYje+qsot/mMig\neWmhVBaBkczaUFlzeJSPHDHw2laBxIiHOrfcnFi3UaMufN+pzSbivLStT5NQtXRMbgvmWvfBmPXj\nggAAIABJREFUcBQIMEG6wCWQYULiNTM6OZ1+t/DZg/BrVz8Mj2w2j3FP5BZyHKUGEfm533psp3G5\nKvILEA7cjwi2Tc928aCBcpvmyuPwRVmjSjfZWyHlBaWgc3CuEwwjE1Pw0TvXwYkxunwPAQQOOtrI\nRGvMX7RpQPvcQaRx/9an9gq/r+Mt7txZ/WgZ1FqT7DRG6XJTAUn4EKGeMRA/ey3UIddFo010vfvI\nk8439QGuSa8ayXQR1ZJobUMhlxPBcBQIMEEY3qJ4rstJxtq9rWS/6/fb2Vbc1qBpVEeW30ZmbxjE\nv7tsV/U38PxmtAufPQh3rBIvNkXqUl5CU+dY+cv64ZO74ccrdsN1D2+rTZ9egGNX0u3ft6/aC1d+\ndhGs3n3MjkJEuBjHbBlnKMWxD1VLjWQo96pK+Rg43rdVNNc4YMdIydXA2Cu46K+6RYh0Sl6y//DJ\n3RQqsSQYjgKBBlHpaWF7lzDF477x6Pbucy3rxnGexEmnRBVMuKPLyWgcx3C07TL//h+sgn+4ba2z\nsgFKDFIq52m4sa167ih853Gxh52yUbn9mW+ayemZUu8hkdxk4WeyADRJRt9raA99Dmy3puPxip1H\nAABg68BJQm1adN47bhYTFIYF4lC1NHzLD6MHaoeh5BMhQzRuUYbqcTfC8NbOHJHxF+dxZDd805d2\n8EXPupHV04pdR/Xl2Q7LJSIYjgIBJmAGC34+IHIe23oYAMpyHDEaNW0bs4jk6C7yZDmOOFW/jIc2\nDMAvf/oheGL7EXMhlq4VG+731UVbYfuhYfij/3gCPnXvBuExqh4Toh0M//ZHq+EXPvmgmpKAM+r+\n0qcfgv3HcTnRmo4nt5wVdK6d0gCj+pzB5UxJykIIsUSikguPI5KExSSatCgkLrfsVRxQR3fuRx6q\nZqgHdzBX49vaggtzZpmbV5IxmWvdB8NRIMAEjo8qai+gtXuHCv/OP59lnhgTU/idfTDXYtI2SuUZ\nCJYmFnfYi4Tx3Q7D2Fa23+gkoZN5CrmzBOfa3sEPw/GRCfjSQ1vgT775JKnc/L32wHr9vDUYDgyp\n5byJ4xi+9NAW2Husd7ZAPpnz/OK4XmGoUnr/cgybFUFtnKGIgEvGSIw3kG5ZRud2/YGQIfg3xTOT\n433bq6B237Pcjr4YpApqar+UtHONHGtOdKnPO3MOAAD89ytf6FgbdwTDUSDABNmAS78ri7k86c5R\nCD1kybF/76uPaUqmwfayhGpB4XL5pLqrm22dxDta8FhICnd3Uzw3ua6Jim3Q7dcv7dtcFbYfGoav\nLtoK7//BKrsFMWHNnuPwmk8+CAufPVi3Kl3wuJPsoTpWUIS+kJOE1BAYU2YcWI7qznFkeyyz8cJm\nLLchSUAdWx5knth8Agi0m1hwwgXz5gIAwHlnzHGnh2OC4SgQYIz2OtiF27kluZknTXcJ2waHycqp\nY2eeMvKaTM/EMD5ld7KYLJbkOR+qDEK8lpSYRJZiry2C8isEnxibhC8+sBmmJYu2vIw4jo0XEhR9\nCt/marWaVMf4JN7D0AeeaXvLJaG7TOyebKln8cbPI4VSnGwMoqLuHEeyU13uRKrDybEpOmHEhrM5\n/XyXjhRNIZ5T8Jk32obGC8+/+tIOlZTUk0kd+lJlfO/+QKDHQOU4IshxI5Srq4fm8aqhaj4jq+v8\ng+r/+/HT8HP/d6H4uNzf1urJk4dWJwWvNcVzZN5tqgjLktyI2Q56MVx93yb42uJtcO8zB5TK+trD\n2+AVH18IQyPihNYy9hwdLZQf4EOWuLzcg2Tw5Bhct3iblxNxFUwuy+1ukwQy8CKK8mI6ufx3VcPL\nEMrt+MTKOm127yynbF2rLaOPtgy8CLHcZg7hjYHSY92krTuN5Fw86DvpnZEuEOgBqsYqCqMDz6Gs\nmziOhW9TuRqoFqwTGRH0dMUkZe6lOQ3l81jVIJO0zZ1P70u9h6Yk4Wh5He96eh8AABw+NW6mpAay\nyYqdANceRpC4vJMP3bwGvvDAZli3b0hylD1MDVY2F0m+LcDoPY4yI7SxjPapLjyO6l7MS8unshw5\ngulashbE8xg73jK+jTkY6nqhK3/J6qJ8TY8jYboEM1kdkhHn2icYjgIBD+A4jCzbfhgtI4bWAJsf\nZKkeEB+5/Rl46cfuI5HFMeFf4eGuusMWorysrO7y66RuPdbuUVvQ59VM+pOs2cquK7lfbHkZiuiV\nxUocx3DDYztg8KRaMm/Kcss4NdEyMrpY4Ofh+rZThV2HT8GPlu8mkUXjBWHp+UEg1s2uaggDV2ok\no9IGCvJIDA0uN6Vg8NxlPTYQ1w/nsLxOOPQNjAq3rNxDpocL6D1JGTSgAv7cEYFAwxGPGXYe0BTj\n09JtiG3QU0UAPv/AZnjxR+9LF0ZUQ+dtq/aWFKleAmaCpHKqrclw9XHmb9QYTxkzRDkKJHmdMIwq\n5h4Svp1SrM38qVd+dhH8+jUPK52HIcm700tsGRiGzyzYCP/7R087LVe3F/pwD1KvLRdvHlSW/c7r\nl8LH7lpHkvgZlxy7/YnWQiyXwpjowh6JynFkqQ475WNlcPVkLoPjElW3LUQ1zvG6EjjrlqeQGsFh\ntz45TpjXywGyeTSmrR2/I9JmVt0KBAKBFtJEaxUDifKkRfMh4OKhcePSXQAAMFmxixQW3yZ2LdSe\nIJRXph7uJj+wjreSoiKF39XQF/Jv9jHzgkMnW+FqdVyDJy/EjEnGINLEtBKSFswmiophjwZlTU7P\nwLcf36l9nqwsiltc1Xj73u+uhIvOau1YU9UPjxnkArMJ+a6obXEU3kJukmPjQ+roBx+6F1UNHxbZ\nIgxVI+gnhY0p2qUcGZ4glWuD0vG4hg7q41yBUmWK3Ti5rliCx1EgwASXu3tgPC1s54exFxbWkvuT\np/crn8Nx4LZlj1HtE1y81OVpK9RCH5V3RBMhCzOr+C4tQ9HQJSzeUjswad6egCIfguq5Ny3bBdfc\nv0lZrko/cJ2TgnZir2qkIwizMpYgxxfDUd05jqRySQwN7lbJHJ6/1uYgFG1BoUdOyuR06++F6w8S\nSK4H3TGM5n710HKkiegKZbtDN4VgOAoEmJAkwDUB8yB/cscRWPXcUXMBCOpIePfln21RP5eifE+2\nc852clCUUfNzcWSi5RXyHQMvilqMLgKPI6GrvaReMWrX217+TqJmZmK4cenOtL/VxTuvXyb8XrVd\nRybUQip9QDkhPUVZDl/oKMtrf06RhKpx9zhqG98sqcnJ4+jCtkedtCyDwvwdfeXoPj9NeNHzzgAA\ngN94+YVoWbaNCXTXngliYKdkC/WLk7jjkyshVC0QYMK/Ldra9Z1+AlzFCXXusHd/80kAANh1zdv0\nClNEppMolpp60JyeiWFWf6QU2vP07mPpG6YyvvfELvjjX7kc5s7qJ9KwHOmDSfG7AgqJrSvDIhV2\ngnJBErJ15JTchdzlG9ofPPlc6W/5N/tZcmy/pmVNfovWyaaDJ2DgxDiMTU7DJ+/ZADsOn4JPvePn\nSctIxqQZzcVxvtfYbhHTxYjNrq2Xy02syClFQ5rp9beS2Lf/NhMhlQ1AkwuDveEo/bQT7sfJMFj3\nyxhVuDy1jOZAKnJzQvraAxmXazbBl34lg+MlSNOLYORyvNgcweMoEPAA5uNIyopdR2H+VQtg68DJ\nulVJmdYYhd95/TJ41zeeSP8tmiz8893r4bqHtxFoRoj2rEYQImirqBzzr1oA9687gJCQgUk2ncog\n0SRj08Hyfn98NMu7IitXtODeefgUQqukTPOr/cyCjcjSFZOAMxro3vqVx+A931kBo20Dw9Aofd4c\nih0KbYXJqBh+SHIcyTzsfF6t5bC3qxo/b1YRJMmxGY0N9nB3kY9va+bmB9T3RDIGURhY6+rDmB1Y\nTV9u2bjW+9YdgP3HR0lksXzhQqzU/KsWwCfufpZWKATDUSDAGlvz5rLxaeGzB+DgULYV9Z2ru8Pn\nrlu8vVTukztaIW9LthxC6WfKGz/fvduUOI8NLuZbZRFZR/LiD9+2Fi3D9gP1f/1wteUSkLmLFNFt\n36/8LOdRmIQFCo5r1O52KX6t+lxrqxsmiinDlUybnhyqousMNY6iyF54VVsuyWIWLUGhDEZePbbk\n2TaiUpYDAPCe76wwPteWpyyXsMH8PZ94HPWG4ZI3H/jhaviD65baK0C3jaXPP3x4LgU3PVHuBW9K\nMBwFAhIODI3C/KsWwCO5rYBdQu0FkhwnGphmZmJ4/w9WFzxuHtwwoCi5yJjyFuVxOhH6/a/hHwh7\njna/jRAl/xS9Ad0s8Bap+423y7kK1pjWSR1Vp7uDmj1PgIrfIQlV05RrqA8VqtX1+muKBtyFz6ol\nFq37fnNNtquaXqhanrr7RB2YhGSbl2V6Xj40Fa9HQTaY95c6QC2iLCcY54TKNRqNEeRGN8atQXzP\n97UHaRdJ5LmA84Btf9Ko0iV4sJ2igANxxye1XK5vB4PhKBCQsGb3cQAAuHnFnlr1WLvnuNJxqLja\n9ufeYyMIKS3Gp2a0yqz+0hxRqJpo4rPYknFw2fYjVuRiEHrkqJ7MZNcI2dv2/E8iPaVvTFU9fhAP\ndZmHiVS1gg+5WZlVUBhv9nW4k3/rsZ1wYozXFukyXM3XKPK6Ubfr15dsh/lXLcjJN/W4MTpNCuf1\nqojM6EGrOMf8PDJwybGTT+I6ZGiKYm2Q8QTqdk08jlzkAuMIl5c5Lux2lLvPmUWq2bK60RIMR4GA\nAnVNMpQ9iTRHd9nVUFypqjY2n8XpokxgwxIV+9wRfB4ZET9avlvrePW36dlxJLl9lEOkeMwkKCYS\ntrpf1e2YGo4065J68uRyoTI+WW1M5jI3d6XGgnUH0QUq7zCmWMY1928CgKxvyk6r6ue//aUlqPDZ\nMvnKybGZzMBt7apGc3326wiV4yj55NGUpVA8FZlfYgqXTR1shaTnRUSEhiNfDIM0OwHTXiu14Y6i\nB09Oz8BdT+8V/jaDGPS495JgOAoEJDB5PnpHfuCr+1kpCgsS6fTjmr3KEkTPG9FExgTK7ly9+xph\nYQhExhnbqqmGqulCPXlyeW9OTqt5ISbsPjICq547Wvr7ynYifqpkmQBV3l5kxQBAawfHR9u54DDh\nONby6ChoU5XjaOvgMNy+SjyxVtIBeW11euTkcxzZChWq+9mqConHkSWvLU64zHHEEi4edIJQtfy8\n7LWXnUNQiB9w6W4cIwW/vmQ7/N0ta+Ehw5QevjKrbgUCgUA51pIQSgbhCNxavG09mJLrEE3edSey\nnbta2awfkzc1mF0z0u8Ur4rLxFWkLRPVKjGd3B7IJa7n4vmlikqOiHzf+o0vLAYAgF3XvE14bOLJ\n98T2I/BH/+kyvILgdjE5PD5VS7mqyHT68G1rYfeREXjzKy9ypxAA1PEu1tRgUchxZElvX0LVcGX4\nZSTDUPXsf/01D8Mvv+g8fbnE/c/1pi1liOYiJN4yguTYviSiF6Htkc7wXuPorSXLt4R5CcTxWvME\nj6NAQAHm93FKXs/5Vy2AwZNjhd9VFv2YSyXZWQQvgkxe8sDtzNmieLIRqt5aVde1ds9x2HNUnK9K\n9mBSfWiJkom7RHoNgtrBhOfpIxec/Kp7v7z3uyvN1IEyY2E3tpKJ63ocVZEm+ieVKinPop1Od1Hi\nIlRk+c5yb6/bV+2FFbvKfwfwb4c4m2VxDlVzUZUYPa0l22VI1TXuOz4K96zd70QXH7AVqpYn9Tji\n6PKiiMuQ3dQbklwusTxacd3yDQp4eFMrz2o6P6RTh5RgOAoEpNR3654an0JvPbly5zHxYs/yqPmD\nJ+m3gNQlWVwl118VqqYlG3e6FKrdyt5x3VJ44+cXk5XbWf5Pa57AihbbqsmmKRa1GBG+GKIpId+V\nxsJN6DJUrSA7LUOtEJs7diW8/werKo+hqC+p92uX/CTvkmo9qelgi7jjk1owjVHLfiXNIGzGuveG\nrlxWsFTKL0gi1fJCUo8jAsGegLnUuOsPGkSb3FCjW4RsCmJirEt2sa77uVVFMBwFAgrUcR93hkfZ\nhnJidmwk20Hpv359WW16AIjbDut2bLM/zBQWheUlbR8cNi5DJDXb6Uu+ImcTqkawMBV7JlG0blUl\nJcbMeiuTu0u0CpTXIBLlookwl1BnAuiNB7q9DilDKLvrpY5QNTMK97Y1jyM6WTbB5Tiy48HAEV+u\nkcscQAR5cuz0OwLvPl8aOAeXtua4qx2XunFNMBwFAhLSnbksjlm6g8/nF26Cv7xRHLLSqWbZw87W\nYkN0Kat3H7dSVhXpA9+9w5VVlrQT6m7KhYrpPr/kSW3d1s7SbYdhYkr/lTRGTfvP+4pQNQXdRe3Q\n35dprr11rGr4noSbnngOPvDDbk+Ux7YeIg9Fq0Jl1y8K3OSAwYfyWEMi/66n9wkO5/NmuM7dgeI4\nzjyDrOU48mMxS52wmALq66Z4CdAEQz4Gmuu3c89jvObqBpfjSDeBZuGDjF66NbjsBlpGMBwFAjWj\nOyBe/8h2WNSOhdUlXWxJDQdGolvnmp9qDeGCWTUkpOSKXIWqiUrH7FKUycUbEaqprqVn9w3Bn96w\nHD5730Zt6bKHa74O6wo/kpEUq2/ws29gqFr/3JdsId9m1XPH4M+/vQK+8MBmQ80ydC7Pxtu+ut4g\nSi+75hmzuccNqRoFOD5nZNjaVY0ifMZJqBpm3Or4bDK2rrGnFt3EHkfJ3zTJsZvfEPb6MHEkguWm\nQK2jmHeTYDgKBBqMzpsG8thwRU5NTBOUrIfqhLvsemyO6zYnF1neJ1n5FTIIzWbH2yGNWwb0E22L\nr0FNNy4hYtq74ZHrgZdxZLi1s8iOQ/LQWmu92vIki2OoWr7/cp1jUngFdda9bj1RLyL1z7UTZuWb\nMQUXqpZ8Ei8cGdYe9wWjD3CuwvpeVOG9k5XPTUJLiS+WOscRhTTRXLgX7uFgOAoEJLhYXpIk6i2R\nUR6qZlcfDOTjrkigg8HdtBrrSsKo+qCn7B997SeQzsIiKf9Vzz+79Ji8tOU7Wrs/cQpNyDyOumVQ\nGuZckNUDvoJ1qlSWe2L7oeGuHSVVcLFLjwhp/xdUSjE5Nq+Zqov+q+4xSlEWhQxLb8tJvCDsgzK+\nWchxxOyWSeFozBJxeHiibhVKIQ/fTL37+LcNh/7js9ecbhl9liwoqvlG6yIYjgIBJXCj1l/euBJu\ne2oPkS7liB6adbyl5UCanyr5d+431QfsX3xnBalOKphMfLQ9V0QLZO1S8fQhdix5yQVnAgDA237h\nUulxsm3Da3sDKPX4UgvBI9GDwtiTyHJcl7L8c2++dglc+a+L3CqEIPWqMDmXVBM8lIsXKo/PVc8d\nhf/n3x6DsUkT71a8twz1ywBSTyYnhlHzcwltZGLBjGjK3MsUEkOvJRk+76qmbczPXSvF3JKCXr83\nOBEMR4GABCqL76JNg/CPtz9DIktE8mDYd3xU+H0n3N5S20R0qb5MAsi9ZBK5wrxPrc+qkCNKUsOR\nQYPIzhg8Ifc2kW6j6uLNVqKHwxdKPnR5rRxHFpJji9rDVhtReea4TBStA4eXpclz7l/u2QAbD5yA\nzQf1Q2JJ9LAlt+ZQPOUyCBIn5vsvdv7CoW+KsNZPLMmtG4oXpWVyV+8+1vpbUpa2XLQEOZy8lamv\nldzjy9JELzXqo/K68b5jg+EoEKgZyqH+u0t3Ff7dOQBxTBRsi2xhKZpc8L1YoWYV+uJ2zdCDsr/2\nt59AOvHrXeUL3o6t3TukJIsihEL8m9q5qLokmSDjZaSyHJYFYGfHS5fDQn5c0p0UU7zQmJ6J4dl9\n1feJ+a5i+ONKQ7CVjWV4SDaLsBSqVueucXplIM4lkOECkuci82u0DRdD6DN7h+APr18Gi3Ob0HDv\nf5TkL1V7Aw9LndhFqKBuCZwMdS4JhqNAQAGqMWtqegY2HjhRlE0jupS9x0a7vuP8DHTxYiHv4LJt\ncBhGJqZoC0Xg0uOlisETY93eO4qLVpXDTDyOOhfNJhMVLm+cRXrYc/VWE6xbNZR1qZXjiEkbUmLU\n9orndE5y/23RVnj7vz8O6xSNrKq4mUy7MHYQhoMRkyWMJpCFF1HJNMLFN0u2m/8OqxHPdg3goTSE\n7jk2kgs39bfHaCfHzl2r7ksKyrEpD324LwGS+RvupWRbPNM5zqy6FQgEOEN9337hwc3wjSU74Gd/\n/1/gZRfNI5Yu5je+sNhJOTa4celOGBo1M+pMTM8AgHwAj+MY3vKlJfDGKy4wKqOTLzywCS/EheFI\n+FTv/u7Kz7byxOy65m3pd7J74ur7N8Lps/vhXb9yudLEob/PPMeRCIrt7T9yh72Q0qxcu/IPDHUb\ni21Tlxcf5RtOlxO1vIFFVHVfX7JdSc6Ow8PwikvOhtPn9EuP66yn9W1vo4GKsE5dbOY40pVME7ZC\nIAMvQizXk7UsyVbm+YTwaFlIAZbgHqLiA9ZevFDIsNy8NvrP6ueOaepgRxdrGwxYLMMUHlqUEwxH\ngYBDnt59HAAADg+Pp4ajCPADBU1yZNrhamh0Eu3J88l7NqD1EMbBt681eRO6dNthdDkAANctzhZ7\n2w1zBRVyOVh6hAjNRgRFjU3OwEfueAbe9SuXKx2feByZvJHG6Vt+wzy0YQAjGAAAjpyS7zqTtasd\nS8WvXf2woEyBHgRt3pmEvgydvrx1cFil5JZc7rOsEvL1IQrVvOb+lhH6tZefK5XzzuuXwW/+3IVw\n43uvpFUQyZYBeRuOTU7DwaExuPCsudqyldu85r4h8pahlEtjkEGLsErc8Ukml+GF+5J7kTPkBgug\nu9dqH5AUyWv5qXs15+CWLtHFvcGlhanHpuHxKTg+QrcTYghVCwQU8GO458Wbr10iXMC6RuZckyzY\n+gxcDYZGJmH+VQvg9lV7hb93hiSqUtwKVu0cVfVlO6dT93F5Pi3z0qSJraW/ubuLRcanFz3vjEwX\niStyr3snqMLVjRuD6X3x+FZzw3e1wU+PxJPqO0t3So/78G1r4Te/+AickrxcKM1xpKmTCVnIBb40\n6jAXSmkuxkXM9YtCXzgafSjeAbC8LsbYehkiYmbGjtymkSWHJpZLPobavdc43cp//I0n4A2fo4s8\nCYajQECCi8VJWQyxVr4PzTJdTBYPD48bnedCt6SEZDIwZfA64wfLnwOA1gKIEieLIkQpqv1S9uCk\n2vkELcPhufPmZg6+qeEIUf6HblmDOLuth+gqNJWKavb8uXHZLrh15Z56CieiFxeMj7e9PMcn7a3I\nSJJHI85LvWXIF1F0cl10PYy3QLoQLXji4ogini8DOerEGSfZ1Agbxfa95nqnTtm51NeKyZOmiq7O\n8peYCGO5gnwd1u83e4ldRjAcBQIK2JzYl8m2UWQ6EIUZCgDo7ebViS2jYr4//AOxUSorQ+07ETaS\n3+q0gmpolIoMl+TnPRQL2sGTZobZPCRjDGFdivQ5VhL2lxS7bXDYSV4qSu5bdwC+83jmkYNtBtS9\ngCy7E5s5jqq+Nz3Ougy8iA553cYUc1n2ofc4QirUY/RSfVnzOPK4Ej1WPcXFpjm6iF76U4Qnc2+v\nYDgKBBjy8KYB+HZuYeECLoOViwdE8p2Ltxi65DU6MESbuDYtw/Jlj01Oy8tvfybPXZ2wvk4Pl12H\nR7R0i+MY9rV3GnTZ5wvJXdNQtfKJhwUNKo+YmYnhX36KzysmLN3wsn7p0w/RKqKAbQ+gD/xwNSze\nfMhqGapUhqrxGyKdeqWaEgFtuFseW2/1bYFaRHV85hmdkD9nSmXGPOuOo06+QZ+UufWZNxxxbaay\na9fVl8ITm3rMc2O449qyvAjJsQOBGsiPgVEUdc0Y/t8bn7JbvlXpvEgepvlFeprwkKPhSGToqjiH\nwoOGcsL1J996Ul5WHAPW1yHRdt/xbAexj925rvK87z/5HGwwzD+Foe5FgUq/eu7oCGweOGkm3+is\nIjr92CQvGVuQlWdSE7aqT9cjURbS3Kmj7mKEok+ajoufvGeDdQMkyWLKSagagWeUwGPzndcvRcsN\nNAtru6ox8V7kTupxQyyXeqqO8bpPED3pZIbuphA8jgIBCRRhMS5o0hqKGqnHUe7Hn3/B2Y40klPX\n23TlUDWFvpbsHmiTZIKS9xqr2s0MAGD5zqN5KdRqlXLOGbO7Sr1xWblXIX14ix3qGnpsjnlleeds\nUUeoGpdFzDuvX6Z9jnqomvlF7j8+Cp+4+1ljr9RHtxyCjQdPtvUwVkMI5eLEyfOGwoUhn+Oo/eem\ng2ZG7kAzwMxjdMvIDwNNn25jxs3M44hGl0wukwdWDlsbwGTyefY0tOEoiqL+KIqejqLo3va/z4+i\n6KEoira2P8/LHfvRKIq2RVG0OYqi38WWHQjYxkY+Fw5wWTS4QHSpWweH4YsPbC54HF1y9mlacq31\nDaO2UdMl0RnzBpjiqlHdr63Ayl0tA9DktFpy3bpj5J935hwAALjyxeenFbB025H096vv3whx7HZ6\nROkRQZKsXFHEyMQUfO+J59DllevhdoCUlufZYM3eEKHIh29bCzc98RysLBiaNUlCyiyFz1CIdZMc\n27yQ5FxKPbkmx7YFx0W3LWxdK81mHPW0g9uQ/PYn8bWSexwpfidDmhy7wbcchcfRBwFgY+7fVwHA\nojiOrwCARe1/QxRFrwKAdwPAqwHgrQBwfRRF/QTlBwLW4T4IlFmmOxPL2rZgX3TWXAAAuPQcPSNM\nHhfbeL7nOyvga4u3weHhidxxtOWaUpcaquWShMW1C8MY3x5Y39ry3mRHvDpIFkCz+iLhpOobS3bA\n9kPD6b+p79Qbl+4iltiijrdi37V0LTJs9rI63NuVd0dktOjU1QTz9ju5XymGF/ohKtGNYjFrH08i\n6lA08xUjb4ShQpbmj3mxpkXUtaua7hhO4snoYB7PksRwRmLU53nNKMNRFEWXAcDbAOCG3NfvAICb\n2n/fBAB/kPv+5jiOx+M43gkA2wDgSkz5gYB1PJ8NfPzu9cLvbS0G+vtaFfZzl5xlRb5/M+8XAAAg\nAElEQVQJsivluFOGC5UYXrYxFDv2mPCFBzYZlRXHagtJ6iZasO6AlTJMhsip6RkYHp/qlqUojDqp\n/do9xwu7nDkPVZNdjkAXl+rp532wr5zq84tinEONL8nuZ5YGXF+GcZrQl+5QNXN9+C7KAupQeI2o\nMkPY/2zhs5G/CvocR90CKduVoi24hqphk2N/BQA+AgD5VeLFcRwnM9SDAHBx++8XAEA+Y+re9ndd\nRFH0PgB4HwDAC1/4QqSKgQAfeA4DzSaOAU6NTwkXm4dyW5rrDvMujU5VRf14xW5diYIyaK9HlvQW\n81DtvIciQLwBNNYCYLVmHqek+8UQs9nNrw4XdoDWrmIPbhiAXde8zZ0CEt5xXSvJ7ntfPx8AxPdC\n2dg9NjkNfVEEc2aZv4erM7zLxwW0tjHL4MGb7t6of2pKFrZBC+Wuai7aH1eHIo8P//pswA3WjLQ5\nsY3vfwxDYF3Mt3XbVbgrbvLZ4C5iPNOJoujtADAYx/GqsmPi1h2sXX1xHH8zjuPXxXH8ugsvvNBU\nxUCADJtjQB3GJIodBVTl1s32Q8Pw6k88ALuPdm/b/n/uynbh0plw2HxjyT1HCMVbEFz5nf/2xRyb\nf2NZXgEc7yEVdPR+cMMAWoYNkhA4UdLdMtVe8fGF8OvXPEyjgOL1111PuqzdYz9ZvojM48dcBs7j\nqPMPGrLQxuaHqtlaiHl2C+Fo6MW6fPJTeBzV1Qym+oq8gjVKRZwrkEbucYSXIQ6V7DZ0Nw1MqNrr\nAeD3oyjaBQA3A8Cboij6AQAMRFF0KQBA+3Owffw+ALg8d/5l7e8CAbb4siSl0POGx3agZVAZPX62\nYQAWbxqsPlCBv/5+qW0bRienjeXachpxEqpWU7ncqCVpZFzcza9WKCZPvgySFpF516mAD72pFlDW\nTi4Mr4lHl3MIDNQc8/OkixNPXvZg5gXZuEkXKmSjyzMZ0XsKF6FqlN59vpDcrydGJ81leFhflGM9\nxzQYVBgbjuI4/mgcx5fFcTwfWkmvH47j+M8A4KcA8J72Ye8BgLvbf/8UAN4dRdHcKIpeDABXAMAK\nY80DgYAxoiHt2oe24OUSjZX/83tPwXtvXEkjTMLkdG4yqnFeFNl7MLh43HzviV0OSqmGJA5c8bjp\nme4QMZcu5zO5N1EzahvBeQVNTD+BIh7iIvSqvGz/JrhOQtXa58wg3hBkBh7it++pfFKx1qB4yRKX\n/G0kyygWIuAD9l7o0RkuXaOrbhwDXLd4G+qFCPcqsrf7XlpAY8HmOBJxDQDcGkXRXwLAcwDwLgCA\nOI7XR1F0KwBsAIApAPibOI7NX/cHAg6hn/jh5U1Nz8DXFm+Dv3rjS5TPsb0u822snMqt4HWaWDXB\nsQl7BCF1VCSLoSd3dG8x/S0CjzNVxKGSMczEWYL1MjqT76ouCr+2eBv8ZM0+eO3l50r1sEW+KJnR\n0aUxi8Zw184FUyFKpSzfJuR+45+VTrd/UHQnaqNHL0Iyf8qJYGnsZKiSiOmZGCoesX5jaa6e9xI2\nfWnIsduKeHb/ENz/7EG4Y/VeYxks79EcwjmorhDBfZSGR2tr5A+oXdUS4jh+JI7jt7f/PhLH8Zvj\nOL4ijuO3xHF8NHfcv8Zx/NI4jn8ujuP7KcoOBGxS5cb/yOZBWLb9sPC3Z/cNwU/X7rehFgAA/GTN\nfvjKz7bCFx/crHxO9qbSrrXd9TPj3xdthclpfTcOVO4FSxf5pzcstyK3ivX7Tzgra3RyGr728FaY\nynl8feCHq+GlH7tPW5bOLk57j41qy6ciXYDG9LuC1UmvegmZsn7/UC3ldg9Xan2QY081NXianIUK\ns0o+LeXn4L44S+AY7teLxHEML/3YffCpezfUrYo1OPeT2pJqa96A0+152ci4uW8H53YAoNFPNPf0\nbWw2wYbHUSDAgpmZGL7x6A74i197EZw5105X/x/fbYVTiXYHevu/Pw4AAL//2udbKXtiqmUoOTU+\nBXPOmKN0Di7ZnRtOjunHVV/70BY490y1OshT3ClDHZuhatyNChSGgq8u2go3LtsFAycyV+j7nz3o\npPz86S5rOj+R4JLj6OipCbh91V74yze82HquG5VL7gUj1Nu++njXd6wnmQxVU62uzuP+QCPXEmX+\nIOoFY9JfmD8qUijULIQKUcijbhNSabR0GjCTTQCaiD0jLa3cpsO9vkTPXIrnMOVOmlynQyQeR4EA\nRxauPwifW7gJrr5/Y92qSMEulm59ai+jEQY/XJ4YMzNujRskusa4HPsyaefIqbYBc2TCfbRyIVeG\nw9lNluMolk+qHParf7z9GfjMgo3wNMGuVxwnit9/8jmYf9UCOHpqouu36ZkY5l+1AD6/cFMNmhWp\nJy1C66HxPsnmATZhn5SZUA/qnGZZf2F40wkgWZAV5KHF9SS9UG3Wctfk/zYtwpMGSF4i4ZLa875Y\nW9qRblyAF2GF4HEUaCxjbUPCKYS7pQtMBxiMwcnWmE4hd1ZfBFOOrDL5cnQfdC53TVi3r57wFluk\niWcJ6tCX3SsKu6oxsToOtXdNSbwXTaCyWT+yeRAOncTtTtbJLSt3AwDAvmOjcH6HR2IS2vrtx3eS\nlmmCJ12YDbrVZVS/nNvEMy8IiuGOepyn90zh3xg+6IiFvF27/vDHYJtQh7bsa4hAQdEarJ6XQG4J\nhqNAQEIyLnB/3urkebEJRTX19UVeuPNw7xOc6Ws/cU0WA509XVdGXXdKXk8lw5GD/kU5vmEn00nY\nby/i20KEAtyLD8X8TIhqpWwTa7uqkUq1B2UISOsfaHEBhujec6IhxNpL0ZxuXOd+ZXppbyrQPgG1\nrmBaRwki9bR365TI8OWFpgkhVC0QkOAi54ZpGTxMRUUoJoiY7Y9d4oue1FAYKZM+b+J505mLB9MK\nTndVy8W+y3dVcw9qkdxujspd1Up+v+2pPcJQMt/YdBCfXL4XPAIw6NZPcrTJMzYZmijGO+pWpQyH\ncAF12AaFUc+XuqMg7S8160GNcPFPXUYPhB51QhKqRqWMJSietWKPo9xEr6EEw1Eg4CmcE8lixsy6\nLPW6xeZtHq/8+EL44M1P0yrEiN1HRmD+VQtgyZZDJPLSiQnFZIzIq+CRzYOwbXAYr1BpWe3POFbr\n4y6N1ii7kbmiuw6fgn+8/Rn42x+tNlfAMh+8eY3ScW/9ymPGZWDfdNocMTl6Q7kIVaM04tkKn/HF\n0EiiZT5UiOFlM1TJO2jmA3Qt8c93rxemTeDa1pzXBNyw1Ya2NkTgRDAcBQIK2BwETBde+fNubufy\nUMWeO28i37yAuhx5dNs4v/gfnZyGu9fsp1aJHNOds57ecwwAWt4hJHq0P7nk+gFohUq95UtLrMkv\n7KqmklIoVzWvuOQsAAC4cv75xDp1FWUuq/L37iN+84uPAADAIHFuI5WyOSHVTrT7izVN/EB9VzUe\nb8xtvQzxpR/QJMemDvcjludBY3DXkaN6o+0NPAoeb9wrsgNTfTEvhbjXkUg93TFBLKP8t6YQDEeB\ngAQuuYOE5FQ7PqK/hb0NmjxYdnLm3P66VXDG3FmtRwUmiXKequTY7/3uCnjrVx4Vn0uiQYsjwxNw\n/7oDysfvOTpiXNZMzkgjDVUT/DSrH+86LiJSDDNTkYGB+yQTwI2OHGuBU9OYehphQtVIwqLQEjrk\nJbpxahwJJIbpvMcRgTxquBunATzQsWYPZLncTLBpEbbv19IcR1ZLFcPonaAQ0b2gnQtK9F1bCMXL\nAq6rz5AcOxBQwNZ4/92lO2FCyf2gG46Dii8TWRG6qv/CZefaUYSYgRNjsH7/ELzpFRcby5iTGI6m\nZ0gMBYlBtuzhunizJCSOsON/ZsFGAABY88+/rXT8m659xLis5FqVd1XLXWd+RzZKknagWlD89fef\ngj1HR4W/1Tk0sH4BABV1o3DDqVwdt5EZ1x/wb4ZVochlRx+q1h5LaMVag2IRxd3jg/tCuVdwYhwz\nLKKXugh3I6XQW0jXcMT7Eq0RDEeBgEM61wCfX7i5Fj1sDeq9OI7O7o9gcprvlf/Xry+DPUdHYdc1\nbzOWMauvZTiaIrrOPkRybBuoth+mnfOTDN1d1Wzv1EEiNgZ4YP2A6alWkY13qmWbhnmqkO1io6hL\n5/mk2vDF6S6n6ZtjEmEUQjJplgzJtqCoQ/Kd6cjdwIjlEeJLf+Gc9Dwu+VtLBvP6p4T7tdrKp+XL\nvYYhhKoFGsWhk+Mw/6oFsHTb4bpVsY7NhYyPYAfqpr5tSDxAqCbeFL0u6btc7G0u3o7ld7YZnZzW\n0iXu+KQiDVVDyBhvhy9W1aH0Vyb9oG6E1VDzfWuraUweX3WEqpF4y5B7HLXwZstnikUarThyOHtY\n3LJyD8y/aoEw0TMnOO9cVgiV9OW+a2OqLuc+zQHR+DtD+sKBJ8FwFGgUa/YcB4BWCBgFFDlAbNGH\nWMFbux6G9aSK/kPSr4ulyGNDNZFIcxwJnq5bBk7KzyXRoIiL+ztfhK6nVWx5MoKZCH/qnvUAALB6\n93EqdZyi2p/uWbsftlb0TVP8GklowO2GqMb41DQsfFY9h1mxDLpwMGvt60nHofYk4Whg4DhHTDg5\nPgUAAMNjUzVrIofkXqP2TCOVZpcyA7nuNVCse7jXm9BbSFeG9DvuNWBOMBwFGkViTKFaYHH26eHo\ncNTcobKb9G02616SMR3HxppSX2NfVJ7j6He+LE6KbRMXb+7TMgzKsp0MFyN1//ExAKg2hsl0n5yh\nSbreSdJvl247UnqMzrUv0EikroO0XTkO9DWjehtcc/8meP8PVsMze4eMyyDZEcySy5Evz1tbOZ44\nwd2bB4C/hxrN7nu0UBgYsOcpy7fk2Wh0Lve+JvpOU2dZniTml48iGI4CjSJZkFLnTuE4UcHQrKuh\ngWJHBY5U7WCmQxzTrGMTESb3KTZEU3S+E48jxGIv9X5g6CnYj3F9bHNqvDx0D0NSb59buAnGJOGB\ndZOGIlrsh9zGK6PbWPMi9hzD74JIYdMkX8ym4wG3VhXD0WDBUCXrcMkpaBVq44lAHsf+bAOUxxHz\nKqLQT9QP8ikJmkowHAUaRV+ffLemJsHR08WXiawIbTdVTy41MabWveW6SF4ddSjqo995nCa0VYbq\npF32FstacmzENEe1b8hKqLI9bT80DDsODSvrJKKs7iam1C0DvtzzKii3G8OLdqET5bbK5J4Avnkc\nsRfYG9yxem/dKkjhGBZK+cytayzVLTd7Npjry/0WleWSVJYhmqu1P5u8Bg2Go0CjqNp1ZXxquicS\nZ9dFc4dKf0nuCcyD7PDwOAC0PY4IDJapZyBCp+efcxoAALzkgjPR+tzgwHCUYHLJtjxSIgKjoqrH\nEaaMN1+7BN507RIYPDkGr/nEA7Bh/wml8/J9lfM8znVif99xebkki1lLeVd8aXdOITTUUnzi4NBY\n3SpIsbXTFUqesAw6WZzxZXwxgSZPWrnlqMl1FwxHgUaReleUDNFX37cJ/vSG5bBONecB4+TYGLdj\na7lSGNaTMtqhan5cbJZPyFxGsnPWvuOj8K/3bcQr1b6vzELVWp9nnTYbAPQnY3XvRqjab/JHUXo/\nCMvCGI406/N+QZ4g1fIf2XwITo5PwcL1B7XKBOjuJy7GqqGRSVi7pzppeObe7m5MeWjDgLOy6oIi\n1EKUwN+lHjKa/FbbJr1aa73QXcg9jgT3fy/UI0CzcxwJ0VRZFMYcd3w2kWA4CjSKNDl27obOD2Db\n2+EOR0cmlORZCwcjGFVuX8XP7bhOY4rrsn15LpLkOGqfuvuoec6QPMl9ZTK5SL0KG5rnQ/iGM/mk\n9jjqkG8y2p05d5bika1S/umOZ7p+Ua0bzGhcRz/5k289Ce+4bmnlcVjNbNpC67y7tgyIwxPd7IJI\nGKqGltAhrwfyaMjwZMhnhy8vuzDw9G7zC8z8LIF7Oi3xPEtPadHxyfPCl3mpCcFwFGgUkWS3pvzv\ndd3UT+44SiZr3/FR43NtXb3POwroPzT8IPXCQyR5pZ5wJgZeTKhalveHQCEH6N4bBVtAcq6lXodZ\niP7VG18MAAC/++qLzctXPK5+bzE9NhxQC6nLC/7YXevg2gc3a5Xj5XiLsmPbv2DK8cVa+IyH7U4B\ntv0j8POewdIL10w9NohzDvoVq+aZuk6gSY5tRy53VF8VBgJe0CfwrsgvNqpyILkGsw4KuzTTor2r\nWnIC83ZI+snizYPqC1nLRALPQF3SNzueTG90vYZiwd+2xi2M2DmzWu+fZvfL30PJdDcypmnCuZfk\ndfvR8t216cEdl204gzCmdkLvcZTI5dyr7cFl/uYbvVBt9Inoe6HWxDT70vEhiOLk2HHpb00hGI4C\nPUViWOLiXYLa6aqmcqVy7YgNIEg8jj50yxpjGdQhm4k8kzCQzoTOvjygVSegouNshabkd7d73/ee\ngslpfUuerW1tRVAay3t10c0FVFuqGhoRZSRFcNxVzbbcXqAXq64X+gv1NQq9SmiLIIPq+ehbGgAT\nbCVi7wWjfjAcBXqKNJRNcX2UDsQYt3pLg2/dYRtCPB4rPVZdCsNeIvQM1MW2Fw41ccdnFfl2U13E\nxnFsNC5sGzwJDxomS1a9HtlxTnLWMO4n0meE4DeWY78mBCnXrJaR7Y5DYDiyFWJqRSp/sNfdu/XW\nq1dujnDbduPQr3rqX/tFeWr8aC6ia9O9XpkMivkG18d8yHEUaCRlHhKZx1Erl8RdT9tPMC0bQFCh\nauanWiN9U+HhI0d3gcB5IZqH4uFD3p5tpYx2VWt/pl44vjSE5kK0EKpmybsq6RuHh9U2C5CBWqNb\n8jiKRNY3DLZ2tbMilQbdS+Y62dUlS3KKl2XP44hzz7EHiTGvF+uuaZcs8gYiblexx5FZGXV1OeMQ\nrKb1lxwybyFVhC/yPPOENyEYjgI9RhYi86Plu+HvbllrvURbb9rnX3Cm+cmWnwhNHjQTfDGO9fXx\nW8klGmESzybn+tEKOC8Jzq7jqpPMNCWYwLKg2g90Qybz1dV5v3KqSqlhUFBfHPtBgnK+KkwYmQsP\ntfYnx13VEnzZGIAb/J6Ibrjs/DPqVsE65LeE0DhFXQgvkucLxdjHFVvvkSh34+RKMBwFGsmJsUn4\n+1u7jUL5nB4qdG79bYKtSf4LGU4CssVhvXqYoO2m6slzgXNTTBlkx07vYc+SEKK2tm1XU9VCUds7\nhKB3UBhQVSdZuHHY+NQAM1zuqmaQ9ksgjEBGIIXECwwvwjsuO+/0ulWgRfD4os9xJPBMMZRl+xm0\nZWCYRE4POBxJjT6qCPuGZy80TQiGo0Aj2XTwpPD7zPlCNTSifGF1y0q13W9kiz3cW1d+iyjK+F7t\nsh2X6ctCtI/Aike+UwmBXN+SEJK84WJ4rcq7xEkOVL2qiSm9VXy+6/OruQyp4ayhOY5McOlpRenl\nx/G+DfQeM01zUSMMI9Mogu3cb82e48Lvm/pSFIOtZ0nc9UfzCIajQE+RLKIpnp//dMc6peNsTRqb\nNgeoG53nSJxrVe5LOJocR7TgHtq+7qpW/NQ7t+3+TOH9kCPzwKx3caxa/hRi0Ossw0SSra7WpDe8\nLmxaLpOp2wppCJiDrc9ebY5emDO68DjyDe0cR555c5tAYRAUexy1667Bo0wwHAW8YWRiCgZPjEmP\nqXoTm/zs8mEgKmpyJoZ9x0dRcml2pCJ+O+Pxk8bY/ZhUC3oovBOo7xeKxLOpDAqFHIBJHG/rfj3n\n9NkAAHDklHlybFWNZMepLmhc7MTFDsX7d6oqpsrR2Owy/xDVccJz2ydzznEUMMfjqYox0027aFGo\nGnER4iozK8UXY0JyzaOT0/UqYhOKcFehx1uLJhtpg+Eo4A3v+94quPKzi1CLp6jDW0GVquMPnRyH\nj9y+FsYUB9rP3LsBXn/NwzAyYT4wUwxMHEOQfCCCyBsjGUVubOqHIOVORb60A8ZbSNX7Qbcm5s5q\nTQFQi2OC+rdlyH9m75D1MlSo7KMEql19/ya8EIcYed7Rq1FeVpoglk5WgAZfFuDc6IlQNQf3WtNv\n54ZfHgCUeBxpypDlOGoywXAU8IbHtx0GALMtvBOyxLq0x3/2vo1w61N7YcEzB7p+Ew0uWwfxSexo\nwkto8WXQTIwp554xO/tSQ3mfQtUochxRN2y6I1qF3AND3V55vibHTqi0IQjfYiWLWGoPwdYnJkm2\nsveH5EDlPEmKZamcbDJ+2ssNh4+LemzrIRplPMDJ4pCwrKat1+sG2yQR9MbiuJMmhF1xwLda1DW0\n9kI3EY7rmhcun9M0txKD4agHOTI8Dh+7ax2MT/nphqh7O+Zv4CRsR/Wm1l1OidbotsYPVKiaZW+N\nOt4I6rxxP3PurNbnnFnpd9oae/JcoDBs0RsY1cLMfu3qh0sXxDP4tXYtmNy3tvI5pcZPgq3R3eSd\nQYx5hHpwxDfjhEmfy3Y5tY9vOdQCgSowL119gfNzyJ+xxBtFjaG4Qll7NvlWC4ajHuTq+zfBj5bv\nhnvWdnvH+I5sQB8anYR71u5vH2erfMF3doqicaHHixDL5T5otvUzNb5FEHnjLr9/SJ4XTAVqF3ed\nHEcb9p8Qfm9q/KzLQyy5ZpOqTE6pOle3LigNx9XRWPXeL3WOScpeZjXtslkH3EPVEnphse0b2BaJ\nwb/7hYJeuGTOl1iXbtSpOZoAxVpNNqdpchUGw1EP4ru7qqn6X/nZlkwG8W0tm+9b8+pBiLUd5sO9\nh2VhCLnvNJSOIe6Jh2sC9aXqrMU6D029DgwNMXVP3qoWoqKxyZaHIM1bNz9uhM565aR13PUHQkaD\nSbuaw4v1fb7URHwZc7jRuOTYAlyGsTaVpl8fAM0aUDqVa/C9Nqv6kECgGeS9JvQt8NkJ0zMxfH6h\neliUrReWdW+hXSGYNZn3B3NFmUBdTdmOaPqCO/OOuZgoLt9xBLYdwuUlS67VzINBLbSvDpR1ah9I\nERbn+txUhqUWIPGaY9I5VNvXpB9gdiY0JTwi+BGaxIxe8J5zGcbq6jzX9IJhluISZeuHJtdgMBz1\nML4ODi4njaLk2E9sPwLfeHSH8HihZpbUpXh7ZD3ZK1OS687Po7QTCBLqY5NfmX8erNx1DCXDVlJm\nFbFlx6jmSerExG7xx9980uCsIhivoay/VnkrmcnF4PIxgnlm1bqrGvJ3leO5jEfKic5NQtVquEju\nz7KAPtw3tLCFr3N+HTjnOPKFZl9dOZQhfU1+KR1C1QLeIc1k76B8ocFGMhOxNfHEvDyyPaZxHzOz\nUD0zL7SRiWn215iA2TErgTwps6HRByC7nrT/e9IOuurm+6ZOTigTnWhk0Rq1qGV01h3H+xfzrGj6\nYgZAz+CMLivdydB+WQE9sO0/OT1Do4hn9MJlh1A1PD3wKBG/fCHMEdnkOgweRz1MRLFNNzOENh3B\ndapOHMYn9Z60ooGE465qCb2a4wi7I9edq/fBgeOtpNO9MAmlNn7OECwAsxxH3HtbC2WvIcHPtsLy\naMJdE1loUdVl+dHUQr6xZDv83msuFf4mvS7Fi/atalyELFL0b1/Gl94C1yZX378J3vCyC4h08Yem\n9eVxwdzLyRUaF1JP/de5aQZbJPMshIjsN6IqfGD9QTh9dj+NMCKC4aiH8WVw+PGK3fDay85N/31s\nZAJ+tHw3fPDNV8Cs/qLTnGyBmzcg/dMd6yrLPXZqAv77DcuVdJR5ddh6WKO2prbuccSnb10wby4c\nHh4vfCcyOuiq/MSOI20ZOP18gPoaY23/G5EMrAS3pF5WiBAdRrdViuq9TpNjqF5Mr2Hw5Bhcff8m\nuHnlHlqFcnDpG017H8WlXgMBLJzmZRRMTAkMR068Ef1COwTLjhqsoEmOLfE4Qktv8dffX0UkiY5g\nOOpBKEJXXPLRO4tGnqvuWAdLthyC17/sAvjVlzyveDDhiHcoZ2zA7WBmhxkCRxdryV6tSDWl3Aus\nkDDdlTqOoWhj+uTY6nI79U/zjjE2psgwMcKphvaZ1sWsPrNnwpRH3nZddeOw3yRJaU+NT0mPE7af\nwBIjai0uuXhs5jiaUbwPKMDkJAvYhXOie870wq5qnPGm+n3RE4Ht/I5Nfm6EHEc9iO8PzBNjkwDg\nzw4RHBNQZzl+qLTpkM+oaUTdxGV4TROgHjNwOY5aYHZmqwNVE6Xo1+Q7W0nKTYbShzYMwMv+z/2w\n6eDJlqyqskgMmH60dScuwsIpXiT4grKXG6KMpMnCYpsfoUXM8GTKjMJJjiPDMnypfl/0xCCcZ2le\neC/cTyKC4SjgHcnbcVFuGTdvIjXjhRkmx06wVV+cxlNRe4neXPu6KHUBucfRTCIXH27py8MbleA6\nvVZiwxF03weqPLThIAAArNlznFAje9RpYESNLaK8eQhduGBiS3N53WkS/h4yyPmCb7tBcmHGl4cl\nAhdX2PRq7IW5sDAfLYGM7DdNYRb5+RecTSovhKr1IL6FqnXS3zYcTU2XhyBRgxFrLTk2wdPL2gOC\n0agpe7PQtGSRtqCecGIMFp0yfFlFY/qcqoecrnGE4j7QzXHE5eljYkji3NWq2gGj+2NbD8Fps/vh\nReefAVd+dpH0WFWDkItQNUxfoxijAgFOTDXd4gGspp5s0DaIWNGCF+J1gdn8SQSntUV/H62PUPA4\nCtTGH16/FF7ziQe0z0sNR4KHoLW3ygjXeI67qsUxwPyrFsDf37qWUKOcfCtSzZAZPTgN7pyhriWd\n+WtnEyVhP9nOeH60YZaQXe040XfWuivGIcZdUdL79be++Ii8XAbdxEgF5RxH9vjzb6+A//b1J2Db\noeHKY23Wcx15zXrh7btvxBDD/KsW4GT0YLP6kt4Bg4v5gGkZdY0l2smxm99NrOdJY1WFxA0aPI4C\ntbF6t1mIQ+Yx5c7jqArZW1bOoWoPbRjACxHA6cEjfyugdlyvQ58cm8AQUsMiEkPmNYTwOCLPNdX6\npDCgVntD2WXn4VO1ll83TnYTIizDl93XwssFfoQmMWPCo80MTPFtHOSILy/jqLlNAVMAACAASURB\nVKHMccTphQO1JsHjKMCaJVsOdX3XuatSnrpu1dtX7S1VgKPHkW04PXikmvBRkzXk7UkRIgWJB48n\njYgwdKnmc9Kf+BDUYdz1hxRU+BBCzemZGG5atgvGp6bRsnRR3mkMcbKTN+0KRdgMVcudjTlZrQTF\ney7gHlGagkA1k4Lt65uGi57hW+8zDWHvNfTryQ+PI+r2DB5HAda85zsrSn9TjVE1sfyu3XMc1u8/\noX2eiMe2dhu/KOA8uHPSTbYozv/GydjFDerFE8VORVmomh8kelYZaYTjmiUjWeYFhZdReRxiJz3d\nskTc+tQe+OajO+Dk2CT87ZuuMCvfUAHZyw5XUDj4cDHShlC13maKIGN5Lz7vRRvKNA7GliNfepwv\nelJDGtLX4EoMhqMepon9muqa3nHdUjK5/3THOpwyJWAmPrbnwpzm2sqGI0Y6U0KzAw1t5WAMUdki\nnCDczSEYw4m13C4EXhUutkZPwBguDg+PAwDAyMQ0gSZ6oNpN0YXHRaigSv1zuh9prplASICU0CRm\nTPaApxZng2BdY6N2uXyr0CqU1cTlJQsA/T0RQtV6EF9yC1QhvC8t3avqoQbuBgvMS7c1e47RKSKA\nz5CpnuMoUA65p0tqCUHIoFEFAABecsGZhNLEqHocyc61ByZkUI+6QtUm2mEas/tb0x6Ot77QCMdp\nAkqoCmYe4tKpIHgc8YOiTXqxWUOOI6IyTJNjE+thC87GN5uYhvoLZSF1AaDryyFULYCmKQ9M0eBm\nc8DbdPAEjNbwtroMTLjPKcvXwWmyjcovEgAAgAfX0yZR17EbdfYlG2E/Lt4OYfIU2bqfshA4hAxV\nbyiCS5DtkFhFYjiaM6uZ78tcvBBSuU9c5DhymQA3PBf4wWh64RW9EKrmxKjsWf/T9qTx7PqooMwF\nxcrjiFiVZs6gAj2B65ezb/3KY/DO65cpHevCcIIpw/Yag9OgyUkXX9k6WL0Ntw7Zrmru20a0sHWh\nxQxiJWovwX7ySfAGHy2hGoyxfLxtOJqLMBxhX0wk4XJaEIWqUdCLHprh8cEPkrf5BDJ8oycMRy7m\n3qbneTKY+KElPZQeRwRp2Mjagbo9g+GoB2lMqJroOyYjHvdJvO0+wGmBoboo9uWh3gRMbSi3PbUH\n9hwdJdfHiceRZllxyd82wCXHVr0e/FVgxpXE42gmjmH+VQvgrqf3ofWhRvXy6nqGU+Y4woWqxWoy\nKLzcwnOBHaFJzFi5026KAg648Thqdgds+vVRIasmTnVIrUswHPUI2waH4dl9Q3WrYR3R7RERzLI5\nxvxyntBy0k1VEz4aNx+dbeAfyIXJff6BzVb0cdJdFYxlLT0EIbjKxk9NlTTagapMDJgJ0PhUKzz3\n6KlJAAD40oN2+hI5wp1CBYc5UYWuFIpQNbt9L/GKtFlGwASS+UUPtmvIcURUhv0iaNGsFO+uryZk\nz8Mm12EwHPUIb/nSEnj7vz9etxqk6E5iUW84c0VVL/xCnDUn3TjpEmiRhUhVH7tu3xAs33HEqj4u\nE2qqLnryw5Ut9WYIFuDZIl4uJPkZY8ifRrgcDY9PAQDAWae1UjtyHBa4OwOrVD8rj2aiZ36AF6Ft\nAnXiW/8LOY7U0F1Tyo5m9fI85DgKNJWP/+RZ+IvvrKg8Lll4xDHAnqMjsGH/ifQ3W+6BupNh/jmO\n7M7uOblpKuOhyv6il+Po+OikTWWcJsc2SdZuS704/dQvoHMMUfbsQ1wMJlTt1HjL4yjJcWRkhKpj\njGBkiaG8T5zsqoYKwWx/+vgsazgcPcADgSrCUMIb0hxHjNqaen4bdlULsOH7Tz6ndfxMHMMbP78Y\nAAB2XfM2ALA3r8/fdyrzXRdjBmZgOnJqgk4RAZwGTVU8VNlbdDyOXMDFvT0G5Hb12ruCmIfjUO5A\nYqvMYvlJXpzsxQM3VFWqy5akUmfKhlGUl5u7xuMyRgUy6h5LAgETQp/jjW7ryJ4NFM8oquccda8L\nHke9jKdjWDJnFr0xtvZm3sBLwDaYsI2vL9lOqEk3nNw0A/zQyXEEYH+R78bjiMe1isrC5TjSMz5h\nQtVIFoyJvgajdS2jGqN+w21ct2lAw/STgF2YdcNAINAAdJ9vssM5vXAIybEDPU/c8Sn+tV56cRKf\nh7FqpTQ1JIHjVeknt217i1jRxlFOMgdl6JIZ8MxlJKdWjUcU148xlnfS0NvdKip15tIbymYbZqFq\n9soImEFhzAvtGnBNXX0u9HVbyELVCMYotARaOQnBcNRjfPreDdk/+KRO0CJd4AvuhrJ7dfuhYRg4\nMWZepupxqWrucqZwhLNRK1A/pm927IWi8rhfXd82WXJsvYK3HxpOcxy51JlyXOE4RglVIrLEUFzu\nbav24IUQ4CS0lFk4bSCD4a0bCLAleE2qoZ/jiE6WVYh1CTmOeoxvP74z+wejjr3quWPa54gGQ9El\nxXEMb752iYFWRRnc4LjwSeCsWxn+aewv3LoHJ30wquheh2mOozdfuwTe9brLtMqmiflHnNvx72AQ\n0GfpNru7G/IkdBRuULRIaNWAazjNMwLd6Oc4sutxROXbMR1C1QJNY+vASfij/1imfZ7oXmCT5JaJ\nHnURHpABGdqJldufFA9SkQwfDZ0UUHhyJW3pog4xoWqU+ZGcolym/DhGm7OhSfpc1TVRtFYwMPKD\n44u8QCDgOYQ5jmgS+NNAPTcLhqNAFx/44Sq4c/VeZ+Ud1dzhS7bY6SWXTM6LXR8n24yrEwVlXhgq\n6tRJVDKXKnI9flEYfVQTbNMs4vFSbI6bB4fGYO+xEWvyMXAb3zCGLDehanHhM8AHb43AgZ6mrvVJ\n6Opq6FaTbAwhyXFE1G4zMzRyEoLhKNDFfesOwt/futZZebq77CSDb10eRyq4zMHAkTAp48OJsUm0\njF+8/FwCTTJ0DTW2uxOn/koZjlXFTFz81CHNcdQhyybcPUh+9epF8IbPLe76HtW9muQqlINm8Y+X\nUVmG/SICmnB+aRYIlFFbcux6ivUO/VD/8t9oDEc0LTc5TWs5CoajQO2YzotVcxxRwHGewtGTJMHH\niV1TvdUmpvAPDXLDiu4DmrBtRMMNl+7qWg+MV0Vnm1TJqNtLwGVIHSm+6esA1SrB9Re9sgLuCG3i\nlovOmlu3CoGAdXTnmfIcR1ht6Naz1GvFYDgK1I6u3Ui224ktzwHVASX1hnJghOC8AGJs0woYQJ1c\nr86+e3y02wOrV7trGvaL8XLS9FrC+M9QTIAorlX7vJ7tYeWgQtUAYOOBEwplIAsBXt6IgRYULRJa\nVZ2GOj06p64+F4YwNbQ9jqSy+FQ69fw97KrWw7iczG4ZOAlXXDRPOJEzntwxujE76fVQNc5GrTI8\nVNkZ1DHSuv2Dsm0e2Xyo6ztO/RUzLutOVpLrxj0LVD15KPIToUXU4qnpIlSNURdWAqPvip1H4MO3\nrbXqCRE8jvjCaVHWC0Rk+zsF6uDrS7bXrYIX6Oc4MvtNvQACGQAwPR08jgIMieMYth8aFv72yOZB\n+J0vPwq3rxIn3Na1G1HsBFTG3mMjMDY5bXy+y/kMp8VuF4xVC+hD3ddW7z5OKg8Ll3vJeaha+9PE\nMHjrU63xnMJrSZW6k2Pz6CWBXUdaCcgHT45bKyMN47RWQsAUkjVZaFhlgscREaHPsUZ3TJCHqhF4\nR6MltJgKoWqBMr66aCssfPZA1/c7D58SHi96i3Dd4m1GZd/61B5487VLYNm2w12/bRtsGZQ2Hjhp\nJLsTm8mx3/C5xfA/b3oKLdfF84HzxIfLQlwHD1VWognXlVyCrQksxzpyaYhxkciR4npmEBMg3ZA6\nSlBFCiuut1dyLj1OfHyWNZ3QJG7p7dGm+Zx3xmyY3R9aWdfzWp4cG6kM0D3npohDBoLhqEF86aEt\n8P4frC589/CmAfitLz4iPF50k3zhgc1GZT+zdwgAALaXGKlk9HWsBuM4hp2HT5Vua5y94e7WnyL8\n7nGB8UvfhTHkOAo0B86J2CngdCu51IUkx1H700UXwZSRnlpDY4fQGlpc7uAXmo4fFPdTaFZ1ULnC\nAikh1x1ztB0E7LYnlXTq52XIcdRwqLx8KCi7yTofSTct2wWfvGeDRE7xs/Cb4EuKh57qREWmGzWc\n1/KcjVoBfepuT9sLby4TuoGTY3DJ2acBgJl3lWmMPip8S9FriaKGaULVCBTRBFWksCOIXpr0Di4N\ncVzGhkCGTotcMG8OHB6esKZLIOA7MSQRKL091lHmOKKASj718zJ4HDWcOl4UDI1MwsjElPLxnTqu\nfO6Y0nk2QtXUCnZQhgJ1L+ZlMFatlOAVUE7Tq4aLEfa9311ZS84gTFEuPY4wRSSPGcwOI02/D3xB\ntRkwY3qdoY0BOTpzn9n9JcuccDMrExyOaAhdjjemm4vYguqlBbWWwXDUcFzvhvAvP10Pr/3Ug/Bf\nvvCI8jnaOsaFD9FP5HCzRAPwNhxx1i2gT9Nbk1N/denhIAv7pZZRe46jREYtoWrUJ3c/M30zfGMW\no05DZ/2q1p5Ap6sHmweeznQSATO4DiWePTqsoZ8c244e1FC3bzAcNRzX432Svf2Qxm4n2ruqpcmx\nBe76XEZAF4YjxqMWX83K8VFnV9Sd48gXl2AKUPmGtCc+yViKKVMtVI0CmiTeiHMNR4kTY5PmhRLB\nLeQK0w6TxNsLi0jnGczqLaD3rLaVn2f+886wIpcjttYR/X29ZZBisz4R0VtN4QVcu0swHDHk+MgE\naoCZnM4yqHMcC254bAc8vVstHE2GMFQNJU9yNsMbmKFKKZw8OAJ46m7PXlq8OU2O3f6k2KK+SgRF\nG6KSYyehRwghQ6OT8MmfrofxqWmt8/7w+mXGZaqu2rgnsB2ZmILRCb16K2NqmnaXGBEhVI0vOvPj\nstsC26xnnTYbKSFw4by5dasQCKRwG+q56ZMQDEfM2HN0BH7xUw/Btx7bYSzjr7+/Svuc/cdH4XML\nNxmXKaNzQvuZBRvhnbmJ9Klx9XxIeYQLEWHeI+LbTzY/j929peRsm+GsWxk+6uyKUDfucBuqhs9x\nlKBqfMLYNyi8LDEi7ly9D25ctgvuWr0PrQc1Vc8512Hrnbzqnx+A//SZh0hkufSA5OAlwNwmyBpb\ndddLbWLrUnvM4ciYy8473XoZoSkYznO56dMmGI6Ysae9Bf3DmwaNZeTPVX24feiWNfAfj2w3LhPD\nH3/zSa3jZcljMYsuSoejXs9xFGgWdS+ekuLrXvy6ALUm1jyXYlc1kIzHorIw0Oyqhpcx4cDjJYXo\n3qMwSP7Oqy5GnT9C5HGkmuCc4lYKT1l+6NzDZc8M7G3V/CdRhi1vRu5ektSYdjnb1VT3/I4L3Dzb\nuemTEAxHTKG6j1UXWi5cv6mQTegI1j/i3zQFu7jdg+GIFq6DNAcwO1FR0Etd3eUkjiTHUYcsm+Ac\nTcpz4+kyMeXP85KS2bN4TBmnFHMc4ZKSE8ggoreW19VoJce2VXk9ZPQIVdhsYghtAcBjrM/DTZ8E\nHrOAQO1wSlJXpUksWezYynEUl/6DrnxdZnpz7RKogbrzfDB9flrB6RhCuDiu6iMuw+HkMvB63PTE\nLrwQVTjN6JnciKfP6Vc6Dpe7i8nFQu95ZlRhsqtaZxVi27eXWsRW9+u13dpMh6Ne8LQO+EMwHDUc\n2bicH8QwE5PpmVhpgkZlPc08jvhM7Dpx4TUQPI5oaWp1UvTFul2ZOe8gSI3T5NgEO6IlT44qGXEc\nw+T0jNNd40TnUoybl517Buw7PgrffNRBeLdBWNaTO47A/esO2NGHAbKkuvmxoq6+FrCLTtMkxgnq\npTejd63WsReqZkUsW7iGqgVa1D3P7YSXNhmz6lYgUKQuy3I/YmT6tasXweDJcUJt1BB6HFkKVVOW\nkbzBJ5BVRTAcBVxRt91mqm4FHIKZvOga02X54pSJ1GTcvmovfPDmNYiCaHZ/w3SlM+b0w8jENFx+\n/unwlzeuhE0HT5oLQ1D2uJ6eiSECgHe38wbuuuZtpOVyeVkjS4795M4j6d+4e4kPYd1YRGscaFde\nFEWFQQo7feolbxl7ybF7pw4BwLjTJfXUF9mbiwWvJl5jPgA/Q1ZCMBwxYcP+E7B18CRcdNZpZDJn\nZmL4+pLy3dnyY3YfwvesDqNRGbaSY3PEM3XZE+qznLqNlNM9FJfp0qOSIj9R8hipmuQ8tvWwcRkJ\nJKFqiJn3rLabweR0DCfHzHYDpaDspclLP3YfvP0XLnWvkGOmJOPBeC7/FGaRxWnS3mvr6yp02ibq\n+Mxk4HTopTahuNaXXzwPtgwMF+XixRrR3xc53ZkRS1JPfVFkZy7mT1VYhdGQDwB8myWEqjHh9776\nGPptbCdLthyCw8PlRp38TSKz/N+9Zh98+t4NsOfoCKV6xqQhB4KBH+dxJMlxpCnXxQDEbZALNJe6\nQ8Xq8jjaMnAS7l7jdut1l/d1MgnFVG8SxuCiiSjsh5iJd39qOJqBfcdH8cqooLlqu/eZLEQtjmO4\n4bEdcPTUBIkqlH0TsxiV5cbOj1UUHmocCN4ARfSSY7dD1TqqELsAD22ih6i+6jK+zRLEGSZju02d\njHtc6jVHpYmgiNCd2XjUJnBd4wWPowYzrrHzi8xwlBi0frp2P6z8P29B64Ul7vgU/WYkV3Lybk2j\nGbcBKFAN10GaA3XXjeouStT8zpcfdV5msqAxWZjotlO2eMLXr4ud9+pOjp2cO+lyF1LENT+zdwg+\ns2AjLNt+BF556VlOVFFdgGCaUuaBmDcy25oPBOpFp2kyj6OocObAiTGcEj200LYVUsYp6XtfBDAN\nrWutexfZTrIE78U+TAWvq60PZs3OluBxxBTX/VdlV7UjEu8lSvJvTIXItpDmcuczUSMQoOiKtYeq\ncbmvHZDlSdO/Zt12StbYOI+j1if3DQHS3ThR4cytc+syZCaorrcm2gauodFJi9oUcXGryux2xeTY\nDRk3+KyvWaDTrH0lHkfHRibh9Nlqu/OJ5Rqf2pOIxixOdZi8qOGkU0LqNWezDIuyy3jZRfNqKLWc\nhjwtrBMMR8ygMsDPv2qBXrkKx2BvqhuX7YKtA3TJREULAJGOqm81jo+4m1wHONLMx4aLrdYDdGAM\nG7rtlBpTSHIcGYtQhqIfYsIuk2ucdHlDKD6/RNdFvRjg4kkr6695I3NTxi2Ga9la0emHye1Dbbjg\nGKpmy4HH2q5qNdWh7HJs6hQSshfhdjVNec9gm2A4csjCZw+wyRPUiWg8unXlnsK/TW+qvOjlO4+a\nCcnrkXyWJAjVZf5VC2DgxBh8+La1KL1a5SdvtQOB5lC3x1FCw+ZNQjBVrdtO2Rb15mWalm0ChQcJ\nLlStdTLHZO29tfNg+W/TDfQ4cjnuXTBvrrvCDDFp1k6DwNxZfSiDCMdnkS2VKOSK6ppVHVrKI/TG\nKy5I/zY1vPdZ0i3QSTOeF7ZplOHoM/du0Pa0ccn7f7Aa3voV9zkzTPnIHc/AxgMn6lajC9mW96YT\nxc0HT5IlEG3pQSYq4IjQZuVwqRsuenBFd6eYLDk2wuPIZXJsRBnJqZhrVS2f1GChKGt0crr7VH0x\naFVcLG5kC7BpohxHvYoPi1OtXdVKQtX6+yLUtfpQT1T45slUWa7EFGbTq8f85XsSRmdHtziOnbVF\n3suP2z20/dCpulXwgkYZjm54fKfW8Vfft9G5oenURPfkLs/yHW2PHKE3TQy3rtwDIxM02wAXixDf\nwToJtvXLNCOd+AtGYVNjUgxqeZ5U5LQ+w5TVN0KLlcPF46gXcOpx1PWHPhThbqpgtlCWPDaUUa1f\nbrcL1fxc5bLq3lE073lV926QATvotGrU8ZkwE8eo+4JnqJqtJNYEMgTfcconlCWgppVLYeyRhVtS\n4HKUzPdRbvfQCoKImF6gUYYjXb7x6I66Vejiyz/bUvrbE9uPwEfueAY+fe8GJVmqg8z41DT8bOOA\n2sEMSOaClLmx4ziGPk5PsUCACAojJhfDEbc3VDZAecRo2vmzUDWEQaZDlk1odlXDXyvVcUoIOj2T\n27E2ZG0447nH0ZxZ3dNylwssH4ZYreTY7ersXMDHMaDmfByfRfZC1exIritnj6xYap3yXWz9flwE\nR9OSYyecNTds8O4TXhuOxian4a6n9zYmjr2K4fGWp9Ghk2q7mz28cVD6ezJQfOmhcmMVOQRtlbS3\naPJoKj0GgH7VbYQVSuHeJee1B+pzz5hdsyZ8aOo4EpJj+wWmqk2NIqgy253Dxc53NH2ZIN7NZhmM\n4XJZ0hxHuR+56KuDaBpSx/r6zDnmO47ZRitULU1e0yGj+ystKLx7fv2lz8vJQ4uzGFJmR0b+O5fv\nbWVFUdchRT9JZNh6uW0yTl56zmnocjkaXwPVeG04uub+TfB3t6yFx7cdLnwfxzHcvWYfTBCHWXHh\nZxsH4dl9Q5XH3fLUHunvyVghM0RVPaD/7IbllXqIysQgCzkwXvzHNG8aZPmXOJGFl9SsSMA6h4bV\nDM0BHmAMmHc+vc/ofAovHBfGEoxxKhneKZJjV72BJ60KzwZpNzmOysmHMzbFgOdyfeXDzk0693Ak\nthu1fzO/1mQNT/XyjXOt0xg/5HJl/e7lF7e2bf+5i89C61FGUjx1/ydJLE4oq7IQO4cHGoTXhqOB\nE2MAAHBitJjzZ9HGQfjgzWukYV++c8/a/XWrAADQZbRzQTIZpJ4S9lKoWjLxasrEmoKm1kRoYr/4\nH99dCQAAY5P6Lz6+umgr3PvMAe3zUEmnCXIHqYIycKV6YpJjJ88euQwuOe7yl0qjEQ9vW1k/KO6q\nZl8Xauq22yTlc646kxxHXfO7GOflQrPTWPY3SS4cayFlVsQWtO28/kvOPi13nMNQTXKPI7PzXnnp\n2WgZNjE1JubPqis5egCH14aj7AFXfIwcH50EAICBoTHXKlklf5O5eqhTlJMfGyhDDfYfH8ULaxND\nTPpw5B72lNwzzNUMBAKaqIYyFyBJjm0uQ70wxKlpiLO5jORcpx5Hwsm1XgFU83MuzwtpqJrnHkci\nA4LLBRaXpdy8ubPgjJJwOa35VbKrWqcMg2C1vMdLVCJXh/w4QtLEAhm/9MJz0WL7LRm1CnNuSREu\nwscodi57Vc7YIysr4cUXnKkkN8mNVLW5kg/kq4PLWBPQw9hwFEXR5VEULY6iaEMUReujKPpg+/vz\noyh6KIqire3P83LnfDSKom1RFG2Oouh3sconN6Rvc4O7nt5beYzLN5Y61vyP3bUO/vePnzYui2Ii\nl4i4e02315V5cmz1XdVU6ot7nwweR92Eqgg0gclpfU8lzPPG5ViCCVVzGVJnO1RNVf7SGjyCXSDr\nr9OeJ8fmspiq+3kYS3Y907IbJZ+i5Ni64TnEi96CPEODxQXz5mQyRGUYSS3msLEVvlgMVev8TXQ8\nUbmS31BeaCKdDfUQYSv9Sgz6OwxSdgkfx+leBuNxNAUA/xDH8asA4FcB4G+iKHoVAFwFAIviOL4C\nABa1/w3t394NAK8GgLcCwPVRFKGy7yWDGeVEcMP+E7D7yAiZPBGfuXejVfmUPLH9SOHfP1q+GxUm\nR9FUMhGmC6BYI8cRlzAEFA7DSwKBgDtMDEeobe4T70VjCepQJLbGXGsmSi5j8OQYrN59DF0OlsPt\n/GZU47yKGBfOMbLdA/M6cvf8FSH2hqi3/DqIoVwXnTlYYgjolDQTx6i+mnocEdWXrVo31S9/66i+\nVJXr0f1dXqxs/i26htNnI5aPUqOU+bVqG7tkXlbGWriBczL3gF2MDUdxHB+I43h1+++TALARAF4A\nAO8AgJvah90EAH/Q/vsdAHBzHMfjcRzvBIBtAHClafkA5TdW8r3JlOH3vvoY/MYXFhtqZJf89bqa\nEH3hgc2k8mxvp4zIjU2THBstwQ1JHQaPowwfFxm9QtI0YZ5RzRcf1M/tRxG+RWGQqSwL8cI1zU9E\n8fKiQsbvfPlR+MPrl+ELQkLtKaAyRtY9jObL92k3yMvOO738R4cDX1kKiDow9Th69fPz+WESA0+H\nDMDdH7Kk2+oyKELV5DJMxebbn8RwJPwu73FUPKKqba5oJ8ymBpf3qjwETnx8N/m+64o41jcwJtd1\nwby52mWlMhSLTHaBDvCAJMdRFEXzAeCXAGA5AFwcx3GSnfMgAFzc/vsFAJDf5mtv+zuRvPdFUfRU\nFEVPHTp0SFJu67PzIcLdiqn6OP6HW9fCx+5aJ5ZR/zPdCFvbKW8dOAl7j40YT3XiOCZ5OPpCUk++\n9qNAIMAEh2MIxtDtMqRunDKkgGDnJ7IcRzRi0Kga+X16GSDd/asGPepG1nRVrSoOKesOVVO91Fdc\n0sptlDdmZCFwikIEFFP8mAkqXqvAcGGon8kCX4owd5daGdk4VtaaeEjaU9PjSOhdmBpu1dA13FAR\nGT5b8teV9NeqcZrJkBRogzYcRVE0DwDuAIAPxXF8Iv9b3OoN2k/uOI6/Gcfx6+I4ft2FF15YXnb7\ns26viZ88vQ/mX7UA3vWNJ5SOV53M3LF6L/xo+W6xDGXt5Czbdthp/VG8xRK9df7tLz8Kb/jc4to9\njlJ5zOeraUJbNkuB+gk1EQjo4/L5gdpVjXBDAKfjuyjHkeKphQ01SF7aqJSJLwejR2EzDvuqkJEs\nolwabmRlsZjDlOg3WWGYFSWdFr0XxISZUYfqmL63LBifhB5HeEVtvVSV5YyqNCopNsBps7uXubIz\ncV5oeMOdqjHFVD6VnKjjE1Mmh6EmoA7K/yuKotnQMhr9MI7jO9tfD0RRdGkcxweiKLoUAAbb3+8D\ngMtzp1/W/g5TPgAA/P2ta+HYyCRGFIoP3bIGAABW7DyqdLzKTeLiof3UrmPw0TvFHk0UHB4e77KG\n00zay4Us3jxY+lsV/QT+d74YZLK37/XqEQio8LmFm+B0wQQwUD8uhxCSkDoWK2I/4eLBoxquzkRd\nLVzmGJrVF8HkdLGS0oWrMy3KKauJL/1MHo4r8sLBJFuW7cKlunR+yysvpCcPgQAAIABJREFUhp9t\nHOiQIS+DBAKxFC9VRRLycvs6jFNVYXeqGl16zumw8/CpDtmts1//sufB0m1HCt+hDEei72S5m4TH\n48vUxeReLwsB1ZJBfqBdosjPZwo1mF3VIgD4NgBsjOP4S7mffgoA72n//R4AuDv3/bujKJobRdGL\nAeAKAFhhWn5Lh+zvT9+7oet3LpMcKqi3tT80bLBtswZvvnZJ13cUhgqZiP3HR81kaiTHbhJ1e+ux\nIlQFaz55T/cYH6gfpx6rBMmxvZsXEDyXVu5yl6jbRfXKiuD20kbdOCH7zZLHR9UW6TUSQ1yeHLui\niUVeOBRhXAUPIc3Vk9hwhQ+9qtrpzVRuvooLIXqaAi8///TS8wqGo44DCl5jBG03R/BmWDTnJw9V\nUzj+/DPlO+O99rJztMp0QWY21VNAdLTO/WzK2afh8yT14hpRBOYV7usB4M8B4E1RFK1p//d7AHAN\nAPx2FEVbAeAt7X9DHMfrAeBWANgAAAsB4G/iOJ7GKF/WYbm07d5jI7BPYMig2VmM1+RIxNBoywss\n3xwkoWqSCpwxtkzFMJvC5SiRxr95AMAfPV1QZ1VwGbMCAV1cjiE4j6NkQwC8Hi6fvzsODTsrqwou\nzwufjH/9ioO7LMeKNWcUmTGDqIpNDVGthL2GhQoMHSI9VBeCYoMMcaMYh6rlrxVvCBEZKfJTY101\nU48v0W+SUD1RqFqVkUxcvtoJsvtPFX2Po8TLqXBC13EvvuDMShkoYoDjulE7gjZRoeBZJjhZFBZJ\nYTgnkYGW0AyMTXBxHD8O5fX45pJz/hUA/tW0zE5MH0h7j40Irc/UvOFziwEA4Cd/83r4xcvPTb/n\n4uVRx5sl23kWTMMQ4hjgpRe2dmm44qJ5sHUQN1nn0cIBHepcjPRFUQihCQQqwOzc5utOkifHprq+\nq2usUjGYuTCC+9SELeOESr1JFti0KqUIPS4slGFyz8VQXMyriHjtZefA2r1DwqTT4nAzNeTGPMVc\nNILvVLejl8qt9Dgyk5uv707PIJ32VPek6/Q46v7bqIo0DbEYrxLRubJ1VmqjzSciFx6np9PLL54H\nWwb4vHDI01fRX/siAJRHSQkUzyXVsbzpeJ00oqoj/GTNfhg8Odb1/Rs+txiu/Oyi9N+2J2F/cN1S\n5fxHLqnDekpR1zIZ04KciSqDbiuLe0vuJeecVlF+pbiAh9TZrFzCAwIBzuCSYycyaHTxgbmzsime\nrY0p6kA1xxEH9MOh3D0MpMmxiZ6IojJUd4LSzTuTeDNUJlZOjlf1OBJ5vLT/odrfxEV1e0ZRoy23\nwmuJMlynaDjrLFPeiLreYiKDRbEMPbnCskRGKk0ZiZ5VxiRZmbM04yhN7vWknjC9IXNujHPf4b3m\nRJDk6QrzdADw3XCk0GVve2qvA02qOTCUC1nzbVcXQmzvaGO6sIjjbEFRJUK6TayveTQCtd5TLhcL\nPhPqqbdBpThKNgQgsBz5MrxT74akssBwUTfcjH9zZpVPpVXbIOr4LPxGPO4lHvCyHC+idky2pddB\npLtSlcTyOhGWJTheZPTpOqFSLt4zq2q9YuxxVPW7ruFS+F2Fm4iCvCqPr+4cRxU6tb+cVdGZRMZH\n3YTVqohzMUnKEt5/mjKEcksPJyMpYv9Qt3OGDJFBrNLjSqsEMRQyQo6jFl4bjnST05XhehKYL254\nfAoeWH9QekxCE/osRVXLjENTApcj1WpLjD1V4RC+hToE1JgUuas5ogG3diBgHUyoWkIvjd/UYxqX\nquPWhlKPAE0hFF4LlUUlZQl/oy1NtK5XLUJq9BEeHxU+AfKhaiLdur98yYVq+WQoqskkZ49cYOtj\n/vPOyH2lJ1kYgifxDNJUrUAxBK5Mj3x/7ZYyq7/CcNT+FOUaw/RNVaShehQyLHnoVEHT/9V0p8lx\nhBYRIgPa4NOM14q4FfODi4rnh+spSF6nn//EA2gZPkEz8Tf7rYr0jXRF3aoU4Wfr9DZTNb7GDm8y\nAoFqVAwGVc9Gms0p6kW1/LHJnOHI0XWHHEdFRIueObP6YGKqaNSLBH9lMoh1ksiVh8XoKyL21tHz\nIWodr5FXJ/93+x+YfE4yo5OqVlV1bdrGop3ZzpgzK/e7prz2Z1mOI31DVLnhrqB7p8dRhVEh0WN2\nXx+MQbWBvLL+25+ouZiwjGp5+T6U1n8+fEtbDfuDsHHurIrvbOnOJcF2E/DW4+joqQn48Yrdwt90\n4zW5G2Gmpmdg1+FT5HLruAko3hRKPY5ESRgULzORWx2qJsmvAGoyAoE81CElgUATse2x2jTyxvCq\nN/MqcJkr+dSGomnWXElom8tpmWwOSFXD4t3Mqs+LIc7qQtXjKPkseKtEhd+KeqgZkyjCZ8SGE4xE\nkTyZkUZNhsggJjNwvfzieXKd0s/sxNNm97XLUtNP+FP7y/4qj6PEcJgrTHYGLjl2efkisrDQuOs7\nVSjGC6fDaWUicMF3BMWSyPBkmm57OeGt4ejbj+9QOk7lhqC+Z3YKjDx5PVTKe+5IJuOz922C3/zi\nI3BwaFxLBkcoPI5kbYqRn5xZtbuVrAiP5rMBAXU9GDx5HgUCtdJL+YkoyHu16CZMFcGl7uTJsd0r\nqbvoFS+Yyg0cuk+Is+bKgwmyvC+i31qfVPVouhCP40w/jMdFZkxCeHKJDDLanjzZCRfMm9P1nWyx\n91JZ+FyFsUI3wXilvI7fzj9zjpLc/ImXnnN6l1xZjqPUmCUwOlRdVVLH+VxIWQhkt8cTzuFIz6tN\nbCRB9FPD400wLSOGGM45fXbp77a870mSYxPo4QJVpxDTKvHWcEQJ5Tzj8a2H4be++AjcubqYlPuq\nO5/RKu/w8ET697LthwEA4NhI9l0cAwycGIOxSfONC+uYYNEYjsplTE53/6bq+qi6XbM0Obbgr4A/\n1PVg8OVNRi8hm9wE6oHC06TqxYASngzvlM/4KOJz2bJpxJFTE+U/WkI2x9BOdlvxLFB6VlQck6yh\nxduHd3ucYKBYiOvmRBLl5FG3EUm8sAqGi8SYoV9Tl513RrusfLnlx+dDzzrpFxlEBFeL8UIoehzR\nLcB1cxyJfq/SR/W47Hjz69P19BKOG6nhNvtKZvSoUjcxUsrA3OuvecE5WscLr6vSCwnf5yi8cPo8\niQxQHusM5TfScHQkZ3RRy0dDNx3acXgYAACe3n288H0+1wBFeTHE8J8/uwj+6ntPoWW5hCKPjK4I\n5TEnzXFUVb7sbadiWQGW1BXD7MsDqZcIxjx+qIz9VWOwT2FOZaheQt5Ihr3qCPiEqsn0+O7SXe4U\nUUDZ6JF+yj0OVDwYVL0wXDzvdA1nCTHkjT6Ki/40J5KgLIEI3VC14rbhSioJBWfGDLkuKmXN7s+W\ncaLDZEZCcVndBjFRHqXs31WWTplukfDvTrmyPFmqzVBMjh21y8x9k9aTokABslxY6serHZf+Vunl\n1P17sgvkay/rNvqoXn9yXWfM6Vc7oU1h5JbdbPmvCIYpGuOTH5NC23p6aziSDVafWbAx/dv1PCcZ\nxGW7mejqNDw+1fVdcv2PbT2sJywvo4aboI5dcVSvMvU4qtBR9mvIceQ3dT0WfHkg9RLhHm4mFOFu\nvkB9qVxqjmIe4QrxG/Ty76pyfKg8KyrndpKyVEnyNL3g3NMrdDEvQxZSJz6hu0yZx5EsevOCeXNz\negjO1byubrNF+e8qJPVeMBwJwq1E3j0JP/+Cs5X0wIToCWWIZEktoq0PE8NdUifiPJLd3+FyHMmN\nviqI17bmHkciLj57bunJuiGNuvVVMEgm3yESgbvEl/e7tpcT3hqOVL12VI6jXCAkcbQTMsORpsy9\nx0YFMvyZOOWxneOIQm51qFq1x5GfrROoy37jywOpl8B6V1TlGQnUA4XNoe7nr2rfLBjJkP05iiJy\nQ5QpXPRIkHoEKHu1qHlQqORTUvcaEMlPPE7K5QMAzGuPb1XPTJLFNOrBnBifEIvk9qcwVE1ViwrP\nK2n4ouC7M+e2PD3mCJJDF+wxEuObrieXSJeq1GlR+tltzZJ5HFXKSL5T7es5PWXGWd2uNq/yOS8z\n+pT3oUJycoV7XiS37NzOesw/T3QNRzjDcFJ+/ju18VIXghR/tUUk6KJrVNXFW8MRV5KdS0S5drB4\n0mel0ISqaXocKdRbFGWT0SoVgycCb154/hnS3y86a27pby62MRWW24Sbu2Ggb/PQpM6J42qTDueX\nF8rlKx6Xv1YSlRUu/Kdr9lOU5BWyW11mnCl+V/1bqyy8x5FsIa5qdJItHP/oly/TltdJHMfpYk/T\n4ai4fbxEz0rvmvS7bk+eRDfMWKBcNcLFdOs7YaiaopFMNbSqmG+o+Luy8anbblToG7J+ImpDZYNR\n+7h+xevX7a7PP/e0nDw9w5boJ5GBQ1cn5dDW3N+ve9F5XedKy4Due0KVLHoUb1RWLpNgQubLlE7d\nIGyGt4Yj1YpR2lXNwiRQ+kbQsLx/X7TN7MSayQ+m0zPlnliq2JqzJ0uOqlAGWfLNpN3rXlj0Mpec\nfVrpbxedNVf+pqSmJ0PwOGJI7h428R4KTeoelXG3CTmOqp5RydhGkgg8kQlqz95TE9Ubdhw7NdGI\ndkiQG2rM36DrPqPkO7Nl9AkMIdIyxaW1/5+d8IpLzuqSYbqbWZyXjTBmyfIkib0bytur2GX1Rvj8\n0aJyTT0iiqFq3WX1CepEpFOnjLLjKBf20hxHAmOpyOOrqh1EIVXi644Kn6rIvLGqtSuXV8wxJTm+\nwkNHfm7rMwZxPUn1NDUIV8ig8FAUQSHDl5QSunmqtOUbndUwRO8o89vYaslSMVQZmj5GS3ZQm5ia\ngZGJ7jxIHKnF40jTyFgl/9P3bqiWFYLVakM2FuYno6qJMW3xyy88N1euHw+kXsXsrVpoU9eojLok\nHkdoCThULyG/+CDZTI5AxsCJMfilTz8EJ8f8mLPYQNWr5eUXtQ0xFYvTzt+qhh7ZIlH1WSRanIjC\ndsSLab0ylA1tAmOWzDNCPaTD/FyhvFRGt566zJ6VX8aJrrU939HMuly2wO88w6QOVQ2csjK0679Q\nftdXKdq5qwRyi/LKBYpDteQyOn+XGcGqKBiY0nmx0qm5NtSrMNG1FvqaqCyC+bGsDtVloNWoRDfZ\neJ7TZrfGAuW5p2k9mJ3mD0q7qgkO+rNvLzcrTyHHDfUE7r99fRm86p8f0JZRx7KGIjGpvRxHbY8j\nRAENeolqTN3rZdVJs6rrsi2eV5F8M1AvccnfqoQ2dY9K7p8meLqovpigzAUURTQvRA4MjRFow5Oz\nT+s2nIgXG9WLybLvVDwIqpB63SqSGZ8EcnPHmb7VjmNzL5A82bV2y1D1bpB7IandExhPHrFxoPVZ\nneOo9anaDlXGRNO2kG09LypXZGAQGp+q5nuSF4UiLyTd/pqvD3EZMt0Uy5CdU9WvNMPBZDv8igyI\nOAOq4v1HMJc65/TZ6d+mY5KLl4FnCZ4hquiGD5oawhpvOFJZyYuOWLHzKKx67ih8/4ldesVpHW1O\nvpy1e4cclYqHwuPIFqo5jmQkpzZgbaJNkhi+bndOWflxLH/gufT86RM8hAN8wHprhDZ1TwzVCzmf\nduQqQ/USijmOCLx98ZHmjTDcdZLc65ec0x0mrboQSp9LFaFtsmeUavhOgvZCV3gt+cW3nrwqdBen\n4lC1xDOiXL5MVtmB2gYGgWyZJ48q4nxOau0gc0LK36aFxNJdx8s1V93prTNULz9OiEPV1JB5F1EY\nKSq6ibR+ZAnTy0bJznMw8+1I0CgyeYkHZOtc42K7ZBSf2XZmTnlPHmODiQOLCcX6SVVCUtZ5Z8yu\nOLLjPE19WLB2z3G4bdUepWMxU5Q/+o8n4ON3r9c6J1ZwOcr/NP958kS+5eUYnVagjglcHZP2R7YM\nKh03EzyOUPzK/PMBoP58PVUDr2x7Vu1JA+JaixO+YGbgRj5XSxMXu01EpZkoXl5gd9xzVT5lv40g\nInkx1uRbSWz00fVgEP8t+67zt6pnsCzHkepDTT0XivlzVrboV0ZgpFHRo6qf6s8V5AZBlZAmkTyh\nF4igDFxofrdxSl9GN8Xk2LrXr1e+sgGXwLtN+XjV7wSeUfLj5Tp1fpV/oapqOMa0e1f5JWVgy8p7\ngFLMt128WE70nIVYSKleX/Yc0CvLO8PRzEwM77huKQycGCeTaWMS+MSOI07LM6GOF691eByp9pVE\nM5ThCHo3OXZija/b40hefNYwFDmOcJOmYCzSZd/x0VrKNbmfQ/vyhOalC16GzfKTnlfwOMLqHFHN\nXRr4cNQ0TsgWe7oLR6EMxaEH8wwUGSlE9YBZCOqGqiXXIwpBUjUSyLx1RB4vRmHMILguWR8yKCNB\nVCdS3SoNfeVy3vLKi5XkZgbO8n4tqlcTw4UoF48sREp3vV70QjEzkqomwlYFs6uW3IDXbXwh83iS\n6GRKmUFKlKZCBRcvxUX3hv65do5P8M5w9Kc36OUeUktWTUci66hs5y3SkuqUoA9FjiNbpDmOEO74\nmcMZ3+u0hSyW3KkelR5HxU+dczvBdOeqyXWADybG5NCm7okVRt5mJMd273FEBeMpgDFSY47oO5kH\nRcXAIV3YJQaJiuWXbnJiuTDhn7myysuvQjs5tuDZLjOcqBvJ1AxMchlypCF1ykaf4mf+b3GoWnmd\nFIwZgjLK/l2qW+HvxHCWl1MUVAhVa/9WzHvULUNWrqhOxKGVuu2aHS8M/ZO0fFKWMFCrJGG2Uv0r\nGtiEdSIdXzJku/WpkvU1sU7Zd/iRqriDn5kMF5EBusb/wrmpDF3DoV5h3hmOZJ48pjCcW1VCkmsA\nMYMzffPIOcdRavRBeRzhmTvLu9sSALKBThQC5hJZ8a2Em62/haFqlnQSQbHLQ8ANfEetQB6VoZtk\ni/raQ9XUjss/bgkcjtjszMYVVQ8WWUhbpfFF8qxQXXSIFtPCsmQyBIYO0bm6C/EXnHt6Jk9gYFDS\nTaCTSISqMYnk+SxoQ1GzKnsGpZ+K/avSk0jtO1VDhJpcyRwon2NJ2K8kigjKFS6mo+5/aPc1iVGn\nUp6ikUTmLVWZE03Vg6h9Vr9k+VHsB+XjlSqiNjT1BtIpq/MeUx/z8Hq8+vlnS39PdKtaR4l2sMxk\nqOmSJELXvS4/V6gaKHl+2HA5kh0isJrrF0OQ6BIxgzO1/3BOTJrlOEIIifGhanxrSE72Zq9ePaqK\nTx+QihMpW6jG9wfqx8yYHNqUIxRer4wfYwVmKEPVgMaDiUuovg1UjQ66xpmqMjp/Ux15VHcay46X\nyxB5POl6+rz4gjO7ytPOwSEKgUI876UGMcXuXLWrmMzod3JsUk8ngVzVxL7CPpw3XHQcIAs3q5Kr\nnONIWHZ3XxMh8+Sq2mlNhaLxTyRPS1wWFlkmo0OeyfgiM/rKrp+ivkR6VH9nXESK7EVt1TW86tKz\nlY5ToUpEsi6pKuv0XLJvQSlqumgdndF8w5GS3Uh+kM5Ex1WIUt2TQJ06yfd/roajD/xwdZoMF/NG\nmuTqeFZRJdkEpd4Fc37A7bTaxyCfjLpMUq064QrUj9GuarlGrdsLr1dwlhyb+SCdjGP5ZxnWYBNF\nNI8mplMAFLKwGV3jhMi7QzWxq2qy02Q4EofWqCHy5FG+fkkhRQ+O8kW/+NzudtA1HGCMSdpv7iuM\nbglbBoZLyxL1l2KbtP4lfFEm8UwqM1x0e7x06yT6XeRdlJfVqV/VMKHcTgq6FXRCuBxVGVZl5cu+\nzc+pO3+t9FBSvtfaZSkajmWGTlXDVVUZqvIS/tdvvrT4RUkIXOcaRTQOvvzieV3nOpnGSeq18lTN\nl/fSTRJk5+kd7h8qc5Sq+ZSOscPVy7S6J3BN9Dg61s5LhQpV43t51hFNcmvRI1d+5wCab1vZVrQu\nECUaDPDEKMdR7m9b7teBIjHETsZg7uN8lq8vU5SLtxB3o5sJskVXVShJ53eiZ0F+hx2VhaDqwg3n\nLaD4nXEJ8kW/7PjCX5pGPfE1CBbz7a9Ue3O1gUfwpSayndaEba3YXrI5VfFcPUOczJNmpjBXK7+H\nqvt693GyU3SrP3+8rnedfr9W+01k4BGdo9teYiOh3jVXHYfxapLvCCcxvlWEe6oY1VSpeoSqJrOX\nP0PUdBEljleh8YYjCnQ8UFxNieqeBJpO/kjyS1iCIlSNYlc1GxPrX7z8XHKZneju3mEL0RvLhBiy\nwbVfMBl3qXnxLR4d//nF5xNK440rRx6TMWF2LlmAaphAAIerxwvj9x8F8npidY4gInkyTUwRJGgk\nIh8WRYG6caL8QNHirF/icVApV3ScVIaSCBDlHyJJNpw3UvSVyxWfW/xslZ/IkJelIld0biwwcLzi\nkrO6j6+Qq+1dJVn0Fxe97XmZYk5HXW+RKiOFvKxyg5QoYXJVEmVxueXzUrGBTU2uUA/Zal6imyif\nU3Gnte65atm/O7+TJufu6z5OdVc1mbcK5r6SJXGv2qJenog8+7vTA7yqDl2uaZKXjEZe6oL7X4bM\n81R6nt7h/kGxq5rOZFR34mrcHUlC1czPNZ2gc06OnahWt1fU5DR9+W62kUwePPbLklGMm+/+XfS2\ndXb7CerS86dywmGI6kPutNn+D/91GyllzOrPdKPwOGJ8qWxwNXL7kqdnmtjjiKKCORmOXNxTFGPU\nrJwRWiXMQ9ULQ7yY1lt0VHktaHthFP4uX5zKzs0fTrGrmqwsVXlVnky6u1QJPWlE37U/1XM6qh5X\nrlPVb6I5WGf7FF+glhspK1WTGQ4F3+FyHIl+r75fdb7rChVU7FfC8gW6yQwWwr4mlNvNyy6aJ/i2\nfPx5zQvOSb9TvTfkhsvyvlY1DlJ4aMp44fln5MpKdJKfo+zJJ0E1tLnrPK2jG0ocx3BibBJuXrFb\n+LtWqJqrHEcEMnDJsQ09jii2g7NE6t5PEKrGzR3f5TaSdedzKeQ46pyMxLk3S7nRL9HZpea2chyp\nere8+ALRg9wvOBuO8h5Hs2RblSjC+Vq5EMcxu7H3/2fvzaMvP67Cznrvt/bearXU2tWS3JIlYcmW\n5R3LlhdMwDCHEMwBwh62CYETBghLhhnOCYHMDJwTMsxkzsAJk8wBkjkYMhPIeDBLWIZgGxssjBcM\neJONZI+tvdXb780f73urblV96tb9vve6+9dtX52j3+v61rfqfmu5W91762KC5mUXOy+iwL/4rQ8u\nj8iKAJUe5zbTXrxksBjbxwSeye918Dji03rhY76PsPDovwvKvFsRXl45tXCiRLhu3AxldpZXbLbn\nvhkNv3V5Ok8eV+xxVJfFajO7nn5q1ZvA7qA1XBmOOt5FY42JIVsT1joZaTiCdr14etef2X/H0Dd6\nr5nGF7sNswx+e9+N8nlnbqynmXdVUdGL7/nyHO8ZVQm8nmFj158HLnvDkUeInIUQfuI/vi/8wJsf\nwuejQtUuVI6jFXR0UTyOzoM3zapAxmMVN6LtlgPpA9vzKxsvhMqZ3B53j+GoClVTliNdbz0ypnF9\n6VORsXC+jHlj3VQXAVlXy8Aq7IvLDOGtKw5TKUEbjjbWlv/Yz+VJ6sNuCRW/2FMltEXjuawn7WSy\nmvF96OHHV9DKaoBosHefoXK2xMRbRoc8rLp9yGEpZCeu1sle28qJ9QmLXRJAxgSjPvweG4JHc7NM\nGB2PdW1MsjykKGcKeZK4r5mndo1vpLMLr+KehdVP8jIrT9Ei7QpkhiNob6xxDmsvYZCiJuhVi57g\nGhqqN5OTO4weXcDv7hssyEPMMrS23jXQMHMc0d645uC27jh7lo9rjbvZ51Q/b9cbC3pd33DFnjlu\nGS6L90UGLmv9fS459hJw/z95a3j61Nnm8zHM8s3v/NgKMOrDKqKpljE+Le5xtEssKgDyTcvguFsM\nRgKRkJwnTeYFN6VT10jcLzZVsYSRwGOyFhX7+d8r9m64ulrG+HG+HLPcJ3FL9D/2VTHM7VVXiK7C\ncLbMut7esK4zXR60sWgVXngXfV9dAnDhDm7s57vFxLfqULXdxt+WBT1PW+vzDea9FdQy3GRlTuXU\nMk5oWmIZmKxn5OE6lnySMt/zshh7CUWG+0jFhgwyVpjLMvNlfZffQ4aMSYtTDwwfHH6uAQNZRFYo\nw1uojU2nh633tkB85MTdqxyPNdzF9zpr3U7Y7CwDb63Wv+dttOvrMjbwwAsWbkvsK6ZT9G7bqOXN\nFWqFRVq0V/evx+vo/s2s/tb6NBzdv2Vg0MaJjFrLiIzb60m2tfjZoucdl78o6hR0zHEz2vjjD386\n+/c7P/KYr0NXx21463sfWexFBUsZSEbU1Z+4qw1HOysIVZPk2CvBaIVwvowU+rcIKLvI46hS2Gea\nCaXi0uPInedhCeruFZqWaXcV9QjG4ns3eGatwnC2TBvne5nmHm2fC1W7ILBLbjXdLbckala2fHLs\n3ReCvUo4MVy/3KMpx6+c56PwhqX0rst21XcbfSZZnRZuFp+zcOuFai1zvT3VH2/gku+vFTH/3LRa\n5UTYeXtS37s2bFwsSF5udVk+hvN/eJ1e5bta3hpltzTWD9x+tFk/w8+p9Fv5jD7++LPGmx1D6xJe\naNSGlfR+bHuadk+tdeJcw9xX/du7ri3DIfblpA1p/dU3IPdzEbX7H+u1hcZn9XxrPT94nHX6F12E\nvMuor55uwTR+XrpvK0UEeDyOxq75y95w5L8qsz1wliHhy//nPxyJ0WrgmdPnlm5jqVC1BVMV7e5b\n1eZ/l0IxtrE7vvN8J6wm48eFVHDpBrE8OXaOS+tWtTLH0SKMdyycr1vV3Ix8hX2O6SudAC+PgbXW\n7r7uoI3TeR4A3f76eQpVW/WtUJc6zMLsgnjFrCTR9HkE4T+a3/5iI4fjGNjFKQoXAqIBPSMvhYpZ\nfKNnxCnbpXqkdJveAurRjUf2VPUt74qx/AMNAgowKXPHTDCuPvSPSp9PcUVF3Jib3AvLJ29RvYj6\nEqzC+hzvNec9D5ISTzL04RyC4p57geT181vFapz8YYZtoxMbU13wKdCFAAAgAElEQVTN6g7oZ4TR\nsncH0fJxdwkb/XvHdQqEKK0D57pqo4GQG86GOXTuKwLKZxWfdW4cpP26uV7wiZn9jSSPMg316VFS\n7/rDe1TZgJvy+CsPz7N8ecBfPHD5G46cAp41cGfO7YR//ycPR4+UywWWMW4sevK4u3Mc1cL2WNht\nX2cJDatsP4TVCD7L9F/iEYItGFMS7bG4X0ivHS+4cYJ6lLtoBYdo6Op+sXMcrSIJqcB9KmRTgHJo\nLQO9td6CVeSjWhYuVML8C2bP6fSzWwxLYzx8e0bI3eJFRXDXtbaR2AN+pT8M9UAAX0AR9zwjDxJW\nUutnVH+sMQHxRO+W+tfYiwHIM2A5Y1abt6N8YLSblVlzzRYpKFlcibO6IA81fwimPShl2I7f+DSp\nfvcO+ar2DANKC9DAFde/6m8mz8bRuUnzH1LUbi8mXddGMqg3OnysXb1ot15/ZDenfFbWesW9hu36\n6I+s3d5aKx+3xrW6ma5GLV9rsK/KcMxZmOG6Sm3Uc10+0+9SG7dd5TwozPZVu9rnblVbEt78roeb\nz37xbR8N3/1LfxL+7Ts+egExOv+w3K1qF77P8w3yTcvdqra7QtVIjlmFEkuQkjCefwVDEn12czmU\nwshspoSW9ExyHHmNCQ/ecVWzfy+cN2Oeu/+67Lar6pvW+MR4MZxms8XdYwksYfhCer5hQtvMcLQ8\nq8V9tXt1+YsCuyU59sVmc4tc0uBZSrvFk7aEVeT/SorTSCuF+gfr8qTstGlqdvsPtGEZU6wy9K4g\nfI3PJ0UsbxfoIFw8YfWBh1Ht6vguhlY5lXr3uEL/ZLjztpvG02vgqfGQ39rBNcplI61v+VzXBhvy\nbhu7rryhT5bxk8A911g2DuhQTINp9Bn+UligPpynnFWNf2ZttJ4Tbmle2/SKQ0B9Y9gzSMZ2B/qn\nWY2s556HpPWtVkhbz0NKHus9tLHeXk8kq1nzpJ9Z8rGXrunuS1y8fMCCy95wtAo559En5zG0f9OJ\npb3UYLlb1Rb0ONrFXlvR6HMRbpuz4OASHgNE8LdKF0sn0KkuEbILobBfd1jc7+FhxsjzRzoOWQvo\noth7TzhJGB4L1k0ln3e9fYJeJubTMFbwJJzEMDYv8yk+AuS5kAlDK1wn1IYYFXvNr3KZ6rbuveFQ\nVTZdwXrxCiOL1GmBdoO+FOBCGTZ2MRsLISQ+NCqnICyUI/s2s8e79bPHeg9SElOhUe5b1ahsCaND\nEuJtY4ap4MX6pOjrMkNJ934/4Etvjr1Rkg5+/DxNlK66vd7NS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vEb2fN6vYM4xsPua7yy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sk9qt9wbRRmrXO7/4DZAcW3vNJR5S8yMKLZ4YOFH+1HSAMs4+ckkajg7AjV2yie44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\nERERka3/D9rDJJe3eY+UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117928ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].plot(figsize=(20, 10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1174e8860>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ko3XW7kpzOQLNhSmXED2etbBPB2Cr92K2ftlm9qHV+2owfsZSpWNVzRszy+8w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\n0AuCICQcEfSCIAgJRwS9IAhCwvn/9wpSACD5zwIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117e1cba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['casual'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118183320>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HiwvrjuRWDPQepHe7yjBMlLgY6GOQW9HsdBbIRXgxJbdioDchuOn7h//ciz4H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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117fab0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['registered'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df['count'] == df['casual'] + df['registered']).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract day, month, year... from datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1\n",
       "1         1\n",
       "2         1\n",
       "3         1\n",
       "4         1\n",
       "5         1\n",
       "6         1\n",
       "7         1\n",
       "8         1\n",
       "9         1\n",
       "10        1\n",
       "11        1\n",
       "12        1\n",
       "13        1\n",
       "14        1\n",
       "15        1\n",
       "16        1\n",
       "17        1\n",
       "18        1\n",
       "19        1\n",
       "20        1\n",
       "21        1\n",
       "22        1\n",
       "23        1\n",
       "24        2\n",
       "25        2\n",
       "26        2\n",
       "27        2\n",
       "28        2\n",
       "29        2\n",
       "         ..\n",
       "10856    18\n",
       "10857    18\n",
       "10858    18\n",
       "10859    18\n",
       "10860    18\n",
       "10861    18\n",
       "10862    19\n",
       "10863    19\n",
       "10864    19\n",
       "10865    19\n",
       "10866    19\n",
       "10867    19\n",
       "10868    19\n",
       "10869    19\n",
       "10870    19\n",
       "10871    19\n",
       "10872    19\n",
       "10873    19\n",
       "10874    19\n",
       "10875    19\n",
       "10876    19\n",
       "10877    19\n",
       "10878    19\n",
       "10879    19\n",
       "10880    19\n",
       "10881    19\n",
       "10882    19\n",
       "10883    19\n",
       "10884    19\n",
       "10885    19\n",
       "Name: datetime, Length: 10886, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.datetime.map(lambda x: x.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0\n",
       "1         1\n",
       "2         2\n",
       "3         3\n",
       "4         4\n",
       "5         5\n",
       "6         6\n",
       "7         7\n",
       "8         8\n",
       "9         9\n",
       "10       10\n",
       "11       11\n",
       "12       12\n",
       "13       13\n",
       "14       14\n",
       "15       15\n",
       "16       16\n",
       "17       17\n",
       "18       18\n",
       "19       19\n",
       "20       20\n",
       "21       21\n",
       "22       22\n",
       "23       23\n",
       "24        0\n",
       "25        1\n",
       "26        2\n",
       "27        3\n",
       "28        4\n",
       "29        6\n",
       "         ..\n",
       "10856    18\n",
       "10857    19\n",
       "10858    20\n",
       "10859    21\n",
       "10860    22\n",
       "10861    23\n",
       "10862     0\n",
       "10863     1\n",
       "10864     2\n",
       "10865     3\n",
       "10866     4\n",
       "10867     5\n",
       "10868     6\n",
       "10869     7\n",
       "10870     8\n",
       "10871     9\n",
       "10872    10\n",
       "10873    11\n",
       "10874    12\n",
       "10875    13\n",
       "10876    14\n",
       "10877    15\n",
       "10878    16\n",
       "10879    17\n",
       "10880    18\n",
       "10881    19\n",
       "10882    20\n",
       "10883    21\n",
       "10884    22\n",
       "10885    23\n",
       "Name: datetime, Length: 10886, dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.datetime.dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ao5rlo4DLu9pnNzNy7grsDtzYDAV9Msl+zaydRw7aZ+BYhwHXeD2fJEmSJI3csCZySfIS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\nDqEzLPmqXl0znVWHiq9y6cD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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d4feb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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z6JMkSZKkGrPokyRJkqQas+iTJEmSpBpznT5J0nrpyfparq0lSVLz2dMnSZIk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\nauz/AhApleBvHzobAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117ed3a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_by_hour(data, year=None, agg='sum'):\n",
    "    data['hour'] = data.datetime.dt.hour\n",
    "    dd = data[ data.datetime.dt.year == year ] if year else data\n",
    "    \n",
    "    by_hour = dd.groupby(['hour', 'workingday'])['count'].agg(agg).unstack()\n",
    "    return by_hour.plot(kind='bar', ylim=(0, 80000), figsize=(15,5), width=0.9, title=\"Year = {0}\".format(year))\n",
    "\n",
    "\n",
    "plot_by_hour(df, year=2011)\n",
    "plot_by_hour(df, year=2012);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S+rHHHnvwy1/+stfdWKdcVEaSJEmSWspAKEmSJEktZSCUJEmSpJbyGUJJkiS1lwsOqeUM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\nAx7pUx7g/wx/d3pqYZJ9quo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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1179030b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3B57RbZ+uY50M3FZ6HSVJkiRJTWp2ltEPA98oM4z+DngvVZE5KyImAQ8BpwJk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\n/wf366Htru4AAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118342940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_by_year(data, agg_attr, title):\n",
    "    data['year'] = data.datetime.dt.year\n",
    "    data['month'] = data.datetime.dt.month\n",
    "    data['hour'] = data.datetime.dt.hour\n",
    "    \n",
    "    by_year = data.groupby([agg_attr, 'year'])['count'].agg('sum').unstack()\n",
    "    return by_year.plot(kind='bar', figsize=(15,5), width=0.9, title=title)\n",
    "\n",
    "\n",
    "plot_by_year(df, 'month', \"Rent bikes per month in 2011 and 2012\")\n",
    "plot_by_year(df, 'hour', \"Rent bikes per hour in 2011 and 2012\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ckosvvnjSpcGSJEgCALDgnX322Xn5y1+egw8+ON2dO+64I+edd17e/OY3T7o0WJLcIwkA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\nEbIARgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1174e8710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[ ['count', 'year'] ].boxplot(by=\"year\", figsize=(15, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KRqPEYjFCoRB5eXmEQiFisRjRaNTv0EQkSyhhFBEREZmk2tvbKS8vH9JWXl5Oe3u7TxGJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1188f3780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RCnHrrbeSmZmJy+UiMzOTW2+91emQokZmZiaLFy/G4/GQnZ2Nx+Nh8eLFZGZm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\nkPSCc25LsPmEmX3KOXfczD4l6WSw/aikCX0eXhxsAwCMYEz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118341940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MdPkPAJdRWFioffv29Wvbt2+fCgsLfYootbS3t+vxxx/XypUrlZWVpZUrV+rx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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a2c0940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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To14XuZRdcI+kMWYi8AiQbq09aYzZCNwLpAN11tpVxpjHgceBQmNMeuj264AJ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\nSZIkSarEIClJkiRJquT/A6dqKN4Y4ZgWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b0a7ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for year in [2011, 2012]:\n",
    "    for workingday in [0, 1]:\n",
    "        dd = df[ (df.datetime.dt.year == year) | (df.workingday == workingday) ]\n",
    "        dd[ ['count', 'month'] ].boxplot(by=\"month\", figsize=(15, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mapping\n",
    "### Weather \n",
    "1. **Clear**, Few clouds, Partly cloudy, Partly cloudy\n",
    "2. **Mist** + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "3. **Light Snow**, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "4. **Heavy Rain** + Ice Pallets + Thunderstorm + Mist, Snow + Fog "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Clear', 'Mist', 'Light Snow', 'Heavy Rain'], dtype=object)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather = {1: 'Clear', 2: 'Mist', 3: 'Light Snow', 4: 'Heavy Rain'}\n",
    "df['weather_label'] = df.weather.map(lambda x: weather[x])\n",
    "\n",
    "df['weather_label'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    weather-1, season-1\n",
       "1    weather-1, season-1\n",
       "2    weather-1, season-1\n",
       "3    weather-1, season-1\n",
       "4    weather-1, season-1\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[ ['weather', 'season'] ].apply(lambda x: 'weather-{0}, season-{1}'.format(x['weather'], x['season']), axis=1).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Value counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012    5464\n",
       "2011    5422\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.year = df.datetime.dt.year\n",
    "\n",
    "df['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7     912\n",
       "6     912\n",
       "5     912\n",
       "12    912\n",
       "8     912\n",
       "11    911\n",
       "10    911\n",
       "4     909\n",
       "9     909\n",
       "3     901\n",
       "2     901\n",
       "1     884\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['month'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  month\n",
       "2011  5        456\n",
       "      6        456\n",
       "      7        456\n",
       "      8        456\n",
       "      11       456\n",
       "      12       456\n",
       "      4        455\n",
       "      10       455\n",
       "      9        453\n",
       "      2        446\n",
       "      3        446\n",
       "      1        431\n",
       "2012  5        456\n",
       "      6        456\n",
       "      7        456\n",
       "      8        456\n",
       "      9        456\n",
       "      10       456\n",
       "      12       456\n",
       "      2        455\n",
       "      3        455\n",
       "      11       455\n",
       "      4        454\n",
       "      1        453\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('year')['month'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2011    1\n",
       "2012    1\n",
       "Name: count, dtype: int16"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('year')['count'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2011    638\n",
       "2012    977\n",
       "Name: count, dtype: int16"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('year')['count'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2011    638\n",
       "2012    977\n",
       "Name: count, dtype: int16"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('year')['count'].agg(np.max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean year  month\n",
      "2011  1         54.645012\n",
      "      2         73.641256\n",
      "      3         86.849776\n",
      "      4        111.026374\n",
      "      5        174.809211\n",
      "      6        196.877193\n",
      "      7        203.614035\n",
      "      8        182.666667\n",
      "      9        174.622517\n",
      "      10       174.773626\n",
      "      11       155.458333\n",
      "      12       134.173246\n",
      "2012  1        124.353201\n",
      "      2        145.646154\n",
      "      3        208.276923\n",
      "      4        257.455947\n",
      "      5        264.109649\n",
      "      6        287.186404\n",
      "      7        267.037281\n",
      "      8        285.570175\n",
      "      9        292.598684\n",
      "      10       280.508772\n",
      "      11       231.980220\n",
      "      12       217.054825\n",
      "Name: count, dtype: float64\n",
      "median year  month\n",
      "2011  1         47.0\n",
      "      2         59.0\n",
      "      3         72.0\n",
      "      4         84.0\n",
      "      5        154.0\n",
      "      6        172.0\n",
      "      7        173.0\n",
      "      8        159.0\n",
      "      9        147.0\n",
      "      10       140.0\n",
      "      11       133.5\n",
      "      12       110.5\n",
      "2012  1         93.0\n",
      "      2        118.0\n",
      "      3        171.0\n",
      "      4        233.5\n",
      "      5        234.5\n",
      "      6        254.5\n",
      "      7        244.0\n",
      "      8        260.0\n",
      "      9        245.5\n",
      "      10       239.5\n",
      "      11       202.0\n",
      "      12       192.0\n",
      "Name: count, dtype: float64\n",
      "amin year  month\n",
      "2011  1        1\n",
      "      2        1\n",
      "      3        1\n",
      "      4        1\n",
      "      5        1\n",
      "      6        1\n",
      "      7        1\n",
      "      8        1\n",
      "      9        1\n",
      "      10       1\n",
      "      11       1\n",
      "      12       1\n",
      "2012  1        1\n",
      "      2        1\n",
      "      3        1\n",
      "      4        1\n",
      "      5        1\n",
      "      6        4\n",
      "      7        2\n",
      "      8        3\n",
      "      9        3\n",
      "      10       2\n",
      "      11       1\n",
      "      12       1\n",
      "Name: count, dtype: int16\n",
      "amax year  month\n",
      "2011  1        219\n",
      "      2        327\n",
      "      3        332\n",
      "      4        452\n",
      "      5        611\n",
      "      6        638\n",
      "      7        596\n",
      "      8        600\n",
      "      9        628\n",
      "      10       625\n",
      "      11       553\n",
      "      12       477\n",
      "2012  1        512\n",
      "      2        539\n",
      "      3        801\n",
      "      4        822\n",
      "      5        873\n",
      "      6        869\n",
      "      7        872\n",
      "      8        897\n",
      "      9        977\n",
      "      10       948\n",
      "      11       724\n",
      "      12       759\n",
      "Name: count, dtype: int16\n"
     ]
    }
   ],
   "source": [
    "for agg_func in [np.mean, np.median, np.min, np.max]:\n",
    "    print(agg_func.__name__, df.groupby(['year', 'month'])['count'].agg(agg_func))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>hour</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weather_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10430</th>\n",
       "      <td>2012-12-01 00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.65625</td>\n",
       "      <td>15.148438</td>\n",
       "      <td>81</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>99</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10431</th>\n",
       "      <td>2012-12-01 01:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.65625</td>\n",
       "      <td>15.148438</td>\n",
       "      <td>81</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>64</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10432</th>\n",
       "      <td>2012-12-01 02:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.65625</td>\n",
       "      <td>15.148438</td>\n",
       "      <td>81</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>Mist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10433</th>\n",
       "      <td>2012-12-01 03:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.65625</td>\n",
       "      <td>13.632812</td>\n",
       "      <td>81</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>Mist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10434</th>\n",
       "      <td>2012-12-01 04:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.65625</td>\n",
       "      <td>14.398438</td>\n",
       "      <td>81</td>\n",
       "      <td>6.003906</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>Clear</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 datetime  season  holiday  workingday  weather      temp  \\\n",
       "10430 2012-12-01 00:00:00       4        0           0        1  10.65625   \n",
       "10431 2012-12-01 01:00:00       4        0           0        1  10.65625   \n",
       "10432 2012-12-01 02:00:00       4        0           0        2  10.65625   \n",
       "10433 2012-12-01 03:00:00       4        0           0        2  10.65625   \n",
       "10434 2012-12-01 04:00:00       4        0           0        1  10.65625   \n",
       "\n",
       "           atemp  humidity  windspeed  casual  registered  count  hour  year  \\\n",
       "10430  15.148438        81   0.000000       9          99    108     0  2012   \n",
       "10431  15.148438        81   0.000000       5          64     69     1  2012   \n",
       "10432  15.148438        81   0.000000       3          47     50     2  2012   \n",
       "10433  13.632812        81   9.000000       1          14     15     3  2012   \n",
       "10434  14.398438        81   6.003906       0           5      5     4  2012   \n",
       "\n",
       "       month weather_label  \n",
       "10430     12         Clear  \n",
       "10431     12         Clear  \n",
       "10432     12          Mist  \n",
       "10433     12          Mist  \n",
       "10434     12         Clear  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=['year', 'month'], ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('df.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datetime,season,holiday,workingday,weather,temp,atemp,humidity,windspeed,casual,registered,count,hour,year,month,weather_label\r\n",
      "2011-01-01 00:00:00,1,0,0,1,9.84375,14.3984375,81,0.0,3,13,16,0,2011,1,Clear\r\n",
      "2011-01-01 01:00:00,1,0,0,1,9.0234375,13.6328125,80,0.0,8,32,40,1,2011,1,Clear\r\n",
      "2011-01-01 02:00:00,1,0,0,1,9.0234375,13.6328125,80,0.0,5,27,32,2,2011,1,Clear\r\n",
      "2011-01-01 03:00:00,1,0,0,1,9.84375,14.3984375,75,0.0,3,10,13,3,2011,1,Clear\r\n",
      "2011-01-01 04:00:00,1,0,0,1,9.84375,14.3984375,75,0.0,0,1,1,4,2011,1,Clear\r\n",
      "2011-01-01 05:00:00,1,0,0,2,9.84375,12.8828125,75,6.00390625,0,1,1,5,2011,1,Mist\r\n",
      "2011-01-01 06:00:00,1,0,0,1,9.0234375,13.6328125,80,0.0,2,0,2,6,2011,1,Clear\r\n",
      "2011-01-01 07:00:00,1,0,0,1,8.203125,12.8828125,86,0.0,1,2,3,7,2011,1,Clear\r\n",
      "2011-01-01 08:00:00,1,0,0,1,9.84375,14.3984375,75,0.0,1,7,8,8,2011,1,Clear\r\n"
     ]
    }
   ],
   "source": [
    "!head df.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_hdf('df.h5', 'df')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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