Section 6 - Data Cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 6: Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Data\n",
    "- things like a 0, empty cell, or a string for a price value are considered missing data\n",
    "- like python falsy values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#anything that is not in this list is a Truthy value\n",
    "falsy_values = (0, False, None, '', [], {})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "any(falsy_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#not a number\n",
    "np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#anything you do with np.nan becomes np.nan\n",
    "a = np.array([1,2,3,np.nan,np.nan,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "inf"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#numpy's infinite type, behaves like a virus, just like nan\n",
    "np.inf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### checking for nan or inf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#same thing for inf\n",
    "np.isnan(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#or checking for both\n",
    "np.isfinite(np.nan), np.isfinite(np.inf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These can take arrays as inputs and return boolean arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### filtering them out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,2,3,np.nan,np.nan,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creates new array with only non nan and non inf elements\n",
    "a[np.isfinite(a)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now can perform normal operations on it\n",
    "a[np.isfinite(a)].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Missing Data with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions to detect null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(np.nan), pd.isnull(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#is a synonym of the above\n",
    "pd.isna(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#opposite one\n",
    "pd.notnull(np.nan), pd.notna(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.notnull(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#can also pass it series and dataframes\n",
    "pd.isnull(pd.Series([1, np.nan, 7]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column A</th>\n",
       "      <th>Column B</th>\n",
       "      <th>Column C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column A  Column B  Column C\n",
       "0     False      True      True\n",
       "1      True     False     False\n",
       "2     False     False      True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#they return series and dataframes correspondingly\n",
    "pd.isnull(pd.DataFrame({\n",
    "    'Column A': [1, np.nan, 7],\n",
    "    'Column B': [np.nan, 2, 3],\n",
    "    'Column C': [np.nan, 2, np.nan]\n",
    "}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Operations with Missing Values\n",
    "- unlike numpy, pandas can do operations with missing values by just ignoring them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,2,np.nan]).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,2,np.nan]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([1,2,3,np.nan,np.nan,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4     True\n",
       "5    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "5    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[s.notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### On series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    NaN\n",
       "4    NaN\n",
       "5    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "5    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### On DataFrames\n",
    "- a little bit harder because can't drop single values, can only drop columns and rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column A</th>\n",
       "      <th>Column B</th>\n",
       "      <th>Column C</th>\n",
       "      <th>Column D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column A  Column B  Column C  Column D\n",
       "0       1.0       2.0       NaN         5\n",
       "1       NaN       8.0       9.0         8\n",
       "2      30.0      31.0      32.0        34\n",
       "3       NaN       NaN     100.0       110"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'Column A': [1, np.nan, 30, np.nan],\n",
    "    'Column B': [2, 8, 31, np.nan],\n",
    "    'Column C': [np.nan, 9, 32, 100],\n",
    "    'Column D': [5, 8, 34, 110],\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 4)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Column A  2 non-null      float64\n",
      " 1   Column B  3 non-null      float64\n",
      " 2   Column C  3 non-null      float64\n",
      " 3   Column D  4 non-null      int64  \n",
      "dtypes: float64(3), int64(1)\n",
      "memory usage: 256.0 bytes\n"
     ]
    }
   ],
   "source": [
    "#know we have 4 rows, so if only have 3 non-null, 1 value is null\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column A</th>\n",
       "      <th>Column B</th>\n",
       "      <th>Column C</th>\n",
       "      <th>Column D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>34</td>\n",
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      "text/plain": [
       "   Column A  Column B  Column C  Column D\n",
       "2      30.0      31.0      32.0        34"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#default drops rows with at least one null value\n",
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column D\n",
       "0         5\n",
       "1         8\n",
       "2        34\n",
       "3       110"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#can also drop columns instead\n",
    "df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column A</th>\n",
       "      <th>Column B</th>\n",
       "      <th>Column C</th>\n",
       "      <th>Column D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column A  Column B  Column C  Column D\n",
       "0       1.0       2.0       NaN         5\n",
       "1       NaN       8.0       9.0         8\n",
       "2      30.0      31.0      32.0        34\n",
       "3       NaN       NaN     100.0       110"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({\n",
    "    'Column A': [1, np.nan, 30],\n",
    "    'Column B': [2, np.nan, 31],\n",
    "    'Column C': [np.nan, np.nan, 100]\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column A</th>\n",
       "      <th>Column B</th>\n",
       "      <th>Column C</th>\n",
       "      <th>Column D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column A  Column B  Column C  Column D\n",
       "0       1.0       2.0       NaN         5\n",
       "1       NaN       8.0       9.0         8\n",
       "2      30.0      31.0      32.0        34\n",
       "3       NaN       NaN     100.0       110"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#only drops row if all values are null\n",
    "df.dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column A</th>\n",
       "      <th>Column B</th>\n",
       "      <th>Column C</th>\n",
       "      <th>Column D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column A  Column B  Column C  Column D\n",
       "2      30.0      31.0      32.0        34"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(how='any')  # default behavior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column A</th>\n",
       "      <th>Column B</th>\n",
       "      <th>Column C</th>\n",
       "      <th>Column D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column A  Column B  Column C  Column D\n",
       "0       1.0       2.0       NaN         5\n",
       "1       NaN       8.0       9.0         8\n",
       "2      30.0      31.0      32.0        34"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#if at least 3 values are non-null, the row is kept\n",
    "df.dropna(thresh=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filling null values\n",
    "- sometimes can be replaced by a 0 or the mean value for that column, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    NaN\n",
       "4    NaN\n",
       "5    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    0.0\n",
       "4    0.0\n",
       "5    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fill with arbitrary value\n",
    "s.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    2.5\n",
       "4    2.5\n",
       "5    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fill with mean\n",
    "s.fillna(s.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    3.0\n",
       "4    3.0\n",
       "5    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filling with close value from above\n",
    "s.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    4.0\n",
       "4    4.0\n",
       "5    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fill with close value from below\n",
    "s.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the bad thing about using close values is that cell might be left blank if it is at extreme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filling null values on DataFrames\n",
    "- similar but specify axis (row or column) to use to fill the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>Column A</th>\n",
       "      <th>Column B</th>\n",
       "      <th>Column C</th>\n",
       "      <th>Column D</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column A  Column B  Column C  Column D\n",
       "0       1.0       2.0       NaN         5\n",
       "1       NaN       8.0       9.0         8\n",
       "2      30.0      31.0      32.0        34\n",
       "3       NaN       NaN     100.0       110"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column A</th>\n",
       "      <th>Column B</th>\n",
       "      <th>Column C</th>\n",
       "      <th>Column D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column A  Column B  Column C  Column D\n",
       "0       1.0       2.0      47.0         5\n",
       "1       0.0       8.0       9.0         8\n",
       "2      30.0      31.0      32.0        34\n",
       "3       0.0      99.0     100.0       110"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna({'Column A': 0, 'Column B': 99, 'Column C': df['Column C'].mean()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column A</th>\n",
       "      <th>Column B</th>\n",
       "      <th>Column C</th>\n",
       "      <th>Column D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column A  Column B  Column C  Column D\n",
       "0       1.0       2.0       NaN         5\n",
       "1       1.0       8.0       9.0         8\n",
       "2      30.0      31.0      32.0        34\n",
       "3      30.0      31.0     100.0       110"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#close values by column\n",
    "df.fillna(method='ffill', axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column A</th>\n",
       "      <th>Column B</th>\n",
       "      <th>Column C</th>\n",
       "      <th>Column D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column A  Column B  Column C  Column D\n",
       "0       1.0       2.0       2.0       5.0\n",
       "1       NaN       8.0       9.0       8.0\n",
       "2      30.0      31.0      32.0      34.0\n",
       "3       NaN       NaN     100.0     110.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#close values by rows\n",
    "df.fillna(method='ffill', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking if there are NAs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking the length\n",
    "- if there are missing values, s.dropna() will have less elements than s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 4)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s), len(s.dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values = len(s.dropna()) != len(s)\n",
    "missing_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### More Pythony solution 'any'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#are any of the values True\n",
    "pd.Series([True,False,False]).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#are all the values True\n",
    "pd.Series([True,False,False]).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this tells us at least one value in s is a null value\n",
    "s.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning not-null values\n",
    "- if values are not null values, but are invalid for some reason"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>?</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex  Age\n",
       "0   M   29\n",
       "1   F   30\n",
       "2   F   24\n",
       "3   D  290\n",
       "4   ?   25"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#have invalid 290 for age and D and ? for sex\n",
    "df = pd.DataFrame({\n",
    "    'Sex': ['M', 'F', 'F', 'D', '?'],\n",
    "    'Age': [29, 30, 24, 290, 25],\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'F', 'D', '?'], dtype=object)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#first get all values of specific column\n",
    "df['Sex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    M\n",
       "1    F\n",
       "2    F\n",
       "3    F\n",
       "4    ?\n",
       "Name: Sex, dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#replace invalid values ('to be replaced', 'replace with')\n",
    "df['Sex'].replace('D', 'F')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    M\n",
       "1    F\n",
       "2    F\n",
       "3    F\n",
       "4    ?\n",
       "Name: Sex, dtype: object"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#can have a dictionary to replace all invalids at once\n",
    "df['Sex'].replace({'D': 'F', 'N': 'M'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>?</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex  Age\n",
       "0   M   29\n",
       "1   F   30\n",
       "2   F   24\n",
       "3   F   29\n",
       "4   ?   25"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#if wanted to replace values in many columns, could use dataframe type\n",
    "df.replace({\n",
    "    'Sex': {\n",
    "        'D': 'F',\n",
    "        'N': 'M'\n",
    "    },\n",
    "    'Age': {\n",
    "        290: 29\n",
    "    }\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>?</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex   Age\n",
       "0   M  29.0\n",
       "1   F  30.0\n",
       "2   F  24.0\n",
       "3   D  29.0\n",
       "4   ?  25.0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#if saw that some ages had an extra 0 at the end\n",
    "df.loc[df['Age']>100, 'Age'] = df.loc[df['Age']>100, 'Age']/10\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duplicates in Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gérard Araud                  France\n",
       "Kim Darroch           United Kingdom\n",
       "Peter Westmacott      United Kingdom\n",
       "Armando Varricchio             Italy\n",
       "Peter Wittig                 Germany\n",
       "Peter Ammon                  Germany\n",
       "Klaus Scharioth              Germany\n",
       "dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#can only invite one ambassador per country\n",
    "ambassadors = pd.Series([\n",
    "    'France',\n",
    "    'United Kingdom',\n",
    "    'United Kingdom',\n",
    "    'Italy',\n",
    "    'Germany',\n",
    "    'Germany',\n",
    "    'Germany',\n",
    "], index=[\n",
    "    'Gérard Araud',\n",
    "    'Kim Darroch',\n",
    "    'Peter Westmacott',\n",
    "    'Armando Varricchio',\n",
    "    'Peter Wittig',\n",
    "    'Peter Ammon',\n",
    "    'Klaus Scharioth '\n",
    "])\n",
    "ambassadors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gérard Araud          False\n",
       "Kim Darroch           False\n",
       "Peter Westmacott       True\n",
       "Armando Varricchio    False\n",
       "Peter Wittig          False\n",
       "Peter Ammon            True\n",
       "Klaus Scharioth        True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tells you which values are duplicated\n",
    "ambassadors.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gérard Araud          False\n",
       "Kim Darroch            True\n",
       "Peter Westmacott      False\n",
       "Armando Varricchio    False\n",
       "Peter Wittig           True\n",
       "Peter Ammon            True\n",
       "Klaus Scharioth       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#default it keeps the first appearance as the non duplicate, can change it though\n",
    "ambassadors.duplicated(keep='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gérard Araud          False\n",
       "Kim Darroch            True\n",
       "Peter Westmacott       True\n",
       "Armando Varricchio    False\n",
       "Peter Wittig           True\n",
       "Peter Ammon            True\n",
       "Klaus Scharioth        True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#or mark all of them as duplicates\n",
    "ambassadors.duplicated(keep=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gérard Araud                  France\n",
       "Peter Westmacott      United Kingdom\n",
       "Armando Varricchio             Italy\n",
       "Klaus Scharioth              Germany\n",
       "dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to drop the duplicates, keep parameters work the same way\n",
    "ambassadors.drop_duplicates(keep='last')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duplicates in DataFrames\n",
    "- happen at row level. 2 rows with same values are duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kobe Bryant</td>\n",
       "      <td>SG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>SF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kobe Bryant</td>\n",
       "      <td>SG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Carmelo Anthony</td>\n",
       "      <td>SF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kobe Bryant</td>\n",
       "      <td>SF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name Pos\n",
       "0      Kobe Bryant  SG\n",
       "1     LeBron James  SF\n",
       "2      Kobe Bryant  SG\n",
       "3  Carmelo Anthony  SF\n",
       "4      Kobe Bryant  SF"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players = pd.DataFrame({\n",
    "    'Name': [\n",
    "        'Kobe Bryant',\n",
    "        'LeBron James',\n",
    "        'Kobe Bryant',\n",
    "        'Carmelo Anthony',\n",
    "        'Kobe Bryant',\n",
    "    ],\n",
    "    'Pos': [\n",
    "        'SG',\n",
    "        'SF',\n",
    "        'SG',\n",
    "        'SF',\n",
    "        'SF'\n",
    "    ]\n",
    "})\n",
    "players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#default = all column values need to be the same\n",
    "players.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#choose which columns to look at to find duplicates\n",
    "players.duplicated(subset=['Name'], keep='last')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the drop_duplicates works the same way and takes the same parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text Handling\n",
    "- most complex type, these are some of the most common cases:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Splitting Columns\n",
    "- when data comes as one single value, but actually have multiple pieces of info in it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1987_M_US _1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990?_M_UK_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992_F_US_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1970?_M_   IT_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985_F_I  T_2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Data\n",
       "0     1987_M_US _1\n",
       "1     1990?_M_UK_1\n",
       "2      1992_F_US_2\n",
       "3  1970?_M_   IT_1\n",
       "4    1985_F_I  T_2"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#should have columns for year, sex, coutry, number of children\n",
    "df = pd.DataFrame({\n",
    "    'Data': [\n",
    "        '1987_M_US _1',\n",
    "        '1990?_M_UK_1',\n",
    "        '1992_F_US_2',\n",
    "        '1970?_M_   IT_1',\n",
    "        '1985_F_I  T_2'\n",
    "]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Data'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/opt/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.index.Int64Engine._check_type\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Data'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-102-d53cfa2c9da5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#split method splits acording to (1st argument), _ in this case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Data'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpand\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/miniconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2798\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2799\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2801\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/range.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    351\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    354\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_index_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"get_indexer\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.index.Int64Engine._check_type\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Data'"
     ]
    }
   ],
   "source": [
    "#split method splits acording to (1st argument), _ in this case\n",
    "df['Data'].str.split('_', expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1987</td>\n",
       "      <td>M</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990?</td>\n",
       "      <td>M</td>\n",
       "      <td>UK</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992</td>\n",
       "      <td>F</td>\n",
       "      <td>US</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1970?</td>\n",
       "      <td>M</td>\n",
       "      <td>IT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985</td>\n",
       "      <td>F</td>\n",
       "      <td>I  T</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0  1      2  3\n",
       "0   1987  M    US   1\n",
       "1  1990?  M     UK  1\n",
       "2   1992  F     US  2\n",
       "3  1970?  M     IT  1\n",
       "4   1985  F   I  T  2"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Country</th>\n",
       "      <th># Children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1987</td>\n",
       "      <td>M</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990?</td>\n",
       "      <td>M</td>\n",
       "      <td>UK</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992</td>\n",
       "      <td>F</td>\n",
       "      <td>US</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1970?</td>\n",
       "      <td>M</td>\n",
       "      <td>IT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985</td>\n",
       "      <td>F</td>\n",
       "      <td>I  T</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year Sex Country # Children\n",
       "0   1987   M     US           1\n",
       "1  1990?   M      UK          1\n",
       "2   1992   F      US          2\n",
       "3  1970?   M      IT          1\n",
       "4   1985   F    I  T          2"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['Year','Sex','Country','# Children']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "Name: Year, dtype: bool"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if a string contains a certain thing\n",
    "df['Year'].str.contains('\\?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2     True\n",
       "3    False\n",
       "4    False\n",
       "Name: Country, dtype: bool"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'].str.contains('U')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    US\n",
       "1    UK\n",
       "2    US\n",
       "3    IT\n",
       "4    IT\n",
       "Name: Country, dtype: object"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing spaces in country names\n",
    "df['Country'].str.replace(' ', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Global API\n",
    "- most codes use this API, some people prefer OOP (object translated API)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(-10, 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x123b90750>]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.title('My Nice Plot')\n",
    "\n",
    "plt.plot(x, x ** 2)\n",
    "plt.plot(x, -1 * (x ** 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'X Squared')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SJSLvMTxmw/FLnRyJRx6P7R3u5Uq7h1GPv/sj2z20hMX0dYQFGLEgIYybEIm8zKnaLoxY7WzxII/384NVONvYw/YON8J2D21iMX0DazJMKK7vRu/wmOooROQiBRXtMOgElqVFqo5C5DSX2zvuYnuH29maG4vb50Wz3UNDWEzfwOpME2x2iSNVHaqjEJGLFFRYsCgpHEG+BtVRiJziY4ezsL3D7Qgh8N3tC9juoSEspm9gUVI4Aox6Hi1O5CU6+kdwvqmXI/HIo71wqJrtHW6O7R7awmL6BowGHZalRrBvmshLfOR4FYrFNHmqiy19+PG+crZ3aADbPbSDxfRNrMk041L7ABq6BlVHISInK6ywIMTPgNz4UNVRiGYd2zu0he0e2sFi+iauHC3OVg8ijyalRGFFO1amm2DQ89JInufFwkts79CYie0eLx2uUR2HroO/MW4iIyoI0SG+KGCrB5FHq24fQFPPMFs8yCP1DI7hpx9UYsPcKLZ3aMzW3FisyzLjfw5UcrqYm2IxfRNCCKzOMOOjynbY7FJ1HCJyksuvPnG+NHmiFwqq0D9ixTdun6M6Ck2REAJ/f/sc9AyN4cUCbkZ0RyymJ2FNpgndg2M439SjOgoROUlBhQWJEf5IjgxUHYVoVrX3j+DXH9VgS24csmNDVMehaZgfH4o758fgxcJL6BoYVR2HrqKkmBZCfF0IcV4IcU4IsUMI4SeESBVCHBNCVAoh/iCEMKrIdi2rHMcK82hxIs80arXjaHUnVmeYVUchmnU/+7AKw2M2fG1jpuooNANf35SFgVErnj9YpToKXcXlxbQQIh7AVwAsllLOB6AH8CCA/wTwIyllBoAuAE+6Otv1mIN9kZsQiv1lraqjEJETHL/Uif4RK26bG6U6CtGsau4Zwm+O1uJTixKQbg5SHYdmICs6GNvz4/HykRq09Q6rjkMTqGrzMADwF0IYAAQAaAZwG4DXHR9/GcB2RdmuacPcaBTVd6O9f0R1FCKaZfvKWuFr0GF1BvulybP8z4FKSCnxlQ1clfYEX9uYCatN4qcfVKqOQhO4vJiWUjYC+CGAOowX0T0ATgHollJaHQ9rABB/recLIZ4SQpwUQpy0WCyuiAwA2JAdBSmBAxfaXPY1icj5pJTYV9aKVRkm+Bv1quMQzZq6jkG8dqIeDy5JQmJEgOo4NAuSIwNx/+JE/P54Hc+/cCMq2jzCAWwDkAogDkAggDsm+3wp5QtSysVSysVms+v6G+fFhSA21I+tHkQepry1Hw1dQ9iQzRYP8iw/3l8OvU7gS7dlqI5Cs+jLt2VAQOC5/RWqo5CDijaPjQAuSSktUsoxAG8AWAUgzNH2AQAJABoVZLsuIQQ2ZEehoKIdw2M21XGIaJbsc/yBvGFutOIk7kcI8V9CiAtCiBIhxJtCiLAJH/tHx4bxi0KI21XmpE+qbOvDW0WNeGxlCqJD/FTHoVkUF+aPzy5Pwp9ON6La0q86DkFNMV0HYLkQIkAIIQBsAFAK4AMA9zke8xiAnQqy3dCG7GgMjtpwpLpDdRQimiX7ylqxID4UMaEsOK5hL4D5UspcAOUA/hEAhBA5GN84Pg/jryz+rxCCPTJu5Nm95fD30ePpdemqo5AT/O2tGTDqdfjxPq5OuwMVPdPHML7R8DSAs44MLwD4BwDPCCEqAUQCeNHV2W5mRVokAox6tnoQeYj2/hEU13ezxeM6pJR7JuxlOYrxVw2B8Va9V6WUI1LKSwAqASxVkZE+6VxjD/58tgVPrk5FRKDbTJmlWWQO9sXjq1Kwq6QJF1p6VcfxekqmeUgp/1VKOVdKOV9K+YjjglwtpVwqpcyQUt4vpXS7sRl+PnqszjBhf1kbpORpiERad+BCG6QENmazxWMSPgfgXcfb8QDqJ3zsupvGyfWe3VuOUH8fPLkmTXUUcqLPr01DkNGA/95TrjqK1+MJiFO0MScazT3DON/EvwSJtG5faStiQ/0wL857T4UTQuxzHKB19W3bhMf8fwCsAH43jc+vZAKTtzpV24UDF9rw1No0hPr7qI5DThQWYMTfrE3D3tJWnKnvVh3Hq7GYnqLb5kZBCGB/GUfkEWnZ8JgNBRXtjp9poTqOMlLKjY5XCa++7QQAIcTjALYA+Kz8y0tyjQASJ3ya624aVzWByVv98P2LMAUZ8cSqFNVRyAU+tzoV4QE++OGei6qjeDUW01NkCvJFfmIY9l9g3zSRlh2p7sDQmA0bc9jicT1CiDsAfBPA3VLKiUNt3wbwoBDCVwiRCiATwHEVGekvDle240h1B/721gwEGA03fwJpXpCvAV+4NR0FFe04xuEIyrCYnoaN2dEoaehBK4/zJNKs/WWtCDDqsSItUnUUd/Z/AQQD2CuEKBZCPA8AUsrzAF7D+CSm9wB8UUrJmaEKSSnxX3suIjbUD59ZlqQ6DrnQoytSEBXsix/uucj9XIqwmJ6Gy5uV2OpBpE1SSuwva8PqDBP8fDjR7XocG8ITpZT5jtvTEz72PSllupRyjpTy3Rt9HnK+AxfaUFTXjS/flsnvaS/j56PHl2/LwImaLhyqaFcdxyuxmJ6GrOggJIT7c0QekUadb+pFc88wp3iQR7DbJf57TzmSIwNw/+KEmz+BPM4DS5IQH+aP/+bqtBIspqdBCIGN2dEorGzH0Chf2STSmv1lbRACWD+X86VJ+94914LS5l58bWMmfPT8te6NjAYdvroxEyUNPXj/PBf6XI0/ddO0MTsaI1Y7Civ5kgqR1uwra0V+YhjMwb6qoxDNiM0u8ezei8iMCsLdeRz17c3uXRiPNHMgnt17ETY7V6ddicX0NC1NjUCwr4GtHkQa09o7jLONPWzxII/wZlEjqiwDeGZTFvQ67x3xSIBBr8PXN2ahvLUfu0uaVMfxKiymp8lo0GFtlhn7L7TBzr8AiTTj8sZhFtOkdaNWO36yvxzz4kJwx/wY1XHIDdy1IBZzY4Lxo73lGLPZVcfxGiymZ2BjThQsfSMoaexRHYWIJmlfWSsSwv2RFR2kOgrRjLx2sh71nUP4xuY5Xn3wEP2FTifwd5vnoKZjEH861aA6jtdgMT0Dt2ZFQSfAVg8ijRgateGjynZszI5m8UGaNjxmw/8cqMAtyeG4dQ5PlqS/2JgdhbzEMDy3vwIjVg5JcAUW0zMQHmjE4uQI7OO8aSJNKKxsx4jVzhYP0rzfHq1Fa+8IV6XpE4QQ+PvNc9DUM4wdx+pUx/EKLKZnaEN2FMqae9HYPaQ6ChHdxP6yVgT7GrA0NUJ1FKJpGxix4mcfVmF1hgkr0nmCJ33SqoxILEuNwP/9oAqDo1bVcTwei+kZ2phz+TREtnoQuTO7XWJfWRvWZplhNPDSR9r1u2O16BgYxTObs1RHITclhMDf3z4H7f0j+D1Xp52Ov1FmKN0chFRTIFs9iNxcSWMP2vtHsCGbB7WQdtnsEq8cqcWy1AgsSgpXHYfc2OKUCCxODsdvjtZy6piTsZieBRvmRuFoVQf6R/hSCpG72l/WCp0A1s9hMU3atb+sFQ1dQ3hsZYrqKKQBj65MQW3HID4s54KfM7GYngUbsqMxarOjoNyiOgoRXcfe0lYsTo5AeKBRdRSiaXvlSC1iQ/2wOYebaOnm7pwfg6hgX7x8uFZ1FI/GYnoWLE4JR6i/D1s9iNxUQ9cgLrT0scWDNK2yrQ+Fle14eHkyDHr++qab89Hr8NllyThYbkG1pV91HI/Fn8ZZ4KPX4dY5ZnxwsQ029iURuZ0DFxynHnI1jzTslSO1MOp1eGBJouoopCEPLUuEj17gN0e5Ou0sLKZnyYbsaHQOjKK4vkt1FCK6yr6yNqSaApFu5qmHpE19w2P406kGbMmLhSnIV3Uc0pCoYD/81YJYvH6yAQPc2+UULKZnybosMww6gb2lbPUgcif9I1YcrerAhrls8SDtev1UAwZGbXhsRYrqKKRBj65IQd+IFW8UNaqO4pFYTM+SUH8fLE2N4LxpIjdTUG7BqM3OFg/SLLtd4jdHapGfGIa8xDDVcUiDFiWFYUF8KF45XAMp2Y4621hMz6IN2dGoaOtHbceA6ihE5LCvrA2h/j5YnMyZvKRNBZXtqG4fwOMch0fTJITAoyuSUdHWj8NVHarjeBwW07Noo2NSAKd6ELkHm13ig4ttuHWOmdMPSLNeOVwDU5ARdy6IUR2FNGxrXhwiAo14+XCN6igeh79dZlFyZCAyo4LY6kHkJorqutA5MIoN2WzxIG2q6xjEgYtt+MzSJPga9KrjkIb5+ejx4JJE7CtrRUPXoOo4HoXF9CzbkB2N45c60Ts8pjoKkdfbV9YGg05gXZZZdRSiaXnlSA30QuCzy5NVRyEPcPn76LdH6xQn8SwspmfZxuwoWO0SBy/yNEQi1faXtWJpagRC/X1URyGassFRK147WY/b58cgOsRPdRzyAPFh/ticE4NXT9RheMymOo7HYDE9yxYmhSMi0Ih9bPUgUqq2YwAVbf1s8SDNequoCb3DVm48pFn16MpkdA+O4e0zTaqjeAwW07NMrxNYPycKH160wGqzq45D5LUubwTeyCPESYOklHjlSA2yY0M4iYZm1Yq0SMyJDsbLHJM3a1hMO8HG7Cj0DI3hZC1PQyRSZX9ZKzKjgpAcGag6CtGUHbvUiQstfXh8ZTKEEKrjkAcRQuDRlck439SLU6xTZgWLaSdYk2WGUa/DvlK2ehCp0DM0huOXOtniQZr18uEahAX4YFt+vOoo5IG258cj2M+Al4/Uqo7iEVhMO0GQrwHL0yOx/wLnTROpcLDcAqtdYlMOWzxIe5q6h7CntBUPLE6Enw/H4dHsC/Q14NOLE/Hu2Wa09g6rjqN5LKadZGN2FC61D6DK0q86CpHX2V/WiohAI/IT2WtK2vO7Y7WwS4mHOQ6PnOiR5cmwSYnfH+OYvJliMe0kt80dXxHjAS5ErjVms+ODC21YPycKeh17TUlbhsds2HG8HhvmRiMxIkB1HPJgKaZA3Jplxu+P12HUyoEJM8Fi2kkSwgMwNyYY+0rZ6kHkSidrutA7bOUUD9Kkd0qa0TkwynF45BKPrkyBpW8E755rVh1F01hMO9GmnGicrO1E18Co6ihEXmN/WSuMeh3W8NRD0hgpJV4+UoN0cyBWZUSqjkNeYF2mGammQLx8uEZ1FE1jMe1EG7OjYZfgRkQiF5FSYk9pK5anRyLI16A6DtGUFNd3o6ShB4+tTOE4PHIJnU7gkeXJOF3XjbMNParjaBaLaSfKTQhFfJg/dpfwlCEiVzjb2IO6zkHctSBGdRSiKXv5cA2CfA24d1GC6ijkRe5bnIAAox4vH6lRHUWzWEw7kRACW/JiUVjRzlYPIhfYdaYJPnqB2+exmCZtsfSN4J2zzbjvlgS+qkIuFeLng3sXxePtM03o6B9RHUeTWEw72dbcOFjtEu+db1Edhcij2e0S75Q0Y22mGWEBRtVxiKZkx/E6jNkkHl3BcXjkeo+uSMGo1Y4/nKxXHUWTWEw72by4EKSaArHrDFs9iJzpdF0XmnqGsSUvVnUUoikZs9nxu2O1WJtlRpo5SHUc8kJZ0cFYmR6J3x6phdXGMXlTxWLayYQQ2Jobi6PVHWjr4ylDRM6yu6QZvgYdNvIIcdKY98+3oLV3BI9xVZoUenRFCpp6hrGvjEMTporFtAtsyYuDXQLvnmWrB5Ez2OwSu0uasX5OFIL9fFTHIZqSlw/XICkiALfO4Wx0UmdjdhTiw/w5Jm8aWEy7QFZ0MOZEB7PVg8hJjlV3oL1/BFvz4lRHIZqS8009OFHThUeWJ/PETlLKoNfh4eXJOFLdgYstfarjaIqSYloIESaEeF0IcUEIUSaEWCGEiBBC7BVCVDj+DVeRzVm25sXiZG0XmrqHVEch8ji7SpoRYNTjtrlc2SNteeVwLfx99Pj04kTVUYjwwJJEGA06vHKkRnUUTVG1Mv0TAO9JKecCyANQBuBbAPZLKTMB7He87zG25I6vmL1TwiM7iWbTmM2Od881Y2N2NPyNetVxiCate3AUbxU3YvvCeIQGsD2J1IsINGJbXhzeON2InqEx1XE0w+XFtBAiFMBaAC8CgJRyVErZDWAbgJcdD3sZwHZXZ3OmFFMgFsSH8gAXoln2UT1O8LYAACAASURBVGU7ugfH2OJBmrOzuAkjVjseXp6kOgrRFY+sSMbQmI31yhSoWJlOBWAB8GshRJEQ4pdCiEAA0VLKy8u2LQCuuSVfCPGUEOKkEOKkxWJxUeTZsTUvFmcaelDbMaA6CpHH2HWmGcF+BqzNMqmOQjQlbxU3Ym5MMObFhaqOQnTFgvhQZEQFYWcRi+nJUlFMGwAsAvAzKeVCAAO4qqVDSikByGs9WUr5gpRysZRysdlsdnrY2XSXo9VjN1s9iGbF8JgNe8634PZ5MfA1sMWDtKOmfQBFdd24Z2G86ihEHyOEwD0L43G8phP1nYOq42iCimK6AUCDlPKY4/3XMV5ctwohYgHA8a/HDTqMD/PHLcnhnOpBNEsOlVvQN2LFllwe1ELa8lZxI4QA7s5nexK5n7sdbXNvs16ZFJcX01LKFgD1Qog5jrs2ACgF8DaAxxz3PQZgp6uzucKW3FhcaOlDZRvHzhDN1K6SZoQH+GBVBls8SDuklHirqBEr0iIRG+qvOg7RJyRGBGBpSgTeON2A8WYBuhFV0zy+DOB3QogSAPkA/n8A3wewSQhRAWCj432Pc9eCWAgx3udJRNM3OGrFvtJW3LkgFj56jswn7Siu70ZNxyC2s8WD3Nj2hfGosgzgfFOv6ihuT8lvICllsaPvOVdKuV1K2SWl7JBSbpBSZkopN0opO1Vkc7aoED8sT43ErpIm/rVHNAMHLrRhaMzGFg/SnLeKGmE06HDH/BjVUYiu664FsTDqdXizqFF1FLfH5RwFtuTFotoygNJm/rVHNF27zjTBHOyLZamRqqMQTdqYzY5dJc3YlB2NED/Olib3FRrgg/VzzXj7TBOsNrvqOG6NxbQCd86PhV4nONWDaJr6hsfwwUUL7loQyyOYSVMKKizoHBhliwdpwvb8eFj6RnC4qkN1FLfGYlqBiEAjVmWYsOsMWz2IpmNvaStGrXZszWOLh6sIIf5OCCGFECbH+0II8ZwQolIIUSKEWKQ6oxa8WdSEsAAfrMvS1mhX8k7r50Yh2M+At9jqcUMsphXZmhuLhq4hnGnoUR2FSHN2lzQjPswfCxPDVUfxCkKIRACbAdRNuPtOAJmO21MAfqYgmqb0j1ixt7QFW3JjYTTw1y+5Pz8fPe5aEIv3z7dgcNSqOo7b4k+zIpvnxcCo13HmNNEUdQ+O4lC5BXflxkLHFg9X+RGAb+Ljh2ltA/CKHHcUQNjlswLo2t4/14LhMTsPaiFN2b4wHgOjNuwtbVUdxW2xmFYk1N8Ha7PMeKekGXY7Wz2IJuv98y2w2iW25vKwC1cQQmwD0CilPHPVh+IB1E94v8Fx39XPf0oIcVIIcdJisTgxqft7q7gRiRH+WJTEV1RIO5amRCAu1I+tHjfAYlqhrXmxaOkdxsnaLtVRiDRj15lmJEcGYH58iOooHkMIsU8Ice4at20A/gnAv0z3c0spX3CMQl1sNntvn3Br7zA+qmzHPfnxEIKvqJB26HQC2xbG41BFO9r7R1THcUssphXamB0NPx+2ehBN1viu8nZszY1jQTKLHLP95199A1ANIBXAGSFEDYAEAKeFEDEAGgEkTvg0CY776Bp2nWmCXQLb2OJBGnTPwnjY7BK7Wa9cE4tphQJ9DdgwNxrvnmvmDEeiSXjvXDPsEtiaxxYPV5BSnpVSRkkpU6SUKRhv5VgkpWwB8DaARx1TPZYD6JFSct7ndbxZ1IjchFCkm4NURyGasqzoYOTEhuDNYhbT18JiWrGtebFo7x/F0WqPPPCRaFbtOtOMzKggzIkJVh2FgD9jfOW6EsAvAPyt2jjuq7y1D+eberE9n6vSpF33LIzHmfpuVFv6VUdxOyymFbt1ThQCjXrsLuFfe0Q30twzhBO1nVyVVsixQt3ueFtKKb8opUyXUi6QUp5Unc9dvVXUCL1O8HuXNG1rXhyEAN7i6vQnGK73ASHEWXx8DNLHSClznZLIy/j56LF5XgzePdeC72ybz9mjRNfxTkkzpAS25Hrn9DVek7XJbpfYWdyE1RkmmIN9VcchmraYUD+sTI/EzuJGfH1jJvetTHCjym0LgK0A3nPcPuu4/dlxo1myNS8WPUNjKKz07rFRRDeyq6QZ8+JCkOa9Pae8JmvQydouNHYPcbY0eYTt+fGo7RhEUX236ihu5brFtJSyVkpZC2CTlPKbjo0oZ6WU38L4SVg0S1ZnmBHq74PdZ7h3h+ha6jsHcaa+G1u8eLY0r8na9GZRIwKMemyeF606CtGM3TE/Br4GHWdOX2UyPQVCCLFqwjsrJ/k8miSjQYc75sVgT2krhsdsquMQuZ1djj0F3tricRVekzVixGrDOyVN2JwTjQDjdbsqiTQj2M8Hm3KisetME8Y4heyKyVyAnwTwv0KIGsec0f8F8DmnpvJCW/Ji0T9ixYcX2epBdLXdZ5qxMCkMiREBqqO4A16TNeKDCxb0DluxnS0e5EHuWRiPrsExHCpnvXLZTYtpKeUpKWUegDwAeVLKfCnlaedH8y4r0iIRGWi8sgJHROOqLP0obe716haPiXhN1o63ihphCjJidYZJdRSiWbM2y4zwAB+8yVaPK25aTAshooUQLwJ4VUrZI4TIEUI86YJsXsWg1+GvFsTiQFkbBketquMQuY3dZ5ohBHDXArZ4ALwma0XP4BgOXGjD1rw4GPTswiHP4aPXYWteHPaWtqJveEx1HLcwmZ/wlwC8D+DyslA5gK85K5A325Ibi6ExG/aVtamOQuQWpJR4+0wjlqREICbUT3Ucd/ESeE12e38+14xRm51TPMgjbV8YjxGrHe+da1EdxS1Mppg2SSlfA2AHACmlFQB3yTnBkpQIRIf4YtcZtnoQAcCFlj5UWQZ42MXH8ZqsAW8WNSLNHIgF8aGqoxDNuoWJYUiODMBbxWz1ACZXTA8IISLhOCxACLEcQI9TU3kpnU5gS24cDl60oJcvnRBhd0kT9DqBO+fHqI7iTnhNdnON3UM4fqkT9+TH82AL8khCCGzLj8fhqg609g6rjqPcZIrpZwC8DSBdCPERgFcAfNmpqbzYltxYjNrs2HO+VXUUIqWklNh1phkr0yNhCuLJcRPwmuzmdjpW67bls8WDPNf2/DhICbzN48VvXEwLIXQA/ACsA7ASwOcBzJNSlrggm1fKTwxDQrg/dnOqB3m5s409qOscxFZO8biC12T3J6XEm6cbcUtyOJIiOcqRPFeaOQh5iWGc6oGbFNNSSjuAn0oprVLK81LKc1JK9h84kRDjrR6FFe3oHBhVHYdImbeLm+CjF7h9Hls8LuM12f2VNveioq2fs6XJK9yTH4fS5l5cbOlTHUWpybR57BdCfEqw8ctl7lkYD6td4o3TDaqjECkxarXjzaJGrJ8ThdAAH9Vx3A2vyW7sraJGGHQCWzjKkbzAlrw46HXC6zciTqaY/jyAPwIYEUL0CiH6hBC9Ts7l1ebEBGNhUhhePVEPKaXqOEQut6+sFR0Do3hoaZLqKO6I12Q3ZbNL7Cxuwq1zohAeaFQdh8jpTEG+WJtpws6iRtjt3luvTOYExGAppU5KaZRShjjeD3FFOG/20JIkVLb141Rtl+ooRC6343gd4kL9sDbLrDqK2+E12X0dqepAW98IZ0uTV9m+MB5NPcM4XtOpOooykzqWSQgRLoRYKoRYe/nm7GDebkteLIJ8DdhxvF51FCKXqu8cRGFlO+5fnAi9jp0M18Jrsnt6s6gRwb4GbMiOUh2FyGU258Qg0KjHW168EXEyx4n/NYBDGD9x69uOf//NubEowGjA3flxeOdsE3qGuL+IvMdrJ8f/gPz0kkTFSdwTr8nuaWjUhvfPt+DOBTHw89GrjkPkMv5GPW6fH4N3zjZjeMw7z4+azMr0VwEsAVArpVwPYCGAbqemIgDjrR7DY3a87eWN/eQ9rDY7XjtZj3VZZsSH+auO4654TXZD+8pa0T9ixXbOliYvtD0/Hn3DVnx4sU11FCUmU0wPSymHAUAI4SulvABgjnNjEQAsSAjFvLgQ7DjOjYjkHT68aEFr7wgeXMKNhzfAa7IbequoETEhfliWFqk6CpHLrUyPhDnY12tnTk+mmG4QQoQBeAvAXiHETgC1zo1Flz24NAmlzb0428jTgsnzvXqiDqYgX/ac3hivyW6ma2AUB8stuDs/jn3+5JUMeh3uzovDgQttXtmaOplpHvdIKbullP8G4J8BvAhgu7OD0bht+XHw99FzIyJ5vJaeYRy40Ib7FyfARz+pvdFeiddk97P/QhusdoktuZwtTd7rrtxYjNmkV7Z6TGYDYtLlG4BLAIoB8EgyFwnx88FdubF4u7gRAyNW1XGInOa1k/WwS+BBbjy8IV6T3c+e8y2ICfHDgvhQ1VGIlMlPCIM52Bd7zreqjuJyhkk85h0AEoAA4AcgFcBFAPOcmIsmeGhpIl4/1YBdZ5rwIA+xIA9kt0v84UQ9VmVEIjkyUHUcd+dd1+R3vwW0nFWd4rpsUuKvazthDvKDeOlZ1XGIlNEB+IOxH+3lo7D/Khw6dz6kNWYBcOf3Z+3TTabNY4GUMtfxbyaApQCOzFoCuqlFSeHIjArCjhNs9SDPVFDZjsbuIW48nARek91Lz9AY7BIID+Sx90ThgUbYpUTPsHf1TU9mZfpjpJSnhRDLnBGGrk0IgQeXJuHfd5eirLkX2bE87Iw8y6vH6xAe4IPN86JVR9Ecj78mz+LqkTP8xx/P4L22Fpx6ahNgYK8/ebcAqw1Pfmcv7o6Jx3/cu0B1HJe5aTEthHhmwrs6AIsANDktEV3TvQvj8Z/vXsCrx+vw7W3zVcchmjWWvhHsLW3F4ytT4GvgYRc3w2uy+7DZJfZfaMP6uVEwspAmgq9Bj1vnRGFfWSu+Z58PnZdMt5nMT3/whJsvxvv1tjkzFH1SeKARd8yPwZtFjV57whB5pj+dboDVLvHgUm48nCRek93EqdoudA6M8hUVogk25UTD0jeC4gbvOUvqpivTUspvuyII3dxDS5Pw9pkm/PlsM+5dlKA6DtGMSTm+8XBJSjgyooJVx9EEXpPdx57zLfDRC6zLMquOQuQ21s+JgkEnsOd8KxYlhauO4xKTafPYhfGd49ckpbx7VhPRdS1Pi0CqKRCvHq9nMU0e4Wh1Jy61D+BL6zNUR9EMXpPdg5QSe8tasTLdhGA/bj4kuiw0wAfL0yKxt7QF37pzruo4LjGZNo9qAEMAfuG49QOoAvDfjhu5iBACDyxJxPGaTlS29auOQzRjr56oQ7CfAX+1gIddTAGvyW6gvLUftR2DbPEguoZNOdGosgygyuIdtcpkiulVUsoHpJS7HLfPAFgjpTwopTzo7ID0cZ9alACDTuAPJ+pURyGake7BUbx7rgX3LIyHv5EbD6eA12Q3sLe0BQCwMZvFNNHVNuWM/1zsLfWOA1wmU0wHCiHSLr8jhEgFwFMVFDEH+2JTTjT+dLoRI1ZuRCTteuN0I0atds6Wnjpek93AntJW5CeGITrET3UUIrcTF+aPBfGh2HO+RXUUl5hMMf11AB8KIT4UQhwE8AGArzo3Ft3Ig0uT0Dkw6jV/8ZHnkVLi1RN1yEsIRU4c56ZPEa/JijX3DKGkoefK6hsRfdKmnGgU1XejrW9YdRSnm8wJiO8ByMT4xforAOZIKffM9AsLIfRCiCIhxG7H+6lCiGNCiEohxB+EEMaZfg1PtSbDhPgwf7x6nCcikjadrutGeWs/HlzKVempctY1mSZvn2Mh43b2SxNd1+Z50ZAS2F/WpjqK0123mBZCLBFCxACAlHIEQB6A7wD4LyFExCx87a8CKJvw/n8C+JGUMgNAF4AnZ+FreCSdbnwjYmFlO2o7BlTHIZqyHcfrEGjUY2tenOoomuGCazJN0p7SVqSZApFuDlIdhchtzYkORmKEv1e0etxoZfrnAEYBQAixFsD3AbwCoAfACzP5okKIBAB3Afil430B4DYArzse8jKA7TP5Gp7u/sUJ0AngDye4Ok3a0js8ht0lTbg7Pw5Bvjedzkl/4bRrMk1ez9AYjlR1YFNONMZ/dRHRtQghsDknBh9VdqB/xKo6jlPdqJjWSyk7HW8/AOAFKeWfpJT/DGCmQ2F/DOCbAOyO9yMBdEspL//fbgAQf60nCiGeEkKcFEKctFgsM4yhXbGh/lg/Jwp/PNWAMZv95k8gchM7i5swPMaNh9PgzGsyTdKHF9tgtUuOxCOahM050Ri12XGo3LPrtRsW00KIy8tGGwAcmPCxaS8nCSG2AGiTUp6azvOllC9IKRdLKRebzd596tSDS5Ng6RvBgQue349EnuPV43XIjg1BbkKo6iha45RrMk3NntJWmIJ8kZ/oHSe7Ec3ELcnhCA/w8fhWjxsV0zsAHBRC7MT4AQEFACCEyMD4y4rTtQrA3UKIGgCvYry94ycAwib8okgA0DiDr+EV1s8xIzrEF68e58xp0oazDT0439SLh5Ym8iXyqXPWNZkmacRqw8GLFmzMjoJex+9fopsx6HXYkB2NAxfaPPpV9OsW01LK7wH4OwAvAVgtpZQTnvPl6X5BKeU/SikTpJQpAB4EcEBK+VmMj3e6z/GwxwDsnO7X8BYGvQ7335KIg+UWNHUPqY5DdFM7TtTBz0eHbfnX7OKiG3DWNZkm70jVeO8nWzyIJm9zTjR6h604fqnz5g/WqBuOxpNSHpVSvimlHJhwX7mU8rQTsvwDgGeEEJUY76F+0Qlfw+M8sCQRdgm8dpIbEcm9DYxY8XZxE/5qQSxC/X1Ux9EkF1+T6Sp7SlsRYNRjZbpJdRQizViTaYafj86jWz0mc2iL00gpP5RSbnG8XS2lXCqlzJBS3u8Y/UQ3kRgRgDWZJrx2oh42u7z5E4gUeaekGf0jVjzE2dKkQXa7xL7SVqzLMsPPR686DpFm+Bv1WJNpxt7SVvzlBTXPorSYptnx4JIkNPUM41CFZ++WJW3bcaIOGVFBWJzMjVukPWcautHWN8IWD6Jp2JQTjaaeYZxv6lUdxSludGjLdZePhBBrnBOHpmNTTjQiA43ciEhu62JLH4rquvHgEm48nC5ek9XaU9oKvU7gtjkspommasPcKOgEPLbV40Yr0x8KIb4phLjyepYQIloI8VsAP3J+NJoso0GHT92SgP1lbWjrG1Ydh+gTdhyvg1Gvw72LElRH0TJekxXaW9qKZakRCA1gvz/RVEUG+WJxSgT2lLaqjuIUNyqmbwGQDqBYCHGbEOKrAI4DOAJgqSvC0eQ9sCQRVrvE66caVEch+pjhMRveLGrE5nnRiAg0qo6jZbwmK1Jl6UdlWz8253BVmmi6NudE40JLH+o6BlVHmXU3Go3XJaX8PMaP/N4H4O8BrJJS/lRK6bnDAjUq3RyEpakRePV4PezciEhu5N1zzegZGuPGwxniNVmdvY7VtI0spommbZPj52dPqee1etyoZzpMCPFzAE8AuAPA6wDeFULc5qpwNDWPrkhGXeegx76MQtojpcQvDl1CmjkQK9IiVcfRNF6T1dlb2op5cSFICA9QHYVIs5IjAzEnOvjKH6ee5EZtHqcBVABYLKXcI6X8GoBHAHxXCLHDJeloSu6YF4OkiAD87GCVx46fIW05VNGO0uZePL02HTqeGDdTvCYrYOkbwem6LmzOiVEdhUjzNs+LxomaTnQOjKqOMqtuVEyvlVL+UEppvXyHlLJYSrkSwAHnR6OpMuh1eGptGs7Ud+NoteeeNETa8fyHVYgJ8cO2hXGqo3gCJddkIcSXhRAXhBDnhRA/mHD/PwohKoUQF4UQtzvr66u2v6wVUv7lJWoimr7NOTGwS+DAhTbVUWbVjXqmr7uTTUr5C+fEoZm675YEmIKM+NnBKtVRyMsV13fjSHUHnlydCl8DD7mYKRXXZCHEegDbAORJKecB+KHj/hwADwKYh/GWk/+dOGXEk+wpbUVCuD+yY4NVRyHSvPnxIYgN9fO4EXk8tMXD+Pno8cSqVBwqt+B8U4/qOOTFnv+wCiF+Bjy0jBsPNewLAL5/+URaKeXl5aRtAF6VUo5IKS8BqIQHThQZGLGisLIdm3NiOB+daBYIIbApJxqHKiwYGrWpjjNrWEx7oIeXJyPI14DnD1arjkJeqsrSj/dLW/DoihQE+RpUx6HpywKwRghxTAhxUAixxHF/PID6CY9rcNz3CUKIp4QQJ4UQJy0WbZ3SeqjcglGrnS0eRLNoc04MhsfsKKxsVx1l1rCY9kCh/j747LIkvFPS5JHzHMn9vXCwGka9Do+vSlEdhW5CCLFPCHHuGrdtAAwAIgAsx/govtfEFJdopZQvSCkXSykXm81mJ/wXOM+e0laEBfhgSUq46ihEHmNZWgSC/Qwe1erBYtpDfW51Kgw6HX5RwNVpcq2WnmG8UdSATy9OhCnIV3Ucugkp5UYp5fxr3HZifMX5DTnuOAA7ABOARgCJEz5NguM+jzFms2N/WSs2zI2GQc9flUSzxUevw21zo7D/QhtsHnIuBq8QHio6xA/3LorHayfr0d4/ojoOeZFffXQJNrvE36xJUx2FZu4tAOsBQAiRBcAIoB3A2wAeFEL4CiFSAWRi/DRGj3HiUid6h61s8SBygk050egcGMWp2i7VUWYFi2kP9tTaNIza7HjpoxrVUchL9AyN4ffH6rAlNw5JkTzgwgP8CkCaEOIcgFcBPOZYpT4P4DUApQDeA/BFKaXn7CbCeIuHr0GHtVkm1VGIPM66LDOMep3HtHqwmPZgaeYg3J4Tg1eO1KB/xHrTxxPN1G+P1qJ/xIrPr+OqtCeQUo5KKR92tH0sklIemPCx70kp06WUc6SU76rMOduklNhzvgVrMk0IMHIDLdFsC/bzwcqMSOwpbfWIQ+ZYTHu4p29NR++wFTuO1amOQh5ueMyGX390CeuyzJgXF6o6DtG0nW/qRVPPME89JHKiTTnRqOscRHlrv+ooM8Zi2sPlJ4ZhRVokfllYjRGrR70KS27m9VMNaO8fxdPr0lVHIZqRPaWt0AlgQ3aU6ihEHmtT9vh+BE9o9WAx7QW+cGs6WntHsLOoSXUU8lBWmx0vHKpGXmIYlqdFqI5DNCN7S1txS3I4IjmNhshpokL8kJ8Yhr1lraqjzBiLaS+wJtOEeXEheP5QFeweMoaG3Mu751pQ1zmIL6xL50lxpGn1nYMoa+5liweRC2yeF42Shh40dQ+pjjIjLKa9gBACT69LR7VlAHtKtf8XILkXKSV+9mEV0syB2MwxYqRxl6+RHIlH5HyX/2jdp/HVaRbTXuLO+TFIigjAzw5WecTOWXIfBRXtKG3uxdNr06HTcVWatG1vaQuyooOQYgpUHYXI42VEBSHNFIi9Gl/oYzHtJQx6HZ5am4Yz9d04Wt2pOg55kOcPViE6xBfbFsapjkI0I73DYzhR04WN2VyVJnKVjTnROFrdgQENj/BlMe1F7rslAaYgI54/WKU6CnmIM/XdOFzVgb9enQZfg151HKIZOVzZAZtdYl2WWXUUIq+xLsuMMZvE0eoO1VGmjcW0F/Hz0eOJVak4WG7B+aYe1XHIAzx/sAohfgY8tCxJdRSiGSuosCDQqMfCpHDVUYi8xi3J4fDz0aGgol11lGljMe1lHl6ejCBfA35+sFp1FNK4Kks/3jvfgkdWjH9PEWldQUU7VqRHwmjgr0YiV/Hz0WNZaiQOVVhUR5k2XjG8TKi/Dz67LAm7S5pQ1zGoOg5p2C8OVcOo1+HxlamqoxDNWG3HAOo6B7GWLR5ELrc2y4xqywAaurRZl7CY9kKfW50Kg06HXxRwdZqmp7V3GG+cbsT9ixNgDubBFqR9h8rHV8XWZLKYJnK1tZkmANBsqweLaS8UHeKHexfF47WT9WjvH1EdhzToV4WXYLXb8dQaHh1OnuFQRTsSwv2REhmgOgqR18mICkJMiB8KNNrqwWLaSz21Ng2jNjte+qhGdRTSmJ6hMfzuWB3uyo1DEgsP8gBjNjuOVHVgTaaZJ3gSKSCEwJpMEwor2mHT4EnNLKa9VJo5CLfnxOCVIzXo1/BsR3K93x6tRf+IFU+vS1MdhWhWFNd3o3/EinVZJtVRiLzW2iwzeoetKGnoVh1lylhMe7Gnb01H77AVO47VqY5CGjE8ZsOvP6rB2iwz5sWFqo5DNCsOlVugE8CKdBbTRKqsyjBBCOBQufb6pllMe7H8xDCsSIvELwurMWK1qY5DGvD6qQa0949wVZo8yqGKduQnhiHU30d1FCKvFRFoxIL4UE32TbOY9nJfuDUdrb0j2FnUpDoKuTmbXeIXBdXIc/wRRuQJugdHUdLQzSkeRG5gbaYZRfXd6B0eUx1lSlhMe7k1mSbMiwvB/35YiVGrXXUccmM7ixtR2zGIL6xL4yYt8hgfVXZASnC+NJEbWJNpgs0ucaRKW0eLs5j2ckIIfOP2OajpGMQrR2pUxyE3NThqxQ/eu4jchFBszolRHYdo1hwqtyDYz4C8BO4BIFJtYVI4Ao36K3PftYLFNGH9nCisyzLjJ/sr0DkwqjoOuaGfH6xGS+8w/mVLDnQ6rkqTZ5BSoqDCglXpJhj0/HVIpJrRoMOK9EjNHd7CqwcBAP7PXdkYHLXhR3vLVUchN9PUPYSfH6rCltxYLE6JUB2HaNZUWQbQ1DOMNRyJR+Q21maZUdc5iNqOAdVRJo3FNAEAMqOD8fCyJPzuWC0utvSpjkNu5AfvXYBdAt+6c67qKESz6vLUgLXcfEjkNi5vBj6kodVpFtN0xdc2ZiHI14DvvlMKKbV3AhHNvtN1XXiruAlPrUlDQjhPOyTPcqjcglRTIBIj+L1N5C5SIgOQEO6vqb5pFtN0RXigEV/bmIWCinZ8cLFNdRxSTEqJ7+wqhTnYF1+4NV11HKJZNWK14Wh1J9ZkssWDyJ2MHy1uxpGqDozZtDFljMU0fcwjK5KRZg7Ed3eXaeabmJzj7TNNKK7vxjdvn4NAX4PqOESz6lRtIDZy1QAAIABJREFUF4bGbJwvTeSG1mWZ0D9iRXG9No4WZzFNH+Oj1+H/3JWN6vYB/OZIreo4pMjQqA3ff/cC5seH4FOLElTHIZp1h8rbYdAJrEjnAURE7mZFugk6Ac20erCYpk9YPycKazJN+PG+cnRxVJ5XeuFQNZp7hvEvW+ZxFB55pIIKCxYlhyOIr7oQuZ1Qfx/kJ4ZpZhMii2n6BCEE/nlLDvpHrPjxPo7K8zbNPUN4/mAV7loQi6WpHIVHnqe9fwTnm3qxlv3SRG5rbZYZJQ3d6B50/0U9lxfTQohEIcQHQohSIcR5IcRXHfdHCCH2CiEqHP+Guzob/UVWdDA+uywZvz1Wh4pWjsrzJv/13kXYpOQoPPJYH1WOr3axX5rIfa3JNENK4KNK9z9aXMXKtBXA30kpcwAsB/BFIUQOgG8B2C+lzASw3/E+KfT1TVkIMOrx3XfKVEchFymu78YbRY3469WpHBdGHutguQXhAT6YH88jxIncVV5CKIL9DJrom3Z5MS2lbJZSnna83QegDEA8gG0AXnY87GUA212djT4uItCIr27IxMFyC0fleYHxUXjnYQryxd+uz1Adh8gpxo8Qb8eqDBP03A9A5LYMeh1WpZtQUGFx+7MvlPZMCyFSACwEcAxAtJSy2fGhFgDR13nOU0KIk0KIkxaL+/+1onWPrkhBqikQ391dylF5Hm5XSTNO142PwuOmLPJUF1v7YOkb4amHRBqwNsuMpp5hVFnc+2hxZcW0ECIIwJ8AfE1K2TvxY3L8T5Br/hkipXxB/r/27jw+ivr+4/jrm4twJZAQIFySIPcVICaA4oWKoAXvH16oqHi0eP2K0moPa2utV2nxqHKIVhSPVsEKHlgQUAlyRAyHJCZBAgghgYQAgRzf3x+7yS9gAiHsZiab9/PxyIPs7OzMe4fNdz47853vWJtorU2MiVFj6G9hIUE8PKY33+ceYO5KDZUXqIpLynhi4Sb6dojgyiEaCk8C1/It3v7SPXTxoYjbVdxUaXm6uw+eOlJMG2NC8RTSc621//ZO3mWMifU+HwuoX4FLjOzdlrNOb8NfF6c3iKtq5eTNWJbJjoJifnNpH536loC2LD2X7m1bEBvZ1OkoInICnaOaEdemuev7TTsxmocBZgGbrLXPVnlqAXCT9/ebgPn1nU2qZ4zhkUt7s7+4hGmL052OIz62q7CYF5Z+z+h+7RkarxtYSOAqLikjJStfo3iINCAjurdhZWY+h0vLnI5SIyeOTJ8J3Aicb4xJ9f6MAZ4ALjTGpAMXeB+LS/RqH8H4pC68vnIrGbuLnI4jPvTkR99RVm751ejeTkcR8atVWfkcKS1XFw+RBuTs7jEcKiljzda9TkepkROjeayw1hpr7QBrbYL3Z6G1Ns9aO9Ja291ae4G1Nr++s8nxPXBhD5qGBvP4Qg2VFyjW5+zjX2tzmHhWHF2iNRSeBLbl6bmEBQeRrJsRiTQYQ7tFExJkWO7iuyHqDohSa21aNGHyyNP57+bdfO7y/ktyYp6h8DbSpkUYPz+vm9NxRPxu2ZY9nBHXmmZhGq1GpKFo0SSEwae1dnW/aRXTclJuGt6V06Kb8cf/bKRUQ+U1aB9+u5PVW/fyy4t60jI81Ok4In61q7CY73btV39pkQbonB4xbNhRyJ6iw05HqZaKaTkpTUKC+fWY3qTvLuLNVT84HUfqqLikjD8v3Ezv2AiuTuzsdBwRv6s4RVwx1JaINBwVf7dfZLizq4eKaTlpF/Vpx7D4aJ79dAsFB0ucjiN1MGtFFtv3HeI3l/bWUHjSKCxPz6VNizB6t49wOoqInKS+HSJp3SyUZVtUTEuAMMbwm0v7sO9QCU9/8p3TceQk5ew9yPNLMhjVtx3Du+konQS+8nLPLcRHdI8hSF8eRRqc4CDDmae799biKqalTvp0iODm4V3558qtLN64y+k4UkslZeXc8+Y6gozhkUv6OB1HpF5s3FlI/oEj6uIh0oCd3SOG3fsP892u/U5H+QkV01JnD13ciz6xEUx59xt+LCh2Oo7UwrTFW1j7wz4ev6I/naM0FJ40Dsu8tyI+63QV0yINVeWtxV3Y1UPFtNRZeGgw068bxOHScu6dt46ycvedepH/90XGHl5Y+j3XJHZi7MAOTscRqTfLt+yhV/uWtI0IdzqKiNRRbGRTurdtUfnl2E1UTMsp6RbTgj+M60dKVj7P/TfD6ThSgz1Fh7nvrVTi2zTn92P7Oh1HpN4cOFzK6q35nNNDQ+KJNHRn94hhVVY+xSXuurW4imk5ZVcO7sjlgzryt8+2kJKZ53QcOUZ5ueV/3/6GgkMlPHfdYN2wQhqVlKw8SsqsxpcWCQAjurfhcGk5q7LcdZNsFdNyyowxPHZZP7pENePeeansPXDE6UhSxcwVmXy+JZffXNqH3rEaFkwal2Vb9hAeGkRi19ZORxGRU5QcF01YcBDLXdbVQ8W0+ESLJiE8d91g8g4cZsq76105dE1j9M22fTz50XeM6tuOG5K7OB1HpN4tT88lOS6a8NBgp6OIyClqGhbMGXGtK2/C5BYqpsVn+nWMZOro3izetItXv8x2Ok6jV1hcwuQ319EuIpwnrxyIMRpfV06eMSbBGLPSGJNqjFltjEnyTjfGmL8bYzKMMeuNMYOdznqs7fsO8X3uAQ2JJxJAzu4ew+Yf97Or0D2jiKmYFp+aeGZXRvZqy+MLN5O2vcDpOI2WtZaH30tj+75D/G18ApHNQp2OJA3Xk8Cj1toE4LfexwCjge7en0nAi87Eq9nyLZ5TwWfr4kORgFFx/YObjk6rmBafMsbw1NUDad08lHveXMeBw6VOR2qU3lmdwwff7OD+C7qT2DXK6TjSsFmgorN9JLDD+/s44DXrsRJoZYyJdSJgTZan76F9RDjd27ZwOoqI+Eiv9i1p06KJq/pNq5gWn4tqHsbfxg8iO+8Av52/wek4jU7G7v38dkEaw7tFc9e5pzsdRxq++4CnjDHbgKeBX3mndwS2VZkvxzvNFcrKLSsy9jCiext1cRIJIEFBhhHd27AifQ/lLrm/hYpp8Yuh8dH84vzu/GttDu+ty3E6TqNRXFLGL95YR/OwEP76PwkEB6mIkBMzxiw2xqRV8zMOuAu431rbGbgfmHWSy57k7Wu9Oje3/o4krc/ZR8GhEkaoi4dIwDm7RxvyDhxh485Cp6MAKqbFj+45/3SSukbxyHtpZO054HScRuFPH25i84/7efqagbTT3d6klqy1F1hr+1XzMx+4Cfi3d9Z3gCTv79uBzlUW08k77dhlv2ytTbTWJsbE1F9huzx9D8boFuIigehM79+1W+6GqGJa/CYkOIhp4xMIDQli8ptrOVzqrjsWBZqP0nbyz5VbuX1EHOf1bOt0HAkcO4BzvL+fD6R7f18ATPCO6jEUKLDW7nQiYHVWpO+hb4cIopqHOR1FRHysbctwesdGsHyLOy5CVDEtftWhVVOevHIAadsL+cui75yOE7By9h7kwXfXM6BTJFNG9XI6jgSW24FnjDHfAI/jGbkDYCGQCWQAM4C7nYn3U8UlZaRu28fwbjoqLRKohsVHs/aHva44UKdiWvzuor7tuXl4V2Z/kcVnm3Y5HSfglJaVc++8VMotTL92EGEh+rMW37HWrrDWDrHWDrTWJltr13inW2vtz6213ay1/a21q53OWmHtD3s5UlZOcpxGshEJVMnxURwuLWd9jvPD8GqvK/Vi6uhe9ImN4JfvfMOPBe4ZaD0QTFuczpqte/nT5f04Lbq503FEHJeSmY8xaFhIkQCW5P37TsnMcziJimmpJ+GhwUy/bhCHS8u5d946ylwynE1D90XGHp5fmsE1iZ0Yl+CaUclEHJWSlUef2Agim+pmRSKBqnXzMHq1b0lKVr7TUVRMS/3pFtOCP4zrR0pWPo/9ZyPWqqA+FRm7i7h3XirxbZrz+7F9nY4j4gqHS8tY98M+kuOinY4iIn6WHBfFmq17KSkrdzSHimmpV1cO7sitZ8Ux58tsfjt/g2sGXG9ovvtxP+Nf/gqwvHTjEJqFhTgdScQV1ucUcLi0nOR4dfEQCXTJ8dEcPFJG2nZn+01rDyz1yhjDI5f0JiTY8NLnmZSUlfP45f0J0s1Fam3DjgJumJlCaHAQb9w+lNN1q2SRShX9J5PUX1ok4J1R0W86K59BXVo7lkNHpqXeGWOYenEv7jn/dOZ9vY1fvvuN+lDX0vqcfVw3I4WmocG8fccwFdIix0jJyqdnu5a01vjSIgEvpmUTusU0d/wiRB2ZFkcYY3jgop6EBgfxzKdbKCmzPHvNQEKD9f2uJmu27uXm2auIbBbKm7cPpXNUM6cjibhKSVk5a7bu5aohnZyOIiL1JDk+mg9Sd1BWbgl26Cy3Khdx1OSR3Zk6uhcffLODyW+s40ipsxcRuNWqrHwmzEohukUYb98xTIW0SDW+3V7AwSNluvhQpBFJjoti/+FSNu4odCyDimlx3J3ndOM3l/bhow0/cvfcNa64m5GbfJmxh5tmr6JdZDhv3TGMDq2aOh1JxJVSMj1DZCXpZi0ijcbQeM+X55Qs57p6qJgWV7j1rDgeu6wfizftZtJrayguUUEN8PmWXG6Z8zWdo5ry1qRhtIsIdzqSiGulZOXRLaY5MS2bOB1FROpJu4hwukY3Y2Wmc+NNq5gW17hx6Gn85cr+LEvP5dZXv+bgkVKnIznqs027uP3V1cTHtODN24eqQBA5jrJyy+rsvSTHq4uHSGOTHBfN19n5jg23q2JaXOV/zujCM1cP5Kvv87j5la8pOtw4C+qP0n7kztfX0Cu2JW/enkx0CxXSIsezcUchRYdLSVYXD5FGJykuioJDJXy3a78j61cxLa5zxeBOTBs/iDVb9zJhVgqFxSVOR6pX/1m/g5+/sZZ+HSN5/bZkWjXTEF8iJ1LRX1IXH4o0PhU3aXJqiDwV0+JKYwd24LlrB7E+p4AbZ6ZQcLBxFNTvrcvhnjfXMaRLa/55azIR4aFORxJpEFZm5nNadDPaR+q6ApHGplPrZnRs1ZSULGf6TauYFtca3T+Wf9wwhE0793PdzJXkHzjidCS/env1Nh54+xuS46KZM/EMWjTRMPAitVFebvk6O19dPEQaseT4KFZl5WNt/febVjEtrnZBn3a8PGEI6buLuG7GSn7IO+h0JJ8rL7fMXJ7Jg++u56zT2zD75jNoFqZCWqS2Nv+4n4JDJeriIdKIDY2LJu/AETJ2F9X7ulVMi+ud27Mtr9x8BtvyD3LBXz/n6Y+/C5iRPtZs3ctlL3zBHz/cxAW92zFjQiJNw4KdjiXSoFT2l47XkWmRxqri73+lA109VExLg3Dm6W347H/P5ZL+sTy3JIORz3zOgm92OHI6xxd2FxbzwNupXPnil+wuPMzfxicwY8IQwkNVSIucrFVZ+XRs1ZROrXVnUJHGqktUM9pFNGGVA8W0ziVLg9E+Mpy//k8C1yd34fcfbOCeN9fx+ldb+d3YPvTtEOl0vFo5XFrGK19kM/2zdErKLD8/rxt3n3s6zdU/WqROrLWsysrnnJ4xTkcREQcZY0iOi2ZlZh7WWowx9bZuHZmWBiexaxTzf34Wf76iPxm5Rfxs+goefu9b11+g+N/Nuxj112U8sWgzw7q14dMHzmbKqF4qpEVOQcbuIvIOHGGo+kuLNHrJ8VHs3n+Y7Hq+vkp7cWmQgoMM1yZ1YUy/WKZ9toXXvtrKB9/s4H8v6sn1yV0ICXbP98TM3CIe+89GlnyXS3xMc16dmMQ5PXQUTcQXKvpHJmkkD5FGr+Ii5JTMPOLaNK+39bqn4hCpg8hmofzuZ31ZdO8I+neK5HcLNnDJ31fwZcYep6Oxv7iEPy/cxKhpy1idvZdHLunNR/eerUJaxIdSMvNoF9GE06LVX1qksesW05w2LZrU+3jTOjItAaFHu5a8fmsyH2/YxR8/3Mh1M1MY0789vx7Tu94vSiovt/x73Xb+8tFmcvcf5prETkwZ1YuYlroluIgvWWtJycpnWHx0vfaPFBF38vSbjiKlnvtNq5iWgGGM4eJ+7Tm3ZwwzlmXy/NIMPtu0m9tGxHFez7b0jo3wW/9kay3b8g/x7fYCZizPJHXbPgZ1acXMCYkM7NzKL+sUaeyy9hwgd/9hDYknIpWS46P48Nud5Ow9ROeo+jmY5qpi2hhzMfA3IBiYaa19wuFI0gCFhwYzeWR3rhzSiT8v2szzS77n+SXfYwzEtWlOvw6R9OsYQb8OkfTtEElks5O7ZXdZuSVrzwE27CggbXsBadsL2bCjgMJiz9jXMS2b8MzVA7l8UEeCgnS0TMRfKobA0s1aRKRCxfUTKVn5ja+YNsYEA88DFwI5wNfGmAXW2o3OJpOGqkOrpky/dhCPXNKbtO0FbNhRSNr2AtZs3cuCb3ZUztc5qil9Yz0Fdt+OkfTrEElFr+aSsnLSdxWRtqOADdsLSNtRyMYdhRwqKQOgSUgQvWIj+NnADvTzvrZn+5aEhehyBBF/S8nKp02LJnSLqb8LjUTE3Xq0bUmrZqGkZOZx1ZBO9bJO1xTTQBKQYa3NBDDGzAPGASqm5ZS0iwinXUQ4I3u3q5yWf+CI98hyYWWh/NGGHyuf/1fTvYQGG6767cccKSsHoHlYMH07RDI+qbP36HYk3WKau2rkEJHGwlpLSmYeyXFR6i8tIpWCggxJXaPq9SJENxXTHYFtVR7nAMnHzmSMmQRMAujSpUv9JJOAE9U8jBHdYxjR/f9H1igsLmHTjkLSdhQS+WUoJWXl3HJmV+/R6gi6RjdXtw0Rl8jZe4gdBcXcqf7SInKM5PhoPtm4i50Fh4iNbOr39bmpmK4Va+3LwMsAiYmJDfNe0uJKEeGhJMdHkxwfDektAOg9prfDqUSkOisz8wCNLy0iP5Vc0W86M5/LBnX0+/rcdH56O9C5yuNO3mkiIiJHScnKp1WzUHq0bel0FBFxmd6xEbQMDyElK69e1uemYvproLsxJs4YEwaMBxY4nElERFwoJSuPpK5R6nolIj8RXNFvOrN++k27ppuHtbbUGPML4GM8Q+PNttZuONnllJSUkJOTQ3Fxsc8zSu2Eh4fTqVMnQkNPbsg5EZHa2LHvENvyD3Hz8DinozQ42kc2TNqvnrykuCg+27yb3YXFtI0I9+u6XFNMA1hrFwILT2UZOTk5tGzZkq5du+oKbwdYa8nLyyMnJ4e4OO3oRMT3/n98afWXPlnaRzY82q/WTXK8Z/z5Vdn5XDqgg1/X5aZuHj5RXFxMdLRuLesUYwzR0dE66iEifpOSlUfL8BB6x0Y4HaXB0T6y4dF+tW76dYigeVhwvXT1CLhiGlAj4TBtfxHxp5TMfJK6RhGs/tJ1oja64dH/2ckLCQ5iSNeoerkIMSCLaSdt27aNuLg48vM934T27t1LXFwc2dnZABQVFZGYmEh8fDw7duw46rXXX389PXv2pF+/fkycOJGSkpL6ji8i4mq7C4vJ3HNAQ+IFiGeffZaJEydWPp47dy6XXHLJUfMsXbqUFi1acNtttx01PTU1lWHDhtG3b18GDBjAW2+9VS+ZpeFIjotiy64i8g8c8et6VEz7WOfOnbnrrruYOnUqAFOnTmXSpEl07dqV0tJSrrnmGm688Uaeeuopxo0bR2FhYeVrr7/+ejZv3sy3337LoUOHmDlzplNvQ0TElSrualbRH1IatnvuuYe1a9fyxRdfsG/fPh555BGmT59e+XxaWhp33303K1euZP/+/Tz66KOVzzVr1ozXXnuNDRs28NFHH3Hfffexb98+J96GuNRQ702dVvn56LSrLkAMFPfffz9Dhgxh2rRprFixgueeew6AO+64g9GjRzN58mQAgoODGT9+PPPnzyc0NJQxY8ZULiMpKYmcnBxH8ouIuFVKVh7Nw4Lp10H9pQNBSEgIL7zwAnfffTdJSUlMnDiR+Ph4ALZv386tt97K+++/T48ePXjjjTe48cYbmT17NhMnTqRHjx6Vy+nQoQNt27YlNzeXVq1aOfV2xGX6d2xFeGgQKzPzubhfrN/WE9DF9KMfbGDjjsITz3gS+nSI4Hc/63vceUJDQ3nqqae4+OKL+eSTTyqHspk1a9ZR81122WVcdtllP3l9SUkJ//znP/nb3/7mu+AiIgEgJTOfIV2jCAnWidVT5dQ+8ljDhw+nd+/eLF68mE2bNlVO79ixIykpKZWPg4ODeeONN6pdxqpVqzhy5AjdunWrW3AJSGEhQQzu0rryjJa/qDXyk0WLFhEbG0taWtpJv/buu+/m7LPPZsSIEX5IJiLSMOUVHSZ9d5GGxAswRUVFrF69mpKSEnJzc0/69Tt37uTGG2/klVdeIShIZY0cLTkums0/FlJw0H/XoQX0kemT/XbsK6mpqXz66aesXLmSs846i/HjxxMbW7vTC48++ii5ubm89NJLfk4pItKwfJ3tObpU0Q9STo1T+8jnn3+eGTNmALBw4UKeeeYZbrjhBtq1a8f999/PO++8U+tlFRYWcskll/CnP/2JoUOH+iuyNGDJ8VHYxZ7244I+7fyyDn2F8zFrLXfddRfTpk2jS5cuTJkyhV/+8pe1eu3MmTP5+OOPefPNN/XtWkTkGCsz8wkPDaJ/R/WJbch+/vOfk5qaSmpqKnl5eXz44Yc89NBDTJo0iezsbD799NNaLefIkSNcfvnlTJgwgauuusrPqaWhSujcirCQIL8OkaeKzcdmzJhBly5duPDCCwFPl41Nmzbx+eefn/C1d955J7t27WLYsGEkJCTwhz/8wd9xRUQajJSsfIac1pqwEO26AkHFwae//vWvhIeHExQUxIsvvsi9997LkSMnHsrs7bffZtmyZcyZM4eEhAQSEhJITU2th+TSkISHBpPQuZVf+00HdDcPJ0yaNIlJkyZVPg4ODmbt2rW1em1paam/YomINGgFB0vY/GMh943sceKZpUEwxrBixYqjpiUmJrJx48Zavf6GG27ghhtu8Ec0CTBD46J4bkkG+4tLaBke6vPl6+u9iIi43qrsfKz19H8UETkZSXHRlFtYvXWvX5avYlpERFwvJTOPsJAgEjqrv7SInJzBp7UiJMiQkumfrh4qpkVExPVSsvJJ6NyK8NBgp6OISAPTLCyEAZ0i/XYRooppERFxtf3FJWzYUcBQjS8tInWUHB/NtzkFHDzi++vTVEyLiIirrd66l3Lr2RmKiNRFclwUpeWWtVv3+XzZKqZFRMTVUjLzCQ02DO7S2ukoItJAJXaNIjjI+KWrh4ppHzvvvPP4+OOPj5o2bdo07rrrrpNazuOPP37U4+HDh9cpz+9//3uefvrpWk+v63pERPwlJSuPAZ1a0TRM/aUDQYsWLY56PGfOHH7xi1+c1DIWLFjAE0884bNM+/bt44UXXqjVvMfmP970f/zjH7z22munlE18o0WTEPp1iPDLRYgqpn3s2muvZd68eUdNmzdvHtdee22tXm+tpby8/CfF9JdffumzjMdTX+sREfcwxlxtjNlgjCk3xiQe89yvjDEZxpjvjDGjqky/2Dstwxgz1V/ZDh4p5ducApLUX1q8SktLGTt2LFOn+u5jdzLF9Mm48847mTBhgs+XK3WTFBdF6rZ9FJeU+XS5KqZ97KqrruLDDz+svHtTdnY2O3bsYMSIEQA89dRTnHHGGQwYMIDf/e53lfP07NmTCRMm0K9fP2699VYOHTpEQkIC119/PXD0N96//OUv9O/fn4EDB1Y2JjNmzOCMM85g4MCBXHnllRw8eLBO+SvWs3TpUs4991yuuuoqevXqxfXXX4+1FoA1a9ZwzjnnMGTIEEaNGsXOnTvrtC4RcY004ApgWdWJxpg+wHigL3Ax8IIxJtgYEww8D4wG+gDXeuf1uTVb91JabklWMd0oZGdnc/755zNgwABGjhzJDz/8AMDNN9/MnXfeSXJyMg8++OBRR7Mr7n6YkJBA06ZN+fzzz8nPz+eyyy5jwIABDB06lPXr1wOes7ITJ07k3HPPJT4+nr///e8ATJ06le+//56EhASmTJlCUVERI0eOZPDgwfTv35/58+fX6f1UPQt87rnn8tBDD5GUlESPHj1Yvnw5AGVlZUyZMqWyNnjppZdOaRtKzZLjojlSVs66H3zbbzqw74C4aCr8+K1vl9m+P4yu+dRSVFQUSUlJLFq0iHHjxjFv3jyuueYajDF88sknpKens2rVKqy1jB07lmXLltGlSxfS09N59dVXGTp0KADvvPNOtbdFXbRoEfPnzyclJYVmzZqRn+85XXHFFVdw++23A/DII48wa9YsJk+efEpvdd26dWzYsIEOHTpw5pln8sUXX5CcnMzkyZOZP38+MTExvPXWWzz88MPMnj37lNYlIs6x1m4Czx3pjjEOmGetPQxkGWMygCTvcxnW2kzv6+Z5563dretOQkpmPsFBhsSuKqZ9zoF9JFB5sKhCfn4+Y8eOBWDy5MncdNNN3HTTTcyePZt77rmH999/H4CcnBy+/PJLgoODmTNnTuXrK/aVH3zwAU8++STDhw/ngQceYNCgQbz//vv897//ZcKECZXzbd68mSVLlrB//3569uzJXXfdxRNPPEFaWlrlPKWlpbz33ntERESwZ88ehg4dytixY6v7GzkppaWlrFq1ioULF/Loo4+yePFiZs2aRWRkJF9//TWHDx/mzDPP5KKLLiIuLu6U1iU/dUZcFMZ4uo4N6+a7C5oDu5h2SEVXj4pietasWQB88sknfPLJJwwaNAiAoqIi0tPT6dKlC6eddlplIX08ixcv5pZbbqFZs2aAp3gHSEtL45FHHmHfvn0UFRUxatSo4y2mVpKSkujUqRPg+eafnZ1Nq1atSEtL48ILLwQ836hjY2NPeV2u076/0wlE3KAjsLLK4xzvNIBtx0xPrm4BxphJwCSALl26nHSA1s3DGNM/lhZNtLsKFE2bNj3qYNGcOXNYvXo1AF999RX//vdIGb9dAAAM5ElEQVS/Abjxxht58MEHK+e7+uqrCQ6uvt98eno6U6ZMYcmSJYSGhrJixQr+9a9/AXD++eeTl5dHYWEhAJdccglNmjShSZMmtG3bll27dv1kedZafv3rX7Ns2TKCgoLYvn07u3bton379qf03q+44goAhgwZQnZ2NuCpDdavX8+7774LQEFBAenp6Sqm/SCyaShj+sXSqqlvbyke2K3TCb4d+8u4ceO4//77Wbt2LQcPHmTIkCGA54/zV7/6FXfcccdR82dnZ9O8efNTWufNN9/M+++/z8CBA5kzZw5Lly49peUBNGnSpPL34OBgSktLsdbSt29fvvrqq1Nevqs59NkR8RdjzGKgukrgYWtt3c5h14K19mXgZYDExER7sq+/9SwVFH7TwNq5mvaTRUVFXHPNNcyYMaNWB3eq27cda+7cueTm5rJmzRpCQ0Pp2rUrxcXFdQ9/zLqrrtday/Tp031yEExO7PnrB/t8meoz7QctWrTgvPPOY+LEiUddeDhq1Chmz55NUVERANu3b2f37t3VLiM0NJSSkpKfTL/wwgt55ZVXKvtEV3Tz2L9/P7GxsZSUlDB37lxfv6VKPXv2JDc3t7KYLikpYcOGDX5bn4j4hrX2Amttv2p+jldIbwc6V3ncyTutpukip2T48OGVF/HPnTu38nqj45k4cSK33HLLUfOOGDGicl+4dOlS2rRpQ0RERI3LaNmyJfv37698XFBQQNu2bQkNDWXJkiVs3bq1rm/phEaNGsWLL75Yuc/fsmULBw4c8Nv6xPcC+8i0g6699louv/zyo0b2uOiii9i0aRPDhg0DPEX366+/Xu1pq0mTJjFgwAAGDx58VHF88cUXk5qaSmJiImFhYYwZM4bHH3+cxx57jOTkZGJiYkhOTj6qUajJH//4R6ZNm1b5OCcn54SvCQsL49133+Wee+6hoKCA0tJS7rvvPvr27XvC14pIg7MAeMMY8yzQAegOrAIM0N0YE4eniB4PXOdYSgkY06dP55ZbbuGpp54iJiaGV1555bjzb926lXfffZctW7ZUXrszc+bMygsNBwwYQLNmzXj11VePu5zo6GjOPPNM+vXrx+jRo3nooYf42c9+Rv/+/UlMTKRXr14nzH7w4MHKrpEADzzwQC3eMdx2221kZ2czePBgrLXExMRU9hOXhsFUjNDQECUmJtqKflYVNm3aRO/evR1KJBX0/yByfMaYNdbaxBPP6X/GmMuB6UAMsA9ItdaO8j73MDARKAXus9Yu8k4fA0wDgoHZ1to/nWg91bXZUr/UNjdc+r9zXk3tto5Mi4g0ctba94D3anjuT8BPCmVr7UJgoZ+jiYi4nvpMi4iIiIjUkYppEREREZE6CshiuiH3Aw8E2v4iIu6lNrrh0f+ZuwVcMR0eHk5eXp4+eA6x1pKXl0d4eLjTUURE5BjaRzY82q+6X8BdgNipUydycnLIzc11OkqjFR4eftTwQCIi4g7aRzZM2q+6W8AV06GhoboFp4iISDW0jxTxvYDr5iEiIiIiUl9UTIuIiIiI1JGKaRERERGROmrQtxM3xuQCW+vw0jbAHh/HqQu35ABlqY5bcoB7srglBzT8LKdZa2P8EcatAqDNBvdkcUsOcE8Wt+QAZamOW3JA3bNU22436GK6rowxq6u7t3pjzQHK4uYc4J4sbskBytKYuGn7uiWLW3KAe7K4JQcoi5tzgO+zqJuHiIiIiEgdqZgWEREREamjxlpMv+x0AC+35ABlqY5bcoB7srglByhLY+Km7euWLG7JAe7J4pYcoCzVcUsO8HGWRtlnWkRERETEFxrrkWkRERERkVMWsMW0MeZqY8wGY0y5MSbxmOd+ZYzJMMZ8Z4wZVcPr44wxKd753jLGhPkg01vGmFTvT7YxJrWG+bKNMd9651t9quutYR2/N8Zsr5JnTA3zXezdThnGmKl+yvKUMWazMWa9MeY9Y0yrGubzy3Y50Xs0xjTx/t9leD8TXX217mPW09kYs8QYs9H72b23mnnONcYUVPl/+62fshx3WxuPv3u3yXpjzGA/5ehZ5b2mGmMKjTH3HTOP37aJMWa2MWa3MSatyrQoY8ynxph077+ta3jtTd550o0xN/kqU6ByY5vtXa4r2m212UctV2129Xkcb7cbbZttrQ3IH6A30BNYCiRWmd4H+AZoAsQB3wPB1bz+bWC89/d/AHf5ON8zwG9reC4baOPn7fN74JcnmCfYu33igTDvduvjhywXASHe3/8C/KW+tktt3iNwN/AP7+/jgbf89H8SCwz2/t4S2FJNlnOB//jzs1GbbQ2MARYBBhgKpNRDpmDgRzzjfNbLNgHOBgYDaVWmPQlM9f4+tbrPKxAFZHr/be39vbW/t1FD/nF7m+1drmPtttrs2r/Hxthm12Z713e73Zja7IA9Mm2t3WSt/a6ap8YB86y1h621WUAGkFR1BmOMAc4H3vVOehW4zFfZvMu/BnjTV8v0kyQgw1qbaa09AszDs/18ylr7ibW21PtwJdDJ1+s4jtq8x3F4PgPg+UyM9P4f+pS1dqe1dq339/3AJqCjr9fjI+OA16zHSqCVMSbWz+scCXxvra3LTT/qxFq7DMg/ZnLVz0NNbcMo4FNrbb61di/wKXCx34IGADe32VXW4fZ2W222h9rs6tV3u91o2uyALaaPoyOwrcrjHH764Y8G9lVpLKqb51SMAHZZa9NreN4Cnxhj1hhjJvlwvcf6hfdUz+waTnvUZlv52kQ835yr44/tUpv3WDmP9zNRgOcz4jfe05KDgJRqnh5mjPnGGLPIGNPXTxFOtK2d+GyMp+ZCpj62SYV21tqd3t9/BNpVM48T2ydQuaHNBne022qz1WYfj9va7UbTZofUPZvzjDGLgfbVPPWwtXZ+feeBWme6luMf3TjLWrvdGNMW+NQYs9n7bctnWYAXgcfw/PE9huf05cSTXYcvslRsF2PMw0ApMLeGxfhku7idMaYF8C/gPmtt4TFPr8VzyqzI22fyfaC7H2K4alsbT//XscCvqnm6vrbJT1hrrTFGQyLVkhvbbHBPu602u2FySZsNLtreja3NbtDFtLX2gjq8bDvQucrjTt5pVeXhOf0R4v1WW908dcpkjAkBrgCGHGcZ273/7jbGvIfntNZJ/0HUdvsYY2YA/6nmqdpsK59kMcbcDFwKjLTeDkzVLMMn2+UYtXmPFfPkeP//IvF8RnzOGBOKp1Gea63997HPV22orbULjTEvGGPaWGv3+DJHLba1zz4btTQaWGut3VVN1nrZJlXsMsbEWmt3ek+R7q5mnu14+gVW6ISnL3Cj5sY2uza56qvdVptdK2qza+CydrtRtdmNsZvHAmC88VztG4fn29CqqjN4G4YlwFXeSTcBvjpqcgGw2VqbU92TxpjmxpiWFb/judAjrbp5T8Ux/aQur2EdXwPdjecq+TA8p2wW+CHLxcCDwFhr7cEa5vHXdqnNe1yA5zMAns/Ef2vaeZwKb5++WcAma+2zNczTvqLvnzEmCc/fsE93ErXc1guACcZjKFBQ5TSaP9R4VLA+tskxqn4eamobPgYuMsa09p6Ov8g7TU6e0202uKDdVptdSW129etxW7vduNpsWw9XmDrxg6exyQEOA7uAj6s89zCeq4G/A0ZXmb4Q6OD9PR5Pg50BvAM08VGuOcCdx0zrACysst5vvD8b8JxS88f2+SfwLbDe+0GLPTaL9/EYPFcof+/HLBl4+iqlen/+cWwWf26X6t4j8Ac8OwqAcO9nIMP7mYj303Y4C88p3PVVtsUY4M6KzwzwC+/7/wbPhT/D/ZCj2m19TA4DPO/dZt9SZfQFP+RpjqehjawyrV62CZ6dwU6gxNue3Iqn7+VnQDqwGIjyzpsIzKzy2onez0wGcIu/tk+g/ODSNtu77Dk43G6jNrvq+tVm/zSLa9ptGmGbrTsgioiIiIjUUWPs5iEiIiIi4hMqpkVERERE6kjFtIiIiIhIHamYFhERERGpIxXTIiIiIiJ1pGJapApjTGdjTJYxJsr7uLX3cVdnk4mIyLHUZosbqJgWqcJauw3PbXuf8E56AnjZWpvtWCgREamW2mxxA40zLXIM7+1h1wCzgduBBGttibOpRESkOmqzxWkhTgcQcRtrbYkxZgrwEXCRGmUREfdSmy1OUzcPkeqNxnNL0n5OBxERkRNSmy2OUTEtcgxjTAJwITAUuN8YE+twJBERqYHabHGaimmRKowxBs/FLPdZa38AngKedjaViIhUR222uIGKaZGj3Q78YK391Pv4BaC3MeYcBzOJiEj11GaL4zSah4iIiIhIHenItIiIiIhIHamYFhERERGpIxXTIiIiIiJ1pGJaRERERKSOVEyLiIiIiNSRimkRERERkTpSMS0iIiIiUkcqpkVERERE6uj/AIGpj9JKf1IKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.title('My Nice Plot')\n",
    "\n",
    "plt.subplot(1, 2, 1)  # rows, columns, panel selected\n",
    "plt.plot(x, x ** 2)\n",
    "plt.plot([0, 0, 0], [-10, 0, 100])\n",
    "plt.legend(['X^2', 'Vertical Line'])\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('X Squared')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x, -1 * (x ** 2))\n",
    "plt.plot([-10, 0, 10], [-50, -50, -50])\n",
    "plt.legend(['-X^2', 'Horizontal Line'])\n",
    "\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('X Squared')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this more complicated plot shows the issues with this Global API. You can't know exactly which line of code creates which line or text in which plot. It depends only on the order that the code is executed. If you had 100 lines for first subplot, would be hard to keep track of where plot 2 starts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OOP Interface\n",
    "- creates figure and axes and keep references to them\n",
    "- will be way more explicit, easier to keep track of\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating figure and axes\n",
    "fig, axes = plt.subplots(figsize=(12, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#in this case have only one axes, can plot multiple things to it\n",
    "axes.plot(\n",
    "    x, (x ** 2), color='red', linewidth=3,\n",
    "    marker='o', markersize=8, label='X^2')\n",
    "\n",
    "axes.plot(x, -1 * (x ** 2), 'b--', label='-X^2')\n",
    "\n",
    "axes.set_xlabel('X')\n",
    "axes.set_ylabel('X Squared')\n",
    "\n",
    "axes.set_title(\"My Nice Plot\")\n",
    "\n",
    "axes.legend()\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'My Nice Plot')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "axes.plot(x, x + 0, linestyle='solid')\n",
    "axes.plot(x, x + 1, linestyle='dashed')\n",
    "axes.plot(x, x + 2, linestyle='dashdot')\n",
    "axes.plot(x, x + 3, linestyle='dotted');\n",
    "\n",
    "axes.set_title(\"My Nice Plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Other types of plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- figures and subfigures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x124b92dd0>)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#only one main figure\n",
    "plot_objects = plt.subplots()\n",
    "\n",
    "fig, ax = plot_objects\n",
    "\n",
    "ax.plot([1,2,3], [1,2,3])\n",
    "\n",
    "plot_objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1008x432 with 4 Axes>,\n",
       " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x124c040d0>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x124ca1790>],\n",
       "        [<matplotlib.axes._subplots.AxesSubplot object at 0x124cd7190>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x124d18510>]],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzoAAAFpCAYAAABUNF3EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAfD0lEQVR4nO3dX4ild30/8PfHrFHq3+KuINnVpHStLrZgOgSLUFNiyyYXuxdaSSD4h+CCbaRUEVIsKvHKSi0IaXWL4h/QGL2QAVdyoZGAGMmE1GASItNozUYha0xzEzSm/fwuzrG/ceY8mZPdM3Mmz75esHCe53yZ8+HLzL73Pc85z1Z3BwAAYEyes+wBAAAAFk3RAQAARkfRAQAARkfRAQAARkfRAQAARkfRAQAARmfbolNVn62qR6rqhwPPV1V9sqrWq+qeqrp08WMCwGxyCoBZ5rmi87kkR5/m+SuTHJ7+OZHk3859LACY2+cipwDYZNui0923J/nl0yw5nuQLPXFHkpdW1SsWNSAAPB05BcAsi/iMzkVJHtpwfHp6DgD2AjkFcB7at5svVlUnMnnbQF7wghf86Wte85rdfHkANrnrrrt+0d0Hlj3HXiGnAPaWc8mpRRSdh5Mc2nB8cHpui+4+meRkkqysrPTa2toCXh6As1VV/7XsGXaBnAJ4ljqXnFrEW9dWk7x9elebNyR5vLt/voCvCwCLIKcAzkPbXtGpqi8nuTzJ/qo6neTDSZ6bJN39qSSnklyVZD3JE0netVPDAsBmcgqAWbYtOt19zTbPd5K/XdhEAPAMyCkAZlnEW9cAAAD2FEUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYnbmKTlUdraoHqmq9qm6Y8fwrq+q2qrq7qu6pqqsWPyoAzCanANhs26JTVRckuSnJlUmOJLmmqo5sWvaPSW7p7tcnuTrJvy56UACYRU4BMMs8V3QuS7Le3Q9295NJbk5yfNOaTvLi6eOXJPnZ4kYEgKclpwDYYp6ic1GShzYcn56e2+gjSa6tqtNJTiV576wvVFUnqmqtqtbOnDlzFuMCwBZyCoAtFnUzgmuSfK67Dya5KskXq2rL1+7uk9290t0rBw4cWNBLA8C25BTAeWaeovNwkkMbjg9Oz210XZJbkqS7v5fk+Un2L2JAANiGnAJgi3mKzp1JDlfVJVV1YSYf4lzdtOanSa5Ikqp6bSYB4po/ALtBTgGwxbZFp7ufSnJ9kluT3J/JXWvuraobq+rYdNn7k7y7qn6Q5MtJ3tndvVNDA8BvySkAZtk3z6LuPpXJhzc3nvvQhsf3JXnjYkcDgPnIKQA2W9TNCAAAAPYMRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABiduYpOVR2tqgeqar2qbhhY87aquq+q7q2qLy12TAAYJqcA2Gzfdguq6oIkNyX5yySnk9xZVavdfd+GNYeT/EOSN3b3Y1X18p0aGAA2klMAzDLPFZ3Lkqx394Pd/WSSm5Mc37Tm3Ulu6u7HkqS7H1nsmAAwSE4BsMU8ReeiJA9tOD49PbfRq5O8uqq+W1V3VNXRWV+oqk5U1VpVrZ05c+bsJgaA3yWnANhiUTcj2JfkcJLLk1yT5N+r6qWbF3X3ye5e6e6VAwcOLOilAWBbcgrgPDNP0Xk4yaENxwen5zY6nWS1u3/T3T9O8qNMAgUAdpqcAmCLeYrOnUkOV9UlVXVhkquTrG5a8/VMfkuWqtqfyVsEHlzgnAAwRE4BsMW2Rae7n0pyfZJbk9yf5JbuvreqbqyqY9NltyZ5tKruS3Jbkg9096M7NTQA/JacAmCW6u6lvPDKykqvra0t5bUBmKiqu7p7Zdlz7EVyCmD5ziWnFnUzAgAAgD1D0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZH0QEAAEZnrqJTVUer6oGqWq+qG55m3VuqqqtqZXEjAsDTk1MAbLZt0amqC5LclOTKJEeSXFNVR2ase1GSv0vy/UUPCQBD5BQAs8xzReeyJOvd/WB3P5nk5iTHZ6z7aJKPJfnVAucDgO3IKQC2mKfoXJTkoQ3Hp6fn/k9VXZrkUHd/Y4GzAcA85BQAW5zzzQiq6jlJPpHk/XOsPVFVa1W1dubMmXN9aQDYlpwCOD/NU3QeTnJow/HB6bnfelGS1yX5TlX9JMkbkqzO+qBnd5/s7pXuXjlw4MDZTw0A/5+cAmCLeYrOnUkOV9UlVXVhkquTrP72ye5+vLv3d/fF3X1xkjuSHOvutR2ZGAB+l5wCYItti053P5Xk+iS3Jrk/yS3dfW9V3VhVx3Z6QAB4OnIKgFn2zbOou08lObXp3IcG1l5+7mMBwPzkFACbnfPNCAAAAPYaRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABgdRQcAABiduYpOVR2tqgeqar2qbpjx/Puq6r6quqeqvlVVr1r8qAAwm5wCYLNti05VXZDkpiRXJjmS5JqqOrJp2d1JVrr7T5J8Lck/LXpQAJhFTgEwyzxXdC5Lst7dD3b3k0luTnJ844Luvq27n5ge3pHk4GLHBIBBcgqALeYpOhcleWjD8enpuSHXJfnmrCeq6kRVrVXV2pkzZ+afEgCGySkAtljozQiq6tokK0k+Puv57j7Z3SvdvXLgwIFFvjQAbEtOAZw/9s2x5uEkhzYcH5ye+x1V9eYkH0zypu7+9WLGA4BtySkAtpjnis6dSQ5X1SVVdWGSq5OsblxQVa9P8ukkx7r7kcWPCQCD5BQAW2xbdLr7qSTXJ7k1yf1Jbunue6vqxqo6Nl328SQvTPLVqvqPqlod+HIAsFByCoBZ5nnrWrr7VJJTm859aMPjNy94LgCYm5wCYLOF3owAAABgL1B0AACA0VF0AACA0VF0AACA0VF0AACA0VF0AACA0VF0AACA0VF0AACA0VF0AACA0VF0AACA0VF0AACA0VF0AACA0VF0AACA0VF0AACA0VF0AACA0VF0AACA0VF0AACA0VF0AACA0VF0AACA0VF0AACA0Zmr6FTV0ap6oKrWq+qGGc8/r6q+Mn3++1V18aIHBYAhcgqAzbYtOlV1QZKbklyZ5EiSa6rqyKZl1yV5rLv/MMm/JPnYogcFgFnkFACzzHNF57Ik6939YHc/meTmJMc3rTme5PPTx19LckVV1eLGBIBBcgqALeYpOhcleWjD8enpuZlruvupJI8nedkiBgSAbcgpALbYt5svVlUnkpyYHv66qn64m6//LLI/yS+WPcQeZW+G2Zth9mbYHy17gL1ETs3Nz9QwezPM3gyzN8POOqfmKToPJzm04fjg9NysNaeral+SlyR5dPMX6u6TSU4mSVWtdffK2Qw9dvZmmL0ZZm+G2ZthVbW27BkWQE7tMnszzN4MszfD7M2wc8mped66dmeSw1V1SVVdmOTqJKub1qwmecf08VuTfLu7+2yHAoBnQE4BsMW2V3S6+6mquj7JrUkuSPLZ7r63qm5Mstbdq0k+k+SLVbWe5JeZhAwA7Dg5BcAsc31Gp7tPJTm16dyHNjz+VZK/foavffIZrj+f2Jth9maYvRlmb4aNYm/k1K6zN8PszTB7M8zeDDvrvSlX7gEAgLGZ5zM6AAAAzyo7XnSq6mhVPVBV61V1w4znn1dVX5k+//2quninZ9or5tib91XVfVV1T1V9q6petYw5l2G7vdmw7i1V1VV13typZJ69qaq3Tb937q2qL+32jMsyx8/UK6vqtqq6e/pzddUy5txtVfXZqnpk6FbJNfHJ6b7dU1WX7vaMyySnhsmpYXJqmJwaJqdm27Gc6u4d+5PJh0L/M8kfJLkwyQ+SHNm05m+SfGr6+OokX9nJmfbKnzn35i+S/N708XvszZZ1L0pye5I7kqwse+69sjdJDie5O8nvT49fvuy599DenEzynunjI0l+suy5d2lv/jzJpUl+OPD8VUm+maSSvCHJ95c98x77vpFTcuoZ7810nZySU890b+TU7OfPKqd2+orOZUnWu/vB7n4yyc1Jjm9aczzJ56ePv5bkiqqqHZ5rL9h2b7r7tu5+Ynp4Ryb/N8T5YJ7vmyT5aJKPJfnVbg63ZPPszbuT3NTdjyVJdz+yyzMuyzx700lePH38kiQ/28X5lqa7b8/kTmNDjif5Qk/ckeSlVfWK3Zlu6eTUMDk1TE4Nk1PD5NSAncqpnS46FyV5aMPx6em5mWu6+6kkjyd52Q7PtRfMszcbXZdJkz0fbLs300uWh7r7G7s52B4wz/fNq5O8uqq+W1V3VNXRXZtuuebZm48kubaqTmdyh6737s5oe94z/ftoTOTUMDk1TE4Nk1PD5NTZO6ucmuv20ixXVV2bZCXJm5Y9y15QVc9J8okk71zyKHvVvkzeFnB5Jr9dvb2q/ri7/3upU+0N1yT5XHf/c1X9WSb/r8rruvt/lz0YPJvJqd8lp7Ylp4bJqQXa6Ss6Dyc5tOH44PTczDVVtS+Ty3SP7vBce8E8e5OqenOSDyY51t2/3qXZlm27vXlRktcl+U5V/SST92qunicf9Jzn++Z0ktXu/k13/zjJjzIJlLGbZ2+uS3JLknT395I8P8n+XZlub5vr76ORklPD5NQwOTVMTg2TU2fvrHJqp4vOnUkOV9UlVXVhJh/iXN20ZjXJO6aP35rk2z391NHIbbs3VfX6JJ/OJDzOl/evJtvsTXc/3t37u/vi7r44k/eFH+vuteWMu6vm+Zn6eia/JUtV7c/kLQIP7uaQSzLP3vw0yRVJUlWvzSRAzuzqlHvTapK3T+9q84Ykj3f3z5c91C6RU8Pk1DA5NUxODZNTZ++scmpH37rW3U9V1fVJbs3kThOf7e57q+rGJGvdvZrkM5lcllvP5ENIV+/kTHvFnHvz8SQvTPLV6edef9rdx5Y29C6Zc2/OS3Puza1J/qqq7kvyP0k+0N2j/+3znHvz/iT/XlV/n8kHPt95PvyDtaq+nMk/KvZP3/f94STPTZLu/lQm7wO/Ksl6kieSvGs5k+4+OTVMTg2TU8Pk1DA5NWyncqrOg70DAADOMzv+H4YCAADsNkUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYnW2LTlV9tqoeqaofDjxfVfXJqlqvqnuq6tLFjwkAs8kpAGaZ54rO55IcfZrnr0xyePrnRJJ/O/exAGBun4ucAmCTbYtOd9+e5JdPs+R4ki/0xB1JXlpVr1jUgADwdOQUALMs4jM6FyV5aMPx6ek5ANgL5BTAeWjfbr5YVZ3I5G0DecELXvCnr3nNa3bz5QHY5K677vpFdx9Y9hx7hZwC2FvOJacWUXQeTnJow/HB6bktuvtkkpNJsrKy0mtrawt4eQDOVlX917Jn2AVyCuBZ6lxyahFvXVtN8vbpXW3ekOTx7v75Ar4uACyCnAI4D217Raeqvpzk8iT7q+p0kg8neW6SdPenkpxKclWS9SRPJHnXTg0LAJvJKQBm2bbodPc12zzfSf52YRMBwDMgpwCYZRFvXQMAANhTFB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB0FB0AAGB05io6VXW0qh6oqvWqumHG86+sqtuq6u6quqeqrlr8qAAwm5wCYLNti05VXZDkpiRXJjmS5JqqOrJp2T8muaW7X5/k6iT/uuhBAWAWOQXALPNc0bksyXp3P9jdTya5OcnxTWs6yYunj1+S5GeLGxEAnpacAmCLeYrORUke2nB8enpuo48kubaqTic5leS9s75QVZ2oqrWqWjtz5sxZjAsAW8gpALZY1M0Irknyue4+mOSqJF+sqi1fu7tPdvdKd68cOHBgQS8NANuSUwDnmXmKzsNJDm04Pjg9t9F1SW5Jku7+XpLnJ9m/iAEBYBtyCoAt5ik6dyY5XFWXVNWFmXyIc3XTmp8muSJJquq1mQSIa/4A7AY5BcAW2xad7n4qyfVJbk1yfyZ3rbm3qm6sqmPTZe9P8u6q+kGSLyd5Z3f3Tg0NAL8lpwCYZd88i7r7VCYf3tx47kMbHt+X5I2LHQ0A5iOnANhsUTcjAAAA2DMUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHQUHQAAYHTmKjpVdbSqHqiq9aq6YWDN26rqvqq6t6q+tNgxAWCYnAJgs33bLaiqC5LclOQvk5xOcmdVrXb3fRvWHE7yD0ne2N2PVdXLd2pgANhITgEwyzxXdC5Lst7dD3b3k0luTnJ805p3J7mpux9Lku5+ZLFjAsAgOQXAFvMUnYuSPLTh+PT03EavTvLqqvpuVd1RVUcXNSAAbENOAbDFtm9dewZf53CSy5McTHJ7Vf1xd//3xkVVdSLJiSR55StfuaCXBoBtySmA88w8V3QeTnJow/HB6bmNTidZ7e7fdPePk/wok0D5Hd19srtXunvlwIEDZzszAGwkpwDYYp6ic2eSw1V1SVVdmOTqJKub1nw9k9+Spar2Z/IWgQcXOCcADJFTAGyxbdHp7qeSXJ/k1iT3J7mlu++tqhur6th02a1JHq2q+5LcluQD3f3oTg0NAL8lpwCYpbp7KS+8srLSa2trS3ltACaq6q7uXln2HHuRnAJYvnPJqbn+w1AAAIBnE0UHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYHUUHAAAYnbmKTlUdraoHqmq9qm54mnVvqaquqpXFjQgAT09OAbDZtkWnqi5IclOSK5McSXJNVR2Zse5FSf4uyfcXPSQADJFTAMwyzxWdy5Ksd/eD3f1kkpuTHJ+x7qNJPpbkVwucDwC2I6cA2GKeonNRkoc2HJ+envs/VXVpkkPd/Y2n+0JVdaKq1qpq7cyZM894WACYQU4BsMU534ygqp6T5BNJ3r/d2u4+2d0r3b1y4MCBc31pANiWnAI4P81TdB5OcmjD8cHpud96UZLXJflOVf0kyRuSrPqgJwC7RE4BsMU8RefOJIer6pKqujDJ1UlWf/tkdz/e3fu7++LuvjjJHUmOdffajkwMAL9LTgGwxbZFp7ufSnJ9kluT3J/klu6+t6purKpjOz0gADwdOQXALPvmWdTdp5Kc2nTuQwNrLz/3sQBgfnIKgM3O+WYEAAAAe42iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjI6iAwAAjM5cRaeqjlbVA1W1XlU3zHj+fVV1X1XdU1XfqqpXLX5UAJhNTgGw2bZFp6ouSHJTkiuTHElyTVUd2bTs7iQr3f0nSb6W5J8WPSgAzCKnAJhlnis6lyVZ7+4Hu/vJJDcnOb5xQXff1t1PTA/vSHJwsWMCwCA5BcAW8xSdi5I8tOH49PTckOuSfHPWE1V1oqrWqmrtzJkz808JAMPkFABbLPRmBFV1bZKVJB+f9Xx3n+zule5eOXDgwCJfGgC2JacAzh/75ljzcJJDG44PTs/9jqp6c5IPJnlTd/96MeMBwLbkFABbzHNF584kh6vqkqq6MMnVSVY3Lqiq1yf5dJJj3f3I4scEgEFyCoAtti063f1UkuuT3Jrk/iS3dPe9VXVjVR2bLvt4khcm+WpV/UdVrQ58OQBYKDkFwCzzvHUt3X0qyalN5z604fGbFzwXAMxNTgGw2UJvRgAAALAXKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDoKDoAAMDozFV0qupoVT1QVetVdcOM559XVV+ZPv/9qrp40YMCwBA5BcBm2xadqrogyU1JrkxyJMk1VXVk07LrkjzW3X+Y5F+SfGzRgwLALHIKgFnmuaJzWZL17n6wu59McnOS45vWHE/y+enjryW5oqpqcWMCwCA5BcAW8xSdi5I8tOH49PTczDXd/VSSx5O8bBEDAsA25BQAW+zbzRerqhNJTkwPf11VP9zN138W2Z/kF8seYo+yN8PszTB7M+yPlj3AXiKn5uZnapi9GWZvhtmbYWedU/MUnYeTHNpwfHB6btaa01W1L8lLkjy6+Qt198kkJ5Okqta6e+Vshh47ezPM3gyzN8PszbCqWlv2DAsgp3aZvRlmb4bZm2H2Zti55NQ8b127M8nhqrqkqi5McnWS1U1rVpO8Y/r4rUm+3d19tkMBwDMgpwDYYtsrOt39VFVdn+TWJBck+Wx331tVNyZZ6+7VJJ9J8sWqWk/yy0xCBgB2nJwCYJa5PqPT3aeSnNp07kMbHv8qyV8/w9c++QzXn0/szTB7M8zeDLM3w0axN3Jq19mbYfZmmL0ZZm+GnfXelCv3AADA2MzzGR0AAIBnlR0vOlV1tKoeqKr1qrphxvPPq6qvTJ//flVdvNMz7RVz7M37quq+qrqnqr5VVa9axpzLsN3ebFj3lqrqqjpv7lQyz95U1dum3zv3VtWXdnvGZZnjZ+qVVXVbVd09/bm6ahlz7raq+mxVPTJ0q+Sa+OR03+6pqkt3e8ZlklPD5NQwOTVMTg2TU7PtWE519479yeRDof+Z5A+SXJjkB0mObFrzN0k+NX18dZKv7ORMe+XPnHvzF0l+b/r4PfZmy7oXJbk9yR1JVpY9917ZmySHk9yd5Penxy9f9tx7aG9OJnnP9PGRJD9Z9ty7tDd/nuTSJD8ceP6qJN9MUknekOT7y555j33fyCk59Yz3ZrpOTsmpZ7o3cmr282eVUzt9ReeyJOvd/WB3P5nk5iTHN605nuTz08dfS3JFVdUOz7UXbLs33X1bdz8xPbwjk/8b4nwwz/dNknw0yceS/Go3h1uyefbm3Ulu6u7HkqS7H9nlGZdlnr3pJC+ePn5Jkp/t4nxL0923Z3KnsSHHk3yhJ+5I8tKqesXuTLd0cmqYnBomp4bJqWFyasBO5dROF52Lkjy04fj09NzMNd39VJLHk7xsh+faC+bZm42uy6TJng+23ZvpJctD3f2N3RxsD5jn++bVSV5dVd+tqjuq6uiuTbdc8+zNR5JcW1WnM7lD13t3Z7Q975n+fTQmcmqYnBomp4bJqWFy6uydVU7NdXtplquqrk2ykuRNy55lL6iq5yT5RJJ3LnmUvWpfJm8LuDyT367eXlV/3N3/vdSp9oZrknyuu/+5qv4sk/9X5XXd/b/LHgyezeTU75JT25JTw+TUAu30FZ2HkxzacHxwem7mmqral8llukd3eK69YJ69SVW9OckHkxzr7l/v0mzLtt3evCjJ65J8p6p+ksl7NVfPkw96zvN9czrJanf/prt/nORHmQTK2M2zN9cluSVJuvt7SZ6fZP+uTLe3zfX30UjJqWFyapicGianhsmps3dWObXTRefOJIer6pKqujCTD3GublqzmuQd08dvTfLtnn7qaOS23Zuqen2ST2cSHufL+1eTbfamux/v7v3dfXF3X5zJ+8KPdffacsbdVfP8TH09k9+Spar2Z/IWgQd3c8glmWdvfprkiiSpqtdmEiBndnXKvWk1ydund7V5Q5LHu/vnyx5ql8ipYXJqmJwaJqeGyamzd1Y5taNvXevup6rq+iS3ZnKnic92971VdWOSte5eTfKZTC7LrWfyIaSrd3KmvWLOvfl4khcm+er0c68/7e5jSxt6l8y5N+elOffm1iR/VVX3JfmfJB/o7tH/9nnOvXl/kn+vqr/P5AOf7zwf/sFaVV/O5B8V+6fv+/5wkucmSXd/KpP3gV+VZD3JE0netZxJd5+cGianhsmpYXJqmJwatlM5VefB3gEAAOeZHf8PQwEAAHabogMAAIyOogMAAIyOogMAAIyOogMAAIyOogMAAIyOogMAAIyOogMAAIzO/wMGvQqYhQ/hfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#multiple subplots\n",
    "plot_objects = plt.subplots(nrows=2, ncols=2, figsize=(14, 6))\n",
    "\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plot_objects\n",
    "\n",
    "plot_objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kf02JlRU5MECBA5OfT9K0x45RFGrgQN3GPHWKJlqq5IJtbIC+fTWfX1yhq1WrgsmtIfzxB00WTV08v3o1FYz/8EPRSbabG732KSmW0eeDMUpn6t4dqFRJeb8gUBRNl/9dZCT9f6pVI4nk6dPpddK1XuvCBSp237OH0t0AcpxbtqS+RYW5eZMcZ1U4O1Pal6OjbtfXhqhMVrMmvcd0USdbtozEL7y81B8zdKhh9nE4OmJrbYv/PvxP5b6P9nwEb2dvzOs8r4Stsjzqe9ZHfc8SVkrklHquPr2Ki7EX8X6j9+Fg64C4dGmtQt6s9aaRLTMPSsFsXg2MUQ3M7t303NubJprPzbOdjU7Ex5PSWvG0E2trWpV2d9cvdW7TJiqoVpfvf+cOOTj5+cr7kpOB7GzdrymFyZNpfF1W1Z8+pdcoXabVxevXgalTKSowuVho1t+f0u0sRVUqNBR4/31yFNShqxMzcSLVrVhZAY8eUURvxAjd62PCwylyVziiZ2sLnD9f1Cl/8YIcW03yypcukV1yIiqT6ePEhIeTvTVqqD8mN5cWIsTrcDgmJJ/lI5+p+LznKHHs7jHMODoD91/cN7UpnFLEoahDGLNvDBgoS+G3vr/ht76/aTwnPScdYXFhyMrLQkJGAnbc2oHEDAtLeZdI6XVi7t4lh0UslvXzo4mHXAXwpiQ2lpTCVKWyWFtTsfbhw7pNIPPyyOHr3x9wcFB9zLlzNJFXlWb03XfkPBVfLZ8wgQrG9YUx+p9ZW5MCmFSn5Msv6TX6+2/9r12Yb76hNKv165Ujee+8Q3Ul3t7yXMvY7NhBr2e/fuqPCQig9D1NdSeFqVq1ICrStClFYfbvpxorXbh1iyKM1asr78vNLej1dPMmPZqiR4y7O0XcPD11c2KePtV+j2RnA+3bk/ogh1MCHI0+iq4buuJhsnL92Np+a7GgywITWGV5LDq3CIvOLcJfYX+Z2pQyz+PUxzgTc8bUZsjClFZT8HDKQzjaSs8quPzkMhr93AinH5zGzfibGLh9IEKflqJyikKUXidGjFI0b06Pb71FksSaVkEtBbGIXF2qTo8elDp37Zr0MW1saGI4Z476YzQV94eG0sTXpliGYnw8panpS3o6OWPTp9NYUtODxKiQHD2J8vMpEjBokDzjmRLGSCChc2fNqV7jx1NERYqsdX4+pW4Wdm7HjSPn7rPPdFOVCw+nWqriMt5paZSGJTZcleLELFtGaWhyEh1NiyEA1Y69qUPI/smTohEmVTg709/EFco4JUSuIheZeZlwsFWzeMWRxMa3N+LXPr9ieOPhpjalzPNN8DcYu2+sqc2QBXsb+yIS58EPgtFhfQfEJKvPTHmt0mvY8s4WNK3cFEFVghA6JhSt/UqnbH/pdmIcHEp+pbYk0ObEvPkmqSZpmzAVx9tbc0+N116j11SVEyMqkxXHUJnl5GR69PcnB0Jq4XxmJk2GCzcT1Bdra5q8zldTSBcTQw6eJajfhYWRMz9ggObjPD3p/pHyesfEkNNz+nTBNkGgSFi1asCnnxYU7GsjPJzus+I4O5PzIDZPbdiQooLVqqkfKyODPgcyMqRdWwq//EI/AP3N334r/dz27dUX9BemRQuyW+prxuEYQM+Anjg78iwqOSrXxx27ewyNf26Me0kGKkyWATydPDGq6Sj4uPqY2pQyj7ezNxp7Nza1GbKw5cYW7Li149VzGysbMMaQkav+e83DyQODGgyCu6M7nOyc0MS7iU6RHEui9Dox9+9TWou4kpyVBbzxBrBxo0nNkgVtToynJ/B//yc9ven5c3J8tK3+2tgAjRsrOzFPn1LkJzBQ+RxRZvn+fWm2FEd0YuzsSHlNaj8WPz/qWyIXtrZAxYqq99nbA1euyK+EZQxOnyanrLDKlyry8ymFTorMshiBCQgour18eWDvXqq9keIMMUbRLnW29exJEbHERHIGfvhBs0iHMWSWa9Qo6qwrFNKdjT//BEaP1n5cixb0nuR1MRwT42rviqrlqyJXYWCvrzLA71d+x5rLa3Dy/klTm1Lm+bT1p5j2+jSkZFt++cDS80ux5vKaV89b+7VG8P+CUbdSXbXnXH16FZGJBTWth6MO42DkQaPaaSpKrxOzcydw5EjBc3t7msDJ1U/FlGRnU0TEzU39McnJJAMspQZo2zZ6rewkyPA1a0YKS4UnbqJ0tbpIDECr//pQqxYVmPfrR5GQo0elnbdkCVChQoHClb6kpFA0b+9e9ceIzo0l9IoZN47SxLSlxVlb02u4a5f2MUUBgOJODEDS2O7udL9oi8YJAkkMDxqken+vXuQ0HD5MtTPa6nWqvpSllMuJiY+nnkXieFu3knMrt7MhpsDxfjGcEuDjfR+rTb1p7tMce9/bi9rutUvYKsuCMYYx+8ZgzL4xmHRokixjZudl49rTa2A8IqszV55cQdM1TXHuoRk1yNaT4P8FY/tA3XrkjTswDqP3FSyYzT89H9+e0SFrwIIovU4MULR+QhAKZJYtnSlTKEVGk9MRFgYMHgz8+6/28TZtosmmKiekOHPnkgxz4ZX1OnWAxYtVn1+7NnVUF+WgdcXOjhyZihWpuaAu0Y78fIouFRcb0IW9e2nCrMlhtLMjVTdLUb6TGqGrXVuaQllkJKV7qRs3MZEcit80K6ogKamgcF8VQUHkfG3aRI6lWB+jDrl7xVy9CkyaVNC81dWVnKr4eO3nHj1K95CmZrEi9euTQtlbbxlmL4cjAVd7V5S3twBpeDMndmoszo08h20Dtsky3i+Xf0GTX5rgadpTWcYrKzxOfYwh/wzBR00/QqC3iuwQC8PO2g7lyxV9f/b9qy/mnVIve/5Tj5/wXZfvXj3f1H8Tdg/ebTQbTUnpdGK2bwfefVe5c7iXF6U+lQVataJ0noNaQoh379KE6f33paX8uLsr92upUYO6vxdvkAlQatvx4+r7eWgjLIwcpBcvqP5BSsPLs2dpAvv8Oa3+GzKJ3bqVnCdttQxubro5MV9+Sa95SbJoEdXCqJLIVkVAgLSGl5GR5Giqu3/c3MjRO3RI8zhLlpCzqi7CYmVFkZCOHel5Ay2dxH186L7T5IACQESE8meFKgr3iAEKollSFMoePyYnrUIF7cfa2FBvHHUqgRyOjCx8YyEWvrFQ7f7em3uXmiJpYyEIArycvfC63+uoU6mOLGMevXsUTbybwNnOWZbxygpZeVlo5NUIHzT5AF7OGnpyWQC5+bmYc3IOLsYWjcpXdKio8b5oWrkpWvoWiNpULV8VFR3UpMNbOKXTiTl6lBo+Ohf7J3t7l45IzOzZ9KMJGxuga1eaOGoKR//5Jz0OGSL9+l99Bfz8c8Hz48e1r0brGxL/7z9SJktPl+7EREZSypSYlqNvKtuLF5S6NHCg9gapnTsXpM5JITaWXreSZONG+j8VV/5SR0AApU5p63+zdSulb6pDEKix5rFjmlPAwsPJIdYUYRw8uMBZ1ibaYWdH19QU0UhLIyGB997TPBZATky5cgWCGbo4MU+e0KPUKNj168DMmdIlrjlaEQRhrSAI8YIg3DC1LZZEI69GqOMuz8S8tPIo5RGWnV+G84/OY3PYZqTnGN6fzMHGAZ38O8HF3kUGC8sONSrWwN739sLFzgXX466b2hyDSMpKwtxTcxHyOKTI9j/e+gNTXp+i8pyEjAQciDyAF1kvXm27/+I+fvzvRyRkJBjVXlNQOp2YixdJWrn4ynBgYOmQWD50iAqctdGjB03mb91Sf0z16sDHHxfUD0jhyBFqZgjQCnbXrgWKTaqYMgVo1Ej6+IURC/tdXcnGxETtkYToaJqod+1Kz3Vp2liY3btpEqmuRqMwa9dSdEUKeXnAunW0Oq+vapuuRERQLZM2VbLCBASQI6CtpsTFRbOqHUDCEenpFCVTx61bqpXJirNqFT2K6WKG4OxM95UUSePoaPr8EB1a0YmRkk725Am9Tk5O0uyKiKDeSzf4fFtG1gPobmojzIm4tDjUWVEHO8PVL0Is6LJA7YSJQ4TFhWHy4cn4K+wvDP1nqEb5W6lsG7gN45qPw6XYSzJYWPYYvms4Pj/2uanNMAhPJ0/kfpmLUU1HST7n/KPz6LW5FyISIl5ti0yMxNQjU3HrmYa5oIVS+pyY9HT64hf7wxRm7lzgn39K3ia5SUzU3ONDROxhERys/phhwwomhVJp1oyK+RUKWjFmTLUymYijI03K9JmwJyfTpNHZmZyE1FTtkYSoKIraVK1KzTt99JS8DAggB09smCoXhQUAHj82fDzGgHnzNIseiE0/+/eXPu4771DtVW0NRb2xsdQLRpuj2KkTFcGrSynLzaUxpDgxERGUaiUl/XHChIL+RuoYMYJSAbWllIlOjIijIzno2sYHpPWIKYx4z/F+MbLBGAsGYCGFayVDniIPTbybwN1RwvcJRy1v1noTCZ8m4Iv2XyB8XDhqutWUZdxxB8ZhzL4xsoxVVphwYAJ6/NkDq3quwnddv9N+gpljY2UDO+ui2QnrQteh1vJayMlXjtR3qNYBZ0eeRUPPhq+2tavWDgmfJqBdVQOFjsyQ0ufEhIbSSr3cE09zQqoT4+tLRcgff6x6/8WL0moBihMUROdFRlKxM6BZFKBWLYo+SEkFK05yMkVhBIHEAaRMXKOiCmo0/v5b/wLp1q3JwZNyzXnzqHeJFAqv3MuhnBUTQyl+3btTsbuq1L0dO6iux9dXeZ867Oy0O4zXrgELF2pPqXJxoZ4q6ppDRkfTPSLFiYmLowijFGxsgNu3Vb8mmZnA0KF0XykU2h2G//5TFidYsgTo1k27HR060IKBVKpVo0gPVygrUQRBGC0IQoggCCHPpKQJWjg+rj7YOmAr2ldrr/aYfXf2wW2hG+4kSqiPK6NYCVZwd3SHp5Mn6laqqzTp1JXnmc/RZUMXtPFrg7X91spkZdkgwD0ADT0b4nW/19HAU0vdpJlz5ckVzDo+SykNzNPJEy19WyIzVznV28XeBa39WsPJriDqX86mHNwd3SFI7bNnQZQ+JyY7m1ZGVUViTp6kSZKm9CpzJy+PajWkODGA+jSf3FySrB2jxyqPuPJ8+TI5je7umqMdovSuPrUpyckkUADQavmHH1Kdgya6dgX69i14np2t+3VDQ2nyK5XsbLqvFArtxxZ2YuT4UBElrgMDSU2tuA35+UCfPsAnn+g+9syZNFFXh7oeMar4v/9TL/Dg5gb89JM0SeyKFbUX64v4+VF0NilJed/hw8DmzfSZYGWlOdUNoFQwr2KFojk50gQdxo2Tnm4I0H3RsqVmJ0bKvcbRCcbYGsZYEGMsyEObDHkZwb+CPwY3GGzwxLw08+/df7H0/FLk5udi0/VNCH0SatB4mbmZyM7LRmDlQDTxlqAaynnFxJYTseiNRXic+hh7bu9Bbr7l9ji6Hncd3575Vini0qt2L/zZ/08l1TIAOBh5EGdjlL/Lfr/yO7be2Go0W01F6XNiunQBQkJUF9BaW1MqSmxsydslF2lpFGWQuqIeH09F+8Wllo8cARISqFBaV+rVo8lhSgpFYpo00TwZFwve9alN+fnngolcuXJUe6Jtxfzbb2nSCFBdgaur7qlsM2ZQc0WpggRubjSplBLZEp2YW7eANm10s0sV167RJPzkSerrYm1NkRFxcm1tDXz9tW6RAJHgYGpWqY7ISHIyKyl3+1aCMXK4rl1T3ufpCYwfTzVacqKpV8w//5BD1KcPpUV+8YX6ccLDgU8/VVa669+/oPZKHYxpF0dQRYsWFHXKylIeb+1aWqhJN7yAmFN2WfLfEgT8FKAyLUWkgWcDrOq1Cv4V/EvOMAtjZ/hOfBP8DawEK4zYNQI7IzQInUjAx9UHZ0aeQbea3XAg8gDCn4XLZGnppnBPnYORB9FvSz/EplrufO+DJh8g98tcVHaWnoo8498ZWHRukdL2NVfWYMP1DXKaZxaUPidG0+qkuIpqyTLLFSrQxHGUxEKv8uVpElpcPWrjRoqgdNejztXGhiZzY8cCGzaQdK8mvLwogqKptkIdjo40wRV/9/DQnJaWnV3UYfHyotVyXVLZnj0j5bBBg6RHSsTIgJSGl9Wq0f+vShXpNmli1qyCXi3OzjTJHTyYVvJv3wZOndIvGgVQhMEBIFsAACAASURBVEWT8xkZScdIfZ169aKGlsW5fNk4HerF4v/iTkxODkWt+valWp369em+VkdICPD998rOiIeH9lS6tDS6d5cu1c32Tz+lsYtLml+9Su+nChWoZonD0RP/Cv5oW7WtpChLnsKAflulnBU9V+DepHuwtrLG7fG3Mb3NdFnGVTAF+v7VF5vDNssynim5k3gHwlwBr/+upV2BAUQnRaPiworYHbEbvWv3xoVRF3RyAMwRK8FKKQ0sMzcTNZfXxPILy5WOPzzsMJa+qfxdc2z4Mex7b5/W6809ORf9tvTD3SQjfB8bgdLlxCQm0qRdlA0ujhidMURmOTOTVm4rVKBV9LFjgfXr9R/P2NjbUwrPwYMFUYWUFFLeGjRIs5ytFOrVA5o21XyMIFAtgbp6CE0sWQJs2VLwvGpVzX1f/v6bir6Ld5HXJQr0zz+UgiVFlUxETO+TklrUujXw66/AyJFUVG4o1tZFC84FgSIvycm0mt+xI6Vq6UNAAIkPqGtCGRcnLZVMtKt7dxIgKN6AdPRo/dLdtFGjBr3GoiMscuIEpWW+8w49j4kBJk5Un2oaHU32F0/PFJ0YTRE7UV5ZSrSqMOXKFXUORWcpMJBewyNHChTSOFoRBOEvAP8BqCMIwiNBED40tU2mpv9r/bGu3zqtxzX/tTmG/aNHJLeMIAjCKynkWm61DO7tsjtiNzqs74CU7BScH3Ue01pPk8NMk2ItUH1lWFyY0a5hZ22HIQ2GoHrF6vBy9kILnxawt9Gz0bYZ8PuV3/H9ue+VtjvYOqBDtQ6oXkE5c6GyS2VUr6i83dnOWWtNTJ4iD3NOzcGe23tw+bGExsxmQOlyYi5dosmWuhVuFxeaGBjixKxYAezfD/TuTZPHrVsLin0ZoyLyUMPyYTVy6hSlzOmyat29OxX4izUpR49SioohzRaDg2mCNX26crqLKhij9DVdWbkS2Fdo9UBbr5ioKIrGiSvw+jgxW7dS1EgXWejq1Um+2NFR+7EZGWRjRgZw86b0a6giIYHqmoqnaLVpQ2l4VatSqpkuqmSF0VbPdPUqpTZJpXt3qk+5VEg2VKGgdC0pRf264uFBiwzFhT4yM8n5fuMNem5lRY5e8bRLkehoSuG0L/aF6OFBUS51Th5Q4MTook4m8uWXpID244907587R9u7dpXe74cDAGCMvccYq8wYs2WM+TLGfje1TZbCsIbD0Lt2b1ObYbYsO78M/4ST8unR6KP487qahVQdcbBxQFCVIJW1D5ZGTbeaUHylQNrnGj4rDaRq+apY2WslGnk1goIpsOf2HovuFXPs3jG1qYlr+61Fnzp9imx78OIBfgn5Bc/SlbMDrsddx9TDU5GYoT5bxMbKBi9mvMDdiXcxsP5Aw4wvIUqfEyMI6iVPBQF4+23D8u6nTCEnYNMmmsg/fw4cOED7kpMpwnHypP7jayM6mlKddCkIF1PGRHnbd96hyXPLlurP0YbYeXzxYs1pOCKzZ9MkrvgKvDYKF/YD5Fxoih5FRdFkU0zB8fQk51WqE5OSQj14dEklA6h7/PbtFJnSxpAhdI/6+RmuTnblCrBmjeo0Nn9/+luuXdO/P1KdOjSO2K+nOIKgnO6kia5dyWEoLLUcE0NOhZTXTh8YU64deestSmETbff1JYfvzBnVY0RHAzVVyKZK6RWja6PLwty7R2loU6eSClrdurqPweGoofHPjTH50GStx01qNQnDGvFIjDp+uvgT9tym2sG1V9dizqk5Bo3Xr24/nPrgFFzsXXD/xX0sv7AcaTnGm/yXBLn5uUZXxyqc8ihAwKAdg7DhmuXWgWx+ZzPOjlQvOMOKZQCce3gOY/ePRXy68vfRw+SH+DnkZzxM0TznKF+uvMpIjrkiYfZpQVy8SF/yYkdvVWzWI7eUMZqsDxtGUZ4uXQr2CULB9cqXp5V4OWRz1SGmK0lVJwNoAtuzJ6VZiRg6YSx8vhQnxt+/QGZZ1WRQFYwpOzHffks/6hDllUUEgRS2pE7+XF0pfUpbQ01DiI+n1CI/P/o9O1t5hV8qYtRPncS1kxM5WPrSuDFNpFURHEwO1OLF0qMMbm7kPP/7L/VtAgpSuIwRiQFo8p+TQ1FMgCJBTk7KznDbtrQAwZiyA/viBaUBFqdFC6rx0fSZY0gkpkcPco6//56ED0qhRGZpJDU7FQ9THqKeh5Ecc5noHdAbr3lIe9+l5aTBwcYB1lY8AlicyAmRrybQK3qsgK21rWxjh8WFYdKhSWjh0wKtfFvJNm5J0/GPjshX5KNupboY13wcmvuoUJA1kPd3vo+o51G49NElCIKAC6MuwMdFzz5xZs6nRz7F3+F/4+6kgqycQQ0GoV21dvBy8lI6vnut7kj/PF2jI7ny4ko42TnBw9EDwQ+CsfCNhUaxXU5KTySGMXJijNEfZs4cUqv64w/NxwmCPKvrmkhMJKfBxUW38/bvp2LyFSsojczQTvE2NsDkyZTuJQV90royM8nxKa9DKL24EwOQE/P229LHqFBBNycRIKfH3R345hvtx8bHU4RIVJiT2vNEFaGhlGYkVXJYTi5epPozXSIxAIlBHDlS8Dz8pfKOsZwYL6+idVRff02vWfH3QJs25MDev688xq1bwOrVytvr1aP7S1NtSlAQpV1WrKi77UOGkOLdhAncgbEgfjz/I+qvqq9R9cscmN9lvqQIy/ab2+HyrQvvFaMGQRBeOS7uju5wtdewqCGBSQcnYfjO4QCALjW64Mn/PUFLHwMyJ8yAQfUHoXft3jh27xgep8rQ5FkFvQJ6YWjDoa+eN/JqZNGNXMfuG/sqwleclr4tMaj+oCLRGCvBCr6uviqdaGsra62RsK03t2Lvnb0IeRyCP679YfafX4BMkRhBELoDWAbAGsBvjLGSb5PKGHUO1zb5nDUL+OsvSg+RwtKlNOkZOZLG10ZJODHu7vpNaPLyaNXcx4cUmQzlxx+lHys6Frr0iklJocfCTkxMDCkzzZihLG2rUFAvksaNi27Py6OJqb+/5qjR06dUO/Ljj7qn2llb03Wk1P3Ex9Okt2FD3dPWihMaql1YwVBmzqTXvbhgxp07FFHSdXJe3MkcPJjS1nR1HKXi50ey6goFvdb//EPyxMXfA23bUrQkJkZ1yqmq94xCQe93Jyf1hfvt2knrf6MKQTBcfINT4vR/rT/qVjLv1D9x8iMlxSewciAWdF6ACuUqyHb9Z+nPUMmxksU34EvMSMSC0wswrNEwBFYOxJ3EO9gZvhOjmo7SewJd0aEirARaY3a0dYSjrYRaSzNnYsuJAIBZ7WcZ7RrFHfLLjy/j1rNbeL+xAfW/JiI3Pxf7I/ejZsWaQB3l/QPqDcCAegOKbPvtym/wcvJSqpURmXtyLnxcfTCqqWp12+D/BSMrLws2VjaY3XG2wX9DSWBwJEYQBGsAKwH0AFAPwHuCIJRcDP3wYZIQtrKiyIC2YnVra0qPkVKbsX491cD07w/88ou0yWa1apS6Yiy8vPSrZcnLo0lYTIxhBf364u1NEz1dIjHe3rRa/mEhESFHR0pFUlUQb2VFzk1x2ejNmykSpE0M4fhx6squr4Pn5qZdnSwzk1bWPT1phX7LFv3rVXJyKAJkbCfm2TPVBe+ivLI+LFtWILXs40NCGcbCz4/uo7g4qoOJiVEtdNCwITk7HToU3X7qFDlaYlpYYTIzyTkWxT1UERurufCfU+po4NkA79Z/16wbRJ6OOQ2nBU44E6OmDqwQtdxqYWa7majsIo9cbVJmEjy/98RXJ76SZTxTEpceh58v//xKkjYiIQKfHfsM916oScOVwJyOc/Bj94JFwr2392LZ+WUG22oqMnIzVHaXl5N8Rb5S3dCWG1vw0d6PlGpHLAFba1s8nPIQn7b5VO0xCqYoUge08OxCbL2pvqHlkbtHcDFWQwNlAOVsysHGynIqTeRIJ2sBIIoxdpcxlgNgC4B+MoyrmexsWnXv3p3yxcU8dm2OhpeXNKWsx48patGtG02CpdR9ACSde+WKtGP1Yd48Eg/QFRubgsn5u+/Ka5MUBIFqWfqoXiFQS2G7AVqtd3RUrVD27Bmtihf/wJLabPPECUolKx7JkYoUJ0ahoPTEwhNlfetv7OwosqWpSaMcBARQ9EiMjIkY4sRcuAAsX06vxy+/UBNaYyEq1cXEUBTG2pr6wxRH3efHpUukWFe4pkzEyYnuR029Yrp3N83CAcdk5ObnIiwuDHFpBihhGhkvJy980vwTVCtfTdLx6TnpKlWP9KGiQ0V8GPghmlVRI8JjQdTzqIf0z9PR/zVaGHmz5ptIm5mGoCpBsl1jf+R+LDm/RLbxSppN1zfBaYETHqU8wuKzizH+wHjZrxGREAGXb13w962/X237tM2nuD/5vuzXMgciEiJQ7pty2BleoF4WMS4Cq3qtUnvO2ZFnsabPGpX7fgn5BRMPTnzl8E05NAW/XzF/AUc5nBgfAIXzpx693FYEQRBGC4IQIghCyDNtzeG0cfs28Prr1ENk3DiKxkgNSUtteFmlCnDjBjXE06Xo2pxD45cuUZGwsdJ2tDFhgvbu5oUJCwM+/rhojYIgkIqUKifm119pX/EGgFLrcY4fJ+dCX+laNzftzS6dnEiprU2bAtvGG/iBbux7TtXrl5tLf2/DhvqN2b07RUYOH6ZeS0ePGm6nOho1ojRSDw/qI9Spk/oaot27KZWssDMaHU3HV1CTSqOt4eWTJ/oV9XMslhdZL9Do50bYdnObqU1RS51KdfB9t+/hV95P0vHN1jTDx/s/lu36v/X9DW/VfUu28UyNmBZnb2MPJzsnvcdhjKHeynpYdalgMvpDtx9wb5L+kR1T07xKc8zpOAdVXKogISMBj1IMqANVQ0WHiljQeQGaeBeI3Hg6ecLb2dsiUxavPb2G4TuHq206WcWlCqa+PhUB7gULidZW1nrXY91NuotrcddevVYXH1/E7cTbeo1VkpRYzIgxtgbAGgAICgrSP7Z37x6lzzg4UBqZriv7ujS81Ccf/fZtSmn66ivjpPl06EBKYzNm6H5u48b6RxnkIC2NirgDA6VFtiIigJ9/Vm6CWK2a6oaXUVE0WXQq9gVSqRLV1WhyYmJiKN1s4kTtdqnjrbfUSxGLpKaS3K+HBzlLTk76F/Z/9hlFSHTp06IPhZ0YUb7c1ha4boD+frdu9Ljk5eqisYr6Abpf5s2jCJ2216pCBXKaz50rSHGLjtac8qfJicnJIceWOzFlCndHd2wbsE3W1Xi5yc7Lhp21neQJ3uwOs+HmII+AyN7bexH5PBIjGo9AZl4mfF19ZRnXFBy/dxx7bu/B/M7z4WTnBAVTYOGZhWjp2xKdq3fWebxcRS6aeDeBp1NBg15DnCJzILByIAIrBwKA0RSvqrhUwcx2M4tsS8pMwuawzehaoyvqVFJRWGLGxKXHIfhBMLLzslXud7V3xXddC8rPw+LCsOXGFkxoOQHezqrl/E8/OI2fLv6EX3r/gooORWtZi/9fNEk7mxNyRGJiARReyvF9uc04VK9OE5Lr13V3YADKXx8xQnv37L59pRXyF0ehoNVcUXFJThijmo2kJPnHLgm2bSP1OFUOiCpUFfYDNEY1FSkQ0dHKReMAOaMBAZqdmJQUmlgXls/WlXHjgM8/13zMP//QhFaMLhkiBHH4MNVbGJtatagwvbhzaAje3iQLLdbaGNOJASiy8uQJRcDEKJgqxIL/s4U+wNX1iBHx9FTvxIgRX316xHAsFivBCgPrDzTrfgtj9o1B7RW1JR//XsP38GatN2W59t47e7H0/FLU+qkWvj71tSxjmoqb8TexNnTtqzoCK8EK35z+Bkej9Ysu21nbYfM7m4sUbSuYAp/9+xl23Nohi80lTdTzKOTmG6iIqoX49Hhk5BbNwkjNScX4g+MR/CDYqNc2Bt1qdsP9yfc1SqArmAIp2TRPuh53HQvPLtSoKPYi6wWuxV1DYqaWjBELQg4n5hKAAEEQqguCYAdgMADVmnByMXUqpXvpg48PFeyra4gJkCNy7Ji0TvTFEfPvjaFQlpZGaTymSgczFKm1KSJiVKO4E/P118AOFR/mquSVRWbNovtGHQ0akFNgSE8VgO4ZTUWEYlNEz5erbPo6MdnZJG5g7KJ+gKKewcFFFw1Wr6aooCFS3WJxvYuL/u9nqbRpQ+99bfVqjo70mopNLxnTnjY3YYJ659WQHjEciybqeRQuxV4ytRlq6VunLya0mCD5+Jz8HIQ/C0d6Trr2g7Wwps8aRIyPwOpeq/FxkHwpaqZgQssJSJmZAnubgrTz59Of49uuGvqZ6YiVYIUdt3bg8uPLso1ZUmTmZqL2T7Ux//R8AEBkYiQ6/9FZkqCELozeOxotfi3aYsPHxQdP/u8JPmz6oZqzLJvum7qj+yYSMhraaCiyZmXBz1V9emifOn1we/xt1HIrOk/acmMLum7oiueZBWnU1+Ouo/um7rgZr0JEyYwwOJ2MMZYnCMJ4AIdBEstrGWPm/VczRpMvdali9+9TXYU+E1pnZ0pJMYYTI9ZbWKoTI6YlRUUBb0pY0UtOpiiKlJ446ekkxqDOiemnQWtCbKqpruZBKsuWkUJeUpL6seLjqa+KszM99/OjKEFGBk2gpXLzJt3DgYGG2awLhZtAhoSQxLIhUt1ffkliChkZxq/rEZ2tkye1O35t21I/JbEJ6SUtE9HianiF8fUlmXZTpnFyTMJn/36Gm89uInycEaLyMiAWokvlbMxZdN7QGf++/y+61DAgYv0SR1tHDG4w2OBxzJHCDo2unLh3AiP3jMSuQbvQ2Lvgc+POhDuvZJctjT/e+uPV3+Jo64js/Gy1aVL6MjZo7KuohIi1lbXa1CpzZ/Wl1bgedx2re6voT/aSUU1HISuvYLFdX1UxBVMgJz+niIS6nbUd4tLj8CLrhV5jlhSy1MQwxg4AOCDHWCVCgwaUylK874XIjRv0qG/RctWq3IlRha4yy3l59LdaFfvgvnEDGDAAWLUK6Pwy59jKCti0Sf1kMS2NJt6NGikXdd+7R87Ppk3UXFBfRMclMVGzE+PpWTBpb9cOmDZN94hGaCg9lpQT8/XXJCMspgLeuaO/Mllh9uzRLoYgB6mp9Cil6WnPnnS/pKVJE/VITKT6rebNlRdGfHyASZN0t5dj8cxqP8usm8UlZSahQrkKkmtimng3waa3N6G+Z32DrpuclYzPj32OkYEj0axKM1yKvYTMvEy0r9beoHFNxaKziyBAKCKFu+3mNtxNuovP2uqeku5q74q2Vdsq1R9ZqgPjYOtQpE+Lj6uPUeotutdSvZi0/eZ2pOem44MmH8h+TWPyOPUxIp9rniu9W79AafbL41+itnttrT1xRu4eiUZejTC51eRX24Y0HIIhDYvOfepWqovQMaF6WF6yWOa7wlDKl9dc2B8WRo/19Gx3ExioW5d5qdjaAm+8oboexBIQBHIWpDox8+errjVwcSEBhcINSx0cgKFD1UfPbt4kVaozKkLYJ05QlKFJE+V9uiA6l5pklkUnRqRtW5Ly1vV+cXWl+h1NtRpy4uREjrn4txkir1wYZ+eSuZ937aK+NKqaWBanc2cSlHB3J8e2TRvghYbVqL176f+oqj7p3j3jykdzzJYm3k3QwqeF9gNNQG5+LtwWuelUj1LRoSKGNhpq8Mp2bGosNt/YjIcptND38f6P8fkxLbWEZsylx5dw8XHR3htHoo/gj2t/6DVesyrNsPHtjUqqcWFxYXhn2zu4k3hHb1tNQfTzaLUKW3KRlZeFiIQIldGdjdc34qeLPxn1+sZgXud5+He4iv5shVAwBZ6mPUVOfg72Re7DhdgLWseNT48vEl2xxB46hbGcjjZy4u1NK8nq8POj5nZS0phUsX69fudpo1Ej4MgR44xdUixapP/rKuLjQ8pehQUCbt6klLDXX1edmqRJZvnECZLeNrS4XIzwaHJixoxRrrVKT6deMa46SCMOHEg/JUXh1++112gRoLb0omCT8/rr9CMVhYJU465dowaZmv43Hh70GB+v7CQtWEDRJilqiJxSxfPM57jw6AJa+bZSUgIyNfksHz+++SNa+bbS6bwHLx7gWcYzg1TX6nnUQ9KMJCiYAgCwrt86VHExck2cEdk+cLvStjV91sgeOREE4VXvodrulvPZ++WJL/Hfo/+KSERPPzod91/cx7aB8kiQ34i/gea/NsfOQTuVZLs3v7MZjrY6pGpbEAciD6DPX31wYdQFhI4JleSQ7Buyr8jzUw9OYfjO4dg9ePcrBTmRxWcX4+jdozjyvvnOO8tmJMbLS3OfmOHDgb/+Kjl7yhLdukmfTM6eXdDVvTA2NuTIFO4Vs3w5SRyrS41wc6Of4k4MY+TEdOxoeF2G6MRoSo966y1ykEWysykasUyHbswKBaXalSSFnZjUVKBHj5IRFTAV48bR3xcVRY5J8ZTGwohOjKqo4dOnvKi/jHL16VX03NwT1+KumdoUJcrZlMPkVpN1dmKmHZ2GIX8bkHJbCHGS39CrIdwdLTRFWg2GODDTjkxDo9WNlLY38GyAOxPuoF21doaYVuJMbzMdq3sVreuoWK4iPBw9ZLuGfwV/bHp7E1r6tFTa52znbJGpeIN3DMbqS+rrYQAg0DsQK3qseCVRrk8/HGc7Z7Sp2gZVy1dVua+iQ0WzjtZY3n9WDry8aKKpqg5BoaDeDoZw8iQQFFQ03UkOFi2i9CF9O7ybAw8fkgS1lEn4/v2q078A5YaXmpTJRFTJLEdGkiBAp07a7dFG5crA9OlA3bqq9zNGdTmFIzX29pRepksN1Z075PjsMa4IYBFq1KCJfGQkOZAHDlBqY2mleXP6jDhwQHvKniYnhje6LLMEVQnCuZHn0Kyy+XWlT81ORUJGgs6Tk5ltZ2LD2xsMuvbS80sx9+TcItt2hu/EiosrDBrXFOQp8vDu9nex707R1e0b8Tcwbv84vZo6BnoHoketHnKZaHKaeDdRqleZ2W4mVvZaKds1KjlWwtBGQ1HZRfmzNvxZOGb+OxNxaZYVDX+W8QxpOWkaj/Fx9cG4FuMQ9TwKI3aNwNM0LU3cAey5vQetfmuF1GyqEw2qEoS/3vlL5ULCx80/xtYBW826WWjZdGI6diRJVFVOzK1blP+/d69h17h8mfLh5eTRI5pY6dtR3hzYv5+iEaLUsCZSUtTXivToQf1iRKKitE82VTkxbm7AypVUzG0o5csDCxeqj1CkptLkuHjDRV1llkNDKYJTkrVR9vYUnWjUSLOEdGmhbVt6zMmR7sSouqe5E1NmcbV3xet+r8PF3sD0WSOw5cYWeCz20HmS3bRyU52jN8W5Hncdlx4XVfzbfXs3VoesNusVX1Wk56QjLD5MaYIcnx6PLTe34EnqE53HHNpoqNqGkKsvrX4lqVsSJGUmSZoYqyM5Kxn/3v1XSTVMbsKfhSP6uepF44cpD/HDfz/g/ov7RrVBbo4NP1ZELEId8enxuBR7CcfvHYe9tXYhGlsrWzjbOSM1JxWMMVkk001J2ayJ6diRflQRFkZRAn9//cc3Vq+YxETLVSYT8fKix/h47b1BkpPVOzGF+3JkZdFrrS0SM306MH580W2VKgGffKL5PF1ITqZIWXEFNEC5R4yIn590sQOAnBg7O/2FJ/Rl+XJ6HDmS7D19umSvX5IEBFC0Ky0NaKmcolAEJydgyxZl5zU/n2pheKPLMsvR6KNwtnPG63461GOVAK39WmNZ92XwcvbS6bysvCycfnAaAe4B8K/gr9e11/Zbq7RtWfdlcLF3MesVX1WUL1depYR25+qdkThdftVFK8EK1lbWyM3Pha21AfL2EhnyzxAcijoENls/5/Ji7EV029QNJ0ecRAf/Dq+234i/gf5b+2N1r9WyyHVPOTwFCRkJCBkdorSvS/UuyJqVZZEpZVJos7YNgqoE4eEUafPNHgE90COAIn0PXjxAjeU1sPHtjUrqZAAJBwStCUK/Ov0wu+NsWe2Wi9L5X9UGY+QQpKvwQG/coJqLOnX0H9+X8hO5E6MCcQIvJRKjyYkB6P/IGEW8GNPuxDRsWHRCyhiwebO8RdeBgeoldTU5MbpGYho0MKxHiz4wBiQkAOHhJX/tkkYQgK5dKQqjTXZbEIBBg5TV2hgDtm4tWgPFKVOMPzgeSy8slXTswjML0WVDlyJ9H4xFfc/6mNhyIuys1fRKU0NaThq6beqGXRG7ZLWnfLnypXaSqSt1V9TFxIMTVe4bEzQG+4fsLxEHBgCGNRyGGW1m6B0ha+nbEseHH0fTykUXeNwd3BFYORDOds5ymIn5nefjh24/qNxnbWVttHvr5P2TCHms7DgZyq1nt9Dpj06SmuUu6roInwTptxBrZ22HWe1mqU15tRKs0Mq3FWpUrKHX+CVB2fzUuHePVuC3qVDGCAsj1SV1jTClYG9PEQfuxCgj1YnJzaXXUN0q9unTpHJ27hylVQUHa6/RSE8HNm6klEGAHocOBfbt03yeLri5qVcnE2smPIoVNPbvT31YFArt4zNGTowpiupXrybbz5+XR17Z3Jk6laJPUr7AL1+me7AwNjbAO+9QCh6nTLJz0E4s6y5NtCOf5eP4veNai3nl4MGLB0jM0D1S4O7gjlMfnMLwxsP1uu7thNvo+1dfXHuqLHaw/up62UQDSoqzMWfRf2t/PHjxoMh2xhg+2f8Jtt7YqvOYgxsMRhu/NnKZqDcKpsDQRkPxXdfv9I6Qudq7olP1TkoplZVdKmPrgK1o6aslyi2RZlWaFYn0FGfuybnYHLZZlmsV5ovjX2DUnlG48uQKDkcdlm3cnPwc5CvyYW2lvXTg7dfexpora7Dq0irJ43f+ozO+Pf0tKrtUxtxOc1GnkvpF+1W9VmntPWNKymY6mZjSpGoF/sYNqlswlO7dqfhcTjp3Vl7FtzRE+7VFP2xtixbuF6dSJXJKHjygPh7tJCi25OWR8tzCGgkfFAAAIABJREFUhZSKdeIEbZejqF/E3V29Opm6SIym9Mbi5OUBU6aQcERJUzjF0pLklfVFyj0lMmsWOakhhVblYmPJUW7dmlLOOGWOeh7SUz4/b/c5MnMzS6S3zOC/B8PZzhlH3z+q03mCIBjUlDIpK0ltbUJSZhJiU2ORmZsJB1sHva9RkiRnJ+NO4h2llX5BEHDi/gm9eurM6ThH4/4ef/ZAA48GWNxtsc5jS4Uxhpa/tUTvgN6Y1X4WsvKy4GSn+2fYvjv74OPioyTdKyep2an479F/aF6luVop8x3hO9C+anuVKVOGcGDIATxMeYjRe0cjPTcdb9R8Q5aoTxPvJgj+X7D2A0F1R3tv70VddzWCQirwdfVFRYeKiHoeheoVqmt1lkQ5dLOMljLGSvynWbNmzOQ4OzM2aVLRbQoFY4sXM7Zrl2lsKgsoFIwdPcpYbKxh46SlUTLZt98ydugQY7t3SzvPw4OxUaPo9/79GatalWySi8GDGQsIUL0vMpKx9esZy84uuj0nh7Fbtxh79kw+O4zBnTtiAp/017usMHw43UuFWbuWXqvoaNPYJAEAIcwE3wGW8CPH99St+FtsTcgappDzM0YGDkYeZIejDut17s34m2zD1Q0yW8RhjDGFQqH1Xpl4YCJbdn6ZUe3Iys1i4/ePZxuvbWSVFlViUw9N1WucKj9UYSN2jlC5b8TOEazj+o4GWEmcfnCaYQ7YwciDao8x9vvv7vO7LDEj0ajXUMeKCysY5oA9SX2i03nZednMfp49+/TIpxqPOxx1mDnOd2ShT0INMdMgNH1PmaFbVUJ4eytHAwQBmDYN6NfPNDZpQpw+WjpirYG2ov6ICIpmXb6ser+TE0U9HjwAliyhdCwpiAplCgVJYXfqZHh/mMJoSierVQsYMUI5VfHJE4oM7dypffy7d1VL+ZYEhSMxPEWqKB4eyv+XJy+Vibg6WZnlcPRhjN43GklZSRqPS8xIRLWl1bDt5jY8Tn1sdLu61+qObjW76XXujls7MHzXcGTmZspsFSGu+pZFbsTfgO08W+yO2K32mGU9lmFiS9U1M3Jhb2OPn3r+hGGNqCZG33vl3MhzmN1BdUF4K99W6Fq9qyFmAgAaezXGyREnVfaIEZFbMCIhIwE9/+yJ0CehAIDqFavDzcENjDFEPY8yePwVF1egw/oOkmqRegT0wK5Bu+Bip5sKooIpsLbfWgxuoLlms5ZbLXzU9CPZ6pfkpuw6MV5eyk5MbCz9yOEsbNxI13jxwvCxALLVwQFYt06e8UzJ8ePa61BiY4HDh0kdSh3VqpETExUlvUajVi1yYsLDydmQM5UMoPqW2WpUPEJCqJ6lOJUrUw8WKTVUU6YA7fVP5zAIW1t6/QYONEy9rzTi4QFkZhYVC3n6lIQpHCwjNaa0IwhCd0EQbguCECUIwmclcc3hjYcjZnIMKpSroPG47PxstK/WHuuuroPPEh9k5GYYzaYXWS8Q8jgEufkqWgxIYEyzMXgw+QHK2ZTT+dyP9nyEmf/OVLv/9yu/w3eJb4mIG8jBorOL8L/d/1O5b83lNfh438c6jVfRoSJmtJmhsUYBoAwaYzl7ufm5iEiIePV8WutpeLPWm3qNVa1CNVSvWF3lvrFBY/FF+y/0GrcwLvYu6ODfQW0qGQAcjDyI4TuH6y1QUJzIxEjciL+hJIwx/eh0tPi1BZ6lG7bQWM6mHCqUqyDJ+apRsQb61e2nU7rfutB1CPgpAP1f668kuqBq/KXdl6KWm2bhpISMBMnXl5Oy68RMmACMGVN02/ff02RYjhvd3p5qIOQq7k9MpN4gjo7yjGdKvv8emDNH8zHJyfSoSZ3s/fcpqvPggXZlMpGAAGpu6e8PxMQAb78t7TypdOlC95YqZsxQlngGyDnw9pZ2r4SGkgKaqZg5U7taV1lEVa8Y3iPGbBAEwRrASgA9ANQD8J4gCEbXKHdzcINfeT+tueRVXKpg49sb8W2Xb/Fzr59lm2yp4kj0ETT/tTmux13X63wvZy9ULV9V79VtTefVdKuJngE9jd5XRC7Sc9LV2vow+SFuPLuh03i+rr6Y32U+6lZSX99wM/4mPL/3xIHIAzqNLZUj0Ufw2srXcOzuMQDkMMWmxOocebvy5ArWX12v0SFljCFfYVjz7uAHwTgbc1bjMQ+SH+B0zGnZ7qvX/V7H/cn3Ud+zfpHto5qOwvzO81HJsZJB449qOgq7B6uPxhmKr6svXO1dEZkorbUDYwzJWclq9++7sw8eiz3U9uoxKuryzIz5YxY1Maro0oWxoCB5xjp3jhLA9u+XZ7xTp2i8o0flGc+UjBjBmK+v5mPEeoK7dzUfFxlJx61fL+3a8fGMPXokbx1MYdLTGbt5k7GMDOV9DRsy1q+f6vNatqT7TxPPntHfunix4XZy5OXxY8ZOny76f2/ThrFOnUxnkwRQRmpiALwO4HCh5zMBzNR0jhzfUxk5GeznSz+zK4+vaDyuJGtm4tPi2dYbW1lOXo5e5ysUCrb2ylp28t5JmS3jZOdls7z8PI3HJGcls1G7R7GLjy4axYa4tDi27Pwylp1HtZsn7p1gmAN2NFq3uccXx75gNl/bqL3PLj66yMp9U44dijxkkL0d1nVgbX5vY9AYupCUmSTp/ZqvyC8Ba/Sn5rKa7O0tb0s6dtg/w1jAcjW1voyx8GfhrP269ux2wm25zCuCpu+pshuJSU0Frl8ntSeRGzeol4gcyN3wUlS8UtVE0dLw8qIVa02rjVIiMYwBl17qqEuNxHh4UNTj/fcL1Mnk5PhxoH59kuouTny8enU5Kb1ixFQ0U0ZiOKqpXBlo27Zo6tjKlcBi4ykIcXTCB0DhN9ijl9uMiiAIGLt/rNZV8zH7xqD1760BAE/TnuJhsszy/IXwcPLAu/Xf1bvXiCAI+Pz459h4faPMlhUQmxJbJmtjfjj3A2zn2WqMXrjau+LXvr+iuY8MKqoq8HTyLNJDqLFXY6zosQJ13HXrnTen4xxETohUe59Vq1AN45uPh4+rYW/DjW9vxK99fjVoDF14f+f76LJBc4POszFn0Wh1IzxKeaTXNfr+1Ref/WvcjNctA7ZgVvtZko59t967mNxqssp9z9KfoW6lujj1wSnUdi951dKy68T89RfQuDHlrQNUlBsXR00E5aByZcDaWj4nRiwWt/Q+MQBN5HNygBQNoV1nZ2o46uqq/phduyi16Z9/gGaqmzUpwRg1Jvzzz4LCazkR/z/Fi/sVCrrH1DkxEycCP6hu1vUK7sSYL5mZwKZNwM2bBdsaN5Z+X3LMAkEQRguCECIIQsgzGQQ0ytmUw6MpjzC9zXSNx7XybfWqeLrZmmb46uRXBl9bFTn5Ofjz+p+ISzOswe/l0Zfxc++fdTrncNRhBP4SqLXw+UDkAfj+6ItN1zcZYmKJ0GtzL/x04SeV+0KfhKLPX32K1Jdoo23VtpjdYbakeqP0HBXNug3kxL0TOBB5oIgDWdGhIsa1GAe/8n46jWVjZQP/Cv5q93s6eWJxt8Vo4GnYnMuvvB9e83hN4zEp2SkY+s9Q7LtjeE+4QfUHaZVq9nL2gpOdk97pa36ufvB0Mm47jaAqQVrrYUT61OmDT5orN9QMfxaOqkurYsuNLQBgcGqgPpRdJ6Z4rxhx8iFXJMbaGhg5EnhN85tLMgEBwOjRyo0SLREpDS9HjSKFMhsNrYzEaJeVFVBOYpGpIAB//02/y13UDxREyor3inn+nBwZdU5Mu3ZA796axx4wgJyv0hCNK23k5VF0b/9+ep6ZSSIc9+6Z1i6OSCyAwrMw35fbisAYW8MYC2KMBXnI9Fnr4+qjNeoxMnDkq/4gP/X4CWObjZXl2sW5Hncdw3YOw6kHpwwap4pLFdhY6dZmzs7aDlVcqsDNQfPnV/da3fHjmz/irbpvGWJiiSBAUFvvpGAKxKbEIjU7VfJ47aq1w+yOaoRhCvH1qa/hvsgdeYo8rcfqwo/nf8SUw1MgoGjdkigGIZU8RR5mn5j9Sr1LHYwxnV6f4iRkJGBt6Fqtin4ONg44/+i8wc47AAxrNAyjmo7SeEwtt1o4/+F5nfpEFWZlr5WY+vpUvc41Fs8znysV73s5e+GToE/QpXoXDPl7CNqua1vyhqnLMzPmj1nUxJw/T/UF+/bR87g4xjZtYuz5c9PaVRZITKS+KMX7pehKXBz9D1u00O08UbDaGIh1K8uXF92emUn1TPfuqT4vOZmxI0cYS0gwjl0c46JQMFauHGPTptHziAi6DzZtMq1dWkDZqYmxAXAXQHUAdgCuAaiv6Ry5vqf23d7HVl9arXZ/viKf5ebnynItbeTm57LQJ6EsKTPJoHHC4sLYrGOzWEpWikyWqcbc+usYmxeZLyTVKgXfD2bfnf6Opeeky3r97LxsdifhjtL2aYenMft59pLv03tJ95jVXCu2LnSdxuN6bOrBWv3WSh9TGWOMHY0+yjAH7Pjd43qPIZXsvGy26domlpmbKfmclKwUdu3pNSNaVTJk52Uz67nW7ItjX6g9Zl3oOvbDuR+Mcn1N31M8EiNGYjw9gaFDgYrqZfr0IidHnnEyMmglvzTg5kYRquL9UgozfTowfLjmccSV0osXdbt+RITqmhU5qPBSSrV4Olm5cqSkpk6aODwc6NYN+O8/1ftPngTWrwdy9ZNF5RgZQSjaK0ZMVfTWvWM3R34YY3kAxgM4DCAcwDbG2E3NZ8nD3+F/Y/7p+Wr33064DYf5DtgVsQsAkJaThv8e/meUPiw2VjZo4t1Eq+SzNiITI/HtmW9x74XxIo0xyTHo9EcnXIzV8fPdgunxZw/0+LOH1uPaVWuHGW1nwNFWXrVSO2s7BLgrtyv4oMkHOqll+VfwR8bnGRhUf5DG40Y0HoHRTUfrbKdIR/+OuDvxLlr6qu8RIxcHIg9QFPO+9CjmO9vewcDtA8WFFEncf3EfNZbVwN7be/Ux0yjYWdthda/VeLsuqbnm5Ofgoz0fFUmV/KDJByaJHnEnRqyJ2bsXuHVL3mvMmUOF6XI4H0OHAk2aGD6OOZCdDSxbBly4oP6Yq1eBO3c0jyMIwMKFwJEjul2/Th35ap+KY2MD/PYb0Ldv0e1RUcCOHUX7iBTG15ce1dVQLV0KzJpFaYoc80SVE8Mlls0GxtgBxlhtxlhNxph6r0JmlvdYjnuT1E/2ne2c8WnrT/FaJUo9Pn7vOFqvbY2wePkXWpaeX4rLj9U0ENaBXrV7IeOLDDTykt70ttmaZvj82OeSj69QrgKSspLwNO2pPiYanbC4MAStCcL5R+fVHvPOtnew/MJyyWNOaDEBY4OkpRJm5mbKkh4FUEbOoB2DXjnSxanvWR9v1npTpxRCext7ONhq7pE1qMEg/C9QdZ8dKdhY2aB6xeqSnLkl/y1R29NHCv3q9MPJESfxRs03JJ8zu8NsrOunW28/K8EKrf1aG70mRlc+avYRmlWhGs8b8TewI3wH7iUV/VzLzss2WhNcdZRdJ8bBAVi7FujTh5KLhg4FVq2S9xru7kBWluYO64xRrcP27ZrHSkwsPbUQVlbA5MnUzFIdycmalclEpk8H3pD+oVIifPihcvH94cPUJFJd805vb3KAVDkxqanAoUPAO+/Qa8cxTzw9C97r4uIId2LKPM52zhonf37l/bCgy4JXDQ5b+7XGvvf2ya70k5KdgmlHpuHo3aMGj2VnbafU6E8TjDG09Wursf9JcVztXXF1zFX0rdNX+8EmgIHB08lT4wQ6Oy8bOfnSszHea/geBtQbIOnYRj83wsRDEyWPrYmEjATcSbyD55nP1R5z+fFlyQ7wb1d+k+S8McbwLP2Z2omvginw1YmvcDvhNgAoNWjdcWsHdobvlGRTSnYKEjMStR+oBkEQ0MG/g9aeT4VpU7UNWvu11qmnUtXyVbGp/6YSiS7pQkZuBkIehyAnPwdNKzfF/Un30SOgIGr4KOURHBc4GlW1UBUGzYgEQRgoCMJNQRAUgiAEyWVUifG//1Eh/8OHNFGUe3VeiszynTtUaL5jh+axEhNLhzIZQM0d3dw0F/ZLdWLMkdu3KZJUmPh4ihyp+x9aWwNVqqi+V/bto+jVwIHy28qRj5UrC97HT55Qw9sKhqXtcCyf+y/u48vjXyqtWoqkZKcUUYOq5FgJvWr3Mjjlqziu9q548dkLySv92lhxcQVWXZK28CcIApb1WIbhjbWkCKs4jzGGHbd2yBZ1kItGXo1wYOgBjdGofUP2YVrraZLGY4yaSkp1euZ2nItRgZoLzKXi4eSB0DGh+KDJB2qPGbFrBOaemitpvINRB7EzQrtzcfbhWXh+74ngB8Eq90ckRGDR2UUIeRyCyMRI1FheAyfvn3y1/4f/fsCKSysk2TSn4xzseW+PpGOL803wN/jhnBb1UDXEJMfgm+BvlBwwS2PP7T1o/mtz7LhF33HlyxWdn1VxqYIv2n2BZpVLVpHT0GXdGwD6A1B9B5o7t28D589TfxhAPmUyESlOzDHqiosFCzSPlZgIVDKsC6xZ4elZUI+kCkt2YiZNAsaMKbotPp4cGG1qa6rulR07aEW/dWt57eTIS40aQNWq9PuMGVSrpWdXc07p4Vn6M3x75lvcSVSdHjvk7yFo8WuLItvC4sK0diHXB2c7Z9mco3139uFQ1CFJxxrS8yUmOQZD/h6Cny6qljIuLSRlJcH3R1/JjuGQhkN0Sm1SR54i75XKmaYow7p+67C8h7TUuL/f/RtH39ce8avnUQ9L31yqNupYz6MeYqfGYkC9AWBgaOjZ8FW/GsYYTn1wClsHbJVkkyFceXIFV+Ouaj9QBdfjrmP2yf9n77zjpCjvP/5+7ihH71V6l+aBqIhiwYItajAaBXtBE7FEY4wSWxTFEmssgVgSIRp/KnaM2AUBAQUEEZFyFBEOjibtuLvn98ezw87ubZnZnd3Z8n2/Xvea3ZlnZp6b3dl5Ps+33cGcn+Y4av/UnKfo9GinpLK2pYJjOh3D7UfdzqjXRzFh3oRq2wtUAX899q/7Xc7ShbsciWForZcArkxlGcVf/mICvC8J+En26ePt8Z2ImA8/NAMfrY3rWaRUwVrnliUGggUvo1Fc7P3nkS6aNoXly0PXxSp0afHww9U/f63NrL64kmU+Cxcat79rrzX3ai7dr0LCDGwzkL1/2UthQeR4tosOuohd+3aFrLtp2k2U7ipl3ujk41cs7p9+Px0adeC8fud5crz3Rr3n2LXmya+e5K7P7uKHa36Im2I5nI6NO/LZxZ+lrLhjojwy8xEmfTuJuVfMjToGenjmw3yw/APePz++2KtVWItnTn2GIe2dTVZVVFWwbPMy2jRok5QwffP7N7n6vav59OJPY7r7ub3+TuJnmtZpynWDr4vZplld8zvao1kP3hsVLBp7/pTz6dOiD7cOdRZntWjjIq5+72oePOFBDj3g0Pg72Hj9t6+7am9neNfhrPnDGto2aOuofafGnTim0zHUr1U/4XOmgtb1WzP2qLE0q9uMc/qcE7FNRVUF67avo2PjjmnrV9pGRV4XEfOE1q2NNWDRIhNY7bXrR4sW8Ic/mKJ3kaisNBXeq6pMHZhoWbYqKuC220x2q1yhZcvYImbqVBM3k400bVo9O5kTEXPoodA/zDVBKfjySyNwhMxmzhxjgdmwAZ5+2kxQCHlPYUFhVAEDcHafs7mo+KKQdQ+c8ACTR0z2tB8vLXqJj1d+7Nnx3MQG9GnZh3P7nkuTosSyfx7e/nBqFNRg977dCbnluIlLcUrzus3p3rR7zEncohpF1KtVz9Hx6teqz5WDrqRfK2ceIYs3Lqb3U735348xYksd0K5hO07pfgrdm1bPSmZn+97tvPTtS1HdIi0W/LyAS9+8NG47i7LdZRHbPvTlQ5zx8hkRP7vyyvJqtWziUVSjCK112t26ahbWdCxgAE7pfgovnPlCRhoHahXW4trDro0qmv/62V/p+njXlNxvUYmWe9n6Az7EuI2F/51ha/MpMCjesay/jKgTo7XWd99tajmsW6f1/PnpP/+2bVpfe63WL7xg+vHoo+nvg1+Ulmq9PbU1Bnzjttu0Vkrriorguh9/NLVxYlFSovWzz5rvhcW+9NSPEDzgzTfNffzVV1o3aaL173/vd4/iQp7UiUnkz8vn1APTH9DPff1ctfW79+3Wa7et1ZVVlZ6dKxZ7K5KszWVjedlyffEbF+v569Pz7CzdWaq7PtZVPzTjIVf7PTrzUd3igRZ6z749KeqZN2zbs02XbC1xXItlz749+sUFL+q129amuGeG1VtXa+5EP/XVUzHbvb30bd36odZ6RdkKR8c97l/HRawV89isx/S5r54bc9901BF6dOaj+tcv/zqpe3TH3h161Guj9IsLXvSwZ5nHvJ/m6We/flbvKt/l6XFjPafiTqVorY/XWveN8Oc8aXimYtVwqKiIbi1Jlr174aco1WQbNjSphi+6yLhXhQeDW+zebY5R4W11Xl9p3hwaNIi8raQEevQwaa+zkWbNjBvYtm3BdV27mto4sfj6a5PZbNky837nThPs/89/pq6vgndYlra1a2HLFslMJuzntSWv8dHKj6qt/2rdV7R7pB3TlofGD2zfu51XFr9CydYST/vhJqOYEz5Y/gHrdqyL2y7cXS4RmtVpxqndT2VAmwHxGwco2VpCcetiLuh/gSd9SCVTlkyh46MdHX/mtWvU5vz+53NAwwMSPue05dMcZ+xq17AdC69ayGUDL4vZ7rQep7H+xvV0btLZ0XFvPuJm7jz6zmrrrz3sWl4666WY+6bDWlFRVUGlrnRleQynXs16LN+yvFrF+0gMnzSc37ziLENdpjGwzUAuHXBp3NTaXpLfTvZWrZgxY2D16tSc48orYfDgyNu++y4oTIqL4ZtvIrf7/HM44IDYdVWyjQULTHrkzRF+QMvKzEA+W0XbaafBm29C3UDqzX37jFiNV2AzPIZq6lSTsrdr19T1VfAOq/jqwoVmKSJGCDDzsplMGjGp2vouTbrw5ClPVhuYb961md+++ls+XOGNS+JDXz7En6b9yZNjWXRp0oV1N6zjlO6nxGxXWVVJk/ubcNenzjJbRcPKcDas8zBH7eesm0OXx7tQuquUvw3/G03qeFvI+uTJJ3P1u1fHbDP3p7kc9MxBjgp2HtHhCCb+aiJtGjj/3Vi/Yz0fragujp2wa98uRrwywvH3QilFv1b9YgrhiqoKV4UdAU7oegLDuw0PWbds8zLXx3HChVMu5Nqp7tJS3zjkRleFPiOhlOLLS7/k+sHxXeSHdx3OsZ2OTep8frJ622pWbV2VtvMlm2L510qptcDhwLtKqeScM9PN4MFw6aVmxj/SYNoL2reHdeuqD8h37TK1RP7yF/N+wABYvNhYbsLZFFDvuZSdbMUKePDByOLRsmBka3ayrl1NsUsrSL+01MT3zIiTbShcxPzf/5mB8VFHpa6vgneIiBGiEG3GuF3Ddvz+kN9XK2zXoVEHFly1gJH9Rnpy/pVbVrJ081JPjuWW8spy7jrmLo7t7M3ArLyynIe+fIiZa2bGbNe9WXduO+o2hncdjtaaxRsXJ5UlLZyDWh20v7ZPNBrWbkinxp0cBbl3a9qNywde7qhwo8Uzc5/hxEknsqdij+N9LOrWrMvMy2Zyy9BbHO+z4OcF3PvFvVEFxv3T7+fQfx7qquDh7n27+Xr912zfux0wwuzAJw/kbzMTS2kci+Z1m7tOLOEV1m/AL+VRasUFuOHwG7j60NjiOJMZ/M/BjlNxe0FSIkZrPUVr3U5rXVtr3UprPTz+XhlEixYmoL+gAHo5L8LlivbtTeC+VcHbYvp0KC+HY44x70eOhP9GSRVoCaxcynZkud5ECu7PdhGzYwe8+64RrxD8H+MF9rdoYWqLrFljRO4778CIEaaGjJD5NGhgRPk5gcwtImKEAJ+s/IRL3ryEvRWhk1TLNi9j487qv4GFBYX0b9XfM7eMJ099MunZ5IjH/erJuFXQ69Ssw5+P/DNHdfRmMqa8spxHZz0atw5J46LG3HnMnTSo3YA3vn+Dvk/3ZfZa77wZxh8/nmsPiz2r36NZD948900GthkY93glW0tcuw9eVHwR0y+Z7kgkRaJvy750a9rNcfvZ62bzl4//wtrtayNu79KkC4e0PcTV93be+nkcPOHg/aK0Ye2GPHnKk4w4cITjYzjl4eEPc+cxdzpu/+p3r3Lgkwd65tY55r0xHDLxkKgi0IrzyGaeOe0Zrjn0mrSdL7/dycDUZ6mqgjop8uGLlmb5ww9N0cehQ837fv3MgLV27erH2LzZZKlq4q053FdyWcSsX29cyj791Lx3KmKUMqJ67VqTqnfXLilwmU0oZe733/wGfv7Z++K5QtayettqPlzxYbWK6OdPOZ+Rr0W2tsxaO4uJ8yamo3sJU7a7LOqA1mLrnq2exqPUr1WfeaPn8cAJD0TcXllVyZVvX8k364Pu2cM6D+PpU5+OWo8kE7jxgxs5efLJ8Rva6NKky/7MbW74bNVnXDf1Orbs3uJqv5H9RrL9lu20b9Q+4vbz+p3HU6c6q3Nj0a9lP14757X9LpX1atXjykFX0qVJF1fHSQWNixrTq3kvVy5+sTi+y/FcUnzJ/ro84WzZs4Va99TiH3P/4cn5/OD0nqc7Eu1eISIm1XEXsUTMkCFQz5Z+cdYsE/8SzubNJv1zLs3IWwP6SAUvW7WCk0/OXstT04C52rKgORUxYKwvjz9uRO3tt8PRR6emj0JqePZZk4ihVSszSSEImBnzNX9YU20wdO+wexk7dGzEfV5f8jrXTL0maReof8z9B8f/+3hXLj5Oue3o2+IWNRz3+TiaPdDMU1euVvVNPOvGnRvZumdryLYfy35kyvdTWFa2bP+6RkWNuGrQVftrjiTLTzt+ouWDLXl50ctx2w59fii3fhS/lskNh9/AQyc+5Lovn6z8xHVh1Pk/z+e1Ja+l1O98AAAgAElEQVS5tvTVr1U/av2S6aunJ5S+uFFRI0YcOIKW9Voye+1sXln8StRBfrK89t1rdHmsS0TrZySO73I8U347xbOEGGf2OpM/HfEnahZGfjYUqAJuGnITB7VOUaKpNLB973Y+XfVpXLc5rxAR89JLMM+7gmLV6NzZxH7Ys59t2mSC+MPrvtx4YzBGxs6IEcZilEs0bGisTlsizAQNHw7vvZe9lier31atGKsukhMR06uXiX3q3h3uugtqJFWPVkg3r7wCV10ldX0ERxzX5biosSI3H3EzG2/amFRWJDB1KmrXqJ3WjEF2zuh1Bg+f+HDS/0c42/Zso/eTvbn9k9tD1vds3pPl1y7nN71DMzztqdjDlCVTPAk6LlSFjDhwBJ0ad4rb9qBWBzlqN6T9kLhJEiJxzdRrGD9jvKt9rht8HcuvXU5RjQjFtePw/DfP8+RXT4asW7V1FUc9fxQPfvmg6+MBLCldwpx1c5j49URPhHs0WtZryeHtD3ckkqp0VUpqylRWVTJt+bSIx25c1Jh7j7uXwe2iJIPKAmasnsGx/zqW+T9HybbrNdFyL6fyL2PqxPjF3r1af/SR1ivC8qhffbXWDRpoXZmemgG+s3u33z1IHY0baz1mjHm9c6epE+Mkp/3cuVqfcYbW774bWmdGyA5GjTK1YgYM8LsnjkDqxKTlObWrfJe+cMqFesqSKfvXbd61Wc9aM8vzmgrpZNuebfq4fx2nJy2Y5Mv5n57ztF5SumT/+xVlK6LWDlm3fZ3mTvT4L8anq3uumLturl6/Y73r/RZtWKQ3/LLBcfud5Ttdn8POGS+doY96/qiQdRWVFfqt799KuGaNVSumorJCL920NKn+ecWiDYt07btr67eXvu3pcd9Z+o6ufXdt/X3p91prcw9Z39nyivK01YxKFWW7yvQHP36gt+3ZFr+xQ2I9p8QSkw7WrDGZxyxq1YJhw4yVxk5xsQkKXxlWvXbJEuNjn2sURZkF+sMfYGD6fCpTQrNmQUtM3bomY5mTnPZffWXSM596qonVErILy9omQf2CjaIaRcxYPSMkfuSTlZ8w+NnBfL/p+4j77Kvcx8MzH+bTVZ8mfF6d4iDhBrUasK9qH5rI56nSVSwpXVItoYFXXDXoKno1N0l5tuzewsAJA/nzh3+O2LZtg7bMvnw2Nxx+Q0r6kgxaa4547ggemfmI6337tOxTLbtdNPZU7KHr4115cEZiFhOAV85+hc8u/ixkXWFBIb/q+auEa9bcf/z9/OO0f1BYUJgxcUt1a9ZlzKFj6N2it6fHPaHrCVw/+Pr9me1GvjaSI547AoAXF75IvXvrsXpbikp+pIEmdZpwQtcTaFi7YVrOJyImHYwebQpaWowfb+qkhFNcbJbhRS9PPtnUVMk1Jk6EWyP4Cq9fD7+kx58yZUyaBHfcYV4//zz8+9/O9rMGwX37SkxFNmKlWW7c2N9+CBmFUoofr/2RMYeO2b9uaMehvHXuW1HT9NYoqMEdn97Be8veS/i8b//wNh0e6cDSTalJr6yU4rOLP+P8/udH3L5+x3p6P9Wb5755LiXnB1Pv5Pfv/p6pP07l/uPvj9oXgEMPODRqPIIbJs6bSLMHmrHhlwgxnWHc/dnddH+ie9x2U347hQsPutB1Xzbt2sQzc59h5ZaVcdvurdjLRQddxGHtDnN9Hovw+JD3f3yfR2c9SnllecLHLG5dzB/+9wdeX/J6wsdwQpWuoscTPfjLxxHc9sPo3KQzD534kOcJBmoV1mL88UH3v5H9RnLFwCsAGHHgCPq36k+Lui08PWe6mf/zfM9qXMVDREw6aN8+WA9lxQq45Rb44ovq7fr2NcH74UUvN2/O3iD3WHz5Jbz4YvX127Zlb2Yyi8GDoUdgRumZZ2DyZGf7WRao8e58nIUMwbpPRYAKcWhZryW/6vmrqHVBlFKsu2Fd1CxcTmhetzlHdDiCDo06JHyMZGhQuwGTfj2JE7qekLJz1C6szbz181ixZQWjDx5Nv1b9oratrKrk/un389p3ryV1zp7Ne3Jun3NpXBR/suLAFgfur1UTDaUUJ3c/mT4t+7juS9nuMn737u/4vCRCUqAwGhU1Yvzx45NKd71j7w6ueueq/eL6nR/e4Ymvnkg4zTPA8i3L42a584ICVcCvevyK/q36x227bvu6tKQ7HtlvJJcMMGnKGxc15pOLPvEtfs0r7vn8Hq5+Lz21biRqOB20b2+Cu/fsgY8C1XXDg/rBuFfNmWOCui3Ky41VIhdFTKtWJnOX1qGuVrkgYr76ygjWc881/2MPhybyzp2NG5kT1zMh8zjtNFN36pBD/O6JkGH8/au/M/enubxw5gsAfLrqU1rWaxnTXSVZl4wh7YcwpP2QpI4Rjxfmv8C9X9zL4t8vrmblaFi7IaP6j0rp+QsLCpl+yXRHFpbCgkL+teBfHN3xaM7qfVbC5zyq41GOhcBvev+mWpKBcLbu2cqijYvo36q/68+8a5OulFxfQvuGkdMeW/yw+Qe2793OoLaDXB0/nLo16/LOD+/Qp4URXH8/5e+U7S5LKnFDh0YduOPoOziz15lJ9c0Jfxsev4hmeWU5XR7vwk1DbuKeYfekvE923BQ7zVTuPe7epEStG8QSkw6sNMtr15rUym3bQs8olX4HDID6thSGuVjo0qJlSyPStm8PXZ8LIubFF+F3vzOvS0udZSazEAGTvbRta9K2jx7td0+EDGPL7i38tOOn/e8vffNS7v787pj7zFo7i2veuyYhV50qXZWWNKct6rbg4LYH80v5L3y55kv+8+1/9m+bvno6P2z+IeV9cOMiNueKOTx92tNJnc/rGfo56+Yw9PmhLPg5gpt5HAoLCunQqMP+ivDReHDGgxzzwjFJfycKCwpZe8NarjksmEWsaZ2mSR2zqEYRI/uN9DyDXTT2VOwJqSMUTkVVBY+f9Dhn9DwjLf3JNXo065G2Oj8iYtKBJWJWr4aPPzZWmGg/OEuXGnczS7zkuoiB6gUvTznFJD7IZpo1g61bTaKGnTuDsRJC7qOUuJMJ1bjt6Nv44IIP9r9/67y3uP2o22PsAcs2L+PfC/8dIn6csqR0CY3GN+KN799wva8bTu1xKi+d9RJN6jRh0sJJXPf+dfu3/eXjv3Dmy6mfXXdDvVr14jeKw+kvn85RzzuzxMz7aR5N7m/CB8s/iNrm4LYH87/z/xfTFS4WX5R8wV8/+2vMNg+d+BBvn/d21DovbtlZvpNuj3cLEa3Zwu/f/T3Hv3h81MmBujXrcuWgKznkALGoJ8LO8p1MWjiJJaVLUn4uETHpoLgYXn7ZWFjKyyO7klmsW2fiIazaNW3bmqDwwdmbNzwqrVubwX64JebBB+Hq9PhTpgyr4OWyQME1N5YYQRBynr4t+3JgiwNjthnZbyRbb97qqM5IOA1qN2Ds0LEMaD0gwR66Z/zx45l7xdz97+877j5eOfuVtJ3fKbd+dCtXvHVFwvuP6DWC3/b5raO2bRq04fx+59OmfvSMhU3rNOXEric6irGJxIw1M7hv+n3sLN8ZtU2jokZRaxK55fOSzxk0cRDtG7WnY6OOnhwznVx72LW8fNbLFKrIBcQXbVzEpl2b0tyr3GFf1T4umHIBb//wdsrPpdIRuBTOoEGD9Ny5c+M3zEUqKqCy0hR6jERZmRnY339/bmYki4f1fcx2l6pJk+CCC4xlrUMgqDZaSmlB8Aml1DytdXJO8jmK18+pJaVLGDN1DPcOu5eW9Voyb/08hncdToPaDTw7h+CMWz+6lY07N/LP0//pd1cA890o3VWacMD97n27qVlYM2Icwuptq7n4jYt5/OTH6duyb7JdBYzl58YPbuSFM1/wPAVxJtD3qb50bNyRd0e+63dXspalm5bSuUnnatnsEiHWc0osMeli1iyYPdtUYI8mYMDM4HfoEMxQVlICM2YY8ZMPbN1q6ug8nZzPsu9Y7n9lZUa8iIARhLymVmEt9lTsYW/lXt7/8X3O/r+z2bZ3W9z97vjkjmoV0uNRtruMGatnpCW7UjZy73H3JiVgEql7E6sK/dNzn+b0l05PuD91ataJGki9cstKSraVeFq3Y2jHoXx1xVdZLWA27tzIAzMeYMvuLdW2PXnKk9x6ZITyD4Jjejbv6YmAiYeImHRx2WXGJex1B3nQBwwI1op56SU48kjjhpZraA3nnAMvvBBct22bEWyxhF42cMQR8O235v+5/vrqLnOCIKQFpdTZSqnFSqkqpZRvVqeuTbsy49IZHNXxKC486EK+ufIb2jZoG3e/L9d+yfyf58dtZ+f5b57nyOePTEtQfTaza98u1/tUVlVSZ1wd7v4sdlIGO72f7M3lb10edfuNh9/Ie6MSrwcEcO8X90asx3N0p6NZds0y39JsZyprtq3h5g9v5pNVn1TbdnSnozmiwxE+9Cp3mPvTXMZPT32pCBEx6WJtIAe6k4Df4mIT7F5ebgL7i4pM1fdcQymTcnrOnOC6bYGZyWzPTtawoan7M38+PPaYqf8jCIIfLAJGAPELaaSJerXqUdy62FE2pmkXTGPi6RNdHf+qQVcx5bdTohbSFEwti06PdqKiyp2XQ6Wu5O5j7+aYTsc43ueS4ks4qdtJUbd3bNwx6VTYb//wNtNXTw9Zt+DnBVTpqrRl/comBrYZyIprVzDiwBEh679Z/w0z18wUK2aSfLbqM2796NaIli4vkTox6aJzZ1iwAI4+On7bP//ZVHtXKncLXVq0agUbbFWPc0XE7Ntnily+/bYRoPWSz4gjCIJ7tNZLgLgpaNPBJW9eQuPajTmo9UH0aNYjZTVc6tWql5aaG9nMkPZDqKiqYE/FHlcZu2oV1mLsUWNdneumI26KuX3a8mm0qNeC4tbFro5rZ8alM0LEypptazhk4iH85ai/cPvRsbPg5SNKKTo36Vxt/fgZ45m9djarrl+V/k7lEFcOupLfH/L7lBfuFHmeLt55B6ZNMzP08SgqCga257qIadkyNMVyroiYggK47joTzySZyQRBABrUakCD2g24Zuo1/HfRfx3t8+2Gbznz5TMdpyu9edrNvPuDBCTHY1jnYdx5zJ2uUw6XV5azs3yn65n6st1lLN20NOK2K9+5kr/NjF+EMRbh1pY2Ddrw3BnPcXHxxUkdN5eprKrkd+/8jsdmPbZ/3SPDH8nIjHrZRv1a9VMuYEAsMemjXTvz55TrrzeD33wQMQsXBt+3b2/SKx9wgH998oLCQmjcGLZsEREjCClGKfUh0DrCprFa6zddHGc0MBqgQwfvYwgeP/lxAG44/Ab2Ve5z2id+LPuRst1lcdvuLN/J69+/Tv1a9Tm1x6lJ9TUfqKiqYO32ta5SWP/vx/9x+sun89XlX7mqIzL0+aG0bdCWaRdMq7bt3ZHvJh0EvWLLCsZ+PJY/Hv5HDm57MDUKanB+//OTOmauU1hQSMm2kpBinW0btHUUqybE529f/o0OjTpwdp+zU3YOETGZyrffwi+/mHiKquhZTbKeHj3g55+D74uL4e9/968/XtK0qRExrVr53RNByGm01jGKb7k6zgRgApgUy14cMxJu6oH0bdmXRb9f5KhtvVr1WDpmaULZs/KR373zO95c+iYb/rjBsbthr+a9GH/c+IiuSLF4+MSHaVEvctHjePWCnFCrsBYz18zk534/M+7zcXRq3IlR/Uclfdxc592R7+7/7BdvXMzcn+bym96/8aQoar7z3PznOKTtISkVMeJOlqkUFxsLxaBBMCQ1ftMZwT33wOe2eNs9e0w8SS7QrBkMHw5vOp4IFgQhh3n1u1dRdylu+/i2mIUJI1G6szTm9n2V+/YHcafDjSMXuKj4Ih496dGY6Y/D6d6sOzcfeTPN6zZ3da7h3YYzsM3Aaut/Kf+F/3z7H1ZvW+3qeOG0a9iOVdev4qRuJ/HOsneqBfkLkbEEzM7ynbzx/Rtc8uYlVOpKn3uVG3w9+mteOPOFlJ5DREymUlxsBvT33APLl/vdm/Rx550mCD4XMoM0bWrcATMgoFgQ8hWl1K+VUmuBw4F3lVL/86svzeoY1+B7vriHwgLnGQtnr51Nl8e78NbSt6K2eX7+83R/ojsbftkQtY0QypEdjmRkv5GuPostu7ewfW9iKfO/3fAtz379bMi6kq0ljHp9FDPXzEzomOEUFhTy5aVf8rfhycXY5BP3fH4P3Z7oxh+H/JGlY5Z6WlMnn6ldI/WlMpISMUqpB5VS3yulFiqlpiilnNvIhdgMGGCWd92V2zP5s2fD4YfD4sXm/bZtJqg/Fwb+jz9u/pePPvK7J4KQt2itp2it22mta2utW2mth/vVl2M7H0vV7VWU/amMohrOC+D2b9Wf8/udz+B2g6O26dS4E8d2OpaW9SQGzw2rt61m4YaF8RsGuPnDm+nxRI+EzjX528mMmTqGX8p/2b+ue7PuLLl6CcO7Jf+1fHTWowx9figAdWvmYFmGFDGk/RBGDxzNvqp9dG/W3e/u5AyLNy7mireuoGRrScrOkawlZhrQV2vdH/gBuCX5LgkA9OwZTMuby4H9lZUwa1awjo4lYnIBrY2AWb/e754IgpAhKKVoUqeJq33q1KzD06c9Tct6LdFaRxwUnNj1RP55+j8zIpV0NvHbV3/LmPfGOG5/Xt/zuO+4+xI61w2H38CaP6wJyYhWq7AWvZr3chUnFY2iGkVMXz2dPRV7kj5WPjGs8zB+d8jvuO+L+1helkeeLylmR/kO3lj6Bmu2r0nZOZISMVrrD7TWVqWoWYCL9FtCTGrWDM7gN3fne5tVWJm7rDTLuSRi3njDLJs2jd1OEIS8oKKqAnWX4rT/nJbwMcZ+PJZBEwfx8y/BhCgfrvjQdYyNYHjg+Ad47KTH4jcMcGznY7lkwCUJnatlvZbVYmkWbVzEiwte9ER4XHnwley4ZYfERCXAqq2r+Me8f7Bx58b4jQVHHHbAYZTeVMqRHY5M2Tm8jIm5FJjq4fGEzZvNMpctMVbmLqvg5fbtzmrpZANTA7dDreRSZwqCkBvUKKjBmEPGcN1h1yV8jEuKL+GmITfRqp757dzwywZOnnwyf/3sr151M68Y2nEoA9oMcNy+ZGtJwjExAPN/ns85/3fO/pTZ7/7wLhe+cSGVVckHkyulXNe9EQztGrbj8oGXu0qbLcQmHVbhuCmWneTfV0qNBSqAyTGOk9L8+znJ7NlmmSuD+kjUr2+Ke1qWmAsvNFaoXODOO2HECJNhThAEAXjilCeS2r97s+786Yg/AbBu+zrq1KzDpxd9SvtG7b3oXt5RWVXJxys/pnnd5o7EzOHPHs6p3U9l4ukTEzpfRVUFn5d8zg+bf2Bwu8GMOXQMZ/U+S2JYfKZdw3aMP368393IOb7f9D0KRc/mPVNyfOW26my1Ayh1MXAlcJzWepeTfQYNGqTnzp2b1Hnzgj17jEvZqTletOy00+DYY+HGG/3uiSDkFUqpeVprUdkRyPTn1L7KffR9ui+dG3fm/fPf97s7WYvWmqYPNOXs3mcz4VcT4rZ/6duXaN+ofcIuMlprqnSVq4xogpDPxHpOJVXsUil1EvAn4GinAkZwQVFR7gsYgHfeCb5etw6aNIG6MislCIIQjZqFNXng+AfEApMkSik+uvAjujTp4qj9ef3OS/p8hcoIGK01by59kwJVwOk9T0/quIKQjyQbE/N3oAEwTSk1Xyn1jAd9EvKVqipo3x7uSyzziyAIQj5xRq8zIhZQFNwxsM1AR9nBpi6bmnRRSjBxTMXPFPPvBf/moS8f4tFZjyZ9TEHIR5KyxGitu3nVESGPuesueO89+PBDk5Y4V7KTCYIgCBnPlt1b+M+3/+G4LsfRq3mviG3KK8v57au/5dy+5zpyO4tFy3ot6da0G03qNGHqqKnsrtid1PEEIV9JSsQIgifs3g3ffANbt5r3ImIEQRCENLG3ci9jpo7h8ZMejypiahXWYt7oeRSo5JO6KqV49ZxX979vULtB0scUhHxERIzgPy1bwr59sDpgphcRIwiCIKSJ1vVbs+YPazigwQEx23ldzX1vxV5u/vBmzu9/PoPaSn4NQXCLl3ViBCExrIKXy5aZpYgYQRAEIY20a9gual2LyqpKbvzfjSzcsNDTc/b8e08em/0YX5R84elxBSFfEBEj+I9V8LJWLXjoIejd29/+CIIgCHnFd6Xfcf3717Np16Zq25ZuXsoz855h6aalnp5z/PHjef2c17ly0JWeHlcQ8gVxJxP8p1MnOP106NULRo70uzeCIAhCnlG6s5SJX0/knD7n0Lxu85BtvVv0pvSm0v2pkb3i3L7neno8Qcg3RMQI/tO9O7z5JpSWwg8/QNeuUCiFwARBEIT0cGSHI9n2523UKIg8LKpbU2qXCUKmIe5kQubw/PPQs6fJViYIgiAIaaKwoDCigPl6/dec8OIJnruSCYKQPCJihMygb1+4+WZjgalXz+/eCIIgCHnGW0vf4uz/Oxut9f51pTtL+WnHT9VczARB8B9xJxMyA+uh0bAhRMkQIwiCIAipYuPOjSzdtJSy3WU0q9sMgOHdhrO422KfeyYIQiTEEiNkBlaaZUmvLAiCIPjA5QMvZ+HvFu4XMOWV5SFWGUEQMgsRMUJmICJGEARByCD+9uXf6Pp4V3aW7/S7K4IgREBEjJAZWCLmjjv87YcgCIKQt9z16V2c99p5APRr1Y9f9/o19WpJnKYgZCISEyNkBsccA1VVcOaZfvdEEARByFNqFtbcn6XstB6ncVqP03zukSAI0RARI2QGZ50F7drB6tXQsaPfvREEQRDykFuH3grAss3LaF2/NQ1qN/C5R4IgREPcyYTM4cQT4b77/O6FIAiCkOdc/vblHP3C0X53QxCEGIglRsgMZs+G7dthzhy/eyIIgiDkMcMnDWfrnq2MP268310RBCEGImKEzKBOHbPcu9fffgiCkFMopR4EfgWUA8uBS7TWW/3tlZDJDGoziDYN2nBy95P97oogCDEQESNkBj16QK9e8OijfvdEEITcYhpwi9a6Qil1P3ALcLPPfRIymHHHjfO7C4IgOEBEjJAZFBXBkiV+90IQhBxDa/2B7e0s4Dd+9UUQBEHwDgnsFwRBEPKFS4Gp0TYqpUYrpeYqpeaWlpamsVuCIAiCW8QSIwiCIGQ1SqkPgdYRNo3VWr8ZaDMWqAAmRzuO1noCMAFg0KBBOgVdFQRBEDxCRIwgCIKQ1Witj4+1XSl1MXAacJzWWsSJIAhCDiAiRhAEQchZlFInAX8CjtZa7/K7P4IgCII3SEyMIAiCkMv8HWgATFNKzVdKPeN3hwRBEITkUX5Y1pVSpUBJEodoDmzyqDvZjFwHg1wHg1wHg1wH59ego9a6Rao7k43Ic8oz5DoY5DoY5DoY5DoYnFyHqM8pX0RMsiil5mqtB/ndD7+R62CQ62CQ62CQ6yDXIBOQz8Ag18Eg18Eg18Eg18GQ7HUQdzJBEARBEARBELIKETGCIAiCIAiCIGQV2SpiJvjdgQxBroNBroNBroNBroNcg0xAPgODXAeDXAeDXAeDXAdDUtchK2NiBEEQBEEQBEHIX7LVEiMIgiAIgiAIQp6SdSJGKXWSUmqpUupHpdSf/e5PulBKPaeU2qiUWmRb11QpNU0ptSywbOJnH1ONUqq9UuoTpdR3SqnFSqnrAuvz7ToUKaW+UkotCFyHuwLrOyulZgfujf8qpWr53dd0oJQqVEp9o5R6J/A+766DUmqVUurbQB2UuYF1eXVfZBLynJLnlDyn5DllR55TqXlOZZWIUUoVAk8CJwO9gfOUUr397VXaeAE4KWzdn4GPtNbdgY8C73OZCuBGrXVvYDBwdeDzz7frsBcYprU+CCgGTlJKDQbuBx7RWncDtgCX+djHdHIdsMT2Pl+vw7Fa62Jbusp8uy8yAnlOyXMKeU6BPKfCkeeUwdPnVFaJGOBQ4Eet9QqtdTnwMnCGz31KC1rrz4GysNVnAP8KvP4XcGZaO5VmtNbrtdZfB17vwPwgHED+XQettf4l8LZm4E8Dw4BXA+tz/joAKKXaAacC/wy8V+ThdYhCXt0XGYQ8p0LJq++hPKcM8pwKIs+pmCR1X2SbiDkAWGN7vzawLl9ppbVeH3j9M9DKz86kE6VUJ2AAMJs8vA4B0/R8YCMwDVgObNVaVwSa5Mu98SjwJ6Aq8L4Z+XkdNPCBUmqeUmp0YF3e3RcZgjynQsnb76E8p+Q5FUCeUwbPn1M1vOyd4B9aa62UyotUc0qp+sBrwPVa6+1mUsOQL9dBa10JFCulGgNTgF4+dyntKKVOAzZqrecppY7xuz8+c6TWep1SqiUwTSn1vX1jvtwXQmaTT99DeU7JcwrkORWG58+pbLPErAPa2963C6zLVzYopdoABJYbfe5PylFK1cQ8GCZrrV8PrM6762Chtd4KfAIcDjRWSlkTE/lwbxwBnK6UWoVx2RkGPEb+XQe01usCy42YwcKh5PF94TPynAol776H8pwKRZ5T8pyC1Dynsk3EzAG6B7I61ALOBd7yuU9+8hZwUeD1RcCbPvYl5QT8SJ8FlmitH7Ztyrfr0CIws4VSqg5wAsbv+hPgN4FmOX8dtNa3aK3baa07YX4LPtZajyLProNSqp5SqoH1GjgRWESe3RcZhDynQsmr76E8pwzynDLIc8qQqudU1hW7VEqdgvEvLASe01qP87lLaUEp9RJwDNAc2ADcAbwBvAJ0AEqAc7TW4UGVOYNS6kjgC+Bbgr6lt2L8jfPpOvTHBMAVYiYiXtFa/1Up1QUz09MU+AY4X2u917+epo+Amf6PWuvT8u06BP7fKYG3NYD/aK3HKaWakUf3RSYhzyl5TiHPKXlOhSHPKe+fU1knYgRBEARBEARByG+yzZ1MEARBEARBEIQ8R0SMIAiCIAiCIAhZhYgYQRAEQRAEQRCyChExgiAIgiAIgiBkFSJiBEEQBEEQBEHIKkTECMCxIeYAACAASURBVIIgCIIgCIKQVYiIEQRBEARBEAQhqxARIwiCIAiCIAhCViEiRhAEQRAEQRCErEJEjCAIgiAIgiAIWYWIGEEQBEEQBEEQsgoRMYIgCIIgCIIgZBUiYgRBEARBEARByCpExAiCIAiCIAiCkFXU8OOkzZs31506dfLj1IIgCEKAefPmbdJat/C7H6lGKdUe+DfQCtDABK31Y7H2keeUIAiC/8R6TvkiYjp16sTcuXP9OLUgCIIQQClV4ncf0kQFcKPW+mulVANgnlJqmtb6u2g7yHNKEATBf2I9p8SdTBAEQchptNbrtdZfB17vAJYAB/jbK0EQBCEZRMQIgpC3TJ4MnTpBQYFZTp7sd4+EVKOU6gQMAGb72xNBEAQhGUTE5AkyWBOEUCZPhtGjoaQEtDbL0aPl3shllFL1gdeA67XW2yNsH62UmquUmltaWpr+DgqCIAiOERGTB8hgTRCqM3Ys7NoVum7XLrNeyD2UUjUxAmay1vr1SG201hO01oO01oNatMjlfAfvASLSBEHIbkTE5AEyWBOE6qxe7W69kL0opRTwLLBEa/2w3/3xl1LgVOAZvzsieM4M4FfAHr87IghpQURMHiCDNUGoTocO7tYLWc0RwAXAMKXU/MDfKX53yh/mB5Y/+doLIRV8BLwT+BOE3EdETB4ggzVBqM7dd1dfV7cujBuX/r4IqUVrPV1rrbTW/bXWxYG/9/zulz9YImaDr70QUoHlIviir70QhHQhIiYPGDcOaoRVBKpTRwZrQn5z3HGh7xs2hAkTYNQof/ojCOlBREzuYomY94BNfnZEENKCiJg8YNQoGDo0dN0dd8hgTchvwt0pzz5b7gkhHxARk7tsAppjaru+7HNfBCH1iIjJE5o1C33fs6c//RCETCFcxGySiUsh59kNfA8o4Gef+yJ4TykwBOiPuJQJ+YCImDxhy5bQ9z/+6E8/BCFTKCkJfb95sz/9EIT0sQioAgYBOwN/Qu6wCWiByWHxFbDU3+4IQooREZMniIgRhFDEEiPkH5Yr2UmBpbiU5Q4aY4lpDozEDO8m+dojQUg1ImLyhHARs2yZP/0QhEwhXMSIJUbIfeYDDYHBgfciYnKHHcA+jCWmLXA8xqWsys9OCUJKERGThUyeDJ06QUGBWU6eHH+fsrLQ92KJEfKdSCKmSp73Qk4zHzgIaBN4LyImd7AykzUPLC8ASoDp/nRHENKAiJgsY/JkuOIK48+vtVmOHh1byFRWwrZtoevWrIE9UtRXyGPCRUxVVfX7RBByhypgAVAMtAqsk+D+3MHyh20RWP4aqIcE+OcK5wMP+N2JjENETJYxdizs3h26btcusz4akQZmWsOKFd72TRCyhV9+qW6dBImLEXKZ5ZhA/mKCA12xxOQO4ZaYesBZwP9hstIJ2YsG3gD+63M/PgZW+dyHUETEZBnhs8fx1kP1eBgLiYsR8pU1ayKvl7gYIXexgvqLgZpAM0TE5BLhlhgwLmXbgLfT3x3BQ8owExDfAnt97Mc5wP0+nr86ImKyjA4d3K2H6CJG4mKEfCWa6BdLjJC7zAdqAL0D71shIiaXsCwxdhFzLHAA4lKW7Vj1APZh0qT7QSWwmaBYzgySFjFKqfZKqU+UUt8ppRYrpa7zomNCZMaNA6VC19Wta9ZHQywxghBKNBEjlhghd5kPHAgUBd63RmJicolSoDbGjcyiEBgFvE9Q5AjZh72o2Tyf+rA1sIwyoPQJLywxFcCNWuvemLyNVyulesfZR0iQUaOgUaPg+9q1YcIEsz4adt//unWDr8USI+QrYokR8o/5GFcyC7HE5BZWocuwWU4uwAzTXk57jwSvsERMETDXpz5YA8kcEzFa6/Va668Dr3cASzD2SyEFVFWZoGSLzp1jCxgItcQcfHDwtVhihHzFLmI6dgy+FkuMkJtsBH5CREwuYxW6DKcvMAD4d3q7I3hICcbCdgT+WWK2hC0zA09jYpRSnTB3y+wI20YrpeYqpeaWlopZM1HKyqCiIvjeycyxXcQMHGjqy4CkWRbyF7uIGTAg+FosMUJusiCwDBcxvwC70t8dIQVYlphIXICZwf/eo3Np4DWCLkZCalkFdAQG4V9wf45aYiyUUvUx3+rrtdbbw7drrSdorQdprQe1aBHtRssMEikmmS42hE2clZXFL9BnFzGtWgWTAEiaZSFfsYuYgQODr8USI+QmVmayg2zrrFoxYo3JDaJZYgDOwwz3HsUIkGR5FfgN8JIHxxLiU4IRMQfjX3C/JWK2YWpOZQaeiBilVE2MgJmstX7di2NGIh3iYvJkUzzSTTHJdPJzWBymkwJ9dhHTtCl07x58L3ExQr5RVRWaYlksMULuMx9oj0mrbNE6sJTg/twgliWmNfA74B/AtSQ3CN0F3Bh4HaHYlpAC7CIG/ImLsT5rjREymYEX2ckU8CywRGv9cPJdiky6xMXYsaZ4pJ14xSTTSbglBuIPvOyB/U2aQLduwfcSFyPkGxs2wL595nWzZqHpycUSk7sopZ5TSm1USvmVo9RHwoP6QSwxuUQ5ZmAZzRID8DhGfPwdk7GsPMFz3Q+swSQQqOZ0I3jOLxgB0RHoDDTBn7iYLVFe+4sXlpgjMA6Xw5RS8wN/p3hw3BDSJS4SKSaZTiKJmHgDL7slpkkTscQI+Y39Xu7QwQgZC7HE5DQvACf53Yn0sxsTCyEiJnexBgGxXPULgIeABzCZyn6FGSC7YSVGxJwXOFfmzMjnLlZmso4Y4TgQf0SM3eqWQyJGaz1da6201v211sWBv/e86JyddImLRIpJppNwdzJwL2LEEhOZTI6FErwjlojZvNlYeoXcQ2v9OXnp/7II4z4ULmJaBpYiYrKfSIUuo3ET8BzwETAMd8UL/4ipPfMA0AixxKQDu4gB/4L77T+dmZPQwdPsZKkkXeJi3DioWTN0XVFR7GKS6UQsMakhlruiiJvcIlzEFBVBvUB9uIoK2C7PZSGnsIL6w0VMTaApEhOTC1hCJJY7mZ1LgNcxg+EjCS2mGI0PA/uMBdoBDRFLTDqwPptOgaUV3P9tmvuxBfObYb3ODLJGxIwbZwYbduJVqk+EUaPguONC191wQ/xaLOkikiUmngtMuIjp3DmYZnn1atjrR7a+DCOau+JFF5m/TE30ILgnXMQANLc9+yUuJn/JzVIA8zEDzk4RtrUm8ywxk4B+mAKNTpmNsSytTUmPMh83lhiL04EPMCL2CGBGjLb7MAkBugA3BNaJJSY9rAJqEUzEYQX3p9ulrAwTkwMiYhJg1Cj4+99D1/3jH6kRFzVqhL7v39/7cySKW0uMfWZZKWjUCGrXljTL4URzS6ysNH92MinRg+CeSCJG4mIEyK5SAM6Zj0mtHOlxn4kFL6diXODc5F94FzOQ/ywlPcp8LBHj1BJjMRT4HDNIHgr8mchuSk9i6pg/gqkaD0YYp0PELAC+TsN5MpUSTGZB6/71K7i/DCNiQURMglx2mbEkWBx/fGrOE+5iFUk4+IVbEbPV5rrYqBEUFprX9rgYcSmDtm3dtc+URA+Ce8QSI+QPVZhBYLgrmUUmipiFgWW1mtkxmBVY+lXN3G82YYK+myawb3/Md+RyTND+IQSLowJsBO4AhmOSAViky53s0sBfvmKlV7ZQGGtMur/rWzBiqgYiYpLAHgNT4sSN0yWVldUtExs3en+eRKiqityXWDPH4a5kFva4GAnuh4svrr6uTh1o2DBy+0xJ9CC4x/67IZaY/EEp9RIwE+iplFqrlLrM7z6lnuXATlIrYpYBhwJezOyUE6wqPytWQxtVwFeB137Uz8gESjGz8zXiNYxCA2AC8A5GtBwCjAcqgVsxtWEewwygLdLhTrYTI6gW400guwb6YOrlZAvhIgaMiElncL/GWGKaYr5nImISpqPts0yFiFm3DsrD0qdniiVm8+bqrk3W+mhEEzFiiQmlY8fq7ydOhKeeMmLGTipisYT0sHNn8H6pWRNaB9yMxRKT+2itz9Nat9Fa19Rat9NaP+t3n1JPtKB+i1bADswgNVGeAOYAbyRxDIulmFiYWjgXMUsxFoFmwDdkUjXx9BGr0KUbTsW48Z0B3IIRM88B1wM9w9pa7mSpTOc4DyOkKoDvPDje1sBxZnpwrHSwF1hPZBGTzuD+XZgJBhExSWMfbKbCpSfSgD5TLDF2MaVsEyKJiBixxIRivwZ33QWrVpl4q1GjjJCxM2FC5iR6yDeSzRS3Zk3wdbt2wQQXYokRcpP5mNn5PlG2W8HCic7U7QZeDLz+OMFj2LFcyUZgLDJOUrlabmeXYeqe/OBBP2KxkExKMWsoxX08TDSaA68AkzF1YVoBt0Vo1wgjMJIRwPGwC9n5UVs5Z11guSZmq8zB6mckEQPpcymz0iuLiEmaVFtiIomYTLHE2DOTde4cfO1UxDS1ucuKJSaUH2zPPbvAA+NqVr9+8P0pnpdyzV28TE89eTJccUVymeIixcOAWGKEXGU+0BuoHWV7sgUvX8MM6HsDn2IGtcnwLSaN64WB91/FaGsxCzOgtmaWUulSVgUMwWTzypDZTcA7S4yFAkZiXAXnYawu4VjrUhkXMwsTTF6P/BQx4TViLNId3G8NJJsgIiZJUi1ili+vvi4TLTF9bBNrmzZFL9AXzRLTpUvQmiNplkMtMT16VN9uT1SUM5lXU0ys2juJMHYs7N4dus5tprhoIkYsMUJuMp/ormSQvIiZCHTF1A7ZhnHnSoaFwIEYoaBw5lI2CzgMI6TqkNqBXSkmTuM74FgyR8iU4q2IsWgORMt60yiwTFVcjMa4fQ3BZNfzQsRYKbjXkFo3OK9YFVh2Cluf7uB+scR4hlhiDJ06BWM1ysuNr38kymxFVu0ixp5muaoKVq70tKtZRWVl6OcebokBaNky+FpEjDOi1d5JND11NPdRN26lYokR0sNk/M+otBH4CWciJpGClz9g0vNeDljF1ZJ1KfsWUyOmEUbMxMtQtjOwz2EYt7liUmuJsWbyr8MMMDNByGiMJcYrdzKnpNoSswbzvRyM+Vznk3y8k/X57SFYIDSTKcEM09tF2JbO4H4RMZ7hh4jZtSu6SEgndjHVunXo7HG0gVc0SwxIXIzFmjXBZA6tWkXOSCaWGPd4ITrsRMsI5yZTnFhihPSwGniexMSBV1hpcmOJGGt2JpGZun8ChcDFGDHUh+REzBbMTLlVmG0wxsoSa8Z8LmZgOzjwfhDGGpSsW1s0rEHwSExtmpX4L2S2YQLf013XyHpQpsoSY1nhLBGznaBlIlHW2V5ng0tZCcYSVjPCtnQG91sDSUvEbCVTEmhknYhp0SJogdi2zfx5hdah7mT2OIhMsMbY+9CqlbOBVywRI3ExBruAi2SFARExieCF6LBz993V17nNFCeWGCE9nBRYfuBjHyz3m4NitKmFGZi4fcCVAy9g6oZYyQGGAV8EtiWCNRjrF1gOxswAx3o4WZaaQwPLgzHWmaUJ9iEeljvSAcAxwHv4L2QSLXSZLKl2J5uFKazZn6AQT9albC1BQZAtIiY8HsYincH9liXGiompwmQ19J+sEzFKpa5WzIYNQYtLo0bQ05ZRMBPiYuzuZOEiRiwxiWMP6o8UDwMiYhJh3DiTxthOnTqJp6ceMqT6uqeecpcpzqklJlqMmSA44yCMleN/PvZhAcYNJV4BxERqxbyNGTxfYVs3DJOpykkwfiTCRcxhgWUsl7JZQDeCA/hBgWWqBnbrMMMmyw3vGFJnkfkBZ5YtawbTL0tMqtzJZmI+z5pAX8x1T1bErCMoiLKhYnUsEZPO4P4yzOdQL3BOyBSXsqwTMZA6lzK7NaJbNyMULDLNEtO6tbPZ42jZyUAsMRZiiUkNo0bB8OGh6268MfH01N9FKBMwdKjz/auqQlMst28ffF23rrMYM0FwRgFwIsYS45fbxWLM4C8eiYiYiZjq3fYb/GhMwHGiLmULMQOkAwLv+2AGTdGC+3Vg22Dbul5AXVIXF7MOY3myF5U8lqCQGYZ36ZdvA85x0C4XLTF7ga+BwwPv62A+Wy9EzABMtr5Mt8RUYixH0USMFdyfjgKvWzCTIQoRMR6QKkuM3ZWsa9dQEZMJlphY7mTRREy0wH4IFTFiiTGIJcZb7PcQRL++TogkYtzc/xs3BmOfmjSBBg1Ct0tcjOAtwzGz5F/7cO5KTJ2VaPVh7LTCXezOKow4uxQTE2PRBBgIfOTiWHasoH6rCFohxk0smiVmLaYQ4GG2dYWYQWoqLTEHRFh/LMY69T0m0YEXptwlwGaCrjzR8MsSY/2ApsISMx/jlmgXqFZwf6JYwfztAn+ZLmJ+wsQ6RRMxYETMIlIf3F9GULyIiEmadFli7Bmp/LbEVFaGCqmWLZOPiUlFmmUv64KkC7HEpI5wcZ3M/bpkSfV1bo4XzZXMQuJiBG85MbD0w6VsBWbQ5kTEtMadJea5wPKSCNuGYdyA3BZArMIMxvqHrT8MM3DdXW2P0MBvOweTuuD+aCIGTIa2ezG1c55M8jyVmBotABHqPoTglyWmEKhPaiwxkT7bARjhkeiP80+B5QEYK2Kmi5hVgWU8EZOO4P4ygm6pImKSxg93Mr8tMZs3G3cYMGKkdm337mThIqaoKOhSU1VlqtQng9d1QdLBvn2h6aXt1ik7ImISI/x7mcx3LFlLTDwRI5YYwVtaYiwTfoiYxYGlU0vMDiILhXAqMSJmOJEHV8Mwg6oZDo5lpyTQh35h6wdjZqMjWbNmY9yCwoXPIIyI+t5lH5ywjsjpbi3+CJwC3Ehy1qDVGBEKsRMbgLEu1MG43qWbhqTGEjMLIzTsNWqSDe63MpO1Cxw702NirIdbpxht0hXcLyLGU1IlYsLdyTLJEhMe1A/x3cn27YNffjGvlYqcOtjL4H6v64Kkg5UrjZULjKCz4iLC8VvEZKOFC0LdGSHx+1XryCLGjSgSS4yQfoZjLBOpyuAUDUvE9HbQ1k3By/cxg8Eromw/EhMv4jYuJjyo3yJWcP8szCCuVth6a2DndazATsyAPZolBsyQ6l8YAXsOiQ/w7QIs3oM5VYUundCI1Fliwi1sVpa9ZEXMAUAHjGUmVam4vcB6WMZK52kF96c6LmYL4k7mIXYRk2jNiUhksiUmPKgf4ouYrbb4wiZNzAA4HC+D+72uC5IOnMTDQHURk87sVdlo4bLwyp1szZrIwfapcicTS4zgDcMxloRki0C6ZTHGUlI/XkPcFbycGGj/qyjb62OER6IiJjwRQWvM/xEe3L8PM/scPtAF6ImxSng9O20fBMeiOfBfzCA00fgYS8Q0wJklJt2uZBapsMT8jHGlCv9sW2CufaIixp4euz1GwKx3uO8FmM+iC+b7fRqmPtJNwGOkJt1wCeZ/rhujjRXcn05LTH2MK6GImIQ54AAoDMQT/vwz7NkTu70TysqCrld16kCbNqGWmEwSMZEsMZEGXbFcySy8tMR4XRckHTiJhwGoVy9opdm7N2jhSgfZaOECI7giiZiqBJI12eNh7GIjVe5kYokRvOFwzEM/3S5li3HmSgbOLTHrgXcwg7dIxfcshmFmht0MbhdiZpUbRNhmFb0Mb7+H0KB+Cyu43+vZaaciBmAIcB/wKvBUAudaCjTDDFDjiRg/LTEN8d4SY1ndIgnUZIL712HEbSOMiAHncTFTMQJmCNAYY8X5CHgCuB44wsWxnBIrvbKdVAf3V2A+Y0vEWBnKRMQkTI0aRshYrPHgu2N3JevWzbhfZVKKZbs7mWWJief+EiszmYWXlphx40ysjp3atROvC5IOnFpiwD+Xsmy0cIERevv2ha4rL0/sXrK7kp14YvD1mjXORZH9enWM8GwQS4zgPbUwg/r38SZjlRMqMDP5TlzJIFisMt6N+QJm9vqyOO2GYQL1P3d4fjCWmPDYFovDMAPEn2zrogX1WwzCDHYrXPQhHm5EDJi4mFOBG3Cfoe57TErhbmS2JaYR3ltiZmJE8oAI24oxWdsSmbm2kjIo3ImYzYG/C4FJmAmJrwP77sbc2yWY76mXFhE3ImYfQRdSr7FceuyDSBExSeN1XIx9AN+1q1k2axbM3rV5M1R4+XvokniWmEgixq0l5oMPkou3GDUKLglLWHPBBYnXBUkHTi0x4J+IyUYLF0S3ZiRyv9pFzCGHBAXHvn2w3qFHQKZaYrI13klwykkY95h05bFfjklP69QSY7kcxBMxL2JqwcT5oWQwptK6U5eyPZjCjuHxMPbjQWhczGygDcHBaDgHYwaYXgb3292RnJBMfIxdxJTG2TebLDG3A2/GaTMLI1YiBagWY4R0IgN2e2Y56wHgZCbQmunsGWGbwriMzsBMWBxF/P/PCRrnIqZLYJmqbGvWbLi90GCOiRil1HNKqY1KqUVeHM8JqRQxlnWiRo3Q2Vk/s1JFCuxv2ND0Ecysd3iKZCci5quw4srJxltYAtCifbRnTIZgt8RkqogZNy7oPmlRt25mW7gguhBIJEOZ3Z2sd+/Q+9/J8XbvDn5mNWoErZl2nFpivBQd2RzvJDjFKgiZLpcyS/E7FTG1MIOSWCJmI2YG/BQHxyvCuNc4FTFLMAPTaCJmAGZm3u5SNgsz860i7mEsMeCtS9k6zKDdSZyRRTNMfMwq4M8O99mK+Sx6EhSM0dIs78XEY/hpiXEjYh7G1BeKVvumAphDdAubZZ35xsU5LdYSFDGNMK6LTgb+SwPLSCLGoi/mO9kH+DXwCMlZXjdixL0TEdMmsHQa3+OWPBAxGDvzSR4dyxFeF7wMdyezyJS4mEiB/UpBU9v3KnzQaBcx9nZ27ryz+rpk4i1+DosNXbcucrtU4nSQuXt30BWxsBA6d459XLuISed3YdQoM3C388gjmW3hAu8sMeGZyQ480P0kht3ltF276qIQnFlivBYd2RrvJLihC2ZGPV0ixpqlPtDFPvEKXn4ZWB7h8HjDMHErTmZ7rKD+aO5kRZgZeMsSsxlj1Yo20AXogREbXrr3xKoRE4shwJmYAqFOsAbNliUGoruU+VXo0qIhRkQ5yfK1F5PhrQy4I0qbRZj02NE+Wytuym1cTBXGHdGeHttprZgfMBn3OsVp1xr4FBiBcSEcQ+LujE7SK1u0xIj5VIkYayCZwyJGa/058cvKeko63Mkgc+JiIrmTQey4GCeWGK/jLcJde9ItYtwMMu3CtVMnqBWeqTMMP9Msh2eWGxzr+Z0heCViNmwIfpfr1zcipFMnd8eL50oGziwxXouObI13ykaUUicppZYqpX5USjmdFveI4cAnpL6yNhgR0wl3FoNWxLbETMfUZBkUo42dYYHlpw7aLgwcO0qRLsAMaudgBoWW+0CkoH6LAkyNHq8tMYmIGDDXbQXOBn6WC1wvwBqMRBMxfhW6tGgUWDrJdGM9EFoAT2MESzjxYp0KMKmW3YqYUsx3x/75ORUxSzGfQ6xkFhZ1gVeAP2ESOlzsqpdBrIeaE0tMDYyQSbUlRmJiPCUd7mSQOZaYSO5kEHv22Elgv9fxFn5bYtwMMt0E9YO/Iib8uq5dG7ldJmH/Ptq/s27dyexWmN69jQXS7f1vbxPtu+3EEuO16MjWeKdsQylViCmjfjIm4v08pZTTyHcPGI6ZYXZbBDIR3GQms2hNbBEzAzMQrx2jjZ1BmBlzJy5l32I+khox2gzGXL/FmIFuAfEF1cF4G9wfr9BlvL6AM8vQ95gBc2dMNq02RI+nygRLDDiL97EGJH8N7Hcd1V2uZmH+l1huEcXAAox1xSmRkjJ0wFlMzFJiu5KFUwDcD1yFETTlLva1cCNiwHxH0u1OtpX0JSuJTtpEjFJqtFJqrlJqbqkHI0AvRcyOHUFLR82aoXEcmWCJqawMnRm2C6tYaZadWGLGjatugUgm3iJ8sP3TT5HbpQo3g0w3Qf3gn4iprKx+Pi8y8qUau4g++ODga7f3qz0e5sCAh4zbmBgnlph69YLZ9Xbvri6GY+2bqOi4/fbq67Ih3ikLORT4UWu9QmtdDrwMnJG+0x+LGZim2qWsAjPocitiYllidmMG30e6OF4NTJCzExGzkOiuZBaW1WUWxq2sH/EtTYMwcQURquS6phLjbpeoJcatiOlGUNTFylBmPRj8FjFO4mKsmaHuGCHzMfBGWJtZmLTk0WKdwIiYXzCWLadYs37h7mQbiW0drcRceweznNU4GpM1bEm8hhEowVzbxg7bp0PE2PvSBHNtUlEfxx1pEzFa6wla60Fa60EtWiR/w9kHDWvXBquuJ8IK273QuXOov3wmWGI2bQqmkW3aNFR0JOtONmoU3H9/8H1BAUyYkHi8Rbg72caNJq1uunAzyMwWS0xpafU0wtlmiRk4MPh61Sp3xULDLTHgfhLDiYhRKr41JpK4UCqyGHHCEWEhBrVrJ3f/CVE5gFDfEXuU7368nmwLUh8jAt738JiR+BF3mcksWmEGorsjbJuDGYw5jYexGIaJJ4j1Y7UJIw6iBfVbdMG4TM3EiJhYrmQWboRDPDZgBm2JipimGOuCk74sxbiSWcQSMdbMpd/uZE4sMdYPajOMlaIvJg21lS65DPO/x/OVLg4s3biURbLEWLPVsb6fqzEix40lxiKZJAROM5NZpFLEbMEIKrultLFtm79krTtZ3brBQWVFRXIz/tFcySBUxPhliYlUI8Yi1qDLiYgBuO66YOB/VRUcfnhi/dy7N/ScFk5T4HrBuHHBjG0WBQWRB5/ZYokJt25Bdlhi7N/Hbt2gQaCO3a5d7lIYRxIx4TEx8USRExED8eNijjqq+jqtE8u4BtXFaP36ImD8xOvJtlCGY6wOqfxBtIL63XrKxSp4OT2wHOLymFZczCcx2sQL6rdQmMHtFIwbi5OgwO4YlzYv4mLc1oiJxMEO+rIPI1jCRczPRI47KcUM42I84FNKIpaYppgB8aPASkzGMgjGOsX7bPtgCpq6EQfrAvvYXGsc1YpxkpksGt0wMTKJ2UOGsQAAIABJREFUFOdMRMRYQttrygh1JYPg9y1HRIxS6iXMFElPpdRapVS8alie4JVLWSwRY3cn88sSEy2oH5y7k0XLTgZmJtnu7jM3wd/8aCIvnXExo0ZVL2SoNZx6avW2bi0xdkHrt4jJNktMs2aJ36/h6ZUBGjc2KcbBuH7FK07pVMTEs8QsssWiFhUFXz/4YGJxMeGf4+bNUmgzRawjtKhIO4Ij0zRhpVp2mqUqERLJTAaxRcyMwPGaRdgWi/6YwU8sl7KFgWU8SwwY68t22+t4WMH9XlhivBAxgzCD9lg5kFZihIx90BwrzfImzDWOkG4xLbixxFj/t/U9Og6TjvhezPWdibNYpyKMSHcjDtZi4r7s18lJrRhrkJCIO1kh5h5Il4ipJGiZ85I8EDFa6/O01m201jW11u201s96cdx42AdFyWTziZZeGTLPEhNLxCRqiQEYZPvdSFTERLO4pFPElJdXHxhqDe++G7pu+/bg51mrlrN6NmKJcUe4iLFbT5xaLjZvDn5ORUWh97zTuJhJk0InKmbOjN42niXGLmIuvDDoJrdnD9xyS/TjRiOSGP3ey/p8gsUcoLtSqrNSqhZwLvBWervQHyMWUhkXs5hgQLgbLBN/+EOuCpNe2U08jEUBRrj9H9HjAr7FuEK1irLdjjVD35BQS0UsBmEGkfscto+GV5YYMNXeo2HPTGYRK82yn4Uuwb0lpjbGOmHxN0wc182YeJi+GOtZPIpx704WnpTBeh/PEtOIYEFYt1j9dBMAvxUjCt2KGEiNlXcL1S19OSZi/CIVlpjwYo2ZZokJdyeLFRPjJDuZhV3EzEtw4irSYBvSK2IWLape9BPgjbD4QbsrWbdukeuGhNOgQTAeadeuyIHfqSCaJcZNXIkfeGGJsVthevUK/ZycHM9KuW3nuuui13VxY4np39/U67H4z39g1qzq+8QikohZurT6OiE5tNYVmMIN/8OMqF/RWidS9jsJCoATgWm4y6zkhu9wHw8D0S0x32EGVW7jYSwexAiqs4jsDmUF9ccK5LY4JNDuMJwPXQ7GxDQkG9y/lmAq20SxAgNjPWAtEWO3xMRKs7wJ/+JhIGiJcSpimhH6WXcG/ghMBj7DmZsgGHHwEyYw3wmR0mPXwVy7eCKmJ86+n5EoxggSNwNUNzViLFIpYvLAEuMXXhW8dBoTs3GjPwNHp+5k9kFXeXlwkF1YGIxHiIbdnWzevMT+z0wQMXPmBF/3s3koTJ1q3I4s3MbDgHG788MaE+m67toVOf4ok/BCxESKh7FwUitm7NjQzx1i13VxY4np29fEyJx1VnDd9de7u3fEEpM+tNbvaa17aK27aq19yv92EmbgGWs2PlH2kVhmMggOzsN/bKx4mEQsMWAGji8H+nU5oTPSVRjLkRNXMjAD5uuBK12c35qdSzYuZh1moJjMkKkpJkFBrL4sxVjF7JmgGmI+n0y0xNTDXBOn7mSR/Nr/DLTFiE03IgZMqmUnRKvxE69WzA8k5kpmkUhwv9v0yiAiJkvxwhKzZ09wIGFVeLdTt64JtgUjDLY5uVc9xqk7mX3QZR/gNm5sBuCx6NAhOIDbti3Uxc4pdneydjbLrV8i5oILggJl50746KPgNrfxMBaZImIgs+NiKiqC94pS5juYiDtZpPTKFk7uf7d1XWJZYiorQ0VVn8BY8YEHgha62bPhpZciHzsSke4NETG5zAmYWd1JKTj2jxghk4iIqY0ZOIdbYmZgrDRdkujXsZi4h/8CT9jWr8DUfnEqYsAEgZ8Vt1WQrhgRkGxcTDKFLu0cHKcv3xM5iLwbkWvFlOKvJUZhrq8bS0w49YFHMN/BYxye96DA0olL2Q5M/yJ9frFqxezECJxEgvot+mKG2W5c3xIRMZaLjlMRU4GZDIj3sNEYoRIuYhpg/i8RMUnhhYhZuTI4c9qhQ+Sq7X7HxcRyJ4s26HIa1G/hRXC/fbBtP5ZfIuaQQ+DXvw6+nzIl+DoRSwz4I2Ls4tD+/cxkERMuogsLvbfEODme27ousSwxK1eaSQ8w96HVtksXY4GxuPDC4IRINLc1C7HE5BstgMuAx6heIyNZLO+4REQMRK4VMx1jhUnUncbiT5iyPDcSLPhpBfXHy0yWDAWYuiMvEcx+lQjJFLq0czDRg/s1xtMxUrxPpDTLVRhh4KclBoyIcZpiOVpyiHMwQiNWkUs7zTBWFCcWDmsAEunzi2WJsQYJyVhi6mJEkFsRU4Q718UijHXEqYhZBkwAXovTbidmYiQ8HqEAM+khIiYpwgcxibhAxXIls/A7LiaWJaZJk6CVZetWMwMO7oL6LZKNi/FbxOzaBYsDz3FLlJ15ZnD7W28F6wllqyWmuDj4OpOD+8NdySC1IiaaZWfcuOpWyFjFJGNZYsJdyeyMHRt02aysNL9FJSUmHieakNmzJ/gdKigI9nPlyshxXUKu8AQmvuNC4s+EumExRmw4DXoPpzWhImYdsIrE42HsKOBfGD//cwLn+TawPlHR5ZSnMTPJxxE73XMsvLTEQGRrzCbMoDCaiFmHsVxZbMVkpMoEEePUEhNrRjXCDHJMnAb3x0rK0B4jwCIVbbQGCclYYsB9EoISjIXI7cSBm1ox1gN4ZZx2ltiO9Lk1wXwH/SWrRUyTJkFXL7e1JyxiZSaz8LvgZSxLTI0aZqYbzMDJEi9ugvotkrXE2C0GdkG0bl16Yom++SYoUnr1MoPKww4LXrNNm2DGDNMXu4jJdEuMXcQcckjwdSZbYiKJmJYtg2mJt26N75q5fXvwf6xZs3rSDScxMaefHvrda98+djHJWJaYWCKmYUPTx3Bixd/Ya1u1bRsUZZWViblzCtlCEWYGtAg4E2cDQCdYmcnqxmsYhXBLjGUxSTQeJpxGmP97C3AeZhbdqqWRSjpjLEodgZNxn5RuOyYpQapFjJXRI5KIsR5S9ir1fhe6tGhEfEuMxgyI3abpjsUAzDWLl2HHelBGEzEQ2RpjfR4uBggRGYBxWYuVWtuO2/TKFm5EzP+3d+7RcpRlvn6+vZO9gVy2gRAIJCRcglwkRM1CLlEQlAFhDAqOMyuj43IcRtfo6OjojGZGRo85enStuXj0zIhHzziePUfGkciIKAZkBrkbASXILcQkECAJJIGQe/b+zh9vf6u+qq6qrqqu6ure/T5rZe3q6t7dleruXe/v+72X9Y2frUSMWw1PEjHqxLSFMe2nlKV1JnP47ken08kOHgwHU3Hz1+JWj9t1Yu6/v3lKfCv8YPvkkwOBuXevBK1Vc5+XLXDWWfJzYACWLg32r1wp58gdz5QpMHs2mem0iNm9W4J5kCDZb1bQa05M3u+rn1a1YEGzSDjySDj0UNl+8cV4UeR/JhYulFqYtGGSaXOXfBFzeszicVKjhaT6G1+EzpkjwtuhKWUTnblI6+G1iCNTRreyh2nP1TiKcGH/nYjAWBT/8EIsBP4JcURuIF89TDvMRrpfnQm8nXw1SWW0V3bMQOqL4kRMXGcyR1ybZXcB6gUn5mUkLalMEbMI+d6safG4Vk4MxNfFPNa4v12R7b4/Wd2Y9VQvYtzFd13qowLhFRdEqogphTJFTDc6MVu3BivJRxwRv9pbloiZMyf4v770UvjctMLa5rS3Y72/GZ1IKYvWwzj8upjvf7+5HqZV0wOfTouYqAvn13L0mhMD+b6vaalkIO9bqw6Fd90VbJ+bYeB4WsvyNCcG8tffqIjpd85HCtVvANptlrYfSX9pV8S8BDQKv7gDaWccc9Fpi3cD729sd0rEgATQtwBvAN4F/K+Mv1emiIHk4v5HEXcu7g9GXJvlXnJi3B/Tsp0YkBFQaWxCAu44MeLOddyK4OO0n0oG+ZoQ7EbE6fwCr+NETJa0F3exfAop8k+iVTqZipi2aVfEZEknq9OJSUslc8QFXkVETDvF/du3S/c2EAdm6tTuETFvfGMw3X39evjud4P78tTDQOdFjC8Mjz463PWtlRMzOiopV1mLzMskScTk6VDWSsRA67qYvCJm2jRJ0QTpaOcK+ffvD89viTueFSua5w2l1d9ERcwrvetlKxFT53urlMmHkID6GuCHLR6bxhNIMNKuiAFJKduJBF1l1MPE8ffAJ5H/eyeZBtwEvBX4E6RrWqugr2wRsxhJ44nmvz+KFJHHDS2bgQiAXnVi0oLhosxDWjPf2eJxafVMxyBhcPRiaglmxLTLrMbrZBExzhEq6sTsI1udiguWx0hvMa0ipnLaETEHDoQDnxMSukjW6cSkzYhxxKXA5O1O5iha3O8H2y49q5MiZvv2wDmaPBnOPDO4b2gILrssuH3ttcF2nnoYCIuYTnwWoiJm7tzgdtrASzfk0TW8aFVkXjZlODF+e+UkEZNWFzM+DnffHdzOImKMiV8UePzxoGnGvHmBKPZZtiw8M2batPT6mzQnJm3g5egovO999b23SpkY4GtIysky4tvoZsEp/nZEjFsl2wzci6TqlFUPE2UYERAJOdyVcgjw78j5Xg7c2OLxVTgx0Dwr6DHSmzJE2yx3kxPTSsRU4cQY5PN5R4vHPU3yezcJCf6jgfxm5P/UTmcyn1eTTcQUaa/syDMrxjUPgPSUMhdIpqWT1Tt1u+dFTDsDLzduDAKTY46RVdM46nRiokFsHGWlk0FxJybuODspYvxjXbgQhofD9/tdynbtCrZ7zYkZGZE6HkgfeLl8eTDs1JFWZF42ZaeTRWfEZHm+Rx4J6mRmzUpepIgStyjQKpXMcdVVwfb556fX3/jfiWOPbU4nSxKoy5cHDpGjk++tUjaHAtcjAdUVyGpqXh5GLudFO5NB4MQ8h6xuG7IPH+w1JgP/B0kxuqXFY1060qElvfZrGj/9VcJ9SDCZtvIfbbO8FRk2WdZxFWU6sAepeUmiChEDImKeInnWC7Rujz035vfL6kzmWIS0z97b4nHrGz+rFDH7gWeQ+U2QXty/DekaFxccz0Dc310x93WOnhcxfhCTVECbhL8i/8ILySuZ3e7ExK0cF+lOBsWL+/3OZHU4MUmpZI5LL42fAdSOE1OHiDGm2Y2JI++Qx7JpN51szx5pNQySMpUkNtNETNSFyVr7FPd9cq27IV3E+M1BWnUYizoxRx0lIhWkJi1pyGnd761SBfORuQ2/Bm4u8PsPIwXj7QS0fjrZHUgR/kgbz9ftTAbOonU6UtpKfhFmIA6Uv0q4FnG+WjkxTxEEwnUPunQ4WzrNjalSxEDye3gA+TynvX/H0ezEOCu8TBFzkGCWUxI/RMRIkc9bVhHzNOKeLEFSF1uJmMOJb/fsAst6U8omlIjJ48SMjsLf/V1we9++5JSMOoddps2IcbRKJ8sjYo45JnBSXn453Io463G63z/mmGBf1SImrjOZz7Rp8KY3Ne/P68S4wY0AO3dWP88j7rxmqYvJW2ReNr6I9tMZs35fH3sscCJOPDFozRwlrSYmbz2Mox0nxhcx69alLwJERYwx2epi5iQsKnbqvVWq4nJkgFyrAXRxtNuZDILhes8A91BdPUw3cS6S5pO2mlzWoEufaHG/+7KniZgFSPDpgs7nqb8eBgKhm1bcn9blqh3OAKaSnFL2HHLO0kSBG3jpW9+PISmPc2N/Iz9ZOpRtAX4E/D7xdVGtyCpi3IX3BMTxaZVOlvSeqYgphdmzg45dL7wQThVKwlr46EelJsYnKSVjxoyg2Pell5pTOaokS2F/melk0eL+rHUxdaeTtXJiINylDGSF/8c/zvc6AwPhlfqq3Zi485rFiYkrMp88ObnIvGySnJjZs4Pv0tatyd/XLKlkkF4TU1TExDkxWUXMyEjw+/v2JX/uDxwIu5dO8Gepi3nnO5v3pTUQUHqFIWSq/Q1IykdW9iO1Eu2KmEOQgHQV0hK3qnqYbuI8pLg5rcNVWYMufV6LpA65P5Tuy562qhZts7yV7hAxWZ2YaeQfaNmKScA5JIsYd4FslU62l3CjhccR0VhETMRxAvL/TxMx30HcmncXfI1pSHphVhEzD5mhlMWJiUNFTCkMDISDumgg43fxmTdPxMvZZyenhcWlZAwMdL6g25G3sD9OxOQp7IdwSlnWupg608meeSYYHDhlSnLQuz8SF4yPFyuI7mRKWVEnZtmy5hX7U05Jr9EokyQRMzgYdgySUqCydCaDZlHk6oCefz4QAZMnh4V5K6JOzO7dQWrYwEBYaMSRJaXsuecCp+moo4JUxyxtlg9GOmLOm5feQEDpJa5EVrR/muN3HkeCn5QvSmaOBpz67wcnxtX83JVwf5Z0pCJEh14+igTaU1N+Jypinqc70smyODEvUH4qmWMJ8BDxXbmyNGWIG3hZVmcyxwDSajlNxPwL0gAgZZWsJVlmxWxA0sPmoiKmS0hKUYl2aNq4UVLI/NSjKEkpGb6A6KSIyZJOVlaLZUdZTszRR0vQB3LOoiKiLHwX5jWvaXYgHF/8YvO+IgXRdYuYLE7MwYPNNRVPPlnde+BjbbKIgdYpZaOj8Ld/G9xOG5Q6OBg+H04U3XNPsO81r0lOR4sj+n165JFAcJx0Uuvn8lu1J81aiqaSObKImFWrgu0f/UjS6FTATBTejASyeVLKXJ59u04MSF2MRQKcfshPPBw4lWQRkyUdqQjR4v5Had2U4XAk3bBXnZgy2yv7LEHeo7tj7ssiYtzn3K2oHUBSrMrqTOZYhIiYuBzjh5HPQlEXxpFFxKxvPG4IcYi2IM5rHNtREdMBkoKiuA5NjsHBYAXXkZaSUVddTN50suefl3S3PXvk9qRJQTerrPgi5v77YWys9e/EBduTJoWF17NZOv8VIEsqGZRXEN0pERM3QBSyOTFr1zbX6+zenS7gy2L37uC1h4ebu/6lFfe7hQf3+QX4539Od8vivv9FU8mg+fuUNZXMkcWJKSpinnkmaDIwNARveEPr41F6iUOA3wZWkj6EzqeMzmQO9we7H1wYx7mIiIkLLstur+xwxf2/QALwLCLGEHQo24PU8fSKE7ON6pyY1yFpX3EpZU8jtS1prx11Yn6DfPfKdGJARMzLxNegfBv5P/xem6+R1YlxF83jGz/XJzx2G1oT0wGSRExacPr00xIczZsndSCtUjLqcGIOHAhWtI0JB88+ftC1bVtzZ7I8E+lB8vNdStiuXekzKxxx6WQQTilzKV9lk1XElFXs7gvaKkXMjh2BczJtWiBGszgxv/pV/P6f5slSKUjUhYl+/tKcmLiFh717092yOFHUjoiJOjFViJhoe2X/d52TuGFD87nwXZglS5Lbwiu9zJXIyvXtGR//MBIQ57AbE3EXun6oh3GchwRicRe6qkQMBMX9zyLDRbOIUDcrxs2I6SUnpioRMwVxtuI6lLl6prQg6EjElXAipuzOZI6k4v4x4P8ClxB8/4pSVMTEpZQdQD6XSU7MdOS8qohpm7ig6NvfTp6zMG+euAXLlknQMz7eOiWjDidm69bg/zBzZrNz5BgeDgLcgwfDgWHeVDJHnqGX+/YFwila+F51XYy1YRET15nMsWJFc9BXpCC6U05M0oygqBMT9zn3RYwf5NchYqKkiZgibln0+Q4cCDtO7ToxWdsrO9pxYoaGwvNsnojMPvRFzJvf3PpYlF7kUmQuw79nfHwZnckc7g9NvzkxEJ9SVqWIWYysgLvXzRI0n4QEoW5FsBucmLpFDMjn9V6aG2JkacowgNQjRUVM2elkpyONCKIi5jbkONtNJQMRMTtJ7rY3jvw/5zduu4tNnDvkxEmSiBlA0htVxLRNtM3qX/0VvDvh81C0i08dTkyWon6HLxz8PPy8Rf2OPMX9/vmYNStck1K1iHnyyaD+54gj4Pjjkx+7bJm4bVndtyTqFjEjIzC1Uf+5Z0/8wEtfxHzoQ8H23Xcnp1iWRSsRk5ZONjeho2WaWxYVMb/6VZCONm9euNV3Ftp1YqI1MXEiM0nEQHJKmbVwizeXT0VMNowx7zDGPGyMGTfGLG79G3VzGCJkViKrtGnsQ9KLyhIx7wA+hcyI6RdORgK1JBEzRDViweVtu1zZLE7MAiQQdSt33eDEHILM3ElKJxtDiu6rqokBcQ73AvdH9mftLHccQU3M48j7XfbxHoLUX0VFzLeRlLzfLuE1WrVZfhZxWNxFcybiZMU5MS6wSFsJn4GKmBLwA5y77w6LlDlzJJBuJ2iFepyYLEX9Dj9Y9EVMUScmT3F/UioZVC9ifBdm8eLWqXN53LckOiVi/PPqixhjWtfF+CLm4ouDDl/798OdrWa7tUk7Tszllzc/vtXCQ/T52kklg/Axb9oUnN+hobBASWLWrMAZfeml8PlwFBExDz0U/O054gh49atbH4sCwBrg7WTPz+oCrkKKypMKzh33IEFiWSLmZGAFEyQ0yIghqIuJsgk4hvR0pKK44v6bkEAyS7Dt/gC5ziXd4MQYxI1JcmJ2IHU/VTsxEK6LsUhNTJYZP25WDJTfmcxnEfCAd/tlpInH79DeoFpHKxHjt1cGee+SOpS5uoQ0MaciphSSgrKFCyUV5Omn2wtaoX4nJqmo3+EHXn4KShki5oEH0ov7kxwD6KyISauHKZMsIsZv7T1/fv42zpB+XtPqYl58MRAHkyfLAMULLwzurzqlLGnQpWPOnKBr3bPPBk0ArA0LEMi28BB1dtoVMSMjgZvod3M75ZRgJlUaxrROKUsTMUkDL3/yk2D7oouCc6ikY619xFqbobKvm7gMKUhO61J2EPgIEmRf1omDmsCcixTXR1ccnqaaVDKQVJyTkBSoU8gmlJyIcZ24usGJAXESkpwYd06rFDFHI+fGFzHbEKcyy/s3FxGsY4iIKTuVzLEISQV0QeRKJPWrjFQyyC9iQFLK4tLJVMR0jGuuid+/YwdMnx5/X17qcGKKppOVIWKOPjoQILt3J7d7hWTHAMKpPP0iYqKtvTdsKDaPJk3EpDkxDz0UbJ92mgTenRQxrZyYyZODz5a1wfHfcgs82HDaDz1Uzm2WhQc37R6kecTt3np7EREzMBAvvrKkkjnS2iyPjycX9kOyE+PXw1x8cfZjUbJjjLnaGLPaGLN6a9X901OZBlyMiJi4rlkAX0ZSU/4nQV2CUgy3kh9t07uJbCv5RXGrhVlX/o9EPhvrkG5Wr6jioAqQ5sR0QsSApJTdiTgwkK+eaS6BgNlMtU4MwC8bP/8FcULKqkErImKcExPNe+6jdDJjzCXGmMeMMWuNMX9ZxnPmIanoN6n9bBHqcGLqTCeLvub55ycH4f5xdjKd7OBBaQHt6BYRE9dhq8g8mqJOjJ9KdsYZ8vOCC4JAf/Xq9Nkr7dJKxEB8StmXvhTse+97w8I8jaGhQCxbG3TBO+wwcWOLEPfap+fI2ElzYrZsCQZWHn54c7MJX8Q8/riInr17w+JM62HCGGNuMcasifm3NM/zWGuvtdYuttYuPjKpHWTHuApxAuKmyW8E/hq4HHhbJw9qgrIYKbr2rWBL9pqKojgRk7U9tmuzDCIKumUdOs2JybKiXwZLkK5tjzduuwtj1poYgFsbP6sWMQ8ix3cr4sKUla54OFLDlSZijkDSFx3HI27Q85HHluXEJC3ClEPb3wBjzCDwVaQS8TTg94wxZYwOzkxZrXPTiAau49W+L0DxdDI/QC0qYkZHwyv6L7yQ7CbkSSdL6hhXhC99KRALg4Od6bwFEng6QbB9u3TD8ilrHk0ZTowL4mfMkKGPIJ/d2yusDigiYh58MHAaBgbgox/N95r+8zle97rkjn6tiBMxeZyYNBGTlkoGcs7c6+/eLY+/804RMgAnn1zu37aJgLX2TdbaV8X8u6HuYyvObyOBdVxKmevW8RWqqdfoNw5DpqX7ImYHMo+lShHj2mnmCZmciKlbZPt0gxMTrYtxq6ZZa2IAXOeUqtLJDkcE04NIQwcLvKvE5zdIal2SiFlP2IWB5A5lTsSkuX1OxKQFdu8Arki5vz3KkPFnAWutteustfuB7wC5Vr/apazWuWkMDQWCYHw8vli3bIo6MT5Fu5MtX94cnCe5CWmF/dOnB0XOe/aU5wCMjsLf/E1we2ysWMpWEQYHw+c7+lkoS1SX4cT4TkSnUsqyiJhoHYvvwlx1VbjNcBb853Occ06+5/CJO+6y0snSUskc0ZQyba3cj8wA3oS0WvaDhO8D/wF8huaARCnOucB9SPcmqLa9suMNyPuZJ2Ryf1y6oajfUXdNDIh7cgRhEWMIUqzScBfU25Cw+MSUx7aLK+7/F0R4lf1aabNi/BkxjqRZMduR93WQZGYg35e0lqf3AlNT7m+PMkTMsQRtHSChEq7KXOOyWue2wq+L6URKWR4nJin1pqgTk8dNSAu2jakmpeyTnwwXXUOxlK2i+M5c9LOwYkVz0fXkyflFdREnZnw83omB7hIxvnPys5/BddcFtz/+8fyvGefEFKmHcUS/T4cdFi+UkmjHiYHm4n6/qF/rYfJhjHmbMeZp4Bzgh8aYm+s+puxciQQXri3rTuCDSAvkD9d1UBOUcxHnxZ3rTogYgwiYPJbxgsbPXnFitiGh5kjFx2CQlDJfxMxC2j+3YgQJtHciQf1wFQfYYBHwCPBrynVhHEkixiIiZn5kv7sdFTHbaJ0C6ALMpJSy55D3obrO9h1LqKw617iM1rmt6GRx/+houKi3VVvcpGCxqIjJ4yakBdtQvogZG0uud8qbslWUtLqYK65oTps744x8n8kDB2TQIogQ9D970OzEuNfbsAF27pTtmTPD78frXx+kVz30UHVCPK+I+c//DLrfXXBBeEZRVuJEzNln538eR/S4Tz89XzewuXODTmabN8PLLwf3ZRExvhNzxx3SIRDEBbzgguzHoYC1dqW1do61dthae5S19rfqPqbsXIGshLqUsk8j3Y2+RrbgTMlOdOhlJ0RMEbrRiZmOODFxaUUvIMFuJ8LNJcjcpM1kb68MIoBccFNVKpnD9cYfQlorl02SiHkBcUyiF8upiNiLSycls8qJAAAc/klEQVRrV8S4+RyvTbi/fcr4VG0i8OJAPjUV9KGqn04V97vuVn7dzcc/np4qVbaIiUvRi3MTrE3vTgblihhr4QMfSL6/U7UCaSLmvvuaRUzeeqCtW4PHH3lkc23H9OnhgZeurXE0lcyfmzNlSjiwv+227MeTh7zpZD6f+ESx14yKmFNOSX7tLESdmDypZCBiwx+8us67PuQVMStXBttnn11ex0WlF5gJnI+klN2PdCR7P9CGQlcSmIMEslERk3NabuV0Y03MCNLye2/MfS9QfSqZY0nj553kb8rgwtiqivodrrj/raR3/irKbOScR1JVYjuTOeJmxZQlYgyBcCufMkTMz4EFxpjjjTFDwO8iCbsTjk45MUW6W5UtYlyKnv/7ixc3uwk7dgRpXVOnBoG1jy9iXOeoonz60/D1r8ffV3YdVBppIuaOO2hi8+Z83fJauVvGxNfFJNXDOKpOKRsby9ZYIk5szpkDl1xS7HWjouipp9qrj4p+n/KKGAinlPl1MXlFjOtkBloP059cibR+vQpZMf3v9R7OhMYferkJEZFVphYV4WjgGuCddR+Ih1tZiUsp66SIeQ1wCJJS1q0iZh7y/v1NRc/vaoCei+zPK2K201pktRIxq5HOe11cE2OtPYgk6d6MJPr9m7X24XaftxvplBNTpLtVUk1M0cJ+EMFys5c9Hvd/bhVsQ3lOzJe/DJ/7XHD7vPMkGK6yDiqJNBHjp/4NejVx992X/fmznNe4upi6RcyOHYGDNDKS3B3sezENl7ZsgX/912Kve3dkvMOuXe01elizpvnY8pJUF5NFxMyfHz9YU0VMP/I2ZDXzN8Df0z2zQSYi5yJpSE9RfXvlohgkAM7R871yXL1LXHF/lhX9shhCek2tarxuERFTdTpZ1e9f0qyYNBFzQuN+b8WsNCemulQyKClJ0Vp7k7X2ZGvtidbaDq2Fd55OOTFFultNmSId1HyGhmRoYDuceSYMNxainnyyOWBP60zmaGfg5eioBHTGwIe9Ota3vEXSoTZsqLYOKokkETM2Fg6or7oq2C5bxLRyYtyMGJ+zzw4+E2vXll9DlCWVDOJdxf37izdm+MxnmvcVbfQwOgpf/Wp435e/nF8QxYkYa7OJmEmTYMGC8L7p0+Gss+Ifr0xkZiPtlq+gmhx6JcC16b2LfDUV/U63ODEgKWVuFSrP+/daZH5KweFiXUOaiJlKvLtyPDLs012cLO2LmGeR+r3qivqheyYl9QSdcmJWrGhehW2VKmVMc9A4Y0a4JqIIQ0PBfBGAe+8N31+lE+Nqg9wwRMdJJ8F3vxu/Ut0pkkTMmjXwUuPv+OzZ8E7P8a/aidm9O0hbGhiA02JGDwwPw5Ilwe2y3ZisIqasWTpVPN/y5bBvX3jfnj35BVFcm+Xt24N5L1Onpte3nBKZf3fhhcXn3ii9zg3A9ehMmKpZiMyMKVJT0c+kOTF1iBhHnvfvLcixdlPDhCKkiZh5xP8NibZZfhkRNa3SyUYazxcnYqov6gcVMbko6sQ4N2FgQH62WtFdtiw842LGjGypUtGUsqL1MFH8YvB77gnfV6WI+dSnmmuDQILAaNOBTpMkYvxUsvPOk4GLjtWrgy5crSjixPz610EziAULks9RlSllWUVM2QNqy3y+sgRRnBOTxYVx+LUwIOl5Sj+jAqZ6JgGvQ+aFbEVFTFaSnJh9yDT4ToqYcwi+K3neP0P31T8VYRYS2kdFzHqS50q5wWxOxLhBl62cmAHkvU8SMYagkUE1qIjJQREnxncTrJWfWXL1/Xauo6PZUqXinJgy8EVM1InJkk529NGBI7RlS/MQzTjuuis5aCxr1kw7JIkYv6j/vPMklc6JuF27RGhkoYgT06oexuGLmG9/W+qJyhoSmlXElD2gtsznK0sQHX988LnfuFHS5bKKmNFR+PGPw/uuu64zw1wVpb85lyAdSUVMNpyIiToxWYPhMnkF4HKp+zEdcBARMklOTBxzG7/n2mjmed9mEC9iVgOnUmVRP6iIyUXUicnSMrdIpzGA33iNIvxWrWlUJWJ8N+Hee8NuQpZge/LkQABGWzI7fLdq2jQRAEl0qo1yGlmcGJe25dcxZE0pK+LEZBUxjz0Wvr1xY3tF8D5ZRUzZA2rLfL6yBNEhhwRCxdVtZRUxy5c3D3Pdu7dzw1wVpX/xp+SqiMmGs4mjToy7IHTSiQG4COniNq3Dr9stRGfF7ESExvyEx09ChIwLPJ0oyRJEJomY6ov6QUVMLqZNk8AEJEd+167Wv1MkNeXAgXA73qyTwqNBYzudyXyOOy4IpHfuDA/hzBJsQ3pKWdSt8gcDRulkG+U0/NS9F14IBnC69/Www6QpAlQnYqJOzC9/GdxOEzF//dfN+4oWwUfJKmKg/AG1ZT1fmYIomlKWVcSUXTOkKEpWvFxuFTEZSXJi6hIxn0Omf/QrURGT1pnMcQL508kgXsQ803j9aov6QUVMLqKT07PUxfir5T5pbsLTTwe1DbNnB8KpFVXVxBiTXBeTJZ0M0kVMnFsF0kXrH/6hvNX6Mpk8OTi/1sqwSd+Fed3rgsYDVYmYkRER1iCr9P77kiZiqgyQ84iYbqYsQVRUxJRdM6QoSlZmAK4rioqYbEwGDqXZiakjnQykOUM/ppI5ioiY4ykvnawzRf2gIiY3eetiPvjB5n2HHpruJhRJJYPq0skguS6mDCcmKXjeuxf+9E/LXa0vk2hKWVwqGciQUFcb8dBD8YLNZ9cucbxAusO9ImUshB8Iu65X06Y1T7D3qTJA3rYt2O5lEVMWfoeyPCKm7JohRVHy8HrEXahiovpEZTrdk07W78wGNiMdxiC7iNkM7CYQMUXTyVYj8qLaon5QEZMbvyh96dLWdQRxLY4/8pH0YLwbRYxfF+NW/PfvD1beBwbCQX2UJBFjbfN8G0e3rzqniRi/pmf6dDj1VNkeG4MHHkh/3qgwTGuTHef0LVyY/jtxATLAn/95+nFlwXdiykpn7GV8J2bt2rCIOTZlkbfsmiFFUfLwOeCnaEe4PIzQPelk/c5sYBzpsAciYoaAoxJ/I2izvB4RJcOIu9aKGcCOyL5fIEX9U7IdbhuoiMnB6KispDu2bm1dEP3DHzbvaxWc+yImaz0MVCtiFi8OOqatWSNOgZ9ON2tWeDp9FD9ge+aZYPt732ueyQG9sersi5h164KalGj6HYRTyqId3qJkdbcgfjU/bsiljwuQo5/DRx5J/70sTJR0srKIppP5Ar5Vi+Wya4YURcnKTDqRCjOxSHJihpH0LqVzRGfFbACOIz3kd22W1xEMuswi4mcgrbT3NG5bOlXUDypicrF8efOcj7SC6B074Gc/a97/xBPpr1PUiYnWxJS5Ej51ahAcWws//3m+YPuYY4JtF8jt3g0f+1iwf9q03lp19kXMjTcGdUwLFzbP9MhTF5PnvCY5Ma1YtkwaKdx0U7Dv61+XYLkdVMSE8UXME08Eg1CHh/X8KIoykYhzYvIEw0p5xImYtFQyCA+8dO9bFtxquUspewZ4jk4U9YOKmFzkLYi++eZA9PjpPVWJmGhQ9Ed/VO5ciWirZT/YTivqh/h0si98ITh3M2dKUN1Lq86+iPFnesS1h65KxMSt5mcRMY5LLgmO98AB+Oxns/9uHCpiwoyMBIsL/vDKOXPSU/4URVF6iyQnRi8EnScqYtbTWsTMQhyz3yCCJGsqT1TEdK6oH1TE5CJvQfSNNwbbS5cG248/nv46RUXMbbeFb2/eXN78D2juUOZ3JmsVbEdFzLp18MUvBvs+//ly0986gS9i/GJ9v6jfccYZsvoO8v76s2WitOvEvOpV6b/jY0w4be9b32qeI5OVPXvkH0hntqnVzrjqGXw3xtEqlUxRFKW3mE58TYyKmM7jAodngb2IMzK/xe8Ygg5l7TgxnSvqBxUxuYgriB4cjK/dGBuDH/0ouP2RjwTb69aFV2V99uwJgtjBweQWzUnHF6Ws+R/QnogZGQnO3e7d8L73BbUwixfDe99bzjF2kqRGBnFOzNAQvPrVwe2fp7Swb8eJmT+/OZWtFeefD29+s2yPj8M118Q/zh9IOn9+sziOujDqNAh+hzKHihhFUSYWI8Q7MdrhpfMMI+f9WcANHWzlxICImHbTyX6BtCjvTB2UipgcuIJoP3VqfBwuuqj5sffeGwR1s2fD618fBKQHDiSnoG3YEGzPnQuTJmU/Pn9Apk9ZA/Je+cogQN6yJVyg3iqdzJiwG+O7Rl/5StA0oJeIEzFz5iQ7c9F0vCTacWI2by7mvH3uc8H2ddeFB2dC80DSDRuaXT5NJYtHnZh6McZ8yRjzqDHmV8aYlcaYlKbliqIUw6WTjXv7tqFOTF24WTFZ2is7fBFTJJ3MIk5M55pi9GDoWC/Llkl3LZcyZC184xvNj/NTyS67TIL0k08O9iXVxRRNJYPqB+QNDIRrO269NdhuFWxDfEvZ97wnHNz3EnEiJi6VzJG1LiaPiPnBD8K39+wplkJ41lnhlMdFiwK3Zd06+NCHmufbRF0+FTHxxImYtPbKSumsAl5lrV0IPA58subjUZQJyAgSxO5q3LZoOlmdFBExJwA7kfewiBOzCdhCp4r6QUVMYT7wgWD7a19r7lrmi5jLL5efCxYE+5LqYtoRMZ0YkOenlO3fH2xnETH+4wEOOUSK+3uVOBETl0rmiIoYa+Mfl0fExKUKFk0hjLaF3rAB3vUuCcK3R2dZNfBdPh10GY86MfVirf2JtdYl8N5Df4/yVpSKmN746VLKXgYOoCKmLnwRMwBkWTnzg86sIsYZ29vpdFE/qIgpzJVXBkHsU0+F58Fs3BjMkxkeDtLNfBGTxYnJMyMGOjMgLxroOlqlk42ONrsPY2Nwyy3lHFcd5BUxJ54YNC/Ytk0cjijj4+H5O0elzaYif8e8NP7pn5r3JQkth+/y6aDLeLQmpqt4L/Cjlo9SFCUnrhjTFfe7VS29GNSDEzHrEQEzOcPv+CImazrZICJgtyOpZIPAmZmPsl1UxBRkeDhcjP6P/xhs+4LmgguCLk15RUxeJwaqH5CXlPqVxTGINjM4cKC8pgN1MDwcdBwDEY5r1iQ/3pjWKWXbt8t5AZg+vdlZi1JmCmGa8Fm0SJoT+AwNhV0+TSeLZ9YsmBIZXKwiplyMMbcYY9bE/FvqPWY5cBBITLY0xlxtjFltjFm9Na2FoKIoEaJOjLsg6MWgHmYD+4Ffki2VDIo5MSCCxzkxnSvqBxUxbfHHfxx0YLr55mBlPS6VDKqviekERxzRvLI8ZUrrdrplOgbdwuhoOEXOWnj/+9PrUVqJmDypZFBuCmGS8Jk7Fx54AL75zXDns0WLwiJZRUw8xoRTyiZNEmGjlIe19k3W2lfF/LsBwBjzHuByYJm1yf6itfZaa+1ia+3iI5PaDyqKEoMTMc6JURFTLy49Zg3ZRcw0wE1NLyJiVtPJehhQEdMWxx8Pl14q29ZKbcyuXeGC98suC7b9QGb9+uYaEeh+EQPNKWWtUsmg+qYDdbB8eXO6Vat6FF/ExHUoyytiykwhTBJEn/988Fp33RXc9/DDwVwYUBGThu9iWQvf+U59x9JvGGMuAT4BvNVau7vV4xVFKYJb4VInpjtwAcQ4rWfE+LjAM8/gvhnAr4CtdLIeBlTEtI1f4P+Nb8BNNwXzT04/PSxEDj00aIk7NhYWLAAvvhgUUA8PZwti6yAqYjrtGHQLRdwlX8TcfbeIDt+5yStioLwUwiyC6NRTA0dx1y5YtSq4T0VMPKOj8OCDwe2xsXKH0Cot+QqyxLjKGPOgMSam+ktRlPaIOjFaE1Mv/upyVicGpEMZ5HdiXOCjIqanuPRSCfZAgrg/+7PgPt+FcaTVxaxfH2y7oYLdSLQu5v77WwdknWg60GmKuEurVoWHQG7cGA5ofRGTxeEqm1aCyBh4+9uD29dfH2yriIknrh6szCG0SjrW2pOstXOttYsa/95f9zEpysQjyYlREVMPRUXMqUhNS56p2c616WxRP7QpYowx7zDGPGyMGTfGdDYRrksYHJQg1LFpU7Dt18M40upieiGVDCSNyGf37mwry1U3Heg0RdylViloRZyYTuOLmB/8IGhEoCImnolYD6YoihLGFcb6NTHTgKH4hysVMw1wHWXyiJiPAfcigiQrTsScDhya4/fap921/jXA24HbSziWnuUP/xAmR7rXDQyEnRVHmhPTTnvlTnLNNc37+nFluYi7lBS4btggjSF6QcQsXhx019q2DW5vfPtVxMQzEevBFEVRwgwigbNzYrahLkzdODcmz8VmKvCqnK/jREznvYy2RIy19hFr7WNlHUyvctRR8NpIGuD4eHynqrSBl73ixOjKckBedyktcD3tNLjhhuD2X/xFd9ZNGANve1tw+/rr5f/vD8TUOTEBE7EeTFEUpZnphNPJdDWrXmYDs6jeHXEiprP1MKA1MaURLdKHeHciqxPTzSJGV5aLExfQOvbtg507g9tbtnRvAbifUrZyJezYIUIGYNq0Zmeyn5mI9WCKoijNjBBOJ1MRUy+/BSxt+aj2cWkjCYMEK6SliMkyRCwLE32I2JYt8fuj7sQJJwQF+089BXv3Bvf1iojRleXixAW0n/lMs5Pn6NY0vSVLYGajnfyzz0pXPoemkjUz0erBFEVRmlEnprtYDlzbgde5HLiVrnRiWg0Ry8pEHyKW1Z0YGgrqXayFJ58MtntFxOjKcntEA9pPfzp+ZoyjG9P0Jk2Cpd4yxte/HmyriFEURelHphNusax5xf3BZODCWl5Z08lKIo87EVcX8/zzsuoOko7T7TUFurJcLoODQavuKN2apufXxdzutfZQEaMoitKPjCBOzBgywV0vBkq1tNti+W3GmKeBc4AfGmNuLueweo887kRcXUzUhfFniSj9Qa+l6V10kQjuKCpiFEVR+hHnxOwALCpilKpptzvZSmvtHGvtsLX2KGvtb5V1YL1IVnciblZMr6SSKdXRa2l6hxwSP9BVRYyiKEo/4pwY129fLwZKtWg6WQ20cmK6eUaMUi29lqbndylzqIhRFEXpR6YDuwDX6ajL8+KVnkdFTA3E1cSoE6P0IpdeCsPD4X0qYhRFUfqRkcbP9Y2fejFQqkVFTA3MmyfdnUDa0778sooYpTeZOhUuvji877Of7c7ZNoqiKEqVTG/8dAGNihilWlTE1MCkSXDiicHttWtVxCi9y+zZ4dvPP9+9QzoVRVGUqoiKGE0nU6pFRUxN+Clljz4KGzYEt1XEKL2EP+jS0a1DOhVFUZSqcOlkv0HCy1fUeCxKP6AipiZ8EfNf/wUHDsj2zJmSoqMovcKmTfH7u3FIp6IoilIVvhMzAw0xlarRT1hN+CJm1apgW10YpddIGsbZrUM6FUVRlCpwTsxTaD2M0glUxNSEPyvmySeDbRUxSq/Ra0M6FUVRlCpwTsw4Wg+jdAIVMTXhOzE+OiNG6TV6bUinoiiKUgUj3rY6MUr1TKr7APqVOXNk4vneveH96sQovciyZSpalO7FGPPfgKXIEvEW4D3W2mfqPSpFmWgcBgwCY6iIUTqBOjE1MTAQbrPsUBGjKIpSOl+y1i601i4CbgQ+XfcBKcrEwxCklKmIUapHRUyN+HUxDhUxiqIo5WKtfcm7OQWwdR2LokxsnIjRmhilejSdrEaidTGunkBRFEUpF2PMCuDdwIvAGxMeczVwNcBx2l5PUQqgTozSOdSJqZGoiDnmGBgerudYFEVRehljzC3GmDUx/5YCWGuXW2vnAqPAB+Oew1p7rbV2sbV28ZFHHtnJw1eUCYIr7lcRo1SPOjE1EhUxmkqmKIpSDGvtmzI+dBS4CbimwsNRlD5F08mUzqFOTI1Ea2IeeABGR+s5FkVRlImKMcZfMloKPFrXsSjKxEadGKVzqBNTIz/9afj2rl1w9dWyre1qFUVRSuMLxphXIi2WNwDvr/l4FGWCojUxSudQEVMjy5c379u9W/ariFEURSkHa+2VdR+DovQH6sQonUNFTI1s3Jhvv6IoiqIoSvdyGbAdGXypKNWiNTE1ktTBUzt7KoqiKIrSe7wBuBYZfKko1aIipkZWrIDDIosVhx0m+xVFURRFURRFiUdFTI0sWwbXXisDLt2gy2uv1XoYRVEURVEURUlDa2JqZtkyFS2KoiiKoiiKkgd1YhRFURRFURRF6SlUxCiKoiiKoiiK0lMYa23nX9SYrcjAsaLMBJ4v6XB6GT0Pgp4HQc+DoOch+zmYZ609suqD6UX0OlUaeh4EPQ+CngdBz4OQ5TwkXqdqETHtYoxZba1dXPdx1I2eB0HPg6DnQdDzoOegG9D3QNDzIOh5EPQ8CHoehHbPg6aTKYqiKIqiKIrSU6iIURRFURRFURSlp+hVEXNt3QfQJeh5EPQ8CHoeBD0Peg66AX0PBD0Pgp4HQc+DoOdBaOs89GRNjKIoiqIoiqIo/UuvOjGKoiiKoiiKovQpPSdijDGXGGMeM8asNcb8Zd3H0ymMMd80xmwxxqzx9h1ujFlljHmi8XNGncdYNcaYucaY24wxvzbGPGyM+XBjf7+dh0OMMfcZY37ZOA+faew/3hhzb+O7cZ0xZqjuY+0ExphBY8wDxpgbG7f77jwYY9YbYx4yxjxojFnd2NdX34tuQq9Tep3S65Rep3z0OlXNdaqnRIwxZhD4KnApcBrwe8aY0+o9qo7xz8AlkX1/CdxqrV0A3Nq4PZE5CHzMWnsacDbwJ433v9/Owz7gQmvtmcAi4BJjzNnA/wD+zlp7ErAd+MMaj7GTfBh4xLvdr+fhjdbaRV67yn77XnQFep3S6xR6nQK9TkXR65RQ6nWqp0QMcBaw1lq7zlq7H/gOsLTmY+oI1trbgW2R3UuBbzW2vwVc0dGD6jDW2mettfc3tncifxCOpf/Og7XWvty4ObnxzwIXAv/e2D/hzwOAMWYOcBnwvxu3DX14HhLoq+9FF6HXqTB99TnU65Sg16kAvU6l0tb3otdEzLHAU97tpxv7+pWjrLXPNrafA46q82A6iTFmPvBq4F768Dw0rOkHgS3AKuBJYIe19mDjIf3y3fh74BPAeOP2EfTnebDAT4wxvzDGXN3Y13ffiy5Br1Nh+vZzqNcpvU410OuUUPp1alKZR6fUh7XWGmP6otWcMWYq8D3gI9bal2RRQ+iX82CtHQMWGWNeAawETqn5kDqOMeZyYIu19hfGmAvqPp6aWWKt3WSMmQWsMsY86t/ZL98Lpbvpp8+hXqf0OgV6nYpQ+nWq15yYTcBc7/acxr5+ZbMxZjZA4+eWmo+ncowxk5ELw6i19vrG7r47Dw5r7Q7gNuAc4BXGGLcw0Q/fjfOAtxpj1iMpOxcC/0D/nQestZsaP7cgwcJZ9PH3omb0OhWm7z6Hep0Ko9cpvU5BNdepXhMxPwcWNLo6DAG/C/xHzcdUJ/8B/EFj+w+AG2o8lspp5JF+A3jEWvu33l39dh6ObKxsYYw5FHgzknd9G3BV42ET/jxYaz9prZ1jrZ2P/C34qbV2GX12HowxU4wx09w2cDGwhj77XnQRep0K01efQ71OCXqdEvQ6JVR1neq5YZfGmLcg+YWDwDettStqPqSOYIz5f8AFwExgM3AN8H3g34DjgA3A71hro0WVEwZjzBLgZ8BDBLmln0LyjfvpPCxECuAGkYWIf7PWftYYcwKy0nM48ADw+9baffUdaedo2PR/bq29vN/OQ+P/u7JxcxLwr9baFcaYI+ij70U3odcpvU6h1ym9TkXQ61T516meEzGKoiiKoiiKovQ3vZZOpiiKoiiKoihKn6MiRlEURVEURVGUnkJFjKIoiqIoiqIoPYWKGEVRFEVRFEVRegoVMYqiKIqiKIqi9BQqYhRFURRFURRF6SlUxCiKoiiKoiiK0lOoiFEURVEURVEUpaf4/4WdaOygpwb0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x432 with 4 Axes>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#can plot things in each subplot by referencing its axes, order doesn't matter.\n",
    "ax4.plot(np.random.randn(50), c='yellow')\n",
    "ax1.plot(np.random.randn(50), c='red', linestyle='--')\n",
    "ax2.plot(np.random.randn(50), c='green', linestyle=':')\n",
    "ax3.plot(np.random.randn(50), c='blue', marker='o', linewidth=3.0)\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#more advanced subplots\n",
    "plt.figure(figsize=(14, 6))\n",
    "\n",
    "ax1 = plt.subplot2grid((3,3), (0,0), colspan=3)\n",
    "ax2 = plt.subplot2grid((3,3), (1,0), colspan=2)\n",
    "ax3 = plt.subplot2grid((3,3), (1,2), rowspan=2)\n",
    "ax4 = plt.subplot2grid((3,3), (2,0))\n",
    "ax5 = plt.subplot2grid((3,3), (2,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 50\n",
    "x = np.random.rand(N)\n",
    "y = np.random.rand(N)\n",
    "colors = np.random.rand(N)\n",
    "area = np.pi * (20 * np.random.rand(N))**2  # 0 to 15 point radii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#are plotting 4 different pieces of info\n",
    "plt.figure(figsize=(14, 6))\n",
    "\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5, cmap='Spectral')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = np.random.randn(1000)\n",
    "\n",
    "plt.subplots(figsize=(12, 6))\n",
    "plt.hist(values, bins=100, alpha=0.8,\n",
    "          histtype='bar', color='steelblue',\n",
    "          edgecolor='green')\n",
    "plt.xlim(xmin=-5, xmax=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- KDE (density plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<scipy.stats.kde.gaussian_kde at 0x12d9a5150>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "density = stats.kde.gaussian_kde(values)\n",
    "density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(12, 6))\n",
    "\n",
    "values2 = np.linspace(min(values)-10, max(values)+10, 100)\n",
    "\n",
    "plt.plot(values2, density(values2), color='#FF7F00')\n",
    "plt.fill_between(values2, 0, density(values2), alpha=0.5, color='#FF7F00')\n",
    "plt.xlim(xmin=-5, xmax=5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- combine plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(12, 6))\n",
    "\n",
    "plt.hist(values, bins=100, alpha=0.8, density=1,\n",
    "          histtype='bar', color='steelblue',\n",
    "          edgecolor='green')\n",
    "\n",
    "plt.plot(values2, density(values2), color='#FF7F00', linewidth=3.0)\n",
    "plt.xlim(xmin=-5, xmax=5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = np.random.rand(1, 5)[0]\n",
    "Y2 = np.random.rand(1, 5)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "barWidth = 0.5\n",
    "plt.bar(np.arange(len(Y)), Y, width=barWidth, color='#00b894')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Real Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing data, changing data type to dates and making column 1 our index\n",
    "df = pd.read_csv(\n",
    "    '/Users/rebeccaceppas/code/data-analysis-course/btc-eth-prices-outliers.csv',\n",
    "    index_col=0,\n",
    "    parse_dates=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bitcoin</th>\n",
       "      <th>Ether</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-02</th>\n",
       "      <td>1099.169125</td>\n",
       "      <td>48.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "      <td>1141.813000</td>\n",
       "      <td>44.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-04</th>\n",
       "      <td>1141.600363</td>\n",
       "      <td>44.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "      <td>1133.079314</td>\n",
       "      <td>44.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-06</th>\n",
       "      <td>1196.307937</td>\n",
       "      <td>43.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Bitcoin  Ether\n",
       "Timestamp                     \n",
       "2017-04-02  1099.169125  48.55\n",
       "2017-04-03  1141.813000  44.13\n",
       "2017-04-04  1141.600363  44.43\n",
       "2017-04-05  1133.079314  44.90\n",
       "2017-04-06  1196.307937  43.23"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1301bf550>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#quick plot to see there are some invalid spikes and data missing for ether\n",
    "df.plot(figsize=(16,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1304c3450>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#zooming into issue for ether\n",
    "df.loc['2017-12': '2017-12-15'].plot(y='Ether', figsize=(16, 9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#want to check if there are null values here\n",
    "df_na = df.loc['2017-12': '2017-12-15']\n",
    "df_na['Ether'].isna().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bitcoin</th>\n",
       "      <th>Ether</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-08</th>\n",
       "      <td>16007.436667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-09</th>\n",
       "      <td>15142.834152</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-10</th>\n",
       "      <td>14869.805000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Bitcoin  Ether\n",
       "Timestamp                      \n",
       "2017-12-08  16007.436667    NaN\n",
       "2017-12-09  15142.834152    NaN\n",
       "2017-12-10  14869.805000    NaN"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding when exactly are the null values found\n",
    "df_na.loc[df_na['Ether'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bitcoin</th>\n",
       "      <th>Ether</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-06</th>\n",
       "      <td>13540.980000</td>\n",
       "      <td>422.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-07</th>\n",
       "      <td>16501.971667</td>\n",
       "      <td>421.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-08</th>\n",
       "      <td>16007.436667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-09</th>\n",
       "      <td>15142.834152</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-10</th>\n",
       "      <td>14869.805000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-11</th>\n",
       "      <td>16762.116667</td>\n",
       "      <td>513.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-12</th>\n",
       "      <td>17276.393333</td>\n",
       "      <td>656.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Bitcoin   Ether\n",
       "Timestamp                       \n",
       "2017-12-06  13540.980000  422.48\n",
       "2017-12-07  16501.971667  421.15\n",
       "2017-12-08  16007.436667     NaN\n",
       "2017-12-09  15142.834152     NaN\n",
       "2017-12-10  14869.805000     NaN\n",
       "2017-12-11  16762.116667  513.29\n",
       "2017-12-12  17276.393333  656.52"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at the context around it\n",
    "df.loc['2017-12-06': '2017-12-12']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "#assuming little flunctuations at those times, can fill in\n",
    "df.fillna(method='bfill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x125630410>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+YcSeR2KMMUmVlk9IxB9NeRZIjDG+kJad7ZH9e9SWMcb0qnR8HolfnpBogcQY4wvp+YREf5yvBRJjjC+k453tfplG3kZtGWN8oWmurT4uR2/yyxMSLZAYY3whLUdtKUT6uhBdYE1bxhh/SMOmLa9lK/VP2GokxhhfSMdp5L0+ktRv3LIaiTHGF6Lhww/DYZPFm0Y+9U/YAokxxhfS8T4Sbxr5vi5F56xpyxjjC+nYtOV1tqf++VqNxBjjC+l2H0k0YPqhKc8CiTHGV/wwiikZYgNIqtfCLJAYY3whkmaTNsZOjZLq52yBxBjjC+n2hMTY4JHq821ZIDHG+ELTExLTpmkrpkbSh+XoCgskxhhfiKRdZ3vz8n5dIxGRG0RkpYh8JCILRCRHRMaKyDsislZEHheRLLdttnu/1q0fE3Ocm136JyJyWkz6bJe2VkRuSqSsxhh/ax61ldoX1WSJrXml+inHHUhEZBRwLTBNVScBQeB84NfAnao6HigHLnO7XAaUu/Q73XaIyES33+HAbOB3IhIUkSBwH3A6MBG4wG1rjElL6dbZ3ryc6uecaNNWBpArIhlAP2ArcBLwpFs/HzjbLZ/l3uPWnywi4tIfU9V6VV0PrAWmu9daVV2nqg3AY25bY0waamra6tti9JrY5qz9tmlLVTcDvwE24gWQvcAyYI+qhtxmpcAotzwK2OT2DbntB8emt9qnvfR9iMgVIlIiIiVlZWXxnpIxJoU136CX2hfVZEmLPhIRGYhXQxgLjATy8Jqmep2qPqCq01R1WlFRUV8UwRjTw5qfR9Knxeg1miajtk4B1qtqmao2Ak8DxwGFrqkLoBjY7JY3A6MB3PoCYFdseqt92ks3xqShSCS9npDYoo8kxZ9ulUgg2QjMEJF+rq/jZGAV8ApwjttmDvCMW17o3uPWv6xeyF0InO9GdY0FJgDvAkuBCW4UWBZeh/zCBMprjPGxdHtCovqojyTu2X9V9R0ReRJ4DwgB7wMPAH8HHhORX7i0eW6XecCfRGQtsBsvMKCqK0XkCbwgFAKuUtUwgIhcDSzGGxH2sKqujLe8xhifS7P7SFrUSPquGF2S0DTyqnoLcEur5HV4I65ab1sHfLOd49wG3NZG+iJgUSJlNMbsH5rm2kr5y2py+KlGYne2G2N8Id2ekBh7mhZIjDEmCdLteSQtgkeKn7MFEmOML6Rb01ZszSvVa2EWSIwxvpDO95FY05YxxiRDuk3a6J+WLQskxhh/SOcnJEZSvG3LAokxxhfSbtRWGs3+a4wxvULTrrM9dq6t1D5nCyTGGF9Ityck2qgtY4zpIenS2R7bxW6jtowxJkF+mlI9WdLpCYnGGNPj/HRRTZYWfSQpftIWSIwxKc9PN+cli1ofiTHGJI+2s7w/s1FbxhiTRC2befqwIL2oRY1kP35CojHG9IqWN+elRyRp2bSV2udsgcQY4yspfk1NmlQPHrEskBhjUp6f+guSJeKjAQYJBRIRKRSRJ0XkYxFZLSLHisggEVkiImvcz4FuWxGRuSKyVkQ+FJGjYo4zx22/RkTmxKRPFZEVbp+5IiKJlNcY409+GsGULC2fkNhnxeiSRGskdwPPq+qhwGRgNXAT8JKqTgBecu8BTgcmuNcVwO8BRGQQ3nPfj8F71vst0eDjtrk8Zr/ZCZbXGONDLUZtpfhFNVk0He4jEZEC4N+AeQCq2qCqe4CzgPlus/nA2W75LOBR9bwNFIrICOA0YImq7lbVcmAJMNutG6Cqb6v3KT4acyxjTBpJz6attpdTUSI1krFAGfBHEXlfRB4SkTxgmKpuddtsA4a55VHAppj9S11aR+mlbaQbY9KMn6ZUTxY/jVRLJJBkAEcBv1fVLwDVNDdjAeBqEj3+CYjIFSJSIiIlZWVlPZ2dMaa3+eiimiwta2GpLZFAUgqUquo77v2TeIFlu2uWwv3c4dZvBkbH7F/s0jpKL24jfR+q+oCqTlPVaUVFRQmckjEmFfnpoposafGERFXdBmwSkUNc0snAKmAhEB15NQd4xi0vBC5xo7dmAHtdE9hiYJaIDHSd7LOAxW5dhYjMcKO1Lok5ljEmjfhpBFPS+KiPJCPB/a8B/iwiWcA64FK84PSEiFwGfA6c67ZdBJwBrAVq3Lao6m4R+Tmw1G33M1Xd7Za/CzwC5AL/cC9jTJrx0wimZGkx43GK18MSCiSquhyY1saqk9vYVoGr2jnOw8DDbaSXAJMSKaMxxv9aXlTTg5/mF7M7240xKS/2G3m61EhaNuel9jlbIDHGpL40HP5rNRJjjEmidHxCop8e5mWBxBiT8mKbtlL9oposfroJ0wKJMSblaVp2tjcvp/qoLQskxpiU56f+gmRpeUNiHxakCyyQGGNSnp/mnUoWe0KiMcb0kNS+pCaP+mhaGAskxpiUF0n3O9tT/JwtkBhjUl56PiExdqRaHxakCyyQGGNSnrazvD/z070zFkiMMSkvHZu27IZEY4xJIj/dnJcsNmrLGGOSKh2f2e6f87RAYoxJeX7qL0iWiNVIjDEmefzUzJMsane2G2NM8rR8HkkfFqQX+Wl+MQskxpiUF/uNPNUvqskSsVFbxhiTPOn+hMRUP+eEA4mIBEXkfRF5zr0fKyLviMhaEXlcRLJcerZ7v9atHxNzjJtd+iciclpM+myXtlZEbkq0rMYYf0rH4b9+mvE4GTWS64DVMe9/DdypquOBcuAyl34ZUO7S73TbISITgfOBw4HZwO9ccAoC9wGnAxOBC9y2xpg046f+gmRpOWqr78rRFQkFEhEpBr4MPOTeC3AS8KTbZD5wtls+y73HrT/ZbX8W8Jiq1qvqemAtMN291qrqOlVtAB5z2xpj0kw6PiGRNOojuQv4ARDtChsM7FHVkHtfCoxyy6OATQBu/V63fVN6q33aS9+HiFwhIiUiUlJWVpbgKRljUk16Nm01L6f6KccdSETkTGCHqi5LYnnioqoPqOo0VZ1WVFTU18UxxiRZi/6CPixHb/LT/GIZCex7HPBVETkDyAEGAHcDhSKS4WodxcBmt/1mYDRQKiIZQAGwKyY9Knaf9tKNMWkkehkVSf2LarK0uAkzxTtJ4q6RqOrNqlqsqmPwOstfVtULgVeAc9xmc4Bn3PJC9x63/mX1fiMWAue7UV1jgQnAu8BSYIIbBZbl8lgYb3mNMf4VvagGRNKoacs/tbBEaiTtuRF4TER+AbwPzHPp84A/ichaYDdeYEBVV4rIE8AqIARcpaphABG5GlgMBIGHVXVlD5TXGJPiorWQoEjaTNrop4d5JSWQqOqrwKtueR3eiKvW29QB32xn/9uA29pIXwQsSkYZjTH+Fb2OBgLp09nup5sw7c52Y0zKi23aSvVv58nipxmPLZAYY1JeJLZpK9Wvqklic20ZY0wSNdVIAtK3BelFfuojsUBijEl50f6CgKT+t/Nk0RajtlL7nC2QGGNSXnoO/21eTvVztkBijEl5sU1bKX5NTZq0uCHRGGN6S7RpJyiSNk1bfroh0QKJMSblRZqatkj9q2qSqI3aMsaY5IleVNOqaQtvbjGwUVvGGJOw6HU0GEivpq2giBdMUvycLZAYY1JeU40kzUZtiYBgNRJjjEmYxvSRpPo9FcmiCiLipoVJ7XO2QGKMSXnR62gwIEQiHW+7v1BVBFcL6+vCdMICiTEm5UVimrbSRUTVO18f3M3fE88jMcaYpGqaRj6NJm1UjTblpXxfuwUSY0zqa9G0leIX1WSJRPtIsOeRGGNMwppHbaVPZ3tE1TejtqxGYoxJec1PSEyf4b+Ab/pI4q6RiMhoEXlFRFaJyEoRuc6lDxKRJSKyxv0c6NJFROaKyFoR+VBEjoo51hy3/RoRmROTPlVEVrh95oqkUU+bMaZJU9NWWj0h0dVIJPX7SBJp2goB31PVicAM4CoRmQjcBLykqhOAl9x7gNOBCe51BfB78AIPcAtwDN6z3m+JBh+3zeUx+81OoLzGGJ9qOWorxa+qSRIdtSU+GGAQdyBR1a2q+p5brgRWA6OAs4D5brP5wNlu+SzgUfW8DRSKyAjgNGCJqu5W1XJgCTDbrRugqm+r9yk+GnMsY0waaW7aSv1v58kSHbXlPcyrr0vTsaR0tovIGOALwDvAMFXd6lZtA4a55VHAppjdSl1aR+mlbaQbY/YToXCEbXvrOt0u+o08vebaAhB3Q2Jqn3PCgURE+gNPAderakXsOleT6PFPQESuEJESESkpKyvr6eyMMUky96U1zPjVS3x57j95/dP2/3ZbPCGxl8rW11SVgOsj2a9rJCKSiRdE/qyqT7vk7a5ZCvdzh0vfDIyO2b3YpXWUXtxG+j5U9QFVnaaq04qKihI5JWNMLwmFIyxY6jVG1DSEufJPy9he0XbtpPmZ7ekzakujkzbuz30kbgTVPGC1qt4Rs2ohEB15NQd4Jib9Ejd6awaw1zWBLQZmichA18k+C1js1lWIyAyX1yUxxzLG+FRlXSMAL67eTlllPQ9eMo1HLj2aUCTC3S+taXOfljcktn1RbexiM5lfRDvbAz4YtZXIfSTHARcDK0RkuUv7IXA78ISIXAZ8Dpzr1i0CzgDWAjXApQCqultEfg4sddv9TFV3u+XvAo8AucA/3MsY4xOqSuyo/btfXMNdL33KGUeMYEXpXsYV5XHiIUVkBgOcM7WYp98r5abTD2VATmaL40Rimrbaatsqr27gy3P/yZa9dXz7i2O45SsT8fvdAhFtroGler9Q3IFEVd/Au+myLSe3sb0CV7VzrIeBh9tILwEmxVtGY0zfuuov7/Hp9iruv3gq1fUh7nzxU6YdOJAXVm4jFFH+97JjyAx6DSMXTD+ABe9u4hfPreKakyYwelA/AMIR5Z9rvP6TQNtxhH98tI0te+s4deIwHnlrA9PHDuKMI0b01mn2iObmvNTvI7E7240xPaIxHGHRim0AXDLvXaaPHURWMMC8bx9NZlAor2lkVGFu0/ZHjCpgyuhCnigp5c21u7js+LGceeQIXvlkB88s3wLs27SlqlTUhfjb8s2MHZLHHy6ayql3vsY9L6/llMOGUdsYpiC3Ze3GL1S94c6RSOr3C1kgMcb0iFVbvEGcV/7bOP745gb+7/3NzJo4rOnC3i+r5eVHRPjrd47l/Y17uGjeO/zsuVV8WLqHra7f4zsnHERjONJ0UQ2FI3zroXd4d73XEv69Uw8mGBCuOWk8Nzz+AZNuXUxuZpB/3jiTyroQQIvAleqifSQqmvKd7RZIjDE9Ytnn5QB8+7gxfPnIEfy1pJTzjh7d4T6ZwQDTxw7ipf88gTuXfMrT73sDNW845WCuO2UC97/2GbWNYb73xAc89Z53m9llx4/l+PFDOOFgb8Tm2VNGsbemkVufXUVDKMKvFq3muQ+2gsDXvzCKMyeP5Ogxg3rwzJNDlaYHW6V6H4nN/muM6RHvrN/FyIIcRhTkcmRxIT8/exKTRhV0ad/Rg/px4+mHMnZIHiccXMSFMw4A4Nxpo8nNDPLUe6VkBIRjxw3mx18+jJmHDiUQ8LpsRYRvHzeW9b86g3FD8ljw7iaGF+QwbEAO8//1Ofe9srZFXmWV9ck98SRpniIl9SeFsRqJMSbpdlXV8/LHO7hoxoFxH2PYgBxe+f6JLdIG5mVx/SkTeOuzXdz7rS+QnRFsd3SWiHDDqQezeOU2fnH2JApyM7npqRUs+mgr4YgSDAiLVmzlqr+8x1+vPJZpKVZLid5HEiD1J6q0GokxJqk276nltr+vpjGsXHjMAUk//pUnHMT8f59Ofk4mWRkdX8K+Mnkk937rKAr7ZSEiHDNuEJV1IT7dXomqcv/r61CF37362T77lpbXMO+N9XxYuiep5VftWp+H4g2dFh9MI281EmNMwhrDEZ5aVsqzH27hX5/tQoGLZxzI+KH5fV20FqJ9Izc8vpzS8lqq6kOMH9qflz/ewU+fXck/1+wkFI4wfmg+b322k5qGMJlB4f6Lp3LSocM6OXr7qupDbC6v5f2N5dz14hoOHZHPvDlHEww016ZUlR2V9VTWhXhyWSnLN+4hPyeT/tkZLN+4hz01DRT2y0r4M+gJkuqjAbpr2rRpWlJS0tfFMCYtNIQihCIRfvj0Cv62fAtjh+TxlSNHcA3yKvkAABvbSURBVO7Royke2K+vi7cPVeWH/7eCDzbt5YhRBUw9cCCnHzGcr9zzBht21TB5dCGjB+by7vrdjBmSx0/OnMh/PfkhZZX1/PbcyXxp/JCmvpiuWr+zmqv/8h4r3Si2Q4bl88n2Si6ecSCnHzGcw0cUUBcKc+2C93ln/W5yMgOEwspBRf255uTxjCzM5fz73+aI4gIeufRo8nN6ZjiziCxT1Wlx7WuBxBjTXVv21PLLRatZvHIbjWHvGvK9Uw/m6pPG+/KO8k+3V/L8R9u48oRxZGcEW6xbuWUv5/z+X95osVMP5pqTJ3TpmKrK7c9/zP2vrSMzKFw8Ywz5ORlce/IE/vv5j7n/9XUADOmfTVZQ2FvbyFcmj2Tdzmp++83JTTdkAjz/0Vau/sv7HFlcwLw5R7Ojsp7fvPAJ35t1MIcOH5CUz8ACSQwLJMb0rH99tovv/O8yGkIRzjt6NANyMznuoMEcM25wXxetx+ypaeDmp1fw0uodHHvQYBT45dcmtah11TWGyQgIDeEIzyzfwifbKnnkrQ2cO62Yq2dO4IDBzduqKq9+UkZFXSNPvbeZusYwN84+lKkHDmyZcY2bLarfoKZgkpMZpCA3k817agGvhrPgihn0z84gIyAEAsLu6gZ+uWg1lXWNHDd+CMUDc8nJCDKsIIeDivq3eY4WSGJYIDEmMXtrGqmoa2RvbSMvrt7Omu1VFPbLZEdlPRkBYfHKbYwdkse8OUczZkheXxe31+yubuDWhStZvbWCbXvrGJKfzeLr/42XP97BohVbeWHVNnIyg2QEhJ1VDQCcN200v/r6Ed1uDmvyv+dAIAjfehzwak53LvmUF1Zt56dfPZyyynp+9+paRhTkUlZZz5D8LE44uIjnP9pORV0jwwfksHF3TYtDfv2oUXx18kjGD+3fIhBaIIlhgcSYZpV1jSzftIfXPimjX1aQb0wt5vNdNXywaQ+nHzF8n87wR/+1gZ8/t6qpuUoEhuXnUF7TQPHAXBrDyomHFPFfpx3SY2317Qo3Qs0uyB/eu/m2Ycmq7Vz+aAmHjRjA6q0VDOmfzUmHFqEK9aEIXz9qFHnZGRx1wMAWHerdds80L5Bc9U6L5LrGMDmZXhPcvDfW89i7Gzlm3CBeXLWDPbUN/NuEIr47czyTiwvYsKuGvbWN1DWGefWTMh5+cz0NoQj9szP463eO5bARXtOYBZIYFkiM8WbD/cu7G/ndK2upbgiTlREgHFHCMTckBAQuPOZAvjfrYDbtruWxpRv58zsbOfnQocyeNJysjADHjhvM0AE5RCIa/7fqZFn6ELzwE/ivtZDVtx35kYhy8h2vsX5nNVf+2zh+MPvQxAJGe24/0AskP1jXpc3DEaUxHGkKMm3ZUVnHyi0V3Pjkh+yubqAoP5uh+dksvOZLcQcSG/5rTAoqr27gs7Iq8rIziKgyqjCX8ppGlq7fzfubyskMBhhVmMsZR4xgw65qPt1exciCHF5fU8byTXtZV1ZFfSjCyYcO5eJjD+SYsYPZXdPAsx9soXhgLlMPHMj9r63j0X9t4MXV29lV3UBDKMIF00fzi7OP2Oei2OdBBKDsU2ishsqtMPigPi1KICD84uxJvL1uFzeccnDPfD6hBqjbAwiEQxDs/HIdDAjBQPtBBGBofg5DD8nhmauPY/5bn1NWWc/mPTUd7tMZq5GYtLW3tpH87IzUuEg6H23eyy8Xread9btb1B5iRSc93FvbuM+6zKBw7EFDOHBQPy6ccUCnI3qWb9rDpX98l35ZGTz93S8ybEBO4ifRUx6/CFY/C3Oeg7Ff6uvS9LyKrXDHod7y99dA/6E9ml0iTVtWIzG+VFZZz4Zd1dQ0hNlT08C4If3pn5NBQKAwN4t+2UG27a1jw65qtu2tIxRRQhHlk20VbC6vZf3OajbsqmHskDxGFeYyfewgtlfUsWZ7FQNyM5k9aTgCDMrLYmBeFgcP67/PbLWd2VFRR1V9iPpQhHVl1VQ3eDPQjizwZqDdWVVPbWOYZZ+X8881ZZRXN9IQjjA0P5vvnDCOow4YSF1jhIB4d4v3y8pg+tiBjBvSn0BA2LirhoUfbGbiyAFMGlXAzsoGhuRnMTS/68FgyuhClvznCQRFGJiXmje7Nanc5n5u7dty9JbqspbLPRxIEmGBxKS0usYw5TUNDOyXxY6Kel5YtY3FK7dR8nl5XM9o6JcVZFxRHuOH5nP2F0bx1me72FFZxx1LPqV/dgaHjcjng9I9vLh6e4v98rKCXHfKBGoawlTVhdhaUcdrn5QxsjCHwn5ZnDFpOFX1IWoawqzeWsGOyvqmG9A6MyAngxMOGUrxwFzyczI4b9poBvfP7nS/Awb34+qTmu9p6E4AiTWkC3mlhAoXQNI1kKQwCyQ+VVUf4r3PyxlZmEtNQ4gdFfXsrm4AgePHD6FfVrBpOoVIRN18Pd6NZP1du7uIMLBfZo/dQFZe3cDqbRUMzc+hPhQmNzPI8IIc+mVl0BCKsGFXNVnBAFX1IcprGjioqD+N4Qjrd1bz5LJSVm+tYMOumn2aeA4dns91J09g8uhCsoMBhg7I5qPNFYQiigB7ahupqgsxvCCbAwfnMbIgl8wMIRgQCnIzW9xwdv0p3s8dlXUMzssmGBDqGsNs2l1DZjBAeU0DZZX1/Pmdjfxy0ceIQK4b4jlr4jAq60Os2V7Jrc+uArw26pGFOYwZnMf1p0xg7JA8AiKMK8qjIDcTVa92EYkowwtyyM4MMjQ/u+kpgaYdkQhUuRpJRboEkp1tL6cgCyR9pD4UZtveOjbvqWXLnjq27KmltLyGusYIh40YwFEHFPJZWTVllfWIeBe67RX1bN1bS019mI27awh1MCVoRkD4wgGFqMLH2yrJyghQ0xCirjHSYrv+2RkcOLgfWRkBtu+toy4UITsjQHZGwO0TprIuRG1DmOzMAEX9s6ltDDNsQA7jivIQhIDAmCF5TC4uZPSgXN76bBfrd1azaMVWSstrW+SXFQxQPDCX0j21NIRaliVWfnYGx40fwmmHD2fUwFzKqxso6JfFl8YPafPehUTndIr9Np+TGWTCMO94Y/DyOunQocz/1+dMHzOII4pbToUeiXhzJOVlB8nLykCEDoNz7B3LpotqdkLEaxqkckvflqW3WI0keURkNnA3EAQeUtXbu3sMVW/YYyiiTbNoZgYDCBBWJRLxfoYjSiSiTcvhiBIKK42RCI3hiLccjtAYVipqG9ld00BFbSONYe+4kYiyt7aRz3fXsKOynj01DRTkZpIREOoaI00jYbZV1LX5DISi/GxyMgMs/GDfP5TCfpkMy89heEEOBw7O4LRJw5kxbjCby2sZlJfJiIJcBuVlsbOqnuWb9rBxdw0rt1SQERC+MnkEobAyIDeTCUP7U9MQJhgQwhFl4+4aPt9VTUM4wrEHDSE3K0B9Y4T6UISGUITcrCD5ORn0y8qgqr6R3dUN5GVlsH5nNe+s8+66DUUi/HVZaYvyZgUDDMrL4jffnAx4TUp1jWE+2V7Jpt01nDJxGBNHDCAcUfplBcnPyfRqKBkBDhjUj4kjBzCgt+9T6EBGMMBlx49tc10gIAwvSOFO6v1BbHNWtK+kI+FGb5/+w0AjUPYxNFRDv8Ew6CDISPH+IIDqHRDM8sqf4oEkpUdtiUgQ+BQ4FSgFlgIXqOqq9vbJG3Wwjrv8HkLhaPCI9Opc/tnuQji8wGs731PT0JQejigRheEDchhZmMvIwhxGFeYysjCX4QU5TWO/t1fU8WHpXg4e1p+Rhbmo0ul02X1tb20jK0r3sm5nFceMHczBw7xpGPw475LpRCTiXdyimv6PpdV7t9xQA41ueGm4AXaugYYqbzkS9l6hOqgt9wJAIAgFo717RRprvXWrn4VPn4fB42HXWvja/d70IbXlULu71XI5VG2HsPuyJoGW5c0p8I4vblht5RbIK/KCTrgR6vZ6ZQjVe8v1lZA3xDu/ASNgwEjvYSHDj/Q6wBuqvXOp2gGRRpCgt78I1FV456oRbx8UgtmQWwg5hV7ZIo0t10eXP3vFG/4bCUP+MDjkDMjIhoyc5p/BLO+VkQ3BTPc+ZjmaHsh0PzNcWrb3QHhi/6v20xsSReRY4FZVPc29vxlAVX/V3j4TDhyh9/z4PwgGhYB4zS6CEAx4F7XoSM9wRFG8x1h6j7NsXi/ivQ/gxmWLEnTrggEIipCdGSA3I0BOZoCgCCLafByvkK5E2vayRrxfvnCj9wsfXd7n/0O9Kn30D07D3r4tliPeNvWVUF/h/dFKoI2XxCwHO1nf2f7uFQi2vx5alr3p/Lv6032O6s4v+kJjHhnXxr5Nn39bx3NliR4rmAVZeZCZ21zm9n8jYxal8/TWF4Z9ytb6HFqdzz7n0cVtif7o6rbt5dHGtuHG5qDQKfE+33CSnkCYkQPHXAlv3t0yj9yB3qvfIMgd5P3MK4JBY72+hUgIhk70Akh1Gax/3Qs6GvH+z/sP85rOqnd6v885hd66jGxvn6w8qCrz/m/3lno1Ig3D7lY3CWbmeTWdpr/VCGTne6/o3wfifR61e7wghXoXdwl466LbRH9O/Kr3ma96xgs4yRSMBqNMCASR/1qz3w7/HQVsinlfChzTeiMRuQK4AmDqiACzN8/tndLFzf2iBLPdN4jM5p9tXcwCGe4VbHXxDjYvZ2RB3ljIHuBdFJsuYNFXq/fRX/T21u/zUrdPY0xauJP9tflbUOs/kO78DAS9P+roZxC7Hpov3i32pYPjZXo3d0nQ+yNtqPK++Xb0QNMWAV67lh4NrPuUo1XZYs+hs/OIXd/htm3t1976DvJovT6Q4V1YA9Fmx9hg0+oziIS8L0jZ+d7FOXrcQQd5F/5gVvP/aTDLCwDBbG+f8g3ez8zc5m/gmf28Y027zDt27kDvuIHOvgC0cuS53du+PXUVXkDKzvfKn932ZIjtikTc70hXau3zXM2t3quhheohVOu+iDY0fxEN1cek1TenRRq92lfEvY/u31jnpUXCwJp4PgUg9QNJl6jqA8ADANOmfkG56dUEj9jBH2y8y9bEY0zXBDNg2MT21w+M//G9SZUzwHvFq7sBMBD0mvt6bHqYe+LeM9UDyWZgdMz7YpfWPgl61VFjjDG9IrV7cL3O9QkiMlZEsoDzgYV9XCZjjDExUrpGoqohEbkaWIw3/PdhVV3Zx8UyxhgTI6UDCYCqLgIW9XU5jDHGtC3Vm7aMMcakOAskxhhjEmKBxBhjTEIskBhjjElISk+REg8RqQS2AXvbWF3QTnpH6zraZwjQ1vzOycwnNr11fvGUuTv7RPNLpMzd2SeT7n2eyTjP2M+0p89zCNDYzX3iySc2vau/M8nIvyufZTLyidUbf4Ox63ry8+zLv3WAQ1Q1vmm0VXW/egElwAPtrGszvaN1nexT0tP5xKa3zq+nzzOaXyJl7s4+3f08k3GesXn29Hl29LvZU+fZ1d+ZZOTflc+yB86zx/8Ge+vz7Mu/9Y4+y6689temrWe7mR7vPt09Vjz5JLvMvbFPX+ffW/v0df7x7NPX+Sd7n3i27+syp2r+cdsfm7ZKNM4ZLFM5L8vP/3na+fk/z97Kz2+f5f5YI3lgP83L8vN/nnZ+/s+zt/Lz1We539VIjDHG9K79sUZijDGmF1kgMcYYkxALJG0QERWR/415nyEiZSLyXC/lX9Ub+XQnTxF5VUQS7vwTkbPd53toosfqYn4/EpGVIvKhiCwXkX2esJnk/IpF5BkRWSMin4nI3e4RCO1tf72IxPWkIvc5/jbm/fdF5NZ4jtXF/MLuM1wpIh+IyPdEOn0+cbLy7pW/iZhzjL7GdLDtifFeE/rqGtNTf38WSNpWDUwSkVz3/lQ6e6CW6aoLgDfczx4lIscCZwJHqeqRwCm0fHRzsvMT4Gngb6o6ATgY6A/c1sFu1wPxPvKuHvi6iAyJc//uqlXVKap6ON7fxOnALb2Ud2+JnmP0taGH8kn4GiMi8czeHtffn4gEO1pvgaR9i4Avu+ULgAXRFSIyXUT+JSLvi8hbInKIS39dRKbEbPeGiEyOJ/PW33ZE5F4R+bZb3iAiPxWR90RkRbK+XXSUZ5KO3x84HrgM7yFlnZ3nGSLysYgsE5G5cXxbGwHsVNV6AFXdqapbRGSqiLzmjrtYREa4/F51NYjlIvKRiEzvZn4nAXWq+keXXxi4Afh3EckTkd+4434oIteIyLXASOAVEXmlm3kBhPBG2tzQeoWIjBGRl11eL4nIASJSICKfR2sRrkybRCSz9f6dUdUdwBXA1eIJisj/iMhSl+eVMWW50f2efiAit8dxntHj9HfnEv29PyvmXFeLyIOutvRCzAU6YR2dGzBARP4uIp+IyB+6WUOL5xrzbRFZKCIvAy918zza+/t7va1zEJEqEfmtiHwAHNvRsS2QtO8x4HwRyQGOBN6JWfcx8CVV/QLwE+CXLn0e8G0AETkYyFHVD3qofDtV9Sjg98D3eyiPZDsLeF5VPwV2icjU9jZ0n/v9wOmqOhUoiiO/F4DRIvKpiPxORE5wF817gHPccR+mZY2hn6pOAb7r1nXH4cCy2ARVrQA2Av8BjAGmuNrRn1V1LrAFmKmqM7t/egDcB1woIq2fL30PMD+aFzBXVfcCy4ET3DZnAotVtTGejFV1Hd4D54biXZz2qurRwNHA5eI92fR0vP/3Y1R1MvDf8eTl1AFfc7/3M4Hfioi4dROA+1xtaQ/wjTjzyJXmZq3/c2ltnptbNx24BpgIHAR8vRt5xXONATgK7/f3BLqnvb+/9s4hD3hHVSer6hsdHdgCSTtU9UO8P/wL2PfBWgXAX0XkI+BOvAsIwF+BM93F6t+BR3qwiE+7n8tcOf3gArw/HtzPjqrXhwLrVHW9e7+gg23bpKpVwFS8b85lwOPAlcAkYImILAd+DBTH7LbA7fs63rfNwu7m244TgftVNeSOvzsZB3WB6lHg2larjgX+4pb/hPdNFLzP4Dy3fL57nwyzgEvcZ/oOMBjv4n4K8EdVrXHlTeS8BfiliHwIvAiMAoa5detVdblbTuRvIrZp62surb1zA3hXVde52ucCmj/nTsV5jQFYEufn2N7fX3vnEAae6sqBU/4JiX1sIfAbvIvA4Jj0nwOvqOrXxOuMexVAVWtEZAle5D8X7yIWrxAtA31Oq/X17meY5P0/dpZn3ERkEF7TzxEionjfZBV4pqfyhKbmpVeBV0VkBXAVsFJV26uqt76xqjs3Wq0CzolNEJEBwAHAhm4cp7vuAt4D/tiFbRfiXYwH4f1+vhxvpiIyDu/3bwfeRf4aVV3capvT4j1+Gy7Eq5lOVdVGEdlA8+9Lfcx2YSBpTVu0f24nktjvC3TzGuNUdzOPjv7+/t5GmaPv69zfT6esRtKxh4GfquqKVukFNHeMfbvVuoeAucBSVS1PIO/PgYkiku2+FZ+cwLFSIc9zgD+p6oGqOkZVRwPr8X4H28rzE2CcNI+aOa/1ATsjIoeIyISYpCnAaqBIvI54RCRTRGK/7Z3n0o/Ha85ob2bVtrwE9BORS9wxgsBv8Wqmi4ErxXWQuj9sgEogvhlXHfft9Am8Jpiot3Dt4HgX4H+6bauApcDdwHNdvVC0JiJFwB+Ae9W7q3kx8P9F+1tE5GARyQOWAJeKG5kWc97xKAB2uCAyEzgwgWN1R3vnBjDdNeEF8H53OmwCakM815h4tPf39yUSPwcLJB1R1VLXjt3afwO/EpH3aVUbUNVlQAVd+3a4D3ehqVfVTXgXh4/cz/fjOV4K5XkB8H+t0p7Cu9jtk6eq1uL1UzwvIsvwLrjduaiDN2Jqvoiscs0hE/Ham88Bfu06EZcDX4zZp879v/6BlhfmTrkL6teAb4rIGuBTvHb9H+J9wdgIfOjy/Zbb7QF3jvF0tsf6Ld7U41HX4F3APwQuBq6LWfc4cBHdb9aK9h+sxGtaegH4qVv3EF6N7D3XHHM/kKGqz+N96y5xTUPd7s+L/n7i9fVMczXLS/D6EXpDm+fm1i0F7sX7grKefX/HOxTPNSZO7f39XUCC5wA2RUrSichIvGrooaoaiWP/ycCDqtrdEUNx64s8u0JE+qtqletQvQ9Yo6p39mB+rwLfV9WSnsrDdF+q/n7uD1zz3PdV9cxEjmM1kiRyTRrvAD+KM4h8B6+z68fJLlsq5dkNl7tvsSvxqvr393F5TC9L8d9P41iNxBhjTEKsRmKMSRkiMlpEXnH9WitF5DqXPkhElog39cwSERno0g8V78a9ehH5fqtj3eCO8ZGILHD3a5geYIHEGJNKQsD3VHUiMAO4SkQmAjcBL7mpZ15y7wF2491D85vYg4jIKJc+TVUn4Q13PR/TIyyQGGNShqpuVdX33HIl3kiiUXj3Zs13m80Hznbb7FDVpUBbd+dn4I00y8Cbz2xLDxc/bVkgMcakJHcP0RfwBrAMU9WtbtU2mu9ob5OqbsarpWwEtuLdE/RCjxU2zVkgMcakHPEmGHwKuN5NA9PE3a/T4Sgh14dyFjAWb2LMPBG5qIeKm/YskBhjUoq7g/wpvIkto3PKbZfmWZpH4E3L0pFT8ObfKnOTUj5NyxtPTRJZIDHGpAx38+k8YLWq3hGzaiEwxy3PwZujrSMbgRki0s8d82S8/hbTA+w+EmNMynBznP0TWAFEb+r9IV4/yRN4E2B+DpyrqrtFZDhQAgxw21cBE1W1QkR+ijd3VAhv6p3/iD6bxiSXBRJjjDEJsaYtY4wxCbFAYowxJiEWSIwxxiTEAokxxpiEWCAxxhiTEHtmu0lbIjIYbwJAgOF4z/ouc+9rVLVHbmBzU398UVX/0hPHN6a32fBfYwARuRWoUtXfdLZtEvI6kSQ8lc6YVGFNW8a0QUSq3M8TReQ1EXlGRNaJyO0icqGIvCsiK0TkILddkYg8JSJL3es4l36Ce875chF5X0TygduBL7m0G0RkjIj8U0Tec68vdjPvR0TkDyJSIiKfiogFKNOrrGnLmM5NBg7De/bFOuAhVZ3uHrp0DXA9cDdwp6q+ISIHAIvdPt8HrlLVN91EhHV4z9JoqpGISD/gVFWtE5EJeI+WndaNvAHGANOBg4BXRGS8qtb13EdiTDMLJMZ0bml0CnMR+QyITke+Apjplk8BJnrTOgEwwAWON4E7ROTPwNOqWhqzTVQmcK+ITMHrpzm4m3kDPKGqEWCNiKwDDgWWJ3DOxnSZBRJjOhc7P1Mk5n2E5r+hADCjjVrA7SLyd+AM4E0ROa2N498AbMerfQTwai3dyRv2nVbdOj9Nr7E+EmOS4wW8piYAXO0CETlIVVeo6q+BpXg1hUogP2bfAmCrq1FcjPdY2O76pogEXL/JOOCT+E7DmO6zQGJMclwLTBORD0VkFfAdl369iHwkIh/iPQ72H8CHQFhEPhCRG4DfAXNE5AO8QFMdR/4bgXfd8b9j/SOmN9nwX2N8TkQeAZ5T1Sf7uiwmPVmNxBhjTEKsRmKMMSYhViMxxhiTEAskxhhjEmKBxBhjTEIskBhjjEmIBRJjjDEJsUBijDEmIf8/wGnBDdXjrBIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fixed hole\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1253c21d0>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#zooming into first spike\n",
    "df['2017-12-25':'2018-01-01'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12fa107d0>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#and second spike\n",
    "df['2018-03-01': '2018-03-09'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from plots know which day they are locates, so now drop data from those 2 days\n",
    "df_cleaned = df.drop(pd.to_datetime(['2017-12-28', '2018-03-04']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x125412190>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cleaned.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Analysis\n",
    "\n",
    "we have visually cleaned out data, we need to use analytical methods to identify outliers now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Central Tendency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bitcoin    6996.004022\n",
       "Ether       469.609068\n",
       "dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#with invalid values\n",
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bitcoin    6323.509186\n",
       "Ether       428.628402\n",
       "dtype: float64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clean, not being affected by outliers\n",
    "df_cleaned.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bitcoin    4594.98785\n",
       "Ether       309.49000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x125417310>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cleaned.plot(kind='hist', y='Ether', bins=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x130f03b50>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cleaned.plot(kind='hist', y='Bitcoin', bins=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using seaborn\n",
    "fig, ax = plt.subplots(figsize=(15, 7))\n",
    "sns.kdeplot(df_cleaned['Ether'], shade=True, cut=0, ax=ax)\n",
    "sns.rugplot(df_cleaned['Ether'], ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x131a959d0>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatter plot with linear regression model fitted\n",
    "fig, ax = plt.subplots(figsize=(15, 7))\n",
    "sns.regplot(x=\"Bitcoin\", y=\"Ether\", data=df_cleaned, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dispersion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142706.6475416667"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#range\n",
    "df['Bitcoin'].max() - df['Bitcoin'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18399.514208333334"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#range is very sensitive to outliers\n",
    "df_cleaned['Bitcoin'].max() - df_cleaned['Bitcoin'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103766537.75278121"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#variance\n",
    "df['Bitcoin'].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4513.757662530282"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#both variance and standard deviation sensible to outliers\n",
    "df_cleaned['Bitcoin'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IQR (interquartile range)\n",
    "- good measure of centered dispersion (Q3 - Q1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6597.494147619047"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Bitcoin'].quantile(.75) - df['Bitcoin'].quantile(.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6548.249242559523"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#can see it's not so sensible to outliers\n",
    "df_cleaned['Bitcoin'].quantile(.75) - df_cleaned['Bitcoin'].quantile(.25)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analytical Analysis of invalid values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- using standard deviation, Z scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "#upper/lowe limits given by twice the standard deviation away from mean\n",
    "upper_limit = df['Bitcoin'].mean() + 2 * df['Bitcoin'].std()\n",
    "lower_limit = df['Bitcoin'].mean() - 2 * df['Bitcoin'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Upper Limit: 27369.17635730169\n",
      "Lower Limit: -13377.16831365621\n"
     ]
    }
   ],
   "source": [
    "print(\"Upper Limit: {}\".format(upper_limit))\n",
    "print(\"Lower Limit: {}\".format(lower_limit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x13216a690>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#anything past these lines could be considered an invalid value\n",
    "fig, ax = plt.subplots(figsize=(15, 7))\n",
    "sns.distplot(df['Bitcoin'], ax=ax)\n",
    "ax.axvline(lower_limit, color='red')\n",
    "ax.axvline(upper_limit, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- using IQRs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6597.494147619047"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr = df['Bitcoin'].quantile(.75) - df['Bitcoin'].quantile(.25)\n",
    "iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "#limits given by twice the iqr range away from mean\n",
    "upper_limit = df['Bitcoin'].mean() + 2 * iqr\n",
    "lower_limit = df['Bitcoin'].mean() - 2 * iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1326e3190>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#still good indicator, but few valid values seem to be invalid\n",
    "fig, ax = plt.subplots(figsize=(15, 7))\n",
    "sns.distplot(df['Bitcoin'], ax=ax)\n",
    "ax.axvline(lower_limit, color='red')\n",
    "ax.axvline(upper_limit, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cleaning invalid values analytically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining upper limit\n",
    "upper_limit = df['Bitcoin'].mean() + 2 * df['Bitcoin'].std()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x132970a10>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph of everything within the limit\n",
    "df[df['Bitcoin'] < upper_limit].plot(figsize=(16, 7))\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}