{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python: Data Munging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resources\n",
    "----\n",
    "\n",
    "- [Pandas documentation](http://pandas.pydata.org/pandas-docs/stable/)\n",
    "- [QGrid](https://github.com/quantopian/qgrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Working with Series\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = Series(range(5,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    6\n",
       "2    7\n",
       "3    8\n",
       "4    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We cna treat Series objects much like numpy vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35, 7.0, 1.5811388300841898)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum(), x.mean(), x.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    25\n",
       "1    36\n",
       "2    49\n",
       "3    64\n",
       "4    81\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    8\n",
       "4    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[x >= 8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series can also contain more information than numpy vectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### You can always use standard positional indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    6\n",
       "2    7\n",
       "3    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Series index\n",
    "\n",
    "But you can also assign labeled indexes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5\n",
       "b    6\n",
       "c    7\n",
       "d    8\n",
       "e    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.index = list('abcde')\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note that with labels, the end index is included"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5\n",
       "b    6\n",
       "c    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['a':'c']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Even when you have a labeled index, positional arguments still work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    6\n",
       "c    7\n",
       "d    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 7, 9)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.a, x.c, x.e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Working with missing data\n",
    "\n",
    "Missing data is indicated with NaN (not a number)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10.0\n",
       "1     NaN\n",
       "2     NaN\n",
       "3    13.0\n",
       "4    14.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = Series([10, np.nan, np.nan, 13, 14])\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Concatenating two series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     5.0\n",
       "b     6.0\n",
       "c     7.0\n",
       "d     8.0\n",
       "e     9.0\n",
       "0    10.0\n",
       "1     NaN\n",
       "2     NaN\n",
       "3    13.0\n",
       "4    14.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = pd.concat([x, y])\n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reset index to default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     6.0\n",
       "2     7.0\n",
       "3     8.0\n",
       "4     9.0\n",
       "5    10.0\n",
       "6     NaN\n",
       "7     NaN\n",
       "8    13.0\n",
       "9    14.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = z.reset_index(drop=True)\n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `pandas` aggregate functions ignore missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72.0, 9.0, 3.2071349029490928)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.sum(), z.mean(), z.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecting missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6   NaN\n",
       "7   NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[z.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecting non-missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     6.0\n",
       "2     7.0\n",
       "3     8.0\n",
       "4     9.0\n",
       "5    10.0\n",
       "8    13.0\n",
       "9    14.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[z.notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replacement of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     6.0\n",
       "2     7.0\n",
       "3     8.0\n",
       "4     9.0\n",
       "5    10.0\n",
       "6     0.0\n",
       "7     0.0\n",
       "8    13.0\n",
       "9    14.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     6.0\n",
       "2     7.0\n",
       "3     8.0\n",
       "4     9.0\n",
       "5    10.0\n",
       "6    10.0\n",
       "7    10.0\n",
       "8    13.0\n",
       "9    14.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     6.0\n",
       "2     7.0\n",
       "3     8.0\n",
       "4     9.0\n",
       "5    10.0\n",
       "6    13.0\n",
       "7    13.0\n",
       "8    13.0\n",
       "9    14.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     6.0\n",
       "2     7.0\n",
       "3     8.0\n",
       "4     9.0\n",
       "5    10.0\n",
       "6     9.0\n",
       "7     9.0\n",
       "8    13.0\n",
       "9    14.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.fillna(z.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Working with dates / times\n",
    "\n",
    "We will see more date/time handling in the DataFrame section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "z.index = pd.date_range('01-Jan-2016', periods=len(z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-01     5.0\n",
       "2016-01-02     6.0\n",
       "2016-01-03     7.0\n",
       "2016-01-04     8.0\n",
       "2016-01-05     9.0\n",
       "2016-01-06    10.0\n",
       "2016-01-07     NaN\n",
       "2016-01-08     NaN\n",
       "2016-01-09    13.0\n",
       "2016-01-10    14.0\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Intelligent aggregation over datetime ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-03    18.0\n",
       "2016-01-10    54.0\n",
       "Freq: W-SUN, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.resample('W').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Formatting datetime objects (see http://strftime.org)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Jan 01, 2016', 'Jan 02, 2016', 'Jan 03, 2016', 'Jan 04, 2016',\n",
       "       'Jan 05, 2016', 'Jan 06, 2016', 'Jan 07, 2016', 'Jan 08, 2016',\n",
       "       'Jan 09, 2016', 'Jan 10, 2016'], \n",
       "      dtype='<U12')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.index.strftime('%b %d, %Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame\n",
    "----\n",
    "\n",
    "Similar to R."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Titanic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv'\n",
    "titanic = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 15)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',\n",
       "       'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town',\n",
       "       'alive', 'alone'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For display purposes, we will drop some columns\n",
    "titanic = titanic[['survived', 'sex', 'age', 'fare',\n",
    "                   'embarked', 'class', 'who', 'deck', 'embark_town',]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         int64\n",
       "sex             object\n",
       "age            float64\n",
       "fare           float64\n",
       "embarked        object\n",
       "class           object\n",
       "who             object\n",
       "deck            object\n",
       "embark_town     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summarizing a data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived                 0\n",
       "sex                   male\n",
       "age                     22\n",
       "fare                  7.25\n",
       "embarked                 S\n",
       "class                Third\n",
       "who                    man\n",
       "deck                   NaN\n",
       "embark_town    Southampton\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.ix[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         survived         age        fare\n",
       "count  891.000000  714.000000  891.000000\n",
       "mean     0.383838   29.699118   32.204208\n",
       "std      0.486592   14.526497   49.693429\n",
       "min      0.000000    0.420000    0.000000\n",
       "25%      0.000000   20.125000    7.910400\n",
       "50%      0.000000   28.000000   14.454200\n",
       "75%      1.000000   38.000000   31.000000\n",
       "max      1.000000   80.000000  512.329200"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived     sex   age     fare embarked  class    who deck  embark_town\n",
       "0         0    male  22.0   7.2500        S  Third    man  NaN  Southampton\n",
       "1         1  female  38.0  71.2833        C  First  woman    C    Cherbourg\n",
       "2         1  female  26.0   7.9250        S  Third  woman  NaN  Southampton\n",
       "3         1  female  35.0  53.1000        S  First  woman    C  Southampton\n",
       "4         0    male  35.0   8.0500        S  Third    man  NaN  Southampton"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.45</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.75</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived     sex   age   fare embarked   class    who deck  embark_town\n",
       "886         0    male  27.0  13.00        S  Second    man  NaN  Southampton\n",
       "887         1  female  19.0  30.00        S   First  woman    B  Southampton\n",
       "888         0  female   NaN  23.45        S   Third  woman  NaN  Southampton\n",
       "889         1    male  26.0  30.00        C   First    man    C    Cherbourg\n",
       "890         0    male  32.0   7.75        Q   Third    man  NaN   Queenstown"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['survived', 'sex', 'age', 'fare', 'embarked', 'class', 'who', 'deck',\n",
       "       'embark_town'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=891, step=1)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Third</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>First</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Third</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>First</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Third</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex   age  class\n",
       "0    male  22.0  Third\n",
       "1  female  38.0  First\n",
       "2  female  26.0  Third\n",
       "3  female  35.0  First\n",
       "4    male  35.0  Third"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[['sex', 'age', 'class']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>G</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    survived     sex   age     fare embarked  class    who deck  embark_town\n",
       "10         1  female   4.0  16.7000        S  Third  child    G  Southampton\n",
       "11         1  female  58.0  26.5500        S  First  woman    C  Southampton\n",
       "12         0    male  20.0   8.0500        S  Third    man  NaN  Southampton\n",
       "13         0    male  39.0  31.2750        S  Third    man  NaN  Southampton\n",
       "14         0  female  14.0   7.8542        S  Third  child  NaN  Southampton"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[10:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using the `ix` helper for indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.0</td>\n",
       "      <td>16.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>58.0</td>\n",
       "      <td>26.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>39.0</td>\n",
       "      <td>31.2750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.0</td>\n",
       "      <td>7.8542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>55.0</td>\n",
       "      <td>16.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age     fare\n",
       "10   4.0  16.7000\n",
       "11  58.0  26.5500\n",
       "12  20.0   8.0500\n",
       "13  39.0  31.2750\n",
       "14  14.0   7.8542\n",
       "15  55.0  16.0000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.ix[10:15, 'age':'fare']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>fare</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>female</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>Third</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>female</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>First</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>male</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Third</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>male</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>Third</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>female</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>Third</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>female</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>Second</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sex     fare   class\n",
       "10  female  16.7000   Third\n",
       "11  female  26.5500   First\n",
       "12    male   8.0500   Third\n",
       "13    male  31.2750   Third\n",
       "14  female   7.8542   Third\n",
       "15  female  16.0000  Second"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.ix[10:15, [1,3,5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>1.00</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1.00</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>1.00</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>F</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0.92</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>child</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1.00</td>\n",
       "      <td>15.7417</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>1.00</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0.67</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>1.00</td>\n",
       "      <td>20.5750</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>1.00</td>\n",
       "      <td>37.0042</td>\n",
       "      <td>C</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>18.7500</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived     sex   age      fare embarked   class    who deck  \\\n",
       "78          1    male  0.83   29.0000        S  Second  child  NaN   \n",
       "164         0    male  1.00   39.6875        S   Third  child  NaN   \n",
       "172         1  female  1.00   11.1333        S   Third  child  NaN   \n",
       "183         1    male  1.00   39.0000        S  Second  child    F   \n",
       "305         1    male  0.92  151.5500        S   First  child    C   \n",
       "381         1  female  1.00   15.7417        C   Third  child  NaN   \n",
       "386         0    male  1.00   46.9000        S   Third  child  NaN   \n",
       "469         1  female  0.75   19.2583        C   Third  child  NaN   \n",
       "644         1  female  0.75   19.2583        C   Third  child  NaN   \n",
       "755         1    male  0.67   14.5000        S  Second  child  NaN   \n",
       "788         1    male  1.00   20.5750        S   Third  child  NaN   \n",
       "803         1    male  0.42    8.5167        C   Third  child  NaN   \n",
       "827         1    male  1.00   37.0042        C  Second  child  NaN   \n",
       "831         1    male  0.83   18.7500        S  Second  child  NaN   \n",
       "\n",
       "     embark_town  \n",
       "78   Southampton  \n",
       "164  Southampton  \n",
       "172  Southampton  \n",
       "183  Southampton  \n",
       "305  Southampton  \n",
       "381    Cherbourg  \n",
       "386  Southampton  \n",
       "469    Cherbourg  \n",
       "644    Cherbourg  \n",
       "755  Southampton  \n",
       "788  Southampton  \n",
       "803    Cherbourg  \n",
       "827    Cherbourg  \n",
       "831  Southampton  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[titanic.age < 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sorting and ordering data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived     sex   age     fare embarked  class    who deck  embark_town\n",
       "0         0    male  22.0   7.2500        S  Third    man  NaN  Southampton\n",
       "1         1  female  38.0  71.2833        C  First  woman    C    Cherbourg\n",
       "2         1  female  26.0   7.9250        S  Third  woman  NaN  Southampton\n",
       "3         1  female  35.0  53.1000        S  First  woman    C  Southampton\n",
       "4         0    male  35.0   8.0500        S  Third    man  NaN  Southampton"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sort_index().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0.67</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived     sex   age     fare embarked   class    who deck  embark_town\n",
       "803         1    male  0.42   8.5167        C   Third  child  NaN    Cherbourg\n",
       "755         1    male  0.67  14.5000        S  Second  child  NaN  Southampton\n",
       "644         1  female  0.75  19.2583        C   Third  child  NaN    Cherbourg\n",
       "469         1  female  0.75  19.2583        C   Third  child  NaN    Cherbourg\n",
       "78          1    male  0.83  29.0000        S  Second  child  NaN  Southampton"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sort_values('age', ascending=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived     sex  age     fare embarked  class    who deck  embark_town\n",
       "164         0    male  1.0  39.6875        S  Third  child  NaN  Southampton\n",
       "386         0    male  1.0  46.9000        S  Third  child  NaN  Southampton\n",
       "7           0    male  2.0  21.0750        S  Third  child  NaN  Southampton\n",
       "16          0    male  2.0  29.1250        Q  Third  child  NaN   Queenstown\n",
       "119         0  female  2.0  31.2750        S  Third  child  NaN  Southampton"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sort_values(['survived', 'age'], ascending=[True, True]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grouping data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex_class = titanic.groupby(['sex', 'class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>who</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>First</th>\n",
       "      <td>94</td>\n",
       "      <td>85</td>\n",
       "      <td>94</td>\n",
       "      <td>92</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Second</th>\n",
       "      <td>76</td>\n",
       "      <td>74</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>10</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Third</th>\n",
       "      <td>144</td>\n",
       "      <td>102</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>6</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>First</th>\n",
       "      <td>122</td>\n",
       "      <td>101</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>94</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Second</th>\n",
       "      <td>108</td>\n",
       "      <td>99</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>6</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Third</th>\n",
       "      <td>347</td>\n",
       "      <td>253</td>\n",
       "      <td>347</td>\n",
       "      <td>347</td>\n",
       "      <td>347</td>\n",
       "      <td>6</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               survived  age  fare  embarked  who  deck  embark_town\n",
       "sex    class                                                        \n",
       "female First         94   85    94        92   94    81           92\n",
       "       Second        76   74    76        76   76    10           76\n",
       "       Third        144  102   144       144  144     6          144\n",
       "male   First        122  101   122       122  122    94          122\n",
       "       Second       108   99   108       108  108     6          108\n",
       "       Third        347  253   347       347  347     6          347"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_class.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Why Kate Winslett survived and Leonardo DiCaprio didn't"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex     class \n",
       "female  First     0.968085\n",
       "        Second    0.921053\n",
       "        Third     0.500000\n",
       "male    First     0.368852\n",
       "        Second    0.157407\n",
       "        Third     0.135447\n",
       "Name: survived, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sex_class.mean()\n",
    "df['survived']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Of the females who were in first class, count the number from each embarking town"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>deck</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embark_town</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cherbourg</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>38</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queenstown</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southampton</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>44</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             survived  sex  age  fare  embarked  class  who  deck\n",
       "embark_town                                                      \n",
       "Cherbourg          43   43   38    43        43     43   43    35\n",
       "Queenstown          1    1    1     1         1      1    1     1\n",
       "Southampton        48   48   44    48        48     48   48    43"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_class.get_group(('female', 'First')).groupby('embark_town').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross-tabulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80</td>\n",
       "      <td>97</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>136</td>\n",
       "      <td>87</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class     First  Second  Third\n",
       "survived                      \n",
       "0            80      97    372\n",
       "1           136      87    119"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(titanic.survived, titanic['class'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can also get multiple summaries at the same time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def my_func(x):\n",
    "    return np.max(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>embarked</th>\n",
       "      <th>survived</th>\n",
       "      <th colspan=\"3\" halign=\"left\">age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>my_func</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embark_town</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cherbourg</th>\n",
       "      <td>43</td>\n",
       "      <td>42</td>\n",
       "      <td>36.052632</td>\n",
       "      <td>37.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queenstown</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southampton</th>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>32.704545</td>\n",
       "      <td>33.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            embarked survived        age               \n",
       "               count      sum       mean median my_func\n",
       "embark_town                                            \n",
       "Cherbourg         43       42  36.052632   37.0    60.0\n",
       "Queenstown         1        1  33.000000   33.0    33.0\n",
       "Southampton       48       46  32.704545   33.0    63.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapped_funcs = {'embarked': 'count', 'age': ('mean', 'median', my_func), 'survived': sum}\n",
    "sex_class.get_group(('female', 'First')).groupby('embark_town').agg(mapped_funcs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['survived', 'sex', 'age', 'fare', 'embarked', 'class', 'who', 'deck',\n",
       "       'embark_town'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing tables\n",
    "\n",
    "See more examples in the Graphics notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1153bf4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_context(font_scale=4)\n",
    "sns.factorplot(x='sex', y='age', hue='survived', col='class', kind='box', data=titanic)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making plots with `pandas`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_datareader import data as web\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "apple = web.DataReader('AAPL', 'google', \n",
    "                        start = datetime.datetime(2015, 1, 1),\n",
    "                        end = datetime.datetime(2015, 12, 31))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>111.39</td>\n",
       "      <td>111.44</td>\n",
       "      <td>107.35</td>\n",
       "      <td>109.33</td>\n",
       "      <td>53204626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>108.29</td>\n",
       "      <td>108.65</td>\n",
       "      <td>105.41</td>\n",
       "      <td>106.25</td>\n",
       "      <td>64285491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>106.54</td>\n",
       "      <td>107.43</td>\n",
       "      <td>104.63</td>\n",
       "      <td>106.26</td>\n",
       "      <td>65797116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>107.20</td>\n",
       "      <td>108.20</td>\n",
       "      <td>106.70</td>\n",
       "      <td>107.75</td>\n",
       "      <td>40105934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>109.23</td>\n",
       "      <td>112.15</td>\n",
       "      <td>108.70</td>\n",
       "      <td>111.89</td>\n",
       "      <td>59364547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2015-01-02  111.39  111.44  107.35  109.33  53204626\n",
       "2015-01-05  108.29  108.65  105.41  106.25  64285491\n",
       "2015-01-06  106.54  107.43  104.63  106.26  65797116\n",
       "2015-01-07  107.20  108.20  106.70  107.75  40105934\n",
       "2015-01-08  109.23  112.15  108.70  111.89  59364547"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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jGlSSZQWNDpcXIj6zxk8Gg+tbNzahWS3FysaKmGOMZge62qoCQXiFkBGZ21xL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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ac20b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apple.plot.line(y='Close', marker='o', markersize=3, linewidth=0.5)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AamuaOHa4kMP78jl2uO93Yy91g6oHdNvm422+V1XIOm4kbJh3r3pCnUaOonrn\ndhozT+I+bRp+nk5s2J1DRKAbQT7dlxAQQ8fGH3LYcbiQX90Uj67ZyrcbjjHpsiiGjeq+bJJOp6W2\npokTacXETQrpcKBSZxRFYViwB4mj/UnPrmD9rmzqG82MCvNEK72hQggx4EoKq9m+5QTbN58g67iR\nvFPlZJ8o6/Pd2J6qqanh9ddf4R//eJ7XX/8/Nm/+CrPZzJgxLdNtzpiRyMSJkwgMDLpgbbDFkO4B\nPZfFopKyO7dX+2qdnAhY9jPqj6ZRveN7xkZ5ExPhxX+/O4nF2rtao2LwOXW6mjXbs5iTFMGIUA+2\nfZWBl68L46eE9fgYcQkh1NeZOHmsdz3yAd7OPHbbRJbMiubbAwU8+fZeThZUdbhtRU0TOw4XsmVf\nHjsOF1Ihn8KFEOKCOJVReuZuqxHrefWg7XE3tjvV1VXcc88yjh8/xh/+8AQrV67mrrvu5f333+al\nl/5xQc7ZX4bE8NuczDJqa5pw7cUnEJexcbhPn0HpJx/hPDaepbOieeqdvXx/qJBZE7qv/ygGt4Ym\nM6+vSyM8wI0Fl0VxZF8BpUW13Lhsok09kN6+LoRGenFkXz4jxwT0qi0ajcJ1U8KJH+7Dfzak8+zK\n/Vw3JZyFlw1Dr9OQVVjNht05HMo0YjnnQqjVKIyL9mVuUgRR59QhFUII0XslhdVsXZeOxdL1Y3kW\ni8qWteksvH1Cm1rQ9vDqq//GYDDwz3/+HzpdS2QLDAzCYDDwhz88yuLFS+16vv406HtAoeVTSH52\n75+d81v6ExQHAyXvv0N4gCtTxwSydvspGpp6PgWjGJw+3HKcqnoT986Ppb6mieTtWcQlhBAQbPtF\nJG5SCCWFNRSfru5Tm4J9XfjDHQksmjGMzcl5PPXOXr7ak8NzK1NIOV7aJnwCWKwqKcdLW9cLIYTo\nu5Tdud2Gz7P6cje2M83NzXz99RYWL765NXyeNX36DF566ZV2t91NJhOvvPIvbrxxLrNnz+Cxxx6m\npKS4df3q1R+xZMl8rrxyOvfcs4zDhw+2rjt1KpNf/Wo5V101ndtuu4nPP/+vXV/P+YZEAAVo7kNY\n1Lq4EHDHT6k7fIiaPbu5ceYw6pssfLXHvm8mcXFJPlrMztQibp89En9PJ77fdBxHJz2TZ0b16ngR\nw31w93TkyL78PrdNq9Fww7RInrgzEbPFyiffnsRs6fqxELPFymtr08gq7FsAFkKIS11tTRPZJ4w2\n7XP2bqxFmCGzAAAgAElEQVS9FBTk09jYwOjRMR2unzAhAb1e32bZ3//+LNu3f8cTT/yNFSvexmw2\n89hjjwBw/PgxXn31XzzyyGN8+OGnxMeP54knHgegqamJRx/9NePGTeC99z7mgQce4p133mTz5i/t\n9nrONyRuwQPo+1jM23X8BNwmT6Vk1QdEPhXL7MRQNu3N5YoJIVKWaQgyVjXw7lcZTI7xZ9rYQE6k\nFZOXVcGcm+Jw6OV7SVEUxiaE8MO3p0ia1WSXh9LD/F0J9nWhuKKhdZmruZ6o+tM4WJsxafRkOQdT\nq3MGWkLoxt05PHBjXJ/PLYQQQ1l1ZQNNjR13XmWfMGJrQRyrVeXooUIio306XG9w1OFuw2Q3tbU1\nALi4uPZo+5qaGjZv/pIXX/w348dPBOCJJ55m8eK57N37Aw0NjSiKQkBAIIGBgdxzzy+YPn0mVquV\nLVu+wtvbh7vvvg+AkJBQli37GR9//CHXXHN9j9tsiyERQDUahdBIrz4fx/+W28h+4g+UfLiSuT9b\nzvZDhazdcYo7r4+hoqaJtKxyGkxmnBx0jInylmA6SFmsVt74Ih1ng45l146isaGZnV9nEh3jT0Qn\nF46eGh0XRPL3WaQdPM3kGb3rST1XRU0ThzJbpu0MbDSSVJFKdF0eWn68MlpQyHQJY7fXWIocfTmY\naaSipknen0II0YmGehMfrthjc8jszr4d2ezbkd3hOkWBnz44DSfn7keIA7i7e6CqKjU1NT3aPi8v\nB1VViY0dc84x3AkLiyA7O5v58xcxbFg0y5bdzIgRo5gx43LmzVuERqMhJyebEycymD17Zuu+Vqul\nXQ+rPQ2JABoR7dOrAUjn07q54X/rHRSueAW3xAPMmx7Jqq0nKK1s5HhepQz8GCI27M4hs6CK3986\nEWdHPV9/cRRVhelXR/f52AZHHaPjAkk/cJqEpAi0ur495ZKWVY5VVRlZm8v84u/Rqe1vw2tRGVWX\ny/D6fNYGzOSEazjp2eVMj7s4SnKIC0M+FAvRe07ODtx635Que0D37cyx+biTLovssge0p+ETIDQ0\nDFdXNzIyjnZ4G/7xxx9h8eKbW793cOj4599qtWK1WjAYDLzxxrscOLCfnTu3s3Hjetas+ZT//Gcl\nFouZSZOm8Mgjv++3WuiDPoBqdRomJoXb7XiukxJxTU6g5IP38br9IQCO5rQf4HR24Mfhk2UsXzCG\niSO7rxcpBl5mQRXrdmQzb1okI8M8yT1VzvG0YmbNGYWzS88vDF0ZmxBCasppMo+VMGpsYJ+O1WAy\nE9ho7DR8nkunWllQ/D0rddfJALohTKohCGEfXd0Od3JxIGV3brvSS13RaBRixgXZpUOs5Xgarrpq\nNp999glz585vMxBpx47v2blzO8uXP9i6LCQkFI1GQ1raERITpwJQVVVJfn4u4eERpKYeISVlL8uW\n3cWECQncd98DzJt3DYcPHyQ8PIIdO74nKCi4dfKVTZs2cuzYUX7960fs8nravb4LctR+omgUZs+P\nsWvZA0VR8L/tDixmM/krV3a7vQz8GDzOllyKCnZj3vRImk0Wvt90nJAIT0bF9S0onsvLx4WwqJaS\nTH39JOnkoCOpIrXb8HmWTrWSVJGKUx+fiRYXp/0ZpVINQYh+4OpmsPmRLHvdjT3XXXfdS11dHY88\n8iAHD6ZQUJDP+vVrePbZv7JkyS1ERES2buvk5MS8eYv4n/95gQMH9pOZeYKnnnqCwMAgEhOnYjAY\nePvtN1i/fg1FRYVs3bqJxsYGoqNHcM01c2hsbOSFF54hNzeb3bt38L//+yLe3n17LK0rg3ImJFWx\nojibUcwaJk6NsFvP1VkaR0e+y6xhbN5eigzelDt44GquZ1RtDmENxfiaqqjXOmLStDwbYVVV6hqa\nmRzTu/qPPVFR08T+jFIy8iopNNbj5uwgIcNG73x5lNziWh79yQRcnRzY890pCnIrmbs0DscezNpg\nC4OTnkPJ+YQN88bV3bHXx3E21eG05TN6PrcSeDVXE37d1Th79OzBdTE4ZBVW89Lqw91WQ7CqKinH\njYwdduFvyct1SQxl7p6OHE8t6tFzolqdhllzRtl9RiQnJ2dmzbqazMwTfPjhe3z88Qfk5eVy220/\nZdmyu1AUhXfeeZPrr7+BwMAgEhImUVCQz1tvreCLL9YQERHJk08+g5ubGz4+PgQHh/L++2/z3ntv\ncerUSX7zm98xfvxE9Ho9kyZNZvPmr3jzzdfYv38fCxcu5s4777a5zT2dCUlRB9HE58v/93msWjO1\nHkYsumai0y7Dy9GD2+6ehk6ntdt5Kmqa+O3/7eTG018T1GjktKMfw+oLuhz4odUovHD/NLtf8OV2\nm33sTivijS/SuWdeLEljAikprOaz91KYcvkwJky13yMcZ6mqyqrXk/ELdGX2gjHd79CJqp3bKX77\nPzbvF/Czn+Mx/bJen1dcfF7+7IhNPZsJI/0uWDUEuS6JS0XW8VK2rO26GL1WqzB7QSxR8igeADqd\nBi+v7qczH1S34MsDc6j0K8Ds0ISqsZI3/AC1lc1s3XLYrudJyyrHChx3CcPZ2sSI+vw24RN+HPhx\ne8FXjKjNxWJVSc8ut2s75HabfZRUNvD+pgySxgSQNCYQi8XKti+P4+3nQnxi6AU559mSTCePlVJb\n3djr41gberevtbGh+43EoFFR08RBG2sSnq2GYG9yXRKXkqiRfiy8fQJRI33RaNrei9JoFKJG+p5Z\nL+HTVoP6Xkmjcw3FoRloDsWQH1Nhl1JM8OPAj2uMyd3e+jx34MfOI4XU1Dfj4eqAp4sDHq4GPF1b\nbkmdfai3p7IKq1mxLq3Hxccfv32i9Dh0oKXkUhquTnpuv2YUAIf35lNWavt0m7YaHRfYWpJpysxh\nvTqGxql3t+81jj2vNScufmerIZxLD7gDWsACVAPN56y3WFV+SCvi8vEhOBm0Nl+DOiLXJXEp8g9y\n57obx1Jb00R+dgXNTWb0Bh2hkV52f+bzUjKoAyhAWWAW7lX+bF2fzk/unoyjU99rVvV24MfXxiCy\nimpoMlnarNfrNHi4OODpasDDxQEP1zPh9JyQ6uFqwM1J3/oJa8PunG4v8mdJ8fHOfbEzm6zTNTx2\n+0ScDDqqKurZuyOb+Emhdp+z93wOhrMlmQpJmBZh02MiqqrScOwoVdu/t/m8FhR21blxYUoHi4HQ\nYPqxqoEzEIyCB6A55yOyFZUq4DQq9WeWrf7uJKu/O4lWo+DqpMfVWY+bk/7M3x1wdTrzvfOPX12d\n9Lg5O2DQt3+/ynVJXMpc3QyMtuOA1UvdoA+gKJA37BDu6T5s++o41yyM7fMn/dFeCsa6PJv2ia7L\nI2nRCHzDAmk0mamqNVFVZ6KytomqWhOVdS1fq2qbKM6rp7LWRG1Dc5tjaBQFNxc9ro56Cox1Np1f\nio+3dzyvki92ZbPgsiiiQ1oK+m776jjOLg4k2qFIfE+MTQjhyP4CMtNLGB3ffV1OVVWpO3SQ8o3r\naTx1EkN4BIaoYTRlnerxOesjRrM6pQw8crh+SkRfmi8uEk4OLZdqT2A4SpvgeZYGBS/AAziJSiVw\n/ZRwIgLdqG1opra+mZqG5jN/N1FS2UBtQzM19c00m9uHSged5sdA6qTHQa/t9WMAcl0SQpxv8AdQ\nwOzQSOBkLad2lJKRWtznTyj63BNosG1slhYVfW4mhAXi6KDD0VtHgLdzl/uYLVaq60xUngmmlXUt\nXzNyK21u89lnUKX4eIv6xmbe+CKN6BAPbkiKBCDjSBEFOZXMXRqP3sF+g9a64untTPhwb47sL2BU\nXGCnH45Uq5WavcmUb1yPqSAfpxEjCXnoYZzHxNGUk03e88+gmruv7ano9Yy5Yyk35FpZ/e1JdFoN\nsyeF2ftliX5ktlipaTDhTOfh81waFIYDxxW4elJYj8JfU7OF2vqWcFrTYPoxrLZ+NZFfWmvjVVGu\nS0KIzg2JAArgP9wJl0oXdmw5QXCYh03zrZ6vvwZ+6LQavN0d8T6vTM+WfXlk5NkeQqX4eAtVVXlv\nUwb1TRZ+Py8WjUahvs7Erm9OMnJMAOHDvPu1PXEJoWz45DBF+VUEhXm2WWdtbqZm9y7Kv9pIc0kx\nzmPj8L/tDpxHjmrdxjEyiqD77qdwxatdhlBFpyPo3uU4RkaxKELFbFZZtfUEeq2GKyaEXLDXJy4M\ns8XKrtQi1u/KxljVSIxWi6aLkbjn0qAwysWhxz2PBr0Wg4cWH4/Onznesi+PVVtP9Oh455LrkhCi\nI0MigGoUDaO9R+A824XCvCq+/uIoC24bj0bTuwEmAz3w4+ztNlvtOVqMv5czsZFe6C7g4JqL3a7U\nIpKPlrB8wRh8PVr+T3ZuzURRYNpVw/u9PWFRXnh6O3Fkf0FrALU2NVG1fRsVm77EXFmJ68QEgu69\nH8fIyA6P4TohgbDH/kj5xvXUHjoIlrbPGWucnAj5zaM4DWt5fYqisGTWcJotVt7blIFWqzAjPviC\nvk5hH+cHz0mj/bnnutF8/clhm3ogNXUmamua7DZIorfXpQMnSgkPcCM61AONHQZCCSGGhiERQON9\nY/E0eABw1bwY1n5wgAO7c0mYHtmr4znHjgWttt0v+S5ptTjH9r7e47nGRHmj1SjtSpx0RVGgps7E\nS6sP4eqkJzHGnykxAZfcRb+4op6VW44zPS6wdWKAnMwyMo+WcNUNo22ah9deFEUhLiGUHVtPUFVc\njnnvDiq3bsFSX4f7lCS8rp+LIbj7cOgYGUXwLx6kuaKC+vQ0rI0NaByd0Dg6Uvjqy5gr2k4ZqygK\nt149ArPFyjsbj6HXapg6Rh6gv1h1FDx/tTieUH9Xjh0u7FEx7HOpKuRnV9ht0ERvr0unjfU8/0EK\nXm4GJsf4MzkmgMhAN7uMyhdCDF6DPoDqNTquiZjV+n1QqAcTkyLYuyOb0ChvAoJtH+ms9/LCNX48\ntQf293gf13Hj0XvZpwyUl5uBcdG+NtXRmzjCj18sGkteSS0/pBezJ72Yb1MK8HE3MDk2gKmxgYT6\nuQzpi77ZYuX1del4ODtw69UjATA1mfl+83HCorwYMebCzVTVnWERzuxWrOz610dEl6XgPmMm3tde\nj97X9tpxei+vdkXmq2LGULZuDa4TJqKc0/OvKAp3XDsKs8XKm+uPotNqmDTav8+vR9hPV8HzLJPJ\nhg/D52hqbO5+ox7q7XXp/kVjOVlQRXJ6CbtTi9iUnIefpyOTYwKYEhNAyBC/LgkhOjaopuJcnbah\nzfcaNNw99jZGeY9oszww1IO8U+WcSC9hdHxgr2o96v39qd61E6zdlxxR9HoCf3Y3Ok/7BFAAP08n\ndqUWtav91xG9TsPP5sTg5eaIh6uBMVHezE4MIybCi2aLyu7UIjbvzWN/Ril1jWa83Ay4ONperupi\nn3bv8+2n2J9RykNLxuHv1XLrffe3JykqqGLukni7lOgqK87h2LYvyDu8m+Ksozh4eOLs6tnp9s1l\nZZSt+YzSd96kSeNIkccoLnv0DjynTEXr3P1MET2l9/en4ssNGIJDMIS0fd5TURTGR/tSVFHPFzuz\nCfN3JcjHfucWvWO2WNlxpJBX16SyK7WI2Egvli8Yy1UJobifN71whbGO7BNlNp+jILeSwrwqaqsa\nQQFnF4d2xbRtcf51SQ94AW6AI2AGzl4xz16XvN0c8XF3JH64D7MTwxgV5omp2dJ6XdqXUUptQzMe\nrgZce/EzerFfl4S41AzJqTiXfnw/0PLMp5+TD6X1Zfxxym8IdGnfs1VZXs/qt/cxIjaAK64f1W59\nT9Qe2N+zgR/33Y/rhIRenaMrKcdLeW1t10WfdVoNyxeMYWIXszCYLVbSssrZk15MyolSTM1Whge7\nMyU2gMSYADxcun6jDIZp9zJyK3jhwwPcePkw5p4Z9V58umW6zWlXDmfc5L6NBM9K+4HT6z7F+1Qp\n2nN+YiwKlA/zI3j+YqLGTG1dbioqovyrDVTv3oXG0RGvq69BM3E6H72fyhXXjyJmnP1HBee/9CLm\nsjIi/vp0m17Q1rZaW4qDH8o08uDieOKG+di9Df2loqaJtKxyGkxmnBx0jIm68POe20u7Hs9Rfsyf\nHtWmx/Msq1Ul+4SRlB9yKS2ssek8igITksIpK66lML8KU5MFrU5DQLA7wWEeBId7EhDsjq6Dep9d\nSTleyrtr0giwqniqtOm9VFWVSgWKNQo/Xdiz61Ly0WJSThhpMlmICHBjcqw/iaP9W5/f7sxguC4J\ncSnq6VScgyqAfnn8W1STwgjPaFx0zjyb/E/cHNx4aOJ9aJT2v3DTD55m21fHue7GsUSN9O3VORuz\nszoe+KHRgNWK5zXX4b/0J719Sd3KKqxm4+4cDnZwkR0f7cscGy+yTSYLBzJLSU4v4cipMqyqSmyk\nN1NiApg40g9nx7Y9B/szSrud+aQnIfhCqm1o5sm3kvH3dOK3t0xAo1GwWKz89539aLUKNy6b2OsB\naQBHvvsc7Ydr0XXRGW7WgOXWBYwcnkDZhvXU7t+L1t0Dr2uuxfPyWWgcWwa2bfzvEWqrGlly1yS7\n33ZsOHWKvGefIvCe+3CfktRxOy1WXvk8lbTscn59Uzyxkf1bEaCvLqbQYWsItiV4NpssZBwp4vC+\nfKoqGggM9cBqsVJiQwiNGunLdTeOBVqCbFlJLadzKzmd19Ir2tRoRqNV8A9yJzjcg+AwTwJDPLot\nUXYqo5Qta9O6vDmk0cA1C8f0eHpCU7OFwyfLSD5WwqFMI81mK8ND3JkcE0DiaH88Xdv+uw6G65IQ\nl6ohGUABKirqMJ8pmpxRnsm/Dr7ObaNvYlrw5HbbqqrKV5+mUlRQzc13T8LZtfc9JOcP/HCOHUP5\n+rXUJO8h8unn0Hl0fhvWHipqmkjPLqehyYyTQUdsZN97fGobmtmXUcKetGKO51Wi1WoYF+3D1NgA\n4of7kF9ax3MrU3o084lOq7ng0+519Avf09WBV9ekcjSngr/eNbm1pNX+XTns3Z7FTXcm4Bvg1utz\nZqX9QMP/vtZl+DxLBRRA5+uL93VzcJ9+GRp9297l/OxyvvjoMAtuHU9wuP3fMwX/+iem4mIin3oG\nRdtxkGg2W/n3p4c5nl/Jw0vHMzLswr537eViCR22hmBbgmddTRNHUgpIP3AaU5OZYaP8GDc5jIBg\nd0oKq1mz8gCWHpRi0uo0LLxtfKezfamqSnlpXWsgPZ1XRWN9MxqNgl+gG8HhngSHexAY4oHDObez\nSwqr+fz9lJ48mYRGA4vumGjzjGMNTWYOZRpJPnrmQ7JVZVS4J5NjA5g0yp/SyoaL6rokhGjrkgig\nAO+lf8wRYzpPTP0tbg7tL+gN9SY+/s9efAPcmLskzq69TpbaWrL+9BiuceMIvPseux13IJRXN5J8\ntIQ96cXkFNfgZNDiZNBRXt3U42MkjPS7INPudfULP8TPhdziWn6xcGzr4JqKsnpWv7WX+MRQpl7R\nt7JLO5/7LX4nez7ootrfjYS/vdRp+FNVlY/f3Iunj3Nr75Q9NeZkk/u3vxB41z24T5ve6XamZgsv\nrT5EVlENj948nuEhHnZviz1lFVZfFKHDlhAcP9ynx8HTWFzLob15ZKaXoNVpiBkXRFxCSLt6xlnH\nS9myNr3LEKrVKsxeENvj3kdoeV9WlNVzOreSwrxKTudWUV9nQlHAL9CNoLCWQHpk10nyT/e83nFE\nqDNzbm/fOdBTdY3NpGSUkny0mPScCjSKgoujnup6U4+PcaGuS0KIjvU0gA6qQUgAjY3NWM8JIcM9\novi+YBelDUYm+Le/yOj1Wrx8Xdi/MwdHZ32vRsV3RuPggMbZmfINX+AcE4veZ/A+U+dk0BEd6sEV\nE0KYHOOP1aqSllXR/Y7nKKloYEZ8sF0HAOzPKOWl1YcpMNa1K0OjqlB15pfklNgAgnxcUFWVTZ+n\nAXDNgjFo+lAPtaw4B/XTDd3MO9OWrt6EZmpCpwOTFEVB0cDhvfmMigvE4GjfwRI6T08a83Kp3bcX\nzytmdfgsKIBWq2HSKH/Ss8vZtDePMVFe7W5zXkw+2HK8x9PTWlWVuobm1jJc9pJVWM1Lqw93G4Kt\nqsq+jFK2HTzNnvTiHwcXTQprM7hIVVVyT5WzffMJfvjuFE2NZiYmRXDVvBiiRvpi6GCgoJePC2HD\nvGlsaKa6oqHNz4RGoxA5wpdZc0cTauOjFYqi4OTsgH+QO8NH+zNucigjxgTg7edCc7OFnJPlpB8s\npLqmGWz4iaiuNjF6XHCbXlRbOOi0RAS6MW1sELMmhOBk0HIws+1gLFdzPaNqcwhrKMbXVEW91hGT\n5sd/uwtxXRJCdK6ng5AGfQB10Drg5uDKV9lfM8w9Aj/n9iHQ09uZxvpmDuzJI2qkr11rQRrCwqlP\nPULtwQN4zLi801/4g4mbswNVtSYOnDfvc3cXelWFED8XIvpwy/tcPf2FD5By3MjYYd4UniwnNeU0\n1ywcg6dP11OhdufYti8wHM2yaR8NUOqpJWjEuE638fJxITWlAFWFsCj7P4NpCAqifON69L6+OIZ3\nPhe87kwIPXKqjC178xgb5Y3HRRhCK2qaeH9TRpsi7AMROmwJwaoKTgYtf7g9oV3wNJstZBwp5uv1\nRzmyrwCDo45pVw5n5rUjCQ7zRKfr+hri4mYgOsaf6AhnXBuN+DtbiAjUctl1MYydEoWLHQZjKYqC\no5Mev0A3ho3yIz4xFMXcxOmCnr3+c46Ep4uCf2jf3+cGBy0lFY2t16XARiOzS5O5tvQHRtXlMbz+\nNCPr8kioPEpAUwWVeldqdc4tP2f+roTb6bokhOhaTwPokPhIOCUwgT1FKXyU8Rl/nPIwDtr2Lzxp\n1jAKcir4et1Rblw2EW03F/meUjQa/G+7g9xnnqLy22/wunq2XY470BpMP478D2w0klSRSnRdHtpz\nYoAFhUyXMHZ7jaXIsWWQ1zsbj/H596fwcDHg4eqAu4sDHmf+tP7d1YCHiwOODtouH4nYsDunR+ET\nWp6z27g9C8eCakbHBRIa2feSWOYGW3/ZtrDU13e5Xu+gJWZcEEcPFTLpskj0No5C7o4hLBzXhEmU\nrV+H+9RpKLrOf8ydDDoeXjqOv686yIsfH+R3t04kxPfiKtGUllXeWvanp+9Fi1Xlm5R8Jo3yR6fT\n4HDmj16nQa/TotMqNj2OU1HTxMHzPpDpAXdAC1iAauDcqps19c24nFNWqKHeRFrKaVJTCmiobyYy\n2oeZ14wkKMzDprY0ZmdRvmE9tYcP4mKxcPZ/q2STlvr48XjPvQHHyKgeH68nFEXBWlrcq33rcgtg\nqn1mIDt7XRpZm8v84u/Rqe2vD1pURtXlMrw+n7UBMznhGi7TgQpxERoSAVRRFH4yahHPJv+TL7O/\nZsHw69tto9NruWpeDJ+9l0Ly9iySZtlvSkbHyCg8Zl5O2drPcEucjM7j4n6erifOTrtn64U+aUwA\nvp5OVNeZqKozcdpYx9HsCqrqTO3CpINO0xJKXR1aAus5IVWrUThwoufPXgJUnSrHycmBpCvt83+r\nc+pdENM6d9/zOnZiCIeS8zmRVkzsePtPkekzfyE5f/kzVbt24Dnzii63dXbU88hPxvPChyn8Y9UB\nfn/bRAK9+9Z7bE+9DR0bduewYXdOh8dU4EwYbfnjoNOe8/cfg+rZ743Vja0h2BkIRsGDljnXz7Ki\nUgWcRqUesFhV0rPLiQ125/DefDJSi1GAUXGBxCeG4tmLf+OalP0Uvd5JaTiLhdoD+6k7cqhPpeGs\njY2YSoppLinGVFTU8rW4mIZyHfhM7f4A52nYu4uc49+i9/FB7+OLztcXvbcPel9fdD6+aJ16PoWx\nk4OOwEZjp++Dc+lUKwuKv2el7jqcDDE2t1sIcWENiQAKEODsx3URV7IxeyuTAsYT4tq+zqJfoBuT\nZ0bxw3enCB/mTUiE/QrH+y66iZp9ezF++gmBdw3uAUnQMu1esKnMpgv9hw7Xs/iK6R2OzldVlYYm\nM1V1JqpqW8JpVZ3pTFBtoqrOxMmCqpZl9Sabpx30BLxQCBjta5eC8wARk2ZSuuZrNDa0xaJARMLM\nbrdz93QicoQPR/YXEDMuyO4lmQwhobhNSqR8/Re4J01Ho+/638TVSc+jP5nA//swhb+fCaH+nj0P\nBhdSs9nSq9Ax7ZrJjB3mTbPZislspfnMH1OzhWaLleZmK82Wtt//uJ2FZrOV2oZmms1WyqobgZb3\n2XCUNsHzLA0KXoAHcBIVC3ByTx6HjfU4uehJmBbBmAnBvX5/NmZndR4+z6GazRSueJWwx/7YaU+o\ntdlEc0kppuIimouLMZWc+VpcjKWq8sfX5OKCQ0AAev8AQlwtHK21onZQ8q4zimolJNwdg5sj5jIj\ntXkHaC4ztilpp3F2ORNGWwKq3ufHcKr38UXj7Nz68zEmypvKytRu3wdn6VQr0ypTiY1c0OM2CyH6\nx5AJoABXR1zBvuKDrDr2GQ8n3N9hbdBxk8PIPVXO1+uPcfPdkzp80L83tK6u+C5eQsl77+Ax4wqc\nRozofqeLmJebgWubMmy60F/bdLzT0lCKouDsqMfZUd/tLDxWq8qGH3L4/PtTPTq3BohAoRKVaG/7\nhCZVVSk/sBdsDMLlw/yICej8uctzxSWE8sVHhyjIqbTLIwPn8563kJwn/0j1ju14zrqy2+3dXRz4\n7S0TeP6DFP7+4QEeu20iPh6Odm9XT9Q2NLP3WAm7Ugs5WVDNogrbQ8f0uAV2K06/43AhH2082mn4\nPJcGhWhAQcHSZGbWnFGMiA3o82M/5RvWdxs+z1LNZso2fIHf4qXnhMziMyGzCHNFOWc/5WkcHdH7\nB+AQGIjTyFGtgdMhIBCt648j9psrKvD5n3UYXXr2/gZQUSiInMHkq0a1PnuvWq1YqqtoNhppLivD\nXGakucxIs9FIfeoRmsuMqM0/PsygcXQ8E0Z90Lq5MqI2t8fnB4iuy8fVXA9cfM83C3EpG1IBVK/R\ncSy1dgwAACAASURBVMvoxfwz5VV2FOxhZmj7YtwajcKVc0fzyVt7+X7TCa6eH2O33iePy2ZS9f02\nSj58j/A//aXTUjyDQXNFBQGlPQuAZwWUnqS5ogK9V9/ClEaj4GXDYJhQFLRADioz7fCBorm2htTX\n/oHLsRxOR3oQmFvVozqgZi0Ez1/c4/OERHji7efCkf35FySAGoKDcZs8lfKNX+B+WfuapB3xdDXw\nu7Mh9ExPaH/NMGS2WDlysoxdqUUcOmnEaoWxw7xZPisU9zfzbDqWvUPHmChvQnoQPs9SUKhF5eY7\nJrbWpu2L5ooKag8dsGmfugMp1B1IaWmPg8OZUBmA25SpOAS0BEy9fwBad/ceXQPrnDXo9cfQWEOx\narq/tmmsZpytmZzI1HHixB4mJoUTPykUnV6LztMLnacXTtHtP6irqoqlpoZmoxFzubFNUK1LS7Op\nKgWARrVSl576/9k7z/A2zitt34PeCYAASbB3sYrqXXKRJblL7rEt23FN7CS72XjjbLwl2SSb/bJO\n2c3GLdnEce+2JEuU1a3eKYqiKLGLvYFgAwGCBDDfD1qFFgsAUW7BfV2+aAHzzgyBlzPPnPec52Bc\nuDjIkWHChLmcfOWr4D+LWWWie6CHTxr3Ms82E5Xs4huQUiVDH6HiyN46IswaIkfx5QsFQRBQJSTi\n2LAeqU6HOnXy8kw/b5xFR87dvAJGFFHGJ6BKTLzk4+s1CrYdbRyxFD9a32kNkIyEBkT6JQL3LMu8\npMpnx6kSap75DyQdDlpXLeSKB/+JdoMApeXjLsV7JfDJFdHMX3wHylGK4EZDEAQkEoHjhxqZkhc9\nadH4C1HGxePYuAGp3hDwfFQrZUzPsLCrpJlDp9qYlRWFaoLuOKEiiiK1LX1s2H+GvxSeZs+JFuQy\nCStmJ/LQDdlcOT0OXV0ZriDnosDkzUUA36CP8sONQYkfOQIz5iSEbEF0ISH9PQKma6/H9tjjWG69\nHdNVS9HPnoM2JxdVYhLyyEgkKlXAD+DHOkrZ5z7G7NMtdGoSx12Kl/h9ZHfs4tj0ZuZcPR2L3ELx\nwQbKS1tRaxSYrdoxjysIAhKlErnJhDI2DnV6BrqpBRjmzkeQSHGVngj6czjsMZI0Mw/5JBWfhgkT\nZmz+ZmyYRiPNmMyepgO0utqZETV11G0irTp6ulyUHG4kPTtq0m7+MpMJb3c33du3Yli4+FwLxkvF\n2eehpryD5oYeuuz9qDSKSbmxjYW7oiKkC70mO3tShLdaKaO+zUlLpwsNw0vsSQiYkWBEwIRAFBCJ\nwABwBpiRYWVJiAU9ot9PzXuv0Pfam3QaZWi+8yhz596MRJAQnZyNMzWGto46VN0uLryF+QToTLOi\nuPs29qhbKe+qZFbMdKQB5skZIzWcPNaM3y9eFksmqU7HkL2D3n17MF61NOCovEYlpyDdwo5jTRwp\nb2d2VhTKT6v1u/o8HC3voLyhmxa7C71GEbTot/e42VbUxMsfl1N4oI6uPg8L8m3cv2IKNy9KIS0u\nAtWnhXBf9FyE4faTZyo7J97wAgQgMkqHJfrSH3BD/Qz0s+egzc0LeZXHL/rpcHdS3lXFgZYj1Avd\nuDX9zCmvxitR4pYbhpvOf4og+rH21zGlYy8HZrppjFGQbc1gyfRppGdH0WV3UbS/nrpqB0azBn2Q\nKR6DLc30FwcXCQY4pkygsNZLXooZ3STliIcJE2Z0/qZsmD6LVq7htoyb+GvZm5TaT5FnGb0CcvGy\nTFoaDrN9/WluvmcaEsnkLMVbbr2dvqOTU5DU3tI7fMGu6hwhvCUSgaT0SGbMTwy61V0gSNShCWeJ\navIKV26Yn0RdVSfJfnHMog8AFSIWiYTr5weem3Yhg52dlD/3DPL6Vipm2Fhw35NYdJYR26TkziMl\ndx6dbXXUHd2Fz+VCqtGQNHPJuZzPx3rS+Z+iF3jr9Aeszr4joJu+XD5syVRW3MzsRcnIFZP/J2m+\ncSW9B/bT88kOTMtXBDwu2qThqbun86vXi/jNW8XctTSdbUebQu7D7vZ4OXK6nX2lrZQ3dKOQS5iZ\nGcW9yzLJTjKN+ff3Rc9Fr9dHW3NvSGOHJsn+5/P4DLx+Ly397TT2NdHgbKaxr4kmZwsDvuFuaGrp\n8DnUJChxaoaYXbafeXWH6FHb8ErkyPxDRLhbaLb5+PgKDe2Rw6tPZ1ehjObh7l/NDd3s317N2jeK\nSc6IZN6VaZgC9OzV5OSBVDqiiGlCpFJueeBant3awM9fPsK3VuaSn/rVbRoSJszXha9lBBQgVhtD\nbW89B1qOsjBuLrJRcpZkMgnWaD1H9tYhk0mwTVJP7OEOSdpPOyTlhtwhqaa8g8J3T+Cwu0btAtTd\n6aKitBWzVYtpgsKeYJHqDXRv30pQ5egSKVH3rA7KVmU8hpwe2kpaESY4BQEBsyBQUGAL2oTbceQA\ndf/9X3jcTppuX8S1t/8DOuXYESuNzogto4C4nFnYMgpGdDwyqYxEqs1sqN2MQqogzZgc0DkYzRqK\nDzagMygvy8OEVKtlqLPzXDHSeL6gn0WvUZCXEsnHh+rZfbyFlk4XMnFkKsSgCE2dLvaVthJn1Y4o\nMvP5/ZTWOPhwdw1/3XiaoooOosxqbl6YwkPXZzM3J5ook3pcsR7aXJRc0lx09Q9Sdaqdo3vr2Lmp\ngrbmvpD2k5YdNSkRUIlaPfwZBDVo7M/A7R2grreB4x0n2dN8gMLaLbxf+RG7m/Zzwn4Kl9dFtMbK\njKipXJN4Bbdm3Mji+Pl80rgXEZF+jZTKJBXlKVI8ml56jb20xLjYM0PFyXQ1/Zrh662AwPUpyzAo\nz5vA6yNUZBfYMEZqqDzZRvHBBtz9Q0TZ9BN64krVajz19Qy2tgT8MWinTSduxTLm58ZQ29rLur21\nKOQS0uOC818NEyZMYPxNL8HDcB5RakQSW+t3MugbJDsyc9Tt9BEqfD4/RfvqSUqLRDtJnWCUiYn0\nnyihvyS0DkntLb0Uvnti3J7PMHxPrq2wk5BqnpQOKGcJ5UIvyOUoY+NQxMVPyoV995ZKuuzjm7qf\nQ4QB9xDp2VEBbe4fHKTm1T/S/8Fa6m1K9I8/yuIZN43qnBAMcTobPr+PwtotxOtiidZOfD5KlQx7\nu5P6age5M2Ivy01RGR+PY9NGJCrVqIUf49HZO8DO4mZU4tipEFoEXKLIwQo7eSkm+lxDbDxYz18K\nT7OzuBkRWDYrgYeuz2bpzAQSo/XIAmyTOtpcHJBqaNcl0a2Opl9hRO4bQCZeYAMvCMOdoJKSA/o8\nRVHEYe/n1PEWDuyoYe+2Ks5UdaLSyMiZFsv0+YlUlbUHqYEFFi3LuORUGXdlBa0vPo/X2RdUDmpH\nqoXkZTfT4+mjuqeWovYSPmnYw7qaj1lTXciBliNUdFUhAon6OObZZrEi6Wpuz7iJaxKvYHrUVNKM\nKVg1FpRSJSqZikZnM22u9nPHGJJLsJvktFnk2E1yhuQjv1MJEg62HsE15CZOb0MpHb5GCYJApFVH\nzvRY5HIppUVNnDjahCAIWGN0SMa5Xsqjoujdtxf8gbkiSBQKdAXTUeo0zMmOxucXWbO7lrYuN/mp\nkQHPwzBhwgTG37wABdDINUiQsKl+B/mWbCKUo0eXbAkRnKnqpOpUO1lTbUgn4YI0oiBJr0edkhrU\n+N1bKnEEKL7EIMVXoMgsFnr37D737/Fu+oJMhio1je6tm3EePoTUYEARE7q/pbPPw65NFUGN6e1y\nkzXVNuENf6Cpkcpf/xxfeRXHF8Yz/9GnSYuaPNusDFMqzf2tbKnfSV5k1ojoz1hotAqKDzVgS4jA\ncBn8N6UaLd7uLnp378J4ZXBR0Ne3VOCy95OJgAYB4TMySEBAjYAF6BdFdp9qZ+vRRjq63czNieG+\n5VO4ZXEKmQnGkAvE5FFR9OzeTa/STLl1HuVR8+nQJdGpjceuS6TRmE2f0ox6qA+VxIu2YDrdmz/G\nVXYSVWoaMsPFf/s+n5/m+i5KDjeye3Mlx/bX097ShyVax7R5iVx5XSb5M+OxxQ9/J/Y2J92dAT4Q\nAckZlktqMuBzu+l4503aX3sFwWqhMFckpckTkC+tVwrrZykpdBzg4zPbONx2jIa+RlQyFenGVBbH\nzePG1OXclnETS+LnM9WaS7IhAbPKiEwy9ndkUZs52FqEPwBLLLlExhMFD2FUGtjVdIDtDbvpH3IR\np4s9tywvkUiwxUeQXWBj0OMbLlQ6MX6hksxoQhkXh/NY0bgiVJDJiLx5Fa7Tp+n5ZDuqpCQUUVHk\nJJuJtWgpPFBHcaWd/NRINKqvZTZamDBfCGEB+inJhkSKO0o52VnOgtjZo17QJBKB2MSI4aUg1xBJ\n6ZOTHzRckNRF97atGBYtQaIMLEJ5OcVXMHRv3cJAVSW9aivllrlj3vQ1fhfJj3wT6213op1awEB9\nHV2FG+gvPobMbEIeFR20EK0p76A2yKIPURy/6EMURTo/2UrTc/9Lj8RD/TeuZOUN3wtIIAaDIAjk\nWbI50XGSfS2HmRk9bVQ3hgvRGZTUVtjpcrjJyIme1PM5izI+ga7NG5EoFKgzRl8R+CxdfR7e/7ic\njAAsiIRPjdg7fX7uvyGbh27IpiDdglGnvOSoruv0KWrK2zluW0a/0jSi8GX44AIuhZFWQzrxV84l\n7e5bUWdl03fkEI6NGxD9flRp6XgG/dSUd3B0Xx27NlVw6ngrrv5BktIjmbM4hSXLM8nIjcYSrbto\nOdhgVFFR2hpQFFQqk3DV9VNCXpVwFh+j+fe/ZaCmBuud36Dumnx2eCvojJCR1ji+CPVKYOPCCBpj\nFGSZM1iZei0r065jZdr1zI+dTU7kFOL1sRgU+qAj/kZlBLHaaI7bT44rQmUSGQ/l3kOeJZtMUzqL\n4+YiESTsbj7A9oZdOAf7idXFoJIN55XK5VIS0yKHC5U6hguV6mvGLlRS2GLR5uXjczrp7eyjXZN4\n/sGYQYwF+cQ8+AiGOfMwzF/AQHUlnR+tRRRF1JlTiLPqmJoWyZ6SVrYfayTVZsAS8eVovBAmzFed\nyy5ABwcHWbVqFRkZGcTFxY14r6amhquvvppvf/vbI16/4YYb+PnPf86zzz7LH/7wB5599lluuOEG\nTEH4RgYrQCWChHh9LJvObEcj15ASMboti1qjQKmScWRvHdYYXUht8kbdb2o63Tu24XU40E2fEdCY\nyyG+gqVr2xY613yAZ8VqDvuzcMqNY9/0IzKImZqBKVKLzGjCMG8B6qxsBqqr6CrcgKvsJHKLBbnV\nOuqxvEM+uhwuWht7OFPVSfmJVspLW/EMBF/AEZ9kJDp2lGhXfz+1L/4vri1bOZWmwfjIwyzNvwFp\nAH6GoSCTSMmNzGJv8wFOOcqZHT193GMJgoBEKuH4oQYyc6MnrZvThUg1Grw93fTs+oSIK6+esDsS\nwNHyDjor7WiC8L+UAsmZFpJjJief1dPcxOkXX+W4bSl+YfzvSxQkNHb4SEg1Y0yOI2LxEvo8AmUH\najhytJ0Dh+zUVNiRy6VMyY9h4dJ05l2ZSnK6hQiTetxCRK1eidmqpbbCPq4IlUoFlq3MIT45eFcD\nb08PbS//hc41H6BKzyT++0+izc2jqqeWMkc5XREy6mwK1IMiEX2+ixwZahKUbJlnoDFm+OJ/RdwC\n5sXOQiPXTFpqR4w2ihxzJv1DLtrddsQLujVIBAkF1lxWZ9/BFPP5VQW5VE6mKY1FsfOQSqTsbj7I\ntoZd9A06idPZzglRlVpOek4UcYlGGmq7KNpfT2ebE0uM7qK/CYdbSnG3mRP+ZDq0iecfjE05eBJz\nMScP54RLlEr0c+cjSCQ4PlrLQFUlmtw8TJEG5udGU17fzfp9dejVcpJj9OG80DBhLpFABaggisE2\nPRwWnz/4wQ/Ytm0br7zyCrNnzz73XlNTEw899BBNTU2Ulpaee93n81FQUMDrr79OfHz8udfNZnNQ\nf/BdXf14vYHl/lzIW+Ufcqj1KP869x8xqUYvNhJFkY3vnaCtpY+7Hp6NRhuYn+NEdO/cQfurL5Pw\nT/8cUP5dyZFG9m6tCvo4toQIElPNaHRKtDoFWp0SrX7YrimYz7jv6GFaXngOcckN7Gy1TpiHCsM3\n3VWrp48oohFFEVfZSewfvo/7TB3ilAKk867GrTTS0+Wm2+Gip8uNs9dzboxcIT0nBNpbgi/8uOqG\nLLLyY0a85q6q5Mzzv8frdnJ0cQLX3vxdYgLIzZwM6nob+F3R8xRY8/hmzt3jfg/eIR+vPLufKXkx\nLLwm/bKcz5DDwZmnn8J8481E3njzhNtv3FNL7Z4zFy27j4cfkbRFyVy7aPQ2kMHgHxig/j9+RpGy\ngDZZ4JFhW0IEUTY9Z6o66XG4kUoFIr12zB3lpE5PJuGOW0Kukr8czhSiKNK7bw8db7+FIJFgvfse\n9HPmnZsv+1uO8Nqpd0aM0bp8JLYOohgSGZQL1McozhX/nOW+7DuZZ5sV0u8ZCN2eHk47KhnwelDJ\nlGSZMzAqIyYc5/a6+aRhL9sbdjPoG2RB7FyWJ1054tosiiKVZe0c3FmDyzlI7vRYZi5MQq1RUFPe\nwdZ1ZeNem84+CKRknn/wdZ0qo+VPL4AgYHv022iysvH6/Ly9vYptRxtZUhDL6uWZ4bzQMGEuAZlM\ngsk0cWF00AK0urqaJ598EoDy8vIRAnTTpk389Kc/JTo6mqqqqhECtKamhptvvpni4mJkQeSffZZQ\nBajb6+bnB35NsiGRx6Y+MOZ2rv5B3v7zYaJteq67PX9SnoZFv5/6X/4cfD4S//Wn4xYk9Xa7Obz7\nDBUn24I+jlavwDvkvyhyKJVJ0GgVaPXDolSjG/lTq1Og0SlRKKW4Kyto+u0z6GbM5HjkoqAisSmZ\nFhZcnUa3w01Pl4seh5vuLjc9Dhd9PQPnokZS/BiMSoxRERjNaiJMGiLMaowmNWqtAkEQcPZ5eP35\nA8FFuyUC9z4+D92ny56i30/H+rV0fbSOFouMtpWLuGX23SgCNIqfLI62HecvJ1/nptQVXJu8dNxt\nD3xSw8ljTdz3xPzL5vPa/ubr9O7fS8r/ewapZvyLxEeFp2ksaQ36GLZsKyuWZSCTSZDKpCFZnImi\nSOv//RH7iVPsjVsVVAEQDEfTUjItJKdHEpdsQiYV6N62BfuH7yPV6Yha/QC6qQVBn9dZnH0eGs90\nMeTxIlfKiE82nZt7wTDY0U77Ky/jOnUS/bz5RN11D1L9yLSQTreDn+z/1Yho40RIBAk/X/DjgATh\nF4XbO8DOxr1sr9+Nx+dhfuwcViRdNUKIeod8nDjaRNH+OgAy82I4Vdwc8oOxt6eblj+9iLv8NJEr\nb8F8/Y0IEgm7S5p5dVM5yTEGvnNLHhGTVJAaJszfGpdNgL755pvU19fz/e9/n4KCAl599dVzAvTp\np59mxowZxMXF8eijj44QoFu2bOHXv/41mzZtCvJXGUmoAhSgqL2EP5e+xmP591NgzRtzuzNVdja+\nV8qSFRnkTo8bc7tgcNfU0PCfPyfq7nsxXn3Nude9Qz6aG7qpr3ZQX+ugx+FGEIJznIGR4svr9eFy\nDtLvHMTl9Jz/2TeIq3+Q/k//f/AzHoVSqYDC04da5kefmkRdTVfIv69EImAwDYvKiLMi06hEWl+B\ne/MavG1t6GbNxrLyFhS20Qs1Pv6glNoKe8DHTMm0cO2tw9/rUFcX9S/+AW91NUfy9KTeupr5CXND\n/n0ulQ21Wyis3cIjefcxPSp/zO2cvQO89vwBFl2TQd7MyZl7n8Xb3U3tj3+I6drrsay8ZdxtD+45\nQ9GeM5d8TIlEQCqTfCpIh3/KZFKkZ/8tlyCVXvBTJsXb0sjAyeP4psygoSP4v/mrrp9C1lTbRa8P\ndXTQ9trLuE6Wop8zD+vd9yDTT7791USIPh9dWzfTufZDpHo90fc9gDZvZOMMURQ52XmaD6sLae0P\n7qF0mjWPR/Pvn8xTvmwMeAfY2biPbQ27GPB6mG+bxfKkq4lUn0/PcrsGObLnDKVFzUHt+8LrwllE\nv5/Oj9biWL8OTU4uMY88hkxvoLqphz98eAKJIPDdW/PH9bUNEybM6Fw2AXohWVlZIwToWfbv33+R\nAH3hhRdYu3YtCQkJlJWVkZKSwo9+9CPy8sYWgqNxKQJUFEVeKHmJRmcL/zL3SdSysc2dd26qoOJE\nK7c/OBNTpHY42lHrYHDQh0IhJT7FHHS0o+2Vl+g9fAjTD/+d5vZB6mscNNd34/P60RmUJKaaSUw1\nE5dkYvuG0yGLr0AZGhoWqi6nh962LprWbmBQoUOaNwO73U13pzuo/QEUzIknb0YcOoNqzKiX6PPR\nu28PnR+tw9vlwDB/AZE3rbooR7S9pZc1rx0LLNIhk7Dq3mlE2Qw4i4/R9OcX6ReGOHhVAjcv+xZx\nuouFyOeJKIq8dPINSuxl/GDG4yQa4sfcdvOak3S2O/nGo3MuWz5ax9tv0rNnFyn/+QxS3dh5w6dL\nWthRWB70/gvmDFePe71+fF7/iJ9er++i13xDfrw+P74hH16fnyHXAJ6OTkSVhiHJcGQ/WBZdk07+\nrNE/Z1EU6Tuwj/a33gBBIOquu9HPW/C55f95GuppffklPHVnMC69Bsuq2y7qmtbY18yHVRs43VVJ\nhjGV+bbZvFH+Pl7/xLnRcomMf5jxOEmGhMv1K1wWBrwedjXtY1v9LtzeAebZZrIi6Woi1cP5tM4+\nD689tz9oO6wLV0YupP9kKa3/9yJIpdgeexxN5hS6+jw8++EJ6tucfPO6KSzI+2KvHWHCfNX40gnQ\np556ir179/KLX/yCmJgY3nzzTQoLCyksLCQqKvB8vN5eNz5faAIUhpeyfrr3GRbFz+WurFVjbjc0\n5OOt/zsMgClSM2q+V3KGhVkLk0Ytehmxr0EfjXVdnDnVSnVRDW6ZDolUIC7RSFJaJElpkZgsI4sE\n2pp7ef/looB+V6lMwm33z5jwPMbC53Zz5pf/ga/fScq//Btys5njhxrYtbky6H0tWZFBwezAbnr+\noSG6d36C/aN1eJ1OTEuuwHLzzchN54s3qss72PT+Ccb7GKQSWHFbPikpEbS8/SY927ZRHa+g86YF\nfGPmPecKHL5oBn1D/Obwc3R7evjx3L/HqBp9abS5vpv3Xyni5rsLSEq7PB1bvD09VP7wSSJXXEvU\nbbePuZ2z18Nf/3cfwVwmBEHgm99bgM4Q2hKmr7+fmp/8K1K9nuSn/4XyMjtbPzoV9H6uuSmb7ILx\nxYO3t5fWN16j98ABtHn52B74JooxiuUmA//gIPZ1a7FvLERps2F78GE0aSPbhXYN9LCu6mP2Nx8h\nSmPhtswbmWrNQRAEittL+VPJa+OKUJlExqNTVzMtKrgH0i8TA14POxv2seXMJ/R73cyPncV1KUux\nVw5O+lwY6uqi6YXncFVWEnXrbURefwNDfpGXN55m9/EWrp2byF1L05EG6eUcJszfKlKpBINh4hz7\nz02A+nw+BgYG0GrPq+Ibb7yRW265hYcffjjUUwiJj05v5bXjH/Af1zxFemTymNvt21HF1vXjX+yk\nMgm3rZ5BVv75i5soitjbnFSVt1N1qp36Ggc+nx+jWUOsZgD5oc0sevrbROaP3iL0LKdPtPD+a0X4\nxon4jnb8YPAPDVH281/irKoi/z//A23SsEtA8aEG1r1dHPT+Vn5jWsAC9Cw+j4eWDRtp+uBD/J5B\nYq5bQfxttyCPiKBz/0EO/f6vnDHkYNcmIl5gGyOIfiz99ST3llFw7000bdqEu6mJPTMMzLrrAZal\nL/nSVbR2uXt4esuvMKoM/PTqH6CUXZyPKooif/rdLnQGFfc8cvnSBs68/CothR8z60/PIx/FJxOG\nc6L/+Jud9PYMBLzfrPwY7vzm7Ik3HAXR7+fUL39F36nTFPz2GVTRUfT2uPn9L7YFnQ/8d/+yFEOA\n1jqOI0epfv6PePv6SFp9D7YbrkOQTq5DQk/pSaqefQFPezsJd95O3K2rRjgRDAwNsPb0Fj4q34JS\npuTO3BtZmrbooi5u1Y46Pjz1MUebSvBdYIUkFSTMjJvKLdnXkmYOrS3tl40Br4ctVbtZd3ozfYP9\nzB5cQv+x4B1Krl2Vx5zFYxfFiT4f9W+8ReN7H2CaOYOM738PmV7P+j21/N+6UqamW3jqvlnoNZ9v\n/niYMF9nPjcBOhrf+973sNlsPP300wEf81IjoAA+v4//PPh7QOTHc/9+VHucoCKQUgkr7ylgYMBL\nfXUndVWd9PV6kMokxCUZSU6LJDEtEqNZDaJI7c9+CqJIyk/+fcIOSW3NvRzdV0dthf2iCGxKpoWZ\nCyaOwI6FKIo0/+mP9B46SOKTP0SbfV4QO3s9vPyHfUHf9B/47iVEvlwuHJs30fnxRgD0s+fQvW8v\nwqd9nwekGro05/tOm1wtqHzDxuAi0KeVsnt5Indd9ei4S9xfNPW9TTxz6A/kW7N5ZOrqUb0YTx1v\nYetHp7jviXmTZgn2Wbx9fVT98ElMVy8l+s67RrwniiJVpzrYuakc79DwUnkgc+FSo/H2wg20v/M2\nCd//B/TTpp97fcO7J6gp7wh4P2lZVq6/fexc29Hwud20v/cuXdu3oUpJIfbBh1ElXPoSts/lou2d\nt+n+ZAfqjAxiH3wIZez5/F6f38e+5sOsq9qEy+vmmqQlXJt8FWr5+OK5a6CH050VuH0e1FIlWZGZ\nmMaIqn/VGfQNsqvxAHsOnCSyckrQ4wOJhgM4S0po+uMLCAoF8Y9/B01GBmW1Dv7wwQk0Shnfv7OA\n+KiRKSuO3gFO1jpwe7yolTJyU8yYDV+OVZcwYb4IAo2Afm7tH+69914WL158zhtUFEUqKipYuHBh\nUPvx+fwh54CeR+DuKbfyzJE/sOXMLq5JvOKiLQ7vOROw0PX5/Hzw6jEAIkxqkjMsJKaZiU0wIrvA\nzPpsLqP1nvto+OXP6dy2DeNV41dFR0bpWL4qd9yK21A/D/sH79Gzby+2xx5HmTFlxH5UGjlJ5kMe\n7AAAIABJREFU6ZFB5aEmpUei0shD/34UKkw3rsRw5VIcHxfStWkjwgXPRyqfC1tf9ahDBcBlNfCt\n5U+hkasnYY5cPmI1Nh7IvZs/nXiFqMrN3Ji6/KJtUqZYUG2VU3ywgUXLJq9L0wjUWoxXX4Nj62Yi\nrllxrltQv9PD7s2V1FbYScm0sGR5Bm3NvWxZG4Dtzc3ZREbpQvr8XRXltL/3LqbrbkCdVzBiHzH5\nCqoqfEjEiaOSfsFHdJ4i+HOQK7HevRrd7Lm0vfwSNT/9N8zXXY/5hpuQyC+OfA11deEqK8XvHkCi\nVqHJyUP+GU9j57GjtL32Kv6BAaLuvY+IK65CkEjwev2IokiZo5wPqzbQ0t/G7OgZ3JS64lzhzUTn\nr5fpmR09c8RrX+Z5fylIkHFl3CJSFqexoeoUghj4crgo+NHGSAL6bFQ5eST+289o+ePznPl/v8Ry\n6+1kLL+Wf31gFv/7/gn+/aXDPHJjDjOnWKlt6WXD/jqOV9nxXfBwJpUIFKRbuGF+UriIKUyYcfjc\nBOhVV13Fn/70J7KyskhKSuKll17C7XazcuXKz+sURpBkSOCK+AVsqNnMdGv+uSR3GE50P1MZuPCC\nYY/2lfdOxxY/cQRCnZqGYfES7B++j27W7IAqcHV65UX+lpdC945tOArXY73zG+jnjL7MO2N+IvXV\nnQEXAc2YP7rJf7BIdTqMS5fh2FQYVO/rqPpu5M4BMH35O5pMs+axMvU61tZsJEZjZVbM9BHvy2RS\ncqbbOHGkiTlLUi6bJZNp+bV0b99K16ZCLLffRfmJVvZuq0YqFVi+Koe0rOH87JRMK6tWT590/8uz\neHt6aHnxedTpGVhW3XrR+wfd+2lI7yChatq4ItQv+GhIL+aQu4kCQhPu6vQMEv/t33EUrsdRuB7n\nkSNEP/Dgue5RA2dqcWxYj7OkGD6NzgMglaKbOg3zDTciM5lof+M1nEePoJ1aQNTq+5Gbz+fzXlhg\nlG5M4alZ3/vKFQx9ETT7mug1thPRFfi1sNfYTv1gHbEEltsrN5tJ+McfYV/zAfZ338ZdUU7Mg4/w\nz/fN5M+Fp3j2wxPMzrJyrLIT7yhBCp9fpKiig5LqTr69MpcZmZcvpzhMmK8yl3RXCya/7uGHH2Zo\naIif/exnOBwOCgoKeOmll1CrvzixcFPqCoo7Snm7Yg2PT33w3O/TWOsI2gZJFKGnyx2QAAWw3noH\nzqNHsb//LjHf/HxzYJ3HjtL+xmsYr1mOafm1Y24XZTOwbGVOwJGvUMXHaNiPH0YI1orKL2IvOYLt\nimWTdh6Xk2VJV9LiauPV0+8SqY68qEtX7vQ4ju2vp/xE65jV3JeKVKfDuGwFLVt3cmAwg8aGPjJz\no1l4TfpFnWeibAauvTVv0vwvzyL6/cPm4KIf22OPX5R72e3p4YS9DL/JT032AawtaRi6o0ZEwUTB\nT6+xnQ5bNQO6HkrsHXR7ekL2wJTI5VhW3oJ+1hzaXv4LDb/6JRFXXY0qLYP2v/4Z0TtKEZDPh/PY\nUZzHixFkUiRKJbbHHkc3+7ybQbenh/U1mznQcgSrJpLH8h9gqiXnS5er/GVlwDtAh60afbc1oGi4\niH94TniDE/eCTIb19jtRZ2TS+uc/Uffzn2D71hM8vjKX19RydhxrOretzusixdWMwj/EoEROrSYW\np0yD1+fnhbUn+fHqGeFIaJgwo3BJAvTUqdELdObPn39R/qcgCDz++OM8/vjjl3LISUUlU3Fn5ir+\neOJljnWcYEbUsAff4KBvgpGjM+QJvHWkVK/HcutttL/2ChGLr0Cddnk633wWd1UlLX98Ad3MWVjv\n/MaE26dkWpl7Syz79lQitqkuuukL0QPMXZRBSvrkPuW3OhqY2MRhtHH1fFVMUwRB4J6s27G7O3nx\nxF/50ay/G2HArdMrScuycuJoE3kz4y6LSBFFkeboaRyw6VA0d3P9HdMmrLyf7Gh857oPcZefJv7J\np5AZL+5SdspRea7v+ICuh4aMImSDSnQ9FiQ+GX6pF2eEHa/ifEctv+jntKPykrsAKePiSPinf6Z7\nxzY63nuHnh3bJx7k9yEO+Yn57t+jzckdPm+vh631O9lWvxOFVMEdmStZFDv3srWC/bqikqmG50B6\n8YTRcBE/IGBpTUEhhFY8pCuYRtJPfkbLi8/R8KtfYr39Tnqcw1eYmAE787tKSe9vQHpBgwAfAlXa\nBPab8mhVWSjcX8d3bg0uHzlMmL8FQu4F/0URbC/4iYjRRtHU18ze5kMssM1BLpXTZe/nTJC92AHS\nsqOC6sWuTEyi/3gx/SdKiFh8xWWPggy2ttD4m/9ClZhE7BPfRRJAR6rijlJeqn6VVkMNXdYGBtR9\nOA2d9JhbaEkqo9VczfG+EmK10ZPS3rJ/yEVzfwu1FUcxV7cHPz4nhfgp0yfe8EuCVJCQb8nhQMsR\nijtKmR09A5nk/Pei1SspPthAVKxh0ouRuh0uNn14krKSNlLMXnJPfUDyTdeE3KIyFPpPlND+6stY\nbr0dw/zR88Grumsoc4z0IvVLfQxo+3DrehjQ9uGXXvzQqJIpidZY0V5iH3RBEFCnpuGurGSoI/A5\nKXo8aGfNZl/LIf504hXKu6q4Mn4Rj+SvJt2YOmrxWZjx0Su0fNK4F4/KSV+EHalXgdKjGdEqVhT8\n9JraaEwtoT/CjrU5DbFTRdoUKzJZ8IJfqtFgmL8QcWCAznVr8LU0IYh+bmvdQdRgN5/9FiWAZaiH\nvL5qOhRGTvUrWDw1FvVlSqMJE+bLRqC94P/mBShAakQyOxr24BzqJ8+SjUqjoPRoU9Bmx4uWZQSV\nqycIAsqERBzr1yEzGFClpIZw9oHh7emm4de/QqrVEv8PP0QagMio623g+ZKXznkOjnXT94t+jttP\nkmPOnHDJUxRF+odcNDlbqOiqprijlL3NB9lS9wlrqgspPLOVfc2HaKaXgnL3RRf38fAJ4LnpKuIt\nyUGM+uJRShVMMaWzrX4nTc4WpkdNPSeYtHolddUOHB1OMvMmJ+ro9/s5fqiBzWvLAFhxSy5T56fS\nu2MrfrcLbX7o7SmDYaizk8bf/RpNVjZR9943pkhsdbVTYj8Z9P6bnC3satrPtoZdnOw8TaOzmb5B\nJxJBgkamDkoADnV10fHma0G1KPO0tfGKrpxd9qPkW3J4LP9+ZkRPRS6RTzw4zKioZCoanc20udrx\nKjz0RraM+mDcHdWIV+HBo3YSmxjBUI2G6lMdJKVHolQFLwQFiQRtXj7VPj2Wsv3k9J8ZEfUcDQki\nmf31VKtjMcVGkRitH3f7MGG+LgQqQMOPZIBJZeSm1Gt5r3Idc2JmkBqRFFIFeCh5cOrUNAyLgitI\nChb/gJum//kdotdL3A9/jFQb2OL2prodAXVdAfD6vWyu28Gj+fcjiiLOoX463HbaXXY63J10nP3p\ntuP2nveUNCj0WNWR2HTRTLXmYlVHEqWxIJfIOXb0X0lr8Ixz1JHUxitZkPzViX5eSJzOxoO59/Bi\nyct8VLOJlWnXAcMPKfmz4ti+/jRdnf2YIkNJTDhPZ4eTTwrLaW/pY+rseOYsTkGuGI4KmVZci+Oj\ntZiuvX5EwczlQPR6aXnxWSQqFTEPPzauHVm2OQOJIDm3DB8IEkHC07P/gd7BPhqcTdT3NnLKUc7O\nxr0AyCVy4nU2EvRxJOjjSdTHYdNGj7kk7iorBX9wFeaC309Ci4fbbwoXGE0mK5Ku4mTn6XPXJq/C\nQ7e1adRt5RIZK6YvxJAXyYZ3Svjg1SJuuCMfS4hisCchE4fKSqq7JaDtZaKf+V2luD3zQzpemDBf\nZ8IC9FOWxM/nUGsRb55+n3+a/fefawW45bbbcRZdnoIk0eul+flnGepoJ+Gpp5FHBiYszhZ+BENx\nRym/OPhbuga6GfCdF5kRCgNWTSTxOhvTrflYNJFY1Ras6khUsrFFe/fCfLzvHEEWwH3fK4Wehfkh\nF518Gci35HBL+g18ULWeaI31XP5ielYU+7dXU3q0icXLM0Pat8/np2h/PUX76ogwqbnlvunExI38\nrExLr6FryyYcG9YTfd8Dl/z7jEfHu28zUFdHwo/+edxWoABGZQT5lhyOd4zvK3whUy052HTR2Ihm\nivl8frXbO0BjXxMNfU3U9zVR3lXN7qYDiIjIJDLitDYS9LEk6uNJMMRh08Ygl8jwuwM34r+QZdHz\nMYXF56SSZEjgodx7+MvJNyZ8QL4xZfk58X/r/TMofLeENa8Xs3xVLomp5nHHjoZ2sB+zuzWoMen9\nDXQP9Qd9rDBhvu6EBeinSAQJd2fdxn8d+T3b6nexPPmqz60CXKY3YLnlNtpfn9yCJFEUaXvlr7hO\nnyL++0+iDMJU+8LCj2DQydXMjZmBVR2JVWPBoo5EKQ2tAGDBnJV82FLN8t1d44pQrwS2LDKxas4X\nY+k1mVydsJjW/jbeOP0+FnUk6cYUpDIJOdNjOX6ogTlLUoNeQmxv6WVHYTndnS6mz0tk5oIkpLKL\nI44SlRrziuuxr3kf83XXI7dcHvuYviOH6N62Bes9q1GnBpZ28tmo13jIJTKWJ1016ntqmYoMUxoZ\npvPtLwe8HhqdzTR8Kkyre86wt/kQIiJSQUqsLoa8zkFCkf4D4dX2y0KBNY8fzHiczXU7KLGXjbhW\nSQQJeZHZ2N12ttbvYlpUPhZ1JBqtgpX3TGPz2jIK3y3hyuumkDU1uJLFFHcLfRMsvX8WKSLJrlYg\nK6hxYcJ83QnngF5AhFLPgG+ArfU7mRk1jbgYCwmpZgbcQ/R2uUekf53tBX/VDVnEJwf/JP1ZlEmT\nX5DUufYDurdtJeaRx9BNmxHU2NEKPwJhacIVLE1cQow2GoNCf1EbwWAwKiNQx8azVl6B0uMjos83\nIifUJ0BNgpLtC0ysWPoQU8yXybD9c0QQBHIip1DZXcPOxn3MiJqKRq7GGKnh+KFGVBr5RZHLsfAO\n+Ti4q5ZPCsvR6pRcf0c+GbnRSCRjzy1lYiI9O3fg7etFN23y0xkGW1tp/v3v0E2fgeX2OwOe50Zl\nBDFqK0UdJeNuJ5PIeCj3nqDmgkwiw6wykRKRSIE1jyXxC7gm8QryLNnE6mIQEakYbCGr7OKCk/Hw\nCdC+fOZXLif5q4JRGcHM6AIWxM4mTmdjiimdmdEF3Jm5kkVxc5keNZXDbUUcbjvGrOhpKKQKpFIJ\n6dlWXP2DHN59BoDYhIiA56G3pgpX6YmgzzUiPxd1atrEG4YJ8zUgXIQUIqkRyRxsPUptTx1zYmag\n06tIz44ia6qNyCgd8UlG0rKjWLQsg5xpsWgvwf/wQgRBQBmfgGPDR8gMEahSxu5bHAjdO3dgf+8d\nLLfdifHKq4MeH2rhx8zoAuL1sUGPG4sYbRRJ8dkUx/rYYXPSYZTSEK2gIknJ7tkRyObN4tYZ3/ha\niM+zSAQJeZZsDrUWUdRewuyYGWhUKro7XcPdiTIs1FZ00NzQQ5e9H5VGcVHxW3NDNxveOUFjrYPZ\ni1O46vosdPqJ2wMKMhmCRELXpo3o580POF84EPweD42/+zWCUkHc331/RB/0QKjtreOEvYxMYxpd\nnh7ECyJREkFCgTWX1dl3TMpckEqkmFRGkg2JFFhz8SlkdFSdxNwbuEVbTYISYc4MkiMmp0FDmNFR\nyVTE62NJjkgkXh+LSjY8z5VSBTmRWexq3MdpRyWzoqchlUgRBIGktEgkUgmHd5/B2echMdU87oPZ\nWQZbmukvPhb0Oepnz0WVGJ4HYf42CBchhYhSquCuzFU8X/ISR9qKmf1ph5rJ9j4cDXVaOoZFiz8t\nSJoVckGSs/gY7a+9gvHqpZiuvS6kfYRa+JF1GYRgkiGBR/Pvpzuzh9OOSga8HlQyJavNGV/pnM/x\n0Mm1PD71mzxz5FleOvkG3576TeKTTVSWtfP6Cwcuisaf7URkNGs4uLOG0qJmYuIMXHd7XtCFSxFX\nXIVj00Yc6z8i5sHJy0luf/1VhjraSfznfwva6mnQN8j6ms3MjCrgobx76faMnAtZl3kuqGQqduVq\nSG72BJyTfDhHw4pxcpzDXH6iNBa+PfVB/ufYi7xS9hYP5d2LRJAgCAIzFyShMyj5pLCc/j4Py1fl\nTuhiosnJA6l0ZAesCfAj0B+bytfzShUmTOiEI6CjEKWx0trfxu6mAyyInYNC+vklcqnS0unevhVv\nd3dIS6Dummqa//e/0U2dRvSDD49bXTwevYNODrQcwSsGbq5fYM1lYezobT0ng7EiHV9XdAodCfpY\nCmu30lPvo3JX76guQKII3Z0uyk+0UlbcTHtrH/OvTmPJikw02uDzbwWZDEEqo2tTIfo5cycsEgqE\nnj27cHy0lpgHHkKbmxf0+C31OznZeZrHpt6PRq753OeCXqFlQ+d+7BFS0ho9SMa5BHklsHFhBM02\nFXdmrvzaz9MvOyZVBDZtDIW1WxjweciJnHLuPUuUjpg4A8WHGjlT1UlyRiQKxdgiVKpW46mvZ7A1\nsCp4gHpTCn9pMZKVZMI0SStmYcJ8WWlq6eWT3TUUHWtixrS4cbcNC9AxSItI5pPGvfR4ephqzaXb\n08OxjlKqumtodbWjV2gvy41FolQiUalwbPgITW4ecnPg+aWDba00/eYZFHHxxH737wIymh+NovYS\nnj/+F9QyFUN+74ilzrGQS2Sszr7jaxuR/KKwaiwIvSpadgL+8ZcIRRG8Xj/LV+WQlW+7pDxiZUIC\nPXt24e1yoJ8xM+T9AHga6ml+7n+JWLSYyBtvDnp836CTv5S+xqLYucyK+WJsts76T56WOaizKVAP\nimPmJG+ZZ6AxRnHZH8jCBE6MNgqtXMv62s1oZOoRbW8NRjVJaZGUHW+mvKSVhBQzas3YD27yqCh6\n9+0NyJZLkMtJe+LbnOr0sfFQPak2A1bjF9d+OszXm64+D0fLOyhv6KbF7kKvUXxuDRCKT7Twwbsn\nKD/cQG9rP33t/VyxYsq4Y8ICdAxUMiVqmYqNZ7ZR7qjig6r1HO8opcxRTon9JJ807qXR2YxFbZ50\n0aVMSg66IMnb20vjr3+FoFKS8IOnkKqDv8gN+b28V7GONdUbKLDm8Z1pD5NoiOe4/eS4S/GhFH6E\nCZzqfX30dAZuA+TziaRnX1pXKkEqRSKT0fVxIfrZc5DqQ/NN9LlcNP72GWRGI7YnvntRn/dAWFu9\nkSZnC4/m34ciREeFycCiNnOwtYg+tUBlkoqTaSrsRtm5nOSds/ScTFfTr5GGH8i+hCQbEvD4PBTW\nbsGmjcGmjT73nkarIC3LSnW5nZIjjUTHGdBHjB5gkBlNKOPicB4rGl+ECgK2xx7HmJ/P3Oxoqpt7\nWL/vDDGRWuIsk5dbHSZMbUsvr2+p4LXN5RRVdFBa4+BYpZ1tRxupb3NiNaova/R9y45qinfUIBn0\njehKFhagl4BjoItj7SU4PN0XRQFFRNpc7RxsLZq0NpRnGVGQFBGBKnn8giT/wACNv/s1vn4nCT/8\nJ2RGU9DHtLs7ee74nynrLOfOKatYmXYdcqmcGG0UOeZM+odctLvtl7XwI8zFOPs87NpUEdSYni4X\nWVNtQXXlGg1FfAK9e/cwZLejnxl8T3VRFGn98x8ZbGok/skfITMEn9Pc7rLz6ql3uD75GrIjQ/NA\nnSyMyghitdHnHsiG5BLsJjltFjl2k5wh+XA8NPxA9uVliimdNlcHm+u2k2lKw6QynntPoZSRkRNF\nc30PRfvrMJo1mK2jC0WFLRZtXj4+p5PB9vaRHbKkUlRp6XgdnaiSklBnZCKTSpidFUVbl5u1u2sx\naOSkXIJ1X5gwZzla3sF/v1tCk73/ohQtUYSWThf7SluJs2qxXWIjk9EoPtEyLD5HeS8sQENkuA3l\nXycswgmmDWUwyM1mhhyddG/fTsSiJfhcLpxFR3BXVDDY0oxUb0CqViP6fLQ8/wcG6uqIf/IplLbg\nK9CL20/wXMlfEAQJ3532CPmWnBFR1/HsThbGzg1HeS4jNeUd1FZ2BjdIhMgoHZboS8vdFKRSBKWC\nro8L0c2cHbSA7N66me4tm4l57HE06aGJsbfKP8DldfPN3LvH7FL0eRJ+IPtqIwgCeZHZVHRX80nj\nXqZac9HJz9+UZTIpGTlR9HS5Obz7DAqFlOhYw6irUDKjCf3suRgWLUEZn4AmOxv97LlE3b0a87Ll\niF4vnevXocnJRW4errKfnmnF5fGyZnctApCZYJwUy70wf5vUtvTy3++W4PVNpFNEiirs5KWaJz0S\n+sG7J5AMjl6UN5EADVfBj0GobSgnE8ttd9B3+BB1v/gp3u7ukZWXUinaqQXgF+kvO0nc3/0DqsSk\noPbv9XtZU1XIjsY9TLfmc2/27ahlYy/dG5UR57rzhPl86HE5QxrX2x/auM8SsXAxjsL1dH60hthv\nfyfgce7qKjreewfTshUh55Ce6a2nqL2E1Vl3fK6FgBNxzpXhc67EDzM5yKVyHst/gN8cfY7niv/M\nP876LnrF+Yc1qVTC0huz0RlU7NteTV/PAAuWpo9p0yQ3mYhYuOii1yNvXoWr/DQtf3yepJ/8DKlG\ni0QQuHtpBhFaBe/vrKHXNcg9yzKRhEVomBDYsL9uhPiUAwZACviAXmDo0/e8Pj+F++v4zq35k3b8\nppZecHqA0OZvWICOQihtKEvsZXR7eib1BuSurEQcGsLbOUoEzOej/1gRAMaly4KuLO50O/jzyddp\n7GvmjsyVXBG3IPwk/iWkY8ge4rgO4NI7agkyGZE33kzbyy/haWgIqJuWr6+PlheeQ5WcguW2O0I6\nriiKrKkqJFYbw1zbpRVBXS7CD2RfXbRyDd8peIhfH32W50te4u+nf2tExzZBEJh3RSp6g5Ldmytx\n9nm45qZsZPLAo/CCTIbt0W9R97Of0PbyS9i+/R0EQUAQBG6Yn4xeo+Dlj0/jdA/xyI05yKShOZaE\n+dukq89DceXw/UEDxCIQAUguEIN+RHqAZkRcwLFKO6U1nWjVcoa8fry+s/+JeH1+hrx+fH5x1Pe8\nPj9er8iQz4/P52fI56enqRdDiOITwgJ0VEJpQ+kX/Zx2VE7aDWngTC2tf3w+oErLnp07MMxfMGGu\n6FlKOk7yyql30MjUPDnziXO9ksN8+VBEeREFP4IY+M1JFPwoogL3KZwIw/yFw1HQdWuI/c73xj+2\n30/L/72IODSE7VtPIIToxFDaeYrK7hqeKHgIiRC+MYeZfCLVZh4veJDfFb3ASyff4LH8+y+aa7nT\n49DqlGxZW8ZHbx3n2tvyxq2Q/yxyi5XoBx6k5fln6dn1CcYrzreIXVIQi1Yl58V1J/mfd4/znVvz\nUY1jARUmzIWcrHXgF0WMQBrCCOF5FgkCJiACqEakWxT57TvHJ9y3VCIgk0mQffpTLpUglQioAYUI\nchFkPj9qV+A2jaMRnu2jMOANvOJ45DjPpJ2DY8N6RG9gX67o9eIoXE/sE+OLA5/fx9rqjWxr2EWB\nJZfV2XeikYctQb7M6A1qeo1NRHQF3gSh19iO3nDp0c+zCDIZ5htX0vbS/zFQXzduqodjw0e4yk4S\n9/0ng7IQuxCf38ea6o1kGtPIMY+fQxQmzKWQqI/nkbzVvFDyV96tWMudmasuWglKzrBw8z3TKHzv\nBB++eowb75qK4QIrJWefh8ZaB4ODPhQKKfEpZnQX5NnpZ87GdeXVdLz1Buq0dJTx5x/4Z06x8oM7\nC/j9+yU88+Yxvn9HAfogBG6Yv13cg140jC0+L0SCQBpwCpHFsxOYnxeDTCq5SGT6vX6cvQP0dQ/Q\n2+2mp8tNb5ebnu4B+nvO6xuZTILeqMItkTDQG7ruCQvQUQjV31MpnZzk3qGuLpzHg2v35jxezFBX\nF3LT6BXwjoEu/lL6OnV9jdyWcRNXxS8KL7l/Bcg2Z/BB7Gb03VYk4sTLf37BR2dsDVnm0DpgjYVh\n3nwchR/RuW4NUffej6usFL97AIlahSYnD7nJRH/ZSTrXrSHyppUhmc2f5UDrEVr727h/VuC94sOE\nCZXcyCy+kXkLb5S/j1llYlnSlRdtEx1r4Nb7ZrDhnRI+eKWI6+8YzqMr2l9PXVXniMLYCzuTRX1a\n6W698xu4KytoefF5Ev/lJ0iU5+8VWUkmfnTPDH73TjH/+VoRT941jcgxLKDChDmLWiEjNgDxeRYJ\nAjYgOkKFfMhPb3v/sMDsdtPTPSw0B9zng14KpYwIk5oIk4qYhAgijGoMRjURJjUanQJBEGhq6WXt\ny0dHWC8FQ1iAjkIobSgBdjTsRiqRMD1qKnJJ6B+tq6w0oKX3Efh8uMpOjpoMX2o/xStlb6OQKvjB\njCdGmDCH+XJjVEaQkRxPzWAxCVXTxhWhfsFHQ3oxmcnxk14MI0ilGOYtoHPth9SWHB85P6VSNNk5\nDNRUo8nOwRyC2fxZPL5BNtRsYVb0tHBqSJjPjYVxc3EMdLGmuhCTMmLUhgcRJjW33Dedje+V8uFr\nx0BkVEcWv1+ktsJOfXUny1bmkJJpRaJQYPvWE9T/4qe0v/U6MQ88NGJMUoyeH983k9+8VcwvXzvK\nD+6aFvYKDTMuKVYtJUGOMQGntlZz6tN/a3QKIoxqTJFaktMiMZiGBabBqEalnrjwM85mAJ0SnIPB\nnj4QtmEalbNdT9pc7QGPSTEkoZGr2dawi73NB/F4PURrrKhC6AXtrqjAVXoi6HGa7GzUqWnn/u3z\n+1hX/TFvV6why5zOd6Y9QpTGEvR+w3yxWNRmdvfsosfQjtSrQOnRjHjiFAU/vaY2mlJOMGTquSwG\n6H1FR+l46/Vh4TmK2dxQezvi0BDmm1YG7cZwIVvrP+FkZzmP5T8QTg8J87mSaUrDPuBg05ntpBtT\niFRfnEIil0uJMKspL2kdtS3uhYgi1FbYSUg1o9Urken1yCIicKxbgzwmBmVc/IjtdWo5s7OjOFre\nwdYjDWQmGDEbwpHQMKPTXOvgTJAWfQIC+TPjuPL6Kcy/Oo0Z85PImmojJdNCbKKRSKsP65soAAAg\nAElEQVRueK4GUWwnV8uor+wcNQYa9gENkbNdTwKJgsolMh7OW83y5KuYGTUVj2+QXc0H2Fa/izZX\nBxHKCEyqwAXBYEsz/cXBLcED6GfPRZU4HN3s9vTwQslLFHWUsCr9eu7IWPmFdpEJEzpnDdCP9hbR\nbW6iy9rAgLoPp6GTHnMLLUlldEc14lV4eCDnGyN6XU8GA2dqaf6f3waUk+w6UYI2Lz+kZgjDLTdf\nZ1HcXGZFTwvlVMOECZlhj9AsqnvOsL1hD/mWnBH2TGfZu62Krk5XQPsURRhwD53rTKZMSGSorY3u\nzR+jmz0HqXZklFOlkDE3J4pTZ7ooPFhHYrSeaLPm0n+5MF87mht6aKhxBD0uMzea5AwL0klyXYiJ\n1tPr9WFv7L1IhIYFaIh8tuvJWHy264lOoSPPks2SuPno5BqOdZSyrWEnJztPI5fIidZYJ6zq/f/s\n3Xd4nNWZ8P/v9BmVkUYa1VFvtiSr2JaNu7HBBmyMaaZDgAABArvp2ST77ibZtF+ybzZvstmEbAIp\nlBAwLmAbF2yDG5aLepetPuq9zmhmnt8fQsZCbWZULJnzuS4uLs8853mOjJDuOefc963w1tNx9Mjo\nlaYJBykIfPARFDodha0l/Hf2H7HYrTyf9iRLg9LFWbp57uoC6A3WRvo9uuj36mTAsxuUEgsMcXRY\nOtEqNKQGJE/rs5teexWruc65ix0O7D09eC9zvQf6nksHMPfW89Q1brkpfH7JZXJSA5LIbsrjtPkc\nS4PSRuxiudOZrKu9/0pnMplMhkdSMt0fn6Y3NxefVWuQyUf+PlApFSxPDKKqoYd3T1cS4KsjPHBq\nTSWE6097S6/LK6AAsYmBU25S8lkxUX6ofTTU1HeD1XZlh04EoFMwla4nKoWKaJ9I1oetIlIfRl13\nPR/UfMRpcyYWu5Ugj8Bxt+cVOh2W6mqsDfVOz9Vr8RK816zhvcuHeKPkHeIMMbyQ9hRB09giVLi2\nJupItT5sFT4aH/ZXHibYI5BQL+ez5icy2N5O06t/cenDkLWpCf2adSh0zm+hX2m5GX0ziX7XtuWm\n8PmmkqtIMSZyuj6T3OYCMoLSUX5ypt+dzmTSZzqTyVUqdHHxtO17F8lqGTNhT6GQk7EwgNZOC7tP\nVOChURJrEk0OhE9ZJYni7HqX0n/kchlrNsVPuU3zWIKDvFm2PJzgACW21np8NFbS1k2cjCqSkCYx\n1a4ncpmcFGMSKcYk6nsbOV57iiNVxzlYeZQlgWlsCF89ZrKF39bb6c3LcWrbU6ZSobr5Rn6T/b9c\n6qxke8xt3By5XtRPvE6NVwB9ZUgGxW2lvFGykyh9+Jhn2Fw13Qlx49l7+X30am9uDHN+jCDMFIPW\nl+fTnuSXF37Hn/Jf49nUx1HIFVjHaTk4mUHLyJ/j2qhoAu7ZQfM//o5HYhKei1JHjVHI5TyxZSHe\nnire+KCMrj4rd6+LEbtZAja7gz8fKUOlkOFld35xIDLOf0SJsOk0UFlB27736M/NJvpK18b7Jxwj\nVkCdpFVqCfMOJcongjDvULdKNXmrvUj5ZHveQ+VBdnMeR6o/pLC1BPVntueVvgY0JhPdWRcnDgCU\nShwP3cnveo/RZ+vnubQnWBa8WPyQ+hySyWQsMMTzccMFitvLuCF46ZQ/hExXQtxEKjqreaf8Xe5N\n2E6UyHwX5gi92ptIfRgHKo/QYekkxZhER2vftG17aqNjGKiooPPYB+hXrEKuHf07RSaTkRzlh0al\nYPeJCjp6LKTE+ovWnZ9zrx4uJae8hR2L9dSZ+8GJ7we5w8aaDF/0IdOfiNx98QLm//fLoaNaV+2W\nRTwoAtA5R6VQEeMTxfqw1YR7m6jtMV/ZnrfarQR5BqJRaChUtrFLWYzGYsen287VoYRdBpfDNRy+\nQc9RbQ2xvlG8kP4UIZ5B1+zrEq49lUJFlD6c9yuPIiGRYJhaQfrpSIibiCRJ/LnwDbQKLQ8suEt8\ncBLmFKPOHz+tL/sqDg/tZpkWkn+hzqXj+eNtew6fB+386EP6y0rQrxi/HXJcmA9GHy17T1ZS29zL\n4ngjiqvOjrZ3W7hQ0kxJTQf1LX14e6jRzcA2q3DtfZRjZveJCh67ZQHWsxdpduiRSY4Jg1C5w86i\nxo/wG2hw63z+RCZKUp0sABXfodfQ8DnStIBkzD0NfFh7ikOfbM8n+MVR0laO3SBn31ofPPvsRDRY\nUQ9KWFUyqoPV9Hoortzn9ujNY2ZsCp8/MT5RbInaxL6KQywwxBNviHH7Xh5Ji0ChALsLW48KBR5J\nziVC5bcWUd5RIVpuCnPWipAM2gc6eK/iEAatL5Fx/lSUtjg9fqJtT6VeT8hTz1D7y1/QdmAf/lu3\njXuf1SkheOlU/G53Pv/1jxxevCeVhrY+9p2pIqe8BftVCzMKuYy0OCNbV0YS/UkxfGH+u1TXyauH\nSrgxPRSTNMhhSzBR7TkE9FZTaUihxTMC6aqfozLJgbG3mqj2PPSWVnpy6iZsWOMOV7o2fpZMklz5\nLHfttbf3YrO5eCZtHukd7ONM/Tneu3yIQceg0+PSAxbxdMpjMzgzYT5xSA5+nfUHmvtb+e7yr+Kp\ncr+Ui/m3v6En64LT13stWTppW1gYqlP7k3O/Qq/25p/Snxarn8KcJUkSrxfv5OOG8zwS+gg5e1ux\nO3H2TqGUc+fD6Vc6Io2nZfdO2vbvI/yb30EXPzqp9WpltR38v7dy8dAq6eixYJtgHkqFnGe3J7Mk\nIWDSuQpzW2ePhR/8+RxGHx1PbUpgz98uYOisIqXh2JVEpAGFB+0eIdjkKpSOQQx99WjtI0uGeaSk\noo2MRKZQIlMokCmVoFRe9eeh12QKJXzy/tCfFUPXfPI+CgX2nh5qfvqjcY8Jrt6zc8KvSQSgc1CH\npZP/c/qnLnViksvk/Meq70x7AXJh/mof6OCnmb8izhDD04sedTvAG6isoOZnP3Y6IS78299FGxU9\n6bWnzGd5vXgn3874JyL0YZNeLwjXkt1h5/e5f+ZyZyUP+D5K1qH6CYNQhUJ2pRPSZCS7nZpf/Axb\nWxuR//YDFF4T72adLWzgpb2FTs1bqZDznUeWiJXQecxmd/DzN7Jo6ejnW/enc+TtfBTWftLyX0Mp\nubb6KPfyRq5WI9ltQz/T7XYkmw3Jbnet9KMTJgtAxRb8HFTUVuZyG1CH5KC4rWzM7Gjh88mg9eXh\nxHv5Q95fOWk+y1rTCrfuo42KJuRLz1H/0u8mDEJlSiUhzzzrVPA51HLzEBlB6SL4FOYFhVzBFxc9\nzK8u/p7dPW9x++3byD5bi9SoRSZ9uu0pISHzHOSG26KJjnNu5VGmUBDy9LNU/eDfaPzLK4Q8/8KE\nHxjPFTc7PW+b3cH+M1V8+e4Up8cIc8vrR8qoMHfxrQfS+fhgGbZBOxsXKejJc33rO2DH/WNWKJEk\nCRwOpCsBqQ3JZodP/j0csA6/j91O9/lMOj887vbXJQLQOWjANuDmOMs0z0SY79ICFrHGtIKdZXuJ\n9Ylyuz6o1+KlhP/L92jb/x49Odkjz4QqFHilpeO35Xangk+AYzUn6BnsY1vMrW7NRxCuBa1Sy3Np\nT/KTzP/i1fpXIQKUwRq8Oo3I7UocChuenUY8e/x4ueYVnvR5iLSAiWshDlP5+xP8xJOYf/sbOo8f\nxXfDTWNe195tIbvM+TOoANnlLbR3WzDMUAkeYeZ8lGPmeFYdj92SQG1+I431XWx/MB1/Twc9/5i+\n8/kymWxoy12hALVzjUAG21pFAHq9cafE09A48cNFGO2euG1c6qjglYLX+VbGi6gUKrfuo42KJvT5\nFxlsb6evsADHQD9yrQ6PpGSXDrV3W3s4XHWc9WGrME5DrVJBmE0dlk76r1oksKktdAR82inMouvB\nUGhC2+HLywWv87Ulz41Z63ksXouX4rvxJprffANdXDya8NGVJAoq2nC4uFVqd0gUVraxOiXEpXHC\ntXV10pHBJnE6t4ENWxcSHDZ01M4rNd218/lp6dOagORWkupVRNrpHJToF+9yRrBcJmfhGB2ZBEGt\nUPFE8kM09bew69K+Kd9PZTDgs3oNhps24bN6jcs/0A5UHkEmk3FL1MYpz0UQZtvBqmPYpfF/4fZ7\ndjKg68bQHI7NYeNQ1TGX7m/ccT/qkBDML/0PDsvoXa1+q3sZx/0W98YJ10Znj4Xf7sojKljP2jgj\nZ45eIv2GcBamfLqL5bf19qGEICfIVCr8ttw+rXNUGQzIk9zvXCcC0DnIV+NDijHJpTGpxiSRgCSM\ny+QVwt1xt/Nh7Wlymwuu2Tya+po5UfcxmyM34KXyvGbzEAR3dFg6yWuZJPlHBu0BNXi3B6EYVJHb\nUkiHpdPpZ8hVakK+9Dy29naaXn911Ps6tXsbl6Iu6Pxhszv47e58JODh9TEcfbeIiBg/blg/sqTe\n8Pl8FIoJ7+fK+XxXnUv2xOZmJCkC0DnqlsgNV/oPT0YlV7I5csMMz0iY79aZVpJiTOLV4rdc+oU4\nnfZePihabgrzlrMJoh3+dYCEb6vpSoKoK9TBIQQ+9Chdp07Q9fHpEe8lR/uhkLtW0UIhl5EUJY67\nzBdvfJJ09PSWRE7uL8HTW8PNdyQhH+O/u2faYpR+/si9vEcHogoFXkuGzu97LV467fPssHRyWlHL\ngdU+bgWh4iPRHBWpD+fJ5Id4ueB1bI7xt06UciVPJD/k9Bkj4fNLJpPxyMId/CTzv/hLwd95cfHT\ns1r8vaKzmqymXB5NvA+1m+dQBeFacjZB1K4apNvQiKE5nNagSrcSRPWrVtNXVEDj3/6KNjoWddBQ\nlzuDt4a0OCMXS53PhE+PM173CUgtNQ1UnjqPrb8Ppc6DqNUZGMPdS7q8lj7KMXMsq47HNidQfrYG\nq8XGPQ8sHdVJa1j3uUxszU2Ef+dfUfr5T+l8vquGP5BdDtfw1iYDywr7iK61oHDyiLIIQOewtIBF\nfG3JcxyqOkZuS+GIT95ymZxUYxKbIzeI4FNwmpfak8eTH+DXWf/L4arjs3YOU5IkdpXvI9QzmOXB\nS2blmYIw3VxJEG0LqCW6ZDm6Xl+3EkRlMhlBjzzGwOXL1P/hd0R851+vnPfbujKS3Eut2OyTr8aq\nlHK2rIx0+fnzxaXz+Zh37cbYeAk9n0Y+zUd2UhQUS+hddxKb4Vwlgmvt6qQjeXMf9TWdbHsgDb2v\nbszrJbud1r278ExNQxc71HZ5rBJLM+XqD2RN/qpRXRtXTzJeBKBzXKQ+nKdTHqPD0klxWxkDNgta\npYaFfvHizKfglgRDHJsjN/BexSESDLFE+8z8L6f81iIudVbwfNoXRctNYd4aThB1Zhu+V9+CVd2H\nX3O42wmicq2OkGeeo/qn/0HLzrcIuP9BAKJD9Dy7PZnf7ymYNAjNWBB43Rahz3n3KKq9rxI0xn8P\nBRJBjeVYXvolOY2PkrZ1bh9TuzrpKN3oxakj5ay/NYHQCN9xx3SdPslgYyMhX3p+Fmf6qbE+kPV6\nKCiKGTtg/izxm2Ce8NX4sCIkgxvDV7MiJEMEn8KUbI3eRIR3GK8UvEG/rX9Gn2V32Nldvp8FhjiS\n/NzPmBSEa82lBFEZtAfUYmgz4YH7CXfaqCgC7r2P9sMH6cnNvvL6koQAvvPIEpYmBIw6E6qQy1ia\nEMDa1BDOFDSQWdTo9vPnqkvn81HtfRXlJB8GlJID1e6/cel8/izNzHVXJx3dnRHO6Q/KSckwkZQe\nOu4Yx+Agre/uwStjGdqIa7PCvdAQhxz3WyiLFVBB+BxSyBU8kfwQP838FX8v2cXjSQ/OWC/2j+vP\n09DXxBeSHhD93oV575bIDRS0Fk94Nn9YT2ADQXUJXCpuJjHN/Rqcvjdvpq+okMaX/4Tm33945Vxf\ndIieL9+dQnu3hcLKNvotNnQaJUlRfhi8NUiSxKDdwR/fK8LgrSE+bPzVtPnGvGv3mCufY1FKDsy7\n9szZrfjhpKMXtiVx+mApYVEGVm2MnXBM54fHsbW3Y9x+1yzNciRzTwPvlL+HA/fbd4oVUEH4nDLq\n/HhwwV2cb8zmbIPzxYxdYbFb2VchWm4K14/hBNHJqpQo5UoeXXI34TF+FOXWT+mZMpmM4CeeAqWC\nhj++hOQYGXgZvDWsTgnh5oxwVqeEXEk4kslkPHFbIjGhen6zM4/G9r4pzWOuaKlpIKDxkktjjI3l\ntNQ0zNCM3DecdPTghlgKT1Si81CxaXsScvn44ZnDYqFt37voV65GHTL+KulM6Lb28EbJO/wk879o\n6W/lrtitTlfs+SwRgArC51hG8GJWBGfwZulumvqcz6p11tHqE/SKlpvCdWY4QTQ9YNGYZ5q91V58\nbclzpAUsIjE1mMa6Ltpaeqf0TIW3NyFPP0t/aQlt+951epxKKeeFu1Pw0qn41T9y6OkfnNI85oLK\nU+eRu7jypkCi8vT5GZqRe4aTjtanhdB3uZ3+vkFuuzcFjXbiKiEdHxzG3teL/x3bZ2mmMOiwcbjq\nON8/83MuNGZzd9xW/vWGr3Nz5HqnPpCNRWzBC8Ln3I6E7VzurOSVgtf5+tIvu/1p9rO6rT0crj7G\nOtFyU7gOjZcg2j/Yz9vl76JWDPXTjoo3otWpKM6pZ9VNcVN6pseChfjdfgete3ejW7AQj4QFTo3z\n0qn4yo5UfvTXC/xmZy7feCAdlXLi4uVzma3fvZVcW9/Mnnd3xdVJRzFKJQWVjdx+fyq+fh4TjrP3\n9dL2/gF81q1HZQyY8XlKkkROcz67yvfRZulgTegKtkZvwkv96bnmiSr2TETx/e9///szNO8ZMTAw\niMPh/pkDQRBGUsqVxPhEcrDqGBaHlcRpShTac2k/5t4Gnkp59MovY0G43miVWsK8Q4nyiSDMO5Rw\nbxNnzJn0DvaRGjBUPLy/z0pJfiOpy8LGLCbuCl18Av0lxXSd+Aj9qtXI1WoG29vpuXie/tJSrPVm\nFN56FLqRmcieOhXx4b7sO1NFY3s/SxIC5u2Z7IaSCjSXJ+lINYbBhWmELrr2iZA2u4NfvZ1L74CN\nu1JDyT5dzdpN8cQlBU06tm3fe/SXlhD63AvItc5lm7ururuWPxe8weHqD4nyieCZlC+wIiRjzJ/n\nvhoflgalsSp0GRH6UBaHJRPvP3HnJRGACoKAj0aPSqFif8URYvSRBHj4T+l+TX3N/K3oLbZGb5q2\ngFYQ5gO5TI7NYed43SlWhy5Ho9Dg5a0hO7MG/0BP/IxTa0Erk8vxSEym4+hh+kqK6cnOoum1v9F7\n8QJ9+Xn0ZmfRcfQIlupqVIGBKH0/LUTup9cS4u/B7hMVOCSJxMj5uTOh9tHT8+EHLp0htCPjSMBy\nvHy9CDTormnw/drhUnLKW/jC+lguHrtMUnooy9ZO3ibT1t1F/R9+j++Gm/BemjFj8+uwdPJW6V7+\nUboblULNY0kPsDV6E95qr0nHapVaIn3DSAmb/Oe+CEAFQQAgSh9BZVc1J8xnuCF4KZoprFq+UfwO\nAzYLX0h6AIV8/m71CYI7QjyDOFZzAqVMQYIhFp2HmtqKNtpaeklYNPXuPAqdDntfHz1nzzDYUA/S\nZ34nShLWhnq6Tp9CYzKNSFQJNXqiVsnZfaICP28NkcHeU57PbNPpPck+mYPvQIfTY+r8Y7gcnMT7\nZ2u4UNKMWqkgxN/T5bamU/VRjpndJyp4YG0Ml85UExCi5+Y7Ep1aGW/d/Q6W6qqh1U/19O8qWe2D\nHKo6zssFr9Ey0MpdcVt5eOG9BHm6ttUvl8vQ6SafnwhABUEAhjJmFxjiOVn3MZXd1WQEpbu1SlDR\nWc075e+xI+EO0aVL+FxSK1R0Wrr5uOE868NWX/kQlnuuloUpwWi0UztnPVBZQdNfXwHHJGftHA56\nsi7iuShlxEponMmHrl4r756uJNbkQ+A4nXbmIpvdwd8OlnChycGi7ktOJSMNyhQYH3uS7belkxhp\noL61jwNnqzmRa8bhkDAZPWflTOwlcye/253P2kXBWCvakctlbHsgDbV68u+HwfZ2Gv74EoZbbsMr\nNW1a5yVJEucbs/lD3l8oaC1mvWkVTy16lDhDjFuNQ0QAKgiCy7RKDSFeweyvOIyHSudylyRJknil\n4A08VDruX3DXvD1jJghTFehh5Ej1h/hpfYnQh+HrpyPvQh1KlQLTBN1tnNH02qtYzXXOXexwYO/p\nwXvZDVdekslkLIrx45K5i0OZ1aTFGdF7zv1z2n0DNv77nVyyylq4746lKENMyEryJgxCJcC6/RES\nN6xAJpNh9NFxQ1IQyxYG0tM3yJHzNRy+UEtP3yDB/h54TJKB7q7OHgu/eCOLEH8PoiU57S293PFg\nOt4+zrV3bXn7H1ibhroeyVXTN8eKzir+mP8qx2tPscAQyzOpXyAjOB2Vwv1niABUEAS3BHoYGbAN\ncLDyKIuMifhonG/jl9dSyOHq4zyWeD+BHjOfoSkIc5WnyoPabjN5rUWsNa1AqVTQ1THA5ZJmUpaG\nuf3hbLC9naZX/zJ6230C1qYm9GvWjUhMkstkpMcZuVjawslcM8sTg9A6sRJ3rbR2DvCfb2Zhbu3j\nn3eksSQhgOCEaLpCY2moacazt21ETx47Mjr8wvHo7yIkOQFd/Mgzid4eahbHB7Aubeh4wokcM++f\nraGhrQ+jjw5fL820zf3qpKObI/ypKG7ilrsWERzmXEdDa3MTjX9+Gf/bt+OZOH4nrg5LJ1nN+ZR3\nXKahrwlvteeY7TIB2gba+XvJLt4uexcvlSdPJD/I5qiNeKomzsJ3xowHoFarlTvvvJP4+HhMJtOI\n9y5fvszGjRt59tlnR7x+8uRJnn/+eX7+85+TmZlJRkYGer1rPWpFACoIMy/eEEt+SyHnm7JZEZzh\nVGkmu8POH/P/hskrhK3Rm8Xqp/C556Px4Uj1h0Tpwwn0CEDnqSYns5aQMB98DO5te/dcPE9v1kXX\nBkkSmrBwtBERI15WKeWkxfrzYbaZ7PJWViYFo1TMvfLglQ1d/OKNLCQJvvXgYmJCP40b/EIDidi4\nlrbDB7FELqAraTmDC9MwPfoYCffegcNqpf3APryWLEXpPTre0KqHOkdtXGLC21PN+eImDpytprSm\nAx9PNQG+U09YGk46un9pOEXnalm1MZYFKc6fBW7+++vYOjsIeepLyJSjfxZXddXwj9I9/L1kFznN\n+RS2lZDbUsDx2lPU9pgx6vyutO8esFk4UHGEPxe+QZe1m3vjt/PAgrsw6qaWeHo1ZwNQt77TrFYr\nX/va1ygvLx/1Xl1dHc899xyDgyOL3dbW1vLiiy9y//33s3PnTry8vHjxxRfdebwgCDNMJVfyRPJD\ndAx08nbZXqfGDLfcvDNuiwg+BQGI9Yki0jucD2pOABAY4o3B6EFRjvudkRz9A+6NGxi7BqafXstX\ndqTR0NbHS3sL5twCT1ZpMz977SJ+ei3feyyD0DGqCNi7umBggKjNG1jxxA4y7r8dY/hQgOe//U5U\nxgAaXv4jkt0+7nO0aiWbMsL56ZdW8Oz2ZPosNn75jxz+/eVMTuXVY7NPXtuyvdvCydx6Dp+v4WRu\nPe3dliudju5eFk7R2RoWpgaTusz5rnAWcx1dH5/Gf+s25JrRq7LZzfn88uLvyGnOH1V/0yE5yPnk\n/aymPM6Yz/GDj3/OkZqP2Bi+jn9f8U1WhS5z65zndHB5vf3SpUt8/etfH/O9gwcP8v3vf5+goNG1\nrN566y0WL17Mo48+CsDPfvYzVq9ezcWLF1myZImr0xAEYYYFeQZyX8KdvFr8Fol+CSwNGv/gu8Vu\n5b2KQywLWkyEt2i5KQgwdNbypoi1vFzwOjXdZsK9Q0lMC+HjY5fp77Oi83D93KVc59yZwVHjJqgZ\nGRHkzXPbF/Hrt3P5+wdlPLRpbpROO3yuhr9/UMaSBQE8dXsSGtXYiULD52E1oaPbUspVaoKe+CI1\nP/sx7Yfex++2rRM+UyGXszxx6IxoSXUH72dW86d9Rbzz0WVuzghjfZoJj88kkVXUd7HvTBU55S3Y\nrwrg5bKhkxJLog005jUSFKJn3S0JLn1Ab92zC6WfHz7rbhz1XlVXDa8UvI7NYZvwHjaHjT/m/w2A\npYFpbI/dgr/OMOGY2eBy2JuZmcnKlSt58803kT5zBuXDDz/k61//Ot/+9rdHjcvOziYj49O6VR4e\nHiQmJpKdne3GtAVBmA0rQjJYGpjGGyU7ae1vG/e6o9Uf0TfYx7aYW2ZxdoIw96UHpGDQ+HK05iMA\nEpKDQAal+Y1u3c8jaREoXMzYVijwSEqe8JLUWH8e3pzAkQu1HD5X49bcpovDIfHa4VLe+KCMW5ZH\n8Nydi8YNPgEs9WZQKFAFjl3IXRcbh2HzrbTu2YXFyeQtmUzGwkgDX9mRxn98cTnJ0X7s+ugy3/if\nU7x5tIy2rqGV6Aslzfz01YtcLG1G7pDwBwIBf0AhgQywVHTgAG65OxmFC0ccBqoq6blwHv9td465\n9X6w6tikwefV4n1jeHLRw3Mi+AQ3VkAffPDBcd/7yU9+AsCZM2dGvdfc3ExgYOCI14xGIw0NDa5O\nQRCEWSKTyXhgwd389NyveKXgDb665Fm6B3soaitjwDaAVqkl3CuUw9XHWRe2Cn/RclMQRlDIFWwI\nX8PuS/vZHnsbvh4+RMcbKcqtJ3WZ68lIKoMBr9R0erIuOD3GKy0dlWHyoGPDYhPN7f38/YMyjD5a\nFifMfiLhgNXGS3sKyLvcxqO3LGDDYtOkY6xmM+rgEGQTBOb+2++iJyeLxlf+RPi/fG/Caz/LFODF\nk1sSuXtdDEfO13Isq44j52tJijJQWNWOxi4RhQwfQH5VKpQDCRugALIHBtnQOUC0C6veLbveQRUc\njH7lqlHvdVg6yWtxrRvUpc5KOiydV86DXmuzlvI2MDCA+jOFU1UqFVar1aX7uPLpQRCEqdMrPXkq\n9WF+kflbfpz5S5r7W0ecNZIhQy6TkRaYhFIp/v8UhM9aF34D+ysOc8J8hrvit/hKn44AACAASURB\nVLBoiYk9r2fT2tjjdCb01QLu2EZvXi6SbXDSa2UqFQHbtjn9/+YDm+Jp7RrgpXcL+O6jGSMSfmZa\ne7eFX76ZTWNbH197II3UWKNT4wbrzWhNoRN/jUotpqeepvLHP6Lzg0MYt0y8FT8Wo6+OB26OZ/va\naD7MNvP2sXK87RKxyEYEnsPkyFAzFIgqHQ4OnK3in+51roZnX2kJffm5mJ7/MirN6JJIpY3lTvdc\nH+aQHJR1lLPStMylca5yNk6btQBUrVaPCjYHBwfR6VzLBNTr50/BXEG4Xjh6B5HJZDT2NY96T0LC\nLkn8d9af+OeVX2R5WPo1mKEgzF0GPLk5dg3HKk7z0OJtpC4O4/iBEsqLmklMGX1ucdL7GVLQfPOr\nlPznfyENjh+EylQqFnzjq/gvSXHp/t9+fDnf+90pfvVWDv/3n9YR6Df10jyTqTB38h9/PocE/PzF\ntUSHOh+Yl9ab8V+ShsEwcZtTw7J0rNu3Ub/rHUzrVuER7t55dQOweaWavUfKxg0+ryZHRixQUtKC\nQy7H32fiOEaSJGr37MIzOorITTcik48O6GTNrgWfV8appUn/nmbLrAWgQUFBtLS0jHitubmZ9HTX\nfll1dfVjdyIbTRCE6VHZWcOvzv1p0k/bgw4bvzrzJ7657MtE+YgOSIJwtVVBN7C/7Bj7Cz9kQ8Qa\nFqYEc/FMNcvXRaHWuP6rWL5gEVHf/R4t771Hd3YWXJ3hrVDgnb4Y4+23I4+Oob291+X7v3h3Cj94\n5Rz/9tJp/vXxDDxnqEA7QO6lVv57Zy5Bfh589b40fHVKp+ds6+rC1tWF5Bfg1Bj9ljto+TiT4l/+\nmqh//T9jBnfOOJVjJlhi0uBzmBwZgZLEqaxa1qZN/KGjJz+ProJCwr/yVTo6x65eIFndm7dklbn1\n/eAKhULu1GLhrAWg6enpXLjw6ZmV3t5eiouL+cY3vuHSfex2BzabCEAFYbbsv/yB0wfdbQ4bBy5/\nwNMpj83wrARhfvFR+bI4IIUjVSdYHbKC+OQgzn5UQUl+I4lpIW7dUxUeRchzL2Bsb6evsIDOEx8y\n2NJCxPf+7cqZT3d/X3polHxlRyo/+dsFfv1WLl+9L21GaoQez6rj1UOlLIrx49ntyWjVSpfm3FdT\nC4AiKMS5cXIlQY9/kZr/7yc0HziA3y23uTXvjvZ+XO1n5Qt0tvdPOE9Jkmh8+220MbFok1PHvTbB\nNw65TO7SNrxcJifeN27OxFCzdmDr3nvvJTMzk5dffpny8nK+853vEBsby9KlS2drCoIguMidg+65\nLYV0WDpnaEaCMH9tjFhLS38ruS2FePtoCY/xoyjX/Zqgw1QGAz6r16BffgP27i6U3t7TMFsI8ffk\nhbtTKK3p4K/vl4yqfDMVDknirWPl/PVgCTcuDuXFe1Lc6sRkNQ9lwKvHyYAfiy4uHsPNm2ndtRNr\nvdnlZwLYuizInFz9HCZHxmC3ZcJrerMvYqmswHjXPRMmqPlqfEgxjt8VaSypxqQ5k4AEUwxAXcne\nCw8P59e//jVvvvkmO3bsoK+vj9/85jdTebwgCDOsqK3MrYPuxW1lMzQjQZi/ovQRxPpEcbR6qCRT\nYmowjXVdtLVMz5ao2hQGdjvWRvdKPI1lQYSBJ7ckcjKvnvdOV07LPa2Ddn6/O5/3z1bzwE3xPLwp\nAYWbW+EWcx3qwKAxyxRNxP/Ou1H6+9Pw55eRHK6vCAbq3avHOtE4yeGgZfcuPBKT8Jig5eawWyI3\nONWlDoaai2yO3OD0PGfDlALQoqIili0bnU21cuVK8vPzR72+fv16Dh48SFZWFn/84x8JCXFv20EQ\nhNkxYHOv68qAbeJP+YLwebUxYh2XOiup7KomKt6IVqeieAqdka6mCR0qWWStq52W+w1buSiYO9dE\ns+tEBR8XTK10YlevlV+8kUXupVa+fHcKm5eFT6lzmrXejHqMAvSTkWs0BD/+RQYuX6L98EGXx/vo\n3esV7zNBANp97izWulr877rHqXtF6sN5MvmhSTsZKT/pbBepn1tn80XNFEEQxqVVuvcpX6t074ez\nIFzvUo1JGHX+HK0+gUIhZ0FKECX5jdOSXKvw9kbh44PFPL0BKMC21VGsXhTMy/uLKK3pcOse9a29\n/Oiv52nuHODbDy9hyTTUGbWa61CHTl4rdCy6+AR8b9pE6+53sDa49iEgLNoPV+NmmQzCosauxyrZ\nbLTu3oVnWjq6mFin7xnvG4NKrsKo9RsViMplctIDFvG1Jc+RFrDItcnOgllLQhIEYf5J9It366D7\nQr/4GZyVIMxfcpmcDeFr2Fn2Lq397SSmhpCTWUtlWQuxCwMnv8EkNKYwLHXOdftxhUwm4wu3LaS1\na4Df7Mzlu48uJcTf+XI+xVXt/Pc7eRi8NXzrocUYJylF5Ax7Tw/2ri40Ia6vgA4z3nUPvbk5NLzy\nJ8K//V2ns+K9vDVExRupKG2Z/OJPRMUb8fIe+8N55+mTDDY3Efr8C07fD+BQ1XEkJL6e8eUrx58G\nbBa0Sg0L/eLn1JnPzxIroIIgjOt6OOguCHPNypBlaBUajteexGD0JNikp2iatuHVpjCsMxCAAigV\ncl64OwW9p5pfvZVDV99Qbe/2bgsnc+s5fL6Gk7n1tH8m0eZUXj3/981sokK8+c4jS6cl+ASutNV0\nZwt+mFyjIejxJxm4fImOI4ddGrtkZQQKhXPLoAqlnCUrI8Z8zzFope3dvXgvW44mfOxrxtI+0MGx\n2pPcHL4OvdobX40PK0IyuDF8NStCMub8z2GxAioIwoRuidxAQWuxU6WY5uJBd0GYazQKNWtMK/io\n9jRbom9mYWoIxw+U0N05gLePe8dertzbZKLjyCEcFgtyzfQfhfHQqvjqjjR+9Nfz/OKNLAJ8dORd\nbsXu+DRDXiGXkRZnZOuKCHIutbL3VCVrU0N49JYF01rKyVpvBrkcVVDwlO7jkbAA35tupmXX23im\npaF28n6BIXo2bU/i4K4CJioQoFDI2HRHIoEhY3eV6vzwOLbODvy33+3SvPdVHEar0HBTxDqXxs0V\nYgVUEIQJDR90nyzbcq4edBeEuWh92CoGHTZOm88RlxiASq2gOG9qCT4A6tAwkCSs9dOzojoWo6+O\nzcsjqGvuJbu8ZUTwCWB3SFwsbeZHf7vA3lOV3LM+hsdvWzjtdUStZjOqwEDkqqkXyTfedS9KXwMN\nr/zJpaz40Ahf5HIZvn465PKRq6FyuYzoBCN3PrKY6HHOuzosFtr2vYd+1WrUwc4H0uaeBj6uP89t\n0Te7fVb/WhMroIIgTCrtk4Psh6qOkdtSOOJMqFwmJ9WYxObIDSL4FAQn+Wp8WBqUxrGak9wYtpq4\nxECKc+tZuipyVCDjCs0n29GWulq0UVHTNNuRKuq72H2iYtLrJGloNTQpym9Kme7jsZrNaELcS0D6\nLLlGQ9ATX6T25z+l44PDGDbd4tS4wux6JGD7Q+k4JKitbGfQYkOlURIWZRj3zOewjg8OY+/rxX/b\ndpfmu/fy+/hrDawJvcGlcXOJCEAFQXBKpD6cp1Meo8PSOa8OugvCXHVT+DoyGy6S3ZxHYloMRTn1\n1FW1Ex7t5/Y95VotKmMA1hnIhB+270wVNiez9u0Oif1nqvjy3a71o3eGpb4On9Vrp+1+HgkL8N14\nMy27duKZOvlWvN3uIO98LQnJQXh4DQWaC1OcX8W09/XS9v5+fNffiMrf6PS48o4K8loKecKJnam5\nTGzBC4Lgkvl20F0Q5qow71AWGOL4oPoEAcFeGIwe05KMpDaZZiQTHoYSjrLLnM/8BsgubxmVmDRV\n9r5e7B0dU0pAGovxnh0ofXxodKJAfXlRE709VtKWubfz037wfSSbDb+t25weI0kSey7tJ9zbxJLA\nVLeeO1eIAFQQBEEQrpGN4Wup6q7hclcViWkhVJS20P9Jdrm7NKawaS9GP6ygog2Hiy057Q6Jwsq2\naZ2H1TzUQlPjZg3Q8QxlxX+R/rJSOo5+MO51kiSRk1lDeIwffgHOl6MaZuvqov3IIXw33ozSx/mu\n8rkthVzurOLO2C2TFqCf6+b37AVBEARhHkvyX0CwRyBHqz8iITkIZFCaP7VWmmqTCVt7O/be6Wnx\nebV+6+TVMMYcZ3Fv3HisZjPIZFPOgB+Lx4KF+G68iZZ33hq3rWldVTutTb2kLw9z6xltB/Yhk8vx\nu3WL02PsDjt7Lh1goSH+uqi1LAJQQRAEQbhG5DI5G8PXkttSSDedRMcbKcqtR3JxlfFqmtChoMhq\nnv5teJ3avTOHOs30nlW01JtRBQQiV6un9b7DjHfvQKn3ofHPY2fFZ2fW4h/oiSly7M5GExlsa6Pz\n2AcYNt+KwsvL6XEfN5ynsa+J7XG3ufzMuUgEoIIgCIJwDS0LXoKnyoNjNadITAuhvaWPRnOX2/dT\nBQeDXI5lBrbhk6P9ULiYpT+cCT+dhlpwTu/5z6vJtVqCHn9yaCv+2Mit+LbmXmout5G23L0+9m3v\n7UWm1eJ782anx1jtVvZdPkxGUDoR3u6tus41IgAVBEEQhGtIrVCxzrSSj+vPYTBp8NZrKM51vyao\nXKVCHRQ8I4lIBm8NaXHOZ2wDpMcZMUxSjshVVrN52s9/fpbHwkR8NtxEy863sDY1XXk951wNnl5q\n4hJdb51qbWqi89QJ/G7bikLnfEeo4zWn6BnsZVuMc+Wh5gMRgAqCIAjCNbYubBUOJE6Zz7IwNYTy\noiYG3TxvCcMtOWcmEWnrykini8qrlHK2rIyc1ufb+/uxtbehnkIPeGcF3LMDhV5/ZSu+r8dCaUEj\nKRlhKNworN+6dxcKb298N9zk9JiewV4OVR9jjWkFRp2/y8+cq0QAKgiCIAjXmLfai+VBS/iw9hRx\ni4wMWu2UFzW7fT+NyYTFXDels6TjiQ7R8+z25EmDUKVCzpfuSCZ6nBaU7rLWD2XAz+QW/DC5Vkvw\nF56kv7SEjuNHybtYh0IhJyk9xOV7Werq6D77Mf5b73Dp7OqhymM4JAe3RTkftM4HIgAVBEEQhDlg\nY8RaOq3dlPSXEB7jR1Gu+zVB1aYwHD092Ls6p3GGn1qSEMB3HlnC0oSAUWdCFXIZSz95f8k4LSin\nwmquA5kMdbDrQaA7PBKT8LlxI407d1JwoY6FqcFotK63/2zd8w5Kf3981jrfu721v50Pa09xc8R6\nvNXOJyzNB/O3hL4gCIIgXEdCPINI8l/ABzUfcV/KwxzeU0hbSy9+RtfrTGpMQ+cjLXV1LtWZdEV0\niJ4v351Ce7eFwso2+i02dBolSVF+037m82pWsxmV0YhcM3PP+KyAe3dQVNqJxWIjZYnrK68DlRX0\nXLxA0BNPIVM6H3rtqziETqljY7jzQet8IVZABUEQBGGOuCl8HXU99ViNHWh1Kord7IykCghEplLN\n2DnQqxm8NaxOCeHmjHBWp4TMaPAJYDGbZ+X85whqLbUB6QR2VyLlfOzy8JZdO1EHh6BfucrpMXU9\n9WQ2XGRL9M1olbMXbM8WEYAKgiAIwhyxwBCHySuEY+YTLFgUREl+I3Yn+65fTSaXow4JnbGWnNeS\ntb4O9QxnwH9WZVkL3b12kmK0NL/9DwabnT+f21daQl9BPv533o1M7nzYtffSAYw6P1aH3uDOlOc8\nEYAKgiAIwhwhk8m4KXwdha0l+CeoGegfpNLF3uvDNKYwrOaZXwGdTY6BAWytrbO+AppzrobgMB8W\nPHwnCk8vGsYpUP9ZkiTRumsnmohIvJYsdfp5Ze2XyG8tZlvMrSjkiqlMfc4SAaggCIIgzCFLg9Lw\nUXtzvieTYJOeIje34dUmE5Y6s1OB0nwxnAE/fMZ1NjTUddJQ20X68nDkWt1QgfqSYjo/PD7p2L6C\nfPrLSl1a/ZQkid2XDhDhHcbiwJQpzn7uEgGoIAiCIMwhSrmS9WGryWy8SFSyHzUV7XR3Drh8H40p\nDMkytGJ4vbCYPynBNEsZ8AA5mbX4GHRExQ/V4PRMSsZn3Y00v/0mgy3jb8VLkkTLrp1oY+PwTEl1\n/nnN+VR2VXNn7Bbksus3TLt+vzJBEARBmKfWmFYgR0atZykqtYLiPNc7I6mvZMJfP9vw1nozSn9/\n5FrtrDyvq6OfitJmUpeFjWi7adxx/ydb8S+PW2u15+IFLFWVGO+6x+mWnXaHnT2XD5Dkt4AFfnHT\n8jXMVSIAFQRBEIQ5xlPlwYqQZZxsOkP0QiPFufU4HK4VlVca/JDrdEN1M68TVnMd6pDZ237PPVeL\nRqtkQUrwiNcVOh1BX3iC/uIiOj88BsBgezudp07QfuQwnSc/ouWdt/BISsZjYaLTzztTf47mvla2\nx942rV/HXCTqgAqCIAjCHLQhfA0n6s7gCO2gJ9dCXVU74dF+To+XyWSoTWHX1wqo2exSMs9UWAYG\nKcqtJ21ZOCrV6EQgz+RF+KxbT9Obf6cn6yJ9xUVgt4+4RuljYKCyAm1U9OTPs1vZX3GYjKDFhHnP\ncpmpa0CsgAqCIAjCHBToYSTVmMTH/acwGD3cSkbSmEzXTSkmh8XCYGvLrJVgKsgyIzkkFi0d/3na\nhIUwaKWvIH9U8AnQX1pMzc9+TE/WhUmfd6zmJL2DfWyL2Tylec8XIgAVBEEQhDlqY8Q6GvqbMMTJ\nqShtob/P6tJ4tSmMwYZ6JJtthmY4e6wN9SBJs9ID3m53kHehjvjkIDw8x+7bPlBZQdOf/zTpvSSb\njfqXfsdAZcW41/RYezlcdZy1YSvx1zm/yj2fiQBUEARBEOaoWJ8oIr3DKdFlA1Ca3+jSeE2oCclm\nw9rUNBPTm1XW4Qz4WagBWlbYRF+PlbTl4eNe07bvPacDe8lmo23/e+O+f7DqKCBxa+RNrk513hIB\nqCAIgiDMUTKZjI0RaynpLSUoxpOi3Ppxs67HMpwJfz0UpLeY61Aa/FDodDP6HEmSyMmsISLWDz+j\n55jXDLa305OT5dJ9e3KyGWxvH/V6a38bH9WeZlPkjXipx37e9UgEoIIgCIIwhy0OSMGg8aXdWEN7\nSx+N5i6nxyq99Sj0+uviHKi13jwr2++1le20NfeStmz81c++wnxwtcC/3U5fYcGol9+rOISHyoMN\n4Wtdneq8JgJQQRAEQZjDFHIFG8LXkOXIxFOvpjjXtZqgGlMY1usgE95qNs9KAlJOZg3GQC9Mkb7j\nXuPod70xAIBjoH/En2u7zZxryGJL9CY0irHPml6vRAAqCIIgCHPcqtBlqBRK5OG9lBc1MWh1PqlI\nfR1kwjusVgabm9DM8PnP1qYeairaSbshfMLi8XKde4Xw5dqRxwf2XD5AgIc/q0KWuXW/+UwEoIIg\nCIIwx+mUOlaFLidfe45Bq53yovFbQH6WJjSMwaZGHFbXMujnktnKgM85V4unt4bYhQETXueRtAgU\no2uDTkihwCMp+cofS9vLKWwt4Y6Y21DIXbzXdUAEoIIgCIIwD9wYtoZeZReeoTKKcp2vCao2mUCS\nsNabZ3B2M2t47jMZgPb2WCgraCQlw4RCMXF4pDIY8EpNd+n+XmnpqAwGYCjRaXf5ASL14aQHLHJ7\nzvOZCEAFQRAEYR7w1xlYHJBCrW8pjXVdtLX0OjVu+NykdR5vw1vNZhS+vig8Zi5LPO9CHQqlnKQ0\n54Jcv623I1M611BSplLht+X2K3/Oas6jqruGu2K3ON0n/nojAlBBEARBmCc2RqzF7FGOUiuj2MnO\nSAqdDqW//7xuyWk1m9HMYA/4QaudwiwziWkhaLTOBZXaqGhCvvTcpEGoTKkk5Jlnr7TjtDvsvHvp\nfZL9FxJviJ3y3OcrEYAKgiAIwjwRpY8gxhBJf2ALJfmN2O3OlQLSmMLmdSKSxVw3o9vvxXn1WC02\nUjPCXBrntXgp4f/yvaH+9J89E6pQ4LXkk/cXf9q//nR9Js39rWyPvW06pj5vORfmC4IgCIIwJ2yM\nWMdfG94hvtqfyrIWYhcGTjpGbQqj++yZWZjd9HMMDjLY1Ig69NaZub9DIvdcLbELA/D2cT27XRsV\nTejzLzLY3k5fYQGOgX7kWh0eSclXznwOG7BZ2FdxmOXBSzB5hUzXlzAviQBUEARBEOaRVGMS3n77\nkBksFOXUOxWAakwm2tvasPf1ofDwmIVZTp/BxgaQJDQztAJaUdpCV8cAm7YnTek+KoMBn9VrJrzm\nWM1J+gf72Rq9eUrPuh6IAFQQBEEQ5hG5TM6G8DV8UH0RqUJDY10X7a29WK121GoFYdF+eHlrRozR\nmIa2lq3mOnRx8ddi2m6b6R7wOedqCAn3ITBEPyP3H9Zt7eFI9XHWha3CX2eYfMB1TgSggiAIgjDP\nrAxZxhGPsyCTeOfVi3BVe3iZHKLijCxZGXElqFIFB4NcjqVu/gWgFnMdCr0ehZfXtN+7oa6Txrou\nbr1n5kshHaw8Csi4JWrjjD9rPhBJSIIgCIIwz9SVd2IqWgySbETwCSA5hraVd72aRUXpUMF6uUqN\nOjBoXrbkHOoBPzMZ8DmZNfj46YiK85+R+w9r6W/jo7ozbI68ES/VzJWSmk9EACoIgiAI80hTfReH\n9haAY+L6kQ67xME9BTTVdwGftOQ0z79MeKvZPCPnPzvb+7lc0kLasonbbk6H9y4fxEvlwYbwic+I\nfp6IAFQQBEEQ5pGTH5Ug2Z27VrLDyRMlwNA50Pm2AirZbFibGlHPQA3Q3HO1aHUqFiwKmvZ7X62m\nu45zjVlsjd6MWqGe0WfNJyIAFQRBEIR5oqfbQmNlj0tjGit66Om2oDaZsHd3Y+vqmqHZTT9rUyPY\n7dNeA3Sgf5DivHqSl4SiVM1sH/Y9lw4Q5BHAipCMGX3OfCMCUEEQBEGYJ0rL6obOfbpCklFaVvdp\nJvw8WgW1fnJkYLoD0IIsM5JDYtGSmeuuBFDcVkZRWyl3xN6GQj6zge58IwJQQRAEQZgnajsa3BpX\n19GAKiAQmVI5r1pyWs1mFN7eKL2nr0SS3eYg/0IdCYuC8fCcuS1xh+Rgz6X9ROsjSDMmz9hz5itR\nhkkQBEEQ5gm73ObeOIUdmUKBOiR0XgWgFrN52ut/lhU20tdrJW25a203J9Nh6aSorYwB2wBapZZB\n+yDV3XV8ZfGzM57kNB+JAFQQBEEQ5gm/MC31sh5kkvMbmJLMgZ9pqPuROiwM6zzqCW+tN6OLT5i2\n+0mSRM65WiJj/TH4T085pKquGg5WHSOvpRCH5Bjxnl7tjVqhmpbnXG/c3oK3Wq1s27aNc+fOXXmt\ntraWJ554gsWLF3P77bdz6tSpEWPuuOMOFi5cSGJi4pV/l5eXuz97QRAEQfgcSQtfQLdvk0tjun2b\nSA1fAIAmNAxLXR2SJE0y6tqTbDasDfXTev6zpqKdtubeaVv9zG7O55cXf0dOc/6o4BOgy9p95X1h\nJLcCUKvVyte+9rVRweOXv/xlAgMD2blzJ3fccQcvvPACDQ1D51UkSaKqqorXXnuNkydPcurUKU6e\nPElMTMzUvwpBEARB+Bzw1fjgmwQOmXN1mCQc+CYOjYOhWqCSZQBbW+tMTnNaDDY3gd2OZhqL0Odk\n1mAM8iI0wnfK96rqquGVgtexOSY+FmFz2Hi54HWqumqm/MzricsB6KVLl7jvvvuorR15huTMmTPU\n1NTwwx/+kJiYGJ555hnS09N5++23AaiursZms5GSkoK/v/+Vf+RykQclCIIgCM66NW015vjcSYNQ\nCQcy5IR1JlxZ8RzOhJ8P50At09wDvrWph9rKdtKWT0/h+YNVxyYNPofZHDYOVR2b8jOvJy5Hf5mZ\nmaxcuZI333xzxBJ+bm4uycnJaDSaK68tXbqU7OxsYChwDQ4ORq0WRVgFQRAEwV2R+nDuW7uJ6uRz\ndBoakGQjt34lmYNOQwNVizKJXetFdUEXHx0sRZIklH5+yLXaeXEO1FpvRu7piUI/PRnwOZk1eOk1\nxC4MmPK9Oiyd5LUUujQmt6WQDkvnlJ99vXA5CenBBx8c8/Xm5mYCAwNHvObv709jYyMwFIAqlUqe\nffZZ8vPziY6O5pvf/CapqaluTFsQBEEQPr/SAhbhu86HQ1XHKDR/iEeHH3K7EofCRp9vG0mhsTwY\n+TCR+nCK9Q0c21eMwyFx420LUJvC5sUKqNVchybUNC2rlb3dFsoKm7hhfQwKxdR3XovaysY88zkR\nh+SguK1MFKT/xLRlwff3949a3VSr1VitVgAuX75Md3c39913H//8z//Mm2++yeOPP86BAwcICnK+\nDdZ0fOMIgiAIwnwX6xfJc36P057YSXFrKRcacyluK+eHq7+Fn85w5bpFi0NRKuUc2VuIDEg0hWGp\nvIxSObd/n1rrzXjExk3LPAuyzCiUclKWmqblfoMOi1vjrJJ1zv+9T5Wzcdq0BaAajYbOzpFLy1ar\nFa1WC8CPf/xj+vv78fQcKnvw/e9/n4sXL7Jnzx6eeeYZp5+j1+uma8qCIAiCMO8Z8CQmJJRQ/0Dy\nPirC01uNwXtkiaGV62Lx9tKy6/WLDAZGEVl/Gl+9Fplibnbnkex2rA0NhN6yCYNhauWSrBYbBVlm\nlq6MJDjEZ1rm59/h3n389fopfz3Xi2kLQIOCgkZlxbe0tBAQMHTWQi6XXwk+h8XExFzZondWV1c/\ndrtry96CIAiCcL3zk/sDkFdbhiZ4dJBjivbllruSObirgG6/FUQXXUZnmt4i79PF0tCANDiI3WCk\nvb13SvfKyazBMmBjYWrwlO81LFwTgQwZEs6Xs5LL5ERoI6dtDnOVQiF3arFw2gLQtLQ0/vd//xer\n1XplK/7ChQtkZAyddXjsscdYvnw5L7zwAjBUlqmkpIRHHnnEpefY7Q5sNhGACoIgCMLVdHIPDBpf\nKjtqSTeOnV8RnRDATZuj+eB9B4f2l3HrY4Fz8mhbf81QySJlUMiUfuc7HBJZZ2uITQxA56melvih\nuquWt8r2uhR8AqQak/BWeosY5hPT9l23fPlyQkJC+Jd/+RfKy8v5wx/+8cjGbAAAIABJREFUQF5e\nHvfeey8AGzdu5K9//StHjx6loqKCH/zgB3R3d3PXXXdN1xQEQRAE4XMt3NtETffEGe7xiyNJ6/yY\nmkYbh/cUzsldRYvZjNzDA4XP1Op1VpQ20905QPry8CnPqdPSzatFb/Hz87+h39bPfQl3opQ7t46n\nkivZHLlhynO4nkxpBfTqzDS5XM7//M//8N3vfpd77rmHiIgIfvvb3xIcHAzA448/jtVq5Uc/+hGt\nra2kpqbyl7/8BQ8Pj6l9BYIgCIIgABDuHcrxmlNIkjRh9niYUYFGXc25S3IO7S5g853Jc2ol1PpJ\nD/ipZMBLkkR2Zg2hEb4EBHu7fR+bw8bx2lMcqDiCXCZnR8J21oTegEKuwFej5+VJitEr5UqeSH6I\nSP3Ug+DriUyaD/24rtLe3iuWrwVBEARhDHkthfw+98/8cOV38L8qE/6zmt54jd6CPORPfJOD7+QT\nFuXHLXclo5gjGdpVP/x3NJGRBH/hSbfvUV/bye5Xs7jt3kVExRldHi9JEvmtRbxT9h4tA22sNa1g\nS/QmvFQjz9dWddVwqOoYuZ/pBS+XyUk1JrE5csPnKvhUKuVOJVpN2xlQQRAEQRCurQjvoU5HNd21\nEwagapOJjqNHiIvw5tZ7Unh/Zx7v78rnlruSUSqvbWa85HBgrTejX7FqSvfJyazB109HZKy/y2Mb\neht5u+xditpKWWCI4+mUxwj1Ch7z2kh9OE+nPEaHpZPitjIGbBa0Sg0L/eKvtEAVRhMBqCAIgiBc\nJ3w0evRqb2q660gPTBn3Oo0pDCQJa309ETGRbNmRwv6383l/Zz633r0IperaBaGDLS1Ig4OoQ93P\n0O9s76OitIX1tya4tI3fN9jH/oojfFh3Gj+NL8+kfIFUY5JT9/DV+Igi8y4QAaggCIIgXEfCvU1U\n90yciKQONQFgratDGxFJWJQfW+5N4cDOPA7szOfWexahukZBqNVcN2KOzujptlBb0YbVaketVlBX\n3YHWQ0XCIuca3TgkB6fMZ3n38kEGHTa2Rd/Choi1qJxMMhJcJ/5mBUEQBOE6EuFt4mTd2QkTkRQ6\nHUo//xEtOcOiDGzdkcq+t3LZ/1YeW+5NQaWe/SDUWm9GrtWiNIx/hGBYU30XF89UU1XeisMxMqXF\n19+DtuZeAkMm7iVf2n6Jt8v2UtdTzw3BS7kj9laxdT4L5sZpY0EQBEEQpkW4t4nuwR46rV0TXqcx\nma6sNg4LjfBl632pNDd0s++tXAat42d3zxSr2Yw6dPIM+Mslzex+NYuK0pZRwSdAR2vfJ+83jzm+\ntb+NP+b9jf+X9RJquYpvZrzAY0n3i+BzlogAVBAEQRCuI+HeQ1vXk9UDVZvCRqyADgsN9+X2+1Jp\naezhvX/kYrXMbhBqMdehDpl4+72pvosjewux2ycu5GO3SxzeU0hT/afBuMVu5d3LB/nh2f/kcmcV\nX0h6gK8tfZ4ofcS0zF9wjghABUEQBOE6YtD44qXypHqSAFRjMmFrbcXe3z/qveAwH7Y9kEZbc++s\nBqH/f3v3Hh1Vfe///7VnJjMhZHK/TyZcAhpE5KZYzrFypKhfW6WntXUdXPZUXYoVOZzWero8pZ6e\nRfurHFmtl9JyftJiLbV+qaJUKfbbop5+laIockdqmZDr5ApkciGZnczM94/A1JjbJExmcnk+1nKV\n7L1n7/fUrMXLz/68P58LHfCDNSB9sLdi0PB5QSAQ0gd7KxQKhbSv9gOte2eDdlf8Scvc1+o/PvVv\nWpS3QBaDOBRr/D8OAMA4YhjG+R2Reo9ufpzd1b1k0ydfw1+QW5CiW/5prs42ntOr2w7J39EZ9Vo/\nqevMaYVMU44BGpBaW/wq+2vjkO5bdrJRP9rztJ49/r81NaVIj1z9kG4p/l9KtDkutmQMEwEUAIBx\npjuAege8xp6XLxmGzOr+R0pz8lO0fMVc+c6069X/fXjEQ6jf213zQCOgVafOaKhb6ISCUrDRoX+d\nv1L3zvmKsiZlXEyZiAICKAAA44zb6VKT36dms6Xfayx2uxJycuX3DjxSmp3n1PIVc9Xia9crzx9S\nR/vIhVDTWy3D4ZAtvf+AaJqBYd37BtdSXZI+Y7ilIcoIoAAAjDNF4UakgUdBHS6X/FUDB1BJysp1\navmKeWpt9uuV5w+q/ZwZPtfa4teJwzU6/H6VThyuUWuLf9h1h/eAt/SMJ20tfnlO1Ovt3X/VwX0V\nw7p3wBr7jn70j3VAAQAYZzITMzTJlqjKlirNzry03+vsrkL5/ufNyO6Zk6zlt8/Vq88f0ivPH9Lf\nLS3WsQPeXmtwWiyGpszI1ILFRYOuwflJZk13AD1d36raap9qq5pVU+VTi69DkuRMTZQtLaBQc0iG\nIt/hKGQE1ZrSKGn6kOrByCGAAgAwzhiGIXeya9ClmByuQgVamtXV3CxbyuBhMTM7WZ+/fZ5e2npA\nO7cd7vOaYDCkUx81qsJzWtd//jJNuyR7wHt2dgZU7+0OmqXmDDWfzlfnlvdlGN0jr1NnZiq/MFV5\nhamanOzQm5Vvq2JXnVLP9r03e1+a0+oVdNBwNJoQQAEAGIfcKS4drD8y4DUO1/ktOb3VEQVQqTsw\ndnUNPg/zwhqc/3jH/B4joeda/aqpaj4/wulTY12rgsGQEhIscgZDuqx4kqZcealy8lP63Ikp0Zao\nhnyPnE3ZsoQG36kpaATUkO9Roo35n6MJARQAgHGoKNml1yv+r1o725ScMLnPaxJycmXYbPJXVymp\nZFZE9/1gb4WCQ1iD890/nVJxSbZqqroDZ3PT+dfpKQ7lFabq0jl5yi9Mlb3Oo5ont2jamhuUkN3/\nNpyzMmbKdL6oyhkH5T45b8AQGjQCqpxxUKazRSUZMyOqGbFBAAUAYBy6sCNSVYu33/BlWK2y5+cP\nuBTTxw1nDc6qsrOqKjurrNxkTSnOVN751+nJzp6vxM8erJFht8uWmTng/dIcqZqTdZkOhY6qdNY7\nyq4pVkpTjozQ3xqXQkZQzWn1asj3qCPZp3lZl7PF5ihDAAUAYBzKTsqSw2pXZUv1gKN/9oK+t+Ts\ny3DW4JSka2+cqdnzB95e0+/1yp6X36sDvi83TrlOx06fUEeyT5UzP5DNdCjZlyVLwKagtUutqY3q\nsnd34ydYbLphynVDLxojimWYAAAYhyyGRYURNSK5ZHqrFYogWQ53Dc5IXtmb3upBt+C8YEqKW3fP\nvl02S/c4Wpfdr6bsap3JK1dTdnU4fNosNt01+3ZNSXEPq26MHAIoAADjVJHTpYoItuQMtrer6+yZ\nQe9n76MpKBIJjoFfuIZCIZk13gG34PykudmX68EF92t2ZkmvcxbDonnnz8/NvnzI9WLk8QoeAIBx\nyu106c2qt9Xe1a5Jtkl9XhPuhK+uVkLGwPMvC6dlyGIxeqz7ORiLxVDh1P6biiSpq6lJwfZ22YcQ\nQKXukdC/L7hax06f0BdnfE5Ww6ZEm0MlGTOZ8znKEUABABinPt6INDO9uM9rbBmZMhyJ8ldXafKc\nKwa8X7LToSkzMnXqo8gbkabMyOzVcPRJprd7moA9P7JX8B/n8Z1SmiNVnylaMuTPIn54BQ8AwDiV\nm5StBEuCKgaYB2pYLHK4CiLuhF+wuEhWa2S7EFltFi1YXDTodaa3WobNpoTsgRet70tpU5mKU6cO\n+XOILwIoAADjlNViVWFy/qCNSHZX5J3wOfkpuv7zlw0aQq1WQ9cvnxXRdpzdW3BG1gHf43OBTlW0\nVKs4bdqQPof4I4ACADCOuZ2Rbclp1ngVCgYjuue0S7L1j3fM17RLsmSx9AyiFouhaZdknT8f2Yim\n3+sd8vxPSSpvrlQgFNB0RkDHHOaAAgAwjrmdhXqr+h35A6YcVnuf1zhchQp1dqqzvl72vMj2WM/J\nT9H/+uLlam3xq6rsrDr9XUpw2FQ4NX3QOZ8fFwqFZHq9mnz5nIg/c0Gpr0yJVodcyZHvC4/RgQAK\nAMA45na6FFJIVS1eFadN7fOaC6OP/uqqiAPoBclOh0rmDD8ABpp9Cp5rG9YIqMdXpmmpU2QxeKE7\n1vBvDACAcSx/co5shnXA1/DWlBRZk53hbvRYMr1eSZIjwkXoLwiGgir1ldOANEYRQAEAGMdsFpsK\nBmlEMgxDdpcr4kakaPKHO+BzhvS52rZ6tXe19zuqi9GNAAoAwDjnjmBHJIfLFfFSTNFker1KyM2T\nYR3aLkse3ylZDIumpAy+zBNGHwIoAADjnNvpUu25epmBzn6vsbsKZdbVKtjZ/zUjoXsLzmEsQN9U\nJneyq9/GKoxuBFAAAMa5IqdLwVBQ3raafq9xFBRKwaA6a2tjVlcoFJLfWz2sBqRSXxmv38cwAigA\nAONcweQ8WQzLgPNA7a7uUUi/N3bzQAMtLQq2tso+xBHQsx1NOt1xlvU/xzACKAAA41yCNUH5k3NV\n0TxAJ3zSZNnSM2I6D/Rve8APbQS01FcmSQTQMYwACgDABOB2ulTZOtiWnLHthDdrvJLVKnvO0Drg\nPb5yZU/KVKrDOUKVYaQRQAEAmACKnIXyttaqK9jV7zUOV2FMR0D93mrZc3Nl2Ia2L05p0ylGP8c4\nAigAABOA2+lSIBSQt63/JiOHq1CdjQ0KdrTHpCZzGHvAd3R1qKq1hgakMY4ACgDABOBKzpchY5BG\npPNbcp7fnWikmV6v7PlDa0A61VyhkEIqTp02QlUhFgigAABMAA6rXbmTc1TZ0n+4tOcXSIYhMwbz\nQAMtLQq0NMsxxBFQT1OZJickKTcpe4QqQywQQAEAmCCKnK4BR0AtdrsScnLkj8E8UH9NdxAe6hJM\nHl+ZpqdOlWEYI1EWYoQACgDABOF2ulTd6lUgGOj3GkdBbBqRTG+1ZLEoISc34s8EggGVNVeomAak\nMY8ACgDABOFOdqkz2KXac/X9XmN3uWKyGL3p9cqekytLQkLEn6lq9coMmDQgjQMEUAAAJgi3s/t1\n90Cv4R2uQgV8PgVaWka0FrPGO6zX7zaLTW5n4QhVhVghgAIAMEEk2hKVk5Sliog64Uf2NbzfO/QA\nWtpUpilOtxIsQ1s3FKMPARQAgAnEnTxwI5I9J1eyWke0Ez7Q1qaAr2lIW3CGQiF5fGW8fh8nCKAA\nAEwgRSmFqmr1KhgK9nnesNlkz8sf0U548/w6o0NZgul0xxk1my00II0TBFAAACYQd7JLZsBU/bnG\nfq9xuAq7u9RHiL+mWjIMJeRF3gHvaSqTJE1PnTJCVSGWCKAAAEwgFxqRKlr6f8XuKCyUv7pKoVBo\nRGowvV4l5OTIkmCP+DMe3ynlT85VUkLSiNSE2CKAAgAwgSQlJCkzMWPgeaAFLgXPnVNXU9OI1GB6\nq4e8BafHV87r93Fk2AHUNE3dcssteu+998LHqqqqdNddd2n+/Pm6+eabtWfPnh6f+fOf/6xbbrlF\n8+bN05133qnKysrhVw4AAIZlsB2RHK7uZY7M6pH5e9qs8Q5p/mdrZ5tq2+o0nQA6bgwrgJqmqQcf\nfFAnT57scfyBBx5QTk6Otm/fruXLl2v16tWqra2VJNXU1OiBBx7Qrbfequ3btys9PV0PPPDAxX8D\nAAAwJG6nS5Ut/Tci2TIzZTgcI9KIFDh3Tl1nzw5pCaZTvnJJUnHatKjXg/gYcgD1eDy67bbbVFXV\nc+7I3r17VVlZqXXr1mn69OlauXKl5s2bpxdffFGS9Jvf/EZz5szRnXfeqeLiYj366KOqrq7uMYIK\nAABGntvpUkegQ43tZ/o8b1gschS4RmQpJjO8B3zkI6CepjKl2lOUmZge9XoQH0MOoPv27dPixYu1\nbdu2HpOTDx8+rNmzZ8vhcISPLVy4UAcPHgyfv+qqq8LnEhMTddlll+nAgQMXUz8AABgit7M7/A04\nD9TlGpERUNPb3QFvz82L+DMeX5mmp02VYRhRrwfxMeStBFasWNHn8YaGBuXk5PQ4lpmZqbq6OklS\nfX19r/NZWVnh8wAAIDac9mSlO9JU2VKthblz+7zGUVColn3vKhQMyrBEr2fZ9HqVkJUty8cGrAbS\nGehURXOl/nHG56JWA+Ivar9R7e3tstt7Lqdgt9tlmqYkqaOjY8DzAAAgdtyDNCLZXS6FTFOdDQ1R\nfa5/iHvAV7RUqysUYAekcSZqm6k6HA75fL4ex0zTVGJiYvj8J8OmaZpKSUkZ0nOsVlaOAgDgYk1J\ndemNirdltRp9vtpOmlIkSeqq9SrJlR+153bWeJVy9adks0X29/mpljI5rHZNSXXJGsWRWIyMSHNa\n1AJobm5ur674xsZGZWdnh883fOK/ohobGzVr1qwhPSclZdLFFQoAADS7YIZe9fxBAYdf2ZMze50P\npSXplDNZljP1Sk+fHJVndp1rV+fp08qYOT3ie5YfrdQlWdOVlTm0ASuMblELoHPnztXmzZtlmmb4\nVfv+/ft15ZVXhs9/8MEH4evb29t1/Phx/cu//MuQntPc3K5AoO9lIwAAQGQyLFmSpMOVf9WC3MQ+\nr7EXuNT011Iln22LyjPbSz2SpK60TJ2N4J7BUFB/afDouqJrIroe8We1WiIaLIxaAF20aJHy8/P1\n8MMPa9WqVXrjjTd05MgRrV+/XpJ06623asuWLdq8ebOuu+46bdy4UUVFRVq0aNGQnhMIBNXVRQAF\nAOBiTLYmK8XuVHlTpa7InN3nNQkFLrX/5S9R+3v3XGX3sk7WnPyI7lnTVqe2znOa5pzC3/3jzEVN\npvj4nBGLxaKf/vSnamho0K233qpXX31VP/nJT5SX173Mgsvl0o9//GNt375dX/7yl9XS0qKNGzde\nXPUAAGDYipwuVbQOvCOSWVerUFdXVJ5ner2yZWVF3AHvaToli2HR1BR3VJ6P0eOiRkA//PDDHj+7\n3W5t3bq13+s//elP6/e///3FPBIAAESJ2+nS29XvKhQK9dmI5HAVSoGAzLra8PacF8P0VssxhD3g\nS33lKkzOV6Kt7ykCGLtoJwMAYIJyO11q6WyVz2zu8/yF3Yr8VdHZEWmoSzB5mk6x//s4RQAFAGCC\nGmxHJOvkybKlp0dlS86g36+uxsaIt+D0+ZvV2HGG/d/HKQIoAAATVLojTckJk1Ux0IL0BS75vRe/\nJadZU9N9v/zIAqjHVyZJmp465aKfjdGHAAoAwARlGMb5HZH6H+F0uAqjMgJq1nSHWEdBZIvalzaV\nKTMxQ2mO1It+NkYfAigAABNYdwD19nve7nKps6FBQb//op7jr66WLSNDlsTINpTx+MrYfnMcI4AC\nADCBuZ0uNfl9ajZb+jx/ofvdvMjX8GaNN+L5nx1dflW1elVMA9K4RQAFAGACKxqkEcmeXyAZhvzV\nFxlAvd6Il2Aqa65QMBSkA34cI4ACADCBZSZmaJItsd8AanE4lJCVfVHzQIOmqc7GhoiXYCr1lSnJ\nNkl5k3OG/UyMbgRQAAAmMMMw5E529RtApe55oBfTCW/W1kihUMSv4D1NZZqeOlUWg5gyXvFvFgCA\nCc6dMnAAdbgK5b+IEdAL80ftEbyCDwQDOtVczvzPcY4ACgDABFeU7NLpjrNq7Wzr87zDVahAU5MC\nra3Dur/p9cqWni5rUtKg13rbauUPmJpOB/y4RgAFAGCCu7AjUlU/yzHZz3fCD/c1vL/GG9Hop9T9\n+t1mWDXFefF7z2P0IoACADDBZSdlyWG1998Jn5srWa0yh9kJb3qrI25A8vhOqSilUAnWhGE9C2MD\nARQAgAnOYlhUmOxSRT87Ihk2m+x5+cOaBxrsNNVZXx/RFpyhUEiepjIVp7L/+3hHAAUAACpyDtaI\n5BrWUkydtXVSKCRHBB3wZzrOymc2s//7BEAABQAAcjtdamg/rfau9j7P2wtc8ldXKxQKDem+/poL\nHfCD7wHv8ZVJEgvQTwAEUAAAMGgjksNVqOC5NgV8TUO6r+mtljU1Vdbk5EGv9fjKlJeUo2T75CE9\nA2MPARQAACg3KVsJlgRV9NeIdKETfoiNSKY38g740vML0GP8I4ACAABZLVYVJuf3Ow80IStLht0+\n5Hmgptcb0fzPc53n5G2rVTHrf04IBFAAACBJcjsL+w2ghsUSngcaqVBXl8z6uoiWYCr1lUsSHfAT\nBAEUAABI6p4HWneuQR1d/j7POwpcQ1qKyayrlYLBiPaA9/jK5LQnK2tSRsT3x9hFAAUAAJK6A2hI\nIVW31vR53lFYKNNbrVAwGNH9TG93Q5Mjgjmgpb7u9T8Nw4i8YIxZBFAAACBJKpicK5th7X9HJFeh\nQqapztONEd3P762W1Zkiq9M54HWdwS6VN1cy/3MCIYACAABJ3Y1IBQM0Ijlc3a/SI92S06zxRjT/\ns7KlWp3BLhXTAT9hEEABAECY29n/lpzW1DRZkiZHPA/U9EYWQEt9ZbJbElSYHNlyTRj7CKAAACDM\n7XSp9ly9zEBnr3OGYUS8JWeoq0tmXW1E8z89TWWamjpFVot1WDVj7CGAAgCAsCKnS8FQUN62vhuR\n7K7CiJZiMuvrpUBg0A74UCh0vgGJ/d8nEgIoAAAIK5icJ4thUUVz//NAzdoahbq6BryPeWEP+EEC\naP25BrV2trH+5wRDAAUAAGEJ1gTlT84dsBNegYDMuroB72N6vbIkJw/aAe/xlcmQoampRcOuGWMP\nARQAAPRQ5CxUZWs/I6AFFzrhB54Hanqr5cgvGHRdT09TmVzJ+ZpkSxxesRiTCKAAAKAHt9Mlb2ut\nuoK9X7Nbk5NlTU2T3ztwAPV7vRHtgFTqK2P9zwmIAAoAAHpwO10KhALyttX2ed7hGnhP+FAgoM66\n2kGXYGo2W1Tf3qjprP854RBAAQBAD4XJ+TJkDDgPdKDF6DsbGhTq6gq/ru9PaVOZJLEA/QREAAUA\nAD3YrXblTc5RZYu3z/MOV6E6G+oV9Pv7PO/3nu+AH2QNUI+vTBmJ6UpPTLu4gjHmEEABAEAvA+2I\n5HC5pFBIZk3fa4Wa3mpZkpJkTU0d8BkeX5mms/7nhEQABQAAvbidLlW31igQDPQ6d6G5qL8tObv3\ngHcN2AFvBkxVtlSz/ucERQAFAAC9FDkL1RXsUu25+l7nLA6HErKzZfbTCW96vXIM0oBU1lypYChI\nB/wERQAFAAC9FCbnS9KAjUh9dcKHgkGZtTWDz/9sKtMkW6LyJ+defLEYcwigAACgl0RbonKSslTR\nTwB1FLj6XIy+s6FBoc7OQdcALfWVaVrqFFkMoshExL91AADQJ3eya8AR0K6zZxVoa+tx3Kzp7pwf\nKIAGQ0GV+sqZ/zmBEUABAECfilIKVdXqVTAU7HXO4Tq/Jae3Z0A1vdWyTJokW1r/Syt5W2vVEehQ\nMR3wExYBFAAA9Mmd7JIZMFV/rqHXOXtevmS19uqE99d4ZR9kD/hSX5mshlVTUtxRrxljAwEUAAD0\nye3sbiTqax6oYbPJnpvbqxHJrK4edAtOj69MRU6X7FZ79IrFmEIABQAAfUpKSFJWYkb/80ALCns0\nIl3ogB9sC05PUxn7v09wBFAAANAvt7P/RiSHyyW/t1qhUEiS1HX6tEKmOeAI6JmOszrrb2L9zwmO\nAAoAAPrVHUD7aUQqLFSwtVWBZp8kyV9zYQ/4/kdAS5vKJIkR0AmOAAoAAPrldrrUEehQY/uZXufs\nBYWSFJ4HalZ7ZTgSZcvI6Pd+Hl+ZcpKy5LQnj0zBGBMIoAAAoF9uZ/doZl+v4ROys2XY7eF5oGZN\ntRwFA3fAe3xlrP8JAigAAOif056sdEdanwHUsFhkzy8Ij4D6vd4Bt+Bs72qXt7VWxbx+n/CiGkDP\nnDmjNWvW6KqrrtKNN96ol19+OXzu+9//vkpKSjRr1qzw/z733HPRfDwAABgBgzUimd4qhUIhmTXe\nARuQSn0VCimk6TQgTXi2aN5s1apVkqStW7eqrq5O3/rWt+R0OrVs2TKVlpbqoYce0he+8IXw9cnJ\nzP8AAGC0K3K69Gbl2wqFQr1er1szMtSx712dfmWHQn6/rCkp/d6n1Fem5ITJypmUNdIlY5SLWgA9\nevSoDh06pN27d8vlcqmkpET33HOPfvazn2nZsmXyeDy65557lJmZGa1HAgCAGHA7XWrrOqczHWeV\nOam7waij7JTO/G6nWg8dkIJBnXn1t5Kkul9sUduBA8r43M1KnNpzrqen6ZSK06YNOEcUE0PUXsFX\nVlYqIyNDLtffll649NJLdfToUbW2tqqurk5Tp06N1uMAAECMfLIRqeWD/apc//+p9cB+KfiJ5ZmC\nQbUe+Nj58wLBgMqaKzWd/d+hKAbQrKwsNTc3y+/3h4/V1NQoEAiotLRUhmFo06ZNWrJkiT7/+c9r\nx44d0Xo0AAAYQamOFKXYnapsqVZH2SnVPr1Joa6uAT8T6upSzf+/SR1lpyRJla3V6gx20gEPSVEM\noHPnzlV2drbWrVun9vZ2lZeX6xe/+IUkqbS0VBaLRcXFxdq8ebO+/OUv65FHHtHu3buj9XgAADCC\nipwuVbRW68zvdg4aPi8IdXXpzK6dkrq330yw2ML7y2Nii9ocULvdrqeeekpf//rXtXDhQmVmZuqe\ne+7R+vXrdcMNN2jp0qVKOT8x+ZJLLlFZWZmef/55LVu2bEjPsVpZOQoAgFibklqo9//ytloPVQ1+\n8ce0HjqoUEuTTjWXa1pqkRLt9hGqEKNBpDktql3wl19+uXbv3q3Tp08rPT1db731ltLT05WUlNTr\n2unTp+vdd98d8jNSUiZFo1QAADAEs/KLVbr71d5zPgcTCCh06qRKW8r0meK/V3r65JEpEGNK1AKo\nz+fT/fffr02bNoU73f/0pz9p0aJFeuqpp3TgwAE988wz4es//PBDTZs29Hkgzc3tCgSG+MsPAAAu\nSqY1W/bO0LA+662pki+hRS6HS2fPtkW5MowmVqslosHCqAXQ1NRUtbe3a8OGDbrvvvv0zjvv6KWX\nXtJzzz2nYDCop59+Ws8884yWLVumt956S6+88oq2bt065OcEAkGBpqddAAAW2klEQVR1dRFAAQCI\nJafVKUtioqTWIX+2PtAiI8FQUXIRf4dDUpR3Qnr88cdVXl6u5cuX65e//KWefPJJzZ49W3PmzNFT\nTz2lHTt26JZbbtFzzz2nH/7wh7riiiui+XgAADBCDMNQaOY0BS1DXMPTatXJLKkgOU9JCUyjQzcj\nFAoNbzw9Ts6ebeO/ngAAiIPfel5T0nM7NaUi8tfoyQsW6mcLOnRJ+gz906VfGPwDGNNsNktE83xp\nKQcAABFxO1368yy7ZItsBp+RkCDH9UtVd65BxalTR7Y4jCkEUAAAEJEip0v1mQnqun25jEFCqGGz\nKX/l11SV1v1zcdrUkS8QYwYBFAAARCQzMUOTbJNUUTRZ7ofXKnnBQslq7XmR1arkBQu7z89fKI/v\nlNIcqcpITI9P0RiVoroOKAAAGL8Mw5Db6VJlS7US53xGBav+RZ1nz+rc8WMKdrTLkjhJSZfNVkL6\n38JmaVM5r9/RCwEUAABEzO0s0IH6I+GfE9LTlfr31/R5rRnoVEVLla7Kmx+r8jBG8AoeAABErCjZ\npTMdZ9XaOXgnfHlzpQKhgKYzAopPIIACAICIuVMKJUlVLd5Bry31lSnR6pArOW+ky8IYQwAFAAAR\ny56UKYfVrsqW6kGv9fjKNC11iiwGcQM98RsBAAAiZjEsKkx2qaKlasDrgqGgSn00IKFvBFAAADAk\nRec74QdS21av9q521v9EnwigAABgSNxOlxraT6u9q73fazy+U7IYFk1JKYphZRgrCKAAAGBI3E6X\nJKlygEYkT1OZ3MkuOaz2WJWFMYQACgAAhiQ3KVsJloQBX8OX+sp4/Y5+EUABAMCQWC1WFSYX9BtA\nm/w+ne44SwMS+kUABQAAQ+YeoBHJ01QmSZrOCCj6QQAFAABD5na6VHeuQR1d/l7nPL4yZU/KVIrd\nGYfKMBYQQAEAwJC5nS6FFFJ1a02vc6VNp9h+EwMigAIAgCErmJwrm2Ht9Rq+o6tDVa01NCBhQARQ\nAAAwZFaLVQXJ+b12RDrVXKGQQipOnRanyjAWEEABAMCw9NWI5Gkq0+SEJOUmZcepKowFBFAAADAs\nRU6Xas/Vywx0ho+V+so0PXWqDMOIY2UY7QigAABgWNxOl4KhoLxt3Y1IgWBAp5orWP8TgyKAAgCA\nYSmYnCeLYVFFc/dr+KpWr8yASQMSBkUABQAAw5JgTVD+5NzwPNBSX7lsFpvczsI4V4bRjgAKAACG\nLTcpRyfOfqQ3K9/WvtoP5JqcrwSLLd5lYZQzQqFQKN5FDMXZs23q6grGuwwAACa08uZK/Z/yN3W4\n4ZhC+luUMGToiuzZunHKdZqS4o5jhYgHm82i9PTJg15HAAUAAENysOGonjn2a3UFu/q9xmax6e7Z\nt2tu9uUxrAzxFmkA5RU8AACIWHlz5aDhU5K6gl3acuzXKm+ujFFlGEsIoAAAIGL/p/zNQcPnBV3B\nLv2h/M0RrghjEQEUAABEpMnv05HG40P6zOHG42ry+0aoIoxVBFAAABCRD8/8VcHQ0PowgqGgTpz5\n6whVhLGKAAoAACLS0dUxzM/5o1wJxjoCKAAAiEiiLXGYn3NEuRKMdQRQAAAQkVkZM2UxhhYdLIZF\nJRkzR6gijFUEUAAAEJE0R6rmZF02pM9ckXWZ0hypI1QRxioCKAAAiNiNU66TLcKtNhMsNt0w5boR\nrghjEQEUAABEbEqKW3fPvn3QEGqz2HTX7NvZjhN9YitOAAAwZOXNlfpD+Zs63Hi8x9JMFsOiK7Iu\n0w3sBT8hsRc8AAAYcU1+n06c+as6uvxKtDlUkjGTOZ8TGAEUAAAAMRVpAGUOKAAAAGKKAAoAAICY\nIoACAAAgpgigAAAAiCkCKAAAAGKKAAoAAICYIoACAAAgpgigAAAAiCkCKAAAAGIqqgH0zJkzWrNm\nja666irdeOONevnll8PnqqqqdNddd2n+/Pm6+eabtWfPnmg+GgAAAGOELZo3W7VqlSRp69atqqur\n07e+9S05nU4tW7ZMq1at0qxZs7R9+3bt3r1bq1ev1muvvaa8vLxolgAAAIBRLmoB9OjRozp06JB2\n794tl8ulkpIS3XPPPfrZz36m5ORkVVVV6YUXXpDD4dDKlSu1d+9evfjii1q9enW0SgAAAMAYELVX\n8JWVlcrIyJDL5Qofu/TSS3X06FG9//77mj17thwOR/jcwoULdfDgwWg9HgAAAGNE1AJoVlaWmpub\n5ff7w8dqamrU1dWl06dPKycnp8f1mZmZqquri9bjAQAAMEZELYDOnTtX2dnZWrdundrb21VeXq5f\n/OIXMgxDfr9fdru9x/V2u12maUbr8QAAABgjojYH1G6366mnntLXv/51LVy4UJmZmbrnnnv06KOP\nymKxqL29vcf1pmkqMTFxyM+xWlk5CgAAYDSKNKdFtQv+8ssv1+7du3X69Gmlp6frrbfeUkZGhoqK\nivT222/3uLaxsVHZ2dlDfkZKyqRolQsAAIA4iNpwos/n0+233y6fz6fMzExZLBb9z//8jxYtWqQr\nrrhCx44d6/HKff/+/Zo3b160Hg8AAIAxImoBNDU1Ve3t7dqwYYMqKyv1wgsv6OWXX9a9996rRYsW\nqaCgQA8//LBOnjypp59+WkeOHNGXvvSlaD0eAAAAY4QRCoVC0bpZWVmZHnnkER09elSFhYV66KGH\ntGTJEkndyzR9+9vf1uHDh1VUVKS1a9fqU5/6VLQeDQAAgDEiqgEUAAAAGAwt5QAAAIgpAigQAdM0\ndcstt+i9994LH6uqqtJdd92l+fPn6+abb9aePXviWCEmik/+Lv77v/+7SkpKNGvWLJWUlIT/ufPO\nO+NbKMaturo6rVmzRldffbWWLFmi9evX91rXu7W1VZ/+9Ke1Y8eOOFWJ0Y4ACgzCNE09+OCDOnny\nZI/jDzzwgHJycrR9+3YtX75cq1evVm1tbZyqxETQ1+/i2rVrtWfPHr399tvas2ePtm3bJofDoX/+\n53+OY6UYz9asWSO/369f//rX+tGPfqQ333xTTz75ZI9rHnvsMTU2NsapQowFBFBgAB6PR7fddpuq\nqqp6HN+7d68qKyu1bt06TZ8+XStXrtS8efP04osvxqlSjHf9/S4mJycrMzMz/M9TTz2lm266SUuX\nLo1TpRjPSktLdfjwYT366KMqLi7WwoULtWbNGu3cuTN8zfvvv693331XWVlZcawUox0BFBjAvn37\ntHjxYm3btk0f79c7fPiwZs+eLYfDET62cOFCHTx4MB5lYgLo73fx4/bu3av9+/frG9/4Royrw0SR\nnZ2tzZs3KyMjI3wsFAqppaVFUvco/Xe/+11997vfVUJCQrzKxBgQ1Z2QgPFmxYoVfR5vaGhQTk5O\nj2OZmZmqq6uLRVmYgPr7Xfy4zZs364tf/KJyc3NjUBEmIqfTqWuuuSb8cygU0q9+9Sv93d/9nSTp\nv//7v3XZZZeFfwb6QwAFhqG9vV12u73HMbvd3msiPhArlZWVeuedd/Sd73wn3qVgAnnsscd04sQJ\nbd++XSdPntRvfvMbvfLKK/EuC2MAARQYBofDIZ/P1+OYaZpKTEyMU0WY6P7whz9o1qxZmj59erxL\nwQSxYcMGbd26VU888YSKi4u1YsUKrVmzpsfreaA/zAEFhiE3N1cNDQ09jjU2Nio7OztOFWGie+ut\nt7Rs2bJ4l4EJ4nvf+56effZZbdiwQcuWLZPX69WBAwe0fv16zZ8/X/Pnz1dNTY2++93vauXKlfEu\nF6MQI6DAMMydO1ebN2+WaZrhV/H79+/XlVdeGefKMFEdOXJEq1atincZmAA2btyobdu26fHHH9f1\n118vScrLy9Mf//jHHtfdcccd+upXv6qbb745HmVilCOAAsOwaNEi5efn6+GHH9aqVav0xhtv6MiR\nI1q/fn28S8MEVF1drba2NhUXF8e7FIxzHo9HmzZt0n333af58+f3WOvT7Xb3uNZqtSojI6NXwyYg\nEUCBiBmGEf6zxWLRT3/6U33729/WrbfeqqKiIv3kJz9RXl5eHCvERPHx30VJOn36tAzDUEpKSpwq\nwkTx+uuvKxgMatOmTdq0aZOk7k54wzD04Ycf9rj2k7+nwMcZof4WlAMAAABGAE1IAAAAiCkCKAAA\nAGKKAAoAAICYIoACAAAgpgigAAAAiCkCKAAAAGKKAAoAAICYIoACAAAgpgigAAAAiCkCKAAAAGKK\nAAoAAICYIoACAAAgpgigAAAAiCkCKAAAAGKKAAoAAICYIoACAAAgpgigAAAAiCkCKAAAAGKKAAoA\nAICYIoACAAAgpgigAAAAiCkCKAAAAGKKAAoAAICYIoACAAAgpkZVAN2+fbtKSkr00ksvxbsUAAAA\njJBRFUB37dqlKVOm6OWXX453KQAAABghoyaAnjlzRu+8844eeOABvf/++6quro53SQAAABgBoyaA\nvvbaa0pJSdHy5cuVk5OjHTt2hM8tXbq0x8/79u1TSUlJ+OfKykrdeeedmjdvnpYvX64tW7Zo6dKl\nMa0fAAAAkRk1AXTXrl36h3/4B0ndgfO3v/3tgNcbhiFJCgQC+trXvqa0tDRt375d9913nzZu3Bg+\nDwAAgNFlVATQ2tpaffDBB1q2bJkk6YYbblBlZaX2798/6Gf37t2r2tpa/eAHP1BxcbE+97nP6Y47\n7hjpkgEAADBMoyKA7ty5U4mJibrmmmskSVdddZVSUlJ6vHbvz0cffaSpU6cqKSkpfGzevHkjVisA\nAAAuzqgIoLt27VJHR4cWLFig2bNna+7cuWpubtbvf/97+f3+Xq/TA4FA+M9Wq1WhUKjH+U/+DAAA\ngNHDFu8CysrKdPz4cT3yyCO6+uqrw8c/+ugjffOb39Qf//hHJSQkqK2tLXyuoqIi/OeZM2eqvLxc\n586dC4+CHj16NHZfAAAAAEMS9wC6c+dOpaWl6bbbblNCQkL4+IwZM7Rp0ya9/PLLuuKKK/TCCy9o\n0aJFOnPmjJ555pnwdYsXL1Z+fr6+853v6IEHHtDJkye1detWpaWlxePrAAAAYBBxfwW/a9cuff7z\nn+8RPi/4p3/6J73zzjtasWKFUlJSdOutt+rRRx/V17/+9fA1hmHoxz/+serr6/WFL3xBmzZt0q23\n3trn/QAAABB/RmiMT5g8c+aMjh8/Hm5gkqSf//zn+tOf/qRf/vKXcawMAAAAfYn7CGg03H///Xr+\n+efl9Xr15z//Wc8++6xuuummeJcFAACAPoz5EVBJeuONN/TEE0+ovLxcmZmZWrFihe699954lwUA\nAIA+jIsACgAAgLFjXLyCBwAAwNhBAAUAAEBMxSWA1tXVac2aNbr66qu1ZMkSrV+/XqZpSpKqqqp0\n1113af78+br55pu1Z8+ePu/x29/+Vl/5yld6HGtublZJSYlmzZqlkpISlZSUaPHixSP+fQAAABC5\nuCxEv2bNGqWlpenXv/61mpqa9O1vf1tWq1X/9m//plWrVmnWrFnavn27du/erdWrV+u1115TXl5e\n+PPvvPOO/vM//1Nz5szpcd+TJ08qPT1dv/vd78LbcX5yG08AAADEV8wDaGlpqQ4fPqw9e/YoIyND\nUncgfeyxx/TpT39aVVVVeuGFF+RwOLRy5Urt3btXL774olavXi1J2rhxo55++mlNnTq11709Ho+m\nTp0avi8AAABGn5i/gs/OztbmzZt7hcSWlhYdOnRIs2fPlsPhCB9fuHChDh48GP5579692rJli264\n4YZe974QQAEAADB6xTyAOp3OHrsWhUIh/epXv9LixYvV0NCgnJycHtdnZmaqrq4u/PNzzz2nK6+8\nss97ezwe1dbW6stf/rKuvfZaPfjgg2poaBiZLwIAAIBhiXsX/GOPPaYPP/xQ3/jGN9Te3i673d7j\nvN1uDzcoDaa0tFRtbW1au3atnnjiCdXX1+u+++4TS50CAACMHnFpQrpgw4YN2rp1q5544gnNmDFD\nDodDPp+vxzWmaSoxMTGi++3atUuGYYRD7FNPPaVrrrlGhw4d0rx586JePwAAAIYubiOg3/ve9/Ts\ns89qw4YNWrZsmSQpNze31yvzxsZGZWdnR3RPh8PRYwQ1IyNDaWlpPV7hAwAAIL7iEkA3btyobdu2\n6fHHH9dNN90UPj537lwdP368xyv3/fv3RzR62draqquvvlrvvfde+FhdXZ3Onj2r6dOnR/cLAAAA\nYNhiHkA9Ho82bdqklStXav78+WpsbAz/s2jRIuXn5+vhhx/WyZMn9fTTT+vIkSP60pe+NOh9k5OT\ntXDhQv3gBz/QkSNHdOzYMT344INasmSJZs6cGYNvBgAAgEgYoRh36Dz99NN6/PHHexwLhUIyDEMf\nfvihKioqtHbtWh0+fFhFRUVau3atPvWpT/W6z8aNG7Vv3z798pe/DB9raWnR+vXr9eabb8o0TS1b\ntkxr166V0+kc8e8FAACAyMQ8gAIAAGBii/syTAAAAJhYCKAAAACIKQIoAAAAYooACgAAgJgigAIA\nACCmCKAAAACIKQIoAAAAYooACgAAgJgigAIAACCmbPEuAADGuqVLl8rr9YZ/TkhIUFZWlpYsWaJ/\n/dd/VXp6esT32rFjh6699lplZGSMRKkAMCqwFScAXKSlS5fqpptu0t133y1J6ujo0EcffaTHHntM\nFotF27ZtU3Jy8qD3ee+99/SVr3xFb7zxhgoKCka6bACIG17BA0AUTJo0SZmZmcrMzJTL5dJ1112n\nLVu2qKamRj//+c8jukcwGJRhGCNcKQDEHwEUAEZIfn6+rr/+ev3ud7+TJH300Uf62te+pkWLFuny\nyy/XsmXL9Mwzz0iS9u3bp69+9asKhUL6zGc+ox07dkiSPvjgA91xxx2aO3eurrvuOq1bt06tra1x\n+04AEA0EUAAYQZdccokqKyvV2tqqu+++W+np6dq2bZt27dqlm266Sf/1X/+lEydOaMGCBfrxj38s\nwzD04osv6rOf/axOnDihu+++W9dee6127typH/7whzp+/LjuueeeeH8tALgoBFAAGEEpKSmSpJaW\nFt155536j//4D02bNk1FRUVavXq1pO6RUZvNptTUVElSenq67Ha7tmzZomuuuUYrV66U2+3WggUL\ntGHDBh08eFDvvfde3L4TAFwsuuABYAS1tLRIktLS0rRixQq9+uqrOn78uCoqKnTixAkZhqFgMNjn\nZ48fP67y8nLNnz+/x3HDMOTxeHTVVVeNeP0AMBIIoAAwgo4dO6apU6eqra1Nt912m7KysrR06VJd\nc801mjNnjpYsWdLvZ4PBoG655Rbdf//9vc4NZWknABhtCKAAMEJqa2v1+uuva+XKldq5c6eam5u1\ne/duWSzds5/+8pe/SJIurIb3yQ74mTNnyuPxyO12h495PB5t2LBBDz30kGbMmBGjbwIA0cUcUACI\ngnPnzqmxsVGNjY2qqqrS7t27de+998rtduuuu+5SXl6e2tvbtWvXLtXU1Ojtt9/WN7/5TRmGIdM0\nJUlJSUkKhUI6fvy4zp07p7vvvlvHjh3TunXr5PF4dODAAT300EOqrKzU1KlT4/uFAeAisBA9AFyk\npUuXqqamJvyzzWZTQUGBPvvZz+ruu++W0+mUJP3oRz/SSy+9pLa2NrlcLn3pS1/S66+/rilTpuj7\n3/++Ojs7tXr1au3du1cPPvig7rzzTr377rt68skndfz4cSUlJWnx4sX61re+pdzc3Hh9XQC4aARQ\nAAAAxBSv4AEAABBTBFAAAADEFAEUAAAAMUUABQAAQEwRQAEAABBTBFAAAADEFAEUAAAAMUUABQAA\nQEwRQAEAABBTBFAAAADEFAEUAAAAMfX/AAzMoaQr3DY1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113714898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Zoom in on large drop in August\n",
    "aug = apple['2015-08-01':'2015-08-30']\n",
    "aug.plot.line(y=['High', 'Low', 'Open', 'Close'], marker='o', markersize=10, linewidth=1)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data conversions\n",
    "----\n",
    "\n",
    "One of the nicest features of `pandas` is the ease of converting tabular data across different storage formats. We will illustrate by converting the `titanic` dataframe into multiple formats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived     sex   age     fare embarked  class    who deck  embark_town\n",
       "0         0    male  22.0   7.2500        S  Third    man  NaN  Southampton\n",
       "1         1  female  38.0  71.2833        C  First  woman    C    Cherbourg"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titanic.to_csv('titanic.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived     sex   age     fare embarked  class    who deck  embark_town\n",
       "0         0    male  22.0   7.2500        S  Third    man  NaN  Southampton\n",
       "1         1  female  38.0  71.2833        C  First  woman    C    Cherbourg"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = pd.read_csv('titanic.csv')\n",
    "t1.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t1.to_excel('titanic.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived     sex   age     fare embarked  class    who deck  embark_town\n",
       "0         0    male  22.0   7.2500        S  Third    man  NaN  Southampton\n",
       "1         1  female  38.0  71.2833        C  First  woman    C    Cherbourg"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2 = pd.read_excel('titanic.xlsx')\n",
    "t2.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Relational Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "\n",
    "con = sqlite3.connect('titanic.db')\n",
    "t2.to_sql('titanic', con, index=False, if_exists='replace')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>None</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived     sex   age     fare embarked  class    who  deck  embark_town\n",
       "0         0    male  22.0   7.2500        S  Third    man  None  Southampton\n",
       "1         1  female  38.0  71.2833        C  First  woman     C    Cherbourg"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3 = pd.read_sql('select * from titanic', con)\n",
    "t3.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### JSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t3.to_json('titanic.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>class</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>embarked</th>\n",
       "      <th>fare</th>\n",
       "      <th>sex</th>\n",
       "      <th>survived</th>\n",
       "      <th>who</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>Third</td>\n",
       "      <td>None</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>man</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>First</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>woman</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  class  deck  embark_town embarked     fare     sex  survived    who\n",
       "0  22.0  Third  None  Southampton        S   7.2500    male         0    man\n",
       "1  38.0  First     C    Cherbourg        C  71.2833  female         1  woman"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t4 = pd.read_json('titanic.json')\n",
    "t4.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>None</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived     sex   age     fare embarked  class    who  deck  embark_town\n",
       "0         0    male  22.0   7.2500        S  Third    man  None  Southampton\n",
       "1         1  female  38.0  71.2833        C  First  woman     C    Cherbourg"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t4 = t4[t3.columns]\n",
    "t4.head(2)"
   ]
  }
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