7. Cheat Sheet Time Series with PANDAS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series with pandas\n",
    "\n",
    "- DateTime Index Basics\n",
    "- Time Resampling\n",
    "- Time Shifting\n",
    "- Rolling and Expanding\n",
    "- Time Series Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DateTime Index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- Python built-in datetime library---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_year = 2020\n",
    "my_month = 1\n",
    "my_day = 2\n",
    "my_hour = 13\n",
    "my_min = 30\n",
    "my_sec = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_date = datetime(my_year,my_month,my_day,my_hour,my_min,my_sec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Day: 2,\n",
      "Hour: 13,\n",
      "Year: 2020,\n",
      "Date: 2020-01-02 13:30:15)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"\n",
    "Day: {my_date.day},\n",
    "Hour: {my_date.hour},\n",
    "Year: {my_date.year},\n",
    "Date: {my_date})\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- Numpy datetime64 object---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2020-03-15', '2020-03-16', '2020-03-17'], dtype='datetime64[D]')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Numpy datetime64 object:\n",
    "D - day level precision\n",
    "Y - Year level precision\n",
    "h- Hours level precision\n",
    "\"\"\"\n",
    "\n",
    "np.array(['2020-03-15','2020-03-16','2020-03-17'], dtype='datetime64[D]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2018-06-01', '2018-06-02', '2018-06-03', '2018-06-04',\n",
       "       '2018-06-05', '2018-06-06', '2018-06-07', '2018-06-08',\n",
       "       '2018-06-09', '2018-06-10', '2018-06-11', '2018-06-12',\n",
       "       '2018-06-13', '2018-06-14', '2018-06-15', '2018-06-16',\n",
       "       '2018-06-17', '2018-06-18', '2018-06-19', '2018-06-20',\n",
       "       '2018-06-21', '2018-06-22'], dtype='datetime64[D]')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Create Array of dates\n",
    "\n",
    "Parameter of arange method:\n",
    "From date\n",
    "To date\n",
    "steps or frequency\n",
    "data type\n",
    "\"\"\"\n",
    "\n",
    "np.arange('2018-06-01','2018-06-23',1,dtype='datetime64[D]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- Pandas datetime object---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-12-31', '2021-12-31', '2022-12-31', '2023-12-31',\n",
       "               '2024-12-31', '2025-12-31', '2026-12-31'],\n",
       "              dtype='datetime64[ns]', freq='A-DEC')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "There is a variety of date formats that pandas will recognize. For example:\n",
    "- Jan 01, 2018\n",
    "- 2020-01-01\n",
    "The freq parameter just specifies if the periods will be yearly, daily, monthly, etc...\n",
    "\"\"\"\n",
    "\n",
    "pd.date_range('2020-01-01', periods=7,freq='Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-02-01', '2018-01-03'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Also, you could use the format parameter to specify the date format using the pd.to_datetime() method\n",
    "\"\"\"\n",
    "pd.to_datetime(['1/2/2018','03/01/2018'], format='%d/%m/%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>-0.771952</td>\n",
       "      <td>-0.747947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>-1.830185</td>\n",
       "      <td>1.788879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>0.088524</td>\n",
       "      <td>-0.069559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2020-12-31 -0.771952 -0.747947\n",
       "2021-12-31 -1.830185  1.788879\n",
       "2022-12-31  0.088524 -0.069559"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.random.randn(3,2)\n",
    "cols = ['A','B']\n",
    "idx = pd.date_range('2020-01-01',periods=3,freq='Y')\n",
    "df = pd.DataFrame(data,index=idx,columns=cols)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "location of min index value: 2,\n",
      "2020-12-31 00:00:00,\n",
      "2020,\n",
      "Int64Index([12, 12, 12], dtype='int64')\n",
      "Int64Index([2020, 2021, 2022], dtype='int64')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"\n",
    "location of min index value: {df.index.argmax()},\n",
    "{df.index.min()},\n",
    "{df.index.year.min()},\n",
    "{df.index.month}\n",
    "{df.index.year.unique()}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_datareader import data, wb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = datetime(2015,1,1)\n",
    "end = datetime(2021,9,1)\n",
    "df = data.DataReader('SBUX','yahoo',start,end)\n",
    "df = df[['Close','Volume']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>40.720001</td>\n",
       "      <td>6886000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>39.939999</td>\n",
       "      <td>11623800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>39.615002</td>\n",
       "      <td>7664400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Close      Volume\n",
       "Date                             \n",
       "2015-01-02  40.720001   6886000.0\n",
       "2015-01-05  39.939999  11623800.0\n",
       "2015-01-06  39.615002   7664400.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When calling `.resample()` you first need to pass in a **rule** parameter, then you need to call some sort of aggregation function.\n",
    "\n",
    "The **rule** parameter describes the frequency with which to apply the aggregation function (daily, monthly, yearly, etc.)<br>\n",
    "It is passed in using an \"offset alias\" - refer to the table below. [[reference](http://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases)]\n",
    "\n",
    "The aggregation function is needed because, due to resampling, we need some sort of mathematical rule to join the rows (mean, sum, count, etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"display: inline-block\">\n",
    "    <caption style=\"text-align: center\"><strong>TIME SERIES OFFSET ALIASES</strong></caption>\n",
    "<tr><th>ALIAS</th><th>DESCRIPTION</th></tr>\n",
    "<tr><td>B</td><td>business day frequency</td></tr>\n",
    "<tr><td>C</td><td>custom business day frequency (experimental)</td></tr>\n",
    "<tr><td>D</td><td>calendar day frequency</td></tr>\n",
    "<tr><td>W</td><td>weekly frequency</td></tr>\n",
    "<tr><td>M</td><td>month end frequency</td></tr>\n",
    "<tr><td>SM</td><td>semi-month end frequency (15th and end of month)</td></tr>\n",
    "<tr><td>BM</td><td>business month end frequency</td></tr>\n",
    "<tr><td>CBM</td><td>custom business month end frequency</td></tr>\n",
    "<tr><td>MS</td><td>month start frequency</td></tr>\n",
    "<tr><td>SMS</td><td>semi-month start frequency (1st and 15th)</td></tr>\n",
    "<tr><td>BMS</td><td>business month start frequency</td></tr>\n",
    "<tr><td>CBMS</td><td>custom business month start frequency</td></tr>\n",
    "<tr><td>Q</td><td>quarter end frequency</td></tr>\n",
    "<tr><td></td><td><font color=white>intentionally left blank</font></td></tr></table>\n",
    "\n",
    "<table style=\"display: inline-block; margin-left: 40px\">\n",
    "<caption style=\"text-align: center\"></caption>\n",
    "<tr><th>ALIAS</th><th>DESCRIPTION</th></tr>\n",
    "<tr><td>BQ</td><td>business quarter endfrequency</td></tr>\n",
    "<tr><td>QS</td><td>quarter start frequency</td></tr>\n",
    "<tr><td>BQS</td><td>business quarter start frequency</td></tr>\n",
    "<tr><td>A</td><td>year end frequency</td></tr>\n",
    "<tr><td>BA</td><td>business year end frequency</td></tr>\n",
    "<tr><td>AS</td><td>year start frequency</td></tr>\n",
    "<tr><td>BAS</td><td>business year start frequency</td></tr>\n",
    "<tr><td>BH</td><td>business hour frequency</td></tr>\n",
    "<tr><td>H</td><td>hourly frequency</td></tr>\n",
    "<tr><td>T, min</td><td>minutely frequency</td></tr>\n",
    "<tr><td>S</td><td>secondly frequency</td></tr>\n",
    "<tr><td>L, ms</td><td>milliseconds</td></tr>\n",
    "<tr><td>U, us</td><td>microseconds</td></tr>\n",
    "<tr><td>N</td><td>nanoseconds</td></tr></table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>53.253869</td>\n",
       "      <td>8.648356e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>56.588889</td>\n",
       "      <td>9.300222e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>57.267849</td>\n",
       "      <td>9.295893e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>57.501873</td>\n",
       "      <td>1.122824e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>81.443214</td>\n",
       "      <td>7.996700e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>82.863992</td>\n",
       "      <td>9.634834e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>111.426964</td>\n",
       "      <td>5.908343e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close        Volume\n",
       "Date                                \n",
       "2015-12-31   53.253869  8.648356e+06\n",
       "2016-12-31   56.588889  9.300222e+06\n",
       "2017-12-31   57.267849  9.295893e+06\n",
       "2018-12-31   57.501873  1.122824e+07\n",
       "2019-12-31   81.443214  7.996700e+06\n",
       "2020-12-31   82.863992  9.634834e+06\n",
       "2021-12-31  111.426964  5.908343e+06"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "The resample below means that the dataframe will take the yearly average of the columns! Since the dataset has 7 years,\n",
    "you will see 7 rows only.\n",
    "\"\"\"\n",
    "df.resample(rule='A').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "custom re-sampling function\n",
    "\"\"\"\n",
    "def first_day(entry):\n",
    "    if len(entry) != 0:\n",
    "        return entry[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>40.720001</td>\n",
       "      <td>6886000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>58.259998</td>\n",
       "      <td>13521500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>55.349998</td>\n",
       "      <td>7809300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>57.630001</td>\n",
       "      <td>7216000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>64.320000</td>\n",
       "      <td>10094000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>89.349998</td>\n",
       "      <td>6473800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>103.099998</td>\n",
       "      <td>8041500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close      Volume\n",
       "Date                              \n",
       "2015-12-31   40.720001   6886000.0\n",
       "2016-12-31   58.259998  13521500.0\n",
       "2017-12-31   55.349998   7809300.0\n",
       "2018-12-31   57.630001   7216000.0\n",
       "2019-12-31   64.320000  10094000.0\n",
       "2020-12-31   89.349998   6473800.0\n",
       "2021-12-31  103.099998   8041500.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample(rule='A').apply(first_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Close'].resample('A').mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Shifting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>40.720001</td>\n",
       "      <td>6886000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>39.939999</td>\n",
       "      <td>11623800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>39.615002</td>\n",
       "      <td>7664400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>40.590000</td>\n",
       "      <td>9732600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>41.244999</td>\n",
       "      <td>13170600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Close      Volume\n",
       "Date                             \n",
       "2015-01-02  40.720001   6886000.0\n",
       "2015-01-05  39.939999  11623800.0\n",
       "2015-01-06  39.615002   7664400.0\n",
       "2015-01-07  40.590000   9732600.0\n",
       "2015-01-08  41.244999  13170600.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>40.720001</td>\n",
       "      <td>6886000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>39.939999</td>\n",
       "      <td>11623800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>39.615002</td>\n",
       "      <td>7664400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-26</th>\n",
       "      <td>115.080002</td>\n",
       "      <td>3447300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-27</th>\n",
       "      <td>115.570000</td>\n",
       "      <td>3881500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-30</th>\n",
       "      <td>114.320000</td>\n",
       "      <td>5188200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-31</th>\n",
       "      <td>115.120003</td>\n",
       "      <td>4202900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>115.610001</td>\n",
       "      <td>3078200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1679 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close      Volume\n",
       "Date                              \n",
       "2015-01-02         NaN         NaN\n",
       "2015-01-05         NaN         NaN\n",
       "2015-01-06   40.720001   6886000.0\n",
       "2015-01-07   39.939999  11623800.0\n",
       "2015-01-08   39.615002   7664400.0\n",
       "...                ...         ...\n",
       "2021-08-26  115.080002   3447300.0\n",
       "2021-08-27  115.570000   3881500.0\n",
       "2021-08-30  114.320000   5188200.0\n",
       "2021-08-31  115.120003   4202900.0\n",
       "2021-09-01  115.610001   3078200.0\n",
       "\n",
       "[1679 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "shift function will shift down the number of rows you decide\n",
    "\"\"\"\n",
    "df.shift(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>39.939999</td>\n",
       "      <td>11623800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>39.615002</td>\n",
       "      <td>7664400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>40.590000</td>\n",
       "      <td>9732600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>41.244999</td>\n",
       "      <td>13170600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>39.895000</td>\n",
       "      <td>27556800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-26</th>\n",
       "      <td>115.120003</td>\n",
       "      <td>4202900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-27</th>\n",
       "      <td>115.610001</td>\n",
       "      <td>3078200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-30</th>\n",
       "      <td>117.489998</td>\n",
       "      <td>6995500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-31</th>\n",
       "      <td>117.449997</td>\n",
       "      <td>4402700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1679 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close      Volume\n",
       "Date                              \n",
       "2015-01-02   39.939999  11623800.0\n",
       "2015-01-05   39.615002   7664400.0\n",
       "2015-01-06   40.590000   9732600.0\n",
       "2015-01-07   41.244999  13170600.0\n",
       "2015-01-08   39.895000  27556800.0\n",
       "...                ...         ...\n",
       "2021-08-26  115.120003   4202900.0\n",
       "2021-08-27  115.610001   3078200.0\n",
       "2021-08-30  117.489998   6995500.0\n",
       "2021-08-31  117.449997   4402700.0\n",
       "2021-09-01         NaN         NaN\n",
       "\n",
       "[1679 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>40.720001</td>\n",
       "      <td>6886000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>39.939999</td>\n",
       "      <td>11623800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>39.615002</td>\n",
       "      <td>7664400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>40.590000</td>\n",
       "      <td>9732600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>41.244999</td>\n",
       "      <td>13170600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Close      Volume\n",
       "Date                             \n",
       "2015-01-31  40.720001   6886000.0\n",
       "2015-01-31  39.939999  11623800.0\n",
       "2015-01-31  39.615002   7664400.0\n",
       "2015-01-31  40.590000   9732600.0\n",
       "2015-01-31  41.244999  13170600.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shift(periods=1,freq='M').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rolling and Expanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---Rolling---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Close'].plot(figsize=(12,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-12</th>\n",
       "      <td>40.302858</td>\n",
       "      <td>1.237937e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-13</th>\n",
       "      <td>40.262143</td>\n",
       "      <td>1.297291e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-14</th>\n",
       "      <td>40.300715</td>\n",
       "      <td>1.264023e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-15</th>\n",
       "      <td>40.325715</td>\n",
       "      <td>1.270626e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-16</th>\n",
       "      <td>40.285000</td>\n",
       "      <td>1.260383e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-20</th>\n",
       "      <td>40.194286</td>\n",
       "      <td>1.225637e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-21</th>\n",
       "      <td>40.301429</td>\n",
       "      <td>9.868857e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-22</th>\n",
       "      <td>40.480715</td>\n",
       "      <td>1.185337e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Close        Volume\n",
       "Date                               \n",
       "2015-01-02        NaN           NaN\n",
       "2015-01-05        NaN           NaN\n",
       "2015-01-06        NaN           NaN\n",
       "2015-01-07        NaN           NaN\n",
       "2015-01-08        NaN           NaN\n",
       "2015-01-09        NaN           NaN\n",
       "2015-01-12  40.302858  1.237937e+07\n",
       "2015-01-13  40.262143  1.297291e+07\n",
       "2015-01-14  40.300715  1.264023e+07\n",
       "2015-01-15  40.325715  1.270626e+07\n",
       "2015-01-16  40.285000  1.260383e+07\n",
       "2015-01-20  40.194286  1.225637e+07\n",
       "2015-01-21  40.301429  9.868857e+06\n",
       "2015-01-22  40.480715  1.185337e+07"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "window is expressed in days\n",
    "The first 7 will be null since we have not reached 7\n",
    "\"\"\"\n",
    "df.rolling(window=7).mean().head(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hhBBCeIHFpNy5xCv3pxDoa8wMD24bSVzyQfdxyVn5Hufl2Rz4WcwoV73iyCBfkrIKaK2OMmj+Y5C1DzqMhxH/gaZ9vPRqGiYJjIUQQgghvMBkUuAKjH/fGM/W+HQsJkXbqECP4w6mFM0eB/tZyLM58bMWZb9Gh/gRmrCKL3xewiffClN/gXajvfMiGjjJMRZCCCGE8ILOsSEAvHt5H0L9rexLzKZ1ZCBXDGzJQ2d34qvrBgIwe2sCSsHwDlG0iQoi1+Yoau/ssPFo5vN87/ssiaYI1J3rJSiuRhIYCyGEEELUIKdTc9d36zmQnE2nxsFM7BHL5QNbANAiPAB/HzM3j2jL0HYR7nM6NQ7BYlI4nE4jlcJqhr3z4Z2+dExdyFv28zkn9ykIblxLr6phksBYCCGEEKIGZebZmbkhnrQcG1ZXy+bBbYwguGuTEPdxhTnEAD/cNAizSeFwQkJGHsGmAph5O5jMHB8/nTfsF5JKCKJ6SY6xEEIIIUQNyrHZ3bfTco1qEsPaR/LN9QPp4apMUejO0e3xMSuC/axYTIo8m4N9R5N4zfo+mI/A//1FZIsh8Otf3nwJp40KA2Ol1CfAJOC41rqba1s48D3QCogDLtZap7r2PQxcBziAO7XWs2tk5EIIIYQQ9UB2flGt4kMpuYAxOzykXcmmG/ee1cF922RSZObZudY8i0nmFTDqMWg1FBPQJTaEFuEBNT72001lUik+A8afsO0hYJ7Wuj0wz3UfpVQX4FKgq+uc95RSZoQQQgghTlO5JzTxqCyLSRFYkMSNlj9YpHvD8Afc+/66axjvX9m3uoYoXCoMjLXWi4GUEzafC3zuuv05cF6x7d9prfO11vuBPcCA6hmqEEIIIUT9k11gr/igUgQ7M/hJPUAAeRQMf7iaRyVKc6o5xjFa66MAWuujSqlo1/amwIpixx12bRNCCCGEOC3luALjxyZ2ZkDr8EqfNzLlB8LJZErBM1wf2b2mhieKqe7Fd6qUbbrUA5W6EbgRoEWLFtU8DCGEEEKIuiEl2wbAWV1iaBkRWMHRLkfWMST1V2Y7+7NJt8XXIoXEvOFUr3KCUioWwPXvcdf2w0DzYsc1A+JLewCt9XStdT+tdb+oqKhTHIYQQghR/6RkF/DyrB3k208t91TULynZRovn8ECfyp3gdMKPV5NnCuRV+8UAEhh7yale5d+Aq123rwZmFtt+qVLKVynVGmgPrKraEIUQQoiG5Yvlcby3cC9fLj9Q20MRNUBrzdoDqQCs2p/Cp0vjCPAxE+RbyS/qd82CtIPMaXor+3QTAHwtUsvAGyoMjJVS3wLLgY5KqcNKqeuAl4CzlFK7gbNc99FabwV+ALYBs4DbtNbycVgIIYQopkmYPwCLdyfV8khETfh21SEu+N8y3pm3m4s/WM7R9DyGtI30aOBRpi2/wA9XQmhzdjc607050FcCY2+o8KOL1vqyMnaV2phba/088HxVBiWEEEI0ZBaTESCt2JdcyyMRNWH70QwAXpuzy73NVImYGK1h4YsQ1Qmu+o1fXl/n3tUqspK5yaJKJGFFCCGE8DKH01iXXmB3lnnMmNcXcc/3G7w0IlFTAn3M3D2mPQ+O71TxwfsXQdIuGHwbBEZ4NAAJ8bPW4ChFIQmMhRBCCC8rDIwB7I7Sg+M9x7OYsf6It4YkqtHR9Dz37VaRgdw9pgPtooPKPyn1APx0HQTFQNcpANwzpn1NDlOUQgJjIYQQwsscuigwzso3atwmZuYTn5bL2gMpZObZ3Pu/X33Q6+MTVXM4Ncd9u9LVJFb8D/LS4erfwWrkoIcFGFUsfMwSrnlLddcxFkIIIUQFnMVmjDPz7Gw+ks6VH5dexOk/P2/mgj7NsEhwVC9orTmcmstlA5rTMSaYzrEhFZ+UnwkbvoEu50JUR/fmRgFWpg5qwUV9m5dzsqhOEhgLIYQQXlY8leJAck6ZQXFMiC8JGfkcTMmhTVQFX8WLWqe1ZuHORLLy7bSNCuL/hrau+KTMY/DpBMhPh4E3eexSSvHcedLxzpvk46cQQgjhZY5iPWF/WnuozOPeu6IPALd+va7MY0Td8dXKg1zz2WoAmocHVO6kDd9Ayl6Y+jM0H1CDoxOVIYGxEEII4WXFUyl+3VBqg1iuHdra/TX8jmOZXhmXqJrCMm0AjUP8KnfSjj8hthe0G1MzgxInRQJjIYQQwsuKL7470dRBLQCIDfUjwMfCLWe2xWJS0j66HtDFfq5BfhVkq2oNsx6BI2ug63k1OzBRaRIYCyGEEF5WPMe40OhO0dwxqh19WzYCYFCbCAA6x4Zgd2r2HM/y6hjFySv+eSfQp4LA+NhmWPEu9LkKBt9eswMTlSaL74QQQggvK0ylmHHrEKa8twyAj/+vP2DMOp7RLoqoYF8AusQGA7DjaCZdm4TWwmhFZTmLRcYVtnDe/AOYLDD6KTBL8466QmaMhRBCCC8rTKXo2SysxD6llDsoBogKMnJV03JtJY71lv1J2fy1+SiXf7iC1XEptTaO+iSgvBnjgyth5XToMB4CI7w3KFEhmTEWQgghvKxwxthkUjx0dieahvmXeayPq0GER/vo7GRI3AGNWkJosxodK8CNX6xhtyuVIz5tIwsfGFnjz1kf2YuVGzGbVNkHLn8H/EJh8jteGJU4GRIYCyGEEF7m0NodON08om25x1rNxnE2h9NIYt3xJ8y8DfLSQJmg/VgY/iA061tj47UVa1ttlUYjZcrMt9M2KpCfbh5S9kH2Ati7ELpfAAHhXhubqBx5dwshhBBe5nCCWZUzo1iMxWzCpFwzxvOfg++vgMBIuOx7GHQrHFkHn4yDn66DtLJrIldFqKs1MUhgXJ7MPBvhgT40CvQp+6CDy6EgE9qP897ARKXJjLEQQgjhZU6tMZ1EfBmoczhj+fXAJug9FSa9aSzY6jgeht0Hcx6HLTNg7zw4/yNoX701cZMy8wnytZCVb6dJOWkfp7usfDtRQb5lH2AvgH9fBYsftB7uvYGJSpOPfUIIIYSXOZy60jPG5GXwlnUa/fVm9nW+ldSR//WsYhAQDue+Czf/CyFN4fupRg5yNdFak5iZzxWDWtCtaQgOp7Pik05TWXl2gv3KqTCx8RvYvxgmvAq+0uK7LpLAWAghhPCSedsTaPXQnxxIzsZU3uKsQlrDV+cz3LSJJ+3/x6j1Z9D7hUWlHxvRFi74COy5sOaTahtzao6NAoeTqCBfIgJ9SckuqLbHbmiy8u3lN/ZY9yVEdTZm/UWdJIGxEEII4SU/rjkMwNztx6nUfHHcEji8mqfsV/OV4yz35oPJOaUfH90Z2p0FS9+EnbOqPF6AV2bvBKBddBARQT4kZUlgfCw9D4DcAgdH03Pd2zPy7AT7lhEYxy0xutz1uRIq+22B8DoJjIUQQohakJFnL/+A3XPh28sgMIqfHJ75qMNfWeDRftjDpDegUWv49RbIS6/SGPccz+TbVQcBGNA6nKggX5Ky8st+7tPA96sPMujFeWw+nM79P25k8Ivzybc7OJCcTYHdSVBpgXFeOnxzKYS3gZ6XeX/QotIkMBZCCCG8wOZwMmvrMff9UP8Kup0tftmoPnHtbPIouaDr4yX7Sz8vrDmcOw1yU4wqFlUIYse8vhiAB8d3JMDHQkSQD/l2J1n5nkF9ToGdT5bsd9dnbsjeX7QPgC3x6SzZkwTAT2sPs/ZAKgA9m4eVPGnLz0YliinTpURbHSeBsRBCCOEFr8/Z5XH/6cldyz44Ix4OrYTeV0BEW/65p2QFg/cW7i37/Ca9oM9VsGo6rP30FEcMnWNDALiob3MAIl0VF4qnU5w7bQldnpjNM39sY872hFN+rvogPdfG/qRsAPYcz6J9tLGA7tEZW4hPM1IqBrQ+IfDNSYHl70FsT2jWz6vjFSdPAmMhhBDCC35Zd5gxnWPY+8IE4l6ayHm9m5Z98NYZxr+dzwWgQ0wwPZqFAvDBlUYjj8L7ZZr0FjTtB8vegZOsJJFb4OCrFQeICvale9NQd4vqCFdgnJyV7z524+GidA1HA58x3haf4b69KyHT3dobjA8Lwb4W/KzmohMyjsIHwyF1P4x4SHKL6wEJjIUQQogalmdzkJCRT89moeW3CgZI3AXznoWWQyGqg3vztMv6cHa3xgxrH8nQdhFk5NrKfxyTCQbfCin7YG35VSq01h55w9MW7OaxX7eweFciwcWqLEQGGY0rkooFxsUV75DXEB3PNBbd9W4Rxs5jmaQWq9CRmlNAYPH8YqcTFjwPmUfhmlnQaYK3hytOgQTGQgghRA0r/Jq9WXglmmNs+h4cBXChZzDbIiKA/03tS4CPhVB/K4dScyueoe08GVoNgz/vg4MryxzbGf9dQMfHZ7E6LoUNh9JIzSkKukOK1eUtbF6xN9FIJ8gpMHKNC9MH0isK1uu5za7Z8UFtIjiemU9cseogyVkFBPoWmy3+91VY/yX0vQaa9/f2UMUpksBYCCGEqGGF5b1iQvwqPnj3bGg+EIIbl3lIowAfEjPzeWLmlvIfy2yFy3+AgAj497VSD/lz01GOpOVSYHdy0fvLOe/dpSzfW9QgJCKoqL1xVLAvzcP9WboniVYP/cm0+XsAmNyzCReZF9J3w+Ow/9+KX2MdtOVIOq0e+pPDqUawO/7NxVz/+Rr3/vQcGx+5FjwObhNR4vwle5KKKlI47LDqQ6N03oRXan7wotpIYCyEEELUoK9WHODTZXEANK4oMM6Ih2ObocPYcg+7YVgbAA6l5pZ7HAA+ATDwFiPgXvBiid37k7NLbksq2la8oYdSiiah/hxxzYC/t3Avw0ybOHvdjbxinU7XhN/gi8nw622Qm1bx2OqQ71YbZekW7DgOwI5jmcwttpiw+HUa2KZogV2/lo3ct92pFHGLIfs49L1a8orrmXLaswghhBCiKo5n5vHYr0Wzuo1DywmMtYZ/Hjdudyw/H7VVZCBD2kaQW1BBLeRCQ26HhC2w6CXodoFH7nJGro3WkYHcPaY9w9pH8cHivXywaB99WoQRG+bPTcPbeDxUkK+F5KwC2qojPGz5hpGmDdhzmvGx+RL2tbuS503TYcNXkJMEl31XbwJDk2ucTg32UnKl9yVmuW/7Wsz4Wkzk2530bx3OGleptkBfC9hyYd4z4BsK7cZ4Z/Ci2siMsRBCCFFDsvMdHvcDfMqZjzq6Ebb8ZFQviOpY4WMH+FhKPH6ZrP4w4VUw+xgl3IrJzLMT7Gfh3F5NCQ/04c5R7RnUJpwHxnXi3cv70KNZmMfxbTnMw44PmOHzBH1Mu/nQMQl9y1J+Cb2S+DxfuORLGPs87JoFc56o3PjqgKLAWJNYyuLCvcUCY4AF95/Jp9f0x9dSFEoF+Vpg43cQvx7Oe9e47qJekRljIYQQooYULk578pwudGtaQXm1HX+AMsGAGyv12IG+Zvfjp2QXcPmHK7jlzLac26uMMnBBUdD9IljzMTTuBn3/D4CMPJvHArtAXwvf3Ti45Plaw/dTeSTuD3LMvixy9uA521QmDh+IX2AoMSF+7lxqBt0CiTtg2dvQ+0qPGeq6qjAwdjg18Wl5HvucTs27C4y60RO7xwLQJMyfJmH+DGodwZtzdwPGz4RNP0BkR+g0yYujF9VFZoyFEEKIGpJnM2Z020QF0b9VBR3Ptv9hlGgLLLmwqzQBPhayCxzk2x1MevtfdhzL5K7vNpR/0tn/hVZnwOzHcCbs4OFfNrP+YBoBPubyz8tJgb//Azv+YH3MBZyR/xa32O7hCFEMaWuMNybE113ODJMZRvzHuL1nbqVeT22zmo3AODWnwCPHWmtNQmZRoPzuFX08zvP3MXPPGCPwD846AAeXQY+L6k0KifAkgbEQQghRQ3IKjMC4wsBz0cuQuB26nlfpxw70MZOYmc/YNxYTn14UuBUP6krwDcYx4XXsJh/U9BEkrfkFqKA9dUEOfHeFkYLR41I293yMFELcuwe1KQyM/UjKKiiqZRzWHCLawb6FlX5NdcG7C/by9O9b3ffz7U4GvzgfgPen9i31nOEdIvEjn6vjHgDfEOh5mVfGKqqfBMZCCCFEDSkMjP2t5QTGBTnw7+vQ+Ryj5m0lBbgqIBxw1dKdf98ILCbF1ysOlHve30cDGZr2NId8WvO2dRpvNP6HR0ZEeh7kdELcUvjjHnilnTELOuUDOP8DYkID3IctfmCku9NbYSm6xMxi+bltRxuBcdrBSr+u2pKRV7SQMbOM203CSl882bNZGNM7rqOxPR4u+QpCm9XcQEWNksBYCCGEqCGFDS/KnZHdOw/sudD/BiMFoZKCfD2PbRMVxFldYpi5Mb7c85Iy80kgnCmpd7Le2Y4paZ/RaHof+HA0/H4XfH0xvNwaPpsAG76FThPh6t+h5yWAZy3mFhFFQXJMiNH8IyGjWH7ukDuMvOlF/63066oN6bk21h9MpU1koHubj2tR3Z7jRYvuyqpDbdr1N8OPfW7ULW4zomYHK2qUBMZCCCFENcopsPPNyoNorTnuChKjgn3LPmHbTPBvZOQXnwSLqeSf8JYRgaTlGHWHU7ILSu2Ml55rzIAmE8rltsdYfvbf0O9aIyjf/gck7YTOk+CCj+GBPXDBh9B6uPv8aNdraRMV6PG40cFG0OgRGIc1h3aj4cDyk3pt3vb071vZcSyTtFwbj07oDMCZHaIAWLY3yX1cRKBPyZNzU+GXGyCkqTTzaACkKoUQQghRTfYcz+KV2TuYvTWBQ6k57EvMIjzQx51uUML6r2DzjzDgJjCf3J/k466UhbFdYnh8UhcAgv0s2Bya/UnZjHx1IQ+d3YmbR7Rl8a5Elu5N4j/jOpGc7VmKLDu4LQws2fijLLGhfjwyoRMTezTx2F44m3o884RSZ016GRU38tLBr4LKHLUkKcv4MJGVZ+fqIa2ICPJhRIco5mxP4IvlRmrKjcPbYDGXMp+49jMoyILzp0N4ay+OWtQECYyFEEKIajLm9UXu2/9baJT3umxA87JPWPE/iO0F454/6ee6oE9T/t5ylKfP7UpsqFEvN9C1yG/6YuO517kaTzzw00YSMvI5lJJDgV3TqXEw/72gB0v2JDGyU/RJPa9SihuHty2xPcTfCCmK5+QCxusDo6NfqzNO6rm8JcD1weX7mwbhYzFxfh8jR7hz4xC2Hc3A12Li4bM7lTxx228w/zloOwpie3hzyKKGSCqFEEIIUQ20Lpm2AHDFwJaln5C02+hG1/NSMJeTg1yG9jHBLHpgpDsohqKWxH9vOQYYtXahKO3ir83HmLs9gWaN/OnZPIzbRrbDbKqesmI+ZhMWk3LXVnaL7Wn8e3RjtTwPwKwtR1m2J6niAyspI89Gv5aN6N2ikcf2Hs2MGe58uxN1Yvk1pxPmPgVRneDCT6ttLKJ2SWAshBBCVIPk7IJSt5fZ2GP9l8a/nSdX2xhCXIv80nKMRX9JWflk5tk4mp7LhX2LKiWM6RxTbc9ZSClFgI+5ZDe+oGgIbgIHqy/P+Oav1nH5Ryur7fHSc23ua1fc9cPKSY3Y/COk7IVh94J/WLWNRdQuSaUQQgghqsG2+Az37fbRQUy/qp/RCa00h1bB0reh+8UQWkanulPQrJExQ+xnNTG0bSR/bDrKH5uOAjCgVTg/rT0MwDk9m5T5GFUR6GspOWMM0P0CWDYNErZCTNcqPcex9LyKDzoJBXYnW+Mz6BIbUmJfYd50iX37F8OMGyGmO3Q+t1rHI2qXzBgLIYQQVfTHpniu+mQVAK9c2IPXL+5F68hAd6WGEjb/ZKRPTHqjWsfRJjKIYD8Lr1zYk0k9Yz32NQsvmXJR3QJcTUemL97rWRHjjHuN17vhmyo/x6bDaVV+jOJ+WWd8WFi0K7HEvmA/K3/ccQbf3DDQc8fStyEoBq6bfdKLJkXdJj9NIYQQooqe+2O7+/ZF/cpZbAeQnwkbvzUaevgGVes4/H3MbH5qHABZ+XagKK+3S2wI/lYz7WOq9zmLC/S1sGBnIgt2JtIyIpBxXRsbOwLCocVg2DPvlBYaFrfFNTNfXR2X/V0LFu8a077U/SVSYQ6vhT1z4MxHwCew1HNE/SWBsRBCCFFF7WOCOJaRx9RBLco/0F5gNNDIz4DBt9XomIKKzQqP6RxNWIAPm58aW3IRWTUq3vq6xLO0Gw1znoCMeAg59VSOwjrNpmp+HYNdra3LlZcOX18Aoc2h/3XV+vyibpBUCiGEEKKK0nNtDGsfybPndiv/wJ1/Ge2VJ70JTfvW+Li2Pj2Ol87vzn8vMEqJWcymaqtCUZpAn6JgPMDnhLm3tqOMfzf/VKXnyLc5AXA4NXaHs0qPBUaOMYC1tBrFJ9r8k9HQ46LPIDCywsNF/VOlwFgpdY9SaqtSaotS6lullJ9SKlwpNUcptdv1b6OKH0kIIYSov1KyC4gM8q14NnbtZ8ZsY5+rvDKuQF8Llw5oQURQOZ33qlFAsVlq54nl62K6QbsxMO8ZSD1wys9RUCwYLqiGwNjmMMZZ2AK6TE6H8fOL6e6VDzWidpxyYKyUagrcCfTTWncDzMClwEPAPK11e2Ce674QQgjRYKXl2GgUUEq74OJWfQj7FkCfq432yw1QYLFUCtuJQatSMO5FcNpg38JTfo7CGd4Tb5+qwnFWOGM883Y4tgkG3lR9Cc6izqlqKoUF8FdKWYAAIB44F/jctf9z4LwqPocQQghRZ+XbHWTl2wkPLKdJR+YxmP0ItB8LQ+7w3uC8rHj6RKlBa2R7CIyqUk3j/BoKjMudMT6+HTZ+A4Nvhz5XVvk5Rd11yoGx1voI8CpwEDgKpGut/wFitNZHXcccBUrtNamUulEptUYptSYxsWSJFCGEEKI+SM81mmmEljdjvHwaOO0w/iWwllHCrQEoXrd56d5SOtMpBS0GwYGlp/wcK/cnu2/nV0NgnO/OMS5jFjgrEX67E3yCjbJzokGrSipFI4zZ4dZAEyBQKTW1sudrradrrftprftFRUWd6jCEEEKIWpWVZzS0CC6rNvC8Z2DZO9DtQoho68WReV/xGeOvVhws/aCWQyHtICTuOunHT8+xkZlX1EDkVAPjLUfS+WzpfqBYKoWpjJBo5q1GO+tz3oTASlSuEPVaVVIpxgD7tdaJWmsb8AswBEhQSsUCuP49XvVhCiGEELVPa81Lf+9gcbFmEIUtkEttmrH/X/j3Neg9Fc6d5q1h1poyO/0V13ky+IXCr7fACQv09IkL9k6QmW/zuJ9vd5RxZPn+79NVPPX7Noa9PJ9le5OxmBSm0qp1bP4Jdv8Dox6D7hee0nOJ+qUqgfFBYJBSKkAZy3BHA9uB34CrXcdcDcys2hCFEEKI2nc8M49/tiXw/qK93P7NOvf2wmAt6MTAeN8i+Op8CGkKZ78CFu9UhqhNvhVVdgCjBfbIR+HIGkja7d6clW+n9cN/8alrJvdEb8zZxWO/bvHY5tFd7yQkZRm1kA+l5LJqf0rpC+92/AU/X2dU0xh40yk9j6h/qpJjvBL4CVgHbHY91nTgJeAspdRu4CzXfSGEEKJeyimw88XyOAY8P4+bvlwLQEaxr/MLZ4w9AuOCHPjjbqM02/XzwCfAm0OuNX7WohnjqOByPgh0nGD8u+tv96YDydkAfLPSMwWjwO7k781HeWvebhbuNGbqz+/dFAD7KQbGJ36IaRlxws9Ha1j4AoS3NX5+p8GHGmGoUlUKrfWTWutOWutuWusrtdb5WutkrfVorXV7178p1TVYIYQQwttu/GItT8zcWmJ7YXOJ7HwjSHanEeSmwftnQMo+mPgahMR6a6i1blzXxky7vDcjO0YRVV7t5LDmRi3gRS8bM+tAQkYeAI0CfPjo330MeXEeAG/N28UtX6/zOL0wbeWRXzbz1tzdnCy708mE7o1pGuYPQOfYkKKdDjvMvA2ObYZh9zXoxZKiJOl8J4QQQpQip8DOkBfnsWSPUV1hbJcYj/1nvbEYgExXYBzk55qFXP4upOyFy3+AtiO9N+A6wM9qZlKPJvj7mCtuvnHJVxAUDbMf5fvVB7n2szUAhAZYee7P7cSn55GRZ+NIai5glFN7ZEInzuvVhDM7Gov2dxzL5I255S/iO5SSw9H0XO7+bj1rD6SSZ3OQZ3PStUkovlYjDOrUONg4ODsJ/rwXNnwNQ+6EnpdW4WqI+qiMJbRCCCHE6W353mTi041ZzKmDWnDvWR35Z9sc9/79ScZX/4UzxkG+FtjxpxEYdzkXOozz/qDrCB+zqeKFcSFNoO81MOdx3vllPmAEu35WM8G+FjLz7cQlZWNSimBfC+ueOMudC7xqf+W/jB728gL37X+2JfDzLUMAaB4eQJvIIPYlZtOhcTA4bPDZJEjcDv1vgLHPntyLFg2CzBgLIYQQLn9siuf+HzfidGq2xmcAEOBj5p4xHQgPLFmnODvfTna+HZMCf1sa/Hw9RLSBsc97eeR1i4/FVLnmG50mAnCWaY1704ZDqTQONdIX9idlk5Vvp2kjf48FcuYTKkhUtjpFToGDnccyjaeODuDVCU15cFQLhh37Al5pawTFU6bDxFcr9Xii4ZEZYyGEEMLl2T+2kZCRT4iflfi0XJqH+/Pvg6Pc+9+f2peIIB8enbGZXQlZ/LjmEO/M30OwnwW17B2w5cIFHxs5tKexSgfGEW3Z6mzJrZbfmOPsy2EdTUJGPv1aNmL38Sz2Jmbzz7YE/K2eZeAsJwTGx9LzaBkRaNzJTYPj28CeB016o3CiMRFLMkPNW7Cv3cQz1nW0/+galCOfWwsfpP04o6xel8lVfv2i/pLAWAghhHCJDPIlISOfT1wlw8Z09mzeOr5bYwB+vGkII15dwFO/bwPg1SaLYOm70O0CiOro3UHXQcF+VjLz7DicusTs7onust3Gbz6Pc6t5Jo/Yb6DA7nSXYft3t1GFItfmOSN84mMeSc2lZYgZvrkI9i/22LffDw45o4hSafgpGxwGh9mE6nIhNO0DeRkQ3saoU6zKH6to+CQwFkIIITDq6BamTxQqLX0CjAViH0ztyyXTVxBJOuOSvoC2o+G8/3ljqHVekzB/7E5NUlY+MSHlV3XYo5vxl3Mgk83Lecl+ORkEuttsrz+YBsANw1p7nGM5oX3zvR/9xZzmnxKcuI6tba4nt3F/1h9IIjluMwEqj/bqCNnan8/tY0nRwQzu3oFXLxhSfS9YNBgSGAshhDjtHc/MY7hrkVaov9UdmF3cr+yUiIHN/DnXtIRnfb8Cey6c/bLUu3Vp7AqG1x1I5ezuFZer+8Q+ngt9F/NnwFNclvsgaTmewfQNw9p43C9MpbCYFP0bm7kv6R18kw/jnDyNiT+EwzaAYMAzoC703MUDT/o1idODLL4TQghx2pu95Rh5NidTB7Vg3n0juH9sB/a+MIF+rcJLP+HQanitE2/5vEdQ47ZGabbIdt4ddB0W6m8F4Jav17H+YGq5x5pNim26FdcW3E+MKYPnLR+TmlPg3u9vNZdoFmI2GeFLU0sa3+bdQj/TLma3fpA9Tc8r9Tn6t2oEwHVntOa1i3p6NCIRojiZMRZCCHHayimwc/mHK9lwKA1/q5lnz+2GUorbR7Uv/QStYd4zsPJ9CIqBKe9jajcGLKWnXJyuCgNjMCpL9G7RqNTjtNbufOL5zj7sbHcdI3a8yUW22XzFWQA0a+SPOiH31+y6P0Uthpxkbjc9QqPgs0gro4zb6xf34s7v1nPHqHaEBcjPSpRNAmMhhBCnrdVxqWw4lAbAjcPblAjAPNjzYfk0WPI6dDgbJrxy2lefKEuIf1F4Udbiux/WHMLX4vnF9aEOV5G0dQFPWz5jkbMHh3SMR5k292OaFaAZr5ZBswH8sacbrDhQ6vM8d143mocHMOPWoaf+gsRpQ1IphBBCnLYOp+YAcPnAFtw+qpxUCKcTvp9qzBZ3GA+XfiNBcTmKzxiX9WHjwZ82cdd3Gzy2BQaF8JDtBhyYuMH8FwB3lPJzsZgU/7F8RycOlNudrktsCFcMbHEKr0CcriQwFkIIcdpadyANgCfP6VLqzCQABTlGm+Dd/8BZzxpBsUn+fJaneN3hCqq1eWgTGUgC4fziGMZUyzxmDN7rLpFXnLJl83/m2cxSZ0C/axnf1Tgm2M/CzufG071pKACJWfnlfwsgxAnk/2whhBCnrZ/XHQbA11LOYqw5j8Paz2DAjTDkDjDJwq2KFA9GTScRmDYJ8wfgaftVJDfqRe/d01DOkl3trPvm468KmGkaA0rRp2UYYATWvhYzL57fHYDEzPwqvApxOpLAWAghxGlHa80nS4wmHmXOaGoNy96B1R/DwJuNnGKZfTxplZkxntg9ltcv7unOR87FD9MZd0JWAuyZ63mw00HIpk9I1KG07DsGKJqhLmwl3T4miF7Nw3j23K7V90LEaUEW3wkhhDjt7EvK5pk/jK51394wqPSDtvwM/zwG7cbAqEe9OLqGpbRUBqerEkWhd6/oU+KYkO4TYH4UzH4YwlpATBdjx7+vYT60jKAJb/Fg/25AUWe82FBjxtnXYubX22SxnTh5MmMshBCiwdLaCMAK7E7u/m49248ane12J2QCcNXglgxsE1HyxE0/wt8PQpM+Ro1i32Cvjfl0kGcvSo+ICfGsUfzced0Y3CYCq48vXPS5keP9wTB4szt8OgEWvAAdzsZ/4P9hcs0wp2QbDVliQ8vvsidERWTGWAghRIN1zWerWbgzkQfGdeTXDfGs2p/CsodHs2hXIsF+Fh6Z0LnkSbvnwi/XQ0w3o8Wz5BRXyYmzw2C03wa4e0x7rhnq2Z1u6qCWTB3U0rjTaihc8xes+QRS9hupFV0mw6Q3Pc65flhrkrLyi84T4hRJYCyEEKLBeWTGZiIDfVi4MxGAV2bvBCC7wMHMDUf4Zd0R+rVqVLID2qHVMPNWCG8DNyyQxh3VwF5aYJxnBMatIgI9SruVKqItjHu+3EMig3x59aKepzxGIQpJKoUQQogGJT3XxjcrD/L2/D0l9vlYTNz13Qby7U5uHtHWc6fTCb/fCWYfuPhLCYqr6O3LegPg1CUD40xXYBzkK/Nzom6RwFgIIUSD8s683R73B7UJB2BK76bu8l3/Gd+JYe2jPE/c8jMc3wajn4DG3bwy1oasV7MwAOZtPw6AzeHk781H0Vq7UymC/SQwFnWLvCOFEEI0GEv3JPGRqwzbjFuHuKsUHEnLYf3BNGasPwLABX2bep64828jrzi6K3Q936tjbqiMts3w28Z43r6sN9Pm7+Gtebv5+Op+2BxOAIL9KkijEMLLJDAWQgjRIGTl27nio5UAvHlJL3o1D3OXCmsc6kdcktH+eUrvpkQHF6te4LAZZdmiOsF1/4BZ/jRWB8sJBYyPpOUCkJxVAK5dMmMs6hp5RwohhPCq3AIHS/YkMbpTtLvcVnU4lp7nvn1e76Yl9p/dvTH+PmbGdS3WYthhgx//D5L3wKXfgm9QtY3ndGcu9rPVWhfGwmg02XlGuTYJjEVdIznGQgghvOr3jfHc8MUaPlqyr1ofN7fACLamlBIUAwT4WJjQPdYjYGPl+7DjDxjzNHQ8u1rHc7orPmNsd2p300CtixbfBcriO1HHSGAshBDCqzLyjGYML/y1g5GvLmTBzuNVfswvl8dxzrQlAFzSv3nlTlr3patZxHg4425p91zNin8AKbA7Ua4548TMfLLybfhbzVjNEoaIukXekUIIIbwqPdfmvr0/KZs7v13vvr8vMQu7a2HWyVi+L9l9u1IlwJJ2w+93QWyvEs0iRPUoERi77r42ZxdrDqRKGoWokyQwFkII4VXFA2MwvlZPyspn8+F0Rr22iE+Xxp3c4+XYOJRiLOwK8rXQrJF/+Sfs/xe+PB+sAXDxFxASe1LPJyqneGBsczg9JuT3JWYTJIGxqIMkMBZCCOFVaTk2WkYEeGxbE5fqToXYl5TNgh3HmTZ/d2mnl3DVJyvZfCSdiT1i2fzUWMICymjMcWQdfHUBfD7JqDxx5QwIiir9WFFlFlNRiJFvd+IuRYHx4UhKtYm6SD6uCSGE8Kq0XBth/lbeveMM7E7Nee8u5fU5O937Z289xrerDgJw+6j25T5Wdr6djYfTAbhhWBt3eTYPyXthzhOwazb4N4Khd8OIB8EnsNpekyip+BpHWynpMSEyYyzqIHlXCiGE8Kr0XBuhAT50axqK1pqIQB92JWQBcPvIdkxbUNTKWWtderDr8vTvWwHo2iSEXs3DinbY8uDYZlj1AWydAdZA6HMljHwMAiNq5HUJT8V/bgUOJz5mz5+jtIMWdZG8K4UQQnhVek4BLcKNVAqlFJ1jQ1iyJ4lOjYMZ1TnaIzDOLnCUG0DFJRtNO6xmEyTuhLglEL8eds+BrGNgskD/62HoXRDSpGZfmCiTza4xmzyzN2XxnaiL5F0phBDCq9JdqRSFJvdqwoGUbC7p35yYED+PYzNybeUGxmk5BQD0tm2A9x4D7QT/cGg+AHpeBk37QFiLGnkdovIKHA5ScwoID/QhJdv4mQX5So6xqHskMBZCCOE1Tqc2AuOAoqDo4n7NubhfUe3hty7txfqDaXy2LI6pH69kzj0jPJtyuBzPyGNXQhbjTKu4O/0TiGgPl38PjVpJTeI6Jt/u5Fh6Hi3CA9yBscwYi7pIqlIIIbxid0ImD/y40T3DJ05PiVn5ODWE+pc9W3hur6Y8PqkLYJT1WncwFYAdxzLIzCsq9fbpgq1cYl7Au9a3yfWLhku+gvDWEhTXQTaHJi3XRlSwr3ubBMaiLpJ3pRDCK37fdJQf1x5ma3wGv99xRqkzgKLhu/eHDQB0bxpa7nFmk2JAq3BWxaWQml3A/qRsxr/5L01C/Xhqclf27tzEuRvupZP1EHmNOhJ+/T8QGFbzL0CckgK7E4fT6dEm2s9qrsURCVE6mTEWQnjF8Yw8ALYdzWBfYlYtj0bUlrikHM7qEsPANhVXhnj7st4AHM/MZ/4Oo210fHoec755jZs2XkwzlchzwY/jd8dyfCQortNsDicOp8ZULDCWD8eiLpLAWFTo6d+30uqhP9l4KK22hyLqIa01368+yHerDxERaDRe2J+UXcujErXF7nQSXlYDjhMUdkbLzrczf0cCCidXmWfzrOVTVuuOjM1/mcmXXA8mmXms6wrsTpwazMXSXCQsFnWRBMaiXPl2h7s963erD5Fb4KjdAYl6Z9vRDP7z82YA7h5jNGtIzMqvzSGJWuRwaizmyoVEAa6v2pftTWb3nj28a32bZ6yfc8y/HXuGT2NYv970aBZWg6MV1aXANWNcfJbYapYQRNQ98q4U5Vq0M9F9+9tVB+n73ByP/d+sPMgnS/Z7e1iiHolLMurMfn7tAC5yVR5IzZYFeKcru1N75JmWx2RSBPkosvetYqbv44wxr+dl2yVsHv8zV4zpz38v7FHDoxXVxcgx1piKzRif01PqSou6RxbfiXJtOZLucT+nwMHrc3ZxTo9YooP9eGSGMRMYG+rH2d1j+WvzUbo2CaFlhLRaFYYDKUbaRN+WjfCzmgn0MbPlSEYtj0rUFrujZKOHMtly+cL8LH1M28jU/rwQ+zaf7Q/lfzLTWO8YqRQaswkendCZRoE++Fjk5yjqHgmMRbkSs/KJDPJlyX9G0unxWQC8PW83b8/bTdcmIe7jbv92PS/k2dxfme97YYLHIgtx+jqUkkNEoI+7ScPQdpEs2pVIns3RYFelp2YXkJlnp0VEQG0Ppc6xO51YK5NKkbwXvrucXnonz9quYI7lTDr4tQES5HdLPWQrlkpxw/A2tT0cIcokH9dEuRIzC4gK9sXPambNY2M89m2NN2b9LhvQAodTu4NiMDpbVVaezcH7i/aSnW+vnkELr3tz7i4unb68xPaV+5L5feNRjwDxikEtybU5WLY3yZtD9JrMPBu9n53D8FcW1PZQ6qQT80zLNPtRyDjKtOin+NgxER0YSYhrMV6AT8P8QNUQ7Xh2PAAfL9nP8cx8j1QKIeqiKs0YK6XCgI+AboAGrgV2At8DrYA44GKtdWpVnkfUjvRcG3O3JzCsfSQAkUG+fH/jIC6ZvsJ9TNMwf547rxsr9yezL7Go0kCOzUEjgLglrF84g8DU7XRoHgvN+kPrYRDZEczG2++Nubv4YNE+An0tXDmo5UmN0eZw8sGivRxKyeVASjYvX9ATpSAzz06XYjPaouYkZubz5tzdAMQlZbNqfwpWi+K8Xk3d75UmYf7u4we1CSfQx8ycbcdp1iiAtlFBDaps013fbXDfrnQQeJrQWmNzVCLHeO1nsOtvGPU4Odnj4eBeTErx5OSudIoN5ox2kV4Zr6g6X1e6xPFMY8Gt/P8g6rqqplK8BczSWl+olPIBAoBHgHla65eUUg8BDwH/qeLzCC9yOjUr9iXz+6Z4wDPPeGCbCB6b2Jnn/twOwF1j2mM2KS7o04xXZu/EjIMzTRsInDMLkjfAsU101yb26Vi0PoDa8pPxQL4hENMVRj3OzHVGQH0qC7JWx6Xw6j+73Pcnvv0vmfl2TAoWPTCS5uHyVXZNe2X2Dvft679Yw57jRo3iwjzzYD8Ld4xq5z7G12JmaLtIvl11kJ9X7eXDYdmMaKogPxPs+WCyGOW3LL7QejiE15+vXfclZrFw53ECfcxkFzg4nJoj+fbFOLXxr6W8HOFNP8Lvd0GLwTDoFiYet/P+or0cSM4h1N/KjcPbemewolqoE2aIZcZY1HWnHBgrpUKA4cD/AWitC4ACpdS5wJmuwz4HFiKBcb3yv0V7eWX2Tvf9ST08Vw5HBBXVIO0QEwyAxaToo3bxeMDP9HZsJn+rD0cj+rAw6CqeThpJHr7Mmzqctva9kLgDDq2EHX/BZxN4y9mJX81DyUvyBdqXGI/Wxl/TE3/BAuTke5aPy3SlYzi1keohgXHNsjuczNwQT6uIAOKSc9hzPIsxnWOYuz2B899bBsAdo9rRqbHn7H3XGD/67PqGy8zzCV2dA6vLeZLmA6HvNdDjEqjsoq1asGxvElM/WonFbOKZc7tx348bWXsgVQLjYuxOJ1DOrOH+f+G3O6DZALhqJlh86dbUmHWf0rupF0cqaorMGIu6riozxm2AROBTpVRPYC1wFxCjtT4KoLU+qpSKrvowhbdorXl/0V73/btGt/eY7QMI8rUCRhpFr+ZhoDUdE/7iep+nKXD48pDtemY4ziD/iGcR/4OpubTt2Aua9IKel8KYp8hd+y2d/3mKgdYd5O34GlY/D/2ug2JBcOuH/+KKgS14fkp3ADLybJiVItDX4m4vW1ynxsHsOJZJvl1qLte0gyk55NudTOrRhGkL9gDw8oU96PNsUVm/UH+r50l753P9tgcJtOzmd8cgfnKM4OWbzicmKhrMVnA6jP/y0mDbTFj3Bfx6M6x8H674CYKivPgKPeXZHBQ4nIT4WUvs+3ntEQJ8LPxzz3B3nd7saqz7vf1oBvFpuYzuHFNtj+ltdofxIbfUxXf5mfD9FRDaDC74yPjGAOMD8a7nzkbiqYZBAmNR11Vl+sUC9AH+p7XuDWRjpE1UilLqRqXUGqXUmsTExIpPEF5xLCOPzLyiRXDn92la4mvPJmF+gBE0k5UIH43mzG2PsVZ34In2v7AwcAL5GEFxowArG58Yi1Kw5XA6doeT6Yv3Go1C/EJJ7nIVvfOnMzb/v+y3tIU/7zO+Rj2yFjDSOgC+XnkQMDqm9XjqH7o+OZvdCZlkuMb6+sU9adbIyGN9YFxHAPJtzpq6THVKUi02y0jIMJ57SLsIvrl+IKsfHUN4oA/vT+1DTIgR2LSLDi46Ie0Q/HA1gWYHB876iDtsd7LI2ZOB/9tLpikYfIPBPwwCIyCiLQy7lyXjZ7Oq53OQuBPe6QvL36uF15lHeq6N0a8tYsDzcz32pefYGPnqQn5ed5ixXWJoEuaPv6vaRl4lA+NDKTn8su4wyaX8LDPybHy4eB9nv/Uv132+hvQcG2viUqr+omqB3fX/c6nl2tZ/BXnpMOV9aOS51sBsUqV+YyTqH0mlEHVdVWaMDwOHtdYrXfd/wgiME5RSsa7Z4ljgeGkna62nA9MB+vXrp6swDlGM1pp5248zslP0KX0yn754n8f90r4G7toklFWPjiY6aTV8dBtkHsM2/jX+OdaHx8Z1J9Tfyk1frmH21gSGtIskNMBKu6gg1h9K47eN8bzw1w42H8mgY0wQGw+n48DMLt2cO/1eYE7vv2DVB7Duc+h2AXk9r3Y/r8OpPdpSn/XGYgAmdo/l/D7NaBMVxCdL9tOtaSjAaTFjvC0+gwlv/8trF/Xkgr7NvP78hR3sooJ8aR9TFACP7xbLuK6NScjIp3Go8UGKw2vg+yuN2eCpv9AyvDV/tc5g8rQl2J2a1/7ZxVOTu5Z4jqmfrgHaEHfHXzD3SZj9MKQdgFGPGYF0DdNac967Szmanlfq/m1HM9wtrjvFGuMpLEOXZ6vce/Dst/4lK99OkK+FLU+Pc2+fteUoz/y+jfhiz33ee0vZn5TN33cNo3Ns3V9g+tPaw8SG+hEb6ucObkssvts9Fxa8aOQVN+tXC6MU3iIlqEVdd8qBsdb6mFLqkFKqo9Z6JzAa2Ob672rgJde/M6tlpKJSvl11iEdmbOaVC3u4u4xVVnJWvrv9c0WiLbnw07XgGwSXfYu13WgeK7a/MIgY17UxAL1bhPHDmsO0jw4C4PeN8fxe7PjWkYGk5Nhgwssw/H70smlkLv2QkC0/849PU9Y4O3JovZUNh5vgazGRby+aDb6wbzNwOull3s/bLZdi/+1FNvsuxjzfH2y3QlhLOLbJ+Ko2rCV0mgRRHU7q2tQ1eTYHMzcccZfIm7s9ocLA2JiVPMLto9pV29eZR9NyAYgO8SuxTyllBMX2Alj5P1j+Llj84KpfIbw1AF2ahDD/vjMZ/soCPlsWx1OTu+Jwau77YQMjOkYxpXex19S0D0z9xchBXTUdspPgwo+r5XWU53hmfomg2OnU7lq6mw6nAUYXr8JOXlazCYtJkVuJwDgjz0aWKzc+K9/Obxvjmex6nJu/Wuc+7oUp3XlkxmZ3EH40PbdeBMb3/7ixxDaPltDph+G7yyGqozFbLBo0s8wYizquqlUp7gC+dlWk2Adcg5Ge8YNS6jrgIHBRFZ9DnIRFu4wJeqeu/CT8D2sOkZlnd9cI7dQ4mOfO60ZeaakI9nzY+ivMfgRyU+CKH42c4RNc3K85mw6nM6KDkQ9auEjvgxNmpAu1ighg0a5EI+AIimZXjweYPL83U81zGWLaynnmpQT8Pp+rTC2ZHBRLTEwsf+3NJ8cSwplH1sGC2ZBgBInmRm34x9mPfkF2Ws5/zngCix/4BEFOEix4HjqfA4Nug+b9K32dCuXbHTz+6xYu6d+Cvi0bnfT5VbU6LoWL3vesGVxao4x8u4PtRzNpHOLHWa8vwu7U5Noc9GvViKGnUO4qLaeAG79cS3qOjfem9qFtVBBxyTk0CrCWzCMGY2Z430JY8gbE/QuxPeHc96BxN4/DWkQE0CEmiEMpuSzbk8SuhEx+3RDPrxvisdlPeB+brUbwFN4WFjwHVj+Y9Ja79F9N2BpfVJWlaZg/R9JySc0pICLISBVZdzCVNpGBvHNZb4/zRlm30v/gfPjdBo1aQ7fzIbS5R/48wNLdRj3n5uH+HErJ5c5v1zOsXSRhAUXXtG/LRlzSvzmP/brZXdkhISMfrXWdTjHQZfweKkw1wemAec+CowAu+RIatfLe4EStkOYsoq6r0l8TrfUGoLTvvUZX5XHFqXly5hZmb00AwOGKadcdTKVtVFDpgYvLgz9tAmB818bEhvrx913DSv9jm7gLvjgXMuONesTjvi01KAaYOqgllw1o4Z6ZPKtLjLvEW2mahwfg1JBdYCfYz8qq/cnk48PHjgl87JhAiyAnw3PnMd60io4BqUSkHuIqazK+Og+WmIy6yJPfgdYjILQ59z3yNxMiYnjnYj9jDIV1kzOPweJXjOB+20xo2hc6TYQ2Z0KT3mWOr1C+3cGl01ew/mAaP6w5zIqHRxelCnhJ8aB4RIco0nIKSC6l1N1Tv23j21UHS2xPyCg9JaAif2w6yqr9Rm7r6NcWce3Q1szfkUCPZmGeB9rzYdF/Yc2nxocn/3DjZ9PnqjIf+87R7bnvh41c/tFKj+0P/ryp9BPOuMdYnLd8mvGzHXrnKb2m4tYfTOVoeh4Tusd6bP9s2QEA1j42hr2J2VwyfTlXfryK728axL7EbM90EQBbHix7h+nqOYgHUsON6zD3SbAGQHBjCG4CIbEQ3JigpEC6qyC+u2IkN324gKx8J1/8nEmgrw+d1TGO6Aj+N3U0ZpNyB8UAD/+yGaszjwvD4+DYRsg4ijPzGIePJdDYtwAfZ76xkK3b+UbZu1KC8ppWuAZgSu+mzFh/xL19uOsDM+s+h03fwbD7JChuwAa3iWD5vmRAZoxF3SctoRuQz5cfcN/Oyre5ZxYndo/l3Sv6lHrOnuOZ7tsLdh5nVKfokkFxQTbMedJYHOMbDBd9Bp0nG7Vmy1H86/qWEYFM7tmE3zbGl3psowBjsV6B3UlaTgGPz9wKwHc3DqJ701Dmbk/gru9MfOU4i98uHUpUszB8wQhCtAN8inKhC5/1ry0J9Gjeicv6t8CR52TZ3nij9NzE12D0E7DsHdgzF+Y+ZZxQOIvccnCJ8W04lMad365nXNcY1h9Mc29/9o9tZV7b6padb+fhXzZ7bAvwMeNv9WfW1mP0e24Os+4eTqRrJnN5GZ3lck5YEJZnc/DVigP8su4Iz57Xlb4tw0s9b8exDIJ9Ldw7tgPT5u/hk6X7AXj38mJVSzZ+B38/aCyi6nIudJ0CHSe4KwyUZVKPJvRrGc6gF+cBMLpTNA9P6MSbc3fzx6ajJU8wW2Dsc0bb4AXPg18I9P2/cp+jIlNc5eX2vjDB/d5duS+ZxbuMxcERQb5EBPny0vnd+c/Pm/nvrB18tcL44FGY+sDxHUaqx+FVrPA7g5f87ubXu8+C1AOw/XfIiIfMo8Z/h1dDxlGGOfIZ5gt8CF8B+GJ8/wZcX3jZ3n8UIjswp30TPt8XRJRKp7vax+C/t4FyfSjyDSXDJ5pj6Sb2aH8GdGhLUPIWmHmb+zXmdbkYvwv+V+0z7Hk2B6v2p7i/iTCbFB0f+5vWkcb/l8M7RBId7Evb6CAuLkzx0hpWfWh8kzDq8Wodj6hbAn2L/lbIjLGo6yQwbgAOpeRw5qsLAaMiw6v/7GTFvhTWxBkNB7cdzSj1vJdn7eC9hUWl2fLtTvcfMgAcNlj9ESx8yZid630lDLv3lBsuFM5ad28aypTeTXnmj20E+1m4bEAL94ybzaGZv6NoveagNhEA9Cw2K9m1SWjRg1pLn6396Kp+XP/FGl76ewcv/b2Dbk1D2HIkg0FtIozA0S/UWLw16jHIOAprP4WVHxjBS4vBMPg21vgPJSkrn/HdYjnv3aUAfPjvfhqH+LH0oVE88NNGlu1JLvHcL/61nb+3HOOnWwYTHVx9s8nL9yaX+GDRtUkIZ3ePxaE1c7Yl8PWKg9w1pj3Z+XaOpOVy0/A2PDCuIxsOpbF0TzJvzN1lLAhz2CBpNxzbzKolC2h07BD/p83s+NBCXut29O7Vl4BuE40ccoz32Ix1RxjSLpJrhrbmon7NufLjlYzuFE2/VuGQnQx/3A3bf4MWQ+DMh6DNiJN6fY1D/XhwfEdahgcytmsMVrOJaZf3ITp4Gz+uOVTyBKVg8tvw83Xwxz0Q061aFm498/tWVsWlYjbBwNYRJfZf3K85X6444A6KAW7qUgAzboaN34Iyw/kfsjGlDxtm7eSL5XFcNbgVDLndfXyB3clNX65hRN9INmzeQGjGbp4+uw1ZKoDbv12PAkw4seLg/gG+tFPxkLSb9gmzeM6aiRMTe52xfO84k/nO3qxydgKHP3npxdKftsLAlmE8cg7off+yc+NyLtn2AxxdZdQIrsYZ2s+WxfHS30ajl+vOaM3jk7qQb3ey45jxwTs80JeHJ5yQA7/tVzi+Dc552+sz2cK7ik+SFNhPj2pBov6SwLgB+HHtYRyu71jHdI5hye4kj+DS4SyZ56e19giKCzUJ8zdKsC17G1Z/DLZsaDvaCHSaD6jSOP19jFmDAa3DiXEt1rp5RFtuG9mOn9ceBoxfmr6u/MPitU5bRhiNOm4c3qZSC8fGdIlhXNcYd2rJliPGh4O0HJt7RtUtJBZGPmJ8Pb98Gmz6Ab6filN35uWC67hZezY4CfAxYzYpmoX5czwzD5vDibXYUuuf1h4mObuAV2bt5JWLelb6+lRkT6LRUW7Fw6P5bvVB2kYFMbF7LCaT4sOr+nH1J6t4Y+4ukrLymdyrCTaHZmCbcCxmE/1ahdOzsS8+C59m8opdsHC/kdcJDNQWUi3hBPko8vJyiTw0Dw4BcxvBgJug9xU89ethFE6e75sNW34hKD+DGf3t4NwKc7+BzT9BVoJxDc98uMIZ4rLcema7EtssZoXNWcYf06BouORreG8QfDkF+l0DY54+6UDL7ih6/OLfvBS+b5b8Z6R7m1KK1y7qxTt/rqF/1jymRu/HPOMPQMHQu6D/9RDWgrFJ2bw4aydPzNxqBMZAZp4NP6uZOdsSWLAzkQU7EwFfLh84AXp0x9+pWXjCS72+12Bo5ZrFd9ggJwWT1Y+znlrieaBrTUBkkA9JWcbPduWBNM49ANAd6M6e8GE8mvs2zLjFWAR5ij+nE2UVK/H48ZL9/Gd8J4/9EYGeNc1J2Qc/XWfMFneXZSgNXfGSn7sSMss5UojaJ4FxA/D2vN0AzLh1CB0bB3PjiDbufK4BrcLdAVVx/Z+f57698pHRDHxhHl1VHGfu+R3mzQR7HnS7wPivw/hqmdHp6FqAN6JDFMPaRzLt8t6Md1WtsFqMX5wFDgdJmUYJsL/vGuY+VylF3EsTT+r5WpTS9W5XQibtXJUxSrD6w/AHYMhdsOZj2v/9HPN972ensxkf2Ccxx9mPLq2bMXWQUWO1SZg/Tm3k7DZrZDxXRp6N3OwMzjTtwLRhAUesM8jPSKRNh27QuLvxn19o6c9fgcTMfPytZmJCfLl7TMmqGu9c3pt7v9/IlysO0NRV07lDTLCRCrPqQ6yrP+YWy0HizP1h4E04orvxdVwIz6ywccXgttw9pgOP/LwJW34OmftW80WblQQsegkWvcTHgM3si/XHUmomKzO0GgrnvQeth5XcX0UWkyr1w52bX4ixCHTRf2HpW0bQNf6/EFr5Tmlb4o0AuLCVc3GRQT7uny8F2bD5RzpuncG0Q4sADfYmMOQOIw0npCg/uXVkIGd1iXHnZQNMnraUAruTMzsWNSl5dEJnrhxsvKeKf+jzt5rJtTkI9C32a9pshWDPBh+bnxrLDV+sYcW+FObeO5xQfx/emLuLb1aWzC8/EjMSOkcb6R4fnwXXzAKfqneHzMizedwvbAsORi3zwg+2buu/BjRc+m21PL+o26zF3tfBfhJ2iLpN3qH13Jxtxoxoj2ah9G5hVEgY0jaC6GBfIoN86d0ijA2H0lh3MJW8AgdD2kWSW+BwN4X48+Y+xGz/gnk+r9PWdBRnnL+RGzr8fogs2Z65Ki7o24yh7SLdaRPFW037uGYUjmfmM33xPtpGBdImsowAtpIGto7gw3/3e2z7euWBEourTvThssM8/1cLYniJ1ztuo0/6HF5Pe58MFUxIs0sgORx+jmPCke0M843H/+NwCI/CWZCNNTOZ9b4J+CpXoLABMrU/7Puq6AkatTIWL/a52piFr8SsXVpOAQt3Hic6xLfMKgQhflbO7dWEudsT+GDRXgJ8TDTZ+A4sfhmcdmg1jFszppIYMIwvRw5kxvojPLFiM0pZePKcrphMiulX9WPT4TQmT8tkUe/LGTs2lQPr5zJzyQbGtvGha//RRlUJv1AwWYyg2Opfo8GNxaSwOXT5FRhiusJFn8P8Z2HF+/D5JBj/ErQf6/GhLiEjj1/WHSnxzcNbc3cRFmBl8YMjybM5iA72Y8uRdCa9s8SYfXU6jPzppW9C0i4IaWZ8iGo7CloMKvODY9+WjZizLYHMPBtKKXepta+LBa3DOkR6VBVpGRHAgFbhNAnz5615uwk/cbb1BMF+Vj6/dgAFdifBro58z5/XjW3xGWwoVvc72NeCzaGNRZBmH/j1FqPT3IWfgH/VqqskZubTLjqIJ8/pwpUfr2LC2/+69z18dmf3uADIzzJSTtqOOqkPL6L+unJwS37dEM/rF/fkrC71t3OjOD1IYFxPaa0Z/OJ8jmXkERZg5Ytri9IcfC1mFj84EqvZxPuL9lLgcHK+a2HRvhcmkJyRyQjTRh5rF0f7r64Fex6pdORV2xlcf8eLhEXUXMvdsio4+FiMwOLbVYc4lpHHb7cPrfIijTFdYlj0wJmYlCI0wMolH6xgy5EM9hzPIsTfQoifFT+rmX93J/LJkv2M6hzDir3J/LnZWOyVQDjb293A0KHPweHVhCx+BTb9CPkZENoca0grViSGEZyeQyt7Fql2H47ktyJJ9+Syy69lhy2a22ceJiFHM+u6DnTigFE94Ogm2P0PbP4RQprCiAeh24XufN7S3PXdBvYmZvPw2Z3KPAbgrA5hDDVtpm/+bq7w/RfTwgRjAVyfq6HNmfRbGsczf2zjru/Wu9NMPr66n8e17hATjMWk2BKfzrK9Tr5c0RZoS68z+tO1o/c7vBd+DXvbN+tYuS+Fdy7rzZDSys0pZSyq7DAevrkEvrnYWFDZ/wajKoNSPDlzK7O2HqN9dBDDO0Th4/qmYk1cKuf1bkqIn9Xd7rlrkxACfEzc3yXTqLO7a5ZRB/vKGdBmZKW+RenuajYz6Z0lXD/MMze/b8tG3D+2I50ae9YiXvSAkbahtebygS3caUcnmnffCHeFEV+LGV9LUXCtlHKnL3x/4yDCAny4/8eNRSkjPS+F3DSjWco3l8A1f1e4mLY8xzPziQ72dX84Ly4yuFhg73QaKS8Z8UZusTgt9G0ZftLf+AlRWyQwrqf2HM/imOuP4kvn9yAswHNWqXAGys9qJpgcwlUGY0xr2fjcc/Rw7uRzHwfOw1YjVaLnJQQH9KVVfGaNBsXl8TEb4/3dtbisRAmwU1S8c9+7l/dm0jtL+GTpfvfXzKseHc2VH68CcOV7egrytRgBQ4tBMPVnYyW90w5mKz5OzX2P/GUcmFZ0TmyoHzd2GU1/4P1GzZny3jLiHSF06jQG2o8xDsrLMGr8Lvqv0QL739eNr+P7XVtqgLJoVyKRQT7cOLyUhY8Ou/FYqz/Eb/ccvvYxUgHifLrAhOeMHE5XEHfFoBY89+c2d1DcPNyfUZ08Z3D8rGY6xASz+UiGu3Nbu+ggzuxQO++NwpndvzYfA+CTpXGlBsabDqehUHRvPgDu38WuGc/TZtt7WLb/jm4+iK1NL2LW1saA4vov1tAyIoBFD4zk21UHycy3Ex3smrm35cLOv1FbZ7A1aClqRzKYfY30jIE3nVRaUeHi0QPJOTz+6xbCA3347Jr+TJ62lL4tGzG4bcnFfYWUUmUGxQBto4JoG1X2h6kXL+jOV8sP0L9VOCaTwmxS7pbMxuBuNtJQfr3FqCRy1jMe1V0qY/PhdMKDfIhPy2Vg63CCfC30bhHG+oNpmBQ4ddH/22QchXnPwOFVRlBc+P+CEELUIRIY1yN5Noc74F2218ghvvesDozv1rj0E2x5DNn9Ktf5FX2Nv98ew3TnBDY423LPrbfTqZkR7HQEOsaeWu5rdSi+0K6mtIkKolPjYP4oVtlhQLFc60IjOkSxyFWiyyO/E4ygyGzMKJpNinn3jeDrFQdZfyiV3QlZZOXbGdK2KGgr7Aj3+8ajDGsfVbRIzy8Eukw2ZjTj/jUapvx1v1FfecANrnJ4JnILHO6FZ9ee0bpkKkHCVvjjXji0AnxDYfBt5Mb2Y1Z2R3q3aw6RnoGOr8XsUQv3wXGlz0B3ig1mwY7j+FnNTOoRy7TLvVOSrjQnvjdOzGcFeGLmFr5YfoBGAVbWPzGWXUl5jF3THz/e52LzQv6Ttphuh+5jkU80fzoH8ZdjAFuSW3Pb1+vc3xDozKMw4zXY/gcUZEJwLKr9OKN8X5dzTyk33GxSHp0aJ3RvTI9mYXx/46BSZ1erU3SwH/eO7ei+bzUr7I4TcrV7XgaHVhoLbVMPwGXfut/fFXl3wR5emb3Tfb+wFfsvtwzheGY+9/+4kX93J6HRRlD86XhIOwhD7iy3prUQQtQmCYzricJ8x+lX9iXIz8Jzf26jWSN/7hxdRh7w8R3w5RQ6Z8YzwzGULbRjob0be3VRTt+b0aXXq60NhV9pA4zsWHMzkxO6x7objdwzpgPvLthDgcPJA+M6kppdwB2j23MsPc8dGJfXGAWMWbsnzukCGNU/Pl26n0v6F7XijnJVwJix/gijO0fTMjyQ2VuPcdeY9kaQrJTxNf/NS4zgZMHz8OPVEBhNQduxPLalCccszbAQRpNQY0EdTgccXA5bfoY1nxizmee+a6RjWP3wB6ZU4lp8c8NAjyD+xNf1y7ojgK3cWUlvMJuK3huD2oRzzNWe2eZw8sgvm3Fo7RorpObYWLEvmUunrwAgKjyML1LGkdnkatj8I9eFreWWnD+41fIba53tObIjkpFWK4HkMm7bDuPbgO4XQPeLodUZVUovKHTbyHa8PmcXUJRXP7BN2TPFNcViMmE/sbqHUnDOWxDbyyi39+4AI1WkglJuWmv+d0JVm8IukIUz3S9M6c6bc3czKH8ZvH0jKBNcN6daSuoJIURNkcC4jkvNLuCeHzaw0PU1/41frnXva1NWwLLgRaMNr38jVgz5iHvm+1PU9sKw5rEx7vJpdUF7V8UKgLtKqbhQXa4a3Aqn1gT5Wjm/T1Mu7t+M7UczPNIJgovNEle08Kk4s0mVyCP1sZiIDfXjaHoet3+z3l1PedqCPQxtF8HHV/cvWnjV/zoeOdiPdknzuDZyG+atP/KaMx8KwOmrcM5tDP8GGN37bNnGwrd+1xrl0YJOPvc3IrDsRX9Tejd1zwae07NJmcd5Q/EZ465NQtl4KB2tNZuPpPOjq8wfGOP8fWO8Oyh+9/I+jOsaw/i3/mXGxmPAMC46/35sUU7+/uYt2h/9ne5qH1H+iqAAf2gxCc64F6Kq9/13x6iiwLhX87BqfeyTYTErcm1lVPfod43RkW/GzfDJeBhwIwy9G1wfSuwOJ9+tPsSE7rGEB/qQkJFPVr6dpyd3ZWTHaFbuTy7x2pqHWnmtxXKY+bSxOPK8/0FUx5LPLYQQdYgExnXcqrgUd1B8olJnM7f/AYtegvbjYMIrZMT7Ams9DpldrDtaXRHka2HOPcP5d3cSPZvVXEqHj8XEjcPbuu/HhvoTWzgT61J8IVpEUOUD47L8++BI2j36N1BUFxdg6Z5knvtzG09P7obZpFh7IIVvVh8GOjL03BtY2egOvp+zhC6mOCY2t3NmTK5RRq/9WGjeH9qdZaRknKTCHNCYkLLfA03C/Nn9/NkcSM4uu7ydl7SLCkIpo61s4xA/cm0OjmXkcSQ1133MFQNbMLJjtDtHHWBiD6P6SGyon7t8WMfGwfgE+XLuLc+TnPUEX644YNQYPokPQCerePpL8eoT3lZh2buOZ8OVv8CsR2De03BwhdEAJ7YHd32/gT83HaXA7uTqIa34eZ3xgaR7s1BaRATQ4sRybIfXGB0l4/41KrBc8pUReAshRB0ngXEdV1hWbVKPWPYmZrP9aAY3jWjDB4v20frEP0Yr3odZ/4HornDxF2D1wy+xZFBdoqZoHdE+Jthj5rg2/XHHGXy+LK5aOtdZzCZWPTKaAS8Y+cyPT+rCs39sA+CrFQf5asVBpl/Zly+KNZa4dPpy+rYMZ6tuxVZHKx6aOgaq6cPMZ9cM4FBKTokFmyeymk20i679n8eQdpHsfX4CSsGsLcYCvMEvznfv/+32ofRoFkacqxQaGI1uCl01uBVNQv2ZOqglEcWuYURQ6fWga8Ifd5xBfFpuxQfWIIvZhM1RQdexpn3h2llGg5/Fr8En40ga9AjzNjUFfEnLKaCta8Fp83B/ejQ94UOsvQDWfwF/PWiUIZz8juQTCyHqFQmM67DMPBvrDqQR4mfhrUt7Y1JG22aLSRET7MflA1sYBzqdsOEr+OdRYxbxwk/crZJ9i+Xu3jGqHRf3a16rs1b1RbemodXatS46xI/+rRqxOi6V/q0a8fvtZ3AwJYfbvlkHFKXIXDagBdcMbcXYNxYzd3sC/Vs14vsbB1e5dF1xof5WQk8MaOq4wtffPqbk7HVhI5dWkYF8d+Mg2kQG0qjYDPBZXWJqvXZqt6ah7sVptcVqPqEqRVmUq4Nfj0vhu8uJ/PcxfvdpyqeO8bw33w5YiA72ZeZtZ3h0NENr+OEq2PU3tBoGl35zSt9oCCFEbZLAuI7alZDJ2DcWAzC8Q5S7ZFVhUHvtGa2NA3NT4Z/HYf2X0KQ3XPChxx+joGJdhkL8rDQvpRuc8I53r+jD/O3H6d40FKUU3ZuF0r/VaB77dQv/uBq1dIkNNrrVuQT4WKo1KK7v2kUH89V1A3FqzVWfrOLcXk08Zr4H1cKitvrCYjJ5tL4uS4HdydoDqQxuGwPXz+Wlae8yNfltXjB9zOXmeWyMmcKEqx70+PBBQY6RfrHrbxj9pBFYV8PCRSGE8DYJjOsQrTXvzN/jXqhTKKqsr9DTj8CHIyErAc64x/iDdEI5rybF8mf96tBiu9NRdLAflw5o4bktxI/pV/Ujz+bgi+VxTO5lVA0Z0jaCZXuT8ZfZ/RLOaG9U0vjl1iElv8oXZbKYjQ6C5TmUksOwlxe47z9zble+SGxHXr8Z3B69mbbL/ku3xDfgwx+Mro0tBhsLP9d8AvsXQ++pHov2hBCivpHA2IsmT1vC5J5NSlQuACMo/mvzsRJB8Yvnd2d0p1IqDhxeA7/fDQXZcP28MksghQUULdCTIKvu8rOaPRYFXj6wBcv2JhMgH2bK1KeG6wA3NIE+FnIK7CW2b41PJyLQl8ahfh51iQGemLkVgObhAUQOuhQGXgI7/jS6Nh5cAVtnGAda/CWfWAjRIEhg7CUOp2bT4XQ2HU4vERg//ftWPl0aBxj5n+m5RQ0MpvRu6pkTnJ8F22bCn/eCbwhc8FG5dUGLr4iXIKv+GNM5hssHtuAeLy0OEw1fsJ+F1BwbuQUOj1KNE99eAhh1iNceSAWgaZg/eTYHydkFAIzr6srRVgo6TzL+09po7Zy4HZoPKreluRBC1BcSGHtJrqu1LsCx9DwahxqL4zYeSnMHxWBUjPjt9jM4lJJDcnaBZ1CcfgS+nAJJO6Fxd7hyJgRWnFMZ7GshM98uM8b1iJ/VzAtTutf2MEQDkpxlBLnXfb6ab24YVGJ/YVA8pnMMH13dD4dTM2vLMfq0DCtR0hAwguTQpsZ/QgjRQEgimJfkFhQFxld+vJL1B40/Qo/P3ALARX2bATCxu1F7tXl4gGfB/J1/w1s9IXU/XPI13LCwUkExFKUdR5dTt1YI0bDl2Y3fQcv2JpOdb6RUHM/M8zjm/rEdeH+q0f7bbFJM7BFbelAshBANlATGXpJXbMZ49/Espry3jPQcG5sOp3PX6Pa8clFPdjw7nhuHn5B/7LDBvGfgp2uhcTe4dYXxNaa58pP9fVytWps1kooUQpyuHj67M/1bGb8LCutBT3l3GQBPT+5K3EsTuX1Ue88SbEIIcZqR34BeklNsxrjQhe8bf5SahBlpFX5Ws0dOMJt+NKpO/PsatBsNl30PEW1LPE5F3r6sNz/ePLj0TnlCiNNC41A/vr9xMP5WM1vi0wE44mo6Mqq0Bb5CCHEakhxjLynMMfYxmyhw1RLd7WpT2yTshK8qk/fCwheNld+NWsP5H0KPi0/5uUP8rPRvFX7K5wshGgaTSdGskT9H0/I4kGx0CnxsYmepby6EEC4SGFeTLUfSOZ6Zh7/VwuC2JXN/03KMhS8fXt2P9Fwbd367HoA7R7dnaFujLitaw86/4I97jDJsg26Fs54Bs8z0CiGqR7CfhVlbjzFnu9FUZlzXxrU8IiGEqDskMK4GdoeTSe8scd+Pe2mi+/bh1BycTkhyrQhvGR5Ai/AAHv1lM7eMbMutZ7ZztXT+Fjb/BHvnQXATozZxdCevvxYhRMO27mAaYJSQjAr2ldliIYQoRgLjanA8M9/jvtYapRT7ErMY9doij32Rwb6YTIrNT48zZoi3zYQV78PBZRAYDWOehsG3ySyxEKJG3DyiLZ8s2c+4bo0Z5uoiKIQQwiCBcTWIdy1g6RIbwrajGbR++C9m3z2caQv2eBzXOMSPIF/XJd/2Gyx5HeLXQ2gLmPg69Lu2REtnIYSoTg+d3YmHzpZvo4QQojRSleIUHE3P9bgfn27UAh3RMcq9bdGu4/y1+ShTejcl1tXM44vrBkDaQfj8HPjhSijIgXEvwl0boP91EhQLIYQQQtQimTE+ST+sOcSDP20iPNCHH24aRLvoYLYcMUofXTO0FbkFDj5bFscLf+0AoF10ELeNbIfN4aRDxkr4637ITYHRT8DgO8DiU5svRwghhBBCuEhgfBL+O2sH/1u4F4CU7ALGvL6Ya4a24tOlcUzu2YToYD+emtyVz5bFAUZ750k9YmkZ6ICV78OCFyAoBq74GZr3r8VXIoQQQgghTiSBcSWtP5jK/xbupUNMEHaHZl+SUQP006VxADx7bjf3sRaTwu7ULHpgpJEu8fHZkLAFupwL5/0PfAJr4yUIIYQQQohySGBcCT+vPczDv2wG4OvrBxEV7MuiXYlc/ckqACKDfAkNKKoiseaxMTg1kH4YZt5uBMWXfgOdJpb28EIIIYQQog6QxXcVOJ6Zx30/bqTA4eQ/4zsRFewLwPD2kfx082AA+rQI8zgnLMCHcDLgm0vh8GqY8KoExUIIIYQQdZzMGFdg3YFUAIa2i+CWM9u6tyul6NcqnLn3jqCxq+qE24r3YfYjgIbLf4D2Z3lxxEIIIYQQ4lRIYFyGPJuD5XuTufmrdQC8MKV7qce1iw7y3LBvIcx6CNqPhZGPQJNeNTtQIYQQQghRLSQwPoHWmgU7j3PtZ2s8tkcE+VbuAZoPhOH3w7D7wOpfAyMUQgghhBA1QQLjEzz082a+X3PIY9u9Z3Uo6lhXEas/jHqsBkYmhBBCCCFqkgTGJ9h1PNPj/spHRhMT4lfG0UIIIYQQoqGQwPgEBXYnw9pHcu3Q1vRp2YhQf2vFJwkhhBBCiHpPAuMT5NkchPhZGdkpuraHIoQQQgghvEjqGJ8gz+bE1yqXRQghhBDidCMR4Any7Q78rObaHoYQQgghhPAyCYxPkGdz4i+BsRBCCCHEaee0D4x3HMvg7Xm70VqjtSbX5sBPUimEEEIIIU47VY4AlVJmpdR6pdQfrvvhSqk5Sqndrn8bVX2Y1c/ucLJgx3HGv/kvr8/ZRU6Bg8TMfBxOLeXZhBBCCCFOQ9UxNXoXsL3Y/YeAeVrr9sA81/06J8/u5Jav17rvP/TLZp78bSsAbSKDyjpNCCGEEEI0UFUKjJVSzYCJwEfFNp8LfO66/TlwXlWeo6YE+VoY0znGff/3jfH8veUYN41ow5C2EbU4MiGEEEIIURuqOmP8JvAg4Cy2LUZrfRTA9W+pBYGVUjcqpdYopdYkJiZWcRin5txeTUtse/jszphMqhZGI4QQQgghatMpN/hQSk0Cjmut1yqlzjzZ87XW04HpAP369dOnOo6qGNEhimA/C5f2b87IjtLQQwghhBDidFaVzndDgclKqQmAHxCilPoKSFBKxWqtjyqlYoHj1THQmuBjMbH5qXG1PQwhhBBCCFEHnHIqhdb6Ya11M611K+BSYL7WeirwG3C167CrgZlVHqUQQgghhBA1rCYK9r4EnKWU2g2c5bovhBBCCCFEnVaVVAo3rfVCYKHrdjIwujoeVwghhBBCCG+RFm9CCCGEEEIggbEQQgghhBCABMZCCCGEEEIAEhgLIYQQQggBSGAshBBCCCEEIIGxEEIIIYQQACita6Ubs+cglEoEDtTS00cCSbX03KcbudbeI9fau+R6e49ca++Ra+1dcr29p6XWOqq0HXUiMK5NSqk1Wut+tT2O04Fca++Ra+1dcr29R66198i19i653nWDpFIIIYQQQgiBBMZCCCGEEEIAEhgDTK/tAZxG5Fp7j1xr75Lr7T1yrb1HrrV3yfWuA077HGMhhBBCCCFAZoyFEEIIIYQAJDAWQgghhBACkMBYCCFEHaOUUrU9htOFXGshPDX4wFgp1aq2x3C6UEr1U0pF1/Y4ThdKqTFKqb61PY7TgVIqtNhtCSRqnqW2B3Aasdb2AE4nSilzbY9BlK/BBsZKqT5KqbnAM/JGrFlKqa5KqWXAk0BYLQ+nwVNK9VZK/Q3MANrV9ngaMqXUQKXUTOAjpdS1SilfLSuWa4xSapBS6muM39vt5Xd3zVFKDVZK/Qi8qpTqIte65riu9TMAWmtHbY9HlK/BBcbK8CjwLfCd1vqqwjeizPTUmLuAGVrrc7TWu0CudU1QSpmVUtOBD4EPgG+Azq59De7/5dqmlOoBvAv8BPwIjEI+iNQYpVQ34B3gDyABuBG4yrVPfp9UI9c3e9OAvzBaEN8FXOvaJ9e6GimlrgY+Bx5TSl3s2ibfiNRhDe6PqWs2xwos0Vp/BO4ZNovM9FQvV6AWDmiMX7IopaYopZoB/q778ku2mrg+4M0ChmmtfwV+BkYqpfy01s5aHVzD1BfYo7X+EpgD+AEHC3fKe7vaDQJ2aK2/xfjwlwNcoZRqpbXWcr2rVU9gl9b6U+A14BfgXKVUB7nW1e4Ixofq8RjXGq21Xa5x3dUgAmOl1Ail1MBim14BmiqlXlVKrQaeBT5XSl1YOyNsOIpfa1eglgMMB0Yppb4CbgKeA950HSMfRqrgxPe21voXrXWu65eqE9gFBNTaABuQUn6P/AlMUUo9D2wGmgFvK6X+A/LerqpSrvdqoLlSqq3WOhvj/Z0O3AByvatCKXWeUuoRpdRE16YNQL9i13o1sAbj97dc6yoodq0nuTYtABK01v8AB5RSz7q2y6xxHVWvA2OlVLBS6heMXMublFKNAFz/o38J9ALu01pPAhYD45VSHWprvPVZOdc6D/gU4yvn2Vrr8cCjQDel1Nm1NuB6rqzr7UoVUq4/XDuA0RgzmTKDeYrKeW8fx5hZswCPaK0HAZ8BZyilBtfWeOu7Uq53uGvXXmAV8KlS6legH0YKi0Up5Vcrg63nlFJRrmt5L5CCcW0v1FonYnzjdIfr0DRgLhCglIqtjbHWd6Vc60+UUlNcE0iFv5tvAu5USsVorW21NFRRgXodGAMFwHxgKhAPXFS4Q2v9NXCx1nqxa9NcIArI8vYgG4gyrzXwHkbqRBSA1voIsARjxkecmlKvt3ZRSpm01oeBlcCFhftqa7D1XHm/R3YAnYBDrk1rgeNAvpfH2JCU9d7O0lo/CNwOfKa1PgfYA/RwfQAXJ68tsFRrPVxr/T5wH3CPa9+3QCel1GhXKlYy0BRjll6cvNKu9QMAWusCpZRZa70V48PeSwAyeVQ31bvAWCl1lesruDCtdT7wEUbQuwvjq6EOruOU1jql2KlnYeTCSmBcSZW91lrrLIyZh6uVUr2UUrcAY4C4Whp6vXQS722T1trpWsCxG8iuvVHXT5W91i7/AE+5ZuQvBbpiBBGikiq43n2LX2+t9SZXDj0YuZkr5NuQynNd6zOVUgEYH+S+cG03A9tc/4GRHvQd8JZSqh3Gt08K8PH+qOunSlzrza77CiP+QGt9PcbfylSgp5KF03WOqg+TTK43VWOMVfhOjK/cAoG7tNZJrmPaA1cDeVrr51zbTMAZwFsYi2b+45oBEmU41Wvt2n4JxlfPXTG+et7q5eHXO1V5b7uC4zeALK3147XyAuqRk7zW+VrrZ13b/IHpQDRgBu7UWm8r+QyiuCr+LumLsVDJAdyotd7r5eHXKxVda9dspUMpNRWYrLW+uNi5DwIdML4ZuUFrvd37r6D+qOK1bgm8AUQAt2mtt3j/FYiK1PlPKq43mQaCgSNa69HArRg5PB8UHqe13o3xia2JUqqdKydNY6wIfVJrfa4ExeWrwrUOVEpZtdbfA4+6rrUExRWo4nvb37X7XgmKK3YK1zpWGXV0A7TWucA1wNVa6zESFFesCu/twvd1HMbv7dESFJevgms9/YTDx2KUH0Qp1RhAa/0ycKvW+gwJistXhWsd5dqWDryktR4hQXHdVWdXRbq+Jn4GMCul/gJCMGYPCkud3AnEK6VGaK0XubbPUEp1xihpFQSMcv0Rk1+s5aimaz0S2C55rhWT6+09VbzWfwNBSqmRroDhWO28ivqjOt7bSqnC39uLaudV1A+ncq0xUgn3K6PZxPlKqfFa68Na64LaeA31RTVd6wla64MYC0xFHVYnZ4yVUiMwZhEaYSy+eBawYdRsHQDuhUbPAE8VO+8ijIoICzAWbMjMTgWq8VrLTEMlyPX2HrnW3iW/t73nVK61K+/1WoxZzBBgpDYW8IpyVOO1PljiwUWdVCdzjJVSw4BW2iisj1LqPYwk9lzgDq11X1f+cDTwNkbu8H7XeWit/62lodc7cq29S66398i19i653t5zCtf6AYxviO8AvtBar6udkdc/cq1PP3Vyxhjj09kPqqh3+1Kghdb6M4yvMu7QRnmZZoBDa70fjF+s8sv1pMm19i653t4j19q75Hp7z8lca6fW+oDWeq/W+m4J1E6aXOvTTJ0MjLXWOVrrfG0Uxgaj1Fqi6/Y1QGel1B8YdRjljVcFcq29S66398i19i653t5zktd6LUgDoFMl1/r0U2cX34E7T0cDMcBvrs2ZwCNAN2C/NppJiCqSa+1dcr29R661d8n19p6Tuda6LuZN1iNyrU8fdXLGuBgnYAWSgB6uT2WPY3xdsUR+uVYrudbeJdfbe+Rae5dcb++Ra+09cq1PE3Vy8V1xSqlBwDLXf59qrT+u5SE1WHKtvUuut/fItfYuud7eI9fae+Ranx7qQ2DcDLgSeF0brURFDZFr7V1yvb1HrrV3yfX2HrnW3iPX+vRQ5wNjIYQQQgghvKGu5xgLIYQQQgjhFRIYCyGEEEIIgQTGQgghhBBCABIYCyGEEEIIAUhgLIQQdYpSyqGU2qCU2qqU2qiUulcpVe7vaqVUK6XU5d4aoxBCNFQSGAshRN2Sq7XupbXuitF+dgLwZAXntAIkMBZCiCqScm1CCFGHKKWytNZBxe63AVYDkUBL4Esg0LX7dq31MqXUCqAzsB/4HHgbeAk4E/AF3tVaf+C1FyGEEPWUBMZCCFGHnBgYu7alAp2ATIwWtHlKqfbAt1rrfkqpM4H7tdaTXMffCERrrZ9TSvkCS4GLtNb7vflahBCivrHU9gCEEEJUSLn+tQLTlFK9AAfQoYzjxwI9lFIXuu6HAu0xZpSFEEKUQQJjIYSow1ypFA7gOEaucQLQE2ONSF5ZpwF3aK1ne2WQQgjRQMjiOyGEqKOUUlHA+8A0beS9hQJHtdZO4ErA7Do0Ewgudups4BallNX1OB2UUoEIIYQol8wYCyFE3eKvlNqAkTZhx1hs97pr33vAz0qpi4AFQLZr+ybArpTaCHwGvIVRqWKdUkoBicB53hm+EELUX7L4TgghhBBCCCSVQgghhBBCCEACYyGEEEIIIQAJjIUQQgghhAAkMBZCCCGEEAKQwFgIIYQQQghAAmMhhBBCCCEACYyFEEIIIYQAJDAWQgghhBACgP8HLPWltMUTSpQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Moving Average\n",
    "it also has an argument center=True to have the window calculated centred in the middle. \n",
    "Then it looks 3 days ahead and 3 days back when your window is 7.\n",
    "The default value is center=False. It just takes current day value + 6 day values back when the window is 7.\n",
    "\"\"\"\n",
    "df['Close'].plot(figsize=(12,5))\n",
    "df.rolling(window=60).mean()['Close'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---Expanding---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Window keeps expanding and shows the average in the 'Y' axis... ????\n",
    "See semicolon at the end of the plot. That allows getting rid of the comments of the matplotlib libraries\n",
    "\"\"\"\n",
    "df['Close'].plot(figsize=(12,5))\n",
    "df['Close'].expanding().mean().plot(figsize=(12,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Time Series Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "axis parameter inside autoscale function: type x,y or both if you want autoscale based in x, y or both\n",
    "\"\"\"\n",
    "title = 'Title'\n",
    "ylabel = 'Y label'\n",
    "xlabel = 'X label'\n",
    "ax = df['Close'].plot(figsize=(12,5),title = title);\n",
    "ax.autoscale(axis='x',tight=True)\n",
    "ax.set(xlabel=xlabel,ylabel=ylabel);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Use datetime index to filter based on data\n",
    "\"\"\"\n",
    "df['Close']['2017':'2019'].plot(ls='--',c='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Formatting follows the Python datetime <strong><a href='http://strftime.org/'>strftime</a></strong> codes.<br>\n",
    "The following examples are based on <tt>datetime.datetime(2001, 2, 3, 16, 5, 6)</tt>:\n",
    "<br><br>\n",
    "\n",
    "<table style=\"display: inline-block\">  \n",
    "<tr><th>CODE</th><th>MEANING</th><th>EXAMPLE</th><tr>\n",
    "<tr><td>%Y</td><td>Year with century as a decimal number.</td><td>2001</td></tr>\n",
    "<tr><td>%y</td><td>Year without century as a zero-padded decimal number.</td><td>01</td></tr>\n",
    "<tr><td>%m</td><td>Month as a zero-padded decimal number.</td><td>02</td></tr>\n",
    "<tr><td>%B</td><td>Month as locale’s full name.</td><td>February</td></tr>\n",
    "<tr><td>%b</td><td>Month as locale’s abbreviated name.</td><td>Feb</td></tr>\n",
    "<tr><td>%d</td><td>Day of the month as a zero-padded decimal number.</td><td>03</td></tr>  \n",
    "<tr><td>%A</td><td>Weekday as locale’s full name.</td><td>Saturday</td></tr>\n",
    "<tr><td>%a</td><td>Weekday as locale’s abbreviated name.</td><td>Sat</td></tr>\n",
    "<tr><td>%H</td><td>Hour (24-hour clock) as a zero-padded decimal number.</td><td>16</td></tr>\n",
    "<tr><td>%I</td><td>Hour (12-hour clock) as a zero-padded decimal number.</td><td>04</td></tr>\n",
    "<tr><td>%p</td><td>Locale’s equivalent of either AM or PM.</td><td>PM</td></tr>\n",
    "<tr><td>%M</td><td>Minute as a zero-padded decimal number.</td><td>05</td></tr>\n",
    "<tr><td>%S</td><td>Second as a zero-padded decimal number.</td><td>06</td></tr>\n",
    "</table>\n",
    "<table style=\"display: inline-block\">\n",
    "<tr><th>CODE</th><th>MEANING</th><th>EXAMPLE</th><tr>\n",
    "<tr><td>%#m</td><td>Month as a decimal number. (Windows)</td><td>2</td></tr>\n",
    "<tr><td>%-m</td><td>Month as a decimal number. (Mac/Linux)</td><td>2</td></tr>\n",
    "<tr><td>%#x</td><td>Long date</td><td>Saturday, February 03, 2001</td></tr>\n",
    "<tr><td>%#c</td><td>Long date and time</td><td>Saturday, February 03, 2001 16:05:06</td></tr>\n",
    "</table>  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Check link in class notebook for all dates tickers\n",
    "set major locator fixes the x date scale\n",
    "byweekday = 0 means 'Monday'\n",
    "\"\"\"\n",
    "\n",
    "ax =df['Close'].plot(xlim=['2017-01-01','2017-03-01'],ylim=[50,60],figsize=(12,5));\n",
    "ax.set(xlabel='')\n",
    "ax.xaxis.set_major_locator(dates.WeekdayLocator(byweekday=0))\n",
    "ax.xaxis.set_major_formatter(dates.DateFormatter('%d'))\n",
    "\n",
    "\n",
    "ax.xaxis.set_minor_locator(dates.MonthLocator())\n",
    "ax.xaxis.set_minor_formatter(dates.DateFormatter('\\n\\n%b'))\n",
    "\n",
    "ax.yaxis.grid(True)\n",
    "ax.xaxis.grid(True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}