cohort_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### visualization\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.offline as py\n",
    "import matplotlib as mat\n",
    "py.init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# data analysis and wrangling\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "#import re\n",
    "from functools import partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# other library\n",
    "import datetime\n",
    "import calendar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ignore sys warning\n",
    "import warnings\n",
    "import os\n",
    "import glob\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohort analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cohort analysis 幫助我們觀察消費者的回購狀況，透過它我們可以迅速發現回購異狀，並以此做更深入的分析，比如比較來源渠道、訂單內容等等。\n",
    "在這一篇 Jupyter 中，我要示範以 python 畫出cohort table。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load data from csvs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由於訂單資料很龐大，因此我先透過 API 將訂單資訊載入到本機端(這一步沒有呈現在這邊)，然後用 for loop 將每個月的訂單 csv 檔案載入並儲存成一個data frame。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "path =r'YOUR PATH' \n",
    "allFiles = glob.glob(path + \"/*.csv\")\n",
    "df = pd.DataFrame()\n",
    "list_ = []\n",
    "for file_ in allFiles:\n",
    "    df_temp = pd.read_csv(file_,index_col=None, header=0)\n",
    "    list_.append(df_temp)\n",
    "df = pd.concat(list_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## spilt cart by different shop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由於訂單資訊並不會分商城儲存，因此我們必須先將不同商城的資訊拆開"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df.order_status != 'Cancel']\n",
    "df_10706 = df.loc[df.shop_id == 10706].reset_index().drop('index', axis=1)\n",
    "del df\n",
    "#df_10706.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TS to TG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由於訂單資訊包含商品資訊，因此會出現單筆訂單多個商品(列)的情況，由於cohort table並不需要商品資訊，因此我們將去除商品資訊與去除重複訂單資訊。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_10706_tg = df_10706[['cart_code', 'member_code', 'order_time', 'order_label', \n",
    "                        'payment', 'promotion_discount', 'ecoupon_discount']]\n",
    "\n",
    "#df_10706_tg.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_10706_tg = df_10706_tg.groupby(['cart_code', 'member_code', 'order_time', 'order_label'], as_index=False).sum()\n",
    "df_10706_tg = df_10706_tg.drop_duplicates()\n",
    "#df_10706_tg.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我們要處理時間資訊，由於原始資料的時間資訊細至毫秒，但是計算cohort table時，我們僅需到月的資訊，因此我們將訂購時間抓至月份。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_10706_tg['order_time'] = df_10706_tg['order_time'].str[:7]\n",
    "df_10706_tg['order_time'] = pd.to_datetime(df_10706_tg['order_time'], format='%Y-%m-%d')\n",
    "#df_10706_tg.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Find first buy day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "計算cohort的第一步在於先找出每位消費者第一次購買的月份，如此我們才可以根據消費者的首購月份計算每個 m + n 的回購"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_10706_tg = pd.merge(df_10706_tg,\n",
    "                       df_10706_tg.groupby('member_code', as_index=False)['order_time'].min(), \n",
    "                       'left',\n",
    "                       'member_code')\n",
    "df_10706_tg.columns = ['cart_code', 'member_code', 'order_time', 'order_label', 'payment', \n",
    "                       'promotion_discount', 'ecoupon_discount', 'first_buy']\n",
    "#df_10706_tg.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define cohort_group & order_period"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "將訂購日期與首購日期轉換成月份，如此方便我們計算cohort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_10706_tg['cohort_group'] = df_10706_tg.first_buy.astype(str).str[:7] \n",
    "df_10706_tg['order_period'] = df_10706_tg.order_time.astype(str).str[:7]\n",
    "#df_10706_tg.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## calculate each cohort_group & order_period metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在這邊我們要分別計算每群不同首購月份的族群在不同月份的貢獻，這邊我們會特別計算人數與訂單數"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>member_code</th>\n",
       "      <th>cart_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cohort_group</th>\n",
       "      <th>order_period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-12</th>\n",
       "      <th>2016-12</th>\n",
       "      <td>137</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01</th>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02</th>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03</th>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04</th>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           member_code  cart_code\n",
       "cohort_group order_period                        \n",
       "2016-12      2016-12               137        140\n",
       "             2017-01                18         25\n",
       "             2017-02                19         20\n",
       "             2017-03                19         28\n",
       "             2017-04                15         18"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts = df_10706_tg.groupby(['cohort_group', 'order_period']).agg({'member_code': pd.Series.nunique,\n",
    "                                                                     'cart_code': pd.Series.nunique})\n",
    "\n",
    "# make the column names more meaningful\n",
    "cohorts.rename(columns={'UserId': 'TotalUsers',\n",
    "                        'OrderId': 'TotalOrders'}, inplace=True)\n",
    "cohorts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## label cohort period"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "這邊我們要將每個月份依照不同的首購月份給予編號，以便我們方便比較。比如對比11月和12月的回購狀況，10月m+1和11月m+1的比較反而更直覺"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>member_code</th>\n",
       "      <th>cart_code</th>\n",
       "      <th>period</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cohort_group</th>\n",
       "      <th>order_period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-12</th>\n",
       "      <th>2016-12</th>\n",
       "      <td>137</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01</th>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02</th>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03</th>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04</th>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           member_code  cart_code  period\n",
       "cohort_group order_period                                \n",
       "2016-12      2016-12               137        140       0\n",
       "             2017-01                18         25       1\n",
       "             2017-02                19         20       2\n",
       "             2017-03                19         28       3\n",
       "             2017-04                15         18       4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts['period'] = cohorts.groupby(level=0).cumcount()\n",
    "cohorts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## reindex cohort"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "這邊我們將以剛重新計算的編號來取代原先的編號"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>order_period</th>\n",
       "      <th>member_code</th>\n",
       "      <th>cart_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cohort_group</th>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-12</th>\n",
       "      <th>0</th>\n",
       "      <td>2016-12</td>\n",
       "      <td>137</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-02</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-04</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    order_period  member_code  cart_code\n",
       "cohort_group period                                     \n",
       "2016-12      0           2016-12          137        140\n",
       "             1           2017-01           18         25\n",
       "             2           2017-02           19         20\n",
       "             3           2017-03           19         28\n",
       "             4           2017-04           15         18"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts.reset_index(inplace=True)\n",
    "cohorts.set_index(['cohort_group', 'period'], inplace=True)\n",
    "cohorts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## calculate user retention by cohort"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "這邊開始我們要根據回購人來計算cohort table，後續我們還會根據訂單數來做一次，詳細說明僅會在這邊呈現。\n",
    "首先我們先取得首購月份的總人數"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cohort_group\n",
       "2016-12     137\n",
       "2017-01     618\n",
       "2017-02     658\n",
       "2017-03    1267\n",
       "2017-04    1244\n",
       "Name: member_code, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts_usr_group = cohorts['member_code'].groupby(level=0).first()\n",
    "cohorts_usr_group.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再來我們先將 cohorts 以剛得到的總人數來計算各個月分的回購比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cohort_group</th>\n",
       "      <th>2016-12</th>\n",
       "      <th>2017-01</th>\n",
       "      <th>2017-02</th>\n",
       "      <th>2017-03</th>\n",
       "      <th>2017-04</th>\n",
       "      <th>2017-05</th>\n",
       "      <th>2017-06</th>\n",
       "      <th>2017-07</th>\n",
       "      <th>2017-08</th>\n",
       "      <th>2017-09</th>\n",
       "      <th>2017-10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.131387</td>\n",
       "      <td>0.114887</td>\n",
       "      <td>0.148936</td>\n",
       "      <td>0.140489</td>\n",
       "      <td>0.127010</td>\n",
       "      <td>0.120062</td>\n",
       "      <td>0.124558</td>\n",
       "      <td>0.124834</td>\n",
       "      <td>0.124312</td>\n",
       "      <td>0.102093</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.138686</td>\n",
       "      <td>0.129450</td>\n",
       "      <td>0.112462</td>\n",
       "      <td>0.112865</td>\n",
       "      <td>0.091640</td>\n",
       "      <td>0.093726</td>\n",
       "      <td>0.086341</td>\n",
       "      <td>0.110226</td>\n",
       "      <td>0.093627</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.138686</td>\n",
       "      <td>0.106796</td>\n",
       "      <td>0.113982</td>\n",
       "      <td>0.086030</td>\n",
       "      <td>0.073955</td>\n",
       "      <td>0.068164</td>\n",
       "      <td>0.090587</td>\n",
       "      <td>0.096282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.109489</td>\n",
       "      <td>0.100324</td>\n",
       "      <td>0.072948</td>\n",
       "      <td>0.061563</td>\n",
       "      <td>0.069132</td>\n",
       "      <td>0.082881</td>\n",
       "      <td>0.065817</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort_group   2016-12   2017-01   2017-02   2017-03   2017-04   2017-05  \\\n",
       "period                                                                     \n",
       "0             1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "1             0.131387  0.114887  0.148936  0.140489  0.127010  0.120062   \n",
       "2             0.138686  0.129450  0.112462  0.112865  0.091640  0.093726   \n",
       "3             0.138686  0.106796  0.113982  0.086030  0.073955  0.068164   \n",
       "4             0.109489  0.100324  0.072948  0.061563  0.069132  0.082881   \n",
       "\n",
       "cohort_group   2017-06   2017-07   2017-08   2017-09  2017-10  \n",
       "period                                                         \n",
       "0             1.000000  1.000000  1.000000  1.000000      1.0  \n",
       "1             0.124558  0.124834  0.124312  0.102093      NaN  \n",
       "2             0.086341  0.110226  0.093627       NaN      NaN  \n",
       "3             0.090587  0.096282       NaN       NaN      NaN  \n",
       "4             0.065817       NaN       NaN       NaN      NaN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts_usr = cohorts['member_code'].unstack(0)                  #將cohort_group作為欄位\n",
    "cohorts_usr = cohorts_usr.divide(cohorts_usr_group, axis=1)      #計算每個月回購率\n",
    "cohorts_usr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 畫出不同首購月份的回購線圖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1e78f5a64a8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P8VcTnZIkSZIkSepnr61axTsHX+jTmMOybmXcH/1Rj++vqalh7969TJ8+nYaG\nBmKxGADjxo2joaGhx3HKy8uZN28eIYRzxurq6sjIyIifZ2RkUFdXB8Czzz7L4cOH+cQnPsG3v/3t\nHs/3Xg26TiWA9hMxhqce5aWfNyU6FUmSJEmSdAU4ceIExcXFrFu3jvT09DPGQgjdFojOp7y8nPnz\n5/dq/lOnTrFs2TLWrl3bq+fej0HXqQSQPu7DREP2wNUdiU5FkiRJkiRdAr3pKOprbW1tFBcXs2DB\ngvg+SGPHjqW+vp5YLEZ9fT1jxozpUaz9+/fT3t7O1KlTAejo6Ih/LioqorS0lNra2vj9tbW1jB8/\nnuPHj3PgwAHuu+8+AF577TWKiop47LHHmDZtWh+u9r8Myk6lKfcUATDk+mNwysKSJEmSJEnqH1EU\nsXjxYrKysli2bFn8elFREWVlZQCUlZUxe/bsHsXbunXrGV1KSUlJ8c27V6xYQSwWIz09nV27dhFF\nEZs3b2b27Nlce+21HDlyhJqaGmpqapgxY0a/FpRgkHYqXZt2Ex3tQxkyopGWV58lPeOuRKckSZIk\nSZIGoZ07d7JlyxZycnLIzc0FYNWqVSxfvpy5c+eyadMmMjMzqaioiD8zYcIEWlpaaG1tpbKykscf\nf5zs7GwAKioq2L59+wXnfOihh1i0aBEnT56ksLCQwsLC/lvgBYQoihIy8Xs1bdq0aPfu3Re974c/\nmEnr0DZGHC8g74FvXoLMJEmSJEnSpXTw4EGysrISncaA0933FkLYE0VRr9qaBuXrbwBtb45n+PCj\nHH7u7USnIkmSJEmSNOgM2qLSiBs+QlJSB0OuGVidWJIkSZIkSQPBoC0q3TG9833CcP0b0Ga3kiRJ\nkiRJUl8atEWlEekTOdW1WffrLz+T6HQkSZIkSZIGlUFbVAphCK0to0hJe509P96R6HQkSZIkSZIG\nlUFbVAJof+sGhqc201Cd6EwkSZIkSZIGl0FdVLruxpmdm3UPD4lORZIkSZIkDUKHDx8mLy+P7Oxs\nJk+ezPr16wFobm6moKCASZMmUVBQwNGjRwFoamoiLy+P1NRUlixZEo9z/PhxcnNz48eoUaN48MEH\nu51zz5495OTkMHHiRJYuXUoUdf5I2cMPP8zo0aPjMf72b/+2X9c+qItKOVN+FYAw8ijRyTcSnI0k\nSZIkSRpskpOTWbt2LVVVVezatYsNGzZQVVXF6tWryc/Pp7q6mvz8fFavXg1ASkoKK1euZM2aNWfE\nSUtLY9+FBIntAAAgAElEQVS+ffEjMzOTOXPmdDtnaWkpGzdupLq6murqanbs+K9tf+bNmxeP8fnP\nf77/Fs4gLyqNSJ/IqY5khlzbQO2L/5rodCRJkiRJ0iATi8WYMmUK0FkYysrKoq6ujm3btlFSUgJA\nSUkJlZWVAAwfPpyZM2eSkpJy3piHDh2isbGRWbNmnTNWX19PS0sLM2bMIITAwoUL47EvteSEzHqJ\nhJBE67FRpKQdYe+P/40bJ3860SlJkiRJkqR+8G8Vhzhy+ESfxhx1Yyqz5t7S4/tramrYu3cv06dP\np6GhgVgsBsC4ceNoaGjocZzy8nLmzZtHCOdu51NXV0dGRkb8PCMjg7q6uvj5I488wlNPPcWHPvQh\nvvOd73DjjTf2eN7eGtSdSgDtJzNITW2mueb8FUBJkiRJkqT348SJExQXF7Nu3TrS09PPGAshdFsg\nOp/y8nLmz5/f6xx+4zd+g5qaGp577jkKCgrinVL9ZVB3KgGMnPBR3mnbzZDURGciSZIkSZL6S286\nivpaW1sbxcXFLFiwIL4P0tixY6mvrycWi1FfX8+YMWN6FGv//v20t7czdepUADo6OuKfi4qKKC0t\npba2Nn5/bW0t48ePB2DkyJHx65///Of58pe/3CfrO59B36mUc0c+AENGHqXjWO1F7pYkSZIkSeq5\nKIpYvHgxWVlZLFu2LH69qKiIsrIyAMrKypg9e3aP4m3duvWMLqWkpKT4xtsrVqwgFouRnp7Orl27\niKKIzZs3x2PX19fHn3vsscfIysrqiyWe16DvVEpL7dysO1zbwC9f+Bdumf7biU5JkiRJkiQNEjt3\n7mTLli3k5OSQm5sLwKpVq1i+fDlz585l06ZNZGZmUlFREX9mwoQJtLS00NraSmVlJY8//jjZ2dkA\nVFRUsH379gvO+dBDD7Fo0SJOnjxJYWEhhYWFAPzVX/0Vjz32GMnJyVx//fU8/PDD/bPoLiGKon6d\noK9NmzYt2r17d6+e+eEjH+GdEMGLucz54v/sp8wkSZIkSdKldPDgwX7vxhmMuvveQgh7oiia1ps4\ng/71N4COt29keOpRWl5Nv/jNkiRJkiRJuqgroqh0/U33kpzcRkgHBlhnliRJkiRJ0uXoiigq3Tb5\nXgDC9UdpO/IfCc5GkiRJkiRp4LsiikrpabdwqiOJIde+xgsv/DDR6UiSJEmSJA14V0RRaciQobQd\nH8nVaUeoeuaFRKcjSZIkSZI04F0RRSWAjnc+QGpqM281jkx0KpIkSZIkSQPeFVNUGnnTfV2bdQfo\naEt0OpIkSZIkaRA4fPgweXl5ZGdnM3nyZNavXw9Ac3MzBQUFTJo0iYKCAo4ePQpAU1MTeXl5pKam\nsmTJknic48ePk5ubGz9GjRrFgw8+2O2ce/bsIScnh4kTJ7J06VKi036UrKKiIp7LZz/72X5c+RVU\nVMq6dSYA4fom3np1b4KzkSRJkiRJg0FycjJr166lqqqKXbt2sWHDBqqqqli9ejX5+flUV1eTn5/P\n6tWrAUhJSWHlypWsWbPmjDhpaWns27cvfmRmZjJnzpxu5ywtLWXjxo1UV1dTXV3Njh07AKiuruZb\n3/oWO3fu5Pnnn2fdunX9uvYrpqh0bfqHiE4NYciIBqpe2J7odCRJkiRJ0iAQi8WYMmUK0FkYysrK\noq6ujm3btlFSUgJASUkJlZWVAAwfPpyZM2eSkpJy3piHDh2isbGRWbNmnTNWX19PS0sLM2bMIITA\nwoUL47E3btzIF77wBa677joAxowZ06drPVtyv0a/jAwZchWtx0dydeoRqn/6CtMKEp2RJEmSJEnq\nK//34b+h8eUX+zTmmMybyVv0Oz2+v6amhr179zJ9+nQaGhqIxWIAjBs3joaGhh7HKS8vZ968eYQQ\nzhmrq6sjIyMjfp6RkUFdXR3QWYwCuOeee+jo6ODrX/86v/Zrv9bjeXvriikqAXS0TiB1xH5ePTo2\n0alIkiRJkqRB5MSJExQXF7Nu3TrS09PPGAshdFsgOp/y8nK2bNnS6xza29uprq7mySefpLa2lo9+\n9KM899xzjBgxotexeuKKKiqN+eB9nGj5OUPShkDrm3DV8ESnJEmSJEmS+kBvOor6WltbG8XFxSxY\nsCC+D9LYsWOpr68nFotRX1/f41fR9u/fT3t7O1OnTgWgo6Mj/rmoqIjS0lJqa2vj99fW1jJ+/Hig\ns2tp+vTpDB06lJtuuolbbrmF6upq7rrrrr5cbtwVs6cSwIcm3QNAGHmEY6/8NMHZSJIkSZKkgS6K\nIhYvXkxWVhbLli2LXy8qKqKsrAyAsrIyZs+e3aN4W7duZf78+fHzpKSk+ObdK1asIBaLkZ6ezq5d\nu4iiiM2bN8djf/KTn+TJJ58E4MiRIxw6dIibb765j1Z6riuqUyk97RaiU0MI1zbw3As7mDnx/0l0\nSpIkSZIkaQDbuXMnW7ZsIScnh9zcXABWrVrF8uXLmTt3Lps2bSIzM5OKior4MxMmTKClpYXW1lYq\nKyt5/PHHyc7OBqCiooLt2y/8A2MPPfQQixYt4uTJkxQWFlJYWAjAr/7qr8ZjJSUl8e1vf5uRI0f2\n08ohRFHUb8H7w7Rp06Ldu3e/5+e3/9MM3jqVzNv7b+E3V/xdH2YmSZIkSZIupYMHD5KVlZXoNAac\n7r63EMKeKIqm9SbOFfX6G0BH+wRSU5toPT4u0alIkiRJkiQNWFdcUWnMB+9l6NBWhqQPgbeaE52O\nJEmSJEnSgHTFFZVuufnDnR+uP8LrL/3fxCYjSZIkSZI0QF1xRaX0tKzOzbrTX+MXLzyR6HQkSZIk\nSZIGpCuuqJSUNIy2E9dzTVoTtc+eTHQ6kiRJkiRJA9IVV1QC6Gi/ibTUJjreGg8D7NfvJEmSJEmS\nLgdXZFFpzMR7GXrVO4T0IURvHE50OpIkSZIkaYA6fPgweXl5ZGdnM3nyZNavXw9Ac3MzBQUFTJo0\niYKCAo4ePQpAU1MTeXl5pKamsmTJknic48ePk5ubGz9GjRrFgw8+2O2ce/bsIScnh4kTJ7J06VKi\nroaZ//bf/lv8+VtuuYURI0b069qvyKLSpMzpnR+ue53al9xXSZIkSZIkvTfJycmsXbuWqqoqdu3a\nxYYNG6iqqmL16tXk5+dTXV1Nfn4+q1evBiAlJYWVK1eyZs2aM+KkpaWxb9+++JGZmcmcOXO6nbO0\ntJSNGzdSXV1NdXU1O3bsAOA73/lO/Pk/+IM/OO/zfeWKLCqlp2cTnQqEa1/jwMGnEp2OJEmSJEka\noGKxGFOmTAE6C0NZWVnU1dWxbds2SkpKACgpKaGyshKA4cOHM3PmTFJSUs4b89ChQzQ2NjJr1qxz\nxurr62lpaWHGjBmEEFi4cGE89um2bt3K/Pnz+2KJ55Xcr9EvU0lJKbS9eT3DU5t4bc91iU5HkiRJ\nkiS9T2/88y9pffXNPo151Q3DGfEbH+zx/TU1Nezdu5fp06fT0NBALBYDYNy4cTQ0NPQ4Tnl5OfPm\nzSOEcM5YXV0dGRkZ8fOMjAzq6urOuOfll1/mpZde4mMf+1iP53wvrshOJYBTHTeTmtZE9M54OHUq\n0elIkiRJkqQB7MSJExQXF7Nu3TrS09PPGAshdFsgOp/y8vL31WVUXl7O/fffT1JS0nuO0RNXZKcS\nwNiJ93LsyM8hbQgdR14gaUx2olOSJEmSJEnvUW86ivpaW1sbxcXFLFiwIL6P0dixY6mvrycWi1Ff\nX8+YMWN6FGv//v20t7czdepUADo6OuKfi4qKKC0tpba2Nn5/bW0t48ePPyNGeXk5GzZs6IulXdAV\n26n0wRvv7vxwfSMvvfiviU1GkiRJkiQNSFEUsXjxYrKysli2bFn8elFREWVlZQCUlZUxe/bsHsU7\ney+kpKSk+ObbK1asIBaLkZ6ezq5du4iiiM2bN58R+4UXXuDo0aN8+MMf7qMVnt8V26mUnp5FFAVC\n+ms8V3WUiTP+MNEpSZIkSZKkAWbnzp1s2bKFnJwccnNzAVi1ahXLly9n7ty5bNq0iczMTCoqKuLP\nTJgwgZaWFlpbW6msrOTxxx8nO7vzDaqKigq2b99+wTkfeughFi1axMmTJyksLKSwsDA+Vl5ezmc+\n85levW73Xl2xRaWkpGtoO3Edw9OaOPrzSYlOR5IkSZIkDUAzZ84kiqJux5544olur9fU1Jw33osv\nvnjROadNm8aBAwe6Hfv6179+0ef7yhX7+hvAqWgiqanN0H4jtL+T6HQkSZIkSZIGjH4tKoUQfi2E\n8B8hhP8MISzvZvzaEMI/hxD2hxCeDyF8rj/zOdu4m2cxbNhJGB7RVr/vUk4tSZIkSZI0oPVbUSmE\nkARsAAqBbGB+COHsn1j7AlAVRdEdwH3A2hDCVf2V09luypgGQDTyCIfcrFuSJEmSJKnH+rNT6W7g\nP6MoejGKolagHDh7q/MISAudu0elAs1Aez/mdIb09MlEEYT0eg5UPXupppUkSZIkSRrw+rOoNB44\nfNp5bde1030PyAJeBZ4D/jCKolNnBwoh/E4IYXcIYffrr7/eZwkmJw+n7c3rSU1t4sQvR/VZXEmS\nJEmSpMEu0Rt1/yqwD7gByAW+F0JIP/umKIr+JoqiaVEUTRs9enSfJnAqTCQ1rRlOjYd3jvdpbEmS\nJEmSpMGqP4tKdcCNp51ndF073eeAR6NO/wm8BNzajzmdY9yEmQwb9hakRrx1+N8v5dSSJEmSJGmA\nO3z4MHl5eWRnZzN58mTWr18PQHNzMwUFBUyaNImCggKOHj0KQFNTE3l5eaSmprJkyZJ4nOPHj5Ob\nmxs/Ro0axYMPPtjtnHv27CEnJ4eJEyeydOlSoigC4JVXXiEvL48777yT22+/ne3bt/fr2vuzqPRz\nYFII4aauzbc/Azx21j2vAPkAIYSxwIeAF/sxp3PcNH4qANF1r3PwpR9dyqklSZIkSdIAl5yczNq1\na6mqqmLXrl1s2LCBqqoqVq9eTX5+PtXV1eTn57N69WoAUlJSWLlyJWvWrDkjTlpaGvv27YsfmZmZ\nzJkzp9s5S0tL2bhxI9XV1VRXV7Njxw4AvvnNbzJ37lz27t1LeXk5v//7v9+va++3olIURe3AEuD/\nAAeBiiiKng8h/F4I4fe6blsJfCSE8BzwBPCVKIqO9FdO3UlPnwzAkPR6Dj5/4FJOLUmSJEmSBrhY\nLMaUKVOAzsJQVlYWdXV1bNu2jZKSEgBKSkqorKwEYPjw4cycOZOUlJTzxjx06BCNjY3MmjXrnLH6\n+npaWlqYMWMGIQQWLlwYjx1CoKWlBYBjx45xww039Olaz5bcn8GjKNoObD/r2l+f9vlV4Ff6M4eL\nSU5Oo+3N6xie1kzL8x9KZCqSJEmSJOk9+pd/+Rdee+21Po05btw4CgsLe3x/TU0Ne/fuZfr06TQ0\nNBCLxeJxGhoaehynvLycefPmEUI4Z6yuro6MjIz4eUZGBnV1nbsNff3rX+dXfuVX+O53v8ubb77J\nj37Uv29kJXqj7svCqTCJ1NRmhjAeTjQmOh1JkiRJkjTAnDhxguLiYtatW0d6+pm/QRZC6LZAdD7l\n5eXMnz+/1zls3bqVRYsWUVtby/bt23nggQc4depUr+P0VL92Kg0UsZtm0tzwM04Njzj28jNcO7n7\ndxYlSZIkSdLlqTcdRX2tra2N4uJiFixYEN8HaezYsdTX1xOLxaivr2fMmDE9irV//37a29uZOrVz\nD+iOjo7456KiIkpLS6mtrY3fX1tby/jx4wHYtGlTfH+lD3/4w7z99tscOXKkx3P3lp1KQGbsTgCi\n6xt5vuaJBGcjSZIkSZIGiiiKWLx4MVlZWSxbtix+vaioiLKyMgDKysqYPXt2j+Jt3br1jC6lpKSk\n+ObdK1asIBaLkZ6ezq5du4iiiM2bN8djf+ADH+CJJzrrGgcPHuTtt99m9OjRfbXUc9ipBKSl3QbA\nkLTXOFT1Jh/5RIITkiRJkiRJA8LOnTvZsmULOTk55ObmArBq1SqWL1/O3Llz2bRpE5mZmVRUVMSf\nmTBhAi0tLbS2tlJZWcnjjz9OdnY2ABUVFWzfvr3bud710EMPsWjRIk6ePElhYWG8S2vt2rX89m//\nNt/5zncIIfDwww/36rW73rKoBAwdmk7bWyNIS2vmWNWtEEXQj1+6JEmSJEkaHGbOnEkURd2Ovds1\ndLaamprzxnvxxRcvOue0adM4cODcX7DPzs5m586dF32+r/j6W5doyIdITWsiKdwAR2sSnY4kSZIk\nSdJlzaJSl1jmDFJS3qRjeETjS08mOh1JkiRJkqTLmkWlLjeOmwJAdF0jBw4/neBsJEmSJEmSLm8W\nlbqkp3du1p2U9hovVr2a4GwkSZIkSZIubxaVugwdOoK2k9eSmtZMdGQSdLQnOiVJkiRJkqTLlkWl\n00RJHyI1tYkhYRxR48FEpyNJkiRJknTZsqh0mnE3zuDqq0/QcQ3U1vw40elIkiRJkqTL3OHDh8nL\nyyM7O5vJkyezfv16AJqbmykoKGDSpEkUFBRw9OhRAJqamsjLyyM1NZUlS5bE4xw/fpzc3Nz4MWrU\nKB588MFu59yzZw85OTlMnDiRpUuXEkURAC+//DL5+fncfvvt3HfffdTW1vbr2i0qnebGcXcC0HHd\naxw4/JMEZyNJkiRJki53ycnJrF27lqqqKnbt2sWGDRuoqqpi9erV5OfnU11dTX5+PqtXrwYgJSWF\nlStXsmbNmjPipKWlsW/fvviRmZnJnDlzup2ztLSUjRs3Ul1dTXV1NTt27ADgi1/8IgsXLuQXv/gF\nf/qnf8pXv/rVfl27RaXTpKd1btadnN7Ay1VvJDgbSZIkSZJ0uYvFYkyZ0vmL8mlpaWRlZVFXV8e2\nbdsoKSkBoKSkhMrKSgCGDx/OzJkzSUlJOW/MQ4cO0djYyKxZs84Zq6+vp6WlhRkzZhBCYOHChfHY\nVVVVfOxjHwMgLy+Pbdu29elaz5bcr9EHmKuuup72t9NJTW3m+LHJ0HYShl6d6LQkSZIkSdJFHDq0\nkuMn+nZ/5LTULG655Ws9vr+mpoa9e/cyffp0GhoaiMViAIwbN46GhoYexykvL2fevHmEEM4Zq6ur\nIyMjI36ekZFBXV0dAHfccQePPvoof/iHf8g//dM/cfz4cZqamhg5cmSP5+4NO5XOEiXdSmpqE0lJ\n4+io35/odCRJkiRJ0gBw4sQJiouLWbduHenp6WeMhRC6LRCdT3l5OfPnz+91DmvWrOGpp57izjvv\n5KmnnmL8+PEkJSX1Ok5P2al0lnE3TqfptZ/RfnUHL770IyZ9YEaiU5IkSZIkSRfRm46ivtbW1kZx\ncTELFiyI74M0duxY6uvricVi1NfXM2bMmB7F2r9/P+3t7UydOhWAjo6O+OeioiJKS0vP2IC7traW\n8ePHA3DDDTfw6KOPAp1FrkceeYQRI0b02TrPZqfSWTLG5ALQcV0jBw7/LMHZSJIkSZKky1kURSxe\nvJisrCyWLVsWv15UVERZWRkAZWVlzJ49u0fxtm7dekaXUlJSUnzz7hUrVhCLxUhPT2fXrl1EUcTm\nzZvjsY8cOcKpU6cA+Na3vsVv/dZv9dUyu2Wn0lnS0zs36x6a1sBrh95JcDaSJEmSJOlytnPnTrZs\n2UJOTg65uZ2NKqtWrWL58uXMnTuXTZs2kZmZSUVFRfyZCRMm0NLSQmtrK5WVlTz++ONkZ2cDUFFR\nwfbt2y8450MPPcSiRYs4efIkhYWFFBYWAvDkk0/y1a9+lRACH/3oR9mwYUM/rbqTRaWzXHXVqM7N\nutOaON5yO5w8Cldfl+i0JEmSJEnSZWjmzJlEUdTt2BNPPNHt9ZqamvPGe/HFFy8657Rp0zhw4MA5\n1++//37uv//+iz7fV3z9rRvR0FtJTW1mSPJoWmt/nuh0JEmSJEmSLjsWlboRGz+Nq69uoe3qUxyq\n6b6qKEmSJEmSdCWzqNSNG0bfSQjQMaKB5w7vTXQ6kiRJkiTpPM736pm615ffl0WlbqSn5wBwVXoj\nTb8MCc5GkiRJkiR1JyUlhaamJgtLPRRFEU1NTaSkpPRJPDfq7sawYaNpfyeN1NQmTrx1B7S8Cuk3\nJDotSZIkSZJ0moyMDGpra3n99dcTncqAkZKSQkZGRp/Esqh0PkNvJS31ECeTRvHWKz/hmtsu3e7p\nkiRJkiTp4oYOHcpNN92U6DSuWL7+dh7jbpjG1dcc452rT1H18pOJTkeSJEmSJOmyYlHpPG4Yndu1\nWfdrHKj7RaLTkSRJkiRJuqxYVDqPdzfrHpbWyPGXUuHUqQRnJEmSJEmSdPmwqHQeV101hvbWVFJT\nmxn29mRo/mWiU5IkSZIkSbpsWFQ6jxACXJVFamozSckjeePlZxKdkiRJkiRJ0mXDotIFxGJTuWb4\nMd5O6eD5w08nOh1JkiRJkqTLhkWlC4iNyiWEiPYRr3Gg7mCi05EkSZIkSbpsWFS6gLS0yQCkpB7h\nnVdGQ3trgjOSJEmSJEm6PFhUuoBhw2K0tw0nNa2JlNbJ0FiV6JQkSZIkSZIuCxaVLiCEQLgqi7TU\nZoYMvY6GmqcSnZIkSZIkSdJlwaLSRYwbeyfXXPMG76R0cKDuJ4lOR5IkSZIk6bJgUekixo26kzAk\noi39NQ6++p+JTkeSJEmSJOmyYFHpItLSbgPg6rTXaa/9ALxzIsEZSZIkSZIkJZ5FpYtISbmBjrZr\nSE1t5pr2LKJX9yU6JUmSJEmSpISzqHQRIQTCsHc3676Wwy8/meiUJEmSJEmSEs6iUg+MGZPLNcPf\n4O1hpzhQ/7NEpyNJkiRJkpRwFpV6oHOz7lO0pdfzH/WvJDodSZIkSZKkhLOo1APp727WnX6EUD8R\n3jyS4IwkSZIk6f9n786D5LwP885/f31Pd789B04CIAnwBqYB6qCoy3d8ahPJTmlt2XGcteJ1eV12\n7HI2a7t214k3m0pcqdrassu2SutaOxtvreLEsizLsiUliqyLlgSQEtVDihdAEjODY+777t/+0QMQ\nIHEMjp4XM/39VHXN9Nvv9DzDIv/gU+/7tCSly1JpA0qlA6ytdlGtjlNZfYTVwa+lHUmSJEmSJClV\nlkobEEIgFFpj3SGfcNKxbkmSJEmS1OEslTaoNdY9wUJxjYFzT6YdR5IkSZIkKVWWShu0d8ejZDJN\nVmpneXHoLMSYdiRJkiRJkqTUWCptUHJhrDsZozBSh0k/BU6SJEmSJHUuS6UN6uq6l7XVEkl1nHLz\nQZYHv5p2JEmSJEmSpNRYKm1QCIFQbI11U6jynGPdkiRJkiSpg1kq3YA9ux6lXJlgvrBG4/zTaceR\nJEmSJElKjaXSDdjd9yiZ7BoryVlOnZ2C5lrakSRJkiRJklJhqXQDarWjAFSSMcrjj8Lo8yknkiRJ\nkiRJSoel0g3o6rqXtbUS1WSM6tp9zL365bQjSZIkSZIkpcJS6QaEkCFTeJhadYJYqPDM6S+kHUmS\nJEmSJCkVlko3aPeuN1GujDNbXGVgdCDtOJIkSZIkSamwVLpBF8a6V6vnGTy7AiuLaUeSJEmSJEna\ndJZKNyhJ6kBrrLs68WY410g5kSRJkiRJ0uazVLpB5fJBms0i1eoYVQ4y8cqX0o4kSZIkSZK06SyV\nblAI2Ytj3c1CFwNDlkqSJEmSJKnzWCrdhF07H6VSHWc2v0pj4vm040iSJEmSJG06S6WbsKv3GJns\nKqvV85w7G2BxKu1IkiRJkiRJm8pS6SZcGOuuJuN0T7+VOPRkyokkSZIkSZI2l6XSTahU7qfZLLTG\nuuM9nHv1i2lHkiRJkiRJ2lSWSjfh0rHutUKJgeGvpB1JkiRJkiRpU1kq3aRdO45RqY4xW1ihMXky\n7TiSJEmSJEmbylLpJu3sO0Ymt8pqZZSJkQrMnE07kiRJkiRJ0qaxVLpJteQoANVkjN6ptxIHT6Sc\nSJIkSZIkafNYKt2kcvl+ms081eo41bCfV1/5m7QjSZIkSZIkbRpLpZuUyeTIFh+iuzrBarFE4/yT\naUeSJEmSJEnaNJZKt2Bn36OUq2PM5FcYmHoVYkw7kiRJkiRJ0qawVLoFO3uPkc2tsFYZZW50F4z7\nKXCSJEmSJKkzWCrdgiSpA1CtjrFj+lFWB4+nnEiSJEmSJGlzWCrdgkrlAZpxfaw7s4+XXv182pEk\nSZIkSZI2haXSLchk8mQLD9BdnWC5UGBg5BtpR5IkSZIkSdoUlkq36MJY93R+hWdmzsLaStqRJEmS\nJEmS2s5S6Rbt6DlKNr9MszzG0ugBOP9s2pEkSZIkSZLazlLpFiW1C2Pd4+yePcbS4FdTTiRJkiRJ\nktR+lkq3qFp5iGbMUa2OU8ns4bnTX0g7kiRJkiRJUtvlrndCCKHvCodnYoyOBwGZTIFs4QF6qhOc\nKRZojD3DsbRDSZIkSZIktdlGrlR6EhgBngdeWP/+5RDCkyGEt7Yz3Faxs+8o5WSM6dwS35odh+X5\ntCNJkiRJkiS11UZKpc8A74kx7owx7gB+CPgE8PPA77Uz3FbR13OMbH6JZnmCOHIQzj6ddiRJkiRJ\nkqS22kip9I4Y46cuPIkxfhp4Z4zxb4Fi25JtIbWkNdadVMfZu1Bn7vQTKSeSJEmSJElqr42USmdC\nCL8aQrh3/fE/AedCCFmg2eZ8W0K1+jAxZqlWxyhl9/DM4JfTjiRJkiRJktRWGymVfgI4AHxs/XHP\n+rEs8KPti7Z1ZDJFMoX76alOslTI0Zh4Lu1IkiRJkiRJbXXdT3+LMY4Cv3iVl1+8vXG2rp29x1id\n+wTT+WVemZ2F+XEoX+mD8yRJkiRJkra+616pFEJ4KITw4RDCp0MIn73w2IxwW0lfzzGyhUViaZLi\n6MMw/GTakSRJkiRJktrmulcqAf8R+BDwB8Bae+NsXUnSD0C1Ok7PuSOMv/pl+h743pRTSZIkSZIk\ntcdGSqXVGOPvtz3JFletPkKMGarVcZq5BxkY/iLfnnYoSZIkSZKkNtnIUPdfhBB+PoRwVwih78Kj\n7RbNXIYAACAASURBVMm2mGy2RKZwHz3VCRaLORpTL0GMaceSJEmSJElqi41cqfSP1r/+s0uOReC+\n2x9na9vZe4yV2U8ynVtkZHENpoeg+0DasSRJkiRJkm67jXz626HNCLId9PYcZeT8R4mlaaojDxEH\njxMslSRJkiRJ0jZ01VIphPA9McbPhhD+/pVejzF+tH2xtqZaUgdaY919545w7vSX2Nv/wymnkiRJ\nkiRJuv2udaXSdwKfBf7eFV6LgKXS61Srh4kxQ606zlr+ARpnP8PetENJkiRJkiS1wVVLpRjjP1//\n+tObF2dry2a7yBYO0V2ZYLiQpTH7Kt/bbEJmI3vokiRJkiRJW8d1244Qwi+FEGqh5Q9CCE+GEL5/\nI28eQvjBEMJzIYQXQwi/dpVzviuE8PUQwkAI4W9u9A+40/T1HKWcjDKZW+TkYhbGXkg7kiRJkiRJ\n0m23kUtoPhhjnAa+H9gB/EPg31zvh0IIWeB3gR8CjgA/HkI48rpzeoDfA94bY+wH/tsbi3/n6es5\nSq64QCjO0jtymObgibQjSZIkSZIk3XYbKZXC+tf3AP9PjHHgkmPX8jjwYozxZIxxGfgI8L7XnfMT\nwEdjjK8CxBjPbyz2nSu5ONY9xv7lh3n19BdSTiRJkiRJknT7baRUOhFC+DStUulTIYQEaG7g5/YD\npy95Prh+7FIPAb0hhM+FEE6EEH7qSm8UQvjZEMLxEMLxkZGRDfzq9CTJEWIM1KoTZPO9NEa+kXYk\nSZIkSZKk224jpdI/Bn4NeFuMcR4oALdrvDsHvBX4b4AfAP7XEMJDrz8pxvjhGONjMcbHdu3adZt+\ndXtks2WyhYN0VydYKGQYmD8Lq8tpx5IkSZIkSbqtrvrpbxfEGJshhFPAQyGE0g289xBw9yXPD6wf\nu9QgMBZjnAPmQgifBx4Fnr+B33PH6es5yvLMf2Eyu8TwQgnONWD/W9KOJUmSJEmSdNts5NPffgb4\nPPAp4DfXv/6LDbz314AHQwiHQggF4APAx193zp8D3xZCyIUQysDbgWc3Hv/O1NtzjFxpjlCYZffI\nYVYHv5Z2JEmSJEmSpNtqI7e//RLwNuCVGON3A28GJq/3QzHGVeAXaJVQzwJ/EmMcCCH8XAjh59bP\neRb4a+Bp4KvAH8QYGzf1l9xBkmo/ANVkjP0rD/HS4JdSTiRJkiRJknR7Xff2N2AxxrgYQiCEUIwx\nfiuE8PBG3jzG+Engk6879qHXPf+3wL/dcOIt4OJYd2WCtcKDNEY/zYb+gUmSJEmSJG0RG7lSaTCE\n0AN8DPhMCOHPgVfaG2try+WqZAv30FOdZKEQaCyNw9JM2rEkSZIkSZJum40Mdf/I+rf/IoTwX4Fu\nWres6Rr6eo6xNP1ZJrOLDC9UYPjrcOjb044lSZIkSZJ0W2zkSiVCCNkQwj7gFPB1YG9bU20DPd11\n8l1zZApzHBips3T6K2lHkiRJkiRJum2ue6VSCOEXgX8OnAOa64cjcKyNuba8WlIHoFodpzJ6P98a\nfoJHU84kSZIkSZJ0u2xkqPuXgIdjjGPtDrOdJEnrE+Bq1QnW8g/QGH/OUkmSJEmSJG0bG7n97TQw\n1e4g200ul5DJ3UNPZYK5AgyszcPs+bRjSZIkSZIk3RZXvVIphPAr69+eBD4XQvhLYOnC6zHG/6PN\n2ba83p46SzOfZyq7yOhsDYaehId/MO1YkiRJkiRJt+xaVyol649Xgc8AhUuOJe2PtvX1dh8l3zVL\nJr/AwdF+Zgcd65YkSZIkSdvDVa9UijH+5mYG2Y6SC2PdyTjV0UM8M/y3PJ5yJkmSJEmSpNvhuptK\nIYTPhBB6LnneG0L4VHtjbQ8XSqWeygSxmNCYegliTDmVJEmSJEnSrdvIUPeuGOPkhScxxglgd/si\nbR/5fI1Mbj/d1dZYdyOswcTLaceSJEmSJEm6ZRspldZCCPdceBJCuBfwcpsN6u05Rld1jKnMIlNT\nPTD8ZNqRJEmSJEmSbtlGSqX/GfhiCOHfhxD+GPg88OvtjbV99HQfJV+eJptf5KHRfsZPP5F2JEmS\nJEmSpFt21aFugBBCAAaAtwDvWD/8yzHG0XYH2y6SpB+AanWcZOwgjTOf5ztSziRJkiRJknSrrnml\nUowxAp+MMY7GGD+x/rBQugG19VKpuzJJs1BlYPZVWFtNOZUkSZIkSdKt2cjtb0+GEN7W9iTbVD7f\nSya3j57KBLOFJgPZDIw+l3YsSZIkSZKkW7KRUuntwBMhhJdCCE+HEL4ZQni63cG2k97uo3QlI0xn\nFlmY7CUOHk87kiRJkiRJ0i255qbSuh9oe4ptrqe7zljlU2Ryyxweq3P29BPc9dZ/lHYsSZIkSZKk\nm7aRK5XiVR7aoCSpA62x7r54L43zT6WcSJIkSZIk6dZs5Eqlv6RVIgWgBBwCngP625hrW7lQKvVW\nJ1ku3kdj8Rzft7IA+a6Uk0mSJEmSJN2c616pFGM8GmM8tv71QeBx4In2R9s+CoU+Mrm9dFcmmM03\neTabh7PfTDuWJEmSJEnSTdvI7W+XiTE+SWu8Wzegp7tOaX2suzm+k+bgibQjSZIkSZIk3bTr3v4W\nQviVS55mgLcAw21LtE311I4yXvnPZHPL9I/388rglzjE/5B2LEmSJEmSpJuykSuVkkseRVobS+9r\nZ6jtKKldGOueoDvcTWO0kXIiSZIkSZKkm3fNK5VCCLtolUgvxhgnNyfS9nRxrLsyyXLhEAMrk/y9\nhQno6k05mSRJkiRJ0o276pVKIYSfAQaA3wG+FUJ476al2oaKhZ1kcrvprowzU1jj2WwBhp9KO5Yk\nSZIkSdJNudbtb78M9McY3wm8C/j1zYm0ffV0H6WUjDGTWaIwsoeVweNpR5IkSZIkSbop1yqVlmOM\nIwAxxpO09pR0C7prR8lXJshmV6hPHuGlwSfSjiRJkiRJknRTrrWpdCCE8NtXex5j/Cfti7U91ZJ+\nQoBKZZxk4gCNic/zSNqhJEmSJEmSbsK1SqV/9rrnJ9oZpBNcGOvuq0yxVLyPRlzk/dPDUNuXcjJJ\nkiRJkqQbc9VSKcb47zYzSCcoFneTye6kVpngZH6V5zMFGHrSUkmSJEmSJG0519pUUhu0xrpHmc0s\n0X1uH4uDX007kiRJkiRJ0g2zVNpk3bU6heoEmcwK/dOH+dbwV9KOJEmSJEmSdMOuWyqFEN69kWPa\nmCSpE0KkUp2gnNnHwNRL0GymHUuSJEmSJOmGbORKpd/Z4DFtQFJrjXXvKE+zXCzSyDZh/GTKqSRJ\nkiRJkm7MVYe6QwjvBN4F7Aoh/MolL9WAbLuDbVfFwh4y2R3UquOcz6/wQrMAQydg5wNpR5MkSZIk\nSdqwa12pVACqtIqn5JLHNPD+9kfbnkIIdNfqlKqjzGeW2Xv+bmYG3VWSJEmSJElby1WvVIox/k0I\n4YvAsRjjb25ipm2vu7vOePJ5MplVjkw8zDNnj/P2tENJkiRJkiTdgGtuKsUY14B9m5SlY9QujHWX\nJyjl7qIx8yqsraQdS5IkSZIkacOueqXSJb4eQvg48B+BuQsHY4wfbVuqbS5JWmPdO6vTzBcP8kIh\nC+cGYN+bUk4mSZIkSZK0MRsplUrAGPA9lxyLgKXSTSoW7yKT7SGpjHM2t8xLawUYftJSSZIkSZIk\nbRnXLZVijD+9GUE6SWus+ygL1WdZyKxw/5l7GTv9BDse+2Da0SRJkiRJkjbkmptKACGEAyGEPwsh\nnF9//GkI4cBmhNvOumt1Csk4Iazx0PwjDJz/RtqRJEmSJEmSNuy6pRLwh8DHaQ127wP+Yv2YbkGS\n1AmZJtXKBIXcbhqL52BpNu1YkiRJkiRJG7KRUmlXjPEPY4yr648/Ana1Ode2d3GsuzLNUiFPo5CH\nM16tJEmSJEmStoaNlEpjIYSfDCFk1x8/SWu4W7egVNpPyHaTVMaZyi/z6nKBOHQi7ViSJEmSJEkb\nspFS6YPAjwJngTPA+wHHu29RCIGeWp1iMspiWKH/zCHODD6RdixJkiRJkqQN2cinv70CvHcTsnSc\nWlJnLHmCENa4b+FhGqOfYV/aoSRJkiRJkjbguqVSCGEX8N8DBy89P8b4wfbF6gxJrU4m06RaniI/\ncxeN1Wm+f24UKjvTjiZJkiRJknRN1y2VgD8HvgD8Z2CtvXE6S+3iWPcUc8UDDBQLMPwUPPh9KSeT\nJEmSJEm6to2USuUY46+2PUkHKpXuJmQSkso4Q7llziwUaA4eJ2OpJEmSJEmS7nAbGer+RAjhPW1P\n0oFCCHSvj3Uvh1XefPZ+Xh7627RjSZIkSZIkXddVS6UQwkwIYRr4JVrF0uL6sQvHdRvUanUKtTFC\nWOPg4kMMjD8LMaYdS5IkSZIk6ZquWirFGJMYY239aybGWFr/Pokx1jYz5HZWS+pkMmtUy1NkCzto\nsAxTp9OOJUmSJEmSdE0b2VQihPBe4DvWn34uxviJ9kXqLEnSD8DO8gwzhQM0CgUYOgE996ScTJIk\nSZIk6equu6kUQvg3tG6Be2b98UshhH/d7mCdoqvrXkKmSlIZYyq3xMR8kZXB42nHkiRJkiRJuqaN\nXKn0HuBNMcYmQAjh3wFPAb/ezmCdIoQM3bV+5pOXWQlrPD78EC/2fpXDaQeTJEmSJEm6ho18+htA\nzyXfd7cjSCerJXWKtTFCaHJg5X4aUy9Bcy3tWJIkSZIkSVe1kVLpXwNPhRD+aP0qpRPAv2pvrM6S\nJHUy2VWqXdOEQh8DuQijz6cdS5IkSZIk6aque/tbjPH/CyF8Dnjb+qFfjTGebWuqDpMkdQB2VqaZ\nnt5HI1+AoSdhtzfBSZIkSZKkO9NVr1QKIfxACOH9ADHGMzHGj8cYPw58Wwjh+zYtYQcolw8SMhWq\n5XGmsossTJdYGPxq2rEkSZIkSZKu6lq3v/0G8DdXOP454H9rS5oOFUKGWnKEQjLCamjyznMP89xZ\nPwFOkiRJkiTdua5VKhVjjCOvPxhjHAUq7YvUmWq1OsXuMaDJXSv305g9DatLaceSJEmSJEm6omuV\nSrUQwhs2l0IIeaCrfZE6U5LUyWZXSLpmoNhDo5CDs420Y0mSJEmSJF3RtUqljwL/Vwjh4lVJIYQq\n8KH113Qb1S6MdZenmSsEnskXYOhEyqkkSZIkSZKu7Fql0v8CnANeCSGcCCGcAE4BI+uv6TYqlw8R\nMl1Uq2NMZReJk2WmhxzrliRJkiRJd6Y33N52QYxxFfi1EMJvAg+sH34xxriwKck6TAhZkuoR5pJh\n1kKTd519mGeSJ3lH2sEkSZIkSZKu4FpXKgEQY1yIMX5z/WGh1Eatse5RoMme5iEaiyOwOJV2LEmS\nJEmSpDe4bqmkzVNbH+uudc0RC90MFAsw/PW0Y0mSJEmSJL3BVUulEMK7178WNy9OZ0uSfgB2lqeY\nLeBYtyRJkiRJumNd60ql317/+sRmBBGUy/cTQolKpTXWnYwmjA59Je1YkiRJkiRJb3DVoW5gJYTw\nYWB/COG3X/9ijPGftC9WZ8pkciTJYWaT8zRD5PHzDzFQfZLvTDuYJEmSJEnS61zrSqW/C3wWWARO\nXOGhNnhtrDuyMx6ksTYLM2fTjiVJkiRJknSZq16pFGMcBT4SQng2xviNTczU0ZJqnVzu31MrzdEs\n7qZRLMDQk/DIe9KOJkmSJEmSdNFGPv1tLITwZyGE8+uPPw0hHGh7sg6V1OrAhbHuyLdyeaJj3ZIk\nSZIk6Q6zkVLpD4GPA/vWH3+xfkxtUCk/QAhFypUxpjKL7D1XY9ixbkmSJEmSdIfZSKm0O8b4hzHG\n1fXHHwG72pyrY2UyOZLqI+STMWKIPDb6CI3xZyHGtKNJkiRJkiRdtJFSaTSE8JMhhOz64yeBsXYH\n62RJrU6pZwSI9HEvA2EVxk+mHUuSJEmSJOmijZRKHwR+FDgLnAHeD/x0O0N1ulpSJ5dbolacZ7VU\nbY11Dz+VdixJkiRJkqSLrvrpbxfEGF8B3rsJWbQuSS6MdU8zNr+DFzJ5moPHyRx9f8rJJEmSJEmS\nWjZypZI2WaXyAIQ85coY05lF7jvTw8vDX007liRJkiRJ0kWWSnegTKZAUn2EXDJKDPDm8UdoTL0E\na6tpR5MkSZIkSQIsle5YtaROqbc11t2TuYdGLsDIs2nHkiRJkiRJAm6gVAohvCOE8NchhM+FEH64\nnaHU2lXK5xbpKSyyUiwzUCzA0Im0Y0mSJEmSJAHXKJVCCHtfd+hXgB8B3gP8y3aGEiRJPwB95Ulm\n8k1OhTwrg8dTTiVJkiRJktRyrSuVPhRC+I0QQmn9+STwflrF0nTbk3W4avUhCDnKlXGmM4scGe7j\nhTNeqSRJkiRJku4MVy2VYow/DDwFfCKE8FPALwNFYAfg7W9tlskUSSoPk62NQYBjEw/TmDsNy/Np\nR5MkSZIkSbr2plKM8S+AHwC6gT8Dno8x/naMcWQzwnW6pFanq6c11l3L3s1AMQ9nn047liRJkiRJ\n0jU3ld4bQvivwF8DDeDHgPeFED4SQrh/swJ2siSpk8/P01NYZrlYplFwrFuSJEmSJN0Zctd47X8H\nHge6gE/FGB8H/mkI4UHgXwEf2IR8Ha2W1AHo65pkZLHGcDPHwuDX6Eo5lyRJkiRJ0rVKpSng7wNl\n4PyFgzHGF7BQ2hSVysNAjq7KGDNTe3jz0C6+tfoUb047mCRJkiRJ6njX2lT6EVqj3DngJzYnji6V\nzRapVh8kWxsHoH/6YRrLYzA/nnIySZIkSZLU6a716W+jMcbfiTF+KMY4vZmh9JokqdPVex6IVLIH\naBQLMPxk2rEkSZIkSVKHu+anvyl9taROIT9HT36FpWKJgWIBhp5KO5YkSZIkSepwlkp3uOSSse7p\n/CqjKzmmB7+acipJkiRJktTp2loqhRB+MITwXAjhxRDCr13jvLeFEFZDCO9vZ56tqFp9BMjSVRlj\nLrPE20/vYWDkaYgx7WiSJEmSJKmDta1UCiFkgd8Ffgg4Avx4COHIVc77LeDT7cqylWWzJSqVB8h0\nt8a5H5l9mIE4D9NDKSeTJEmSJEmdrJ1XKj0OvBhjPBljXAY+ArzvCuf9IvCnwPk2ZtnSakmdrt4R\nIFLO72uNdQ851i1JkiRJktLTzlJpP3D6kueD68cuCiHsB34E+P1rvVEI4WdDCMdDCMdHRkZue9A7\nXVKrU8zP0JtbY6FYpFEswtCJtGNJkiRJkqQOlvZQ9/8J/GqMsXmtk2KMH44xPhZjfGzXrl2bFO3O\nkST9APR2TTCdW2FxIcPosGPdkiRJkiQpPbk2vvcQcPclzw+sH7vUY8BHQggAO4H3hBBWY4wfa2Ou\nLSepHgEylKrjzM/s4p2D+2js/hbf1WxCJu1eUJIkSZIkdaJ2NhJfAx4MIRwKIRSADwAfv/SEGOOh\nGOPBGONB4D8BP2+h9EbZbBeVyv1kaq2x7gfmH6KRWYOxF1NOJkmSJEmSOlXbSqUY4yrwC8CngGeB\nP4kxDoQQfi6E8HPt+r3bVZLU6eo9T4hQujjW7a6SJEmSJElKRztvfyPG+Engk6879qGrnPvftTPL\nVpck/ZQKf0ZvbpX5YoWBQoE4dILwph9PO5okSZIkSepADvJsEbXkKAA9XVNM5ZfJzGYZGv5ayqkk\nSZIkSVKnslTaIqrVw0CgWBljMazwzuF7aEyfhNXltKNJkiRJkqQOZKm0ReRyFcrl+8h0t8a67194\nkIFcBs41Uk4mSZIkSZI6kaXSFlJL6pT7RggR8oXdrbHu4SfTjiVJkiRJkjqQpdIWkiR1SoVJenNN\n5os5nikUWBv0E+AkSZIkSdLms1TaQpKkDkBP1yRTuWWqkxlePnM85VSSJEmSJKkTWSptIUlyGIBi\neYylsMq7zhyiMT8ESzMpJ5MkSZIkSZ3GUmkLyeUSyl2HoKc11n1w6YH1XaWvp5xMkiRJkiR1Gkul\nLSap1an0jpKJgWxhFwMFx7olSZIkSdLms1TaYpKkTldxnJ5Mk7lilucKBVYGv5Z2LEmSJEmS1GEs\nlbaYJOkHoKc8yWRuiZ1jGZ4/942UU0mSJEmSpE5jqbTF1NY/Aa5QGWclrPGuM/czsDIOsyMpJ5Mk\nSZIkSZ3EUmmLyeUSurruhe4JAO5euX99rNtdJUmSJEmStHkslbagJKlT6TtPJgZCcWerVBo6kXYs\nSZIkSZLUQSyVtqBa0k+5OEZvgLlihpPZPPODx9OOJUmSJEmSOoil0haUrO8qdZcnmMwucfdojm+N\nfANiTDmZJEmSJEnqFJZKW9CFUilfGWc1rPGOMw/QYAkmX0k5mSRJkiRJ6hSWSltQPt9NqXQ39EwC\nsL/5gLtKkiRJkiRpU1kqbVG1pE617zzZmCEWehkoFmHIT4CTJEmSJEmbw1Jpi0qSOuXiCD0BZouB\noUyWqSHHuiVJkiRJ0uawVNqiktr6WHdpgqnsIvedLTAw/iysraacTJIkSZIkdQJLpS2qlvQDkKuO\nsxqavH3kIQayEUafSzmZJEmSJEnqBJZKW1Q+30uptJ+4Pta9N963PtbtrpIkSZIkSWo/S6UtLEnq\nJH0j5GKGtUI3jVLJT4CTJEmSJEmbwlJpC6sldSqlc/TEwGwhMB4DI8OOdUuSJEmSpPazVNrCkvVd\npVq5Ndb94JkSjemTsLKQcjJJkiRJkrTdWSptYUnS+gS4bGWCtdDk7aMP08jn4Ow3U04mSZIkSZK2\nO0ulLaxQ2EGxeBfN3tZY9y4OMeBYtyRJkiRJ2gSWSltcLalT6ztPPmZZKdZolErEQXeVJEmSJElS\ne1kqbXFJ0k+1dHZ9rDsyvwaDZ71SSZIkSZIktZel0hZ3YVepWppkKrPIkaEKAwtnYGEy5WSSJEmS\nJGk7s1Ta4pLaUQAy1QmaIfLY+MM0igUYfirlZJIkSZIkaTuzVNriioWdFAt7iOtj3X3hII1CAYZO\npBtMkiRJkiRta5ZK20BSq9O94zyFmGOlWOWZUok1PwFOkiRJkiS1kaXSNpAkdSrFM3Q3AzOFSHMl\ncuqcpZIkSZIkSWofS6VtoJbUCSFS7WqNdR8brNJYnYbp4bSjSZIkSZKkbcpSaRtIkn4AQmWCGCJv\nnTjSGuv2FjhJkiRJktQmlkrbQLG4h0JhF82+KQBq2XsYKBZh2FJJkiRJkiS1h6XSNpEkdXp2nKfU\nzLNULPNcocDK4PG0Y0mSJEmSpG3KUmmbqCV1qsVhkmZgJt8kvxR5fvSb0GymHU2SJEmSJG1Dlkrb\nRJL0E0KTammK6cwibxrspsEyjJ9MO5okSZIkSdqGLJW2iSSpAxCrE8QAb5nub411u6skSZIkSZLa\nwFJpmygW95LP76DZNw1AOXOARqkEQydSTiZJkiRJkrYjS6VtIoRArVand+dZupp5FotdnMzlmB9y\nrFuSJEmSJN1+lkrbSFLtJykOkTQzzBTWqCxEnh1/DtZW0o4mSZIkSZK2GUulbSSp1QmhSbnYGut+\n62AfjRxw/pm0o0mSJEmSpG3GUmkbqSVHAYjVSQCOzdQZKBbcVZIkSZIkSbedpdI2UizeRT7fS3PH\nFABdubtolLoslSRJkiRJ0m1nqbSNhBBIkjp9O89RbhaYL5Y4ncswNfRk2tEkSZIkSdI2Y6m0zSRJ\nnaQ4SLKWYTq/Ss9MZGDmFCzPpR1NkiRJkiRtI5ZK20wtqRPCGl3FKWYzSzw2uItGIQ9nvpF2NEmS\nJEmStI1YKm0zSdIPwFrS2lU6utBPw7FuSZIkSZJ0m1kqbTOl0gFyuW7ijmkA8rm9DHR1gbtKkiRJ\nkiTpNrJU2mZCCNSSOn07z1JZKzBfKHI+wPnh42lHkyRJkiRJ24il0jaUJHVqxdNU17JM51fYMQ2N\npRGYG0s7miRJkiRJ2iYslbahJOknhFW6itPMZZZ5x+CB1q7SsLfASZIkSZKk28NSaRtKkjoAK+tj\n3Y8sHWagUHBXSZIkSZIk3TaWSttQV9c95HIJcec0RMjmdjHQ1UUcdFdJkiRJkiTdHpZK21AIgaTa\nz46dZ0iaReaKeaaIDJ7/OsSYdjxJkiRJkrQNWCptU0mtTq10mvJqhqncCnvGA421WZg6nXY0SZIk\nSZK0DVgqbVNJUifDCsXiLAuZZd599p7WWLe7SpIkSZIk6TawVNqmahfHuicBuH/pMI1iCYZOpBlL\nkiRJkiRtE5ZK21RX171ks1XYOUOIQL6PZ4sF1oa9UkmSJEmSJN06S6VtKoQMSdLPzl3DrbHuQp5F\nIifPfxOaa2nHkyRJkiRJW5yl0jZWS+rUSq/StZplKr/MXSOBRmYVRl9IO5okSZIkSdriLJW2sQtj\n3YXCDIthhW8/9wADxYK7SpIkSZIk6ZZZKm1jSdIPwHIyA8C9qw/SKHWBu0qSJEmSJOkWWSptY+Xy\nIbLZCuyaJsRAzPXyXD7H8tDxtKNJkiRJkqQtzlJpGwshQ1I9wq5dw9TWiswWc6zFyPPjz8PqUtrx\nJEmSJEnSFmaptM0ltTrdpVfpWssylVvinvMZGvkMnG2kHU2SJEmSJG1hlkrbXFLtJ8MSucIsS2GV\n7xx5mIZj3ZIkSZIk6RZZKm1zSa0OwFIyDcC+1fsZ6Co71i1JkiRJkm6JpdI2VynfRybTBbtmyMTA\naq6Hk7kM8451S5IkSZKkW2CptM2FkCVJDrPn4lh3hrAWeWb2NCxOpR1PkiRJkiRtUZZKHSBJ6nSX\nXqa00hrrPng2y0AhD8NfTzuaJEmSJEnaoiyVOkAtqa+Pdc+zHNb47vGjrbFud5UkSZIkSdJNslTq\nAEnSGuueT2YA2LV6L42uip8AJ0mSJEmSbpqlUgcol+8nkymR2TNFNmZYyScMZmFy+Km0o0mSJEmS\npC3KUqkDZDI5qtXD7N01TLJWZKaYIb8aGVgehZlzaceTJEmSJElbkKVSh6gldbpLr9C1kmMyu8T9\nZ3LuKkmSJEmSpJtmqdQhkqROhgUy+TlWwxrfNfEmGsWiu0qSJEmSJOmmWCp1iCTpB2C2NgtAGNfp\nVAAAIABJREFU79oBBkplGPJKJUmSJEmSdOMslTpEpfIAmUyB3J5psjHDciFhJBM5d/YpiDHteJIk\nSZIkaYuxVOoQmUy+Nda9c5jaapGZQqCwEmnEBZg4lXY8SZIkSZK0xVgqdZAkqdPTdYrSSo7J7CKH\nh0sMFAveAidJkiRJkm6YpVIHSZJ+MsxDYYG10OQ7pt5Mo9RlqSRJkiRJkm6YpVIHqSV1AGaS1lh3\n0ryLgWKROHQ8zViSJEmSJGkLslTqIJXKg4RQoLB3inzMspivMh0ip0casLaadjxJkiRJkrSFWCp1\nkEymQLX6EHt3XRjrhq6lSCMbYeTZtONJkiRJkqQtxFKpwyRJnd7SyxSWc0xlFzk6XKXhWLckSZIk\nSbpBlkodppbUyTBLM7/IWmjyrpk3M9BVhqETaUeTJEmSJElbiKVSh0kujHV3zwFQXtvDs4U8q5ZK\nkiRJkiTpBlgqdZhq9SFCyFPcM0khZlkollkgcnLiBVieTzueJEmSJEnaIiyVOkwmU6RaeYi7dg+R\nrBaZKUQqC5GBQg7OPp12PEmSJEmStEVYKnWgJOmnp/QyxeUcU5lFHju7w7FuSZIkSZJ0QyyVOlBS\nO0qWGVZyyzRD5G3TR2mUq451S5IkSZKkDWtrqRRC+MEQwnMhhBdDCL92hdf/QQjh6RDCN0MIXw4h\nPNrOPGpJkn4AprtbG0qFuIvncxmWhy2VJEmSJEnSxrStVAohZIHfBX4IOAL8eAjhyOtOOwV8Z4zx\nKPAvgQ+3K49eU608Qgg5ynsnKDZzzBe6WCXy3OwQzI+nHU+SJEmSJG0B7bxS6XHgxRjjyRjjMvAR\n4H2XnhBj/HKMcWL96d8CB9qYR+uy2SKVyoPs3T1EslpgphCpzcXWrtLwU2nHkyRJkiRJW0A7S6X9\nwOlLng+uH7uafwz81ZVeCCH8bAjheAjh+MjIyG2M2LmSpJ/e0ssUVlpj3e88t9exbkmSJEmStGF3\nxFB3COG7aZVKv3ql12OMH44xPhZjfGzXrl2bG26bSpI6WaZYzq0RQ+TRmX4GygkMWypJkiRJkqTr\na2epNATcfcnzA+vHLhNCOAb8AfC+GONYG/PoErWkDsBkrTXWnYs7OJmNzA2fgBjTjCZJkiRJkraA\ndpZKXwMeDCEcCiEUgA8AH7/0hBDCPcBHgX8YY3y+jVn0OtXqYULIUtk3TqmZZ67YRQSeWZmE6eG0\n40mSJEmSpDtc20qlGOMq8AvAp4BngT+JMQ6EEH4uhPBz66f9BrAD+L0QwtdDCMfblUeXy2ZLlMv3\ns2/XENXVAjP5NXpnIgOFAgydSDueJEmSJEm6w+Xa+eYxxk8Cn3zdsQ9d8v3PAD/Tzgy6ulpSp3fu\ns0wuv5vRyhzfOXIvjT2LrV2lI+9NO54kSZIkSbqD3RFD3UpHa6x7kqXsGgR4ZOZhGl0Vr1SSJEmS\nJEnXZanUwZJaa6x7oqc11h3oZSjTZOLsN6DZTDOaJEmSJEm6w1kqdbCkehjIkOyboKuZZ65QhBgZ\nYAnGXkw7niRJkiRJuoNZKnWwbLZMpXI/+3cNUl0pMF1osmsq0CgWWrtKkiRJkiRJV2Gp1OGSpJ/e\nrlfILxeYySzyd8YeYKBUdldJkiRJkiRdk6VSh0uSOtk4xly2taF0/8wDNEoloqWSJEmSJEm6Bkul\nDpckrbHuyd7WWHczdDMampwbGYDV5TSjSZIkSZKkO5ilUodLqkeAQPe+MSrNArPF9bHuXIBzjbTj\nSZIkSZKkO5SlUofL5SqUy/exb9cwlZUC0/lV9k9kHeuWJEmSJEnXZKkkkqSfvq5TZJfzzGaW+L7x\nR2h0VWHIUkmSJEmSJF2ZpZKoJXWycZTZbOv5gdmDDBTzjnVLkiRJkqSrslTSxbHuiZ7WWPdqpsYM\nTV6deAmWZtKMJkmSJEmS7lCWSiJJjgDQu3+M6lqB2UKBECONYh7OfCPldJIkSZIk6U5kqSRyuYSu\nroPs3z1EebXAVH6FQ+OF1li3t8BJkiRJkqQrsFQS0NpV6iu9TGY5z3xmme8drzNQqTnWLUmSJEmS\nrshSSQAktTrZeJ7pTABgz9x+ns0FVi2VJEmSJEnSFVgqCXhtrHu8dxEiLGcSFom8NH8GZkdSTidJ\nkiRJku40lkoCIKn2A7Bz/yhJs8hsMU+mGRkoFmDYq5UkSZIkSdLlLJUEQD5fo6vrntZY90qeqdwK\nD49XaJSK7ipJkiRJkqQ3sFTSRUlSZ0fpZcJygYXMMn9n7AiNcs1PgJMkSZIkSW9gqaSLakmdbDzL\nVMgC0LewjxeykaWTn4UPfzf85T+Fp/4Yzj0DzbWU00qSJEmSpDTl0g6gO8eFse6xvgUqy7CUqbJK\n5Lm3fIBjo6/CN/4DfO0PWifny3DXo7DvzbDvLa2vffdBxp5SkiRJkqROYKmki5KkNda9+8AImRd7\nmCnmyK5FGvd/G8f+7k9AswljL8LwU63x7uGn4Pgfwurvtd6g2A37Hn2tZNr/Fui+G0JI8a+SJEmS\nJEntYKmki/L5Hkqlu9m/e5ipZ3czWVzk0fFeBsYGWidkMrDrodbj0R9rHVtbhZFvvVYyDT0JT/wu\nNFdar5d3XF4y7XszJHvT+QMlSZIkSdJtY6mkyyRJPzuWnmZy+XEWSzN8x/hh/t/hJ/jjZ/6YQ92H\nONR9iL2VvWTC+m1u2Rzsrbceb/mp1rHVJTjXWC+Znmp9fem/QGyu/5J967fNvRn2r98+V+5L5w+W\nJEmSJEk3xVJJl6kldUZG/prx0PpXo29xH4trDX7ra7918ZxStsS9tXsvlkwHawc51H2Ie2v3Us6X\nIVeE/W9tPd62/kPLc3D2m69dzTT8FDz3l6/94p57L7+a6a43Qam2iX+5JEmSJEm6EZZKusyFse6J\n3kWSlcBcpsSXPvAlxhfHOTV1ipenX+bU1ClOTZ2iMdrg0698muaFK5CAuyp3XSyZLi2ddpd3E+55\nB9zzjtd+2eIUnPnGayXT8JPwzMdee33Hg6+VTPveAnuPQqG8Wf8oJEmSJEnSNVgq6TIXxrr33H2e\n3AvdTBczxKUldnTtYEfXDh7b+9hl5y+tLfHq9KsXi6YLpdPHXvwY86vzF88r58pvuLLpUPch7rnn\n7RQPfcdrbzg3tl4wrZdMpz4PT/+H1mshC7sPw743vbbTtKe/dWWUJEmSJEnaVJZKukyh0EepuI8D\nu4eZemYn46UF5r45QPK2t17x/GK2yIO9D/Jg74OXHY8xcn7+/GVXNp2aOsWJcyf4xMlPXDwvEzLs\nq+x7w5VNhx7/IH2l/5EQAkyfufwT5771SXjqj1tvkC20iqULVzPtezPseqS19SRJkiRJktrG//PW\nGyS1OjtWBphcfhvLXTOc+sIJjl2lVLqaEAJ7KnvYU9nD2+96+2Wvza/M88r0K2+4ne6rZ7/K0trS\nxfNqhdrlVzY98C4OvfUfcKC6n/zri6Zv/ic4/n+3fjDXBXcdu/xT5/rub316nSRJkiRJui0slfQG\nSVJnZOTTjGbyAJw+M8ax2/j+5XyZwzsOc3jH4cuON2OTs3NnL7uy6dT0Kb48/GX+/KU/v3heLuQ4\nkBxoFU19hzh48IMcqh3kUDPQPfL8a2PgJ/4IvvL7rR8q1uCuRy8fA++5F0K4jX/ZtcUYmV5YZWxu\nifG5Zcbmlhlff4zNLjM+t3Tx2FozcqC3iwO95Ytf7+7r4u6+MrVSftMyS5IkSZJ0NZZKeoPa+lj3\nWN8ifcuB8RiY/qu/otTfT/7uu1u3pLVBJmTYV93Hvuo+3r3/3Ze9NrM8w8tTL3Nq+lTr63rp9IWh\nL7DaXL14Xl+pr3Vl0+F3cujxD3CIHIfmJtk3cpLsma/DVz4Ea8utk7v6Li+Z9r0FandtOO9aMzIx\nv8zEJQXR2Nwy468riC4cn5hbZrUZr/he5UKWvkqBHZUCu5Mi2UxgcGKBJ14aY2557bJza6Ucd/e1\nyqa7e8uvfb/+tVzwP2tJkiRJUvuFGK/8P7l3qsceeyweP3487Rjb2vLyKF/44ts5fvrHKL5QI0+O\n7/nbp8kuTkM+Ujy0n+Ijh+jqP0JXfz/5e+4hpHRr2WpzleHZ4TcMhZ+aOsXE0sTF8wqZAvfU7uFQ\n7V4OZiscWl7mvqnzHDz/IpXzz0JsFTdrlT3M7zzGeHc/w5XDnCo8xJmVyiVl0fLFK40mF1a42n8+\nSSnHjkqBvkqBvkqx9X21cMmxAjsqxYvHSvnsFd8nxsjk/AqDEwucnpjn9Pj8xe8HJxYYnJhncaV5\n2c/sqBRaVzf1lbm79/LCaX9P11V/lyRJkiSpc4UQTsQYH7v+mZf8jKWSruSLX3o3p2ceZPKzdc6V\nZ8nFLL2xQm+zQm+s0her9CwFSouLsLZAppgh091FfncP+bt3U7h7D9mkQKaSJ1vJk6nkCbnNLZ7O\nzIzxzfMv8NzYS7w0eZLB2Vc4t3iaqZWzRF4rYjJrNcrLVXYvwcMrCzy+Mso7V85xV3OVDDAYd/Js\neICXiw8zXDnMZPcRyrW+1wqiavHi9zsqBXrKBQqb9LfGGBmZXWoVTeOvFU2nx1tfhyYXWFm7/L/x\nPbVi63a6S2+r6y1zoLfM/9/enQdZlublff++Z7n7kvtWVV1VPT3d9MIwSzPTM2KxhZHAIsDyFkiy\nBAgLEYFlcNhhLQ5ZIYcdYYWQbJmQrZABgS2EhJAIY9lGIIzAgzzjmYHZqqf3rj33zLtvZ3n9xzl5\n897MW12VPZV5q7KeT8SJ895zz733zZNZ1ZVP/97fWZ3J4bvqPSUiIiIiIvKkUagkD82Xvvxn2dx7\nnX/1S/8ZSwt3KMz02W80aEcBoXu4/C0TO1Rtjrkoz3xUZtaZZc6WyXK874/Jujilw5BpNHByiv6x\n55zMYUWNtZZWPzyyxOxg2Vl/Qn+iAd0gOjYHAM+JmKk2KZZ28XM74G8zcDZox3cZ2M7wvKzxueyV\neTq2PN2qcbW5zZUg4HIQkp/7wPgd51Y/BJniQ/wOPBxRbNlq9oYh0629gyqnZLxe7zK6Is8xsFrN\nj1U3jVY7LVdyuM7Z9aESERERERGRs/F+QiU1X5GJyuWX2Nn5TXazMYs7z1KpVHjq6QLVpTyR3WZ3\n9222d+6ws7vL3qDJdiYHmV3gOgDZOKbaC5mrdZjtwGzgM+tWyC5cIK4sYDJlYpvB9i3mHn2GBgYa\nDtRszG4cs0dMDXtsa7vgFn3ypaRq6AOLpZElZum+lCxDmytmqOS8iX2hrLXs9naPNQr/Sv06/zyq\nYfMLw3PXsFzd+wxX7vwLrgYBV8OIq5XLLKx8DHMh7dO0/BJ42dP49jww1zGsVvOsVvN8/OrcseeD\nKGaj3ju2rO7WXof/581tNhv9sfN917A2kz+2rO6g4mmxlD21nlsiIiIiIiLyaFGlkky0s/NbfOnL\n/yE//bmf4OXwZZ7OZaltdug0BocnGSjP5qgserjeHs3eLWqNO7TaewzimDhXIM7k4KDfkrVkun3m\n93aZrdWo1uvkmh12MwvcnXua7ZlL1CureOV5ljM+S67LvOMwg6EcG/KhJRvEuNE9fmZdc1jpVDpe\nCeUeOW5yHuYBq256YY8bjRvHGoVfr79LN+oNzyvGlqvBgKuDkKthxJXiGlcXXuSpi58kc+njsPgN\n4D4+d2/rhxF39rvHAqdb+13u7HfYaQ3Gzs96zvjd6tJldZfmkmOzBV+hk4iIiIiIyCNIy9/koen3\nt/n0777Cp7f+OP/vxnfyo9/+geQOZrUejZ0u/b0+USPAbYfkepZqAFkOw4IgbtOON+hHd3GdHXyn\nBRmPOJsnyhWwfhbScMGJY8rNJtX9JGia6XZZWlxk4YMfJP/SS+RefJHsB57GeElhXTyIiFsBcTsg\naif74bh15HE7wPYnL4PDAacwOXByRsOo9LhT8I+FUNZaNjubY9VN1/de493aO2wGjcOPspaLYciV\nMOZqZpar1ae5uvJRrlz+dmZXP4ZxH8/m2Z1ByJ1hE/GRfk61ZF/vBmPnFzPuWHXT2DK7uQKV3OMT\nuImIiIiIiJwnCpXkofr0pz/Fne4L/Pnf/KPDY45huLRseAezYoa5gs+s51EKLNlOjGmFhPUBnd0e\nje0eYRBh431suAFsYNmkHzeIM1nibB5KVWwuTzASTHlhSKVep1qrM9Nps1ipsnLlCrMvvkj+G18i\n+/TTGP/+IYQNYqLO5MApbgdER47bbjj5jQw4BW9iP6jRUMopZnCLPt3MgBvtG1yvvcu7m7/Pu9tf\n5d3WLW4ETQYj2VQ1jrlqclwtrHBl7lmurn2CK2sf52LlEr7jg7Vg4/Etjo4cs8kd7O553uh7TDov\nPn5s7Dx7j899jy2O6AYBtVafWqdPrd2n3u7R6PZpdAY0On3CKMIhxsHimJi8Z6jmPGZyDpWcSyXr\nUsk5lLMupYzBN0yYoz32uQ90bY5+3dkyXPxmeOoTcOFlyJbe558cERERERGRx49CJXmovvSlP0O7\nc4Ps2j+kmk/6E1XzPs4JGzXHsaW136O+2aW21aG22aG21WF/vU596xZxuE4cbmCjdWJaSTVTtoBb\nXcDkiwyMIeDw5zTT7ycVTc0W8/k8y2urrD7/PDMf+hDZZ555oKDpvdgoJu6EEwOnSUFU3AngHn+M\nTM47rHRKwycKLm2vw1bvLe62XuVG7w3eCG7yqrPDhn/4Rp61XAxCLodh2iA84EoQcjUImI9izuMi\nshiDxRDjEFtDTLJFOFgM1jgY42AcF+O4uI6D47q4rovnuhjjgOOCce6/jZ1noL0DW18DLBgXVl6C\nS68kIdOlV6B6YdqXR0RERERE5NQoVJKH6p13/hbvXv8pvv3bvoTnnc6dzaIgpr7Tpb7VobbZZfvW\nJlvvvkl96zqD9m3icAMYELseNl/BqSzjFCvErkOXgGgkWcl3Osw0Gsx5HosLC6xevcrqRz5C+Ru+\nAZPJnMr8AWxsibtJCBW3xsOnsTBq5Dnu0ZwcHyKvS89t0XTb7LoNNp06t80+e26Dhtem4bYI/B7V\nnMdyPstT2RmuZme5nJ3lcm6Bgps9EqC4h8HJPUOVg+cnBDLOPV5/7FzzHoGO+wCf7wyXREKytHC7\n1R/2cbq9P7K8br/DnVqX4Eh/reVKdqyJ+Oh4pZrDd533/mZ2a3D7c3DzM3Drs3D78xB2k+eql+DS\nJ+CpV5L98ovJ1yAiIiIiInIOKFSSh2p75zf58pd/hI999B8xM3Oin6uHYtALqW20uf3aO9x5/TV2\nbr1Nc+c6QXcTsFjAZuagsIJTnsHkfEIT0HMGxAfZhLWUWy1m45iFSoXlixdZe/ElVj7yYfx8/sy/\npmRKFtuL0h5Qg2PB0/BxWi0VdwLsIJ74XjGWppsETQ23Rd1tE2Qj3KJPtlygVK0yO7vA8uIKS/Or\n+OUcJuuei2bZUWzZbPTGQqekt1MyXq93x7I71zGsVHITA6eLs3mWKznco1V4UQAbX4abn4Vbn0n2\nrY3kuUwZLn3zYTWTlsyJiIiIiMhjTKGSPFT9/iaf/t1P8ewH/zKXLv3gtKczNOh1uf3qG1z/8jU2\n3nqDvTvv0O/spc864C5AfhWbm8Xm8pAJCJ0WfS84bA4exVQHfeazWZaWl1l95hnWPvxhZhcXcZz7\nVLNMgQ0iooOQKQ2a4nZI1A4YtLq0aw16rQ5xO8DpQm7g49nJVTSRiRlkQ2zewStmyVeK5MqFYTPy\nYa+okf5RxnceuyAqiGI26r2JgdOt/Q6bjf6x12Q8h5znkPNd8hmXnOeS85PHOd8l5xkusM2zg2t8\noHeNy+0vs9h9B4MlNi618rPUFj5KY/Fluqsv41QuHH+vdOy75rG7piIiIiIicn4pVJKHylrLp3/3\nFebmvpUXX/jJaU/nPbVr+6y/9Qbrb77Gna+9xua7bxIOegA4bg43u0psF4mzC8S5IpEfEHtNIrdF\n6B/eHc6LYmaNYWl2huXLl1l74QWWL1ygVCo9VgGAtZa4H7K3t8P69m22dzep1XZp1xsMml3oxpTC\nAtWoRDUsUY3LlMMCDvcI1DwHt+gN75Y3bE4+Ejwd3kkvOc94j144N6oXRNytdbmVLqvbavTphRG9\nQUQviJNxENENYnpBRD+I6Abpc+m4H8TkoiYfcd7iY87rvGze4MPO2xRMEljdtgt8IX6Wz8fP8oX4\nWV6zTxGn19gxkD8Iq3yXrO+MPE7GWT8JoPIZJw2lkoAqexB8TTrfP/5cznNP3AtNRERERESeLAqV\n5KH74pd+mF7vDq984temPZUTsXHM3t3brL/5Outvvc76W2+wc/M6Nk6WkeXK8+Qrl3DcFXqDKt2o\nQOT3iLw2odcm8lrE7mHYlLGGuVye1bVl1p59luULF1hcXCQ/pSV0X68wDrnbusv1xnWu169zvXGd\nm/UbbO1tMmh1qUQlKlGRalTmkrvKmrPCEgvMxhVKYZ7swMd07b3vlAeYrDuseHJHQicnDZ2Gx0pp\nOFXwMY9h8BHFll6QBFC9MKbX62E3vkL27ufIb3ye0vbnyXW3ABi4Rbaq38id8oe4UfhG3s09TyPO\nDYOq5D0iuqPB1iB93/T5e7Xjup+DKqx85qDqyk2rpkZDqJGqrGMB1ZHnjr2XMxyrCktERERE5PGj\nUEkeurff+e9YX/9lPvXJf4njfH13VZu2oNdj8923WH/rDTbeTIKm5u42AI7rMrv6FOWFK2SLF4h7\nM9T3YmrdNj13NGxqY53D/kY54zNXnmHt6lNcvHqJpaUlFhcX8b/OO9BNUztoc6Nxg+v169xo3ODd\nxrvDx52wMzwv5+a4Ur7Ms7lneCbzNFf8i6yYZRaZI9N302V6Ydof6qBxeYgdRJM/2ICTH6mGGqmC\ncguHYdThMQ+T8x79IMpaqN1Mm3+nfZm2XiW5y5wDyy8dNv9+6hWoXnyPt7IMopheEE+snOqNPB4N\nurqDJKzqB4fj96rCOnifQTS5l9f9uI4ZhlW5I4HUQUCV9V3mChmeX63w4lqF51bK5Hw1PhcRERER\nmRaFSvLQxXHw2IdJ76W1v8f6W68PQ6aNt98k6CV3+8oWi6x84FkWLz9DYeYp3EGJxvUaWxu77PV6\ntLyYwO+lYVMHTPpnyULeKzA3u8DK2iqXrlzgwqVV5ubmcN3H95dmay3b3e0kaKqnQVNa6XSndYfI\nHoZFc7k5rlSucKV6hcuVy8PxpdIlvNgl7gTHwqaxxyP9o6J2ANE9/p5ySIKmNHRyjyzFOwifRo+Z\nzCPQH6pbS+4sd+szSdh05wsQpIFd5WLS+PvSK0nINMW7zEWxpX9QOZVWS3UHEf0wCZ0OA6ojQVYQ\nTw65jrxuu9Gn2U+q3VzH8MxiiRfWkpDphbUKL65WqRbO798/IiIiIiKPEoVKIl+nOI7Yu30r6c+U\nhk07t25ibVKxUV1eYfWZ51h95lmWn/4g2TDH3pffZfuNu2xs1diLIpo5l34OQr9D5HYhzS+MNeQz\nFWYq8ywtLnLhqTWuPHOR+YXZR7I5+EkEUcCt1i1u1NOgaWRZ3V5vb3iea1wulC4kQVP1ShI2pYHT\nYn5xYthjrcUO4pEG5cF40/KD8CltXh53kvO4V5GNZ9LKp/GKKLfoU/6Op6YTOEUBbHwFbn02rWj6\nLDTXk+cyZbj48mE108WXIVs++zmeAmstt/a6vLpe59rdRrrVx5qoX5zN8+JahRfXqsP9ciU7/WBQ\nREREROScUagkcgoGvS6b77zF+puvs5GGTa29XQAc12PpylVWnnmO1Q8+x8oHnqXkuHS+8iq7X3yT\n7bc3WK932cvmaJUKdAsug+yA2D38pdlYl7xXoVKcZWF+kbULK1z+wEWWL83heY9vZdOBxqBxLGy6\n0bjBjcYNelFveF7BKxwLmy5Xkyqnol880Wfa2GJ74eQ75nXGw6iDJXrGwNp/+cmH/eW/PwdL5kZD\nps1rvJ8lc4+jnVZ/GDBdu9vg1bsN3t1pD5+fL2Z44aCaKQ2brs4X1YxcREREROTroFBJ5Iw093bY\nePONtAn462y+/RZBPwlIcqUyK888y+ozz7L6zHOsPPMsXrdH79o1etdepfXV19i8vs2mV2R/dp5m\npUynnKGfCYidtPG1NSxufyuV+TwzS3mqiwWqS3lmlpJ9eT6H6z7e1U2xjdlsbw7DpoO+Tdcb17nb\nuovl8O+mpfzSMGC6XLnM1epVrlSusFZaw3O8hzIfG9tHuz9Trw63P5f0ZLr1Gbj9BQjSoGVsydwn\nktBpSkvmTkurH/LaemMsbHpjs0mQLo0sZNxhf6YX1yq8sFrl2ZUS2XMQzIqIiIiInAWFSiJTEscR\nu7duJiHTm2+w8dbr7Ny+mVScADMrq2nA9ByrH3yWxctPQ61G79VX6aZhU+/aNfabfbaXnqJRnqfS\nLtObuUgnv0jHmyHksLeMMVCuelSXCsxcqCTB01KBmaU85bkczmMeOPWjPjcbN4d9m4Y9nOrXafbq\nOLHBiQ1ZPC4U1rhYuMDF/Bpr+VVWckssZxcpOgXiKCQKQ6IwGO7j8OBYmI6T5xzXY2Z5hZnlVWZW\nVilUZx7tJVZRCJtfOQyZbn4WmneT5zKlZJncQch08ZvPzZK5UYMw5s2t5rCa6drdOq/ebdBOm8F7\njuGZpdLI0rmkuqmcU58mEREREZGjFCqJPEIG3Q4bb7/FxttvpEvnXqe1n/QXcj2PpSsfYOWDz6Y9\nmp6jurxCtLdH79o1+m+/Q7i9nWxbWwTb23T2OrRtkW5+kU5+kW5+KdkXlojc7PBzDTHFbEilbKjO\nZ5lZKzN7eZ7ZpxepLBQmLhGy1o4EMONhSxQEx46NBzPBkeDmyOtHHwcjwU509PUj5wb3CIGi8FS+\nV47rEcfRMAQE8LM5ZlZWhyHTcL+ySnluAfOo9cGyFuq3xkOmza9yuGTuxcPm35c+ATOXpj3jUxHH\nlpt7nbGKpmt3G+y0DpecXp4vpNVMh8vnliq5Kc5aRERERGT6FCqJPOKauztpNVPSn2n0z5u2AAAg\nAElEQVTjnTcJ+8kvu/lyJV029xyzaxewUXQkbAkIu12CRp2g0SBstZKt06bf6dPvRQxCSxAbQuMS\nuR6R46bLyCKwMRBibIghAmKsibE2HjYif9gc18P1ks3xPFzPPxz7/vC55Fj62B0510/2zsh5kx67\nngeuSz1ssDPYZbO3zWZvi7u9De521tka7BI7drgtlpa5UL3E5ZkrXJ69ytXqVS4WLlDoGFpb2+xv\nrFPbvEt9c4P9jXUaWxtE4WGg5Xoe1aWVI2HTGjPLK1QWl5P5PArec8nchTRgSquZll4E9xGZ9ynY\navTG+zStN7ix2xk+v1DKDquZDoKmp+Ymh7AiIiIiIueRQiWRx0wcRezcujFsAL7+5uvs3rk1VjEz\nieO6I8GKPx7WeB6O4+JYi4lj7CAiGsREA0sYGqLIJbQZQieHNZmkigUXY8EP+2SCDjkG5HxLIWco\nVTIUZopk5mbx5+bIzi+QWZjHn5vHzWQmBz2+j+N6j8zysU7Q4Wbz5thd6Q6ah7eC1vA8z3islda4\nVLnEpdIlLpWT7WLxAtVBnu7Obho03aW2sU59c539zfVhMAhgHIfK4lISNi2vJkvqVtaYWVmluryC\nn8lOmuLZ0JK5MY1ewNeGd51LAqe3tlqEcfLnr5T1eH61zItr1bQpeIUPLpXJeI9YlZqIiIiIyEOg\nUEnkHOh3OjR3tg4rd0YqehzPx3Xdh7L0ylpLc6vJ3pub7N/Ypb7Ror7bp9GC1iBDzGGDYycOyHV3\nKHS3yXe3KHS2yfd3KOdCSrNZ/KUlvMVFvOF+EW8xHc/PYR6Vyp0jrLXs9na5Xr/OzeZNbjVvcbOR\n7G83b9MMmmPnLxWWhkHTpfIlnio/xcXSRebjMtF+6zBo2linllY79dvtsfcozc0fVjgtH1Y4zays\nki2c7C53X7dJS+a2riVVbQ9pyZy1lqAf0W0GdFsDeum+2wyIwph8OUOhcrjlKxn8zPSaa/fDiDc2\nWry6frh07mvrDTppn6aM6/DB5dKwoumFtQrPr1YoZR/Nn3ERERERkQelUElEHgobW9r1PrWtLvWt\nDvvrLWq369S3uzTqIXF8WIHk2JBCWCff2SRfv5uETt0t8t1tsv06xjG483P4i0eDp6XD8GlpEW9+\n/pEKn6y11Pv1JGhKA6eDsOlW8xbb3e2x8yuZymHQVL44DJ6WzCxeI6C+tUlt43BJXW3jLp16bew9\n8uXK2FK6ZJ8sr8uXK2dT+dVrJEvmbn0Wbn4Gbn9+bMmcvfgJBqufojv7UXrZq3Q7Ed1WQLc5OBIc\npcdaAVFwsuWVfs6lUM5QqGaSfRo2jQZPB2PPP/0AKoot13fbw2qmV9Owaa89AJLG+Vfmi8NqpoPl\ncwulKValiYiIiIickEIlETl1Nra0an1qWx3qaeh0ED7Vt7vE0eHfKa5jKfp9irZBob9LvrlOduc6\nuc23yfRrjEUkxuDOz49UOi0er4BaXMRbWMD40797VyfocLt1exg0HVQ43WreYr29TmSj4bk5N8fF\n8sWxsOmp8lOs+ovk2tDa2k4rmw4rnZq7O2PLIDP5wrGG4Qfj0szc+65es9bS74T0RkKgsX2jT29v\nn26tRbcd0R1kiO3k6+9lHPLlDPmSP9znyhnyZZ98aXyfK/m4nkO3GdBp9Ok0BnQaA7rNweG4cTju\ndyY3ac/kXArVLPmyT6GSPVb1NHxczuD6D2/ZmrWWzUZ/pBl4sr+93x2es1zJJtVMq4dh06W5/COz\nLFREREREZJRCJRGZqji2tPZ742HT9uTAyfMN5YqTLKFzOhTCGoXOJrnabZztm0Tb20Q7u8f7SxmD\nOzt7ZKndYfDkH4wXFjCZzBlfgUQQB2y0NsYqnEYrnXpRb3iua1xWiivDoOkgdFrNLVPtZent7A8D\np9pmUuHU2N4ijg5DKy+Tpbq0nFQ2La1QmlsmV14gk5/HeGX67Zhea0CnGdAbBkZpVVErGPu+jPKz\nbhIEjQZEJZ+81yHfv06++TXytS+Q2/8ieWcf3wmTht9PfeKwN1P1UlLK83WKgphOMw2d6gM69wif\nOo0Bg+7kACpb8CYutysc2fLlDO777JtU7wRcWz+sZrp2t87b222itE9TOeeN3XXuxQsVPrBYwnfV\np+mxYy0EHejsQWc32br7h+OD43EIlbV0u5Bs1QtQXgV3+gG5iIiIyAGFSiLyyIpjS2uvN1Lh1KW2\nnYwb213ieCRwyrpUF/PMLOYol6Dk9SnSpNDfxattEu1sE25tEW6n+91diI8vsXJnZ48vuRsNotKl\nd84Zhk/WWra72+NhUyPdt25R79fHzl/ILXA1+wyXvKssu2vMs0wlnMGth/T39mnubtGpb9Fv7TDo\n7REHNWA0VHEwTgXjzOBmZskWFyhUFijOLVNZWKZYLaQVRGl4VM6QK/nky/6DLy3rNeDO5w97M936\n3OGSufLaeMi0/I2nfpe5MEh6OA3Dp3o/rYA6UhXVGDDoRRPfI1vw7hk+5csZitVscr0qPu59AqFe\nEPH6RnOsoum1jQa9dFlgxnP4hpXysKLphbUqz6+WKWQeneWg594wIBoJgzp70N07HhKNHg9793hD\nA/kZKMyDcaG5Dv3G8XNKS2nQtAbVi+PBU2UtCZ686YTjIiIi8uRRqCQij6U4imnu9Ueqm5KwqbbV\nobnTGwuc/KxLdSlPdbHAzFKe6lKB6nyWot/D7+wR7eyMBE7bh8HT9jbhzg5Ex0MEt1rFnZvDZDIj\nm4/xfUwmg5PJYPzMkefTcw6eH91Gz/X9sfOs5zOIXHoDQ69v6PWh142T5WetgEa9Q73eotPoE3Qs\ntudg7ORKn77XJcr2MfmYbNGlWMlRrRSZyXnkzICwv0+vtUOntkVrb4va5jqDbufwDYyhPL/A7Moq\n1bRx+OzKGtW0cXgml39/39AohM2vHvZluvVZaNxJv4FFuPgxeOqTsPKNkK1AtgSZcrpPt4fQjP5B\nhIMoCZnSCqh7Lb/rNAYE/ckBVK7oH1tqV6gm4dNYX6iyj5MGUGEU8+5Oeyxouna3Qb0bAOAYuLpQ\nPKxoSvezRQUM93XSgKizmzz3IAFRYR7yc+l4Lt2OHp9PzneOhLK9BjTuJn8WGneScf32yLG79w+e\nDqqcFDyJiIjIKVCoJCLnThzFNHZ7w2V0w/5NW10auz3saOCUc5lZKlBdzFNdyifj9HG+7EMcE+3v\njwVNQbqPajXsIMAOBhO3OBgce544JjYOgV8i8EsM/DJBpsQgfRz4ZQaZ9LhfIsiUCLxCcme1Cfyw\njR92yMQdMnGXrO2ToU/WGZBxQjLuAEyHmBYD06RNh4btUo871GyLvhMTuobAg9hzKRVnqBbnmSkv\nMlteYq68RCUzT8ZmaLfbNBp1GvV96nu71Pd26LbG73ZXnJmluryahk5J4/DZ5VWqK6vkS+WTfSNr\nt0ZCps/AZnqXuXvxi5AdCZqy5XRfGtmX73NO+tjLPpQleMEgOhY0HQ+f+nSaAeGkAMokAdRoxdMw\ndKomoVPLwI1Wj9drba6tN3n1bp27tS4OFmMta5UML66UeH6lzPPLJZ5fKrJazgIW4hgbxcl1jWNs\nnOzHxq6LW6ngViqPRG+y+xoLiA5CoJEg6F7HHzQgGoZBo0HRkZBoUkB0Wt4zeEqP3zN4OrK8bjSI\nUvAkIiIiD0Chkog8UaIoprkzsqRuGDx1aO72xtoxZXIu1aWR6qZh6JQnV/SHzZOj8KBqaEC3ke7T\n/kPdVkC3MRhWFXWb924gDZDNGXJZyGUtWd+S8yOybkTGDciagKwZJKGR7eJHXUwwwAYD4mFwdY+Q\nKwjeM+h6vwLHoZP16GR82lmfbi5LO5eh47v0vPFfqn0LJcel5HiUvAzlTI5yLk85XySfL+Bks8MK\nLeP749VcfgbjRJjuLnbQgUEXgi62n+wJetigB4MuNuxD0INBHxv2IOhDOMAGfYjCw++xNck4fWyt\nScYWLA44Gazjg+OD8bGOBybZrPHA8bC4yVIl42GNAziAm4ytScaYJKSxMcQWoghr00AnjpJjcUyI\nS58cfZOn7xTomzwDt0DfLTJwCvS9EgOvSN8rETsTftm3MX7QIjNokhk0kn3QIDto4A+aZAeN4XE/\naGE4+X/LnUIBp1pNKvUqFdxqJXlcSY9Vk/Dp8FgFt1rFKZffX2P49wqIjoVEI72Jov493tBAfnZC\nEHQ8IIpzs4S5OcJMhcA6RLEljGKC2BJFliCOCSNLeGxv6YcB7aBNa9CmFTbpBG3aQYtO2KYbtulE\nLXpRm17UoRe16Udt+nGHge0QxB1iYvJOhbw7Q8mbpeTNUPZnqWRmqWZmmcnOMZebZzY3Q97zyXjO\n4eYejrOum4yjFm5zfTx4atyB+p2TBU9Hl9wpeBIREXniKVQSEUlFYUxz9zBwqqXNwusTAqdswSNb\n9Om1gns2eTaGtNdQ2rT64G5mx+52ltzhLFfycZyzv8uXDcPD6qo0mIoHfWrNLTZrd9hq3GW7fpfd\nxiZ7zU3qrR363RZ+BF4IfgQVk2fOLTPrlKiaIhWTp2Az2MihPwho9vu0gj7NMKBtQ9rWMnorPze2\nFIKQ4iCg0B1Q6A8oDEKK/YBcEHJqV8Ux4DhJQGjAOGY4L2NIxxZj0rTJ2DSMiTHE6bGRc41NXpI+\nNgevd1xwky0Ze+B6GNcDz0/2rp+MPT8dJ2Eabgb8DMbPJb/AOz6h49O3Gfpxlp7N0o99enEm2Yc+\nvcijF3r0Q4/ITgpzLLENGMR9gjggtAMiAvI5qBQdFqsea3MZLi3lKGVi4kaDqFEnrjeI6nWi9HFU\nS8ZxvY7t3yvIAWsMFEvYUgkKechnIOdB1sHxLU4mxvECPLeP73bw3TY50yDv1PGd/sSiMYuh41bo\nuBVabjXZnDINp0LDVGmYMnVTZp8KNSrs2yK1uEhgnWEgFMWWIIqH+zC2hDYgtB2s6WHcHsZJ9jg9\njNMfHsPpYdx+8rzTg4NznR7GHdz3R89aBxvlIM5h42RPnMOxObCG2G2B08J46WaOV7JZa7BRARuW\nsFEZGxaH4zgsYaNS8jgs4dgyGSczFj5lR8KoqumyYvZYYpeleJcFu8N8tM1cuE013KYabJGL2sfm\n0M3O08uv0i+sMCisEpZWiUqrxJULUF7Dqa7hZ3LHgq+s5+iuhiIiIueAQiURkQcQBTGN3e7YUrp+\nJ5gQFB2GR7mCn4QU51Br0OJ26za3mre42bg5vEvdreYt1tvr2JEqmIJX4GL54vBudRfLF7lQWGOu\nX8BvhDS3NqltblDbuEttY5361gZReBjUOZ5HdW6Byuw8M3PzfMsf+aMYz0sqXxxnuD82NiYJcoyT\nfB9cNznmOIfjr4e1SZXUoAX9ZrpvHT6edOy9zgm7D/a5jnd86d5wPL6cz2bKBKZMJyzRCYp0+nk6\nfZ9u16PTMbRaMbW9Pu1Gn7ATYiYUrYXG8gsXLJEdCV7imEzUpWqbzJomc6bJQlRjKaixENSYD5pU\ngxblQYdS0CMbDMj2Awgs0cAhHjhEA0M0cIgGB1Vdk8XG0M/4dP0s7UyeZqZII1Oinq3QyZbo5gr0\nskW6+SK9XIZ+waNbcOgVHcJMlIZBPazTA6dLTI/YdIlNl4hkC+kQ2h6B7RAT3Pdb4JssWbdA3i2S\n80oU3CJ5r0jRK1LwSxT9EiW/SNEvUc6UKWdKlP0ylWyZaq5ENVuh4OfIuC6uY/AcMzFQttYyiGL6\nQcRet85WZ5ftzg473V12u7vs9fbY7+9R7+9RH+zTCPZphfsM4slL+XxTIGuqZEwFz1bwqODEZZy4\nDFEJ0jAqCooEoc8gTD5/ECZbJmqxYPdYM7usmD3WSPfp41WzR8V0jn3utq1y186zYedYt3Os23nW\n7RzbzgK7zgI1dx7jZ0cqrdy00mpyBdaxUGzCcxlv9Hn3PZ5L9r7r4BhwHaOw62GwFqIBDNpJteGg\nk9yMYdBJHged5C6HL/07057puWetTQpirSW2lqx3RktzReSJoVBJREQeqkE04E7rzvBOdQdh083m\nTe407zCID6s4PONxoXwhCZ1Kl7hUvsTF4gUW4wr5JnR2dqltrFPbXKe2sU7Q6/HD/8P/PMWv7hRF\nYRIwjQVPjZFxCwbNCY8nHWuBndwk/Bi/kAZQJQbeIm2zSC2cZ6dfZrdbphe4PD33ryhFdYpRnUKY\nbJ6dXI1jMQz8KoPMLIPsDEF2ljA7S5SbJcrNEefmiHOzxIV5TGGOOD8HeASNHYL6DkFjm7CxS1Sv\nETVr0KhjG01Mq43TbOO2enjtPtnOgGw3wnmPf5IMXGjloZ2DVg7aOUO/4DMoZYiKOeJSAVsp4pTL\nONUq/swM2Zl5MjNzlAszlPwSpUwSDBX9ImW/TDFTxHce7d5SnaDDXm+P3d4ue91kfxBCHYx3e8nj\no3ePPJBzc8zn55nLzTGfmx+OZ7NzVDNzlP1Zin6VkjdLxhQJYxiEMWG3gWncxTTv4LXW8VvrZDrr\nZLsb5DsbFPqbZMPWsc9renPsewvsuYvsuotsOwtsM88mc6wzz0Y8Sztyk4AriumnQdcgijmNf5aO\nBkyOAdcYHGMw6fFkbHAdcCY+N/k1SXHkyDg9xxl5n2OfPRwb3PQ1o589cR4mCSpH53H8sy2ZOF1O\nHffJxL2kN1/cxYt7ZKIeftzFj7t4UQ8/7uBHPdwoWX7thV3cqIcXdXGjDm7YxY26uGEyNvf5Oyj2\nCrz6Q6+lYQdEsR0GIGPjNAyx1hLFHBsPt7HH6TgeDVRIHx9/PrIHn5e87+h4+Hnp65LPvtdnTJ5H\n8vVMGE94z8PPPnJeOq8Hnoc9mPvhNS9lPb76V//ww/8DIyJPNIVKIiJyZmIbs9XZGgZOB1VOB+FT\nMxhv/L1UWOJS+dKwymmxsIhrkiqjg72Dczg2zuGGc/zYyHOOcY69z+g5B691jYvh+PsMXzvpOaZc\n7WBt0ni6nwZTJ6mYOnpOHB7pNzQ31pS6n6vQyuRp+Vlark/TQDvs0gyatAYtmkGT9qBNK2jRHDRp\nBa1kG7SGz3cfoErLczzKfplSpjQMe0pukbk4x0zfo9r3qAwcSj0odGPy7YhsN8BvD/BaXZxmF1ot\nonqyjC9uH1/KNWrYP6pSGfaLcioV3OrMsJ+UW63iHO0nVS5j3MenEiCIAvZ6e2OB015vbxg8jYZR\n+719oglBgWc8ZnOzzOfnxwKosXH63GxuFi/oTmgono4P+jxNCruKS0fuaLeGrVwgKq0yKKzSzy8z\nMD6DcDx0GgzH0T2fC6L4gcKH0RDjaPgwHNvDICKORwML0hAg+UV/bDwaBIx8tokCMrZLNu6Rsb10\n3yUb98naHjmb3KAhZ3vk6JGxffK2T47e4Z5+uvUomGRcoE/e3H+Z5qjYGjpk6ZKlY7Nj4y45Oum4\nRyYd55Lnh+ekr7FZOun5N+3y+/3RfejeK+Q7DOMOw0A3De2Ojc34cceQBn8j45HPMPcIC500GByb\n18HYebB5HP3sjOfwI9/2gWlfahE5ZxQqiYjII8FaS61fG4ZMR7ed7s60p3gi9wy2OBJm3eO5YbCF\ng+M4k0OvCcfuFZLdL2wzjM/pYBzF0WEIlAZD7eAwJAri+y8Xy3v5wyDIL02sADp4bngsUx57TdbN\nPtSgzgYBUbOZhEyNtFdUPe0VlQZP0cHxI/2kbO9ed4oDjMEpl4+EUfdqbl4ZHneqMzjFwiO99Cq2\nMfV+fazS6Vj4NPJ4tCpx1Ex2ZmLgNBZGuVnmBj1y7Z2RhuIPEjwtHrmj3ZFG45W15O6OD8vBMtig\nc3ypV9C99/Kv4fFJz3cPxw/w52uMm0mrD4vg50fGBcgUsH4B6xeTvZfH+kViP0/sFZLNzxN5BWIv\nR+QWiL08kZcncgtETpYYuFcFzvupNjoMXu5R6XUsVBkPfFyHkSquNFBxxgOVSZVfk6rJHuU/eyIi\njzKFSiIi8lg4WNIT2zjZiNOlAFH6i0tyLI7Hn4ttMh597uDY8LXE4+8zslkmH4viaPjcsTnYw88Y\ne+2EeUU2mvwZB3M+ciyy0eSv/chnHD02el3G5jzhM0bPd3CSYCftC1T0i8PxWNXQhEConClT8AuP\n/HKxk4r7/YlhVDwcTw6jokYDgvcICTwPt1zGW1jg6f/9V8/uCzoF1lraQXti4DRp3AqOL4kDKPrF\ne1c/eQXmo5j5oMdct0WptYNp3nnA4GkNKumd7KoXIFdNQ6DRcGdCyHMsCOrASe+k6BeGIQ9+Md2P\nhz9jx9/z3NHXFJIm/yIiImfo/YRK3mlNRkRE5F4KfoGCX5j2NERwslmcpSVYWjrR66y12E7nMHSa\nGEbVIJrQMf0xY4wZhpGXK5fve34v7B0uwzsaPqWh1I3GDX5v8/eo9WtjNwM4kHEySeA0M8f86seY\nz/0h5vwS87jMxzHzgz5zvTbznT1mmts4++/C9U8fD54c70iAk+6zZSivnCAQOtjnD8dePrlZgIiI\nyBNMoZKIiIjICRljMMUiTrGIv7o67ek8UnJejrXSGmultfueG8YhtX5tGDgdW4rX22W7s81ru6+x\n19sjtOGx93CMw+z8LPMXv5n5TJXFTJX/+pW/jMmUwMucxpcoIiIiKYVKIiIiIjIVnuOxkF9gIb9w\n33OttTQGjbHA6dhyvO4e9c5dTGHuDGYvIiIiCpVERERE5JFnjKGarVLNVnmap6c9HREREQG0EFxE\nRERERERERE5MoZKIiIiIiIiIiJyYQiURERERERERETkxhUoiIiIiIiIiInJiCpVEREREREREROTE\nFCqJiIiIiIiIiMiJKVQSEREREREREZETU6gkIiIiIiIiIiInplBJREREREREREROTKGSiIiIiIiI\niIicmEIlERERERERERE5MYVKIiIiIiIiIiJyYgqVRERERERERETkxBQqiYiIiIiIiIjIiSlUEhER\nERERERGRE1OoJCIiIiIiIiIiJ6ZQSURERERERERETkyhkoiIiIiIiIiInJhCJREREREREREROTGF\nSiIiIiIiIiIicmIKlURERERERERE5MQUKomIiIiIiIiIyIkpVBIRERERERERkRMz1tppz+FEjDFN\n4PVpz+MJswDsTHsSTxhd87Ona372dM3Pnq752dM1P3u65mdP1/zs6ZqfPV3zs6drfvaes9aWT/IC\n77Rmcopet9a+PO1JPEmMMZ/XNT9buuZnT9f87Omanz1d87Ona372dM3Pnq752dM1P3u65mdP1/zs\nGWM+f9LXaPmbiIiIiIiIiIicmEIlERERERERERE5sccxVPq7057AE0jX/Ozpmp89XfOzp2t+9nTN\nz56u+dnTNT97uuZnT9f87Omanz1d87N34mv+2DXqFhERERERERGR6XscK5VERERERERERGTKFCqJ\niIiIiIiIiMiJPVahkjHmu4wxrxtj3jLG/IVpz+e8M8b8rDFmyxjz1WnP5UlhjLlkjPktY8yrxphr\nxpgfn/aczjtjTM4Y8/8ZY76UXvO/Ou05PQmMMa4x5veNMf9s2nN5UhhjrhtjvmKM+eL7uV2snJwx\nZsYY88vGmNeMMV8zxnxy2nM6z4wxz6U/3wdbwxjzE9Oe13lnjPlP0v9+ftUY84vGmNy053TeGWN+\nPL3e1/Qzfjom/R5kjJkzxvyGMebNdD87zTmeN/e45v9e+nMeG2Nenub8zqN7XPO/nv675cvGmF8x\nxszc730em1DJGOMCfxv4buAF4I8ZY16Y7qzOvZ8Dvmvak3jChMB/aq19AXgF+DH9nJ+6PvAHrbXf\nBHwY+C5jzCtTntOT4MeBr017Ek+gf91a+2Frrf5hdjb+FvBr1tpvAL4J/cyfKmvt6+nP94eBjwEd\n4FemPK1zzRhzAfiPgZettS8BLvD9053V+WaMeQn4M8DHSf5e+R5jzDPTndW59HMc/z3oLwC/aa39\nIPCb6WN5eH6O49f8q8C/DfzOmc/myfBzHL/mvwG8ZK39EPAG8Bfv9yaPTahE8hfnW9bad6y1A+Af\nAt835Tmda9ba3wH2pj2PJ4m1dt1a+3vpuEnyC8iF6c7qfLOJVvrQTzfdweAUGWMuAn8E+Olpz0Xk\ntBhjqsC3AT8DYK0dWGtr053VE+U7gLettTemPZEngAfkjTEeUADuTnk+593zwGettR1rbQj8Nskv\n3fIQ3eP3oO8Dfj4d/zzwb53ppM65SdfcWvs1a+3rU5rSuXePa/7r6d8tAJ8BLt7vfR6nUOkCcGvk\n8W30y7acY8aYK8BHgM9OdybnX7oU64vAFvAb1lpd89P13wP/ORBPeyJPGAv8C2PMF4wxPzLtyTwB\nrgLbwN9Ll3r+tDGmOO1JPUG+H/jFaU/ivLPW3gF+ErgJrAN1a+2vT3dW595XgW81xswbYwrAvwlc\nmvKcnhTL1tr1dLwBLE9zMiJn4E8D/9f9TnqcQiWRJ4YxpgT8E+AnrLWNac/nvLPWRulyiYvAx9PS\ncjkFxpjvAbastV+Y9lyeQN+S/px/N8nS2m+b9oTOOQ/4KPA/WWs/ArTRUokzYYzJAN8L/ONpz+W8\nS3vKfB9JiLoGFI0x/8F0Z3W+WWu/Bvw14NeBXwO+CERTndQTyFprUWW7nGPGmP+CpDXLL9zv3Mcp\nVLrDeAp/MT0mcq4YY3ySQOkXrLX/dNrzeZKkS1N+C/USO01/APheY8x1kmXMf9AY8/enO6UnQ1pR\ngLV2i6TPzMenO6Nz7zZwe6Ty8ZdJQiY5fd8N/J61dnPaE3kC/BvAu9babWttAPxT4FNTntO5Z639\nGWvtx6y13wbsk/Q9kdO3aYxZBUj3W1Oej8ipMMb8IPA9wJ9IA9T39DiFSp8DPmiMuZr+H6jvB351\nynMSeaiMMYak/8bXrLV/c9rzeRIYYxYP7mpgjMkD3wm8Nt1ZnV/W2r9orb1orb1C8vf4/22t1f/V\nPmXGmKIxpnwwBv4QyRIKOSXW2g3gljHmufTQdwCvTnFKT5I/hpa+nZWbwCvGmM+0HYsAAAOKSURB\nVEL6b5jvQA3pT50xZindP0XST+kfTHdGT4xfBX4gHf8A8L9NcS4ip8IY810kbSq+11rbeZDXeKc7\npYfHWhsaY/4j4J+T3FniZ62116Y8rXPNGPOLwL8GLBhjbgN/xVr7M9Od1bn3B4A/CXwl7fED8Jes\ntf/nFOd03q0CP5/eYdIBfslaq9vcy3mzDPxK8jsfHvAPrLW/Nt0pPRH+HPAL6f8Mewf4oSnP59xL\nQ9PvBP7stOfyJLDWftYY88vA75Esk/h94O9Od1ZPhH9ijJkHAuDHdBOAh2/S70HAfwv8kjHmh4Eb\nwL8/vRmeP/e45nvATwGLwP9hjPmitfYPT2+W58s9rvlfBLLAb6T/bvyMtfZH3/N9HqCaSURERERE\nREREZMzjtPxNREREREREREQeEQqVRERERERERETkxBQqiYiIiIiIiIjIiSlUEhERERERERGRE1Oo\nJCIiIiIiIiIiJ6ZQSUREROQUGWN+1Bjzp05w/hVjzFdPc04iIiIiD4M37QmIiIiInFfGGM9a+3em\nPQ8RERGR06BQSUREROQ9GGOuAL8GfAH4KHAN+FPA88DfBErADvCD1tp1Y8y/BL4IfAvwi8aYMtCy\n1v6kMebDwN8BCsDbwJ+21u4bYz4G/Gz6kb9+Rl+aiIiIyNdFy99ERERE7u854H+01j4PNIAfA34K\n+HettQeB0H8zcn7GWvuytfZvHHmf/wX489baDwFfAf5KevzvAX/OWvtNp/lFiIiIiDxMqlQSERER\nub9b1trfTcd/H/hLwEvAbxhjAFxgfeT8f3T0DYwxVWDGWvvb6aGfB/6xMWYmPf476fH/Ffjuh/8l\niIiIiDxcCpVERERE7s8eedwErllrP3mP89unPB8RERGRqdPyNxEREZH7e8oYcxAg/XHgM8DiwTFj\njG+MefG93sBaWwf2jTHfmh76k8BvW2trQM0Y8y3p8T/x8KcvIiIi8vApVBIRERG5v9eBHzPGfA2Y\nJe2nBPw1Y8yXSBpzf+oB3ucHgL9ujPky8GHgv0qP/xDwt40xXwTMw568iIiIyGkw1h6t5hYRERGR\nA+nd3/6ZtfalKU9FRERE5JGiSiURERERERERETkxVSqJiIiIiIiIiMiJqVJJREREREREREROTKGS\niIiIiIiIiIicmEIlERERERERERE5MYVKIiIiIiIiIiJyYgqVRERERERERETkxP5/5e2Qo2qF2h0A\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e78f51ecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cohorts_usr[['2017-01', '2017-02', '2017-03', '2017-04', '2017-05', '2017-06', '2017-07', '2017-08', '2017-09']].plot(figsize=(20,10))\n",
    "plt.title('Cohorts: User Retention')\n",
    "plt.xticks(np.arange(0, 12.1, 1))\n",
    "plt.xlim(0, 12)\n",
    "#plt.ylim(0, 0.2)\n",
    "plt.ylabel('% of Cohort Purchasing')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 呈現 cohort table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e7da3f5f60>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fiZ/je/IQ5/ZuLdBu4Kez1GI+/Ivv0dbRoVnXvgDcD7rDpkWzadi+O+Pmr8LC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\nKwUNRERERERERKRWChqIiIiIiIiISK0UNBARERERERGRWiloICIiIiIiI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4oYKDg+uxWlyuWGlwRklJid555x099NBD6tixo6699lrdddddio+Pr+/S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\nPY1D1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e7dd5b5898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cohorts_usr.columns = ['1612', '1701', '1702', '1703', '1704', '1705',\n",
    "                       '1706', '1707', '1708', '1709', '1710']\n",
    "sns.set(style='white')\n",
    "plt.figure(figsize=(20, 12))\n",
    "plt.title('Cohorts: User Retention', size=24)\n",
    "mat.rcParams.update({'font.size': 14})\n",
    "plt.tick_params(labelsize=14)\n",
    "sns.heatmap(cohorts_usr.T, mask=cohorts_usr.T.isnull(), annot=True, fmt='.2%', cmap='Paired')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## compute order retention by cohort"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下列展示以訂單數為主要衡量回購基礎的算法，方法與上述相同，在此不贅述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cohort_group\n",
       "2016-12     140\n",
       "2017-01     666\n",
       "2017-02     699\n",
       "2017-03    1389\n",
       "2017-04    1370\n",
       "Name: cart_code, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts_crt_group = cohorts['cart_code'].groupby(level=0).first()\n",
    "cohorts_crt_group.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cohort_group</th>\n",
       "      <th>2016-12</th>\n",
       "      <th>2017-01</th>\n",
       "      <th>2017-02</th>\n",
       "      <th>2017-03</th>\n",
       "      <th>2017-04</th>\n",
       "      <th>2017-05</th>\n",
       "      <th>2017-06</th>\n",
       "      <th>2017-07</th>\n",
       "      <th>2017-08</th>\n",
       "      <th>2017-09</th>\n",
       "      <th>2017-10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1786</td>\n",
       "      <td>0.1186</td>\n",
       "      <td>0.1745</td>\n",
       "      <td>0.1447</td>\n",
       "      <td>0.1350</td>\n",
       "      <td>0.1351</td>\n",
       "      <td>0.1417</td>\n",
       "      <td>0.1328</td>\n",
       "      <td>0.1326</td>\n",
       "      <td>0.1118</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.1429</td>\n",
       "      <td>0.1486</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>0.1166</td>\n",
       "      <td>0.0942</td>\n",
       "      <td>0.1024</td>\n",
       "      <td>0.0927</td>\n",
       "      <td>0.1164</td>\n",
       "      <td>0.1004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.1141</td>\n",
       "      <td>0.1202</td>\n",
       "      <td>0.0878</td>\n",
       "      <td>0.0723</td>\n",
       "      <td>0.0697</td>\n",
       "      <td>0.0954</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1286</td>\n",
       "      <td>0.1156</td>\n",
       "      <td>0.0744</td>\n",
       "      <td>0.0634</td>\n",
       "      <td>0.0693</td>\n",
       "      <td>0.0853</td>\n",
       "      <td>0.0673</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort_group  2016-12  2017-01  2017-02  2017-03  2017-04  2017-05  2017-06  \\\n",
       "period                                                                        \n",
       "0              1.0000   1.0000   1.0000   1.0000   1.0000   1.0000   1.0000   \n",
       "1              0.1786   0.1186   0.1745   0.1447   0.1350   0.1351   0.1417   \n",
       "2              0.1429   0.1486   0.1230   0.1166   0.0942   0.1024   0.0927   \n",
       "3              0.2000   0.1141   0.1202   0.0878   0.0723   0.0697   0.0954   \n",
       "4              0.1286   0.1156   0.0744   0.0634   0.0693   0.0853   0.0673   \n",
       "\n",
       "cohort_group  2017-07  2017-08  2017-09  2017-10  \n",
       "period                                            \n",
       "0              1.0000   1.0000   1.0000      1.0  \n",
       "1              0.1328   0.1326   0.1118      NaN  \n",
       "2              0.1164   0.1004      NaN      NaN  \n",
       "3              0.0976      NaN      NaN      NaN  \n",
       "4                 NaN      NaN      NaN      NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts_crt = np.round(cohorts['cart_code'].unstack(0).divide(cohorts_crt_group, axis=1), 4)\n",
    "cohorts_crt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cohort_group</th>\n",
       "      <th>period</th>\n",
       "      <th>2016-12</th>\n",
       "      <th>2017-01</th>\n",
       "      <th>2017-02</th>\n",
       "      <th>2017-03</th>\n",
       "      <th>2017-04</th>\n",
       "      <th>2017-05</th>\n",
       "      <th>2017-06</th>\n",
       "      <th>2017-07</th>\n",
       "      <th>2017-08</th>\n",
       "      <th>2017-09</th>\n",
       "      <th>2017-10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1786</td>\n",
       "      <td>0.1186</td>\n",
       "      <td>0.1745</td>\n",
       "      <td>0.1447</td>\n",
       "      <td>0.1350</td>\n",
       "      <td>0.1351</td>\n",
       "      <td>0.1417</td>\n",
       "      <td>0.1328</td>\n",
       "      <td>0.1326</td>\n",
       "      <td>0.1118</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.1429</td>\n",
       "      <td>0.1486</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>0.1166</td>\n",
       "      <td>0.0942</td>\n",
       "      <td>0.1024</td>\n",
       "      <td>0.0927</td>\n",
       "      <td>0.1164</td>\n",
       "      <td>0.1004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.1141</td>\n",
       "      <td>0.1202</td>\n",
       "      <td>0.0878</td>\n",
       "      <td>0.0723</td>\n",
       "      <td>0.0697</td>\n",
       "      <td>0.0954</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.1286</td>\n",
       "      <td>0.1156</td>\n",
       "      <td>0.0744</td>\n",
       "      <td>0.0634</td>\n",
       "      <td>0.0693</td>\n",
       "      <td>0.0853</td>\n",
       "      <td>0.0673</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.0691</td>\n",
       "      <td>0.0672</td>\n",
       "      <td>0.0619</td>\n",
       "      <td>0.0737</td>\n",
       "      <td>0.0704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>0.0691</td>\n",
       "      <td>0.0815</td>\n",
       "      <td>0.0756</td>\n",
       "      <td>0.0628</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.0571</td>\n",
       "      <td>0.0646</td>\n",
       "      <td>0.0587</td>\n",
       "      <td>0.0583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.0691</td>\n",
       "      <td>0.0658</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.0929</td>\n",
       "      <td>0.0646</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.0857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort_group  period  2016-12  2017-01  2017-02  2017-03  2017-04  2017-05  \\\n",
       "0                  0   1.0000   1.0000   1.0000   1.0000   1.0000   1.0000   \n",
       "1                  1   0.1786   0.1186   0.1745   0.1447   0.1350   0.1351   \n",
       "2                  2   0.1429   0.1486   0.1230   0.1166   0.0942   0.1024   \n",
       "3                  3   0.2000   0.1141   0.1202   0.0878   0.0723   0.0697   \n",
       "4                  4   0.1286   0.1156   0.0744   0.0634   0.0693   0.0853   \n",
       "5                  5   0.1000   0.0691   0.0672   0.0619   0.0737   0.0704   \n",
       "6                  6   0.1071   0.0691   0.0815   0.0756   0.0628      NaN   \n",
       "7                  7   0.0571   0.0646   0.0587   0.0583      NaN      NaN   \n",
       "8                  8   0.1000   0.0691   0.0658      NaN      NaN      NaN   \n",
       "9                  9   0.0929   0.0646      NaN      NaN      NaN      NaN   \n",
       "10                10   0.0857      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "cohort_group  2017-06  2017-07  2017-08  2017-09  2017-10  \n",
       "0              1.0000   1.0000   1.0000   1.0000      1.0  \n",
       "1              0.1417   0.1328   0.1326   0.1118      NaN  \n",
       "2              0.0927   0.1164   0.1004      NaN      NaN  \n",
       "3              0.0954   0.0976      NaN      NaN      NaN  \n",
       "4              0.0673      NaN      NaN      NaN      NaN  \n",
       "5                 NaN      NaN      NaN      NaN      NaN  \n",
       "6                 NaN      NaN      NaN      NaN      NaN  \n",
       "7                 NaN      NaN      NaN      NaN      NaN  \n",
       "8                 NaN      NaN      NaN      NaN      NaN  \n",
       "9                 NaN      NaN      NaN      NaN      NaN  \n",
       "10                NaN      NaN      NaN      NaN      NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts_crt.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 劃出每個月訂單回購變化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1e791e545f8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GkSNH2Lt3L6tXr2bWrFkMGjTovHXnz59PVlYW//mf/8nx48d57rnnOHLkCHfffXeb9wwY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\nUDPpG/qFhuHXBME1ZQyu6sfR05/6OpK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KpBJjGIaqOjtUl55RxbRL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\nZr1T7odOn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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e790cd27b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cohorts_crt[['2017-01', '2017-02', '2017-03', '2017-04', '2017-05', '2017-06', '2017-07', '2017-08', '2017-09']].plot(figsize=(20,10))\n",
    "plt.title('Cohorts: Cart Retention')\n",
    "plt.xticks(np.arange(0, 12.1, 1))\n",
    "plt.xlim(0, 12)\n",
    "#plt.ylim(0, 0.2)\n",
    "plt.ylabel('% of Cohort Purchasing')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 畫出以訂單為基準的cohort table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e79194ecc0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i/nX59lAgvVedwNzIgMUD6irlo1pWoW99J3w2XmTsz+fpWbciY1u7AaU7hnmy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M0caNG7V48WL17t3b1nHsTlpamkaPHq3CwkJz28GDB+Xr60vBoI5Wrlyp\nTZs26b333tN7772nAQMGqEOHDnrvvfdsHc1ufPTRR7r++utrrWA7dOiQvLy8eFNXR7GxsTp37pyO\nHj1qbktLS5OHh4eaNWtmw2T2KTk5WfHx8baOYZcCAwN1+vRp5eXlmdvS0tIkiXvx6+jUqVMaNGiQ\nCgsL5e/vL4PBoE8++eSizfxQNzExMTpy5IhKS0vNbXv27FFsbKwNU+FqRtHgPHd3d917772aMWOG\n9u3bp23btumtt97SQw89ZOto+B1JSkrSO++8ozFjxqhDhw7Kz883f6Fu4uPjFRERoYkTJyotLU2f\nfvqp5s+frxEjRtg6mt0IDQ1VeHi4+cvLy0tubm4KDw+3dTS7ER8fL5PJpGnTpunYsWP67LPP9OKL\nL2ro0KFycHCwdTy7cM011+iPf/yjnn32WR04cEDffvutXnrpJd13330U83+FlJQURUZG2jqGXYqN\njVWbNm00YcIEHTlyRElJSZo6dar69etHMbqOvL29VV5erjlz5ig9PV3//e9/9dprr2nkyJG2jmaX\nunbtqpCQEE2cOFEpKSlatmyZkpOT2eQU9cbBZDKZbB2isSgrK9Nzzz2njz76SB4eHhoyZIiGDBli\n61h2q23btlqxYgU7//8Cc+fOveRO9QcPHuSNch3l5ORoxowZ+uabb+Th4aEHHnhAw4cP58Par7Rw\n4UL973//08qVK20dxa4cOnRICQkJOnDggDw9PTVw4ECNGjWK38NfoKSkRLNmzdJHH30kZ2dn3Xvv\nvRo3bhx7k/wKHTt21CuvvKKbb77Z1lHsUm5urmbPnq2vvvpKLi4u6tOnj8aPH8895L/A8ePHNW3a\nNO3bt09BQUEaNWqU+vbta+tYduPn76t/+OEHTZ48WcnJyWrZsqWeffZZ3XjjjTZOiasVRQMAAAAA\nAGARtycAAAAAAACLKBoAAAAAAACLKBoAAAAAAACLKBoAAAAAAACLKBoAAAAAAACLKBoAAAAAAACL\nKBoAAFDP9u/fr8mTJ/+i17Rt27ae0gAAANSdg8lkMtk6BAAAqK1t27Y6evSorWMAAIDfOWdbBwAA\nwB58/fXXWrx4sZydnZWTk6OOHTtq1qxZ+uCDD/TOO++ourpa7du31/Tp0+Xq6qru3burffv2Kigo\n0IQJE7R06VKtXLlSx44d07Rp03Ty5Ek1adJEkydPVseOHZWZmamnn35apaWliomJ+f/27tCluSiM\n4/j3ohNRwSCsWQSDQRDBol1YcWAwOWyrpgmKjIHReK0LYhlLu1rGxD/AwUD2Bwy0DRSTwcF2DS/6\nolxlb3hn+X7SCecc7lN/PM+5v12uJEkS4HiCJElDa7fbFItF6vU6r6+vlMtlqtUqlUqFKIqYm5uj\nXC4D8Pz8TD6fJ4oixsf/ZvSFQoFcLsfV1RWHh4fs7+/T6/U4OTlhe3ubKIpYXV39rRIlSZI+MTSQ\nJGlIa2trLCwsEAQB2WyWMAy5v79nZ2eHbDbLzc0NnU7nY//XjoGXlxceHh7Y3NwEYGVlhdnZWTqd\nDs1mk0wmA8DW1hapVGp0hUmSJH3D8QRJkoY0Njb2sY7jmH6/TyaT4fj4GPgTCvT7/Y89k5OTn87H\ncczXp4Te73lfAwRBQBAE/6UGSZKkf2GngSRJQ2q1WnS7XQaDAbVajaOjI66vr3l6eiKOY0qlEufn\n59+en5mZYX5+nkajAcDd3R2Pj48sLi6yvr7O5eUlAI1Gg16vN5KaJEmSfmKngSRJQ0qn0xwcHNDt\ndtnY2GB3d5epqSn29vYYDAYsLS2Rz+d/vOP09JRSqUQYhqRSKcIwZGJigmKxSKFQoFKpsLy8zPT0\n9IiqkiRJ+p6/XJQkaQi3t7ecnZ1xcXHx258iSZI0Mo4nSJIkSZKkRHYaSJIkSZKkRHYaSJIkSZKk\nRIYGkiRJkiQpkaGBJEmSJElKZGggSZIkSZISGRpIkiRJkqREb7zzUT7Myu8eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e79194e198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cohorts_crt.columns = ['1612', '1701', '1702', '1703', '1704', '1705', '1706', '1707', '1708', '1709', '1710']\n",
    "sns.set(style='white')\n",
    "plt.figure(figsize=(20, 12))\n",
    "plt.title('Cohorts: User Retention', size=24)\n",
    "mat.rcParams.update({'font.size': 14})\n",
    "plt.tick_params(labelsize=14)\n",
    "sns.heatmap(cohorts_crt.T, mask=cohorts_crt.T.isnull(), annot=True, fmt='.2%', cmap='Paired')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 總結"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cohort analysis只是幫助你及早發現回購或顧客關係維持出狀況，因此這只是你分析的第一步！後續一般而言我還會進一步仔細研究以下差異，但礙於下列步驟會牽扯太多資訊，因此在此簡單條列給各位：\n",
    "1. 觀察表現不好月份的購買商品排行，這部分還可以結合商品的回購率來比較\n",
    "2. 觀察訂單的來源渠道，觀察這些渠道的變化，再進一步尋找是否該渠道的操作不同以往\n",
    "3. 觀察表現不好的月份的廣告排名，觀察是否是新的廣告形式、族群、內容所導致的結果\n",
    "4. 若你有做NPS，去觀察是否是服務、商品等原因導致消費者不回購\n",
    "5. 觀察商城的產品狀況，如主打活動商品維和、庫存狀態和效期"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最後希望這些步驟能幫助到需要回購分析的朋友，若要轉載再麻煩標明出處唷！"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}