Task 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b4a95e67",
   "metadata": {},
   "source": [
    "# Task 2 - Experimentation and Uplift testing\n",
    "Now we have to evaluate the performance of a store trial which was performed in stores 77, 86 and 88.\n",
    "\n",
    "This can be broken down by:\n",
    "\n",
    "- total sales revenue\n",
    "- total number of customers\n",
    "- average number of transactions per customer\n",
    "\n",
    "Create a measure to compare different control stores to each of the trial stores to do this write a function to reduce having to re-do the analysis for each trial store. Consider using Pearson correlations or a metric such as a magnitude distance e.g. 1- (Observed distance – minimum distance)/(Maximum distance – minimum distance) as a measure.\n",
    "\n",
    "Once you have selected your control stores, compare each trial and control pair during the trial period. You want to test if total sales are significantly different in the trial period and if so, check if the driver of change is more purchasing customers or more purchases per customers etc.\n",
    "\n",
    "Main areas of Focus are :\n",
    "\n",
    "- Select control stores – Explore data, define metrics, visualize graphs\n",
    "- Assessment of the trial – insights/trends by comparing trial stores with control stores\n",
    "- Collate findings – summarize and provide recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba42e7e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7866f5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "qvi = pd.read_csv(\"QVI_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "66dd2149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>175</td>\n",
       "      <td>NATURAL</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>2018-09-16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>58</td>\n",
       "      <td>Red Rock Deli Chikn&amp;Garlic Aioli 150g</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>150</td>\n",
       "      <td>RRD</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>2019-03-07</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>52</td>\n",
       "      <td>Grain Waves Sour    Cream&amp;Chives 210G</td>\n",
       "      <td>1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>210</td>\n",
       "      <td>GRNWVES</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>2019-03-08</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>106</td>\n",
       "      <td>Natural ChipCo      Hony Soy Chckn175g</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>175</td>\n",
       "      <td>NATURAL</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>2018-11-02</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>96</td>\n",
       "      <td>WW Original Stacked Chips 160g</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160</td>\n",
       "      <td>WOOLWORTHS</td>\n",
       "      <td>OLDER SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LYLTY_CARD_NBR        DATE  STORE_NBR  TXN_ID  PROD_NBR  \\\n",
       "0            1000  2018-10-17          1       1         5   \n",
       "1            1002  2018-09-16          1       2        58   \n",
       "2            1003  2019-03-07          1       3        52   \n",
       "3            1003  2019-03-08          1       4       106   \n",
       "4            1004  2018-11-02          1       5        96   \n",
       "\n",
       "                                PROD_NAME  PROD_QTY  TOT_SALES  PACK_SIZE  \\\n",
       "0  Natural Chip        Compny SeaSalt175g         2        6.0        175   \n",
       "1   Red Rock Deli Chikn&Garlic Aioli 150g         1        2.7        150   \n",
       "2   Grain Waves Sour    Cream&Chives 210G         1        3.6        210   \n",
       "3  Natural ChipCo      Hony Soy Chckn175g         1        3.0        175   \n",
       "4          WW Original Stacked Chips 160g         1        1.9        160   \n",
       "\n",
       "        BRAND              LIFESTAGE PREMIUM_CUSTOMER  \n",
       "0     NATURAL  YOUNG SINGLES/COUPLES          Premium  \n",
       "1         RRD  YOUNG SINGLES/COUPLES       Mainstream  \n",
       "2     GRNWVES         YOUNG FAMILIES           Budget  \n",
       "3     NATURAL         YOUNG FAMILIES           Budget  \n",
       "4  WOOLWORTHS  OLDER SINGLES/COUPLES       Mainstream  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qvi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "304ecfe2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 264834 entries, 0 to 264833\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   LYLTY_CARD_NBR    264834 non-null  int64  \n",
      " 1   DATE              264834 non-null  object \n",
      " 2   STORE_NBR         264834 non-null  int64  \n",
      " 3   TXN_ID            264834 non-null  int64  \n",
      " 4   PROD_NBR          264834 non-null  int64  \n",
      " 5   PROD_NAME         264834 non-null  object \n",
      " 6   PROD_QTY          264834 non-null  int64  \n",
      " 7   TOT_SALES         264834 non-null  float64\n",
      " 8   PACK_SIZE         264834 non-null  int64  \n",
      " 9   BRAND             264834 non-null  object \n",
      " 10  LIFESTAGE         264834 non-null  object \n",
      " 11  PREMIUM_CUSTOMER  264834 non-null  object \n",
      "dtypes: float64(1), int64(6), object(5)\n",
      "memory usage: 24.2+ MB\n"
     ]
    }
   ],
   "source": [
    "qvi.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6c7639e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "qvi[\"DATE\"] = pd.to_datetime(qvi[\"DATE\"])\n",
    "qvi[\"YEARMONTH\"] = qvi[\"DATE\"].dt.strftime(\"%Y%m\").astype(\"int\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54921267",
   "metadata": {},
   "source": [
    "Compile each store's monthly:\n",
    "\n",
    "- Total sales\n",
    "- Number of customers,\n",
    "- Average transactions per customer\n",
    "- Average chips per customer\n",
    "- Average price per unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "06512963",
   "metadata": {},
   "outputs": [],
   "source": [
    "def monthly_store_metrics():\n",
    "    store_yrmo_group = qvi.groupby([\"STORE_NBR\", \"YEARMONTH\"])\n",
    "    total = store_yrmo_group[\"TOT_SALES\"].sum()\n",
    "    num_cust = store_yrmo_group[\"LYLTY_CARD_NBR\"].nunique()\n",
    "    trans_per_cust = store_yrmo_group.size() / num_cust\n",
    "    avg_chips_per_cust = store_yrmo_group[\"PROD_QTY\"].sum() / num_cust\n",
    "    avg_chips_price = total / store_yrmo_group[\"PROD_QTY\"].sum()\n",
    "    aggregates = [total, num_cust, trans_per_cust, avg_chips_per_cust, avg_chips_price]\n",
    "    metrics = pd.concat(aggregates, axis=1)\n",
    "    metrics.columns = [\"TOT_SALES\", \"nCustomers\", \"nTxnPerCust\", \"nChipsPerTxn\", \"avgPricePerUnit\"]\n",
    "    return metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed0cc0ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3169 entries, 0 to 3168\n",
      "Data columns (total 7 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   STORE_NBR        3169 non-null   int64  \n",
      " 1   YEARMONTH        3169 non-null   int64  \n",
      " 2   TOT_SALES        3169 non-null   float64\n",
      " 3   nCustomers       3169 non-null   int64  \n",
      " 4   nTxnPerCust      3169 non-null   float64\n",
      " 5   nChipsPerTxn     3169 non-null   float64\n",
      " 6   avgPricePerUnit  3169 non-null   float64\n",
      "dtypes: float64(4), int64(3)\n",
      "memory usage: 173.4 KB\n"
     ]
    }
   ],
   "source": [
    "qvi_monthly_metrics = monthly_store_metrics().reset_index()\n",
    "qvi_monthly_metrics.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ac67f6",
   "metadata": {},
   "source": [
    "- Pre-Trial Observation as this filter only stores with full 12 months observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e89819b",
   "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>STORE_NBR</th>\n",
       "      <th>YEARMONTH</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>nCustomers</th>\n",
       "      <th>nTxnPerCust</th>\n",
       "      <th>nChipsPerTxn</th>\n",
       "      <th>avgPricePerUnit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>201807</td>\n",
       "      <td>206.9</td>\n",
       "      <td>49</td>\n",
       "      <td>1.061224</td>\n",
       "      <td>1.265306</td>\n",
       "      <td>3.337097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>201808</td>\n",
       "      <td>176.1</td>\n",
       "      <td>42</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>3.261111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>201809</td>\n",
       "      <td>278.8</td>\n",
       "      <td>59</td>\n",
       "      <td>1.050847</td>\n",
       "      <td>1.271186</td>\n",
       "      <td>3.717333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>201810</td>\n",
       "      <td>188.1</td>\n",
       "      <td>44</td>\n",
       "      <td>1.022727</td>\n",
       "      <td>1.318182</td>\n",
       "      <td>3.243103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>201811</td>\n",
       "      <td>192.6</td>\n",
       "      <td>46</td>\n",
       "      <td>1.021739</td>\n",
       "      <td>1.239130</td>\n",
       "      <td>3.378947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>201812</td>\n",
       "      <td>189.6</td>\n",
       "      <td>42</td>\n",
       "      <td>1.119048</td>\n",
       "      <td>1.357143</td>\n",
       "      <td>3.326316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>201901</td>\n",
       "      <td>154.8</td>\n",
       "      <td>35</td>\n",
       "      <td>1.028571</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>3.685714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>201807</td>\n",
       "      <td>150.8</td>\n",
       "      <td>39</td>\n",
       "      <td>1.051282</td>\n",
       "      <td>1.179487</td>\n",
       "      <td>3.278261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    STORE_NBR  YEARMONTH  TOT_SALES  nCustomers  nTxnPerCust  nChipsPerTxn  \\\n",
       "0           1     201807      206.9          49     1.061224      1.265306   \n",
       "1           1     201808      176.1          42     1.023810      1.285714   \n",
       "2           1     201809      278.8          59     1.050847      1.271186   \n",
       "3           1     201810      188.1          44     1.022727      1.318182   \n",
       "4           1     201811      192.6          46     1.021739      1.239130   \n",
       "5           1     201812      189.6          42     1.119048      1.357143   \n",
       "6           1     201901      154.8          35     1.028571      1.200000   \n",
       "12          2     201807      150.8          39     1.051282      1.179487   \n",
       "\n",
       "    avgPricePerUnit  \n",
       "0          3.337097  \n",
       "1          3.261111  \n",
       "2          3.717333  \n",
       "3          3.243103  \n",
       "4          3.378947  \n",
       "5          3.326316  \n",
       "6          3.685714  \n",
       "12         3.278261  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observ_counts = qvi_monthly_metrics[\"STORE_NBR\"].value_counts()\n",
    "full_observ_index = observ_counts[observ_counts == 12].index\n",
    "full_observ = qvi_monthly_metrics[qvi_monthly_metrics[\"STORE_NBR\"].isin(full_observ_index)]\n",
    "pretrial_full_observ = full_observ[full_observ[\"YEARMONTH\"] < 201902]\n",
    "\n",
    "pretrial_full_observ.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7c95caeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcCorrTable(metricCol, storeComparison, inputTable=pretrial_full_observ):\n",
    "  control_store_nbrs = inputTable[~inputTable[\"STORE_NBR\"].isin([77, 86, 88])][\"STORE_NBR\"].unique()\n",
    "  corrs = pd.DataFrame(columns = [\"YEARMONTH\", \"Trial_Str\", \"Ctrl_Str\", \"Corr_Score\"])\n",
    "  trial_store = inputTable[inputTable[\"STORE_NBR\"] == storeComparison][metricCol].reset_index()\n",
    "  for control in control_store_nbrs:\n",
    "    concat_df = pd.DataFrame(columns = [\"YEARMONTH\", \"Trial_Str\", \"Ctrl_Str\", \"Corr_Score\"])\n",
    "    control_store = inputTable[inputTable[\"STORE_NBR\"] == control][metricCol].reset_index()\n",
    "    concat_df[\"Corr_Score\"] = trial_store.corrwith(control_store, axis=1)\n",
    "    concat_df[\"Trial_Str\"] = storeComparison\n",
    "    concat_df[\"Ctrl_Str\"] = control\n",
    "    concat_df[\"YEARMONTH\"] = list(inputTable[inputTable[\"STORE_NBR\"] == storeComparison][\"YEARMONTH\"])\n",
    "    corrs = pd.concat([corrs, concat_df])\n",
    "  return corrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "76ab2c8b",
   "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>YEARMONTH</th>\n",
       "      <th>Trial_Str</th>\n",
       "      <th>Ctrl_Str</th>\n",
       "      <th>Corr_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201807</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.070414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201808</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.027276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201809</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201810</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.020045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201811</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.030024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>201812</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.063946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>201901</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201807</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "      <td>0.142957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  YEARMONTH Trial_Str Ctrl_Str  Corr_Score\n",
       "0    201807        77        1    0.070414\n",
       "1    201808        77        1    0.027276\n",
       "2    201809        77        1    0.002389\n",
       "3    201810        77        1   -0.020045\n",
       "4    201811        77        1    0.030024\n",
       "5    201812        77        1    0.063946\n",
       "6    201901        77        1    0.001470\n",
       "0    201807        77        2    0.142957"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_table = pd.DataFrame()\n",
    "for trial_num in [77, 86, 88]:\n",
    "    corr_table = pd.concat([corr_table, calcCorrTable([\"TOT_SALES\", \"nCustomers\", \"nTxnPerCust\", \"nChipsPerTxn\", \"avgPricePerUnit\"], trial_num)])\n",
    "    \n",
    "corr_table.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "187dddd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculateMagnitudeDistance(metricCol, storeComparison, inputTable=pretrial_full_observ):\n",
    "    control_store_nbrs = inputTable[~inputTable[\"STORE_NBR\"].isin([77, 86, 88])][\"STORE_NBR\"].unique()\n",
    "    dists = pd.DataFrame()\n",
    "    trial_store = inputTable[inputTable[\"STORE_NBR\"] == storeComparison][metricCol]\n",
    "    for control in control_store_nbrs:\n",
    "        concat_df  = abs(inputTable[inputTable[\"STORE_NBR\"] == storeComparison].reset_index()[metricCol] - inputTable[inputTable[\"STORE_NBR\"] == control].reset_index()[metricCol])\n",
    "        concat_df[\"YEARMONTH\"] = list(inputTable[inputTable[\"STORE_NBR\"] == storeComparison][\"YEARMONTH\"])\n",
    "        concat_df[\"Trial_Str\"] = storeComparison\n",
    "        concat_df[\"Ctrl_Str\"] = control\n",
    "        dists = pd.concat([dists, concat_df])\n",
    "    for col in metricCol:\n",
    "        dists[col] = 1 - ((dists[col] - dists[col].min()) / (dists[col].max() - dists[col].min()))\n",
    "    dists[\"magnitude\"] = dists[metricCol].mean(axis=1)\n",
    "    return dists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "16eb27b4",
   "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>TOT_SALES</th>\n",
       "      <th>nCustomers</th>\n",
       "      <th>nTxnPerCust</th>\n",
       "      <th>nChipsPerTxn</th>\n",
       "      <th>avgPricePerUnit</th>\n",
       "      <th>YEARMONTH</th>\n",
       "      <th>Trial_Str</th>\n",
       "      <th>Ctrl_Str</th>\n",
       "      <th>magnitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.935431</td>\n",
       "      <td>0.980769</td>\n",
       "      <td>0.958035</td>\n",
       "      <td>0.739412</td>\n",
       "      <td>0.883569</td>\n",
       "      <td>201807</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.899443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.942972</td>\n",
       "      <td>0.951923</td>\n",
       "      <td>0.993823</td>\n",
       "      <td>0.802894</td>\n",
       "      <td>0.886328</td>\n",
       "      <td>201808</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.915588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.961503</td>\n",
       "      <td>0.836538</td>\n",
       "      <td>0.992126</td>\n",
       "      <td>0.730041</td>\n",
       "      <td>0.703027</td>\n",
       "      <td>201809</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.844647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.988221</td>\n",
       "      <td>0.932692</td>\n",
       "      <td>0.989514</td>\n",
       "      <td>0.940460</td>\n",
       "      <td>0.590528</td>\n",
       "      <td>201810</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.888283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.962149</td>\n",
       "      <td>0.951923</td>\n",
       "      <td>0.874566</td>\n",
       "      <td>0.730358</td>\n",
       "      <td>0.832481</td>\n",
       "      <td>201811</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.870296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.944193</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>0.868716</td>\n",
       "      <td>0.857966</td>\n",
       "      <td>0.770744</td>\n",
       "      <td>201812</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.880632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.964375</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.790956</td>\n",
       "      <td>0.551428</td>\n",
       "      <td>0.678728</td>\n",
       "      <td>201901</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.797098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.895138</td>\n",
       "      <td>0.884615</td>\n",
       "      <td>0.933787</td>\n",
       "      <td>0.680831</td>\n",
       "      <td>0.848622</td>\n",
       "      <td>201807</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "      <td>0.848599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TOT_SALES  nCustomers  nTxnPerCust  nChipsPerTxn  avgPricePerUnit  \\\n",
       "0   0.935431    0.980769     0.958035      0.739412         0.883569   \n",
       "1   0.942972    0.951923     0.993823      0.802894         0.886328   \n",
       "2   0.961503    0.836538     0.992126      0.730041         0.703027   \n",
       "3   0.988221    0.932692     0.989514      0.940460         0.590528   \n",
       "4   0.962149    0.951923     0.874566      0.730358         0.832481   \n",
       "5   0.944193    0.961538     0.868716      0.857966         0.770744   \n",
       "6   0.964375    1.000000     0.790956      0.551428         0.678728   \n",
       "0   0.895138    0.884615     0.933787      0.680831         0.848622   \n",
       "\n",
       "   YEARMONTH  Trial_Str  Ctrl_Str  magnitude  \n",
       "0     201807         77         1   0.899443  \n",
       "1     201808         77         1   0.915588  \n",
       "2     201809         77         1   0.844647  \n",
       "3     201810         77         1   0.888283  \n",
       "4     201811         77         1   0.870296  \n",
       "5     201812         77         1   0.880632  \n",
       "6     201901         77         1   0.797098  \n",
       "0     201807         77         2   0.848599  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_table = pd.DataFrame()\n",
    "for trial_num in [77, 86, 88]:\n",
    "    dist_table = pd.concat([dist_table, calculateMagnitudeDistance([\"TOT_SALES\", \"nCustomers\", \"nTxnPerCust\", \"nChipsPerTxn\", \"avgPricePerUnit\"], trial_num)])\n",
    "    \n",
    "dist_table.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2deaf535",
   "metadata": {},
   "source": [
    "- We'll select control stores based on how similar monthly total sales in dollar amounts and monthly number of customers are to the trial stores by using correlation and magnitude distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a92ce273",
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_corr_dist(metricCol, storeComparison, inputTable=pretrial_full_observ):\n",
    "    corrs = calcCorrTable(metricCol, storeComparison, inputTable)\n",
    "    dists = calculateMagnitudeDistance(metricCol, storeComparison, inputTable)\n",
    "    dists = dists.drop(metricCol, axis=1)\n",
    "    combine = pd.merge(corrs, dists, on=[\"YEARMONTH\", \"Trial_Str\", \"Ctrl_Str\"])\n",
    "    return combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "641fa218",
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_metrics_table1 = pd.DataFrame()\n",
    "for trial_num in [77, 86, 88]:\n",
    "    compare_metrics_table1 = pd.concat([compare_metrics_table1, combine_corr_dist([\"TOT_SALES\"], trial_num)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f076ae17",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_weight = 0.5\n",
    "dist_weight = 1 - corr_weight"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b97eee25",
   "metadata": {},
   "source": [
    "- Determining the top five highest composite score for each trial based on Total sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "42e1d394",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Trial_Str  Ctrl_Str  Corr_Score  magnitude  CompScore\n",
      "218         77       233         1.0   0.986477   0.993238\n",
      "239         77       255         1.0   0.979479   0.989739\n",
      "177         77       188         1.0   0.977663   0.988831\n",
      "49          77        53         1.0   0.976678   0.988339\n",
      "120         77       131         1.0   0.976267   0.988134 \n",
      "\n",
      "     Trial_Str  Ctrl_Str  Corr_Score  magnitude  CompScore\n",
      "356         86       109         1.0   0.966783   0.983391\n",
      "401         86       155         1.0   0.965876   0.982938\n",
      "464         86       222         1.0   0.962280   0.981140\n",
      "467         86       225         1.0   0.960512   0.980256\n",
      "471         86       229         1.0   0.951704   0.975852 \n",
      "\n",
      "     Trial_Str  Ctrl_Str  Corr_Score  magnitude  CompScore\n",
      "551         88        40         1.0   0.941165   0.970582\n",
      "538         88        26         1.0   0.904377   0.952189\n",
      "582         88        72         1.0   0.903800   0.951900\n",
      "517         88         4         1.0   0.903466   0.951733\n",
      "568         88        58         1.0   0.891678   0.945839 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mirza\\AppData\\Local\\Temp\\ipykernel_18056\\2888102123.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  grouped_comparison_table1 = compare_metrics_table1.groupby([\"Trial_Str\", \"Ctrl_Str\"]).mean().reset_index()\n"
     ]
    }
   ],
   "source": [
    "grouped_comparison_table1 = compare_metrics_table1.groupby([\"Trial_Str\", \"Ctrl_Str\"]).mean().reset_index()\n",
    "grouped_comparison_table1[\"CompScore\"] = (corr_weight * grouped_comparison_table1[\"Corr_Score\"]) + (dist_weight * grouped_comparison_table1[\"magnitude\"])\n",
    "for trial_num in compare_metrics_table1[\"Trial_Str\"].unique():\n",
    "    print(grouped_comparison_table1[grouped_comparison_table1[\"Trial_Str\"] == trial_num].sort_values(ascending=False, by=\"CompScore\").head(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1b2d8c19",
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_metrics_table2 = pd.DataFrame()\n",
    "for trial_num in [77, 86, 88]:\n",
    "    compare_metrics_table2 = pd.concat([compare_metrics_table2, combine_corr_dist([\"nCustomers\"], trial_num)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf340e68",
   "metadata": {},
   "source": [
    "- Determining the top five highest composite score for each trial based on no. of customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7c7388a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Trial_Str  Ctrl_Str  Corr_Score  magnitude  CompScore\n",
      "218         77       233         1.0   0.993132   0.996566\n",
      "38          77        41         1.0   0.976648   0.988324\n",
      "101         77       111         1.0   0.968407   0.984203\n",
      "105         77       115         1.0   0.967033   0.983516\n",
      "15          77        17         1.0   0.965659   0.982830 \n",
      "\n",
      "     Trial_Str  Ctrl_Str  Corr_Score  magnitude  CompScore\n",
      "401         86       155         1.0   0.986772   0.993386\n",
      "467         86       225         1.0   0.969577   0.984788\n",
      "356         86       109         1.0   0.969577   0.984788\n",
      "471         86       229         1.0   0.964286   0.982143\n",
      "293         86        39         1.0   0.961640   0.980820 \n",
      "\n",
      "     Trial_Str  Ctrl_Str  Corr_Score  magnitude  CompScore\n",
      "736         88       237         1.0   0.987818   0.993909\n",
      "705         88       203         1.0   0.944629   0.972315\n",
      "551         88        40         1.0   0.942414   0.971207\n",
      "668         88       165         1.0   0.935770   0.967885\n",
      "701         88       199         1.0   0.932447   0.966224 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mirza\\AppData\\Local\\Temp\\ipykernel_18056\\1649250243.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  grouped_comparison_table2 = compare_metrics_table2.groupby([\"Trial_Str\", \"Ctrl_Str\"]).mean().reset_index()\n"
     ]
    }
   ],
   "source": [
    "grouped_comparison_table2 = compare_metrics_table2.groupby([\"Trial_Str\", \"Ctrl_Str\"]).mean().reset_index()\n",
    "grouped_comparison_table2[\"CompScore\"] = (corr_weight * grouped_comparison_table2[\"Corr_Score\"]) + (dist_weight * grouped_comparison_table2[\"magnitude\"])\n",
    "for trial_num in compare_metrics_table2[\"Trial_Str\"].unique():\n",
    "    print(grouped_comparison_table2[grouped_comparison_table2[\"Trial_Str\"] == trial_num].sort_values(ascending=False, by=\"CompScore\").head(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c7f1a661",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial_Str  Ctrl_Str\n",
      "77         233         0.994902\n",
      "           41          0.986020\n",
      "           46          0.984762\n",
      "dtype: float64 \n",
      "\n",
      "Trial_Str  Ctrl_Str\n",
      "86         155         0.988162\n",
      "           109         0.984090\n",
      "           225         0.982522\n",
      "dtype: float64 \n",
      "\n",
      "Trial_Str  Ctrl_Str\n",
      "88         40          0.970895\n",
      "           26          0.958929\n",
      "           72          0.954079\n",
      "dtype: float64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for trial_num in compare_metrics_table2[\"Trial_Str\"].unique():\n",
    "    a = grouped_comparison_table1[grouped_comparison_table1[\"Trial_Str\"] == trial_num].sort_values(ascending=False, by=\"CompScore\").set_index([\"Trial_Str\", \"Ctrl_Str\"])[\"CompScore\"]\n",
    "    b = grouped_comparison_table2[grouped_comparison_table2[\"Trial_Str\"] == trial_num].sort_values(ascending=False, by=\"CompScore\").set_index([\"Trial_Str\", \"Ctrl_Str\"])[\"CompScore\"]\n",
    "    print((pd.concat([a,b], axis=1).sum(axis=1)/2).sort_values(ascending=False).head(3), '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7dae27f",
   "metadata": {},
   "source": [
    "Similarities based on total sales:\n",
    "\n",
    "- Trial store 77: Store 233, 255, 188\n",
    "- Trial store 86: Store 109, 155, 222\n",
    "- Trial store 88: Store 40, 26, 72\n",
    "\n",
    "Similarities based on No. of Customers:\n",
    "\n",
    "- Trial store 77: Store 233, 41, 111\n",
    "- Trial store 86: Store 155, 225, 109\n",
    "- Trial store 88: Store 237, 203, 40\n",
    "\n",
    "Final SImilarities based on Highest average of both features combined:\n",
    "\n",
    "- Trial store 77: Store 233\n",
    "- Trial store 86: Store 155\n",
    "- Trial store 88: Store 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ecc12ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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992rPnj0e32fv3r3605/+pObNm+v5559X48aNFRQUpC+++EL33Xdfuf5HRkaWe43AwECXcYcOHVKTJk3KjXM4HBXusyRFRUUpJCSkwppPdaZ/q9ND3NnUfyYhISEKDw8vV8fJ212cKjo6Wn5+fs46K8tut2vgwIEaOHCgJGnHjh3q3r27pkyZouHDh6tu3boen3um3mRkZLisc7dfBw8e1K+//upyjuipKrp1x969e/Xoo4/q6aefVkBAgH799VdJcoaxX3/9VYGBgQoODnb+u7jr0+HDhxUREeHxfSSpW7duLrejSUpKOu9z1c72s/jjjz8qJCTkjDWeq7P9Lps/f76k368uPtlru92uLl266O2339acOXMq/Lx40rRpU1199dVVVicgEfYs42z/yl22bJn8/f313nvvuVxMsGLFiiqtp2HDhhoxYoSSk5O1Y8eOCr94Tv6HJzs7u9y2AwcOyMfHR/Xq1XNZf/r+RkVFyWaz6dNPP3V7kcGZbiORlZWlVq1aOYPeSScvHti+fbvi4+O1a9cuFRYWOtef6uqrr9b69etVUFCgOnXquH2fFStW6NixY3rnnXfUqFEjl/c/V5GRkcrJySm33t260/n6+qpbt2764IMPtH//fjVo0OCM7yX9/m91+tgDBw4oKiqqEpWfHXef7cjISH3++ecyxrhsz83N1YkTJ6qsjtatW2vAgAFKTU3V7t27y81Snl6T5PlzfHpN7vYrKipKkZGRWrVqldv3CAsL8/j+P/zwgwoLCzV27FiNHTu23PZ69epp7NixSk1Ndd537+uvv1avXr1cxn399dce78t30ssvv+xyAVBV9NvX11c33HCDVq1a5fGzuH//fmVmZqpnz57O2bTq5u67LD8/X2+//bYkuf0ukKT09HSNGjXqgtToqU7gJA7j1jI2m01+fn4uX5SFhYV6/fXXz+n1jh49qoKCArfbTl6BGBsbK+n/AtfpszrNmzfXJZdcovT0dJcrgo8dO6a3337beYVuRRITE2WM0c8//6yrr7663NK2bdsKnx8bG6sdO3aU25eTVwOf/A/PyX3ZvHmzyzhjjDZv3qx69eqVC4ynOvkf+FPDp/n/N2k+VzfccIN27Nihr776ymV9enr6WT1/8uTJMsZo+PDhKi4uLre9pKREK1eulCTnqQJLlixxGbNlyxbt3LnTeUVpZXj6XFSkW7duKigoKPdHyuLFi53bK+PQoUNu9136/Upn6cyf406dOik4OLhcb/bv36+PP/74rGpKTEzUoUOHVFpa6vZz3Lx5c4/Pbd++vdauXVtuadeunRo3bqy1a9c6D/Fdcskluuaaa7RkyRKVlpY6X2Pz5s3atWvXGe8V2bx5c5e6qup+gyc/i6NGjXKpS/r90PHf/vY3GWM0efLkKnm/U1Xmuyw9PV2FhYXO+/qdvkRFRZ3XodyqqhM4iZm9WqZ3796aNWuWBg0apBEjRujQoUN69tlnz/kGqrt27dJNN92kAQMGKD4+XvXr11deXp7ef/99zZs3T127dlXnzp0l/X5T0ODgYC1dulQtW7ZUnTp1FBsbq9jYWM2cOVN33HGHEhMTNXLkSBUVFemZZ57Rr7/+ela3Wbjuuus0YsQIDR06VFu3btX111+v0NBQZWdna8OGDWrbtq3+9re/eXx+cnKybr31VvXo0UMPPPCAoqKitHnzZk2fPl2tWrVSz549Jf3+1/Ptt9/uvE1Jr169VFRUpEWLFumzzz7TE088UeEsa48ePRQQEKCBAwfqoYce0m+//aYXX3xReXl5ley8a+2vvfaaevfurSeffFIxMTFaunSpM6ScSadOnfTiiy9q1KhR6tChg/MQUElJib788kvNmzdPbdq0UZ8+fdS8eXONGDFCaWlp8vHxUc+ePfXjjz/qkUceUVxcnB544IFK11/R58KTIUOG6IUXXlBSUpJ+/PFHtW3bVhs2bNC0adPUq1cvde/evVI1rF27VmPHjtUdd9yhzp07KzIyUrm5uXrjjTe0atUqDRkyxBn4T856zZs3T2FhYQoKClKTJk0UGRmpRx55RH//+981ZMgQDRw4UIcOHdLUqVMVFBSkxx577Ix1DBgwQEuXLlWvXr00duxYXXPNNfL399f+/fu1du1a3XLLLR5vmFu3bl23v3hTt25dnThxoty2GTNmqEePHvrLX/6iUaNGKTc3V5MmTVKbNm00dOjQSvWvqlx33XVKTU1VcnKyunTpotGjR6thw4bOmyp//vnnSk1NdX6nVKXKfJfNnz9f9erV0/jx493ebmnIkCGaNWuWvvrqK5dzaVeuXOl2dvbUc0+/++67cn9MSr//wdmgQYNK1Qk4eevKEJwbT1fjurtp6sltp19R+9prr5nmzZubwMBA07RpUzN9+nQzf/58I8nlMv6zuRo3Ly/PPPnkk+bGG280l1xyiQkICDChoaGmffv25sknnyx3q4g33njDtGjRwvj7+xtJ5rHHHnNuW7FihenYsaMJCgoyoaGhplu3buazzz5zef6Zrmp77bXXTMeOHU1oaKgJDg42l156qRkyZIjZunVrhfthzO831k1ISDAOh8MEBwebyy+/3IwbN8787//+r8u4wsJC88wzz5grrrjChIWFmYiICHPttdeaJUuWuL1y93QrV6407dq1M0FBQeaSSy4xEyZMMB988EG5Kzw9/bsmJSWZRo0auaz75ptvTI8ePUxQUJCJiIgww4YNM//+978rdfPfrKwsk5SUZBo2bOj8d7zyyivNo48+6ry1iDG/X/E5Y8YMc/nllxt/f38TFRVl7rzzznI3k61M/Z4+FxVdXXno0CFz7733mvr16xs/Pz/TqFEjM3ny5HI3hj6bq3H37dtnHn74YXPdddcZh8Nh/Pz8TFhYmOnYsaNJS0srd5uP1NRU06RJE+Pr61vuSuJXX33VXHHFFSYgIMDY7XZzyy23mB07dpTrgaf9KikpMc8++6zzM1KnTh3TokULM3LkSPPdd99VuB/uVPT9sHr1anPttdc6PzdDhgxxe5VrVaropsonbdq0yfTr18/ExMQYPz8/Ex0dbW6//XazcePGCp93Plfjnu132VdffWUkmeTkZI+v9e233xpJzts2nfze8rQYc+arcadMmVKpOoFT2Yw5hzvpAgAA4KLAOXsAAAAWxjl7AABLKy0trfDnIG022wW7uhfwBmb2AACWdumll8rf39/jci5XkQMXE2b2AACWtnLlShUVFXncXtH9CwEr4AINAAAAC+MwLgAAgIVZ9jBuWVmZDhw4oLCwsGr5wWwAAFD1jDE6evSoYmNj5ePDnFRVsGzYO3DggOLi4rxdBgAAOAf79u074+914+xYNuydPOF23759Cg8P93I1AADgbBw5ckRxcXFcOFOFLBv2Th66DQ8PJ+wBAHCR4RSsqsPBcAAAAAsj7AEAAFgYYQ8AAMDCCHsAAAAWRtgDAACwMMIeAACAhRH2AAAALIywBwAAYGGEPQAAAAsj7AEAAFgYYQ8AAMDCCHsAAAAWRtgDAACwMMIeAACAhfl5uwAAFUixV9Hr5FfN6wAALjrM7AEAAFhYpcPeJ598oj59+ig2NlY2m00rVqzwOHbkyJGy2WxKTU11WV9UVKT7779fUVFRCg0NVd++fbV//36XMXl5eRo8eLDsdrvsdrsGDx6sX3/9tbLlAgAA1GqVDnvHjh1Tu3btNGfOnArHrVixQp9//rliY2PLbUtOTtby5cu1bNkybdiwQQUFBUpMTFRpaalzzKBBg5SVlaVVq1Zp1apVysrK0uDBgytbLgAAQK1W6XP2evbsqZ49e1Y45ueff9bo0aP14Ycfqnfv3i7b8vPzNX/+fL3++uvq3r27JGnJkiWKi4vTmjVrdNNNN2nnzp1atWqVNm/erI4dO0qSXnnlFXXq1Em7du1S8+bNK1s2AABArVTl5+yVlZVp8ODBmjBhglq3bl1ue2ZmpkpKSpSQkOBcFxsbqzZt2mjjxo2SpE2bNslutzuDniRde+21stvtzjGnKyoq0pEjR1wWAACA2q7Kw96MGTPk5+enMWPGuN2ek5OjgIAA1atXz2V9TEyMcnJynGOio6PLPTc6Oto55nTTp093nt9nt9sVFxd3nnsCAABw8avSsJeZmannn39eCxculM1mq9RzjTEuz3H3/NPHnGry5MnKz893Lvv27atc8QAAABZUpWHv008/VW5urho2bCg/Pz/5+fnpp59+0rhx49S4cWNJksPhUHFxsfLy8lyem5ubq5iYGOeYgwcPlnv9X375xTnmdIGBgQoPD3dZAAAAarsqDXuDBw/Wf//7X2VlZTmX2NhYTZgwQR9++KEkqUOHDvL391dGRobzednZ2dq+fbs6d+4sSerUqZPy8/P1xRdfOMd8/vnnys/Pd44BAADAmVX6atyCggJ9//33zsd79uxRVlaWIiIi1LBhQ0VGRrqM9/f3l8PhcF5Ba7fbNWzYMI0bN06RkZGKiIjQ+PHj1bZtW+fVuS1bttTNN9+s4cOH6+WXX5YkjRgxQomJiVyJCwBnwi+vADhFpcPe1q1bdcMNNzgfP/jgg5KkpKQkLVy48KxeY/bs2fLz81P//v1VWFiobt26aeHChfL19XWOWbp0qcaMGeO8ardv375nvLcfAAAAXNmMMcbbRVSHI0eOyG63Kz8/n/P3cPFihgbngs8NLmL897vq8du4AAAAFkbYAwAAsDDCHgAAgIUR9gAAACyMsAcAAGBhlb71CgDUCFxxCgBnhZk9AAAACyPsAQAAWBhhDwAAwMI4Zw8AUOM1nvR+lbzOj0/3rpLXAS4mhL2zwJcMAAC4WHEYFwAAwMKY2UPNUBW30eAWGheNqpgt/zGoCgpB7cMte1ALMbMHAABgYYQ9AAAAC+Mw7oXEocpahUOVAICagJk9AAAACyPsAQAAWBhhDwAAwMIIewAAABZG2AMAALAwwh4AAICFEfYAAAAsjLAHAABgYYQ9AAAACyPsAQAAWBg/lwYAAKrmJz0lftazBmJmDwAAwMIIewAAABbGYVwAAC5ijSe9XyWv82NQlbwMaiBm9gAAACyMsAcAAGBhhD0AAAALI+wBAABYGGEPAADAwgh7AAAAFkbYAwAAsDDCHgAAgIUR9gAAACys0mHvk08+UZ8+fRQbGyubzaYVK1Y4t5WUlGjixIlq27atQkNDFRsbqyFDhujAgQMur1FUVKT7779fUVFRCg0NVd++fbV//36XMXl5eRo8eLDsdrvsdrsGDx6sX3/99Zx2EgAAoLaqdNg7duyY2rVrpzlz5pTbdvz4cW3btk2PPPKItm3bpnfeeUe7d+9W3759XcYlJydr+fLlWrZsmTZs2KCCggIlJiaqtLTUOWbQoEHKysrSqlWrtGrVKmVlZWnw4MHnsIsAAAC1V6V/G7dnz57q2bOn2212u10ZGRku69LS0nTNNddo7969atiwofLz8zV//ny9/vrr6t69uyRpyZIliouL05o1a3TTTTdp586dWrVqlTZv3qyOHTtKkl555RV16tRJu3btUvPmzStbNqoJv8kIAEDNVu3n7OXn58tms6lu3bqSpMzMTJWUlCghIcE5JjY2Vm3atNHGjRslSZs2bZLdbncGPUm69tprZbfbnWNOV1RUpCNHjrgsAAAAtV21hr3ffvtNkyZN0qBBgxQeHi5JysnJUUBAgOrVq+cyNiYmRjk5Oc4x0dHR5V4vOjraOeZ006dPd57fZ7fbFRcXV8V7AwAAcPGptrBXUlKiAQMGqKysTHPnzj3jeGOMbDab8/Gp/9vTmFNNnjxZ+fn5zmXfvn3nXjwAAIBFVEvYKykpUf/+/bVnzx5lZGQ4Z/UkyeFwqLi4WHl5eS7Pyc3NVUxMjHPMwYMHy73uL7/84hxzusDAQIWHh7ssAAAAtV2Vh72TQe+7777TmjVrFBkZ6bK9Q4cO8vf3d7mQIzs7W9u3b1fnzp0lSZ06dVJ+fr6++OIL55jPP/9c+fn5zjEAAAA4s0pfjVtQUKDvv//e+XjPnj3KyspSRESEYmNj1a9fP23btk3vvfeeSktLnefYRUREKCAgQHa7XcOGDdO4ceMUGRmpiIgIjR8/Xm3btnVenduyZUvdfPPNGj58uF5++WVJ0ogRI5SYmMiVuAAAAJVQ6bC3detW3XDDDc7HDz74oCQpKSlJKSkpevfddyVJ7du3d3ne2rVr1bVrV0nS7Nmz5efnp/79+6uwsFDdunXTwoUL5evr6xy/dOlSjRkzxnnVbt++fd3e2w8AAACeVTrsde3aVcYYj9sr2nZSUFCQ0tLSlJaW5nFMRESElixZUtnyAAAAcAp+GxcAAMDCCHsAAAAWRtgDAACwMMIeAACAhRH2AAAALIywBwAAYGGEPQAAAAsj7AEAAFgYYQ8AAMDCCHsAAAAWRtgDAACwMMIeAACAhRH2AAAALIywBwAAYGGEPQAAAAsj7AEAAFgYYQ8AAMDCCHsAAAAW5uftAgAAv2s86f0qeZ0fg6rkZQBYBDN7AAAAFkbYAwAAsDDCHgAAgIUR9gAAACyMsAcAAGBhhD0AAAALI+wBAABYGGEPAADAwgh7AAAAFkbYAwAAsDDCHgAAgIUR9gAAACyMsAcAAGBhhD0AAAALI+wBAABYGGEPAADAwgh7AAAAFkbYAwAAsDDCHgAAgIVVOux98skn6tOnj2JjY2Wz2bRixQqX7cYYpaSkKDY2VsHBweratat27NjhMqaoqEj333+/oqKiFBoaqr59+2r//v0uY/Ly8jR48GDZ7XbZ7XYNHjxYv/76a6V3EAAAoDardNg7duyY2rVrpzlz5rjdPnPmTM2aNUtz5szRli1b5HA41KNHDx09etQ5Jjk5WcuXL9eyZcu0YcMGFRQUKDExUaWlpc4xgwYNUlZWllatWqVVq1YpKytLgwcPPoddBAAAqL38KvuEnj17qmfPnm63GWOUmpqqKVOm6Pbbb5ckLVq0SDExMUpPT9fIkSOVn5+v+fPn6/XXX1f37t0lSUuWLFFcXJzWrFmjm266STt37tSqVau0efNmdezYUZL0yiuvqFOnTtq1a5eaN29+rvsLAABQq1TpOXt79uxRTk6OEhISnOsCAwMVHx+vjRs3SpIyMzNVUlLiMiY2NlZt2rRxjtm0aZPsdrsz6EnStddeK7vd7hxzuqKiIh05csRlAQAAqO2qNOzl5ORIkmJiYlzWx8TEOLfl5OQoICBA9erVq3BMdHR0udePjo52jjnd9OnTnef32e12xcXFnff+AAAAXOyq5Wpcm83m8tgYU27d6U4f4258Ra8zefJk5efnO5d9+/adQ+UAAADWUqVhz+FwSFK52bfc3FznbJ/D4VBxcbHy8vIqHHPw4MFyr//LL7+UmzU8KTAwUOHh4S4LAABAbVelYa9JkyZyOBzKyMhwrisuLtb69evVuXNnSVKHDh3k7+/vMiY7O1vbt293junUqZPy8/P1xRdfOMd8/vnnys/Pd44BAADAmVX6atyCggJ9//33zsd79uxRVlaWIiIi1LBhQyUnJ2vatGlq1qyZmjVrpmnTpikkJESDBg2SJNntdg0bNkzjxo1TZGSkIiIiNH78eLVt29Z5dW7Lli118803a/jw4Xr55ZclSSNGjFBiYiJX4gIAAFRCpcPe1q1bdcMNNzgfP/jgg5KkpKQkLVy4UA899JAKCws1atQo5eXlqWPHjlq9erXCwsKcz5k9e7b8/PzUv39/FRYWqlu3blq4cKF8fX2dY5YuXaoxY8Y4r9rt27evx3v7AQAAwL1Kh72uXbvKGONxu81mU0pKilJSUjyOCQoKUlpamtLS0jyOiYiI0JIlSypbHgAAAE7Bb+MCAABYGGEPAADAwgh7AAAAFkbYAwAAsDDCHgAAgIUR9gAAACyMsAcAAGBhhD0AAAALI+wBAABYGGEPAADAwgh7AAAAFkbYAwAAsDDCHgAAgIUR9gAAACyMsAcAAGBhhD0AAAALI+wBAABYGGEPAADAwgh7AAAAFkbYAwAAsDDCHgAAgIUR9gAAACyMsAcAAGBhhD0AAAALI+wBAABYGGEPAADAwgh7AAAAFkbYAwAAsDDCHgAAgIUR9gAAACyMsAcAAGBhhD0AAAAL8/N2AQAAAJVhjNGJEydUWlrq7VK8xtfXV35+frLZbGccS9gDAAAXjeLiYmVnZ+v48ePeLsXrQkJCVL9+fQUEBFQ4jrAHAAAuCmVlZdqzZ498fX0VGxurgICAs5rZshpjjIqLi/XLL79oz549atasmXx8PJ+ZR9gDAAAXheLiYpWVlSkuLk4hISHeLsergoOD5e/vr59++knFxcUKCgryOJYLNAAAwEWlolms2uRs+0C3AAAALIywBwAAYGFVfs7eiRMnlJKSoqVLlyonJ0f169fXXXfdpYcfftg53WiM0dSpUzVv3jzl5eWpY8eOeuGFF9S6dWvn6xQVFWn8+PF64403VFhYqG7dumnu3Llq0KBBVZcMAAAuco0nvX9B3+/Hp3tf0Pc7H1U+szdjxgy99NJLmjNnjnbu3KmZM2fqmWeeUVpamnPMzJkzNWvWLM2ZM0dbtmyRw+FQjx49dPToUeeY5ORkLV++XMuWLdOGDRtUUFCgxMTEWn1PHQAAcPGbPn26bDabkpOTneuMMUpJSVFsbKyCg4PVtWtX7dixo0rer8rD3qZNm3TLLbeod+/eaty4sfr166eEhARt3bpV0u87k5qaqilTpuj2229XmzZttGjRIh0/flzp6emSpPz8fM2fP1/PPfecunfvriuvvFJLlizR119/rTVr1lR1yQAAABfEli1bNG/ePF1xxRUu689mIuxcVXnY69Kliz766CPt3r1bkvTVV19pw4YN6tWrlyRpz549ysnJUUJCgvM5gYGBio+P18aNGyVJmZmZKikpcRkTGxurNm3aOMecrqioSEeOHHFZAAAAaoqCggLdcccdeuWVV1SvXj3n+rOZCDsfVR72Jk6cqIEDB6pFixby9/fXlVdeqeTkZA0cOFCSlJOTI0mKiYlxeV5MTIxzW05OjgICAlwacfqY002fPl12u925xMXFVfWuAQAAnLP77rtPvXv3Vvfu3V3Wn81E2Pmo8gs03nzzTS1ZskTp6elq3bq1srKylJycrNjYWCUlJTnHnX7Ha2PMGe+CXdGYyZMn68EHH3Q+PnLkCIEPAADUCMuWLdO2bdu0ZcuWctsqmgj76aefzvu9qzzsTZgwQZMmTdKAAQMkSW3bttVPP/2k6dOnKykpSQ6HQ5KcV+qelJub69xJh8Oh4uJi5eXluczu5ebmqnPnzm7fNzAwUIGBgVW9OwAAAOdl3759Gjt2rFavXl3hL12cy0TY2ajyw7jHjx8vd0dnX19flZWVSZKaNGkih8OhjIwM5/bi4mKtX7/eGeQ6dOggf39/lzHZ2dnavn27x7AHAABQE2VmZio3N1cdOnSQn5+f/Pz8tH79ev3jH/+Qn5+fc7Lr9FPVTp0IOx9VPrPXp08fPfXUU2rYsKFat26tL7/8UrNmzdLdd98tSc5LjadNm6ZmzZqpWbNmmjZtmkJCQjRo0CBJkt1u17BhwzRu3DhFRkYqIiJC48ePV9u2bcsd5wYAAKjJunXrpq+//tpl3dChQ9WiRQtNnDhRTZs2dU6EXXnllZL+byJsxowZ5/3+VR720tLS9Mgjj2jUqFHKzc1VbGysRo4cqUcffdQ55qGHHlJhYaFGjRrlvKny6tWrFRYW5hwze/Zs+fn5qX///s6bKi9cuFC+vr5VXTIAAEC1CQsLU5s2bVzWhYaGKjIy0rn+TBNh56PKw15YWJhSU1OVmprqcYzNZlNKSopSUlI8jgkKClJaWprLzZgBAADcuZh+0cKds5kIO1dVHvYAAABQsXXr1rk8PpuJsHNV5RdoAAAAoOYg7AEAAFgYYQ8AAMDCCHsAAAAWRtgDAACwMMIeAACAhRH2AAAALIywBwAAYGGEPQAAAAvjFzQAAMDFL8V+gd8v/8K+33lgZg8AAKCanThxQg8//LCaNGmi4OBgNW3aVI8//rjKysqcYwoKCjR69Gg1aNBAwcHBatmypV588cXzfm9m9gAAAKrZjBkz9NJLL2nRokVq3bq1tm7dqqFDh8put2vs2LGSpAceeEBr167VkiVL1LhxY61evVqjRo1SbGysbrnllnN+b2b2AAAAqtmmTZt0yy23qHfv3mrcuLH69eunhIQEbd261WVMUlKSunbtqsaNG2vEiBFq166dy5hzQdgDAACoZl26dNFHH32k3bt3S5K++uorbdiwQb169XIZ8+677+rnn3+WMUZr167V7t27ddNNN53Xe3MYFwAAoJpNnDhR+fn5atGihXx9fVVaWqqnnnpKAwcOdI75xz/+oeHDh6tBgwby8/OTj4+PXn31VXXp0uW83puwBwAAUM3efPNNLVmyROnp6WrdurWysrKUnJys2NhYJSUlSfo97G3evFnvvvuuGjVqpE8++USjRo1S/fr11b1793N+b8IeAABANZswYYImTZqkAQMGSJLatm2rn376SdOnT1dSUpIKCwv197//XcuXL1fv3r0lSVdccYWysrL07LPPnlfY45w9AACAanb8+HH5+LjGLl9fX+etV0pKSlRSUlLhmHPFzB4AAEA169Onj5566ik1bNhQrVu31pdffqlZs2bp7rvvliSFh4crPj5eEyZMUHBwsBo1aqT169dr8eLFmjVr1nm9N2EPAABc/Gr4L1qkpaXpkUce0ahRo5Sbm6vY2FiNHDlSjz76qHPMsmXLNHnyZN1xxx06fPiwGjVqpKeeekr33nvveb03YQ8AAKCahYWFKTU1VampqR7HOBwOLViwoMrfm3P2AAAALIywBwAAYGGEPQAAAAsj7AEAAFgYYQ8AAFxUjDHeLqFGONs+EPYAAMBFwd/fX9LvNyjG//XhZF884dYrAADgouDr66u6desqNzdXkhQSEiKbzeblqi48Y4yOHz+u3Nxc1a1bV76+vhWOJ+wBAICLhsPhkCRn4KvN6tat6+xHRQh7AADgomGz2VS/fn1FR0erpKTE2+V4jb+//xln9E4i7AEAgIuOr6/vWYed2o4LNAAAACyMsAcAAGBhhD0AAAALI+wBAABYGGEPAADAwgh7AAAAFlYtYe/nn3/WnXfeqcjISIWEhKh9+/bKzMx0bjfGKCUlRbGxsQoODlbXrl21Y8cOl9coKirS/fffr6ioKIWGhqpv377av39/dZQLAABgWVUe9vLy8nTdddfJ399fH3zwgb755hs999xzqlu3rnPMzJkzNWvWLM2ZM0dbtmyRw+FQjx49dPToUeeY5ORkLV++XMuWLdOGDRtUUFCgxMRElZaWVnXJAAAAllXlN1WeMWOG4uLitGDBAue6xo0bO/+3MUapqamaMmWKbr/9dknSokWLFBMTo/T0dI0cOVL5+fmaP3++Xn/9dXXv3l2StGTJEsXFxWnNmjW66aabqrpsAAAAS6rymb13331XV199tf7yl78oOjpaV155pV555RXn9j179ignJ0cJCQnOdYGBgYqPj9fGjRslSZmZmSopKXEZExsbqzZt2jjHnK6oqEhHjhxxWQAAAGq7Kg97P/zwg1588UU1a9ZMH374oe69916NGTNGixcvliTl5ORIkmJiYlyeFxMT49yWk5OjgIAA1atXz+OY002fPl12u925xMXFVfWuAQAAXHSqPOyVlZXpqquu0rRp03TllVdq5MiRGj58uF588UWXcTabzeWxMabcutNVNGby5MnKz893Lvv27Tu/HQEAALCAKg979evXV6tWrVzWtWzZUnv37pUkORwOSSo3Q5ebm+uc7XM4HCouLlZeXp7HMacLDAxUeHi4ywIAAFDbVXnYu+6667Rr1y6Xdbt371ajRo0kSU2aNJHD4VBGRoZze3FxsdavX6/OnTtLkjp06CB/f3+XMdnZ2dq+fbtzDAAAAM6syq/GfeCBB9S5c2dNmzZN/fv31xdffKF58+Zp3rx5kn4/fJucnKxp06apWbNmatasmaZNm6aQkBANGjRIkmS32zVs2DCNGzdOkZGRioiI0Pjx49W2bVvn1bkAAAA4syoPe3/84x+1fPlyTZ48WY8//riaNGmi1NRU3XHHHc4xDz30kAoLCzVq1Cjl5eWpY8eOWr16tcLCwpxjZs+eLT8/P/Xv31+FhYXq1q2bFi5cKF9f36ouGQAAwLKqPOxJUmJiohITEz1ut9lsSklJUUpKiscxQUFBSktLU1paWjVUCAAAUDvw27gAAAAWRtgDAACwMMIeAACAhRH2AAAALIywBwAAYGGEPQAAAAsj7AEAAFgYYQ8AAMDCCHsAAAAWRtgDAACwMMIeAACAhRH2AAAALIywBwAAYGGEPQAAAAsj7AEAAFgYYQ8AAMDCCHsAAAAWRtgDAACwMMIeAACAhRH2AAAALIywBwAAYGGEPQAAAAsj7AEAAFgYYQ8AAMDCCHsAAAAWRtgDAACwMMIeAACAhRH2AAAALIywBwAAYGGEPQAAAAsj7AEAAFgYYQ8AAMDCCHsAAAAWRtgDAACwMMIeAACAhRH2AAAALIywBwAAYGGEPQAAAAur9rA3ffp02Ww2JScnO9cZY5SSkqLY2FgFBwera9eu2rFjh8vzioqKdP/99ysqKkqhoaHq27ev9u/fX93lAgAAWEq1hr0tW7Zo3rx5uuKKK1zWz5w5U7NmzdKcOXO0ZcsWORwO9ejRQ0ePHnWOSU5O1vLly7Vs2TJt2LBBBQUFSkxMVGlpaXWWDAAAYCnVFvYKCgp0xx136JVXXlG9evWc640xSk1N1ZQpU3T77berTZs2WrRokY4fP6709HRJUn5+vubPn6/nnntO3bt315VXXqklS5bo66+/1po1a6qrZAAAAMuptrB33333qXfv3urevbvL+j179ignJ0cJCQnOdYGBgYqPj9fGjRslSZmZmSopKXEZExsbqzZt2jjHAAAA4Mz8quNFly1bpm3btmnLli3ltuXk5EiSYmJiXNbHxMTop59+co4JCAhwmRE8Oebk809XVFSkoqIi5+MjR46c1z4AAABYQZXP7O3bt09jx47VkiVLFBQU5HGczWZzeWyMKbfudBWNmT59uux2u3OJi4urfPEAAAAWU+VhLzMzU7m5uerQoYP8/Pzk5+en9evX6x//+If8/PycM3qnz9Dl5uY6tzkcDhUXFysvL8/jmNNNnjxZ+fn5zmXfvn1VvWsAAAAXnSoPe926ddPXX3+trKws53L11VfrjjvuUFZWlpo2bSqHw6GMjAznc4qLi7V+/Xp17txZktShQwf5+/u7jMnOztb27dudY04XGBio8PBwlwUAAKC2q/Jz9sLCwtSmTRuXdaGhoYqMjHSuT05O1rRp09SsWTM1a9ZM06ZNU0hIiAYNGiRJstvtGjZsmMaNG6fIyEhFRERo/Pjxatu2bbkLPgAAAOBZtVygcSYPPfSQCgsLNWrUKOXl5aljx45avXq1wsLCnGNmz54tPz8/9e/fX4WFherWrZsWLlwoX19fb5QMAABwUbogYW/dunUuj202m1JSUpSSkuLxOUFBQUpLS1NaWlr1FgcAAGBh/DYuAACAhRH2AAAALIywBwAAYGGEPQAAAAsj7AEAAFgYYQ8AAMDCCHsAAAAWRtgDAACwMMIeAACAhRH2AAAALIywBwAAYGGEPQAAAAsj7AEAAFgYYQ8AAMDCCHsAAAAWRtgDAACwMMIeAACAhRH2AAAALIywBwAAYGGEPQAAAAsj7AEAAFgYYQ8AAMDCCHsAAAAWRtgDAACwMMIeAACAhRH2AAAALIywBwAAYGGEPQAAAAsj7AEAAFgYYQ8AAMDCCHsAAAAWRtgDAACwMMIeAACAhRH2AAAALIywBwAAYGGEPQAAAAsj7AEAAFgYYQ8AAMDCCHsAAAAWVuVhb/r06frjH/+osLAwRUdH69Zbb9WuXbtcxhhjlJKSotjYWAUHB6tr167asWOHy5iioiLdf//9ioqKUmhoqPr27av9+/dXdbkAAACWVuVhb/369brvvvu0efNmZWRk6MSJE0pISNCxY8ecY2bOnKlZs2Zpzpw52rJlixwOh3r06KGjR486xyQnJ2v58uVatmyZNmzYoIKCAiUmJqq0tLSqSwYAALAsv6p+wVWrVrk8XrBggaKjo5WZmanrr79exhilpqZqypQpuv322yVJixYtUkxMjNLT0zVy5Ejl5+dr/vz5ev3119W9e3dJ0pIlSxQXF6c1a9bopptuquqyAQAALKnaz9nLz8+XJEVEREiS9uzZo5ycHCUkJDjHBAYGKj4+Xhs3bpQkZWZmqqSkxGVMbGys2rRp4xxzuqKiIh05csRlAQAAqO2qNewZY/Tggw+qS5cuatOmjSQpJydHkhQTE+MyNiYmxrktJydHAQEBqlevnscxp5s+fbrsdrtziYuLq+rdAQAAuOhUa9gbPXq0/vvf/+qNN94ot81ms7k8NsaUW3e6isZMnjxZ+fn5zmXfvn3nXjgAAIBFVFvYu//++/Xuu+9q7dq1atCggXO9w+GQpHIzdLm5uc7ZPofDoeLiYuXl5Xkcc7rAwECFh4e7LAAAALVdlYc9Y4xGjx6td955Rx9//LGaNGnisr1JkyZyOBzKyMhwrisuLtb69evVuXNnSVKHDh3k7+/vMiY7O1vbt293jgEAAMCZVfnVuPfdd5/S09P173//W2FhYc4ZPLvdruDgYNlsNiUnJ2vatGlq1qyZmjVrpmnTpikkJESDBg1yjh02bJjGjRunyMhIRUREaPz48Wrbtq3z6lwAAACcWZWHvRdffFGS1LVrV5f1CxYs0F133SVJeuihh1RYWKhRo0YpLy9PHTt21OrVqxUWFuYcP3v2bPn5+al///4qLCxUt27dtHDhQvn6+lZ1yQAAAJZV5WHPGHPGMTabTSkpKUpJSfE4JigoSGlpaUpLS6vC6gAAAGoXfhsXAADAwgh7AAAAFkbYAwAAsDDCHgAAgIUR9gAAACyMsAcAAGBhhD0AAAALI+wBAABYGGEPAADAwgh7AAAAFkbYAwAAsDDCHgAAgIUR9gAAACyMsAcAAGBhhD0AAAALI+wBAABYGGEPAADAwgh7AAAAFkbYAwAAsDDCHgAAgIUR9gAAACyMsAcAAGBhhD0AAAALI+wBAABYGGEPAADAwgh7AAAAFkbYAwAAsDDCHgAAgIUR9gAAACyMsAcAAGBhhD0AAAALI+wBAABYGGEPAADAwgh7AAAAFkbYAwAAsDDCHgAAgIUR9gAAACyMsAcAAGBhNT7szZ07V02aNFFQUJA6dOigTz/91NslAQAAXDRqdNh78803lZycrClTpujLL7/Un/70J/Xs2VN79+71dmkAAAAXhRod9mbNmqVhw4bpnnvuUcuWLZWamqq4uDi9+OKL3i4NAADgouDn7QI8KS4uVmZmpiZNmuSyPiEhQRs3biw3vqioSEVFRc7H+fn5kqQjR46cdy1lRcfP+zUk6YjNVMGLnP/+VCV641lV9KZK+iLRmwpfqOb0pkb9/0miNxW+EL3x/ELn15uT/902porqgWymhnbzwIEDuuSSS/TZZ5+pc+fOzvXTpk3TokWLtGvXLpfxKSkpmjp16oUuEwAAVIN9+/apQYMG3i7DEmrszN5JNpvN5bExptw6SZo8ebIefPBB5+OysjIdPnxYkZGRbsdfaEeOHFFcXJz27dun8PBwb5dTo9Ab9+iLZ/TGM3rjGb3xrCb1xhijo0ePKjY21qt1WEmNDXtRUVHy9fVVTk6Oy/rc3FzFxMSUGx8YGKjAwECXdXXr1q3OEs9JeHi41/+PVFPRG/foi2f0xjN64xm98aym9MZut3u7BEupsRdoBAQEqEOHDsrIyHBZn5GR4XJYFwAAAJ7V2Jk9SXrwwQc1ePBgXX311erUqZPmzZunvXv36t577/V2aQAAABeFGh32/vrXv+rQoUN6/PHHlZ2drTZt2ug///mPGjVq5O3SKi0wMFCPPfZYuUPNoDee0BfP6I1n9MYzeuMZvbG2Gns1LgAAAM5fjT1nDwAAAOePsAcAAGBhhD0AAAALI+wBAABYGGEPAADAwgh7AAAAFlaj77MH6yotLZWvr6/z8RdffKGysjJdeeWVtf4+T3v37lV2drZ8fX3VuHFjRUVFebukGuOnn35STk6ObDabYmJiLsp7bgLAhcbMXjUICwvTsGHDtHHjRm+XUuP8+OOP6tChgwIDA9W7d28dOXJEPXr00LXXXqvOnTurVatW2r17t7fL9Iq5c+eqUaNGatKkiTp37qyOHTsqJiZGXbp0UWZmprfL86rZs2crLi5OTZs2VadOnXTttdeqadOmiouLU2pqqrfLq7G++uorlz+qapv3339f99xzjx566CF9++23Ltvy8vJ04403eqmymq22f26siLBXDY4dO6bPP/9cXbp0UcuWLfXcc88pNzfX22XVCOPHj1dYWJhWrFihOnXqqFevXjpx4oT27dunn3/+Wc2aNdPEiRO9XeYF9+yzz+rJJ5/Ugw8+qLlz56p58+ZKSUnR+++/r6ZNm+r666/X1q1bvV2mVzzxxBNKSUnR6NGjlZmZqZ9//ln79+9XZmamRo8erZSUFD355JPeLrPGqq33zU9PT9ctt9yinJwcbdq0SVdeeaWWLl3q3F5cXKz169d7scKarbZ+bqyKX9CoBj4+PsrJyVF2drZeffVVpaenq6CgQImJibrnnnt08803y2azebtMr4iOjtbq1avVvn175efnq169evrkk0/UpUsXSdK2bdvUq1cv5eTkeLnSC6tJkyaaO3euevbsKUnavXu3OnfurJycHPn5+Wns2LHauXOnVq9e7eVKL7y4uDilpaXp1ltvdbt9+fLlGj16tH7++ecLW1gNcPvtt1e4PT8/X+vWrVNpaekFqqjmuOqqqzR06FDdf//9kqR//etfGjp0qFJTUzVs2DAdPHhQsbGxtbI3fG5qH2b2qlG7du2Ulpam7OxsLVy4UPn5+UpMTFTDhg316KOPers8r/jtt99kt9sl/X6429fXV2FhYc7t4eHhOn78uLfK85rc3Fy1bNnS+bhZs2bKz8/XL7/8Ikm6++67tWnTJm+V51WHDh1S8+bNPW6//PLLlZeXdwErqjlWrlzp/P+Uu6VOnTreLtFrdu/ercTEROfjfv36aeXKlXrggQf00ksvebEy7+NzUwsZVDkfHx9z8OBBt9v27NljHn74YRMXF3eBq6oZrr32WvPwww8bY4x57bXXTExMjJk0aZJz++OPP246dOjgrfK8pn379mbevHnOxx999JEJCQkxZWVlxhhjvv32WxMWFuat8rwqPj7e3HHHHaakpKTctpKSEjNo0CATHx9/4QurAdq2bWteffVVj9u//PJL4+PjcwErqjnq169vNm3aVG79unXrTJ06dcyUKVNqbW/43NQ+XI1bDUwFR8YbN26sJ554Qo8//vgFrKjmSElJ0a233qqZM2fK19dXH374oe655x599NFH8vX11ZYtW5Senu7tMi+4yZMn684779SaNWsUFBSkd955R2PGjHEe7l+3bp3atGnj5Sq9Iy0tTQkJCYqOjlZ8fLxiYmJks9mUk5OjTz75RIGBgcrIyPB2mV7RoUMHbdu2TcOGDXO7PTAwUA0bNrzAVdUM11xzjT744ANde+21Luvj4+O1cuVKl1m/2obPTe3DOXvVYOrUqZowYYJCQkK8XUqNtGfPHm3btk1XX321GjVqpIMHD+qFF17Q8ePH1bt3b91www3eLtErPvjgAy1ZskRFRUW66aabNHz4cOe2Q4cOSZIiIyO9VZ5XHT16VEuWLNHmzZud53M6HA516tRJgwYNUnh4uJcr9I6ioiKVlpbyXePG+vXrtXHjRk2ePNnt9nXr1mnRokVasGDBBa7M+/jc1D6EPQAAAAvjMG41KigoUGZmpstNYDt06MDJr6I3ntCXyispKVF2djaHndw4ceKEDhw4QG/coDd839QqXj1j0KKKi4vNmDFjTHBwsLHZbCYwMNAEBAQYm81mgoODzdixY01xcbG3y/SKkpISeuMGfTl3WVlZnEzuAb3xrDb3hu+b2odbr1SD8ePH6+2339aCBQt0+PBh/fbbbyoqKtLhw4e1YMECvfPOO5owYYK3y/SKcePG0Rs36AuAC4Xvm9qHc/aqwR/+8Ae9+eabHn+K56OPPtKAAQOc91CrTeiNe/TFs6uuuqrC7YWFhdq9e3etvAEsvfGM3njG903twzl71aCwsLDCH6+PjIxUYWHhBayo5qA37tEXz7755hsNGDBATZo0cbs9Ozu71v6eMr3xjN54xvdN7cPMXjXo06ePCgsLtXTpUsXExLhsO3jwoAYPHqygoCC9++67XqrQe+iNe/TFs6uvvlrDhg3T3/72N7fbs7Ky1KFDh1o5Q0NvPKM3nvF9U/sws1cN5s6dq169eqlBgwZq06aNy01gt2/frlatWun999/3dpleQW/coy+edenSRbt27fK4PSwsTNdff/0FrKjmoDee0RvP+L6pfZjZqyZlZWX68MMP3d4ENiEhQT4+tffaGHrjHn0BcKHwfVO7EPYAAAAsjMO41SQ3N1c7duxQhw4dFB4eroMHD2rRokUqKytT79691bZtW2+X6DX0xj364hm98YzeeEZvKvbDDz9ow4YNys7Olq+vr5o2baru3bvX2p8ftDTv3eLPutauXWtCQ0ONzWYz9evXN1999ZVp0KCBadasmWnevLkJDAw0H374obfL9Ap64x598YzeeEZvPKM3nhUUFJh+/foZm81mbDab8fHxMQ6Hw/j6+po6deqYOXPmeLtEVDHCXjW47rrrzH333WeOHj1qnnnmGdOgQQNz3333ObePHz/edO7c2YsVeg+9cY++eEZvPKM3ntEbz0aMGGGuu+46k5WVZb799lvz5z//2Tz00EPm2LFjZv78+SYkJMQsXbrU22WiChH2qkF4eLj5/vvvjTG//yyNn5+f+fLLL53bd+/ebex2u3eK8zJ64x598YzeeEZvPKM3nkVFRZmtW7c6Hx8+fNgEBQWZY8eOGWOMmTNnjmnfvr23ykM14HKbahAQEKDffvtNklRcXKyysjLnY+n3G1r6+/t7qzyvojfu0RfP6I1n9MYzeuPZiRMnXM7Lq1Onjk6cOKFjx45JkhISEvTtt996qzxUA67GrQa33nqrSktLNWnSJC1evFjbtm1TTEyM3nzzTdlsNiUlJamgoEAffPCBt0u94OiNe/TFM3rjGb3xjN54lpCQoMsvv1xz5syRJD377LOaNWuWDhw4IEn68ssvlZCQwM+lWYm3pxataPfu3eayyy4zNpvNtG7d2vz888+mb9++xs/Pz/j5+Zk//OEPJjMz09tlegW9cY++eEZvPKM3ntEbzzIzM01ERIRxOBymYcOGJiAgwLzxxhvO7XPmzDFDhgzxYoWoaszsVaNDhw4pMjLS+fijjz5SYWGhOnXq5LK+NqI37tEXz+iNZ/TGM3rjXnZ2tt577z0VFRXpxhtvVKtWrbxdEqoRYQ8AAMDCuEDDC/Ly8rR48WJvl1Ej0Rv36Itn9MYzeuMZvfGM3lgPM3te8NVXX+mqq65SaWmpt0upceiNe/TFM3rjGb3xjN54Rm+sh59LqwZHjhypcPvRo0cvUCU1D71xj754Rm88ozee0RvP6E3tw8xeNfDx8ZHNZvO43Rgjm81WK/9qojfu0RfP6I1n9MYzeuMZval9mNmrBmFhYZoyZYo6duzodvt3332nkSNHXuCqagZ64x598YzeeEZvPKM3ntGb2oewVw2uuuoqSVJ8fLzb7XXr1lVtnVClN+7RF8/ojWf0xjN64xm9qX24GrcaDBo0SEFBQR63OxwOPfbYYxewopqD3rhHXzyjN57RG8/ojWf0pvbhnD0AAAALY2YPAADAwjhnr5ocO3ZM6enp2rhxo3JycmSz2RQTE6PrrrtOAwcOVGhoqLdL9Bp64x598YzeeEZvPKM3ntGb2oXDuNXgm2++UY8ePXT8+HHFx8crJiZGxhjl5uZq/fr1Cg0N1erVq2vlbxHSG/foi2f0xjN64xm98Yze1D6EvWpwww03yOFwaNGiRQoICHDZVlxcrLvuukvZ2dlau3atlyr0HnrjHn3xjN54Rm88ozee0Zvah7BXDUJCQrR161aPfxVt375d11xzjY4fP36BK/M+euMeffGM3nhGbzyjN57Rm9qHCzSqQb169fTdd9953P7999+rXr16F7CimoPeuEdfPKM3ntEbz+iNZ/Sm9uECjWowfPhwJSUl6eGHH1aPHj0UExMjm82mnJwcZWRkaNq0aUpOTvZ2mV5Bb9yjL57RG8/ojWf0xjN6UwsZVIunn37a1K9f39hsNuPj42N8fHyMzWYz9evXNzNmzPB2eV5Fb9yjL57RG8/ojWf0xjN6U7twzl4127Nnj3JyciT9flfyJk2aeLmimoPeuEdfPKM3ntEbz+iNZ/SmdiDsAQAAWBgXaFSTwsJCbdiwQd988025bb/99psWL17shapqBnrjHn3xjN54Rm88ozee0ZtaxrtHka1p165dplGjRs5zIeLj482BAwec23NycoyPj48XK/QeeuMeffGM3nhGbzyjN57Rm9qHmb1qMHHiRLVt21a5ubnatWuXwsPDdd1112nv3r3eLs3r6I179MUzeuMZvfGM3nhGb2ohb6dNK4qOjjb//e9/XdaNGjXKNGzY0PzP//xPrf6rid64R188ozee0RvP6I1n9Kb24T571aCwsFB+fq6tfeGFF+Tj46P4+Hilp6d7qTLvozfu0RfP6I1n9MYzeuMZval9CHvVoEWLFtq6datatmzpsj4tLU3GGPXt29dLlXkfvXGPvnhGbzyjN57RG8/oTe3DOXvV4LbbbtMbb7zhdtucOXM0cOBAmVp6xxt64x598YzeeEZvPKM3ntGb2of77AEAAFgYM3sAAAAWRtgDAACwMMIeAACAhRH2AAAALIywB0CSZIxR9+7dddNNN5XbNnfuXNntdi1evFg2m83tkpOT4/Kc/fv3KyAgQC1atHD7fqc+t06dOmrXrp0WLlzoMmbdunWy2WyqV6+efvvtN5dtX3zxhfP5pyotLdXs2bN1xRVXKCgoSHXr1lXPnj312WefuYxbuHChbDabbr75Zpf1v/76q2w2m9atW+ccU9FyclzdunU97ueKFSvcbgOAC4GwB0DS76FkwYIF+vzzz/Xyyy871+/Zs0cTJ07U888/r4YNG0qSdu3apezsbJclOjra5fUWLlyo/v376/jx4+WC1kkLFixQdna2vvrqK/31r3/V0KFD9eGHH5YbFxYWpuXLl7use+2115z1nGSM0YABA/T4449rzJgx2rlzp9avX6+4uDh17dq1XOjy8/PTRx99pLVr17qt769//avLPnbq1EnDhw93Wde5c2f3DQWAGoKwB8ApLi5Ozz//vMaPH689e/bIGKNhw4apW7duuuuuu5zjoqOj5XA4XBYfn//7OjHGaMGCBRo8eLAGDRqk+fPnu32/unXryuFw6NJLL9Xf//53RUREaPXq1eXGJSUl6bXXXnM+Liws1LJly5SUlOQy7q233tK//vUvLV68WPfcc4+aNGmidu3aad68eerbt6/uueceHTt2zDk+NDRUQ4cO1aRJk9zWFxwc7LKPAQEBCgkJKbcOAGoywh4AF0lJSerWrZuGDh2qOXPmaPv27Zo3b16lXmPt2rU6fvy4unfvrsGDB+utt97S0aNHPY4vLS3VW2+9pcOHD8vf37/c9sGDB+vTTz91/lD722+/rcaNG+uqq65yGZeenq7LL79cffr0Kfca48aN06FDh5SRkeGyPiUlRV9//bX+9a9/VWofAeBiQdgDUM68efP0zTffKDk5WS+//HK5Q7QNGjRQnTp1nEvz5s1dts+fP18DBgyQr6+vWrdurcsuu0xvvvlmufcZOHCg6tSpo8DAQP31r39VRESE7rnnnnLjoqOj1bNnT+c5fa+99pruvvvucuN2795d7iegTjq5fvfu3S7rY2NjNXbsWE2ZMkUnTpzw3JQzyM/Pd+nJyQUAvI2wB6Cc6OhojRgxQi1bttRtt91Wbvunn36qrKws53LqeXa//vqr3nnnHd15553OdXfeeafLYdiTZs+eraysLGVkZKh9+/aaPXu2LrvsMrc13X333Vq4cKF++OEHbdq0SXfcccc57dvpF3RI0sSJE/XLL7+4rfFshYWFufTk5AIA3ubn7QIA1Ex+fn7y83P/FdGkSROPV5+mp6frt99+U8eOHZ3rjDEqKyvTN998o1atWjnXOxwOXXbZZbrsssv0z3/+U1deeaWuvvpqlzEn9erVSyNHjtSwYcPUp08fRUZGlhtz+eWX65tvvnFb186dOyVJzZo1K7etbt26mjx5sqZOnarExES3zz8THx8fj0EVALyJmT0AVWr+/PkaN26cy+zWV199pRtuuKHCmbPLLrtMf/7znzV58mS32319fTV48GCtW7fO7SFcSRowYIC+++47rVy5sty25557TpGRkerRo4fb595///3y8fHR888/fxZ7CQAXD8IegErLzc1VTk6Oy1JSUqKsrCxt27ZN99xzj9q0aeOyDBw4UIsXL1ZJSYnH1x03bpxWrlyprVu3ut3+xBNP6JdffnF7L0Dp97B32223KSkpSfPnz9ePP/6o//73vxo5cqTeffddvfrqqwoNDXX73KCgIE2dOlX/+Mc/Kt8QAKjBCHsAKq158+aqX7++y5KZman58+erVatWbm+kfOutt+rw4cNuZ91Oatu2rbp3765HH33U7faAgABFRUW5Pe9O+v18vLfeektTpkzR7Nmz1aJFC/3pT3/STz/9pLVr1+rWW2+tcL+SkpLUtGnTCscAwMXGZowx3i4CAAAA1YOZPQAAAAsj7AEAAFgYYQ8AAMDCCHsAAAAWRtgDAACwMMIeAACAhRH2AAAALIywBwAAYGGEPQAAAAsj7AEAAFgYYQ8AAMDCCHsAAAAW9v8ADk2EMN3EwFkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "trial_control_dic = {77:233, 86:155, 88:40}\n",
    "for key, val in trial_control_dic.items():\n",
    "    pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"].isin([key, val])].groupby(\n",
    "        [\"YEARMONTH\", \"STORE_NBR\"]).sum()[\"TOT_SALES\"].unstack().plot.bar()\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "    plt.title(\"Trial Store \"+str(key)+\" and Control Store \"+str(val)+\" - TOT_SALES\")\n",
    "    plt.show()\n",
    "    pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"].isin([key, val])].groupby(\n",
    "    [\"YEARMONTH\", \"STORE_NBR\"]).sum()[\"nCustomers\"].unstack().plot.bar()\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "    plt.title(\"Trial Store \"+str(key)+\" and Control Store \"+str(val)+\" - nCustomer\")\n",
    "    plt.show()\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71a5b0fb",
   "metadata": {},
   "source": [
    "- Next we'll compare the performance of Trial stores to Control stores during the trial period. To ensure their performance is comparable during Trial period, we need to scale (multiply to ratio of trial / control) all of Control stores' performance to Trial store's performance during pre-trial. Starting with TOT_SALES."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b7b3389a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ratio of Store 77 and its Control store.\n",
    "sales_ratio_77 = pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 77][\"TOT_SALES\"].sum() / pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 233][\"TOT_SALES\"].sum()\n",
    "\n",
    "#Ratio of Store 86 and its Control store.\n",
    "sales_ratio_86 = pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 86][\"TOT_SALES\"].sum() / pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 155][\"TOT_SALES\"].sum()\n",
    "\n",
    "#Ratio of Store 77 and its Control store.\n",
    "sales_ratio_88 = pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 88][\"TOT_SALES\"].sum() / pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 40][\"TOT_SALES\"].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bfd65533",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LUW4uvha7d+9WrVq18nwOALAaZuhR4NhsNrm7uzvdmeLs2bN6//33r+t4J0+e1KlTp3Jtu3hnj8jISEn/P1RfPjtbqVIllShRQvPmzXO6087p06e1YMECx51vrqZdu3Yyxuj3339X3bp1c2wxMTF5up6MjAzNmTNH9erVU/Xq1a/ad8mSJfrrr7/Uv3//PB1b+v8h7tJfMIwxevfdd/N8jMs1adJEO3bs0Hfffee0f968eXl6/ogRI2SM0cMPP6zMzMwc7VlZWVq6dKkkOZZ1zZkzx6nP5s2btWvXLsedWvLjSuPiapo1a6ZTp07l+EV09uzZjvb8OHz4cK7XLv19ByHp2uO4QYMG8vHxyfHaHDx4UKtWrcpTTe3atdPhw4eVnZ2d6ziuVKnSVZ9/8X1MSkrSO++8o759+17znJf6+eefdfDgwTzdreni8qu89AUAK2OGHgXOvffeqzfeeEM9e/bUwIEDdfjwYb3++uv5msG+1E8//aRWrVqpe/fuio2NVUREhI4ePaply5ZpxowZaty4sRo2bChJKleunHx8fDR37lxVqVJF/v7+ioyMVGRkpCZMmKAHH3xQ7dq10yOPPKKMjAy99tprOnbsWK5//r9co0aNNHDgQPXt21dbtmzRPffcIz8/P6Wmpmr9+vWKiYnRo48+es3jLF68WEeOHMnTEprExET5+Pjka616ixYt5OnpqR49emj48OE6d+6cpk2blmNJUX7Ex8frvffe07333qsxY8YoPDxcc+fOdQTRa2nQoIGmTZumwYMHq06dOnr00UdVrVo1ZWVl6dtvv9WMGTNUvXp1tW/fXpUqVdLAgQM1depUFSlSRG3atNG+ffv0wgsvKCoqSk8++WS+67/auLiS3r1766233lKfPn20b98+xcTEaP369Ro7dqzatm2r5s2b56uG1atX69///rcefPBBNWzYUCEhIUpPT9cHH3yg5cuXq3fv3o6/SFz8RW/GjBkKCAiQt7e3oqOjFRISohdeeEHPPfecevfurR49eujw4cMaPXq0vL29NWrUqGvW0b17d82dO1dt27bVv//9b9WrV08eHh46ePCgVq9erY4dO171cx1PPPGEEhMT1a9fP8XExDh9w7GXl5djJv3777/Xk08+qfvvv19ly5ZVkSJFtH37dk2aNEkhISEaNmyY43mjRo3SH3/8oXvuuUclSpTQsWPHtHz5cr377rt64IEHVKdOnXy91gBgOa76NC7+ua50l5tq1arl2j+3O9W89957plKlSsbLy8uULVvWjBs3ziQmJhpJTredzMtdbo4ePWrGjBljmjZtakqUKGE8PT2Nn5+fqVmzphkzZkyO2+x98MEHpnLlysbDw8NIMqNGjXK0LV682NSvX994e3sbPz8/06xZM/PVV185Pf/iXW4uvT3i5ddWv3594+fnZ3x8fEy5cuVM7969zZYtW656HRe1aNHC+Pn5mRMnTly13/79+02RIkVM796983TcSy1dutTUqFHDeHt7mxIlSpinn37afPbZZznunHKl97VPnz6mdOnSTvt27txpWrRoYby9vU1wcLDp37+/+e9//5uvL0Datm2b6dOnjylVqpTjfaxVq5Z58cUXHbdlNObvO/O8+uqrpmLFisbDw8OEhoaahx56yHGryeup/0rj4mpfpHb48GEzaNAgExERYdzd3U3p0qXNiBEjcnzRUV7ucnPgwAHz/PPPm0aNGhm73W7c3d1NQECAqV+/vpk6dao5f/68U//Jkyeb6Oho4+bmluMOPTNnzjR33HGH8fT0NEFBQaZjx45mx44dOV6DK11XVlaWef311x1jxN/f31SuXNk88sgj1/zistKlSzvdtebS7dLXPC0tzTz00EOmXLlyxtfX13h6epqyZcuaQYMG5bgbzpIlS0zz5s1NeHi4cXd3N/7+/qZevXrm//7v/0xWVtZV6wGAfwKbMdfxTT0AAAAACgTW0AMAAAAWRqAHAAAALIxADwAAAFgYgR4AAACwMAI9AAAAYGEEegAAAMDCLPnFUhcuXNChQ4cUEBCQ61eTAwCAgscYo5MnTyoyMlJFijCnCNwslgz0hw4dUlRUlKvLAAAA1+HAgQOObzYGcOMsGegDAgIk/f0/hMDAQBdXAwAA8uLEiROKiopy/DsO4OawZKC/uMwmMDCQQA8AgMWwXBa4uVjABgAAAFgYgR4AAACwMAI9AAAAYGEEegAAAMDCCPQAAACAhRHoAQAAAAsj0AMAAAAWRqAHAAAALIxADwAAAFgYgR4AAACwMAI9AAAAYGEEegAAAMDCCPQAAACAhRHoAQAAAAtzd3UBAG6vMs8uc3UJ17Rv/L2uLgEAAMtghh4AAACwMAI9AAAAYGEEegAAAMDCCPQAAACAhRHoAQAAAAsj0AMAAAAWRqAHAAAALIxADwAAAFgYgR4AAACwMAI9AAAAYGEEegAAAMDCCPQAAACAhRHoAQAAAAsj0AMAAAAWRqAHAAAALIxADwAAAFgYgR4AAACwMAI9AAAAYGHuri4AAFC4lXl2matLyJN94+91dQkAkCtm6AEAAAALI9ADAAAAFkagBwAAACyMQA8AAABYGIEeAAAAsDACPQAAAGBhBHoAAADAwgj0AAAAgIUR6AEAAAALI9ADAAAAFkagBwAAACyMQA8AAABYWL4C/bhx43TnnXcqICBAYWFh6tSpk3766SenPnFxcbLZbE7bXXfd5dQnIyNDQ4YMUWhoqPz8/NShQwcdPHjwxq8GAAAAKGTyFejXrl2rxx57TBs3blRKSorOnz+vli1b6vTp0079WrdurdTUVMf26aefOrXHx8dr0aJFmj9/vtavX69Tp06pXbt2ys7OvvErAgAAAAoR9/x0Xr58udPjpKQkhYWFaevWrbrnnnsc+728vGS323M9xvHjx5WYmKj3339fzZs3lyTNmTNHUVFR+vzzz9WqVav8XgMAAABQaN3QGvrjx49LkoKDg532r1mzRmFhYapYsaIefvhhpaenO9q2bt2qrKwstWzZ0rEvMjJS1atX14YNG3I9T0ZGhk6cOOG0AQAAALiBQG+M0VNPPaV//etfql69umN/mzZtNHfuXK1atUoTJ07U5s2b1bRpU2VkZEiS0tLS5OnpqWLFijkdLzw8XGlpabmea9y4cQoKCnJsUVFR11s2AAAA8I+SryU3l3r88cf1/fffa/369U77u3Xr5vjv6tWrq27duipdurSWLVum++6774rHM8bIZrPl2jZixAg99dRTjscnTpwg1N9MCUGuruDaEo67ugIAAIAC6bpm6IcMGaIlS5Zo9erVKlmy5FX7RkREqHTp0tqzZ48kyW63KzMzU0ePHnXql56ervDw8FyP4eXlpcDAQKcNAAAAQD4DvTFGjz/+uBYuXKhVq1YpOjr6ms85fPiwDhw4oIiICElSnTp15OHhoZSUFEef1NRU/fDDD2rYsGE+ywcAAAAKt3wtuXnsscc0b948/fe//1VAQIBjzXtQUJB8fHx06tQpJSQkqEuXLoqIiNC+ffv03HPPKTQ0VJ07d3b07d+/v4YOHaqQkBAFBwdr2LBhiomJcdz1BgAAAEDe5CvQT5s2TZLUuHFjp/1JSUmKi4uTm5ubtm/frtmzZ+vYsWOKiIhQkyZN9OGHHyogIMDRf9KkSXJ3d1fXrl119uxZNWvWTMnJyXJzc7vxKwIAAAAKkXwFemPMVdt9fHy0YsWKax7H29tbU6dO1dSpU/NzegAAAACXuaH70AMAAABwLQI9AAAAYGEEegAAAMDCCPQAAACAhRHoAQAAAAsj0AMAAAAWRqAHAAAALIxADwAAAFgYgR4AAACwMAI9AAAAYGEEegAAAMDCCPQAAACAhbm7uoB/ujLPLnN1Cde0z9vVFQAAAOB6MUMPAAAAWBiBHgAAALAwAj0AAABgYQR6AAAAwMII9AAAAICFEegBAAAACyPQAwAAABZGoAcAAAAsjEAPAAAAWBiBHgAAALAwAj0AAABgYQR6AAAAwMII9AAAAICFEegBAAAACyPQAwAAABZGoAcAAAAsjEAPAAAAWBiBHgAAALAwAj0AAABgYQR6AAAAwMII9AAAAICFEegBAAAACyPQAwAAABZGoAcAAAAsjEAPAAAAWBiBHgAAALAwAj0AAABgYQR6AAAAwMII9AAAAICFEegBAAAACyPQAwAAABZGoAcAAAAsjEAPAAAAWBiBHgAAALAwAj0AAABgYQR6AAAAwMII9AAAAICFEegBAAAACyPQAwAAABbm7uoCACCHhCBXV5A3CcddXQEAAPmboR83bpzuvPNOBQQEKCwsTJ06ddJPP/3k1McYo4SEBEVGRsrHx0eNGzfWjh07nPpkZGRoyJAhCg0NlZ+fnzp06KCDBw/e+NUAAAAAhUy+Av3atWv12GOPaePGjUpJSdH58+fVsmVLnT592tFnwoQJeuONN/Tmm29q8+bNstvtatGihU6ePOnoEx8fr0WLFmn+/Plav369Tp06pXbt2ik7O/vmXRkAAABQCORryc3y5cudHiclJSksLExbt27VPffcI2OMJk+erJEjR+q+++6TJM2aNUvh4eGaN2+eHnnkER0/flyJiYl6//331bx5c0nSnDlzFBUVpc8//1ytWrW6SZcGAAAA/PPd0Idijx//e/1ocHCwJGnv3r1KS0tTy5YtHX28vLwUGxurDRs2SJK2bt2qrKwspz6RkZGqXr26o8/lMjIydOLECacNAAAAwA0EemOMnnrqKf3rX/9S9erVJUlpaWmSpPDwcKe+4eHhjra0tDR5enqqWLFiV+xzuXHjxikoKMixRUVFXW/ZAAAAwD/KdQf6xx9/XN9//70++OCDHG02m83psTEmx77LXa3PiBEjdPz4ccd24MCB6y0bAAAA+Ee5rkA/ZMgQLVmyRKtXr1bJkiUd++12uyTlmGlPT093zNrb7XZlZmbq6NGjV+xzOS8vLwUGBjptAAAAAPIZ6I0xevzxx7Vw4UKtWrVK0dHRTu3R0dGy2+1KSUlx7MvMzNTatWvVsGFDSVKdOnXk4eHh1Cc1NVU//PCDow8AAACAvMnXXW4ee+wxzZs3T//9738VEBDgmIkPCgqSj4+PbDab4uPjNXbsWFWoUEEVKlTQ2LFj5evrq549ezr69u/fX0OHDlVISIiCg4M1bNgwxcTEOO56AwAAACBv8hXop02bJklq3Lix0/6kpCTFxcVJkoYPH66zZ89q8ODBOnr0qOrXr6+VK1cqICDA0X/SpElyd3dX165ddfbsWTVr1kzJyclyc3O7sasBAAAACpl8BXpjzDX72Gw2JSQkKCEh4Yp9vL29NXXqVE2dOjU/pwcAAABwmRu6Dz0AAAAA1yLQAwAAABZGoAcAAAAsjEAPAAAAWBiBHgAAALAwAj0AAABgYQR6AAAAwMII9AAAAICFEegBAAAACyPQAwAAABZGoAcAAAAsjEAPAAAAWBiBHgAAALAwAj0AAABgYQR6AAAAwMII9AAAAICFEegBAAAACyPQAwAAABZGoAcAAAAsjEAPAAAAWBiBHgAAALAwAj0AAABgYQR6AAAAwMII9AAAAICFEegBAAAACyPQAwAAABZGoAcAAAAsjEAPAAAAWBiBHgAAALAwAj0AAABgYQR6AAAAwMII9AAAAICFEegBAAAACyPQAwAAABZGoAcAAAAsjEAPAAAAWBiBHgAAALAwd1cXAAAAcJExRufPn1d2drarSwFcys3NTe7u7rLZbNfsS6AHAAAFQmZmplJTU3XmzBlXlwIUCL6+voqIiJCnp+dV+xHoAQCAy124cEF79+6Vm5ubIiMj5enpmaeZSeCfyBijzMxM/fnnn9q7d68qVKigIkWuvFKeQA8AQF4kBLm6gmtLOO7qCq5bZmamLly4oKioKPn6+rq6HMDlfHx85OHhod9++02ZmZny9va+Yl8+FAsAAAqMq81CAoVNXn8e+KkBAAAALIxADwAAAFgYa+gBAECBVubZZbf1fPvG33tLj5+cnKz4+HgdO3Ysz88pU6aM4uPjFR8ff8vqulRcXJyOHTumxYsX35bz4cYwQw8AAHCdbDbbVbe4uLgcz+nWrZt27959y2r69ttv1a5dO4WFhcnb21tlypRRt27d9Ndff92yc8K1mKEHAAC4TqmpqY7//vDDD/Xiiy/qp59+cuzz8fFx6p+VlSUfH58c+2+W9PR0NW/eXO3bt9eKFStUtGhR7d27V0uWLOH+/v9gzNADAABcJ7vd7tiCgoJks9kcj8+dO6eiRYvqo48+UuPGjeXt7a05c+YoOTlZRYsWdRzjl19+UceOHRUeHi5/f3/deeed+vzzz6+rng0bNujEiROaOXOmatWqpejoaDVt2lSTJ09WqVKlJEnZ2dnq37+/oqOj5ePjo0qVKmnKlClXPa4xRhMmTFDZsmXl4+OjGjVq6OOPP3a0Hz16VA8++KCKFy8uHx8fVahQQUlJSdd1Dcg/ZugBAABuoWeeeUYTJ05UUlKSvLy8tHLlSqf2U6dOqW3bthozZoy8vb01a9YstW/fXj/99JMjhOeV3W7X+fPntWjRIt1///25fjnXhQsXVLJkSX300UcKDQ3Vhg0bNHDgQEVERKhr1665Hvf555/XwoULNW3aNFWoUEFffvmlHnroIRUvXlyxsbF64YUXtHPnTn322WcKDQ3Vzz//rLNnz+ardlw/Aj0AAMAtFB8fr/vuu++K7TVq1FCNGjUcj8eMGaNFixZpyZIlevzxx/N1rrvuukvPPfecevbsqUGDBqlevXpq2rSpevfurfDwcEmSh4eHRo8e7XhOdHS0NmzYoI8++ijXQH/69Gm98cYbWrVqlRo0aCBJKlu2rNavX6933nlHsbGx2r9/v2rVqqW6detK+vtDvLh9WHIDAABwC10MuVdy+vRpDR8+XFWrVlXRokXl7++vH3/8Ufv377+u873yyitKS0vT9OnTVbVqVU2fPl2VK1fW9u3bHX2mT5+uunXrqnjx4vL399e77757xfPt3LlT586dU4sWLeTv7+/YZs+erV9++UWS9Oijj2r+/PmqWbOmhg8frg0bNlxX7bg+BHoAAIBbyM/P76rtTz/9tBYsWKBXXnlF69at07Zt2xQTE6PMzMzrPmdISIgeeOABTZw4Ubt27VJkZKRef/11SdJHH32kJ598Uv369dPKlSu1bds29e3b94rnu3DhgiRp2bJl2rZtm2PbuXOnYx19mzZt9Ntvvyk+Pl6HDh1Ss2bNNGzYsOuuH/mT70D/5Zdfqn379oqMjJTNZstxf9K4uLgct2y66667nPpkZGRoyJAhCg0NlZ+fnzp06KCDBw/e0IUAAABY0bp16xQXF6fOnTsrJiZGdrtd+/btu2nH9/T0VLly5XT69GnH+Ro2bKjBgwerVq1aKl++vGOmPTdVq1aVl5eX9u/fr/LlyzttUVFRjn7FixdXXFyc5syZo8mTJ2vGjBk37RpwdfleQ3/69GnVqFFDffv2VZcuXXLt07p1a6dPNnt6ejq1x8fHa+nSpZo/f75CQkI0dOhQtWvXTlu3bpWbm1t+SwIAALCs8uXLa+HChWrfvr1sNpteeOEFx6x4fn3yySeaP3++unfvrooVK8oYo6VLl+rTTz91ZLPy5ctr9uzZWrFihaKjo/X+++9r8+bNio6OzvWYAQEBGjZsmJ588klduHBB//rXv3TixAlt2LBB/v7+6tOnj1588UXVqVNH1apVU0ZGhj755BNVqVLlul8T5E++A32bNm3Upk2bq/bx8vKS3W7Pte348eNKTEzU+++/r+bNm0uS5syZo6ioKH3++edq1apVfksCAAD/YLf6m1tdbdKkSerXr58aNmyo0NBQPfPMMzpx4sR1Hatq1ary9fXV0KFDdeDAAXl5ealChQqaOXOmevXqJUkaNGiQtm3bpm7duslms6lHjx4aPHiwPvvssyse9+WXX1ZYWJjGjRunX3/9VUWLFlXt2rX13HPPSfp78nbEiBHat2+ffHx8dPfdd2v+/PnXdQ3IP5sxxlz3k202LVq0SJ06dXLsi4uL0+LFi+Xp6amiRYsqNjZWr7zyisLCwiRJq1atUrNmzXTkyBEVK1bM8bwaNWqoU6dOTp+6vigjI0MZGRmOxydOnFBUVJSOHz+uwMDA6y3/trjdX1d9PfZ593R1CdeWcNzVFfxjMCZvIsblTWGFMSlZZFwW8DF54sQJBQUF5frv97lz57R3715FR0fL29vbRRUCBUtefy5u+odi27Rpo7lz52rVqlWaOHGiNm/erKZNmzoCeVpamjw9PZ3CvCSFh4crLS0t12OOGzdOQUFBju3S9VoAAABAYXbTA323bt107733qnr16mrfvr0+++wz7d69W8uWXX0GxhiT65cfSNKIESN0/Phxx3bgwIGbXTYAAECBN3fuXKdbR166VatWzdXlwUVu+RdLRUREqHTp0tqzZ4+kv7/BLDMzU0ePHnWapU9PT1fDhg1zPYaXl5e8vLxudakAAAAFWocOHVS/fv1c2zw8PG5zNSgobnmgP3z4sA4cOKCIiAhJUp06deTh4aGUlBTHt5Glpqbqhx9+0IQJE251OQAAAJYVEBCggIAAV5eBAibfgf7UqVP6+eefHY/37t2rbdu2KTg4WMHBwUpISFCXLl0UERGhffv26bnnnlNoaKg6d+4sSQoKClL//v01dOhQhYSEKDg4WMOGDVNMTIzjrjcAAAAA8ibfgX7Lli1q0qSJ4/FTTz0lSerTp4+mTZum7du3a/bs2Tp27JgiIiLUpEkTffjhh06/TU6aNEnu7u7q2rWrzp49q2bNmik5OZl70AMAAAD5lO9A37hxY13tTpcrVqy45jG8vb01depUTZ06Nb+nBwAAAHCJm36XGwAAAAC3D4EeAAAAsLBbfpcbAACAG5IQdJvPV/C+cbdx48aqWbOmJk+efN3HSE5OVnx8vI4dO3bT6iqIbDabFi1apE6dOt2W892M9+ZGMUMPAABwg9LT0/XII4+oVKlS8vLykt1uV6tWrfT111+7urTrlpmZqQkTJqhGjRry9fVVaGioGjVqpKSkJGVlZd208yQkJKhmzZo37Xj5sXr1ajVp0kTBwcHy9fVVhQoV1KdPH50/f94l9VwvZugBAABuUJcuXZSVlaVZs2apbNmy+uOPP/TFF1/oyJEjri7tumRmZqpVq1b67rvv9PLLL6tRo0YKDAzUxo0b9frrr6tWrVq3PYRnZWXd1C/P2rFjh9q0aaMnnnhCU6dOlY+Pj/bs2aOPP/5YFy5cuGnnuR2YoQcAALgBx44d0/r16/Xqq6+qSZMmKl26tOrVq6cRI0bo3nvvdfQZOHCgwsPD5e3trerVq+uTTz6R9PeXcPbo0UMlS5aUr6+vYmJi9MEHH1z1nJmZmRo+fLhKlCghPz8/1a9fX2vWrHHqk5ycrFKlSsnX11edO3fW4cOH83xNkydP1pdffqkvvvhCjz32mGrWrKmyZcuqZ8+e2rRpkypUqCBJysjI0BNPPKGwsDB5e3vrX//6lzZv3uw4zpo1a2Sz2fTFF1+obt268vX1VcOGDfXTTz85ahw9erS+++472Ww22Ww2JScnS/p76cz06dPVsWNH+fn5acyYMZKkadOmqVy5cvL09FSlSpX0/vvv5/m6LpWSkqKIiAhNmDBB1atXV7ly5dS6dWvNnDlTnp6ekm7Ne/Pbb7+pffv2KlasmPz8/FStWjV9+umn13UNFxHoAQAAboC/v7/8/f21ePFiZWRk5Gi/cOGC2rRpow0bNmjOnDnauXOnxo8f7/j+nXPnzqlOnTr65JNP9MMPP2jgwIHq1auXNm3adMVz9u3bV1999ZXmz5+v77//Xg888IBat26tPXv2SJI2bdqkfv36afDgwdq2bZuaNGniCMR5MXfuXDVv3ly1atXK0ebh4SE/Pz9J0vDhw7VgwQLNmjVL33zzjcqXL69WrVrl+MvEyJEjNXHiRG3ZskXu7u7q16+fJKlbt24aOnSoqlWrptTUVKWmpqpbt26O540aNUodO3bU9u3b1a9fPy1atEj//ve/NXToUP3www965JFH1LdvX61evTrP13aR3W5Xamqqvvzyyyv2uRXvzWOPPaaMjAx9+eWX2r59u1599VX5+/vnu/5LseQGAADgBri7uys5OVkPP/ywpk+frtq1ays2Nlbdu3fXHXfcoc8//1z/+9//tGvXLlWsWFGSVLZsWcfzS5QooWHDhjkeDxkyRMuXL9d//vMf1a9fP8f5fvnlF33wwQc6ePCgIiMjJUnDhg3T8uXLlZSUpLFjx2rKlClq1aqVnn32WUlSxYoVtWHDBi1fvjxP17Rnzx41btz4qn1Onz6tadOmKTk5WW3atJEkvfvuu0pJSVFiYqKefvppR99XXnlFsbGxkqRnn31W9957r86dOycfHx/5+/vL3d1ddrs9xzl69uzpCP8XH8fFxWnw4MGS/v6C04vLgC794tO8eOCBB7RixQrFxsbKbrfrrrvuUrNmzdS7d28FBgZKujXvzf79+9WlSxfFxMRIch4L14sZegAAgBvUpUsXHTp0SEuWLFGrVq20Zs0a1a5dW8nJydq2bZtKlizpCPOXy87O1iuvvKI77rhDISEh8vf318qVK7V///5c+3/zzTcyxqhixYqOvw74+/tr7dq1+uWXXyRJu3btUoMGDZyed/njqzHGyGazXbXPL7/8oqysLDVq1Mixz8PDQ/Xq1dOuXbuc+t5xxx2O/46IiJD09weJr6Vu3bpOj3ft2uV0Pklq1KhRjvPlhZubm5KSknTw4EFNmDBBkZGReuWVVxx/LZBuzXvzxBNPaMyYMWrUqJFGjRql77//Pt+1X44ZegAAgJvA29tbLVq0UIsWLfTiiy9qwIABGjVqlNMMb24mTpyoSZMmafLkyYqJiZGfn5/i4+OVmZmZa/8LFy7Izc1NW7dudSzbueji0g1jzA1dS8WKFa8Zki+e4/Lgn9svA5d+mPViW14+eHpxac+l8nK+/ChRooR69eqlXr16acyYMapYsaKmT5+u0aNH35L3ZsCAAWrVqpWWLVumlStXaty4cZo4caKGDBly3dfADD0AAMAtULVqVZ0+fVp33HGHDh48qN27d+fab926derYsaMeeugh1ahRQ2XLlnWst85NrVq1lJ2drfT0dJUvX95pu7hspWrVqtq4caPT8y5/fDU9e/bU559/rm+//TZH2/nz53X69GmVL19enp6eWr9+vaMtKytLW7ZsUZUqVfJ8Lk9PT2VnZ+epb5UqVZzOJ0kbNmzI1/muplixYoqIiNDp06cl3Zr3RpKioqI0aNAgLVy4UEOHDtW77757Q3UzQw8AAHADDh8+rAceeED9+vXTHXfcoYCAAG3ZskUTJkxQx44dFRsbq3vuuUddunTRG2+8ofLly+vHH3+UzWZT69atVb58eS1YsEAbNmxQsWLF9MYbbygtLe2KIbVixYp68MEH1bt3b02cOFG1atXSX3/9pVWrVikmJkZt27bVE088oYYNG2rChAnq1KmTVq5cmef185IUHx+vZcuWqVmzZnr55Zf1r3/9y3Fdr776qhITE1WzZk09+uijevrppxUcHKxSpUppwoQJOnPmjPr375/nc5UpU0Z79+51LE0KCAiQl5dXrn2ffvppde3aVbVr11azZs20dOlSLVy4UJ9//nmez3fRO++8o23btqlz584qV66czp07p9mzZ2vHjh2aOnWqJN2S9yY+Pl5t2rRRxYoVdfToUa1ateqGfyEh0AMAgIKtAH5z66X8/f1Vv359TZo0ybGuPCoqSg8//LCee+45SdKCBQs0bNgw9ejRwzG7PX78eEnSCy+8oL1796pVq1by9fXVwIED1alTJx0/fuXrTkpK0pgxYzR06FD9/vvvCgkJUYMGDdS2bVtJ0l133aWZM2dq1KhRSkhIUPPmzfX888/r5ZdfztM1eXl5KSUlRZMmTdI777yjYcOGydfXV1WqVNETTzyh6tWrS5LGjx+vCxcuqFevXjp58qTq1q2rFStWqFixYnl+/bp06aKFCxeqSZMmOnbsmJKSkhQXF5dr306dOmnKlCl67bXX9MQTTyg6OlpJSUnX/ABvburVq6f169dr0KBBOnTokPz9/VWtWjUtXrzY8QHeW/HeZGdn67HHHtPBgwcVGBio1q1ba9KkSfmu/1I2c6OLrFzgxIkTCgoK0vHjxx2fQi6oyjy7zNUlXNM+756uLuHaCvj/zK2EMXkTMS5vCiuMScki47KAj8mr/ft97tw57d27V9HR0fL29nZRhUDBktefC9bQAwAAABZGoAcAAChkqlWr5nRbxUu3uXPnurq8m2Ls2LFXvMaL983/p2ANPQAAQCHz6aefKisrK9e28PDw21zNrTFo0CB17do11zYfH5/bXM2tRaAHAAAoZEqXLu3qEm654OBgBQcHu7qM24IlNwAAoMCw4L06gFsmrz8PBHoAAOByF79J9MyZMy6uBCg4Lv48XPpNu7lhyQ0AAHA5Nzc3FS1aVOnp6ZIkX19f2Ww2F1cFuIYxRmfOnFF6erqKFi0qNze3q/Yn0AMAgALBbrdLkiPUA4Vd0aJFHT8XV0OgBwAABYLNZlNERITCwsKueAcWoLDw8PC45sz8RQR6AABQoLi5ueU5yADgQ7EAAACApRHoAQAAAAsj0AMAAAAWRqAHAAAALIxADwAAAFgYgR4AAACwMAI9AAAAYGEEegAAAMDCCPQAAACAhRHoAQAAAAsj0AMAAAAWRqAHAAAALIxADwAAAFgYgR4AAACwMAI9AAAAYGEEegAAAMDCCPQAAACAhRHoAQAAAAsj0AMAAAAWRqAHAAAALIxADwAAAFgYgR4AAACwMAI9AAAAYGEEegAAAMDCCPQAAACAhRHoAQAAAAsj0AMAAAAWRqAHAAAALCzfgf7LL79U+/btFRkZKZvNpsWLFzu1G2OUkJCgyMhI+fj4qHHjxtqxY4dTn4yMDA0ZMkShoaHy8/NThw4ddPDgwRu6EAAAAKAwynegP336tGrUqKE333wz1/YJEybojTfe0JtvvqnNmzfLbrerRYsWOnnypKNPfHy8Fi1apPnz52v9+vU6deqU2rVrp+zs7Ou/EgAAAKAQcs/vE9q0aaM2bdrk2maM0eTJkzVy5Ejdd999kqRZs2YpPDxc8+bN0yOPPKLjx48rMTFR77//vpo3by5JmjNnjqKiovT555+rVatWN3A5AAAAQOFyU9fQ7927V2lpaWrZsqVjn5eXl2JjY7VhwwZJ0tatW5WVleXUJzIyUtWrV3f0uVxGRoZOnDjhtAEAAAC4yYE+LS1NkhQeHu60Pzw83NGWlpYmT09PFStW7Ip9Ljdu3DgFBQU5tqioqJtZNgAAAGBZt+QuNzabzemxMSbHvstdrc+IESN0/Phxx3bgwIGbVisAAABgZTc10NvtdknKMdOenp7umLW32+3KzMzU0aNHr9jncl5eXgoMDHTaAAAAANzkQB8dHS273a6UlBTHvszMTK1du1YNGzaUJNWpU0ceHh5OfVJTU/XDDz84+gAAAADIm3zf5ebUqVP6+eefHY/37t2rbdu2KTg4WKVKlVJ8fLzGjh2rChUqqEKFCho7dqx8fX3Vs2dPSVJQUJD69++voUOHKiQkRMHBwRo2bJhiYmIcd70BAAAAkDf5DvRbtmxRkyZNHI+feuopSVKfPn2UnJys4cOH6+zZsxo8eLCOHj2q+vXra+XKlQoICHA8Z9KkSXJ3d1fXrl119uxZNWvWTMnJyXJzc7sJlwQAAAAUHvkO9I0bN5Yx5ortNptNCQkJSkhIuGIfb29vTZ06VVOnTs3v6QEAAABc4pbc5QYAAADA7UGgBwAAACyMQA8AAABYGIEeAAAAsDACPQAAAGBhBHoAAADAwgj0AAAAgIUR6AEAAAALI9ADAAAAFkagBwAAACyMQA8AAABYGIEeAAAAsDACPQAAAGBhBHoAAADAwgj0AAAAgIUR6AEAAAALI9ADAAAAFkagBwAAACyMQA8AAABYGIEeAAAAsDACPQAAAGBhBHoAAADAwgj0AAAAgIUR6AEAAAALI9ADAAAAFkagBwAAACyMQA8AAABYGIEeAAAAsDACPQAAAGBhBHoAAADAwgj0AAAAgIUR6AEAAAALI9ADAAAAFkagBwAAACyMQA8AAABYGIEeAAAAsDACPQAAAGBhBHoAAADAwgj0AAAAgIUR6AEAAAALI9ADAAAAFkagBwAAACyMQA8AAABYGIEeAAAAsDACPQAAAGBhBHoAAADAwgj0AAAAgIUR6AEAAAALI9ADAAAAFkagBwAAACyMQA8AAABYGIEeAAAAsLCbHugTEhJks9mcNrvd7mg3xighIUGRkZHy8fFR48aNtWPHjptdBgAAAFAo3JIZ+mrVqik1NdWxbd++3dE2YcIEvfHGG3rzzTe1efNm2e12tWjRQidPnrwVpQAAAAD/aLck0Lu7u8tutzu24sWLS/p7dn7y5MkaOXKk7rvvPlWvXl2zZs3SmTNnNG/evFtRCgAAAPCPdksC/Z49exQZGano6Gh1795dv/76qyRp7969SktLU8uWLR19vby8FBsbqw0bNlzxeBkZGTpx4oTTBgAAAOAWBPr69etr9uzZWrFihd59912lpaWpYcOGOnz4sNLS0iRJ4eHhTs8JDw93tOVm3LhxCgoKcmxRUVE3u2wAAADAkm56oG/Tpo26dOmimJgYNW/eXMuWLZMkzZo1y9HHZrM5PccYk2PfpUaMGKHjx487tgMHDtzssgEAAABLuuW3rfTz81NMTIz27NnjuNvN5bPx6enpOWbtL+Xl5aXAwECnDQAAAMBtCPQZGRnatWuXIiIiFB0dLbvdrpSUFEd7Zmam1q5dq4YNG97qUgAAAIB/HPebfcBhw4apffv2KlWqlNLT0zVmzBidOHFCffr0kc1mU3x8vMaOHasKFSqoQoUKGjt2rHx9fdWzZ8+bXQoAAADwj3fTA/3BgwfVo0cP/fXXXypevLjuuusubdy4UaVLl5YkDR8+XGfPntXgwYN19OhR1a9fXytXrlRAQMDNLgUAAAD4x7vpgX7+/PlXbbfZbEpISFBCQsLNPjUAAABQ6NzyNfQAAAAAbh0CPQAAAGBhBHoAAADAwgj0AAAAgIUR6AEAAAALI9ADAAAAFkagBwAAACyMQA8AAABYGIEeAAAAsDACPQAAAGBhBHoAAADAwgj0AAAAgIUR6AEAAAALI9ADAAAAFkagBwAAACyMQA8AAABYGIEeAAAAsDACPQAAAGBhBHoAAADAwgj0AAAAgIUR6AEAAAALI9ADAAAAFkagBwAAACyMQA8AAABYGIEeAAAAsDACPQAAAGBhBHoAAADAwgj0AAAAgIUR6AEAAAALI9ADAAAAFkagBwAAACyMQA8AAABYGIEeAAAAsDACPQAAAGBhBHoAAADAwgj0AAAAgIUR6AEAAAALI9ADAAAAFkagBwAAACyMQA8AAABYGIEeAAAAsDACPQAAAGBhBHoAAADAwgj0AAAAgIUR6AEAAAALI9ADAAAAFkagBwAAACyMQA8AAABYGIEeAAAAsDACPQAAAGBhBHoAAADAwgj0AAAAgIW5NNC//fbbio6Olre3t+rUqaN169a5shwAAADAclwW6D/88EPFx8dr5MiR+vbbb3X33XerTZs22r9/v6tKAgAAACzHZYH+jTfeUP/+/TVgwABVqVJFkydPVlRUlKZNm+aqkgAAAADLcXfFSTMzM7V161Y9++yzTvtbtmypDRs25OifkZGhjIwMx+Pjx49Lkk6cOHFrC70JLmSccXUJ13TCZlxdwrVZ4L22CsbkTcS4vCmsMCYli4zLAj4mL/67bYwFXkvAQlwS6P/66y9lZ2crPDzcaX94eLjS0tJy9B83bpxGjx6dY39UVNQtq7EwCXJ1AXkx3hJV4iaxzLvNuCxULPFuW2RMnjx5UkFB1qgVsAKXBPqLbDab02NjTI59kjRixAg99dRTjscXLlzQkSNHFBISkmt/5N2JEycUFRWlAwcOKDAw0NXlAIxJFEiMy5vDGKOTJ08qMjLS1aUA/yguCfShoaFyc3PLMRufnp6eY9Zekry8vOTl5eW0r2jRoreyxEInMDCQf6RQoDAmURAxLm8cM/PAzeeSD8V6enqqTp06SklJcdqfkpKihg0buqIkAAAAwJJctuTmqaeeUq9evVS3bl01aNBAM2bM0P79+zVo0CBXlQQAAABYjssCfbdu3XT48GG99NJLSk1NVfXq1fXpp5+qdOnSriqpUPLy8tKoUaNyLGkCXIUxiYKIcQmgILMZ7h0FAAAAWJbLvlgKAAAAwI0j0AMAAAAWRqAHAAAALIxADwAAAFgYgR4AAACwMAI9AAAAYGEEegAFjjFGFy5ccHUZgJPk5GQdP37c1WUAQA4E+kJm2bJlGjBggIYPH64ff/zRqe3o0aNq2rSpiypDYXT+/Hk9//zzio2N1ahRoyRJr732mvz9/eXj46M+ffooMzPTxVUCfxs4cKAOHTrk6jIAIAcCfSEyb948dezYUWlpafr6669Vq1YtzZ0719GemZmptWvXurBCFDajR4/WzJkzVbduXX388cd69NFHNXXqVM2YMUMzZ87UqlWrNHnyZFeXiUImODg41+38+fNq0KCB4zEAFBTuri4At8/rr7+uSZMmaciQIZKkjz/+WH379tW5c+fUv39/F1eHwmjevHmaOXOm2rVrp0cffVSVKlXSvHnz1K1bN0mSt7e3XnrpJQ0fPtzFlaIwycrKUmxsrB544AHHPmOM46+bJUqUcGF1AJCTzRhjXF0Ebg9/f39t375d0dHRjn1r1qxRhw4dNGHCBHXu3FmRkZHKzs52YZUoTHx8fLR7925FRUU5Hn/77beqXLmyJGnv3r2qUaOGTpw44coyUcj8/PPP6tmzp6pUqaK33npL/v7+kiQPDw999913qlq1qosrBABnLLkpRAIDA/XHH3847WvcuLGWLl2qp59+WlOnTnVRZSisgoKCdOzYMcfj2rVrKyAgwPE4IyNDNpvNBZWhMCtfvrw2bNggu92umjVr6quvvnJ1SQBwVSy5KUTq1aunzz77THfddZfT/tjYWC1dulTt2rVzUWUorKpWrapvvvlGMTExkpQjOG3fvl0VKlRwRWko5Nzd3fXqq6+qVatW6tmzpx588EF+uQRQYDFDX4g8+eST8vb2zrWtcePG+uSTT9S7d+/bXBUKs+nTp+uee+65YntWVhbr5+FSTZs21TfffKMff/xRfn5+cnNzc3VJAJADa+gBAAAAC2PJTSF06tQpbd26VWlpabLZbAoPD1edOnUcH/wCbjfGJAqiy8el3W5X7dq1GZcAChwCfSGSlZWlYcOG6d1339W5c+fk6ekpY4yysrLk7e2tgQMH6rXXXpOHh4erS0UhwZhEQXT+/HkNHTqUcQnAMlhDX4gMGzZMCxYsUFJSko4cOaJz584pIyNDR44cUVJSkhYuXKinn37a1WWiEGFMoiAaOnQo4xKApbCGvhApXry4PvzwQzVt2jTX9i+++ELdu3fXn3/+eZsrQ2HFmERBxLgEYDXM0BciZ8+eVWho6BXbQ0JCdPbs2dtYEQo7xiQKIsYlAKthhr4Qad++vc6ePau5c+cqPDzcqe2PP/5Qr1695O3trSVLlrioQhQ2jEkURIxLAFZDoC9EDhw4oLZt2+rHH39U9erVFR4eLpvNprS0NP3www+qWrWqli1bppIlS7q6VBQSjEkURIxLAFZDoC9kLly4oBUrVmjjxo1KS0uTJNntdjVo0EAtW7ZUkSKswsLtxZhEQcS4BGAlBHoAAADAwrgPfSH066+/av369UpNTZWbm5vKli2r5s2bKzAw0NWloZBiTKIgYlwCsAoCfSFy+vRpxcXFacGCBZIkm82msLAw/fnnn/Lx8dH48eP12GOPubhKFCaMSRREjEsAVsMiwELkqaeeUmpqqr799lvt2rVLnTt3Vu/evXXixAlNmTJFw4cP17x581xdJgoRxiQKIsYlAKthDX0hUrx4cS1fvlx16tSRJB09elSRkZE6fPiwfH199dZbb2nmzJn69ttvXVwpCgvGJAoixiUAq2GGvhA5f/6809pPf39/nT9/XqdPn5YktWzZUj/++KOrykMhxJhEQcS4BGA1BPpC5M4779SUKVMcj6dMmaLixYurePHikqRTp07J39/fVeWhEGJMoiBiXAKwGj4UW4iMHz9eLVq00IIFC+Tp6am0tDTNmjXL0b5hwwa1bdvWhRWisGFMoiBiXAKwGtbQFzKpqan65JNPlJGRoaZNm6pq1aquLgmFHGMSBRHjEoCVEOgBAAAAC2MNPRyOHj2q2bNnu7oMwIExiYKIcQmgoGGGHg7fffedateurezsbFeXAkhiTKJgYlwCKGj4UGwhcuLEiau2nzx58jZVAvyNMYmCiHEJwGqYoS9EihQpIpvNdsV2Y4xsNhuzTrhtGJMoiBiXAKyGGfpCJCAgQCNHjlT9+vVzbd+zZ48eeeSR21wVCjPGJAoixiUAqyHQFyK1a9eWJMXGxubaXrRoUfEHG9xOjEkURIxLAFbDXW4KkZ49e8rb2/uK7Xa7XaNGjbqNFaGwY0yiIGJcArAa1tADAAAAFsYMPQAAAGBhrKEvZE6fPq158+Zpw4YNSktLk81mU3h4uBo1aqQePXrIz8/P1SWikGFMoiBiXAKwEpbcFCI7d+5UixYtdObMGcXGxio8PFzGGKWnp2vt2rXy8/PTypUrVbVqVVeXikKCMYmCiHEJwGoI9IVIkyZNZLfbNWvWLHl6ejq1ZWZmKi4uTqmpqVq9erWLKkRhw5hEQcS4BGA1BPpCxNfXV1u2bLnirNIPP/ygevXq6cyZM7e5MhRWjEkURIxLAFbDh2ILkWLFimnPnj1XbP/5559VrFix21gRCjvGJAoixiUAq+FDsYXIww8/rD59+uj5559XixYtFB4eLpvNprS0NKWkpGjs2LGKj493dZkoRBiTKIgYlwCshiU3hcyrr76qKVOmOO7aIEnGGNntdsXHx2v48OEurhCFDWMSBRHjEoCVEOgLqb179yotLU3S3996GB0d7eKKUNgxJlEQMS4BWAGBHgAAALAwPhRbyJw9e1br16/Xzp07c7SdO3dOs2fPdkFVKMwYkyiIGJcArIQZ+kJk9+7datmypfbv3y+bzaa7775bH3zwgSIiIiRJf/zxhyIjI5Wdne3iSlFYMCZREDEuAVgNM/SFyDPPPKOYmBilp6frp59+UmBgoBo1aqT9+/e7ujQUUoxJFESMSwBWwwx9IRIeHq7PP/9cMTExjn2PPfaYPvnkE61evVp+fn7MOuG2YkyiIGJcArAa7kNfiJw9e1bu7s5v+VtvvaUiRYooNjZW8+bNc1FlKKwYkyiIGJcArIZAX4hUrlxZW7ZsUZUqVZz2T506VcYYdejQwUWVobBiTKIgYlwCsBrW0BcinTt31gcffJBr25tvvqkePXqIFVi4nRiTKIgYlwCshjX0AAAAgIUxQw8AAABYGIEeAAAAsDACPQAAAGBhBHoAAADAwgj0QCFgjFHz5s3VqlWrHG1vv/22goKCNHv2bNlstly3tLQ0p+ccPHhQnp6eqly5cq7nu/S5/v7+qlGjhpKTk536rFmzRjabTcWKFdO5c+ec2v73v/85nn+p7OxsTZo0SXfccYe8vb1VtGhRtWnTRl999ZVTv+TkZNlsNrVu3dpp/7Fjx2Sz2bRmzRpHn6ttF/sVLVr0ite5ePHiXNsAALhdCPRAIWCz2ZSUlKRNmzbpnXfecezfu3evnnnmGU2ZMkWlSpWSJP30009KTU112sLCwpyOl5ycrK5du+rMmTM5wvRFSUlJSk1N1Xfffadu3bqpb9++WrFiRY5+AQEBWrRokdO+9957z1HPRcYYde/eXS+99JKeeOIJ7dq1S2vXrlVUVJQaN26cI1i7u7vriy++0OrVq3Otr1u3bk7X2KBBAz388MNO+xo2bJj7CwoAQAFCoAcKiaioKE2ZMkXDhg3T3r17ZYxR//791axZM8XFxTn6hYWFyW63O21Fivz//1UYY5SUlKRevXqpZ8+eSkxMzPV8RYsWld1uV7ly5fTcc88pODhYK1euzNGvT58+eu+99xyPz549q/nz56tPnz5O/T766CN9/PHHmj17tgYMGKDo6GjVqFFDM2bMUIcOHTRgwACdPn3a0d/Pz099+/bVs88+m2t9Pj4+Ttfo6ekpX1/fHPsAACjoCPRAIdKnTx81a9ZMffv21ZtvvqkffvhBM2bMyNcxVq9erTNnzqh58+bq1auXPvroI508efKK/bOzs/XRRx/pyJEj8vDwyNHeq1cvrVu3Tvv375ckLViwQGXKlFHt2rWd+s2bN08VK1ZU+/btcxxj6NChOnz4sFJSUpz2JyQkaPv27fr444/zdY0AAFgJgR4oZGbMmKGdO3cqPj5e77zzTo7lNCVLlpS/v79jq1SpklN7YmKiunfvLjc3N1WrVk3ly5fXhx9+mOM8PXr0kL+/v7y8vNStWzcFBwdrwIABOfqFhYWpTZs2jjX27733nvr165ej3+7du1WlSpVcr+ni/t27dzvtj4yM1L///W+NHDlS58+fv/KLcg3Hjx93ek0ubgAAFAQEeqCQCQsL08CBA1WlShV17tw5R/u6deu0bds2x3bpuvdjx45p4cKFeuihhxz7HnroIaclMxdNmjRJ27ZtU0pKimrWrKlJkyapfPnyudbUr18/JScn69dff9XXX3+tBx988Lqu7fIP0UrSM888oz///DPXGvMqICDA6TW5uAEAUBC4u7oAALefu7u73N1z//GPjo6+4l1d5s2bp3Pnzql+/fqOfcYYXbhwQTt37lTVqlUd++12u8qXL6/y5cvrP//5j2rVqqW6des69bmobdu2euSRR9S/f3+1b99eISEhOfpUrFhRO3fuzLWuXbt2SZIqVKiQo61o0aIaMWKERo8erXbt2uX6/GspUqTIFX8ZAQDA1ZihB5BniYmJGjp0qNMs9XfffacmTZpcdQa8fPny6tKli0aMGJFru5ubm3r16qU1a9bkutxGkrp37649e/Zo6dKlOdomTpyokJAQtWjRItfnDhkyREWKFNGUKVPycJUAAFgLgR6Ak/T0dKWlpTltWVlZ2rZtm7755hsNGDBA1atXd9p69Oih2bNnKysr64rHHTp0qJYuXaotW7bk2v7yyy/rzz//zPVe+dLfgb5z587q06ePEhMTtW/fPn3//fd65JFHtGTJEs2cOVN+fn65Ptfb21ujR4/W//3f/+X/BQEAoIAj0ANwUqlSJUVERDhtW7duVWJioqpWrZrrl0l16tRJR44cyXX2/KKYmBg1b95cL774Yq7tnp6eCg0NzXUdvPT3+viPPvpII0eO1KRJk1S5cmXdfffd+u2337R69Wp16tTpqtfVp08flS1b9qp9AACwIpsxxri6CAAAAADXhxl6AAAAwMII9AAAAICFEegBAAAACyPQAwAAABZGoAcAAAAsjEAPAAAAWBiBHgAAALAwAj0AAABgYQR6AAAAwMII9AAAAICFEegBAAAACyPQAwAAABb2/wBEVWv9VDNQ4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trial_full_observ = full_observ[(full_observ[\"YEARMONTH\"] >= 201902) & (full_observ[\"YEARMONTH\"] <= 201904)]\n",
    "scaled_sales_control_stores = full_observ[full_observ[\"STORE_NBR\"].isin([233, 155, 40])][[\"STORE_NBR\", \"YEARMONTH\", \"TOT_SALES\"]]\n",
    "\n",
    "def scaler(row):\n",
    "    if row[\"STORE_NBR\"] == 233:\n",
    "        return row[\"TOT_SALES\"] * sales_ratio_77\n",
    "    elif row[\"STORE_NBR\"] == 155:\n",
    "        return row[\"TOT_SALES\"] * sales_ratio_86\n",
    "    elif row[\"STORE_NBR\"] == 40:\n",
    "        return row[\"TOT_SALES\"] * sales_ratio_88\n",
    "\n",
    "scaled_sales_control_stores[\"ScaledSales\"] = scaled_sales_control_stores.apply(lambda row: scaler(row), axis=1)\n",
    "\n",
    "trial_scaled_sales_control_stores = scaled_sales_control_stores[(scaled_sales_control_stores[\"YEARMONTH\"] >= 201902) & (scaled_sales_control_stores[\"YEARMONTH\"] <= 201904)]\n",
    "pretrial_scaled_sales_control_stores = scaled_sales_control_stores[scaled_sales_control_stores[\"YEARMONTH\"] < 201902]\n",
    "\n",
    "percentage_diff = {}\n",
    "\n",
    "for trial, control in trial_control_dic.items():\n",
    "    a = trial_scaled_sales_control_stores[trial_scaled_sales_control_stores[\"STORE_NBR\"] == control]\n",
    "    b = trial_full_observ[trial_full_observ[\"STORE_NBR\"] == trial][[\"STORE_NBR\", \"YEARMONTH\", \"TOT_SALES\"]]\n",
    "    percentage_diff[trial] = b[\"TOT_SALES\"].sum() / a[\"ScaledSales\"].sum()\n",
    "    b[[\"YEARMONTH\", \"TOT_SALES\"]].merge(a[[\"YEARMONTH\", \"ScaledSales\"]],on=\"YEARMONTH\").set_index(\"YEARMONTH\").rename(columns={\"ScaledSales\":\"Scaled_Control_Sales\", \"TOT_SALES\":\"Trial_Sales\"}).plot.bar()\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "    plt.title(\"Trial Store \"+str(trial)+\" and Control Store \"+str(control))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cb49746f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{77: 1.2615468650086281, 86: 1.1315014357363697, 88: 1.043458345854219}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentage_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b661ebed",
   "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>c_STORE_NBR</th>\n",
       "      <th>YEARMONTH</th>\n",
       "      <th>c_ScaledSales</th>\n",
       "      <th>t_STORE_NBR</th>\n",
       "      <th>t_TOT_SALES</th>\n",
       "      <th>Sales_Percentage_Diff</th>\n",
       "      <th>trial_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>233</td>\n",
       "      <td>201902</td>\n",
       "      <td>249.762622</td>\n",
       "      <td>77</td>\n",
       "      <td>235.0</td>\n",
       "      <td>-0.060907</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>233</td>\n",
       "      <td>201903</td>\n",
       "      <td>203.802205</td>\n",
       "      <td>77</td>\n",
       "      <td>278.5</td>\n",
       "      <td>0.309755</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>233</td>\n",
       "      <td>201904</td>\n",
       "      <td>162.345704</td>\n",
       "      <td>77</td>\n",
       "      <td>263.5</td>\n",
       "      <td>0.475075</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>155</td>\n",
       "      <td>201902</td>\n",
       "      <td>864.522060</td>\n",
       "      <td>86</td>\n",
       "      <td>913.2</td>\n",
       "      <td>0.054764</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>155</td>\n",
       "      <td>201903</td>\n",
       "      <td>780.320405</td>\n",
       "      <td>86</td>\n",
       "      <td>1026.8</td>\n",
       "      <td>0.272787</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>155</td>\n",
       "      <td>201904</td>\n",
       "      <td>819.317024</td>\n",
       "      <td>86</td>\n",
       "      <td>848.2</td>\n",
       "      <td>0.034642</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>40</td>\n",
       "      <td>201902</td>\n",
       "      <td>1434.399269</td>\n",
       "      <td>88</td>\n",
       "      <td>1370.2</td>\n",
       "      <td>-0.045781</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>40</td>\n",
       "      <td>201903</td>\n",
       "      <td>1352.064709</td>\n",
       "      <td>88</td>\n",
       "      <td>1477.2</td>\n",
       "      <td>0.088458</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>40</td>\n",
       "      <td>201904</td>\n",
       "      <td>1321.797762</td>\n",
       "      <td>88</td>\n",
       "      <td>1439.4</td>\n",
       "      <td>0.085182</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    c_STORE_NBR  YEARMONTH  c_ScaledSales  t_STORE_NBR  t_TOT_SALES  \\\n",
       "7           233     201902     249.762622           77        235.0   \n",
       "8           233     201903     203.802205           77        278.5   \n",
       "9           233     201904     162.345704           77        263.5   \n",
       "19          155     201902     864.522060           86        913.2   \n",
       "20          155     201903     780.320405           86       1026.8   \n",
       "21          155     201904     819.317024           86        848.2   \n",
       "31           40     201902    1434.399269           88       1370.2   \n",
       "32           40     201903    1352.064709           88       1477.2   \n",
       "33           40     201904    1321.797762           88       1439.4   \n",
       "\n",
       "    Sales_Percentage_Diff trial_period  \n",
       "7               -0.060907        trial  \n",
       "8                0.309755        trial  \n",
       "9                0.475075        trial  \n",
       "19               0.054764        trial  \n",
       "20               0.272787        trial  \n",
       "21               0.034642        trial  \n",
       "31              -0.045781        trial  \n",
       "32               0.088458        trial  \n",
       "33               0.085182        trial  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp1 = scaled_sales_control_stores.sort_values(by=[\"STORE_NBR\", \"YEARMONTH\"], ascending=[False, True]).reset_index().drop([\"TOT_SALES\", \"index\"], axis=1)\n",
    "temp2 = full_observ[full_observ[\"STORE_NBR\"].isin([77,86,88])][[\"STORE_NBR\", \"YEARMONTH\", \"TOT_SALES\"]].reset_index().drop([\"index\", \"YEARMONTH\"], axis=1)\n",
    "scaledsales_vs_trial = pd.concat([temp1, temp2], axis=1)\n",
    "scaledsales_vs_trial.columns = [\"c_STORE_NBR\", \"YEARMONTH\", \"c_ScaledSales\", \"t_STORE_NBR\", \"t_TOT_SALES\"]\n",
    "scaledsales_vs_trial[\"Sales_Percentage_Diff\"] = (scaledsales_vs_trial[\"t_TOT_SALES\"] - scaledsales_vs_trial[\"c_ScaledSales\"]) / (((scaledsales_vs_trial[\"t_TOT_SALES\"] + scaledsales_vs_trial[\"c_ScaledSales\"])/2))\n",
    "def label_period(cell):\n",
    "    if cell < 201902:\n",
    "        return \"pre\"\n",
    "    elif cell > 201904:\n",
    "        return \"post\"\n",
    "    else:\n",
    "        return \"trial\"\n",
    "scaledsales_vs_trial[\"trial_period\"] = scaledsales_vs_trial[\"YEARMONTH\"].apply(lambda cell: label_period(cell))\n",
    "scaledsales_vs_trial[scaledsales_vs_trial[\"trial_period\"] == \"trial\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "509f5638",
   "metadata": {},
   "source": [
    "Check significance of Trial minus Control stores TOT_SALES Percentage Difference Pre-Trial vs Trial.\n",
    "\n",
    "- Step 1: Check null hypothesis of 0 difference between control store's Pre-Trial and Trial period performance.\n",
    "\n",
    "- Step 2: Proof control and trial stores are similar statistically\n",
    "\n",
    "Check p-value of control store's Pre-Trial vs Trial store's Pre-Trial. If <5%, it is significantly different. If >5%, it is not significantly different (similar).\n",
    "\n",
    "- Step 3: After checking Null Hypothesis of first 2 step to be true, we can check Null Hypothesis of Percentage Difference between Trial and Control stores during pre-trial is the same as during trial."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03b9a55b",
   "metadata": {},
   "source": [
    "Check T-Value of Percentage Difference of each Trial month (Feb, March, April 2019). Mean is mean of Percentage Difference during pre-trial. Standard deviation is stdev of Percentage Difference during pre-trial. Formula is Trial month's Percentage Difference minus Mean, divided by Standard deviation. Compare each T-Value with 95% percentage significance critical t-value of 6 degrees of freedom (7 months of sample - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "009d025c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Store 40\n",
      "Ttest_indResult(statistic=-0.5958372343168558, pvalue=0.5722861621434027) \n",
      "\n",
      "Store 155\n",
      "Ttest_indResult(statistic=1.4291956879290917, pvalue=0.1972705865160342) \n",
      "\n",
      "Store 233\n",
      "Ttest_indResult(statistic=1.191102601097452, pvalue=0.2944500606486209) \n",
      "\n",
      "Critical t-value for 95% confidence interval:\n",
      "[-4.30265273  4.30265273]\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind, t\n",
    "\n",
    "# Step 1\n",
    "for num in [40, 155, 233]:\n",
    "    print(\"Store\", num)\n",
    "    print(ttest_ind(pretrial_scaled_sales_control_stores[pretrial_scaled_sales_control_stores[\"STORE_NBR\"] == num][\"ScaledSales\"],\n",
    "                   trial_scaled_sales_control_stores[trial_scaled_sales_control_stores[\"STORE_NBR\"] == num][\"ScaledSales\"],\n",
    "                   equal_var=False), '\\n')\n",
    "    #print(len(pretrial_scaled_sales_control_stores[pretrial_scaled_sales_control_stores[\"STORE_NBR\"] == num][\"ScaledSales\"]), len(trial_scaled_sales_control_stores[trial_scaled_sales_control_stores[\"STORE_NBR\"] == num][\"ScaledSales\"]))\n",
    "    \n",
    "alpha = 0.05\n",
    "print(\"Critical t-value for 95% confidence interval:\")\n",
    "print(t.ppf((alpha/2, 1-alpha/2), df=min([len(pretrial_scaled_sales_control_stores[pretrial_scaled_sales_control_stores[\"STORE_NBR\"] == num]),\n",
    "                       len(trial_scaled_sales_control_stores[trial_scaled_sales_control_stores[\"STORE_NBR\"] == num])])-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d3df324c",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = pretrial_scaled_sales_control_stores[pretrial_scaled_sales_control_stores[\"STORE_NBR\"] == 40][\"ScaledSales\"]\n",
    "b = trial_scaled_sales_control_stores[trial_scaled_sales_control_stores[\"STORE_NBR\"] == 40][\"ScaledSales\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b5a4e89",
   "metadata": {},
   "source": [
    "- Null hypothesis is true. There isn't any statistically significant difference between control store's scaled Pre-Trial and Trial period sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e4ffe7e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial store: 77 , Control store: 233\n",
      "Ttest_indResult(statistic=-1.2533353315065932e-15, pvalue=0.999999999999999) \n",
      "\n",
      "Trial store: 86 , Control store: 155\n",
      "Ttest_indResult(statistic=3.1048311203382156e-15, pvalue=0.9999999999999976) \n",
      "\n",
      "Trial store: 88 , Control store: 40\n",
      "Ttest_indResult(statistic=-5.69358613974361e-15, pvalue=0.9999999999999956) \n",
      "\n",
      "Critical t-value for 95% confidence interval:\n",
      "[-2.44691185  2.44691185]\n"
     ]
    }
   ],
   "source": [
    "# Step 2\n",
    "for trial, cont in trial_control_dic.items():\n",
    "    print(\"Trial store:\", trial, \", Control store:\", cont)\n",
    "    print(ttest_ind(pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == trial][\"TOT_SALES\"],\n",
    "                   pretrial_scaled_sales_control_stores[pretrial_scaled_sales_control_stores[\"STORE_NBR\"] == cont][\"ScaledSales\"],\n",
    "                   equal_var=True), '\\n')\n",
    "\n",
    "alpha = 0.05\n",
    "print(\"Critical t-value for 95% confidence interval:\")\n",
    "print(t.ppf((alpha/2, 1-alpha/2), df=len(pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == trial])-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "819e9518",
   "metadata": {},
   "source": [
    "- Null hypothesis is true. There isn't any statistically significant difference between Trial store's sales and Control store's scaled-sales performance during pre-trial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7d36f14d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial store: 77 , Control store: 233\n",
      "201902 : -0.7171038288055838\n",
      "201903 : 3.035317928855674\n",
      "201904 : 4.708944418758219\n",
      "\n",
      "\n",
      "Trial store: 86 , Control store: 155\n",
      "201902 : 1.4133618775921597\n",
      "201903 : 7.123063846042147\n",
      "201904 : 0.8863824572944234\n",
      "\n",
      "\n",
      "Trial store: 88 , Control store: 40\n",
      "201902 : -0.5481633746817577\n",
      "201903 : 1.0089992743637823\n",
      "201904 : 0.9710006270463672\n",
      "\n",
      "\n",
      "Critical t-value for 95% confidence interval:\n",
      "1.9431802803927816\n"
     ]
    }
   ],
   "source": [
    "# Step 3\n",
    "for trial, cont in trial_control_dic.items():\n",
    "    print(\"Trial store:\", trial, \", Control store:\", cont)\n",
    "    temp_pre = scaledsales_vs_trial[(scaledsales_vs_trial[\"c_STORE_NBR\"] == cont) & (scaledsales_vs_trial[\"trial_period\"]==\"pre\")]\n",
    "    std = temp_pre[\"Sales_Percentage_Diff\"].std()\n",
    "    mean = temp_pre[\"Sales_Percentage_Diff\"].mean()\n",
    "    #print(std, mean)\n",
    "    for t_month in scaledsales_vs_trial[scaledsales_vs_trial[\"trial_period\"] == \"trial\"][\"YEARMONTH\"].unique():\n",
    "        pdif = scaledsales_vs_trial[(scaledsales_vs_trial[\"YEARMONTH\"] == t_month) & (scaledsales_vs_trial[\"t_STORE_NBR\"] == trial)][\"Sales_Percentage_Diff\"]\n",
    "        print(t_month,\":\",(float(pdif)-mean)/std)\n",
    "    print('\\n')\n",
    "    \n",
    "print(\"Critical t-value for 95% confidence interval:\")\n",
    "conf_intv_95 = t.ppf(0.95, df=len(temp_pre)-1)\n",
    "print(conf_intv_95)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba3b07e2",
   "metadata": {},
   "source": [
    "There are 3 months' increase in performance that are statistically significant (Above the 95% confidence interval t-score):\n",
    "\n",
    "- March and April trial months for trial store 77\n",
    "\n",
    "- March trial months for trial store 86"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d49ac8e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for trial, control in trial_control_dic.items():\n",
    "    a = trial_scaled_sales_control_stores[trial_scaled_sales_control_stores[\"STORE_NBR\"] == control].rename(columns={\"TOT_SALES\": \"control_TOT_SALES\"})\n",
    "    b = trial_full_observ[trial_full_observ[\"STORE_NBR\"] == trial][[\"STORE_NBR\", \"YEARMONTH\", \"TOT_SALES\"]].rename(columns={\"TOT_SALES\": \"trial_TOT_SALES\"})\n",
    "    comb = b[[\"YEARMONTH\", \"trial_TOT_SALES\"]].merge(a[[\"YEARMONTH\", \"control_TOT_SALES\"]],on=\"YEARMONTH\").set_index(\"YEARMONTH\")\n",
    "    comb.plot.bar()\n",
    "    cont_sc_sales = trial_scaled_sales_control_stores[trial_scaled_sales_control_stores[\"STORE_NBR\"] == control][\"TOT_SALES\"]\n",
    "    std = scaledsales_vs_trial[(scaledsales_vs_trial[\"c_STORE_NBR\"] == control) & (scaledsales_vs_trial[\"trial_period\"]==\"pre\")][\"Sales_Percentage_Diff\"].std()\n",
    "    thresh95 = cont_sc_sales.mean() + (cont_sc_sales.mean() * std * 2)\n",
    "    thresh5 = cont_sc_sales.mean() - (cont_sc_sales.mean() * std * 2)\n",
    "    plt.axhline(y=thresh95,linewidth=1, color='b', label=\"95% threshold\")\n",
    "    plt.axhline(y=thresh5,linewidth=1, color='r', label=\"5% threshold\")\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "    plt.title(\"Trial Store \"+str(trial)+\" and Control Store \"+str(control)+\" - Total Sales\")\n",
    "    plt.savefig(\"TS {} and CS {} - TOT_SALES.png\".format(trial,control), bbox_inches=\"tight\")\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "715ee47c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ratio of Store 77 and its Control store.\n",
    "ncust_ratio_77 = pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 77][\"nCustomers\"].sum() / pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 233][\"nCustomers\"].sum()\n",
    "\n",
    "#Ratio of Store 86 and its Control store.\n",
    "ncust_ratio_86 = pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 86][\"nCustomers\"].sum() / pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 155][\"nCustomers\"].sum()\n",
    "\n",
    "#Ratio of Store 77 and its Control store.\n",
    "ncust_ratio_88 = pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 88][\"nCustomers\"].sum() / pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 40][\"nCustomers\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "91291480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zZ4+ysrI0dOhQDR06tND+ihUraujQoZo6dar9vrZbt25Vhw4dHPpt3bq12PveAnAeLEVAuWCz2eTq6ioXFxd7W1ZWlt57771/dL7jx4/rxIkTRe4r+CZ4WFiYpP8Ll+fO1tWrV09Vq1ZVYmKiw50ZTp48qY8//th+p4Tz6dixo4wx+v3333XjjTcW2ho2bHje48PCwrRt27ZC11JwV4aCEFdwLd9//71DP2OMvv/+e1WsWLFQOD5bQZg5O2ib///Ah3+qVatW2rZtm3788UeH9sTExIs6fsyYMTLGaODAgcrJySm0Pzc3V5999pkk2Ze7zJs3z6HPhg0btGPHDvs3+y9FcePifFq3bq0TJ04U+gfZ3Llz7fsvxZEjR4q8dunvO05IFx7HLVq0kJeXV6HP5uDBg1qxYsVF1dSxY0cdOXJEeXl5RY7jevXqFXtskyZNtHLlykJb48aNVaNGDa1cudK+NKhq1aq6+eabNW/ePOXl5dnP8f3332vnzp3l8l7MAEoWM7YoF+6880699tpr6tWrlwYNGqQjR47olVde+cc3Y9+5c6fatWunHj16KCoqSlWqVFF6erqWLFmimTNnKjo6Wi1btpQkXXfddfLy8tL777+v66+/Xr6+vgoLC1NYWJgmT56s3r17q2PHjho8eLCys7P18ssv69ixY5o4ceIF67j11ls1aNAgDRgwQBs3btRtt90mHx8fHTp0SGvWrFHDhg31n//8p9jjhw0bpq5du6pt27Z64oknVKlSJX3//feaMGGCIiIi1L59e0l/z0R369bNfmusDh06KDs7W3PmzNF3332nF1544byz523btpW7u7t69uypUaNG6fTp03rzzTeVnp5+iZ+8Y+3vvvuu7rzzTr344osKCQnR+++/bw9kF9KiRQu9+eabGjJkiJo3b25fOpKbm6stW7Zo5syZatCggTp16qR69epp0KBBmj59uipUqKD27dtr7969evbZZxUeHq4nnnjikus/37goTt++ffX666+rX79+2rt3rxo2bKg1a9YoLi5OHTp0UJs2bS6phpUrV2ro0KHq3bu3WrZsqeDgYKWlpWn+/PlaunSp+vbta//HTcFs5syZM+Xn5ydPT0/VrFlTwcHBevbZZ/X000+rb9++6tmzp44cOaLx48fL09NT48aNu2AdPXr00Pvvv68OHTpo6NChuvnmm+Xm5qaDBw9q5cqV6tKli/2BJ+cKDAws8kmBgYGBOnPmTKF9kyZNUtu2bXXPPfdoyJAhSktL01NPPaUGDRpowIABl/T5AbCgUvziGpxIcXdFKOoG7AX7zr2zwbvvvmvq1atnPDw8TK1atcyECRPM7NmzjSSH23VdzF0R0tPTzYsvvmhuv/12U7VqVePu7m58fHxMkyZNzIsvvljo9kTz58839evXN25ubkaSGTdunH3fokWLTGRkpPH09DQ+Pj6mdevW5rvvvnM4vuCuCGffVurca4uMjDQ+Pj7Gy8vLXHfddaZv375m48aN570OY/6+SX9MTIwJDQ01Xl5epm7dumb48OHmr7/+cuiXlZVlXn75ZdOoUSPj5+dngoKCzC233GLmzZtX5B0UzvXZZ5+Zxo0bG09PT1O1alUzcuRI8+WXXxb6pn1xP9d+/fqZ6tWrO7Rt377dtG3b1nh6epqgoCDz4IMPmk8//fSSHiSQnJxs+vXrZ6pVq2b/OTZt2tQ899xz9ttZGfP3N+8nTZpk6tata9zc3EylSpXM/fffb79F1z+pv7hxcb4Hkhw5csQ8/PDDpkqVKsbV1dVUr17djBkzptBDJi7mrggHDhwwzzzzjLn11ltNaGiocXV1NX5+fiYyMtJMnz7dnDlzxqH/1KlTTc2aNY2Li0uhOzrMmjXLNGrUyLi7u5uAgADTpUsXs23btkKfQXHXlZuba1555RX7GPH19TX169c3gwcPNrt37z7vdRTlfH8+LFu2zNxyyy32cdO3b1/z559/XvJ7ALAemzH/4O72AAAAQBnDGlsAAABYAsEWAAAAlkCwBQAAgCUQbAEAAGAJBFsAAABYwiUH22+++UadOnVSWFiYbDbbeZ/8NHjwYNlstkLP/87OztZjjz2mSpUqycfHR507d9bBgwcvtRQAAADA7pIf0HDy5Ek1btxYAwYM0N13311sv0WLFmn9+vVF3qx82LBh+uyzz7RgwQIFBwdr+PDh6tixozZt2uTwZKni5Ofn648//pCfn99FP5IVAACULmOMjh8/rrCwMFWowC+NcQVczk1wJZmFCxcWaj948KCpWrWq+fnnn0316tXNlClT7PuOHTtm3NzczIIFC+xtv//+u6lQoYJZunTpRb3vgQMHjCQ2NjY2Nja2crid+2AUoKSU+CN18/Pz1adPH40cOVI33HBDof2bNm1Sbm6uYmJi7G1hYWFq0KCB1q5dq3bt2hU6Jjs7W9nZ2fbX5v8/U+LAgQPy9/cv6UsAAABXQGZmpsLDw+Xn51fapcCiSjzYTpo0Sa6urnr88ceL3J+amip3d3dVrFjRoT0kJESpqalFHjNhwgSNHz++ULu/vz/BFgCAcoZlhLhSSnSBy6ZNmzRt2jQlJCRc8qA1xhR7zJgxY5SRkWHfDhw4UBLlAgAAwEJKNNh+++23SktLU7Vq1eTq6ipXV1ft27dPw4cPV40aNSRJoaGhysnJUXp6usOxaWlpCgkJKfK8Hh4e9tlZZmkBAABQlBINtn369NFPP/2k5ORk+xYWFqaRI0fqq6++kiQ1b95cbm5uSkpKsh936NAh/fzzz2rZsmVJlgMAAAAncslrbE+cOKFff/3V/jolJUXJyckKCgpStWrVFBwc7NDfzc1NoaGhqlevniQpICBADz74oIYPH67g4GAFBQVpxIgRatiwodq0aXOZlwMAAABndcnBduPGjWrVqpX99ZNPPilJ6tevnxISEi7qHFOmTJGrq6vuvfdeZWVlqXXr1kpISLioe9gCAAAARbGZgntnlSOZmZkKCAhQRkYG620BACgn+PsbVxqP/QAAAIAlEGwBAABgCQRbAAAAWALBFgAAAJZAsAUAAIAlEGwBAABgCQRbAAAAWALBFgAAAJZwyU8eA1C+1XhqSWmXcEF7J95Z2iUAAMohZmwBAABgCQRbAAAAWALBFgAAAJZAsAUAAIAlEGwBAABgCQRbAAAAWALBFgAAAJZAsAUAAIAlEGwBAABgCQRbAAAAWALBFgAAAJZAsAUAAIAlEGwBAABgCQRbAAAAWALBFgAAAJbgWtoFAACcW42nlpR2CRdl78Q7S7sEABdAsIUUG1DaFVxYbEZpVwAAAMo4liIAAADAEgi2AAAAsASCLQAAACyBYAsAAABLINgCAADAEgi2AAAAsASCLQAAACyBYAsAAABLINgCAADAEnjy2BVWHh4VudeztCsAAAC4fMzYAgAAwBIItgAAALAEgi0AAAAsgWALAAAASyDYAgAAwBIu+a4I33zzjV5++WVt2rRJhw4d0sKFC9W1a1dJUm5urp555hl98cUX2rNnjwICAtSmTRtNnDhRYWFh9nNkZ2drxIgRmj9/vrKystS6dWu98cYbuvbaa0vswgAAKFGxAaVdwYXFZpR2BUCpuuQZ25MnT6px48aaMWNGoX2nTp3S5s2b9eyzz2rz5s365JNPtGvXLnXu3Nmh37Bhw7Rw4UItWLBAa9as0YkTJ9SxY0fl5eX98ysBAACAU7vkGdv27durffv2Re4LCAhQUlKSQ9v06dN18803a//+/apWrZoyMjI0e/Zsvffee2rTpo0kad68eQoPD9fXX3+tdu3a/YPLAAAAgLO74g9oyMjIkM1mU2BgoCRp06ZNys3NVUxMjL1PWFiYGjRooLVr1xYZbLOzs5WdnW1/nZmZeaXLBlCaysOvfCV+7QsAZcwV/fLY6dOn9dRTT6lXr17y9/eXJKWmpsrd3V0VK1Z06BsSEqLU1NQizzNhwgQFBATYt/Dw8CtZNgAAAMqhKxZsc3Nz1aNHD+Xn5+uNN964YH9jjGw2W5H7xowZo4yMDPt24MCBki4XAAAA5dwVCba5ubm69957lZKSoqSkJPtsrSSFhoYqJydH6enpDsekpaUpJCSkyPN5eHjI39/fYQMAAADOVuLBtiDU7t69W19//bWCg4Md9jdv3lxubm4OXzI7dOiQfv75Z7Vs2bKkywEAAICTuOQvj504cUK//vqr/XVKSoqSk5MVFBSksLAwde/eXZs3b9bnn3+uvLw8+7rZoKAgubu7KyAgQA8++KCGDx+u4OBgBQUFacSIEWrYsKH9LgkAAADApbrkYLtx40a1atXK/vrJJ5+UJPXr10+xsbFavHixJKlJkyYOx61cuVLR0dGSpClTpsjV1VX33nuv/QENCQkJcnFx+YeXAQAAAGd3ycE2Ojpaxphi959vXwFPT09Nnz5d06dPv9S3BwAAAIp0RW/3BQAAAFwtBFsAAABYAsEWAAAAlkCwBQAAgCUQbAEAAGAJBFsAAABYAsEWAAAAlkCwBQAAgCUQbAEAAGAJBFsAAABYAsEWAAAAlkCwBQAAgCUQbAEAAGAJBFsAAABYAsEWAAAAlkCwBQAAgCUQbAEAAGAJBFsAAABYAsEWAAAAlkCwBQAAgCUQbAEAAGAJBFsAAABYAsEWAAAAlkCwBQAAgCUQbAEAAGAJBFsAAABYAsEWAAAAlkCwBQAAgCUQbAEAAGAJBFsAAABYAsEWAAAAlkCwBQAAgCUQbAEAAGAJBFsAAABYAsEWAAAAlkCwBQAAgCUQbAEAAGAJBFsAAABYAsEWAAAAlkCwBQAAgCUQbAEAAGAJBFsAAABYwiUH22+++UadOnVSWFiYbDabFi1a5LDfGKPY2FiFhYXJy8tL0dHR2rZtm0Of7OxsPfbYY6pUqZJ8fHzUuXNnHTx48LIuBAAAAM7tkoPtyZMn1bhxY82YMaPI/ZMnT9Zrr72mGTNmaMOGDQoNDVXbtm11/Phxe59hw4Zp4cKFWrBggdasWaMTJ06oY8eOysvL++dXAgAAAKfmeqkHtG/fXu3bty9ynzFGU6dO1dixY9WtWzdJ0pw5cxQSEqLExEQNHjxYGRkZmj17tt577z21adNGkjRv3jyFh4fr66+/Vrt27S7jcgAAAOCsSnSNbUpKilJTUxUTE2Nv8/DwUFRUlNauXStJ2rRpk3Jzcx36hIWFqUGDBvY+58rOzlZmZqbDBgAAAJytRINtamqqJCkkJMShPSQkxL4vNTVV7u7uqlixYrF9zjVhwgQFBATYt/Dw8JIsGwAAABZwRe6KYLPZHF4bYwq1net8fcaMGaOMjAz7duDAgRKrFQAAANZQosE2NDRUkgrNvKalpdlncUNDQ5WTk6P09PRi+5zLw8ND/v7+DhsAAABwthINtjVr1lRoaKiSkpLsbTk5OVq9erVatmwpSWrevLnc3Nwc+hw6dEg///yzvQ8AAABwqS75rggnTpzQr7/+an+dkpKi5ORkBQUFqVq1aho2bJji4uJUp04d1alTR3FxcfL29lavXr0kSQEBAXrwwQc1fPhwBQcHKygoSCNGjFDDhg3td0kAAAAALtUlB9uNGzeqVatW9tdPPvmkJKlfv35KSEjQqFGjlJWVpSFDhig9PV2RkZFatmyZ/Pz87MdMmTJFrq6uuvfee5WVlaXWrVsrISFBLi4uJXBJAAAAcEaXHGyjo6NljCl2v81mU2xsrGJjY4vt4+npqenTp2v69OmX+vYAAABAka7IXREAAACAq41gCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALKHEg+2ZM2f0zDPPqGbNmvLy8lKtWrX0/PPPKz8/397HGKPY2FiFhYXJy8tL0dHR2rZtW0mXAgAAACdS4sF20qRJeuuttzRjxgzt2LFDkydP1ssvv6zp06fb+0yePFmvvfaaZsyYoQ0bNig0NFRt27bV8ePHS7ocAAAAOIkSD7br1q1Tly5ddOedd6pGjRrq3r27YmJitHHjRkl/z9ZOnTpVY8eOVbdu3dSgQQPNmTNHp06dUmJiYkmXAwAAACdR4sH2X//6l5YvX65du3ZJkn788UetWbNGHTp0kCSlpKQoNTVVMTEx9mM8PDwUFRWltWvXFnnO7OxsZWZmOmwAAADA2VxL+oSjR49WRkaG6tevLxcXF+Xl5emll15Sz549JUmpqamSpJCQEIfjQkJCtG/fviLPOWHCBI0fP76kSwUAAICFlPiM7QcffKB58+YpMTFRmzdv1pw5c/TKK69ozpw5Dv1sNpvDa2NMobYCY8aMUUZGhn07cOBASZcNAACAcq7EZ2xHjhypp556Sj169JAkNWzYUPv27dOECRPUr18/hYaGSvp75rZKlSr249LS0grN4hbw8PCQh4dHSZcKAADKGGOMzpw5o7y8vNIuBWWEi4uLXF1di50APVuJB9tTp06pQgXHiWAXFxf77b5q1qyp0NBQJSUlqWnTppKknJwcrV69WpMmTSrpcgAAQDmRk5OjQ4cO6dSpU6VdCsoYb29vValSRe7u7uftV+LBtlOnTnrppZdUrVo13XDDDdqyZYtee+01PfDAA5L+XoIwbNgwxcXFqU6dOqpTp47i4uLk7e2tXr16lXQ5AACgHMjPz1dKSopcXFwUFhYmd3f3i5qhg7UZY5STk6PDhw8rJSVFderUKTSBerYSD7bTp0/Xs88+qyFDhigtLU1hYWEaPHiwnnvuOXufUaNGKSsrS0OGDFF6eroiIyO1bNky+fn5lXQ5AACgHMjJyVF+fr7Cw8Pl7e1d2uWgDPHy8pKbm5v27dunnJwceXp6Ftu3xIOtn5+fpk6dqqlTpxbbx2azKTY2VrGxsSX99gAAoBw732wcnNfFjgtGDwAAACyBYAsAAABLKPGlCAAAACWpxlNLrur77Z1451V9P5QcZmwBAACusC1btuiee+5RSEiIPD09VbduXQ0cOFC7du0qkfMnJCQoMDCwRM5VnhFsAQAArqDPP/9ct9xyi7Kzs/X+++9rx44deu+99xQQEKBnn322tMu7KvLy8uzPNLiSCLYAAACXITo6Wo8//rhGjRqloKAghYaG2u/8dOrUKQ0YMEAdOnTQ4sWL1aZNG9WsWVORkZF65ZVX9Pbbb0sqesZ10aJFDvfy/fHHH9WqVSv5+fnJ399fzZs318aNG7Vq1SoNGDBAGRkZstls9rtPSVJ6err69u2rihUrytvbW+3bt9fu3bvt5yx4388//1z16tWTt7e3unfvrpMnT2rOnDmqUaOGKlasqMcee8zhaXA5OTkaNWqUqlatKh8fH0VGRmrVqlVFnjciIkIeHh7at2+fVq1apZtvvlk+Pj4KDAzUrbfeqn379pXYz4I1tgAAAJdpzpw5evLJJ7V+/XqtW7dO/fv316233qoTJ07or7/+0qhRo4o87lKWD/Tu3VtNmzbVm2++KRcXFyUnJ8vNzU0tW7bU1KlT9dxzz2nnzp2SJF9fX0lS//79tXv3bi1evFj+/v4aPXq0OnTooO3bt8vNzU3S3+H7v//9rxYsWKDjx4+rW7du6tatmwIDA/XFF19oz549uvvuu/Wvf/1L9913nyRpwIAB2rt3rxYsWKCwsDAtXLhQd9xxh7Zu3ao6derYzzthwgTNmjVLwcHBCgoKUtOmTTVw4EDNnz9fOTk5+uGHH0r0QRwEWwAAgMvUqFEjjRs3TpJUp04dzZgxQ8uXL1dQUJAkqX79+pf9Hvv379fIkSPt5yoIkJIUEBAgm82m0NBQe1tBoP3uu+/UsmVLSdL777+v8PBwLVq0SPfcc48kKTc3V2+++aauu+46SVL37t313nvv6c8//5Svr68iIiLUqlUrrVy5Uvfdd59+++03zZ8/XwcPHlRYWJgkacSIEVq6dKni4+MVFxdnP+8bb7yhxo0bS5KOHj2qjIwMdezY0f5e119//WV/LmdjKQIAAMBlatSokcPrKlWqKC0tTcaYEnuPJ598Ug899JDatGmjiRMn6rfffjtv/x07dsjV1VWRkZH2tuDgYNWrV087duywt3l7e9uDpiSFhISoRo0a9lnfgra0tDRJ0ubNm2WMUd26deXr62vfVq9e7VCTu7u7w+cSFBSk/v37q127durUqZOmTZumQ4cO/fMPpAgEWwAAgMtU8Gv9AjabTfn5+apbt64k6Zdffjnv8RUqVCgUgnNzcx1ex8bGatu2bbrzzju1YsUKRUREaOHChcWes7hQbYxx+PV/UbUXdz2SlJ+fLxcXF23atEnJycn2bceOHZo2bZr9GC8vr0LLDOLj47Vu3Tq1bNlSH3zwgerWravvv/++2Gu4VARbAACAKyQmJkaVKlXS5MmTi9x/7NgxSdI111yj48eP6+TJk/Z9ycnJhfrXrVtXTzzxhJYtW6Zu3bopPj5e0t+zo2d/uUuSIiIidObMGa1fv97eduTIEe3ateuylgA0bdpUeXl5SktLU+3atR22s5dCnO/4MWPGaO3atWrQoIESExP/cS3nItgCAABcIT4+Ppo1a5aWLFmizp076+uvv9bevXu1ceNGjRo1Sg8//LAkKTIyUt7e3nr66af166+/KjExUQkJCfbzZGVl6dFHH9WqVau0b98+fffdd9qwYYM9oNaoUUMnTpzQ8uXL9ddff+nUqVOqU6eOunTpooEDB2rNmjX68ccfdf/996tq1arq0qXLP76munXrqnfv3urbt68++eQTpaSkaMOGDZo0aZK++OKLYo9LSUnRmDFjtG7dOu3bt0/Lli277JB9Lr48BgAAyrTy/iSwLl26aO3atZowYYJ69eqlzMxMhYeH6/bbb9eLL74o6e/1p/PmzdPIkSM1c+ZMtWnTRrGxsRo0aJAkycXFRUeOHFHfvn31559/qlKlSurWrZvGjx8vSWrZsqUefvhh3XfffTpy5IjGjRun2NhYxcfHa+jQoerYsaNycnJ022236Ysvvii01OBSxcfH68UXX9Tw4cP1+++/Kzg4WC1atFCHDh2KPcbb21u//PKL5syZoyNHjqhKlSp69NFHNXjw4Muq5Ww2U5Krmq+SzMxMBQQEKCMjQ/7+/qVdznld7ccA/hN7PXuVdgkXFptR2hVYBmOyBDEuS0R5GJNSORmXZXxMnu/v79OnTyslJUU1a9aUp6dnKVWIsupixwdLEQAAAGAJBFsAAABYAsEWAAAAlkCwBQAAgCUQbAEAAGAJBFsAAABYAsEWAAAAlkCwBQAAgCUQbAEAAGAJPFIXAACUbbEBV/n9yt4T3KKjo9WkSRNNnTr1H58jISFBw4YN07Fjx0qsrrKGGVsAAIDLlJaWpsGDB6tatWry8PBQaGio2rVrp3Xr1pV2acVKSEiQzWbTHXfc4dB+7Ngx2Ww2rVq16qrU0b9/f3Xt2rVEzsWMLQAAwGW6++67lZubqzlz5qhWrVr6888/tXz5ch09erS0SzsvV1dXLV++XCtXrlSrVq1Ku5zLxowtAADAZTh27JjWrFmjSZMmqVWrVqpevbpuvvlmjRkzRnfeeae9z6BBgxQSEiJPT081aNBAn3/+uSTpyJEj6tmzp6699lp5e3urYcOGmj9//nnfMycnR6NGjVLVqlXl4+OjyMjIQjOsCQkJqlatmry9vXXXXXfpyJEjhc7j4+OjAQMG6Kmnnjrv+x08eFA9evRQUFCQfHx8dOONN2r9+vWSip5xHTZsmKKjo+2vP/roIzVs2FBeXl4KDg5WmzZtdPLkScXGxmrOnDn69NNPZbPZLnummBlbAACAy+Dr6ytfX18tWrRIt9xyizw8PBz25+fnq3379jp+/LjmzZun6667Ttu3b5eLi4sk6fTp02revLlGjx4tf39/LVmyRH369FGtWrUUGRlZ5HsOGDBAe/fu1YIFCxQWFqaFCxfqjjvu0NatW1WnTh2tX79eDzzwgOLi4tStWzctXbpU48aNK/JcsbGxql27tj766CN179690P4TJ04oKipKVatW1eLFixUaGqrNmzcrPz//oj6fQ4cOqWfPnpo8ebLuuusuHT9+XN9++62MMRoxYoR27NihzMxMxcfHS5KCgoIu6rxFIdgCAABcBldXVyUkJGjgwIF666231KxZM0VFRalHjx5q1KiRvv76a/3www/asWOH6tatK0mqVauW/fiqVatqxIgR9tePPfaYli5dqv/9739FBtvffvtN8+fP18GDBxUWFiZJGjFihJYuXar4+HjFxcVp2rRpateunX0mtm7dulq7dq2WLl1a6HxhYWEaOnSoxo4dW+Ra18TERB0+fFgbNmywh87atWtf9Odz6NAhnTlzRt26dVP16tUlSQ0bNrTv9/LyUnZ2tkJDQy/6nMVhKQIAAMBluvvuu/XHH39o8eLFateunVatWqVmzZopISFBycnJuvbaa+2h9lx5eXl66aWX1KhRIwUHB8vX11fLli3T/v37i+y/efNmGWNUt25d+2yxr6+vVq9erd9++02StGPHDrVo0cLhuHNfn2306NE6fPiw3n333UL7kpOT1bRp0388k9q4cWO1bt1aDRs21D333KN33nlH6enp/+hcF0KwBQAAKAGenp5q27atnnvuOa1du1b9+/fXuHHj5OXldd7jXn31VU2ZMkWjRo3SihUrlJycrHbt2iknJ6fI/vn5+XJxcdGmTZuUnJxs33bs2KFp06ZJkowxl1R7YGCgxowZo/Hjx+vUqVMO+y5Uf4UKFQq9X25urv2/XVxclJSUpC+//FIRERGaPn266tWrp5SUlEuq8WIQbAEAAK6AiIgInTx5Uo0aNdLBgwe1a9euIvt9++236tKli+6//341btxYtWrV0u7du4s9b9OmTZWXl6e0tDTVrl3bYSv4dX5ERIS+//57h+POfX2uxx57TBUqVLCH4wKNGjVScnJysXd4uOaaa3To0CGHtuTkZIfXNptNt956q8aPH68tW7bI3d1dCxculCS5u7srLy/vvLVdLIItAADAZThy5Ihuv/12zZs3Tz/99JNSUlL0v//9T5MnT1aXLl0UFRWl2267TXfffbeSkpKUkpKiL7/80r7etXbt2kpKStLatWu1Y8cODR48WKmpqcW+X926ddW7d2/17dtXn3zyiVJSUrRhwwZNmjRJX3zxhSTp8ccf19KlSzV58mTt2rVLM2bMKHJ97dk8PT01fvx4/fe//3Vo79mzp0JDQ9W1a1d999132rNnjz7++GP7PXpvv/12bdy4UXPnztXu3bs1btw4/fzzz/bj169fr7i4OG3cuFH79+/XJ598osOHD+v666+XJNWoUUM//fSTdu7cqb/++sthtvdS8eUxAABQtpXBJ4GdzdfXV5GRkZoyZYp+++035ebmKjw8XAMHDtTTTz8tSfr44481YsQI9ezZUydPnlTt2rU1ceJESdKzzz6rlJQUtWvXTt7e3ho0aJC6du2qjIzirzs+Pl4vvviihg8frt9//13BwcFq0aKFOnToIEm65ZZbNGvWLI0bN06xsbFq06aNnnnmGb3wwgvnvZZ+/frp1Vdf1fbt2+1t7u7uWrZsmYYPH64OHTrozJkzioiI0Ouvvy5JateunZ599lmNGjVKp0+f1gMPPKC+fftq69atkiR/f3998803mjp1qjIzM1W9enW9+uqrat++vSRp4MCBWrVqlW688UadOHFCK1eudLhV2KWwmUtdhFEGZGZmKiAgQBkZGfL39y/tcs6rxlNLSruEC9rr2au0S7iwMv6HWnnCmCxBjMsSUR7GpFROxmUZH5Pn+/v79OnTSklJUc2aNeXp6VlKFaKsutjxwVIEAAAAWALBFgAAAJZAsAUAAIAlEGwBAABgCQRbAABQZpTD77TjKrjYcUGwBQAApc7NzU2SCj31CpD+b1wUjJPicB9bAABQ6lxcXBQYGKi0tDRJkre3t2w2WylXhdJmjNGpU6eUlpamwMBAubi4nLf/FQm2v//+u0aPHq0vv/xSWVlZqlu3rmbPnq3mzZvbixw/frxmzpyp9PR0RUZG6vXXX9cNN9xwJcoBAADlQMHjYAvCLVAgMDDQPj7Op8SDbXp6um699Va1atVKX375pSpXrqzffvtNgYGB9j6TJ0/Wa6+9poSEBNWtW1cvvvii2rZtq507d8rPz6+kSwIAAOWAzWZTlSpVVLly5ct6rCqsxc3N7YIztQVKPNhOmjRJ4eHhio+Pt7fVqFHD/t/GGE2dOlVjx45Vt27dJElz5sxRSEiIEhMTNXjw4JIuCQAAlCMuLi4XHWSAs5X4l8cWL16sG2+8Uffcc48qV66spk2b6p133rHvT0lJUWpqqmJiYuxtHh4eioqK0tq1a4s8Z3Z2tjIzMx02AAAA4GwlHmz37NmjN998U3Xq1NFXX32lhx9+WI8//rjmzp0rSUpNTZUkhYSEOBwXEhJi33euCRMmKCAgwL6Fh4eXdNkAAAAo50o82Obn56tZs2aKi4tT06ZNNXjwYA0cOFBvvvmmQ79zv+lojCn2249jxoxRRkaGfTtw4EBJlw0AAIByrsSDbZUqVRQREeHQdv3112v//v2S/u8bj+fOzqalpRWaxS3g4eEhf39/hw0AAAA4W4kH21tvvVU7d+50aNu1a5eqV68uSapZs6ZCQ0OVlJRk35+Tk6PVq1erZcuWJV0OAAAAnESJ3xXhiSeeUMuWLRUXF6d7771XP/zwg2bOnKmZM2dK+nsJwrBhwxQXF6c6deqoTp06iouLk7e3t3r16lXS5QAAAMBJlHiwvemmm7Rw4UKNGTNGzz//vGrWrKmpU6eqd+/e9j6jRo1SVlaWhgwZYn9Aw7Jly7iHLQAAAP6xK/LksY4dO6pjx47F7rfZbIqNjVVsbOyVeHsAAAA4oRJfYwsAAACUBoItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwhCsebCdMmCCbzaZhw4bZ24wxio2NVVhYmLy8vBQdHa1t27Zd6VIAAABgYVc02G7YsEEzZ85Uo0aNHNonT56s1157TTNmzNCGDRsUGhqqtm3b6vjx41eyHAAAAFjYFQu2J06cUO/evfXOO++oYsWK9nZjjKZOnaqxY8eqW7duatCggebMmaNTp04pMTHxSpUDAAAAi7tiwfaRRx7RnXfeqTZt2ji0p6SkKDU1VTExMfY2Dw8PRUVFae3atUWeKzs7W5mZmQ4bAAAAcDbXK3HSBQsWaPPmzdqwYUOhfampqZKkkJAQh/aQkBDt27evyPNNmDBB48ePL/lCAQAAYBklPmN74MABDR06VPPmzZOnp2ex/Ww2m8NrY0yhtgJjxoxRRkaGfTtw4ECJ1gwAAIDyr8RnbDdt2qS0tDQ1b97c3paXl6dvvvlGM2bM0M6dOyX9PXNbpUoVe5+0tLRCs7gFPDw85OHhUdKlAgAAwEJKfMa2devW2rp1q5KTk+3bjTfeqN69eys5OVm1atVSaGiokpKS7Mfk5ORo9erVatmyZUmXAwAAACdR4jO2fn5+atCggUObj4+PgoOD7e3Dhg1TXFyc6tSpozp16iguLk7e3t7q1atXSZcDAAAAJ3FFvjx2IaNGjVJWVpaGDBmi9PR0RUZGatmyZfLz8yuNcgAAAGABVyXYrlq1yuG1zWZTbGysYmNjr8bbAwAAwAlc8UfqAgAAAFcDwRYAAACWQLAFAACAJRBsAQAAYAkEWwAAAFgCwRYAAACWQLAFAACAJRBsAQAAYAkEWwAAAFgCwRYAAACWQLAFAACAJRBsAQAAYAkEWwAAAFgCwRYAAACWQLAFAACAJRBsAQAAYAkEWwAAAFgCwRYAAACWQLAFAACAJRBsAQAAYAkEWwAAAFgCwRYAAACWQLAFAACAJRBsAQAAYAkEWwAAAFgCwRYAAACWQLAFAACAJRBsAQAAYAkEWwAAAFgCwRYAAACWQLAFAACAJRBsAQAAYAkEWwAAAFgCwRYAAACWQLAFAACAJRBsAQAAYAkEWwAAAFgCwRYAAACWQLAFAACAJRBsAQAAYAkEWwAAAFgCwRYAAACWUOLBdsKECbrpppvk5+enypUrq2vXrtq5c6dDH2OMYmNjFRYWJi8vL0VHR2vbtm0lXQoAAACcSIkH29WrV+uRRx7R999/r6SkJJ05c0YxMTE6efKkvc/kyZP12muvacaMGdqwYYNCQ0PVtm1bHT9+vKTLAQAAgJNwLekTLl261OF1fHy8KleurE2bNum2226TMUZTp07V2LFj1a1bN0nSnDlzFBISosTERA0ePLikSwIAAIATuOJrbDMyMiRJQUFBkqSUlBSlpqYqJibG3sfDw0NRUVFau3ZtkefIzs5WZmamwwYAAACc7YoGW2OMnnzySf3rX/9SgwYNJEmpqamSpJCQEIe+ISEh9n3nmjBhggICAuxbeHj4lSwbAAAA5dAVDbaPPvqofvrpJ82fP7/QPpvN5vDaGFOorcCYMWOUkZFh3w4cOHBF6gUAAED5VeJrbAs89thjWrx4sb755htde+219vbQ0FBJf8/cVqlSxd6elpZWaBa3gIeHhzw8PK5UqQAAALCAEp+xNcbo0Ucf1SeffKIVK1aoZs2aDvtr1qyp0NBQJSUl2dtycnK0evVqtWzZsqTLAQAAgJMo8RnbRx55RImJifr000/l5+dnXzcbEBAgLy8v2Ww2DRs2THFxcapTp47q1KmjuLg4eXt7q1evXiVdDgAAAJxEiQfbN998U5IUHR3t0B4fH6/+/ftLkkaNGqWsrCwNGTJE6enpioyM1LJly+Tn51fS5QAAAMBJlHiwNcZcsI/NZlNsbKxiY2NL+u0BAADgpK74fWwBAACAq4FgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCwAAAEsg2AIAAMASCLYAAACwBIItAAAALKFUg+0bb7yhmjVrytPTU82bN9e3335bmuUAAACgHCu1YPvBBx9o2LBhGjt2rLZs2aJ///vfat++vfbv319aJQEAAKAcK7Vg+9prr+nBBx/UQw89pOuvv15Tp05VeHi43nzzzdIqCQAAAOWYa2m8aU5OjjZt2qSnnnrKoT0mJkZr164t1D87O1vZ2dn21xkZGZKkzMzMK1toCcjPPlXaJVxQps2UdgkXVg5+1uUFY7IEMS5LRHkYk1I5GZdlfEwW/L1tTDn4LFEulUqw/euvv5SXl6eQkBCH9pCQEKWmphbqP2HCBI0fP75Qe3h4+BWr0ZkElHYBF2NiuagSJaTc/LQZl06lXPy0y8mYPH78uAICyketKF9KJdgWsNlsDq+NMYXaJGnMmDF68skn7a/z8/N19OhRBQcHF9kfFy8zM1Ph4eE6cOCA/P39S7scgDGJMolxWTKMMTp+/LjCwsJKuxRYVKkE20qVKsnFxaXQ7GxaWlqhWVxJ8vDwkIeHh0NbYGDglSzR6fj7+/OHNcoUxiTKIsbl5WOmFldSqXx5zN3dXc2bN1dSUpJDe1JSklq2bFkaJQEAAKCcK7WlCE8++aT69OmjG2+8US1atNDMmTO1f/9+Pfzww6VVEgAAAMqxUgu29913n44cOaLnn39ehw4dUoMGDfTFF1+oevXqpVWSU/Lw8NC4ceMKLfUASgtjEmUR4xIoH2yGe24AAADAAkr1kboAAABASSHYAgAAwBIItgAAALAEgi0AAAAsgWALAAAASyDYAgAAwBIItgDKHGOM8vPzS7sMwEFCQoIyMjJKuwwA50GwdTJLlizRQw89pFGjRumXX35x2Jeenq7bb7+9lCqDMzpz5oyeeeYZRUVFady4cZKkl19+Wb6+vvLy8lK/fv2Uk5NTylUCfxs0aJD++OOP0i4DwHkQbJ1IYmKiunTpotTUVK1bt05NmzbV+++/b9+fk5Oj1atXl2KFcDbjx4/XrFmzdOONN+qjjz7Sf/7zH02fPl0zZ87UrFmztGLFCk2dOrW0y4STCQoKKnI7c+aMWrRoYX8NoOwptUfq4up75ZVXNGXKFD322GOSpI8++kgDBgzQ6dOn9eCDD5ZydXBGiYmJmjVrljp27Kj//Oc/qlevnhITE3XfffdJkjw9PfX8889r1KhRpVwpnElubq6ioqJ0zz332NuMMfbfdlWtWrUUqwNwPjxS14n4+vpq69atqlmzpr1t1apV6ty5syZPnqy77rpLYWFhysvLK8Uq4Uy8vLy0a9cuhYeH219v2bJF9evXlySlpKSocePGyszMLM0y4WR+/fVX9erVS9dff71ef/11+fr6SpLc3Nz0448/KiIiopQrBFAcliI4EX9/f/35558ObdHR0frss880cuRITZ8+vZQqg7MKCAjQsWPH7K+bNWsmPz8/++vs7GzZbLZSqAzOrHbt2lq7dq1CQ0PVpEkTfffdd6VdEoCLxFIEJ3LzzTfryy+/1C233OLQHhUVpc8++0wdO3YspcrgrCIiIrR582Y1bNhQkgoFiK1bt6pOnTqlURqcnKurqyZNmqR27dqpV69e6t27N//IAsoBZmydyBNPPCFPT88i90VHR+vzzz9X3759r3JVcGZvvfWWbrvttmL35+bmsr4Wper222/X5s2b9csvv8jHx0cuLi6lXRKA82CNLQAAACyBpQhO6MSJE9q0aZNSU1Nls9kUEhKi5s2b278gAVxtjEmUReeOy9DQUDVr1oxxCZRhBFsnkpubqxEjRuidd97R6dOn5e7uLmOMcnNz5enpqUGDBunll1+Wm5tbaZcKJ8GYRFl05swZDR8+nHEJlEOssXUiI0aM0Mcff6z4+HgdPXpUp0+fVnZ2to4ePar4+Hh98sknGjlyZGmXCSfCmERZNHz4cMYlUE6xxtaJXHPNNfrggw+KfWzu8uXL1aNHDx0+fPgqVwZnxZhEWcS4BMovZmydSFZWlipVqlTs/uDgYGVlZV3FiuDsGJMoixiXQPnFjK0T6dSpk7KysvT+++8rJCTEYd+ff/6pPn36yNPTU4sXLy6lCuFsGJMoixiXQPlFsHUiBw4cUIcOHfTLL7+oQYMGCgkJkc1mU2pqqn7++WdFRERoyZIluvbaa0u7VDgJxiTKIsYlUH4RbJ1Mfn6+vvrqK33//fdKTU2VJIWGhqpFixaKiYlRhQqsTsHVxZhEWcS4BMongi0AAAAsgfvYOqE9e/ZozZo1OnTokFxcXFSrVi21adNG/v7+pV0anBRjEmUR4xIofwi2TuTkyZPq37+/Pv74Y0mSzWZT5cqVdfjwYXl5eWnixIl65JFHSrlKOBPGJMoixiVQfrFIyIk8+eSTOnTokLZs2aIdO3borrvuUt++fZWZmalp06Zp1KhRSkxMLO0y4UQYkyiLGJdA+cUaWydyzTXXaOnSpWrevLkkKT09XWFhYTpy5Ii8vb31+uuva9asWdqyZUspVwpnwZhEWcS4BMovZmydyJkzZxzWhvn6+urMmTM6efKkJCkmJka//PJLaZUHJ8SYRFnEuATKL4KtE7nppps0bdo0++tp06bpmmuu0TXXXCNJOnHihHx9fUurPDghxiTKIsYlUH7x5TEnMnHiRLVt21Yff/yx3N3dlZqaqjlz5tj3r127Vh06dCjFCuFsGJMoixiXQPnFGlsnc+jQIX3++efKzs7W7bffroiIiNIuCU6OMYmyiHEJlE8EWwAAAFgCa2xhl56errlz55Z2GYAdYxJlEeMSKLuYsYXdjz/+qGbNmikvL6+0SwEkMSZRNjEugbKLL485kczMzPPuP378+FWqBPgbYxJlEeMSKL+YsXUiFSpUkM1mK3a/MUY2m41ZCFw1jEmURYxLoPxixtaJ+Pn5aezYsYqMjCxy/+7duzV48OCrXBWcGWMSZRHjEii/CLZOpFmzZpKkqKioIvcHBgaKCXxcTYxJlEWMS6D84q4ITqRXr17y9PQsdn9oaKjGjRt3FSuCs2NMoixiXALlF2tsAQAAYAnM2AIAAMASWGPrZE6ePKnExEStXbtWqampstlsCgkJ0a233qqePXvKx8entEuEk2FMoixiXALlE0sRnMj27dvVtm1bnTp1SlFRUQoJCZExRmlpaVq9erV8fHy0bNkynomOq4YxibKIcQmUXwRbJ9KqVSuFhoZqzpw5cnd3d9iXk5Oj/v3769ChQ1q5cmUpVQhnw5hEWcS4BMovgq0T8fb21saNG4udZfj55591880369SpU1e5MjgrxiTKIsYlUH7x5TEnUrFiRe3evbvY/b/++qsqVqx4FSuCs2NMoixiXALlF18ecyIDBw5Uv3799Mwzz6ht27YKCQmRzWZTamqqkpKSFBcXp2HDhpV2mXAijEmURYxLoPxiKYKTmTRpkqZNm2b/lq/093PPQ0NDNWzYMI0aNaqUK4SzYUyiLGJcAuUTwdZJpaSkKDU1VdLfT9GpWbNmKVcEZ8eYRFnEuATKF4ItAAAALIEvjzmZrKwsrVmzRtu3by+07/Tp05o7d24pVAVnxphEWcS4BMonZmydyK5duxQTE6P9+/fLZrPp3//+t+bPn68qVapIkv7880+FhYUpLy+vlCuFs2BMoixiXALlFzO2TmT06NFq2LCh0tLStHPnTvn7++vWW2/V/v37S7s0OCnGJMoixiVQfjFj60RCQkL09ddfq2HDhva2Rx55RJ9//rlWrlwpHx8fZiFwVTEmURYxLoHyi/vYOpGsrCy5ujr+yF9//XVVqFBBUVFRSkxMLKXK4KwYkyiLGJdA+UWwdSL169fXxo0bdf311zu0T58+XcYYde7cuZQqg7NiTKIsYlwC5RdrbJ3IXXfdpfnz5xe5b8aMGerZs6dYmYKriTGJsohxCZRfrLEFAACAJTBjCwAAAEsg2AIAAMASCLYAAACwBIItAAAALIFgCzgBY4zatGmjdu3aFdr3xhtvKCAgQHPnzpXNZityS01NdTjm4MGDcnd3V/369Yt8v7OP9fX1VePGjZWQkODQZ9WqVbLZbKpYsaJOnz7tsO+HH36wH3+2vLw8TZkyRY0aNZKnp6cCAwPVvn17fffddw79EhISZLPZdMcddzi0Hzt2TDabTatWrbL3Od9W0C8wMLDY61y0aFGR+wAAVx/BFnACNptN8fHxWr9+vd5++217e0pKikaPHq1p06apWrVqkqSdO3fq0KFDDlvlypUdzpeQkKB7771Xp06dKhQqC8THx+vQoUP68ccfdd9992nAgAH66quvCvXz8/PTwoULHdreffddez0FjDHq0aOHnn/+eT3++OPasWOHVq9erfDwcEVHRxcKmK6urlq+fLlWrlxZZH333XefwzW2aNFCAwcOdGhr2bJl0R8oAKBMItgCTiI8PFzTpk3TiBEjlJKSImOMHnzwQbVu3Vr9+/e396tcubJCQ0MdtgoV/u+PCmOM4uPj1adPH/Xq1UuzZ88u8v0CAwMVGhqq6667Tk8//bSCgoK0bNmyQv369eund9991/46KytLCxYsUL9+/Rz6ffjhh/roo480d+5cPfTQQ6pZs6YaN26smTNnqnPnznrooYd08uRJe38fHx8NGDBATz31VJH1eXl5OVyju7u7vL29C7UBAMoPgi3gRPr166fWrVtrwIABmjFjhn7++WfNnDnzks6xcuVKnTp1Sm3atFGfPn304Ycf6vjx48X2z8vL04cffqijR4/Kzc2t0P4+ffro22+/1f79+yVJH3/8sWrUqKFmzZo59EtMTFTdunXVqVOnQucYPny4jhw5oqSkJIf22NhYbd26VR999NElXSMAoHwi2AJOZubMmdq+fbuGDRumt99+u9Ayg2uvvVa+vr72rV69eg77Z8+erR49esjFxUU33HCDateurQ8++KDQ+/Ts2VO+vr7y8PDQfffdp6CgID300EOF+lWuXFnt27e3r8F999139cADDxTqt2vXrkKPOC1Q0L5r1y6H9rCwMA0dOlRjx47VmTNniv9QLiAjI8PhMynYAABlC8EWcDKVK1fWoEGDdP311+uuu+4qtP/bb79VcnKyfTt7XeyxY8f0ySef6P7777e33X///Q5LCQpMmTJFycnJSkpKUpMmTTRlyhTVrl27yJoeeOABJSQkaM+ePVq3bp169+79j67t3C+bSdLo0aN1+PDhImu8WH5+fg6fScEGAChbXEu7AABXn6urq1xdi/7fv2bNmsXeBSAxMVGnT59WZGSkvc0Yo/z8fG3fvl0RERH29tDQUNWuXVu1a9fW//73PzVt2lQ33nijQ58CHTp00ODBg/Xggw+qU6dOCg4OLtSnbt262r59e5F17dixQ5JUp06dQvsCAwM1ZswYjR8/Xh07dizy+AupUKFCsaEcAFB2MGML4KLNnj1bw4cPd5i1/PHHH9WqVavzzojWrl1bd999t8aMGVPkfhcXF/Xp00erVq0qchmCJPXo0UO7d+/WZ599Vmjfq6++quDgYLVt27bIYx977DFVqFBB06ZNu4irBACUVwRbAA7S0tKUmprqsOXm5io5OVmbN2/WQw89pAYNGjhsPXv21Ny5c5Wbm1vseYcPH67PPvtMGzduLHL/Cy+8oMOHDxd5r13p72B71113qV+/fpo9e7b27t2rn376SYMHD9bixYs1a9Ys+fj4FHmsp6enxo8fr//+97+X/oEAAMoNgi0AB/Xq1VOVKlUctk2bNmn27NmKiIgo8qEMXbt21dGjR4ucTS3QsGFDtWnTRs8991yR+93d3VWpUqUi18lKf6+f/fDDDzV27FhNmTJF9evX17///W/t27dPK1euVNeuXc97Xf369VOtWrXO2wcAUL7ZjDGmtIsAAAAALhcztgAAALAEgi0AAAAsgWALAAAASyDYAgAAwBIItgAAALAEgi0AAAAsgWALAAAASyDYAgAAwBIItgAAALAEgi0AAAAsgWALAAAASyDYAgAAwBL+H9SlvFDS2akDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scaled_ncust_control_stores = full_observ[full_observ[\"STORE_NBR\"].isin([233, 155, 40])][[\"STORE_NBR\", \"YEARMONTH\", \"nCustomers\"]]\n",
    "\n",
    "def scaler_c(row):\n",
    "    if row[\"STORE_NBR\"] == 233:\n",
    "        return row[\"nCustomers\"] * ncust_ratio_77\n",
    "    elif row[\"STORE_NBR\"] == 155:\n",
    "        return row[\"nCustomers\"] * ncust_ratio_86\n",
    "    elif row[\"STORE_NBR\"] == 40:\n",
    "        return row[\"nCustomers\"] * ncust_ratio_88\n",
    "\n",
    "scaled_ncust_control_stores[\"ScaledNcust\"] = scaled_ncust_control_stores.apply(lambda row: scaler_c(row), axis=1)\n",
    "\n",
    "trial_scaled_ncust_control_stores = scaled_ncust_control_stores[(scaled_ncust_control_stores[\"YEARMONTH\"] >= 201902) & (scaled_ncust_control_stores[\"YEARMONTH\"] <= 201904)]\n",
    "pretrial_scaled_ncust_control_stores = scaled_ncust_control_stores[scaled_ncust_control_stores[\"YEARMONTH\"] < 201902]\n",
    "\n",
    "ncust_percentage_diff = {}\n",
    "\n",
    "for trial, control in trial_control_dic.items():\n",
    "    a = trial_scaled_ncust_control_stores[trial_scaled_ncust_control_stores[\"STORE_NBR\"] == control]\n",
    "    b = trial_full_observ[trial_full_observ[\"STORE_NBR\"] == trial][[\"STORE_NBR\", \"YEARMONTH\", \"nCustomers\"]]\n",
    "    ncust_percentage_diff[trial] = b[\"nCustomers\"].sum() / a[\"ScaledNcust\"].sum()\n",
    "    b[[\"YEARMONTH\", \"nCustomers\"]].merge(a[[\"YEARMONTH\", \"ScaledNcust\"]],on=\"YEARMONTH\").set_index(\"YEARMONTH\").rename(columns={\"ScaledSales\":\"Scaled_Control_nCust\", \"TOT_SALES\":\"Trial_nCust\"}).plot.bar()\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "    plt.title(\"Trial Store \"+str(trial)+\" and Control Store \"+str(control))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "959aeaa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{77: 1.2306529009742622, 86: 1.1354166666666667, 88: 1.0444876946258161}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncust_percentage_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "40df6925",
   "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>c_STORE_NBR</th>\n",
       "      <th>YEARMONTH</th>\n",
       "      <th>c_ScaledNcust</th>\n",
       "      <th>t_STORE_NBR</th>\n",
       "      <th>t_nCustomers</th>\n",
       "      <th>nCust_Percentage_Diff</th>\n",
       "      <th>trial_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>233</td>\n",
       "      <td>201902</td>\n",
       "      <td>45.151007</td>\n",
       "      <td>77</td>\n",
       "      <td>45</td>\n",
       "      <td>-0.003350</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>233</td>\n",
       "      <td>201903</td>\n",
       "      <td>40.134228</td>\n",
       "      <td>77</td>\n",
       "      <td>50</td>\n",
       "      <td>0.218913</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>233</td>\n",
       "      <td>201904</td>\n",
       "      <td>30.100671</td>\n",
       "      <td>77</td>\n",
       "      <td>47</td>\n",
       "      <td>0.438370</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>155</td>\n",
       "      <td>201902</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>86</td>\n",
       "      <td>107</td>\n",
       "      <td>0.118812</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>155</td>\n",
       "      <td>201903</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>86</td>\n",
       "      <td>115</td>\n",
       "      <td>0.200957</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>155</td>\n",
       "      <td>201904</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>86</td>\n",
       "      <td>105</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>40</td>\n",
       "      <td>201902</td>\n",
       "      <td>127.610209</td>\n",
       "      <td>88</td>\n",
       "      <td>124</td>\n",
       "      <td>-0.028697</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>40</td>\n",
       "      <td>201903</td>\n",
       "      <td>120.464037</td>\n",
       "      <td>88</td>\n",
       "      <td>134</td>\n",
       "      <td>0.106388</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>40</td>\n",
       "      <td>201904</td>\n",
       "      <td>121.484919</td>\n",
       "      <td>88</td>\n",
       "      <td>128</td>\n",
       "      <td>0.052228</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    c_STORE_NBR  YEARMONTH  c_ScaledNcust  t_STORE_NBR  t_nCustomers  \\\n",
       "7           233     201902      45.151007           77            45   \n",
       "8           233     201903      40.134228           77            50   \n",
       "9           233     201904      30.100671           77            47   \n",
       "19          155     201902      95.000000           86           107   \n",
       "20          155     201903      94.000000           86           115   \n",
       "21          155     201904      99.000000           86           105   \n",
       "31           40     201902     127.610209           88           124   \n",
       "32           40     201903     120.464037           88           134   \n",
       "33           40     201904     121.484919           88           128   \n",
       "\n",
       "    nCust_Percentage_Diff trial_period  \n",
       "7               -0.003350        trial  \n",
       "8                0.218913        trial  \n",
       "9                0.438370        trial  \n",
       "19               0.118812        trial  \n",
       "20               0.200957        trial  \n",
       "21               0.058824        trial  \n",
       "31              -0.028697        trial  \n",
       "32               0.106388        trial  \n",
       "33               0.052228        trial  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp1 = scaled_ncust_control_stores.sort_values(by=[\"STORE_NBR\", \"YEARMONTH\"], ascending=[False, True]).reset_index().drop([\"nCustomers\", \"index\"], axis=1)\n",
    "temp2 = full_observ[full_observ[\"STORE_NBR\"].isin([77,86,88])][[\"STORE_NBR\", \"YEARMONTH\", \"nCustomers\"]].reset_index().drop([\"index\", \"YEARMONTH\"], axis=1)\n",
    "scaledncust_vs_trial = pd.concat([temp1, temp2], axis=1)\n",
    "scaledncust_vs_trial.columns = [\"c_STORE_NBR\", \"YEARMONTH\", \"c_ScaledNcust\", \"t_STORE_NBR\", \"t_nCustomers\"]\n",
    "scaledncust_vs_trial[\"nCust_Percentage_Diff\"] = (scaledncust_vs_trial[\"t_nCustomers\"] - scaledncust_vs_trial[\"c_ScaledNcust\"]) / (((scaledncust_vs_trial[\"t_nCustomers\"] + scaledncust_vs_trial[\"c_ScaledNcust\"])/2))\n",
    "\n",
    "scaledncust_vs_trial[\"trial_period\"] = scaledncust_vs_trial[\"YEARMONTH\"].apply(lambda cell: label_period(cell))\n",
    "scaledncust_vs_trial[scaledncust_vs_trial[\"trial_period\"] == \"trial\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d7b3ab",
   "metadata": {},
   "source": [
    "Check significance of Trial minus Control stores nCustomers Percentage Difference Pre-Trial vs Trial.\n",
    "\n",
    "- Step 1: Check null hypothesis of 0 difference between control store's Pre-Trial and Trial period performance.\n",
    "\n",
    "- Step 2: Proof control and trial stores are similar statistically\n",
    "\n",
    "- Step 3: After checking Null Hypothesis of first 2 step to be true, we can check Null Hypothesis of Percentage Difference between Trial and Control stores during pre-trial is the same as during trial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fce97501",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Store 40\n",
      "Ttest_indResult(statistic=0.644732693420032, pvalue=0.5376573016017127) \n",
      "\n",
      "Store 155\n",
      "Ttest_indResult(statistic=1.3888888888888882, pvalue=0.204345986327886) \n",
      "\n",
      "Store 233\n",
      "Ttest_indResult(statistic=0.8442563765225701, pvalue=0.4559280037660254) \n",
      "\n",
      "Critical t-value for 95% confidence interval:\n",
      "[-4.30265273  4.30265273]\n"
     ]
    }
   ],
   "source": [
    "# Step 1\n",
    "for num in [40, 155, 233]:\n",
    "    print(\"Store\", num)\n",
    "    print(ttest_ind(pretrial_scaled_ncust_control_stores[pretrial_scaled_ncust_control_stores[\"STORE_NBR\"] == num][\"ScaledNcust\"],\n",
    "                   trial_scaled_ncust_control_stores[trial_scaled_ncust_control_stores[\"STORE_NBR\"] == num][\"ScaledNcust\"],\n",
    "                   equal_var=False), '\\n')\n",
    "    \n",
    "alpha = 0.05\n",
    "print(\"Critical t-value for 95% confidence interval:\")\n",
    "print(t.ppf((alpha/2, 1-alpha/2), df=min([len(pretrial_scaled_ncust_control_stores[pretrial_scaled_ncust_control_stores[\"STORE_NBR\"] == num]),\n",
    "                       len(trial_scaled_ncust_control_stores[trial_scaled_ncust_control_stores[\"STORE_NBR\"] == num])])-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d8fca8ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial store: 77 , Control store: 233\n",
      "Ttest_indResult(statistic=0.0, pvalue=1.0) \n",
      "\n",
      "Trial store: 86 , Control store: 155\n",
      "Ttest_indResult(statistic=0.0, pvalue=1.0) \n",
      "\n",
      "Trial store: 88 , Control store: 40\n",
      "Ttest_indResult(statistic=-7.648483953264653e-15, pvalue=0.999999999999994) \n",
      "\n",
      "Critical t-value for 95% confidence interval:\n",
      "[-2.44691185  2.44691185]\n"
     ]
    }
   ],
   "source": [
    "# Step 2\n",
    "for trial, cont in trial_control_dic.items():\n",
    "    print(\"Trial store:\", trial, \", Control store:\", cont)\n",
    "    print(ttest_ind(pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == trial][\"nCustomers\"],\n",
    "                   pretrial_scaled_ncust_control_stores[pretrial_scaled_ncust_control_stores[\"STORE_NBR\"] == cont][\"ScaledNcust\"],\n",
    "                   equal_var=True), '\\n')\n",
    "\n",
    "alpha = 0.05\n",
    "print(\"Critical t-value for 95% confidence interval:\")\n",
    "print(t.ppf((alpha/2, 1-alpha/2), df=len(pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == trial])-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "84c89126",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial store: 77 , Control store: 233\n",
      "201902 : -0.19886295797440687\n",
      "201903 : 8.009609025380932\n",
      "201904 : 16.114474772873923\n",
      "\n",
      "\n",
      "Trial store: 86 , Control store: 155\n",
      "201902 : 6.220524882227514\n",
      "201903 : 10.52599074274189\n",
      "201904 : 3.0763575852842706\n",
      "\n",
      "\n",
      "Trial store: 88 , Control store: 40\n",
      "201902 : -0.3592881735131531\n",
      "201903 : 1.2575196020616801\n",
      "201904 : 0.6092905590514273\n",
      "\n",
      "\n",
      "Critical t-value for 95% confidence interval:\n",
      "1.9431802803927816\n"
     ]
    }
   ],
   "source": [
    "# Step 3\n",
    "for trial, cont in trial_control_dic.items():\n",
    "    print(\"Trial store:\", trial, \", Control store:\", cont)\n",
    "    temp_pre = scaledncust_vs_trial[(scaledncust_vs_trial[\"c_STORE_NBR\"] == cont) & (scaledncust_vs_trial[\"trial_period\"]==\"pre\")]\n",
    "    std = temp_pre[\"nCust_Percentage_Diff\"].std()\n",
    "    mean = temp_pre[\"nCust_Percentage_Diff\"].mean()\n",
    "    #print(std, mean)\n",
    "    for t_month in scaledncust_vs_trial[scaledncust_vs_trial[\"trial_period\"] == \"trial\"][\"YEARMONTH\"].unique():\n",
    "        pdif = scaledncust_vs_trial[(scaledncust_vs_trial[\"YEARMONTH\"] == t_month) & (scaledncust_vs_trial[\"t_STORE_NBR\"] == trial)][\"nCust_Percentage_Diff\"]\n",
    "        print(t_month,\":\",(float(pdif)-mean)/std)\n",
    "    print('\\n')\n",
    "    \n",
    "print(\"Critical t-value for 95% confidence interval:\")\n",
    "conf_intv_95 = t.ppf(0.95, df=len(temp_pre)-1)\n",
    "print(conf_intv_95)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0cbbcf7",
   "metadata": {},
   "source": [
    "There are 5 months' increase in performance that are statistically significant (Above the 95% confidence interval t-score):\n",
    "\n",
    "- March and April trial months for trial store 77\n",
    "\n",
    "- Feb, March and April trial months for trial store 86"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c72afbc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for trial, control in trial_control_dic.items():\n",
    "    a = trial_scaled_ncust_control_stores[trial_scaled_ncust_control_stores[\"STORE_NBR\"] == control].rename(columns={\"nCustomers\": \"control_nCustomers\"})\n",
    "    b = trial_full_observ[trial_full_observ[\"STORE_NBR\"] == trial][[\"STORE_NBR\", \"YEARMONTH\", \"nCustomers\"]].rename(columns={\"nCustomers\": \"trial_nCustomers\"})\n",
    "    comb = b[[\"YEARMONTH\", \"trial_nCustomers\"]].merge(a[[\"YEARMONTH\", \"control_nCustomers\"]],on=\"YEARMONTH\").set_index(\"YEARMONTH\")\n",
    "    comb.plot.bar()\n",
    "    cont_sc_ncust = trial_scaled_ncust_control_stores[trial_scaled_ncust_control_stores[\"STORE_NBR\"] == control][\"nCustomers\"]\n",
    "    std = scaledncust_vs_trial[(scaledncust_vs_trial[\"c_STORE_NBR\"] == control) & (scaledncust_vs_trial[\"trial_period\"]==\"pre\")][\"nCust_Percentage_Diff\"].std()\n",
    "    thresh95 = cont_sc_ncust.mean() + (cont_sc_ncust.mean() * std * 2)\n",
    "    thresh5 = cont_sc_ncust.mean() - (cont_sc_ncust.mean() * std * 2)\n",
    "    plt.axhline(y=thresh95,linewidth=1, color='b', label=\"95% threshold\")\n",
    "    plt.axhline(y=thresh5,linewidth=1, color='r', label=\"5% threshold\")\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "    plt.title(\"Trial Store \"+str(trial)+\" and Control Store \"+str(control)+\" - Number of Customers\")\n",
    "    plt.savefig(\"TS {} and CS {} - nCustomers.png\".format(trial,control), bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a392d579",
   "metadata": {},
   "source": [
    "We can see that Trial store 77 sales for Feb, March, and April exceeds 95% threshold of control store. Same goes to store 86 sales for all 3 trial months.\n",
    "\n",
    "- Trial store 77: Control store 233\n",
    "- Trial store 86: Control store 155\n",
    "- Trial store 88: Control store 40\n",
    "\n",
    "Both trial store 77 and 86 showed significant increase in Total Sales and Number of Customers during trial period. But not for trial store 88. Perhaps the client knows if there's anything about trial 88 that differs it from the other two trial.\n",
    "Overall the trial showed positive significant result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e54c8e6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}