eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(path): return pd.read_csv(\n",
    "    path, sep=';', index_col='timestamp', parse_dates=True)\n",
    "\n",
    "\n",
    "data_frames = {\n",
    "    'monday': get_data('./data/monday.csv'),\n",
    "    'tuesday': get_data('./data/tuesday.csv'),\n",
    "    'wednesday': get_data('./data/wednesday.csv'),\n",
    "    'thursday':  get_data('./data/thursday.csv'),\n",
    "    'friday': get_data('./data/friday.csv')\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_no</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:03:00</th>\n",
       "      <td>monday_1</td>\n",
       "      <td>dairy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:03:00</th>\n",
       "      <td>monday_2</td>\n",
       "      <td>dairy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:04:00</th>\n",
       "      <td>monday_3</td>\n",
       "      <td>dairy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:04:00</th>\n",
       "      <td>monday_4</td>\n",
       "      <td>dairy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:04:00</th>\n",
       "      <td>monday_5</td>\n",
       "      <td>spices</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>friday_1500</td>\n",
       "      <td>dairy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>friday_1507</td>\n",
       "      <td>checkout</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>friday_1508</td>\n",
       "      <td>checkout</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>friday_1509</td>\n",
       "      <td>drinks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>friday_1510</td>\n",
       "      <td>spices</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24877 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     customer_no  location\n",
       "timestamp                                 \n",
       "2019-09-02 07:03:00     monday_1     dairy\n",
       "2019-09-02 07:03:00     monday_2     dairy\n",
       "2019-09-02 07:04:00     monday_3     dairy\n",
       "2019-09-02 07:04:00     monday_4     dairy\n",
       "2019-09-02 07:04:00     monday_5    spices\n",
       "...                          ...       ...\n",
       "2019-09-06 21:50:00  friday_1500     dairy\n",
       "2019-09-06 21:50:00  friday_1507  checkout\n",
       "2019-09-06 21:50:00  friday_1508  checkout\n",
       "2019-09-06 21:50:00  friday_1509    drinks\n",
       "2019-09-06 21:50:00  friday_1510    spices\n",
       "\n",
       "[24877 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = None\n",
    "for key in data_frames:\n",
    "    current_df = data_frames[key]\n",
    "    current_df['customer_no'] = current_df['customer_no'].map(lambda x: f'{key}_{x}')\n",
    "    \n",
    "    if df is None:\n",
    "        df = current_df\n",
    "    else: \n",
    "        df = pd.concat([df , current_df])\n",
    "df\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add time elements to the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_no</th>\n",
       "      <th>location</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>day_number</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:03:00</th>\n",
       "      <td>monday_1</td>\n",
       "      <td>dairy</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:03:00</th>\n",
       "      <td>monday_2</td>\n",
       "      <td>dairy</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:04:00</th>\n",
       "      <td>monday_3</td>\n",
       "      <td>dairy</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:04:00</th>\n",
       "      <td>monday_4</td>\n",
       "      <td>dairy</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:04:00</th>\n",
       "      <td>monday_5</td>\n",
       "      <td>spices</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>friday_1500</td>\n",
       "      <td>dairy</td>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>friday_1507</td>\n",
       "      <td>checkout</td>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>friday_1508</td>\n",
       "      <td>checkout</td>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>friday_1509</td>\n",
       "      <td>drinks</td>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>friday_1510</td>\n",
       "      <td>spices</td>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24877 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     customer_no  location  hour  minute  day_number     day\n",
       "timestamp                                                                   \n",
       "2019-09-02 07:03:00     monday_1     dairy     7       3           2  Monday\n",
       "2019-09-02 07:03:00     monday_2     dairy     7       3           2  Monday\n",
       "2019-09-02 07:04:00     monday_3     dairy     7       4           2  Monday\n",
       "2019-09-02 07:04:00     monday_4     dairy     7       4           2  Monday\n",
       "2019-09-02 07:04:00     monday_5    spices     7       4           2  Monday\n",
       "...                          ...       ...   ...     ...         ...     ...\n",
       "2019-09-06 21:50:00  friday_1500     dairy    21      50           6  Friday\n",
       "2019-09-06 21:50:00  friday_1507  checkout    21      50           6  Friday\n",
       "2019-09-06 21:50:00  friday_1508  checkout    21      50           6  Friday\n",
       "2019-09-06 21:50:00  friday_1509    drinks    21      50           6  Friday\n",
       "2019-09-06 21:50:00  friday_1510    spices    21      50           6  Friday\n",
       "\n",
       "[24877 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hour'] = df.index.hour\n",
    "df['minute'] = df.index.minute\n",
    "df['day_number'] = df.index.day\n",
    "df['day'] = df.index.day_name()\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[df['day']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total number of customers in each section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_no</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>day_number</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>checkout</th>\n",
       "      <td>7417</td>\n",
       "      <td>7417</td>\n",
       "      <td>7417</td>\n",
       "      <td>7417</td>\n",
       "      <td>7417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dairy</th>\n",
       "      <td>4679</td>\n",
       "      <td>4679</td>\n",
       "      <td>4679</td>\n",
       "      <td>4679</td>\n",
       "      <td>4679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drinks</th>\n",
       "      <td>3905</td>\n",
       "      <td>3905</td>\n",
       "      <td>3905</td>\n",
       "      <td>3905</td>\n",
       "      <td>3905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <td>5122</td>\n",
       "      <td>5122</td>\n",
       "      <td>5122</td>\n",
       "      <td>5122</td>\n",
       "      <td>5122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spices</th>\n",
       "      <td>3754</td>\n",
       "      <td>3754</td>\n",
       "      <td>3754</td>\n",
       "      <td>3754</td>\n",
       "      <td>3754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          customer_no  hour  minute  day_number   day\n",
       "location                                             \n",
       "checkout         7417  7417    7417        7417  7417\n",
       "dairy            4679  4679    4679        4679  4679\n",
       "drinks           3905  3905    3905        3905  3905\n",
       "fruit            5122  5122    5122        5122  5122\n",
       "spices           3754  3754    3754        3754  3754"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('location').count()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total number of customers in each section over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>customer_no</th>\n",
       "      <th>minute</th>\n",
       "      <th>day_number</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">checkout</th>\n",
       "      <th>7</th>\n",
       "      <td>454</td>\n",
       "      <td>454</td>\n",
       "      <td>454</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>677</td>\n",
       "      <td>677</td>\n",
       "      <td>677</td>\n",
       "      <td>677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>455</td>\n",
       "      <td>455</td>\n",
       "      <td>455</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>427</td>\n",
       "      <td>427</td>\n",
       "      <td>427</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">spices</th>\n",
       "      <th>17</th>\n",
       "      <td>269</td>\n",
       "      <td>269</td>\n",
       "      <td>269</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>346</td>\n",
       "      <td>346</td>\n",
       "      <td>346</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>246</td>\n",
       "      <td>246</td>\n",
       "      <td>246</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               customer_no  minute  day_number  day\n",
       "location hour                                      \n",
       "checkout 7             454     454         454  454\n",
       "         8             677     677         677  677\n",
       "         9             455     455         455  455\n",
       "         10            427     427         427  427\n",
       "         11            342     342         342  342\n",
       "...                    ...     ...         ...  ...\n",
       "spices   17            269     269         269  269\n",
       "         18            346     346         346  346\n",
       "         19            389     389         389  389\n",
       "         20            246     246         246  246\n",
       "         21            122     122         122  122\n",
       "\n",
       "[75 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['location', 'hour']).count()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total amount of customers in the store at any given time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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></th>\n",
       "      <th></th>\n",
       "      <th>customer_no</th>\n",
       "      <th>location</th>\n",
       "      <th>day_number</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Friday</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">7</th>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Wednesday</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">21</th>\n",
       "      <th>45</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4363 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       customer_no  location  day_number\n",
       "day       hour minute                                   \n",
       "Friday    7    0                 3         3           3\n",
       "               1                 3         3           3\n",
       "               2                 3         3           3\n",
       "               3                 3         3           3\n",
       "               4                 3         3           3\n",
       "...                            ...       ...         ...\n",
       "Wednesday 21   45                5         5           5\n",
       "               46                4         4           4\n",
       "               47                4         4           4\n",
       "               48                3         3           3\n",
       "               49                4         4           4\n",
       "\n",
       "[4363 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minute_location_df = df.groupby([ 'day', 'hour', 'minute' ]).count()\n",
    "# minute_location_df.to_csv('./data/amount_of_customers_at_time.csv')\n",
    "# minute_location_df.index.names\n",
    "# minute_location_df.reset_index()\n",
    "minute_location_df\n",
    "# minute_location_df.loc['Tuesday', 12, 9]['customer_no']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['dairy', 'spices', 'fruit', 'checkout', 'drinks'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations = df['location'].unique()\n",
    "locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# minute_location_df[minute_location_df['location'] == 'checkout']['minute'].rolling(1000).mean().plot()\n",
    "for location in locations:\n",
    "    df.query(f'location == \"{location}\"') \\\n",
    "    .groupby(['hour', 'minute']) \\\n",
    "    .count()['customer_no'] \\\n",
    "    .rolling(80) \\\n",
    "    .mean() \\\n",
    "    .plot(label=location, ylabel='Amount of Customers',legend=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time each customer spent in the market"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_customer_time_in_market_for_day(name, cus_df):\n",
    "    max_hour = cus_df['hour'].max()\n",
    "    min_hour = cus_df['hour'].min()\n",
    "    max_minute = cus_df['minute'].max()\n",
    "    min_minute = cus_df['minute'].min()\n",
    "    mins_in_market = max_minute - min_minute\n",
    "    hour_in_market = max_hour - min_hour\n",
    "    hour_in_market = max_hour - min_hour\n",
    "    first_stop = cus_df.iloc[0]['location']\n",
    "    following_stop = None\n",
    "    try:\n",
    "        following_stop = cus_df.iloc[1]\n",
    "    except IndexError:\n",
    "        pass\n",
    "\n",
    "    return_df = pd.DataFrame({\n",
    "        'customer_no': [name],\n",
    "        'time_in_market_mins': [hour_in_market * 60 + mins_in_market],\n",
    "        'first_stop': [first_stop],\n",
    "        'following_stop': [following_stop['location'] if following_stop is not None else None]\n",
    "    }).set_index('customer_no')\n",
    "    return return_df\n",
    "\n",
    "\n",
    "customers_gr = df.groupby('customer_no')\n",
    "\n",
    "\n",
    "customers_df = None\n",
    "for name, custom_df in customers_gr:\n",
    "    current_df = get_customer_time_in_market_for_day(name, custom_df)\n",
    "    if customers_df is None:\n",
    "        customers_df = current_df\n",
    "    else:\n",
    "        customers_df = pd.concat([customers_df, current_df])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total number of customers in the supermarket over time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Over a day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>time_in_market_mins</th>\n",
       "      <th>first_stop</th>\n",
       "      <th>following_stop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_no</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>friday_1</th>\n",
       "      <td>5</td>\n",
       "      <td>dairy</td>\n",
       "      <td>spices</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friday_10</th>\n",
       "      <td>5</td>\n",
       "      <td>fruit</td>\n",
       "      <td>checkout</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friday_100</th>\n",
       "      <td>1</td>\n",
       "      <td>fruit</td>\n",
       "      <td>checkout</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friday_1000</th>\n",
       "      <td>1</td>\n",
       "      <td>fruit</td>\n",
       "      <td>checkout</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friday_1001</th>\n",
       "      <td>10</td>\n",
       "      <td>fruit</td>\n",
       "      <td>drinks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wednesday_995</th>\n",
       "      <td>112</td>\n",
       "      <td>fruit</td>\n",
       "      <td>dairy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wednesday_996</th>\n",
       "      <td>110</td>\n",
       "      <td>fruit</td>\n",
       "      <td>dairy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wednesday_997</th>\n",
       "      <td>105</td>\n",
       "      <td>dairy</td>\n",
       "      <td>spices</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wednesday_998</th>\n",
       "      <td>6</td>\n",
       "      <td>dairy</td>\n",
       "      <td>fruit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wednesday_999</th>\n",
       "      <td>1</td>\n",
       "      <td>fruit</td>\n",
       "      <td>checkout</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7445 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               time_in_market_mins first_stop following_stop\n",
       "customer_no                                                 \n",
       "friday_1                         5      dairy         spices\n",
       "friday_10                        5      fruit       checkout\n",
       "friday_100                       1      fruit       checkout\n",
       "friday_1000                      1      fruit       checkout\n",
       "friday_1001                     10      fruit         drinks\n",
       "...                            ...        ...            ...\n",
       "wednesday_995                  112      fruit          dairy\n",
       "wednesday_996                  110      fruit          dairy\n",
       "wednesday_997                  105      dairy         spices\n",
       "wednesday_998                    6      dairy          fruit\n",
       "wednesday_999                    1      fruit       checkout\n",
       "\n",
       "[7445 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customers_df.hist(bins=40)\n",
    "customers_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Over a week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customers_gr = df.groupby('day').count()\n",
    "\n",
    "min_val = customers_gr[['customer_no']].min()\n",
    "max_val = customers_gr[['customer_no']].max()\n",
    "customers_gr[['customer_no']].plot.bar()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  The distribution of customers of their first visited section versus following sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='first_stop,following_stop'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EJIaIiIj0EpMYIiIi0ktMYoiIiEgvMYkhIiIivcQkhoiIiPQSkxgiIiLSS0xiiIiISC8xiSEiIiK9xCSGiIiI9BKTGCIiItJLTGKIiIhILzGJISIiIr3EJIaIiIj0EpMYIiIi0ktMYoiIiEgvMYkhIiIivcQkhoiIiPQSkxgiIiLSS0xiiIiISC8xiSEiIiK9xCSGiIiI9BKTGCIiItJLTGKIiIhILzGJISIiIr3EJIaIiIj0EpMYIiIi0kufnMQcOXIEn3/+OVxcXKBSqbB582bpsZSUFAQFBcHb2xuWlpZwcXHB119/jYcPH2rsIykpCQMGDEDBggVhaWkJf39/3L9/X6NMfHw8AgICoFaroVarERAQgOfPn/+rgyQiIqK855OTmFevXqFChQpYuHBhlsdev36N8+fPY8yYMTh//jw2bdqEGzduwN/fX6NcYGAgwsLCsH79ehw7dgwvX75Ey5YtkZqaKpXp3LkzIiIisGvXLuzatQsREREICAj4F4dIREREeZFKCCH+9ZNVKoSFhaF169Y5ljl79iyqV6+O6OhoFC1aFAkJCXBwcMDq1avRsWNHAMDDhw/h6uqKHTt2oEmTJoiMjISXlxdOnTqFGjVqAABOnTqFWrVq4dq1a/D09Pxg3RITE6FWq5GQkABra+t/e4hEpKfcRm7/18+NmtZCizUhok/xKd/fsveJSUhIgEqlgo2NDQAgPDwcKSkpaNy4sVTGxcUF5cqVw4kTJwAAJ0+ehFqtlhIYAKhZsybUarVUJrOkpCQkJiZq3IiIiCjvkjWJ+fvvvzFy5Eh07txZyqbi4uJgYmICW1tbjbKOjo6Ii4uTyhQqVCjL/goVKiSVyWzq1KlS/xm1Wg1XV1ctHw0RERHlJrIlMSkpKfjyyy+RlpaGRYsWfbC8EAIqlUq6n/HvnMpkNGrUKCQkJEi3mJiYf195IiIiyvVkSWJSUlLQoUMH3L17F3v37tW4puXk5ITk5GTEx8drPOfRo0dwdHSUyvz1119Z9vv48WOpTGampqawtrbWuBEREVHepfUkJj2BuXnzJvbt2wd7e3uNx6tUqQJjY2Ps3btX2hYbG4vLly+jdu3aAIBatWohISEBZ86ckcqcPn0aCQkJUhkiIiLK34w+9QkvX77ErVu3pPt3795FREQE7Ozs4OLigi+++ALnz5/Htm3bkJqaKvVhsbOzg4mJCdRqNXr27ImhQ4fC3t4ednZ2GDZsGLy9vdGwYUMAQJkyZdC0aVP07t0bS5cuBQD06dMHLVu2/KiRSURERJT3fXISc+7cOfj6+kr3hwwZAgDo2rUrxo0bhy1btgAAKlasqPG8gwcPwsfHBwAQHBwMIyMjdOjQAW/evIGfnx9CQkJgaGgolf/ll18wcOBAaRSTv79/tnPTEBERUf70n+aJyc04TwxR/sZ5Yoj0U66aJ4aIiIhIDkxiiIiISC8xiSEiIiK9xCSGiIiI9BKTGCIiItJLTGKIiIhILzGJISIiIr3EJIaIiIj0EpMYIiIi0ktMYoiIiEgvMYkhIiIivcQkhoiIiPQSkxgiIiLSS0xiiIiISC8xiSEiIiK9xCSGiIiI9BKTGCIiItJLTGKIiIhILzGJISIiIr3EJIaIiIj0EpMYIiIi0ktMYoiIiEgvMYkhIiIivcQkhoiIiPQSkxgiIiLSS0xiiIiISC8xiSEiIiK9xCSGiIiI9BKTGCIiItJLTGKIiIhILzGJISIiIr3EJIaIiIj0EpMYIiIi0ktMYoiIiEgvMYkhIiIivcQkhoiIiPQSkxgiIiLSS0xiiIiISC8xiSEiIiK99MlJzJEjR/D555/DxcUFKpUKmzdv1nhcCIFx48bBxcUF5ubm8PHxwZUrVzTKJCUlYcCAAShYsCAsLS3h7++P+/fva5SJj49HQEAA1Go11Go1AgIC8Pz5808+QCIiIsqbPjmJefXqFSpUqICFCxdm+/iMGTMwZ84cLFy4EGfPnoWTkxMaNWqEFy9eSGUCAwMRFhaG9evX49ixY3j58iVatmyJ1NRUqUznzp0RERGBXbt2YdeuXYiIiEBAQMC/OEQiIiLKi1RCCPGvn6xSISwsDK1btwbwrhXGxcUFgYGBCAoKAvCu1cXR0RHTp0/HN998g4SEBDg4OGD16tXo2LEjAODhw4dwdXXFjh070KRJE0RGRsLLywunTp1CjRo1AACnTp1CrVq1cO3aNXh6en6wbomJiVCr1UhISIC1tfW/PUQi0lNuI7f/6+dGTWuhxZoQ0af4lO9vrfaJuXv3LuLi4tC4cWNpm6mpKerXr48TJ04AAMLDw5GSkqJRxsXFBeXKlZPKnDx5Emq1WkpgAKBmzZpQq9VSmcySkpKQmJiocSMiIqK8S6tJTFxcHADA0dFRY7ujo6P0WFxcHExMTGBra/veMoUKFcqy/0KFCkllMps6darUf0atVsPV1fU/Hw8RERHlXrKMTlKpVBr3hRBZtmWWuUx25d+3n1GjRiEhIUG6xcTE/IuaExERkb7QahLj5OQEAFlaSx49eiS1zjg5OSE5ORnx8fHvLfPXX39l2f/jx4+ztPKkMzU1hbW1tcaNiIiI8i6tJjHu7u5wcnLC3r17pW3Jyck4fPgwateuDQCoUqUKjI2NNcrExsbi8uXLUplatWohISEBZ86ckcqcPn0aCQkJUhkiIiLK34w+9QkvX77ErVu3pPt3795FREQE7OzsULRoUQQGBmLKlCnw8PCAh4cHpkyZAgsLC3Tu3BkAoFar0bNnTwwdOhT29vaws7PDsGHD4O3tjYYNGwIAypQpg6ZNm6J3795YunQpAKBPnz5o2bLlR41MIiIiorzvk5OYc+fOwdfXV7o/ZMgQAEDXrl0REhKCESNG4M2bN+jbty/i4+NRo0YN7NmzB1ZWVtJzgoODYWRkhA4dOuDNmzfw8/NDSEgIDA0NpTK//PILBg4cKI1i8vf3z3FuGiIiIsp//tM8MbkZ54khyt84TwyRflJsnhgiIiIiXWESQ0RERHqJSQwRERHpJSYxREREpJeYxBAREZFeYhJDREREeolJDBEREeklJjFERESkl5jEEBERkV5iEkNERER6iUkMERER6SUmMURERKSXmMQQERGRXmISQ0RERHqJSQwRERHpJSYxREREpJeYxBAREZFeYhJDREREeolJDBEREeklJjFERESkl5jEEBERkV5iEkNERER6iUkMERER6SUmMURERKSXmMQQERGRXmISQ0RERHqJSQwRERHpJSYxREREpJeYxBAREZFeYhJDREREeolJDBEREeklI6UroCS3kdv/9XOjprXQYk2IiIjoU7ElhoiIiPQSkxgiIiLSS0xiiIiISC8xiSEiIiK9xCSGiIiI9BKTGCIiItJLTGKIiIhIL2k9iXn79i3+97//wd3dHebm5ihevDgmTJiAtLQ0qYwQAuPGjYOLiwvMzc3h4+ODK1euaOwnKSkJAwYMQMGCBWFpaQl/f3/cv39f29UlIiIiPaX1JGb69OlYsmQJFi5ciMjISMyYMQMzZ87EggULpDIzZszAnDlzsHDhQpw9exZOTk5o1KgRXrx4IZUJDAxEWFgY1q9fj2PHjuHly5do2bIlUlNTtV1lIiIi0kNan7H35MmTaNWqFVq0eDejrZubG9atW4dz584BeNcKM3fuXIwePRpt27YFAISGhsLR0RFr167FN998g4SEBCxfvhyrV69Gw4YNAQBr1qyBq6sr9u3bhyZNmmi72kRERKRntN4SU7duXezfvx83btwAAFy4cAHHjh1D8+bNAQB3795FXFwcGjduLD3H1NQU9evXx4kTJwAA4eHhSElJ0Sjj4uKCcuXKSWUyS0pKQmJiosaNiIiI8i6tt8QEBQUhISEBpUuXhqGhIVJTUzF58mR06tQJABAXFwcAcHR01Hieo6MjoqOjpTImJiawtbXNUib9+ZlNnToV48eP1/bhEBERUS6l9ZaYDRs2YM2aNVi7di3Onz+P0NBQzJo1C6GhoRrlVCqVxn0hRJZtmb2vzKhRo5CQkCDdYmJi/tuBEBERUa6m9ZaY4cOHY+TIkfjyyy8BAN7e3oiOjsbUqVPRtWtXODk5AXjX2uLs7Cw979GjR1LrjJOTE5KTkxEfH6/RGvPo0SPUrl0727impqYwNTXV9uEQERFRLqX1lpjXr1/DwEBzt4aGhtIQa3d3dzg5OWHv3r3S48nJyTh8+LCUoFSpUgXGxsYaZWJjY3H58uUckxgiIiLKX7TeEvP5559j8uTJKFq0KMqWLYs///wTc+bMQY8ePQC8u4wUGBiIKVOmwMPDAx4eHpgyZQosLCzQuXNnAIBarUbPnj0xdOhQ2Nvbw87ODsOGDYO3t7c0WomIiIjyN60nMQsWLMCYMWPQt29fPHr0CC4uLvjmm2/www8/SGVGjBiBN2/eoG/fvoiPj0eNGjWwZ88eWFlZSWWCg4NhZGSEDh064M2bN/Dz80NISAgMDQ21XWUiIiLSQyohhFC6EnJITEyEWq1GQkICrK2tsy3jNnL7v95/1LQW//q5RCQ/vr+J9NPHfH+n49pJREREpJeYxBAREZFeYhJDREREeolJDBEREeklJjFERESkl5jEEBERkV5iEkNERER6iUkMERER6SUmMURERKSXmMQQERGRXmISQ0RERHqJSQwRERHpJSYxREREpJeYxBAREZFeYhJDREREeolJDBEREeklJjFERESkl5jEEBERkV5iEkNERER6iUkMERER6SUmMURERKSXjJSuABFRXuM2cvu/fm7UtBZarAlR3saWGCIiItJLTGKIiIhILzGJISIiIr3EJIaIiIj0EpMYIiIi0ktMYoiIiEgvMYkhIiIivcQkhoiIiPQSkxgiIiLSS0xiiIiISC8xiSEiIiK9xCSGiIiI9BKTGCIiItJLTGKIiIhILzGJISIiIr1kpHQFiHTFbeT2f/3cqGkttFgTIiLSBrbEEBERkV6SJYl58OABvvrqK9jb28PCwgIVK1ZEeHi49LgQAuPGjYOLiwvMzc3h4+ODK1euaOwjKSkJAwYMQMGCBWFpaQl/f3/cv39fjuoSERGRHtJ6EhMfH486derA2NgYO3fuxNWrVzF79mzY2NhIZWbMmIE5c+Zg4cKFOHv2LJycnNCoUSO8ePFCKhMYGIiwsDCsX78ex44dw8uXL9GyZUukpqZqu8pERESkh7TeJ2b69OlwdXXFypUrpW1ubm7S30IIzJ07F6NHj0bbtm0BAKGhoXB0dMTatWvxzTffICEhAcuXL8fq1avRsGFDAMCaNWvg6uqKffv2oUmTJlniJiUlISkpSbqfmJio7UMjIiKiXETrLTFbtmxB1apV0b59exQqVAiVKlXCTz/9JD1+9+5dxMXFoXHjxtI2U1NT1K9fHydOnAAAhIeHIyUlRaOMi4sLypUrJ5XJbOrUqVCr1dLN1dVV24dGREREuYjWk5g7d+5g8eLF8PDwwO7du/Htt99i4MCBWLVqFQAgLi4OAODo6KjxPEdHR+mxuLg4mJiYwNbWNscymY0aNQoJCQnSLSYmRtuHRkRERLmI1i8npaWloWrVqpgyZQoAoFKlSrhy5QoWL16Mr7/+WiqnUqk0nieEyLIts/eVMTU1hamp6X+sPREREekLrbfEODs7w8vLS2NbmTJlcO/ePQCAk5MTAGRpUXn06JHUOuPk5ITk5GTEx8fnWIaIiIjyN60nMXXq1MH169c1tt24cQPFihUDALi7u8PJyQl79+6VHk9OTsbhw4dRu3ZtAECVKlVgbGysUSY2NhaXL1+WyhAREVH+pvXLSYMHD0bt2rUxZcoUdOjQAWfOnMGyZcuwbNkyAO8uIwUGBmLKlCnw8PCAh4cHpkyZAgsLC3Tu3BkAoFar0bNnTwwdOhT29vaws7PDsGHD4O3tLY1WIiIiovxN60lMtWrVEBYWhlGjRmHChAlwd3fH3Llz0aVLF6nMiBEj8ObNG/Tt2xfx8fGoUaMG9uzZAysrK6lMcHAwjIyM0KFDB7x58wZ+fn4ICQmBoaGhtqtMREREekiWtZNatmyJli1b5vi4SqXCuHHjMG7cuBzLmJmZYcGCBViwYIEMNSQiIiJ9x7WTiIiISC8xiSEiIiK9xCSGiIiI9BKTGCIiItJLTGKIiIhILzGJISIiIr3EJIaIiIj0EpMYIiIi0ktMYoiIiEgvMYkhIiIivcQkhoiIiPQSkxgiIiLSS0xiiIiISC8xiSEiIiK9xCSGiIiI9JKR0hXIr9xGbv/Xz42a1kKLNSEiItJPbIkhIiIivcQkhoiIiPQSkxgiIiLSS0xiiIiISC8xiSEiIiK9xCSGiIiI9BKTGCIiItJLTGKIiIhILzGJISIiIr3EJIaIiIj0EpMYIiIi0ktMYoiIiEgvMYkhIiIivcQkhoiIiPQSkxgiIiLSS0ZKV4CIiEifuY3c/q+fGzWthRZrkv+wJYaIiIj0EpMYIiIi0ktMYoiIiEgvMYkhIiIivcQkhoiIiPQSkxgiIiLSS7InMVOnToVKpUJgYKC0TQiBcePGwcXFBebm5vDx8cGVK1c0npeUlIQBAwagYMGCsLS0hL+/P+7fvy93dYmIiEhPyDpPzNmzZ7Fs2TKUL19eY/uMGTMwZ84chISEoFSpUpg0aRIaNWqE69evw8rKCgAQGBiIrVu3Yv369bC3t8fQoUPRsmVLhIeHw9DQUM5qk4w4nwIREWmLbC0xL1++RJcuXfDTTz/B1tZW2i6EwNy5czF69Gi0bdsW5cqVQ2hoKF6/fo21a9cCABISErB8+XLMnj0bDRs2RKVKlbBmzRpcunQJ+/btk6vKREREpEdkS2L69euHFi1aoGHDhhrb7969i7i4ODRu3FjaZmpqivr16+PEiRMAgPDwcKSkpGiUcXFxQbly5aQymSUlJSExMVHjRkRERHmXLJeT1q9fj/Pnz+Ps2bNZHouLiwMAODo6amx3dHREdHS0VMbExESjBSe9TPrzM5s6dSrGjx+vjeoTERGRHtB6S0xMTAwGDRqENWvWwMzMLMdyKpVK474QIsu2zN5XZtSoUUhISJBuMTExn155IiIi0htaT2LCw8Px6NEjVKlSBUZGRjAyMsLhw4cxf/58GBkZSS0wmVtUHj16JD3m5OSE5ORkxMfH51gmM1NTU1hbW2vciIiIKO/SehLj5+eHS5cuISIiQrpVrVoVXbp0QUREBIoXLw4nJyfs3btXek5ycjIOHz6M2rVrAwCqVKkCY2NjjTKxsbG4fPmyVIaIiIjyN633ibGyskK5cuU0tllaWsLe3l7aHhgYiClTpsDDwwMeHh6YMmUKLCws0LlzZwCAWq1Gz549MXToUNjb28POzg7Dhg2Dt7d3lo7CRERElD/JOk9MTkaMGIE3b96gb9++iI+PR40aNbBnzx5pjhgACA4OhpGRETp06IA3b97Az88PISEhnCOGiIiIAOgoiTl06JDGfZVKhXHjxmHcuHE5PsfMzAwLFizAggUL5K0cERER6SVFWmKIiEgenBWb8hMuAElERER6iS0x+RB/qRERUV7AJIaIiEhP5fcfpbycRERERHqJLTFERKT38nuLRH7FlhgiIiLSS0xiiIiISC8xiSEiIiK9xD4xRESkFeyXQrrGlhgiIiLSS0xiiIiISC8xiSEiIiK9xCSGiIiI9BKTGCIiItJLTGKIiIhILzGJISIiIr3EJIaIiIj0EpMYIiIi0ktMYoiIiEgvMYkhIiIivcQkhoiIiPQSkxgiIiLSS0xiiIiISC8xiSEiIiK9xCSGiIiI9BKTGCIiItJLTGKIiIhILzGJISIiIr3EJIaIiIj0EpMYIiIi0ktMYoiIiEgvMYkhIiIivcQkhoiIiPSSkdIVICJ5uY3c/q+fGzWthRZrQkSkXWyJISIiIr3EJIaIiIj0EpMYIiIi0ktMYoiIiEgvab1j79SpU7Fp0yZcu3YN5ubmqF27NqZPnw5PT0+pjBAC48ePx7JlyxAfH48aNWrgxx9/RNmyZaUySUlJGDZsGNatW4c3b97Az88PixYtQpEiRbRdZSKSCTsVE5GctN4Sc/jwYfTr1w+nTp3C3r178fbtWzRu3BivXr2SysyYMQNz5szBwoULcfbsWTg5OaFRo0Z48eKFVCYwMBBhYWFYv349jh07hpcvX6Jly5ZITU3VdpWJiIhID2m9JWbXrl0a91euXIlChQohPDwc9erVgxACc+fOxejRo9G2bVsAQGhoKBwdHbF27Vp88803SEhIwPLly7F69Wo0bNgQALBmzRq4urpi3759aNKkibarTURERHpG9j4xCQkJAAA7OzsAwN27dxEXF4fGjRtLZUxNTVG/fn2cOHECABAeHo6UlBSNMi4uLihXrpxUJrOkpCQkJiZq3IiIiCjvkjWJEUJgyJAhqFu3LsqVKwcAiIuLAwA4OjpqlHV0dJQei4uLg4mJCWxtbXMsk9nUqVOhVqulm6urq7YPh4iIiHIRWZOY/v374+LFi1i3bl2Wx1QqlcZ9IUSWbZm9r8yoUaOQkJAg3WJiYv59xYmIiCjXk23ZgQEDBmDLli04cuSIxogiJycnAO9aW5ydnaXtjx49klpnnJyckJycjPj4eI3WmEePHqF27drZxjM1NYWpqakch0JERESZ5IbRh1pviRFCoH///ti0aRMOHDgAd3d3jcfd3d3h5OSEvXv3StuSk5Nx+PBhKUGpUqUKjI2NNcrExsbi8uXLOSYxRERElL9ovSWmX79+WLt2Lf744w9YWVlJfVjUajXMzc2hUqkQGBiIKVOmwMPDAx4eHpgyZQosLCzQuXNnqWzPnj0xdOhQ2Nvbw87ODsOGDYO3t7c0WomIiIjyN60nMYsXLwYA+Pj4aGxfuXIlunXrBgAYMWIE3rx5g759+0qT3e3ZswdWVlZS+eDgYBgZGaFDhw7SZHchISEwNDTUdpWJiIhID2k9iRFCfLCMSqXCuHHjMG7cuBzLmJmZYcGCBViwYIEWa0dERER5BddOIiIiIr3EJIaIiIj0EpMYIiIi0ktMYoiIiEgvMYkhIiIivcQkhoiIiPQSkxgiIiLSS0xiiIiISC8xiSEiIiK9xCSGiIiI9BKTGCIiItJLTGKIiIhILzGJISIiIr3EJIaIiIj0EpMYIiIi0ktMYoiIiEgvMYkhIiIivcQkhoiIiPQSkxgiIiLSS0xiiIiISC8xiSEiIiK9xCSGiIiI9BKTGCIiItJLTGKIiIhILzGJISIiIr3EJIaIiIj0kpHSFSDKD9xGbv/Xz42a1kKLNSEiyjvYEkNERER6iUkMERER6SUmMURERKSXmMQQERGRXmISQ0RERHqJSQwRERHpJSYxREREpJeYxBAREZFeYhJDREREeolJDBEREeklJjFERESkl5jEEBERkV7K9UnMokWL4O7uDjMzM1SpUgVHjx5VukpERESUC+TqJGbDhg0IDAzE6NGj8eeff+Kzzz5Ds2bNcO/ePaWrRkRERArL1UnMnDlz0LNnT/Tq1QtlypTB3Llz4erqisWLFytdNSIiIlKYkdIVyElycjLCw8MxcuRIje2NGzfGiRMnspRPSkpCUlKSdD8hIQEAkJiYmGOMtKTX/7p+79vvx2BsxmZsxmZsxmbsnB8TQnx4RyKXevDggQAgjh8/rrF98uTJolSpUlnKjx07VgDgjTfeeOONN97ywC0mJuaDuUKubYlJp1KpNO4LIbJsA4BRo0ZhyJAh0v20tDQ8e/YM9vb22Zb/kMTERLi6uiImJgbW1tafXvH/gLEZm7EZm7EZO7/GFkLgxYsXcHFx+WDZXJvEFCxYEIaGhoiLi9PY/ujRIzg6OmYpb2pqClNTU41tNjY2/7ke1tbWOj/5jM3YjM3YjM3Y+Tm2Wq3+qHK5tmOviYkJqlSpgr1792ps37t3L2rXrq1QrYiIiCi3yLUtMQAwZMgQBAQEoGrVqqhVqxaWLVuGe/fu4dtvv1W6akRERKSwXJ3EdOzYEU+fPsWECRMQGxuLcuXKYceOHShWrJjssU1NTTF27Ngsl6h0gbEZm7EZm7EZm7E/TCXEx4xhIiIiIspdcm2fGCIiIqL3YRJDREREeolJDBEREeklJjFERESkl5jEEBERkV7K1UOsdUUIgcOHD+Po0aOIiorC69ev4eDggEqVKqFhw4ZwdXWVvQ4xMTEascuWLSv70DQlj/v69etYt25dtrGbNGmCdu3ayXb8CQkJCAsLyzG2LiZTVOJ854bjTpeUlKTzYZ8pKSmIi4uTjtvOzk72mEq+x5Q+30lJSThz5kyW2O7u7rLGVTK2kp9rmen6PabY+f7vSzXqr9evX4vJkyeLwoULCzMzM1GjRg3Rtm1b0aVLF9GsWTPh6uoqDA0NRbNmzcTJkye1Hj8qKkqMHDlSFCtWTBgYGAiVSiXdTE1NRcOGDcXGjRtFamqqVuMqedznz58XDRs2FKampsLX11cEBQWJ+fPni59++klMnz5dBAQECDc3N2FnZyemTZsm/v77b63FfvjwoejVq5cwNzcX7u7uomPHjmLIkCFi9OjR4rvvvhOfffaZsLCwEGXKlBHr16/XWtx0Sp1vpY9bCCF27dolunbtKooXLy6MjIyEgYGBKFCggKhXr56YNGmSePDggSxxX7x4IZYsWSLq168vzM3NhYGBgXQrWrSo6NWrlzhz5ozW4yr5HlP6fB8/flx8+eWXwszMTBgYGAg7OztRuHBh6fUvWbKkmDFjhkhMTMwzsZX8XEun1HtMyfMthBD5OokpUqSIaNeundi6datITk7OtkxUVJSYMmWKKFq0qFi2bJnWYg8cOFBYWVmJdu3aidDQUBEZGSkSExNFSkqK+Ouvv8T+/fvFuHHjhKenpyhbtqxWP2iVPO6iRYuKBQsWiKdPn7633IkTJ0T79u3F5MmTtRbbwcFBDB06VFy6dCnHMq9fvxZr164V1atXFzNnztRabCXPt5LHHRYWJkqVKiUcHR1F9+7dxeLFi8WWLVvE3r17xYYNG8SYMWOEj4+PMDU1Fd9884149OiR1mLPmTNH2NvbiypVqojx48eLnTt3iosXL4qbN2+K06dPi+XLl4tu3boJtVotmjRpIm7cuKG12Eq+x5Q83/7+/sLZ2VkMHTpUHD58WLx69Urj8du3b4uQkBDRpEkT4eTkJPbs2ZMnYiv5uabke0zJ1zxdvk5i3vcmzywpKUmrH3LDhg376H+m7du3i19//VVrsZU87qSkJFnLv8+nvnm1+WZX8nwredzVqlUTW7Zs+WDr0v3798Xw4cPFrFmztBb7iy++EBcvXvxguTdv3ogff/xR/PTTT1qLreR7TMnzvXDhwo9+z16+fFmrX2pKxlbyc03J95iSr3k6ztj7j3v37sHV1RUqlUpjuxACMTExKFq0qEI1k1duO+7nz59rZfXxDzly5Ahq164NIyPNbmFv377FiRMnUK9ePdnroIT8etxEunL37l2d9Puhdzg66R/u7u54/Phxlu3Pnj2T/R/yzZs3eP36tXQ/Ojoac+fOxe7du2WNCyh73NOnT8eGDRuk+x06dIC9vT0KFy6MCxcuyBrb19cXz549y7I9ISEBvr6+ssYeN24coqOjZY2REyWPO7PU1FREREQgPj5e9lg9evTAixcvsmx/9eoVevToIWtsNzc3TJgwAffu3ZM1TnZCQ0Oxfft26f6IESNgY2OD2rVry/4/GBMTg/v370v3z5w5g8DAQCxbtkzWuABQvHhxPH36NMv258+fo3jx4rLGLlmyJHx9fbFmzRr8/fffssb6EF2+xxR7zbXetqOnVCpVts2qUVFRwsLCQtbYjRo1EosXLxZCCBEfHy8cHR1FkSJFhJmZmVi0aJGssZU8bnd3d3H8+HEhhBB79uwRNjY2Yvfu3aJnz56iUaNGssbO6bivX78urKysZI1duXJlYWhoKBo0aCB++eUX8ebNG1njZaTkcQ8aNEj8/PPPQggh3r59K+rUqSNUKpWwtLQUBw8elDW2gYGB+Ouvv7Jsf/z4sTA0NJQ19vz586Vz3rBhQ7Fu3TpZOnZmp1SpUmL//v1CiHf9MczNzcXSpUvF559/Ltq0aSNr7Lp164pVq1YJIYSIjY0V1tbWolatWsLe3l6MHz9e1tgqlSrb8x0XFydMTExkjX3p0iUxePBgUahQIaFWq0WfPn3E6dOnZY2ZTsn3mFKveb4fYj1kyBAAgEqlwpgxY2BhYSE9lpqaitOnT6NixYqy1uH8+fMIDg4GAPz2229wdHTEn3/+id9//x0//PADvvvuO63HzA3HHRsbKw0x3bZtGzp06IDGjRvDzc0NNWrUkCVm27ZtAbw77m7dumkMQUxNTcXFixdlH3oaHh6OixcvYuXKlRg8eDD69euHL7/8Ej169EC1atVkiZkbjvu3337DV199BQDYunUr7t69i2vXrmHVqlUYPXo0jh8/rvWYiYmJEO/6/uHFixcwMzOTHktNTcWOHTtQqFAhrcfNaMCAARgwYAAuXLiAFStWYODAgejbty86d+6MHj16oHLlyrLFjomJQcmSJQEAmzdvxhdffIE+ffqgTp068PHxkS0uAFy+fBnVq1cHAGzcuBHlypXD8ePHsWfPHnz77bf44YcftB5zy5Yt0t+7d++GWq2W7qempmL//v1wc3PTetyMypUrhzlz5mDGjBnYunUrQkJCULduXXh4eKBnz54ICAiAg4ODLLGVeI8p/Zrn+yTmzz//BPCuD8ilS5dgYmIiPWZiYoIKFSpg2LBhstbh9evXsLKyAgDs2bMHbdu2hYGBAWrWrClbk29uOG5bW1vExMTA1dUVu3btwqRJk6Q6paamyhIz/Q0mhICVlRXMzc2lx0xMTFCzZk307t1bltgZlS9fHsHBwZg5cya2bt2KlStXok6dOvD09ESvXr3QrVs3jQ+D/yo3HPeTJ0/g5OQEANixYwfat2+PUqVKoWfPnpg/f74sMW1sbKBSqaBSqVCqVKksj6tUKowfP16W2JlVqFAB8+bNw6xZs7Bo0SIEBQVh8eLFKFeuHAYNGoTu3btn6Zv2XxUoUABPnz5F0aJFsWfPHgwePBgAYGZmhjdv3mg1VmYpKSlSsrxv3z74+/sDAEqXLo3Y2FhZYrZu3RrAu/PatWtXjceMjY3h5uaG2bNnyxI7MyMjI7Rp0wbNmzfHokWLMGrUKAwbNgyjRo1Cx44dMX36dDg7O2s1phLvMcVfc9naePRMt27dREJCgiKxvb29xbx588S9e/eEtbW1OHHihBBCiHPnzglHR0dZYyt53P369RPFihUTDRs2FPb29uLFixdCCCHWr18vKlWqJGvscePGiZcvX8oa42MkJSWJ9evXi8aNGwsjIyNRr1494enpKaysrGSZw0PJ4y5atKjYvXu3ePv2rXB1dRVbt24VQrwbtWBjYyNLzEOHDomDBw8KlUolNm3aJA4dOiTdTpw4IdvcGdlJTk4WGzZsEE2bNhWGhoaiTp06YsWKFWLSpEnCyclJdOrUSesxO3fuLCpXrix69uwpLCwsxJMnT4QQQvzxxx+ibNmyWo+XUfXq1UVQUJA4cuSIMDMzExEREUIIIU6ePCkKFy4sa2w3Nzfx+PFjWWN8yNmzZ8V3330nbG1tRZEiRcTo0aPFnTt3xLFjx0SDBg1EtWrVtB5TifdYOqVecyYxucCvv/4qjI2NhYGBgUZfkClTpoimTZsqWDN5JScni5kzZ4qBAweK8+fPS9uDg4O1Otw1Nzp37pzo16+fsLOzE87OziIoKEjcvHlTenzWrFmiUKFCCtZQ+8aOHSvUarUoXbq0KFq0qNQvZPny5aJmzZqyxo6KihJpaWmyxshJeHi46N+/v7C3txeFChUSQ4cOFZGRkRplzpw5I8zMzLQeOz4+XvTr10/4+/uLnTt3Stt/+OEHMWnSJK3Hy+jgwYPCxsZGGBgYiO7du0vbR40aJXt/HCXNnj1blCtXThgbG4tWrVqJrVu3Zhn+fPPmTVn6Yin5HlMKh1j/o0GDBu99/MCBA7LGj4uLQ2xsLCpUqAADg3eDxs6cOQNra2uULl1aq7Hatm2LkJAQWFtbS30lcrJp0yatxs4t3N3d39t0f+fOHdlily9fHpGRkWjcuDF69+6Nzz//HIaGhhplHj9+DEdHR6Slpf3neJUrV8b+/ftha2uLSpUqvfe4z58//5/jvc9vv/2GmJgYtG/fHkWKFAHwbgSNjY0NWrVqJVvcI0eOvPdxOYeWGxoaolGjRujZsydat24NY2PjLGVevXqF/v37Y+XKlVqNnZycrHGpOKMnT56gYMGCWo2XWWpqKhITE2Fraytti4qKgoWFhax9kSZMmPDex+Xoj5POw8MDPXr0QPfu3aVLO5klJydj3bp1WS6/aIMu32Pz589Hnz59YGZm9sHLVQMHDtRq7HT5vk9MugoVKmjcT0lJQUREBC5fvizLP1pG+/fvh5+fX5Z/+OrVq2PhwoVaT2LUarX0RabNfhf/xurVq7F06VLcuXMHJ0+eRLFixTB37ly4u7vL+qUWGBiocT8lJQV//vkndu3aheHDh8sWFwDat2+PHj16oHDhwjmWcXBw0EoCAwCtWrWS+iakX79WyhdffAEAGkNP5X5/Aci2E2vGZE6uPlipqalYvnw5/P3937tWk6WlpdYTGODdtAWbNm2Sfhil++uvv+Dn54fLly9rPWZGQgiEh4fj9u3b6Ny5M6ysrGBiYqIxkEAOYWFhGvdTUlJw9+5dGBkZoUSJErIlMW/fvkWXLl3w1Vdf5ZjAAO/6ocnxfx8TEyO9xzLq2rUrTp06pfV4wcHB6NKlC8zMzKTBKdlRqVSyJTG8nPQBY8eOFUOHDpU1hlqtznaa+eDgYNmHvSpp0aJFomDBgmLSpEnC3Nxc3L59WwghxMqVK4WPj48idVq4cKHo1q2bbPtPTk4W7u7u4sqVK7LFyK3evn0rJkyYIFxcXIShoaF0vv/3v/9Jw0Ll8vz5c43b48ePxZ49e0SNGjXEvn37ZI1tamoq7ty5I2uMnFSvXj3L/3NsbKwoXbq0aNeunayxo6KiROnSpYWFhYXG+R40aJD45ptvZI2dnYSEBNGmTRtp2LdcChQoIO7evStrjJx4enpK/Z4yOnbsmFCr1bqvkA4wifmAmzdvCltbW1ljrFixQhQsWFDji23mzJnC2tpaHDlyRNbYSipTpowICwsTQrx746d/yF26dEnY29srUqfbt2/Lnji6uLiIq1evyhojNxo/frwoXry4WLNmjUbSumHDBsWu1x8+fFhUrlxZ1hhVq1aVPVHKyZMnT4SXl5cIDAwUQryber5UqVKiffv2Wl9oNLNWrVqJr776SiQlJWm8vw8dOiRKliwpa+ycXLp0SRQrVkzWGK1atRIrV66UNUZOevXqJSpXrqyx2OLhw4eFlZWVmDNnjiJ1khsvJ33AyZMnNeaWkEP37t3x9OlTNG7cGMeOHcOGDRswZcoU7Ny5U/a5O5TsG3L37l1UqlQpy3ZTU1O8evVKtrjv89tvv7232V8bBgwYgOnTp+Pnn3/OMv2/3AwMDN57vuW6rAIAq1atwrJly+Dn54dvv/1W2l6+fHlcu3ZNtrjv4+DggOvXr8saY/LkyRg2bBgmTpyIKlWqwNLSUuNxa2tr2WLb29tj9+7dqFu3LgBg+/btqFy5Mn755Zcsl5i07dixYzh+/HiWPjnFihXDgwcPZI2dk+fPnyMhIUHWGM2aNcOoUaNw+fLlbM93+lBzOSxbtgzt27dHixYtsGfPHpw8eRL+/v6YNGkSBg0aJFtcAB+c+XrFihWyxGUS84/MHVyFEIiNjcW5c+cwZswY2eMPGzYMT58+RdWqVZGamoo9e/bINuFbRkr2DXF3d0dERASKFSumsX3nzp3w8vKSNXbmDq5CCMTFxeHx48dYtGiRrLFPnz6N/fv3Y8+ePfD29s7yISdnZ+rs+gr8+eefCA0NlX2+lAcPHkgTr2WUlpaGlJQUWWNfvHhR4376+3vatGlZ+sNpW9OmTQG8+/LK/D+nUqlkTRwBoEiRIti7dy/q1q2LRo0aYfXq1VqfjyY7aWlp2R7b/fv3pXmx5JK5k2n6+V69erV0PuSSPjnpnDlzsjwm9/lWqVRYt24dWrRoAT8/P1y8eBFTp05F//79ZYuZLvPSBikpKbh8+TKeP3/+wYEz/wWTmH9k7uBqYGAAT09PTJgwAY0bN9Z6vOx6cjs7O8PCwgL16tXD6dOncfr0aQDy9eoGkGN2/uOPP+LcuXOyxQWA4cOHo1+/fvj7778hhMCZM2ewbt06TJ06FT///LOssTN3cDUwMICDgwN8fHy03pE6MxsbG7Rr107WGDnJrrP0F198gbJly2LDhg3o2bOnbLHLli2Lo0ePZklaf/3112xb5LSpYsWKUKlUEJkGY9asWVO2X4jpDh48KOv+M7O1tc02SXn9+jW2bt0Ke3t7aVt262hpS6NGjTB37lxprSSVSoWXL19i7NixaN68uWxxAWTpZJr+/u7atStGjRola2xtdcj/WJkTdAAYO3YsOnXqhK+++gr16tWTypQvX162emT+gQS8ey369u0r69pJHGKtkI9dXFGlUsl6SScnd+7cQcWKFZGYmChrnJ9++gmTJk1CTEwMAKBw4cIYN26crF+mlNXt27dRvnx5WS/jbd26FQEBARg1ahQmTJiA8ePH4/r161i1ahW2bduGRo0ayRY788zX6V9qcl8qVkJoaOhHl5VzZNjDhw/h6+sLQ0ND3Lx5E1WrVsXNmzdRsGBBHDlyRPblHvKL9EvEGb/KM95P/1sXrX7ZuX79Onx8fGSbpZlJTCbh4eGIjIyESqWCl5eX7L8Qc6sZM2Zg0aJFiIqK0km8J0+eIC0tTacfbKmpqdi8ebPG+fb3988yZ0te9+bNG4waNQo7d+6UvX/I7t27MWXKFISHhyMtLQ2VK1fGDz/8IEtrp5IuXryIcuXKwcDAINtfyhnJ+etYaW/evMH69es1zneXLl00lr2Q2/3796FSqd47pcF/peR8KZ+yNE3mVlBd2LFjB7p27YrHjx/Lsn8mMf949OgRvvzySxw6dAg2NjYQQiAhIQG+vr5Yv369bAt2Ke1DfUP69OkjW+y7d+/i7du38PDw0Nh+8+ZNac0Nudy6dQvNmzfHgwcP4OnpCSEEbty4AVdXV2zfvh0lSpTQarzcMuFc5ksN4p+FES0sLLBmzRpZOx0q7fDhw5g1a5aUtJYpUwbDhw/HZ599pvVYBgYGiIuLQ6FChbL9pZxOF7+O09LScOvWLTx69CjLpQ45J/lTUlpaGiZNmoTZs2fj5cuXAAArKysMHToUo0eP1nqnZnd3d5w7dw729vbvbWVXqmVdF9IXFU6X3g9p+/bt6Nq1KxYuXChLXPaJ+ceAAQOQmJiIK1euoEyZMgCAq1evomvXrhg4cCDWrVsnW+zU1FSEhIRg//792X7QyDlbsJJ9Q7p164YePXpkSWJOnz6Nn3/+GYcOHZIt9sCBA1GiRAmcOnVKGo309OlTfPXVVxg4cCC2b9+u1Xi5ZcK5uXPnatxPP981atTQmFVVDmfPnkVaWlqWDuunT5+GoaEhqlatKlvsNWvWoHv37mjbti0GDhwIIQROnDgBPz8/hISEoHPnzlqNd/fuXemHz927d7W6709x6tQpdO7cGdHR0VmSKLkTqKlTp8LR0THLqJUVK1bg8ePHCAoKki326NGjsXz5ckybNg116tSBEALHjx/HuHHj8Pfff2Py5MlajZfxHCt5vgHgxo0bOHToULbfJXLOVJy+qHC69M+W2bNnf3Dk0n+is8HcuZy1tXW2E86dPn1a9kmC+vXrJywtLUWHDh3EoEGDRGBgoMZNLikpKSIkJETExsbKFuN9rKysNNYLSnfz5k3ZX3MLCwtx8eLFLNsjIiKEpaWlrLGVkpKSIsaNGyfu3bunSPxq1aqJX3/9Ncv233//XVSvXl3W2KVLl852nozZs2eL0qVLyxpbSRUqVBDt27cXV69eFfHx8Vkm/ZNTsWLFxPHjx7NsP3XqlHBzc5M1trOzs/jjjz+ybN+8ebNwcXGRNbaSli1bJgwNDYWjo6OoUKGCqFixonSTc1HdtLQ0ERUVJV69eiVbjJywJeYfaWlp2a5pYmxsLHtv8/Xr12Pjxo2y99jPzMjICN999x0iIyN1GjedSqXCixcvsmxPSEiQvYnd1NQ029gvX77Mca0ZfWdkZIRZs2bpZJr/7Fy9ehWVK1fOsr1SpUq4evWqrLHv3LmDzz//PMt2f39/fP/997LGTnf16lXcu3cPycnJWeogl5s3b+K3337Ldmi73OLi4uDs7Jxlu4ODg2ydPNM9e/Ys25bk0qVLyzoiK939+/exZcuWbM93dkOvtWXSpEmYPHmyrK1c2RFCwMPDA1euXMnSsi43eWc70iMNGjTAoEGD8PDhQ2nbgwcPMHjwYPj5+cka28TERJEPGQCoUaNGlmZAXfnss88wdepUjYQlNTUVU6dOlSbnkkvLli3Rp08fnD59GuLdzNU4deoUvv32W9n7haSmpmLWrFmoXr06nJycYGdnp3GTk5+fn6yX6d7H1NQUf/31V5btsbGxsk/65+rqiv3792fZvn//fri6usoa+86dO6hQoQLKlSuHFi1aoHXr1mjdujXatGmDNm3ayBq7Ro0auHXrlqwxcuLq6orjx49n2X78+HG4uLjIGrtChQrZ9sFYuHCh7PMC7d+/H56enli0aBFmz56NgwcPYuXKlVixYgUiIiJkjR0fH4/27dvLGiM7BgYG8PDwwNOnT3Uem5eT/nHv3j1RqVIlYWxsLIoXLy5KlCghjI2NReXKlUVMTIyssWfNmiX69u0r0tLSZI2TnY0bN4rixYuLBQsWiBMnTogLFy5o3OR05coVYW9vL0qUKCG6desmunXrJkqUKCEcHBzEpUuXZI0dHx8v/P39hUqlEiYmJsLExEQYGBiI1q1bi/j4eFljjxkzRjg7O4uZM2cKMzMzMXHiRNGzZ09hb28v5s2bJ2vsJUuWCCcnJzF06FCxdu1a8ccff2jc5NSxY0dRv359jcsY8fHxon79+qJ9+/ayxl60aJEwMTER3377rVi1apVYvXq1+Oabb4SpqalYsmSJrLFbtmwpWrVqJR49eiQKFCggrl69Ko4ePSqqV68u+7IimzZtEl5eXmLlypXi3LlzOn1/T5s2Tdjb24sVK1aIqKgoERUVJZYvXy7s7e3FlClTZI196NAhYWlpKcqUKSN69OghevbsKcqUKSMKFCgg+2terVo1MWbMGCHE/y+n8uLFC+Hv7y8WLVoka+wePXqIxYsXyxojJ9u2bRN169aV/bM7M45OymTv3r24du0ahBDw8vJCw4YNpTH2cmnTpg0OHjwIOzs7lC1bNstlLTlncM2ul74u5xV4+PAhFi5ciAsXLsDc3Bzly5dH//79ZW+RSHfr1i1ERkZK57tkyZJ48+aNrENAS5Qogfnz56NFixawsrJCRESEtO3UqVNYu3atbLHfNypD7vP94MED1KtXD0+fPpWmLoiIiICjoyP27t0re4tIWFgYZs+eLV0+TR+dJOdq6QBQsGBBHDhwAOXLl4darcaZM2fg6emJAwcOYOjQobK2hCr5/hZCYOTIkZg/f750ScXMzAxBQUGydjBN9/DhQ/z4448an+d9+/aVvRUo43va1tYWx44dQ9myZXHhwgW0atVK1mkrpk6dijlz5qBFixbw9vbO8l0i58Sptra2eP36Nd6+fQsTE5Msn6FyXcZjEvOPqVOnZjuTY2pqKr766itZRyd17979vY+vXLlSttgfmmNAiXkFdKFfv3748ccfs2x/9eoVWrRoIeslF0tLS0RGRqJo0aJwdnaW1rO5c+cOKlWqJPvaLkp69eoVfvnlF42ktVOnTtn2R9OmmJiYHJOkU6dOoWbNmrLFtrW1RXh4OIoXL44SJUrg559/hq+vL27fvg1vb2+8fv1atti54f398uVLREZGwtzcHB4eHtIoPTnt378/x24ACxculHUaficnJxw4cABeXl4oW7Yspk6dCn9/f1y4cAF16tSRhnzLQcnh3R+aZFG2vng6bffJxQoVKiSWLl2qse3t27fiiy++yHOjFypVqiSePXsmhHi3srAue5RfuHBBWj03c9O2Lpu6S5YsKUaPHq2x7eXLl6Ju3bqibt26ssYuVaqUOHXqlBBCiLp164qpU6cKIYRYv369cHBw0Ho8W1tb8fjxYyGEEN27d9dY4Ta/8PT0FE+ePMmy/dixY7KPhKtbt660WnunTp1E06ZNxbFjx8TXX38typYtK2vs/EqtVmc72jQ4OFj2VepbtWolli1bJoQQYvjw4aJkyZJi0qRJonLlysLPz0/W2Lo2ePBg8fLlSyHEu9WyU1JSdF4HtsT8Izw8HA0bNsTSpUvRoUMHpKSkoGPHjrh27RoOHDgAJycnpauoNebm5rh58yaKFCkCQ0NDxMbG6mym3NwyCdjdu3dRt25dDBs2DIMHD8aLFy/QpEkTGBkZYefOnVkWZdSmkSNHwtraGt9//z1+++03dOrUCW5ubrh37x4GDx6MadOmaTVegQIFcPHiRRQvXhyGhoaIi4vT2eSNW7ZsQbNmzWBsbIwtW7a8t6ycHap79+6N8+fP49ChQ9Lig0eOHEHLli0xfvx4DB48WLbYu3fvxqtXr9C2bVvcuXMHLVu2xLVr12Bvb48NGzZofXE8JV/ztm3bIiQkBNbW1lkW1c1MzsvkK1euxIgRI3D48GFpMdlZs2Zh4sSJ2LZtmywTHKa7c+cOXr58ifLly+P169cYNmwYjh07hpIlSyI4ODhPtW4bGxvj/v37cHR01Pl3STomMRkcOnQIrVq1wqpVq7B8+XLcvn0bBw4cgKOjo9ZjKTmDa61atVCgQAHUrVsX48ePx7Bhw1CgQIFsy2r72nV0dDSKFi0KlUqleFP35cuX4ePjgzFjxmD9+vUwNTXF9u3bZU1gsnPq1CmcOHECJUuWlOWLvFGjRvjrr79QpUoVhIaGomPHjjn2+dH2YoiZk9ac6KJ/Rvv27fHo0SPs2bMHJ0+ehL+/PyZNmpTjIqhyevbsWY4LNf5XSr7m3bt3x/z582FlZaXoZXLgXdIyd+5cHDt2DBs2bMCUKVOwc+dO1K5dW9a4ujZkyBBMnDgRlpaWWWbNzUzbw7s9PDzQoUMHNG7cGL6+vggLC8tx0ky5ZodmEpPJli1b0K5dO5QpUwYHDhxAwYIFZYkzfvx4DB8+HBYWFhg/fvx7y44dO1arsa9fv46xY8fi9u3bOH/+PLy8vLId4qpSqWSdAj83OHXqFBo2bIgaNWpg27ZtOl3TRVf++usvBAcH4/bt29i0aROaNGmSY7+E7FaizStSUlLQokULvHr1ChcvXsTUqVNl7RtByhs1ahR++uknpKamYteuXVlmi84L0pMHGxsb+Pj45JgYq1Qqrc/+vnnzZnz77bd49OhRjq3q6bHl+pGSr5OYnJo7T506hZIlS2okMHI1faampuLYsWMoX7687NO+ZyfjrzYlXL9+HQsWLJDWsyldujQGDBgAT09PrcfKqcUrOjoahQoV0khgtJ28fahZPyM5L6tkXOMlr8tu4cUXL16gU6dOaNGiBb777jtpu7YXYfzQpZSM5PpsSUlJQePGjbF06VKUKlVKlhgf49GjR7h+/TpUKhVKlSol22dNTgsvzpo1C/Xq1UP16tWlbdoepfMprWq6mGxP116+fAlra2tcv349x/OrVqtliZ2vk5gPNXdmJGfTp5mZGSIjI9/bszwvSu8PUrVqVdSqVQvAuwTy7NmzWLt2rdYnbfpQi1dG2m79ytysn92vlvQPQbmHtStp//79CA4O1khaAwMD0bBhQ63Hyq7PVcb7cg41zvjZIoRAWFgY1Gq1tD5UeHg4nj9/jrZt28r62eLg4IATJ07ofBZVAEhMTES/fv2wfv166fU1NDREx44d8eOPP2r9S+1jPz/lGKWTcWTO06dPMWnSJDRp0kT6XDt58iR2796NMWPGyNb/6u3btzAzM0NERATKlSsnS4z3OXz4MOrUqSP7xJVZ6LonMWVVtWpVsW/fPqWroXPu7u7SpFAZ/fDDD8Ld3V2BGunG3r17ReXKlcWuXbtEQkKCSExMFLt27RJVq1YVe/bsUbp6slmwYIEwMjISX375pZg3b56YN2+e6NSpkzA2NhYLFizQerz0CdY+5ianESNGiF69eom3b99K296+fSv69Okjhg0bJmvsIUOGiKCgIFlj5KR9+/bCw8Mjy/+5p6en7JMbKqlt27bZ/j8vWLBAtGrVStbYxYsXFxEREbLGyG2YxPzjzp074saNG1m237hxQ9y9e1fW2Lt37xYVK1YUW7duFQ8fPhQJCQkat7zK3Nw82wUgb9y4IczNzWWNfebMGWmYc0anTp0SZ8+elTV22bJlxdGjR7NsP3LkSJ4bzp+Ri4tLth/uCxcuFM7OzgrUSDcKFiworl27lmX7tWvXhJ2dnayx+/fvL6ytrUXlypVFnz59xODBgzVucrKwsMjx/9zCwkLW2EqytLTM8XNN7sVlV6xYIZo1ayaePn0qa5zchGsn/aNbt244ceJElu2nT59Gt27dZI3dtGlTXLhwAf7+/ihSpAhsbW1ha2sLGxsbRfrJ6IqPjw+OHj2aZfuxY8dkHQIJvJvsLiYmJsv2Bw8eoF+/frLGvn37drZN6Wq1WtbZPJWWmJiIpk2bZtneuHFjJCYmyhp76tSp2Y68WrFiBaZPny5r7Ldv32a7yGpkZKTsi8tevnwZlStXhrW1NW7cuIE///xTusm9jo+9vX2O/+dyf6598cUX2U5VMHPmTNnXFrK3t8+2g/zmzZtl74s2f/58HD16FC4uLvD09ETlypU1bnkRV7H+x59//ok6depk2V6zZk3ZRzAcPHhQ1v3nVv7+/ggKCkJ4eLg0Y+qpU6fw66+/Yvz48RqdYbXd2VXJFZWrVauGwMBArFmzRlrlNy4uDkOHDtXofJjX+Pv7IywsDMOHD9fY/scff2S7wrQ2LV26NNvlHMqWLYsvv/xS1lV/u3fvjh49euDWrVsa/+fTpk37pH55/4aSny3/+9//MGTIEKxatUrj/3z48OEYM2aMrLEPHz6cbb+2pk2bYtasWbLGHj9+PHr27IlDhw5p9PXbtWsXfv75Z1ljt27dWtb950b5umNvRmq1GocOHZLWdEkXHh4OHx8fvHjxQqGa5V3vm8MiIzk6Xtrb22Pbtm3Sh0y6EydOoEWLFoiPj9dqvIxu3bqFNm3a4Pr16yhatCgA4N69eyhVqhQ2b96s2Irmcps0aRJmzZqFOnXqaHy4Hz9+HEOHDoW1tbVUVtujR3LqPH/nzh14eXnh77//1mq8jNLS0jBr1izMmzcPsbGxAABnZ2cMGjQIQ4cOhaGhoWyxlVSpUiXcunULSUlJGv/npqamWToaa3s0oLm5OSIiIrKMcrx27RoqVaqEN2/eaDVeZqdPn8b8+fM11mUbOHBgnhzirTQmMf9o2bIlLCwssG7dOulDJTU1FR07dsSrV6+wc+dOrcbLbvhnTrQ9/PNjubu7o0GDBpgwYQIKFy6sSB3k8uWXXyIuLg5//PGH1OT9/PlztG7dGoUKFcLGjRtljS+EyHaxUTkXGv0QAwMD+Pj4YObMmahSpYrW96/k6BEPDw+MHTsWX331lcb21atXY+zYsbKuKZNR+mWzjAmbtuWG4d2AsqMBq1Wrhs8//zzLZJ3jxo3D1q1bER4ertV49H49evSAr68vAgICtL5vJjH/uHr1KurVqwcbGxupP8bRo0eRmJiIAwcOaH3IWsbhnx/64lJqyO24ceMQHR2NI0eO4Pbt2zqJ+fz5c9jY2MgeR+kVlXOjkJAQREdHY8+ePTh+/LjS1dGq6dOnY+bMmZg5c6Y0zf/+/fsxYsQIDB06NNvFX/VVbhneraT0SUs7d+6scb7XrVuHX3/9NU9ddtGHOWp8fHwQHR0Na2trXLhwQav7ZhKTwcOHD7Fw4UKNFXb79+8POzs7rcfKOOX+n3/+iWHDhmH48OEa8wrMnj0bM2bMyFNvuIymT58ONzc3dOzYEQDQvn17/P7773B2dsaOHTtQoUIFWeMrtaIyvZOamopLly6hWLFisnf0FEJg5MiRmD9/PpKTkwG8u8QUFBSk9aU1cpOgoCA8e/YMS5Ys0Whh7tu3L6ytrTFz5kzZYsfExEClUqFIkSIAgDNnzmDt2rXw8vJCnz59ZIubbvv27ZgyZQoiIiKk9/fYsWNRv3592WPrUm6Yo+ZjXb9+XfsTmSoxJIo0VatWTWzfvj3L9u3bt4vKlSvLGvvgwYOy7v993N3dxfHjx4UQQuzZs0fY2NiI3bt3i549e4pGjRopVq/8JCEhQYSFhYmrV6/KHmvQoEHi559/FkK8myeldu3aQqVSCUtLS539H7548UKcOXNGXLp0Sfz99986iakkJYd3161bV6xatUoIIURsbKywsrIStWrVEvb29mL8+PGyxs6vlJyjRilMYjI4cuSI6NKli6hVq5a4f/++EEKIVatWZTvXgTaZmZll+yVy9epVYWZmJmtsU1NTUbx4cTFx4kRx7949WWNlZmZmJsUcOHCg6NOnjxBCiOvXrwsbGxvZ469atUrUqVNHODs7SxOezZkzR2zevFn22Epp37699CH3+vVr4eHhIYyNjYWRkZH47bffZI1duHBhaQ6esLAw4eLiIq5fvy5Gjx4tateuLWvsdDdv3hS7du0Sr1+/FkIIkZaWppO4SrGxsRFhYWFZtoeFhcn+HrOxsZESqHnz5knnePfu3TqZzDI+Pl789NNPYtSoUdK8KeHh4dJne16k5Bw1ISEhYtu2bdL94cOHC7VaLWrVqiXrhJKcJ+Yfv//+O5o0aQJzc3OcP38eSUlJAN6ttTJlyhRZY5cpUwaTJk3SGCGRlJSESZMmoUyZMrLGfvjwIQYNGoRNmzbB3d0dTZo0wcaNG6UmdznZ2tpKc7Xs2rVLmnpeCCF7P6DFixdjyJAhaNasGeLj46V4tra2mDt3rqyxlXTkyBGpz1dYWBiEEHj+/Dnmz5+PSZMmyRr7yZMncHJyAgDs2LED7du3R6lSpdCzZ09cunRJ1thPnz6Fn58fSpUqhebNm0ujhHr16oWhQ4fKGltJ6cO7Z82ahWPHjuHYsWOYNWsWevXqJfvw7pSUFGmh0X379knTJJQuXVp6/eVy8eJFlCpVSuoL9fz5cwDv/ufzUv+nzJSco2bKlCnS+nMnT57EwoULMWPGDBQsWFDey1iypUd6pmLFiiI0NFQIIUSBAgXE7du3hRBC/Pnnn8LR0VHW2KdPnxaFChUSBQsWFH5+fsLPz08ULFhQODg4iNOnT8saO6M///xTDBgwQBQsWFDY2dmJAQMGyDqFdb9+/USxYsVEw4YNhb29vXjx4oUQQoj169eLSpUqyRZXCCHKlCkj/ULNeL4vXbok7O3tZY2tpIytXwEBAdKU9NHR0bL/UitatKjYvXu3ePv2rXB1dRVbt24VQghx+fJl2VsFAgICRJMmTURMTIzG+d69e7fw8vKSNfb7hIaGilu3bsm2/9TUVDF9+nTh4uIiVCqVUKlUwsXFRUyfPl1jGQQ5VK9eXQQFBYkjR44IMzMz6bPk5MmTonDhwrLG9vPzE8OHDxdCaL6/jx8/LooVKyZr7Pfp3r27dIlNDitXrhQGBgaiefPmYuLEiWLixImiRYsWwtDQUKxcuVK2uEK8m4E9OjpaCPFuqY2AgAAhxLv3d8GCBWWLyyTmH+bm5tLyAhn/6W/fvi1MTU1lj//q1SuxdOlSMXjwYBEYGCiWLVsmXr58KXvczB48eCDGjh0rTE1NhaWlpTA0NBR169YVly9f1nqs5ORkMXPmTDFw4EBx/vx5aXtwcLD46aeftB4vIzMzM6mJM+P5vnHjhuyX8N5HpVIJX19fce7cOVn27+HhITZs2CBevnwpHBwcxP79+4UQQkRERMievI0dO1ao1WpRunRpUbRoUalPyvLly0XNmjVlje3o6Ch9iWY833fu3JE9eXsflUolTExMRP/+/WWPpetlTA4ePChsbGyEgYGB6N69u7R91KhRok2bNrLGtra2lpLDjOc7KipKJ5/nOalfv75wc3MT5cuXly3GqVOnROfOnUWlSpVExYoVRefOnbNdYkXbHBwcpM/xjI0Ct27dkvU9xhl7/+Hs7Ixbt27Bzc1NY/uxY8dQvHhx2eNbWFjopMd+dlJSUvDHH39gxYoV2Lt3L6pWrYqFCxeiU6dOePbsGYKCgtC+fXutz2RrbGyMYcOGZdkeGBio1TjZcXd3R0REBIoVK6axfefOnfDy8pI9fk5WrFiB6OhoDBw4UJZhzoGBgejSpQsKFCiAYsWKwcfHB8C7y0ze3t5aj5fRuHHjUK5cOcTExKB9+/bSpQZDQ0OMHDlS1tivXr2ChYVFlu1PnjyR6qGEtLQ0REVFYffu3bLHknNumuz4+PjgyZMnSExM1Bh91qdPn2zPhTaZmZllu5TF9evX4eDgIGvs9zl06JBUD7nUqFEDv/zyi2z7z0mjRo3Qq1cvVKpUCTdu3ECLFi0AAFeuXMnyvapVsqVHemb69OnCy8tLnDp1SlhZWYmjR4+KNWvWCAcHB1lW2M0t+vfvL+zt7YW9vb0YNGiQuHTpUpYy0dHRQqVSKVA7+axYsUIULlxYrF+/XlhaWop169aJSZMmSX/nZefOnRObNm2SLt8JIcS2bdvEsWPHFKyVvJo3by7+97//CSHe/TK/c+eOSE1NFe3btxft2rVTuHakbb179xatW7cWycnJ0vmOjo4WlSpVEoMGDVK6enlSfHy86Nevn/D39xc7d+6Utv/www9i0qRJssVlEpPB999/L8zNzaVrx2ZmZtIHX17VoEEDsXbtWpGUlJRjmZSUFHHo0CEd1ko3li1bJooWLSqd7yJFikhDgHVJl8OcL1y4kONj2Y1iySuuXLkiHBwcRNOmTYWJiYn44osvRJkyZYSjo6OsfVKEEMLAwED89ddfWbY/efJEGBgYyBo7v0pISBB16tQRNjY2wtDQULi6ugpjY2NRr1492S/TKzVKJ7/iZHeZvH79GlevXkVaWhq8vLxQoEABpaskm5SUFPTp0wdjxozRySWz3OrJkydIS0tDoUKFdBKvQ4cOqFevHvr37483b96gQoUKiIqKghAC69evR7t27WSL7ezsjOPHj2c537///ju+/vprvHr1SrbYSouLi8PixYsRHh6OtLQ0VK5cGf369ZMWJ5SLgYEB4uLisvx/PXz4ECVKlJB9HZ/87MCBAzh//rx0vtNHQMrJ09MTixcvRoMGDXDy5En4+flh7ty52LZtG4yMjGRd6kFpR48exdKlS3Hnzh38+uuvKFy4MFavXg13d3fUrVtXlpjsE5OJhYWFND13XmdsbIywsDDZV5TN7QoWLKjTeEeOHMHo0aMBaA5zDg0NxaRJk2RNYr777jv4+fnhxIkT0pf3hg0b0KNHD4SEhMgWNzdwcnL6pPV8/qv58+cDeLcW1M8//6zxgyg1NRVHjhxB6dKldVaf/KhBgwbSsgO6EhMTIy3iunnzZnzxxRfo06cP6tSpI/VBy4t+//13BAQEoEuXLtlOU7Jjxw5Z4jKJ+cerV68wbdo07N+/H48ePUJaWprG47paIE7X2rRpg82bN2PIkCFKV0Wn/vrrLwwbNkw635kbJOWcpyYhIUFaymLXrl1o164dLCws0KJFCwwfPly2uADwww8/4OnTp2jYsCGOHj2KXbt2oVevXli9erWsyVNu8Pz5c5w5cybb9/fXX3+t9XjBwcEA3s17lHHafwAwMTGBm5sblixZovW4H2vVqlWoU6cOSpQooVgd5LR///4cP89XrFghW9wCBQrg6dOnKFq0KPbs2SPNkWJmZpanW90mTZqEJUuW4Ouvv8b69eul7bVr18aECRNki8sk5h+9evXC4cOHERAQAGdnZ0VXE85I7pWkS5YsiYkTJ+LEiROoUqUKLC0tNR4fOHCg1mN+DLmPu1u3brh37x7GjBmj8/Pt6uqKkydPws7ODrt27ZLe8PHx8TAzM5M9/rx58xAQEICaNWviwYMHWLduHVq1aiV7XCVt3boVXbp0watXr2BlZaVxvlUqlSxJzN27dwEAvr6+2LRpk+zrQ32qbt26wdjYGH369MGCBQt0GlvuBGr8+PGYMGECqlatqvP3t2KjdD5AzpWkgXcjrurVq5dlu7W1tTTZoBzYJ+YfNjY22L59O+rUqaN0VTTIvZK0u7t7jo+pVCrFWqDkPm4rKyscPXoUFStW1Pq+P2TRokUYNGiQNMz5/PnzMDAwwIIFC7Bp0yYcPHhQq/G2bNmSZVtKSgoGDx6Mxo0bSzOpAtD4W5cMDAzg4+ODmTNnokqVKlrff/pMvVOmTJF9eK8+SR/e/c033+g0roGBgawJlLOzM2bMmCHbF/b7PH/+HP/73/8QExOD7777Dk2bNgUAjB07FiYmJtKlZF2TcyVpAChRogSWLl2Khg0bwsrKChcuXEDx4sWxatUqTJs2TetTdKRjEvMPd3d37NixQ/Zp/il38PLywi+//IJKlSopEj88PBz37t1Do0aNpL4S27dvh42NjdYTaQODj1tdRKVSyb7cQ05CQkIQHR2NPXv2yDI/jqWlJS5duqSzDuxDhgzBxIkTYWlp+cFLtXPmzNFJnXIbORMoe3t7nDlzJs9eKvsvZFlJGsCMGTMQGhqKFStWoFGjRtixYweio6MxePBg/PDDD+jfv7/WYwJMYiRr1qzBH3/8gdDQUJ3/Ujt06FCe7vCVEyWPe8+ePZg9ezaWLl2q8ybeixcvonz58tk+tnnzZrRu3Vqn9ckP2rZtiy+//BIdOnTQSTxfX1+EhYXBxsYGvr6+OZZTqVQ4cOCAbPUwNDREbGxslpFRT58+RaFChRRLWuUWFBSEAgUKKDZoQYlROrnB6NGjERwcLK0DaGpqimHDhmHixImyxczXSUylSpU0rpXeunULQgi4ubnB2NhYo+z58+dlq4eZmRkKFy6M7t27o2vXrnB1dZUtFpB7fiXq+rhtbW01zverV6/w9u1bWFhYZDnfz549k60e+XmYc0aJiYk4cOAAPD09ZWkBzXgZ7fHjx5gwYQK6d+8Ob2/vLOdbqctoclNyeLeuE6iMn2VpaWkIDQ1F+fLlUb58+SznW87PtYyjdFavXo2rV6+iePHiWLRoEbZt2ybbKB0ACA0NRcGCBaV+OCNGjMCyZcvg5eWFdevWZZmhXA66nqYkX3fszS2/eB8+fIg1a9YgJCQE48aNg5+fH3r27InWrVvDxMRE6/H+/PNPpKSkSH/nRO7OcLo+7tyyOrXSw5yVGrWReX6cqlWryjo/Tnbv7+xGSSh5GU0uuWF4d06/j5OSkmT7XMsovb/b5cuXNbbL/bmm1Cgd4N1K0osXLwbw/ytJp89RM3jwYFnnqElISEBqairs7Ow0pil59uwZjIyMZFv2Il+3xORGERERWLFiBdatW4e0tDR06dIFPXv2RIUKFZSumqzy23EPGjQI+/bt0/kw5w+N2ggLC5MttpOTE3bv3o0KFSpg7dq1GDt2LC5cuIDQ0FAsW7bsvQm1PvP19X3vF6ccl5PSO+xHR0ejSJEi2Q7vnjBhAmrUqKH12OkJ1ODBgzFx4sRsE6ioqKg8e74tLCxw9epVuLm5aXRwvXPnDry8vKRLLXLFvnbtGooWLYqgoCDExsZi1apVuHLlCnx8fPD48WPZYjdr1gyff/45+vbtq7F9yZIl2LJli3wtUDqeITjXOnPmTLYrfZ46dUqcPXtWp3XR5UrSuYkuj3v79u1i165dWbbv3r1b7NixQ+vxsvPVV18JDw8PYWFhITZv3qyTmE5OTmLVqlU6iZWZmZmZuHfvnhBCiICAABEUFCSEeLc2l5IrScstMDBQ49avXz9Rp04doVarxcCBA2WN7ePjI549eyZrjMzc3NyEm5ubUKlUwtXVVbrv5uYmSpUqJRo3biz7qsrPnz8XT58+zbL96dOnsq/kXbx4cbF3714hhOYK2qGhoaJMmTKyxlZqJWkhhLC1tc126ZTIyEhhZ2cnW1wmMf+oVq2a+PXXX7Ns//3330X16tVlj5+cnCx+/fVX0axZM2FkZCRq1qwpfvrpJ/Hy5Utx79490alTJ9neAGfOnBHDhw8XHTt2FG3atNG4yU2p4/b29hbbt2/Psn3nzp2ifPnyWo/3xx9/ZLn99ttvwtXVVfTs2VNju5zs7OxkXysoJx4eHmLDhg3i5cuXwsHBQezfv18IIURERISwt7eXNfaAAQPEvHnzsmxfsGCBYgsCjh07VgwdOlSR2LqgRAKVrmnTpuLHH3/Msn3x4sWiWbNmssZWcjHhzp07i8qVK4uePXsKCwsL8eTJEyHEu8+fsmXLyhrbwsJCXLx4Mcv2ixcvCnNzc9niMon5h6WlpZQxZ3Tnzh1RoEABWWMruZL0unXrhLGxsWjRooUwMTERLVu2FJ6enkKtVotu3bppPV5GSh63mZmZuHv3bpbtd+/eFRYWFlqPl77I5Iduci8IOGLECDFhwgRZY+Tkxx9/FEZGRsLGxkZUqFBBpKamCiGEmD9/vvDx8ZE1touLizh37lyW7eHh4aJw4cKyxs7JzZs3ha2trdb3O3jwYGmRw8GDB7/3llcp1SqQTqnFhJVaSVoIIerXry/69++fZXvfvn1F3bp1ZYubrzv2ZmRqaoq//vory4iR2NhYGBnJ+zJdvXoVCxYsQLt27XLs8Obi4qL1SdCAdx3BgoOD0a9fP1hZWWHevHlwd3fHN998I/vCeEoet1qtxp07d7IMr75161aWWYu1IXMHWqX8/fffWLZsGfbt26fzURt9+/ZFjRo1pPlx0uevKV68OCZNmiRbXODdiBi1Wp1lu7W1NZ48eSJr7JycPHlSlhmaley4n1tGPiYlJeHt27dZtqekpOhk6v/Jkydj9OjROl9M2MbGBgsXLsyyXRdrhk2ePBkNGzbEhQsX4OfnB+DdIIKzZ89iz549ssVlEvOPRo0aYdSoUfjjjz+kD7vnz5/j+++/R6NGjWSLm5KSgqJFi6JGjRrv7bFvZGSE+vXraz3+7du3peF4pqamePXqFVQqFQYPHowGDRrI9s+v9HH7+/sjMDAQYWFh0oRYt27dwtChQ/PscFvg3Rw1So3auHjxIqpUqZJlRt4WLVpg8+bNssYuWbIkdu3alWXCrZ07d8o+AV7btm017gshEBsbi3Pnzskyj0nGpF+OHwDvk1tGPlarVg3Lli3LMhvwkiVLZJkROiOlRumkU2qOmjp16uDkyZOYOXMmNm7cCHNzc5QvXx7Lly+Hh4eHbHE5OukfDx48QL169fD06VNpFteIiAg4Ojpi7969ss5hYmNjg/Pnz+tsNtGMXF1dsWPHDnh7e6NChQoYOXIkOnXqhJMnT6Jp06ZISEiQLbaSx52QkICmTZvi3LlzKFKkCADg/v37+Oyzz7Bp0ybY2NjIGl+pYc5KUnJ+nBUrVqB///4YPny4tKrx/v37MXv2bMydOxe9e/eWLXb37t017hsYGMDBwQENGjRA48aNZYubnx0/fhwNGzZEtWrVsm0V+Oyzz2SLrdgoHSg7R41SmMRk8OrVK/zyyy+4cOGClEV26tQpS5O7tqVPwKXEStKdO3dG1apVMWTIEEyePBnz5s1Dq1atsHfvXlSuXFnWeQWUPG7g3S/ivXv3apzv7BYw0zYlhzkracKECVi5cmWO8+O0b99e1viLFy/G5MmT8fDhQwCAm5sbxo0bJ8vij+lSU1Nx7NgxeHt7SyuX65ISw7tzi4iICMycORMRERHS+3vUqFGytgoAgJ2dHY4fP55lAsdr166hTp06ePr0qWyxK1WqhMGDB+Prr7/WGN4dERGBpk2bIi4uTqvxEhMTpZalxMTE95blPDF52OTJkzFr1iz4+fnpfCXpZ8+e4e+//4aLiwvS0tIwa9YsHDt2DCVLlsSYMWNkXXlXyeNWkq4Xp2vbti1CQkJgbW2d5dJGZnImrYBy8+Nk9PjxY5ibm+ukjwLwbmbqyMjI9y62KpfBgwdr3E9JSUFERAQuX76Mrl27Yt68ebLFzq8JlKWlJU6dOgVvb2+N7ZcuXUKNGjXw+vVr2WLreo6ajLMyGxgYZHu+hRCyTijJPjG5wM8//wwbGxuEh4cjPDxc4zGVSiXrl3nGX4cGBgYYMWIERowYIVu8jJQ8biUlJyejdu3aOounVqulD5fsOrfq0rx58xAQEICaNWviwYMHWLduHVq1aqXTOjg4OOg0nre3N+7cuaNIEhMcHJzt9nHjxuHly5eyxs68QnzmBCqvUrI/jrOzM27dupVlwMKxY8dkuWx/4MAB6TtE1/2v0rElJh/6ULNfRnJ3QsuPlFqcTgiBe/fuwcHBQWeLnGZcvyhdSkoKBg8ejMaNG2t0os6rHar37NmDoKAgTJw4MdsWRyXeY7du3UL16tVlXSMsJ+kJ1KxZs3QeWxeU7I+j1ErSSmISkw/l1OyXnby2pkxuMGjQIKxatUrni9OlpaXBzMwMV65ckb1fQLr0YdQfkhfXL0qX8TXI+L6Tu5n9fVavXo2goCCpf5AuKZlA6YpS/XEAZVaSThcfH4/ly5cjMjISKpUKZcqUQffu3WXtD8bLSQpRcj6FjM1+UVFRGDlyJLp164ZatWoBeDd/RWhoKKZOnarVuEDumUdCSUoNczYwMICHhweePn2qsyQmt8yPoySlmtkB3Q/v/hhyzY+Tm1SsWBG//PKLIrGVmqPm8OHD8Pf3h1qtloaWz58/HxMmTMCWLVtkmSoDYBKjGCXnU8j4zzRhwgTMmTMHnTp1krb5+/vD29sby5Yt0/q169wyj4SSlPxSmzFjBoYPH47FixejXLlyitUjr8vYmTo6OhodO3aEqampzuuRuQ+UgYEBPD09MWHCBNmHd+fGBEouuWGUDqDsHDX9+vVDx44dsXjxYmnB0dTUVPTt2xf9+vXL8oNNW3g56SO4u7ujQYMGmDBhAgoXLqx0dbTKwsICFy5cyPLL/MaNG6hYsaKsPelzKwMDA/j4+GDmzJmyd8TTNVtbW7x+/Rpv376FiYkJzM3NNR6Xu4k/N86Ps2rVKtSpU0ea9FAbTExMEB0dDWdnZ40RHLqk9PDu3Do/To8ePeDr66vV0YG5YZQOoOwcNebm5oiIiICnp6fG9uvXr6NixYqyzZTMlpiP0LVrV0RHR6NevXq4ffu20tXRKldXVyxZsgSzZ8/W2L506VJZJ/jLzVasWIHo6GgMHDgQx48f18o+c8sw57lz58q27w/50Pw4SunWrRuMjY3Rp0+fLCNK/q3SpUtj1KhR8PX1hRACGzduzPFXsFzz1BgaGqJJkyaIjIzUeRKTmpqKbt26KZZAvc+dO3dw8OBBzJo1CxcuXNDKPnPDKB0AOH36dLaX4X18fDB69GhZY1euXBmRkZFZkpjIyMgsI9W0iS0xucTZs2fx66+/4t69e0hOTtZ4TM4vtR07dqBdu3YoUaIEatasCQA4deoUbt++jd9//x3NmzeXLTag3HHrWvfu3TF//nxYWVll+YWa2cqVK3VUK93S9fw4nyIqKgq7d+/GN998o5X9nThxAkOGDMHt27fx7NkzWFlZZZu0qVQqWVu/qlWrhmnTpkmjZHRJyflxPsb169ezfOHqOyXnqNmwYQNGjBiBAQMGaHyX/Pjjj5g2bZrG5H/ly5fXWlwmMf84dOgQfHx8FIm9fv16fP3112jcuDH27t2Lxo0b4+bNm4iLi0ObNm1k/1K7f/8+Fi9ejMjISAgh4OXlhW+//Vb2lhiljzujxMREHDhwAJ6enllm2tQmJYY555Yh9fb29jhz5oxWL9voAwMDA8TFxen8chKg7PBuJRMopSkxSgd41+Li7e2dpUWxX79+uHjxIo4ePSpb7A+NRFSpVPJcUpNtfWw9Y2pqKooXLy4mTpwo7t27p9PY3t7eYuHChUIIIQoUKCBu374t0tLSRO/evcUPP/yg07rokpLH3b59e7FgwQIhhBCvX78WHh4ewtjYWBgZGYnffvtNtripqanC2NhY3LhxQ7YYmalUKmFgYPBRNzmNGDFCTJgwQdYYOTEwMBB//fVXlu1PnjyR/bijoqJEWlqarDFyolKppFvG85x+X067d+8WFStWFFu3bhUPHz4UCQkJGjc5hYSEiG3btkn3hw8fLtRqtahVq5aIioqSNfahQ4eEtbW1cHV1FW3atBFt2rQRRYsWFdbW1uLQoUOyxj527JgwMzMTn332mRg3bpwYN26c+Oyzz4SZmZk4cuSIrLGjoqI++qZNbIn5x7Nnz7BmzRqEhITg4sWL8PPzQ8+ePdG6dev3rrKsDZaWlrhy5Qrc3NxQsGBBHDx4EN7e3oiMjESDBg0QGxur1Xj37t1D0aJFP7r8gwcPZOnQrOvjzsjJyQm7d+9GhQoVsHbtWowdOxYXLlxAaGgoli1b9t6RU/9V2bJlsXz5cqnJVW6HDx+W/v7QkHo5Z1JVan4cIOfWkIcPH6JEiRJa73SYW95jGc99duQa9gooOz+Op6cnFi9ejAYNGuDkyZPw8/PD3LlzsW3bNhgZGcl6qbpcuXKoXbt2tqN0jh8/LtsonXRKzlGjCK2mRHnEn3/+KQYMGCAKFiwo7OzsxIABA0RERIRs8YoUKSIuXrwohBCifPnyYu3atUIIIU6cOCGsra21Hq9QoUKiV69e4vTp0zmWef78uVi2bJkoW7asmD9/vtbrIITujzsjMzMzqcUtICBABAUFCSGEiI6OFpaWlrLG3rZtm6hbt664dOmSrHGy06BBA+l1zuiXX34R9evXlzW2j49PjjdfX19ZYs6bN0/MmzdPGBgYiMmTJ0v3582bJ+bMmSNat24tKlasqPW4Sr7H2rRpI7V0hIaGir///ltr+/4Uhw4deu9NTubm5iI6OloI8a4FMCAgQAghxOXLl0XBggVljW1mZiauXbuWZfu1a9eEmZmZrLGVpFTrF5OYHDx48ECMHTtWmJqaCktLS2FoaCjq1q0rLl++rPVYnTp1ErNnzxZCCDFp0iTh4OAgevXqJYoVKybatGmj9XhPnz4VQ4cOFba2tqJQoUKiefPmolevXqJ///6iS5cuolKlSsLExETUrl1b7NixQ+vx0+n6uDPy8PAQGzZsEC9fvhQODg5i//79QgghIiIihL29vayxbWxshImJiTAwMBBmZmbC1tZW4yYnc3PzbC9lXb9+XZibm8saWwlubm7Czc1NqFQq4erqKt13c3MTpUqVEo0bNxanTp3Selwl32PGxsbi4cOHQoicL6PJJbckUA4ODuL8+fNCCCEqVqwoQkNDhRBC3Lp1S/YfKbVr1xZhYWFZtoeFhYmaNWtqPV7GS3OZL9np8hJeqVKlpM/REydOCHNzc7F06VLx+eefy/p5zstJGaSkpOCPP/7AihUrsHfvXlStWhU9e/ZEp06d8OzZMwQFBSEiIgJXr17ValylVpL++++/sWPHDhw9ehRRUVF48+YNChYsiEqVKqFJkyayT4am5AraixYtwqBBg1CgQAEUK1YM58+fh4GBARYsWIBNmzbJOkwyNDT0vY/LeUnH09MTLVu2zDKkfujQodi2bRuuX78uW2wl+fr6YtOmTbL+T2VHifdY+fLlUblyZfj6+kqj4nQ1vDs3zI8DAF26dMG1a9dQqVIlrFu3Dvfu3YO9vT22bNmC77//XtZLOroepZNb5qixsLDAtWvXULRoUQQFBSE2NharVq3ClStX4OPjg8ePH8sSl0nMPwYMGIB169YBAL766iv06tUrywfMvXv34ObmxqnU84jw8HDcu3cPjRo1kqbl3r59O2xsbFCnTh2FaycPXQ+pzy3z4+QnSg7vVjKByuj58+f43//+h5iYGHz33Xdo2rQpAGDs2LEwMTGRdc4UXY/SOXz4MOrUqQMjIyNF+0AVKlQIu3fvRqVKlVCpUiUMHjwYX3/9NW7fvo0KFSrItmo6k5h/+Pn5oVevXmjXrl2OHXnfvn2L48ePa+UfIbcMe9W13HLcFy9ezPFX0ObNm9G6dWutxsstxw3odki9kvPjcJ0u3Q/vzi3z4ygpOjr6o8sWK1ZMxprollKtX0xi8O4yUp8+fTBmzBgUL15cJzHz60rSueW4nZ2dcfz48Szn+/fff8fXX3+NV69eaTVebjlupQgF5sfx9fVFWFgYbGxs4Ovrm2M5lUqFAwcO6KROuhYdHY2iRYsqMjuykvPjAMDRo0exdOlS3LlzB7/++isKFy6M1atXw93dHXXr1lWkTrqg1Bw1irV+ydbbRs+o1Wpx+/ZtncXL2Es/JCREODk5iZEjR4o//vhD/PHHH2LkyJHC2dlZhISE6KxOupBbjnv8+PHCzc1N6gAphBDr168XFhYWYuPGjVqPp+Rxp4/S+Fj379/Xeh2UmB8nv8oN51sIZefH+e2334S5ubno1auXMDU1lT7bf/zxR9GsWTNZY+fXOWqUwiTmH926dZNGyuiaksNelaT0cQ8cOFB4eXmJp0+fil9++UWYm5vLOtFdOl0fd24ZUu/l5SVOnjwpy77p/yl5vnNLApVxRFL6RJpCvJs+w9HRUZaY6ZQapSOEEGXLlhW9e/cWb9++lba9fftW9OnTR5QtW1br8XLD+eblpH9MnjwZs2bNgp+fX7bTcw8cOFC22Pl1JenccNwBAQE4ffo0Hjx4gLVr16JVq1ayx9T1cT979gxTpkzBihUrYGxsjKpVq8LFxQVmZmaIj4/H1atXceXKFVStWhX/+9//0KxZM63GT7d9+3ZMmzYNixcvln3kW2a+vr7vvaSSly4nKXm+HR0d4e/vj969e6N69erZlklISMDGjRsxb948fPPNNxgwYIDW4qezsLDA1atX4ebmBisrK1y4cAHFixfHnTt34OXlhb///lvrMTPGVmKUDqD7laRzw/nmKtb/+Pnnn2FjY4Pw8HCEh4drPKZSqWRNYvLrStK6Pu4tW7Zk2da6dWscPnwYnTp1gkqlksr4+/trPX46XR+3nZ0dZs2ahUmTJmU73LdLly46GVL/1Vdf4fXr16hQoQJMTExgbm6u8bicHT0zr6KbkpKCiIgIXL58WdYh7UpQ8nxHRkZiypQpaNq06QcTqJkzZ8qWMDs7O+PWrVtwc3PT2H7s2DHZ+z0WKFAAT58+RdGiRbFnzx4MHjwYwLsFMbWdRGSm65Wkc8P5ZktMLqD0StJK0fVxf2joYzq551PIr+dbyflxcjJu3Di8fPkSs2bN0nnsvEzpOahmzJiB0NBQrFixAo0aNcKOHTsQHR2NwYMH44cffkD//v1li52f5qhJp+T5ZhKTSyi1krTSeNz567hzm1u3bqF69ep5drhvfjZ69GgEBwdLl45MTU0xbNgwTJw4Uda4+WmOmtwgXycxnEeCSF65aX6c7KxevRpBQUF4+PChzmOT/F6/fo2rV68iLS0NXl5e0qSWeVV+nKMmX/eJ+fPPP5GSkiL9nRM55ljILavc6lpuOu79+/dj//79ePToUZZZmFesWKHVWLnpuHXJxsYmV8yPk3m2YCEEYmNjce7cOYwZM0a2uKSMhIQEpKamws7ODlWrVpW2P3v2DEZGRlpPmHPL+zuvJCaf4uM6CeRRBw8ehI2NjfR3Tjc5Ri5Uq1YNvXv3xpkzZ3Isk5CQgJ9++gnlypXLM1Oy55bjHj9+PBo3boz9+/fjyZMniI+P17hpW245bl1Lf/8cOHAAK1asQKFChTBixAiEhYUhLCwMI0aMgKOjo9aTxszUarXGzc7ODj4+PtixYwfGjh0ra2zSvS+//BLr16/Psn3jxo348ssvtR4vt7y/Q0NDsX37dun+iBEjYGNjg9q1a39SK40+ydeXk5SUW4a96lpuOW5nZ2fMmDEDAQEBsuw/s9xy3EpKX9qjU6dOGtvXrl2LZcuW4dChQ7LETU1NxbFjx+Dt7S37rKWUO9jZ2eH48eMaHVkB4Nq1a6hTpw6ePn2q1Xi55f3t6emJxYsXo0GDBjh58iT8/Pwwd+5cbNu2DUZGRnnmx1FGTGIyOHv2LH799Vfcu3cPycnJGo/JdfKV7sWvFKWP297eHmfOnEGJEiVkjZOZ0setJCXnBTIzM0NkZCTc3d1li0G5h6WlJU6dOgVvb2+N7ZcuXUKNGjVk+19T+v2t5Bw1itH69Hl6at26dcLY2Fi0aNFCmJiYiJYtWwpPT0+hVqtFt27dlK4eadmIESPEhAkTlK5GvlKqVCkxZMiQLNuHDBkiSpUqJWvsqlWrin379skag3KP+vXri/79+2fZ3rdvX1G3bl0FaqQbDg4O4vz580IIzVmLb926JSwtLZWsmmzydcfejKZMmYLg4GD069cPVlZWmDdvHtzd3fHNN9/A2dlZ6eqRlv39999YtmwZ9u3bh/Lly8PY2FjjcY5G077g4GC0a9cOu3fvznZ+HDlNnjxZGl6b3YzceWmleHp3vhs2bIgLFy7Az88PwLuO/GfPnsWePXsUrp18GjVqhF69eqFSpUq4ceMGWrRoAQC4cuVKlon/8gpeTvqHpaWldKILFiyIgwcPwtvbG5GRkWjQoAFiY2OVriJpUX5d1VhpSs2Pk3H+jIyjpUQemzOD/l9ERARmzpyJiIgImJubo3z58hg1alSWy5l5iZJz1CiFScw/XF1dsWPHDnh7e6NChQoYOXIkOnXqhJMnT6Jp06ZISEhQuopE9C8dPnz4vY/Xr19fRzUhIm3i5aR/fPbZZ9i7dy+8vb3RoUMHDBo0CAcOHMDevXul5kgi+jRKzp/Rtm1bhISEwNraGtHR0ejYsSNMTU21sm/KfRITE6XLgh+aZDEvXT7MLXPUKIUtMf949uwZ/v77b7i4uCAtLQ2zZs3CsWPHULJkSYwZMwa2trZKV5H+o4xfapknP8ssLw5FVIKSq9yamJggOjoazs7OMDQ0RGxsLAoVKqSVfVPuk/EcGxgYZDvJYl68fJgbVpJWElti/pFx/ggDAwOMGDECI0aMULBGpG1qtVr6YFOr1QrXJn9QcpXb0qVLY9SoUfD19YUQAhs3bszxF/jXX3+ttbikjAMHDkif4wcPHlS4NrqTG1aSVlK+bonJ7eu6kDyEELh37x4cHBxgYWGhdHXyBSXmzzhx4gSGDBmC27dv49mzZ7Cyssr217lKpeICkKT3lJ6jRin5OonJqckxO3mp+TG/S0tLg5mZGa5cuZKnRyrQ/zMwMEBcXBwvJ+Uj8fHxWL58OSIjI6FSqVCmTBl0796dszbnMfn6clLGJseoqCiMHDkS3bp1Q61atQAAJ0+eRGhoKKZOnapUFUkGBgYG8PDwwNOnT5nE5BN3796Fg4OD0tUgHTl8+DD8/f2hVqulBSDnz5+PCRMmYMuWLRyNlofk65aYjJRa14WUsX37dkybNg2LFy/Os82s+V1+H7WRn5UrVw61a9fG4sWLYWhoCOBda3rfvn1x/PhxXL58WeEakrYwifmHkuu6kO7Z2tri9evXePv2LUxMTGBubq7xOPtI6L/8PmojPzM3N0dERAQ8PT01tl+/fh0VK1bEmzdvFKoZaVu+vpyUkaurK5YsWYLZs2drbF+6dKnss4mS7s2dO1fpKpDM8vuojfyscuXKiIyMzJLEREZGomLFispUimTBlph/7NixA+3atUOJEiWyXdelefPmCteQiP6N/DpqIz/bsGEDRowYgQEDBmh8nv/444+YNm0aypQpI5UtX768UtUkLWASk4FS67qQbnBIPVH+kHGtrOyoVKo8OfFdfsQkhvINDqknyh+io6M/umyxYsVkrAnJLV/3ieHohfyFQ+qJ8gcmJvlHvm6J4eiF/ItD6onyrtDQUBQsWBAtWrQAAIwYMQLLli2Dl5cX1q1bxyQnD8nXScyzZ88wZcoUrFix4oOjF/73v/9x9EIewiH1RHmXp6cnFi9ejAYNGuDkyZPw8/PD3LlzsW3bNhgZGXGB1zwkXycx6Th6If/x9PREy5YtswypHzp0KLZt24br168rVDMi+q8sLCxw7do1FC1aFEFBQYiNjcWqVatw5coV+Pj44PHjx0pXkbQkX/eJSWdmZoa2bduibdu2SleFdCQ4OBjt2rXD7t27sx1ST0T6q0CBAnj69CmKFi2KPXv2YPDgwQDefdZzoru85f3j0IjyqObNm+PmzZto1aoVnj17hqdPn6JVq1a4ceMG5wQi0nONGjVCr1690KtXL9y4cUPqG3PlyhW4ubkpWznSKl5OIiKiPOX58+f43//+h5iYGHz33Xdo2rQpAGDs2LEwMTHB6NGjFa4haQuTGMo3OKSeiChv4eUkyjeqVauG3r1748yZMzmWSUhIwE8//YRy5cpxBAORHrl3794nlX/w4IFMNSFdYksM5RscUk+Ud3Her/yJSQzlOxxST5T38EdK/sQkhoiI8gz+SMlfmMQQERGRXmLHXiIiItJLTGKIiIhILzGJISIiIr3EJIaIiIj0EpMYIiIi0ktMYoh0SAiBPn36wM7ODiqVCjY2NggMDFS6Woq5du0aatasCTMzM1SsWPGjntOtWze0bt1auu/j4yPra6hSqbB582bZ9k9E/x6TGCId2rVrF0JCQrBt2zbExsbixo0bmDhx4n/apza/ZENCQmBjY6OVfX2MsWPHwtLSEtevX8f+/ft1FvdTxMbG5pqJ0XR9fohyOyOlK0CUn9y+fRvOzs6oXbv2R5VPTk6GiYmJzLVSzu3bt9GiRQsUK1ZM6arkyMnJSekqEFEO2BJDpCPdunXDgAEDcO/ePahUKri5uWW5FOLm5oZJkyahW7duUKvV6N27N5KTk9G/f384OzvDzMwMbm5umDp1qlQeANq0aSPt80MuXLgAX19fWFlZwdraGlWqVMG5c+dw6NAhdO/eHQkJCVCpVFCpVBg3bhwAID4+Hl9//TVsbW1hYWGBZs2a4ebNm9I+01sINm/ejFKlSsHMzAyNGjVCTExMjvVQqVQIDw/HhAkTNGJdunQJDRo0gLm5Oezt7dGnTx+8fPnyo1/n99VVCAEHBwf8/vvvUvmKFSuiUKFC0v2TJ0/C2NhYipmxpSsqKgoqlQqbNm2Cr68vLCwsUKFCBZw8eVKjDj/99BNcXV1hYWGBNm3aYM6cOR/dgpJbzg+RPmASQ6Qj8+bNw4QJE1CkSBHExsbi7Nmz2ZabOXMmypUrh/DwcIwZMwbz58/Hli1bsHHjRly/fh1r1qyRkpX0faxcufK9+8yoS5cuKFKkCM6ePYvw8HCMHDkSxsbGqF27NubOnQtra2vExsYiNjYWw4YNA/AuATt37hy2bNmCkydPQgiB5s2bIyUlRdrv69evMXnyZISGhuL48eNITEzEl19+mWM9YmNjUbZsWQwdOlSK9fr1azRt2hS2trY4e/Ysfv31V+zbtw/9+/f/2Jf5vXVVqVSoV68eDh06BADSmjopKSm4evUqAODQoUOoUqUKChQokGOM0aNHY9iwYYiIiECpUqXQqVMnvH37FgBw/PhxfPvttxg0aBAiIiLQqFEjTJ48+aPrn1vOD5FeEESkM8HBwaJYsWLS/fr164tBgwZJ94sVKyZat26t8ZwBAwaIBg0aiLS0tGz3CUCEhYV9dB2srKxESEhIto+tXLlSqNVqjW03btwQAMTx48elbU+ePBHm5uZi48aN0vMAiFOnTkllIiMjBQBx+vTpHOtSoUIFMXbsWOn+smXLhK2trXj58qW0bfv27cLAwEDExcUJIYTo2rWraNWqlfR4xtfwY+o6f/58Ua5cOSGEEJs3bxZVq1YVbdu2FT/++KMQQojGjRuLoKAg6fkZX9+7d+8KAOLnn3+WHr9y5YoAICIjI4UQQnTs2FG0aNFC4zi7dOmS5XXNSW46P0S5HVtiiHKZqlWratzv1q0bIiIi4OnpiYEDB2LPnj3/af9DhgxBr1690LBhQ0ybNg23b99+b/nIyEgYGRmhRo0a0jZ7e3t4enoiMjJS2mZkZKRR99KlS8PGxkajzIdERkaiQoUKsLS0lLbVqVMHaWlpuH79+kc9/0N19fHxwZUrV/DkyRMcPnwYPj4+8PHxweHDh/H27VucOHEC9evXf2+c8uXLS387OzsDAB49egQAuH79OqpXr65RPvP998nN54cot2ESQ5TLZPwCB4DKlSvj7t27mDhxIt68eYMOHTrgiy+++Nf7HzduHK5cuYIWLVrgwIED8PLyQlhYWI7lRQ5rxAohoFKpNLZlvp/TtvfFyqn8x+znY+parlw52Nvb4/Dhw1ISU79+fRw+fBhnz57FmzdvULdu3ffGMTY2zlKvtLS0HI8hp3plJzefH6LchkkMkR6wtrZGx44d8dNPP2HDhg34/fff8ezZMwDvvlBTU1M/aX+lSpXC4MGDsWfPHrRt2xYrV64EAJiYmGTZl5eXF96+fYvTp09L254+fYobN26gTJky0ra3b9/i3Llz0v3r16/j+fPnKF269EfXy8vLCxEREXj16pW07fjx4zAwMECpUqU+6vkfqmt6v5g//vgDly9fxmeffQZvb2+kpKRgyZIlqFy5MqysrD66zpmVLl0aZ86c0diW8XX5GLn1/BDlNkxiiHK54OBgrF+/HteuXcONGzfw66+/wsnJSRrt4ubmhv379yMuLg7x8fHv3debN2/Qv39/HDp0CNHR0Th+/DjOnj0rfdm5ubnh5cuX2L9/P548eYLXr1/Dw8MDrVq1Qu/evXHs2DFcuHABX331FQoXLoxWrVpJ+zY2NsaAAQNw+vRpnD9/Ht27d0fNmjU/6VJKly5dYGZmhq5du+Ly5cs4ePAgBgwYgICAADg6On7w+R9bVx8fH6xduxbly5eHtbW1lNj88ssv8PHx+ej6ZmfAgAHYsWMH5syZg5s3b2Lp0qXYuXPnR7V45PbzQ5TbMIkhyuUKFCiA6dOno2rVqqhWrRqioqKwY8cOGBi8e/vOnj0be/fuhaurKypVqvTefRkaGuLp06f4+uuvUapUKXTo0AHNmjXD+PHjAQC1a9fGt99+i44dO8LBwQEzZswA8G70U5UqVdCyZUvUqlULQgjs2LFD47KKhYUFgoKC0LlzZ9SqVQvm5uZYv3699Hj68OT0kUHZsbCwwO7du/Hs2TNUq1YNX3zxBfz8/LBw4cKPfr0+pq6+vr5ITU3VSFjq16+P1NTUD/aH+ZA6depgyZIlmDNnDipUqIBdu3Zh8ODBMDMz++BzlTw/RPpIJT7lYi0RUTZCQkIQGBiI58+f51jm0KFDaNOmDe7cuQNbW1vdVS4X6N27N65du4ajR48qEv9jzg+RPuKMvUSkE7t27cL333+fLxKYWbNmoVGjRrC0tMTOnTsRGhqKRYsWKV0tojyHSQxRHlO2bFlER0dn+9jSpUvRpUsXHdfonWnTpikSVwlnzpzBjBkz8OLFCxQvXhzz589Hr169AOTe80Okj3g5iSiPiY6O1pipNSNHR8f/NPKG/jueHyLtYRJDREREeomjk4iIiEgvMYkhIiIivcQkhoiIiPQSkxgiIiLSS0xiiIiISC8xiSEiIiK9xCSGiIiI9NL/AaMRMklbQSMGAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# customers_df.groupby(['first_stop', 'following_stop']).plot.bar()\n",
    "to_plot_df = customers_df.groupby(['first_stop', 'following_stop']).count()\n",
    "\n",
    "to_plot_df.plot.bar()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-09-06 21:50:00')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index().iloc[-1]['timestamp']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Markov Probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_next_step(name, cus_df):\n",
    "    ## Add the first step\n",
    "    \n",
    "    cus_df = pd.concat([\n",
    "        pd.DataFrame({\n",
    "            'timestamp': [cus_df.reset_index().iloc[-1]['timestamp'] -pd.Timedelta(minutes=1)],\n",
    "            'customer_no': [name],\n",
    "            'location': ['entrance'],\n",
    "        }).set_index('timestamp'),\n",
    "        \n",
    "        custom_df,   \n",
    "        \n",
    "    ])\n",
    "    \n",
    "    \n",
    "    cus_df['next_location'] = cus_df['location'].shift(-1)\n",
    "\n",
    "    if len(cus_df[cus_df['location'] == 'checkout']) == 0:\n",
    "        cus_df = pd.concat([\n",
    "            \n",
    "            custom_df,   \n",
    "            \n",
    "            pd.DataFrame({\n",
    "                'timestamp': [cus_df.reset_index().iloc[-1]['timestamp'] + pd.Timedelta(minutes=1)],\n",
    "                'customer_no': [name],\n",
    "                'location': ['checkout'],\n",
    "                'next_location': ['exit']\n",
    "            }).set_index('timestamp')\n",
    "        ])\n",
    "        \n",
    "    else:\n",
    "\n",
    "        cus_df.loc[cus_df['location'] == 'checkout', 'next_location'] = 'exit'\n",
    "    return cus_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "next_locations_df = None\n",
    "for name, custom_df in df.drop(['hour','minute','day'], axis=1).groupby('customer_no'):\n",
    "    current_df = get_next_step(name, custom_df)\n",
    "    if next_locations_df is None:\n",
    "        next_locations_df = current_df\n",
    "    else:\n",
    "        next_locations_df = pd.concat([next_locations_df, current_df])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "next_locations_df.to_csv('./data/customer_transitions.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Can start from here after dataframe has been saved to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "next_locations_df = pd.read_csv('./data/customer_transitions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>timestamp</th>\n",
       "      <th>customer_no</th>\n",
       "      <th>location</th>\n",
       "      <th>day_number</th>\n",
       "      <th>next_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-09-06 07:04:00</td>\n",
       "      <td>friday_1</td>\n",
       "      <td>entrance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dairy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-09-06 07:10:00</td>\n",
       "      <td>friday_10</td>\n",
       "      <td>entrance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fruit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-09-06 08:03:00</td>\n",
       "      <td>friday_100</td>\n",
       "      <td>entrance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fruit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-09-06 17:19:00</td>\n",
       "      <td>friday_1000</td>\n",
       "      <td>entrance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fruit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-09-06 17:28:00</td>\n",
       "      <td>friday_1001</td>\n",
       "      <td>entrance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fruit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32298</th>\n",
       "      <td>2019-09-04 17:19:00</td>\n",
       "      <td>wednesday_995</td>\n",
       "      <td>entrance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fruit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32304</th>\n",
       "      <td>2019-09-04 17:02:00</td>\n",
       "      <td>wednesday_996</td>\n",
       "      <td>entrance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fruit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32308</th>\n",
       "      <td>2019-09-04 17:13:00</td>\n",
       "      <td>wednesday_997</td>\n",
       "      <td>entrance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dairy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32315</th>\n",
       "      <td>2019-09-04 16:58:00</td>\n",
       "      <td>wednesday_998</td>\n",
       "      <td>entrance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dairy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32319</th>\n",
       "      <td>2019-09-04 16:53:00</td>\n",
       "      <td>wednesday_999</td>\n",
       "      <td>entrance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fruit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7417 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 timestamp    customer_no  location  day_number next_location\n",
       "0      2019-09-06 07:04:00       friday_1  entrance         NaN         dairy\n",
       "4      2019-09-06 07:10:00      friday_10  entrance         NaN         fruit\n",
       "7      2019-09-06 08:03:00     friday_100  entrance         NaN         fruit\n",
       "10     2019-09-06 17:19:00    friday_1000  entrance         NaN         fruit\n",
       "13     2019-09-06 17:28:00    friday_1001  entrance         NaN         fruit\n",
       "...                    ...            ...       ...         ...           ...\n",
       "32298  2019-09-04 17:19:00  wednesday_995  entrance         NaN         fruit\n",
       "32304  2019-09-04 17:02:00  wednesday_996  entrance         NaN         fruit\n",
       "32308  2019-09-04 17:13:00  wednesday_997  entrance         NaN         dairy\n",
       "32315  2019-09-04 16:58:00  wednesday_998  entrance         NaN         dairy\n",
       "32319  2019-09-04 16:53:00  wednesday_999  entrance         NaN         fruit\n",
       "\n",
       "[7417 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next_locations_df[next_locations_df['location'] =='entrance']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>next_location</th>\n",
       "      <th>checkout</th>\n",
       "      <th>dairy</th>\n",
       "      <th>drinks</th>\n",
       "      <th>exit</th>\n",
       "      <th>fruit</th>\n",
       "      <th>spices</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>checkout</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dairy</th>\n",
       "      <td>0.392389</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222318</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.189852</td>\n",
       "      <td>0.195442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drinks</th>\n",
       "      <td>0.538956</td>\n",
       "      <td>0.027256</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.217794</td>\n",
       "      <td>0.215994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entrance</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.286639</td>\n",
       "      <td>0.153566</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.378050</td>\n",
       "      <td>0.181745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <td>0.500784</td>\n",
       "      <td>0.236966</td>\n",
       "      <td>0.136417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spices</th>\n",
       "      <td>0.251672</td>\n",
       "      <td>0.323616</td>\n",
       "      <td>0.272800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.151912</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "next_location  checkout     dairy    drinks  exit     fruit    spices\n",
       "location                                                             \n",
       "checkout       0.000000  0.000000  0.000000   1.0  0.000000  0.000000\n",
       "dairy          0.392389  0.000000  0.222318   0.0  0.189852  0.195442\n",
       "drinks         0.538956  0.027256  0.000000   0.0  0.217794  0.215994\n",
       "entrance       0.000000  0.286639  0.153566   0.0  0.378050  0.181745\n",
       "fruit          0.500784  0.236966  0.136417   0.0  0.000000  0.125833\n",
       "spices         0.251672  0.323616  0.272800   0.0  0.151912  0.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P = pd.crosstab(\n",
    "    next_locations_df['location'], \n",
    "    next_locations_df['next_location'], normalize='index')\n",
    "P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'P' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/2d/jjks8mqd6wj1vm8dxd7jbnl80000gn/T/ipykernel_7360/3034674200.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'P' is not defined"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(P.corr())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k, row in P.iterrows():\n",
    "    row_sum = sum(row)\n",
    "    assert round(row_sum, 5) == 1, f'The sum of row was {row_sum}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create choice probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "probs_dict = P.to_dict(orient='index')\n",
    "# probs_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert internal values, which is a dict to a list\n",
    "probs = {}\n",
    "for key in probs_dict.keys():\n",
    "    probs[key] = list(probs_dict[key].values())\n",
    "# probs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entrance\n",
      "['checkout', 'dairy', 'drinks', 'exit', 'fruit', 'spices']\n",
      "[0.0, 0.2866388027504382, 0.15356613185924228, 0.0, 0.3780504247000135, 0.18174464069030605]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'fruit'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import random\n",
    "\n",
    "current_state = 'entrance'\n",
    "states = list(probs_dict[current_state].keys())\n",
    "states\n",
    "\n",
    "print(current_state)\n",
    "print(states)\n",
    "# print(probs.keys())\n",
    "print(probs[current_state])\n",
    "# print(probs)\n",
    "\n",
    "\n",
    "random.choices(states, weights=probs[current_state])[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Customer at entrance\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# while current_state != 'exit':\n",
    "print('Customer at', current_state)\n",
    "state_probs = probs[current_state]\n",
    "\n",
    "choices = list(probs_dict[current_state].keys())\n",
    "current_state = random.choices(choices, weights=state_probs)[0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 1., 0., 0.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "initial_state = np.array([1,\t0,\t0,\t0,\t0,\t0])\n",
    "np.dot(initial_state, P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "TILE_SIZE = 32\n",
    "\n",
    "class Customer:\n",
    "    \"\"\"\n",
    "    a single customer that moves through the supermarket\n",
    "    in a MCMC simulation\n",
    "    \"\"\"\n",
    "    def __init__(self, name, supermarket, avatar_id, row=0, col=0, initial_state='entrance'):\n",
    "        \"\"\"\n",
    "        supermarket: A SuperMarketMap object\n",
    "        avatar : a numpy array containing a 32x32 tile image\n",
    "        row: the starting row\n",
    "        col: the starting column\n",
    "        \"\"\"\n",
    "    \n",
    "        self.name = name\n",
    "        self.state = initial_state\n",
    "        \n",
    "        ## Properties for animation\n",
    "        self.supermarket = supermarket\n",
    "        self.avatar = supermarket.get_tile(avatar_id) \n",
    "        self.row = row\n",
    "        self.col = col\n",
    "\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return f'<Customer {self.name} in {self.state} {id(self)}>'\n",
    "\n",
    "    def next_state(self, probs_dict):\n",
    "        '''\n",
    "        Propagates the customer to the next state.\n",
    "        Returns nothing.\n",
    "        '''\n",
    "        \n",
    "        probs_arr = {}\n",
    "        \n",
    "        for key in probs_dict.keys():\n",
    "            probs_arr[key] = list(probs_dict[key].values())\n",
    "           \n",
    "        next_location_choices = list(probs_dict[self.state].keys())\n",
    "                \n",
    "        probability_weights = probs_arr[self.state]\n",
    "                \n",
    "        self.state = random.choices(next_location_choices, weights=probability_weights)[0]\n",
    "        return self.state\n",
    "    \n",
    "      \n",
    "    def draw(self, frame):\n",
    "\n",
    "        y = self.row * TILE_SIZE\n",
    "        x = self.col * TILE_SIZE\n",
    "\n",
    "        one = y + self.avatar.shape[0]\n",
    "        two = x + self.avatar.shape[1]\n",
    "\n",
    "        frame[y:one, x:two] = self.avatar\n",
    "\n",
    "        return frame\n",
    "    \n",
    "    def move(self, location):\n",
    "        assert(type(location) is tuple)\n",
    "        new_row = location[0]\n",
    "        new_col = location[1]\n",
    "\n",
    "        if self.supermarket.contents[new_row][new_col] == '.':\n",
    "            self.col = new_col\n",
    "            self.row = new_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Sections:\n",
    "    \n",
    "    def __init__(self, dairy, drinks, spices, fruit, checkout):\n",
    "        assert type(dairy) is list\n",
    "        assert type(checkout) is list\n",
    "        assert type(drinks) is list\n",
    "        assert type(spices) is list\n",
    "        assert type(fruit) is list\n",
    "        \n",
    "        self.dairy = dairy\n",
    "        self.drinks = drinks\n",
    "        self.spices = spices\n",
    "        self.fruit = fruit\n",
    "        self.checkout = checkout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class SuperMarket:\n",
    "    \n",
    "    def __init__(self, name, transition_matrix, map, sections):\n",
    "        self.name = name\n",
    "        self.map = map\n",
    "        \n",
    "        assert name != None, 'The name should have a value'\n",
    "        assert transition_matrix != None, 'There should be a transition_matrix on construction'\n",
    "        assert sections != None, 'There should be a section on construction'\n",
    "        \n",
    "        self.customers = []\n",
    "        self.total_visits = 0\n",
    "        \n",
    "        self.transition_matrix = transition_matrix\n",
    "        self.sections = sections\n",
    "        \n",
    "        self.probs_dict = transition_matrix.to_dict(orient='index')\n",
    "        self.probs_arr = {}\n",
    "        \n",
    "        for key in self.probs_dict.keys():\n",
    "            self.probs_arr[key] = list(self.probs_dict[key].values())\n",
    "            \n",
    "    \n",
    "    def __repr__(self):\n",
    "        return f'<SuperMarket {self.name}>'\n",
    "    \n",
    "    \n",
    "    def add_customers(self, new_customers: list):\n",
    "        self.total_visits += 1\n",
    "        self.customers = [*self.customers, *new_customers]\n",
    "        \n",
    "        \n",
    "    def remove_customers(self, customer_arr):\n",
    "        for customer in customer_arr:\n",
    "            self.customers.remove(customer)\n",
    "        \n",
    "        \n",
    "    def tick_minute(self):\n",
    "        current_status = [ ]\n",
    "        \n",
    "        for customer in self.customers:\n",
    "            \n",
    "            new_customer_state = customer.next_state(self.probs_dict)\n",
    "            \n",
    "            if new_customer_state == 'exit':\n",
    "                # print('Removing customer with state', new_customer_state)\n",
    "                self.remove_customers([customer])\n",
    "                \n",
    "            current_status = [*current_status, { 'id': customer.name, 'state': new_customer_state }]\n",
    "                \n",
    "        return current_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ WARN:0@69.436] global /Users/runner/work/opencv-python/opencv-python/opencv/modules/imgcodecs/src/loadsave.cpp (239) findDecoder imread_('<built-in function dir>/tiles.png'): can't open/read file: check file path/integrity\n"
     ]
    }
   ],
   "source": [
    "from faker import Faker\n",
    "from tiles_skeleton import SupermarketMap, MARKET\n",
    "import cv2\n",
    "\n",
    "faker = Faker()\n",
    "\n",
    "time_simulation_in_minutes = 30\n",
    "max_customers = 10\n",
    "\n",
    "background = np.zeros((500, 700, 3), np.uint8)\n",
    "tiles = cv2.imread(f\"{dir}/tiles.png\")\n",
    "\n",
    "sections = Sections(\n",
    "    dairy=[],\n",
    "    spices=[],\n",
    "    drinks=[],\n",
    "    fruit=[],\n",
    "    checkout=[(4, 8) , (8, 8) , (12, 8)]\n",
    ")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/2d/jjks8mqd6wj1vm8dxd7jbnl80000gn/T/ipykernel_22895/758791553.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msupermarket_map\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSupermarketMap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMARKET\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtiles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Desktop/spiced_2022_data_science_course/markov-cane-student-code/week_8/project/tiles_skeleton.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, layout, tiles)\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mTILE_SIZE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mncols\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mTILE_SIZE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m         )\n\u001b[0;32m---> 42\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextract_tile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/spiced_2022_data_science_course/markov-cane-student-code/week_8/project/tiles_skeleton.py\u001b[0m in \u001b[0;36mprepare_map\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprepare_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 129\u001b[0;31m         \u001b[0;34m\"\"\"prepares the entire image as a big numpy array\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m             \u001b[0;31m# print('row', row, 'line', line)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/spiced_2022_data_science_course/markov-cane-student-code/week_8/project/tiles_skeleton.py\u001b[0m in \u001b[0;36mget_tile\u001b[0;34m(self, char)\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtiles\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mTILE_SIZE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mTILE_SIZE\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_tile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/spiced_2022_data_science_course/markov-cane-student-code/week_8/project/tiles_skeleton.py\u001b[0m in \u001b[0;36mextract_tile\u001b[0;34m(self, row, col)\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mTILE_SIZE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mTILE_SIZE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'y'\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "supermarket_map = SupermarketMap(MARKET, tiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'SuperMarket' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/2d/jjks8mqd6wj1vm8dxd7jbnl80000gn/T/ipykernel_1371/1896771819.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msupermarket\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSuperMarket\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'REWE'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuper_market_map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msections\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'SuperMarket' is not defined"
     ]
    }
   ],
   "source": [
    "supermarket = SuperMarket('REWE', P, super_market_map, sections)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "for minute in range(0, time_simulation_in_minutes):\n",
    "    \n",
    "    total_visits = supermarket.total_visits\n",
    "    \n",
    "    supermarket.add_customers([  \n",
    "            Customer(str(f'{faker.name()} ID:{i}'), supermarket.map, '1')                     \\\n",
    "            for i \\\n",
    "                in range(\n",
    "                        total_visits,\n",
    "                        random.randint(\n",
    "                                total_visits + 1 ,\n",
    "                                total_visits + 1 + max_customers \n",
    "                            )\n",
    "                    )\n",
    "        ])\n",
    "\n",
    "    customer_states = supermarket.tick_minute()\n",
    "    # print(f'Minute{minute} \\n{customer_states}\\n')\n",
    "    \n",
    "    while True:\n",
    "        frame = background.copy()\n",
    "        supermarket.map.draw(frame)\n",
    "\n",
    "        # https://www.ascii-code.com/\n",
    "        key = cv2.waitKey(1)\n",
    "        \n",
    "        if key == 113: # 'q' key\n",
    "            break\n",
    "\n",
    "        cv2.imshow(\"frame\", frame)\n",
    "\n",
    "\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "    # market.write_image(\"supermarket.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting opencv-python\n",
      "  Downloading opencv_python-4.6.0.66-cp36-abi3-macosx_10_15_x86_64.whl (46.4 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.4/46.4 MB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.14.5 in /Users/earyzhe/opt/anaconda3/lib/python3.7/site-packages (from opencv-python) (1.21.5)\n",
      "Installing collected packages: opencv-python\n",
      "Successfully installed opencv-python-4.6.0.66\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.2.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m22.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# !pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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