EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7f3cc7a2-1367-4c37-89f2-3d1cde866b58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing the relevant libraries\n",
    "import IPython.display\n",
    "import json\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import squarify\n",
    "%matplotlib inline\n",
    "# import missingno as msno\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# D3 modules\n",
    "from IPython.core.display import display, HTML, Javascript\n",
    "from string import Template\n",
    "import datetime\n",
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "6b1b94b8-8835-44a6-9a73-d816743c0041",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtypes = {'store_nbr': np.dtype('int64'),\n",
    "          'item_nbr': np.dtype('int64'),\n",
    "          'unit_sales': np.dtype('float64'),\n",
    "          'onpromotion': np.dtype('O')}\n",
    "\n",
    "items = pd.read_csv(\"inputs/items.csv\")\n",
    "holiday_events = pd.read_csv(\"inputs/holidays_events.csv\")\n",
    "stores = pd.read_csv(\"inputs/stores.csv\")\n",
    "oil = pd.read_csv(\"inputs/oil.csv\")\n",
    "transactions = pd.read_csv(\"inputs/transactions.csv\")\n",
    "# Read in the full training data just to get prior information and here is the output:\n",
    "# Output: \"125,497,040 rows | 6 columns\"\n",
    "pd_train = pd.read_csv(\"inputs/train.csv\", nrows=6000000, parse_dates=['date'], dtype=dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8f115e7-94e8-46b0-808b-f8cf69a3198e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "edae623a-6e73-4ad2-ae78-3c8406a052d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#add missing date\n",
    "min_oil_date = min(pd_train.date)\n",
    "max_oil_date = max(pd_train.date)\n",
    "\n",
    "calendar = []\n",
    "\n",
    "d1 = min_oil_date\n",
    "d2 = max_oil_date\n",
    "\n",
    "delta = d2 - d1         # timedelta\n",
    "\n",
    "for i in range(delta.days + 1):\n",
    "    calendar.append(datetime.date.strftime(d1 + timedelta(days=i), '%Y-%m-%d'))\n",
    "\n",
    "calendar = pd.DataFrame({'date':calendar})\n",
    "\n",
    "oil = calendar.merge(oil, left_on='date', right_on='date', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "adf018d8-e0fa-47ba-a13a-46ab5d4cd280",
   "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>date</th>\n",
       "      <th>dcoilwtico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>93.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>92.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>93.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  dcoilwtico\n",
       "0  2013-01-01         NaN\n",
       "1  2013-01-02       93.14\n",
       "2  2013-01-03       92.97\n",
       "3  2013-01-04       93.12\n",
       "4  2013-01-05         NaN"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oil.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "71144319-bf6a-4d92-8de6-ed9886d01172",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date           0\n",
      "dcoilwtico    44\n",
      "dtype: int64 \n",
      "\n",
      "Type :  \n",
      " date           object\n",
      "dcoilwtico    float64\n",
      "dtype: object\n"
     ]
    },
    {
     "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>date</th>\n",
       "      <th>dcoilwtico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>93.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>92.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>93.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  dcoilwtico\n",
       "0  2013-01-01         NaN\n",
       "1  2013-01-02       93.14\n",
       "2  2013-01-03       92.97\n",
       "3  2013-01-04       93.12\n",
       "4  2013-01-05         NaN"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check how many NA\n",
    "print(oil.isnull().sum(), '\\n')\n",
    "\n",
    "#Type\n",
    "print('Type : ', '\\n', oil.dtypes)\n",
    "\n",
    "#Print the 3 first line\n",
    "oil.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8578f1fa-84bc-439b-af8b-5e74277ff82a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Check index to apply the formula\n",
    "na_index_oil = oil[oil['dcoilwtico'].isnull() == True].index.values\n",
    "\n",
    "#Define the index to use to apply the formala\n",
    "na_index_oil_plus = na_index_oil.copy()\n",
    "na_index_oil_minus = np.maximum(0, na_index_oil-1)\n",
    "\n",
    "for i in range(len(na_index_oil)):\n",
    "    k = 1\n",
    "    while (na_index_oil[min(i+k,len(na_index_oil)-1)] == na_index_oil[i]+k):\n",
    "        k += 1\n",
    "    na_index_oil_plus[i] = min(len(oil)-1, na_index_oil_plus[i] + k )\n",
    "\n",
    "#Apply the formula\n",
    "for i in range(len(na_index_oil)):\n",
    "    if (na_index_oil[i] == 0):\n",
    "        oil.loc[na_index_oil[i], 'dcoilwtico'] = oil.loc[na_index_oil_plus[i], 'dcoilwtico']\n",
    "    elif (na_index_oil[i] == len(oil)):\n",
    "        oil.loc[na_index_oil[i], 'dcoilwtico'] = oil.loc[na_index_oil_minus[i], 'dcoilwtico']\n",
    "    else:\n",
    "        oil.loc[na_index_oil[i], 'dcoilwtico'] = (oil.loc[na_index_oil_plus[i], 'dcoilwtico'] + oil.loc[na_index_oil_minus[i], 'dcoilwtico'])/ 2    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4e2a5042-4520-4f55-b4d3-9b4961f9dbf3",
   "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>date</th>\n",
       "      <th>dcoilwtico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>93.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>93.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>92.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>93.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>93.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  dcoilwtico\n",
       "0  2013-01-01       93.14\n",
       "1  2013-01-02       93.14\n",
       "2  2013-01-03       92.97\n",
       "3  2013-01-04       93.12\n",
       "4  2013-01-05       93.16"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oil.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "47ee99cb-8329-477f-98c3-911f55c6b9b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEBCAYAAABhZ/5bAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABH90lEQVR4nO3dd2DU9f0/8Ofty43sRQYJCQFDAggyBaEqilgcSBVERaSKtvq1otZVlSrQ1qo/Ra1iixUHgqmrjopiCw6WyJCwyZ5c1l1u7/fvj8vnyJG73F1yK8nr8U/rzTc3Xnnd6/1+v948xhgDIYSQAYcf7QEQQgjpGwrghBAyQFEAJ4SQAYoCOCGEDFAUwAkhZIAShuNBzWYzjhw5grS0NAgEgnA8BSGEDDoOhwOtra0oLS2FVCr1e/uwBPAjR47gpptuCsdDE0LIoLdp0yZMmjTJ7+3CEsDT0tLcg8jMzAzHUxBCyKBz5swZ3HTTTe4Y6k9YAjhXNsnMzEROTk44noIQQgatQEvPNIlJCCEDFAVwQggZoCiAE0LIAOW3Bm61WvHoo4+ivr4eCoUCTz75JJqamvDcc89BKBRi+vTpWLlyZSTGSgghpBu/AbysrAwymQxlZWWoqqrC6tWr0d7ejueeew6FhYVYsmQJTp48idGjR0divIQQQrr4LaFUVFRg1qxZAICCggJUVlaiuLgYGo0GNpsNFouFNusQQkgU+A3gxcXF2L59OxhjOHToEFQqFYqKinDXXXfhyiuvxLBhw1BQUBCJsQ5qt7yxF89sPRHtYRBCBhC/AXzhwoVQKBRYsmQJtm3bhuHDh+Mf//gHvvjiC3zzzTfIy8vDP//5z0iMddCqaNHh+9NteHd3Lcw2R7SHQwgZIPwG8PLyckyfPh2bN2/GFVdcgZEjR0Imk0EmkwEA0tPTodVqwz7Qwezzw80AAJ3Fjm3HVFEeDSFkoPA7iZmXl4d169Zh/fr1UCqVWLt2LQ4fPozly5dDIpFAqVTiL3/5SyTGOigxxvD54WZMGZGMhg4jPjzQgKvGZ0V7WISQAcBvAE9OTsbGjRs9Lrvssstw2WWXhWtMQ8pJlQ4VLXqsvrYUzRoT1n9biRadGelK/53ICCFDG23kibIvDjeDzwPmlWbiuok5cDLg3weboj0sQsgAQAE8yr443IwLC1ORqpBgZLoCE4YnYuOuGhit9mgPjRAS4yiAR5HF7kBVmwFTRyS7L3t0XjEaNSY8//WpKI6MEDIQUACPok6jDQCQJBe7L5syIhk3TR2ON3dW41C9JmzPffKMDoyxsD0+IST8KIBHkZoL4DKxx+UPzzsP6UopVv37SFie93CDBnNf/A5fHjkTlscnhEQGBfAoUhutAIBEmcjj8nipCEumDsfPDZ3QmW0hf95vT7YCALafaAn5YxNCIocCeBRpujLwcwM4AIzNTgAAHGsK/SapXZXtAIDvT7e5yygmqwNOJ5VUCBlIKIBHkaYrAz+3hAIAJdnxAIAjIQ7gZpsD++vUSFNKcEZrRmWrHiarAxc/twMv/68ipM9FPDHG6I8kCSkK4FGk7iUDT1dKka6U4GhjZ0ifc3+tGla7E/deMhKAKwv/4EADzmjNqGjVh/S5iKc3fqhG6R+/wt+2V/Ta86bTZMNNG/Zg097aCI6ODERhOdSYBEZjtEIs5CNO5L0db2l2Ao40hTaA76psg5DPw4KJOXjjh2p8e6oVNW0GAECbzhLS5yKe9lS1w+Zw4tmvTuKd3bWYW5KBWaPSMCpDiazEOAj4PBitdvx64z78VKtGmkKCm6bmRXvYJIZRAI8ijdGGJJkIPB7P6/WlWfHYcbIFJqsDceLQ9FzfWdGO8bmJUEiEuKgoDe/scWV5SqkQbXoK4OF0UqXDFaXDsHhyLt74oRrv/1SPt3a7Xn+xgI/c5DgAQHWbAUqJEHoLbeYivaMAHkVqo9Vr/ZtTkp0AJwNOnNFiwvCkfj+f1mzD4QYN7r7YVT6ZWZSKd/bUIicpDhcVpWHrkeZ+PwfxzmCxo77DhBsuyMWMkamYMTIVZpsDP9drUN1mQHW7ATVtBpzpNOP/3XA+Nv9YB52ZAjjpHQXwKNIYbUiI61n/5pR2rUQ50hSaAL6roh1OBkwvTAHg+t8kmQj/d8lINHeaoTbaYHM4IRLQ1EionW5xzS+MylS6L5OKBJhakIKpBSk9bv/54SY0d5ojNj4yMNE3NYo0pt4z8KwEKZJkopBNZH58sAGpCgkm57u27sdLRdj/+GVYNHk4UhUSAEC73hqS5yKeTp3RAQBGZyj93NJFQSUUEgAK4FGkNtqQJPedgfN4vJBNZLbrLfjv8RYsmJDlkWHz+a76OxfAqQ4eHidVOkhFfOQmywK6vUIqhJ5KKMQPCuBRwhiDxmhFQpzvDBwASrIScPKMDoZ+ZmP/PtQEu5PhVxfker0+TekaRysF8LA4pdKhKF0JAd/7hPW5FBIRdJSBEz8ogEeJ0eqAzcGQ5GUNeHeXjUmHzcHwxeH+TTB+sL8B43ISMDrT+094dwZOSwnD4uQZHUYFWD4BXKuCrHYnLHY6I5X4RgE8StS97MLsbuLwJIxMV2DLvroe1x1u0MBqd/p9rqNNnTjWrMWvLsjxeZuzJZTea+AdBis+OtBAnQyDoDZY0aKzYHSmIuD7KKWu9QVURiG9oQAeJVwflAQ/GTiPx8Piybk4UKfBKZXOfXl9hxHX/G0nnv/6pN/n+nB/I8QCPq4a5/usTblECJlY0GsN/KeaDly57nvcX/YzDoax1e1gw71vwWTgCklXAKcyCukFBfAo0fhoJevNggnZEAl4eH9fvfuyHyrawBjw1u4atOh8Lzez2p345FAj5oxJ9+g77k2qQuI1gDPG8PfvKrHo73vcJwWpDbRaJVBcAPdVvvKGC+C0Fpz0hgJ4lJwtofSegQNAikKCy8dk4qMDDe6a6K7KdsRLhbA5GNbvqPJ53+0nW9BhsPZaPuGkKsQ9AninyYY73t6PP/3nBC4rzsB7d0xzX04Cc1Klg1IqRGZ84AdVK6SUgRP/KIBHCdeJ0F8JhbNoci7URhu2HVOBMYbdlW24tDgD103Ixrt7a6HSes/CP9jvWvs9qyjN73OkKiRo7TaJyRjD77YcxLenWrDqqjF47eaJyEmK6xo/BfBAHW7oxJhh8T5bJnijlLg+F1QDJ72hAB4l7l7gfpYRcmaOTEV2Yhze31ePUyo92vRWTC9Mwb2XFsHhZHhnd8/OdW16C7afaMF1E7MhDGB3ZapS4jGJ+eGBRuw42Yo/XFmM22aMAI/Hg1IqAo8HaCgDD4jJ6sCxJi0uyAtuJy1l4CQQFMCjRG20QSERQiwM7C3g83m4YVIuvj/d5q6FX1iYgtxkGUqy4nGgTt3jPp92rf1eONF/+QRwZeBqoxV2hxMqrRlPf3YUk/OTsHR6vvs2Aj4P8VIRtBTAA3K4QQO7kwUfwN01cHqdiW8UwKNEY7R67QPem+sn5YDHAzbuqkZeigw5Sa5dfWOzE1De2NnjsIA9Ve3IT5EFPHmWphCDMddSwb9uPQmL3Ym//mq8e7cmJyFO5C4Bkd7t7/rDGmwvG24ZIW3miS27K9uxp6o92sNwowAeJRqTLegAnpUYh9mj0uBkruybMy4nATqzHbUdRo/bH2vWoqSrIVYg0pSuteANGhO2HmnGggnZGJEq73G7RJmISigBOlCrRkGaHMl+VgCdSyLkQyTgUQ08QJ0mG9Z8fgztYdxJvK+mA7f+80f88dOjYXuOYFEAjxJ/rWR9WTzZtRV+5sizk5JjsxMBuH6uczpNNjSoTSjJig/4sbnNPB8daIDB6sCVY4d5vV1CnIhWoQSAMYb9tWpc0IdOkjwejxpaBeGH023Y8EM1frvpAGwO/5vbglXTZsCKt3+C1eFEVasB9jA8R1/4DeBWqxUPPPAAbrjhBixfvhw1NTWora3FsmXLcNNNN+G2226DWt2z/kp6pzHakNiHAD63JBPv/HoK5pVmui8rylBAIuSjvOFs06vjza6zNMcM60sAb0SiTORuO3uuhDgROmkVil/VbQaojTZMDLL+zaGGVoFrULt+fe6t7sDqz4+F9LE1RiuWb9wHAPi/S0bC6nD2+LUbLX77gZeVlUEmk6GsrAxVVVVYvXo1bDYb7r//fpx//vn46quvUFNTg6Sk/verHmiMVjs27anDmzurcdO0PPdBCYFQG61I7KUXuC88Hg8XnbMkUCTgY0xWPA53azvLnWY/JpgMvKuEYrQ6MH/cMJ99wRNllIEHYn+tK7EJdgKTQw2tAlevNiJRJsINk3Lx9++qMHNkKi4vyfR/Rz+sdifufGc/GtQmbLpjKsQCPl7+XwVOq/QoTAu8NUK4+M3AKyoqMGvWLABAQUEBjh49io6ODmzfvh233HILDh06hHHjxoV9oLFEb7HjtR2VmPnMdqz9z3G06CzYV9MR8P2dToZOky2gTTyBGpedgKONnXB0TWQea9YiVSFBujLwzSNysQBSkesjMc9H+QTomsQ02agfih8H6jRQSoUY2ccvulJCGXig6jtMyEmKw0NzRyM3OQ7/+N735rZg/OHjcuyt7sCz14/D5PxkjEx3vZcVLTo/94wMvwG8uLgY27dvB2MMhw4dglqtxunTpzF9+nS8/fbb6OzsxMcffxyJscaEg3VqzP7rdjyz9QTGZifgw99Mx6XF6WjSmAJ+DK3ZBsbQpxKKL2NzEmGwOlDd5jr55ViTNqjsG3Bl96kKCeKlQswoTPV5u8Q4MRxORvVZP440dmJ8TmKPVTyBUkiF0Fnol04gGtRG5CbJIBTwcev0fOyrUXuUFPviaFMn/rW/Ab/5RSGuOT8bgKtnUHZinPuEpWjzG8AXLlwIhUKBJUuWYNu2bSgtLYVcLse0adPA4/Fw8cUX48iRI5EYa9Ttr+3ALW/8CIVUiE/unoG3lk/BBXnJyEqMQ6PaFHBG+q+fGgAA5wXRG8OfcTmu1SaHGzphtTtxukUXVP2bM7ckE7dfVNDr+nRu9yiVUXrXqrMgMyHwX0DnUlAGHhDGGBrUJvcu4Rsm50IuFuDNndX9ety3dtUgTiTAXbMKPS4vylDgtGqABPDy8nJMnz4dmzdvxhVXXIHhw4cjPz8fP/30EwBg3759KCoqCvtAo62iRY+lb/yINKUEW1ZMw/m5ie7rshPjYLA6oDX5/7I1aUx44ZtTmFOcjgtH+s5yg1WYpkCcSIB9NWpUtOhhc7CgM3AAeGL+GNx7ae/vJ3eOJ22n940xhg6DFSmKvv/KUkhpFUogWvUWWOxO92lH8VIRrp+Ui88ON6HFR4sJfzoMVnxyqAkLJmb3aHdRlK5AZaveXa6MJr+TmHl5eVi3bh3Wr18PpVKJtWvXQq1W46mnnoLD4UBOTg4efPDBSIw1ql7YdgoA8N4dUzEsIc7juuxE1383akx+e5us/vwYnIxh1VUlIR2fgM/DFaWZ2LKvzt2Qqi8ZeCC4yVfKwH3TWeywOpxIlUv6/BhKqZC6Efrw/74+iR2nWvHvu2egvsNVvuQycABYdmE+Nu6qwQcHGvDbXwS+uICzZV8drHYnll2Y3+O6onQlLHYn6juMyPeyTyKS/Abw5ORkbNy40eOyjIwMbN68OVxjijnHm7X4orwZ/3fJyB7BG3BtsAFcAby3rHf7yRZ8eeQMfj93dMBnIwbjz9eNRXOnCduOqSAV8b1uwgkFKqH4xx0OHewGnu6UEiEsdiesdmfALReGgo07q/HS/yoAAJWtBvcSwtyks9+p/FQ5RmUo8GN1B377i+Ae3+5w4t3dtbiwMMVrD/eiDNdE5ukWfdQDOH0qAvDCtlNQSoW4fWaB1+u5AN7bRKbZ5sCqfx9FQZoct180IizjlIoE2HDrZEwYnojJ+ckBn78YLK4BF5VQfOswuH4F9auE0tUPpb/noQ4mX5Y346nPj2Hi8EQArkNGGtSu7112kmdydUFeMvbXqnu0mPCnstWApk6zzx5C3EqU0zGwEoUCuB/7azvw9TEVbp9Z4LM8kqoQQyzk9xrAX9tRiboOI9ZcUwqJUBCu4UIhEeKDuy7Em8smh+05uBYAGhP1Q/GF6+rIbY7qC4W0q6UsBXAArq3sv3v/ECbkJmLT7dOQIhfjx5oONKiNSFWIIRN7FhQm5ydBZ7bjVJCBtrbdAOBsoD6XUirCsAQpKmJgIpMCeC9+rtfgtjf3ITsxDrfNzPd5Ox6Ph+zEODT6COC17Qa89m0lrjk/K6QTl74I+LyA2sf2lVQkgFjIpxJKL7gSSigycKqDu9Zd3/7WT8hJisMbt05GnFiASflJ+KlGjfoOE7KTepYkJ+UlAwD21QS3U7y23VWSyUvxXeYsylDGxFJCCuA+HGvS4uYNe5EgE2HLimmIl/Y+OZmVKPUZwMt+qofDyfDYlcXhGGpUJNJ2+l5xJZR+1cCl1FIWcK3ouee9gxAJ+HjrtinuowEn5yejrsOI8sZO5Cb1nJvKTY5DmlKC/UFssgOA2g4DEuJEve7TyEuWoV4d/e30FMB9+Nf+eticTmxZMT2gCcfsxDifJZRvjrVgcn4SMoI4UivWJcpEVAPvRZveCqVE2K9yGR1s7PJDRRtOnNHhkXnneXwXJ+W7MuxOk83dWrk7Ho+HyflJ+Kk2+Ay8t+wbcP1h7jTZor6UkAK4Dy1aC7IS49xLBP3JSoxDi84Cq90Ju8MJs811dmVduxEnVTrMKc4I53AjjjoS9q69n2vAATqVh7Ph+2qkKiS4arxne4eSrHh364fcZO/f0wvyktGgNuFMZ+Drwes6jBjuJ2lLlrt650f7O0AB3AeV1oyMIPqIZCXGgTHgTKcZf/j4CC5/4TsYrXZ8c1wFALhszGAL4GLqCd6LDoOlX+UToHsJZegG8NMqHb491Ypbp+f1+DUjEvAxIdfVKMxbBg64JjIB4KfawMooNocTDWqT3wycm8jvMER3Ip8CuA8qnRkZ8YGvIMjpytR/btDgo4MNqOsw4qX/VuCb4yoUpSuQlxLd9aKhligToZNO5fGpXW9FSj9WoADdDjYewhn4P3dWQyLk46ZpeV6v5wK0txo44NrMJhMLsLMisFN0mjQmOJzM7/eV++OsjvJ3gAK4F4wxqLSWoGrW3FrwF785BZuDYcbIFGz4vgp7qzswZ5Bl3wCVUPxp01uR2s8SilTEh4A/tE/l2XrkDK4cO8znr5mbp+Xh0Xnn+dy0JhTwMa90GD452Ah1ANmyewWKnxIKdxgLZeAxqNNkg9XuRHoQAZxrWlTZasBFRal4afEEyCVCOJxs0NW/AdcqFIPVEZbTTwY6p5NBbbT2u4Qy1E/lsTucUBttvdaj0+OluHN2IXg835vW7ppdAJPNgbd21/h9Tm4NeMAZOAXw2KPSupaApSsD/wksFQncZ0ounZ6PFIUEaxeU4uLRaR6NrwYL2k7vG7c6IaUffVA4CokQ2iG6jFDdtcqpv5PBRRlKzClOx1u7amC09v7HsLbdCKmI7/e7787AqYQSe1p0rhnrYJf95Sa5Vq1ccl46AGD+uCy8eduUsG1pjybqSOhbewi20XOUQ/hYNa6+3JezY8911+xCqI02lO2r7/V2tV0rUPz1cI8TCxAnEkQ9A/fbzGoo4jLwYCYxAeBP140FgEEZsM/FbXLopO30PbSHYBs9Zyh3JOTqy/0tRQGuNeOT8pLwj+9dxx/6Oi6wrt2I4cmBLThIlovRYaBlhDFH1dVDOJjjyADgvMx4nJcZnhausSaBWsr61B7CwDOUJ4u57DYUGTjgysIbNSZ8cbjZ6/WMMdR2GJDvZwkhJ0kugoZKKLGnRWtGvFSIOHH4mk4NdBTAfWvXh66EkhAnHrKvMVdfDsUfQgC45Lx0FKUrsP7bSq+nZ7XoLDDbnH7XgHOSZGKqgccildYS1AqUoYg2mfjmzsBDkDm6WhYMzTIVl4Enhujwbz6fhztnF+LEGR12nGrtcT23hHB4gHs2kmTiqNfAKYB7EewmnqGIOuX51q63IlEmCklHyKG8XLPDYINcLIBUFLpfwlePz8KwBCk2eDm1vqZrCWGgJRRXDZwCeMxp0VqC2kY/FElFAogFfArgXrQbLEgJ0c/+obxcs8NgQXIIylDdiYWujT3eDnqoazdCwOe5N+X5kyQTQ2u2R/WPKwXwczDG0KIzUwklAK4VEkMvsPgTim30nKG8XLPDaAtJGepchelymG1ONJ9z4HFthxHZiXE+V6icK1ke/feGAvg51EYbbA5GJZQA0Knp3rUbrCHLwPuzXPOJT46g7Kfe1z3HMrXB6u79HUoFqa6TdqpaPQ9kqG03BDyBCcA9tmj2Q6EAfo6+LiEciobyGmVfzDYHmjUm967c/krsYwbeqrPgnT212HrkTEjGEQ0dBmt4MvA01yRlVavB4/JA+oB3lxwD/VAogJ+DC+CUgfunlIiohHKOTw81wWB1YF7pMP83DkBfl2tuP9ECAEH1wY41amN4MvA0pQQKidAjA+802tBpsiEvwE08QLcMnAJ47GjRcbswKQP3R0EZuAfGGN7cVYPRGUpMK0gOyWO6D5AOMgP/7wlXH3qVdmAGcLPNAaPVEbI14N3xeDwUpMlR1XY2A6/tcP3/4cFk4O4SCtXAY0ZL1wc+VD+BBzMqoXjaV6PG8WYtls3I77U7XjCUUhF4PAR1eIbF7sD3p9sg4PPQbrDCYneEZCyRFMo+KN4UpMo9Sig1XWvA84Po28/9caUaeBTVdxg9ygAqrQWJMlFI154OVvFSKqF0t3FXNRLiRLj2/OyQPaaAz0O8NLjDM/ZWdcBodeCyrjbGLV29fQYSrp9MODJwAChIU6BRY4LJyh192JWBB3D+LUciFEAuFlANPFoqWnS4/IXv8PzXp9yXNXcGd5TaUMb1qva2LXmoadKY8NVRFRZPzg15C4Zg+6H870QLpCI+rp3g+kMyEMso6hBvoz9XYZprJUp1Vxmltt2IdKUk6PcuSR7d3ZhDNoCbbQ7c895BmGwO95sIuL6I2T6OZyKelFIhnAwwWgfeT/RQe3dPLRhjuNnH0V/9kSgTBVVC+e8JFWaOTEV+qiubbB6AE5lnOxGGZhv9uQq4lShtronM2nZjUOUTTrI8uv1Q/AZwq9WKBx54ADfccAOWL1+Ompoa93Xr16/HypUrwzm+sPnLlydw4owO2YlxaNSY3Jc3akzISqQMPBBKqevLNdTr4GabA5t/rMOc4gzkBvETPFAJcaKAJzHVBivqO0yYOiIFmV0T8bGagTPGYPCxjyDUnQjPNSJVDh7v7FLC2g5DUBOYHK4fSnWbAc99dRJmW2STGb8BvKysDDKZDGVlZXj88cexevVqAMC3336LHTt2hHt8YbHtmAobd9Vg+YwRmFuSiUa1CYwx6C12dJpsyE4M/ZdwMFK4G1oN7Tr4p4eaoDbasGxGflgeP5gSCreyojBdjoQ4EaQifswuJXx/Xz2m/um/XjeDdRht4PHOLqMMNalIgKyEOFS16mG2OaDSWvyeg+lNslyMRo0JS/+5F69sr8BHBxrDMFrf/AbwiooKzJo1CwBQUFCAyspK1NbW4v3338e9994b9gGG2plOM37/wc8oyYrHw/NGIzspDiabA2qjDU1dmThl4IFxdyQcwrsxGWPY2LV0cHpBSlieI1EWeADnyoEjUhXg8XjIjJfiTIxm4P/a3wC9xY7qczbUAK4MPCEuNA3BfOGWEtZ1dB1k7ONg5N4kycRo01vRqrNgeLIMb+6sjuickN9Xp7i4GNu3bwdjDIcOHYJKpcIf//hHPP300xAIBtZKDYeT4b73D8Jqd+LlGydAIhQgu6txTaPahEa1K4DnUA08IPHUUhbHm3U41qzFLdPzQrZ08FyJcWJojNYezZe8qW7TQ8jnuT/DGfHSmCyhNGlM2F+rBnC2C2B3Hcbw7MLsrjBNgWNNWvzm3f0A/J9E701WohQ8HvDS4gn43aVFON2ixw8VbaEeqk9+j1RbuHAhKisrsWTJEkycOBHDhw9He3s7Vq5cCa1Wi5aWFvz973/HihUrIjHefvnuVCv2VHXgz9eNRUHXLDT3QW/UGNHWtXSJSiiBUUi4GvjQLaEcqHMFodmj0sL2HIkyEZwM0FvtiJf2XlKoajVgeLLM3ZApM0HqHmMs+bLbFv9abwFcbw3bChTOosm56DBY0W6wIDdZhtGZyqAf4+Zpebj4vHQUpilgsTvw5y9P4J8/VOOiovB9HrrzG8DLy8sxffp0PPbYYygvL0dTUxNeeOEFAMDevXuxZcuWmA3eNocTzRqze3Lio4ONSJSJsHBijvs2XAbeoDah3WCFkM+jTTwB4kooQ/XQXQA4VK9Bilwc1l9t8dx2eqPNbwCvbjNgRLdSQGa8FCqtBYyxsP1C6IsvDjeheFg81AarexNNd2qjNSwTwt0VD4vHSzdO6NdjSEUC95JEiVCAm6cNx4vfnEaD2oicpPAngn5LKHl5eXjrrbewaNEirFu3Do888kjYBxUqZT/V4xfPbcfhBg10Zhu+PnoGV43Lglh49p+dKBNBJhagUeMqoQxLlA6JQ4lDgU7lcQXw8bmJYQ2OiQH2Q3E6WY8AnhEvhdXujOp273M1aUw4UKfBL8dmIi9F5j0DD1Mjq3BbdmE+lk7Pcx94Em5+nyU5ORkbN270et3UqVMxderUUI8pZMobOuFkwFOfHcOiSbmw2J1YMNFzlxyPx3MtJVSb0GGwIiuB6t+BkouF4PEGXwnFbHNg65EzGJ2pRPEw34dUa802VLbqcfX4rLCOh2sp628pYbPWDIvdiRFp3TLwBNeEfHOnKewliUD9p9x1qPCVY4ehvsOE/51s8bieMRa2RlbhligT4+lrSiP2fJH5MxElp1Q6SEV87K9Vo7JVjxGpckzITexxu+wk11rwDoMV0wvDs5JgMOLzeVCIhYNmFUqHwYp3dtfinT01aNNbcel56Xhj2WSfty9v6ARjwPlePlOh5G5o5acnOLeag+t3DZwN4CqtGSVZCWEaYXC+KG/GmGHxKEhTIC9VhtafLDBY7JB3Za1GqwM2BwvZWZiD2aAN4IwxnFbp8asLcvBzfSfKGzuxfMYIrz91sxPjsL9WDYPFjpwAj1MiLoOhoZXZ5sBfvjyBLfvqYLY5cfHoNDR3mqHS9b5641C9BgAwPswBPNBTeaq7dhUWpHnWwAHgTGds9ENp1JhwsE6D388dDQDu9q11HUb3rx2D1fV5koe4JcFgNGi30qu0FugsdozOUGLNtaUoHhaPX12Q4/W22Ulx0JntcDIEfB4ecVEM8GPVNEYrbt6wF2/trsHV47OwbeUsvHnbFIzLSfDbBOpgnQYFafKwbTbhBNoTvKrNAJlYgPRuk/BpSgl4PMTMWvAvu5VPALgPUOheBzdbXWdMxokHbX4ZMoP2FTql0gEAijKUGJ+biC9/d5HP22Z3C9rUByU4SqlowB6rprfYccPru1HTZsTLN07A/HFna9npSina9BY4nMzrpDZjDIfqNZg1KjXs45SKBJCK+H4DODeB2f1XpkjAR6pCAlWM7Mb8orwZJVnx7olWLoB3X4litLk+T3HUEdSvQZuBcwF8VIb/tZ3dl4BRBh6cgVxC+e9xFU6p9HhliWfwBlyZq5P5Pi6rqdOMNr3F65xKOHCbeXpz7goUTqzsxmxQG3GwTuPOvgFXApCqEHtk4FyLVxmVUPwatAH8tEqPVIU4oJn37ht3simAB0UhGbgBfGdFG+KlQlza1Te7O64M0eKjDn6ksRMAMDYnMWzj685fQyur3Yn6DiMKvATwZLn/4B8JX5a7Nu/8cqzncXN5KXLUtJ3NwLkATj35/Ru0AfxUiw4j0xX+bwjXl1Uk4CFVIaYPTZCUUtGADOCMMfxwug0XFqZ6LZGkd52J2qrzXgev6/rJ7y3jDYcEP/1Q3vihGk4GlGT3XGmikMbGSqHvTrdidIYS+ee8ZueuBTfZKAMP1KAM4IwxVKj0AZVPANdyuGEJcVQ+6YP4ATqJWdNuRFOnGTOKvNew0xSu1RstPgJ4vdoIpVQY9glMTmIvGfj/Tqjw169O4KrxWbh8TM9fE/FSYVR2yz7+STm2HVO5//uUSoeS7J7r6vNT5GjqNLtbsRqphBKwATuJ2WmyoaJFj4oWHU6r9GjuNKPDYMXYnATcemE+dBY7igIM4ABww6Qc9zpUEjiFRAiL3Qmr3emxwzXWcQ2HZo70EcCVnhn49pMt2F3ZjseuLAbgOoovNwJbpTk5STJ8d7q1x+tc0aLH7zYfwphh8fjrwnFel8lGo8zFGMP7++rRqrPgsjEZ6DTZoNJaUJTe8zvJTWTWdxhRlKGkEkoQYi5iVbTosPnHejgZQ26SDMsuzAe/6yfu/lo1Pvu5CduOqTwOYZAI+chOioNcLMTfv6vCnqp2AMCoAEsoAHDPJUWh/YcMEe5+KBY7nGYGhUQ4IL54O0+3ITsxDvk+mvjHiQVQSoTuAP7B/gb8p7wZ980pgkwsRIPa5LHeOtymjEjCP3dWo7xRgwvyXCfed5psWPH2T5CI+Pj70kk+jwNTSEQw2RywO5xhbc/ancXuhM3BcLRJC8D1vQaAURk9v5PcPBV36hCVUAIXcwH8eLMOH+xvgMPpOmDByRhuv6gA7++rw8MflkMs5GP2qDTcMj0PRekKFKUrkZ0U565j/nXrCby6oxJAYCtQSP9wp/J0GCy48R97UZIVjzeXTY6pxknncjgZdlW24YrSzF7HmRYvcU9iVrbowZir219JVjwa1KawdiA81+R8V9DeW92BC/KS4XAy3Lv5IOrVRrx3x7ReJ9+5gzcMFgcSZJEJ4FzG36A2odNowymVa5ORt+8k98uXW47KlVBCfbboYBRzAfyq8Vm4anwWGGNY8c5+PLP1BCQiAZ7+7CguKkrFazdf0GujmN/PHQ2L3YmDdeoB2UthoOGCw6eHmtCqs2DHyVb8+1CT+0DdWHS0qRNasx0zfJRPOOlKCVp1rrXg3Ek3FS16ZMRLYbI5wt4tr7sUhQRF6QrsrerAb38BbNlXh29PtWLtglJ3cPdFKeEO3rAhIULb07vvDTja3IlTKh3iRAKvf2jkXRt2jBZX4OYycKmQArg/MVu05PF4eGbhOCTLxXjikyPITozDKzdO9Nvli8fj4Yn5Y/DRb2dEaKRDG1dCee/HOqQqJDg/NxFPfXYU7frY2LrtzY/VHQCAaX5O0ElTStGis6BBbYTV7todeLpFh3q1awVKbnJkJ72nFiRjf60adocT7+yuRWl2PJZMGe73fgqpZ4YbCd0nTY81aVHRosfIdIW7HNqdXOIK1Nz5mCarHVIR3+ttiaeYDeCAqza2bvEETBieiH8snRSx7IEEjutP3aa34trzs/DXX42D3mLHfe8fCvgYsEg7WK9BdmIcMuJ7PzqPy8ArWlw///k81/6C+q4juCLR77m7KSNSoLfYsWlvHU6c0eGmqYGdAsQlPZFciaKznH3vjzVpcUqlQ5GX+jfQbXxcALc5IKNt9AGJ6QAOuLKkj387I6gVJSRyuv8iWnhBDkZlKLH6mlLsrmzHNa/8gOPN2h73cToZ9td2RPTswO4O1Wlw/vBEv7dLU0pgtDrwc4Nr086UEcmoaNWjIUpH700d4SqVPLP1BBQSYcBtbKNxdilXA8+Il2BPVTtUWovPOSkuWButZ2vgtI0+MDEfwEls44LDmGHx7m5yi6cMx+YV02C0OrDg1Z34+GCDx33e2l2Dha/txr6ayB/11aI1o1FjCmgLPLcbc09lO1IVYkzKS0ZtuxGVra5dvpHOEjPipchPkble1wnZAS97jcbBG1y2P3VECpq6+rB4W4ECAGIhH2IBH/quGrjZ5qAJzABRACf9khAnQk5SHG6bke9x+eT8ZHx+70yMz0nEyvd/xqp/H4Hd4USHwYoXtp0CAJzs6lcTSQe7WsBOGJ7k97bcWvCD9WoUpilQlKGAw+nawRnp8gln6ghX3X7JVP+1bw53dmkkSyhcOWTKiLMTrN7WgHNkEoFHBk5LCANDhSbSL0IBH98/dLHXWmy6UopNt0/FM1tP4B/fV6NRY0aSTASD1QGxgO8+gCCSDtZpIBLwUJLl+6QdTrrSVSO3ORhGpivcrRladBZM9TMBGi53/aIQE4Yn9npS0LnOTmJGbk6CC+DTClwB3NcKFI5cLDxbA7c6BsReglhAAZz0W28TaUIBH3/45RjkJsuw6tOjYMx1buDe6g5UdR1AEEkH69QYMyw+oADRva92YZoChWkK8HgAY5Gvf3NGpMqD7r8iEwnA40V4EtNsh1jAR2GaAkqJECPS5L2uKlFIhB7LCGPl+LdYRyUUEhFLp+fj1SUTcel56Vg5ZxQK0uSobotsBm53OHG4oTOg8gngOspMJHAFnZHpCkhFAvf2+Uhuo++vaBx9pzPboJAKwePxcNvMEbhhUm6vt5dJBO6TeGgSM3CUgZOImTd2GOZ1tRItSJXjy/JmWOwOSCK0YeOUSg+TzYEJAaxAAVy/LNIUEjR1mlHYVT4pSlegrsMY8TXg/aWIcEMrvcXunjy9/7JRfm+vkHiWUGgSMzCUgZOoKEiTw8ngXlMdTp1GG9Z/W4nHPykHAEzIDSwDB1wTmTKxAFldhwNzdfCBlIEDrpUokd7I42/TXXcyscCjhEIZeGAoAydRwZ2cXtlqwMheVieEwh8+Kcfnh5tRmCbHXbMLg8qeR2cqoZSK3HX++eOy0KKzRK0G3lfdM9xI0FmCC+DyczJwWoUSGArgJCpGdHXyC3cd3Gi145vjKtw8bTjWXDs26PuvXTAWDufZDUdjcxLwwqLzQzjCyFBIez8QItT0ZjuyEnvf6dqdXCyE0WqH08koAw8ClVBIVMRLRUhVSFDVGt6VKDtOtsJsc3qcwxgMkYA/KJa0KSVC6CN48IbOYgs6AzdYHLDY6UT6YFAAJ1FTkBr+lSj/KW9Gilzs3gAzVEW6hKI3292thgOhkAhgdTjdvxKohBIYCuAkagrS5KgK42Yes82B/51owdzSTK/nXg4lkVyFwpirlz+3gSgQXFuCtq4ullRCCYzfV9hqteLRRx9FfX09FAoFnnzySTQ3N+PFF1+EUChESkoKnnnmGcTFDaxJHRJ9I1LlaDdY0WkMT5/qHSdbYbQ6cGVp38ong4lCIoTB6oDDycL+x4w7jSeYEgp3W3cApww8IH5f4bKyMshkMpSVlaGqqgqrV69GQ0MDNm3ahNTUVDz//PP417/+haVLl0ZivGQQKUhzrUSpatMHvLnGl1adBT/VdKCqzYBGjQmNahOONmmRJBO5t3MPZdyabIPV7m4BHC5cqUYZTAbe1RO8TW8FQBl4oPy+whUVFZg1axYAoKCgAJWVlSgrK0Nqqus0E7vdDolE0ttDEOIVtyW8pt3Q5wBusjpw6z9/xI81He7LkuViZCfGYXJ+En45bljEzoGMZe6zS83hD+Bc18NgJzGBsxk41cAD4/cVLi4uxvbt2zFnzhz8/PPPUKlUSElxTQh9/fXX2Lt3L+67775wj5MMQpldm2O4g4P7Yk91O36s6cDtM0fgynHDMDpDGXCb1aGE60gYiZayXK09uElM13vGfRakFMAD4veTvnDhQlRWVmLJkiWYOHEiSkpKIBAIsHHjRmzduhUbNmygDJz0iVwsgFTE718Ar2yHWMDHA5ePprppLyLZkZA7jSfYnZgAZeDB8vsKl5eXY/r06XjsscdQXl6OpqYmvPbaazh69Cg2btwIqTTwxfqEdMfj8ZDWdWxZX+2uasf5wxMpePvBBdPIZuD9mMSkGnhA/L7CeXl5WLduHdavXw+lUomnnnoKc+fOxZgxY3DHHXcAAObNm4clS5aEfbBk8ElVSNwTV8HSmm040tiJ/7ukKMSjGnyUETzYmHuO4DLwrgCu65rEpD/IAfH7CicnJ2Pjxo0elx05ciRc4yFDTJpCgtr2vjW0+rGqA04GTC8c2pt0AhHJg43dk5j9yMDpUOPA0PQ8iao0pQSt+r6VUHZXtUMi5AfcHnYoU8R4Bi4V8cHnAR1GWkYYDArgJKpSFRKojVbYHM6g77ursh0X5CVFrJ/4QKYQR64Gzp3GE0wPGR6PB7lYCMZchxwP9Z2zgaIATqIqTSkBY0CHIbg6uNpgxfFmLaZH6WzKgYbP50WsH4reYguqfMLhln9S9h04CuAkqriT34NdiXK0SQsAuCCvfzs4hxKFRAhdBDoSBnuYA4fbjUlLCANHAZxEVaqiK4AHWQdv0pgAALnJA+tknGhSROhUHl0fA7iCMvCgUQAnUZXexwy8UWMCjwdkxNM+hEC5MvAIBPAgOxFyuMyblhAGjgI4iSp3Bh5kAG/uNCFNIYFYSB/hQEXqXExXvxXKwCOBPv0kquLEAigkQvf630A1aczISqQWxsFQSCLTE1wf5HmYHG7tN2XggaMATqKuL9vpmzpNQZ25SFwZuDYSk5h9LKFwq1BoEjNwFMBJ1KUpggvgjDE0a8wYlkAZeDDSlBK0661wdjukOdQYY9CZbe7uh8FQdK1CoRJK4CiAk6hLVYqDKqFojDaYbA4qoQQpXSmF3cncux3DgTuNJ5hGVpyzJRTaRh8oCuAk6oLNwJs6XUsIsxKohBIMbsWPSmsO23P05TAHDk1iBo8COIm6VIUEWrMdZpsjoNs3aVwBiDLw4KTHuwJ4Sz/a9/rDnSqfEBd8CYU28gSPAjiJOm43Znsv2+mdTgaT1RXgm7sy8GE0iRmUdGXXCUja2Azg7gycAnjAKICTqAtkO/27e2sx45n/wWi1o1FjgljAR6qcToIKBvc6t+jCV0LhVrnE9yGAy8VUQgkWBXASdYFs5tlfq0aHwYpdFe1o1piRmSAFnzrWBUUqEiAhThTWEoo2BCUUysADRwGcRB2XGfa2EuW0Sg8A+O8JFZppDXifpSslaInxEgrVwANHAZxEXapCAgGfh8oWvdfrHU6GytauAH68BY1qE7JoDXifpMdLoApjCaXTyJVQgl+FMjJdgStKMjEpPznUwxq0KICTqBML+bj0vHR8dLDR60qURrUJFrsTU/KT0aKzoKnTTBOYfZSulIY1A9eabZCK+H06ZEMmFmL9LRcgm1YXBYwCOIkJt16Yjw6DFf8pb+5x3ekWHQDg9otGgNdV9qYlhH2T3tW2gLHw7MbsNNn6VD4hfUMBnMSECwtTUJgmx1u7a3tcd7qrtDK1IAXn5yYCAJVQ+ihNKYHV4XTXqkONAnhkUQAnMYHH42Hp9Hz8XK/Bz/Uaj+sqWvRIV0qQECfCpeelAwCykyiA9wXXPz1cK1EogEcWBXASM66bmA25WIC/fnUC9m6HHJ9u0aMoQwHAVWr5y3VjUZSuiNYwBzRuO3246uCdJjvipRTAI4UCOIkZSqkIT141Bjsr2rH682MAXN3tKlv0KEpXum+zeMpw8Hi0Brwv0t0ZeHhWomgpA48oavtFYsqiycNR0aLHP76vRmG6ApeNyYDeYkchZdwhcbahVXgycK3J1qddmKRvKICTmPPIvGJUtRrw1GfHUN9hBAAqmYSIXCKEXCwISwbucDLoLHbKwCOISigk5gj4PKy7cQKK0hX4x/fVAFybPEhopMdLwzKJ2Z9t9KRv/GbgVqsVjz76KOrr66FQKPDkk09Co9Fg7dq1EAgEmDlzJu65555IjJUMIQqJEG8sm4xrXtkJJ2NIkYujPaRBI10pCUtHwv40siJ94zeAl5WVQSaToaysDFVVVVi9ejXa2trw8ssvIzc3FytWrMCxY8cwZsyYSIyXDCHZiXEou3MaWnQWmrQMofR4KcobNCF/3P70QSF947eEUlFRgVmzZgEACgoKUF5eDqvViuHDXSsBZs6ciV27doV9oGRoKkhTYFpBSrSHMaikKyVoCcNuTArgkec3gBcXF2P79u1gjOHQoUPQ6XSQyWTu6+VyOXQ6XVgHSQgJndykOBitjpDXwSmAR57fAL5w4UIoFAosWbIE27Ztw3nnnQeTyeS+3mAwID4+PqyDJISETkl2AgDgSGNnSB+XAnjk+Q3g5eXlmD59OjZv3owrrrgC+fn5EIlEqKurA2MMP/zwAyZNmhSJsRJCQmDMsHjweMCRRm1IH1drch1o3JdWsqRv/L7SeXl5WLduHdavXw+lUom1a9eiubkZDz74IBwOB2bOnInx48dHYqyEkBCQS4QoSJXjSFPoM3CRgEdHokWQ3wCenJyMjRs3elyWkZGBsrKycI2JEBJmpdkJ+LG6I6SPyTWyohVDkUMbeQgZgsZmJ6C509zrMXbBom30kUcBnJAhqCQr9BOZWrONOhFGGAVwQoagkmzXyrGjTaGbyKRe4JFHAZyQISheKkJ+iiykGTgF8MijAE7IEFWSnYByCuADGgVwQoaosdkJaFCboDFa+/1YTiejwxyigAI4IUNUqXsis/91cIPVDiejTTyRRgGckCGqtGsiMxQbemgbfXRQACdkiEqUiZGTFBeSOniHwVWGoQAeWRTACRnCSrMScDQEAXxnRTsAYHxuYr8fiwSOAjghQ9jYnATUtBvdp+n01TfHVSjNjsewhLgQjYwEggI4IUNYSVbXhp5+TGS26S04UKfGnOKMUA2LBIgCOCFDWGlXb/Cj/ZjI/N+JFjAGCuBRQAGckCEsVSHBsARpvyYyvzmmwrAEqTubJ5FDAZyQIa4kK6HPW+rNNge+P92GOcUZ1EY2CiiAEzLEjc1OQFWbAQaLPej7fnX0DEw2B+aMofJJNFAAJ2SIK82OB2PA1a/8gP/bfBANamNA9+s02bD2i+MYMyweMwpTwjxK4g0FcEKGuJlFqbj3kpHITZbhs5+b8M0xVUD3++vWE2jTW/CXhWMhFFAoiQZ61QkZ4iRCAe6/fDT+fovrcHKd2X8pZV9NBzbtrcNtM0ZgXE5imEdIfKEATggBAIiFfEiEfOj81MItdgce/agc2YlxuP+yUREaHfGGWocRQtyUUhF0fnZlrt9RhYoWPd5cNhlyCYWQaKIMnBDiFi8VQttLCaWiRY+/ba/AVeOzcPF56REcGfGGAjghxE0pFfZaA//b9gpIhHw8OX9MBEdFfKEATghx662EYrU78c1xFa4ozUSaUhLhkRFvKIATQtx6y8B3V7VDZ7ZjbklmhEdFfKEATghxcwVw7xn4V0fPQCYWYGZRaoRHRXyhAE4IcXOVUHpm4E4nw7ZjKvxidBqkIkEURka88bsGyGaz4ZFHHkFjYyP4fD5Wr14Nq9WKVatWQSAQID8/H2vXrgWfT38LCBnolFIhjFYH7A6nx+7Kg/VqtOosVD6JMX6j7rfffgu73Y4tW7bg7rvvxosvvohXXnkFd999NzZv3gyr1YodO3ZEYKiEkHBTSl1nWurP2czz1VEVRAIeLR2MMX4D+IgRI+BwOOB0OqHX6yEUClFcXAyNRgPGGAwGA4RCWsxPyGCglLq+y93LKK06CzbvrcPFo9MRL6VDi2OJ38grk8nQ2NiIefPmQa1WY/369WhqasLTTz+N1157DUqlElOnTo3EWAkhYRbfFcC7n5H53FcnYbI58PC886I1LOKD3wC+ceNGzJw5Ew888ACam5tx6623QqfTYdOmTSgqKsKmTZvwl7/8BatWrYrEeAkhYcSVULgMvLyhE2X763H7zBEoTFNEc2jEC78llPj4eCiVSgBAQkIC7HY7lEolFArXm5meng6ttu8HohJCYse5JZS/fnUCKXIx/u/SomgOi/jgNwNftmwZHnvsMSxZsgQ2mw0rV67EsGHDsHLlSgiFQohEIqxevToSYyWEhNnZDNxVQjnWpMXc0kyqfccovwFcLpdj3bp1PS7fsmVLWAZECIme7hm4xe5Au8GKzHhplEdFfKHF24QQNy6Aa002tOosAEABPIZRACeEuEmEAoi7DnVQac0AgPR4alwVqyiAE0I8xHf1Q1FpXRl4BmXgMYsCOCHEg1IqgtZsx5lOVwZOJZTYRQGcEOKBaymr0pkhFvKRKKMVKLGKAjghxAPXUlbVaUZGvAQ8Hi/aQyI+UAAnhHhQSlwtZVVaCzKUVD6JZdSFihDigcvAnU6G4qz4aA+H9IIycEKIB+5QB5XWTBl4jKMMnBDigTvUAQAyE2gNeCyjDJwQ4oHbjQnQGvBYRwGcEOKhe+OqdCqhxDQK4IQQD90z8MwECuCxjAI4IcRDfNzZDDyD+qDENArghBAPXAaulAohE9M6h1hGAZwQ4oE71IEmMGMfBXBCiAcuA6cmVrGPAjghxAMXwKkPeOyjAE4I8SARCjAsQYrzMpXRHgrxg2YoCCE9fHP/bEiElN/FOgrghJAe5BIKDQMB/YklhJABigI4IYQMUBTACSFkgKIATgghAxQFcEIIGaAogBNCyAAVlrVCDofrNI8zZ86E4+EJIWRQ4mImF0P9CUsAb21tBQDcdNNN4Xh4QggZ1FpbW5GXl+f3djzGGAv1k5vNZhw5cgRpaWkQCAShfnhCCBmUHA4HWltbUVpaCqnUfzOxsARwQggh4UeTmIQQMkAFXQO32Wx47LHH0NjYCKvVit/85jcYOXIkHnnkEfB4PBQVFWHVqlXg811/G2pra3HPPffgs88+AwC0tLTg97//PWw2GxISEvDss89CoVB4PEdHRwcefPBBmM1mpKen489//jPi4uLc191444349NNPIZH0bHdZVlaGLVu2QCAQIC4uDna73T3On3/+GZ988glycnKiPs6NGzfiiy++AADMnDkTDQ0N7td0+vTpOH36NDo7O8P+mnYfx+zZs3HPPfd4fd87OjqwePFijB07Fs3NzTCbzRCJRLDZbKivr8eIESNQXFwctnFu2rQJH330EXg8HpYvX44rr7zS53svFAqxYsUKfPXVV6iurkZVVRWysrIglUrhcDgQFxcX1tc0kLF6e//r6upQWVmJjIwMiEQiOJ1OKBSKsH9WnU4nVqxYgUsvvRQ33nhjQO+/1WrFr371K7z55ptITk4O++d0zZo1OHDgAORyOQDg1VdfhVLp2S3xlVdewY4dOyAUCvHQQw9h8+bN7u9UamoqSktLsXPnzpgY6zPPPIMDBw7Abrdj0aJFuOGGGzw+G21tbXjwwQe9vhceWJA++OADtmbNGsYYY2q1ms2ePZvdeeedbM+ePYwxxp544gn29ddfM8YY+/jjj9mCBQvYhRde6L7/mjVr2Mcff8wYY+yll15ib775Zo/nWL16Nfvwww8ZY4y9/vrr7tt899137JprrmETJkxgZrO5x/1aWlrY/PnzmcViYe+++y6bMmUKs1gs7MyZM2zcuHFs/PjxbOXKlVEfZ11dHVuwYAGz2+3M6XSyOXPmsAceeIAxxtjjjz/OiouL2cyZM8P+mp47jkWLFrHjx4/3uC/37yktLWVPPfUUY4yxv/3tb2zixInszjvvZC+++CJbvXp12MbZ3t7OfvnLXzKr1cp0Oh2bNWsWczqdHvfr/t5rtVp20UUXsaeffpo99NBD7JNPPmGzZ89m119/PbvxxhvD+poGMlZf7/+6devYq6++ymbPns1uueUWdtlll4V1rJznn3+eXX/99ey9997rcT/GvL//mzZtYiUlJay0tDQi3/3Fixez9vZ2r+NjjLEjR46wW265hTmdTtbY2MguvvhitmbNGtbe3s6WLl3KiouL2fz582NirLt372a//e1vGWOMWSwWNmfOHKbRaJjJZGL3338/u+yyy9izzz7r8/7dBV1CueKKK/C73/2OC/4QCAQ4evQopkyZAgCYNWsWdu3aBQBISEjAu+++63H/xx57DFdffTWcTieam5t7/GUCgP379+Oiiy7q8Xh8Ph9vvvkmEhMTvY7t8OHDmDBhAsRiMa699lqcf/75OHHiBMxmMxQKBUQiEYYNGxb1cWZmZmLDhg0QCATg8XhQKBS47bbbAADjxo1DcnIyOjs7w/6anjsOu93u9dcC9+9JSUlxZ+iHDh2CTCbD0aNHcdttt2H37t1hG2dycjI++eQTiEQitLW1QSKRgMfjedyv+3uvVCpRXFyMyy+/HA8//DAuvPBCCAQC1NbWIjMzM6yvaSBj9fX+L1u2DAsWLIBAIMDp06eRlJQU1rECwNatW8Hj8dzXeePt/VcqlUhPT4fdbg/759TpdKK2thZPPvkkFi9ejA8++MDr/WbOnAkej4esrCwoFAosXboUBoMBd9xxB+RyOZqbm2NirBMmTMCf/vQn9387HA4IhUJYLBYsWLAAd911l7e3waugA7hcLodCoYBer8e9996L++67D4wx94dULpdDp9MBAC6++GLIZDKP+/N4PDgcDsyfPx979+7FtGnTejyHXq93v2DdH2/GjBnuD7U3594vISEBra2tePzxx/HII4/EzDhFIhGSk5PBGMMzzzyDsWPHoqSkBHq9Hp9++ikWLlzoHkM4x3ruOMaMGYMRI0b0uC/37+Hz+ZDL5dDr9Th48CBuvfVWMMagUCig0+nC+poKhUK8++67WLRoEa6++upe7we4vpQOhwNisRj3338/lixZAp1O5w5A0Ryrr/efz+fj97//PZYvXw6tVosHHnggrGM9deoUPv/8c3dC5ou397+srAwrV650P384x2k0GnHzzTfj2WefxYYNG/Dee+/hxIkTPe7XvcShVCrhdDqRlJSE119/HVOnTo3I9z+QsUokEiQkJMBms+GRRx7BokWL3PFq5syZvb0VPfRpHXhzczPuvvtuLFmyBFdddRWeffZZ93UGgwHx8fG93l8kEuE///kPdu3ahYcffhj33Xcf1q1bBwD49a9/DYVCAYPBAKlU6vfx/vCHP6Curg5JSUm45pprYDAY3Ne1t7fjueeew69//WtcddVVePrpp2NinC+99BIsFgsee+wxyOVyrFq1yuM1zc3NxcaNGyMy1nPH4W2s3Z05cwYrV65ETk4OpkyZgrffftv9eOF+TW+++WbccMMNuOOOO7Bnzx589tlnPt97g8EAi8WCpUuXYtq0afjggw+QkJCAgoKCsL+m/sba2/t/ySWX4L333oNCoXBni+Ea6yeffAKVSoVbb70VjY2NEIlEyM7OxldffeX3/ee++w899JDHax6OccbFxWHp0qXumv20adNw4sQJvPDCCzAajRg1ahTy8vJ6vP9Go9E91ubmZuzZsycmxvrEE0+gs7MT9957L6ZMmYI777yz13H0KqBCSzetra3siiuuYLt27XJfdm4N/IsvvvC4T/fa0qpVq9ju3bsZY4wdOnSI3XzzzT2e4+mnn/aoLb3++use11988cW91sDNZjOrrq5mJSUlbMeOHe7rr776ao8aeLTG6XQ62fLly923P/c13bNnT48aeDjGeu44/Jk1axabO3cu27VrF3vjjTfYSy+95K6BP/nkk2EbZ2VlJbv77ruZ0+lkTqeT3X777Wzv3r0e9+v+3mu1WjZnzhw2d+5ctmHDBjZ//nzW0NAQkc9pIGP19f7/61//YnPnzmXHjx+P+HfqpZde8lkD53R//zklJSVhH2dFRQWbP38+s9vtzGq1skWLFrFTp0553K+8vJwtXbqUORwO1tjYyObNm+fxnXrppZd61MCjNVaTycSuvfZa9u9//7vHYzLG2IcffhhwDTzodeBr1qzBl19+6c5kAFfGtmbNGthsNhQUFGDNmjUeG3hmzJiBnTt3AgAqKyvxxz/+EYCrtvbkk0+isLDQ4zna2trw8MMPw2AwICkpCc8//7zHT5xLLrkEX375pc9VKO+//z6amppgs9lQXFzsvm7atGkoKytDZmZmVMe5bds23H///Tj//PMBuGbAzWYzRo8eDQDQarVITU2FxWIJ62u6c+dOj3EAwP33348JEyb0eF0BYOLEiZBKpSgsLITD4UBVVRUyMjLQ2NiI4cOHY/To0WF7TV955RV899137nqtt9Uy3HvPGENqaiqOHj0Kg8EAxhhEIhHy8vKgUqmQnZ0d1vff31h9vf88Hg9GoxESiQR5eXloaGhAfn5+RD6rL7/8MlJTU32uQgE833/OqVOnUFRUFPbv/oYNG/Dll19CJBLhmmuu8TrOl19+Gd999x2cTieys7Oxf/9+d5xqaGjA1Vdfjf3790d9rBs3bsQrr7ziEZv+9Kc/ITc3FwDw0UcfoaqqKqBVKLSRhxBCBijayEMIIQMUBXBCCBmgKIATQsgARQGcEEIGKArghBAyQFEAJ4OWxWLBJZdc4vP6999/HzabLYIjIiS0KICTIev111+H0+mM9jAI6bOwHKlGSLQYDAY8+OCD0Gq1GD58OADgxx9/xCuvvALGGAwGA55//nn89NNPaG1txcqVK/Hqq6+6L3M6nVi2bBnmzZsX5X8JIf5RBk4GlS1btmDUqFHYtGkTFi9eDAA4ffo0nn32Wbzzzju4/PLLsXXrVlx//fVIS0vDCy+8gG+//RYNDQ3YvHkz3n77baxfvx5arTbK/xJC/KMMnAwqNTU1mD17NgBg/PjxEAqFyMjIwNq1ayGTyaBSqTBx4kSP+5w6dQpHjx7FLbfcAgCw2+1obGz02+yIkGijAE4GlcLCQhw6dAhz5szBsWPHYLfb8cQTT2Dbtm1QKBR4+OGHwXWP4PF4cDqdKCgowNSpU7F69Wo4nU68+uqr7r4UhMQyKqGQQeXGG29EfX09brzxRmzatAkikQhXX301brrpJixevBgGgwEtLS0AgEmTJmHFihW45JJLIJPJsGTJElx33XUA0OP4LEJiETWzIoSQAYoycEIIGaAogBNCyABFAZwQQgYoCuCEEDJAUQAnhJABigI4IYQMUBTACSFkgKIATgghA9T/B6gN+FgVhuivAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the oil values\n",
    "oil_plot = oil['dcoilwtico'].copy()\n",
    "oil_plot.index = oil['date'].copy()\n",
    "oil_plot.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "fb430d77-2328-4e4c-b2b6-1e0687efe7fe",
   "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>date</th>\n",
       "      <th>type</th>\n",
       "      <th>locale</th>\n",
       "      <th>locale_name</th>\n",
       "      <th>description</th>\n",
       "      <th>transferred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-03-02</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Local</td>\n",
       "      <td>Manta</td>\n",
       "      <td>Fundacion de Manta</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Regional</td>\n",
       "      <td>Cotopaxi</td>\n",
       "      <td>Provincializacion de Cotopaxi</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-04-12</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Local</td>\n",
       "      <td>Cuenca</td>\n",
       "      <td>Fundacion de Cuenca</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-14</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Local</td>\n",
       "      <td>Libertad</td>\n",
       "      <td>Cantonizacion de Libertad</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-04-21</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Local</td>\n",
       "      <td>Riobamba</td>\n",
       "      <td>Cantonizacion de Riobamba</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date     type    locale locale_name                    description  \\\n",
       "0  2012-03-02  Holiday     Local       Manta             Fundacion de Manta   \n",
       "1  2012-04-01  Holiday  Regional    Cotopaxi  Provincializacion de Cotopaxi   \n",
       "2  2012-04-12  Holiday     Local      Cuenca            Fundacion de Cuenca   \n",
       "3  2012-04-14  Holiday     Local    Libertad      Cantonizacion de Libertad   \n",
       "4  2012-04-21  Holiday     Local    Riobamba      Cantonizacion de Riobamba   \n",
       "\n",
       "   transferred  \n",
       "0        False  \n",
       "1        False  \n",
       "2        False  \n",
       "3        False  \n",
       "4        False  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holiday_events.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "17a12273-4cd4-4b68-8b08-904d8b5e935d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date           datetime64[ns]\n",
       "type                   object\n",
       "locale                 object\n",
       "locale_name            object\n",
       "description            object\n",
       "transferred              bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holiday_events['date'] = pd.to_datetime(holiday_events['date'], format=\"%Y-%m-%d\")\n",
    "oil['date'] = pd.to_datetime(oil['date'], format=\"%Y-%m-%d\")\n",
    "holiday_events.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "65b5c413-b4d2-4ad7-9336-eef613844993",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge train\n",
    "# pd_train = pd_train.drop('id', axis = 1)\n",
    "# pd_train = pd_train.merge(stores, left_on='store_nbr', right_on='store_nbr', how='left')\n",
    "# pd_train = pd_train.merge(items, left_on='item_nbr', right_on='item_nbr', how='left')\n",
    "pd_train = pd_train.merge(holiday_events, left_on='date', right_on='date', how='left')\n",
    "pd_train = pd_train.merge(oil, left_on='date', right_on='date', how='left')\n",
    "pd_train = pd_train.drop(['description', 'locale_name'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "745ff536-4210-4026-b1a1-2d4c89777394",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_train[:1000].to_csv(\"train_merged.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d05082d-cacf-4c92-843e-abe4a8145bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_train[0:100].to_csv(\"train_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "53118815-b10d-4cfd-9df6-ea31f1f6aa78",
   "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>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>item_nbr</th>\n",
       "      <th>unit_sales</th>\n",
       "      <th>onpromotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>103665</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>105574</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>105575</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>108079</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>108701</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  id        date  store_nbr  item_nbr  unit_sales  onpromotion\n",
       "0           0   0  2013-01-01         25    103665         7.0          NaN\n",
       "1           1   1  2013-01-01         25    105574         1.0          NaN\n",
       "2           2   2  2013-01-01         25    105575         2.0          NaN\n",
       "3           3   3  2013-01-01         25    108079         1.0          NaN\n",
       "4           4   4  2013-01-01         25    108701         1.0          NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_2 = pd.read_csv(\"train_dataset.csv\")\n",
    "train_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "771b8cd2-b9c1-4fa5-8907-c706ea558938",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of               id       date  store_nbr  item_nbr  unit_sales  onpromotion\n",
       "0              0 2013-01-01         25    103665         7.0          NaN\n",
       "1              1 2013-01-01         25    105574         1.0          NaN\n",
       "2              2 2013-01-01         25    105575         2.0          NaN\n",
       "3              3 2013-01-01         25    108079         1.0          NaN\n",
       "4              4 2013-01-01         25    108701         1.0          NaN\n",
       "...          ...        ...        ...       ...         ...          ...\n",
       "5999995  5999995 2013-05-24         14    454596         3.0          NaN\n",
       "5999996  5999996 2013-05-24         14    456870         4.0          NaN\n",
       "5999997  5999997 2013-05-24         14    456875         3.0          NaN\n",
       "5999998  5999998 2013-05-24         14    457471         8.0          NaN\n",
       "5999999  5999999 2013-05-24         14    457556        10.0          NaN\n",
       "\n",
       "[6000000 rows x 6 columns]>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77e72d39-b90c-4676-99e6-3720c950db5f",
   "metadata": {},
   "source": [
    "Taking a peek, we note that the training data only consists of 6 rather measly columns and coupled with the fact that we have approx 125 million rows, there does seem to be a discrepancy in the number of features that we are going to provide our learning model to train on. However that's where the other supplementary files comes in to play as we will most definitely have to join the fields \"store_nbr\" (store number) and \"item_nbr\" as well as dates ( bring in daily oil prices). So there is actually quite a lot of potential and avenues for feature enhancement and engineering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "089b6984-8219-4287-aa0d-886ee3ae7fc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nulls in Oil columns: ['date' 'dcoilwtico'] => [False  True]\n",
      "======================================================================\n",
      "Nulls in holiday_events columns: ['date' 'type' 'locale' 'locale_name' 'description' 'transferred'] => [False False False False False False]\n",
      "======================================================================\n",
      "Nulls in stores columns: ['store_nbr' 'city' 'state' 'type' 'cluster'] => [False False False False False]\n",
      "======================================================================\n",
      "Nulls in transactions columns: ['date' 'store_nbr' 'transactions'] => [False False False]\n"
     ]
    }
   ],
   "source": [
    "print(\"Nulls in Oil columns: {0} => {1}\".format(oil.columns.values,oil.isnull().any().values))\n",
    "print(\"=\"*70)\n",
    "print(\"Nulls in holiday_events columns: {0} => {1}\".format(holiday_events.columns.values,holiday_events.isnull().any().values))\n",
    "print(\"=\"*70)\n",
    "print(\"Nulls in stores columns: {0} => {1}\".format(stores.columns.values,stores.isnull().any().values))\n",
    "print(\"=\"*70)\n",
    "print(\"Nulls in transactions columns: {0} => {1}\".format(transactions.columns.values,transactions.isnull().any().values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "645b623b-7ff0-45a6-b460-bf1a0933cff4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "fill": "tonexty",
         "fillcolor": "rgba(0, 0, 216, 0.3)",
         "line": {
          "color": "rgb(20, 15, 200, 0.8)"
         },
         "mode": "lines",
         "name": "Oil prices",
         "type": "scatter",
         "x": [
          "2013-01-01",
          "2013-01-02",
          "2013-01-03",
          "2013-01-04",
          "2013-01-07",
          "2013-01-08",
          "2013-01-09",
          "2013-01-10",
          "2013-01-11",
          "2013-01-14",
          "2013-01-15",
          "2013-01-16",
          "2013-01-17",
          "2013-01-18",
          "2013-01-21",
          "2013-01-22",
          "2013-01-23",
          "2013-01-24",
          "2013-01-25",
          "2013-01-28",
          "2013-01-29",
          "2013-01-30",
          "2013-01-31",
          "2013-02-01",
          "2013-02-04",
          "2013-02-05",
          "2013-02-06",
          "2013-02-07",
          "2013-02-08",
          "2013-02-11",
          "2013-02-12",
          "2013-02-13",
          "2013-02-14",
          "2013-02-15",
          "2013-02-18",
          "2013-02-19",
          "2013-02-20",
          "2013-02-21",
          "2013-02-22",
          "2013-02-25",
          "2013-02-26",
          "2013-02-27",
          "2013-02-28",
          "2013-03-01",
          "2013-03-04",
          "2013-03-05",
          "2013-03-06",
          "2013-03-07",
          "2013-03-08",
          "2013-03-11",
          "2013-03-12",
          "2013-03-13",
          "2013-03-14",
          "2013-03-15",
          "2013-03-18",
          "2013-03-19",
          "2013-03-20",
          "2013-03-21",
          "2013-03-22",
          "2013-03-25",
          "2013-03-26",
          "2013-03-27",
          "2013-03-28",
          "2013-03-29",
          "2013-04-01",
          "2013-04-02",
          "2013-04-03",
          "2013-04-04",
          "2013-04-05",
          "2013-04-08",
          "2013-04-09",
          "2013-04-10",
          "2013-04-11",
          "2013-04-12",
          "2013-04-15",
          "2013-04-16",
          "2013-04-17",
          "2013-04-18",
          "2013-04-19",
          "2013-04-22",
          "2013-04-23",
          "2013-04-24",
          "2013-04-25",
          "2013-04-26",
          "2013-04-29",
          "2013-04-30",
          "2013-05-01",
          "2013-05-02",
          "2013-05-03",
          "2013-05-06",
          "2013-05-07",
          "2013-05-08",
          "2013-05-09",
          "2013-05-10",
          "2013-05-13",
          "2013-05-14",
          "2013-05-15",
          "2013-05-16",
          "2013-05-17",
          "2013-05-20",
          "2013-05-21",
          "2013-05-22",
          "2013-05-23",
          "2013-05-24",
          "2013-05-27",
          "2013-05-28",
          "2013-05-29",
          "2013-05-30",
          "2013-05-31",
          "2013-06-03",
          "2013-06-04",
          "2013-06-05",
          "2013-06-06",
          "2013-06-07",
          "2013-06-10",
          "2013-06-11",
          "2013-06-12",
          "2013-06-13",
          "2013-06-14",
          "2013-06-17",
          "2013-06-18",
          "2013-06-19",
          "2013-06-20",
          "2013-06-21",
          "2013-06-24",
          "2013-06-25",
          "2013-06-26",
          "2013-06-27",
          "2013-06-28",
          "2013-07-01",
          "2013-07-02",
          "2013-07-03",
          "2013-07-04",
          "2013-07-05",
          "2013-07-08",
          "2013-07-09",
          "2013-07-10",
          "2013-07-11",
          "2013-07-12",
          "2013-07-15",
          "2013-07-16",
          "2013-07-17",
          "2013-07-18",
          "2013-07-19",
          "2013-07-22",
          "2013-07-23",
          "2013-07-24",
          "2013-07-25",
          "2013-07-26",
          "2013-07-29",
          "2013-07-30",
          "2013-07-31",
          "2013-08-01",
          "2013-08-02",
          "2013-08-05",
          "2013-08-06",
          "2013-08-07",
          "2013-08-08",
          "2013-08-09",
          "2013-08-12",
          "2013-08-13",
          "2013-08-14",
          "2013-08-15",
          "2013-08-16",
          "2013-08-19",
          "2013-08-20",
          "2013-08-21",
          "2013-08-22",
          "2013-08-23",
          "2013-08-26",
          "2013-08-27",
          "2013-08-28",
          "2013-08-29",
          "2013-08-30",
          "2013-09-02",
          "2013-09-03",
          "2013-09-04",
          "2013-09-05",
          "2013-09-06",
          "2013-09-09",
          "2013-09-10",
          "2013-09-11",
          "2013-09-12",
          "2013-09-13",
          "2013-09-16",
          "2013-09-17",
          "2013-09-18",
          "2013-09-19",
          "2013-09-20",
          "2013-09-23",
          "2013-09-24",
          "2013-09-25",
          "2013-09-26",
          "2013-09-27",
          "2013-09-30",
          "2013-10-01",
          "2013-10-02",
          "2013-10-03",
          "2013-10-04",
          "2013-10-07",
          "2013-10-08",
          "2013-10-09",
          "2013-10-10",
          "2013-10-11",
          "2013-10-14",
          "2013-10-15",
          "2013-10-16",
          "2013-10-17",
          "2013-10-18",
          "2013-10-21",
          "2013-10-22",
          "2013-10-23",
          "2013-10-24",
          "2013-10-25",
          "2013-10-28",
          "2013-10-29",
          "2013-10-30",
          "2013-10-31",
          "2013-11-01",
          "2013-11-04",
          "2013-11-05",
          "2013-11-06",
          "2013-11-07",
          "2013-11-08",
          "2013-11-11",
          "2013-11-12",
          "2013-11-13",
          "2013-11-14",
          "2013-11-15",
          "2013-11-18",
          "2013-11-19",
          "2013-11-20",
          "2013-11-21",
          "2013-11-22",
          "2013-11-25",
          "2013-11-26",
          "2013-11-27",
          "2013-11-28",
          "2013-11-29",
          "2013-12-02",
          "2013-12-03",
          "2013-12-04",
          "2013-12-05",
          "2013-12-06",
          "2013-12-09",
          "2013-12-10",
          "2013-12-11",
          "2013-12-12",
          "2013-12-13",
          "2013-12-16",
          "2013-12-17",
          "2013-12-18",
          "2013-12-19",
          "2013-12-20",
          "2013-12-23",
          "2013-12-24",
          "2013-12-25",
          "2013-12-26",
          "2013-12-27",
          "2013-12-30",
          "2013-12-31",
          "2014-01-01",
          "2014-01-02",
          "2014-01-03",
          "2014-01-06",
          "2014-01-07",
          "2014-01-08",
          "2014-01-09",
          "2014-01-10",
          "2014-01-13",
          "2014-01-14",
          "2014-01-15",
          "2014-01-16",
          "2014-01-17",
          "2014-01-20",
          "2014-01-21",
          "2014-01-22",
          "2014-01-23",
          "2014-01-24",
          "2014-01-27",
          "2014-01-28",
          "2014-01-29",
          "2014-01-30",
          "2014-01-31",
          "2014-02-03",
          "2014-02-04",
          "2014-02-05",
          "2014-02-06",
          "2014-02-07",
          "2014-02-10",
          "2014-02-11",
          "2014-02-12",
          "2014-02-13",
          "2014-02-14",
          "2014-02-17",
          "2014-02-18",
          "2014-02-19",
          "2014-02-20",
          "2014-02-21",
          "2014-02-24",
          "2014-02-25",
          "2014-02-26",
          "2014-02-27",
          "2014-02-28",
          "2014-03-03",
          "2014-03-04",
          "2014-03-05",
          "2014-03-06",
          "2014-03-07",
          "2014-03-10",
          "2014-03-11",
          "2014-03-12",
          "2014-03-13",
          "2014-03-14",
          "2014-03-17",
          "2014-03-18",
          "2014-03-19",
          "2014-03-20",
          "2014-03-21",
          "2014-03-24",
          "2014-03-25",
          "2014-03-26",
          "2014-03-27",
          "2014-03-28",
          "2014-03-31",
          "2014-04-01",
          "2014-04-02",
          "2014-04-03",
          "2014-04-04",
          "2014-04-07",
          "2014-04-08",
          "2014-04-09",
          "2014-04-10",
          "2014-04-11",
          "2014-04-14",
          "2014-04-15",
          "2014-04-16",
          "2014-04-17",
          "2014-04-18",
          "2014-04-21",
          "2014-04-22",
          "2014-04-23",
          "2014-04-24",
          "2014-04-25",
          "2014-04-28",
          "2014-04-29",
          "2014-04-30",
          "2014-05-01",
          "2014-05-02",
          "2014-05-05",
          "2014-05-06",
          "2014-05-07",
          "2014-05-08",
          "2014-05-09",
          "2014-05-12",
          "2014-05-13",
          "2014-05-14",
          "2014-05-15",
          "2014-05-16",
          "2014-05-19",
          "2014-05-20",
          "2014-05-21",
          "2014-05-22",
          "2014-05-23",
          "2014-05-26",
          "2014-05-27",
          "2014-05-28",
          "2014-05-29",
          "2014-05-30",
          "2014-06-02",
          "2014-06-03",
          "2014-06-04",
          "2014-06-05",
          "2014-06-06",
          "2014-06-09",
          "2014-06-10",
          "2014-06-11",
          "2014-06-12",
          "2014-06-13",
          "2014-06-16",
          "2014-06-17",
          "2014-06-18",
          "2014-06-19",
          "2014-06-20",
          "2014-06-23",
          "2014-06-24",
          "2014-06-25",
          "2014-06-26",
          "2014-06-27",
          "2014-06-30",
          "2014-07-01",
          "2014-07-02",
          "2014-07-03",
          "2014-07-04",
          "2014-07-07",
          "2014-07-08",
          "2014-07-09",
          "2014-07-10",
          "2014-07-11",
          "2014-07-14",
          "2014-07-15",
          "2014-07-16",
          "2014-07-17",
          "2014-07-18",
          "2014-07-21",
          "2014-07-22",
          "2014-07-23",
          "2014-07-24",
          "2014-07-25",
          "2014-07-28",
          "2014-07-29",
          "2014-07-30",
          "2014-07-31",
          "2014-08-01",
          "2014-08-04",
          "2014-08-05",
          "2014-08-06",
          "2014-08-07",
          "2014-08-08",
          "2014-08-11",
          "2014-08-12",
          "2014-08-13",
          "2014-08-14",
          "2014-08-15",
          "2014-08-18",
          "2014-08-19",
          "2014-08-20",
          "2014-08-21",
          "2014-08-22",
          "2014-08-25",
          "2014-08-26",
          "2014-08-27",
          "2014-08-28",
          "2014-08-29",
          "2014-09-01",
          "2014-09-02",
          "2014-09-03",
          "2014-09-04",
          "2014-09-05",
          "2014-09-08",
          "2014-09-09",
          "2014-09-10",
          "2014-09-11",
          "2014-09-12",
          "2014-09-15",
          "2014-09-16",
          "2014-09-17",
          "2014-09-18",
          "2014-09-19",
          "2014-09-22",
          "2014-09-23",
          "2014-09-24",
          "2014-09-25",
          "2014-09-26",
          "2014-09-29",
          "2014-09-30",
          "2014-10-01",
          "2014-10-02",
          "2014-10-03",
          "2014-10-06",
          "2014-10-07",
          "2014-10-08",
          "2014-10-09",
          "2014-10-10",
          "2014-10-13",
          "2014-10-14",
          "2014-10-15",
          "2014-10-16",
          "2014-10-17",
          "2014-10-20",
          "2014-10-21",
          "2014-10-22",
          "2014-10-23",
          "2014-10-24",
          "2014-10-27",
          "2014-10-28",
          "2014-10-29",
          "2014-10-30",
          "2014-10-31",
          "2014-11-03",
          "2014-11-04",
          "2014-11-05",
          "2014-11-06",
          "2014-11-07",
          "2014-11-10",
          "2014-11-11",
          "2014-11-12",
          "2014-11-13",
          "2014-11-14",
          "2014-11-17",
          "2014-11-18",
          "2014-11-19",
          "2014-11-20",
          "2014-11-21",
          "2014-11-24",
          "2014-11-25",
          "2014-11-26",
          "2014-11-27",
          "2014-11-28",
          "2014-12-01",
          "2014-12-02",
          "2014-12-03",
          "2014-12-04",
          "2014-12-05",
          "2014-12-08",
          "2014-12-09",
          "2014-12-10",
          "2014-12-11",
          "2014-12-12",
          "2014-12-15",
          "2014-12-16",
          "2014-12-17",
          "2014-12-18",
          "2014-12-19",
          "2014-12-22",
          "2014-12-23",
          "2014-12-24",
          "2014-12-25",
          "2014-12-26",
          "2014-12-29",
          "2014-12-30",
          "2014-12-31",
          "2015-01-01",
          "2015-01-02",
          "2015-01-05",
          "2015-01-06",
          "2015-01-07",
          "2015-01-08",
          "2015-01-09",
          "2015-01-12",
          "2015-01-13",
          "2015-01-14",
          "2015-01-15",
          "2015-01-16",
          "2015-01-19",
          "2015-01-20",
          "2015-01-21",
          "2015-01-22",
          "2015-01-23",
          "2015-01-26",
          "2015-01-27",
          "2015-01-28",
          "2015-01-29",
          "2015-01-30",
          "2015-02-02",
          "2015-02-03",
          "2015-02-04",
          "2015-02-05",
          "2015-02-06",
          "2015-02-09",
          "2015-02-10",
          "2015-02-11",
          "2015-02-12",
          "2015-02-13",
          "2015-02-16",
          "2015-02-17",
          "2015-02-18",
          "2015-02-19",
          "2015-02-20",
          "2015-02-23",
          "2015-02-24",
          "2015-02-25",
          "2015-02-26",
          "2015-02-27",
          "2015-03-02",
          "2015-03-03",
          "2015-03-04",
          "2015-03-05",
          "2015-03-06",
          "2015-03-09",
          "2015-03-10",
          "2015-03-11",
          "2015-03-12",
          "2015-03-13",
          "2015-03-16",
          "2015-03-17",
          "2015-03-18",
          "2015-03-19",
          "2015-03-20",
          "2015-03-23",
          "2015-03-24",
          "2015-03-25",
          "2015-03-26",
          "2015-03-27",
          "2015-03-30",
          "2015-03-31",
          "2015-04-01",
          "2015-04-02",
          "2015-04-03",
          "2015-04-06",
          "2015-04-07",
          "2015-04-08",
          "2015-04-09",
          "2015-04-10",
          "2015-04-13",
          "2015-04-14",
          "2015-04-15",
          "2015-04-16",
          "2015-04-17",
          "2015-04-20",
          "2015-04-21",
          "2015-04-22",
          "2015-04-23",
          "2015-04-24",
          "2015-04-27",
          "2015-04-28",
          "2015-04-29",
          "2015-04-30",
          "2015-05-01",
          "2015-05-04",
          "2015-05-05",
          "2015-05-06",
          "2015-05-07",
          "2015-05-08",
          "2015-05-11",
          "2015-05-12",
          "2015-05-13",
          "2015-05-14",
          "2015-05-15",
          "2015-05-18",
          "2015-05-19",
          "2015-05-20",
          "2015-05-21",
          "2015-05-22",
          "2015-05-25",
          "2015-05-26",
          "2015-05-27",
          "2015-05-28",
          "2015-05-29",
          "2015-06-01",
          "2015-06-02",
          "2015-06-03",
          "2015-06-04",
          "2015-06-05",
          "2015-06-08",
          "2015-06-09",
          "2015-06-10",
          "2015-06-11",
          "2015-06-12",
          "2015-06-15",
          "2015-06-16",
          "2015-06-17",
          "2015-06-18",
          "2015-06-19",
          "2015-06-22",
          "2015-06-23",
          "2015-06-24",
          "2015-06-25",
          "2015-06-26",
          "2015-06-29",
          "2015-06-30",
          "2015-07-01",
          "2015-07-02",
          "2015-07-03",
          "2015-07-06",
          "2015-07-07",
          "2015-07-08",
          "2015-07-09",
          "2015-07-10",
          "2015-07-13",
          "2015-07-14",
          "2015-07-15",
          "2015-07-16",
          "2015-07-17",
          "2015-07-20",
          "2015-07-21",
          "2015-07-22",
          "2015-07-23",
          "2015-07-24",
          "2015-07-27",
          "2015-07-28",
          "2015-07-29",
          "2015-07-30",
          "2015-07-31",
          "2015-08-03",
          "2015-08-04",
          "2015-08-05",
          "2015-08-06",
          "2015-08-07",
          "2015-08-10",
          "2015-08-11",
          "2015-08-12",
          "2015-08-13",
          "2015-08-14",
          "2015-08-17",
          "2015-08-18",
          "2015-08-19",
          "2015-08-20",
          "2015-08-21",
          "2015-08-24",
          "2015-08-25",
          "2015-08-26",
          "2015-08-27",
          "2015-08-28",
          "2015-08-31",
          "2015-09-01",
          "2015-09-02",
          "2015-09-03",
          "2015-09-04",
          "2015-09-07",
          "2015-09-08",
          "2015-09-09",
          "2015-09-10",
          "2015-09-11",
          "2015-09-14",
          "2015-09-15",
          "2015-09-16",
          "2015-09-17",
          "2015-09-18",
          "2015-09-21",
          "2015-09-22",
          "2015-09-23",
          "2015-09-24",
          "2015-09-25",
          "2015-09-28",
          "2015-09-29",
          "2015-09-30",
          "2015-10-01",
          "2015-10-02",
          "2015-10-05",
          "2015-10-06",
          "2015-10-07",
          "2015-10-08",
          "2015-10-09",
          "2015-10-12",
          "2015-10-13",
          "2015-10-14",
          "2015-10-15",
          "2015-10-16",
          "2015-10-19",
          "2015-10-20",
          "2015-10-21",
          "2015-10-22",
          "2015-10-23",
          "2015-10-26",
          "2015-10-27",
          "2015-10-28",
          "2015-10-29",
          "2015-10-30",
          "2015-11-02",
          "2015-11-03",
          "2015-11-04",
          "2015-11-05",
          "2015-11-06",
          "2015-11-09",
          "2015-11-10",
          "2015-11-11",
          "2015-11-12",
          "2015-11-13",
          "2015-11-16",
          "2015-11-17",
          "2015-11-18",
          "2015-11-19",
          "2015-11-20",
          "2015-11-23",
          "2015-11-24",
          "2015-11-25",
          "2015-11-26",
          "2015-11-27",
          "2015-11-30",
          "2015-12-01",
          "2015-12-02",
          "2015-12-03",
          "2015-12-04",
          "2015-12-07",
          "2015-12-08",
          "2015-12-09",
          "2015-12-10",
          "2015-12-11",
          "2015-12-14",
          "2015-12-15",
          "2015-12-16",
          "2015-12-17",
          "2015-12-18",
          "2015-12-21",
          "2015-12-22",
          "2015-12-23",
          "2015-12-24",
          "2015-12-25",
          "2015-12-28",
          "2015-12-29",
          "2015-12-30",
          "2015-12-31",
          "2016-01-01",
          "2016-01-04",
          "2016-01-05",
          "2016-01-06",
          "2016-01-07",
          "2016-01-08",
          "2016-01-11",
          "2016-01-12",
          "2016-01-13",
          "2016-01-14",
          "2016-01-15",
          "2016-01-18",
          "2016-01-19",
          "2016-01-20",
          "2016-01-21",
          "2016-01-22",
          "2016-01-25",
          "2016-01-26",
          "2016-01-27",
          "2016-01-28",
          "2016-01-29",
          "2016-02-01",
          "2016-02-02",
          "2016-02-03",
          "2016-02-04",
          "2016-02-05",
          "2016-02-08",
          "2016-02-09",
          "2016-02-10",
          "2016-02-11",
          "2016-02-12",
          "2016-02-15",
          "2016-02-16",
          "2016-02-17",
          "2016-02-18",
          "2016-02-19",
          "2016-02-22",
          "2016-02-23",
          "2016-02-24",
          "2016-02-25",
          "2016-02-26",
          "2016-02-29",
          "2016-03-01",
          "2016-03-02",
          "2016-03-03",
          "2016-03-04",
          "2016-03-07",
          "2016-03-08",
          "2016-03-09",
          "2016-03-10",
          "2016-03-11",
          "2016-03-14",
          "2016-03-15",
          "2016-03-16",
          "2016-03-17",
          "2016-03-18",
          "2016-03-21",
          "2016-03-22",
          "2016-03-23",
          "2016-03-24",
          "2016-03-25",
          "2016-03-28",
          "2016-03-29",
          "2016-03-30",
          "2016-03-31",
          "2016-04-01",
          "2016-04-04",
          "2016-04-05",
          "2016-04-06",
          "2016-04-07",
          "2016-04-08",
          "2016-04-11",
          "2016-04-12",
          "2016-04-13",
          "2016-04-14",
          "2016-04-15",
          "2016-04-18",
          "2016-04-19",
          "2016-04-20",
          "2016-04-21",
          "2016-04-22",
          "2016-04-25",
          "2016-04-26",
          "2016-04-27",
          "2016-04-28",
          "2016-04-29",
          "2016-05-02",
          "2016-05-03",
          "2016-05-04",
          "2016-05-05",
          "2016-05-06",
          "2016-05-09",
          "2016-05-10",
          "2016-05-11",
          "2016-05-12",
          "2016-05-13",
          "2016-05-16",
          "2016-05-17",
          "2016-05-18",
          "2016-05-19",
          "2016-05-20",
          "2016-05-23",
          "2016-05-24",
          "2016-05-25",
          "2016-05-26",
          "2016-05-27",
          "2016-05-30",
          "2016-05-31",
          "2016-06-01",
          "2016-06-02",
          "2016-06-03",
          "2016-06-06",
          "2016-06-07",
          "2016-06-08",
          "2016-06-09",
          "2016-06-10",
          "2016-06-13",
          "2016-06-14",
          "2016-06-15",
          "2016-06-16",
          "2016-06-17",
          "2016-06-20",
          "2016-06-21",
          "2016-06-22",
          "2016-06-23",
          "2016-06-24",
          "2016-06-27",
          "2016-06-28",
          "2016-06-29",
          "2016-06-30",
          "2016-07-01",
          "2016-07-04",
          "2016-07-05",
          "2016-07-06",
          "2016-07-07",
          "2016-07-08",
          "2016-07-11",
          "2016-07-12",
          "2016-07-13",
          "2016-07-14",
          "2016-07-15",
          "2016-07-18",
          "2016-07-19",
          "2016-07-20",
          "2016-07-21",
          "2016-07-22",
          "2016-07-25",
          "2016-07-26",
          "2016-07-27",
          "2016-07-28",
          "2016-07-29",
          "2016-08-01",
          "2016-08-02",
          "2016-08-03",
          "2016-08-04",
          "2016-08-05",
          "2016-08-08",
          "2016-08-09",
          "2016-08-10",
          "2016-08-11",
          "2016-08-12",
          "2016-08-15",
          "2016-08-16",
          "2016-08-17",
          "2016-08-18",
          "2016-08-19",
          "2016-08-22",
          "2016-08-23",
          "2016-08-24",
          "2016-08-25",
          "2016-08-26",
          "2016-08-29",
          "2016-08-30",
          "2016-08-31",
          "2016-09-01",
          "2016-09-02",
          "2016-09-05",
          "2016-09-06",
          "2016-09-07",
          "2016-09-08",
          "2016-09-09",
          "2016-09-12",
          "2016-09-13",
          "2016-09-14",
          "2016-09-15",
          "2016-09-16",
          "2016-09-19",
          "2016-09-20",
          "2016-09-21",
          "2016-09-22",
          "2016-09-23",
          "2016-09-26",
          "2016-09-27",
          "2016-09-28",
          "2016-09-29",
          "2016-09-30",
          "2016-10-03",
          "2016-10-04",
          "2016-10-05",
          "2016-10-06",
          "2016-10-07",
          "2016-10-10",
          "2016-10-11",
          "2016-10-12",
          "2016-10-13",
          "2016-10-14",
          "2016-10-17",
          "2016-10-18",
          "2016-10-19",
          "2016-10-20",
          "2016-10-21",
          "2016-10-24",
          "2016-10-25",
          "2016-10-26",
          "2016-10-27",
          "2016-10-28",
          "2016-10-31",
          "2016-11-01",
          "2016-11-02",
          "2016-11-03",
          "2016-11-04",
          "2016-11-07",
          "2016-11-08",
          "2016-11-09",
          "2016-11-10",
          "2016-11-11",
          "2016-11-14",
          "2016-11-15",
          "2016-11-16",
          "2016-11-17",
          "2016-11-18",
          "2016-11-21",
          "2016-11-22",
          "2016-11-23",
          "2016-11-24",
          "2016-11-25",
          "2016-11-28",
          "2016-11-29",
          "2016-11-30",
          "2016-12-01",
          "2016-12-02",
          "2016-12-05",
          "2016-12-06",
          "2016-12-07",
          "2016-12-08",
          "2016-12-09",
          "2016-12-12",
          "2016-12-13",
          "2016-12-14",
          "2016-12-15",
          "2016-12-16",
          "2016-12-19",
          "2016-12-20",
          "2016-12-21",
          "2016-12-22",
          "2016-12-23",
          "2016-12-26",
          "2016-12-27",
          "2016-12-28",
          "2016-12-29",
          "2016-12-30",
          "2017-01-02",
          "2017-01-03",
          "2017-01-04",
          "2017-01-05",
          "2017-01-06",
          "2017-01-09",
          "2017-01-10",
          "2017-01-11",
          "2017-01-12",
          "2017-01-13",
          "2017-01-16",
          "2017-01-17",
          "2017-01-18",
          "2017-01-19",
          "2017-01-20",
          "2017-01-23",
          "2017-01-24",
          "2017-01-25",
          "2017-01-26",
          "2017-01-27",
          "2017-01-30",
          "2017-01-31",
          "2017-02-01",
          "2017-02-02",
          "2017-02-03",
          "2017-02-06",
          "2017-02-07",
          "2017-02-08",
          "2017-02-09",
          "2017-02-10",
          "2017-02-13",
          "2017-02-14",
          "2017-02-15",
          "2017-02-16",
          "2017-02-17",
          "2017-02-20",
          "2017-02-21",
          "2017-02-22",
          "2017-02-23",
          "2017-02-24",
          "2017-02-27",
          "2017-02-28",
          "2017-03-01",
          "2017-03-02",
          "2017-03-03",
          "2017-03-06",
          "2017-03-07",
          "2017-03-08",
          "2017-03-09",
          "2017-03-10",
          "2017-03-13",
          "2017-03-14",
          "2017-03-15",
          "2017-03-16",
          "2017-03-17",
          "2017-03-20",
          "2017-03-21",
          "2017-03-22",
          "2017-03-23",
          "2017-03-24",
          "2017-03-27",
          "2017-03-28",
          "2017-03-29",
          "2017-03-30",
          "2017-03-31",
          "2017-04-03",
          "2017-04-04",
          "2017-04-05",
          "2017-04-06",
          "2017-04-07",
          "2017-04-10",
          "2017-04-11",
          "2017-04-12",
          "2017-04-13",
          "2017-04-14",
          "2017-04-17",
          "2017-04-18",
          "2017-04-19",
          "2017-04-20",
          "2017-04-21",
          "2017-04-24",
          "2017-04-25",
          "2017-04-26",
          "2017-04-27",
          "2017-04-28",
          "2017-05-01",
          "2017-05-02",
          "2017-05-03",
          "2017-05-04",
          "2017-05-05",
          "2017-05-08",
          "2017-05-09",
          "2017-05-10",
          "2017-05-11",
          "2017-05-12",
          "2017-05-15",
          "2017-05-16",
          "2017-05-17",
          "2017-05-18",
          "2017-05-19",
          "2017-05-22",
          "2017-05-23",
          "2017-05-24",
          "2017-05-25",
          "2017-05-26",
          "2017-05-29",
          "2017-05-30",
          "2017-05-31",
          "2017-06-01",
          "2017-06-02",
          "2017-06-05",
          "2017-06-06",
          "2017-06-07",
          "2017-06-08",
          "2017-06-09",
          "2017-06-12",
          "2017-06-13",
          "2017-06-14",
          "2017-06-15",
          "2017-06-16",
          "2017-06-19",
          "2017-06-20",
          "2017-06-21",
          "2017-06-22",
          "2017-06-23",
          "2017-06-26",
          "2017-06-27",
          "2017-06-28",
          "2017-06-29",
          "2017-06-30",
          "2017-07-03",
          "2017-07-04",
          "2017-07-05",
          "2017-07-06",
          "2017-07-07",
          "2017-07-10",
          "2017-07-11",
          "2017-07-12",
          "2017-07-13",
          "2017-07-14",
          "2017-07-17",
          "2017-07-18",
          "2017-07-19",
          "2017-07-20",
          "2017-07-21",
          "2017-07-24",
          "2017-07-25",
          "2017-07-26",
          "2017-07-27",
          "2017-07-28",
          "2017-07-31",
          "2017-08-01",
          "2017-08-02",
          "2017-08-03",
          "2017-08-04",
          "2017-08-07",
          "2017-08-08",
          "2017-08-09",
          "2017-08-10",
          "2017-08-11",
          "2017-08-14",
          "2017-08-15",
          "2017-08-16",
          "2017-08-17",
          "2017-08-18",
          "2017-08-21",
          "2017-08-22",
          "2017-08-23",
          "2017-08-24",
          "2017-08-25",
          "2017-08-28",
          "2017-08-29",
          "2017-08-30",
          "2017-08-31"
         ],
         "y": [
          93.14,
          92.97,
          93.12,
          93.2,
          93.21,
          93.08,
          93.81,
          93.6,
          94.27,
          93.26,
          94.28,
          95.49,
          95.61,
          96.09,
          95.06,
          95.35,
          95.15,
          95.95,
          97.62,
          97.98,
          97.65,
          97.46,
          96.21,
          96.68,
          96.44,
          95.84,
          95.71,
          97.01,
          97.48,
          97.03,
          97.3,
          95.95,
          96.69,
          94.92,
          92.79,
          93.12,
          92.74,
          92.63,
          92.84,
          92.03,
          90.71,
          90.13,
          90.88,
          90.47,
          91.53,
          92.01,
          92.07,
          92.44,
          92.47,
          93.03,
          93.49,
          93.71,
          92.44,
          93.21,
          92.46,
          93.41,
          94.55,
          95.99,
          96.53,
          97.24,
          97.1,
          97.23,
          95.02,
          93.26,
          92.76,
          93.36,
          94.18,
          94.59,
          93.44,
          91.23,
          88.75,
          88.73,
          86.65,
          87.83,
          88.04,
          88.81,
          89.21,
          91.07,
          93.27,
          92.63,
          94.09,
          93.22,
          90.74,
          93.7,
          95.25,
          95.8,
          95.28,
          96.24,
          96.09,
          95.81,
          94.76,
          93.96,
          93.95,
          94.85,
          95.72,
          96.29,
          95.55,
          93.98,
          94.12,
          93.84,
          94.65,
          93.13,
          93.57,
          91.93,
          93.41,
          93.36,
          93.66,
          94.71,
          96.11,
          95.82,
          95.5,
          95.98,
          96.66,
          97.83,
          97.86,
          98.46,
          98.24,
          94.89,
          93.81,
          95.07,
          95.25,
          95.47,
          97,
          96.36,
          97.94,
          99.65,
          101.92,
          103.09,
          103.03,
          103.46,
          106.41,
          104.77,
          105.85,
          106.2,
          105.88,
          106.39,
          107.94,
          108,
          106.61,
          107.13,
          105.41,
          105.47,
          104.76,
          104.61,
          103.14,
          105.1,
          107.93,
          106.94,
          106.61,
          105.32,
          104.41,
          103.45,
          106.04,
          106.19,
          106.78,
          106.89,
          107.43,
          107.58,
          107.14,
          104.9,
          103.93,
          104.93,
          106.48,
          105.88,
          109.11,
          110.17,
          108.51,
          107.98,
          108.67,
          107.29,
          108.5,
          110.62,
          109.62,
          107.48,
          107.65,
          108.72,
          108.31,
          106.54,
          105.36,
          108.23,
          106.26,
          104.7,
          103.62,
          103.22,
          102.68,
          103.1,
          102.86,
          102.36,
          102.09,
          104.15,
          103.29,
          103.83,
          103.07,
          103.54,
          101.63,
          103.08,
          102.17,
          102.46,
          101.15,
          102.34,
          100.72,
          100.87,
          99.28,
          97.63,
          96.9,
          96.65,
          97.4,
          98.74,
          98.29,
          96.81,
          96.29,
          94.56,
          94.58,
          93.4,
          94.74,
          94.25,
          94.56,
          95.13,
          93.12,
          93.91,
          93.76,
          93.8,
          93.03,
          93.35,
          93.34,
          95.35,
          94.53,
          93.86,
          93.41,
          92.05,
          92.55,
          93.61,
          95.83,
          96.97,
          97.14,
          97.48,
          97.1,
          98.32,
          97.25,
          97.21,
          96.27,
          97.18,
          96.99,
          97.59,
          98.4,
          99.11,
          98.62,
          98.87,
          99.18,
          99.94,
          98.9,
          98.17,
          95.14,
          93.66,
          93.12,
          93.31,
          91.9,
          91.36,
          92.39,
          91.45,
          92.15,
          93.78,
          93.54,
          93.96,
          94.51,
          96.35,
          97.23,
          96.66,
          95.82,
          97.49,
          97.34,
          98.25,
          97.55,
          96.44,
          97.24,
          97.4,
          97.84,
          99.98,
          100.12,
          99.96,
          100.38,
          100.27,
          100.31,
          102.54,
          103.46,
          103.2,
          102.53,
          103.17,
          102.2,
          102.93,
          102.68,
          102.88,
          105.34,
          103.64,
          101.75,
          101.82,
          102.82,
          101.39,
          100.29,
          98.29,
          98.57,
          99.23,
          98.43,
          100.08,
          100.71,
          99.68,
          99.97,
          100.05,
          99.66,
          100.61,
          101.25,
          101.73,
          101.57,
          99.69,
          99.6,
          100.29,
          101.16,
          100.43,
          102.57,
          103.55,
          103.37,
          103.68,
          104.05,
          103.7,
          103.71,
          104.33,
          104.35,
          101.69,
          101.47,
          102.2,
          100.85,
          101.13,
          101.56,
          100.07,
          99.69,
          100.09,
          99.74,
          99.81,
          101.06,
          100.52,
          100.32,
          100.89,
          102.01,
          102.63,
          101.74,
          102.31,
          102.95,
          102.8,
          104.31,
          104.03,
          105.01,
          104.78,
          103.37,
          104.26,
          103.4,
          103.07,
          103.34,
          103.27,
          103.17,
          103.32,
          105.09,
          105.02,
          105.04,
          107.2,
          107.49,
          107.52,
          106.95,
          106.64,
          107.08,
          107.95,
          106.83,
          106.64,
          107.04,
          106.49,
          106.46,
          106.07,
          106.06,
          105.18,
          104.76,
          104.19,
          104.06,
          102.93,
          103.61,
          101.48,
          101.73,
          100.56,
          101.88,
          103.84,
          103.83,
          105.34,
          104.59,
          103.81,
          102.76,
          105.23,
          105.68,
          104.91,
          104.29,
          98.23,
          97.86,
          98.26,
          97.34,
          96.93,
          97.34,
          97.61,
          98.09,
          97.36,
          97.57,
          95.54,
          97.3,
          96.44,
          94.35,
          96.4,
          93.97,
          93.61,
          95.39,
          95.78,
          95.82,
          96.44,
          97.86,
          92.92,
          95.5,
          94.51,
          93.32,
          92.64,
          92.73,
          91.71,
          92.89,
          92.18,
          92.86,
          94.91,
          94.33,
          93.07,
          92.43,
          91.46,
          91.55,
          93.6,
          93.59,
          95.55,
          94.53,
          91.17,
          90.74,
          91.02,
          89.76,
          90.33,
          88.89,
          87.29,
          85.76,
          85.87,
          85.73,
          81.72,
          81.82,
          82.33,
          82.8,
          82.76,
          83.25,
          80.52,
          82.81,
          81.27,
          81.26,
          81.36,
          82.25,
          81.06,
          80.53,
          78.77,
          77.15,
          78.71,
          77.87,
          78.71,
          77.43,
          77.85,
          77.16,
          74.13,
          75.91,
          75.64,
          74.55,
          74.55,
          75.63,
          76.52,
          75.74,
          74.04,
          73.7,
          65.94,
          68.98,
          66.99,
          67.3,
          66.73,
          65.89,
          63.13,
          63.74,
          60.99,
          60.01,
          57.81,
          55.96,
          55.97,
          56.43,
          54.18,
          56.91,
          55.25,
          56.78,
          55.7,
          54.59,
          53.46,
          54.14,
          53.45,
          52.72,
          50.05,
          47.98,
          48.69,
          48.8,
          48.35,
          46.06,
          45.92,
          48.49,
          46.37,
          48.49,
          46.79,
          47.85,
          45.93,
          45.26,
          44.8,
          45.84,
          44.08,
          44.12,
          47.79,
          49.25,
          53.04,
          48.45,
          50.48,
          51.66,
          52.99,
          50.06,
          48.8,
          51.17,
          52.66,
          53.56,
          52.13,
          51.12,
          49.95,
          49.56,
          48.48,
          50.25,
          47.65,
          49.84,
          49.59,
          50.43,
          51.53,
          50.76,
          49.61,
          49.95,
          48.42,
          48.06,
          47.12,
          44.88,
          43.93,
          43.39,
          44.63,
          44.02,
          46,
          47.4,
          47.03,
          48.75,
          51.41,
          48.83,
          48.66,
          47.72,
          50.12,
          49.13,
          52.08,
          53.95,
          50.44,
          50.79,
          51.63,
          51.95,
          53.3,
          56.25,
          56.69,
          55.71,
          56.37,
          55.58,
          56.17,
          56.59,
          55.98,
          55.56,
          57.05,
          58.55,
          59.62,
          59.1,
          58.92,
          60.38,
          60.93,
          58.99,
          59.41,
          59.23,
          60.72,
          60.5,
          59.89,
          59.73,
          59.44,
          57.3,
          58.96,
          60.18,
          58.88,
          57.29,
          57.51,
          57.69,
          60.25,
          60.24,
          61.3,
          59.67,
          58,
          59.11,
          58.15,
          60.15,
          61.36,
          60.74,
          59.96,
          59.53,
          60.01,
          59.89,
          60.41,
          59.62,
          60.01,
          61.05,
          60.01,
          59.59,
          59.41,
          58.34,
          59.48,
          56.94,
          56.93,
          52.48,
          52.33,
          51.61,
          52.76,
          52.74,
          52.19,
          53.05,
          51.4,
          50.9,
          50.88,
          50.11,
          50.59,
          49.27,
          48.11,
          47.98,
          47.17,
          47.97,
          48.77,
          48.53,
          47.11,
          45.25,
          45.75,
          45.13,
          44.69,
          43.87,
          44.94,
          43.11,
          43.22,
          42.27,
          42.45,
          41.93,
          42.58,
          40.75,
          41,
          40.45,
          38.22,
          39.15,
          38.5,
          42.47,
          45.29,
          49.2,
          45.38,
          46.3,
          46.75,
          46.02,
          45.92,
          44.13,
          45.85,
          44.75,
          44.07,
          44.58,
          47.12,
          46.93,
          44.71,
          46.67,
          46.17,
          44.53,
          44.94,
          45.55,
          44.4,
          45.24,
          45.06,
          44.75,
          45.54,
          46.28,
          48.53,
          47.86,
          49.46,
          49.67,
          47.09,
          46.7,
          46.63,
          46.38,
          47.3,
          45.91,
          45.84,
          45.22,
          44.9,
          43.91,
          43.19,
          43.21,
          45.93,
          46.02,
          46.6,
          46.12,
          47.88,
          46.32,
          45.27,
          44.32,
          43.87,
          44.23,
          42.95,
          41.74,
          40.69,
          41.68,
          40.73,
          40.75,
          40.55,
          39.39,
          39.27,
          40.89,
          41.22,
          40.57,
          40.43,
          40.58,
          39.93,
          41.08,
          40,
          37.64,
          37.46,
          37.16,
          36.76,
          35.65,
          36.31,
          37.32,
          35.55,
          34.98,
          34.72,
          34.55,
          36.12,
          36.76,
          37.62,
          36.36,
          37.88,
          36.59,
          37.13,
          36.81,
          35.97,
          33.97,
          33.29,
          33.2,
          31.42,
          30.42,
          30.42,
          31.22,
          29.45,
          28.47,
          26.68,
          29.55,
          32.07,
          30.31,
          29.54,
          32.32,
          33.21,
          33.66,
          31.62,
          29.9,
          32.29,
          31.63,
          30.86,
          29.71,
          27.96,
          27.54,
          26.19,
          29.32,
          29.05,
          30.68,
          30.77,
          29.59,
          31.37,
          31.84,
          30.35,
          31.4,
          31.65,
          32.74,
          34.39,
          34.57,
          34.56,
          35.91,
          37.9,
          36.67,
          37.62,
          37.77,
          38.51,
          37.2,
          36.32,
          38.43,
          40.17,
          39.47,
          39.91,
          41.45,
          38.28,
          38.14,
          37.99,
          36.91,
          36.91,
          36.94,
          35.36,
          34.3,
          34.52,
          37.74,
          37.3,
          39.74,
          40.46,
          42.12,
          41.7,
          41.45,
          40.4,
          39.74,
          40.88,
          42.72,
          43.18,
          42.76,
          41.67,
          42.52,
          45.29,
          46.03,
          45.98,
          44.75,
          43.65,
          43.77,
          44.33,
          44.58,
          43.45,
          44.68,
          46.21,
          46.64,
          46.22,
          47.72,
          48.29,
          48.12,
          48.16,
          47.67,
          48.12,
          48.04,
          49.1,
          49,
          49.36,
          49.1,
          49.07,
          49.14,
          48.69,
          49.71,
          50.37,
          51.23,
          50.52,
          49.09,
          48.89,
          48.49,
          47.92,
          46.14,
          48,
          49.4,
          48.95,
          49.16,
          49.34,
          46.7,
          45.8,
          47.93,
          49.85,
          48.27,
          49.02,
          46.73,
          47.37,
          45.22,
          45.37,
          44.73,
          46.82,
          44.87,
          45.64,
          45.93,
          45.23,
          44.64,
          44.96,
          43.96,
          43.41,
          42.4,
          42.16,
          41.9,
          41.13,
          41.54,
          40.05,
          39.5,
          40.8,
          41.92,
          41.83,
          43.06,
          42.78,
          41.75,
          43.51,
          44.47,
          45.72,
          46.57,
          46.81,
          48.2,
          48.48,
          46.8,
          47.54,
          46.29,
          46.97,
          47.64,
          46.97,
          46.32,
          44.68,
          43.17,
          44.39,
          44.85,
          45.47,
          47.63,
          45.88,
          46.28,
          44.91,
          43.62,
          43.85,
          43.04,
          43.34,
          43.85,
          45.33,
          46.1,
          44.36,
          45.6,
          44.65,
          47.07,
          47.72,
          47.72,
          48.8,
          48.67,
          49.75,
          50.44,
          49.76,
          49.76,
          50.72,
          50.14,
          50.47,
          50.35,
          49.97,
          50.3,
          51.59,
          50.31,
          50.61,
          50.18,
          49.45,
          48.75,
          49.71,
          48.72,
          46.83,
          46.66,
          45.32,
          44.66,
          44.07,
          44.88,
          44.96,
          45.2,
          44.62,
          43.39,
          43.29,
          45.86,
          45.56,
          45.37,
          45.69,
          47.48,
          48.07,
          46.72,
          46.72,
          45.66,
          45.29,
          49.41,
          51.08,
          51.7,
          51.72,
          50.95,
          49.85,
          50.84,
          51.51,
          52.74,
          52.99,
          51.01,
          50.9,
          51.93,
          52.13,
          52.22,
          51.44,
          51.98,
          52.01,
          52.82,
          54.01,
          53.8,
          53.75,
          52.36,
          53.26,
          53.77,
          53.98,
          51.95,
          50.82,
          52.19,
          53.01,
          52.36,
          52.45,
          51.12,
          51.39,
          52.33,
          52.77,
          52.38,
          52.14,
          53.24,
          53.18,
          52.63,
          52.75,
          53.9,
          53.55,
          53.81,
          53.01,
          52.19,
          52.37,
          52.99,
          53.84,
          52.96,
          53.21,
          53.11,
          53.41,
          53.41,
          54.02,
          53.61,
          54.48,
          53.99,
          54.04,
          54,
          53.82,
          52.63,
          53.33,
          53.19,
          52.68,
          49.83,
          48.75,
          48.05,
          47.95,
          47.24,
          48.34,
          48.3,
          48.34,
          47.79,
          47.02,
          47.29,
          47,
          47.3,
          47.02,
          48.36,
          49.47,
          50.3,
          50.54,
          50.25,
          50.99,
          51.14,
          51.69,
          52.25,
          53.06,
          53.38,
          53.12,
          53.19,
          52.62,
          52.46,
          50.49,
          50.26,
          49.64,
          48.9,
          49.22,
          49.22,
          48.96,
          49.31,
          48.83,
          47.65,
          47.79,
          45.55,
          46.23,
          46.46,
          45.84,
          47.28,
          47.81,
          47.83,
          48.86,
          48.64,
          49.04,
          49.36,
          50.32,
          50.81,
          51.12,
          50.99,
          48.57,
          49.58,
          49.63,
          48.29,
          48.32,
          47.68,
          47.4,
          48.13,
          45.8,
          45.68,
          45.82,
          46.1,
          46.41,
          44.79,
          44.47,
          44.73,
          44.24,
          43.34,
          42.48,
          42.53,
          42.86,
          43.24,
          44.25,
          44.74,
          44.88,
          46.02,
          45.11,
          45.52,
          44.25,
          44.4,
          45.06,
          45.48,
          46.06,
          46.53,
          46.02,
          46.4,
          47.1,
          46.73,
          45.78,
          46.21,
          47.77,
          48.58,
          49.05,
          49.72,
          50.21,
          49.19,
          49.6,
          49.03,
          49.57,
          49.37,
          49.07,
          49.59,
          48.54,
          48.81,
          47.59,
          47.57,
          46.8,
          47.07,
          48.59,
          47.39,
          47.65,
          48.45,
          47.24,
          47.65,
          46.4,
          46.46,
          45.96,
          47.26
         ]
        }
       ],
       "layout": {
        "autosize": true,
        "showlegend": false,
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Daily oil prices from Jan 2013 till July 2017"
        },
        "xaxis": {
         "autorange": true,
         "range": [
          "2013-01-01",
          "2017-07-03"
         ],
         "type": "date"
        },
        "yaxis": {
         "autorange": true,
         "range": [
          0,
          116.4421052631579
         ],
         "title": {
          "text": "Daily Oil price"
         },
         "type": "linear"
        }
       }
      },
      "image/png": "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",
      "text/html": [
       "<div>                            <div id=\"e9ffe54d-3255-4a2b-b7d6-8f973953a465\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"e9ffe54d-3255-4a2b-b7d6-8f973953a465\")) {                    Plotly.newPlot(                        \"e9ffe54d-3255-4a2b-b7d6-8f973953a465\",                        [{\"fill\": \"tonexty\", \"fillcolor\": \"rgba(0, 0, 216, 0.3)\", \"line\": {\"color\": \"rgb(20, 15, 200, 0.8)\"}, \"mode\": \"lines\", \"name\": \"Oil prices\", \"type\": \"scatter\", \"x\": [\"2013-01-01\", \"2013-01-02\", \"2013-01-03\", \"2013-01-04\", \"2013-01-07\", \"2013-01-08\", \"2013-01-09\", \"2013-01-10\", \"2013-01-11\", \"2013-01-14\", \"2013-01-15\", \"2013-01-16\", \"2013-01-17\", \"2013-01-18\", \"2013-01-21\", \"2013-01-22\", \"2013-01-23\", \"2013-01-24\", \"2013-01-25\", \"2013-01-28\", \"2013-01-29\", \"2013-01-30\", \"2013-01-31\", \"2013-02-01\", \"2013-02-04\", \"2013-02-05\", \"2013-02-06\", \"2013-02-07\", \"2013-02-08\", \"2013-02-11\", \"2013-02-12\", \"2013-02-13\", \"2013-02-14\", \"2013-02-15\", \"2013-02-18\", \"2013-02-19\", \"2013-02-20\", \"2013-02-21\", \"2013-02-22\", \"2013-02-25\", \"2013-02-26\", \"2013-02-27\", \"2013-02-28\", \"2013-03-01\", \"2013-03-04\", \"2013-03-05\", \"2013-03-06\", \"2013-03-07\", \"2013-03-08\", \"2013-03-11\", \"2013-03-12\", \"2013-03-13\", \"2013-03-14\", \"2013-03-15\", \"2013-03-18\", \"2013-03-19\", \"2013-03-20\", \"2013-03-21\", \"2013-03-22\", \"2013-03-25\", \"2013-03-26\", \"2013-03-27\", \"2013-03-28\", \"2013-03-29\", \"2013-04-01\", \"2013-04-02\", \"2013-04-03\", \"2013-04-04\", \"2013-04-05\", \"2013-04-08\", \"2013-04-09\", \"2013-04-10\", \"2013-04-11\", \"2013-04-12\", \"2013-04-15\", \"2013-04-16\", \"2013-04-17\", \"2013-04-18\", \"2013-04-19\", \"2013-04-22\", \"2013-04-23\", \"2013-04-24\", \"2013-04-25\", \"2013-04-26\", \"2013-04-29\", \"2013-04-30\", \"2013-05-01\", \"2013-05-02\", \"2013-05-03\", \"2013-05-06\", \"2013-05-07\", \"2013-05-08\", \"2013-05-09\", \"2013-05-10\", \"2013-05-13\", \"2013-05-14\", \"2013-05-15\", \"2013-05-16\", \"2013-05-17\", \"2013-05-20\", \"2013-05-21\", \"2013-05-22\", \"2013-05-23\", \"2013-05-24\", \"2013-05-27\", \"2013-05-28\", \"2013-05-29\", \"2013-05-30\", \"2013-05-31\", \"2013-06-03\", \"2013-06-04\", \"2013-06-05\", \"2013-06-06\", \"2013-06-07\", \"2013-06-10\", \"2013-06-11\", \"2013-06-12\", \"2013-06-13\", \"2013-06-14\", \"2013-06-17\", \"2013-06-18\", \"2013-06-19\", \"2013-06-20\", \"2013-06-21\", \"2013-06-24\", \"2013-06-25\", \"2013-06-26\", \"2013-06-27\", \"2013-06-28\", \"2013-07-01\", \"2013-07-02\", \"2013-07-03\", \"2013-07-04\", \"2013-07-05\", \"2013-07-08\", \"2013-07-09\", \"2013-07-10\", \"2013-07-11\", \"2013-07-12\", \"2013-07-15\", \"2013-07-16\", \"2013-07-17\", \"2013-07-18\", \"2013-07-19\", \"2013-07-22\", \"2013-07-23\", \"2013-07-24\", \"2013-07-25\", \"2013-07-26\", \"2013-07-29\", \"2013-07-30\", \"2013-07-31\", \"2013-08-01\", \"2013-08-02\", \"2013-08-05\", \"2013-08-06\", \"2013-08-07\", \"2013-08-08\", \"2013-08-09\", \"2013-08-12\", \"2013-08-13\", \"2013-08-14\", \"2013-08-15\", \"2013-08-16\", \"2013-08-19\", \"2013-08-20\", \"2013-08-21\", \"2013-08-22\", \"2013-08-23\", \"2013-08-26\", \"2013-08-27\", \"2013-08-28\", \"2013-08-29\", \"2013-08-30\", \"2013-09-02\", \"2013-09-03\", \"2013-09-04\", \"2013-09-05\", \"2013-09-06\", \"2013-09-09\", \"2013-09-10\", \"2013-09-11\", \"2013-09-12\", \"2013-09-13\", \"2013-09-16\", \"2013-09-17\", \"2013-09-18\", \"2013-09-19\", \"2013-09-20\", \"2013-09-23\", \"2013-09-24\", \"2013-09-25\", \"2013-09-26\", \"2013-09-27\", \"2013-09-30\", \"2013-10-01\", \"2013-10-02\", \"2013-10-03\", \"2013-10-04\", \"2013-10-07\", \"2013-10-08\", \"2013-10-09\", \"2013-10-10\", \"2013-10-11\", \"2013-10-14\", \"2013-10-15\", \"2013-10-16\", \"2013-10-17\", \"2013-10-18\", \"2013-10-21\", \"2013-10-22\", \"2013-10-23\", \"2013-10-24\", \"2013-10-25\", \"2013-10-28\", \"2013-10-29\", \"2013-10-30\", \"2013-10-31\", \"2013-11-01\", \"2013-11-04\", \"2013-11-05\", \"2013-11-06\", \"2013-11-07\", \"2013-11-08\", \"2013-11-11\", \"2013-11-12\", \"2013-11-13\", \"2013-11-14\", \"2013-11-15\", \"2013-11-18\", \"2013-11-19\", \"2013-11-20\", \"2013-11-21\", \"2013-11-22\", \"2013-11-25\", \"2013-11-26\", \"2013-11-27\", \"2013-11-28\", \"2013-11-29\", \"2013-12-02\", \"2013-12-03\", \"2013-12-04\", \"2013-12-05\", \"2013-12-06\", \"2013-12-09\", \"2013-12-10\", \"2013-12-11\", \"2013-12-12\", \"2013-12-13\", \"2013-12-16\", \"2013-12-17\", \"2013-12-18\", \"2013-12-19\", \"2013-12-20\", \"2013-12-23\", \"2013-12-24\", \"2013-12-25\", \"2013-12-26\", \"2013-12-27\", \"2013-12-30\", \"2013-12-31\", \"2014-01-01\", \"2014-01-02\", \"2014-01-03\", \"2014-01-06\", \"2014-01-07\", \"2014-01-08\", \"2014-01-09\", \"2014-01-10\", \"2014-01-13\", \"2014-01-14\", \"2014-01-15\", \"2014-01-16\", \"2014-01-17\", \"2014-01-20\", \"2014-01-21\", \"2014-01-22\", \"2014-01-23\", \"2014-01-24\", \"2014-01-27\", \"2014-01-28\", \"2014-01-29\", \"2014-01-30\", \"2014-01-31\", \"2014-02-03\", \"2014-02-04\", \"2014-02-05\", \"2014-02-06\", \"2014-02-07\", \"2014-02-10\", \"2014-02-11\", \"2014-02-12\", \"2014-02-13\", \"2014-02-14\", \"2014-02-17\", \"2014-02-18\", \"2014-02-19\", \"2014-02-20\", \"2014-02-21\", \"2014-02-24\", \"2014-02-25\", \"2014-02-26\", \"2014-02-27\", \"2014-02-28\", \"2014-03-03\", \"2014-03-04\", \"2014-03-05\", \"2014-03-06\", \"2014-03-07\", \"2014-03-10\", \"2014-03-11\", \"2014-03-12\", \"2014-03-13\", \"2014-03-14\", \"2014-03-17\", \"2014-03-18\", \"2014-03-19\", \"2014-03-20\", \"2014-03-21\", \"2014-03-24\", \"2014-03-25\", \"2014-03-26\", \"2014-03-27\", \"2014-03-28\", \"2014-03-31\", \"2014-04-01\", \"2014-04-02\", \"2014-04-03\", \"2014-04-04\", \"2014-04-07\", \"2014-04-08\", \"2014-04-09\", \"2014-04-10\", \"2014-04-11\", \"2014-04-14\", \"2014-04-15\", \"2014-04-16\", \"2014-04-17\", \"2014-04-18\", \"2014-04-21\", \"2014-04-22\", \"2014-04-23\", \"2014-04-24\", \"2014-04-25\", \"2014-04-28\", \"2014-04-29\", \"2014-04-30\", \"2014-05-01\", \"2014-05-02\", \"2014-05-05\", \"2014-05-06\", \"2014-05-07\", \"2014-05-08\", \"2014-05-09\", \"2014-05-12\", \"2014-05-13\", \"2014-05-14\", \"2014-05-15\", \"2014-05-16\", \"2014-05-19\", \"2014-05-20\", \"2014-05-21\", \"2014-05-22\", \"2014-05-23\", \"2014-05-26\", \"2014-05-27\", \"2014-05-28\", \"2014-05-29\", \"2014-05-30\", \"2014-06-02\", \"2014-06-03\", \"2014-06-04\", \"2014-06-05\", \"2014-06-06\", \"2014-06-09\", \"2014-06-10\", \"2014-06-11\", \"2014-06-12\", \"2014-06-13\", \"2014-06-16\", \"2014-06-17\", \"2014-06-18\", \"2014-06-19\", \"2014-06-20\", \"2014-06-23\", \"2014-06-24\", \"2014-06-25\", \"2014-06-26\", \"2014-06-27\", \"2014-06-30\", \"2014-07-01\", \"2014-07-02\", \"2014-07-03\", \"2014-07-04\", \"2014-07-07\", \"2014-07-08\", \"2014-07-09\", \"2014-07-10\", \"2014-07-11\", \"2014-07-14\", \"2014-07-15\", \"2014-07-16\", \"2014-07-17\", \"2014-07-18\", \"2014-07-21\", \"2014-07-22\", \"2014-07-23\", \"2014-07-24\", \"2014-07-25\", \"2014-07-28\", \"2014-07-29\", \"2014-07-30\", \"2014-07-31\", \"2014-08-01\", \"2014-08-04\", \"2014-08-05\", \"2014-08-06\", \"2014-08-07\", \"2014-08-08\", \"2014-08-11\", \"2014-08-12\", \"2014-08-13\", \"2014-08-14\", \"2014-08-15\", \"2014-08-18\", \"2014-08-19\", \"2014-08-20\", \"2014-08-21\", \"2014-08-22\", \"2014-08-25\", \"2014-08-26\", \"2014-08-27\", \"2014-08-28\", \"2014-08-29\", \"2014-09-01\", \"2014-09-02\", \"2014-09-03\", \"2014-09-04\", \"2014-09-05\", \"2014-09-08\", \"2014-09-09\", \"2014-09-10\", \"2014-09-11\", \"2014-09-12\", \"2014-09-15\", \"2014-09-16\", \"2014-09-17\", \"2014-09-18\", \"2014-09-19\", \"2014-09-22\", \"2014-09-23\", \"2014-09-24\", \"2014-09-25\", \"2014-09-26\", \"2014-09-29\", \"2014-09-30\", \"2014-10-01\", \"2014-10-02\", \"2014-10-03\", \"2014-10-06\", \"2014-10-07\", \"2014-10-08\", \"2014-10-09\", \"2014-10-10\", \"2014-10-13\", \"2014-10-14\", \"2014-10-15\", \"2014-10-16\", \"2014-10-17\", \"2014-10-20\", \"2014-10-21\", \"2014-10-22\", \"2014-10-23\", \"2014-10-24\", \"2014-10-27\", \"2014-10-28\", \"2014-10-29\", \"2014-10-30\", \"2014-10-31\", \"2014-11-03\", \"2014-11-04\", \"2014-11-05\", \"2014-11-06\", \"2014-11-07\", \"2014-11-10\", \"2014-11-11\", \"2014-11-12\", \"2014-11-13\", \"2014-11-14\", \"2014-11-17\", \"2014-11-18\", \"2014-11-19\", \"2014-11-20\", \"2014-11-21\", \"2014-11-24\", \"2014-11-25\", \"2014-11-26\", \"2014-11-27\", \"2014-11-28\", \"2014-12-01\", \"2014-12-02\", \"2014-12-03\", \"2014-12-04\", \"2014-12-05\", \"2014-12-08\", \"2014-12-09\", \"2014-12-10\", \"2014-12-11\", \"2014-12-12\", \"2014-12-15\", \"2014-12-16\", \"2014-12-17\", \"2014-12-18\", \"2014-12-19\", \"2014-12-22\", \"2014-12-23\", \"2014-12-24\", \"2014-12-25\", \"2014-12-26\", \"2014-12-29\", \"2014-12-30\", \"2014-12-31\", \"2015-01-01\", \"2015-01-02\", \"2015-01-05\", \"2015-01-06\", \"2015-01-07\", \"2015-01-08\", \"2015-01-09\", \"2015-01-12\", \"2015-01-13\", \"2015-01-14\", \"2015-01-15\", \"2015-01-16\", \"2015-01-19\", \"2015-01-20\", \"2015-01-21\", \"2015-01-22\", \"2015-01-23\", \"2015-01-26\", \"2015-01-27\", \"2015-01-28\", \"2015-01-29\", \"2015-01-30\", \"2015-02-02\", \"2015-02-03\", \"2015-02-04\", \"2015-02-05\", \"2015-02-06\", \"2015-02-09\", \"2015-02-10\", \"2015-02-11\", \"2015-02-12\", \"2015-02-13\", \"2015-02-16\", \"2015-02-17\", \"2015-02-18\", \"2015-02-19\", \"2015-02-20\", \"2015-02-23\", \"2015-02-24\", \"2015-02-25\", \"2015-02-26\", \"2015-02-27\", \"2015-03-02\", \"2015-03-03\", \"2015-03-04\", \"2015-03-05\", \"2015-03-06\", \"2015-03-09\", \"2015-03-10\", \"2015-03-11\", \"2015-03-12\", \"2015-03-13\", \"2015-03-16\", \"2015-03-17\", \"2015-03-18\", \"2015-03-19\", \"2015-03-20\", \"2015-03-23\", \"2015-03-24\", \"2015-03-25\", \"2015-03-26\", \"2015-03-27\", \"2015-03-30\", \"2015-03-31\", \"2015-04-01\", \"2015-04-02\", \"2015-04-03\", \"2015-04-06\", \"2015-04-07\", \"2015-04-08\", \"2015-04-09\", \"2015-04-10\", \"2015-04-13\", \"2015-04-14\", \"2015-04-15\", \"2015-04-16\", \"2015-04-17\", \"2015-04-20\", \"2015-04-21\", \"2015-04-22\", \"2015-04-23\", \"2015-04-24\", \"2015-04-27\", \"2015-04-28\", \"2015-04-29\", \"2015-04-30\", \"2015-05-01\", \"2015-05-04\", \"2015-05-05\", \"2015-05-06\", \"2015-05-07\", \"2015-05-08\", \"2015-05-11\", \"2015-05-12\", \"2015-05-13\", \"2015-05-14\", \"2015-05-15\", \"2015-05-18\", \"2015-05-19\", \"2015-05-20\", \"2015-05-21\", \"2015-05-22\", \"2015-05-25\", \"2015-05-26\", \"2015-05-27\", \"2015-05-28\", \"2015-05-29\", \"2015-06-01\", \"2015-06-02\", \"2015-06-03\", \"2015-06-04\", \"2015-06-05\", \"2015-06-08\", \"2015-06-09\", \"2015-06-10\", \"2015-06-11\", \"2015-06-12\", \"2015-06-15\", \"2015-06-16\", \"2015-06-17\", \"2015-06-18\", \"2015-06-19\", \"2015-06-22\", \"2015-06-23\", \"2015-06-24\", \"2015-06-25\", \"2015-06-26\", \"2015-06-29\", \"2015-06-30\", \"2015-07-01\", \"2015-07-02\", \"2015-07-03\", \"2015-07-06\", \"2015-07-07\", \"2015-07-08\", \"2015-07-09\", \"2015-07-10\", \"2015-07-13\", \"2015-07-14\", \"2015-07-15\", \"2015-07-16\", \"2015-07-17\", \"2015-07-20\", \"2015-07-21\", \"2015-07-22\", \"2015-07-23\", \"2015-07-24\", \"2015-07-27\", \"2015-07-28\", \"2015-07-29\", \"2015-07-30\", \"2015-07-31\", \"2015-08-03\", \"2015-08-04\", \"2015-08-05\", \"2015-08-06\", \"2015-08-07\", \"2015-08-10\", \"2015-08-11\", \"2015-08-12\", \"2015-08-13\", \"2015-08-14\", \"2015-08-17\", \"2015-08-18\", \"2015-08-19\", \"2015-08-20\", \"2015-08-21\", \"2015-08-24\", \"2015-08-25\", \"2015-08-26\", \"2015-08-27\", \"2015-08-28\", \"2015-08-31\", \"2015-09-01\", \"2015-09-02\", \"2015-09-03\", \"2015-09-04\", \"2015-09-07\", \"2015-09-08\", \"2015-09-09\", \"2015-09-10\", \"2015-09-11\", \"2015-09-14\", \"2015-09-15\", \"2015-09-16\", \"2015-09-17\", \"2015-09-18\", \"2015-09-21\", \"2015-09-22\", \"2015-09-23\", \"2015-09-24\", \"2015-09-25\", \"2015-09-28\", \"2015-09-29\", \"2015-09-30\", \"2015-10-01\", \"2015-10-02\", \"2015-10-05\", \"2015-10-06\", \"2015-10-07\", \"2015-10-08\", \"2015-10-09\", \"2015-10-12\", \"2015-10-13\", \"2015-10-14\", \"2015-10-15\", \"2015-10-16\", \"2015-10-19\", \"2015-10-20\", \"2015-10-21\", \"2015-10-22\", \"2015-10-23\", \"2015-10-26\", \"2015-10-27\", \"2015-10-28\", \"2015-10-29\", \"2015-10-30\", \"2015-11-02\", \"2015-11-03\", \"2015-11-04\", \"2015-11-05\", \"2015-11-06\", \"2015-11-09\", \"2015-11-10\", \"2015-11-11\", \"2015-11-12\", \"2015-11-13\", \"2015-11-16\", \"2015-11-17\", \"2015-11-18\", \"2015-11-19\", \"2015-11-20\", \"2015-11-23\", \"2015-11-24\", \"2015-11-25\", \"2015-11-26\", \"2015-11-27\", \"2015-11-30\", \"2015-12-01\", \"2015-12-02\", \"2015-12-03\", \"2015-12-04\", \"2015-12-07\", \"2015-12-08\", \"2015-12-09\", \"2015-12-10\", \"2015-12-11\", \"2015-12-14\", \"2015-12-15\", \"2015-12-16\", \"2015-12-17\", \"2015-12-18\", \"2015-12-21\", \"2015-12-22\", \"2015-12-23\", \"2015-12-24\", \"2015-12-25\", \"2015-12-28\", \"2015-12-29\", \"2015-12-30\", \"2015-12-31\", \"2016-01-01\", \"2016-01-04\", \"2016-01-05\", \"2016-01-06\", \"2016-01-07\", \"2016-01-08\", \"2016-01-11\", \"2016-01-12\", \"2016-01-13\", \"2016-01-14\", \"2016-01-15\", \"2016-01-18\", \"2016-01-19\", \"2016-01-20\", \"2016-01-21\", \"2016-01-22\", \"2016-01-25\", \"2016-01-26\", \"2016-01-27\", \"2016-01-28\", \"2016-01-29\", \"2016-02-01\", \"2016-02-02\", \"2016-02-03\", \"2016-02-04\", \"2016-02-05\", \"2016-02-08\", \"2016-02-09\", \"2016-02-10\", \"2016-02-11\", \"2016-02-12\", \"2016-02-15\", \"2016-02-16\", \"2016-02-17\", \"2016-02-18\", \"2016-02-19\", \"2016-02-22\", \"2016-02-23\", \"2016-02-24\", \"2016-02-25\", \"2016-02-26\", \"2016-02-29\", \"2016-03-01\", \"2016-03-02\", \"2016-03-03\", \"2016-03-04\", \"2016-03-07\", \"2016-03-08\", \"2016-03-09\", \"2016-03-10\", \"2016-03-11\", \"2016-03-14\", \"2016-03-15\", \"2016-03-16\", \"2016-03-17\", \"2016-03-18\", \"2016-03-21\", \"2016-03-22\", \"2016-03-23\", \"2016-03-24\", \"2016-03-25\", \"2016-03-28\", \"2016-03-29\", \"2016-03-30\", \"2016-03-31\", \"2016-04-01\", \"2016-04-04\", \"2016-04-05\", \"2016-04-06\", \"2016-04-07\", \"2016-04-08\", \"2016-04-11\", \"2016-04-12\", \"2016-04-13\", \"2016-04-14\", \"2016-04-15\", \"2016-04-18\", \"2016-04-19\", \"2016-04-20\", \"2016-04-21\", \"2016-04-22\", \"2016-04-25\", \"2016-04-26\", \"2016-04-27\", \"2016-04-28\", \"2016-04-29\", \"2016-05-02\", \"2016-05-03\", \"2016-05-04\", \"2016-05-05\", \"2016-05-06\", \"2016-05-09\", \"2016-05-10\", \"2016-05-11\", \"2016-05-12\", \"2016-05-13\", \"2016-05-16\", \"2016-05-17\", \"2016-05-18\", \"2016-05-19\", \"2016-05-20\", \"2016-05-23\", \"2016-05-24\", \"2016-05-25\", \"2016-05-26\", \"2016-05-27\", \"2016-05-30\", \"2016-05-31\", \"2016-06-01\", \"2016-06-02\", \"2016-06-03\", \"2016-06-06\", \"2016-06-07\", \"2016-06-08\", \"2016-06-09\", \"2016-06-10\", \"2016-06-13\", \"2016-06-14\", \"2016-06-15\", \"2016-06-16\", \"2016-06-17\", \"2016-06-20\", \"2016-06-21\", \"2016-06-22\", \"2016-06-23\", \"2016-06-24\", \"2016-06-27\", \"2016-06-28\", \"2016-06-29\", \"2016-06-30\", \"2016-07-01\", \"2016-07-04\", \"2016-07-05\", \"2016-07-06\", \"2016-07-07\", \"2016-07-08\", \"2016-07-11\", \"2016-07-12\", \"2016-07-13\", \"2016-07-14\", \"2016-07-15\", \"2016-07-18\", \"2016-07-19\", \"2016-07-20\", \"2016-07-21\", \"2016-07-22\", \"2016-07-25\", \"2016-07-26\", \"2016-07-27\", \"2016-07-28\", \"2016-07-29\", \"2016-08-01\", \"2016-08-02\", \"2016-08-03\", \"2016-08-04\", \"2016-08-05\", \"2016-08-08\", \"2016-08-09\", \"2016-08-10\", \"2016-08-11\", \"2016-08-12\", \"2016-08-15\", \"2016-08-16\", \"2016-08-17\", \"2016-08-18\", \"2016-08-19\", \"2016-08-22\", \"2016-08-23\", \"2016-08-24\", \"2016-08-25\", \"2016-08-26\", \"2016-08-29\", \"2016-08-30\", \"2016-08-31\", \"2016-09-01\", \"2016-09-02\", \"2016-09-05\", \"2016-09-06\", \"2016-09-07\", \"2016-09-08\", \"2016-09-09\", \"2016-09-12\", \"2016-09-13\", \"2016-09-14\", \"2016-09-15\", \"2016-09-16\", \"2016-09-19\", \"2016-09-20\", \"2016-09-21\", \"2016-09-22\", \"2016-09-23\", \"2016-09-26\", \"2016-09-27\", \"2016-09-28\", \"2016-09-29\", \"2016-09-30\", \"2016-10-03\", \"2016-10-04\", \"2016-10-05\", \"2016-10-06\", \"2016-10-07\", \"2016-10-10\", \"2016-10-11\", \"2016-10-12\", \"2016-10-13\", \"2016-10-14\", \"2016-10-17\", \"2016-10-18\", \"2016-10-19\", \"2016-10-20\", \"2016-10-21\", \"2016-10-24\", \"2016-10-25\", \"2016-10-26\", \"2016-10-27\", \"2016-10-28\", \"2016-10-31\", \"2016-11-01\", \"2016-11-02\", \"2016-11-03\", \"2016-11-04\", \"2016-11-07\", \"2016-11-08\", \"2016-11-09\", \"2016-11-10\", \"2016-11-11\", \"2016-11-14\", \"2016-11-15\", \"2016-11-16\", \"2016-11-17\", \"2016-11-18\", \"2016-11-21\", \"2016-11-22\", \"2016-11-23\", \"2016-11-24\", \"2016-11-25\", \"2016-11-28\", \"2016-11-29\", \"2016-11-30\", \"2016-12-01\", \"2016-12-02\", \"2016-12-05\", \"2016-12-06\", \"2016-12-07\", \"2016-12-08\", \"2016-12-09\", \"2016-12-12\", \"2016-12-13\", \"2016-12-14\", \"2016-12-15\", \"2016-12-16\", \"2016-12-19\", \"2016-12-20\", \"2016-12-21\", \"2016-12-22\", \"2016-12-23\", \"2016-12-26\", \"2016-12-27\", \"2016-12-28\", \"2016-12-29\", \"2016-12-30\", \"2017-01-02\", \"2017-01-03\", \"2017-01-04\", \"2017-01-05\", \"2017-01-06\", \"2017-01-09\", \"2017-01-10\", \"2017-01-11\", \"2017-01-12\", \"2017-01-13\", \"2017-01-16\", \"2017-01-17\", \"2017-01-18\", \"2017-01-19\", \"2017-01-20\", \"2017-01-23\", \"2017-01-24\", \"2017-01-25\", \"2017-01-26\", \"2017-01-27\", \"2017-01-30\", \"2017-01-31\", \"2017-02-01\", \"2017-02-02\", \"2017-02-03\", \"2017-02-06\", \"2017-02-07\", \"2017-02-08\", \"2017-02-09\", \"2017-02-10\", \"2017-02-13\", \"2017-02-14\", \"2017-02-15\", \"2017-02-16\", \"2017-02-17\", \"2017-02-20\", \"2017-02-21\", \"2017-02-22\", \"2017-02-23\", \"2017-02-24\", \"2017-02-27\", \"2017-02-28\", \"2017-03-01\", \"2017-03-02\", \"2017-03-03\", \"2017-03-06\", \"2017-03-07\", \"2017-03-08\", \"2017-03-09\", \"2017-03-10\", \"2017-03-13\", \"2017-03-14\", \"2017-03-15\", \"2017-03-16\", \"2017-03-17\", \"2017-03-20\", \"2017-03-21\", \"2017-03-22\", \"2017-03-23\", \"2017-03-24\", \"2017-03-27\", \"2017-03-28\", \"2017-03-29\", \"2017-03-30\", \"2017-03-31\", \"2017-04-03\", \"2017-04-04\", \"2017-04-05\", \"2017-04-06\", \"2017-04-07\", \"2017-04-10\", \"2017-04-11\", \"2017-04-12\", \"2017-04-13\", \"2017-04-14\", \"2017-04-17\", \"2017-04-18\", \"2017-04-19\", \"2017-04-20\", \"2017-04-21\", \"2017-04-24\", \"2017-04-25\", \"2017-04-26\", \"2017-04-27\", \"2017-04-28\", \"2017-05-01\", \"2017-05-02\", \"2017-05-03\", \"2017-05-04\", \"2017-05-05\", \"2017-05-08\", \"2017-05-09\", \"2017-05-10\", \"2017-05-11\", \"2017-05-12\", \"2017-05-15\", \"2017-05-16\", \"2017-05-17\", \"2017-05-18\", \"2017-05-19\", \"2017-05-22\", \"2017-05-23\", \"2017-05-24\", \"2017-05-25\", \"2017-05-26\", \"2017-05-29\", \"2017-05-30\", \"2017-05-31\", \"2017-06-01\", \"2017-06-02\", \"2017-06-05\", \"2017-06-06\", \"2017-06-07\", \"2017-06-08\", \"2017-06-09\", \"2017-06-12\", \"2017-06-13\", \"2017-06-14\", \"2017-06-15\", \"2017-06-16\", \"2017-06-19\", \"2017-06-20\", \"2017-06-21\", \"2017-06-22\", \"2017-06-23\", \"2017-06-26\", \"2017-06-27\", \"2017-06-28\", \"2017-06-29\", \"2017-06-30\", \"2017-07-03\", \"2017-07-04\", \"2017-07-05\", \"2017-07-06\", \"2017-07-07\", \"2017-07-10\", \"2017-07-11\", \"2017-07-12\", \"2017-07-13\", \"2017-07-14\", \"2017-07-17\", \"2017-07-18\", \"2017-07-19\", \"2017-07-20\", \"2017-07-21\", \"2017-07-24\", \"2017-07-25\", \"2017-07-26\", \"2017-07-27\", \"2017-07-28\", \"2017-07-31\", \"2017-08-01\", \"2017-08-02\", \"2017-08-03\", \"2017-08-04\", \"2017-08-07\", \"2017-08-08\", \"2017-08-09\", \"2017-08-10\", \"2017-08-11\", \"2017-08-14\", \"2017-08-15\", \"2017-08-16\", \"2017-08-17\", \"2017-08-18\", \"2017-08-21\", \"2017-08-22\", \"2017-08-23\", \"2017-08-24\", \"2017-08-25\", \"2017-08-28\", \"2017-08-29\", \"2017-08-30\", \"2017-08-31\"], \"y\": [93.14, 92.97, 93.12, 93.2, 93.21, 93.08, 93.81, 93.6, 94.27, 93.26, 94.28, 95.49, 95.61, 96.09, 95.06, 95.35, 95.15, 95.95, 97.62, 97.98, 97.65, 97.46, 96.21, 96.68, 96.44, 95.84, 95.71, 97.01, 97.48, 97.03, 97.3, 95.95, 96.69, 94.92, 92.79, 93.12, 92.74, 92.63, 92.84, 92.03, 90.71, 90.13, 90.88, 90.47, 91.53, 92.01, 92.07, 92.44, 92.47, 93.03, 93.49, 93.71, 92.44, 93.21, 92.46, 93.41, 94.55, 95.99, 96.53, 97.24, 97.1, 97.23, 95.02, 93.26, 92.76, 93.36, 94.18, 94.59, 93.44, 91.23, 88.75, 88.73, 86.65, 87.83, 88.04, 88.81, 89.21, 91.07, 93.27, 92.63, 94.09, 93.22, 90.74, 93.7, 95.25, 95.8, 95.28, 96.24, 96.09, 95.81, 94.76, 93.96, 93.95, 94.85, 95.72, 96.29, 95.55, 93.98, 94.12, 93.84, 94.65, 93.13, 93.57, 91.93, 93.41, 93.36, 93.66, 94.71, 96.11, 95.82, 95.5, 95.98, 96.66, 97.83, 97.86, 98.46, 98.24, 94.89, 93.81, 95.07, 95.25, 95.47, 97.0, 96.36, 97.94, 99.65, 101.92, 103.09, 103.03, 103.46, 106.41, 104.77, 105.85, 106.2, 105.88, 106.39, 107.94, 108.0, 106.61, 107.13, 105.41, 105.47, 104.76, 104.61, 103.14, 105.1, 107.93, 106.94, 106.61, 105.32, 104.41, 103.45, 106.04, 106.19, 106.78, 106.89, 107.43, 107.58, 107.14, 104.9, 103.93, 104.93, 106.48, 105.88, 109.11, 110.17, 108.51, 107.98, 108.67, 107.29, 108.5, 110.62, 109.62, 107.48, 107.65, 108.72, 108.31, 106.54, 105.36, 108.23, 106.26, 104.7, 103.62, 103.22, 102.68, 103.1, 102.86, 102.36, 102.09, 104.15, 103.29, 103.83, 103.07, 103.54, 101.63, 103.08, 102.17, 102.46, 101.15, 102.34, 100.72, 100.87, 99.28, 97.63, 96.9, 96.65, 97.4, 98.74, 98.29, 96.81, 96.29, 94.56, 94.58, 93.4, 94.74, 94.25, 94.56, 95.13, 93.12, 93.91, 93.76, 93.8, 93.03, 93.35, 93.34, 95.35, 94.53, 93.86, 93.41, 92.05, 92.55, 93.61, 95.83, 96.97, 97.14, 97.48, 97.1, 98.32, 97.25, 97.21, 96.27, 97.18, 96.99, 97.59, 98.4, 99.11, 98.62, 98.87, 99.18, 99.94, 98.9, 98.17, 95.14, 93.66, 93.12, 93.31, 91.9, 91.36, 92.39, 91.45, 92.15, 93.78, 93.54, 93.96, 94.51, 96.35, 97.23, 96.66, 95.82, 97.49, 97.34, 98.25, 97.55, 96.44, 97.24, 97.4, 97.84, 99.98, 100.12, 99.96, 100.38, 100.27, 100.31, 102.54, 103.46, 103.2, 102.53, 103.17, 102.2, 102.93, 102.68, 102.88, 105.34, 103.64, 101.75, 101.82, 102.82, 101.39, 100.29, 98.29, 98.57, 99.23, 98.43, 100.08, 100.71, 99.68, 99.97, 100.05, 99.66, 100.61, 101.25, 101.73, 101.57, 99.69, 99.6, 100.29, 101.16, 100.43, 102.57, 103.55, 103.37, 103.68, 104.05, 103.7, 103.71, 104.33, 104.35, 101.69, 101.47, 102.2, 100.85, 101.13, 101.56, 100.07, 99.69, 100.09, 99.74, 99.81, 101.06, 100.52, 100.32, 100.89, 102.01, 102.63, 101.74, 102.31, 102.95, 102.8, 104.31, 104.03, 105.01, 104.78, 103.37, 104.26, 103.4, 103.07, 103.34, 103.27, 103.17, 103.32, 105.09, 105.02, 105.04, 107.2, 107.49, 107.52, 106.95, 106.64, 107.08, 107.95, 106.83, 106.64, 107.04, 106.49, 106.46, 106.07, 106.06, 105.18, 104.76, 104.19, 104.06, 102.93, 103.61, 101.48, 101.73, 100.56, 101.88, 103.84, 103.83, 105.34, 104.59, 103.81, 102.76, 105.23, 105.68, 104.91, 104.29, 98.23, 97.86, 98.26, 97.34, 96.93, 97.34, 97.61, 98.09, 97.36, 97.57, 95.54, 97.3, 96.44, 94.35, 96.4, 93.97, 93.61, 95.39, 95.78, 95.82, 96.44, 97.86, 92.92, 95.5, 94.51, 93.32, 92.64, 92.73, 91.71, 92.89, 92.18, 92.86, 94.91, 94.33, 93.07, 92.43, 91.46, 91.55, 93.6, 93.59, 95.55, 94.53, 91.17, 90.74, 91.02, 89.76, 90.33, 88.89, 87.29, 85.76, 85.87, 85.73, 81.72, 81.82, 82.33, 82.8, 82.76, 83.25, 80.52, 82.81, 81.27, 81.26, 81.36, 82.25, 81.06, 80.53, 78.77, 77.15, 78.71, 77.87, 78.71, 77.43, 77.85, 77.16, 74.13, 75.91, 75.64, 74.55, 74.55, 75.63, 76.52, 75.74, 74.04, 73.7, 65.94, 68.98, 66.99, 67.3, 66.73, 65.89, 63.13, 63.74, 60.99, 60.01, 57.81, 55.96, 55.97, 56.43, 54.18, 56.91, 55.25, 56.78, 55.7, 54.59, 53.46, 54.14, 53.45, 52.72, 50.05, 47.98, 48.69, 48.8, 48.35, 46.06, 45.92, 48.49, 46.37, 48.49, 46.79, 47.85, 45.93, 45.26, 44.8, 45.84, 44.08, 44.12, 47.79, 49.25, 53.04, 48.45, 50.48, 51.66, 52.99, 50.06, 48.8, 51.17, 52.66, 53.56, 52.13, 51.12, 49.95, 49.56, 48.48, 50.25, 47.65, 49.84, 49.59, 50.43, 51.53, 50.76, 49.61, 49.95, 48.42, 48.06, 47.12, 44.88, 43.93, 43.39, 44.63, 44.02, 46.0, 47.4, 47.03, 48.75, 51.41, 48.83, 48.66, 47.72, 50.12, 49.13, 52.08, 53.95, 50.44, 50.79, 51.63, 51.95, 53.3, 56.25, 56.69, 55.71, 56.37, 55.58, 56.17, 56.59, 55.98, 55.56, 57.05, 58.55, 59.62, 59.1, 58.92, 60.38, 60.93, 58.99, 59.41, 59.23, 60.72, 60.5, 59.89, 59.73, 59.44, 57.3, 58.96, 60.18, 58.88, 57.29, 57.51, 57.69, 60.25, 60.24, 61.3, 59.67, 58.0, 59.11, 58.15, 60.15, 61.36, 60.74, 59.96, 59.53, 60.01, 59.89, 60.41, 59.62, 60.01, 61.05, 60.01, 59.59, 59.41, 58.34, 59.48, 56.94, 56.93, 52.48, 52.33, 51.61, 52.76, 52.74, 52.19, 53.05, 51.4, 50.9, 50.88, 50.11, 50.59, 49.27, 48.11, 47.98, 47.17, 47.97, 48.77, 48.53, 47.11, 45.25, 45.75, 45.13, 44.69, 43.87, 44.94, 43.11, 43.22, 42.27, 42.45, 41.93, 42.58, 40.75, 41.0, 40.45, 38.22, 39.15, 38.5, 42.47, 45.29, 49.2, 45.38, 46.3, 46.75, 46.02, 45.92, 44.13, 45.85, 44.75, 44.07, 44.58, 47.12, 46.93, 44.71, 46.67, 46.17, 44.53, 44.94, 45.55, 44.4, 45.24, 45.06, 44.75, 45.54, 46.28, 48.53, 47.86, 49.46, 49.67, 47.09, 46.7, 46.63, 46.38, 47.3, 45.91, 45.84, 45.22, 44.9, 43.91, 43.19, 43.21, 45.93, 46.02, 46.6, 46.12, 47.88, 46.32, 45.27, 44.32, 43.87, 44.23, 42.95, 41.74, 40.69, 41.68, 40.73, 40.75, 40.55, 39.39, 39.27, 40.89, 41.22, 40.57, 40.43, 40.58, 39.93, 41.08, 40.0, 37.64, 37.46, 37.16, 36.76, 35.65, 36.31, 37.32, 35.55, 34.98, 34.72, 34.55, 36.12, 36.76, 37.62, 36.36, 37.88, 36.59, 37.13, 36.81, 35.97, 33.97, 33.29, 33.2, 31.42, 30.42, 30.42, 31.22, 29.45, 28.47, 26.68, 29.55, 32.07, 30.31, 29.54, 32.32, 33.21, 33.66, 31.62, 29.9, 32.29, 31.63, 30.86, 29.71, 27.96, 27.54, 26.19, 29.32, 29.05, 30.68, 30.77, 29.59, 31.37, 31.84, 30.35, 31.4, 31.65, 32.74, 34.39, 34.57, 34.56, 35.91, 37.9, 36.67, 37.62, 37.77, 38.51, 37.2, 36.32, 38.43, 40.17, 39.47, 39.91, 41.45, 38.28, 38.14, 37.99, 36.91, 36.91, 36.94, 35.36, 34.3, 34.52, 37.74, 37.3, 39.74, 40.46, 42.12, 41.7, 41.45, 40.4, 39.74, 40.88, 42.72, 43.18, 42.76, 41.67, 42.52, 45.29, 46.03, 45.98, 44.75, 43.65, 43.77, 44.33, 44.58, 43.45, 44.68, 46.21, 46.64, 46.22, 47.72, 48.29, 48.12, 48.16, 47.67, 48.12, 48.04, 49.1, 49.0, 49.36, 49.1, 49.07, 49.14, 48.69, 49.71, 50.37, 51.23, 50.52, 49.09, 48.89, 48.49, 47.92, 46.14, 48.0, 49.4, 48.95, 49.16, 49.34, 46.7, 45.8, 47.93, 49.85, 48.27, 49.02, 46.73, 47.37, 45.22, 45.37, 44.73, 46.82, 44.87, 45.64, 45.93, 45.23, 44.64, 44.96, 43.96, 43.41, 42.4, 42.16, 41.9, 41.13, 41.54, 40.05, 39.5, 40.8, 41.92, 41.83, 43.06, 42.78, 41.75, 43.51, 44.47, 45.72, 46.57, 46.81, 48.2, 48.48, 46.8, 47.54, 46.29, 46.97, 47.64, 46.97, 46.32, 44.68, 43.17, 44.39, 44.85, 45.47, 47.63, 45.88, 46.28, 44.91, 43.62, 43.85, 43.04, 43.34, 43.85, 45.33, 46.1, 44.36, 45.6, 44.65, 47.07, 47.72, 47.72, 48.8, 48.67, 49.75, 50.44, 49.76, 49.76, 50.72, 50.14, 50.47, 50.35, 49.97, 50.3, 51.59, 50.31, 50.61, 50.18, 49.45, 48.75, 49.71, 48.72, 46.83, 46.66, 45.32, 44.66, 44.07, 44.88, 44.96, 45.2, 44.62, 43.39, 43.29, 45.86, 45.56, 45.37, 45.69, 47.48, 48.07, 46.72, 46.72, 45.66, 45.29, 49.41, 51.08, 51.7, 51.72, 50.95, 49.85, 50.84, 51.51, 52.74, 52.99, 51.01, 50.9, 51.93, 52.13, 52.22, 51.44, 51.98, 52.01, 52.82, 54.01, 53.8, 53.75, 52.36, 53.26, 53.77, 53.98, 51.95, 50.82, 52.19, 53.01, 52.36, 52.45, 51.12, 51.39, 52.33, 52.77, 52.38, 52.14, 53.24, 53.18, 52.63, 52.75, 53.9, 53.55, 53.81, 53.01, 52.19, 52.37, 52.99, 53.84, 52.96, 53.21, 53.11, 53.41, 53.41, 54.02, 53.61, 54.48, 53.99, 54.04, 54.0, 53.82, 52.63, 53.33, 53.19, 52.68, 49.83, 48.75, 48.05, 47.95, 47.24, 48.34, 48.3, 48.34, 47.79, 47.02, 47.29, 47.0, 47.3, 47.02, 48.36, 49.47, 50.3, 50.54, 50.25, 50.99, 51.14, 51.69, 52.25, 53.06, 53.38, 53.12, 53.19, 52.62, 52.46, 50.49, 50.26, 49.64, 48.9, 49.22, 49.22, 48.96, 49.31, 48.83, 47.65, 47.79, 45.55, 46.23, 46.46, 45.84, 47.28, 47.81, 47.83, 48.86, 48.64, 49.04, 49.36, 50.32, 50.81, 51.12, 50.99, 48.57, 49.58, 49.63, 48.29, 48.32, 47.68, 47.4, 48.13, 45.8, 45.68, 45.82, 46.1, 46.41, 44.79, 44.47, 44.73, 44.24, 43.34, 42.48, 42.53, 42.86, 43.24, 44.25, 44.74, 44.88, 46.02, 45.11, 45.52, 44.25, 44.4, 45.06, 45.48, 46.06, 46.53, 46.02, 46.4, 47.1, 46.73, 45.78, 46.21, 47.77, 48.58, 49.05, 49.72, 50.21, 49.19, 49.6, 49.03, 49.57, 49.37, 49.07, 49.59, 48.54, 48.81, 47.59, 47.57, 46.8, 47.07, 48.59, 47.39, 47.65, 48.45, 47.24, 47.65, 46.4, 46.46, 45.96, 47.26]}],                        {\"showlegend\": false, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"autotypenumbers\": \"strict\", \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Daily oil prices from Jan 2013 till July 2017\"}, \"yaxis\": {\"title\": {\"text\": \"Daily Oil price\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('e9ffe54d-3255-4a2b-b7d6-8f973953a465');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace = go.Scatter(\n",
    "    name='Oil prices',\n",
    "    x=oil['date'],\n",
    "    y=oil['dcoilwtico'].dropna(),\n",
    "    mode='lines',\n",
    "    line=dict(color='rgb(20, 15, 200, 0.8)'),\n",
    "    #fillcolor='rgba(68, 68, 68, 0.3)',\n",
    "    fillcolor='rgba(0, 0, 216, 0.3)',\n",
    "    fill='tonexty' )\n",
    "\n",
    "data = [trace]\n",
    "\n",
    "layout = go.Layout(\n",
    "    yaxis=dict(title='Daily Oil price'),\n",
    "    title='Daily oil prices from Jan 2013 till July 2017',\n",
    "    showlegend = False)\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='pandas-time-series-error-bars')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421a6095-b022-40d2-8a9f-47ca34a9877a",
   "metadata": {},
   "source": [
    "Takeaway from the plots\n",
    "\n",
    "This plot shows that the daily oil price is on a general downward trend from Jan 2013 till July 2017. Where the price of oil started out 2013 by increasing and even busting the 100 dollar mark for a good few months in 2013 and 2014, it reached the middle of 2014 where there was a drastic drop in the price of oil. Via some quick open-source research (i.e Googling), this trend checks out as it seems oil prices were kept fairly stable from 2010 till mid-2014 after which it drastically fell ( due to a confluence of reasons such as weak demand due to poor economic growth and surging alternative sources of crude oil from shale/tar sands).Stores data\n",
    "With regards to the \"stores.csv\" file, the data dictionary on the Kaggle competition simply states that it contains metadata on the city, state, the store type and a column termed \"cluster\". Now this cluster column is a grouping to stores that are similar to each other and as we can see from the latter analysis, there are a total of 17 distinct clusters. With regards to the number of stores, there are a total of 54 stores (based off a unique list of store_nbr) and therefore I presume that all the unit sales and transactions are generated off the data collected from these 54 stores."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d64da0-1424-4a46-92d7-dfb151332c6b",
   "metadata": {},
   "source": [
    "Stores data\n",
    "\n",
    "With regards to the \"stores.csv\" file, the data dictionary on the Kaggle competition simply states that it contains metadata on the city, state, the store type and a column termed \"cluster\". Now this cluster column is a grouping to stores that are similar to each other and as we can see from the latter analysis, there are a total of 17 distinct clusters. With regards to the number of stores, there are a total of 54 stores (based off a unique list of store_nbr) and therefore I presume that all the unit sales and transactions are generated off the data collected from these 54 stores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "13e2902f-8127-4753-bb4c-dd4635036611",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2880x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(25, 21))\n",
    "marrimeko=stores.city.value_counts().to_frame()\n",
    "ax = fig.add_subplot(111, aspect=\"equal\")\n",
    "ax = squarify.plot(sizes=marrimeko['city'].values,label=marrimeko.index,\n",
    "              color=sns.color_palette('cubehelix_r', 28), alpha=1)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(40,25)\n",
    "plt.title(\"Treemap of store counts across different cities\", fontsize=18)\n",
    "plt.show();\n",
    "fig.savefig('plot.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8ec4f442-33e0-4516-9c09-c7ba43e5e4b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2880x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(25, 21))\n",
    "marrimeko=stores.state.value_counts().to_frame()\n",
    "ax = fig.add_subplot(111, aspect=\"equal\")\n",
    "ax = squarify.plot(sizes=marrimeko['state'].values,label=marrimeko.index,\n",
    "              color=sns.color_palette('viridis_r', 28), alpha=1)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(40,25)\n",
    "plt.title(\"Treemap of store counts across different States\", fontsize=18)\n",
    "plt.show()\n",
    "fig.savefig('plot2.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6392c56c-bc85-46d3-82d8-ea0bada2a33f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Pichincha', 'Santo Domingo de los Tsachilas', 'Cotopaxi',\n",
       "       'Chimborazo', 'Imbabura', 'Bolivar', 'Pastaza', 'Tungurahua',\n",
       "       'Guayas', 'Santa Elena', 'Los Rios', 'Azuay', 'Loja', 'El Oro',\n",
       "       'Esmeraldas', 'Manabi'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stores.state.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1d64560-6cb0-44dc-aae3-e633115628db",
   "metadata": {},
   "source": [
    "Inspecting the allocation of clusters to store numbers\n",
    "\n",
    "We can now generate will be that of our store numbers ordered against their respective store clusters so that we can observe if there are any apparent trends or relationships in the data. To do so, I will take our stores Python dataframe and group it based on the columns \"store_nbr\" and \"cluster\" via the groupby and pivot statement. After which, I will unstack the grouping which means that I will pivot on the level of store_nbr index labels, returning a DataFrame having a new level of columns which are the store clusters whose inner-most level relate to the pivoted store_nbr index labels. This technique is commonly used for producing stacked barplots in Python but since we only have unique store_nbr numbers, therefore we will simply get barplots of store numbers ordered by their relevant clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9c369656-a2a2-4d27-981a-81ff64337452",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unhide to see the sorted zip order\n",
    "neworder = [23, 24, 26, 36, 41, 15, 29, 31, 32, 34, 39, \n",
    "            53, 4, 37, 40, 43, 8, 10, 19, 20, 33, 38, 13, \n",
    "            21, 2, 6, 7, 3, 22, 25, 27, 28, 30, 35, 42, 44, \n",
    "            48, 51, 16, 0, 1, 5, 52, 45, 46, 47, 49, 9, 11, 12, 14, 18, 17, 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d5ed192-49cc-4bb5-902e-e07c7402ea5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finally plot the seaborn heatmap\n",
    "plt.style.use('dark_background')\n",
    "plt.figure(figsize=(15,12))\n",
    "store_pivot = stores.dropna().pivot(\"store_nbr\",\"cluster\", \"store_nbr\")\n",
    "ax = sns.heatmap(store_pivot, cmap='jet', annot=True, linewidths=0, linecolor='white')\n",
    "plt.title('Store numbers and the clusters they are assigned to')\n",
    "plt.savefig('plot2.png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b5e52d-565f-4688-a9ca-eeb7c1b0d672",
   "metadata": {},
   "source": [
    "Takeaways from thse plot\n",
    "\n",
    "From visualising the store numbers side-by-side based on the clustering, we can identify certain patterns. For example clusters 3, 6, 10 and 15 are the most common store clusters based off the fact that there are more store_nbrs attributed to them then the others while on the other end of the spectrum, we have clusters 5 and 17 which are only related to the stores 44 and 51 respectively.\n",
    "\n",
    "Stacked Barplots of Types against clusters\n",
    "\n",
    "Here it might be informative to look at the distribution of clusters based on the store type to see if we can identify any apparent relationship between types and the way the company has decided to cluster the particular store. Again we apply the groupby operation but this time on type and on cluster. This time when we pivot based off this grouped operation, we are able to get counts of each distinct cluster distributed and stacked on top of other clusters per store type as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "26e5ca33-ffcd-4303-aec5-a5562f5e7f64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.style.use('seaborn-white')\n",
    "plt.style.use('dark_background')\n",
    "type_cluster = stores.groupby(['type','cluster']).size()\n",
    "type_cluster.unstack().plot(kind='bar',stacked=True, colormap= 'PuBu', figsize=(13,11),  grid=False)\n",
    "plt.title('Stacked Barplot of Store types and their cluster distribution', fontsize=18)\n",
    "plt.ylabel('Count of clusters in a particular store type', fontsize=16)\n",
    "plt.xlabel('Store type', fontsize=16)\n",
    "plt.show()\n",
    "plt.savefig('plot3.png', dpi=300);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7f9595-3326-41ed-8c97-8f6d6b430336",
   "metadata": {},
   "source": [
    "Takeaway from the plots\n",
    "\n",
    "Most of the store types seem to contain a mix of the clusters, especially with regards to store type \"D\". Only type \"E\" stores seem to fall within the single cluster of cluster 10. However with regards to our initial plan of trying to suss out relationships between store types and clusters, it seems that there is nothing apparent that stands out. If we think of store types, one would normally think of categories such as convenience store types, huge general store types or bulk buy types.\n",
    "\n",
    "Stacked barplot of types of stores across the different cities\n",
    "\n",
    "Another interesting distribution to observe would be the types of stores that Corporacion Favorita has decided to open for each city in Ecuador as well as the absolute number of stores for that city. All these three things can be achieved by turning to our usual groupby operation and pivoting again by unstacking and plotting as follows:# plt.style.use('dark_background')\n",
    "plt.style.use('seaborn-white')\n",
    "city_cluster = stores.groupby(['city','type']).store_nbr.size()\n",
    "city_cluster.unstack().plot(kind='bar',stacked=True, colormap= 'viridis', figsize=(13,11),  grid=False)\n",
    "plt.title('Stacked Barplot of Store types opened for each city')\n",
    "plt.ylabel('Count of stores for a particular city')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7b0e387b-80e9-4b72-8133-99632350bbd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwcAAALICAYAAAAqkYyaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAByK0lEQVR4nO3dd3yNd//H8ffJspXYxN7jVrOoVaNWVewdapVaLWpvalWtUrNGRWqrKm5aqqiqGremVIoYMSqJLRGyrt8fOTm/xGjaxrnOwev5ePTR5Izr+8nJcXK9r++yGIZhCAAAAMBLz8XRBQAAAABwDoQDAAAAAJIIBwAAAACsCAcAAAAAJBEOAAAAAFgRDgAAAABIIhwA+BeOHz8uHx8fvf3222rcuLG6d++uM2fO2O7v2rWrbt68+a+OfejQITVu3DhZ9U2YMEFz58597PZNmzapfPny8vb2VpMmTdSoUSP17NlTISEhyWovob9b/7x587Rr165/dOw///xTjRs3VpMmTfS///0v0X1hYWEaNWqU3n77bTVp0kRNmzbV+vXrbfevX79efn5+/6i9f+KHH37QnDlz7Hb859GOHTvk4+PzxPvGjh2r2rVra9asWSZX9WRz587VhAkT/vXzV69ercWLF0uy/3sNgH25OboAAM+XyMhI9ezZU8uWLVPJkiUlSV9//bV69Oih3bt3y9XVVQcOHHBwlU9XoUIFLVq0yPb9uHHj9Omnn+qjjz4ytY5Dhw6pUKFC//g5mTNn1ooVKx67b8aMGUqdOrW2bNkii8Wi4OBgtWnTRjly5FC1atV09OhRFS5c+BlV/7jffvtNd+7csdvxXzRr167VDz/8oOzZszu6lGeiXbt2tq/t/V4DYF+EAwD/SEREhO7du6f79+/bbmvSpInSpk2rmJgYjRo1SpLUuXNnLV68WAEBAVq0aJEiIyN18+ZNNW3aVB988IEkacOGDVq+fLlcXFyUMWNGTZs2LVFbR44c0eDBgzVjxgyVK1dO33//vRYsWKCoqCilTJlSQ4cOVdmyZRUWFqaRI0cqICBAWbNmlaurq8qXL5/kzxIVFaWwsDDlzp1bknT9+nWNGTNGN27cUGhoqHLlyqXZs2crU6ZMql27tkqXLq0//vhDAwcO1JQpU1S3bl0dOXJE9+7dU5cuXdS+fftEx793757Gjx+vgIAAWSwWVa9eXQMHDtTatWt14sQJffzxx3J1ddWbb76Z6Hlr166Vr6+vXFxclDlzZo0ePVrBwcGaPXu27t27Jx8fH/n6+iZ6TmhoqDJlyqSoqCh5eHgoW7Zsmjt3rjJkyKDvvvtO33//vQ4cOKCUKVOqdevWmjp1qg4ePChXV1eVLl1aw4cPV9q0aR/7OUuXLq0JEybozz//VFRUlN566y316tUrUdu//vqr1qxZo5iYGKVLl07+/v5q0KCB2rRpI0lasGCBbt26pWLFimnbtm2KjY1VcHCwsmXLpqlTpypbtmy6d++eJk2apNOnTysqKkpVqlTRkCFD5Obmpk8//VTfffed3N3dlTFjRk2ZMkVZs2b9W6+1m5ubSpQooc6dO+vQoUO6f/++Bg4cqHr16kmKu8q9evVqxcbGKkOGDBo9erQKFiyoYcOGKW3atPrjjz907do1FShQQDNnzlSaNGkUGBioSZMm6fbt24qJiZGPj49atmwpSZozZ46++eYbZciQQXnz5n3i+659+/YyDEM9evTQ2LFj9corr2jChAm6ffu2LBaLunbtqqZNm+rQoUOaNGmSUqdOrfv372vDhg3y8PCwHSc4OPipv5uFCxdq165devjwoSIiIjR06FC9+eabio6O1vTp0/XDDz/I1dVVZcuW1dixYyVJ586dk4+Pj0JDQ5U5c2bNnDnzsdf5ac9ftGiRbt26pSpVqiR6r61cuVKjR49WtWrVJEmjRo1S4cKF1blz5ye+NgCcgAEA/9CyZcuM0qVLG7Vr1zY+/PBDY/369cb9+/dt9xcpUsS4ceOGERsba3Ts2NE4f/68YRiGce3aNaN48eLGjRs3jFOnThmVKlUyrl69ahiGYSxfvtwYPXq08fPPPxtvvfWWcfDgQaNu3brGqVOnDMMwjPPnzxuNGzc2bt68aRiGYZw+fdqoWrWqER4ebkyaNMkYMmSIERsba9y4ccOoUaOG8emnnz5W98aNG41y5coZTZo0Md5++23jtddeM6pXr25cvnzZMAzDWLFihbFo0SLDMAwjNjbW6N69u7F06VLDMAyjVq1axrx582zHqlWrljF69GgjNjbW+PPPP41KlSoZAQEBtvoNwzCGDBliTJw40YiNjTUePnxodO3a1Xb8jh07Gv/9738fq/Gnn34y6tata9y4ccNWc8OGDY3Y2Fhj48aNxrvvvvvE38mpU6eMevXqGWXLljW6du1qzJs3zzh37pzt/qFDhxqff/65YRiGMWfOHKNv375GZGSkERMTYwwbNswYPXr0E39OHx8fY/fu3YZhGMaDBw8MHx8fY9u2bY+1/+mnnxrjx483DMMwvvvuO6NFixaGYRhGTEyMUatWLSMwMNDYuHGjUaZMGVtd06dPN/r162cYhmEMGzbMWLlypWEYhhEdHW18+OGHxuLFi42rV68a5cqVMx4+fGgYhmEsXbrU+O677x5r/69e6yJFihgLFiywvU7ly5c3bty4YRw6dMho37697b27f/9+o2HDhrbXq02bNsbDhw+NyMhIo2nTpsaGDRuMqKgoo1GjRsaJEycMwzCMu3fvGg0bNjT+97//Gd99953RqFEj4969e0ZUVJTx7rvvGh07dnzi7yv+30hUVJRRp04dY+fOnYZhxP0bqV69unHs2DHj559/NooVK2Z7fz7qab+by5cvGz4+PkZERIRhGIaxdetWo3HjxoZhGMYXX3xhdOjQwYiIiDBiYmKM999/3/jqq6+MTz/91Khdu7btfffee+8leh/E+6vnx//+E77Xli9fbvTv398wDMO4d++eUblyZePOnTtP/HkAOAd6DgD8Y126dFGrVq10+PBhHT58WEuWLNGSJUu0YcMGpUuXzvY4i8WihQsX6ocfftDWrVsVGBgowzAUERGhgwcPqlq1asqRI4ck6Z133pEUN3Tm2rVr6tWrl9q1a6dixYpJkg4cOKCQkBDb4+KPHxQUpIMHD2rEiBGyWCzy9PR87Ep8QgmHFcXGxmrlypXq3r27tm/frs6dO+vIkSNavny5Lly4oDNnzujVV19N9NyE2rdvL4vFouzZs6t69eo6cOCAbaiVJO3bt0+rV6+WxWKRh4eH2rZtqy+++ELvvvvuU+vbv3+/GjVqJE9PT0lS8+bNNWnSJF2+fPmvfiUqVqyYduzYoZMnT+rw4cM6cOCAFi5cqDlz5qh27dqJHrtv3z4NGDBA7u7ukiQfHx/16dPnsZ/z/v37Onz4sO7cuWObT3D//n0FBASoUaNGT62lVq1a+uijjxQQEKDg4GB5eXmpQIECOn78uKpWrar8+fNLklq3bi1vb29JcXMWfvvtN23YsEGS9ODBA0lStmzZVKxYMTVr1kw1atRQjRo1VKVKlcfaTOq17tixo+11KlKkiA4fPqxff/1VFy9eVNu2bW3HuXPnjm7fvi1Jql69uu1KfZEiRXTnzh1duHBBQUFBGjFihO05Dx480O+//67AwEC9+eabSps2rSSpRYsWj/XwPOrChQt6+PChrScjW7Zsqlevnvbv369KlSopR44cypUr12PPS+p3M23aNH3zzTe6ePGifv31V4WHh0uSfvrpJ3l7eytlypSSpNmzZ0uKm3NQtWpV2/uuWLFiT5w39FfPf5LmzZvrs88+082bN7Vjxw698cYbSp8+/V++JgAci3AA4B85evSo/ve//6l79+6qVauWatWqpYEDB+rtt9/WgQMH1KBBA9tj79+/r2bNmqlu3bqqUKGCWrRooV27dskwDLm6uspisdge++DBA125ckWS5OrqqsWLF6t3795q2LChSpcurdjYWFWpUsV2MiLFTdCNH/ZgGIbtdldX17/1s7i4uKhNmzaaMmWKbty4oeXLl8vf318tWrRQpUqVFB0dnei4qVOnTvR8N7f//wiNjY2Vi0viNR5iY2Mf+z46Ovova0rYXsLb/up50dHRGj9+vAYNGqRSpUqpVKlS6tKli+bPn6+1a9c+Fg6eVFdUVJTt+/ifMzY2VoZhaM2aNUqVKpUk6ebNm0qRIsVf/gyurq5q27atNmzYoJCQkEQn3wl/N7GxsbbvY2NjNWfOHBUsWFCSdPfuXVksFrm4uGjVqlX67bffdPDgQU2ePFmVKlWyDV/7q58p4Wv2pHZjY2Pl7e2twYMH224PCQnRK6+8Ikm2E2ApLogahqGYmBilT59eX3/9te2+69evK126dJo+ffo/fh8+WreU+Pf96Hsu4fOe9rs5efKkevfurXfeeUdVq1ZVxYoVNX78eEmJ37PxtcfXkPC++J/3UX/1/CdJnz69GjRooC1btuibb76xDWEC4LxYrQjAP+Lp6akFCxboyJEjtttCQ0MVERGhIkWKSIo7KYqOjtbFixcVFhamDz74QLVr19Yvv/yiyMhIxcbGqlKlSjp48KBtpaA1a9Zo+vTpkqQsWbKoXLlyGjp0qAYPHqyIiAhVrlxZBw4cUGBgoCRp7969atKkiR4+fKjq1atrw4YNio2N1Z07d7R79+6//fN89913ypUrlzw9PfXjjz+qc+fOatq0qTJlyqSffvpJMTExT33u5s2bJUlXr17VgQMHVKNGjUT3V6tWTX5+fjIMQ5GRkVq3bp1ef/31RK/Ro6pVq6bt27fbrtpu3LjxL8evS3EnbBcuXND8+fNtJ/nR0dG6dOmSSpQo8Vh71atX15o1axQVFaXY2Fj5+fmpatWqjx03bdq0KlOmjJYvXy4p7oS9Xbt2T3x9H/15WrVqpV27dunkyZOJenJ+/vlnBQcHS4r7ndeqVcv2c69YscL2Wr333ntatWqVAgIC1LhxYxUsWFA9e/bUO++8oz/++OOJr9vTXmvp/39XJ0+e1Pnz51WxYkVVrVpV27Zts70HV69eneRY+Pz58ytFihS2cBC/gtSJEydUvXp17dixQ3fv3lVsbGyiAPFXx3N3d9e3334rKW4ewc6dOxPV/iR/9bs5fPiwLSC+9tpr2r17t+19XKVKFW3dutX273DcuHHatm1bknXG+zvPf/S90KFDB61cuVKGYah06dJ/uy0AjkHPAYB/JH/+/Prss880a9YsXbt2TSlSpFC6dOk0YcIEFShQQJL05ptvqn379po3b57eeOMNNWzYUOnTp1eePHlUqFAhXbx4UdWrV9fgwYPVvXt3SXGBYPLkybpw4YKtrWbNmmnnzp2aOnWqxo8frwkTJmjgwIEyDENubm5asGCBUqdOrX79+mns2LFq2LChPD09bSHlSY4cOSJvb29ZLBZFR0crQ4YM+uyzz+Ti4qI+ffro448/1vz58+Xq6qpy5copKCjoqce6fPmymjdvrgcPHmjUqFEqUKCAQkNDbfePGjVKH330kd5++21FRUWpevXqtgmjtWrV0rRp0xQVFaVmzZrZnlO1alW988476ty5s2JjY+Xp6alFixY91ivxqDlz5mj69OmqX7++UqVKJcMwVLduXdtwoRo1amjixImSpPfee0/Tpk1T06ZNFR0drdKlS2v06NFPPO4nn3yiiRMn6u2331ZkZKRtKdVHValSRf369ZO7u7tGjx6tTJkyqVSpUipYsKBt+JIUN2xm8ODBCg0NVaFChWzLZ44cOVKTJk2yvVavv/66unfvLnd3dzVs2FAtWrRQ6tSplTJlysd6DZJ6rSXp2LFjWrdunWJjYzVr1iy98sorql69unr06KGuXbvKYrEobdq0mjdvXqIerUd5eHho/vz5mjRpkj7//HNFR0fr/ffft02A/+OPP9SiRQulT59exYoV061bt/7y9+bu7q758+fro48+0ty5cxUTE6M+ffqocuXKOnTo0F8+92m/m+vXr+vbb79Vo0aN5O7uripVqujOnTsKCwtT27ZtdeXKFTVv3lyGYei1116Tj4+PFixY8Jdtxfs7z0/4XuvZs6eKFSumV155JVEPEgDnZTGe1G8IAPhLtWvX1pw5c/Sf//zH0aU4pZs3b6ply5by8/OzzSvZtGmTdu7cmWgpWTMULVpUBw8etI2nh7mCgoLk4+OjHTt22IZAAXBeDCsCADxT69atU6NGjdSpUydbMMDLac6cOWrXrp2GDh1KMACeE/QcAAAAAJBEzwEAAAAAK8IBAAAAAEnP0WpFDx480IkTJ5QlS5a/vYY5AAAAgMRiYmIUGhqqUqVKJdrTRXqOwsGJEyfUoUMHR5cBAAAAvBD8/PxUoUKFRLc9N+EgS5YskuJ+iOzZszu4GgAAAOD5dO3aNXXo0MF2fp3QcxMO4ocSZc+eXV5eXg6uBgAAAHi+PWmoPhOSAQAAAEgiHAAAAACwIhwAAAAAkEQ4AAAAAGBFOAAAAAAgiXAAAAAAwIpwAAAAANjZw4cPtX79ekeXkSTCAQAAAGBnoaGhz0U4eG42QQMAAACeVwsXLtTZs2dVtGhRbd26VYULF9bevXu1Z88eZcqUSefOndONGzd09+5djRo1ShUqVNB///tfrVixQi4uLipfvrw+/PBDu9dJzwEAAABgZ7169VKhQoU0efJkffXVV5KkjRs3qlWrVpKklClTauXKlZo+fbomTJig27dva+7cuVqxYoVWr16t4OBgHThwwO51Eg4AAAAAkzRs2FDff/+9bty4oeDgYJUsWVKSVLlyZUlS4cKFdf36dQUFBenmzZt699135ePjo8DAQAUFBdm9PoYVAQAAAHbm4uKi2NhYpU6dWpUqVdKkSZPUpEkT2/0nT56Ut7e3Tp8+rWzZssnLy0s5cuTQsmXL5O7urk2bNql48eJ2r5NwAAAAANhZpkyZFBUVpenTp6t169Zq3769xo0bZ7v/1KlT6ty5syIiIjRx4kR5enrqnXfekY+Pj2JiYpQrVy41bNjQ7nUSDgAAAAA7S5Eihb7++mtJkr+/v+rXr6/06dPb7m/UqJHatWuX6Dne3t7y9vY2tU7CAQAAAGCSVatWacOGDZo9e7ajS3kiwgEAAABgko4dO6pjx46JbuvXr5+DqnkcqxUBAAAAkEQ4AAAAAGBFOAAAAAAgiXAAAAAAwIoJyQAAAEAyvOnS6pke77vY9X/7sUuWLNEXX3yh3bt3K0WKFMlum54DAAAA4Dm1ZcsWNWrUSNu2bXsmxyMcAAAAAM+hQ4cOKU+ePGrbtq38/PyeyTEJBwAAAMBzaP369WrVqpUKFCggDw8P/frrr8k+JnMOAAAAgOfMnTt3tG/fPt28eVO+vr4KCwvTqlWr9OqrrybruIQDAAAAqPMv3ZL1/C9eW/qMKsHfsWXLFrVo0UJDhw6VJEVERKhOnTq6efOmPD09//VxGVYEAAAAPGfWr18vb29v2/epUqVSvXr1tG7dumQdl54DAAAAIBn+ydKjz8qWLVseu23cuHHJPi49BwAAAAAkEQ4AAAAAWBEOAAAAAEgiHAAAAACwIhwAAAAAkEQ4AAAAAGDFUqYAAABAMlRr8ckzPd6PGz9M8jGHDh3SBx98oEKFCskwDEVGRmrcuHEqUaJEstomHAAAAADPocqVK2vWrFmSpB9//FFz5szRokWLknVMhhUBAAAAz7m7d+/K09Mz2ceh5wAAAAB4Dv3888/y8fFRZGSkAgIC9NlnnyX7mHYLB7/++qs++eQT+fr6asCAAbp+/bok6cqVK3r11VdtXSCSZBiGatSooXz58kmSypQpo0GDBtmrNAAAAOC5l3BY0blz59S2bVvt27dPKVOm/NfHtEs4WLJkibZs2aJUqVJJkq3oO3fuqFOnTho+fHiixwcFBalkyZJauHChPcoBAAAAXmiZM2d+JsexSzjIkyeP5s6dqyFDhiS6fe7cuerYsaOyZs2a6PaTJ08qODhYPj4+SpkypYYPH64CBQrYozQAAADghRA/rMjFxUXh4eEaNmxYsnoNJDuFg/r16+vy5cuJbrtx44YOHjz4WK+BJGXJkkXvvvuuGjZsqCNHjmjw4MHauHGjPUoDAAAAnqm/s/Tos1apUiUdPHjwmR/XtAnJO3bsUOPGjeXq6vrYfaVKlbLdXqFCBYWEhMgwDFksFrPKAwAAAF56pi1levDgQdWoUeOJ982bN09ffPGFJCkgIEA5cuQgGAAAAAAmMy0cnD9/Xrlz5050W9euXRUZGal3331Xhw8fVseOHTVlyhRNmTLFrLIAAAAAWNltWJGXl5fWrVtn+37btm2PPWbZsmWSJA8PDy1evNhepQAAAAD4G9ghGQAAAIAkwgEAAAAAK9NWKwIAAABeRAU+nfFMj3eu/6C/9bgzZ85o+vTpioiI0P3791WzZk3169cvWQv70HMAAAAAPGfu3r2rgQMHasSIEfL19dW6det0+vRprVmzJlnHpecAAAAAeM7s3r1blSpVUr58+SRJrq6umjZtmtzd3ZN1XMIBAAAA8JwJCQl5bJuANGnSJPu4DCsCAAAAnjM5c+bUtWvXEt126dIlHT58OFnHJRwAAAAAz5latWpp//79CgoKkiRFRUVp6tSpOn36dLKOy7AiAAAA4DmTNm1aTZ06VaNGjZJhGAoPD1etWrXUvn37ZB2XcAAAAAAkw99devRZK1WqlFauXPlMj8mwIgAAAACSCAcAAAAArAgHAAAAACQRDgAAAABYEQ4AAAAASCIcAAAAALBiKVMAAAAgGTr/0u2ZHu+L15Ym+ZhDhw7pgw8+UKFChWQYhqKjo9WpUyc1atQoWW0TDgAAAIDnUOXKlTVr1ixJUnh4uHx8fJQ/f34VL178Xx+TYUUAAADAcy5NmjRq06aNduzYkazjEA4AAACAF0CmTJl069atZB2DcAAAAAC8AK5evars2bMn6xiEAwAAAOA5FxYWpvXr16tBgwbJOg4TkgEAAIDn0M8//ywfHx+5uLgoJiZG/fr1U4ECBZJ1TMIBAAAAkAx/Z+nRZ61SpUo6ePDgMz8uw4oAAAAASCIcAAAAALAiHAAAAACQRDgAAAAAYEU4AAAAACCJcAAAAADAiqVMAQAAgGSIvVbkmR7PJfvpJB9z6NAhffDBBypUqJDttowZM+rTTz9NVtuEAwAAAOA5VLlyZc2aNeuZHpNhRQAAAAAk0XMAAAAAPJd+/vln+fj42L6vWbOmunfvnqxjEg4AAACA5xDDigAAAADYDT0HAAAAwHPo0WFFkrRkyRKlTJnyXx+TcAAAAAAkw99ZevRZq1Spkg4ePPjMj8uwIgAAAACSCAcAAAAArAgHAAAAACQRDgAAAABYEQ4AAAAASCIcAAAAALAiHAAAAACQRDgAAAAAYEU4AAAAACCJcAAAAADAinAAAAAAQBLhAAAAAIAV4QAAAACAJMIBAAAAACvCAQAAAABJhAMAAAAAVoQDAAAAAJIIBwAAAACsCAcAAAAAJBEOAAAAAFgRDgAAAABIIhwAAAAAsCIcAAAAAJBEOAAAAABgRTgAAAAAIIlwAAAAAMCKcAAAAABAEuEAAAAAgBXhAAAAAIAkwgEAAAAAK8IBAAAAAEmEAwAAAABWhAMAAAAAkuwYDn799Vf5+PhIkn7//XdVr15dPj4+8vHx0fbt2xM99sGDB+rXr5/at2+vHj166ObNm/YqCwAAAMBTuNnjoEuWLNGWLVuUKlUqSdLJkyfVpUsXde3a9YmPX716tYoUKaJ+/fpp27Ztmj9/vkaNGmWP0gAAAAA8hV16DvLkyaO5c+favj9x4oR++OEHdejQQSNGjFBYWFiixx89elTVq1eXJNWoUUMHDx60R1kAAAAA/oJdwkH9+vXl5vb/nRKlS5fWkCFD5Ofnp9y5c+uzzz5L9PiwsDClS5dOkpQmTRrdu3fPHmUBAAAA+AumTEh+8803VapUKdvXv//+e6L706ZNq/DwcElSeHi40qdPb0ZZAAAAABIwJRx069ZN/v7+kqSDBw+qZMmSie4vV66c9u7dK0nat2+fypcvb0ZZAAAAABIwJRyMGzdOkydPlo+Pj44dO6bevXtLkrp27arIyEi1a9dOZ86cUbt27bR27Vr17dvXjLIAAAAAJGCX1YokycvLS+vWrZMklSxZUmvWrHnsMcuWLbN9/emnn9qrFAAAAAB/A5ugAQAAAJBEOAAAAABgRTgAAAAAIIlwAAAAAMCKcAAAAABAEuEAAAAAgBXhAAAAAIAkwgEAAAAAK8IBAAAAAEmEAwAAAABWhAMAAAAAkggHAAAAAKwIBwAAAAAkEQ4AAAAAWBEOAAAAAEgiHAAAAACwIhwAAAAAkEQ4AAAAAGBFOAAAAAAgiXAAAAAAwIpwAAAAAEAS4QAAAACAFeEAAAAAgCTCAQAAAAArwgEAAAAASYQDAAAAAFaEAwAAAACSCAcAAAAArAgHAAAAACQRDgAAAABYEQ4AAAAASCIcAAAAALAiHAAAAACQRDgAAAAAYEU4AAAAACCJcAAAAADAinAAAAAAQBLhAAAAAIAV4QAAAACAJMIBAAAAACvCAQAAAABJhAMAAAAAVoQDAAAAAJIIBwAAAACsCAcAAAAAJBEOAAAAAFgRDgAAAABIIhwAAAAAsCIcAAAAAJBEOAAAAABgRTgAAAAAIIlwAAAAAMCKcAAAAABAEuEAAAAAgBXhAAAAAIAkwgEAAAAAK8IBAAAAAEmEAwAAAABWhAMAAAAAkggHAAAAAKwIBwAAAAAkEQ4AAAAAWBEOAAAAAEgiHAAAAACwIhwAAAAAkEQ4AAAAAGBFOAAAAAAgiXAAAAAAwIpwAAAAAEAS4QAAAACAFeEAAAAAgCTJzV4H/vXXX/XJJ5/I19dXp06d0sSJE+Xq6ioPDw9NmzZNmTNnTvT4Zs2aKW3atJIkLy8vTZkyxV6lAQAAAHgCu4SDJUuWaMuWLUqVKpUkadKkSRo9erSKFy+uNWvWaMmSJRo+fLjt8Q8fPpRhGPL19bVHOQAAAAD+BrsMK8qTJ4/mzp1r+37mzJkqXry4JCkmJkYpUqRI9PiAgABFRESoa9eu6tSpk44fP26PsgAAAAD8Bbv0HNSvX1+XL1+2fZ81a1ZJ0rFjx7Rq1Sr5+fklenzKlCnVrVs3tWrVShcuXFCPHj20Y8cOubnZbdQTAAAAgEeYdva9fft2LViwQIsXL5anp2ei+/Lnz6+8efPKYrEof/78ypAhg0JDQ5UjRw6zygMAAABeeqasVvT1119r1apV8vX1Ve7cuR+7f8OGDZo6daokKTg4WGFhYcqSJYsZpQEAAACwsns4iImJ0aRJkxQeHq5+/frJx8dHn376qSRpyJAhunr1qlq2bKl79+6pXbt2GjBggCZPnsyQIgAAAMBkdjsD9/Ly0rp16yRJv/zyyxMf8/HHH9u+njFjhr1KAQAAAPA3sAkaAAAAAEmEAwAAAABWhAMAAAAAkggHAAAAAKwIBwAAAAAkEQ4AAAAAWBEOAAAAAEgiHAAAAACwIhwAAAAAkEQ4AAAAAGBFOAAAAAAgiXAAAAAAwIpwAAAAAEAS4QAAAACAFeEAAAAAgCTCAQAAAACrJMPBjh07FB0dbUYtAAAAABwoyXBw4sQJNW/eXNOmTVNgYKAZNQEAAABwgCTDwYcffqjNmzerUqVKmj17ttq2batNmzYpKirKjPoAAAAAmCTJcGAYhn788Udt3rxZV65cUYMGDXTr1i316tXLjPoAAAAAmMQtqQfUq1dPFSpUkI+Pj8qXL2+7/ezZs3YtDAAAAIC5kgwHw4YNU506dWzfb9++XY0aNdKUKVPsWhgAAAAAcz01HOzZs0fHjh3Ttm3b9Ouvv0qSYmJi9P3336tRo0amFQgAAADAHE8NB8WKFdPt27eVIkUK5c+fX5JksVjUuHFj04oDAAAAYJ6nhoMsWbKoWbNmatiwoVxc2CsNAAAAeNE9NRwMHTpUM2bMUKNGjWSxWCTFrVxksVi0e/du0woEAAAAYI6nhoMZM2ZIknbv3q1r164pR44c8vf3V+nSpU0rDgAAAIB5khwvNHbsWG3fvl2StGXLFk2aNMnuRQEAAAAwX5Lh4Pfff1e3bt0kSaNGjdLvv/9u96IAAAAAmO9vzTS+deuWJOnu3buKiYmxa0EAAAAAHCPJTdD69OmjFi1aKEOGDLp7967Gjh1rRl0AAAAATJZkOKhVq5Zq1KihW7duKVOmTLaViwAAAAC8WJIMB5Lk6uqqzJkz27sWAAAAAA7E7mYAAAAAJP2NcDB16lQz6gAAAADgYEmGg7Nnz+ru3btm1AIAAADAgZKccxAYGKhKlSrJ09PTNhn5xx9/tHthAAAAAMyVZDjYs2ePGXUAAAAAcLAkw8Hx48e1adMmRUVFSZJCQkK0dOlSuxcGAAAAwFxJzjkYN26cXnvtNYWFhSlnzpzKkCGDCWUBAAAAMFuS4SBjxoxq3Lix0qZNq379+ik4ONiMugAAAACYLMlw4OLiojNnzigiIkLnzp3TnTt3zKgLAAAAgMmSDAfDhg3TmTNn5OPjow8//FAtWrQwoy4AAAAAJnvqhOTIyEhJUt68eZU3b15J0po1a8ypCgAAAKba/3OJ5B3gtWdTBxzrqeGgQYMGslgsMgwj0e0Wi0W7d++2e2EAAAAAzPXUcPD999+bWQcAAAAAB0tynwMfHx/bzsjxVq5cabeCAAAAADhGkuFg/PjxkiTDMHTy5EmdOnXK7kUBAAAAMF+S4aBAgQK2rwsWLKgNGzbYtSAAAAAAjpFkOFi7dq3t65CQEN2/f9+uBQEAAABwjCTDQWhoqO3rlClTavbs2fasBwAAAICDJLkJ2ttvv618+fKpb9++7I4MAAAAvMCSDAdDhw6Vl5eXJKlmzZoaOXKk3YsCAAAAYL4kw4EklSlTRpJUsWJFxcbG2rMeAAAAAA6S5JyD9OnTa+3atSpTpoz8/f2VJk0aM+oCAAAAYLIkew6mTp2qs2fPavr06QoMDNTkyZPNqAsAAACAyZ7ac3Dt2jVlz55dd+7cUfv27W2337lzR56enqYUBwAAAMA8Tw0Hy5cv1/DhwzVmzBhZLBYZhiFJslgsWrlypWkFAgAAADDHU8PB8OHDJUldunRR7dq1bbdv377d/lUBAAAAMN1Tw8GePXt07Ngxbdu2TcePH5ckxcbGavfu3WrUqJFZ9QEAAAAwyVPDQbFixXT79m2lSJFC+fPnlxQ3pOitt94yrTgAAAAA5nlqOMiRI4eaNWumgIAANWvWzMyaAAAAADhAkkuZBgYG6u7du2bUAgAAAMCBktwELTAwUJUqVZKnp6csFosk6ccff7R7YQAAAADMlWQ42LNnjxl1AAAAAHCwJMPB8ePHtWnTJkVFRUmSQkJCtHTpUrsXBgAAAMBcSc45GDdunF577TWFhYUpZ86cypAhgwllAQAAADBbkuEgY8aMaty4sdKmTat+/fopODjYjLoAAAAAmCzJcODi4qIzZ84oIiJC586d0507d8yoCwAAAIDJkgwHw4YN05kzZ+Tj46MPP/xQLVq0MKMuAAAAACZLckJy4cKFFR0drfPnz2vKlCkqWrSoGXUBAAAAMFmSPQezZ8/W+PHjdfz4cY0dO1aff/753zrwr7/+Kh8fH0nSxYsX1a5dO7Vv315jx45VbGxsosc+ePBA/fr1U/v27dWjRw/dvHnzX/woAAAAAJIjyXCwb98+ffnllxoxYoT8/Py0Y8eOJA+6ZMkSjRo1Sg8fPpQkTZkyRR988IG+/PJLGYah3bt3J3r86tWrVaRIEX355Zdq2rSp5s+f/y9/HAAAAAD/VpLhIHv27AoPD5ckRUdHK3PmzEkeNE+ePJo7d67t+5MnT+q1116TJNWoUUM//fRToscfPXpU1atXt91/8ODBv/8TAAAAAHgmkpxzEBISovr166tYsWI6e/as3N3d1bZtW0nSmjVrnvic+vXr6/Lly7bvDcOQxWKRJKVJk0b37t1L9PiwsDClS5fuqfcDAAAAsL8kw8GcOXOS3YiLy/93UISHhyt9+vSJ7k+bNq2td+JJ9wMAAACwvyTDQa5cuZLdSIkSJXTo0CFVqlRJ+/btU+XKlRPdX65cOe3du1elS5fWvn37VL58+WS3CQAAAOCfSXLOwbMwdOhQzZ07V23atFFUVJTq168vSeratasiIyPVrl07nTlzRu3atdPatWvVt29fM8oCAAAAkECSPQf/lpeXl9atWydJyp8/v1atWvXYY5YtW2b7+tNPP7VXKQAAAAD+hiTDwe7du+Xn56fo6GgZhqHbt2/rm2++MaM2AAAAACb6W5ug9evXTzly5FCzZs3YIRkAAAB4QSUZDrJmzaqyZctKkpo3b67g4GC7FwUAAADAfEmGA3d3dx0+fFjR0dHav3+/bt26ZUZdAAAAAEyWZDgYP368oqOj9d5772ndunV67733zKgLAAAAgMmSnJCcLVs2ZcuWTZI0d+5cuxcEAAAAwDFM2ecAAAAAgPNLMhzcu3fPjDoAAAAAOFiS4eDdd981ow4AAAAADpbknINXXnlFX3zxhfLnzy8Xl7gsUa1aNbsXBgAAAMBcSYaDjBkzKiAgQAEBAbbbCAcAAADAiyfJcDBlyhSdPn1aZ8+eVf78+VW8eHEz6gIAAABgsiTDga+vr7Zu3arSpUtr2bJlatiwobp162ZGbQAAAABMlGQ42Lp1q/z8/OTm5qaoqCi1bduWcAAAAAC8gJJcrcgwDLm5xWUId3d3ubu7270oAAAAAOZLsuegXLly6t+/v8qXL6+jR4+qbNmyZtQFAAAAwGRP7Tk4fPiwJGnAgAFq3ry5oqOj1bx5cw0dOtS04gAAAACY56nh4KOPPtL9+/fVvXt3Va1aVT4+Pnr99dcVGRlpZn0AAAAATPLUYUXVqlVTkyZNFBISogYNGkiKm39gsVi0e/du0woEAAAAYI6nhoPBgwdr8ODB+uyzz9SnTx8zawIAAADgAEmuVkQwAAAAAF4OSYYDAAAAAC+HfxQOYmNj7VUHAAAAAAdLMhxs2bJF27Zt01dffaWqVatq6dKlZtQFAAAAwGRJhoOVK1fq9ddf15YtW7R3717t2bPHjLoAAAAAmCzJcJAyZUpJUpo0aeTh4aHo6Gi7FwUAAADAfEmGg9y5c6tNmzZq0aKF5s2bp6JFi5pRFwAAAACTPXWfg3hTpkxReHi40qRJo1KlSilLlixm1AUAAAAT5dxrJO8A/Z9NHXCsJMPBmTNnNHbsWN29e1dNmjRR4cKFVatWLTNqAwAAAGCiJIcVffTRR5oyZYoyZsyoli1bau7cuWbUBQAAAMBkf2ufg7x588piscjT01Np0qSxd00AAAAAHCDJcPDKK69ozZo1ioiI0LZt25Q+fXoz6gIAAABgsiTDweTJk3X58mVlzJhRJ06c0KRJk8yoCwAAAIDJkpyQPHbsWM2YMcOMWgAAAAA4UJI9B5GRkQoICNDDhw8VGRmpyMhIM+oCAAAAYLIkew4uXLig3r172763WCzavXu3XYsCAAAAYL4kw8E333wjSbpx44YyZMggV1dXuxcFAAAAwHxJDis6dOiQ6tSpo27duunNN9/UgQMHzKgLAAAAgMmS7DmYPXu2vvzyS2XLlk3BwcHq27evqlatakZtAAAAAEyUZM+Bq6ursmXLJknKli2bUqRIYfeiAAAAAJgvyZ6DtGnTytfXVxUrVtThw4f1yiuvmFEXAAAAAJMl2XMwffp0Xb16VbNmzdLVq1c1ZcoUM+oCAAAAYLIkew58fX01dOhQ2/czZszQoEGD7FoUAAAAAPM9NRysX79eGzZsUGBgoPbt2ydJiomJUXR0NOEAAAAAeAE9NRx4e3urSpUqWrRokXr16iVJcnFxUaZMmUwrDgAAAIB5njrnwMPDQ15eXho+fLhcXV2VIkUKbdq0SaGhoWbWBwAAAMAkSU5I7t+/v06cOKGPP/5Y7u7uGjNmjBl1AQAAADBZkuHgwYMHqlOnjq5du6Z3331XMTExZtQFAAAAwGRJhoOoqCh98cUXKlmypM6ePauIiAgz6gIAAABgsiTDwdChQxUSEqL33ntPP//8s0aOHGlGXQAAAABMluQ+B+XKlVO5cuUkSR07drR7QQAAAAAcI8meAwAAAAAvh6eGg8OHD0uSIiMjTSsGAAAAgOM8NRx89NFHun//vrp166aoqChFRkba/gMAAADw4nnqnINq1aqpSZMmCgkJUf369W23WywW7d6925TiAAAAAJjnqeFg8ODBGjx4sD777DP16dPHzJoAAAAAOECSE5KbN2+u/v3766233lKfPn105coVM+oCAAAAYLIkw8Ho0aPl7e2t1atXq1mzZhoxYoQZdQEAAAAwWZLh4OHDh6pTp47Sp0+vunXrKiYmxoy6AAAAAJgsyU3QYmJi9Mcff6ho0aL6448/zKgJAJ652GtFkn0Ml+ynn0ElAAA4ryTDwejRozVixAiFhoYqa9as+uijj8yoCwAAAIDJkgwHxYsX18aNG82oBQAAAIADJTnnAAAAAMDLgXAAAAAAQNLfCAfz589P9P2MGTPsVgwAAAAAx3nqnIP169drw4YNCgwM1L59+yTFrVwUHR2tQYMGmVYgAAAAAHM8NRx4e3urSpUqWrRokXr16iVJcnFxUaZMmUwrDgAAAIB5njqsyMPDQ15eXho/frxu3Lihq1ev6vLly/r111/NrA8AAACASZJcyrR///66ceOGcuTIIUmyWCyqWLGi3QsDAAAAYK4kw8H169e1Zs0aM2oBAAAA4EBJrlaUP39+BQcHm1ELAAAAAAdKsufg6NGjqlWrljw9PW23/fjjj/+4oU2bNumrr76SJD18+FCnTp3SgQMHlD59eknSRx99pGPHjilNmjSS4pZQTZcu3T9uBwAAAMC/k2Q4+Pbbb59JQ82bN1fz5s0lSePHj1eLFi1swUCSTp48qc8//zxRCAEAAABgniTDwfDhwx+7bcqUKf+6wd9++01nz57V2LFjbbfFxsbq4sWLGjNmjK5fv66WLVuqZcuW/7oNAAAAAP9ckuGgUaNGkiTDMPT7778rJCQkWQ0uWrRIffr0SXTb/fv31bFjR3Xp0kUxMTHq1KmTSpUqpWLFiiWrLQAAAAB/X5LhoHr16rava9Sooa5du/7rxu7evavz58+rcuXKiW5PlSqVOnXqpFSpUkmSKleurICAAMIBAAAAYKIkw0HCycehoaG6fv36v27s8OHDqlKlymO3X7hwQR988IE2b96s2NhYHTt2TM2aNfvX7QAAAAD455IMB9u2bbN97eHhocmTJ//rxs6fPy8vLy/b98uXL1eePHlUp04deXt7q3Xr1nJ3d5e3t7cKFy78r9sBAAAA8M8lGQ6mTJmi06dP6+zZs8qfP7+KFy/+rxvr3r17ou+7dOmS6L5H7wcAAABgniTDga+vr7Zu3arSpUtr2bJlatiwobp162ZGbQAAAABMlGQ42Lp1q/z8/OTm5qaoqCi1bduWcAAAAAC8gFySeoBhGHJzi8sQ7u7ucnd3t3tRAAAAAMyXZM9B+fLl1b9/f5UvX15Hjx5V2bJlzagLAAAAgMmSDAdDhw7VDz/8oMDAQDVv3lxvvPGGCWUBAAAAMNtfDitau3atoqOj9cYbb+jVV1/Vn3/+aVZdAAAAAEz21HAwd+5cHThwQFFRUZKk7Nmz68CBA5o3b55pxQEAAAAwz1PDwb59+zRnzhylSpVKkuTl5aVZs2Zpz549phUHAAAAwDxPDQepU6eWxWJJdJu7u7vSpElj96IAAAAAmO+p4SBlypS6dOlSotsuXbr0WGAAAAAA8GJ46mpFH374oXr37q0qVaood+7cunr1qn788UdNmzbNzPoAAAAAmOSpPQeFCxfWl19+qRIlSigiIkIlS5bU6tWrVaJECTPrAwAAAGCSv9znIF26dGratKlJpQAAAABwpL/c5wAAAADAy4NwAAAAAEAS4QAAAACAFeEAAAAAgCTCAQAAAAArwgEAAAAASYQDAAAAAFaEAwAAAACSCAcAAAAArAgHAAAAACQRDgAAAABYEQ4AAAAASCIcAAAAALAiHAAAAACQRDgAAAAAYEU4AAAAACCJcAAAAADAinAAAAAAQBLhAAAAAIAV4QAAAACAJMIBAAAAACvCAQAAAABJhAMAAAAAVoQDAAAAAJIIBwAAAACsCAcAAAAAJBEOAAAAAFgRDgAAAABIIhwAAAAAsCIcAAAAAJAkuTm6AAAwQ5eg6sk+xhfZn0EhAOCkUn11yNElwAnQcwAAAABAEuEAAAAAgBXhAAAAAIAkwgEAAAAAK8IBAAAAAEmEAwAAAABWhAMAAAAAkggHAAAAAKwIBwAAAAAkEQ4AAAAAWBEOAAAAAEgiHAAAAACwIhwAAAAAkEQ4AAAAAGBFOAAAAAAgiXAAAAAAwIpwAAAAAEAS4QAAAACAFeEAAAAAgCTCAQAAAAArwgEAAAAASYQDAAAAAFaEAwAAAACSCAcAAAAArAgHAAAAACQRDgAAAABYEQ4AAAAASCIcAAAAALByM7OxZs2aKW3atJIkLy8vTZkyxXbfunXrtGbNGrm5uem9995TrVq1zCwNAAAAeOmZFg4ePnwowzDk6+v72H2hoaHy9fXVxo0b9fDhQ7Vv315Vq1aVh4eHWeUBAAAALz3ThhUFBAQoIiJCXbt2VadOnXT8+HHbff7+/ipbtqw8PDyULl065cmTRwEBAWaVBgAAAEAm9hykTJlS3bp1U6tWrXThwgX16NFDO3bskJubm8LCwpQuXTrbY9OkSaOwsDCzSgMAAAAgE8NB/vz5lTdvXlksFuXPn18ZMmRQaGiocuTIobRp0yo8PNz22PDw8ERhAQAAAID9mTasaMOGDZo6daokKTg4WGFhYcqSJYskqXTp0jp69KgePnyoe/fuKTAwUEWKFDGrNAAAAAAyseegZcuWGj58uNq1ayeLxaLJkyfL19dXefLkUZ06deTj46P27dvLMAwNGDBAKVKkMKs0AAAAADIxHHh4eGjGjBmJbitXrpzt69atW6t169ZmlQMAAADgEWyCBgAAAEAS4QAAAACAFeEAAAAAgCTCAQAAAAArwgEAAAAASYQDAAAAAFaEAwAAAACSCAcAAAAArAgHAAAAACQRDgAAAABYEQ4AAAAASCIcAAAAALAiHAAAAACQRDgAAAAAYEU4AAAAACCJcAAAAADAinAAAAAAQBLhAAAAAIAV4QAAAACAJMIBAAAAACvCAQAAAABJhAMAAAAAVoQDAAAAAJIIBwAAAACsCAcAAAAAJBEOAAAAAFi5OboAADDD/p9LJP8gryX/EAAAODN6DgAAAABIIhwAAAAAsCIcAAAAAJBEOAAAAABgRTgAAAAAIIlwAAAAAMCKcAAAAABAEuEAAAAAgBXhAAAAAIAkwgEAAAAAK8IBAAAAAEmEAwAAAABWhAMAAAAAkggHAAAAAKwIBwAAAAAkEQ4AAAAAWBEOAAAAAEgiHAAAAACwIhwAAAAAkEQ4AAAAAGBFOAAAAAAgiXAAAAAAwIpwAAAAAEAS4QAAAACAFeEAAAAAgCTCAQAAAAArwgEAAAAASYQDAAAAAFaEAwAAAACSCAcAAAAArAgHAAAAACRJbo4uAC+mzr90S/Yxvnht6TOoBAAA4PmS3POo5JxD0XMAAAAAQBLhAAAAAIAV4QAAAACAJMIBAAAAACvCAQAAAABJhAMAAAAAVoQDAAAAAJIIBwAAAACsCAcAAAAAJBEOAAAAAFgRDgAAAABIIhwAAAAAsHIzq6GoqCiNGDFCV65cUWRkpN577z3VqVPHdv+KFSu0fv16eXp6SpLGjx+vAgUKmFUeAAAA8NIzLRxs2bJFGTJk0PTp03X79m01bdo0UTg4ceKEpk2bplKlSplVEgAAAIAETAsHDRo0UP369SVJhmHI1dU10f0nT57U4sWLFRoaqjfeeEM9e/Y0qzQAAAAAMjEcpEmTRpIUFham/v3764MPPkh0/1tvvaX27dsrbdq06tu3r/bs2aNatWqZVR4AAADw0jN1QvKff/6pTp06ydvbW2+//bbtdsMw1LlzZ3l6esrDw0M1a9bU77//bmZpAAAAwEvPtHBw/fp1de3aVYMHD1bLli0T3RcWFqbGjRsrPDxchmHo0KFDzD0AAAAATGbasKKFCxfq7t27mj9/vubPny9JatWqlSIiItSmTRsNGDBAnTp1koeHh6pUqaKaNWuaVRoAAAAAmRgORo0apVGjRj31/qZNm6pp06ZmlQMAAADgEWyCBgAAAEAS4QAAAACAFeEAAAAAgCTCAQAAAAArwgEAAAAASYQDAAAAAFaEAwAAAACSCAcAAAAArAgHAAAAACQRDgAAAABYEQ4AAAAASJLcHF3As9T5l27JPsYXry19BpUAcDY59xrJP0j/5B/C0Qp8OiPZxzjXf9AzqATO8DfLGWoA8Lj9P5dI3gFe+/dPpecAAAAAgCTCAQAAAAArwgEAAAAASYQDAAAAAFaEAwAAAACSCAcAAAAArAgHAAAAACQRDgAAAABYEQ4AAAAASCIcAAAAALAiHAAAAACQRDgAAAAAYEU4AAAAACCJcAAAAADAinAAAAAAQBLhAAAAAIAV4QAAAACAJMIBAAAAACvCAQAAAABJhAMAAAAAVoQDAAAAAJIIBwAAAACsCAcAAAAAJBEOAAAAAFgRDgAAAABIIhwAAAAAsCIcAAAAAJBEOAAAAABgRTgAAAAAIIlwAAAAAMDKzdEFPEtXK99N/kFik/f0ai0+SXYJP2788LmvIXBa8WTXoI3Je3rnX7olu4QvXluarOcX+HRGsms4139Qsp4fe61IsmtwyX46Wc93htch1VeHkl1DcjnD6+AMnOE96QyfD/t/LpHsGvRa8p7uDH83neHfRXJreBb/Lp2hBmfgDK9Dcj8fkvvZIEk59xrJO0D/f/9Ueg4AAAAASCIcAAAAALAiHAAAAACQRDgAAAAAYEU4AAAAACCJcAAAAADAinAAAAAAQBLhAAAAAIAV4QAAAACAJMIBAAAAACvCAQAAAABJhAMAAAAAVoQDAAAAAJIIBwAAAACsCAcAAAAAJBEOAAAAAFgRDgAAAABIIhwAAAAAsCIcAAAAAJBEOAAAAABgRTgAAAAAIIlwAAAAAMCKcAAAAABAEuEAAAAAgBXhAAAAAIAkwgEAAAAAK8IBAAAAAEmEAwAAAABWpoWD2NhYjRkzRm3atJGPj48uXryY6P5169apefPmat26tfbs2WNWWQAAAACs3MxqaNeuXYqMjNTatWt1/PhxTZ06VQsWLJAkhYaGytfXVxs3btTDhw/Vvn17Va1aVR4eHmaVBwAAALz0TAsHR48eVfXq1SVJZcqU0YkTJ2z3+fv7q2zZsvLw8JCHh4fy5MmjgIAAlS5d2vaYmJgYSdK1a9ee2ka0W2Sy67x8+XKynh8TeY8anKSGB6EPHF6D7jr+dTBCk12CLNHP/+vgDJ8PzvA6OEMNzvCe5PMhDv8unk0NyW7fSWpI7vvhRXkdkvv58CxqSO55VFI1xJ9Px59fJ2QxDMNIVut/08iRI1WvXj3VrFlTkvTGG29o165dcnNz09dff63Tp09r8ODBkqQhQ4aoadOmev31123PP3LkiDp06GBGqQAAAMALz8/PTxUqVEh0m2k9B2nTplV4eLjt+9jYWLm5uT3xvvDwcKVLly7R80uVKiU/Pz9lyZJFrq6u5hQNAAAAvGBiYmIUGhqqUqVKPXafaeGgXLly2rNnjxo1aqTjx4+rSJEitvtKly6t2bNn6+HDh4qMjFRgYGCi+yUpZcqUjyUbAAAAAP9c3rx5n3i7acOKYmNjNW7cOJ0+fVqGYWjy5Mnat2+f8uTJozp16mjdunVau3atDMNQz549Vb9+fTPKAgAAAGBlWjhwpNu3bysoKEheXl7y9PR0dDkAAACAUzJtWJGjbN++XXPmzFHBggV15swZ9e3bV97e3o4uy2EuXLigixcvqmjRosqWLZssFoujS3rp/Pnnn9q6dasePnxou61v374OrAgA4Oxu3rypDBkyyMWF/WujoqLk7u7u6DJMFxAQoJEjR+ratWvKkiWLJk2apJIlSz7zdl74cPDFF19o06ZNSpMmjcLCwtS5c2eHhIOwsDB99tlnCgwMVL58+dS7d29lyJDB1BpWrVql7777Tnfu3FHTpk0VFBSkMWPGmFrD7t275efnp+joaBmGodu3b+ubb74xtYawsDAtWbJEISEhqlWrlooWLfrUcXf28P7776tKlSrKkSOHaW0+SUBAgCIiIuTi4qKZM2eqV69eqlKliqk1hISE2N4LISEhKlu2rCntDhw48KnBeMaMGabUkNDt27f1448/Jnotevbs+dLUMG/evKfeZ3Zwjo6O1m+//ZbodWjcuPFLV4MzvCed4TNq7969OnPmjPLly6e6deua2na8n3/+WSNHjlTatGl17949TZw4UVWrVjWt/ZiYGK1Zs0Znz55Vvnz51K5dO9P3oVq9erVWrFhhez+6ubnp22+/NbWG4OBg3bt3T66urlqyZIl8fHxUvHhxU2uYNGmSJk2apGLFiunUqVMaP3681qxZ88zbeeHDgcViUZo0aSTFrYqUIkUKh9QxYsQIVahQQU2aNNEvv/yiYcOGaeHChabWsG3bNvn5+alz585655131KJFC1Pbl6TZs2drwoQJWrNmjSpVqqSffvrJ9BpGjBihGjVq6PDhw8qcObNGjhypVatWmdZ+mjRpNGDAANPae5px48Zp9OjRmjt3rgYMGKDp06eb+od3xIgROn78uCIiIhQREaE8efJo3bp1prTdtm1bU9r5u/r27asCBQro9OnTSpEihVKlSvVS1ZA5c2ZJcZtlenl5qVy5cvrtt9/0559/mlZDvL59+yoqKkohISGKiYlR1qxZTT8xd5YaHP2edPRn1IwZM3ThwgWVL19emzdv1pEjRzRs2DDT2o83Z84cffnll8qWLZuCg4PVt29fU8PB6NGjlS5dOlWtWlW//PKLRo0apY8//ti09iXpyy+/lK+vrxYsWKAGDRroiy++MLV9SRo0aJD69u2rL7/8UvXr19fkyZPl6+treh3FihWTJBUvXty26uez9sL3TeXOnVtTp07Vrl27NHXqVOXJk8chddy6dUudOnVS8eLF1blzZ929e9f0GgzDkMVisV0xdcQO1FmzZrVdHW7evLmCg4NNr+H27dtq2bKl3NzcVK5cOcXGxprafuHChbVt2zadO3dO58+f1/nz501tP56Hh4cKFy6sqKgolSlTxvSu6oCAAG3btk3VqlXT9u3bTQ3u4eHheu2112yvf8L/HMEwDE2YMEH58+fX8uXLdfv27ZeqhrZt26pt27a2hSuaNGmikSNHJlri2iy3bt3S0qVLVbp0aW3atCnR8L+XqQZneE86+jPq8OHDmjt3rt555x3NnTtXR48eNbX9eK6ursqWLZskKVu2bKZf5Lx48aKGDx+uunXrasSIEQoKCjK1fSnu3CFr1qwKDw9XpUqVdO9e8jfO+6csFosqVqyou3fv6q233nLI8C4XFxft2bNH9+7d0/fff2+387gXvudgypQpWrt2rX766ScVLFhQH374oUPqePjwoUJDQ5UlSxZdv37d9BNSSWrcuLE6dOigq1evqkePHg7pInV3d9fhw4cVHR2t/fv369atW6bXIEmBgYGS4nYINHvfjFOnTunUqVO27y0Wi1auXGlqDfHtDhkyRDVq1ND27dtNH7+ZMWNGWSwW3b9/3/SFAuJPdEJDn8FWvc+Aq6urHj58qIiICFkslifuWPky1BC/eESePHl07tw5h5wApEyZUpIUERGhlClTOmReljPU4AzvB0d/RkVHRys2NlYuLi62i2uOkDZtWvn6+qpixYo6fPiwXnnlFVPbj38fpEqVSg8ePHDIeyFdunTatWuXLBaL1qxZ45CwGh0drenTp6tChQr6+eefFRUVZXoNkydP1rRp0zRjxgwVLFhQEydOtEs7L/xqRRMmTEg0rn7IkCGmd4dJ0k8//WTrmgsLC9PEiRNNHzspxZ0UnzlzRvnz51fRokVNbz84OFjnzp1TlixZNGfOHDVo0EBvvfWWqTX88ccfGjNmjAIDA1WgQAGNHTvWLhN6/sq9e/d05coV5c6d2zbszWw3b97Ub7/9pho1aujQoUMqVqyYqfNgZs6cqVdeeUXXr1/XtWvXdOnSJW3YsMG09iXp6tWrj92WM2dOU2uQpJ07d+rixYvKmDGj5s6dq/Lly2vWrFmm13DhwgV5eno6rIYjR45o/PjxunHjhrJnz65x48apdOnSptbg5+enW7duycPDQ7t27VLq1Km1YsWKl64GZ3hPOvozatmyZdq5c6deffVV+fv7q0GDBnrnnXdMaz/evXv3NH/+fJ07d04FCxZUz549TQ0IW7Zs0bx581S4cGGdPXtW/fv3N/3vdlhYmC5duiRPT08tX75ctWrVUqVKlUyt4cKFCzpw4IBatWqlXbt26T//+Y9y585tag3xf7Piw6qbm5syZsz4zIPzCxsO/Pz8tGDBAt2+fTvRh0nBggUdMlbtt99+03/+8x/dvHnTYcupXrp0SR9//LHOnz+vIkWKaPDgwaZPig0KCpK/v78aN26sTz75RG3btpWXl5epNTjazp07tWDBAsXExKhBgwayWCzq3bu36XU4w4TDsLAwpUyZUvv27VPp0qVtY8/N0qZNG1ksFsXGxury5cvKmzevVq9ebWoNUtwf3yZNmkiKe03Spk1reg0JOUMNkuNXJPnjjz+UL18+h81Vc2QN8X+zpLj3w++//67XXnvN1Boc/RkVGRmpCxcu6Ny5cypQoMBjm7Pa25MuXsQz8yLGtWvXlDJlSl26dEleXl66deuWChQoYFr7UtzvYu3atbpw4YIKFy6sVq1amd7r7wwLBbz99tsKDg5WgQIFdP78eaVKlUrR0dEaPHjwM11s54UNB/EWLlyoXr16OboMDRgwQFeuXFGTJk3UpEkTpU+f3vQafHx81L17d5UrV06HDx+Wr6+vli9fbmoNbdu21bBhw1SmTBkdOXJEc+fONS2sVatW7an3/fjjj6bUIMW9BitXrlS3bt20cuVKtWjRQps2bTKt/XgdO3Z8bMKhGZPknWllmoTu3r2r0aNHa86cOaa33bFjR1MnxT9JvXr1Eg0XcHNzU44cOTR48GDTetbWrFmj5cuXO2RFkvnz56t3795PXMnK3d1db7zxhho0aGDXGpxhFa0jR47o7NmzWrFihbp06SIpbhNTPz8/bd261ZQa4jnqMyre22+/rcqVK6tVq1amBwMp7uKFFBeSwsPDVaRIEZ05c0aZM2fWV199Zff2T58+reDgYH3yyScaPHiwpLj3wowZM/T111/bvf2EBgwYoFy5cqlMmTI6evSobt68qWnTpplaQ69evR5bKMDsHr333ntPkyZNkqenp+7cuaNRo0Zp4sSJ6tGjh9avX//M2nnh5xy0bdtWW7dudejVUUmaNWuW7ty5o61bt+r999+Xp6enWrdubWq3mKurq2rWrClJql27tkN6UCSpTJkykqQKFSqYOvfCzADwV1xdXeXh4WGbHO6IVUCk/59wOHz4cE2aNEnt27c3pV1nWpkmoXTp0unSpUsOaTsyMlJNmzZV/vz5bZPczF5StXLlymrQoIEqVKig//3vf1q/fr1atGihjz76yLTeFD8/P4etSFK7dm1JT17JKioqStOnT7d7OHCGVbTSp0+v69evKzIy0jYnx2Kx2E4OzeSoz6h4X3/9tfbv36958+bp1q1batKkiRo1amTaUNC1a9dKkvr06aNp06Ypbdq0un//vgYOHGhK+3fv3tX27dt148YNbdu2TVLce8Hs34MkXb9+3TasrW7duurYsaPpNdy6dUtr167VyJEjNXr0aFt4NtONGzdso0/ih+XaY++LFz4cOMNybPGuX7+uq1ev6tatWypYsKB27typ9evX65NPPrFru/EnxalSpdKSJUtUsWJF+fv7mz6EQ4r7w7N27VqVKVNG/v7+DhlvP3z48MdumzJlimntly9fXgMHDlRwcLDGjBlj67o3m6MmHMafAH377bcaN26cJKlJkyYO+aCNH1ZkGIZu3rzpkHlAkvTuu+86pDcxofPnz+v111+XJFWqVEnz589XlSpV/rKn51l7dEUSM9uOXx6wRIkST9yTxoyx7vHDdp40nMasIT1FihRRkSJF1KpVK9sKOY7i6EnRLi4uqlGjhiRpw4YN8vX11caNG9W4cWNTT06vXbtmG+aXOnVq0xZSqFChgipUqKCTJ0+aPi8vXmRkpCTJy8tL/v7+Kl26tAICApQvXz7Ta3GGhQJKliypgQMHqkyZMjp+/LiKFy+u7du3K1OmTM+0nRc+HDj6ykO8Vq1aKWXKlGrdurXef/992/JT3bp1s3vb8Yk/Q4YMOnfunM6dOyfJMUuZTp06VQsWLNB3332nQoUKafLkyabX0KhRI0lx743ff/9dISEhprY/cOBA7du3TyVKlFDBggVVq1YtU9uP16FDB61YsUJVq1ZVzZo1Vb58eVPbd4aVaWbOnGn7OkWKFA4JzJK0dOlSh8x1SMjDw0OrV69W2bJl9b///U8eHh46ceKEqSdkzrAiyYgRI1SxYsXH9qQx8+TIGS5qHTx4UIsWLVJkZKRt8uPu3btNrcHRn1Eff/yxdu/erddee009evRQ6dKlFRsbq+bNm5saDqpVq6aOHTuqVKlS8vf3N22lwfgFXSZMmPDYibA9Nt56kvh5eYZh6NChQ3J3d1dUVJRD5gHVq1dP8+bNU7FixdS6dWulTp3a9BrGjh2r3bt3KzAwUN7e3qpZs6bOnTv3zM8jXvg5B506ddKSJUs0dOhQzZo1S61bt36m47L+rgsXLihfvnwO3/48LCws0ZrZzzpt/h03btxIVIMjVodJqGvXrlq2bJlp7V26dEl79uxJ9Br06NHDtPafxBETUJ1hZRpnmf8Qv/NrwmFFfzVHxh5u3bqlhQsXKjAwUEWKFFGPHj3k7+8vLy8vFSxY0JQawsLCFBQUpEyZMmn58uWqXbu26ZNgfXx8Em1s1L59e3355Zem1tChQwf5+fkluqhl1slYvLfeekvz589PtGiF2ReUHp0UbfZn1Lp16/TWW2891sN9+fJl0xfSOHHihC5evKiCBQvaerns7fr168qcObOuXLny2H25cuUypQZn5aiFAsLCwrRv3z5bj4okNW3a9Jm388L3HDj6ykO8a9euqVu3bg7b/lyShg4dqqNHjypdunS2K0FmTGpKaNy4cdq3b5+yZs1qq8HsP3oJ5x6Ehobq+vXrprbfu3dv1atXz2HDSHx8fJ7aHWrmfgsVKlTQN998Y1p7T3Lx4kWlSpVKlSpV0okTJ3Tq1Cm7jyt/kowZMyogIEABAQG228wOBxkzZtS7775rC60RERG2OUpmmTlzpm3p6WHDhmnIkCGmhwNn2JPG0cNppLgNRPPmzWt6uwktW7Ys0UIeZgsJCXls0Y6+ffuaHgyCg4O1YsUK3bx5Uw0aNNDDhw/16quv2r3d+J7UJ50nmL14xJo1a7R27dpEF9W2b99uag2nTp16rAYzhyRLcecPWbNmtYV2ew1teuHDQf369SXFXRVr2LChw5bnc/T255J07tw57dq1y9Q2H+Xv769du3Y5rOdE+v9hVlLclTCzhzblyJFD/fr1M7XNhMaPHy9J+uyzz1SnTh2VL19e/v7+2rNnj6l1bN68WYsXL070QWv2sIXr16/b/vi/9dZb6tq1q0MmhT76B8bsoW6SY4N7wqWnE65OZFaPRULvv/++2rZta9uTxhELWDjDRa2UKVOqe/fuKl68uO0ExKyJsPEcvZBH/Mlx/BBURwRFSbbJr/Pnz1eFChU0bNgwrVu3zrT2neF1WLlypRYvXmz6BnAJDRs2TB07dlT27NkdVoNhGHafpyq9BOFg7969mjBhgtKnT6/79+9rwoQJpm+cITl++3NJKl26tG29ZkfJmzevHj586NCJ4Y4+EatVq5Y++eQTFSpUyHabPboFnyb+93/9+nXb/Is333wz0VAKMyxZskQLFiwwfa+NhO7cuaOLFy8qb968CgwM1IMHDxxSx5w5c7R69WpFRUXpwYMHypcvX6IQawZHBvcOHTqoQ4cOTrH0dNWqVbV7927dvHlTGTNmVKtWrdSqVStTa4i/qCXJYRe1zO41ehpHLeQhPb56VPfu3e3e5pM8ePBAVapU0YIFC1SgQAHTzx+c4XUoWrSocuTIYfreBgllzpzZ9M+CRxUtWlS//vqrihcvbrvNHsP9XvhwMG/ePK1fv16enp4KDQ1Vnz59TE3c8Ry9/Xl8DS1btkw0icbs5T3//PNP1apVy9Zd7YhhRY4+Edu+fbsKFCigwMBASfbrFvw71q9fr9KlS+t///uf6ZtNOcOwheHDh6tv3766deuWvLy8bKsnme3777/Xvn37NHnyZHXp0sXWu2MmZwjue/fudXg4iBe/XKAjpuWtX79eK1asSBRWze5Ve/vttx/b8Mls8Qt5tGrVyvSFPKS4FbzihYaG/uWmZPaUIkUK7d+/X7GxsTp+/Ljpcz8Svg4hISEOeR0qV66sunXrKnfu3LaeTTOHwUpx8ywWL16cqDfN7OGfv/zyi77//nvb9/ZaKOCFDwdp0qSxfchnyZLFYX/4pk+frvnz52vWrFkqWLCgQ1bpOXTokH755Re5uTnu12722u1P4ugTMQ8PD4ec/D3qk08+0cKFC7Vjxw4VKlTIlCtxCTnDsIWKFSsmmvcQFRVlavvxsmTJIg8PD4WHhytv3rwOqcMZgvsrr7yiL774wqETsx/liPC+evVqLV68WFmyZDG97Xh9+/Z9bMMns3eDnT59+hOXrFy6dKnd2w4LC9PgwYOVOnVqGYahlClTaujQoXZv90kmTpyoadOm6datW1q2bJnpFzGGDh2q69evK1OmTMqQIYP69OmjiIgIU8+n1q5dq9mzZytdunSmtfmoqKgonT9/PlFYMvvzacuWLaa088KGg/glCmNiYtSzZ0/buGpHLN8pxa1N3KhRI9sEs9OnT6tixYqm1pAvXz7duHHDoWtXu7m5afr06baJVUWLFjV91YPMmTM79EQsZ86cWrRokUqUKOGQqw8JP9gSLsd3+/ZtZcyY0bQ6nGHYgiN35E0oe/bs2rBhg1KlSqUZM2bo7t27ptfgDMHdkROzn7Q7sWEYDtkYL2PGjA5fDcYZNnwKDAzUxIkTFRUVJcMwdPv2bVMWMVi1apWWLVsmV1dX9e/f37bXgaPs3LlT48aNM33EQVRUlKZMmaIbN24oS5Ysunr1qooXL64ff/xRhQsXNnXX6GzZsuk///mPQ4Y9RkdHy83NzaEX9eKXlY3fmyche1zEeWHDQf78+RP9X5Lq1KnjqHLUv39/3bt3T1myZLF1iZkdDo4dO6batWsnOgE0e1iRoydWSXETguNPxD755BPTT8Sio6N14cIFXbhwwXabmeEgfjWYR5ndTesMwxYcuSNvQhMmTNC1a9fUoEEDffXVVw45UXeGFUmmTJmi8+fPKygoSEWLFlXWrFlNa/tpE9HNnKAef1ErMjJS3bp1S3QBwexetUc3fHKE2bNna8KECVqzZo0qVaqkn376yZR2t27dqh07digsLExDhgxxeDiIiYlRly5dlD9/flMnZH/22WfKlCmTbdiKYRgaOXKkbty4YWowkOL+TXh7e6tw4cK2fxNmfU4OHTpUM2bMsO25IMn0vT969+4tKfHePPb0woaDZs2aSZLu3bunX375JdGKKI5w69Yt09fKfpQjrog+ytETqwICAuTp6akdO3aoXLlySpcuneknYq+88oqGDRtmapsJPW3iccJ1k83gDMMWHLkjb0J37tzRypUrdeHCBRUuXNghvXvOsCLJqlWr9N133+nOnTtq1qyZLl68+NQw+6yZvWTqkzzpopaj1KtXT5999pmKFSumNm3aOGRIbtasWVW2bFmtWbNGzZs3N23pbQ8PD3l4eMjT09NhQw0T6tq1q7p27Sp/f38tXbpUY8aM0c6dO+3e7qFDhxJtzmixWBQcHKxbt27Zve1HOWLVsHjx5wgJx/qbLf7z+caNG9q2bVuic1p7DDN7YcNBvK5du6pQoUK2cWoWi8W2QouZcubMqT///NOhK7McP35cmzZtsn3YhYSEmDJ2MyFHTqz673//qyVLlqhdu3Z69dVXdfXqVa1bt045cuRItHKQvZ09e1Z379512D4H8R4dUuPu7m7KH5x4zjBswRl25JXirky98cYbatq0qY4cOaKhQ4dq/vz5ptbgDCuSbNu2TX5+furcubM6d+6sFi1amF6DI8Vf1Dp+/Lj8/f3VqVMnDRo0SF27djWthuHDh9u+jo2NlYuLi7JmzeqQuWru7u46fPiwoqOjtX//foeclDrDPrEPHjzQzp07tXnzZhmGYdpS2E8awjNr1iyHLBpQpEgR/fjjj4l6ms0K9E8ayhPP7HlZQ4cOVY8ePex+/vDCh4N06dKZvklFQvHDRSIjI7Vjxw5lyJDBdp/ZQ3rGjRun7t27a+fOnSpSpIjpV4olx06sWrlypVatWpVotaZmzZrpvffeM207eiluHG2lSpXk6elp+8Ax+70gOX5IzaPDFhwx8fOjjz5SUFCQBg4cqOXLl2vUqFGm1yDFbbzVvn17SVKxYsVMDWnxnGFllviu+vj3gqPmiDnaxIkTNWvWLEnSBx98oGHDhsnPz8+Utk+cOKEHDx6oSZMmKlu2rENPjsePH69z587pvffe05w5c/Tee++Z0u7Zs2c1aNAgGYZh+zqeI4b8NWnSRPXr19e4ceNMXeEtZcqUCgoKUp48eWy33b592yG9SH379lWBAgV0+vRppUiRwtQazBrK83fkzZtXzZs3t3s7L3w4qFatmlavXp3oyrCZY/0dcdL3NBkzZlTjxo114MAB9evXL9FkVLNkz57d9kfPbG5ubomCgRS3vKvZ6yabvdnY0zh6SE3CYQutW7d+7HdjhkGDBqlVq1YqWrSoQ4Z6xZ+QZ8yYUdu3b1fFihXl7+9v+g6sUtxcFIvF4tCVWRo3bqwOHTro6tWr6tGjh6mh3Zm4u7vbTshy585t6iTMb775RqdPn9aWLVu0ePFiVaxYUU2aNDH1pDRhUI3fcMrMORezZ8+2fe2ITREftX37dl26dEkXLlxQihQplC1bNlMupgwYMEC9evVS69at5eXlpUuXLmnDhg2aPn263dt+lGEYmjBhgoYPH65JkybZLqaYIX5xgIsXL2rHjh2JRl9MmDDBtDqkuD1QBgwYkGiDSHvMDXvhw8GRI0cUGRmpw4cPS5JDJgJLzjGkx8XFRWfOnFFERITOnTunO3fumNq+lHji7e3bt5U7d27997//NaXtp32Ymj222hneC5Ljh9QULFhQlSpVksViUc2aNR2y58GQIUO0ceNGzZ07V9WqVVOrVq2euHSivcSfkEtx3dNmd1En9PnnnyswMFAlSpTQrl279Prrr5teQ8eOHVWlShWdOXNG+fPnV9GiRU2vwRnkzJlTM2fOVJkyZeTv72/qxGwpbgjHhx9+KEk6fPiwZsyYoWvXrpm2eMSYMWMUFhYmV1dX21KiknmLJjjD/JOE1qxZY5uL07RpUwUFBZkyF6dUqVJavny5Nm/erH379ilXrlxaunSpQ3YIdnV11cOHD20rPsbExJhew6BBg/Tmm2/q2LFjypo1q+7fv296DX5+fqpXrx7DipLr/v37WrFihaPLcIohPcOGDdOZM2fk4+OjDz/80CHjeRP2pFy5csXUq9WPdg9LcVcj4jcjM4szvBckxw+pmTt3ripXrixJDjsJLFiwoIYMGaKbN29q0qRJaty4sSpWrKj+/furbNmydm//ab97RwyxGjx4sGrWrKkSJUro/Pnz+u9//2v6EIpLly5p9uzZOn/+vIoUKaLBgwc7dJ6Wo0yZMkWrV6/W3r17VahQIdtKJWYKCwvTd999p61btyoiIkJNmjQxre369es71VKijpZwLs4777xj6t/ubNmyOXQycLwOHTpoxYoVqlq1qmrWrKny5cubXkPq1KnVs2dPXbhwQVOmTDG19yJehgwZ9O6779q9nRc+HBQuXFhbt25NtCScI1aCcIYhPfv27VOzZs3k6empTZs2md7+o3LlyqVz586Z1l7CruKEzO42dob3ghS3vO6yZcskySFDaiwWi/r06ZNowyuzl2vcu3evvvrqKwUGBsrb21sjRoxQdHS0evToYcpmM840ljU4ONh20tGjRw/5+PiYXsOIESPUvXt3lStXTocPH9aIESO0fPly0+twNFdXV7366qsqWbKkDMPQt99+a9pKXtu3b9f27dt19epV1atXT+PHjzd9mNvWrVu1c+dO3bt3zymWEnU05uLEBUYpbiGLhg0bKm3atKbXYLFYFBoaqvDwcN2/f98hPQcZM2bUmDFjEp3TtmnT5pm388KHg/gNdSwWi27duqULFy7ot99+M70OZxjSkzp1avXp00dZsmRRixYtVKNGDdOvUCbcaCgkJESZMmUyrW1n6Sp2hveCJKVPn167du1KdHJuZnB2hpVotmzZovbt2z/23jBrNRBHb3SVkMVi0fnz55U/f34FBQU5ZClTV1dX2+Z4tWvXdti+E47myGV+Bw4cqAIFCqhYsWI6ffp0ojliZvUkeXh4yN3d3WmWEnU05uLEXciZMGGC0qdPr/v372vChAmm7fcQr2/fvtq1a5dq166tWrVqmTIx+FHxw2+vX79u13Ze+HDg6+srf39/rVq1SoGBgWrZsqVD6nCGIT3t2rVTu3btdObMGS1cuFBjx45VixYt1KlTJ1N2Xly7dq1atGhhW56uaNGi6tatm93bdTbO8F6Q4tZLTnjy9TJugjZ16lSdOHFChw8fttXQuHFjvfnmm6bX4mjDhw/XgAEDdP36dWXNmtXU3UDjhxumSpVKS5YssU3Mjl/b+2XjyGV+zfwM+DucYSlRR4ufi3P69Gnlz59fxYoVc3RJpps3b57Wr18vT09PhYaGqk+fPqbNgTl58qRGjhyp9evX69atWxo7dqwyZMigChUqmNJ+Qn379tUPP/xgm5dlr6D4woaDyMhIbdu2TV9++aXc3d0VFham3bt3O2ynx8KFCys2NlYXLlzQ9OnTE800N8vdu3e1bds2ff3110qXLp1GjhypmJgY9ezZ0+4TIefOnaszZ85o2rRpSpUqlXLmzKmpU6dq4cKF6tOnj13bdia3b99W4cKFVbhwYV2/fl2ff/65PD09HVKLr6+v7t27pytXrih37txKkyaNqe07wyZo/fr1c3gNzuLVV1/V5s2bbd+becV227ZtkuLG0547d8423PBlHD4hOXZ3YmfoYXW2pUQdLeHcvMDAQO3atcv03csdLU2aNLa/lVmyZDF1KdOPP/5YU6dOlbu7u2bPnq3PP/9cefPmVffu3VWnTh3T6pDi3v8XL15UuXLltHnzZh09etQuK8u9sOGgdu3aaty4saZPn658+fKpe/fuDgsGkjR//nzt379f//nPf7R8+XI1aNBA77zzjqk1tGzZUk2aNNHMmTOVM2dO2+2nTp2ye9v79u3TunXrbEOKvLy8NGvWLLVt2/alCQe//PKLhg4dqs2bN+uVV15RQECARo8erenTpzvkCsTOnTu1YMECxcTE2LaFN3PiozNsguYMNTiLRzfFc3NzM21X9YkTJ8rNzc1hk/OdjTPsTuxIzraUqKM5w+7ljhI/Lyv+Qmb58uXl7+9v6oWD2NhYFStWTMHBwYqIiFDJkiUlPXmTOHs7fPiw7WJu586d1bp1a7u088KGg86dO+ubb77RlStX1LJlS4d3Te7du1erV6+Wi4uLoqOj1b59e9PDwc6dOxPNMQgJCVHWrFk1YMAAu7edOnXqx+Y3uLu7m3612pFmz54tX19f2xCuatWqadmyZRo5cqS+/PJL0+tZvny51q1bp27duql3795q0aKFqeHAGTZBc4YanIUjN8UbOnSoZsyYYQupUtyJUExMjPbu3WtaHY7mTLsTO5Iz9F44E2fYvdxR4ufBxf/fYrGYfrU+/t/f/v37VaVKFUlxPavh4eGm1iFJ0dHRts+G+Inq9vDCfuL06NFDPXr00C+//KL169frxIkTmj59ury9vVWkSBHT68mUKZMiIiKUJk0aRUVFOWQoyaeffqrVq1crKipKDx48UL58+Wzd+faWMmVKXbp0Sblz57bddunSpZfqZMzV1fWxVT8STgZ2RD0eHh62VTDMvjpZr149zZs3z6GboDlDDc7CkZvixQ8V+f777xPd7gyT1s3kTLsTw3kk3BQuJCTEIbuXO0qzZs0kxZ0Ur127VmfPnlW+fPnUrl0702qoUqWK2rZtq2vXrmnBggUKCgrShAkT1KhRI9NqiNeoUSO1a9dOr776qvz9/e1Wg8V4ST597t69q6+//lobN25MNK7W3tq0aSOLxaIbN24oIiJCRYsWVWBgoDJkyGBqHZLk7e2t9evXa/LkyerSpYvGjx9vW8rS3s6cOaOBAweqSpUqyp07t65evaoff/xR06ZNU4kSJUypwdE6duyolStXJgoDMTExat++vdauXWt6PTNnztTly5d18uRJVapUSalTp3bIkqaS9McffyhfvnxKkSKFQ9p3lhoc6YMPPlDjxo313XffqWzZsvLz89M333zj0JpatmypDRs2OLQGs8XvTuzv7++Q3YnhfFq3bq3r168rU6ZMypAhg5o2baratWu/VMPNRowYofTp06tChQr65ZdfdPv2bX388cemtR8YGKi0adMqW7ZsCgoK0h9//OGwhStOnz6tc+fOqUCBAna72P3ShANHuXLlylPvM3sZw+7du+vzzz/X4MGDNX36dPn4+MjX19e09u/du6fdu3crJCREOXPm1BtvvOGQtYodZd26dfrpp5/Uq1cveXl56dq1a5o/f75Kly5t+hCzePv27dPp06dVsGBB1apVy9S2Ew6hiDdlypSXrgZnERYWpqCgIGXKlEnLly9X7dq1HT6842UMBwkdPnxYvr6+pu5ODOcRFRWlKVOmaO/evcqcObOuXr2qWrVqKSoqSl26dHHIKAhH6dChg/z8/Gzft23b1qE7yjvCDz/8oO3bt+vWrVvKnj27GjVqZBvm9Ky9sMOKnEV8ALh48aJ27NhhWwEkJCREEyZMMLWW7Nmza8OGDUqVKpU++eQT3b1719T206VLp6ZNm5rapjNp3bq10qZNq8mTJyskJES5cuVSixYtHNI1KcUN67pw4YJtRZCzZ8+qR48eprUf/3PHT7JzxFKmzlCDs5g5c6bGjBkjKW653SFDhpgWDhLufxLPMAxdunTJlPadjSN3J4bz+Oyzz5QpUybt3r1bUty/iZEjR+rGjRsvVTCQpIcPHyoiIkKpUqXSgwcPFBMT4+iSTOXn56d9+/apU6dOypQpk65evapFixYpKCiITdCeZ4MGDdKbb76pY8eOKWvWrKbvrLd27VqNGTNGoaGhypcvnwICApxqd9aXRaNGjRwWBh7Vu3dv1atXT+nTp3dI+9WrV7d9XaNGDXXt2vWlrMHR/Pz8tGDBAt2+fTvR6kRmLrf8tBVpXraVapxhd2I4j0OHDmn16tW27y0Wi4KDg3Xr1i0HVuUYnTp1kre3twoXLqyzZ8+atlGls/jmm2/k5+cnV1dXSVKxYsVUrVo1de3alXDwPEudOrV69uypCxcuaMqUKWrfvr1pbcfvMdCkSRPlypVLhmFoxYoVunPnzkuzjCgelyNHDod+wMZvfCXF9aTZe8dHZ63B0Tp06KAOHTpo4cKF6tWrl0NqcPTwJWfhDLsTw3k8abGKWbNmOezfqSM1adJENWrU0KVLl+Tl5aWMGTM6uiRTubu724JBPA8Pj8due1YIByaxWCwKDQ1VeHi47t+/b2rPAXsM4Elq1aqlTz75RIUKFbLdZuawr23btik4OFjZsmWTxWJxyFh/Z6jBWbRt21Zbt25NtGN1z549HV3WS8XZdieGY6VMmVJBQUHKkyeP7bbbt2+/VBOR58+fr969ez9x6OHLFJiftrKjvaYNEw5M0rdvX+3atUve3t6qW7euvL29TWubPQacQ8Ll6B4Vv4azmbZv364CBQooMDBQ0tM/fJ61s2fPasKECVq5cqUaNGig8PBwXbt2TW+88YZtc5mXoQZn07dvXxUoUECnT59WihQpXqoTEGdBDwoSGjBggHr16qXWrVvLy8tLly5d0oYNGzR9+nRHl2aaatWq6dtvv1Xr1q1tPSmhoaGaNGmSgysz18mTJx8bZmkYhu3v97NGOLCzgIAAzZ49W5kyZdJbb71l23CsaNGiptXAHgPOIX6y56MsFotDrhh6eHho/Pjxprf7ySefaPDgwZKkLFmyyNfXVxcvXtSoUaNUr169l6YGZ2MYhiZMmKDhw4dr0qRJpg59BPC4UqVKafny5dq8ebP27dunXLlyaenSpcqePbujSzPNsmXL5OrqquvXr+vNN9+Ul5eXJk+erM6dOzu6NFNt2bLF1PYIB3Y2btw49evXzza+/6uvvpKnp6e6d+9u2hCODz/8UL17937iHgMwz9OWjX348KHJlcTJmTOnFi1apBIlStiCYrVq1ezebkREhP7zn/9IilvBSpLy5s2r6Ohou7ftTDU4G1dXV9uKIBaL5aVbDQRwRtmyZXuph/cFBQVp06ZNioyMVIsWLeTu7q6VK1eaumCCMzB76XvHbM36EnF3d1fVqlXVqFEjFS1aVPny5VP69OlN3Ym1cOHC+vLLL1WiRAlFRESoZMmSWr169Uuz+Ziz+OCDD2xfJ9x8zszlQxOKjo7WhQsXtH37dm3bts203bIThqH58+fbvo7fov5lqcHZdOjQQV988YWqVq2qmjVrKkeOHI4uCcBLLn4vJA8PD8XGxmrZsmUvXTBwhJf3L6FJEg7d8fDwsH0dGxtrah0v+x4DzuDGjRu2r3/44QfbspmO2odwypQpiomJkWEYOn78uEqXLm1Ku1mzZpW/v3+i9vz9/ZUlSxZT2neWGpxN/fr1bV83aNDAYRvzAcCTxO8QDfsjHNjZ2bNnNWjQINtGU/Ff22sSCZ4PCQOBo+Z+TJo0SQULFtTVq1d18uRJZcmSRVOnTrV7u4MHD1bv3r1VuXJl5c2bV5cuXdLBgwe1cOFCu7ftTDU4s/ihVgDgSE86h4r3Mq1WFC8gIEAjR47UtWvXlCVLFk2aNMkui2gQDuxs9uzZtq8TzjR/2Tb3QeIQ4AyTwX/77TeNHDlSPj4+8vX1NW2CV+7cubV+/Xp9//33unz5skqVKqX333/f1KF2zlCDs3OG9yiAl9vTzqFeVpMmTdKkSZNUrFgxnTp1SuPHj9eaNWueeTuEAztjaTrEc7ZepNjYWJ04cUJeXl6KjIxUeHi4aW2nTJnS4TtFO0MNzuBJ64cbhqFLly45qCIAiMM51OOKFSsmSSpevLjd5skRDgCTOFsvkre3t8aPH6/Jkydr+vTpatasmUPqgGM97f3HVToAcC4uLi7as2ePKlSooMOHDyeay/osWQxHzYYE4BAffPCBLagsXbpU3bp1kyR16tSJHVoBAHBSV65c0bRp03Tu3DkVLFhQQ4cOVc6cOZ95OyxlCrxkEq6atHfvXtvXXCcAAMB5/fTTT/r000+1detWzZkzR7t27bJLOwwrAl5izrBqEgAAeLqtW7fq+++/16FDh/Tzzz9Lips3ePr0aXXq1OmZt0c4AF4yzrZqEgAAeLrq1asrS5Ysun37ttq0aSMpbv5B7ty57dIecw6Al8zrr7+uKlWqyDAM/fzzz7avDx06pAMHDji6PAAA8BQ3btzQw4cPbd/bY84B4QB4yfzyyy9PvY9l4wAAcE7jx4/X3r17lTVrVhmGIYvFYpd9DggHAAAAgJNr3ry5NmzYIBcX+64nxGpFAAAAgJPLmzdvoiFF9sKEZAAAAMDJ/fnnn6pVq5by5s0rSQwrAgAAAF5WV65ceey2XLlyPfN26DkAAAAAnFx0dLR27NihqKgoSVJISIgmTJjwzNthzgEAAADg5AYNGiRJOnbsmC5fvqzbt2/bpR3CAQAAAODkUqdOrZ49eypbtmyaOnWqrl+/bpd2CAcAAACAk7NYLAoNDVV4eLju37+v+/fv26UdwgEAAADg5Pr27avvvvtO3t7eqlu3rqpUqWKXdlitCAAAAHhO3L17Vy4uLkqbNq1djk/PAQAAAOCkTp48qaZNmyoqKkrffvut6tevrxYtWuj777+3S3uEAwAAAMBJffzxx5o6darc3d01e/Zsff7559q4caMWL15sl/bY5wAAAABwUrGxsSpWrJiCg4MVERGhkiVLSpJcXOxzjZ+eAwAAAMBJubnFXcvfv3+/bRJyVFSUwsPD7dOeXY4KAAAAINmqVKmitm3b6tq1a1qwYIGCgoI0YcIENWrUyC7tsVoRAAAA4MQCAwOVNm1aZcuWTUFBQfrjjz/05ptv2qUtwgEAAAAAScw5AAAAAGBFOAAAAAAgiXAAAHgG9u3bp7Vr10qS1q5dq6ioKAdXBAD4N5hzAAB4pmrXrq3//ve/SpEihaNLAQD8QyxlCgD4xx48eKDhw4fr6tWrioqKUv369XXv3j3lzZtXoaGhGjBggAoVKqRs2bKpQ4cOunPnjrp06aJNmzY5unQAwF9gWBEA4B9bs2aNcuXKpbVr12rmzJm2XoJWrVopS5YsmjVrllq1aqXNmzdLkrZu3aq3337bgRUDAP4OwgEA4B87d+6cypQpI0nKly+f0qdP/9hjcufOrTRp0ujs2bP65ptv5O3tbXKVAIB/inAAAPjHChYsqN9++02SdOnSJc2cOdN2n8ViUWxsrCSpdevWmj9/vrJlyyZPT0+H1AoA+PsIBwCAf6xt27a6fPmyOnbsqCFDhqhLly62+ypUqKB3331XhmGobt26+umnn9SyZUsHVgsA+LtYrQgAYDcRERHq2LGj1q9fLxcXrkcBgLPjkxoAYBfHjh1T69at1aNHD4IBADwn6DkAAAAAIImeAwAAAABWhAMAAAAAkggHAAAAAKwIBwAAAAAkEQ4AAAAAWBEOAAAAAEiS/g80dkWi3kVnTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 936x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.style.use('dark_background')\n",
    "plt.style.use('seaborn-white')\n",
    "city_cluster = stores.groupby(['city','type']).store_nbr.size()\n",
    "city_cluster.unstack().plot(kind='bar',stacked=True, colormap= 'viridis', figsize=(13,11),  grid=False)\n",
    "plt.title('Stacked Barplot of Store types opened for each city')\n",
    "plt.ylabel('Count of stores for a particular city')\n",
    "plt.show()\n",
    "plt.savefig('plot5.png', dpi=300);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d22678-b95d-438d-aef0-5603e0659194",
   "metadata": {},
   "source": [
    "Takeaways from the plot:\n",
    "\n",
    "As observed from the stacked barplots, there are two cities that standout in terms of the variety of store types on offer - Guayaquil and Quito. These should come as no surprise as Quito is the capital city of Ecuador while Guayaquil is the largest and most populous city in Ecuador. Therefore one would think it logical to expect Corporacion Favorita to target these major cities with the most diverse store types probably to capture different ends of the market (if we think store types as being high-end/premium/wholesale/discount etc) as well as opening up the highest number of stores evinced from the largest counts of store_nbrs attributed to those two cities.\n",
    "\n",
    "Holiday Events data\n",
    "Trudging on, we can inspect the \"holiday_events.csv\" file which contains data on the national, regional and local level of Ecuador. According to the data dictionary, we should pay special attention to the \"transferred\" column.\n",
    "\n",
    "A holiday that is transferred officially falls on that calendar day, but was moved to another date by the government. A transferred day is more like a normal day than a holiday. To find the day that it was actually celebrated, look for the corresponding row where type is Transfer. For example, the holiday Independencia de Guayaquil was transferred from 2012-10-09 to 2012-10-12, which means it was celebrated on 2012-10-12. Days that are type Bridge are extra days that are added to a holiday (e.g., to extend the break across a long weekend). These are frequently made up by the type Work Day which is a day not normally scheduled for work (e.g., Saturday) that is meant to payback the Bridge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "21c661e4-b2b1-4084-9d92-4c9ac8e23d6c",
   "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>date</th>\n",
       "      <th>type</th>\n",
       "      <th>locale</th>\n",
       "      <th>locale_name</th>\n",
       "      <th>description</th>\n",
       "      <th>transferred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-03-02</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Local</td>\n",
       "      <td>Manta</td>\n",
       "      <td>Fundacion de Manta</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Regional</td>\n",
       "      <td>Cotopaxi</td>\n",
       "      <td>Provincializacion de Cotopaxi</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-04-12</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Local</td>\n",
       "      <td>Cuenca</td>\n",
       "      <td>Fundacion de Cuenca</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date     type    locale locale_name                    description  \\\n",
       "0  2012-03-02  Holiday     Local       Manta             Fundacion de Manta   \n",
       "1  2012-04-01  Holiday  Regional    Cotopaxi  Provincializacion de Cotopaxi   \n",
       "2  2012-04-12  Holiday     Local      Cuenca            Fundacion de Cuenca   \n",
       "\n",
       "   transferred  \n",
       "0        False  \n",
       "1        False  \n",
       "2        False  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holiday_events.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "639908b0-4575-4a2f-81ba-62e17aac7b1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn-white')\n",
    "# plt.style.use('dark_background')\n",
    "holiday_local_type = holiday_events.groupby(['locale_name', 'type']).size()\n",
    "holiday_local_type.unstack().plot(kind='bar',stacked=True, colormap= 'magma_r', figsize=(12,10),  grid=False)\n",
    "plt.title('Stacked Barplot of locale name against event type')\n",
    "plt.ylabel('Count of entries')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4f8c01dc-b3ab-4a10-8612-d8cadd87ac00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Holiday', 'Transfer', 'Additional', 'Bridge', 'Work Day', 'Event'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holiday_events.type.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff224103-0a2e-4832-b231-0b4b98b1019c",
   "metadata": {},
   "source": [
    "Transactions data\n",
    "\n",
    "PERIODICITY IN TRANSACTION PATTERN\n",
    "\n",
    "Inspecting the transactions file, we must be aware that the transactional data is only included for the training timeframe according to the competition's data dictionary. Furthermore the columns in this file relate only to the count of sales transactions for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "af00b516-d035-4ca4-8968-9aeb1717c74c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        date  store_nbr  transactions\n",
      "0 2013-01-01         25           770\n",
      "1 2013-01-02          1          2111\n",
      "2 2013-01-02          2          2358\n",
      "============================================================\n",
      "There are 83488 rows and 3 columns in the transactions data\n"
     ]
    }
   ],
   "source": [
    "print(transactions.head(3))\n",
    "print(\"=\"*60)\n",
    "print(\"There are {0} rows and {1} columns in the transactions data\".\n",
    "      format(transactions.shape[0], transactions.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2a888b4e-29d7-4644-950d-c7f3129db23f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn-white')\n",
    "plt.figure(figsize=(13,11))\n",
    "plt.plot(transactions.date.values, transactions.transactions.values, color='darkblue')\n",
    "plt.axvline(x='2015-12-23',color='red',alpha=0.3)\n",
    "plt.axvline(x='2016-12-23',color='red',alpha=0.3)\n",
    "plt.axvline(x='2014-12-23',color='red',alpha=0.3)\n",
    "plt.axvline(x='2013-12-23',color='red',alpha=0.3)\n",
    "plt.axvline(x='2013-05-12',color='green',alpha=0.2, linestyle= '--')\n",
    "plt.axvline(x='2015-05-10',color='green',alpha=0.2, linestyle= '--')\n",
    "plt.axvline(x='2016-05-08',color='green',alpha=0.2, linestyle= '--')\n",
    "plt.axvline(x='2014-05-11',color='green',alpha=0.2, linestyle= '--')\n",
    "plt.axvline(x='2017-05-14',color='green',alpha=0.2, linestyle= '--')\n",
    "plt.ylim(-50, 10000)\n",
    "plt.title(\"Distribution of transactions per day from 2013 till 2017\")\n",
    "plt.ylabel('transactions per day', fontsize= 16)\n",
    "plt.xlabel('Date', fontsize= 16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7b484506-c834-4af8-836f-603b10503f14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2013-01-01', '2013-01-02', '2013-01-02', ..., '2017-08-15',\n",
       "       '2017-08-15', '2017-08-15'], dtype=object)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.date.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "94b4ce70-1216-4797-9a2b-251e2374c4a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 770, 2111, 2358, ..., 2255,  932,  802], dtype=int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.transactions.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc25f5d8-8ac7-4045-8d5a-05fd2dd96c6f",
   "metadata": {},
   "source": [
    "Takeaway from the plots\n",
    "\n",
    "Interestingly when plotting transactions on a year-to-year basis, we can already pick out two different periodic spikes in transactions from the data (one in the solid Red line and the other the dotted Green line). The bigger yearly periodic spike in transactions seem to occur at the end of the year in December, specifically on the 23 December every year-end. Perhaps this is due to some sort of Christmas sale/discount that Corporacion Favorita holds every December thereby explaining the bump in transactions on this date. The weaker periodic spike given by the green dotted lines seems to occur around the middle of the months of May , specifically on the sunday in the second week of May. Perhaps another similar scheme is being implemented by Corporacion Favorita on those sundays.\n",
    "\n",
    "Items data\n",
    "\n",
    "Moving onto the \"items.csv\" file, we can see that there are not many columns in the data. There is a unique item number identifier \"item_nbr\" which I presume relates to mappings to grocery items such as \"Housebrand tomatoes pack of 6\" or maybe \"4 blueberry muffins\" etc. The family column should be pretty self-explanatory in the sense that this relates to parent category that that particular item relates to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "37892b1d-43c7-47d2-80a8-5deb0a8ffc8e",
   "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>item_nbr</th>\n",
       "      <th>family</th>\n",
       "      <th>class</th>\n",
       "      <th>perishable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>96995</td>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>1093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99197</td>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>1067</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103501</td>\n",
       "      <td>CLEANING</td>\n",
       "      <td>3008</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103520</td>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>1028</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103665</td>\n",
       "      <td>BREAD/BAKERY</td>\n",
       "      <td>2712</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_nbr        family  class  perishable\n",
       "0     96995     GROCERY I   1093           0\n",
       "1     99197     GROCERY I   1067           0\n",
       "2    103501      CLEANING   3008           0\n",
       "3    103520     GROCERY I   1028           0\n",
       "4    103665  BREAD/BAKERY   2712           1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "65be0dde-69d3-4c11-ba19-94c95fb7afda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "marker": {
          "color": [
           1334,
           613,
           446,
           306,
           242,
           153,
           134,
           108,
           91,
           84,
           77,
           73,
           55,
           54,
           45,
           41,
           31,
           26,
           26,
           21,
           20,
           20,
           19,
           17,
           15,
           14,
           14,
           8,
           6,
           4,
           1,
           1,
           1
          ],
          "colorscale": [
           [
            0,
            "rgb(12,51,131)"
           ],
           [
            0.25,
            "rgb(10,136,186)"
           ],
           [
            0.5,
            "rgb(242,211,56)"
           ],
           [
            0.75,
            "rgb(242,143,56)"
           ],
           [
            1,
            "rgb(217,30,30)"
           ]
          ],
          "reversescale": false
         },
         "orientation": "v",
         "type": "bar",
         "x": [
          "GROCERY I",
          "BEVERAGES",
          "CLEANING",
          "PRODUCE",
          "DAIRY",
          "PERSONAL CARE",
          "BREAD/BAKERY",
          "HOME CARE",
          "DELI",
          "MEATS",
          "HOME AND KITCHEN I",
          "LIQUOR,WINE,BEER",
          "FROZEN FOODS",
          "POULTRY",
          "HOME AND KITCHEN II",
          "EGGS",
          "CELEBRATION",
          "LAWN AND GARDEN",
          "PREPARED FOODS",
          "LADIESWEAR",
          "AUTOMOTIVE",
          "LINGERIE",
          "BEAUTY",
          "PLAYERS AND ELECTRONICS",
          "SCHOOL AND OFFICE SUPPLIES",
          "PET SUPPLIES",
          "GROCERY II",
          "SEAFOOD",
          "MAGAZINES",
          "HARDWARE",
          "BABY CARE",
          "BOOKS",
          "HOME APPLIANCES"
         ],
         "y": [
          1334,
          613,
          446,
          306,
          242,
          153,
          134,
          108,
          91,
          84,
          77,
          73,
          55,
          54,
          45,
          41,
          31,
          26,
          26,
          21,
          20,
          20,
          19,
          17,
          15,
          14,
          14,
          8,
          6,
          4,
          1,
          1,
          1
         ]
        }
       ],
       "layout": {
        "height": 800,
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Counts of items per family category"
        },
        "width": 800,
        "xaxis": {
         "autorange": true,
         "range": [
          -0.5,
          32.5
         ],
         "type": "category"
        },
        "yaxis": {
         "autorange": true,
         "range": [
          0,
          1404.2105263157896
         ],
         "showgrid": false,
         "showline": false,
         "showticklabels": true,
         "type": "linear"
        }
       }
      },
      "image/png": "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",
      "text/html": [
       "<div>                            <div id=\"afc8e822-203e-49e1-907b-89f7e4b11cce\" class=\"plotly-graph-div\" style=\"height:800px; width:800px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"afc8e822-203e-49e1-907b-89f7e4b11cce\")) {                    Plotly.newPlot(                        \"afc8e822-203e-49e1-907b-89f7e4b11cce\",                        [{\"marker\": {\"color\": [1334, 613, 446, 306, 242, 153, 134, 108, 91, 84, 77, 73, 55, 54, 45, 41, 31, 26, 26, 21, 20, 20, 19, 17, 15, 14, 14, 8, 6, 4, 1, 1, 1], \"colorscale\": [[0.0, \"rgb(12,51,131)\"], [0.25, \"rgb(10,136,186)\"], [0.5, \"rgb(242,211,56)\"], [0.75, \"rgb(242,143,56)\"], [1.0, \"rgb(217,30,30)\"]], \"reversescale\": false}, \"orientation\": \"v\", \"type\": \"bar\", \"x\": [\"GROCERY I\", \"BEVERAGES\", \"CLEANING\", \"PRODUCE\", \"DAIRY\", \"PERSONAL CARE\", \"BREAD/BAKERY\", \"HOME CARE\", \"DELI\", \"MEATS\", \"HOME AND KITCHEN I\", \"LIQUOR,WINE,BEER\", \"FROZEN FOODS\", \"POULTRY\", \"HOME AND KITCHEN II\", \"EGGS\", \"CELEBRATION\", \"LAWN AND GARDEN\", \"PREPARED FOODS\", \"LADIESWEAR\", \"AUTOMOTIVE\", \"LINGERIE\", \"BEAUTY\", \"PLAYERS AND ELECTRONICS\", \"SCHOOL AND OFFICE SUPPLIES\", \"PET SUPPLIES\", \"GROCERY II\", \"SEAFOOD\", \"MAGAZINES\", \"HARDWARE\", \"BABY CARE\", \"BOOKS\", \"HOME APPLIANCES\"], \"y\": [1334, 613, 446, 306, 242, 153, 134, 108, 91, 84, 77, 73, 55, 54, 45, 41, 31, 26, 26, 21, 20, 20, 19, 17, 15, 14, 14, 8, 6, 4, 1, 1, 1]}],                        {\"height\": 800, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"autotypenumbers\": \"strict\", \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Counts of items per family category\"}, \"width\": 800, \"yaxis\": {\"showgrid\": false, \"showline\": false, \"showticklabels\": true}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('afc8e822-203e-49e1-907b-89f7e4b11cce');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = (list(x) for x in zip(*sorted(zip(items.family.value_counts().index, \n",
    "                                         items.family.value_counts().values), \n",
    "                                        reverse = False)))\n",
    "trace2 = go.Bar(\n",
    "    y=items.family.value_counts().values,\n",
    "    x=items.family.value_counts().index,\n",
    "    marker=dict(\n",
    "        color=items.family.value_counts().values,\n",
    "        colorscale = 'Portland',\n",
    "        reversescale = False\n",
    "    ),\n",
    "    orientation='v',\n",
    ")\n",
    "\n",
    "layout = dict(\n",
    "    title='Counts of items per family category',\n",
    "     width = 800, height = 800,\n",
    "    yaxis=dict(\n",
    "        showgrid=False,\n",
    "        showline=False,\n",
    "        showticklabels=True,\n",
    "#         domain=[0, 0.85],\n",
    "    ))\n",
    "\n",
    "fig1 = go.Figure(data=[trace2])\n",
    "fig1['layout'].update(layout)\n",
    "py.iplot(fig1, filename='plots')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76e23be3-f39d-4ab2-a3bf-761e0987a7dc",
   "metadata": {},
   "source": [
    "Takeaways from the plot\n",
    "\n",
    "As we can see from this barplot, the y-axis shows the counts of items while the x-axis displays the different family categories that the various retail items fall under, sorted from largest number of item counts to smallest number of items. As we can see from the plot, the top 3 family categories are the GROCERY I, BEVERAGES and CLEANING categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a716a01c-dbf4-4bd8-8ae6-a6b6124f6890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "marker": {
          "color": [
           133,
           110,
           100,
           98,
           81,
           70,
           65,
           62,
           61,
           60,
           59,
           54,
           53,
           51,
           51,
           48,
           42,
           41,
           41,
           40,
           40,
           39,
           39,
           37,
           36,
           36,
           36,
           35,
           35,
           34,
           33,
           32,
           31,
           31,
           31,
           30,
           30,
           30,
           29,
           29,
           29,
           28,
           28,
           27,
           27,
           27,
           27,
           26,
           26,
           26,
           25,
           24,
           24,
           23,
           23,
           23,
           22,
           22,
           22,
           22,
           21,
           21,
           21,
           20,
           20,
           20,
           20,
           19,
           19,
           19,
           19,
           19,
           19,
           19,
           18,
           18,
           18,
           18,
           17,
           17,
           17,
           17,
           17,
           17,
           17,
           17,
           17,
           16,
           16,
           16,
           16,
           16,
           15,
           15,
           15,
           14,
           14,
           14,
           14,
           13,
           13,
           12,
           12,
           12,
           12,
           11,
           11,
           11,
           11,
           11,
           10,
           10,
           10,
           10,
           10,
           10,
           10,
           10,
           9,
           9,
           9,
           9,
           9,
           9,
           9,
           9,
           9,
           9,
           9,
           9,
           8,
           8,
           8,
           8,
           8,
           8,
           8,
           8,
           8,
           7,
           7,
           7,
           7,
           7,
           7,
           7,
           7,
           7,
           7,
           6,
           6,
           6,
           6,
           6,
           6,
           6,
           6,
           5,
           5,
           5,
           5,
           5,
           5,
           5,
           5,
           5,
           5,
           5,
           5,
           5,
           5,
           5,
           5,
           5,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           4,
           3,
           3,
           3,
           3,
           3,
           3,
           3,
           3,
           3,
           3,
           3,
           3,
           3,
           3,
           3,
           3,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           2,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1,
           1
          ],
          "colorscale": [
           [
            0,
            "rgb(0,0,131)"
           ],
           [
            0.2,
            "rgb(0,60,170)"
           ],
           [
            0.4,
            "rgb(5,255,255)"
           ],
           [
            0.6,
            "rgb(255,255,0)"
           ],
           [
            0.8,
            "rgb(250,0,0)"
           ],
           [
            1,
            "rgb(128,0,0)"
           ]
          ],
          "reversescale": true
         },
         "orientation": "v",
         "type": "bar",
         "x": [
          1016,
          1040,
          1124,
          1034,
          1122,
          1072,
          1032,
          1148,
          1120,
          3020,
          2302,
          1026,
          3106,
          1042,
          1030,
          1028,
          1114,
          3034,
          2006,
          1190,
          4126,
          1074,
          1004,
          1142,
          2502,
          1126,
          2420,
          1144,
          3046,
          2008,
          2028,
          1318,
          1002,
          3110,
          2018,
          2116,
          1062,
          1044,
          3026,
          3032,
          4138,
          3040,
          4114,
          1136,
          3004,
          1036,
          2016,
          3008,
          1118,
          1014,
          2124,
          2708,
          1045,
          1076,
          3038,
          2644,
          1010,
          1068,
          5324,
          2718,
          1132,
          2170,
          7780,
          3044,
          1084,
          1048,
          2174,
          6269,
          2032,
          3016,
          2636,
          1060,
          4118,
          1092,
          1116,
          2172,
          2166,
          1096,
          2026,
          3024,
          3014,
          1088,
          1086,
          2712,
          2416,
          3018,
          5446,
          3108,
          2632,
          1058,
          2004,
          2162,
          6242,
          1094,
          2012,
          2702,
          2020,
          1236,
          2014,
          4176,
          2306,
          1008,
          2756,
          1006,
          1078,
          2228,
          1052,
          3022,
          2226,
          2112,
          2130,
          3028,
          7034,
          2630,
          3030,
          1012,
          2128,
          1080,
          2752,
          2142,
          2714,
          2102,
          2002,
          1056,
          2030,
          1138,
          2716,
          1022,
          6516,
          2304,
          6257,
          6330,
          1330,
          6920,
          2010,
          1050,
          2022,
          2108,
          1039,
          6350,
          2218,
          3090,
          1082,
          1063,
          1146,
          2034,
          2986,
          6392,
          2164,
          2210,
          7002,
          2104,
          4122,
          6810,
          2152,
          1035,
          2220,
          6258,
          6848,
          6806,
          6482,
          1066,
          2114,
          6517,
          5322,
          4254,
          4252,
          2966,
          4130,
          6922,
          1093,
          6344,
          2178,
          4104,
          1038,
          2222,
          6448,
          1024,
          2652,
          6212,
          6352,
          1070,
          1386,
          2722,
          2662,
          2786,
          2246,
          1310,
          4162,
          2650,
          1152,
          6706,
          6918,
          6246,
          3104,
          7016,
          6936,
          6329,
          2168,
          3060,
          3015,
          1003,
          3010,
          5308,
          6328,
          1077,
          6414,
          2962,
          2750,
          1087,
          1338,
          3012,
          2208,
          1033,
          3102,
          2704,
          6222,
          6206,
          2506,
          6232,
          6808,
          6266,
          4222,
          6262,
          4214,
          1387,
          1115,
          6338,
          2642,
          2690,
          2980,
          2960,
          2956,
          2654,
          2904,
          6954,
          2372,
          2720,
          2646,
          2504,
          2664,
          3006,
          1027,
          2074,
          1302,
          1054,
          1364,
          1320,
          1312,
          6353,
          1067,
          2850,
          5224,
          5222,
          2854,
          6253,
          1154,
          6960,
          1013,
          1064,
          2156,
          1334,
          1025,
          2802,
          1314,
          6824,
          1073,
          1326,
          1075,
          2954,
          2782,
          5325,
          2970,
          1083,
          6924,
          1079,
          2906,
          2806,
          6155,
          6412,
          6022,
          2864,
          2784,
          2640,
          2412,
          2340,
          6426,
          6322,
          4255,
          6238,
          6230,
          4139,
          2024,
          1380,
          6404,
          1336,
          1328,
          1316,
          1156,
          5250,
          3029,
          3005,
          4250,
          6267,
          4210,
          4198,
          6223,
          1005,
          1029,
          1041,
          3116,
          2242,
          2238,
          2214,
          6260,
          6248,
          2122,
          4141,
          2070,
          1370,
          1306,
          1150,
          5244,
          5192,
          1046,
          1018,
          3035,
          6393,
          6301,
          4228,
          4212,
          6233,
          2103,
          4140,
          1153,
          1089,
          6241
         ],
         "y": [
          133,
          110,
          100,
          98,
          81,
          70,
          65,
          62,
          61,
          60,
          59,
          54,
          53,
          51,
          51,
          48,
          42,
          41,
          41,
          40,
          40,
          39,
          39,
          37,
          36,
          36,
          36,
          35,
          35,
          34,
          33,
          32,
          31,
          31,
          31,
          30,
          30,
          30,
          29,
          29,
          29,
          28,
          28,
          27,
          27,
          27,
          27,
          26,
          26,
          26,
          25,
          24,
          24,
          23,
          23,
          23,
          22,
          22,
          22,
          22,
          21,
          21,
          21,
          20,
          20,
          20,
          20,
          19,
          19,
          19,
          19,
          19,
          19,
          19,
          18,
          18,
          18,
          18,
          17,
          17,
          17,
          17,
          17,
          17,
          17,
          17,
          17,
          16,
          16,
          16,
          16,
          16,
          15,
          15,
          15,
          14,
          14,
          14,
          14,
          13,
          13,
          12,
          12,
          12,
          12,
          11,
          11,
          11,
          11,
          11,
          10,
          10,
          10,
          10,
          10,
          10,
          10,
          10,
          9,
          9,
          9,
          9,
          9,
          9,
          9,
          9,
          9,
          9,
          9,
          9,
          8,
          8,
          8,
          8,
          8,
          8,
          8,
          8,
          8,
          7,
          7,
          7,
          7,
          7,
          7,
          7,
          7,
          7,
          7,
          6,
          6,
          6,
          6,
          6,
          6,
          6,
          6,
          5,
          5,
          5,
          5,
          5,
          5,
          5,
          5,
          5,
          5,
          5,
          5,
          5,
          5,
          5,
          5,
          5,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          4,
          3,
          3,
          3,
          3,
          3,
          3,
          3,
          3,
          3,
          3,
          3,
          3,
          3,
          3,
          3,
          3,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1
         ]
        }
       ],
       "layout": {
        "height": 1400,
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Number of items attributed to a particular item class"
        },
        "width": 800,
        "xaxis": {
         "autorange": true,
         "range": [
          1001.5,
          7780.5
         ],
         "type": "linear"
        },
        "yaxis": {
         "autorange": true,
         "range": [
          0,
          140
         ],
         "showgrid": false,
         "showline": false,
         "showticklabels": true,
         "type": "linear"
        }
       }
      },
      "image/png": "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",
      "text/html": [
       "<div>                            <div id=\"695b7143-80d1-4b20-94a6-a0d4d31d4a97\" class=\"plotly-graph-div\" style=\"height:1400px; width:800px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"695b7143-80d1-4b20-94a6-a0d4d31d4a97\")) {                    Plotly.newPlot(                        \"695b7143-80d1-4b20-94a6-a0d4d31d4a97\",                        [{\"marker\": {\"color\": [133, 110, 100, 98, 81, 70, 65, 62, 61, 60, 59, 54, 53, 51, 51, 48, 42, 41, 41, 40, 40, 39, 39, 37, 36, 36, 36, 35, 35, 34, 33, 32, 31, 31, 31, 30, 30, 30, 29, 29, 29, 28, 28, 27, 27, 27, 27, 26, 26, 26, 25, 24, 24, 23, 23, 23, 22, 22, 22, 22, 21, 21, 21, 20, 20, 20, 20, 19, 19, 19, 19, 19, 19, 19, 18, 18, 18, 18, 17, 17, 17, 17, 17, 17, 17, 17, 17, 16, 16, 16, 16, 16, 15, 15, 15, 14, 14, 14, 14, 13, 13, 12, 12, 12, 12, 11, 11, 11, 11, 11, 10, 10, 10, 10, 10, 10, 10, 10, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"colorscale\": [[0.0, \"rgb(0,0,131)\"], [0.2, \"rgb(0,60,170)\"], [0.4, \"rgb(5,255,255)\"], [0.6, \"rgb(255,255,0)\"], [0.8, \"rgb(250,0,0)\"], [1.0, \"rgb(128,0,0)\"]], \"reversescale\": true}, \"orientation\": \"v\", \"type\": \"bar\", \"x\": [1016, 1040, 1124, 1034, 1122, 1072, 1032, 1148, 1120, 3020, 2302, 1026, 3106, 1042, 1030, 1028, 1114, 3034, 2006, 1190, 4126, 1074, 1004, 1142, 2502, 1126, 2420, 1144, 3046, 2008, 2028, 1318, 1002, 3110, 2018, 2116, 1062, 1044, 3026, 3032, 4138, 3040, 4114, 1136, 3004, 1036, 2016, 3008, 1118, 1014, 2124, 2708, 1045, 1076, 3038, 2644, 1010, 1068, 5324, 2718, 1132, 2170, 7780, 3044, 1084, 1048, 2174, 6269, 2032, 3016, 2636, 1060, 4118, 1092, 1116, 2172, 2166, 1096, 2026, 3024, 3014, 1088, 1086, 2712, 2416, 3018, 5446, 3108, 2632, 1058, 2004, 2162, 6242, 1094, 2012, 2702, 2020, 1236, 2014, 4176, 2306, 1008, 2756, 1006, 1078, 2228, 1052, 3022, 2226, 2112, 2130, 3028, 7034, 2630, 3030, 1012, 2128, 1080, 2752, 2142, 2714, 2102, 2002, 1056, 2030, 1138, 2716, 1022, 6516, 2304, 6257, 6330, 1330, 6920, 2010, 1050, 2022, 2108, 1039, 6350, 2218, 3090, 1082, 1063, 1146, 2034, 2986, 6392, 2164, 2210, 7002, 2104, 4122, 6810, 2152, 1035, 2220, 6258, 6848, 6806, 6482, 1066, 2114, 6517, 5322, 4254, 4252, 2966, 4130, 6922, 1093, 6344, 2178, 4104, 1038, 2222, 6448, 1024, 2652, 6212, 6352, 1070, 1386, 2722, 2662, 2786, 2246, 1310, 4162, 2650, 1152, 6706, 6918, 6246, 3104, 7016, 6936, 6329, 2168, 3060, 3015, 1003, 3010, 5308, 6328, 1077, 6414, 2962, 2750, 1087, 1338, 3012, 2208, 1033, 3102, 2704, 6222, 6206, 2506, 6232, 6808, 6266, 4222, 6262, 4214, 1387, 1115, 6338, 2642, 2690, 2980, 2960, 2956, 2654, 2904, 6954, 2372, 2720, 2646, 2504, 2664, 3006, 1027, 2074, 1302, 1054, 1364, 1320, 1312, 6353, 1067, 2850, 5224, 5222, 2854, 6253, 1154, 6960, 1013, 1064, 2156, 1334, 1025, 2802, 1314, 6824, 1073, 1326, 1075, 2954, 2782, 5325, 2970, 1083, 6924, 1079, 2906, 2806, 6155, 6412, 6022, 2864, 2784, 2640, 2412, 2340, 6426, 6322, 4255, 6238, 6230, 4139, 2024, 1380, 6404, 1336, 1328, 1316, 1156, 5250, 3029, 3005, 4250, 6267, 4210, 4198, 6223, 1005, 1029, 1041, 3116, 2242, 2238, 2214, 6260, 6248, 2122, 4141, 2070, 1370, 1306, 1150, 5244, 5192, 1046, 1018, 3035, 6393, 6301, 4228, 4212, 6233, 2103, 4140, 1153, 1089, 6241], \"y\": [133, 110, 100, 98, 81, 70, 65, 62, 61, 60, 59, 54, 53, 51, 51, 48, 42, 41, 41, 40, 40, 39, 39, 37, 36, 36, 36, 35, 35, 34, 33, 32, 31, 31, 31, 30, 30, 30, 29, 29, 29, 28, 28, 27, 27, 27, 27, 26, 26, 26, 25, 24, 24, 23, 23, 23, 22, 22, 22, 22, 21, 21, 21, 20, 20, 20, 20, 19, 19, 19, 19, 19, 19, 19, 18, 18, 18, 18, 17, 17, 17, 17, 17, 17, 17, 17, 17, 16, 16, 16, 16, 16, 15, 15, 15, 14, 14, 14, 14, 13, 13, 12, 12, 12, 12, 11, 11, 11, 11, 11, 10, 10, 10, 10, 10, 10, 10, 10, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}],                        {\"height\": 1400, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"autotypenumbers\": \"strict\", \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Number of items attributed to a particular item class\"}, \"width\": 800, \"yaxis\": {\"showgrid\": false, \"showline\": false, \"showticklabels\": true}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('695b7143-80d1-4b20-94a6-a0d4d31d4a97');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = (list(x) for x in zip(*sorted(zip(items['class'].value_counts().index, \n",
    "                                         items['class'].value_counts().values), \n",
    "                                        reverse = False)))\n",
    "trace2 = go.Bar(\n",
    "    x=items['class'].value_counts().index,\n",
    "    y=items['class'].value_counts().values,\n",
    "    marker=dict(\n",
    "        color=items['class'].value_counts().values,\n",
    "        colorscale = 'Jet',\n",
    "        reversescale = True\n",
    "    ),\n",
    "    orientation='v',\n",
    ")\n",
    "\n",
    "layout = dict(\n",
    "    title='Number of items attributed to a particular item class',\n",
    "     width = 800, height = 1400,\n",
    "    yaxis=dict(\n",
    "        showgrid=False,\n",
    "        showline=False,\n",
    "        showticklabels=True,\n",
    "#         domain=[0, 0.85],\n",
    "    ))\n",
    "\n",
    "fig1 = go.Figure(data=[trace2])\n",
    "fig1['layout'].update(layout)\n",
    "py.iplot(fig1, filename='plots')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e1bd0538-20fe-4889-84bf-230fc5de5700",
   "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>item_nbr</th>\n",
       "      <th>family</th>\n",
       "      <th>class</th>\n",
       "      <th>perishable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>96995</td>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>1093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99197</td>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>1067</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103501</td>\n",
       "      <td>CLEANING</td>\n",
       "      <td>3008</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103520</td>\n",
       "      <td>GROCERY I</td>\n",
       "      <td>1028</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103665</td>\n",
       "      <td>BREAD/BAKERY</td>\n",
       "      <td>2712</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_nbr        family  class  perishable\n",
       "0     96995     GROCERY I   1093           0\n",
       "1     99197     GROCERY I   1067           0\n",
       "2    103501      CLEANING   3008           0\n",
       "3    103520     GROCERY I   1028           0\n",
       "4    103665  BREAD/BAKERY   2712           1"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9191fe1a-4ec8-40cb-b8f8-145464809d2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAALnCAYAAACk3aC0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAACvm0lEQVR4nOzdd3hURf/+8XsJCS0UAygd4RFU2qOIKAooiPQuHQFFFAHpvUPoLfQmTYXQQm+KAgKGJkVBUIo8Su9FklBS9vz+yC/7TSABMns2Rny/risXyyY7mc3unr13zsxnHJZlWQIAAACQKCn+7g4AAAAA/0QEaQAAAMAAQRoAAAAwQJAGAAAADBCkAQAAAAMEaQAAAMAAQRp4gv38889q1qyZatSooerVq6tVq1Y6ceKE6/stW7bU9evXjdres2ePqlev7lb//P39NXny5AeuX7FihV555RXVqlVLNWvWVNWqVdW6dWtdvnzZrd8X2+P2f8qUKdq0aVOi2r5w4YKqV6+umjVr6qefforzvfLly+uXX35JVHuP4/nnnzd+LJ8ktWrV0q1bt4xuu3XrVk2cONHmHiVOUFCQAgMD4/1ev379dPjw4STuEYCHIUgDT6jw8HC1bt1avXr10tq1a7Vu3TrVqFFDH3/8saKioiRJO3bs+Jt7mbASJUpo9erVWrNmjTZs2KDs2bNr0qRJSd6PPXv2KDIyMtG3yZIli9asWaOXX37ZQz1DfFavXq0MGTIY3faXX37RX3/9ZXOPEmf//v26e/duvN/buXOn2PoBSF5S/t0dAOAZd+7cUUhIiG7fvu26rmbNmvL19VVUVJT69esnSWrRooU+//xzHT16VDNnzlR4eLiuX7+u2rVrq1OnTpKkZcuWad68eUqRIoWeeuopjRo1Ks7v2rdvn7p3765x48apePHi2rJli6ZPn66IiAilTp1aPXv21Msvv6zQ0FD17dtXR48e1dNPPy0vLy+98sorj7wvERERCg0NVe7cuSVJV69e1YABA3Tt2jVduXJFOXPm1IQJE5Q5c2aVL19exYoV07Fjx9SlSxeNGDFCFSpU0L59+xQSEqIPP/xQTZo0idN+SEiIBg8erKNHj8rhcKhMmTLq0qWLlixZosOHD2v06NHy8vLSu+++G+d2S5Ys0fz585UiRQplyZJF/fv316VLlzRhwgSFhISoWbNmmj9/foL3K77b58uXT2FhYRo6dKgOHDggLy8vVahQQZ07d9aff/4pf39/3b59W5cvX9YLL7ygCRMmKFWqVHHaDQoK0qJFi+R0OpUpUyb1799f//nPf+L8zJ49ezR+/Hjlzp1bJ06cUHh4uAYMGKDXX39df/zxR4K/p2jRovrggw+0detWhYaGqnv37vrmm290/PhxPf3005oxY4bSpk2rkydPatiwYbp586aioqLUrFkz1atX74G/wffff5/g8+7zzz/XsmXLlC5dOpUoUUKbN2/Wli1bHtq/559/Xrt27dLWrVv13XffKUWKFDp16pS8vb01atQoFSxYUN9++62mT58uh8MhLy8v9ejRQz4+Plq8eLGioqKUPn16de7cOU4/E7o/Xbt2VaFChfTRRx9JkhYtWqQ9e/ZowoQJCb4OJk+erHPnzunKlSs6d+6c/Pz8NH78eB06dEhbtmzRjh07lDp1ajVt2tT1+8ePH6/Lly+rW7duGjJkiFq3bq3t27crffr0sixLlStX1sSJEzVs2DD95z//0eHDh3Xjxg3VqlVLHTp0kCQdOHBAY8eO1Z07d+RwONS+fXuVK1cuwecngMdgAXhizZ071ypWrJhVvnx5q1u3blZQUJB1+/Zt1/cLFixoXbt2zXI6ndb7779v/fHHH5ZlWdbFixetF1980bp27Zr122+/Wa+99pp1/vx5y7Isa968eVb//v2t3bt3W9WqVbN27dplVahQwfrtt98sy7KsP/74w6pevbp1/fp1y7Is6/jx49abb75phYWFWcOGDbN69OhhOZ1O69q1a1bZsmWtSZMmPdDv5cuXW8WLF7dq1qxp1ahRwypZsqRVpkwZ6+zZs5ZlWdYXX3xhzZw507Isy3I6nVarVq2sOXPmWJZlWeXKlbOmTJniaqtcuXJW//79LafTaV24cMF67bXXrKNHj7r6b1mW1aNHD2vIkCGW0+m07t27Z7Vs2dLV/vvvv299/fXXD/Rx586dVoUKFaxr1665+lylShXL6XRay5cvtz755JN4H5Ny5cpZhw4deujthw8fbnXu3NmKjIy07t27ZzVt2tTavXu3NXLkSGvVqlWWZVlWeHi4Vb16deubb76J81ju2bPHatKkietx/uGHH6wqVao80I/du3dbL774ovXrr79almVZc+bMsZo2bWpZlvXI3/Pll19almVZM2fOtF5++WXr4sWLVlRUlFWnTh1rzZo1VkREhFW1alXr8OHDlmVZ1q1bt6wqVapYP/30U5w+POx5t337dqtSpUrWX3/9ZTmdTqt3795WuXLlHqt/165ds5YvX2698sor1oULFyzLsix/f3+rR48elmVZ1jvvvOPqyw8//GBNnjzZsizLmjRpkjV48OAH/lYPuz+7du2yqlev7vrZevXqWTt27Hjo62DSpEnWO++8Y4WEhFiWZVmtW7e2Jk6caFmWZfXs2dOaPXv2A32wrP977liWZbVp08ZasGCBZVnRz8UGDRpYlhX9fP3444+t8PBw66+//rIqVapkbdmyxbp586ZVsWJF68yZM66/ddmyZa1z587F+7sAPB5GpIEn2Icffqj69etr79692rt3r2bNmqVZs2Zp2bJlSp8+vevnHA6HZsyYoa1bt2rdunU6efKkLMvSnTt3tGvXLpUuXVrZs2eXJH3wwQeSokc0L168qE8//VSNGzfWCy+8ICl6usjly5ddPxfT/unTp7Vr1y716dNHDodDfn5+D4zwxlaiRAnNnDlTkuR0OvXVV1+pVatW2rBhg1q0aKF9+/Zp3rx5+vPPP3XixAn997//jXPb2Jo0aSKHw6Fs2bKpTJky2rFjhwoXLuz6/vbt27Vo0SI5HA75+PioUaNG+vLLL/XJJ58k2L8ffvhBVatWlZ+fnySpbt26GjZsmM6ePfuwh+Sxbr9z50717t1bXl5e8vLy0oIFCyRJr776qnbs2KFZs2bpzz//1OXLl+OccZCi5/meOnVKjRo1cl33119/6ebNm8qUKVOcn82RI4defPFFSVKhQoW0cuVKSVL37t0f+nsqVaokScqTJ48KFiyoZ555RpKUK1cu/fXXX/rzzz91+vRp9enTx3Wbu3fv6tdff9VLL73kuu5hz7tt27apcuXKrmkaTZs21e7dux+rfzEKFy6sbNmyue7fd999J0mqVq2aPvvsM7311lt688039fHHHz/0sXrY/WncuLHu3bunX375RWnSpNH169dVqlQpLVy4MMHXgSSVLFlSvr6+rr4ldkpJ06ZNNWbMGDVt2lRLlixR48aNXd9r2LChvL295e3trcqVKys4OFgpUqTQlStX1K5duzj9OXbsmHLkyJGo3w3g/xCkgSfU/v379dNPP6lVq1YqV66cypUrpy5duqhGjRrasWOHKleu7PrZ27dvq06dOqpQoYJKlCih9957T5s2bZJlWfLy8pLD4XD97N27d3Xu3DlJkpeXlz7//HO1bdtWVapUUbFixeR0OlWqVClNmDDBdZsLFy7o6aeflqQ4czy9vLwe676kSJFCDRs21IgRI3Tt2jXNmzdPhw4d0nvvvafXXntNkZGRcdpNmzZtnNunTPl/hzqn06kUKeIuD3E6nQ/8/1Hzoq145qpalvXY86kfdvuUKVPG+ZtfuHBBqVOn1uDBgxUVFaUqVaro7bff1oULFx5ox+l0qlatWurevbvr/5cvX1bGjBkf+H2pU6d2XXY4HK62unTp8tDf4+3tHe/lGFFRUcqQIYNWr17tuu7q1atxPrxJD3/epUyZMsHnyqP696j717lzZ9WrV0/BwcFasWKFPv/8c61YseKB2z/O/XE4HKpXr55Wr14tb29v1atXTw6H46Gvg++++y7Bvj2uN954w/VBd9++fXGmW8V+vluWpRQpUigqKkr/+c9/FBQU5PrepUuXXB/kAJhhsSHwhPLz89P06dO1b98+13VXrlzRnTt3VLBgQUnR4SQyMlKnTp1SaGioOnXqpPLly+vHH39UeHi4nE6nXnvtNe3atctVMWPx4sUaM2aMJClr1qwqXry4evbsqe7du+vOnTt6/fXXtWPHDp08eVKStG3bNtWsWVP37t1TmTJltGzZMjmdTv3111/avHnzY9+f7777Tjlz5pSfn5+Cg4PVokUL1a5dW5kzZ9bOnTtdCyjjs2rVKknS+fPntWPHDpUtWzbO90uXLq3AwEBZlqXw8HAtXbpUb7zxRpy/0f1Kly6tDRs2uCplLF++XJkyZVLevHkf6/487PalSpXSypUr5XQ6FR4erg4dOmjv3r0KDg5Wu3btVLVqVTkcDh08ePCB+/3mm29q/fr1rsdr0aJFatGixWP1Kcbj/J6HyZcvn1KlSuUKnjFVTO6vOPGw591bb72lb7/9ViEhIZKi5+nb0b/IyEiVL19et2/fVuPGjTVw4ECdPHlSkZGRCT7Wj7o/derU0ZYtW7Rx40bVrVtXkh76OniYhPpw//ccDoeaNGmivn37qnr16nHmya9Zs8b1Gvv6669Vvnx5vfTSSzp16pT27t0rSfrtt99UqVIlWyvhAP9GjEgDT6h8+fJp6tSpGj9+vC5evKhUqVIpffr08vf3V/78+SVJ7777rpo0aaIpU6bo7bffVpUqVZQhQwblyZNHzz33nE6dOqUyZcqoe/fuatWqlaTo8Dx8+HD9+eefrt9Vp04dbdy4USNHjtTgwYPl7++vLl26uEYWp0+frrRp06p9+/YaOHCgqlSpIj8/P1egj8++fftUq1YtORwORUZGKlOmTJo6dapSpEihdu3aafTo0Zo2bZq8vLxUvHhx1ynz+Jw9e1Z169bV3bt31a9fP+XPn19Xrlxxfb9fv34aOnSoatSooYiICJUpU0affvqpJKlcuXIaNWqUIiIiVKdOHddt3nzzTX3wwQdq0aKFnE6n/Pz8NHPmzAdGuxPysNt/9tlnGjZsmGrVqqWoqChVrVpVFStWdJ2az5gxo9KkSaNXX331gftdpkwZffzxx2rZsqUcDod8fX01ZcqUOCPcj9K5c+dH/p6H8fHx0bRp0zRs2DDNnj1bkZGR6tix4wMLS59//vmHPu8aNGighg0bKnXq1CpQoIDSpEnjdv9SpkypPn36qFu3bq6R/+HDh8vHx0elSpVS+/bt5e3trf79+z/2/cmaNasKFSqkyMhI1zSXAgUKJPg6eJiyZctqyJAhkqTWrVvH+V7MotOhQ4eqdOnSqlOnjkaNGqWGDRvG+bm7d++qXr16CgsLU5MmTVSqVClJ0qRJkzR69Gjdu3dPlmVp9OjRypkz52P93QDEz2El9nwSAPyDlC9fXhMnTlTRokX/7q4gEX755Rf99NNPat68uSRp3rx5OnjwYJypEv9269ev18qVKzV79mzXdc2aNVPTpk3jTN0C4DmMSAMAkp18+fJp1qxZWrp0qRwOh7Jnz+4aqUV0YL569Wq8GxoBSDqMSAMAAAAGPLbY8ODBg2rWrFmc69auXRtnLtfSpUtVt25dNWjQQN9//70k6fr162rZsqWaNGmiTp066c6dO57qIgAAAGDMI0F61qxZ6tevX5zVyb/++quWLVvmKvFz5coVzZ8/X4sXL9acOXMUEBCg8PBwTZs2TdWrV9fChQtVqFAhLVmyxBNdBAAAANzikTnSefLk0eTJk9WjRw9J0o0bNxQQEKA+ffq4VkIfOnRIL7/8snx8fOTj46M8efLo6NGj2r9/v2ulctmyZRUQEBCnoL0UvSL58OHDypo162PXoQUAAAASKyoqSleuXFGRIkXi1ICXPBSkK1Wq5NrdKyoqSn379lXv3r3j1LkMDQ2NU5w/Xbp0Cg0NjXN9unTpXDVEYzt8+LCaNm3qia4DAAAADwgMDHxg51yPV+04cuSITp06pUGDBunevXv6/fffNWzYML3++usKCwtz/VxYWJjSp08vX19fhYWFKXXq1AoLC3NtDxtb1qxZJUXfoZjtXwEAAAC7Xbx4UU2bNnXlz9g8HqSLFSum9evXS4reFKFLly7q27evrly5ogkTJujevXsKDw/XyZMnVbBgQRUvXlzbtm1T3bp1tX379gcK+Ev/t1VstmzZlCtXLk/fBQAAAPzLxTed+G+rI501a1Y1a9ZMTZo0kWVZ6ty5s1KlSqU2bdqoZ8+eWrp0qZ566imNGzfu7+oiAAAAkKB/ZB3ps2fP6p133tHmzZsZkQYAAIDHPCx3eqyONAAAAPAkI0gDAAAABgjSAAAAgAGCNAAAAGCAIA0AAIC/zWeffZbg93r16qXt27c/VjuTJ0/WokWLHrj+zTffNO7boxCkAQAA8LeZMmXK390FY39bHWkAAAA8OVasWKFNmzYpLCxMN27cULt27fTUU09p/Pjx8vLyUu7cueXv76+1a9dq+fLlcjqd6tChg7p166YdO3YoMDBQq1atUooUKVS0aFH169dPkrRkyRLNnj1boaGhGjRokIoVK6Zx48bp8OHDunnzpl544QWNGDFCkrRp0yZ9/fXXunv3rvr166dixYq5+nfs2DENHTpUkpQpUyYNHz5c6dOnd+s+E6QBAABgizt37mjevHm6fv266tevrxQpUmjp0qXKnDmzJkyYoJUrVyplypTKkCGDpk+fHue2K1as0MCBA1WsWDEtXLhQkZGRkqTChQurbdu2WrFihVasWKH8+fMrQ4YMmjdvnpxOp6pVq6ZLly5JknLmzCl/f3+dOHFCPXr00MqVK13t9+/fX8OHD9dzzz2noKAgzZ49W507d3br/hKkAQAAYItXX31VKVKkUJYsWZQmTRqdOnVKnTp1kiTdvXtXb7zxhvLmzat8+fI9cNsRI0Zo7ty5Gj16tF566SXF7BlYuHBhSVKWLFl09+5dpUqVStevX1eXLl2UNm1a3b59WxEREa7fL0kFChTQlStX4rR/8uRJDR48WJIUERGhZ5991u37S5AGAACALY4cOSJJunr1qu7du6c8efJo2rRpSp8+vTZv3qy0adPqwoULSpHiwWV6S5cu1eDBg5UqVSp99NFH+umnnyRJDocjzs9t375dFy5c0IQJE3T9+nV99913rtB96NAh1ahRQ8eOHVOOHDni3C5fvnwaNWqUcuTIof379z8QtE0QpAEAAGCLq1evqkWLFgoJCdHAgQOVIkUKffLJJ7IsS+nSpdPo0aN14cKFeG/7/PPPq0mTJkqXLp2eeeYZ/fe//9WKFSse+LlixYpp2rRpatq0qRwOh3Lnzq3Lly9Lit7Ou3nz5goPD5e/v3+c2w0aNEg9e/ZUZGSkHA6Hhg0b5vb9dVgxEf4f5GF7ngMAACDprVixQv/73//UrVu3v7srtnpY7qT8HQAAAGCAqR0AAABwW926df/uLiQ5RqQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMsNgQAAAAHlO6xjZb2wte+9ZDv+90OjVo0CAdO3ZMPj4+Gjp0qPLmzWtrH2IwIg0AAIAnxqZNmxQeHq4lS5aoa9euGjlypMd+F0EaAAAAT4z9+/erTJkykqSXXnpJhw8f9tjvYmoHgGTpcU8FPuoUHwDg3yU0NFS+vr6u/3t5eSkyMlIpU9ofexmRBgAAwBPD19dXYWFhrv87nU6PhGiJIA0AAIAnSPHixbV9+3ZJ0s8//6yCBQt67HcxtQMAAABPjHfffVc7duxQo0aNZFmWhg8f7rHfRZAGAACAxyT1WpYUKVLI398/aX5XkvwWAAAA4AlDkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMUP4OAAAAHrPe+3lb26sWceyxfu7gwYMaO3as5s+fb+vvj40gDQAAgCfKrFmztGbNGqVJk8ajv4epHQAAAHii5MmTR5MnT/b47yFIAwAA4IlSqVIlpUzp+YkXBGkAAADAAEEaAAAAMECQBgAAAAxQtQMAAAAe87jl6uyWK1cuLV261KO/gxFpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADDgsSB98OBBNWvWTJL022+/qUmTJmrWrJk++ugjXb16VZK0dOlS1a1bVw0aNND3338vSbp+/bpatmypJk2aqFOnTrpz546nuggAAAAY80iQnjVrlvr166d79+5JkoYNG6b+/ftr/vz5evfddzVr1ixduXJF8+fP1+LFizVnzhwFBAQoPDxc06ZNU/Xq1bVw4UIVKlRIS5Ys8UQXAQAAALd4JEjnyZNHkydPdv0/ICBAL774oiQpKipKqVKl0qFDh/Tyyy/Lx8dH6dOnV548eXT06FHt379fZcqUkSSVLVtWO3fu9EQXAQAAALd4JEhXqlRJKVOmdP3/6aefliQdOHBACxYs0AcffKDQ0FClT5/e9TPp0qVTaGhonOvTpUunkJAQT3QRAAAAcEvKR/+IPTZs2KDp06fr888/l5+fn3x9fRUWFub6flhYmNKnT++6PnXq1AoLC1OGDBmSqosAAADAY0uSqh2rV6/WggULNH/+fOXOnVuSVKxYMe3fv1/37t1TSEiITp48qYIFC6p48eLatm2bJGn79u165ZVXkqKLAAAAQKJ4fEQ6KipKw4YNU/bs2dW+fXtJ0quvvqoOHTqoWbNmatKkiSzLUufOnZUqVSq1adNGPXv21NKlS/XUU09p3Lhxnu4iAAAAkGgeC9K5cuXS0qVLJUk//vhjvD/ToEEDNWjQIM51WbJk0Zw5czzVLQAAAMAWbMgCAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGPBYkD548KCaNWsmSTp16pQaN26sJk2aaODAgXI6nZKkKVOmqF69emrUqJEOHTr00J8FAAAAkhOPBOlZs2apX79+unfvniRpxIgR6tSpkxYuXCjLsrR582YdOXJEP/74o4KCghQQEKDBgwcn+LMAAABAcuORIJ0nTx5NnjzZ9f8jR46oZMmSkqSyZctq586d2r9/v0qXLi2Hw6EcOXIoKipK169fj/dnAQAAgOTGI0G6UqVKSpkypev/lmXJ4XBIktKlS6eQkBCFhobK19fX9TMx18f3swAAAEBykySLDVOk+L9fExYWpgwZMsjX11dhYWFxrk+fPn28PwsAAAAkN0kSpAsVKqQ9e/ZIkrZv364SJUqoePHiCg4OltPp1Pnz5+V0OuXn5xfvzwIAAADJTcpH/4j7evbsqf79+ysgIED58+dXpUqV5OXlpRIlSqhhw4ZyOp0aMGBAgj8LAAAAJDcOy7Ksv7sTiXX27Fm988472rx5s3LlyvV3dweAB5Suse2xfi547Vse7gkA4N/sYbmTDVkAAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwEDKpPpFERER6tWrl86dO6cUKVJoyJAhSpkypXr16iWHw6ECBQpo4MCBSpEihaZMmaKtW7cqZcqU6tOnj4oVK5ZU3QQAAAAeS5IF6W3btikyMlKLFy/Wjh07NGHCBEVERKhTp0567bXXNGDAAG3evFk5cuTQjz/+qKCgIF24cEHt27fX8uXLk6qbAAAAwGNJsqkd+fLlU1RUlJxOp0JDQ5UyZUodOXJEJUuWlCSVLVtWO3fu1P79+1W6dGk5HA7lyJFDUVFRun79elJ1EwAAAHgsSTYinTZtWp07d05VqlTRjRs3NGPGDO3du1cOh0OSlC5dOoWEhCg0NFSZMmVy3S7mej8/v6TqKgAAAPBISRakv/jiC5UuXVpdu3bVhQsX1KJFC0VERLi+HxYWpgwZMsjX11dhYWFxrk+fPn1SdRMAAAB4LEk2tSNDhgyuQJwxY0ZFRkaqUKFC2rNnjyRp+/btKlGihIoXL67g4GA5nU6dP39eTqeT0WgAAAAkO0k2Iv3BBx+oT58+atKkiSIiItS5c2cVKVJE/fv3V0BAgPLnz69KlSrJy8tLJUqUUMOGDeV0OjVgwICk6iIAAADw2JIsSKdLl04TJ0584PoFCxY8cF379u3Vvn37pOgWAAAAYOSRUzsuXbqk33//XX/88Yf69Omj3377LSn6BQAAACRrjwzSXbt21dWrVzV+/Hi9+eabGj58eFL0CwAAAEjWHhmkHQ6HXn31Vd26dUvVqlVTihTsKg4AAAA8MhVHRkZqzJgxKlGihHbv3h2nZB0AAADwb/XIID1ixAjlzp1bn3zyia5fv65Ro0YlRb8AAACAZO2RQTp37tzy8fHR9OnTlTlzZqVLly4p+gUAAAAka48M0gMGDND58+e1c+dOhYWFqWfPnknRLwAAACBZe2SQPn36tDp27KhUqVKpfPnyCgkJSYp+AQAAAMnaI4N0VFSUrl+/LkkKDQ2lagcAAACgx9jZsFOnTmrcuLGuXLmihg0bqk+fPknRLwAAACBZe2SQLlmypDZu3Kjr16/rqaeeksPhSIp+AQAAAMlagkHa399fAwYMUMOGDR8Iz4sXL/Z4xwAAAIDkLMEg3bZtW0nS8OHDlTp16iTrEAAAAPBPkGCQzpIliySpX79+WrRoUZJ1CAAAAPgneOQc6bRp02r48OHKly+fq2JHw4YNPd4xAAAAIDl7ZJB++eWXJUnXrl3zeGcAAACAf4pHBukUKVK45ktL0rhx4zzaIQAAAOCfIMEgHRQUpGXLlunkyZPavn27JMnpdCoiIkJdu3ZNsg4CAAAAyVGCQbpWrVoqVaqUZs6cqU8//VRS9Oh05syZk6xzAAAAQHKV4H7fPj4+ypUrlwYPHqxr167p/PnzOnv2rA4ePJiU/QMAAACSpUfOke7QoYOuXbum7NmzS5IcDodeffVVj3cMAAAASM4eGaSvXr3KToYAAADAfRKc2hEjX758unTpUlL0BQAAAPjHeOSI9IEDB1SuXDk99dRTcjgckqTg4GCPdwwAAABIzh4ZpDdu3JgU/QAAAAD+UR4ZpE+cOKGBAwfq1q1bqlmzpgoUKKBy5colRd8AAACAZOuRc6SHDh2qESNG6KmnnlK9evU0efLkpOgXAAAAkKw9MkhLUt68eeVwOOTn56d06dJ5uk8AAABAsvfIIJ0xY0YtXrxYd+7c0fr165UhQ4ak6BcAAACQrD0ySA8fPlxnz57VU089pcOHD2vYsGFJ0S8AAAAgWXvkYkNfX19169YtKfoCAAAA/GM81hxpAAAAAHElGKT37t0rSQoPD0+yzgAAAAD/FAkG6aFDh+r27dv66KOPFBERofDwcNcXAAAA8G+X4Bzp0qVLq2bNmrp8+bIqVarkut7hcGjz5s1J0jkAAAAguUowSHfv3l3du3fX1KlT1a5du6TsEwAAAJDsPXKxYd26ddWhQwdVq1ZN7dq107lz55KiXwAAAECy9sgg3b9/f9WqVUuLFi1SnTp11KdPn6ToFwAAAJCsPTJI37t3T++8844yZMigChUqKCoqKin6BQAAACRrjwzSUVFROnbsmCS5/gUAAAD+7R65s2H//v3Vp08fXblyRU8//bSGDh2aFP0CAAAAkrVHBukXX3xRy5cvT4q+AAAAAP8YbBEOAAAAGCBIAwAAAAYeGaSnTZsW5//jxo3zWGcAAACAf4oE50gHBQVp2bJlOnnypLZv3y4puoJHZGSkunbtmmQdBAAAAJKjBIN0rVq1VKpUKc2cOVOffvqpJClFihTKnDlzknUOAAAASK4SnNrh4+OjXLlyafDgwbp27ZrOnz+vs2fP6uDBg0nZPwAAACBZemT5uw4dOujatWvKnj27JMnhcOjVV1/1eMcAAACA5OyRQfrq1atavHhxUvQFAAAA+Md4ZNWOfPny6dKlS0nRFwAAAOAf45Ej0vv371e5cuXk5+fnui44ONijnQIAAACSu0cG6W+//TYp+gEAAAD8ozwySPfu3fuB60aMGOGRzgAAAAD/FI8M0lWrVpUkWZalX3/9VZcvX/Z4pwAAAIDk7pFBukyZMq7LZcuWVcuWLT3aIQAAAOCf4JFBOvbCwitXrujq1ase7RAAAADwT/DIIL1+/XrXZR8fHw0fPtyjHQIAAAD+CR4ZpEeMGKHjx4/r999/V758+fTiiy8mRb8AAACAZO2RQXr+/Plat26dihUrprlz56pKlSr66KOPkqJvAAAAQLL1yCC9bt06BQYGKmXKlIqIiFCjRo0I0gAAAPjXe+QW4ZZlKWXK6Lzt7e0tb29vj3cKAAAASO4eOSL9yiuvqEOHDnrllVe0f/9+vfzyy0nRLwAAACBZe2SQ7tmzp7Zu3aqTJ0+qbt26evvtt5OgWwAAAEDy9tCpHUuWLFFkZKTefvtt/fe//9WFCxeSql8AAABAspZgkJ48ebJ27NihiIgISVK2bNm0Y8cOTZkyJck6BwAAACRXCQbp7du3a+LEiUqTJo0kKVeuXBo/fry+//77JOscAAAAkFwlOEc6bdq0cjgcca7z9vZWunTpPN4pPL7SNbY91s8Fr33Lwz0BAAD4d0lwRDp16tQ6c+ZMnOvOnDnzQLgGAAAA/o0SHJHu1q2b2rZtq1KlSil37tw6f/68goODNWrUKONfNnPmTG3ZskURERFq3LixSpYsqV69esnhcKhAgQIaOHCgUqRIoSlTpmjr1q1KmTKl+vTpo2LFihn/TgAAAMATEhyRLlCggBYuXKhChQrpzp07Kly4sBYtWqRChQoZ/aI9e/bop59+0qJFizR//nxdvHhRI0aMUKdOnbRw4UJZlqXNmzfryJEj+vHHHxUUFKSAgAANHjzY+M4BAAAAnvLQOtLp06dX7dq1bflFwcHBKliwoNq1a6fQ0FD16NFDS5cuVcmSJSVJZcuW1Y4dO5QvXz6VLl1aDodDOXLkUFRUlK5fvy4/Pz9b+gEAAADY4ZEbstjlxo0bOn/+vGbMmKGzZ8+qTZs2sizLNec6Xbp0CgkJUWhoqDJlyuS6Xcz1BGkAAAAkJ0kWpDNlyqT8+fPLx8dH+fPnV6pUqXTx4kXX98PCwpQhQwb5+voqLCwszvXp06dPqm4CAAAAj+WhOxva6ZVXXtEPP/wgy7J06dIl3blzR6VKldKePXskRdetLlGihIoXL67g4GA5nU6dP39eTqeT0WgAAAAkO0k2Il2uXDnt3btX9erVk2VZGjBggHLlyqX+/fsrICBA+fPnV6VKleTl5aUSJUqoYcOGcjqdGjBgQFJ1EQAAAHhsSRakJalHjx4PXLdgwYIHrmvfvr3at2+fFF0CAAAAjCTZ1A4AAADgSUKQBgAAAAwQpAEAAAADBGkAAADAAEEaAAAAMECQBgAAAAwQpAEAAAADBGkAAADAAEEaAAAAMECQBgAAAAwQpAEAAAADBGkAAADAAEEaAAAAMECQBgAAAAwQpAEAAAADBGkAAADAAEEaAAAAMECQBgAAAAwQpAEAAAADBGkAAADAAEEaAAAAMECQBgAAAAwQpAEAAAADBGkAAADAAEEaAAAAMECQBgAAAAwQpAEAAAADBGkAAADAAEEaAAAAMECQBgAAAAwQpAEAAAADBGkAAADAAEEaAAAAMECQBgAAAAwQpAEAAAADBGkAAADAAEEaAAAAMECQBgAAAAwQpAEAAAADBGkAAADAAEEaAAAAMECQBgAAAAwQpAEAAAADBGkAAADAAEEaAAAAMECQBgAAAAwQpAEAAAADBGkAAADAAEEaAAAAMECQBgAAAAwQpAEAAAADBGkAAADAAEEaAAAAMECQBgAAAAwQpAEAAAADKf/uDgB/t9I1tj3WzwWvfcvDPQEAAP8kjEgDAAAABgjSAAAAgAGCNAAAAGCAIA0AAAAYIEgDAAAABgjSAAAAgAGCNAAAAGCAIA0AAAAYIEgDAAAABgjSAAAAgAGCNAAAAGCAIA0AAAAYIEgDAAAABgjSAAAAgIEkD9LXrl3TW2+9pZMnT+rUqVNq3LixmjRpooEDB8rpdEqSpkyZonr16qlRo0Y6dOhQUncRAAAAeKQkDdIREREaMGCAUqdOLUkaMWKEOnXqpIULF8qyLG3evFlHjhzRjz/+qKCgIAUEBGjw4MFJ2UUAAADgsSRpkB41apQaNWqkp59+WpJ05MgRlSxZUpJUtmxZ7dy5U/v371fp0qXlcDiUI0cORUVF6fr160nZTQAAAOCRkixIr1ixQn5+fipTpozrOsuy5HA4JEnp0qVTSEiIQkND5evr6/qZmOsBAACA5CRlUv2i5cuXy+FwaNeuXfrtt9/Us2fPOCPNYWFhypAhg3x9fRUWFhbn+vTp0ydVNwEAAIDHkmQj0oGBgVqwYIHmz5+vF198UaNGjVLZsmW1Z88eSdL27dtVokQJFS9eXMHBwXI6nTp//rycTqf8/PySqpsAAADAY0myEen49OzZU/3791dAQIDy58+vSpUqycvLSyVKlFDDhg3ldDo1YMCAv7OLAAAAQLz+liA9f/581+UFCxY88P327durffv2SdklAAAAIFHYkAUAAAAwQJAGAAAADBCkAQAAAAMEaQAAAMAAQRoAAAAwQJAGAAAADBCkAQAAAAMEaQAAAMAAQRoAAAAwQJAGAAAADBCkAQAAAAMEaQAAAMAAQRoAAAAwQJAGAAAADBCkAQAAAAMEaQAAAMAAQRoAAAAwQJAGAAAADBCkAQAAAAMEaQAAAMAAQRoAAAAwQJAGAAAADBCkAQAAAAMEaQAAAMAAQRoAAAAwQJAGAAAADBCkAQAAAAMEaQAAAMAAQRoAAAAwQJAGAAAADBCkAQAAAAMEaQAAAMBAyr+7AwAAAPhnW+/9/GP9XLWIYx7uSdJiRBoAAAAwQJAGAAAADBCkAQAAAAMEaQAAAMAAQRoAAAAwQJAGAAAADBCkAQAAAAMEaQAAAMAAQRoAAAAwQJAGAAAADBCkAQAAAAMEaQAAAMAAQRoAAAAwQJAGAAAADBCkAQAAAAMEaQAAAMAAQRoAAAAwQJAGAAAADBCkAQAAAAMEaQAAAMAAQRoAAAAwQJAGAAAADBCkAQAAAAMp/+4OAE+a9d7PP9bPVYs45uGeAAAAT2JEGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADCQMql+UUREhPr06aNz584pPDxcbdq00XPPPadevXrJ4XCoQIECGjhwoFKkSKEpU6Zo69atSpkypfr06aNixYolVTcBAACAx5JkQXrNmjXKlCmTxowZo5s3b6p27dp64YUX1KlTJ7322msaMGCANm/erBw5cujHH39UUFCQLly4oPbt22v58uVJ1U0AAADgsSRZkK5cubIqVaokSbIsS15eXjpy5IhKliwpSSpbtqx27NihfPnyqXTp0nI4HMqRI4eioqJ0/fp1+fn5JVVXAQAAgEdKsjnS6dKlk6+vr0JDQ9WhQwd16tRJlmXJ4XC4vh8SEqLQ0FD5+vrGuV1ISEhSdRMAAAB4LEm62PDChQtq3ry5atWqpRo1aihFiv/79WFhYcqQIYN8fX0VFhYW5/r06dMnZTcBAACAR0qyIH316lW1bNlS3bt3V7169SRJhQoV0p49eyRJ27dvV4kSJVS8eHEFBwfL6XTq/PnzcjqdTOsAAABAspNkc6RnzJihW7duadq0aZo2bZokqW/fvho6dKgCAgKUP39+VapUSV5eXipRooQaNmwop9OpAQMGJFUXAQAAgMeWZEG6X79+6tev3wPXL1iw4IHr2rdvr/bt2ydFtwAAAAAjbMgCAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGEj5d3cAAADgcZWuse2xfi547Vse7gnAiDQAAABghCANAAAAGCBIAwAAAAYI0gAAAIABgjQAAABggCANAAAAGKD8HTxqvffzj/2z1SKOebAnAAAA9mJEGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwABBGgAAADDAzoYAgL9F6RrbHuvngte+5eGeAIAZRqQBAAAAAwRpAAAAwABBGgAAADBAkAYAAAAMEKQBAAAAAwRpAAAAwADl74B/gPXezz/Wz1WLOObhngAAgBiMSAMAAAAGCNIAAACAAaZ2AAAAj2FqGp5kjEgDAAAABhiRBoBkoHSNbY/9s8Fr3/JgTwAAj4sRaQAAAMAAI9IAAPxDcSYD+HsxIg0AAAAYIEgDAAAABgjSAAAAgAGCNAAAAGCAIA0AAAAYIEgDAAAABih/B8Btj1uCi/JbAJB4bLOefDEiDQAAABggSAMAAAAGCNIAAACAAeZIA8ATirnrAOBZBGngX4iFKwAAuI+pHQAAAIABgjQAAABggCANAAAAGGCONP5xWEAFAACSA0akAQAAAAOMSAMAkADOgOFJ9LiVmySqNz0KI9IAAACAAUakAQBPDEaQASSlJypI/xMOoP+EPgIAPIP3AODJ8kQFaQAAgL8bH5jc90/5GxKkAfwr/FMOygCSHscHmGKxIQAAAGAgWY5IO51ODRo0SMeOHZOPj4+GDh2qvHnz/t3dAgAAwD/Q45b8S2y5v2QZpDdt2qTw8HAtWbJEP//8s0aOHKnp06f/3d0CAOCJxzQH4PElyyC9f/9+lSlTRpL00ksv6fDhw3G+HxUVJUm6ePFi3Osjrj5W+2fPnrWhl2bs7mNyv8/XE/EM+7vus93tPe59TsxjYnebdrfniedhcn+c67fa81g/FzT7tcf6ucftn/TkHB/+jc+bv6s9T7TJ8Sbp2vu73gM80eY/8W8Ykzdj8mdsDsuyrMdrOun07dtXFStW1FtvRX/affvtt7Vp0yalTBn9V9i3b5+aNm36d3YRAAAA/yKBgYEqUaJEnOuS5Yi0r6+vwsLCXP93Op2uEC1JRYoUUWBgoLJmzSovL6+/o4sAAAD4F4iKitKVK1dUpEiRB76XLIN08eLF9f3336tq1ar6+eefVbBgwTjfT5069QOfCAAAAABPSKjoRbKc2hFTteP48eOyLEvDhw/Xf/7zn7+7WwAAAIBLsgzSAAAAQHL3r9iQJTw8PFm3BzwJNm3aJEkKCQnRqFGjNH78eN2+fftv7lW08PDwBL/w99m27f/KrN24ccN1efHixX9Hdx5w/vz5BL/w9/n111/jvT7mGITHc/36ddflrVu3aufOnX9jb+J39OhRSVJERIQCAwMVFBQkp9P5N/cqricqSHfq1Ml1ee7cua7LrVq1ShbtSdIPP/xgfNt/qnHjxsVbMiY5unTpks6fP68LFy641Y7d99nTQfDEiRP6888/jW8/duxYrV69WlFRURoyZIhu376tp556SoMGDTJqb8mSJQl+mahcubKqVKmiypUrx/mqUqWKUXueULp06QS/TIwbN04BAQHxfpm6efOmhg8fLqfTqePHj6tu3bpq3Lix/ve//xm1N2fOHNfljh07ui5v2LDBuI+SdOTIEZ0+fdr1/3v37mnMmDGJbqdz587q0qWLOnfu7Lr83nvvqWLFisZ9S+4fHuJz69YthYaGJpv2Ro4c6br84Ycfui5/9dVXRu3Z/bw+c+aM2rVrp8jISO3du1dvvvmm3n33Xf30009G7Un2v6esXbtWDRs2VEREhKZMmaLp06crMDBQ06ZNs+13uGvevHnq37+/IiMjNWrUKO3YsUPHjh3T8OHD3Wo3PDxc165ds6mXT1iQjv2H2bp1q+uy6ewVu9uTpClTpmjo0KG2BaB79+7pyy+/lGVZunjxojp06KBu3brpypUrxm3afVCJiIhQ48aNderUKeM+xbZq1aoEv0z8/vvvat68uSSpRYsW6ty5sxo3bqxvv/3WuI923+eY0Bf7y50guGPHDr399tuKiIjQokWL1KZNG/Xo0UNBQUFG7e3bt0+TJ0+WZVnatm2bevXqpQ8++EBnzpwxau/KlSsJfpnYsmWLNm/erC1btsT52rx5s1F7kjR06FDX5TVr1rgut2vXzqi94ODgBL9M5M+fX/ny5Yv3y9TAgQOVK1cuSdH3v1mzZurXr5+GDRtm1F7sY2lClxNryJAhGjp0qNq1a6cNGzboyJEjqlq1qkJCQhLd1pIlS7R48WItWbJE8+fPV4kSJZQlSxa3Qq8nPjyEhIRo3rx5Wr58uWu07tixY2rUqJFRe0eOHFHt2rUVERGhb7/9VpUqVdJ7772nLVu2JIv2Yj8/IiMj470+Mex+Xg8ZMkR169ZVypQpNXLkSI0ePVoLFixw60Os3e8pgYGBWr16tby9vbV48WJNnjxZkydPjpN1EsvuY+I333yjxYsXK0WKFFq3bp1Gjhypfv36PbC3yOO6efOmOnTooCpVqqhVq1YqU6aMevTo4faHxGRZtcMOsV9QDocj2bS3aNEizZ49W40bN9aIESMeqEiSWEOHDlXatGnldDo1ePBgFS1aVAUKFNCgQYM0depUozYHDhyoV155xdV+s2bNVLBgQQ0bNizOm8Dj6tWrlw4cOKAuXbqoYcOGql27tut7Pj4+iW7v5MmTcf5vWZZWrFih1KlTx2n7cY0dO1bdu3eXJGXNmlXz58/XqVOn1K9fP+ORJ7vvs+kbTkKmTp2qoKAgeXt7a9asWZo3b56yZ8+uZs2aqX79+oluL126dJKkQ4cOqUCBAkqTJo2k6IO/ic8++8zodgl52Eh2w4YNjdo8fvy46/KyZctUs2ZNSdGjbclBnTp1bG/zypUrat68uUJDQ3Xs2DHVrl1bDodDd+7cMWov9rE0ocuJdeDAAa1cuVIhISH64IMPdPv2bQ0dOlSlSpUybvPo0aPq1auXSpUqpeXLlxu9hmN44sNDx44dVaRIEf3666+6cOGCsmTJoilTpqhXr15G7Y0ePVojR46Ut7e3JkyYoNmzZytv3rxq1aqVypcv/7e3Z/fzxu7n9e3bt/XOO+/oxo0bunjxot58801JcmtKgt3vKalSpVLatGn1+++/y8/PT08//bQkKUUK8/FVu4+J6dKlk5eXl44cOaLcuXMrQ4YMksxfK8OHD9e7776rSZMmua4LCgqSv7+/Ro8ebdSm9IQFabsOxJ5qT4p+kn7yySd666231KJFC+XNm1eWZcnhcBiNcpw4cUKLFy/WvXv3tH//fk2aNEne3t5xpqIklt0HFSm6pKG/v78++OADzZw503WfTUYEu3bt6rp8+vRp9ezZU2+//bb69Olj1Lc7d+6oaNGikqT06dNLii5zE3ukw4Sd9zkhLVq00Jdffpno26VMmVJZs2bVmTNn5O3t7SrrY3oQTZkypYKDg7Vy5UrXh4+9e/e6Dnx/N3fO0CQkoRBk17HCXQ+bEmI6yh3zAWnv3r0qUaKE676aHhvu3LmjP//8U06nU3fv3o1z2ZSvr6+k6NdyaGio5syZ4xptTCyn06kZM2Zo3bp18vf3t6Xsqic+PISFhalLly6yLEuVK1dWzpw5tXr1amXOnNmoPafTqRdeeEGXLl3SnTt3VLhwYUnmxwe727MsSxEREbIs64HLJux+XqdKlUqStGvXLr3++uuuPpucFYnNzvcUh8Oh0NBQbdy4UWXLlpUUfRbenfc9u4+JDodDf/zxh1auXOn6wPXnn38a7x9y5swZ1ahRI8519evX19q1a43ai/FEBenff/9dXbt2lWVZcS7fP4L5d7UXY9euXfL399f7779vNIIaW8xI4IEDB1S0aFF5e3tLip7yYcrug0pkZKSmTp2qDRs2aOrUqSpZsqRx32ILDAzUl19+qd69e6tcuXLG7cT+W8WeHxZ7E6DE8tR9vp/pKSmHw6HIyEht3brVFbjCwsKMA0zfvn0VEBCgLFmyqFGjRvrhhx80ZswYTZgwwag9u9k9wi155oO2nUzD8sM8/fTTCggIUHBwsNq2bavQ0FB9+eWXev75543aS506tfr37y8pOnzEvmwq9mPx9NNPG4doKfpsxfnz59WqVSudPHkyzrHf9EyGJz48xIxIOhwOpUqVStOnT3frbxhz7Pvhhx9cI/kRERFxNkr7O9s7d+6cKleu7AprMZdNX4d2P68LFCigLl266MiRIxoyZIguX76sSZMmuUK1CbvfUz788EPVqFFDGTJk0Ny5c3Xo0CF16tTJ9Ro0YfcxsWPHjurRo4eyZMmizp0768cff1T37t01ceJEo/Zi8tH93O3rExWkY79px54bZjpPzO72JGnAgAH66aefNG7cOBUqVMi4nRjp0qXTkiVLtHHjRlWvXl1Op1Nr1qxR9uzZjdu0+6BSt25dFStWTCtWrHAFf3dcunRJvXv3VsaMGRUUFKSMGTO61d7TTz+tQ4cOqVixYq7rDh06pKxZsxq3afd9TojpAaBOnTqqWrWqIiMj9eWXX+r48ePq3r27mjVrZtRenjx54rxeypQpozJlyhi1JUUvrOnUqVOy3rn0zJkzCggIkGVZcS6fPXvWqL0pU6Yk+D1PfBAwMWjQIC1fvlyffvqpKlSooJ9//lk3btzQgAEDjNqbP39+vNe7cwbh/sci9rzULl26JKqtsmXLyuFwKCwszDj03c/THx4yZcrkVluSVKpUKTVq1EgXL17U9OnTdfr0afn7+xuvyUiovapVqxq1l9BUN9MPI3Y/r3v27Knt27frww8/VNGiRXXs2DE999xzxsdXyf73lLfeekvff/+96/8+Pj5aunSpsmTJYtym3cfEYsWKxVm389JLL2nTpk0JBuJHifngev+ZC3fOtktPWB3pJk2aqGHDhqpSpYpbc9hiO3LkiNKnT688efJIih69nDRpkmtObWKNGjVKnTt3tq1/169f15w5c5QlSxZ98MEH2r17t7766isNHjzYNecpse7du6fly5crS5Ysqlixon7++WetW7dOXbp0Udq0aRPd3tatW/X2228/cP2VK1eMwmqJEiXk4+Oj119//YEgOW7cuES3d+bMGbVt21avv/668ubNqzNnzmjXrl2aMWOGcuTIkej2JPvvc3xzfC3L0ueff248fzo0NFQ+Pj7y8fFxLeQz/XAXHh6u8ePHa+PGjQoPD1e6dOlUrVo1tW3b1mhkf+TIkTpw4IDGjBmT4G5Sf7eVK1cm+D2T+cn3T+26c+eOZs2apZw5cxovArVbQqN+586dU86cORPd3pEjRzR58mRlzJhRPXv2lJ+fnwIDAzVjxgzjCkcJPS4OhyPRZwDtvr+e8sorr6hAgQKus6cxl02nDErRa1F8fX31zDPP6PTp0zp27Jjeffdd4z7a2d65c+c0b948ZciQQR9//LHSpEmjbdu2aejQofruu+8S3Z4nHueTJ0+6NpI7ffq07t6969aaKLvfU2La3LBhg27cuKFs2bKpWrVqbo2a231MnDlzplq3bi0p+kx+zNmMgQMHavDgwYlu72EfZBL6UP9YrCfIr7/+ag0ZMsSqWLGiNWzYMOv33393qz1/f3+rUaNGVvXq1a3169dbhw8ftsqXL2/179/fuM2OHTu61afHde/ePePbHj16NN7rV61aZdTeL7/8YrVu3drq0aOHde3aNcuyLGvBggVW6dKljdrbs2dPgl+m7ty5Y61fv96aOXOmtWbNGissLMy4Lcuy/z5Pnjw5wS9T//vf/yzLsqwvv/zSmjx5sjVlyhQrNDTUqC1/f39r+vTp1t27dy3LsqyQkBBr/Pjx1tChQ437t3//fqtu3brWkiVLrHv37rm+TIwdO9aKjIw07ktCrl27Fue54nQ6rcDAQLfb3bdvn1W5cmVrypQpHum3qWbNmrkujxw5Mt7rE6NOnTrWDz/8YC1ZssTq16+f1aVLF+v999+3/vzzT7f7GtvmzZutli1bJvp2dt9fy7Kszp07W126dIn3y9TZs2cT/DJx5MiReK//7rvvjNobMmSI0e0S0rBhQ2vx4sVWQECANXLkSGvcuHFWpUqVrL179xq1Z/fj/M0331gVK1a0bt26ZVlW9LGsYsWKxn8/y7L/PWXBggXWJ598YgUHB1u//fabtXnzZqtFixbW4sWLjftoWVac3HXq1Cnr2LFjxm3F/vsndDk5eKKmdrz44ovq16+fwsPDtXnzZo0cOVJ3795VvXr1VKtWrUS354nV37ELoNuhU6dOrlPqc+fOVcuWLSVF17o2ranZoUMHzZo1yzUKHx4ersGDB+uXX34x+jsOGDBAXbp00fnz512bdFy+fFkLFiww6l/JkiUVGhqq7777TufOnVOOHDlUsWJF1yKjxIrZXOGll17SSy+9JCm6TM7NmzeNR6Ttvs/t2rVLcMTExNKlS7V27VrNnz9fq1atUoMGDXTw4EF9/vnn6ty5c6LbO3LkSJyRL19fX3Xq1MmtU5l2LqyJKR1l5wj3zJkztWzZMkVFRWnYsGHKmzevOnfuLF9fXzVp0sSozYiICAUEBGjXrl1uT//yxFQRK9YJzCNHjsR7fWKkSZPGNUd/6tSpql27tsaOHWvL/MqbN28qKChIS5cuVZ48eVSvXr1Et2H3/ZXcmxqYkN9//11vvfWWpOja1E899ZSk6LMcJr9v5MiRrvePDz/8UPPmzZMUXae5QoUKiW4vdjUHOzgcDtcc9fLly+vVV1/V6tWrjae02P04z507V0uWLHEtXi9evLgWLlyoNm3aGP39JPvfU9auXavAwEDX9LkXXnhBpUuXVsuWLY3n/2/cuFEBAQFatmyZ0qdPr6tXr6p3797q3r270f22Eli86I6Yx+bOnTvy9vZWkyZN9NFHH7nV5hMVpGP4+PioSpUqKl68uBYsWKBhw4YZBUA7V3/HiJk3FJ/Ezt+THqx1HROk3XnSDRs2TG3bttWcOXN0+/ZtderUSa+++qqWLVtm1J7db5Z//vmn2rVrp/LlyytXrlw6ceKEZs2apWnTphnVyO3cubMcDofrb+ZwOHTq1CmFhIQY16u0+z63aNHC9cY2atQo9ezZU5LUu3dvow9Ma9eu1eeffy4pep59o0aNVKdOHTVu3NgoSNu9iMPuhTV2l46SpPXr12v9+vW6ceOGunTpoqtXr+rjjz82CmxS9G5tvXv3VpkyZVylCd1x/1zH2FNF7JhzbdmwKj/2HPinn37a6Ll3v8OHDyswMFAHDhxQlSpVlC1bNqOynfez4/5K0QMB169fV+rUqeNMlVu4cKHx83zOnDmuIN2xY0fXMWHDhg1GQTr2fbWjTvOlS5cSLEFpEtpiTxfLlCmTRo4caduCXzseZx8fH2XKlCnOdZkzZ3Zr7rrd7yne3t4PrEHx8fFxa12K3R8g7F68+MUXX+iPP/7Q8uXL5evrq9DQUA0fPlyzZ892a6O9Jy5I3717Vxs3btTq1asVGhrqVtF3O1d/x0idOrVbGyI8jF0H+hIlSqh///5q2bKlwsPD1bdv33jnZj0uu98sR40apXHjxumFF15wXVe9enWNGjVKM2bMSHR7sQ/w4eHhmjRpksLCwjRr1izjPtp9nz0xMhZTnSVmwU+qVKmMR/UluUpQxWbaP08s1rS7HGHGjBnl4+OjZ555RpcuXdLEiRNdZb1MNGjQQOnSpdPevXtdI/mWG/NcYweo/fv3q1+/fmratKk+/fRT4z7a/cZmxSpdljp16jjPIdMPOI0aNdJHH32ktWvXysfHRx9//LFx/zxRmcUTZzISGrkzff3ZXaIvIiLC1hKUsfvh6+vr9mPjidK5d+/eVerUqV3X3blzx7iuvmT/e0pC99Od9xS7P0DEfACzLCvO5cuXLxu1t3HjRgUGBrrKLvr6+mrw4MF6//33CdIxevbsqd27d6t8+fLq1q2b21Ux7Fz9HSNLliy2bpTgqRJcr732mvr166cJEya4NmcxZfebZWhoaJwQLUmFCxfWX3/95VY/7d50we6AELvtGKaP+b1791whrXHjxq52TWuI3l+Oyt3+denSxdaFNZ4oRxj7vmXPnt2tEC1FH+TtZudUESn6Q1yjRo1ci9piLpuWBI153kjRz79KlSpJklsfcBYuXKigoCBVr15d7777rm7fvm3UjmT//ZXsP5Mh2R98Yx+/7r9swq6zIDEOHDjgGp29efNmnJrpJmUf7X6cmzdvro8//lgtWrRQ7ty5dfHiRc2ePVvvv/++UXuS/e8pMff5/t/hznPb7g8QNWrUcH0Ai325evXqRu15e3s/ULvc29vbrVK30hMWpF977TUNGjTINdLmrg4dOsR72Z3AWqRIEbf6dD9P1Lpu2LCha6rD6dOnVatWLVcFEJORsfvfLGPX/DR5s0zoYB4VFZXotiTPbLpwf7B0NyDY/YGpbNmyGjt2rLp27aoUKVLIsixNmDDBdXo4sezeeTFLliz69NNPbavm4IkR7vtHSGKf2TA5XW33PFe7p4pIcbf9tUNCz5uffvrJuM1ixYqpWLFiun37ttavX699+/apfv36qlWrVqKDjN33V7L/TIZkf23qhx2/TDzzzDMPXHfjxg0FBQXpk08+SXR7CU25u3jxYqLbkux/nCtUqCA/Pz8FBQXp8uXLypkzp7p27epag2PC7vdRTzy37f4AYXfZT4fDoWvXrsXZqOjq1atu7eYoPWHl78aNG5fgC910BPl+W7ZsUWBgoPGcu71797ouxxTPf+6554zD/48//pjg90xH3R62gC05lHwaNmyYnn32WTVt2tR13cKFC3XixAkNHDgw0e3Vr1/ftenC/eX9TBdd2C2mvJUUHbiee+451wemffv2Jbq9qKgoBQQE6Ouvv1amTJl08+ZNVapUSd27dzc+qGzcuFELFizQ+fPnlSNHDjVt2tR14E+sunXruhbW/PLLL66FNUOHDjVaLOiJ0lF2L+Zr3ry5a25rQpcTo0iRIkqXLp2effZZ13HRnakiMZYsWaL33ntPKVOm1L59+3TixAnXWQ13hIeHuxZAhYeHa926dW63GePYsWNatmyZ+vbtm+jbHj16VBs3bnSVCKtcubKeffZZ477EfjybNWvmXtmt/89jZb084NChQwoMDFRwcLAqVapkXKs5tt27d7vmxe/YsSPRt3/YKPbDdghNSpcuXYr3A4k7zp07p5UrV7oW7depU8ftKawHDhyI8wGibt26xh8g7N6ddffu3Ro9erQ+/fRT5cmTR2fPntX06dPVpUsX1zbuJp6oEen8+fN7pF07Vn/HWLRoUZz/3759WydOnFD//v2N5iGXLFlSt27dUoYMGfTtt9+6TmNWq1bNuI85c+a09QVmdy3Izp07q3///lq8eLHy5Mmjc+fOKU+ePBo9erRR/zyx6UJCC0olsw91a9asUUhIiObMmSNfX1+VKFFClStXNh5l9PLyUvfu3dWlSxfduHFDmTJlcuv01qpVq/T1119r0KBByp07t/7880+NGTNGYWFheu+99xLdnt0La+we4ZbiD8vHjh1TYGCgUXt2z3P1xFSRyZMn68SJE6pZs6ZSpkypbNmy6YsvvtD169fVrl07ozbPnj2rwMBAff3117IsS+PHj1fx4sWN+7h8+XLXc+7EiRMqUKCAnn/+eaONm77++mvNmjVLjRo1UpEiRXT+/Hm1b99eHTt2NK6+YPeZDMn+sJzQh0SHw2H0OIeHh2v9+vUKDAyUj4+PQkNDtXnz5jhTABLr9u3bWrlypRYtWqQrV66oX79+RvsISNHTbRJiEqTtDoCS1L17d+NKXPE5dOiQ+vbtq6ZNm+qll17SqVOn9Omnn2rYsGH673//a9xu8eLF3Xr9xmb37qyvv/66Ro4cqSVLlmjZsmXKli2bhgwZ4v7meImvmJd8LVu2zNb2fvnlF6tXr15WxYoVrfHjx1vvv/++re3HuHHjhtWwYUOj23733XdWnTp1LMuyrJo1a1pjx461WrZsac2aNcu4PwcPHrSqV69uLVq0yPrhhx+sBQsWWNWqVbN+/vlno/YSqv/o7t/z+vXr1sGDB62LFy+61Y4nrFixIt6vlStXGrW3YcMGq27dutbSpUutTZs2WV999ZVVo0YN47qk4eHh1tKlSy3LsqwuXbpYzZo1s5o3b26dOnXKqL0mTZo8UOM5NDTU+Hkd+3lSr149ozZi82S94sjISGvDhg1W06ZNrUqVKlmzZ882asfumqlbt251Xb5+/brr8qJFi4zas6zox8LpdMa5Ljw83Kpbt65Re61bt7aaNm1qLV261AoJCbE++ugj477FsPPv2KhRowdqyoeEhLh17PJETfjYdZpXr17tuty2bVuj9hYtWhTna+7cuVapUqWMX4tvvvmmNWLECOuPP/6wLMty+3H29/e3qlatagUEBFh//PGHLc+b+Nhdz9wddtdObtmypXXu3Lk4150+fdr64IMPjNt88803XV+lS5e2SpUqZb3//vuux90u3bp1c+v2Z8+etfbs2WOdOXPGlv48USPSq1evNhr9Soidq78fxp3RwPnz57ummWTIkEFdu3ZVSEiIPvzwQ+NVqBMnTtTMmTNdNZRLly6tsmXLasCAAa56oolhJTC6Zjq6eH8Zpd9++8112WRExxOjB/EtKI0ZrUzs7mpSdP3W+fPnx5l6UqdOHeOyQmPGjHFN4Th//ryGDRumPXv2aNq0aRo5cmSi2/Py8npgwUu6dOmMSylZNi+s8US94itXrmjJkiVavXq1XnrpJYWHh+ubb74xbs/uea52l0STpLRp0z7wN/P29nZr3rmXl5fu3r0rp9NpWyWQR11+XClTpnxgupevr69bJcLsrgkvRR9bYixbtkw1a9aUJN26dcuoPbsrvrRo0UJr167VuXPnVK9ePbdrAu/fv1+FCxfWf//7X+XJk8fWhfaxde3a1ajsa+wpnLE5HA7jNTgJLQ40naoVHh7+wD4JuXPnVnh4uFH/pPjfL/ft26fBgwcbZYeE/O9//zO6XVhYmLp27aqbN28qZ86cOnXqlPz8/BQQEOBWxaonKkgntI+6JKOSc3au/n6Y27dvKzQ01Oi2TqfTtSgpZk50+vTp3VpwafcLzO6FcnaWUZLsP30UW1RUlL799lsFBgbq6tWrql+/vlE7dr+hHz161BWsUqZMqfz58yt//vzGtcKdTqfCwsLiBKrQ0FDjBaB2L6zxRL3iihUrqnnz5lq5cqV8fX3dKp8kRZfG7N+/v6ToUoSxL5uwM1DG7uOZM2eUO3du13Vnzpwxfl3PmDFDFy5c0PLly1W/fn3dvn1b27dvV+nSpY3n6ttZwSKh2zidzsR37P+zuyb8/ewYrJDsrfjy8ccf6+OPP9aPP/6ooKAgHT58WGPGjFGtWrWMts1etWqVay7uyJEjXetFYrbktovpa+X+KZwOh0O7d+9WeHh4giH7UZ577jnjqSvxie85bFmWW0E6PiVKlHCr7F98TJ/X48aNU+XKleMMZgUFBWn06NHy9/c37s8TFaT/+OMPDRgwIN4SXCYHKDtXf8e4f0FkeHi4duzYYVw/9N69e67L7du3d102DTCS/S8wu2tBFi1a1Li6RHw8MXpg92il3W/osW/XtWtX12XTT+VNmzbVZ599pm7durnmrY8ePdr4dWJ3FRC7R7il6EWvy5YtU4sWLfTee++5/WZh9zxXu0uiSVK3bt3Utm1blSpVSrlz59b58+cVHBysUaNGGbeZPXt2ffbZZ2rXrp1++OEHLVu2TAMGDNDWrVuN2rt586Z27Nghp9Opv/76S8HBwbIsy6g8ZkwlpNhiQpup2O9PdtWEt3uwwhMVX6TowZ6YdT2rV69Wjx49tGrVKqO2YubihoaGas2aNerevbskacWKFbb0VTL/W8ZeI3Pz5k0NHjxYBQoU0IgRI4z74uPjY+ti/zfeeENjx45Vly5dlCJFCjmdTo0fP96tRXcJMV17FN8gl2VZxgOPR48efWBxa/369Y0HkGI8UUH6hRdesHUyfoy0adOqfv36ql+/vo4fP66lS5cat3X/gshUqVKpYcOGxpu0vPTSS1qwYEGcwLJo0SK3yuzY/QKzuxZk7FPWdvDE6IHdo5V2v6HHHIx8fX1dz5XQ0FDjN/MqVarI19dXkydP1pkzZ5QtWzY1a9ZM5cqVM2rP7gWqdo9wS9Eb2VStWlVnz57VsmXLdObMGXXq1Em1atUyut/NmjVL8I3b5Lhm91QRSSpQoIAWLlyozZs36/LlyypcuLDatWvn1mnRmzdvKlOmTHI4HCpUqJAGDRrkVqgsXLiwq+JHoUKFXAvJTEZUJ0yYEO/1dm3zbdfocew9D+7f/8CE3ZsD/frrr3H+/hkyZFCzZs2UPXt2o/5J0YNFXl5e8vX1Va1atVS/fn2dOHHCqK0uXbo88PeP+Vu6Y9u2bRo+fLiaN28ep8qUiYSKHIwePVo9evRIdHutW7fWxIkTVb58eWXMmFG3bt1S5cqV3Tpbd3/wDQ8P13fffaeXX37ZqL2EFoGatpfQFFp3pmpJT1iQtltERIQmT56sdu3aKVWqVPr++++1f/9+9erVy7jN+ObOfv/99xo6dKhRSb3OnTurT58+Wr58uXLnzq2zZ88qV65cxhUsJPtfYO+++66ef/75B65fvXq1UXuxRxfvZzK66InRA7tHK+1+Q2/SpInat2+vnj17up437owgS1KZMmX02muvuUrKubPxzI4dO1xBevr06a4gbTo3zu4Rbil6dOOFF15Qrly51KlTJ3Xo0EFbt27V0qVLjYJ0zAcEy7LUq1cvt0Z5JfunikjR8+mluKU1b926pVu3bj0wHexx/Pjjj+rZs6dWrVqljBkz6ujRo+rfv7/GjBkTp9ZrYlSoUEGlSpV6YCqUiZIlS+rIkSNKnz698uTJIyn6LOCkSZOMy4t6YhOthPY8iH2WMjHsrvgycuRI14fBDz/80DVf9quvvjJa43H8+HG1a9dOy5YtU8aMGbVr1y6NHDnSaGdbKeHjqOnx9fbt2xo+fLhOnjypWbNmuZ477qhVq1a81z+sBO7DpEyZUl27dlXXrl11/fp1pU+f3u0zD/cH31SpUql48eLGm9CNGDFCoaGh8vLysmV/kEyZMumXX35R0aJFXdf98ssvRhV9YnuigvTEiRMlRX8Kunr1qrJkyeLWm/mIESOUMmVK18Hu5Zdf1o4dOzRy5Ej169fPrb7aVVIvTZo0Gj9+vK5evapz584pW7ZsrkL/pqWF7H6BdejQIc7BJDw8XIMHD9Yvv/yS4MHhYQ4ePBhnRFGS26OLkr2jB3aPVtqxE19s1apVk6+vr8aNG6dz584pe/bsatq0qXHN1IiICI0YMULbt29XlixZdPnyZb311lvq06eP0XPH7gWqdo9wS9Efli5cuKBXX31VZcqUUenSpVW+fHmVL1/eqL3YZ6vSpEnjdjlPT9QP7ty5s2uzJin68Th16pRCQkIS3CTjYSZMmKD58+e73shKly6tuXPnqm/fvlq4cKFRHzdt2qTRo0frmWeeUZkyZVSmTJkHdkJ9XEOGDNGvv/6q0NBQtWnTRnnz5lWHDh3cOv3tid0S7w8qBw4cUEREhF577TWj9v74448Ev2cyvSD2azj27qmmZx6GDRumgIAA1/MmZgOUoUOH6osvvkh0e0WKFNGKFSuUNm1a1a5d2+0NOqpXr6579+6pVq1aD0wbsGtPCzvYWftfkmvwKTw8XCEhIcYfhmPE7NmRMmVK9e/fX2XKlHGrve7du6tt27Z67bXXXANIu3bt0vTp091q94kK0hkyZJC/v7+2bdumrFmzuv1mfuTIkTgVIjJlyqS+ffsaLxiTondkiikcX6VKFWXLls14c5fYsmTJoixZsmj37t0aOnSocWH6GHa+wIYNG6a2bdtqzpw5un37tjp16qRXX33VeF7Sf//7X1tDgidGD6S4GznUrVtXGTJkUFBQkPF0Bzt16tTJtZPhnDlz9NFHH0ky3/xj6tSpypw5szZt2iQp+rTr1KlTNXHiRHXr1i3R7dk9amf3CLcUHVTDw8P1008/uRZROZ1OlSxZ0rimsp3snioixa2YEx4erkmTJiksLEyzZs0yas/Ly+uB+vT58uVzK8jEvJmfPXtWP/74o7788kudPn1aefPm1fDhwxPV1oEDB7Ry5UqFhITogw8+0O3btzV06FDX88eEJ3aUW7NmjUaNGqWMGTOqatWq2rhxo9KnT6+iRYuqd+/eiW7P7rrKds/XdzqdcUYVpeg506Zn/nr16qU8efLo1q1b+vPPP90Ou6ZnAh4mvg83lmXFWSeVGAnV/r99+7bq1q1r1OZff/2l/v3768iRI8qYMaOuXLmiUqVKacCAAUbTv9auXatvvvlGoaGh6tGjh9tBOnfu3Fq2bJm2bt2qM2fOqFixYurcubPbZ6+eqCA9ZcoUZc6c2TUq6e6beXynQB0Oh1unGDxRUu/+wvT9+/d3a3Wv3S+wEiVKqH///mrZsqXCw8PVt29fo81nHsV05ydPjB7Et5HD0qVL1bFjR6P27Hbt2jXX5W3btrmCtOkI0Z49e+LMNffy8lKHDh3UoEEDo/bsXqBq9wh3DB8fHxUuXFh//fWXwsLCdOTIkTjlGBMj9mLe+6cvmZxZs3uqSGxHjx5Vr169VKpUKS1fvtz4zJ9lWXI6nXGCc1RUlC2r/O/du+d6XGLm0iZWzG3Sp0+v0NBQzZkzx+2d33LmzKk//vhD+fLl01dffaVbt27J4XDogw8+MG7zyy+/1MaNGxUSEqLatWtry5YtSps2rfGOkx9//HG8Z0QOHDhg1F7s5/P9l00ktMg69mh3Yty4cUOTJk2SZVn68MMPjdqIrU6dOrIsS3v37tX58+eVPXt2lSxZ0q3jTUI7QGbKlMmovaCgIM2bN8/12i1YsKAmTJigjz76yDhIDxs2TO+++64mTZoU5/f4+/sbTTf18fGRj4+P/Pz8bDkmBAUFqX79+qpUqVKcM9rueqKCtN1v5n5+fvHOp3EnSNtdUm/IkCHavXu3KlSooClTpmjo0KHGi/hieOIF9tprr6lfv36aMGGCXnnlFbf6FzPXM4a728MmNHrgzovsq6++0oIFC2yr++xJdgTLhM74mNZHt3uBqifmpc6dO1fbtm1TSEiISpUqpbfffltdu3Y1ngZVuXLlONMmKlWq5OqvyZQlu6eKSNEBZsaMGVq3bp38/f2Nq9rEqFmzprp06aJPP/1UuXLl0sWLFzVt2jRVqVLFuM0hQ4boxx9/VM6cOVW2bFl17949Trm+xIj9XHn66afdDtGStHTpUq1du1bz58/XqlWr1KBBAx08eFCff/658TqUtGnTytfXV76+vipQoICrDKXpB5xq1aqpadOm6t27d5yFWBMmTDA6mxHfYl93lC1bVqNGjVLbtm2VPn16hYWFacqUKXr99deN2ot5nB0Oh1ulDWNcvXpVrVu3Vt68eZUrVy5t2bJFI0eO1MyZM/X0008btWn3VC27a/9L0Ytea9SoEee6+vXra+3atcZtxnC39rgUPcIdM6MgdhlKdz1RQdruN/NevXqpbdu2yp49u6vU07lz51xzsU3YXVLPE4Xp7X6BNWzY0BUQTp8+rVq1ailr1qzGK8ALFixo6yh8fAshtmzZYrx5iuSZjRzsZHewTJUqlU6fPh1nWszp06eN5+l/9tlnrmoOUnQ5QS8vL/n5+Rm1Z/cItyRNmzZNZcqUUevWrfXqq6+6vVDHEwsi7dawYUOdP39erVq10smTJ+PM6zXZDKlBgwby9fXV8OHDdfnyZeXMmVPvvfeeqlatatzHXbt2KU+ePHrnnXdUtmxZo7NUMe6vgBF7YbLp2aq1a9fq888/lxR9XG3UqJHq1Kmjxo0bGwfp2K9hd+f3StHrgcLDw/XBBx9o0qRJrr0KTMOM6VzthHzyySeaNWuW6tSpo7t37ypjxoyqXbu268xaYiU0Yi6ZfRgZOXKkunXrFmcK0Pbt2zVixAiNHz/eqI+xp2o5HA6lTp1aRYsW1UcffWQ0uGd37X8p4Qxm+h4TU60qZj1B7MpVJu/3dtXTv98TFaTtfjPPli2bli1bpv379+vy5cuqVKmSXnrpJVuCR+ySeseOHTOeL+yJwvR2v8Biv/nEZlqGyxOj8JJ9C0Alz2zkYKf4DlDuLHjq2rWr2rRpo3r16rkWccQUujdxfzWHY8eOuao5mIyC2j3CLUUHtn379mn79u0KCAhQ1qxZVbZsWb311ltGFSxCQ0Ndix99fX21du1abdmyRUOHDjXaOdDuqSKSXGUnw8LCjGvD3i9mYa5dNmzYoDNnzmjbtm3q37+/bt68qZIlS+qtt97Sq6++mqi2EqqG4e57QEzwibnfqVKlcquE4IEDB1xzl2/evOm6bFI7W4oeCPD399fChQvVqFEjTZw40XjBphS93uju3buqUaOGq3SZu3WzP/nkE33yySfGbcQWM2Ie0yd3y2NevHjxgXn0ZcuW1bRp04z7eP+i6JjNiwYOHGh0nLW79r+U8KZ4d+7cMWovdrUqO0pOeuLMpCQ5LDtj+d/s6NGj6tq1a7xv5oULF/67uydJWr58uWsb8xMnTqhAgQKSoud3f/bZZ261HVOYPiaUmxam//rrr7V06VLXCyymNFrdunUfOG1j4syZMwoMDNSaNWu0c+fORN++du3aKliwoCpXrqy3335brVu3Nl7sJD24AHT//v1un0Z74403HjiQWpalPXv2uLUI1C4PK5lkWiHk2rVrWr16tc6cOaNnnnlGderUMR4NbNKkiUaPHh3nVPoff/zhVjUHO0e447N9+3bNnDlTBw4cMJon3bVrVxUtWlQtWrSQw+FQZGSkvvzyS/32228aO3ZsotsrX758nKkiMdytbnO/Y8eOKTAw0K2dwTwlNDRUO3fu1Jdffqlff/1VP/30k9ttxpytMl0k3qBBAy1ZsiTOG7llWWratKnxc9tusRcdx5R87dixo5YuXWp8Ovz48eNas2aNDh06pFdffVU1a9ZU3rx57ex2spHQou1GjRoZnYV9GHfaDA4O1oIFC1y1/99//323FsPH1ByPj8l76u3bt22tphLzvmxZlnbv3h3nPdqddWVPVJCW7H0z94TYL7CELtth3759bs1fjHmBnT17Vs8884yaNWvm9gLBbdu2acGCBTpw4IA++eQT1alTx3i+WMwo/P79+2VZlmbMmGE8Cl+kSBF99NFHateunWsBqDvBXPJMUE3Ovv/+e1urkTRr1izeA+/777+vBQsWJLq9+0e4g4OD3RrhlqLXS+zfv1979+7VH3/8oRdeeEGlSpXSm2++aTQi3bhx4wc2B5Kip0zErpaRHERFRenbb79VYGCgrl69qvr16xufVrfbN998o3379unAgQNKkSKFSpUqpTfeeEOvvPKK8Uh8fGerTOdxT5kyRXfu3FHXrl2VIkUKWZalCRMmKG3atK7KMnYJDg6Wt7d3oqdW3H8MvHTpkjp27Khjx47Z8mFk7969mj9/vi5evOjWBmd22bt3r0aMGKF06dJp6NChbgf8Xr16qVKlSnGOiVu3btXatWtt3eZbip6DHBQUlOjbXbx4UdmyZbO1L3br0KGDq5pKpkyZ3K6mktD7ssPhSPTZqjisJ8i5c+cS/DJx6dIlm3toWe+///4jLyfG9u3brerVq1tNmza1Tp8+bUVERFijRo2y3nrrLeM+/vbbb1br1q2tPn36WMHBwVbJkiWtkiVLWitXrjRqb86cOVb16tWtrl27Wjt37rRatmxp3Lf7hYSEWIGBgVadOnWsOnXqGLVx8OBBq1+/fta7775rjR492mrSpIlt/fu3aNasmetyx44d3W6vadOmVlRUVJzrIiMjrQYNGhi117hxY+vMmTNxrvvf//5nNW7c2LiPLVq0sD7//HPr8OHDltPpNG4nRkLHgKZNmxq1FxISYnXp0sUKCQmxLMuy1qxZY3Xq1MkKDQ017uPly5etyZMnWxUqVLC6detm1a9f37gty0r4GPvzzz8bt9mnTx9r/fr11rVr14zbiPHLL79YvXr1sipWrGiNHz/e+DgdW2RkpDV69GirXLlyVp06daxy5cpZI0eOtCIjI91u+34zZ860tmzZYl24cMHttsLDw60ZM2a41UZISIi1YsUKq2XLllbjxo2t+fPnu92v2Lp162Z0u0aNGlm///67tW/fPqt9+/Zu9+PatWtWkyZNrNatW1vDhg2z2rRpYzVo0MCt5+S9e/fifIWEhFjLli2zPvnkE6P2Yh+z3X1cY5szZ45VsWJFq0yZMlb58uWt2bNnG7cV83pzOp1WixYtbOphXJs3b3Y7kzxRc6TLly+vnDlzKmvWrJL+bw6W6aK2Jk2aqFevXrZWWbC7nuaYMWM0adIknTt3TuPGjdO1a9eUPXt2410DJWnQoEFq3769/vrrL3322WdauXKl/Pz81KpVK6PFd3PnzlW1atVUt25dPf/885o7d65x3+7n6+urJk2aqEmTJtq3b59RG3YvAP03smKd2IpdWs+U3dUcPFGv+IsvvtC1a9f0/fffa+fOncqWLZveeustZciQwai9PHnyaNOmTXGON5s3b3YdzxJr4MCBKlq0qGt+dZUqVXT58mUNHDjQaKqIJFWsWFHNmzfXypUr5evrq1atWhm1E6Nbt26uM3Hdu3fXmDFjJEWfZjU9Qzd06FBt3rxZfn5+Cg0N1dSpU+Xj46PWrVsnul6sJ8qVenl5qXv37urSpYtu3LihTJkyGS+IfxQ75hCHh4dr7dq1CgwMVHh4uNGo+YYNG7RhwwadP39eFStW1ODBg22pgHK/h20k8zDe3t6uM5qTJ092ux9+fn4KDAzU4cOHdebMGVWuXFnFixd3q837q/qkTp1ahQsXNp5SFfuYHbvOvju++OIL/fHHH1q+fLl8fX0VGhqq4cOHa/bs2UbHCrurqcSwcz2U9IQtNpw0aZI2bNige/fuqXLlyqpYsaJbpeoWLFig/v37a+vWrerbt68tW1TevHlTwcHBsiwrzmXTRSEZMmRQvnz5lC9fPvXt21ft2rUzLvcXw9vb27Vz11dffaVnn31WkoyLlm/ZskUbN27UsGHDdPfuXd25c0chISFKnz69UXs//PCDRo8erYwZM2rEiBHKnj27AgICtGHDBm3dutWoTcm+BaD/RnYv4mjQoIHSpUtnWzUHywP1ivfs2aOBAweqUqVKypw5s44cOaLJkydrxIgRRiUee/bsqS5dumjq1KnKlSuXLly4ID8/P+MFm+fPn49zGjllypT66KOPjKprxBg2bJiWLVumFi1a6L333nO7tmvsN/OLFy/Ge31iBQQE6M8//9Tbb78tf39/pUmTRs8884wGDRqU6L+l3eVKpeipHQkxXSczdOhQ1267a9asUc2aNSVJ7dq109SpU43aPHv2rAIDA/X111/LsiyNHz/eOAx26dJF+fPn1wsvvKDjx4/HqVxh91QHd9kR2CIjI7VlyxZlyJDB9eH/6tWrGjp0aJwFdIlhd1UfOxfbxdi4caMCAwNdx1lfX18NHjxY77//vlGQtmyupuKpDfGeqCBdsWJFVaxYUSEhIfrmm2/UuXNnZcyYUdWrVzfaESdbtmyaNWuWVq9e/cD2yaZzdQoXLuzaNapw4cKuBYGFChUyai92MMiePbvbIVqK+wKL/WQ1PcD4+PioRo0aqlGjhk6dOqWlS5eqVq1aKlKkSJzC7Y/L7lH4+BaAPv/8867tZ/FoscuExVyOYfpaqVatmipUqKCrV68qa9asxvNbJc/UK54yZYoCAwPjbIPbsmVL9ejRw2ib4vTp02v27Nk6f/68Ll++rOzZs7u1viOhUU53yvTFVNg4e/asli1bpjNnzqhTp06qVauWrXPk3XmT37t3rxYvXqzIyEht27ZNW7duVZo0aYw2J/HE2aosWbLE+f+dO3c0a9Ys5cyZ0zhIHz9+3HV52bJlriB969Yto/Y+/fRThYaGqlatWlq3bp06derk1oiqnet/pOi53/ezLEuhoaFG7cVXHjOGyQfPbt26ycvLS1euXNHvv/+uXLlyqW/fvmrevLlR/2LYuePwzZs3tWPHDjmdTv31119x/qYmu1dK0ceW+8/yeXt7G59xub+airu19T1xhkl6woJ0jPTp06t+/fp67rnnNG/ePPXu3TveF97juH79urZt26aMGTMqX758bvetefPmmjhxojJnzqxq1aq56oaabOMqRR+E//zzTzmdTjmdzjilZ0z7a3dptNjy5s2r7t27q3Pnzvr++++N2rB7FH716tWuID1kyBDXQf9hCwYRV0JlwkxFRERoxIgR2r59u7JkyaLLly/rrbfeUp8+fYyCoN0j3FL0G3fsEC3JePGs9H8bBOTIkcNoseL97J4qEluuXLnUqVMndejQQdu2bVNQUJBRkPZEOaqYqSyHDh1SgQIFXGcS3Rk9j3226vjx424tkItdxmv//v3q16+fmjZtqk8//dS4zdgj+LEvu/M39fLy0t27d+V0Ot1+bOxeYJ3QFuYxpfUSK6HymKZOnz6tFStWKDw8XO+99568vb311VdfuVWWNqEdh8PCwlzvX4lRuHBhrVu3TlL0QF7sv6lpkHY4HLp27Vqc4+LVq1eNp9DZPQrviTNM0hMYpI8ePap169Zp+/btKlSokOrXr29cAH39+vUaO3asWrVqpaZNm9rSv8GDB6tDhw66efOm2rVr5/b849SpU7t2+kuVKpXrssPhMB4FSKh2o2kdxwsXLmjOnDny8/PTO++8o/bt2ysyMlKDBg0yas/uUfiE3oTcOb38bxPfpjbumDp1qjJnzqxNmzZJip6GMXXqVE2cOFHdunUzatPOEW4p4Y0vkkutcLunikjxjwR6e3urSZMmRu3ZXf9Yih6JDw4O1sqVK1WxYkVJ0aPUpnPXd+7cqTfeeEOjR4/WjRs35HA44mwMYSIiIkIBAQHatWuXxo0bZ3xGMobdH0hmzJihCxcuaPny5apfv76rZnHp0qVt2fDFXSNGjHjguuvXrxtPx3O39Oz9YmqC+/j4yOl0au7cucZbecd42I7DJkE6vr+hu9q0aaOPP/5Yn376qat07vTp043PSsYeYIy9CU2tWrWMnoeeWg/1RAXpatWquf4dPXq0UqVKJSn606HJ6OxXX32luXPn2jISHcPb21tvvPGGq3135x/bvW2oZP/oQY8ePVSjRg399ddfatasmSZNmqRs2bKpZ8+eKlu2bKLbs3sU3u4FoHDfnj174pSC8/LyUocOHYw/NNk9wi3pgSksklw74Jm4f+eu2Ezmkdo9VURKeCRQMhvFOnz4sDvdiVffvn0VEBCgLFmyqFGjRvrhhx80ZswYo7mp06ZN04kTJ/TGG29o3759at++vfbt26dp06a5Bi0S69dff1Xv3r1VpkwZBQUFub0jphT/1Cp3notS9CDFZ599pnbt2umHH37QsmXLNGDAALfWoXjCoUOHFBgYqB07drg+OCVW7Oeuw+FQVFSU/vOf/2jIkCGu92hTmTNndjtES57Z0ttur7/+ukaOHKklS5Zo2bJlypYtm4YMGWL8QfH+M4a3b9/WDz/8oF9//VV9+/Y17qfd66GeqCAds7nCrl27tGvXLtf1pqOzixYtivOp58CBA4qIiHBru1O75x97YpGJ3ZxOpysAffPNN3r99dclmX94sHsUPr65Yu4sAIX7EgoXpnPtPDHCndAUlvbt2xu19/TTT7u1EPB+dk8VkRIexTp16pRRe55YeJcnT544oblMmTJGa2Sk6PeSmPnuqVKlUpkyZfTGG2+ofv36Ru1J/zfNaO/eva4NLKz/v4ue6cYaCU2tMn0uxnb58mU999xz6t27t9tncewSHh6u9evXKzAwUD4+PgoNDdWmTZuMdzGO70zLvn37NHjwYM2bNy/R7dm9tbXkmS29PaFgwYIPfMjctm2ba1fUxIjvdVupUiXjARVPrYd6ooK03aOz69ev18iRI5UxY0ZVrVpVGzduVPr06VWkSBH16dPHqE275x8fO3bMddmORSaeEPsTc+yDgOkBwO7HOaG5Yu6ebv03OX/+fILfMwlxqVKl0unTp5UnTx7XdadPnzZ+o7R7hFuKns5y/fp1pU6d2vWh0LKseDdVeRzp06f/x27W07VrV6NRnQULFihDhgyqVq2asmXLZst0KrsHF2KOXy1atHD937TikBS9YMxudk+t+v333+Xv76+vvvpKLVq0UMaMGXXp0iX16dPHeNTXTuXLl1f16tU1duxYPfvss2rVqpXxsSEhJUqUMJ5Xb/fW1pJntvSWos9m3LhxQ88884zbZ6xWrFihgIAApU6dWpMmTVLu3LnVr18//e9//zMK0gkxnV7kqfVQT1SQlqJrFi9evFh37951zd0z3XHriy++0MaNGxUSEqLatWtry5YtSps2rdHq7xh2zz+Oza5FJnaz+7RjQnOuBw8ebDTylNAom2ld6n+jzp07x6lx6nA4dOrUKYWEhBidvu/atavatGmjevXqKXfu3Dp79qyCgoKM5/faPcItSTNnztSyZcsUFRWlYcOGKW/evOrcubOrtnliubtz6P3sniryMKYBODg4WD/88IPWrVun3377TRUrVlSlSpXifOBOLDsrWERERCg8PFw+Pj6uRZvh4eFujQI+rNZxzpw5jdqMb1pNWFiY7t69a7Rd/dixY9W9e3dJUtasWTV//nydOnVK/fr1SxZBukWLFlq7dq3OnTunevXqeWw9S1hYmNHtvv32W9eHObtUqVJFlmVp3Lhxunz5smvHYdNqOWfPnlWnTp3k7e2tzJkz6/z580qTJo3Gjx9vvGh63rx5Wr9+va5cuaKRI0fq8uXLeuedd4zr1sdn9+7dxtOhPLUe6okK0jHFwFesWGFLMfC0adPK19dXvr6+KlCggOvg7s7pLbtHnP4Jc3rtPu34sDnXpqdw4zNy5EhqST+m2OWiwsPDNWnSJIWFhRlvtf7CCy/oq6++0urVq7Vjxw4988wzmjt3rvGIid0j3FL0Gav169frxo0b6tKli65evaqPP/7YuLj/888/n2B1IZP5x3ZPFXkY02NPypQpVa5cOZUrV05hYWH67rvv1LVrV9cbugk7K1jUqFFDffr0Uf/+/ZUxY0bdunVLw4cPV/Xq1Y36JiU8zzwiIsK4WsL9z5tFixZp7ty56tWrl1F7d+7cUdGiRSXJNfqeN29eRUZGGrVnt48//lgff/yxfvzxRwUFBenw4cMaM2aMatWqpYIFCya6vfv/fuHh4fruu++Mq4DE/jBnl8DAQM2ZM0deXl7q37+/0fqi2EaOHKlevXqpRIkSrut27Nghf3//h065ephMmTIpY8aMypgxo06ePKlBgwa5NRJ9/+vB4XAod+7cGjJkiFF7nloP9UQFabuLgcf+4yaHlcrx8cQiE7tt2rRJxYsX19tvv+1W+Z8Yds+5TghVOxLv6NGj6tWrl0qVKqXly5e79aHT4XCoUaNGcR7XhQsXGo322j3CLUkZM2aUj4+PnnnmGV26dEkTJ05U4cKFjduzeyGfJ6aKdOnS5YE3nZhjj7uOHDmiAwcO6Pz5864NoUzY+QbZtGlTORwOvf/++/rrr7+ULl06NW3a1K0ziAmdAXN3dzUpuh5y3759lS5dOi1ZssS1biix7t2757o8bdo012VP7cBoqmTJkipZsqRu3bql1atXq0ePHlq1alWi27n/tZcqVSoVL17ceMrM/bWoYzP9cLt27Vp98803Cg0NVY8ePdwO0tevX48ToiXpzTffNB78kOK+3nLkyOH2dA7TssUJsXtDvBjJ61XhJruLgXuiNJPdPLnIxC6TJ0/WgQMHtGLFCp08eVJ58uTR22+/rZIlSxo9NnbPuU5Ich3hT46cTqdmzJihdevWyd/f/4EDdGLZPW3C7hFuKe7zI3v27G6FaEnq06dPvHNvTddP2D1VREp4CpppsDx06JDWr1+vnTt36qWXXlL16tU1ePBgt157dg8uNGnSxLi8X2K4+8F99erVmjJlijp27OjWiLkUfTbj0KFDKlasmOu6Q4cO2VKD3A7xrcl455139M477xi1d/+Hm1u3bilFihTG2SEiIsLtWtT38/HxkY+Pj/z8/NzeUVRK+EORO+U7Y4fT0NBQtzd5CQ8P16JFi9S8eXNdvnxZw4YNk4+Pj3r27Gn0XLx/Q7yFCxfKy8vL7fVQT1SQtrsYeEJzOy9dumTUnifs3LlTDRs2dDu4eFKKFClUokQJVx/PnDmjrVu3avHixUY7G9r9RtmwYcN4R9n+97//GbX3b9SwYUOdP39erVq10smTJ+OEP5MRGLunTUj2jnBLcXdDu3z5stu7oX3yySf64osvXGU7pehwNGbMGKORGbunikhSkSJFtGLFCqVNm1a1a9d2+0xdgwYN9J///EdlypSRt7e3duzYoR07dkgy3xHznzC4EB93Pjy0b99eBw4cUJcuXZQpUya3A0z37t3Vtm1bvf7668qbN6/OnDmjXbt2acaMGcZ9tJPdazKOHDmivn37KigoSN9//70GDhyoDBkyqEePHkbh3J1dKh+HHWdLY0Lv/e26M1BYuHBhLV682BVO3d3kxd/fX+nSpZPT6dSgQYNUtGhRFShQQIMGDTJaOPz++++rT58+WrZs2QOPszueqCBtdzHw++3evdu1T3vMwf7vVqlSJc2ePVv+/v567733VKdOHeONBzzp6NGj2rhxo27cuKFs2bKpcuXKrtJPiWX3G+W4ceMUHBys119/Xd7e3rpw4YKOHz/u9qmzf5OyZcvK4XAoLCzMeIFObHZPm7B7hFuyfze0ypUrq02bNvr888/ldDrl7++v3377zbhKjd1TRSSpV69eypMnj27duqU///zT7WOrJzaFiNlPILnyxPSYdOnSqWzZsvEukDZ5rHPnzq2goCBt2bJFZ8+eVZEiRdSxY0fbp8+ZsntNxujRozVy5Eh5e3trwoQJmjVrlqsaiEmQdrf6RXzsLqkXe3Q2NndGZwsXLqy5c+faNo/7999/1+LFi3Xv3j3t379fkyZNkre3t+bOnWvU3ujRozVq1CjX4zx79mzlzZvX+HGO8UQF6ddff12jRo3S4sWLbSkGLkUXAF+5cqUWLVqkK1euqF+/fraveHdHhQoVXLu1rVq1Si1atNBzzz2XrEapv/76a82aNUuNGjVSkSJFdP78eXXo0EEdOnSIs33x43rzzTfjXVV88OBBo/6tWrVKx48fV61atZQmTRpZlqUvvvhCN27cULt27Yza/Lf57LPP4h1RO3funFF7dk+b8MQId3wjTseOHVNgYKBRey1atFBERITatm2rq1ev6rXXXtPixYuNV6jbPVVEkm7cuKFJkybJsix9+OGHxu3EqFOnjizL0t69e3X+/Hllz55dJUuWdGt0tnLlyg+MVsbUad68ebNRm3aWCLN7eowUvXDMbqlTp3ZtiBEaGqrly5dr0aJF2rBhg+2/y5RdazKcTqdeeOEFXbp0SXfu3FGRIkUkma+NGjt2rE6ePOlaE3TmzBnduXPHaCFkDLtL6iX0ITY8PNy4zXXr1rkqndkxjztm6uaBAwdUtGhR17Ew9hz+xLj/cY55X3F7Gqf1BJkxY4at7fn7+1tVq1a1AgICrD/++MP66KOPbG3fE+7du2cFBARYRYoU+bu74tKoUSMrLCwsznUhISHW+++/b9Res2bNXJe7desW7/WJUa9ePcvpdMa5Ljw83Kpbt65Re/9Gsf/2I0eOjPf6xKhYsaK1ePFia9GiRa7LMV8mYj/X3nnnHevw4cNG7cQnMjLS2rBhg9W0aVOrUqVK1uzZs91qb/r06VaLFi3c7lejRo2su3fvxrlu1apV1ptvvmncZuzH0/Sxje3KlStW3bp1rc6dO1vjxo2z2rdvb9WuXdu6dOmS223b4cyZM9Z7771nNWrUyGrXrp1Vp04dq0mTJsmmfzEaNGhgNWzYMN4vd5w4ccIaOHCg9dprr1n9+/e3Dh48aFOP3RMVFWVNnTrVqlKlirV371632/vggw8sy7KsoKAgq3fv3pZlRb8H1KxZ06i9b775xqpYsaJ169Yty7Isa//+/VbFihWt7777zu2+2qVjx46uy3PmzHFddud1Hfu2zZs3N24nRvv27a3FixdbH374obV8+XIrKirKWrlypdW2bVuj9ux+nGM8USPSO3bsUOvWrW1rb//+/SpcuLD++9//Kk+ePMl68dm+ffu0evVq7d+/XxUqVHjoad2kljJlygdOCfr6+hpvbWrFmh928eLFeK9PjDRp0jzw2Hp7e7tVy/bfJvbf/siRI/Fenxh2T5uwe4Rbkq5cuaIlS5Zo9erVeumllxQeHq5vvvnGuL1x48a5Rk///PNPtW3bVs8995wks/nCdk8VkaIfz4iICFmWFeeyZFYWdOTIkerWrZtKlSrlum779u0aMWKEcfk7Sdq6das2bNjgmkpWrVo1V3WfxPbP7hJhnnD/VvXuiqmAFRERobp16+qPP/6Qv7+/rb/DHXavyShVqpQaNWqkixcvavr06Tp9+rT8/f0f2KL6cc2dO1dLlixxnREqXry4Fi5cqDZt2hidhfWEa9euuS5v3bpVLVu2lGRftSo72hk0aJDmzJmjMmXKqE6dOtq9e7c2btxo/Fy0+3GO8UQF6fgmz8cwmSe2atUqHThwQEFBQRo5cqRrB0I7SrjZZdKkSVq3bp2effZZ1a9fXwMHDkx2JYoS+gDizurgxPyeR0mTJo3OnDmj3Llzu647c+ZMsv7glJzFPoCa/g1jT5u4dOmSoqKi5HA4lD17dqP27F4YKEkVK1ZU8+bNtXLlSvn6+hqV2Iwtf/78rssdO3Z0qy3J/qkiUvRUncqVK7se45jLptMmLl68GCdES9Hz7WOXXEuswMBAbd++Xc2bN3dtNDFjxgydOnUq0Y+1J0qEecLw4cNVtmxZlSlTxpbt4Hv27KnmzZvrww8/1FNPPaVvv/3Whl7aJ6asml1rMj755BO988478vX11TPPPKPTp0+rYcOGevfdd43a8/HxUaZMmeJclzlz5jgLiZMTO47Zkv3zuP38/FwbA0nRQfj+40Vi2P04x0heictN169fT3Ak1nRxTfHixVW8eHGFhoZqzZo1rgd1xYoVxv20k8Ph0BdffGHLwdNT4tthLeZDiQm7N6Hp1q2b2rZtq1KlSil37tw6f/68goODNWrUKLfb/rew+zG5f4viTJky6eLFi8ZbFNs9wi1Jw4YN07Jly9SiRQu99957bpeksnubZ0lq1aqVIiMjtXv3bvXs2dPt9rZs2WJDr/6PJ+rzr127VoGBga4zXi+88IJKly6tli1bJjpIe6JEmCe0aNFCe/bsUc+ePRUaGqqSJUuqTJkyKlmypNGZgo0bN2rlypVq2rSpChYsqBs3bnig1+ZiPmifO3fONbc+V65cbrUZe4AsT548cTZvSiyHw6G7d+/G2fDpzp07tpSts4snNnPzxNbodrPzcY7hsOwax08GmjVr5tZpy0cJDw93BUB36w7aZfny5a6940+cOKECBQpIkqZMmeLR8juJ8bB97E02jChSpIjr0/7Nmzddl//66y/98ssvJl1USEiINm/erMuXLytHjhx6++235evra9TWv9Err7yiAgUKuEYiYi6fPHnSaKv1Tz/9VO3atVPRokVdr+uYLYrdfY3bMcId29mzZ7Vs2TKtWbNGxYoVU+3atT1SwzmxYk8VWbt2rQoVKuTWVBFJ2rt3r0aMGKF06dJp6NChyps3r1t97NWrlypVqhRnm+OtW7dq7dq1xou6E3ofaNGihb788stEtVWzZs0HSmNZlqWxY8dq9erVRv3ztPDwcG3fvl1z587Vb7/9pp9++smt9nbt2qWlS5fq4MGDqlSpki0fyNwVFhamrl276ubNm8qZM6dOnTolPz8/BQQEJIvj9qZNm/Tll1+qRYsWyp07ty5evKjZs2erYcOGbtf4tssbb7yhUqVKybIs7d6923V5z549yaYq2T/FEzUibTrnNiFHjx7VhAkTlDlzZlWrVk2dO3eWJPXu3TvZBOnVq1e7gvSQIUP01VdfSXp4eE1q94fl0NBQVyUUkxXgJnVCHyV9+vSqXbu27e3+W6xZs8bW9uzeotjuEW5JcXZQe/bZZ9W2bVvdvXtX3bp1M/rwYDe7p4pI0XNxx4wZo5s3b2rcuHFGdeBj69Gjh9q3b68lS5a4SpZeu3ZN06dPN24zodE1kzEjT5QI8wSn06kDBw7o+++/165du+Tr66u3335bAwYMMGqvd+/ecf6fOnVqFSxYUEFBQckiSI8bN06VK1eOc8yO2anUZP7s5cuX460EZapChQrKnDmzli5dqsuXLytnzpzq2rWrXnrpJdt+h7sSGj1OTiPJCT0uBw8e1H//+9+/oUfxe6KC9BdffGFre4MGDVL79u31119/qV27dlq5cqX8/PzUqlWrZBO6Yr85JHQ5ufj999+1YMECffPNN6pYsaLtJZtMRpxgjy5duqh3794PvFF8+umnRps42L1F8dixY13TsrJmzRpnhNs0SMeemrR+/XpVr15dlmWpcePGRu1J8ddbf/bZZ43a8sRUEW9vb9ep0cmTJ7vdnp+fnwIDA3X48GGdOXNGlStXVvHixd1q88iRIw+EAdOpZJ6oc+0JpUqV0uuvv65q1aqpTZs2bo/KHj58WHfv3lXNmjX18ssvu95PmjZtakd33Xb06NEHPiTUr19fy5YtM2qvW7durkEoO1iWpZdfflkvv/xynOvPnTunnDlz2vZ73FGyZEkdOXJE6dOnd01vuHfvniZNmmR0ptgTYj8u3bt315gxYyRFf5Cy8/Fy1xMVpGPPg3Y4HIqKitJ//vMfDRkyxOjNyNvbW2+++aYk6auvvnK1kVyK0ksJz3NKTgvlkmoFeGhoqO1t4vHcuHFDPXv2VKtWrVS/fn3X9aYLgezeotjuEW5Jceb9//zzz25vThJfvfX27durY8eOyWalf2x2zBOOPaovSadPn9bp06clyXiwIqGzI3fv3k10W82aNYv3WOpwOJLVh/aWLVsqODhY8+fP18mTJ/XWW2+5NWq+du1aHT9+XGvWrNHnn3+uV199VTVr1nR7Ko9dEvpAbfdZaVMtWrRwBb1Ro0a5RvF79+6dbALgkCFD9Ouvvyo0NFRt2rRR3rx51aFDB1fmSQ7srtDlKU9UkI6vYse+ffs0ePBgzZs3L9HtxT6Axl6wkZwWmsTe2z72ZXe2+bRbUq0AT04fHv5tsmXLpvHjx6tDhw46cuSIBgwYoBQpUhg/JnZvUWz3CPf97HjuffXVV1qwYEGcD+p16tRJViWzYlc/ibkcw6T6SUKj+u78Pe8f8Ttz5owCAwO1Zs0a7dy5M1FtDR48OM7/jx49quHDhyebea4xWrdurdatWyskJETBwcFasGCBTpw4oeeee854VL1gwYLq1q2bpOi58ePGjdPFixe1dOlSO7tuJFOmTPrll19cH44l6ZdfflHGjBmN2kvoLIbD4dDixYsT3Z7d5UA94cCBA1q5cqVCQkL0wQcf6Pbt2xo6dKhbVTGSSnJ7r3+ignR8SpQoYbxSNr5SLu5Um/CE2HP4Chcu7Komkpzm8H377bdasWKFbSvAY795x7AsS9evX3erXbgnc+bM+vLLLzV06FD9v/buPKqpM/0D+DdioogiAi2WCrZ2QYtjtVP1h4pWbCsqiGvFurRVqduo4IIrWjdELVo4VqsWbNW4ASoCSrUuKFSL1s44OnWtyGYLVaMGxCDJ7w9OMomAo+HGewPfzzmec7k53PMIIXny3ud9npEjR1arflboEcVCr3BbgtD91gFhS0UA4bufCL2qbywtLQ1bt27F2bNn8fnnn1dY/X4a+jpznU6HDRs2YO/evVi1apVkbn0/Li8vD7du3UJxcTHkcnm1u6Ko1WocOnQIycnJePDgAfr27StQpNUTGhqK8ePHo2PHjnBzc0Nubi5Onjxpdm3966+/brGJxUK1lhOavvynUaNGUKvViImJqXbnE6FJ9S7742p8Ig2Yf8vfGorxR44ciaioqEo3RErFiy++iHHjxmHcuHGGHeA+Pj5m7wCv6s17wIAB1Q2VzKR/s6hbty6++OILJCQkYOjQodVagTEeUVxdQq9wA+V14fquGEL0TBW637olSkWEHotuTKg3ytjYWOzZswceHh4YNWoUtFpttQZ1ZWVlYdasWXjzzTcRHx8vyUFNn3/+OS5fvoxWrVqhc+fOmDRpUrXmHezfvx/79+9Hfn4+PvzwQyxcuFBSSVazZs0QHx+PY8eOIScnB2+//TZCQkLM/qCtUCgErV22hgTQOK4XX3xRUr9fvbNnzxpKdlUqleFYSnfcgRrW/u7x0g6NRoODBw/Czs4OYWFhIkVlWYGBgZg8eTJUKhXmzp1rsiFSCrfgqnL79m3s27cPn3766TN/b1W3fqW0kaO2OX36NNq3b29y7ty5c/jqq68QGxsrUlSmSkpKDCvcL730Enr06FGt/Q5Ct3XUt6MyVp12VEOHDkVMTIzJ/1FfD1ndFoJlZWU4ePAglEol/vrrLwwePBijR4+u1jVHjhwpSP1oly5d0KdPHwwYMAAeHh4ICgoye4DKli1b8N1332H27Nno2rWryWPm9Ge2lDNnzqBt27aClSq1bNkSLVq0QMuWLQGYJl2WWrl9VhcvXkTLli2h0WgQFxcHhUKBgQMHmrUKn5iYiICAAMFiE7odqCV0794d/v7+0Ol0SE5Ohr+/v+ExIe8M1QY1akX68TZF+pY9Dx48ECkiy5PL5ejUqRMA6W6IXL9+vWFF6OTJk/Dy8oKjoyOuX79u1vWsYSNHbdO+fXvDbeC8vDy4urriww8/lEwSDQi7wg2Ylyw/ifEdMGPm3gGzRKmI0GPRhV7VB8qHxvzwww9YunQpSkpK8ODBA9y/f9+wyfRZ6PfWhIeHY9myZYY7LOZOcrSUx6cv6m3YsAFA+WbEZ0mypf46umnTJuzfvx/bt2/HihUrkJ+fD1dXV4SHh2PevHnPfD3jTcdCzGMQuh2oJUyePLnSYymtoD969AjR0dGYOHEi6tWrh6NHj+KXX35BcHCwpCY4SycSARhvqjh37hy2bt2KmJgY9OzZU8SoLMsaNkRmZGQYEul169YZVt3MTaStYSNHbZOVlYWJEyfCx8cHzZo1w5UrV7Bx40asXbsWr776qtjhWQWhb60KXSoCCD8W3RIlcwqFAv7+/vD398eNGzcQFxeHgIAAtG7d+pnr9qua5FjdISfPS6tWreDp6Yl79+7B0dHxqb9PqjXgeqmpqdixYwdkMhmSk5Nx8OBB2Nvbm/0cEnoeQ1V3Ro1buImtsvaYR44cgVKplEx73/DwcNjY2Bhey9q1a4eMjAxERESY9YHJUmpUIq3RaJCSkoJt27ZBLpdDrVbj8OHDJmM6axpr2BBpyV7XUt3IUdssX74ckZGRhlvBAODn54fly5dXqw65NgkJCTGszgLlz+cbN27g/v37Zg0henyFFzC/n7Ke0GPRLZ2wNW/eHNOnT8eUKVOqfTdCo9EYxo9rNBokJycLFKXlaLXaZ0qgrYWdnR1sbGxw4cIFuLm5wd7eHoD57ynPax6DuYtHlqRSqRAXF4ddu3bB3d0dgwYNEjskgwsXLpg0F3BwcMDcuXNNWqxKQY1KpH18fODn54eVK1filVdewZgxY2p0Eg1Yx4ZIoXtdW8NGjtpGrVabJNFAeRcZqW0KkTLjNwyNRoPo6GgUFRWZXd8rdKkIAPTu3Ru9e/c2jEXPyclBcHAwAgICTMZ8S41cLjckW88qNzcXSqUSBw4cgE6nw+rVq6s9NEZou3fvxqpVq1C/fn1ER0fDzc0N8+bNw++//45u3bqJHZ7gZDIZrl+/jj179sDHxwdA+V0xc8uWrGEeg9DOnz8PpVKJs2fPolevXmjatCliYmLEDstEvXr1KpyTyWSwtbUVIZqq1ahE+pNPPkFSUhLy8vIwaNCgWnGrX+q34IDKe8/qdDoUFBSYdT19z0/9Krz+WEqr8LVNVX9rZWVlzzkS63fx4kXMmjULXl5eSEhIMHtTmyV34Tdr1gzBwcGYPHkyjh07hri4OEkn0oB5SdG4ceOgVqsREBCA5ORkBAcHSy6JBsprhlNSUlBYWIiIiAgUFBSgR48e+PLLL8UOzSKmTJmC0NBQODs7IyQkBJmZmZgxYwaioqLMup5KpUJGRga0Wi3u3r1b7XkMlc200Ol0khoaFhgYiNGjRyMpKQkKhQJBQUFih1SBo6Njpf3CmUhbUFBQEIKCgpCZmYm4uDicP38eK1euREBAAN58802xw6u1quo9a+5Qg8TERKSnp+P//u//IJfLcfPmTVy+fLnCrnp6flq1agWlUmkyQnjbtm3w9PQUMSrrotVq8c033yA5ORmLFi2qcgPZ0xK6VKQyderUgY+Pj2FVUAr0GxiN6XQ65OTkmHU9GxsblJSUQKvVSnaF0sHBAY0bN0bjxo1x7do1fPHFFzVyJVqvTZs2iIuLM3zdtm1b/Pjjj2Z/cPf09DSU6rz11luGxgXmzmN4vPGB3uMjw8W0bds2xMXFwc/PDx988AGKi4vFDqmCWbNmYcKECXjppZfg5uaG/Px85OXlmf2ByVJqVPu7x927dw+JiYlISEgwqxk/WVZaWppZL/Zr1qzB5cuXsXz5ctja2iI3NxcRERFo1aoVJk6caIFI6X8pLi5GWFgYLl++DHd3d+Tl5cHd3d3wO6L/bfDgwcjPz8eYMWMqdNswZ2qgMX2pSFpaGpYtW4bWrVtX63pSJnRbwps3byIhIQFJSUkoLi7G0qVL0aVLl2oPOxGScevAYcOGCdLXW8ry8vKwadMm2NvbIygoCLa2tkhLS8OSJUtw6NAhs66pVqthY2Nj0dcrjUYjqbaJQPlrd0pKCuLj46HVahEQEIDhw4eLHZaBVqvFL7/8goKCAri6uqJt27aS+0BboxNpkoY9e/YgMjKy0vq9pKSkZ77e4MGDsWvXLpM/ptLSUgQGBiIhIUHI0OkZ3blzBzk5OXBxcYGLi4vY4ViVNWvWVPmYOS249IxLRUJCQqr1Rl5QUIAXX3yxwvl//etfePvtt82+rjXQ6XQ4ceIE4uPjce7cORw7dkzskAz69u2LmTNnQqvV4ssvv8SMGTMMj+mHWNQkgYGB6N+/P/Lz86HRaCCXy3Hw4EEsWbLErDs5SqUSMTExqFu3LsLCwuDt7V2t+IKDgw17FGJjYzFq1CgAwvVKF0JxcTF2796NBg0aoF+/fqhTpw4uXbqEuLg4yXTEqKxERk9Kz+saVdpB0hQbGyto/Z6trW2FT6RyuVySE8dqi/v37yM+Ph729vbo37+/4UV5wYIF2LFjh9jhWQWhpwYKXSoCANOnTzckAsatvCIjIyWTIFiKTCZD165d0bVrV9y6dUvscExUVZoASCvhEIpMJjPcpfHx8UH79u2RmJhY6ea0p5GUlITU1FSo1WqEhoZWO5E2fn4cO3bMkEhLad1y1qxZcHd3x71795CVlYWpU6fCw8NDMkk0UHWJDCCt5zUTabI4oev3bG1tkZOTAzc3N8O5nJwcyd3uqU2mTJmC1q1b4z//+Q9u3rwJZ2dnrFmzxqwR8LVdZVMDzTFkyBBDqci1a9dMNuOaWypinAj88ccflZ6vDZycnMQOwYR+hoJGo8Fff/0FZ2dnyZUQCMl4GIeDgwMiIiKq9fqvUCigUCjg6OhY7ZaOj5Nqi9Y7d+4gOjoaOp0On332mdjhVGrx4sWSGrxSFelHSFbP+MXD1dW12ptgpk+fjgkTJsDLy8uwASE9PR3Lly+vbqhkpqKiIkydOhU6nQ6+vr54+eWXkZiYKLmEQ8qEnhqo/zsrKipCUVGRUGFWSkoJQm306NEjhIeH4/jx43B2dkZBQQHee+89zJ49G3K5XOzwBGf8fGvYsKGgzz8hPhRaQws9fVwymUxSA9yMjRo1ynCnKyEhwTA0R2qYSJPFqVQqQzshtVptUvdkzu2ZN954A9u2bcPhw4dRUFAAT09PTJw4EQ0bNhQybHoG+tUvmUyGevXqYd26dWbfZq2thJ4aKHSpCGAdCYKQZs+eXeVjxpN0xbZmzRo4OTnhxx9/BFB+V+Prr79GVFQUpk+fLnJ0wjt79qzhvUOlUpm8jzyprrYqlQ020zNnVL21DEorLS2FTqczOQYgmbsZxh9qjKdPSg0TabI4T09Pk3ZCu3fvBlDebN3cOqdGjRpJZowpmSZVDg4OTKLNIPTUQD2hSkWAqhOYmjp45/FpiAUFBYiMjMTf//53kSKq3M8//4zt27cbvraxscHkyZPx0UcfiRiV5QjVvlGvqsFmQl9PSoPS8vLy4OvrCwCGO4k6nQ4ymQyHDx8WObpy1vJhnYk0WdzIkSMRFRUFJycn9OnTByEhIQCevNpD1qWqITkymYybDZ+S0FMDhS4VAYRPYKTOeNNZcnIy1q1bh5kzZyIgIEDEqCqqqnzDGupLzWF8m//KlSt44403AJSvzJvT4UbowWbWMCjtyJEjYofwPz148ABZWVnQarUoKSlBVlaWYZX61VdfFTm6/6qZf2UkKQsXLsTkyZOhUqkwceJE7NmzB46OjhgzZgxXlWuIffv2iR1CjSHU1EChS0UAy7XokzKVSoUFCxZArVZDqVRKsq1jvXr1kJ2dDXd3d8O57Oxs1K9fX8SoLMf4Nv/ixYsNdbRP6iFOptavX4+xY8cCAE6ePAkvLy8AwIIFC7Bw4UIxQzOoX78+wsLCAJQ/x/XHMplMUl2CmEiTxcnlcnTq1AkAsHnzZrzyyisAUGHoBFmvxMRETJgwAUDVvYbp2VR3aqAlSkW2bt0Ke3t79OnTB02bNq3x3TqOHDmCiIgIfPbZZxg6dKjY4VRp2rRpGD9+PAYNGgQ3Nzfk5uYiLi4OK1asEDs0izB+3lV1TE+WkZFhSKTXrVtnSKR///13McMysWXLFrFDeCrSGc1ENZZxnZPxJgap7hSmZ3fq1CnDcU3c3GSNevfujdjYWERFRaGgoMBQKnL06FGzr5meno7Zs2cjOzsbR48ehUKhgL+/v6RqP4U0YcIEFBYW4uuvv0aXLl1M/klJy5YtsXnzZuh0OmRkZKCkpASxsbHw9PQUOzSLqGrTq5Rqao8dO4YZM2YgKCgIYWFhOHnypNghmajqA4iUfoaTJ082HKelpYkYyZNxRZoszhp2MFP1cFWo+iw1NVCoUhGgvOa2e/fu6N69O4qKinDo0CFMmzYNtra2WL16tdkxStVvv/0mqcSiKvn5+QAAX19fQ+ccR0dHkaOyHJVKhYyMDGi1Wty9e9fQFUoqm16VSiWOHz+OkSNHwsnJCfn5+Vi/fj2ys7PN7uEuNKl+ADF2584dw3FMTEy1W+daChNpsjhr2MFM1WMNL8pSZ+mpgdUtFXnchQsXcPbsWeTn56Nz586CXFNqunXrhvbt28Pb2xtdunSBs7Oz2CFVKiQkxOTvrqioCBqNBitWrKiRo9urmuT41ltviRmWQVJSEpRKJWxsbACU3zHo0qULRo0aJZlE+s8//8TOnTuh0+lMjgsKCsQOrVJSXqBhIk0WZw07mKl62LWj+qxhauC5c+eQkpKCn376CW3btoWfnx8WLlxYYz88/fjjj/j111+RmZmJkJAQlJaWokOHDvD29kb79u3FDs9g586dFc5lZ2dj9uzZ1eobLlXLli2DWq2GjY0NbG1txQ6nArlcbkii9RQKRYVzYvL390dhYWGFYz8/PzHDqsC4v7UUe10DTKSJSADs2mE55iapligV+eijj/Daa6/B29sbcrkcGRkZyMjIAABMnTrVrGtKmUKhQMeOHdGxY0fcvn0bmZmZ2Lx5M3bt2mWyL0CK3N3da+wHHKVSiZiYGNStWxdhYWEmbQqloKqfu5Q+FFfVZUdKtcj6Xtf6n1vPnj0BQFK9rgEm0kQkgJdfflnsEKye0OUxligVkdI0v+fh/PnzSEtLw/HjxwGUT2INDQ1FmzZtRI7sfysrK8P9+/fFDsMikpKSkJqaCrVajdDQUMkl0vo7dMakti9oz549iIyMRP369REdHQ03NzfMmzcPv//+u2Rqka2h1zXARJqISBKEnhpoiVKR/v37Q6fT4fTp08jPz8dLL72EDh061NiVz8GDB6NXr16IjIxEs2bNxA6nSo+Xdmg0Ghw5cgTvv/++SBFZlkKhgEKhgKOjo2ATQIVkDXfoYmNjkZKSgsLCQkRERKCgoAA9evTAl19+KXZoJs6dO4ft27cjLy8Prq6uGDp0qOTq/plIExFJwPOaGlidpPevv/7C2LFj0bx5czRr1szQZ3n9+vU1snf4tm3bcPz4cUyfPh12dnbw9vaGt7c3XnvtNbFDM6Gvb9WrV68egoKCDP37azIplUvo6buoVEYqd+8cHBzQuHFjNG7cGNeuXcMXX3whmZVovePHjxumVbq5uSErKwuLFy/GP/7xD7z33ntih2fARJqISAKEnhpoiU4qERERmD59umF4A1D+Zrds2bIa2f6uXbt2aNeuHaZMmYJbt27hxIkTmD9/Pv744w9J1Wjqnx95eXnIy8uDi4sLmjdvjps3b0KhUMDJyUnkCIVVWUtVvcjISBEjK7d9+3aTr2UyGU6dOgWNRoPTp0+LFJUp49cEV1dXySXRAPDtt99iw4YNcHBwAFA+FrxNmzaYMmUKE2kiIjIl9NRAoUtFgPISEeMkGgC6du2KtWvXmh+ohOl0Ovz22284c+YMzpw5g6ysLHh4eGDw4MFih2aiqKgI06ZNg0qlgqurK7KystCkSRM0aNAAM2fOFDs8wVXVUlUqVq1aZThWqVRYuHAh3njjDUntMVCpVIb+22q1Gunp6YbHpDRwSJ9E6zk5OUnuLgQTaSIiCUhPT8eJEyeQnJyM3377DR9++CF69uwJOzs7s65niVKROnVq1zBcb29vtGrVCp06dcLEiRPh4eEBAHj48KHIkZmKjIyEr68v+vXrZzi3ZMkS5OTkSLq221zW0lI1LS0N4eHhGDlyJIYNGyZ2OCY8PT1N+m/rjwHpJNKPHj2CRqMxaXWn0WgkVxfPRJqISAKEnhoodKkIUH4L+OjRoyaTEY8dOyaZuk+hHTlyxORNPCcnB0qlEvv27cNPP/0kYmSmLl68iPnz55ucy83NrVA7Tc9HcXExwsPDce3aNWzcuBHu7u5ih1SB1HtxA+U9refOnYt58+ahcePGuHfvHpYsWYI+ffqIHZoJJtJERBIjxNRAoUtFACA0NBSTJk3Czp074e7ujtzcXNy6dQvr1q2r9rWlSJ9Ep6WlYevWrTh79iw+//xz7N27V9zAHlPZ73bt2rUYPny4CNGQn58fHj58iICAAMTHx5s8JpV+61LvxQ0AH3/8MerUqYPhw4fjzp07aNiwIUaMGCG51X0m0kREEiD01EChS0UAwNHREUqlEufPn0dOTg58fX3xzjvvmH09qYuNjcWePXvg4eGBUaNGQavVYuzYsWKHVYGjoyP+/e9/429/+5vh3Pnz59GkSRMRo6q9Jk2aVOl5KbWJlHovbr3AwEBJ1sEbk+mkVrVNRFQLtWzZ0mRqoPGbbnVXsfSlIqmpqWaXigB44kqscX1uTdGlSxf06dMHAwYMgIeHB4KCgrBx40axw6ogOzsb48ePh5eXF9zc3JCTk4OTJ0/im2++gZubm9jhEcrLhPSrwFIwcuRIw2CmTz75BN9//73IEVkvrkgTEUmAJXf0C1EqAsBkMltKSgr8/Pyg0+kktdImpCNHjuCHH37A0qVLUVJSggcPHuD+/fto1KiR2KGZcHd3R3x8PA4fPoz8/Hy0bdsWU6dORYMGDcQOrVZTqVSIi4vDrl274O7ujkGDBokdUqW4nlo9XJEmIpIIIacGVlYqIuQUwhEjRmDLli2CXMsa3LhxA3Fxcdi/fz9at26N6OhosUMyMG5d9jipdGCoTc6fPw+lUomzZ8+iV69e+OWXXyT3t9KpUyd4eXlBp9Ph1KlTJm0tpdCL21hOTg7u3LkDFxcXuLi4iB1OBUykiYgk4PGpgVlZWcjJyTF7aqAlS0UA01vDtUlpaSm+//57jBkzRuxQDGbPnl3p+dLSUsmNfK4NWrdujdGjR2PixIlQKBSSLAnKzMys8jGptBfMzc1FcHAw5HI5nJyckJ+fbyhNk9IkVZZ2EBFJgNBTA6U0/KEmkcvlSE1NlVQiXdXvWqqlBDXdtm3bEBcXBz8/P3zwwQcoLi4WO6QKpJIsP0lERARmzZqFd99913AuIyMDixYtemJ7z+eNiTQRkQQIPTWwf//+gpaKAOUr2TKZTLKjmZ8Xa7mRay1x1jRt2rRBmzZtUFxcjJSUFJw5cwaDBw9GQEAAWxI+g9u3b5sk0QDQuXNnya3uM5EmIpIAoacGPl4qcuTIEURERJhdKgKYjmOWeksqS7KWzZXWEmdN1aBBAwwePBiDBw/GpUuXEBcXJ3ZIVqVu3cpTVK1W+5wjeTIm0kREEiD01EChS0UA67gdLCT9CrwxnU6HnJwckSKqnLXEWZt5eHhg3rx5YodhVVQqVYWNtDqdDnfv3hUpospxsyERkQTcvn0bkyZNQqNGjSpMDXR0dHzm6w0fPhxbt26tcD4wMBA7duwQIuQazxo2ZAHWEyfRs6hqEy0grT0gTKSJiCREPzXQxcWlWlMDq+qqwUSayPIuXryIli1borS0FLt27YJCocDAgQMFL+Ei8bG0g4hIAh6fGpidnY3s7GwA5k0NFLpUhIiezqZNm7B//35s374dy5cvR35+PlxdXREeHs7yjmcwYsSISuv8ZTKZpCYxMpEmIpIAoacGhoaGYtKkSdi5c2eFUhEispzU1FTs2LEDMpkMycnJOHjwIOzt7Wv1Bl1zLFy40OTrixcvIjw8HH5+fiJFVDkm0kREEmDcSu6f//xntYemODo6QqlUGkpFfH19q1UqQkRPx87ODjY2Nrhw4QLc3Nxgb28PgO0In1WLFi0AlP/cNmzYgL1792LVqlWSq/tnIk1EJDFCtC0TulSEiJ6OTCbD9evXsWfPHvj4+AAAsrKyYGNjI3Jk1icrKwuzZs3Cm2++ifj4eNjZ2YkdUgVMpImIaiChS0WI6OlMmTIFoaGhcHZ2RkhICDIzMzFjxgxERUWJHZpV2bJlC7777jvMnj0bXbt2BQBoNBoAgEKhEDM0E+zaQUQkAcZTA0+dOmXS/7m6UwNHjBiBLVu2VDdEIjKDRqOBTCZDWVkZ6tevL3Y4VkO/mg/A8NqoPz58+LBYYVXAFWkiIgmw5NRArkITPT95eXnYtGkT7O3tERQUBFtbW6SlpWHJkiU4dOiQ2OFZjSNHjlR6/tdff33OkTwZE2kiIgmQ2gYaIjLPtGnT0L9/f+Tn5yM6OhpyuRwHDx6U1BARa6PRaJCUlASlUgmNRoPk5GSxQzJgIk1EVAMZl4pcvXrVpCtIdUtFiKhqMpkMQ4YMAVBentC+fXskJiaiXr16IkdmfXJzc6FUKnHgwAHodDqsXr1act2HmEgTEdVAliwVIaKq1a3739TKwcEBERERLK8yw7hx46BWqxEQEIDk5GQEBwdLLokGmEgTEdVILBUhEodx0tywYUMm0dVgY2ODkpISaLVayf4c2bWDiIiISCCtW7eGg4MDAEClUhmOASA9PV2coKzUzZs3kZCQgKSkJBQXF2Pp0qXo0qUL6tSpI3ZoBkykiYiIiEiydDodTpw4gfj4eJw7dw7Hjh0TOyQD6aT0RERERFYuISHBcHzlyhXD8Zo1a8QIp0aQyWTo2rUroqOjTX6+UsBEmoiIiEggiYmJhuPFixcbjjMzM8UIp8ZxcnISOwQTTKSJiIiIBGJcMVvVMZnvu+++EzsEE0ykiYiIiARi3F2iqmMyn5SGsQBsf0dEREQkGJVKhYyMDGi1Wty9exfp6enQ6XS4e/eu2KHVCFJb2WciTURERCQQT09Pw6rpW2+9hZSUFMMxPT2NRiN2CE+FiTQRERGRQD799FN4eHiIHYbV8/X1hUwmq7ACLbUSGfaRJiIiIhKIr68vAgMD8emnn4odCj0H3GxIREREJJD4+Hhcv34do0ePRmFhodjhWLUffvgBI0aMQI8ePTBixAikpqaKHVIFXJEmIiIiElhmZibmzJmDt99+23AuMjJSxIisy969e3HgwAGEhobCzc0NWVlZWLlyJXx9fTFw4ECxwzNgIk1EREQkoGvXrmHu3Llo0aIF+vXrZzjfoUMH8YKyMsOGDcOmTZugUCgM54qKijB69Gjs2LFDxMhMcbMhERERkUA2bNiAHTt2YP78+XjvvffEDsdq2djYmCTRAGBnZwcbGxuRIqoca6SJiIiIBHL+/HkkJCSYJNGXL1/G/PnzxQvKCmm1WhQVFZmcU6vVKCsrEymiyjGRJiIiIhJIdHQ0mjRpgrKyMhw4cADDhw/H5MmT0bx5c7FDsyrDhg3DP/7xD1y4cAH379/HxYsXMXnyZAwfPlzs0EywRpqIiIhIIIWFhdi5cycSExPRtm1b3LhxA7t27RI7LKt04sQJKJVK5OTkoGnTphg+fDi6d+8udlgmmEgTERERCaRdu3YYOXIkgoKC0LBhQ4wZMwbffvut2GFZLY1Gg8LCQrzwwgsVaqalgJsNiYiIiASydOlSxMfH45NPPsHAgQNRWloqdkhWqbS0FMuWLcPx48fh7OyMgoICdOvWDXPmzIFcLhc7PAOuSBMREREJLDc3F/Hx8di3bx/atGmDgIAAyZUlSNlXX30FuVyOiRMnAgDKysrw9ddfQ6PRYPr06SJH919MpImIiIgsRKvV4tixY4iPj8fatWvFDsdqDB06FNu3b69w/qOPPpJUzTm7dhARERFZSJ06deDj4wM7OzuxQ7EqVZVv1K0rrapkJtJEREREFnb9+nWxQ7Aq9erVQ3Z2tsm57Oxs1K9fX6SIKiettJ6IiIiIar1p06Zh/PjxGDRoENzc3JCbm4u4uDisWLFC7NBMMJEmIiIiEkh6enqFczqdDmq1WoRorFfLli2xefNmJCYmIiMjAy4uLoiNjYWLi4vYoZlgIk1EREQkkJSUlErPt2vX7jlHYt3y8/MBAL6+voZzZWVlyM/Ph6urq1hhVcCuHUREREQCUqvVsLGxga2trdihWK0hQ4ZAJpNBp9Ph2rVreP3116HT6SCTybBjxw6xwzNgIk1EREQkEKVSiZiYGNStWxdhYWHw9vYWOySrN2LECGzZskXsMCrFrh1EREREAklKSkJqaip27NiB77//XuxwagSZTCZ2CFViIk1EREQkEIVCAYVCAUdHR44HrwW42ZCIiIjIAlg9a76dO3cajv/880+Tr4cMGSJGSJViIk1EREQkkKtXr2LatGnQ6XSGY73IyEgRI7MuhYWFhmN/f3+Tr6WEmw2JiIiIBJKZmVnlYx06dHiOkVg/lUoFBwcHAOWJtY2NDRwdHcUN6jFMpImIiIhIUjIzMzFz5kzs3bsXjRs3Rnp6OsLCwrBy5Uq8++67YodnwESaiIiIiCTl448/xooVK9CsWTPDuevXr2Pu3LnYtm2biJGZYtcOIiIiIpIUGxsbkyQaAF599VXUqSOt1FVa0RARERFRrafT6aDVak3OlZWVSa6lIBNpIiIiIpKUvn37YurUqbh48SLUajWuXr2KGTNmoFevXmKHZoI10kREREQkOSkpKdi5cycKCgrw8ssvY+DAgejdu7fYYZlgIk1EREREknbv3j3UqVMHDRs2FDsUEyztICIiIiJJuXDhAvr164fS0lIcOnQIPXv2xMCBA3H48GGxQzPBRJqIiIiIJGXFihWIiIiAXC7H6tWrsXHjRiQkJGDjxo1ih2aCI8KJiIiISFK0Wi1atmyJP//8Ew8ePEDr1q0BgO3viIiIiIiepG7d8rXeEydOwMvLCwBQWlqKoqIiMcOqgCvSRERERCQpXl5eCAwMxB9//IF169YhOzsbixYtYtcOIiIiIqL/5dq1a2jYsCFcXFyQnZ2NS5cu4YMPPhA7LBNMpImIiIiIzMAaaSIiIiIiMzCRJiIiIiIyAxNpIiIr9ejRI4wYMQKBgYG4e/fuM3//7t27cfjwYfz8888ICQmxQIRERDUbu3YQEVmpgoICFBUVYffu3WZ9/4ABAwAAP//8s5BhERHVGkykiYis1IIFC5CVlYU5c+bg9u3bePjwIQoLCxEcHIz3338f/v7+ePfdd3Hp0iW0aNECTk5OOHPmDBQKBTZs2IBvvvkGzs7OaNGiBQAgPT0du3btQnR0NAAgMDAQUVFRcHFxEfO/SUQkWSztICKyUgsWLMDrr78OPz8/fPbZZ9i0aRMWLVoEpVIJACgqKoKfnx+2bduGM2fO4J133oFSqURpaSmuXr1a4XqdO3fG5cuXcffuXVy5cgVNmjRhEk1E9ARckSYisnIvvPAC1q1bh/j4eMhkMjx69MjwmKenJwDA3t4er732muH44cOHFa4jk8nQt29fJCcnIzc3F4MGDXo+/wEiIivFFWkiIisXFRWFgIAArFy5Eh07doTxeACZTPZM1xo4cCBSU1Nx+vRpdOvWTehQiYhqFK5IExFZOV9fX6xYsQIbNmxA06ZNcefOHbOv5eLiAjs7O7Rt2xZ16/ItgojoSTjZkIiITIwdOxZz5sxB8+bNxQ6FiEjSWNpBREQAgJKSEgwYMAAtWrRgEk1E9BS4Ik1EREREZAauSBMRERERmYGJNBERERGRGZhIExERERGZgYk0EREREZEZmEgTEREREZmBiTQRERERkRn+Hx2VXkT4StQrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn-white')\n",
    "fam_perishable = items.groupby(['family', 'perishable']).size()\n",
    "fam_perishable.unstack().plot(kind='bar',stacked=True, colormap= 'coolwarm', figsize=(12,10),  grid=False)\n",
    "plt.title('Stacked Barplot of locale name against event type')\n",
    "plt.ylabel('Count of entries')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2c2c4023-a111-48ba-99c8-b7fa5ee7d8a4",
   "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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>item_nbr</th>\n",
       "      <th>unit_sales</th>\n",
       "      <th>onpromotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>103665</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>105574</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>105575</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>108079</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>108701</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id       date  store_nbr  item_nbr  unit_sales  onpromotion\n",
       "0   0 2013-01-01         25    103665         7.0          NaN\n",
       "1   1 2013-01-01         25    105574         1.0          NaN\n",
       "2   2 2013-01-01         25    105575         2.0          NaN\n",
       "3   3 2013-01-01         25    108079         1.0          NaN\n",
       "4   4 2013-01-01         25    108701         1.0          NaN"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "431e913f-5ace-40f7-8a5c-b2488ea06bb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "id\tdate\tstore_nbr\titem_nbr\tunit_sales\tonpromotion\n",
    "0\t0\t2013-01-01\t25\t103665\t7.0\tNaN\n",
    "1\t1\t2013-01-01\t25\t105574\t1.0\tNaN\n",
    "2\t2\t2013-01-01\t25\t105575\t2.0\tNaN\n",
    "3\t3\t2013-01-01\t25\t108079\t1.0\tNaN\n",
    "4\t4\t2013-01-01\t25\t108701\t1.0\tNaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b7e31901-6012-4c8f-9f4f-c47ff873d5e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn-deep')\n",
    "plt.figure(figsize=(13,11))\n",
    "plt.plot(train.date.values, train.unit_sales)\n",
    "plt.ylim(-50, 10000)\n",
    "plt.ylabel('transactions per day')\n",
    "plt.xlabel('Date')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77f1eb15-0f08-446f-997b-b6969b4e8a8f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}