Datathlon 2018 entry - Hristo Piyankov.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About\n",
    "<br>\n",
    " <b>Entry:</b> Data Science society Datathlon 2018\n",
    " <br><b>Case:</b> SAP Case\n",
    " <br><b>Dataset:</b> <a href=\"https://fdibatusofia-my.sharepoint.com/personal/pnikolov_fdiba_tu-sofia_bg/Documents/Forms/All.aspx?slrid=ce11499e-b05e-5000-10fd-9e61d4c16eca&FolderCTID=0x012000658541D2E1754F44876F9448FAF6F5CB&id=%2Fpersonal%2Fpnikolov_fdiba_tu-sofia_bg%2FDocuments%2FDatathon_2018%2FSAP\">available here</a>\n",
    " <br><b>Authors:</b> Hristo Piyankov (hpiyankov@gmail.com)\n",
    " <br><b>Notes:</b> not all caclulations and graphs are carried out in python, due to time constraints"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Business understanding\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal of the study is to udnerstand drivers behind sales up-lift with relation to the company's own pricing strategies, promostions and competitors.'Identify possible action points for optimisation of sales and revenue. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data understading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample from the provided dataset with basic statistics is shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"/home/hpiyankov/Documents/Datathlon/Datathlon 2018/\"\n",
    "df = pd.read_excel(path+\"data.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 146 entries, 0 to 145\n",
      "Data columns (total 11 columns):\n",
      "Week                 146 non-null int64\n",
      "VOLUME_OF_SALES      146 non-null int64\n",
      "ACTUAL_PRICE         146 non-null float64\n",
      "COMPETITOR1_PRICE    146 non-null float64\n",
      "COMPETITOR2_PRICE    146 non-null float64\n",
      "COMPETITOR3_PRICE    146 non-null float64\n",
      "COMPETITOR4_PRICE    146 non-null float64\n",
      "COMPETITOR5_PRICE    146 non-null float64\n",
      "COMPETITOR6_PRICE    146 non-null float64\n",
      "COMPETITOR7_PRICE    146 non-null float64\n",
      "TYPE_OF_PROMOTION    84 non-null object\n",
      "dtypes: float64(8), int64(2), object(1)\n",
      "memory usage: 12.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Week</th>\n",
       "      <th>VOLUME_OF_SALES</th>\n",
       "      <th>ACTUAL_PRICE</th>\n",
       "      <th>COMPETITOR1_PRICE</th>\n",
       "      <th>COMPETITOR2_PRICE</th>\n",
       "      <th>COMPETITOR3_PRICE</th>\n",
       "      <th>COMPETITOR4_PRICE</th>\n",
       "      <th>COMPETITOR5_PRICE</th>\n",
       "      <th>COMPETITOR6_PRICE</th>\n",
       "      <th>COMPETITOR7_PRICE</th>\n",
       "      <th>TYPE_OF_PROMOTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>103188</td>\n",
       "      <td>1.011</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>93420</td>\n",
       "      <td>1.003</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>78636</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>36840</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>74880</td>\n",
       "      <td>1.072</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Week  VOLUME_OF_SALES  ACTUAL_PRICE  COMPETITOR1_PRICE  COMPETITOR2_PRICE  \\\n",
       "0     1           103188         1.011              0.674                0.0   \n",
       "1     2            93420         1.003              0.715                0.0   \n",
       "2     3            78636         0.990              0.707                0.0   \n",
       "3     4            36840         0.955              0.703                0.0   \n",
       "4     5            74880         1.072              0.711                0.0   \n",
       "\n",
       "   COMPETITOR3_PRICE  COMPETITOR4_PRICE  COMPETITOR5_PRICE  COMPETITOR6_PRICE  \\\n",
       "0                0.0                0.0                0.0                0.0   \n",
       "1                0.0                0.0                0.0                0.0   \n",
       "2                0.0                0.0                0.0                0.0   \n",
       "3                0.0                0.0                0.0                0.0   \n",
       "4                0.0                0.0                0.0                0.0   \n",
       "\n",
       "   COMPETITOR7_PRICE TYPE_OF_PROMOTION  \n",
       "0                0.0               NaN  \n",
       "1                0.0               NaN  \n",
       "2                0.0               NaN  \n",
       "3                0.0               NaN  \n",
       "4                0.0               NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data consist of company's price and sales volumes data, on a weekly level, promotion information, plus information for 7 competiors and their pricing. Data is rather scarce at only 146 observations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic modifications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Needed data modifications:\n",
    "<br>\n",
    "<ul>\n",
    "<li> Competitors data is coded as 0 for missing data. Those need to be recoded to nulls and proper aggregations done.</li>\n",
    "<li> Type of promition to be recoded to dummy variables.We have 5 promitions and months with no promitions, thus 5 dummy variables are created:PROMOTION_A...PRMOMOTION_E.</li>\n",
    "<ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outliers and normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 800
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "Image(filename = path+\"BoxPlt.png\", width=800, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is not signifficantly dispersed and the averages of the boxplots, are well within 3-4x of each other. There are a few outliers, but they are not too far off the averages. Based on this, neither normalization, nor outlier handling is really nessecary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sales fluctuations (looking for seasonality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename = path+\"Volume.png\", width=1000, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Legend:</b>\n",
    "<ul><li> Promotion: (pink) periods, when there was any promotion active.\n",
    "    <li> Volume: (blue) sales volume during this period</li>\n",
    "    <li> Volume per price: (orange) (sales volume) x (price for the period) </li>\n",
    "    <li> Double weighted: (gray) (sales volume) x (price for the period) x MIN(competitor price for the period). </li></ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Intepretation:</b><ul>\n",
    "    <li>Price does not appear to be a signifficant factor when it comes to sales volumes. The sales and the price weighted sales are almost identical</li>\n",
    "    <li>Competitors minial price or the lack thereof (surprisingly) also does not seem to play a major role in the sales volume. One notable exception are weeks 88 - 121 where there is a notable inversion in te trend of competitor prices - it does lead to sales increase, which however is barely noticable.</li>\n",
    "    <li>The major factor driving sales fluctuations appears to be the prmotions. The positive takeaway here is that they do not seem to be overwhelmingly price-related (or at least to a very low extend)</li>\n",
    "    <li>Further analysis of the sales fluctionations needs to split the data set between periods of promitions and non promotions</li></ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Correlations:</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " | Sales | Price | Min Competitor price\n",
    "------------- |:-------------:| -----:|\n",
    "Price |-0.71|  |  |\n",
    "Min Competitor price |-0.06 |0.16 |\n",
    "Has promotion |0.53|-0.63 |0.03 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Intepretation:</b><ul>\n",
    "    <li>It is safe to say that Sales fluctuations are tightly correlated with the price, which in turn is mainly driven by the promotions.</li>\n",
    "    <li>Competitors do not appear to be a signifficant factor. Their price is not correlated with the Sales at all, and has bary any correlation with out price. Last but nto least they do not appear to react in any way to our promortions.</li>\n",
    "    <li>The major factor driving sales fluctuations appears to be the prmotions. The positive takeaway here is that they do not seem to be overwhelmingly price-related (or at least to a very low extend)</li>\n",
    "    <li>Further analysis of the sales fluctionations needs to split the data </ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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so0jIVPCiJjsHL2qyc/CiJjtH1kUdc+sRMjfYUpOdgxd1JnxaLYdRF/XDDz88LB0ldAF46TFd5IV0NLF6QIt6bH4oHYT0hVCZFLnriiG6pfoeOr2kvp5spaW2O196YDz9UB2h5VaUrtdb1mmakjqE0LKHzS+t05a3lOi2hubHAsm50LAs/3svg5zlKeBFTXYOXtRk51hd1PKHEELIPGFDTQghM4cNNSGEzBw21GRRyCiON5KTO7oz9SgQIS1gQ00WR05DLb9z9SwpHfntlSOkF2yoyaLQDWTpsiC/IcDqCEizebacV5aQ1rChJoSQmcOGmhBCZg4bakIImTlFDbXnk6OfjhBC+kKLmhBCZg4bakIImTlsqAkhZOawoSaEkJnDhpoQslUeuvpXV5+45Wduw7ChJoRsHWmkH7v5w8MvYmFDTQjZOtJQn7r2WcMvYslqqBEnzThqQkhrznzyD+n6SECLmhByhKktWzTSbKjDsKEmZGag0Trxih8dUvJ55GOvdxs9pNl0S64eyNFPDRZK+tl7vhStY99hQ03IzIAroIYffO8bq7InXvVvh5TzeHXefy5N0nVe6bpz9GP7hHSbH3ro7COzaKhxMnhCCDk4eOL0/aPuBSnruS8kXeq22HWVrlvry7KI1xvw6j1771fX6TZ//dB55b8eUvaXWVnUclJEHrzqKUMK2Ud6+UiXFK87ZhsfuuYZbnlJE8vWIulowL//9euL1631ZTlU3kuXtJOvvXC9bJE0XA+yDPF+7zKzaqiFfTjoJEzvm2+MW2FKxmyjxCN75UPHVdLQgEujGCsrDwGL1oeeh00Xg0zre+Xs9lgdr8wussiGWnQgZHn0sphzWcJ1M3YbvfKhh5SkIV3+e40xGlWxuC26Tl2XJZQOvHxt4f/w0VMbOvL7iZP/NPzaXZINtY6RniKOOnUyBe+ExRDdEn1ySI/jhjq3eT62ue5cxm6jVz503+gGXP6H3hAMbZOkf/+um9bLMb0YqXIPf+CKDR35LenChZded3DhJYeya8zSovb8aJbQSbX88OzjK91cfejm6qeQC1hGr5cIjoM3ADWGVse2Fqzfdr3nxNjjHtonL10PXsaORaxONJayHNN77EsfH35tIvmedYz65L+19kOuml1jdg31yTf8XNaBLzk5ocGVEKKbe5OILsQDeS0b66nClnqtQ+rc5rwOep9kubUrxgt5E04E0j1EJ2SwnPmbtxw8ftsnh18+oXWk0mPbJnme6+PE8R8/Uj5Uh6R7bhUg+d4+67phuYN9ib+eXUOdO+ocOqkesKpzEd3cm1d3G0Ok8rHPuduIsKUHOoctlWxTCVJnzcscrdD75O0j0lLpuK5O/elzV781tqwg1qSX7iF63jF69Mb3rfJS9YTyU+mxeiUPERqaRz5yfJUn1rD8F/EexKfffUm6/nOGmgVlQmVjde4Ks2uohZwD//B7X1x0gkp0cx8WguiJpLp0KU6+9slF2yg3cYl+Ddi31qCx6YnULxash+ShIQntY+j46jT01EJ61vqEqyUH9JpCpOqRfLE2LaFySI/VGzpvYuVKOnzIIl6DC70QITcG0kJlY3V6wI+dI8CO1en/Fozb2fzcNI/FNtRCyQkqPZkl2zD2pgIl2xgKwWqJ1C9S4rbJ7R203na7ztg2SLo0JPDNenqwDi2SJq4HARb1oze8a/VbI+m2sZI0kdhDHYTWDyRPXhYJIfnwG2skXXpkFqwrtk4hlC/pIrLP3qAfiNUfuqYlTVw9NXXuCnvVUHsXaIjSbYjpS17spgK56wSl+qVI/bGbLoTop94ma73tUp8IxhZi9WOftAXoYdPt2EDsBRrPJQZdT98jppcyDqTB9PIlLdSApyxeIZQv6SLS2MYeMrH6Qy5KSYsNGsbq3BW6N9SXXn3rsJRP7oEvOUGi6/nXQoi+3IgpsA2xbZE8GXBJEavDQ68b0gpt3dTUmyrTclsFqU/XGatf+1LhvpDQNYutQ37rBlD+x3o2XvmS0NKUXiz/zKevdfNjDTgkRijflo3pxfDy0UjH6swdr1oqWQ01fCilPhbP15ODHHj91L/lzgcPrv7QXcOvQ0oH4EoGcoTc0WTonHz9z5xbvmC1bIl12zSiU3LBoU504cWH2ArbIJWSKlNTZwypT0QG9nLGL6APK1IaA4ukS0MMtD5+6/8Wm57St8T0cl6H9/JCfmYxYlL1CZLvRUTZsqF6cuq34N6Va9Ij9PAJAX2IjsbRElof2jy0e6HfQOfbdI3N13S3qIXSQHTbzUFZ21iXDM4IJbo5oVS6QYBuyJ8bqwfY/U5h111SNoXUhYdGTb1SRo5PCMn3fLu1SH1oREVSfmDoCYhasNjzgWXvv3TbLbqsYMulSOnpCY08QnleOlw1qfEI0Qk91HS93joESZceTYhYudA5jfVqdoVJGmrhzm89nN1Qy0HXBx7l0GDrekQv1wptfTKlPs/f55G77pJtxL6jTEnZFLouWS6tGyGEIXTs7Vi0OyF3W61eqIynI/91j0v+e64tXVbQ+jYixMOW94jphPJi6bFGVIBf3yJpOj22jtg9EysXI5W/dCZrqIVfuvyzq0b29JmzQ4qPHHQR6WKJvrWkLTknCXXm6OZSUleubkmdugsnlJRNoevCOrwub4zY9rS0grB9djmG1QuV8XRQFr9DDyVJg+tEuxxyu+qikzJCYoOKXrodENXotCdfcv2GUSSgrH1xxdYpy9LD0dS6a1AGeY+ffWK1XS+55iur3xKFI3neOMOuMGlDjRP/1CtuPPydcIkg73lXfXFI2aTUV23BSdeSS8k6c7expE7d1RdyY6tz9tXWU/p2J9xCsTIl9cWQenS0hwheFsF+2msIekAvA9uo4T+uOYTpQSfWeHlv71l9i+iEYsGBvv7tOZU0Cx4qtgdgGzvcF7lgG4Asey8CxUAduh4POZd6X1GmJFhgaUzaUAtycI+/83B9+mCPQXzV6E69+S/uzK5XIlKgm7sdeD0YPuzcpzgupphVWjP3hOiKjxXklJV9jbmiQtuA9FQDowltj2fZ6QghuBaQH7LwhNA6gJSRhkej6w5ZemjURLSLSdDLOG/WnaF7DajDY7Vf54wWi5TR6wmhr3+AsjnlBejaxi50jVhQHtcGfsu2lSBlSntu+8DkDfUnb/7u+uTnXgQ5SOO/uuBV3T/1gusPrrvVP+nQ9W4QjdbDsoAL0buwRbxegOiXNHIpPItP/wZ6m+T/01/9ucN0tU96GXXk3DAXXfn5dTkPqccbaNONIMTWg3RB93w00EkdV102x9rXQBfnGr9T5fWcHzFdbJsWS4kB0hKsM9arJf2ZvKEWcLH1vOg+ev231xeZBmk91y2E1iE37F3vOh7dhhtvPxHNB6FurAV1reVi/5jIWADcUrn87AtvWJV99hu+sK5HA9+s9SvmYhs4rAOS04CEGvlcYn7dFFJGoltiZWPbZQ2QVqDOVL14EMs1qVmVTRg5IULXCkBeKH8fSTbUiOuz/0Es9k+Quq3ICfiZc9au/Pfyd0Fwod10y+1H0nHDv/+KSw/+04vPd+e1jsjTXvmZI919K1bfk7X+uYbZy6+RD37iqLtIRNK/fNtXj/yGYH9FvPyUnHjZk9blvfyW8uKrP+du4503fWq1/vtf9WNH0j35rdf/w0ZabPtlXZe/7fNuXgvB/th9Giu5da70Gl5/ObKLyH5txaIWIWlqjhXK9DrGYmW97j13DL/SSCNV63cMvVY8R0LHHQ21BbrSK9lVvONBytlOQ238vYTsMqH5qQnJZSsNNSGEkHzYUBNCyMxhQ00IITOHDTUhhMwcNtSEEDJzmjTUpXHUFAqF0kt2EdkvWtSEEDJjujbUz372sw+e+cxnrkTzW7/1WwePPvrowT/90+bctxdddNHBI488MvzKI2TR6/SYTk55jZeOekJlPKxuqGys3pL1CaX6gi2D7dHpWPZ09f8UoperC0JlYnXp9JiOV0dIX4jllRKqS6fn6Fi8PEmD5FK6DkGn5+hoJL20zK6wExb1mJNXWjaWDtF4aUJMt1e6UJPuYdPxuzTd0jLdy/PSkTZWP5QOeqePQer06q1J1/+Bly7LVk9Ipdu8UPousRMNNSG7SKjhWUKDtORtnyNsqAkhZOawoSaEkJmzbqgpFAqFMk/5Z0ODTQghZIawkSaEkBnDRpoQQmYMG2lCCJkxbKQJIWTGsJEmhJAZw0aaLAJ5c41vr5F9hI00WSReg60b8lCjrvMtXhmk2TyrR0gv2EiTxWAbxlijGWtEY+VAqQ4hvWAjTRaFNIxoHHMaUg9dPlZO6xGyLdhIk0VgG1T7W9BpXr6g03Lz5T8E6GVCesJGmuwtuQ0tG2SyTdhIE0LIjGEjTQghM4aNNCGEzBg20oQQMmPYSBNCyIzZaKQxki3/9bL+L3hphBBC2uJa0tLwhhphnQfsb0IIIW0IWtKAjTQhhGwP+qQJIWTGsJEmhJAZw0aaEEJmDBtpQgiZMWykCSFkxrCRJoSQGcNGmhBCZgwbaULI1jjzN285eODyf37ww0dPDSnEwkaaELI1pIGGEB820oSQrYEG+uRrLxxSiIWNNCFka5x+9yW0ohOwkSaEbA26OtKwkSaEbA020mk2Gmk9ox2W7X/BSyOEkBLYSKdxLWlpeEONsM4D9jchhOTARjpN0JK2/4H89tIIIaQUNtJp6JMmhKyQF0sev+2Tw69pYCOdho00ITPhh2cfXzVYp/70uUNKG9AQPnrDu4aUTR75yPGiBvORj70+Sz/1RmHJOvcVNtKEzAQ0WLWNlpQ74ZRFnQ9e9ZQhZZNHb3xf0bqhm9KP6T32pY9n1bHvsJEmZCY8dM0zRjVaKGvLP/SHT99I+/7Xr9/QleVT1z5r+BXnB9/7xkr/xKv+7ZDig3V4bxQ+dPWvrvNJGDbShMyIMQ1WqMGDxap5+L0v3tCX5TOf/MPhV5pcfbtuIOnyUJD/T5z8pyH1EEnzegX7yNYbaTkZoZNIyL4x9l7wyksD6KWffM1PHEmX5e/fddPwK43oP/yBK1bLMX+6t25htb7BorfrlTTIvrPVRprdHUKOMvZeCJX30q2FLctPnL5/+JVG9E++4efWyxCLlyYgXf7bQc1QXfvIVhtpPH21ENKDpcxbLNtou/4lhO4hL91a2KGyIU4c//F1mZg/XdLO3vvV4dd5oCv/YZFrdF2yLILGHL/3gVn4pHHA9+WgE5+ecbpLucZk+8TCrSW0fznppccG/mQQW4dEj1igL/+9AUtbt4hEqOxbD3w2A4dywM986prhF9k3SuN0S0Hdc5+3eHUfFAzeWULHLyc9pBMC5wzE1mEtZUkTSVng4IHLLzg48fIfWS3rHvg+MKtGOjf8h+wepXG6NSzhph57H4T2MSc9pCNRFg9c/qTh13keu/nDWeUlHb5rcP9LD8+11AvL2GLrPvPpa4df56+XfWA2jbT2b4XIfcuJEI8lXDdjr+9Q2Zx0T0fcC5Lu5UlEBtJh3XrRIV4jjHolukOsbK9+nSbL2q8diljZRWbTSIdOlEbyIWR5yA0sD9ptsYTrxvp5SwmVzUmP6aTKo8H2XDXWLWKRMl6+Tkvl7zIbjXRo9jsBM+BBWmK7Th7rt5zOWd1keci5ExE/5DaQdZ+950vDr3kSarBykbKeNRuqU6fHdELziaAMtttz1aTubcRKW3RaKv/tf3X3wXW35ocPLoloI+01xqG0sUhoVOxEghwdwM/F19F7op9tWdOybu3XnCMS3SLbWUtoH0N16vSQzolX/GiyPHoAnl7KNRHKlzSEI4byhTf/xZ0HF15y3Up2kWAjDaZqpIXYiQSiI0/mHEQXQvLpddyk8djmuZB1y4dPsSyyLas+xFhfq5SVV74tko4GT6PXFVpvzBWJdPkP8QilAy9f0hCOGMoXPnr9t/erkd4mqRMpiE7ujSW6IrlhV9CPTelYgtf1WwKxsKgxwF21LWTduBZ0dMHcGHOMVvtoIikESfeMG72u0Hpj1j3S5T/EI5QOvHxJk94wli2pOneFxTXSaEByKdUViU3pqIG+B/JC+XOn13Zv83igSy48+MafXy2LP7QV3sxyoORFnTHHSBpor7ykyQCeRdLFwtaRGh6hPKTLf318LbG6BS8fdWLZkqpzV1hcI50zwKgp0T31rt/O1k+99YS83PpykfqmmB1M1iOWb2taH48SMEYh9Iiz9WaWE0pf1InppXpnsXA2uWYtki4uhdTxCOUhXf7HGvpY3YKXL2lID+XvA4trpIWSk1N6InP1c956Kl13Dlhnj7o1Un+PAb7e2x1Dd9tlUNTbFkkTqZ3w5+Tv/YcNvdIXdUJ6hy+VxOsJGTHi5vHSJU0eXqkQ2FAe0uU/7gmPWN2Cl68tcy8/VeeuwEbaUFp3TD+nrhMvS994mhLdWtahjq/40SGlHb23PYaOIAodR6RblxfScUzw2xKyJiUtZQUDr7yAdYbyBXnhw8uXAVMvXdJku0JuEhDKQ7r9b4nVLXj5COuLHdOW7qopqAmy2ItGuuREltYd05e8VCTK/UMdpevtiViRpduUS+9tT4H1h/YPb7VaoI+pPPHbe5DFyucQ05O8VKy3Vz4Uf43wOhFvwBF4ZQWk2/+WUDrw8tHziZ0Tz8+ey423n4hGhSCSDQ2r18B6+VZPp4d0YsyukfYC8S3eCQshul5IUojSumP6kpeynmJdxBCl+qXAFdBjPb23HTd0aD1Ih44XQ++VhT7Ab2/+5ZzyMWJ6kpeKVvLKh6xRuBREEJ7o4ZUVkG7/W0LpwMtHOGJsMDT2YLFcevWt60b5UK43v8/L01/9Obdx1ej0VD4IpcWYXSONkJsY3gkLkTMniEZ0cx4UgujG6k51IUHJ9gml+qVgv3qsZ9vbjnTopMLSAPSB/a3JKR8jpqcHP0OE8r10WNgishwiVaf9bwmlg1g5Ea8xxvwfY7joys8fvO49dwy/joLG0/7X6DSb75W3aYJe9phdI50T/iZ63iTiHqGuUgjRLY3DDhHqYlpEp2QO4Zw6xyD1hwbWYuT41yXPe6miFvt2ZGrqAGyb/F+Vc3o60NFAH9jfmpzyMVJ6tfleOixskZhxIvmhifv1Czj4b8mp30PSRbxB7JwH1i4wq0Y698koOjIanUNoICUEun8p9MUdemDoizeG6JS8gp1T5xikfvilS8jxr0teyk9fAtaHdaamF0Ce/I9FPNhxDEnTuva3xkuP6VtETx4+IVL1hPK9dB2llFqnZ2lLOu4F/PZYlY+8kh8rJ+I18Hq9u8ysGuncJ2ONCyOX3BOvY19jlndOXaHGIoTo9ohhBlJ/7gNGk+Nfl/ySB1IK+3ZkqueEPPkfmp1N0uw2SprWtb81XnpM3yJ6XqMEUvWE8mPpOXWG4qx1jzFUj6TX+rxDeUIsb1eYVSOd20DmuhFA6YnM0UfDihcYQuTUVdptE11YNbLc+uUWbEvJNoFUGcmvqTeGrlP+x2ZJ1Hqh680bS5DfOs3+1th0PPBC+hbR86xWIPmxh3RoPbH0UB4I9XIlTcdYy//QYGpswDNWDnV7xPJ2hW6NdO3UgTkHXU5myckpPZE5+qIj1lZu9zpGqUtmte7BlyrLkFagLvlfarGnyoTidccg9eltlpdHQmg9/V/juXrkt07Db8/ilXTtOsALJrp8DNGL9TYk37NqQWg9sfRQHggZRpKmH2ry33v9PeVGlLzQsUyV23U2GmlvJBIgzcvTjJk6MPegl5yc0hOZoy868K3G9HPXXbKN9qYoKZuDrtsbsIkhZWLuHzRYLZH69DbHkHztlvH0PbeN/BbR51zE68JrPQGWpkjO/B2iF7M6H7r62EonNAWv5HnE0kN5QGKzPR24HpEn/70IrVRvUfL0MQO6bo9YXg6yXaFzgjYu1tZNQbSR1v+B/PbSNGOmDrQHXWIbLTVv6ZWQo691Yvo9ttF2L0vK5qDrLn3rMGUp1/i6Y6Dx0NscQ/IxERJ+e9h0+S0iFiEeNKEQS0nTA9vQW5XP8Md7n5vSoK6QTk16KE/j6cBCRp789x7S+pV8D8nzggF03R6xPAt6vRD9mr0V6a3rNtC2cYJOs/m5ZUJ6mi6NNJAYxAsvva7I7SEHCKCht4196Vt6uXpATt4jf3PV8MsHdWI7QuuQ9NioOQiV95BwPejH1l0L6tMPg1xyBw9bYSc1yll37kCXRn6jsUXDFJqUaKWrLGH5LQ1vqFG3pI675Nl1aCRPHoaWUJ2oL4WnYyePwliNJeWilDw5rhZdt0csz4LwTIhuR6zIg1y3gbqN0+ke0Lf5uWmWrgOHNW4POUBg3Uifa+g1aAhy530WXa8r5ZE7YxnyoRvSl/ScdYfKh4B+bN21oL5QFzdFqkzL7ZW6rPsnhuRD8NtDp2OAEY27iKxT8MqjEQeyLG6jkF/XAks9Rixf8kKuAw9JT61P8HSwrciL9aRi65A8b8BX1+0Ry+tNqnFtRddGusbtoQ/6n3z8f59vqE09JQNQoic3Tg65M5a1XndufQD68l+kZUie3pbS7RJSZWrqDCF14ZzlNIKeD9VDp6NeHaWBmF+vvP0CjSxLdz93wDvHJST5oRe/JC/kOqgZnAOeDh5guMZjX9+JrSPUy5C0WLlY3q7QtZEG0rje+a2Hh19hYCHDbyfl5JVNr5EWck9Qi9dHLa3XXbp9oq/9sTm+zlywLfZ85ILyIVL5JaAu+Q+JIfui9eR/aP4OpCMWW0BZhIshXWMbWVmGy8vT90jpxaxtSQ/FNHu9T0lPrU+IrU8eZEKs9xVbR8jFI2mxcpLX0kCZI5M00ld/6K6NBtYDJwQnBWXuue/MavkXL/vs6jeInTzN6Q+98pzuBcOvNuSuO7eLKzqlr4aj8UDD0wJYRoL8h5Qg+t4IPyitLwbqgm86FpomwNLT7oqQawANj9UXAXpZE9IJ6Vty9EI6Ib+wpNUMzoGQjqTrCAlPD9EdoYiU0H0iabF7V/Jz3Z5LZZJGWvAsYYt9eyylL3o5IWKoMxYaVgJGiXPIjWYQnRKLFfskZTAg0gJ9s9jzkUuokQCttlVAXTjOqWlp8RBCYyXLpwIDVhiYs/oiQC9rQjohfUuOXkhH9sfLkzTvxSdJh35o+k7oQE8jaehZCCGdUHlBD4ZrUAb3rt22WJ27wiSN9LPf8IWNg5t62eWpL79x40KxpF4BBjiRoQYd2/b+v8+b+CdnYEeTo5tq2CzYJ8wbklM2NX+ugIYZlO6rkAqRLK0vBuryXDNe+KYgerD8Qsddv0kq/9HLkWWtL8veg8HqAL0cI0dPdGDt632NW6Wb50WnHX/n19xrJFRWsGkhHRE8+CwhYwblcO9i2372hTesQxW9crvEJI3042efOHLiU1EfyAvlg9J4aYtej4iEDOZwKmCpeOT6dVPB/ha73zllQzegRupB1x6UbJeQCpGU9NCkVKVgHVgffuvzahEd+Ihj83fA6tT6sqz1D2NtN784rnWwjLI6zwPGR0rvxMuetNbT+6pdVppQnTb9V674h4MnX1L/oQxvHSmwDSnXBfbzgVOPr++tmvUticncHQIuolTUB/Js6J3FawxC1pMH1vNTv3P9wVOvuHFI9cELEBDbkIXQZWJAJ9clY+tM1Q9kf2M3oNRj/ZZ2XSlw84SQPFiAHrnr01a+dc3EriFd98Pv/d3Vb+srRV0QoH9jUFjnA1sG/0P6mlw9ff1jfwWk5fhqQ9Yo6spBl0Vdtr4UKBOKWNlnttJICyUXQQjbGMgNqS/WELfc+eDBK/7k9rWuPJVT6JcmIDnk+nXvfynq3bTKPGydsiwj6zHkaxPYZ3F9WHA8H3zrrwwph2Bden0pRDf0uq3kad+odn1JoyENuFi4QsxFk4qcCQ1Y6zKyDNHoNJ1nde1vYHU8sH12GzHGEJsoSgg9DCVNJKfBC1mjsk3v+cQ3h19hUBbl7e9cpPcQcoXsO4tupIFYhrjYITGg85w33TKk+Kz0Lr5u3YjIRX/ilf8qeBGGrPhTGe4RNDipgS9gtwG/Y9aT7M93Hnhs5Xv33gRFHbpeIZSO4/i8q744pJwH387z0PVJY416BGk0Hr3pA+uyMReN6HhzZ2hsOesiw7LXQCAPuoIse4NvGt3rEnR5DfZLS4iSHmKLBg+93RTYT72PsqwHEsk4ttJIf/Lm72ZdALngAheB/zt2UUNXLJkQq1faBz2IELM8tJ7F6o9F6tONBbbJWk+rbTr3oMGyoMcEtHz2snD33WNddqhfE4tqwTq0SD0aSQNyLuRBLOcW6LIxfuoFhw/w02fOrn6XTCngnWv8jpXPfVUdoaVaPHJ7iK1ZrdM5t2RaJm+k5aWW1hccGmYQuqiRZtNDaP1UmbWe4wMV5EaVhqsVaATQAHjW08aDZrjh9JiAlRJS5UKNE9Dbb5F07b6R3sxqXef2QR7AKBsqD+w2hlwEIew6ctYpnHjlv1npxV7u+Osb71ttV+g9gNIeYi6rugLXKdDXTktwHvHQtIgrMmY87SOTN9IvueYrq//SUPQCF5eIBmkSvtMLrEP2U3NqcHnoG88jlQ9yGgtxZ6Aur07dOMj/HN+8BvXhoWhvPGyf5EE3F+tvDvVsUkA3NTAcQrYh5d4IIWVD4XCCbFdOj0+kJbn1ejohP3ouKBsqjzzPhbavBBtpb/KQnBmbYsiBj52g3mDdpY1RCViH3Ue5USGr/EDDZsvr3ynrR7PSP2d1Yl9RRwvWVu1QH5Zt/bKvEimCvNKHo23c9HpKjkUOoYejPm+lSJlTkfEIuy6L7SGWgv2x8f9IrwFlx5YPPTSRT2v6PNFGWjfIWPYaavtb+NrXvrYhH/zEreuT4OXvgjzrtf/g7qO+2ZHv6X35tq9u5FvR+p487ZWfydYtEa/7LelY/qXLbjiij/1944tetcq/6Zbbj+TH5Bufeu+6vJffWrAPIjo9dxt+/503H7z3r790JA1lQ+XtuloL9keuBy+/Rj79D7et6vw/X3L0XFvBuuV+8PJ7yS6S7e4obaRD4OTtOzgOKYsCOliOocPVcvRLsXWnXAhooHJCwSx28K03Y6zW0MtZeCnl1DlrWoN11a4vF7FGa908Y8H+ceBxPJP6pIUpLs5dAD5YL545RM4bhWMobcjGhoJJ4+5NCDQ3Yi9nSSNtX9yBbsmxXBo11y/xmb6RHnyxpA9yc8j0rmT72HhpsO2xGbIsJm2kx3yglpClMbXLhuwmkzbSqTk7CNk1luKyIfNlcncHIYSQfNhIE0LIjGEjTQghM4aNNCGEzBg20oQQMmPYSBNCyIxhI00IITOGjTQhhMwYNtKEEDJj2EgTQsiMadJI584nTaFQKD1lF+nWSBNCCBkP3R2EEDJjujfSZ86cGZbO88gjjwxLh9jfhJB5wF7y9unSSEuj+8xnPnMlz3rW+U8HSfp99913cO+99x48/PDDQ+rBwfOf//xV2nOf+9wh5ZDQBSLpsTyPUJncumI6Xh0hfSGWV0qoLp2eo2Px8iQNkkvpOgSdnqOjkfSaMhbUEyrjYXVDZWP1lqxPKNUXbBlsj07Hsqer/6cQvVxdECoTq6t0HUuimyX94IMPrv6/5CUvWf3X/OZv/uawdIinA7wTEzshpfpCKN9LT9U1hljdNk9+x7YvVleKFnXEkHpL6g7p56bFgL4tF6unZB2i20I/Vkcoz6aH6gZenldHCK/+lL4lpi+E8lPllsxe+6RxYu0JTqV7lNbhpUM0LdKx7Onq/xpJ65Xu6Qmh8h6pdJsfS4dovDShJN3TE1ql1xLaTp0uy956Y+kesXSvrlbpu8SsG+nQgS9NT5FTH5atbmydOfXWUlK3pPVO1/9BKB3kpJeWFcasT9NiHfLb0/XSkVaS7hFKBznlauquKVNC7/rnyl5b0oTsI0tu7PaxoWYjTUgnpOGAaJaQ7qUJS0nfJdhIE0LIjGEjTQghM4aNNCGEzBg20oQQMmP+2a7OHEUIIYQQQkhPxI6mMU0IIYQQQkgFNKYJIYQQQgiphMY0IYQQQgghldCYJoQQQgghpBIa04QQQgghhFRCY5oQQmZA688Y7PJnEQghZE7QmCaEkIbUGrFzNaZplBNCSBwa04QQ0hgxQD0jVKdZHasf+m3TQ4ie1tW/Y3kWmx4rG8sT9LJgfxNCyBKhMU0IIR3RBiWWtQC77Anw0ixenq0D9KwHafLfE0IIWTo0pgkhpCHWQNS/7XIoTwj9tmWsHrB5nq6XZgnpeOk2zfutsb8JIWSJ0JgmhJA9ZYwx65WlcUwI2UdoTBNCCCGEEFIJjWlCCCGEEEIqoTFNCCGEEEJIJTSmCSGEEEIIqYTGNCGEEEIIIZXQmCaEEEIIIaSSYmMaUx/ZKZD0b0+nNI0QQgghhJC5U2RMe4ZuzCAek5ZCtplCoVAoFAplV4UsAzlXSWPaM3BjhnAsLUeHEEIIIYSQJZBlTBNCCCGEEEI2oTFNCCGEEEJIJTSmCSGEEEIIqYTGNCGEEEIIIZXQmCaEEEIIIaQSGtOEEEIIIYRUQmOaEEIIIYSQSmhME0IIIWQv+f7Xrz848bJ/fvDA5UeFkBJoTBNCCCFkL3n4vS8+uN8Y0jSmSSk0pgkhhBCytzzyd3+0NqJP/elzh1RC8qExTQghhJC95eRrL1wb02c+fe2QSkg+NKYJIYQQsrfAkBZ5+ANXDKmE5ENjmhBCCCF7izamT137rCGVkHyKjeljx44d+S/Isv2t/wulaYQQQgghPZHZPLQxLUJIKUXGtGccC7Ls5Y1JSyHbTKFQKBQKhVIr33nr0zaMaU9vW0KWgZyrpDHtGbgxQziWlqNDCCGEENIba0iLEFJKljFNCCGEkPnyw7OPHzEIx07xJnWcWNf3pIOHrnnGkNOG79910yo++ZGPvX5I2Q44Xg9e9ZT1MiGl0JgmhBBCHGBcaXn0hncNuf0pMTi9bT3xih8dcssQw/L+l9r6njTkjudovYdSa6yP+YLhEyf/aa0vx7qkLCEaGtOEEEKIA4wrLWJoToG37pjBKXlW/4nT9w+5m5z5m7cc0f3ho6eGnEO0p1ZEjNZW6HohtR7qMV8wlHVCXxvWhJRCY5oQQggJ8OBbfvm8wRUxTluDdWpJGZziNYdujIeu/tUj9YbKIL3X3Muo/7GbPzyk1CEhLide/e/W9eUCfXjwc8vPJUSFzAca04QQQkgAMfRyjaweYN05BmduqIKNrxaRrwBakDfW2A2B+lsYpTXnCfpnPvmHR37/4HvfWP32gI6W1vHkZHnsvTE9Jt6KEELIbiOG1TafC1h3rne4ZFtTnmzkxYzLMaB++6EUpGtJxarXhGlAHyEu+B3rPEBHCz3UZO+N6VC8FWTsG9GEEEKWDZ4H2wDrzo3Vhv7Ze740pIRJebJjeS1A/dYrjnQtOfsPXRv/7fHoje9b6wP8hqc6xlr309cOKYe0nlWFLIO9N6YFufjtyxZaat+IJoSQqeFoW3twDMX4nJqTb/i5onMIXTEWcwjVLcZ4KK8VsX174PIL1nm5serQf/y2Tw4pYbx143eOAQxdcchpkK4FNgTvzd2FxrSh9I1oQshuIvd+z3l2ezFmdgPig2M45bR4QAy7knMI3dKwkLP3fnVIOWSKWHHZxtA6kF6yfujLTCUpcG8/9Lb/MqScLy+GdoqQbsyG4L25u9CYDoCL/JGPHB9SCCH7goxU9Zxntze1sxsQHxzHXrNaxJCQg5LzCN0cg1CAvo0Tjhm6IcSIlfjnHM+wENs3pJesH/o2Blsjz/TzneSj64h5yi0xXaR714vcmydf8xPBsmSZ0JgOgAs9t0GKwWl0CFkePefZnYIpPIv7Ao5ji+dBKbkzdIASg1CArjX8SuqJGagx5J4K6ep65OXCHE4c//FgfUDHSmsR5Dmtf8fI8aqHOhWl55TMHxrTAfRQzRhQhxZOo0MI6Q0/QtGOUgO1NSXrLvUoQ9d2FELpHjEDNUboGtXpIrHZNTSlITEW/SGbFLHOKtLFCx0iVJYsExrTAUqH1kKgDi30UBPSH7nPcM/Ba7ZvYP/53sc4akIeWrI+jxke2tIRiVBHAWkls1GgTE7MMkAZjd4HES9cwmPsc7vk2EmMuaeb++ImdLbxUitpT7YxfezYsWHpkNhvLI9J2zYt32SODWURQvqAe87KPoF9fuxLHx9SSA3eNGpTsj6PGR7a0nmxQx0FpOVMEwdQJsebDVBGT+Wnt6mkvrHhE6XlPd1cgx469vheeMl15+Xi6w6ed9UXhxwyZ6qNaY3keQbxmLQUss29BRf71277sptfIqjrzpuvc/Nr5Bufeq87zY6nS6G0EHutidzz4Te5utuWO2/61JHtvP9VP+bq7bJg37/1Z5dt5H3rz19+8J23Pu3gG59410Ye5aj843V/uT6WXn5viZ1HT0q29e6Pvc3VR9pdn/7gkfSY3PeWp7p1xQT6sh1I++6VP7lOh+gyMSnVt1JSHrr6GMk9lVMHdEQfaU975WeOGtODQU3mj5y/pDEtBq4Wmwbsb2FM2rbBxd7Cq4O6WoZ49J5mh5PPE4u+HiC5H5PYBvICELZzH8G+P3T1rw4p9S+L7TvbPE5Yd2yWCg30c0IIQuEKSMv5AAqoCbOAvn42Ik1LLtDP+WiNB8rnhNRAV0+ZiDTv8+yaUHjNRVd+/ogxfePtJ4YcMmeyjOl9BRd6brxWDNRlb5yxaIP3wTf/wpDaBr3NkLl8wIbxsNuj5mMK2wTbuo94D+zal8W2hX4pTKTEuGsJ1m/nY54CaXex/hygmxuiYeuufXn1BxWfXoe+7iggTUsu0K+dExzlczoi0NU2gpfmEQqvIcuExnQEXOipHmYO2vhrDeptXXfPD9iM/RKULQeZK9Iw78r0iEs43hpsa87DcdeQ0SvvXOn3OOZ8TYpHHdupJYbkn/e8t/vYDtadE7fcmpIp2wTonn73JUNKHOjjHhkzooNyOZ5dAfr6OYu0MZ5u+2XCXFA+5wuS0NUx3UhLXSclLzuS+UNjOkJpAxajpseeS88erniBetQ9NkRFH08RmV90rujthCx5ekS9H9swLErBtuZ66XaJ0ItzunOferlrm+Fedt0iMedGz4/toL6SkUrRb2HYlxqV0M11BEEf3twaIxbYulJ4oyf4rT3ksWnmNNBPXdchUD7nPKe2PUavZyvZDjSmI5z59LVNL3bU1frjD+JNaLmdFtQtx6Mlj936N+u6Txz/d0NqPi07KD2HknW9kCV7qPV+5MZwbhNsq44b3hdC8bD2gzQxtB4kFu7l6Y/9DHeJF08bOCKt2lvUl2uktTTsS2eE0h79HKALb+4YRxLK5bYNnjNI/8Zy7siSHlGtAWVztt9ue2l4DHTlWUaWDY3pCLVxYyFQVw/PDurOHVorAXWLtEQPQdfWjbJjGqOaoeQaUO+SP1GvH+qQueN5j/YJb9+R5uVZSsO9rK6IfknVy08Z2yVtsXaCtHzHo+Y6sp2WWsNejnfJuqWNKdGHLjoKpTHamtLjZL3g1jmE5dyRpbEhlSibc+3YkZ/SKRShO7azOYa3/9XdR154PH3m7JCTxk4cMadJJKbeFhrTCXCxtzBSez7UUW+PoeySr0KVgDohNd5glC35SICldCi5lnXdwwNrieiHNGTujH24zgUxMsRbVjqqgX3Xnj2kQXKAbo6HP/aSKtK1aGM7BHRTbbH+Cp5IK0Lx5zmg3JgRKdSR8ywqjce1zyYs14w8lRry1utujWss526LzL6ly5eiO0Ap7Pco9LWXA3RrjnMOr3vPHUcM5VZy6dW3ruqHwRr6L1ij1ssr0YnlCS3SaqAxnQAXe4vYUOl9or7WoN4ehpo2NluCOiE1L4ygbAvjNzQk3orSodc5oh+6S9mX3mFQU6CPNyQ3/hb6uqOt6xHJGUKXBz70U+i6PWpmhIF+KtwMepBWjAn7KzHQQqC8vByYosSTL9hwBSzXjKKV3m92W+11VuolL913ixd2EkPrYrm0bK5+ax4/+8SGofzUK24cctNYQzTHQPXySnRieUKLtBpoTCfAhZ77VnSKXjdOyYOuBtTd0vONOrWUUuoJSIG6esSwWY/LEsH2a6O650uIrWLZUX6p6GMAyfVyQl97v3Q9Ijl1lXj8dN3e9aHzc+cDhn7Ki6frFsnx5OYwplPWoqOO8rltMPRzXtyznmws197buq4ctD6W8bvm2Qb9mnNf2k5DV3upc0M5xa5AmX1hjME6Z2hMJ8CF3upiR1050+6UYBvD1qDuVvXrN5nHPGjGDulZUFfLTgPYJQ+pDp1IGTe1tIxlR9kcD+w20PsHsXGUSC/9GIWuU9APfUjuiBb0Y50a7RkUsdeHvg9EckekZMYelImh6xYZEwJmQZ01jN0elM+936Cfc81bby6WazsiKJ9rjEP/iEE67GeNEwL6NZ2B0mkB9bqwnPt8L42xJvOFxnSC1j1H1JUTI1hKy+20tPaqWuMfyzUzhuh6xoK6eoTLCC23dRtg+yXOsXfYSstYdpRvaVi1RO8jRLcRY+b9tZ49fS/bvBTQjXU2tYEA0ehhdC8/RO7wu65XpOW0maizxsiUaxfla0DZ3PLQzXUM6Lr1cg0on9tRgr5+LmC7a5wQ0K8JU7EdixTQ1ddn7vVRui4yX2hMJ7BG31hKH14loN4Wnz/3QP25833GsMdhzNvjKFfjhbD0PD/CelvNOZIHhqx7zAtKU6CPzVRelZbxpr06SS146K1PPb+dr/+ZIfWQMcfAdoT1NV56DqEb69RoBwREY/Nsfojcthg6Y14YDIH6atoa7XWtobRtgq6UywH69nqpAeVz64CuHvHSM58gLRfo57ws61GyPuhqKQFlakcByDygMZ2gdc9xjIcpBertMfWegPpbePdQF4ybMeEaKJf70IjRuvNkQd3ixQBI0zLHj7p4xoD93YOxRojQwiDvDbbP206k1by7YT17WBaDuHTKtRwDFflaNEgrnfVBh4aF0Pta49FMgfr0/VsCyteMwGlDMwfolurnhtPEKO3IQFd3GHQoEdKkLchBv9NRA8rmGLh6XZASUKaFM4hsj0Ua00fePr34uoPnXfXFIacPuNhb9Rxb1wfGNiApxniPLahHD0EirXQu1tb73bIuC+pGJ0JAmpY5eqg9jxV+934QYD2187G28Lb1BtvnbSfSal+M1XViGQYhfovxmSLHQEW+fjlY3osA6zTVSco1LnVZD3uesdy67a71eI4xVEs7+rWebC21lG6rXqdXDmm59//YUQmUzWnXdHgHpASUqe2gkXmQbUzbNzDtdCL2t/4vlKaFuOjKzx81pgeDuie42Gu8CZZWsxN49DYYxkwNZUE93kO2NC629X6jrh7hMqEHHNJEcqcKmxpv2/Fb8noytiPXw0vZGmwfBO1NqWHigfL6q6EwFPA7twMHfX3vapCvQ0hQt16/gOXcex76cs972Gs0pV/KmLhncRKceNlhWS25lI6SlraL0NVSS+l0qinvLtJyY7DHTkOLsjnXjV4XpASUKX3utULWff6T92WjotZeS9lv22KK7VqcMS14BvUzX/PZ1c70EFxk33nr0zbyjv+x2pZLD/9bHch333R+qFmLp1sjd958XfM6raD+r996s5ufI952fuvPLlun4cbWZWLSer9R17f/+zPd/DHyzfedH9pG2t0fe9s6bbXea37zSJm5CLbv/lf92DpNX9Nat7V846//ePR6ptjOWrnnw29ab58Wyfve8Z848rtGUP7uj751vSz3jc777pU/uVHOE+jf+84XRvP1fYlr5lt//vJ1mvy+9x0XH/mdEuh6bbHOf+DlP3LktxxDq1sj0iagTi8/Jt/+H889uH8oq8XTDcm6zG1fdvO1fOMT5408L9+KfT7lXg8hQT13ffqDbr4WfR1AdD7ScrfpH6/7S7eeXEFZOWdevhbZP+iLhK7NkIg+ynr5Y9Cdv5BsfvI+TwRrr3n2m2fnAZ0W0ovpxPKEFmm5yLlKGtNSqRaQ+i3YMkJuWi5rY1akQ9hHzBtxZN1KPFrOThAC9ebGlpUg9Z7vwT6pOq7X85jgt5VcoN9iv8d4n1J40wB6Dd4cwbZp79CUUzthPbUjBihfGkY0BfoasPcHlmtmJgCowxtd8NJixEYJrPfUzoZkwxxKp8VMhXQhD7HlrWdjKvX2WuQZcOLV/666DpTLCckp9WTrGHaRXC9wCNSTM+KhjytEoz9pn0upvgZldTheDOiL5HizNWOvqbHo90lEStpHz46z/0M6gs63/4GXnpPm5Qk1ZXLIMqbnzBRhHzomyvKP9z68sf5fe9VNq89tXv2huwatTVBfy2mbBNQ75sHrITfcZg/2SUNuGd7LYD9Qw78ipccF5Vrstw7F6QHqRjgHftc8MKYE22bjCKfaZqxHjLkaUL71vdECbJuNY9Yv3Y0J/0EdWkBphyj2IpwNSbEvXGNZG2pIyzEQY22xgDzE1ophENMvpVW4EOrIicnVoFyuwQb9nJhxfe5W6xgZ1oh6cgxSfZ68MrFrLgT0c64rS6lDBboipQ4d/e7AvlFqsM6ZxRvTgmdQtyQ208TPvvCGI+vU2wDxPOXaI9NiqjmAOmsNjhhjerAalG85YwXqbLHfpd6yUlC3jVnVhlPti3Y9wbbZBzPSSw2DUsbG7KNsrrdpSrBt0qnUv1vMrCB4H8ABpTN6xDyenrGL33pkTrcdSMuZhSg1GxLycBwFpLV4F6E0FjhE7RztKJM7YxP0c0Zz9HkVqTFCNSUjHnbd9mW8mtmeoF/qKRZKR2ugm6tvGVOWzIOdMKY1MGDFyG2BvkkgMHROnzm7Xt93HnhslYbfa7n0uqCHGvW1NHzHvsU8Bdi+lgZj6/1GXfqh3ArULQ8MO8SHZfyeCyEDSm8vpGdHAOuoCeeZq+ff855qI9rm1WCH8G19SMs1oNb6pkPtGf/4HeoMlU5bGNP18pBWY1R5oD7bqSwF9ZRcyyiTGx4I/dzpTKEvMpbSzq9et4yWaGIduBDQr5kqVt8vOUA3V9+CsrUOKrJ9ds6YFmDIXnHt7UNKPfomgUjjL2ijuQbdQLSY1UMPhSG2eY5gG1vOWuEZJGNAXa0ewBrUrWNPRQQxRPXvuWCH7wHStOD+6MGYddiOy1zwvGA69EJk7Nzudghf1qlBek58q7Cuxxgq63RVP9K0aELXVgjoWsM/1AYgrdWIBOobOxKj7/9cSj3a0D35up8eUuJA36v/7X9195FnnjiTQtQ8i6Av4s0Ug7xUJ0augzGzpggl12SL5y7KzzEEjeSxc8b0jbefOHjyJdcfuelFxoCbRbwBMADFE426Hz/7xCqtBtxEImPRdWnJRfZDH7OXXPOVIacdrWIOPVBvi44J6uoREqCNJ4iOT0Sa90CpoeQhGCIWq6ofJl5+Sx66+tiRdZWc67nGJmKbbNgT0kXGhoLpjruIjYdFeu71Lnooo0Ga7oTa8DARC9Jzzid0bUc39K6Dnu+6Bagrt+MRQod15V7HqZhxDfS0pELrtK7m2W/4wpE2BBJC16MlRkoPealOjIxSerOmlFDynNLr0AK84/b+vz/sELQw/Mk82Dlj+vg7v+ZevJojeYWzf0jIxrrsMBXeGM4YQ2SMITj2Jb71filpFS4DSofPSkC9doiwBm3wtkZ7n711IK1FTHnpQzBEzgs56+2+uu6DFim8uF+RElCm5XsKY8E2STwwQJoWHT5T0w7pumxHrfR6f/ijvzfoXzCkHOLVr0cERGRdFnj0Tv/PFw0pYVCPHZ3wwnhaeA0tqC/X2xsD2wT5hUv/1/nz6jybSjymul5IqgOgdTXW0SLy1CtuHHI3qXkWaX0P5OV4b+X+RifOxl+n0HHxIqkwkdS+2uMmIu95CS0MfzIPds6YFuTGf/rxz60uWvFSa8bO/rFRdpAaWhgHY/D2w8oDp+qNDu8DNaVvSeei1wMZE7srDy086LSR0wLrJRTRtJzOq/QhGALb4xlCQD+EesRN24ecSG7sKEC5Hh/kqUE/iDVI06KNR3tOITF0XZaSGT10PRAxZLWnVWOvd+1ZFMPIGpQiMVJece1dR5qVWry6aju9ofb/yDlVz6ZSI6+EEg8ptu09n/jmkNKG2Hp77rtFrwcy9r0mdH6l7bXPVNm3WsOfzIedNKYBbvq/vvG+IeWQ//Ti8zNwiEhoSC733HfmSNlfvOyzQ045OcaBXlepFz3FkboHQY95LKEHBaR1Y+itw3qucql5wJei67XbWTq7Qoxb7nzQPc+lYHtSMeSn3vGctW4rT6BlTHw8yj34ll8eUvJYGRvqgQojWFMTTpMTPqPDy4Bth558TlJgPXZdpUPNVk9EtjU244LW1djYcE/Hor3NGqRpgyTHQyr3SGoqU6DrgqS8vSG89v/ul/37I+dVP5usrkirl9dLPKQfvf7b6+1rhT6nXruBPC2t9t2iRzggLd/vIbvJXhjT1hOH9BYvKLbGPpA3pMCLbvGGhiFejzkH2V55EF1369HGxntQnLj8SRtpLdEN8oN/+GtDajk1D/hSdL3W893SC4Pzi06YPuc56O2AxLzOnr5IjNA1FAP1lhwbvT2QB9/ySytvu1w7+tjYjqsYG3JdiK4uD55dGU6DekpDsjRYl2xDCG+UCIwZaoauXBOxjgHSbVhIDQ+/93dVfef3Bb9TMbUafa4g//X3b842rqekt5FX4iHVx6sFdr8ggAYumTs7bUy/+S/u3Ljh9bC3LM+J0APZSu12e3WJ1LA6tsYgPyKOMVLywK6JCRW0Vzf11vc2OX8MNo2L83nnJeWF8QxSfT1p1sdV5Nx50saDDQvxtiXm8S+JlQxdQzmEvJMx9HaF5LOXPeX8tpiOqzY2RPQMG144zc+/+DND7iYpT1wpWKdnAOaEk5UYUhr9DkQq/EJL69AIAcsl970+XyEpGQ08Uta0gbvGah/P3b//5fjnijrCHiXtBiFzZKeNafG0omED4o22aXPBeyBrr/qG17qgsdbHAjImREUP9QUlYoyUeD605HCq8GWqqcG2adHGRYkXJmSQvvWD/7hOv+3u815I952BhLz7dy9dbYMXbjCG0DWUC45NbXw2ymt554svWW9HKPwrNcWevo9/6gXXu153vU4tNaRmMLIjHSKlseYxbN1y/2lsvoh0JmoI7YvuRJeC4yXhgPr4reTcPRTzUPccSexBSUhLjDEdYUJ2kZ02pgXc5HhZAr97TPvWm9KXJzd0z8kfvP8fh9z22O0riUW32JhQiDYMY+DBCiPVPvRKp4cbW16DbdNSa1y4BmniIVdqUNeE/+Rw57fOf4pfjE0si0c9B+2lnBqsN+RhjY7cnEt/xu+8f12HyBhPXM4MRj3RLx7KSEsqrr4F9iulJbNcaJ7+6sMX1WuOl1yn+nh7MqYNbI23fbWe85qOcCtDvpRUZzOFbO+63HBPE2LZG2MaU7zh9xhjaJuUGKxaDyLll4i3L5ir0+PhD796/XDFMLotn0vooQnsiIJ01Ho/OBDWodcrjPX2ToU+ZrIvglzLF15c9tDDORbjypYTiV0jY0i9BJk1cnNOWiHHE23D695zx5BaT0nnEcdBS20YRwlYl3REYvHaMbB/uZ10TWoksTdeuxND667knGE4lWG7se5zMlUIzNjOpldWhBBNtTF97NixYek8Ok2W7W/9X4iltUI/FPRNtS+gV137guFcwHnTEusY4MFqZV2+wGjzHpoinkEbklYPjqUNr1qjLOYhKn3o2SH/v/3dX9so27PzaGd80S/1WVrOAtSbVOfRoo8BpHakpQQ95Z6EemA5F3GwpPZtzuhzA2n9XYBWbGxrA0Me7W9OvPaYzuaS7l2yPZp4pkNGsWck56a1wjOEdvmlkF2mpGMQe6FlrKdi1Sg7Bm1QRj44rEHqyRzZNMo2DWkRTclDT59fyPeu+JFV2d6dx5yX+paK12bK8ZyKI+tOvBdij3/uFxzF0451LHWUUo7LkWN1TpbsMMmF8dpkjozyTEM0Ni1HR/DSWmFvun1ocEicMZ6KKUMpSr2Ec8Izyn7lnFFWe9wtJS9qtqb3S337Cq6NIxJ5L+RovHbdi829QoFIH5YSykb2iyae6bmjh/N405ElsW0vISFTYw3q1Et8JeE2ul6IrI8QQsawF8b0kcYzMWxICCFkGdSE2+zKeySEkPmw88Z06bAhIYSQZcBwG0LIHNgLz3TpsCEhhBBCCCE57IUxTQghhBBCSA9oTBNCCCGEEFIJjWlCCCGEEEIqoTFNCCGEEEJIJTSmCSGEEEIIqYTGNCGEEEIIIZXQmCaEEEIIIaQSGtOEEEIIIYRUQmOaEEIIIYSQSmZhTB87dmxYOrpMCCGEEELInKExTQghhBBCSCWzMKYFMaJpSBNCCCGEkCUxG2OaEEIIIYSQpbEzxvTznve8YekojzzyyMGzn/3sg2c+85kr+fVf//WVPPe5zx00jnLRRRcdPP/5zz+49957V2VzgFddSwidn9LVQDelr/VydTWpMgLKxXS1jpZWeHWLhEjlW3SdOWWRn6sv5OpqvRL9HLQeysXK6vyUbohQGdRnJUQq36LrzCmrdVL6Og+6MX1B6+XqalJlBJSL6WodLa3w6hYJkcq36DpzyiI/V1/I1dV6Jfo5aD2Ui5XV+SndEKEyqM9KiFS+RdeZU1brpPR1HnRj+oLWy9XVpMoIKJejS+bH4oxpMXCf/vSnH5w+ffqI/PZv//ag4fOc5zxnZUB/73vfOzh58uSQuokY0prvfve7B8961rMOHn744SHlKN6Fn3vjgJg+8nL0c+sMIWVi5ZCvdUL6SLf6IXJ0LLl1e8TKeumx9ZRuA/R1uZz6c/RteqxeQfIhJegysbJeXmpdOfUKpXWX6gOtU1J/rm5ML4SUSdVvdUL6SLf6IXJ0LLl1e8TKeumx9ZRuA/R1uZz6c/RteqxeQfIhJegysbJeXmpdOfUKpXWX6gOtU1J/rm5ML4SUSdVvdWL6ZJ7sjGeakF6UNmxsCAmZDhgikBRj9HMo1Z+S0m2awz6UHssa/RJK9cl+QGN6JLhxrYTwdEVCeLoiHp6eSAjkab1W+jo9pKNJ1Wfx9GNlS/RteqxeYax+ihL9nnVrdLmcOnL0S7elVB9IuZKyOfo6v4c+sOVS5Oi3qDOnnKaVfu/1erTcdk9CeLoiITxdkRygF9K36al6S/WB1sspk6Ofu25Qqi9MsQ4yL2hMN2DsjdDyxiupy9MtrXvs+mKUbkuMUn1Nz3VNsR+915HSl3wtKUp0hVL9HpSuu0YfZfRyiBp9kKuf0tFY/dL6U/qEELJNaEwTQgghmZQa9nPqCHDbt8OSt53kQWOaEELIYulpqHi6sfIt9GOU6LfYllL9GCX6LbalVD9GiX6LbSndPrJ9aEwTQgghhBBSCY1pQgghhBBCKqExTQghhBBCSCVrY5pCoVAoFAqFQqGUC41pCoVCoVAoFAqlQhjiQQghhBBCSCE0pAkhhBBCCKmAhjQhhBBCCCEV0JAmhBBCCCGkAhrShBBCCCGEVEBDmhBCCCGEkApoSBNCCCGEEFIBDWlCCCGEEEIqoCFNCCGEEEJIBTSkCSGEEEIIqYCGNCGEEEIIIRXQkCaEEEIIIaQCGtKEEDIDjh07Niy1oXV9hBBCNqEhTQghDRED1koOuXq5tK6PEELIJjSkCSGkIbUG7FwNaRrkhBAShoY0IYQ0JGV4Sr6nE0orSdcg3+p6Zb00AelWNDpN/7d6IJZHCCFLg4Y0IYQ0JGVEAqtj80K6oWVLbh1Cqh5N7Lcs298glkcIIUuFhjQhhHTAGo4Wm5cyMpEWq1OTqkPnx+q0ebGyXj1Ii+URQshSoSFNCCEdsYamFk1MDwLsbw8v39ah/4eI1WPzUrqeEELIkqEhTQghHYGxaI3G2O9cAzOm5+V560itK1aPzSvRJYSQXYCGNCGENCRkXMp/bxno3zY/Z9kSq0NAvk23ePmhsjbNLofyCCFkqdCQJoSQhsBgtIajkMrTxPS8dAvyY7qpOgSUt7peWaR5+gLSvTxCCFkiNKQJIWRPGWPQhgxlQgjZJ2hIE0LIHjLGMxwqS0OaELJv0JAmhBBCCCGkAhrShBBCCCGEVEBDmhBCCCGEkApoSBNCCCGEEFIBDWlCCCGEEEIqoCFNCCGEEEJIBTSkCSGEEEIIqYCGNCGEEEIIIRXQkCaEEEIIIaQCGtKEEEIIIYRUQEOaEEIIIYSQCrIM6WPHjq1EgzSdHtMDOTqEEEIIIYTMnSKPtDWINV6eTcvRIYQQQgghZAnQkCaEEEIIIaSCakMaxAxim5ajk4NsL4VCoVAoFMouC5k/cp6yDOmQkRsziG1ajg4hhBBCCCFLIMuQtgZuzCCO5Qm5aYQQQgghhMyZbI80IYQQQggh5Dw0pAkhhBBCCKmAhjQhhBBCCCEV0JAmhBBCCCGkAhrShBBCCCGEVEBDmhBCCCGEkApoSBNCCCGEEFIBDWlCCCGEEEIqoCFNCCGEEEJIBTSkCSGEEEIIqYCGNCGEEEIIIRXQkCaEEEIIIaQCGtKEEEIIIYRUQEOaEEIIIYSQCmhIE0IIIWQv+eHZxw8euPyfr+XUnz53yCEkDxrShBBCCNlLtBENefSGdw25hKShIU0IIYSQvcQzpE+84keHXELS0JAmhBBCyF7iGdIPXvWUIZeQNDSkCSGEELKXPHTNMzYM6SdO3z/kEpKGhjQhhBBC9pYHLr+ARjSphoY0IYQQQvYW7Y0mpBQa0oQQQgjZW2hIkzHQkCaEEELIXvKD732DhjQZBQ1pQgghhOwlj938YRrSZBQ0pAkhhBCylzzykeM0pMkoaEgTQgghZC+ROaNpSJMxZBnSx44dW4mmNi1HhxBCCCGkN9qIpiFNaijySMPY1UZvSVqODiGEEELIFNCQJmOhIU0IIYSQvcQa0j88+/iQQ0geizSkZXspFAqFQqFQxog1pO+8+TpXb1tC5o+cpyxDOmTwxgxim5ajQwghhBAyBdaQ/v5dNw05hOSRZUh7Bq41hO1/YUwaIYQQQkgvzt7zpQ1D+vHbPjnkEpJHtkeaEEIIIfuBeGZPXfusg0c+9vohZfd49IZ3bRjS8oEWQkqgIU0IIYQsnDN/85YjBuEPHz015JQjxjPqOTH8b41srxjq2/QAP/zeF6/3E3Lmk3845BKSBw1pQgghxKGlcdqT73/9+oMTLztqEIrUYusReeiaZwy545H6YKA/cPmTRtUts2zo7Tz1p88dctKcfMPPHSkrIl86JKQEGtKEEEKIobVxWkOu1/ahq391YzvHbKtXl0gLxFA9b0SPr9vWIyIhGzlAXxvUD3/giiGXkDxoSBNCCCGG1sZpKbKuXK+thCjcv9Y9L7XYekRaxUrLJ7nvf6mt/0lDbjlH6zmUE6/40SE3DvR1iId0XAgpgYY0IYQQYmhtnJZQ6rW14Q0iJ1974ZC7SSoc4gff+8aR/BPHf3zIGc+jN77vSN2QWry6xFjPAfr6pUPxThNSAg1pQgghxFBqnLakxmurt/eht//GkOpztN5DseEQMmsH8loj+6fXLWE0tYinXtcl8sTp+4fcONA/e+9X18s0pEkpNKQJIYQQhxLjtCW1XlvopWae0HVCbDiETAOHvB6I171V/XqWkUc/82dDahx9bgXxuuvfMeYw4wiZDzSkCSGEkAAwrs58+tohZRpqvLbQTb0wp+uF2HAIMcaR14PW9aOu3OnrrMddv3AYQ/JbzThCdgMa0oQQQkgAGFfihZwa7WnNAbqpF+ZywiFK111Ka4836sqddcMa8nLM9G+P1jOOkN1g7w3p1EsXhBBC9hc8G7YxLZp4wbH+HKCbE+ebCofQM1n0QDzsofpr5u+Gbu6sG/KsRxkhZ39rYtfJ7rP3hvTRG+JQcuegJIQQstvgubCNadFKvbbQLdX3wiFyPLRjCL3MWDt/N/RsiEoIGxMtHSX926P1jCNkN6Ah7dwUuXNQEkII2W3wXNjGbA6lM2fkxvkC6Hre9tK6Snni5D+59dfO3w290jmkZX1Cbsx2yxlHyG5AQ1rdECI2/mmun4QlhBDSH/08mJqQsRmi1IsMXc/bjrzcumrw6q+dv7u2E4FPgpd4/2NG91I+K0/asfeGtH3p4sTlTzryW4QQQpYC3/toS2/PbIqSdeeEJ2ig63nbY3mtwDo09voVyZm/u7YTIQa0UGJIh3RTYSm8N3eTvTekBbx0ceLl//LIRQ4hhJCl4LVhfO+jnt6xwilK1p0bngCg6+kj3fNWtwLrEM+75vE7PrPOe+iaXx9S49R2IrDukjCakG4qLMXL4725fGhIK7b5SVhCyLyQh6UYEdLRXhK2/RLhex/1lBporcG6rbHpoV+GyyHmbUe6Fz/dCqzj7D1fGlIO0R5f70VIj5JOhEz1Z3VLwmj0J9Q1KRvCpovw3lw+NKQVtUNKhJDdQk8NhvcmlgK2W0vuTAZkk1Ivb2uwbunYpYhNKecR87YjPdeQrQHrsPump/3LNeSLYpw/dc2ge8GQ4hvXISTu2dNN2RA2T4T35vKhIW2QG+Hk7/2H9UVOCNk/9IMOspQvmOV8bIPkU2Kg9QDrlu1IcfberxZta8zbjvSeoQdYh903/fnw3NCS3E5ErJOcUx6EdHVYiu0E8N7cTWhIO2y74SSEbBf9oNOyFFIf2yD5lHp5W4N153iGS7yqQszbjvTHvvTxISVOTSgU1mH37fS7L1nn5b7sGAq3sEBHCzrJ+J0DdG3ITcp+4L25e9CQdgi9SEAI2Q9w/2spMRDmALa759D8PlDq5W0N1p0b4lCyrbGYaqTnhJTEvLwxUMbeW/alvRxC4RYWXa8WnZcTjw7djbCUSOcEIH8bn50n7aEh7VDy0kEOcrNIT/3x2z45pBBC5oz2bonIV9CWBra958ti+0Dr50EpWHfuVGnQzzEG5ZkEfUtJPdDVkhMKBV17jUpcsa4rlxx9XS8Ehjx+53QeoGs99rFwGYB88byT5UNDOkDqRshF6jj/kZcnLSbOkhCybNCG5caYkjA4ltsA6859dkA/xxiU2TKgbwmle0DXSgro2WtU1yGSC/Rjxn+sk4y0nK8VQtfGkMu+IC8E8vFVRbJsaEgHwIUuN10t8sKE/VKiCCGE9AbtTc8PauwLOJY53tnWlJ5H6OfENoe87Xr2iRygqyUnFAq6dt90PSK5QD+nE+GB8jkvdkLXhmdob3oI5HPqu92AhnQAXOhjwjFkWpv7X3pYz3l50pBLCOnNPodV6XaHjAPH0c53PAXiMS05j9CVKeRy8OoOGdghakOhoG+nmdV1ieQC/RxD2APlc94rgK4NS0F6rOMjedAjy4eGdABc5LmNkYd+kUMLIaQ/cq/tc1gVH9btwHHcRodMHDIl5xG6uS/HQl9728Wji/RcJMShtEyok4A0Ly8G9GtfsEX5Em96KCwlFlKVE/5BlkOWIX3s2LGVaEK/dfqYtG2Di3zsizq6ERTJib0ihIyDYVV8WLcEx1GcI1NTeh6hK1/ZywH62tteMwVsjfEd6uwhzcuLAf3a53ZJeeiGwlJideh5si233PngwaVX33pw9YfuGlLI3Mn2SFsjN/bbM4xlOUdnLuAij/Uqc1nXlfnWNSFkHAyr2v6nrXcJHMdtTCVYeh6hm/vsgr72tudM4WYpDQcRQp0EpHl5MaBf+9wuKQ9dEQ3SYteK/nKjRoznCy+57lAuPfxP5s8oQzpkBGPZpuXozAVc5LE4p1xQV+sptOznSGmok95IzLG+5mTu1jnCsKo6Y4j44DjWejrHUHoeoZv77IK+9rbHPKYxSsuEOglI8/JiQL/2uY3yOZ/tTnnTY3HaoWkH10a0kudd9cUhl8yVakMaxAxim5ajk4Nsb2/53vGfWF/oXn6JfPfKn2xWlxbUqeWeD7/J1aVQxso3PvXegxMv27zmPN05yHdfd/5BJyLb7+ntqtz9sbcFz9Fdn/7gwXfe+rSDb/3ZZRt5lE3BcZRj5uX3lNh59OT+V/1YkT50v/m+4+u0b1/zm0V1QFDmzps+5eZbufedL3TXgzTInTdfdyQ/JLqMl58SlJVntpevRa4Fb11I+8fr/vJIuhY5Pl5Zz5AWIfNGzt3iDOkpCA051dBriBV1auF0OqQX9mtjkDmDbdzGkPy2CcWsyotUSC/5Ct0+g+PVYoSylNLY49qXE7W3vfb5hzK5nxUPeduRBsmdzi7kJc5Fnp+55VPe9NRonVd2w4i+9DrGSi+ApCENAxgC7G8hJy1HZw60NH5r3mbOAXVqyRmSymUpw/hkGuTlpfvV9QCZM9hGbSTsC6GYVaRp4Yei4uhjNTU/UFPL5VBqBENXyoFagxRlcjuu3kuN+rqF5BrmYx1gJfud6gSkgJ5+rt5z35kjhvQvXvbZIYfMmWyP9L5RGpcWI/b1qDHIww/1Qp44ff+QOw6ZXcQbxp8T/PT6tNiYfBE7/+vcwHbuq6GI/dcgzQoJM9bTOQYxtErWXeoEgq72tiOtdIQT5XI7rvKcQRmgPfCQ3GloxzrASgxxrxNQ0umB3jbmJidtoSEdQHrAuTdEDi3r0px6x3PWdbcyooW5D+PLtvDT69Pz+B2fWV8LD/7BLw+p8wXb2vpF36WA/ZdOEECaltw5h6dmLi9Uj/V0jqVk3aVOIOhqffzWxnUOKJf76WsvbEU/eyG51+dYB9jpd1+SXd7rBIReIvSAXq63ncwXGtIBzt771ewbIgfU1br3WTrsl0vvYfwxD0jOEbw9tBdGDwXPlW16EucA9l3aCaDbDJE5dzL0dkIkVC6GGGdybbbsHLQM9asB69YfTQlROge0d4/gd+k9jnK6rhhe+JEOhYSIgZtD6b5bSs6zZyOEprXzgF5uGAyZLzSkA2hDrwWoq8eE/i23E/Qexrd1i6QekGCJcwTvShiK9vgs4cXWbXsStw323b6shfS5Hxe9nZDYddfrRUr9vsg2wLpzXrrzvLwxvHsEv3NDNEBNx9Xq63MIGePhLqHEoy0jwFa3ZNpA6JUeYzI/aEhHwIXe4iW7njcN6pYecku0MX3ydT81pLYB9WrJNcyWNkewbNuuhKFoj82cjzmonQ93V8C+i6dOg/S5Hxe9nZDYC9Wefov7rfaF8Vbecaw7Jwyg9MMonhcWv0u9pSWhEcDqy2go0iC5TpzSfbfoZ0sOVrek4w49KUOWDQ3pCLjQc7wAKVBXy1k1AOq2D8sWoO7W3kfUq6Xk2IiuLjvXT6/vWhiKflAsYT96zZizFLDv1iBCes5x2ebsPaUvVFtdyFhq3plp6R1HPbmjdtDPwfPC4nfpCGqJRxdAH2Er+pyXerjHjiSXhoZAF9tesr3QkzJk2dCQjoALvYWBKnGIqK81qLdlTCDQjVpLSh+QHig356GxJYahxNAPCpG5470QtE9g3+09gnSRmGFcM3tPa8NbG6Rn/t93DKk+er2QFu1iTciA3gZIrXcc5eXY5gD9HDzjEb9LQ9FKDVEB+nh/SLcxJR5eUKqvKT3P0MW243eO46mnTUCmhYZ0BFzkpcNbHvIyXa+bBvX2GCLqGRv4wOUXrOuumXEEZXPj50KMefExxdLCUFIsbT/GDvUuHey7bRuQLhIbcSudvafG8M4B9cgIT4xeL1LWXEd6O7TUgLK5TgPow1MawzMe8bt0NLZmqle7LvyWcA4d5pEL9HP23VL68j500eHA75xnUi8nFZkeGtIRcJG38HjWDHnlgnp7vH3f06OHekX0rAK56PJj0PVAcodQc1hKGEoOej9ElsCStrU12Hc7fIx0kdiIW+nsPTmGd43HGro5zgJdd0tK69TbAan1jqN8rrME+jmGsGc84nepMSrn0taVAvq4DvFbrqUaRw70SzsBQmmHCboIgcFvuW9SeLHppA+9P/hHQzoCLvKxHk+hp0FaEpdVSuvZSzSoVyR3wn2NfmiPQW8HpHVMeM2Q5xzRx0ikpgM0NdjWGg/V0pHrGPsPtLEgEjPuSmfvSRnetR5r6OXEk5bUWwLqzA1Vsd7xB152wZBTDurI2X8B+rFOEvCMX/u7BJTNHWWEPkZ+8VteXKx5xwH6OfvuUbI+6NptzxnF7ulcy+Xxs08c+ZLiS675ypCTh/0q9Zy+UD3lttCQjoCLvIVR5U2V04qaN6VLQN01PfwYqFekJnawVY9ebwek9UuhcuxQ95LRx0ik9mE1JdjW1tfvEtCjIUBfiyIpL6cY0ydf++SNejxShneOx9rD6xCE0PW27OjV1tmiw4/yuXVAN8egE3TdNV5lDcrm3m/QR4du/fsjx4s+cAKgn7vvlpL1QRej1vid0y7OwbmijWjI+/8+3+EQM6RtHkB6TNf7bf/bfO830rw8gN86rRQa0hFae3pRV2vPWO+eLequbZhCoF5IKa1mZGjx4mOK0iHDuaKPkUiLF7l6g21dgtEfQ4a5xegteQHMezdDP8BFcrycJR+oevz2v1/rPvjmXxhSDykNFQElxqiut+U5R52lHbIW75mUPougWxNTPbatQtncYw99a4zK86bmw2jQz913C8rnPKehi84ofudcI7pD2wvPUG4hAMZn6L9gDVQvL6Qfy4/lCbEyQiytFBrSEVq/IIi6Wn8StKbXXgLqzo3Py0E3kJBSWjZEYhCirtMZ8W01tNrWbYJ9gLS8JnqBbW3dEZwS2f6auci9URvd8bZ5MXJ1dQfXhsWVhooAbz9C6LrFiG0F6iw1zmuMQYvcZyV1QDf3/oS+tKlj21WUzT320Me24rcc55qRXOjXtk0on2MMQxedUfzOMcKncK54RnALAdYYzTFOvbyQfiw/lifEygixtFJoSEfQxlULUFdrL17vGxJ1t6zfesVq6h47BGlBXbVTVKVA/TmN7FzBPkBax5L3ANua+5nhuTFmLnJvtEobpTYvBnRTBob+CI5X9yN/90frPAkZycHbjxDQE6k1pjxQZ02HDGUxTVopJR0JAbq5IWrQl3ZZt801oGzu7EfQt8YorjP8zgX6OSMtHihfYkiL6N+5lOq35nlXfXHDSH7g1ONDbhpreOYYp15eSD+WH8sTYmWEWFopNKQjlH7lKAXqatm4A9QtHp/WyPai/lbo4c4xdaNsi1CMsduSAnXvwqwdrV70nAJsZ+u496mQ7a6di9wzirxQphygm5rRRrcXXt16SrPcjliucaedCqv6G85khDprQgZQttaJUtKREKCbu//Ql5e+S9dlQdlcQ3atP4xM4DccDvZ3CuiL1ICycs2l0CE3NQ4tXXZbXP2hu1YG9FOvuLHIiBasERr7DZBudTWxfCzbNP1b0Gkx3dByCTSkI4wd4rKgrlb1aVBvTi+6lLENq4cOm4HUGJjrsg32u7dxiLprZiiZA/pBUeohq8GGAdTO763rWCJj5iL32jD9IQibFwO6qam9tHHh1a1fgPTyPXLbYq2Xo18C6qtxhKBsbWeu1Kmjz0EO0BVDf+y9ra+vHKy+LYvfuW186b5bUDZn5EF3GnOvUY0uuy/UGqtzhoZ0hJoeZoyehhrqxXyWLalpIFLYh61IjbcGZVsYp72NQ9Rd49GaA/o6aD1a44H6tdTM7z32wToFdoTGTrF28jU/cSQ/t9PptWG6HpsXA7pyPGPoekUsqXyP3FhZ7bnO0S8B9dUYw2OvwdJPlJeOIkJXwp/GvhukRzxy0MfGO8/4neMhFsaOoKJsToy3fmbkjppodFmyXGhIJyi9MWLo2MHWoN5eRhrqbxU6gvpkOG+9nHhAe6Bsi/0WYxz19QB113i05oB+UNR8wawU1K+lJia79UvDrcmZW3lMJ9yWw2/tGc5B36sxoBPSTeWHyNHXo2c5+iWgvppwkbHv25Q6M3T4TA7QFSO41BC2lDoktOHrtSv4neMhFkrXb0HZnGeKvt5qRm7XZRu+FEumh4Z0AlzoLWKYanqsuaDe3i/KtYrvRX3SiI7x1qBc7ZCppofnXYO6azoMc8A+KPRyD1C/lprzrL29c0QbyVo0SKvpSKAs2jD81gZHzkdGcjsk0AnppvJDQD82j7PeJ0grxrRTYzuepaOjpYY7dGUfx+ynUGpQ6nPmzUCF3zmGrVBj0GpQNsfhoUfmSjsvAvSX+iI0OYSGdAJc6C1imPSXrlqDenvULaDuVj1n1CcN/hhvTcmHGlLUTLVUQmns4NzQDzwByy0/eqHxXoqream0Zwe2BTlzKyOtJk4cZdGG4bc2OHLat1wDBToQe85S+SGgH5s+VHs3Ia0Y66lFWZkOr4aSdZeOrnlx8zWed6F0OlY9UuuFjOF37kje2PsdZaWDm0Lva82okS5LlgsN6QS40FvFHqO+1rNrjBn6zQF1t/J4oz5p9MTLjd+ljH24WVBXjoeuFP2QXyL2WGNZzmEvTr/vsvV6aoxoofdIw1hy5lZGek2MOMrCAF3/VgZHzjnMvU+hA7FGus3PnVcf+mLQh9DeVEiL0URh7BdkUbb2fQ6Uz2mbSmOqdZgPpHbkrNR7rjto3ugRfuduz9j7vWR9eqShxqmjy5LlQkM6AS70mhfhPFBfjgeoBOstbA3qblU/6sJxwO9SA7b1fqOu1udHiG2rPEDE0C75at3UhN6ub3VvhMB6aj15pQ/2bWCNaQvSa44BysKAW9eljICcc5g7YgMdiDUcbX7u9QP92BC/rheSa6inGPuOC8rWeh9RPmcEqNSY9Dz5uR5gD9SRg+7QeW0kfucam2Pvd5TNKa/XBSnpgOiOH1kuNKQT4CKvnXrLgvpqvRIhWn0uO0TrF7ZQFzzz+F3q+W89e8R6Oyo8fylCQ+Pyu+ardVODbceDAr/HPHBzwHrGhBWhjjmDbRTRxlKuARsCZcUQ1L+l04rl3HMI/ZiXFzoQbfh6XzTNvd6hH9tW6GgDJebBLiE3tCXE2NAulM3p5Jcak9qAhcQ6LClQR+w6AXqkQxv0oMbYLNXXlK4PupCS9tDbX7I8aEgnwEUO42EsqK/1ywWlHohSSmPuYniNPH6XHpfW+426xjxEQmjPCxDjpvardVODbcODAr97D0tiPWPiCFFHzoN9G2ijVkR3KEuH6S0oi3tL14Xl3PYN+jEvL3Qg+gVRfQ9oyQG6sW2FjjZQWjlBvPu3hFYe7ZwwHKFkXbqTABnTAUEdOS+n6zbcM2JrjE3o14RQlq4PuhB0WHMYe02QebBIQ/qWOx88uPTqW1df5OlNy5fZBNRlYyDHoh/EPWg53Zln/OJ3qVGmh8RbgLp6eIW9YyhGRu1X66YG24dORu+4fNDiJU2Uz/HmbQPtlRPRHcpWBpicL/vCc+6UdgC6odEB3Un22k69L6UGS46nEPl6Pa3a2rGd9jHvgggom2vgQj/n3QKvg5NrsHugjpyRV33NQPSLjtpbngv0a16ELh19hS6kZLS5pYOKbI/FGdL4nOVKLj3835PWxkLOw6AW1Fv7UlYK1F8ax2zxwlDGvDSIci28jairZjtSeJ0dHZqiZY5g2/Agr3nA1dDCa4PyrV4abo31CGpDYmyboeu0MyrUet+knIc2Nr17Wq/Pe7EsRs62Il8MlNZtrTeSVgrK17RVKJs7Wgb9nLh6fd4gOd7kEKijdFshehRD3xu5QL+m41zarkEXUvKeS+kMJ2SeZBnS9tvkAn6H/gtj0kKsjWglz7vqi0Nue1obCzVzTeaCent53VD/2BfivGM6pmeOcmMaftDby4q69ZDjidf8+3W6SIv96AG2D56q1vHpIVo8bFC+94uRtWgjEQLwu9azqg1K24kt7aRANzRypD2b3n2uvdSl5zWnLUa+hJ70aGvH1GdfKC0NOVmXC3RiLNDPaU88r7CMoNWCOkq3dV1OHRt9TeUC/RqveqnhDl1IiRfcjhBtA7EX5DzVtI3WZsux4bZNj23M9kjrlXvLOWk5Oik8Q1qkFzW94RBjG9IUqLdkaKkE1D/WGPE8VWMaFJSDp3QMrTtOFtStH1J26qm5Yre9ZbhPCqyndjQE5XMf7FOjDUwIwG8xDGuQfUYd1nAu7QylOpq6vfTqxm+pp/Qlypy2GPlybfYYNh9TH8pqKXmpGWW0tzbGeh2ZozDQh4wZ4UMdemQlhl6viLTDQHvLc4F+zTOh9MV96JaU0dSWa4E8y7F+vKtTgrXZcmy4bdB7u5ZvSF/a1yOd0xt++1/dvYrZvu7WeEgF6tHScnYI1KkboZagfnkIjsGLnRRwI5+5/p1DSh6oq9bQ0PSOWUPd2lOCNMhcwfbp0KGpthnrqQ3NQHnxzs4RbJ8WgN813jVBdw61US2UGimpjqb2Ant14zfaKPzOMdpyXrpc13fuGq0xwFKs6x8RmqEl5Nn3WJcpNE5zjUnoQ8ZQGlaj1yuit1l7y3OBfs2zsNQDDt2SMhqUGxsy6aE7zq1EP2etzWbtOvvf4uVbXasj/700EEsTbH4sL5fFGdL33Hdm05g+J1++7aurnWktd336g+sLyMs/sh0XX3fwrNf+g6snoi/Gtbz8R1zdGkGd333dz7n5Y0W2Fevw8nPF2075raeAu+8tTz1SJibr+q78STe/RFLne6yg7m++7/hGGkTrz0m87UPanTd96kh6a8F6vv3fn+nmp6TVtdtLsG33vvOFG9uJ33fefN2RMrnyrT9/+boOuUewLHl3fPHGI79TcvfH3hbV/+6bzo+ufO22L2/o4vc9H37Tkd9SL3RCcudn/nqt7+WL6Pyv33pzUr9UUJ+0E15+TFBWS0lbbc9dSqB77zsudvOtQB/i6eTKd976tKJ69HpFjlwPznWUEujLdnj5MSl9BkC3pIwWlPvH6/7SzR+D3q6WAqzNFvot/z37TqdpXZCbH8sTYmWEWFoOcp4WZUhrHj/7xNqI/cnfub7LLB6x4cc3/8Wd6xcetYTQFyIkd5guh5CntxUP/dFFQ/0XDCl1YBvRs5Ve85gp4MRDU1omROlwcymoW3tKkAaZIyGvENJqvaW5YD12/bn0jn0fC7ZNe13Fo/qDBjGUOsRCC8DvHI+Yngfaw7ZBWIYHF79l3/RvaQNygL6Hd43id40H2QP11VzvOqQNokd3UpS+kA3d3HAm6EPGoEMGctDrFcH1AZCeC/RrRqBS17gFuiVlNCgXm1KyFhs2mCcXOGnnRb+rYW02a9eF8oH+7ZXJzY/lCbEyQiwth6QhLZVpAfa3kJOWo1PCc990y3kjttMsHriALBdd+fnz64ZcHF7/2IY0hR22bcnYWCoN6sHwndzsY6aAa73fqKvHUBvq1g83pEHm+LKhbBO2T4O0sXHzKUqnpLLokIM5gm3THTmZ1aI0htlDnzuI7sAjzRovIaDvgTwYMPiNaxq/8bItfueGi0Hfaze9UA78HvuCNEB9NbG3gm5HH/n4m4bUPEo/UQ7dXGMS+pAxlF63er0ituMTSg+h6yrF65DF0OvKLaNBudpragy6oy6SGzYEPFsu5z/w0lM6nm5ummDzU3o5ZHuk58qGIXtOWsdM4yKz0wh99Ppvu+uPoRtSkZaUvoFfgt5miHQMakB5PNxSHrMUrfcbdeUaFiWgbvTqbUMm0tsorSH0YESa7hj0YKxBKQ+pMeV7Yh/cWJb7q9R48vCuMT1PNdJyvazQ9+4P5OF6wG85/p6BUjqKBl1v3V5sK363MlJQX64H3QN1lBotpe0cdGv0c8uE8Do1MfR6vTJIz22TS2O0LSVl9bpq1odyNfHcS6fEWJ0zO2lIi7QEF7o39PJkJ7Tj6g/fFX35sHToKJfSlyRKQL1WakBZ7V2wQ1AlXtnW+426Wr4ICmwDr98Qh7QM92mF7gBqkFby0lQNY0Nuet4bY7Hbpo3LFmFLev5yiO6seWkxoO/dH+u6BkMTv2VEwJvuTgxumxYDunLMLF5nCb91x2EMLeqrHV0p7QyWGpPQLSkTorVXF+neefcova4sJWX1umrWh3LofO4TNKRngjViJbyjdaw0LnTPq4H1XnHt7Ue3Q+TisHccdbY01npOR4Z6tdR6TlG+Fa33G3X18BDYBl57HEu/MjcloYf/lLHHWE9N6Euph2xKrKdRH2vI2M6VrU88/ABpuQ9y6Ou39wHy0K7htxh1niFYahxC12uL5X61deG3rL8FqK92NE6ovRZLO4OlxiR0S8rEQD16zvwQer0iFqR7593DuxZKQFnt7Amh11WzPpRrdY2S6Vm8Ie3N4pGahq4UXOjWsNIvO37ngccOXnzNl7NfPkSdpcN7KVBva8bGUoFe3viWdaKuMQ/LELaB197H1iEqLdEjBpqxD6wSsJ6aYfWxHu2e2GPrjVKMDfex9ekhcqTlPshj+shD2Jb2qHudsVDsfQjo2rZY8AxH2UabNgbUNdboQT0lsdulBnjpvQndkjIxUI8NifTQ6xWxIN077x6lHTQLymLO/Bj6C501H01qMepEtsviDWlNynitQd8kIvoFNP25cqHk5cNeBgjqzOlJb4NeQ+wt9xt1td5Gwc5TjWUx2r2h77mA7bLbdupPnnMkr8cLmgDrqP3CH8rPDWwXOqe6swkpMbg8bH36PikdCYnpI1063oI2bj2jVoed5ABdr5Pr1d8ixlzTyuhBHXJ8cikNl6j19peUiYF6csIx9HpFLEjPPV5jnzMomxOTrTu+JecTyLWM8mSZ7JQh/dc33rc2YMVbPBbxlpx42eEFrgVog1koeflQN4o5Q1+5oM6xD95e6I5JS1rud89wBWssY1mOi/7yZctrogXYLu2JS90frQl5xXNBWRh5cwHbJR5bgDTI2A6KrU8jD38vPUQsxtem61EWiPXmIj3nvEDXi8lHnu5ojfVMWsZeg6C08wJKypQak7ojklsmBurJCcfQHRRv3Ui3106IsaFcKJvTCdDHOddjrunlVCPTsVOGtADjVbzDY9EGlRaAdemYbOuVvvH2E0POJqgvN+4rB0xNd/p/vmhImRexh/AYWh7Lng2b9SphGZ4P/J5bRwjbpT0uqfujNWM7YSg712OrP+2PNMhYYvWVhhSFjFMvfEYbGRDdYRCQnmO0eF5ngHTtrS4NHUlR2ukIob2YJZSUKTUmtWe0dtRHg2fRQ2+/aEgJkzLiY3kW7ZAQsddbDiib8zyR4zzmuRu6n8hy2DlDWr/0NxZ5meb+4QLXIjxw6vH1ek6fObtKKwX1tZrxYFXXUKfMw+wNf26b2INwDKizxdv5U30mXDf4AL9r4oB74m1X7P7owdiXSlFWG6xzANulh5GRBhlLrL7SqQVDxqlnuOlzBrHGCdJzvHkxQ3Zdj3oJsnVsfKtOtr73S8LRSsqIhx/Pg5zOow6DsS+36veBRF5yzVeGHB+po+RZpM+riKXkuaHrgZS+1I9yOYa06I157paOHJD5sXOGdOkNH8P2bEXQU3/de+5Yr6OW0JRiNYiBc/5mPi9zA9vVwuOhWdebOfQXo9STUwrq9rxS+N36+IwF26UfSLH7oxdYT014BsrWDL/2BNulQZqXV0OsvtLrPWSchgwCpEGsUYd0a7x5xLznSA8Z6rLdY2npPUQ9JS+SoozudIUQvRIDTz+PxLDV6Gcq5P1/7xvzNc8i3UHxdGMdKIuuB1LqrEK5VFuRs69v/6u7jxw37Xhr4T0n22fnDGl9wUJCN3wOcqEjLk7fVKj7Z194w5BSjn7RZmwMpGzjmK8DCrEbvhXYNttQj0UMONQ9lt4zPKBuLxa7VQwmaNWxxDZZI0juD+0tynnLvZaxD511ucbX3hhCXvbUUHcpui5bX831Dn3tGQ0ZmUiDWG9qyQt8sZAIpNsQEaSLJ30sLb2HqKekLuinwmBqjFk9EmcNSN2GQELPvppnke4geUavNrRTQE9LTidNg3KptiK1rxLa+eRLrt84duBouUMp9Z6T7bMXhrS94W+588HVB1Nq55teGZzDNHd/8vH/PaTWgZtn7PRWY78OmLrhW4HtGru/FmnwSvc5BurqMQsF6tYCWo5SCPZ8itR0LLFNIU8w8luFKXlgHVpKHjoo02LUohWhMKKHrj62ThfxrkNph2IfftLoukQsSM+93qGvP1IVMnaQ5uUJJe9NxLznSLfeWhiULd4baTla5R2v1LMJ+qmQgxpjVncSbP1eOxJ6D6nmWaQ7Yd79WTISoGO9IaWjESiX8uKn9vXZb/iCe+yAV7ZnG0r6sBeGtL7h9ZR1MIZLWJcVufi6g//vm+s/R956WEd7M0VKPDCpG74V2LaUR6WU1gYo6soZQi0FdUMw7ZnQ+uUo75zWjKKktqn18fdA/VpKHjotRy1a4U3PJtdAajaUI+c08uEnkPJwIz33el8bp++5dEgJd2aR5uUJp9//siHvgiEljH1ZV4N07fGW3+c9s+PfG5G6Ud/Yl1a//81bh+06lC+//jfOn9PAswm64sGNUWPM6k6CbZ/l+lpv2yDynlCI0meRNuLlOrLo/BzQHsk9X2pEx6a89Yjt6/F3fm3juIkAXQ4i9ZFlsXOGdOqGt3kiqYcQePNf3Jn9wZUcvJtoymGdo6Ecm95okdZgP1sbqKff+bwjx3GsJxn19DgfejtF7BfikN7CG+6d09IZbWQ7sE0xoGOHhVuB+rWUPHTkAY1yc8Ez7lOzodS0Q3rfdV0A6TkdXNHzjNNQRwVpXp7ugKHOFF49gk2vCW9IIeVbGeZ6myBvfNGrjpxT+2yCXs5L1SXGrHXqPPS2pw8559FOqN964xeG1DZoB4LXfrQcCYiR04ktwYbWiTz1ihuH3Dbec7J9ds6QFvQNf+xVRw02fUFrycFObbeSwAdXcrA3kMhUwzqhUA4t+oYvJeRtx2/Jb0Xrxk9AHQ++5ZeHlHbobRQR75EG6XrYvBYZMbHnNeZJ8sj9GmWrj1WEGPvQKYmznApsj+4QpGZDqWmH9L57Q+fIE8M2Rsw4xbKtX+uJaGyeSMo4hZ72PHudPTmmY98b0bQ2zG09It+5/F9unFtQ6iktQdcL8ZwIEnZit6sF2lD2wlYe+chrjmxby33X9JjSU4xp3LMySYFljPeczIOdNKQFMQK9Gx5pa7n06DzQMUo+uJKDvVlFrIdtbDx3iGcHQjliN3wJ3r6d+dv/Z73cktaNX42XrATUDbEvXyHdeqpr0LPLQEoRgx7bFAMGtxyzh/77r3WZr1mfmzOfumZIzaMkztLSOgwLoD7thbPrEpGHLKhph/S+nzJD5yX7FjNO1+VN/dpTLaLR6VpiQEePanmdvbHvjVhaG+ZH6zmUWy/76fPnVD2bejgLNLZeEc+po6d9/c4Djw2p49EhMw//5ZVD6iG9910z9ZSeZDfYWUNaxyZp7rnvzDpd5Bcv++yQk4f1BsU+uJIi5WHTnvVQzFwtqditsdj9Ejnx8n+5Xm5J68bP1iMyZgjXkhuvqmOna/HOsXwBtIQSA3S13YPu2KHvENgW2+lMIR0CbFupkS9ltOEmxqH22NXOjoJ9sSEVYtzK/kmeN9Rt26G/vfm7Q46PjjG19SFdSyikKWacYtnWL4Y18qxxhnQtKa849PQxk/OJdA2OIUSMMkvuS5utDXOv/f/lS/9/63Oqn029P37k1R26v7B9uSGROcj6Qu1G733XpDqxhHjsrCGtPxeukSndkC4Pv20TG9bBdmoZ23jhofHTL9gM6xgTymGxjZGVVh49oXXjZ+uCtEL2PVavfnCMBefWejFzKRlO7hGT6qGNoxJEP2bkx0Z/sC6sV46DNgr1sYXkzI6C+mo/W451pe7d2NA50rXEQsyscfrgWw4NLvy29etRBDGqNbLfyBPJ6TxCV65NIIY/0kvQ5yvnpc0cw7yEWPuv6e0p9Yz60Pa0Du9ItRtTe4lTnVhCLDtrSMuwk3eza4NibngvJnhSy5F6zj005P/Pv+Qzq/81oRwxw8NrmK20fJFPGj95GKHuMehthKS8ZCVow0LEcuqaXz+SXxsPqF8mFcTjht93fuvhVVqM0iFVefiUDn3XhC5pz2AuoYe1GGOnzhl3H37He9bHxhv9wbpQ7pGPvW61v2BdVklqdhQxVFBfLTlfcrUdzQff+itDziE6b62j9i2EHlkRsGy96/Ibx/7UnzxnSK3D7gs65HJ+kZZL6KXNkmkFp6K1s8Aj16hvHd6Rajem2HdCxrCzhrSAm13COYAMuSJ9bmC7glIQz21pPeOIF3ZijSLdMNuGUCT1YmXNAw11j/kMdI2XrAQ9zC6iaRkPuD4/5wTg90/+zvXJa6l0SLV06Nu7hnJB3bmeXP9hfdRD/dnLnnJ+e86J9k5Kx1Dq0GW1saHLQST8IoZMHYe6xoD1iVHtobcZojuxJd5IjfZy606B/SiPpMVGAkrAOrTIvuhRnlxseMyGZHioLb3eaRHEoMQ1uG1PKY5Ri/COnHZjTvtOiGUvDGnxQgOkicwNvW0hefYb6qYdch8aI2Yc2ahLi2MU2UZSRBrGEEfqK3ig6frnihggMCxOXvWLQ+ohNfGAoYc3jp/+aND//YZbzh/X4TzpDov2Yn/0Jf9X8ZCqNTZjQ9/r7VBSep5zRwrsw1qO/3nj7rzY7dHERhJku23Z2OwoUr6VcSlhHVinh95miO3E5nojLahPG7J6BGWKmS5kX/R1l4v70uYIZ8OYjuHSkPYC+9mi01DSbhAyN/bCkNaeGi9tLmDbtMCrpQ3h57zpluLGq+ZN/xheXVa0UVTi9RrjPa8Z9p8a2baQEVUaDxh6eL/nE990j9ta15NVuM/52PmfueTvNraj5ZDqkXUryUFvUy72Ye0NJ6+3I2AgiJEIfTvbypFzcU5Cnd7WxuXpR8+/9yFiX3S06xGJdWJL0FMeQjSynt4zXcg6xJjG7xKOvfJ8J8SVAmeDV76Fx3aOvPG9ajagHe80EJJiLwxp/fYz0rSXei6kvFpH8gobLzkGR8qfkzEzjti6QqLJ9XqN9Z7jgTomvKMXKSOqNB5w4zidk//6+zcf6YhotN5KlJ4nYkzDAG09pLqxvoDx6uF9EbCE0HAytiU2mw90pdPjker0tjYusS4t+kXH2tCNHPRsGRBNabhPitC+YNmbIzuGdHZwzEqdDb3faelBq/hvbz93tdNASIqdNqT1A03QDZ/M3jFH4NWS4Vo7NIxt1xJrvPQwPSR3Wq4UdhrBDSkwiixjvef6ITs3coyoknhA7zitxelsbegckWm+bgk+dN29G+sKeXEtOj63FhxjSO5wcs6HZ47s13AeYMR8+UPvOLLeVF0pjqxrEPuiY23oRg6p/ag9ziF0iA3mEsfvU2ZWkBQ4XrjubCc+5mzQeq6MaAN7cGTbCsLlPI7UpWSO1E5NqWnVASG7yU4b0jBKcYN/8ubvHvm9NLDtVjxCXy7sxdj5uS2uV/qc5GDDO8Y2pC0aYtDaQ6e3KyT6gZnsABlpOSWiRa9HewZzwbHr8eGXGPpl0RB63zbknBHztZf/7LoOkTHGpbcOuX+mQsIq9CjLFGBdeBEYv0tHTXC8akbn9PH2ZGwb2JLWL5vbenI6DdsyRje29ZzkTE0JjpQd2QEhu8lOG9Jyw+IGEHKmipoz2Pa1RBovbZhoWSIrz7p5CKRGFFYP2XPy4Jt+/uAlL7j6SFmR6oa0orylpYfO2zZPQtgOyw1ffmCdNvbrljH0FFo4lvgNSXVYcPxODV5IOwLTc9QJ6w59xl1vx0oaGjEWebDbumMvOrZGjkMo5r8XNrQHy3YO6xhy3eF41aCPN2TKDkwJrmOiIFzOUuo40bpTG6NH1j1IampK0LoDQnaTnTak9cdXBLl5lnwjlDRevb9cOCXiLbrw4jLvujxU9cPdTm2W25AKupxX3jPgenpfMEvHsVfetF5nyBjN8RRtA2+miSPbPUisw6JDLKYegcF6QzG5djs2ZIQR44HRNy8krJSSEZjWL07moj8Jrt8rsHNYx5D2E/tYw7Y7MCUdx9Yvm5ewbWPUrlckt8PTugNCdpNqQ/rYsWPD0nmQFvov5Ka1Ahf/bXefWi+PGZpfCvZhKNJzmL4npd710MPdq0MkdT14ZdAQhwy4tTT2vuhwJTyc9HltHWLTEu+a1F5vmycS6/Dory5OPQKTmrc4J4RmrnjbGurQ5MT89wLr05+w159xT4F9GzODU8sOTAk1HUdrFNaEs9SwbWN0TIdnmx0QshyaGdIpwziU5um0BBe+zKWL5ZiXa5cQwwWNWM9h+t6UetdDD3evDkjsmvAa4tf9+ddXXuH//LJ/2Mjr6X2x9YpMOUxagmc4W9HH3ctHh8Xjkb987ZFz/AuX/q+N8r04+52vH1k3vrAXYltGTA16OyGhDk3rmP8SsC499Z2dkjCEvjZbfJ1vapYUutfDGC398M2YDs+S7l2yHUYZ0iEjGMs5aZ5OCtneXMHF/1uvP2/w3HTL7a4uZZ7y5du+uj53kF+67AZXV+Tuj751/WDVgnK2LpGfecH1bl2Q43/seFVELo14oyEXX+fWWSNu/efE0922eNtpRR/3Z712s1MSule/8an3ul+A1GVj18hYsesVuefDb3J1lyb6GEKe9srPuLoi333d+U+Fi8i58fRy5S//7sur9lruOS8fcu87Lj6yXhFPzxN9P3v5c5cXX/25I+dn7vsi14/ezg9+4lZXL0eOtMWD08LT2xUh80fO06gY6ZCBrP8LoTRPpyXrG04JWR6l3vXYC332ehCJeT4tXvmU1JL06s40Blpwt9eIPe65nqPQFyBLrpExeOu2XwtcKtuM/cX5X8lgJIUQ77M9B7ms1xGpf8547YLcM/uA3W+RuY7Kkf1g5w1pGf6xNx3Zb8YaCrasFjHgWg4F6no8mVMMtMXbXiu1BlrpFyBbY9crIp23XWHMUPgY7PUhEjOS7DlIYV/Qu+ov7hxylkepc2FX0OdPCyHbotqQtkawUJvm6bRCx0bzhiNgjKFgr6eeXuGNdZ2TkhlHtonXYcGnhccaaKVfgGxNz68F7jP2eoGEsOcgRs0LemR+2PM351E5sh+M9kjPHXkpQd90HAIiY5lyZgy9HkhJGMq26enZLPkCZA96fi1wX7HXespI0l85TBnSS3pBj4SZ88xEZD/ZeUNa0Dfdez7xzSGVkPkzNgyFkCVRaiRJB0Yb0rHZU3Zpbn1CyHzYH0N6eHFFGmpClkRPry4hS0Yb0ZBHb3jXkHuUfX5BjxDSj503pGGErCTxFjghhJDl4BnSsdlT9vUFPUJIP3bekF4b0UoYJ00IIcvHM6R3afYUQsj82UtDWoQQQsiy4ewphJBts3+GNKfKIYSQnYGzpxBCtsnOG9KcKocQQgghhPRg5w1pQgghhBBCekBDmhBCCCGEkApoSBNCCCGEEFIBDWlCCCGEEEIqoCFNCCGEEEJIBTSkCSGEEEIIqYCGNCGEEEIIIRXQkCaEEEIIIaQCGtKEEEIIIYRUQEOaEEIIIYSQCmhIE0IIIYQQUgENaUIIIYQQQiqgIU0IIYQQQkgFNKQJIYQQQgipgIY0IYQQQgghFdCQJoQQQgghpAIa0oQQQgghhFRAQ5oQQgghhJAKZmFIHzt27Mh/QgghhBBC5s7WDWltPNOQJoQQQgghS2GRhrRsL4VCoVAoFMouC5k/cp7okSaEEEIIIaQQGtKEEEIIIYRUsHVDWhADmkY0IYQQQghZErMwpAkhhBBCCFkaNKQJIYQQQgipYCcM6R/+8IcryeF3f/d3h6VNpI6HH354+EWWAsOCCFkGvFcJIbvGThjSp0+fPjhx4sTw6yjPf/7zDy666KKDZz7zmSv5tV/7tYPf+I3fcA1mqUd0n/vc52Yb1Ijv1hID+Tn6Oi9X30qMHB2g9VAuVlbnp3RDhMqgPishUvkWXWdOWa2T0td50I3pC1ovV1eTKiOgXExX62hphVe3SIhUvkXXmVMW+bn6Qq6u1ivRz0HroVysrM5P6YYIlUF9VkKk8i26zpyyWielr/OgG9MXtF6uriZVRkC5mK7W0dIKr26REKl8i64zpyzyc/WFXF2tV6Kfg9ZDudyyZD4szpC2hrGI/BYj2OM5z3nOwR133HFw5syZlc5v//Zvr/57HuwHH3xwZUCLSDkxuO+7774hN5/QjYD03BulxQ2V2hYhZz2125K7HsnzxMOmh/SAzo/VGyNWBnl2PR6l6/bqjNWhdXL0gdYJ6efoaETHkxCxvBipeoXSuqFfW04YU1ZTWmfpekHueiTPEw+bHtIDOj9Wb4xYGeTZ9XiUrturM1aH1snRB1onpJ+joxEdT0LE8mKk6hVK64Z+bTlhTFlNaZ2l6yXzZHGGtBi4d91118oYhtx7770HJ0+eHDQ2kfyLL7744Hvf+97KkA4hhrSu55FHHlmt70UvetGQsol3I+TcZLnYMrE6kKd1Qvol9QqSDylBl4mV9fJS68qpVyitu1QfaJ2S+nN1Y3ohpEyqfqsT0ke61Q+Ro2PJrdsjVtZLj62ndBugr8vl1J+jb9Nj9QqSDylBl4mV9fJS68qpVyitu1QfaJ2S+nN1Y3ohpEyqfqsT0ke61Q+Ro2PJrdsjVtZLj62ndBugr8vl1J+jb9Nj9QqSDyHLZXGGtPCCF7xgWDrkoYceWhnBKcSgFsM4RG49Fn0zlN4QOfoldVu9nDKgRFeI6evt0Msl5JZJ6dWuW0uMsfWnyNUTPL3csiCmr/NkOVV36bpBbbkU2Oaa+lNldN059Vu9nDKgRFeI6evt0Msl5JZJ6dWuW0uMsfWnyNUTPL3csiCmr/NkOVV36bpBbbkU2Oaa+lNldN059Vu9nDKgRFco1SfbZ5GGNCG7AhtZQsYh9wQkhxJ9rdtD31JapmYdvei17T2Piej21i+hVJ/MAxrSHZjTzRDbFskr2dYSfU83Vlbre2UtVrelvtbREsLTFfHw9ERCeLoiKUp0hRx9nddDH5ToCjn6JfUJc9KHrpUQnq5ICJ2f0hWgoyVGia5Qou/lx8qU6PesO0Rv/RBSj5YUnm6sXIm+1oVOSFeo1de00oeulRCerghZFjSkR+LdBCIenp5ICE9XJISnK+Kh00M6mlp9+a+XQ3g6If2c+jS1+rmU6FvdVNlSfaD1csrk6OeuG5Tqg5xt0eToW51UvaX6oERXyNHX+Tl1l+qD2nJCSN+rM1Z3qb5HK/0p1utJCE9XxMPTEwlh82K6Fujmlknpl6xbKNUHulxOHbn6veol84WG9Ei8Cz90M5TeJC30c7clta5SfYvo55TROiH9mnWXImVKytXo6/8pcvUlHwJiZUr0Pd0UVjdWFnlap5W+l55TtyambxHdVvpIz62zVB9o/Vg5Tyekn6OjqdG3EmNO+l5eb/0QpXVbRLeVfkk9Qqk+0OVy6qjRz9EDpfpkPtCQ7kDsZii9UcbeWCXlW26bl1eqX0LLbdf0qhf01hd6riNHV3QgKbRuS/2cujSl+r3R25PaV6FEX+endIHVi5XJrROU6hNCyDahIU0IIWRnKDXC52S0c9u3w5K3nWwfGtKEEEJ2Bhp024HbTvYVGtKEEEIWiRg0noTwdEU8PD2REJ6uSAhPVySEpyvi4emJhPB0RUJ4uiIhPF0RD09PJISnKxLC0xUJ4emKeHh6IiE8XRGyLGhIE0IIWSSe0REzRFroxyjRb7EtpfoxSvRbbEupfowS/RbbUqofo1SfzA8a0oQQQgghhFRAQ5oQQgghhJAKaEgTQgghhBBSAQ1pQgghhBBCKqAhTQghhBBCSAU0pAkhhBBCCKmAhjQhhBBCCCEVrA1pCoVCoVAoFAqFUiY0pCkUCoVCoVAoFAqFQqFQKBQKhTKZrJ3ShBBCCCGEEEIIIYQQQkgv6JQmhBBCCCGEEEIIIYQQMhl0ShNCCCGEEEIIIYQQQgiZDDqlCSGEEEIIIYQQQgghhEwGndKEEEIIIYQQQgghhBBCJoNOaUIIIYQQQgghhBBCCCGTQac0IYQQQgghhBBCCCGEkMmgU5oQQgghhBBCCCGEEELIZNApTQghhBBCCCGEEEIIIWQy6JQmhBBCCCGEEEIIIYQQMhl0ShNCCCGEEEIIIYQQQgiZDDqlCSGEEEIIOcexY8dWMlfmvn2EEEIIIYTkQqc0IYQQQgiZPXDIWmlJjzpbMvftI4QQQgghJBc6pQkhhBBCyGyJOWJbO2lb19eauW3f3I8XIYQQQgiZL3RKE0IIIYSQWTK103PuTta5bd/cjxchhBBCCJkvdEoTQgghhJBZAqdnqeNTl/MkRG5+TCyeDqQUXU7XY8USStfk6ADohgTY3x6ejk7DsichPF0rhBBCCCFku9ApTQghhBBCZo3nVBSpIVa2Ng9YnZwyJaC+WJ0hnVi5UJkUOWViOsiz+aF0TUgnVU7I0SGEEEIIIX2hU5oQQgghhCwOOBZjzkWtY8UjlKfL5YgmlldKbh2eHtJy03PIKReqP5QuhNItVg+/c4UQQgghhGwPOqUJIYQQQshisQ7GlNOxJi9WphTUVVNfbrmQHtJ1Xkg3h9yy0NO6sbKxPI3Vyy1HCCGEEEK2D53ShBBCCCFksZQ6JmP5oTykh8rVUFNfznaU5Kd0U5SU17qpcsjP0dHklCOEEEIIIfOATmlCCCGEEDJLtJMxJB6enhWPWJ6gy4dE4+VrKcWrw5MUpfohbD2QELV6IQnh6VohhBBCCCHbhU5pQgghhBBC9ohtOWdz17uNbSOEEEIIIdNCpzQhhBBCCCF7xLacvrnr3db2EUIIIYSQ6aBTmhBCCCGEkD1hWw7fkvVuaxsJIYQQQsh00ClNCCGEEEIIIYQQQgghZDLolCaEEEIIIYQQQgghhBAyGXRKE0IIIYQQQgghhBBCCJkMOqUJIYQQQgghhBBCCCGETAad0oQQQgghhBBCCCGEEEImg05pQgghhBBCCCGEEEIIIZNBpzQhhBBCCCGEEEIIIYSQyaBTmhBCCCGEEEIIIYQQQshk0ClNCCGEEEIIIYQQQgghZDLolCaEEEIIIYQQQgghhBAyGXRKE0IIIYQQQgghhBBCCJmMJk7pY8eOrUTjpQmhdI3VCZXJ0cvREXL1CCGEEEIIIYQQQgghhNTTxCkdwnPsppy9oXybnqOXoyPk6hFCCCGEEEIIIYQQQggZRzendMrRq0XjpQk2PUcvR0fI1RsLjjGFQqFQKBQKhUKhUCgUCmV5QghpA+6ppk7pUmeu1g+Vtek5ejk6Qq4eIYQQQgghhBBCCCGEkHE0dUrXOnF1uVAdNj1HL0dHyNUjhBBCCCGEEEIIIYQQMo4mTmk4b0OiSeWDHB0hRy9HR8jVI4QQQgghhBBCCCGEEFJHE6c0IYQQQgghhBBCCCGEEJIDndKEEEIIIYQQQgghhBBCJoNOaUIIIYQQQgghhBBCCCGTQac0IYQQQgghhBBCCCGEkMmgU5oQQgghhBBCCCGEEELIZNApTQghhBBCCCGEEEIIIWQy6JQmhBBCCCGEEEIIIYQQMhl0ShNCCCGEEEIIIYQQQgiZDDqlCSGEEEIIIYQQQgghhEwGndKEEEIIIYQQQgghhBBCJoNOaUIIIYQQQgghhBBCCCGTQac0IYQQQgghhBBCCCGEkMmgU5oQQgghhBBCCCGEEELIZNApTQghhBBCCCGEELKHfP+umw7O/M1bDk5d+6yVPPKx1x88ftsnh1xCCOkHndKEEEIIIYQQQgghe4Q4nx+4/J+v5MTw//zyk1bLD13zjEGbEELaQ6c0IYQQQgghhBBCyJ7wyEeObzijQ0LHNCGkF3RKE0IIIYQQQgghhOwJD171lJXD+f6X+o7o83IYMU0IIT2gU5oQQgghhBBCCCFkT3j0xvcZ53NYZJoPQgjpAZ3ShBBCCCGEEEIIIXvED773jYOTv/+TriNa5MTxHz/4/tevH7QJIaQ9dEoTQgghhBBCCCGE7Bn6Y4daHvvSxwcNQgjpB53ShBBCCCGEEEIIIXvGQ1f/qu+UvvnDgwYhhPSDTmlCCCGEEEIIIYSQPeKHZx93HdIiZz75h4MWIYT0g05pQgghhBBCCCGEkD3i+3fd5DqkRR75yPFBixBC+kGnNCGEEEIIIYQQQsgeIdHQnkNa5OEPXDFoEUJIP+iUJoQQQgghhBBCCNkjTv3pc12HtMipa581aBFCSD/olCaEEEIIIYQQQgjZI04c/3HXIS1y8g0/N2gRQkg/mjiljx07thKNlybYdE8vR0fI0cvREXL1CCGEEEIIIYQQQpbK2Xu/6jqjISdfe+GgSQgh/WjilA6R6+jV6Tk6Qo5ejo6Qq0cIIYQQQgghhBCyZB698X2uM1oLIYT0pptT2nPohpy8Oj1HR8jRy9ERcvXGgmNMoVAoFAqFQqFQKBQKhbINufcdF7uOaC133nydW5bCaW8JaQXuqaZO6VInr07P0RFy9HJ0hFw9QgghhBBCCCGEkCUjc0Z7jmgtMsUHIYT0pKlTOuXEDeXr9BwdIUcvR0fI1SOEEEIIIYQQQghZKk+cvt91Qlv5/l03DSUIIaQPTZzScN6GxJLKF3J0hBy9HB0hV48QQgghhBBCCCFkaTx+2yddJ7SVx27+8FCCEEL60MQpTQghhBBCCCGEkHFIdOqZv3nLwalrn7WSRz72+pUTcZv88Ozjq23yHJcip/70uQc/fPTUoE3mjncuT7ziRzfSznz62qEEIYT0gU5pQgghhBBCCCFFzNF5umTk+MEZeEI5Bg+Xn7RafuiaZwza0/H9r19/uB0vO79NIXn0hncNpebDEhzqU99Lch3Z4/DIR45vpJ355B8OJQghpA90ShNCCCGEEEIIyWKuztMlA4egPp4hmfrYPnT1r7rb4YlE286JuTvUt3UvYT1aZKoOm/bwB64YShBCSB/olCaEEEIIIYQQkmTOztPe9Iy4ffCqp6zquP+lft3n5dBROSUPv/fFq3Xef2Q7fJH9mBNzdqhv616SqGxbv0Rnu+nnrmlCCOkJndKEEEIIIYQQQpLM2Xnak94Rt4/e+D63Lk8kuraWmmkixBkvTlFvW7ScfO2FB0+cvn8oNQ/m7FDf1r0k80Tbdcg18cTJf9pIl2uEEEJ6Qqc0IYQQQgghhJAkUzlP58YUEbc/+N431o5KT04c//GVc7yGFtNEiHP69PsuW5eFnHztk2fnjAZzdqhv6146/e5LNuqXgQnZf5t+8g0/N5QihJA+0ClNCCGEEEIIISSLns7TuTJlxK18XM7WOeaDcy2niUDEuJYlzDsszmlx7J549b/b2P5HP/Nng9b0JO+lV/2b5veSOODteuCQt+kihBDSEzqlCSGEEEIIIYQUoaNvIfKxtF1kyohb74Nz4liupeU0Ed62ydQPS8Fz+EvE8hyYYtvECW7XoQdRJDLa5su1X4OU6zUHOyFkd6BTmhBCCCGEEEJIEe7ctCMiepfAFBG33gfnZMqFWlpOE+E6Tivm0N4Wsq12++dyzXrnqfW2eYMKOtLdm6ZG5poupfcc7ISQ3YFOabKm5sMXhBBCCCGEkP0j5eDaZXo6Z70PzqWm1UiRnCbilf86a5oIOb+27JKi48dcs737ylNMjYKpXLToaGzZL5sv+13KFHOwE0J2AzqlyZFX72o/fEEIIYQQQgjZH7yIXnFq7QM9p7GQaGxbd8sPznkOdYl6z6GV03JbuNfsnz53yPWZqq989t6vruuGtL6fvIEJGbAA3qBDjeN9yjnYCSHLhk7pPaflhy8IIYQQQggh+4EX0bsvDqbeUa3ex+ha4TnUU9N2AM+pWTO9w7bwrtmY43fKvrLMr2zrbDkY4dUvHyXVeHNA10TCTzkHOyFk2dApvefAsMj58IU8jOWhLcKpPQghhBBCCNlvbJ/BOrl2ld5Rra3m9vXwooVz56yW82vLLglxgNrtjzl+S/rK8n8sMpWFrbsV3kCKjRJ3o+hHzGu9noP9Nf9+o97cOdjleuUUo4TsLnRK7znpD19csF7u+boSIYQQQggJw445mSPi0EP/ACKOqF2n1LlZijgLbf2tpskYM2e1Lddyn6fC7oNIiJYficyhZyR6jsN5TBR9jJqPosp6oVvrh+Bzk5D5Q6c0CX74Ao2/fgiEhI5pQgghhJD2tOiYE9ILufZwTUKWNJ3DGOx+i7Si5wcFa+esdp3ZV//qkLsc3IGUR08NuZus+spv/E8bZSASPZ7zkcgceg5GiFM2Vbf8tjotpqXxnN2xesdOm8LnJiHLgU5pEmTq15UIIYQQQsh5+O0PMndk2gd7LbZyos0dz7k5ZVTrGGqm4fAclqmPBM6RmoEUb99FWp4ToedghDc1iH2rwT3H146flqa03jF+CD43CVkWdEqTIFO/rkQIIYSQ+SKdV+8jSBBxTsSizUg5DBAgcwcOIC2tnGhzx4s8PXvPl4bccZRGlpZSM01E722aipyIYYu37yKt97/XYIRcl7ZezyHrRcPLtTIWr95YdP4YPwSfm4QsCzqlSZTQ1B6QE6/8181eVyKEEELIPMEHkk68zLcHtDx6w7uGUmQsDBAgc6d3RO+c8aJaW81X2zsqucYxuyvnuiYa2dt3kRZRxJrWjv+awWSr02recFtv6qOoh9Om/PxGOUho2hQ+NwlZFnRKk2xqPlBACCGEkOUj84ZaGyAk8oowaUcyQOBV/4YBAmRr7Er0bA2eo1IcYi3oFbEKahyzuxIVX3PevOMl0sphC1pOn1E7mOxN89ECdy7vxEdRsQ9aHn7vi4fcMOnAOj43CZkLdEqTbGTk3zbo+2J0EkIIIfuMdALluX+/sQM8aek4IUfp6QQjpIbeEb1zxovIbBmwY+tORZaW4LUlqW3Hc0BLKro6lymnhxIHrK0/te9eZLlI60HY0mkuYtQOJnvO49TULjl4xzBVb825stRc64SQ6aBTmmQjI462QefHAQghhJDdRxwG3sehrJx87YUHT5y+fyhFWuNFpYojh5Bt4TnRxBm2D/QO2KmJLM3Fa0skEjqG51SU/uFYaiN6a6mJ7vfOBaT1M8/WX+v4rh1M7nWeawY1ZHoNW6Y0Or/mfBNCpoNOaVKEbdBbjtjXMOWoOiGEELLvyHNXOon6tVhxREsHj87o/rivMrNzTbaMvSalTdgHvI/H1U614OENBLaIWBVqItxdJ3mDftbU00PV7LvV19Lq45bAjVSueL7WDiaffvclGzotIuJrpn+R82LLlG5L7/uUEDIOOqVJEa0eki2YelSdEEIIIWSbeG+tsXNNto04tex1uQ+0nGrBo+d0GTUR7lZfpAVTTw/l7XusHfX0tbT6uCXwIpXHOL5LB5MlXa9bpDQ62aNmGg3X91A4MCMDJ7aOlvcpIWQcdEqTIryHZCvjqBR+dIkQQggh+4Q4F6yNw8412TaeTV7qOFoqdr9FWtFi6oIYtu5YhLvnmG3V9tRG9NYidWzUHdkXL7JaS+vgJ88p3NrxHcNzHm9r2hSrL1KDvN3doh5CSHvolCZFzOmry1OPqhNCCCHbRjrvnLZqv2HnmsyNFq/YLxU3krPRW6RnPn3tRt0tP9BWEuE+xRQIK+f02359Yz0P/fdfa/5mrl2HSAjPmaql9bz+vZzCufT6gKfn3C+NUK/t03uDHi3mySaEjKepU/rYsWNrCaF1tFhS+SBHL0dHyNXbZ+SBaBv0lsZRCVOPqhNCCCHbhNNWEWGfo1LJPOn1uv8ScKdauPerQ+44aiJLSyhpS3p/1BH03mfgDSaEBnS9wQEtEijVEu8YTPlB217noNTJ7DmxZb7rGrw2SmwqQsj2aeKU1k7clEO3Nt+m5+jl6Ai5esR/OLR+EJcizulT73jOxnY9+OZfoDOaEELIzsBpq4jQ6yNUhNTiRXZuK2hlano6u0ojS0spiXDvFT1rEYe+XU/riGyh5COS3jQqWqSulmy7v+2tv9YZbLH1yps/IVo65702KjZ4//jZJw7e/ld3H1x4yXWuvOSarxycPnN20CaEjKGJU1qTcuTW5tv0HL0cHSFXj7R9jaYlngEjnfclIA9+ecCKwSUihs+U84YRQkhP2Ma1g9NWEWFOU6kRInjOI7lO9wF5vtl9b3U/ev2ulnPIl7QlrlPvxvcNue2QYCO7npjjshaxR+x6Qg55byBQS8tzImz7g7bedddq/W6E+rlz7uHdW7XXXEkbdePtJ1aO5ydfcv2GM9rK+/+ebykRMpbJndIWqx8qX6OXoyPk6o0Fx3jpYht0EU9vanng5T+yuV23fdnVnYN8688uW2/nCbXNh8tPWi3f95anumUpFApl7sI2roOce6bJMcOxDMn3jv/EwR1fvNGvg7J4uecDv79xzr/5vuOuLoUyhdz16Q9uXJPfvuY3Xd1dk3s+/KaNfW95P9q6pb/j6dWIbKetP7Tt977j4g3db/z1H7u6Y+W7V/7kxrruvPk6V7dWvP25+2Nvc3VznrteuWo596y39csxcXU7iBzrXuv3jmXo3EobYnWlrfF0U+K1Ud9569Nc3We+5rOuA9qTn33hDYOnhxBSC+69rTmlBV0mVN6m5+jl6Ai5euQQ94MegdedpqRkxHvbIDJBO2pC0vqVMEII6Q3buL4EPwZ1za9z2qo94LEvfXzj3Mfm+5TrxYs4g/DjmGQsXmTlvrTtXgRmyzmQS+Y+LqVk26fsZ03x4cySyG/vg5BWWveFt/1BW7vuVlOClUw/JW98Wd3a4+y1UaEI9+Pv/JrrgPbkois/P5QihNQyqVPay8tJ83SEHL0cHSFXj8x3LkOvwyUfppgjeMje/9Kj27sph9GEc0XOO1/LJ4RYdqWNmzPuq6jn2mCy+5y950sb5z70ajU/jkmmwJtyofWUBnNFbGG776H7sQbPGdzqQ4ol2z5lUJI3h3PrqUJKPt5v9Txp3Rfe9gdtvfPdgpIpY8QRbnXHYOsK1SfzST/vqi+6TmgtT73ixoMHTvlTjxBC8mnulCa7T8v5nVrifRW61UcZWiPHy25rSOboZNDGYui1fEY/ErK/LL2NWwK9HSFkvpREfPHjmGQqth3ZuS1K7scaen5I0dv20PcIWjsIY0wx6JobJe4dI09CjtVath0E1jJKWeNFqHuDAT3uq9J9Euf01R+6axUNrR3Rr3vPHXRGE9IQOqVJMXONzurx8OqJfMTCezhCxLhvZXS2hK/l18NXqMk+sWrj3vjz7rUuMtc2biks7ZlH2mLPvYgHP47ZH3EU8a2xfk6sJWD3u+UAj2c3tnSA2rq9jwpOHQk/xaCru45zdrjF0/MkFGVdS0lEcQ/keNv1t3CK5w4G9LgG5vq2NyH7Dp3SpJjch/g2EAPJbtvc59ec8/H0QKeDU4+UwVeoyT7itW8i4igj45kyco3MC9fecRyAq/nHr3nGhq4VmTOV85GXIXYDjh/fGlvWt11a492Pcu+1oGSaiRpytt0bBO15bUtbZNfX2gnu7ZPn9PScqN4c0y3nERdkCkq7jtaO7xi9IvRz+71uENxHjg+5dfQe4CGE1EGnNClmztFZ3gjo3KNVJDLWbrMYO3OFU4/UwVeoyT4Sai/m8sxYOtuec5Jsj1IH4ON3fOaczgUbZeQaojO6nCneGlva21WeE2tfHD7e/diqLfamJ2zpAPUGrey2e47E3lMkeo7fVo5+Idfx7U034fU3PYf2GHIjinuh+0uQFvez50eQ55DFO+5jg3Y8m1TaWELIdqFTmlQx1+gsd1R5AQ8bL0phzp1ETj1SDl+h7sfSOu77hNepgfCcjAftihZOibIflDoAPQeHyJROjl2i91tjS3y7ynMiTRnZuU16TgtQ8mHTGnK23Ws/xkatpvAc/XIsWmLrF7F4ba13rbcebHcjihs7vmP0vJ9tvV4wlnfcx9o32xhcIYSkoVOaVDHX6KxtP8BrEaeZ3e5WxmxP5Ovfdrvn7FDd5tQj4jj1olGs8BXqMpbYcd8nvM4uZAlt3NzxOo28zveDUoeBpy/iRajVIPezDA6KzSUiA1K7PK9y77fGPDs7JHN5u8p1XFbse4o5DkT3nP/Xiyxt6QDNiYgtbW9aUDrwVoMXFGSvHc857vU3RVrS+7yn8O7nVoFe3nG3eH0mCYoaQ26UNiFkWuiUJlXMNTpLDFW7XUuYCsE19j597ZA7b7yvrc/VqTqHqUdWzuk/umhjfWIU0RldzhI77vtE7I2KpbRxc8Z1AnWOXiPzwDv3sahnz8EDGYN2aPWcEmuOTu/kW2Ov/NfVtvES367yHHWtvx8w14Ho3k5bW7dIK3LmLp7CQWyZYk7lnKlLPAeq6ITSW2LrF5kK3GtaYs+YEjzb3R47r3/ZAlsn+waEbB86pUkVnvE1l+gsr4MwdmS1N4996eMb29zqwd8bb4BC9meuyLVw8vU/s7HNkCmmHvE6bt5HPrbB0qLdlthx3ye8qZ4grZ0V+4jblpy7b8nuU/ockevC6kNqHSlTTIk1ldN7LK0daEt8u0quI7t9rdujuQ5Elw4SldLTAZqz7T2nJwnhOUVbT7XgtYt2v2y+iJBTdixTOL5D9LQvUm8IS5tm8+VYtGCbx5QQ4kOnNKliztFZ2xjNH4tnyC/FgeZFH889Us+7fiFT0NPYqmUpHX8Lp0WZL167pmXb1/wuMMe2hExDqd3gdcQhtQOPCALoNSVWS6e3ODx6DrhKXXabWjglQx+ofPCN/2l2zzSZ9sBuZ+v2aK4D0T0deILnAG01v3LOtnt2Vm9H3hR9o1Sf0dsGXNNT9De9866dtz3x9r3VVBepY9fzftrmMSWE+NApTarobXyNYYlOUsGLKFwCEnlst3vuThF0dD2ZKirYcxBsq4PZsuO/LcQ5LU6Gk7/3Hze2+ZGPv2nQIlPiPSes8GOH4+n1iiuZP/a8hyJDvanNtNRG9PaeEquF03uqAVfPFmpRb2gQfa52Vm9bdq4D0T0deILnxGtlr8Ycr8CzWeVc9MauU6Ql7pu/59o1EPvIpFd2zNsRHq7zdsK3Ue26W7U7qWPntXtyLFrg9QFbDyYsiVvufPDg7X9198GlV9+6kqs/dNfBdbf2bTuPHTu2Fo9YHtlkF44XndKkijlHZ3kf32tpGPbCG7lt/ZXpXojxb7ddjNy54h1ryFQDGHP6uGXvaLcp6RWtRsoJOVO0MDplPJ6DZu5TVpE2uI4iZ6DHs4u0jGkj5VrDM8SVV/yr6imxxjq9px5wtXXKgNFYPOcNZI6Det412cMexEB06NrbxrGx2yC2cSu866DltIm2bjvAlcrvRc7cw2OQY2jrP+IcjUyv6Nk4rfsQrvN2wu9x2HWLtCDldHb3W52XMUwxV/kSEOfzhZdctymXnl9+3lVfHLTbop2onjN1F5ysPdjl40KnNKlmztFZS4w6XvLIrTuv9Iy33XOiQ1p2ImJ4X47f1rzsvaPdpqRXtBopx+tUWJmyc7WrSEfOHtep3vgg28UdzL73q0PuebzBOi2tBu69OWDHfi9B2vTa70BMPeDqOmRHRut69zdkjoN63jXZezs9G1RHu06FZ1u2wnPiiR3ZitgAV04kdS96P99SztGY09p7G6z192G87ZvSDu/RpgmpY+ed91YR4rsQvHL6zNmDN//FnQdPf/XnjjqUS0U5oHuIbN/xd37t4IFTR9+q0M5Vz9Gak+bpWKCTKufpaWyep4s0T89L0+ToCCXpNi2mk9KbAjqlSTVzjs7ahlE8FtfwmChqdyyeU3OuD3jPuLYyRYT63D5u2TPabWrstreIViPleJ0KK/zY4XjciCI6+ydHIje9wUaIdLhbR29695jnsPEiw6y0wH2LrsFAr2cfiaQcQFMPuPawPWNvds3xPveuSTl/PfHsqdbOwRx6RvV6Az4tbUbvOkOfznUgntOfAvf51jCqNeUc9dp0XM9ef0Ls6JZ429f6Y48xvOuixTXtHTs9OOqt1xtwrcELXpnqem7FU6+40XUCz1k0KUdo6jcIpYPcciE9IZRn0z29nDRPR/DSc3Vb600BndKkmtzO0DaYUxRqLrF5y+aO94CfKoqiFM/As9Iy+iSEd8zmMs1MykifO70iO0gZXqfCylzbiSXhOexaOitIGjiMTrzs6HnwpKUt4jlsvPo9e81KK+eZ2/6OrNvbT5Gc9kOetSdf+2S3vEgs0rqUHm+8eccTMsf7fBuDZKE506eewkPsJLsNYk+1wJuCp2UfwYs2x7ZvM4ii97o956g+rrHjIti81kEQrvN2wrf/el7Ttl49gOm1ey3nMLd1T/WWbCvuue/MwUVXft51/jaTRlHU4kC/8fYTw5Yf4jk9dZrNt79BKB3klgvpCaE8m+7p5aR5OoKXnqvbWm8K6JQm1fQevR6Da8QsICLPbvNUc7a1wHttsVUntyXeq3hWpnIOe+ueA3OeMz4Hzxnayogm+Xhtgpc2x3lRl8SSBzR3BS9CMiQtn+vegIQ3qJozQNQqqMBz4ohNNoaYUz1nwFGioL2yrZ9rsVf9a7H1aWkdldmC3GuyNRI9atc79RQe3nU6dlAC9LbLYhHBPa7rXNzvBDV0yqaOq9d2IoJccJ2njW0aW/+UDtSe17RnDwreIFPrtnqK8zZ3Uo7tX7zssxvO5FaknKFevk3zdCzQSZXz0jQ239NvmebpCCXpNi1HB4TSe0KnNKnGMzynGjlP4Y0st36g9cCbPmGOjl2PnoZLS2KdWy1TRNZ6U+C0ej1tLEt2Hi55fvZdIdSp6Bl1sw1k26UzLx1XEXF+Tf3GkNyX9pgu4Xm3S8ARe785D560dCTK9Wfr9wbgvQ64lVZOJteBNdIpGXOq57QfsfItn/XuFAsjAiI8W9ZKy+jBFrjX5AR9Ay8YZcppDoTewTo9v5cTczx7+zWlTWXX3Tpgx9YvAlznpbrnvLaltR0fct5OgXfuWw32hKa76f1WgOCetwmmbiTTsg3nKqmDTmlSzdyjs7yH+BSOxjF4EUZzmRIlhTtIMaIz1gvPEewZRmKg92bOjnzPYFuK83CbUT3kkFCnwuvgLHH+Yx15eULty+Hy4UfTpnzF1h1EmpmzainUDDTIsfaeLVbkPLW0QzynpXfdWR1PWjkOPafkWNvQu74hOc9q78PckJY2lns+Rrx55R1LK3N7Lm9rvlZvIFRkynbQs4NbfhvGc5C2ClyJTZPh2apT9k167rfgOp6HIAybbqfn8I5N6++veM+Wlvsfo6c9HQpS6D1li+Cdt7Fv9JD5Qaf0cqBTmlQz9+gs71W+uTt4vY8RLcWZ5nXGpCM5N7xIE7kubNoUcyi7xt4Er7nm4L3KOYWjvgWto9VIOaFOhXevLe3cIBJfO6NDMpVjWpw+dt1zeetiKbQYaDj1Z88/p3fBuuyq/PF/t7r2ewyKe444a4d5z2bPSdtq2ipvm8ZENnq2ppaUs8Lbfy2tn7m2/jH77jk5rcztuSzXud3GqfoGnpNrSkeTN4jQMlrba+dbDUp4247BBPf5MmFUae83rEJOX6/tsO1kz0hi4AUstdz/GG7AUSMHsesYPrc+N3ihcV94inUQQvKhU5qMwh1dnjAqIcYSHzieM23q1w/H4F0Peu61beMZmPIq9bYibGKdgG3T0xDtTU5HgvQlNMDmnZs5DWbmgGmW7n/p0f3YlENH5hS4nTtG/WTTaqDBc/b2fkPLiyLWeM8Zb9BepBXe20e1gyTeW3laUm27N0CmpfUz17ODpN2rwRsctjLH57K3nVMgzkC73iltaNf2aDgwGXLitSD2bG55TdfgBkk0dPyGnP2ujW4CVrxrrnVf05sTv9V5T+Edg1aBBKF+es/rHCy5j0PILkKnNBnFtkfPY8zZ4RdimxEmLfAMidYRA2OIRWl6kRi9I+vdtw1mEl3uTb8wVdRnC+y2t56DkMSJdSo8JxpelV0CXic0JNKZnIJQ547k0WKgwWszW84fHSIU5Qdcp8nfvGW9z1paOZpaOhU854GVGN69YaUlLaM6vQhJK1NcY6Vsy4kZiqqfKljGC3BoacO77XzD6a9s3SKx9Knw2oCW06KE2quc9bpv5jV2bvacQiNFz+l4Qsc3NEjQkrlPQUrIvkGnNBlFy45Hazzj1M4FNkeW7LDxDIw5vZofc9x4Rl9rw9Jj2xEoMex2iSyFOR/XfSDWqej9Ku4USEfNc+pB5FnTel7JGKHpUkgeLQYavGfIFIMSXtSzvp+8KEN5VnsOz1YDsTnOnFxynMqxKGyvvbHSMpiiZVSjN+DgyVzeUARTOJVCeOd7yrdGvLclWuHeVw3bGM9u8pySLR3tOci1Y7ehpQPRa2PkmRDrM4Ap5lDf5vNd7Ga77lbn3zuv8jxz7ffGbxy5QUETX9eEkPPQKU1GkfPA3iae02BO00l4bNOYH4trvMxoXulYJ9zb9ikGMVyHwoTOrBjeK9hzv3/ALjg+l4zbqRgGBdznxgI/dihsa+ofixel27pjvOukBhoeuOJfRNtmr83p/baNkHKChpzWPe03902byimUvOAHKzGnrzfQb6XlvMyhyPQavG1fgo3oBqxM5Bj2jv+UwRE930DwnHgt9827tjyH6NTPlt5vkYYikd3r2LQ1Uzg3tx3Va9fdqm/k9bu8gbhefbGeA0iEkDLolCajcKdDmFF0Vo5BMTfcudMadph6M+eBgFRnwZ0Hs/N0NHN20G2zYzmWkmg1cSB69x1EnE1LeVthLthjqKdPcZ8bM3qjopRtR+YJ7mvjMxoQXBKeExcSw/uI7hTtRsihAkLPvd7Rd97xqMFzlFkJOX09Z5YnLdsfz3Eo11Qp7j39hp9zn21zsxE9u2aqbfTO+ZTTd/UcNAg58Vrh2XyebbSN53XPt0jFNrR1y7HIPZe9nZveeZ8yqtcLMmhBaFDfSq+pAz0H+FICbwjZNeiUJqOQxts26L0eHjV4ERMt5yHrQcg4WgpyfO32y3mYA3a7bEfFM75Dnd1WzNlBF/pY3RLIjVbDfIAnXnZU15MlDQ5tE68DJY4xsO0OVmu8a63GCTUWr+PIwZRyPAcDJBQp7UWy1UYGl5JyLts8EcG13xpucyvnnHddWwlFDaJ91+INPrdsf1LtXy7e+ZH9XIKN6G3jlLaD52ya4q0FwXPshgbESwkNVLTCG0zw7uOWU4bk4m1Hq6ARbyBJBpu9tkfub4vXpnh6Y7D1i0xF7nGoIfa8hfRq37x7dS5vqhKyb9ApTUZjG/Rer9nU4HYUZ+Q09+jdUeyN10HehoPGkvPhPu96qelMltCqA9uDkKG+BLxt965DrzMRkimjrZZMzrHvGfU0NbLddl+2ca307LTvC97zV0tokNIdwOs8oAm85xactN7zRTuxbJ5IK1pE9HpOOM+JEXorwHOySVpPJ4tg6xYpJfQm4hJsRG8wYErHuff2wFSD/e411/Dtt55Rud4AqydTDjCAns5+r52UNjT3bQ/vbamaAbgYvdusGD1tC+9NHiu9rjfvXmXwCSHbgU7pCXj87BMHb/+ruw8uvOQ6V15yzVcOTp85O2gvD/dB2fiDBGOw2yYyd5a4zcB9dXIGAxW50UWes0w6gT3JNXynpvc8fj3JdfZLR1Xy7je6nsxlsGDuuFFyxkE3RSduSrYZmQd6dtr3hZRTJuT88zrtU0Vcxdppd2BxcFgLnkOglaPDjeAudAyG5krPddDIYJjVk/sS7b6WllPutDiunmMVzpm52gxg23Pcb3MKD+/51zKyuOc96w2EeLKN50rsfhhL6HqxaSH713tDtPUxcp8xE9lMnm3Rat3eflnpdb259+rM36YmZFdp6pQ+duzYWmJovZBujo6Qo5ejI+TqlXDj7SdWjucnX3L9hjPayvv/fpoRz9Zs80GZg+csmHv0mLvN54z8peAZzdvefnGK2W3yRsTd6Uc6j5x799BcrtElR7Ta7RaxSCSed79ZkeMgHReSxr3XzBQ+XoTKUj92KHgd5qk7N0uebmcueJ1vK1775+qda1umwlu/kOp0e87ZVoMp3sBg6aBmaGoSz9nsDQKEnNfu/drQcehuX6Fd7EWawzkzZ5tBaHHux4Ln+gm1DafffWn3wUJvIKhllHbPAV3vrQtPttHH846r9/ZbLbZuT+S+83DtmcbP3m0OOvd0uuc8c3tdb941FTrHhCyRlj7N3jRxSusdju18KE+n5+gIOXo5OkKuXg3PfsMXXAe0Jz/7whuGUsui58OqBaff84IjRukDl19w8PAHXz7kzpNtGh8t2IZjN0WuIe9FivSesmLO99DcB51ilEQUnfqz55/Lv2BD/8QV/4LO6EJyHDLefday4z413iv1U78h4h7Tc88Sko/nxLRiI2q9jq04xKYk5HxNDf70dqaMHdQMbV9qvwRvWh1M8+E531qes5yBuRSxdtSdGqWw/t7Y7ROZCn18tP1/uPyk1XKve9R7FrR0dPXsH3iDCZ6E7Kie9B7oyGn7Q89TOf65urX0bqtj9Fy3V7eVXtdb72tq7sjAufesgkjft1cQUsrH1sIHt0/swvFq4pTWxA5KKE+n5+gIOXo5OkKuXg3H3/k11wHtyTN/7/NDqWXhRZxM9aCMsU2jdCzeK8Qto3h6M8d5pd1Ou+Ns9OawFOkZ9Tbn850bYT5HSqLVvFc1IUtxws+FnI/+7GJnwNvvKaMX3blmZ/qMmyPeK9ye6EhjoWeHPZfQ4KHnwNKRoqFI5Fa4g8EF05qEHHA5g1reYIF2Dto8kVZ4TqrSZ3qsHZ3CCTYW1+bq5FzSYJD/aDCKLz3aR3cwpOGzrXd7Y+v2ZFv0nLZGrgVbt5XQcU61NS1w2xTzLOqFt+5W15xXt5be0+5469wHvHn/Q9Kjz5fysXn5SNNiSeUDnR/T9/QsSE/l62Wt56VpdL6nY/O1jv0NtG4sXy97ei2hU/ocuXo1yHzSz7vqi64T2spPv+C6g7+9/ivrk7IU+can3rvRgH37fzzX1bXy5du+evD777zZPR4i/+1NNx584Uu3u2Vjcu87X7jajhyj9L63PNWtY5ty16c/uLGd33nr01zdOcodX7xxY/vvf9WPubpTyNdvvXlje753/Cc2dW/78sG3/vzlG7qQb//3Z67q2ig3Uv7xur/cWNdczvfdH3vbxrbd+46LXd25iXcu7/7oWzf0vOOvReqxZShheeDlP7JxDD09uQetXo/7ayrxrrdv/dllrm4vsevfZru7NPnGJzYH2L/7uk3HmqTpcmJDWB15hmud3iJtst0Gabu9bbvzpk+ty8myzf/um55ypO4x8s33bb4FJGmerifyHLTl5djeefN1G+n2vNzz4Tdt6Oi2PFS3rqNWPBtO7AdPNySxdrT3eWsh3vGVZ62n21Jwz97/0qPr3pTD4BSvjrGyua526+ltk333yp/cqF/LNp8pco3b7dHt2Rjxrlcrnv0o4rZH546jp1srLdqUWvGeja2uOW+/tNh2vbV4z3g5n56uJ9vCG7AtkRMv89N7iR2kgY8tJTGsTk4ZENK16SE9IZRn0z29nDRPR/DSc3Vb67UE9xSd0hl6YxDn9NUfuuvgois/v3a4/vyLP+PONf0nH//fQ6ll4EW85XwJvOd823htP9conRtetO4cPhZYghs1uKV5pXOiGDBqnPOgbj1qPOfz7X2waCnRl7nRal7kkRZpT0geJRHQPefG3AbevTL1tZP7RgjZRL9dBZG23puCAhGfoTdrpiYUPeltu8Xmezq1eNFYJdNhxa7nVNSkG2Wtpl7x3gJqNa99STvo4UXt2/I9o0Zb4B1/HaXfC+/Ns5D0eiPNvW4bRYm79mzDKea8ty60bNMeSt3TY8hx8sXe8vD0W1Lb125Bz0hwb7+09H7LtuSNyjmRuk9TgqC9nI+8txD7/Er52Lx8pFnRxPI0oXybHtITQnk23dPLSfN0BC89V7e1XksmdUoLNt/Tz9ERcvRydIRcvdbcdvepQ8frpZuOWMhLrvnKwekzZ4cS88Q2Pjmv2zy743zbczBKx9LTqO2JdNK3NUdViBznpOdED0mP18nmfL7tdoksgdzOW85rm0u49+ZASefFdaQt+GOHgucEnHIwzuuoLKGDNQe8dkCmX/E+Boj5e12na6POegneM87rbNuOoVAy934prnN1mNc5B1tWD9Z617qeLie1X94UIC0dIGOcxt6c1/a6Cl2vc8Fr33Hf9EamMvLOP+TEq/5t1ME4lp7tsFzDtu6WzsmUc3Yb7RsIDb61wKvbSuxZPoUNb+ufKnjFveYaBaeEBnYh0p/siddflefpriPHPafvI8/rHoENKT+bzff0vTRNLD+UZ9NDekIoz6Z7ejlpno7gpefqttZrSXOnNCkD0cIXdogWnpKah3HJfNsSYV5KyigVefSz/3PQnh/u6G1HI7oF6KBvI9o4Rs6HBOF4yBk17hEp4jk+pogqysFz2Mv9NXc8Q9o6ZVIGMWSqzvTS8Zxj0sn1yJkXdml40bZTOtrn/NHUOROLeHav6eE67RltW4I3GOQ5RT2HUu9nT62zRp4xtpx2hKSudZtnB5PHOsxTeM/NXCdVzlzf3v7P6TnlBYe0ciCW4G1H7+jLnhG9gq275XWbcs6GnudT0HMOfOmT2LqtxAJqeg5EAK8tlWdXb7zno7WlxyDOdVs/pLf94rZTnR3hc0LOrdit2l8i7YncVz2c0SDl3PTykYb0lI7N0+j8mH4oXaPLe7peem6agPRQPvB0QmW0bizfEkpvAZ3SW6ZntPCU1LyGnTvf9k9eev3BrXc9NJSqxzM4ehumY+gZEdCLbUcbh8gxFuXBvM1R4zmf794drJ6kotU8x6jrzGn4euwu417HASddzqDB0vAiZ1tGsaXwnnNzf27MAc+pCwduzHnpPfO2Ea3q3UueeA6c3s8ez+md8/xwB63U9nvOBLwBlRNpLHiBC60GXL39zg0skDbTlrXnxBssmerDZzmkzt+UeE6vnm+wePdUywEDzznZCu+60rLN50lqoGoMqf1O9VmmsJO9vsxUASJ2vSKtiAWQtXbsW7xn/5x9A6QNPZ2rpA46pbdM72jhqfCiw3JGN2+580F3X0XWc003nNpkasN0DEuMIkQnbFvRxiHEgWDXH4ou2NaosefMmothlNNBniupaDU3stXpUIqQNKUdM/fenHh6n9Z4z5mebYfGa0fm/tyYA949r9s4r9PsdWanHGy12G3xxGu3e0YfCu5ASUYkWmqAJeZ4jjmsNW571Sgyz7umZJ9yyNkubw77Vg66FuQODEyBdy5aXuMWz8Hp3Xu1eAEUuVH4KbxniJZW90ctdntatblee64l1WfxrrHWb8147ULuQNdY3CjtRraa52yHtLquQ3gDulMGEhBCDqFTesuURAs/cKr/Kzq11L7+cunVt27s61/feN/BPfedOfjpFwxO6YvbTW2ypGhp70E59yjCbUcbeyzlOM55O90owoVEDqei1Txnk3T2PSN5KuN/yXjHLTbwV/OWzdzxrrlcZ9RY2MGqw70O1f3uTZXgXevbbBc9p4EVz6HkRh82vGbc50eGczI1PYfYGzYfc6zmOptd53WjaGPPMZlbt/v8cdpFqyMyF+Zk04jzzG6LSK8B0N5R4tJvsfW3em56gx1atm0Hee2cXGtj8a5XLak2y73fnYGwMWxz/mOvTWpx3AWvvYZMgV3nNgeXCdlX6JSeCeKcvvpDd62iobWzdR0tfE7EgTtXzlz/rvWXXA/lgoOH3vZfhlwfL0r66a/+3JDbb2oTL4ptrk4Qb1tDEb5zYlvRxh5uh3imDtW5nm/39fUZOvY9YtFq3n7BqeFFh7fuYOwiXocx1vHH+dHPj4f+6KJVp36peA6JHCdcK+y62cFK47a96rr1zqknUw0+eHhOAyuhqUU83Va4zuOMazLHMRtyUHmD497gmBtt3Mgh79oeme1AruMtdz+3hd02kW3hvhXVae7Y3lHiqQGbMXh2kZZtX1+9HPKp/U4NKnj3e+ugJzcArGEEfoyc9rgWz04XmaqfkdveEkL6Qaf0jPGcsuK4nhPayNNOhcPlJ62WQ68TevsnUdKg19Qm3mh2S2OxJT2NgH3Bi46fyogrZc7ne6nTLMSi1bw8THXgRQ9OZSAvFc/5JNdNiDHPj7mDfdEy1f3CDlYZOY5JOXdWx5NtOmxi0WaQ0KCw98ZIy2tGjqetP3Wscq5j762E0ABCCHGQW90Wg8FjBnNtORGPns7JFrjnMHAN9kauHbstcu57DPx762ppP3hOvJZ2ra1by7afJV60cKvBQFuvltTx9c5567eUekfgx/CeL63mzPZscZGpAojY1yZk+9ApPWMkevqpV9y44YDNnaqiNzCGtTMhJNaxcN2t92/slzipNblTm8gxKp3axDOU5/gA8iI7Wn4sZR9wDakZddo0cz7fSzXaZBvtdmMQyrs29PH22ompPiqzRDxHPo61ZczzYwl40VxTtTvsYJWRO4WDF5WqBW9ZbItQtBkktn0h524rap7DVt+Lrvb22bv3Ym1IauqWMXgR+ClKHJqeM8e7dreF1xZtc+DGuzZ6vd1g1yPSCu+8t3ROerYPZNv0vOZj+53z/LZlYoPyNTx60weMzXTBwYN/8MtDbl+8tjbnmOTg2eki0ieaAtijWlrtGyEkDzqlZ444Wz1H7I23nxg0tgcia+5/6dGGfFMOI9404oC2+ySOag9vahNxRL/uPXdUz7PtGTUh58k2kMgNLxoAIp2oJUSpzgHPkbDt1w9D9DS2x9IzOqUnsWg1L/pbRwF5huoS9nlblETxjHl+LAHvXm79Km8Ib3CLHawwuQ7T2DNZZKrzG8K75rTEHLOewyEVGVhC6bMtN+pQnMdWz5OYw67nvnv2R2pgMzaQanGj/CPneWq8e2ubcxJ702q0dhwCz8HZKkq85BqpwRtMEAkNjlhkesa3/9Xdq2knRaQPF+rj5TJFv8i7XyE5A7veOW8RiT+Ht8p6ffBcjs8jH37VRt2QKfq7S/6YOyG7Ap3SC0Ac0Gvn7aXn5Y3vvWP0Q34MXnRRSPRop0zRsd6fQcRJPTWe8dAyMqgWdLJOvOzotnlCB1ka79XcuTLnDqbnVGgZmdMTL1rNi+oVR6nGc3jMafBqbpQY9rXPj6UQmu5hCrxjyw5WGNd55LyiHormgui3LLZBavswNZGHvIa9od+wfXefbZFX27198bbfc157Ejs3bjvf6LVxzymbsjNLn7VWV2QueE5Eb8BnSjyHa49t8tbTIiBi5cT7yGs26oa0cOJ51+2q7oT9I85n279bifRbLz5cljdgS5mqXxRyxot4zwSLV37s23UIjtj2W2U9+gAtz+uYgZAzn7pmIwL9oWt/c8glhEwBndILoNdDvgXysPXmIoSIM0geOhr5mKHdl21EfntRfa3n/6rBm3sxJDkfCxpDj2iHKfE6rNbxODfs9vY+x7nMPSIrhhf94kVBe5F7VkekReRLa+Zwr3rHNNbZTz0/HnjZv9h4fiwJdFB1Z+fUu367++Cn59DbdhTvXPGeEbHISaurZazzYSwpB21sYMIbpGttD5UMEJcMrHiDjlZCH3gUvAGk2FQnJcg227pl8C6GWyZy7jybsYXzswXiSLLbFtuXKZjK9vccu2OfZ1M5Z71rUCTmhHzzX9x52KeTvqnp41kp7bNO1S8KOeNFcvCmQcqJsI4xl7fK3IHCkU7pFud1jI9kDhHohJBD6JSeOT0f8tvAi5IWB8q28B6I246WhlFzv9kuT3o5WOc8EFKC1/mIRYvNAc9JlxOhMQV2u0SWgNfR8BwZ3r0vUUc5ettiTveqFyVU0iHzBg/Gdnq2xTY7O0scjNsWboRw5BmB9kCfU4mqauXEHMv5bdqUVDSoV6YlJe2Dvn8goe2PRTdCUvRy7NZEF3rPq9i5K9Wfkt4R+LV4b0eMdR5aekSJT+Wc9a5bEdmnEHqKxagMdkkJU/WLQs743GlLepzzubxV5gWmSNs7hrHndYyPZC4R6ISQQ+iUnjktHvIyJ7NE0G2UGeQl13zl4PSZs4N2W1LrFvnMVx4YtKcHTkv9UDp55f+xVaeTRGF6zhkrEs3V4yvmuzQQUhpxNAfcDmajL1yPxesQbTs6MIdQR8OK98qrF+nlRVRvg7ndq7nTIISQts8bLBgT8bUN5tDZ8dZFNvGcn6HrbexAwxS2mDdPPiTldOs9IFpyrL3BwBIHtpYcJ5X3lkeL6Vi8OYxTjhzPye7tu7SXvefYHYts98Z2jXRktUA7+nAvn/y9n1hdS2L/t7hXPdthrP05lXPWO28isSj/j17/bfdYHZHBVpHB9BKm6hd550wk95rtcc6FmreSW/P4uX7IUZvmgoMTV/7HIbeOsed1jI8Ex3NXv2tCyNKgU3rmjH3IYz7qJ19y/dEyjrz/79tGY25z3TmM7WD2Rh7Wso3aEJEHszgtWzmjvVf+//Pln3XP0YZURDtMDQx4LXOKcvXAvLz6mnzobb8+i+2es8M8RijqR0voXveiTuU+nAM9I5NqsMdJpBQvGkck9vr93JhDZ2fsAMG+kBshO3agYSp7KNbBT53/Xs/LGudpyfWbat9jkbm9Hbve1CCpqMucfZ9qGocxrGzYznMf15Jj/3/2sqe496eW2L3aI0p8rBMvl8e+8FHT1l2w+p+K+r3nvjNRu+RnX3TDqOkae/eLQm1J7nmT9rK27FyZoq9ce17H+EjmEoFOCDmETukFkHrI//QLrg8+5J/9hi+4ZTz52RfeMJRqwzbXnWLfX9vJeeU/KpXRDi2RSA7pTEoEg4gYDbYDrQ0cyJwdM1MYf2NY6heqvWg1K3IthfCuoznM2dkzMqkUua/sMZLjVoPXMZRXkbcd9ZfLHDo7JZGm+4p0hO0xCr3yjjagdqBhKnvIcyxDUnhvlIxt32udp15+iNBAFiTklJ3KsSvOFVufXHshrK6IZappHGqZs9O8xP5POaZj9+qZ699l1nHBwYN/8MtD7jh6OWdb2qAPnHrcPWbfeeCxQWN+BCPEI/ahBm2RPnYnX/fTsw+GCbGEvnLKR/KLl3026COZQwQ6IeQQOqUXhrxS5jW6IY6/82uuvifSqLdkm+tOMbaDuWRKXvkPSewh35sSoxl5a50tdM5ymbvxJ52gh9/7u+72iMzhVeEQXrSalZjDTl9zkNTHqqbirz7zHfcehTz5nPztzd8dtPvhdebGfFzPO+biiFkKyc7Oq36sa2fHi/5MRbm1JGfQcNt416xsq8fYgYap7CHvvhFJRecKiOzUz6AH3/LLo85bjfO0ZoDL6msJte1TOXblmrL1hQY1c/d9qmkcapmz07zU/vfuUYh3r+p7MGWjzokeNqg3cD7naf+8+08kZ+Bkqec9Rum9QgghtdApvUDkgW4f8jINg4c4sT19K0+94sbVqHZLtrnuFC0i2aaYH7IHsRHlIzLD6TlKjOYH/59f2Uibs0E4Z+NvzlFPuXjRalpieM6ruVxLMkC0ce8akTZW2queeNHNobYzlwff9l+i9/qcB0I0XgRq71d6vfORG+01Bt0xl/Za7h04qMX5JOk59443tdR1t7aZtgqURgaPiaqayh6S9tfbNrlXYvRyqNQ4T732NjXAFTsvoTZiKseu3Ou2vtC0V96+e+dOBonlfFhdK/LcazXdWwlzdpqX2P/vfPEl7n0q4t2rS4gsDYF7qLUNKu23PXbv+cQ3h9z5sIo+/8vXmn09LzF7Y8nnPQanuCCETAWd0gvEc4T+ycf/95Dr8yvnjCdb5j+/9B8OXveeO7o7hKUzJh1K7QwVY26KdccY08Gcan7IHrzhz+9wt/OIzGB6Do9So/mogXjBwUPX/uZQ0/yYs/E391eFc/Ci1SAph40Ah5qWbTtE1289KJEO4BXX3r6R/uw3fGEo1R5xpJz6o99Qx+Zw/km5pms5PxByeC/HZK4DISAUqd/r+tnWWw3omGvnt1wDq0jpc3myXZhzM9Qxl2eOvXZXoqaWahVpJ8dBHxeRntHrAuyhZ/7e0cFh2BJjnTU4vlZiz4yeDpVc5+mJV/2btfO0ZoDLc/yKxCLEp3Lslgx+uPt+7vyEWDnSzh0bbcvmTuPQM7BiqmNbS479f+b2Qxu4pO/Sy7E7Bb1sUDlW+rqCzGkaj7GBF0s+7yk4xQUhZArolF4gEkFkH+6xTtptd5/a0B/zeui+4UVsPf34TRvHNCRTz5ctpDobKdnm9Bwx8ozmQ4eYiO5kHy4fGoSlHeupSBl/D7ziX23F+Jv7q8I5hJwWIjlTcUiknujqa+rUnz5va1MSeO26CDrL4oS2eeKsbol0THEsWt9ruzAQovGuv1hUbi1jO9djHFVouzA1gdeWyevRGOCxlEwt1cIx7Q40nZ1uoDzkrPnrG+8bNMp59KYPHLkXcwaIcJ56OlQ856kVuTYEb+qZ1ACX970DkZwphMY4dnMomRbFdWB3mCpqqsCK3sd2bvRy7E5FLwfk3KfxGGtvLP28E0LItqFTeqHYh7uIdCY9vMgj6XSSONGIrQxDHjL1AEBJZ+Npr7opeN30Ymx0TsxoRqfvaKfcl7k6poF02LztRsd9SuYe9ZSD19mHpD5aKJ0I6Opr63B5OwMdT3/15zbuHRtlKVFdVkfatRb0fl2190DI2HaoFO9jbNLBb01u5zp23v72d5928AuX/i/3uEA8RxU65mgHvHvu5JX/cfXf65hPObWUdz7k2E3Nnd96eHP/zknpoPCYNmobDhXP8SwR9QIGALXI2xgh5Pk01+8dlJ4Xb/AqNNXHGJ6d++HNyADRXKeo2zaMLPWZ8zQeLewNnndCCKmHTumF4j3cQ50Yz3khHSESpsXHAEXEMfz5r58cap2G7M7GOZk6ijvbYV7ZEYJB2Cvia2pHlucsmNr5qVly1BOi1aykHIO9na81yHRN9trzBr/kepV7fKxjwXtb5Nu//3+u9rnXvdZzIGSqKEGLN4VM648P5nauTwznJSZvfNGr3GMiEnp2JN/2OCdnPv2OQfsoXjTdhgzXcs3gilxTnjMUIlM9bMN5KXNlb+znOcm101q0UdtwqMi9a9cjzzxvO0KDsWPfDOhJzXnx2ojUoGkN2R/evHjaNhJMbWuR/sx5Go9dCLwghJAlQ6f0QpHRZftg9zppXhQOp+5IUxqxZfWtsyM153dLpvrKfw3ZDvPKjlDPiK85ObJ6vM67y6wi6T7w8o3jCIlF0s1toEM6cJ7ePfedGTTOg2s2ej9FHNZPcwY0V3KuzJUvvNLsf1jGRFf2GAjJbofOScuBO2++39bRuanOdY7zDvKdy/+le0xEcp8d3hy5sX2W6zj1/P3pF1xfPLA+Z+elIM+M9T7KPXlOfvJ3rj9443vvSH7gsXcb1QucEy3elCoiIca+dt+TmvMiAyM2v8dAiTx3cj68mSOtgxu2ZWuR/nzw/703anNse7Chh71BCCEkDZ3SC8WbT1Q62hbP2SFpJM6YiK0j52YLxtff3fI9d31W5NV+zD87FSUO85TURurVRnxty5ElhrC3H9uYxmOJjHVGzW2gw3MkhNr0rGs2NQAUacOe8TvvP7j1sp92j4XIXF9X3ebAnRcdGpuaoJZQ5/rBN/6n1XLOa8pybr1jUvrs8ByHJRHioUGbksHeOTsvBbEjvH1c3X+JDzxuY/qNVnjTVViJfaxwzt87qDkvNj31Js9Y/uAD/7hxzf3ciz5z8F+OZwZmnJMxbeQSv9lC6lgPknOwgRBCiIFO6QUjxph9kFsnpxdxJE5TkiYVsRX7GOANX37gUK/zq4/WoD/2yrgxn/pyeG/S0Tnp4wVp7SxKUeLIesZrPjeUaoPXuQ29hk2O0sIZtfWBjohj+GfOPQdCg1stB4GC0mB+36kpiRL8+I3fGUq1wfsgW87H2FqRiqSG/NMr/8PBf/u9T62Pw5hnhzjdbf2lzraxg71zdl62+MDjUuczlesxFB0NwVzTHrnX87Zeu0+el1f92Pq8yECzzY+9VTAWuU+8a0zSc9vI0gEqEB2EmbEduARK376aimx755xwsKEOb5An9aYNIYTMATqlF8wV196+8SDXDx9v6g4xIEl/ehtfOVFVIrUdht6I0Sz7oJ3+sq2vfffXD37rjbes00Kyjf0qcWSJtJ6yJXcaj20bpXMziufsjMp2GlcObsWv2fyOf1AGJ5qc46Uh1+bG/gxiI9dbXr/yKr537U395sPUryl7H6+TDyGWkDXYG3Ds/swlf3fw5Vf/ysY2WNmG8zI2+H1EFjgAlIM3xYsWmZs5xdTXcw3yJo7eLxE9IOUN3vQcsJJBJnuN2bY8ZKuNCW5o9c2WbdiBc2fOU59s8w2lXUfuUe846j5haEBzLHMdBCGELAs6pReMGBS28RdjD3gPiSU6D5ZIT+OrRVTV3OnREWqFt22/9NJ/2OwEnDs/P/uiGw6e/9YvrfTHOrZS03hs0ygVtr3+EHOOpJNr6akvv3HzmFVIbHDLu2Z/5dz99Msv+4cjdXgi89p66SKxt0XmjNc+y0CiIPepzZNr+P963c3NBlm8KQu8aRV2KerJiwIVKZkvN2uwNzGAI87pL/7RK2blvOz9gce5I200ppXx5MG3PGUrH6DsgUSs2/3Dhww957x8mLMH3rcJ5Bkiz4re6OdQVAa7YY524FzJaiMHmToaWa6t8CD5eakdbJD699E5us0+4ZwHQQghy4JO6QWT+oihZ/hx6o5p6Gl8lRr0ZBrknAcdjI0cs2c+++fRL/n/7e/+2sEvXPq/NtevpIdjuJVR3NMJN7dIOrnvxanrHSMRaRummNdTrlvrsN7ljr/sqz5u2F85DsB1TEMa3MvihNL3rYieOsbbxpU0ake2hdx/dr/l/sul1XQ0c3w1fMx0YVPSuo0eO+f/0ohFS8ubAzZPpu7qgfem5VQf5N73QRjQw95ZQjRyD5tjn52jsefGEenQJ5zzIAghZFlM7pQ+duyYKx6tdIQcvRydueE5NeShLh0cmy4PfTItPYwvGvTzpHe0wvnO++GX+mPyxhe9yl0vjNLWHaGxRrFsw4auiHLC/X9eEY4onnMEjLQBsegdLbIfGikr14qnq0XalF10ILdAd/xlrnd77KSjZI/dFJFH3nQ84oDa5TdhZGDImz8YkaIp4vdD2hkB2ZYzZsnktNE11+PcP0DZAy9aWqbu8N6g6DEPuPehdOlLTMlSBmF60OteEvbVZthn5+g2+4SckoUQ0oqtOaVjhHR0eo6OkKOXozNXvAfCX99438F7PvHNjfTWDyOyPfbZoJ8rYx2zKUo679+5/F9urjfk5GrQERpjFGc74Tp/NLQHJdE7f/D+fxxKbdJjcGtXCEWbyX99fNeirncReePI0vteFh6/7ZOre1W/+XDiNT9x8IZXv31zPZ6MWPc20ZGi2PeTr/nxVRS1HJMcvPshdzoaDuCU03OgZM5z/vfCm6ZDBqm8gSr5UGJrpJ20542v1k/DVIOO+2Yz7LtzdFt9wn0dBCGEtIdOabOsCaXPCc8ZJEaHN2pMJyUh/egdrVDSeb/1sp/eXLdIx45QyiheScb6x8jcImD4amM/ok5nbzkg3vXe+17W01gccUqv/h++CfHZy56yuU7IiHVvm5x9lznga+EATnuS7TqkYqBkznP+9+TkG37O3U8rcnxagoFSLU9/9eeGXNKbnvfSPjN356hsX+yNuaXPd83nLiFkLJM7pS2e4zfkDNbpOTpCjl6OTgtwjFvKp//htiMPtpD8ny+5wS1PoVDaidyPT3vlZ9x7UETuww9+4la3bFJu+/LBfW95qttx1fK94z9xcMcXbzxSNrZNR2ToCOmyY+X333mzqj//Ffvzkl9G9tPbhinkL//uy6t9/a3X/8NKjv/x5w9+Lfe4n5NtbvvS5BX/Y+j4jB3kiFzvve7le9/5wtV9qh2yIQk5pke1I1uUkn2Xts6rgzK9vP0Dt7jX4REZ7kVp97w6knLu+fatP7vs4LuvO++slWfZve+4eON5NqV8+bavHn2GGflvb7rx4Atfut0tG5O7P/a2I9e7J7L/Xtkx8szXbE759ycf+aKrS2kvk9xLeyxyv8px08/uX7rshoPL3/b5g5tuKb9PW4g8q2U7ct6Y+8O/uMWtgzJfIYS0AffUVj90aJ2/IWewTs/REXL0cnTmjEQ5eA83LfLKGCFk+dR8sG+bc84Jv/n6/IhhT37qd9LG/LYiYOR4eduzOp6D0zMl29r2pZIdbRaTjtd7DNy397900wl1VA6jhneJfd73pbOPU4aVTL9UM/1FKlpapvNoAaY4uui1587f0O5B5G0eMi2cfm+/4BtzhBCSZlKntOfkDTl+bbqnl6Mj5Ojl6MwVeT3Ge7hpoYFDyH6zzY5Qi/n+5vh6YMn8kD9zrrPxzN+bz7YvmZJBlpBsq+MvHzP0HFCeyODTLrHP+06WR29nEuaVD4kMNI8hZ8A09LwlhLSBHwMkhJA0kzqlSR/kw4beww0inW9CCNkWc5/vr5aYk/+IcH7I5iw52kw+XqbfdLBy4viPH3z/69cP2rvFPu87WRZTOJNiHzA+88k/HLTKmeqDeoSQOLtq/xJCSEvolN4B5CHmPeAgnLqDEDIHdu1jKNueFoUQQkgfpnAm6Wjpo3OtX3Dw8F9eOWiVwwFTQubFrtm/hBDSEjqld4SYAXrdrbvzxXJCCJkTnB+SEEJ2l57OJJmmxnNKHy4fzq3+0DXPGLTz4YApIYQQQpYCndI7gBjM//cbw190fsk1Xzk4febsoE0IIf//9u5tx5LaCgPwm+U9QCBOAomTgDuEcpunyV3eIYqUJyC54YZzIs4QiCJ2WJ0xMWbZu1y7XDM9fJ/0a7pd5d+ekTKNVlrTAMDD8u2f/vCrYXQvewbT/g9TAOA+MJS+5+I/KOM/Llf9dHAAAOA45d9W/+fv80H0//O/75gGAHgcGUrfc6t/OjgAAHCcf/31j83wuZ/4Zz4AAB5HhtL33Bk/HRwAADjOf/7x3s/fMZ3lsz/87vLvv//5wdsAAI8fQ+l77oyfDg4AAAAAcBRD6cfEyp8ODgAAAABwFENpAAAAAABOYygNAAAAAMBpDKUBAAAAADiNoTQAAAAAAKcxlAYAAAAA4DSG0gAAAAAAnMZQGgAAAACA0xhKAwAAAABwGkNpAAAAAABOYygNAAAAAMBpDKUBAAAAADiNoTQAAAAAAKcxlAYAAAAA4DSG0gAAAAAAnMZQGgAAAACA0xhKAwAAAABwGkNpAAAAAABOYyhdeeKJJ34RAAAAAACOZSj9k94Q2nAaAAAAAOBYhtI/MZQGAAAAADiHofRPzhpKlz9jERERERERERG5fwGOUf43ZSh9wlAaAAAAAOC3zlD6gXYAbSANAAAAAHA8Q2kAAAAAAE5jKA0AAAAAwGkMpR9zP/744+W77767+xUAAAAA4GEzlH6EfPnll5d33nnn8sUXXzxYuS6Gzd9+++3lm2++SfPBBx9cXn311cv777//89rWAXX0xt6XXnrp8uGHH97tXaX8G95ZVljZX3ff2t921TnDkeccdf9eR73ePlvh6DNu7Tvj913OyLLKqjPq3iO62746ZzjynKPu3+uo19tnKxx9xq19Z/y+yxlZVll1Rt17RHfbV+cMR55z1P17HfV6+2yFo8+4te+M33c5I8sqq86oe4/obvvqnOHIc466f6+jXm+frXD0Gbf2nfH7LmdkWWXVGXXvEd1tX50zHHnOw7g//NYZSi92bWhcJwa/b7zxxuXzzz9/sPu6r7/++vL0009fnnnmmcvzzz9/eeGFF+4+fvbZZ+8SHz/55JN3vz733HOXV1555e6sLWI4Hvf5+OOPL5988sndgDo+jzPi41iL39sttv6Fv+cLQ9lTp6zX6mczsj1Z157+3p56bU9vKPuytLK1a3o97Xq2dk2vI+tqPz/abH99zy2ZVe+5tau1tWfveWVfnbJeq59tlb2f9ezpDr199dqt3Vla2do1vZ52PVu7pteRdbWfH222v77nlsyq99za1dras/e8sq9OWa/Vz7bK3s969nSH3r567dbuLK1s7ZpeT7uerV3T68i62s+PNttf33NLZtV7bu1qbe3Ze17ZV6es1+pnW2XvZz17ukNvX712a3eWVrZ2Ta+nXc/Wrul1ZF3t50eb7a/vuSWz6j23drW29uw9r+yrU9Zr9bOtsveznj3dobevXru1O0srW7um19OuZ2vAsQylF4uh8VNPPXU3OG4Hxm1iaPziiy/e7dkqht4xZP70008vb7/99uW11167+/iHH364ex6/vvXWW1OdRQyl33zzzV8NyWMQHWfEgDvuHMPw+Pijjz6a/mdCZv6Sn/2CkL3f69jzxWZl/0zPUd0hntXPZ7tDtqfXM9vf6545c6R0bc0evb17+4rR/nJmyayZPUf193pm+1d2h5mu2f7R+/Gsfj7bHbI9vZ7Z/l73zJkjpWtr9ujt3dtXjPaXM0tmzew5qr/XM9u/sjvMdM32j96PZ/Xz2e6Q7en1zPb3umfOHCldW7NHb+/evmK0v5xZMmtmz1H9vZ7Z/pXdYaZrtn/0fjyrn892h2xPr2e2v9c9c+ZI6dqaPXp79/YVo/3lzJJZM3uO6u/1zPav7A4zXbP9o/fjWf18tjtke3o9e/qB7QylF4shbQxwX3755buB8ffff//gya/FcPn111+/fPbZZw9W5sXAOL7jOr6TOQbKMTCOwfFXX3314I3tYhgdd752n/Kd4DHEnh1Kh/iLfktmZXt6PXv6Q7nbteyR9URa2drIlvdH511T762T6a2P9Hrb9frZjK179/bXyllbzxw54j4j7V172SPb1+vac0a527XslXVFWtnayJb3R+ddU++tk+mtj/R62/X62Yyte/f218pZW88cOeI+I+1de9kj29fr2nNGudu17JV1RVrZ2siW90fnXVPvrZPprY/0etv1+tmMrXv39tfKWVvPHDniPiPtXXvZI9vX69pzRrnbteyVdUVa2drIlvdH511T762T6a2P9Hrb9frZjK179/bXyllbzxw54j4j7V172SPb1+vac0a527XslXVFWtnayJb3R+ddU++tk+mtA8cwlD5RDG9HQ9t4du2dWTEsjuzpXHEfAAAAgD1WD4oNouE8htLcGyu/ONznL2z+XHJHd0dfydFWdocz+lda2X8f7x6ddY62sn9ld1jZv7I7HNF/y94tVvbf57uH0t+eUa+3z2b0Our19tlWvf31evtsVttV51ZZZ8kRst6SW2R9WfbKurLskfVk2SvryrLSfe7f213+XEt69vSv7A4r+7Peeq19NivrqNfaZzOy/fVa+2xW1lGvtc+2yjpGmZV1jAKsYyjNvbHyC8LqLzaP0t3j/V5a2do1bWedVrY20vbVaWVr17SdpaP8WtTPtip76pT1Wv1sRtlXp6zX6mdblT1bMyvrGGVGtn+UWVnHKDOy97OePd1hZf/K7rCyf2V3WNlfv1/27+npWdm/sjus7G87Rv3t51tkHXVq7efXZPvr1NrPt8h6Qr3We+eald1hZf/Mnket/77ffSazso5RZmT7R5mV7ev1tZ9f0+vorc/q9fTWZ7QdWWcx2x1W9q/sDiv7R11htq+1uh/YzlCah6p8QdiaGdn+UWZlHaPMyPaPMmP0fts32x1W9q/sDtmeXs9s/8rusLo/xL6Zc2at7F/ZHVb0z/TtOWdl/8rusLJ/ZXdY2T96P57V2WNl/8rusLI/29PretT6V3aH3p4j+ld2h5X98f6WPVvfa63sX9kdVvbP7JntDiv7V3aHa3vq82f7V3aHlf3Z+72O2e6wsn9ld1jdX8TekuKWvtbqfmDMUJqHrv0i0LPni8PK7rCyf1X31s6t57dW9q/sDtmeXs9s/8rusLq/FR11jrayf2V3OKq/7SnJ9NZH2t6STG+9p+0syfTWr2m7e9kj68myV9aVZdaePTNW9t/nu4for1O06/WzGb2Odr1+tlVvf7teP5uR9URa2do1bWdJK1vbou0taWVrW7XddY6Q9ZbcKussOULWW3KLLftvOWNl/6PQvad/ZXdY2b+1e6+V/ff57j3Rd3RnbXU/8EuG0gAAAAAAnMZQGgAA4BG0+jv2Vn/H4UrunnP3PnfPuXvf6n74rTOUBgAAOEkMOWYyK+sYZUa2f5RZWccoM7L9o8zKOkaZke0fZVbWMcqMbP8os7KOUWZk+0eZlXWMMiPbP8qsrGOUGdn+UWZlHaMA6xhKAwAAnGRm0LFnILKyf2V3WNm/sjus7F/ZHVb2r+wOK/tXdoeV/Su7w8r+ld1hdT+wnaE0AADAybYMO24ZiKzsd/c+d8+5e5+75+7z3YFtDKUBAAAAADiNoTQAAAAAAKcxlAYAAAAA4DSG0gAAAAAAnMZQGgAAAACA0xhKAwAAAABwGkNpAAAAAABO8/NQ+t133/1L+UREREREREREREREZF3+9t5/AfH19saPEmIaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename = path+\"SalesByType.png\", width=1000, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Intepretation:</b><ul>\n",
    "    <li> Blue dots = Sales volume during no promotions; Orange dots = Sales volume during promotions</li>\n",
    "    <li>The frequent switch between promotion and non-promotion periods, makes it hard to judge weather we can talk about seasonality or not. Overall, even if seasonality is present, it is very hard to detect and thus of no signifficant consequence.</li>\n",
    "    <li>An interseting observaton is the sharp jump and subsequent sharp drop after each prmotion start. This is worth examining further.</li>\n",
    "    </ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eFsS1FFBrKaDWUkCtpYBaSwG1lgJqLQXUWgqotRRQaymg1lJAraXgqbXlhyU3T6jWaPwXiCrpgjM0BdRaCqFae3m+47O7O3PLTd2IEVNxN0vHwL7BTtCCfYMipJK4BolUeZboq9z7R7aK9HJmG0YJ1dr5v/n05QNZeRJnK876eyotCcHKqkSppwohJZ9JFSn03ODRqFo1Jr3zYiYnTi6SOZbIsPO6FofgGfqS5rtkoMIE0XFNuwefk+yoREodLjUTUz3hc0sldWSxVSoXn7onj6XSapGQBrWcpeIKPpcWS1QWjrLaOh+Tak2uxJ4yaANqwrFTcmZp3Bq0CmskBpXYzmUvR3ZXZSdcRF4pKmgZLWlslcnkJ9rX6sWzk9NUQYJ5t19rete4IBOOShJUmTSjo3JsNg35WkFQaymg1lJAraVw8ZW/Vw9AZuBqoBBwNVAIuBooBFwNFAKuBgqxQ1cLjrsTwVHIEN5h9xESFtk6u3M19xhncTVO7EJwtR1gghqbc1H+mWMv7NJGWYWCZSGDNEmdFknt5hNW9UdJNePCrthnW41uQav/BElixg3aHUijDNJKsiuKqZjLhEgJjU7TXPjjrtjfBZRjZloj01YOLoi0k1km+Mz8iZnIIG08LSdcJf+MkWbi/67YZ1SbhvaFvTnFMsDVQCHgaqAQcDVQCLgaKARcDRQCrgYK4biaHH0UQz6dCQBzsV1NeFTPy8SEoBe/ASDIMaZgXE34k+tjJgXAXNBWA4WAq4FCwNVAIeBqoBBwNVAIuBooBFwNFAKuBgoBVwOFgKuBHgfG7J/u7wFXAz24q+X/TWm4GigEXA0UAq4GCgFXA4WAq4FCOK4mH4W0H45UEwDmYruadKohV6OnfAEgyDGmYFxN+JPrYyYFwFzQVgOFgKttlz+dTi9OJLscGT+QTJyfviJpLeBqdRF2nz5PTwySXbirucf1QzAvN3wgeVHganURdh9J2DH+/OF7kryw5Z6S6MLX+DPJiwJX2xZBx+CGFyTXCVytEf7MXI3ErLAreqYVx7ra+q1LMJ/z68lHceyKPp1IV+Oty7rDNBjn7SpHMTaqsUKy1uXDR48pDbYIO4qvSYzj+uZMUjxJbbUr/pgmyfvhGTtAizSHtgMfRDk+ePDidPqJNBGkdQsWefZ8FVZptWyWa+Fp/PqbUBFprtYM67RaquU8zYnen96SFMPOXS291bIBvosOPosG7gyuFnENAiXhflPRFX++q+EaVC0fUlrvi5EhqjFX28g1CPF3TYyrid93oz/zTCRNmgDjz6tiRTXpUD0vExOCnvLtcP/x4v6zzb+xY/qR5CLcX//6XySGubhgxfpAZQZ5IMeYQsfVXB8zqTjYIS3bHl1hTHnOuPk+ydBW82C3R+O73AkUH1OmcXMwnWVczWZaiEu717Eectw8hZFHGNtleVdjIe5HklwujsdbElPvdWwQvqOdvoldEVvH3G/oNbsKuFoI5xo0cq+D70Abo/o/qkNhcCrCYXsR0NxvqMnVHrw5XpM0Ct+BIg/AJ8FL955k4vY4fedYZn9evt6Njs5cMkhUrOlqw2zomR1eUvfdEuZpVqA63/yVpDg8EXDD1Otq235m57nV/HL9brfU62o1cf4hLSwRzNNuSNwxcLUJ/ICwlAG42hQQljLQrKtNfKA0yBlv6syi1wFt19Vm9ir2+abOfB6betvOYMfKFL+r2gZPtKu1EdXwVMUW2aKr8f4gOoSbY4uulv5UBVgR42r0wDeb6olJgRX5dolffCqPFdWkqw1+4Zsene5z/6+8Oeg+9g3y8Kniqr0nx5hCnKuFsTq5cfwTDa8xnhw+kbRpSrXVfhvyxBu4WlPQ7WJPgCrlajzoYUi0ffTt4hXvFlwdfk8SaBnmaXS7OGdUu6ju0oex3YqZ42o8Vlbla9UVaEN8c/WOpKWYdQENPRMveVi8ebbPsd1gj0sybWyAZ/qWZA85juWCbbU/ZCjebEaOQwvwah7YR3MDfIivhzN5jmW09+V2tdpaS7w+oirEhb8W8JzkpUmuuqV6XMMFio4kmV1tZmtpgRg07zjw3Zn6OjCv+iuSPfwyUjXcrOyei97Y4kTGm1h2gXKQ2dVGWkv/GHlekddwp4q55gnJEzm/iX3pJPmRWxb1PpMoSjpwmM8jR86uOs9Fz1k82P4SN7FI9mK1/scacblbvgu21Tx8P/ZobD8G8foYiBUPzq//QZJi7KWTfuhMfuR2rdfuwu2vJ4OVJWpTtf6nNeLyUdbVJMn3Sx1kM8r344PMMPTSCd8627zdEmGKyY/c8qV1mLVf9/QRau6s1VsZaf0vyhqulud04gHljXiz3P/jg2FE6OSLewPS2PsvvPCDkcMiuBG+ktWO+Uqs4WrJ5AmHkmBLJOdb9eHmzg7v023K1Uq3LkBOqnS1+C5kRdidUmDRcTVxN959MFIIRdn0dwvW6pTWT9fV5DO4vUdxy8IOVt7vFkx7BkX2aefynHdWQJ/+BVQ616qulh0eacYH/XkuuMlibKpbkM7wMyigBDtxNbA+cLUV4cNu++lCwNVW5DlcDYD8wNVAIeBqoBBwNVAIuBooBFwNFAKuBgoBVwOFgKuBQsDVQCHgaqAQcDVQiAmu1v/+HwDxjLtaC0/hggqAq4FCoK0GCgFXA4WIc7Xz+cGX8/lOJl4+P9/dvfxvmbjkr/WZC61JcIPVsbByiXkglzHoiZwHNsJlhZC0gduk7CYEKpe9dT53Mulc/L+2BDbI1+Uk9IqFWhvoz4PIp0WdMCsSmFzOvncI5BKSMvD1KhObW7vIFzEJA18kvM0gkVHt7svlly/q4zp3X85fLsnvnC3zItp7pkXH0CmwncsyBBNM6CS0bPS9BElM1hNBJ5NK2bnspV34NmybvRlbNLmEpBM059iZ3CX0hM+dxa19767Ln6u3Xo1jcSqY5hKtjmG5C2iwNJbBKbIjhwzB1bpLWImAwdHSXOAmOodRJ1yDOSQiQRKjY/Dn4gaVsmWJm7LwGuyFhazSHYNJ2g7lJOxcoljexHRyuZqzeTvBZ3axWPlJEnorl4Ux2GI/obBEjsnmGDq5rIrkf5bVkaXZkQRBA8NJjuUSM2UKJpROMGhwFVYuy8BEy6BxEnYuLhijXT/TQbcAFAKutjr2NSCcsOVphiKLRwBXW5/eUXMU+gDTXOM1TMvFybT4dOBqoBBwNVAIuBooBHM1AIoAVwOFwAUUlAGuBgoBVwOFuPgKH4kFRUBUA4WAq4FC4AIKCoGoBgoBVwOFwAUUFAJRDRRi567me8Av/qG/eEpsozb2dwF1nlXO4moJfrNPV9tZVOMH2Rxo3yGPdgN3lZOI3kYD7NLVDL5DnuQGcLVRdncBpYNM7/yIPyGSgtJcwUWpoIuuVJNVzITIc4kUTVUWaWEpNddGoaAs+2F/3QLlBvL4yxmbmAMv9FIhldJFKCVk7mRK1jPxZ6fZlKekRihoM3wmVrAr9tgDlW6inIWHGSabQy8EoSAdKcRcaZSO/ym90mmZz8R2+H+1GZpJ+57Y5RCuPNT8aNNMoA6/mAlDT0Eymyod/1N6pdMyn/FtOQqakbAjdjmuJg80/ZljTpKxKQVpxMySpULPtE7LbCZ82lboGUn7YX89UAoyJtZIkRSUFmJXQUqRkLL8EzOhFFOlFki1lKRRzLR2R+AeaBjlC3vziYXY5QV0InC1rMDVgmgPg6tlARdQUAhENVAIuBooBC6goBCIaqAQcDVQCFxAQSEQ1UAh4GqgELiAgkIgqoFCGFdTj7hoyRIAmI19AeXOpWZ8ogVpBmAO9gWUvEp7mBakGYA5dFxNOxefaEFYOfRVcAAIcowpWBdQ6VGDrgZAMla3QP5x5zI+piYAzAWDHaAQGMIFhUBUA4WAq4FC4AIKCoGoBgoBVwOFwAUUFAJRDRQCrgYKgQsoKASiGigEXA0UAhdQUAhENVAIuBooBC6goBCIaqAQcDXQ43A4/I7EjOACCrp8w1ztQHJGENVAl09wNVCGJ0u5Gi6goMOTw68k5QRRDRQCrgYKgQsoKASiGigEXA0UAhdQUAhENVAIuBooBC6goBAmqokvebsTkwJgLvYFtPu5SJMCYDb2BRSuBhYkLqrRZ50BIMgxpmC5mnSqQVcDIBlzARUOxV3LcjY9AWAuGFcDhYCrgUJgCHfDvPrwPUkdbo/HjyTWA6Ladjkx3pPscH1kkKwIumUx4GqVEeES3NVekOzAglrX1V6E3JJbfiJxWXABrYtgpPLA874l2eFd39V4Xq9bvuIWkpcFUa0ugi5B2DHozyzvP0l2eXe8JUnB1+t1y7fcQvKywNXqIugSknAMOr9+RZKXH0PLfR9cY25wAa2LcKQSBGPQMxYM0zzm/ZBr5wRRbVMEYxD3waELbwXA1bZFKAY9DfngbLKNkuAC2gjM056SmJXwKEksiGpgEB4t81yZY11t/UFnMJ8/x40T5+k3RF5A84VTsB4xRzE4ShJNZFTjPl55RweMss5RTHC1DySDrcKPYqHBNIvICyiF02+u3sk02CL8KA6ME4c53/yVpASSeqD8A5ZPSN4REY3pNjn3b+NHkOxq35K8H9AleiNd7Tt5aYslaQiXu9oVyfuBVfDOu0Q3zNNukxvsSVHta+ZqJG6dVUaYNgtztRtZEQlhLcnV2iF2hCmpMb0hJp153NN+JjmCnd8D5bWGcUINr44JZ96HpEfEdx7VeN3u/KJow6tjsTPv4qt7knZJ0xdF1jqIjD7c1RYbod+5q7VMwgsq+W54esjgarsf2ayUci+oTGO+q2Fks1LKvaAyjfmuxncInbgaKfaCyjTyuBo6cWCU+a62oU4cWpVrYlxNfMn7UnwqUkqW0AY8/qJVuRra1eTHIelPfi5SCyLD9uGuhlblalgXUO5R9Neuq+ER4tXouJp2Lj7RgjBwrn18vv/8+eNnmxen07+TWIaP/+/X/01imI//YK528ZFKDbJAjjGFOFebRrnvKCm+PezyseBt4boaOZfxMTWJovwwNX9Ws+xjweebXT8Rk4LlatkoP0zNXa3oY8Ev5aPPIIIlXM0dpo5/viCe4o8F/wBXi2YRV7Mp33ArAX/KHq4Wx+KuNq3h9h2LfSRuA+ZpNyTGMfJ1x4ZZ3NWmNdx4ptckN815UnVsGLr557nHtLirhZ8vsH/HgR+Apo+A4gPbz+4dC1YRzXRn+XEUN//WcLUQzu848BL+hWQfJfoWRfB83dH3gxabhe+eOJVqcjX3dxyGX8JpqG/BdqTzdUfPD1oQG2zX8QMlbv7V5GrPgjXcp7ZnlzvMc4nglzC22K7TbyfU5GoPnh/fkDRKbc8uu3hc4kJ8cWAizwN5fe26rXApHkdzWNHVRvjnq9fPSKzu2WUXj0vwQDX5RAqx3Fe7V6FeV9vO6zEel+CuZi6K1zdnkuJgq13kq93rUK+r8eO3kXO67xLc0/RnA3i7/znJO6ZuV9vug4y31lAZ97vpLbdmqdfVFn3TuihwNUG9rlYTqY0tCR+jJXHPwNUmgMZWDuBqE+BXQMSlucDVJsA9DY2tucDVJoDGVg7adTV8NaEymnU1Piw352YDfnBmFv1boG272oxxOf4OFt4sjee35oWi7dxunwn3tBk3G7ir7e8HZ+bD641ek9yNq8282WBVGYiA15sMa/u5gM6koR+cKQr3tN9xoedpcDWQl6vD7/ms72mbdLXzDzN+lRKsxQZdbd6vUoK12KCr4XsZ22SDrka/Sgk2xhbbaszV0r6XAdZki64GNolxNXHTSn4zkkmdCVgP617PprGimnAr6VlyYlITwO3ppeCjoi3cuIhzNfpWdp+PvD7+kxILcy+gRPtc8Ko9VLrD6V/4Ns7FJyY1Dq+OtNvT814R2QG8asW9no2T09WSojx/RQS+NsjV4Y8kbRrtavJ7HvzPTExqlOTb03w8FqNke8CKaosSvAhwV5v9IRVQDdQeEpdDl0Ku9g2/wJLsgldEmsJ6ZZZfFqUkKeRqn4KuBpqCX6RE6JCNLy4pCrnaE7jaPuCeRk3vlaIa87VfSQItY7WH1nI1sC8yv5zHmoCfSawDPJ5bMzNc7RfVAqwFPJ5bNTNcjfdrqzqyeDw3nfOjxyQtxgxXGw0ipR/2wM/Z+Zj0DNLDsRGCDMdyTlst9L19gpe+7LcImKft8PHcb4fvxvPDMHp3+ornItkHN889lnNcbQRePHyLYHEe83om2Qc3j4a1P0xYSfdYxga6hV1t9Uf6Rk75Bhi7EcPN41XAMj0i0QdfSedYclVUoFvQ1Wr4FsHYKT/GBoZPRm/E0Pvms/AcS77ZqItWZler7dDMvPdacvgkueqeLHTtGCkQr9moDed1tepGtmbee40aPhm5Vl8Mr8qpOk+vcWTx/Iwdy+iLVl5XmzuyxY5W5gdO553yMW83j12redUMPJjnVB1fVafcI4sTGd+uyj5KmdfVxg7N++GPnvmO1vlq8bHFAdj+TB0+GbtW8yM3cOicquOr6qzLWTzoUXy5qefWiFtm/4hA5rYaK97QoRn5lKPnaP2mrxol/sWY1AFK+6o2dq3mrqJ/4cyDXXV8VZ2LsbM4t3s9ihuGCmHfFAiuhLALlIPMrjYCd7WBsOY5WmNjix7ifzuFbyNpgJIff31VG7tW279wNoan12gvzgvsrRWuH2gwOjcFuBxZtbMo72o/k+yjf7RGRyteve5+M54f/6FGRr/5zreRNNg8tqml4AX2e9TwyIZz4oZXsgxlXe3Bh9NPkW1Xltc/tnh+w/vi3Hk734znh3+gAe1xXq6Z2sJxGo98U0MXxcVIHCtzbwrkGHCLoLCrCaKObAjqi3NX61ySR16MoQahPWoU0293G48jF0V5OnhY6yYGK/vQTYFFWcvVJh/aENQX554W98142SAcGDXqX5JtYhqPwY3MvYmxSdZytdnnND0xlPDNeNEgDI8acecd+BmXGC8JbmTmTYxtsoarJTcSnEYeO4oz+uLhUSPuakPey8ow9RpEp0OfmTcxtskqrpYKP0CzG3lE0FO5p+X6zfjgRpa6b1kzm3O1xYNBO78ZXxmbc7VVOm4gA5tytdIjQSAnVbralr/uV/xhn81Qo6vF38Ssh4es7PiIl5eOq4m33/m3sehzkWpSFH60NhsZNl34RfG4muNj4uOR0lYMfrDWCAzB20gx8MKvclu0erquJj1rXVfL/3W/SV8XGXvCeSJvIp4V2hW9tprlZWLiuNpFGe7vSciEGLUnOYz4MATJ6dxffCZpD8R8HijO1TbKtK+LBG8jgSx4LqCdiRC2zbNpTsQyZX3CGTj0olqTvMP4w/rsw9VABcDVQCHgaitykflNy7qBq60I763spxEJV1sR7mr7GV6Bq60I97T93MSCq63Jrm5iwdVAIeBqoBBwNVAIuBooBFwNFAKuBgoBVwOFgKuBQsDVQCHgaqAQcDVQCLgaKARcDRQCrgYKAVcDhYCrgULA1UAh4GqgEHA1UAi4GigEXA0UAq4GCgFXA4WAq4FCjLtaA5/yAzUw6mr8u5EkAjADuBooRJyr0XedAZDM+sJ3F0Q1kIcJ3QK4GsjBuKsBkAW4GigEXA0UIs7Vzg8e3H1RP9V5/vL8fHdHCYYZ6+VdCW8DzzFcMkjsYhk6mazFrQQTLIO7iJUIb9Cis7iDtrg5htZrW0y23kZMyjJMysWk2Fyd9QYSTHAs9q44+onEuRpzrLuXd/QTxHfM7c5nSohCUQGcfee7pZKuQU/43MnFM0rZzUU2kqx1saReRMyMiUkqwefGYBHcegdhIymwJ2ZFDLFaJ5/Kxf8bA8NnEHOd4CuihJCUgZusBGMsl5gpgyiflUsnWHajFiiDu1eTiXO1/zk/ePlAedfdgzPboP7payarsmiJ44omZekZ/gUIZyFKGEngGGw6+XQup76sHE5+N5dl4zO3um2DFBlMsjKZlKO3N2K0DCfBcH3FwJJKE1yXnStoYDi5LLW1Xtcynci22pcvXy7VNfP85e7lFxXinAKwQlnHwRVNyi59J5cx2HI3YZYRsm8RW5ZZtIXmEpPJWhHDzmUvbuudjYi5ZTUrs3M5G3EW6eay8vG0mltaJpu0mI/n4pAssnsNYhEl64nAWno6sd2Cly9fksRk8R/skBKuNkiwACGDrXfOlGm7ElrcXZU3YSvd9dBcwlJmEessdwxM0iKXwwmSXAOX/YkeQYsyiGWdBOEa+GaU7OyVu4t2Lk5fjmFxVxOlsgopJYZIqKTQ27mMQST7CVtmMEkZ+H+fgWESdp2ylJ0llBDZfItzSaXYXGfqJfwGASVcnb2LdsKWOwleLpVL/Cdcg9HLhErbiU4uA5ft9FRyuZq9w7bMsfbRKqSVgcOSVi6Dk7ITZgGOSdl5GE42N5dTRiU7Bja38ljJjoHhM0zOZRuMyWRh9HIpbINAHwb609gGC5Y0GpNwc9l1xUyucRrZXE1PrLnC2cdAJpa0cxncnfQnhGerlO3otuwaaN7DNbD81rr4GuyElpnkJBR2YjBXJxth5ZHoxWmucQy2dZpB4SQ4lmI44wSyXkABCANXq4NpYSKYa1rIWWrxacDVVodfP9VV05bDCVtOyGXLcxePAK62OuKoqcMoZ5J+IphLaSblChqm5XIMEcDV6kAdv/4BtvHm4ngXK7b4NOBqoBBwNVAIuBooBFwNFAKuBgoBVwOFgKuBQlx8Ra/EA7Aon+FqAIC2YEGNLqUAALB5ENQAAE2BoAYAaAoENQBAU1wjqAEAWgItNQBAUyCoAQCaAt1PAEBToKUGAGgKBDUAQFOg+wkAaAq01HbM1C+LpnyBtEqa2REwBIJa68jvIjMobVFPUPMWLz8IarsA3c/GGTqP6wlqioW3hKC2C9BSa5zueWw32ozN1RpZwpNaa4x2NieLTqpEzyCTrlplNDpbprRMChyjnBvBymmlaG5bQXsgqLWOOaUZUlSazjkuZkoWU0KtQs5MdiuTm0Une7mVgc+5oJN8auZKb+x6zmeSjo0JlBxIiYlUgVZB93MP6POe6J/pAi75Tnk3h8riZOxlkQk7k98g527KykaiTlsGleA7Z/KYDFxyU3xiNKBV0FLbCerEJ0gnpwad5oLGJFUOLSo6WXTSyuU3yLmbsrKRqNOWgSdYyvwJkyg90U3xfDIXaBkEtb0gz3Ej65n3NHeUJmEWlFFC08mik1Yuv0HO3ZSVjUSdtgwMFa34hCzdDCRIVF7QNOh+No46hcVcntAyECid0uosUpQqiVpAa7nCztDLYqwqY9egBFetcmshtBjBzGS3ckhJG5yUmYJmQUutdfj5rk5tSqnT2tVSwpYJnnS13RzdLLZZqFW6J8i5pVZ5hdTNbQSJzmKydpZ0Up7MoD0Q1EACvaiAMAGqAd1PEE+/qYOgBqoBLTUAQFMgqAEAmgLdTwBAU6ClBgBoCgQ1AEBToPsJAGgKtNQAAE2BoAYAaAp0PwEATYGWGgCgKRDUAABNge4nAKAp+i018WqyfD9Zv6XMBaMSkkcFAACrE+p+6sClE1bwYlOPCgAA1ifQ/VSRSn5jxsQtLXlUXa4vLvAf//Ef/4P/KVRkxt9Ss0MUk03c0pJHBQAA6+MLam6AYikTt7TkUQEAwPr0u5+iz8lQolYKwUgeFQAArA2eUwMANAWCGgCgKfDwLQCgKdBSAwA0BYIaAKAp0P0EADQFWmoAgKZAUAMANAW6nwCApkBLDQDQFAhqAICmQPcTANAUaKkBAJoCQQ0A0BTofgIAmgItNQBAUyCoAQCaAt1PAMA4D7+5evT4N5SoG7TUAABj/OZAPCFFzSCoAQDG+JVi2uFAippB9xMAMMZjCmmHK1LUDFpqAIBRvpYx7RElqwZBDQDQFOh+AgCaAi01AEBTIKgBAJoC3U8AQFOgpQYAaAoENQBAU6D7CQBoCrTUAABNgaAGAGiKfvfz8lJNxFQnx1UAALA6oZaaE7O0MKgCAID1CQQ1EaN0uDJxa0jV5friHv/xH//xP/ifQkVm/Hc/7UDFpiY5pAIAgPXxtdRUgNLhysStIRUAAKxPP6hdEkrWSiEMqgAAYG3w8C0AoCnwnBoA2+B8fXPzy5kSIAyCGgDl+e4k+PmflB7nfHt7FCCsjYHuJwDF+V8ypp1OT0kxDoW04/GWFF6+eyFWGxEsWbh89fr1q2eUaAG01AAIE9+imsRbudrT6QUpxrlRUW0oqP2NVhsRLJ/JMHg6fU+K7YOgBkCQhBaVS6AV9D2t9vQdKSZwLWPaDSW9JARLvci/kGL7oPsJQJCEIGEz0Ap6/+F0evsTJbKhg+VfSDHOU1ri9IEU2wctNQCCpLSoLBJaQSmDYjbv2TbjguWPYoOnv1GyARDUABhgVosqvhX0ihY4vSYFiAfdTwAWI7oVNLO/CzhoqQFQD+8ppkUMiq3M3P7yAiCoAVAT8YNiq5LwEMnioPsJAEimxv7ysi21CpumAOyShc7FhIdIFmfRoPY39ZgObuUAsCrL3Vatr7+8aPcTt3IAqIM9nYuLttS2dysHgDbR52LaY8SbYuG7n1bT9LcHwe9EAgBQltLdxAvxBv7tZ0oWpNjdz8cyph0OV6QA7YIbROBfxev3DPkGfsnvGxV7Tu0TxbTDgRSgWfCuDzDffzuyxDNyiDLfNyoW1J5QSEP/s31wgwg8OFNIO/Lw8oEc4vR3aVyWgg/fPvn1cPj0R0qAGliom7inQWkQ5OKH4/FW/l5x2e8bFWupgfrY07NLIJqclzx6s7/Mu1QIajsG3UQQRj86v7mvR+Ldzx2DbuJmOb/6sPTNxO1e8tBS2zXoJm6SIjcTt/voPIIaAFtDN6KWvZm41UteDd1PPKoJQAwN/lhKTipoqeFRTQDiKHozcXN8XD+o4R4cACAf9+sHNdyDAwDko4KghntwC4BxSrBbPEHtkqEkOReCTGjJowLVsN0HJwGYy2dfS02FKHdughebelSgHjBOCfaL9+6nilC8zcZFE7e05FEtTIFnqNthuw9OAjAX73NqToRiCRO3tORRdfl8EcM9/+MTPz/TSXr6iRQN8p/y20z/h5Lz+Lf/ezr9/d8/yooFoEooVGRmsKUmYAkTt7TkUS2K/iDTiRTtob8N/IgUAIAE+mNqos/JUKJWCsFIHtWC7OAZ6j18G/jiVnwQdaErNACczbz7OfAM9fnV66cNjLbt4NvAv4hPoTLkd+sBWILtf3romXp6ocjnzxflydXh8Knlz507360HYBm2/5UOPdr2L6SYQ8nfvNkh7yikof8JFsT7nNqmyDja1lCjr1bEd+tL/hQkXq1oiynj99tvqanRtr9Rcgovr29ufjlTwoBHVptj6qsV5+ubN9d9jwAr07kmsYg2Iajt8XPeZzW003Vi3ehL/QjSM/ReK2PadSroEWBdPNekfbTUotHD1bek0Mz7TJX+yPJ7UoDVmfZqRdgjwKp4rkkTgtr2x9Ti+St58PENKTKR9ZYFyMSUT8DcqKg23SMwVlcCzzUJLTUvDz9LD879sBQ+sjyDaUFiqaGv61iPwGdQCtG7JmFMrSwJtyyAZFKQqGjoC/eUKmaPLbW5fCfdGR/AyMikIFHR0Bc+g1IxexxTmwl+KGYBJgWJgaGvC2kq6Mr4XHO1oKUWjW5UtPvBkDWYEiSCQ19/VeEOL5UCjKnFoxsVP5MCrI78+AeHFGDHoKWWAHoetXGmkIaXSgHG1EAuaFTrnpKlubg5Hm+vKQF2DVpqIAv6iWaMaoGVwZgayIIe1MKoFlgZtNRAFjCqBWoBY2ogE8U/lQaAF7TUtga9JIkn2QHwgzG1jaFfklznfYbftv7bMKAu3n1zdfXNu4eUEm+zj77Sjpbaxlj3fQb926RXpABgQd59S/72hBSCsaiGMbWNse77DHv4bVJQD1fkbYdvScFBS6091nyfYQe/TQoq4g/kbnbPYPyDahhTA1E8+fVw+PRHSgCwLF/LmPaIklNCGlpqAIDNcElQMgDG1Iqw8ouRAOwItNRKgBcjASgGxtRKgBcjASgGWmolwIuRABQDY2plwIuRABTC11Izdxf0jQZzx2FIBcA+cV/mAevSH1NjAUqHLSfNp0MqAPaJ/2UesBbeMTWKUDpcmbg1pMoCXpgGm8P7Mg9YDe+YGkUoHa5M3BpSdbm/iOX+4hF5x+E/4pfeAPfsH2iO/yCnPfx6zyAlmACFisxU1lJb4YXp8j+DC1qj9zIPWBPvc2oqQumYpYVBVQbKvzCtn4v9gRQAgE1T3XNqT65mvTD98OurR9/8hhJTwHOxAGwG/qTFaAOqrefUfhN/G+oZhTT0PwGoDu9L0xTWgsGtrTcK9G2omCG552/wXCwANeJ9aVo11YJBra13P33flAMAbBTv4JAIaqIfGuiJttVSw20oABrC/9L0WPcT734CAKrFfWmaGmmUkLM+a7XUztc/3Pz1TAkAAMjFOmNqZ9VXRlgLgW/lApDGKi21h6xJKbklDejwC1UQvpULQCTrjKnd0Bl7fEMK0AHPBIPJ5PvwURvfk1hpTO1anrCpzZD2v+XxTlYQngneBbOiUsYPH839Af5KzstNPqc2t+43AZ4J3gszo1LGDx/N/J5ELeflJp9TW+FbHrWCT65un5lRKeMT5zO/JzHxvFy8PbfJ59TKf8ujUvDJ1RaYG5VyPnE+6wf4p52Xy7fnNvpGway6b4eVP7mKx07y0M57MFPOy+X7WVWPqeGkGWHdd12X+4nmmZ3qeU92h71uWrHaf6583nm5fD+r5pba2r9r/u7xo6vH7yhRJ6te45d67GRmp3rmk91Br5tWrLzPlVc5ZDr7vFy6n1XzmNrsk+bZq9evXz2jRCwYrxpjqZ9ontepnvtkd9DrJhVrcOuxIWo5F5wVLJe6mGWj5pbazJPm2Yl4T4o4Jp5au/75q5k/0RzoqM3sVM98sjvoddOKFd56fIhaash0blOY9rDaZyjrfk5t1knzlmLa6e+kiGOaD5d5NKfE4GLx6BzuqM3sVM98sjvoddOKFdx6fIiaO2QaOqSzg+XMi9nSbPTu5xSeUkw7fSBFJJN8uMgjc7MHMSb0Noo/OLm7F4ATQtS0MBoieEjnBsvaafl7aj/KmPZUJBYa9Z97K+e7F6yEL05/oaSfmYMYk3ob5R9o3t0LwPNCVDzhQ1q6JIVpuKVmk/CLLFOJvpVj9yX/xmMa57UwBZg5iDGpt7HcjfZgv3ZuNzGeKm8mLsZun1Fv6zcKgujz+hMp1sPpS+pxv5M0hrhgzZrkQQyntxEenlvoRvti/drogcb93c/e6TPqO2mpFR8xCuP0Jd9TSDv9LI3LYPU2Ur7TNqmLHGSpfm38QONSNxNBZezlNwpWGEUIjOJ1+pLvWWPt7U9CLEHC8Ny0LnKQpTpB8XvS+vg4IHbSUivPQF9nVl9yJgnDc1O7yEGW6QQl7MkKVzawAjsZUytPtX2d6O+0lekiJ7DmxQFUDFpqC9FSX6dwFxmAWexlTK086OsAsApoqYHKOV+/wU/EgggwpjaNEm9fAg/4iVgQy2BL7VKgRVfwqZpl9tuXIJG9vSEK5jM4pqZjlRTYVAs+VcMkPN4FsrC7N0TBbEZaao7gRDCPqmGq/4QUJ+PgU0W97fJviIKNMzqmJqKVJ4J5VB2uPzfDxeeLa3aa395c3JNmAvef76/v2X9KLsw9L6DgevYm+QNgktsLUq0Eq/iPFxImg9agUJGZ8bufgQjmUYE1eUNxKMfgE3rbYMMMjamJQGVilk4GVWBFblQkyhDUNtHbBsAPnlNrhqyDT3gFCWwWPKcGAGgKtNQAAE2Bdz/BTsHrV62ClhrYJXj9ql0wpgZ2iX5qBa9fNQdaamCX6Cdg8PpVc2BMDewTvH7VLGipAQCaAmNqAICmQEsNANAUGFMDADQFWmoAgKbAmBoAoCnQUgMANAXG1AAATYGWGgCgKTCmBgBoCrTUAABNgTE1AEBToKUGAGgKjKkBAJoCLTUAQFNgTA0A0BRoqQEAmgJjagCApkBLDQDQFBhTAwA0BVpqAICmwJgaAKAp0FIDADQFxtQAAE2BlhoAoCkyjKldXso/AABYn/ktNRnPENUAAFUwf0wtGNSuPwMAQBgKFZlBSw0A0BTzx9QQ1AAAFZHj7uclg0QAAFgXPKcGAGgKPKcGAGiK5d/9PDNINEid15RIzg7wnjvTm9t3jHxsh0LHasGW2vl/WMQ6311+ufty58au8x1T3T2/+8Kmnagmhuf4IF1n/6VKTh2EiiCVRGrktINQCkhBkFJAKolIefThVYUXkSo5dZAqOXUROgEpiOA2rEUo7cE1UXYBqQhSDq2quy4x8SzT1yhEZgEpFEblWZeCVBKR8uitRSjtYeqqrM2TQmFUE9clVXIaZPa6hE5ACoXQeA1i4rFIlZyGcC0iL0GqJVlwTO1895JPRJPMjV3nOz655MqXroXvM+13p1rERE75RKNqydUygkvYSdcysi4jaqxUZ6HhRbx7IiYhg8S3hBENRqn2iSDPEpBKMrzvvlUNrEtPOquThm52hqXoLMGTcoHOQmolHTUppNa1GV23vBakkoiUWczCSnYW4Um5no5BT7wGy6yRJZKQSmJl9hkss8JKdQwsSWt3DbQuI2qkwTIrqKgCUklU0tUuxpJjajxsPb87n18ygVQS1oY7s9Yaa6a9fM4jn8GqqU618D9ZV66BIdR9Lf/zLmFq3EgK/7pERqHtLDGwquAi/M9XrrBBJzurEmmh8hlI6VqsjJ1FuI2pPFo16dqC6+J7QcaOgeZyUxYm2TWoOWsakGgQa/GpSdtdF09KnccgcQ3BVVnpjkWlmLpjEEhRTBU8RSbXYK+6s4hAimKq4CkydZcgwV4pR6SkyjXInNLsWnhqZCO9dfEFPNplWHRM7aWIXP0+JmufCZ2Ia6Qi9G53958qUc76eNRDS3Ct18Dx6oNLBA0hi1R5LEFDcFUjBimIqUGnPQvxxUiwCK5qZF2MzupC2Th8K2pDFmYT/oW96sC6hF4KYmrQ6d4yYkUxGzGr8i3E6RrkSnzrGigXEbMuCaU0WtFfIrhIcCNDK+NLkLAwuPuZhfDRCloKHWAwBX6Ces7RAYMh/gjHG3KtK7gr4X1MWYTmPYKGvNT7nNrUmmF1S5qpBnk4uHm2QagIUhGkFJBKQjoBqSQi5dEnGAYW6WmIeINcP00dpEpOHaRKTl2EjkNpDan7hoRFSO3ZOAmWJAgabIKW2QartK4l0SB0nT0JGhIWEQaCVBLSCUi1JJW11GjHBaSSkE5AKolISuVEg7AIfdeSZhBzObMIWsIGPenY4g1hizR0K5ERb1AWLWoSDWLa2U7QkLDIiEEw0cDWYCCVhHQCUklIJyCVhHQCUhGU5OrOIjSLNEjVREPYEjSwtGVzCBqWoLLfKJD7LnArIGxQc3ntMAQNMil1nkWiDAzhij0tJ2jxG7hTcwMXxVSjs5NZEzQELTqlNqWINwgLFbm/SKRBWoTIJ5qgIWGRoIEpFaRQkJZBCo3R9NZFQgYDzXkxSJIkGqRqoiFsCRoEvsoSBA3ZqW5MTe93twJCBqOfagiuKsEgCR+toGXoAPdt3CG8PhE0BCzBrcYbxCbktLeVaIPReRbxGxIWCRpAS+DdTwAMwWgXDoPxi1S6kXW3nhHc/QTAkHAyZjyx193IulvPSGVjagCUh/dKFaSSkE5AKoKUAlJJSCcglYR0AlJJSCcgFUFKAakkpBOQSkI6AakkpBOQSkI6AakIUgpIJSGdgFQS0glIJSGdgFRLgpYaAOZU65xzQUPCIpVuZN2tLwLG1AAATYGWGgCgKTCmBgBoCrTUAABNgTE1AEBToKUGAGgKjKkBAJpCtNTury/wD//wD/9a+HdxcfH/AbAPgjjh0Qq3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename = path+\"SalesByPromotionWeek.png\", width=1000, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Intepretation:</b><ul>\n",
    "    <li>Blue dots = Sales volume during the first week of a promotion; Orange dots = Sales volume during third week of a promotion; Gray dots = Sales volume during 3+ week of a promotion</li>\n",
    "    <li>When we further break down the promotions by week running, it becomes quickly apparent that the first week of the prmotion is almost always the \"star\" followed by a bottom in week 2. There is some fluctuation in week 3, but again it is rather on the down side. </li>\n",
    "    <li>The next question here is, does it make sense to run a promotion past week 1 ?</li>\n",
    "    </ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename = path+\"SalesByPromotionWeek2.png\", width=1000, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Intepretation:</b><ul>\n",
    "    <li> Horisontal axis = Running promotion or not; Bar colors = consecutive week of the prmotion; Bar height = average sales volume.</li>\n",
    "    <li>It quickly becomes apparent that week 1 is always the strongest week in any promotion. </li>\n",
    "    <li>Sales in weeks 3+ for promotion types A and B are almost indistinguishable from having no promotion at all.</li>\n",
    "    <li>Promotion types C, D and E still see some uplift in weeks 3+</li>\n",
    "    <li>All this drives the questions - does it financially make sense to run promotions past week 1?</li>\n",
    "    </ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename = path+\"PriceWeightProm.png\", width=1000, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Intepretation:</b><ul>\n",
    "      <li> Horisontal axis = Running promotion or not; Bar colors = consecutive week of the prmotion; Bar height = average sales volume; Yellow line: average sales volume during non-promotion period.</li>\n",
    "    <li>Answer to the above question is: if there is any additional costs to running a prmotion, apart from the price reduction, it generally does not make sense to run it past week 1. </li>\n",
    "    <li>Even if there is no additonal cost, any form of Sales usually has implicit costs, like cost of goods, cost of FTEs etc. Any reduction in prices, generally drives the margins thin, and unless there is good enough incentive (a lot more sales) it does not make sense to reduce the prices.</li>\n",
    "    </ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Final observations</b><ul>\n",
    "    <li>Majority of the variable enginnering efforts need to be concentrated around our own price fluctuations and promotions. </li>\n",
    "    <li>Competitor activity is rather insignifficant, so the variables created, which are related to it can be minimal.</li>\n",
    "    <li>The target variable, which will be optimized needs to reflect both the sales and the price. Given that the price is driven by promotions, an open topic remains what is the cost of those promotions? Is it only the reduction in the price? are there any advertising costs associated with it? We need to know this because, past week 1 of the promotions the effects wear off rapidly, in case there is any running cost associated with running the promotions past week 1, it might make sense not to keep them.</li>\n",
    "    </ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variable enginnering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the analysis and observations above, below is the final list of variables used for the modelling part."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variable | Type | Description | Reasoning | Imputation of missing variables\n",
    "------:|-------:|------:|------:|-------:|\n",
    "WSales|Target, continious|Sales X Price|Our target variable needs to reflect the cost of running a promotion| None |\n",
    "PROMOTION_A...PROMOTION_E|Binary|5 Dummy variables for all of the campaigns + nulls || None |\n",
    "HAS_PROMOTION|Binary|Flag if the customer is in any prmotion || None |\n",
    "TIME_SINCE_LAST_PROM|Continious|Number of days between current and previous promotion ended|To track if spacing between prmotions plays a role| Fixed value: -1\n",
    "LAST_PROM_DUR|Conitious|Duration of the last prmotion|Monitor impact of previous promotion duration on the current sales | Fixed value: 0\n",
    "PROMOTION_WEEK|Continious|Consecutive week in which the promotion is running || None |\n",
    "PROMOTION_MAV5|Continious|Moving average of HAS_PROMOTION for the last 5 weeks|Measure the customer's saturation to promotions |Constant: 0, only applicable to first 4 weeks.\n",
    "PROMOTION_MAV10|Continious|Moving average of HAS_PROMOTION for the last 10 weeks|Measure the customer's saturation to promotions | Constant: 0, only applicable to first 4 weeks, then MAV 5-9\n",
    "PRICE|Continious|Original price variable| |None|\n",
    "PRICE_CHANGE|Continious|Current price / previous period price -1 |One major effect of prmotions is change in price, we want to capture the immediate change here | Constant: 0, only applicable to first week.\n",
    "PRICE_SLOPE5|Continious|Slope of the price for the last 5 periods| Majority of capmpaigns run for an average of 3 days. We want to capture the effects of having multiple campaigns close to one annother, as opposed newer campagins.| Constant 0, only applicable to first 4 weeks.\n",
    "PRICE_SLOPE10|Continious|Slope of the price for the last 10 periods|Same as above, just a longer period | Constant: 0, only applicable to first 4 weeks, then MAV 5-9\n",
    "HAS_COMPETITOR|Binary| Flag if currenty any competitor has an active price |\n",
    "COMPETITOR_MIN|Continious|MIN(Competitor 1...7 price) | For price sensitive customers | 5 period forward moving average\n",
    "COMPETItOR_AVG|Continious|AVG(Competitor 1...7 price) | Median would make more sense but data is too scarce. | 5 period forward moving average\n",
    "PRICE_TO_COMP_MIN|Continious| Our price / MIN(Competiror price) -1| Monitor spread to competitors | None\n",
    "PRICE_TO_COMP_AVG|Continious| Our price / AVG(Competiror price) -1| Monitor spread to competitors | None\n",
    "COMP_PRICE_CHANGE_MIN|Continious|Current competitor min price / previous period competiotr min price -1 | How do competitor discouns affect us |  Constant: 0, only applicable to first week.\n",
    "COMP_PRICE_CHANGE_AVG|Continious|Current competitor avg price / previous period competiotr  avg price -1| How do competitor discouns affect us |  Constant: 0, only applicable to first week.\n",
    "COMP_SLOPE5_MIN|Continious|Slope of the minimum competior price for the last 5 periods| Monitor the rate of the competior price change| Constant 0, only applicable to first 4 weeks.\n",
    "COMP_SLOPE5_AVG|Continious|Slope of the minimum competior price for the last 5 periods| Monitor the rate of the competior price change| Constant 0, only applicable to first 4 weeks.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_excel(path+\"final.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 146 entries, 0 to 145\n",
      "Data columns (total 25 columns):\n",
      "Wprice                   146 non-null float64\n",
      "PROMOTION_A              146 non-null int64\n",
      "PROMOTION_B              146 non-null int64\n",
      "PROMOTION_C              146 non-null int64\n",
      "PROMOTION_D              146 non-null int64\n",
      "PROMOTION_E              146 non-null int64\n",
      "HAS_PROMOTION            146 non-null int64\n",
      "TIME_SINCE_LAST_PROM     146 non-null int64\n",
      "LAST_PROM_DUR            146 non-null int64\n",
      "PROMOTION_WEEK           146 non-null int64\n",
      "PROMOTION_MAV5           146 non-null float64\n",
      "PROMOTION_MAV10          146 non-null float64\n",
      "PRICE                    146 non-null float64\n",
      "PRICE_CHANGE             146 non-null float64\n",
      "PRICE_SLOPE5             146 non-null float64\n",
      "PRICE_SLOPE10            146 non-null float64\n",
      "HAS_COMPETITOR           146 non-null int64\n",
      "COMPETITOR_MIN           146 non-null float64\n",
      "COMPETITOR_AVG           146 non-null float64\n",
      "PRICE_TO_COMP_MIN        146 non-null float64\n",
      "PRICE_TO_COMP_AVG        146 non-null float64\n",
      "COMP_PRICE_CHANGE_MIN    146 non-null float64\n",
      "COMP_PRICE_CHANGE_AVG    146 non-null float64\n",
      "COMP_SLOPE5_MIN          146 non-null float64\n",
      "COMP_SLOPE5_AVG          146 non-null float64\n",
      "dtypes: float64(15), int64(10)\n",
      "memory usage: 28.6 KB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wprice</th>\n",
       "      <th>PROMOTION_A</th>\n",
       "      <th>PROMOTION_B</th>\n",
       "      <th>PROMOTION_C</th>\n",
       "      <th>PROMOTION_D</th>\n",
       "      <th>PROMOTION_E</th>\n",
       "      <th>HAS_PROMOTION</th>\n",
       "      <th>TIME_SINCE_LAST_PROM</th>\n",
       "      <th>LAST_PROM_DUR</th>\n",
       "      <th>PROMOTION_WEEK</th>\n",
       "      <th>...</th>\n",
       "      <th>PRICE_SLOPE10</th>\n",
       "      <th>HAS_COMPETITOR</th>\n",
       "      <th>COMPETITOR_MIN</th>\n",
       "      <th>COMPETITOR_AVG</th>\n",
       "      <th>PRICE_TO_COMP_MIN</th>\n",
       "      <th>PRICE_TO_COMP_AVG</th>\n",
       "      <th>COMP_PRICE_CHANGE_MIN</th>\n",
       "      <th>COMP_PRICE_CHANGE_AVG</th>\n",
       "      <th>COMP_SLOPE5_MIN</th>\n",
       "      <th>COMP_SLOPE5_AVG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>172624.464</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010248</td>\n",
       "      <td>1</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.718286</td>\n",
       "      <td>0.444853</td>\n",
       "      <td>0.094272</td>\n",
       "      <td>-0.007299</td>\n",
       "      <td>-0.028781</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.009114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>63162.528</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018394</td>\n",
       "      <td>1</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.696571</td>\n",
       "      <td>0.813559</td>\n",
       "      <td>0.228876</td>\n",
       "      <td>-0.132353</td>\n",
       "      <td>-0.030231</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>-0.001586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>57399.840</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006697</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.737286</td>\n",
       "      <td>1.060000</td>\n",
       "      <td>0.397016</td>\n",
       "      <td>0.059322</td>\n",
       "      <td>0.058450</td>\n",
       "      <td>-0.0080</td>\n",
       "      <td>-0.002586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>158749.272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009400</td>\n",
       "      <td>1</td>\n",
       "      <td>0.514</td>\n",
       "      <td>0.712571</td>\n",
       "      <td>0.717899</td>\n",
       "      <td>0.239174</td>\n",
       "      <td>0.028000</td>\n",
       "      <td>-0.033521</td>\n",
       "      <td>-0.0112</td>\n",
       "      <td>-0.003500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>73502.520</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005582</td>\n",
       "      <td>1</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.659857</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>0.379086</td>\n",
       "      <td>-0.182879</td>\n",
       "      <td>-0.073978</td>\n",
       "      <td>-0.0206</td>\n",
       "      <td>-0.010086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>148944.576</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>1</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.490598</td>\n",
       "      <td>0.103797</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.197229</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.010943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>70645.608</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004915</td>\n",
       "      <td>1</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.765250</td>\n",
       "      <td>0.566957</td>\n",
       "      <td>0.177393</td>\n",
       "      <td>-0.017094</td>\n",
       "      <td>-0.031329</td>\n",
       "      <td>0.0221</td>\n",
       "      <td>0.013336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>142544.556</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008879</td>\n",
       "      <td>1</td>\n",
       "      <td>0.563</td>\n",
       "      <td>0.787750</td>\n",
       "      <td>0.497336</td>\n",
       "      <td>0.070136</td>\n",
       "      <td>-0.020870</td>\n",
       "      <td>0.029402</td>\n",
       "      <td>0.0253</td>\n",
       "      <td>0.025575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>71467.488</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004291</td>\n",
       "      <td>1</td>\n",
       "      <td>0.569</td>\n",
       "      <td>0.755000</td>\n",
       "      <td>0.606327</td>\n",
       "      <td>0.210596</td>\n",
       "      <td>0.010657</td>\n",
       "      <td>-0.041574</td>\n",
       "      <td>0.0276</td>\n",
       "      <td>0.018804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146753.040</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007473</td>\n",
       "      <td>1</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.770750</td>\n",
       "      <td>0.675214</td>\n",
       "      <td>0.271489</td>\n",
       "      <td>0.028120</td>\n",
       "      <td>0.020861</td>\n",
       "      <td>-0.0006</td>\n",
       "      <td>-0.004875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Wprice  PROMOTION_A  PROMOTION_B  PROMOTION_C  PROMOTION_D  \\\n",
       "136  172624.464            1            0            0            0   \n",
       "137   63162.528            1            0            0            0   \n",
       "138   57399.840            0            0            0            0   \n",
       "139  158749.272            0            0            1            0   \n",
       "140   73502.520            0            0            1            0   \n",
       "141  148944.576            0            0            1            0   \n",
       "142   70645.608            0            0            0            0   \n",
       "143  142544.556            0            0            0            1   \n",
       "144   71467.488            0            0            0            1   \n",
       "145  146753.040            0            0            0            0   \n",
       "\n",
       "     PROMOTION_E  HAS_PROMOTION  TIME_SINCE_LAST_PROM  LAST_PROM_DUR  \\\n",
       "136            0              1                     1              2   \n",
       "137            0              1                     1              2   \n",
       "138            0              0                    -1              3   \n",
       "139            0              1                     1              3   \n",
       "140            0              1                     1              3   \n",
       "141            0              1                     1              3   \n",
       "142            0              0                    -1              3   \n",
       "143            0              1                     1              3   \n",
       "144            0              1                     1              3   \n",
       "145            0              0                    -1              2   \n",
       "\n",
       "     PROMOTION_WEEK       ...         PRICE_SLOPE10  HAS_COMPETITOR  \\\n",
       "136               2       ...             -0.010248               1   \n",
       "137               3       ...             -0.018394               1   \n",
       "138               0       ...             -0.006697               1   \n",
       "139               1       ...             -0.009400               1   \n",
       "140               2       ...             -0.005582               1   \n",
       "141               3       ...              0.000164               1   \n",
       "142               0       ...             -0.004915               1   \n",
       "143               1       ...             -0.008879               1   \n",
       "144               2       ...              0.004291               1   \n",
       "145               0       ...              0.007473               1   \n",
       "\n",
       "     COMPETITOR_MIN  COMPETITOR_AVG  PRICE_TO_COMP_MIN  PRICE_TO_COMP_AVG  \\\n",
       "136           0.544        0.718286           0.444853           0.094272   \n",
       "137           0.472        0.696571           0.813559           0.228876   \n",
       "138           0.500        0.737286           1.060000           0.397016   \n",
       "139           0.514        0.712571           0.717899           0.239174   \n",
       "140           0.420        0.659857           1.166667           0.379086   \n",
       "141           0.585        0.790000           0.490598           0.103797   \n",
       "142           0.575        0.765250           0.566957           0.177393   \n",
       "143           0.563        0.787750           0.497336           0.070136   \n",
       "144           0.569        0.755000           0.606327           0.210596   \n",
       "145           0.585        0.770750           0.675214           0.271489   \n",
       "\n",
       "     COMP_PRICE_CHANGE_MIN  COMP_PRICE_CHANGE_AVG  COMP_SLOPE5_MIN  \\\n",
       "136              -0.007299              -0.028781           0.0145   \n",
       "137              -0.132353              -0.030231           0.0036   \n",
       "138               0.059322               0.058450          -0.0080   \n",
       "139               0.028000              -0.033521          -0.0112   \n",
       "140              -0.182879              -0.073978          -0.0206   \n",
       "141               0.392857               0.197229           0.0146   \n",
       "142              -0.017094              -0.031329           0.0221   \n",
       "143              -0.020870               0.029402           0.0253   \n",
       "144               0.010657              -0.041574           0.0276   \n",
       "145               0.028120               0.020861          -0.0006   \n",
       "\n",
       "     COMP_SLOPE5_AVG  \n",
       "136         0.009114  \n",
       "137        -0.001586  \n",
       "138        -0.002586  \n",
       "139        -0.003500  \n",
       "140        -0.010086  \n",
       "141         0.010943  \n",
       "142         0.013336  \n",
       "143         0.025575  \n",
       "144         0.018804  \n",
       "145        -0.004875  \n",
       "\n",
       "[10 rows x 25 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Wprice  PROMOTION_A  PROMOTION_B  PROMOTION_C  PROMOTION_D  \\\n",
      "count     146.000000   146.000000   146.000000   146.000000   146.000000   \n",
      "mean    86657.821233     0.089041     0.095890     0.212329     0.123288   \n",
      "std     34254.778199     0.285783     0.295454     0.410364     0.329899   \n",
      "min     35182.200000     0.000000     0.000000     0.000000     0.000000   \n",
      "25%     63188.616000     0.000000     0.000000     0.000000     0.000000   \n",
      "50%     71419.518000     0.000000     0.000000     0.000000     0.000000   \n",
      "75%    104487.300000     0.000000     0.000000     0.000000     0.000000   \n",
      "max    178385.088000     1.000000     1.000000     1.000000     1.000000   \n",
      "\n",
      "       PROMOTION_E  HAS_PROMOTION  TIME_SINCE_LAST_PROM  LAST_PROM_DUR  \\\n",
      "count   146.000000     146.000000            146.000000     146.000000   \n",
      "mean      0.054795       0.575342              0.760274       3.061644   \n",
      "std       0.228362       0.495992              2.018209       1.365701   \n",
      "min       0.000000       0.000000             -1.000000       0.000000   \n",
      "25%       0.000000       0.000000             -1.000000       2.000000   \n",
      "50%       0.000000       1.000000              1.000000       3.000000   \n",
      "75%       0.000000       1.000000              2.000000       4.000000   \n",
      "max       1.000000       1.000000              8.000000       7.000000   \n",
      "\n",
      "       PROMOTION_WEEK       ...         PRICE_SLOPE10  HAS_COMPETITOR  \\\n",
      "count      146.000000       ...            146.000000           146.0   \n",
      "mean         1.273973       ...             -0.000374             1.0   \n",
      "std          1.445662       ...              0.009352             0.0   \n",
      "min          0.000000       ...             -0.018394             1.0   \n",
      "25%          0.000000       ...             -0.006771             1.0   \n",
      "50%          1.000000       ...             -0.000203             1.0   \n",
      "75%          2.000000       ...              0.004809             1.0   \n",
      "max          7.000000       ...              0.020515             1.0   \n",
      "\n",
      "       COMPETITOR_MIN  COMPETITOR_AVG  PRICE_TO_COMP_MIN  PRICE_TO_COMP_AVG  \\\n",
      "count      146.000000      146.000000         146.000000         146.000000   \n",
      "mean         0.602986        0.745602           0.698517           0.368315   \n",
      "std          0.097343        0.128441           0.374629           0.241522   \n",
      "min          0.255000        0.486500           0.175412          -0.164370   \n",
      "25%          0.533000        0.667250           0.484756           0.180725   \n",
      "50%          0.612000        0.703500           0.601865           0.380383   \n",
      "75%          0.683000        0.762687           0.888941           0.543607   \n",
      "max          0.755000        1.077500           3.415686           1.314491   \n",
      "\n",
      "       COMP_PRICE_CHANGE_MIN  COMP_PRICE_CHANGE_AVG  COMP_SLOPE5_MIN  \\\n",
      "count             146.000000             146.000000       146.000000   \n",
      "mean                0.011750               0.003863        -0.000893   \n",
      "std                 0.195541               0.080169         0.023569   \n",
      "min                -0.395735              -0.209677        -0.065000   \n",
      "25%                -0.052747              -0.040615        -0.010575   \n",
      "50%                -0.007293              -0.005479        -0.000900   \n",
      "75%                 0.051046               0.036464         0.009575   \n",
      "max                 1.760784               0.447071         0.090100   \n",
      "\n",
      "       COMP_SLOPE5_AVG  \n",
      "count       146.000000  \n",
      "mean          0.000466  \n",
      "std           0.019823  \n",
      "min          -0.068533  \n",
      "25%          -0.008189  \n",
      "50%           0.000000  \n",
      "75%           0.010367  \n",
      "max           0.081550  \n",
      "\n",
      "[8 rows x 25 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df2.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of this summary, we can see that HAS_COMPETITOR is 1 for all values, so go ahead and drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2.drop(\"HAS_COMPETITOR\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(146, 24)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our main goal in the modeling part is not nessecarily to obtain the \"perfect model\" for predicting the sales volume, but rather to udnerstand how the different variables interact with each other in order to best structure future promotions and pricing.\n",
    "\n",
    "Doing so, we need to use models which offer full transparency of the modeling process as opposed to \"back box\" model appraches.\n",
    "<br>\n",
    "<br>\n",
    "<b> Good candidates:</b>\n",
    "<br>Linear regression and it's \"derivatives\" (GLM, Lasso, Least squares, Polynomial, Feed-forward/Backwards/Stepwise)\n",
    "<br>Decision trees\n",
    "<br>Gradient boosting\n",
    "<br>*(Note: Association analysis (Market basket) is an interesting candidate, however the data processing for it is substatially different that all other models)*\n",
    "<br>\n",
    "<br><b>\"Maybe\" candidates:</b>\n",
    "<br>Random forest\n",
    "<br>\n",
    "<br><b>Bad candidates:</b>\n",
    "<br>Neural networs\n",
    "<br>Support vector machines\n",
    "<br>Ensemble models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We might want to remove highy correlated variables up-front, to simplyfy later processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff7a5b90f28>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "f, ax = plt.subplots(figsize=(14, 11))\n",
    "corr = df3.corr()\n",
    "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True),\n",
    "            square=True, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears we do have some highly correlated variables, so let's look in detail (looking at cutoff above 0.75 correlation in either way)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>TIME_SINCE_LAST_PROM</td>\n",
       "      <td>HAS_PROMOTION</td>\n",
       "      <td>0.751904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>PROMOTION_WEEK</td>\n",
       "      <td>HAS_PROMOTION</td>\n",
       "      <td>0.759700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>HAS_PROMOTION</td>\n",
       "      <td>TIME_SINCE_LAST_PROM</td>\n",
       "      <td>0.751904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>HAS_PROMOTION</td>\n",
       "      <td>PROMOTION_WEEK</td>\n",
       "      <td>0.759700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>PROMOTION_MAV10</td>\n",
       "      <td>PROMOTION_MAV5</td>\n",
       "      <td>0.789757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>PROMOTION_MAV5</td>\n",
       "      <td>PROMOTION_MAV10</td>\n",
       "      <td>0.789757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>PRICE_TO_COMP_MIN</td>\n",
       "      <td>COMPETITOR_MIN</td>\n",
       "      <td>-0.759608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>PRICE_TO_COMP_AVG</td>\n",
       "      <td>COMPETITOR_AVG</td>\n",
       "      <td>-0.764302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>COMPETITOR_MIN</td>\n",
       "      <td>PRICE_TO_COMP_MIN</td>\n",
       "      <td>-0.759608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>COMPETITOR_AVG</td>\n",
       "      <td>PRICE_TO_COMP_AVG</td>\n",
       "      <td>-0.764302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>COMP_PRICE_CHANGE_AVG</td>\n",
       "      <td>COMP_PRICE_CHANGE_MIN</td>\n",
       "      <td>0.797666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>COMP_PRICE_CHANGE_MIN</td>\n",
       "      <td>COMP_PRICE_CHANGE_AVG</td>\n",
       "      <td>0.797666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>COMP_SLOPE5_AVG</td>\n",
       "      <td>COMP_SLOPE5_MIN</td>\n",
       "      <td>0.772463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>COMP_SLOPE5_MIN</td>\n",
       "      <td>COMP_SLOPE5_AVG</td>\n",
       "      <td>0.772463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Index               variable     value\n",
       "151   TIME_SINCE_LAST_PROM          HAS_PROMOTION  0.751904\n",
       "153         PROMOTION_WEEK          HAS_PROMOTION  0.759700\n",
       "174          HAS_PROMOTION   TIME_SINCE_LAST_PROM  0.751904\n",
       "222          HAS_PROMOTION         PROMOTION_WEEK  0.759700\n",
       "251        PROMOTION_MAV10         PROMOTION_MAV5  0.789757\n",
       "274         PROMOTION_MAV5        PROMOTION_MAV10  0.789757\n",
       "402      PRICE_TO_COMP_MIN         COMPETITOR_MIN -0.759608\n",
       "427      PRICE_TO_COMP_AVG         COMPETITOR_AVG -0.764302\n",
       "448         COMPETITOR_MIN      PRICE_TO_COMP_MIN -0.759608\n",
       "473         COMPETITOR_AVG      PRICE_TO_COMP_AVG -0.764302\n",
       "501  COMP_PRICE_CHANGE_AVG  COMP_PRICE_CHANGE_MIN  0.797666\n",
       "524  COMP_PRICE_CHANGE_MIN  COMP_PRICE_CHANGE_AVG  0.797666\n",
       "551        COMP_SLOPE5_AVG        COMP_SLOPE5_MIN  0.772463\n",
       "574        COMP_SLOPE5_MIN        COMP_SLOPE5_AVG  0.772463"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr[\"Index\"] = corr.index\n",
    "melted = corr.melt(id_vars=[\"Index\"])\n",
    "melted[(melted[\"Index\"] != melted[\"variable\"]) & (abs(melted[\"value\"]) > 0.75)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The overall COMPETITOR_MIN appears to be quite simmilar to the COMPETITOR_AVG, so here we will drop all variables related to COMPETITOR_AVG (under the assumption, that COMPETITOR_MIN makes more business sense).\n",
    "<br>\n",
    "<br>\n",
    "Also HAS_PROMOTION is highly correlated to PROMOTION_WEEK (as it should be). We will keep only PROMOTION_WEEK, as it represents more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = df3.drop([\"HAS_PROMOTION\",\"PROMOTION_MAV10\",\"PRICE_TO_COMP_AVG\",\"COMPETITOR_AVG\",\"COMP_PRICE_CHANGE_AVG\",\"COMP_SLOPE5_AVG\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wprice</th>\n",
       "      <th>PROMOTION_A</th>\n",
       "      <th>PROMOTION_B</th>\n",
       "      <th>PROMOTION_C</th>\n",
       "      <th>PROMOTION_D</th>\n",
       "      <th>PROMOTION_E</th>\n",
       "      <th>TIME_SINCE_LAST_PROM</th>\n",
       "      <th>LAST_PROM_DUR</th>\n",
       "      <th>PROMOTION_WEEK</th>\n",
       "      <th>PROMOTION_MAV5</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>PRICE_CHANGE</th>\n",
       "      <th>PRICE_SLOPE5</th>\n",
       "      <th>PRICE_SLOPE10</th>\n",
       "      <th>COMPETITOR_MIN</th>\n",
       "      <th>PRICE_TO_COMP_MIN</th>\n",
       "      <th>COMP_PRICE_CHANGE_MIN</th>\n",
       "      <th>COMP_SLOPE5_MIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>172624.464</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.786</td>\n",
       "      <td>-0.080702</td>\n",
       "      <td>-0.0126</td>\n",
       "      <td>-0.010248</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.444853</td>\n",
       "      <td>-0.007299</td>\n",
       "      <td>0.0145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>63162.528</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.856</td>\n",
       "      <td>0.089059</td>\n",
       "      <td>-0.0399</td>\n",
       "      <td>-0.018394</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.813559</td>\n",
       "      <td>-0.132353</td>\n",
       "      <td>0.0036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>57399.840</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.030</td>\n",
       "      <td>0.203271</td>\n",
       "      <td>-0.0121</td>\n",
       "      <td>-0.006697</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.060000</td>\n",
       "      <td>0.059322</td>\n",
       "      <td>-0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>158749.272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.883</td>\n",
       "      <td>-0.142718</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>-0.009400</td>\n",
       "      <td>0.514</td>\n",
       "      <td>0.717899</td>\n",
       "      <td>0.028000</td>\n",
       "      <td>-0.0112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>73502.520</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.030578</td>\n",
       "      <td>0.0275</td>\n",
       "      <td>-0.005582</td>\n",
       "      <td>0.420</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>-0.182879</td>\n",
       "      <td>-0.0206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>148944.576</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.872</td>\n",
       "      <td>-0.041758</td>\n",
       "      <td>-0.0088</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.490598</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.0146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>70645.608</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.901</td>\n",
       "      <td>0.033257</td>\n",
       "      <td>-0.0269</td>\n",
       "      <td>-0.004915</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.566957</td>\n",
       "      <td>-0.017094</td>\n",
       "      <td>0.0221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>142544.556</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.843</td>\n",
       "      <td>-0.064373</td>\n",
       "      <td>-0.0089</td>\n",
       "      <td>-0.008879</td>\n",
       "      <td>0.563</td>\n",
       "      <td>0.497336</td>\n",
       "      <td>-0.020870</td>\n",
       "      <td>0.0253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>71467.488</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.084223</td>\n",
       "      <td>-0.0021</td>\n",
       "      <td>0.004291</td>\n",
       "      <td>0.569</td>\n",
       "      <td>0.606327</td>\n",
       "      <td>0.010657</td>\n",
       "      <td>0.0276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146753.040</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.072210</td>\n",
       "      <td>0.0229</td>\n",
       "      <td>0.007473</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.675214</td>\n",
       "      <td>0.028120</td>\n",
       "      <td>-0.0006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Wprice  PROMOTION_A  PROMOTION_B  PROMOTION_C  PROMOTION_D  \\\n",
       "136  172624.464            1            0            0            0   \n",
       "137   63162.528            1            0            0            0   \n",
       "138   57399.840            0            0            0            0   \n",
       "139  158749.272            0            0            1            0   \n",
       "140   73502.520            0            0            1            0   \n",
       "141  148944.576            0            0            1            0   \n",
       "142   70645.608            0            0            0            0   \n",
       "143  142544.556            0            0            0            1   \n",
       "144   71467.488            0            0            0            1   \n",
       "145  146753.040            0            0            0            0   \n",
       "\n",
       "     PROMOTION_E  TIME_SINCE_LAST_PROM  LAST_PROM_DUR  PROMOTION_WEEK  \\\n",
       "136            0                     1              2               2   \n",
       "137            0                     1              2               3   \n",
       "138            0                    -1              3               0   \n",
       "139            0                     1              3               1   \n",
       "140            0                     1              3               2   \n",
       "141            0                     1              3               3   \n",
       "142            0                    -1              3               0   \n",
       "143            0                     1              3               1   \n",
       "144            0                     1              3               2   \n",
       "145            0                    -1              2               0   \n",
       "\n",
       "     PROMOTION_MAV5  PRICE  PRICE_CHANGE  PRICE_SLOPE5  PRICE_SLOPE10  \\\n",
       "136             0.8  0.786     -0.080702       -0.0126      -0.010248   \n",
       "137             0.8  0.856      0.089059       -0.0399      -0.018394   \n",
       "138             0.6  1.030      0.203271       -0.0121      -0.006697   \n",
       "139             0.8  0.883     -0.142718        0.0300      -0.009400   \n",
       "140             0.8  0.910      0.030578        0.0275      -0.005582   \n",
       "141             0.8  0.872     -0.041758       -0.0088       0.000164   \n",
       "142             0.6  0.901      0.033257       -0.0269      -0.004915   \n",
       "143             0.8  0.843     -0.064373       -0.0089      -0.008879   \n",
       "144             0.8  0.914      0.084223       -0.0021       0.004291   \n",
       "145             0.6  0.980      0.072210        0.0229       0.007473   \n",
       "\n",
       "     COMPETITOR_MIN  PRICE_TO_COMP_MIN  COMP_PRICE_CHANGE_MIN  COMP_SLOPE5_MIN  \n",
       "136           0.544           0.444853              -0.007299           0.0145  \n",
       "137           0.472           0.813559              -0.132353           0.0036  \n",
       "138           0.500           1.060000               0.059322          -0.0080  \n",
       "139           0.514           0.717899               0.028000          -0.0112  \n",
       "140           0.420           1.166667              -0.182879          -0.0206  \n",
       "141           0.585           0.490598               0.392857           0.0146  \n",
       "142           0.575           0.566957              -0.017094           0.0221  \n",
       "143           0.563           0.497336              -0.020870           0.0253  \n",
       "144           0.569           0.606327               0.010657           0.0276  \n",
       "145           0.585           0.675214               0.028120          -0.0006  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and validation sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the dataset is quite small and we don't intend to do any hyperparameter tunning or manual pruning of decision trees, we will generate only train and validation sets (no cross validation) with a test set of 20%.\n",
    "\n",
    "Although it could be contravertial, we will append the data to itself multiple times. The reason for this is that the small data set, can generate huge variance, when splitting by test/train. We will avoid this by arificially multpying the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(584, 18)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for k in range(2):\n",
    "    df4 = df4.append(df4)\n",
    "df4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "target = df4[\"Wprice\"]\n",
    "features = df4.drop(\"Wprice\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pydotplus\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import tree\n",
    "from sklearn.externals.six import StringIO  \n",
    "from sklearn.tree import export_graphviz\n",
    "import collections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Comments:</b> Since our data is quite small, let's go ahead and play with with tree settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best R^2 =0.7251315267662963 was achieved at 50 samples per split and  20 minimum leafs\n"
     ]
    }
   ],
   "source": [
    "max_score = 0\n",
    "splits = 0\n",
    "leafs = 0\n",
    "for split in range(30,500,20):\n",
    "    for leaf in range(20,500,10):\n",
    "        clf = tree.DecisionTreeRegressor(min_samples_split=split, min_samples_leaf=leaf,random_state=0,max_depth=4)\n",
    "        clf = clf.fit(X_train,y_train)\n",
    "        score = clf.score(X_test, y_test)\n",
    "        if score > max_score:\n",
    "            max_score = score\n",
    "            splits = split\n",
    "            leafs = leaf\n",
    "print(f'Best R^2 ={max_score} was achieved at {splits} samples per split and  {leafs} minimum leafs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeRegressor(min_samples_split=splits, min_samples_leaf=leafs,\n",
    "  random_state=0,max_depth=4)\n",
    "clf = clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 113,
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize data\n",
    "dot_data = StringIO()\n",
    "export_graphviz(clf, out_file=dot_data, \n",
    "                feature_names=X_train.columns,\n",
    "                filled=True, rounded=True,\n",
    "                special_characters=True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "Image(graph.create_png(), width=1000, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Comments:</b> Makority of the predictors are focused around either the price change, the slope of the price change or the time since last promotion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Least Squares regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 Wprice   R-squared:                       0.629\n",
      "Model:                            OLS   Adj. R-squared:                  0.615\n",
      "Method:                 Least Squares   F-statistic:                     44.77\n",
      "Date:                Sun, 11 Feb 2018   Prob (F-statistic):           2.40e-85\n",
      "Time:                        10:59:01   Log-Likelihood:                -5309.2\n",
      "No. Observations:                 467   AIC:                         1.065e+04\n",
      "Df Residuals:                     449   BIC:                         1.073e+04\n",
      "Df Model:                          17                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=========================================================================================\n",
      "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------\n",
      "const                  1.095e+05   2.12e+04      5.176      0.000    6.79e+04    1.51e+05\n",
      "PROMOTION_A            3.398e+04   5696.069      5.965      0.000    2.28e+04    4.52e+04\n",
      "PROMOTION_B            2.172e+04   6118.028      3.550      0.000    9696.916    3.37e+04\n",
      "PROMOTION_C            2.101e+04   6046.732      3.475      0.001    9128.344    3.29e+04\n",
      "PROMOTION_D            3.647e+04   6828.779      5.340      0.000     2.3e+04    4.99e+04\n",
      "PROMOTION_E            3.357e+04   6796.348      4.939      0.000    2.02e+04    4.69e+04\n",
      "TIME_SINCE_LAST_PROM   -640.5780    979.340     -0.654      0.513   -2565.237    1284.081\n",
      "LAST_PROM_DUR         -2637.0155    962.551     -2.740      0.006   -4528.679    -745.352\n",
      "PROMOTION_WEEK        -6665.3167   1424.352     -4.680      0.000   -9464.540   -3866.093\n",
      "PROMOTION_MAV5         1.767e+04   6914.808      2.555      0.011    4078.220    3.13e+04\n",
      "PRICE                  -263.5584   2.75e+04     -0.010      0.992   -5.43e+04    5.38e+04\n",
      "PRICE_CHANGE          -1.382e+05   1.18e+04    -11.666      0.000   -1.61e+05   -1.15e+05\n",
      "PRICE_SLOPE5          -5.072e+04   4.54e+04     -1.118      0.264    -1.4e+05    3.85e+04\n",
      "PRICE_SLOPE10         -5.037e+04   1.46e+05     -0.346      0.729   -3.36e+05    2.36e+05\n",
      "COMPETITOR_MIN        -3.422e+04   3.52e+04     -0.972      0.332   -1.03e+05     3.5e+04\n",
      "PRICE_TO_COMP_MIN     -1.302e+04   9204.139     -1.415      0.158   -3.11e+04    5064.950\n",
      "COMP_PRICE_CHANGE_MIN -1064.7800   5157.643     -0.206      0.837   -1.12e+04    9071.337\n",
      "COMP_SLOPE5_MIN       -2.617e+05   5.46e+04     -4.795      0.000   -3.69e+05   -1.54e+05\n",
      "==============================================================================\n",
      "Omnibus:                       35.854   Durbin-Watson:                   1.969\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               44.888\n",
      "Skew:                           0.633   Prob(JB):                     1.79e-10\n",
      "Kurtosis:                       3.838   Cond. No.                         606.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X2 = sm.add_constant(X_train)\n",
    "est = sm.OLS(y_train, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Comments:</b> Let's see if we can drop some of the variables with high P score in order to obtain a better result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "X3 = X2.copy(deep=True)\n",
    "\n",
    "while True:   \n",
    "    loop_est = sm.OLS(list(y_train), X3)\n",
    "    result = loop_est.fit()\n",
    "    pvals = result.pvalues.sort_values(ascending=False)\n",
    "    location = 0\n",
    "    if pvals.index[0] == \"const\":\n",
    "        location = 1\n",
    "    if pvals.iloc[location] < 0.05:\n",
    "        break;\n",
    "    X3 = X3.drop(pvals.index[location],axis=1)\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 Wprice   R-squared:                       0.627\n",
      "Model:                            OLS   Adj. R-squared:                  0.617\n",
      "Method:                 Least Squares   F-statistic:                     63.62\n",
      "Date:                Sun, 11 Feb 2018   Prob (F-statistic):           2.99e-89\n",
      "Time:                        10:59:05   Log-Likelihood:                -5310.4\n",
      "No. Observations:                 467   AIC:                         1.065e+04\n",
      "Df Residuals:                     454   BIC:                         1.070e+04\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const              1.201e+05   1.82e+04      6.590      0.000    8.43e+04    1.56e+05\n",
      "PROMOTION_A        3.403e+04   4691.640      7.253      0.000    2.48e+04    4.32e+04\n",
      "PROMOTION_B        2.014e+04   4655.153      4.327      0.000     1.1e+04    2.93e+04\n",
      "PROMOTION_C        2.019e+04   4307.788      4.687      0.000    1.17e+04    2.87e+04\n",
      "PROMOTION_D        3.486e+04   5027.855      6.933      0.000     2.5e+04    4.47e+04\n",
      "PROMOTION_E        3.296e+04   6221.379      5.297      0.000    2.07e+04    4.52e+04\n",
      "LAST_PROM_DUR     -2950.8359    882.120     -3.345      0.001   -4684.380   -1217.291\n",
      "PROMOTION_WEEK    -6381.1857   1339.599     -4.764      0.000   -9013.769   -3748.602\n",
      "PROMOTION_MAV5     1.749e+04   5831.488      3.000      0.003    6031.946     2.9e+04\n",
      "PRICE_CHANGE      -1.352e+05   1.02e+04    -13.301      0.000   -1.55e+05   -1.15e+05\n",
      "COMPETITOR_MIN    -4.707e+04   2.24e+04     -2.103      0.036    -9.1e+04   -3094.088\n",
      "PRICE_TO_COMP_MIN -1.646e+04   5701.069     -2.887      0.004   -2.77e+04   -5253.745\n",
      "COMP_SLOPE5_MIN   -2.706e+05   5.27e+04     -5.137      0.000   -3.74e+05   -1.67e+05\n",
      "==============================================================================\n",
      "Omnibus:                       31.362   Durbin-Watson:                   1.986\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               37.144\n",
      "Skew:                           0.603   Prob(JB):                     8.60e-09\n",
      "Kurtosis:                       3.675   Cond. No.                         206.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "est_2 = sm.OLS(y_train, X3)\n",
    "est2_2 = est_2.fit()\n",
    "print(est2_2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Comments:</b> Although all the variables now are highly signifficnt, and the overall preiciton qulity is pretty much the same. It is worth noting that again the price change is a strong predictor. \n",
    "\n",
    "Let's go ahead and try to improve the R^2 by adding polynomials, to see if there will be additional meaningfull result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polynomial regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# create a Linear Regressor   \n",
    "lin_regressor = LinearRegression()\n",
    "\n",
    "# pass the order of your polynomial here  \n",
    "poly = PolynomialFeatures(2)\n",
    "\n",
    "# convert to be used further to linear regression\n",
    "X_transform = poly.fit_transform(X_train)\n",
    "X_tst = poly.fit_transform(X_test)\n",
    "\n",
    "# fit this to Linear Regressor\n",
    "lin_regressor.fit(X_transform,y_train) \n",
    "\n",
    "# get the predictions\n",
    "y_preds = lin_regressor.predict(X_tst)\n",
    "score = lin_regressor.score(X_transform, y_train)\n",
    "print(score)\n",
    "Score2 = lin_regressor.score(X_tst, y_test)\n",
    "print(Score2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Comments:</b> Obviously the train set currenty is signifficantly overfitted+ we get an R^2 = 1 which is obviously an issue. Let's try to fix that by going stepwise.\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_feature_names = ['x'.join(['{}^{}'.format(pair[0],pair[1]) for pair in tuple if pair[1]!=0]) for tuple in [zip(X_train.columns,p) for p in poly.powers_]]\n",
    "output_df = pd.DataFrame(X_transform, columns = target_feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>PROMOTION_A^1</th>\n",
       "      <th>PROMOTION_B^1</th>\n",
       "      <th>PROMOTION_C^1</th>\n",
       "      <th>PROMOTION_D^1</th>\n",
       "      <th>PROMOTION_E^1</th>\n",
       "      <th>TIME_SINCE_LAST_PROM^1</th>\n",
       "      <th>LAST_PROM_DUR^1</th>\n",
       "      <th>PROMOTION_WEEK^1</th>\n",
       "      <th>PROMOTION_MAV5^1</th>\n",
       "      <th>...</th>\n",
       "      <th>COMPETITOR_MIN^2</th>\n",
       "      <th>COMPETITOR_MIN^1xPRICE_TO_COMP_MIN^1</th>\n",
       "      <th>COMPETITOR_MIN^1xCOMP_PRICE_CHANGE_MIN^1</th>\n",
       "      <th>COMPETITOR_MIN^1xCOMP_SLOPE5_MIN^1</th>\n",
       "      <th>PRICE_TO_COMP_MIN^2</th>\n",
       "      <th>PRICE_TO_COMP_MIN^1xCOMP_PRICE_CHANGE_MIN^1</th>\n",
       "      <th>PRICE_TO_COMP_MIN^1xCOMP_SLOPE5_MIN^1</th>\n",
       "      <th>COMP_PRICE_CHANGE_MIN^2</th>\n",
       "      <th>COMP_PRICE_CHANGE_MIN^1xCOMP_SLOPE5_MIN^1</th>\n",
       "      <th>COMP_SLOPE5_MIN^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.264196</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.014392</td>\n",
       "      <td>-0.005757</td>\n",
       "      <td>0.515379</td>\n",
       "      <td>0.020101</td>\n",
       "      <td>-0.008040</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>-0.000314</td>\n",
       "      <td>0.000125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065025</td>\n",
       "      <td>0.871</td>\n",
       "      <td>-0.100912</td>\n",
       "      <td>-0.016575</td>\n",
       "      <td>11.666913</td>\n",
       "      <td>-1.351705</td>\n",
       "      <td>-0.222020</td>\n",
       "      <td>0.156606</td>\n",
       "      <td>0.025723</td>\n",
       "      <td>0.004225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.452929</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.055182</td>\n",
       "      <td>-0.010028</td>\n",
       "      <td>0.093692</td>\n",
       "      <td>0.025098</td>\n",
       "      <td>-0.004561</td>\n",
       "      <td>0.006723</td>\n",
       "      <td>-0.001222</td>\n",
       "      <td>0.000222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.335241</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.015399</td>\n",
       "      <td>0.025765</td>\n",
       "      <td>0.639447</td>\n",
       "      <td>0.021267</td>\n",
       "      <td>0.035585</td>\n",
       "      <td>0.000707</td>\n",
       "      <td>0.001184</td>\n",
       "      <td>0.001980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.480249</td>\n",
       "      <td>0.414</td>\n",
       "      <td>-0.044015</td>\n",
       "      <td>0.007346</td>\n",
       "      <td>0.356890</td>\n",
       "      <td>-0.037943</td>\n",
       "      <td>0.006332</td>\n",
       "      <td>0.004034</td>\n",
       "      <td>-0.000673</td>\n",
       "      <td>0.000112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 171 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   const  PROMOTION_A^1  PROMOTION_B^1  PROMOTION_C^1  PROMOTION_D^1  \\\n",
       "0    1.0            0.0            0.0            1.0            0.0   \n",
       "1    1.0            0.0            0.0            1.0            0.0   \n",
       "2    1.0            1.0            0.0            0.0            0.0   \n",
       "3    1.0            0.0            0.0            0.0            0.0   \n",
       "4    1.0            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   PROMOTION_E^1  TIME_SINCE_LAST_PROM^1  LAST_PROM_DUR^1  PROMOTION_WEEK^1  \\\n",
       "0            0.0                     1.0              3.0               1.0   \n",
       "1            0.0                     2.0              4.0               3.0   \n",
       "2            0.0                    -1.0              0.0               3.0   \n",
       "3            0.0                    -1.0              2.0               0.0   \n",
       "4            0.0                    -1.0              2.0               0.0   \n",
       "\n",
       "   PROMOTION_MAV5^1        ...          COMPETITOR_MIN^2  \\\n",
       "0               0.8        ...                  0.264196   \n",
       "1               0.6        ...                  0.065025   \n",
       "2               0.6        ...                  0.452929   \n",
       "3               0.4        ...                  0.335241   \n",
       "4               0.4        ...                  0.480249   \n",
       "\n",
       "   COMPETITOR_MIN^1xPRICE_TO_COMP_MIN^1  \\\n",
       "0                                 0.369   \n",
       "1                                 0.871   \n",
       "2                                 0.206   \n",
       "3                                 0.463   \n",
       "4                                 0.414   \n",
       "\n",
       "   COMPETITOR_MIN^1xCOMP_PRICE_CHANGE_MIN^1  \\\n",
       "0                                  0.014392   \n",
       "1                                 -0.100912   \n",
       "2                                  0.055182   \n",
       "3                                  0.015399   \n",
       "4                                 -0.044015   \n",
       "\n",
       "   COMPETITOR_MIN^1xCOMP_SLOPE5_MIN^1  PRICE_TO_COMP_MIN^2  \\\n",
       "0                           -0.005757             0.515379   \n",
       "1                           -0.016575            11.666913   \n",
       "2                           -0.010028             0.093692   \n",
       "3                            0.025765             0.639447   \n",
       "4                            0.007346             0.356890   \n",
       "\n",
       "   PRICE_TO_COMP_MIN^1xCOMP_PRICE_CHANGE_MIN^1  \\\n",
       "0                                     0.020101   \n",
       "1                                    -1.351705   \n",
       "2                                     0.025098   \n",
       "3                                     0.021267   \n",
       "4                                    -0.037943   \n",
       "\n",
       "   PRICE_TO_COMP_MIN^1xCOMP_SLOPE5_MIN^1  COMP_PRICE_CHANGE_MIN^2  \\\n",
       "0                              -0.008040                 0.000784   \n",
       "1                              -0.222020                 0.156606   \n",
       "2                              -0.004561                 0.006723   \n",
       "3                               0.035585                 0.000707   \n",
       "4                               0.006332                 0.004034   \n",
       "\n",
       "   COMP_PRICE_CHANGE_MIN^1xCOMP_SLOPE5_MIN^1  COMP_SLOPE5_MIN^2  \n",
       "0                                  -0.000314           0.000125  \n",
       "1                                   0.025723           0.004225  \n",
       "2                                  -0.001222           0.000222  \n",
       "3                                   0.001184           0.001980  \n",
       "4                                  -0.000673           0.000112  \n",
       "\n",
       "[5 rows x 171 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_blank = output_df.drop(\"\",axis=1)\n",
    "Xtr2 = sm.add_constant(no_blank)\n",
    "Xtr2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.699\n",
      "Model:                            OLS   Adj. R-squared:                  0.693\n",
      "Method:                 Least Squares   F-statistic:                     106.1\n",
      "Date:                Sun, 11 Feb 2018   Prob (F-statistic):          2.83e-112\n",
      "Time:                        11:10:31   Log-Likelihood:                -5260.1\n",
      "No. Observations:                 467   AIC:                         1.054e+04\n",
      "Df Residuals:                     456   BIC:                         1.059e+04\n",
      "Df Model:                          10                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "========================================================================================================\n",
      "                                           coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------------------\n",
      "const                                 6.149e+05   8.48e+04      7.249      0.000    4.48e+05    7.82e+05\n",
      "PROMOTION_MAV5^1xPRICE_CHANGE^1        -1.9e+05   1.36e+04    -13.939      0.000   -2.17e+05   -1.63e+05\n",
      "PRICE^1                              -9.836e+05    1.7e+05     -5.800      0.000   -1.32e+06    -6.5e+05\n",
      "PROMOTION_D^1xCOMP_SLOPE5_MIN^1      -2.703e+05   9.99e+04     -2.705      0.007   -4.67e+05   -7.39e+04\n",
      "PRICE_CHANGE^2                        2.674e+05   4.67e+04      5.729      0.000    1.76e+05    3.59e+05\n",
      "PROMOTION_A^1xTIME_SINCE_LAST_PROM^1  1.419e+04   1971.462      7.197      0.000    1.03e+04    1.81e+04\n",
      "COMPETITOR_MIN^1xCOMP_SLOPE5_MIN^1   -4.298e+05   7.48e+04     -5.747      0.000   -5.77e+05   -2.83e+05\n",
      "PRICE^2                               4.481e+05   8.42e+04      5.325      0.000    2.83e+05    6.14e+05\n",
      "PROMOTION_D^1xPRICE_TO_COMP_MIN^1      2.53e+04   4188.255      6.040      0.000    1.71e+04    3.35e+04\n",
      "PROMOTION_E^1xTIME_SINCE_LAST_PROM^1  1.496e+04   2933.394      5.101      0.000    9198.299    2.07e+04\n",
      "LAST_PROM_DUR^2                       -428.4009    102.027     -4.199      0.000    -628.902    -227.900\n",
      "==============================================================================\n",
      "Omnibus:                       67.141   Durbin-Watson:                   2.070\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              122.115\n",
      "Skew:                           0.842   Prob(JB):                     3.04e-27\n",
      "Kurtosis:                       4.855   Cond. No.                     3.39e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.39e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "blank_df = Xtr2[\"const\"]\n",
    "iterable = Xtr2.drop(\"const\",axis=1)\n",
    "lastR2 = 0\n",
    "col_to_add = \"\"\n",
    "\n",
    "for m in range(10):\n",
    "    for k in iterable.columns:\n",
    "        test_df = pd.concat([blank_df,iterable[k]],axis=1)\n",
    "        est = sm.OLS(list(y_train), test_df)\n",
    "        est2 = est.fit()\n",
    "        if est2.rsquared > lastR2:\n",
    "            lastR2 = est2.rsquared\n",
    "            col_to_add = k\n",
    "        test_df = test_df.drop(k,axis=1)\n",
    "        \n",
    "    blank_df = pd.concat([blank_df,iterable[col_to_add]],axis=1) \n",
    "    \n",
    "    loop_est = sm.OLS(list(y_train), blank_df)\n",
    "    result = loop_est.fit()\n",
    "    pvals = result.pvalues.sort_values(ascending=False)\n",
    "    location = 0\n",
    "    if pvals.index[0] == \"const\":\n",
    "        location = 1\n",
    "    if pvals.iloc[location] > 0.05:\n",
    "         blank_df = blank_df.drop(pvals.index[location],axis=1)    \n",
    "    \n",
    "    \n",
    "est = sm.OLS(list(y_train), blank_df)\n",
    "est2 = est.fit()   \n",
    "print(est2.summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Comments:</b> We end up with a (relatively) small enough subset of signifficant parameters, on a model having acceptable R2 value. However with the polynomial terms, we are not sure that ll the variables have compareble ranges, so let's go ahead and fix that.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COMPETITOR_MIN^1xCOMP_SLOPE5_MIN^1     -3.143025e+07\n",
      "PROMOTION_D^1xCOMP_SLOPE5_MIN^1        -2.076131e+07\n",
      "PRICE^1                                -1.487011e+07\n",
      "PROMOTION_MAV5^1xPRICE_CHANGE^1        -3.810381e+06\n",
      "LAST_PROM_DUR^2                        -4.547857e+01\n",
      "PROMOTION_A^1xTIME_SINCE_LAST_PROM^1    2.259503e+04\n",
      "PROMOTION_E^1xTIME_SINCE_LAST_PROM^1    3.574012e+04\n",
      "PROMOTION_D^1xPRICE_TO_COMP_MIN^1       1.088079e+05\n",
      "PRICE^2                                 3.183628e+06\n",
      "PRICE_CHANGE^2                          1.524925e+07\n",
      "const                                            inf\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "descr = blank_df.describe().transpose()\n",
    "descr[\"range\"] = descr[\"max\"]-descr[min]\n",
    "mean_range = np.mean(descr[\"range\"])\n",
    "weights = mean_range/descr[\"range\"]\n",
    "coeffs = pd.concat([result.params, weights],axis=1)\n",
    "final_weights = (coeffs[0]*coeffs[\"range\"]).sort_values()\n",
    "print(final_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Comments:</b> Resuls of the Polynomial stepwise regression are debatable and might need further, in-depth analysis. The results suggest that:\n",
    "<ul><li>Any price change is a good one.</li>\n",
    "    <li>Promotion D success is mainly related to the actions of our competitors</li>\n",
    "    <li>Promotions A and E play well, when there was no other promotion in the near past</li>\n",
    "    <ul>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Summary:</b> we have explored the several approaches to determining the major drivers and effects of promotional campagins carriend out in the dataset. Both exploratory analysis, simple decision trees and various regressions suggest promotional activities with short duration, and steep price changes are what drives the sales.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strategy for sales, based on the result suggests:\n",
    "<li>Frequent but short promotions.\n",
    "<li>Duration of the promotions not more than a week.\n",
    "<li>Gap between the promotions of around 2 weeks.\n",
    "<li>Price drop of at least 6.8% on each promotion."
   ]
  }
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