Retail_Data_Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy as sc\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc(\"axes.spines\", top=False, right=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = pd.read_csv('Features data set.csv')\n",
    "sales = pd.read_csv('sales data-set.csv')\n",
    "stores = pd.read_csv('stores data-set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8190 entries, 0 to 8189\n",
      "Data columns (total 12 columns):\n",
      "Store           8190 non-null int64\n",
      "Date            8190 non-null object\n",
      "Temperature     8190 non-null float64\n",
      "Fuel_Price      8190 non-null float64\n",
      "MarkDown1       4032 non-null float64\n",
      "MarkDown2       2921 non-null float64\n",
      "MarkDown3       3613 non-null float64\n",
      "MarkDown4       3464 non-null float64\n",
      "MarkDown5       4050 non-null float64\n",
      "CPI             7605 non-null float64\n",
      "Unemployment    7605 non-null float64\n",
      "IsHoliday       8190 non-null bool\n",
      "dtypes: bool(1), float64(9), int64(1), object(1)\n",
      "memory usage: 711.9+ KB\n"
     ]
    }
   ],
   "source": [
    "features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "   Store        Date  Temperature  Fuel_Price  MarkDown1  MarkDown2  \\\n",
       "0      1  05/02/2010        42.31       2.572        NaN        NaN   \n",
       "1      1  12/02/2010        38.51       2.548        NaN        NaN   \n",
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       "\n",
       "   MarkDown3  MarkDown4  MarkDown5         CPI  Unemployment  IsHoliday  \n",
       "0        NaN        NaN        NaN  211.096358         8.106      False  \n",
       "1        NaN        NaN        NaN  211.242170         8.106       True  \n",
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       "4        NaN        NaN        NaN  211.350143         8.106      False  "
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   "source": [
    "features.head()"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 421570 entries, 0 to 421569\n",
      "Data columns (total 5 columns):\n",
      "Store           421570 non-null int64\n",
      "Dept            421570 non-null int64\n",
      "Date            421570 non-null object\n",
      "Weekly_Sales    421570 non-null float64\n",
      "IsHoliday       421570 non-null bool\n",
      "dtypes: bool(1), float64(1), int64(2), object(1)\n",
      "memory usage: 13.3+ MB\n"
     ]
    }
   ],
   "source": [
    "sales.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "   Store  Dept        Date  Weekly_Sales  IsHoliday\n",
       "0      1     1  05/02/2010      24924.50      False\n",
       "1      1     1  12/02/2010      46039.49       True\n",
       "2      1     1  19/02/2010      41595.55      False\n",
       "3      1     1  26/02/2010      19403.54      False\n",
       "4      1     1  05/03/2010      21827.90      False"
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     "execution_count": 11,
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   ],
   "source": [
    "sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45 entries, 0 to 44\n",
      "Data columns (total 3 columns):\n",
      "Store    45 non-null int64\n",
      "Type     45 non-null object\n",
      "Size     45 non-null int64\n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 1.1+ KB\n"
     ]
    }
   ],
   "source": [
    "stores.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "   Store Type    Size\n",
       "0      1    A  151315\n",
       "1      2    A  202307\n",
       "2      3    B   37392\n",
       "3      4    A  205863\n",
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   ],
   "source": [
    "stores.head()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's combine these datasets in one dataframe for it will be helpful in some cases!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 421570 entries, 0 to 421569\n",
      "Data columns (total 16 columns):\n",
      "Store           421570 non-null int64\n",
      "Dept            421570 non-null int64\n",
      "Date            421570 non-null object\n",
      "Weekly_Sales    421570 non-null float64\n",
      "IsHoliday       421570 non-null bool\n",
      "Temperature     421570 non-null float64\n",
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      "MarkDown1       150681 non-null float64\n",
      "MarkDown2       111248 non-null float64\n",
      "MarkDown3       137091 non-null float64\n",
      "MarkDown4       134967 non-null float64\n",
      "MarkDown5       151432 non-null float64\n",
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      "Type            421570 non-null object\n",
      "Size            421570 non-null int64\n",
      "dtypes: bool(1), float64(10), int64(3), object(2)\n",
      "memory usage: 51.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data = pd.merge(sales,features,on=['Store','Date','IsHoliday'],how='left')\n",
    "data = pd.merge(data,stores,on=['Store'],how='left')\n",
    "data.head()\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keeping both kinds could be helpful in some cases!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   Store  Dept        Date  Weekly_Sales  IsHoliday  Temperature  Fuel_Price  \\\n",
       "0      1     1  05/02/2010      24924.50      False        42.31       2.572   \n",
       "1      1     1  12/02/2010      46039.49       True        38.51       2.548   \n",
       "2      1     1  19/02/2010      41595.55      False        39.93       2.514   \n",
       "3      1     1  26/02/2010      19403.54      False        46.63       2.561   \n",
       "4      1     1  05/03/2010      21827.90      False        46.50       2.625   \n",
       "\n",
       "   MarkDown1  MarkDown2  MarkDown3  MarkDown4  MarkDown5         CPI  \\\n",
       "0     -999.0     -999.0     -999.0     -999.0     -999.0  211.096358   \n",
       "1     -999.0     -999.0     -999.0     -999.0     -999.0  211.242170   \n",
       "2     -999.0     -999.0     -999.0     -999.0     -999.0  211.289143   \n",
       "3     -999.0     -999.0     -999.0     -999.0     -999.0  211.319643   \n",
       "4     -999.0     -999.0     -999.0     -999.0     -999.0  211.350143   \n",
       "\n",
       "   Unemployment Type    Size  \n",
       "0         8.106    A  151315  \n",
       "1         8.106    A  151315  \n",
       "2         8.106    A  151315  \n",
       "3         8.106    A  151315  \n",
       "4         8.106    A  151315  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_noNan = data.fillna(-999)\n",
    "data_noNan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot some of the basic numerals and notice their varience over time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ISAEAgKA0JMI/VAV901L7C+iPwp6MatHX58GQvRDjS7xr9JDaPhJSAApBMpXUgf9zYNBhwCcL1i3QN5/+pq/bOGefc3TZoZf5ug1sqy5RF3QICFhTsjDKPLQU0Ye/6prrtKFxQ+tkA5KUcAnFI3GlXEomk5m1mZwg6ZKtk3DFI3ENKRlSMImgbFi1apUuvvhivfXWW6qoqNDIkSN1880364ADDtBee+2lpqYmTZo0SXfccYc+++wznXrqqVqwYEFWYyAhFWJhr2GUC37Oshf2hJSfBd8BIFeYwbKw/eXTv/i+jQcXPUhCKgC1zbVBh4CAhf1cukWhJNbyWcqldNhDh/W5nYsPvljnjT8vCxGFn3NOZ5xxhqZNm6aHH35YkjR//nytXr1au+22m+bNm6dEIqHJkyfr8ccf18EHH+xLHAzZCzF6SHXNz6LmyzYt863tMGu5CgEAucAPgcLGxbfCtalxU9AhIGCF8v3N95T/sjXE99mlz2alnULw4osvKh6P68ILL2xddsABB2jMmDGtt2OxmI488kgtXrzYtzjoIhFic1fNDTqErMrmdLrvrH5H02ZNk5PLWpvthX3WJb9em5OqTtKCdQv09JlP+9K+H2a8NkMvV7/s6zamVE3RwvUL9dQZT/nS/q3v3Kp7F9zrS9tAPiuUK+zoGHWGCte7694NOgQErFD2bxJS/svWa+zn6Jne+uWbv8z6rKN7D9lbPzn0J52us2DBAk2YMKHTderq6vT888/rF7/4RTbDa4OEVIiVF5UHHULWZLurfC7GBp+222l67fPXfGn7sX96bJshdSmlFGnXqdHJyWSt91v6f05OzjlFI1E552Rmcs5LQL2+8nVd9/frWm9nm3OudVw32mp5r/ziZwIWyFeFcoUdHWNYV+EaWjJUNQ01QYfRLZVllUGHUJA+3fRp0CFkxYotK4IOoeBlKyEVj1JDtzs++eQTHXjggTIzTZ06VVOmTNHSpUt92RYJqRA7dPShmv8v8zsspNeSlOiJjh7TkGjQwx8+vLVbdfr3dHOyWWbmJU3SD2l5bEvyo7a5VuVF5UqmkmpONas4VqyIIl68Gb/L9xu6n06sOrFHsXabj7/PW5ILfz79z6oaXOXfhrJsZe1KSf4lL1JKhbJYoN/JHJJFgD+aUrlLSJ2737neH87bp+sT9RoQG7D1+Ncu6TwwPlCNyUY1JBvknFM8Elc8Gm+dpbUp1aQHFz2o3St2z9lzCBtqhBWur+/1df12/m81bZzXo71lyH8sElMsElNNfY2KokUaVDSotQB6m33MpIgiMjMlU8k2x1nnXJs2ZRnnuc6rR1MSK9GKLSs0tGSoJHkX8dL3Z7r3/Xt1QtUJ/r0Q/diogaOCDkGSdHLVyX16fMtnCP7J1uzm+TipU1c9mfyy77776k9/+lOH97XUkMoFElIhF7GIr71RyorKdP74831r3y8tJywkAbbVkizyq4dUym3bkyvfhTGBBsCTrZPU7rhkwiVZb3PpxqXa3LQ56+0Wio2NG4MOAT679JBLgw6hUw998FDQIRS8m469SSfsEkzS77AHD9PwAcP71EbL743rjr5Op+12WjbCauOGt27QIx89kvV2wyRrPaTyMCEVlMmTJ+vyyy/XzJkzNX36dEnSu+++q40bc3vcDdevRiCPhDXZ1ZIs8q2HlEspEgnfV4tfCbrW9nPweQnrZxLoi9dXvh50CPDRovWLfN8GP1CC4fdxF/kvHz4D2bwoyQVO/5CQyj4z02OPPaY5c+Zot91207777qv//M//1KhRue25mNMeUmY2RtIDkkbK6xA70zl3i5kNkfQHSVWSlko6yzn3hXl79S2STpFUJ+k7zrl3chkzwi0XP9DDdvChh9S2/K7t1LodHz8rYfscAtmy2+DdcrKdSZWTcrIdtFVRXKENjRt83cZ+w/bztX1sX66Ov8hvYf8c+J1Y4xxPWavbSw2ptnbccUc98si2ve8WLFiwzbKqqqoOl/dVrofsJST9yDn3jpmVS3rbzJ6T9B1JzzvnrjezyyRdJuknkqZI2iP932GS7kz/CwsxsZMAACAASURBVHQqF1/c+XBVpy/8StaFtai57zWkQv55AfJVccybMeeYnY7RQSMOaq1jWBQtUnG0WFuat7TZ/zKPD5ubNm8zQUg8Et9m5r4x5WN04i4+1TpEp87a6yzNfHemfnDQD9q8d+3rXrZ/j4ujxYpYRBsaN2hgbKCKY8WqT9S3qbvZlGzSnfPv1BE7HpGbJ4M26NWLfJGtc7SwJ9by2QHDD8hKO3vusGdW2kH25DQh5ZxbKWll+u/NZrZI0k6Spko6Nr3a/ZJekpeQmirpAed9S7xhZhVmNjrdDtClXCQBwnbwaYn3/Nnhqw3ml5z1kArZZwU9s2TjEp35xJlKumTW2x41cJRmnTlL0Ug0622HXvpr/oL9L9BBIw4KNhZkXctFjgv2vyDrbadcSnfOvzPr7aJ7WmYBzneNyUbdu+Be3bvg3h4/tqMEd0cunXippu07rTfhhdr2JoTIpWxsOydlGXz+TXP3e3frlndu6dVjBxUN0qamTZ2us//w/XXvSfeqKFrUq220uOeke3TIqEN6/Li1dWs1+Y+TNahoUJ+2j+wLrBuDmVVJOkjS3yWNzEgyrZI3pE/yklXLMx5WnV7Wvq3pZjbXzOZKGuZXzAgPfvhvXxhO/gpRIZysoHNTH5/qSzJKklbVrtK97/f8x1B/wvd+4eK9LVyF/t52JxklSTfMvcHnSPJcgXwM/Po8+72fNCYbe52MktRlMkqS3l37ru56965eb6Ov59H8/slfgSSkzKxM0qOSLnbOtfkEp3tD9egT55yb6Zyb6JybKGld9iJF2OWkhlTIjqJhizcXcnWQ8vO1530tfNkq6FloGPYDAOFUKBfSwj5xTW1zrW9tZ8rGrKmFdL5bMJ//Pj6PnCekzCwuLxn1oHPu/9KLV5vZ6PT9oyWtSS9fIWlMxsMr08uATuXiy4ofQYWlUA4KKFwl0ZKgQwByzskV1A8QtMV7Cyn8Q/Za2/LrAqfPL09dc52/G0grjZX2+rHZ+t2VL+f7JSUlqqmpyZt4ess5p5qaGpWU9P4cNdez7Jmk30la5Jy7KeOuJyVNk3R9+t8nMpZfZGYPyytmvpH6Ucg7ITuXako1BR1Cv+V3T6ywJ0mbkk1aWLNwm2FvKZdqUyi/fTHjqEXVnGpW1KKtyyMWaVO8uDnVrMZEow4eefA2RazDIhbJ9Twk4RD2zz26IWTHWXQP+y4K5jOQg6fhZ+KiLpGbhNSA2IA+txH00LtEKqFFNYu2+T3V/ly1vahFVRIrUV1znfe5L5Y2rNug6lXVbZKisUgsdJNDlZSUqLKystePz/XZ7VGSvi3pPTObl152ubxE1CNmdp6kZZLOSt/3jKRTJC2WVCfp3NyGi7Bilr3ta0g0BB1CXgr7LHtBH6CzYcL/TsjJdt6b9l5OtpNtJKQ61rJvFcI+gG2F9ViLbmK37dfyoah5Zhx9FdYaUv8/e/cdJlV59g/8e8/szBZ6Ly64CyoSxQaKBWyJNbHEGkvEFjSa5htN0BSwxOj7MxpjVIJRUTQYgxpjiVFfRcEGCIpSRKILrNL79pk59++PmV1mly2zM+c5bb6f6+Ji58w5z7ln5tT7PMUXNaQ8ci6Y+NpEzF833+g6/Hqdmi2nR9mbi7ZPPd9sZX4FcK3RoCjQOMre7pxqJ+4nfvsNKT9FQhG3Q/A07sfBxd82mLxyg+kVfhl10ARXP7cNq/Z7H1INCWdaT5REbKghleMPluv3aDoZlY/8VR+MKEONByuTB2+/VjN2YrjTM4afYXwddjNeQ8qn2wt5R5+iPm6H4Enct4KNv2+wMdmY54K2exvrQioY+0ku9yA5j7IXkO8wiFj/n4LJwWOO355mjS8djzfOfaPV4eljiRgi4bZrYcQSMUCS/fOEJdw0vfEgb6mFSDjiuxtnv/2G5E0DuwxEWfcy3HrUrc2mt/bUOz1prqqoS9ShS6RLU/9Y6f1kzVs3DzfOuRFdo12d+SA+xYvNYGKn5sHll2TjvIvmdTg6WGsPQuNWHPWJekTDUURCEQgEItKsf0MA+Mviv2DWill5va37/XM7UdvPif3l9+N/jzEDxjS9bmubTdd4P9F4X9DavvDF9i9w1WtX2RIjr9mDhwkpCjSTJwg/VzXvV9LP7RA8x5HmnQZPon6/mAuCEELoX9IfA7sMtLXcgSXJ8vxy80ZkN96ABJNfEjDFBcU59X3Tkf4l/QGkrkO8/3XYyivnNbuuAY31IeXQoDgDSgbYfg0D7OqjKqff26ZNxSvbHO3CJnsUSE5e4PjhYora58Rv6OcEJmXG1EUOb8bbx07NA46HzkDjfutMNxNe5+Z2YMc1oCO/ncFVGO9cPlWsHdfC2cbIY413MSFFgZbPJ3fqHCe2FSa+gs0vT/uDit99MHG/CjCergDk97EraNcsfh1lz/SDHTuSrnZdpwdtmwsCJqQokFhDijrDiacmphNe3A69weS2xIuo1vHBQ/DxyXZw8dy1Sz4ey7zymXONwyufI1deTqjlS6fm54843+0QHMc+pCjQOMoeZcyBn9MvJ0O3DO4yGGMGjsHvxv3OSPmjHhtlpNxGphJG3G4yxK8pkJiIDS5eRyU1JVzz+Otw8zxnZ8Lb6EOpANzT2LEetx9QjOo7Ct2j3TH1hKmuxhEkrCFFgeTkwcrtAyPlzpGmdPl8pZknTDct4jbUOiYsgo3bfbDxGiq/+5AKymf2/XkoFb6xJnupcnP5nuz6jm3Z5njYshUTUhRsJjsA9PvJh5pxpA+pADQNNMnPsQMA1MxvzBu29hnvjJWIjOB1FDVy+zzn9VH2AGdqSHm5yV6uZdkVA49b9mNCigKJNybkOabPXwHY5P3eebHf4/c7fvfBxP2Kgq6p9ojfH8pkwQs39344vphO2Pmhn1Mv7R9+2Gb8hAkpCrQgtLcmZwRllD0/U1XXn5Lmwvg2xENOq3gsDj4/Hxeobdx3m/NCcsYtQbk+MlpDyuD2YXqUvab12NGHlMvbCo9b9mNCiihHbh8YKXdBakrn5wtav9eEMJVQ8/N34gjDfV+Qu/x8TKOO8fjG7wBw/zvw+ih7pr8f403fG/vtd/F4bmvn9dxnbcWEFAUaa0hRpvx+0xOUk6Ofkwrs1JzIjKAc36g5HtOay8fvwwuf2dbji8FDlZ9r8tvZcb/b14le2GaDhgkpCiQ7RnPo7LrIvzjKnkcE4CvijbPz2Kl58PG3DSa/N9O2i5PXrJ7l883A9G9nvCmd4SZ7dpRr2yh7NpTD45a9mJCiQHIkwZDPFw4BFJRR9vzM7032TGNSk/IRt/tg4zE/v7+DoF1L+/W3dOrBTi6/t1cePgVtm/UCJqQo0PxcvZWcE6QEpp9v3vwcO2CwDykmMtvl9+2G2qeqvq89QdSefL6O9EKSwZbaOz7vQ8o0vzSLzDwEDwQRIExIUSDxQEFeZHK7DMI27/fmG0yMuMOp0YHIPUE4vlHr+Nvuks/nkKAcv71cwyijco31aZ57H1K2NdnL4/3Mq5iQokBjMyzKRJBG2fM7P9+cGO/UnNXE2+XnbYfaxmNncPG3TcrnPqS88plzjcPvNaRM11Tzwgh3vF/zLiakKJAcSTB45CRK9nCkA3x2nt4uP8cOGGyyx0RLu/y+3VDHeCMRYPxpm+TzsSwo5zm/Hqv81IeUFwRle/UKJqQo2Lxz7CIPC0KiyK8XQS35+SRvvIYUD2jt8vO2Q23jw5/g4m9LgTmvOfQxjH1fjS32TI/mZ0P8uZ7rvV4bLh8xIUWBFIQEAzmLzTvd5/c+pKBmfmMmWtrHY3GwcfTNYONva0//On7n9nZg13dv7HMY/nqMN9mzow+pHH+joHWsHiRMSFGgcZQ9yogTP2H+XmdmLAgX4zweuMChJ7vkHu5XwRSEY74d8rkPqUZuHr/tOL44tS0b69TcR7X43T7X5/N+aoqjCSkReURENojIp2nTpojIVyLyUerfqWnv3SgiK0XkMxE5yclYyeccOFbxQipYgtKHlJ83S7/XhOAxwV1+3naI8pXbN5dekM/HrqDd3Jvanp3q28l4k71cfm+bNhUvNBuk5pyuITUdwMmtTL9HVQ9K/XsZAETkGwC+B2C/1DIPiEjYsUgpEBxJMvBiyveCcGIJwmfwO1MJKR5j2sdEYLD5vikvtSloyQjKX05ty6bOd0412ctFrjHa9dl4zWE/RxNSqvo2gC0Zzn4GgKdUtV5VvwSwEsBhxoKjQHGiPT4vpILF9AmGJ7CO+f3G03T8POZ0wL+bDlFe8nutWLvkc5M9r1wbeSWOtji1nxhLSAVsG+dxy15e6UPqRyKyONWkr1dq2h4A1qTNU5mathsRmSgiC0RkAYC+hmMlH3DyQMGDkv859huyKWm7/H5zYip+P38nTgjKBS61zs/HNOoYj2+75PO27uZ2YMeDJMf6kDJcQ8rLvBKjV+IIEi8kpB4EMBzAQQDWAvhDZwtQ1WmqOkZVxwDYZHN85GNGa0jxgBQoxmtIGb5p9nPNoiaGRqlzEm+unGe6qQG5j78tBVk+b99Bu5Y2XcPIGIcGB7Gl/6YsYwxSx+pB43pCSlXXq2pCVS0AD2FXs7yvAAxJm7U0NY2oQ04eKPL5QiIonNpeuK20z/c1pEw32QvYhbvd/LztUNu43Qcbb+x2ycdt3anOtDONI+vlnfrtDK3GqT6kcvmevFIb2itxBEmB2wGIyCBVXZt6+V0AjSPw/QvA30TkbgCDAewNYJ4LIZKP8aBBGTO8qeTjhWa+MZZQ4/1au/y+b9XGa7F402Ic9uRhzc5ZdYk6AEBxQXGr57L6RD0UimgoChFBSEKojdeiKFwEEUHciiMkIVyx/xX44UE/dOzz2M3vfcu98N8XcMt7t+w2XaEIya7nwi1vzGvjtc3mj4ai6Brtitp4bbN5a+O1uO2o23DGXmeY+gjG8BotyQ/967xd+TZ+8fYvYKnVNC19Gy0KFzWbX0SQsBJosBrQq7AXdjTsQCQUaTquFYWLYKmFBqvBmQ/QDjvP234fZc/UauzcxnP9LrwQAzXnaEJKRGYCOBZAXxGpBDAZwLEichCSt4MVAK4CAFVdIiJPA1gKIA7gWlVNOBkv+ZejfUj5+EKZkgTiyE2tE9uln2/O/Rw7kLrIMfgTe/lmxQv8eixeuGEhgN0TEI3amt6o5Q1d4w1fowc+fgBn7nUmBnUdlEOU7vLrxb+q4qa5N9lSVoPVgC11rY8L9Ot3fu3LhBQlOTEQT66u/b9r232/5XEn3db6rQCARGLXbVzL+ddXr88hOvdxlL322TnKnpsxkBmOJqRU9YJWJj/czvy/A/A7cxERZY83h9QZXr7Q9Aq/14QAzFzw8CKqfTwWk1d1lEzMdzwvJvEYD+zbZ1+3Q7CFX/uQcqovRlv6kPLA/uL3a1Wvcb3JHpEJXjhYkb84UkOKJ7B2OdWH1KjHRhkr+5FPH8F1o68zVr4Jm2o34binj3M7jJwlWIm6TSc+c6LbIXjaAx89gAc+esDtMDzJUgsHPn6g22GQixoS5pvVdYt0M76OtvjhnmFHww4AwOgnRhtdT9yKGynXCzWk7CqHD8Hs53qn5kQmcZQ9yoQjiSKn+rv0+YnSDxeGbjF1zHm78m0j5TotYTEhRWS3nQ073Q4h8Lzeh1RjMsSkrtGuxtfhBFPXMB9t+MhIuS3VW/VGyrVlG3doJEByHhNSFEwOHqt4YAwGJy4ETSZbgpLI8fv+dNjAwzqeqZNM/7bb6rcZLd8p0XDU7RCIAmd7/Xa3QyCXObENtOwU3Wk515ox/NTRqf2wOFzsyHpy4fb1Lisk2I8JKQo0k0kGrz7Jos5jZ+PeEIR9alAXcx1Hm9qGttUFIyEVCUXcDiErEw+Y6HYIRG0KSsLaD7x6nVCfMFNrplH3aHd3H0bZuWpDH8OphFQkbPY8mss27pUme4D7SbGgYR9SFEiNB4oNNRt2G7mjo35qEppANByFqjYNbxuSEBTa7IZ5Q82GZusif1Nop7cVIFkro7HNfeP20rJqskKxuXYzehT2sDvs3Wyo2YDCcGHG84ckhGg4ippYDUQEAkE4FG7W/Knx81hqNRsNSCBIaAJhCbdbfuPycSvetHxIQk3fV2NZdYk63+9PA7oMsL3Mxu+v5fGste0zfVrj3+FQGJZaTb9f+u+rUHy44UPbY3aDXxNSPz74x/jxwT82UvaEf09oGsWP3PPBhR+gJFJipOzGPvGyGaUsGo7CUgsxKwaBtNqsZsXWFfYESm1q/N4bf8P07z8kyboD4VDyPJuwErudO9P/bjkNaPs6VUSaygeS5/j0dTeWtbZqLQDg/m/ej6NLj87x03qPpRY21mzE+ur1zb6/xnNn476Rfg2jUIQlDBFBwkoYHyWwrRE27Tawy0Aj5aZfx6yrXtdsmwxJCAlNJK8XU9eVBaGCZt9347LJwnKPYUPNhmbXtPWJ+qbr0oJQAYoLihG34k33fiLStG9srNmIYT2GZRcEtYoJKQq0Py36E/606E9uh0EeN2PpDADAt2Z9y+VIsvfiFy8CAL793LddjiQ3jy55FP8z5n/cDiNr4/YYZ6zs2z+4Hbd/cLux8v2uW9S9TnG96ujSowORkBreY7iRcp1KgJtKRqXz6/mrIMRbkYrtFQCAC1++0N1AOvDe1+8FMiG1qXYT5nw1x5Z96J+f/xNHDj7ShqiaO3OvM/HcyudsL7clUw92Go+105dMx/Ql03Mqq6qhKqfln1z2JJ5c9mROZby++vWclqfmeBagQHKy6i/7LfG/mniN2yHkrHJnpdsheN78i+ZjXfW63aprW2o1e0rcsuZRgRRAoYhbcUTCESSsRPKpqCaa5rPUQrdoN/Qv6W973LFEzPYyW/P8mc83+9ytPV1PnxaSEEoKSlAbr4VCURAqQCwRSz5RTM0XDUURklCrTT5aPtFv7XuHoKn2BrCrZl16HALB4K6DeSxuxeX7X45Tyk/Z7ftP3wda1rBIn16XqEM0HEVRuAj1iXqUFJSgJl6TXF5Ty6RqVrYso6MafI21DhKaQIEUNNX6sGA1iysSimBw18G2fSfpRATvXPAONtduboovLdhmT+JbfhZLLRSGC5uOC+kjkaV/TpPNeJ1097F3Y6+eezW9FgjCEkZc463WyglLuFnt4YJQAaLhKBoSDU21DsISRmm3Uuc/jMdsqt3kdggZMd10LwhOKDvBSLlTjpyCiQdMRMxqfj3Q0XnaUqupJmRhuBC18dqmedLPu43nUVPsTP5nWzvJ7zXwg4wJKQokJ/uiiYZ4E0Tuq0vUuR2CLXoW9jRWdlFBEcp6lBkr3xSnRrliFfTgETF7kxEE3aPd0T3a3e0wPG/fXvtiSPchbocRSH5J9Awosb9JetCMHjDaSLkhCTF5m9Il0sXtEMhm7NScAqnBauh4Jpv4fVQwCobiAu+PjJKJW4+61e0QPCehiY5nIiIyqEeR+T4Q85VfbrBN9S8UJGw63rr0Wui5yva+y6uDBhATUhRQZd3LcOjAQ42v53fjfmd8HWTev8/6tyPref7M542V/cKZLxgr2ykje4/EMaXHuB2G5xy1x1HG+tBp9Kfj2NcekR/9+fg/G1/HLw/9JWuRGTTpsEluh9Ch/iX9cdrw09wOw4jrx1xvSzkX7HuBbwfXMK0kUoLv7vXdnMvJ5bcqLijG6cNPzzkGAHj0pEdtKYeSJAjDbKcTkQWqOiaDWYP1wYmIiIiIiIiI3JdRdTbWkCIiIiLyqLKyMrz+Okf0ISIiouAJYg2pV1T1ZLfjcJKI/FlVf+R2HETUedx/iZwnIgpgb1VdmTZtCoC9VPXiTpZldB8WkQoAV6qqL7JSInIpkvGOczsWokzwPEzkX9x//S9wNaTyLRmVcr7bARBR1rj/Evkb92Eif+M+TORf3H99LnAJqTy1ze0AiChr3H+JPEZEjhWRShH5uYhsEJG1InJZ2vuFInKXiKwG0EtEpopIcYtlf5G27JkicqqIrBCRLSJyU1pZU0Rkloj8XUR2ishCETmwjbgKReSPIvJ16t8fRaQw9d6nInJa2rwREdkkIgeLSJmIqIhcJiJrRGSriFwtIoeKyGIR2SYif26xrstFZFlq3v+IyJ5p72lq+c9Ty94vSSMBTAVwhIhUiQiPb+QH3E6J/Iv7r88xIRUM290OgIiyxv2XyJsGAugBYA8AVwC4X0R6pd67A8A+AA4CsDg1z29bLFuUNv0hABcDGA1gPIDfiEh52vxnAPgHgN4A/gbgnyLS2nBNvwJweGq9BwI4DMCvU+89nlpHo1MBrFXVRWnTxgLYG8knyn9MlfctAPsBOE9EjgEAETkDwE0AzgLQD8AcADNbxPIdAIcCOADAeQBOUtVlAK4G8J6qdlXVnq18BiKv4XmYyL+4//ocE1LBMM3tAIgoa9x/ibwpBuAWVY2p6ssAqgCMEBEBMBHAdaq6BckaQbcD+F6LZX+nqjEATwHoC+BeVd2pqksALEUyodToQ1WdlZr/biSTWYe3EtNFqZg2qOpGADcD+H7qvScAnCoi3VOvvw9gRovlb1XVOlV9FUA1gJmpsr5CMul0cGq+qwH8XlWXqWo89fkOSq8lBeAOVd2mqqsBvIlkkozIj3geJvIv7r8+V+B2AJQ7VeWOSORT3H+JXJEA0LIGUgTJRFKjzalkTKMaAF2RrDFUAuDDZG4KQHJo43CLZROpv2tT/69Pe782VVajNY1/qKolIpUABrcS92AAq9Jer2qcT1W/FpF3AJwtIs8BOAXAT1ss3zKGtmLaE8C9IvKHtPcFyRpfjetfl/ZeDZp/HiLf4HmYyL+4//ofE1JERESUb1YDKAOwLG1aOYAVGSy7CcnkzX6pmkV2GNL4h4iEAJQC+LqV+b5GMlm0JPV6aIv5HgNwJZLXd+/lEN8aJGt4PZnFssEavpmIiIiMYZM9IiIiyjd/B/BrESkVkZCIfAvAaQBmdbSgqlpI9gl1j4j0BwAR2UNETsohntEicpaIFAD4GYB6AO+3Mt/MVNz9RKQvkv1TPZH2/j8BHIJkzajHc4hnKoAbRWQ/ABCRHiJybobLrgdQKiLRHNZPREREeYAJKSIiIso3twB4F8BcAFsB/C+Ai1T10wyX/yWAlQDeF5EdAF4HMCKHeJ5HsqPxrUj2/XRWqj+plm4DsADJjtQ/AbAwNQ0AoKq1AJ5BsrbXs9kGo6rPAbgTwFOpz/cpkk0AM/EGkjW41onIpmxjICIiouATVdasJiIiInKDiEwBsJeqXtzRvBmW91sA+9hVHhEREZEp7EOKiIiIKABEpDeAK7Br5D0iIiIiz2KTPSIiIiKfE5EfINkZ+b9V9W234yEiIiLqCJvsERERERERERGRo1hDioiIiIiIiIiIHBW4PqRE5BVVPTmDWVk1jIiIiIiIiIjIXpLJTEGsIdXX7QCIiIiIiIiIiKhtgashRUSUj+qWLcOX3z3L6Dr2/WQxJBIxug4iIsrc9hdewNc3/MLoOroedxyGPPiA0XUQuaXi4otRu+BDW8rad9lSiGRUKYSIUoJYQ4qIKO+YTkYBQENFhfF1EBFR5kwnowCg6s03ja+DyC12JaMAwKqutq0sonzBhBQREWVELcvtEIiIyAXxzZvdDoGIiAKITfaIiCgziYTbERARkQs0FnM7BCLvi8fdjsCXYrEYKisrUVdX53YolIWioiKUlpYikmW3HkxIERFRRjTBGlJERHlJOTg1UUdYkzw7lZWV6NatG8rKytgHl8+oKjZv3ozKykqUl5dnVQab7BERBYETnY0rL7SIiPISb7SJOsaa5Fmpq6tDnz59mIzyIRFBnz59cqrdxoQUEVEASMj84Vx5oUVElJdYQYqoY6whlT0mo/wr19+OCSkioiBw4kTOCy0iojzFjBRRh/jgjqjTmJAiIgoCBxJSrCFFRJSnWEWKqEOsIeVfFRUV2H///ZtNmzJlCu666y7HY5k9eza+853vOL7e9syePRvvvvuukbKZkCIiCgIHElJWTY3xdRARkQfxgQRRx7ifUECZTEhxlD0iogAQmG9QESoqMrwGIiIiIn9iDancrbv9dtQvW25rmYUj98XAm27Kevljjz0WY8eOxZtvvolt27bh4Ycfxvjx45FIJDBp0iTMnj0b9fX1uPbaa3HVVVdh9uzZmDx5Mnr27IlPPvkE5513HkaNGoV7770XtbW1+Oc//4nhw4fj0ksvRVFRERYsWIAdO3bg7rvv3q1m1JYtW3D55Zfjiy++QElJCaZNm4b9998fI0aMwLvvvot+/frBsizss88+eO+993DDDTeguLgYixYtwoYNG/DII4/g8ccfx3vvvYexY8di+vTpAIBXX30VkydPRn19PYYPH45HH30UXbt2RVlZGSZMmIAXXngBsVgM//jHP1BUVISpU6ciHA7jiSeewH333Yfx48fn8pM0wxpSRERB4ECn5kRElJ+UTfaIOsaEVGDF43HMmzcPf/zjH3HzzTcDAB5++GH06NED8+fPx/z58/HQQw/hyy+/BAB8/PHHmDp1KpYtW4YZM2ZgxYoVmDdvHq688krcd999TeVWVFRg3rx5eOmll3D11VfvNlrd5MmTcfDBB2Px4sW4/fbbcckllyAUCuHiiy/Gk08+CQB4/fXXceCBB6Jfv34AgK1bt+K9997DPffcg9NPPx3XXXcdlixZgk8++QQfffQRNm3ahNtuuw2vv/46Fi5ciDFjxuDuu+9uWmffvn2xcOFC/PCHP8Rdd92FsrIyXH311bjuuuvw0Ucf2ZqMAlhDiogoGDg6CRERmcJ8FFGH2Ndm7nKpyZSLtkaKa5x+1llnAQBGjx6NiooKAMlaRosXL8asWbMAANu3b8fnn3+OaDSKQw89FIMGDQIADB8+HCeeeCIAYNSoUXjzzTebyj/vvPMQCoWw9957Y9iwYVi+vHntsLlz5+KZZ54BABx//PHYvHkzduzYgcsvvxxnnHEGfvazn+GRwNLgoAAAIABJREFURx7BZZdd1rTMaaedBhHBqFGjMGDAAIwaNQoAsN9++6GiogKVlZVYunQpjjrqKABAQ0MDjjjiiKbl0z/rs88+29mvstOYkCIiCgInElJ8Qk5ElJ+UNT+IOqItareQf/Tp0wdbt25tNm3Lli0oLy8HABQWFgIAwuEw4vE4gGTN0fvuuw8nnXRSs+Vmz57dND8AhEKhptehUKhpeWD3RFhbibGWhgwZggEDBuCNN97AvHnzmmpLpceavt70dYfDYZxwwgmYOXNmq2W39llNYhsPIqIgYEKKiIhM4fGfqGPhsNsRUJa6du2KQYMG4Y033gCQTEa98sorGDduXJvLnHTSSXjwwQcRi8UAACtWrEB1dXWn1vuPf/wDlmXhv//9L7744guMGDGi2fvjx49vSjbNnj0bffv2Rffu3QEAV155JS6++GKce+65CHdi2zv88MPxzjvvYOXKlQCA6upqrFixot1lunXrhp07d3bmo2WMCSkiogBggz0iIjKGCSmijnE38bXHH38ct956Kw466CAcf/zxmDx5MoYPH97m/FdeeSW+8Y1v4JBDDsH++++Pq666qtM1ioYOHYrDDjsMp5xyCqZOnYqiFgMITZkyBR9++CEOOOAATJo0CY899ljTe6effjqqqqqaNdfLRL9+/TB9+nRccMEFOOCAA3DEEUfs1lSwpdNOOw3PPfccDjroIMyZM6dT6+uIBK2TQhFZoKpjMpg1WB+ciPLaisOPQGLbNqPrGPrIw+hy5JFG10FERJlbtu9IR9ZT/q/nUbTPPo6si8hJdu5DZf94GsWp/nooc8uWLcPIkc4cy7zk0ksvxXe+8x2cc845WS2/YMECXHfddbYniLLRxm+Y0fNy9iFFRBQE7NSciIhM4WNcoo4FrKIHedcdd9yBBx98sFnfUX7FhBQRURAwIUVERKZYHD2MqEMWO/+nzE2fPj3rZSdNmoRJkybZF4yL2IcUEVEQOJCQCloTbyIiyowmeKNN1BFeJ2WP351/5frbMSFFRBQEISdG2TO/CiIi8iBlQoqoQ7xOykpRURE2b97MpJQPqSo2b968W2fsncEme0REQRDr3KgeWeGFAhFRfkqwyR5Rh5i4zUppaSkqKyuxceNGt0OhLBQVFaG0tDTr5ZmQIiIKACkuBgyPssdHf0RE+UktHv+9qnrePKy+ZILx9ey7+GNINGp8Pb7GB3dZiUQiKC8vb3cetSx8NuZQaE1NTusasfBDhEpKciqD7MWEFBERZYYXWkREeWnVhRcaKbfLuHEY+teHjJSdL5xIRgHAhrvvwYBJv3RkXX6l7NTcmJr33885GQUAn40eg5HLltoQEdnFE31IicgjIrJBRD5Nm9ZbRF4Tkc9T//dyM0YiIk9zIlnEhBQREdmoeu5ct0OgDFnVVW6H4H28TDImsdOm7Y/Xsp7jlRpS0wH8GcDjadMmAfg/Vb1DRCalXjMtT0TUCieqH7OzSSIiIntZ1dVYc+2PUL9yJazqaiAeh0QisGpq0OXIIxDftg3R0iHY+eqrCPXoAVhW8qbagdF100lh9p0W5w32IWVMYsd2t0MgQzxRQ0pV3wawpcXkMwA8lvr7MQBnOhoUEZGPdPvWt8yvhH2IEBF5Stdjj3U7BMrRmqt/iJr330di0yZobS00FoOVappU/e57qF+6DDtffRUAYG3fDmvnTlhVVcn/d+50LlA+lOoYvyNjQjmM4kbe5pUaUq0ZoKprU3+vAzCgrRlFZCKAiamXfU0HRkTBkti2DevvuBPxLZubP3GMxVEwcCBCRYXNawc1/m0p1EoA8QQSW7dCU0/GQiVdEO7Vs2k+q6oa0aFD0OeKKxDq0sXMhxABCgow8tNPbC+69tMlqDjnHLAuOhGRt4RKihEtK8PwV/5tpPxdx38ypf6LL9wOISNqBXOkxVCXLuh5zjkYcOOkrMuo+fBDrLroYiak2rH9pZew48WXEO7VCxKNAApIOISi/Ueh/r8rUfPe+yg54nDULVmK6NChyYVCAgmFoKrY+epr7n4AMsbLCakmqqoi0uYerqrTAEwDABFZ4FhgRBQInx9zLLS+3vh6Nk17yEjCyLjGHB0vtIiIPEUNN90KdeFoVKZpPO52CBnRWMztELwrtQ9yNMrWxbduxdc/v76Nd2c2/VW3NNnZeM377zsQFXmFJ5rstWG9iAwCgNT/G1yOh4gCyolkFADAJxedLUnjzQ4TUkRE3qIwmpCSSNRY2ZQU6d/P7RAyonUOXSv5Ea+T2qV1dW6H0KTbCQ50cUGd4uUaUv8CMAHAHan/n3c3HCKiPNX45I8XWoFUs3AhVl14kdF19DjjdAy+806j6yDKWyY7tw5oMy1PKYi4HUFGdrz4IgqHD0u+EAFUoaoQkWbXB5K2PcbWrUdBnz6ACKQgDABQy9p1XVFbh+7f+TaKRoxw7oMYsOvBHTs1b1U47HYETQr33tvtEKgFTySkRGQmgGMB9BWRSgCTkUxEPS0iVwBYBeA89yIkCpbKn/y0qYNME4b/5xVE99zTWPnkMD75CzTTySgA2P78v5iQIjLB8HFZGxqMlu8HW5/6O9ZNmeJ2GJ6w8d4/2V7m5ocewsjly2wv11G8TmqXeCghxYer3uOJhJSqXtDGW990NBCiPFCzcKHRZBQA/Pekk/1/cUG7SKp1t+G+EVZfcSWq33nH6DpGfLjAXMfyRHlo3e23Y+vjM4yuo8u4cRj614eMrsO3VHf182eCydpXPsFkFHUolLxOUos1pDwvkdtvtPFPf8KmBx60KZjdlf3jaRSPGmWsfC/ych9SRGRA3VImiqiTmu5HzCakTCejAOCz0WOMr4Mon5hORgFA9dy5xtfhW6kmU6ZEhwwxVjZRo9j69e6t3JYaM401pGwoKog8lKjTRG79uZpMRgFAxbn51yjMEzWkiEzYcPc92DZr1q4TjQg0HofGYggVFe1+AhIBRJDYsgUAEO7Vq6mNvBQVJTvkSysLloUBv7oJPU4/3cFPlTs2pQsog1WQ2al5sIW6doVVVeV2GHlLLQuJbduSf8dikIICaDyRvL+xLEg02my6hEOpWjGSHPUqFGr+HgBNWE39tYRKShAqLnbr45EjDHZqHo0aq/G89uabsfOV/xgpm/yl6u230evcc5v6pMpVejkZlZnrOkNNwxG3G0u+8lIzuYLevd0OgVpgQooCqfajj7B52rQ230/U1nZYRmLr1g7n+foXv/RdQipUwhuTwDJ1wcOEVLDl+YWy25aPOgBImO04mk2o27biqHFIbN5srPzup56CPe6+21j5fq6Ske836XYztZ/Ht27F50ccaaTsRut+81us+81vja6jPfUrPstp+cZtufKaa+0Ip02+PZbbeP1Y9vTfUXzAAZ1ezqqpwWeHjOa1rAcxIUXtqlu6FF+edbbRdUhREUYsmA8psG9zTOzYYVtZQWPn90x5InWh9dX//Bxf/c/PXQ4md8v2HWms7H3mz0O4Wzdj5Rvh0MWZye8dAPZdugQSMtMTwebp07HhDv92yh5btw6RgQNtL9cKQIfXJpNRALDj5X+j5znnNNW4btIyGZOq9dY0TygMqJXsk0YBCQkSO6sQ7ta12XxVs99CqGcPo5/BFKu+HomtW40fGyg34Z493Q7BuOp338tp+fqVK22KJKDsvM7INpHNEaM9i3emPpbYsQPbn3sODatWJ4fTTHvCatXVJZulNSoIJ6v8p3bGhooKJLbvQHTPPRHu2QOQEKQgnGwmkNpRw316Y9N9fzb+ObSuDmuuuQZD26nR1Gkh74zm4Dl8IkmdZegmvy0Dfvub5jdn8TgQ2TUstrS4qLCqqqF1dQh1KYGkmiZJuCB5zEs1YUIojHWTJxuPfc3Eq1A282/G12OnoDTXW/6N/dwOwbMSO3YYSUhVzzXf71sjPyctVl9+hdHyExs3GS3flO3PPOt2CJQBETFWM6f+yy/xxSmnGinbSSVjD3c7BG/zQhIodS278Q93Y/szzyYHuCkIJxP+0QjC3bqj4csv0bB6NcK9e6OgTx9IUeGuSqgtPsOA3/6m6W9JNaGXSGT3hFfqdWxNJcI9uiPUpSsQDkFCISR27ESoW1eICKz6esTWVKLPlWbPF17EhJSP1S7+BOt/f0dOZdQtWWJTNLmpfnuOreVJiEkXIrtIJOrYuooPPhi9L7zQSNlOJKRqFy0yvg67RYcNQ8MXX7gdBhlkasjtgn79jJRLRPkhKP3bFfQKfi2ynNjZqXm2ZaU9kG+oqGh31sSmTUhsaj/Rb+paNR8xIeVj4R7+rKLtCIdrdBAFmUQjHc9kEw1AEyC/6X7KKdh0//0oPuigXRdsIrDqaiEFkV3NfNOe+kkkgtjatYitWYMu48fD2rkTalmIr12LULduyWaLoRBi69YhvnatC5+KmjF8Thz8h7vQ7dhjm03rsFmEZSW3qXDapWgivluzco6MGVyRoUMRW73a7TDIRYHpRiIon8MQOytIaTy7UfLYZ513ce/xscjAAW6HYKuWN6KaSLT7VFctq1l/IZpqsijhsKM3tRw9gzpjt20zFEo1LWt9W1cg2Rw3HG4aR0ktKzmyVvp8sZixKtEScS4hZdXXObYuam7PmX8zcizzc1OroDB90xcqKUk2fyDqhMigQUxI5bmgJKRM9V8YGGpfDSnNdhAQ3qt5VjCOAnkqaFXllx9woNshZMeykn14EWXAj9u5k032xGD/bz2++11sf+45Y+UDQPHo0UbLN8lUYr3XJd/H1sdnGCmbMuPnmyUpLoZmMDJuPiv9831uh5CVcPfubodAbitw7oFXEJh6CF793ntYfdnltpdrRLZN9nx8Hgw6JqSIcmVzQipRVYX65ct31XZJdeysiWSH8209TdJEoqlT+lBJMaSwCImtW6DxeLNRv2o/XmxbrG2J7jXc+DrIORJx8FRh41O0lgb//nYM/v3txspffshoFI8aZax8vxp4000YeNNNxsrf+cYbxofa9j1jtRzNd1QbKilBwucJqbJnZqF4P3a631KoJBj9B1H2HL2+CILGAV9s5ptkFJC818mCnx/MBB2PAkQ5UlXYeWpYMeZQG0tr25C/TEXXY46xvdxVl10Grau3vVxyj5NV6v08HK+0HNadHBHu1cvtEGxhYhSrrU89hXVTbjbfrJxNIdoXi7kdgSdJtNDtEDrF1EhzdZ+twJdnnGGkbK9zskuAQEh12ZDXrCyb7JFnMSFFlKts2zIHlITCsHiyCBYnL34S5mpImWZVV2PLY49hy2OPGSl/xOKPEYoaaD7p8ySak01KyXlFI0ag+t133Q4jJ5E993Q7BE9i7ZgkKcjfbh9Ya6WT7Bytzqey7kOKPItnAqIc7Xj5ZYRKSppeWzU1sOrqUNCnLwDd7WbPqqlBfOMmhHv2TData7zBVEV88xbH4q565x0jNaQQDgGWv25wo3vuiYZVq9wOw7Mc7bSfF1tt+syH/Y85IbLHYLdDIIN6XXyR8YRU/0m/RJ9LLzW6DmpFQDq0zlWomE0XKTN2t8rwo+jQoW6HQDbjmYAoR2t/9Wu3Q8hK7cJFRsoVCfmu1ljJYYehYdUqDJ0+PTkhlYDReGr0O0k9wdutJolCY3GEigqhjTV7RABBsmuV1Pwai2HND35g+mMEgjrQJw0FS0GvXth32dLW3+ygvw2NxTpuMqKK5d9g/z9u6Xb88Rix+ONWf6eMfr8OltFYzEzNQ+qQhHkbAgCRwYPRe8KEnGrXls2aZWNE5Fk+r9FcuM8+GPCrX+36HFYCCBdA6+sg0UJorCH5UL9Xr119RaWdwwv69UPhsHIXIieTeCYgylNqqFmdxhpQt3Qptjw+I1lbKpEACgqS/UqFJFkjLGEBIUnWpGo80QgQW70a4T59ESqMAiKwqqsR6pYahSd1Y7n+ttuMxA0AXQ4fa6zsUPfusHbsMFZ+YPisdh15Q5u1+Dqo3SeZJCLYP1LbHLo5aithlNHv18Ey2ZRBZLcBN07CgBsnuR0GeZ3Pa5GXHHoouow9zO0wyGOYkPK5IQ9Nw9fX3wBNO0BpLJZ84tfYjKyVC0arqgrhHj2SbycSgAgkFErWTbCs5KhulgWtqXHgU5ArDN34V7/7HgBg/e3mRjPzI/aTkJnE1q1uh0BEneRos14KjhC3G6LOUJ8/tNOGBrdDIA9iQsrnuo4fj30+eN9Y+VtnzsS6m28xVj65yGfN6vwusW2b2yH4Qnp/bEREFFyhQn+NskfkOvV3DSlTrTPI3/jIntrV7cQT3Q6BDLFY+81RBYMHuR2CL2h9vdshEO2m9IEHjJYfStVYJsonPc85x+0QMtbz/PPdDiGwBtx0Y7vvh/v0QZdjjjYaw/DXX8u5jIE335zdgp3pB8/nTfb6//znbodAHsQaUtSugj59MHL5MqPrqJ43D6svmWB0HbQ7U8Omhnv0QGL7diNl+1nZjBlY+c1vuR2G51l1dW6HQLSbbscfZ/xcaEJjgvfz8WZv5tTnHe2SOyKDB/tyvyJ79b7kEvS+5BK3w8hZr/PPQ6/zzzNS9qapU7Hxj/dixdjDjZTfaNDvbkPPs882ug6ilpiQItd1OewwIxcky/YdaXuZQaLxuJmCo50b8ShfRPbYgxfeGfBzDam+P/4RNt33Z7fDIGoS37jRmRX5vF8TIiIva6hY5cyKQmFn1kOUhgkponxlKCEVikTBFuKUrZ7n+qcJR0v9rr0W/a691kjZTLBTNgoGOtRUmP2CEBEZI51p1pfLegqYkCLneT4hJSIVAHYCSACIq+oYdyMiCgZjNaQ42lIg7bNgPhq+/LL5xNSInGhrBEERaCwOiRRAooXQ+rTmeBLa1TlnapsJFRUhOny4gej9r3DkSNQvYw076hynbmI0zoQUEZEpEo06syKOCE0u8HxCKuU4Vd3kdhBErSk+5JDkTXlKbP06aEMM0SFDWk3OhIqLkzf2BQWQSAThnj0hItBEAvFNmxBbs8aRuAff9f+MlKuxmJFyyV3hrl1RPGqU22HkrWHPPet2CORDUuDMZZ4mDD3gICIiWPXO9K8pTEiRC/ySkCLyrLK/Pel2CJ7S68ILsfGPf3Q7DCKivGesJmxLPh/5iYjIy6J77OHMigLeh9Sg392Gtb/6dc7lDPnrX22Ihhr5ISGlAF4VEQXwF1Wd1nIGEZkIYGLqZV8ngyOi5vpcNRHdTjwBVnUNkrsvAAiQiAPhgubTWgoJ0Dj6n4SSNcwaa5+l/q4491yzH4CIKCDCPbo7sh422SMiMod9SNmj59lncxRBD/JDQmqcqn4lIv0BvCYiy1X17fQZUkmqaQAgIgvcCJKIkkQEhcOGGSt/xOKPUffxx7AaGprWBwBWQwNCaW3sWw5DrrEYrOpqhIqLIZEIJBxumk/r6lB88MHGYiYicoNTNzHs1JyIyBzHjuVsskcu8HxCSlW/Sv2/QUSeA3AYgLfbX4qIgioUjaLk0EPdDoOIyPOc6kMqWSOWiIiMcKqGVDjYNaTImzydBhWRLiLSrfFvACcC+NTdqIiIiIh8wKlOzWMNjqyHiCgfOVdDigkpcp7Xa0gNAPBcqklOAYC/qeor7oZERERE5H1OjZhk1dc7sh4ionwkrYzabWQ9YU/XVaGA8nRCSlW/AHCg23EQERER+U20rMyR9RQOG+7IeoiI8lHR/vs7sp6IU6P5EaXxdEKKiIiIiLITGTQIw/79MmKVX6FphFNVaCIBjcWa9zGlmhzNNPV3bP16RIfuCa2vg9bXI9StG2BZyXlEoIkEYCkK+vVF8ahRjn82IqJ8UbTvvhj2wr8QW7c+baomj8nA7p2Rp47nGosjsWUzpLAoOaBPNDmoj8bjzY73almI9OuH6NChjnweonRMSFFg9b70UmyZPt3oOorHjDZaPhERUS4Ky8tRWF7udhhERJSDwr33RuHee7sdBpHtpOXQ6H4nIgtUdUwGswbrgxMRERERERERuS+jzs/YcxkRERGRQyoqKiAiiMfjbodCRERE5KogNtnblOF8zgxX4AAR+bOq/sjtOIio87j/EnmXiFQAGAxgsKpuSpu+CMBBAMoBXN+Zfbi8vLwMwJeRSCSiqs2yUiJSBuBLANWpSdUA5gO4V1Vfy/JjZEVEpgE4BsDeAC5X1elOrp/IKTwPE/kX91//C1wNKVU92e0YXHC+2wEQUda4/xJ525cALmh8ISKjAJSkvZ/xPiwimT4I7KmqXZEcafg1AM+JyKWZrscmHwO4BsBCh9dL5DSeh4n8i/uvzwUuIZWntrkdABFljfsvkbfNAHBJ2usJAB5Pe90gIotEZIeIrBGRKY1viEiZiKiIXCEiqwG80bJwETlbRCpEZLdxvVV1nareC2AKgDtFJJRaZqSIzBaRbSKyREROT00vT01rnO8hEdmQtq4ZIvKz1N+zReRWEXlHRHaKyKsi0jdt3fer6v8BqOv8V0bkKzwPE/kX91+fY0IqGLa7HQARZY37L5G3vQ+geyoJFAbwPQBPpL1fhWTCqieAbwP4oYic2aKMYwCMBHBS+kQRuQzAnQC+paqfthPDswD6AxghIhEALwB4NTXtxwCeFJERqvolgB0ADk4tdzSAKhEZmRbHW2nlXgjgslQ5UQDXt/dFEAUUz8NE/sX91+eYkAqGaW4HQERZ4/5L5H2NtaROALAMwFdp7/1BVT9RVUtVFwOYiWTiJ90UVa1W1dq0aT8DcAOAY1V1ZQfr/zr1f28AhwPoCuAOVW1Q1TcAvIhdzQrfAnCMiAxMvZ6Vel0OoDuSTfEaPaqqK1JxPY1kv1hE+YbnYSL/4v7rc0Hs1DzvqCp3RCKf4v5L5AszALyNZCfmj7d472MReRPA/kjWMioE8I8W86xppcwbANyiqpUZrH+P1P9bABwAYI2qWmnvr0qb5y0ApwOoTMU8G8D3kWx6N6fFcuvS/q5BMtFFlFd4HibyL+6//scaUkRERETtUNVVSHZufiqSzefS/Q3AvwAMUdUeAKZi95F8tZViTwTwaxE5O4MQvgtgA4DPkKwtNaSxn6iUodhVa+stAOMBHJv6ey6Ao7B7cz0iIiIiVzEhRURERNSxKwAcr6rVLaZ3A7BFVetE5DAk+2XKxBIAJwO4v7FT8pZEZICI/AjAZAA3pmo3fYBkbaZfiEhERI4FcBqApwBAVT8HUAvgYgBvqeoOAOsBnI1OJKREJCoiRUgm1yIiUtQiCUZERESUE15YEBEREXVAVf+rqgtaeesaALeIyE4Av0WyL6ZMy/wYwHcAPCQip6S9tU1EqgF8gmStrHNV9ZHUMg1IJqBOAbAJwAMALlHV5WnLvwVgs6quSXstABZmGhuSnabXAjgSyT46apHsJJ2IiIjIFqLaWi1yIiIiIiIiIiIiM1hDioiIiIiIiIiIHMWEFBEREREREREROYoJKSIiIiIiIiIichQTUkRERERERERE5KgCtwOwm4i8oqonZzAre3MnIiIiIiIiIrKXZDJTEGtI9XU7ACIiIiIiIiIialvgakgRERER+cW2DTV48rfvGyt/6H69cdqPDzJWPhEREVG2glhDioiIiMgXTCajAGD1ki1GyyciIiLKFhNSRERERERERETkKDbZIyIiIiIiIiLfisViqKysRF1dnduh5JWioiKUlpYiEolktTwTUkRERERERETkW5WVlejWrRvKysogktEAb5QjVcXmzZtRWVmJ8vLyrMpgkz0iIiIiIiIi8q26ujr06dOHySgHiQj69OmTU600JqSIiIiIiIiIyNeYjHJert85m+wREZEnJGIWdm6pg6o2m64KtHeuExGEwgIrobst26wcC+jerwgFkbBdIRMRERERUZaYkCIiIk+Y+uPZjqzn2qnHO7IeIiIiIsofIoKLLroITzzxBAAgHo9j0KBBGDt2LF588cWMy5k9ezbuuuuu3ZaZPn06brjhBpSWlqKqqgrDhg3D5MmTceSRR9r6OdKdfPLJeP/99zFu3LhOfYZMsckeOSYRs9BQF3c7DCIiIiIiIiJbdenSBZ9++ilqa2sBAK+99hr22GOPTpURj7d/v3z++edj0aJF+PzzzzFp0iScddZZWLZsWdYxd+SGG27AjBkzjJXPGlLUrsrPtuL5exYZX084EsLV9x1rfD1EREREREQUXHOeXoFNa6psLbPvkK4Yf94+Hc536qmn4qWXXsI555yDmTNn4oILLsCcOXMAAPPmzcNPf/pT1NXVobi4GI8++ihGjBiB6dOn49lnn0VVVRUSiQRuvvnmpvLmz5+PiRMnYtasWbut67jjjsPEiRMxbdo03HPPPfjoo49w9dVXo6amBsOHD8cjjzyCWCyGU045BR9++CE+/vhjHHTQQVi1ahWGDh2K4cOH45NPPsE111yD7t27Y8GCBVi3bh3+93//F+eccw4A4Jvf/CZmz55tz5fYCtaQonat/HCDI+tJxCxH1kNERERERERkwve+9z089dRTqKurw+LFizF27Nim9/bdd1/MmTMHixYtwi233IKbbrqp6b2FCxdi1qxZeOutt5qmvfvuu7j66qvx/PPPY/jw4a2u75BDDsHy5csBAJdccgnuvPNOLF68GKNGjcLNN9+M/v37o66uDjt27MCcOXMwZswYzJkzB6tWrUL//v1RUlICAFi7di3mzp2LF198EZMmTTLx1bSKNaSIiIiIiIiIKBAyqclkygEHHICKigrMnDkTp556arP3tm/fjgkTJuDzzz+HiCAWizW9d8IJJ6B3795Nr5ctW4aJEyfi1VdfxeDBg9tcX+OAPtu3b8e2bdtwzDHHAAAmTJiAc889FwBw5JFH4p133sHbb7+Nm266Ca+88gpUFePHj28q58wzz0QoFMI3vvENrF+/PvcvIkOsIUVEREREREREZIPTTz8d119/PS644IJm03/zm9/guOOOw6effooXXngBdXV1Te916dKl2byDBg1CUVERFi1qv/ucRYsWYeTIke3Oc/TNAWS4AAAgAElEQVTRRzfVijrjjDPw8ccfY+7cuc0SUoWFhU1/tzdqtd2YkKJ2tTPSOhERERERERGlufzyyzF58mSMGjWq2fTt27c3dXI+ffr0dsvo2bMnXnrpJdx4441t9uH01ltvYdq0afjBD36AHj16oFevXk39Vc2YMaOpttT48ePxxBNPYO+990YoFELv3r3x8ssvY9y4cbl9UBuwyR4RERERERERkQ1KS0vxk5/8ZLfpv/jFLzBhwgTcdttt+Pa3v91hOQMGDMCLL76IU045BY888ggA4O9//zvmzp2LmpoalJeX45lnnmmqIfXYY481dWo+bNgwPProowCAsrIyqCqOPvpoAMC4ceNQWVmJXr16dRjD+PHjsXz5clRVVaG0tBQPP/wwTjrppIy/i46Ik9WxnCAiC1R1TAazBuuDG/LWzM/w6VtfObKua6ce78h6iMib7r/6DUfWw2MNeYkT2z23eSIiCrply5Z12HSNzGjju8+osRVrSBERERFRVphIJiIiomwxIUXtYh9SRERERET5y7IUmsihcUkIgLX7ZIVCQoJwmN0aE+UrJqSoXWzXSERERNR5lqV48Jo3HVkXa5CRSaa344tvPQI9+hUbXQflB1WFCKtUOCnXLqCYkCIiCoDqbfWYPukdo+u4/P+NQ3G3qNF1EBEFRe3OBrdDCDxVxXN/WIi1K7cbXc9FtxyOnv1LjK4jn21dW82EFOWsqKgImzdvRp8+fZiUcoiqYvPmzSgqKsq6DCakqF3clYn8YdHrq42vY+k7X2P0yWXG10PUWf956FOs/HCD0XV8+9oDUDaqr9F1EFHnrK/YYTwZBQBP/vZ91kIj8rjS0lJUVlZi48aNboeSV4qKilBaWpr18kxIERERkW9tqtxpPBkFAC/dv5g3pEQek4i10jEREeWlSCSC8vJyt8OgTmJCitrH6o6BxGHGg4d7KuWreANvSImIiIj8iEMaEBFRRnLss5CIiMhWfG4aEPwdifJWTjWkRKQCwE4ACQBxVR0jIr0B/B1AGYAKAOep6lZJ9ix2L4BTAdQAuFRVF6bKmQDg16lib1PVx1LTRwOYDqAYwMsAfqq5duNOncMTBJE/OHBV/sHzX+CD578wvh4iIqLM8EKViMjP7Giyd5yqbkp7PQnA/6nqHSIyKfX6lwBOAbB36t9YAA8CGJtKYE0GMAaAAvhQRP6lqltT8/wAwAdIJqROBvBvG2ImIiIiB+zYXIsZv3rP7TCIiIiIyGNM9CF1BoBjU38/BmA2kgmpMwA8nqrh9L6I9BSRQal5X1PVLQAgIq8BOFlEZgPorqrvp6Y/DuBMMCFFGXrqtnnYXFllfD3sK4ky8cWijfj3Xz5xOwwixzEZRXmLdfrNYwUpIiJfy7UPKQXwqoh8KCITU9MGqOra1N/rAAxI/b0HgDVpy1amprU3vbKV6UQZcSIZRZQpJqOIiIiIiIh2ybWG1DhV/UpE+gN4TUSWp7+pqioixp8PpZJhjQmxvqbXR0RERERERERE2cuphpSqfpX6fwOA5wAcBmB9qikeUv9vSM3+FYAhaYuXpqa1N720lemtxTFNVceo6hgAm1qbh7LDmtAdYz/7RERE1BIvD4gyIxwukShvZV1DSkS6AAip6s7U3ycCuAXAvwBMAHBH6v/nU4v8C8CPROQpJDs1366qa0XkPwBuF5FeqflOBHCjqm4RkR0icjiSnZpfAuC+bOMlIiIiIgqi+69+w2j51zx4nCeTBt6LiLLCH5Iob+XSZG8AgOdSJ6cCAH9T1VdEZD6Ap0XkCgCrAJyXmv9lAKcCWAmgBsBlAJBKPN0KYH5qvlsaOzgHcA2A6QCKkezMnB2aO40niI4p+D0RERFRYG35uhp99ujqdhhEnmU6KQxwICUKpqwTUqr6BYADW5m+GcA3W5muAK5to6xHADzSyvQFAPbPNkYics+2DTXNJzQ2XWgneSciKIiGEKtLAAKItNLkQYHufYsQCuc6JsMu2zfW4InfvG9beUQUTNMnvbPrhSp6D+6Cqq31aKiNtzp/QWEY4YIQqrfVJ19HQqje3oAuPaIAgPq6hPGYiezg2e4JPFhri8hvLEvx4DVvGl/PBZPHovegLsbXQ/6Sa6fmRHmPFaRa9+RvzSZ47HxKNP+lCtvKIqLgakwsNb3e3tCp5RuX7uxy5FceTeJkwav5KCLK3cbVOx1Zz8ybP2AtL9qNfVUMKJCEqRYiIiKiTmMSxzxWkAoG/oxE+Ys1pIhypawj5Xf89YiIvM1k/yznTBqDAWXdjZVPRGQHJ/qpInIaE1JEHrP0na/x5ozlboeRX5iRojxWW9WAR66f63YYRK554U8f4cq7j3Y7DCIiorzDhJTP7dxSh02VVc0nqkItQMLNG9yl1xyPREOwLIUV12Z9TafX9VFVrPxwvanQqQ1MRrmAdf4pjy1+o9LtEMgwPlVvX31NnN8Rdcj0NnLNg8dBeD1C5GmffbAOrz+61Fj5+40fjGMv2tdY+V7EhJTPPX7Tu26HkPfYRQQRERER5aJqaz269S5yOwx3MA9HPmEyGQUAS+Z8zYQUERERdd7Tt893bKQaIiJHefXpW4ASGcpe8IkoD3GUPaJc8frB9wJ0PUsuYjKKiIio8ziqN1H+YkKKiIjXQURERETu4HUYUd5ikz2iXLGGlO/xOoiIiKhtT98+f7dp5Qf2xZcfb8qp3G9d9g2MGDswpzKIiMi/WEOKiIiIiIg6JddkFGBDB8EBeijIEfaIKB+xhhRRjjRIV0P5iheBeYXDuxMRBcP2jbVuh0BERDlgQoooR3/58Vtuh0BEREREeejzBevx6l+XuB1GbvhckChvsckeEREvhIiIiMiHfJ+MIqK8xoQUEeU95qOIiIj8J0gt7tXK4y4g8vijE+U7NtkjIl966YHFAHZdjDbUJRApDLd5cVpXFUNR1wh69i/Bzi11AAAroYjHLKxestmJkImIiIhapXmclPn6820YMrK322EQkQuYkCIiX6pYnPvoPkRERETkrrqqmNshEJFL2GSPiIiIiIj8J0BN9oLU/JCIKFNMSBERERERERERkaOYkCIiIiIiIt+RIFWRIiLKQ0xIERERERERERGRo5iQIiIiIiIi39F8HpqOiCgAOMoeecb9V7/R6WUKoiHEGywD0RARERGRl1kJJqSCgIlFovzFGlLka0xGERERERH51+cLNrgdQk4siwk1omwxIUVEREREROSigmjY7RBc01AXdzuE3LCGF1HWmJAiIiIiIiL/CdAge5HC/E1IiQTohySiTmFCioiIiIiIyE15nJPp0a/Y7RCIyCWe79RcRE4GcC+AMIC/quodLodEREREREQ22LmlrtnrRMxCrD6B4m5RhMKChto4QmGBhHbP2FRtrXcqTDKoS8+oa+vevrEGbz/1OWL1u5oNNtQlsLmyCgAwaHgP1NXEEQoJuvctQl11DCLS1BG7iKB6G7fDTLU3iFVJjygSMQv1Na034Tz8zGEYfXKZkXWTe8TLoxqISBjACgAnAKgEMB/ABaq6tJ1lFqjqmAyK9+4H7wTuWERERERERET+d+3U490OwS4Z1fv0epO9wwCsVNUvVLUBwFMAznA5JiIiIiIiIiIiyoHXm+ztAWBN2utKAGNbziQiEwFMTL3s60BcnjHquFJ88mal0XWIAGf/onmlM1UFBJB2Ep8F0RAScQuWpVALgCoScQuRwuab3aw7F5gIu0l6J5FqKSxLEY6EIGh9UIxYfQLR4oKm+RuniQAFaWXF6hJG4ybqrC49C3HpHUcZKfvrlduw+I3Gw7FAJLl/JBKKgkjzZxvJ/Sq571RtrUdDbRx77NMLtVUxiCSPKQBgWbvmAwT/XejvYZ+pbSf9YH/sNbq/kbJVFX+b8gG2ra8xUr4TDj9zGPYaPaDZNCthQULSZme/9TUxxBssdOlZCCB1fkskz29JiniDhadunWcydPKAHv2KcfGtRxgp20pYePDa2UbKJiKi5g47rdztEBzn9SZ75wA4WVWvTL3+PoCxqvqjdpbJqyZ7RERERERBkohbCBd4vSEH2cFKWIjHLABAKCSwUg+DmxLsumt7aHnfGgoLCiLmRie0EhZCYW6HpliWph5UNn/wkkhYCHfye7csRaiVfuYyld4nGNkmoy/T6zWkvgIwJO11aWoaERERke9UVFSgvLwcsVgMBQVevwwjcgeTUfkjFA4h2kHyIb21g5OYjDKrrQRSZ5NR7ZWVKSai3OP1K6H5APYWkXIkE1HfA3BhB8tsyrDswGx1IvLn9mqNEZF3cf8l8i4RqQAwGMBgVd2UNn0RgIMAlAO4vjP7cHl5eRmALyORSERVmw0lJCJlAL4EUJ2aVI3ktdC9qvpalh+j00RkHwD/D8CRSI5yPB/AT1T1M6diIHIKz8NE/sX91/88nfZNXaj9CMB/ACwD8LSqLulgmZOdiM1jznc7ACLKGvdfIm/7EsAFjS9EZBSAkrT3M96HRSTTB4E9VbUrgAMBvAbgORG5NNP12KAngH8BGAFgAIB5AJ53cP1ETuJ5mMi/uP/6nKcTUgCgqi+r6j6qOlxVf+d2PB61ze0AiChr3H+JvG0GgEvSXk8A8Hja6wYRWSQiO0RkjYhMaXxDRMpEREXkChFZDeCNloWLyNkiUiEi+7d8T1XXqeq9AKYAuFNEQqllRorIbBHZJiJLROT01PTy1LT/z96dh0lVnnkf/95VvbLTKCi0DsiL26gj6hsdh0WTMSoSNAlGzSQSzYQ3MWoyE3WUxDUbZuLERMckJEHcghqNo1FjxAxgo3FBUdSAcQNpZG2gm15ru98/6nRR3fRGd1dVV/fvc13QdZ5zzvPcVV2nqvquZ2k+7ldmtjWtrXvM7JvB7WVm9l0ze87MdpvZ02a2X9DuS+7+G3ff4e5R4CfAYWY2qluPoEjfpvdhkfyl6zfP9fmElHRJda4DEJFu0/Ur0re9AAwLkkBhktMH3Ju2v5ZkwmoEcBbwNTM7p1Ud04EjgNPTC83sIuBm4J/d/c0OYvg9MJpkUqgQ+APwdFB2GXCfmR3m7h8ANcDk4LxpQK2ZHZEWx/K0ej8PXBTUUwRc0U7704DN7l7VQYwi+UrvwyL5S9dvnlNCqn9YkOsARKTbdP2K9H3NvaROIzmFQPoCK7e4+xvunnD31cBikomfdDe4e527N6SVfRO4EjjF3d/tpP2Pgp9lwEnAEGC+u0fc/X+Bx9kzrHA5MN3MDgi2Hwq2JwDDgNfT6r3T3f8WxPUgyXmxWjCzcuC/gX/vJEaRfKX3YZH8pes3z/X1Sc2lC9xdF6JIntL1K5IX7gGeJTmJ+d2t9r1uZkuBo0j2MioGftfqmA1t1HklcJO7V3ah/XHBzx3AMcAGd0+k7V+fdsxyYBZQGcS8DPgi0AhUtDpvc9rtepKJrhQz259kT6w73H1xF+IUyTt6HxbJX7p+8596SImIiIh0wN3Xk5zcfAbJ4XPpfktyAvCD3H048Av2XsnX26j2k8B3zOyzXQjh08BW4G2SvaUOap4nKnAwe3ptLQemAqcEt1cA/8Tew/U6ZGYjSSajHtMcniIiIpIJSkiJiIiIdO7LwMfdva5V+VBgh7s3mtnHSM7L1BVvAWcA/908KXlrZjbGzC4FrgeuCXo3vUiyN9NVZlZoZqcAnwLuB3D3d4AG4AvAcnevAbYAn6WLCSkzG0ZyhePn3P3qLt4fERERkX2ihJSIiIhIJ9z9PXdf2cauS4CbzGw3cB3JuZi6WufrwEzgV2Z2ZtquXWZWB7xBslfWue6+MDgnQjIBdSawHbgDuNDd16advxyocvcNadsGvNrF0D4N/F/gIjOrTft3cFfvm4iIiEhnzL2tXuQiIiIiIiIiIiKZoR5SIiIiIiIiIiKSVUpIiYiIiIiIiIhIVikhJSIiIiIiIiIiWaWElIiIiIiIiIiIZFVBrgPobWb2lLuf0YVDNZu7iIiIiIiIiEjvsq4c1B97SO2X6wBERERERERERKR9/a6HlIjIvopFItx7zTepqvxwn88dUjaK2h1VXTr2/Bt/xLjDj9znNkRERERERPqbTntImdlCM9tqZm+mlZWZ2RIzeyf4OTIoNzP7mZm9a2arzey4tHPmBMe/Y2Zz0sqPN7M3gnN+ZmbWURsiIr1txf13dSsZBXQ5GQVw//VXdasNERERERGR/qYrQ/YWAa3nZLoa+LO7TwL+HGwDnAlMCv7NBX4OyeQScD1wIvAx4Pq0BNPPga+knXdGJ22IiPSqul27ch2CiIiIiIjIgNLpkD13f9bMxrcqPhs4Jbh9F7AM+I+g/G53d+AFMxthZgcGxy5x9x0AZrYEOMPMlgHD3P2FoPxu4Bzgjx20ISIiIiIiIiICQDQapbKyksbGxlyHMqCUlJRQXl5OYWFht87v7hxSY9x9U3B7MzAmuD0O2JB2XGVQ1lF5ZRvlHbWxFzObS7JHFmhScxERkQHlLw8tZvUzfwTSltB1Z8QBY6nesil1nAflJGcHoG7nDgqKiikZPBgPykOhMO4J3B0j2YPSPcEXfngrYw75P9m8WyIiItJFlZWVDB06lPHjxxPMAiQZ5u5UVVVRWVnJhAkTulVHjyc1d3c3M+/8yMy14e4LgAUAZrYyk7GIiIhI37Ft/Qc8/7v72txXt2tnp+fHIk3URpo6Pe7ea77Jtx54fJ/jExERkcxrbGxUMirLzIxRo0axbdu2btfRlTmk2rIlGIpH8HNrUL4ROCjtuPKgrKPy8jbKO2pDREREBICmurpchyAiIiJ9gJJR2dfTx7y7PaQeA+YA84Ofj6aVX2pm95OcwLza3TeZ2Z+AH6RNZP5J4Bp332FmNWZ2EvAicCFwWydtSBa5O28//yy7Nu8Z8oAZ8ViMUCiEhUKpJ2Fy6rDkk9LdiUcjYCFC4RDxaIyCoqLkvkQiVQ9AIh7n+LPOoXjQoKzeNxERERERERHJjU4TUma2mOTk4vuZWSXJ1fLmAw+a2ZeB9cDngsOfBGYA7wL1wEUAQeLpu8DLwXE3NU9wDlxCciW/UpKTmf8xKG+vDcmi159+kj8v/HnG2/nLQ7/VUAgRERERERHJS2bGv/zLv3DvvfcCEIvFOPDAAznxxBN5/PGu/627bNkyfvzjH+91zqJFi7jyyispLy+ntraWQw45hOuvv56TTz65V+9Hs9dee42vfe1r1NTUEA6H+fa3v815553Xq210ZZW9C9rZ9Yk2jnXg6+3UsxBY2Eb5SuCoNsqr2mpDsmvLB+/lOgQR6QOqt25m49q/tihzdzyRIFRQkF7Y4phYNEq4oICiklKiafP0GHt6VabXd/g/TSNc0L1VOkREREREcmXw4MG8+eabNDQ0UFpaypIlSxg3blznJ6aJxWId7j/vvPO4/fbbAVi6dCmf+cxnWLp0KUcccUS3427PoEGDuPvuu5k0aRIfffQRxx9/PKeffjojRozotTZ6PKm5iIj0f7++7F+z0s5Td/xEvSVl32i6CBEREekjZsyYwRNPPMHs2bNZvHgxF1xwARUVFQC89NJLfOMb36CxsZHS0lLuvPNODjvsMBYtWsTvf/97amtricfj3Hjjjan6Xn75ZebOnctDDz20V1unnnoqc+fOZcGCBfzkJz/htdde46tf/Sr19fVMnDiRhQsXEo1GOfPMM3nllVd4/fXXOfbYY1m/fj0HH3wwEydO5I033uCSSy5h2LBhrFy5ks2bN/OjH/2I2bNnc+ihh6baGjt2LKNHj2bbtm1KSImI9CZNgCiSx3T9ioiISJqlixawdf37vVrn6L87hFO/NLfT484//3xuuukmZs6cyerVq7n44otTCanDDz+ciooKCgoKeOaZZ5g3bx4PP/wwAK+++iqrV6+mrKyMZcuWAfD8889z2WWX8eijj3LwwQen6kl33HHH8ctf/hKACy+8kNtuu43p06dz3XXXceONN3LrrbfS2NhITU0NFRUVnHDCCVRUVDBlyhRGjx7NoGAe502bNrFixQrWrl3LrFmzmD17dot2XnrpJSKRCBMnTuz2Y9gWJaRERERERERERHromGOOYd26dSxevJgZM2a02FddXc2cOXN45513MDOi0Whq32mnnUZZWVlqe82aNcydO5enn36asWPHttte8xQY1dXV7Nq1i+nTpwMwZ84czj33XABOPvlknnvuOZ599lnmzZvHU089hbszderUVD3nnHMOoVCII488ki1btrRoY9OmTXzxi1/krrvuIhQKdfORaZsSUiJ9TM32rSz8xlzinYwf7omi0kFcuvB+rJdfUERERERERHKpKz2ZMmnWrFlcccUVLFu2jKqqqlT5tddey6mnnsojjzzCunXrOOWUU1L7Bg8e3KKOAw88kMbGRlatWtVhQmrVqlWdzh81bdo0KioqWL9+PWeffTY333wzZsZZZ52VOqa4uDh1O32e15qaGs466yy+//3vc9JJJ3V63/eV/hqVDmkkRPb96usXZzQZBRBpqKfi/rsz2oaIiIiIiMhAc/HFF3P99ddz9NFHtyivrq5OTXK+aNGiDusYMWIETzzxBNdcc01qCF9ry5cvZ8GCBXzlK19h+PDhjBw5MjWs75577kn1lpo6dSr33nsvkyZNIhQKUVZWxpNPPsmUKVM6jCESifDpT3+aCy+8cK8hfL1FPaREBqjaqu25DkFERERERKRfKS8v5/LLL9+r/KqrrmLOnDl873vfa9E7qT1jxozh8ccf58wzz2ThwoUAPPDAA6xYsYL6+nomTJjAww8/nOohddddd6UmNT/kkEO48847ARg/fjzuzrRp0wCYMmUKlZWVjBw5ssP2H3zwQZ599lmqqqpSCbRFixZx7LHHdvmx6Iy1XnY735nZSnc/oQuH9q87niFPL7iNN/78p6y0pZW1km45b2ZW2jliyinMuOyKrLTV1z15249Zs2JZVtrK1+d5tp6XkL+PkeTGxrV/5f7rr8pKW3puioiI9E1r1qzpdOiaZEY7j32XxlppyJ6IiIjkL40tFxEREclLSkiJiOgPWhERERERkazSHFLSb9VX76Kq8sMWZckRqo6ZJZMQbQxZDRcW4gknkYin9puFcE+0OCcejzN89BhGHtD+qgciIiIiIiKSee7B33mSNT2dAkoJKemQdW3oZ5/087lfyEo7+TqnSH+bP64n8vdZLm1JJOLUV1dTXDqIwpKSXIcjIiIiIhlWUlJCVVUVo0aNUlIqS9ydqqoqSnrweVsJKRER6VMyOYH6oSdN4VP/dnXG6pfs02dOERERKS8vp7Kykm3btuU6lAGlpKSE8vLybp+vhJSIiAwYf3thRa5DkF6njJSIiMhAV1hYyIQJE3IdhuwjTWouHdPnfBERERERERHpZUpIiYiIiIiIiIhIVikhJR3ShHAiIiIiIiIi0tuUkBIRUeJVJG/pixMRERGR/KSElMgA5e65DkFEpOeUjxIRERHJS1plT0Qki245b2auQxAREREREck5JaREZMDTkB8RERERGaheeeJR3nr2zwAYhnuCht01xCIRhu03Gsf37AtuRxrqiUUijP67CdTuqAIzLBTCLEQiEd9zrDsFRUWcd8N8wgWFObuP0jcpISWd0B/qIiIimbLhr2/w4I3XZLSNbz3weEbrFxGR/FVV+SHL7v5Vu/sba3d3eP4HO3d0qZ2F3/wqX7n9N/sUm/R/PUpImdk6YDcQB2LufoKZlQEPAOOBdcDn3H2nJbsg/BSYAdQDX3L3V4N65gDfCar9nrvfFZQfDywCSoEngW+4Jr7JLvUcERHp15rq67n9os9ltI1Z/z6PSSeenNE28lWmk1EiIpJ52ZiSIVNfLjTW1mak3tZqtm3JSjuSX3qjh9Sp7r49bftq4M/uPt/Mrg62/wM4E5gU/DsR+DlwYpDAuh44AXDgFTN7zN13Bsd8BXiRZELqDOCPvRCziIiIAC/+z4MZb+Ox//pBxj5Im3ryikgGPXvfnbz82MMZb+ff7/+DphAQkQEnE0P2zgZOCW7fBSwjmZA6G7g76OH0gpmNMLMDg2OXuPsOADNbApxhZsuAYe7+QlB+N3AOSkiJ9A51NhQRwBOJrLSjCf1zJ9OP/f/7+V0MKRuVkbrrdu3kF//vixmpu9ncny9iaNl+vV6vJxL81wWzer3eXBjIyZJsJKMAIg0NFA8alJW2RET6ilAPz3fgaTN7xczmBmVj3H1TcHszMCa4PQ7YkHZuZVDWUXllG+V7MbO5ZrbSzFYCvf+JQkT6uYH5IVtEpDd8+NbqjNX9xM/+M2N1N8vUsMndO6oyUm8u7Ni4ofODRERE9lFPe0hNcfeNZjYaWGJma9N3urubWca7Ybj7AmABQJCUGjA2rv0rby1/JtXZxQzcnXgsRkFhYXK1g7QVDprV11QzZGQZiUQCTyTLQ6FQatUESH6z9+bSJVm9P/nI3Qfst4YiIiKZFItGMt5GPBrLeBv5LpGlnpQiIjKw9Cgh5e4bg59bzewR4GPAFjM70N03BUPytgaHbwQOSju9PCjbyJ4hfs3ly4Ly8jaOlzT3X39VrkMQERGRAeyPt99CxW8X7VkIJZEgkUi0XN47+MKs2eDhI0jE4zTW1tJUX0dBURFFpaXEIpEWC6rUVqVPU5pfsjUcVkREJF91OyFlZoOBkLvvDm5/ErgJeAyYA8wPfj4anPIYcKmZ3U9yUvPqIGn1J+AHZjYyOO6TwDXuvsPMaszsJJKTml8I3NbdeEVERPJVU30dDTU1Lcqae76atRp9n+wqC0CooIBEPE6LbrSQ2nZPsP3DdZkMXQaI2n0cntY60RRpqKe+eldvhtRl6b3De1NTfV1G6hURkdz473/9PI27azo/sAcytQhMX9WTHlJjgEeCoUoFwG/d/Skzexl40My+DKwHmteSfhKYAbwL1AMXAQSJp+8CLwfH3dQ8wTlwCbAIKCU5mbkmNBeR3qcRl9KHeSLB7Redl+swRPqvDAH6LDsAACAASURBVC3yoeH8IiL9S6aTUQNRtxNS7v4+8A9tlFcBn2ij3IGvt1PXQmBhG+UrgaO6G6OItE9r7InkB83dIiIiIhmj5LnkUE9X2RORfJWhb4RFRERERCRP6G8CyaGerrInItLC7h3bWfC1L3X7/KLSQUQa6js85l9+8BMOmDip2220pmEVIiIDmP4YExERyQklpESkV/UkGQV0mowCuG/evw24Cf9k4FLCVCSzanfu4L55/xbkpRx3JxaJUFhcnFoZcM91mPy55f13GHPI/yHa2EgsGqVu1w5Khw1n8PARqXO2rfsg+3dG8ljfTYwuu/vXvPLE/2SmcjP+ffFjeq8TGaCUkBIRERHpgucevK/FVBuRhgYKS0pblDUnNcAIFxQQCoeJNNTjiQThwiIadtdQOnQoANGmpmyGLx3Y/N47+3zOlvffbbFdW7V9r9UDRfqDjCWjANyp/OsbHPT3x2SuDRHps5SQEhGRAeX+668KbhlmRlNdLYWlg9K+nW35LXVhSSmhcJjaHVU01dUSaWykoaaa/Q76O4oHDyEeixJramL0hIn8879eQmFxSVbvj2TPCw8vznUIIrmhYY2SQfFoNNchDGzqnSY5pISUyEClD5cyQG1c+9deqWf7hvV7bW/fsJ4vzv9pr9QvIiIiknH6m0BySKvsiYiI9JKtH7zX+5Xqi0sRERER6YfUQ0pERH/xi4iI5KUNb61m67r3W5QlEglwx0LBd+/uYNZi4uzC4hIwiNTX4+6ECwrADE8kUhPTq+dIduhRFhm4lJASEREREZG8s+OjSh68aV6uwxDJilvOm5nrEER6nYbsifSUvj3Le5rLUUREJP/s1qqGIiJ5TT2kRERERERE8tArTzzKsrt/leswRES6RT2kRAYo14h9ERERkbzWL5JRGm0gMmApISUyUOm9X0RERKRPcCVlRGQA0pA9kR76rwtm5TqEbnnnpec1OaKIiIh06u6rLstY3d964PGM1S0iIn2bElLSZ1T8dlGL7Vg0SkFhYWrG6ealepu/QYpHI5SNO4jdVduJx2LUV++ioLCI4kGDqNu1M6uxS/Y9cOPVLbYjDQ0UlZZipM1QbpbqBl5fU03drp2UDBlCyZChlAweQiIeIxqJsOlva7MZuoiIiIiIyICnhJT0GS89+lCuQ5A8UvnXN7t1XmPtbmBT7wYjIiIiIt1SV70r1yGISI4oISUiItKLNBRWRESk62KRSK5D6BHN/yXSfZrUXERERERERKQb3BO5DkEkbykhJSIiIiIikkPqZSMiA5ESUiIiIiIikndKBg/JdQi9xhPqZSMiA48SUiIiIiIikncGjxiZ6xCkN6h3mMiA1ecnNTezM4CfAmHg1+4+P8chiYiIiIiISC/46J21HPXxT4I7BUVFAMRjURKJBLgTChcQLsjMn60127fx0v/8jmhTI2YGJIdPRpsaiUejFJaUUjxoEO+/8hJFpYM4cNJhgLWoQ3NIdd2rf/wDkExAeiLBjo8qGTHmQIpKB1FfvYv6mmri0QjD9htNYUkpAGbJ/8YeegQHTJyUu+AlI6wvj1c2szDwN+A0oBJ4GbjA3f/awTkr3f2ELlTfd+/4PtBqTiIiIiIiIiL571sPPJ7rEHqLdX5I3x+y9zHgXXd/390jwP3A2TmOSUREREREREREeqCvJ6TGARvStiuDshbMbK6ZrTSzlcB+2QpORERERERERKQ3JOLxXIeQVX1+DqmucPcFwAJIDtnLcThZlY0ufZ5IJMdw772HjnrimVmXlrB1dxLx2L6f745ZCAuFUiuTeDAS0zAcpygYe5wpiUSceGzv2EOhMIlE+y8mHoyJDxcWkYjH8EQCC4dJxOKECwoIFYQJhcKZizseJxaN7FVurcfEtzOyNVxQSDwWbbd+MyMR73g8vbXx1Gn9627vmOJBgzqsuyPJ51vycfZEAgsl8/LRSNNeK9ykP7faYhiEDBLe7mPVXI9hqXkRMiXTQ7CtrV+IpHT3tTKRiBMOF5CIx1PPR3dPvQaaGRaybr0mNJ8vmdEbj28iEc/Y633za0K+PwdikbT3K7OuTYAcHGchI5FI7PX+ljosZMSje7+ftfm7bd5Oa9/CYby9Px7MCIULKCgs7DzeHog2Ne7VLtDp42ShcHLum06OCxcUEgr3/DmaiMfbraf14+3uyfdfI6Ofh/o6dw+exx33IUjE493+DJA+b1JHMjWHU3visVhW2mz+nNf6MW7+fB8Kh/P+NVQ6l8n3YulYX09IbQQOStsuD8okiywUItzJG2GPZfjDWqaEQmFCRW2/eIW7eHm1eLPNbL4iJRQOUxTuWbIu2x9MeouZpWJP//BRWFScq5B6jT4w5VZ3XyubXyt64w++1vScyKzuPL7r1q1jwoQJRKNRCgoKMvoBuL/8/nuazO/sEQ4X5OdnkGaFxSW5DqFLOnqNa/1cNTMsA6+J+cbM2v52rpVMvH/kWrY+Z7aX7MvXz7nSPUpG5U5fv9JeBiaZ2QSSiajzgc93cs72LtbdPz6lAWZ2u7tfmus4RGTf6foV6bvMbB0wFhjr7tvTylcBxwITgCv25RqeMGHCeOCDwsLCQndv0cXWzMYDHwB1QVEdyc9CP3X3Jd28G/vMzPYDHgUOJ5nPWUPyfj6XrRhEskXvwyL5S9dv/uvTc0gFH9QuBf5E8sPQg+7+VifnnJGN2PqY83IdgIh0m65fkb7tA+CC5g0zOxpIHzfc5WvYzLr6ReAIdx8C/AOwBHjEzL7U1XZ6QS1wMbA/MBK4GfjDPsQvkk/0PiySv3T95rk+nZACcPcn3f1Qd5/o7t/PdTx91K5cByAi3abrV6Rvuwe4MG17DnB32nbEzFaZWY2ZbTCzG5p3mNl4M3Mz+7KZfQj8b+vKzeyzZrbOzI5qvc/dN7v7T4EbgJvNLBScc4SZLTOzXWb2lpnNCsonBGXNx/3KzLamtXWPmX0zuL3MzL5rZs+Z2W4zezroGYW7N7r72+6eINmjPE4yMVXWjcdPpK/T+7BI/tL1m+f6fEJKuqQ61wGISLfp+hXp214AhgVJoDDJ6QPuTdtfSzJhNQI4C/iamZ3Tqo7pwBHA6emFZnYRyd5H/+zub3YQw++B0cBhZlYI/AF4Oii7DLjPzA5z9w+AGmBycN40oNbMjkiLY3lavZ8HLgrqKQKuaBXfaqAReAz4tbtvRaT/0fuwSP7S9ZvnlJDqHxbkOgAR6TZdvyJ9X3MvqdNITiGQvsDKLe7+hrsn3H01sJhk4ifdDe5e5+4NaWXfBK4ETnH3dztp/6PgZxlwEjAEmO/uEXf/X+Bx9gwrXA5MN7MDgu2Hgu0JwDDg9bR673T3vwVxPUhyXqwUdz8mOOfzwIpOYhTJV3ofFslfun7znOYC6AfcXReiSJ7S9SuSF+4BniU5ifndrfa9bmZLgaNI9jIqBn7X6pgNbdR5JXCTu1d2of1xwc8dwDHAhmA4XbP1accsB2YBlUHMy4AvkuzpVNHqvM1pt+tJJrpacPdGYLGZrTGz19z99dbHiOQzvQ+L5C9dv/lPPaREREREOuDu60lObj6D5PC5dL8lOaTtIHcfDvyCvVfy9Taq/STwHTP7bBdC+DSwFXibZG+pg5rniQoczJ5eW8uBqcApwe0VwD+x93C9fVUIHNKD80VERERaUEJKREREpHNfBj7u7nWtyocCO9y90cw+RnJ4W1e8BZwB/HfzpOStmdkYM7sUuB64Jujd9CLJ3kxXmVmhmZ0CfAq4H8Dd3wEagC8Ay929BtgCfJYuJqTM7CQzm2JmRWZWamb/AYwJ2hYRERHpFRqyJyIiItIJd3+vnV2XALeY2e0kEz4PkpzgvCt1vm5mM4EnzCxKcn4qgF1mZkAdsBI4192fCs6JmNmngDuAa0j2jLrQ3demVb0cOMndN6RtHw682rV7SzHwM5I9oqLAG8BZ7v5Rh2eJiIiI7ANzb6sXuYiIiIiIiIiISGZoyJ6IiIiIiIiIiGSVElIiIiIiIiIiIpJVSkiJiIiIiIiIiEhWKSElIiIiIiIiIiJZ1e9W2TOzp9z9jC4cqtncRURERERERER6l3XloP7YQ2q/XAcgIiIiIiIiIiLt63c9pERE9pUnnJo/f0h0Ux34ns6THktgIYNwO7n7hOOROKGhRXg0Ae54LEHTO7soGD2Igv1K8Wic6OY6wiNKGHXB4RSUlWTpXomIiIiIiPRd5t6/Rq6Z2Up3P6ELh/avOy4i3Va9ZD27//xhVtoqnz81K+2IiIiIiIjkyIAdsicisk9im+tyHYKIiIiIiMiAoiF7IiIiIiIiIpK3otEolZWVNDY25jqUAaWkpITy8nIKCwu7db4SUiIiIiIiIiKStyorKxk6dCjjx4/HrEujxaSH3J2qqioqKyuZMGFCt+rQkD0RERERERERyVuNjY2MGjVKyagsMjNGjRrVo15pSkiJiIiIiIiISF5TMir7evqYKyElIiIiIiIiIiJZpYSUiIiIiIiIiEgPmBlf+MIXUtuxWIz999+fmTNn7lM9y5Yta/OcRYsWsf/++zN58mQmTZrE6aefzvPPP9/juNuzfv16jjvuOI499lj+/u//nl/84he93oYmNRcRERERERER6YHBgwfz5ptv0tDQQGlpKUuWLGHcuHH7VEcsFutw/3nnncftt98OwNKlS/nMZz7D0qVLOeKII7odd3sOPPBA/vKXv1BcXExtbS1HHXUUs2bNYuzYsb3WhnpIiYiIiIiIiIj00IwZM3jiiScAWLx4MRdccEFq30svvcQ//uM/MnnyZE4++WTefvttINnzadasWXz84x/nE5/4RIv6Xn75ZSZPnsx77723V1unnnoqc+fOZcGCBQC89tprnHTSSRxzzDF8+tOfZufOnWzdupXjjz8egNdffx0z48MPPwRg4sSJ1NfX86UvfYnLL7+ck08+mUMOOYSHHnoIgKKiIoqLiwFoamoikUj05kMFqIeUiIiIiIiIiPQTu/7wHpGP6nq1zqKxgxnxqYmdHnf++edz0003MXPmTFavXs3FF19MRUUFAIcffjgVFRUUFBTwzDPPMG/ePB5++GEAXn31VVavXk1ZWRnLli0D4Pnnn+eyyy7j0Ucf5eCDD07Vk+64447jl7/8JQAXXnght912G9OnT+e6667jxhtv5NZbb6WxsZGamhoqKio44YQTqKioYMqUKYwePZpBgwYBsGnTJlasWMHatWuZNWsWs2fPBmDDhg2cddZZvPvuu/znf/5nr/aOAiWkRERERERERER67JhjjmHdunUsXryYGTNmtNhXXV3NnDlzeOeddzAzotFoat9pp51GWVlZanvNmjXMnTuXp59+usMkkLun6t61axfTp08HYM6cOZx77rkAnHzyyTz33HM8++yzzJs3j6eeegp3Z+rUqal6zjnnHEKhEEceeSRbtmxJlR900EGsXr2ajz76iHPOOYfZs2czZsyYHjxCLSkhJSIiIiIiIiL9Qld6MmXSrFmzuOKKK1i2bBlVVVWp8muvvZZTTz2VRx55hHXr1nHKKaek9g0ePLhFHQceeCCNjY2sWrWqw4TUqlWrOp0/atq0aVRUVLB+/XrOPvtsbr75ZsyMs846K3VM89A82JPkSjd27FiOOuooKioqUr2neoPmkBIRERERERER6QUXX3wx119/PUcffXSL8urq6tQk54sWLeqwjhEjRvDEE09wzTXXpIbwtbZ8+XIWLFjAV77yFYYPH87IkSNTw/ruueeeVG+pqVOncu+99zJp0iRCoRBlZWU8+eSTTJkypcMYKisraWhoAGDnzp2sWLGCww47rLO7v0/UQ0pExHIdQN9XefXeY9YzYfiMCQydVp6VtkREREREelt5eTmXX375XuVXXXUVc+bM4Xvf+16L3kntGTNmDI8//jhnnnkmCxcuBOCBBx5gxYoV1NfXM2HCBB5++OFUD6m77rqLr371q9TX13PIIYdw5513AjB+/HjcnWnTpgEwZcoUKisrGTlyZIftr1mzhm9961uYGe7OFVdcsVeSraesre5Y+czMVrr7CV04tH/dcRHptqp7/0rDm1WdH9gLyudP7fygPihbCSnI38dIRERERHJjzZo1nQ5dk8xo57Hv0lf+nfaQMrOFwExgq7sfFZSVAQ8A44F1wOfcfaeZGfBTYAZQD3zJ3V8NzpkDfCeo9nvufldQfjywCCgFngS+4e7eXhtduVMiIiIiA50nnI+ufx6P9v4yzenG/WAKFlJXUxEREdk3XRmytwi4Hbg7rexq4M/uPt/Mrg62/wM4E5gU/DsR+DlwYpBcuh44gWTPpFfM7LEgwfRz4CvAiyQTUmcAf+ygDREREZGUnY++S91fNu3zeaGhRSR2R7p07P5f+weK/27YPreRSw1vbc94MgqgcU0VpX+/X8bbERERkf6l00nN3f1ZYEer4rOBu4LbdwHnpJXf7UkvACPM7EDgdGCJu+8IklBLgDOCfcPc/QVPjh28u1VdbbUhIiIiAkBkw+5uJaOALiejALb9/PVutZFTsezMTuBxzYIgIiK519+mI8oHPX3Mu7vK3hh3b/70txkYE9weB2xIO64yKOuovLKN8o7a2IuZzTWzlWa2EtBXdCIiIgNEojGW6xBEREQkx0pKSqiqqlJSKovcnaqqKkpKSrpdR49X2Qvme8rob72zNtx9AbAAkpOaZzIWEREREREREek7ysvLqaysZNu2bbkOZUApKSmhvLz7K2R3NyG1xcwOdPdNwbC7rUH5RuCgtOPKg7KNwCmtypcF5eVtHN9RGyIiIiJJmktbRERkwCssLGTChAm5DkP2UXeH7D0GzAluzwEeTSu/0JJOAqqDYXd/Aj5pZiPNbCTwSeBPwb4aMzspWKHvwlZ1tdWGiIj0Y/HdkeS/muZ/TWm3g3+7W/2ri7Y4L7ajce9ja5rweOYneBYRERERkc512kPKzBaT7N20n5lVklwtbz7woJl9GVgPfC44/ElgBvAuUA9cBODuO8zsu8DLwXE3uXvzROmXkFzJr5Tk6np/DMrba0NERPqxTd9/MaP1l8+fmtH6paWapRuo+dO6XIchIiIiIn1Mpwkpd7+gnV2faONYB77eTj0LgYVtlK8EjmqjvKqtNkRERCR/KBklIiIiIm3p8aTmIn1V/etb2fnQOy3K3B3ijhWEkvOOtDFVvkcTWHE4uZHwVBmAFQWjXD1ZNvSUgxh+xvhejTteF2XX/7xLvDaKpc2NkmiKY0XhVFnrBSSim+vwSJzi8cPwmONNMUKDClscawbxmgihoYWM+pcjCA8p6tXYRURERERERLpCCSnpt3Ysfrvdfc0Jpnb3N8XbLo+0PG/3sg29npDa9N0XenR+03vVnR+0vYGmD6oZdPT+PWqr3zDNiiwiIiIiIpJNSkiJDFDepMmdRUREREQGMo8l2Pid5zLezujLJ1M0dkjG25H8ooSUdCi2vYHNP16Z8XYGHT+GsnMPzXg7kq6N8YoiIiIiIrJPtty2iujG2sw1EDLKfzAlI1U3ra/JSL2tbf3ZKi0sI3tRQko6VPPnD7PSTv0rW5SQEhER6U0ajZxzkY21RCp3JzdS3wM137B2f0cWSu7wuEM8QaIhRnR7A0VjhyTnuQyqiG2tp/SoURQfMiJTd0FEOuEJz2wyClLz2or0N0pIiQxUel8TkUAiEt/nD7uJ2igOhIcW7nk9STiElAURgeQiJVtvW9WrdTa8tm2vstrnP1KvgwyK7Whkd0UlxD31Wte8SA7h5OudNc9FmfbyZ8VhwkOLiO1sTC6YY0Z0Wz0FZSXJxXUAHBLROKWHlzHo2NFZvFciIn2DElIiIllUeXVFRuvXHyWyrxrf3sH2O9/KdRgDVmxXI5vnv5zrMCQDvDGW6xCkF2z+Ue9en5F1ew+PanhtG6VH7bcnUSUiMkAoISUiIgNK4992tvgW2+OOhTvo1RMyzCARTUAsgUcSxHY1UXjAoOQfD57srh8qLaDooKGpoTb5ovHtnbkOYUBTMkpEAHB1XReRgUcJKelYfv1dJSLSqe0L38xY3VqgQURERESka5SQEhmo9EVcv5TpIYHSMS3QIAPRjt+uZcdv12a0DQ1HFhER6X+UkBIZoFwZKZG84Aln47wVuQ5DRPqwTH8ZMe6HU/ZM3C0Z4a6BCSIy8CghJSIi0od5LJHrEERybtPNLyVvNH+X4iRXOEufd8eCbQcrCGGlBRSUldC4dgfeFE8dFh5ZnKwiEqd4wnDKzjscK9Rk0h2JbW+gcP9BuQ5DcqT+ta3seuJ9Wk7A2Maqqp52w4xBx+5Pw2vb8HgCzEjURgkNKUyeF0uQaIwzdHo5w08fn507Im1SrllySQmpPBeviRDbXg+0mgvRwTr4bGWFYTzhEE+kzvOGGFZakHpRcof6V7dmJnDJPXWQEhGRPBHf2dSt86IbdndYV8ObVewIvc2ozx/R7dhE+rsd97/drfNqn924V1miNtpie/fSDZQevR9FY4d0qw0RyW9KSOW5TT94MdchiIiIiOSt6Ka6zFSsbgciXZJoiOU6BBHJEfVPFhERERGR9qlXtUg/puS55I4SUiIDlT5cioiIiIiISI5oyJ6IiIiIiLRry3+9QsF+pXjzxKMJp2BkCfHqPfNxeasbodICQqUFNL2/C4K1GcKjSgCIVzUCsP8l/0DxwcOycA9ERKQvUkJKpIfcPU+XQs5MF6mGN7dTde+ajNTdbOipB2lFFhER6RWxbQ1suW1VciNYpc9jieTKe81vlelv82ZEN9ZSOHYwHkngsQTx3RFCgwsJDylMnZOxualyJLa9ocV2ZxPNx9sqCxJRzbbd8Trl86f2NDQREclTSkiJDFQZGrKX6WQUJFdk2b10Q8bbERGRgSG6sXbfz/moZcIpURMhURPprZBkoOnm57JEJE7D6u14NC0FaGDh5MwsHku0KG9ux8IhCBmeSGDhEImGGN4Yg7ARKilIHZ+oz8KE45pGIrfy8Xt16TeUkBIZoHY9+h67Hn0v12GIiOSNTT98kfRP7vG6KOFBBa1WU0v2sMHACsMQNhK1EXBINMYh4YSHF4GZVpYS6QUNb22naX0Ne65NTw0RJETLZIftuREqDgPBCm/ueCRBIhonPKyY1HWcBz667vlchyBZsuN3f9uzYUDckz9D1ubz1SNxEnVRwmUlJBpihEoLiG1rIDysCCsKp46rf2VLxmMXaY8SUtJntPhmh2Sv+Y5HwllwXMtX4OQ5SvWLiEjvilfv3fsl3o0eMW3VIyL7LvJRLVX3ZL5ntkhf0O3E0fvVvRuISC9SQkr6jI3X5uk3PME34SIiIiKSPd1JCIuISN+hhJSIiEgvqry6ItchiIhI3smTMYIZMZDvu8jAFsp1ACIiIiIiIiIiMrCoh5RID2363gvJiauCYXuJ2igAocGFex9se/a3FhpcSKKu7X0iIiIi0oqmTBARyWtKSIn0UHvL4e5rcknJKBEREREZcDRiT/JEfHeEXY++23KVXDM8msAKrOWKXGkLb3kiueqhFYawkCVXRkx4sEJiclVPKwwxfOYhFO4/KIv3KPeUkBIRERERkbzTrzpIKSkj0udt+v6LGa2/8e1XKJ8/NaNt9DWaQ0pERERERPKP9auUlIjIgKOElIiIiIiIiIiIZJUSUiIiIiIiIiIiklVKSImIiIiIiIiISFYpISUiIiIiIvlHU0j1D5rQXWTAUkJKRERERERERESySgkpERERERERERHJKiWkREREREQk//SnIXsatiYiA1CfT0iZ2Rlm9raZvWtmV+c6HhERERERERER6Zk+nZAyszDw38CZwJHABWZ2ZG6jEhERERGR3OtPXaRERAYec++7/UPN7B+BG9z99GD7GgB3/2EH56x09xO6UH3fveNd5HFn47dX5DoMEREREREREemh8vlTcx1Cb+nSNwZ9uocUMA7YkLZdGZS1YGZzzWylma0E9stWcDnX1397IiIiIiIiIiJtKMh1AL3B3RcACyDZQyrH4WSNmTHuu/9EbEdDWzuhg95v0U11hEoLCI8swWMJLGTE66KESgqwgrRkpid7YoWHFLasIBQck/Bk7rNVUx5NEN8dITSkkFBxGHfwhlibde1eXknt8x/twz3vuuJJIwiVFiQfjyBOjyWwglbZvObHygyPJkjURQkNKsCKwuBOojaKlRa0OM+b4jSu3ZGRuMPDixg0eUwy4CC53Nyb0axVsjnY9FgCEp6M0YxEYwyPxAkNKkyLO1nf7mc3QCIjoUsHBp94AEXjh7f4usDd8UgCKw7v9TWCA8QSJJriyevSDBJOvCZCwehByedt8Hzwpji7/ufdjMY/ZNo4QsVpbxsGocGFJGqjbb4OABTsV4IVhIjXRFI/MQgPLya+q6nFsfHdkdTzOCXhydeSQYVYYfA8bn59C+67FRiJ+hgFo0qJ10Yg7sm46qLJ1ypPXhex7Q00vV/dy4+KdMXgjx1A4bghLcosHIJQ8v2iteimOkKDCiDheMwJDy/CisLEq5taPhcA3IluqccKwy3qCA8uwEoLiO9owhN7PzktBJ7wZBwJT77GOsnX+5Jw8vWf5HtX5KNaopW1vfBI9E9jvnlc6vFqlqiLEhpc2M4ZzY+9Jd+Tw6EW78MtjovE2XLrq70ecwsFIYilPQ9DhhWF8KZ4q6C7UFeLF3goLB9C8cQRyfvnace0dRuSj2PCie9qIjQk+f7tkTieSL5XhErCELbUx4NEfYz6lVv28Q73P2UXHJa2FXwGiiYIDy1kzy+l+TNV8gGP10QIDSkiVBjCYwkSjTFIQGhwQYvjYlWNRDbWYuG0X27cie1qIjysqOV7UzN3GtfuhLBRengZHk8+v6wgRLw2mvw9Nh8acxr/WtWLj4ZI9+x38VF7Ojw4aX8/tf5Dj2R53Ek0xpOfYQtCtPvCBmz/9ZsZjLyPKDCIdfxGYSUFeGOs3f1l5x3W7r7+SkP2REREZJwtGgAAIABJREFURLJk3bp1TJgwgWg0SkFBv/heUERERKS1fjFk72VgkplNMLMi4HzgsRzHJCIiIgPA+PHjKSoqYvv27S3KJ0+ejJmxbt26Xm1v3bp1mBlDhgxhyJAhjBkzhpkzZ7JkyZJebWdf3H333ZgZv/71r3MWg4iIiPRPfbqHFICZzQBuBcLAQnf/fifHP+XuZ2QluD7CzG5390tzHYeI7DtdvyJ9l5mtA5qA2939tqDsaOAh4FBgAnBFV69hMysAyoEPgEJ3j7XaPz59n5kdAJwHfB+41N0X9fxedZ2ZjQT+AsSAW91dWSnpd/Q+LJK/dP3mv77eQwp3f9LdD3X3iZ0lo4LjB1QyKnBergMQkW7T9SvSt90DXJi2PQe4O237i2a2ysxqzGyDmd3QvMPMxpuZm9mXzexD4H9bV25mnzWzdWZ2VOt97r7Z3X8K3ADcbGah4JwjzGyZme0ys7fMbFZQPiEoaz7uV2a2Na2te8zsm8HtZWb2XTN7zsx2m9nTZtZ6YZgfAj8DtiPSf+l9WCR/6frNc30+ISVdsivXAYhIt+n6FenbXgCGBUmgMMnpA+5N27+bZMJqBHAW8DUzO6dVHdOBI4DT0wvN7CLgZuCf3b2jGV9/D4wGDjOzQuAPwNNB2WXAfWZ2mLt/ANQAk4PzpgG1ZnZEWhzL0+r9PHBRUE8RcEVabB8DTgB+0UFcIv2B3odF8peu3zynhFT/oGWjRPKXrl+Rvq+5l9RpwBpgY9q+ze7+hrsn3H01sJhk4ifdDe5e5+7py+J+E7gSOMXdO1sis3kp2jLgJGAIMN/dI+7+v8DjwAXBMcuB6cFwP0gOL5xuZhOAYcDrafXe6e5/C+J6EDgWIEi83UFymKDWZJX+Tu/DIvlL12+e0/Iu/cOCXAcgIt2m61ek77sHeJbknFF3t9q3xMyWAkeR7GVUDPyu1TEb2qjzSuAmd6/sQvvjgp87gGOADa0SRevTjlkOzAIqg5iXAV8EGoGKVudtTrtdTzLRBXAJsNrdX+hCbCL5Tu/DIvlL12+eUw+pfsDddSGK5CldvyJ9n7uvJznZ+AySw+fSfY7kCsAHuftwkkPcWi913NYKMp8EvmNmn+1CCJ8GtgJvk+wtdVDzPFGBg9nTa2s5MBU4Jbi9Avgn9h6u15FPAJ82s81mthk4GbjFzG7v4vkieUPvwyL5S9dv/lMPKREREZHOfRkY6e51wWp5zYYCO9y9MZh36fMk53fqzFvAGcCfzCzq7o+1PsDMxgDnAtcD33D3hJm9SLI301VmdgvJZNOngP8L4O7vmFkD8AXgh+5eY2ZbgM+SHIbXFV8CStK2f09y6N9vuni+iIiISKeUkBIRERHphLu/186uS9jTe2g5ybmYRnSxztfNbCbwhJlFSc5PBbDLzAyoA1YC57r7U8E5ETP7FMnk0jUke0Zd6O5r06peDpzk7hvStg8HXu1iXC0miTWzCFDj7pqrQ0RERHqNubfVi1xERERERERERCQzNIeUiIiIiIiIiIhklRJSIiIiIiIiIiKSVUpIiYiIiIiIiIhIVikhJSIiIiIiIiIiWdXvVtkzs6fc/YwuHKrZ3EVEREREREREepd15aD+2ENqv1wHICIiIiIiIiIi7et3PaRERERERPqCHTt2sG3btk6PM0t+kezuRKNRtm7dyn777UdpaSklJSXU19cTDoeJx+MtzovH4xx88MEMGTIkI/GLiIhkkhJSIiIiIiK9rKmpiZ/97GdZaeuGG27ISjsiIiK9qT8O2RMRERERyaloNJrrEERERPo09ZASERERERERkbwVjUaprKyksbEx16EMKCUlJZSXl1NYWNit85WQEhEREREREZG8VVlZydChQxk/fnxqXj7JLHenqqqKyspKJkyY0K06NGRPRERERERERPJWY2Mjo0aNUjIqi8yMUaNG9ahXmhJSIiIiIiIiIpLXlIzKvp4+5kpIiYiIiIiIiIhIVikhJSIiIiLSy/RNvYjIwGJmfOELX0htx2Ix9t9/f2bOnLlP9SxbtqzNcxYtWsT+++/P5MmTmTRpEqeffjrPP/98j+PuTE1NDeXl5Vx66aW9XrcmNRcRkU7V1taydevWFmXujrsTCrX/3UY8HiccDlNQUEAsFuuwDXdn/PjxhMPhXolZJB/E43E2bdpELBbD3VPl0WiUcDjc7vUVj8cZPHhwi3kbzCxVh7un5nYYNmxYZu+EiIiIMHjwYN58800aGhooLS1lyZIljBs3bp/q6Ozz8nnnncftt98OwNKlS/nMZz7D0qVLOeKII7odd2euvfZapk2blpG6lZASEZFO/fjHP85aWzfccEPW2hLJtfvuu4/3338/o23omhIREcmOGTNm8MQTTzB79mwWL17MBRdcQEVFBQAvvfQS3/jGN2hsbKS0tJQ777yTww47jEWLFvH73/+e2tpa4vE4N954Y6q+l19+mblz5/LQQw/t1dapp57K3LlzWbBgAT/5yU947bXX+OpXv0p9fT0TJ05k4cKFRKNRzjzzTF555RVef/11jj32WNavX8/BBx/MxIkTeeONN7jkkksYNmwYK1euZPPmzfzoRz9i9uzZALzyyits2bKFM844g5UrV/b646WElIiIiEiOZDoZJSIiMtD88Y9/ZPPmzb1a5wEHHMCZZ57Z6XHnn38+N910EzNnzmT16tVcfPHFqYTU4YcfTkVFBQUFBTzzzDPMmzePhx9+GIBXX32V1atXU1ZWxrJlywB4/vnnueyyy3j00Uc5+OCDU/WkO+644/jlL38JwIUXXshtt93G9OnTue6667jxxhu59dZbaWxspKamhoqKCk444QQqKiqYMmUKo0ePZtCgQQBs2rSJFStWsHbtWmbNmsXs2bNJJBJ861vf4t577+WZZ57pjYdxL0pIiYiIiPRjzcP3REREJLOOOeYY1q1bx+LFi5kxY0aLfdXV1cyZM4d33nkHMyMajab2nXbaaZSVlaW216xZw9y5c3n66acZO3Zsu+01D9Wvrq5m165dTJ8+HYA5c+Zw7rnnAnDyySfz3HPP8eyzzzJv3jyeeuop3J2pU6em6jnnnHMIhUIceeSRbNmyBYA77riDGTNmUF5e3sNHpX1KSImIiEheW79+PZs2bQJoMQ9TaWlpao6l9PJmJSUlRCIREolEqqx14iYSiVBXV8eUKVMYOnRoJsIXERGRXtSVnkyZNGvWLK644gqWLVtGVVVVqvzaa6/l1FNP5ZFHHmHdunWccsopqX2DBw9uUceBBx5IY2Mjq1at6jAhtWrVqk7nj5o2bRoVFRWsX7+es88+m5tvvhkz46yzzkodU1xcnLrd/JnpL3/5CxUVFdxxxx3U1tYSiUQYMmQI8+fP79Lj0BVKSImIiEjG7N69mwULFrB79+59Oq+wsJDi4mJqa2szFNm+efHFFzUXk4iIiHTq4osvZsSIERx99NGp4XeQ7MXUPMn5okWLOqxjxIgR/OY3v+G0005j8ODBLZJXzZYvX86CBQtYunQpw4cPZ+TIkVRUVDB16lTuueeeVG+pqVOn8u1vf5tp06YRCoUoKyvjySef5Ic//GGHMdx3332p24sWLWLlypW9mowCaH9ppC4ws3Vm9oaZvWZmK4OyMjNbYmbvBD9HBuVmZj8zs3fNbLWZHZdWz5zg+HfMbE5a+fFB/e8G56q/uYiISB655ZZb9jkZBclV5vpKMirftdU7TERERDKjvLycyy+/fK/yq666imuuuYbJkyd3upoewJgxY3j88cf5+te/zosvvgjAAw88wLHHHsuhhx7KD37wAx5++OFUD6m77rqLK6+8kmOOOYbXXnuN6667DoDx48fj7qmV8qZMmcKIESMYOXJkb93lbrOefEgxs3XACe6+Pa3sR8AOd59vZlcDI939P8xsBnAZMAM4Efipu59oZmXASuAEwIFXgOPdfafZ/2/vzuPsqMu873+vOuf0lu5sJCSSgFnYlSWSUQRkHWWRB9DREXyEjAvRGdHxefT2RmdUBvR+xNdzz42io6LDsHiLqMjIjowaCCAQlhCWAFlIoEPWTro76e6zX/cfp87J6U5v6e6zdX/er1e/+tSvqn51VZ2qOnWu86tf2VOSvijpSUn3SfqBu98/RExPu/viYYTP1RkADFM5W4bQCmV8GU/vZynWpRzb55vf/KaCYFS/QWIEuru79b3vfa8syxpPxxkAjMTq1auHvHUNpTHAth9WY6JSXJ1cKOnm8PXNki4qKr/Fc56QNNXM3ibpbEkPuftOd98l6SFJ54TjJrv7E57Lmt1SVBcAAAAAAABq1Gj7kHJJfzAzl/RTd79B0ix33xyO3yJpVvh6jqQ3i+ZtDcsGK2/tp3wfZrZU0tJwcMaI1wb92r59e6FT2Lx8y7rB7qKMxWJKp9Nyd2WzWXV1damrq0uzZ8/ep66DDjpI0ejYdmmWyWR6PblguPLrVNx60Mz6veUhEokoFouNPEhgjGSzWd1zzz1at25dYX81MyWTSUUiEUUikX7nq6+v19atW9XS0qIgCOTu6uzs1JQpU2Rmymazw2pSDAAAAAD7Y7QZgFPcfZOZHSjpITN7pXiku3uYrCqpMBF2g5S7Za/Uy5tIXnzxRf32t78ty7LGurn5NddcM6b1DYRm8qgGTz31lJ599tkRz9+3j5+Ojo7RhgRgAli2bFmvDltL4dRTT9WZZ55Z0mUAAGpf/gdZlM9o+6kcVULK3TeF/7eZ2Z2S3i1pq5m9zd03h7fdbQsn3yTp4KLZ54ZlmySd3qd8WVg+t5/pUUYbNmyodAgAhmE8JZBWrlzZ68Mtm80W+r/p7yIjCALV19crkUgok8n0O52ZKZ1O66ijjlJTU1MJoweqTyk7NS91MkqSHnnkERJSAFBCTz75pJ577rleZV1dXUqn05oyZUq/89TX1ysIAkUikcIPm0EQFFrY57m7otGoPvnJT475HTHFGhoa1NbWpgMOOICkVJm4u9ra2tTQ0DDiOka8R5jZJEmBu+8OX39A0tWS7pK0RNJ3w/+/D2e5S9IVZvYr5To17wiTVg9K+h/5p/GF9XzN3XeaWaeZnahcp+aXSbp+pPECAGrDf/7nf5as7rvvvptWjQAAAKG2tjbdf//Azw3r6ekZk+V8//vf15e//OUxqas/c+fOVWtrq7Zv316yZWBfDQ0Nmjt37tATDmA0KcpZku4Ms49RSb909wfMbIWkX5vZpyVtlPS34fT3KfeEvbWSuiV9UpLCxNM1klaE013t7jvD1/8g6SZJjZLuD/+AqkLTUAAAAAC1qG9fwaXSt3uIsRaLxTR//vySLgNjb8QJKXdfL+m4fsrbJJ3VT7lL+vwAdd0o6cZ+yp+W9M6RxggAAAbX1tam66+nAfJ4Vq4+FQEAAPZH6W7ixLhAyx+gNnCsYqRWrlxZ6RCAIZXyVtslS5bwqzoGVK7bvP/5n/+5pP3rAEA1CiodAFDrStlZLAAAKK3bb7+9JPXyQwH2RzqdrnQIAFB2pOEBAJjA+NIMABjMddddp/b29pLVT+uwyuI6AJXEkQ8AAIAJazy0dC71bWX/9E//pFgsVtJlTHTVvB+WMhklSevWrdMRRxxR0mUAqE4kpAAAAAAMqK2tTbNnz650GBinqjkZBxT785//rIcffrhk9c+ePVuf+9znSlZ/NSIhBQBl8Pzzz+vOO++sdBjAPmiqj4mOL8MAgOEoZTJKkrZs2VLS+qsRCSkMii8qwNggGYXR+MUvfqG1a9dWOgwAExRJO2D84vseKomn7AGjxEUagFIjGQWUTqk+x/mSBwDA4GghBWDCy2azevjhh/XWW2/1Kk+lUopEIopEIgPOO3PmTO3atUvpdLrwpSYIcrn+/HBPT0+JIgcAYGL75S9/qddee63SYWAU+HEXmLhISNW4FStW6N57793v+WKxWK8v0MBE9vjjj4/4nnAuggGgtnEtVLs2bdo0bj6H2Q8BTEQkpGrcSJJRUq7lB4Cc7du3VzoEAACwn7ieRbX49a9/rZdffrlk9be0tOjLX/5yyeoHKoU+pAAAADBh0TJlaGwjYHClTEZJ0u7du0taP1ApJKQAAAAAAJiAeAADKolb9oBRGutfDdva2nT99dePaZ39+cY3vjFoZ90TCR/EADBx0fqndvH5PT5wDAIT14hbSJnZwWb2ZzN72cxeMrN/DMuvMrNNZrYy/DuvaJ6vmdlaM3vVzM4uKj8nLFtrZlcWlc83syfD8tvNrG6k8QK1ohzJKEm6//77y7IcAAAAAAD6Gs0te2lJX3b3oyWdKOnzZnZ0OO5/ufvx4d99khSOu1jSOySdI+nfzCxiZhFJP5J0rqSjJV1SVM+1YV2HStol6dOjiBdAka6urkqHUDX4hRUAJi5aZwAAUBkjvmXP3TdL2hy+3m1mqyXNGWSWCyX9yt0Tkl43s7WS3h2OW+vu6yXJzH4l6cKwvjMlfTyc5mZJV0n68UhjBgAAGKmrrrqq0iGgBEhIDa1atxE/KAGjx3GEShqTPqTMbJ6kRZKelHSypCvM7DJJTyvXimqXcsmqJ4pma9XeBNabfcrfI+kASe3unu5n+r7LXyppaTg4Y5SrA0wI2Wy20iEAAABA1Zv0K4eJvO77Y8WKFb2SR/lr+SAI+t2GkUikME0ymVRd3d7eb4qn37JlS6lCBoY06oSUmTVLukPSl9y908x+LOkaSR7+/5+SPjXa5QzG3W+QdEMYz9OlXBYwXvDhvxe/DAEAAKCa3XvvvZUOARhzo0pImVlMuWTU/3b330mSu28tGv8zSfeEg5skHVw0+9ywTAOUt0maambRsJVU8fRA1SCxAwAA+uL6AACAwY04IWW5JgX/Lmm1u/9rUfnbwv6lJOlDkl4MX98l6Zdm9q+SDpJ0mKSnJJmkw8xsvnIJp4slfdzd3cz+LOkjkn4laYmk3480XgC9caEMAEBOT0/Pfk1vZoXPUXfvt6VtPB4fk9iqQSKRqHQI/aKFMwDUttG0kDpZ0qWSXjCzlWHZ15V7St7xyt2yt0HSZyXJ3V8ys19Lelm5J/R93t0zkmRmV0h6UFJE0o3u/lJY33+X9Csz+7ak55RLgAEYAySk9uKCdmJ55plnJO1939PptCKRSGG477ERjUZlZkokEkqlUqqrq1M0GlUmk5GZKZvNKpPJqLm5WUceeaQikUh5VwjAqF177bWVDqGqZTKZSocAABiHRvOUvUeVa93U132DzPMdSd/pp/y+/uYLn7z37r7lAEavVAmpLVu26Cc/+UlJ6s778Ic/rGOPPbaky8D4dffdd5es7oULF+rSSy8tWf0AAIw3/EgKTFxBpQMAML6UOhklSb/73e/GtD5aSGGsrFu3rtIhAMCYq9aEwXj6/K7WbQwApTTqp+wBqE3r1q3Tj3/8Y0m5C7pMJqP6+nql0+le0+UvkFKplHbu3KkZM2aoq6tLzc3NhcfJmlnhliYAAAAAAIZCQgoYpa1btyoWixWGM5mM3F3RaP+HVxAEcncFQSAz65UAymazJY+32NatW4eeqI8dO3ZI2v8OYAEAQG2i9Q4AoBRISAGj9POf/7zSIWCUxlOTfwAAxhoJqeq1c+dO/eAHPxjx/NOmTdOuXbvGMKL9x/4FTFz0IQUAAACg5vCDkkaVjJJU8WQUgImNFlIAAAAABpTJZJTJZPZ7PjMbsPVLPpkUBPw+DgATFQkpABMev7ACADCw3/zmNyWr+6qrripZ3QCA6kZCCkBNGukF7GC/1gIAgNrBD0rjA9dlwMRFQgrAhMJFD0qNX/sBAACAoZGQQtXgSxwAAAAmIn4wAzAR0YsgAAAAAAAAyoqEFAAAAAAAAMqKhBQAAACAmkOn5uMDtysCExcJKQAAAAA1p7W1tdIhjJlsNlvpEACg7Ko+IWVm55jZq2a21syurHQ8AAAAADCWJnIroY6OjkqHMCokE4GRq+qElJlFJP1I0rmSjpZ0iZkdXdmoAAAAAFQat+yNDxM5GQdMdFbNJwAze6+kq9z97HD4a5Lk7v/fIPM87e6Lh1F99a74frjqqqsqHQIAAAAAAINauHBhIQEZiUTU3d2tVCqlSCSizZs3S5IOOeQQxWIxuXsh6ZzJZLRhw4ZKhV1W4+j7/bB+MYiWOopRmiPpzaLhVknvqVAsAAAAAABgBNatWzfkNG+88UYZIkG1qPaE1LCY2VJJS8PBGZWMpdwuv/xyPf/8873K3F2xWEzpdHrA+bZv365sNqt58+app6dHkpRMJlVXV9ernh07dmjKlCmaP39+r/mz2azMbNCm0t3d3WpqapK7F+6tTiQSampq6jXd8uXLtWPHjuGt8H4688wzNXv27F5l+Wy7mfXbRDiZTKq+vr4wbTab1ebNmzV16lQ1NzcX5kmlUrr33nvV3d095nHPmTNHkydP7hVzLBZTJpMpbMviXw0kqbOzU+6uqVOnyt3V3d2t7u5uRaNRTZs2rTBdT0+PksmkjjvuuMJ65uuLRqO97oMv3j7urq1bt2r27NmFfaW9vV1TpkyRmSmVSmndunV6+9vfvs97LOV+2QiCge8STiaTkqRYLCZJhTrzw/myjo4OrV27VplMZp/56+rq+t0nk8mktm3bpiAINGPGDMXjcXV2dmr69OlqbGyUJB1++OE66KCDes031H7u7urq6lJTU1Nh3bLZrLZt26bJkyerqalJ2WxWb731ljKZjB599NEB13+sfOITn+gV71DrkM1mlUgk1NDQICm3jfPHrpRbR3dXY2NjYZr8dPlxQRAM2Nx+z549isVihW3h7urp6VE6nVZLS0uvabu7u7V69erCdHmZTEaRSKSw3L7y+24mkykcs2+99ZamT5+uSZMmFeZLJBKKRCL71JVMJhWLxQrDfdclFosVtlN++2zZskULFiwoLDedTisSiaijo6Owbxaf+yQVllu87EQiodbWVjU2Nqqnp0czZsxQKpVSJpPRpEmT1NbWpmw2qxkzZhTWJS8ejyudTqu5ublQX/59SSaThb/8/llXV6dEIqHdu3frgAMOKNSTSCS0cOFCzZw5s9f2zW+Hgc6VO3bskLurqampsG+0t7eroaGhEGs6nVY8HldLS8s+713x+9q3/vx2bmtr0/Tp09XU1CQzU1dXlxobG3vtc+6uVCpVOJbz58ZsNqtMJtPrHFK8Tvn4zEyRSKTwfq1fv14HHXRQr3XYs2ePGhsbe72H7q50Oq1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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Date', 'Temperature', 'Fuel_Price', 'CPI', 'Unemployment', \n",
    "    'MarkDown1', 'MarkDown2', 'MarkDown3', 'MarkDown4', 'MarkDown5']].plot(x='Date', subplots=True, \n",
    "    figsize=(20,15),title=['Temperature', 'Fuel_Price', 'CPI', 'Unemployment','MarkDown1', 'MarkDown2', 'MarkDown3', 'MarkDown4', 'MarkDown5'])\n",
    "sns.despine(offset=10,bottom=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The change spectrum of all Markdowns are pretty much similar! though the intensity varies!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at total stores and the departments in them!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "store_department = data.groupby(['Store'])['Dept'].nunique().reset_index()\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.barplot(data=store_department,x='Store',y='Dept',palette='bright')\n",
    "sns.despine(offset=10)\n",
    "plt.title('Number of departments per store')\n",
    "plt.ylabel('Number of departments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, number of departments per store lie between 60-80!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets look at the stores with maximum sales!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_sale_stores = data.groupby(['Store'])['Weekly_Sales'].sum().reset_index()\n",
    "sns.barplot(data=max_sale_stores.nlargest(5,columns='Weekly_Sales'),x='Store',y='Weekly_Sales')\n",
    "plt.title('Sales comparison of top selling stores')\n",
    "sns.despine(offset=10)\n",
    "plt.ylabel('Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=max_sale_stores.nsmallest(5,columns='Weekly_Sales'),x='Store',y='Weekly_Sales')\n",
    "plt.title('Sales comparison of bottom selling stores')\n",
    "plt.ylabel('Sales')\n",
    "sns.despine(offset=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J0sIY9BxDkiVJzgAuBb5SVSfPMs6BSVYkWQFsM2Q9kqT5DRoMVbWyqnYHlgF7JLnHLOMcWVXLq2o5cPmQ9UiS5rcoVyVV1VXA14G9FqM9SdJNN+RVSdsm2bo93gx4LHDOUO1JkhbGkFclbQ98OMkSugA6pqqOHbA9aVYP+b8PmXYJg/jWId+adglaTw15VdIPgPsMNX1J0jD85rMkqcdgkCT1GAySpJ41DoYkt0iy5RDFSJKmb6JgSPLxJFsm2Rw4E/hRkpcNW5okaRom3WPYraquAZ4CfAm4A/DMwaqSJE3NpMGwcbtD6lOAL1TVn/CGeJK0Xpo0GN4LnA9sDpyYZGfgmqGKkiRNz0RfcKuqdwLvHOt1QZJHDlOSJGmaJj35vF2SDyT5UuveDXj2oJVJkqZi0kNJRwHHAUtb94+BFw1RkCRpuiYNhm2q6hjgRoCqugFYOVhVkqSpmTQYfpvkdrQrkZI8ELh6sKokSVMz6d1VXwJ8AbhTkm8B2wL7DlaVJGlqJr0q6bQkDwd2BQKc277LIElaz6w2GJI8dY5Bd01CVX12gJokSVM03x7DE1czrACDQZLWM6sNhqp6zmIVIklaO0z8055JHg/cHdh01K+qDh+iKEnS9Ez6zecjgKcBh9CdfP5rYOcB65IkTcmk32N4cFU9C7iyql4HPAi463BlSZKmZdJguL79/12SpcANwPbDlCRJmqZJzzEcm2Rr4K3Aqa3f+4cpSZI0TfN9j+H+wIVV9frWfWvgh8A5wL8MX54kabHNdyjpvcAfAZI8DHhz63c1cOSwpUmSpmG+Q0lLquqK9vhpwJFV9RngM0nOGLY0SdI0zLfHsCTJKDweDXxtbNjE34GQJK075tu4Hw18I8nldFcmfRMgyZ3xttuStF6a75YYb0zyVbpLU4+vqmqDbkH3ZTdJ0npm3sNBVfXdWfr9eJhyJEnTNukX3CRJGwiDQZLUYzBIknoMBklSz2DBkGTHJF9P8qMkZyU5dKi2JEkLZ8gvqd0AHFZVpyXZAjg1yVeq6kcDtilJupkG22Ooql9V1Wnt8bXA2cAOQ7UnSVoYi3JbiyS7APcBTp5l2IHAga1zm8WoR5I0t8FPPrdbdX8GeFFVXTNzeFUdWVXLq2o5cPnQ9UiSVm/QYEiyMV0ofKyqPjtkW5KkhTHkVUkBPgCcXVVvH6odSdLCGnKP4SHAM4FHJTmj/e0zYHuSpAUw2MnnqjoJyFDTlyQNw28+S5J6DAZJUo/BIEnqMRgkST0GgySpx2CQJPUYDJKkHoNBktRjMEiSegwGSVKPwSBJ6jEYJEk9BoMkqcdgkCT1GAySpB6DQZLUYzBIknoMBklSj8EgSeoxGCRJPQaDJKnHYJAk9RgMkqQeg0GS1GMwSJJ6DAZJUo/BIEnqMRgkST0GgySpx2CQJPUYDJKkHoNBktRjMEiSegwGSVLPYMGQ5INJLk1y5lBtSJIW3pB7DEcBew04fUnSAAYLhqo6EbhiqOlLkoax0bQLSHIgcGDr3GaatUiS1oKTz1V1ZFUtr6rlwOXTrkeSNnRTDwZJ0trFYJAk9Qx5uerRwHeAXZP8MskBQ7UlSVo4g518rqr9hpq2JGk4HkqSJPUYDJKkHoNBktRjMEiSegwGSVKPwSBJ6jEYJEk9BoMkqcdgkCT1GAySpB6DQZLUYzBIknoMBklSj8EgSeoxGCRJPQaDJKnHYJAk9RgMkqQeg0GS1GMwSJJ6DAZJUo/BIEnqMRgkST0GgySpx2CQJPUYDJKkHoNBktRjMEiSegwGSVKPwSBJ6jEYJEk9BoMkqcdgkCT1GAySpJ5BgyHJXknOTfLTJK8Ysi1J0sIYLBiSLAHeDewN7Absl2S3odqTJC2MIfcY9gB+WlXnVdUfgU8ATx6wPUnSAkhVDTPhZF9gr6p6Xut+JvCAqjp4xngHAge2zk2r6h6DFCRJmshG0y6gqo4Ejpx2HZKkzpCHki4CdhzrXtb6SZLWYkMGw/eAuyS5Q5JbAk8HvjBge5KkBTDYoaSquiHJwcBxwBLgg1V11lDtSZIWxmAnnyVJ6ya/+SxJ6jEYJEk9BoMkqcdgkCT1TP0LbjdFki8D20y5jG2Ay6dcw9rCZbGKy2IVl8Uqa8OyuLyq9ppkRK9KuomSrKiq5dOuY23gsljFZbGKy2KVdW1ZeChJktRjMEiSegyGm84b/63isljFZbGKy2KVdWpZeI5BktTjHoMkqcdgkCT1GAwTSLJpklOSfD/JWUle1/o/OslpSc5IclKSO0+71iEl2bXN6+jvmiQvSvJPSc5J8oMkn0uy9bRrXQxJDk1yZlsnXtT63TbJV5L8pP2/zbTrXAxzLAvXi1XL4rVJLhp77+wz7TpXx3MME0gSYPOqui7JxsBJwKHAR4AnV9XZSV4I7FFV+0+x1EWTZAndDy89ANgV+Fq71fpbAKrq5dOsb2hJ7kH3O+Z7AH8EvgwcRPcztVdU1ZuTvAK4zQa8LO6I68VoWTwDuK6q/nmK5U3MPYYJVOe61rlx+6v2t2XrvxVw8RTKm5ZHAz+rqguq6viquqH1/y7dr/Wt7+4GnFxVv2vz/g3gqcCTgQ+3cT4MPGVK9S2mWZeF60VvvVinGAwTSrIkyRnApcBXqupk4HnAF5P8Engm8OZp1rjIng4cPUv/5wJfWuRapuFM4KFJbpfkVsA+dD9lu11V/aqN82tgu2kVuIjmWhbjNvT1AuDgdljtg2v7IUaDYUJVtbKqdqf71LNH22V8MbBPVS0DPgS8fZo1Lpb2U61PAj41o/+rgBuAj02jrsVUVWcDbwGOpztccAawcsY4o73K9dp8y8L1gpXAe4A7AbsDvwLeNq0aJ2EwrKGqugr4OrA3cO+25wDwSeDBUytsce0NnFZVl4x6JNkfeALwt7WBnLiqqg9U1f2q6mHAlcCPgUuSbA/Q/l86zRoXyxzLwvWiLYuquqR9uLwReB/dOYi1lsEwgSTbjq6oSLIZ8FjgbGCrJHdto436bQj2Y+wwUpK9gH8AnlRVv5taVYssye3b/53ojiN/HPgC8Ow2yrOBz0+nusU127Jwvegti+3HRvlLukNOay2vSppAknvRnUhcQhemx1TV4Un+EjgcuJHuk8Fzq+q86VU6vCSbA78A7lhVV7d+PwU2AX7TRvtuVR00pRIXTZJvArcD/gS8pKq+muR2wDHATsAFwN9U1RVTLHNRzLEsXC9WLYuP0h1GKuB84AVj56LWOgaDJKnHQ0mSpB6DQZLUYzBIknoMBklSj8EgSeoxGLReSbKy3b3yrHY33MOSDL6eJ9k/ydL1pR1t2AwGrW+ur6rdq+rudF863Bt4zZANtjvN7g8sxgZ7sdrRBsxg0Hqrqi6luw32weksab8R8L12M7MXACR5RJITk/xnknOTHDHay0jyniQrxn+Ho/U/P8lbkpxG903w5cDH2t7KZm34m1r3iiT3TXJckp8lOWhsOi8bq2f0Ox+7JDk7yftau8e3ae47SztvTvKj9vx14pbOWvsZDFqvtW+iLwFuDxwAXF1V9wfuDzw/yR3aqHsAhwC70d3sbHSr5FdV1XLgXsDD27fgR35TVfetqn8DVtDdD2j3qrq+Df9Fu/HiN4GjgH2BBwKjAHgccJfW9u7A/ZI8rD33LsC7257PVcBfVdWnx9sBbkV3e4W7V9W9gDfc/CUmGQzasDwOeFa7ffrJdLctuEsbdkpVnVdVK+nuA7Vn6/83ba/gdODudMEx8sl52vtC+/9Dunv0X1tVlwF/aPfeelz7Ox04DfiLsXp+XlVntMenArvMMv2rgd8DH0jyVGCDuR+RhrXRtAuQhpTkjnS3Pb4UCHBIVR03Y5xH8Oe3x662N/FS4P5VdWWSo4BNx8b57TzN/6H9v3Hs8ah7o1bPm6rqvTPq2WXG+CuBzWZOvP0y2h50P5q0L3Aw8Kh5apLm5R6D1ltJtgWOAN7Vbvl8HPB37edZSXLXdlNA6H5j4w7t3MLT6H6+dUu6jf/VSbajO5E9l2uBLdawxOOA5ya5datnh9GdOSdppz1vq6r6It1vg9x7DduXZuUeg9Y3m7VDRRvT/TjMR1n1A0rvpzskc1qSAJex6qc3vwe8C7gz3e9tfK6qbkxyOnAOcCHwrdW0exRwRJLrgQdNUmhVHZ/kbsB3unK4ju63gVeu5mnj7ewNfD7JpnR7Hy+ZpF1pPt5dVRu8dijppVX1hGnXIq0NPJQkSepxj0GS1OMegySpx2CQJPUYDJKkHoNBktRjMEiSev4/ITQDjPVh7e8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_department_sales = data.groupby('Dept')['Weekly_Sales'].sum().reset_index().nlargest(5,columns='Weekly_Sales')\n",
    "sns.barplot(data=max_department_sales,x='Dept',y='Weekly_Sales')\n",
    "plt.title('Sales comparison of top selling departments')\n",
    "plt.ylabel('Sales')\n",
    "plt.xlabel('Departments')\n",
    "sns.despine(offset=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay these five departments are the best selling ones but this is not really any usefull information unless attributed with the store. So lets plot the top 5 departments of top 5 selling stores!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we already know the top selling stores! [2,4,13,14,20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store  Dept\n",
       "2      1        4401251.25\n",
       "       2        9425547.92\n",
       "       3        2499148.56\n",
       "       4        6521896.32\n",
       "       5        4369410.09\n",
       "       6         973598.70\n",
       "       7        5788330.70\n",
       "       8        8395153.83\n",
       "       9        4915748.62\n",
       "       10       5554957.19\n",
       "       11       3343724.12\n",
       "       12       1106240.32\n",
       "       13       6594868.17\n",
       "       14       3047921.01\n",
       "       16       3861740.69\n",
       "       17       1972092.15\n",
       "       18       1320694.62\n",
       "       19        579641.91\n",
       "       20        822052.58\n",
       "       21       1417264.40\n",
       "       22       1847990.98\n",
       "       23       4171398.17\n",
       "       24       1188267.73\n",
       "       25       2187829.70\n",
       "       26       1336724.69\n",
       "       27        262827.51\n",
       "       28        114244.59\n",
       "       29       1016942.02\n",
       "       30        712305.22\n",
       "       31        555520.25\n",
       "                  ...     \n",
       "20     52        466761.31\n",
       "       54         31289.80\n",
       "       55       2833685.59\n",
       "       56        771711.24\n",
       "       58        594281.59\n",
       "       59        240390.92\n",
       "       60         83894.30\n",
       "       67       2493569.31\n",
       "       71       1203350.06\n",
       "       72      13154550.44\n",
       "       74       3187344.60\n",
       "       77          3150.74\n",
       "       78            29.00\n",
       "       79       5519408.07\n",
       "       80       5701519.74\n",
       "       81       4713078.80\n",
       "       82       4350270.76\n",
       "       83        804367.77\n",
       "       85        714110.01\n",
       "       87       6335146.57\n",
       "       90      13707777.99\n",
       "       91      11224526.19\n",
       "       92      23542625.04\n",
       "       93       7553057.47\n",
       "       94       9030211.90\n",
       "       95      21537795.62\n",
       "       96           244.27\n",
       "       97       3694556.90\n",
       "       98       2757665.96\n",
       "       99         35030.77\n",
       "Name: Weekly_Sales, Length: 390, dtype: float64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_sale_stores_departments = data[data['Store'].isin([2,4,13,14,20])]\n",
    "max_sale_stores_departments = max_sale_stores_departments.groupby(['Store','Dept'])['Weekly_Sales'].sum()\n",
    "max_sale_stores_departments = max_sale_stores_departments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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