PCA wholesale customers segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA on wholesale customers data.\n",
    "\n",
    "## A customer segmentation analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset used can be found [here](https://archive.ics.uci.edu/ml/datasets/Wholesale+customers).\n",
    "It is originated from a larger database referred on: \n",
    "\n",
    "*Abreu, N. (2011). Analise do perfil do cliente Recheio e desenvolvimento de um sistema promocional. Mestrado em Marketing, ISCTE-IUL, Lisbon*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of variables: 8 \n",
      "Number of observations: 440\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dataset = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00292/Wholesale%20customers%20data.csv'\n",
    "df = pd.read_csv(dataset)\n",
    "\n",
    "print('Number of variables:', df.shape[1], '\\nNumber of observations:', df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variable description:\n",
    "\n",
    "- FRESH: annual spending (m.u.) on fresh products (Continuous)\n",
    "- MILK: annual spending (m.u.) on milk products (Continuous)\n",
    "- GROCERY: annual spending (m.u.) on grocery products (Continuous)\n",
    "- FROZEN: annual spending (m.u.) on frozen products (Continuous) \n",
    "- DETERGENTS_PAPER: annual spending (m.u.) on detergents and paper products (Continuous) \n",
    "- DELICATESSEN: annual spending (m.u.) on and delicatessen products (Continuous) \n",
    "- CHANNEL: customers Channel - Horeca (Hotel/Restaurant/Cafe) or Retail channel (Nominal) \n",
    "- REGION: customers Region - Lisbon, Oporto or Other (Nominal) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30379</td>\n",
       "      <td>13252</td>\n",
       "      <td>5189</td>\n",
       "      <td>321</td>\n",
       "      <td>51</td>\n",
       "      <td>1450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5113</td>\n",
       "      <td>1486</td>\n",
       "      <td>4583</td>\n",
       "      <td>5127</td>\n",
       "      <td>492</td>\n",
       "      <td>739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12238</td>\n",
       "      <td>7108</td>\n",
       "      <td>6235</td>\n",
       "      <td>1093</td>\n",
       "      <td>2328</td>\n",
       "      <td>2079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>18827</td>\n",
       "      <td>3677</td>\n",
       "      <td>1988</td>\n",
       "      <td>118</td>\n",
       "      <td>516</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9155</td>\n",
       "      <td>1897</td>\n",
       "      <td>5167</td>\n",
       "      <td>2714</td>\n",
       "      <td>228</td>\n",
       "      <td>1113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Channel  Region  Fresh   Milk  Grocery  Frozen  Detergents_Paper  \\\n",
       "141        1       3  30379  13252     5189     321                51   \n",
       "329        1       2   5113   1486     4583    5127               492   \n",
       "281        2       3  12238   7108     6235    1093              2328   \n",
       "144        1       3  18827   3677     1988     118               516   \n",
       "321        1       2   9155   1897     5167    2714               228   \n",
       "\n",
       "     Delicassen  \n",
       "141        1450  \n",
       "329         739  \n",
       "281        2079  \n",
       "144         201  \n",
       "321        1113  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    298\n",
       "2    142\n",
       "Name: Channel, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1 - HoReCa\n",
    "# 2 - Retail\n",
    "df['Channel'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    316\n",
       "1     77\n",
       "2     47\n",
       "Name: Region, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1 - Lisbon\n",
    "# 2 - Oporto\n",
    "# 3 - Other\n",
    "df['Region'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe7b91f45c0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b+dEiMpItm3OfHzu2b6NV6ygAWrWOYkuO82fyxAm8+vobOFPl1k3Qv64EIPbA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\nT3dmdX0ADzcjKeYMks0ZhPh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XTNuxOKKU8tdaX1JKDQKes1VJBF7TWm9TSlUFjgKvAL2A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\nMcYYE8H/A3Xk8NY8TUYTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7b91d8c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_context('talk')\n",
    "%matplotlib inline\n",
    "\n",
    "# Subset to continous vars only (I'll deal with nominal vars later)\n",
    "cont_vars = ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']\n",
    "\n",
    "# Plot a correlation heatmap\n",
    "sns.heatmap(df[cont_vars].corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Fresh          Milk       Grocery        Frozen  \\\n",
       "count     440.000000    440.000000    440.000000    440.000000   \n",
       "mean    12000.297727   5796.265909   7951.277273   3071.931818   \n",
       "std     12647.328865   7380.377175   9503.162829   4854.673333   \n",
       "min         3.000000     55.000000      3.000000     25.000000   \n",
       "25%      3127.750000   1533.000000   2153.000000    742.250000   \n",
       "50%      8504.000000   3627.000000   4755.500000   1526.000000   \n",
       "75%     16933.750000   7190.250000  10655.750000   3554.250000   \n",
       "max    112151.000000  73498.000000  92780.000000  60869.000000   \n",
       "\n",
       "       Detergents_Paper    Delicassen  \n",
       "count        440.000000    440.000000  \n",
       "mean        2881.493182   1524.870455  \n",
       "std         4767.854448   2820.105937  \n",
       "min            3.000000      3.000000  \n",
       "25%          256.750000    408.250000  \n",
       "50%          816.500000    965.500000  \n",
       "75%         3922.000000   1820.250000  \n",
       "max        40827.000000  47943.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive stats for each variable\n",
    "\n",
    "df[cont_vars].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although values are all measured as annual spending, they have very disparate std (~13000 for Fresh, ~2800 for Delicassen) which calls for value scaling before undergoing PCA. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import scale\n",
    "\n",
    "# Scale vars (with z-score)\n",
    "X_std = scale(df[cont_vars], axis=0, with_mean=True, with_std=True, copy=True)\n",
    "\n",
    "# Confirm if vars are scaled if == 1\n",
    "np.std(X_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Start PCA object and fit to data using sklearn library\n",
    "pca = PCA()\n",
    "pca.fit(X_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ezsWnjCUQCIQ7NGNMK1raEuPRzn4zEZkG3IKbyRQH3Kuqt4tILvAAcCiuCNBT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\naiKHjOnD1HF5jBiQYdNhje/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7b3d8ecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do a scree-plot to decide how many components to retain\n",
    "\n",
    "expvar = pca.explained_variance_ratio_*100\n",
    "plt.ylabel('Proportion of explained variance (%)')\n",
    "plt.xlabel('Components')\n",
    "ncomps = ['PC'+str(i+1) for i in range(0, pca.n_components_)]\n",
    "plt.xticks(range(6), ncomps)\n",
    "plt.title('Scree plot')\n",
    "plt.plot(expvar)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'inflexion point' in the scree-plot is a rule of thumb on how many components to retain (also known as the 'elbow rule'). Based on the scree-plot I should retain the first three components. Lets check the proportion of variation of the original data these three components explain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of explained variance by the three first components: 84.7937053045 %\n"
     ]
    }
   ],
   "source": [
    "print('Proportion of explained variance by the three first components:', \n",
    "      expvar[0]+expvar[1]+expvar[2],'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first three components explain most of the variance (~85%) of the original dataset. That's fair enough. I'll retain these 3 components for further analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loadings\n",
    "Component loadings are the correlation between each component and the original variables. It is a measure of the variation of a variable that is explained by the component."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "            \n",
       "                color:  black;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow4_col2 {\n",
       "            \n",
       "                color:  black;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow4_col3 {\n",
       "            \n",
       "                color:  black;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow4_col4 {\n",
       "            \n",
       "                color:  black;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow4_col5 {\n",
       "            \n",
       "                color:  black;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow5_col0 {\n",
       "            \n",
       "                color:  black;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow5_col1 {\n",
       "            \n",
       "                color:  green;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow5_col2 {\n",
       "            \n",
       "                color:  green;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow5_col3 {\n",
       "            \n",
       "                color:  black;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow5_col4 {\n",
       "            \n",
       "                color:  black;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow5_col5 {\n",
       "            \n",
       "                color:  black;\n",
       "            \n",
       "            }\n",
       "        \n",
       "        </style>\n",
       "\n",
       "        <table id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdf\" None>\n",
       "        \n",
       "\n",
       "        <thead>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                \n",
       "                <th class=\"blank level0\" >\n",
       "                  \n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col0\" colspan=1>\n",
       "                  PC1\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col1\" colspan=1>\n",
       "                  PC2\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col2\" colspan=1>\n",
       "                  PC3\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col3\" colspan=1>\n",
       "                  PC4\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col4\" colspan=1>\n",
       "                  PC5\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col5\" colspan=1>\n",
       "                  PC6\n",
       "                \n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </thead>\n",
       "        <tbody>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                \n",
       "                <th id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdf\"\n",
       "                 class=\"row_heading level0 row0\" rowspan=1>\n",
       "                    Fresh\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow0_col0\"\n",
       "                 class=\"data row0 col0\" >\n",
       "                    0.0697438\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow0_col1\"\n",
       "                 class=\"data row0 col1\" >\n",
       "                    0.688862\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow0_col2\"\n",
       "                 class=\"data row0 col2\" >\n",
       "                    -0.69876\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow0_col3\"\n",
       "                 class=\"data row0 col3\" >\n",
       "                    -0.177708\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow0_col4\"\n",
       "                 class=\"data row0 col4\" >\n",
       "                    -0.0260203\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow0_col5\"\n",
       "                 class=\"data row0 col5\" >\n",
       "                    -0.00904023\n",
       "                \n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                \n",
       "                <th id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdf\"\n",
       "                 class=\"row_heading level0 row1\" rowspan=1>\n",
       "                    Milk\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow1_col0\"\n",
       "                 class=\"data row1 col0\" >\n",
       "                    0.886546\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow1_col1\"\n",
       "                 class=\"data row1 col1\" >\n",
       "                    0.10852\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow1_col2\"\n",
       "                 class=\"data row1 col2\" >\n",
       "                    0.05195\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow1_col3\"\n",
       "                 class=\"data row1 col3\" >\n",
       "                    -0.0654639\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow1_col4\"\n",
       "                 class=\"data row1 col4\" >\n",
       "                    0.441796\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow1_col5\"\n",
       "                 class=\"data row1 col5\" >\n",
       "                    -0.00954582\n",
       "                \n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                \n",
       "                <th id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdf\"\n",
       "                 class=\"row_heading level0 row2\" rowspan=1>\n",
       "                    Grocery\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow2_col0\"\n",
       "                 class=\"data row2 col0\" >\n",
       "                    0.942066\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow2_col1\"\n",
       "                 class=\"data row2 col1\" >\n",
       "                    -0.19062\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow2_col2\"\n",
       "                 class=\"data row2 col2\" >\n",
       "                    -0.0932395\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow2_col3\"\n",
       "                 class=\"data row2 col3\" >\n",
       "                    0.0795775\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow2_col4\"\n",
       "                 class=\"data row2 col4\" >\n",
       "                    -0.168363\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow2_col5\"\n",
       "                 class=\"data row2 col5\" >\n",
       "                    0.181115\n",
       "                \n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                \n",
       "                <th id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdf\"\n",
       "                 class=\"row_heading level0 row3\" rowspan=1>\n",
       "                    Frozen\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow3_col0\"\n",
       "                 class=\"data row3 col0\" >\n",
       "                    0.0832499\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow3_col1\"\n",
       "                 class=\"data row3 col1\" >\n",
       "                    0.797614\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow3_col2\"\n",
       "                 class=\"data row3 col2\" >\n",
       "                    0.15346\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow3_col3\"\n",
       "                 class=\"data row3 col3\" >\n",
       "                    0.577142\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow3_col4\"\n",
       "                 class=\"data row3 col4\" >\n",
       "                    -0.0149294\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow3_col5\"\n",
       "                 class=\"data row3 col5\" >\n",
       "                    -0.00392399\n",
       "                \n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                \n",
       "                <th id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdf\"\n",
       "                 class=\"row_heading level0 row4\" rowspan=1>\n",
       "                    Detergents_Paper\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow4_col0\"\n",
       "                 class=\"data row4 col0\" >\n",
       "                    0.892274\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow4_col1\"\n",
       "                 class=\"data row4 col1\" >\n",
       "                    -0.333036\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow4_col2\"\n",
       "                 class=\"data row4 col2\" >\n",
       "                    -0.117162\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow4_col3\"\n",
       "                 class=\"data row4 col3\" >\n",
       "                    0.128949\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow4_col4\"\n",
       "                 class=\"data row4 col4\" >\n",
       "                    -0.181533\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow4_col5\"\n",
       "                 class=\"data row4 col5\" >\n",
       "                    -0.172118\n",
       "                \n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                \n",
       "                <th id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdf\"\n",
       "                 class=\"row_heading level0 row5\" rowspan=1>\n",
       "                    Delicassen\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow5_col0\"\n",
       "                 class=\"data row5 col0\" >\n",
       "                    0.404441\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow5_col1\"\n",
       "                 class=\"data row5 col1\" >\n",
       "                    0.657905\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow5_col2\"\n",
       "                 class=\"data row5 col2\" >\n",
       "                    0.450699\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow5_col3\"\n",
       "                 class=\"data row5 col3\" >\n",
       "                    -0.414501\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow5_col4\"\n",
       "                 class=\"data row5 col4\" >\n",
       "                    -0.168203\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_87b5e990_f9c0_11e6_b594_408d5c5c2cdfrow5_col5\"\n",
       "                 class=\"data row5 col5\" >\n",
       "                    -0.0188542\n",
       "                \n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </tbody>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<pandas.formats.style.Styler at 0x7fe7b3b039e8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# The interpretation of component loadings varies. Some rule of thumb say a value around \n",
    "# 0.5 is fair enough to be considered for loadings interpretation. I decided for 0.45.\n",
    "\n",
    "def colordf(val):\n",
    "    color = 'green' if val > 0.45 else 'red' if val < -0.45 else 'black'\n",
    "    return('color: %s' % color)\n",
    "\n",
    "loadings = pd.DataFrame(pca.components_.T * np.sqrt(pca.explained_variance_))\n",
    "loadings.index = cont_vars\n",
    "loadings.columns = ['PC'+str(i+1) for i in range(len(loadings.columns))]\n",
    "s = loadings.style.applymap(colordf)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AncAV6exrgbsjYjTwSZJQHpPOuxy4KiJ2Av4bsAUwGtgP+CywU7rcd4BD02XW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7b35d3400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3) = plt.subplots(3, sharex=True, sharey=True)\n",
    "loadings['PC1'].plot(kind=\"barh\", title=\"PC1\", ax=ax1, figsize=(6,10))\n",
    "loadings['PC2'].plot(kind=\"barh\", title=\"PC2\", ax=ax2)\n",
    "loadings['PC3'].plot(kind=\"barh\", title=\"PC3\", ax=ax3, xlim=[-1,1])\n",
    "f.subplots_adjust(hspace=0.35)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The first component (PC1) loads highly on Milk, Grocery, Detergents/Paper and somewhat high on Delicassen.\n",
    "- The second component (PC2) loads highly on Fresh, Frozen and Delicassen.\n",
    "- The third component (PC3) loads highly and negatively on Fresh and positevely on Delicassen.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axes._subplots.AxesSubplot at 0x7fe7b32c3f98>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x7fe7b32c3f98>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x7fe7b32c3f98>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x7fe7b32c3f98>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x7fe7b32c3f98>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x7fe7b32c3f98>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VKlx2xBUTmEzSWHNJV5ImgRUDK1i07PZR992x7Hsu70olZ+GTpEngkdVLWD6wfNR9yweW\ns/TJR8Y5kaTxZOGTpElg3/79mN07e9R9s3tns8/O+45zIknjycInSZPArN5ZzJ/zqlH3zZ/zKmb1\nzhrnRJLGkzdtSNIkcdHhlwCMepeupHKz8EnSJNE7pZfLj7iSFQMrWPrkI+yz876e2ZMmCQufJE0y\ns3pnWfSkScZr+CRJkkpuq2f4IqIP+D/Aq4FfA5/MzEUNcwYy08e0S5IktaFmzvBdAPwJcB3wS+Df\nIuLYhjmVVgeTJElSazRzDd+bgVdl5lKAiLgO+MeI+HVm/nsxpzpWASVJkrR9mjnDN4PamT0AMvMW\n4GTgnyJi3lgFkyRJUms0U/juBT5QP5CZ1wHnAd+JiIPHIpgkSZJao5nCdwZwTkRcXD+YmX8DnAv8\nB7BD66NJkiSpFbZ6DV9mLiqWbvcYZd/fRcRC4F3bGyQiDgG+COwGDAJnZ+a1o8w7HjgbmAIsB07J\nzHu25RiSJEmTSVPP4cvMZdSWdp8lIqZn5i8y81PbEyIipgE3ARdn5lzgKOCSiHhRw7yXAl8A3pKZ\newGfA26KiKnNHkOSJGmyaarwRcTfAZ8eZdcPIuKvW5DjcGA4M68CyMwHgIXAcQ3zjgW+mZmLi3lf\no/Y1vGYbjiFJkjSpNPPg5fcCbwXeMcruE4BvRsTizPzH7cgxD1jSMLYEOHCUeXc3jD0MHECt+DVz\njC3q6mrvRwqO5DNna5izdTohI5iz1do9n6SaZp7DdyJwamZ+r3FHZv6wKIQfAran8PUB6xvG1gPT\nt2FepcljbFF/f9+2TJ8w5mwtc7ZOJ2QEc0qaXJopfPsB//8W9n8L+PJ25lgL7Nww1g+s2YZ5XU0e\nY4tWr17H8HD7Pke6q6tCf3+fOVvEnK3TCRnBnK02klNSe2um8PUAw1vYX2X7P1ptMXBmRHRl5sif\nNQ+4b5R5MbIREd3UCuliaoWvmWNs0fBwlaGh9v3hOsKcrWXO1umEjGBOSZNLMzdtPAi8fgv7jwIe\n2M4ctwED1JaPiYgDqd2EcXXDvGuA10fEHxbbJwOri9c3ewxJkqRJpZnC9yXg0og4tHFHRPwxcAm1\nZ9/93jJzEDgGOCUiHqV2PeAJmflQRJwXER8v5v0UOAW4ISJ+CRwPHJ2Zm7Z0jO3JJkmS1Okq1erW\nlwoi4kvAu4E7gQS6gRcCLwW+kJkLxjLkOKquXLm2rZdPursrzJw5HXO2hjlbpxMygjlbrcjprbpS\nm2v2wcvvobY8eg+wO7AL8D3gFSUqe5IkSaXUzE0bRMSfUHsW3ybgvMy8fUxTSZIkqWW2eoYvIv4M\n+BdgDvA84NaIOHqsg0mSJKk1mlnS/QhwXGYenZlHUvt0jbPGNJUkSZJappnCtx+1M3wjbqTuWXiS\nJElqb80Uvp7M3DiykZkbgKljF0mSJEmt1NRdupIkSepcTX20WkS8n2d/fFp341hmfr7V4SRJkrT9\nmil8y4APb2WsClj4JEmS2tBWC19m7j0OOSRJkjRGvIZPkiSp5Cx8kiRJJWfhkyRJKjkLnyRJUslZ\n+CRJkkrOwidJklRyFj5JkqSSs/BJkiSVnIVPkiSp5Cx8kiRJJWfhkyRJKjkLnyTpd6xYUeGuu7pY\nsaIy0VEktUDPRAeQJLWPgQFYsGAHFi3qYfnyLmbPHmb+/E1cdNHT9PZOdDpJvy8LnyTpGQsW7MCN\nN059Znv58i5uvHEqlQpcdtnTE5hM0vZwSVeSBNSWcRctGv08wB139Li8K3UwC58kCYBHHqmwfPno\n/1tYvryLpUstfFKnsvBJkgDYd98qs2cPj7pv9uxh9tmnOs6JJLWKhU+SBMCsWVXmz9806r758zcx\na5aFT+pU3rQhSXrGRRfVbswY7S5dSZ3LwidJekZvL1x++dOsWDHI0qUV9tmn6pk9qQTapvBFxEeB\n04vNJcDJmbl0lHkzgS8Ah1Bbkr4HeG9m/iYiPgF8CHii7iW3Z+Z7xjK7JJXNrFkWPalM2qLwRcQx\nwAeAwzLz8Yj4OPAN4LBRpn8JeAqYB1SA64DzgZOL/Tdk5gljHlqSJKlDtMtNG8cBV2Xm48X2BcBB\nEfGCUeZeCZyVmUOZuQn4NvDiccopSZLUcdriDB+1s3ULRzYyc31EPA4cADxUPzEzvzny+4ioAG8B\nbq+b8pKI+B6wB/AA8OHMfLjZIF1d7f2cqZF85mwNc7ZOJ2QEc7Zau+eTVDNuhS8ijgUuHmXXk8Wv\n6xvG1wPTt3C8CnARsDvwp8Xwj4Fp1JZ41xa/fisiXpSZG5vJ2d/f18y0CWfO1jJn63RCRjCnpMll\n3ApfZn6D2nV5vyMi7gV2bhjuB9ZsZn4vcDUwE3hNZj5V/Bk3AjfWzfsrajdx7A/c10zO1avXMTzc\nvhcqd3VV6O/vM2eLmLN1OiEjmLPVRnJKam/tsqS7GIiRjYjYFZhVjD9LROwA3AQsA46tP3MXEfsB\nv83MlcVQpfhnsNkgw8NVhoba94frCHO2ljlbpxMygjklTS7tctPGV4HjImLPYvsjwB2Z+cgoc8+h\ndpfuCaMs034KuDQiphTbZ1A7s9f0NXySJEll0xaFLzNvoXa93R0R8RhwILU7dwGIiAcj4oXF5v+i\n9riWB4rxB4slYYD3AlUgI+Kh4jhvycyh8fpaJEmS2k27LOmSmZ8HPr+ZffvX/X6HLRzjt8A7W59O\nkiSpc7XFGT5JkiSNHQufJElSyVn4JEmSSs7CJ0mSVHIWPkmahAZWDLDsrscYWDEw0VEkjYO2uUtX\nkjT2Ng5s5N8XfJtli37JwPIBemf3Mmf+czn8oiOY0jtl6weQ1JEsfJI0ifz7gm/z8I35zPbA8oHa\ndgWOuOxNE5hM0lhySVeSJomBFQMsW/TLUfctu+OXLu9KJWbhk6RJYvUjKxlYPnqpG1g+wJNLV41z\nIknjxcInSZNE/74z6Z3dO+q+3tm97LzPLuOcSNJ4sfBJ0iTRO6t2g8Zo5sx/Lr2zRi+DkjqfN21I\n0iRy+EVHAIx6l66k8rLwSdIkMqV3Ckdc/iYGVtSu2dt5n108sydNAhY+SZqEemf1WvSkScRr+CRJ\nkkrOwidJklRyFj5JkqSSs/BJkiSVnIVPkiSp5Cx8kiRJJWfhkyRJKjkLnyRJUslZ+CRJkkrOwidJ\nklRyFj5JkqSSs/BJkiSVnIVPkiSp5Cx8kiRJJWfhkyRJKjkLnyRJUsn1THSAERHxUeD0YnMJcHJm\nLh1l3n8AATxZN3xBZn4pIrqAzwDHAlXgh8B7MvO3Y5ldkiSpnbVF4YuIY4APAIdl5uMR8XHgG8Bh\nm3nJmZn5lVHG3w+8HjgAWANcBnwReGfLQ0uSJHWIdlnSPQ64KjMfL7YvAA6KiBf8Hse5ODOfyswq\n8FngrRHR18KskiRJHaUtzvAB84CFIxuZuT4iHqd2pu6hUeYfGxHvB2YA3wE+lplriuPUz38E6Ab2\nA37cTJCursrv9QWMl5F85mwNc7ZOJ2QEc7Zau+eTVDNuhS8ijgUuHmXXyLV46xvG1wPTR5l/M7CW\n2nLtdOCfgc8BJwF99cfJzOGIGNzMcUbV398ZJwPN2VrmbJ1OyAjmlDS5jFvhy8xvULsu73dExL3A\nzg3D/dSuw2s8zvl1m6si4jzg68X22vrjRMQOwLTRjrM5q1evY3i42uz0cdfVVaG/v8+cLWLO1umE\njGDOVhvJKam9tcuS7mJqd94CEBG7ArOKcerGe4AXAvdn5qZiuBsYbDjOrcX2POBpanf9NmV4uMrQ\nUPv+cB1hztYyZ+t0QkYwp6TJpV1u2vgqcFxE7FlsfwS4IzMfGWXuQuA0gIjYEVgAXF93nFMjYufi\nES1/AfxDZg6MaXpJkqQ21haFLzNvAc4H7oiIx4ADqd1xC0BEPBgRLyzO6h0NvD0illC7EeMh4GPF\n1MuBfwXuBx6l9vW9f9y+EEmSpDZUqVZdKqhTXblybVsvn3R3V5g5czrmbA1ztk4nZARztlqR01t1\npTbXFmf4JEmSNHYsfJIkSSVn4ZMkSSo5C58kSVLJWfgkSZJKzsInSZJUchY+SZKkkrPwSZIklZyF\nT5IkqeQsfJIkSSVn4ZMkSSo5C58kSVLJWfgkSZJKzsInSZJUchY+SZKkkrPwSZIklZyFT5IkqeQs\nfJIkSSVn4ZMkSSo5C58kSVLJWfgkSZJKzsInSZJUchY+SZKkkrPwSZIklZyFT5IkqeQsfJIkSSVn\n4ZMkSSo5C58kSVLJWfgkSZJKzsInSZJUcj0THWBERHwUOL3YXAKcnJlLR5l3OzCrbmgKsHdmdkfE\nJ4APAU/U7b89M98zNqklSZLaX1sUvog4BvgAcFhmPh4RHwe+ARzWODczX9Xw2nN4dgG8ITNPGMO4\nkiRJHaVdlnSPA67KzMeL7QuAgyLiBVt6UUQ8HzgJ+PgY55MkSepYbXGGD5gHLBzZyMz1EfE4cADw\n0BZe9xnggsxcXTf2koj4HrAH8ADw4cx8uNkgXV2VbQo+3kbymbM1zNk6nZARzNlq7Z5PUs24Fb6I\nOBa4eJRdTxa/rm8YXw9M38Lx9gfmA39WN/xjYBpwPrC2+PVbEfGizNzYTM7+/r5mpk04c7aWOVun\nEzKCOSVNLuNW+DLzG9Suy/sdEXEvsHPDcD+wZguHPA24JjOfrvszbgRurDvuX1G7iWN/4L5mcq5e\nvY7h4WozUydEV1eF/v4+c7aIOVunEzKCOVttJKek9tYuS7qLgRjZiIhdqd2IsXgLr3kzcHL9QETs\nB/w2M1cWQ5Xin8FmgwwPVxkaat8friPM2VrmbJ1OyAjmlDS5tMtNG18FjouIPYvtjwB3ZOYjo02O\niD2AvYF7GnZ9Crg0IqYU22dQO7PX9DV8kiRJZdMWhS8zb6F2vd0dEfEYcCC1O3cBiIgHI+KFdS+Z\nA2ysO5M34r1AFciIeKg4zlsyc2hMvwBJkqQ2VqlWXSqoU125cm1bL590d1eYOXM65mwNc7ZOJ2QE\nc7ZakdNbdaU21xZn+CRJkjR2LHySJEklZ+GTJEkqOQufJElSyVn4JEmSSs7CJ0mSVHIWPkmSpJKz\n8EmSJJWchU+SJKnkLHySJEklZ+GTJEkqOQufJElSyVn4JEmSSs7CJ0mSVHIWPkmSpJKz8EmSJJWc\nhU+SJKnkLHySJEklZ+GTJEkqOQufJElSyVn4JEmSSs7CJ0mSVHIWPkmSpJKz8EmSJJWchU+SJKnk\nLHySJEklZ+GTJEkqOQufJElSyVn4JEmSSs7CJ0mSVHI9Ex1gRETsAlwG/CkwKzN/s5l5vcAlwBFA\nFbgFOC0zNxT7PwqcXkxfApycmUvHOL4kSVLbaoszfEXZuwt4oInpnwLmAPsAexe//2RxnGOADwB/\nlJlzgX8HvjEGkSVJkjpGWxS+wtuAK5uYdxzwN5k5mJmbgAuB/16376rMfLzYvgA4KCJe0PK0kiRJ\nHaItlnQzcxWwKiL23tK8iJgJzAYeqhteAuwREf3APGBh3XHXR8TjwAENr9msrq7KtoUfZyP5zNka\n5mydTsgI5my1ds8nqWbcCl9EHAtcPMquJzNz3yYP01f8ur5ubOT304v963m29cW+ZlT6+/u2PqsN\nmLO1zNk6nZARzClpchm3wpeZ32D7r6dbW/y6M7C8+H1/8euaYv/ODa/pL/ZJkiRNSu10Dd9WFUu/\ny4CoG54HPJaZTwKL6/dFxK7ArGJckiRpUuqowlf4KrAgIqZGxA7AAuCKun3HRcSexfZHgDsy85EJ\nyClJktQW2qLwRcTbI+JB4N+KoTsj4sGIOLTY/2BEvLDY9wngZ8BS4BHgfuBcgMy8BTgfuCMiHgMO\npHbnriRJ0qRVqVarE51BkiRJY6gtzvBJkiRp7Fj4JEmSSs7CJ0mSVHIWPkmSpJJri49WGy8RsQtw\nGfCnwKzM/M1m5vUClwBHAFXgFuC0zNxQ7P8ocHoxfQlwcmYubWHOpo4fEbdTe87giCnA3pnZHRGf\nAD4EPFG3//bMfM8E5PwPas9HfLJu+ILM/FJEdAGfAY6l9l7/EHhPZv52AnLOBL4AHELtL0P3AO/N\nzN+MxfsZEYcAXwR2AwaBszPz2lHmHQ+cTe3f73LglMy8Z1uOsT22Ief7qL3P3cA64GOZeWvxkYlL\ngWx4ySsov15wAAAgAElEQVQ399/gWOWMiBOKOY/WDf8sM48s9r+B2udz91F7kPsHM/M7rcq4DTk/\nDJzS8NK5wKnA7YzD+1nkOAX4W+CczPybzcyZ0O9PSc2ZNIWvKHt30dynfXwKmAPsAwwD/wp8EvhY\nRBwDfAA4LDMfj4iPF8c8rEU5mz5+Zr6q4bXn8OwCeENmntCKXNuTs3BmZn5llPH3A6+n9nnHa6gV\n8i8C75yAnF8CnqL2MO8KcB21x/ycXOxv2fsZEdOAm6i9L1dFxDxgUUT8JDMX1817KbUS+orMXBwR\n7wJuiojnUyulWz3GOOU8Cvg4cGhmPhYRbweuj4jdR+Zk5v6tyLQ9OQv/mZmvGeUYe1D7d35MZn43\nIl4N/EtEPD8zV4xnzsy8kFrxHHndC6j9xfMmYGYxZ8zez+LPvITaz5MHtzBnQr8/JTVvsi3pvg24\nsol5xwF/k5mDmbmJ2g/e/16376rMfLzYvgA4qPiB3Aq/1/GLH7AnUfuf7nho1ftwHHBxZj6VmVXg\ns8BbI6JVHyC6LTmvBM7KzKHi3/u3gRe3KEejw4HhzLwKIDMfABbyu8+NPBb45sj/IDPza9T+u33N\nNhxjPHI+ArwjMx8rtr8FzAD2bmGWVuTckrcAP8nM7xbHuI3ap/S8tQ1yXkLte/OpFmbZmq9n5jvY\n8kdTTvT3p6QmTZozfMXHsq0qlpc2q1jWmw08VDe8BNgjIvqpnf1ZWHfc9RHxOLUzVPWv+X39vsf/\nDLVl0tV1Yy+JiO8BewAPAB/OzIdbkPH3yXlsRLyfWgn4DrXlvjXFcernP0JtSXA/4MfjmTMzvzny\n+4ioUCsAt9dNaeX7OY/a91W9JdQeFt447+6GsYeL/F1NHmN7NJUzM3/aMOdt1Ja/H6Z2tpyIuBp4\nGTAAXJiZ14x3zsJzI+Lb1MroL6l9L/6I3/1eHDnGAROUE4CIOJraWb2vN4yP5ftJZn6/iWkT/f0p\nqUmlKnwRcSxw8Si7nszMfZs8zMiZpfV1YyO/n17sX8+zrS/2bXfOUf7srR4/IvYH5gN/Vjf8Y2Aa\ntSXJtcWv34qIF2XmxnHOeXOR4bJi/z8Dn6N2RvJZ72dmDkfE4GaOM9Y5R45XAS4Cdqd2vSe04P1s\n0Oz30ZbmVZo8xvbY5u/3iHgNtffvzzNzMCLWUjt7ellm/iAi5gO3RsRjxVm08cz5MLWzj5+mVkhP\nB24uzvhu93/bLcxZ7wzg3OIMONS+/8b6/WzWRH9/SmpSqQpfZn6D5q7R25K1xa87U7sAGaC/+HVN\nsX/nhtf0s+Vlj2fZUs6IuPf3OP5pwDWZ+XTdn3EjcGPdcf+K2k0H+wP3jWfOzDy/bnNVRJzHf52t\neNb7WXw+8rTRjjPWOYv5vcDV1M6ovGZkCa0V72eDZr+PtjSvq8ljbI9t+n4vLuD/DPDOuqXR31Ar\n9xTbiyLiJuBooFUFpamcxVmr+jNXn4+IM4BXFMf4g1GO8asWZWw654iIOBh4PvAvI2Pj9H42a6K/\nPyU1abJdw7dVxdLvMmp3lY6YBzyWmU9Su6bnmX0RsSu1C5tbdRHy73P8N1O3bFm8br9ieXpEpfhn\ncDxzRkRPRLwkIur/ctFdl+NZx6H2Xj/N7y4FjWnOYt8O1C4yXwscUXwvjOxr9fu5GNivuEt5xLxR\ncjXmH1nuXrwNx9geTf8ZEfFuap91/eqRsleMz4yI/Rqm138PjFvOiJhb3JwxWpbG78VRjzEeOeu8\nGbglM4dGBsbp/WzWRH9/SmqShW90XwUWRMTUogQsAK6o23dcROxZbH8EuCMzH2nhn9308Yv/ee1N\n7REi9T4FXBoRU4rtM6idiWrVNXzbknMhtbOQRMSO1N7P6+uOc2pE7Fz8j+EvgH/IzIEJyHkOtbt0\nTxhlmbbV7+dt1K69OhEgIg6kdpH71Q3zrgFeHxF/WGyfDKwuXt/sMbZHU39GRBwAnAe8NjMbHxfy\ncuDOiNi3mPti4I3UnTEdr5zUlnCvG7kpqCipQ8CdwA3UCsrrin1voPYolBsmIOeI+fzuf9vj8X42\na6K/PyU1qVKtVrc+qwSi9piIc6k9K+p51P5HPQQcn5n/GREPAv9fZt5flLwvAG+i9my4m4AFIyUg\nIj4A/C9qf6u+j9pz3R5v/DO3I+tmj1+fs9g+GLgzM6c2HGNXao83OQTYRO3RCh/M1j4vsKmcRcaR\na+KGqd39ekZmriuul/sUcHxx2NuBU1t5N+I25HwaWMF/LesDPJ2ZLxmL9zMiXgJcTu0mkA3Ubh64\noVjyXpeZ5xbz3kntPdqB2vPj3jNyk8TmjvH7Zvp9c0bEZdTuvmz87+DDmbkwIhYA7y3GNlC7Ju26\nCcg5jdr1o68HNlI7m/+hzPxxcYzXAp8HdgJWUXv+ZjM3L7Q0Z93c+4DzG2/IGOv3szhTd3+xOZfa\nfxMr+a/y2zbfn5KaM2kKnyRJ0mTlkq4kSVLJWfgkSZJKzsInSZJUchY+SZKkkrPwSZIklZyFT5Ik\nqeQsfJIkSSVXqs/SlTpFRPwceA61h39D7WHOS4DPZua1xZwK8B7gFGqf2TtUzLkC+LvMrNYd72jg\ny8CizHzL+HwVkqRO4Rk+aeL8RWZOy8xpwK7UPq3gKxHxxmL/5cBfUft82pnAbsWcc4BLRw4SERcA\n5wONH2kmSRLgGT6pLWTm08A/RcT/AN4aEeupfS7p/My8s27qDRGxCjgmIqZm5iDwG+Bl1D76rX+8\ns0uS2p+FT2ovPdSWd/8UuKuh7AGQmf8B/Efd9nkAETE+CSVJHcfCJ7WBiNgROAb4Y+D1wBm4RCtJ\nahGv4ZMmzmcjYkNEbAB+Dfwv4J2ZeRu1s3zdE5pOklQanuGTJs5fZOZFm9n3MPDq8QwjSSovz/BJ\n7ekfgZdGxOsbd0TEIRHx04jYaQJySZI6kGf4pDaUmXdGxOeBayLifcC/AhXgSGqPZPlSZj41kRkl\nSZ3Dwie1qcz8YETcTe3avi8Xw/cDH8zMrwNExF78180dU4qxDcXrp41vYklSu6pUq9Wtz5IkSVLH\n8ho+SZKkkrPwSZIklZyFT5IkqeQsfJIkSSVn4ZMkSSo5C58kSVLJWfgkSZJKzsInSZJUchY+SZKk\nkrPwSZIklZyFT5IkqeQsfJIkSSVn4ZMkSSo5C58kSVLJWfgkSZJKzsInSZJUchY+SZKkkrPwSZIk\nlZyFT5IkqeQsfJIkSSVn4ZMkSSo5C58kSVLJ9Ux0gEYRcQrwt8A5mfk3m5lzPHA2MAVYDpySmfcU\n+w4BvgjsBgwCZ2fmteORXZIkqR211Rm+iLgEeB3w4BbmvBT4AvCWzNwL+BxwU0RMjYhpwE3AxZk5\nFzgKuCQiXjT26SVJktpTWxU+4OuZ+Q5gzRbmHAt8MzMXA2Tm16h9Ha8BDgeGM/OqYt8DwELguLEM\nLUmS1M7aakk3M7/fxLR5wN0NYw8DB1Arfksa9i0BDtz+dJIkSZ2prQpfk/qA9Q1j64HpQGUL+7aq\nWq1WK5XKdgeUpEnGH5xSm+vEwrcW2LlhrJ/aMnDXFvZtVaVSYfXqdQwPV7c75Fjp6qrQ399nzhYx\nZ+t0QkYwZ6uN5JTU3jqx8C0GYmQjIrqB/YrxLuDMiOjKzOFiyjzgvmYPPjxcZWiofX+4jjBna5mz\ndTohI5hT0uTSbjdtNOMa4PUR8YfF9snAauC24p8B4ESAiDiQ2o0cV09ATkmSpLbQNmf4ijN19xeb\nc4EDIuJk4IZibF1mnpuZPy2e1XdDROwAPAocnZmbiuMcA1weEecAG4ATMvOhcf1iJEmS2kjbFL7M\nHAL2b3LuPwD/sJl99wKHtTCaJElSR+vEJV1JkiRtAwufJElSyVn4JEmSSs7CJ0mSVHIWPkmSpJKz\n8EmSJJWchU+SJKnkLHySJEklZ+GTJEkqOQufJElSyVn4JEmSSs7CJ0mSVHIWPkmSpJKz8EmSJJWc\nhU+SJKnkLHySJEklZ+GTJEkqOQufJElSyVn4JEmSSs7CJ0mSVHIWPkmSpJKz8EmSJJWchU+SJKnk\nLHySJEklZ+GTJEkqOQufJElSyVn4JEmSSs7CJ0mSVHIWPkmSpJKz8EmSJJVcz0QHGBERhwBfBHYD\nBoGzM/PahjkfBk5peOlc4FTgdmApkA37X5mZvxmT0JIkSR2gLQpfREwDbgLOzMyrImIesCgifpKZ\ni0fmZeaFwIV1r3sBcEvx2pnFnP3HNbwkSVKba5cl3cOB4cy8CiAzHwAWAsdt5XWXAGdl5lNjnE+S\nJKljtcUZPmAesKRhbAlw4OZeEBFHUzur9/WG8auBlwEDwIWZec22BOnqqmzL9HE3ks+crWHO1umE\njGDOVmv3fJJq2qXw9QHrG8bWA9O38JozgHMzs1psrwWuBC7LzB9ExHzg1oh4LDNvazZIf3/fNsSe\nOOZsLXO2TidkBHNKmlzapfCtBXZuGOsH1ow2OSIOBp4P/MvIWHFjxkl124si4ibgaKDpwrd69TqG\nh6tbnzhBuroq9Pf3mbNFzNk6nZARzNlqIzkltbd2KXyLgTMjoiszh4uxecB9m5n/ZuCWzBwaGYiI\nmcCumVm/NNxN7Y7fpg0PVxkaat8friPM2VrmbJ1OyAjmlDS5tMtNG7dRu+buRICIOJDajRxXb2b+\nfOCehrGXA3dGxL7FMV4MvBG4cSwCS5IkdYq2KHyZOQgcA5wSEY8C/wickJkPRcR5EfHxhpfMAX7d\ncIyFwLnAwohIamXx3Zn5g7H/CiRJktpXpVp1qaBOdeXKtW29fNLdXWHmzOmYszXM2TqdkBHM2WpF\nTm/VldpcW5zhkyRJ0tix8EmSJJWchU+SJKnkLHySJEklZ+GTJEkqOQufJElSyVn4JEmSSs7CJ0mS\nVHIWPkmSpJKz8EmSJJWchU+SJKnkLHySJEklZ+GTJEkqOQufJElSyVn4JEmSSs7CJ0mSVHIWPkmS\npJKz8EmSJJWchU+SJKnkLHySJEklZ+GTJEkqOQufJElSyVn4JEmSSs7CJ0mSVHIWPkmSpJKz8EmS\nJJWchU+SJKnkLHySJEklZ+GTJEkqOQufJElSyfVMdIAREXEI8EVgN2AQODszr22Yc0Ix59G64Z9l\n5pHF/jcAFwJ9wFrgg5n5nbFPL0mS1L7a4gxfREwDbgIuzsy5wFHAJRHxolGm/2dm7l/3z0jZ2wO4\nDjg9M/cC3gdcHxGzxunLkCRJakttUfiAw4HhzLwKIDMfABYCx23DMd4C/CQzv1sc4zZgMfDWFmeV\nJEnqKO2ypDsPWNIwtgQ4cJS5z42IbwN7A78EPpaZPyqO8dAoxzhgW4J0dVW2Zfq4G8lnztYwZ+t0\nQkYwZ6u1ez5JNe1S+PqA9Q1j64HpDWMPA98CPg08AZwO3BwRL9iGY2xRf3/ftkyfMOZsLXO2Tidk\nBHNKmlzapfCtBXZuGOsH1tQPZOb3ge/XDX0+Is4AXlEc4w9GOcavtiXI6tXrGB6ubstLxlVXV4X+\n/j5ztog5W6cTMoI5W20kp6T21i6FbzFwZkR0ZeZwMTYPuK9+UkTMBTZm5hN1w93U7updDJzacNx5\nwDbdpTs8XGVoqH1/uI4wZ2uZs3U6ISOYU9Lk0i43bdwGDAAnAkTEgdRu5Li6Yd7pwHUR0VfMezcw\nBNwJ3ADsFxGvK/a9AZhbjEuSJE1abXGGLzMHI+IY4PKIOAfYAJyQmQ9FxHnAusw8Fzgb+BxwX0Rs\nBJYBR2bmUwAR8VZqy7w7AauAozNz5UR8TZLGRmXTJroHBxmaOpVqT1v8CFPJ3H333V3A7sBOE51F\nasJTwK8OPvjg4S1NqlSrLhXUqa5cubatl0+6uyvMnDkdc7aGOVtnzDMODzNj2a+YMjBA96Yhhnq6\n2djby5o5u0NX84sVnfBeQsflLM2tuvfc8+OZvb0zLttxx759uru7p010HmlrhoaGNqxfv27pwMCa\nU1/60gM3e5LLvx5L6ggzlv2KaU/9131c3ZuG6C621+w5Z6JiqUTuvvvurunT+y/bZZdZ+1QqFait\nNkltb9q03n2Ay+6+++53bu5MX7tcwydJm1XZtIkpAwOj7psyMEBl06ZxTqSS2n3HHftGyp7UMSqV\nCjvu2LcPtUsRRmXhk9T2ugcH6d40NPq+TUN0Dw6OcyKV1E4u46pTFd+7m73u1MInqe0NTZ3KUE/3\n6Pt6uhmaOnWcE0lSZ7HwSWp71Z4eNvb2jrpvY2+vd+tK0lZY+CR1hDVzdmfDTjOeOdM31NPNhp1m\n1O7SldRyJ5zwZ/vfe+89z/qb1gUXnP+cL3/50t229toPfvC9+25u36JFt8/48Iff/7zG8fe85/h4\n4IH7XVIfIxY+SZ2hq4s1e85h1fP2ZtXez2XV8/au3Z27DY9kkdS8ww9/3cpbb715l/qxRYu+v8uR\nRx61xefbDg8P87nP/d0jY5tO28p1EEkdpdrTwyaXcKUxd+SRR638n//zpP2BxwHuvfee3pkzdx18\n5JGHp/3lX35k356eKdXp06dv+sxnLvrZj370g+nXXnv1bk8//XTXe9/7/sf+8i8/st/NN//Hvbff\nftuMv//7S59TPxdg3bo1PQsWvG/fFSuW7zB//itXnXbagmc+MnXNmjVdn/zkWXuvWfNUz9DQcOVD\nH/rooy984YvWf+pTn3juww8/1Dc4ONj1pjcdvfy4447/zQS9NR3JvxpLkqTfMWvW7E2zZ+/29P/9\nvz/qBbj11pt3ee1rX79yzZqnus866xM///KXv5p9fX1Dt9323Z0AHnvs0R0vueRLSw466GXPPENp\nc3MfffTRHf/3//700iuvvPaB73zn23+watXKZ+7K+upXr9jtkEMOe+qyy77y0Ec+csYvLr74b/dc\ntWpl949+9J/9V1557YNXXHHNA0NDQz47Zxv512RJkjSqP/7jI1beeuvNMw866GUDP/jBnf2XX/4/\nH7z//vt6P/vZ8+YODw9Xli//1Q4HHnjwmr6+vsG5c/deP3Xq1Gd9LMxOO+08NNrc5z1v33UzZswY\nBnjOc/Zc/4tf/HyHkdc89NCDvXfd9dup3/nOLbsCDA5urOyyy8yh2bN3e3rBgvft+9/+22tWvf3t\nx3p2bxtZ+CRJ0qj+5E+OXHXiicft8eMf/9+Vc+Y8Z8Muu+wy9NnP/p+9P/3pC5fsv/8BGz75yY/P\nHZnb0zPldz7hYXNzGx9u3bj9/vd/+NFDD335uvqxSy+94qF77rm7d+HCf931pptumH3VVV9/sFVf\n52Tgkq4kSRrVjBkzhvfaa++Br3zly3u87nVHrATYsGFD93Ofu9fgypW/7V68+CczNm3auNnl1c3N\n/dnPHukbGBjoevrppyuPP/7Yjnvttc/TI6/Zf/8D1t1223d3Ach8YNoVV1w++9FHfz716quvnHXQ\nQS8bOPPMs3/5618/scPQ0OgPY9foPMMnSZI263Wve8PKCy44b5/zzrvgZwBvetPRy0855X/sP2fO\nczYcd9x/f+JrX7tqzvHHn7RstNeONvdd7zph2T77PG/dOeecufeyZct2eMMb3rSiv7//mfb2rned\nsPycc87c593vflcMDQ1VFiz4i0d3333OxsWLfzL9pJPeNXN4eKjy9rcf+6vu7tEfxq7RVarV6tZn\nTR7VlSvXMjTUvu9Jd3eFmTOnY87WMGfrdEJGMGerFTlLcQH93Xffvf/uu8/9p6lTp22Y6CzSthoc\n3DDtV7969O0HH3zwqEvdLulKkiSVnIVPkiSp5Cx8kiRJJWfhkyRJKjkLnyRJUslZ+CRJkkrOwidJ\nklRyFj5JkqSSs/BJkqTt8spXvuzgG264buZE59Dm+dFqkiSVyHvec3w8+OAD07u7e37nI1qOPPLN\nyz/60bMem4hcmlgWPkmSSuYVr/hvK88//4KlW5u3cePGypQpU9r3s/vUMhY+SZLGSNcTy3q6H/jp\ntKF5B2wY3mPOponOA/CWt7zhD1/72iN++8Mf3rXzmjVP9dx44833rV+/vnLBBefvuWjR92cODj7d\ntdtue2z48z8//okjjzxqNcDKlb/t/vSnz51733337rRhw9Ndu+666+Db3vaOX//Zn73rNyPHHRgY\n6P7Yxz60z913/7B/6tQdht/0pqOWn3bagicm7itVPQufJEktVlm7tmvGqSfuNeVHP5zRtWrllOFd\nZm7c+LJD1qy57MpfVKdPH57ofP/2b9/e9ayzPvGzl73s0HUAn/nMp577858v7b300r9/YM6cPQdv\nvfXm/vPP/9/P23XXP3josMP+aO0XvnDhc5566qmea6/958U77bTT0O2337bTX//1x/c96KCD10bM\n2wBw003/PPtDH/roL84774KlX/vaV2ZdfvkX5772ta9fNW/eCzdM7Fcr8KYNSZJabsapJ+61w623\nzOxatXIKQNeqlVN2uPWWmTNOPWmvic4GsO+++6079NCXr+vq6mLNmjVd//Zv35510kmnPD537t6D\nPT09vPGNb1598MGHPHnjjdf/AcCqVaumVCoVpk2bNtzV1cWrX334U9/5zvfvGSl7AIce+vLVf/RH\nr1jb1dXF2972jt8CPPzwkh0n6mvUs3mGT5KkFup6YlnPlB/9cMZo+6b86D9ndD2xrGesl3fvuON7\nM1/zmj/apXH8tNM++AuAPfbYY3Bk7Oc/X7rD8PAwf/VXH9sPKs/MrVaH2W+/WAdwyinvW3bWWR99\n/lFHHfGSF73oD9cceujLn3zTm45ZudNOOz1ztvI5z9nz6ZHfT5s2bRhgw4YNnlhqExY+SZJaqPuB\nn04bObPXqGvVyind+eAOY134tnTTxjXXfGVOT8+UZ4paV1etk33+85c++OIXHzgw2msOOOBF6//p\nn/71vh/+8AfT77pr0U7//M/X7XbttVfPufTSKx54znP23AhQqdjt/h979x3eZNW/AfxO0rTpoouW\n7sE6BVkCIkMQRUARkCGK4KugICiggDL8gdvKEhmCigtREVSmCIrgeFGGA0WZBwqlpYsWukeaNMnv\njyS8oQYIkLZpen+ui6s8zzk5ufsQ6JdznuHKXKbgE0LcBOAtAI0A6AA8L6X8zE6/JwBMBKACUApg\nhpRyhxAiHkAKAFnlJbdIKc+BiIioBhhatNQag4L19oo+Y1Cw3iASK+y9rrbExcVXKJVKHD162Me2\n4EtPT/MMD4/UeXh4oKCgQOXr62vs3LlrSefOXUsmTZqSed9997T67rtvgkaPHptTm/nJMS5Rjgsh\nNAA2A1gmpYwFMADAciFEqyr9BgCYDaCPlFIAeA3AeiGEj7WPlDKxyi8We0REVGOMEZGV+o43Fdtr\n03e8qdhVrta18vPzM/bq1Tt3zZpPIw4e/NvbYDBg797dfo8++lDLbdu2BJlMJjz66IMtFi6cG1VY\nWKgymUw4cuSwd0lJsUdMTJxLFa90aa4yw3cbAKOUchUASCmPCiG2ARgB4P9s+p0EcJ+U0nrTyK0A\n/AHEA7A7DU1ERFTTilesTMW40bB3lW5tZ7Nn2rRZ6W+8MQ8zZkxpXlZWrmrYsKHuwQdHZQwcODgf\nAJKS5icvXfpGzH33DWxdWVmpCA4O0Y0c+XDGHXf0Kazt7OQYhclU+/dbFEJMBTBASnmbzb4XAbST\nUg66zOtGAlgAc8EXCfOS7qcAOsJcAL4hpVx9FVFMBQWlMBpr/5hcilKpQGCgL5jTOZjTeepCRoA5\nnc2SU3Hlnq5v//79ieHhses8PTVOu42IMivTQyWPeRlEYoWrzeyRe9HptJrs7LR7O3TocMxeu6vM\n8PkCKK+yrxyA36VeIIToCWAxgJFSSp0QogTASgArpJS/CiG6AtghhEiXUv7X0SCBgb5XHb42MKdz\nMafz1IWMAHNSzTBGRFay0CNX4CoFXwmAgCr7AgHYPQdCCPEQgPkA7pdS/gAAlnP1HrH2kVLuEUJs\nBjAQgMMFXx353zRzOglzOk9dyAgwp7NZcxKRa3OVgu8QgGeFEEoppfVS8RYADlbtKIR4FMAsALdK\nKaXN/mAAIVLKEzbdVTBf8eswo9EEg8F1/3G1Yk7nYk7nqQsZAeYkovrFJa7ShXkGrgzAaAAQQrSD\n+UKOT2w7CSFaApgD4HbbYs+iM4C9Qogmlr5tANwFYFP1RiciIiJybS4xw2c5B+8eAO8KIV4AoAUw\nSkp5XAgxB0CplPJVAE8B8AbwrRDCdoipUsptQohXAWyztGkBPCql/LVGvxkiIiIiF+MSBR8ASCn/\nBnCznf3P2vx+HIBxlxljMcwXchARERGRhass6RIRERFRNWHBR0REROTmWPARERERuTkWfERERERu\njgUfERFRPbV8+ZKIIUPubgUAaWmnPW+5pWOHXbt+8geAsWMfEjNnPp1QuwnJWVjwERERuZGxYx8S\nt9zSscN3331T9QlWAIAxY8zte/b87D9hwlNZGzZsPVTTGanmseAjcmMKXS488vdAocut7ShEVIMC\nAgL1X3+9ObTq/lOnTnplZWV61UYmql0s+IjckaEM/v+MRtDeLgj6404E7e0C/39GA4ay2k5GVK9k\nlWR6/JC20y+rJLNG73vbsWOnwn/+OdAgOztLbbt/06Z1DTt0uKnQur106RuRAwb0aePImF99tTHo\njjtuufG33/bx4cl1EAs+Ijfkf3gCNGfXQ6XLAQCodDnQnF0P/8MTazkZUf1QoitRjtw6LKHn511b\nDv96iOj5edeWI7cOSyjRldTIz92QkIZ6IVqUbNy4LsS6z2Aw4Mcfvw/p2/euvKsd75dfdvkvXbow\n/rnnXj7ZqVPnUuempZrAgo/IzSh0uVDn/2y3TZ2/i8u7RDVg3I7RcTtStwfnV+SpASC/Ik+9I3V7\n8Lgdj8TVVIZ+/Qac++67bxqaTCYAwE8//RDg4eFh7Nq1e/HVjHPo0EHvV155rsnUqTNSbr319qJq\nCUvVjgUfkZtRlZ64MLP3rzZdDlSlJ2s4EVH9klWS6fFH9u/+9tr+yP7Nv6aWd/v27ZdfVlbq8csv\nu2r5gyYAACAASURBVPwBYOvWr0LuuKPPeYVC4fAYubk5njNnTml2440dC/v1G1BQbWGp2rHgI3Iz\nBt9mMHiG2W/zDIPBt0kNJyKqX47mHdFYZ/aqyq/IU8u8YzVy0YRGozH16HHb+S1bNjbMyzuv+uuv\nPwLvuWfouasZ48QJ6dehQ6fCvXt/Cdq3b49fdWWl6seCj8jNmDxDoQ/qbrdNH9QdJs9/XbhHRE7U\nIrilNsgrWG+vLcgrWC+CEytqKsugQfee++OP3wM3blzXMDGxZXF0dIzdXJfSuXPXvJdeei21X7+B\nOUlJLzbOyzuvqq6sVL1Y8BG5oeIblkPbaOiFmT6DZxi0jYai+IbltZyMyP1F+EVWdgy/ye55ch3D\nbyqO8IusrKksN9zQqjw6Olr7xRdrIvr1639Vs3sAoFSa67upU6dnBAYG6V944f8SrOcEUt3Cgo/I\nHal8UNxmJfK77EV+x++Q32UvitusBFQ+tZ2MqF5Y0Xtlau+4vnnWmb4gr2B977i+eSt6r0yt6Sx3\n3dU/FwD69Ol3zefgqdVq08svzzl19Ohh/w8+WNHIeemopihYqV/ElJdXAoPBdY+JSqVAcLAfmNM5\nmNN56kJGgDmdzZLT8asAXNj+/fsTw8Nj13l6arTOGjOrJNND5h3zEsGJFTU5s0f1j06n1WRnp93b\noUOHY/baa/RGkERERPVJhF9kJQs9cgVc0iUiIiJycyz4iIiIiNwcCz4iIiIiN8eCj4iIiMjNseAj\nIiIicnMs+IiI6pncslzsy9yD3LLc2o5CRDXkmm/LIoRQSCld9+ZQRER0kTJ9GSb/OAF7Mn9GTlkO\nwnzC0DWyOxbfthw+at6Um8idXbHgE0L4ApgHoDWAz6SUK4QQ8wFMFEIYAGwC8ISU0u5jZIiIyDVM\n/nECNiWvv7CdU5aDTcnroYACK/p8WIvJiKi6ObKkuwhALwC/A3hWCPF/AO4G8BCAsQBaAphTbQmJ\niOi65ZblYk/mz3bbdmfu4vIukZtzZEn3bgC3SClThBBfAPjJsv0nAAghfrPsm1hdIYmI6PqcLDiB\nnLIcu205ZTlIKTyJUJ/QGk5F1SU7O0v94Yfvhu/f/3tAfn6eWqVSmRo2DNPdckuP/DFjxmd7eXnx\nlKx6xpGCLwDAacvv9wNQAzhg054KINC5sYiIyJmaBDZDmE+Y3aIvzCcMCQFNaiEVVYdTp056TZz4\nWGJiYsvipKT5yUK00Gq1WsXu3bsaLF++JPbAgT/9V6xYeVypvHiRr7KyEh4eNfvEVb1er1Cr1Sw+\na4AjS7rJAPoAgJTSAGC0lNJo0z4IwMlqyEZERE4S6hOKrpHd7bZ1jezO2b1qkpWl8PjhB5VfVpai\nxiqp+fOTYkNCQnQLFy49lZjYUqtQKODt7W26446+ha+/vvT4zTd3KSwpKVF++eXakDvv7Nn2448/\nDO3du3u7des+bwgAe/fu9hs9emRi79492vXv37vNjBlTEs6ezb6QPzs7Sz116qTGvXt3b9e3763t\npk17qnFubs6F9i1bNgUNHz645W23dWk/eHC/1nPmvBxTWlqqBIC0tNOet9zSscNnn33ccODAPm2e\nf35m/KBBd7VetGh+lO33cODAnz633NKxg5RHNTV13NydIwXfPADrhRADAEBK+am1QQjxCYDVAOZW\nTzwiInKWxbctx6CmQxHmEwbAPLM3qOlQLL5teS0ncz8lJVCOHKlJ6NnTp+Xw4T6iZ0+fliNHahJK\nSqr3dmjnz5/zOHz4YIMhQ+47q1Ao/tXepEnTijFjxp9t0KCBEQB0Or0yOfm4z+bN2/+5774Hzp06\nddJr5sypzbt1656/efO3/3zwwSdHCgsLPZ566vHmJpN5Iu75559NMBoNii+++Org2rWbDhYXF6tm\nz57eGAD27//Nd9Gi+fFjxz6RsWPHz38uXPjm8UOHDvrNm/dKjG2OH37YGbJixUdHk5IWpPTufee5\nH37YGWIwGC60b9++Lbhp02alQrTQVuPhqleu+D8OKeUaIUQmgHw7zakA+kspd15vECHETQDeAtAI\ngA7A81LKz+z0ewjA8zAvLecAeExK+dfVjEFEVB/5qH3wbp+VyC3LRUrhSSQENOHMXjUZN04Tt2OH\nOti6nZ+vVO/YoQweNw5YvVqbUl3ve/p0ipfJZEJCQmOHCiWdrkL5wAP/Oevj42MEgC+/XBsaFtao\nYsyY8WcBwMfHx/jYY09kPPnk+BYHDvzp4+3tbTxy5JD/229/eCQoKMgAADNmzE47evSwt8lkwtq1\nqxvdfHOXgl69ehcCQOPGTSpGjXo085VXnm+q0+nSrO/bo0fPvIiISD0ADBky7NwXX3wW8dNPPwT0\n6tW70Gg04pdfdgWPGPFQprOPT33m0BSzlPK/Qgh7fedKKUuuN4QQQgNgM4BnpZSrhBAtAOwRQvwj\npTxk0+9GAG8C6CalPCSEeBDAZiFEU5hnK684BhFRfRfqE8pCrxplZSk8/vhD5W+v7Y8/VP5ZWQqP\niAhTZXW8t/V8OIOh8qLpvQceGNoyKytTAwAmkxHDhj2Q1ahRuA4A4uISdP/LnuEVFRV9UbHYpEmz\nCgA4cybNy9PT02R+TXyFtT0hoXFFQkLjCgDIyEjXpKef8e7Zs0tQ1WwZGemeKpXSBABRUTEX3jMi\nIlJ/440dCrdu3dywV6/ehb/+usevrKxU1b//PXnXezzofxyaWhZCvA3z0m5VvwohXnZCjtsAGKWU\nqwBASnkUwDYAI6r0Gw7ga2sBZ1leVgLoeRVjEBERVZujR5Wa/Hyl2l5bfr5SLaXSq7reOzY2vkKl\nUpmOHz/ubbt/zZr1R376ae+fP/2098/mzUWp0Wi8UBB6enoa/z3S/xiN/242mUz/Xi8GoFAoTX37\n9suxvpf1165dv+23FoUAoFarLxq0f/9B5/bv/z0gL++86rvvvgnu0qVbvr+//2Vz0dVx5MbLjwMY\nDOA+O82jAHwthDgkpfziOnK0AHCiyr4TANrZ6be/yr5kmO8FqHRwjMtSKu1+hl2GNR9zOgdzOk9d\nyAgwp7O5er7a0KKFURsUZNTbK/qCgox6IYwV9l7nDIGBgYYOHW4qXLdubfiQIcPO27v9itF46Yti\no6NjtHv37r5odi45+bgGABISGmutM4jJycc1HTrcVAoAKSmnvL7//rvAUaPGnI2MjKo4dSr5ose2\nFBQUqEwmI4KCgg24hNtvv6Nw6dKF+q1bvwreu3d30IsvvsaLQZ3MkSXd0QDGSSl3VW2QUv5uKQin\nALiegs8XQHmVfeUA/K6in8LBMS4rMND3arrXGuZ0LuZ0nrqQEWBOqj4REabKjh0NxTt2KIOrtnXs\naCiuruVcq+nTZ6WNH/9I4sSJjzV7/PFJ6W3b3lhmMplw5Mghn3Xr1oaeOpXs+8ADD2bn5+f/qwYY\nNuyB3C1bNjV6//13Go0c+XBOXt55j3ffXR7dpEnT0tat25YDQIsWLUvee+/tyLi4+BS12tO0ePGC\n6LKyUo8xY8afvf/+EWenTJmQ+MknH4UOGzb8XGFhgeqVV56PNxqNirfeer/qpMwFSqUSvXvfee6T\nT1ZGNWgQUNm5c9frPl2MLuZIwdcMwDeXad8K4P3rzFEC8/3+bAUCqPq4tsv1Uzo4xmUVFJRe9n8/\ntU2pVCAw0Jc5nYQ5nacuZASY09msOeliK1ZoU8eNM5+zl5+vVAcFGfUdOxqKV6zQplb3e4eHR+g/\n+mjNkQ8+eCd87txXEvLyznuaTCY0bBiqa9v2xqIPP1x9OCGhccWXX64NqfrauLh43QsvvJr84Yfv\nRq1evSrK39+/sm3b9oVTpkxLt/Z56aU5p+bPT4odPnxwa5VKZWrVqk3Rq6/OPwkAHTt2Kp027f9O\nffbZxxEffLAixttbY2jXrkPh9On/d+ZKuQcPvvfcF198Fjl06P3Zzj0iBDhW8HkAuNw6ugnm2bXr\ncQjmx7Ypbe7x1wLAQTv9hHVDCKGCuSA9BHPB58gYl2U0mmAwuO4/rlbM6VzM6Tx1ISPAnFS9/Pxg\nXL1am5KVpfCQUuklhLGiumf2bAUGBhqefnpmBoCMS/UZNmz4+WHDhp+vuv/223sX3X5776JLvS4y\nMkq/ePFbl1xyHThwcP7AgYPt3dkDsbHxul9++aPqqVkAgPPnz6k9PDxMQ4fed+5SY9O1c+SijWMA\nel+mfQCAo9eZ478AymBePoYQoh3MF2F8UqXfagC9hRCtLdtjABRYXu/oGERERDUiIsJU2bOnobQm\ni726KCMjXT1//mtxd93VP6dhw1Aeq2rgSMH3HoB3hBCdqjYIIXoBWA7zve+umZRSB+AeAI8JIdJg\nPh9wlJTyuBBijhBitqXfEQCPAdgohDgD4CEAA6WUlZcb43qyERERUfV5+eXnYv/zn/taxcXFl0+e\nPO2SM5J0fRTWO2dfjhDiPQCPAtgLQAJQAbgBwI0A3pRSTq7OkDXIlJdX4tLLJyqVAsHBfmBO52BO\n56kLGQHmdDZLTre4VHf//v2J4eGx6zw9NXy6A9U5Op1Wk52ddm+HDh2O2Wt36D58UsqxMC+P/gUg\nHEAQgF0w3wDZXYo9IiIiIrfk0JM2hBB9Yb4XXyWAOVLKn6s1FRERERE5zRVn+IQQDwD4CkAkgMYA\ndgghBlZ3MCIiIiJyDkeWdJ8BMEJKOVBK2Q/mp2vMqtZUREREROQ0jhR8zWCe4bPaBJt74RERERGR\na3Ok4POQUuqtG1JKLQDP6otERES1LTdXgX37lMjNdYsLcInqPYcu2iAiovqhrAyYPNkLe/Z4ICdH\nibAwI7p2rcTixRXw8antdER0rRx6tJoQYhIufnyaquo+KeVSZ4cjIqKaNXmyFzZt+t8iTk6OEps2\neUKhAFasqKjFZER0PRwp+DIBTL3CPhMAFnxERHVYbq4Ce/bY/7Gwe7cHcnN1CA113ZtAk9nYsQ+J\nY8eO+qlUHv/6w+rXr3/O9Omz0msjF9WuKxZ8Usr4GshBREQ1qCy3DAUn8xDYJBg+oea12pMnFcjJ\nsX9qd06OEikpChZ8dUS3bj3y5s5dmHKlfnq9XqFWq/mHWg/wHD4ionpEX6bHj5O/Q+aeMyjLKYNP\nmA8iu8bgtsV90KSJJ8LCjHaLvrAwIxISWBdcLaU200NVckRj8GupNWoiK2s7DwAMGnRn69tv73P+\n99/3BRQXF3ls2vTtwbKyMuWSJa9H/fbbvoDi4iJ1WFijimHDhmcPHjwsDwCef/7ZuF27fgqxjmEy\nGWEwGBRr1qw/GBMTp8vLO69asOC12D///CPAaDQhOjpG+9hjj6d36XJLCQDMnPl0gsFQqWjdum3x\nunWfh5eWlngI0aLkpZdeSwkNDXOJ4+LuWPAREdUjP07+Dsmb5IXtspwy87YC6LPibnTtWnnROXxW\nXbtWcnbvKigqS5T+B0fHqQt/91fq89RGdbBeH3BTcXHrlakmDz9jbef7/vvvQmbNevFUx46dSgEg\nKemF2OTkE77z5y9KjotLqPj+++8C5sx5uYla7Wnq3/+e/JdfnpMKIBUAdDqd4pFHRiY2aBBQGR0d\nqwOA556b2VitVhs//fTLQw0aBBi++OKzhs8++0zzjz5acyg+PkGnUilNf/99sEFMTKz28883HcrL\nO+8xduzDLVeufC+cS8w1w6Fn6RIRUd1XlluGzD1n7LZl7j6DstwyLF5cgUGDdAgLM9ckYWFGDBqk\nw+LFvGDjavgfHB3ndW57sFKfpwYApT5P7XVue7D/wUfiajsbADRp0qy0U6fOpUqlEkVFRcpdu34K\nGTny4cxmzYTW09PTdNdd/Qtat25b+O23W0OqvvaNN+ZF5efnq1966bUUhUKB48ePaf7++68GTz31\nTHpoaFill5eX6T//GZ0bHR1Tvnnz+obW1ymVStPEiVMyvb29TVFR0frExBYlqamnNTX7nddfnOEj\nIqonCk7moSynzG5bWU4ZClPyEdHJB+++W4HcXB1SUhRISDBxZu8qKbWZHurC3/3ttakLf/NXajM9\nqnt5d/fuXcE9e3YJqrp/woSnUgEgIiJCZ92Xmnray2QyoXHjplrbvtHRMRV//vlHA9t933+/I2Dr\n1q8avfrq/BPWpdhTp05qAGD06JE32PY1mYyIjY0vt26HhYXplMr/zTN5enoZi4uLeF/fGsKCj4io\nnghsEgyfMB+7RZ9PmA8CEv5XH4SGstC7VqqSIxrrzF5VSn2eWlVyzKu6C77LXbSxevVHkR4e6gvL\nygqF+Q5rJtPFf95Vt7Ozs9Svvz4n/p57hmbfeuttRdb9SqXSBAAbN279Oygo2HCpTAqFkh+oWsQl\nXSKiesIn1HyBhj2RXWMuXK1L18fg11JrVAfr7bUZ1cF6g1+iS62Px8XFVyiVSiQnH/e23X/mTJom\nMjJKCwBGoxEvvPBsQlhYo4rJk5/JsO2XkNC4AgAOHTp40QcoNfW0Z9WikWoPCz4ionrktsV90HSQ\ngE+Y+WezT5gPmg4SuG1xn1pO5j6MmshKfcBNxfba9AE3FbvK1bpW/v7+xp49e51bs+aTiOTkE14V\nFRWKzZs3BB869E+DgQOHnAOAFSuWh586ddLn5ZfnnPLwuHhxsFkzoW3Tpl3RO+8siz516qSXwWDA\nN99sDRw16oFW+/f/7lsr3xT9C5d0iYjqEbWPGn3evRtlueZz9gISgjizVw2KW69MxcHRsHeVbm1n\ns2f69FlnXn99TsykSY8l6vV6ZUREpHbWrBdP9urVuxAAtm/fFqrValUPP/xAK9vXzZnz+vEuXbqV\nvPzynJTXX58b8/jjj7TQ6/WK8PCIiqefnplivQqYap+C060XMeXllcBgcN1jolIpEBzsB+Z0DuZ0\nnrqQEWBOZ7PkVFy5p+vbv39/Ynh47DpPT432yr0dY74P3zEvg19ihavN7JF70em0muzstHs7dOhw\nzF47Z/iIiIiqiVETWclCj1wBz+EjIiIicnMs+IiIiIjcHAs+IiIiIjfHgo+IiIjIzbHgIyIiInJz\nLPiIiIiI3BwLPiIiIiI3x4KPiIiIyM2x4CMiIiJyc3zSBhERkRsZO/YhcezYUT+VysMEAGq1hzEy\nMlrbvfut+Q8+OCpHo9E49Ky+jIx09e7duxrcd9+I89Wb+Pr9+uteP71er7jllh7Fzhhv1qxp8bt2\n/RRiPYaACX5+fpXNm7coefzxSRnNmjWvcMb71CSXKfiEENMBTLRsngAwRkqZYqdfMIA3AdwE8wzl\nXwAel1KeE0K8CGAKgCybl/wspRxbndmJiIhcSbduPfLmzl2YAgClpaXKffv2+L///ttRP/64M2T5\n8vdlYGCg4Upj7Ny5Pei///0x6FoLPr1er1Cr1TXyIOjPP1/dKCYmrtxZBR8ANGsmSj788FNp3c7M\nzFAvWrQgZurUieLTT788HBAQcMVj6GzXc0xdouATQtwD4EkAN0spM4QQswGsBXCzne7vASgC0AKA\nAsCXAOYCGGNp3yilHFXtoYmIiK6gJKvYI+/oeU1wixCtX4R/rTxT19fX19irV+/Cjh07lTz88PCW\nS5YsiHrhhaS01NTTnosWzY85fPigv1KpQkJC49KJE6ekt2rVunzhwrlRmzatDweAnj27tH/jjTeP\ntW/fsWzLlk1Bq1evijh7NlsTGBik79Spc8GTTz6d4evra0xLO+05YsS9rZ944snUtWs/jbzhhtbF\nc+YsTPn1171+S5e+EZOdnamJjo4pf+yxJzJmzJjaPClpwYlbb72tyGAwYPnyJZE7d24PKSkp8QgN\nDdUNHDg4Z8SIh3IVCgXWrl3d8OOPP4h67rmXTy5ZsjA2J+esV6NGERXTpj17un37jmWjR49MPHFC\n+v7xx2+B27ZtCduxY9eBf/454PPmm4uiU1NTfIxGoyImJrZ8/PhJ6Tff3KXkWo9jZGSU/umnZ5y5\n994Bbfbv/8339tt7F509m+0xf35S7OHDB/0rKyuV4eER2scem5Deo0fPYgCYMWNqgre3t1Gj0Rj+\n+98fQoxGo6JNm3ZF//d/L6QGBQUbAODgwX+8ly17IyY5+YSvl5fGKERiyZQp087ExsbrAGDQoDtb\n3357n/O//74voLi4yGPTpm8PXkt+VzmHbwSAVVLKDMv2QgDthRDN7fRdCWCWlNIgpawE8B2ANjWU\nk4iI6Ip0JTrl1pEbEz7v+UnLr4dvEJ/3/KTl1pEbE3Qlulr7uRsQEGDo129Azi+//BxsMBgwY8aU\npkFBwfr167f+s2HD1n+aNWte9swzk5oXFhaqnn56ZkaPHj3PN2smSn76ae+f7dt3LNu//zffRYvm\nx48d+0TGjh0//7lw4ZvHDx066Ddv3isxtu/zww87Q1as+OhoUtKClLy886pZs6Y1EyKx9Kuvvvt7\n5sznTr/11pvRAKBWm5dL33//nfBdu34MnjfvjRM7duz6a/LkaakfffR+1KZN64IBQKVSmcrKylQb\nN64LXb78veNbt35/IDAwUL9o0YI4AFi5cvWxhg0b6gYPHpa1Y8euAwDwyivPJ7Rs2ark6693/r11\n6/cHOnfuWpCU9GKCwXB9k3LW13t4mGfZkpJejM/Ly/Ncs2bDoW+++fFAhw43Fb388uymRUVFSnM/\nlWn37l1BUVExFVu27Pj7nXdWHk1JOeXz6qsvxAFAcXGxcsaMyc3btLmx+Ouvdx5YvXrdIW9vH8PT\nTz/ZrLLyf/8/+P7770ImTZqatmHDtmsq9gAXmeGDebZum3VDSlkuhMgA0BLAcduOUsqvrb8XQigA\nDALws02XtkKIXQAiABwFMFVKmexoEKVScU3fQE2x5mNO52BO56kLGQHmdDZXz1dbdozbGpe6IyXY\nul2Rr1Wn7kgJ3jFuG+5ePehfpyvVlMaNm2rLy8tUGzeuC8nKytSsWrX2qJeXlwkApk6dkbFz5/aG\n27dvDbS3jLt27epGN9/cpaBXr96F5rGaVIwa9WjmK68831Sn06VZ+/Xo0TMvIiJSDwC7dv0UUFFR\noZw4cXKGr6+vsUWLG7T9+9+Tu3z54jhr/82b1zd6+OFHM1q0uEELAF26dCu5446+577++qvQwYOH\n5QFAZWWl4tFHx2WFhDSsBIDOnbsWrlr1QdSlvs+CggK1SqWCp6enCQDGjZuYPW7cxOxrPW4mkwkZ\nGWc833hjfkyjRuEVHTt2KgWA+fMXnzQYDApfX18jAPTrN+D8+vVfhJ84ccy7Qwdzn5CQhrr//GdU\nLgAkJDSuuPvue3JWrfogurKyEps3rw/x8FAbJ0x4KgsAvL29DdOnzzozYEDvdvv27fG3LlE3adKs\ntFOnzqXXmh+owYJPCDEcwDI7TYWWr+VV9pcD8LvMeAoAiwGEAxhm2X0AgAbmJd4Sy9etQohWUkq9\nIzkDA30d6VbrmNO5mNN56kJGgDmp+pRkFXtk/5Hlb68t+49M/5KsYo/aWt7V6SoUAJCdneVpMBgU\nffv2vNG23WQyIisr08veazMy0jXp6We8e/bsEmSnzVOlUpoAICoqRmfdn52d5enj42MIDg65MLXW\npk27C4VLYWGhqqioyOOtt5bGvv32slibJAgMDLzo53ZCQpMLF0p4eWmMOt2lZ0snTZqSunTpwvid\nO7eHtG17Y1G3bt0L+/S5q0CpdHyC9cQJ6dezZ5f21m1/f//Kli1bFS9atOy4j4+PEQAOHvzb5/33\n345KS0vzLi8vU1n7VlRUXHijmJg4re24UVFRFZWVekVubo46LS1Nk5+f52n7PgCgVCpNmZnpntbt\niIgIHa5TjRV8Usq1MJ+X9y9CiL8BBFTZHQjA7smXQggfAJ8ACAbQU0pZZHmPTQA22fR7DuaLOBIB\nODQNWlBQCqOxRs4xvSZKpQKBgb7M6STM6Tx1ISPAnM5mzUn/k3f0vKYiX6u211aRr1XnyfNetVXw\nHTt2xDcoKFjv5+dn0Gg0xp07f/nL0dcqFEpT3779cmbNevGMvfa0tNOeAKBWq43WfUajEebT7W3H\nsf29wgQAM2c+d+quu/oXXO79lUqlw38RBg4cnH/77b0Ld+/e1eC33/Y1WLLk9bgNG74Me/vtD46r\nVKorD4B/X7RRVUlJiXL27OnN2ra9sXD16oUng4NDDMnJJ7xGjXqglW0/k8mIi7dNCgDQaLyNSqXC\nFB0dU75mzYYjl8vi4aE2Xq7dEa6ypHsIgLBuCCFCAIRa9l9ECOEFYDOATADDbWfuhBDNAJyXUuZZ\ndiksvxyujI1GEwwG1/3H1Yo5nYs5nacuZASYk6pPcIsQrVeQRm+v6PMK0uiDRUit3NLj3Llcjx07\ntje8885+uXFx8VqtVqtMSTnllZDQ+EKe06dTPOPjE+z+zIyMjKo4dSrZx3ZfQUGBymQywnoBQlWh\noaH6srJSVVFRkbJBgwZGADh06J8LYzRo0MDYoEFApZTHfGwLvszMDHVISMNK63LztXyvDRuGVvbt\n26+gb99+BenpadnDhw9pfeTIYe/WrdtUXVG8JsnJxzWlpaWqUaPGZFtnMI8cOeRTtV9GRrrGdjs9\n/YyXRqMxBgUFGWJiYiu+++5bL9vjYzKZkJaW6hkXF3/ds3q2XOWijY8BjBBCRFu2nwGwW0p50k7f\nF2C+SneUnWXaJADvCCGsf8lmwjyz5/A5fERERNfDL8K/MrxjhN0VqvCOEcU1Pbun1WoVP/64s8Hj\njz8qwsMjKsaPn5TVo8dtRVFR0dr585Nis7Oz1DqdTrF69arQUaNGtDpzJtUTMM9A5eWd98zLO68q\nKytT3n//iLPHj0u/Tz75KFSr1SrOns32mD17esKsWdMbX+q9u3S5pUipVJreeefNyPLycsXx48c0\n27ZtCbXtc889g89+882WsN27d/kbDAYcPnzIe8KEsYkfffReI0e/Ry8vjTEzM0OTn5+vOnMm1XPo\n0P5tNm1aH6zT6RSVlZX4/fff/NRqtSkqKsppRVR4eIReqVTi11/3+huNRuzd+4vf9u3bGgJAItAt\nIwAAIABJREFUVlbWheXYs2fPen388Yeh5eXlipMnk722bv0qtHv3W88DwIABg897eXkZ5817JTY/\nP19VVlamXLJkYeT48aNblJSUOLVGc4kZPinldiHEXAC7hRAqmIu0EdZ2IcQxAEOllIcBPA0gF8BR\nIS5MClZIKdsCeBzAWwCkEKISwDEAg6SUNX6vHCIiqr96r7g7dce4rcj+I8u/Il+r9grS6MM7RhT3\nXnF3ak28/+7du4Kt59qpVEpTWFh4xR139D0/evTYs9YLGebNW3Ri0aL5MQ8+OKwVAERHx5QnJc07\nERMTpwOAfv36n9+3b0/g/fcPbj1t2rMpffrcVTht2v+d+uyzjyM++GBFjLe3xtCuXYfC6dP/z+4S\nr2VM/cyZz5/68MMVUXfffUdokyZNS0eNGpM5e/aMZkqlygQAY8Y8nq3VapXz5iXFFxUVqQMDA/S9\nevU9N3bsEw5fZNG//6CcVavejx458t5WH3302eGZM58/9dlnqyKWLVsUp1AoTdHR0dqXXppzwvZc\nwusVHh6hHzv2ibS1az+NWL16VVSrVm2KXnrptZR585Ji3357aaxKZf7+br65c0F6+hnN4MF3tTGZ\noGjXrn3hlCkz0gHzDOe8eW+cWLZscfTQof3bKJUKNGnSrHTBgqXH/fz8rnsZ15bCZOJSgQ1TXl6J\nSy+fqFQKBAf7gTmdgzmdpy5kBJjT2Sw53eJS3f379yeGh8eu8/TUaK/c2zElWcUeefK8V7AIqait\n8/ZqW2VlJRQKBaznzu3d+4vftGmTxWefrTtovdecu5o1a1o8ACQlLThd3e+l02k12dlp93bo0OGY\nvXZXWdIlIiJyO34R/pWxPeNL62uxV1FRoRg4sE/bBQtei9ZqtYrz5895fPzxyoj4+IQy60wi1QyX\nWNIlIiIi9+Pl5WV68cXXkt9+e2lM//53tFOrPY1CJJbMnLngpEJR8xPDK1YsC1+zZnXk5fokJc07\n0a2b8x7R5ipY8BEREVG16dSpc2mnTp3tLjPWtOu9AfPVqomlXEdxSZeIiIjIzbHgIyIiInJzLPiI\niIiI3BwLPiIiIiI3x4KPiIiIyM2x4CMiIiJycyz4iIiIiNwcCz4iIiL6l7FjHxIzZz6dAAAbNnwR\n3LNnl/YGAx9NX1fxxstERERuZOzYh8SxY0f9VCoPEwCo1R7GyMhobffut+Y/+OCoHI1Gc9UPZx4y\n5L68IUPuy3N+WqopLPiIiIjcTLduPfLmzl2YAgClpaXKffv2+L///ttRP/64M2T58vdlYGAgp+rq\nGRZ8RERE1USp13uotDqNQeOpNarVlbWRwdfX19irV+/Cjh07lTz88PCWS5YsiHrhhaS01NTTnosW\nzY85fPigv1KpQkJC49KJE6ekt2rVurzqGF9+uTZkyZLX43/6ad9+Dw8PZGdnqefPfy3m4MEDDZRK\nJdq0aVc0ffqstNDQsEoAePfdtxp9++3W0IKCfLWfn5/httvuOD958rQMhUIBk8mEJUsWRv74486Q\n4uIiDx8fX8Mtt/TIe/rpmRlqtdr0zz8HfN58c1F0amqKj9FoVMTExJaPHz8p/eabu5QAwMGD/3gv\nW/ZGTHLyCV8vL41RiMSSKVOmnYmNjdcBwIABfdoMGTIsOy3ttGb37p+DFQolbrmlR97s2S+l1cbz\ne10Fz+EjIiJyMoXBqGyQlp4QdPJ0y8Az6SLo5OmWDdLSExQGY6393A0ICDD06zcg55dffg42GAyY\nMWNK06CgYP369Vv/2bBh6z/NmjUve+aZSc0LCwtVVxrr+eefTTAaDYovvvjq4Nq1mw4WFxerZs+e\n3hgAtm3bEvjppx9Fz5z5XMr33+/+65VX5iVv2bIpbN26tSEAsGXLpqAdO75tuHjxW8d/+GHPX4sX\nvyX37/894Msv14QAwCuvPJ/QsmWrkq+/3vn31q3fH+jcuWtBUtKLCQaDAcXFxcoZMyY3b9PmxuKv\nv955YPXqdYe8vX0MTz/9ZLPKSnM9rVIpTRs2fBnetWv3wm+++fHArFkvnty+fVvo99/vCKjGw+vy\nWPARERE5mX9GZpxXSWmw0mhUA4DSaFR7lZQG+2dkxtVmrsaNm2rLy8tUGzeuC8nKytTMnPncmQYN\nGhh9fX2NU6fOyFAoFNi+fWvg5cY4duyI5siRQ/6PPjo+MygoyBAUFGSYMWN22j33DM0xmUy48867\nCzZv3n6gU6fOpQqFAm3b3lgWHR2jPXLkkC8AnD9/Tq1QABqNxggATZs2q/jii82HR4x46BwAFBQU\nqFUqFTw9PU1eXl6mceMmZn/11faDKpUKmzevD/HwUBsnTHgqy9vb2xQUFGSYPn3WmezsLM2+fXv8\nrRmFSCzp3fvOQpVKhR49ehb7+voZTp1K1lTnsXV1XNIlIiJyIqVe76EuK/e316YuK/dX6vUetbW8\nq9NVKAAgOzvL02AwKPr27XmjbbvJZERWVqbX5cY4fTpFAwBxcfEV1n0JCY0rEhIaVwBAUVGRaunS\nhVF//fVHQGFhodpkAgyGSkVMTGw5AAwZct/5ffv2BA4fPrh18+aJJe3bdyzq339gXkxMnA4AJk2a\nkrp06cL4nTu3h7Rte2NRt27dC/v0uatAqVQiLS1Nk5+f59mzZ5f2tpmUSqUpMzPd07odGRlVYdvu\n6elp1Gq1V5y5dGcs+IiIiJxIpdVprDN7VSmNRrWqQudVWwXfsWNHfIOCgvV+fn4GjUZj3Lnzl7+u\ndSyTyWT3hLiFC+dGHziwP+DFF19LbteufZlSqcTo0SMSre0BAQGGFStWHj927Ihm9+6fA/bt2xPw\nxRefRc6e/fLJXr16Fw4cODj/9tt7F+7evavBb7/ta7BkyetxGzZ8Gfb22x8cVyoVpujomPI1azYc\nuVw2hUJ51Vciuzsu6RIRETmRQeOpNSqVenttRqVSb/DyrLDXVt3Oncv12LFje8M77uhzLi4uXqvV\napUpKacums07fTrF81Kvt4qPT9ACQHLy8QtLpCkpp7zef/+dRpWVlZDyqF+3bj3y2rfvWKZUKlFW\nVqbMzMy40LeiokJRXFysTExsqX300XFn33tv1fFu3XrkbdmysaE1p5+fn7Fv334Fzz33ctp77606\neuTIIf8jRw57x8TEVpw9e9arqKjoQv1iMpmQmnr6irnrOxZ8RERETmRUqyv1Pt7F9tr0Pt7FNT27\np9VqFT/+uLPB448/KsLDIyrGj5+U1aPHbUVRUdHa+fOTYrOzs9Q6nU6xevWq0FGjRrQ6cyb1ssVT\nYmJLbYsWLUvee+/tyHPncj0KCwtVixcviP711z2BHh4eCA0Nqzh27KhfaWmp8uzZbI9Zs6YlBAc3\n1J07l+tpMpmQlPRC7NSpE5ump6d5AkB2dpY6I+OMJjIyquLMmVTPoUP7t9m0aX2wTqdTVFZW4vff\nf/NTq9WmqKgo3YABg897eXkZ5817JTY/P19VVlamXLJkYeT48aNblJSUsKa5DC7pEhEROVlxVGQq\nMjLN5+wZjWqjUqnX+3gXF0dFptbE++/evSu4Z88uQYD5qtWwsPCKO+7oe3706LFnPT09TQAwb96i\nE4sWzY958MFhrQAgOjqmPClp3gnruXSX89JLc07Nn58UO3z44NYqlcrUqlWboldfnX8SACZNmpL+\n6qsvJgwY0LttaGiYbvz4iWe0Wq1y4cK5CU899XjTV16Zl7JgwWsx48Y9klhWVurh5+df2blz1/xJ\nk6ZmeHt7m2bOfP7UZ5+tili2bFGcQqE0RUdHa196ac6J4OAQgzn3GyeWLVscPXRo/zZKpQJNmjQr\nXbBg6XE/Pz9jtR1QN6AwmbjMbcOUl1cCg8F1j4lKpUBwsB+Y0zmY03nqQkaAOZ3NktMtbm62f//+\nxPDw2HWenhqts8ZU6vUeqgqdl8HLs6K2ztuj+kGn02qys9Pu7dChwzF77ZzhIyIiqiZGtbqShR65\nAq53ExEREbk5FnxEREREbo4FHxEREZGbY8FHRERE5OZY8BEREZkVGQwGp12hS1STLJ/doku1s+Aj\nIiIyyy4vL03h7cqorjGZTCgvL00BkH2pPrwtCxEREYAOHToY//rrwDgAK7y9fRNUKpXmii8iqmUG\ng0FbXl6aUlZWPK5Dhw6XvPm0yxR8QojpACZaNk8AGCOlTLHT7ycAAkChze6FUsr3hBBKAPMBDAdg\nAvA7gLFSyvPVmZ2IiNzDjTe2y9u/f//9JSUF4QAa1HYeIgcUAci+XLEHuEjBJ4S4B8CTAG6WUmYI\nIWYDWAvg5ku85Fkp5Ud29k8C0BtASwDFAFYAeAvA/U4PTUREbsnygzPT8ovILbjKOXwjAKySUmZY\nthcCaC+EaH4N4yyTUhZJKU0AFgAYLITwdWJWIiIiojrFJWb4ALQAsM26IaUsF0JkwDxTd9xO/+FC\niEkA/AHsBDBDSllsGce2/0kAKgDNABxwJIhS6dqPhLTmY07nYE7nqQsZAeZ0NlfPR0RmNVbwCSGG\nA1hmp8l6Ll55lf3lAPzs9P8WQAnMy7V+ADYAWALgEQC+tuNIKY1CCN0lxrErMLBuTAYyp3Mxp/PU\nhYwAcxJR/VJjBZ+Uci3M5+X9ixDibwABVXYHwnweXtVx5tps5gsh5gBYY9kusR1HCOEFQGNvnEsp\nKCiF0ei6l+QrlQoEBvoyp5Mwp/PUhYwAczqbNScRuTZXWdI9BPOVtwAAIUQIgFDLftjs9wBwA4DD\nUspKy24VAF2VcXZYtlsAqID5ql+HGI0mGAyu+4+rFXM6F3M6T13ICDAnEdUvrnLRxscARgghoi3b\nzwDYLaU8aafvNgATAEAI4Q1gMoD1NuOME0IEWG7RMg3A51LKsmpNT0REROTCXKLgk1JuBzAXwG4h\nRDqAdjBfcQsAEEIcE0LcYJnVGwjgXiHECZgvxDgOYIal67sAtgA4DCAN5u9vUo19I0REREQuSMFH\nyFzElJdX4tLLJyqVAsHBfmBO52BO56kLGQHmdDZLTl6qS+TiXGKGj4iIiIiqDws+IiIiIjfHgo+I\niIjIzbHgIyIiInJzLPiIiIiI3BwLPiIiIiI3x4KPiIiIyM2x4CMiIiJycyz4iIiIiNwcCz4iIiIi\nN8eCj4iIiMjNseAjIiIicnMs+IiIiIjcHAs+IiIiIjfHgo+IiIjIzbHgIyIiInJzLPiIiIiI3BwL\nPiIiIiI3x4KPiIiIyM2x4CMiIiJycyz4iIiIiNwcCz4iIiIiN8eCj8jNKHJz4bFvDxS5ubUdhYiI\nXIRHbQcgIicpK4P/5AlQ7/kZqpwcGMLCoO/aHcWLlwM+PrWdjoiIahELPiI34T95AjSb1l/YVuXk\nQLVpPaBQoHjFh7WYjIiIahuXdIncgCI3F+o9P9ttU+/exeVdIqJ6jgUfkRtQnTwBVU6O/bacHKhS\nTtZwIiIiciUs+IjcgKFJMxjCwuy3hYXBkNCkhhMREZErYcFH5AZMoaHQd+1ut03ftTtMoaE1nIiI\niFwJL9ogchPFi5cDgP2rdImIqF5zmYJPCDEdwETL5gkAY6SUKXb6/QzAdrpCDSBeSqkSQrwIYAqA\nLJv2n6WUY6snNZEL8fFB8bsrocjNhSrlJAwJTTizR0REAFyk4BNC3APgSQA3SykzhBCzAawFcHPV\nvlLK7lVe+wIuLgA3SilHVWNcIpdmCg1FJQs9IiKy4Srn8I0AsEpKmWHZXgigvRCi+eVeJIRoCuAR\nALOrOR8RERFRneUSM3wAWgDYZt2QUpYLITIAtARw/DKvmw9goZSywGZfWyHELgARAI4CmCqlTHY0\niFKpuKrgNc2ajzmdgzmdpy5kBJjT2Vw9HxGZ1VjBJ4QYDmCZnaZCy9fyKvvLAfhdZrxEAF0BPGCz\n+wAADYC5AEosX7cKIVpJKfWO5AwM9HWkW61jTudiTuepCxkB5iSi+qXGCj4p5VqYz8v7FyHE3wAC\nquwOBFB8mSEnAFgtpayweY9NADbZjPsczBdxJAI46EjOgoJSGI0mR7rWCqVSgcBAX+Z0EuZ0nrqQ\nEWBOZ7PmJCLX5ipLuocACOuGECIE5gsxDl3mNf0BjLHdIYRoBuC8lDLPskth+aVzNIjRaILB4Lr/\nuFoxp3Mxp/PUhYwAcxJR/eIqF218DGCEECLasv0MgN1SSrvPgxJCRACIB/BXlaYkAO8IIdSW7Zkw\nz+w5fA4fERERkbtxiYJPSrkd5vPtdgsh0gG0g/nKXQCAEOKYEOIGm5dEAtDbzORZPQ7ABEAKIY5b\nxhkkpTRU6zdARERE5MIUJhOXCmyY8vJKXHr5RKVSIDjYD8zpHMzpPHUhI8CczmbJyUt1iVycS8zw\nEREREVH1YcFHRERE5OZY8BERERG5ORZ8RERERG6OBR8RERGRm2PBR0REROTmWPARERERuTkWfERE\nRERujgUfERERkZtjwUdERETk5ljwEREREbk5FnxEREREbo4FHxEREZGbY8FHRERE5OZY8BERERG5\nORZ8RERERG6OBR8RERGRm2PBR0REROTmWPARERERuTkWfERERERujgUfERERkZtjwUdERETk5ljw\nEREREbk5FnxEREREbo4FHxEREZGbY8FHRERE5OZY8BERERG5ORZ8RERERG6OBR8RERGRm2PBR0RE\nROTmPGo7gJUQIgjACgDDAIRKKc9dop8PgOUA+gAwAdgOYIKUUmtpnw5goqX7CQBjpJQp1RyfiIiI\nyGW5xAyfpdjbB+CoA92TAEQCSAAQb/n9S5Zx7gHwJIAuUspYAD8CWFsNkYmIiIjqDJco+CyGAFjp\nQL8RAF6XUuqklJUA3gDwH5u2VVLKDMv2QgDthRDNnZ6WiIiIqI5wiSVdKWU+gHwhRPzl+gkhggGE\nAThus/sEgAghRCCAFgC22YxbLoTIANCyymsuSalUXF34GmbNx5zOwZzOUxcyAszpbK6ej4jMaqzg\nE0IMB7DMTlOhlLKJg8P4Wr6W2+yz/t7P0l6Oi5Vb2hyhCAz0vXIvF8CczsWczlMXMgLMSUT1S40V\nfFLKtbj+8+lKLF8DAORYfh9o+VpsaQ+o8ppASxsRERFRveRK5/BdkWXpNxOAsNndAkC6lLIQwCHb\nNiFECIBQy34iIiKieqlOFXwWHwOYLITwFEJ4AZgM4EObthFCiGjL9jMAdkspT9ZCTiIiIiKX4BIF\nnxDiXiHEMQDfW3btFUIcE0J0srQfE0LcYGl7EcApACkATgI4DOBVAJBSbgcwF8BuIUQ6gHYwX7lL\nREREVG8pTCZTbWcgIiIiomrkEjN8RERERFR9WPARERERuTkWfERERERuziWetFFTLM/sXQFgGIBQ\nKeW5S/TzAbAcQB8AJgDbAUyQUmot7dMBTLR0PwFgjJQyxYk5HRpfCPEzzLedsVIDiJdSqoQQLwKY\nAiDLpv1nKeXYWsj5E8y3yym02b1QSvmeEEIJYD6A4TAf698BjJVSnq+FnMEA3gRwE8z/GfoLwONS\nynPVcTyFEDcBeAtAIwA6AM9LKT+z0+8hAM/D/OebA+AxKeVfVzPG9biKnE/AfJxVAEoBzJBS7rA8\nQScFgKzyklsu9XewunIKIUZZ+qTZ7D4lpexnab8T5sc1+sJ8X8+npJQ7nZXxKnJOBfBYlZfGAhgH\n4GfUwPG05HgMwCIAL0gpX79En1r9fBKRY+pNwWcp9vbBsZs/JwGIBJAAwAhgC4CXAMwQQtwD4EkA\nN0spM4QQsy1j3uyknA6PL6XsXuW1L+DiAnCjlHKUM3JdT06LZ6WUH9nZPwlAb5gff1cMc0H+FoD7\nayHnewCKYL63owLAlzBf9T3G0u604ymE0ADYDPNxWSWEaAFgjxDiHynlIZt+N8JchHaTUh4SQjwI\nYLMQoinMRekVx6ihnAMAzAbQSUqZLoS4F8B6IUS4tY+UMtEZma4np8VvUsqedsaIgPnP/B4p5Q9C\niFsBfCWEaCqlzK3JnFLKN2AuPK2vaw7zfzw3Awi29Km242l5z+Uw/3ty7DJ9avXzSUSOq29LukMA\nrHSg3wgAr0spdVLKSpj/4f2PTdsqKWWGZXshgPaWf5Cd4ZrGt/wD+wjMP3RrgrOOwwgAy6SURVJK\nE4AFAAYLIZz1PKmrybkSwCwppcHy5/4dgDZOylHVbQCMUspVACClPArzc6Cr3kZoOICvrT8gpZSf\nwvz3tudVjFETOU8CuE9KmW7Z3grAH0C8E7M4I+flDALwj5TyB8sY/4X5pu2DXSDncpg/m0VOzHIl\na6SU9+HyTyqq7c8nETmo3szwWZ7SkW9ZXroky7JeGIDjNrtPAIgQQgTCPPuzzWbcciFEBswzVLav\nuVbXOv58mJdJC2z2tRVC7AIQAeAogKlSymQnZLyWnMOFEJNgLgJ2wrzcV2wZx7b/SZiXBJsBOFCT\nOaWUX1t/L4RQwFwA/GzTxZnHswXMnytbJ2C+d2TVfvur7Eu25Fc6OMb1cCinlPJIlT5DYF7+ToZ5\nthxCiE8AdARQBuANKeXqms5pESOE+A7mYvQMzJ/FP/Dvz6J1jJa1lBMAIIQYCPOs3poq+6vzeEJK\n+YsD3Wr780lEDnKrgk8IMRzAMjtNhVLKJg4OY51ZKrfZZ/29n6W9HBcrt7Rdd047733F8YUQiQC6\nAnjAZvcBABqYlyRLLF+3CiFaSSn1NZzzW0uGFZb2DQCWwDwjedHxlFIahRC6S4xT3Tmt4ykALAYQ\nDvP5noATjmcVjn6OLtdP4eAY1+OqP+9CiJ4wH7+RUkqdEKIE5tnTFVLKX4UQXQHsEEKkW2bRajJn\nMsyzj/NgLkgnAvjWMuN73X+3nZjT1kwAr1pmwAHz56+6j6ejavvzSUQOcquCT0q5Fo6do3c5JZav\nATCfgAwAgZavxZb2gCqvCcTllz0ucrmcQoi/r2H8CQBWSykrbN5jE4BNNuM+B/NFB4kADtZkTinl\nXJvNfCHEHPxvtuKi42l5XJ7G3jjVndPS3wfAJzDPqPS0LqE543hW4ejn6HL9lA6OcT2u6vNuOYF/\nPoD7bZZGz8Fc3MOyvUcIsRnAQADOKlAcymmZtbKduVoqhJgJoJtljIZ2xsh2UkaHc1oJIToAaArg\nK+u+GjqejqrtzycROai+ncN3RZal30yYryq1agEgXUpZCPM5PRfahBAhMJ/Y7KyTkK9l/P6wWba0\nvK6ZZXnaSmH5pavJnEIIDyFEWyGE7X8uVDY5LhoH5mNdgX8vBVVrTkubF8wnmZcA6GP5LFjbnH08\nDwFoZrlK2aqFnVxV81uXuw9dxRjXw+H3EEI8CvOjD2+1FnuW/cFCiGZVutt+BmospxAi1nJxhr0s\nVT+LdseoiZw2+gPYLqU0WHfU0PF0VG1/PonIQSz47PsYwGQhhKelCJgM4EObthFCiGjL9jMAdksp\nTzrxvR0e3/LDKx7mW4jYSgLwjhBCbdmeCfNMlLPO4buanNtgnoWEEMIb5uO53maccUKIAMsPhmkA\nPpdSltVCzhdgvkp3lJ1lWmcfz//CfO7VaAAQQrSD+ST3T6r0Ww2gtxCitWV7DIACy+sdHeN6OPQe\nQoiWAOYAuF1KWfV2IZ1hfj52E0vfNgDugs2MaU3lhHkJ90vrRUGWItUAYC+AjTAXKHdY2u6E+VYo\nG2shp1VX/Pvvdk0cT0fV9ueTiBxUb56lK8y3iXgV5ntFNYb5B7UBwENSyt+EEMcADJVSHrYUeW8C\nuBvme8NtBjDZWgQIIZ4E8DTM/6s+CPN93TKqvud1ZL3k+LY5Ldv/397dh9lZ1/e+f6+ZJMQJyhia\nqLRF2Ihfg1bQFvWk5VLawq5WebBVKT1lowJuFTU+49FC977wgFpsRKAFekkRUatlA1Y5Pp0qpgRt\n6/GBIHwJKd0KWDMaAkkmMMnMOn/c99LFcpKskHtm7nXP+3VduSbr/v3WL59ZrAyf3E/rN4FbM3NR\nzxoHUtze5GhgJ8WtFd6S1d4vsK+cZcbOOXFTFFe/npOZ28rz5d4PnFYuuwZ4XZVXI+5FzkeAMX5x\nWB/gkcw8ciZez4g4EriC4iKQhykuHri+POS9LTPPL+e9iuI12o/i/nFndi6S2NUajzXTY80ZEZdT\nXH3Z+/fgbZl5U0SsAl5fbnuY4py0z85BzsUU548eB+yg2Jv/1sz8brnG7wIXA08AHqC4/2Y/Fy9U\nmrNr7m3Ahb0XZMz061nuqbu9fHgwxd+JTfyi/Nbm/SmpP/Om8EmSJM1XHtKVJElqOAufJElSw1n4\nJEmSGs7CJ0mS1HAWPkmSpIaz8EmSJDWchU+SJKnhGvVZutKgiIj/AH6V4ubfUNzMeT3wocz8ZDmn\nBZwJnEXxmb2T5ZyPAX+dme2u9U4A/hZYm5knzc53IUkaFO7hk+bOOzNzcWYuBg6k+LSCv4uIF5fj\nVwB/TvH5tEuBJ5VzzgP+prNIRFwEXAj0fqSZJEmAe/ikWsjMR4B/iIj/BpwcEdspPpd0ZWbe2jX1\n+oh4ADgxIhZl5gTwU+C3KD76bXS2s0uS6s/CJ9XLAorDu68AvtlT9gDIzK8DX+96fAFARMxOQknS\nwLHwSTUQEY8DTgR+DzgOOAcP0UqSKuI5fNLc+VBEPBwRDwM/Ad4OvCozb6bYyzc8p+kkSY3hHj5p\n7rwzM1fvYuxu4IWzGUaS1Fzu4ZPq6TPAcyLiuN6BiDg6In4QEU+Yg1ySpAHkHj6phjLz1oi4GLg2\nIt4A/CPQAl5CcUuWKzPzobnMKEkaHBY+qaYy8y0R8W2Kc/v+ttx8O/CWzPwUQEQ8lV9c3LGw3PZw\n+fzFs5tYklRXrXa7vedZkiRJGliewydJktRwFj5JkqSGs/BJkiQ1nIVPkiSp4Sx8kiRJDWfhkyRJ\najgLnyRJUsNZ+CRJkhrOwidJktRwFj5JkqSGs/BJkiQ1nIVPkiSp4Sx8kiRJDWfhkyRJajgLnyRJ\nUsNZ+CRJkhrOwidJktRwFj5JkqSGs/BJkiQ1nIVPkiSp4Sx8kiRJDWfhkyRJajgLnyTcfwiTAAAg\nAElEQVRJUsMtmOsAvSLiLOCvgPMy8y93Mec04FxgIbAROCszv1OOHQ1cBjwJmADOzcxPzkZ2SZKk\nOqrVHr6IuBT4feDO3cx5DvBR4KTMfCrwEeDGiFgUEYuBG4FLMvNg4GXApRHxrJlPL0mSVE+1KnzA\npzLzlcCW3cw5Bfh8Zq4DyMxPUHwfLwKOBaYy8+py7A7gJuDUmQwtSZJUZ7U6pJuZ/9zHtBXAt3u2\n3Q0cQVH81veMrQeO2vd0kiRJg6lWha9PS4DtPdu2A/sDrd2M7VG73W63Wq19DihJ84w/OKWaG8TC\ntxU4oGfbKMVh4KHdjO1Rq9Vi8+ZtTE219znkTBkaajE6usScFTFndQYhI5izap2ckuptEAvfOiA6\nDyJiGDi83D4EvCcihjJzqpyyArit38WnptpMTtb3h2uHOatlzuoMQkYwp6T5pW4XbfTjWuC4iPiN\n8vEZwGbg5vLXOPBqgIg4iuJCjmvmIKckSVIt1GYPX7mn7vby4cHAERFxBnB9uW1bZp6fmT8o79V3\nfUTsB/wQOCEzd5brnAhcERHnAQ8Dp2fmXbP6zUiSJNVIbQpfZk4Cz+hz7t8Df7+Lse8Bz68wmiRJ\n0kAbxEO6kiRJ2gsWPkmSpIaz8EmSJDWchU+SJKnhLHySJEkNZ+GTJElqOAufJElSw1n4JEmSGs7C\nJ0mS1HAWPkmSpIaz8EmSJDWchU+SJKnhLHySJEkNZ+GTJElqOAufJElSw1n4JEmSGs7CJ0mS1HAW\nPkmSpIaz8EmSJDWchU+SJKnhLHySJEkNZ+GTJElqOAufJElSw1n4JEmSGs7CJ0mS1HAWPkmSpIaz\n8EmSJDWchU+SJKnhLHySJEkNZ+GTJElquAVzHaAjIo4GLgOeBEwA52bmJ3vmvA04q+epBwOvA9YA\n9wDZM/47mfnTGQktSZI0AGpR+CJiMXAj8J7MvDoiVgBrI+L7mbmuMy8zPwx8uOt5Twe+VD53aTnn\nGbMaXpIkqebqckj3WGAqM68GyMw7gJuAU/fwvEuB92bmQzOcT5IkaWDVYg8fsAJY37NtPXDUrp4Q\nESdQ7NX7VM/2a4DfAsaBD2fmtXsTZGiotTfTZ10nnzmrYc7qDEJGMGfV6p5PUqEuhW8JsL1n23Zg\n/9085xzg/Mxsl4+3AlcBl2fmtyJiJfCViLg3M2/uN8jo6JK9iD13zFktc1ZnEDKCOSXNL3UpfFuB\nA3q2jQJbppscEb8JPA34XGdbeWHGa7oer42IG4ETgL4L3+bN25iaau954hwZGmoxOrrEnBUxZ3UG\nISOYs2qdnJLqrS6Fbx3wnogYysypctsK4LZdzH8p8KXMnOxsiIilwIGZ2X1oeJjiit++TU21mZys\n7w/XDnNWy5zVGYSMYE5J80tdLtq4meKcu1cDRMRRFBdyXLOL+SuB7/RsewFwa0QcVq7xbODFwA0z\nEViSJGlQ1KLwZeYEcCJwVkT8EPgMcHpm3hURF0TE+3qechDwk541bgLOB26KiKQoi6/NzG/N/Hcg\nSZJUX61220MFXdqbNm2t9eGT4eEWS5fujzmrYc7qDEJGMGfVypxeqivVXC328EmSJGnmWPgkSZIa\nzsInSZLUcBY+SZKkhrPwSZIkNZyFT5IkqeEsfJIkSQ1n4ZMkSWo4C58kSVLDWfgkSZIazsInSZLU\ncBY+SZKkhrPwSZIkNZyFT5IkqeEsfJIkSQ1n4ZMkSWo4C58kSVLDWfgkSZIazsInSZLUcBY+SZKk\nhrPwSZIkNZyFT5IkqeEsfJIkSQ1n4ZMkSWo4C58kSVLDWfgkSZIazsInSZLUcBY+SZKkhrPwSZIk\nNZyFT5IkqeEWzHWAjog4GrgMeBIwAZybmZ/smXN6OeeHXZv/PTNfUo7/AfBhYAmwFXhLZn515tNL\nkiTVVy328EXEYuBG4JLMPBh4GXBpRDxrmun/kpnP6PrVKXtPAT4LnJ2ZTwXeAFwXEctm6duQJEmq\npVoUPuBYYCozrwbIzDuAm4BT92KNk4DvZ+Y/lWvcDKwDTq44qyRJ0kCpyyHdFcD6nm3rgaOmmfvr\nEfFl4BDgR8C7M/PfyjXummaNI/YmyNBQa2+mz7pOPnNWw5zVGYSMYM6q1T2fpEJdCt8SYHvPtu3A\n/j3b7ga+AHwA+DFwNvDFiHj6XqyxW6OjS/Zm+pwxZ7XMWZ1ByAjmlDS/1KXwbQUO6Nk2Cmzp3pCZ\n/wz8c9emiyPiHOC3yzV+ZZo1/nNvgmzevI2pqfbePGVWDQ21GB1dYs6KmLM6g5ARzFm1Tk5J9VaX\nwrcOeE9EDGXmVLltBXBb96SIOBjYkZk/7to8THFV7zrgdT3rrgD26irdqak2k5P1/eHaYc5qmbM6\ng5ARzClpfqnLRRs3A+PAqwEi4iiKCzmu6Zl3NvDZiFhSznstMAncClwPHB4Rv1+O/QFwcLldkiRp\n3qrFHr7MnIiIE4ErIuI84GHg9My8KyIuALZl5vnAucBHgNsiYgdwP/CSzHwIICJOpjjM+wTgAeCE\nzNw0F9+TJLV27mR4YoLJRYtoL6jFj1tJ81Sr3fZQQZf2pk1ba334ZHi4xdKl+2POapizOoOQEWYp\n59QUj7//P1k4Ps7wzkkmFwyzY2SELQc9GYb6O7AyYK+nl+pKNec/OSWpYo+//z9Z/NAvrjkb3jnJ\ncPl4y68dNFexJM1jdTmHT5IaobVzJwvHx6cdWzg+TmvnzllOJEkWPkmq1PDEBMM7J6cf2znJ8MTE\nLCeSJAufJFVqctEiJhcMTz+2YJjJRYtmOZEkWfgkqVLtBQvYMTIy7diOkRGv1pU0J/zJI0kV23LQ\nkwGmv0pXkuaAhU+SqjY0xJZfO8j78EmqDX8CSdIMaS9YwE6LnqQa8Bw+SZKkhrPwSZIkNZyFT5Ik\nqeEsfJIkSQ1n4ZMkSWo4C58kSVLDWfgkSZIazsInSZLUcBY+SZKkhrPwSZIkNZyFT5IkqeEsfJIk\nSQ1n4ZMkSWo4C58kSVLDWfgkSZIazsInSZLUcBY+SZKkhrPwSZIkNZyFT5IkqeEsfJIkSQ1n4ZMk\nSWo4C58kSVLDLZjrAB0RcTRwGfAkYAI4NzM/Oc28NwBnA8PANuDdmfmViDgEuAfInqf8Tmb+dCaz\nS5Ik1VktCl9ELAZuBN6TmVdHxApgbUR8PzPXdc17GfA+4HmZeW9E/DFwXUQ8uTMnM58x2/klSZLq\nrC6HdI8FpjLzaoDMvAO4CTi1Z94G4JWZeW/5+AvA44FDZimnJEnSwKnFHj5gBbC+Z9t64KjuDZn5\ng545Lwd+DNwNHAQQEdcAvwWMAx/OzGv3JsjQUGtvps+6Tj5zVsOc1RmEjGDOqtU9n6RCXQrfEmB7\nz7btwP67ekJEvAhYDfxpZk5ExFbgKuDyzPxWRKwEvhIR92bmzf0GGR1dstfh54I5q2XO6gxCRjCn\npPmlLoVvK3BAz7ZRYMt0kyPiNOCDwKsy858AygszXtOZk5lrI+JG4ASg78K3efM2pqbae5d+Fg0N\ntRgdXWLOipizOoOQEcxZtU5OSfVWl8K3DnhPRAxl5lS5bQVwW+/EiHgt8F7ghZmZXduXAgdmZveh\n4WGKK377NjXVZnKyvj9cO8xZLXNWZxAygjklzS91uWjjZopz7l4NEBFHUVzIcU33pIg4ArgA+N3u\nsld6AXBrRBxWzn028GLghpmNLkmSVG+12MNXnoN3InBFRJwHPAycnpl3RcQFwLbMPB94C/A44IsR\n0b3E2zLzpog4H7ipHHsYeG1mfmtWvxlJkqSaabXbHiro0t60aWutD58MD7dYunR/zFkNc1ZnEDKC\nOatW5vRSXanm6nJIV5IkSTPEwidJktRwFj5JkqSGs/BJkiQ1nIVPkiSp4Sx8kjSAWhNjLHhgLa2J\nsbmOImkA1OI+fJKkPk2O8/jb38jCB9YwPLGRyUXL2fHEY9jyzEtheGSu00mqKQufJA2Qx9/+Rhb/\n5LqfPx6e2MjwT64DWmx59sfmLpikWvOQriQNiNbEGAsfWDPt2MIHvuHhXUm7ZOGTpAExvG09wxMb\npx+b2Mjwtg2znEjSoLDwSdKAmFxyOJOLlk8/tmg5k0sOm+VEkgaFhU+SBkR70TJ2PPGYacd2PPEY\n2ouWzXIiSYPCizYkaYBseealANNfpStJu2Dhk6RBMjzClmdfRWtijOFtG5hccph79iTtkYVPkgZQ\ne9Eydlr0JPXJc/gkSZIazsInSZLUcBY+SZKkhrPwSZIkNZyFT5IkqeEsfJIkSQ1n4ZMkSWo4C580\nj4yNj/HN+9cyNj4211EkSbPoMd94OSJamdmuMoykmTG+Y5xVX3sja+9fw8bxjSwfWc7Kg45h9bGX\nMrJwZK7jSZJm2B4LX0QsAT4A/Abwycy8PCI+CJwdEZPADcAbMnPLzEaV9Fit+tobueHu637+eOP4\nRm64+zpatLj8+I/NYTJJ0mzo55DuXwG/B/wr8J6I+L+APwROA84EjgAumLGEkvbJ2PgYa+9fM+3Y\nLfd/w8O7kjQP9HNI9w+B38nMeyLiM8DXy8f/H0BE/Eu57eyZCinpsduweT0bxzdOO7ZxfCP3PLiB\nZSN+JqskNVk/e/gOAP6j/P23gYXAd7vG/zcwWm0sSVU5bPRwlo8sn3Zs+chyDj3gsFlOJEmabf0U\nvruB4wEycxJ4dWZOdY2fBGyYgWySKrBsZBkrDzpm2rGVBx3j3j1Jmgf6KXwfAK6LiJcBZOYnOgMR\ncQ1wLXDhzMSTVIXVx17KSU/7o5/v6Vs+spyTnvZHrD720jlOJkmaDXs8hy8zPxUR9wMPTDP8v4GX\nZuZX9zVIRBwNXAY8CZgAzs3MT04z7zTgXIpDyxuBszLzO3uzhjTfjCwc4Yrjr2JsfIx7HtzAoQcc\n5p49SZpH+rrxcmbeDPxgmqELKyp7i4EbgUsy82DgZcClEfGsnnnPAT4KnJSZTwU+AtwYEYv6XUOa\nz5aNLON5T3mBZU+S5pm+Cl9E/DXFod1e34qI/1lBjmOBqcy8GiAz7wBuAk7tmXcK8PnMXFfO+wTF\n9/CivVhDkiRpXunnxsuvB04GXjnN8OnA5yNiXWZ+Zh9yrADW92xbDxw1zbxv92y7m+JegEN9rrFb\nQ0OtvZk+6zr5zFkNc1ZnEDKCOatW93ySCv3ch+/VwOsy8xu9A5n5r2UhfCuwL4VvCbC9Z9t2YP+9\nmNfqc43dGh1dsjfT54w5q2XO6gxCRjCnpPmln8J3OPD/7Gb8C8Df7mOOrRT3++s2CvR+XNvu5g31\nucZubd68jamp+n5E8NBQi9HRJeasiDmrMwgZwZxV6+SUVG/9FL4FwNRuxtsUe9f2xTqKj20b6rrH\n3wrgtmnmRedBRAxTFNJ1FIWvnzV2a2qqzeRkfX+4dpizWuasziBkBHNKml/6uWjjTuC43Yy/DLhj\nH3PcDIxTHD4mIo6iuAjjmp551wLHRcRvlI/PADaXz+93DUmSpHmln8J3JfA3EfG83oGI+D3gUop7\n3z1mmTkBnAicFRE/pDgf8PTMvCsiLoiI95XzfgCcBVwfET8CTgNOyMydu1tjX7JJkiQNula7vedD\nBRFxJfBa4FYggWHgmcBzgI9m5qqZDDmL2ps2ba314ZPh4RZLl+6POathzuoMQkYwZ9XKnF6qK9Vc\nvzdePpPi8Oh3gCcDTwS+Afx2g8qeJElSI/Vz0QYR8V8p7sW3E7ggM9fMaCpJkiRVZo97+CLiT4DP\nAQcB/wX4SkScMNPBJEmSVI1+Dum+Azg1M0/IzJdQfLrGe2c0lSRJkirTT+E7nGIPX8cNdN0LT5Ik\nSfXWT+FbkJk7Og8y82Fg0cxFkupnbKzFN785xNiYFyNKkgZPXxdtSPPV+DisWrUfa9cuYOPGIZYv\nn2Llyp2sXv0IIyNznU6SpP709dFqEfEmHv3xacO92zLz4qrDSXNt1ar9uOGGX+zQ3rhxiBtuWESr\nBZdf/sgcJpMkqX/9FL77gbftYVsbsPCpUcbGWqxdO/1fkVtuWcDY2ATLltX3hriSJHXssfBl5iGz\nkEPaJ+Nj42zesInRw5YysqyaY60bNrTYuHH601w3bhzinntaFj5J0kDwHD4NtB3jO/jaqi9z/9of\nMb5xnJHlIxy08tc5dvXxLBxZuE9rH3ZYm+XLp6YtfcuXT3HooZY9SdJg6Ouj1aS6+tqqL3P3Dcn4\nxnEAxjeOc/cNydfe+uV9XnvZsjYrV+6cdmzlyp3u3ZMkDQz38GlgjY+Nc//aH007dv8tP2J8bHyf\nD++uXl1cmDHdVbqSJA0KC58G1uYNm36+Z6/X+MZxHrzngX0ufCMjcMUVjzA2NsE997Q49NC2e/Yk\nSQPHwqeBNXrYUkaWj0xb+kaWj3DAoU+s7M9atsyiJ0kaXJ7Dp4E1sqy4QGM6B6389cqu1pUkadC5\nh08D7djVxwNMe5WuJEkqWPg00BaOLOT4K/6Q8bHinL0DDn2ie/YkSeph4VMjjCwbsehJkrQLnsMn\nSZLUcBY+SZKkhrPwSZIkNZyFT5IkqeEsfJIkSQ1n4ZMkSWo4C58kSVLDWfgkSZIazsInSZLUcBY+\nSZKkhqvNR6tFxLuAs8uH64EzMvOeaeYtBT4KHE1RWL8DvD4zfxoRfwG8Ffhx11PWZOaZM5ldkiSp\nzmpR+CLiRODNwPMz876IeB/waeD500y/EngIWAG0gM8CFwJnlOPXZ+bpMx5akiRpQNTlkO6pwNWZ\neV/5+CLguRHx9GnmXgW8NzMnM3Mn8GXg2bOUU5IkaeDUYg8fxd66mzoPMnN7RNwHHAHc1T0xMz/f\n+X1EtICTgDVdU46MiG8ATwHuAN6WmXfPYHZJkqRam7XCFxGnAJdMM/Rg+XV7z/btwP67Wa8FrAae\nDLyi3PxdYDHFId6t5dcvRMSzMnNHPzmHhlr9TJsznXzmrIY5qzMIGcGcVat7PkmFVrvdnusMRMT3\ngEsy88qubT8G/ntm3jjN/BHgGmAp8PLMfGAX6z6OovgdlZm39RFl7l8MSRo8tj6p5upySHcdEJ0H\nEXEgsKzc/igRsR9wI3A/cEr3nruIOBz4WWZuKje1yl8T/QbZvHkbU1P17X1DQy1GR5eYsyLmrM4g\nZARzVq2TU1K91aXwfRy4KiJWZ+a9wDuAWzJzwzRzz6O4Svf0zOz9Kfh+gIj407IIngPcBvR9Dt/U\nVJvJyfr+cO0wZ7XMWZ1ByAjmlDS/1OIq3cz8EsX5drdExL3AURRX7gIQEXdGxDPLh2+nuF3LHeX2\nO8tDwgCvpzgsmxFxV7nOSZk5OVvfiyRJUt3UZQ8fmXkxcPEuxp7R9fv9drPGz4BXVZ9OkiRpcNVi\nD58kSZJmjoVPkiSp4Sx8kiRJDWfhkyRJajgLnyRJUsNZ+CRJkhrOwidJktRwFj5JkqSGs/BJkiQ1\nnIVPkiSp4Sx8kiRJDWfhkyRJajgLnyRJUsNZ+CRJkhrOwidJktRwFj5JkqSGs/BJkiQ1nIVPkiSp\n4Sx8kiRJDWfhkyRJajgLnyRJUsNZ+CRJkhrOwidJktRwFj5JkqSGs/BJkiQ1nIVPkiSp4Sx8kiRJ\nDWfhkyRJajgLnyRJUsNZ+CRJkhpuwVwH6IiIdwFnlw/XA2dk5j3TzPs6EMCDXZsvyswrI2II+CBw\nCtAG/hU4MzN/NpPZJUmS6qwWhS8iTgTeDDw/M++LiPcBnwaev4unvCcz/26a7W8CjgOOALYAlwOX\nAa+qPLQkSdKAqMsh3VOBqzPzvvLxRcBzI+Lpj2GdSzLzocxsAx8CTo6IJRVmlSRJGih1KXwrgLs6\nDzJzO3AfxZ666ZwSEd+OiLsi4rKIePx06wAbgGHg8BnILEmSNBBm7ZBuRJwCXDLNUOdcvO0927cD\n+08z/4vAVorDtfsD/wv4CPAaYEn3Opk5FRETu1hnWkNDrX6nzolOPnNWw5zVGYSMYM6q1T2fpMKs\nFb7M/DTFeXm/JCK+BxzQs3mU4jy83nUu7Hr4QERcAHyqfLy1e52I2A9YPN06uzI6OhhHf81ZLXNW\nZxAygjklzS+1uGgDWEdx5S0AEXEgsKzcTtf2BcAzgdszc2e5eRiY6FnnK+XjFcAjFFf99mXz5m1M\nTbUfw7cwO4aGWoyOLjFnRcxZnUHICOasWienpHqrS+H7OHBVRKzOzHuBdwC3ZOaGaebeRHHrlY9E\nxOOAVcB1XeucHRHXUOzVeyfw95k53m+Qqak2k5P1/eHaYc5qmbM6g5ARzClpfqnFRRuZ+SXgQuCW\niLgXOIriilsAIuLOiHhmuVfvBOCPI2I98F2KizTeXU69AvhH4HbghxTf35tm7RuRJEmqoVa77b8c\nu7Q3bdpa639NDw+3WLp0f8xZDXNWZxAygjmrVub0yg2p5mqxh0+SJEkzx8InSZLUcBY+SZKkhrPw\nSZIkNZyFT5IkqeEsfJIkSQ1n4ZMkSWo4C58kSVLDWfgkSZIazsInSZLUcBY+SZKkhrPwSZIkNZyF\nT5IkqeEsfJIkSQ1n4ZMkSWo4C58kSVLDWfgkSZIazsInSZLUcBY+SZKkhrPwSZIkNZyFT5IkqeEs\nfJIkSQ1n4ZMkSWo4C58kzaLW2BgLvrmW1tjYXEeRNI8smOsAkjQvjI/z+FVvZOHaNQxv3Mjk8uXs\nWHkMW1ZfCiMjc51OUsNZ+CRpFjx+1RtZfMN1P388vHEjwzdcB60WWy7/2BwmkzQfeEhXkmZYa2yM\nhWvXTDu28JZveHhX0oyz8EnSDBvesJ7hjRunH9u4keF7NsxyIknzjYVPkmbY5GGHM7l8+fRjy5cz\neehhs5xI0nxj4ZOkGdZetowdK4+ZdmzHymNoL1s2y4kkzTe1uWgjIt4FnF0+XA+ckZn3TDNvDdD9\n03EhcEhmDkfEXwBvBX7cNb4mM8+cmdSS1J8tqy8FmP4qXUmaYbUofBFxIvBm4PmZeV9EvA/4NPD8\n3rmZeUzPc8/j0QXw+sw8fQbjStLeGxlhyxVX0RobY/ieDUweeph79iTNmroc0j0VuDoz7ysfXwQ8\nNyKevrsnRcTTgNcA75vhfJJUifayZex83gsse5JmVV0K3wrgrs6DzNwO3AccsYfnfRC4KDM3d207\nMiK+ERHrI+JzZSmUJEmat2btkG5EnAJcMs3Qg+XX7T3btwP772a9ZwArgT/p2vxdYDFwIbC1/PqF\niHhWZu7oJ+fQUKufaXOmk8+c1TBndQYhI5izanXPJ6nQarfbc52BiPgecElmXtm17cfAf8/MG3fx\nnI8CE5n59t2s+ziK4ndUZt7WR5S5fzEkafDY+qSaq8VFG8A6IDoPIuJAigsx1u3mOS8FzujeEBGH\nAz/LzE3lplb5a6LfIJs3b2Nqqr69b2ioxejoEnNWxJzVGYSMYM6qdXJKqre6FL6PA1dFxOrMvBd4\nB3BLZk57+/mIeApwCPCdnqH3l+N/Wh7CPQe4Dbi73yBTU20mJ+v7w7XDnNUyZ3UGISOYU9L8UouL\nNjLzSxTn290SEfcCR1FcuQtARNwZEc/sespBwI6uPXkdr6c4LJsRcVe5zkmZOTmj34AkSVKN1eIc\nvhppb9q0tdb/mh4ebrF06f6YsxrmrM4gZARzVq3M6Tl8Us3VYg+fJEmSZo6FT5IkqeEsfJIkSQ1n\n4ZMkSWo4C58kSVLDWfgkSZIazsInSZLUcBY+SZKkhrPwSZIkNZyFT5IkqeEsfJIkSQ1n4ZMkSWo4\nC58kSVLDWfgkSZIazsInSZLUcBY+SZKkhrPwSZIkNZyFT5IkqeEsfJIkSQ1n4ZMkSWo4C58kSVLD\nWfgkSZIaz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7b3461390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colorlist = ['red','green', 'blue', 'orange', 'purple', 'pink']\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3)\n",
    "[loadings.ix[[i]].plot.scatter(x='PC1', y='PC2', figsize=(6,20),\n",
    "                               ax=ax1, \n",
    "                               ylim=[-1,1],\n",
    "                               xlim=[-1,1],\n",
    "                               s=50, \n",
    "                               color=colorlist[i % len(colorlist)])\n",
    "                               for i,l in enumerate(cont_vars)]\n",
    "\n",
    "[loadings.ix[[i]].plot.scatter(x='PC1', y='PC3',\n",
    "                               ylim=[-1,1],\n",
    "                               xlim=[-1,1],\n",
    "                               ax=ax2, \n",
    "                               s=50,\n",
    "                               label=cont_vars[i],\n",
    "                               color=colorlist[i % len(colorlist)])\n",
    "                               for i,l in enumerate(cont_vars)]\n",
    "\n",
    "ax2.legend(loc='center right', \n",
    "           bbox_to_anchor=(1.7, 0.5), \n",
    "           title='Variables',\n",
    "           frameon=True,\n",
    "           fontsize=15)\n",
    "\n",
    "[loadings.ix[[i]].plot.scatter(x='PC2', y='PC3',\n",
    "                               ylim=[-1,1],\n",
    "                               xlim=[-1,1],\n",
    "                               ax=ax3, \n",
    "                               s=50,\n",
    "                               color=colorlist[i % len(colorlist)])\n",
    "                               for i,l in enumerate(cont_vars)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### PCA scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>Region</th>\n",
       "      <th>Channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.193291</td>\n",
       "      <td>-0.305100</td>\n",
       "      <td>-0.140878</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.434420</td>\n",
       "      <td>-0.328413</td>\n",
       "      <td>0.319007</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.811143</td>\n",
       "      <td>0.815096</td>\n",
       "      <td>1.523416</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.778648</td>\n",
       "      <td>0.652754</td>\n",
       "      <td>0.163012</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.166287</td>\n",
       "      <td>1.271434</td>\n",
       "      <td>0.066279</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       PC3  Region  Channel\n",
       "0  0.193291 -0.305100 -0.140878       3        2\n",
       "1  0.434420 -0.328413  0.319007       3        2\n",
       "2  0.811143  0.815096  1.523416       3        2\n",
       "3 -0.778648  0.652754  0.163012       3        1\n",
       "4  0.166287  1.271434  0.066279       3        2"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a dataframe with PCA scores for each observation \n",
    "# then append the respective Region and Channel.\n",
    "\n",
    "dfs = pd.DataFrame(pca.fit_transform(X_std))\n",
    "dfs.columns = ['PC'+str(i+1) for i in range(len(dfs.columns))]\n",
    "dfs = dfs[['PC1', 'PC2', 'PC3']]\n",
    "dfs['Region'] = df.Region\n",
    "dfs['Channel'] = df.Channel\n",
    "\n",
    "# Check the dataframe\n",
    "dfs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA scores plot (by Region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wdR0iEchm/RKFkfnpKSy+Zxurm+7HOb0d60x5tA/3LTyPYl8fmcOHKfb13XL/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5yPDxhwshtrSROFc2dpuHMbxTPnURUF63Q3y7fuANNFxFIgkybT2WmnX7zxBt5Vq3C2t08Z1E4s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\nSOIrhBBCCCFmhf8fV0iQulgWS4AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7b0c98390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.pairplot(dfs, size=3,  palette=\"muted\", vars=[\"PC1\", \"PC2\", \"PC3\"], \n",
    "                 hue='Region', diag_kind=\"kde\", markers='.', \n",
    "                 plot_kws=dict(s=55, alpha=.8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There aren't obvious differences between customers in different regions. The three regions have very similiar distributions for each component extracted. Lisbon (region 1) has slightly higher PC1 values than Oporto (2) and Others (3). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA score plots (by Channel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZL64/HIps6Oof7xoA4JX0PKmKfM0r9rfGAW+GZXLE58ehqVxIxvHZ7FzJpJgv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\n1S90kQrqfg25IIsdZr2dG7xEDOvkYcgVIR7FbVDSpf66ZwzswUi1Q7G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vAIAPrFEdSxyXW/fyGayVper6593Ze2B7G1yAlvYk1We8LddiULiKzOq8nqFEk+TWfAXw262ng1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\nzhZ3zvTjJCZK3CIhBEhtXiEW41hvYbJbR87ErlmdC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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7b280f3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.pairplot(dfs, size=3,  palette=\"husl\", vars=[\"PC1\", \"PC2\", \"PC3\"], \n",
    "                 hue='Channel', diag_kind=\"kde\", markers='.', \n",
    "                 plot_kws=dict(s=55, alpha=.8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scores interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Customers who spent more on Milk, Groceries and Detergents/Paper (PC1) tend to be Retail customers (Channel 2). \n",
    "\n",
    "- There are no differences amongst customer channels represented by the third component, PC3, which mostly represents customers who buy mostly Delicassen and not any Fresh products at all. \n",
    "\n",
    "- Customers who spent more on Fresh, Frozen and Delicassen (PC2) tend to be slightly more HoReCa customers (Channel 1). \n",
    "\n",
    "- There seems to be no difference in the customers regions by their segment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    @font-face {\n",
       "        font-family: \"Computer Modern\";\n",
       "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunss.otf');\n",
       "    }\n",
       "    @font-face {\n",
       "        font-family: \"Computer Modern\";\n",
       "        font-weight: bold;\n",
       "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunsx.otf');\n",
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       "    @font-face {\n",
       "        font-family: \"Computer Modern\";\n",
       "        font-weight: bold;\n",
       "        font-style: oblique;\n",
       "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunso.otf');\n",
       "    }\n",
       "    div.cell{\n",
       "        width:800px;\n",
       "        margin-left:16% !important;\n",
       "        margin-right:auto;\n",
       "    }\n",
       "    h1 {\n",
       "        font-family: Helvetica, serif;\n",
       "    }\n",
       "    h4{\n",
       "        margin-top:12px;\n",
       "        margin-bottom: 3px;\n",
       "       }\n",
       "    div.text_cell_render{\n",
       "        font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
       "        line-height: 145%;\n",
       "        font-size: 115%;\n",
       "        width:800px;\n",
       "        margin-left:auto;\n",
       "        margin-right:auto;\n",
       "    }\n",
       "    .CodeMirror{\n",
       "            font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
       "    }\n",
       "    .prompt{\n",
       "        display: None;\n",
       "    }\n",
       "    .text_cell_render h5 {\n",
       "        font-weight: 300;\n",
       "        font-size: 18pt;\n",
       "        color: #4057A1;\n",
       "        font-style: italic;\n",
       "        margin-bottom: .5em;\n",
       "        margin-top: 0.5em;\n",
       "        display: block;\n",
       "    }\n",
       "    \n",
       "    .warning{\n",
       "        color: rgb( 240, 20, 20 )\n",
       "        }\n",
       "          \n",
       "    .output_png {\n",
       "        display: table-cell;\n",
       "        text-align: center;\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "</style>\n",
       "<script>\n",
       "    MathJax.Hub.Config({\n",
       "                        TeX: {\n",
       "                           extensions: [\"AMSmath.js\"]\n",
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       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
       "                },\n",
       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
       "                \"HTML-CSS\": {\n",
       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
       "                }\n",
       "        });\n",
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This chunk is related to Jupyter notebook aesthetics.\n",
    "\n",
    "import json\n",
    "from IPython.core.display import HTML\n",
    "from pylab import rcParams\n",
    "%matplotlib inline\n",
    "\n",
    "s = json.load( open(\"/home/hypathia/bmh_matplotlibrc.json\") )  #edit path to json file\n",
    "rcParams.update(s)\n",
    "rcParams['figure.figsize'] = 6, 6\n",
    "\n",
    "def css_styling():\n",
    "    styles = open(\"/home/hypathia/custom.css\", \"r\").read() #or edit path to custom.css\n",
    "    return HTML(styles)\n",
    "\n",
    "css_styling()\n"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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}