PCA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## PCA and other test on the computed Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import operator\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import seaborn as sns; sns.set()\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Good_Book.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "new_df = df.drop([\"Year\", \"New ROI\", \"Movie_Title\", \"IMDB_Rating\", \"Female_Male_Dialogues_Ratio\"],\n",
    "                axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 324 entries, 0 to 323\n",
      "Data columns (total 20 columns):\n",
      "Genre1_Tag_Female         324 non-null float64\n",
      "Genre1_Tag_Male           324 non-null float64\n",
      "Genre2_Tag_Female         324 non-null float64\n",
      "Genre2_Tag_Male           324 non-null float64\n",
      "Genre3_Tag_Female         324 non-null float64\n",
      "Genre3_Tag_Male           324 non-null float64\n",
      "#_of_Female_Cast          324 non-null int64\n",
      "#_of_Male_Cast            324 non-null int64\n",
      "#_of_Female_Dialogue      324 non-null int64\n",
      "#_of_Male_Dialogue        324 non-null int64\n",
      "#_of_Female_Crew          324 non-null int64\n",
      "#_of_Male_Crew            324 non-null int64\n",
      "Sentiment_Female          324 non-null float64\n",
      "Sentiment_Male            324 non-null float64\n",
      "noun_count_male           324 non-null float64\n",
      "noun_count_female         324 non-null float64\n",
      "verb_count_male           324 non-null float64\n",
      "verb_count_female         324 non-null float64\n",
      "adjective_count_male      324 non-null float64\n",
      "adjective_count_female    324 non-null float64\n",
      "dtypes: float64(14), int64(6)\n",
      "memory usage: 50.7 KB\n"
     ]
    }
   ],
   "source": [
    "new_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### PCA for dimensionality reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components = 3, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "principalComponents = pca.fit_transform(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "principalDf = pd.DataFrame(data = principalComponents, \n",
    "                           columns = ['PC1', 'PC2', 'PC3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.6593168 , 0.323233  , 0.01674345])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_ ## The amount of variance explained by each of the selected components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.22798185e-05, -3.31850519e-05, -1.87189843e-05,\n",
       "        -2.71742871e-05,  8.76860307e-06,  3.90910173e-06,\n",
       "         2.77222617e-03,  1.44208532e-02,  1.46282827e-01,\n",
       "         9.89077199e-01, -3.81539560e-03, -9.86319492e-03,\n",
       "         4.90633159e-05,  4.50197588e-05,  2.06573498e-04,\n",
       "         1.15683171e-04,  2.72904946e-04,  2.01637617e-05,\n",
       "         6.03400872e-05,  3.84616809e-05],\n",
       "       [ 2.94420479e-05,  1.35710224e-05, -1.84752004e-05,\n",
       "        -3.15018050e-05, -4.56138517e-05, -5.39084924e-05,\n",
       "         9.66136692e-03, -3.19896157e-03,  9.88627634e-01,\n",
       "        -1.46532918e-01, -4.75476805e-03, -3.18566385e-02,\n",
       "         7.36164872e-05,  1.04677770e-04,  1.08874153e-05,\n",
       "         1.02354454e-03, -1.29096550e-04,  1.14851045e-03,\n",
       "        -7.42908870e-05,  2.54253555e-04],\n",
       "       [-5.49375686e-05, -1.58490330e-05,  9.93402139e-05,\n",
       "         1.40330393e-04,  4.17188676e-04,  4.47294905e-04,\n",
       "        -9.84995742e-03, -4.91707311e-03,  3.30332151e-02,\n",
       "         5.88777191e-03,  3.14957752e-01,  9.48442025e-01,\n",
       "        -3.25524254e-04, -2.06426734e-04, -1.69470853e-03,\n",
       "        -1.50995977e-03, -1.58999366e-03, -1.79246919e-03,\n",
       "        -6.25718914e-04, -6.64363662e-04]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "val = pca.components_.mean(axis = 0)\n",
    "cols = new_df.columns\n",
    "weights = {}\n",
    "\n",
    "for vals, col in zip(val, cols):\n",
    "    weights[col] = vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('noun_count_male', -0.0004924158735068233),\n",
       " ('verb_count_male', -0.00048206175559903033),\n",
       " ('adjective_count_male', -0.00021322323807656897),\n",
       " ('verb_count_female', -0.00020793165732414725),\n",
       " ('adjective_count_female', -0.00012388280877614178),\n",
       " ('noun_count_female', -0.000123577353941298),\n",
       " ('Sentiment_Female', -6.761481686476837e-05),\n",
       " ('Genre1_Tag_Female', -1.9258446393614348e-05),\n",
       " ('Sentiment_Male', -1.8909735290283163e-05),\n",
       " ('Genre1_Tag_Male', -1.1821020817148753e-05),\n",
       " ('Genre2_Tag_Female', 2.071534306110072e-05),\n",
       " ('Genre2_Tag_Male', 2.7218100423744237e-05),\n",
       " ('Genre3_Tag_Female', 0.00012678114247919358),\n",
       " ('Genre3_Tag_Male', 0.000132431838188373),\n",
       " ('#_of_Female_Cast', 0.0008612118912131027),\n",
       " ('#_of_Male_Cast', 0.00210160617589418),\n",
       " ('#_of_Female_Crew', 0.1021291960216452),\n",
       " ('#_of_Male_Dialogue', 0.2828106842326955),\n",
       " ('#_of_Male_Crew', 0.30224073051550904),\n",
       " ('#_of_Female_Dialogue', 0.3893145585816442)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_d = sorted(weights.items(), key=operator.itemgetter(1))\n",
    "sorted_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "female_df = new_df.drop([\"#_of_Male_Crew\", \"#_of_Male_Cast\", \"#_of_Male_Dialogue\", \"Genre3_Tag_Male\", \n",
    "                        \"Genre2_Tag_Male\", \"Genre1_Tag_Male\", \"Sentiment_Male\", \"adjective_count_male\",\n",
    "                        \"noun_count_male\", \"verb_count_male\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 324 entries, 0 to 323\n",
      "Data columns (total 10 columns):\n",
      "Genre1_Tag_Female         324 non-null float64\n",
      "Genre2_Tag_Female         324 non-null float64\n",
      "Genre3_Tag_Female         324 non-null float64\n",
      "#_of_Female_Cast          324 non-null int64\n",
      "#_of_Female_Dialogue      324 non-null int64\n",
      "#_of_Female_Crew          324 non-null int64\n",
      "Sentiment_Female          324 non-null float64\n",
      "noun_count_female         324 non-null float64\n",
      "verb_count_female         324 non-null float64\n",
      "adjective_count_female    324 non-null float64\n",
      "dtypes: float64(7), int64(3)\n",
      "memory usage: 25.4 KB\n"
     ]
    }
   ],
   "source": [
    "female_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pcaf = PCA(n_components = 10, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "principalComponentsf = pcaf.fit_transform(female_df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "principalDff = pd.DataFrame(data = principalComponentsf) ## Loading vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.336560</td>\n",
       "      <td>-0.096107</td>\n",
       "      <td>-0.615534</td>\n",
       "      <td>-0.146796</td>\n",
       "      <td>0.569522</td>\n",
       "      <td>0.108055</td>\n",
       "      <td>0.064188</td>\n",
       "      <td>0.001241</td>\n",
       "      <td>0.002284</td>\n",
       "      <td>5.507202e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.842685</td>\n",
       "      <td>-0.372949</td>\n",
       "      <td>-0.428391</td>\n",
       "      <td>0.050359</td>\n",
       "      <td>-0.239187</td>\n",
       "      <td>-0.337697</td>\n",
       "      <td>-0.119079</td>\n",
       "      <td>0.013065</td>\n",
       "      <td>0.003050</td>\n",
       "      <td>5.507202e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.053196</td>\n",
       "      <td>-0.368038</td>\n",
       "      <td>-0.016277</td>\n",
       "      <td>0.052387</td>\n",
       "      <td>-0.377802</td>\n",
       "      <td>0.344173</td>\n",
       "      <td>0.040438</td>\n",
       "      <td>0.001345</td>\n",
       "      <td>-0.003997</td>\n",
       "      <td>5.507202e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.274309</td>\n",
       "      <td>0.631149</td>\n",
       "      <td>-0.063105</td>\n",
       "      <td>-0.359605</td>\n",
       "      <td>-0.237292</td>\n",
       "      <td>-0.097649</td>\n",
       "      <td>0.319733</td>\n",
       "      <td>-0.001913</td>\n",
       "      <td>0.001304</td>\n",
       "      <td>5.507202e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.131738</td>\n",
       "      <td>0.788178</td>\n",
       "      <td>0.055742</td>\n",
       "      <td>-0.375002</td>\n",
       "      <td>-0.041315</td>\n",
       "      <td>0.081114</td>\n",
       "      <td>-0.317213</td>\n",
       "      <td>0.009715</td>\n",
       "      <td>-0.000413</td>\n",
       "      <td>5.507202e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.955162</td>\n",
       "      <td>-0.270797</td>\n",
       "      <td>0.794949</td>\n",
       "      <td>-0.178874</td>\n",
       "      <td>0.282127</td>\n",
       "      <td>-0.106792</td>\n",
       "      <td>0.033475</td>\n",
       "      <td>-0.008887</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>5.507202e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.245986</td>\n",
       "      <td>0.761075</td>\n",
       "      <td>0.073732</td>\n",
       "      <td>0.750208</td>\n",
       "      <td>0.089103</td>\n",
       "      <td>-0.014908</td>\n",
       "      <td>0.022752</td>\n",
       "      <td>-0.012856</td>\n",
       "      <td>0.000428</td>\n",
       "      <td>5.507202e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.030715</td>\n",
       "      <td>-0.376686</td>\n",
       "      <td>0.080885</td>\n",
       "      <td>0.069948</td>\n",
       "      <td>-0.035365</td>\n",
       "      <td>0.027499</td>\n",
       "      <td>-0.019573</td>\n",
       "      <td>-0.022668</td>\n",
       "      <td>0.076301</td>\n",
       "      <td>5.507202e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.988795</td>\n",
       "      <td>-0.365188</td>\n",
       "      <td>0.011104</td>\n",
       "      <td>0.033223</td>\n",
       "      <td>-0.024373</td>\n",
       "      <td>-0.009795</td>\n",
       "      <td>-0.030812</td>\n",
       "      <td>-0.139113</td>\n",
       "      <td>-0.048242</td>\n",
       "      <td>5.507202e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.008032</td>\n",
       "      <td>-0.330637</td>\n",
       "      <td>0.106895</td>\n",
       "      <td>0.104151</td>\n",
       "      <td>0.014583</td>\n",
       "      <td>0.005998</td>\n",
       "      <td>0.006091</td>\n",
       "      <td>0.160071</td>\n",
       "      <td>-0.031249</td>\n",
       "      <td>5.507202e-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  0.336560 -0.096107 -0.615534 -0.146796  0.569522  0.108055  0.064188   \n",
       "1  0.842685 -0.372949 -0.428391  0.050359 -0.239187 -0.337697 -0.119079   \n",
       "2  1.053196 -0.368038 -0.016277  0.052387 -0.377802  0.344173  0.040438   \n",
       "3 -0.274309  0.631149 -0.063105 -0.359605 -0.237292 -0.097649  0.319733   \n",
       "4 -0.131738  0.788178  0.055742 -0.375002 -0.041315  0.081114 -0.317213   \n",
       "5  0.955162 -0.270797  0.794949 -0.178874  0.282127 -0.106792  0.033475   \n",
       "6  0.245986  0.761075  0.073732  0.750208  0.089103 -0.014908  0.022752   \n",
       "7 -1.030715 -0.376686  0.080885  0.069948 -0.035365  0.027499 -0.019573   \n",
       "8 -0.988795 -0.365188  0.011104  0.033223 -0.024373 -0.009795 -0.030812   \n",
       "9 -1.008032 -0.330637  0.106895  0.104151  0.014583  0.005998  0.006091   \n",
       "\n",
       "          7         8             9  \n",
       "0  0.001241  0.002284  5.507202e-17  \n",
       "1  0.013065  0.003050  5.507202e-17  \n",
       "2  0.001345 -0.003997  5.507202e-17  \n",
       "3 -0.001913  0.001304  5.507202e-17  \n",
       "4  0.009715 -0.000413  5.507202e-17  \n",
       "5 -0.008887  0.000535  5.507202e-17  \n",
       "6 -0.012856  0.000428  5.507202e-17  \n",
       "7 -0.022668  0.076301  5.507202e-17  \n",
       "8 -0.139113 -0.048242  5.507202e-17  \n",
       "9  0.160071 -0.031249  5.507202e-17  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principalDff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.72696786e-01, 2.60055153e-01, 1.36136317e-01, 1.00935978e-01,\n",
       "       7.46563913e-02, 3.03111050e-02, 2.50885102e-02, 5.11170425e-03,\n",
       "       1.01760735e-03, 3.36991940e-33])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcaf.explained_variance_ ## The amount of variance explained by each of the selected components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71.4199936230092 %\n"
     ]
    }
   ],
   "source": [
    "var_ratios = pcaf.explained_variance_ratio_\n",
    "su = var_ratios[:2].sum() * 100\n",
    "print(su, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.03351962,  0.24114351,  0.31774961, -0.20099391, -0.15069149,\n",
       "         0.25509436, -0.02273441, -0.49232897, -0.47582748, -0.49220459],\n",
       "       [-0.09804917, -0.27748674, -0.2781917 ,  0.38347424,  0.48275991,\n",
       "        -0.22541532,  0.4533791 , -0.26854657, -0.26135612, -0.24310135],\n",
       "       [-0.62027059, -0.44698294, -0.0828566 , -0.10804445, -0.00770129,\n",
       "         0.62624575, -0.01661596,  0.0358798 , -0.02807555,  0.04834338],\n",
       "       [-0.19868207,  0.01163241,  0.00773797, -0.4121764 , -0.43349619,\n",
       "        -0.25280532,  0.72828671,  0.04863233,  0.00932043,  0.07588065],\n",
       "       [ 0.62959798, -0.35112979, -0.52948091, -0.33947416, -0.10831502,\n",
       "         0.24747538,  0.02153536, -0.07762228, -0.0656132 , -0.03084186],\n",
       "       [ 0.20937058, -0.64438764,  0.66151929, -0.18515189,  0.15744803,\n",
       "        -0.20060646, -0.02508215,  0.05377588, -0.01752887,  0.01336008],\n",
       "       [ 0.12349973, -0.26059039,  0.07320965,  0.66455277, -0.67749132,\n",
       "         0.0523539 ,  0.03166179, -0.04624222, -0.07041207,  0.00404139],\n",
       "       [-0.04736609,  0.01827582, -0.04628067, -0.04410433,  0.00185801,\n",
       "        -0.12856623, -0.13771951, -0.12148095, -0.67038424,  0.70261178],\n",
       "       [ 0.09492384,  0.08599535,  0.0267277 ,  0.05819287,  0.05189392,\n",
       "         0.12504195,  0.11083568,  0.80812853, -0.4769827 , -0.2613355 ],\n",
       "       [ 0.31355495,  0.2252948 ,  0.29756636,  0.18574189,  0.24092267,\n",
       "         0.54002576,  0.4794855 , -0.05438481,  0.13382204,  0.35698154]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcaf.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "val = pcaf.components_.mean(axis = 0)\n",
    "cols = female_df.columns\n",
    "weights = {}\n",
    "\n",
    "for vals, col in zip(val, cols):\n",
    "    weights[col] = vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Sentiment_Female', 0.00014014906764678664),\n",
       " ('Genre1_Tag_Female', 0.00016337922149142444),\n",
       " ('Genre2_Tag_Female', 0.0005294677469812494),\n",
       " ('Genre3_Tag_Female', 0.0006240214892043449),\n",
       " ('adjective_count_female', 0.007285649953057652),\n",
       " ('noun_count_female', 0.02588795117976136),\n",
       " ('verb_count_female', 0.027101296618462948),\n",
       " ('#_of_Female_Cast', 0.3255711525652901),\n",
       " ('#_of_Female_Dialogue', 0.3317715978929498),\n",
       " ('#_of_Female_Crew', 0.340302661933211)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_d = sorted(weights.items(), key=operator.itemgetter(1))\n",
    "sorted_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0593773276680565\n"
     ]
    }
   ],
   "source": [
    "print(val.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Onto Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters = 4, random_state = 42).fit(principalDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 1, 2, 0, 0, 0, 1, 0, 2, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 3,\n",
       "       0, 2, 1, 1, 1, 0, 1, 3, 0, 1, 1, 3, 1, 3, 0, 0, 1, 3, 0, 1, 3, 1,\n",
       "       1, 1, 2, 1, 0, 0, 3, 2, 2, 2, 0, 3, 3, 0, 0, 1, 0, 3, 3, 2, 2, 1,\n",
       "       1, 3, 1, 1, 3, 2, 1, 1, 2, 1, 2, 1, 1, 1, 0, 0, 2, 2, 1, 1, 1, 0,\n",
       "       3, 1, 1, 1, 0, 1, 3, 1, 1, 1, 1, 0, 0, 3, 0, 2, 0, 0, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 2, 1, 0, 1, 1, 1, 3, 0, 3, 3, 2, 3, 1, 0, 0, 2,\n",
       "       0, 2, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 2, 3, 0, 0, 0, 2, 0, 2,\n",
       "       3, 1, 1, 1, 3, 2, 1, 2, 2, 3, 2, 3, 1, 1, 1, 1, 3, 0, 3, 0, 3, 0,\n",
       "       1, 0, 1, 0, 0, 2, 0, 1, 0, 2, 1, 0, 2, 2, 3, 0, 0, 0, 1, 2, 2, 0,\n",
       "       0, 0, 3, 2, 0, 0, 1, 1, 2, 2, 0, 2, 1, 1, 0, 3, 3, 0, 0, 0, 1, 0,\n",
       "       1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 2, 0, 2, 2, 1,\n",
       "       2, 1, 0, 0, 0, 1, 1, 3, 1, 0, 3, 1, 1, 3, 0, 1, 1, 2, 0, 3, 1, 0,\n",
       "       1, 0, 0, 0, 3, 1, 0, 2, 1, 0, 1, 1, 0, 3, 0, 1, 3, 3, 0, 3, 1, 3,\n",
       "       1, 2, 2, 0, 1, 1, 2, 2, 3, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
       "       1, 2, 0, 0, 1, 3, 3, 0, 3, 1, 0, 0, 0, 1, 0, 1], dtype=int32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_kmeans = kmeans.predict(principalDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [15.0, 10.0])\n",
    "plt.scatter(principalDf.iloc[:, 0], principalDf.iloc[:, 1], c = y_kmeans, s = 50, cmap = 'viridis')\n",
    "centers = kmeans.cluster_centers_\n",
    "plt.scatter(centers[:, 0], centers[:, 1], c = 'black', s = 200, alpha = 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "## For only female chars\n",
    "kmeansf = KMeans(n_clusters = 4, random_state = 42).fit(principalDff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 0, 0, 2, 2, 2, 1, 1,\n",
       "       0, 2, 0, 0, 2, 2, 0, 1, 0, 2, 2, 1, 0, 1, 2, 0, 0, 1, 0, 2, 3, 0,\n",
       "       0, 0, 2, 2, 0, 0, 1, 2, 0, 1, 2, 1, 1, 0, 0, 0, 2, 1, 1, 2, 1, 0,\n",
       "       0, 1, 2, 0, 1, 2, 0, 2, 0, 0, 2, 2, 2, 2, 0, 0, 1, 2, 0, 0, 0, 2,\n",
       "       1, 0, 0, 2, 2, 2, 1, 0, 2, 2, 0, 2, 2, 1, 0, 2, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 2, 2, 2, 2, 0, 2, 0, 0, 1, 2, 1, 3, 0, 3, 2, 0, 0, 1,\n",
       "       2, 0, 2, 2, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 1, 2, 0, 0, 2, 0, 2,\n",
       "       1, 2, 2, 0, 1, 1, 0, 0, 0, 1, 2, 1, 2, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 2, 2, 0, 0, 1, 0, 0, 0, 0, 2, 2, 2,\n",
       "       0, 0, 2, 0, 0, 2, 0, 2, 0, 0, 0, 2, 0, 0, 2, 3, 1, 1, 2, 2, 2, 2,\n",
       "       0, 2, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 0, 2, 0, 0, 0, 1, 2, 1, 0, 0,\n",
       "       1, 0, 0, 0, 2, 0, 0, 1, 0, 2, 1, 2, 0, 1, 2, 2, 2, 1, 0, 1, 0, 2,\n",
       "       0, 0, 0, 0, 1, 0, 0, 2, 0, 2, 0, 0, 0, 1, 0, 0, 1, 1, 2, 1, 2, 1,\n",
       "       0, 1, 1, 0, 2, 0, 1, 0, 1, 2, 2, 0, 2, 2, 0, 2, 0, 0, 2, 0, 0, 0,\n",
       "       2, 2, 2, 0, 0, 1, 1, 0, 2, 0, 0, 2, 2, 0, 0, 2], dtype=int32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeansf.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_kmeansf = kmeansf.predict(principalDff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZUb1Rs1LXffSAaiINyS6vz3McR/dveaVVcPtUxI7qnxVvJqEqAAAAdBYzb0iKaDiql//6ql55cJ6a61o0fGKpLrzhPE08bkK75/xh88sK262DhyVHTbGwHt3+jr414vSeLjul1Vstqmon5DqSltdv7NV6AAAA0DXMvKHXWTFLd1z0az159zOq3lKj5vpmrVnwse6+6j7Nf+yduOfsijaqMrw7/niy9dbuNT1ZclpwydSemBafI0dR2+q9ggAAANAlhDf0usXPL9WmlZsVCUVbvR5piejBm+co0hJpc07MsTrKHbJlJ7rMtJPt9mtkRvvPtFmOrVs+fpgABwAA0EcR3tDr3njkLYWbw3GPGaahVe+0nUVbXrdRUcUPFaYMHZE7OqE1pqtvDp8tn+mJe8yWo/LmHZq3a2UvVwUAAIDOILyh18WbWdtXNNx6Rm5Fwyb9eevcdt/vNd26cuhxCakt3Y3LGqrvDD9Tpoy4x8NOVC9XLevlqgAAANAZhDf0uiPPOEzejPizP9FwTIGjxrZ67ZGKt9s0KvmUIUN3jr1cpf6ChNeZrgZ6s+V3eds93mzFnxUFAABAchHe0OtOuOJ4+bMyZLpa336+DK9OuGKm8opa79m2vmVnu2P5TI+y3f4eqTNdjcosVqyd59rcMjU1d2QvVwQAAIDOILyh12XlZepnL9+qCccE5Pa65c/yyZ/l0xnXztYX77yyzftzXO2HM8uxlO3O6Mly006Wy68ziw6Tz2g7++k2Xbpg0FFJqAoAAAAHwj5vSIqiYYX64ZM3qr6mQc31zSoYOlAeX/yllGcXH6GHtr2p8H6bS5syNCG7RHnuzN4oOa1cU3qSTMPU8zuXym2YsuSo0JOjG0eeq0G+/GSXBwAAgDgIb0iq3IIc5RbkdPies4uO0KLatVrbvF0he08zE5/pUYbp1fVlZ/dGmWnHZZj6culJumLIcdoUqlKWy69S30AZRvxGJgAAAEg+whv6PI/p0h3jLtfC2rV6pWaFwlZU0/PH6tTCQ5XVwZJKHFiGy6vxWSXJLgMAAACdQHhDSnAZpmYMCGjGgECySwEAAACSgoYlAAAAAJACCG8AAAAAkAIIbwAAAACQAghvAAAAAJACaFgCIGneqyvX45ULVBHerWJvni4cNF3H5I9jywIAAIA4EhLeAoHA3ZKKgsHgFxMxHpCqNrbs1N+3vq7lDRslGToyd5S+VHqiSv0FyS6tz3lk+9t6vHKhwp/s3bcr2qh7Nj6nMwoP01eGnZzk6gAAAPqebi+bDAQCJ0v6YvdLAVJbeXOlvrfmn1pav14xx1bMsbSobq3+56MHtSVUk+zy+pTqSL0e2/7u3uD2qZAd1QtV72tzS3WSKgMAAOi7uhXeAoHAQEl3SLozMeUAqev+La8qZEfl7POaIylkR/TA1nnJKqtPenv3mnaPWY6lN3et7sVqAAAAUkN3l03+RdIPJQ1LQC1AyoraMa1u3Br3mCNpad16OY7Ds1yfCNlRxRwr7jFLjprtcC9XBHTNztpGvbtmo6IxW4ePLtHoISyNBgD0vIMOb4FA4CuStgSDwdcCgcAXu3p+QUH2wV4a6BOKinL2/nfYinbwTsmRo6KiHMLbJ2a4Anpix0K1WJE2xzJdXh1fOqHV9xe9h+97xxzH0R+eeVv/mrdMhiFZtqM5b7yvw8eW6p6vnyOfhz5gfRH3NdIR93X/1J3/y1wqaUggEFguaaCk7EAgcG8wGLy+MyfX1DTKtp0DvxHog4qKclRV1dDqtdGZg7S2uTLu+yfnDFd1dWNvlJYSSp0ClfoKtKFlZ6sZOLdMDXRna5yGtvn+oufFu6/R2hsfluuR15crEvvsvo1Ztt4LbtGdc17Vt8+ekcTqEA/3NdIR93XqM03joCazDvqZt2AweGowGJwUDAanSvqxpGc7G9yAdPTVYafIZ7T9eYjP9OhLJScmoaK+yzAM3THuMh2VN1oew6VM0yeP4dKU3DL9avzVchlsQYm+6dH5yxWKxtq8HolZmrtsrcJxjgEAkCis7wASZGL2MP107GW6f8sr2thSJUkakzVYXx92qsZmDUlydX1PlsuvH46+UHXRZu2M1KnQm6sBnqxklwV0aPvu9n/SbUja3diiwQNYygQA6BkJCW/BYPBBSQ8mYiykBsdxVL21RoZhqKBkIM9yfWJSzjD97pBr1GJFZEjyu7zJLqnPy/NkKs+Tmewy2hWJWVoU3KydtY0qKczTtDGlcrmYGeyvBmZnqmJXfdxjluMoL9PfyxUBAPoTZt7QZcte/UB//8FDqq/a8wEmrzhP1/zq85py4qQkV9Z3ZBDa0sLqzTv0ozn/lWXbilq2PC6XMrxu/fJLZ2p40YBkl4ckuGjGZP3l5UVtlke6XaaOm1CmDJ8nSZUBAPoDfnyMLlk5f7Xu+/IfVb2lRpFQVJFQVFWbq3XvF3+v1e+0v3cXkGoaW8L64UMvqzEUUUskpphlqyUS1e7GFt309xcVs+xkl4gkOOOI8Tpm/HD5PG59ut4gw+vWsMJ8XXcOzUoAAD2LmTd0yZzbH1Mk1LYtfiQU1cM//bd+/t8fJ6EqIPFeW7FOVpyOuI6kUCSmxR9v1rETynq9LiSXaRq6+eKTFNxWpTc+KFc4Zmn6uGE6cmypXCY/DwUA9CzCGzotFolpy0fxN6KWpA0rNsm2bJk8D4Q0sGHHrnY7B4ajMW2pqpMm9HJR6DMCJUUKlBQluwwAQD/Dp2x0mukyO2xMYrpMGSaNS5AeBg/IkcflinvM53GrMK/vNlkBAADpifCGTjNdpqacOClugDNMQ4fPnkLXSaSNUw8bp45u5xkTRvZeMQAAACK8oYuu/vnlysjJaLU00nSbyszJ0BU/viSJlQGJVZCTqRsumCWv2yXPJ/e7z+2S3+PW7VecKr+XVecAAKB38ekDnWLFLEXDUQ0qK9Zdr92up+99Vu+9tFyGIR155uE6//pzVFhakOwy01bIiuiNXau1pK5cmS6vTimYrENzRjDT2cNmTRqlicMH6eWlQVXU1Gvk4AE67bCA8rLYywsAAPQ+whs61Li7UQ/9+FEt/M9iWZatnIHZuuC75+qrv/mSvnYvwaE3VEfqdf2af6jZCitk7+n0+W5tUEfkjtIPRp0vkwDXowpzs3TViYcnuwwAAADCG9oXaYnoR2f8XNVba2RFLUlS3c56PfzTf2vX9t269JYLe+S6G1duVvn765U9IFtTT54sX6avR66TKn694TnVRptk67O29SE7qvfq1+u1mg91auGhSawOAAAAvYXwhna989RC1e6o3RvcPhVujujFP8/VmdfOVs7A7IRdr6muWXdfdZ82frhJ0p4GKXIcfeMPX9W0M/vnzMeuaKPWNG1rFdw+FbajembnYsIbAABAP0HDErRrwdOLFG6OxD3m9ri0+p01Cb3e77/2J61fvkGRlogiLRGFGkMKNYX1x2/er63BbQm9VqqojTbJbcRvV7/neHMvVoMDqW1s0dqKatU2tSS7FAAAkIaYeUO7jI422zaU0D3dqjZX66MFHysWabspciwS04t/nquv3fulhF0vVQz25cty7HaPj8go7MVq0J765pB+/fSber+8Qh6Xqahl68jRJfreBbOUk9G/l/0CAIDEYeYN7TruomPafd4sFrU0aeaEhF1ra3Cb3O20XrctWxtWbEzYtVJJpsunEwsmymu0/d74TLcuGzIjCVVhX7bt6MYHXtDSddsUjVlqDkcVjVlasm6rvv/3F+Q4bZe8AgAAHAzCG9p19LnTNKisSJ79QpUvw6sLvneeMnMzE3at/EH5sq32Z5gGDh2QkOu0WBG9VvOhHq9coCV16zqc1eorrh12mg7PHSmv4ZbP8CjD9MpruPWV0pN1aM6IZJfX7y1dt1U7ahsV2+/+jVm2tu2q1/L1FUmqDAAApBuWTaJdHp9Htz13s5745TN645G31dLYosFlg3TRTefp2AuOTui1yiYP14DB+arcsEP79+bwZfp0+ldP7fY13qsr113rn5YkRW1LXtOtbLdfvxh3pQb78rs9fk/xmm79aMxF2haq0crGrfKZbk3LG60sF3uN9QUfbNyulkg07rFQJKYPNlbqsNElrV63LFuvrlirZxauUn1zWIGSIl0+a6rGDmUZLAAAaB/hDR3KyM7Q1T+7XFf/7HI5jtNjm0IbhqHv/v3b+sm5dykaiSnSEpFhGvL4PDrxquM16fhDujReefMOPbj1dX3QuEmGDE3JKdMHDRsVcT7rnNliRxSKRHXr2kf014nX9vkNr0v8BSrxsxF6X+P3euQyDVl22+WRbpepjP1mri3b1m0Pz9UHGysVju55xrOmoUnvrduiH1x0oo6dUNYbZQMAgBREeEOn9XS4KR1fot8u+ZXmP/a2Vr+zRnlFeTrhipkac/ioLo0TbKrQzR8/rLD92WzI0vryOM32JUeOdkebtKpxiyblDD/g2LbjaEXDRm2r3yWFpJkDJijPk7jlo0g9J0wepUfnL5dlW22OmYah4ye1vn8XrtmsD/cJbpLkOFI4aunup97Uv78/TB53+x1GAQBA/0V4Q5+SlZepM752ms742mkHPcafNs9tFdykNisxWx9zpG3hXQcMb7XRJv3g43+pKlKviB2T23Dp/7bO09eHnarTi6YedL1IbSUFeTpv+kQ9t3i1QvsEMp/HrQuPnazBA3Javf/FpWtavW9/H2zcriPGlPZYvUg8x3FUVd8kl2GoIDcr2eUAANIY4Q1ppdkKa33Lji6dYxqGCj25B3zfHeVPqSK0W5b2NKaIOHs+gN+/9RWNzhyksVlDul4w0sJXZh+lQ4YP0uNvr1Dl7gYNGZirS2ZO0dGBtj8QaA7Hfz7uU+09P4e+6d2PNup/X1yguqaQHEmD83P0nXNnaHIZ/x4AABKP8Ia0YjuOurq402d6NDW3rNVrjuPovfpyvVD1vuqizRqRUaS1Tdv3Brd9RWxLT+1YrO+POu/gC0fKO3bCCB074cDdP6eNKdW6impFYm2XWUZjtiaUFvdEeegBC4Ob9YsnXlc4+tmf5ZbqWv3woZd19zVnK1BSlMTqAADpiK0CkFay3X4N9Q1s97ghQ25jz/NEftOjLJdPPx17iVzGZ38VHMfRbzY+r1+sf0ZL6sr1cfN2zatZqajaftiW9jw3tylU1ekaQ3ZUi+vWaUHtx6qPNXf6PKSHs6ZNkNftavNDBp/HpVmTRrLsLkU4jqO/vLSwVXD7VDhq6e+vLElCVQCAdMfMG9LO14edqp+se3zvssZP+U2Pbhl9gYKN21QVqdeYzCE6sWCiMl2tNyJfUl+ud2uDCu3z3Fy8GbdPGZKG+jq3D91LVcv0t62vyfzko3vMsXRW8RH6cslJfb7bJRIjL8uve796rn7x+OvaUl0rt8tUzLJ12mHjdO0ZxyS7PHRSYyiinbWN7R7/cFNlL1YDAOgvCG9IO1Nzy3T7mIv1ly2vamuoRpI0LmuIrh1+msZkDtYRuR13r3xh5/utgtuBeE2Pzh901AHf915duf669VWF7dah8sWqZcp3Z+qiwXxw7y+GF+Xrf795vip3N6iuOaTSgjxl+b3JLgtd4DbNDhshuU0WtgAAEo/whrQ0JbdM/zvxK2q2wjJlyO/q/Afj2lhTh8e9hlsRJyaXTLkMU5cOPlYTs4cdcNw5FW+1CW6SFLajerxygc4fNL3V8k2kv8EDctp0o0RqyPB5NGFYkVZuatsgyTQMHTexrPeLAgCkPcIb0tr+SyI7Y1L2MG1o3hl3qaTHcOlLpSeoRg3yxNw6aeAkDfW3/4zdvja1tP9cXMS2VBttUoGXD/JAqvj22TN0/V+fUzgak+3smYdzmYay/F594eQjk1wdACAdEd7QL+0M12lh3VrZjq3Dc0dpeEbh3mPnFU/Ty9XLZdmtw5vHcOmI3FE6t3iaiopyVFXV0KVrZrp8isTi7+9lyzmooAkgeUYOGqg/XPs5PfT6Ui1Zu1WmYWjmxJG68oTDVEjjGQBADyC8oV9xHEd/3fqqXqpavuf3cvSPbW/qqPwxunHkuXIbLhX78vTTsZfqrvKnFbKjMiTFHFuH547Ujd3YDuCMoql6snJRm0YqLhk6PHekMrqwtBNA31BamKebLz4p2WVgkoecAAAgAElEQVQAAPoJwhs6FFy8Vs//8SVVrN2u4rJinf2N0zVx5oRkl3XQXqn5QC9Xr2gToJbUrtPDFW/r8yWzJEkTs4fpn4dep9WNW9VgtWh05iAVe/O6de2LBh+jJXXl2hqq2dsQxWe4leX269vDT+/W2AAAAEh/hDe06+W/vqJH73hSkVBEcqTt5Tv00btBnXvdmbrge+cmu7yD8ljluwrH6SQZdmJ6dud7unLozL1NQ0zD0KScAzci2V99rFkvVy3X+/UblO32a3bhFB2RO1p+06NfBz6vt3d/pFdrPlTUiWlG/nidWngoSyYBAABwQIQ3xLV7R60e+dkTioZbB51IS0T/+d0LOvaC6Ro8clCSqjt4VeH6do/FHEuNVkh57swDjrO+cYeW1KxXrjtDU3PL9m78vbmlWjcGH1LEju2d3VtWv0FH5I7SD0adL4/p0okFk3RiwaTEfEH9iOM4WrBmk55ZuEq7Gpo1vrRYFx13qMqKO7fHHgAAQKojvPVz0XBUS19eplXvrFHOwGwde8HRKh03VIueXdLuOXbM1jtPLNSFNx7881/JkufJ1K5o/I11DRnKNDueAWuyQvr5uicVbN4u85OzXIapW0adrym5ZfrFhmfUZIVa7f8UsqNaWr9e83ev1gkDJybqS+lXHMfRr5+er7dXbVAouicUb9tVr/mrNuiWi0/U0eNHJLlCAACAnkd468dWvfWR7r76PkVaIntfe+a3z2vCMQEFpo9VLBK/M6IVs9RY2/FeaH3VucVH6pGKtxXe75k3j+HSSQWT5DFdHZ5/Z/nTWt20TTHHavX6T8qf0G2jL1JlaHfcjXtDdlTP7niP8HaQlq+vaBXcJMm2HYXtmH755Bt67PtXyevu+M8OAAAg1bEjcD9VU7FLv7rqt62CmyTJkT5aENRH7wbly4zf/dCf5deEY8b1QpWJtSvaqMnZwzUhu0R+0yPjk9f9pkcjMor0ldKTOzy/IrRLqxu3tglukmTZll6sXiaX0X6AqIs1d6f8fu2lpWtaBbf9LSvf1ovVAAAAJAczb/3UK3+f1+7Mmhyp/P31KhpeqJ2bq2VFPwsrpttU9sAsHT57ai9V2n11sWbds+E5fdCwSR7Tragd09jMISr1D5RpmDomf5wOzx0l0zA6HGdDy065DVOROFNrMdmqDNcq6sT/npoyNDZrSCK+nH6prjnc7jHHcdQUirR7HAAAIF0w89ZPlS/bIMeOt8BvD9Nl6tJbLlRg+jh5/B5l5mbI6/do9NSRuv25W+T2pEbutxxbNwXnaEX9RkUdS81WWFHH0sfN21XevEPfHn66jswbfcDgJkn5nqy4SyI/VezN06yBh8hrtP3eeEyXLh58TDe+kv5t6sih7S6LtGxHgdKiXq4IAACg96XGJ3AkXGFpQYfHHdvR0DFDdOuTN6pqS7V2bqxS4bACDSor7qUKE2NpXblqIg2KyW71esyxtC28Sx80btaUnM41u5iQVapMl1ctdttZHp/p0dnFR2hCVolarIgW162T23DtXZr53ZHnaHRm6nXn7CvOPHK8Hn/nA0VirZeset0uTRk5VCUF3duDDwAAIBUQ3vqpU790kt55cmG7SyeLRhSpdHzJnv8eVqiiYYW9WV7CrGjYFDdsSVLYjmpVw5ZOhzfTMHTr6At1y8ePyJKtiB2TIclrejS78FAdmj1chmHoltEXaEe4VsGmCmW6fJqSM0Iek79q3ZGX5dc9Xz5bP3v0NdXUN8llGopYtqaPG6YbLpjV5fFilq3aphZl+73yez09UDEAAEDi8Ymynxo1pUznX3+Onvz1f2RbrWel/Nl+Xf/At5JUWWJlunxyyZS138ybJLkNlzJc8ZuytGdc1lD9ddK1mt+8Sot2rNMAT7bOLDpME7Nbb+Y9yJevQb78btWO1kYOGqj/+38XacOOXaptCmlE8QAV5Bx4T759WbatOa8v09MLVsq2bdmOo6MDw3XdOccpL8vfQ5UDAAAkhuE4HT3F0yPKJG2oqWmU3cEzV+gdm1Zt1hN3/0cblm+UN8OrGRcerdlfOUXZ+VnJLi0htoZqdN3qB/ZumL0vr+HW/ZO+riJvbpfHLSrKUVVVQyJKRC+65+k39ebKDQrv07nS7TJVlJel+791obwp8ixnT+G+RjrivkY64r5OfaZpqKAgW5JGStrY2fP69ycVaMTE4freg9d1+bydm6r0n/te0LJXV8jldum4i47RWd+YrewB2T1Q5cEr9RfonOIj9HzV+wrb0b2v+0yPLh509EEFN6SmnbWNev3D9Yru99xczLK1u7FF81dt0ClTxyapOgAAgAMjvPVDzQ0teufJhdqyeouKhhdp5iXHKr+48w0ftny0VbeffafCoYjs2J7liC/86b966/F3deertyu3IKdL9bQ0tuiNh9/Su08vlmkYmnHR0Tr+0uPkz/J1aZz2XFN6kiZmD9MTlQu1I1Krob6BunjwMToib1RCxu8LbMfRppYqxRxLZRnFB9xsvD9avqFCLsNQNM6xUCSmt1dvJLwBAIA+jfDWz6x7f73uuuQe2ZalcHNEHp9HT979jL7xh69o+jnTOjXG3274h1oaQ61ei0Viqquq19O/eVZfuOPKTtdTV1WvW2f/VA27GvduGL5p9Ra9dP8r+tnLP0rY8s3p+WM1PT89P5gvrlun3216US1WRIYMGZKuHjpL5w46Mtml9Slu05TRwZYQXjc7pwAAgL6NTyv9SCwS0y8vv1ctDS0KN+8JStFwVJFQVH/69t+0a/vuA47RWNukDSs2xj1mRS298+TCLtU057ZHVbujbm9wk6RIS0TVW2v07zuf7NJYPclxHK1s2KxHtr+tJysXqaL5wN+r3rC6cat+Uf60dkebFLKjarEjarYjerDiDc2tXtHpcZqtsOZWr9DDFW/pzV2rFbXb2cA9hR05tlSW3bZxjST5vW6dPKXnw/2mnbv1u2ff1nf/9px+88x8lVfW9Pg1AQBA+mDmrR95/5UVsvZ73udTjuPojX/N1wU3nNfhGNFQRIbLlKLxx4mGO/+h37ZsLXruvbg1WVFLbz3+rq751ec7PV5PabbCunXto9rUUqWwHZXLcGnO9vk6v3iaPl9yQlJre6jiTYXjNGMJ21H9c9ubOqXg0ANuQL6ifqN+Wv6EJClkR5VhevWnzXN157jLNSqN9qbLzfTr6hMP15w3lrVqWOJzuxQoKdKRY0t79PqvLV+n+559S1FrT5fLj7bs1BsflOurp0/XOUcd0qPXBgAA6YGZt36kektNu/u6RcMxVZRXHnCMvOI8ZeW135593LQxna4nGo622aZgX+HmiJLQDbWN3296WeubdyhkR+VozwbfETumZ3a+pwW1Hye1tmBTRbvHmqywdkcbOzy/Ptasn5Y/oZAdVeiThi4tdkQNVot+uPZRxZz4IT1VXTJzim66cJZGDhoor9ulwtxMXXHCYbrj6tPlMnvun8P65pB+++xbCscs2Z/c07bjKByzdP/Li7SzruM/JwAAAImZt35l8Khiub3uuAHO6/do+CHD4pzVmmmauuzWi/TATQ+1WuooSd4Mry65+YJO1+PN8Cp/UJ52VcRfgjh0zJAOn1HqDY2xkBbUBhWNE2LCdlT/3v6ujskfl4TK9vAaboUVP5DbcuQzO96A+tXqD9sNyFE7pkW16zRjQKDbdfYlxx0yUscdMrJXr/nmyvVq7062HUfzVqzTZcdP7dWaAABA6mHmrR+ZctLkPR0c43yKNExDsy47rlPjHH/JDH3hjiuUPSBL/iyfvBleFQ0v1A3//H8afVjnPxQbhqGLvv85eTPabpTtzfDq4h+c3+mxekp1tEFuo/3OjZXh2l6spq2TCibHrc+QND5rqLLdHW88vTlUHXfZpSRF7JgqwrsSUWa/V9cUUridJct7tipo7uWKAABAKmLmrR9xuV265fEbdMeFdyvcElG4OSxvhleGpO8+eJ3yijq/59mJVx6v4y+doe3rKuXyujV4ZPFBzZKdcNlM1Vc16Kl7npXLbUoyZFu2Lv3hhZp+TvK7JRZ4sjtcOljkS+4+cZcPmaGFtR9rd7Rp70bkLpnyuzy6bsQZBzy/xD9QXsOlSJyv0Wu6Ndibn/Ca+6MxQwqU4fWoJdJ2owK/161ASXESqgIAAKmG8NbPlAZK9Ptlv9b7c1eoYu12DRwyQEedfYT8WR3P0MTjcrtUOr6k2zWde92ZOu2akxRcvE6RUERVm6q0ZkFQFR9X6IQrZmrU1N5d4ravHHeGpuWN1uK6dYo5rZ/P85seXTzomCRVtkeOO0O/P+QaPbdzqebVfKiYY2t6/lhdOGi6CjuxAfmJAydpzrb5cY+ZhqGj03R7hd42bdww5Wb6FI7G9j7zJkmGIfk9bh13SFnyigMAACnDSEJDiDJJG2pqGmXbyW9Ggb5je3mlbj/7TkVCEYWbIzJMQx6fRyddPUuf/9nlnRpjd7RJ79YGFbIiOiS7VOOzSrr93FxDrEU/+Phf2hGuU4sdkUuG3KZbpxUcqq8POzXpz+UdrMpwrb4fnKP6WMveWTtJcsuU1/ToZ+Mu1fis7odz7LGztlG3zvmvduxukGEYcuRoYHamfn71bJUU5CW7PElSUVGOqqoakl0GkFDc10hH3NepzzQNFRRkS9JISRs7ex7hDb3Kill6/n9f1sv3v6L66gYNGJyvs791hk778kn6/qwfq2Lt9rgNNAaNLNblt16ko85ufynlU5WL9M+KN2XIkOXYcpsujcwo1s/GXqpMl69bdduOo/fr12t5/Ub5XR6dN/pI5bS033Wzr3McR9euul8V4d2y1fr77TZM/TrweY3NGpKk6tKX4zhat71G22rqNDg/R4HSoj4V/vkwgHTEfY10xH2d+g42vLFsEr3GcRzde80ftXL+6r2dKndt363H7nhCq99Zo+qt1e12PtyxYaf+dN3ftH7FJl32wwvbHF9ev1Fztr/VqiukZdsqb67UvRtf0A9Hd74LZjymYejIvNE6Mm+0JKkoO0dVLan7j2awuULV0YY2wU2SHEd6teYDwlsPMAxDY4cWauzQwmSXAgAAUhDdJtFr1i1dr1VvrW6zxUC4JaLlr35wwBmIcHNEL/1lrqo2V7c59njlAoXtts0goo6lJXXrVBelm9++KsO17baut2RrY0vb7zEAAACSi5k39JrFLyxVpKVtwJL2zMpFQvGP7Ss6wqM/Ln9eY1yjNC1/jAJZQyVJW0I17Z7jMd2qjNQqz9N3ljlWhHbpscp3tbRuvTymWycPnKTPDTrqgK39E6XYmxdnzm0PU4aG+Qf2Sh0AAADoPMIbeo1tWXLajQxS8YgiVW+tibuJuGNKzTcOVmx6lt7zbdd7ldv11M7Fmpw9TLeOvkhF3lzVROMvY4zaMRV6chL2dXRXeXOlbgrOUcSO7V22+MSOhZq3a6Xum/Al5bgzeryGCVklGuDOUmWkts2fiNtw6ezi5G/TAAAAgNZYNolec8Tsw+TLiN84xOVx6ev3XaNRU8rk9rb9mUL4vHzFpmdJflOOITmSwnZUHzRs1r+2v6ULBk2Xz/S0HVemDskuVYG374S3+za+qJAdbfW8WdSxVBNt1OOVC3ulBsMw9JOxlyjPnSn/J983j+GSx3Dp2uGnqiyjqFfqAAAAQOcR3tBrJhwb0MgpI+Txtw5ZXr9XU06cpMBRY3X787fotmdv1oDB+TLdn92ekQsHSP62t2vEiemFnUt1dN5YnVZwqLyGW+YnT3NlmF4VeXN1w8hzD6reFiuiN3at0vM7lyrYVNFuM5Wu2B1t0uZQ/OfJYo6leTUfdvsanVXiL9CDk7+l60acoc8VT9NVQ4/X3yZ9Q7MLp/ZaDQAAAOg8lk2i1xiGoR88+j09ducTmvfQfMWiMXkzvDrtmpN04Q3n7X3f6MNG6lfzf66/3fCg3v/vcplut5wBrnbHDTsxhZ2orh1+mmYXTtXru1aqyQpram6ZjskfJ7fR/rnteWfXGt2z6XmZn2w7YBqGhvsL9dOxl3ZrWWPYjso0TGmfrpj72ne/td7gMd06YeBEnTBwYq9eF91T29SiNVt2yuN2aXLZEHndXb/HAQBA6iG8oVd5/R5d/dPLdeVtl6qlMaSMbL9MV9sZtay8TH3nr99UU12zanfW6aamf6vWit8x0mu45Te9kqSRmcUamXlSl+va3FKtxyrf1cqGzfKYLu0M18na92kwR1rfvFN3rn9Kd427ssvjf6rImyuf6Y7bGdOQNCl72EGPjfRn2bb+9OJCvbw0KM8+M9P/c+5xmjV5dBIrAwAAvYFlk0gK02UqKy8zbnDbV1ZepkrGDtEFg6fLZ7T9WYPXcOus4sNldmOj4w8aNul/1jyo+btWqzraoO3h2tbB7RMxWVrTWKHt4d0HfS2XYerzQ2fFfT7Pa7p15dDjD3pspL+H5i3V3GUfK2pZag5H9/6655n5WrWpMtnlAQCAHkZ4Q1xVm6s157ZHdfs5d+oP3/iLPl6yLqn1fG7QUZqWP0Y+0y1DhgxJftOjyTnDdNWQgw88juPo1xueU3i/BiLtcRsubenmHmhnFB2mL5eeqGyXXxmmVz7TrcHefN0+5hKNzhzUrbGRviLRmJ5esErhaNulteGopTlvLEtCVQAAoDexbDKFVKzbrtf++aaqNldr5JQROunK45VXnJfw63z45ir95gu/lxWzFItaWrukXO+9tExnfv00XXLzBXHPcRxHK+ev1sv3v6Kail0aOaVMZ107W6XjSxJSk8swdfOo81XeXKmFtWtlObam54/VuMwhB9zcuyPrW3aoyQp1+v2WY2ngQXSuDNlRLdgd1K5ok8oyinRG4eGaXThVW0O75DVcGuIb0K2vA+mvsraxw3tkbQUbqwMAkO4Ibyli7gOv6eGf/FtWzJIVs7Xi9Q/17O9e1PcfuV7jjx6XsOtEw1H99sv/q3BLZO9rjuMo0hLRi3+ZqyPPPFyjppS1OW/ObY9p3kNvKNy857ytwQoteGaxvvnHr+qos45IWH2jMwdrdObghI3XYkVldmECOuzE9MyOxZo2bFSnz1lat153rn9KhgxF7Ji8plu57gzdNe4KWvKj03IyvIpZ8RvdSFJ2hrcXqwEAAMnAsskUULFuu/71k38rEorKitmSpGgoqnBzWL/+/O/ibmp9sFbMW9luS/xoOKp5D73Z5vXyZRv02j8/C26SZFu2Ii0R/enbf1O4OZyw+hJtVGaxYu10fmzPu7uD+uPH/+3Ue6sjDbpj/ZMK2VG12BFZstViR1QVqdetax9NyPYD6B8GZGdqbEmR4k2++TwunTPtkN4vCgAA9CrCWwqY99B82Z+Etv3Zlq1lr36QsGs17GqQbcW/lmM72l1Z2+b11/81X9Fw2+6JkmSYRkLrS7RMl0/nFB8Rt4GIofhL1MJOTE9sXhi3Y+T+XqpaJjtOQLPlaFe0Uasat3a9aPRbN14wSzkZvlZbA/g9bo0ZUqhzphPeAABIdyybTAFVW6plxeLPDlkxS7U72gaqg1U2eUS7x9w+t7Lys/SbL/1B0VBUR555mGZccIzqq+vl2PFnkGzLVlNtU8Lq6wlfLDlRLsPUf3a+J5cMWbJV5MlVdbRBoXYCWsSOaXNLtcZmDelw7PUtOxRtZ2bPcaRt4RpNymF7AHTO0IG5+r//d7FeWLJGC4Ob5Pd6dPrhAc2cOFLuA3RuBQAAqa9b4S0QCNwm6ZJPfvtCMBi8qfslYX+jppZpxWsfKBJqGyRcLlOlgcQ0BZGkkYeO0LDxJdq4crOsaOvQYccsLXlh6d5lkGsWBvXMvc/rxCtn6oM3Vimyz3Ny+xp9eOefD0sG0zD0hZITdOngY7UlVKNMl1cl/gJ9ZeWftD0cPxg7kp7esVg3jTov7vFPDfENkEumLLWdzTQNQwWerjc/Qf+Wm+nX5bOm6vJZU5NdCgAA6GUH/aPaQCBwiqTTJB0maaqkIwKBwPmJKgyfOfGKmXH3QzNdpvIH52v8MYlrWCJJNz18vUYfNkpev0f+bP/eXy63q9Xza+HmiHbvqNXHS8rl8Xm0/ypDt9etsskjVDZpeELr6yl+l1djs4aoxF8gSTqn6MgO37+g9mNF7Y6flzuz6DC5jPh/zbymW4fljjy4YgEAANDvdGedzXZJ3wsGg5FgMBiV9JGk1PiUnmJyC3N10yPXKys/U/5sv7x+j3xZPhWXFemHj9+Y8BbzOQOzdftzN+vnc3+sr/3mi7pxzneUlZ+paLhtYxQ7Zmv1O2t008P/o8EjB8mX6VNGToY8Po8OOTagG+d8J6G19aazig/v8LgjRyE7/myj4zhqskIq9Obqa8NOkddwy23seU7JZ3qUafr0kzGXtBvsAAAAgP0d9LLJYDC46tP/DgQCYyVdKunYRBSFtsZPH6c/ffhbLX/tQ9XurFXJuKEaf/S4Ht0brDRQsndJZuPu9p9bc7ldyi/K0z3v3qlNKzerdmedSsYOVdHwwh6rrTe4DZeG+gaoIrw77nGf6VGWy9/m9UW1a/W3ra9pR6ROcqRDskv1o9EXalXjVlVF6jU2a7BOKpgU91wAAACgPUZ3W5UHAoGJkl6QdFswGPxHJ04pk7ShWxdFj7JilrYEK+T1ezRk1CAZhqFvH32zgovXxX1/RrZfT1Y/II+3bcfGVPfitmX6xepnFLJaP2/oNz26ZvSJ+uLoE1q9Pq9ypW774PFWnSgNSRkun+bM+LZKMwt6oer+x3EcPbtglf720mJtr6lXbqZPl8yaomtOP0peD32ZAABAnzVS0sbOvrlb4S0QCMyQ9KSk/wkGg4928rQySRtqahplt9OhEMkz9++v6d93PS07Zsm2HA0Ykq+v//YahZpC+u2X/7dNUxKPz60zvnaqLrv14iRV3LMcx9Gcivl6csciuQxTjiTbsXVmyeH6SvHJMveZ+XQcR1/48I+qiTa0GceUoRMLJuq7Zef0YvX9x//NXaxnF61WKPrZ0l6v26VxJUW6+0tnyTR7boY6nRQV5aiqqu39C6Qy7mukI+7r1GeahgoKsqUuhrfuNCwZJukZSVd0IbihD3v1H6/r4Z88rua6ZoWawoqEItqxYad+cdlvNHDwAF3+o4vk9Xvl9n02k2FbtuY+ME/z5rTdvDsdGIahq0tm6cHJ39I3hp+mrw87VX+ddK1+OOn8VsFNknZE6tQYa4k7ji1Hi2vLe6PkfqemoVlPL1zVKrhJUiRmqXx7tZauYy89AACQHrqznugGSX5JvwkEAp++9udgMPjnbleFXhcNR/XwTx6P2+4/GorqyXue1fUPfEu+DJ8e+P5De49ZMVtWLKx/3vqIcgbmaNqZHTf56C2WY2trqEZuw9RQ38BuPxuY78nSKQWHdvge0zDU0Vzy/mGvs3ZFG/WfHUu0uG6dPKZLswum6JTCQ+NuLN4fvbd2i1yGoXg78rVEYnr9g3JNG8deegAAIPV1p2HJdySlbitB7BVpiei2s+9UqCkU97jjOFr9zhpJ0tP3PqdYpG3XyUhLRP++66k+Ed7mVq/QA1tfV9SJyZGjPHeW/t+IM3q8LX+RJ1eF3py4DU5cMjVzwIQuj7k1VKPvrvmHInZs72bf/xeapxerl+uewNXyu7zdrjvVWbbTYWi27LZ77AEAAKQi+pRD//ndC9q2tqLD93j9HkVaIqrZuqvd92z7uEJ2kj8oz6tZqT9vnqsGq0UhO6qwHdPOSJ1+tu4JBZs6/hq7yzAMfXv46fIZrX8mYspQltunSwYf0+Ux7934vJqt8N7gJklhO6aK0C49tWNxt2tOB0eMKZHdzrO7GV63Zk5iLz0AAJAeCG/Qqw++rlicPdw+5fa6dfwlM+T2uuNuFv4pr9/To1sXHIjjOPr7ttcVdtp+LWEnpoe29fxzeVNyy3TnuCs0KXuYXDLlM906YeBE/X7CNSrw5nRprNpok8qbd8SdVYo4Mf23enliik5xg/JzdPKhY+Tbr6ukx2Vq8IBcHR0YkaTKAAAAEose2lBzfXOHx/OKcnXWt06X6TI17ewjtOi5JbJjrWfYXG6Xjr1gelLD2+5Ykxpj8Zd+StLqpt5pXDE+u0S/DFzV7XGarbBchtlq1m1ftbFmbWzZqbKM4m5fK9V959zjNGRgjp5450O1RKIyDUMnTxmjr50+Xe4OfuAAAACQSghv0KCRg1SxdnvcY4bL0Df/+FVl52dJkq7+6WVasyCoxtom/X/27jMwqipt4Pj/3umT3kMLCSUBQi/SERBEUQHF3ntvWFFXXXVtr8radlXsYlkbdgUEEWmK9JqEFggQ0num3PZ+CATCzEASUkg4v09mbntmvAnz3HPO8yjuqhIRVruF0OjQZm8XYJXM6ASetmmRWtbtHmsLQyJwMqwaGtO2fMgVbUdxXvzgJozsxCPLEheP6suFI/pQ6fFit1pE0iYIgiAIQqsjvt0ITL1vMjZHgMIXusHzF/+bjx79DMMwCI8N4/lFT3HePeeQ0KM9CakdOP+BKTz3+5OERtVtWmBDCzbb6eps6zfdMSMzJjK1yWOqr+2VOdyz5cMazb798Roqs/b9wS5XXhNFdmKTZYlgh00kboIgCIIgtEotayhCaBRDp5xCTmYu38z4AVVRMQ5rnm4Y4HV7WfjxH6SO6M6ACX0JDg9i8l1nM/musxvk+gV7C/n2lR9Z+fNqAAZO7M+Uu88mqm1knc91e8czuC/tI7y6gnZgtZhFMhFmdnJJmxENEm9jy/YU8UD6LNzHSNwO0gyNX/LWcHPC6Y0cmSAIgiAIgtCcxONpAYApd5/Nk788ErAgiafSw0//ndPg183JzGX6mMf4/ZM/KMkrpSSvlN8/+YPpYx4nd1fdR5MSHTG83uM6xkX1JtISTIwllHPjTuG1HtcSZnE2ePyN4Yvs5Sh64AIyR9IwyPWWNmJEgiAIgiAIwolAjLwJ1TyVXmwOK5WKy+/2/L0FDX7NT/75OZVlrsTNNfcAACAASURBVBqjfZqqU1laySdPfM60926v8znjbeHcmTixIcNsUqtKd1SPGtaGVTKTEtS2ESMSBEEQBEEQTgRi5E2oFtsxBuUoLQPaJTdsgqDrOmt+XVcjcTvI0A1Wz13b7H3jmoNVNtVpf1mSmBDdp5GiEQRBEARBEE4UInkTqoXHhtF7dCpmq++ArNVhZdIdDTuaZegGuhZ4hEnXDL+JXWt3WlQvrAEqY5qQsUlmbJIFp2wlxGTnqa4XE24JauIoBUEQBEEQhKYmpk0KNdzy+vU8d9EMstL2onpVTBYThm5w8T/Op/vQlHqdsyinmLnvzGfdgg3YQ+ycdsVohkwehNlipkOP9uzelOX3uITUDpjMdRuFCqREqWSvp5BISzDxtvAGOWdjmRw7iAUFG8n3llb3eJMAq2zh8c7nY5Mt7HLnEWEJpn9oEmapYT4jgC3le5ibv44StZJewQmMj+5NiNnRYOdvLXTdYFdeEYZh0DE2ApMsnoMJgiAIgtD4RPIm1OAMdfLEz4+wdeV20ldsxRFs55SzBhAaHVqv82Vt2cM/Jz2L4lZQvVVTMjPX7+a3jxcx/bN7cATb/R5ntVu49LHj7xvn1ry8vOtn/izOwCKbUXWNjo4YpneacsImcU6TjZe7Xc3XOX8yv2ADXl2lZ3AHLm07ks7OOKCqEXh9eHQFRdcIMtlqNFQ3DIO3sn5lXsF6vLqCAawrzeTz/cv4v5TL6eiIaYi31ios3rSD139cjltRkJCwmk3cfOYQxvbp0tyhCYIgCILQykmG0eTT0hKBnQUF5egn4ZS41shT6aE4t4TQ6BAcwTVHaR4c8xhZm/f4HGN1WOk3vg9rfl2H1+X12d59aAqPfvvgUa+7rjSTb3L/Zr+niERHDOfFDSb5iMId/8j4jI3lWdUjWAAyEqFmB+/0vAWHKUB/u2OIiQkhL6+sXsc2h2xPEW/snsfaskwkINISwjXtRjMqsgcAq0p28MyO2T7tCSSgjS2Cmak31Uj2TlYrt+7hic9+xatqNV63WUw8dMFYhnbr2EyRNYyWdl8LQm2I+1pojcR93fLJskRUVDBAEpBZ2+PEyJtQb163wqzHPmPxF8uQJAlN1Rh4Rj+ue/EqgsKc5GTmkrMjx/+xLi8rf1mNpmh+t29bvQN3hQd7kM3v9o/3/sHs3BXVTaz3uAtZUbKNmzqMZ0J0XwB2ufLYVL6nRuIGoGPg1hUWFmxkYmz/+r79FqPAW8bdWz6gQvNgHKhimest4eXMn3DpXiZE9+X73JV++8oZQKFSzg5XDp2d8U0c+Ynn3V9X+CRuAB5F4515K1p88iYIgiAIwolNLNQQ6m3G1a/xx+dL8bq8eCo9qF6VlXPW8MQ5z6CpGpWlLkyWwOuxAiVuALJJoryo3O+2LHcBX+f8VZ24ARgYeHSV13b9wk95q/HqKlvK9xJorMitK6wu21mr99nSzc75C7emVCduB3kMlff2LEQzdPKVwH3iZEmmSKlo7DBPeLpusHN/YcDt+wpK8Sq1788nCIIgCIJQVyJ5E+olc+Nu0v7MQHHXHK1RvSr5ewpYO389bTrHoan+S/1LshRwvRtUrcE6uM5uT/peNv6xmcLsIgAW5G9AM/wnfgbwdtZ8rlr/OhWaG1nyf4tLQLAp8PVbk+XFGaj4/7w0QyfTlUuysw2mAH8OFF0lwR7dmCG2CJIEpgBN7Ku3i8IlgiAIgiA0IjFtUqiXTYs3o/mZPgbgrvCwZv56BpzRj3FXj2H+Bwt91rVZbBYm3XUW3/77BzyVvmveVI/KC5e9TGF2EQX7CjFbTCgehd6jeyI/2PGoTawVQ0PRXHybswLd8J88WmUL46N71+Edt1zyUdaqGRjIyJwbN5hFRZvRjuirZ5FM9AlJJNYW1thhnvAkSWJkjyQWbdyBfsRaYVmSGJyccNTkThAEQRAE4XiJbxpCvZitFuQAX1QlWcLqqCoEcumjF3DqJSOw2Mw4QhzYg+0ERwRz1zu3MumOiUy4bhwWmxnZVDPB0HWDTUu2kL19P16Xl8pSF4pHZd3vG9kzfTV22XLMGF26wlkxA7DJ5hrTJ+2yhRER3egR1L7e778lGR2RiiVAOwG7bKWjI4YERzQPJE3BLltwyFZskgWrZCYlqC0PdprcxBGfuK6fcAqhThvmw+59s0km2GHl5olDmjEyQRAEQRBOBqLapFAvBfsKuWfIdBSP7xofm9PKI18/QJf+napfKyssZ8fandiCbCQP7FIj8du2egf/POcZ9ABTLI9ktVtQX+1MSXsd/SgjcFZMjInqSaFSzm53PoquEmcLZ3LcIEaEdzuu6oktqcpTmerits3vUqxUoHHoM7ZJZu5NOofhEd2qX/PoCitLdlCuuUl2tiHJGetzPs3QmV+wgR9yV1KmukgOasOF8cPoGtSmSd5PcyuucPH10g0s2rgDAxjVI4mpw3sRGeJs7tCOW0u6rwWhtsR9LbRG4r5u+US1SaFJRbWNZOLNE5jz9q81pj3anFb6n96Xzv2SauwfEhlMn7G9/J5r/44crDYLbtVTu4tLEhcU9mZB8j52VuYGTOAUNBYWbsJrqFX9uGQzbe2Rx524tTQhZgevdL+Gj/Yu4o+izSi6RpegeK5qeyp9QhNr7GuTLQyPCNyMXTN0ntj2JRvLs6oLxhQUl7GqZAf3d5rM0PDkxnwrJ4TwIAfXnX4K151+SnOHIgiCIAjCSUYkb0K9XfTwVDr2TODbl38kb1ce4XFhTLxlAmMuG1Wn5MhsNVdVe6gl2SQTFxXFq93PYE1pJk9s+8KnHQBUFS/xGuqB/zbw6ApLi9LoG5LI2Kietb5eaxBhCeKuxInclTjxuM7zV/FWNh2WuEHV5+wxVGZk/sinve/CIgeuMCoIgiAIgiDUn0jehOMyZNIghkwadFzn6D06NWDxE38MXafvaVXFRvqFJnJ/4iRezPwBiaokwiZZ8Bi+PcugqkXA7Jy/TrrkraHMyV/rtx8cAIbBxvLd9AtN8r+9lXJ7FXJLKggPshPqPDkqmAqCIAiC0DxE8naCcJW7KC+qIDw2DIvt2MU4WhNnqJOLHp7KF8/OrlGVUjbJGIaBcWBtpCRLWGwWbnr52hrNu4dHdqN3aEcWFW6mUCknyGTjs+yluHTfKpZQ1XRaqJ9K7ehTWys1/595a6SoGm/N+ZN5qzOQZRlV0+iT1JZ7zx3VKta/CYIgCIJw4hHJWzMrLSjj3fs/Ys2v6zCZZZAkxl8zloseOg+T+eSZfjbxptOJS4xl9ovfsW9rNkERQYy/ZixtO8cz7/3fKMwuIql3R86+7UwSeyb4HB9idnB27AAAKjQ3H+9bHPBa7eyRjfY+WruBYZ3ZXplTPR31cIqh0T24XTNE1Tye/XIhf2/NwqtqcKCP3pode7lr5ve8e+f5WC3iz6sgCIIgCA1LfLtoRopH4fGJT5O/pwBN1VAPDFrMe3cBhfsKuf2Nm5o3wCY2YEJfBkzo6/P6oLMG1Ok8QSY7oyK780fhFp8kwyZbuCh+2HHFeTI7M6Yf3+SsQNHUGmVibJKZERHdibQEN1tsTWlPfslhidshmm5Q5nKzaOMOxvdr/cVbBEEQBEFoWqLPWzP664eVlOSV+Kz38rq8/P3zanJ35dX5nF6XlyVfLefbf//A8m9XoHgCrE9q5W5JmECP4PbYZDMWyYRNtmCRTFzWZgQDwzo3d3gtVpjZyYvdriTJEYdVMuOUbVglM+Oie3Nn4pnNHV6TWZ+ZHbDGjsur8mf67qYNSBAEQRCEk4IYeWtGK39ZjbvC/xoiWZLYtHgLsR1jan2+jL+38fwl/8bQDTwuDzaHlfenz+KhL+4jqXfHhgq7RbDLFp5OvoTtlTlsKNuFTbYwNDyZcEtQc4fW4nWwR/Faj2vZ7ymmTHXRzh6J02Q79oGtiNVsQg6QvUmAw3pyrVsVBEEQBKFpiOStGVnt1oDbJFnCZK39mrfKMhfPXzwDV7m7+jV3hQcqPDxzwQu8uupFcnflYjKZaJfS9qTpc9bZGUdnZ1xzh9EqxdvCibeFN3cYzeKU5A5ouv/+gjarmXF9uzZxRIIgCIIgnAxE8taMhp8/lL9/Xo2n0nf0TVN1+o3rU+tzLf/mL3Rd97vNU+nl1l53I8kyhm7gDHVw/UtX029c73rHfrIqUSuZk7eWzTuzsOtWzojpS9+QxJMmGa4twzDwGipWydwqP5tQp51rxg3kgwWr8CiH1lXaLGb6d2pHn6Q2zRidIAiCIAitlUjemlGvU3uQMrgraX9m1CiRb3NYOffeSYRE1r74w570fXgq/ZdpV71qdTEUAE+lh1eu+w/Tv7iXboNrV1Rh37ZsivYX07ZrGyLiWs5oi2EYLCrazJf7l5PrKSHaGsr58UMYG9mzzklFpiuPB9JnoehadSGUlaXbGRLWlfuSJrXKJKWu3LrCR3sXMTd/LV5dJdhs5/y4IZwbNzjgNMOW6rxhvegQE86ni9aSlVdMeJCdc4f25IwBKeJeEARBEAShUUiG4X/qTyNKBHYWFJSjB5h2dDJRFZV57/3GnJm/UlZYTpvOcZx7zyQGTexfp/PMeXs+//vXV3jdte+zFRTm5JkF/ySmQ3TAffbvzOHl6/7L/h05mC0mFI9Kn7E9ueX163EEO+oUY3N4K+tX5uWvq9FY2iZbGBuZyu0d61Zg46ZNM9njLvB53S5buCfxbIZHdPPZpugahUoZQSY7webW3cBZNwzuT5/FjiNaCdhkC6dG9OCuxInNGJ1QGzExIeTllTV3GILQoMR9LbRG4r5u+WRZIioqGCAJyKztcSJ5ayXKCsu5o/99NUbwaiMkKpgXFj9NaFSIzzZXuYu7T5lOeVF5daNsALPNTPLALvxj9gPHHXdj2usu5PbN7/rtSWaVzLzS/RoSHIET18PtcuUxLe1DPLr/6p09gzvwfMrl1T/rhsGn2Uv4NmcFOga6odM7pCN3djyTaGto/d7QCe7vkm08t+M73H6ao1slM2+k3nDSrpFrKcSXAaE1Eve10BqJ+7rlq2/yJloFtBIhkcHc/e6tWB1WrPaqSndWx7Er3rnLPcz/YKHfbUu+XI7X5a2RuAGoHpVtq3ewe1PW8QfeiJYVp6MZ/tcBqobGH0Wba32uYqUC81F+XYqUiho/v5k1j9k5f+LSvXh0BcXQWFO6k2lpH1Kp+a8w2tItK0r3m7gdtLJkexNGIwgnnv1FZbz6w1KueOkzrnn5Cz5dtIaKOsyWEARBEASRvLUifU/rzaurXuDCh87jtCtHc8k/LqDbkK5VtcsDUDwKf/3wt99tG//Y7LeYCgCGwdZVJ/aXca+uouM/edMx8Oq+I3KBJDhi/I7gAchIpAS1rf65SKlgXv46PEecX8egQnUzP39Dra/bsoh1XoIQyM6cQm7572zmrEojt6SCfYWlfLZoLbe9+Q3lrtb5QEcQBEFoeCJ5a0VK80vJ31PA8KlDue6FK5lw/ThumHENNufRe3Dtzchm60rfRCw4MhhJ9v+FXDabcISc2Gve+oYmYpP9jz7aZQv9QzvV+lwRliCGhqdglXxr/FhkE1Pjh1T/vKk8C7Pkv82Dx1BZVpxe6+u2JMMjUrAH+LwBTgnr0uDX3Lovn4c/+oVJT77PeU9/yMvfLaagrLLBryMIx+vf3y6m0qPUaDHhVTXySyr4fPG6ZoxMEARBaElE8tYKVJRUMuPq17mj/308PfUF7hxwPy9c9vKBAijxPPfbE5gtgQuL6prOcxe9RHlReY3XR18yEovN/5dxXdPpf3rtWxk0hx5B7enkiMNyRCJlkUx0sEfTJ6RujcvvSpxIv9BErJIZp8mKQ7bilK08kDSFRMehZupHXu9INrl1FnntH9qJzs54nwTXJlsYF9WLWFtYg15vQ+Z+7n33R1Zt24tH1ajwKMxbk8Ftb3xDUblI4IQTR3GFix37fYsdASiazq9rtzZxRIIgCEJLJZK3Fs4wDJ6e+n+sXbAexaPiKnOheBTWL9rEk5OfQ9d04hJjufGVazBbAycNmqbzxxfLarzWdWBnRl44rMbInSRLWB1WbphxNfagE7t6oiRJPNX1IkZHpmKVzNhlCxbJxIiIbjyTfEmdy7nbZQuPdbmA/6Zez0Op5/Jgp8l82ucuhoTXbMjcJ6QjOv6L8dhlC+OiWmd/PVmS+FfXi5kSN4ggU9U9E2kJ5up2o7k1YUKDX++V7xfX6LEGoOkGZS4Pn/2xtsGvJwj15faqyHLgf26PvI8FQRAEIZDWOQRwAqkoqWTNr+vwVHroPiyFtl0atnnv5iVp7N+Rg+o94kusolGwt4B1CzfSb1xvRkwdSsaKbcz/cCH+8gqvy8vOdZk+r1/7/BX0Pa03c2bOo2BfEYm9Ejjn9okk9a7bqFVzsZus3J14Fjd1GE+RUk64JQin6ejTSI+ljS2C3jEJAas82U1Wbmw/jplZv+I5bJ2cVTKT5IhlWETKcV3/RGaVzVzVbjRXtRuNZuiYpMZ5PpRXUkFOcbnfbaqm8/uGHdw6cVijXFsQ6iomLAibxeQ3SZOA1IT4pg9KEARBaJFE8taI5n+4kFmP/Q+TSUbXDQzDoNepqdw585bqipDHa8uf6bgDFBVxV3jYvGQL/cZVjfR0H5rCkq+W4y53++xrspiIbh9V/XP+ngK2r9mJPdhOnzE9GTChb4PE21wcJisOU2STXe+MmL7E2cL4ZN9iMl15BJnsnB3bnymxgxotoTnRNOb71HSdow2carr/QjWC0BxMssyVYwfw9twVPgmc1WLiyrF16+spCIIgnLxE8tZINi9N4+PHP0dxKxzeGWzDok18+PAnXP/SVXWetuePPciO2WL2GXkDMB1RVGTAhL68fa//88gmmdGXjkTxKLxxxzusmrMWs8UEUtX0w1tev6HFJ3BNrV9oEv1Ck5o7jOO2113A7JwVbCrPIsTk4KzY/oyM6N6sSWhsWDDBdhsexXdtmyxJnNK1QzNEJQiBnT2oO4qqMWvhagyjasp7iMPGtCkjSW4Xc+wTCIIgCAKiSXejeeaCF9j4xxb/GyXAAKvDyojzh3LhQ+f5bZJdG3lZ+dw3/BEUj2/zaIvdwnO/PUGbzoem5GxavIUXr3wFXTNQPAomswmTWeaKf13CaVeM5q273mP5d3/hddU8n9Vh5cHP7mb/jlxyduayY30m21fvQJJlBkzoy/n3TyEmoXYNr1uDk6U55rqyXTyx7UtUXUU7MN/WLlvoE5LIPzpPRW6ABxD1tWjDDl76dhEeRavxusNq4fWbp9A+umELpJwMTpb7ujkpqkZmbhEWs4mOMeEN8hBPODpxXwutkbivW776NukWyVsjubnn3ZTmlR5zP5PFRER8OM8tfBJnPUvvf/V/3/LTG3PwVB5q9mpz2hh/7VguffQCn/2Lc0v4bdYiMjfsIiI+Ak+Fm1XzqtblaYpGoHtCkiUkSULXak5Jk00y9mA7T897jLjEWL/Hel1e/vz+bzL+3kZ4bBgjLxwWcN+WoDH/aGqGjkvz4jTZapUcGYbRKF8ANUPnyvWvUaz6jm7ZZQv3JJ7N8IhuDX7duli8aQdvz11BYZkLwzDo2i6aO84ZTuf4qGMfXE/lLg+rt+9F0w36JLUhMsTZaNdqauLLgNAaiftaaI3Efd3y1Td5E9MmG0l4bFitkjdN0SjNK+W3WYs4+9Yz6nWt8x+YQuf+nfjhtZ/YvzOXmIRozrltIgPP7BcwtvPunYSr3MXDpz1Bwb5Cv9Muj2ToBoafaie6puMqc/Hpk19y97u3smNtJqX5pXTo3p6wmFDmvruAL56dja7r6KqObJb54T+/cNFD5zHx5oavQthSuTUv7+1dyPyC9WiGjlW2MDl2IJe0GeEzRdGleZm17w/m5a/DpXuJs4ZxaZsRnBbVq8ESufSKfT6Nxqtj1RV+zlvT7MnbyNROjOiRRHGFC4vJRLDj+IrRHMuXS9bz0W+rMMkyYKDqBmf2T+GWiUORA/REFARBEARBaCgieWskE286nfenz6oxGhaI162w5Kvl9U7eAPqN611dmKS2Fnz4O4X7i2qVuB2LoRusmruGuwY9QHlhOZIso3gUZJOM4lZqjObpalUS98Vz39BtaAqd+iQe9/VbOt0weCjjU3a6clGMqmmAquZh9v6/2Osu5MFOU6r3VXSNB9JnkeUuqN43x1vCf7PmkeMt4bK2IxskpnLNjUTghKRMdTXIdY6XJElEBDf+6NeSzTuZtXA1XlUDDk3VnLsmg6hQJxePEmtCBUEQBEFoXCdH2btmMOKCofQb16eqR1otHsgbzTCF9I8vlqG4fdfK1Zeu6uRnFeCu8OAqc6F6Vbwub8BpmIpHYc7bvzbY9U9Uuq7jdrvRj1IBcXXpDna786uTsYM8hsqfxVvJch9q8Lu0OI19niLffXWFL/cvb7CkqoszHsXwn9ibJRN9QhMb5DotxQfzV/ot9e5RVL5csl5UuBQEQRAEodGJkbdGIssyd8y8mS3L0vnj86VUlFSy7rcNfke5LHYLQyYPavIYNVU79k5UrXWrTXIpSVLARM0fQzfYvzO31vu3JKqqkp6exsKF88nISK9+PTm5G2PGnEZKSjfM5kO/fn8WZ+DW/SfSBgarSrbTwV61jmtR4eaA+5olE2tKdzIqssdxv4dISzAjIrqztCgN7xFJnFmSmRQ78Liv0VL8tGILWfklAbd7VY3SSneTjAAKgiAIgnDyEslbI5IkiR7Du9FjeNW6oLnvzuezp77C6zo0lVI2ywSFORl/9Zgmj2/gmf35ZeY8VI+fNgMWE536JhEcEUT+7jyy0vYd9Vy1TfAOJ5tlErq3r9MxUFWgo0itwCKZCDHXr8hLY8rO3sfMmW+Qn5+H0xlEu3btqxPbrKzdvPnm60RHx3DjjbfQpk1bAGRJPliE1Id0YPtBR0+QDXS/Z6laJzc3f131mrqh4clMih1IuCUo4Nnu6ngmJkliUeFmLJIZHZ0ws5PpnaYQYw099ofRCqTvzeOtuX8edR/DMHDarE0UkSAIgiAIJyuRvDWhCdeNIzgimC+emU3BvkJkWWLAmf258qmLCY4IbvJ4zrxxPAs/XkSFotVIvKwOKxdOP5eJN09g8ZfLeO+Bj456nvC4MNp2bcPmpWn+s48AzBYzZ9wwrk4xLy1K4509CyhSKtAxCDHZiTAH09EZwzkxA+gW3K5O52to2dn7mDHjBUwmmQ4dEmpskySJqKgooqKiKCwsYMaMF7jnnvtp06YtIyK6saBgg98RNQMYHNal+udRkd3ZUL7b776qodMvxLe3XIXmZtqWD8n3luI5MIqWnVPEL3lrmNH9KtrYIvy+H4tsZlri2VzbbiyZrlyCzQ46OWJPqvLmXy9df2Cdm38mWWJot47YLOLPqSAIgiAIjUt822hiw88bwrBzB+Op9GC2mjE34xe+8NgwnprzKO89MIsty9KQ5KpRwAsePJcxl48C4Je35h216MpFD09l8l1nsXlpGttX76hVgZaDbvz31bRPOXaydbAU/pLCLczY9WONCojFaiXFaiU73bksL85gatzgBivYUVeqqjJz5huYTDKRkUcvVR8ZWZXAzZz5Bo888ji9ghPoEdyeTWVZ1ckVgE22MC6qF3G28OrXRkZ05/P9y9nvKUY9bN2bTbZwdkx/wiy+U/f+l72MXG9JjXVyiqGhaW5e3zWHp5MvOWq8YRYnfSyJx/oIWqXdecUcbbAz1GnntrOHNV1AgiAIgiCctETy1gwkScIeZG/uMACIS4zloS/uxVXuwlvpJSQ6BFk+NEWvODfwOh9HiIPeY3oC0H1YCsPPG8LS2X/hqfQc87qOYDvDzh0ScLuqqHz/6s/MfWc+ZYXlhLcJp+CNtnisgStjenSFr/f/ydDwZDo5444ZQ0NLT08jPz/PZ8QtkMjIKLKydpOenkZqak8e73IBX2X/yfd5KylTXURZQ7gwfhhnRtesYmiRzbyUciXv7fmNhYWbUA2NMLOTi9oM5+yY/n6vNT9/vU+BEwAdg43lu6nUPDhNjVtmv6VqGxVGZk6R30Fls0niqctPJzzoxJu+K7QuhmGwfX8BxeVukuIiiAoNPN1ZEARBaL1E8iYA4Ah24Aj2/QKa0KMDxTn+EzjVqxLbMQaoSkive/EqBk7sz9x3F1CUXUTengJcpb6VDyVZYsAZ/nvQQdWXlBeveJUty9JRPFVTAwuVcsoND8cqkKoYGnPy13JrQv36xxmGwZaKvex1FxJrDaVXSEefRtmKrvJr9no25mQRZQ1hZER3gs12Fi6cj9NZty9UTqeThQsXkJraE7Nk4uK2w7m47fBjHhdstnNn4kRu73gmXkPFJpmPOpUxUIETABkZt66I5C2AqUN7snJrFh6lZvIrSxKJsZF0bRvTTJEJJ4vt2QU89b/5FFW4MEkSXk1ncHIH7j9vNHar+GdcEAThZCL+6gtHNeXus0n7M6NGkRWoqpA5bMpggsIOTdGTJIm+p/Wm72lV/eY2LNrES1e9VuNYSZKwB9uZev/kgNdcNWcNGxZtqlkARTNq1XJBx6BQKa/lu6sp11PCo1v/R75SVhUrEk6TjSe7XkiiIxaA3a58pmd8gmKoVGpebLKFt/cs4IGO55CRkU67dnUrwBIZGUVGRhq6rtcY8awtWZKwS5Zj7tfFGc/mij1+tzlMVsLN4il+IKkd47l8dH9mLVyNrhuouo7DaiHYbuXxS8Y3d3hCK1dc7uK+936k0lPzAcyKjCye+XIBT15WvwdVgiAIQssk+rwJR9VtSDJX/etSrHYL9iA7NqcVi91Cr1E9uOa5y/0eYxgG21ZtZ/W8dfQY3o24pKrERzbJ9B3Xm3/NeZS4xFi/x+q6zpt3vutTuVIu0pD3HrsnnYREj6C6V7DUDJ0HMz5hn6cIt67g1hVcupcCpYzp6Z/g1rxohs4jWz+jRK2kUqtKSD26gkdXeG7rbDy6UudCv7iLwwAAIABJREFUHgerUHq9tV8rWB9XtBuFTfZ9VmOTLVzWdqTP6KJQ04Uj+/D2Hedz2eh+TBmSyj1TRvLBtIuIDW/6QkPCyeXHv7egaL49BL2qxprt+9hXWNoMUQmCIAjNRYy8NbD8PQXM/3AhuzdlEd8pjtOuGkO7rm2aO6zjMubyUQyePIi189fjqfCQMqQrbbv4f0+6rvPf295h1ZzVeN0Khm5gD7LTpks8j38/ndCoo5eX37R4S8A1c47Xcql4ph3YJAiQbEjA+OjedXp/UNUku0x1+S2zrxgai4q2EGUJxqX5T7IMk8xedyGdjKQ6JXAHi7FYrY1bZr53SEemdTyb/+yeg2roSFSNUl7cZjgTowNPYRUOiY8I4dLR4rMSmta6nftQAlQ7NckSGXvzaBt5crTtEARBEETy1qDW/LqOV254A13TUb0qpkWb+G3WIq565jLGXDaqucM7Ls4QB8POHXzM/RZ9toRVc1bXqDrprnCTuyuPd++fxbT3bjvq8dvX7ETXfZ8yA5jT3IQ8sJfy59thOPwnSGfG9KtX77edlbl4AqwLc+sKGRX7SHTE1KjueDhV0rF1iKSwsJCoqKNXmjxcYWEBycnd6jVlsq5GRnZnWEQKGRX70AydLkFtsMvHnnIpCELzCXUGLm4lSRLBdrFWVRAE4WQipk02EFe5i1dvfAOvy4vqraqIqKk6XrfCBw9/Qv6eAr/HFewtZMuydHJ35TVluDWoisqWZems+20D5cUVx3Wun96Y67ddgKZorJ2/jsrSyqMeHxwRhMUaOKHoYIpkYsJAbH7WeQXL9nq3CYiwBGP1M60QwCyZiLaEEGcLxyyZ/O5jkUwMHjWCysq6fX6VlZWMGXNaneOtL5Mk0z24PT1DEkTiJggtwMSB3bAHaCkjSxJ9O7Vt4ogEQfBh6Nj07wjTLiBCO41Q7Xosxp/NHZXQSomRtway4sdVAafyGbrBH58v4bx7DxXpKCss5/Vb3mLLsnQkWULxKJjMJpIHduGC6efSbUhy08T90yrenvb+gdEuCdWrMv6aMVz6+IX1Gg0qzikOuM1kNlGSX4Yz1LcP2UGDzxnErMf+53ebbJKZ9t5txHWIwynb+CFvJRISqqHR2RnHPYnnIAHz8tfh0rz0CG5P16DaTVkdHpHCG1nz/F8XidOiexFlCcEmm3HpvsmpjMSVgycx84/tFBYWHLPPG1SNukVHx5CS0q1WMQrCsei6jtfrxWq1NslortD4+ndux4jURJZsysStVD0YNMkSZpOJ6ReMwWwS/58FoVkZBsH6dKwsRcINgMwazPq9VEi345GP3kdVEOpKJG8NpCS3pLqs/ZFUr0rB3qLqnw3D4OnzX2BP+l509dAUQU3R2LI8necunsFVT1/a6FMtt67czn9ve9unkuSCD3/HEWxn6v1T6nzOuKQ4dq7L9LtN13Qi4sL9bjsoJDKYa5+/gvenf4zqVdEPLNS32C2c/8CU6rV2V7cfzSVth5PjKSHYbCfSEsxPeat5J2sBsiShGTqyJNHZGc8TXS48Zhl8p8nGA0mT+b+d36LpBioaJiTMkokbO4wn1hoGwLXtx/JK5s9oVMUlI2FC5t7Ec2jjjOTGG29hxowXjpnAFRYWoGk6N954C2az+DUU6k9VVdLT01i4cD4ZGenVrycnd2PMmNNISekm7rEWTJIk7jv3VEb0SOK7PzdRVO6iW/tYpg7vRULM0f+eCoLQ+Myswsqy6sTtIAk3QcZreI2zMCSxLlVoOOJf9AbSoXt7rHYr7nK3zzab00anvonVP29emkZOZm6NxO1wXpeXDx/5lCGTB/ntvdZQvn7xO5/EDcDj8vLTG/OYdOdZWGx1m1o35e6z+e9tM32mTlrsFkZeOAx70LHXZ5x68Qg69kzgl7fmkbVlD/FJcZxx43iSB3WpsZ9NtpDgiAZgY1kW72YtwGuoVNccMWBrRTb/zvyJRzqfd8zrDgnvyhs9buDHvNXsrMylnT2Ss2L6V19jaWEa/9k1pzpxg6ovVkmOWAZHdAWgTZu23HPP/cyc+QZZWbtxOp1ERkZVV5UsLCygsrKS6OgYbrzxFtq0EVOehPrLzt7HzJlvkJ+fh9MZRLt27avvtays3bz55uviXmsFJEliaLeODO3WsblDEQThCDb9J8D3u18VExZjMV7prKYMSWjlRPLWQPqM7UVweBCeSo9PmXuzxcTwqUOqf966crvfpOlwsknm1/cXYnNYsQXZGHhGP4IjDpUlLy0ow1PpIaptJLKfaTO6pvt9/XA71u48ylaD3N35da6UOWhif86+7Uy+f/VnJFmqjqPbkGSueOrSWp8nsWcCt7x2fa33/3L/MjyG6vO6Ymj8XbKNYqWCcMuxe5nF2cK5rv1Yn9dVQ+OV3T/7XEMzdHa781lcuIUxUT2BqgTukUcePzAasoCMjLTqqpKtZTRkj7uAQqWc9vYoIi2iXH5zyM7ex4wZL2AyyXTokFBjmyRJREVFERUVRWFhATNmvMA999wvErh6Wp62i1m/rWJ3XjFBditnDerORSP7YAuwFk0QhJOHRCWSn0rVVXSfETlBOF7iX54GIptk/jH7AZ696CVKckuqvqzLMvYgGw9+Ng170KGKYY4QB2arGcUduG+Zp9LDl89/gyxLyGYT70//mMufuJhug7vy9r0fkLlhN7JJxua0cuH08zjtytGoisq3L//I3HcWUFFcQUhUMGfdfAZn33aG30TOEeKgvMh/gQ1N1Wo04K6LqfdNZuzlo1g5Zy1et5eeI7vTMTXh2Aceh93u/IDbLLKZbE9RjeRNNwxWl+5gQcFGPLqXoeEpjIrsji1AEY9NZVkYhv8/zm5dYW7+uurkDcBsNpOa2pPU1J6tah3SPnchz+z4hn3uQsyyCa+uMiisM/cknoPD1LjtDgLJL63g+782syEzm4hgB2cN6k7/zu3q3HOvJVFVlZkz38Bkko+5vjIysiqBmznzDR555PEW/dCgOXyzfCPvz1+J58B6s+IKN18uWc+qbXt46bpzxJozQTjJKdJwLMZSZFx+thoo0oAmj0lo3cS/4g0otmMMM5Y/S/qKrWRv2090+yhSR3T3SZyGnDOQT5/44qjnMnQDTdfQADxVXxo+euRTTGYZ72FJn9fl5aNHP2Pz0jRWzVlTY1tZQTmzZ3zPrk27ueOtm32uMe6qMXz9wrc1joGqp/aJvToSHhtWx0/gkIj4CMZfPabex9dVtCWUXK//ZrWqrhFlCTn0s6HxxLYv2Vy+B/eB9gDrynbxafYSZnS7igg/I3T+RvUO5/ZTxOQgWZax2wOX+24pKjUP96Z/RJnqxsDAo1V9Jn+XbOep7V/xTHLtR1YbypasXB768GdUTa9uZLxq215O7dWJaZNHttoELj09jfz8PJ8Rt0AiI6PIytpNenoaqak9j32AAECF28t7v/6N94g+a15VIzOniKVbMjm1Z6dmik4QhBOBRzodh/EWBl4kDv2tMLChMARdSmy22ITWSTwybGCSJNFtcDJjLhtFr1NT/Y54hcWGccmj52Ox1209maZqPokWgOJWWP7tCr/bvC4vq+auJWvLHp9tE64fR0KPDtich9ahWWwW7MF2Bp01gJW/rAnYMPtEc27cKX5HzWQkujjjibUdSkR/zF3FprKs6sQNqkbPCrxlvLrrZ7/n7xbUFiVAjzerZOaUsK7H+Q5OfAsKNuLRVIwjpocohkZa+V4yXblNGo+uGzz1v/m4vGp14gbgVlQWbdjBym2+93xrsXDhfJxO34cMiqaxaVcO3/+1mW+Wb+SvjCzK3VUPFpxOJwsXLmjqUFu0tTv2BRxZcysq78z9i70FJU0clSAIJxTJTon8AQqDMLCiE4SBDQ9nUyY/19zRCa2QSN6ayRk3jOehL+6l58juWGwWJFnC6rBitpoxWfz3Eqsvzauyet46n9etdguPfTeda56/nJTBXemY2oHYxBgUr8LsF7/jjdvf5ubUu1ny9fIGjacxDA1PZlxUL2yymYNjLXbZQqQlmAc7Ta6x7w+5q/yOpGnorCndSbnqOz891OzkjOi+Pv3lJMAqmzkrpn9DvZUT1trSnXiMwFN908r3NWE0sCUrh8oAFV7disoPK7Y0aTxNRdd1MjLSiYyMrPG6omr8sjKd9ZnZlFa6qXB72bYvnx9XbKGo3EVkZBQZGWkH2oIItaHqOgFmSwOQW1LBLf/9hj/TdjVdUIIgnHAMKYYy038okn+mVH6fQnk+FaaHQWqe5QRC6yamTTaDjL+38fWL37Fj7U4cIQ7Ou/cchkwahKvcjdli5pHTn0TD/yhPfeiGgeLxP63PbDUz6sLhjLpwOO/c9yFLvlyG6lFRPYeSm3fu/ZC4jrF0Hdi5wWJqaJIkcWvCBE6P7sOC/A2Ua276hSYyIqK7T/PtEjVwo3CTJFOmuQg2+05zvKHDOBwmK9/nrkSWZBRdpZMzjnsTzybMUr/1gS1JiNmBBH6XZcuSjLOJ17yVVHoCtVYEoKjs6A3hWyqvt+p3+cgpoZuzcih3e2skZ4ZhoGoay9N2MXFgNwzDwOv1toppvE2hd2I8qnb0v8UeReXZLxfy+YOXYbeKxveCcDIzpAg0Ipo7DKGVE8lbE1vx06oavdUqiiv5ZsYP/P3Tah7/4SEsNgud+iaS8fc2n6qV9WXoBr/MnM/QKYNpl+y/2lxlaSWLv1jmt1ed1+3l25d/4P6P726QeBpTF2c8XRLij7pPB3sUGZXZAbcfvj7ucCZJ5qp2o7m4zXA8TgWlVCPK6n/f1uj06D78UbQFj+57j+gYDArr4ueoo1tZsp0v9y9nv6eYNrYIzo8fwsCw2j0k6BQfiar5H0Uym2R6JMTVOZ6WwGqtSpIPVjA9aHt2QcBRteIKN5UeL5IkVR8vHFtEsJOzBnXnl1Xp1QVL/JEkieVpuxnT+8R9wCUIgiC0DmLaZBNSFZW3p73v0ybA61bYu3UfS2f/CcDtb95EcMSxy9rXhavMxQtXvBKwYuL+nbmYrQFyeQMyN+xu0HgCcVd42L0pi4J9hY12jUvajvC7Ps4mVU1/PHKkzmc/2ULnkLiTKnED6B7UjjGRqdgP++wkJGySmTsTzqxztclZexfxzI5v2FieRb5Sxoby3Tyz4xs+2be4VsfHR4TQr3NbLGbfP2NmWebcoa2zMIcsyyQnp1BYWPN3RD3KdEhZgvz8fJKTu7X4iqdN7aYzhnDpqX0xyYGHeVVdo6TSX6U5QRAEQWhYx/WveEpKyqUpKSmbU1JStqWkpNzWUEG1VltXbkcLMFLgqfSy8JOqL61RbSN5ZeULARM42SwTEhmMPdhOn7G9GDJ5EFbHsb8452bmMX3MY2xdud1nW1h0KKo38JPl0OjGTVQ0VePjx//Hzal38cSkZ7lnyEM8esZTZG/f3+DXOiWsC1e0HYVVMuOQrdhlCxbJxNCIFK5qN7re59UNA0VvuOmuJxpJkrg94QzuS5pEr+AE2toiGBGRwv91u4LRUal1Ole2p4ivc/7yGcXz6Apf7l9Ojqe4Vud56Pyx9E1qh9Vswmmz4LRZCHPaefLy04mPaL3J9Zgx46isrNnmIzYscL89WZIwVIUxY05r7NBaHVmWuHhUX26cMBhbgPXIJlmmS5voJo5MEARBOBnVe9pkSkpKO+BpYADgAZalpKQsTE9P39xQwbU2ikc5aulyr+tQZUe708Y/f3yYp6Y8j6fSg7vCg9VhxTAMbn39egafM6h6X8Mw+OWteXz/2s+U5pcdNYasLXt5cspzPPrNgyQPOjTNLapdJAmp7dmxNtNnuqbNaWXC9ePq+nbr5P3pH7Pkq+U1RiW3r9nJvcMfBiA+KY6pD0wm8cxkJKCNLeK4ysCfG3cK46J6sap0B4qu0SskgXhbeL3OVaiU83bWApYVp6EZBm1tEVzVbjTDI1LqHd+JSpIkhoYnMzQ8+bjO80fhFvQAo8AGBouL0jg/fojf7Ydz2Cz864oJZBeWsi27gBCHjV6J8Zha+ehSSko3oqNjKCwsqO7z1juxDdlFZT4PiEwmmU5RTmJjY0lJ6dYc4bYK4/slM2vhajxKzQc0JlkiPiKE1FY6TVcQBEE4sUiBptEdS0pKylXAqPT09OsO/PwoIKWnpz95jEMTgZ0FBeXoDbSmq6WoKKnk1t7T/DbnttjMnH3bmVzw4Lk1Xle9Kit/Wc2uzXuIjA9n6JRTCI7w/4Rd13XuGvBAraYcxneOY8ayZ2u8lpOZy+MTn8Zd6alOomxOGz1H9WDae7f5bXvQEEpyS7hz4P0onqP3UsMmo1wahXZRNCEmB7clTOCU8OYp0R8TE0JeXhllqotbN79DiVKBdlgpD5tk5qaE8UyI7tss8Z3oPtjzO1/mBK5ielH8MK5sd2oTRtQ0vIrKsrRd5BSX0z4qjMEpCfVu8pydvY8ZM16o0ah7f1EZy9N24VFUJEnCMKBTlIOk2Ajuued+2rTxv+b1oIP3teDfzpxCHvt4LmUuDyChGwaJsRE8cdl4IoJbf9Gilkrc10JrJO7rlk+WJaKiggGSgMzaHnc8BUvaAodXfcgGTqntwQeCPanExIRw7p0T+f4/c3BXHBplkyQJe5CdSx6YTESM71Svc66v3VSndb9voryovFb77t+eQ2iwFZvjUI+3mJgQPtr2GnM/+J2/flpFUFgQZ1w7loET+jTqOpmMpVuw2CzHTt48OuZP8qk8MwRPsMpzO79jxoArGRTVPEUCYmJC+G7bCso1d43EDaqaer+3dyEXJQ/l99zNfLhjEdmuIuId4VyZdCqnt+ndahtI18ZwkvkpfxWVmm8VVKfJyvD2KcQ08lTdprZu+z7u+M+36LqOR1GxWczYrRbevGsqXdrVfcpdTEwKTz/9BK+88gp5eftxOp10ahdNp3ZRFJZVkp+fjxmNNvHx3HXXXbRr166W563f515UVkmF20t8ZGi9E9ITXUxMCL88ewPrduwjt7icpPgoutbj/53Q9Op7XwvCiUzc1yen40ne/H3zrHUDoZNx5A1g0rRz0JH48b9zwDBQFY2kPh25+dXrUSX5uJ6i/PjOAjxu/y0B/Nm9I4/w2DCf10deOoqRl46q/rmgoMJnHwBXuQtZlms0+a6ryjIXi77+C1e5b281v0xgXluJOiIEj67w8qafeLn7NfW+fn0dfOL1y561eHX/Saem6zy08jP+KtlWvbarrGw/T2+czV/Z27gl4fSmDPmE0tloQ5QlBI9WhHbYnw0zJmIsoSTpca3qiWK5y8Ntr82u0Zeu0qNQ6VG44d9f8ul9l9Yr4bFaQ5k27SHS09NYuHABGRlp1VUok5O7MWbMaaSkdMNsNtfq86zPk9ys/GJmfPsHW/fmY5JlzCaZS07ty9RhvVrtA4p2oaG0Cw0FaFX3aWslRiiE1kjc1y3fYSNvdXI8ydteYORhP7cBmrZLbwskyzLn3TuJSXdMJC8rH2eIgzA/CVR9eN1e/024Aqjv96oNizYx67H/sW9bNhjQpX8nrnrmMpJ6d6zTeUrzS/nHhKcoySupU1sE6bBHBNsq96MbBvIJ+CXRMAyWFWegGjXXyLh1hXn56zgrpj8JjpPzqb0sSTyfcjkv7fyB9WW7sEhmFEOld0hH7ks654T8/3k8FqzbhhbgHvcqVX3YRqYm1evcZrOZ1NSepKb2RNd1vF4vVqu1SapKFpRVcvfM76nweDEMUDQdFJj122rcXpXLx7T+5vWCIAiC0JSOJ3mbD/wzJSUlBqgApgI3NkhUJwGz1UybzkfvRwZV7QV+fnMec9+eT2lBGdEdoph811mcevEIn6faAyb0Y/XctTWmZAYim2RCo0PrHPeGRZt46cpX8R62bi/j7208Ofk5nvz5ETp0b1/rc332r68oyilGU+pQoVEFtc+htSUmSfY7BNxURkX04Iv9y1EM39E3DT1gawbN0FhSlMaljhGNHeIJK8zs5MmuF1GolJPrLSHWGkakpemnU5dWulmyOZMKt5fuHWJJTYhr8BGjzJzCgH3CPIrK3oKSBrmOLMtN1oDb7VX597d/UOH2+jwzcisqXyxZz/nDe4nG1YIgCILQgOr9aDY9PX0v8AiwEFgLfJqenr6ioQITqkZuZlz1OrNf+r4qyVE1cnbm8uHDn/DJPz/32X/QxP6Ex4VjClDO+nATb55Qry+oH/3j0xqJ20Fel5cvnp1d6/MYhsGyb/46auJ25MiBYZPwTArHCKt6fxISQ8NTmnVq1jmxAwgx2zEd8atkk8z0C0mqMSXwcBqG32bXJ6NISzDdgto1S+L2899pXPriZ7z5y5+8P38lj3w0hzve+o7SylpO462lNpGhWM3+fy9tFjMxYQ3b17GxbcnK5ZIXPmHl1j0BB/tNssTWfflNGpcgCIIgtHbHNa8mPT390/T09J7p6enJ6enp/9dQQQlV0pZnsGV5uk9Tb0+ll1/fX0jB3ppVJc1WM0/89Aj9T++LxWbG5rRVVYg8IrcZftEQLnrkPL/X3LF2J7Nf+p5v//0DuzfvqbGtvLiC/Ttz/R5nGAbrF22q9XszDAPFEzh5kSQYdt5gLDYLslnGCDfjuSYGz7VVVfVMyISY7Fzbfkytr9kYQswOXul+DaMiu2OWTEhAB3sU93eazNT4wTUaWh/OLlvoG5rYpLEKNaVl5fLmnOUoqoZHUdF0HbeisjOnkOe+Wtig1xrfr2vAacqSBMO712/KZHNwe1Ue/ugXKj3KUWdpGwZYTMd+kCQIgiAIQu0dz7RJoZEt/24FHpf/KZCSBKvnrWX8NWNrvB4SGcy0926jssxFeWE54XHheNxu5nwxl7/X/Um5XkI6a5g27fYaBQ3QYcY1r7N5aVp1P7pvX/mJQRP7c8vr1yPLMrIsHXVNnSzXfgSsYG/hgVLm/k8Y3SGaW/9zAze9ci0el5dyq5cvc5azvCgDSZIYHp7CBfFDibI2f6WlSEsw9yVN4t7Ec9AxMElVz0QMw6CjI4YdlTkoh617s0gm2tki6RuS2EwRCwBfLF2PV/Ud+VU1nQ2Z+8ktKT9q4+u6iAh2Mv38MTz31UJ0AxRVw2YxIUkST152OnZry/lTvHjzzoA9+g5nNZtEJUZBEARBaGAt5xvDSUj1qgGTJeNApUqoKvyx8NPF7FibSXT7KMZecSrturbBGeIgO3sfM2e+QX5+Hs7gIDpGJlYnTVlZu3nzzdeJjo4hzpXA5iVbqqdEGhh4XV7+/nk1SW/PZ+JNp+MMdZLQoz071+/yiUeSJQZM6EdpQRkluSVEtY/CGeII+N5mv/h94DcuwYUPVY0MmswmnCEOnDi4LeEMbks4o5afXtOTJAnTYcOckiTxdNdL+M/uOSwtSsckyaiGxtDwFO7oeIbPdE/DMFhenME3OSvI85bS0RHNBfHD6BnSoanfykkhM6eQQDmIxSSzr6C0wZI3gGHdE/ng7ouYtyaDfYWlJMVGMq5fV0Ic9a/W2hyyC0pxe4/e1sNmMTFtyshW3yxdEARBEJqaSN5OYAPO6Mef363wW4BEkiR6j+lJxt/beO6il9A1Ha9bwWSWWfDhQi7+xwX0ObtHdRPfDh0SfI6PiooiKiqKgoJ83p31Jh3UZOxSzYTL6/Ly03/nMPGmqrL2Vz97OU+f/0KNqZySLGF32ijKKeb2vvditprRFI0hkwdx7fNX+G0lsGrumoAVJmVZpvvQlDp/Xicih8nKfUmTuDXBQ4FSTqQliCCT/4ISb2TNY0HBBtwH1sLlKaVsKN/Nde3HclbMgKYM+6QQHx7C3oJSv9sUVWNLVg7z120lMtjJhP7JtIs6/qqwkSFOLh7Vshu3x0eGYLeaAyZwHaLDuGfKKHokxDVxZIIgCILQ+onHoiewfuN6E98pDrOtZo5tdVjpd3pf4hJjeOHyV3BXeKpHzDS1Kon79KnPmfF8VeIWGRl11OuEBoehawZZbEU3fAtsFOcUV/9314Gdefz7h+h1aiomswmLzczAif0ICg8iY8U2VK+Ku9yN4lH487u/eenKV/1eM9B0SagabTva9pbIabLRwR4VMHHbWpHN/IL11YnbQR5d5Z2s3yhVK5sizJPKecN6Ybf4Pr+SAFU3+N/idfy6ZitfLV3Pzf+ZzReL1zV9kCegUalJAVs52C1mXrlxskjcBEEQBKGRiOTtBCabZB799kFGnj8Mq92CxWbBEWLnzJvGc/sbN7J+4UZ0P2t2AErUIjb9vemYiRtUFTqxmx14cVOB70hEWEzNEYek3h256OHz6DG8GyCxbv4GivYXoR0Ri+JRyFi5ncwNvtMs+5/eF9nk/wtgRHw4kW0ijhl3a7KgYANe3f//S1mSWF68tYkjav0Gdm3P5MGpWM2m6mTk4Do03TCqR5Y03cCranz8+xrS9+Y1yLUVVePHFZu5+T9fc8VL/+P/vv6d3XlFDXLuxma3Wnjq8gk4rJbq5NdmMWO3mHnistMJslubOUJBEARBaL3EtMkTnCPYwQ0zrubqZy/DVeYiKDwI04GS41uWpwfs6ZanZhPiCbzm7HCSJNGpbyJbVm+hQMshhPDqbVaHlYk3n15j/y3L03n+kn/7VMH0R9d0Ni9NJ7FXzQbeU++bzMo5a3CXuWuMslkdVq5+9rJmLf/fHCo0D0aABY6aoePSjt27T6i7a08fxNi+XZi/ZiulLjcxYUF8tWQDbj892byqyrfLN/Lg+cdX4VRRNR54/ye27y/Ac2Dd6sIN21myOZOnLj+dPkltj+v8hzMMgx9XbOHzJevIL60g1GFn0uAeXDSyD5YArQtqo2fHeD69/xJ+37CDPXnFtI0OY0yvziJxEwRBEIRGJpK3FsJiqxp5O2jz0jTmvrPA7/RCwzBwyeUkt+9c6/OnjuhOUU4x+/6fvfuOj6pKGzj+u/dOy6R3QgmBAAmEIlUQBAGFRRRQVxTb2vtrW3Fde1nL2l17b4vdFQugCIIgTZASWkIIhCSkF1I7lHw0AAAgAElEQVSn3fL+EQiEzKSRBpzv58MfmVvOM8kkzDPnnOfJ2Y+hGciyjNlq5pTJgzn7xql1zn337o+alLhBzeyh1V7/DV1Uz0j+9dODfPzApyQv345hGMT2785lj17MwPEDmhz3IZqhs/pAKosKN1GluRgSGMeMqBFEdIJqlE0xNCiO1QdS6y2bBJAlmaQAUbSkrcRFhXLt1FEArNy+12fVVMOA3NKKYx5v6ZbdpOeV1CZuALpu4NJVnvlmOf/9+5xW+/DitQWrWbwprbZBeFm1ky9WbmF7Zj5PXlG/aE5z2K0Wzh6R2CpxCoIgCILQNCJ5O059eN+8mmqUXujoSJJE/JCm946SFZnxs8eyfdN2zkycgs1qY/i0ofQeElfnvOKcEoqyipt8X0M3GPGXoV6PVZRUkre3ALPFhKwoFGQWsXfrPpJO79+sN5WaofN4+tdsrcisTX4yHIUsLNzI0wmX0sfepcn36ijjQvvz0f7fcOsq+hEzcGZJoY+9C339YzowupNH94hgNB+FdBRZond02DGPsWD9ztpk6mhVTje7c4vp2/XYS+znlVbw08ZdeI5azuxWNXZk5ZOckduqs3yCIAiCILQ9kbx1QkXZxSx44yc2L92KxWZh4qWnM+myCVj8amawKksryU3P83m9jMzACUlYvMx4NcQwDIIjg7j0wYuQfZT41jwaUhP7uVn9LJz39xkER9Wv0pedup+nZj+Hq/rQDF5N0vW/575DliSm39z0lgC/leyok7gBqIaGamg8vWc+7yTd0OmXYVpkE88n/o1/75nP7uo8TJKCx1AZFdyHO+Kmd3R4J41e0WH0jAohPbe4XhJnUmRmjRl4zGNUN9CcXpYkHA0cb451qZk+j7ncKiu27RXJmyAIgiAcZ0Ty1slkbs/i0RlP4XZ50A4uq/r8iW9Y/ulKHvnxfmz+jfeEstqtjBg3hKysLMLDGy9YckhJSTH9+iX6TNwAInqE4x9s97psUpIk7EF+yCaFmPhoZt5+DkPPHOz1PvNf/LG2QuaRXNVuPn38KzRNZ8rVk7D5e6/OeKQfC//0utwQoNRTyT5nEXF+kY3ep6NFWAK5pedf2Fudj59iJdG/KyFm/44O66Tz6CVTuOfDhRSVVeLRdEyKjGEYzD1/ArGRIY3foBFD47uRW1rudYbPo+nExzT9d7YhNY20vc8iGoCu168sKwiCIAhC5yaSt07mjdvew1HprPOY2+Emd08+P73zC7PuOIeA0AC69o0ha0d2veslWWLYlCFMnHg6b775arOSt+rqaiZOnNzgObIsc/njc3jztvfqJXBWfyuP/fQgMb0bLxO+/fedPvu8GbrB18/O57fPfufxnx9ssNk3QIXq8B2vJDd4vLPIchbzRPo3FLjLMUkybl1lWFAv7u41A7tyfDVxPt6FBdp559YL2LI3l7ScIoL9bYztH9dqxTj+OnYQSzbvwnHUsmer2cT0kYmtNs6Ivt15/5f1Xo/ZLCZOGxDXKuMIgiAIgtB+RKuATqQ4p4SctFyvxzxOD8vmraj9+qqnLqtdRllLApu/lQvvPZ+EhEQiIiIpKWna/rSSkmIiIiJJSGi8AMHoGSO5+dVriYyNwGQxoZgV+o3swyPf/7NJiRvUtBFoiOpSKcoq4odXFjZ4XqmnCpfm+16qrnb6WbcqzcnclE/Idhbj0j1UaS48hsbG8r08vvvrjg7vpCRJEqf07sqF4wYzZWi/Vq2i2CU0kGeumk638CCsZhN2qxmrSeHcUf25dsqoVhunR0QIY/vHYTXXrSppMSn0jApleHz3VhtLEARBEIT2IWbeOhFnpRPFJOPxURXeWXV4Ri5xdD/u++puPn3sS9LWpyPJEkMmDeKSh2fXJlDXX38TL7zwLCUlxQ32eyspKUbTdK6//iZMpqa9JEadM4KR04dTUVyByWLCHmRv8vPcvnInriZUq/S4VZZ/9jsX3XfB4Vg9lWiGToQ5ENXQuDvlY0rVKq/XWyUTZ4QnEWhqWsuEjrKkaCtuQ623wM1jaKRU5ZDhKCDOL6pDYhPaRr9ukbx324VkFZVR7XTTMyoUvyOqybaWuRdM4LPfgvl2zTacbhVFkZk6rB/XnDXKZ1VNQRAEQRA6L5G8tQG3w83GX7ZQXlROz4Gx9BvZp07BjIJ9haRv2otfoI2kcf1rWwBEx0WBj8IakiSRcGq/Oo/1G9mHR364D13TQaLeXrWYmK7cdddc3n77DbKyMrHb7YSFhSNJEoZhUFJSTHV1NRERkVx//U3ExDSveIEkSQRFBDXrGoCFby+u3c/XGFd1TSa7ozKbV/f9RI6rBAmJIJMfY0L6cUCtqlOd8UinBMVxU4+pXo91JlsqMnD52LMnASmVOa2avBlGTUc5uZMXcTnRSZLUKnvoGqLIMpdNHMYlE4bicHuwWUwoDexpFQRBEAShcxPJWyvbsmwbL1/7OlBTmVE2yUTFRvDPL+/GHmTn1ZveYsvSrShmBQlAkrj51WsZ/pehmCwmZt42nfkv/XBEFcYaZpuZ8/8+w+uYsuL7zVhMTFfuv/9hUlNTWLZsKbt2pWAYBpIk0a9fIhMnTiYhIbHJM26toTCzqMnn9h0eT3p1Pg+kfYZLP7xHqMhTwYLCjT4TNxMypwT1wiy3vBFxewk22ZHwXlpClmQCTI0XbWmK/c4S3steyoayPejoJPh35ZrukxkQIJbPnehkWRINtAVBEAThBCCSt2NgGAYpa3ax+tt1qG6VviPi+fiBT+tWUXTB/rRcnrn0Jbr368qWX7ficXnq7Pl65ca3eGzhA8QO6M6M285G9aj8+NoiZEVB13XsgX7c+J9riBsY26I4TSYTSUkDSUoaSH5GAYveXUzmtmxMBOA3zr9dEzeAnkk92L8rx2fBkkMsfhYu/McsPjnY/+xovhI3AEVWsMrHtgyt0F3OipKdVGoO+vt3Y3hwPIrU+rMWUyKG8FvpTq+zbwYGI4Kb3mzdl1xXKXekfIhDc9V+11Kqcnhg12c83Hc2QwJ7HvMYnUW1y015tYuwAD8s5uP3T1yV001ReRWhAX4E2VsngT8ZabrOim17+X7ddsqrXQyIjebCcYPbfNZTEARBENqCZBgNv4FuA3HA3uLiSvRG3rx3Zpqq8cKVr7JjVQouhwsMUMyKz+WAFpsZXTNQvTTnlRWZMbNGccvr19c+5qp2sW97FhY/Cz2TerRKn7Itv27lpatfQ1N1VI+KJEuYrWbOu/NcZt7efr3EMrbu45Fzn/LabkBWJCw2C1Z/K9e/eDVDzxzM+Zue87ms0BeLpPDhoFsJNjd9L96Rvslbyyc5NQViPIaGn2whxOzPMwmXEWYOIDIykMLCihbd25vX9v3EryXbalseyEiYJYW74s5hXFj/Y77/s3u/57eSHRheEt4etnDeTLrey1XHlwqHi//8sIo1OzNQZBkDmDY8gWunjMJs6vwzsIc43Sqv/riK5dv2YJJlVE1jeJ/u/P288W2exLX267qj6brBY5//wqb0HJwH//bKkoTZpPDwnDMZ3kfMOp8MTrTXtSCAeF2fCGRZIjw8AKAXkNHk69oqoBPdord/YfvvO2v2ZB18P9zYPi7Z5D0B0zWd3Rv31HnMarfSb2Qf4gbGtkri5na4efna13E53LUJpKEbuB1uvn3he7JT9h/zGE0VN6gn1zx7BWabGavdisliwmq30nNgLHPn3clD393La1teqO0RZ2pgtktCwiLVnV2xymYu7zqhxYnbtoos5uX+jsfQ8Bg1P1OH7qbAdYCn0r9t0T0bc3PsVP7RexZDA+PoYQtnQtgAnk/8W6skbgB/HEjzmrgB5LkOcMDjvejL8ULTdO569wdW78zAo+k4PSouj8qiDSn864ulHR1eszw872d+27YHj6rhcHvwaDobdmdz57s/oGmiN1tzrEnZx6Y9hxM3qOl/5/KoPP31cjTR604QBEE4zhy/a4o62E/v/OJ15sgX1aM1uDctODII1a2yc00qrmo3fUfEExzZ/GIgvmz8ZYvPYiiqR+PXeb9xxeOXtNp4jTn9wtMYfMZAvvr3t6SsScXmb2PSFRNIPLUvVnvdvmbjQhJZUrwVjfpvtPrYu3Bu1HC+zf+DEk8l3WxhXNxlLMODe7c4tv/lr8XtZaZPw2B3dR65rlIiCWzx/b2RJIlRwX0YFdynVe97SGNz3MfvHHiNNan7KCyrRD0quXGpGhvT95NRUEpcVGgHRdd0aTlF7MgqwK3W/SBI1XQKyypZm5rJWNGfrckWrN+J011/tQOAR9XYkZnPoLiYdo5KEARBEFpOJG8tVF7UvKlqXdMxW02oXvI9q91Kn2G9uTHp9pp9YJKE6vZw+uyxXPX0ZSgmBbfTQ3lROYFhAfWSm6bFW46mep8Z1DWdkpzSZt/zkOryanJ25xEYFlBTMRPQdZ3kZdvY9MsWTGYTp84YSd8R8bWziM4qF09f/AJ5e/JrK0rufzCXH15ZyGOLHiQwLKD2/pd2PZ21ZWlUqs7aBE5CwiqbuCV2Kn39Y5gcPqjF8R9tv7PEZzJjkhXyXWWtNlZ7GRnch99Ld3rdJxhlCSbU7N8BUbWedalZ9ZpeH2IYBpvS9x8Xydum9P31ErdDXB6Ndan7RPLWDBUOH31XqPksq8rZ9A/gBEEQBKEzEMlbC0XGRpC7O69Z1xxdQRJqEre+I+JZ8tHyejN5v3+9BrPVjK5p/Pb5KqBmqePoGSO58ulL8Qtoev+yngNj67USOMTiZ6HfyObP+KhulY8e+JQVX6zCZDaheVS69O7C9S9eyfv3fELO7lycVS4kSeLX/65g0BkDuOPdW5AVmW+em09OWg4e1+E33K5qF0X7S/jkwc+4+bXrah8PtwTy6oBr+Dx3FStKdqKjMzSoF5fGnE6sX0Sz425MV1sY+13eEzi3phJpad1Zt/ZwedfT2VC2G4furvO8rJKJm2KndFhcRytwl/F5zirWlO1CQuK0kH5cFDOWSEvDs9BWs4IkgbctvLIsHTd73orKqxs8XlzhaKdITgyn9O7K3vwSPF6Wm3pUjb5dW//vhyAIgiC0JbHnrYXOu/NcrPaWld6WJLAH2Rk5fRh3vHczZpsZt5dPgN0ON4vfX8qyT1fidrhxO9x4XB7WfPcHj896Br0Z+zX6jexDVM8IFFP9H7nJrDD+4nHNfh5v3/E+K79cjcfpwVHhwO30kLkzi0fOeZLMndk4q2o+9TYMA1e1i+Rl2/np3SUALJu3sk7idojm0Vj7/fp6hV3CzAHcHDuVz0+5gy9PuYt/9j6vTRI3gAuiT8Uie/9cQ0Xj7tRPWJKb3CZjt5WutjBe6H8lw4J6IyMhI9HX3oVH+s5maFCvVh1LM3T+l7eOy5Nf4dw/n+bKra/xY8Gf6I0UR8p1lfJ/O95nSXEy5aqDMrWaxUXJ3LrjPfJcBxq89oxB8Vh9VE01DBiT0LJKre3Nz9Lw52kW8/GRhHYWM0cnYVLqf88sJoVxSb0IDzq+Z5wFQRCEk49I3lpo7AWjmXLNZMxWMxY/C2arCZPFhCQ3XlzEMMBV7cTtVPnz582krU/3uenI0A3Uo5Ic1a2StyePrb/taHK8kiRx35d3E5vUA4ufBVuAFVuAjZDoEO7/5h4CQpr3JqYkt5R1P/5Zf9+fUbOHTvWyhM3tcLPwzZ8BcDQwg2AYRrP2E7a2gYGxjA8d4PN4uerg0a3fsKEs3ec5Lt3DL0XJ/CdjIR/v/439zpK2CLVZetjCeazvRcwfdg//GzqXl/pfxeA2aBHw7z3z+W/OCko8legYFLrLeT97Ga/sW9jgde9kLaFKc6Ed8cugoVOtuXgv+9cGr02KjWZE3+5Yj2oNYDWbuHDs4OPmTXpUSIDPJtoSEBshyts3R0SQP89ePZ2YsEBsZhP+VgtmRWHCoN7cNWt8R4cnCIIgCM0mlk22kCRJzHngQqZcPZmNi7egeVQGnTGQTx74lJ1rd+FxNlzaXlN1tixNrkn2WlAtwlnlYtMvWxgycWCTrwmOCuaJxQ+TsS2T/ak5hHYJIXFMP5/LKRuye+MeTGalTr+6pji0V7BLr2hy070vO/UPtuMX2PQloW0h19XwHkCX7uGD/cu89mDLcZYwN/UTnLoHp+7BhMy3+X9wScw4LowZ01YhN5kiySjHXsDUq11VOWwo34PLqJu8uwwPy0t2cEGX0XS3hde7TjN01pele62IqWOw7kBabXN5byRJ4r7Zk1i4PoX/rdlGaWU1MWFBXDJhKKcnte7MYlsaN6AXbyxai7eikmaTwplD+7Z/UMe5vl0j+OD22WQUlFJe7SQuKoxgf9E3TxAEQTg+ieTtGIV3DeOsKyfWfn3XR7fx6aNfsPyz3/G4PI02om7seEMK9hWwc3UqVeXVzH/xRzJ3ZGG1W5lw8TjOv3sGdh8JUNzA2BY3/D7E5t/8oikA4d3CALjwH7N464736+0DtPpZmHXnua3SHqEldlfn8b+8deyozG703H2OQjRDr9O42zAMHkv/mjK1ujYNUdHB0Pks73cGBvagf8CJ21tqVWmq10qdUJOgrT2Qxl+71E/edMNXI4ODx9HRMVDw/bpQZJlzTx3Auaf6njXt7IL9bdwyfQxvLFyDW9Vq9/AdmkHsIWbeWkSSJHpFh3V0GIIgCIJwzETy1sosNjNXPnUZlzx8ERsWbuTtuz5o8hJAWZZr97GZbWYwaHBma+vyHWxfmVLnHNWtsviDpWxemswTix+qV5my8kAVK79YxZ4tGUR0D+eMS06vrRDZHANOS/S5RFQ2yciyXG/ppNVuqW0GPnrmKAqzi/nmmfkoJgWDmv1uU66ZzNRrJjc7ntbwS1Eyb2T9jFvXfPZEO5IiychHJRO7qnPIcx3wXuxEV/muYP0Jnbxphu99mBo6Cws30j+gG0kBPdhbXcC3+evY6ygg2hpCF0swuW7ve9vi7V3qJMknsmnDE4nvEs7Xq7aSUVBKTGgg5582kCG9unZ0aIIgCIIgdDDJaKSIQBuIA/YWF1eiH8Os0/Hi5/eW8OmjXyGbapKZhhp5R3QPp0vvaByVToaeNRjVpTL/pR9bNK7Fz8KlD8/mrKsm1T62e+Menpr9PJqq4Xa4UcwKsiJzxeNzmHzFGc0eY8OiTbx601t1ZhitdgsJp/bDZFbYtmIHmqYjyxIgMenyCVzxrzl1ZtWqy6vZtnInuqaTNK5/nRYB7alCdXBF8qu4De/l5o+mIDEutD/39J5Z5x4373iXEk+lz+v62Lvwcv+rjjneziq5Yh+P7v4Kp4/ZN6ipcDktciiLCjfhMTR0DCTAJCnohlGvn59VMvFw39kMaYP9eUJdkZGBFBY2rw2KIHR24nUtnIjE6/r4J8sS4eEBAL2AjKZeJ2be2tjUa85k7Pmj2fDTJgqzivjhlUVei3nIikzS6f254aWrax/buTqVhW8tblHxDrfDzW+fr6pN3jRV49nLXqpTKETzaGgejU8e/Iz+pyXQtU/zmtWOmDaUR374J/NfWkD6pj0EhgUw5ZrJjJ89FlmRydyRTfLybSgmheFTTyGqZ2S9e9iD7IyaPrzZz6+1rT6QWpNUNuHzBJMkE2T24+ruk+o8/uye7yltIHGTkYjzq/89OJEMCogl3h5NWlWez0TYZajML1hf5zED8BgaJmTCTAFUak4AQs3+3Bw7VSRugiAIgiAIiOStXQSEBnDGnNMBSFufTsraXfUSOJPFxPQbp9Z5LHFMP7r26ULWzmw0teltAQ7R1MNjJC/f7rU0f815Oks/Ws7lj89p9hhxg3pyx3s3ez0WO6A7sQOOjyWClaoTVfc9Kxoo25BlGZtsZnxYf64dMAm1rCbTMwyDl/Yt4M+KPQ2OYZYUzose1apxdzaSJPF434v5IHsZPxb+2exaPAYGZ4QlMT1qGBI1DcQ7av+jIAiCIAhCZyOSt3Z22zs38cwlL5K5IxsMA9mkYOgGN71yDd0Tu9U5V5Ik7vt6Lk/Nfp69WzKaNY7ZZubUc0fWfl2SU4LurYQdNbNyBZmFXo/pms6yT1fw09tLKC8up0did86781ySTu/frHg6uwT/rphlBc1L7zw/2cx1sWcyOXxQ7WOhlgAKqVmusKR4KytKdjY6xq09pxHn1/z9hccbq2zmxtgpbChL97mHzRcNgzK1mi5WUZhDEARBEAThaCJ5a2cBIf48tvAB9mzeS/rmDAJC/Bl61hCf1RsDQvzp2qdLs5I3WZHwC/DjzL+dUftY174xB/ee1We2mujppfqkYRi8ePVrbFuxvbYq5I5VKezeuIfLH7u4RfvkOqukgB7EWEPJchTVVIc8SEbCJls4PdR7sprjLOHdrKWN7pWLtUUwKbzpbR1OBKND+vFD4Z+ohu8ZzaPZZDMDA3u0YVSCIAiCIAjHr5OjfFsn1PuUXpx15UTGzBrVaNl9dyM945BAkmqWXipmhSGTBvHE4ocICD1c/CNxdD9CokO8VoiUFcVrIrb1t+1sW7GjXjl/t8PNJw9+TnUDjbaPN5Ik8US/OQwI6I5FMmGXrVhlEz39Ink28XIscv3PORYVbuKWHe9RqTsbvLdFMnFp19PbKvRO67zoUdhkc73i/hbJRJg5ANNRf34kpIPLUo/fUv+CIAiCIAhtScy8dYCygjJ+nbeCvVv2ERkbweQrJjRYLGTEtKFsXb4NZ5Wr/kEJzrxyEufdMR1ZUbDarV6TQUmS+OeXf+df5z9DRXEFqkfFZDEDcNcHtxIaXX+Z2oovVuGq9jImNe0AtizdyphZJ84ermCTnacSLiXXVUqOs5QISyA9fRQYyaoq5u2sJU2qTnl+9CjGhSa2dridXrglkBcSr+DVzJ/YUZmNjIxdsXJZ19M5LTSBJ9L/R1pVLiZJQUMn2hLMg/EXYJPNHR26IAiCIAhCpySSt3aWsnYX/57zIrqu43F6UEwySz9axiUPz2bK1d77m5167kj+99x3eFwlaOrhJWgWPwsjzx7G1U9f1qSxI3tE8OK6p9m+cifZKfsJjgpmxF+GYvGzeD3fa7J4kKEbuBy+jx/PYqyhxFhDGzxnfvZ69AZ6msHhdgKXd5vQmuEdV7rZwnmq36VUaU6cmodQcwDywQIkzyRcxn5nCftdJUSYA+ltj+7gaAVBEARBEDo3kby1I4/Lw3OXv1xnNktTdTRVZ96jXzJw/ACvM3AWm5lHF9zPe/d8zKZfklFMCkgw5epJzL73/GbFIMsygyYkMWhCUqPnDj1zMNtX1l82CTWFTPqPSWjW2CeSnOqSOnvjjmaSZPraY7it57R2jKrz8lds+Cu2eo93s4XRzRbWAREJLVXldLN40y52ZOYTFmhn2vAE4qLFz1AQBEEQ2oNI3trR5qVbfTYm11WdXz9ZwWWPXuT1eFBEEHe+fyuOSgeVpVWERAVjtrbt8rKxF4zhfy98j9t5uAk31CSTp5w5hOi4E79yoi/9g7uxsiDF67JJkyRzVbdJzIwaQXJlJl/lrmafs4gIcyDnRY/i9ND+ovy9cFzak1fM3PcX4NF0XB4VWZJYuCGFi8cP4dIzhnV0eIIgCIJwwhPJWzs6kH+gzrLHI2mqRlFWUaP38Avwwy/Ar7VD88rtcNGtbwyleYfLvcuKxOmzT+NvT1zq9RrVo7L99xSqSivpNSSOmPgu7RJre5vRfQTv7V5Wr6m3jESkOYgZUSP4sfBPPti/DJdek+CVeCp5ed9CNpXv5fa46R0QtdAa8kor+GLFZtamZiLLMmcMimf2uMEE+9efWTyRGIbBw/MWU+k8PBOvGwZuVeOLlckMj+9OYo+T9wMdQRAEQWgPInlrR90TuiEr3gt8Wmxmeg3p2c4R+aa6VR74y+MUZRXXeVzXDfIzCjFZ6r90tq3YwcvXvYGuahjUJKSJo/txx3s3t1vC2V5CLP481vciHtn9JbphoBk6iiQTYrbzZN9LqNQcvJ+9rN7MnFP38FvpTqZGnkKifzcfdxc6q8zCA9zx9nc4PSrawdno+Wu38euWNF676TzCAu0dHGHb2ZGZT4WPfa5uVeW7ddtF8iYIgiAIbUwkb+0ocUw/QruEUJBRWK9htqzITLx0fAdFVt/a7/+gKLu4/gEDtq3cQdbObHr07177cH5GAc9f8R9cjrr743auSeWVG97innl3tHXI7W5AQHfmDb6dDeXpFLsr6OEXweCAWCRJYnHRlprCHF5Wybp1lV+Lt4nkrZPKKjrAFyu2kJyRi91qYfqIRKYOT8BiUnjlh1VUuzx1fqyqplNW7eSTZRu5fca4Dou7rRVVVPtc7msYkHegsp0jEgRBEISTj+jz1o4kSeL+r+YSFReJzd+KxWbGFmDDP9jOPZ/dSVBEUEeHWOvX/67wmngAYMDP7y2t89Cit39B9bIkVHWpbF+5k4J9hW0QZcczywpjQvpxTtRwhgT2rH1z69Q9aD6qURoYVGktq9Tp1D0sLd7K+9m/8kPBBsrV6hbHfqw8usqvxVt5OO0LHk77gl+Lt+LRG2+d0Jltzcjj1jfms3TLbvIPVLI3v4R3Fv/BPR8soLzayfbMfK+/FppusHxrervH2556Roag6d5f0yZZpk9MeDtHJAiCIAgnHzHz1s7Cu4Xx/KonSVm7i/27cgiJCuGUyYO8LkPsSNXlDTfgLskpqfN12vrdaB7v+/lMFhNZO7OJ6um9Z9rxxqNrvLt7KV9krKFcrSbCEsTsLqcxLeKU2uQtKaAHcr321DVsspnhQb2aPW56dT737foU1dBw6h4skon3s5fxj14zGR3a75ieU3NVay7uTvmYfHcZTr2mify2yiy+ylvL84lXYFcabjzfGRmGwTPfLMPpqZuAujwqe/JK+GVTGg3VmfH42M96ooiLDqNXdBhpOUW1S0YPURSZWaMbr2ArCIIgCMKxETNvHUCSJPqPSeDMv01kxLShTU7cDMNg1/rdrF/4J7npeW0aY98R8Q0ejx/eu87XIV3qN/k+xNANAuHhjjoAACAASURBVMMDWyWujmYYBo/u/oqP9qygTK3GAArd5bybvZQ3s36pPS/eHk1iQDfMklLnehmJQMWPcaH9mzWuamg8mPY5lZqzNllyGypuQ+WZvd9R7K445ufWHB/uX06Oq7Q2FqiZFcx1lfLh/uXtGktr2ZNfQrmPPV0uj8rS5N2EBfje09a/x4nfp+7RS6bQMzIUm9mExaTgZzFjs5i4b/YkuoUHd3R4giAIgnDC61zTPYJP+7Zn8vwVr1BZWokkS2gejfhhvbnz/VsICA1o9fFm3j6dXz/5rU6LgENkRWbSUfvzplw9mR2/p9TpYXeIPdiv0WTweJFcmcnOqmxcRyQtAC7dw+KiLZwfPYpoa00i+1D8X3klcxGrSlMxSyY8hkqifzfm9pqBRW7er96Gsj24fSxJ1DH4uWgLl3Rtn/1WhmGwpDgZj1F/psljaCwpSuamHlOOu3YIDpcHpYGYHS4P100dxXPf/obrqFlmq1nhqjNHtHWIHS4kwI/Xbz6PHVkF7M4tIthuY3RCT2ydbOWAIAiCIJyoxP+4HUDXdLYs20bWzmxCooIZOX1Yg9UYK0sreXzWM1SX193ftGv9bp699GUeXXh/q8cY0S2cSx+ezWePf12nvYHJYuLSR2YT2iW0zvmDz0hi3F/H8PvXa3A5XGCA2WpGMSvc9cGtbfJGfkdlNl/mrWGfo4AIcxCzokdxWki/Nk0afi/ZWS9xO9IfZbs5N6rmTbxNsTC310xu7OEgz3WAMHMA4ZaWzUDmuw54TZagJmHKcnopLtNGVEP3mUhCzYygho4Jxec5nVGv6DA8mvc9XYosMTS+G+MH9satarz98zpcHhXDgGC7jTtmjmNA7Ik/8wY1KweSYqNJOkmeryAIgiB0JiJ5a2eFmUU8fv6/qSytwuN0Y7aa+eDeT7jt7ZsYetYQr9cs/+x3VE/9N8uaR2Pfjiz2Ju+j1+DWbzNw9o1T6TeyDwveXEzu7ly69onh7Jum0mdY73rnSpLE1c9czphZo1jy0TLKCsvpf1rN0tCQqNZfTrWocBPvZC/FrddU/itwl7MnI59NYUnc2nNaq493iGYYDdRxMdCN+kcDTX74KzbWl+3m9cyfqdJcjAiOZ2rEEAJNTWuh0MUagllSUL0kcGZJoadfRN1YDIMdVdlkOooINwcwLLg3Jql1kimzrBBuDqTI432pZrg5sNXGak/+NgszRg3gh/U76s2smU0KF44dBMCZp/Rl4uB49heXocgyXcOCjrtZRkEQBEEQjk8ieWtHhmHw1MXPU7y/pHY5oqbWLDN8+bo3eO73J4joXr9iW8raXbiPKsFfS4K9WzLaJHkD6DM8ntvfualJ50qSxICxiQwYm9gmsRxSoTp4O2uJ1x5qv5ZsY0rEEPr5d22TsU8L7cdvpdvr7PU6REJiRHD9xFYzdB5P/5qtFZm11+2qyuGbg8U9utrCGh13RHA8VtmEQ6//OpCRmBJxOPEvcpfzQNrnFLrLMQwDWZIxSTIP9bmQAQHd613fEpfEjOOt7CX1ZiGtsplLYo7fcvnXTBmFJEt8v3Y7iiKj6QZhAX7c+9eJxIQdrgaryDKxkaEN3EkQBEEQBKH1iYIl7Sj1jzRK8w543UemazpLPlrm9bqQqGAk2fsn+4osExDW+nve2lLBvkK2rdhBfkZBi65fV5ZW00PNC4+usaR467GE16BhQb2JtUXU27NmlU2cFppAN1v95HtJ8VaSj0jcAFyGSoXm5Nm93zdpXEWS+VffOQQqfvjJltoxrZKJe+PPI8xc8xowDIP7d33GfmcJTt2Dy1Bx6G4qNCcPpX1Bmad1WgtMiRjCeVEjsUgm/GQLfrIFi2TivKiRdRLJ440sS1w7ZRRf/OMynrxiGq/cMJMP7pgtmk8LgiAIgtApiJm3dpSXnu+zd5rqVtm3LcvrsUmXT+D3r9d4nX0zgFMmD27FKNtOWUEZL1//Bumb9mK2mPC4VXoN6snt795Ubw9dQxya22cPNR2DStXZWiHXI0sST/W7hM9LVvF99p9ohoZVNjMrahQXxZzm9ZrvC9Z73SdnYJDhKKTQXU6kpfEef73sUXw8+FZWlaaQ6Swi0hLE6aH96yy93F6ZRZGnAt3LC00zdBYXb+HCLmOa8Yy9kySJy7tNYGb0SDaXZwBwSlAcQSbf1RiPJ35WM/1FwiYIgiAIQicjkjcfSvNK2bk6FbPNzKAJSdj8bcd0v5LcUmRF9jmDJptkont5f7PYe0gcU6+dzOL3luKqPiKBk2Dg+AFUHajE0ozk51iVF5Xz83tL2bBoE4pZYfxFYzljzunY/H339tJ1nQfP/lftklGPsyaZ2b1xD4/OeJrnVz+JYmraPqkBAd0b7KE2tAU91JrDpliYO2AGl0dMwKm78ZOtPmcCgQYbaZskmTK1uknJG4BFNjExfKDP4xmOQq/77qCmkEhaVW6TxmmqIJOd8WEDWvWegiAIgiAIgncieTuKrum8e/dHrPpmLYpZQQI0Tefyxy5m8hVnNPt+aRvSeeeuD8jPKECSJTxO71X6TCaFs66a5PM+cx64kP5jEvjP9W/iqnJhGAYYsHnJFub+nsKjC++nW9+YZsd3NI/Lw/60XKx2K116RdUrxFCwr5AH//I4zioXHldNApaTlsuSD5fx2KIHsAfWL8BRVVbNo+c+SVFW/YqIuqZTXlTOxsVbGHn2sCbFGG/vQl//rqRW7a9TgVFGwq5YGR9W00PNMAySKzNJqdyPXbEwNjSxdnlha1AkGX+l8aS+jz2G9WW7vU66qoZOV2vrJd5h5gAUSfY6w6sgNzlJFARBEARBEDofkbwd5cun/sfqb9fhcXlqkxOATx76nKiekQyakNTke2Wn7OfJC5+tO1t2kMliQnWrmCwmJFniiicuaTT5StuQjuZRaxK3g1S3huZx8PYd7/Pogpa3DDAMgx9f+4lvX/yhNmEN6xLCja9cS7+RfWrPe3fuR1QeqKqzb8/tcFO4r5DvXvqROQ9eWO/eL13zGjlpvpuKO6tc7PojrcnJG8AjfS7kpYwFrCtLwyybUHWNeHs09/SeiVU2U6E6uG/Xp+S4SnHpKmZJ4b3sX7mq20RmRo9s8jit4aKY09hSkYHrqPL6VsnEmRGDsSu+Zyyba2RwvM9ZQEWS+Uvk0DqPFbrLSa/OJ8jkR6J/twZnEAVBEARBEISOJZK3I7idHn5+f6nXvWVuh5tvnvuuWcnb189+h9vpvSeYYlYYe8FoIrqHM+HicV6rTB5t+byVeFz1Z+4Mw2Bv8j7Ki8oJimjZzMqPr/3E/57/rk6imbe3gKdmP8e/fn6Ibv26Ul3hIGV1qteCKx63yvLPfmfOgxfidrhxVjkJCAsgP6OQXet3o+ve96gBmMwK/iH+zYrXT7Hwz/jzOOCpItdVSrg5kCjr4ZYEz+z9jkxHMSo1M3OHKlN+lPMb8fYuDAzs0azxjkWifzduj53OfzIXIh1c7qkaGmNCE7iu+5mtOpZZNvFg/F95ZPeXaIaOx9BQkFAkhSu7TaTHwYIqTt3D83u/Z31ZOmbJhIGBTTZzX/z5rVaRUhAEQRAEQWhdInk7QkluSYPHs1P2N3g8Nz2PBW/8TOq6XQSGBbI3OcNrogNg6Aaz7jiH6LimF0VwVrt8HlNMMs4qF0ERPk/xSXWrzH/pB68zhB6XyvyXfuSW16/HVeVEUmTweG8W7ax08MKVr7B56VYkCWz+NoZNGYLJbKrd4+aNJMuMPX908wMHQsz+hJjrJn5F7nK2VmTWJm5Hcusevs5fe8zJW5m7mt3VeYSZA5q0FHNC+ADGhPZjc0UGTs1N/4DubbaEcVBgLO8MvJGfCjezuzqPKEsQZ0cOI/aIXnDP7/2eDWV78Bha7dJTh+7mwbTPeWPAdXUSYaHtFRyoZH1aTcGikX17EBVyfFWQFQRBEAShfYjk7QgBIf5oPhIToMHZoQ0/beLVG99C9ajoqg7k4qOmBlCzt2zZvBVMumwCUT0j6x13VrmQJLDaDy+p6zO8N1uXb/d6P5PFTHi3xvuFeZOTlouPGhfoms62lTsBCI4Mxma3+kzEdN1g4y9bDj5/8LgqWf2/dT4LaABIssT5f59BeLcwNi1JZsuvW7HYzIyeOYreQ+Ja9HzyXAcwyyY8Wv2fpQFkOYpadF8Ap+bmlcxFrD6wCxMyHkOjf0A35vaa2WgSZ5FNjAru0+A5rSXMHMAlXb33Wyt0l7O+LL3OfsFDVF3nu4L1XNejdWcEBe8Mw+DVH1fz86ZdtUtW31i0lqlD+3HrOaeJ5t+CIAiCINQhkrcjBIQG0P+0BLav3Imu1V3mZ/GzMPWayUBN4lVd7iAg1J+Utbv48L557E/NqX9D3zkLhm7w42uLWPjmYmbdcQ7n/30GUNML7uMHPq1tGxA3uCdXPnEJfYbH89e5s0hdl1ZvWafVbuG8u85tcrXGo1n9rfWe75E0T82SQ1mROf/uGXz++Ne4jopBNslIklQv+fW4vRdoAZBlmUsfvYgJF4/l/imPkr+3oCZplSUWv/8ro6YP58ZXrkGWm9eOMNIShKr7TsK7WEOadb8jPZb+NTsqs/EYGoe+A9srsvh7yse8nXQ9Zrnz/0rtqc7HLJm8J29obKv03rJCaH3frd3OL5vT8Kh1fxa/bE6jR0Qws8b4riwqCIIgCMLJRzTpPsoNL11NcGRQnRkvq7+VPsN7M/6isbxz14dc1+9Wbhs+l2v63MJTs5/3nrgdyceH57pmoLpVvn9lAVt/286u9bt5avbz7N2yD13T0TWdPZv28q8LniV90176jojntrdvJCgiCFuADb9AP6x2KzNvm860689q8XOOjosiooFZu+oKB6l/pAEw5erJzLzjHCx+FvwC/fALsOEfYicqNhLVR6JmtVuw+Fmw2Mw13w5ZwuJn4S/Xn8m068/ig3v/y/5duTirapaFGrqB2+Fm/cI/Wf7pyuY/H2sIfexdvLYTsMlmzo8+tdn3BEivzielcn+9pEfDoEJ1sOpAaovu294CTX4YDXyyEHyC9Go7Hny+cgsuT/3fG5dH5YuVWzogIkEQBEEQOrPOP03QzsJiQnlu1RP8/tUa1i/ciNXPwoQ5p3PK5EE8PP0JslP2Nzib5E1wRBCVB6p8Lsl0Ozz8+PpPOModPoulfPrYlzz47T8YNuUUXk9+gYyt+3C7PPQa1LNOotlSF8ydxSs3vOn1mK7qLHzjZxJG9UWSJGbdcQ5/ue4s0jftwWQxET+0F0/+9Tny9uR7vV5WZK59/ioKMvJJWZtGcFQQky8/g74j4nFWOfnjxz+9Jn6uajcLXv+JSZdNaPbzubf3LOamfkK56sChu1GQUSSZmVEjGR7cu9n3A9hWkem1+TXU7BfbULaHM8KaXtCmoyT6d8Mmm3Ho9V9rNtnMOZFNr/optJxH1ThQ6fB5vLTSgarpmBTxGZsgCIIgCDVE8uaFX4AfZ101qU7ftU2/bCE3Pa/ZiZvJYmL0zFEs+XBZg+flpudRvN93wZSUtbvQdR1ZlpEVmehe0aguDxY/S7Pi8cUe5IfV34qryntRlKMTM5u/laRx/Wu/Hn/RWPYmZ3gteqLrBsOnDMHiZ2Hm7XWPlRdVIDfw5rQ070AznsVh4ZZA3h54A2sPpJFcsY9AxcbE8IF0tzVe1dMXm2JBlmTwstxQRsK/FUv+tyVZkrgv/nweTPscVddrC7vYZDOjQ/oxsp325Z3sTIqMzWLG4fa+h9RmMaPIYs9be3N7VDbuycHh8jAgNorokMCODkkQBEEQaonkrYn+/Hlz7bK+5jBZTIyZOZJl81agqb73YcX07tJg8iZJEpIkkbUzm/fmfkz65r3IskRgWCBzHrqwxdUaD4mKi/K5702SJLr169rg9aedP5pFb/9CbnpenVk0i5+Fyx65yGeSGRwZhNFAG4GIHi0on3mQSVIYF5rIuNDEFl1foTooV6uJsARhlc2MDunLm5mLvZ5rlk1MDj9+9icNCOjOG0nX8X3+BrZWZhJi8md61DBGBsWLIhntRJIkzh6RyA9/7MB91N8Gi0lh+ohE8bNoZyu27eGF+SuRJDAM0HSd0QmxzL3gDCwt3FMsCIIgCK1JJG9NpJiVmr1rDRQhOZKsyIR3DeO2d26iZ1KPBmeXJEni3FunoaoqO1d52TclwaAJSRRmFvHIOU/iqHQCoAEluaW8c9eHaB6N8ReNbf4TOyimdzS9BvUkfdMeNLVuMmW2mTn7pqkNXm+xmXl0wX18+8IPLJu3Emelk24JXbnwH7MYNuUUn9dZ7VZOn30aK75cXa+KpdVuYdbt01v8nBpiGEbtG+Mqreb76a/YACjxVPJSxgKSK/ahSAoGBmdHDOXK7mdwZbcz+ChneZ2G2zbZzPjQ/vTzbzjB7WyiLMFc22NyR4dxUvvb5OHsyMwjo6AUx8EPPfwsJuKiQrli8vAOju7ksmt/Ic99u6LeHsS1qZm8+uMq7po1voMiEwRBEITDJKOBMu5tJA7YW1xcie6jB1pnlLJ2F/+e8yKuBnqtASDVJDK3vH4DI6YNrU0Qvn5mPj++tshr0+7xF4/lxpevIXN7Fg+f+2TNGMbh+9n8bTy24H4WvrWYFV+s8jpDFhQeyOtbX2wwSWzMgYIyHpv1NAfyynBWObHYzBgGXPboRXWWkLY2t8PNc5e/zK4N6ageFVmRkZCYcvUkLnl4dqvNPjh1D5/l/M6iok1Uay5CTAHISJSqlUiSRKwtgqu7T+LljAWUeirRjsjUrZKJ00ITuLvXDDaXZ/Bt8Tr2VBQQbg7gvOhTGR/aX8ySCC2i6Tob0rL5bdseACYM7M2Ivt1RmllltTVERgZSWFjR7uN2Bo999gurU/Z5bZtiNil8NvcSAv2Oj6XRQl0n8+taOHGJ1/XxT5YlwsMDAHoBGU29TiRvTWQYBs9d/jLbf0+pU1TEbDMTGh1SszdLgmFThjD7nxcQ0zu6zvW6rvPR/Z+yfN4KJFlG13QMw2D2vedx7q1n156XtTObz/71NVt/q+nnNmTiQGbcNp1df6Tx1TPzffZYM9vMnHXlJLr26cKoc4YTENqyJr+6rrN1+XZ2b9xDQIg/o2eMJDiqfRo279m8l2XzVpCyLg1nhZOouEjOvmEKw6aecsyJkWbo3J3yMRmOAtxe9qwdYpJkZCSv51gkE28mXUe0NUT80RROSCfz6/qy5z+jsKzK6zG71cxTV0wjsUdUO0cltIaT+XUtnLjE6/r4J5K3dqCpGoveWsyit3+hvLiCiG5hzLz9HCbMGdfk5KKssJzUdbswWUwkjevfaKXIDT9t5KWrX2+wD9shsiJjtprQdYOr/305Ey723qS5s1ry0TL++/AXuJ3u2plHq93KGZeczt+euOSY7r26NJXnM37AqXtPfpvCJpu5KXYKZ4YPFn80hRPSyfy6vvXN+aTlFHk9ZjEpvH3rBcSEBbVzVEJrOJlf18KJS7yuj38tTd7EnrcjGIZB6h9prP5mLa5qN0PPGsyIacMwWWq+TYpJ4ZxbpnHOLdNaPEZwZBCjzhnRpHMrSip58arXMJqY5OqaXlvt8YN7/0vPpB7EDerZ4ljbU0VJJZ889Hm9mUVXtYtl81YwYc444gbGtvj+v5XuOKbEDUBCwiKJXxlBOBHNGp3EKz+swnnUnjdJgh6RISJxEwRBEDoF0UDoIF3T+c91b/Dvi19g6ce/sfKr1bx95wfcM+FByos75pON+S//2OTE7Wgel4eFb/zcyhG1nQ2LNiL7KIvucXlY+cXqY7p/a8wwa4bOiOD4Y76PIAidz6TBfRjWpxs28+EPaKxmhQCblfsubLs9v4IgCILQHGIa4aBl81aweUkyriP2szmrXBRmFfHu3z/krg//r91j2r1hd4uvNXSDzJ3ZxxzD3uR95KbnEd4tjH4j+7RZUQ5npdNnE3NDN6g8UHlM9x8Xmsif5XsanX2TgADFD7fhqVNR0iqbuabbJOzHSS+3xmiGTp7rAGZZIcrSPnsaBaEzk2WJBy86kw27s/lpYyqVDhcj+3Zn6rAEguy2jg5PEARBEACRvNVa+ObPdRK3QzSPxualW6kqq8Y/2N6uMYVEh7b8Yqmmd1tLleSW8swlL5GfkY8ky2AYBIYFMnfe7XRP6NbyuHzoOyIexaygekngbP5WBo4fcEz3HxOSwOe5q8lxleDxUbBEAmyyhaf6zWFbZRbfF2ygTK2mpy2SOV3HMiyo9zHF0Fn8UpTMB/uX4dI96IZBlCWY2+KmkRTQo6NDE4QOJcsSo/r1YFQ/8bsgCC1mOJEpxCAEQxJN7gWhtbU4eUtISBgLvASYgWLg6tTU1H2tFVh7Kyss93nMZFaoKKlo1+TNMAx6D+nJ+gV/tuh6i83CtOvPatG1uq7z+Hn/pjCzqE6hFGe1i8dn/ZuXNzyLzb91Z6Dih/UmdkAP9ibvq9PkW1Zk/ILsnHruyGO6v1lWeC7xct7L/pVfi7ehGTp+ioVwcyA5zhIkSWJEcG+u6HYGPWzh9LJHc25U0/YmHk+WFG3ljcyfcRmHv8fZrmIeTPuC5xIup7c9uoGrBUEQBMEHw4NdfxEb32EgIaHiZhxV8v0Y0jF8GC0IQh3HMvM2D5iRmpqanJCQcDXwH2Bm64TV/qJ7RbN3S4bXY7puEBod0q7xfHTfPH77/HffJ0hgMpuwB9tRXR50TcdZ5UIxKcgmmRn/dzb9xyS0aOxtK3ZQVlBev8KlAW6nhzXz1zHx0tZtWCtJEv/4/C7euOUdkpdvx2w14XGr9Ezqwe3v3IzFZj7mMeyKlf/rOY2bY6fi1lVssvmk6s2mGwYf7l9WJ3E7xK17+G/OSh7q89cOiEzojJxulUqnixB/P0zH0D9SEISTQ4B+LxbWIOHi0P+sFlZi0q/mgPwlSMf+/7ggCC1M3hISEqzAA6mpqckHH0oG2n9TWCs6785zefWmt+r0cIOahtvjLxrbaEl/gIyt+8jYmklgeCBDJg6srVLZXPt35bD805VeG3oDnH3DFNxOD+sWrEfzqAyakESvIXHk7cknODKI8ReNJSa+S4vGBti3LQuPq/4SUqip/pi2Ib3VkzcAe6Aff//4Ng4UlJG3N5+wLqFE9Yxs9XEUScZPsbTqPXXDYHHRFj7NXUmJpxIJiVMC47imxyTi/LwvX93nKOSz3FUkV+zDKps5K3wws6JHttm+uiJ3OVWa9ybzBrC1IrNNxhWOL+XVTl79cTWrd+5DlkCWZWacOoC/TRqOIpI4QRC8UIy9tYnbkSRUZIqwGMtwS1M6KDpBOLG0KLtITU11Af8FSEhIkIFHgPnNucfBvgadxrQrxlOSVcinT3yDLMvouo4kSZwyeRB3vnEdFqvvT4zKiyu4f/qT7N2WhXTwzY4kSzz09d0Mmzyo2bH89EYymuq9r5tikmsSO5endnnh+gV/smXZNp5f9gj9hh97NcSuPSMwWy1oqrPeMZNFoVvvKCIj224de2RkIH2TurfZ/VvLoe+Bqmvc9Me7bDlweNWwgcHGir3sSPmYd0bfQEJQ1zrXbi7N4K7Uj3BrKvrBpnZf5a/h9/KdfDjmFvxNrZ/AyS4wJKO2h97RrCZTm/5chc7Po2rM/WAB+4vK8NTOvGt8t3Y75U4XT1zd8jYpgtCRxN+2tmVUJWP4KMwtUU2gdTVyyAXtG9RJQLyuT06NJm8JCQkXAi8e9XBKamrqmQkJCRbgo4P3ebI5A3fGJt1Trp/CqbNO5c+fN+NxeUga15/uid0oK3cC9ROZQx4550nSN++tVy3xoZlP88yKfxHZI6JZcZQWVaCp3otqaKqOo8pZp4WArhs4K508d+2bPLH4oWaN5U3/CUnouvfkUZJlRpw78qRvDHlkc8xv8/9g6wHvs1ZO3cMzyd/zdMKltY8ZhsHD27/EqdWdWXXrKrnVB/hgx3IuijmtTeKOtUaQ7siv97hJkhkfMuCk/7me7DZm5JBfWnFE4lbD6VFZuimN2amD6XpEvzNdN1ixfQ/frd1OWZWT/rFRXDhuCHFRYn+L0HmIZsZtz6rr2JG99p8ykHA5TVSJn0GrEq/r498RTbqbd11jJ6Smpn6Vmpra/ah/ZyYkJAQAP1GTuM1MTU09tg7InURwVDCTLp/A1GvPpHti41UVs1P2k7Et02uZe03VWPz+r82OYeD4AQ0WBPHV+y1rZ3ar9KSzB9m5+dVrsdgsKCYFqCkcYrFZmHP/X4k+hiqWJ6Lv89fXzp55s70yC49++PWR4yqlxO299YHbUPmlONnrsdbwfz2nYZPNyBze62eSFEJM/l4TRt0w2FCWzue5q1hQuJEytbrNYmtvVU43Szan8cMfO9iTV9zR4XQKy5N343DX3xMJNc2qN6Xvr/1a1w2e+HIpL85fyY6sAvaXlPPrlnRue+s71qdltVfIgiB0Am5pPBLeP3QGGy757HaNRxBOZMdSsOS/wG7ghtTU1M41hdaOMndmI/vYB6K6NdI37Wn2PZNO70+X3l3ITt1fp/KiyWpC82g+kzdJlvC4WieHHnXOCHr0787P7y0lc3sW0b2imHrNZOIG9WyV+59IylVHo+cYRyR3Lt2DLMk+ly+6G+lFdyz6+sfwcv+r+Cx3FRvL9mCSFSaGDeSCLqcSbKpbTbXYXcG9u+ZR6qnCqbuxSCbezVrKzbFTOSticJvF2B4W/ZnC6wvWoMgSmm4gSdC/exSPXjoFm+Xk3VRvNvn+L0FCwqQotV+v25XJht3ZOD2H/0bphoHLo/L018v44p7LRKETQThJGFI41dK12I33kY5YqWRgw81oVIZ2YHSCcGJpacGSodRUltwBbEpISADISU1NPek+WgmJ8t3gWJIkwruGNfuesizzwLf38O7dH/Hnoo0oJgVN1Rl1znAqSitJXrbN6xv/gBB/XNUutvy6lYgeEXTrG9PssY8UE9+FK5+8tPETO7ESTyULCzeytSKTEJM/0yKHtd1vogAAIABJREFUMiSwZ6tWmezhF05adZ7P4/H2aCzy4V+1HrYIfI0uI3FKUFyrxeZNd1s4c3vNaPS8x9O/Js91oHZW8VCVyjcyfybeHn3cthXYvi+PNxauwX3U0uTtWfk8/+0K7r9ocgdF1vGmjUxkyZ+76iRkh2iGwalH9D9buCEFp49ZOl032JqRy9D41u8JKQhC5+SUr0EzeuOnv4dCJgZhOKRLcUnn10zdC4LQKlpasGQT+Hz/eVJJHNMPm92Ks7L+njizzcxZV01q0X3tgX7c9taNVJdXU5pfRmiXEOyBfmRsyyRlza56VTHNNjMWPwv3TX4ExWxC86h0T+zGnR/c2qIEsjlK8w+w7L8ryNiaSXSvSCZdfgYxvTv+jX1aVS7/3PUpqqHVNuZeX57OhNAB3NZzWqslcHNixvHUnvl4vJTgV5C5vkfdfntmWWFOzDj+m7sCl173Gots4qIuTd/vZhgG6Y58qjUXvf2iCTDZWvYkjrLPUUimo8jrclCPoTG/YD13xZ3TKmO1ty9WbsHtZZmzR9VZk7KPA1UOQvz9OiCyjndqYiwDYqPZnpmH64jvkdVsYva4wYQEHP6+VDq8Vy49pMpHxVpBEE5cHmkiHmXi/7d33wFSVef/x9/3Tt1G2UJfOlw6IsUGCgooxoKxtxgTa/JNNMXERKMmX5P8LNF8E41GTUyx18SGBUGwUwQVkaHD0rfALlum3vv7YwF33dmFXXZndnY+r7/cOXNnnoXjZZ455zxPssMQ6dBct912W6LfswtwfU1NGKcDbLY0DINhx1h8+J9FmKZBLGpjmAZev5dTr57J8ecfd1iv7/F56JSXg2dftcsu3Toz7JihrFmyjmB1CI/XTVbnTLw+D7t37iEajhENR4lFbcqLK1j08hJmfOfERrd2Hq4VC1dy22m/ZdWHq9myaivrl2/k7X+9Q5dunRkwJnlbLB3H4cer/klFrKZeAhJzbLaGyhiY2Z3e/pYntVlZPqqraz+c9vHn4TXcfL53c71UJ9vl59dDzmNUTmGD64dl9cZneghUbcNtuDAx6O3P5eZBZzPgEFe0vqgs4obAv3mt+BPeLVvF8zs/YnekiiM7DcA8zMT0y6qtfLxnzYGkty6H2nYLswpScxvM399a0mhi4fe6mTSkkILO7asabqJkZ/uYOLAPXrebzcV7iMZsCvO7cM2sozn9qBH1nrurvJLAlmJicbdxG3x35iSy/K3bkkOkJerer0U6Cs3r1GcYBpmZXoD/A/Yc6nWHc+ZN9hk0bgB//Pj/Me+xhaxetIauPbty0remMnBs/zZ5v2FHDeXu935L6bYyouEoW9ds476rH8L+WnsBO2ZTubuKT95YzqTTJrR6HOGaMPdcfh+hOjePWCRGLBLjH794nFFTRlDQt3mVNltLoHoblbH4FUKDdoRXdn3CxM6DW+39vtnjKGbkj2FJ+TqqYiFG5/SlX0bjPeoMw+Cs7pM4vdt4tgV34zc9dPM1vgX367YGy/jVmqcJfe183Fuln2JicHXfGY1ceWi6eTs3WoTFwKCXr21Xc9tSfqdMdpXHLxgTjdnk5mTGHUsXbpfJuZPHcO7kps81nj5pOP/58IsG20+9bhfHDOtHQeestgxTREQkLSl5ayWd8jsx+/rEbiPbvx3y3Wc+IFjVSKJSFWT14rWHlbyFgxFee/AN3v7nO1RVVNN3eB/OvuFM9pZV0tjyqePYzH9iIefd+M0Wv+/hqIhU16uo+HW7I/E/vB+OHHcG0/JGNesat+Gib0bzE9zndnxE1G64TTNkR3m9ZDmX9J5ClqvlWygHZnSjm7czRcHSesVWALyGizO7t/6XAYnyzWNHs/6FBQ3OdZmGwcAeefToWr9vTtneauYuX8OOPZUM7J7LtDGDtKIEdM3O5K7vfoPfPT2P4ooq3KZJOBrjhFED+eEZk5MdnoiISIek5K0DyOqShcfniVtp0u1xkZ3b8i1g0UiU2795J5u/2Ew4WPv6qxev5Q+X/ZlxM8cSaaRgQTQco2RL8sqv98/sFnfLH9SeQxuWnZqFFBzHwTAMPq/cRKyRlTG34WJjTTEjsxtu1zxUhmFwy+BzuGHVv6mxwwTtCG5cGAZc0ut4hmWl5p8fwOQR/VmyZgvzP19HOBrFcWq3S2b6PNx0Xv0zqgtXrOeuFxbgUNvA2u9x87e3FvH7y2YxrI9aZgzqkccjPzyHTcV7qKgK0q9bVzpntc65y/bMth1Wby2mJhxhcK98cjIab+0iIiLSmpS8tbKayho+eOFjtq7ZTvd+BRx39tFkd23b8zNHnzmRp25/Lu6YYZoc982jW/zai15ZStGXWw4kbvuFa8J88sZy3B533B533gxvUs+8dfN25shOA/ikYkODJM5tupjdbWKSImuZxeVr+efWBWys2YXXcOM1Gy9nb2OT5Tr8D5M9fV15dPT3eH9PgJWVW+jizuKkvFF093U57NdOJsMw+NHsKcwab/HmstXsrQkxfnAfpo4ehN/71S2xuLyKu15YUG9b4P7Vupv//QZP3nARHrerweunG8Mw0qop97J1W7nj+XcIhqOYBkRiNqdOGMZVpxyFy1RrBBERaVtK3lrR6sVrueOCe7Bth1B1CG+Glydvf44fPfo/jJ3WvO10zdG1excuvu08nvjNs0SCERzHAQO8fi/n3HAmBYVfbctbsXAlL98/h+1rd1DQN5/Tvj+LcdMbP9uy4Kn3CFXHryrn9rjw+DyEg+EGvedcLpMp5x1esZbDdcOAM/n9+hf5fO9mXEbthyqXYXLjwNn0OoxiJYk2r3QF922ec6AyZciJEok11gy19kzfz1c9xrS8UVzYczKdPbVnuKJOjI/2rOGd0i+IYTOl63CmdB2Gx2z8NuAx3UzNHcnU3JGt+0u1A8MKuzGssPHVs9eXrqr9fymOaMxm0eoijhvRv42ik/Zo467d3PrEW4S+tuV2ztIAbtPkylOOSlJkIiKSLpS8tZJwTZg7LryXmjotA/aX8//jd+7jT0vvJucwti8ezMzvnMTAsf159cE32bZ6Gz0G9eDUa2ZiTRpy4Dkv/fk1XrznpQMFRkq2lLJ++QZOvmIGF9x0dtzXjQSbbhh99g2zefUvr1NRUoEdszHdLtweFz97/HqyuyS3YEGGy8tvhpzPtmAZa6p30MmdwZicfgcSuVQQdWI8WPRmg5YCNg4GBiYGMewG11XaIeYUL+ODPQH+POK7+EwPNwYeoyhYSnBfkZPP9m7imR0fcLf1rVZrMdCRbC2tIBJr+GcLEI3F2Lmn9c9NSvv21ILlRKINvzgJRaK8vGgll0w7kgxf+jZ5FxGRtqfkrZUsenUpjh3/g57jwLvPfsCpV89s0xgGjx/EdQ9fG3esbPtunr/rvw3OxYWqw8x56E1OuOA4eg7q0eC6CaceyfpPNzboKwe159qOOm080y+bysr3V7Ft7Q5ye3Zl7ImjcHvaz9Tq5c9NqZW2utZW7cBurCgMDp09WeR7clhXs5OYU3/+RbEpj9bw/I6PAdhUU0K4Ti+6oB1he2gPj2x5m+v7f6PtfokU1b97V7xfuhpUUwRwu1z0zuuUhKgkmVZsbvz/R5fLZFPxbp2FFBGRNpU6SxDtXPHmknol8+sK14TZsX5ngiOq7+OXFjc6Zkdt3n/ho7hjUy+cTEZORoM+cb5ML9MuOZ5O+Z0wDIORk4cz49vTGH/yEe0qcUt1Dk4TNTMh0+XjxoGzcTXyv3LUiTG/bAWvlyyrl7jVHV9QtrJB4idw8pFD4/bLM4AMn5sJQ/okPihJqixf41VGY7ZNtl+FS0REpG0peWsl3Qd0w5cZ/x92X4aX3kN6Jjii+mqqgkQbqQwZi8aorqiJO5bZKZPbX/8Vo6YMx+1148v0kZHj5/Tvn8q3br+wLUMWYHBmw9XQ/TyGiyldhxF1Yk025Y45NtWx+OcWAWLYDfrFSW0p/FsvmoHf68bvdWMYkOH10DnLzx3fPlXFKdLQ6ZOG4/PEL1LTrXM2ffIPvVejiIhIS2iJpJVMmHUkj974GBDnQ7JpMPncYxMeU13WpCH4Mr0EqxrG58/yMfwYC4CKkgpWfbQal9vFyCnD8Wf5yeudy41P/4TKPVVUl1eT27Mrbu/hT51wTZh5jy1g/uPvEg6GGXviKE773izy++S1+DWjTowPdgeYX/YFMae2KMcJuSOojAXZFtxNnjebnr7UqYznMd1c3mcaj2x5u965NxODLJef07tNINvlx2u6D5xlq8vEYEKngays2sL20J6475Ht8pNhqm9ZPEcO6s1TN1zMeys3UFJRTWFBZ462+uF2KXFLRycfaTH/8/Ws3VZyoPKo22Xicbm48dxpSY5ORETSgdFYNbU21B/YUFpaiW0n/L3b1PpPN/L78+4mFokRrArhy/JhGAY//dcPGXHcsKTG5jgOv5x+G1sC2+qV9ne5TfIL87nr3dt56vbneOvR+bg9LjAgFrW56NZzmXn5Sa0eT7AqxG2n/44d63ceOE/n8rjw+jzc8tKN9BvZt/mvGQvzs9WPsTVYdiCR8ZkeDCBqx/CZHiJOjP4ZBfxi4Fl087X8W/KCghyKi/e2+PrmWli2kn9sfYficAWmYTCp8xCuLpxOvrf23NVbJZ/xQNEbDQqbZJhe/jT8cr6s3Mr9RW80WGHzmR4u7XU8Z3WflLDfRdqvRM/rVBSN2cz7bC2vLVlFdTDM+CF9OOuYUXTr3LYtYaTlNK+lI9K8Tn2maZCXlw0wANh4qNcpeWtl4Zowi15dyo71O8kvzOPoMybiP8SmtdvWbuej/y4mXBNm5JThjJwyHLMVt2ZV7q7k/u89zMr3v9zX1DvKkImD+MEDV7Pg6fd54Q8vNShM4s3wct3D1zJuxthWiwPgv//3Ci/c83Lcapb9Rhby+3m/bvZr/n3LfF7atbjR5tz7mRh08WTx8Khr8DfRL60pybhpOo5DyI7gNl24jYZbt+aVruDRrfOpjAaxcRiU2Z3/6XsKAzO74zgOj29/j+d3fLSv2qZD1LE5tWAcV/aZjtHEtktJH/owIB2R5rV0RJrXqU/JWwpzHId/3/IUb//rHexIjFjMxp/lo8fAHtz8wg1kdsps1ffbvXMPxZtLyOudS16vXGLRGNeMuI6q8uq4zx8wph+/fevWVo3huok/o3hzSdwxj8/DPR/+nrzezasQef7ye6mMBQ/+RMBverimcCYz8hvvcdeU9nrTdByH0kglXtNFJ3fDeVMRreaTig3YjsMRnfqT69FqgXylvc5rkcOheS0dkeZ16mtp8qYzb22sfFc5H728hOryaoZMGMTIKcMbrHLM+eubvPn3t7GjX1X8C1aF2BLYysM/+QfXPfy9Vo2pa/cudO3e5cDPFaV7CYcaL1ixJbCtVd8fiHv2bj+X20VNZfwCKk2paqIoR4P3tyMsq9jQ4uStvTIMg3xvTqPjndyZHbLhtoiIiEg6UPLWht7421ye+PWzYEA0HMWX4SW/MJ+bX/gZnfJqP2C/+sAbPPHrZ4i3AhoNR/nkjeVUlVeT1bl1V9/qysjOwIk1vgqa2Smj1d/TOmoIS+csi/t7Y0D3/s3vldTL15WtobJDeq6JQY679X8vEREREZG2opJpB+E4DnvLKptcKYpn9eK1PPm/zxEJRYgEIzi2Q7AqxLbV2/nFibfy2C1P8d5zH/LsHS/GT2D2cXnc7N6++3B/jSb5s3wcMX00prvhdPD4PJx02dRWf89v/uQMPP6G5818GV7O+MGpeHyNn0XbGizl3o2vcPnn93PtFw/z352LCdtRLuo1GZ95aN9HeEwX0/NGtzh+EREREZFE08pbEz548WOe/N9nKd9VjuPA8GMtLr/jUnoO7H7Qa1+5fw7hYMOm3bZts3vHHuY89BaGaWDbTTdHjkVidO3Z9qXtv3Pnt1h/8m+o2l1FaF/REl+mj95De3HG/5za6u/Xf1RffvLPH/DgD/9G9d4aTNPEjsU47XuncOZ132j0ui8rt3DzmqcI21FsapPef257h/llK7jTupSdoXKe2v7+gaIcYTuGg3PguVB73u2kvNEMyUpu7z0RERERkeZQwZJGvPPku/zjF4/Xq75oGAYZORncseA3Bwp9fPLmcj78zyIAjj5zEuNPPgKX28VPjv0l29ftOKwYTJfJUWdM4AcPXnNYr3OoglVB3n3mAz56aTFur5sTzj+OSadPwO1puxzfcRw2fVFEuCZMv5GF+DJ9TT73ihUPsiPcsF+Zz3Tz7V7TOKP7BPZGa1i+dyO24zA2px87Qnt4dseHbKwpJt+bw+zukzi685DDqrCog8LSEWleS0ekeS0dkeZ16lPBklYUi8Z4/LZnGpTNdxyHUHWIl++bwwU3ncPtZ93BtrXbD2ypXDb3M3oM7M6FN50Tdwtic/Uc1J3v3nXZYb/OofJn+Zlx+YnMuPzEhL2nYRj0H3VoPd2KgqXsjlbFHQvZUV4vWcYZ3SeQ485gStfhB8a6eLL41eBzWiVeEREREZFkUfIWx9avNbKuKxaNsfT1ZbjcLopWbSES+qopcqgqxKYVm7nrkv/DMFu+qmMYBt365XPnwtvVf6uOGjuMi8b/PGrshttURdJRzLZZtLqIRYHNeNwuThg9iBGF3XQ/ERERSXFK3uIwXWaTRURMl8n8xxbUS9wOcGoTvOYwTANfpo9YJIrpdpHXK5ebnvupPmh9Tb+MAmLE/3sxMTgip39iA5J2Ze32El74YAWbdu2mT35nzjpmFMP6NL9qaaqrCob5yd9eZsfuvdSEoxgGvPHJaiYM6cMvzzsRl6k6VSIiIqlKyVscvYb2JLNTBqHqhhUm3T43x8yexEt/fu2QX890m2R3yaKqvLrBip4vw8t3774Ml9tFRUkFfUcUMuyYoQ0SN9u2+eCFj5nz0FtUFFfQb3RfZl/3DQaPH9SyXzIF+U0PZ3WbyIu7FhOy6/el85puzulxTJIik2R7bfEqHnz9QyJRG9txWLejlA9XbeLy6RM565hRyQ4voe575X2KSsqJxmqLITkOBCNRFq/ZwquLvuSMo9XnT0Qk1di2TTgcxuv1YupLuLSm5C0O0zS54u7L+L8rH6h37s3ldpGTm8M3rj2Fd5/5gN07GhbOiMeO2gyZMJhwTZjAojXEojFMlwkOzLp6JpPPaTrpcByHP1/9V5bP/ZRQdW08pdvLWLFwJVf84TImn50+ScvFvY7HwOCFXYtwYRDDIdedxU8GnE5vf26yw6tnY00xL+z4iNXV28n35HBG94lM7DRIK6qtbHdlNQ/M+ZBwnRVvx4FQJMbf31rMcSP6061zdhIjTJxgOMJ7KzceSNzqCkWiPP/hCiVvbWDjzjKWrt2K22VyzLB+dOuSHvNNRNpWNBolEFjF/PlzWb06cODxoUOHcfbZZ1BQUIjbrY/y6UZ/440YN2MsNz71Y57+3fOs/WQ9Xp+HY795NOf8bDY5udnMvv40Hv91w6Im8bjcJt36FXDpby5g4+eb+HzhSrw+DxNOPZK8XgdPOFYsXMnyuZ8dSNwAcCBcE+ZvP/0XE2cd2WSVxo7ENAwu6X085/Q8hk01xWSYXgr9ee0uIfpwz2ruWv9fIk4MG4eiYClfVm1lWu4ovt/35HYXbypbuGJDo2O24zDv07VccPwRCYwoeSqqQ03Ord2VNQmMpuOLRGP87pl5LFm7BcdxMAyDh99cxBmThnPlyUfp/3MRabHt27fx0EMPUFJSTGZmFr1798EwDBzHoahoM/feey9ZWZ256qpr6dmzV7LDlQRS8taEYUcP5daXfhF3bPq3p7Fjwy7m/mMepsuF49iEayJxn+tyuzjx0hMA6D+6H/1H92tWHO88+W7cLZwApsvgs3dWMPHU8c16zVTnNz1YWe3zZhWyI9y94SVCTv0zkUE7wryyFZyYN4oR2X2SFF3HU14drLfqVlc0ZlNeFUxwRMnTJcvf5HiPrjkNHlu/o/TAqtFpx43Ag7bjHKpH5y5h6dotDebfq4tXMbBHHtOPGJKkyEQklW3fvo177rkLl8uksLB+RW7DMMjLyyMry0dRUe3zfvzjG5TApRElby1kGAaX/uYCTvv+KXw673MAsjpn8pfvP4Jt20SCEVxuE5fbxYW3nEvvIS1vCF21p7rRMcd2qKlMnw+nqWBp+XqMRqpihu0Ir5csV/LWiob2LiDD66Em3PDLkwyvm2GF6VO0xOtxM2u8xWtLVjVIKHweNxed8NUKZCQa4/an32bZ+q3EbAcDg7+/tZhvTBzG1accrVWjg4hEY7y65EtCcb44CEaiPLlguZI3EWm2aDTKQw89gMtlkpub1+Rzc3PzKCsr5aGHHuCmm27VFso0ob/lw9S1exemXjjlwM9/XDSEeY8tYMOnmygozOfEb51wWIkbwBEnjWbVR6vjbtGMRW2sSfqA0J5UxoI4jVTFdIDySPxeddIyE4f0oUuWn1A0im1/9eduGgZZfh/HDe+fvOCS4IqZk9i+ey/L123F3reVzwFmHzWSE0YNPPC8R+cu4ZN1WxskeXOWBBjYI4+Z44YmOPLUUlEdrDffvm5XeWUCoxGRjiIQWEVJSXGDFbfG5ObmUVS0mUBgFSNHpleBrnSl5K2VdS7oxFk/Or1VX/P484/jxXtfIRKK4NT5sOD1exgzbRTd+6fPykIqsLJ6YTfSasJnuhnbqX9iA+rgXKbJ3d89jdueeIvNxXtwmQYx26FXbid+ffEM3K702gbocbv4zcUz2bCzjE/WbsXt3ldEo07Rlkg0xquLv4y73TQYifLUwuVK3g4iO6Ppc8YH28IqIhLP/PlzyczMatY1mZmZzJ//tpK3NKHkLQVkdsrkN6/dxJ+uepAtq7bi9rqJhiMcfeYkvnPnt5IdnnxNv4wChmf3ZmXlFiLOVx+ODcBjuJmZNzZ5wXVQ+Z2yuO+a2WzctZttpRX06JrNwB5Nbzfp6AZ0z2VA9/gFkSpqQo1+wQCwa49WjQ7G53EzdfQg5n+2lsjXqnv6PK60a1EhIofPtm1Wrw7Qu3fzjlbk5uaxevUqbNtWG4E0oOQtRXTv343fvnkLxZtLKC+poMfA7mR3ad43M5I4Nw86m7s3vMwnFevxGm6i2BR4O3HTwG+S7dY38m2lf7eu9O/WNdlhtHs5fm+T412zMxIUSWq79tSjWbejlG2l5Qcaovvcbo4c1Jszv9aSIRazWbujFMd2GNQzD4/blaSoRaS9Codrj8c098zx/iqU4XAYv1+fMTo6JW8ppqBvPgV985MdhhxEpsvHLYPPoSRcQVGwlK6eLPpnaHurtA9ej5tpYwYx71OtGh2OTJ+XP199JkvXbuXDVZvwuExOGD2IEYXd6n34mv/ZOu5/9QOiMZv9D393xiROmzQ8SZGLSHvk9dZ+sba/9cih2v/8/ddLx6bkTaQN5Xs7ke/tlOwwRBq4dtYxrN9RxpaScmrCkdpVI4+b8XFWjaRxLtNk0tBCJg0tjDu+ZM0W7v3PwgZVKR9642Oy/B6mjRmciDBFJAWYpsnQoRZFRUXk5R361v+yslKGDh2mLZNpQsmbiEgayvB5+NNVZ7J03RY+XLUZj8tk9pTR9MjOUpuAVvT3uYvjthMIRaI8OncJU0cP0p+3iBwwbdp0HnzwvmYlb9XV1UybdlIbRiXtiZI3EZE0ZZoGE4cUMnFI7apRQUEOxcV7kxxVx7JhR1mjYyUVVdSEI2T6tNVJRGpZ1jDy8wsoKys9aJ83qF11y88vwLKGJSA6aQ+0vioiItJGvE0UJjEw8LhUuEREvuJ2u7nqqmuJxWzKykqbfG5ZWSmxmM1VV12rBt1pRMnbPsWbS1izZB17y1QiW0REWsfU0YNwxTmHYhq1DeZVdVJEvq5nz178+Mc3kJGRSVHRZkpLS3D2tXdxHIfS0hI2btxIRkYmP/7xDfTs2SvJEUsipX2avmtTMfdd81c2rSyq7Z8WinLUaeO54g/fxpuhrSwiItJyl8+YwNK1WyivDh5oiu5xmWT6vHzvG8cmOToRaa969uzFTTfdSiCwivnz32b16lUHqkoOHTqMs88+g4KCQq24pSHDaaJRaxvpD2woLa3EthP+3vUEq4JcP+lG9pbtxakTi8fnYeSU4fzs8euTGJ20ZzobJB2R5nXbqKwJ8dKilcz7bB227XD8yP7MPnoUXdRPLyE0r6UjsG2bcDiM1+vFNE3N6w7ANA3y8rIBBgAbD/W6tE7X3332Q0LVoXqJG0AkFOGL975k65rt9B7SM0nRiYhIR5Cd4eOiE8Zx0Qnjkh2KiKQo0zTVgFuANE/ePpv/OaHqUNwx0zBY/fEaJW8iaaS4vIoXP/ycRauL8LrdnDJ+KCcfaeHzpPWtUkRERNqJtP5Ektkps9GxSCSKP1vfcIiki427dvOjh18iHI0RjdkAPPLmIuYsDXDvFWfg96b17VJSWHUozNzla/hw1Wb8Xjczxw1l0tDCuIVURESkfUvrTyNDJgzm3Wc+iDtmR23yCw+9QaKIpLY/vLCAmlCEupuoQ5EYW0rKefHDFVx4whFJi02kpYrLq7juof9SGQwTikQB+GTdVob36cbtl56C26UETkQklaT1XbtkS0njgwZ8sXBl4oIRkaQprahiw87dxCuhFI7GmLN0VcJjEmkNf/jPAnZX1RxI3ACC4Sgri3by8iL9GycikmrSOnlrimkaJL4Qp4gkQ1UogttlNDpeE44kMBqR1lFRHWTFxh1xKzuHIjFe+ljJm4hIqknr5O3ImUfgy/TFHXN7PYybOTbBEbW9SCjCtrXbKd9VnuxQRNqNnl1zMIz4yZsBDC/sltiARFpBeXUQt6vxJuAV1cEERiMiIq0hzc+8DcKaNIRVHwUIB7/6Zt3r9zL6hJH0H9U3idG1Ltu2eeHul3jtwTcAiEZiDBjTj2v+fAU9B3ZPcnQiyeVxuzh/8lieXLicYJ3tZQBej5tLph6ZpMhEWq5b52ya6uWJX1L7AAASL0lEQVTav3tuAqMREZHWkNYrb4Zh8NN//5BTrpqBP9uPYRpk5GRw2vdP4bpHrk12eK3qiV8/w6t/eZ1gVYhgVYhoOMraT9Zzy6zbqSipSHZ4CRe2oywtX89He9ZQHq1OdjjSDpx//FjOPnYUPo+bTJ+HDK+HrlkZ/Or8kxjauyDZ4Yk0m8/j5rRJw/F5Gq6++TxuLpmqvnMiIqkmrVfeAGoqg2xbs4NYJIo3w0ssEqW8uBw75oAn2dG1jso9Vbz16HwiofrndhzbIVwT5q1H53P2DWcmNKY1S9Yx//GF7C3by8jJwzn+/OOabN3QmuaVruAvm99g/ya5iBNjVsE4ruwzHbORrXPS8RmGwbdOmsC5k8eydnsJXo+bIT3zMU3NCUld35k+kfKqIO+sWI/bNAAD27G5+pSjGDeod7LDExGRZjKa2lLRRvoDG0pLK+Meok6kaCTKz6fewq5NxcQisQOPe/weRhw7jJ8/+aMkRtd6lr/9GX+++q/U7K2JOz5gTD9++9atLXpt27Z582/zeOX+OezesYec/BxmXTmd074/C5e74be9juPwj188zoKn3iMSiuDYDr4MLx6/h1tf/mWbN0X/bO8mblvzDCGn/tY4n+nh7O5HcXGvKYf0OgUFORQX722LEEWSRvO649pVXsmKTTvwul2MH9SHDF8H+XbyEGheS0ekeZ36TNMgLy8bYACw8ZCva6uAUsEnbyynbNvueokbQCQY4csPAmz+oihJkbUur98LcYug7xvP9Lb4tR/+8T94+nfPUbZ9N47jUFFcwYv3vsK9l98f96zF8rc/Z+HT7xOuCePsS95DNWEq91Txx+/c3+I4DtUT295rkLgBhOwIL+5cRMSOxblKRCS1deuczYljBjN5xIC0StxERDqatE7elr65nFB1KO5YJBThi/e+THBEbWPopMEYRvy/al+ml2kXH9+i1922djsfvPgxoepwvcfDNWG+eO9L1i5d3+Ca1x9+K/6fuQPFRSUUfbmlRbEcqnXVOxsdc3AoiaTf+T8RERERSQ1pnbxVNLHc7DgOe8s6xnK02+Pmynsuw5tRf4XN6/dQOKwPx84+qkWvu/SN5dgxO+5YqCbEx68safB42bayJuJ0saeNWxhkueK3hgCIOnaT4yIiIiIiyZTWyZsrTgWuusw4Z7ZS1VGnT+SXz/6U0VNHktUli4K++Zx9w2xufvHnuL0tq1vj2E5TuzHjJnYDxvTHdMWfdpFQlF5tfObt1IJxeI2Gv6+JwYjs3nRyJ6ZoioiIiIhIc6V1tcn8Po33uDFdJp3ychIYTdsbOnEwv3j6J632ekecOJoX/vASsWjDc2L+TB8TTmlYhvq0753ColeXEq6pv9XS7XMz6vgR5PVq275Ds7tP4qPyNWyqKSZo11bf9BpuMlxeru/3jTZ9bxERERGRw5HWK2/HffMYvBnxD2673C4mzFJj3qb0HVnI2Gmj9hVE+YrH56H/6H4MP9aKe821f/4uvkwf/mw/3gwv3gwvQ8YP4vsPXNXmMXtNN3dal/CDfrMYk9OPYVm9uLjXZP468iq6+7q0+fuLiIiIiLRUWq+8DR4/kPEnj+OTN5fXK7rhy/Ry6tUnk9uzaxKjSw0/eOgaXvzDy7z+t7mEa8K4PW6mXXI8F9x0DkYjPdOOOn0iR5w0huVvf07VnioGHzmQviMLExaz23AxNXckU3NHJuw9RUREREQOV1r3eYPaPmXzH1/Iaw+8wZ6d5XTrV8DsH53OUadPSHZoKcW2bYKVQfxZ/kbPtHUk6q8iHZHmtXREmtfSEWlep76W9nlL65U3ANM0OenSqZx06dRkh5LSTNMks5OKfYiIiIiItJWOv0QiIiIiIiLSASh5ExERERERSQFK3vaxYzbVFdWNNp0WERERERFJprQ/8xYJRXjm/73A2/98h0g4isfnYfq3p3Hez89qcfNqERERERGR1pbW2YnjONx96Z8IfLyacLC2YXMsEuPNR+ZStHILP3/yR0mOUEREREREpFZab5tcs2QdqxevPZC47RcORlj1UYB1yzYkKTIREREREZH60jp5Wzb3M8I14bhj4WCEZXM/TXBEIiIiIiIi8aV18maaBhjxxwzDSItm0yIiIiIikhoOOzuxLGucZVmh1ggm0SbMOhKPzxN3zO11M+HkcQmOSEREREREJL7DSt4sy8oE7gO8rRNOYg0Y049xM8bizagfvjfDy4RTxtF3ZGGSIhMREREREanvcFfe/gDc2xqBJMsPHryac244ky7dOmMYBl17dOG8G8/ie/dfmezQREREREREDjAcx2nRhZZlnQGcHQgELrMsywkEAo2cHmugP6AyjiIiIiIiku4GABsP9ckH7fNmWda5NFxdWwV0AqY3J7K6Sksrse2WJY4iyVZQkENx8d5khyHSqjSvpSPSvJaOSPM69ZmmQV5edrOvO2jyFggEngWerfuYZVlXAL8AFlqWtf+x5cCUQCCgmSQiIiIiItLKDpq8xRMIBB4BHtn/875tk0e0WlQiIiIiIiJSjxqZiYiIiIiIpIBWSd6aUaxEREREREREWkArbyIiIiIiIilAyZuIiIiIiEgKUPImIiIiIiKSApS8iYiIiIiIpIAWtQoQkY5pb7SGF3cuYl7pCqJOjPGdB3JBz+Po6eua7NBERERE0p6SNxEBoCJazQ9XPsqeaBURJwbAvNIVvL87wJ3WJQzM7J7kCEVERETSm7ZNiggAT2//gN11EjcAG4caO8x9m19PYmQiIiIiAkreRGSf+WVfEK2TuNW1rnon5dHqBEckIiIiInUpeRMRAMJ2tNExE6PJcRERERFpe0reRASAUdmFGI2MZbl85HlyEhqPiIiIiNSn5E1EALik9/F4zYY1jHymm8t6T8U0GkvtRERERCQRlLyJCACDM3tw2+Dz6OHtgs9w4zc95Lj8XNVnBjPyxyQ7PBEREZG0p1YBInLAmJx+PDLqGraHdhN1bHr7c3EZ+o5HREREpD1Q8iYi9RiGQS9/brLDEBEREZGv0VfqIiIiIiIiKUDJm4iIiIiISApQ8iYiIiIiIpIClLyJiIiIiIikACVvIiIiIiIiKUDJm4iIiIiISApQqwAREREREel4nBhuPsOgnBjDsI0eyY7osCl5ExGRRlVUB1myZgsx2+aIgb0p6JyV7JBEREQOyu0sI8f+GRAEDAwihJlMpfm/YPiTHV6LKXkTEZG4nlywjMcXLMdtmoBD1HaYPnYwPzx9MqZpJDs8ERGRuExnK53sH2BQU+9xL++Rbd9CpevOJEV2+HTmTUREGliwYj1PLvyUSDRGTThCTThKJBpj3mfrePrdT5MdnoiISKP89hNAtMHjBmG8vIvp7Ex8UK1EyZuIiDTw2PxPCEUa/sMXikR57v3PiNl2EqISERE5OA/LMIjEHXPw4mJ1giNqPUreRESkga2lFY2OhaIx9taEEhiNiIjIobPp2uSoQ5eExdLalLyJiEgDORneJsczvZ4ERSIiItI8QfM8HDLijjlkE2VkgiNqPUreRESkgdMmjcDrdjV43O0ymTKiP16P6l2JiEj7FOF4QpyAXSeBc/DgkEGleQcYqZsC6V9fERFp4PzJY1i2bivrdpQSDNeeffN73eTlZHLtqcckOToREZEmGAZV5u2EeQ+//SwGu4kwnpB5Qcr3elPyJiIiDXg9bu76zjdYtLqItz9dSzRmM2Vkf6aMHBh3RU5ERKRdMQwiTCHimpLsSFqVkjcREYnLZZocM6wfxwzrl+xQREREBCVvIpJmguEIC1asp6i4nJ65OUwdPYgsf9PFOURERETaAyVvIpI2vizaxS//NQfbdghGovg8bh56/WNuu2gG4wb1TnZ4IiIiIk1K3VIrIiLNEIpEuenfr1MdihDc13w6FIkSjES57Ym31LdMRERE2j0lbyKSFt5fuRHbtuOOOTjM+3RtgiMSERERaR4lbyKSFrbv3kvNvpL3XxeKxCgq2ZPgiERERESaR8mbiKSFnl1zyPDGP+br87gozO+S4IhEREREmkfJm4ikheNG9Mc049/yDAxOHDs4wRGJiIiINI+SNxFJCz6Pm99degpZPg/+fStwfo8bv8fNry+eSU6GL8kRioiItBOOjcdZSHbsp+TEvo/PfhacqmRHJahVgIikkWGF3XjihotY+MUGtpTsoUfXTpwwaqD6vImIiOznRMmxr8fDcgxqAPA4n5LpPEK5+S9so3uSA0xvSt5EJK34vR5mjhua7DBERETaJZ/zHG6WYRA88FhtEhcmy76Vva4HkxecaNukiIiIiIjUynCexqyTuO1nEMPDpxjO7iREJfspeRMREREREQAMGm+d4+BuclzanpI3EREREREBIMaQRscMbGx6JDAa+TolbyIiIiIiAkC1eQUO/gaPO/gJcjYYGUmISvZT8iYiIiIiIgBEjUlUGT/FwY9NFjaZOHgJcRLV5g+THV7aU7VJERERERE5IGSeRcg5GS8fgRMkaozDNnomOyxByZuIiIiIiHydkUmYE8FIdiBSl7ZNioiIiIiIpAAlbyIiIiIiIilAyZuIiIiIiEgKUPImIiIiIiKSApS8iYiIiIiIpAAlbyIiIiIiIilAyZuIiIiIiEgKUPImIiIiIiKSApS8iYiIiIiIpAAlbyIiIiIiIilAyZuIiIiIiEgKUPImIiIiIiKSApS8iYiIiIiIpAAlbyIiIiIiIilAyZuIiIiIiEgKUPImIiIiIiKSApS8iYiIiIiIpAAlbyIiIiIiIinAnYT3dAGYppGEtxZpPZrD0hFpXktHpHktHZHmdWqr8/fnas51huM4rR9N0yYD7yb6TUVERERERNqZKcB7h/rkZCRvPmAisB2IJfrNRUREREREkswF9AQWA6FDvSgZyZuIiIiIiIg0kwqWiIiIiIiIpAAlbyIiIiIiIilAyZuIiIiIiEgKUPImIiIiIiKSApS8iYiIiIiIpAAlbyIiIiIiIilAyZuIiIiIiEgKcCc7AJFUY1nWRcDNgBe4NxAI3J/kkEQOmWVZtwLn7fvx1UAg8DPLsqYD9wAZwNOBQODmfc89AngY6AwsBK4JBALRJIQtclCWZd0FFAQCgW83Nncty+oLPAZ0AwLAxYFAoDJpQYs0wbKs04HbgCzgjUAgcJ3u16KVN5FmsCyrN/BbYDIwFrjKsqwRyY1K5NDs+0d/JjAOOAIYb1nWhcDfgTOB4cBEy7Jm7bvkMeAHgUBgKGAAVyY+apGDsyzrJODbdR5qbO7+BfhLIBAYBiwBfpXIOEUOlWVZA4EHqb03jwaO3Hdv1v06zSl5E2me6cC8QCBQFggEqoDngHOSHJPIodoO/CQQCIQDgUAE+BIYCqwJBAIb9n1L+xhwrmVZ/YCMQCDw0b5r/wGcm4ygRZpiWVYutV+q/W7fz3HnrmVZHuB4au/bBx5PaLAih+4salfWtuy7X58PVKP7ddrTtkmR5ulF7Qfg/bYDk5IUi0izBAKBL/b/t2VZQ6j9MPAnGs7pPsSf630SEKZIc/0VuAko3PdzY3M3H6ios5VMc1ras8FA2LKsN4AewMvAF+h+nfa08ibSPEacx+yERyFyGCzLGgm8BfwUWBfnKTaa65ICLMu6AigKBAJv13m4sbmrOS2pxE3tbp9LgKOp/aJ4QJznaW6nGa28iTTPVmBKnZ97AtuSFItIs1mWdRzwPHB9IBB4yrKsE6j9Vne//XN6ayOPi7Qn5wM9LctaDuQC2YBD/LlbDHSyLMsVCARiaE5L+7YDmBsIBIoBLMv6D7VbIWN1nqP7dRrSyptI88wFTrIsq8CyrEzgbOD1JMckckgsyyoE/gNcFAgEntr38Me1Q9Zgy7JcwEXAnEAgsAkI7kv2AL4FzEl40CJNCAQCMwKBwKhAIHAEcAvwUiAQuJw4c3ffuaF3qU34Djye8KBFDs0rwMmWZXXZd2+eRe15Td2v05xW3kSaIRAIbLUs6yZgPrWtAh4JBAKLkhyWyKH6KeAH7rEsa/9jD1Jbpe/5fWOv8VVBh4uBhy3LygGWUXs+TiQVNDZ3vwf807Ksm4HNwIVJik+kSYFA4GPLsu4E3gM81G51fwBYhe7Xac1wHCfZMYiIiIiIiMhBaNukiIiIiIhIClDyJiIiIiIikgKUvImIiIiIiKQAJW8iIiIiIiIpQMmbiIiIiIhIClDyJiIiIiIikgKUvImIiIiIiKQAJW8iIiIiIiIp4P8DYcxNttv8apAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [15.0, 10.0])\n",
    "plt.scatter(principalDff.iloc[:, 0], principalDff.iloc[:, 2], c = y_kmeansf, s = 50, cmap = 'viridis')\n",
    "centers = kmeansf.cluster_centers_\n",
    "plt.scatter(centers[:, 0], centers[:, 2], c = 'black', s = 200, alpha = 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "cl = pd.read_csv(\"New_Bechdel_Class_Labels.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "cl[\"All_param_cluster_label\"] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "cl[\"Female_param_cluster_label\"] = kmeansf.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "cl.to_csv(\"New_Bechdel_Class_Labels.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Normalising"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# n_df = pd.read_csv(\"Normalised_New_Bechdel_Class_Labels.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# n = n_df.drop([\"Year\", \"New ROI\", \"Movie_Title\", \"Female_Male_Dialogues_Ratio\", \"IMDB_Rating\", \"Bechdel_Scores\",\n",
    "#               \"labels\", \"All_param_cluster_label\", \"Female_param_cluster_label\"],\n",
    "#                 axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# n.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# cols = n.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# for it in cols:\n",
    "#     n[\"N_\"+it] = (n[it] - n[it].min())/(n[it].max() - n[it].min())\n",
    "#     n.drop([it], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# d = pd.DataFrame(data = n_df, columns = [\"Year\", \"New ROI\", \"Movie_Title\", \"Female_Male_Dialogues_Ratio\", \n",
    "#                                       \"IMDB_Rating\", \"Bechdel_Scores\", \"labels\", \"All_param_cluster_label\",\n",
    "#                                       \"Female_param_cluster_label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# n_df = pd.concat([n, d], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# n_df.to_csv(\"Normalised_New_Bechdel_Class_Labels.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## PCA and Clustering on Normalised Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "n_df = pd.read_csv(\"Normalised_New_Bechdel_Class_Labels.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df = n_df.drop([\"Year\", \"New ROI\", \"Movie_Title\", \"IMDB_Rating\", \"Bechdel_Scores\",\n",
    "              \"labels\", \"All_param_cluster_label\", \"Female_param_cluster_label\"],\n",
    "                axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 324 entries, 0 to 323\n",
      "Data columns (total 20 columns):\n",
      "N_Genre1_Tag_Female         324 non-null float64\n",
      "N_Genre1_Tag_Male           324 non-null float64\n",
      "N_Genre2_Tag_Female         324 non-null float64\n",
      "N_Genre2_Tag_Male           324 non-null float64\n",
      "N_Genre3_Tag_Female         324 non-null float64\n",
      "N_Genre3_Tag_Male           324 non-null float64\n",
      "N_#_of_Female_Cast          324 non-null float64\n",
      "N_#_of_Male_Cast            324 non-null float64\n",
      "N_#_of_Female_Dialogue      324 non-null float64\n",
      "N_#_of_Male_Dialogue        324 non-null float64\n",
      "N_#_of_Female_Crew          324 non-null float64\n",
      "N_#_of_Male_Crew            324 non-null float64\n",
      "N_Sentiment_Female          324 non-null float64\n",
      "N_Sentiment_Male            324 non-null float64\n",
      "N_noun_count_male           324 non-null float64\n",
      "N_noun_count_female         324 non-null float64\n",
      "N_verb_count_male           324 non-null float64\n",
      "N_verb_count_female         324 non-null float64\n",
      "N_adjective_count_male      324 non-null float64\n",
      "N_adjective_count_female    324 non-null float64\n",
      "dtypes: float64(20)\n",
      "memory usage: 50.7 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "female_df = df.drop([\"N_#_of_Male_Crew\", \"N_#_of_Male_Cast\", \"N_#_of_Male_Dialogue\", \"N_Genre3_Tag_Male\", \n",
    "                        \"N_Genre2_Tag_Male\", \"N_Genre1_Tag_Male\", \"N_Sentiment_Male\", \"N_adjective_count_male\",\n",
    "                        \"N_noun_count_male\", \"N_verb_count_male\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 324 entries, 0 to 323\n",
      "Data columns (total 10 columns):\n",
      "N_Genre1_Tag_Female         324 non-null float64\n",
      "N_Genre2_Tag_Female         324 non-null float64\n",
      "N_Genre3_Tag_Female         324 non-null float64\n",
      "N_#_of_Female_Cast          324 non-null float64\n",
      "N_#_of_Female_Dialogue      324 non-null float64\n",
      "N_#_of_Female_Crew          324 non-null float64\n",
      "N_Sentiment_Female          324 non-null float64\n",
      "N_noun_count_female         324 non-null float64\n",
      "N_verb_count_female         324 non-null float64\n",
      "N_adjective_count_female    324 non-null float64\n",
      "dtypes: float64(10)\n",
      "memory usage: 25.4 KB\n"
     ]
    }
   ],
   "source": [
    "female_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pcaf = PCA(n_components = 3, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "principalComponentsf = pcaf.fit_transform(female_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "principalDff = pd.DataFrame(data = principalComponentsf, \n",
    "                           columns = ['PC1', 'PC2', 'PC3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.295529</td>\n",
       "      <td>-0.052132</td>\n",
       "      <td>0.082297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.082149</td>\n",
       "      <td>0.054233</td>\n",
       "      <td>-0.347332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.264626</td>\n",
       "      <td>-0.225641</td>\n",
       "      <td>-0.072794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.041754</td>\n",
       "      <td>0.096580</td>\n",
       "      <td>0.142124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.198120</td>\n",
       "      <td>-0.122346</td>\n",
       "      <td>-0.170241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       PC3\n",
       "0 -0.295529 -0.052132  0.082297\n",
       "1  0.082149  0.054233 -0.347332\n",
       "2  0.264626 -0.225641 -0.072794\n",
       "3  0.041754  0.096580  0.142124\n",
       "4  0.198120 -0.122346 -0.170241"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principalDff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.32957382, 0.18297113, 0.1143717 ])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcaf.explained_variance_ratio_ ## The amount of variance explained by each of the selected components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.06422003,  0.36987552,  0.88786603, -0.07307202, -0.07766879,\n",
       "         0.21446709, -0.05601128, -0.04476067, -0.07238809, -0.05488986],\n",
       "       [-0.19735979, -0.20203668, -0.11824754, -0.55034585, -0.34054705,\n",
       "         0.25596609, -0.13437792, -0.30174879, -0.50379487, -0.24281124],\n",
       "       [-0.10751776, -0.24697803, -0.04989859,  0.10473583,  0.10383742,\n",
       "         0.85140243, -0.21474753,  0.1833713 ,  0.27774172,  0.1463968 ]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcaf.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "val = pcaf.components_.mean(axis = 0)\n",
    "cols = female_df.columns\n",
    "weights = {}\n",
    "\n",
    "for vals, col in zip(val, cols):\n",
    "    weights[col] = vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('N_#_of_Female_Cast', -0.17289401426094342),\n",
       " ('N_Sentiment_Female', -0.1350455742399014),\n",
       " ('N_#_of_Female_Dialogue', -0.10479280678955767),\n",
       " ('N_verb_count_female', -0.09948041270223752),\n",
       " ('N_Genre1_Tag_Female', -0.08021917630894665),\n",
       " ('N_noun_count_female', -0.05437938840747098),\n",
       " ('N_adjective_count_female', -0.050434763614638466),\n",
       " ('N_Genre2_Tag_Female', -0.026379727191435016),\n",
       " ('N_Genre3_Tag_Female', 0.239906633549728),\n",
       " ('N_#_of_Female_Crew', 0.4406118686985923)]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_d = sorted(weights.items(), key=operator.itemgetter(1))\n",
    "sorted_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "## For only female chars\n",
    "kmeansf = KMeans(n_clusters = 4, random_state = 42).fit(principalDff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 2, 1, 2, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 2, 2, 1, 1, 1,\n",
       "       2, 1, 0, 3, 1, 0, 0, 1, 1, 0, 2, 1, 0, 0, 1, 0, 2, 0, 2, 1, 0, 1,\n",
       "       1, 2, 0, 0, 1, 2, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 0, 1, 0, 3, 1, 2, 1, 2, 2, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 0, 0, 1, 1, 2, 3, 0, 3, 0, 0, 0, 0, 1, 2, 1, 3, 2, 0, 3,\n",
       "       1, 1, 3, 1, 1, 1, 0, 0, 1, 1, 1, 1, 2, 1, 1, 0, 3, 0, 0, 1, 2, 1,\n",
       "       1, 0, 1, 2, 0, 1, 1, 0, 1, 0, 1, 2, 1, 3, 0, 1, 0, 2, 2, 1, 3, 1,\n",
       "       0, 1, 1, 0, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1, 2, 1, 2, 0, 1, 0, 1, 1,\n",
       "       3, 0, 0, 2, 3, 0, 1, 0, 2, 1, 0, 0, 3, 0, 2, 0, 1, 3, 0, 0, 2, 0,\n",
       "       3, 1, 0, 1, 0, 0, 2, 1, 3, 3, 2, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 2, 1, 0, 0, 0, 1, 2, 2, 0, 1, 1, 0, 3, 0, 1, 0, 0, 0, 1,\n",
       "       2, 3, 0, 1, 0, 2, 2, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 2, 1, 0, 2,\n",
       "       3, 1, 1, 3, 0, 1, 1, 1, 1, 1, 2, 1, 3, 1, 0, 3, 1, 1, 1, 2, 1, 1,\n",
       "       1, 0, 2, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 2, 1, 2, 1, 0, 1, 1,\n",
       "       0, 1, 2, 1, 0, 1, 2, 2, 1, 1, 1, 0, 1, 2, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeansf.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_kmeansf = kmeansf.predict(principalDff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [15.0, 10.0])\n",
    "plt.scatter(principalDff.iloc[:, 0], principalDff.iloc[:, 1], c = y_kmeansf, s = 50, cmap = 'viridis')\n",
    "centers = kmeansf.cluster_centers_\n",
    "plt.scatter(centers[:, 0], centers[:, 1], c = 'black', s = 200, alpha = 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "n_df[\"Female_param_cluster_label\"] = kmeansf.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "n_df.to_csv(\"Normalised_New_Bechdel_Class_Labels.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering without PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.read_csv(\"Good_Book.csv\")\n",
    "new_df = new_df.drop([\"Year\", \"New ROI\", \"Movie_Title\", \"IMDB_Rating\", \"Female_Male_Dialogues_Ratio\"],\n",
    "                axis = 1)\n",
    "female_df = new_df.drop([\"#_of_Male_Crew\", \"#_of_Male_Cast\", \"#_of_Male_Dialogue\", \"Genre3_Tag_Male\", \n",
    "                        \"Genre2_Tag_Male\", \"Genre1_Tag_Male\", \"Sentiment_Male\", \"adjective_count_male\",\n",
    "                        \"noun_count_male\", \"verb_count_male\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[231.90356234,  41.01386458, 129.55418806, 454.28749871],\n",
       "       [ 46.66680224, 318.99349696, 148.65231661, 732.28583481],\n",
       "       [ 46.71149925, 227.01322641,  56.97987142, 640.27044596],\n",
       "       ...,\n",
       "       [ 64.56301257, 332.17321046, 163.06068925, 744.92828538],\n",
       "       [  6.39739872, 274.95998378, 104.62400917, 688.26838179],\n",
       "       [ 57.90744964, 214.92782948,  44.55219288, 628.27758144]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## For only female chars\n",
    "kmeansf = KMeans(n_clusters = 4, random_state = 42)\n",
    "kmeansf.fit_transform(female_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_kmeansf = kmeansf.predict(female_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-573187.6256993541"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeansf.score(female_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 0, 0, 2, 2, 2, 1, 1,\n",
       "       0, 2, 0, 0, 2, 2, 0, 1, 0, 2, 2, 1, 0, 1, 2, 0, 0, 1, 0, 2, 3, 0,\n",
       "       0, 0, 2, 2, 0, 0, 1, 2, 0, 1, 2, 1, 1, 0, 0, 0, 2, 1, 1, 2, 1, 0,\n",
       "       0, 1, 2, 0, 1, 2, 0, 2, 0, 0, 2, 2, 2, 2, 0, 0, 1, 2, 0, 0, 0, 2,\n",
       "       1, 0, 0, 2, 2, 2, 1, 0, 2, 2, 0, 2, 2, 1, 0, 2, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 2, 2, 2, 2, 0, 2, 0, 0, 1, 2, 1, 3, 0, 3, 2, 0, 0, 1,\n",
       "       2, 0, 2, 2, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 1, 2, 0, 0, 2, 0, 2,\n",
       "       1, 2, 2, 0, 1, 1, 0, 0, 0, 1, 2, 1, 2, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 2, 2, 0, 0, 1, 0, 0, 0, 0, 2, 2, 2,\n",
       "       0, 0, 2, 0, 0, 2, 0, 2, 0, 0, 0, 2, 0, 0, 2, 3, 1, 1, 2, 2, 2, 2,\n",
       "       0, 2, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 0, 2, 0, 0, 0, 1, 2, 1, 0, 0,\n",
       "       1, 0, 0, 0, 2, 0, 0, 1, 0, 2, 1, 2, 0, 1, 2, 2, 2, 1, 0, 1, 0, 2,\n",
       "       0, 0, 0, 0, 1, 0, 0, 2, 0, 2, 0, 0, 0, 1, 0, 0, 1, 1, 2, 1, 2, 1,\n",
       "       0, 1, 1, 0, 2, 0, 1, 0, 1, 2, 2, 0, 2, 2, 0, 2, 0, 0, 2, 0, 0, 0,\n",
       "       2, 2, 2, 0, 0, 1, 1, 0, 2, 0, 0, 2, 2, 0, 0, 2], dtype=int32)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeansf.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_df = pd.read_csv(\"New_Bechdel_Class_Labels.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_df[\"Direct_Cluster_FV\"] = kmeansf.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_df.to_csv(\"New_Bechdel_Class_Labels.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Keyser's Test and Bartlett's Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "## The Kaiser-Meyer-Olkin Measure of Sampling Adequacy is a statistic that indicates the proportion of variance in \n",
    "## your variables that might be caused by underlying factors.\n",
    "## High values (close to 1.0) generally indicate that a factor analysis may be useful with your data. \n",
    "## If the value is less than 0.50, the results of the factor analysis probably won't be very useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "new_df = pd.read_csv(\"Good_Book.csv\")\n",
    "new_df = new_df.drop([\"Year\", \"New ROI\", \"Movie_Title\", \"IMDB_Rating\", \"Female_Male_Dialogues_Ratio\"],\n",
    "                axis = 1)\n",
    "female_df = new_df.drop([\"#_of_Male_Crew\", \"#_of_Male_Cast\", \"#_of_Male_Dialogue\", \"Genre3_Tag_Male\", \n",
    "                        \"Genre2_Tag_Male\", \"Genre1_Tag_Male\", \"Sentiment_Male\", \"adjective_count_male\",\n",
    "                        \"noun_count_male\", \"verb_count_male\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 324 entries, 0 to 323\n",
      "Data columns (total 10 columns):\n",
      "Genre1_Tag_Female         324 non-null float64\n",
      "Genre2_Tag_Female         324 non-null float64\n",
      "Genre3_Tag_Female         324 non-null float64\n",
      "#_of_Female_Cast          324 non-null int64\n",
      "#_of_Female_Dialogue      324 non-null int64\n",
      "#_of_Female_Crew          324 non-null int64\n",
      "Sentiment_Female          324 non-null float64\n",
      "noun_count_female         324 non-null float64\n",
      "verb_count_female         324 non-null float64\n",
      "adjective_count_female    324 non-null float64\n",
      "dtypes: float64(7), int64(3)\n",
      "memory usage: 25.4 KB\n"
     ]
    }
   ],
   "source": [
    "female_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def KMO(df):\n",
    "    corr = df.corr().as_matrix() ## Correaltion matrix for the dataframe\n",
    "    invcor = np.linalg.inv(corr) ## Inverse matrix\n",
    "    d = np.linalg.inv(np.diag(np.sqrt(np.diag(invcor)))) ## Get the D 1/2 matrix\n",
    "    antiimage = np.matmul(np.matmul(d, invcor), d) ## Get the anti-image matrix\n",
    "    num = 0; den = 0\n",
    "\n",
    "    ## Calculate the KMO using formula\n",
    "    for rows in range(antiimage.shape[0]):\n",
    "        for cols in range(antiimage.shape[1]):\n",
    "            if rows != cols:\n",
    "                num += (corr[rows][cols] ** 2)\n",
    "                den += ((corr[rows][cols] ** 2) + (antiimage[rows][cols] ** 2))\n",
    "\n",
    "    kmo = num / den\n",
    "    return kmo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of the KMO Test:  0.69836676830795 :: Rounded of value:  0.7\n"
     ]
    }
   ],
   "source": [
    "kmo = KMO(female_df)\n",
    "print(\"Value of the KMO Test: \", kmo, \":: Rounded of value: \", round(kmo, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "## Bartlett's test of sphericity tests the hypothesis that your correlation matrix is an identity matrix, \n",
    "## which would indicate that your variables are unrelated and therefore unsuitable for structure detection. \n",
    "## Small values (less than 0.05) of the significance level indicate that a factor analysis may be useful with your data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import bartlett"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Statistic:  inf :: P-value:  0.0\n"
     ]
    }
   ],
   "source": [
    "k, pval = bartlett(*female_df.as_matrix()[:, 0:10])\n",
    "print(\"Test Statistic: \", k, \":: P-value: \", pval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def BToS(X, y = None):\n",
    "        \"\"\"\n",
    "        Bartlett's test of sphericity tests the hypothesis that your correlation matrix is an identity matrix, \n",
    "        which would indicate that your variables are unrelated and therefore unsuitable for structure detection. \n",
    "        Small values (less than 0.05) of the significance level indicate that a factor analysis may be useful with your data.\n",
    "        \"\"\"\n",
    "        n = X.shape[0]\n",
    "        p = X.shape[1]\n",
    "        corr = X.corr()\n",
    "        lhs = (n - 1) - (2 * p - 5) / 6 ## Formula of the first equation\n",
    "        rhs = np.log(np.linalg.det(corr)) ## Formula of the second equation\n",
    "        chi_square = -(lhs * rhs) ## Chi-Square value from this\n",
    "        degree_of_freedom = (p ** 2 - p) / 2 ## Degrees of freedom\n",
    "\n",
    "        return chi_square, degree_of_freedom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1208.6388464991983, 45.0)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BToS(female_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Varimax Rotation on dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "loadings = pcaf.components_.T * np.sqrt(pcaf.explained_variance_ratio_)\n",
    "loadings = pd.DataFrame(loadings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def varimax(loadings, normalize=True, max_iter=500, tolerance=1e-5):\n",
    "        df = loadings.copy()\n",
    "        column_names = df.index.values\n",
    "        index_names = df.columns.values\n",
    "        n_rows, n_cols = df.shape\n",
    "        if n_cols < 2:\n",
    "            return df\n",
    "        X = df.values\n",
    "        if normalize:\n",
    "            normalized_mtx = df.apply(lambda x: np.sqrt(sum(x**2)),\n",
    "                                      axis=1).values\n",
    "            X = (X.T / normalized_mtx).T\n",
    "        rotation_mtx = np.eye(n_cols)\n",
    "\n",
    "        d = 0\n",
    "        for _ in range(max_iter):\n",
    "            old_d = d\n",
    "            basis = np.dot(X, rotation_mtx)\n",
    "            transformed = np.dot(X.T, basis**3 - (1.0 / n_rows) *\n",
    "                                 np.dot(basis, np.diag(np.diag(np.dot(basis.T, basis)))))\n",
    "            U, S, V = np.linalg.svd(transformed)\n",
    "            rotation_mtx = np.dot(U, V)\n",
    "            d = np.sum(S)\n",
    "            if old_d != 0 and d / old_d < 1 + tolerance:\n",
    "                break\n",
    "\n",
    "        X = np.dot(X, rotation_mtx)\n",
    "\n",
    "        if normalize:\n",
    "            X = X.T * normalized_mtx\n",
    "        else:\n",
    "            X = X.T\n",
    "        loadings = pd.DataFrame(X, columns=column_names, index=index_names).T\n",
    "\n",
    "        return loadings, rotation_mtx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "rotated_loading, rotationMtx = varimax(loadings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.846130\n",
       "1   -0.905585\n",
       "2    0.170904\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotated_loading.sum(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.036868</td>\n",
       "      <td>-0.084421</td>\n",
       "      <td>-0.036361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.212340</td>\n",
       "      <td>-0.086421</td>\n",
       "      <td>-0.083525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.509711</td>\n",
       "      <td>-0.050581</td>\n",
       "      <td>-0.016875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.041950</td>\n",
       "      <td>-0.235411</td>\n",
       "      <td>0.035420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.044589</td>\n",
       "      <td>-0.145669</td>\n",
       "      <td>0.035117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.123122</td>\n",
       "      <td>0.109490</td>\n",
       "      <td>0.287935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.032155</td>\n",
       "      <td>-0.057480</td>\n",
       "      <td>-0.072625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.025696</td>\n",
       "      <td>-0.129073</td>\n",
       "      <td>0.062014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.041557</td>\n",
       "      <td>-0.215499</td>\n",
       "      <td>0.093929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.031511</td>\n",
       "      <td>-0.103863</td>\n",
       "      <td>0.049510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "          0         1         2\n",
       "0  0.036868 -0.084421 -0.036361\n",
       "1  0.212340 -0.086421 -0.083525\n",
       "2  0.509711 -0.050581 -0.016875\n",
       "3 -0.041950 -0.235411  0.035420\n",
       "4 -0.044589 -0.145669  0.035117\n",
       "5  0.123122  0.109490  0.287935\n",
       "6 -0.032155 -0.057480 -0.072625\n",
       "7 -0.025696 -0.129073  0.062014\n",
       "8 -0.041557 -0.215499  0.093929\n",
       "9 -0.031511 -0.103863  0.049510"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loadings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
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