IS 204 analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "from scipy.stats import norm\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from scipy.stats import norm\n",
    "from scipy.stats import t, f\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy.stats import chi2\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Research Questions\n",
    "1) To what extent does a strong relationship exist between tempo and genre of music?\n",
    "\n",
    "2) What is likely one of the best combinations of independent variables for predicting genre?\n",
    "\n",
    "3) Does a classification model better at pedicting the genre with all independent variables or the selected ideal combination based on question 2 \n",
    "\n",
    "Question 1: bar graph, box plot, Anova Testing\n",
    "\n",
    "Question 2: correlation table: selecting top 5 correlated variables and running either backwards or forwards selection to figure out a parsimonous model/ideal cmombination for predicting genre\n",
    "\n",
    "Question 3: Pick a classification algorithm and figure out whether the parsimonious model combo or all variables are better for selecting genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  1.15852525e+00  1.36740213e+00  6.27975538e+00 -4.95187180e+00\n",
      " -5.37403357e+00  1.06751040e+00  2.50981117e+00 -1.53382783e+00\n",
      " -8.49939175e-01 -3.96305910e+00 -4.32417963e-02 -3.71884995e+00\n",
      " -4.45212369e+00  6.37623836e-01 -2.74136767e+00  5.05645234e-01\n",
      " -6.64660924e-02 -8.44580667e+00 -5.86026399e-02  2.62213686e+00\n",
      " -1.69635106e+00 -4.35541093e-01 -8.20097696e-01 -3.27188237e+00\n",
      "  5.28086246e-01 -2.48170811e+00 -1.02820853e+00 -1.80709312e+00\n",
      " -1.46741902e+00 -3.11545039e+00 -2.10700255e+00 -4.25919321e-01\n",
      " -3.86366331e+00 -4.72846574e+00 -3.20544575e+00  7.33369607e-01\n",
      " -1.88220751e+00 -1.45169930e+00 -3.14006228e+00 -1.86731784e+00\n",
      " -2.29358577e+00 -2.36418988e+00  2.25776738e+00 -1.18923805e+00\n",
      " -9.09181102e-01 -6.17380910e-01 -2.80267695e+00  2.25196541e+00\n",
      " -5.41697675e-01 -2.62742751e+00 -8.15287338e+00 -1.17059912e+00\n",
      " -1.97684159e+00  2.93630385e+00  3.08322104e-01 -4.29863670e+00\n",
      " -5.82928313e+00 -5.05583772e+00 -5.33255874e+00 -1.35253642e+00\n",
      " -2.21343789e+00 -1.40398684e+00  1.67448082e+00 -3.70055873e-01\n",
      " -2.79238782e-01 -1.63200688e-01  7.54422366e+00 -4.78767685e+00\n",
      " -3.39514817e+00 -4.22099848e+00]\n",
      "['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n"
     ]
    }
   ],
   "source": [
    "#determined no null values\n",
    "for i in df.columns:\n",
    "    print(df[i].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['filename', 'tempo', 'beats', 'chroma_stft', 'rmse',\n",
       "       'spectral_centroid', 'spectral_bandwidth', 'rolloff',\n",
       "       'zero_crossing_rate', 'mfcc1', 'mfcc2', 'mfcc3', 'mfcc4', 'mfcc5',\n",
       "       'mfcc6', 'mfcc7', 'mfcc8', 'mfcc9', 'mfcc10', 'mfcc11', 'mfcc12',\n",
       "       'mfcc13', 'mfcc14', 'mfcc15', 'mfcc16', 'mfcc17', 'mfcc18', 'mfcc19',\n",
       "       'mfcc20', 'label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>tempo</th>\n",
       "      <th>beats</th>\n",
       "      <th>chroma_stft</th>\n",
       "      <th>rmse</th>\n",
       "      <th>spectral_centroid</th>\n",
       "      <th>spectral_bandwidth</th>\n",
       "      <th>rolloff</th>\n",
       "      <th>zero_crossing_rate</th>\n",
       "      <th>mfcc1</th>\n",
       "      <th>...</th>\n",
       "      <th>mfcc12</th>\n",
       "      <th>mfcc13</th>\n",
       "      <th>mfcc14</th>\n",
       "      <th>mfcc15</th>\n",
       "      <th>mfcc16</th>\n",
       "      <th>mfcc17</th>\n",
       "      <th>mfcc18</th>\n",
       "      <th>mfcc19</th>\n",
       "      <th>mfcc20</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>blues.00081.au</td>\n",
       "      <td>103.359375</td>\n",
       "      <td>50</td>\n",
       "      <td>0.380260</td>\n",
       "      <td>0.248262</td>\n",
       "      <td>2116.942959</td>\n",
       "      <td>1956.611056</td>\n",
       "      <td>4196.107960</td>\n",
       "      <td>0.127272</td>\n",
       "      <td>-26.929785</td>\n",
       "      <td>...</td>\n",
       "      <td>14.336612</td>\n",
       "      <td>-13.821769</td>\n",
       "      <td>7.562789</td>\n",
       "      <td>-6.181372</td>\n",
       "      <td>0.330165</td>\n",
       "      <td>-6.829571</td>\n",
       "      <td>0.965922</td>\n",
       "      <td>-7.570825</td>\n",
       "      <td>2.918987</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>blues.00022.au</td>\n",
       "      <td>95.703125</td>\n",
       "      <td>44</td>\n",
       "      <td>0.306451</td>\n",
       "      <td>0.113475</td>\n",
       "      <td>1156.070496</td>\n",
       "      <td>1497.668176</td>\n",
       "      <td>2170.053545</td>\n",
       "      <td>0.058613</td>\n",
       "      <td>-233.860772</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.250578</td>\n",
       "      <td>3.959198</td>\n",
       "      <td>5.322555</td>\n",
       "      <td>0.812028</td>\n",
       "      <td>-1.107202</td>\n",
       "      <td>-4.556555</td>\n",
       "      <td>-2.436490</td>\n",
       "      <td>3.316913</td>\n",
       "      <td>-0.608485</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blues.00031.au</td>\n",
       "      <td>151.999081</td>\n",
       "      <td>75</td>\n",
       "      <td>0.253487</td>\n",
       "      <td>0.151571</td>\n",
       "      <td>1331.073970</td>\n",
       "      <td>1973.643437</td>\n",
       "      <td>2900.174130</td>\n",
       "      <td>0.042967</td>\n",
       "      <td>-221.802549</td>\n",
       "      <td>...</td>\n",
       "      <td>-13.037723</td>\n",
       "      <td>-12.652228</td>\n",
       "      <td>-1.821905</td>\n",
       "      <td>-7.260097</td>\n",
       "      <td>-6.660252</td>\n",
       "      <td>-14.682694</td>\n",
       "      <td>-11.719264</td>\n",
       "      <td>-11.025216</td>\n",
       "      <td>-13.387260</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>blues.00012.au</td>\n",
       "      <td>184.570312</td>\n",
       "      <td>91</td>\n",
       "      <td>0.269320</td>\n",
       "      <td>0.119072</td>\n",
       "      <td>1361.045467</td>\n",
       "      <td>1567.804596</td>\n",
       "      <td>2739.625101</td>\n",
       "      <td>0.069124</td>\n",
       "      <td>-207.208080</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.613248</td>\n",
       "      <td>0.384877</td>\n",
       "      <td>2.605128</td>\n",
       "      <td>-5.188924</td>\n",
       "      <td>-9.527455</td>\n",
       "      <td>-9.244394</td>\n",
       "      <td>-2.848274</td>\n",
       "      <td>-1.418707</td>\n",
       "      <td>-5.932607</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blues.00056.au</td>\n",
       "      <td>161.499023</td>\n",
       "      <td>74</td>\n",
       "      <td>0.391059</td>\n",
       "      <td>0.137728</td>\n",
       "      <td>1811.076084</td>\n",
       "      <td>2052.332563</td>\n",
       "      <td>3927.809582</td>\n",
       "      <td>0.075480</td>\n",
       "      <td>-145.434568</td>\n",
       "      <td>...</td>\n",
       "      <td>7.457218</td>\n",
       "      <td>-10.470444</td>\n",
       "      <td>-2.360483</td>\n",
       "      <td>-6.783623</td>\n",
       "      <td>2.671134</td>\n",
       "      <td>-4.760879</td>\n",
       "      <td>-0.949005</td>\n",
       "      <td>0.024832</td>\n",
       "      <td>-2.005315</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         filename       tempo  beats  chroma_stft      rmse  \\\n",
       "0  blues.00081.au  103.359375     50     0.380260  0.248262   \n",
       "1  blues.00022.au   95.703125     44     0.306451  0.113475   \n",
       "2  blues.00031.au  151.999081     75     0.253487  0.151571   \n",
       "3  blues.00012.au  184.570312     91     0.269320  0.119072   \n",
       "4  blues.00056.au  161.499023     74     0.391059  0.137728   \n",
       "\n",
       "   spectral_centroid  spectral_bandwidth      rolloff  zero_crossing_rate  \\\n",
       "0        2116.942959         1956.611056  4196.107960            0.127272   \n",
       "1        1156.070496         1497.668176  2170.053545            0.058613   \n",
       "2        1331.073970         1973.643437  2900.174130            0.042967   \n",
       "3        1361.045467         1567.804596  2739.625101            0.069124   \n",
       "4        1811.076084         2052.332563  3927.809582            0.075480   \n",
       "\n",
       "        mfcc1  ...     mfcc12     mfcc13    mfcc14    mfcc15    mfcc16  \\\n",
       "0  -26.929785  ...  14.336612 -13.821769  7.562789 -6.181372  0.330165   \n",
       "1 -233.860772  ...  -2.250578   3.959198  5.322555  0.812028 -1.107202   \n",
       "2 -221.802549  ... -13.037723 -12.652228 -1.821905 -7.260097 -6.660252   \n",
       "3 -207.208080  ...  -0.613248   0.384877  2.605128 -5.188924 -9.527455   \n",
       "4 -145.434568  ...   7.457218 -10.470444 -2.360483 -6.783623  2.671134   \n",
       "\n",
       "      mfcc17     mfcc18     mfcc19     mfcc20  label  \n",
       "0  -6.829571   0.965922  -7.570825   2.918987  blues  \n",
       "1  -4.556555  -2.436490   3.316913  -0.608485  blues  \n",
       "2 -14.682694 -11.719264 -11.025216 -13.387260  blues  \n",
       "3  -9.244394  -2.848274  -1.418707  -5.932607  blues  \n",
       "4  -4.760879  -0.949005   0.024832  -2.005315  blues  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1: To what extent does a strong relationship exist between tempo and genre of music?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Box plot of tempos for each genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = 'label', y = 'tempo', data = df)\n",
    "sns.set(rc = {'figure.figsize':(20,15)})\n",
    "plt.xticks(fontsize = 15)\n",
    "plt.yticks(fontsize = 15)\n",
    "plt.xticks(rotation = 30)\n",
    "plt.xlabel(\"Genres\", fontsize = 20)\n",
    "plt.ylabel(\"Tempo\", fontsize = 20)\n",
    "plt.title(\"Relationship Between Tempo and Genre\", fontsize = 25)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Bar Graph of Average Tempo for each genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "averages = []\n",
    "for i in df.label.unique():\n",
    "    averages.append(df.tempo[df.label == i].mean())\n",
    "\n",
    "sns.barplot(x = df.label.unique(), y = averages)\n",
    "sns.set(rc = {'figure.figsize':(20,15)})\n",
    "plt.xticks(fontsize = 15)\n",
    "plt.yticks(fontsize = 15)\n",
    "plt.xticks(rotation = 30)\n",
    "plt.title('Average Tempos For Various Genres', fontsize = 20)\n",
    "plt.xlabel('Genres', fontsize = 20)\n",
    "plt.ylabel('Average Tempos', fontsize = 20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Association Between Tempo and Genre with ANOVA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we would like to use Anova testing to determine if, for all western music, does there exist an association between the tempo and the 10 listed genres. Specifically, we would like to test this association by assessing if we have enough evidence to suggest that at least two genres average tempos are different from each other. The averages that we are considering in this case will be\n",
    "\n",
    "* $\\mu_{blues}$ = the average tempo of *all* blues compositions\n",
    "* $\\mu_{classical}$ = the average tempo of *all* classical compositions\n",
    "* $\\mu_{country}$ = the average tempo of *all* country compositions\n",
    "* $\\mu_{disco}$ = the average tempo of *all* disco compositions\n",
    "* $\\mu_{hiphop}$ = the average tempo of *all* hiphop compositions\n",
    "* $\\mu_{jazz}$ = the average tempo of *all* jazz compositions\n",
    "* $\\mu_{metal}$ = the average tempo of *all* metal compositions\n",
    "* $\\mu_{pop}$ = the average tempo of *all* pop compositions\n",
    "* $\\mu_{reggae}$ = the average tempo of *all* reggae compositions\n",
    "* $\\mu_{rock}$ = the average tempo of *all* rock compositions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0$:  𝜇𝑊𝑎𝑙𝑙𝑖𝑛𝑔𝑓𝑜𝑟𝑑 = 𝜇𝑀𝑖𝑛𝑜𝑟 = 𝜇𝐵𝑒𝑙𝑙𝑡𝑜𝑤𝑛 = 𝜇𝐵𝑟𝑜𝑎𝑑𝑤𝑎𝑦 = 𝜇𝐹𝑟𝑒𝑚𝑜𝑛𝑡\n",
    "\n",
    "$H_A$: At least one of the genre tempo averages does not = each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>tempo</td>      <th>  R-squared:         </th> <td>   0.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   5.249</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 14 Dec 2021</td> <th>  Prob (F-statistic):</th> <td>5.26e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:26:23</td>     <th>  Log-Likelihood:    </th> <td> -4737.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th> <td>   9496.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   990</td>      <th>  BIC:               </th> <td>   9545.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     9</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>          <td>  120.7422</td> <td>    2.777</td> <td>   43.478</td> <td> 0.000</td> <td>  115.293</td> <td>  126.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>label[T.classical]</th> <td>    6.2901</td> <td>    3.927</td> <td>    1.602</td> <td> 0.110</td> <td>   -1.417</td> <td>   13.997</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>label[T.country]</th>   <td>   -9.8834</td> <td>    3.927</td> <td>   -2.517</td> <td> 0.012</td> <td>  -17.590</td> <td>   -2.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>label[T.disco]</th>     <td>   -0.4004</td> <td>    3.927</td> <td>   -0.102</td> <td> 0.919</td> <td>   -8.107</td> <td>    7.307</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>label[T.hiphop]</th>    <td>   -6.7659</td> <td>    3.927</td> <td>   -1.723</td> <td> 0.085</td> <td>  -14.473</td> <td>    0.941</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>label[T.jazz]</th>      <td>   -5.6741</td> <td>    3.927</td> <td>   -1.445</td> <td> 0.149</td> <td>  -13.381</td> <td>    2.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>label[T.metal]</th>     <td>    4.8687</td> <td>    3.927</td> <td>    1.240</td> <td> 0.215</td> <td>   -2.838</td> <td>   12.576</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>label[T.pop]</th>       <td>   -7.9822</td> <td>    3.927</td> <td>   -2.032</td> <td> 0.042</td> <td>  -15.689</td> <td>   -0.275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>label[T.reggae]</th>    <td>    8.6220</td> <td>    3.927</td> <td>    2.195</td> <td> 0.028</td> <td>    0.915</td> <td>   16.329</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>label[T.rock]</th>      <td>   -0.4798</td> <td>    3.927</td> <td>   -0.122</td> <td> 0.903</td> <td>   -8.187</td> <td>    7.227</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>71.375</td> <th>  Durbin-Watson:     </th> <td>   2.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  89.166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.638</td> <th>  Prob(JB):          </th> <td>4.34e-20</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.716</td> <th>  Cond. No.          </th> <td>    10.9</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  tempo   R-squared:                       0.046\n",
       "Model:                            OLS   Adj. R-squared:                  0.037\n",
       "Method:                 Least Squares   F-statistic:                     5.249\n",
       "Date:                Tue, 14 Dec 2021   Prob (F-statistic):           5.26e-07\n",
       "Time:                        07:26:23   Log-Likelihood:                -4737.9\n",
       "No. Observations:                1000   AIC:                             9496.\n",
       "Df Residuals:                     990   BIC:                             9545.\n",
       "Df Model:                           9                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept            120.7422      2.777     43.478      0.000     115.293     126.192\n",
       "label[T.classical]     6.2901      3.927      1.602      0.110      -1.417      13.997\n",
       "label[T.country]      -9.8834      3.927     -2.517      0.012     -17.590      -2.176\n",
       "label[T.disco]        -0.4004      3.927     -0.102      0.919      -8.107       7.307\n",
       "label[T.hiphop]       -6.7659      3.927     -1.723      0.085     -14.473       0.941\n",
       "label[T.jazz]         -5.6741      3.927     -1.445      0.149     -13.381       2.033\n",
       "label[T.metal]         4.8687      3.927      1.240      0.215      -2.838      12.576\n",
       "label[T.pop]          -7.9822      3.927     -2.032      0.042     -15.689      -0.275\n",
       "label[T.reggae]        8.6220      3.927      2.195      0.028       0.915      16.329\n",
       "label[T.rock]         -0.4798      3.927     -0.122      0.903      -8.187       7.227\n",
       "==============================================================================\n",
       "Omnibus:                       71.375   Durbin-Watson:                   2.048\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               89.166\n",
       "Skew:                           0.638   Prob(JB):                     4.34e-20\n",
       "Kurtosis:                       3.716   Cond. No.                         10.9\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = smf.ols('tempo~label', data=df).fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a significance level of $\\alpha=0.05$\n",
    "\n",
    "Since the p value (5.26 x $10^{-7}$: Prob(F-statistic)) is below $\\alpha$, we can reject the null hypothesis and conclude that it is highly likely that differences in averages did not occur due to chance, and that the average tempo of each of the 10 genres is not the same. In other words, it is very likely that there exists an association between tempo and genre."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion - Although, it is evident that the average tempos are not equal for all the genres and there exists an association (based on the anova test), there is still not a strong association. We have made this conclusion because the R squared value is quite low (absolute value close to 0). The higher (closer to 1 or -1) the absolute value of R squared, the stronger the association."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.base.Index"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2 - Feature Selection (Through Recursive Feature Selection): Which top 5 independent variables/features are the best for predicting genre of music composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False  True  True False  True False False  True False False  True\n",
      " False False False False False False False False False False False False\n",
      " False False False False]\n",
      "Index(['tempo', 'beats', 'chroma_stft', 'rmse', 'spectral_centroid',\n",
      "       'spectral_bandwidth', 'rolloff', 'zero_crossing_rate', 'mfcc1', 'mfcc2',\n",
      "       'mfcc3', 'mfcc4', 'mfcc5', 'mfcc6', 'mfcc7', 'mfcc8', 'mfcc9', 'mfcc10',\n",
      "       'mfcc11', 'mfcc12', 'mfcc13', 'mfcc14', 'mfcc15', 'mfcc16', 'mfcc17',\n",
      "       'mfcc18', 'mfcc19', 'mfcc20'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "X = df.drop(columns=['filename', 'label'])\n",
    "y = df.label\n",
    "rfe = RFE(estimator=DecisionTreeClassifier(), n_features_to_select=5)\n",
    "model = DecisionTreeClassifier()\n",
    "pipeline = Pipeline(steps=[('s',rfe),('m',model)])\n",
    "# fit the model on all available data\n",
    "pipeline.fit(X, y)\n",
    "print(rfe.support_)\n",
    "print(X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3, 4, 6, 8, 12\n",
    "Based on RFE model, \"chroma_stft\", \"rmse\", \"spectral_bandwidth\", \"mfcc1\", \"mfcc4\" are the 5 best independent variables to use to predict from the 10 given genres of music compositions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3: Are using all features or top 5 features the best way to classify genre of a music composition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Visualization of data using pairplot to determine which machine learning algorithm will work the best for classification\n",
    "Decision Tree: Not a lot of overlap\n",
    "Random Forest: If a lot of overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import tree\n",
    "from itertools import repeat\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6210000000000001\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted Result</th>\n",
       "      <th>blues</th>\n",
       "      <th>classical</th>\n",
       "      <th>country</th>\n",
       "      <th>disco</th>\n",
       "      <th>hiphop</th>\n",
       "      <th>jazz</th>\n",
       "      <th>metal</th>\n",
       "      <th>pop</th>\n",
       "      <th>reggae</th>\n",
       "      <th>rock</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual result</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>blues</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classical</th>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disco</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hiphop</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jazz</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metal</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pop</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reggae</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rock</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Result  blues  classical  country  disco  hiphop  jazz  metal  pop  \\\n",
       "Actual result                                                                  \n",
       "blues                15          0        2      0       0     3      2    0   \n",
       "classical             0         21        0      0       0     0      0    0   \n",
       "country               0          0       16      0       1     1      1    0   \n",
       "disco                 0          1        0      9       4     0      0    2   \n",
       "hiphop                1          0        0      1       8     0      1    1   \n",
       "jazz                  1          3        1      1       1    15      0    1   \n",
       "metal                 1          0        0      0       3     0     14    0   \n",
       "pop                   1          0        1      2       2     2      0   16   \n",
       "reggae                0          0        3      0       2     1      1    0   \n",
       "rock                  1          0        0      8       0     2      1    1   \n",
       "\n",
       "Predicted Result  reggae  rock  \n",
       "Actual result                   \n",
       "blues                  0     2  \n",
       "classical              0     0  \n",
       "country                0     1  \n",
       "disco                  1     2  \n",
       "hiphop                 3     2  \n",
       "jazz                   0     0  \n",
       "metal                  1     2  \n",
       "pop                    1     1  \n",
       "reggae                 4     0  \n",
       "rock                   2     3  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using all independent variables\n",
    "# Creating Random Forest Classifier Model and running it 30 times for greater accuracy \n",
    "predictions = 0\n",
    "avg_score = 0.0\n",
    "ntimes = 30\n",
    "clf = 0\n",
    "for i in repeat(None, ntimes):\n",
    "\n",
    "    # train model with 80% of the data\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "    # prediction\n",
    "    # Creating a random forest classifier\n",
    "    clf = RandomForestClassifier(n_jobs = 2, random_state = 0, n_estimators = 40)\n",
    "    # Training Classifier\n",
    "    clf.fit(X_train, y_train)\n",
    "    predictions = clf.predict(X_test)\n",
    "    # compute model accuracy\n",
    "    avg_score += accuracy_score(y_test, predictions)\n",
    "\n",
    "avg_score/= ntimes\n",
    "print(avg_score)\n",
    "\n",
    "# Creating confusion matrix and assigning it to \"array\"\n",
    "array_all = pd.crosstab(y_test, predictions, rownames = ['Actual result'], colnames = ['Predicted Result'])\n",
    "\n",
    "\n",
    "# Picking list of x axes labels of heat map\n",
    "actual_result = df.label.unique()\n",
    "# Picking list of y axes labels of heat map\n",
    "predicted_result = df.label.unique()\n",
    "#Printing confusion matrix\n",
    "array_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Converting confusion matrix dataframe to an array\n",
    "confusion_matrix = np.array(array_all)\n",
    "confusion_matrix\n",
    "# Code for printing heat map\n",
    "fig, ax = plt.subplots()\n",
    "# Inputting 2D array: confusion_matrix(converted from dataframe to array) into design for heat map\n",
    "im = ax.imshow(array_all, cmap = 'YlGnBu')\n",
    "#im = ax.imshow(confusion_matrix, cmap = 'Greens')\n",
    "#im = ax.imshow(confusion_matrix, cmap = 'Cool')\n",
    "cbar = plt.colorbar(im)\n",
    "tick_font_size = 15\n",
    "cbar.ax.tick_params(labelsize = tick_font_size)\n",
    "\n",
    "# Ticks for x and y axis\n",
    "ax.set_xticks(np.arange(len(predicted_result)))\n",
    "ax.set_yticks(np.arange(len(actual_result)))\n",
    "\n",
    "# Assigning x and y axes labels of heat map\n",
    "ax.set_xticklabels(actual_result)\n",
    "ax.set_yticklabels(predicted_result)\n",
    "# Rotate the tick labels and set their alignment (for a better visual)\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "         rotation_mode=\"anchor\", fontsize = 15)\n",
    "plt.setp(ax.get_yticklabels(), fontsize = 15)\n",
    "plt.xlabel('Actual', fontsize = 20)\n",
    "plt.ylabel('Prediction', fontsize = 20)\n",
    "#plt.figure(figsize=(30,12))\n",
    "\n",
    "# Putting confusion matrix array values in respective spots of heat map\n",
    "for i in range(len(actual_result)):\n",
    "    for j in range(len(predicted_result)):\n",
    "        text = ax.text(j, i, confusion_matrix[i, j],\n",
    "                       ha=\"center\", va=\"center\", color=\"black\")\n",
    "# Assigning title for heat map\n",
    "ax.set_title(\"Confusion Matrix: Comparison of Predicted Vs Actual Genres For All Variables\", fontsize = 20)\n",
    "fig.tight_layout()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'blues': {'precision': 0.75, 'recall': 0.625, 'f1-score': 0.6818181818181818, 'support': 24}, 'classical': {'precision': 0.84, 'recall': 1.0, 'f1-score': 0.9130434782608696, 'support': 21}, 'country': {'precision': 0.6956521739130435, 'recall': 0.8, 'f1-score': 0.7441860465116279, 'support': 20}, 'disco': {'precision': 0.42857142857142855, 'recall': 0.47368421052631576, 'f1-score': 0.45, 'support': 19}, 'hiphop': {'precision': 0.38095238095238093, 'recall': 0.47058823529411764, 'f1-score': 0.42105263157894735, 'support': 17}, 'jazz': {'precision': 0.625, 'recall': 0.6521739130434783, 'f1-score': 0.6382978723404256, 'support': 23}, 'metal': {'precision': 0.7, 'recall': 0.6666666666666666, 'f1-score': 0.6829268292682926, 'support': 21}, 'pop': {'precision': 0.7619047619047619, 'recall': 0.6153846153846154, 'f1-score': 0.6808510638297872, 'support': 26}, 'reggae': {'precision': 0.3333333333333333, 'recall': 0.36363636363636365, 'f1-score': 0.34782608695652173, 'support': 11}, 'rock': {'precision': 0.23076923076923078, 'recall': 0.16666666666666666, 'f1-score': 0.1935483870967742, 'support': 18}, 'accuracy': 0.605, 'macro avg': {'precision': 0.5746183309444178, 'recall': 0.5833800671218223, 'f1-score': 0.5753550577661428, 'support': 200}, 'weighted avg': {'precision': 0.6043856386367257, 'recall': 0.605, 'f1-score': 0.6008178256824569, 'support': 200}}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "target_names = df.label.unique()\n",
    "report_all = classification_report(y_test, predictions, target_names = target_names, output_dict=True)\n",
    "print(report_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\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>blues</th>\n",
       "      <th>classical</th>\n",
       "      <th>country</th>\n",
       "      <th>disco</th>\n",
       "      <th>hiphop</th>\n",
       "      <th>jazz</th>\n",
       "      <th>metal</th>\n",
       "      <th>pop</th>\n",
       "      <th>reggae</th>\n",
       "      <th>rock</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>macro avg</th>\n",
       "      <th>weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.605</td>\n",
       "      <td>0.574618</td>\n",
       "      <td>0.604386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.605</td>\n",
       "      <td>0.583380</td>\n",
       "      <td>0.605000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1-score</th>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>0.744186</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.638298</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>0.605</td>\n",
       "      <td>0.575355</td>\n",
       "      <td>0.600818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.605</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               blues  classical    country      disco     hiphop       jazz  \\\n",
       "precision   0.750000   0.840000   0.695652   0.428571   0.380952   0.625000   \n",
       "recall      0.625000   1.000000   0.800000   0.473684   0.470588   0.652174   \n",
       "f1-score    0.681818   0.913043   0.744186   0.450000   0.421053   0.638298   \n",
       "support    24.000000  21.000000  20.000000  19.000000  17.000000  23.000000   \n",
       "\n",
       "               metal        pop     reggae       rock  accuracy   macro avg  \\\n",
       "precision   0.700000   0.761905   0.333333   0.230769     0.605    0.574618   \n",
       "recall      0.666667   0.615385   0.363636   0.166667     0.605    0.583380   \n",
       "f1-score    0.682927   0.680851   0.347826   0.193548     0.605    0.575355   \n",
       "support    21.000000  26.000000  11.000000  18.000000     0.605  200.000000   \n",
       "\n",
       "           weighted avg  \n",
       "precision      0.604386  \n",
       "recall         0.605000  \n",
       "f1-score       0.600818  \n",
       "support      200.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_all = pd.DataFrame(report_all)\n",
    "df_dict_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "blues        0.750000\n",
       "classical    0.840000\n",
       "country      0.695652\n",
       "disco        0.428571\n",
       "hiphop       0.380952\n",
       "jazz         0.625000\n",
       "metal        0.700000\n",
       "pop          0.761905\n",
       "reggae       0.333333\n",
       "rock         0.230769\n",
       "Name: precision, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_all = df_dict_all.iloc[0,:10]\n",
    "columns_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5470000000000002\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Predicted Result</th>\n",
       "      <th>blues</th>\n",
       "      <th>classical</th>\n",
       "      <th>country</th>\n",
       "      <th>disco</th>\n",
       "      <th>hiphop</th>\n",
       "      <th>jazz</th>\n",
       "      <th>metal</th>\n",
       "      <th>pop</th>\n",
       "      <th>reggae</th>\n",
       "      <th>rock</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual result</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>blues</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classical</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disco</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hiphop</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jazz</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metal</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pop</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reggae</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rock</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Result  blues  classical  country  disco  hiphop  jazz  metal  pop  \\\n",
       "Actual result                                                                  \n",
       "blues                 6          0        5      0       0     2      0    0   \n",
       "classical             1         14        1      0       0     0      0    0   \n",
       "country               1          1        7      1       0     2      0    1   \n",
       "disco                 0          0        0     10       4     0      2    2   \n",
       "hiphop                1          0        1      1      11     2      4    1   \n",
       "jazz                  2          1        2      0       0     9      1    2   \n",
       "metal                 2          0        0      1       1     0     19    0   \n",
       "pop                   0          0        1      1       1     2      0   15   \n",
       "reggae                2          1        0      1       2     1      2    1   \n",
       "rock                  2          0        4      3       2     5      0    1   \n",
       "\n",
       "Predicted Result  reggae  rock  \n",
       "Actual result                   \n",
       "blues                  0     0  \n",
       "classical              0     0  \n",
       "country                3     2  \n",
       "disco                  1     4  \n",
       "hiphop                 3     0  \n",
       "jazz                   0     0  \n",
       "metal                  0     1  \n",
       "pop                    2     0  \n",
       "reggae                 9     1  \n",
       "rock                   2     4  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using selected 5 independent variables variables\n",
    "# Creating Random Forest Classifier Model and running it 30 times for greater accuracy \n",
    "predictions = 0\n",
    "avg_score = 0.0\n",
    "ntimes = 30\n",
    "clf = 0\n",
    "X_smaller = X.iloc[:, [2,3,5,7,11]]\n",
    "for i in repeat(None, ntimes):\n",
    "\n",
    "    # train model with 80% of the data\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_smaller, y, test_size=0.2)\n",
    "\n",
    "    # prediction\n",
    "    # Creating a random forest classifier\n",
    "    clf = RandomForestClassifier(n_jobs = 2, random_state = 0, n_estimators = 40)\n",
    "    # Training Classifier\n",
    "    clf.fit(X_train, y_train)\n",
    "    predictions = clf.predict(X_test)\n",
    "    # compute model accuracy\n",
    "    avg_score += accuracy_score(y_test, predictions)\n",
    "\n",
    "avg_score/= ntimes\n",
    "print(avg_score)\n",
    "\n",
    "# Creating confusion matrix and assigning it to \"array\"\n",
    "array_few= pd.crosstab(y_test, predictions, rownames = ['Actual result'], colnames = ['Predicted Result'])\n",
    "\n",
    "# Picking list of x axes labels of heat map\n",
    "actual_result = df.label.unique()\n",
    "# Picking list of y axes labels of heat map\n",
    "predicted_result = df.label.unique()\n",
    "#Printing confusion matrix\n",
    "array_few"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EHD58GIUKFULt2rVhYmKChg0b4tWrV8pzU0pRUVHo0KED/vrrL1hZWaF79+4oXrw4/Pz8MHToUCQkJKBJkyZo3LgxAKBu3brw8vJK9wJfRrK7/2aFovKryC8jc+bMQYcOHVSmLV26FN7e3nj9+jXc3d3h7u6Ox48fo3fv3ti/f3+qZfz999/w8vKCpaUlevbsiWLFimHnzp1pDga0Y8cOBAYGomvXrrC3t4e9vT0AYNy4cZg2bRri4+PRpUsXNG/eHNevX0eXLl1w+fJl5eevX7+OQYMG4enTp2jZsiV69OgBfX19LFq0CNOmTcvGt5Q17969Q8eOHbFu3ToUKVIE3bt3h729PY4cOYIOHTrg9u3bqT6T1vFAHVq1agV9fX1lRTKlwMBAJCUlKc+V2dnP06O4Ry+tCxjx8fE4duwYSpUqpeyuq65z08mTJ9GzZ0/cunULTZo0Qa9eveDg4IDLly+jb9++aQ5s0K9fPzx58kRZsT906BA6d+6Mly9fZljms2fPokuXLrhy5QoaNmyIHj16ICkpCdOmTcOUKVOU8339+hX9+/fH/v37lcd7R0dHHD58GF26dEnzPEi5TPiB1apVS5BKpcL79++z9blr164JUqlUaNeunfD27Vvl9Ldv3wqtW7cWpFKp8L///U85XSqVClKpVFizZo1yWlJSktCnTx9BKpUKZ8+eVU4fN26cIJVKhfv37yun7d69W5BKpcKGDRtS5dKjRw9BKpUKHz58EARBEObMmSNIpVLh8uXLKvMNGDBAkEqlwqNHjwRBEISwsDBBKpUKgwcPVs7j4eEhSKVSYceOHSqfDQgIEKRSqeDh4aGc5uvrK0ilUqFhw4Yq30FQUJAglUqFjh07ZvwlfpP7rFmzBKlUKjx9+lRlnl9//VXo06ePIAiC4OjoKDRs2FD53qFDhwSpVCosWrRI5TOxsbFC7dq1hUqVKgmfPn1STm/YsKHg6OiYZg6zZ89OlZ9UKhXatm2r/DsxMVHo3LmzIJVKhTNnzghhYWGCvb29UKdOHeHdu3eZljctHTp0EKRSqXDv3r1sfe758+dC5cqVhQYNGgjPnz9XTv/48aNyPe7du1c5vWHDhoJUKhV8fHyU0z58+CBUrVpVkEqlwvDhw4WkpCRBEAQhISFBcHV1FaRSqfL7U2wvUqlUWLt2rUq8Tp06CVKpVLhy5YogCILw6tUroWLFikL37t2FhIQElbz79+8vSKVS4fz588ppiuWeOnVKZd4rV66o5BwcHCxIpVJh7NixKvMFBgYKUqlUmDNnjjL/pk2bCpUrV1bZtwRBEObPny9IpVJhwoQJymmKfa5Dhw7Cx48fldMPHDggSKVSwdvbO811oJDddZHWPp4exbyK7/ZbJ06cEKRSqVC3bl3h69evgiD8t2+2b99eSExMVJm/f//+gkwmE/7++2+V6X/99ZcglUqFuXPnKqd1795dqFSpknDp0iXltLdv3wotW7YUpFKpMG7cOOV0RcwTJ04IgiAI79+/FxwdHYVatWqp7NNv374V6tatK9SoUUOQy+WCIKQ+hgmCIKxZs0aQSqXCvHnzVPL8559/hF9++UX49ddfldOWLl0qSKVSYdmyZcpp8fHxwm+//aY8RmXF2LFjBalUKly/fl1l+oMHD9Lc7tzd3QV7e3shNjZWOS02NlaoU6eOULNmTeX+lJ6///5bkEqlwuTJk5XTTp48KUilUmHo0KGp5h8zZkya54Hff/9dkEqlwrFjxwRBSPt88e2+lNK322NO9t+Ux8m0yOVyoWLFikLVqlUznC89t2/fFmQymdCjRw+VY3p0dLTg6uoqVK1aVXkeSnmsOnLkiEoOrVq1EqRSqfD48WOVeWUymfDgwQOVmEeOHBGkUqnw22+/CfHx8crpz58/F2rUqCG4uLgo97lhw4YJUqlUZf+Xy+WCm5ubUKlSJZVtJC2K/Wfw4MGCr69vmq+U+8eECRMEqVQqLF68WGU5Z86cEWQymdC0aVPlusvoeJAeqVQqODo6ppuLYj9POX/KbaB3795CpUqVhKioKJX5OnbsKNjb2yuPs9nZzzPSvXt3QSaTCRERESrTFfvT0qVLBUFQ37lJEAShWbNmQo0aNVKVefXq1YJUKhUWLlyonKY45nXu3Fn48uWLcvrWrVuV25zCt/vnp0+fhJo1awq1atUSwsLClPMlJiYqt8MzZ84IgiAIp0+fVim/wtq1awWpVCps3rw5va+UcskP3RKk6EKW3Ssie/bsAQCMHTtWpXWocOHCGDVqFAAor3Qq5M+fX6VlQCKRKK/+h4eHZz/5dCi6Udy5c0dl+uzZs3H58mVUqFAhzc9FRETgypUrqF69eqpRebp164YqVargypUryu57Cr/++qvKd1CtWjWYm5tnu0xNmzYFAJXWoJcvX+LOnTsqV19Tqly5Mnx8fFKNYFOgQAFUrlwZiYmJ+PDhQ7biZ0RPTw+zZs2CkZERZs2ahcmTJ+PTp0+YNWsWChUqlKU438rpNnjgwAEkJCRg6NChKt3oTExMMHnyZACpt0EA8PT0VP7f3NxceQ9S7969lQNP6Ovr45dffgGAVFekSpUqpfJ9m5iYwNvbG8B/3Q4MDQ0xb948TJo0Cfr6+iqfd3JyApD6Zvj8+fOjfv36GZZZsW0/e/ZMpetWkyZNcPLkSYwePRoAcPPmTYSGhqJVq1aoV6+eyjKGDx+OYsWK4eDBg6m6qXTv3h0mJibKvxX5ZLYt53RdZMfevXuxbNky5WvhwoUYMGAAhg8fjnz58mHatGkwNDRU+Yyrqyv09P47BL9+/Rrnzp1D/fr10aBBA5V5e/TogRIlSii76kVGRuLatWtwcXFBrVq1lPMVLlw40xZlIPlKZWxsLDw8PFRGjCtcuDAmTJiAfv36ZTgk9q5du2Bubo6RI0eqTK9SpQqaN2+OO3fuICQkBEByi4y5uTkGDhyonC9fvnwYN25ctgZTUXSX+faKcnojSwmCgC9fvijzAJKPPbt27cKpU6cyja1ovWjVqpVymouLCwoVKoQzZ87gzZs3yulyuRwnTpxA2bJlVfZhABg4cCAGDRoES0vLrBU0EznZfzPz7t07JCUlwczMLM33N2zYoLJ9K16K4+OuXbsgCALGjh2rMviHhYUF+vfvj8+fP6dqeShdujRatGih/NvAwEC5LX+7T5cpUybVyICKe7MmTZqEfPn+G+NJ0QoeGRmJS5cuAUj7vGtgYIA1a9bg6tWrKFCgQBa+JeDUqVNYvnx5mi/FdyGXy3H48GGUKlUKw4cPV/l8/fr10bRpU4SGhqZ67MS3x4PMxMbGpptLZkPht23bFomJiSrdiV+8eIHbt2+jcePGyuNsdvbzjLRr1w6CIKS570okEuW+q85z06hRozBv3rxUo+QpWqDS2md+++03lXudu3TpgnLlyuH48ePpdqM8ffo0oqOj0bdvX1hZWSmn6+nppfr9qdgug4OD8fXrV+W83bp1w5kzZ9CtW7cMy0Xf74ceHa5QoUKIiopCTExMtoYefvjwIfT09ODo6JjqPcW0b585UbJkyVQ/UhQnhNzsM+zu7o6tW7diwYIF2Lx5M+rVq4d69eqhTp06Kj/wvqVoqk1vmOpq1arhzp07ePjwocqOl9aQuAUKFEjz/oKMVKtWDZaWljh+/DgGDBgAIPl+mfS6wililytXDl+/fsXt27fx7NkzPH/+HPfu3VPeZ5CYmJil+CnLlJHy5ctj+PDhmD9/PkJDQ9G1a9dUP7Szw8LCAqGhoVmurCkoti/FgTulChUqwNzcPNU2aGBgkKrrlWKb+Lb8igPzt9umg4ODyg8CIPmElTInCwsLtGnTBklJSXj06BGePHmCsLAwBAcHp/rRoFC8ePFUJ6VvyWQyODg44ObNm6hTpw5q1KihHNI+ZeVDsS2n9d0YGhqiSpUqOHnyJJ4+faryw+fbbTmr+2dO1kV2KSonCoaGhvjpp5/QvHlzeHp6ws7OLtVnvl2n9+/fhyAIeP/+PZYtW5ZqfgMDA0RERCAyMlKZr62tbar5HBwcMs1X8XlFt6KUMhsC+uPHj3j27BksLS2xcuXKVO8rKgcPHjxA6dKlERoaiho1asDAwEBlvqJFi8LKyirLI6XVrFkTJUuWxLFjxzBp0iTo6ekpf1il7E6j0LlzZ0ydOhVdunSBTCZDvXr1UL9+fTg6Omb6YzMuLg6nTp2CpaUlatSooZxuaGiIZs2aYfv27di7d6/y3onnz5/j06dPaX6fpUqVSvUj8nvkZP/NjOIiUXrPRdmwYUOqey2B5POZubm58p6/48eP48yZMyrzvHr1CgBSdTcqW7ZsquWlt0+ndfy/d+8ejIyMEBAQkOo9xX03Dx48QIMGDdCxY0ecPHkSI0eOxNKlS+Hi4oJ69eqhZs2aqc77GZk9e3amo8M9e/YMX758QbVq1dLczhwdHXHs2DE8fPhQZZvN6jlOoVSpUjh9+nS2PqPg6uqKadOm4ciRI+jevTuA/y4uKLrCZWc/T+/irULz5s3h4+ODI0eOKB+BERcXh7///huOjo7K84O6zk16enpwdXUFkFzBDgkJwfPnz/H48WPlPZ/fLlcikaS5P9vZ2eHZs2epzk8Kd+/eBZC8faZ1HNfX11cef2vXro3SpUvj5MmTqF27NmrXro169eqhQYMGKFGiRIZlotzxQ1eCSpcujaioKPz7778ZVoJiY2Px+fNnFC1aFEDyzmVkZJTmwc3MzAzGxsb4/PmzyvS05lVcKRRy8cF7FStWxI4dO+Dn54ezZ88qn7tgYmICDw8PeHt7p3mFUlFpSe9KnaLs3/aXTa9c2S2TRCKBq6srtm7dilevXqF48eI4duwYnJ2d072JNikpCatWrcKGDRuUlYgiRYrAwcEBpUqVwpMnT7KcR3aeAeXq6ooFCxZAEIQs/SDMiJWVFW7evInnz5+n+UNWQS6XIzIyUnkwz8r6+vbGx4zKmNUT9bf3nQDJlV5jY2OVAQeOHz+OhQsXIjQ0FEByZcvW1hYVK1bEpUuXUq2XrHz/EokE69atw9q1a3Hw4EGcO3cO586dg4+PD2rXro2ZM2fCyspK+d2kd/VVsS1nto9mdf/MybrILn9//2wPQf3td6r4AXrr1i3cunUr3c+9f/8+wxbKggULZhpb8fmsXgFPSfF9RkVFYfny5enO9+HDB+V+n15LasGCBTN9kKKCRCJB27Zt4efnh6tXr6JWrVrKkaWGDBmS6rjZpUsXFClSBP7+/ggKCkJwcLBylNHx48dnWNkLDAzE169fERUVle4w0Lt27VJWghTlzMn3mRPZ3X8zY2hoCEtLS0RFRSEyMjLVceTbUS+HDBmCU6dOKf9WHFtWr16dboxvLyRldM79Vlojj8bGxiIhISHTbRBIboHx9/fHunXrcOnSJWzatAmbNm1CoUKF4OXllWsP6QRyfq4W4zmHCqampmjcuDEOHz6sXN9HjhxBkSJFlI8FyM5+npkCBQqgcePGOHToEMLCwpQ//L98+ZKqBVcd5yYgubXFx8dHeQHWwMAANjY2sLW1RWhoaKrlFixYMM1tVHEsS2/gCsW+kNYgXAqK78zY2Bg7duzAypUrERgYiOPHj+P48ePKStuMGTNy3IuFsuaHrgS5uLjgxo0buHjxYoY/Zrdv34758+dj8ODB8Pb2hqmpKT5//oyYmJhUN51+/foVX758ydUhrjP6MfbtDzkguSK0ZMkSyOVy3Lx5E+fOncOePXvg5+eHYsWKpdkEqtjx0roaB/z3o0adO0zTpk2xZcsWnDhxAk2bNsWtW7cwY8aMdOdfv349lixZgho1aqB///6oVKmSskuI4obD3CYIgnLAAHNzc8yePRsuLi45foipi4sLDh48iIsXL6J169bpznfq1Cl4e3ujbdu2mD9/vsr6Siv2hw8f1LKuUjapK8jlcpVt/vbt2xgxYgSKFy+ORYsWoUqVKihdujQkEglWr16tvOKWE6amphgxYgRGjBiBZ8+e4eLFizh48CAuXbqEkSNHYufOnaJvy5paF9mlaPUbMmSIcgTE9Ciurn87kh6ADLuxfRsrrRO5XC6Hnp5eqhbFbz9bvXr1NK/Cp6T4oZdWnlnNNaV27drBz88PgYGBqFWrVqruNN9ydXWFq6srYmJicPXqVZw+fRoHDx7EqFGj8PPPP0Mqlab5OcWocG3btk3z2U6nT59GaGgorl27Bicnp0x/GH369CnDlv7snEPUtf82btwY27Ztw4kTJ9CjR49sfdbExAT6+vq4fft2qhY/dTExMYGpqWmqlqf01KhRAzVq1MCnT59w/fp1nDlzBnv37oWPjw+sra0z7VKVVT/CuTor2rZti0OHDuHo0aOoV68eHjx4gJ49eyr3++zs51nh7u6OQ4cO4ciRIxg4cCAOHjwIY2Njle706tq24+Li0KdPH8TGxmLcuHGoXbs2ypcvD0NDQ9y+fRuHDh1K8zOCIKSqmCtG4k1v/Sm+t40bN6p0VU5P4cKFMWnSJEycOBHBwcE4f/489u/fj2PHjkFPT0+U51/qsh/6nqA2bdrAwMAAmzdvTvck+vnzZ+zcuRMAUKdOHQBQNlGm9VyOoKAgCIKQaijj76E46H97QhcEIdWIbfv27cPMmTMhCAIMDQ3h7OyMMWPGKJtN03uWiOJq5I0bN9J8/9q1a5BIJLlarm/VqFEDFhYWOHHiBE6cOAE9Pb10u8IBycPT6uvrY+XKlahXr56yAiQIgnKksNxsZQOALVu24OrVq+jUqRMmT56Md+/eYfr06TleXqNGjVCoUCEcPHgw3aExExMTsXnzZgBZ2wb//fdfREVFZdqFICe+vdcMSD6xCIKgHDXn8OHDSEpKwtSpU9GqVStYW1srD/Tfs14ePnyIuXPnKlsxypUrpxwVrGzZsvjnn38gl8sz3JaTkpIQFBQEExOT7x6VTUFT6yK7ZDIZgP+6U3zL19cXq1evhlwuR+XKlSGRSNL8DtP7fEqKH/9pPXdn3bp1qFq1qvKK6bfMzMxQsmRJPH78OM1RyPbt24dly5bhxYsXyJ8/P2xsbHD//v1U88bExChHzsuqcuXKwd7eHqdOnUJ8fDxOnDihfBZJSnK5HCtXrlQ+tsDc3Byurq6YPXs2Bg8ejKSkJNy8eTPNGGFhYQgKCoK1tTXmz5+PGTNmpHopLlQpzj3lypWDgYFBmt9nZGQkHBwclBdn0mrtSO8cosgnJXXtv507d4a+vj78/PxUhqlOy7ddh2QyGRITE9McYevWrVtYsGBBqntgvpdMJsOrV6/SfP7MmTNnsHjxYmW3o7/++kv5Y9LExAT16tXDlClTMHXqVADpn3dzonz58jAyMsKdO3fS7Kp77do1AFDruTor6tSpgyJFiuD06dPK4avbtGmjfD87+3lW1K5dG8WKFcPx48fx4cMHXLlyBU2bNlVpPVXXtn3lyhW8efMG3bt3R58+fVCxYkVlK4/iYuy3y01ISEg11Hp8fDzu3r0LExOTNLtzAhkfx9+/f48//vhDeb/htWvX4OPjg+fPn0MikaBixYro378/du7cCRMTk1zfZyi1H7oSVLp0aXh6euLdu3fo169fqmfhxMbGYvTo0QgNDUXDhg2Vff4VfXYXLVqkcjCPjo7GvHnzAEA5Zn5uKF++PIDkoRpT3uOyZcuWVEMc3rp1C5s3b051k6jiQJJyiOWUSpYsCWdnZ9y9exdbtmxReW/nzp24ceMGnJ2dUbx48e8tTrr09fXRuHFjBAUFYc+ePXB2ds6whcXIyAiJiYmpTqh//vmn8sbXhIQE5XQDAwOVv7PrxYsXWLBgASwtLTF69Gi4ubmhVq1aOHr0aLaeJ5OSmZkZvL29ER8fj379+qkM8wwkt7zMnDkT169fR+XKlZWtRW5ubsiXLx/8/PxUfsR8+vRJ2XqWm9ugwu3bt1VuPo2Li8PChQuhp6envFqu6FqS8sZuALh8+bLyilhO1oNcLsf69euxYsUKlRNKXFwcPnz4AEtLSxgaGsLR0RFlypTB8ePHcfbsWZVl+Pr6IiIiAi1atMhWX/2MaGpdZFfp0qXh5OSEc+fOpXqmxr59+/Dnn3/i/Pnzyq5LLi4uuHLlisq2HRcXl2HXFYUmTZrAxMQE/v7+Kjehv3//Htu3b4epqamyP7ziB3p8fLxyPnd3d7x//x4LFixQ+UH8+PFjzJgxAxs2bFBeKXV3d8enT5+UXVSB5B8cCxcuzNF25ubmhjdv3mDDhg2Ijo5O9WwgILmr1aFDh7B06dJUlQhFedM71u7fvx+CIKj8IPyWu7s79PT0lM8RMzIyQrNmzfDkyZNUjx/w8/MDAOVVYcWV9pTfZ5kyZaCvr48rV66otPycOXMm1TO21LX/Vq5cGUOGDEFUVBQ8PDzSfI5TTEwMFixYoNxvFfe8KI4ts2bNUrnfNC4uDtOmTcOaNWuyfP9nVrm7u0MQBMycOVOlsvH69WtMnToVq1evVrbKXLhwAX5+fqm6mWa2LeSEoaEhWrVqhdevX8PX11flvXPnziEwMBBlypRBtWrVci1mTuTLlw8tW7ZEUFAQDh06hDJlyqQaXjo7+3lm9PT00KZNG9y7dw/bt29HQkJCqhZcdW3biuV+O/jBy5cvlcfLtJa7fPlyle127dq1ePXqFdzd3dO9D8nV1RUFChTA2rVrUz0Tav78+fD391de/ImKisKmTZuwfv16lfnevHmDr1+/5tqFQErfD90dDgBGjhyJt2/fYs+ePWjcuDEaNGgAa2trREZG4uLFi4iOjka1atWUlRsAygdgbdiwAW3btkXDhg0BJD+XICoqCv3790/zJumcqly5Mn755RfcvHkT3bp1g5OTE4KDg3HlyhVUrVpV5ZkA/fr1Q2BgIEaPHo2jR4+iTJkyCA8Px/Hjx2FpaZlhN4QZM2age/fumD59Ok6cOAGZTIZHjx7h4sWLKFq0KGbOnJlrZUpP06ZNsWvXLty7dy/DrnBAcnP7rVu30LVrV7Ro0QIGBga4evUq7t27hyJFiuDt27cqlcSiRYsiNDQUo0ePRt26ddP8cZMeQRCUo8H5+Pgou0FOmzYNbdu2xfTp05UtWTExMfjrr79gZmaWaiSntHTt2hWvX7/GihUr0Lp1a9StWxc2NjZ4//49rly5gpcvX6J8+fJYsWKF8gdO6dKlMW7cOPzxxx9wd3dX/ug8d+4cwsLC0KpVq2yVL6vMzMzw22+/ITAwEMWKFcOZM2cQFhaGIUOGKFtEWrZsiQ0bNmD69Om4du0aLC0tERwcjAsXLsDCwiLVeskqxcMhjx07pnyYYkJCAk6ePIl3794pH1ynp6eHOXPmoG/fvhg0aBAaNmwIa2tr3Lx5E7du3YKNjQ3Gjh2ba9+JptZFTij28REjRqBevXqoUKECnj17hjNnzqBQoULKK9cAMGXKFHTp0gXe3t5o0qQJihUrhr///jtLI0wVKlQIU6ZMwYQJE+Du7o7GjRvD1NQUR48eVd4DoKiEKu4PmThxIurUqQMPDw8MGDAAFy5cwKZNmxAUFIQaNWogJiYGR48exefPn7FgwQLlFd5evXrh9OnT2LRpE+7evYuqVavi1q1bePToEYoUKZLt76hVq1aYPXs2VqxYAWNjY5URxlL67bffMHToULi7u6N58+YoWLAg7t69iytXrqBGjRrKVttvKR6qrLhBPC0lSpRArVq1cPHiRRw4cADdu3fH2LFjERQUhN9//x3Hjx9HhQoVcOfOHVy7dg1NmjRR3oOk+D63bt2KDx8+KJ+R06RJExw7dgwdO3ZE/fr1ERYWhtOnT8PR0VGlpUJd+y8ADB06FEZGRliyZAnatm2LatWqoXLlyjAyMkJoaCguXryIL1++oFixYpg8ebKy8lCzZk307NkTmzZtQqtWrVC/fn0YGhri5MmTiIiIQJcuXbJ9z1xm2rdvj9OnT+PYsWMIDg6Gi4sLEhISEBgYiPfv32PUqFHKezSHDRuGq1evwsPDA82bN0exYsXw+PFj/P3337CxsclwXefEmDFjcOPGDaxZswbXrl2Dg4ODcn2amppi/vz52RoZUV3atm2LTZs24fHjx/Dy8kr1fnb286xwd3fH2rVrsWLFCpQsWTLVA3fVtW07OjqiVKlS2L9/P969e4eKFSsiIiICp06dgpGRESQSSarlGhkZ4d69e+jYsSNq1qyJhw8f4uLFiyhfvrxyxNW0mJubw8fHB6NHj1aeb4oWLYpr167hn3/+QZUqVZSDQzRp0gQODg7YunUrHj16BHt7e8TFxSkvbH07uiDlvh+6JQhIbn2YPXs21q1bh/r16+Phw4fYtGkTTp8+jbJly2L69OnYvHlzqnt/xo8fj/nz56NUqVI4ePAgAgMDUa5cOSxbtkw5VG9uWrVqFdzd3REaGorNmzfj8+fP+Ouvv1JdWbGyssLWrVvRsmVL3L17Fxs2bMC1a9fQtm1b7NixI80b2xXKli2L3bt3o1OnTnj8+DE2b96M0NBQ9OzZE/v27UvVJUQdatWqBXNzc+jr6ytHW0lPt27d8Pvvv6NQoULYuXMnDh48CFNTUyxatEhZgUrZEjBmzBhUqFABR48eTfPhehnZvn07Ll++DBcXF5UhbcuWLYtBgwbh7du3ykpiTEwMli9fDn9//ywvf8SIEdi+fTtatWqF58+fY9u2bQgMDISFhQVGjx6NvXv3phrNxcPDA2vWrMEvv/yC48ePY+/evShUqBB8fHywcOHCbJUvq2rUqIE//vgDjx49ws6dO2Fubo558+ap3GNSqVIlrF69Gr/88gtOnjyJHTt24M2bNxg+fDj2798PPT29VC00WTVv3jyMGjUKiYmJ2L59O/bs2YPSpUtj5cqVKg9XrFatGnbt2oWWLVvi5s2bCAgIwPv37zF48GDs3Lkz1/vLa2Jd5ET58uWxZ88edOrUCcHBwfD390dwcDDc3Nywa9culS40pUuXxvbt29GyZUtcu3YNu3fvRuXKldMcySkt7u7uWL9+PSpVqoRjx45hx44dsLKywurVq1X27UGDBqFq1aq4ePGi8t6A/Pnzw9/fH8OGDcPXr1+xZcsWnD17FtWqVYO/v7/K/XOGhoZYv349BgwYgMjISGzduhVA8j2DORk2umDBgmjYsCE+f/6svOqalsaNG2PdunWwtbXF33//DX9/f7x69QpDhw7F6tWr06ws3rx5E//++y/s7OzS7e6ioOhxoBiqWfGwz86dOyvX3cuXLzF48GAsXrxY+TknJyd0794dHz58QEBAgLI7zqxZs9CzZ0+8f/8emzZtQnh4OHx9fVM9HkCd+69EIkH//v2V9218+fIFhw4dwqZNm3D//n24uLhg7ty5OHnyZKq8Jk+ejHnz5qFEiRI4cOAA9u7di59++gmzZs1SqbznFolEAl9fX0yaNAnGxsbYuXMnAgMD8fPPP+PPP/9UjmIKJF+g2bx5M+rUqYMrV65gw4YNCA4OhoeHBwICAjK8XysnChcujB07dqBPnz6IiorC5s2bcefOHbRr1w579uxJ94GeYku5nafV8pmd/Twrfv75Z9ja2uLz589wc3NLVRFU17ZtYmKCDRs2oGnTprh37x42b96M+/fvo23btjhw4AAqVqyI69evq9zTZ2hoiL/++guFChVCQEAAHjx4gM6dO2PLli2ZPuC4RYsW2Lx5M2rWrInz589j8+bNiIuLw5AhQ7Bx40ZlC6WhoSFWrVqF/v37Izo6GgEBATh69CiqVq2KTZs2pXuhhnKPRMjtmzKISHQvXrxA48aN0bhxY6xYsULT6RARERH90H74liAiIiIiIqLcxEoQERERERHpFFaCiIiIiIhIp/CeICIiIiIi0ilsCSIiIiIiIp3CShARERERkRaIfh+X+UwEgN3hfghHw67gU8JXUWO2L1cfe57l7FkSOY9ZWtR4/ykP4KmoEa+8DhU1nkLNoo1w5fVpkWOWFTXef8Rfr5ojfllj5GGixlMwN6yPGLm4x6bkuJo4PnEb1k4sq3rlA6D+5yLmZY1/nYYXEW9FiWVVoghO7Z4mSqzclk/TCRDwKeErPiZ8Fj2u+DETRI6nudhfEsVfn5qLrTvrVbPELauAL6LG03xsTW1L3Ia1E8tKmvMi4i2ev3ij6TR+eKwEERERERFpCYlEDxKJOHe8iBVHHfJu5kRERERERDnAliAiIiIiIi0hgR4kIrVziBVHHfJu5kRERERERDnAliAiIiIiIi3Be4KyJu9mTkRERERElANsCSIiIiIi0hISSMRrCYJElDjqwJYgIiIiIiLSKawEERERERGRTmF3OCIiIiIiLSGRSCCRiNNNTaw46sCWICIiIiIi0ilsCSIiIiIi0hp6EK+dI++2p+TdzImIiIiIiHKALUFERERERFqCD0vNmrybORERERERUQ6wEkRERERERDqF3eGIiIiIiLQEu8NlTd7NnIiIiIiIKAfYEkREREREpDUkkIjWzsGHpRIREREREeUJbAkiIiIiItISvCcoa/Ju5umQyWTYv39/uu+PHz8enp6e4iVEREREREQ/FK2rBFHGwoKfYVaPsXBwcMCU9sPx7G6IplNSm8OHz8DOrg1kMhk6dhyOmJg4TaekNiumr0Xn6r1hb2+Pfk2GYfrAuZpOSW10ab3qUlm3bzmFOtUHw97eHk3rj8TNoEeaTkltdGm96lJZAUV57SCTNdP68upSWUk7sRKkQ75+/oJ5fSahVb8OuHnzJtyGdIXfqHmaTkstoqKi0bv3BOzevQzBwcEoX740xo9foOm01Obe9Qf4feVY3Lp1C2tPLsPUVeM0nZJa6NJ61aWyhgSHYcqEtdh90Ae3bt3C6PFd0bPzTE2npRa6tF51qaxAyvLuRnDwMa0ury6VNS9SdIcT65VX5d3MM/D48WN07NgRtra2cHNzw8WLF9Oc7+rVq5DJZHj16lW60+RyOebMmYO6deuiWrVq6NGjB27duqWc/82bN/Dy8kKNGjVgb28PT09PPHjwQK3ly6m7F26gaOkSqNqgBgCgWuOaGLp0ooazUo/jxy/AyakKKlQoCwAYPLgrAgIOQhAEzSamBvKv8Qi5+xQ7/PagatWqmNJvFiJfvNZ0WmqhS+tVl8pqaGQA35XeKF6iCADAwVGKyFfvIJfHaziz3KdL61WXygqkLG8FANpdXl0qK2kvrawE+fv7o0uXLti/fz8cHR0xePBglYpOdowdOxbXrl3DkiVLsHv3btSsWRM9e/bEs2fPAADTp09HQkICtm7dij179sDU1BTDhg3LzeLkmleh4ShkaYG1ExejevXqmOs5EUmJiZpOSy3Cwl6hdOniyr+trIojJiYOsbEfNZiVeryNfItqdezQb0Iv3Lp1C5WryTC5t49Wnox0ab3qUlnLlC2OZi2dAQCCIGDS2FVo0bomDA0NNJxZ7tOl9apLZQV0q7y6VNa8iC1BWaOVo8P17NkTv/76KwBg8uTJOH/+PLZu3YqRI0dmazn//vsvAgMDcejQIeXVDi8vLwQFBWHDhg2YMWMG/v33X8hkMlhZWcHIyAgzZszA48ePkZSUBD29rG0Y7cvVz14Bc+h5wSAcOLcVf//9N5ydnbF//34MHDgQ//77L4yMjETJQSxJSYUBxAGQ/v+U5H/19WUATNUev0EJaeYz5ZYSQNdTfZV/rpyxDgWXFkTZr7+gXLly4uUhAk2vVzFpuqwFDUXchv/fx48f0al7J4SFfcLRo0dR0LCQ6Dmom6bXq5h0qaxAyvICyWVNAKCd5dWlspL20spKkIODg/L/enp6qFy5MkJCsj8AwP379wEAnTp1Upkul8shl8sBAEOGDMG4ceNw/PhxODk5oV69emjTpk2WK0AAsOfZWXxM+Jzt/LIrVP8tipYriUeF38EZQExlI3ySf8bi03+h1M/Wao/fs0J5tcdQsLY2wNWrIQAeAZAiPPw8LCwKwtQ0XJT4ZyLEG3Diyf1neHL/GZp2aIQGJVrhTMRhJCQl4Hr0Ofyb/77a4zcoUUHtMRQ0vV7FpOmyfpA/EyWOQtjz1+jSfipsK9fAvmMTITG+ig9y8eIXNBTngoGm16uYdKmsQMryAsAjhIeHa215NVvWfADE+z2RF0mQ/LhUsWLlVXm3DSsD+vr6Kn8LggBDQ8MsfTYxRfcwA4Pkrhjbtm3Dvn37lK8jR45gwYLkGwCbN2+O8+fPw8fHB5aWllixYgVat26NN2/e5FJpco9dvep4Ex6pHBHu4bU7gEQCyxRN2tqiadO6uHLlNkJCQgEAfn7b4ObWWLNJqYmengTLfl+FiOfJXT73/3UE5SuVhWXJnzScWe7TpfWqS2V9Fx2LVk3GoE27Oti2bRuMjbWrZTolXVqvulRWIGV5k8+x2lxeXSoraS+tbAm6f/8+GjRoAACIj4/HnTt3lN3jUlJUcuLi/hvWMTQ0VPl/RRe4t2/fonbt2srp06dPh42NDbp06YKFCxeibdu2aNOmDdq0aaOc93//+x9atmyphtLlXCHLwhixYgr+mvYndvy+Ch/xFcOXT4ahUdYqiHlJ0aJFsGHDbHToMBxyuQQ2NsXh76+dw0aXq1gWw30GYWKvGZghWQBTy/z4fcUYTaelFrq0XnWprOtWH8KLsCgc2n8JgQfskSjEAgAOHJ2DwkXMNZxd7tKl9apLZQVSlrcD5PI42NhYa215damspL0kgpbdPS2TyWBqaooZM2agUqVKWLNmDU6dOoVjx46hcOHCGD9+PF69eoWNGzciLi4OLi4uaNy4MYYPH47Q0FDMnDkTz58/x9mzZ1G8eHGMHDkS//zzD6ZMmYJy5cph9+7dWL16NdavX49atWrBw8MDnz9/xuTJk1G4cGEcPHgQK1aswOHDh1GmTJks5SxWd7iUelZojk0hR0WOqanmaymSu2OIR8zucCkpusOJG1O87nCqxF+vmiN+WcXuDqdQ0LAZPsiPaSCuJu6f4zasnVhW9WJ3uMw4NvZBWPg7UWKVLmWBoFOTRYmV27SyO9yQIUOwZs0auLm54dmzZ1i3bh0KFy6car4CBQpg/vz5uHv3Llq2bAlfX1+MG6f6fBUfHx/Ur18fEydOROvWrXHu3DksW7YMtWrVAgAsXLgQVlZWGDhwIFq2bImTJ0/izz//zHIFiIiIiIiIxKV13eGCg4MBAP369Uvz/Tlz5qj83aRJEzRp0iTNZQCAqakppkyZgilTpqS5PEtLSyxevPh7UiYiIiIiyhUSiDd0tSQPt6fk3cyJiIiIiIhyQOtagoiIiIiIdJWYDzHNyw9LzbuZExERERER5QBbgoiIiIiItIYE4rVz5N3HpbIliIiIiIiIdApbgoiIiIiISFRTpkxBYmIi/vjjDwBAo0aNEB4enua8f//9N0qWLJlq+tmzZzFgwIA0pxcvXjzD+KwEERERERFpCxEHRkAO4giCAF9fX2zfvh0dOnRQTt+1axcSExOVf3/+/BkeHh6oXr16mhUgIPmxNpUrV8bq1atVphcpUiTTPFgJIiIiIiIitQsLC8PEiRMREhKSqmJTuHBhlb+nTp0KfX19zJw5M93lhYSEQCqVwtLSMtu58J4gIiIiIiItoRgiW6xXdty4cQMlSpTAwYMHYWVlle58Dx8+xI4dOzBlyhQYGxunO19ISAhsbGyylYMCW4KIiIiIiCjHIiIiVLqyAYC5uTnMzc1Vprm5ucHNzS3T5S1btgyOjo6oX79+uvMkJibi6dOnuHv3Ltq2bYvo6GhUqVIFY8aMQfny5TONwUoQEREREZGWkEAPEpE6eynidO/ePdWgBl5eXhg2bFi2lxkWFobTp0+nus/nW8+fP8fXr18hl8vh4+MDuVyOlStXonv37jh06FCm9wWxEkRERERERDkWEBCQZktQThw8eBAlSpRA3bp1M5yvXLlyuHr1KszNzaGnl1wZW758ORo0aID9+/ejT58+GX6elSAiIiIiIsqxEiVK5NqyTp06hRYtWkAiyfxBrIUKFVL529jYGKVLl0ZERESmn+XACEREREREWkIikYg4MELmFZXs+PTpEx48eICaNWtmOu/Jkyfh4OCA6Oho5bS4uDiEhoaiQoUKmX6elSAiIiIiItK44OBgJCYmQiqVpvl+dHQ0YmNjAQBOTk4oUKAAxowZg4cPH+LevXsYMWIELCwssjT4AitBRERERERaIrklSLxXboqKigKQupubQocOHfDHH38AAAoWLIiNGzfCwMAAHh4e6NmzJ0xMTPDXX3/ByMgo01i8J4iIiIiIiES1adOmVNOaNm2K4ODgdD9z+vRplb9tbGzg5+eXo/isBBERERERaYmcPMT0e2LlVXk3cyIiIiIiohxgJYiIiIiIiHQKu8MREREREWkJCfQgEamdQ6w46pB3MyciIiIiIsoBtgT9AFpZm0NAftHjdixvIWq88v1uixpP4elaqeix76+yFjVeSjWL/qSx2KQ9ChqW08nYRER5HQdGyJq8mzkREREREVEOsCWIiIiIiEhrSERsocndh6WKiS1BRERERESkU1gJIiIiIiIincLucEREREREWoJDZGdN3s2ciIiIiIgoB9gSRERERESkLSR6yS+xYuVReTdzIiIiIiKiHGBLEBERERGRluDDUrMm72ZORERERESUA2wJIiIiIiLSEhIJIJGI8xBTkcKoBVuCiIiIiIhIp7ASREREREREOoXd4YiIiIiItAQflpo1eTdzIiIiIiKiHGBLEBERERGRluAQ2VmTdzMnIiIiIiLKAbYEERERERFpi+QxssWLlUexJYiIiIiIiHQKK0FERERERKRT2B2OiIiIiEhb6EG8Zo483JySh1MnIiIiIiLKPrYEERERERFpDREHRgAHRiAiIiIiIsoTfthK0LJly+Dq6ipKrEaNGmHFihW5sqyePXti0qRJubIsIiIiIqJsUQyRLdYrj/phK0Fi2rVrFzw9PTWdhmgEQYCnpycWLwzQdCq5ThAERJ37Ex/uHEj1XuTJBXhzaZ0GshKHNq/XlA4fPgM7uzaQyWTo2HE4YmLiNJ2S2rCs2oll1V7J5bWDTNZM68urS2Ul7cRKEIDChQvDxMRE02mI4uGDZ2jh6oUdO3ZoOpVcJ3//Aq8CZ+Dj08up3nv/z358iXyggazEoc3rNaWoqGj07j0Bu3cvQ3BwMMqXL43x4xdoOi21YFlZ1rxOl8oKpCzvbgQHH9Pq8upSWUl7abwSFBcXh+nTp6N27dpwcHBA37598fTp01TzXb16FT169ICDgwNsbW3h5uaGc+fOKd8/c+YM2rVrBzs7O9StWxczZ87E169fAQCfPn3ChAkTULt2bVSpUgWdOnXC5cv//VD+tjvc2bNn0bFjR1StWhWNGjXC2rVrle89fPgQ/fv3R/Xq1WFra4tmzZph3759avhm1MNv5W54eLZGp06dNJ1Krou5fwwFKjSEaflaKtP//vtvfH5xC+YVxeleqQnavF5TOn78ApycqqBChbIAgMGDuyIg4CAEQdBsYmrAsrKseZ0ulRVIWd4KALS7vLpU1jxJT+RXHqXx1L29vXH58mUsXLgQu3fvhomJCfr164eEhATlPBEREejfvz8cHR1x4MAB7Nq1CyVKlMC4ceMgl8sRHR0NLy8vdOnSBYGBgZg/fz6OHDmCNWvWAAB8fX3x+PFjrFu3DkeOHEGlSpXg5eWFT58+pcrn5s2bGDRoEOrUqYN9+/ZhwoQJ+PPPP7Fjxw58+vQJffr0QdGiRbFjxw7s378fTk5OmDx5Mt68eSPad/Y9lviORrceLTSdhlr8VLsvzCrUU5mW8DEaI0aMgGWD4YBE45u72mjzek0pLOwVSpcurvzbyqo4YmLiEBv7UYNZqQfLyrLmdbpUVkC3yqtLZSXtpdEhsp8+fYrz58/D398fzs7OAIAZM2Zg1apVePfunXK++Ph4jBgxAn369IHk/2/A8vT0RK9evfD27Vu8e/cO8fHxKF68OEqVKoVSpUph7dq1yi5u//77L0xNTWFlZQUzMzOMGzcOzZo1g76+fqqcNm3ahOrVq8Pb2xsAUK5cOUydOhX6+vr4/PkzPD090bNnTxgbGwMABg4ciJ07dyI0NBQ//fRTjr4HI32HHH3u+6yEgZ418us7ixbx6VpxYnl6HoatrS1GjGiHxo0bY9qSJWjUqBGmTXuJN2/eYPnyjqLkoRnir1cxJSUVBhAHQPr/U5L/1deXATDVUFbqwbKyrHmdLpUVSFleILmsyRdztbG8ulTWvEiQAIJIAxYIeXdcBM1Wgh49egQAsLOzU06zsLDA+PHjsWzZMuU0a2trtGvXDn/99ReCg4Px77//4sGD5Ps7EhMTUalSJbRo0QIDBw5E8eLFUadOHTRp0gQNGzYEAPTt2xdDhgxBrVq14ODgABcXF7Rt2xZGRkZp5lSvnmprQrt27ZT/79atG/bt24cHDx4gNDQUDx8+VOaRU18Tb0KAPMefz6n4pOf4knhVtHiVBz4XJU7UpVAcfJCIRWfn4fWN+/jtt9/wIOw9Ej+/hyAk4a8TD2DpMkitOdxfZa3W5WdE7PWaX99CtFjW1ga4ejUEwCMAUoSHn4eFRUGYmoaLloNYWFaWNa/TpbICKcsLAI8QHh6uteXVbFnzASgvQhzSdhrtH5QvX9bqYI8ePULz5s1x4cIF/Pzzzxg8eDAWLVqkfF8ikWDJkiU4fPgwPDw8EBERAS8vL0ycOBEAUL16dZw9exaLFy9G+fLlERAQgBYtWiAkJCRVrIxyioyMRJs2bbB//36UKlUKnp6eWL9+fTZLTWLJX0wK6y4rcevWLZRynw+ziq4wLVdb7RUgUp+mTeviypXbCAkJBQD4+W2Dm1tjzSalJiwry5rX6VJZgZTlTf5toc3l1aWy5kkSkV95lEZbgmxsbAAAd+/ehZOTE4DkgRJcXV0hk8mU823fvh0lSpRQGaBg27ZtAJKHBb5z5w4OHTqECRMm4Oeff0bfvn2xZs0aLF++HLNnz8by5cvh4OAAV1dXuLq64uvXr3BxccGZM2eUN/WlzOnu3bsq0xYvXoyQkBBUr14dHz9+REBAgLIr3fnz55V5EJF6FS1aBBs2zEaHDsMhl0tgY1Mc/v5zNZ2WWrCsLGtep0tlBVKWtwPk8jjY2FhrbXl1qaykvTRaCSpXrhwaN26M6dOnY9q0abCwsMCSJUtgZmaGKlWqIDw8uVm1ePHiCA8Px8WLF1G2bFlcv34dixcvBgDI5XKYmZkhICAARkZG6NChAz5+/Ii///5b2c0uPDwcBw4cwMyZM2FlZYVLly4hNjYWVatWTZVTnz590KFDB6xYsQKtWrXCw4cP4e/vj0mTJsHExARxcXE4duwYqlatiocPH+KPP/5Q5pGXbNy4UdQuU2KyrDc0zekW1bR75DRAu9erQsuW9dGyZX0k90N/pOl01Ipl1U4sq/ZKLm9/sKxEPz6NVoIAYM6cOZg9ezaGDBmCxMREODk5Ye3atdi/f79yHg8PDzx58gQjR45EYmIibGxsMH36dEyYMAF37txBu3bt8Oeff8LX1xf+/v4wMDCAi4sLJkyYAACYPHky5s6di1GjRuH9+/coU6YMZs+ejRo1aqTK55dffsGyZcvg6+uLFStWoHjx4hg5ciQ6dOigbHXy8fHBp0+fYG1tjSFDhmD16tW4c+dOqnuJiIiIiIhEpSdJfokVK4+SCOzHpXGaGBghv76z6C0GYg2M8K2nazuifL+dosbU1MAImlivYg6MoEo3riwnY1m1E8uqnVhW9eLACJlp0GULwl/FZT5jLihVvADObOsmSqzcpvGWICIiIiIiyi0SQKQhsvPyyAja+/RIIiIiIiKiNLAliIiIiIhIW4g5dHXebQhiSxAREREREekWtgQREREREWkLjg6XJWwJIiIiIiIincJKEBERERER6RR2hyMiIiIi0hYSEYfIFm0o7tzHliAiIiIiItIpbAkiIiIiItIWHCI7S9gSREREREREOoUtQURERERE2oJDZGcJW4KIiIiIiEinsBJEREREREQ6hd3hiIiIiIi0Sd7tpSYatgQREREREZFOYUsQEREREZGWECQSCCI9xFSsOOrAliAiIiIiItIpbAkiIiIiItIWHCI7S9gSREREREREOoWVICIiIiIi0insDkdEREREpC0kEG+I7LzbG46VIBLP/VXWOhN7/j8xosZT+N1B/Nhj7EQNp5RfH/iS+E7kmBaixtOkD/JnGolb0FCqkdgFDcuJHlNTxN5vAM3srwDw5kuk6DGtTKV48fGh6HHNDIxEj6mJ/VWC/DA3LC9qTNJOrAQREREREWkLCQCxhq7Owy1BvCeIiIiIiIhENWXKFEyaNEllWocOHSCTyVRe386T0ufPn/H777/D2dkZ1atXx+TJk/Hx48csxWdLEBERERGRtvjBh8gWBAG+vr7Yvn07OnTooDL98ePHWLBgAWrWrKmcbmxsnO6ypkyZgnv37mHVqlVISEjAxIkTMWXKFCxcuDDTPFgJIiIiIiIitQsLC8PEiRMREhKCkiVLpnrv8+fPsLe3h6WlZabLevXqFQ4dOoSNGzfC3t4eAODj4wMPDw+MHTsWxYoVy/Dz7A5HRERERKRNJCK9sunGjRsoUaIEDh48CCsrK5X3Hj16hPz586NUqVJZXpaenh6qVaumnFatWjXo6+sjKCgo08+zJYiIiIiIiHIsIiICiYmJKtPMzc1hbm6uMs3NzQ1ubm5pLiMkJARmZmYYPXo0/ve//8HCwgLt27dHr169oKeXut0mMjIShQsXhoGBgXJavnz5ULhwYURERGSaMytBRERERESUY927d0d4eLjKNC8vLwwbNizLy3j8+DE+ffqEunXrYuDAgbhx4wbmzZuH2NhYDB8+PNX8nz9/hpFR6qHhDQ0N8fXr10zjsRJERERERKQtJBIRh8hOjhMQEJBmS1B2zJ07F58+fVJ+TiaTITY2Fn5+fhg2bBgk35Qpf/78kMvlqZYjl8thYmKSaTxWgoiIiIiIKMdKlCjx3cvIly9fqoqTTCbDx48fERsbm+q94sWLIzo6GomJidDX1wcAJCQkIDo6GkWLFs00HgdGICIiIiLSFoqWILFeuaRTp07w8fFRmXbnzh0ULVo0zVYlR0dHJCQk4ObNm8ppQUFBSEpKgqOjY6bxWAkiIiIiIiKNcnV1xfbt27Fv3z48f/4cO3fuxNq1a1XuB4qOjkZsbCwAoFixYmjRogUmTZqEoKAgXL9+Hb///jvc3NwyHR4bYHc4IiIiIiLtoQfxmjlyMU6/fv2QL18+rFy5Ei9fvkTJkiUxYcIEdOzYUTlPhw4dUKNGDcyZMwdA8nOBfHx8MGDAAOTLlw/NmjXDxIkTsxSPlSAiIiIiIhLVpk2bVP6WSCTo3bs3evfune5nTp8+rfK3qakpZs+ejdmzZ2c7PrvDERERERGRTmFLEBERERGRttDAENl5EVuCiIiIiIhIp7AliIiIiIhIW0j+/yVWrDyKLUFERERERKRT2BJERERERKQlBIkEgp44TTQC7wkiIiIiIiLKG1gJIiIiIiIincLucERERERE2oJDZGcJW4KIiIiIiEinsCWIiIiIiEhbcIjsLGFLUDoSEhKwceNGTadBRERERES5jJWgdBw5cgSzZ8/WdBpqIQgCPD09sXhhgKZTUTtdKOutQyewossgrOgyCPb29ljUuiem1WiBuLfvNJ2a2ujCegWAw4fPwM6uDWQyGTp2HI6YmDhNp6Q227ecQp3qg2Fvb4+m9UfiZtAjTaekNrq0XgHd2V8BYKPfYfzyyy9o7DQMfTr/gTev32s6JbXQpf01T9KTiPvKo1gJSocgCJpOQS0ePniGFq5e2LFjh6ZTUTtdKat9a1cM2eaHIdv8cO3aNRQoUhitxg5FgSIWmk5NLXRlvUZFRaN37wnYvXsZgoODUb58aYwfv0DTaalFSHAYpkxYi90HfXDr1i2MHt8VPTvP1HRaaqFL6xXQnf0VAP65+RirfPfh0qVLOHVtGcrZlMSCmVs0nVau06X9lbTbD10JiouLw/Tp01G7dm04ODigb9++ePr0KQDg1KlTaN++PapWrYoGDRpg2bJlSEhIAABcvXoVMpkMr169Ui7r22mNGjXC+vXrMWjQIFStWhV16tTB8uXLlfOOHTsWACCTybBnzx7s2bMHzZo1w7Rp0+Do6IjffvsNNWvWxNq1a1VyXrp0Kdzc3NT+3eSU38rd8PBsjU6dOmk6FbXTpbIqzJ07FwUKF4JTh9aaTkVtdGW9Hj9+AU5OVVChQlkAwODBXREQcFArL9AYGhnAd6U3ipcoAgBwcJQi8tU7yOXxGs4s9+nSegV0Z38FADuHn3Hu9koULFgQX77I8erlWxQqYqbptHKdLu2vpN1+6EqQt7c3Ll++jIULF2L37t0wMTFBv379cOTIEQwbNgwtWrTA/v37MXbsWGzatCnb3deWLl2Khg0b4tChQ/D09MSyZctw/fp1ODg4YMqUKQCACxcuoGXLlgCA0NBQxMXFYd++fRg6dChat26NAwcOKJcnCAIOHDgAd3f33PsSctkS39Ho1qOFptMQhS6VFQA+vvuAhQsXosXoQZpORa10Zb2Ghb1C6dLFlX9bWRVHTEwcYmM/ajAr9ShTtjiatXQGkHwcnTR2FVq0rglDQwMNZ5b7dGm9ArqzvyoYGOTDvn37UEPaB1cv3kPnHo01nVKu06X9Nc+S4L9hstX+0nRhc+6HHR3u6dOnOH/+PPz9/eHsnLyzzZgxA6tWrcLSpUvRokUL9O/fHwBQtmxZvH//Hn/88Qe8vb2zHKNhw4bo3LkzAKB///5YvXo1bt26herVq6NAgQIAAEtLS5XPDBkyBKVLlwYAtG/fHps2bcKjR48glUoRFBSEyMhItG3bNltlNdJ3yNb8uWMlDPSskV/fWQOxxSZ+WX/XwCqdNWsW3NzcsKi1h/jBNUK7t+GkpMIA4gBI/39K8r/6+jIApmqPX9BQmvlMuezjx4/o1L0TwsI+4ejRoyhoWEj0HNRN0+sVAPLrixLmG5rZX63E+UpVY7YD2rWLwZo1a9DLfTYeP34MPb0f+ppzjujC/kra7YetBD16lHyTnZ2dnXKahYUFxo8fj61bt6Jnz54q8zs5OSEhIUHZXS4rypYtq/K3mZkZ4uPTb86VSCSwsrJS/l25cmXIZDLs378fY8aMwYEDB+Di4oLChQtnOQcA+Jp4EwLk2fpMbohPeo4viVdFj6sJYpd1/j8xosVS+HPjOmxfsx4zb54QNe4YO3NR46Uk9nrNry/efVbW1ga4ejUEwCMAUoSHn4eFRUGYmoaLEv+D/JkocRTCnr9Gl/ZTYVu5BvYdmwiJ8VV8EPGwWNCwnChxNL1eAeBLomYGTdHEOefNl0jRYj17EoGoyHdo7zoeLz4eQNPOP2HQoH9x78UWWBRR/3HSzMBI7TEUNLm/SpAf5ob1xQmWV3GI7Cz5YS9N5MuXfv0sf/78qaYlJiZm+DnF+ykZGhqmmpZRv2w9Pb1Un2nfvj0OHz4MuVyOwMBAtG/fPt3PE6nL55hYRIeFo3bt2ppOhXJJ06Z1ceXKbYSEhAIA/Py2wc1N+7rWAMC76Fi0ajIGbdrVwbZt22BsLN6PObHp0nrVNa9fRWOo5wK8efMGALB3+1nIKluLUgESky7tr6TdftiWIBsbGwDA3bt34eTkBCB5oARXV1e8f/8eN27cQI8ePZTzBwUFwcDAANbW1ggJCVHOrxAaGpqt+BJJ1qq2bdq0wfz587F+/Xro6emhfn1enSDxRYe9hNlPRWBgwD7Z2qJo0SLYsGE2OnQYDrlcAhub4vD3n6vptNRi3epDeBEWhUP7LyHwgD0ShVgAwIGjc1BYy35A6tJ61TXOdX7BsDEd0aBBAwh6n1CsRGGs3TZR02nlOl3aX/MsiYhDV2fx9/KP6IetBJUrVw6NGzfG9OnTMW3aNFhYWGDJkiUwMzPDvHnzMGDAAFSqVAmurq548OABfH190bFjR5iZmUEqlcLExAR+fn4YPnw4QkNDsWHDhmzFNzVN7kh8584dlC9fPt35ihQpgnr16mHlypXo2LFjmq1LP6KNGzfqTFc4XShrqV9k8D6wUdNpiEoX1mvLlvXRsmV9JN83or3P4Rg9vitGj+8KACho2Awf5Mc0nJF66cp6TUkX9lcA8OjfAhO9V+LFxwOZz5xH6dr+Strrh+0OBwBz5sxBlSpVMGTIEHTq1Anx8fFYu3YtXFxcMHfuXOzbtw+tW7fG/Pnz4eHhgUmTJgEAChQogPnz5+Pu3bto2bIlfH19MW7cuGzFdnZ2Ro0aNdC1a9dMn2/Qrl07fPnyBe3atctpUYmIiIiIvh8flpolP2xLEACYm5unO+x127ZtMxyFrUmTJmjSpInKtODgYOX/T58+neozKacVKFAAmzZtUnk/vft9oqKiIJPJYGtrm24+RERERET0Y/ihK0E/urt37+LJkyfKbndERERERPTjYyXoO9y4cQMLFy5E8+bN0aFDB02nQ0REREQ6TpAkv8SKlVexEvQdPDw84OGhKw+mJCIiIiLSDqwEERERERFpCzEHLMjDAyP80KPDERERERER5Ta2BBERERERaQuJRLyHmObhh6WyJYiIiIiIiHQKK0FERERERKRT2B2OiIiIiEhbSEQcGIHd4YiIiIiIiPIGtgQREREREWkLPYjXzJGHm1PycOpERERERETZx5YgIiIiIiJtwSGys4QtQUREREREpFNYCSIiIiIiIp3C7nBERERERNpCT8QhssWKowZsCSIiIiIiIp3CliAiIiIiIi0hSABBpAELhLzbEMSWICIiIiIi0i1sCSIiIiIi0hYSiNfMwZYgIiIiIiKivIGVICIiIiIi0insDvcDMNIvCCBB9Lj59S1Ej6kpYpf1dwfNfbe/O5QRNV7tva9FjadwyR1odCBe1JirGwSLGk/B1kKKu+/EjW1rIRM1XkoFDcuJHvOD/JnoMQsaSjUSVxPy6wNfE9+LHven/MVEj6mpuI9jxD8WFzQEwj7KRY1poKcPc0NRQ+Y9HCI7S9gSREREREREOoUtQURERERE2kIiSX6JFSuPYksQERERERHpFLYEERERERFpC94TlCVsCSIiIiIiIp3CliAiIiIiIm0hgXgPMc27DUFsCSIiIiIiIt3CShAREREREekUdocjIiIiItIWEgkEsQYs4BDZREREREREeQNbgoiIiIiItAWHyM4StgQREREREZFOYUsQEREREZG2kEjEu1eH9wQRERERERHlDawEERERERGRTmF3OCIiIiIibaEH8Zo58nBzSh5OnYiIiIiIKPvYEkREREREpC04MEKWsCWIiIiIiIh0CluCiIiIiIi0hQTiPcQ07zYEsSWIiIiIiIjENWXKFEyaNEll2ubNm9G8eXPY29ujZcuW2LlzZ4bLCAgIgEwmU3lVrlw5S/HZEkRERERERKIQBAG+vr7Yvn07OnTooJy+ZcsWLFy4ENOmTYODgwOuXr2K6dOnw8DAAO3atUtzWY8ePUKjRo0wY8YM5TRJFu9TYiWIiIiIiEhb6EnE6w6XzThhYWGYOHEiQkJCULJkSZX3tm3bhm7dusHNzQ0AYG1tjZs3b2LPnj3pVoJCQkJQs2ZNWFpaZj/1bH+CiIiIiIgom27cuIESJUrg4MGDsLKyUnlv8uTJ6NKli8o0PT09xMTEpLu8x48fw8bGJke56ERL0KtXr1C/fn34+/tj7969ePXqFTZu3KjptIiIiIiIcpUgkUAQaehqRZyIiAgkJiaqvGdubg5zc3OVaW5ubsqWnm/VqFFD5e+XL1/i8OHD6NGjR5rzR0ZG4sOHDzh37hyWLVuGz58/w8nJCWPGjEGxYsUyzV3nWoImTZqEpUuXajoNjTl8+Azs7NpAJpOhY8fhiImJ03RKasOyahdBEPB42R94uW8rACAxMRHP1i3FzWHdcWNIF7w6tk+zCarRvn37UNO6s6bTUCtd2IYVtm85hTrVB8Pe3h5N64/EzaBHmk5JbXSprEDyccrT0xOLFwZoOhVR6MKxibKme/fuaNy4scrrr7/+yvHyoqOjMXDgQPz0008YMGBAmvOEhIQAAPLly4fFixdj9uzZCA0NhaenJ758+ZJpDJ2rBJmZmaFgwYKaTkMjoqKi0bv3BOzevQzBwcEoX740xo9foOm01IJl1a6yfnoRivtTvfH24t/KaatWrcKXiBewX/IX7OatQcShnYgNua/BLNXj3ycvMXr0aCQJgqZTURtd2IYVQoLDMGXCWuw+6INbt25h9Piu6Nl5pqbTUgtdKisAPHzwDC1cvbBjxw5NpyIKXTg25Vl6Ir+QPErbqVOnVF69evXKUfphYWHo2rUrYmJisH79epiZmaU5X926dXH58mX88ccfqFSpEmrXro0VK1bg2bNnOHv2bKZxtLISFB4ejgEDBsDBwQGNGjXC+fPnle+NHz8enp6eAJKvJM+dOxcuLi6wtbVFmzZtEBgYqJxXEARs3LgRTZs2RdWqVeHm5qbypT569Aj9+/eHk5MTatSogbFjxyI6Olq0cmbX8eMX4ORUBRUqlAUADB7cFQEBByFo4QGMZdWusr4K3IuijVqgSJ2Gyml79+5F0UYtIdHPh3wFzPBTncZ4c/a4BrPMfZ8/fcWEgYuwaNEiTaeiVrqwDSsYGhnAd6U3ipcoAgBwcJQi8tU7yOXxGs4s9+lSWQHAb+VueHi2RqdOnTSditrpyrGJsq5EiRKwsrJSeX3bFS4r7t27h86dO0NPTw/btm1D6dKlM5y/cOHCKn8XLVoUFhYWiIiIyDSW1lWC4uPj0a9fP3z+/Blbt27FrFmzsHr16jTn3bJlC06cOIFly5bh6NGjaN68OUaNGoWwsDAAwJo1a+Dr64shQ4bg4MGDaN68OYYOHYqQkBC8ePECXbt2RcGCBREQEIAVK1bg4cOH6NOnT6o+kT+KsLBXKF26uPJvK6viiImJQ2zsRw1mpR4sq3aVtXz/kbBs0FxlWlhYGAyLFFX+bVjEEvK3UWKnplYzfvsTHT2bwc7OTtOpqJUubMMKZcoWR7OWzgCSL7RNGrsKLVrXhKGhgYYzy326VFYAWOI7Gt16tNB0GqLQlWMTievJkyfo06cPSpUqhS1btqBEiRIZzu/v74+6desiPv6/Cyvh4eGIjo5GhQoVMo2ndQMjXLp0Cc+ePcO6deuUQ+9Nnjw5zf6E//77L4yNjVGqVClYWlpiyJAhsLOzQ6FChSAIAvz9/dG7d2/lsHyDBw9GQkICPn36hL1798Lc3ByzZ8+GgUHyAX3x4sVo2bIlzp8/jwYNGmQj6/LfWeqsSUoqDCAOgPT/pyT/q68vA2AqSg5iYVnFK+sld2nmM+USz/3FYGtbDqPd60I6LglrGlRFzZo1AQBr3z7Esain2OleV7R81GnFihX4yaQsfh/mi9DQUOhBH7YWbTSdllpoehsGgIKG4m3HAPDx40d06t4JYWGfcPToURQ0LCRqfDHpUlmTrYSBnjXy6zuLGtXWQpw4unRsyrMkkuSXWLFyybhx42BoaIh58+YhISEBUVHJFzb19fWVLT5RUVEwMTGBqakpGjRogMWLF2PSpEkYOHAg3r9/jz/++AOOjo6oU6dOpvG0rhIUEhICCwsLlbHHq1atmua83bp1w4kTJ1CvXj3Y2trCxcUFbdq0gZmZGaKjoxEVFZXqKsewYcMAAMuXL0eVKlWUFSAAsLGxgYWFBR49epTNStBTAAnZmD9nrK0NcPVqCIBHAKQIDz8PC4uCMDUNV3tssbGs4pW19t7XosQBgMfPI3FGMMWevRdgbW2NXntPo0hE8r4TdvIyEr9IUHvvBbXFX93gndqW/a2V65bgy6evqFilPPQS8+Pz58+oWKU8VmyfgqL/371InWwtZGqPoaDpbRgAPsifiRYr7PlrdGk/FbaVa2DfsYmQGF/FB7lo4UWl6bIa6RcSL1gK8UnP8SXxqqgxH8eIcyzW9LHJQM8YsoJN1B6HxPXs2TPcuXMHANC8uWrPD2tra5w4cQJA8n1AXl5eGDZsGKytrbFhwwYsXLgQHTt2hIGBARo1aoTx48dnKabWVYIkEkmqfuQpKyoplS9fHidPnsTly5dx8eJFHD58GKtWrcLatWtha2ubYZz8+fOnOT0pKSndeJrWtGldjBo1FyEhoahQQQo/v21wc2us6bTUgmXVzrKm5Obmhqn+21DYqTYSv3zG2wunUG7QKE2nlWu2nlyo/H+BD1VQ2bYSdp3TzpEtdWkbfhcdi1ZNxqCbhytmz9iED/Jjmk5JbXSprLpEl45NeZZExIelfkdL0KZNm5T/L1euHIKDgzP9zLfz2NvbqywnO7SuElSpUiW8e/cOoaGhKFu2LADg7t27ac4bEBCAQoUKoVWrVqhXrx7GjRuHtm3b4tixY6hVqxYsLS1x584d1K9fX/mZnj17on79+rCxscH+/fsRHx+vrPQ8fvwYHz58yPFDm9StaNEi2LBhNjp0GA65XAIbm+Lw95+r6bTUgmXVzrKmNHjwYMw6dh63f+sNISEBxVzbouAvDppOi3JAl7bhdasP4UVYFA7tv4TAA/ZIFGIBAAeOzkHhItm/ifhHpktlJaK8RyJo2fA7iYmJ6NixI/T19TFlyhTEx8dj6tSpePToUaqHpS5fvhybN2/G7NmzIZVKcf/+fYwZMwZTp06Fu7u7cp7p06ejSpUqOHToEPz8/LB7925YWFigefPmaNCgAQYOHIgPHz7Ax8cHgiBg165d2WwNEqc7nCopkrue6AKWVZ3E7A6X0iX3umrt+pYWMbvDpWRr0QZ33x0UOaZ43eFUaWZ/FbM7nEJBw2Y60zqiqbJqojtcfn1n0bvCAeJ1h0tJE8cmdofLXJ1Vl/AiJvPn5OQGK/P8uDiwtiixcpvWtQTp6+tjzZo1mD59Ojw8PFCgQAF4e3tj4sSJqeYdNGgQvnz5gunTp+PNmzcoUaIEhg0bBnd3dwCAh4cHvnz5gvnz5ytHmvDz81OOOLF+/XrMnz8fv/76K4yNjdGoUSOMGTPmh+0OR0REREREWlgJAoAiRYrA19dXZdqvv/4KAHB2/m+0lnz58mH06NEYPXp0msvR09PDoEGDMGjQoDTft7Ozy3E/RCIiIiKiXCf5/5dYsfIorXtOEBERERERUUZYCSIiIiIiIp2ild3hiIiIiIh0kaAngSDSENlixVEHtgQREREREZFOYUsQEREREZG2kEi+6yGm2Y6VR7EliIiIiIiIdApbgoiIiIiItIUEgFj36uTdhiC2BBERERERkW5hJYiIiIiIiHQKu8MREREREWkLCcTrpsbucERERERERHkDW4KIiIiIiLSEnl7yS6xYeVUeTp2IiIiIiCj72BJERERERKQtRHxWKu8JIiIiIiIiyiNYCSIiIiIiIp3C7nBERERERFpCImJ3ONG63akBW4KIiIiIiEinsCWIiIiIiEhLSCQSSERqohErjjqwJYiIiIiIiHQKW4KIiIiIiLQE7wnKGrYEERERERGRTmFL0A/ga+IHCJCLGjO/PvAl8Z3IMS1EjadJYn+3CppYr6fbGogaT5Oxq/kXFDWewv3eQKd94sa+3P2ZqPEUChpK8UEufuywj+IegwGgoKFm4v5sXlT0mABgpF9I9JhvvkSKHtPKVDNxbS0qih4zOa5M5Ij86Uq5g1sSEREREZGWkEDE7nDihFELdocjIiIiIiKdwpYgIiIiIiItIdFLfokVK6/Kw6kTERERERFlH1uCiIiIiIi0BIfIzhq2BBERERERkU5hSxARERERkZaQSAA9tgRlii1BRERERESkU1gJIiIiIiIincLucEREREREWoIDI2QNW4KIiIiIiEinsCWIiIiIiEhLsCUoa9gSREREREREOoUtQUREREREWkIikUAiUhONWHHUgS1BRERERESkU1gJIiIiIiIincLucEREREREWkIiASQiNXPk4d5wbAkiIiIiIiLdwpYgIiIiIiItwSGysyZHlaAnT55g//79CA8Ph1wuhyAIqeaRSCRYtmzZdydIRERERESUm7JdCfrf//6Hfv36IT4+Ps3Kj0JeHjKPiIiIiCgvYktQ1mS7EuTr64uEhAR4e3ujfv36KFCgACs8RERERESUZ2S7EnT37l20bNkSAwcOVEc+REREREREapXtSpCRkREsLS3VkQsREREREX0HdofLmmwPkV23bl1cuHABiYmJ6sgnQzKZDPv370/3/fHjx8PT0zPLy2vUqBFWrFiRC5kREREREVFeke1K0NixY/Hp0yd4e3sjKCgI0dHRiIuLS/MltkmTJmHp0qWix81rBEGAp6cnFi8M0HQqanX48BnY2bWBTCZDx47DERMj/jYpJl1Zr4D2l1UQBLxcNxtvj25TTkv8FIunU3rj87OHGsxMfbZvOYU61QfD3t4eTeuPxM2gR5pOSe327duHmtadNZ2G2mn7/prSRr/D+OWXX9DYaRj6dP4Db16/13RKapN8jrWDTNZMJ86xeYlEAuiJ9NKplqBu3brh06dPOHHiBHr06IE6derAyckp1atGjRrqyDdDZmZmKFiwoOhx85KHD56hhasXduzYoelU1CoqKhq9e0/A7t3LEBwcjPLlS2P8+AWaTkttdGW9Atpf1q8vQ/F8/kjEXPtbOe3IkSMInTkIXyOeazAz9QkJDsOUCWux+6APbt26hdHju6Jn55maTkut/n3yEqNHj0ZSBqOsagNt319T+ufmY6zy3YdLly7h1LVlKGdTEgtmbtF0Wmrx3zl2N4KDj2n9OZa0U7YrQSVLlsTPP/8MJycnVK9ePd2Xo6OjOvLFkydP0LNnT1SpUgWNGjXCrl27lO+l7A539epVVK5cGUePHkWjRo3g4OCAgQMHIiIiQmV5kZGRGDRoEKpWrYq6devCz89P5f1Tp06hffv2qFq1Kho0aIBly5YhISEhWzF+JH4rd8PDszU6deqk6VTU6vjxC3ByqoIKFcoCAAYP7oqAgIMZDuuel+nKegW0v6zvTu9DobotYO7UUDnN19cXJfpOgEGhnzSYmfoYGhnAd6U3ipcoAgBwcJQi8tU7yOXxGs5MPT5/+ooJAxdh0aJFmk5F7bR9f03JzuFnnLu9EgULFsSXL3K8evkWhYqYaTottfjvHFsBgPafY/MaxT1BYr3yqmwPjLBp0yZ15JFlAQEBmDlzJmbNmoW//voLv//+O5ydnVG6dOlU8yYmJmLhwoXw8fFBoUKFMGPGDPTr1w/79+9HvnzJRd+9ezcmTZqESZMmITAwEAsXLoSjoyOcnJxw/PhxeHt7Y+TIkXB1dcX9+/cxbdo0vH//Hr///nuWY/xIlviOBgCc+3ulhjNRr7CwVyhdurjybyur4oiJiUNs7EeYmxfQYGbqoSvrFdD+shbv4Q0A+PjghnLa0aNHUXnDOQ1lpH5lyhZHmbLJ+6sgCJg0dhVatK4JQ0MDDWemHjN++xMdPZvBzs5O06monbbvr98yMMiHffv2oU/fPjA0MsDoyd00nZJa6No5lrRTtluCvhUXF4c3b94oW0fUrUePHmjZsiVKly6NYcOGISkpCQ8ePEh3/gkTJqB27dqoXLky5s2bh8ePH+Py5cvK95s1a4auXbuidOnSGDBgAMzMzHD37l0AwOrVq9GiRQv0798fZcuWRcuWLeHt7Y1t27YhNjY2yzFIfElJSWlO19f/7k2eiNTk48cv6NSpE54+iYCvn7em01GLbeuOQF9fH+49XDWdCqlJu3bt8M/zzfhtYlf0aDct3fNRXsZzLGmDHDVVJCQkYM2aNdi1axdevnypnG5tbQ13d3f069dPba0gZcuWVf5fcf/Ply9f0p0/5b1J1tbWKFy4MB49egQXFxcAQLly5VTmNzc3Vy4vJCQE7dq1U3nfyckJCQkJePr0aZZjZMZI3yFL8+WulTDQs0Z+fWcNxFY/a+tquHr1CQApACA83BQWFhYwNbUXJX5+fVHCpEG716sq8ct6v7dooeB5dj1sbctjdO96/x+7HspON8LOttVQvXp18RIRyfPnz9GmTRtUqlQJ587cgLGxsajxCxqKE+f4jpn49OkTejT8HXK5HF8/y9Gj4e84cuQISpYsKU4SGqGZY5OVqXixHj9+jFevXsGqLmBl2hajBrfChBErYSp3QZEiRcRLRAT/nWMBQIrw8H9FPcdSxjhEdtZku6Yil8vRt29fXL9+HUZGRqhYsSKKFi2KDx8+4OHDh1i6dCkuXryIjRs3Ql8/938J6umlvsqQUR/UbytjSUlJKsvIaHn58+dP9Z5iaPCUy80sRma+Jt6EAHmW588t8UnP8SXxqmjx8utbiBaraVMbjBp1ESEhx1GhQlP4+c2Cm1tDAOKMOPUl8Z0ocdIi9nrVJLHLWs3/q2ixXj6OROBnE6zfcA73e9dD5Q3n8DLuKzoeuAHjO5/UHv9y989qj6HwLjoW9et5oZuHK2bP2IQP8mOQi3xIDPsoTsD1x35X/r/AhyqobFsJm/+eiWgEIfpdkCg5/GxeVJQ439LEsenNl0jRYt1+eg9evRfizu2H+GJ8Cbu2nIassjU+57+IFx/VH9/KtKL6g/y//86xIahQQYCf30IRz7H5AJQXIQ5pu2xXgjZs2IBr166hTZs2mDBhAgoXLqx8Ly4uDn/88Qf27duHTZs2ZeuZPepy9+5d5VXTZ8+e4f3796hUqVKWPmtjY4MbN26gR48eymlBQUEwMDCAtbU17t+//90xSD2KFi2CDRtmo0OH4ZDLJbCxKQ5//7maTouI0rBu9SG8CIvCof2XEHjAHolCcnfjA0fnoHARcw1nR5Q1znV+wbAxHdGgQQMIep9QrERhrN02UdNpqcV/59gOkMvjYGNjzXPsD0QikUCiJ04TjSQPNwVluxJ04MABSKVSzJ07N1VrR4ECBeDj44N79+5h7969P0QlaPr06Zg+fToMDAwwY8YMVKlSJcvDdw8ePBgDBgxApUqV4OrqigcPHsDX1xcdO3aEmdl/I758TwxN2bhxo9a3FrRsWR8tW9ZHcpc47X/mCKAb61VB28tasu+EVNN+nr9dA5mo3+jxXTF6fFcAQEHDZvggP6bhjMRRtmxZ/C9M+4eOBrR/f1Xw6N8CE71X4sXHA5pORe2Sz7H9oSvnV9I+2a4EhYWFoWvXrul299LX10fNmjWxc+fO704uN7Rr1w7e3t6Ii4tDw4YNMWnSpCx3VXNxccHcuXOxatUqLF26FEWLFoWHhwcGDhyYazGIiIiIiHIL7wnKmmxXgoyNjfHmzZsM53n79i0MDXP/LtPg4OAMp82ZMyfV+61atULfvn3TXN7p06cznda2bVu0bds2w7wyikFERERERD+WbDdXODo64uTJk3j48GGa79+/fx8nTpxAtWrVvjs5IiIiIiLKOj4sNWuy3RI0aNAgnDt3Dj179kSvXr3g6OgIMzMzREZGIigoCFu3bkVSUhIGDx6sjnyJiIiIiIi+S7YrQXZ2dliyZAkmTpyI5cuXq4wKIQgCzMzMMG/ePI0/CdvZ2TnN7nN5LQYRERERkbaZMmUKEhMT8ccffyinXbhwAfPnz8ezZ89QpkwZjB49GvXr1093GZ8/f8asWbNw/PhxJCYmonnz5pgwYQJMTTN/SFiOnmjapEkT1KxZEydPnkRwcDDi4uJgamqKihUrokmTJihQoEBOFktERERERN/hRx8YQRAE+Pr6Yvv27ejQoYNy+uPHjzF48GAMGTIETZs2xcGDBzF06FDs3bsXFSpUSHNZU6ZMwb1797Bq1SokJCRg4sSJmDJlChYuXJhpHjmqBAHJw2G3a9cupx8nIiIiIiIdEhYWhokTJyIkJAQlS5ZUec/f3x/29vbKW2q8vb0RFBQEf39/zJw5M9WyXr16hUOHDmHjxo2wt7cHAPj4+MDDwwNjx45FsWLFMswl00qQIiFF9zZ/f/8sFRIAPDw8sjwvERERERF9H4kEEOlZqdluCbpx4wZKlCiBRYsW4bffflN57/r162jRooXKNGdnZxw+fDjdZenp6akMxlatWjXo6+sjKCgILVu2zDCXTCtBs2bNgpeXl7ISNGvWLEgkEgiCkOHnJBIJK0FERERERFouIiICiYmJKtPMzc1hbm6uMs3NzQ1ubm5pLuPVq1epWm+KFi2KV69epTl/ZGQkChcuDAMDA+W0fPnyoXDhwoiIiMg050wrQbNnz0alSpWUfysqQURERERE9GPRxD1B3bt3R3h4uMp7Xl5eGDZsWJaX9eXLl1TPGTU0NMTXr1/TnP/z588wMjJKNT2jz6SUaSXI3d1d5e/27dtnulBFYkREREREpN0CAgLSbAnKDiMjI8THx6tMk8vlMDY2TnP+/PnzQy6Xp5oul8thYmKSabxsPyy1cePG2LRpU4bzLF++HI0aNcruoomIiIiIKI8pUaIErKysVF7ZrQSVKFECr1+/Vpn2+vXrdAc4KF68OKKjo1UqXwkJCYiOjkbRokUzjZdpS9CLFy8QFxen/Ds8PBxPnjzBw4cP05w/Pj4ely9fZksQEREREZHIJBJAku1mjpzHyi2Ojo64du2ayrSrV6+ievXq6c6fkJCAmzdvKucJCgpCUlISHB0dM42XaSXo9u3bGDVqlPI+IIlEgu3bt2P79u3pfkYQBNSpUyfT4ERERERERD169MCvv/4KX19ftGrVCocOHcLt27cxbdo05TzR0dEwMDCAmZkZihUrhhYtWmDSpEmYNWsWBEHA77//Djc3t0yHxwayUAlq1aoV7t+/j+joaAiCgH379qFixYoqgyWkZGBggKJFi6J79+5ZLzUREREREX23H/1hqemRyWRYvnw55s+fjzVr1qB8+fLw8/ODjY2Ncp4OHTqgRo0amDNnDoDk5wL5+PhgwIAByJcvH5o1a4aJEydmKV6WHpY6ZswY5f//97//oX379hz+moiIiIiIciStMQYaNGiABg0apPuZ06dPq/xtamqK2bNnY/bs2dmOn6VKUFrBo6OjIZfLUbx4ceV7Bw4cQM2aNbN0MxIREREREeUuiUQi2uNs8vJjc3J029Ty5ctRr1497N+/XzktPj4e48ePz9LocURERERERJqS7UrQ7t27sXz5ctjY2MDOzk45XSKRYM6cOahQoQJmzZqFAwcO5GqiREREREREuSHblaCAgADY2Nhg586dqFWrlnJ6vnz50LZtW2zbtg3W1tbYuHFjbuZJRERERESZUAyMINYrr8p2JejZs2eoX78+DA0N03zf0NAQDRs2xOPHj787OSIiIiIiotyW7YERTE1NERERkeE8b9++hbGxcY6TIiIiIiKi7MurQ2SLLdstQU5OTjh58iT++eefNN9/+PAhTpw4kaUntRIREREREYkt2y1BgwYNwt9//w0PDw+4ubnB3t4eBQoUwMePH/HPP/9g3759EAQBXl5e6siXiIiIiIjSI+a9Onm4JSjblSCZTIaVK1di0qRJ2L59O3bs2KF8TxAElChRArNmzULlypVzNVFt9jUxBgK+iBozvz7wNfG9qDE1Jb8+8CXxnagxNfXdamK9FjQsJ2q8lPLrW4ga735vUcN9E7t45jPlorYnIkWNp3DAFeh51kT0uDsaFRI9JgD8bC7+c/U0cXzS1DnHyrSi6DE1GZeIsi7blSAAqFWrFk6ePIlbt27h4cOHiImJgYmJCaRSKZycnKCvr5/beRIREREREeWKHFWCAEBPTw/VqlVDtWrVcjMfIiIiIiLKIT1J8kusWHlVppUgf39/2NvbKx+M6u/vn+WFe3h45DwzIiIiIiIiNci0EjRr1ix4eXkpK0GzZs2CRCKBIAgZfk4ikbASREREREQkIrYEZU2mlaDZs2ejUqVKKn8TERERERHlVZlWgtzd3TP8m4iIiIiIfgx6EKAnybjHVm7Gyquy/bBUIiIiIiKivCzTlqCc3tcjkUjw119/5eizRERERESUfRIR7wkS7aGsapBpJeh///tfxgvIlw9mZmb4/PkzvnxJfuCnkZERjIyMcidDIiIiIiKiXJRpJejatWsqf798+RJ9+/ZF+fLlMXr0aNja2kJPL7lXXUhICBYuXIgHDx5g48aNakmYiIiIiIjoe2R6T5CZmZnKa/ny5ShQoABWr14NOzs7ZQUIACpUqIBly5bB3NwcPj4+ak2ciIiIiIhU6Yn8yquynfvFixdRr1495M+fP833DQwMULt2bdy4ceO7kyMiIiIiIsptmXaH+5axsTEiIiIynOfx48cwMzPLcVJERERERJR9ehIRh8gWKY46ZLslqFatWjh16hT279+f5vsbN27EpUuX0Lhx4+9OjoiIiIiIKLdluyVo5MiRuHLlCsaPH4+1a9fC1tYWpqamiIuLw82bN/H8+XOUKVMGI0aMUEe+RERERESUDj0Rh8gWK446ZLsSVKpUKezevRuLFi3CiRMnEBISonzPzMwMXbp0wciRI2Fubp6riRIREREREeWGbFeCAKBYsWKYO3cufHx88Pz5c8TExMDc3BxlypRBvnw5WiQREREREZEovmtkO7lcjpiYGHz48AE2Njb4+PFjbuVFRERERETZJIF4w2Pn4d5wOasEvXnzBiNHjoSzszO6deuGIUOGAAC2bNkCV1dXXL9+PVeTJCIiIiIiyi3ZrgRFR0ejc+fOCAwMhJ2dHSpXrgxBSB4ez9jYGC9fvkT//v0RHByc68kSEREREVH6FAMjiPXKq7JdCfL19UVERARWrlyJLVu2oGHDhsr3PD09sX79eiQkJGDlypW5migREREREVFuyPYoBqdPn4arq6tK5SclZ2dnNG3aFEFBQd+dHBERERERZZ1EIkAi0kNMxYqjDtluCXr37h1Kly6d4TzFihVDdHR0jpMiIiIiIiJSl2xXgooXL4779+9nOM8///yD4sWL5zgpIiIiIiIidcl2JahZs2a4fPkytm3blub7GzZsQFBQEJo0afLdyRERERERUdZxYISsyXYlaNCgQfj5558xffp0tGnTBoGBgQCA8ePHo02bNpg3bx6sra0xaNCgXE82N8lkMuzfv1/TaYhu+5ZTqFN9MOzt7dG0/kjcDHqk6ZTUShAEeHp6YvHCAE2nola6tF4PHz4DO7s2kMlk6NhxOGJi4jSdktroUlmf7duN0326w97eHkGzpkMeE6PplNSKxybtlLzP2kEma6b1+6wulZW0U7YrQQUKFMDWrVvRpUsXhIeH48mTJxAEAfv27cO///4LNzc3bN26Febm5urIN9dcuHABzZs313QaogoJDsOUCWux+6APbt26hdHju6Jn55maTkttHj54hhauXtixY4emU1ErXVqvUVHR6N17AnbvXobg4GCUL18a48cv0HRaaqFLZX1z6wYe79iCWnMX49atWyhaoyb+WTJf02mpDY9N2um/fXY3goOPafU+q0tlzYvEelCq4pVXZTv3Fy9eoECBApg6dSquXbuGQ4cOYcuWLdi3bx+uX7+OOXPmoHDhwurINVdZWlrCyMhI02mIytDIAL4rvVG8RBEAgIOjFJGv3kEuj9dwZurht3I3PDxbo1OnTppORa10ab0eP34BTk5VUKFCWQDA4MFdERBwUPmsMm2iS2X9EBKMnxwcYWxZFABQok49RF69hKR47duGAR6btPHYBKTcZysA0O59VpfKStor20Nke3h4oEqVKli6dCn09fXx888/qyMvtZPJZJg3bx6aN2+ORYsW4fjx44iKikKBAgXQsGFDTJkyBcbGxmjUqBHCw8NTfX7YsGEoWbIkJkyYkObyf8SHxZYpWxxlyiYPWCEIAiaNXYUWrWvC0NBAw5mpxxLf0QCAc39r9zOrdGm9hoW9QunS/w26YmVVHDExcYiN/Qhz8wIazCz36VJZC8kq4em+3fgU+QoAEHb8CJLi4yGP+YD8RX7ScHa5j8cm7Ts2Abq1z+pSWfMiiUSAHofIzlS2K0Fv3rzJdIjsvGTu3Lm4cOEC5s+fj+LFi+Off/7B+PHjIZPJ4OnpiV27diExMVE5/6JFi3D+/Hl07NgRBQsWhIuLi/K98PBw9OnTB7169dJEUbLs48cv6DegE0KfR2DXQR9Np0O5RBfWa1JSUprT9fXzcoN82nSprEXs7CHr4Ylr0yeh+pK5kNSqBwMzc+gZaOePZV2jC8cmQLf2WV0qK2mvbFeCnJyccOnSJcjlchgaGqojJ1FVrVoVrVq1gqOjIwDAysoKW7ZswaNHyTdvpuzat2vXLhw+fBibNm1CsWLFAAD58+cHAMTFxWHy5MmoXbs2hg8fnq0czA3r50ZRsuT58+do06YNKlWqhHNnbsDY2Fi02JqzEgZ61siv7yxaxPz6ooUCoDvr1dq6Gq5efQJACgAIDzeFhYUFTE3tNZqXOmi6rAdcpaLEAYDY2FhEliuBnxfNBQBERkai8pa/ENi+FSSSPDz0UKbEPzYB4h6fdOXYBKTcZwFAivDwf3Xg+ARoe1lJe2W7EtSxY0f4+PigWbNmcHFxgZWVlbIi8C0PD4/vTlDd3NzccOHCBcybNw+hoaF4/Pgxnj9/DisrK5X5rl27hmnTpsHHxwdVq1ZVeS8pKQm//fYb9PT0MG/evGyftGPkZyHgy3eXJTPvomNRv54Xunm4YvaMTfggPwa5XO1hlYz0C4kX7BvxSc/xJfGqaPG+Jr4XLZam12tBw3KixWra1AajRl1ESMhxVKjQFH5+s+Dm1hCA9o04pemytj0RKUocAIgLe47L40aiwRp/BLZrjpoDBsO8bn24nbwgWg47Gmnmop7YxyZAvOOTpo9NgKaOTyGoUEGAn99CHTg+aaKs+QCUFyFO3qUH8Yauzsttf9muBHl7eyv/n9HINhKJJE9UgiZNmoRTp07B3d0dTZs2xciRIzFjxgyVecLCwuDl5QUPDw+0a9cu1TLmzJmDO3fuYNeuXTAxMREp8+xbt/oQXoRF4dD+Swg8YI9EIRYAcODoHBQu8mOP5kfp06X1WrRoEWzYMBsdOgyHXC6BjU1x+PvP1XRaaqFLZS1Q2ho/d+6GC8MHQjYuPxLL/YwqXiM1nRZ9J106NgEp99kOkMvjYGNjrbX7rC6VlbSXRMjmUB579+7N8rzu7u7ZTkgsMpkMc+bMwfjx47Fs2TI0bdoUAJCQkIAmTZrA2dkZc+fORWxsLDp37gwrKyv4+flBT0+1zrtjxw7MmDEDGzduRPXq1XOUi1gtQSkVNGyGD/JjosbUVEtQfn1nrb3S+i1NrFcxr7SqkkIbr7CmTfyyitkSlNIBVxe0PXFe9LiaaAnSxLEJ0MzxSRPHpuS4mjg+8dikXmwJysxvV67gzdevosT6ycgIi2rWFCVWbstyS1BERAQuXLiAd+/eoVixYqhXrx4sLCzUmZvaSSQSFChQAKdOnULFihURFxeHVatWISIiAnK5HImJifD29oYgCJg6dSqio6OVwz8aGBggODgYM2bMwLhx41C+fHlERUUpl12wYEGtuGeKiIiIiEjbZKkStHTpUqxZs0ZllLT8+fNj3Lhx6NKli9qSUzeJRIIlS5Zg7ty5aN26NQoXLox69eqhT58+OHnypLLiBwCNGjVS+WyNGjVQqlQpxMfHw8fHBz4+qiPe+Pv7w9lZ3JtdiYiIiEi36UlEvCcoD49dk2kl6MCBA1i5ciWMjY3RokULFCtWDM+fP8fp06cxffp0lClTBrVq1RIj11yTkJAAILk1x8XFRWWYa4Vx48YByPx5P3PmzMn9BImIiIiISG0yrQTt3LkT5ubm2LVrF6ytrZXT79y5gx49eiAgICBPVYIiIyNx8+ZNAEDJkiU1nA0RERERUe6RSCDiw1JFCaMWmVaCHj16hObNm6tUgACgSpUqaNCggbJCkVds3rwZmzdvhpubW6qhromIiIiISPtlWgn6+PEjihQpkuZ7ZcuWxenTp3M9KXUaNWoURo0apek0iIiIiIhIQzKtBCUkJEBfP+3HSxsYGCjvryEiIiIiIs3iwAhZk5cf9EpERERERJRtWX5OEBERERER/dj0IF4rR15uTclSJejhw4fYt29fqukPHjwAgDTfA4B27drlNC8iIiIiIiK1yFIl6NSpUzh16lSq6YKQPPzehAkTUk2XSCSsBBERERERiUhPIog2RLZYcdQh00qQl5eXGHkQERERERGJgpUgIiIiIiLSKRwYgYiIiIhIS0hEHCJbwiGyiYiIiIiI8ga2BBERERERaQk+LDVr2BJEREREREQ6hS1BRERERERagg9LzZq8nDsREREREVG2sRJEREREREQ6hd3hiIiIiIi0hJ5EgJ5EEC1WXsWWICIiIiIi0ilsCSIiIiIi0hISiPiwVHHCqAUrQUREREREpFZXr16Fh4dHmu85OzvD398/1fQRI0bg6NGjKtNq1aqFjRs3fnc+rASRaPLrW+hMbE2WtaBhOVHjfUl8J2o8hfz6mostNk2U9YBrMVHjaTq2sfVU0WN+fr4VFuWWaCDudNFjAuIfm3SNJo6Hmjg2SWAII31RQ+Y5P+oQ2Q4ODrhw4YLKtIsXL2LChAno379/mp959OgRRo0aBXd3d+U0Q0PDnKSaCitBRERERESkVoaGhrC0tFT+HRsbiwULFqBv375wcXFJNb9cLsfz589hZ2en8rncwoERiIiIiIhIVCtWrIChoSGGDh2a5vtPnz5FQkICbGxs1BKfLUFERERERFpCTyLewAiKOBEREUhMTFR5z9zcHObm5ml+7u3bt9i8eTOmTZsGY2PjNOd59OgRDAwMsGzZMpw7dw5GRkZo3rw5hgwZAiMjo+/OnZUgIiIiIiLKse7duyM8PFxlmpeXF4YNG5bm/Fu3bkWRIkXQtm3bdJf5+PFjAED58uXRvXt3PHr0CHPmzMGrV68wd+7c786ZlSAiIiIiIi0hkQiQiPQQU0WcgICANFuC0nPgwAG0b98eBgYG6c7j7e2NPn36oFChQgAAmUwGfX19jBw5EuPHj4eFxfcNQsVKEBERERER5ViJEiWyPG9ISAj+/fdftGrVKsP59PT0lBUgBalUCgB49eoVK0FERERERJRMIuI9QZIcxLl+/TosLS0zHfBgxIgRSEhIwJ9//qmcdvfuXRgaGsLa2jr7gb/B0eGIiIiIiEgUDx48ULbopCSXyxEVFQW5XA4AaNasGU6dOoUNGzbg+fPnOHr0KObOnYs+ffrA1NT0u/NgSxARERERkZb4UR+WqvD69WsULFgw1fSbN2/Cw8MD/v7+cHZ2RsuWLSGXy7Fu3TosXrwYRYoUgYeHBwYOHPj9iYOVICIiIiIiEomfn1+a052dnREcHKwyrV27dmjXrp1a8mB3OCIiIiIi0ilsCSIiIiIi0hJ6EgF6Ig2RLVYcdWBLEBERERER6RS2BBERERERaQk9EYfIFiuOOrAliIiIiIiIdApbgoiIiIiItMSP/rDUHwVbgoiIiIiISKewEkRERERERDqF3eGIiIiIiLSE/v+/xIqVV7EliIiIiIiIdApbgoiIiIiItAQflpo1bAkiIiIiIiKdwpYgIiIiIiItoQcRH5YqThi1yMu5ExERERERZRsrQUREREREpFNYCcrE48ePcebMmSzPP378eHh6eqotn++1fcsp1Kk+GPb29mhafyRuBj3SdEpqc/jwGdjZtYFMJkPHjsMRExOn6ZTURpfKCgCCIMDT0xOLFwZoOhW105Wy6sI2LAgC5G+vICHmIQAgOjoa8jcX8TXiML6+OoaEWO07HuvCek0pubx2kMmaaX15deXYlBdJJMnd4cR4SUTqdqcOrARlYsiQIbhz546m08gVIcFhmDJhLXYf9MGtW7cwenxX9Ow8U9NpqUVUVDR6956A3buXITg4GOXLl8b48Qs0nZZa6FJZAeDhg2do4eqFHTt2aDoVtdOVsurCNpwU/wHxUX8j6XOYctrIkSMh0csHw+ItYFisCZK+RCDxc7gGs8xdurBeU/qvvLsRHHxMq8urK8cm0m6sBGVCEPLu0H/fMjQygO9KbxQvUQQA4OAoReSrd5DL4zWcWe47fvwCnJyqoEKFsgCAwYO7IiDgoFatTwVdKisA+K3cDQ/P1ujUqZOmU1E7XSmrLmzDiXGPoW9aDnrGpZXTgoKCoGdSFhKJHiQSfejlL4nET2EZLCVv0YX1mtJ/5a0AQLvLqyvHprxKXyLuK6/SqkqQTCbDzp070aVLF1SpUgUtW7bErVu3sGXLFtSvXx/VqlXDb7/9BrlcrvzM/7V33/FRVVsbx3+TRkLoIL0IEQJIR5p0FFCK9CpCkNBDqEpTmiC9g3RBmtKkI9Kk914CoRNa6BBamJTz/sGbkVz0Xkoyw8w83/vJlZw5M3vtnDKzZu2zz/79+2nYsCH58uXjk08+YeTIkTx79gyAr776ipCQECZMmECFChUAuHLlCoGBgRQrVowPP/yQChUqMH36dJv093VleT8tlasUA54nd72/ncLn1Yrj4eFu48ji3uXLoWTKlNbye8aMaQkLe8TDh49tGFX8cKa+AowZ143GTT63dRhW4Sx9dYZ92D15YVy9s8ZaVqxYMaKfXMQwojGiI55XiaLDbRRh3HOG7foiZ+qvs5ybxLE5VBIEMGrUKFq1asXy5ctJlCgRrVq1YuPGjUybNo3Bgwezbt06Fi9eDMDJkydp0aIFFStWZOXKlQwcOJC//vqLfv36ATB+/HgyZMjA119/bXlO27ZtMZvNzJ49mzVr1lCjRg2GDx/OyZMnbdXl1/b4cTj169fn/LnrjJvcydbhxIvo6Oh/XO7q6nC7vFP1VRyTs+7DI0eOBEyYQ/8k4vZ2XDzT4khvy862XZ2tv/Lustb1QDE/9srh7hNUv359S9WmRo0aDBgwgH79+pEpUyZy5MjB9OnTOXPmDAAzZsygbNmytGjRAoAsWbLQv39/GjduTOfOnUmdOjWurq4kTJiQFClSEB4eTq1atahatSpp0qQBoH379kyePJng4GBy5cr1RjEn8SgbBz1/NSEhIVSvXp1cuXKxdfNBvLy8rNa2NWXOXIg9e84BOQC4etWb5MmT4+1dwKZxxQdb99XT1SrN/INJuLtkxtO1mK0CsCLH7qut92GApyG/WqUdPz8/8uTJQ7du3QgJCeFmyD5SpEgBwNChQwkNDWX06NFWiSW+vQvb1Zr+7i9ADq5eveQE52LHPjeJY3O4JChz5syWf3t5eeHi4kLGjBktyzw9PS3D4U6ePMmlS5coWLCg5fGYsbvnzp0jderUsV7b09OTJk2asGbNGo4ePcqlS5c4efIk0dHR//oN0KsIM2/BIP6HQNy7+5CyZQJo3LQigwfM4YH5T14YGRjvknpk/d8rxZFKlXzo2nUHZ86sI3v2Skye/CM1apQHHG/2JVv3NTzqnlXa+ScR0SGER+2xWfvWZO2+eromt1pbtt6HAbwy97VKO+Y7u3FZdoTvxx2gc5OsDBu/GPfkhTGiwjHf3IB7ihJMXtIoXmN4GtI/Xl8/xruwXa3p7/6eIXt2g8mTRzrFudja5yYTHiRwLfi/VxT5HxwuCXJzi90lk8mE6V/m73N3d6dmzZq0bNnypcfee++9l5Y9fvyYL7/8kqioKCpXrkyxYsXInz8/5cuXj5vg49mMqau4cvkWq5bv5I8VBYgyHgKwYu0QUqRMYuPo4lbq1CmZOXMwdesGYjab8PFJy+zZQ20dVrxwpr6KY3LWfbhnz54MHTObZ9f/AAzckuTBJUFKW4cVZ5xtu/7d37qYzY/w8cns0P2Vd5fJZOBiss6EHCYrtRMfHC4Jeh0ffPAB586dI0uWLJZlhw4dYtq0afTv35+ECRPGSqC2b9/OyZMn2bNnD8mSJQPg/PnzREdH28XsL916NKJbj+ffMCb1qMwD8582jih+ValSlipVyvJ8KIZjfvMYw5n6GmPWrFlOUwVyhr46yz7skbK45d+JEyfGI1VpG0YT/5xlu8Z43t+WOENfwTnOTeK4nPpqvZYtW3L06FEGDx7MuXPn2Lt3L927d+fhw4eWSpC3tzcXL17kxo0bpEuXDoCVK1dy9epVdu3aRadOnQBizTgnIiIiImILmhjh1Th1EuTr68uUKVM4ePAgNWvWpFOnThQpUoQJEyZY1vHz82Pr1q188cUX5MmTh2+//ZZp06ZRpUoVBgwYwBdffEGxYsUc5oaqIiIiIiKOzmTYwzguB2etiRFeZIvhcNacGCE25xiG8Zz1+2qri3E9XYs5zTAMW/TVmhMjxGab49VaEyO86GnIr3hljt9JEP65XetMjBCbzsPxzRbnYlucmzQxwv8258xWHkZY53NlYndPvspexiptxTWnrgSJiIiIiIjzceqJEUREREREHIk1r9XRNUEiIiIiIiJ2QkmQiIiIiIg4FQ2HExERERFxELpZ6qtRJUhERERERJyKKkEiIiIiIg7CFXC10oQFrtZpJl6oEiQiIiIiIk5FlSAREREREQehKbJfjSpBIiIiIiLiVJQEiYiIiIiIU9FwOBERERERB2Gy4nA4k4bDiYiIiIiI2AdVgkREREREHIQmRng1qgSJiIiIiIhTUSVIRERERMRBuJrA1WRYrS17pUqQiIiIiIg4FSVBIiIiIiLiVDQcTkRERETEQbhgvSqHPVdT7Dl2ERERERGR16ZKkIiIiIiIg9DNUl+NKkEiIiIiIuJUVAl6ByTxyAREWr3dpB5Zrd6mswiPumeTdj1dbde2OJYH5gs2aTepRw6btP00pL/V27RVux/U3G31Ns8uy2GTdg8sTGP1Nm21D9vqPd3TNbmVW9RH1/9FN0t9NaoEiYiIiIiIU1ESJCIiIiIiTkU1RRERERERB+FqMnA1GVZry16pEiQiIiIiIk5FlSAREREREQdhwopTZFunmXihSpCIiIiIiDgVVYJERERERByEpsh+NaoEiYiIiIiIU1ElSERERETEQagS9GpUCRIREREREaeiJEhERERERJyKhsOJiIiIiDgIV9PzH2u1Za9UCRIREREREaeiSpCIiIiIiIMwmQxcTIbV2rJXqgSJiIiIiIhTUSVIRERERMRBuGC9Koc9V1PsOXYREREREbETZ8+exdfX96Wf/fv3/+P6x44do2HDhuTPn59KlSqxbNmyOItFlSAREREREYl3p0+fJnny5KxcuTLW8mTJkr207t27d/H396datWoMGjSInTt30rt3b1KlSkWpUqXeOhYlQSIiIiIiDsLF9PzHWm29jtOnT/PBBx/w3nvv/c91Fy1aRKJEiejduzcuLi74+PgQFBTEzz//HCdJkIbDiYiIiIhIvDtz5gzZsmV7pXX3799PkSJFcHH5O10pWrQoBw8exDDeflY6VYJERERERByEixVvlhpTCbp+/TpRUVGxHkuSJAlJkiSJtezMmTM8e/aM+vXrc/XqVbJnz06XLl3Ily/fS68dGhpK7ty5Yy1LnTo1T58+5d69e6RIkeKtYlcSJCIiIiIib+zLL7/k6tWrsZYFBATQoUMHy+/h4eFcvnyZFClS8O233+Lh4cHcuXNp0qQJS5cuxcfHJ9bzw8PD8fDwiLUs5nez2fzWMSsJEhERERFxEC5WvFlqTDvz5s37x0rQizw9Pdm3bx8eHh6WZGbIkCGcOHGC+fPn8/3337+0/n8mOzG/e3l5vXXsSoJEREREROSNpUuX7pXWS5QoUazfXVxc+OCDD7h+/fpL66ZNm5Zbt27FWnbz5k0SJkxI4sSJ3zzYmLbf+hXErqxevZl8+arj6+tLvXqBhIU9snVI8caZ+gpgGAZ+fn6MHjnP1qHEO/XV8SyYv5GSH7WlQIECVCrbmUMHTts6pHjjDOcmwzC4eXAa98+usSy78EcAl//63vLz8PJOG0YY95xpH4aY/Tgfvr6VHXY/lrh1/PhxChUqxPHjxy3LoqKiOHXqFNmzZ39p/cKFC7N///5YkyDs2bOHQoUKxZos4U0pCXIit27dpXnznixZMp7g4GCyZctEjx4jbB1WvHCmvgKcOnmBzysGsHDhQluHEu/UV8dzJvgyfXpOZ8nKgRw+fJhuPRrxVYMfbB1WvHCGc5P54TWu7RzKo2t7LcuCg4NxdU9IpvI/WH4SZ/rYhlHGLWfah+HF/XgJwcF/OuR+bM9c+Hua7Hj/eY24cubMSYYMGejTpw9HjhzhzJkz9OzZk3v37tG0aVPMZjO3bt2yDHmrW7cud+/epW/fvpw7d445c+awatUq/P394+zvJE5i3brtFCmSl+zZ3wegbdtGzJu3Mk6mGXzXOFNfASZPWkJTv2rUr1/f1qHEO/XV8XgkcGfcpE6kTZcSgIKFc3Aj9B5mc4SNI4t7znBuenBhA0kylyZR+qKWZTt37gSTC1d3DObyX725G7wMw4i2YZRxy5n2YXhxP37+7b0j7scS99zc3Jg+fTpZs2alTZs21KtXj9u3bzN37lxSpkzJoUOHKFWqFIcOHQIgVapUTJ8+naCgIGrWrMncuXMZOnQoJUqUiJt44uRV7JSvry/9+/dn4cKFnD17lg8++ICePXtSpEgRyzqLFy9m1qxZhISEkCZNGpo1a0aTJk0A+P3335k0aRJ+fn789NNPhIeHU758efr06fPSxWDvgsuXQ8mUKa3l94wZ0xIW9oiHDx+TJEmi//JM++NMfQUYM64bAFv/mmTjSOKf+up4sryflizvPz9eDcOg97dT+LxacTw83G0cWdxzhnPTe/maAvD0VpBlWWRkJAnfy0PKDxsQHW0mdPcoXNy8SOZT2VZhxiln2ofBOfZje/Yu3yw1TZo0jBw58h8fK1asGMHBwbGWFShQgMWLF79peP+V01eChg0bRsOGDVm6dCl58uShRYsWXL58GYCZM2fyww8/0KxZM1asWEGLFi0YNmwYP//8s+X5169fZ8GCBUycOJEpU6Zw/PhxOnfubKvu/FfR0f/8rZurq+PtBs7UVxFH8fhxOPXr1+f8ueuMm9zJ1uHEC2c9N7Vs2ZJU+ZpgcnXH1d2bpD6f8fj6AVuHFeecYR8G592PxbE4dSUIoH79+pahJn379mXHjh0sXLiQLl26MH36dJo1a0a9evUAeP/997l8+TLTp0+nefPmAERERDB8+HB8fX0tr+Hn58f58+df+Y648KrrvZ3MmQuxZ885IAcAV696kzx5cry9C1ilfWuydV89Xa3SzD+YhLtLZjxdi9kqACtSX+OTtffhkJAQqlevTq5cudi6+WCcTH/6LrL1uQng7LIcVmnHz28TefLkoVu3psyZM4ffBxSw3BBx8eKETJ58kg3LmlolFmtwln0YXtyPAXJw9eolh/08YY9MWK/KYaWCU7xw+iToxaFvrq6u5MmTh9OnT3P37l1u375NwYIFX1p/+vTp3LlzB4CkSZNaEiB4XrYDOH369GskQeeByLfpxiupVMmHrl13cObMOrJnr8TkyT9So0Z5wPFmsLF1X8Oj7lmlnX8SER1CeNQem7VvTepr/HkWdd9qbd27+5CyZQJo3LQigwfM4YH5T+LgPnivJalHVqu0Y+tzE8AHNXdbpZ2bB8+xan84k7fPpo7vCdp8O4a0RTtgREdyfddIEmcswQc1Z8drDAcWponX14/hTPswvLgfnyF7doPJk0dacT92w1pfHotjc/okyM0t9p8gOjoak8lEggQJ/nH9mBtBxTzvP58f83hcTN0X11KnTsnMmYOpWzcQs9mEj09aZs8eauuw4oUz9VXE3s2Yuoorl2+xavlO/lhRgCjjIQAr1g4hRcp37/rKt+Gs56a+ffsy6bddXN7UG4wovNMXIXGWsrYOK8440z4ML+7HdTGbH+Hjk9kp9mNxLE6fBB0/fpyyZZ+fiCMjIzl+/Di1atUiUaJEpE2bloMHD1K+fHnL+gcOHOC9994jadKkANy9e5dr166RPn16AI4cOQJArly5rNyTV1OlSlmqVCnL86EYjlcBepEz9TXGrFmznKYyor46jm49GtGtRyMAknpU5oH5TxtHFL+c5dyUulBLy78TJkxI6oJxM63tu8jZ9mGI2Y9b4sj7sL0ymZ7/WKste+X0SdDMmTPJli0bOXLkYMaMGYSFhdGgQQMA2rZty+DBg8mcOTNFixZlz549zJ07l8DAQEz/v9UNw6B79+706tWLR48e0b9/fypXrkymTJls2S0REREREfkXTp8E1a9fn8mTJ3PhwgXy5cvH7NmzSZPm+Rjihg0bEh4ezpQpU+jfvz+ZMmWiR48eNG7c2PJ8V1dXPvnkE/z8/ACoUqUK3377rS26IiIiIiJOzoT1Jiyw40KQkiBfX9//mrT4+flZEpy3WUdERERERN4NTp8EiYiIiIg4Cl0T9GrevSnMRERERERE4pFTV4KCg4Pf6vm1a9emdu3acRSNiIiIiMjbccF6VQ57rqbYc+wiIiIiIiKvTUmQiIiIiIg4FaceDiciIiIi4khMJgOTybBaW/ZKlSAREREREXEqqgSJiIiIiDgI3Sz11agSJCIiIiIiTkWVIBERERERR2HFm6XacylIlSAREREREXEqSoJERERERMSpaDiciIiIiIiD0MQIr0aVIBERERERcSqqBImIiIiIOAgX0/Mfa7Vlr1QJEhERERERp6JKkIiIiIiIg9A1Qa9GlSAREREREXEqSoJERERERMSpaDiciIiIiIiDMJme/1irLXulSpCIiIiIiDgVVYJERERERByEJkZ4NUqC3gFh5ssYhFu1zaQeOXhgvmDVNhO4JrNqezE8XSE86p6V20xu1fZs2ba1/7a29Czqvk3a9XS1ftu2Ol5t1ba1z4dgm/MwwPElvlZv01bt1t9ktnqbKyrCV1sSWr3dhRWsfy62xfurCQ8SuFq1SXFQSoJERERERByEKkGvRtcEiYiIiIiIU1ElSERERETEQZgAF2vNDmedZuKFKkEiIiIiIuJUlASJiIiIiIhT0XA4EREREREHoYkRXo0qQSIiIiIi4lRUCRIRERERcRAmk4EJw2pt2StVgkRERERExKmoEiQiIiIi4iB0TdCrUSVIREREREScipIgERERERFxKhoOJyIiIiLiIEwmKw6Hs+PxcKoEiYiIiIiIU1ElSERERETEQbhgvSqHPVdT7Dl2ERERERGR16ZKkIiIiIiIo7DiNUH2PEe2KkEiIiIiIuJUlASJiIiIiIhT0XA4EREREREHYcKKU2RbqZ34oEqQiIiIiIg4FVWCREREREQchG6W+mpUCRIREREREaeiSpCIiIiIiIPQNUGvRpUgJ7Ng/kZKftSWAgUKUKlsZw4dOG3rkOKVYRj4+fkxeuQ8W4cSr1av3ky+fNXx9fWlXr1AwsIe2TqkeOUs21XHq2PSdnVcF5YtwdfXly1tvubAj/0xh4XZOqR440zbVRyTkiAncib4Mn16TmfJyoEcPnyYbj0a8VWDH2wdVrw5dfICn1cMYOHChbYOJV7dunWX5s17smTJeIKDg8mWLRM9eoywdVjxxlm2q45Xx6Tt6rhuHz7I2YXz2bhxI2Un/0zqosU5Oma4rcOKF860XcVxaTicE/FI4M64SZ1Imy4lAAUL5+BG6D3M5gg8PNxtHF3cmzxpCU39qvF+ljy2DiVerVu3nSJF8pI9+/sAtG3biPz5azBxYl9M9nzF4r9wlu2q49Uxabs6rgdngklVsDAZM2aEkxdIV7IMR0cPIzoiAhd3x9q2zrRd7ZGLCQwrtmWv7KIS5Ovry9ixYylTpgxlypTh1q1bPHjwgJ49e1KsWDGKFi1Ky5YtOX/+fKznTZ8+nXLlypE/f37at2/PwIED+eqrryyPHzlyhIYNG5IvXz4+//xzFi1ahK+vL1euXAHgypUrBAYGUqxYMT788EMqVKjA9OnTY7WxcOFCKleuTL58+ahevTpLly6N/z/IG8ryfloqVykGPC9j9/52Cp9XK+6Qb7wAY8Z1o3GTz20dRry7fDmUTJnSWn7PmDEtYWGPePjwsQ2jij/Osl11vDombVfHlcw3F7cPH+TSpUsAXF63huiICMxhD2wcWdxzpu0qjstuKkGLFi1i2rRpREREkDJlSho1akSiRImYPn06Xl5ezJkzh8aNG/PHH3+QPHly5syZw8SJE+nXrx958+blt99+Y/bs2RQpUgSAGzdu0Lx5c6pUqcKgQYM4c+YMffv2jdVm27ZtyZAhA7Nnz8bT05Nly5YxfPhwSpYsSa5cuZg/fz7jx4+nb9++5M6dm0OHDvHDD8+HNdSqVcvqf6NX9fhxOP6t6nMx5DqLVw60dTjylqKjo/9xuaurXXzHIf+DjlfHpO3qeFLmK4BvEz9q1arF+UdPyFy5Cu6JkzhcFUjefZoY4dXYTRJUq1YtcuXKBcDOnTs5duwYe/fuJVGiRAD079+f3bt3s3DhQlq3bs3MmTPx8/OjRo0aAPTq1Yv9+/dbXm/BggUkT56c/v374+rqio+PD7dv37YkMeHh4dSqVYuqVauSJk0aANq3b8/kyZMJDg4mV65cTJ48mYCAAD777DMAMmfOzLVr15g8efJrJUFJPMq+/R/oFYWEhFC9enVy5crF1s0H8fLyslrbtjMJd5fMeLoWs3Ug8SJz5kLs2XMOyAHA1aveJE+eHG/vAlZp39PVKs38A+tvV2v3Vcer9Vhz22q7Ws+KitZr6+HDh9zImo4PRg0Fnn/Zmnv+L/xRu6pDDk1+zrHfX8Wx2U0SlClTJsu/g4KCiIqKonTp0rHWefbsGefOnePevXtcvXqVAgUKxHq8cOHCnDp1yvIaefPmxdXVNdbjMTw9PWnSpAlr1qzh6NGjXLp0iZMnTxIdHU10dDR3797lxo0bDB06lBEj/r4IPTIykqioKMxmMx4eHq/UtzDzFgzCX/lv8abu3X1I2TIBNG5akcED5vDA/Cdmc7w3a5HANZn1GvsPEdEhhEftsVp7nq7JrdZWpUo+dO26gzNn1pE9eyUmT/6RGjXKA9aZcSo86p5V2vkn1t6uz6LuW60tHa/W265gvW1r6+0Kttu2ttiu9TdZ74/76HIIu7p35sa5szTZc4RjE0aTpFRZamzYbpX2F1Z4tc8ccc3a29WEBwlcC1qtPXtkMhmYrHRVkMlkrauP4p7dJEEJEiSw/Nvd3Z1kyZL946wkCRMmxM3tebcM4983jKur678OIwJ4/PgxX375JVFRUVSuXJlixYqRP39+ypcvb4kB4Pvvv6do0aIvPT8mhnfJjKmruHL5FquW7+SPFQWIMh4CsGLtEFKkTGLj6ORNpU6dkpkzB1O3biBmswkfn7TMnj3U1mHJW9Lx6pi0XR1XokyZ+aBBY4oVK8blR49J8WFe8gZ0tnVYIvIv3r1P6q8ge/bs3L9/H4AsWbIAEBUVRbdu3ahYsSJVqlQhffr0HDlyhHLlylmed/ToUUt1xtfXl+XLlxMVFWWpBh05csSy7vbt2zl58iR79uwhWbJkAJw/f57o6GgMwyBx4sSkSZOGK1euUK9ePcvzfv31V06ePMmAAQPi8S/wZrr1aES3Ho0ASOpRmQfmP20ckXXMmjXL6t8+WluVKmWpUqUsz4fEOfY9R2I4+nbV8eqYtF0dW9YadVgxYQxfrN9m61Cswlm2qz1y1AGYcckur5wuUaIEBQoUoFOnTuzfv58LFy7w3XffsWnTJnLkeH5dhL+/P7/88gsrV67kwoULjBo1isOHD1teo3Hjxty9e5f+/ftz7tw5Nm7cyNixYwEwmUykS5cOgJUrV3L16lV27dpFp06dADD//9iFtm3bMmvWLBYsWEBISAgrV65kyJAhvPfee9b7Y4iIiIiIyGuxy0qQyWRi4sSJDB06lHbt2mE2m8mVKxczZszggw8+AJ4nOffu3WPo0KGEhYVRrlw5Pv30U8LDn197kypVKqZOncqPP/5IjRo1yJIlC40bN2bChAm4u7uTL18+vv32W6ZNm8aIESNInz49derUYevWrRw7doxGjRrRqFEjzGYzM2bM4IcffiBNmjS0a9eOVq1a2fLPIyIiIiIi/4VdJEHBwcEvLUuVKhXDh//7nZi3bdtGvXr1CAgIsCxr0aKFZaa3s2fP4ubmFuu+PqtXr8bDw4MUKVJY1m/RokWs1/X394/1e7NmzWjWrNnrd0pEREREJI6ZTFacIvs1G7p9+zbDhw9nx44dhIeHkz9/frp3724ZyfWf6taty7Fjx15aNmjQoDcN2cIukqA3sWzZMq5cuUKfPn1IliwZmzdvZteuXcyYMQOA69ev065dO4YOHUqBAgW4cuUK48aNo0qVKu/kpAYiIiIiIvYqOjqagIAADMPgp59+ImHChIwfPx4/Pz9Wr15N8uSxZ9Y1DIOzZ88yYsQIihcvblkeV7cVcNhP+99//z0//vgjLVu25PHjx2TLlo2RI0dSokQJAEqXLk337t0ZO3Ys165dI1myZHz++ed07qyZXERERETEPr2rN0s9deoUhw4dYs2aNfj4+AAwfPhwihYtypYtW6hZs2as9S9fvszTp08pUKBAvFxv77BJUPLkyf/rcDmAJk2a0KRJEytFJCIiIiLinNKlS8eUKVPImjWrZVnMjYQfPHjw0vqnT5/G09OTDBkyxEs8DpsEiYiIiIg4G2tO/RzT1vXr14mKior1WJIkSUiS5O/7nyVPnjzWrWsA5syZQ3h4OKVKlXrptc+cOUPixInp1q0be/fuJXny5NSuXZtmzZrh4vL2vVQSJCIiIiIib+zLL7/k6tWrsZYFBATQoUOHf33Oxo0bGTVqFM2bN7cMj3vR2bNnefLkCaVKlaJ169YcPHiQYcOG8fDhQwIDA986ZiVBIiIiIiLyxubNm/ePlaB/8/vvv/P9999TpUoVvvnmm39cZ+jQoTx58sTyOr6+vjx8+JDJkyfToUMHy1C6N6UkSERERETEQZh4/amr36YteH69z6uaNGkSY8aMoUmTJnz33Xf/msy4ubm9lEj5+vry+PFjHj58+F+TrFdhzWGDIiIiIiLipKZNm8aYMWMIDAzk+++//6/VnPr16zNw4MBYy44dO0bq1KnfOgECVYJERERERBzIuzlJ9qlTpxg9ejR16tShfv363Lp1y/KYt7c3Li4uPHz4kBQpUuDq6krFihUZN24cefLkoVChQuzZs4fp06fTu3fvOIlcSZCIiIiIiMSrNWvWEBUVxZIlS1iyZEmsxzp27EjatGnp2bMnGzduJGPGjPj7++Pm5sakSZO4du0a6dOnp2fPntSrVy9O4lESJCIiIiLiIEz//z9rtfWqunTpQpcuXf7rOrVr1/77tU0mmjdvTvPmzd84vv9G1wSJiIiIiIhTURIkIiIiIiJORcPhREREREQchMnkgslKdQ5rtRMf7DdyERERERGRN6BKkIiIiIiIw3g3p8h+16gSJCIiIiIiTkWVIBERERERh2G9KbJVCRIREREREbETSoJERERERMSpaDiciIiIiIhDsd9hataiSpCIiIiIiDgVVYLEajxdkztN28fvBVu1vRh5kuewetvJPAyrthcjozfcDr9h1TYTuyewanu2tPvmbZu0Wy6dbdouly671dsESOqR1epthkfds3qbtjKn7BOnafds2H2rt5knOZwNu2nVNt1dvPBNatUm7Y7J5IK16hy6WaqIiIiIiIidUCVIRERERMRh6Gapr0KVIBERERERcSqqBImIiIiIOAiTFStB1rspa9xTJUhERERERJyKkiAREREREXEqGg4nIiIiIuIgNBzu1agSJCIiIiIiTkWVIBERERERh2G9m6Xacz3FfiMXERERERF5A6oEiYiIiIg4CJMJrHdNkP1SJUhERERERJyKkiAREREREXEqGg4nIiIiIuIwrDdFtj0PiFMlSEREREREnIoqQSIiIiIiDkI3S301qgSJiIiIiIhTUSVIRERERMRh6Gapr8J+IxcREREREXkDSoJERERERMSpaDiciIiIiIiD0MQIr0aVIBERERERcSqqBImIiIiIOAiTSZWgV6FKkIiIiIiIOBUlQW9pz549+Pr6EhoaautQXsmC+Rsp+VFbChQoQKWynTl04LStQ4o3q1dvJl++6vj6+lKvXiBhYY9sHVK8W7ZsGcUzN7B1GPFq1uTVfPJRAHny5OHrBoO4ffO+rUOKN850vP7UfzoNPmpOgQIF8P+0A/1bD7V1SPHG2c5NhmHg5+fH6JHzbB1KvHKm4zWGM7zn2CeTlX/sk5IgJ3Im+DJ9ek5nycqBHD58mG49GvFVgx9sHVa8uHXrLs2b92TJkvEEBweTLVsmevQYYeuw4tWlc9fo1q0b0YZh61DizdFDZ5kybhlLNw7l+PHjZPVJz4gf5ts6rHjhTMcrwIn9J/l+0rccPnyY6RvG03dKd1uHFC+c7dx06uQFPq8YwMKFC20dSrxytuMVnOM9RxybkiAn4pHAnXGTOpE2XUoAChbOwY3Qe5jNETaOLO6tW7edIkXykj37+wC0bduIefNWYjjoyfrpk2f0bD2KUaNG2TqUeJWv4AdsPTKJJEm9CQ8PJ/TaHZKlTGzrsOKFMx2v5mcRnDl+noWTfyd//vz08f+RG1du2jqseOFs56bJk5bQ1K8a9evXt3Uo8cqZjldwnvcccWxOlwT5+voyduxYypQpQ5kyZbh16xb37t2jT58+lC5dmvz589OsWTOCgoIszzEMg1mzZlGpUiXy589PjRo12LJlyz++/vbt28mbNy+zZs2yUo9eXZb301K5SjHgeZ96fzuFz6sVx8PD3caRxb3Ll0PJlCmt5feMGdMSFvaIhw8f2zCq+DOgy0Tq+VUmX758tg4l3rm7u7F25W4yZszInh0naNDkE1uHFC+c6Xi9c+MOhUrmw79nMw4fPkzuQr5813ygQyYGznZuGjOuG42bfG7rMOKdMx2v4FzvOfbJhAkXq/xoOJydWbRoEVOmTGHChAmkSJGCr7/+mmPHjjFmzBgWLlxI8uTJadKkCVeuXAFg2rRpjBs3jnbt2rFy5Uo+++wz2rdvz5kzZ2K97t69ewkICOCbb77Bz8/PBj17NY8fh1O/fn3On7vOuMmdbB1OvIiOjv7H5a6ujrfL/zZjDa6urtRqUtHWoVjNZ9WLc/v2bbr0akSTmv3+dXs7Amc4XtNlTsuQuf3J/EFGTCYTDdrW5tql64RevmHr0OKcM52bnJEzHK/O+J4jjskpp8iuVasWuXLlAmDLli0EBQWxdu1asmbNCsCwYcOoVKkS8+bN49tvv2X27Nk0b96cmjVrAtC2bVsiIyN58uSJ5TUPHz5Mr1696Nq1K1999dVrxZPEo2zcdOwVhISEUL16dXLlysXWzQfx8vKyWtvWlDlzIfbsOQfkAODqVW+SJ0+Ot3cBq7SfJ3kOq7QDsG7hDzx58oQm5b/HbDbz7KmZJuW/Z82aNaRPn95qcVjD2bNnCQ0NpVSpUgB0bTuWnh0n4W0uTcqUKW0cXdyz5fFaLp3VmuLo0aMcOXLEcu4sl64qLrhSOkNlMqbLaL1ArMDW5yYAT1erNfWCSbi7ZMbTtZhVW7VmX239/prUwzrtONN7jv2y5oQF9lsJcsokKFOmTJZ/nz59mmTJklkSIAAPDw/y5cvHmTNnuHfvHrdu3Xqp5NuhQwfg+exwAN9++y1ms5mMGV//DTvMvAWD8Dfpymu5d/chZcsE0LhpRQYPmMMD85+YzfHerEVSj6z/e6U4UqmSD1277uDMmXVkz16JyZN/pEaN8oB1Zus5fi/YKu0A/Pzn95Z/J3qQl9x5cjH3rx+4ywHu3jsQ7+0n87DekKUj508Q0Hwkf+4cQ74sTRj/c1d8c2fmqecOrlhhNFFi9wTx38j/s/XxeuhOpNXaunDnIoEduuOWw0yjYi3oPKQ9WXJm4qzrEc5eP2KVGMqly26Vdmx9bgIIj7pntbZeFBEdQnjUHqu2+SzqvlXasfXxCnD5sXUatPV7jruLF75JP433dsTxOWUSlCDB3x9kPD09/3Gd6Oho3NzccHd/tfG8nTp14uLFi/Tt25ciRYqQKFGiOIk1Ls2Yuoorl2+xavlO/lhRgCjjIQAr1g4hRcokNo4ubqVOnZKZMwdTt24gZrMJH5+0zJ7tuFPuOotiJT+kwzf1qPd5b7w8RpAijRvTf+tl67DihTMdr1lzvk/gwDb0ajaAAaYReL/nyfc/fWPrsOKFzk2OyZmOV3n3WfNmqfZcCTIZjnjl6X/h6+vLsGHDqFGjBgC7du3Cz8+PP/74g2zZsgFgNpupWLEi1atXp1u3bpQqVYqGDRsSEBBgeZ2vvvqKsmXLkjdvXpo2bcqWLVtImDAhVapUoXz58vzww6tPjWmtStCLknpU5oH5Tyu3ab1KUGw5sOa3rGDdStCL8iSvzvF7K63apjUrQS/K6P0FVx6vsGqb1qwEvcgWx6s1K0EvKpeuKpuvr7ZBu9apBMVm/XMT2KYS5OlazOpVILBeJehFtjhewXqVoBfZ4j1HlaD/zRx9FLDW/uCBh4t9TpDh9FdiFi9enIIFC9KtWzcOHDjA6dOn6dmzJ2FhYTRo8PwGYP7+/syaNYvVq1cTEhLCTz/9xJEjRyhbNva1PEmSJKFXr14sXLiQXbt22aI7IiIiIuLUdLPUV+H0SZDJZGLChAlkzZqV1q1b06BBA+7fv8/8+fMt1w41bdoUf39/hg8fTrVq1di4cSOTJ08me/aXvzmsUqUKZcuW5fvvv+fp06fW7o6IiIiIiPwPTndNUHDwy8OUUqVKxciRI//1OS4uLrRp04Y2bdq89FixYsVees2pU6e+faAiIiIiIhIvnC4JEhERERFxVM9vYmqtwV72O6jMfiMXERERERF5A6oEiYiIiIg4DE2R/SpUCRIREREREaeiSpCIiIiIiIMwvfD/8u9UCRIREREREaeiJEhERERERJyKhsOJiIiIiDgIk0kTI7wKVYJERERERMSpqBIkIiIiIuIwdLPUV2G/kYuIiIiIiLwBVYJERERERByESTdLfSWqBImIiIiIiFNREiQiIiIiIk5Fw+FERERERByGhsO9ClWCRERERETEqagSJCIiIiLiIHSz1FejSpCIiIiIiMS7qKgoRo4cSalSpShYsCCBgYHcvn37X9c/duwYDRs2JH/+/FSqVIlly5bFWSxKgkREREREHIaJv2+YGt8/r1cJGj9+PEuXLmXo0KHMnTuX0NBQOnTo8I/r3r17F39/fz788EN+//13vvrqK3r37s327dtfq81/o+FwIiIiIiISr8xmM7Nnz+a7776jZMmSAIwaNYpPPvmEgwcPUqhQoVjrL1q0iESJEtG7d29cXFzw8fEhKCiIn3/+mVKlSr11PKoEiYiIiIhIvDp16hSPHz+maNGilmUZM2YkQ4YM7N+//6X19+/fT5EiRXBx+TtdKVq0KAcPHsQwjLeOR5Wgd4CJBDZq19PKLdpyd7Nu2+4uXlZtz5Ztu5re/kT05m1bt68mPKzaXuy2rXu8erpGWbW92G3b4vix1fnJ+u3aaj+2RbvWf5+zXbvuLq5Wb/N5u9Y9Xt1Mttmm9sSEO9absOD5Oez69etERcV+30iSJAlJkiSx/B4aGgpAmjRpYq2XOnVqy2MvCg0NJXfu3C+t+/TpU+7du0eKFCniIHKxqcQexW3SbhKPsjZp1zayWbU136TWbS9225/arG1rS5ewoq1DsBprH6/FU1u1uf9ou4LtGrc6658rEtjmszIJXAvaoE2rNwnY5v01iY2+o3Gm9xz7kdmqrYWHh1OjRg0ePHgQa3lAQECs632ePn2Ki4sL7u7usdbz8PDg2bNn//i6Hh4eL60Lz4fWvS0lQSIiIiIi8kbMZjO///77S8tfrAIBeHp6Eh0dTWRkJG5uf6cgZrMZL6+XK4qenp4vJTsxv//T+q9LSZCIiIiIiLyR/xz29m/SpUsHwK1btyz/Brh58+ZLQ+QA0qZNy61bt2Itu3nzJgkTJiRx4sRvGbUmRhARERERkXiWM2dOvL292bt3r2XZlStXuHr1KkWKFHlp/cKFC7N///5YkyDs2bOHQoUKxZos4U0pCRIRERERkXjl4eFB48aNGTZsGFu3buXEiRN06dKFokWLUqBAAcxmM7du3bIMeatbty53796lb9++nDt3jjlz5rBq1Sr8/f3jJB6TERdzzImIiIiIiPwXkZGRjBgxgqVLlxIZGUnp0qXp06cPKVKkYM+ePTRt2pTZs2dTrFgxAA4fPszAgQMJDg4mffr0BAYGUrVq1TiJRUmQiIiIiIg4FQ2HExERERERp6IkSEREREREnIqSIBERERERcSpKgkRERERExKkoCRKHde/ePVuHICLyj549e2brEEREnJqSIHFIixYtonfv3pw+fdrWoVhNdHQ0AJrwUexRVFRUrN8deT9evHgxnTp10hc14lAc+ZgVx6QkSBzS48ePuXTpEjNnzuTMmTO2DscqYu6e7EyJn6O/6b7YP0fua3R0NK6urgCMHj2aTZs2YTKZbBxV/Llx4wbXrl1jyJAhTpcIOfJ+7GxituX9+/cBHPqYFcekJEgc8k3Jz8+PZs2aceLECaZNm+Y0idDOnTtp27YtFy5csHUo8ebF/dWR33Sjo6Nj9S8iIsKG0cSf6OhoSwI/YcIEpkyZwoYNGyyPOaK2bdtSq1Ytzpw5w6BBg5wqEYrZpx3xfSfGs2fPMJvNlrveO9J+/GJfTCYTmzdvpnfv3pa+itgTN1sHINZjGAYmk4mgoCAuX77M7du3+fLLLx3ug2TMh6r69esTGRnJb7/9xrRp02jZsiXZs2e3dXjxytPTE4ArV66QNWvWWB8wHUHMPnzo0CEOHTrExYsXKVGiBB999BHvvfeercOLUzHbbebMmezfv59r165RoUIFqlWrRtasWW0cXdx4cf8cNGgQK1eupEKFCty9exdwzCQ3ps9NmzYlMjKSNWvWMGjQIHr37k3y5MltHV68Wb58OZcvX+b+/fvUrFmTPHny2DqkeDFz5kyOHDlCSEgIPj4+NG7cmIIFC9o6rDjxyy+/sHbtWn755Rc8PDwACAoKwsXFBQ8PD4d7vxHHp73VScR8eFy7di3Nmzdn0qRJbNq0iZs3b9o6tDhlGAYuLi6Wb6UaN25M48aNOXnypMNVhP7p28VChQpRunRphgwZwqNHjxzuDclkMrF+/XpatmzJX3/9xcmTJ+nSpQtDhgzh+PHjtg4vTry4XcePH8/kyZPJnDkzhQsXZubMmQwcOJA9e/bYMMK4E7N/9u/fnxUrVjB79mzKly9vOS85WhIUc36KiIjAxcWFr7/+murVq3Pu3DmHrggNGTKEoUOHsnfvXvbv30/9+vWZOnWqZRiVoxg1ahRTpkyhRIkSNGrUiBMnTtCqVSsePHgA2H/1y8fHh/PnzxMYGGh5jw0LC7P828XF5aVr+0TeZY71CUn+Vcy35/369aN379789ttvjB49mgQJErBjxw7u3btn9yX7mERv3759jB8/nqFDh/Lw4UMaNmxI06ZNHSoRivkwBXD06FEuXbpkeaxRo0YkTpzY8kHZ3rfriy5evMjQoUPp0aMHs2bNYtGiRfz000/s3r2buXPnWj5s2LOY7Xrq1CkiIyMZMmQI3bt357vvvmP+/PlcvHiRX375hSdPntg40jf34j65adMmVq5cyaxZs8iRIwdJkybl/v37PH36NNYQm0ePHtki1DgTc346cOAAEydOtGzD5s2bU6NGDYdNhC5cuMClS5eYPHkyM2fOZNmyZQQEBDBmzBjWr18POMY56ty5c2zZsoXx48fToEED0qRJw61bt+jbty9nz57l8uXLdp/UlyxZknHjxnHs2DHat29PdHQ06dKlIzw8nNDQUADLtX0x7D3xE8emJMiJXLx4kfz58/PFF18QERHBtGnTaNy4MS1atKBp06Zcu3bN1iG+sRcrXS1atGDfvn0sXLiQZs2ace7cOerVq2dJhH7++WdOnTpl65DfWExfAebNm4e/vz8dO3Zkzpw53L9/n5w5c5I6dWp+/fVXAIeqBj148ICECRNSunRp4Pnfonz58vTv359ly5Zx8OBBG0cYN/bv30/NmjWZMmUKkZGRAERGRpIzZ07GjBnD5s2b2bFjh42jfDMvDpnZvHkzbm5urF69mly5cgHg5eXFw4cPCQ8Ptwy5adGiBb///rvNYn5bL56fvv76a7Zt28bgwYPp3Lkz58+fx8/Pz5IIDR061DIc0N6tXbuWJk2aEBwcTLJkySwfkNu1a0fz5s0ZMWIEN2/edIhz1JMnT7hz5w558uRh06ZNdOzYkU6dOlG5cmV+/fVXFi1aRHR0tF0nBSaTiWLFijFq1ChOnDhBhw4diI6OZs+ePXTq1IlKlSrx3XffsXjxYkuCa++Jnzg2+z/zyCszm82cPn2aAQMG8Nlnn7Fv3z7Kly/Ppk2buHLliuWkZY9MJhMHDx6kb9++DBgwgN9++41Zs2YRFBTEd999x4ULFyyJ0I4dO5g/f75dXsj54sXyo0aN4uDBg3Tt2pVPP/2UUaNG0a5dOyZNmkS7du24cOECGzdutHHEcevWrVucPn0as9mMq6urJUH49NNPyZYtG0ePHrVxhG8m5oNRzH9z5sxJ586dcXd35+LFi5b1oqOj8fX1JVu2bNy6dcsWob6VFxOgsWPH0qZNGxInTkyaNGks1YCUKVOSIEECy3qtW7fm/PnzNGrUyGZxvy2TycT+/fvp06cP3333HUuWLGH69Ols27aNUaNGcfnyZfz8/KhVqxZ79+5lzJgxdl0didmPkyRJwocffsitW7csy2Luj1S9enVcXV3t+su3F7m7u5M6dWpmzJjBN998Q48ePfjyyy9xd3fnxo0b3L17FxcXF7tMCmK23ePHjwkLC6NIkSKMHj2akydPMnToUIoVK0azZs0oU6YMN2/eZODAgfTv35/r16/bOHKR/04TIziomG8eo6OjiYiIIEGCBNSoUYNjx45x+vRpqlatSoMGDciWLRsmk4lChQqRIkUKW4f9Vo4fP07RokWpWbMmt2/fZsyYMdSvX59Dhw7RtWtXRo4cSb169fD29iZv3ryWb5ntRVRUlOWb1K1bt3Ls2DG6d+9Ozpw5AahSpQqrVq1i2bJlLFiwgMjISPbv388nn3xilxesxuzD165d48GDB3h7e5MzZ07y5s3LuHHj6NKlC+nSpSM6Ohqz2UzixInJnDmzrcN+bS9um8ePH+Pm5oaHhwetW7fGbDYzcuRI0qdPT9WqVYHn+4HJZCJJkiS2DPu1vdjPgQMHsmjRIlKkSMHly5cpWLCg5bGYyQGuXLlC9+7duXjxIuvWrcPd3Z3IyEjc3N79t63Q0FASJ06Mt7c3ERERuLu7s3PnTkqUKEG9evW4desWM2bMoGrVqmzZsoV+/frRt29fmjZtipeXF8WLF7e74/VFMR/0P/74Y1KkSEHv3r1p3749s2fPtrzPJEiQgGTJkpEgQQJbhvpWjh07RkREBHnz5iVnzpykTJmSCRMm0KZNGxo0aABAeHg4hmHY5bkJ/j4Pb9y4kdmzZ3Px4kWqVq1KkyZNGDx4MH379uXJkydUrFiRzz//HHg+BbyrqyupUqWycfQi/53JsOfarPyjmJPW1q1bWbVqFceOHaNIkSIUL16cKlWqAM8/SN29exdXV1fmzZvH/PnzWbhwIZkyZbJx9G+ud+/ehIaGMmPGDMaNG8etW7f45ptvCAoKws/PDx8fH1q0aEHt2rVtHeprCQ0NJW3atJbf9+/fT9u2bUmZMiU///wz6dKlIyoqCjc3N8twixkzZrBnzx4OHjzIr7/+akmU7EXMPrx+/XqGDBkCQLJkyfjhhx/YunUrf/75J1myZKF9+/a4urqyfPlylixZwq+//mpX+/CLQxunT5/Ozp07uXbtGsmSJaN79+7kyJGDmTNnMmHCBBo0aECKFCk4fvw4oaGhLF261C4Sgv80ePBgli1bxi+//MKwYcPIly8fnTp1sjx+8+ZNateuTWRkJMmSJWPlypV2lQABLFu2jCdPntC4cWNu3LhBmjRp6NSpEwkSJOCHH35g4sSJ3L59m0GDBrFx40bat29PwYIF+fLLL6lWrZqtw38rixcv5sSJEzx48ABfX19at25NUFAQgwcP5vbt2/To0YPo6Gjmzp3L48ePmT9/vl0mfKNGjWLFihXcunWLwoUL07dvX1KlSkXbtm0JDQ2lWrVqpEiRgs2bN3Pnzh27PV4Btm/fTkBAAG3btiVNmjRERUVRrVo1EiRIwJ49e+jRowfZs2dn7NixeHl52TpckVdmn0ek/Fcx39p069aNr776ipIlS7J06VKWL19OunTpKFiwILNnz+a3337Dw8ODx48fM336dLv88Hjz5k2ePXuGt7c3nTt35vDhw4SEhHDw4EFq1apFkiRJePbsGfny5SNdunR89NFHtg79tY0dOxZ4/uFx//79mM1mvvjiC+bPn8/evXupWbMmLi4uluEzrq6utGrVitq1azNkyBDWrl2Lr69vrMkU3nUmk4nDhw/Tq1cvunTpQqlSpbhx4wa+vr7kzp0bV1dXdu/eTfXq1fHx8cFsNjN16lS72ofh72/Mx4wZw4IFC2jXrh1hYWEcPnyY5s2bM2TIENq1awfATz/9hI+PD82aNaNGjRq4ubnZVWIAz2cJW7x4MXPmzCFnzpwkS5bMcv+jmL4kS5aMHDlyYDabmTlzpt0lQIZhsHv3bvbv309QUBCrV69m586dNG/enKtXr3Lp0iWOHj1K3bp1MQyDZ8+ekSVLFtKnT0/+/PltHf5bGTFiBEuWLKF8+fI8ePCAn3/+me3btzNp0iR69+7Nd999R+vWrSlTpgz58uWjVatWlnOXvZyb4O/jtU+fPmTLlo2mTZsyatQoOnfuzJw5c+jfvz/79u0jOjqarFmzMm3aNNzc3GJV8+2BYRhERESwevVqGjVqROvWrWM9fuHCBcLDwxk6dCjNmzenR48elvcrEXtgH+8q8soMwyAsLIxZs2bRoUMHvv76a8LDwxk+fDgtW7YkefLkBAUF0ahRIxIlSkSqVKnw9fUlffr0tg79lb1YJZg4cSI3b96kaNGi+Pv78+mnn7Js2TLOnj1LjRo1ANi7dy/Zs2enT58+djX0IqafH330EX369OHQoUNERkaycuVKMmTIQHh4OD169CBRokR8+umnlvVj/psqVSq8vb0JCgrCZDLZzVj0F2fRKly4sOVakJgEJyQkBIDhw4dz7tw5EiVKxHvvvWe3Qy8uX77M1q1bGTZsmGXCB7PZzMCBA+nZsye5cuWiffv2eHh4MGrUKJ49e2Y3icGLlS6AjBkzMm/ePEtlMnXq1JYb+8b0ZeTIkeTNm5fAwEDLdV/vej9fZDKZGDJkCF9++SWLFy+mevXqeHl5kT9/fvLnz8/ChQu5efOmZXjjiRMnKFu2LIGBgSRKlMjG0b+5ffv2sW7dOiZOnEihQoWIjo7mwIEDdO/enc6dOzNt2jT69OnDmDFjCAoKYtCgQXh5eREeHm65v5k9OHv2LOvXr2fcuHEUK1aM4OBgoqKi2LlzJ7du3WLo0KEMGDAAeD6jYcw2tbf9GJ7vyx4eHly5ciVWkmo2m/Hw8GD06NG4ubkxatQofvnlF1KmTGnDaEVen/189SKvxGQy4enpycOHDylWrBhXr16lUqVKlClThvbt27No0SJmzJiBp6cn9erVo3z58naVAMHzPu7du5fu3bvTqFEjRo0aZbm+CeD999/HZDLRqVMnevXqxcKFC/nqq6/sKgGCv6sEderUoVixYly8eJEPPvgALy8vsmTJQsuWLalTpw6BgYFs2LAhVgIEzycRePDgAQ8ePODp06d2MytRTPxhYWFcv37dMjVyTKVr27ZtTJ06FcMwKFKkCLly5bLbBAiez3h38eJFy81eDcPA3d2dzp07kzFjRubPn4/JZKJRo0a0b9+ewYMH8/PPP7/zH6henMTj8uXLhIeH07BhQ3LmzGmp/nh4eFim1oXns8Dt3LmTDh064OrqSnR09Dvfz//07NkzzGYzLi4u5M2bl6CgIObNm2eZ0jxZsmScO3eOESNG0LNnTxYtWkSdOnXsOgECuHbtGh4eHpYE18XFhYIFC9KrVy+OHz/O1q1byZcvH+3btyddunSWoYL2lADB81ngnj59Svr06Tl37hzjx4+nT58+bNy4kdOnTzN06FB+++03AMs2NQzD7vbjGOHh4aRJk4br169z48YNDMOwXE9bsGBBrl69SkREBB999JHD3MRZnIeSIAcS82ExLCyMR48esWXLFsuMLT/88AMmkwk3NzfOnTtnmaHHHvz555+x7oMDz6s7FStWpEGDBhQvXpwSJUqQMGFCzp49y5o1a/j666+5c+cOd+7csQy9sScvJixRUVEULVqUgIAAdu3aRY8ePYDnyZ6/vz+1a9emc+fOrF69Ota37k+fPiVt2rQMGDAALy+vd7oS9E8JWqZMmbhx4wYHDhwgMjLS8k1k4cKFyZAhg93O7vefvL29SZEiBceOHbNMemAymUiUKBGJEye2rJc4cWL8/f1p2rQpU6ZMISwszJqhv5YXhzdNnDiRVq1aUbduXX766Sfu3buHu7s7ABkyZLA8p0WLFly9epXff//dMnTInoZIxUiQIAEeHh7MmTOHRYsW4ePjw+zZs1m6dCmPHz+mVKlStGzZkh07dnD9+nVmz56Nr6+vrcN+a1FRUTx9+pQbN25Ylrm5uZErVy4iIyMt034XLlyY7777DhcXF/z9/YmKirKLL2jOnz8PPJ+448MPP8QwDDZu3EjKlCnJnTs3iRIlIm3atBw+fJiTJ0/G6tO7fO59UUzMoaGhnD9/nrNnz+Lp6Unbtm05cOAA48aNi3WD9UuXLpEuXTpbhSvy1uzzqwl5yYkTJ/Dz82PatGkUKFCA+vXrM2rUKD7++GMGDhxoWe/mzZt88MEHdvHhwjAMDh06xMiRI5kzZ06sx06fPh3rZBwzA9PmzZsJCgqiffv2NG/enCdPnpAwYUJrh/5WXvwAeeXKFZIlS0abNm0ASJMmjWWoxZAhQ8iaNSutW7fmyZMnzJ8/n6pVq1qqQZkzZ6ZLly7v/Cx4Lw5/O3jwIDdv3qRHjx7UrVuX9evX06tXLwYOHEiBAgVInjw5q1atAp4nD/bmxRuhAmTNmpXMmTOTJUsWZs2aRcaMGSlRogSAJQl48UOGl5cXHTp0wN/f/52cHS7mOIzp5/Dhw1m4cCGdOnUiODiYNWvW8PTpU1q2bEmKFClIkyYNjx8/5ssvv+TGjRv88ccfdjPU70Ux+/DRo0c5ePAg9+/fJ0uWLNSqVYtx48bRuXNn5s6di4uLC/Xr16dGjRp07drVLs9PL4qOjiYyMhIPDw8KFSpk+dKpQ4cOlln+PDw8eP/992MNlcqfPz8jRoyIde+gd9nevXtp2rQpEydO5JNPPuGHH34gceLErF27lkaNGpEjRw4ePHiAj48PderUoVy5ci9V5t91Lw4zHzFiBBEREVy/fp2SJUvSqlUrZs6cSfPmzbl8+bJlGvv169czf/58y5caIvbGft5l5L9KmTKl5dqBKVOm0KBBA65fv86vv/7K2LFjSZgwIaGhoXZ10oqZunvBggUkT56cs2fPYjKZ8PHxoXTp0syePZstW7ZQtmxZS38yZcrE7du3LfePsbcPGC9OXjBu3DjWrVvH48ePady4MTVq1KBevXq4uLjQv39/TCYTAwcO5ODBgzRr1oy8efMCsb91fJcToJhkL+Ymkj179qRYsWKYzWZCQkLImjUrkydPplOnTvTo0YNkyZKRJk0agoODmTVr1juZBPyTadOmkS9fPooVKwbAsGHD+OOPP7h9+zZly5alc+fOTJgwgS+//JJBgwZRuHBhfHx82LBhAw8ePKBJkybA3x9SvLy83skZmPz9/fn666/5+OOPgeczSm3YsIFffvmF3Llzs3v3bhYvXsy2bduIioqiXbt2JEmShEuXLpEkSRK7TYDg+TG3bt06+vXrR968eUmWLBmTJ09m9+7d/Pjjj4wePZpOnToxbdo0fv31V86dO8eOHTtImjSprUN/YzNmzODYsWOcPXuWqlWrUr16dcaNG0fr1q15/PgxZcuWJW3atEycOBHDMCz7Rcx+HHO+etfNmDGD+/fvA9CjRw8GDx7Mp59+yp07d7h8+TKPHz/mypUrDBgwgIcPH1KuXDm7nOwh5l5WvXr1okOHDhQpUoQrV64wZ84chg4dSt++fVm+fDlz584lNDSUJEmS8Ntvv5E9e3Zbhy7yxjRFth2KjIzE1dX1pW+YQkND6du3L0ePHuXnn3/mgw8+YN68eZbhJe+99x6dO3e2m6FhMW8i0dHR3Lhxw/INW4cOHQBo3749qVKlolGjRpQvXx54Pm3p4cOHmTRpkt1VCl5805w5cyaTJ08mMDCQ48ePs3HjRurUqUPz5s1JnTo1S5YsYeDAgURERJArVy4WLFhgN2+8u3fvpnjx4pbfT58+TevWrenYsSM1a9YkMjKS69evs3fvXnx8fChQoACbN2/m4sWLuLu7U6ZMGbuZBe706dPUrVuXcuXK0aZNG06fPs2YMWPo1asXT58+ZcyYMWTNmpVevXqRMWNGxo4dy5EjR4DnCf3AgQNxd3e3i1mlBg4cyLfffmtJvP/44w8mTpxomahk2LBhVKxYkdDQUObNm0fDhg1p2LAhO3bsoE6dOnY5212MCxcu4OfnR6tWrSxVrcqVK9O2bVuqV69uue5yypQp3Llzhzp16tj1ELiJEycye/ZsGjZsyM2bNzl48CApU6akR48eRERE0K9fP+7cuUPKlClJlSoVU6dOtZv9+EUTJkxgzpw5dO/enbCwMI4fP87atWsZMWIEn332GaNHj2bKlClkyZKFxIkT8+uvv+Lu7m4X5+EXxcQ7fPhwbty4wYgRIyyP7d+/nxEjRvDee+8xfvx4yzFqr8eqSCyG2I0jR47E+n3Pnj3G2bNnYy27fv260bJlS6N48eLGsWPHDMMwjPv37xsRERHGkydPrBZrfJg5c6ZRtmxZo2/fvkZYWJhx+vRpo0GDBkaVKlWML774wmjTpo3x0UcfGUFBQbYO9a2cPXvW6NGjh7Fp0ybLsmnTphklS5Y0hgwZYty4ccMwDMM4efKksWDBAiMiIsIwDMOIjo62SbyvY9WqVUaDBg2M27dvW+LdunWrUbt2bePZs2fGnTt3jO+//9747LPPDF9fX+OTTz4xVq1aZeOo387u3buNChUqGJ07dza+/fZbY+HChZbHjh49alSqVMnw8/MzTpw4YVn+8OFDy79jtu+76j/3u2nTphkrVqww5s2bZ/Tp08e4du2aMWrUKGPQoEHGo0ePjHv37hkFCxY0SpQoYXzzzTeW573r/fxvDh06ZNSvX98wDMO4fPmyUaZMGeP77783Hj58aHTs2NH4/fffLetGRkbaKsw4ceHCBaNbt27GunXrLMu2bdtmfP3118bXX39tOY6vXLliXLhwwbJ/2Nv2DQsLMxo0aBDreDWbzcbQoUONDz/80Ni8ebNhGIYRFBRk7Nq1y7Jd7amfMdvm5s2bhmEYRuvWrY2uXbsahvG8rzGPL1u2zMiZM6dx6dKll54rYs/s56sKJ7d582YCAwOZMWMG8HxIQcysaDEXbAKkTZuWPn36kC5dOrp06cLRo0dJmjQpbm5u7+QQmn9j/H+B8u7du1y4cAHDMPDz86NNmzaWMctp0qRh7NixdOrUifz58/PRRx+xYMECcuXKZePoX09MXw3DYNeuXdStW5e1a9fGmrzC398fPz8/Vq5cyaxZswgJCSFnzpzUr1/f8q3cuz72fNu2baRNm5ZRo0aRMmVKLl++DDyfJjksLAx/f38qVarEmTNnqFWrFvv37ydx4sQcPHjQxpG/GcMwMAyDYsWKMWjQIA4fPszy5cu5cuWKZZ28efMycuRIrl27xujRo9m6dStgX7NK/ed+99dffzF48GCSJ09Oo0aNSJAgAevWrSN//vx4e3tz5coV8uTJQ9++fS03wgXe+X7+J+OFQRRPnz7l1q1bbN++naZNm1K2bFn69euHu7s7ly9f5sSJE5Z17akS8p8uXbrEZ599xp9//hlrealSpWjSpAlHjx7lwIEDpEiRggwZMlhm6rTHWf4ePnzI+fPnLSMKYvrQvn17cufOTdeuXdmyZQu5cuWiaNGiuLq6Wm5abS9MJhN//PEHjRo14tGjR/j6+rJ9+3Zu3rxpGZoKzz9XZMuWLdZniHf9/UbkVSgJshPvv/8+xYsXZ/Xq1cyaNQuTycTo0aPJlSsXzZs359y5c5Z1M2bMSPbs2QkJCaFTp052NRMc/D1mfMOGDfj5+dGwYUP69u1LdHQ0DRs2JCAggA0bNjBy5EgiIyOpWLEiAwYMoEWLFpZpsu1JzJuJyWSiRIkStGrVisjISMvsUTFirrn4+eef2bZtW6zXeNffeK9du0bLli2ZO3cu3t7eBAcHExAQwPTp0y13lU+XLh0tW7ZkzJgxtGrVikSJEpEtWzaSJEliSSjsRcz00DExFy9enGHDhpE+fXoOHDjAgQMHLOvmyZOHUaNGcfDgQXbt2hXrdezpg0ZMX+fNm0e+fPn44YcfuHTpEkeOHOHZs2f4+PgQFhbGpEmTSJEiBZUqVcLFxYWoqCgbR/56Yvr54MED4Pnw5Bw5cpAyZUoCAgIoUKAAAwYMwMXFhQQJEuDl5RVrFjx7FjOBh9lsJjg4ONZ7S/ny5UmRIgUnT5586Xn2NDRs+/btAJab165YsYJ79+5Z+uDt7U26dOnw9PSkffv2HD161PKYvSW4169fZ9WqVfj5+eHh4UHVqlXJkCEDrVu35saNG5ZrbTdv3kzChAntbjpzkf/JBtUneU0xZeeQkBCjZ8+eRrVq1YxZs2YZhmEYoaGhRqNGjYwyZcoYFy9etDxn0KBBxo4dO4zbt2/bJOa3tXXrViNfvnzG1KlTjX379hkhISFGVFSU8fTpU8MwDGPp0qVGmTJljG+//da4cOGCbYONA7NmzTLq1atn2dZjx441SpYsaYwZM8YIDQ2Nte6KFSvsckjNrl27jCJFihjdunUz9u3bZ3Tp0sWoVauWMW/ePMs6z549M/bt22ecO3fOGDVqlFGkSBHj3LlzNoz69UVFRVn+HRISYpw7d864d++eYRjPh02VL1/eaNeunXHw4MFYzzt37pxdbtcXvdj31q1bGyVLljRWrFhhlCpVyihWrJjxySefGNWrVzfMZvNL69uTjRs3GrVq1TIaNWpkzJo1y4iIiDCWL19uFCtWzOjZs6dlHx42bJhRvHjxWMOIHMG2bduMPHnyGGvWrDGePXtmGMbzYdfVq1c31qxZY+Po3tzhw4eNzz77zBg5cqRhGM+HgVWrVs348ccfjUePHhmG8fwc1aJFC+P33383AgMDjWbNmhlPnjyxu+Fhd+7cMXLmzGkUL17c2LVrl2X5xo0bjUaNGhkfffSR0apVK6N58+ZG4cKF7X6Yucg/0cQIdsL4/+rIpUuXmDJlCseOHaNOnTr4+flx48YNOnXqxOXLl6lbty43btxgw4YNLFmyhMyZM9s69NdiGAYRERF88803vP/++3Tu3JkHDx6wbt06Vq9ezfXr12nSpAlfffUVs2bNYsGCBcyePdtyo0l7tWPHDrp06ULevHmZNm0aJpOJMWPGsGTJEurWrUvDhg1JkyZNrOfYy0XGhmEQHR2Nq6srO3fupFWrVrRo0YLSpUvz+++/c+LECapXr46/vz8bNmxg/PjxPHr0CC8vL4YNG0bu3Llt3YU3Mnr0aFavXs2jR4+Ijo6mTZs2NG3alKCgIDp16kTu3Lnx9/enQIECsZ5nL9v137x4Ubi/vz+XL1+mXbt23L59m7Rp01KpUiW7nQUO4OjRozRr1owGDRoQHBzMjRs3qFixIp06dWL58uX89ttvHD58GB8fHyIjIxk9erTd7sP/zdatW2nXrh3Vq1cnU6ZMHDlyhNDQUJYsWWKX2/Xnn39mz5497N27F5PJxNdff01AQABTp05l5cqVmM1mChYsyOnTp4mIiGD58uVMmTKFvXv3MnPmTFuH/0Z27NhBixYtaN68uWXaeoCrV6+yevVqrly5QsqUKalevbpdjrIQ+V+UBL3DjP8fAhQz61dUVBTu7u5cvXqViRMncvToUerWrYufnx937txh4MCBnD59mqRJk/L999/b5bUxMcN/evbsycOHD6lfv75lRppMmTLh7u7O1q1bWbVqFWnSpOHBgwd2N83sP80cZBgG+/bto0uXLuTIkYMZM2ZgMpkYO3YsS5cupWLFirRt29byJmVPYrbrunXr2L17N5s2bSI0NJTGjRtTtWpVlixZQlBQEPXq1ePLL7/k5MmTmM1mMmTIQKpUqWwd/it78UP9pk2b6N69O8OHDydx4sRs2bKF+fPnU6dOHXr27Mm+ffvo1auX5Ro+R5tm9sV9vEWLFpw9e5ZevXpRvnx5PDw87C4BevHctHLlSk6fPk3Xrl2B5/dC2rx5M59++int27cH4OTJk3h4eJA6depY98dxNDt37uTrr7/mww8/pGzZsgQGBgLY3fadPHkyM2bM4LvvvsPb25uNGzcSFBREhQoV6NixI4cOHWLt2rXcvXuXNGnS0LVrV0wmE/379+fBgwcMGTIEd3d3uxq+GmPHjh34+/vTo0cPatWqZTe3HhCJC/ZzlnIix48fJ1OmTCRNmhSTycS2bdvYtGkTISEhfPbZZ5QoUYLu3bszbNgwFi9eDICfnx+jR4/m1q1beHt729X9cWI+YERGRlrGIOfMmZOlS5fSunVrKlSoQP369Slbtiy3b98mODiY+/fvkyZNGrs8Ycd8ONywYQMVKlSw3CunSJEijBw5ki5duuDv78+0adPo2LEjDx8+5MqVK5abD9obk8nE3r176d69O926daNkyZI8evSIfv36ERkZSZ06dQBYsmQJjx8/plWrVjaO+PWsXr2aqlWrWj70/frrr4SFhdG0aVPKlSsHQOHChUmWLBmjR4+mSJEifPrpp3z33XcsWbIEHx8fG0YfP16crn3GjBm0adOGfv36kSJFCooUKWJX14jEnJ+2b9/Ozp07OXbsGFmzZrU8/s0332AYBhs2bMAwDJo1a0b+/PltGLH1fPzxx8ycOZOWLVtSq1YtSxXTXhIgwzB49OgRe/bsoVu3btSoUQOAIkWKsGDBApYsWYKrqysBAQEULFiQp0+fcunSJQ4dOsT69etZuXIl8+fPf6fvx/a/lCxZkqlTp9KmTRtcXV2pWbOmZWIWEYdn7fF38t+FhIQYvr6+lmt+duzYYeTJk8fo1q2b0apVK6NChQrGV199ZRw5csS4d++e0bNnT6NWrVrG+PHjbRz5m4kZR71t2zajY8eOhp+fn7F7927DMAzj/PnzluucYsadDx061KhWrZpx9+5d2wQcRy5dumT4+voaHTp0iHVdRGRkpLF161YjT548RpcuXSyPxfyd7G3ceYwJEyYYX3/9daxlJ0+eNAoUKGD079/f2LdvnxEYGGh8+eWXdrVtJ0+ebAQGBlq206NHj4xatWoZvr6+Rs+ePQ3D+HvfNQzDCAgIMJo0afLStT/2em3M//Jiv+rUqWO0bt3ahtG8uc2bNxt58uQx6tWrZ5QqVcooUaKEcf369VjrjBgxwihTpowxduxYh92e/+bFazhfnN7dHjx79syoVq2aMWbMmFjLnz59ajRp0sTIly+fMWLECMMwnl/31LdvX6NkyZJGkyZNHOo6mZhtOG3aNLvbhiJvyn6+jnMSmTJlYurUqYwcOZI5c+awcuVKOnbsyPDhw5kyZQr9+vXDy8uLCRMmEBkZSdu2bcmUKRM7d+603NXanphMJtavX0/79u1577338PHxsdxYMGvWrKRPn55JkybRpUsXOnfuzOLFixk6dKjdVUWM/xh1mjFjRn766Sf27NlDly5diI6OBp7PLuTr60uGDBlYvXo1gwYNArDMNGaPwy3g+Y18Hz9+bPndbDaTM2dO+vbty/z581m9ejXNmzdn1KhRdrVta9SowciRI3FxceHo0aN4e3szYcIEypUrx9atW7l58yYeHh6WGdDSpk1LypQpX7rmx54qI68jpiIE8MEHH3D//n2ePHli46hez71799i1axfff/89CxcuZMyYMWTOnJl69eoRGhpqWa9r167UqVOHWrVqOez2/DelS5dm9OjRzJgxw+5m+4uIiCBz5sycOnWK27dvW87Vnp6e5M6dm8yZM7Nv3z7mzZtH0qRJ6dq1K0uXLmXSpEl2N+T8v4nZhtOnT7e7bSjyppzrTG0nypQpw8SJE/nxxx/ZsGFDrCFfpUuXpnHjxgQFBXH48GEyZcpEt27dGDNmDMmSJbNd0G/o9u3bTJgwgW+++YbevXvTqVMnrl69yvDhwxk4cCAuLi6kSZOGBAkSkCJFCn799Ve7u8g4ZrpkeD4laWhoKDdu3KBChQoMHTqUnTt30rlzZ8v6SZIkoXDhwqxYsYJevXpZlttrAgTwySefcPToUVauXAlgGT7i5eVF9uzZWb9+PRkyZCB16tS2DPO1pU2bFjc3N/766y8CAgKYOXMm6dOnp3///mTMmJHGjRsTGhpKeHg4ERERBAcH2+V1XW/DxcWFsLAwoqOj6devn10N1b179y5lypThjz/+sMRduHBhevbsSYYMGahXrx43btywrB8YGEimTJlsFa5NVahQgY0bN9rFNZqnTp1i//79nDp1Cm9vbzp27Mj27dsZM2aM5T5ez5494+LFi1SpUoWMGTPy119/ERkZSeLEiXnvvfcccsiYPW1DkbigiRHeYXv27KFZs2a0aNGCwMBAEiRIYHmsXr165M6dm379+tn1h+OQkBDatGnDkCFDSJYsGUOHDiUkJIQnT54QFRVFvnz5GDduHIBdVkJejHnChAls3ryZq1ev4ubmhp+fHw0aNODAgQN069aNvHnzUq5cOTZt2sTjx49ZsGCB5T4q9jxbGEB4eDiDBg1i48aNdO/enRo1amA2mxk3bhzR0dG0b9/eclNCe/Cf++K5c+eYNm0ap06dolatWjRr1owbN27QsWNHzp49S/r06Xn//fc5d+4cy5Yts1z75kwiIiLsst8xM2j5+/vTsmVLywfEI0eOMHz4cI4ePcr69etfmr1R3k2jR49m7dq1uLi48PDhQ3LkyME333zD3bt3CQgI4IMPPsDDw4OIiAgePHjAn3/+yerVq5k0aRK//fabQyY/Is5KlaB3WLFixZgxYwa//PILGzduxGw2A88rC15eXmTKlMkuk4IXZc6cGQ8PD9q2bUv16tUJCwvjq6++YvXq1dSuXZunT5/aKNK4EbN9fvrpJ+bNm0fLli3p3bs3DRo0YNSoUUycOJGyZcsyfvx4rl69arkQd/78+ZahRPaeAMHzoSUdO3akUqVK9OjRg88//5xGjRqxaNEiqlWrZlcJUFRUlGW73r17l4cPH+Lj40OHDh3InTs3ixcv5pdffiFNmjSMGTOGYsWKcfr0aSpXrszq1atj3YndmdhjAgTPLxyfMWMGM2bMYMWKFYSFhQGQP39+OnfuTJEiRez+POUsfvnlFxYvXsyAAQP4448/qFWrFgcPHuTJkyeULFmS5cuXU7lyZXLlysWnn37K6tWrAdi/fz+ZM2e2231YRP6ZKkF2IOZ+DH5+fqRLl46QkBAWL17MokWL7Gru/phvz48cOcKJEycwmUw0atSIW7dusWrVKtKmTcvnn39uWb9Pnz7cv3+fESNG2PXsOw8ePCAwMJAGDRpQpUoVy/Jly5bRo0cPhgwZQs2aNTGbzdy/f5/33nvPMluevcyy9KrMZjNHjx7l4MGDpEyZkqJFi9rN8KEtW7aQP39+y7DTCRMmsH37dm7fvk27du2oXbs2169fZ/z48Rw7doy6devSrFkzQkND+eabbwgJCWHOnDl2d+8ueW7btm20adOGnj178sUXX1iGKT979ixWlV7ePTHvPV26dMHHx4f27duzfv16evToQa9evciSJQsHDx6MNTNlcHAwwcHBBAUFsWjRIubNm0fOnDlt2AsRiXM2mY5BXtv27dsNX19fo2zZssagQYOMU6dO2Tqk1xIzq9kff/xhFChQwPjss88MX19fo3PnzpaZsh4+fGjMmzfPWL9+vTFgwACjUKFCdtdPw3h5pq+bN28aBQsWNJYvXx5rnaioKCMwMNBo1aqV8eTJk//6GmJbQUFBhq+vrzF37lwjKirKmDFjhlG0aFFjwoQJRkBAgJErVy5j8uTJRnR0tHHt2jWjV69exhdffGH89NNPhmE8n/WxWbNmRv78+Y0LFy7YtjPyxl6cQSssLMzW4cgrOnv2rGEYhlG3bl1jw4YNxoEDB4wCBQoYv/76q2EYhrF06VKjQIECxpUrVwzDeP5e1KdPH6N27dpG8+bNjZMnT9osdhGJP471NbMDK1myJNOmTaNnz54EBgbazbhk4/8LjSaTid27d9OrVy++++47qlevzh9//EH37t1xdXVl0KBB3Llzh3Xr1nH9+nVSp07NvHnz8PX1tXEPXs+LN4nctm0b9+/f5969e+TPn59Tp05Rrlw5kiRJYlnH29sbwzDw8vKK9TrONrvUuy5XrlxMnz6dNm3aEBERQVBQEGPGjKFEiRIATJw4kdGjR2MYBq1bt6ZDhw4MGjSI8+fPA89nfezXrx9DhgyxZTfkLcXMoNWrVy/q1atn63DkFQwZMoQtW7awZs0a8ubNyzfffENkZCSDBg2ievXqwPPhulmzZrW8ryZKlIguXbrg7e2N2Wy2q8k8ROTVaTicnXn8+LFdXD8xf/58smTJQsmSJS2JwciRIwkLC6N///7cvXuXwMBAUqRIwd69eyldujQ//vgj0dHRmM1mXF1d7fqNZ9iwYaxcuZLUqVNb+hEUFMS3335L1apVSZQoEU+fPqVt27bkzZvXcvd5ebdt3bqVVq1a4e7uzsSJEylTpozlsQkTJjBhwgTLzW7v3btH8uTJYyW0jjjE0RnZy3nY2R07dowJEybQsmVLChcuzMmTJxkyZAiXLl1i6dKlJE2alGfPnhEYGIibmxuTJk2y+9sRiMir07uxnbGHN97Q0FDWrl3LlStXGDp0KEWKFCEyMpLDhw+TMWNGAGbMmEG2bNno2rUrK1asYNCgQdy6dYuAgAA++ugjG/fg7axZs4ZVq1Yxbdo00qVLR6JEiXB1dWX06NEMHjyYdevWkTp1ai5dukRYWBgdO3a0dcjyisqUKcPs2bNp2rQpx44do2DBgiROnBiAgIAAXF1dGTVqFKlTp6ZmzZpA7OqgEiDHYA/nYWcXEhJCvXr1SJgwIe3bt8dkMpErVy78/PyYMWMGn3zyCVmzZiUiIgIXFxcWL16MyWSKdbyKiGPTkS5xLm3atAQGBpInTx569erF7t27cXNzo2PHjpQqVYpTp05x+vRpPv74Y5ImTUqCBAnImTMnT548cYhpZi9evEj27NnJnj07SZMmtQwJLFiwIOHh4eTPn5+wsDAKFSrEsmXLcHNz083p7EjRokWZPn06EydOZPny5Tx69MjyWNu2bfnxxx+pVq2aZZk+UIlYX+bMmZk1axZPnjzhr7/+4u7du5hMJipUqMC4cePo2bMnn3/+OU2bNuX333+3zNqo41XEeehrSYlTMfe0+eijj/Dw8GDq1Kn06dOHAQMGULx4caKiopgxYwZXrlzhs88+A+DMmTOULVuW9u3b2/UscDFDKEJDQ3n8+LFlauuYN9VEiRLh6elJ/fr1SZUqlaUqoCFS9qdUqVJMmTKFNm3aAFCzZk3L9QS1a9cGtF1FbK148eJMnz6dli1bkiJFCmrUqEGSJElImTIl9evXj7VuVFSUjlcRJ6OvPCROxXzgv3jxIm5ubmTNmpW8efPy/fffs3fvXlxdXXnvvfd49uwZP/30E4MGDWLlypVUrVrVrhMg+PueQNWqVePw4cPMnTsX+PtvEhERYbkR34tvtnrjtU+lS5dm8uTJDB06lPnz5/PkyZNYj2u7itheqVKlmDp1KkOGDGHFihWxKrcvcoT7sYnI69HECBLn/vzzT3744QdSpkzJs2fPqFSpEmfOnOHs2bMMGTKE999/nx9//JFTp06RMGFCfvjhB4e6/4JhGIwdO5YpU6bQuXNnPv/8c1xcXBg0aBBms5lp06bpolsHsnHjRn7++Wfmzp2r7Sryjtq2bRvt2rWjQ4cONGnSxK4n3hGRuKEkSOLUiRMn8Pf3p3v37nz00Ud4e3vj7e3NjRs36NevHyEhIYwZM4YPP/yQR48eER0dbbnpoCN5+vQpCxYsYOzYsSRJkgQvLy+SJEnCvHnzcHd318W3DiZmKKRmlRJ5d+kLCxF5kZIgiVNLly5l/vz5zJ49O9a9b86ePUtgYCC+vr7s3LmTCRMmUKRIERtGah0hISFcuXIFd3d3ChUqhKurq64VcVBKgETeffrCQkRi6JOYxKmbN2/y8OFD3N3dgb+nB3ZxceH8+fPUqlULV1dXUqdObeNIrSNz5sxkzpzZ8rsuvnVc+kAl8u5TAiQiMTQeR+LUxx9/zKVLl5g3bx7wfFIAwzAwDIPcuXNTtWpVfvzxR7JkyWLjSG1DF9+KiNiWEiARAVWCJI7lzZuX9u3bM2TIEKKjo6lXrx4Af/zxB0+ePMHT09PuZ4ETEREREfuma4Ikzj158oS5c+cybtw40qdPj5eXF3fu3GHq1Knkzp3b1uGJiIiIiJNTEiTxJjg4mOPHj+Pl5UX+/PnJkCGDrUMSEREREVESJCIiIiIizkUTI4iIiIiIiFNREiQiIiIiIk5FSZCIiIiIiDgVJUEiIiIiIuJUlASJiIiIiIhTURIkIiIiIiJORUmQiIid8/f3x9fXl5YtW771az18+JC5c+fGQVT/W48ePfD19eXkyZNWaU9ERCSGkiARETt269Ytdu7ciZeXF9u3byc0NPStXq9y5cosWrQojqITERF5NykJEhGxYytXriQqKgp/f3+io6NZvHjxW73enTt34igyERGRd5eSIBERO7Zs2TKSJk2Kv78/iRMn5vfff8cwDFuHJSIi8k5TEiQiYqdOnTpFcHAwJUqUwNPTk08//ZSrV6+yY8eOf1x/yZIl1KtXj4IFC1KyZEnatm3LqVOnANizZw++vr6W1/X19WX8+PEAVKhQgY8++uil14t5zqBBg2ItP336NN988w1ly5YlT548FCpUiIYNG/Lnn3/GZfdFRETemJIgERE7tWzZMgCqVKkS67//dE1Pnz596NWrF3fu3KFGjRqUK1eOnTt30qhRI06dOkWGDBkICAgAIFWqVAQEBFC0aNHXjuno0aPUq1ePzZs3U6pUKZo3b06pUqU4duwYgYGB/PXXX2/YWxERkbjjZusARETk9UVFRbFq1Sq8vb0pV64cAB9//DEpU6Zk48aN3L17lxQpUgCwa9cuFixYwEcffcSUKVNIlCgRAHXq1KFx48aMGTOGyZMn06FDByZMmECqVKno0KHDG8U1duxYIiMj+f333/Hx8bEsX7NmDZ07d2bVqlWUL1/+7TovIiLyllQJEhGxQzt27ODWrVtUrFiRBAkSAODm5sZnn31GREQEy5cvt6y7evVqALp27WpJgAAKFSpEly5d4jQp8fPzY/jw4bESIIBixYoBmnhBRETeDaoEiYjYoZgkp2rVqrGWV69enXnz5rF48WKaN28OPL/Gx9XVlbx58770Oq1atYrTuEqXLg08n7r71KlThISEcOHCBQ4cOAA8r2CJiIjYmpIgERE78+jRIzZs2ADwrzdIPXv2LAcPHqRQoUKEhYWRIEEC3N3d4z22a9euMXDgQDZt2oRhGLi4uPD+++9TuHBhgoKC4r19ERGRV6EkSETEzqxdu5bw8HDy5s1L7ty5X3r8woUL7N27l0WLFlGoUCESJkzIs2fPiIyMxM0t9mn/6dOneHl5/c82o6OjX1oWHh4e63fDMGjdujVnz56ldevWfPrpp2TPnh1PT09u376tm7CKiMg7Q0mQiIidiRkK16NHj3+cuvratWt88sknrF27lt69e5MjRw5OnjxJUFAQ+fLli7Vuu3btOHbsGNu2bfvXZMjd3Z1nz55hGAYmk8myPCQkJNZ6wcHBnD59msqVK9O5c+dYj507dw5A9zASEZF3giZGEBGxI1evXmXfvn1kyJCBwoUL/+M66dOnp3jx4jx58oTVq1fzxRdfADB69OhY1ZtDhw6xd+9eChYsaEmA3N3diYiIiPV62bJlIzIykq1bt1qW3b9/n3nz5sVaz8PDA4C7d+/GWn7//n2GDRsGQGRk5Jt0W0REJE6pEiQiYkeWL1+OYRhUr149VlXmP9WuXZudO3eyaNEiFi9eTJ06dViyZAk1atSgdOnSPH78mNWrV+Pt7U2fPn0sz0udOjXnz5+nb9++lC1blgoVKlC/fn02bdpEp06dqF69Oh4eHqxdu5YsWbJw4cIFy3Pff/998uXLx759+2jcuDGFChXi3r17bNiwAbPZjJeXF/fu3YvXv4+IiMirUCVIRMSOxAyFi6nu/JuKFSuSOHFijh07RnBwMIMGDaJv3754enqyYMEC1q9fT5kyZfj111/JlCmT5Xl9+vQhY8aMLFmyhI0bNwJQvnx5RowYQebMmVm6dCnr16+nVq1ajBkzJlabLi4u/PTTT9SuXZsrV64wZ84c9u/fT5kyZViyZAklS5bk4sWLLw2jExERsTaToQHaIiIiIiLiRFQJEhERERERp6IkSEREREREnIqSIBERERERcSpKgkRERERExKkoCRIREREREaeiJEhERERERJyKkiAREREREXEqSoJERERERMSpKAkSERERERGnoiRIREREREScyv8Bx58QeceIzFIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Converting confusion matrix dataframe to an array\n",
    "confusion_matrix = np.array(array_few)\n",
    "confusion_matrix\n",
    "# Code for printing heat map\n",
    "fig, ax = plt.subplots()\n",
    "# Inputting 2D array: confusion_matrix(converted from dataframe to array) into design for heat map\n",
    "im = ax.imshow(array_few, cmap = 'YlGnBu')\n",
    "#im = ax.imshow(confusion_matrix, cmap = 'Greens')\n",
    "#im = ax.imshow(confusion_matrix, cmap = 'Cool')\n",
    "cbar = plt.colorbar(im)\n",
    "tick_font_size = 15\n",
    "cbar.ax.tick_params(labelsize = tick_font_size)\n",
    "\n",
    "# Ticks for x and y axis\n",
    "ax.set_xticks(np.arange(len(predicted_result)))\n",
    "ax.set_yticks(np.arange(len(actual_result)))\n",
    "\n",
    "# Assigning x and y axes labels of heat map\n",
    "ax.set_xticklabels(actual_result)\n",
    "ax.set_yticklabels(predicted_result)\n",
    "# Rotate the tick labels and set their alignment (for a better visual)\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "         rotation_mode=\"anchor\", fontsize = 15)\n",
    "plt.setp(ax.get_yticklabels(), fontsize = 15)\n",
    "plt.xlabel('Actual', fontsize = 20)\n",
    "plt.ylabel('Prediction', fontsize = 20)\n",
    "#plt.figure(figsize=(30,12))\n",
    "\n",
    "# Putting confusion matrix array values in respective spots of heat map\n",
    "for i in range(len(actual_result)):\n",
    "    for j in range(len(predicted_result)):\n",
    "        text = ax.text(j, i, confusion_matrix[i, j],\n",
    "                       ha=\"center\", va=\"center\", color=\"black\")\n",
    "# Assigning title for heat map\n",
    "ax.set_title(\"Confusion Matrix: Comparison of Predicted Vs Actual Genres For Five Variables\", fontsize = 20)\n",
    "fig.tight_layout()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'blues': {'precision': 0.35294117647058826, 'recall': 0.46153846153846156, 'f1-score': 0.4000000000000001, 'support': 13}, 'classical': {'precision': 0.8235294117647058, 'recall': 0.875, 'f1-score': 0.8484848484848485, 'support': 16}, 'country': {'precision': 0.3333333333333333, 'recall': 0.3888888888888889, 'f1-score': 0.358974358974359, 'support': 18}, 'disco': {'precision': 0.5555555555555556, 'recall': 0.43478260869565216, 'f1-score': 0.4878048780487805, 'support': 23}, 'hiphop': {'precision': 0.5238095238095238, 'recall': 0.4583333333333333, 'f1-score': 0.4888888888888889, 'support': 24}, 'jazz': {'precision': 0.391304347826087, 'recall': 0.5294117647058824, 'f1-score': 0.45, 'support': 17}, 'metal': {'precision': 0.6785714285714286, 'recall': 0.7916666666666666, 'f1-score': 0.7307692307692307, 'support': 24}, 'pop': {'precision': 0.6521739130434783, 'recall': 0.6818181818181818, 'f1-score': 0.6666666666666666, 'support': 22}, 'reggae': {'precision': 0.45, 'recall': 0.45, 'f1-score': 0.45, 'support': 20}, 'rock': {'precision': 0.3333333333333333, 'recall': 0.17391304347826086, 'f1-score': 0.22857142857142854, 'support': 23}, 'accuracy': 0.52, 'macro avg': {'precision': 0.5094552023708034, 'recall': 0.5245352949125328, 'f1-score': 0.5110160300404203, 'support': 200}, 'weighted avg': {'precision': 0.5153314659197012, 'recall': 0.52, 'f1-score': 0.5115120631401119, 'support': 200}}\n"
     ]
    }
   ],
   "source": [
    "target_names = df.label.unique()\n",
    "report_five = classification_report(y_test, predictions, target_names = target_names, output_dict=True)\n",
    "print(report_five)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>blues</th>\n",
       "      <th>classical</th>\n",
       "      <th>country</th>\n",
       "      <th>disco</th>\n",
       "      <th>hiphop</th>\n",
       "      <th>jazz</th>\n",
       "      <th>metal</th>\n",
       "      <th>pop</th>\n",
       "      <th>reggae</th>\n",
       "      <th>rock</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>macro avg</th>\n",
       "      <th>weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.678571</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.509455</td>\n",
       "      <td>0.515331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.524535</td>\n",
       "      <td>0.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1-score</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.848485</td>\n",
       "      <td>0.358974</td>\n",
       "      <td>0.487805</td>\n",
       "      <td>0.488889</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.730769</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.228571</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.511016</td>\n",
       "      <td>0.511512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>20.00</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.52</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               blues  classical    country      disco     hiphop       jazz  \\\n",
       "precision   0.352941   0.823529   0.333333   0.555556   0.523810   0.391304   \n",
       "recall      0.461538   0.875000   0.388889   0.434783   0.458333   0.529412   \n",
       "f1-score    0.400000   0.848485   0.358974   0.487805   0.488889   0.450000   \n",
       "support    13.000000  16.000000  18.000000  23.000000  24.000000  17.000000   \n",
       "\n",
       "               metal        pop  reggae       rock  accuracy   macro avg  \\\n",
       "precision   0.678571   0.652174    0.45   0.333333      0.52    0.509455   \n",
       "recall      0.791667   0.681818    0.45   0.173913      0.52    0.524535   \n",
       "f1-score    0.730769   0.666667    0.45   0.228571      0.52    0.511016   \n",
       "support    24.000000  22.000000   20.00  23.000000      0.52  200.000000   \n",
       "\n",
       "           weighted avg  \n",
       "precision      0.515331  \n",
       "recall         0.520000  \n",
       "f1-score       0.511512  \n",
       "support      200.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict = pd.DataFrame(report_five)\n",
    "df_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "blues        0.352941\n",
       "classical    0.823529\n",
       "country      0.333333\n",
       "disco        0.555556\n",
       "hiphop       0.523810\n",
       "jazz         0.391304\n",
       "metal        0.678571\n",
       "pop          0.652174\n",
       "reggae       0.450000\n",
       "rock         0.333333\n",
       "Name: precision, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_few = df_dict.iloc[0,:10]\n",
    "columns_few"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(columns_few)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assigning x labels for bar graphs (Ranges of cholestrol numbers)\n",
    "X_labels = df.label.unique()\n",
    "\n",
    "# Y axis: Finding number of people who did not suffer a heart attack with cholestrol ranges given above\n",
    "few_var = columns_few\n",
    "\n",
    "all_var = columns_all\n",
    "\n",
    "X_axis = np.arange(len(X_labels))\n",
    "\n",
    "# Setting ideal positions for visual alignment in code for plotting bars\n",
    "plt.figure(figsize=(30,12))\n",
    "plt.bar(X_axis - 0.2, few_var, 0.35, label = '5', color = 'blue')\n",
    "plt.bar(X_axis + 0.2, all_var, 0.35, label = 'all', color = 'orange')\n",
    "\n",
    "\n",
    "#Setting labels  \n",
    "\n",
    "plt.xticks(X_axis, X_labels, fontsize = 30)\n",
    "plt.yticks(fontsize = 30)\n",
    "plt.xticks(rotation = 45)\n",
    "plt.xlabel(\"Genres\", fontsize = 35)\n",
    "plt.ylabel(\"Precision Rate\", fontsize = 35)\n",
    "plt.title(\"Precision Rate For Five vs All Variables\", fontsize = 35)\n",
    "\n",
    "plt.legend(fontsize = 30, shadow = True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}