data-exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration\n",
    "The purpose of this notebook is to explore more in depth the _Spotify Tracks_ dataset and propose preprocessing techniques for each of the features present in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 114000 songs in the dataset\n",
      "The dataset has a total of 20 features\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>track_id</th>\n",
       "      <th>artists</th>\n",
       "      <th>album_name</th>\n",
       "      <th>track_name</th>\n",
       "      <th>popularity</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>explicit</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>track_genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5SuOikwiRyPMVoIQDJUgSV</td>\n",
       "      <td>Gen Hoshino</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>73</td>\n",
       "      <td>230666</td>\n",
       "      <td>False</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.4610</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.746</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1430</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3580</td>\n",
       "      <td>0.715</td>\n",
       "      <td>87.917</td>\n",
       "      <td>4</td>\n",
       "      <td>acoustic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4qPNDBW1i3p13qLCt0Ki3A</td>\n",
       "      <td>Ben Woodward</td>\n",
       "      <td>Ghost (Acoustic)</td>\n",
       "      <td>Ghost - Acoustic</td>\n",
       "      <td>55</td>\n",
       "      <td>149610</td>\n",
       "      <td>False</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.1660</td>\n",
       "      <td>1</td>\n",
       "      <td>-17.235</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0763</td>\n",
       "      <td>0.9240</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.267</td>\n",
       "      <td>77.489</td>\n",
       "      <td>4</td>\n",
       "      <td>acoustic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1iJBSr7s7jYXzM8EGcbK5b</td>\n",
       "      <td>Ingrid Michaelson;ZAYN</td>\n",
       "      <td>To Begin Again</td>\n",
       "      <td>To Begin Again</td>\n",
       "      <td>57</td>\n",
       "      <td>210826</td>\n",
       "      <td>False</td>\n",
       "      <td>0.438</td>\n",
       "      <td>0.3590</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.734</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0557</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1170</td>\n",
       "      <td>0.120</td>\n",
       "      <td>76.332</td>\n",
       "      <td>4</td>\n",
       "      <td>acoustic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6lfxq3CG4xtTiEg7opyCyx</td>\n",
       "      <td>Kina Grannis</td>\n",
       "      <td>Crazy Rich Asians (Original Motion Picture Sou...</td>\n",
       "      <td>Can't Help Falling In Love</td>\n",
       "      <td>71</td>\n",
       "      <td>201933</td>\n",
       "      <td>False</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.0596</td>\n",
       "      <td>0</td>\n",
       "      <td>-18.515</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.9050</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.1320</td>\n",
       "      <td>0.143</td>\n",
       "      <td>181.740</td>\n",
       "      <td>3</td>\n",
       "      <td>acoustic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5vjLSffimiIP26QG5WcN2K</td>\n",
       "      <td>Chord Overstreet</td>\n",
       "      <td>Hold On</td>\n",
       "      <td>Hold On</td>\n",
       "      <td>82</td>\n",
       "      <td>198853</td>\n",
       "      <td>False</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.4430</td>\n",
       "      <td>2</td>\n",
       "      <td>-9.681</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0526</td>\n",
       "      <td>0.4690</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0829</td>\n",
       "      <td>0.167</td>\n",
       "      <td>119.949</td>\n",
       "      <td>4</td>\n",
       "      <td>acoustic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 track_id                 artists  \\\n",
       "0  5SuOikwiRyPMVoIQDJUgSV             Gen Hoshino   \n",
       "1  4qPNDBW1i3p13qLCt0Ki3A            Ben Woodward   \n",
       "2  1iJBSr7s7jYXzM8EGcbK5b  Ingrid Michaelson;ZAYN   \n",
       "3  6lfxq3CG4xtTiEg7opyCyx            Kina Grannis   \n",
       "4  5vjLSffimiIP26QG5WcN2K        Chord Overstreet   \n",
       "\n",
       "                                          album_name  \\\n",
       "0                                             Comedy   \n",
       "1                                   Ghost (Acoustic)   \n",
       "2                                     To Begin Again   \n",
       "3  Crazy Rich Asians (Original Motion Picture Sou...   \n",
       "4                                            Hold On   \n",
       "\n",
       "                   track_name  popularity  duration_ms  explicit  \\\n",
       "0                      Comedy          73       230666     False   \n",
       "1            Ghost - Acoustic          55       149610     False   \n",
       "2              To Begin Again          57       210826     False   \n",
       "3  Can't Help Falling In Love          71       201933     False   \n",
       "4                     Hold On          82       198853     False   \n",
       "\n",
       "   danceability  energy  key  loudness  mode  speechiness  acousticness  \\\n",
       "0         0.676  0.4610    1    -6.746     0       0.1430        0.0322   \n",
       "1         0.420  0.1660    1   -17.235     1       0.0763        0.9240   \n",
       "2         0.438  0.3590    0    -9.734     1       0.0557        0.2100   \n",
       "3         0.266  0.0596    0   -18.515     1       0.0363        0.9050   \n",
       "4         0.618  0.4430    2    -9.681     1       0.0526        0.4690   \n",
       "\n",
       "   instrumentalness  liveness  valence    tempo  time_signature track_genre  \n",
       "0          0.000001    0.3580    0.715   87.917               4    acoustic  \n",
       "1          0.000006    0.1010    0.267   77.489               4    acoustic  \n",
       "2          0.000000    0.1170    0.120   76.332               4    acoustic  \n",
       "3          0.000071    0.1320    0.143  181.740               3    acoustic  \n",
       "4          0.000000    0.0829    0.167  119.949               4    acoustic  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "songs = pd.read_csv('data/spotify_data.csv', index_col=0)\n",
    "print(f\"There are {len(songs)} songs in the dataset\")\n",
    "print(f\"The dataset has a total of {len(songs.columns)} features\")\n",
    "songs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the dataset\n",
    "\n",
    "* Eliminate song duplicates\n",
    "* Drop columns with missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 89740 unique songs in the dataset\n"
     ]
    }
   ],
   "source": [
    "songs = songs.drop_duplicates(['track_id'])\n",
    "# Drop rows with missing values\n",
    "songs.dropna(inplace=True)\n",
    "print(f\"There are {len(songs)} unique songs in the dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>explicit</th>\n",
       "      <th>key</th>\n",
       "      <th>mode</th>\n",
       "      <th>time_signature</th>\n",
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      "text/plain": [
       "   explicit  key  mode  time_signature\n",
       "0     False    1     0               4\n",
       "1     False    1     1               4\n",
       "2     False    0     1               4\n",
       "3     False    0     1               3\n",
       "4     False    2     1               4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "categorical_features = songs[['explicit', 'key', 'mode', 'time_signature']]\n",
    "categorical_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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3CBfr2LGjPvnkE/Xt21f33Xefli9fLg8PD7322msaPHiwbr31Vg0cOFA33HCDDh8+rFWrVqlTp07FPnQYMmSI/vnPf0oSp5mjOMfcTB1wjKJHhRQtrq6uhq+vr/G3v/3NmDNnjs0jKYpc/KiQ1NRUo1+/foa/v7/h6upq+Pv7Gw899JDx448/2qz3ySefGEFBQYaLi4vN4yTuvPNOo2XLliXmV9qjQt577z1jwoQJhre3t+Hu7m6Eh4cbP//8c7H1Z86cadx4442G1Wo1OnXqZHz77bfFxrxUbhc/KsQw/nycxujRow1/f3+jWrVqRtOmTY2XX37ZfORVEV3wWI4LlfYIk4tlZWUZw4YNM+rVq2e4uroarVu3LvERHGV5VIhhGMa///1vo3PnzkaNGjWMGjVqGM2bNzeioqKMffv2GYZhGPfee69Rq1Yt49ChQzbrffLJJ4Yk46WXXjIM43+P9nr55ZeNmTNnGg0aNDCsVqvRpUsX4/vvv7dZ90oeJ2YYhvHbb78Z0dHRxo033mi4uroa9evXNyIiIoxjx47ZbPPCeTh//rzxxBNPGDfccINhsVh4tBgAVBDeI1w6t+vtPUJJOX3yySeGi4uL8eCDD5qPBlu/fr0RFhZmeHp6Gm5ubkbjxo2NoUOHGt9++22xMY8ePWo4OzsbN9988xXlgKrFYhgOvKMBAPxFHDp0SIGBgXr55ZfNT7MBAACKHDt2TH5+foqJidGkSZMcnQ6uMVzjDQAAAABXKTExUQUFBRo8eLCjU8E1iGu8AQAAAKCc1q1bZ97Bvn///mV+0gmqBgpvAAAAACin2NhYbdq0SZ06ddK8efMcnQ6uUVzjDQAAAACAHXGNNwAAAAAAdsSp5hWksLBQGRkZqlWrliwWi6PTAQBcAwzD0MmTJ+Xv72/zPHj89XCcBwBcrCzHeQrvCpKRkaEGDRo4Og0AwDXoyJEjql+/vqPTwFXgOA8AKM2VHOcpvCtIrVq1JP056R4eHg7OBgBwLcjNzVWDBg3MYwT+ujjOAwAuVpbjPIV3BSk67czDw4MDMgDABqcm//VxnAcAlOZKjvNccAYAAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4AwAAAABgRxTeAAAAAADYEYU3AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAduTi6ARQ3OHDh3Xs2LEKG69evXpq2LBhhY0HAAAAALhyFN7XmMOHD6t58xY6c+aPChvT3b269u7dQ/ENAACAKqVZUCsdzcgotd/P31/7du+sxIxQVVF4X2OOHTumM2f+UIdHJ8vDr9FVj5d79JC2LHhOx44do/AGAABAlXI0I0N9Z6wqtX/1uPBKzAZVGYX3NcrDr5HqNGzm6DQAAAAAAFeJm6sBAAAAAGBHFN4AAAAAANgRhTcAAAAAAHbk0MK7oKBAkyZNUmBgoNzd3dW4cWNNnTpVhmGYMYZhKCYmRn5+fnJ3d1doaKj2799vM87x48c1aNAgeXh4yMvLS5GRkTp16pRNzA8//KAuXbrIzc1NDRo00IwZM4rl88EHH6h58+Zyc3NT69attXr1avvsOAAAAACgynBo4f3SSy/ptdde0/z587Vnzx699NJLmjFjhubNm2fGzJgxQ3PnzlVCQoK2bNmiGjVqKCwsTGfPnjVjBg0apF27diklJUUrV67Uxo0bNXLkSLM/NzdXvXr1UkBAgNLS0vTyyy9rypQpeuONN8yYTZs26aGHHlJkZKS2b9+u/v37q3///tq5k8cLAACAitMsqJU8vOqUujQLauXoFAEAFcyhdzXftGmT+vXrp/DwP2/j36hRI7333nvaunWrpD+/7Y6Li9PEiRPVr18/SdI777wjHx8fLV++XAMHDtSePXuUnJysbdu2qX379pKkefPmqW/fvnrllVfk7++vpKQk5efna8GCBXJ1dVXLli2Vnp6uWbNmmQX6nDlz1Lt3b40dO1aSNHXqVKWkpGj+/PlKSEio7KkBAADXKR5vBABVj0O/8b7jjjuUmpqqH3/8UZL0/fff66uvvlKfPn0kSQcPHlRmZqZCQ0PNdTw9PdWhQwdt3rxZkrR582Z5eXmZRbckhYaGysnJSVu2bDFjunbtKldXVzMmLCxM+/bt0++//27GXLidopii7VwsLy9Pubm5NgsAAAAAABdz6Dfe48ePV25urpo3by5nZ2cVFBTohRde0KBBgyRJmZmZkiQfHx+b9Xx8fMy+zMxMeXt72/S7uLioTp06NjGBgYHFxijqq127tjIzMy+5nYtNnz5dzz33XHl2GwAAAABQhTj0G+9ly5YpKSlJS5Ys0XfffadFixbplVde0aJFixyZ1hWZMGGCcnJyzOXIkSOOTgkAAAAAcA1y6DfeY8eO1fjx4zVw4EBJUuvWrfXzzz9r+vTpioiIkK+vryQpKytLfn5+5npZWVlq166dJMnX11fZ2dk2454/f17Hjx831/f19VVWVpZNTNHry8UU9V/MarXKarWWZ7cBAAAAAFWIQ7/x/uOPP+TkZJuCs7OzCgsLJUmBgYHy9fVVamqq2Z+bm6stW7YoJCREkhQSEqITJ04oLS3NjFm3bp0KCwvVoUMHM2bjxo06d+6cGZOSkqJmzZqpdu3aZsyF2ymKKdoOAAAAAADl4dDC++6779YLL7ygVatW6dChQ/r44481a9Ys/d///Z8kyWKxaNSoUXr++ee1YsUK7dixQ0OGDJG/v7/69+8vSWrRooV69+6tESNGaOvWrfr6668VHR2tgQMHyt/fX5L08MMPy9XVVZGRkdq1a5eWLl2qOXPmaMyYMWYuTz31lJKTkzVz5kzt3btXU6ZM0bfffqvo6OhKnxcAAAAAwPXDoaeaz5s3T5MmTdI//vEPZWdny9/fX3//+98VExNjxowbN06nT5/WyJEjdeLECXXu3FnJyclyc3MzY5KSkhQdHa2ePXvKyclJAwYM0Ny5c81+T09PrV27VlFRUQoODla9evUUExNj86zvO+64Q0uWLNHEiRP1zDPPqGnTplq+fLlateJZmgAAAACA8nNo4V2rVi3FxcUpLi6u1BiLxaLY2FjFxsaWGlOnTh0tWbLkkttq06aNvvzyy0vG3H///br//vsvGQMAAAAAQFk49FRzAABw/dq4caPuvvtu+fv7y2KxaPny5Tb9hmEoJiZGfn5+cnd3V2hoqPbv328Tc/z4cQ0aNEgeHh7y8vJSZGSkTp06ZRPzww8/qEuXLnJzc1ODBg00Y8aMYrl88MEHat68udzc3NS6dWutXr26wvcXAIDSUHgDAAC7OH36tNq2bav4+PgS+2fMmKG5c+cqISFBW7ZsUY0aNRQWFqazZ8+aMYMGDdKuXbuUkpKilStXauPGjTaXiuXm5qpXr14KCAhQWlqaXn75ZU2ZMkVvvPGGGbNp0yY99NBDioyM1Pbt29W/f3/1799fO3futN/OAwBwAYeeag4AAK5fffr0UZ8+fUrsMwxDcXFxmjhxovr16ydJeuedd+Tj46Ply5dr4MCB2rNnj5KTk7Vt2za1b99e0p/3h+nbt69eeeUV+fv7KykpSfn5+VqwYIFcXV3VsmVLpaena9asWWaBPmfOHPXu3Vtjx46VJE2dOlUpKSmaP3++EhISSswvLy9PeXl55uvc3NwKmxcAQNXDN94AAKDSHTx4UJmZmQoNDTXbPD091aFDB23evFmStHnzZnl5eZlFtySFhobKyclJW7ZsMWO6du0qV1dXMyYsLEz79u3T77//bsZcuJ2imKLtlGT69Ony9PQ0lwYNGlz9TgMAqiwKbwAAUOkyMzMlST4+PjbtPj4+Zl9mZqa8vb1t+l1cXFSnTh2bmJLGuHAbpcUU9ZdkwoQJysnJMZcjR46UdRcBADBxqjkAAMBFrFarrFaro9MAAFwn+MYbAABUOl9fX0lSVlaWTXtWVpbZ5+vrq+zsbJv+8+fP6/jx4zYxJY1x4TZKiynqBwDA3ii8AQBApQsMDJSvr69SU1PNttzcXG3ZskUhISGSpJCQEJ04cUJpaWlmzLp161RYWKgOHTqYMRs3btS5c+fMmJSUFDVr1ky1a9c2Yy7cTlFM0XYAALA3Cm8AAGAXp06dUnp6utLT0yX9eUO19PR0HT58WBaLRaNGjdLzzz+vFStWaMeOHRoyZIj8/f3Vv39/SVKLFi3Uu3dvjRgxQlu3btXXX3+t6OhoDRw4UP7+/pKkhx9+WK6uroqMjNSuXbu0dOlSzZkzR2PGjDHzeOqpp5ScnKyZM2dq7969mjJlir799ltFR0dX9pQAAKoorvEGAAB28e2336p79+7m66JiOCIiQomJiRo3bpxOnz6tkSNH6sSJE+rcubOSk5Pl5uZmrpOUlKTo6Gj17NlTTk5OGjBggObOnWv2e3p6au3atYqKilJwcLDq1aunmJgYm2d933HHHVqyZIkmTpyoZ555Rk2bNtXy5cvVqlWrSpgFAAAovAEAgJ1069ZNhmGU2m+xWBQbG6vY2NhSY+rUqaMlS5Zccjtt2rTRl19+ecmY+++/X/fff/+lEwYAwE441RwAAAAAADui8AYAAAAAwI4ovAEAAAAAsCMKbwAAAAAA7IjCGwAAAAAAO6LwBgAAAADAjii8AQAAAACwIwpvAAAAAADsiMIbAAAAAAA7ovAGAAAAAMCOKLwBAAAAALAjCm8AAAAAAOyIwhsAAAAAADui8AYAAAAAwI4ovAEAAAAAsCMKbwAAAAAA7IjCGwAAAAAAO3JxdAIAAAC4PjULaqWjGRml9vv5+2vf7p2VmBEAOAaFNwAAAOziaEaG+s5YVWr/6nHhlZgNADgOp5oDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAduTQwrtRo0ayWCzFlqioKEnS2bNnFRUVpbp166pmzZoaMGCAsrKybMY4fPiwwsPDVb16dXl7e2vs2LE6f/68TcyGDRt06623ymq1qkmTJkpMTCyWS3x8vBo1aiQ3Nzd16NBBW7dutdt+AwAAAACqDocW3tu2bdPRo0fNJSUlRZJ0//33S5JGjx6tTz/9VB988IG++OILZWRk6N577zXXLygoUHh4uPLz87Vp0yYtWrRIiYmJiomJMWMOHjyo8PBwde/eXenp6Ro1apSGDx+uNWvWmDFLly7VmDFjNHnyZH333Xdq27atwsLClJ2dXUkzAQAAAAC4Xjm08L7hhhvk6+trLitXrlTjxo115513KicnR2+//bZmzZqlHj16KDg4WAsXLtSmTZv0zTffSJLWrl2r3bt3691331W7du3Up08fTZ06VfHx8crPz5ckJSQkKDAwUDNnzlSLFi0UHR2t++67T7NnzzbzmDVrlkaMGKFhw4YpKChICQkJql69uhYsWFBq7nl5ecrNzbVZAAAAAAC42DVzjXd+fr7effddPfroo7JYLEpLS9O5c+cUGhpqxjRv3lwNGzbU5s2bJUmbN29W69at5ePjY8aEhYUpNzdXu3btMmMuHKMopmiM/Px8paWl2cQ4OTkpNDTUjCnJ9OnT5enpaS4NGjS4+kkAAAAAAFx3rpnCe/ny5Tpx4oSGDh0qScrMzJSrq6u8vLxs4nx8fJSZmWnGXFh0F/UX9V0qJjc3V2fOnNGxY8dUUFBQYkzRGCWZMGGCcnJyzOXIkSNl3mcAAAAAqCzNglrJw6tOqUuzoFaOTvG65eLoBIq8/fbb6tOnj/z9/R2dyhWxWq2yWq2OTgMAAAAArsjRjAz1nbGq1P7V48IrMZuq5Zr4xvvnn3/W559/ruHDh5ttvr6+ys/P14kTJ2xis7Ky5Ovra8ZcfJfzoteXi/Hw8JC7u7vq1asnZ2fnEmOKxgAAAAAAoLyuicJ74cKF8vb2Vnj4/z5hCQ4OVrVq1ZSammq27du3T4cPH1ZISIgkKSQkRDt27LC5+3hKSoo8PDwUFBRkxlw4RlFM0Riurq4KDg62iSksLFRqaqoZAwAAAABAeTn8VPPCwkItXLhQERERcnH5Xzqenp6KjIzUmDFjVKdOHXl4eOiJJ55QSEiIOnbsKEnq1auXgoKCNHjwYM2YMUOZmZmaOHGioqKizNPAH3vsMc2fP1/jxo3To48+qnXr1mnZsmVatep/p1iMGTNGERERat++vW6//XbFxcXp9OnTGjZsWOVOBgAAAADguuPwwvvzzz/X4cOH9eijjxbrmz17tpycnDRgwADl5eUpLCxMr776qtnv7OyslStX6vHHH1dISIhq1KihiIgIxcbGmjGBgYFatWqVRo8erTlz5qh+/fp66623FBYWZsY8+OCD+vXXXxUTE6PMzEy1a9dOycnJxW64BgAAAABAWTm88O7Vq5cMwyixz83NTfHx8YqPjy91/YCAAK1evfqS2+jWrZu2b99+yZjo6GhFR0dfPmEAAAAAAMrgmrjGGwAAAACA6xWFNwAAAAAAdkThDQAAAACAHVF4AwAAAABgRxTeAAAAAADYEYU3AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHLo5OAAAAABWrWVArHc3IuGSMn7+/9u3eWUkZAUDVRuENAABwnTmakaG+M1ZdMmb1uPBKygYAwKnmAAAAAADYEYU3AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4AwAAAABgRxTeAAAAAADYEYU3AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHFN4AAMAhCgoKNGnSJAUGBsrd3V2NGzfW1KlTZRiGGWMYhmJiYuTn5yd3d3eFhoZq//79NuMcP35cgwYNkoeHh7y8vBQZGalTp07ZxPzwww/q0qWL3Nzc1KBBA82YMaNS9hEAAInCGwAAOMhLL72k1157TfPnz9eePXv00ksvacaMGZo3b54ZM2PGDM2dO1cJCQnasmWLatSoobCwMJ09e9aMGTRokHbt2qWUlBStXLlSGzdu1MiRI83+3Nxc9erVSwEBAUpLS9PLL7+sKVOm6I033qjU/QUAVF0ujk4AAABUTZs2bVK/fv0UHh4uSWrUqJHee+89bd26VdKf33bHxcVp4sSJ6tevnyTpnXfekY+Pj5YvX66BAwdqz549Sk5O1rZt29S+fXtJ0rx589S3b1+98sor8vf3V1JSkvLz87VgwQK5urqqZcuWSk9P16xZs2wKdAAA7IVvvAEAgEPccccdSk1N1Y8//ihJ+v777/XVV1+pT58+kqSDBw8qMzNToaGh5jqenp7q0KGDNm/eLEnavHmzvLy8zKJbkkJDQ+Xk5KQtW7aYMV27dpWrq6sZExYWpn379un3338vMbe8vDzl5ubaLAAAlBffeAMAAIcYP368cnNz1bx5czk7O6ugoEAvvPCCBg0aJEnKzMyUJPn4+Nis5+PjY/ZlZmbK29vbpt/FxUV16tSxiQkMDCw2RlFf7dq1i+U2ffp0PffccxWwlwAA8I03AABwkGXLlikpKUlLlizRd999p0WLFumVV17RokWLHJ2aJkyYoJycHHM5cuSIo1MCAPyF8Y03AABwiLFjx2r8+PEaOHCgJKl169b6+eefNX36dEVERMjX11eSlJWVJT8/P3O9rKwstWvXTpLk6+ur7Oxsm3HPnz+v48ePm+v7+voqKyvLJqbodVHMxaxWq6xW69XvJAAA4htvAADgIH/88YecnGzfijg7O6uwsFCSFBgYKF9fX6Wmppr9ubm52rJli0JCQiRJISEhOnHihNLS0syYdevWqbCwUB06dDBjNm7cqHPnzpkxKSkpatasWYmnmQMAUNEovAEAgEPcfffdeuGFF7Rq1SodOnRIH3/8sWbNmqX/+7//kyRZLBaNGjVKzz//vFasWKEdO3ZoyJAh8vf3V//+/SVJLVq0UO/evTVixAht3bpVX3/9taKjozVw4ED5+/tLkh5++GG5uroqMjJSu3bt0tKlSzVnzhyNGTPGUbsOAKhiONUcAAA4xLx58zRp0iT94x//UHZ2tvz9/fX3v/9dMTExZsy4ceN0+vRpjRw5UidOnFDnzp2VnJwsNzc3MyYpKUnR0dHq2bOnnJycNGDAAM2dO9fs9/T01Nq1axUVFaXg4GDVq1dPMTExPEoMAFBpKLwBAIBD1KpVS3FxcYqLiys1xmKxKDY2VrGxsaXG1KlTR0uWLLnkttq0aaMvv/yyvKkCAHBVHH6q+S+//KJHHnlEdevWlbu7u1q3bq1vv/3W7DcMQzExMfLz85O7u7tCQ0O1f/9+mzGOHz+uQYMGycPDQ15eXoqMjNSpU6dsYn744Qd16dJFbm5uatCggWbMmFEslw8++EDNmzeXm5ubWrdurdWrV9tnpwEAAAAAVYZDC+/ff/9dnTp1UrVq1fTZZ59p9+7dmjlzps2NTmbMmKG5c+cqISFBW7ZsUY0aNRQWFqazZ8+aMYMGDdKuXbuUkpKilStXauPGjTanj+Xm5qpXr14KCAhQWlqaXn75ZU2ZMkVvvPGGGbNp0yY99NBDioyM1Pbt29W/f3/1799fO3furJzJAAAAAABclxx6qvlLL72kBg0aaOHChWZbYGCg+f+GYSguLk4TJ05Uv379JEnvvPOOfHx8tHz5cg0cOFB79uxRcnKytm3bpvbt20v685qxvn376pVXXpG/v7+SkpKUn5+vBQsWyNXVVS1btlR6erpmzZplFuhz5sxR7969NXbsWEnS1KlTlZKSovnz5yshIaFY7nl5ecrLyzNf5+bmVvwEAQAAAAD+8hz6jfeKFSvUvn173X///fL29tYtt9yiN9980+w/ePCgMjMzFRoaarZ5enqqQ4cO2rx5syRp8+bN8vLyMotuSQoNDZWTk5O2bNlixnTt2lWurq5mTFhYmPbt26fff//djLlwO0UxRdu52PTp0+Xp6WkuDRo0uMrZAAAAAABcjxxaeP/000967bXX1LRpU61Zs0aPP/64nnzySS1atEiSlJmZKUny8fGxWc/Hx8fsy8zMlLe3t02/i4uL6tSpYxNT0hgXbqO0mKL+i02YMEE5OTnmcuTIkTLvPwAAAADg+ufQU80LCwvVvn17TZs2TZJ0yy23aOfOnUpISFBERIQjU7ssq9Uqq9Xq6DQAAAAAANc4h37j7efnp6CgIJu2Fi1a6PDhw5IkX19fSVJWVpZNTFZWltnn6+ur7Oxsm/7z58/r+PHjNjEljXHhNkqLKeoHAAAAAKA8HFp4d+rUSfv27bNp+/HHHxUQECDpzxut+fr6KjU11ezPzc3Vli1bFBISIkkKCQnRiRMnlJaWZsasW7dOhYWF6tChgxmzceNGnTt3zoxJSUlRs2bNzDuoh4SE2GynKKZoOwAAAAAAlIdDC+/Ro0frm2++0bRp03TgwAEtWbJEb7zxhqKioiRJFotFo0aN0vPPP68VK1Zox44dGjJkiPz9/dW/f39Jf35D3rt3b40YMUJbt27V119/rejoaA0cOFD+/v6SpIcffliurq6KjIzUrl27tHTpUs2ZM0djxowxc3nqqaeUnJysmTNnau/evZoyZYq+/fZbRUdHV/q8AAAAAACuHw69xvu2227Txx9/rAkTJig2NlaBgYGKi4vToEGDzJhx48bp9OnTGjlypE6cOKHOnTsrOTlZbm5uZkxSUpKio6PVs2dPOTk5acCAAZo7d67Z7+npqbVr1yoqKkrBwcGqV6+eYmJibJ71fccdd2jJkiWaOHGinnnmGTVt2lTLly9Xq1atKmcyAAAAAADXJYcW3pJ011136a677iq132KxKDY2VrGxsaXG1KlTR0uWLLnkdtq0aaMvv/zykjH333+/7r///ksnDAAAAABAGTj0VHMAAAAAAK53FN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAduTw53gDAAAAqDzNglrpaEZGqf1+/v7at3tnJWYEXP8ovAEAAIAq5GhGhvrOWFVq/+px4ZWYDVA1cKo5AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHbEXc0BAACASsBjvIDKc639e6PwBgAAACoBj/ECKs+19u+NU80BAAAAALAjCm8AAAAAAOyIwhsAAAAAADui8AYAAAAAwI4ovAEAAAAAsCMKbwAAAAAA7IjCGwAAAAAAO6LwBgAAAADAjii8AQAAAACwIwpvAAAAAADsiMIbAAAAAAA7ovAGAAAAAMCOKLwBAAAAALAjCm8AAAAAAOyIwhsAAAAAADui8AYAAAAAwI4ovAEAAAAAsCMKbwAAAAAA7IjCGwAAAAAAO6LwBgAAAADAjii8AQAAAACwI4cW3lOmTJHFYrFZmjdvbvafPXtWUVFRqlu3rmrWrKkBAwYoKyvLZozDhw8rPDxc1atXl7e3t8aOHavz58/bxGzYsEG33nqrrFarmjRposTExGK5xMfHq1GjRnJzc1OHDh20detWu+wzAAAAAKBqcfg33i1bttTRo0fN5auvvjL7Ro8erU8//VQffPCBvvjiC2VkZOjee+81+wsKChQeHq78/Hxt2rRJixYtUmJiomJiYsyYgwcPKjw8XN27d1d6erpGjRql4cOHa82aNWbM0qVLNWbMGE2ePFnfffed2rZtq7CwMGVnZ1fOJAAAAAAArlsOL7xdXFzk6+trLvXq1ZMk5eTk6O2339asWbPUo0cPBQcHa+HChdq0aZO++eYbSdLatWu1e/duvfvuu2rXrp369OmjqVOnKj4+Xvn5+ZKkhIQEBQYGaubMmWrRooWio6N13333afbs2WYOs2bN0ogRIzRs2DAFBQUpISFB1atX14IFC0rNOy8vT7m5uTYLAAAAAAAXc3jhvX//fvn7++umm27SoEGDdPjwYUlSWlqazp07p9DQUDO2efPmatiwoTZv3ixJ2rx5s1q3bi0fHx8zJiwsTLm5udq1a5cZc+EYRTFFY+Tn5ystLc0mxsnJSaGhoWZMSaZPny5PT09zadCgwVXOBAAAAADgeuTQwrtDhw5KTExUcnKyXnvtNR08eFBdunTRyZMnlZmZKVdXV3l5edms4+Pjo8zMTElSZmamTdFd1F/Ud6mY3NxcnTlzRseOHVNBQUGJMUVjlGTChAnKyckxlyNHjpRrDgAAAAAA1zcXR268T58+5v+3adNGHTp0UEBAgJYtWyZ3d3cHZnZ5VqtVVqvV0WkAAAAAAK5xDj/V/EJeXl66+eabdeDAAfn6+io/P18nTpywicnKypKvr68kydfXt9hdzoteXy7Gw8ND7u7uqlevnpydnUuMKRoDAAAAAIDyuqYK71OnTuk///mP/Pz8FBwcrGrVqik1NdXs37dvnw4fPqyQkBBJUkhIiHbs2GFz9/GUlBR5eHgoKCjIjLlwjKKYojFcXV0VHBxsE1NYWKjU1FQzBgAAAACA8nJo4f3Pf/5TX3zxhQ4dOqRNmzbp//7v/+Ts7KyHHnpInp6eioyM1JgxY7R+/XqlpaVp2LBhCgkJUceOHSVJvXr1UlBQkAYPHqzvv/9ea9as0cSJExUVFWWeBv7YY4/pp59+0rhx47R37169+uqrWrZsmUaPHm3mMWbMGL355ptatGiR9uzZo8cff1ynT5/WsGHDHDIvAAAAAIDrh0Ov8f7vf/+rhx56SL/99ptuuOEGde7cWd98841uuOEGSdLs2bPl5OSkAQMGKC8vT2FhYXr11VfN9Z2dnbVy5Uo9/vjjCgkJUY0aNRQREaHY2FgzJjAwUKtWrdLo0aM1Z84c1a9fX2+99ZbCwsLMmAcffFC//vqrYmJilJmZqXbt2ik5ObnYDdcAAAAAACgrhxbe77///iX73dzcFB8fr/j4+FJjAgICtHr16kuO061bN23fvv2SMdHR0YqOjr5kDAAAAAAAZXVNXeMNAAAAAMD1hsIbAAAAAAA7ovAGAAAAAMCOHHqNNwAAAIDrT7OgVjqakVFqv5+/v/bt3lmJGQGOReENAAAAoEIdzchQ3xmrSu1fPS68ErMBHI9TzQEAgMP88ssveuSRR1S3bl25u7urdevW+vbbb81+wzAUExMjPz8/ubu7KzQ0VPv377cZ4/jx4xo0aJA8PDzk5eWlyMhInTp1yibmhx9+UJcuXeTm5qYGDRpoxowZlbJ/AABIFN4AAMBBfv/9d3Xq1EnVqlXTZ599pt27d2vmzJmqXbu2GTNjxgzNnTtXCQkJ2rJli2rUqKGwsDCdPXvWjBk0aJB27dqllJQUrVy5Uhs3btTIkSPN/tzcXPXq1UsBAQFKS0vTyy+/rClTpuiNN96o1P0FAFRdnGoOAAAc4qWXXlKDBg20cOFCsy0wMND8f8MwFBcXp4kTJ6pfv36SpHfeeUc+Pj5avny5Bg4cqD179ig5OVnbtm1T+/btJUnz5s1T37599corr8jf319JSUnKz8/XggUL5OrqqpYtWyo9PV2zZs2yKdAvlJeXp7y8PPN1bm6uPaYAAFBFlOsb75tuukm//fZbsfYTJ07opptuuuqkAACA41TWcX7FihVq37697r//fnl7e+uWW27Rm2++afYfPHhQmZmZCg0NNds8PT3VoUMHbd68WZK0efNmeXl5mUW3JIWGhsrJyUlbtmwxY7p27SpXV1czJiwsTPv27dPvv/9eYm7Tp0+Xp6enuTRo0KDC9hsAUPWUq/A+dOiQCgoKirXn5eXpl19+ueqkAACA41TWcf6nn37Sa6+9pqZNm2rNmjV6/PHH9eSTT2rRokWSpMzMTEmSj4+PzXo+Pj5mX2Zmpry9vW36XVxcVKdOHZuYksa4cBsXmzBhgnJycszlyJEjV7m3AICqrEynmq9YscL8/zVr1sjT09N8XVBQoNTUVDVq1KjCkgMAAJWnso/zhYWFat++vaZNmyZJuuWWW7Rz504lJCQoIiKiwrZTHlarVVar1aE5AACuH2UqvPv37y9JslgsxQ6I1apVU6NGjTRz5swKSw4AAFSeyj7O+/n5KSgoyKatRYsW+ve//y1J8vX1lSRlZWXJz8/PjMnKylK7du3MmOzsbJsxzp8/r+PHj5vr+/r6Kisryyam6HVRDAAA9lSmwruwsFDSnzc+2bZtm+rVq2eXpAAAQOWr7ON8p06dtG/fPpu2H3/8UQEBAWYevr6+Sk1NNQvt3NxcbdmyRY8//rgkKSQkRCdOnFBaWpqCg4MlSevWrVNhYaE6dOhgxjz77LM6d+6cqlWrJklKSUlRs2bNbO6gDgCwv2ZBrXQ0I+OSMX7+/tq3e2clZVQ5ynVX84MHD1Z0HgAA4BpRWcf50aNH64477tC0adP0wAMPaOvWrXrjjTfMx3xZLBaNGjVKzz//vJo2barAwEBNmjRJ/v7+5rfzLVq0UO/evTVixAglJCTo3Llzio6O1sCBA+Xv7y9Jevjhh/Xcc88pMjJS//rXv7Rz507NmTNHs2fPrpT9BAD8z9GMDPWdseqSMavHhVdSNpWn3I8TS01NVWpqqrKzs81PyIssWLDgqhMDAACOUxnH+dtuu00ff/yxJkyYoNjYWAUGBiouLk6DBg0yY8aNG6fTp09r5MiROnHihDp37qzk5GS5ubmZMUlJSYqOjlbPnj3l5OSkAQMGaO7cuWa/p6en1q5dq6ioKAUHB6tevXqKiYkp9VFiAABUtHIV3s8995xiY2PVvn17+fn5yWKxVHReAADAQSrzOH/XXXfprrvuKrXfYrEoNjZWsbGxpcbUqVNHS5YsueR22rRpoy+//LLceQIAcDXKVXgnJCQoMTFRgwcPruh8AACAg3GcBwCgYpXrOd75+fm64447KjoXAABwDeA4DwBAxSpX4T18+PDLntIFAAD+mjjOAwBQscp1qvnZs2f1xhtv6PPPP1ebNm3MR3MUmTVrVoUkBwAAKh/HeQAAKla5Cu8ffvjBfJ7mzp22z1fjRmsAAPy1cZwHAKBilavwXr9+fUXnAQAArhEc5wEAqFjlusYbAAAAAABcmXJ94929e/dLnmq2bt26cicEAAAci+M8AAAVq1yFd9F1X0XOnTun9PR07dy5UxERERWRFwAAcBCO8wBQsZoFtdLRjIxS+/38/bVv985S+/HXV67Ce/bs2SW2T5kyRadOnbqqhAAAgGNxnAeAinU0I0N9Z6wqtX/1uPBKzAaOUKHXeD/yyCNasGBBRQ4JAACuERznAQAonwotvDdv3iw3N7eKHBIAAFwjOM4DAFA+5TrV/N5777V5bRiGjh49qm+//VaTJk2qkMQAAIBjcJwHAKBilavw9vT0tHnt5OSkZs2aKTY2Vr169aqQxAAAgGNwnAcAoGKVq/BeuHBhRecBAACuERznAQCoWOUqvIukpaVpz549kqSWLVvqlltuqZCkAACA43GcBwCgYpSr8M7OztbAgQO1YcMGeXl5SZJOnDih7t276/3339cNN9xQkTkCAIBKxHEeAICKVa67mj/xxBM6efKkdu3apePHj+v48ePauXOncnNz9eSTT1Z0jgAAoBJxnAcAoGKV6xvv5ORkff7552rRooXZFhQUpPj4eG66AgDAXxzHeQAAKla5vvEuLCxUtWrVirVXq1ZNhYWFV50UAABwHI7zAABUrHIV3j169NBTTz2ljIwMs+2XX37R6NGj1bNnz3Il8uKLL8pisWjUqFFm29mzZxUVFaW6deuqZs2aGjBggLKysmzWO3z4sMLDw1W9enV5e3tr7NixOn/+vE3Mhg0bdOutt8pqtapJkyZKTEwstv34+Hg1atRIbm5u6tChg7Zu3Vqu/QAA4K/OHsd5AACqsnIV3vPnz1dubq4aNWqkxo0bq3HjxgoMDFRubq7mzZtX5vG2bdum119/XW3atLFpHz16tD799FN98MEH+uKLL5SRkaF7773X7C8oKFB4eLjy8/O1adMmLVq0SImJiYqJiTFjDh48qPDwcHXv3l3p6ekaNWqUhg8frjVr1pgxS5cu1ZgxYzR58mR99913atu2rcLCwpSdnV2O2QEA4K+too/zAABUdeW6xrtBgwb67rvv9Pnnn2vv3r2SpBYtWig0NLTMY506dUqDBg3Sm2++qeeff95sz8nJ0dtvv60lS5aoR48ekv58rmiLFi30zTffqGPHjlq7dq12796tzz//XD4+PmrXrp2mTp2qf/3rX5oyZYpcXV2VkJCgwMBAzZw508zzq6++0uzZsxUWFiZJmjVrlkaMGKFhw4ZJkhISErRq1SotWLBA48ePL88UAQDwl1WRx3kAAFDGb7zXrVunoKAg5ebmymKx6G9/+5ueeOIJPfHEE7rtttvUsmVLffnll2VKICoqSuHh4cUO5mlpaTp37pxNe/PmzdWwYUNt3rxZkrR582a1bt1aPj4+ZkxYWJhyc3O1a9cuM+biscPCwswx8vPzlZaWZhPj5OSk0NBQM6YkeXl5ys3NtVkAAPgrs8dxHgAAlLHwjouL04gRI+Th4VGsz9PTU3//+981a9asKx7v/fff13fffafp06cX68vMzJSrq6v5/NAiPj4+yszMNGMuLLqL+ov6LhWTm5urM2fO6NixYyooKCgxpmiMkkyfPl2enp7m0qBBgyvbaQAArlEVfZwHAAB/KlPh/f3336t3796l9vfq1UtpaWlXNNaRI0f01FNPKSkpSW5ubmVJ45owYcIE5eTkmMuRI0ccnRIAAFelIo/zAADgf8pUeGdlZZX4eJEiLi4u+vXXX69orLS0NGVnZ+vWW2+Vi4uLXFxc9MUXX2ju3LlycXGRj4+P8vPzdeLEiWI5+Pr6SpJ8fX2L3eW86PXlYjw8POTu7q569erJ2dm5xJiiMUpitVrl4eFhswAA8FdWkcd5AADwP2UqvG+88Ubt3Lmz1P4ffvhBfn5+VzRWz549tWPHDqWnp5tL+/btNWjQIPP/q1WrptTUVHOdffv26fDhwwoJCZEkhYSEaMeOHTZ3H09JSZGHh4eCgoLMmAvHKIopGsPV1VXBwcE2MYWFhUpNTTVjAACoCiryOA8AAP6nTHc179u3ryZNmqTevXsXOz38zJkzmjx5su66664rGqtWrVpq1aqVTVuNGjVUt25dsz0yMlJjxoxRnTp15OHhoSeeeEIhISHq2LGjpD9PeQsKCtLgwYM1Y8YMZWZmauLEiYqKipLVapUkPfbYY5o/f77GjRunRx99VOvWrdOyZcu0atUqc7tjxoxRRESE2rdvr9tvv11xcXE6ffq0eZdzAACqgoo8zgMAgP8pU+E9ceJEffTRR7r55psVHR2tZs2aSZL27t2r+Ph4FRQU6Nlnn62w5GbPni0nJycNGDBAeXl5CgsL06uvvmr2Ozs7a+XKlXr88ccVEhKiGjVqKCIiQrGxsWZMYGCgVq1apdGjR2vOnDmqX7++3nrrLfNRYpL04IMP6tdff1VMTIwyMzPVrl07JScnF7vhGgAA17PKPs4DAFBVlKnw9vHx0aZNm/T4449rwoQJMgxDkmSxWBQWFqb4+PirKlY3bNhg89rNzU3x8fGKj48vdZ2AgACtXr36kuN269ZN27dvv2RMdHS0oqOjrzhXAACuN/Y+zgMAUFWVqfCW/lfo/v777zpw4IAMw1DTpk1Vu3Zte+QHAAAqEcd5AAAqXpkL7yK1a9fWbbfdVpG5AACAawTHeQAAKk6Z7moOAAAAAADKhsIbAAAAAAA7ovAGAAAAAMCOKLwBAAAAALAjCm8AAAAAAOyIwhsAAAAAADui8AYAAAAAwI4ovAEAAAAAsCMKbwAAAAAA7IjCGwAAAAAAO6LwBgAAAADAjii8AQAAAACwIwpvAAAAAADsiMIbAAAAAAA7ovAGAAAAAMCOKLwBAAAAALAjCm8AAAAAAOyIwhsAAAAAADui8AYAAAAAwI4ovAEAAAAAsCMKbwAAAAAA7IjCGwAAAAAAO6LwBgAAAADAjii8AQAAAACwIwpvAAAAAADsiMIbAAAAAAA7ovAGAAAAAMCOKLwBAAAAALAjCm8AAAAAAOyIwhsAAAAAADui8AYAANeEF198URaLRaNGjTLbzp49q6ioKNWtW1c1a9bUgAEDlJWVZbPe4cOHFR4erurVq8vb21tjx47V+fPnbWI2bNigW2+9VVarVU2aNFFiYmIl7BEAAH+i8AYAAA63bds2vf7662rTpo1N++jRo/Xpp5/qgw8+0BdffKGMjAzde++9Zn9BQYHCw8OVn5+vTZs2adGiRUpMTFRMTIwZc/DgQYWHh6t79+5KT0/XqFGjNHz4cK1Zs6bS9g8AULVReAMAAIc6deqUBg0apDfffFO1a9c223NycvT2229r1qxZ6tGjh4KDg7Vw4UJt2rRJ33zzjSRp7dq12r17t9599121a9dOffr00dSpUxUfH6/8/HxJUkJCggIDAzVz5ky1aNFC0dHRuu+++zR79uxSc8rLy1Nubq7NAgBAeVF4AwAAh4qKilJ4eLhCQ0Nt2tPS0nTu3Dmb9ubNm6thw4bavHmzJGnz5s1q3bq1fHx8zJiwsDDl5uZq165dZszFY4eFhZljlGT69Ony9PQ0lwYNGlz1fgIAqi4KbwAA4DDvv/++vvvuO02fPr1YX2ZmplxdXeXl5WXT7uPjo8zMTDPmwqK7qL+o71Ixubm5OnPmTIl5TZgwQTk5OeZy5MiRcu0fAACSgwvv1157TW3atJGHh4c8PDwUEhKizz77zOyvzBuqxMfHq1GjRnJzc1OHDh20detWu+wzAAD405EjR/TUU08pKSlJbm5ujk7HhtVqNd+fFC0AAJSXQwvv+vXr68UXX1RaWpq+/fZb9ejRQ/369TNPDausG6osXbpUY8aM0eTJk/Xdd9+pbdu2CgsLU3Z2duVNBgAAVUxaWpqys7N16623ysXFRS4uLvriiy80d+5cubi4yMfHR/n5+Tpx4oTNellZWfL19ZUk+fr6FvtQvuj15WI8PDzk7u5up70DAOB/HFp433333erbt6+aNm2qm2++WS+88IJq1qypb775plJvqDJr1iyNGDFCw4YNU1BQkBISElS9enUtWLDAIfMCAEBV0LNnT+3YsUPp6enm0r59ew0aNMj8/2rVqik1NdVcZ9++fTp8+LBCQkIkSSEhIdqxY4fNh+UpKSny8PBQUFCQGXPhGEUxRWMAAGBv18w13gUFBXr//fd1+vRphYSEVNoNVfLz85WWlmYT4+TkpNDQ0EvedIW7nQIAcHVq1aqlVq1a2Sw1atRQ3bp11apVK3l6eioyMlJjxozR+vXrlZaWpmHDhikkJEQdO3aUJPXq1UtBQUEaPHiwvv/+e61Zs0YTJ05UVFSUrFarJOmxxx7TTz/9pHHjxmnv3r169dVXtWzZMo0ePdqRuw8AqEIcXnjv2LFDNWvWlNVq1WOPPaaPP/5YQUFBlXZDlWPHjqmgoKDEmKIxSsLdTgEAsL/Zs2frrrvu0oABA9S1a1f5+vrqo48+MvudnZ21cuVKOTs7KyQkRI888oiGDBmi2NhYMyYwMFCrVq1SSkqK2rZtq5kzZ+qtt95SWFiYI3YJAFAFuTg6gWbNmik9PV05OTn68MMPFRERoS+++MLRaV3WhAkTNGbMGPN1bm4uxTcAAFdpw4YNNq/d3NwUHx+v+Pj4UtcJCAjQ6tWrLzlut27dtH379opIEQCAMnN44e3q6qomTZpIkoKDg7Vt2zbNmTNHDz74oHlDlQu/9b74hioX3328rDdUcXZ2lrOzc4kxRWOUxGq1mqewAQAAAABQGoefan6xwsJC5eXlKTg4uFJuqOLq6qrg4GCbmMLCQqWmpnLTFQAAAADAVXPoN94TJkxQnz591LBhQ508eVJLlizRhg0btGbNGpsbqtSpU0ceHh564oknSr2hyowZM5SZmVniDVXmz5+vcePG6dFHH9W6deu0bNkyrVq1ysxjzJgxioiIUPv27XX77bcrLi5Op0+f1rBhwxwyLwAAAACA64dDC+/s7GwNGTJER48elaenp9q0aaM1a9bob3/7m6Q/b6ji5OSkAQMGKC8vT2FhYXr11VfN9YtuqPL4448rJCRENWrUUERERIk3VBk9erTmzJmj+vXrF7uhyoMPPqhff/1VMTExyszMVLt27ZScnFzshmsAAAAAAJSVQwvvt99++5L9lXlDlejoaEVHR18yBgAAAACAsrrmrvEGAAAAAOB6QuENAAAAAIAdUXgDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4AwAAAABgRxTeAAAAAADYEYU3AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4AwAAAABgRxTeAAAAAADYEYU3AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAduTi6AQAAAAAe2oW1EpHMzJK7ffz99e+3TsrMSMAVQ2FNwAAAK5rRzMy1HfGqlL7V48Lr8RsAFRFnGoOAAAAAIAdUXgDAAAAAGBHDi28p0+frttuu021atWSt7e3+vfvr3379tnEnD17VlFRUapbt65q1qypAQMGKCsryybm8OHDCg8PV/Xq1eXt7a2xY8fq/PnzNjEbNmzQrbfeKqvVqiZNmigxMbFYPvHx8WrUqJHc3NzUoUMHbd26tcL3GQAAAABQtTi08P7iiy8UFRWlb775RikpKTp37px69eql06dPmzGjR4/Wp59+qg8++EBffPGFMjIydO+995r9BQUFCg8PV35+vjZt2qRFixYpMTFRMTExZszBgwcVHh6u7t27Kz09XaNGjdLw4cO1Zs0aM2bp0qUaM2aMJk+erO+++05t27ZVWFiYsrOzK2cyAAAAAADXJYfeXC05OdnmdWJiory9vZWWlqauXbsqJydHb7/9tpYsWaIePXpIkhYuXKgWLVrom2++UceOHbV27Vrt3r1bn3/+uXx8fNSuXTtNnTpV//rXvzRlyhS5uroqISFBgYGBmjlzpiSpRYsW+uqrrzR79myFhYVJkmbNmqURI0Zo2LBhkqSEhAStWrVKCxYs0Pjx4ytxVgAAAAAA15Nr6hrvnJwcSVKdOnUkSWlpaTp37pxCQ0PNmObNm6thw4bavHmzJGnz5s1q3bq1fHx8zJiwsDDl5uZq165dZsyFYxTFFI2Rn5+vtLQ0mxgnJyeFhoaaMRfLy8tTbm6uzQIAAAAAwMWumcK7sLBQo0aNUqdOndSqVStJUmZmplxdXeXl5WUT6+Pjo8zMTDPmwqK7qL+o71Ixubm5OnPmjI4dO6aCgoISY4rGuNj06dPl6elpLg0aNCjfjgMAAAAArmvXTOEdFRWlnTt36v3333d0KldkwoQJysnJMZcjR444OiUAAAAAwDXIodd4F4mOjtbKlSu1ceNG1a9f32z39fVVfn6+Tpw4YfOtd1ZWlnx9fc2Yi+8+XnTX8wtjLr4TelZWljw8POTu7i5nZ2c5OzuXGFM0xsWsVqusVmv5dhgAAAAAUGU49BtvwzAUHR2tjz/+WOvWrVNgYKBNf3BwsKpVq6bU1FSzbd++fTp8+LBCQkIkSSEhIdqxY4fN3cdTUlLk4eGhoKAgM+bCMYpiisZwdXVVcHCwTUxhYaFSU1PNGAAAAAAAysOh33hHRUVpyZIl+uSTT1SrVi3zempPT0+5u7vL09NTkZGRGjNmjOrUqSMPDw898cQTCgkJUceOHSVJvXr1UlBQkAYPHqwZM2YoMzNTEydOVFRUlPmN9GOPPab58+dr3LhxevTRR7Vu3TotW7ZMq1atMnMZM2aMIiIi1L59e91+++2Ki4vT6dOnzbucAwAAAABQHg4tvF977TVJUrdu3WzaFy5cqKFDh0qSZs+eLScnJw0YMEB5eXkKCwvTq6++asY6Oztr5cqVevzxxxUSEqIaNWooIiJCsbGxZkxgYKBWrVql0aNHa86cOapfv77eeust81FikvTggw/q119/VUxMjDIzM9WuXTslJycXu+EaAAAAAABl4dDC2zCMy8a4ubkpPj5e8fHxpcYEBARo9erVlxynW7du2r59+yVjoqOjFR0dfdmcAAAAAAC4UtfMXc0BAAAAALgeUXgDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4AwAAAABgRxTeAADAIaZPn67bbrtNtWrVkre3t/r37699+/bZxJw9e1ZRUVGqW7euatasqQEDBigrK8sm5vDhwwoPD1f16tXl7e2tsWPH6vz58zYxGzZs0K233iqr1aomTZooMTHR3rsHAICJwhsAADjEF198oaioKH3zzTdKSUnRuXPn1KtXL50+fdqMGT16tD799FN98MEH+uKLL5SRkaF7773X7C8oKFB4eLjy8/O1adMmLVq0SImJiYqJiTFjDh48qPDwcHXv3l3p6ekaNWqUhg8frjVr1lTq/gIAqi4XRycAAACqpuTkZJvXiYmJ8vb2Vlpamrp27aqcnBy9/fbbWrJkiXr06CFJWrhwoVq0aKFvvvlGHTt21Nq1a7V79259/vnn8vHxUbt27TR16lT961//0pQpU+Tq6qqEhAQFBgZq5syZkqQWLVroq6++0uzZsxUWFlbp+w0AqHr4xhsAAFwTcnJyJEl16tSRJKWlpencuXMKDQ01Y5o3b66GDRtq8+bNkqTNmzerdevW8vHxMWPCwsKUm5urXbt2mTEXjlEUUzRGSfLy8pSbm2uzAABQXhTeAADA4QoLCzVq1Ch16tRJrVq1kiRlZmbK1dVVXl5eNrE+Pj7KzMw0Yy4suov6i/ouFZObm6szZ86UmM/06dPl6elpLg0aNLjqfQQAVF0U3gAAwOGioqK0c+dOvf/++45ORZI0YcIE5eTkmMuRI0ccnRIA4C+Ma7wBAIBDRUdHa+XKldq4caPq169vtvv6+io/P18nTpyw+dY7KytLvr6+ZszWrVttxiu66/mFMRffCT0rK0seHh5yd3cvMSer1Sqr1XrV+wYAgMQ33gAAwEEMw1B0dLQ+/vhjrVu3ToGBgTb9wcHBqlatmlJTU822ffv26fDhwwoJCZEkhYSEaMeOHcrOzjZjUlJS5OHhoaCgIDPmwjGKYorGAADA3vjGGwAAOERUVJSWLFmiTz75RLVq1TKvyfb09JS7u7s8PT0VGRmpMWPGqE6dOvLw8NATTzyhkJAQdezYUZLUq1cvBQUFafDgwZoxY4YyMzM1ceJERUVFmd9YP/bYY5o/f77GjRunRx99VOvWrdOyZcu0atUqh+07AKBq4RtvAADgEK+99ppycnLUrVs3+fn5mcvSpUvNmNmzZ+uuu+7SgAED1LVrV/n6+uqjjz4y+52dnbVy5Uo5OzsrJCREjzzyiIYMGaLY2FgzJjAwUKtWrVJKSoratm2rmTNn6q233uJRYgCASsM33gAAwCEMw7hsjJubm+Lj4xUfH19qTEBAgFavXn3Jcbp166bt27eXOUcAACoC33gDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4AwAAAABgRxTeAAAAAADYEYU3AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB05tPDeuHGj7r77bvn7+8tisWj58uU2/YZhKCYmRn5+fnJ3d1doaKj2799vE3P8+HENGjRIHh4e8vLyUmRkpE6dOmUT88MPP6hLly5yc3NTgwYNNGPGjGK5fPDBB2revLnc3NzUunVrrV69usL3FwAAAABQ9Ti08D59+rTatm2r+Pj4EvtnzJihuXPnKiEhQVu2bFGNGjUUFhams2fPmjGDBg3Srl27lJKSopUrV2rjxo0aOXKk2Z+bm6tevXopICBAaWlpevnllzVlyhS98cYbZsymTZv00EMPKTIyUtu3b1f//v3Vv39/7dy50347DwAAAACoElwcufE+ffqoT58+JfYZhqG4uDhNnDhR/fr1kyS988478vHx0fLlyzVw4EDt2bNHycnJ2rZtm9q3by9Jmjdvnvr27atXXnlF/v7+SkpKUn5+vhYsWCBXV1e1bNlS6enpmjVrllmgz5kzR71799bYsWMlSVOnTlVKSormz5+vhISEEvPLy8tTXl6e+To3N7fC5gUAAAAAcP24Zq/xPnjwoDIzMxUaGmq2eXp6qkOHDtq8ebMkafPmzfLy8jKLbkkKDQ2Vk5OTtmzZYsZ07dpVrq6uZkxYWJj27dun33//3Yy5cDtFMUXbKcn06dPl6elpLg0aNLj6nQYAAAAAXHeu2cI7MzNTkuTj42PT7uPjY/ZlZmbK29vbpt/FxUV16tSxiSlpjAu3UVpMUX9JJkyYoJycHHM5cuRIWXcRAAAAAFAFOPRU878yq9Uqq9Xq6DQAAAAAANe4a/Ybb19fX0lSVlaWTXtWVpbZ5+vrq+zsbJv+8+fP6/jx4zYxJY1x4TZKiynqBwAAAACgvK7ZwjswMFC+vr5KTU0123Jzc7VlyxaFhIRIkkJCQnTixAmlpaWZMevWrVNhYaE6dOhgxmzcuFHnzp0zY1JSUtSsWTPVrl3bjLlwO0UxRdsBAAAAAKC8HFp4nzp1Sunp6UpPT5f05w3V0tPTdfjwYVksFo0aNUrPP/+8VqxYoR07dmjIkCHy9/dX//79JUktWrRQ7969NWLECG3dulVff/21oqOjNXDgQPn7+0uSHn74Ybm6uioyMlK7du3S0qVLNWfOHI0ZM8bM46mnnlJycrJmzpypvXv3asqUKfr2228VHR1d2VMCAAAAALjOOPQa72+//Vbdu3c3XxcVwxEREUpMTNS4ceN0+vRpjRw5UidOnFDnzp2VnJwsNzc3c52kpCRFR0erZ8+ecnJy0oABAzR37lyz39PTU2vXrlVUVJSCg4NVr149xcTE2Dzr+4477tCSJUs0ceJEPfPMM2ratKmWL1+uVq1aVcIsAAAAAACuZw4tvLt16ybDMErtt1gsio2NVWxsbKkxderU0ZIlSy65nTZt2ujLL7+8ZMz999+v+++//9IJAwAAAABQRtfsNd4AAAAAAFwPKLwBAAAAALAjnuMNALhuHD58WMeOHauQserVq6eGDRtWyFgAAKBqo/AGAFwXDh8+rObNW+jMmT8qZDx39+rau3cPxTcAALhqFN4AgOvCsWPHdObMH+rw6GR5+DW6qrFyjx7SlgXP6dixYxTeAADgqlF4AwCuKx5+jVSnYTNHpwEAAGDi5moAAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4AwAAAABgRxTeAAAAAADYEYU3AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4AwAAAABgRxTeAAAAAADYEYU3AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4XyQ+Pl6NGjWSm5ubOnTooK1btzo6JQAAUAE4xgMAHIXC+wJLly7VmDFjNHnyZH333Xdq27atwsLClJ2d7ejUAADAVeAYDwBwJBdHJ3AtmTVrlkaMGKFhw4ZJkhISErRq1SotWLBA48ePt4nNy8tTXl6e+TonJ0eSlJube1U5nDp1SpJ0/Od9Op935qrGkqTczMOSpLS0NHPsq+Hk5KTCwsKrHqcqjnct53atj3ct53atj3ct51bR4+3bt09Sxfz9LPrbeerUqav6u160rmEYV5UPrl5ZjvGS/Y7z0p+/D+fOnL5k/9Vu53LbqKjtXG0elZEDeTgmj6q0r+RRsTlUVB6Vsa9lOs4bMAzDMPLy8gxnZ2fj448/tmkfMmSIcc899xSLnzx5siGJhYWFhYXlssuRI0cq6WiGkpT1GG8YHOdZWFhYWK58uZLjPN94/3/Hjh1TQUGBfHx8bNp9fHy0d+/eYvETJkzQmDFjzNeFhYU6fvy46tatK4vFUu48cnNz1aBBAx05ckQeHh7lHud6xNyUjrkpHXNTOuamdBU1N4Zh6OTJk/L396/A7FBWZT3GSxzn7aWq77/EHEjMQVXff+n6mYOyHOcpvMvJarXKarXatHl5eVXY+B4eHn/pX0J7Ym5Kx9yUjrkpHXNTuoqYG09PzwrKBpWJ47x9VfX9l5gDiTmo6vsvXR9zcKXHeW6u9v/Vq1dPzs7OysrKsmnPysqSr6+vg7ICAABXi2M8AMDRKLz/P1dXVwUHBys1NdVsKywsVGpqqkJCQhyYGQAAuBoc4wEAjsap5hcYM2aMIiIi1L59e91+++2Ki4vT6dOnzTugVgar1arJkycXO70NzM2lMDelY25Kx9yUjrm5/lwLx3iJ362qvv8ScyAxB1V9/6WqOQcWw+AZJxeaP3++Xn75ZWVmZqpdu3aaO3euOnTo4Oi0AADAVeIYDwBwFApvAAAAAADsiGu8AQAAAACwIwpvAAAAAADsiMIbAAAAAAA7ovAGAAAAAMCOKLwdID4+Xo0aNZKbm5s6dOigrVu3XjL+gw8+UPPmzeXm5qbWrVtr9erVlZRp5SvL3Lz55pvq0qWLateurdq1ays0NPSyc/lXVtbfmyLvv/++LBaL+vfvb98EHaisc3PixAlFRUXJz89PVqtVN99883X776qscxMXF6dmzZrJ3d1dDRo00OjRo3X27NlKyrbybNy4UXfffbf8/f1lsVi0fPnyy66zYcMG3XrrrbJarWrSpIkSExPtnieuL+X9O349mD59um677TbVqlVL3t7e6t+/v/bt2+fotBzmxRdflMVi0ahRoxydSqX65Zdf9Mgjj6hu3bpyd3dX69at9e233zo6rUpTUFCgSZMmKTAwUO7u7mrcuLGmTp2q6/le15c73hqGoZiYGPn5+cnd3V2hoaHav3+/Y5K1NwOV6v333zdcXV2NBQsWGLt27TJGjBhheHl5GVlZWSXGf/3114azs7MxY8YMY/fu3cbEiRONatWqGTt27KjkzO2vrHPz8MMPG/Hx8cb27duNPXv2GEOHDjU8PT2N//73v5Wcuf2VdW6KHDx40LjxxhuNLl26GP369aucZCtZWecmLy/PaN++vdG3b1/jq6++Mg4ePGhs2LDBSE9Pr+TM7a+sc5OUlGRYrVYjKSnJOHjwoLFmzRrDz8/PGD16dCVnbn+rV682nn32WeOjjz4yJBkff/zxJeN/+ukno3r16saYMWOM3bt3G/PmzTOcnZ2N5OTkykkYf3nl/Tt+vQgLCzMWLlxo7Ny500hPTzf69u1rNGzY0Dh16pSjU6t0W7duNRo1amS0adPGeOqppxydTqU5fvy4ERAQYAwdOtTYsmWL8dNPPxlr1qwxDhw44OjUKs0LL7xg1K1b11i5cqVx8OBB44MPPjBq1qxpzJkzx9Gp2c3ljrcvvvii4enpaSxfvtz4/vvvjXvuuccIDAw0zpw545iE7YjCu5LdfvvtRlRUlPm6oKDA8Pf3N6ZPn15i/AMPPGCEh4fbtHXo0MH4+9//btc8HaGsc3Ox8+fPG7Vq1TIWLVpkrxQdpjxzc/78eeOOO+4w3nrrLSMiIuK6LbzLOjevvfaacdNNNxn5+fmVlaLDlHVuoqKijB49eti0jRkzxujUqZNd83S0Kym8x40bZ7Rs2dKm7cEHHzTCwsLsmBmuJ1d7jLveZGdnG5KML774wtGpVKqTJ08aTZs2NVJSUow777yzShXe//rXv4zOnTs7Og2HCg8PNx599FGbtnvvvdcYNGiQgzKqXBcfbwsLCw1fX1/j5ZdfNttOnDhhWK1W47333nNAhvbFqeaVKD8/X2lpaQoNDTXbnJycFBoaqs2bN5e4zubNm23iJSksLKzU+L+q8szNxf744w+dO3dOderUsVeaDlHeuYmNjZW3t7ciIyMrI02HKM/crFixQiEhIYqKipKPj49atWqladOmqaCgoLLSrhTlmZs77rhDaWlp5umvP/30k1avXq2+fftWSs7Xsqrytxj2URHHuOtNTk6OJF13x+zLiYqKUnh4eLG/J1XBihUr1L59e91///3y9vbWLbfcojfffNPRaVWqO+64Q6mpqfrxxx8lSd9//72++uor9enTx8GZOcbBgweVmZlp8+/B09NTHTp0uC7/Nro4OoGq5NixYyooKJCPj49Nu4+Pj/bu3VviOpmZmSXGZ2Zm2i1PRyjP3FzsX//6l/z9/a+7g1l55uarr77S22+/rfT09ErI0HHKMzc//fST1q1bp0GDBmn16tU6cOCA/vGPf+jcuXOaPHlyZaRdKcozNw8//LCOHTumzp07yzAMnT9/Xo899pieeeaZykj5mlba3+Lc3FydOXNG7u7uDsoMfwUVcYy7nhQWFmrUqFHq1KmTWrVq5eh0Ks3777+v7777Ttu2bXN0Kg7x008/6bXXXtOYMWP0zDPPaNu2bXryySfl6uqqiIgIR6dXKcaPH6/c3Fw1b95czs7OKigo0AsvvKBBgwY5OjWHKKpnqkKtI1F44zrx4osv6v3339eGDRvk5ubm6HQc6uTJkxo8eLDefPNN1atXz9HpXHMKCwvl7e2tN954Q87OzgoODtYvv/yil19++boqvMtjw4YNmjZtml599VV16NBBBw4c0FNPPaWpU6dq0qRJjk4PwHUiKipKO3fu1FdffeXoVCrNkSNH9NRTTyklJaXKvk8pLCxU+/btNW3aNEnSLbfcop07dyohIaHKFN7Lli1TUlKSlixZopYtWyo9PV2jRo2Sv79/lZmDqozCuxLVq1dPzs7OysrKsmnPysqSr69viev4+vqWKf6vqjxzU+SVV17Riy++qM8//1xt2rSxZ5oOUda5+c9//qNDhw7p7rvvNtsKCwslSS4uLtq3b58aN25s36QrSXl+b/z8/FStWjU5OzubbS1atFBmZqby8/Pl6upq15wrS3nmZtKkSRo8eLCGDx8uSWrdurVOnz6tkSNH6tlnn5WTU9W9Oqm0v8UeHh58243Luppj3PUmOjpaK1eu1MaNG1W/fn1Hp1Np0tLSlJ2drVtvvdVsKygo0MaNGzV//nzl5eXZHJeuR35+fgoKCrJpa9Gihf797387KKPKN3bsWI0fP14DBw6U9Odx9ueff9b06dOrZOFd9PcvKytLfn5+ZntWVpbatWvnoKzsp+q+i3IAV1dXBQcHKzU11WwrLCxUamqqQkJCSlwnJCTEJl6SUlJSSo3/qyrP3EjSjBkzNHXqVCUnJ6t9+/aVkWqlK+vcNG/eXDt27FB6erq53HPPPerevbvS09PVoEGDykzfrsrze9OpUycdOHDA/DBCkn788Uf5+fldN0W3VL65+eOPP4oV10VvBI3r+FEnV6Kq/C2GfZT3GHc9MQxD0dHR+vjjj7Vu3ToFBgY6OqVK1bNnz2LH5vbt22vQoEFKT0+/7otu6c/j78WPkPvxxx8VEBDgoIwqX2nH2Qvfk1QlgYGB8vX1tfnbmJubqy1btlyffxsdfHO3Kuf99983rFarkZiYaOzevdsYOXKk4eXlZWRmZhqGYRiDBw82xo8fb8Z//fXXhouLi/HKK68Ye/bsMSZPnnxdP06sLHPz4osvGq6ursaHH35oHD161FxOnjzpqF2wm7LOzcWu57ual3VuDh8+bNSqVcuIjo429u3bZ6xcudLw9vY2nn/+eUftgt2UdW4mT55s1KpVy3jvvfeMn376yVi7dq3RuHFj44EHHnDULtjNyZMnje3btxvbt283JBmzZs0ytm/fbvz888+GYRjG+PHjjcGDB5vxRY8TGzt2rLFnzx4jPj6ex4mhTC737/F69/jjjxuenp7Ghg0bbI7Zf/zxh6NTc5iqdlfzrVu3Gi4uLsYLL7xg7N+/30hKSjKqV69uvPvuu45OrdJEREQYN954o/k4sY8++sioV6+eMW7cOEenZjeXO96++OKLhpeXl/HJJ58YP/zwg9GvXz8eJ4aKM2/ePKNhw4aGq6urcfvttxvffPON2XfnnXcaERERNvHLli0zbr75ZsPV1dVo2bKlsWrVqkrOuPKUZW4CAgIMScWWyZMnV37ilaCsvzcXup4Lb8Mo+9xs2rTJ6NChg2G1Wo2bbrrJeOGFF4zz589XctaVoyxzc+7cOWPKlClG48aNDTc3N6NBgwbGP/7xD+P333+v/MTtbP369SX+/Siaj4iICOPOO+8stk67du0MV1dX46abbjIWLlxY6Xnjr+1S/x6vdyX9e5NUpf8dVbXC2zAM49NPPzVatWplWK1Wo3nz5sYbb7zh6JQqVW5urvHUU08ZDRs2NNzc3IybbrrJePbZZ428vDxHp2Y3lzveFhYWGpMmTTJ8fHwMq9Vq9OzZ09i3b59jk7YTi2FU8fMHAQAAAACwI67xBgAAAADAjii8AQAAAACwIwpvAAAAAADsiMIbAAAAAAA7ovAGAAAAAMCOKLwBAAAAALAjCm8AAAAAAOyIwhuAXSQmJsrLy8t8PWXKFLVr1+6K17dYLFq+fHmF5wUAAErWrVs3jRo1ytFpANclCm8AleKf//ynUlNTrzj+6NGj6tOnjyTp0KFDslgsSk9Pt1N2AAAAgP24ODoBAFVDzZo1VbNmzSuO9/X1tWM2AAAAQOXhG2+giiosLNT06dMVGBgod3d3tW3bVh9++KEMw1BoaKjCwsJkGIYk6fjx46pfv75iYmIkSRs2bJDFYtGqVavUpk0bubm5qWPHjtq5c2ep2yvpVPMFCxaoZcuWslqt8vPzU3R0tNl34anmgYGBkqRbbrlFFotF3bp1q7iJAAAAJVq1apU8PT2VlJSkI0eO6IEHHpCXl5fq1Kmjfv366dChQ5KkjRs3qlq1asrMzLRZf9SoUerSpYsDMgeuPRTeQBU1ffp0vfPOO0pISNCuXbs0evRoPfLII9q4caMWLVqkbdu2ae7cuZKkxx57TDfeeKNZeBcZO3asZs6cqW3btumGG27Q3XffrXPnzl3R9l977TVFRUVp5MiR2rFjh1asWKEmTZqUGLt161ZJ0ueff66jR4/qo48+uoo9BwAAl7NkyRI99NBDSkpK0gMPPKCwsDDVqlVLX375pb7++mvVrFlTvXv3Vn5+vrp27aqbbrpJixcvNtc/d+6ckpKS9OijjzpwL4BrB6eaA1VQXl6epk2bps8//1whISGSpJtuuklfffWVXn/9dS1ZskSvv/66hgwZoszMTK1evVrbt2+Xi4vtn4zJkyfrb3/7myRp0aJFql+/vj7++GM98MADl83h+eef19NPP62nnnrKbLvttttKjL3hhhskSXXr1uUUdAAA7Cw+Pl7PPvusPv30U91555169913VVhYqLfeeksWi0WStHDhQnl5eWnDhg3q1auXIiMjtXDhQo0dO1aS9Omnn+rs2bNX9J4AqAoovIEq6MCBA/rjjz/MorlIfn6+brnlFknS/fffr48//lgvvviiXnvtNTVt2rTYOEVFuyTVqVNHzZo10549ey67/ezsbGVkZKhnz55XuScAAKAiffjhh8rOztbXX39tfiD+/fff68CBA6pVq5ZN7NmzZ/Wf//xHkjR06FBNnDhR33zzjTp27KjExEQ98MADqlGjRqXvA3AtovAGqqBTp05J+vParRtvvNGmz2q1SpL++OMPpaWlydnZWfv376/Q7bu7u1foeAAAoGLccsst+u6777RgwQK1b99eFotFp06dUnBwsJKSkorFF52V5u3trbvvvlsLFy5UYGCgPvvsM23YsKGSsweuXRTeQBUUFBQkq9Wqw4cP68477ywx5umnn5aTk5M+++wz9e3bV+Hh4erRo4dNzDfffKOGDRtKkn7//Xf9+OOPatGixWW3X6tWLTVq1Eipqanq3r37ZeNdXV0lSQUFBZeNBQAA5de4cWPNnDlT3bp1k7Ozs+bPn69bb71VS5culbe3tzw8PEpdd/jw4XrooYdUv359NW7cWJ06darEzIFrG4U3UAXVqlVL//znPzV69GgVFhaqc+fOysnJ0ddffy0PDw/Vq1dPCxYs0ObNm3Xrrbdq7NixioiI0A8//KDatWub48TGxqpu3bry8fHRs88+q3r16ql///5XlMOUKVP02GOPydvbW3369NHJkyf19ddf64knnigW6+3tLXd3dyUnJ6t+/fpyc3OTp6dnRU0HAAC4wM0336z169erW7ducnFx0bRp0/Tyyy+rX79+io2NVf369fXzzz/ro48+0rhx41S/fn1JUlhYmDw8PPT8888rNjbWwXsBXFu4qzlQRU2dOlWTJk3S9OnT1aJFC/Xu3VurVq1So0aNFBkZqSlTpujWW2+VJD333HPy8fHRY489ZjPGiy++qKeeekrBwcHKzMzUp59+an47fTkRERGKi4vTq6++qpYtW+quu+4q9ZR2FxcXzZ07V6+//rr8/f3Vr1+/q9t5AABwSc2aNdO6dev03nvvadKkSdq4caMaNmyoe++9Vy1atFBkZKTOnj1r8w24k5OThg4dqoKCAg0ZMsSB2QPXHotR9KBeALhCGzZsUPfu3fX777/Ly8vL0ekAAIBrRGRkpH799VetWLHC0akA1xRONQcAAABwVXJycrRjxw4tWbKEohsoAYU3AAAAgKvSr18/bd26VY899lixx5UC4FRzAAAAAADsipurAQAAAABgRxTeAAAAAADYEYU3AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4AwAAAABgRxTeAAAAAADYEYU3UE5TpkyRxWKplG1169ZN3bp1M19v2LBBFotFH374YaVsf+jQoWrUqFGlbKu8Tp06peHDh8vX11cWi0WjRo1ydEql+ivMJwAAACoOhTcgKTExURaLxVzc3Nzk7++vsLAwzZ07VydPnqyQ7WRkZGjKlClKT0+vkPEq0rWc25WYNm2aEhMT9fjjj2vx4sUaPHiwo1MCAFwBPsi+tlTEB9lF76sOHTpU4flVhkOHDslisSgxMdHRqeA6QuENXCA2NlaLFy/Wa6+9pieeeEKSNGrUKLVu3Vo//PCDTezEiRN15syZMo2fkZGh5557rszF7dq1a7V27doyrVNWl8rtzTff1L59++y6/au1bt06dezYUZMnT9Yjjzyi4OBgR6cEAFUOH2Rf27ldibJ8kD1t2jQtX7688pKrojZt2qQpU6boxIkTjk4FV8HF0QkA15I+ffqoffv25usJEyZo3bp1uuuuu3TPPfdoz549cnd3lyS5uLjIxcW+/4T++OMPVa9eXa6urnbdzuVUq1bNodu/EtnZ2QoKCnJ0GgAA/flBdmBgoM6dO6fMzExt2LBBo0aN0qxZs7RixQq1adPGjJ04caLGjx9fpvGLPixu1KiR2rVrd8Xr2ftDbOnSub355psqLCy0ew5X48IPsi9n2rRpuu+++9S/f3+b9sGDB2vgwIGyWq12ytK+AgICdObMmWvm/c+mTZv03HPPaejQofLy8nJ0OignvvEGLqNHjx6aNGmSfv75Z7377rtme0mnxqWkpKhz587y8vJSzZo11axZMz3zzDOS/jyd7bbbbpMkDRs2zPw2oOg0pm7duqlVq1ZKS0tT165dVb16dXPdi0+NK1JQUKBnnnlGvr6+qlGjhu655x4dOXLEJqZRo0YaOnRosXUvHPNyuZV0atzp06f19NNPq0GDBrJarWrWrJleeeUVGYZhE2exWBQdHa3ly5erVatWslqtatmypZKTk0ue8ItkZ2crMjJSPj4+cnNzU9u2bbVo0SKzv+g0wYMHD2rVqlVm7pc6va0opw8++EBBQUFyd3dXSEiIduzYIUl6/fXX1aRJE7m5ualbt24ljvXBBx8oODhY7u7uqlevnh555BH98ssvxeKK9tvNzU2tWrXSxx9/XGJOhYWFiouLU8uWLeXm5iYfHx/9/e9/1++//35F8wQA15I+ffrokUce0bBhwzRhwgStWbNGn3/+ubKzs3XPPffYnDHm4uIiNzc3u+bzxx9/SJJcXV0d+mF2tWrVrvliNDs7+6qLO2dnZ7m5uVXaJQQVrehsDWdnZ0enYlenT592dApVCoU3cAWKTrO61Cflu3bt0l133aW8vDzFxsZq5syZuueee/T1119Lklq0aKHY2FhJ0siRI7V48WItXrxYXbt2Ncf47bff1KdPH7Vr105xcXHq3r37JfN64YUXtGrVKv3rX//Sk08+qZSUFIWGhpb5FPgrye1ChmHonnvu0ezZs9W7d2/NmjVLzZo109ixYzVmzJhi8V999ZX+8Y9/aODAgZoxY4bOnj2rAQMG6LfffrtkXmfOnFG3bt20ePFiDRo0SC+//LI8PT01dOhQzZkzx8x98eLFqlevntq1a2fmfsMNN1xy7C+//FJPP/20IiIiNGXKFO3Zs0d33XWX4uPjNXfuXP3jH//Q2LFjtXnzZj366KM26yYmJuqBBx6Qs7Ozpk+frhEjRuijjz5S586dbU4DW7t2rQYMGCCLxaLp06erf//+GjZsmL799tti+fz973/X2LFj1alTJ82ZM0fDhg1TUlKSwsLCdO7cuUvuCwD8FfBB9vX1QbbFYtHp06e1aNEiM7Zofkq6xrtRo0a66667tGHDBrVv317u7u5q3bq1NmzYIEn66KOP1Lp1a7m5uSk4OFjbt28vts29e/fqvvvuU506deTm5qb27dtrxYoVV7T/F7rU75dU+jXeRR/YX/hh+sU/06J1X3nlFb3xxhtq3LixrFarbrvtNm3bts1mvB9++EFDhw7VTTfdJDc3N/n6+urRRx+1eX80ZcoUjR07VpIUGBho83O51LXoFotFU6ZMsRnHYrFo9+7devjhh1W7dm117tzZ7H/33XfNLxTq1KmjgQMHFvs3gKtkADAWLlxoSDK2bdtWaoynp6dxyy23mK8nT55sXPhPaPbs2YYk49dffy11jG3bthmSjIULFxbru/POOw1JRkJCQol9d955p/l6/fr1hiTjxhtvNHJzc832ZcuWGZKMOXPmmG0BAQFGRETEZce8VG4RERFGQECA+Xr58uWGJOP555+3ibvvvvsMi8ViHDhwwGyTZLi6utq0ff/994YkY968ecW2daG4uDhDkvHuu++abfn5+UZISIhRs2ZNm30PCAgwwsPDLznehTlZrVbj4MGDZtvrr79uSDJ8fX1txp0wYYIhyYzNz883vL29jVatWhlnzpwx41auXGlIMmJiYsy2du3aGX5+fsaJEyfMtrVr1xqSbObzyy+/NCQZSUlJNnkmJyeX2A4A16rLHU+PHDliSDLuu+8+s+3i4+nOnTsNV1dXo3379sacOXOMhIQE45///KfRtWtXwzAMIzMz04iNjTUkGSNHjjQWL15sLF682PjPf/5jGMafxzdfX1/jhhtuMJ544gnj9ddfN5YvX272lXQ8bd26tdGmTRtj1qxZxvjx4w03Nzfj5ptvNv744w8z9kqOp5fL7eLjaWFhodGjRw/DYrEYw4cPN+bPn2/cfffdhiRj1KhRNtuRZLRt29bw8/Mzpk6dasTFxRk33XSTUb16dePYsWOX/Ln88ccfRosWLYxq1aoZo0ePNubOnWt06dLFkGTExcWZuS9evNioV6+e0a5dOzP3U6dOlTjm4sWLDavVanTp0sWM3bRpk2EY//s9uPA4GxAQYDRr1szw8/MzpkyZYsyePdu48cYbjZo1axrvvvuu0bBhQ+PFF180XnzxRcPT09No0qSJUVBQYK6/c+dOw9PT0wgKCjJeeuklY/78+UbXrl0Ni8VifPTRR5fc/wtd7vfLMAzj4MGDxd4TrVy50rBYLObvyaRJk4zatWsbrVq1svmZFq17yy23GE2aNDFeeuklY8aMGUa9evWM+vXrG/n5+WbsK6+8YnTp0sWIjY013njjDeOpp54y3N3djdtvv90oLCw0DOPP90wPPfSQIcmYPXu2zc+lpDyLSDImT55svi76dxYUFGT069fPePXVV434+HjDMAzj+eefNywWi/Hggw8ar776qvHcc88Z9erVMxo1amT8/vvvVzy3uDQKb8C4ssL7xhtvNJo0aWK+vviNQtEYb731ls2B4kKXK7ytVquRl5dXYl9JbxQmTJhgE1dYWGj4+fkZYWFhZps9Cu+RI0cazs7ONgWqYRjG5s2bixXUkoy+ffsWG9PDw8MYPXp0sfYL9erVy/D19S02n++9954hyfj0009t9rMshffFOaWnpxuSjKioKJv2og8ZUlNTDcMwjE2bNhmSjFdffbXYuM2bNzeCg4MNwzCMjIwMQ5Ixfvz4YnFBQUE28/nkk08anp6eRnZ2tvHrr7/aLDVr1jSGDx9+RfsFAI7GB9lV64PsGjVqlDgnpRXekszi3DAMY82aNYYkw93d3fj555/N9qIPw9evX2+29ezZ02jdurVx9uxZs62wsNC44447jKZNm15RvoZxZb9fJRW0rVu3NurXr2+cPHnSbNuwYUOxD9OL1q1bt65x/Phxs/2TTz4p9t7lwg92ihS9x9m4caPZ9vLLLxebz9LyLFJa4f3QQw/ZxB06dMhwdnY2XnjhBZv2HTt2GC4uLsXaUX6cag5coVOnTqlWrVql9j/44IPq1KmThg8fLh8fHw0cOFDLli0r001UbrzxxjJde9a0aVOb1xaLRU2aNLH74zt+/vln+fv7F5uPFi1amP0XatiwYbExateufdnrl3/++Wc1bdpUTk62f6pK205ZXJyTp6enJKlBgwYlthflWrTNZs2aFRuzefPmZn/Rfy/+GZW07v79+5WTkyNvb2/dcMMNNsupU6eUnZ1d5v0DgGtVzZo1L3l386Lriz/55JNy34jMarVq2LBhVxw/ZMgQm2PafffdJz8/P61evbpc279Sq1evlrOzs5588kmb9qefflqGYeizzz6zaQ8NDVXjxo3N123atJGHh4d++umny27H19dXDz30kNlWrVo1Pfnkkzp16pS++OKLCtibywsKClJISIj5ukOHDpL+vAzhwuNyUXvRfh0/flzr1q3TAw88oJMnT+rYsWM6duyYfvvtN4WFhWn//v0l3melJOX5/crIyNCOHTs0ZMgQ1axZ02y/88471bp16xLXefDBB1W7dm3zdZcuXWz2SZJ5w15JOnv2rI4dO6aOHTtKkr777rsryq2sHnvsMZvXH330kQoLC/XAAw+Y83rs2DH5+vqqadOmWr9+vV3yqIq4qzlwBf773/8qJydHTZo0KTXG3d1dGzdu1Pr167Vq1SolJydr6dKl6tGjh9auXXtFN+i48A9wRSntxiYFBQWVdtOQ0rZjXHT9WmUqLSdH5FpYWChvb28lJSWV2H+569UB4K/k1KlT8vb2LrX/wQcf1FtvvaXhw4dr/Pjx6tmzp+69917dd999xT6ILQ0fZBffjr0+yC6L8n7ofeDAARmGoUmTJmnSpEkljp2dna0bb7zxsjmU5/eraH5Keh/YpEmTEovki/e1qAi/8Gd1/PhxPffcc3r//feLfciek5Nz2X0pj8DAQJvX+/fvl2EYJX5RIP01nmzzV0HhDVyBxYsXS5LCwsIuGefk5KSePXuqZ8+emjVrlqZNm6Znn31W69evV2hoaIXf3XP//v02rw3D0IEDB2we01K7du0Sn/v4888/66abbjJflyW3gIAAff755zp58qTNm4W9e/ea/RUhICBAP/zwgwoLC20OhhW9nbLmJEn79u1Tjx49bPr27dtn9hf99+KfUVHchRo3bqzPP/9cnTp1ssuHLwBwreCD7KtzLX6QXRbl/dC76Jvpf/7zn6W+F7vU79SFKuL360pcyc/qgQce0KZNmzR27Fi1a9dONWvWVOH/a+/e46qo9/2PvwHl4gXwErdEpSwVr1vc4kotLRKNOlnm1rYpKWq6wVR2mpShaWVZXhOli4r7lMdLZ2elhhKmluINJe9mpRsrF+RWQUlBYX5/7B9zXIoXkAHT1/PxmMfDNd/Pmvmsr8j4XmvWTFGRunXrdl2fxl/tZ/JKLv23UVRUJCcnJ3355Zcl9nzxJ/y4MZxqDlzD2rVrNWnSJAUFBalv375XrDtx4sRl64rv35mfny9Jql69uiSVGITL4h//+IfD6XqffPKJjh07pu7du5vr7r77bm3evFkFBQXmuhUrVlx2pcrS9PbII4+osLBQs2fPdlg/ffp0OTk5Oez/RjzyyCOy2+1asmSJue7ChQt69913VaNGDT3wwAPlsp/SaNu2rXx8fJSYmGj+vUrSl19+qf379ysiIkKS5O/vr9atW2vhwoUO71qnpKRo3759Dtv8y1/+osLCQk2aNOmy/V24cKHcfl4AoLKV9o3sadOmad++fXr99de1du1a87TXinoj++KrVV/tjeyLlfaN7F9//fWyU++teCP70KFDl4W5G91PRd0urPiDgqpVqyosLKzE5WpfB7zUtX6+LlU8Pz/88MNlYyWtux4nT55Uamqqxo4dq1dffVVPPPGEHn74YYcPRYpdaZ6LP0W/9OeyNGcw3H333TIMQ0FBQSXOa/Gp77hxBG/gIl9++aU++ugjJSUl6a233lJ4eLjCwsLk6+urzz///Kr3GZ04caLatGmjV155RR9++KHeeOMNDRkyRPXq1TNv13D33XfL29tbiYmJmjdvnhYvXqzDhw+Xud/atWurY8eOmjFjhuLi4tS/f381atRIgwcPNmsGDRqkrKwsdevWTYmJiRo9erQGDx7s8B2x0vb22GOPqUuXLnr55Zf13HPPac6cOerRo4eWLFmiESNGXLbtshoyZIiaNm2qZ599Vi+88IJmz56tsLAwbdy4Ua+99lqpDrLlpWrVqnrrrbe0a9cuPfDAA5o5c6ZeeuklPfXUU2rYsKFGjRpl1k6ePFlZWVnq2LGjpk+frldeeUW9evVSs2bNHLb5wAMP6LnnntPkyZP1yCOPaMaMGUpISNDIkSPNswsA4I+ON7Iv90d/I7t69eoV8uawj4+POnfurPfee0/Hjh27bPy333677m1dz8/XpQICAtS8eXP94x//0JkzZ8z169ev1+7du6973xcr/nT50rMVZsyYcVntlX6mPD09VbduXW3YsMFh/Zw5c667jyeffFIuLi569dVXL+vFMIxr3voV149TzYGLxMfHS5JcXV1Vu3ZttWjRQjNmzNCAAQOuGfL+67/+S0eOHNH8+fN1/Phx1a1bVw888IBeffVV87tKVatW1cKFCxUXF6ehQ4fqwoULWrBgwWXft7leL730knbt2qXJkyfr9OnTeuihhzRnzhxVq1bNrAkPD9fUqVM1bdo0jRw5Um3bttWKFSv097//3WFbpenN2dlZn3/+ueLj47VkyRItWLBADRs21Ntvv33Zdm+Eh4eH1q1bp7Fjx2rhwoXKzc1V48aNtWDBghLvpVpRnn32WVWrVk1vvvmmXnzxRVWvXl1PPPGE3nrrLfOiLZLUrVs3LVu2TOPGjVNcXJzuvvtuLViwQJ999pl539JiiYmJCgkJ0XvvvaeXXnpJVapUUcOGDfXMM8+oQ4cOFfsCAeAGffnllzpw4IAuXLigrKwsrV27VikpKWrQoMF1vZG9YcMGRUREqEGDBsrOztacOXOu+EZ2zZo1Vb16dYWGhpb5eFr8RvaAAQOUlZWlGTNmlPhG9ieffKJu3brpL3/5i3788Ud99NFHV30j+1q9XfxG9pEjR9SqVSutWbNGn332mUaOHFmub2S/9957evbZZ5Wenq6GDRvqk08+0caNGzVjxowyv5EdEhKir776StOmTVNAQICCgoLMC6OVt4SEBHXs2FEtWrTQ4MGDdddddykrK0tpaWn6+eef9d13313Xdq7n56skb7zxhh5//HF16NBBAwYM0MmTJzV79mw1b97cIYxfL09PT91///2aMmWKzp8/rzvvvFNr1qwp8UOPkJAQSdLLL7+sPn36qGrVqnrsscdUvXp1DRo0SG+++aYGDRqktm3basOGDfr++++vu4+7775br732muLi4nTkyBH16NFDNWvW1OHDh/Xpp59qyJAheuGFF0r9+lCCyriUOgAAAG49xbeRKl5cXV0NPz8/4+GHHzZmzpx52W0oDePy24mlpqYajz/+uBEQEGC4uroaAQEBxtNPP218//33Ds/77LPPjODgYKNKlSoOt1R64IEHjGbNmpXY35VuJ/Y///M/RlxcnOHj42N4eHgYERERDre3KjZ16lTjzjvvNNzc3IwOHToY27dvv2ybV+vt0tuJGYZhnD592hg1apQREBBgVK1a1bjnnnuMt99+27yPczGVcMtLw7jybc4ulZWVZQwYMMCoW7eu4erqarRo0aLE21CV5nZiBw4cMO6//37Dw8PDkGT2caXbiZW03ZJeV/Ftst5++22H9T/++KPRv39/w8/Pz6hatapx5513Go8++qjxySefXFe/hnF9P19Xuk3X4sWLjSZNmhhubm5G8+bNjc8//9zo2bOn0aRJk2v2XvxaL77F188//2w88cQThre3t+Hl5WX06tXLvB3pxXWGYRiTJk0y7rzzTsPZ2dlhbn///XcjKirK8PLyMmrWrGn85S9/MbKzs694O7Er3Ubtf//3f42OHTsa1atXN6pXr240adLEiI6ONg4ePHh9E4trcjKMP8jVGAAAAADgJtK6dWvdcccdSklJqexWcJPjO94AAAAAcBXnz5/XhQsXHNatW7dO3333nTp37lw5TeEPhU+8AQAAAPzh2e32q457eHiY190prSNHjigsLEzPPPOMAgICdODAASUmJsrLy0t79uxRnTp1yrRd3D4q/RPvX375Rc8884zq1KkjDw8PtWjRQtu3bzfHDcNQfHy8/P395eHhobCwsMtu+XDixAn17dtXnp6e8vb2VlRU1GUXOdi1a5c6deokd3d3BQYGasqUKZf1smzZMjVp0kTu7u5q0aKFVq1aZc2LBgAAAFCu/P39r7qMGDGizNuuVauWQkJC9OGHH2r48OFKSkpSRESEvv32W0I3rkulXtX85MmT6tChg7p06aIvv/xSd9xxhw4dOmTek06SpkyZolmzZmnhwoUKCgrSK6+8ovDwcO3bt8+8Imbfvn117NgxpaSk6Pz58xowYICGDBmiRYsWSZJyc3PVtWtXhYWFKTExUbt379bAgQPl7e2tIUOGSJI2bdqkp59+WpMnT9ajjz6qRYsWqUePHtqxY4eaN29e8ZMDAAAA4Lpd63vWAQEBZd62l5eXw+3YgNKq1FPNx44dq40bN+qbb74pcdwwDAUEBOjvf/+7eRn7nJwc+fr6KikpSX369NH+/fsVHBysbdu2qW3btpKk5ORkPfLII/r5558VEBCguXPn6uWXX5bdbperq6u57+XLl+vAgQOSpN69eysvL08rVqww99++fXu1bt1aiYmJVk4DAAAAAOAWVqmfeH/++ecKDw9Xr169tH79et15553629/+Zt4z8fDhw7Lb7QoLCzOf4+XlpdDQUKWlpalPnz5KS0uTt7e3GbolKSwsTM7OztqyZYueeOIJpaWl6f777zdDt/Sfexu/9dZbOnnypGrVqqW0tDTFxsY69BceHq7ly5eX2Ht+fr7y8/PNx0VFRTpx4oTq1KkjJyen8pgeAMAfnGEYOn36tAICAuTsXOnf7sINKCoq0q+//qqaNWtynAcASCrdcb5Sg/dPP/2kuXPnKjY2Vi+99JK2bdum559/Xq6uroqMjDQvkODr6+vwPF9fX3PMbrfLx8fHYbxKlSqqXbu2Q01QUNBl2ygeq1Wrlux2+1X3c6nJkyfr1VdfLeMrBwDcTo4ePap69epVdhu4Ab/++qsCAwMruw0AwE3oeo7zlRq8i4qK1LZtW73xxhuSpD/96U/as2ePEhMTFRkZWZmtXVNcXJzDJ+Q5OTmqX7++jh49Kk9Pz0rsDABws8jNzVVgYKBq1qxZ2a3gBhX/HXKcBwAUK81xvlKDt7+/v4KDgx3WNW3aVP/7v/8rSfLz85MkZWVlyd/f36zJyspS69atzZrs7GyHbVy4cEEnTpwwn+/n56esrCyHmuLH16opHr+Um5ub3NzcLlvv6enJARkA4IBTk//4iv8OOc4DAC51Pcf5Sv3CWYcOHXTw4EGHdd9//70aNGggSQoKCpKfn59SU1PN8dzcXG3ZskU2m02SZLPZdOrUKaWnp5s1a9euVVFRkUJDQ82aDRs26Pz582ZNSkqKGjdubF5B3WazOeynuKZ4PwAAAAAAlEWlBu9Ro0Zp8+bNeuONN/TDDz9o0aJFev/99xUdHS3pP+8cjBw5Uq+99po+//xz7d69W/3791dAQIB69Ogh6T+fkHfr1k2DBw/W1q1btXHjRsXExKhPnz7mLQP++te/ytXVVVFRUdq7d6+WLFmimTNnOpwqPmLECCUnJ2vq1Kk6cOCAJkyYoO3btysmJqbC5wUAAAAAcOuo1NuJSdKKFSsUFxenQ4cOKSgoSLGxseZVzaX/XClu/Pjxev/993Xq1Cl17NhRc+bM0b333mvWnDhxQjExMfriiy/k7Oysnj17atasWapRo4ZZs2vXLkVHR2vbtm2qW7euhg8frhdffNGhl2XLlmncuHE6cuSI7rnnHk2ZMkWPPPLIdb2O3NxceXl5KScnh1PQAACSODbcSvi7BABcqjTHhkoP3rcKDsgAgEtxbLh18HcJALhUaY4N3FQUAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAEC5Kyws1CuvvKKgoCB5eHjo7rvv1qRJk2QYhlljGIbi4+Pl7+8vDw8PhYWF6dChQw7bOXHihPr27StPT095e3srKipKZ86ccajZtWuXOnXqJHd3dwUGBmrKlCmX9bNs2TI1adJE7u7uatGihVatWmXNCwcAoAQEbwAAUO7eeustzZ07V7Nnz9b+/fv11ltvacqUKXr33XfNmilTpmjWrFlKTEzUli1bVL16dYWHh+vcuXNmTd++fbV3716lpKRoxYoV2rBhg4YMGWKO5+bmqmvXrmrQoIHS09P19ttva8KECXr//ffNmk2bNunpp59WVFSUdu7cqR49eqhHjx7as2dPxUwGAOC252Rc/NYzyiw3N1deXl7KycmRp6dnZbcDALgJ3M7HhkcffVS+vr6aN2+eua5nz57y8PDQRx99JMMwFBAQoL///e964YUXJEk5OTny9fVVUlKS+vTpo/379ys4OFjbtm1T27ZtJUnJycl65JFH9PPPPysgIEBz587Vyy+/LLvdLldXV0nS2LFjtXz5ch04cECS1Lt3b+Xl5WnFihVmL+3bt1fr1q2VmJhYYv/5+fnKz883H+fm5iowMPC2/LsEAJSsNMd5PvEGAADl7r777lNqaqq+//57SdJ3332nb7/9Vt27d5ckHT58WHa7XWFhYeZzvLy8FBoaqrS0NElSWlqavL29zdAtSWFhYXJ2dtaWLVvMmvvvv98M3ZIUHh6ugwcP6uTJk2bNxfsprineT0kmT54sLy8vcwkMDLyR6QAA3OaqVHYDAABYJTMzU8ePHy/Tc+vWrav69euXc0e3j7Fjxyo3N1dNmjSRi4uLCgsL9frrr6tv376SJLvdLkny9fV1eJ6vr685Zrfb5ePj4zBepUoV1a5d26EmKCjosm0Uj9WqVUt2u/2q+ylJXFycYmNjzcfFn3gDwM3kRo5zJeHYZx2CNwDglpSZmakmTZrq7Nnfy/R8D49qOnBgP/8BKaOlS5fq448/1qJFi9SsWTNlZGRo5MiRCggIUGRkZGW3d01ubm5yc3Or7DYA4Ipu9DhXEo591iF4AwBuScePH9fZs78rdOB4efo3LNVzc48d0Zb5r+r48eP856OMRo8erbFjx6pPnz6SpBYtWuhf//qXJk+erMjISPn5+UmSsrKy5O/vbz4vKytLrVu3liT5+fkpOzvbYbsXLlzQiRMnzOf7+fkpKyvLoab48bVqiscB4I/oRo5zJeHYZy2CNwDglubp31C16zeu7DZuO7///rucnR0vJePi4qKioiJJUlBQkPz8/JSammoG7dzcXG3ZskXDhg2TJNlsNp06dUrp6ekKCQmRJK1du1ZFRUUKDQ01a15++WWdP39eVatWlSSlpKSocePGqlWrllmTmpqqkSNHmr2kpKTIZrNZ9voBoKJwnPtj4OJqAACg3D322GN6/fXXtXLlSh05ckSffvqppk2bpieeeEKS5OTkpJEjR+q1117T559/rt27d6t///4KCAhQjx49JElNmzZVt27dNHjwYG3dulUbN25UTEyM+vTpo4CAAEnSX//6V7m6uioqKkp79+7VkiVLNHPmTIfvZ48YMULJycmaOnWqDhw4oAkTJmj79u2KiYmp8HkBANye+MQbAACUu3fffVevvPKK/va3vyk7O1sBAQF67rnnFB8fb9aMGTNGeXl5GjJkiE6dOqWOHTsqOTlZ7u7uZs3HH3+smJgYPfTQQ3J2dlbPnj01a9Ysc9zLy0tr1qxRdHS0QkJCVLduXcXHxzvc6/u+++7TokWLNG7cOL300ku65557tHz5cjVv3rxiJgMAcNsjeAMAgHJXs2ZNzZgxQzNmzLhijZOTkyZOnKiJEydesaZ27dpatGjRVffVsmVLffPNN1et6dWrl3r16nXVGgAArMKp5gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAADAEg0bNpSTk9NlS3R0tCTp3Llzio6OVp06dVSjRg317NlTWVlZDtvIzMxURESEqlWrJh8fH40ePVoXLlxwqFm3bp3atGkjNzc3NWrUSElJSZf1kpCQoIYNG8rd3V2hoaHaunWrZa8bAIBLEbwBAIAltm3bpmPHjplLSkqKJKlXr16SpFGjRumLL77QsmXLtH79ev3666968sknzecXFhYqIiJCBQUF2rRpkxYuXKikpCTFx8ebNYcPH1ZERIS6dOmijIwMjRw5UoMGDdLq1avNmiVLlig2Nlbjx4/Xjh071KpVK4WHhys7O7uCZgIAcLur1OA9YcKEy94Fb9KkiTnOO+EAAPxx3XHHHfLz8zOXFStW6O6779YDDzygnJwczZs3T9OmTdODDz6okJAQLViwQJs2bdLmzZslSWvWrNG+ffv00UcfqXXr1urevbsmTZqkhIQEFRQUSJISExMVFBSkqVOnqmnTpoqJidFTTz2l6dOnm31MmzZNgwcP1oABAxQcHKzExERVq1ZN8+fPv2Lv+fn5ys3NdVgAACirSv/Eu1mzZg7vhn/77bfmGO+EAwBwaygoKNBHH32kgQMHysnJSenp6Tp//rzCwsLMmiZNmqh+/fpKS0uTJKWlpalFixby9fU1a8LDw5Wbm6u9e/eaNRdvo7imeBsFBQVKT093qHF2dlZYWJhZU5LJkyfLy8vLXAIDA298EgAAt61KD95VqlRxeDe8bt26knTTvxMOAACu3/Lly3Xq1Ck9++yzkiS73S5XV1d5e3s71Pn6+sput5s1F4fu4vHisavV5Obm6uzZszp+/LgKCwtLrCneRkni4uKUk5NjLkePHi31awYAoFilB+9Dhw4pICBAd911l/r27avMzExJuunfCecUNAAArt+8efPUvXt3BQQEVHYr18XNzU2enp4OCwAAZVWpwTs0NFRJSUlKTk7W3LlzdfjwYXXq1EmnT5++6d8J5xQ0AACuz7/+9S999dVXGjRokLnOz89PBQUFOnXqlENtVlaW/Pz8zJpLr+1S/PhaNZ6envLw8FDdunXl4uJSYk3xNgAAsFqlBu/u3burV69eatmypcLDw7Vq1SqdOnVKS5curcy2rgunoAEAcH0WLFggHx8fRUREmOtCQkJUtWpVpaammusOHjyozMxM2Ww2SZLNZtPu3bsdrrmSkpIiT09PBQcHmzUXb6O4pngbrq6uCgkJcagpKipSamqqWQMAgNUq/VTzi3l7e+vee+/VDz/8cNO/E84paAAAXFtRUZEWLFigyMhIValSxVzv5eWlqKgoxcbG6uuvv1Z6eroGDBggm82m9u3bS5K6du2q4OBg9evXT999951Wr16tcePGKTo6Wm5ubpKkoUOH6qefftKYMWN04MABzZkzR0uXLtWoUaPMfcXGxuqDDz7QwoULtX//fg0bNkx5eXkaMGBAxU4GAOC2dVMF7zNnzujHH3+Uv78/74QDAHAL+Oqrr5SZmamBAwdeNjZ9+nQ9+uij6tmzp+6//375+fnpn//8pznu4uKiFStWyMXFRTabTc8884z69++viRMnmjVBQUFauXKlUlJS1KpVK02dOlUffvihwsPDzZrevXvrnXfeUXx8vFq3bq2MjAwlJydf9jUzAACsUuXaJdZ54YUX9Nhjj6lBgwb69ddfNX78eLm4uOjpp592eCe8du3a8vT01PDhw6/4TviUKVNkt9tLfCd89uzZGjNmjAYOHKi1a9dq6dKlWrlypdlHbGysIiMj1bZtW7Vr104zZszgnXAAAMpB165dZRhGiWPu7u5KSEhQQkLCFZ/foEEDrVq16qr76Ny5s3bu3HnVmpiYGMXExFy7YQAALFCpwfvnn3/W008/rX//+9+644471LFjR23evFl33HGHpP+8E+7s7KyePXsqPz9f4eHhmjNnjvn84nfChw0bJpvNpurVqysyMrLEd8JHjRqlmTNnql69eiW+E/7bb78pPj5edrtdrVu35p1wAAAAAEC5qNTgvXjx4quO8044AAAAAOCP7qb6jjcAAAAAALcagjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAABL/PLLL3rmmWdUp04deXh4qEWLFtq+fbs5bhiG4uPj5e/vLw8PD4WFhenQoUMO2zhx4oT69u0rT09PeXt7KyoqSmfOnHGo2bVrlzp16iR3d3cFBgZqypQpl/WybNkyNWnSRO7u7mrRooVWrVplzYsGAKAEBG8AAFDuTp48qQ4dOqhq1ar68ssvtW/fPk2dOlW1atUya6ZMmaJZs2YpMTFRW7ZsUfXq1RUeHq5z586ZNX379tXevXuVkpKiFStWaMOGDRoyZIg5npubq65du6pBgwZKT0/X22+/rQkTJuj99983azZt2qSnn35aUVFR2rlzp3r06KEePXpoz549FTMZAIDbXpXKbgAAANx63nrrLQUGBmrBggXmuqCgIPPPhmFoxowZGjdunB5//HFJ0j/+8Q/5+vpq+fLl6tOnj/bv36/k5GRt27ZNbdu2lSS9++67euSRR/TOO+8oICBAH3/8sQoKCjR//ny5urqqWbNmysjI0LRp08yAPnPmTHXr1k2jR4+WJE2aNEkpKSmaPXu2EhMTK2pKAAC3MT7xBgAA5e7zzz9X27Zt1atXL/n4+OhPf/qTPvjgA3P88OHDstvtCgsLM9d5eXkpNDRUaWlpkqS0tDR5e3uboVuSwsLC5OzsrC1btpg1999/v1xdXc2a8PBwHTx4UCdPnjRrLt5PcU3xfkqSn5+v3NxchwUAgLIieAMAgHL3008/ae7cubrnnnu0evVqDRs2TM8//7wWLlwoSbLb7ZIkX19fh+f5+vqaY3a7XT4+Pg7jVapUUe3atR1qStrGxfu4Uk3xeEkmT54sLy8vcwkMDCzV6wcA4GIEbwAAUO6KiorUpk0bvfHGG/rTn/6kIUOGaPDgwX+YU7vj4uKUk5NjLkePHq3slgAAf2AEbwAAUO78/f0VHBzssK5p06bKzMyUJPn5+UmSsrKyHGqysrLMMT8/P2VnZzuMX7hwQSdOnHCoKWkbF+/jSjXF4yVxc3OTp6enwwIAQFkRvAEAQLnr0KGDDh486LDu+++/V4MGDST950Jrfn5+Sk1NNcdzc3O1ZcsW2Ww2SZLNZtOpU6eUnp5u1qxdu1ZFRUUKDQ01azZs2KDz58+bNSkpKWrcuLF5BXWbzeawn+Ka4v0AAGA1gjcAACh3o0aN0ubNm/XGG2/ohx9+0KJFi/T+++8rOjpakuTk5KSRI0fqtdde0+eff67du3erf//+CggIUI8ePST95xPybt26afDgwdq6das2btyomJgY9enTRwEBAZKkv/71r3J1dVVUVJT27t2rJUuWaObMmYqNjTV7GTFihJKTkzV16lQdOHBAEyZM0Pbt2xUTE1Ph8wIAuD1xOzEAAFDu/vznP+vTTz9VXFycJk6cqKCgIM2YMUN9+/Y1a8aMGaO8vDwNGTJEp06dUseOHZWcnCx3d3ez5uOPP1ZMTIweeughOTs7q2fPnpo1a5Y57uXlpTVr1ig6OlohISGqW7eu4uPjHe71fd9992nRokUaN26cXnrpJd1zzz1avny5mjdvXjGTAQC47RG8AQCAJR599FE9+uijVxx3cnLSxIkTNXHixCvW1K5dW4sWLbrqflq2bKlvvvnmqjW9evVSr169rt4wAAAW4VRzAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALHTTBO8333xTTk5OGjlypLnu3Llzio6OVp06dVSjRg317NlTWVlZDs/LzMxURESEqlWrJh8fH40ePVoXLlxwqFm3bp3atGkjNzc3NWrUSElJSZftPyEhQQ0bNpS7u7tCQ0O1detWK14mAAAAAOA2c1ME723btum9995Ty5YtHdaPGjVKX3zxhZYtW6b169fr119/1ZNPPmmOFxYWKiIiQgUFBdq0aZMWLlyopKQkxcfHmzWHDx9WRESEunTpooyMDI0cOVKDBg3S6tWrzZolS5YoNjZW48eP144dO9SqVSuFh4crOzvb+hcPAAAAALilVXrwPnPmjPr27asPPvhAtWrVMtfn5ORo3rx5mjZtmh588EGFhIRowYIF2rRpkzZv3ixJWrNmjfbt26ePPvpIrVu3Vvfu3TVp0iQlJCSooKBAkpSYmKigoCBNnTpVTZs2VUxMjJ566ilNnz7d3Ne0adM0ePBgDRgwQMHBwUpMTFS1atU0f/78K/adn5+v3NxchwUAAAAAgEtVevCOjo5WRESEwsLCHNanp6fr/PnzDuubNGmi+vXrKy0tTZKUlpamFi1ayNfX16wJDw9Xbm6u9u7da9Zcuu3w8HBzGwUFBUpPT3eocXZ2VlhYmFlTksmTJ8vLy8tcAgMDyzgDAAAAAIBbWaUG78WLF2vHjh2aPHnyZWN2u12urq7y9vZ2WO/r6yu73W7WXBy6i8eLx65Wk5ubq7Nnz+r48eMqLCwssaZ4GyWJi4tTTk6OuRw9evT6XjQAAAAA4LZSpbJ2fPToUY0YMUIpKSlyd3evrDbKzM3NTW5ubpXdBgAAAADgJldpn3inp6crOztbbdq0UZUqVVSlShWtX79es2bNUpUqVeTr66uCggKdOnXK4XlZWVny8/OTJPn5+V12lfPix9eq8fT0lIeHh+rWrSsXF5cSa4q3AQAAAABAWVVa8H7ooYe0e/duZWRkmEvbtm3Vt29f889Vq1ZVamqq+ZyDBw8qMzNTNptNkmSz2bR7926Hq4+npKTI09NTwcHBZs3F2yiuKd6Gq6urQkJCHGqKioqUmppq1gAAAAAAUFaVdqp5zZo11bx5c4d11atXV506dcz1UVFRio2NVe3ateXp6anhw4fLZrOpffv2kqSuXbsqODhY/fr105QpU2S32zVu3DhFR0ebp4EPHTpUs2fP1pgxYzRw4ECtXbtWS5cu1cqVK839xsbGKjIyUm3btlW7du00Y8YM5eXlacCAARU0GwAAAACAW1WlBe/rMX36dDk7O6tnz57Kz89XeHi45syZY467uLhoxYoVGjZsmGw2m6pXr67IyEhNnDjRrAkKCtLKlSs1atQozZw5U/Xq1dOHH36o8PBws6Z379767bffFB8fL7vdrtatWys5OfmyC64BAAAAAFBaN1XwXrduncNjd3d3JSQkKCEh4YrPadCggVatWnXV7Xbu3Fk7d+68ak1MTIxiYmKuu1cAAAAAAK5Hpd/HGwAAAACAWxnBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAFhiwoQJcnJycliaNGlijp87d07R0dGqU6eOatSooZ49eyorK8thG5mZmYqIiFC1atXk4+Oj0aNH68KFCw4169atU5s2beTm5qZGjRopKSnpsl4SEhLUsGFDubu7KzQ0VFu3brXkNQMAUBKCNwAAsEyzZs107Ngxc/n222/NsVGjRumLL77QsmXLtH79ev3666968sknzfHCwkJFRESooKBAmzZt0sKFC5WUlKT4+Hiz5vDhw4qIiFCXLl2UkZGhkSNHatCgQVq9erVZs2TJEsXGxmr8+PHasWOHWrVqpfDwcGVnZ1fMJAAAbnsEbwAAYJkqVarIz8/PXOrWrStJysnJ0bx58zRt2jQ9+OCDCgkJ0YIFC7Rp0yZt3rxZkrRmzRrt27dPH330kVq3bq3u3btr0qRJSkhIUEFBgSQpMTFRQUFBmjp1qpo2baqYmBg99dRTmj59utnDtGnTNHjwYA0YMEDBwcFKTExUtWrVNH/+/Cv2nZ+fr9zcXIcFAICyIngDAADLHDp0SAEBAbrrrrvUt29fZWZmSpLS09N1/vx5hYWFmbVNmjRR/fr1lZaWJklKS0tTixYt5Ovra9aEh4crNzdXe/fuNWsu3kZxTfE2CgoKlJ6e7lDj7OyssLAws6YkkydPlpeXl7kEBgbe4EwAAG5nBG8AAGCJ0NBQJSUlKTk5WXPnztXhw4fVqVMnnT59Wna7Xa6urvL29nZ4jq+vr+x2uyTJbrc7hO7i8eKxq9Xk5ubq7NmzOn78uAoLC0usKd5GSeLi4pSTk2MuR48eLdMcAAAgSVUquwEAAHBr6t69u/nnli1bKjQ0VA0aNNDSpUvl4eFRiZ1dm5ubm9zc3Cq7DQDALYJPvAEAQIXw9vbWvffeqx9++EF+fn4qKCjQqVOnHGqysrLk5+cnSfLz87vsKufFj69V4+npKQ8PD9WtW1cuLi4l1hRvAwAAqxG8AQBAhThz5ox+/PFH+fv7KyQkRFWrVlVqaqo5fvDgQWVmZspms0mSbDabdu/e7XD18ZSUFHl6eio4ONisuXgbxTXF23B1dVVISIhDTVFRkVJTU80aAACsRvAGAACWeOGFF7R+/XodOXJEmzZt0hNPPCEXFxc9/fTT8vLyUlRUlGJjY/X1118rPT1dAwYMkM1mU/v27SVJXbt2VXBwsPr166fvvvtOq1ev1rhx4xQdHW2eBj506FD99NNPGjNmjA4cOKA5c+Zo6dKlGjVqlNlHbGysPvjgAy1cuFD79+/XsGHDlJeXpwEDBlTKvAAAbj98xxsAAFji559/1tNPP61///vfuuOOO9SxY0dt3rxZd9xxhyRp+vTpcnZ2Vs+ePZWfn6/w8HDNmTPHfL6Li4tWrFihYcOGyWazqXr16oqMjNTEiRPNmqCgIK1cuVKjRo3SzJkzVa9ePX344YcKDw83a3r37q3ffvtN8fHxstvtat26tZKTky+74BoAAFYheAMAAEssXrz4quPu7u5KSEhQQkLCFWsaNGigVatWXXU7nTt31s6dO69aExMTo5iYmKvWAABgFU41BwAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsVKWyG8DlMjMzdfz48TI9t27duqpfv345dwQAAAAAKKsyBe+77rpL27ZtU506dRzWnzp1Sm3atNFPP/1ULs3djjIzM9WkSVOdPft7mZ7v4VFNBw7sJ3wDAMqM4zwAAOWrTMH7yJEjKiwsvGx9fn6+fvnllxtu6nZ2/PhxnT37u0IHjpenf8NSPTf32BFtmf+qjh8/TvAGAJQZx3kAAMpXqYL3559/bv559erV8vLyMh8XFhYqNTVVDRs2LLfmbmee/g1Vu37jym4DAHAb4TgPAIA1ShW8e/ToIUlycnJSZGSkw1jVqlXVsGFDTZ06tdyaAwAAFYfjPAAA1ihV8C4qKpIkBQUFadu2bapbt64lTQEAgIrHcR4AAGuU6Tvehw8fLu8+AADATYLjPAAA5avMtxNLTU1VamqqsrOzzXfIi82fP/+GGwMAAJWH4zwAAOWnTMH71Vdf1cSJE9W2bVv5+/vLycmpvPsCAACVhOM8AADlq0zBOzExUUlJSerXr1959wMAACoZx3kAAMqXc1meVFBQoPvuu6+8ewEAADcBjvMAAJSvMgXvQYMGadGiReXdCwAAuAlwnAcAoHyV6VTzc+fO6f3339dXX32lli1bqmrVqg7j06ZNK5fmAABAxeM4DwBA+SpT8N61a5dat24tSdqzZ4/DGBdgAQDgj43jPAAA5atMwfvrr78u7z4AAMBNguM8AADlq0zf8S4vc+fOVcuWLeXp6SlPT0/ZbDZ9+eWX5vi5c+cUHR2tOnXqqEaNGurZs6eysrIctpGZmamIiAhVq1ZNPj4+Gj16tC5cuOBQs27dOrVp00Zubm5q1KiRkpKSLuslISFBDRs2lLu7u0JDQ7V161ZLXjMAAAAA4PZSpk+8u3TpctVTzdauXXtd26lXr57efPNN3XPPPTIMQwsXLtTjjz+unTt3qlmzZho1apRWrlypZcuWycvLSzExMXryySe1ceNGSVJhYaEiIiLk5+enTZs26dixY+rfv7+qVq2qN954Q5J0+PBhRUREaOjQofr444+VmpqqQYMGyd/fX+Hh4ZKkJUuWKDY2VomJiQoNDdWMGTMUHh6ugwcPysfHpyxTBADAH1Z5HecBAMB/lCl4F3/vq9j58+eVkZGhPXv2KDIy8rq389hjjzk8fv311zV37lxt3rxZ9erV07x587Ro0SI9+OCDkqQFCxaoadOm2rx5s9q3b681a9Zo3759+uqrr+Tr66vWrVtr0qRJevHFFzVhwgS5uroqMTFRQUFBmjp1qiSpadOm+vbbbzV9+nQzeE+bNk2DBw/WgAEDJP3n/qUrV67U/PnzNXbs2LJMEQAAf1jldZwHAAD/UabgPX369BLXT5gwQWfOnClTI4WFhVq2bJny8vJks9mUnp6u8+fPKywszKxp0qSJ6tevr7S0NLVv315paWlq0aKFfH19zZrw8HANGzZMe/fu1Z/+9CelpaU5bKO4ZuTIkZL+c6/S9PR0xcXFmePOzs4KCwtTWlraFfvNz89Xfn6++Tg3N7dMrxsAgJuNFcd5AABuZ+X6He9nnnlG8+fPL9Vzdu/erRo1asjNzU1Dhw7Vp59+quDgYNntdrm6usrb29uh3tfXV3a7XZJkt9sdQnfxePHY1Wpyc3N19uxZHT9+XIWFhSXWFG+jJJMnT5aXl5e5BAYGlup1AwDwR1OW4zwAACjn4J2WliZ3d/dSPadx48bKyMjQli1bNGzYMEVGRmrfvn3l2ZYl4uLilJOTYy5Hjx6t7JYAALBUWY7zAACgjKeaP/nkkw6PDcPQsWPHtH37dr3yyiul2parq6saNWokSQoJCdG2bds0c+ZM9e7dWwUFBTp16pTDp95ZWVny8/OTJPn5+V129fHiq55fXHPpldCzsrLk6ekpDw8Pubi4yMXFpcSa4m2UxM3NTW5ubqV6rQAA/BGU53EeAACU8RPvi0+x9vLyUu3atdW5c2etWrVK48ePv6GGioqKlJ+fr5CQEFWtWlWpqanm2MGDB5WZmSmbzSZJstls2r17t7Kzs82alJQUeXp6Kjg42Ky5eBvFNcXbcHV1VUhIiENNUVGRUlNTzRoAAG4nVh7nAQC4HZXpE+8FCxaUy87j4uLUvXt31a9fX6dPn9aiRYu0bt06rV69Wl5eXoqKilJsbKxq164tT09PDR8+XDabTe3bt5ckde3aVcHBwerXr5+mTJkiu92ucePGKTo62vw0eujQoZo9e7bGjBmjgQMHau3atVq6dKlWrlxp9hEbG6vIyEi1bdtW7dq104wZM5SXl2de5RwAgNtJeR3nAQDAf9zQd7zT09P10Ucf6aOPPtLOnTtL/fzs7Gz1799fjRs31kMPPaRt27Zp9erVevjhhyX956qqjz76qHr27Kn7779ffn5++uc//2k+38XFRStWrJCLi4tsNpueeeYZ9e/fXxMnTjRrgoKCtHLlSqWkpKhVq1aaOnWqPvzwQ/NWYpLUu3dvvfPOO4qPj1fr1q2VkZGh5OTkyy64BgDA7eRGj/MXe/PNN+Xk5GTeVUSSzp07p+joaNWpU0c1atRQz549L/vqV2ZmpiIiIlStWjX5+Pho9OjRunDhgkPNunXr1KZNG7m5ualRo0ZKSkq6bP8JCQlq2LCh3N3dFRoaetlX1QAAsFKZPvHOzs5Wnz59tG7dOvP716dOnVKXLl20ePFi3XHHHde1nXnz5l113N3dXQkJCUpISLhiTYMGDbRq1aqrbqdz587X/A9DTEyMYmJirloDAMDtoLyO88W2bdum9957Ty1btnRYP2rUKK1cuVLLli2Tl5eXYmJi9OSTT2rjxo2S/nOr0YiICPn5+WnTpk06duyY+vfvr6pVq+qNN96QJB0+fFgREREaOnSoPv74Y6WmpmrQoEHy9/c332RfsmSJYmNjlZiYqNDQUM2YMUPh4eE6ePCgfHx8bnC2AAC4tjJ94j18+HCdPn1ae/fu1YkTJ3TixAnt2bNHubm5ev7558u7RwAAUIHK8zh/5swZ9e3bVx988IFq1aplrs/JydG8efM0bdo0PfjggwoJCdGCBQu0adMmbd68WZK0Zs0a7du3Tx999JFat26t7t27a9KkSUpISFBBQYEkKTExUUFBQZo6daqaNm2qmJgYPfXUUw73Ip82bZoGDx6sAQMGKDg4WImJiapWrdpVb42Wn5+v3NxchwUAgLIqU/BOTk7WnDlz1LRpU3NdcHCwEhIS9OWXX5ZbcwAAoOKV53E+OjpaERERCgsLc1ifnp6u8+fPO6xv0qSJ6tevr7S0NEn/uX1ZixYtHL76FR4ertzcXO3du9esuXTb4eHh5jYKCgqUnp7uUOPs7KywsDCzpiSTJ092uMBcYGBgqV43AAAXK1PwLioqUtWqVS9bX7VqVRUVFd1wUwAAoPKU13F+8eLF2rFjhyZPnnzZmN1ul6urq8MtQyXJ19dXdrvdrLn0eivFj69Vk5ubq7Nnz+r48eMqLCwssaZ4GyWJi4tTTk6OuRw9evT6XjQAACUoU/B+8MEHNWLECP3666/mul9++UWjRo3SQw89VG7NAQCAilcex/mjR49qxIgR+vjjj+Xu7m5Vq5Zxc3OTp6enwwIAQFmVKXjPnj1bubm5atiwoe6++27dfffdCgoKUm5urt59993y7hEAAFSg8jjOp6enKzs7W23atFGVKlVUpUoVrV+/XrNmzVKVKlXk6+urgoICnTp1yuF5WVlZ8vPzkyT5+flddpXz4sfXqvH09JSHh4fq1q0rFxeXEmuKtwEAgNXKdFXzwMBA7dixQ1999ZUOHDggSWratOll37ECAAB/POVxnH/ooYe0e/duh3UDBgxQkyZN9OKLLyowMFBVq1ZVamqqevbsKUk6ePCgMjMzZbPZJEk2m02vv/66srOzzauPp6SkyNPTU8HBwWbNpXc3SUlJMbfh6uqqkJAQpaamqkePHpL+cyp9amoqdzMBAFSYUgXvtWvXKiYmRps3b5anp6cefvhh857bOTk5atasmRITE9WpUydLmgUAANYpz+N8zZo11bx5c4d11atXV506dcz1UVFRio2NVe3ateXp6anhw4fLZrOpffv2kqSuXbsqODhY/fr105QpU2S32zVu3DhFR0fLzc1NkjR06FDNnj1bY8aM0cCBA7V27VotXbpUK1euNPcbGxuryMhItW3bVu3atdOMGTOUl5enAQMGlMu8AQBwLaUK3jNmzNDgwYNL/J6Tl5eXnnvuOU2bNo3gDQDAH1BFH+enT58uZ2dn9ezZU/n5+QoPD9ecOXPMcRcXF61YsULDhg2TzWZT9erVFRkZqYkTJ5o1QUFBWrlypUaNGqWZM2eqXr16+vDDD817eEtS79699dtvvyk+Pl52u12tW7dWcnLyZRdcAwDAKqUK3t99953eeuutK4537dpV77zzzg03BQAAKp7Vx/l169Y5PHZ3d1dCQoISEhKu+JwGDRpcdir5pTp37qydO3detSYmJoZTywEAlaZUF1fLysoq8fYixapUqaLffvvthpsCAAAVj+M8AADWKFXwvvPOO7Vnz54rju/atUv+/v433BQAAKh4HOcBALBGqYL3I488oldeeUXnzp27bOzs2bMaP368Hn300XJrDgAAVByO8wAAWKNU3/EeN26c/vnPf+ree+9VTEyMGjduLEk6cOCAEhISVFhYqJdfftmSRgEAgLU4zgMAYI1SBW9fX19t2rRJw4YNU1xcnAzDkCQ5OTkpPDxcCQkJXCEUAIA/KI7zAABYo1TBW/q/q4uePHlSP/zwgwzD0D333KNatWpZ0R8AAKhAHOcBACh/pQ7exWrVqqU///nP5dkLAAC4SXCcBwCg/JTq4moAAAAAAKB0CN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAMASc+fOVcuWLeXp6SlPT0/ZbDZ9+eWX5vi5c+cUHR2tOnXqqEaNGurZs6eysrIctpGZmamIiAhVq1ZNPj4+Gj16tC5cuOBQs27dOrVp00Zubm5q1KiRkpKSLuslISFBDRs2lLu7u0JDQ7V161ZLXjMAACUheAMAAEvUq1dPb775ptLT07V9+3Y9+OCDevzxx7V3715J0qhRo/TFF19o2bJlWr9+vX799Vc9+eST5vMLCwsVERGhgoICbdq0SQsXLlRSUpLi4+PNmsOHDysiIkJdunRRRkaGRo4cqUGDBmn16tVmzZIlSxQbG6vx48drx44datWqlcLDw5WdnV1xkwEAuK0RvAEAgCUee+wxPfLII7rnnnt077336vXXX1eNGjW0efNm5eTkaN68eZo2bZoefPBBhYSEaMGCBdq0aZM2b94sSVqzZo327dunjz76SK1bt1b37t01adIkJSQkqKCgQJKUmJiooKAgTZ06VU2bNlVMTIyeeuopTZ8+3exj2rRpGjx4sAYMGKDg4GAlJiaqWrVqmj9/fqXMCwDg9kPwBgAAlissLNTixYuVl5cnm82m9PR0nT9/XmFhYWZNkyZNVL9+faWlpUmS0tLS1KJFC/n6+po14eHhys3NNT81T0tLc9hGcU3xNgoKCpSenu5Q4+zsrLCwMLOmJPn5+crNzXVYAAAoK4I3AACwzO7du1WjRg25ublp6NCh+vTTTxUcHCy73S5XV1d5e3s71Pv6+sput0uS7Ha7Q+guHi8eu1pNbm6uzp49q+PHj6uwsLDEmuJtlGTy5Mny8vIyl8DAwDK9fgAAJII3AACwUOPGjZWRkaEtW7Zo2LBhioyM1L59+yq7rWuKi4tTTk6OuRw9erSyWwIA/IFVqewGAADArcvV1VWNGjWSJIWEhGjbtm2aOXOmevfurYKCAp06dcrhU++srCz5+flJkvz8/C67+njxVc8vrrn0SuhZWVny9PSUh4eHXFxc5OLiUmJN8TZK4ubmJjc3t7K9aAAALsEn3gAAoMIUFRUpPz9fISEhqlq1qlJTU82xgwcPKjMzUzabTZJks9m0e/duh6uPp6SkyNPTU8HBwWbNxdsorinehqurq0JCQhxqioqKlJqaatYAAGA1PvEGAACWiIuLU/fu3VW/fn2dPn1aixYt0rp167R69Wp5eXkpKipKsbGxql27tjw9PTV8+HDZbDa1b99ektS1a1cFBwerX79+mjJliux2u8aNG6fo6Gjz0+ihQ4dq9uzZGjNmjAYOHKi1a9dq6dKlWrlypdlHbGysIiMj1bZtW7Vr104zZsxQXl6eBgwYUCnzAgC4/RC8AQCAJbKzs9W/f38dO3ZMXl5eatmypVavXq2HH35YkjR9+nQ5OzurZ8+eys/PV3h4uObMmWM+38XFRStWrNCwYcNks9lUvXp1RUZGauLEiWZNUFCQVq5cqVGjRmnmzJmqV6+ePvzwQ4WHh5s1vXv31m+//ab4+HjZ7Xa1bt1aycnJl11wDQAAqxC8AQCAJebNm3fVcXd3dyUkJCghIeGKNQ0aNNCqVauuup3OnTtr586dV62JiYlRTEzMVWsAALBKpX7He/Lkyfrzn/+smjVrysfHRz169NDBgwcdas6dO6fo6GjVqVNHNWrUUM+ePS+7QEpmZqYiIiJUrVo1+fj4aPTo0bpw4YJDzbp169SmTRu5ubmpUaNGSkpKuqyfhIQENWzYUO7u7goNDb3sgi4AAAAAAJRWpQbv9evXKzo6Wps3b1ZKSorOnz+vrl27Ki8vz6wZNWqUvvjiCy1btkzr16/Xr7/+qieffNIcLywsVEREhAoKCrRp0yYtXLhQSUlJio+PN2sOHz6siIgIdenSRRkZGRo5cqQGDRqk1atXmzVLlixRbGysxo8frx07dqhVq1YKDw93uKALAAAAAAClVamnmicnJzs8TkpKko+Pj9LT03X//fcrJydH8+bN06JFi/Tggw9KkhYsWKCmTZtq8+bNat++vdasWaN9+/bpq6++kq+vr1q3bq1JkybpxRdf1IQJE+Tq6qrExEQFBQVp6tSpkqSmTZvq22+/1fTp083vgE2bNk2DBw82L7SSmJiolStXav78+Ro7duxlvefn5ys/P998nJuba8kcAQAAAAD+2G6q24nl5ORIkmrXri1JSk9P1/nz5xUWFmbWNGnSRPXr11daWpokKS0tTS1atHC4QEp4eLhyc3O1d+9es+bibRTXFG+joKBA6enpDjXOzs4KCwszay41efJkeXl5mUtgYOCNvnwAAAAAwC3opgneRUVFGjlypDp06KDmzZtLkux2u1xdXeXt7e1Q6+vrK7vdbtZcelXS4sfXqsnNzdXZs2d1/PhxFRYWllhTvI1LxcXFKScnx1yOHj1athcOAAAAALil3TRXNY+OjtaePXv07bffVnYr18XNzc28hygAAAAAAFdyU3ziHRMToxUrVujrr79WvXr1zPV+fn4qKCjQqVOnHOqzsrLk5+dn1lx6lfPix9eq8fT0lIeHh+rWrSsXF5cSa4q3AQAAAABAWVRq8DYMQzExMfr000+1du1aBQUFOYyHhISoatWqSk1NNdcdPHhQmZmZstlskiSbzabdu3c7XH08JSVFnp6eCg4ONmsu3kZxTfE2XF1dFRIS4lBTVFSk1NRUswYAAAAAgLKo1FPNo6OjtWjRIn322WeqWbOm+X1qLy8veXh4yMvLS1FRUYqNjVXt2rXl6emp4cOHy2azqX379pKkrl27Kjg4WP369dOUKVNkt9s1btw4RUdHm6eCDx06VLNnz9aYMWM0cOBArV27VkuXLtXKlSvNXmJjYxUZGam2bduqXbt2mjFjhvLy8syrnAMAAAAAUBaVGrznzp0rSercubPD+gULFujZZ5+VJE2fPl3Ozs7q2bOn8vPzFR4erjlz5pi1Li4uWrFihYYNGyabzabq1asrMjJSEydONGuCgoK0cuVKjRo1SjNnzlS9evX04YcfmrcSk6TevXvrt99+U3x8vOx2u1q3bq3k5OTLLrgGAAAAAEBpVGrwNgzjmjXu7u5KSEhQQkLCFWsaNGigVatWXXU7nTt31s6dO69aExMTo5iYmGv2BAAAAADA9bopLq4GAAAAAMCtiuANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AACwxefJk/fnPf1bNmjXl4+OjHj166ODBgw41586dU3R0tOrUqaMaNWqoZ8+eysrKcqjJzMxURESEqlWrJh8fH40ePVoXLlxwqFm3bp3atGkjNzc3NWrUSElJSZf1k5CQoIYNG8rd3V2hoaHaunVrub9mAABKQvAGAACWWL9+vaKjo7V582alpKTo/Pnz6tq1q/Ly8syaUaNG6YsvvtCyZcu0fv16/frrr3ryySfN8cLCQkVERKigoECbNm3SwoULlZSUpPj4eLPm8OHDioiIUJcuXZSRkaGRI0dq0KBBWr16tVmzZMkSxcbGavz48dqxY4datWql8PBwZWdnV8xkAABua1UquwEAAHBrSk5OdniclJQkHx8fpaen6/7771dOTo7mzZunRYsW6cEHH5QkLViwQE2bNtXmzZvVvn17rVmzRvv27dNXX30lX19ftW7dWpMmTdKLL76oCRMmyNXVVYmJiQoKCtLUqVMlSU2bNtW3336r6dOnKzw8XJI0bdo0DR48WAMGDJAkJSYmauXKlZo/f77Gjh1bgbMCALgd8Yk3AACoEDk5OZKk2rVrS5LS09N1/vx5hYWFmTVNmjRR/fr1lZaWJklKS0tTixYt5Ovra9aEh4crNzdXe/fuNWsu3kZxTfE2CgoKlJ6e7lDj7OyssLAws+ZS+fn5ys3NdVgAACgrgjcAALBcUVGRRo4cqQ4dOqh58+aSJLvdLldXV3l7ezvU+vr6ym63mzUXh+7i8eKxq9Xk5ubq7NmzOn78uAoLC0usKd7GpSZPniwvLy9zCQwMLNsLBwBABG8AAFABoqOjtWfPHi1evLiyW7kucXFxysnJMZejR49WdksAgD8wvuMNAAAsFRMToxUrVmjDhg2qV6+eud7Pz08FBQU6deqUw6feWVlZ8vPzM2suvfp48VXPL6659EroWVlZ8vT0lIeHh1xcXOTi4lJiTfE2LuXm5iY3N7eyvWAAAC7BJ94AAMAShmEoJiZGn376qdauXaugoCCH8ZCQEFWtWlWpqanmuoMHDyozM1M2m02SZLPZtHv3boerj6ekpMjT01PBwcFmzcXbKK4p3oarq6tCQkIcaoqKipSammrWAABgJT7xBgAAloiOjtaiRYv02WefqWbNmub3qb28vOTh4SEvLy9FRUUpNjZWtWvXlqenp4YPHy6bzab27dtLkrp27arg4GD169dPU6ZMkd1u17hx4xQdHW1+Ij106FDNnj1bY8aM0cCBA7V27VotXbpUK1euNHuJjY1VZGSk2rZtq3bt2mnGjBnKy8szr3IOAICVCN4AAMASc+fOlSR17tzZYf2CBQv07LPPSpKmT58uZ2dn9ezZU/n5+QoPD9ecOXPMWhcXF61YsULDhg2TzWZT9erVFRkZqYkTJ5o1QUFBWrlypUaNGqWZM2eqXr16+vDDD81biUlS79699dtvvyk+Pl52u12tW7dWcnLyZRdcAwDACgRvAABgCcMwrlnj7u6uhIQEJSQkXLGmQYMGWrVq1VW307lzZ+3cufOqNTExMYqJiblmTwAAlDe+4w0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUqNXhv2LBBjz32mAICAuTk5KTly5c7jBuGofj4ePn7+8vDw0NhYWE6dOiQQ82JEyfUt29feXp6ytvbW1FRUTpz5oxDza5du9SpUye5u7srMDBQU6ZMuayXZcuWqUmTJnJ3d1eLFi20atWqcn+9AAAAAIDbT6UG77y8PLVq1UoJCQkljk+ZMkWzZs1SYmKitmzZourVqys8PFznzp0za/r27au9e/cqJSVFK1as0IYNGzRkyBBzPDc3V127dlWDBg2Unp6ut99+WxMmTND7779v1mzatElPP/20oqKitHPnTvXo0UM9evTQnj17rHvxAAAAAIDbQpXK3Hn37t3VvXv3EscMw9CMGTM0btw4Pf7445Kkf/zjH/L19dXy5cvVp08f7d+/X8nJydq2bZvatm0rSXr33Xf1yCOP6J133lFAQIA+/vhjFRQUaP78+XJ1dVWzZs2UkZGhadOmmQF95syZ6tatm0aPHi1JmjRpklJSUjR79mwlJiZWwEwAAAAAAG5VN+13vA8fPiy73a6wsDBznZeXl0JDQ5WWliZJSktLk7e3txm6JSksLEzOzs7asmWLWXP//ffL1dXVrAkPD9fBgwd18uRJs+bi/RTXFO+nJPn5+crNzXVYAAAAAAC41E0bvO12uyTJ19fXYb2vr685Zrfb5ePj4zBepUoV1a5d26GmpG1cvI8r1RSPl2Ty5Mny8vIyl8DAwNK+RAAAAADAbeCmDd43u7i4OOXk5JjL0aNHK7slAAAAAMBN6KYN3n5+fpKkrKwsh/VZWVnmmJ+fn7Kzsx3GL1y4oBMnTjjUlLSNi/dxpZri8ZK4ubnJ09PTYQEAAAAA4FI3bfAOCgqSn5+fUlNTzXW5ubnasmWLbDabJMlms+nUqVNKT083a9auXauioiKFhoaaNRs2bND58+fNmpSUFDVu3Fi1atUyay7eT3FN8X4AAAAAACirSg3eZ86cUUZGhjIyMiT954JqGRkZyszMlJOTk0aOHKnXXntNn3/+uXbv3q3+/fsrICBAPXr0kCQ1bdpU3bp10+DBg7V161Zt3LhRMTEx6tOnjwICAiRJf/3rX+Xq6qqoqCjt3btXS5Ys0cyZMxUbG2v2MWLECCUnJ2vq1Kk6cOCAJkyYoO3btysmJqaipwQAAAAAcIup1NuJbd++XV26dDEfF4fhyMhIJSUlacyYMcrLy9OQIUN06tQpdezYUcnJyXJ3dzef8/HHHysmJkYPPfSQnJ2d1bNnT82aNcsc9/Ly0po1axQdHa2QkBDVrVtX8fHxDvf6vu+++7Ro0SKNGzdOL730ku655x4tX75czZs3r4BZAAAAAADcyio1eHfu3FmGYVxx3MnJSRMnTtTEiROvWFO7dm0tWrToqvtp2bKlvvnmm6vW9OrVS7169bp6wwAAAAAAlNJN+x1vAAAAAABuBQRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAGCJDRs26LHHHlNAQICcnJy0fPlyh3HDMBQfHy9/f395eHgoLCxMhw4dcqg5ceKE+vbtK09PT3l7eysqKkpnzpxxqNm1a5c6deokd3d3BQYGasqUKZf1smzZMjVp0kTu7u5q0aKFVq1aVe6vFwCAKyF4AwAAS+Tl5alVq1ZKSEgocXzKlCmaNWuWEhMTtWXLFlWvXl3h4eE6d+6cWdO3b1/t3btXKSkpWrFihTZs2KAhQ4aY47m5ueratasaNGig9PR0vf3225owYYLef/99s2bTpk16+umnFRUVpZ07d6pHjx7q0aOH9uzZY92LBwDgIlUquwEAAHBr6t69u7p3717imGEYmjFjhsaNG6fHH39ckvSPf/xDvr6+Wr58ufr06aP9+/crOTlZ27ZtU9u2bSVJ7777rh555BG98847CggI0Mcff6yCggLNnz9frq6uatasmTIyMjRt2jQzoM+cOVPdunXT6NGjJUmTJk1SSkqKZs+ercTExBL7y8/PV35+vvk4Nze33OYFAHD74RNvAABQ4Q4fPiy73a6wsDBznZeXl0JDQ5WWliZJSktLk7e3txm6JSksLEzOzs7asmWLWXP//ffL1dXVrAkPD9fBgwd18uRJs+bi/RTXFO+nJJMnT5aXl5e5BAYG3viLBgDctgjeAACgwtntdkmSr6+vw3pfX19zzG63y8fHx2G8SpUqql27tkNNSdu4eB9XqikeL0lcXJxycnLM5ejRo6V9iQAAmDjVHAAA4BJubm5yc3Or7DYAALcIPvEGAAAVzs/PT5KUlZXlsD4rK8sc8/PzU3Z2tsP4hQsXdOLECYeakrZx8T6uVFM8DgCA1QjeAACgwgUFBcnPz0+pqanmutzcXG3ZskU2m02SZLPZdOrUKaWnp5s1a9euVVFRkUJDQ82aDRs26Pz582ZNSkqKGjdurFq1apk1F++nuKZ4PwAAWI3gDQAALHHmzBllZGQoIyND0n8uqJaRkaHMzEw5OTlp5MiReu211/T5559r9+7d6t+/vwICAtSjRw9JUtOmTdWtWzcNHjxYW7du1caNGxUTE6M+ffooICBAkvTXv/5Vrq6uioqK0t69e7VkyRLNnDlTsbGxZh8jRoxQcnKypk6dqgMHDmjChAnavn27YmJiKnpKAAC3Kb7jDQAALLF9+3Z16dLFfFwchiMjI5WUlKQxY8YoLy9PQ4YM0alTp9SxY0clJyfL3d3dfM7HH3+smJgYPfTQQ3J2dlbPnj01a9Ysc9zLy0tr1qxRdHS0QkJCVLduXcXHxzvc6/u+++7TokWLNG7cOL300ku65557tHz5cjVv3rwCZgEAAII3AACwSOfOnWUYxhXHnZycNHHiRE2cOPGKNbVr19aiRYuuup+WLVvqm2++uWpNr1691KtXr6s3DACARTjVHAAAAAAACxG8AQAAAACwEMEbAAAAAAAL8R1vAAAAAJUmMzNTx48fL7ft1a1bV/Xr1y+37QHlgeANAAAAoFJkZmaqSZOmOnv293LbpodHNR04sJ/wjZsKwRsAAABApTh+/LjOnv1doQPHy9O/4Q1vL/fYEW2Z/6qOHz9O8MZNheANAAAAoFJ5+jdU7fqNK7sNwDJcXA0AAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsFCVym4AAAAAAICryczM1PHjx8tte3Xr1lX9+vXLbXvXQvAGAAAAANy0MjMz1aRJU509+3u5bdPDo5oOHNhfYeGb4A0AAAAAuGkdP35cZ8/+rtCB4+Xp3/CGt5d77Ii2zH9Vx48fJ3gDAADgj+2PfmoogJuLp39D1a7fuLLbKBOCNwAAAMrdrXBqKACUF4L3JRISEvT222/LbrerVatWevfdd9WuXbvKbgsAANwgjvEV61Y4NRQAygvB+yJLlixRbGysEhMTFRoaqhkzZig8PFwHDx6Uj49PZbcHAADKiGN85fkjnxoKAOWF4H2RadOmafDgwRowYIAkKTExUStXrtT8+fM1duzYSu4OAACU1c10jOd7zwBw+yF4/38FBQVKT09XXFycuc7Z2VlhYWFKS0u7rD4/P1/5+fnm45ycHElSbm7uDfVx5swZSdKJfx3UhfyzpXpurj1TkpSenm5up7ScnZ1VVFRU4c9l3xW/7z9q3+z79tr3jTz34MGDkm7s9+mZM2du6Pd68XMNwyjzNnDjSnuMl6w7zh89elRt2/5Z586V7mfyatzc3PXf//0P+fr63vC2bvR3xcVu5N9gScrj/zmXKs/XW57butm3x8/JzbGt22nurHqtFXqcN2AYhmH88ssvhiRj06ZNDutHjx5ttGvX7rL68ePHG5JYWFhYWFiuuRw9erSiDmcoQWmP8YbBcZ6FhYWF5fqX6znO84l3GcXFxSk2NtZ8XFRUpBMnTqhOnTpycnIq83Zzc3MVGBioo0ePytPTszxavWUwN1fG3FwZc3NlzM2VldfcGIah06dPKyAgoBy7Q0XgOF/xmJsrY26ujLm5MubmyirjOE/w/v/q1q0rFxcXZWVlOazPysqSn5/fZfVubm5yc3NzWOft7V1u/Xh6evIP5AqYmytjbq6Mubky5ubKymNuvLy8yqkblFVpj/ESx/nKxNxcGXNzZczNlTE3V1aRx3nnG9rLLcTV1VUhISFKTU011xUVFSk1NVU2m60SOwMAADeCYzwAoLLxifdFYmNjFRkZqbZt26pdu3aaMWOG8vLyzCugAgCAPyaO8QCAykTwvkjv3r3122+/KT4+Xna7Xa1bt1ZycnK5XCX0erm5uWn8+PGXnd4G5uZqmJsrY26ujLm5Mubm1nMzHOMlfrauhrm5MubmypibK2Nurqwy5sbJMLjHCQAAAAAAVuE73gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN6VICEhQQ0bNpS7u7tCQ0O1devWq9YvW7ZMTZo0kbu7u1q0aKFVq1ZVUKcVrzRz88EHH6hTp06qVauWatWqpbCwsGvO5R9ZaX9uii1evFhOTk7q0aOHtQ1WotLOzalTpxQdHS1/f3+5ubnp3nvvvWX/XZV2bmbMmKHGjRvLw8NDgYGBGjVqlM6dO1dB3VacDRs26LHHHlNAQICcnJy0fPnyaz5n3bp1atOmjdzc3NSoUSMlJSVZ3iduLWX9PX6rK8u/x9vF5MmT9ec//1k1a9aUj4+PevTooYMHD1Z2WzeFuXPnqmXLlvL09JSnp6dsNpu+/PLLym7rpvPmm2/KyclJI0eOrOxWbgoTJkyQk5OTw9KkSZMK2TfBu4ItWbJEsbGxGj9+vHbs2KFWrVopPDxc2dnZJdZv2rRJTz/9tKKiorRz50716NFDPXr00J49eyq4c+uVdm7WrVunp59+Wl9//bXS0tIUGBiorl276pdffqngzq1X2rkpduTIEb3wwgvq1KlTBXVa8Uo7NwUFBXr44Yd15MgRffLJJzp48KA++OAD3XnnnRXcufVKOzeLFi3S2LFjNX78eO3fv1/z5s3TkiVL9NJLL1Vw59bLy8tTq1atlJCQcF31hw8fVkREhLp06aKMjAyNHDlSgwYN0urVqy3uFLeKsv4evx2U9t/j7WT9+vWKjo7W5s2blZKSovPnz6tr167Ky8ur7NYqXb169fTmm28qPT1d27dv14MPPqjHH39ce/furezWbhrbtm3Te++9p5YtW1Z2KzeVZs2a6dixY+by7bffVsyODVSodu3aGdHR0ebjwsJCIyAgwJg8eXKJ9X/5y1+MiIgIh3WhoaHGc889Z2mflaG0c3OpCxcuGDVr1jQWLlxoVYuVpixzc+HCBeO+++4zPvzwQyMyMtJ4/PHHK6DTilfauZk7d65x1113GQUFBRXVYqUp7dxER0cbDz74oMO62NhYo0OHDpb2WdkkGZ9++ulVa8aMGWM0a9bMYV3v3r2N8PBwCzvDreRGj3G3i+v593g7y87ONiQZ69evr+xWbkq1atUyPvzww8pu46Zw+vRp45577jFSUlKMBx54wBgxYkRlt3RTGD9+vNGqVatK2TefeFeggoICpaenKywszFzn7OyssLAwpaWllfictLQ0h3pJCg8Pv2L9H1VZ5uZSv//+u86fP6/atWtb1WalKOvcTJw4UT4+PoqKiqqINitFWebm888/l81mU3R0tHx9fdW8eXO98cYbKiwsrKi2K0RZ5ua+++5Tenq6efrrTz/9pFWrVumRRx6pkJ5vZrfL72JYozyOcYAk5eTkSNIt93+dG1VYWKjFixcrLy9PNputstu5KURHRysiIuKyYxekQ4cOKSAgQHfddZf69u2rzMzMCtlvlQrZCyRJx48fV2FhoXx9fR3W+/r66sCBAyU+x263l1hvt9st67MylGVuLvXiiy8qICDglvsFU5a5+fbbbzVv3jxlZGRUQIeVpyxz89NPP2nt2rXq27evVq1apR9++EF/+9vfdP78eY0fP74i2q4QZZmbv/71rzp+/Lg6duwowzB04cIFDR069JY81by0rvS7ODc3V2fPnpWHh0cldYY/gvI4xgFFRUUaOXKkOnTooObNm1d2OzeF3bt3y2az6dy5c6pRo4Y+/fRTBQcHV3ZblW7x4sXasWOHtm3bVtmt3HRCQ0OVlJSkxo0b69ixY3r11VfVqVMn7dmzRzVr1rR03wRv3BLefPNNLV68WOvWrZO7u3tlt1OpTp8+rX79+umDDz5Q3bp1K7udm05RUZF8fHz0/vvvy8XFRSEhIfrll1/09ttv31LBuyzWrVunN954Q3PmzFFoaKh++OEHjRgxQpMmTdIrr7xS2e0BwG0tOjpae/bsqbjvo/4BNG7cWBkZGcrJydEnn3yiyMhIrV+//rYO30ePHtWIESOUkpJy2/+fuCTdu3c3/9yyZUuFhoaqQYMGWrp0qeVniRK8K1DdunXl4uKirKwsh/VZWVny8/Mr8Tl+fn6lqv+jKsvcFHvnnXf05ptv6quvvrolLx5R2rn58ccfdeTIET322GPmuqKiIklSlSpVdPDgQd19993WNl1ByvJz4+/vr6pVq8rFxcVc17RpU9ntdhUUFMjV1dXSnitKWebmlVdeUb9+/TRo0CBJUosWLZSXl6chQ4bo5ZdflrPz7fvtpCv9Lvb09OTTblzTjRzjAEmKiYnRihUrtGHDBtWrV6+y27lpuLq6qlGjRpKkkJAQbdu2TTNnztR7771XyZ1VnvT0dGVnZ6tNmzbmusLCQm3YsEGzZ89Wfn6+w/+Bbnfe3t6699579cMPP1i+r9v3f1GVwNXVVSEhIUpNTTXXFRUVKTU19YrfR7HZbA71kpSSknLLfX+lLHMjSVOmTNGkSZOUnJystm3bVkSrFa60c9OkSRPt3r1bGRkZ5vJf//Vf5tWYAwMDK7J9S5Xl56ZDhw764YcfzDcjJOn777+Xv7//LRO6pbLNze+//35ZuC4+OBuGYV2zfwC3y+9iWKOsxzjAMAzFxMTo008/1dq1axUUFFTZLd3UioqKlJ+fX9ltVKqHHnrosv8Htm3bVn379lVGRgah+xJnzpzRjz/+KH9/f+t3VimXdLuNLV682HBzczOSkpKMffv2GUOGDDG8vb0Nu91uGIZh9OvXzxg7dqxZv3HjRqNKlSrGO++8Y+zfv98YP368UbVqVWP37t2V9RIsU9q5efPNNw1XV1fjk08+MY4dO2Yup0+frqyXYJnSzs2lbuWrmpd2bjIzM42aNWsaMTExxsGDB40VK1YYPj4+xmuvvVZZL8EypZ2b8ePHGzVr1jT+53/+x/jpp5+MNWvWGHfffbfxl7/8pbJegmVOnz5t7Ny509i5c6chyZg2bZqxc+dO41//+pdhGIYxduxYo1+/fmb9Tz/9ZFSrVs0YPXq0sX//fiMhIcFwcXExkpOTK+sl4A/mWv8eb2fX+vd4Oxs2bJjh5eVlrFu3zuH/Or///ntlt1bpxo4da6xfv944fPiwsWvXLmPs2LGGk5OTsWbNmspu7abDVc3/z9///ndj3bp1xuHDh42NGzcaYWFhRt26dY3s7GzL903wrgTvvvuuUb9+fcPV1dVo166dsXnzZnPsgQceMCIjIx3qly5datx7772Gq6ur0axZM2PlypUV3HHFKc3cNGjQwJB02TJ+/PiKb7wClPbn5mK3cvA2jNLPzaZNm4zQ0FDDzc3NuOuuu4zXX3/duHDhQgV3XTFKMzfnz583JkyYYNx9992Gu7u7ERgYaPztb38zTp48WfGNW+zrr78u8fdH8XxERkYaDzzwwGXPad26teHq6mrcddddxoIFCyq8b/yxXe3f4+3sWv8eb2clzYskfv8YhjFw4ECjQYMGhqurq3HHHXcYDz30EKH7Cgje/6d3796Gv7+/4erqatx5551G7969jR9++KFC9u1kGLf5+YMAAAAAAFiI73gDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAOocJ07d9bIkSMruw0AACrVunXr5OTkpFOnTlV2KyW62fsD/kgI3gAAAEAFuPSN5/vuu0/Hjh2Tl5dX5TV1FZXV35EjR+Tk5KSMjIwK3S9gJYI3AAAAUAlcXV3l5+cnJyenym6lRDd7f9ejoKCgslsAJBG8AVykc+fOGj58uEaOHKlatWrJ19dXH3zwgfLy8jRgwADVrFlTjRo10pdffmk+Z/369WrXrp3c3Nzk7++vsWPH6sKFC+Z4Xl6e+vfvrxo1asjf319Tp069bL/5+fl64YUXdOedd6p69eoKDQ3VunXrKuIlAwBQIZ599lmtX79eM2fOlJOTk5ycnJSUlORwKndSUpK8vb21YsUKNW7cWNWqVdNTTz2l33//XQsXLlTDhg1Vq1YtPf/88yosLDS3fSPH0X/961967LHHVKtWLVWvXl3NmjXTqlWrJJV8qvkHH3ygwMBAVatWTU888YSmTZsmb29vc3zChAlq3bq1/vu//1sNGzaUl5eX+vTpo9OnT5s1ycnJ6tixo7y9vVWnTh09+uij+vHHH83xoKAgSdKf/vQnOTk5qXPnzpJK/qpajx499Oyzz5qPGzZsqEmTJql///7y9PTUkCFDJEnffvutOnXqJA8PDwUGBur5559XXl7edc0RUB4I3gAcLFy4UHXr1tXWrVs1fPhwDRs2TL169dJ9992nHTt2qGvXrurXr59+//13/fLLL3rkkUf05z//Wd99953mzp2refPm6bXXXjO3N3r0aK1fv16fffaZ1qxZo3Xr1mnHjh0O+4yJiVFaWpoWL16sXbt2qVevXurWrZsOHTpU0S8fAABLzJw5UzabTYMHD9axY8d07NgxBQYGXlb3+++/a9asWVq8eLGSk5O1bt06PfHEE1q1apVWrVql//7v/9Z7772nTz75xHzOjRxHo6OjlZ+frw0bNmj37t166623VKNGjRJrN27cqKFDh2rEiBHKyMjQww8/rNdff/2yuh9//FHLly/XihUrtGLFCq1fv15vvvmmOZ6Xl6fY2Fht375dqampcnZ21hNPPKGioiJJ0tatWyVJX331lY4dO6Z//vOf13wdF3vnnXfUqlUr7dy5U6+88op+/PFHdevWTT179tSuXbu0ZMkSffvtt4qJiSnVdoEbYgDA//fAAw8YHTt2NB9fuHDBqF69utGvXz9z3bFjxwxJRlpamvHSSy8ZjRs3NoqKiszxhIQEo0aNGkZhYaFx+vRpw9XV1Vi6dKk5/u9//9vw8PAwRowYYRiGYfzrX/8yXFxcjF9++cWhl4ceesiIi4uz6JUCAFDxHnjgAfP4ZxiG8fXXXxuSjJMnTxqGYRgLFiwwJBk//PCDWfPcc88Z1apVM06fPm2uCw8PN5577jnDMG78ONqiRQtjwoQJJY5d2l/v3r2NiIgIh5q+ffsaXl5e5uPx48cb1apVM3Jzc811o0ePNkJDQ6/Yw2+//WZIMnbv3m0YhmEcPnzYkGTs3LnToe7S+TMMw3j88ceNyMhI83GDBg2MHj16ONRERUUZQ4YMcVj3zTffGM7OzsbZs2ev2BdQnqpUZugHcPNp2bKl+WcXFxfVqVNHLVq0MNf5+vpKkrKzs7V//37ZbDaH73516NBBZ86c0c8//6yTJ0+qoKBAoaGh5njt2rXVuHFj8/Hu3btVWFioe++916GP/Px81alTp9xfHwAAN7Nq1arp7rvvNh/7+vqqYcOGDp9C+/r6Kjs7W9KNH0eff/55DRs2TGvWrFFYWJh69uzp8H+Bix08eFBPPPGEw7p27dppxYoVDusaNmyomjVrmo/9/f3NfiXp0KFDio+P15YtW3T8+HHzk+7MzEw1b978mj1fS9u2bR0ef/fdd9q1a5c+/vhjc51hGCoqKtLhw4fVtGnTG94ncC0EbwAOqlat6vDYycnJYV1xyC4+SN6oM2fOyMXFRenp6XJxcXEYu9KpbgAA3KqudRwuXld8HL7R4+igQYMUHh6ulStXas2aNZo8ebKmTp2q4cOHl+truPj/DY899pgaNGigDz74QAEBASoqKlLz5s2veSE0Z2dnGYbhsO78+fOX1VWvXt3h8ZkzZ/Tcc8/p+eefv6y2fv3613w9QHkgeAMos6ZNm+p///d/ZRiGGcg3btyomjVrql69eqpdu7aqVq2qLVu2mAe2kydP6vvvv9cDDzwg6T8XTiksLFR2drY6depUaa8FAACrubq6OlwUrTyUx3E0MDBQQ4cO1dChQxUXF6cPPvigxODduHFjbdu2zWHdpY+v5d///rcOHjyoDz74wOz322+/dahxdXWVpMvm6o477tCxY8fMx4WFhdqzZ4+6dOly1X22adNG+/btU6NGjUrVK1CeuLgagDL729/+pqNHj2r48OE6cOCAPvvsM40fP16xsbFydnZWjRo1FBUVpdGjR2vt2rXas2ePnn32WTk7/9+vnnvvvVd9+/ZV//799c9//lOHDx/W1q1bNXnyZK1cubISXx0AAOWrYcOG2rJli44cOeJwivWNuNHj6MiRI7V69WodPnxYO3bs0Ndff33FU6+HDx+uVatWadq0aTp06JDee+89ffnll6W63VitWrVUp04dvf/++/rhhx+0du1axcbGOtT4+PjIw8NDycnJysrKUk5OjiTpwQcf1MqVK7Vy5UodOHBAw4YNc7ji+pW8+OKL2rRpk2JiYpSRkaFDhw7ps88+4+JqqFAEbwBlduedd2rVqlXaunWrWrVqpaFDhyoqKkrjxo0za95++2116tRJjz32mMLCwtSxY0eFhIQ4bGfBggXq37+//v73v6tx48bq0aOHtm3bxulfAIBbygsvvCAXFxcFBwfrjjvuUGZmZrls90aOo4WFhYqOjlbTpk3VrVs33XvvvZozZ06JtR06dFBiYqKmTZumVq1aKTk5WaNGjZK7u/t19+rs7KzFixcrPT1dzZs316hRo/T222871FSpUkWzZs3Se++9p4CAAD3++OOSpIEDByoyMlL9+/fXAw88oLvuuuuan3ZL/7l+zfr16/X999+rU6dO+tOf/qT4+HgFBARcd9/AjXIyLv2iBAAAAABch8GDB+vAgQP65ptvKrsV4KbGd7wBAAAAXJd33nlHDz/8sKpXr64vv/xSCxcuvOIn5AD+D6eaAwAAALeo7t27q0aNGiUub7zxRqm3t3XrVj388MNq0aKFEhMTNWvWLA0aNMiCzoFbC6eaAwAAALeoX375RWfPni1xrHbt2qpdu3YFdwTcngjeAAAAAABYiFPNAQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACw0P8D7J8/TZqScNgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "for i, feature in enumerate(categorical_features.columns, 1): \n",
    "    ax = plt.subplot(2, 2, i)\n",
    "    sns.histplot(data=categorical_features, x=categorical_features[feature])\n",
    "    ax.set_title(f'Distribution of {feature}')\n",
    "    plt.tight_layout(w_pad=3) # adds padding between the subplots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 11 numerical features\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></th>\n",
       "      <th>popularity</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>89740.000000</td>\n",
       "      <td>8.974000e+04</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>33.198808</td>\n",
       "      <td>2.291444e+05</td>\n",
       "      <td>0.562166</td>\n",
       "      <td>0.634458</td>\n",
       "      <td>-8.498994</td>\n",
       "      <td>0.087442</td>\n",
       "      <td>0.328285</td>\n",
       "      <td>0.173415</td>\n",
       "      <td>0.216971</td>\n",
       "      <td>0.469474</td>\n",
       "      <td>122.058134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20.580640</td>\n",
       "      <td>1.129458e+05</td>\n",
       "      <td>0.176692</td>\n",
       "      <td>0.256606</td>\n",
       "      <td>5.221518</td>\n",
       "      <td>0.113278</td>\n",
       "      <td>0.338321</td>\n",
       "      <td>0.323849</td>\n",
       "      <td>0.194885</td>\n",
       "      <td>0.262864</td>\n",
       "      <td>30.117651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.586000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-49.531000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.730400e+05</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.457000</td>\n",
       "      <td>-10.322250</td>\n",
       "      <td>0.036000</td>\n",
       "      <td>0.017100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.098200</td>\n",
       "      <td>0.249000</td>\n",
       "      <td>99.262750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.132955e+05</td>\n",
       "      <td>0.576000</td>\n",
       "      <td>0.676000</td>\n",
       "      <td>-7.185000</td>\n",
       "      <td>0.048900</td>\n",
       "      <td>0.188000</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.132000</td>\n",
       "      <td>0.457000</td>\n",
       "      <td>122.013000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>2.642930e+05</td>\n",
       "      <td>0.692000</td>\n",
       "      <td>0.853000</td>\n",
       "      <td>-5.108000</td>\n",
       "      <td>0.085900</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.097625</td>\n",
       "      <td>0.279000</td>\n",
       "      <td>0.682000</td>\n",
       "      <td>140.077000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>5.237295e+06</td>\n",
       "      <td>0.985000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.532000</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>0.996000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>243.372000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         popularity   duration_ms  danceability        energy      loudness  \\\n",
       "count  89740.000000  8.974000e+04  89740.000000  89740.000000  89740.000000   \n",
       "mean      33.198808  2.291444e+05      0.562166      0.634458     -8.498994   \n",
       "std       20.580640  1.129458e+05      0.176692      0.256606      5.221518   \n",
       "min        0.000000  8.586000e+03      0.000000      0.000000    -49.531000   \n",
       "25%       19.000000  1.730400e+05      0.450000      0.457000    -10.322250   \n",
       "50%       33.000000  2.132955e+05      0.576000      0.676000     -7.185000   \n",
       "75%       49.000000  2.642930e+05      0.692000      0.853000     -5.108000   \n",
       "max      100.000000  5.237295e+06      0.985000      1.000000      4.532000   \n",
       "\n",
       "        speechiness  acousticness  instrumentalness      liveness  \\\n",
       "count  89740.000000  89740.000000      89740.000000  89740.000000   \n",
       "mean       0.087442      0.328285          0.173415      0.216971   \n",
       "std        0.113278      0.338321          0.323849      0.194885   \n",
       "min        0.000000      0.000000          0.000000      0.000000   \n",
       "25%        0.036000      0.017100          0.000000      0.098200   \n",
       "50%        0.048900      0.188000          0.000058      0.132000   \n",
       "75%        0.085900      0.625000          0.097625      0.279000   \n",
       "max        0.965000      0.996000          1.000000      1.000000   \n",
       "\n",
       "            valence         tempo  \n",
       "count  89740.000000  89740.000000  \n",
       "mean       0.469474    122.058134  \n",
       "std        0.262864     30.117651  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.249000     99.262750  \n",
       "50%        0.457000    122.013000  \n",
       "75%        0.682000    140.077000  \n",
       "max        0.995000    243.372000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_features = songs.select_dtypes(include='number')\n",
    "shared_features = set(categorical_features.columns).intersection(numerical_features.columns)\n",
    "numerical_features.drop(shared_features, axis=1, inplace=True)\n",
    "print(f\"There are {len(numerical_features.columns)} numerical features\")\n",
    "numerical_features.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['popularity', 'duration_min', 'danceability', 'energy', 'loudness',\n",
       "       'speechiness', 'acousticness', 'instrumentalness', 'liveness',\n",
       "       'valence', 'tempo'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_features.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting duration to minutes so that it is easier to interpret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>popularity</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "      <td>89740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>33.198808</td>\n",
       "      <td>3.819073</td>\n",
       "      <td>0.562166</td>\n",
       "      <td>0.634458</td>\n",
       "      <td>-8.498994</td>\n",
       "      <td>0.087442</td>\n",
       "      <td>0.328285</td>\n",
       "      <td>0.173415</td>\n",
       "      <td>0.216971</td>\n",
       "      <td>0.469474</td>\n",
       "      <td>122.058134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20.580640</td>\n",
       "      <td>1.882430</td>\n",
       "      <td>0.176692</td>\n",
       "      <td>0.256606</td>\n",
       "      <td>5.221518</td>\n",
       "      <td>0.113278</td>\n",
       "      <td>0.338321</td>\n",
       "      <td>0.323849</td>\n",
       "      <td>0.194885</td>\n",
       "      <td>0.262864</td>\n",
       "      <td>30.117651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.143100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-49.531000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.884000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.457000</td>\n",
       "      <td>-10.322250</td>\n",
       "      <td>0.036000</td>\n",
       "      <td>0.017100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.098200</td>\n",
       "      <td>0.249000</td>\n",
       "      <td>99.262750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>3.554925</td>\n",
       "      <td>0.576000</td>\n",
       "      <td>0.676000</td>\n",
       "      <td>-7.185000</td>\n",
       "      <td>0.048900</td>\n",
       "      <td>0.188000</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.132000</td>\n",
       "      <td>0.457000</td>\n",
       "      <td>122.013000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>4.404883</td>\n",
       "      <td>0.692000</td>\n",
       "      <td>0.853000</td>\n",
       "      <td>-5.108000</td>\n",
       "      <td>0.085900</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.097625</td>\n",
       "      <td>0.279000</td>\n",
       "      <td>0.682000</td>\n",
       "      <td>140.077000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>87.288250</td>\n",
       "      <td>0.985000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.532000</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>0.996000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>243.372000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         popularity  duration_min  danceability        energy      loudness  \\\n",
       "count  89740.000000  89740.000000  89740.000000  89740.000000  89740.000000   \n",
       "mean      33.198808      3.819073      0.562166      0.634458     -8.498994   \n",
       "std       20.580640      1.882430      0.176692      0.256606      5.221518   \n",
       "min        0.000000      0.143100      0.000000      0.000000    -49.531000   \n",
       "25%       19.000000      2.884000      0.450000      0.457000    -10.322250   \n",
       "50%       33.000000      3.554925      0.576000      0.676000     -7.185000   \n",
       "75%       49.000000      4.404883      0.692000      0.853000     -5.108000   \n",
       "max      100.000000     87.288250      0.985000      1.000000      4.532000   \n",
       "\n",
       "        speechiness  acousticness  instrumentalness      liveness  \\\n",
       "count  89740.000000  89740.000000      89740.000000  89740.000000   \n",
       "mean       0.087442      0.328285          0.173415      0.216971   \n",
       "std        0.113278      0.338321          0.323849      0.194885   \n",
       "min        0.000000      0.000000          0.000000      0.000000   \n",
       "25%        0.036000      0.017100          0.000000      0.098200   \n",
       "50%        0.048900      0.188000          0.000058      0.132000   \n",
       "75%        0.085900      0.625000          0.097625      0.279000   \n",
       "max        0.965000      0.996000          1.000000      1.000000   \n",
       "\n",
       "            valence         tempo  \n",
       "count  89740.000000  89740.000000  \n",
       "mean       0.469474    122.058134  \n",
       "std        0.262864     30.117651  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.249000     99.262750  \n",
       "50%        0.457000    122.013000  \n",
       "75%        0.682000    140.077000  \n",
       "max        0.995000    243.372000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_features['duration_ms'] = numerical_features['duration_ms'] / (1000 * 60)\n",
    "numerical_features.rename(columns={'duration_ms': 'duration_min'}, inplace=True)\n",
    "# Apply it also to the original dataset\n",
    "songs['duration_ms'] = songs['duration_ms'] / (1000 * 60)\n",
    "songs.rename(columns={'duration_ms': 'duration_min'}, inplace=True)\n",
    "numerical_features.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features we could consider normalising:\n",
    "* duration_min\n",
    "* loudness\n",
    "* tempo\n",
    "* popularity? (not sure about this one but since almost all numerical features are in a range between 0-1 it could make sense to normalize it)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding outliers\n",
    "\n",
    "* The tree algorithms and SVMs are robust to outliers but Naive Bayes and Logistic Regression might perform better if we deal with outliers\n",
    "\n",
    "Some features that might have outliers:\n",
    "* The average song duration is 3.85 min but there is a song in the dataset that lasts around 87 min\n",
    "* Speechiness has a mean of 0.088 but the max speechiness is 0.965\n",
    "\n",
    "We can find more outliers by visualizing the values of the features with a box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 12))\n",
    "for i, feature in enumerate(numerical_features.columns, 1): \n",
    "    ax = plt.subplot(4, 3, i)\n",
    "    sns.boxplot(data=numerical_features, x=numerical_features[feature])\n",
    "    ax.set_xlabel(None)\n",
    "    ax.set_title(f'Distribution of {feature}')\n",
    "    plt.tight_layout(w_pad=3) # adds padding between the subplots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 12))\n",
    "for i, feature in enumerate(numerical_features.columns, 1): \n",
    "    ax = plt.subplot(4, 3, i)\n",
    "    sns.histplot(data=numerical_features, x=numerical_features[feature])\n",
    "    ax.set_xlabel(None)\n",
    "    ax.set_title(f'Histogram of {feature}')\n",
    "    plt.tight_layout(w_pad=3) # adds padding between the subplots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From looking at the box plots, almost all features have outliers.\n",
    "\n",
    "The only ones without outliers are: \n",
    "* Valence\n",
    "* Acousticness\n",
    "* Energy\n",
    "\n",
    "### What to do with the outliers?\n",
    "\n",
    "* We can discuss this together or ask the tutors :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding redundant variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>popularity</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>popularity</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.023157</td>\n",
       "      <td>0.064275</td>\n",
       "      <td>0.013725</td>\n",
       "      <td>0.071674</td>\n",
       "      <td>-0.047086</td>\n",
       "      <td>-0.038828</td>\n",
       "      <td>-0.127477</td>\n",
       "      <td>-0.013857</td>\n",
       "      <td>-0.011508</td>\n",
       "      <td>0.007280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_min</th>\n",
       "      <td>-0.023157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.064139</td>\n",
       "      <td>0.063258</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>-0.060751</td>\n",
       "      <td>-0.108478</td>\n",
       "      <td>0.117372</td>\n",
       "      <td>0.008109</td>\n",
       "      <td>-0.147018</td>\n",
       "      <td>0.026004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>danceability</th>\n",
       "      <td>0.064275</td>\n",
       "      <td>-0.064139</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.143913</td>\n",
       "      <td>0.271838</td>\n",
       "      <td>0.109519</td>\n",
       "      <td>-0.177040</td>\n",
       "      <td>-0.194750</td>\n",
       "      <td>-0.132237</td>\n",
       "      <td>0.492578</td>\n",
       "      <td>-0.021329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>energy</th>\n",
       "      <td>0.013725</td>\n",
       "      <td>0.063258</td>\n",
       "      <td>0.143913</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.758774</td>\n",
       "      <td>0.141332</td>\n",
       "      <td>-0.732569</td>\n",
       "      <td>-0.181038</td>\n",
       "      <td>0.188940</td>\n",
       "      <td>0.256317</td>\n",
       "      <td>0.258629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loudness</th>\n",
       "      <td>0.071674</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>0.271838</td>\n",
       "      <td>0.758774</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.055295</td>\n",
       "      <td>-0.582664</td>\n",
       "      <td>-0.434719</td>\n",
       "      <td>0.077238</td>\n",
       "      <td>0.289095</td>\n",
       "      <td>0.228660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>speechiness</th>\n",
       "      <td>-0.047086</td>\n",
       "      <td>-0.060751</td>\n",
       "      <td>0.109519</td>\n",
       "      <td>0.141332</td>\n",
       "      <td>0.055295</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005124</td>\n",
       "      <td>-0.099659</td>\n",
       "      <td>0.225100</td>\n",
       "      <td>0.032935</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acousticness</th>\n",
       "      <td>-0.038828</td>\n",
       "      <td>-0.108478</td>\n",
       "      <td>-0.177040</td>\n",
       "      <td>-0.732569</td>\n",
       "      <td>-0.582664</td>\n",
       "      <td>0.005124</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.096072</td>\n",
       "      <td>-0.019073</td>\n",
       "      <td>-0.103781</td>\n",
       "      <td>-0.220252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instrumentalness</th>\n",
       "      <td>-0.127477</td>\n",
       "      <td>0.117372</td>\n",
       "      <td>-0.194750</td>\n",
       "      <td>-0.181038</td>\n",
       "      <td>-0.434719</td>\n",
       "      <td>-0.099659</td>\n",
       "      <td>0.096072</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.087157</td>\n",
       "      <td>-0.330756</td>\n",
       "      <td>-0.056885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>liveness</th>\n",
       "      <td>-0.013857</td>\n",
       "      <td>0.008109</td>\n",
       "      <td>-0.132237</td>\n",
       "      <td>0.188940</td>\n",
       "      <td>0.077238</td>\n",
       "      <td>0.225100</td>\n",
       "      <td>-0.019073</td>\n",
       "      <td>-0.087157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012322</td>\n",
       "      <td>-0.006113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valence</th>\n",
       "      <td>-0.011508</td>\n",
       "      <td>-0.147018</td>\n",
       "      <td>0.492578</td>\n",
       "      <td>0.256317</td>\n",
       "      <td>0.289095</td>\n",
       "      <td>0.032935</td>\n",
       "      <td>-0.103781</td>\n",
       "      <td>-0.330756</td>\n",
       "      <td>0.012322</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.090617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tempo</th>\n",
       "      <td>0.007280</td>\n",
       "      <td>0.026004</td>\n",
       "      <td>-0.021329</td>\n",
       "      <td>0.258629</td>\n",
       "      <td>0.228660</td>\n",
       "      <td>0.004033</td>\n",
       "      <td>-0.220252</td>\n",
       "      <td>-0.056885</td>\n",
       "      <td>-0.006113</td>\n",
       "      <td>0.090617</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  popularity  duration_min  danceability    energy  loudness  \\\n",
       "popularity          1.000000     -0.023157      0.064275  0.013725  0.071674   \n",
       "duration_min       -0.023157      1.000000     -0.064139  0.063258  0.000360   \n",
       "danceability        0.064275     -0.064139      1.000000  0.143913  0.271838   \n",
       "energy              0.013725      0.063258      0.143913  1.000000  0.758774   \n",
       "loudness            0.071674      0.000360      0.271838  0.758774  1.000000   \n",
       "speechiness        -0.047086     -0.060751      0.109519  0.141332  0.055295   \n",
       "acousticness       -0.038828     -0.108478     -0.177040 -0.732569 -0.582664   \n",
       "instrumentalness   -0.127477      0.117372     -0.194750 -0.181038 -0.434719   \n",
       "liveness           -0.013857      0.008109     -0.132237  0.188940  0.077238   \n",
       "valence            -0.011508     -0.147018      0.492578  0.256317  0.289095   \n",
       "tempo               0.007280      0.026004     -0.021329  0.258629  0.228660   \n",
       "\n",
       "                  speechiness  acousticness  instrumentalness  liveness  \\\n",
       "popularity          -0.047086     -0.038828         -0.127477 -0.013857   \n",
       "duration_min        -0.060751     -0.108478          0.117372  0.008109   \n",
       "danceability         0.109519     -0.177040         -0.194750 -0.132237   \n",
       "energy               0.141332     -0.732569         -0.181038  0.188940   \n",
       "loudness             0.055295     -0.582664         -0.434719  0.077238   \n",
       "speechiness          1.000000      0.005124         -0.099659  0.225100   \n",
       "acousticness         0.005124      1.000000          0.096072 -0.019073   \n",
       "instrumentalness    -0.099659      0.096072          1.000000 -0.087157   \n",
       "liveness             0.225100     -0.019073         -0.087157  1.000000   \n",
       "valence              0.032935     -0.103781         -0.330756  0.012322   \n",
       "tempo                0.004033     -0.220252         -0.056885 -0.006113   \n",
       "\n",
       "                   valence     tempo  \n",
       "popularity       -0.011508  0.007280  \n",
       "duration_min     -0.147018  0.026004  \n",
       "danceability      0.492578 -0.021329  \n",
       "energy            0.256317  0.258629  \n",
       "loudness          0.289095  0.228660  \n",
       "speechiness       0.032935  0.004033  \n",
       "acousticness     -0.103781 -0.220252  \n",
       "instrumentalness -0.330756 -0.056885  \n",
       "liveness          0.012322 -0.006113  \n",
       "valence           1.000000  0.090617  \n",
       "tempo             0.090617  1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_features.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Highly correlated features\n",
    "\n",
    "- loudness <-> energy (0.76)\n",
    "- acousticness <-> energy (-0.73)\n",
    "- loudness <-> acousticness (-0.58)\n",
    "- valence <-> danceability (0.49)\n",
    "- loudness <-> instrumentallness (-0.43)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring string features\n",
    "* We could apply some preprocessing techniques we will learn on the _Text Mining_ lecture (this week 18.10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 31437 artists in the dataset\n",
      "There are 46589 albums in the dataset\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></th>\n",
       "      <th>artists</th>\n",
       "      <th>album_name</th>\n",
       "      <th>track_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gen Hoshino</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ben Woodward</td>\n",
       "      <td>Ghost (Acoustic)</td>\n",
       "      <td>Ghost - Acoustic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ingrid Michaelson;ZAYN</td>\n",
       "      <td>To Begin Again</td>\n",
       "      <td>To Begin Again</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kina Grannis</td>\n",
       "      <td>Crazy Rich Asians (Original Motion Picture Sou...</td>\n",
       "      <td>Can't Help Falling In Love</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chord Overstreet</td>\n",
       "      <td>Hold On</td>\n",
       "      <td>Hold On</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  artists                                         album_name  \\\n",
       "0             Gen Hoshino                                             Comedy   \n",
       "1            Ben Woodward                                   Ghost (Acoustic)   \n",
       "2  Ingrid Michaelson;ZAYN                                     To Begin Again   \n",
       "3            Kina Grannis  Crazy Rich Asians (Original Motion Picture Sou...   \n",
       "4        Chord Overstreet                                            Hold On   \n",
       "\n",
       "                   track_name  \n",
       "0                      Comedy  \n",
       "1            Ghost - Acoustic  \n",
       "2              To Begin Again  \n",
       "3  Can't Help Falling In Love  \n",
       "4                     Hold On  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_features = songs[['artists', 'album_name', 'track_name']]\n",
    "# This number is inaccurate because some tracks have lists of artists separated by ;\n",
    "print(f\"There are {len(songs['artists'].unique())} artists in the dataset\")\n",
    "print(f\"There are {len(songs['album_name'].unique())} albums in the dataset\")\n",
    "string_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring class distribution\n",
    "\n",
    "* Check balance of the song genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 113 unique genres\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count     113.000000\n",
       "mean      794.159292\n",
       "std       226.234493\n",
       "min        74.000000\n",
       "25%       709.000000\n",
       "50%       873.000000\n",
       "75%       974.000000\n",
       "max      1000.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"There are {len(songs['track_genre'].unique())} unique genres\")\n",
    "grouped_by_genre = songs.groupby(['track_genre']).size()\n",
    "grouped_by_genre.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "995"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_by_genre.to_dict()['anime']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class balance\n",
    "\n",
    "* The classes are imbalanced (to be expected because of the large number of classes)\n",
    "* We could consider groupping music genres together by the parent genre\n",
    "    * This could simplify our classification task\n",
    "    * For example: reggaeton (63 songs) and latino (332 songs) could be groupped together to form a bigger class\n",
    "* Some genres I found to be a bit weird:\n",
    "    * singer-songwriter\n",
    "    * comedy\n",
    "    * language genres (german, swedish, french, spanish, iranian, turkish)\n",
    "    * study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "track_genre\n",
       "reggaeton      74\n",
       "indie         134\n",
       "house         210\n",
       "punk          226\n",
       "metal         232\n",
       "             ... \n",
       "ambient       999\n",
       "alt-rock      999\n",
       "afrobeat      999\n",
       "cantopop      999\n",
       "acoustic     1000\n",
       "Length: 113, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_by_genre.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>track_genre</th>\n",
       "      <th>popularity</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>acoustic</td>\n",
       "      <td>42.483000</td>\n",
       "      <td>3.581616</td>\n",
       "      <td>0.549593</td>\n",
       "      <td>0.435368</td>\n",
       "      <td>5.045000</td>\n",
       "      <td>-9.447843</td>\n",
       "      <td>0.816000</td>\n",
       "      <td>0.043247</td>\n",
       "      <td>0.566816</td>\n",
       "      <td>0.038336</td>\n",
       "      <td>0.153244</td>\n",
       "      <td>0.424023</td>\n",
       "      <td>119.010624</td>\n",
       "      <td>3.885000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>afrobeat</td>\n",
       "      <td>24.407407</td>\n",
       "      <td>4.140698</td>\n",
       "      <td>0.669408</td>\n",
       "      <td>0.702938</td>\n",
       "      <td>5.556557</td>\n",
       "      <td>-7.789599</td>\n",
       "      <td>0.510511</td>\n",
       "      <td>0.086622</td>\n",
       "      <td>0.270893</td>\n",
       "      <td>0.252876</td>\n",
       "      <td>0.184724</td>\n",
       "      <td>0.698475</td>\n",
       "      <td>119.242057</td>\n",
       "      <td>3.945946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alt-rock</td>\n",
       "      <td>33.896897</td>\n",
       "      <td>3.922610</td>\n",
       "      <td>0.534601</td>\n",
       "      <td>0.754027</td>\n",
       "      <td>5.732733</td>\n",
       "      <td>-6.194009</td>\n",
       "      <td>0.640641</td>\n",
       "      <td>0.055076</td>\n",
       "      <td>0.122168</td>\n",
       "      <td>0.054151</td>\n",
       "      <td>0.210340</td>\n",
       "      <td>0.518179</td>\n",
       "      <td>124.654403</td>\n",
       "      <td>3.938939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>alternative</td>\n",
       "      <td>22.218673</td>\n",
       "      <td>3.443440</td>\n",
       "      <td>0.595523</td>\n",
       "      <td>0.684523</td>\n",
       "      <td>5.066339</td>\n",
       "      <td>-6.087376</td>\n",
       "      <td>0.511057</td>\n",
       "      <td>0.093618</td>\n",
       "      <td>0.185147</td>\n",
       "      <td>0.017173</td>\n",
       "      <td>0.209104</td>\n",
       "      <td>0.476821</td>\n",
       "      <td>119.748902</td>\n",
       "      <td>3.955774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ambient</td>\n",
       "      <td>44.208208</td>\n",
       "      <td>3.954176</td>\n",
       "      <td>0.367966</td>\n",
       "      <td>0.237257</td>\n",
       "      <td>4.949950</td>\n",
       "      <td>-18.596266</td>\n",
       "      <td>0.619620</td>\n",
       "      <td>0.041553</td>\n",
       "      <td>0.776701</td>\n",
       "      <td>0.675993</td>\n",
       "      <td>0.129298</td>\n",
       "      <td>0.167345</td>\n",
       "      <td>111.156413</td>\n",
       "      <td>3.653654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   track_genre  popularity  duration_min  danceability    energy       key  \\\n",
       "0     acoustic   42.483000      3.581616      0.549593  0.435368  5.045000   \n",
       "1     afrobeat   24.407407      4.140698      0.669408  0.702938  5.556557   \n",
       "2     alt-rock   33.896897      3.922610      0.534601  0.754027  5.732733   \n",
       "3  alternative   22.218673      3.443440      0.595523  0.684523  5.066339   \n",
       "4      ambient   44.208208      3.954176      0.367966  0.237257  4.949950   \n",
       "\n",
       "    loudness      mode  speechiness  acousticness  instrumentalness  liveness  \\\n",
       "0  -9.447843  0.816000     0.043247      0.566816          0.038336  0.153244   \n",
       "1  -7.789599  0.510511     0.086622      0.270893          0.252876  0.184724   \n",
       "2  -6.194009  0.640641     0.055076      0.122168          0.054151  0.210340   \n",
       "3  -6.087376  0.511057     0.093618      0.185147          0.017173  0.209104   \n",
       "4 -18.596266  0.619620     0.041553      0.776701          0.675993  0.129298   \n",
       "\n",
       "    valence       tempo  time_signature  \n",
       "0  0.424023  119.010624        3.885000  \n",
       "1  0.698475  119.242057        3.945946  \n",
       "2  0.518179  124.654403        3.938939  \n",
       "3  0.476821  119.748902        3.955774  \n",
       "4  0.167345  111.156413        3.653654  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_df = songs.groupby('track_genre').agg({\n",
    "    'popularity': 'mean',\n",
    "    'duration_min': 'mean',\n",
    "    'danceability': 'mean',\n",
    "    'energy': 'mean',\n",
    "    'key': 'mean',\n",
    "    'loudness': 'mean',\n",
    "    'mode': 'mean',\n",
    "    'speechiness': 'mean',\n",
    "    'acousticness': 'mean',\n",
    "    'instrumentalness': 'mean',\n",
    "    'liveness': 'mean',\n",
    "    'valence': 'mean',\n",
    "    'tempo': 'mean',\n",
    "    'time_signature': 'mean'\n",
    "}).reset_index()\n",
    "\n",
    "agg_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore aggregations to find more info about outliers\n",
    "- Speechiness outliers are almost all exclusively of the comedy class\n",
    "    - Average speechiness of 0.76 (second highest genre had only 0.26)\n",
    "    - Question: comedy genre are just standups/podcasts? Or are they songs?\n",
    "- Sleep's valence is close to 0\n",
    "- Sleep and comedy have the lowest average bpm\n",
    "    - Sleep is the genre with the most tempo == 0\n",
    "- Sleep is the genre with the lowest danceability\n",
    "- Emo and comedy are the genres with most explicit songs\n",
    "- Children and kids have the highest danceability (> 0.9)\n",
    "- Romance, piano and classical have the highest accousticness (> 0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "By popularity\n",
      "Top 5\n",
      "k-pop  -  59.42\n",
      "pop-film  -  59.10\n",
      "metal  -  56.42\n",
      "chill  -  53.74\n",
      "latino  -  51.79\n",
      "Bottom 5\n",
      "detroit-techno  -  11.13\n",
      "latin  -  9.86\n",
      "jazz  -  9.79\n",
      "romance  -  3.55\n",
      "iranian  -  2.22\n",
      "------------------\n",
      "By duration_min\n",
      "Top 5\n",
      "detroit-techno  -  6.21\n",
      "minimal-techno  -  6.19\n",
      "chicago-house  -  6.11\n",
      "iranian  -  5.33\n",
      "techno  -  5.31\n",
      "Bottom 5\n",
      "disney  -  2.60\n",
      "sad  -  2.54\n",
      "study  -  2.36\n",
      "children  -  2.31\n",
      "grindcore  -  2.21\n",
      "------------------\n",
      "By danceability\n",
      "Top 5\n",
      "kids  -  0.78\n",
      "chicago-house  -  0.77\n",
      "latino  -  0.76\n",
      "reggaeton  -  0.74\n",
      "minimal-techno  -  0.73\n",
      "Bottom 5\n",
      "opera  -  0.31\n",
      "iranian  -  0.30\n",
      "black-metal  -  0.30\n",
      "grindcore  -  0.27\n",
      "sleep  -  0.17\n",
      "------------------\n",
      "By energy\n",
      "Top 5\n",
      "death-metal  -  0.93\n",
      "grindcore  -  0.93\n",
      "happy  -  0.91\n",
      "metalcore  -  0.90\n",
      "hardstyle  -  0.90\n",
      "Bottom 5\n",
      "romance  -  0.30\n",
      "opera  -  0.30\n",
      "ambient  -  0.24\n",
      "new-age  -  0.21\n",
      "classical  -  0.20\n",
      "------------------\n",
      "By key\n",
      "Top 5\n",
      "edm  -  5.91\n",
      "chicago-house  -  5.83\n",
      "detroit-techno  -  5.82\n",
      "happy  -  5.75\n",
      "indie  -  5.75\n",
      "Bottom 5\n",
      "new-age  -  4.66\n",
      "children  -  4.55\n",
      "soul  -  4.52\n",
      "disney  -  4.44\n",
      "jazz  -  4.30\n",
      "------------------\n",
      "By loudness\n",
      "Top 5\n",
      "j-idol  -  -3.78\n",
      "drum-and-bass  -  -4.17\n",
      "hardstyle  -  -4.28\n",
      "party  -  -4.46\n",
      "metalcore  -  -4.60\n",
      "Bottom 5\n",
      "piano  -  -16.85\n",
      "new-age  -  -18.53\n",
      "ambient  -  -18.60\n",
      "classical  -  -19.92\n",
      "sleep  -  -24.19\n",
      "------------------\n",
      "By mode\n",
      "Top 5\n",
      "honky-tonk  -  0.96\n",
      "country  -  0.89\n",
      "children  -  0.87\n",
      "world-music  -  0.86\n",
      "sertanejo  -  0.85\n",
      "Bottom 5\n",
      "deep-house  -  0.47\n",
      "drum-and-bass  -  0.47\n",
      "hardstyle  -  0.46\n",
      "romance  -  0.35\n",
      "turkish  -  0.33\n",
      "------------------\n",
      "By speechiness\n",
      "Top 5\n",
      "comedy  -  0.76\n",
      "j-dance  -  0.21\n",
      "dancehall  -  0.19\n",
      "funk  -  0.17\n",
      "hardcore  -  0.16\n",
      "Bottom 5\n",
      "gospel  -  0.04\n",
      "mandopop  -  0.04\n",
      "cantopop  -  0.04\n",
      "bluegrass  -  0.04\n",
      "honky-tonk  -  0.04\n",
      "------------------\n",
      "By acousticness\n",
      "Top 5\n",
      "classical  -  0.92\n",
      "romance  -  0.86\n",
      "tango  -  0.85\n",
      "opera  -  0.83\n",
      "new-age  -  0.83\n",
      "Bottom 5\n",
      "drum-and-bass  -  0.03\n",
      "heavy-metal  -  0.01\n",
      "metalcore  -  0.01\n",
      "grindcore  -  0.01\n",
      "death-metal  -  0.01\n",
      "------------------\n",
      "By instrumentalness\n",
      "Top 5\n",
      "study  -  0.79\n",
      "minimal-techno  -  0.75\n",
      "sleep  -  0.72\n",
      "new-age  -  0.71\n",
      "detroit-techno  -  0.70\n",
      "Bottom 5\n",
      "party  -  0.00\n",
      "reggaeton  -  0.00\n",
      "comedy  -  0.00\n",
      "sertanejo  -  0.00\n",
      "pagode  -  0.00\n",
      "------------------\n",
      "By liveness\n",
      "Top 5\n",
      "comedy  -  0.66\n",
      "pagode  -  0.55\n",
      "sertanejo  -  0.47\n",
      "samba  -  0.45\n",
      "sleep  -  0.36\n",
      "Bottom 5\n",
      "singer-songwriter  -  0.15\n",
      "piano  -  0.15\n",
      "chicago-house  -  0.14\n",
      "minimal-techno  -  0.14\n",
      "ambient  -  0.13\n",
      "------------------\n",
      "By valence\n",
      "Top 5\n",
      "salsa  -  0.81\n",
      "forro  -  0.76\n",
      "rockabilly  -  0.74\n",
      "ska  -  0.72\n",
      "samba  -  0.71\n",
      "Bottom 5\n",
      "black-metal  -  0.19\n",
      "new-age  -  0.18\n",
      "ambient  -  0.17\n",
      "iranian  -  0.15\n",
      "sleep  -  0.06\n",
      "------------------\n",
      "By tempo\n",
      "Top 5\n",
      "drum-and-bass  -  155.15\n",
      "happy  -  152.96\n",
      "hardstyle  -  146.69\n",
      "forro  -  140.35\n",
      "j-idol  -  136.08\n",
      "Bottom 5\n",
      "new-age  -  108.13\n",
      "classical  -  108.03\n",
      "opera  -  104.67\n",
      "comedy  -  100.81\n",
      "sleep  -  84.46\n",
      "------------------\n",
      "By time_signature\n",
      "Top 5\n",
      "dancehall  -  4.08\n",
      "j-dance  -  4.06\n",
      "funk  -  4.05\n",
      "reggae  -  4.01\n",
      "drum-and-bass  -  4.01\n",
      "Bottom 5\n",
      "romance  -  3.68\n",
      "opera  -  3.67\n",
      "ambient  -  3.65\n",
      "comedy  -  3.60\n",
      "sleep  -  3.13\n",
      "------------------\n"
     ]
    }
   ],
   "source": [
    "def print_aggregations(agg_df):\n",
    "    # Skip track_genre column\n",
    "    for column_name in agg_df.columns[1:]:\n",
    "        print(f\"By {column_name}\")\n",
    "        aggregation = agg_df.sort_values(by=column_name, ascending=False)[['track_genre', column_name]].to_numpy()\n",
    "        print(f\"Top 5\" )\n",
    "        for genre, agg_value in aggregation[0:5]:\n",
    "            print(f\"{genre}  -  {agg_value:.2f}\")\n",
    "        print(f\"Bottom 5\")\n",
    "        for genre, agg_value in aggregation[-5:]:\n",
    "            print(f\"{genre}  -  {agg_value:.2f}\")\n",
    "        print(\"------------------\")\n",
    "print_aggregations(agg_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "track_genre\n",
       "disney         1\n",
       "samba          1\n",
       "j-idol         1\n",
       "pop-film       1\n",
       "guitar         1\n",
       "            ... \n",
       "j-dance      289\n",
       "hardcore     291\n",
       "dancehall    295\n",
       "emo          447\n",
       "comedy       651\n",
       "Length: 103, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find genre with the most explicit songs\n",
    "explicit_filter = songs[songs['explicit'] == True]\n",
    "explicit_filter.groupby(['track_genre']).size().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tempo outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "track_genre\n",
       "indian                1\n",
       "study                 1\n",
       "pop-film              1\n",
       "pop                   1\n",
       "party                 1\n",
       "                     ..\n",
       "rock-n-roll          13\n",
       "singer-songwriter    16\n",
       "children             17\n",
       "rockabilly           18\n",
       "piano                29\n",
       "Length: 96, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_filter = songs[songs['tempo'] > 200]\n",
    "current_filter.groupby(['track_genre']).size().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "track_genre\n",
       "idm                    1\n",
       "psych-rock             1\n",
       "kids                   1\n",
       "jazz                   1\n",
       "indian                 1\n",
       "hardcore               1\n",
       "singer-songwriter      1\n",
       "grindcore              1\n",
       "funk                   1\n",
       "german                 1\n",
       "country                1\n",
       "chill                  1\n",
       "children               1\n",
       "opera                  2\n",
       "trip-hop               2\n",
       "blues                  2\n",
       "anime                  2\n",
       "disney                 2\n",
       "study                  3\n",
       "romance                3\n",
       "classical              4\n",
       "piano                  4\n",
       "world-music            5\n",
       "guitar                 5\n",
       "new-age                5\n",
       "iranian                6\n",
       "show-tunes             6\n",
       "ambient                7\n",
       "comedy                12\n",
       "sleep                146\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_filter = songs[songs['tempo'] < 50]\n",
    "current_filter.groupby(['track_genre']).size().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duration outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "track_genre\n",
      "minimal-techno      1\n",
      "country             1\n",
      "rock-n-roll         1\n",
      "r-n-b               1\n",
      "ska                 1\n",
      "                 ... \n",
      "sleep             158\n",
      "disney            200\n",
      "children          214\n",
      "classical         228\n",
      "grindcore         407\n",
      "Length: 98, dtype: int64\n",
      "\n",
      "track_genre\n",
      "honky-tonk          1\n",
      "indie               1\n",
      "rockabilly          1\n",
      "latin               2\n",
      "chill               2\n",
      "                 ... \n",
      "black-metal       254\n",
      "iranian           307\n",
      "chicago-house     454\n",
      "minimal-techno    478\n",
      "detroit-techno    489\n",
      "Length: 109, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "current_filter = songs[songs['duration_min'] < 1.5]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values())\n",
    "print()\n",
    "current_filter = songs[songs['duration_min'] > 6]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most popular genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "track_genre\n",
      "dance        176\n",
      "latino       102\n",
      "alt-rock      76\n",
      "indie-pop     69\n",
      "hip-hop       67\n",
      "            ... \n",
      "j-dance        1\n",
      "club           1\n",
      "children       1\n",
      "brazil         1\n",
      "turkish        1\n",
      "Length: 61, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "current_filter = songs[songs['popularity'] > 75]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Danceability outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "track_genre\n",
      "jazz             1\n",
      "romance          1\n",
      "show-tunes       1\n",
      "opera            2\n",
      "ambient          3\n",
      "world-music      3\n",
      "guitar           4\n",
      "iranian          4\n",
      "sleep          138\n",
      "dtype: int64\n",
      "\n",
      "track_genre\n",
      "alt-rock           1\n",
      "happy              1\n",
      "drum-and-bass      1\n",
      "idm                1\n",
      "british            1\n",
      "                ... \n",
      "dancehall         51\n",
      "hip-hop           56\n",
      "chicago-house     58\n",
      "children         129\n",
      "kids             183\n",
      "Length: 62, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "current_filter = songs[songs['danceability'] < 0.05]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values())\n",
    "print()\n",
    "current_filter = songs[songs['danceability'] > 0.9]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loudness outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "track_genre\n",
      "jazz             1\n",
      "show-tunes       2\n",
      "idm              2\n",
      "anime            3\n",
      "children         4\n",
      "guitar           5\n",
      "british          5\n",
      "french           7\n",
      "german           8\n",
      "opera           17\n",
      "world-music     20\n",
      "disney          22\n",
      "new-age         23\n",
      "iranian         24\n",
      "piano           72\n",
      "classical       85\n",
      "ambient         92\n",
      "sleep          277\n",
      "dtype: int64\n",
      "\n",
      "track_genre\n",
      "hip-hop               1\n",
      "latino                1\n",
      "hard-rock             1\n",
      "malay                 1\n",
      "grindcore             1\n",
      "german                1\n",
      "metal                 1\n",
      "power-pop             1\n",
      "punk                  1\n",
      "heavy-metal           1\n",
      "r-n-b                 1\n",
      "synth-pop             1\n",
      "rockabilly            1\n",
      "dance                 1\n",
      "salsa                 1\n",
      "sleep                 1\n",
      "reggae                1\n",
      "rock-n-roll           2\n",
      "disco                 2\n",
      "death-metal           2\n",
      "pop-film              2\n",
      "goth                  2\n",
      "ska                   2\n",
      "blues                 2\n",
      "edm                   2\n",
      "k-pop                 3\n",
      "alternative           3\n",
      "techno                3\n",
      "electronic            3\n",
      "progressive-house     4\n",
      "french                4\n",
      "metalcore             4\n",
      "garage                4\n",
      "industrial            4\n",
      "j-pop                 4\n",
      "j-dance               5\n",
      "party                 6\n",
      "dancehall             6\n",
      "club                  8\n",
      "anime                 9\n",
      "j-rock                9\n",
      "hardcore             10\n",
      "iranian              10\n",
      "hardstyle            11\n",
      "dubstep              12\n",
      "idm                  16\n",
      "funk                 16\n",
      "j-idol               18\n",
      "breakbeat            18\n",
      "dub                  25\n",
      "happy                36\n",
      "drum-and-bass        67\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "current_filter = songs[songs['loudness'] < -30]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values())\n",
    "print()\n",
    "current_filter = songs[songs['loudness'] > -1]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Speechiness outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "track_genre\n",
      "psych-rock      3\n",
      "guitar          3\n",
      "gospel          3\n",
      "ambient         3\n",
      "acoustic        4\n",
      "             ... \n",
      "hardcore      253\n",
      "kids          277\n",
      "dancehall     391\n",
      "j-dance       394\n",
      "comedy        864\n",
      "Length: 112, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "current_filter = songs[songs['speechiness'] > 0.2]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acousticness outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "track_genre\n",
      "death-metal     1\n",
      "grindcore       5\n",
      "black-metal    13\n",
      "sleep          20\n",
      "dtype: int64\n",
      "\n",
      "track_genre\n",
      "comedy           1\n",
      "garage           1\n",
      "folk             1\n",
      "emo              1\n",
      "indian           1\n",
      "club             1\n",
      "turkish          1\n",
      "sad              1\n",
      "show-tunes       1\n",
      "malay            1\n",
      "chill            2\n",
      "j-dance          2\n",
      "bluegrass        2\n",
      "swedish          2\n",
      "german           2\n",
      "acoustic         3\n",
      "french           5\n",
      "honky-tonk       6\n",
      "british          9\n",
      "jazz            10\n",
      "anime           10\n",
      "world-music     14\n",
      "idm             15\n",
      "iranian         21\n",
      "opera           31\n",
      "guitar          32\n",
      "tango           50\n",
      "sleep           88\n",
      "disney         105\n",
      "new-age        119\n",
      "ambient        130\n",
      "romance        141\n",
      "piano          197\n",
      "classical      235\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "current_filter = songs[songs['acousticness'] == 0.0]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values())\n",
    "print()\n",
    "current_filter = songs[songs['acousticness'] > 0.99]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instrumentalness outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "track_genre\n",
      "sleep        289\n",
      "ambient      101\n",
      "new-age       80\n",
      "disney        76\n",
      "iranian       74\n",
      "classical     72\n",
      "piano         66\n",
      "idm           65\n",
      "grindcore     51\n",
      "guitar        44\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "current_filter = songs[songs['instrumentalness'] > 0.95]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values(ascending=False)[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Liveness outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "track_genre\n",
      "indie               1\n",
      "house               1\n",
      "piano               1\n",
      "guitar              1\n",
      "dubstep             1\n",
      "                 ... \n",
      "kids               60\n",
      "breakbeat          68\n",
      "detroit-techno     68\n",
      "salsa              85\n",
      "chicago-house     116\n",
      "Length: 111, dtype: int64\n",
      "\n",
      "track_genre\n",
      "indie                  2\n",
      "house                  2\n",
      "singer-songwriter      3\n",
      "pop                    3\n",
      "rock                   4\n",
      "                    ... \n",
      "samba                229\n",
      "sleep                251\n",
      "sertanejo            297\n",
      "pagode               405\n",
      "comedy               713\n",
      "Length: 112, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "current_filter = songs[songs['liveness'] < 0.05]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values())\n",
    "print()\n",
    "current_filter = songs[songs['liveness'] > 0.6]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Valence outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "track_genre\n",
      "acoustic         1\n",
      "mandopop         1\n",
      "latino           1\n",
      "j-rock           1\n",
      "j-pop            1\n",
      "              ... \n",
      "idm            166\n",
      "black-metal    167\n",
      "ambient        218\n",
      "iranian        353\n",
      "sleep          714\n",
      "Length: 85, dtype: int64\n",
      "track_genre\n",
      "world-music        1\n",
      "edm                1\n",
      "grunge             1\n",
      "ambient            1\n",
      "drum-and-bass      1\n",
      "                ... \n",
      "kids              80\n",
      "disco             98\n",
      "children         135\n",
      "salsa            139\n",
      "forro            145\n",
      "Length: 99, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "current_filter = songs[songs['valence'] < 0.05]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values())\n",
    "current_filter = songs[songs['valence'] > 0.95]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "track_genre\n",
      "world-music            1\n",
      "ambient                1\n",
      "sad                    1\n",
      "r-n-b                  1\n",
      "classical              1\n",
      "progressive-house      1\n",
      "disney                 1\n",
      "dub                    1\n",
      "dubstep                1\n",
      "grindcore              1\n",
      "folk                   1\n",
      "k-pop                  2\n",
      "idm                    2\n",
      "afrobeat               2\n",
      "emo                    2\n",
      "dancehall              2\n",
      "club                   2\n",
      "brazil                 2\n",
      "french                 2\n",
      "garage                 3\n",
      "german                 3\n",
      "iranian                3\n",
      "funk                   4\n",
      "jazz                   5\n",
      "show-tunes             7\n",
      "kids                  10\n",
      "children              10\n",
      "comedy               745\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "current_filter = songs[songs['speechiness'] > 0.75]\n",
    "print(current_filter.groupby(['track_genre']).size().sort_values())"
   ]
  }
 ],
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