explore_labels.ipynb

{
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   "file_extension": ".py",
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   "version": "3.9.2-final"
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   "language": "python"
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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n",
    "PATH = \"cmumosei_labels/CMU_MOSEI_Labels.hdf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = h5py.File(PATH, \"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "labels = {}\n",
    "\n",
    "for filename in f[\"All Labels\"][\"data\"].keys():\n",
    "    labels[filename] = f[\"All Labels\"][\"data\"][filename][\"features\"][:]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[23.199 30.325]]\n"
     ]
    }
   ],
   "source": [
    "labels = {}\n",
    "\n",
    "for filename in f[\"All Labels\"][\"data\"].keys():\n",
    "    print(f[\"All Labels\"][\"data\"][filename][\"intervals\"][:])\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from dataclasses import make_dataclass\n",
    "\n",
    "\n",
    "Record  = make_dataclass(\"Record\", [(\"video_id\", str),\n",
    "                                        (\"begin\", float),\n",
    "                                        (\"end\", float),\n",
    "                                        (\"sentiment\", float),\n",
    "                                        (\"happy\", float),\n",
    "                                        (\"sad\", float),\n",
    "                                        (\"anger\", float),\n",
    "                                        (\"surprise\", float),\n",
    "                                        (\"disgust\", float),\n",
    "                                        (\"fear\", float)]\n",
    "                                        )\n",
    "records = []\n",
    "for filename in f[\"All Labels\"][\"data\"].keys():\n",
    "    intervals = f[\"All Labels\"][\"data\"][filename][\"intervals\"][:]\n",
    "    labels = f[\"All Labels\"][\"data\"][filename][\"features\"][:]\n",
    "    for interval, label in zip(intervals, labels):\n",
    "        args = [filename] + list(interval) + list(label)\n",
    "        records.append(Record(*args))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          video_id    begin      end  sentiment     happy       sad  anger  \\\n",
       "0      --qXJuDtHPw   23.199   30.325   1.000000  0.666667  0.000000    0.0   \n",
       "1      -3g5yACwYnA   82.753  100.555   1.000000  0.666667  0.666667    0.0   \n",
       "2      -3g5yACwYnA  119.919  125.299   0.666667  0.000000  0.000000    0.0   \n",
       "3      -3g5yACwYnA    4.840   14.052   0.000000  0.666667  0.666667    0.0   \n",
       "4      -3g5yACwYnA   13.211   27.521   0.000000  0.333333  0.333333    0.0   \n",
       "...            ...      ...      ...        ...       ...       ...    ...   \n",
       "23254  zwTrXwi54us   47.444   49.312   0.000000  0.000000  0.000000    0.0   \n",
       "23255  zwTrXwi54us   48.511   53.652   0.000000  0.000000  0.000000    0.0   \n",
       "23256  zwTrXwi54us   52.652   57.942   1.000000  1.000000  0.000000    0.0   \n",
       "23257  zwTrXwi54us   57.980   69.326   0.666667  0.333333  0.000000    0.0   \n",
       "23258  zx4W0Vuus-I   15.057   38.327   1.000000  1.000000  0.000000    0.0   \n",
       "\n",
       "       surprise  disgust      fear  duration max_emotion  \n",
       "0           0.0      0.0  0.000000     7.126       happy  \n",
       "1           0.0      0.0  0.666667    17.802       happy  \n",
       "2           0.0      0.0  0.000000     5.380       happy  \n",
       "3           0.0      0.0  0.333333     9.212       happy  \n",
       "4           0.0      0.0  0.000000    14.310       happy  \n",
       "...         ...      ...       ...       ...         ...  \n",
       "23254       0.0      0.0  0.000000     1.868       happy  \n",
       "23255       0.0      0.0  0.000000     5.141       happy  \n",
       "23256       0.0      0.0  0.000000     5.290       happy  \n",
       "23257       0.0      0.0  0.000000    11.346       happy  \n",
       "23258       0.0      0.0  0.000000    23.270       happy  \n",
       "\n",
       "[23259 rows x 12 columns]"
      ],
      "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>video_id</th>\n      <th>begin</th>\n      <th>end</th>\n      <th>sentiment</th>\n      <th>happy</th>\n      <th>sad</th>\n      <th>anger</th>\n      <th>surprise</th>\n      <th>disgust</th>\n      <th>fear</th>\n      <th>duration</th>\n      <th>max_emotion</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>--qXJuDtHPw</td>\n      <td>23.199</td>\n      <td>30.325</td>\n      <td>1.000000</td>\n      <td>0.666667</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>7.126</td>\n      <td>happy</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-3g5yACwYnA</td>\n      <td>82.753</td>\n      <td>100.555</td>\n      <td>1.000000</td>\n      <td>0.666667</td>\n      <td>0.666667</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.666667</td>\n      <td>17.802</td>\n      <td>happy</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-3g5yACwYnA</td>\n      <td>119.919</td>\n      <td>125.299</td>\n      <td>0.666667</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>5.380</td>\n      <td>happy</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-3g5yACwYnA</td>\n      <td>4.840</td>\n      <td>14.052</td>\n      <td>0.000000</td>\n      <td>0.666667</td>\n      <td>0.666667</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.333333</td>\n      <td>9.212</td>\n      <td>happy</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-3g5yACwYnA</td>\n      <td>13.211</td>\n      <td>27.521</td>\n      <td>0.000000</td>\n      <td>0.333333</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>14.310</td>\n      <td>happy</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>23254</th>\n      <td>zwTrXwi54us</td>\n      <td>47.444</td>\n      <td>49.312</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>1.868</td>\n      <td>happy</td>\n    </tr>\n    <tr>\n      <th>23255</th>\n      <td>zwTrXwi54us</td>\n      <td>48.511</td>\n      <td>53.652</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>5.141</td>\n      <td>happy</td>\n    </tr>\n    <tr>\n      <th>23256</th>\n      <td>zwTrXwi54us</td>\n      <td>52.652</td>\n      <td>57.942</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>5.290</td>\n      <td>happy</td>\n    </tr>\n    <tr>\n      <th>23257</th>\n      <td>zwTrXwi54us</td>\n      <td>57.980</td>\n      <td>69.326</td>\n      <td>0.666667</td>\n      <td>0.333333</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>11.346</td>\n      <td>happy</td>\n    </tr>\n    <tr>\n      <th>23258</th>\n      <td>zx4W0Vuus-I</td>\n      <td>15.057</td>\n      <td>38.327</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>23.270</td>\n      <td>happy</td>\n    </tr>\n  </tbody>\n</table>\n<p>23259 rows × 12 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(records)\n",
    "df[\"duration\"] = df[\"end\"] - df[\"begin\"]\n",
    "df[\"max_emotion\"] = df[[\"happy\", \"sad\", \"anger\", \"surprise\", \"disgust\", \"fear\"]].idxmax(axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              begin           end     sentiment         happy           sad  \\\n",
       "count  23259.000000  23259.000000  23259.000000  23259.000000  23259.000000   \n",
       "mean      49.402565     57.007279      0.151554      0.480001      0.157287   \n",
       "std       45.334759     45.614323      1.111520      0.629221      0.324369   \n",
       "min       -0.487528      0.572000     -3.000000      0.000000      0.000000   \n",
       "25%       15.127000     22.822000     -0.333333      0.000000      0.000000   \n",
       "50%       37.526000     45.301000      0.000000      0.333333      0.000000   \n",
       "75%       70.327000     78.306000      1.000000      0.666667      0.333333   \n",
       "max      264.191000    272.644000      3.000000      3.000000      3.000000   \n",
       "\n",
       "              anger      surprise       disgust          fear      duration  \n",
       "count  23259.000000  23259.000000  23259.000000  23259.000000  23259.000000  \n",
       "mean       0.155725      0.047881      0.119495      0.040981      7.604714  \n",
       "std        0.362210      0.168202      0.319611      0.152526      5.044470  \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.119728  \n",
       "25%        0.000000      0.000000      0.000000      0.000000      4.402000  \n",
       "50%        0.000000      0.000000      0.000000      0.000000      6.467574  \n",
       "75%        0.000000      0.000000      0.000000      0.000000      9.421000  \n",
       "max        3.000000      3.000000      3.000000      1.666667    109.393651  "
      ],
      "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>begin</th>\n      <th>end</th>\n      <th>sentiment</th>\n      <th>happy</th>\n      <th>sad</th>\n      <th>anger</th>\n      <th>surprise</th>\n      <th>disgust</th>\n      <th>fear</th>\n      <th>duration</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>23259.000000</td>\n      <td>23259.000000</td>\n      <td>23259.000000</td>\n      <td>23259.000000</td>\n      <td>23259.000000</td>\n      <td>23259.000000</td>\n      <td>23259.000000</td>\n      <td>23259.000000</td>\n      <td>23259.000000</td>\n      <td>23259.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>49.402565</td>\n      <td>57.007279</td>\n      <td>0.151554</td>\n      <td>0.480001</td>\n      <td>0.157287</td>\n      <td>0.155725</td>\n      <td>0.047881</td>\n      <td>0.119495</td>\n      <td>0.040981</td>\n      <td>7.604714</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>45.334759</td>\n      <td>45.614323</td>\n      <td>1.111520</td>\n      <td>0.629221</td>\n      <td>0.324369</td>\n      <td>0.362210</td>\n      <td>0.168202</td>\n      <td>0.319611</td>\n      <td>0.152526</td>\n      <td>5.044470</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-0.487528</td>\n      <td>0.572000</td>\n      <td>-3.000000</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      <td>0.000000</td>\n      <td>0.119728</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>15.127000</td>\n      <td>22.822000</td>\n      <td>-0.333333</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      <td>4.402000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>37.526000</td>\n      <td>45.301000</td>\n      <td>0.000000</td>\n      <td>0.333333</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>6.467574</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>70.327000</td>\n      <td>78.306000</td>\n      <td>1.000000</td>\n      <td>0.666667</td>\n      <td>0.333333</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>9.421000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>264.191000</td>\n      <td>272.644000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>1.666667</td>\n      <td>109.393651</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_red = df[[\"happy\", \"sad\", \"anger\", \"surprise\", \"disgust\", \"fear\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/home/amosbrazzoli/.local/share/virtualenvs/CMU-MultimodalSDK-GOYrgKsa/lib/python3.9/site-packages/scipy/stats/_distn_infrastructure.py:2751: RuntimeWarning: invalid value encountered in true_divide\n  pk = 1.0*pk / np.sum(pk, axis=axis, keepdims=True)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          happy       sad  anger  surprise  disgust      fear   entropy\n",
       "0      1.000000  0.000000    0.0       0.0      0.0  0.000000  0.000000\n",
       "1      0.333333  0.333333    0.0       0.0      0.0  0.333333  1.098612\n",
       "2           NaN       NaN    NaN       NaN      NaN       NaN       NaN\n",
       "3      0.400000  0.400000    0.0       0.0      0.0  0.200000  1.054920\n",
       "4      0.500000  0.500000    0.0       0.0      0.0  0.000000  0.693147\n",
       "...         ...       ...    ...       ...      ...       ...       ...\n",
       "23254       NaN       NaN    NaN       NaN      NaN       NaN       NaN\n",
       "23255       NaN       NaN    NaN       NaN      NaN       NaN       NaN\n",
       "23256  1.000000  0.000000    0.0       0.0      0.0  0.000000  0.000000\n",
       "23257  1.000000  0.000000    0.0       0.0      0.0  0.000000  0.000000\n",
       "23258  1.000000  0.000000    0.0       0.0      0.0  0.000000  0.000000\n",
       "\n",
       "[23259 rows x 7 columns]"
      ],
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     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "from scipy.stats import entropy\n",
    "\n",
    "df_norm = df_red.div(df_red.sum(axis=1), axis=0)\n",
    "values = df[[\"happy\", \"sad\", \"anger\", \"surprise\", \"disgust\", \"fear\"]]\n",
    "df_norm[\"entropy\"] = entropy(values, axis=1)\n",
    "df_norm"
   ]
  },
  {
   "source": [
    "## Entropy distribution of row-normalised emotions"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f921ac04370>"
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     },
     "metadata": {},
     "execution_count": 10
    },
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.displot(df_norm, x=\"entropy\", bins=10)"
   ]
  },
  {
   "source": [
    "## Sentiment Distribution"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f92a0fa5400>"
      ]
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 360x360 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.displot(df, x=\"sentiment\", kind=\"kde\")"
   ]
  },
  {
   "source": [
    "## Sentiment Emotion Distribution"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f921ed1a9a0>"
      ]
     },
     "metadata": {},
     "execution_count": 12
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 436.375x360 with 1 Axes>",
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Wohq8U3RjV4iKfO+All9iUbRS4yEi1SLymojcKSI7RORuEblIRJ4XkZ0icor9sVpEXhGRF0Rkof3eT4vIHfbXy0Rks4gM3cLBOp8rIneIyIv2fS63j18rIveKyOMisk9ErheRz9jXrBGREvu65fbrjSJyj4gUi8g7gBXA3XblJr+IrBKRFfZ7rrGrOm0Wke/2i6VbRL4pIq/a96wcz7+VJjKVVULR+ITNWASraxGG1lts6gpRnu8dcGykGY5KjWIe8ANgkf3x/4CzgM8BNwOvAWcbY04EbgG+Zb/vJ8A8EXkb8Dvgo8aYkapWfxl40hhzCnA+cJuIJLZ7WAq8HVgJfBPotZ+1Gniffc3vgS8aY44HNgFfNcb8A1gPvNsYs9wYE0g8TESmAd8FLgCWAytF5Ar7dC6wxhhzAvAM8OHx/CNp0WCVVULR+IStIYPDXYuDK+A3dYWG7GFWlOumPWDtKD1RY3Rq0ttrjNkEICJbgP8YY4yIbAKqgULgLnsGuAHcAMaYuD1GtRG43Rjz/CjPuAS4TEQ+Z7/2ATPtr58yxnQBXSLSATxgH98EHC8ihUCRMeZp+/hdwN/H+J5WAquMMU3293U3cA5wLxAGHrSvewm4eIx7AdoiU1kmFJngMbJE12Lv4K7FYN/asYQiv4dwNE4gojtFq3Hr3xcd7/c6jtUQ+TpWslkKvBUrCSXMB7rpN041AgGutFtOy40xM40x28b5/IkWMYcLksbG+wxNZCqrhGPxCU1kRf6hFfDD0ThtvZG+tWMJiYkfI23EqdRRKORwoYlrEwftltJPsVo6pfaY1UgeBT4ldjeBiJw43ocbYzqANhE52z70XiDROusC8od524vAuSJSZheXv6bfe46KJjKVVUKRiR0j87gc5HldA8a+WnqsP1oHj5EV5SRabzpOpibM94Bvi8grDGy9/Aj4uTFmB/BB4DsiUjHCPb6O1SW50e6+/PoRxvB+rHG1jVhjXl+zj98J/DIx2SNxsTGmAbgJeAp4FXjJGHPfET5zAB0jU1klFI2R45nYH/vi3IH1Fhs7rURWMSSRWa23Dm2RqXEwxuzDmmyReH3tCOcW9HvbV+zzH+h3bS3WpJGRnhMAPjrM8TuxklHidfVw54wxG4DThnn/P4F/9jt0Xr9zfwb+PMx78vp9/Q/gHyPF3Z+2yFRWsWYtTuyP/eAK+Ikp9iO3yDSRKTWRtEWmskpogteRgdXS6j/9vql7+ESWmOHYHtCuRXXsich1wOBqSs8bYz6ZingmkiYylVXCE7yODKAkx82+5p6+14muxbK8gYms0K+TPVTqGGN+h7WmbNLRrkWVVULRGB7nxLfI+s9abOoOUpzjHrJezed24nc7R9xRWil1dDSRqaySjK7FklwPXaEoYXvTzqauEBX5vmGvLcoZvlq+UuroaSJTWcWafj+xP/aJzTMTFe8PdQ4tT5VgjadpIlNqImkiU1nFWhA9sWNkM0qsUlQ1rb19n2eU+Ie9tsjv1q5FlXbsAsWbUx3H0dJEprJGNBYnFjcTWmsRYKZdU7G2rZeOQITWnjDVpbnDXlts11tUSk2ccf1Gi8i/ROTNIqKJT2WsUHRid4dOmFLgw+0Ualp7+2YvVpcNn8gK/dq1qNKWU0R+LSJbROQxe+uVD4vIOntblX8mtoKxt5b5pYist7eYeYt9/FoRuc/esmWniHzVPv41Ebkx8SB7q5YJ21h5vNPv/w+4DvipiPwd+J0xZvtEBaHUsZCsROZ0CNOL/FYia7ES2ewREllRjtW1qBXw1Uiqb3rox1ilnibShn3fefONY1wzH7jGGPNhEfkbcCXwL2PMrwFE5BtY5a5+lggVOAWYCzwlIonqIadgVR3pBdaJyEPAHcC/gB/bDaKr7esmxLh+o40xTxhj3g2cBOwDnrA3cbtORNyjv1up9JCYVeh1T+wYGVjjZLWtvext7kEEZpYMu4chxTluonFDT1gr4Ku0s9cuNwXWFirVwFIRedbeNubdwHH9rv+bMSZujNkJ7MHaLw3gcWNMi1366l/AWXZJrRa7IPElwCvGmJaJCnzcC6JFpBR4D1Z141eAu7E2eHs//WpoKZWuQlEreUz0OjKwEtfDmxrY19zDtEI/vhGSZf9q+XlerUeghhpHyylZ+m/ZEgP8WPUUrzDGvGrvb3Zev2sMA5kxjv8Gq0L/FKwW2oQZ7xjZPcCzQA7wVmPMZcaYvxpjPgXkjf5updJDX9fiBK8jA6tF1tYbYVN9B9Vlw7fG4HC9xQ6d8KEyQz7QYPe8vXvQuXeKiENE5gJzgMRw08UiUmJXvL8CSGzqeQ9wKdbGmo9OZJDj/ZPw18aYh/sfEBGvMSZkjFkxkQEplSyhSGKMbOK7FhNdibubejhtTumI1yUq4OtWLipD/DewFmiyP/ffX6wGa2+xAuBjxpigPe77IlbV+yrgj8aY9QDGmLCIPAW0G2MmtG99vInsG8DDg46txhozUyojhGPW785ET/YAOGlmMQsr8+kORTl/4UjbPunmmio9DbNlzPf7nf7FCG97whjzsWGO1xljrhh80J7kcRrwzqOPdHijJjIRmQJMB/z2IF1imlUBVjejUhnjcIts4hPZlEIfj376nDGvK+xLZNoiU9lDRJYADwL32JNDJtRYLbI3YA3OVQE/7He8C7h5ooNRKpkSY2QTvSD6SCQme2iLTGWy/pt8Djp+J/024+x3fCvWOFpSjJrIjDF3AXeJyJX2bp9KZazErMVkjJGNl8flINfj1MLBSk2gsboW32OM+SNQLSKfGXzeGPPDYd6mVFpK5qzFI1GU49HNNZWaQGP9RifKE+RhzVYZ/KFUxkhWZY8jZVX3GLlF9rf1tby0v/UYRqRUZhura/F2+/P/HJtwlEqedBgjAyjO8Yw42WP17ha+8I+NnDWvjD9+6NRjHJlSmWm8C6K/JyIFIuIWkf+ISJOIvCfZwSk1kUKR1I+RgTVzcbgWWSQW56Z/bQTgpf1tRGLxYx2aUkckXbZ/Ge+fppcYYzqBt2DVWpwHfD5ZQSmVDOFYenQtFucMv5XL9oNd7G/p5eIllQQiMTbXd6QgOqUyz3h/oxNdkG8G/m6M0d8wlXGC9jqyZNRaPBJFfqtrMR4fWJLutYNdAHzwrNkAvLhXx8nUsSEiuSLykL1dy2YRuUpEbrG3cNksIr8Su2yHiJxsX/cq8MkUhw6Mv7LHgyLyGhAAPi4i5UAweWEpNfFCkRhelwOHI7XbpxTluIkb6ApFKfQf3jxi+8FOPC4HK2YVM6c8lxf3tvLRc+emMFKVErcW/pgkbOPCrR03jnL+UuCAMebNACJSiFXF/mv26z9g9cg9APwOuN4Y84yI3DbBcR6V8W7jchNwBrDCGBMBeoDLkxmYUhMtGImNWJX+WErUW+wYNE722sEu5lfk4XI6WF5VxNaGzlSEp7LTJqxiv98VkbPtXrfzRWStvYXLBcBxIlIEFBljnrHf94cUxTvAkewjsQhrPVn/9/x+guNRKmmCkTi+FK8hg8P1Ftt6w8wsPVzpbfvBLs6aXwbA9GI/jV0horE4rhR3hapjbPSWU1IYY3aIyEnAm4BviMh/sLoNVxhjakXkVsB3rOMar/HOWvwD8H2s/cdW2h9a9V5llEAkhj+NWmSt/abgt/WEaewKsWiKtTxzSqGPWNzQ1B0a9h5KTSQRmQb02gUwbuNwQfhmEckD3gFgjGkH2kXkLPv84K1dUmK8LbIVwBJjzOAN05TKGOnStTil0PrD9mDH4WHm7YesiR4LpxQAMNW+pqEjyNRC/zGOUGWhZcBtIhIHIsDHsfYS2wwcBNb1u/Y64A4RMcBjxzjOYY03kW3G2tWzIYmxKJVUgUgMbxoksop8Lw6xklTCzsZuAOZXWPvUTimwklf/ZKdUshhjHmXoZpfrga8Mc+1LwAn9Dn0hiaGNy3gTWRmwVURepN922MaYy5ISlVJJEIrE8afBGJnb6aA830tDe6Dv2J6mbnI8zr6W2LQi6/OBftcopYY33kR2azKDUOpYCEZjlOR6Uh0GAFML/QNaZHuaephdlou9VIdCvxuf26EtMqXGYbzT75/Gqujhtr9eB7ycxLiUmnCBcAxfistTJUwr8nGg43Bra3dTN3PK8/pei4iV7Do1kSk1lvHOWvww8A/gdvvQdODeJMWkVFIEozH8nvRIZFML/TS0BzHGEIzEqG8PMKcsd8A1Uwp8A7oflVLDG++AwSeBM4FOAHur6opkBaVUMqTLOjKwZiUGIjE6A1H2tfRgDMwpH5jIphb5tGtRqXEY7291yBjTt+jFXhStU/FVRgmG02P6PdA3pf5AR4A9TT0AzO3XtWhd4+NQV4hYXH/VlBrNeBPZ0yJyM+AXkYuBv2PV3FIqYwSjaZTIihLrxALsabKm3g9ukU0psBZFt+iiaKVGNd5EdhPQhFWP66PAwwyzvkCpdBWNxYnETPpM9ki0yNqDvLivjTnlueR4Bk4iLsvzAmh1DzWpiGVC+/jHO2sxjjW54xPGmHcYY36tVT5UJgnau0P7PekxRlae78XvdvLi3lbW7GnhvAVDh5zL8q1E1tI9/G7SSk0kEblXRF4SkS0i8hH7WLeIfNPetmWNiFTax+farzeJyDdEpLvffT5vb/+yUUT+xz5WLSLbReT3WAU2Zkxk7KOuI7P3n/kqcD120hORGPCzRHl/pTJB0N4dOl26Fp0O4fLl0/jLuloAzl1YPuSaUnvNW7O2yLLKsruW/ZgkbOOy6f2bbhzjmg8YY1pFxA+sE5F/ArnAGmPMl0Xke8CHgW8APwF+Yoz5s4h8LHEDEbkEmA+cAghwv4icA9TYx99vjFkzwd/bmC2yT2PNVlxpjCkxxpQApwJnisinJzoYpZKlL5GlSdciwPtOrwbA53Zw6uySIecTLTJNZOoY+S97s8w1WC2m+UAYeNA+/xJQbX99OtZcCYA/9bvHJfbHK1hrjRfZ9wHYn4wkBmNX9ngvcLExpjlxwBizR0Teg1Us8kfJCEqpidaXyNJkHRnAkmkFnLew3K7iMTSufK8Lj8uhXYtZZhwtpwknIucBFwGnG2N6RWQV1rYtkX7DSDHGzhkCfNsYc/uAgyLVWPtYJsVYQbn7J7EEY0yTiLiHe4NS6SgYscbIfK70GCNL+N21K/vKUg0mIpTlenSyhzoWCoE2O4ktAk4b4/o1wJXAX4Gr+x1/FPi6iNxtjOkWkelY1fSTaqzf6tH+FNQ/E1XGCKTZGFnCSEksoSzfS7O2yFTyPQK4RGQb8B2sRDWaG4HPiMhGYB7QAWCMeQyrq3G1vbP0P4D8ZAWdMFaL7AQRGW6/dSGNdwtVarBE12K6lKgar7I8L4e03qJKMmNMCHjjMKfy+l3zD6zEBFAPnGaMMSJyNbCw33U/wZoMMtjSiYt4oFETmTEms37rlRrB4a7FzPqRLsvzsOVAR6rDUGqwk4H/tWe2twMfSGUw493GRamMdrhrMb3GyMZSmuelpTtMPG5wOEbvhlTqWDHGPMvAzTVTKqm/1SJyqb0IbpeI3DTM+c+IyFZ74dx/RGRWMuNR2Svd1pGNV1mel2jc0BlM+ni5UhkraYlMRJzAz7H6XZcA14jIkkGXvQKsMMYcj9X3+r1kxaOyWyhjE5kuilZqLMlskZ0C7DLG7LEr5/8FuLz/BcaYp4wxvfbLNUBVEuNRWSyQoZM9yhP1Frt05qJSI0lmIpsO1PZ7XWcfG8kHgX8Pd0JEPiIi60VkfVNT0wSGqLJFuq4jG0uiuoeuJVNqZGkx2cOuFLICOHe488aYXwG/AlixYoUWK1ZHLBiJ4XIILmdmJbLKfGuVS6NOwVfHkIjcCnQDBcAzxpgnkvy8K4AdxpitR/P+ZCayegZWOK6yjw0gIhcBXwbOtdcyKDXhApEY/gwbHwMo8Ftlqpq69FdDHXvGmFuO0aOuwKrpeFSJLJl/nq4D5ovIbBHxYJUxub//BSJyInA7cJkxpjGJsagsF4zE8WZgIhMRKvJHXxTdFYzQ2qNjaOr1EZEvi8gOEXkOe4GziNwpIu+wv/5Ov1nm37ePDbudi4icJyIP9rv3/4rItcPdR0TOAC4DbhORDSIy90hjT1qLzBgTFZHrsWpvOYE7jDFbRORrwHpjzP3AbVgrx/9ul+qpMcZclqyYVPYKRmIZt4YsobLAR+MILbK/ravl6w9tpSzPy5OfPXfMklcq/W1btPjHJGEbl8WvbbtxpJMicjJWY2M5Vl54GavafeJ8KfA2YJFdzaPIPjXsdi6jPGfIfYwx7SJyP/CgXT3kiCV1jMwY8zDWbtL9j93S7+uLkvl8pRKCGdq1CFCR72VnY/eQ49FYnK89aPXE7G3uobY1wMzSnGMdnpoczgbuScwitxNLfx1AEPit3dJKtLZOx+oWBKvG4vfHeM5I93ld0mKyh1LJZrXIMjeRPbdryCYUbGvoojsU5VMXzONnT+5izZ4WTWSTwGgtp1Sxe9hOAS4E3oG12fIFo7wlysChK99R3mdcMrOvRakj1BOOZdwasoSKAh9dwSiBcGzA8bV7WwD4f6fOpCTXwxr7tVJH4RngChHxi0g+8Nb+J0UkDyi0e9k+zeHyVIntXGDgdi77gSUi4rW7IS8c4z5dvI4q+ZrIVFboCUXJ82ZmB0SFvZassWvghI8X97YysySHqYV+Tp1dwto9rakIT00CxpiXsfYWexVrPe+6QZfkAw/a27Y8B3zGPn4jw2/nUgv8Ddhsf35ljPv8Bfi8iLySVpM9lEonPaEouRmayCoL7LVkXSFmleYCEI8b1u1r5cLFlQCsrC7h35sPcqgz2He9UkfCGPNN4JujXHLKMMdG287lC8AXxnMfY8zzWKUMj0pm/mYrdYS6QzHyvJnatWi3yDoPz1zc39pLW2+EFbOKAZhdbiW4mtZeTWTqWEqL7Vw0kams0BuOkuvJzB/3Cru6R/+1ZFsPWPvdLp1eCMCMYmuSR21rLyurS45xhCpbpct2LjpGpia9eNzQG45lbNdicY4bj8tBQ0eg79jWhg5cDmFehbWBb1WxH4Da1sCw91BqMtNEpia9nnAUIGMne4gIc8py2d3U03ds64FO5lXk9S0p8LmdVOR7qW3rHek2Sk1amsjUpNcTsqatZ2qLDGB+ZT47G7v6Xm9t6GTJ1IIB18woyaG2VROZyj6ayNSk1x2yWmS5GTrZA2B+RR61rQF6w1Gau0Mc6gyxZNqgRFbsp65NuxZV9tFEpia9nlBmdy2ClcgAdjf2sMWe6DG4RTazJIeGjgCRWPyYx6cyn4j8l4hsE5G7Ux3Lkcrc32ylxqmnr0WWuT/u8yutRLazsYutBzrxOB0sn1k04JqqkhziBhrag1qqSh2NTwAXGWPqjvYGIuIyxkQnMKZx0RaZmvS6J0GLbFZpLm6nsLOxm6e2N3LqnBJyBi0n6JuCrxM+1BESkV8Cc4B/29u53CEiL9qVNi63r6kWkWdF5GX74wz7+Hn28fs5yv3EXq/M/c1WapwSsxYzuUXmdjqYXZbLo1sOsqeph3efOmvINdOLrCn49e06TpbJfv6xJ39MErZx+eQvL7hxpJPGmI+JyKXA+Vhlo540xnzArpP4oog8ATQCFxtjgiIyH/gzsMK+xUnAUmPM3gmOe1y0RaYmve6+WYuZO9kD4NozZrPHnoJ//qKKIecTFUAOdYy8CadS43AJcJOIbABWYVWunwm4gV+LyCbg7wwsKfViqpIYaItMZYHJMNkDrCr3xTluthzoZHZZ7pDzPreT0lwPDaPsJq3S32gtp2NEgCuNMdsHHBS5FTiEVcnDgbWvWEIPKaQtMjXp9YSiOISM3Vizvzcum8rn3rBwxPOVBT4OaotMvT6PAp+y6yciIifaxwuBBmNMHHgvkDa/UJrI1KTXHbLqLNq/l5Pa1EIfDZrI1OvzdaxuxI0issV+DfB/wPtF5FVgESluhfWX2X0tSo1DJm/hcqSmFPp4pbY91WGoDGSMqe738qPDnN8JHN/v0Bft46uwxtJSRltkatLrCcUyfqLHeE0p8NHaEyYYiY19sVKThCYyNel1Z/Du0EdqSuHQLV+Umuw0kalJL9u6FgGd8KGyiiYyNel1Z1Eim5pIZNoiU1lEE5ma9HrCUXI9WTJGVmhV99CZiyqbaCJTk5412SM7WmR5Xhf5XhcNWqZKZRFNZGpSM8bQFYyQ73OnOpRjZnqxX+stqrQkIg/b9RsnVHb8maqyVlcoSiRmKM31pDqUY6ZKN9hUx8h4t22xq4SIMeZNyYhDW2RqUmvrCQNQkkWJbHqRn3pNZOoIiEiuiDwkIq+KyGYRuUpE9olImX1+hYissr++VUT+ICLPA38QkWtF5D4RWSUiO0Xkq/Z11SKyXUR+D2wGZiTuOdzz7PecLCJPi8hLIvKoiEwdT/zaIlOTWksWJrKq4hy6QlE6AhEK/dnTpTpZ/OCqt/yYJGzj8tm/PnjjKOcvBQ4YY94MICKFwHdHuX4JcJYxJiAi1wKnAEuBXmCdiDwENAPzgfcbY9bY9x3xeSLiBn4GXG6MabKT2zeBD4z1zWmLTE1qWdkiK7ZmLtbpBptq/DYBF4vId0XkbGNMxxjX32+M6d/sf9wY02If+xdwln18fyKJjeN5C7GS4eP2FjJfAarGE7y2yNSklp0tMnuDzbYAx00rTHE06kiN0XJKCmPMDhE5CXgT8A0R+Q8Q5XBjxzfoLYMLBpsRXg9bWHiE590DbDHGnH6k8WuLTE1q2dgiqyrOARh2wseaPS388LHt1LZqa00dJiLTgF5jzB+B27B2fN4HnGxfcuUYt7hYREpExA9cATx/FM/bDpSLyOn2NW4ROW488WuLTE1qrT1hPC4HOVmyIBqgOMeN3+0cMgU/Hjfc/K9N7Gnu4TfP7WXV586jomDwH9oqSy0DbhOROBABPg74gd+KyNcZu7r9i8A/sboC/2iMWS8i1UfyPGNMWETeAfzUHqNzAT8GtowVvCYyNam19oQpzfVkxV5kCSJiT8Ef2Op6ekcTe5p7+OBZs/ntc3tZtb2Jd62ckaIoVToxxjyKtaHmYAuGufbWYa6rM8ZcMei6fVhjXv2PVdtfDvs8Y8wG4JyxIx5IuxbVpNbaE6Y4J3u6FRNmleawu2ng8MTvXthHZYGXL166iIp8L0/vbEpRdEpNLE1kalJr7Q1Tmpd9iWzp9EJ2N3XTE7LWqrb3hnl+VzNXnlSFx+Xg7PnlPLezmVh88Bi9UkfGGHOnMeb6VMagiUxNatnaIju+qhBjYMuBTgBWbW8iFjdcvKQSgHMWlNERiLCxrj2FUSo1MTSRqUmttSecVTMWE5ZOt6bdJxLVE9sOUZbn5YSqIgBOm1Nqnx9ruZBS6U8TmZq0wtE4XcFoViayinwfUwt9bKrvIBiJ8fT2Ji5cVIHDIfZ5LwU+Fzsbu1IcqVKvnyYyNWm192bfGrL+lk0v5NXadv7+Uh1doSiXnzit75yIML8ynx2HulMYoVITQxOZmrQau0IAWVX5vr+z5pexr6WXbzy4leUzijjd7k5MWFCZx65GTWQq82kiU5NWonrFjJKcFEeSGu85dRbXnDKDUDTOpy6YN2Qt3byKfFp7wjR3h1IUoVITQxdEq0lrv53IZpZmZyJzOIRvXrGMj54zl+qy3CHn51fkAbDjUBdled5jHZ5SE0ZbZGrS2t/SS0muh4Is2h16MIdDhk1iAAsq8wG0e1FlPE1katKqae1hZpZ2K45HZYGXfK+LnTrhQ2U4TWRq0trX3MusLO1WHA8RYU55Lvtaht1pQ6mMoYlMTUrhaJyGjgCztEU2qtlluexp0kSmMpsmMjUp1bX1Ejcws3T48SFlmV2Wx4GOAMFILNWhKHXUNJGpSSkxY1G7Fkc3uzwXY6yJMUplKk1kalLabc/Eq9YW2ajm2DMa9zbrhA+VuTSRqUnp1boOphX6KM/X9VGjSUzN39Os42Qqc2kiU5PShto2TphRlOow0l6e10VFvpe9OuFDZTBNZGrSaekOUdsaYLkmsnGZXZbL7ibtWlSZSxOZmnRetffg0hbZ+CyozGfnoW6M0d2iVWbSRKYmnQ21HTjE2sZEjW3R1Hy6QlHq2wOpDkWpo5LURCYil4rIdhHZJSI3DXP+HBF5WUSiIvKOZMaissfL+9tYOKWAXO84amLH4xDO7vGhRVMKAHitYfhNNrWlptJd0hKZiDiBnwNvBJYA14jIkkGX1QDXAn9KVhwqu0RjcV6uaWNldfHYF+9+Cn5xOny7Cv72PuhtTX6AaWjhFKt48GsHO4ecu+eVOlZ+8z88uuXgsQ5LqXFLZovsFGCXMWaPMSYM/AW4vP8Fxph9xpiNQDyJcagssq2hi95wjBXVJaNf2LgN/vL/IB6FlR+C7f+Gez5mtdCyTJ7XxcySHLYdHNgi23moiy/9axOdgQgf++NLvLC7OUURKjW6ZCay6UBtv9d19rEjJiIfEZH1IrK+qalpQoJTk9O6fVaratQWWSwCf78WPHlw7UPwptvgDd+CnY/CS3ccm0DTzKIp+bzWMLBF9q2Ht+F3O3niM+eS53Fx3ysHUhSdUqPLiMkexphfGWNWGGNWlJeXpzoclcbW72+lqtjP1EL/yBe9+mdoeg3e8iPIn2IdW/khmHkGPPsjiIaPTbBpZPHUAvY299DRGwGgpqWXVTuaeO/p1cwszeHcheX857VG4nEdL1PpJ5mJrB6Y0e91lX1MqaR5eX87J88apTUWDcHT34PpJ8OiNx8+LgJnfwY662DT35MfaJq5YFEFcQOPbzsEwN1r9+MQ4f+dMhOAixZX0twd6lvaoFQ6SWYiWwfMF5HZIuIBrgbuT+LzVJZr6Q5xsDPI0mmjTLvf9gB01MK5N1nJq795F0HlUlj7y+QGmoaOrypkepGfRzY30NId4u61NVx63BSmFPoAOG9hOU6H8ORrjSmOVKmhkpbIjDFR4HrgUWAb8DdjzBYR+ZqIXAYgIitFpA54J3C7iGxJVjxq8ttmTx9fMq1g5IvW/w6Kq62kNZgInPheOLgRDm1NTpBpSkS4dOkUntnRzC33bSEQifHpixf0nS/K8bB4aj4battTF6RSI0jqGJkx5mFjzAJjzFxjzDftY7cYY+63v15njKkyxuQaY0qNMcclMx41uW1t6ACs8Z5hNe2A/c/BSe8Hxwg/+kuvBIcLNv4lSVGmr3ecXIXLKTy0qYGrVs5gXkXegPNLpxWyub5D15WptJMRkz2UGo+tBzqZWuijJNcz/AUv3WklqRPfM/JN8sqt1trGv2fdVPzFUwtYe/OF/PUjp3HLWwYv+YTjphfS1hvhQEcwBdEpNTJNZGrS2NrQyZKRWmORILz6J1j0FsirGP1GS6+ErgNQv37ig0xz+T43p84pxed2Djm31O6y3VzfcazDUmpUmsjUpBCMxNjd1DPy+NjW+yDQBiuuG/tmC94ADrf1HtVn8dQCnA7RRKbSjiYyNSnsONRFLG5GbpG9dCeUzIHqc8a+ma8Q5p4P2+4HHQ/q43M7mVeexyZNZCrNaCJTk8LWA1ZVimFbZI2vQc0Lo0/yGGzxZdBeAw2vTmCUmW/JtIIRiwsrlSqayNSksLWhkzyvixnFOUNPvnSn1VW4/N3jv+HCN4E4rVaZ6rNwSj4HO4O092Zf9ROVvjSRqUlh64FOFk/Nx+EYtMg53GNN8lj8VmtG4njllkL1mbBVuxf7W9RXKV9bZSp9aCJTGS8eN2wbacbixr9CsANO/eiR33jxZdCyE5q2v/4gJ4nE3mXbNZGpNKKJTGW8mtZeesKxoeNjxsDa22HqCTDj1CO/8eK3AqLdi/1UFngp9Lu1RabSiiYylfG22tuPLJk6qMbi3qetKvenfmxoXcXxyJ9iJcCtmsgSRISFU/LZPswmnEqliiYylfG2HujE6RDmVw4sqcTa2yGnDI57+9HffPFb4dAmaN3z+oKcRBZNyWfHoW4tVaXShiYylfG2NnQyrzxvYDWK1r3Wrs8rPgBu39HffPFb7YdoqyxhydQCukNR9rf0pjoUpQBNZGoS2Hqgc+j42HM/BKfbSmSvR/EsmLpcx8n6WVZldeFu1IXRKk1oIlMZLbEH2YAZi237YMOf4ORroWDq63/Iksug/iXoqHv995oEFlTm43E52KSbbKo0oYlMZbRh9yBb9R1rMfNZn56Yhyy+3H7YgxNzvwzndjpYMrWAjXXaIlPpQROZymhD9iCrfxle/TOc9nEomDYxDymbB+WLtXuxn+OrrL3J4nGd8KFSTxOZymgbatupKvZbe5DF4/DIlyC3HM7+7MQ+aMllsP8F6G6c2PtmqGXTC+kJx9jT3J3qUJTSRKYylzGGl/a3cfKsYuvA+t9C7Rq4+GvgG6EK/tFafBlg4DXtXgRYWV0CwHM7m1MciVKayFQGO9AR5FBniJNmFkPLbnjiVph7AZxwzcQ/rPI4axsYnYYPQHVZLvMq8nh826FUh6KUJjKVuV7a3wbASdNy4O/XWtPt3/rTo6viMRYRq1W271noaZn4+2egi5dUsnZPKx2BSKpDUVlOE5nKWC/vb8PvdrLoxS/BwY1w+f9B0Yxxvz9u4uzr2Mf21u2EY+PYluT4qyAehY1/eR1RTx4XLa4kGjc8vnVgq0wngKhjzZXqAJQ6GsYYnt3ZxIm5zbi3/gMuuhUWvWlc743EI/xt+9/49cZf0xK0Wldep5drFl3DR47/CPme/OHfWLkEqlbCS3fBaZ9ITssvg5w4o4hFU/L5zr+3cUJVIau2N3HX6n00dob4xPlzueHC+UiW/xupY0MTmcpIW2qa2N3Uwwdc/4Szr4czbxzX+1qDrXzu6c+x7uA6Tp1yKjfMuQG/y88zdc9w15a7eLruaf7vwv+jKr9q+Buc9H64/3qoWQOzTp+4bygDORzCT685kcv+9zku/tEzAJw5r5RFU/L58RM78bmdfOzcuSmOUmUDybTCnytWrDDr169PdRgqlepf5ht33stdXSez7sJdFF30mXG1jrrD3bzvkfdR01nDV0//Km+d+9YB59cdXMeNT92Iz+Xj7jfdzZTcKUNvEu6BHy6GOefBu34/Qd9QZttU18GG2jbmV+Zz2pxS4nHDh36/npf2t/HCTReQ682qv5e1CZoCOkam0l5Ld4i1e1rYvWkt/OMDNP7q7dzbvYTzZropuviz40pisXiMLzzzBfa07+GnF/x0SBIDWDllJXe84Q56Ij1c/5/r6Y0MUxTXk2uVvtr2ALTXTMB3l/mWVRXy3tOrOW1OKWC11K6/YB4dgQh/WVeb4uhUNtBEptLatn31XPDdx7jqV2u48O5mrnz5BN7l+AG9rkJuuPzMcd/n++u/z7P1z3LzqTdzxrQzRrxuYclCfnDuD9jRtoPvvPid4S865SOAwOr/O8LvJnucNLOYU6pLuOuFfbrdi0o6TWQq/YR7YPO/6Pjjtbz39qfxR9r4XeXf+cLSLqJTl+MtKOdX71vJ0umFY98L+Otrf+WP2/7Iexa/h3ctfNeY1585/Uw+fPyHuWfXPTyy75GhFxRWWWvV1t8BnQeO9LvLGu84uYqa1l42aZV8lWRZ1Xmt0lQsCo1bYf/zsO852P0kRHr5veM9NJtCHri6nGXL38f5wCeO8NbP1z/Pt1/8NudWncvnVnxu3O/7+AkfZ/WB1Xx77bc5bcppFPmKBl5w7hdg41/hme/DW354hFFlh0uOq+Tme4SHNjZwfFVRqsNRk5i2yNSxFw1Zm14+9hW4443wnRlw+9nwyE1wcBOccDW9/+8B7uByLlhUwbLlpxzVY3a17eJzT3+OuUVz+e4538XpcI79JpvL4eLWM26lM9TJbetvG3pB8Sw46X3w8u+hbf9RxTfZFeV4OGt+GQ9ubNDuRZVUmsjUsdOyGx78NHx/Afz5alj7K4hHrITw9t/Ap7fAjRvhLT/i/vZq2nojfPy8o5u+3Rps5fonr8fn8vHzC39Orjv3iO+xoHgB1y29jvt338/qA6uHXnDO50Ac8Mz3jirGbPDmZVOpbw/wqm75opJIuxZV8nU3Wf+zX38HONyw6M1wwtUw+1xweYZ9yz2v1DOnPJcViYLARyAUC3HDkzfQHGjmzkvvHH4a/Th99ISP8vj+x/na6q/xr8v/hd/lP3yyYBqs/CCs/SWc+jGYsuyonzNZXbJkCjc7N/HQxgMsn1GU6nDUJKUtMpU84V545jb46Ymw7rfWYuIbXoV3/BbmXzxiEqtvD7B2bytXLJ9+xJUhIvEIn3/682xo2sC3zvoWS8uWvq5vwev0csvpt1DXXccvNvxi6AXnfB78xfDQ56xtZNQAhTluzp5fzkPavaiSSBOZmnjRELz4a/jZSfDkN2DOufCJNdakiPzKMd9+34Z6AK5YPv2IHhuLx/jKc1/hqdqnuPnUm7mk+pKjCn+wlVNWcuX8K7lr611sbdk68GROCVz0P9b2MS/9bkKeN9m8edlUDnQEeaW2PdWhqElKE5maOIE2WPMLqwX28OegaBZc9whcfTeULxjXLYwx3PtKPSfPKmZmac64Hx03cb6+5us8vPdhbjjpBq5ZNLFbuXz65E9T4ivhv5//byKxQdXeT3yPVenjsf+G1r0T+tzJ4KIllXicDh7e2JDqUNQkpYlMvT7GWHUH7/kY/GCRNfOwcAa89174wCNHXI9wW0MXOw51c8XyaeN+T2+kl8+u+iz/3PlPPrzsw3xo2YeO8JsYW6G3kFtOu4UdbTu4fePtA0+KwGX/Cw4n/PODEB1HJf0sUuh3c86CMh7e1KCV8VVSaCJTR6dxm9Vt+LOT4I43wLYHYfm74aPPwgcfhbnnH1V1+Hs31ONyCG8+fnyJbHvrdq556Br+U/Mfvrjyi/zXSf91xM8cr/Nnns9lcy/jN5t+M7SLsWgGXP5zqH8J/v15K8GrPm8+XrsXVfLorEU1PuEeqH3RWqy86wlrAbM4YPY5cPZnYckV4M17XY8IRmL886U6zl9UQUnu8BNBEtqCbfxm02+4e9vdFPuKuf3i2zl9WvKr0X9h5RdYc2ANX37uy/z1LX/F4+wX55LL4Mwb4PmfQP40OO+LSY8nU1y0uBKf28FdL+zj5KOYiarUaDSRqaEiQWjYAHXroeFV66N5B2Cs6fMzT4M3fs9KXuOYvDFe9284QEtPmOvOqB72vDGGba3buHfXvdy36z4C0QBvm/82Pn3Sp4dW3kiSQm8hXz3jq3zyP5/kW2u/xa1n3Drwggtvhe5GWPUtyC2zpucr8n1uPnTWHP73qV1cd2Y1J87UZKYmjiYyZWmvgW0PYrY9AHXrkLg1oSGSV0FP2Ty6Zr2b9tK5NJbPI+L24BIXjrYtuDq243P6KPAWUOCxPnLduUc8bT4ai/Ob5/awaEo+p8+1qqjH4jH2d+5nS8sWXml8hafrnqaxtxG3w80bqt/AB5d+kHnF8yb8n2Is51Sdw4eXfZhfb/o1x5UdxzsXvPPwSYcDLvsZ9LbCQ5+1jmkyA+Bj583lL+tq+dSfX+G7Vx5PWZ6X/S09+D1Ols8oIt/nTnWIKkPpfmRZxBhDXXcdezv2UttVS3fDBqbuf5GlTXuY09sJwA63m+dyfGzwetno89LiHH9ZpwSXuCjyFVHkLaLYV0yRt4gSX0nfa7/LjyA4HU4EIRQL8cD6GE9vzOP8FbvJK95JXXcddV11BGNBAHJcOZwx7QzOnXEu51adS7EvtX/Rx+IxPvnkJ1nbsJbfveF3LK9YPvCCcC/84zrY8Yi11uz8L2f9jtIAG2rb+fgfX6KhIzjgeGWBlx+8czlnzS9LUWQTRv8jp4AmskmuprOGVbWrePHgi2xs2khxVyOX9PRycU8vCyJWq2tPfinbKuZTO/0E4iXVFHoLyXPn4XV58Tl9eJwevE7ra7fTjQMHMROzPuIxAtEAneFOusJddIY7aQ+10xZs6/vcFmqjPdhOe6idWKiUcOtZxKOFOJzdOLyHiEdKiLSdhqdwI1PnPEapv5QZeTOoyq9iQfEClpQuYXbhbFyO9OpA6Ah1cPWDVxOIBrjrjXcxq2DWwAtiUXjwRnjlD3D8VfCWH4Nn/EsKJquO3gir9zQTjhlmleTQ2hvmWw9to6a1l3s/eSaLpxakOsTXQxNZCmgim4QO9hzkvl338dDeh9jbsZc54QhXRVxc2NlJZbADAwS91cR8JxDJPxHjr0Bcbpz5eThLSnAWl+AqK8VZUnLEXYSj+cuL+/nKvVtwO4UZpV4OdYTpCMRwCrztpKl89a3LMq57aU/7Hq595Fp8Lh+/fcNvmZE/Y+AFxlgV8p/6JlQeB1f9AUrmpCbYNNbcHeJNP3mWPK+Lh284G5/7yHsC0oQmshTQRDZJBKNBnqx5knt33cuahjX44zE+3uPjrS0HKTXdGAO9TR66avx01vmIBcf+H4Xk5OCpqsI9cwaeGTPxzJqJd+5cPPPm4So+sq69f71cx2f//ipnzSvjR1ctpyzPizGGzkAUxFprlKm2tWzjw49/GLfDzS8u+gWLShYNvWjnE9YaM2Pg7bfDwjce+0DT3HM7m3nPb9fy+Tcs5JPnH/uxzwmiiSwFNJFlMGMMG5s3ct+u+3hk7yN0Rbo4MZ7Lp+o6OTF0AJfLEOz0E3Adj5l7Ka45y3BPm4aruBhHTg7idmNiMUw0iolEiHd2Em1tI9bWSrSxiXBdLZGaWutzbR0mFOp7trO0FO+8efbHXLzz5o2Y4NbsaeG9v13LyuoS7rh2ZSb/tT2iXW27+OgTH6Uj1MFNp9zElfOvHNqabdsHf3ufNQv0tE/ChbeA25eSeNPVh+5az+rdzaz6/PmU53tTHc7R0ESWAprIMowxhpquGh7f/zj37bqPfZ378OLm/W05vL1mL9Pyu8FA0LEYOfcGvOdehThe/7p3E48TbWggtGcPoZ27CO3exe59h7jTOZsXy+bji4Y5vWEz72rdxMxZlXjnz8c7fz57yqt5/5PNlBf4uOfjZ1KYk7ktr7E0B5q5+dmbWd2wmjfNfhNfOuVLQ5cFRILWPmzrfg3li63W2dQTUhJvOtrd1M0bfvQM71o5g2+9LSN3E9BElgKayDJAS6CFFw++yOoDq1nTsIaGHqtm3cpIBe/fepCV8UPkFEWIxb1EZ70VzxVfRUpnJjWmVdsb+dSfXiFuDG+YV0R3awdPHozgIM4V7a9x6vYX2Ost4c4lb8QfDfHjXf9ixtQS3NOn466qwj19Gp7p03FPn46ztHRCkm06iJs4v9n0G36+4efkufP4+Akf56pFV+F2DErgO5+A+z4BPU1wykfg/JvBV5iaoNPMrfdv4fer9/HvG85h4ZT8VIdzpDSRpYAmsjQUiAZ4+dDLrGlYw+oDq9neth2AfGcuy4MVXLC9kTNb91MxJYDTbYh5pyFn/xeOU95/TGbF/XtTA5/68yssqMzn1+9fwfQia4+uA+0Bbnt0O/e/eoCYXVPvpCLhlpw6yve9Rrimlkh9PbG2toE3dLtxl5fjmjIFV2UF7sopuCorcU+pxFVZiauiEndFOeIZvdpHOtnZtpPb1t3G6obVzMifwbsXv5u3zXsbOe5+/30CbVaZr3W/tRZPn/N5a6ubLO9ubOsJc+5tT7F8ZjG//8DR7Q6eQprIUkATWRqIxWNsa93W1+J6pfEVIvEIboeb431zOeGgl2Uv7OO4nv0Uz+nFVxTFiJv4/LfgPPsTULVywtYobT/YxQu7mwlH4xxfVcSK6mLcTqu1ZIzh96v38z8PbOHEmcX87rqVFAwzy7CxK8jG2g5K8jwsryrC4RgYW7ynh8iBA4Tr6ojUHyB66CCRg4eIHrI+IocOYYLBIfd1lpbirrSTW2UF7ilTrCQ3dQqe2bNxVVaOe5ZlNByjbnsb9dvbaG8M0NUSIB4Hp0vIKfBSPCWH0ul5TF9QREGZf+wbDsMYw7P1z3L7xtvZ2LSRfHc+b5rzJt40+00sr1iOQ+xW6IFX4NGvwP7nrNJWZ94AJ74bvBnXGpkwv3l2D994aBt3XreS8xZWpDqcI6GJLAU0kaVId7ib5w88zzN1z/Bs3bO0haxWysLC+ZwUmc7SbT1UP7qZQmczRfOCFMwIIBLHVCxDTvkgLL0SfBO33qY3HOWL/9zEA68eGHC8NNfDeQsrmFbk4/ldzbxc085Fiyv5ydXLyfUmZ12XMYZ4ZyeRQwOTW/TgISKNh4geaiR68CCx9vYB73Pk5dmzKufinTsP7/x5+I47DldJSd81h/Z1svHJWva80kQ0EsfpclBY4aegzI/TKUSjcXraQ7Qf7CUasTbKzC/xMX1RMTMXlzBjSQm+3CMf53u16VXu3nY3T9Y8SSgWojKnkkurL+XCWReyrGwZLnHC3mdg1Xeg5gXwFsJJ74VTPwpFye0mTkfhaJxLfvQ00bjhgevPoniM2ptpRBNZCmgiO4aC0SCraldx/+77WX1gNVETpcBTwJllK1nRlM+iF+rwPPMyTmeAokVxiueHcTk6MN5C5ISr4MT3wtTjJzyuxq4gH7prPZvrO7j+/Hm8+7RZeF0O1uxp5YGNB3hhVzPtgQizSnL48DlzuHrlTJyO1P++xkMhoo2NROoPEN67h9Cu3YR27SK0ezex5ua+69xVVUSnz6fBTKUuMpVg+WzmnTqNWccVMWVOPr48Pw7HwJmUJm5obeihfkcb9dvbqd/RRqg3ighUzi5g5nGlzDyulIqZ+cgR/Fv0RHpYVbuKf+/9N88feJ5o3PoZOH3a6Zw1/SzOmn4WZS37YPXPYet91psWv9UaR5t1RlZVB3mlpo2rbl/DybOKuf19Jw/b+k9D2fMfKI1oIksyYwwvN77MA7sf4NF9j9Id6aYyp5JLik5jxV4nM5/cRmTDJlz+GEXLvBTOjeEx1g7JzDrTGjNZchm4j657ayw7DnVx3e/W0doT5mfXnMhFS4YWATbGEDekRfIaDxOP0757JwefXUPTM1tgTw35HTX47FZvTIS2XB8teX5a83y0+304/T5yC4sorKikoHwK5bOqqZg9l4rqOXh8fuJxQ+O+TvZvaaFmSyuN+zvBgC/PzcwlJXZiK8GfN/6WQ0eog9UNq3mu7jmeP/A8zQEr+S4uWcyZ08/kvPy5LN2zGufLf4BQBxTPtrbKOeFqa9uYLPCvl+v4/D82UlXs5xPnzeX8RRVU5Kf1GGJm/JJMMprIkqS2s5YH9jzA/bvvp767Hr/Lz/k5J3LeXj9zH9tGbF8NLn+MklOKKJgZxB3Zb72xchkcdwUc9zYonZvUGP+z7RA3/nUDfreTO65dydLpmTVrLhoO09ZQT0t9La31tbTU19FaV0t7I4hrCQ73fEScEK/Dn1dHSW47JYEweU0teGrqcDYcBMB4PIRmz6Rz+lQO5Xpp6Wijt6PdeogIJdOqmDp/IdMWLGbagkWUTp9BsCdKzdZWara2ULu1lUBXBAQqZhUw87gSZh1XSkV1wZDxwZHETZztrdt5/sDzPFv3LK82vUrMxPA5fZxWdjzvjHk5ueE1cutest4w7USYf4n1Me1Ea1PPSerFva185d5N7DjUDUBVsZ/T5pTy9pOmc/qc0gmtPjMB0iqYbKGJbAJ1hDp4bP9jPLD7AV5pfAVBONk9l/P2+Dnh37vwtnThzhdKz6wgf2onrsBu642Vy+C4y2HJ26AseRUN4nFDZzDC5vpO/vxiDQ9tamDx1AJ+02/mYToK9fbSeqCW1vo6Wupq+hJXx6FDGGONYzlcU/EXngCO2cSifpxuw6zjcjnhwmqmzZ8y7H2jbW30rl9Pz3PP071qFdFDh0AE3/HL8Jx6Kr2zZtAcj3Bwz04O7NxOsMsqrOzNzWXq/EVMm7+IaQsWUzlnPh3NMWq2tFCzpYVDezsxBry5LqoWFlM5u5DK6gLKZ+bj9o4v4XSFu1h/cD1rD65lzYE17O6wflYWSw7XmlxO6WqntGUvYuLgK7Im/FSthBkrYepyyCkZ9f6ZxhjDxroO1u1r5ZWadp7Z0URXKMqps0v4+hVLWVCZNhNjNJGlgCay1ykYDbKqbhUP73mYZ+ufJRqPMosSzt/t59TH6iltj+CfkUvxqRXkFrfg6rKm0iczeUVjcTbWd7B6dwtr9rRQ1xagrTdMRyDSt3Fxvs/FdWdU88kL5uF1pfaveWMMgc4O2g8dpONQA+2NB+k4dLDvdXdba9+1DqeLoinTKKxcgMszk0i4mI5mB4GuGA6HULW4mHknVzJvRQVuz/i/L2MModdeo+upp+he9TTBjRsBcJaXkXfmWeSefTbRebM5ePAAB7Zv48CObTTX1YAxiDgomzGTaQsXM23BYoqnz6Wz2U3dtnbqd7bT1WLNwBSBkml5VM4uoKwqj9LpeZROz8U7jkXijb2NrG1Yy5qGNaxpWENjbyOFsRjnhmJcFHdzXCBAeXcLgvUf2ORVIhWLoWIJlC+C0nnWpJH8qeBMr+LLRyMYifHXdbX8+Ikd9IRi3HDRfD56zhxczpSvR9RElgKayI5Cd7ibtQ1rebL2Sf6z/wl6or2URL2c+ZqDM1/sZEF3jILjSihYmIvXVYcjcMh6Y+VSq9vwKJJXJBZny4FOXtrfRlOXVSoqx+PE73bi8zjxuRy09IR5paaNF3a30BWMArBoSj7zKvIozvFQlOOm0O9mbkUep1SXTMisw1g0Sqi3h3AgQDjQSzgYIBoKEQmHiAaD1udQiIj9EQ0HCXZ309PRTm97O72d7fR2dBCPRQfcN7e4jPyymeQWTsebV4nLW0Ismkt3mzUJIxKMAeAv8DB9fhGzlpVSvazsqGYUDifa1ET3c8/T8+yz9Dz/PLGODnA48C5ciH/pcfiWLsM5by4tkSANtfs4sOM1Gna+RjgQAMDhdFJYOZWSadMpKJuJwz2NUCCH7jYHrQ1hwoFY37Pyir2UTMujdFpu3wzKgjI/+SVeHMP8jzlR3WVz82a2tmxlS8sWtrVswxHuZlkoxIJwhAXhKAuicWaHgnjtVitAXBz05hQTyq8kVjAdimbiLJ6Nt3QeOcVzcORPtab9p1d33Yiau0P8972b+ffmg5xQVcj333kC81PbOsuMf7hJJqmJTEQuBX4COIHfGGO+M+i8F/g9cDLQAlxljNk32j1TkcgC0QCvtb7Gy4de5tnd/2FDxxZixMkPC2/cHuTC+gjznULezFy8/nYcMasLCk8ezDkP5l8M8y6CwqpxP7OtJ8y2g528vL+NtXtbeWl/G71h639+bqcgCOFYfMj7phf5OXt+GWfPL+e0OSWU5g2tV2eMIRoKEQ4mkk+QSCBAOBQg3NtLOBCwk1MvoUAv4d5eQr29hAM9hHoD9mfreDQSHnR3AVwgHkQ8IF77swdx+HC6c3B5cnF58nG5c3G4/IjDC3gwuInHHMQiEA5EGfyj6c1xWa2YabmUVuUxbX4RRZU5SR8jMbEYwU2b6H72OQIbNhDYvJl4R0ffeWdJCZ4ZM3BNnULE46HXxOiNRekMBejo7qK9o40QhqjTQcThwDgEl7cUf8EsXJ5KkBJisXwiIR/GHE5cIuDPd5BX7CG/xENusY/8khwKynLJK/aTW+TFl+fG6XRgjKEl2EJNZw01XTXUdtXSEmihtbcZR0cd/q6D5PW0UB4OMi0aZVo0xrRolIpYjMGpMuBw0O720eXNoddXQNSbj/Hmg78Ip78EV04ZnpwyvHlTyMmfSl7uVHJyShF3Tspaew9uPMB/37u5r3X2wbNmp6qmpyayFEhaIhMRJ7ADuBioA9YB1xhjtva75hPA8caYj4nI1cDbjDFXjXbfiUhkcRMnEo8QioYIhXoJB3vo7m2jrbuR9u5DtLfX09xZz8GeOjpDh+g17eTH45TEYH5HnGk9bnLifqIePx2SQ4fJpVNy6fFNpcs/g17/VGI55eArwpCY9WfAGOJxgzHWRzweJx439EZi9IZj9Ebi9IbjtAVjtAYPJ6nqfAdLS5wsKYAFuVEKJEosEiYcidIbCtMbitEbiuGNh/HGwsTCMaLRKLFIlGgkRiwSIxKOEAuHiYQjRMNRrL8tnCAuBBeIa9Br62uny4fD6cXh9OBwuBGH27rWvt4YByYumLgQjwtmaG4dlsvtwO134fE58fhceBJf+132ayd5xT7yir19n705rrQY2DfGEKmtJbh1G+GaGiK1NYRraq01b21tA5LcsO93u4l7PMTcLqJOB1Gng7BA2BiCxknUnUvcmUvcmUfclU/cWYBx5RN35BF3eog73MQdbow4MCJAHJwxxBHD4YgjToO4BHEKTic4XA4cLsHhgpjLEHKHCbpCBJ0hwo4evNKM17TgMx3kmE5y493kx3soiAUoioXIi0fJi8cYT1s3ghB2OAk7nEQdLqIOD3Gnh6jDQ9TpIe60YsfpwdgfuDzg9CJuL+L0IS4fuD2I24/D5cPh9oHLi8N+LZ4cnC4/Do8fpzsHh9uP051LR9TNNx6u4bGtTRTnuLl06VRWVhczuyyXmSU5FPjdfYv7kyj1P6BZKJmJ7HTgVmPMG+zXXwIwxny73zWP2tesFhEXcBAoN6MEdaSJ7NCeXfzl1i+CgYA7yp/O3kO/P3q5rKubrza34oIhf5m+PXQrm81sIjgxQ84OzxWP4DJRHDhBrKnYiZ/s/j/hh48JbgMeA24jeABfXCiNC+UxB1NiDvzm2P5uOJyC0+3A5XbYn52HX7v6H7c+O91OXC4HTs/ha4ZLTB6f9bXb78SZ+rGMpDGRCNG2NmKtrcQ6Ool3dxHr6iLe1W1/3U28K3Gsi1i3fa6ri1h3N8bunkwnBkPEZwjmGSJ5caI5caI5YPxxjDeOcUPcbcBtEJfB4bQ+nA6DWwxeDF5j8McNbmPwGIMH+7MBjxnvb9gYcRp4wSziV/E38HR8OcQH9kj4p/4JT8EmHCI4nIf/MBL7N7L/H0pXzLuCm0+9+UhD0ESWAslMZO8ALjXGfMh+/V7gVGPM9f2u2WxfU2e/3m1f0zzoXh8BPmK/XAhsH/S4MqCZ9JFu8UD6xaTxjE7jGV26xtNsjLk01cFkm4yYvmSM+RXwq5HOi8h6Y8yKYxjSqNItHki/mDSe0Wk8o9N4VH/J7N+pB/qXH6iyjw17jd21WIg16UMppZQal2QmsnXAfBGZLSIe4Grg/kHX3A+83/76HcCTo42PKaWUUoMlrWvRGBMVkeuBR7GmyN1hjNkiIl8D1htj7gd+C/xBRHYBrVjJ7miM2O2YIukWD6RfTBrP6DSe0Wk8qk/GLYhWSiml+pu8c6CVUkplBU1kSimlMtqkS2Qi8lkRMSJSluI4vi4iG0Vkg4g8JiLTUhzPbSLymh3TPSJSlOJ43ikiW0QkLiIpm7YsIpeKyHYR2SUiN6Uqjn7x3CEijfYay5QTkRki8pSIbLX/e92Q4nh8IvKiiLxqx/M/qYwnQUScIvKKiDyY6liy0aRKZCIyA7gEqEl1LMBtxpjjjTHLgQeBW1Icz+PAUmPM8Vilw76U4ng2A28HnklVAHYZtZ8DbwSWANeIyJJUxWO7E0inBbVR4LPGmCXAacAnU/xvFAIuMMacACwHLhWR01IYT8INwLZUB5GtJlUiA34EfAFI+QwWY0xnv5e5pDgmY8xjxphEifk1WOv6UhnPNmPM4Aotx9opwC5jzB5jTBj4C3B5KgMyxjyDNYM3LRhjGowxL9tfd2H9z3p6CuMxxphu+6Xb/kjp75aIVAFvBn6Tyjiy2aRJZCJyOVBvjHk11bEkiMg3RaQWeDepb5H19wHg36kOIg1MB2r7va4jhf+TTnciUg2cCKxNcRxOEdkANAKPG2NSGg/wY6w/oMdZMltNtIwoUZUgIk8Aw233+2XgZqxuxbSIxxhznzHmy8CX7YLJ1wNfTWU89jVfxuouujuZsYw3HpUZRCQP+Cdw46DehmPOGBMDltvjvPeIyFJjTErGFEXkLUCjMeYlETkvFTGoDEtkxpiLhjsuIsuA2cCrdvXqKuBlETnFGHPwWMczjLuBh0lyIhsrHhG5FngLcOGxqKByBP8+qTKeMmpZT0TcWEnsbmPMv1IdT4Ixpl1EnsIaU0zV5JgzgctE5E2ADygQkT8aY96Toniy0qToWjTGbDLGVBhjqo0x1VhdRCclM4mNRUTm93t5OfBaqmKBvk1OvwBcZozpTWUsaWQ8ZdSymlh/Gf4W2GaM+WEaxFOemHErIn6s/Q5T9rtljPmSMabK/v/O1Vhl9jSJHWOTIpGlqe+IyGYR2YjV5ZnSacvA/wL5wOP2koBfpjIYEXmbiNQBpwMP2XvTHVP25JdEGbVtwN+MMVuOdRz9icifgdXAQhGpE5EPpjIerBbHe4EL7J+bDXbrI1WmAk/Zv1frsMbIdMp7ltMSVUoppTKatsiUUkplNE1kSimlMpomMqWUUhlNE5lSSqmMpolMKaVURtNEpiY9EVnef8q4iFyW7Er3InKeiJyRzGcopSyayFQ2WA70JTJjzP3GmO8k+ZnnAZrIlDoGdB2ZSmsikgv8Dat8lBP4OrAL+CGQBzQD1xpjGkRkFVZB2/OBIuCD9utdgB+r/NS37a9XGGOuF5E7gQBWMdwKrILK78NaqL3WGHOtHcclwP8AXmA3cJ0xpltE9gF3AW/FqsT+TiCItcNADGgCPmWMeTYJ/zxKKbRFptLfpcABY8wJxpilwCPAz4B3GGNOBu4Avtnvepcx5hTgRuCr9vYstwB/NcYsN8b8dZhnFGMlrk9jlaj6EXAcsMzuliwDvgJcZIw5CVgPfKbf+5vt478APmeM2Qf8EviR/UxNYkolUUYVDVZZaRPwAxH5LtYGpW3AUqxSW2C10hr6XZ8oavsSUD3OZzxgjDEisgk4ZIzZBCAiW+x7VGFtvPm8/UwPVhmp4Z759iP43pRSE0ATmUprxpgdInIS1hjXN4AngS3GmNNHeEvI/hxj/D/fiffE+32deO2y7/W4MeaaCXymUmqCaNeiSmsiMg3oNcb8EbgNOBUoF5HT7fNuETlujNt0YRVMPlprgDNFZJ79zFwRWZDkZyqlxkkTmUp3y4AX7R2Bv4o13vUO4Lsi8iqwgbFnBz4FLLErt191pAEYY5qAa4E/21XXVwOLxnjbA8Db7GeefaTPVEqNn85aVEopldG0RaaUUiqjaSJTSimV0TSRKaWUymiayJRSSmU0TWRKKaUymiYypZRSGU0TmVJKqYz2/wEWeIXpN8CeegAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.displot(df, x=\"sentiment\", hue='max_emotion', kind=\"kde\")"
   ]
  },
  {
   "source": [
    "## Row-maximum emotion stats"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/home/amosbrazzoli/.local/share/virtualenvs/CMU-MultimodalSDK-GOYrgKsa/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  warnings.warn(\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='max_emotion', ylabel='count'>"
      ]
     },
     "metadata": {},
     "execution_count": 13
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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vB/YF5gFXDLEpO4zvnPisUVdhiz31ndeNugqSpmAUPZHJ/AlwbpJ3A9cAp7f46cDZSVYD6+nuyKKqbkhyHnAjsAE4tqp+MvxqS9LMNdIkUlWXApe2+VuZ4O6qqvoh8OpJtj8ZOHn6aihJ2hSfWJck9WYSkST1ZhKRJPVmEpEk9WYSkST1ZhKRJPVmEpEk9WYSkST1ZhKRJPVmEpEk9WYSkST1ZhKRJPW2PY3iK/XyvL9+3qirsMW+8aZvjLoK0jZhT0SS1JtJRJLUm0lEktSbSUSS1JtJRJLUm0lEktTb0JNIkv2TfDXJjUluSPKWFt8zyfIkt7Sfe7R4kpyaZHWSVUkOGtjX4lb+liSLh90WSZrpRtET2QD8YVUdCBwKHJvkQOB44JKqmgdc0pYBXgbMa9MS4CPQJR3gBOA5wCHACeOJR5I0HENPIlV1Z1Vd3ea/D9wE7AcsAs5sxc4EDm/zi4CzqrMCmJ3kKcBLgeVVtb6q7gWWAwuH1xJJ0kiviSSZCzwbuBzYp6rubKvuAvZp8/sBdwxstqbFJotPdJwlSVYmWblu3bpt1wBJmuFGlkSS/BTwWeCtVfW9wXVVVUBtq2NV1WlVNb+q5o+NjW2r3UrSjDeSJJLkcXQJ5Jyq+lwLf7edpqL9vLvF1wL7D2w+p8Umi0uShmQUd2cFOB24qareP7BqGTB+h9Vi4IKB+FHtLq1Dgfvbaa+LgQVJ9mgX1Be0mCRpSEYxiu/zgNcB1yX5Zov9KfAe4LwkxwDfBn6rrbsIeDmwGngAeD1AVa1PchJwZSt3YlWtH0oLJEnACJJIVf0TkElWHzZB+QKOnWRfS4Gl2652kqQt4RPrkqTeTCKSpN5MIpKk3kwikqTeTCKSpN5GcYuvpC3wtee/YNRV2CIv+PrXRl0FDZE9EUlSbyYRSVJvJhFJUm8mEUlSbyYRSVJvJhFJUm8mEUlSbyYRSVJvJhFJUm8mEUlSbw57ImlkPvSHXxx1FbbYcX/1ylFXYbtiEpGkaXLya48YdRW22Ds+ef4Wlfd0liSpN5OIJKm3HT6JJFmY5OYkq5McP+r6SNJMskMnkSQ7A38DvAw4EDgyyYGjrZUkzRw7dBIBDgFWV9WtVfVj4Fxg0YjrJEkzRqpq1HXoLckRwMKq+p9t+XXAc6rquI3KLQGWtMWnAzcPsZp7A/82xOMN02O5bWD7dnS2b9v6L1U1tnFwRtziW1WnAaeN4thJVlbV/FEce7o9ltsGtm9HZ/uGY0c/nbUW2H9geU6LSZKGYEdPIlcC85IckGQX4DXAshHXSZJmjB36dFZVbUhyHHAxsDOwtKpuGHG1NjaS02hD8lhuG9i+HZ3tG4Id+sK6JGm0dvTTWZKkETKJSJJ6M4lMQZK5Sa4fdT12FP6+hivJu5K8LcmJSV48hOMdPuqRIZK8OclNSc4ZZT22R0kuSjJ7WMfboS+sS9ubJKG71vjgsI9dVe8c0qEOBy4EbhzS8Sbye8CLq2pN3x0kmVVVG7ZhnabFVOs58N57+RCq9RB7IlO3c5KPJbkhyVeS7Jbkd5NcmeTaJJ9N8gSAJGck+WiSlUm+leQVLX50kguSXJrkliQntPiJSd46fqAkJyd5y0haOSDJE5P8fWvf9Ul+O8k7W5uvT3Jae+OS5OBW7lrg2BFX/VGSfCHJVe31W9JiP2i/62uTrEiyT4v/XFu+Lsm7k/xgYD9/1Nq/Ksn/bbG5bRDQs4DreeSzS9PVnne099Y/0Y3CMP6+O6LNvyfJja2ef7mpdiV5YZILB/b9oSRHT7SfJL8CvAp4X5JvJvm56W7rxpJ8FPhZ4Evt97A0yRVJrkmyqJWZm+Qfk1zdpl8ZaOs/JlnGkJPgJH9PtyfZu62fn+TSNv+uJGcn+QZw9iY+Ox713hvf50THa9scnORr7e/h4iRP2aqGVZXTZiZgLrAB+KW2fB7wWmCvgTLvBt7U5s8AvkyXpOcBa4BdgaOBO4G9gN3aiz6/7f/qtu1OwL8O7nuE7f5N4GMDy7sDew4snw28ss2vAp7f5t8HXD/q+m/Ulj3bz/Hf+15ADdT/L4A/a/MXAke2+TcCP2jzC+huq0x7nS4Ent9evweBQ4fUloOB64AnAE8GVgNva++7I1rbbubhuy9nb6ZdLwQuHNj/h9p7dbL9nAEcMeLX83a6YT/+H/Da8foB3wKe2H43u7b4PGDlQFv/HThgBHWe6O/pdmDvtjwfuLTNvwu4CtitLR/N5J8dj3jvDfxuJjre44B/BsZa7LfpHo3o3S57IlN3W1V9s81fRffiPbP9V3Md8DvAMwbKn1dVD1bVLcCtwM+3+PKquqeq/gP4HPCrVXU7cE+SZ9N9UF1TVfdMe4s27zrgJUnem+TXqup+4L8luby1+UXAM9Kdf51dVV9v2509ovpuyptbL2kFXU9hHvBjug9WePg1BXgu8Jk2/3cD+1jQpmuAq+le03lt3berasV0VX4jvwZ8vqoeqKrv8egHbO8HfgicnuQ3gAdafLJ2TWay/WxPFgDHJ/kmcCndP2tPpfuw/Fh7n36GbpTvcVdU1W1DridM/Pe0Kcva58S4R312tPhk772Jjvd04JnA8vY7+zO6kT5685rI1P1oYP4ndP8NnAEcXlXXtu7/CwfKbPwATm0m/nG6/zZ+Bli61bXdBqrqW0kOAl4OvDvJJXSnquZX1R1J3kX3R7tdS/JC4MXAc6vqgXbKYFfgP6v9O0b3mm7u7yHAn1fV3260/7l0/91uF6p7CPcQ4DC6nslxdAl/Mht45KntXXvuZxQC/GZVPWJQ1fbe/C7wi3Rt++HA6pG8VpP8PQ3+7jf+W9q4npN9dkzYnkmO93nghqp6bs9mPIo9ka3zJODOJI+j64kMenWSndo545/l4ZGDX5JkzyS70V2g/EaLfx5YCPwy3RP4I5dkX+CBqvok3Smqg9qqf0vyU3QfLFTVfcB9Scb/M9r4dzFquwP3tgTy88Chmym/gu5UAHRD6Yy7GHhDaztJ9kvy09u8tpv3deDwdNflngS8cnBlq9/uVXUR8Pt0H6Qwebu+DRyY5PGtV3nYZvbzfbr3/vbgYuBNyUPX5p7d4rsDd1Z3g8Pr6Ea0GKlJ/p5upzs9CQ+/NpOZ7LNjS453MzCW5LmtzOOSPGMTu9kseyJb5/8AlwPr2s/BP6zvAFfQnbN+Y1X9sL3PrwA+S9eF/GRVrQSoqh8n+SpwX1X9ZHhN2KRn0V1AfRD4T+B/0715rwfuohu7bNzrgaVJCvjKkOu5OV8G3pjkJro/os2ddnor8Mkk72jb3g9QVV9J8l+By9pr+QO6a2NDfb2q6uoknwauBe7mka8DdO/DC5LsSvef+h+0+FuZuF13JDmP7nW9je503ab2cy7dqaI3010b+ddt38opOwn4ALAqyU509X8F8GHgs0mOomvr9tBTnOjvaTe604Un0Z2O25RHfXa0XvCUj9c+Z44ATk2yO10O+ADQe7gohz2ZBknOoLtQef5G8aPpTgUdN8E2O9GdZ391u46iEUl3l91/VFUleQ3dxegd/svOHqvtmgk29dkxavZEtgPpHty6kO5iqQlk9A4GPtROkdwHvGG01dlmHqvt0gjZE5Ek9eaFdUlSbyYRSVJvJhFJUm8mEUlSbyYR6TGgDdC378DyxzPi4do1M3h3lvQY0IZyedv4w6vSsNgTkXhoSO1/STec+reSnJPkxUm+kW7o7UPadFm6Icf/Ocn4EOy/n2Rpm39WumG3nzDJcZ6YiYcuPzrdcPXL0w3lfVySP2hlViTZs5X7pba8Ksnnk+zRnkCeD5yTbnj23dINGT6/bXNkuuHfr0/y3oG6TDgUvrRFhj0cspPT9jjx8HD/z6L75+oquoEwAywCvkA3hM2sVv7FwGfb/E5041n9OrASeN4mjjPZ0OVH0w3p/iRgjG5Ikje2cqcAb23zq4AXtPkTgQ+0+UvpnmhmcBnYl24InjG6h4v/gW7QUJhkKHwnpy2ZfGJdethtVXUdQJIbgEuqqtINJz6XblC/M5PMo/sAfhxAVT3YhqVYBfxtVW1qYLwFwKuSvK0tjw9dDvDVqvo+8P0k9wNfbPHrgF9oYx3NrqqvtfiZPDy0+2R+me47Kta1dp1D9x0oX+DRQ+G/ZDP7kh7FJCI9bHC4/wcHlh+k+1s5ie6D/tfbwHeXDpSfRzcg475s2mRDlz9nCsff1rZ0KHzpUbwmIk3d7sDaNn/0eLD1EE6l+w9/r3aNYjKTDV2+WdV9qdC9SX6thV4HjPdKJhue/QrgBem+LnVn4MiBbaStZhKRpu4vgD9Pcg2P/K/9FOBvqupbwDHAezL594ycRHcabFU7ZXbSFtZhMd3w3quAX6K7LgLdF6R9dPzC+njhqroTOB74Kt3Q8VdV1QVbeExpUt7iK0nqzZ6IJKk3L6RJ0yDJ64G3bBT+RlUdO4r6SNPF01mSpN48nSVJ6s0kIknqzSQiSerNJCJJ6u3/A7qLdyQLUdqBAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.countplot(df[\"max_emotion\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          video_id    begin      end  sentiment     happy       sad  anger  \\\n",
       "0      --qXJuDtHPw   23.199   30.325   1.000000  1.000000  0.000000    0.0   \n",
       "1      -3g5yACwYnA   82.753  100.555   1.000000  0.333333  0.333333    0.0   \n",
       "2      -3g5yACwYnA  119.919  125.299   0.666667       NaN       NaN    NaN   \n",
       "3      -3g5yACwYnA    4.840   14.052   0.000000  0.400000  0.400000    0.0   \n",
       "4      -3g5yACwYnA   13.211   27.521   0.000000  0.500000  0.500000    0.0   \n",
       "...            ...      ...      ...        ...       ...       ...    ...   \n",
       "23254  zwTrXwi54us   47.444   49.312   0.000000       NaN       NaN    NaN   \n",
       "23255  zwTrXwi54us   48.511   53.652   0.000000       NaN       NaN    NaN   \n",
       "23256  zwTrXwi54us   52.652   57.942   1.000000  1.000000  0.000000    0.0   \n",
       "23257  zwTrXwi54us   57.980   69.326   0.666667  1.000000  0.000000    0.0   \n",
       "23258  zx4W0Vuus-I   15.057   38.327   1.000000  1.000000  0.000000    0.0   \n",
       "\n",
       "       surprise  disgust      fear  duration max_emotion   entropy  \n",
       "0           0.0      0.0  0.000000     7.126       happy  0.000000  \n",
       "1           0.0      0.0  0.333333    17.802       happy  1.098612  \n",
       "2           NaN      NaN       NaN     5.380       happy       NaN  \n",
       "3           0.0      0.0  0.200000     9.212       happy  1.054920  \n",
       "4           0.0      0.0  0.000000    14.310       happy  0.693147  \n",
       "...         ...      ...       ...       ...         ...       ...  \n",
       "23254       NaN      NaN       NaN     1.868       happy       NaN  \n",
       "23255       NaN      NaN       NaN     5.141       happy       NaN  \n",
       "23256       0.0      0.0  0.000000     5.290       happy  0.000000  \n",
       "23257       0.0      0.0  0.000000    11.346       happy  0.000000  \n",
       "23258       0.0      0.0  0.000000    23.270       happy  0.000000  \n",
       "\n",
       "[23259 rows x 13 columns]"
      ],
      "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>video_id</th>\n      <th>begin</th>\n      <th>end</th>\n      <th>sentiment</th>\n      <th>happy</th>\n      <th>sad</th>\n      <th>anger</th>\n      <th>surprise</th>\n      <th>disgust</th>\n      <th>fear</th>\n      <th>duration</th>\n      <th>max_emotion</th>\n      <th>entropy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>--qXJuDtHPw</td>\n      <td>23.199</td>\n      <td>30.325</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>7.126</td>\n      <td>happy</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-3g5yACwYnA</td>\n      <td>82.753</td>\n      <td>100.555</td>\n      <td>1.000000</td>\n      <td>0.333333</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.333333</td>\n      <td>17.802</td>\n      <td>happy</td>\n      <td>1.098612</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-3g5yACwYnA</td>\n      <td>119.919</td>\n      <td>125.299</td>\n      <td>0.666667</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.380</td>\n      <td>happy</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-3g5yACwYnA</td>\n      <td>4.840</td>\n      <td>14.052</td>\n      <td>0.000000</td>\n      <td>0.400000</td>\n      <td>0.400000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.200000</td>\n      <td>9.212</td>\n      <td>happy</td>\n      <td>1.054920</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-3g5yACwYnA</td>\n      <td>13.211</td>\n      <td>27.521</td>\n      <td>0.000000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>14.310</td>\n      <td>happy</td>\n      <td>0.693147</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>23254</th>\n      <td>zwTrXwi54us</td>\n      <td>47.444</td>\n      <td>49.312</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.868</td>\n      <td>happy</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>23255</th>\n      <td>zwTrXwi54us</td>\n      <td>48.511</td>\n      <td>53.652</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.141</td>\n      <td>happy</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>23256</th>\n      <td>zwTrXwi54us</td>\n      <td>52.652</td>\n      <td>57.942</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>5.290</td>\n      <td>happy</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>23257</th>\n      <td>zwTrXwi54us</td>\n      <td>57.980</td>\n      <td>69.326</td>\n      <td>0.666667</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>11.346</td>\n      <td>happy</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>23258</th>\n      <td>zx4W0Vuus-I</td>\n      <td>15.057</td>\n      <td>38.327</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>23.270</td>\n      <td>happy</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>23259 rows × 13 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "df_normalised = df\n",
    "df_normalised[[\"happy\", \"sad\", \"anger\", \"surprise\", \"disgust\", \"fear\"]] = df_norm[[\"happy\", \"sad\", \"anger\", \"surprise\", \"disgust\", \"fear\"]]\n",
    "df_normalised['entropy'] = df_norm[\"entropy\"]\n",
    "df_normalised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"reworked/labels.csv\")\n",
    "df_normalised.to_csv(\"reworked/labels_normalised.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              begin           end     sentiment         happy           sad  \\\n",
       "count  23259.000000  23259.000000  23259.000000  19773.000000  19773.000000   \n",
       "mean      49.402565     57.007279      0.151554      0.511425      0.175489   \n",
       "std       45.334759     45.614323      1.111520      0.448191      0.314403   \n",
       "min       -0.487528      0.572000     -3.000000      0.000000      0.000000   \n",
       "25%       15.127000     22.822000     -0.333333      0.000000      0.000000   \n",
       "50%       37.526000     45.301000      0.000000      0.500000      0.000000   \n",
       "75%       70.327000     78.306000      1.000000      1.000000      0.250000   \n",
       "max      264.191000    272.644000      3.000000      1.000000      1.000000   \n",
       "\n",
       "              anger      surprise       disgust          fear      duration  \\\n",
       "count  19773.000000  19773.000000  19773.000000  19773.000000  23259.000000   \n",
       "mean       0.137091      0.042222      0.092555      0.041218      7.604714   \n",
       "std        0.274598      0.140532      0.211215      0.149359      5.044470   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.119728   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      4.402000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      6.467574   \n",
       "75%        0.125000      0.000000      0.000000      0.000000      9.421000   \n",
       "max        1.000000      1.000000      1.000000      1.000000    109.393651   \n",
       "\n",
       "            entropy  \n",
       "count  19773.000000  \n",
       "mean       0.333656  \n",
       "std        0.417229  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.673012  \n",
       "max        1.747868  "
      ],
      "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>begin</th>\n      <th>end</th>\n      <th>sentiment</th>\n      <th>happy</th>\n      <th>sad</th>\n      <th>anger</th>\n      <th>surprise</th>\n      <th>disgust</th>\n      <th>fear</th>\n      <th>duration</th>\n      <th>entropy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>23259.000000</td>\n      <td>23259.000000</td>\n      <td>23259.000000</td>\n      <td>19773.000000</td>\n      <td>19773.000000</td>\n      <td>19773.000000</td>\n      <td>19773.000000</td>\n      <td>19773.000000</td>\n      <td>19773.000000</td>\n      <td>23259.000000</td>\n      <td>19773.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>49.402565</td>\n      <td>57.007279</td>\n      <td>0.151554</td>\n      <td>0.511425</td>\n      <td>0.175489</td>\n      <td>0.137091</td>\n      <td>0.042222</td>\n      <td>0.092555</td>\n      <td>0.041218</td>\n      <td>7.604714</td>\n      <td>0.333656</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>45.334759</td>\n      <td>45.614323</td>\n      <td>1.111520</td>\n      <td>0.448191</td>\n      <td>0.314403</td>\n      <td>0.274598</td>\n      <td>0.140532</td>\n      <td>0.211215</td>\n      <td>0.149359</td>\n      <td>5.044470</td>\n      <td>0.417229</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-0.487528</td>\n      <td>0.572000</td>\n      <td>-3.000000</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      <td>0.000000</td>\n      <td>0.119728</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>15.127000</td>\n      <td>22.822000</td>\n      <td>-0.333333</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      <td>4.402000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>37.526000</td>\n      <td>45.301000</td>\n      <td>0.000000</td>\n      <td>0.500000</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>6.467574</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>70.327000</td>\n      <td>78.306000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.250000</td>\n      <td>0.125000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>9.421000</td>\n      <td>0.673012</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>264.191000</td>\n      <td>272.644000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>109.393651</td>\n      <td>1.747868</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "source": [
    "### Explorative Regression Emotions -> Sentiment"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n==============================================================================\nDep. Variable:              sentiment   R-squared:                       0.460\nModel:                            OLS   Adj. R-squared:                  0.460\nMethod:                 Least Squares   F-statistic:                     3298.\nDate:                Thu, 11 Mar 2021   Prob (F-statistic):               0.00\nTime:                        09:32:42   Log-Likelihood:                -28301.\nNo. Observations:               23259   AIC:                         5.662e+04\nDf Residuals:                   23252   BIC:                         5.667e+04\nDf Model:                           6                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nconst          0.0927      0.014      6.699      0.000       0.066       0.120\nhappy          0.8665      0.016     53.137      0.000       0.835       0.898\nsad           -0.5191      0.022    -24.136      0.000      -0.561      -0.477\nanger         -0.6874      0.024    -28.711      0.000      -0.734      -0.641\nsurprise      -0.1338      0.042     -3.155      0.002      -0.217      -0.051\ndisgust       -2.0110      0.030    -67.810      0.000      -2.069      -1.953\nfear           0.0776      0.040      1.926      0.054      -0.001       0.157\n==============================================================================\nOmnibus:                      168.819   Durbin-Watson:                   1.563\nProb(Omnibus):                  0.000   Jarque-Bera (JB):              262.243\nSkew:                           0.013   Prob(JB):                     1.13e-57\nKurtosis:                       3.520   Cond. No.                         9.70\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "import statsmodels.api as sm\n",
    "\n",
    "df =df.fillna(0)\n",
    "X = df[[\"happy\", \"sad\", \"anger\", \"surprise\", \"disgust\", \"fear\"]]\n",
    "w = df[\"duration\"]\n",
    "Y = df[\"sentiment\"]\n",
    "\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y, sample_weight=w)\n",
    "\n",
    "#print('Intercept: \\n', regr.intercept_)\n",
    "#print('Coefficients: \\n', regr.coef_)\n",
    "\n",
    "# with statsmodels\n",
    "X = sm.add_constant(X) # adding a constant\n",
    " \n",
    "model = sm.OLS(Y, X, weight=w).fit()\n",
    "predictions = model.predict(X) \n",
    "\n",
    "print_model = model.summary()\n",
    "print(print_model)\n"
   ]
  },
  {
   "source": [
    "Label Structure\n",
    "\n",
    "Vector of 7 floats: emotions + sentiment\n",
    "\n",
    "[\"sentiment\", \"happy\", \"sad\", \"anger\", \"surprise\", \"disgust\", \"fear\"]"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "alignment compatible \t b'true'\ncomputational sequence description \t b'\"Labels for CMU-MOSEI Dataset\"'\ncomputational sequence version \t b'1.0'\ncontact \t b'\"abagherz@andrew.cmu.edu\"'\ncreator \t b'\"Amir Zadeh\"'\ndataset bib citation \t b'\"@inproceedings{cmumoseiacl2018, title={Multimodal Language Analysis in the Wild: {CMU-MOSEI} Dataset and Interpretable Dynamic Fusion Graph}, author={Zadeh, Amir and Liang, Paul Pu and Vanbriesen, Jon and Poria, Soujanya and Cambria, Erik and Chen, Minghai and Morency, Louis-Philippe},booktitle={Association for Computational Linguistics (ACL)},year={2018}}\"'\ndataset name \t b'\"CMU-MOSEI\"'\ndataset version \t b'1.0'\ndimension names \t b'[\"sentiment\", \"happy\", \"sad\", \"anger\", \"surprise\", \"disgust\", \"fear\"]'\nfeatureset bib citation \t b'\"@online{amt, author = {Amazon},title = {Amazon Mechanical Turk},year = {2017},url = {https://www.mturk.com}}\"'\nmd5 \t b'null'\nroot name \t b'\"All Labels\"'\nuuid \t b'\"bbce9ca9-e556-46f4-823e-7c5e0147afab\"'\n"
     ]
    }
   ],
   "source": [
    "for key in f[\"All Labels\"][\"metadata\"].keys():\n",
    "    print(key,\"\\t\", f[\"All Labels\"][\"metadata\"][key][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}