Read and Visualize Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import seaborn as sns   \n",
    "import matplotlib.pyplot as plt   \n",
    "import plotly.graph_objs as go  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset shape: (30000, 7)\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>text</th>\n",
       "      <th>Anger</th>\n",
       "      <th>Fear</th>\n",
       "      <th>Happiness</th>\n",
       "      <th>Hatred</th>\n",
       "      <th>Sadness</th>\n",
       "      <th>Wonder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>کرونا رو شکست میدهیم؟\\nمرحله بعد چه گوهی میخوا...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>اگر در چند ماه اخیر تصمیم داشته اید وارد بورس ...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>یکی از پدرسوختگی های #برانداز اینه که ظاهرا ژس...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>یکی از دوستای دبستانم</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@username اینقدر گرفتار مسایل میشی که تخصص از ...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  Anger  Fear  Happiness  \\\n",
       "0  کرونا رو شکست میدهیم؟\\nمرحله بعد چه گوهی میخوا...      4     3          1   \n",
       "1  اگر در چند ماه اخیر تصمیم داشته اید وارد بورس ...      2     4          1   \n",
       "2  یکی از پدرسوختگی های #برانداز اینه که ظاهرا ژس...      5     3          2   \n",
       "3                              یکی از دوستای دبستانم      1     0          0   \n",
       "4  @username اینقدر گرفتار مسایل میشی که تخصص از ...      2     1          0   \n",
       "\n",
       "   Hatred  Sadness  Wonder  \n",
       "0       3        3       4  \n",
       "1       2        4       2  \n",
       "2       4        5       2  \n",
       "3       1        0       0  \n",
       "4       1        0       4  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('dataset.csv')\n",
    "df = df.drop_duplicates()\n",
    "print('Dataset shape:', df.shape)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "emotion_labels = ['Anger', 'Fear', 'Happiness', 'Hatred', 'Sadness','Wonder']\n",
    "yes_label_counts = [0 for e in emotion_labels]\n",
    "\n",
    "for _, row in df.iterrows():\n",
    "    for e_id in range(len(emotion_labels)):\n",
    "        e = emotion_labels[e_id]\n",
    "        if row[e]>2:\n",
    "            yes_label_counts[e_id] += 1\n",
    "\n",
    "plt.bar(emotion_labels, yes_label_counts)\n",
    "plt.xlabel('Emotion')\n",
    "plt.ylabel('Number of Tweets with Emotion')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tweets_with_emotion_count = {}\n",
    "sum_of_all_tweets = 0\n",
    "for _, row in df.iterrows():\n",
    "    number_of_emotions_tweet_has = 0\n",
    "    for e_id in range(len(emotion_labels)):\n",
    "        e = emotion_labels[e_id]\n",
    "        if row[e]>2:\n",
    "            number_of_emotions_tweet_has += 1\n",
    "    if not number_of_emotions_tweet_has in tweets_with_emotion_count.keys():\n",
    "        tweets_with_emotion_count[number_of_emotions_tweet_has] = 0\n",
    "    tweets_with_emotion_count[number_of_emotions_tweet_has] += 1\n",
    "    sum_of_all_tweets+= 1\n",
    "\n",
    "x_val, y_val = [k for k in tweets_with_emotion_count.keys()], [tweets_with_emotion_count[k]/sum_of_all_tweets for k in tweets_with_emotion_count.keys()]\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.bar(x_val, y_val, color='red')\n",
    "plt.xlabel('Number of emotions the Tweet has')\n",
    "plt.ylabel('% of Tweets with this emotion count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "emotion_cooccurance = {k1: {k2: set() for k2 in emotion_labels} for k1 in emotion_labels}\n",
    "emotion_occurance = {k:set() for k in emotion_labels}\n",
    "\n",
    "for row_index, row in df.iterrows():\n",
    "    for e1 in emotion_labels:\n",
    "        for e2 in emotion_labels:\n",
    "            if e1 == e2:\n",
    "                continue \n",
    "            if row[e1]>2 and row[e2]>2:\n",
    "                emotion_cooccurance[e1][e2].add(row_index)\n",
    "                \n",
    "        if row[e1]>2:\n",
    "            emotion_occurance[e1].add(row_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "li = []\n",
    "all_the_labels = []\n",
    "for k1 in emotion_cooccurance.keys():\n",
    "    all_the_labels.append(k1)\n",
    "\n",
    "for k1 in all_the_labels:\n",
    "    this_row = []\n",
    "    for k2 in all_the_labels:\n",
    "        union_of_the_two = emotion_occurance[k1].union(emotion_occurance[k2]) \n",
    "        #intersection_of_the_two = emotion_occurance[k1].intersection(emotion_occurance[k2]) \n",
    "        other_way_of_counting_intersection = len(emotion_cooccurance[k1][k2])\n",
    "        this_row.append(other_way_of_counting_intersection/(len(union_of_the_two)))\n",
    "    li.append(this_row)\n",
    "\n",
    "sample_data = np.array(li)  \n",
    "ax = sns.heatmap(sample_data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlAAAAIICAYAAAChP9DfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABLW0lEQVR4nO3dd5xcVfn48c+zJb1usgkhoSQC0qSGHjqodEFQxAKKRgUsiAUsSPPr14KA2AjCF0QFReQnIopK74TQQw0JJbT0tmm7O+f3x9wkuyHZnQmZ2dndz/v1mtfecu69z7mZ3TxzzrlnIqWEJEmSClfV0QFIkiR1NiZQkiRJRTKBkiRJKpIJlCRJUpFMoCRJkopkAiVJklSkmjJcw3kSJEmqTNHRAXRWtkBJkiQVyQRKkiSpSCZQkiRJRTKBkiRJKpIJlCRJUpFMoCRJkopkAiVJklQkEyhJkqQimUBJkiQVyQRKkiSpSCZQkiRJRTKBkiRJKpIJlCRJUpFMoCRJkopkAiVJklQkEyhJkqQimUBJkiQVyQRKkiSpSCZQkiRJRTKBkiRJKpIJlCRJUpFMoCRJkopkAiVJklQkEyhJkqQimUBJkiQVyQRKkiSpSCZQkiRJRTKBkiRJKpIJlCRJUpFMoCRJkopkAiVJklQkEyhJkqQimUBJkiQVyQRKkiSpSCZQkiRJRTKBkiRJKpIJlCRJUpFMoCRJkorUZgIVEdURcUe5gpEkSeoM2kygUkrNQC4iBhZz0ogYHxGPRMQjEyZMeFcBSpIkVZpIKbVdIOJvwI7Af4CGFdtTSl8u8BptX0CSJHWU6OgAOquaAsr8NXtJkiSJAlqgACKiN7BxSun5dbiGLVCSJFUmW6DWUbtP4UXEEcDjwL+y9R0i4qYSxyVJklSxCpnG4BxgV2AeQErpcWBMySKSJEmqcIUkUI0ppfmrbcuVIhhJkqTOoJBB5JMj4gSgOiI2B74M3F/asCRJkipXIS1QXwK2AZYB1wILgK+WMCZJkqSKVtBTeO+ST+FJklSZfApvHbXbhRcRf+edSdB84BHgspTS0lIEJkmSVKkK6cKbCiwCLs9eC4CFwBbZuiRJUrdSyFe5TEwp7bKmbRExOaW0TTvXsAtPkqTKZBfeOiqkBapfRGy8YiVb7petLi9JVJIkSRWskGkMzgDujYiXyGeqo4FTIqIvcHUpg5MkSapEhX4XXk9gy2z1+SIHjtuFJ0lSZbILbx0VmkDtCWxKixarlNLvCryGCZQkSZXJBGodFTKNwTXAe8h/oXBztjkBhSZQkiRJXUohT+E9C2yd1n3GTVugJEmqTLZAraNCnsJ7Gtig1IFIkiR1FoU8hTcUeCYiHib/fXgAKaV0VOnCkiRJqlyFdOHt23IV2Bs4voAJNFewC0+SpMpkF946arcLL6V0F/mvbzkcuAo4APhNacOSJEmqXGvtwouILYCPZa9ZwJ/It1jtX6bYJEmSKtJau/AiIgfcA5ycUpqSbZuaUhpT5DXswpMkqTLZhbeO2urCOwZ4E7gjIi6PiAPxRkuSJBU0iLwvcBT5rrwDyE+geWNK6d8FXsMWKEmSKpMNI+uooK9yWVk4YjBwHPDRlNKBBR5mAiVJUmUygVpHRSVQ68gESpKkymQCtY4KmYlckiRJLZhASZIkFckESpIkqUgmUJIkSUUygZIkSSqSCZQkSVKRTKAkSZKKZAIlSZJUJBMoSZKkIplASZIkFckESpIkqUgmUJIkSUUygZIkSSpSTTku8tZVfyzHZbq0DU46oaNDkCRJGVugJEmSimQCJUmSVCQTKEmSpCKZQEmSJBXJBEqSJKlIJlCSJElFMoGSJEkqkgmUJElSkUygJEmSimQCJUmSVCQTKEmSpCKZQEmSJBXJBEqSJKlIJlCSJElFMoGSJEkqkgmUJElSkUygJEmSimQCJUmSVCQTKEmSpCKZQEmSJBXJBEqSJKlIJlCSJElFMoGSJEkqkgmUJElSkUygJEmSimQCJUmSVCQTKEmSpCKZQEmSJBXJBEqSJKlIJlCSJElFMoGSJEkqkgmUJElSkUygJEmSimQCJUmSVCQTKEmSpCLVdHQA69tDL03h0v/+i1wux2E77MTH9xjXav/ypib+5+b/xwtvvsGA3n34/oeOZcSgQUyc9hIT7ryNxuZmaqur+eL+B7PTpqMB+O/kp/j9A/cSwNB+/fnOkccwqE+fDqidJEmqBF2qBao5l+Pif9/Cjz/yca4efyq3PfM0L8+a2arMP554jP69evHHL36Z43bdncvu/C8AA3v34YfHfoyrPvtFzjr8Q/zg7zcC0JTLcel//8XFJ5zI/332i4wZNpwbJz1c9rpJkqTK0W4CFRHVEfHTcgTzbj37xuuMHFzHhoMHU1tdzQFbbcO9LzzXqsx9Lz7PB7bdHoB9t9yaR1+eSkqJLTYYwdD+/QEYPbSeZU2NLG9qgpRICZY2LielxOLlyxjSr3/Z6yZJkipHu114KaXmiBjXXrlKMGvRQoYNGLByvb7/AJ594/XWZRYuYNiAgQDUVFXRt2cv5i9Z0qpL7q7nn2WLDUbQoyZ/e772wcP49G9/Ta/aHoyqq+Or7z+0DLWRJEmVqtAxUI9FxE3A9UDDio0ppb+uqXBEjAfGA1x22WUc2aPfu42zbKbNnMFld/yXnx7/CQCampv526OP8NvPfJ4NBw3mkn//kz88cC+f2mufDo5UkqTO68VxH0jFlN/83lujVLGsi0ITqF7AbOCAFtsSsMYEKqU0AZiwYvWtq/64zgEWY2i//sxYsGDl+syFC1Z2y60s038AMxbMZ9iAATTlcjQsW8rA3r0BmLFgAd+94U98+4gPMXJwHQAvvv0WwMr1/bfahj88cG85qiNJkipUQQlUSunTpQ5kfdhyw5FMnzubN+fNZWj/Adz+7GS+d+QxrcrstfkW3Pr0E2w7aiPueu4ZdtxkNBHBwqVLOfP6P/L5/Q/ifaM2Xlm+vv8AXp41k3mLGxjUpy+PTHuJTYYOLXfVJElSBSkogYqIXsDJwDbkW6MASCl9pkRxrZOaqiq+evChfP2635NLiUO324HR9cO44u472HLEhuy1+Xs5dPud+MHfb+SEX/+c/r178/2jjgXgxkkP8/rcOVx9711cfe9dAPz0+E8ytH9/Thq3L1/6/VXUVFUxfOAgzjrsqI6spiRJnV907okAIqX2uyAj4nrgOeAE4Dzg48CzKaWvFHCNsnXhdWUbnHRCR4cgSep6Omxc0Yt7H1LcGKh7/llRY6AKTf82Syl9D2hIKV0NHAbsVrqwJElSlxZR3KugU8YHI+L5iJgSEWeuYX/PiPhTtv+hiNi0xb7tIuKBiJgcEU9lvW9rVWgC1Zj9nBcR2wIDgWEFHitJklRSEVEN/BI4BNga+FhEbL1asZOBuSmlzYCLgB9lx9YAvwe+kFLaBtiPVbnPGhWaQE2IiMHA94CbgGeAHxd4rCRJUqntCkxJKU1NKS0HrgNWH7R8FHB1tvwX4MCICOD9wJMppScAUkqzU0rNbV2s0Kfwfpst3gWMKagakiRJa1O13oc0jQRea7E+nXcON1pZJqXUFBHzgSHAFkCKiFuBeuC6lFKbDUUFtUBFxPCIuCIi/pmtbx0RJxdyrCRJ0uoiqop8xfiIeKTFa/x6DKcGGEf+IblxwNERcWBbBxTahXcVcCuwYbb+AvDVdQpRkiSpSCmlCSmlsS1eE1Yr8jqwUYv1Udm2NZbJxj0NJD9R+HTg7pTSrJTSYuAWYKe24ik0gRqaUvozkMsq0QS02TcoSZJURhOBzSNidET0AI4nP267pZuAE7PlY4HbU34+p1uB90VEnyyx2pf8eO+1KvSrXBoiYgj5r28hInYH5hd4rCRJUmvreQxUNqbpNPLJUDVwZUppckScBzySUroJuAK4JiKmAHPIJ1mklOZGxM/IJ2EJuCWl9I+2rldoAvU18lnbeyLiPvIDrI4tvnqSJEmlkVK6hXz3W8ttZ7dYXgoct5Zjf09+KoOCtJlARcTGKaVXU0qPRsS+wHvJz1r6fEqpzfkRJEmS1qrAyTErVXtjoP5fi+U/pZQmp5SeNnmSJEndWXsJVMv00PmfJEmSaH8MVFrLsiRJ0rqrKnQigMrUXgK1fUQsIN8S1TtbJltPKaUBJY1OkiSpArWZQKWUqssViCRJ6ka6+CBySZIkrcYESpIkqUiFTqQpSZK03oRdeJIkSd2LLVCSJKn8Ovk0Bp07ekmSpA5gAiVJklQku/AkSVL5OYhckiSpe7EFSpIklV+VLVCSJEndii1QkiSp/KJzt+F07uglSZI6gC1QkiSp7MIxUJIkSd2LCZQkSVKR7MKTJEnl50SakiRJ3YstUJIkqfycxkCSJKl7sQVKkiSVXyefxqAsCdQGJ51QjstIkiSVRVkSqDkPPFyOy3RpdXvs6n18l+r22LWjQ5AkdRF24UmSpPJzGgNJkqTuxRYoSZJUdlHVudtwOnf0kiRJHcAESpIkqUh24UmSpPJzELkkSVL3YguUJEkqPweRS5IkdS+2QEmSpPJzDJQkSVL3YgIlSZLKL6K4V0GnjA9GxPMRMSUizlzD/p4R8ads/0MRsWm2fdOIWBIRj2ev37R3LbvwJElSpxcR1cAvgYOB6cDEiLgppfRMi2InA3NTSptFxPHAj4CPZvteSintUOj1bIGSJEldwa7AlJTS1JTScuA64KjVyhwFXJ0t/wU4MGLdBmOZQEmSpLKLqqqiXgUYCbzWYn16tm2NZVJKTcB8YEi2b3REPBYRd0XE3u1dzC48SZJU8SJiPDC+xaYJKaUJ6+n0bwIbp5RmR8TOwP+LiG1SSgvWdoAJlCRJKr8ie86yZKmthOl1YKMW66OybWsqMz0iaoCBwOyUUgKWZdeZFBEvAVsAj6ztYnbhSZKkrmAisHlEjI6IHsDxwE2rlbkJODFbPha4PaWUIqI+G4RORIwBNgemtnUxW6AkSVKnl1JqiojTgFuBauDKlNLkiDgPeCSldBNwBXBNREwB5pBPsgD2Ac6LiEYgB3whpTSnreuZQEmSpPKrWv8zkaeUbgFuWW3b2S2WlwLHreG4G4AbirmWXXiSJElFsgVKkiSVX3TuNpzOHb0kSVIHMIGSJEkqkl14kiSp/EowiLycbIGSJEkqki1QkiSp/NbtO3wrhi1QkiRJRTKBkiRJKpJdeJIkqeyik88DZQIlSZLKz6fwJEmSuhdboCRJUvn5FJ4kSVL30uVaoB548kku/uM1NOdyHLnPfnzq8CNa7V/e2Mh5l1/Gcy9PY2C/flzwxdMYUV/P/EUL+fYvLuXZaVM5dNzefP2TJ6485pQf/oDZ8+fRs7YHABd/45vUDRhY1nqVm/dRkqS161IJVHMux4XXXM0l3/gWw+rq+My5Z7P3jjsxeuTIlWX+fvdd9O/Tl7/8+EL+8+AD/PL6P3HBKafRo7aW8cd8mJemT2fq69Pfce5zPv9Ftho9ppzV6TDeR0lSyVV17k6wzh39ap6Z+hKjhg9n5LBh1NbUcNBuu3P3Y5NalbnnsUc5dNw4APbfZVceeWYyKSV69+zF9lu8l561tR0RekXxPkqS1LaCWqAi4j3A9JTSsojYD9gO+F1KaV7pQivezLlzGVZXt3J92OA6Jk99abUycxheNwSAmupq+vXuw/xFixjUv3+b577gisupjir2G7sLnz7yKKKTD35ri/dRklRynfzvf6FdeDcAYyNiM2AC8Dfgj8ChayocEeOB8QCXXXYZx75vh3cfaQc65wtfZNjgOhqWLOHbv/g5/7z/Pg7da1xHh9XpeB8lSV1FoV14uZRSE3A0cGlK6RvAiLUVTilNSCmNTSmNHT9+/PqIsyD1gwczY86clesz5s6hfvDg1crU8fac2QA0NTezaMliBvbr1+Z5hw3Ot8b07d2b9+++B8+s1hrT1XgfJUlqW6EJVGNEfAw4Ebg521Zxg1y2Gj2G195+izdmzqCxqYn/PvQge++4U6sy43bYkVvuvReAOyY+zM5bbd1mN1JTczPzFi7MLzc1cd8TjzNm5KjSVaICeB8lSaUWVVHUq9IU2oX3aeALwA9SStMiYjRwTenCWjc11dWc8YlP8dWf/oRcLsfhe+/DmJGjmPDXG9hq9Gj23nEnjthnX86d8BuO/eYZDOjbj/O/eOrK448+43Qali6hqamJux+dxCVf/xYbDB3CV3/6Y5qam8nlcuyyzTYctd/+HVjL0vM+SpLUtkgpFXdAxGBgo5TSkwUekuY88HDRgam1uj12xfv47tTtsWtHhyBJlabDmnZe/cqZRSUgG1/yvxXVDFVQF15E3BkRAyKiDngUuDwiflba0CRJkipToWOgBqaUFgDHkJ++YDfgoNKFJUmSVLkKTaBqImIE8BFWDSKXJElaNxHFvSpMoQnUecCtwEsppYkRMQZ4sXRhSZIkVa6CnsJLKV0PXN9ifSrw4VIFJUmSurgKnJqgGIUOIt8iIm6LiKez9e0i4rulDU2SJKkyFdqFdzlwFtAIkE1hcHypgpIkSapkhU6k2Sel9PBqM003lSAeSZLUHUShbTiVqdAEalZEvAdIABFxLPBmyaKSJEldWiV+PUsxCk2gTgUmAFtGxOvANOATJYtKkiSpghX6FN5U4KCI6AtUpZQWljYsSZKkylVQAhURPclPW7Ap+Uk1AUgpnVeyyCRJUtdVgZNjFqPQLry/AfOBScCy0oUjSZJU+QpNoEallD5Y0kgkSVL3UdW5n8IrNPr7I+J9JY1EkiSpkyi0BWoccFJETCPfhRdASiltV7LIJEmSKlShCdQhJY1CkiR1L518EHmbXXgRMSBbXLiWlyRJUkWIiA9GxPMRMSUizlzD/p4R8ads/0MRselq+zeOiEUR8fX2rtVeC9QfgcPJP32XyHfdrZCAMe1dQJIk6R3WcwtURFQDvwQOBqYDEyPippTSMy2KnQzMTSltFhHHAz8CPtpi/8+AfxZyvTYTqJTS4dnP0YVXQZIkqex2BaZkk38TEdcBRwEtE6ijgHOy5b8Av4iISCmliPgQ+W9aaSjkYoWOgSIijiE/mDwB96SU/l+hx0qSJJXYSOC1FuvTgd3WVial1BQR84EhEbEU+Bb51qt2u++g8JnIfwVsBlybbfpCRBycUjq1kOMlSZJaiiLngYqI8cD4FpsmpJQmrKdwzgEuSiktigK7FgttgToA2CqllAAi4mpg8rpEKEmSVKwsWWorYXod2KjF+qhs25rKTI+IGmAgMJt8S9WxEfFjYBCQi4ilKaVfrO1ihSZQU4CNgVey9Y2ybZIkScVb/9MYTAQ2j4jR5BOl44ETVitzE3Ai8ABwLHB71ji096qw4hxgUVvJExSeQPUHno2Ih7P1XYBHIuImgJTSkQWeR5Ikab3LxjSdBtwKVANXppQmR8R5wCMppZuAK4BrImIKMId8krVOCk2gzl7XC0iSJJVDSukW4JbVtp3dYnkpcFw75zinkGsVlECllO6KiA3IPyKYgIkppbcKOVaSJOkdqrrwTOQrRMRngYeBY8j3GT4YEZ8pZWCSJEmVqtAuvG8AO6aUZgNExBDgfuDKUgUmSZK6sChuGoNKU2gCNZvW3323MNsmSZJUvE7ehVfMNAYPRcTfyI+BOgp4MiK+BpBS+lmJ4pMkSao4hSZQL2WvFf6W/ey/fsORJEmqfIU+hXduqQORJEndyPqfSLOsCv0uvHrgm8A2QK8V21NKB5QoLkmSpIpV6BD4PwDPAaOBc4GXyU+ZLkmSVLSIqqJelabQiIaklK4AGlNKd6WUPkP+C4YlSZK6nUIHkTdmP9+MiMOAN4C6Qi9St8euxcalNfA+SpJUGQpNoC6IiIHAGcClwADg9EIvMu14Jy1/t0ZfdyUzbvx7R4fRqQ07+gjmPPxIR4fR6dXtOrajQ5DUFXSHeaBSSjdni/OB/UsXjiRJUuVrM4GKiEvJT5y5RimlL6/3iCRJUtfXxacxaNnfcS7w/RLGIkmS1Cm0mUCllK5esRwRX225LkmS1F0VOogc2ujKkyRJKkpV5c3tVIzOHb0kSVIHaG8Q+UJWtTz1iYgFK3YBKaU0oJTBSZKkLqorDyJPKfUvVyCSJEmdhV14kiRJRSpmELkkSdJ6EZ18JnJboCRJkopkC5QkSSq/6NxtOJ07ekmSpA5gC5QkSSq/Tj6NgS1QkiRJRbIFSpIklZ9P4UmSJHUvJlCSJElFsgtPkiSVn9MYSJIkdS+2QEmSpLLzq1wkSZK6GRMoSZKkItmFJ0mSys+ZyCVJkroXW6AkSVL5VXXuNpzOHb0kSVIHsAVKkiSVny1QkiRJHS8iPhgRz0fElIg4cw37e0bEn7L9D0XEptn2XSPi8ez1REQc3d61TKAkSVKnFxHVwC+BQ4CtgY9FxNarFTsZmJtS2gy4CPhRtv1pYGxKaQfgg8BlEdFmL50JlCRJKr+I4l7t2xWYklKamlJaDlwHHLVamaOAq7PlvwAHRkSklBanlJqy7b2A1N7FTKAkSVJXMBJ4rcX69GzbGstkCdN8YAhAROwWEZOBp4AvtEio1sgESpIklV1URXGviPER8UiL1/j1GU9K6aGU0jbALsBZEdGrrfI+hSdJkipeSmkCMKGNIq8DG7VYH5VtW1OZ6dkYp4HA7NWu82xELAK2BR5Z28VsgZIkSeUXVcW92jcR2DwiRkdED+B44KbVytwEnJgtHwvcnlJK2TE1ABGxCbAl8HJbF+tyLVC9t9+WuhNPIKqChbffw/ybbmldoKaG+lM/S8/Rm9C8qIGZl/yappmzqakfwsgLf0DjG28BsOzFl5h9xTVEjx4M++oXqRk+DHI5Fj/6BHOv/UsH1Ky8Hnr+OS75+9/IpRyH77Ibn9jvgFb7lzc18YM/X8vzr09nQJ8+nPuxTzKiro6JL77Ab/71D5qamqmpqeaUQw5n5802B+CMKy9n9oIFNOdybD96NKcfdQzVnXwekLY88OQTXHzNNTTnchy533586ogjW+1f3tjIeZf9muemvczAfv244LQvMaK+nvkLF/LtSy/h2alTOXTvffj6iSe949zf+NmFvDFjBn/43x+9Y58kdUcppaaIOA24FagGrkwpTY6I84BHUko3AVcA10TEFGAO+SQLYBxwZkQ0AjnglJTSrLau17USqAiGfOYTvPWDC2maPYcN/+dsFk96nMbX31hZpP/+e5Nb1MD0r55F3z12ZfAJxzHzkt8A0PT2DN4485x3nHb+zbey9JnnoLqaEd/7Br13eB9LHn+qXLUqu+Zcjp/97UYuOnk89QMH8rlfXMJeW23N6OEbrCzzj4kP0b93b677xln894nH+M2//sG5J3ySgX368qMTP8PQAQOZ+tabnHHl5dz47bMBOO+ET9K3Vy9SSnzv97/jjqee4KDtd+yoapZUcy7HhVdfxSXfOothdXV85uzvsfdOOzF65KiVZf5+153079uXv1z4M/7zwAP88k/XcsFpX6ZHbS3jP3wcL01/janTp7/j3HdOnEjvXj3LWR1JWv9K8GXCKaVbgFtW23Z2i+WlwHFrOO4a4JpirtWlPv733GwMjW/NoGnGTGhupuH+h+gzdodWZfqM3ZFFd98PQMNDj9B7m63aPGdavjyfPAE0N7Ns2ivU1A0uRfgV49nXXmXkkCFsOGQItTU1HLj9Dtz7zORWZe55ZjIf3GksAPttux2TprxISoktRo5k6ICBAIwevgHLGhtZ3pR/kKFvr/x4vOZcjsbmJoLO/U3cbXnmpZcYNXw4I4cNo7amhoN23527J01qVeaeRydx6Lh9ANh/1115ZPJkUkr07tWL7d/7XnrW1r7jvIuXLuXaf93Cp4/6UDmqIUlaizYTqIhYGBEL1vYqV5CFqq4bRPPsOSvXm+fMfUeyU1M3iKYVZXI5ckuWUNW/X35ffT0b/vD7bHD2t+i55ebvOH9Vn9702WkHljz9bOkqUQFmLpjPsIGDVq7XDxzErAXzW5WZtWA+wwbly9RUV9O3V2/mL17cqsydTz/JFiNH0aNmVUPn166YwBHnn0Ofnr3Y733blawOHW3m3DkMqxuycn1YXR0z585tXWbOXIYPqQPy97Bfnz7MX7SozfNO+Mv1fOyQQ+nVwxYoSepIbSZQKaX+KaUBwCXAmeTnTxgFfAu4eG3HtXzUcMKEtgbMV46mufN57bSv88ZZ5zLnmusY9qXPE71bPMFYVUX9l7/Agn/9N9/CpTZNe/stfvPPW/jG0R9utf1nJ4/n/33nbBqbmnj0pSkdFF3n9MIrL/P6jBnsN3aXjg5Fkt69qijuVWEK7cI7MqX0q5TSwpTSgpTSr3nn7J4rpZQmpJTGppTGjh+/XqdpaFPznHlUZ5/oAarrBtM0p/Wn/qY586hZUaaqiqrevcktXARNTeQWNQCwfNorNL49g9oRq8b8DP3ciTS++TYL/vmf0lekg9UPGMiM+fNWrs+cP29lt9wKQwcMZMa8fJmm5mYali5hYJ8+AMyYP49vX3MV3/nI8YwcMvQd5+9ZW8u4rbfh3meeLlkdOlr94DpmzFn1ZOyMOXOoH9y6NbS+bjBvZ62hTc3NLFq8mIH9+q31nE+/OIXnpk3l6NO/wufPP5dX33qTU35wQWkqIElqU6EJVENEfDwiqiOiKiI+DjSUMrB1seyladRuMJya+qFQXU3fPXdj8aTHW5VZPOlx+u2zJwB9dxvLksn58U1V/fuvHNBWM6ye2g2G0/R2vqVp8EeOpqpPb+b87tryVaYDbTlqI6bPnsUbc2bT2NTEbU88zritt2lVZtzW2/CvR/PTY9z59JPs9J7NiAgWLlnCN//vCr7wwcPYbtPRK8svXraMWQvyvb5Nzc088PyzbFw/rHyVKrOtxozhtbfe4o0ZM2hsauK/Dz7I3jvt3KrMuB134pZ77wbgjocfZuettyHaGFR5zEEH8fdLf8mNF13CZd/7PhtvMIJffee7Ja2HJJXM+p/GoKwKfQrvBPLdeJeQ/36Y+7JtlSWXY/b//Z4Nvv01qKpi4R330jj9DQYd9yGWT32ZxZMeZ9Edd1N/6ucYdfEPyS1qYMbPLwOg11ZbMPi4D5GamyElZv32d+QaGqiuG8ygY45g+etvsOEPvw/AgltvY9Ed93RkTUuqprqa0488mjOuvJxcLnHY2F0YPXwDfvvvf7HlqI0Yt/U2HDZ2Vy7487Uc/5MfMqB3H8752CcA+Ov99/H67Flcddt/uOq2fGvdz07+HCnBWb+7kuVNzaSUY8cxm3HUbnt0ZDVLqqa6mjM+dRJf/cmPyOVyHL7PvowZNYoJN/yFrUaPZu+dduaIfffj3N/8mmPP+BoD+vXl/FO/tPL4o0//Cg1LltDU1MTdkx7hkm+d2eoJPklSx4qU2v2+vHcrTTv+M6W+Rpc3+rormXHj3zs6jE5t2NFHMOfhtU4qqwLV7Tq2o0OQtP502OCit676Y1EJyAYnnVBRA6EKahOLiC0i4raIeDpb3y4i7DuQJEnrpNjvwqs0hXYqXg6cBTQCpJSeZNXsnZIkSd1KoWOg+qSUHl5tgGtTCeKRJEndQQlmIi+nQlugZkXEe8gPICcijgXeLFlUkiRJFazQFqhTgQnAlhHxOjAN+HjJopIkSapg7SZQEVFN/luJD4qIvkBVSmlh6UOTJEldVlXlze1UjHYTqJRSc0SMy5YrbvJMSZKkciu0C++xiLgJuJ4WM5CnlP5akqgkSVLX1skHkReaQPUCZgMHtNiWABMoSZLU7RSaQP02pXRfyw0RsVcJ4pEkSap4hY7gurTAbZIkSe2LKO5VYdpsgYqIPYA9gfqI+FqLXQOA6lIGJkmSVKna68LrAfTLyvVvsX0BcGypgpIkSV1bdOVpDFJKdwF3RcRVKaVXyhSTJElSRSt0EPniiPgJsA35J/IASCkdsPZDJEmS1qICxzUVo9D2sz8AzwGjgXOBl4GJJYpJkiSpohWaQA1JKV0BNKaU7kopfYbWc0JJkiQVriqKe1WYQrvwGrOfb0bEYcAbQF1pQpIkSapshSZQF0TEQOAM8vM/DQBOL1lUkiSpa4su/BTeCimlm7PF+cD+pQtHkiSp8rU3keal5L/zbo1SSl9e7xFJkiRVuPZaoB5psXwu8P0SxiJJkrqLChwYXoz2JtK8esVyRHy15bokSVJ3VeggcmijK0+SJKkY0U0m0pQkSVKmvUHkC1nV8tQnIhas2AWklNKAUgYnSZJUidobA9W/XIFIkqRupJPPA9W5o5ckSeoAxQwilyRJWj86+TQGtkBJkiQVyQRKkiSpSHbhSZKk8nMeKEmSpO7FFihJklR+VZ27DadzRy9JkpSJiA9GxPMRMSUizlzD/p4R8ads/0MRsWm2/eCImBQRT2U/D2jvWiZQkiSp/CKKe7V7uqgGfgkcAmwNfCwitl6t2MnA3JTSZsBFwI+y7bOAI1JK7wNOBK5p93oplfw7gv0SYkmSKlOHjeSe+c//FJUf1B9ycJuxRsQewDkppQ9k62cBpJR+2KLMrVmZByKiBngLqE8tkqHIf8vxbGBESmnZ2q5XljFQL477QDku06Vtfu+tvPmr33Z0GJ3aiFM+y9zHn+zoMDq9wTtsx9TjTuzoMDq1Mddf3dEhSB0uipxIMyLGA+NbbJqQUprQYn0k8FqL9enAbqudZmWZlFJTRMwHhpBvgVrhw8CjbSVP4CBySZLUCWTJ0oR2C74LEbEN+W6997dX1jFQkiSpK3gd2KjF+qhs2xrLZF14A8l31xERo4AbgU+llF5q72ImUJIkqfyiqrhX+yYCm0fE6IjoARwP3LRamZvIDxIHOBa4PaWUImIQ8A/gzJTSfYVczARKkiR1eimlJuA04FbgWeDPKaXJEXFeRByZFbsCGBIRU4CvASumOjgN2Aw4OyIez17D2rqeY6AkSVL5FTmIvBAppVuAW1bbdnaL5aXAcWs47gLggmKuZQuUJElSkUygJEmSimQXniRJKr8CZhevZLZASZIkFckWKEmSVH6FTU1QsTp39JIkSR3ABEqSJKlIduFJkqSyK/bLhCuNLVCSJElFsgVKkiSVn9MYSJIkdS8mUJIkSUWyC0+SJJVfVeduwzGBkiRJ5ecYKEmSpO7FFihJklR+zgMlSZLUvZhASZIkFckuPEmSVHYRnbsNp3NHL0mS1AFsgZIkSeXnNAaSJEndiwmUJElSkezCkyRJ5ec8UJIkSd2LLVCSJKn8nMZAkiSpezGBkiRJKpJdeJIkqfw6+SDyLpdA9dltLPVf+QJUVbPg5n8y9/d/brU/amsZ/t1v0PO9m5NbsIA3z/4fmt56G4Ae7xnNsG98maq+fSGX47XPfQmiihHnf4fakRuScjka7nuQ2b+5siOqVlY9NxrFgHG7Q1Ww+JnnaXjsydYFqqoYdNB+1NYPIbd0GfP+fTvNCxdRO6yegfuNW1ls4cRHWTbtFQAG7r83PTfZmNySJcz601/LWZ0O8cDjj3HRVf9HLpfjyAMO5FMfOrrV/uWNjZz7y0t5fupUBvTvzwVfOZ0Nhw1j/sKFnPWzC3n2pSkctt9+fP0znwVg6bJlfPuiC3n97bepqqpi3M47c+oJn+iIqpVV7x3ex5BPf5yoqmLBbXcx///9o3WBmhqGfWk8PcdsSvPCRcy46Fc0zZxFTf1QRl38QxrfeBOAZS+8xKzLrwZgg++cQfWgQUR1NUuffZ5ZV/wOcqncVZPUiXWtBKqqivqvncrrp59F04xZbPzbS2m490GWv/zqyiIDDv8AuYWLeOX4T9PvwH0Z+sWTeev7/wPVVWzwvW/y1gU/YfmUqVQN6E9qaiZqq5h77Q0seewJqKlh1CU/os/uY1n84CMdWNESi2DAPnsy5+//pHlRA0OPPYplL79K09x5K4v02eq95JYtY+YfrqfXZmPov8euzPv37TTOmcOs6/8fpERVn94M/cgxzHj5VUiJJc+9SMNTzzDowH07rGrl0pxr5qdXXsHPv/M9hg2p49NnncXeY8cyetRGK8vcdPvtDOjbj7/8/Bf85777+OUff88Pvvo1etTWMv6jH2Xqa68x9bVXW53344cfyc7bbktjUyOnnX8e9z/2GHvuuGO5q1c+VcHQkz/Fm+f/mKY5cxj5w3NY/MhjNE5/Y2WRAQfsQ25RA6996Zv03XM36j7xEWZc9CsAmt6awevfOPsdp337Z78kLVkKwPAzTqPv7rvScP9D5amTJADCmcgrR6+t3kvj9DdoeuMtaGpi4X/vpO+4PVqV6TtuDxb88z8ALLrzHvrsvAMAfXbZmWUvTWP5lKkA5BYshFyOtGxZPnkCaGpi6QsvUlNfX7Y6dYTaYfU0z19Ac3YPlkyZSs/Rm7Qq02v0Jix57kUAlr40jZ4jN8zvaGqGlP8kH9XVrY5Z/uZbpGXLSl+BCvDMlCmMGr4BI4cPp7amloP33Iu7J7ZOuu95ZCKH7ptPJvfffXceefppUkr07tWLHbbcih61ta3K9+rZk5233RaA2ppa3jt6NDPmzC5PhTpIz83G0PjW2zTNmAlNzTTc9xB9x+7UqkyfXXZi4V33AtDw4ER6b7t1u+ddkTxRXQ01NYCtT5KKU3QCFRGDI2K7UgTzbtXUD8n/oc2saMZvXWboqjLNOZobGqgaOIAeG42ClNjwwh+w0RW/YPAJx73j/FX9+tJvr91ZPOmxktajo1X37UPzooaV67lFDVT37dOqTFXfPjQvWpRfSYnc8uVEr55APgEbevyHGXr8h5l/170rE6ruZOacOQwbMmTl+rAhdcycO/sdZYYPyb8/a6qr6denD/MXLizo/AsbGrh30iR22fZ96y/oClRTN5im2XNWrjfNmUP1kMHvLDMrK5PLkVu8hKr+/fL7htUz8sfnMeLcs+i15RatjtvgO19nk99eSlq6lIYHJ5a2IpK6nIK68CLiTuDIrPwkYEZE3JdS+tpayo8HxgNcdtll7L9+Yi2tmmp6b7ctr37uS6Slyxh5yf+y9PkXWTLp8fz+6io2OOcs5l3/t3wLl9aqccZMZl13AzWDBzHwgH1Y9up0aG7u6LC6jKbmZr7384v5yAcPZeTw4R0dTsVqmjuPV794OrlFDfQYsykbfOPLvPa1b69sfXrrBz8lamsZ9uXP03vbrVny5OQOjljqXgbvtEOn7sMrtAVqYEppAXAM8LuU0m7AQWsrnFKakFIam1IaO378+PURZ0GaZs6mZtiq7rWa+qE0zZy1WplZq8pUV1Hdty+5+QtomjGTJU88RW7+AtKyZSx+YCI9t9hs5XHDvvlVGl97nXnX31iWunSk5obFVPfru3K9ql9fmhsWtyqTa1hMdb/8p3wiqOrRg7S0dfdc09x5pMYmautatxh0B/V1dcyYvarFacbsOdQPHvKOMm/Pzr8/m5qbWbR4MQP792/33P874TI22mAExx922PoNugI1zZlLzZC6les1dXU0z577zjJDszJVVVT16U1u4SJoaiKXtaQun/oyjW/PoHbEBq2OTY2NNEx8jD67tO4WlKT2FJpA1UTECOAjwM0ljOddWfrc8/TYaCQ1I4ZDTQ39D9qPhvsebFWm4b4HGXDIwQD0229vFj+aH9+0+OFJ9BizKdGzJ1RX0XvH7VYOPh/yuROp7tuXmT//TXkr1EEaZ8ykeuAAqvv3g6oqem82ZuWTdCssffkVem+5OQC93jOaZa/nB/VW9+8H2cDA6n79qBk8kKYCu6W6kq3esxmvvfUmb8x4m8amRv5z/33sPXZsqzJ7jx3LLXfdBcAdDz7I2G22bXdQ5W+uu5ZFixdz+oknlSr0irJsyjRqRwynZthQqKmm71670fBI6y70xY88Rv99809+9t19F5Y8/SwAVQP6r3xMumZYPbUjNqBpxkyiV0+qBw3MH1xVRZ+dt6fx9TfLVylJXUKkAsanRMRxwPeAe1NKp0TEGOAnKaUPF3CN9OK4D7zLMAvXZ/ddsmkMqljwj38z93fXUnfyp1j23As03Pcg0aOW4d/7Jj0334zcgoW8ec7/rOyS6//+Axj8yeMhJRoeeJjZv76CmvqhjL7xDyx/+VVSYyMA8264iQU3/6tsdQLY/N5befNXvy3b9XpuPIoB4/aACJY89wKLJj1Ov112onHmLJa9/CpUVzPowH1XTWPwnztoXrCQ3ltsRt+dtodcDlJi4SOPrUy+Bh28Pz02HEFVr17klixh4cRJLHn2hbLVacQpn2Xu40+2X3A9uf+xR7no6qvI5XIcvt/+fPqYDzPhz9ex5Zj3sM/YXVi2fDnn/uJSXnh5GgP69eP8r5y+skvuQ6edwuLFi2lsaqJf3778/DvfpW/vPhx5yhfYZMOR9KjN974f+4FDOOrAA8tWJ4DBO2zH1ONOLNv1eu+4HUNOyk9jsPCOu5n3178z+KNHs+yll1n8yGNEbS31XxpPz9Gb0LyoIT+NwYyZ9N1tLIM/egypuQlyibl/vpHFkx6neuAAhp95OlFbS0SwZPKzzL7qj/n3bJmMuf7qsl1Laken7kbrSAUlUO9SWROorqrcCVRXVO4EqqsqdwLVFZlAqYKYQK2jgrrwIuLHETEgImoj4raImBkRXX8GP0mSpDUodAzU+7NB5IcDLwObAd8oVVCSJEmVrOBB5NnPw4DrU0rzSxSPJElSxSv0q1xujojngCXAFyOiHlhaurAkSZIqV0EtUCmlM4E9gbEppUZgMXBUKQOTJEmqVIUOIu8DnAL8Otu0ITB27UdIkiR1XYWOgfo/YDn5ViiA14ELShKRJElShSs0gXpPSunHQCNASmkxzh0hSZK6qUITqOUR0RtIABHxHmBZ24dIkiR1TYU+hfd94F/ARhHxB2Av4KRSBSVJklTJCkqgUkr/iYhHgd3Jd919JaU0q6SRSZIkVahCW6AAegFzs2O2jghSSneXJixJkqTKVVACFRE/Aj4KTAZWfGV5AkygJElSt1NoC9SHgPemlBw4LkmSur1Cn8KbCtSWMhBJkqTOotAWqMXA4xFxGy2mL0gpfbkkUUmSJFWwQhOom7KXJElSt1foNAZXlzoQSZKkzqLNBCoiniKbfXxNUkrbrfeIJEmSKlx7LVCHZz9PzX5ek/38BG0kVpIkSV1ZmwlUSukVgIg4OKW0Y4td38pmJj+zlMFJkiRVokKnMYiI2KvFyp5FHCtJktSlFPoU3snAlRExkPx34c0FPlOyqCRJkipYoU/hTQK2zxIoUkrzSxqVJElSBSv4y4Qj4jBgG6BXRACQUjqvRHFJkiRVrILGMUXEb8h/mfCXyHfhHQdsUsK4JEmSKlahA8H3TCl9CpibUjoX2APYonRhSZIkVa5CE6gl2c/FEbEh0ASMKE1IkiRJla3QMVA3R8Qg4MfApGzbb0sSkSRJUoVr76tcdgFeSymdn633A54CngMuKn14kiRJlae9LrzLgOUAEbEP8L/ZtvnAhNKGJkmSVJna68KrTinNyZY/CkxIKd0A3BARj5c0MkmSpArVXgtUdUSsSLIOBG5vsa/gOaQkSZK6kvaSoGuBuyJiFvkn8e4BiIjNyHfjSZIkdTttJlAppR9ExG3kpyz4d0opZbuqyE+qKUmS1O202w2XUnpwDdteKE04kiRJlS9WNSqVTMkvIEmS1kl0dACdVVkGgr/+40vKcZkubeQ3v8Ks2+/u6DA6taEH7MPs+9/RoKoiDdlzd978hbOYvBsjThvP9O9e0NFhdHqjLvhuR4egbqzQr3KRJElSxgRKkiSpSCZQkiRJRTKBkiRJKpIJlCRJUpFMoCRJkopkAiVJklQkEyhJkqQimUBJkiQVyQRKkiSpSCZQkiRJRTKBkiRJKpIJlCRJUpFMoCRJkopkAiVJklQkEyhJkqQimUBJkiQVyQRKkiSpSCZQkiRJRTKBkiRJKpIJlCRJUpFMoCRJkopkAiVJklQkEyhJkqQimUBJkiQVyQRKkiSpSCZQkiRJRTKBkiRJKpIJlCRJUpFMoCRJkopkAiVJklQkEyhJkqQimUBJkiQVyQRKkiSpSCZQkiRJRarp6ADWt56jN2HggfsSETQ8OZlFDz3SukB1NYMPez89hg8jt2Qpc266heYFC6ndYDiDPnAgABGw4L6HWPriSwAM//ynScuXk3IJUo6Zv7uu3NUquwcnP83Ff76OXMpxxF5788kPHNJq//LGRs6/+kqef/UVBvbtx3mfHc+IIUOZv2gR37n8Nzz3ysscsvuenHH8CSuP+c/Eh/jdv/5JBAwdOIizP30yg/r1L3fVyubBp57k4j/+geZcjiP22ZdPHXZ4q/3LGxs5//IJPPfKywzs14/zv3gKI4bW5+/hLy/l2WnTOHSvcZzxyU+tPObU//0hs+fPo2dtDwAu+vo3qBswoKz1KreeG49iwN57QgSLn3mOhkefaF2gqopBB+9Pbf1QckuXMe/W/9K8cBG1w+oZuP/e+TIRLHx4EsumvkxVv74MOmh/qvr0hpRYPPk5Fj/5dPkr1oF6bj6GQYd+gKgKGiY9zsK7729doLqaumOPpMeGI8gtXsLsP/2V5nnzV+0eOIDhX/4CC26/m0X3PVjm6KXK0LUSqAgGHbQfs/58I80LFzHsU8ezdMpUmmbPWVmk7/u2IS1dxtuXX03vLbdgwH7jmHvTP2maNZuZv7sWUqKqbx+GnfRx3poyFVICYNZ1N5BbsrSjalZWzbkcF173Ry7+8ukMGzyYz/7vDxi33faMHrHhyjI3338v/fv04c/n/Q//nfgwv7rxBs7/7OfpUVvL5444iqlvvM7UN95YWb6puZmL//wn/vD9cxnUrz+//OtfuOHOOzj58CM7oool15zL8dNrfsclX/8mw+rqOPm8c9h7hx0ZPXLkyjJ/v+du+vfty/U/+gn/eehBfvXnP3P+Kafm7+HRH2bq69OZOn36O879/fFfYKvRo8tYmw4UwYB9xzHnb/+geVEDQz9yNMumvULT3Hkri/TZektyy5Yx8/d/otfm76H/nrsx79bbaJwzh1l/vjH/O92nN0OPP5YZ016BXI4F9z1A08zZRG0tQz96NMtfm97qnF1aBIOPOISZ//cHmhcsYNgXTmbJsy/QNHPWyiJ9d96B3JKlvHXRr+j9vq0Z+IEDmPOnG1fuH3jIwSx9cUpHRC9VjHa78CKiKiL2LEcw71aPEcNpmjef5vkLIJdj8bMv0GuzMa3K9Np8DIuffgaAJc+/SM+NNwIgNTWtTJaipmvllcV69uVpjKqvZ2R9PbU1NRw4dhfueeLxVmXueeJxDt09/7bYb6edmfTcc6SU6N2zJ9tvtjk9amtXO2sCEkuXLSelRMPSJQwdOKgc1ekQz0ydyqhhwxk5bBi1NTUctOtu3PPYo63K3PPooxyy1zgA9h+7C488+8yqe7jFFmu4h91P7fB6mufPp3nBQsjlWPLiS/Qcs2mrMr3GbMKS514AYOmUqfQclSWpTc2rfqera8i/ByG3eAlNM2cDkBobaZozj6p+fctSn0rQY9SGNM2eQ/PcedCcY8lTk+m91RatyvTeagsWP/YkAEsmP0vPMasS9l5bbUHz3Hk0zZiF1J21mymklHIR8UtgxzLE865U9etH88KFK9ebFy6ix4YbtCpT3a8vTQsW5VdSIi1bRlXvXuSWLKV2xHAGH3Iw1QP6M/cf/175x5eUGPKRoyElGp54msVPdO3m/pnz5jFscN3K9WGDBzN52rQ1lBkMQE11NX1792Z+w6K1dsnVVNfw9Y99gk9ecA69e/Rk1LBhnHH8x0tXiQ42c+5chtetuof1dXU889JLrcvMW1Vm5T1ctIhB/dvu1vzBFb+luqqK/caO5aQjjiQi1n8FKkR13740L2xYuZ5b1EDt8GGtylS1LJMSueXLiV49SUuXUTu8noEH7Et1//7M++8dq36nV5y/fz9q64fS+NaMktelUlQP6J//kJlpXrCQHqM2XHuZXPZ3sk9vUlMT/ffek1lX/YH+4/YoZ9hSxSm0qeW2iPgw8NeUVvsLtAYRMR4YD3DZZZdx2LsIsJwa33ybGVf+npq6wQw+7P0snfoyNDcz84/Xk1vUkO8G+MjRNM2ew/Lpb7R7Pq3S1NzEjXffyf99+3uMHFrPz/50Ldf86xZOOvTw9g/WSud8/vPUD66jYckSvv3LS/nX/fetbMXSOzW+PZNZ1/6FmsGDGHjQfix75TVobgYgamsYfMjBLLjnflJjYwdH2jkMOGAfFt3/EGm590sq9Cm8zwPXA8sjYkFELIyIBWsrnFKakFIam1IaO378+PUSaCFyixZR3eLTe3X/fjQvXNSqTPOiBmoG9MuvRBA9e75jbFPTnLmk5Y3U1g/Jzpv/dJtbvIQlL75EjxGtW7W6mvpBg5gxd9W4sRlz51I/aNAayswF8uObGpYsYWDffms954uvvQbAqPphRAQH7jyWp6a+tNbynV394MG8PWfVPZw5Zw71WYvdyjKDVpVZeQ/7rf0e5s+bb7Hq27s37999D56ZOnU9R15ZmhsaqO6/qnutql9fmhsaWpXJtSwTQVWPHqSly1qVaZo7j9TYSO2Q7N+gKhh8yMEseWFK/oNSN9K8YCHVA1c9eFA9oH++i3RtZaqyv5OLl9Bj1EgGfuBANjjjNPrtsSsD9t2LvruNLWf4UsUoKIFKKfVPKVWllGpTSgOy9Yp79Gf5m29TM3hQ/he/qoo+W23B0imt/4NZOmUqfbbdGoDe792cZa/m/2OvHjgg//gd+T8oNUMG0zx/AVFbQ/TIj0WJ2hp6broxjbNml7FW5bflJpsyfcYM3pg1k8amJm57ZCLjttu+VZlx2+3ALQ/mn9y589FJ7Pze97bZlTR00GBefvNN5mZdrA8/+wybbjCidJXoYFuNHs30GW/zxsz8Pfzvww8xbsfWveB777gj/7zvXgDueGQiO2+1VZv3sKm5mXnZ/WtqauK+Jx5nzKhRpatEBWh8eybVAwfmPxhVVdF78/ewbNorrcosnfYKvbfMj+HptdkYlk1/HSB/zIrf6f79qBk8iKYsURh4wL40zZlHw+NPlbE2lWH5629QM6SO6sGDoLqK3u/bZuUYshWWPPcCfXbcDoDe22zFsizJnPnb3/HWhb/grQt/waIHHmbBXffRsPqTzlI3UVAXXuT/qn8cGJ1SOj8iNgJGpJQeLml0xUqJef+9k6HHfQgiaHjqGZpmz6H/uN1pfOttlk6ZRsOTk6k77AMM/9yJ5JYuZc5N/wSgx8gN6f/hsaTmHJCY9+87yC1ZSvXAAQw5Outmqqpi8TPPv+MPeFdTU13N6cefwNcuvZjmXOLwPfdizIYjufzvf2PLjTdh7+134PC9xnH+VVfwkbO/zYA+fTn35FUtjR/+zpk0LF1CU3Mz9zzxGBd9+XRGj9iQTx92OKf+7MfUVFezQd0QvvOpT3dgLUurprqar338k5x+4U9ozuU4fO99GDNyFJff+Fe23HRT9t5xJw7fZx/OmzCB4771DQb07ct5Xzhl5fHHfP2M/D1sauLuxx7l4jO+wQZDh3L6hT+hqbmZXC7H2K234ch99+u4SpZDSiy4+z7qjjoEooolzzxP05y59Nt1ZxpnzGLZy6+w+JnnGXTw/tR/4qPkli1j3q23AdBjww3ou9P2kMtBgvl33psfFzViOH223ILGWbMZ+tFjAFj44MR89153kEvMu/lfDD3xY0RVFQ2THqdpxiwGHLgvy19/g6XPvUjDpMepO/YoNjj9FHJLljC7xRN4kvKigCFNRMSvgRxwQEppq4gYDPw7pbRLAddIr//4kncZpkZ+8yvMuv3ujg6jUxt6wD7Mvt85a96tIXvuzpu/mNDRYXRqI04bz/TvXtDRYXR6oy74bkeH0BV03adQSqzQQeS7pZR2iojHAFJKcyOiRwnjkiRJqliFDiJvjIhqsolUIqKefIuUJElSt1NoAvVz4EZgWET8ALgX+J+SRSVJklTBCurCSyn9ISImAQeS7y/9UErp2ZJGJkmSVKHaTKAioq7F6gzg2pb7Ukpz3nmUJElS19ZeC9Qk8uOeAtgYmJstDwJeBbrJN5pKkiSt0uYYqJTS6JTSGOC/wBEppaEppSHA4cC/yxGgJElSpSl0EPnuKaVbVqyklP4J7FmakCRJkipbofNAvRER3wV+n61/HPDbdCVJUrdUaAvUx4B68lMZ3AgMy7ZJkiR1O4VOYzAH+EqJY5EkSeoUCv0y4S2ArwObtjwmpXRAacKSJEmqXIWOgboe+A3wW6C5dOFIkiRVvkITqKaU0q9LGokkSVInUegg8r9HxCkRMSIi6la8ShqZJElShSq0BerE7Oc3WmxLwJj1G44kSVLlK/QpPL+yRZIkKVPoU3i1wBeBfbJNdwKXpZQaSxSXJElSxSq0C+/XQC3wq2z9k9m2z5YiKEmSpEpWaAK1S0pp+xbrt0fEE6UISJIkqdIV+hRec0S8Z8VKRIzB+aAkSVI31WYLVER8FbgfOJN8q9O0bNemwGdKGpkkSVKFaq8LbxRwMbAV8CIwB7gDuCGl9EZpQ5MkSapMbSZQKaWvA0RED2AssCewH3BWRMxLKW1d8gglSZIqTKGDyHsDA4CB2esN4KlSBSVJklTJ2hsDNQHYBlgIPER+PNTPUkpzyxCbJElSRWrvKbyNgZ7AW8DrwHRgXoljkiRJqmjtjYH6YEQE+VaoPYEzgG0jYg7wQErp+2WIUZIkqaK0OwYqpZSApyNiHjA/ex0O7AqYQEmSpG6nvTFQXybf8rQn0Eh+DNT9wJU4iFySJHVT7bVAbQpcD5yeUnqz9OFIkiRVvvbGQH2tXIFIkiR1FoV+F54kSZIyJlCSJElFMoGSJEkqkgmUJElSkSI/zVP3FhHjU0oTOjqOzs77+O55D9cP7+O75z1cP7yPXZctUHnjOzqALsL7+O55D9cP7+O75z1cP7yPXZQJlCRJUpFMoCRJkopkApVn//T64X1897yH64f38d3zHq4f3scuykHkkiRJRbIFSpIkqUhdLoGKiA9FRIqILTs6lq4iIpoj4vEWr007OqaOEBGLVls/KSJ+UYLr3BIRg9b3eTuTdbnXEbFfROy5vq/dmUTEdyJickQ8mf2u7lbgcZtGxNOljq+ziIiLIuKrLdZvjYjftli/MCLe1XfFRsTLETH03ZxDHavLJVDAx4B7s58lExFtfhFzF7MkpbRDi9fL7+Zk3ezeFS2ldGhKaV5Hx9EJ7QesMYHqDu+5iNgDOBzYKaW0HXAQ8FrHRtVp3Uf2XoqIKmAosE2L/XsC95czoIioLuf11L4ulUBFRD9gHHAycHy2bb+IuDMi/hIRz0XEHyIisn2HZtsmRcTPI+LmbHvfiLgyIh6OiMci4qhs+0kRcVNE3A7c1jG1rAwRsXNE3JXdu1sjYkS2/XMRMTEinoiIGyKiT7b9qoj4TUQ8BPy4Q4MvgYg4IiIeyt4v/42I4dn2cyLimoh4ICJejIjPZdv3i4i7I+IfEfF8dm+qsn0vR8TQrFXg2Yi4PGtV+HdE9M7KvCci/pXd/3tWtLhGxHER8XR2/+/Otm2TvZcfz1omNu+Yu7R+rOleZ62iXwBOz+q59+rvuTbu2ejs3+epiLigI+v2Lo0AZqWUlgGklGallN6IiLOz38mnI2JCi79/O2fvkyeAU1ecJPs799fsXr0YET9use/92b16NCKuz/7mEhH/GxHPZO+vn2bb3vFe7ETuB/bIlrcBngYWRsTgiOgJbAUMzN6DT2X/X/SElb+/52b36KkW77Mh2e/w5Mi3ZsWKi0XEJ1r8jl4WWbIUEYsi39r1RIt4VClSSl3mBXwcuCJbvh/Ymfyn0vnAKPIJ4wPkk6xe5D+djc7KXwvcnC3/D/CJbHkQ8ALQFzgJmA7UdXRdy3xfm4HHs9eNQG12f+uz/R8FrsyWh7Q47gLgS9nyVcDNQHVH12c93YfHgVeBX2T7BrPqoYzPAhdmy+cATwC9yX+KfQ3YMHtfLgXGANXAf4Bjs2NezspuCjQBO2Tb/9zifXkbsHm2vBtwe7b8FDByxXs3+3kp8PFsuQfQu6PvZQnv9ddbnKPVe66Ne3YT8Kls+VRgUUfXfx3vWb/sXr0A/ArYN9te16LMNcAR2fKTwD7Z8k+Ap7Plk4CpwEDyfydfATbK3pN3A32zct8CzgaGAM+3+DdZ8b57x3uxM72AacDGwOfJJ+fnA4cCewETs9/lLbKyvwO+mi2/zKq/e6cAv82Wfw6cnS0fBqTsnm4F/B2ozfb9qsX7MQEf6eh74WvNr67WrP0x4JJs+bps/Wbg4ZTSdICIeJz8f0yLgKkppWlZ+WtZNWPs+4EjI+Lr2Xov8r9IAP9JKc0pYR0q0ZKU0g4rViJiW2Bb4D/Zh9lq4M1s97bZp/hB5P+g39riPNenlJrLEXCJrH4fTgLGZqujgD9FviWuB/k/viv8LaW0BFgSEXcAuwLzyL8vp2bnupZ8Yv+X1a45LaX0eLY8Cdg0+9S/J3B9dv8BemY/7wOuiog/A3/Ntj0AfCciRgF/TSm9uE61L691vderuz6l1NzOPdsL+HC2fA3wo/VRgXJLKS2KiJ2BvYH9yd+jM8m3nHwT6APUAZMj4h7ySc2KlqFrgENanO62lNJ8gIh4BtiE/O/01sB92T3sQf69NZ/8h4ErIt+Kf3N2jjW9FzuT+8m/Z/YEfgaMzJbnk/8gvSSl9EJW9mryyffF2fqK+k4CjsmW91mxnFL6R0TMzbYfSP7D/sTsvvYGZmT7moEb1nO9tJ50mQQqIuqAA4D3RUQi/596Av4BLGtRtJn26x3Ah1NKz692jd2AhvUWdOcVwOSU0pqalK8CPpRSeiL7T2+/Fvu68r27FPhZSummiNiPfGvICqvPFZLa2d7S6u/d3uRbUue1TDBWniClL2Tv08OASRGxc0rpj1k31mHALRHx+ZTS7QXVqjK1da9Xt+I9t9Z7lukS87lkH1DuBO6MiKfIt55sB4xNKb0WEeeQ/0DYnjX9zQzyHyDfMb40InYlnwgcC5wGHLCW9+Lsda5c+a0YB/U+8l14rwFnAAvI3+MPr/XIVfev0P9vrk4pnbWGfUs7+YfOLq0rjYE6FrgmpbRJSmnTlNJG5D+Z7r2W8s8DY2LVE2UfbbHvVuBLLcYK7FiimDur54H6yA9aJSJqI2LFAMv+wJsRUUu+S7W7GAi8ni2fuNq+oyKiV0QMIZ9QTsy275qNv6ki//67t5ALpZQWANMi4jiAyNs+W35PSumhlNLZwExgo4gYQ7619efA38j/h9qZre1eLyT//nuHtu4Z+f8oj8+WO+17NiLeG63Ht+1A/ncVYFbWCncsQMo/pDAvIsZl+wup94PAXhGxWXa9vhGxRXbegSmlW4DTgbW+F99VBcvvfvKD8ueklJqznodB5Mci3UC+NXizrOwngbvaOd/dwAkAEXEI+a5oyHctHxsRw7J9dRGxyfqsiEqjKyVQHyM/PqelG1jL03hZl8opwL8iYhL5P77zs93nkx/n82RETM7WlUkpLSf/h/hH2eDGx1n19NP3gIfI/6f0XIcE2DHOId89NAmYtdq+J4E7yP8HdH5K6Y1s+0TgF8Cz5JP91d+/bfk4cHJ2/ycDR2Xbf5INXH2a/H8ATwAfAZ7Ouq+3JT9eozM7hzXf678DR2cDcdf0wWlt9+wrwKlZi83I0oVdcv2AqyMbzE2+u+0c4HLyLSi3sip5B/g08MvsfRG0I6U0k/z4qGuz8z8AbEk+ab0523YvsOLx/jW9FzuTp8iPUXpwtW3zsyEhnyb/PnwKyAG/aed85wL7ZP+nHEN+XB8ppWeA7wL/zu7hf8g/EKAK161nIo+Iftm4gQB+CbyYUrqoo+NS15F1mSxKKf10te37kR/wfHgHhCVJepe6UgvUuvhc9ulrMvlugcs6NhxJktQZdOsWKEmSpHXR3VugJEmSimYCJUmSVCQTKEmSpCKZQEmSJBXJBEqSJKlIJlCSJElF+v+L9LbxH/rO+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = np.triu(np.ones_like(sample_data, dtype=bool))\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "cmap = sns.diverging_palette(int(120), int(10), as_cmap=True)\n",
    "sns.heatmap(sample_data, mask=mask, center=0, annot=True, cmap = cmap,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, xticklabels=all_the_labels,\n",
    "        yticklabels=all_the_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}