get_outcomes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\Courses & Assignments\\Natural Language Processing\\NLP Final Project\\data_preprocessing.py:17: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(self.filepath, encoding=\"latin-1\")\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries and modules\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import data_preprocessing\n",
    "import model\n",
    "from data_preprocessing import DataPreprocessing\n",
    "from model import ModelTraining\n",
    "\n",
    "# Step 1: Data Preprocessing\n",
    "# Initialize data preprocessing with the dataset\n",
    "dp = DataPreprocessing(filepath=\"data/go_emotions_dataset.csv\")\n",
    "\n",
    "# Load and preprocess the data\n",
    "df = dp.load_data()\n",
    "df = dp.preprocess_data(df)\n",
    "\n",
    "# Split the data into training and validation sets\n",
    "dp.split_data(df)\n",
    "\n",
    "# Retrieve processed data and label mappings\n",
    "train_df, val_df, label2id, id2label = dp.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the total count for each emotion\n",
    "emotion_counts = df[dp.emotion_cols].sum()\n",
    "\n",
    "# Plot a bar chart showing the frequency of each emotion\n",
    "plt.bar(emotion_counts.index, emotion_counts.values)\n",
    "plt.title(\"Frequency of Each Emotion\")  # Add a title to the chart\n",
    "plt.xlabel(\"Emotion\")  # Label for the x-axis\n",
    "plt.ylabel(\"Count\")  # Label for the y-axis\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "\n",
    "# Add text annotations showing the exact count on top of each bar\n",
    "for index, value in enumerate(emotion_counts.values):\n",
    "    plt.text(index, value, str(value), ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "# Adjust layout and display the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       anger       0.22      0.17      0.19        36\n",
      "  excitement       0.62      0.58      0.60       277\n",
      "         joy       0.60      0.66      0.63       351\n",
      " nervousness       0.41      0.34      0.37        87\n",
      "       pride       0.42      0.31      0.36        58\n",
      "      relief       0.58      0.38      0.46        58\n",
      "     sadness       0.67      0.76      0.71       314\n",
      "\n",
      "    accuracy                           0.60      1181\n",
      "   macro avg       0.50      0.46      0.47      1181\n",
      "weighted avg       0.59      0.60      0.59      1181\n",
      "\n",
      "Random Forest Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       anger       0.11      0.06      0.07        36\n",
      "  excitement       0.58      0.61      0.59       277\n",
      "         joy       0.58      0.59      0.59       351\n",
      " nervousness       0.49      0.26      0.34        87\n",
      "       pride       0.45      0.29      0.35        58\n",
      "      relief       0.52      0.29      0.37        58\n",
      "     sadness       0.62      0.78      0.69       314\n",
      "\n",
      "    accuracy                           0.58      1181\n",
      "   macro avg       0.48      0.41      0.43      1181\n",
      "weighted avg       0.56      0.58      0.56      1181\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Step 2: Model Training\n",
    "mt = ModelTraining(label2id=label2id, id2label=id2label)\n",
    "\n",
    "# Train traditional models\n",
    "results_traditional = mt.train_traditional_models(train_df, val_df)\n",
    "\n",
    "# Print the classification reports for the two models\n",
    "print(\"Logistic Regression Report:\\n\", results_traditional['logreg']['report'])\n",
    "print(\"Random Forest Report:\\n\", results_traditional['rf']['report'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Leo\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\transformers\\tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
      "  warnings.warn(\n",
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "c:\\Users\\Leo\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\transformers\\training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0d78873a9cc484380940d696ef34498",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2804 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attempted to log scalar metric loss:\n",
      "1.5946\n",
      "Attempted to log scalar metric grad_norm:\n",
      "18.14244842529297\n",
      "Attempted to log scalar metric learning_rate:\n",
      "4.9108416547788875e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.03566333808844508\n",
      "{'loss': 1.5946, 'grad_norm': 18.14244842529297, 'learning_rate': 4.9108416547788875e-05, 'epoch': 0.04}\n",
      "Attempted to log scalar metric loss:\n",
      "1.2833\n",
      "Attempted to log scalar metric grad_norm:\n",
      "4.054147243499756\n",
      "Attempted to log scalar metric learning_rate:\n",
      "4.821683309557775e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.07132667617689016\n",
      "{'loss': 1.2833, 'grad_norm': 4.054147243499756, 'learning_rate': 4.821683309557775e-05, 'epoch': 0.07}\n",
      "Attempted to log scalar metric loss:\n",
      "1.2025\n",
      "Attempted to log scalar metric grad_norm:\n",
      "14.148418426513672\n",
      "Attempted to log scalar metric learning_rate:\n",
      "4.732524964336662e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.10699001426533523\n",
      "{'loss': 1.2025, 'grad_norm': 14.148418426513672, 'learning_rate': 4.732524964336662e-05, 'epoch': 0.11}\n",
      "Attempted to log scalar metric loss:\n",
      "1.184\n",
      "Attempted to log scalar metric grad_norm:\n",
      "9.670722007751465\n",
      "Attempted to log scalar metric learning_rate:\n",
      "4.64336661911555e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.14265335235378032\n",
      "{'loss': 1.184, 'grad_norm': 9.670722007751465, 'learning_rate': 4.64336661911555e-05, 'epoch': 0.14}\n",
      "Attempted to log scalar metric loss:\n",
      "1.1122\n",
      "Attempted to log scalar metric grad_norm:\n",
      "5.017050266265869\n",
      "Attempted to log scalar metric learning_rate:\n",
      "4.554208273894437e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.1783166904422254\n",
      "{'loss': 1.1122, 'grad_norm': 5.017050266265869, 'learning_rate': 4.554208273894437e-05, 'epoch': 0.18}\n",
      "Attempted to log scalar metric loss:\n",
      "1.1459\n",
      "Attempted to log scalar metric grad_norm:\n",
      "7.266663551330566\n",
      "Attempted to log scalar metric learning_rate:\n",
      "4.4650499286733236e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.21398002853067047\n",
      "{'loss': 1.1459, 'grad_norm': 7.266663551330566, 'learning_rate': 4.4650499286733236e-05, 'epoch': 0.21}\n",
      "Attempted to log scalar metric loss:\n",
      "1.1084\n",
      "Attempted to log scalar metric grad_norm:\n",
      "6.193224906921387\n",
      "Attempted to log scalar metric learning_rate:\n",
      "4.3758915834522115e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.24964336661911554\n",
      "{'loss': 1.1084, 'grad_norm': 6.193224906921387, 'learning_rate': 4.3758915834522115e-05, 'epoch': 0.25}\n",
      "Attempted to log scalar metric loss:\n",
      "1.0578\n",
      "Attempted to log scalar metric grad_norm:\n",
      "6.905110836029053\n",
      "Attempted to log scalar metric learning_rate:\n",
      "4.286733238231099e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.28530670470756064\n",
      "{'loss': 1.0578, 'grad_norm': 6.905110836029053, 'learning_rate': 4.286733238231099e-05, 'epoch': 0.29}\n",
      "Attempted to log scalar metric loss:\n",
      "1.0839\n",
      "Attempted to log scalar metric grad_norm:\n",
      "13.53052806854248\n",
      "Attempted to log scalar metric learning_rate:\n",
      "4.197574893009986e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.3209700427960057\n",
      "{'loss': 1.0839, 'grad_norm': 13.53052806854248, 'learning_rate': 4.197574893009986e-05, 'epoch': 0.32}\n",
      "Attempted to log scalar metric loss:\n",
      "1.0488\n",
      "Attempted to log scalar metric grad_norm:\n",
      "6.5799241065979\n",
      "Attempted to log scalar metric learning_rate:\n",
      "4.108416547788873e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.3566333808844508\n",
      "{'loss': 1.0488, 'grad_norm': 6.5799241065979, 'learning_rate': 4.108416547788873e-05, 'epoch': 0.36}\n",
      "Attempted to log scalar metric loss:\n",
      "1.0093\n",
      "Attempted to log scalar metric grad_norm:\n",
      "5.568687915802002\n",
      "Attempted to log scalar metric learning_rate:\n",
      "4.01925820256776e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.39229671897289586\n",
      "{'loss': 1.0093, 'grad_norm': 5.568687915802002, 'learning_rate': 4.01925820256776e-05, 'epoch': 0.39}\n",
      "Attempted to log scalar metric loss:\n",
      "1.1019\n",
      "Attempted to log scalar metric grad_norm:\n",
      "7.541095733642578\n",
      "Attempted to log scalar metric learning_rate:\n",
      "3.930099857346648e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.42796005706134094\n",
      "{'loss': 1.1019, 'grad_norm': 7.541095733642578, 'learning_rate': 3.930099857346648e-05, 'epoch': 0.43}\n",
      "Attempted to log scalar metric loss:\n",
      "1.0047\n",
      "Attempted to log scalar metric grad_norm:\n",
      "8.129934310913086\n",
      "Attempted to log scalar metric learning_rate:\n",
      "3.840941512125535e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.463623395149786\n",
      "{'loss': 1.0047, 'grad_norm': 8.129934310913086, 'learning_rate': 3.840941512125535e-05, 'epoch': 0.46}\n",
      "Attempted to log scalar metric loss:\n",
      "1.0165\n",
      "Attempted to log scalar metric grad_norm:\n",
      "4.367265701293945\n",
      "Attempted to log scalar metric learning_rate:\n",
      "3.751783166904422e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.4992867332382311\n",
      "{'loss': 1.0165, 'grad_norm': 4.367265701293945, 'learning_rate': 3.751783166904422e-05, 'epoch': 0.5}\n",
      "Attempted to log scalar metric loss:\n",
      "1.0224\n",
      "Attempted to log scalar metric grad_norm:\n",
      "6.170989990234375\n",
      "Attempted to log scalar metric learning_rate:\n",
      "3.66262482168331e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.5349500713266762\n",
      "{'loss': 1.0224, 'grad_norm': 6.170989990234375, 'learning_rate': 3.66262482168331e-05, 'epoch': 0.53}\n",
      "Attempted to log scalar metric loss:\n",
      "1.0559\n",
      "Attempted to log scalar metric grad_norm:\n",
      "6.646623611450195\n",
      "Attempted to log scalar metric learning_rate:\n",
      "3.573466476462197e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.5706134094151213\n",
      "{'loss': 1.0559, 'grad_norm': 6.646623611450195, 'learning_rate': 3.573466476462197e-05, 'epoch': 0.57}\n",
      "Attempted to log scalar metric loss:\n",
      "1.0045\n",
      "Attempted to log scalar metric grad_norm:\n",
      "7.4467902183532715\n",
      "Attempted to log scalar metric learning_rate:\n",
      "3.484308131241084e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.6062767475035663\n",
      "{'loss': 1.0045, 'grad_norm': 7.4467902183532715, 'learning_rate': 3.484308131241084e-05, 'epoch': 0.61}\n",
      "Attempted to log scalar metric loss:\n",
      "0.9848\n",
      "Attempted to log scalar metric grad_norm:\n",
      "8.018450736999512\n",
      "Attempted to log scalar metric learning_rate:\n",
      "3.3951497860199716e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.6419400855920114\n",
      "{'loss': 0.9848, 'grad_norm': 8.018450736999512, 'learning_rate': 3.3951497860199716e-05, 'epoch': 0.64}\n",
      "Attempted to log scalar metric loss:\n",
      "0.9906\n",
      "Attempted to log scalar metric grad_norm:\n",
      "5.614974498748779\n",
      "Attempted to log scalar metric learning_rate:\n",
      "3.305991440798859e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.6776034236804565\n",
      "{'loss': 0.9906, 'grad_norm': 5.614974498748779, 'learning_rate': 3.305991440798859e-05, 'epoch': 0.68}\n",
      "Attempted to log scalar metric loss:\n",
      "0.9405\n",
      "Attempted to log scalar metric grad_norm:\n",
      "5.6915082931518555\n",
      "Attempted to log scalar metric learning_rate:\n",
      "3.216833095577747e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.7132667617689016\n",
      "{'loss': 0.9405, 'grad_norm': 5.6915082931518555, 'learning_rate': 3.216833095577747e-05, 'epoch': 0.71}\n",
      "Attempted to log scalar metric loss:\n",
      "1.0394\n",
      "Attempted to log scalar metric grad_norm:\n",
      "5.648483753204346\n",
      "Attempted to log scalar metric learning_rate:\n",
      "3.127674750356633e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.7489300998573466\n",
      "{'loss': 1.0394, 'grad_norm': 5.648483753204346, 'learning_rate': 3.127674750356633e-05, 'epoch': 0.75}\n",
      "Attempted to log scalar metric loss:\n",
      "1.0105\n",
      "Attempted to log scalar metric grad_norm:\n",
      "6.856000900268555\n",
      "Attempted to log scalar metric learning_rate:\n",
      "3.0385164051355208e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.7845934379457917\n",
      "{'loss': 1.0105, 'grad_norm': 6.856000900268555, 'learning_rate': 3.0385164051355208e-05, 'epoch': 0.78}\n",
      "Attempted to log scalar metric loss:\n",
      "0.9737\n",
      "Attempted to log scalar metric grad_norm:\n",
      "7.016152381896973\n",
      "Attempted to log scalar metric learning_rate:\n",
      "2.9493580599144083e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.8202567760342369\n",
      "{'loss': 0.9737, 'grad_norm': 7.016152381896973, 'learning_rate': 2.9493580599144083e-05, 'epoch': 0.82}\n",
      "Attempted to log scalar metric loss:\n",
      "0.9665\n",
      "Attempted to log scalar metric grad_norm:\n",
      "6.495640277862549\n",
      "Attempted to log scalar metric learning_rate:\n",
      "2.8601997146932952e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.8559201141226819\n",
      "{'loss': 0.9665, 'grad_norm': 6.495640277862549, 'learning_rate': 2.8601997146932952e-05, 'epoch': 0.86}\n",
      "Attempted to log scalar metric loss:\n",
      "0.9964\n",
      "Attempted to log scalar metric grad_norm:\n",
      "7.684788703918457\n",
      "Attempted to log scalar metric learning_rate:\n",
      "2.7710413694721828e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.891583452211127\n",
      "{'loss': 0.9964, 'grad_norm': 7.684788703918457, 'learning_rate': 2.7710413694721828e-05, 'epoch': 0.89}\n",
      "Attempted to log scalar metric loss:\n",
      "1.0121\n",
      "Attempted to log scalar metric grad_norm:\n",
      "4.490509986877441\n",
      "Attempted to log scalar metric learning_rate:\n",
      "2.68188302425107e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.927246790299572\n",
      "{'loss': 1.0121, 'grad_norm': 4.490509986877441, 'learning_rate': 2.68188302425107e-05, 'epoch': 0.93}\n",
      "Attempted to log scalar metric loss:\n",
      "0.9775\n",
      "Attempted to log scalar metric grad_norm:\n",
      "7.3732452392578125\n",
      "Attempted to log scalar metric learning_rate:\n",
      "2.5927246790299575e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.9629101283880172\n",
      "{'loss': 0.9775, 'grad_norm': 7.3732452392578125, 'learning_rate': 2.5927246790299575e-05, 'epoch': 0.96}\n",
      "Attempted to log scalar metric loss:\n",
      "1.0083\n",
      "Attempted to log scalar metric grad_norm:\n",
      "5.980914115905762\n",
      "Attempted to log scalar metric learning_rate:\n",
      "2.5035663338088444e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "0.9985734664764622\n",
      "{'loss': 1.0083, 'grad_norm': 5.980914115905762, 'learning_rate': 2.5035663338088444e-05, 'epoch': 1.0}\n"
     ]
    },
    {
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       "model_id": "c7d97f1bfb5e4d88985d44fc8d8aa754",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/74 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attempted to log scalar metric eval_loss:\n",
      "0.998440682888031\n",
      "Attempted to log scalar metric eval_accuracy:\n",
      "0.6367485182049111\n",
      "Attempted to log scalar metric eval_f1:\n",
      "0.5167051361584505\n",
      "Attempted to log scalar metric eval_precision:\n",
      "0.5474739879586745\n",
      "Attempted to log scalar metric eval_recall:\n",
      "0.497712967750179\n",
      "Attempted to log scalar metric eval_runtime:\n",
      "37.784\n",
      "Attempted to log scalar metric eval_samples_per_second:\n",
      "31.257\n",
      "Attempted to log scalar metric eval_steps_per_second:\n",
      "1.959\n",
      "Attempted to log scalar metric epoch:\n",
      "1.0\n",
      "{'eval_loss': 0.998440682888031, 'eval_accuracy': 0.6367485182049111, 'eval_f1': 0.5167051361584505, 'eval_precision': 0.5474739879586745, 'eval_recall': 0.497712967750179, 'eval_runtime': 37.784, 'eval_samples_per_second': 31.257, 'eval_steps_per_second': 1.959, 'epoch': 1.0}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7841\n",
      "Attempted to log scalar metric grad_norm:\n",
      "4.851502418518066\n",
      "Attempted to log scalar metric learning_rate:\n",
      "2.414407988587732e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.0342368045649073\n",
      "{'loss': 0.7841, 'grad_norm': 4.851502418518066, 'learning_rate': 2.414407988587732e-05, 'epoch': 1.03}\n",
      "Attempted to log scalar metric loss:\n",
      "0.8121\n",
      "Attempted to log scalar metric grad_norm:\n",
      "6.860970973968506\n",
      "Attempted to log scalar metric learning_rate:\n",
      "2.3252496433666192e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.0699001426533523\n",
      "{'loss': 0.8121, 'grad_norm': 6.860970973968506, 'learning_rate': 2.3252496433666192e-05, 'epoch': 1.07}\n",
      "Attempted to log scalar metric loss:\n",
      "0.8252\n",
      "Attempted to log scalar metric grad_norm:\n",
      "6.619368076324463\n",
      "Attempted to log scalar metric learning_rate:\n",
      "2.2360912981455064e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.1055634807417973\n",
      "{'loss': 0.8252, 'grad_norm': 6.619368076324463, 'learning_rate': 2.2360912981455064e-05, 'epoch': 1.11}\n",
      "Attempted to log scalar metric loss:\n",
      "0.761\n",
      "Attempted to log scalar metric grad_norm:\n",
      "9.418994903564453\n",
      "Attempted to log scalar metric learning_rate:\n",
      "2.146932952924394e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.1412268188302426\n",
      "{'loss': 0.761, 'grad_norm': 9.418994903564453, 'learning_rate': 2.146932952924394e-05, 'epoch': 1.14}\n",
      "Attempted to log scalar metric loss:\n",
      "0.8099\n",
      "Attempted to log scalar metric grad_norm:\n",
      "7.126413345336914\n",
      "Attempted to log scalar metric learning_rate:\n",
      "2.0577746077032812e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.1768901569186876\n",
      "{'loss': 0.8099, 'grad_norm': 7.126413345336914, 'learning_rate': 2.0577746077032812e-05, 'epoch': 1.18}\n",
      "Attempted to log scalar metric loss:\n",
      "0.8107\n",
      "Attempted to log scalar metric grad_norm:\n",
      "4.922411918640137\n",
      "Attempted to log scalar metric learning_rate:\n",
      "1.9686162624821684e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.2125534950071326\n",
      "{'loss': 0.8107, 'grad_norm': 4.922411918640137, 'learning_rate': 1.9686162624821684e-05, 'epoch': 1.21}\n",
      "Attempted to log scalar metric loss:\n",
      "0.8124\n",
      "Attempted to log scalar metric grad_norm:\n",
      "5.348957061767578\n",
      "Attempted to log scalar metric learning_rate:\n",
      "1.8794579172610556e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.2482168330955776\n",
      "{'loss': 0.8124, 'grad_norm': 5.348957061767578, 'learning_rate': 1.8794579172610556e-05, 'epoch': 1.25}\n",
      "Attempted to log scalar metric loss:\n",
      "0.767\n",
      "Attempted to log scalar metric grad_norm:\n",
      "6.628406047821045\n",
      "Attempted to log scalar metric learning_rate:\n",
      "1.790299572039943e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.2838801711840229\n",
      "{'loss': 0.767, 'grad_norm': 6.628406047821045, 'learning_rate': 1.790299572039943e-05, 'epoch': 1.28}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7809\n",
      "Attempted to log scalar metric grad_norm:\n",
      "8.410733222961426\n",
      "Attempted to log scalar metric learning_rate:\n",
      "1.7011412268188304e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.3195435092724679\n",
      "{'loss': 0.7809, 'grad_norm': 8.410733222961426, 'learning_rate': 1.7011412268188304e-05, 'epoch': 1.32}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7878\n",
      "Attempted to log scalar metric grad_norm:\n",
      "5.869395732879639\n",
      "Attempted to log scalar metric learning_rate:\n",
      "1.6119828815977176e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.355206847360913\n",
      "{'loss': 0.7878, 'grad_norm': 5.869395732879639, 'learning_rate': 1.6119828815977176e-05, 'epoch': 1.36}\n",
      "Attempted to log scalar metric loss:\n",
      "0.783\n",
      "Attempted to log scalar metric grad_norm:\n",
      "9.127420425415039\n",
      "Attempted to log scalar metric learning_rate:\n",
      "1.522824536376605e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.3908701854493581\n",
      "{'loss': 0.783, 'grad_norm': 9.127420425415039, 'learning_rate': 1.522824536376605e-05, 'epoch': 1.39}\n",
      "Attempted to log scalar metric loss:\n",
      "0.8693\n",
      "Attempted to log scalar metric grad_norm:\n",
      "9.472625732421875\n",
      "Attempted to log scalar metric learning_rate:\n",
      "1.433666191155492e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.4265335235378032\n",
      "{'loss': 0.8693, 'grad_norm': 9.472625732421875, 'learning_rate': 1.433666191155492e-05, 'epoch': 1.43}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7672\n",
      "Attempted to log scalar metric grad_norm:\n",
      "8.43954849243164\n",
      "Attempted to log scalar metric learning_rate:\n",
      "1.3445078459343796e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.4621968616262482\n",
      "{'loss': 0.7672, 'grad_norm': 8.43954849243164, 'learning_rate': 1.3445078459343796e-05, 'epoch': 1.46}\n",
      "Attempted to log scalar metric loss:\n",
      "0.802\n",
      "Attempted to log scalar metric grad_norm:\n",
      "5.044028282165527\n",
      "Attempted to log scalar metric learning_rate:\n",
      "1.2553495007132669e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.4978601997146934\n",
      "{'loss': 0.802, 'grad_norm': 5.044028282165527, 'learning_rate': 1.2553495007132669e-05, 'epoch': 1.5}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7681\n",
      "Attempted to log scalar metric grad_norm:\n",
      "6.793483257293701\n",
      "Attempted to log scalar metric learning_rate:\n",
      "1.166191155492154e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.5335235378031382\n",
      "{'loss': 0.7681, 'grad_norm': 6.793483257293701, 'learning_rate': 1.166191155492154e-05, 'epoch': 1.53}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7533\n",
      "Attempted to log scalar metric grad_norm:\n",
      "8.013359069824219\n",
      "Attempted to log scalar metric learning_rate:\n",
      "1.0770328102710415e-05\n",
      "Attempted to log scalar metric epoch:\n",
      "1.5691868758915835\n",
      "{'loss': 0.7533, 'grad_norm': 8.013359069824219, 'learning_rate': 1.0770328102710415e-05, 'epoch': 1.57}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7097\n",
      "Attempted to log scalar metric grad_norm:\n",
      "9.53731632232666\n",
      "Attempted to log scalar metric learning_rate:\n",
      "9.878744650499287e-06\n",
      "Attempted to log scalar metric epoch:\n",
      "1.6048502139800287\n",
      "{'loss': 0.7097, 'grad_norm': 9.53731632232666, 'learning_rate': 9.878744650499287e-06, 'epoch': 1.6}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7503\n",
      "Attempted to log scalar metric grad_norm:\n",
      "8.356176376342773\n",
      "Attempted to log scalar metric learning_rate:\n",
      "8.98716119828816e-06\n",
      "Attempted to log scalar metric epoch:\n",
      "1.6405135520684735\n",
      "{'loss': 0.7503, 'grad_norm': 8.356176376342773, 'learning_rate': 8.98716119828816e-06, 'epoch': 1.64}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7901\n",
      "Attempted to log scalar metric grad_norm:\n",
      "9.985661506652832\n",
      "Attempted to log scalar metric learning_rate:\n",
      "8.095577746077033e-06\n",
      "Attempted to log scalar metric epoch:\n",
      "1.6761768901569187\n",
      "{'loss': 0.7901, 'grad_norm': 9.985661506652832, 'learning_rate': 8.095577746077033e-06, 'epoch': 1.68}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7899\n",
      "Attempted to log scalar metric grad_norm:\n",
      "7.874309062957764\n",
      "Attempted to log scalar metric learning_rate:\n",
      "7.203994293865907e-06\n",
      "Attempted to log scalar metric epoch:\n",
      "1.7118402282453637\n",
      "{'loss': 0.7899, 'grad_norm': 7.874309062957764, 'learning_rate': 7.203994293865907e-06, 'epoch': 1.71}\n",
      "Attempted to log scalar metric loss:\n",
      "0.748\n",
      "Attempted to log scalar metric grad_norm:\n",
      "6.313715934753418\n",
      "Attempted to log scalar metric learning_rate:\n",
      "6.31241084165478e-06\n",
      "Attempted to log scalar metric epoch:\n",
      "1.7475035663338088\n",
      "{'loss': 0.748, 'grad_norm': 6.313715934753418, 'learning_rate': 6.31241084165478e-06, 'epoch': 1.75}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7836\n",
      "Attempted to log scalar metric grad_norm:\n",
      "8.198199272155762\n",
      "Attempted to log scalar metric learning_rate:\n",
      "5.420827389443652e-06\n",
      "Attempted to log scalar metric epoch:\n",
      "1.783166904422254\n",
      "{'loss': 0.7836, 'grad_norm': 8.198199272155762, 'learning_rate': 5.420827389443652e-06, 'epoch': 1.78}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7342\n",
      "Attempted to log scalar metric grad_norm:\n",
      "6.641572952270508\n",
      "Attempted to log scalar metric learning_rate:\n",
      "4.529243937232525e-06\n",
      "Attempted to log scalar metric epoch:\n",
      "1.818830242510699\n",
      "{'loss': 0.7342, 'grad_norm': 6.641572952270508, 'learning_rate': 4.529243937232525e-06, 'epoch': 1.82}\n",
      "Attempted to log scalar metric loss:\n",
      "0.8227\n",
      "Attempted to log scalar metric grad_norm:\n",
      "7.642160415649414\n",
      "Attempted to log scalar metric learning_rate:\n",
      "3.6376604850213982e-06\n",
      "Attempted to log scalar metric epoch:\n",
      "1.854493580599144\n",
      "{'loss': 0.8227, 'grad_norm': 7.642160415649414, 'learning_rate': 3.6376604850213982e-06, 'epoch': 1.85}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7689\n",
      "Attempted to log scalar metric grad_norm:\n",
      "5.503686904907227\n",
      "Attempted to log scalar metric learning_rate:\n",
      "2.7460770328102713e-06\n",
      "Attempted to log scalar metric epoch:\n",
      "1.8901569186875893\n",
      "{'loss': 0.7689, 'grad_norm': 5.503686904907227, 'learning_rate': 2.7460770328102713e-06, 'epoch': 1.89}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7949\n",
      "Attempted to log scalar metric grad_norm:\n",
      "4.484691619873047\n",
      "Attempted to log scalar metric learning_rate:\n",
      "1.8544935805991443e-06\n",
      "Attempted to log scalar metric epoch:\n",
      "1.925820256776034\n",
      "{'loss': 0.7949, 'grad_norm': 4.484691619873047, 'learning_rate': 1.8544935805991443e-06, 'epoch': 1.93}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7493\n",
      "Attempted to log scalar metric grad_norm:\n",
      "4.42490816116333\n",
      "Attempted to log scalar metric learning_rate:\n",
      "9.629101283880171e-07\n",
      "Attempted to log scalar metric epoch:\n",
      "1.9614835948644793\n",
      "{'loss': 0.7493, 'grad_norm': 4.42490816116333, 'learning_rate': 9.629101283880171e-07, 'epoch': 1.96}\n",
      "Attempted to log scalar metric loss:\n",
      "0.7013\n",
      "Attempted to log scalar metric grad_norm:\n",
      "6.478468894958496\n",
      "Attempted to log scalar metric learning_rate:\n",
      "7.132667617689017e-08\n",
      "Attempted to log scalar metric epoch:\n",
      "1.9971469329529246\n",
      "{'loss': 0.7013, 'grad_norm': 6.478468894958496, 'learning_rate': 7.132667617689017e-08, 'epoch': 2.0}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9da6c701d6814f90b44fdb6803bdf6f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attempted to log scalar metric eval_loss:\n",
      "0.9764068126678467\n",
      "Attempted to log scalar metric eval_accuracy:\n",
      "0.6502963590177815\n",
      "Attempted to log scalar metric eval_f1:\n",
      "0.5378698569567948\n",
      "Attempted to log scalar metric eval_precision:\n",
      "0.5611424125048258\n",
      "Attempted to log scalar metric eval_recall:\n",
      "0.5239711582819748\n",
      "Attempted to log scalar metric eval_runtime:\n",
      "50.9517\n",
      "Attempted to log scalar metric eval_samples_per_second:\n",
      "23.179\n",
      "Attempted to log scalar metric eval_steps_per_second:\n",
      "1.452\n",
      "Attempted to log scalar metric epoch:\n",
      "2.0\n",
      "{'eval_loss': 0.9764068126678467, 'eval_accuracy': 0.6502963590177815, 'eval_f1': 0.5378698569567948, 'eval_precision': 0.5611424125048258, 'eval_recall': 0.5239711582819748, 'eval_runtime': 50.9517, 'eval_samples_per_second': 23.179, 'eval_steps_per_second': 1.452, 'epoch': 2.0}\n",
      "Attempted to log scalar metric train_runtime:\n",
      "10430.5391\n",
      "Attempted to log scalar metric train_samples_per_second:\n",
      "4.3\n",
      "Attempted to log scalar metric train_steps_per_second:\n",
      "0.269\n",
      "Attempted to log scalar metric total_flos:\n",
      "1775006305657200.0\n",
      "Attempted to log scalar metric train_loss:\n",
      "0.9244163468288797\n",
      "Attempted to log scalar metric epoch:\n",
      "2.0\n",
      "{'train_runtime': 10430.5391, 'train_samples_per_second': 4.3, 'train_steps_per_second': 0.269, 'train_loss': 0.9244163468288797, 'epoch': 2.0}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7145de5f8daf47cc9ee10e18ea72eb9c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attempted to log scalar metric eval_loss:\n",
      "0.9764068126678467\n",
      "Attempted to log scalar metric eval_accuracy:\n",
      "0.6502963590177815\n",
      "Attempted to log scalar metric eval_f1:\n",
      "0.5378698569567948\n",
      "Attempted to log scalar metric eval_precision:\n",
      "0.5611424125048258\n",
      "Attempted to log scalar metric eval_recall:\n",
      "0.5239711582819748\n",
      "Attempted to log scalar metric eval_runtime:\n",
      "53.6696\n",
      "Attempted to log scalar metric eval_samples_per_second:\n",
      "22.005\n",
      "Attempted to log scalar metric eval_steps_per_second:\n",
      "1.379\n",
      "Attempted to log scalar metric epoch:\n",
      "2.0\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cdf79a81784b4c8097aac771c260e521",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BERT Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "     sadness       0.72      0.85      0.78       314\n",
      "  excitement       0.66      0.61      0.63       277\n",
      "         joy       0.65      0.69      0.67       351\n",
      " nervousness       0.54      0.36      0.43        87\n",
      "       pride       0.58      0.48      0.53        58\n",
      "      relief       0.44      0.38      0.41        58\n",
      "       anger       0.32      0.31      0.31        36\n",
      "\n",
      "    accuracy                           0.65      1181\n",
      "   macro avg       0.56      0.52      0.54      1181\n",
      "weighted avg       0.64      0.65      0.64      1181\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train BERT Model\n",
    "results_bert = mt.train_bert_model(train_df, val_df)\n",
    "\n",
    "# Print the classification reports for BERT model\n",
    "print(\"BERT Report:\\n\", results_bert['report'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Model  F1_score\n",
      "0  Logistic Regression  0.474001\n",
      "1        Random Forest  0.430954\n",
      "2                 BERT  0.537870\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the performance of traditional models (Logistic Regression, Random Forest)\n",
    "# and the BERT model using metrics such as Macro-F1 scores.\n",
    "mt.compare_models(results_traditional, results_bert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BERT Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "     sadness       0.79      0.92      0.85        12\n",
      "  excitement       0.86      0.75      0.80         8\n",
      "         joy       0.67      0.86      0.75         7\n",
      " nervousness       0.67      0.80      0.73        10\n",
      "       pride       0.80      0.67      0.73         6\n",
      "      relief       1.00      0.33      0.50         3\n",
      "       anger       1.00      0.50      0.67         4\n",
      "\n",
      "    accuracy                           0.76        50\n",
      "   macro avg       0.83      0.69      0.72        50\n",
      "weighted avg       0.79      0.76      0.75        50\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Load the BERT tokenizer\n",
    "tokenizer = model.BertTokenizerFast.from_pretrained(\"bert-base-uncased\")\n",
    "\n",
    "# Load and preprocess the test data\n",
    "test_data = pd.read_csv(\"data/GPT_generated_data_with_emotions.csv\")\n",
    "test_data['example_very_unclear'] = \"FALSE\"  # Set unclear examples to FALSE\n",
    "test_data = dp.preprocess_data(test_data)  # Preprocess the data\n",
    "\n",
    "# Tokenize the test data for BERT\n",
    "test_encodings = tokenizer(test_data['text_clean'].tolist(), truncation=True, padding=True, max_length=128)\n",
    "\n",
    "# Map emotion labels to IDs\n",
    "test_labels = test_data['label'].map(mt.label2id).values\n",
    "\n",
    "# Prepare HuggingFace test dataset\n",
    "hf_test_dataset = model.HFDataset.from_dict({\n",
    "    'input_ids': test_encodings['input_ids'],\n",
    "    'attention_mask': test_encodings['attention_mask'],\n",
    "    'labels': test_labels\n",
    "})\n",
    "\n",
    "# Predict with the BERT model\n",
    "test_preds = mt.bert_trainer.predict(hf_test_dataset).predictions\n",
    "pred_labels = np.argmax(test_preds, axis=1)  # Get predicted labels\n",
    "\n",
    "# Generate classification report and Macro-F1 score\n",
    "report = model.classification_report(test_labels, pred_labels, target_names=mt.emotion_cols)\n",
    "f1 = model.f1_score(test_labels, pred_labels, average='macro')\n",
    "\n",
    "# Print the evaluation report\n",
    "print(\"BERT Report:\\n\", report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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    {
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     "metadata": {},
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    {
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     "metadata": {},
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    {
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     "metadata": {},
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    {
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     "metadata": {},
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    },
    {
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       "model_id": "bf34292202fa4578ba5ed937d3d292b1",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate the list of years from 2017 to 2023\n",
    "years = list(range(2017, 2024))\n",
    "\n",
    "# Initialize a dictionary to store DataFrames for each year\n",
    "dataframes = {}\n",
    "\n",
    "# Loop through each year, process the data, and make predictions\n",
    "for year in years:\n",
    "    # Dynamically generate the file name for the current year\n",
    "    file_name = f\"data/UEFA_F{year}_merged_labeled.csv\"\n",
    "\n",
    "    # Load the CSV file, drop rows with missing \"flair\", and select relevant columns\n",
    "    dataframes[year] = pd.read_csv(file_name).dropna(subset=['flair'])[['text', 'flair']]\n",
    "\n",
    "    # Clean the \"flair\" column: remove colons and replace underscores with spaces\n",
    "    dataframes[year]['flair'] = dataframes[year]['flair'].str.strip(':').str.replace('_', ' ')\n",
    "\n",
    "    # Ensure \"text\" is treated as a string and clean it\n",
    "    dataframes[year]['text'] = dataframes[year]['text'].astype(str)\n",
    "    dataframes[year]['text_clean'] = dataframes[year]['text'].apply(dp.clean_text)\n",
    "\n",
    "    # Tokenize the cleaned text for BERT\n",
    "    year_encodings = tokenizer(dataframes[year]['text_clean'].tolist(), truncation=True, padding=True, max_length=128)\n",
    "\n",
    "    # Prepare the HuggingFace dataset for prediction\n",
    "    hf_year_dataset = model.HFDataset.from_dict({\n",
    "        'input_ids': year_encodings['input_ids'],\n",
    "        'attention_mask': year_encodings['attention_mask']\n",
    "    })\n",
    "\n",
    "    # Use the trained BERT model to predict emotion labels\n",
    "    year_preds = mt.bert_trainer.predict(hf_year_dataset).predictions\n",
    "\n",
    "    # Add the predicted labels to the DataFrame\n",
    "    dataframes[year]['labels'] = np.argmax(year_preds, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the list of target teams and emotions\n",
    "teams = ['Real Madrid', 'FC Barcelona', 'Chelsea', 'Manchester United', 'Liverpool', 'Tottenham Hotspur',\n",
    "         'Arsenal', 'Manchester City', 'Juventus', 'Inter Milan', 'AC Milan', 'Bayern Munich']\n",
    "emotions = list(dp.label2id.keys())  # Get the list of emotions from the label-to-ID mapping\n",
    "\n",
    "# Initialize DataFrames to store yearly counts of teams and emotions\n",
    "count_teams = pd.DataFrame(index=years, columns=teams)  # Count of texts for each team by year\n",
    "count_emotions = pd.DataFrame(index=years, columns=emotions)  # Count of texts for each emotion by year\n",
    "\n",
    "# Dictionary to store emotion counts for each team across years\n",
    "teams_emotions = {}\n",
    "\n",
    "# Process data year by year\n",
    "for year in years:\n",
    "    # Filter data to include only the specified teams\n",
    "    dataframes[year] = dataframes[year][dataframes[year]['flair'].isin(teams)]\n",
    "\n",
    "    # Map numeric labels to emotion names\n",
    "    dataframes[year]['emotions'] = dataframes[year]['labels'].apply(lambda x: dp.id2label[x])\n",
    "\n",
    "    # Count the number of texts per team and update the DataFrame\n",
    "    count_teams.loc[year] = dataframes[year].groupby('flair').count().reindex(teams)['text'].values\n",
    "\n",
    "    # Count the number of texts per emotion and update the DataFrame\n",
    "    count_emotions.loc[year] = dataframes[year].groupby('emotions').count().reindex(emotions)['text'].values\n",
    "\n",
    "    # Create a pivot table showing emotion counts per team and store in the dictionary\n",
    "    teams_emotions[year] = (dataframes[year].groupby(['flair', 'emotions']).size()\n",
    "                                            .reset_index(name='count')  # Convert groupby result to a DataFrame\n",
    "                                            .pivot(index='flair', columns='emotions', values='count')  # Pivot table\n",
    "                                            .fillna(0).astype(int)  # Fill missing values with 0 and cast to int\n",
    "                                            .reindex(teams)[emotions])  # Reindex to ensure consistent ordering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a horizontal stacked bar plot for team text frequencies\n",
    "count_teams.plot(kind='barh', stacked=True, figsize=(9, 6), colormap='tab20')\n",
    "\n",
    "# Add title and axis labels\n",
    "plt.title(\"2017 - 2023 Frequency of Social Media Texts for Different Teams\")\n",
    "plt.ylabel(\"Year\")  # Y-axis represents the years\n",
    "plt.xlabel(\"Count\")  # X-axis represents the number of texts\n",
    "\n",
    "# Add a legend to identify the teams\n",
    "plt.legend(title=\"Teams\", bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "# Adjust layout to ensure elements fit properly\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pqRa1e3p6GmfPnrXYrtud4nTzqVZLly41JBnjx4+36PfYY48ZJpPJOHz4sLlNkuHo6GjRtn//fkOSMW3aNHObl5eXERUVlcM7fnudO3c2HB0djSNHjpjbTp06ZXh4eBgtWrQwt93qFNrc+OuvvwxHR0ejW7duueqfte9zejg5OWWrycfHx0hOTja3jxo1ypBk1KtXz8jIyDC39+jRw3B0dDT/fOfls3SrUzGzavjnfs/6fP3111/mtv379xt2dnbG008/bW4bM2aMIcno27evxZhdunQxSpcubX4+efJkQ5Jx7ty53Lx9FipVqmT06tXL/DzrvQ0PDzcyMzPN7UOHDjXs7e0t3seczJo1y/yZbNmypfHaa68Z27Zts3j/DCP3n/HffvvNsLOzM7p06ZJtjH/W9/DDD1uc3nft2rVsp4P+/fffhp+fX7b3Mze/t//5+U5ISDAkGdu2bTMMwzCmT59uuLu7G5cuXTJ69eplcSrmhQsXDG9vb+OZZ56xGC8pKcnw8vKyaK9fv75Rrlw5i/d43bp1hqRsn62ba758+XK2mrN+B+f0f9mt3Px7KjMz06hataoRERFh8X5fvnzZCAwMtDh1+vr168ZDDz1k+Pn5GX/++acRFRVllChRItsp07c6FbNevXrZLgsA8O/BqZi4K7/88ouioqIUEhKiXr16mduvXLkiSTlOZOHs7GzRJ78yMzM1f/58NWjQQMHBwXc1Vm689dZb+u677/T222/L29vb3H7lypVbTjbg7Ox82+2cN2+eZs2apeHDh+c4q2fNmjW1fv16LV26VC+++KLc3NzyNKHAmDFj5OPjo7Jlyyo0NFRHjhzRO++8k+2IzjPPPGMxKcV3332n9PR0DRkyxGKSlmeeeUaenp4Wp5dKUokSJfTss8+anzs6OurZZ5/V2bNntXfvXknSqlWrVLZsWfXo0cPcz8HBwTyxzpYtWyzGjIyMlI+PT6639WarVq2Svb29Bg8ebNE+fPhwGYah1atXW7SHh4crKCjI/Lxu3bry9PTU77//bm7z9vbW999/r1OnTuW6juvXr2vdunXq3LmzKleubG4vV66cnnzySW3fvl2pqal53bxsFi1apPT09Dyfhjl9+nStX7/e4nHzeyNJjz/+uLy8vMzPH3jgAUnSf/7zH4vJJx544AGlp6frf//7n6S8f5Zy4/Tp04qPj1fv3r0tTturW7euHnnkkRzPCBg4cKDF8+bNm+uvv/4yv/dZP9PLli1TZmZmnmvKyYABAywmWWrevLmuX7+u48eP33a5vn37as2aNQoNDdX27dv15ptvqnnz5qpatarFhDm5/YwvXbpUmZmZev3117NNuvTP+m5mb29v/t2WmZmp8+fP69q1a7r//vtzdfrx7dSqVUt169bVV199JenG78JOnTrleFRs/fr1Sk5OVo8ePfTnn3+aH/b29nrggQfMp4ZmfS569epl8Vl95JFHVLNmzTvW9M/JqzIyMvTXX3+pSpUq8vb2vqvtjY+P12+//aYnn3xSf/31l7n+S5cuqVWrVtq6dav5M2dnZ6fZs2fr4sWLatu2rWbMmKFRo0bdcnbcm3l7e+vnn3/Wb7/9lu96Adgugh3yLSkpSe3bt5eXl5cWLVpkEQyy/oNMS0vLtlzW7QL++Z9ofmzZskX/+9//cv1F9vz580pKSjI/UlJScr2uBQsW6NVXX1W/fv303HPPWbzm4uJink3uZlevXr3ldm7btk39+vVTRESEJkyYkGMfT09PhYeHq1OnTnrnnXc0fPhwderUSfv3789V3QMGDND69eu1YcMG7d27V2fPntWLL76Yrd/NsyJmffGsXr26Rbujo6MqV66c7Ytp+fLl5ebmZtFWrVo1STJfn3L8+HFVrVo12xfLrFB+85g5zdSYF8ePH1f58uXl4eGRq/X981rQLCVLltTff/9tfj5x4kQlJCTI399fTZo00dixYy2CX07OnTuny5cvZ3svs2rJzMzUyZMnc71dtzJ37lyVKlVKbdu2zdNyTZo0UXh4uMWjZcuW2frd/P5kfXH29/fPsT3rfcvrZyk3bjWmdOM9zfrSfLv6S5YsaVHnE088oWbNmql///7y8/NT9+7d9fXXX99VyLvTOm8nIiJCa9euVXJysrZu3aqoqCgdP35cHTp00NmzZyXl/jN+5MgR2dnZ5Src3OyLL75Q3bp1zddr+fj4aOXKlXn6/XkrTz75pBYuXKjDhw9r586devLJJ3PslxVSwsLC5OPjY/FYt26dxfshKcc/kuX0WbnZlStX9Prrr5uvVyxTpox8fHyUnJx8V9ubVX+vXr2y1f/ZZ58pLS3NYvygoCCNHTtWu3fvVq1atfTaa6/lel1vvPGGkpOTVa1aNdWpU0cjR47UgQMH8l07ANvCNXbIl5SUFLVt21bJycnatm2bypcvb/F6uXLlJN34C+rNTp8+rVKlSt31tPRz586VnZ2dxRGg2+natavFUaFevXrl6j5A69ev19NPP6327dvnOCFIuXLldP36dZ09e9ZiYoP09HT99ddf2d4bSdq/f78effRR1a5dW4sWLcr1dNtdu3bVU089pfnz56tevXp37F+1alWFh4ffsd/dhuzCUNQ13WoafeMfk1B069ZNzZs315IlS7Ru3Tq9++67euedd7R48eI8B6qCdOLECW3btk0DBgwosCn3b3ar9yc371txcKc6XVxctHXrVm3atEkrV67UmjVrtGDBAoWFhWndunX5us1CQbw3rq6uat68uZo3b64yZcpo3LhxWr16tcUZEoVlzpw56t27tzp37qyRI0fK19dX9vb2iomJsZjeP7969OihUaNG6ZlnnlHp0qVvea1fVrj+8ssvc7yFx93eriDLoEGDFBsbqyFDhigkJEReXl4ymUzq3r37XQX8rGXffffdW16XePP1pFmT2pw6dUp//fXXbW9d8k8tWrTQkSNHtGzZMq1bt06fffaZJk+erI8++kj9+/fP9zYAsA0EO+TZ1atX1bFjR/3666/67rvvcvwr8H333ScfHx/t2bMn22t3e9G9dONI4DfffKPQ0NAcg1NO3nvvPYu/lOdmue+//15dunTR/fffr6+//jrHLxBZ27Jnzx61a9fO3L5nzx5lZmZm29YjR46oTZs28vX11apVq/I0QUZaWpoyMzML5K/lt5M1O2NiYqLF6YPp6ek6evRotrB46tQpXbp0yeKoXdZN67MmNahUqZIOHDigzMxMi6N2v/zyi8U6b+d2p43ltA3fffedLly4YHFEIy/ry0m5cuX0/PPP6/nnn9fZs2fVsGFDTZgw4ZbBzsfHR66urkpMTMz22i+//CI7O7tsR73y6quvvpJhGAU6G2ZByctnKbf7959j3uyXX35RmTJlsh1Bzg07Ozu1atVKrVq10vvvv6+33npLo0eP1qZNm3L1B5LClnU6XtYfzHL7GQ8KClJmZqYOHjyYp9+9ixYtUuXKlbV48WKLfTNmzJi73RRJN45oNmvWTJs3b9Zzzz13y4CWdYq0r6/vbfdD1vbmdBpiTp+Vmy1atEi9evXSe++9Z267evVqvmchzpJVf9YZGHfy0Ucfaf369ZowYYJiYmL07LPPatmyZRZ9bvezUqpUKfXp00d9+vTRxYsX1aJFC40dO5ZgB/wLcCom8uT69et64oknFBcXp4ULF972ZquRkZFasWKFxWlmGzZs0K+//qrHH3/8rupYtWqVkpOT8/RFtlGjRhanm93ptKRDhw6pffv2CggI0IoVK255BCksLEylSpXSzJkzLdpnzpwpV1dXtW/f3tyWlJSk1q1by87OTmvXrr3lNWTJycnKyMjI1v7ZZ59Jyj4DZ0ELDw+Xo6OjPvjgA4ujC7NmzVJKSorFNkk3Zu/8+OOPzc/T09P18ccfy8fHR40aNZIktWvXTklJSRYzAl67dk3Tpk2Tu7u7Hn744TvWlfVlPTdftLJuzP7hhx9atE+ePFkmkynPR9iuX7+eLVD7+vqqfPnyOZ5ynMXe3l6tW7fWsmXLLKZNP3PmjObNm6eHHnpInp6eearlZvPmzVPFihX10EMP3dU4hSEvnyU3N7dc/dGiXLlyql+/vr744guLz0JCQoLWrVtn8QeW3MrpFg1ZIeh2+7cwbNiwIcf2rGsHs04rzO1nvHPnzrKzs9Mbb7yR7cjT7Y4eZh1x/Gef77//XnFxcXncolsbP368xowZo0GDBt2yT0REhDw9PfXWW2/l+Hsx6xYS//xc/PNztH79eh08ePCOtdjb22d7P6ZNm6br16/ndnNy1KhRIwUFBWnSpEk5XiP9z1tgHD16VCNHjlRkZKReeeUVTZo0Sd9++63++9//Wizj5uaW4+/Bv/76y+K5u7u7qlSpUuSfYQDWwRE75Mnw4cP17bffqmPHjjp//rzmzJlj8fo/p/V/5ZVXtHDhQrVs2VIvvPCCLl68qHfffVd16tRRnz59LJb78ssvdfz4cfN9pbZu3arx48dLkp566qlsR1fmzp0rJycnRUZGFsZm6sKFC4qIiNDff/+tkSNHZpvgISgoyBxqXVxc9OabbyoqKkqPP/64IiIitG3bNs2ZM0cTJkywmNyhTZs2+v333/Xiiy9q+/bt2r59u/k1Pz8/83TUmzdv1uDBg/XYY4+patWqSk9P17Zt27R48WLdf//9t719QkHw8fHRqFGjNG7cOLVp00aPPvqoEhMTzfeSu3n95cuX1zvvvKNjx46pWrVqWrBggeLj4/XJJ5+YTw0cMGCAPv74Y/Xu3Vt79+5VQECAFi1apB07dmjKlCnZrhPKSVBQkLy9vfXRRx/Jw8NDbm5ueuCBB3K8Hq9jx45q2bKlRo8erWPHjqlevXpat26dli1bpiFDhlhMlJIbFy5cUIUKFfTYY4+pXr16cnd313fffafdu3db/IU/J+PHjzffI+35559XiRIl9PHHHystLU0TJ07MUx03S0hI0IEDB/Tyyy/n6YhmltWrV5uP8PzTgw8+aHGELb/y8llq1KiRFixYoGHDhqlx48Zyd3dXx44dcxz33XffVdu2bRUSEqJ+/fqZb3fg5eVlcW+y3HrjjTe0detWtW/fXpUqVdLZs2c1Y8YMVahQocgDc6dOnRQYGKiOHTsqKChIly5d0nfffafly5ercePG5vckt5/xKlWqaPTo0eZJWLp27SonJyft3r1b5cuXV0xMTI51dOjQQYsXL1aXLl3Uvn17HT16VB999JFq1qyZp0mcbufhhx++4x91PD09NXPmTD311FNq2LChunfvLh8fH504cUIrV65Us2bNzOE2JiZG7du310MPPaS+ffvq/PnzmjZtmmrVqnXHmjt06KAvv/xSXl5eqlmzpuLi4vTdd99lu/VHXtnZ2emzzz5T27ZtVatWLfXp00f33Xef/ve//2nTpk3y9PTU8uXLZRiG+vbtKxcXF/MfCp999ll98803euGFFxQeHm4+06RRo0aaOXOmxo8frypVqsjX11dhYWGqWbOmQkND1ahRI5UqVUp79uwx36IFwL+AVebihM3Kmtr+Vo+bJSQkGK1btzZcXV0Nb29vo2fPnkZSUlKexr15ev+UlBTD2dnZ6Nq1a2Ftpnla7ls9/jm1eZZPPvnEqF69uuHo6GgEBQUZkydPtpja2jCM2475z6mrDx8+bDz99NNG5cqVDRcXF8PZ2dmoVauWMWbMGOPixYu5rv9O0+ZnTct+81TaWT788EOjRo0ahoODg+Hn52c899xzFrd6MIwb+65WrVrGnj17jJCQEMPZ2dmoVKmS8eGHH2Yb78yZM0afPn2MMmXKGI6OjkadOnWy3brgTrUvW7bMqFmzplGiRAmLKcVvvt2BYdyYJn3o0KFG+fLlDQcHB6Nq1arGu+++m+N+yek2Bv+cxj4tLc0YOXKkUa9ePcPDw8Nwc3Mz6tWrZ8yYMSPHOm/2448/GhEREYa7u7vh6upqtGzZ0ti5c2eetj0nL7/8siHJOHDgQK6XMYzb3+7gn+/rrWratGmTIclYuHBhjuPe/JnKzWfp4sWLxpNPPml4e3tbTE+f0+0ODMMwvvvuO6NZs2aGi4uL4enpaXTs2DHbrU+ybndw820Msuo8evSoYRiGsWHDBqNTp05G+fLlDUdHR6N8+fJGjx49crwNyc1udbuDm9+DrPfsTrcs+eqrr4zu3bsbQUFB5p//mjVrGqNHjzbfFiRLbj/jhmEYn3/+udGgQQPDycnJKFmypPHwww8b69evN79+8+0OMjMzjbfeesuoVKmS4eTkZDRo0MBYsWJFjj9ryuPtDm7n5tsdZNm0aZMRERFheHl5Gc7OzkZQUJDRu3dvY8+ePRb9vvnmGyM4ONhwcnIyatasaSxevDhXNf/999/m30/u7u5GRESE8csvv2Tbv3dyq9uy7Nu3z+jatatRunRpw8nJyahUqZLRrVs3Y8OGDYZhGMbUqVMtbkeT5cSJE4anp6fRrl07c1tSUpLRvn17w8PDw+L/j/HjxxtNmjQxvL29DRcXF6NGjRrGhAkTzLedAXBvMxlGMbvCHYBNCQ0N1Z9//qmEhARrlwIAAPCvxTV2AAAAAGDjCHYAAAAAYOMIdgAAAABg47jGDgAAAABsHEfsAAAAAMDGEewAAAAAwMZxg/JcyszM1KlTp+Th4ZGvmwADAADAOgzD0IULF1S+fHnZ2XFcA/cmgl0unTp1Sv7+/tYuAwAAAPl08uRJVahQwdplAIWCYJdLHh4ekm78QvD09LRyNQAAAMit1NRU+fv7m7/PAfcigl0uZZ1+6enpSbADAACwQVxOg3sZJxkDAAAAgI0j2AEAAACAjSPYAQAAAICN4xo7AAAAwMquX7+ujIwMa5eBYsbR0THXt+gg2AEAAABWYhiGkpKSlJycbO1SUAzZ2dkpMDBQjo6Od+xLsAMAAACsJCvU+fr6ytXVlZk7YZaZmalTp07p9OnTqlix4h0/GwQ7AAAAwAquX79uDnWlS5e2djkohnx8fHTq1Cldu3ZNDg4Ot+3L5CkAAACAFWRdU+fq6mrlSlBcZZ2Cef369Tv2JdgBAAAAVsTpl7iVvHw2CHYAAAAAYOMIdgAAAACKrbFjx6p+/frWLqPYI9gBAAAAsNC7d2+ZTKZsjzZt2hTqek0mk5YuXWrRNmLECG3YsKFQ13svYFZMAAAAANm0adNGsbGxFm1OTk5FXoe7u7vc3d2LfL22hiN2AAAAALJxcnJS2bJlLR4lS5aUdOPI2scff6wOHTrI1dVVwcHBiouL0+HDhxUaGio3Nzc9+OCDOnLkiMWYM2fOVFBQkBwdHVW9enV9+eWX5tcCAgIkSV26dJHJZDI/v/lUzMzMTL3xxhuqUKGCnJycVL9+fa1Zs8b8+rFjx2QymbR48WK1bNlSrq6uqlevnuLi4sx9jh8/ro4dO6pkyZJyc3NTrVq1tGrVqgJ+B4sWwQ4AAABAnr355pt6+umnFR8frxo1aujJJ5/Us88+q1GjRmnPnj0yDEPR0dHm/kuWLNELL7yg4cOHKyEhQc8++6z69OmjTZs2SZJ2794tSYqNjdXp06fNz282depUvffee5o0aZIOHDigiIgIPfroo/rtt98s+o0ePVojRoxQfHy8qlWrph49eujatWuSpKioKKWlpWnr1q366aef9M4779j8UUFOxcyj2mPWys6Je42gaBwL327tEoA8OxS91Nol/CusqhdUZOuK6h1TZOuyZTucOhfKuCf8txXKuNbSp2p5a5eAXFqxYkW2sPPKK6/olVdekST16dNH3bp1kyS99NJLCgkJ0WuvvaaIiAhJ0gsvvKA+ffqYl500aZJ69+6t559/XpI0bNgw7dq1S5MmTVLLli3l4+MjSfL29lbZsmVvWdekSZP00ksvqXv37pKkd955R5s2bdKUKVM0ffp0c78RI0aoffv2kqRx48apVq1aOnz4sGrUqKETJ04oMjJSderUkSRVrlw5/29UMcEROwAAAADZtGzZUvHx8RaPgQMHml+vW7eu+d9+fn6SZA5KWW1Xr15VamqqJOnQoUNq1qyZxTqaNWumQ4cO5bqm1NRUnTp1Klfj/LO+cuXKSZLOnj0rSRo8eLDGjx+vZs2aacyYMTpw4ECuayiuCHYAAAAAsnFzc1OVKlUsHqVKlTK/7uDgYP531o20c2rLzMwsooot3a6W/v376/fff9dTTz2ln376Sffff7+mTZtmlToLCsEOAAAAQKELDg7Wjh07LNp27NihmjVrmp87ODjo+vXrtxzD09NT5cuXv+M4ueHv76+BAwdq8eLFGj58uD799NM8LV/ccI0dAAAAgGzS0tKUlJRk0VaiRAmVKVMmX+ONHDlS3bp1U4MGDRQeHq7ly5dr8eLF+u6778x9AgICtGHDBjVr1kxOTk7mWThvHmfMmDEKCgpS/fr1FRsbq/j4eM2dOzfXtQwZMkRt27ZVtWrV9Pfff2vTpk0KDg7O13YVFwQ7AAAAANmsWbPGfG1alurVq+uXX37J13idO3fW1KlTNWnSJL3wwgsKDAxUbGysQkNDzX3ee+89DRs2TJ9++qnuu+8+HTt2LNs4gwcPVkpKioYPH66zZ8+qZs2a+vbbb1W1atVc13L9+nVFRUXpjz/+kKenp9q0aaPJkyfna7uKC5NhGIa1i7AFqamp8vLykv+Qr5kVE0WGWTFhi5gVs2gwK2bxw6yYuWONWTGzvselpKTI09OzyNd/K1evXtXRo0cVGBgoZ2dna5eDYigvnxGusQMAAAAAG0ewAwAAAAAbR7ADAAAAABtHsAMAAAAAG0ewAwAAAAAbR7ADAAAAABtHsAMAAAAAG2fVYBcTE6PGjRvLw8NDvr6+6ty5sxITEy36XL16VVFRUSpdurTc3d0VGRmpM2fOmF/fv3+/evToIX9/f7m4uCg4OFhTp061GGP79u1q1qyZSpcuLRcXF9WoUcPmb0AIAAAAAFlKWHPlW7ZsUVRUlBo3bqxr167plVdeUevWrXXw4EG5ublJkoYOHaqVK1dq4cKF8vLyUnR0tLp27aodO3ZIkvbu3StfX1/NmTNH/v7+2rlzpwYMGCB7e3tFR0dLktzc3BQdHa26devKzc1N27dv17PPPis3NzcNGDDAatsPAAAAAAXBqsFuzZo1Fs9nz54tX19f7d27Vy1atFBKSopmzZqlefPmKSwsTJIUGxur4OBg7dq1S02bNlXfvn0txqhcubLi4uK0ePFic7Br0KCBGjRoYO4TEBCgxYsXa9u2bQQ7AAAAIB969+6t5ORkLV261NqlQFYOdjdLSUmRJJUqVUrSjaNxGRkZCg8PN/epUaOGKlasqLi4ODVt2vSW42SNkZN9+/Zp586dGj9+/C37pKWlKS0tzfw8NTU1T9sCAAAA5FfAyyuLdH3H3m6f52WmTp0qwzAKoRrkR7EJdpmZmRoyZIiaNWum2rVrS5KSkpLk6Ogob29vi75+fn5KSkrKcZydO3dqwYIFWrky+w9DhQoVdO7cOV27dk1jx45V//79b1lPTEyMxo0bl/8NAgAAAO5hXl5e1i4B/1BsZsWMiopSQkKC5s+fn+8xEhIS1KlTJ40ZM0atW7fO9vq2bdu0Z88effTRR5oyZYq++uqrW441atQopaSkmB8nT57Md10AAADAvaZ3797q3LmzpBtnuw0ePFi+vr5ydnbWQw89pN27d0uSDMNQlSpVNGnSJIvl4+PjZTKZdPjw4aIu/Z5ULIJddHS0VqxYoU2bNqlChQrm9rJlyyo9PV3JyckW/c+cOaOyZctatB08eFCtWrXSgAED9Oqrr+a4nsDAQNWpU0fPPPOMhg4dqrFjx96yJicnJ3l6elo8AAAAAGT34osv6ptvvtEXX3yhH3/8UVWqVFFERITOnz8vk8mkvn37KjY21mKZ2NhYtWjRQlWqVLFS1fcWqwY7wzAUHR2tJUuWaOPGjQoMDLR4vVGjRnJwcNCGDRvMbYmJiTpx4oRCQkLMbT///LNatmypXr16acKECblad2ZmpsU1dAAAAADy7tKlS5o5c6beffddtW3bVjVr1tSnn34qFxcXzZo1S9KNo3uJiYn64YcfJEkZGRmaN29etokQkX9WvcYuKipK8+bN07Jly+Th4WG+bs7Ly0suLi7y8vJSv379NGzYMJUqVUqenp4aNGiQQkJCzBOnJCQkKCwsTBERERo2bJh5DHt7e/n4+EiSpk+frooVK6pGjRqSpK1bt2rSpEkaPHiwFbYaAAAAuHccOXJEGRkZatasmbnNwcFBTZo00aFDhyRJ5cuXV/v27fX555+rSZMmWr58udLS0vT4449bq+x7jlWD3cyZMyVJoaGhFu2xsbHq3bu3JGny5Mmys7NTZGSk0tLSFBERoRkzZpj7Llq0SOfOndOcOXM0Z84cc3ulSpV07NgxSTeOzo0aNUpHjx5ViRIlFBQUpHfeeUfPPvtsoW4fAAAAgBv69++vp556SpMnT1ZsbKyeeOIJubq6Wruse4ZVg11upkd1dnbW9OnTNX369BxfHzt27G2vlZOkQYMGadCgQfkpEQAAAMBtBAUFydHRUTt27FClSpUk3TjVcvfu3RoyZIi5X7t27eTm5qaZM2dqzZo12rp1q5UqvjcVm9sdAAAAALA9bm5ueu655zRy5EiVKlVKFStW1MSJE3X58mX169fP3M/e3l69e/fWqFGjVLVqVYs5M3D3isWsmAAAAABs19tvv63IyEg99dRTatiwoQ4fPqy1a9eqZMmSFv369eun9PR09enTx0qV3rs4YgcAAAAUM8febm/tEu4oLS1N7u7ukm5cPvXBBx/ogw8+uO0y//vf/+Tg4KCnn366KEr8V+GIHQAAAIBcu3btmg4ePKi4uDjVqlUrV8ukpaXpjz/+0NixY/X444/Lz8+vkKv89yHYAQAAAMi1hIQE3X///apVq5YGDhyYq2W++uorVapUScnJyZo4cWIhV/jvxKmYAAAAAHKtfv36unz5cp6W6d27t/l2ZigcHLEDAAAAABtHsAMAAAAAG0ewAwAAAAAbR7ADAAAAABtHsAMAAAAAG8esmHmUMC5Cnp6e1i4D/xrF/+akwM2Cf4mxdgn/CsHWLgDZtNIRa5cA4F+MI3YAAAAACl1oaKiGDBlifh4QEKApU6bkaYxPPvlE/v7+srOzy/Oy9zqO2AEAAADFzXejinZ94UV/tsXu3bvl5uaW6/6pqamKjo7W+++/r8jISHl5eRVidbaHYAcAAADgrqSnp8vR0TFPy/j4+OSp/4kTJ5SRkaH27durXLlyeVr234BTMQEAAADkSWhoqKKjozVkyBCVKVNGERERSkhIUNu2beXu7i4/Pz899dRT+vPPP285xs2nYiYnJ6t///7y8fGRp6enwsLCtH//fknS7NmzVadOHUlS5cqVZTKZdOzYscLcRJtDsAMAAACQZ1988YUcHR21Y8cOvf322woLC1ODBg20Z88erVmzRmfOnFG3bt1yPd7jjz+us2fPavXq1dq7d68aNmyoVq1a6fz583riiSf03XffSZJ++OEHnT59Wv7+/oW1aTaJUzEBAAAA5FnVqlU1ceJESdL48ePVoEEDvfXWW+bXP//8c/n7++vXX39VtWrVbjvW9u3b9cMPP+js2bNycnKSJE2aNElLly7VokWLNGDAAJUuXVrSjVM4y5YtW0hbZbsIdgAAAADyrFGjRuZ/79+/X5s2bZK7u3u2fkeOHLljsNu/f78uXrxoDm9Zrly5oiNHuJVIbhDsAAAAAOTZP2e0vHjxojp27Kh33nknW7/cTHRy8eJFlStXTps3b872mre3992U+a9BsAMAAABwVxo2bKhvvvlGAQEBKlEi7xGjYcOGSkpKUokSJRQQEFDwBf4LMHkKAAAAgLsSFRWl8+fPq0ePHtq9e7eOHDmitWvXqk+fPrp+/fodlw8PD1dISIg6d+6sdevW6dixY9q5c6dGjx6tPXv2FMEW2D6CHQAAAIC7Ur58ee3YsUPXr19X69atVadOHQ0ZMkTe3t6ys7tz5DCZTFq1apVatGihPn36qFq1aurevbuOHz8uPz+/ItgC22cyDMOwdhG2IDU1VV5eXkpJSZGnp6e1ywEAAEAuFdfvcVevXtXRo0cVGBgoZ2dna5eDYigvnxGO2AEAAACAjSPYAQAAAICNI9gBAAAAgI0j2AEAAACAjSPYAQAAAICNI9gBAAAAgI0j2AEAAACAjSPYAQAAAICNI9gBAAAAgI0j2AEAAAAoVgICAjRlyhRrl2FTSli7AAAAAACWDtUILtL1Bf9yqEjXdye7d++Wm5ub+bnJZNKSJUvUuXNn6xWVB5s3b1bLli31999/y9vbu0jWSbADAAAAUKz4+PhYuwSbw6mYAAAAAPIsMzNTMTExCgwMlIuLi+rVq6dFixbJMAyFh4crIiJChmFIks6fP68KFSro9ddfNy+/fPlyNW7cWM7OzipTpoy6dOlifu2fp2IGBARIkrp06SKTyWR+LknLli1Tw4YN5ezsrMqVK2vcuHG6du2a+XWTyaSPP/5YHTp0kKurq4KDgxUXF6fDhw8rNDRUbm5uevDBB3XkyBGLbcvNuJ999pm6dOkiV1dXVa1aVd9++60k6dixY2rZsqUkqWTJkjKZTOrdu/ddv993QrADAAAAkGcxMTH673//q48++kg///yzhg4dqv/85z/aunWrvvjiC+3evVsffPCBJGngwIG67777zMFu5cqV6tKli9q1a6d9+/Zpw4YNatKkSY7r2b17tyQpNjZWp0+fNj/ftm2bnn76ab3wwgs6ePCgPv74Y82ePVsTJkywWP7NN9/U008/rfj4eNWoUUNPPvmknn32WY0aNUp79uyRYRiKjo4298/tuOPGjVO3bt104MABtWvXTj179tT58+fl7++vb775RpKUmJio06dPa+rUqQXwjt+eyciK0bit1NRUeXl5KSUlRZ6entYuBwAAALlUXL/HXb16VUePHlVgYKCcnZ0tXivu19ilpaWpVKlS+u677xQSEmJu79+/vy5fvqx58+Zp4cKFevrppzVkyBBNmzZN+/btU9WqVSVJDz74oCpXrqw5c+bkOH5AQICGDBmiIUOGSMr5Grvw8HC1atVKo0aNMrfNmTNHL774ok6dOmVe7tVXX9Wbb74pSdq1a5dCQkI0a9Ys9e3bV5I0f/589enTR1euXMn3uJcuXZK7u7tWr16tNm3aFNg1drf7jNyMa+wAAAAA5Mnhw4d1+fJlPfLIIxbt6enpatCggSTp8ccf15IlS/T2229r5syZ5lAnSfHx8XrmmWfuqob9+/drx44dFkfSrl+/rqtXr+ry5ctydXWVJNWtW9f8up+fnySpTp06Fm1Xr15VamqqPD098zWum5ubPD09dfbs2bvaprtBsAMAAACQJxcvXpR045TK++67z+I1JycnSdLly5e1d+9e2dvb67fffrPo4+LiUiA1jBs3Tl27ds322j+Pbjk4OJj/bTKZbtmWmZmZ73GzxskawxoIdgAAAADypGbNmnJyctKJEyf08MMP59hn+PDhsrOz0+rVq9WuXTu1b99eYWFhkm4c7dqwYYP69OmTq/U5ODjo+vXrFm0NGzZUYmKiqlSpcncbc5OCGNfR0VGSstVcmAh2AAAAAPLEw8NDI0aM0NChQ5WZmamHHnpIKSkp2rFjhzw9PVWmTBl9/vnniouLU8OGDTVy5Ej16tVLBw4cUMmSJTVmzBi1atVKQUFB6t69u65du6ZVq1bppZdeynF9AQEB2rBhg5o1ayYnJyeVLFlSr7/+ujp06KCKFSvqsccek52dnfbv36+EhASNHz8+39tWEONWqlRJJpNJK1asULt27eTi4iJ3d/d815QbzIoJAAAAIM/efPNNvfbaa4qJiVFwcLDatGmjlStXKiAgQP369dPYsWPVsGFDSTdmkPTz89PAgQMlSaGhoVq4cKG+/fZb1a9fX2FhYfrhhx9uua733ntP69evl7+/v/kavoiICK1YsULr1q1T48aN1bRpU02ePFmVKlW6q+0qiHHvu+8+jRs3Ti+//LL8/PwsZt0sLMyKmUvFdTYlAAAA3F5x/R6XlxkP8e+Ul88IR+wAAAAAwMYR7AAAAADAxjF5Sh7VHrNWdk6u1i4DRexY+HZrlwAUmUPRS61dwr/OqnpB1i5BUb1jrF3CPW+HU+d8L3vCf1vBFWJlfaqWt3YJwD2JI3YAAAAAYOMIdgAAAABg4wh2AAAAAGDjCHYAAAAAYOMIdgAAAABg4wh2AAAAAGDjCHYAAAAACt2xY8dkMpkUHx9/yz6bN2+WyWRScnJykdV1r+A+dgAAAEAx894THYp0fcMXrCj0dfj7++v06dMqU6ZMoa/r34hgBwAAAKBQpaeny9HRUWXLlrV2KfcsTsUEAAAAkCehoaGKjo5WdHS0vLy8VKZMGb322msyDEOSFBAQoDfffFNPP/20PD09NWDAgBxPxVy1apWqVasmFxcXtWzZUseOHcu2ru3bt6t58+ZycXGRv7+/Bg8erEuXLhXRltoOgh0AAACAPPviiy9UokQJ/fDDD5o6daref/99ffbZZ+bXJ02apHr16mnfvn167bXXsi1/8uRJde3aVR07dlR8fLz69++vl19+2aLPkSNH1KZNG0VGRurAgQNasGCBtm/frujo6ELfPlvDqZgAAAAA8szf31+TJ0+WyWRS9erV9dNPP2ny5Ml65plnJElhYWEaPny4uf/NR+NmzpypoKAgvffee5JkHuOdd94x94mJiVHPnj01ZMgQSVLVqlX1wQcf6OGHH9bMmTPl7OxcuBtpQzhiBwAAACDPmjZtKpPJZH4eEhKi3377TdevX5ck3X///bdd/tChQ3rggQcs2kJCQiye79+/X7Nnz5a7u7v5ERERoczMTB09erSAtuTeYNVgFxMTo8aNG8vDw0O+vr7q3LmzEhMTLfpcvXpVUVFRKl26tNzd3RUZGakzZ86YX9+/f7969Oghf39/ubi4KDg4WFOnTrUYY/HixXrkkUfk4+MjT09PhYSEaO3atUWyjQAAAMC/kZub212PcfHiRT377LOKj483P/bv36/ffvtNQUFBBVDlvcOqwW7Lli2KiorSrl27tH79emVkZKh169YWF0MOHTpUy5cv18KFC7VlyxadOnVKXbt2Nb++d+9e+fr6as6cOfr55581evRojRo1Sh9++KG5z9atW/XII49o1apV2rt3r1q2bKmOHTtq3759Rbq9AAAAwL3i+++/t3i+a9cuVa1aVfb29rlaPjg4WD/88EO2Mf6pYcOGOnjwoKpUqZLt4ejoeHcbcI+x6jV2a9assXg+e/Zs+fr6au/evWrRooVSUlI0a9YszZs3T2FhYZKk2NhYBQcHa9euXWratKn69u1rMUblypUVFxenxYsXmy+qnDJlikWft956S8uWLdPy5cvVoEGDwttAAAAA4B514sQJDRs2TM8++6x+/PFHTZs2zXy9XG4MHDhQ7733nkaOHKn+/ftr7969mj17tkWfl156SU2bNlV0dLT69+8vNzc3HTx4UOvXr7c4kINido1dSkqKJKlUqVKSbhyNy8jIUHh4uLlPjRo1VLFiRcXFxd12nKwxcpKZmakLFy7ctk9aWppSU1MtHgAAAABuePrpp3XlyhU1adJEUVFReuGFFzRgwIBcL1+xYkV98803Wrp0qerVq6ePPvpIb731lkWfunXrasuWLfr111/VvHlzNWjQQK+//rrKly9f0Jtj84rNrJiZmZkaMmSImjVrptq1a0uSkpKS5OjoKG9vb4u+fn5+SkpKynGcnTt3asGCBVq5cuUt1zVp0iRdvHhR3bp1u2WfmJgYjRs3Lu8bAgAAANyl4QtWWLuEO3JwcNCUKVM0c+bMbK/ldD+6gIAA833usnTo0EEdOnSwaOvTp4/F88aNG2vdunV3X/A9rtgcsYuKilJCQoLmz5+f7zESEhLUqVMnjRkzRq1bt86xz7x58zRu3Dh9/fXX8vX1veVYo0aNUkpKivlx8uTJfNcFAAAAAIWpWByxi46O1ooVK7R161ZVqFDB3F62bFmlp6crOTnZ4qjdmTNnVLZsWYsxDh48qFatWmnAgAF69dVXc1zP/Pnz1b9/fy1cuNDi9M6cODk5ycnJKf8bBQAAAABFxKrBzjAMDRo0SEuWLNHmzZsVGBho8XqjRo3k4OCgDRs2KDIyUpKUmJioEydOWNzj4ueff1ZYWJh69eqlCRMm5Liur776Sn379tX8+fPVvn37wtsoAAAA4B63efNma5eAm1g12EVFRWnevHlatmyZPDw8zNfNeXl5ycXFRV5eXurXr5+GDRumUqVKydPTU4MGDVJISIiaNm0q6cbpl2FhYYqIiNCwYcPMY9jb28vHx0fSjdMve/XqpalTp+qBBx4w98laBwAAAADYMqteYzdz5kylpKQoNDRU5cqVMz8WLFhg7jN58mR16NBBkZGRatGihcqWLavFixebX1+0aJHOnTunOXPmWIzRuHFjc59PPvlE165dU1RUlEWfF154oUi3FwAAAAAKg9VPxbwTZ2dnTZ8+XdOnT8/x9bFjx2rs2LG3HYNDxQAAAADuZcVmVkwAAAAAQP4Q7AAAAADAxhHsAAAAAMDGEewAAAAAwMYVixuUAwAAAPg/V1f/VKTrc25bp0jXh4LHETsAAAAA94yMjAxrl2AVBDsAAAAAebZmzRo99NBD8vb2VunSpdWhQwcdOXJEknTs2DGZTCYtXrxYLVu2lKurq+rVq6e4uDiLMT799FP5+/vL1dVVXbp00fvvvy9vb2+LPsuWLVPDhg3l7OysypUra9y4cbp27Zr5dZPJpJkzZ+rRRx+Vm5ubJkyYUOjbXhwR7AAAAADk2aVLlzRs2DDt2bNHGzZskJ2dnbp06aLMzExzn9GjR2vEiBGKj49XtWrV1KNHD3Mo27FjhwYOHKgXXnhB8fHxeuSRR7KFsm3btunpp5/WCy+8oIMHD+rjjz/W7Nmzs/UbO3asunTpop9++kl9+/Yt/I0vhrjGDgAAAECeRUZGWjz//PPP5ePjo4MHD8rd3V2SNGLECLVv316SNG7cONWqVUuHDx9WjRo1NG3aNLVt21YjRoyQJFWrVk07d+7UihUrzGOOGzdOL7/8snr16iVJqly5st588029+OKLGjNmjLnfk08+qT59+hTq9hZ3HLEDAAAAkGe//fabevToocqVK8vT01MBAQGSpBMnTpj71K1b1/zvcuXKSZLOnj0rSUpMTFSTJk0sxrz5+f79+/XGG2/I3d3d/HjmmWd0+vRpXb582dzv/vvvL9Bts0UcsQMAAACQZx07dlSlSpX06aefqnz58srMzFTt2rWVnp5u7uPg4GD+t8lkkiSLUzXv5OLFixo3bpy6du2a7TVnZ2fzv93c3PKzCfcUgl0eJYyLkKenp7XLQJFrb+0CgCIT/EuMtUv41wm2dgEoEq10xNolAAXmr7/+UmJioj799FM1b95ckrR9+/Y8jVG9enXt3r3bou3m5w0bNlRiYqKqVKlydwX/CxDsAAAAAORJyZIlVbp0aX3yyScqV66cTpw4oZdffjlPYwwaNEgtWrTQ+++/r44dO2rjxo1avXq1+cieJL3++uvq0KGDKlasqMcee0x2dnbav3+/EhISNH78+ILeLJvGNXYAAAAA8sTOzk7z58/X3r17Vbt2bQ0dOlTvvvtunsZo1qyZPvroI73//vuqV6+e1qxZo6FDh1qcYhkREaEVK1Zo3bp1aty4sZo2barJkyerUqVKBb1JNs9kGIZh7SJsQWpqqry8vJSSksKpmAAAADakuH6Pu3r1qo4eParAwECLMPNv9swzz+iXX37Rtm3brF1KsZCXzwinYgIAAACwikmTJumRRx6Rm5ubVq9erS+++EIzZsywdlk2iWAHAAAAwCp++OEHTZw4URcuXFDlypX1wQcfqH///tYuyyYR7AAAAABYxddff23tEu4ZTJ4CAAAAADaOYAcAAAAANo5gBwAAAAA2jmAHAAAAADaOYAcAAAAANo5gBwAAAAA2jmAHAAAAADaO+9gBAAAAxcyGjUFFur5WYUeKdH03S09Pl6Ojo1VrsHUcsQMAAACQJ6GhoRo8eLBefPFFlSpVSmXLltXYsWPNrycnJ6t///7y8fGRp6enwsLCtH//fvPrY8eOVf369fXZZ58pMDBQzs7O+uSTT1S+fHllZmZarKtTp07q27ev+fnMmTMVFBQkR0dHVa9eXV9++aX5tWPHjslkMik+Pt6iFpPJpM2bN0uS/v77b/Xs2VM+Pj5ycXFR1apVFRsba7H84sWL1bJlS7m6uqpevXqKi4uzqGn79u1q3ry5XFxc5O/vr8GDB+vSpUvm12fMmKGqVavK2dlZfn5+euyxx8yvLVq0SHXq1JGLi4tKly6t8PBwi2Xzi2AHAAAAIM+++OILubm56fvvv9fEiRP1xhtvaP369ZKkxx9/XGfPntXq1au1d+9eNWzYUK1atdL58+fNyx8+fFjffPONFi9erPj4eD3++OP666+/tGnTJnOf8+fPa82aNerZs6ckacmSJXrhhRc0fPhwJSQk6Nlnn1WfPn0slrmT1157TQcPHtTq1at16NAhzZw5U2XKlLHoM3r0aI0YMULx8fGqVq2aevTooWvXrkmSjhw5ojZt2igyMlIHDhzQggULtH37dkVHR0uS9uzZo8GDB+uNN95QYmKi1qxZoxYtWkiSTp8+rR49eqhv3746dOiQNm/erK5du8owjHzsAUucigkAAAAgz+rWrasxY8ZIkqpWraoPP/xQGzZskIuLi3744QedPXtWTk5OkqRJkyZp6dKlWrRokQYMGCDpxumX//3vf+Xj42Mes23btpo3b55atWol6cbRrTJlyqhly5bmcXr37q3nn39ekjRs2DDt2rVLkyZNMve5kxMnTqhBgwa6//77JUkBAQHZ+owYMULt27eXJI0bN061atXS4cOHVaNGDcXExKhnz54aMmSIeds/+OADPfzww5o5c6ZOnDghNzc3dejQQR4eHqpUqZIaNGgg6Uawu3btmrp27apKlSpJkurUqZO7N/wOOGIHAAAAIM/q1q1r8bxcuXI6e/as9u/fr4sXL6p06dJyd3c3P44ePaojR/7vWr5KlSpZhDpJ6tmzp7755hulpaVJkubOnavu3bvLzu5GbDl06JCaNWtmsUyzZs106NChXNf93HPPaf78+apfv75efPFF7dy587bbVq5cOUnS2bNnJUn79+/X7NmzLbYtIiJCmZmZOnr0qB555BFVqlRJlStX1lNPPaW5c+fq8uXLkqR69eqpVatWqlOnjh5//HF9+umn+vvvv3Nd++0Q7AAAAADkmYODg8Vzk8mkzMxMXbx4UeXKlVN8fLzFIzExUSNHjjT3d3NzyzZmx44dZRiGVq5cqZMnT2rbtm3m0zBzIysA/vPUxoyMDIs+bdu21fHjxzV06FCdOnVKrVq10ogRI265bSaTSZLM1/5dvHhRzz77rMW27d+/X7/99puCgoLk4eGhH3/8UV999ZXKlSun119/XfXq1VNycrLs7e21fv16rV69WjVr1tS0adNUvXp1HT16NNfbeMttv+sRAAAAAOD/a9iwoZKSklSiRAlVqVLF4nHztWw3c3Z2VteuXTV37lx99dVXql69uho2bGh+PTg4WDt27LBYZseOHapZs6YkmY8Anj592vz6PydSyeLj46NevXppzpw5mjJlij755JM8bd/BgwezbVuVKlXMM3uWKFFC4eHhmjhxog4cOKBjx45p48aNkm4ExWbNmmncuHHat2+fHB0dtWTJklyv/1a4xg4AAABAgQkPD1dISIg6d+6siRMnqlq1ajp16pRWrlypLl26mK9tu5WePXuqQ4cO+vnnn/Wf//zH4rWRI0eqW7duatCggcLDw7V8+XItXrxY3333nSTJxcVFTZs21dtvv63AwECdPXtWr776qsUYr7/+uho1aqRatWopLS1NK1asUHBwcK6376WXXlLTpk0VHR2t/v37y83NTQcPHtT69ev14YcfasWKFfr999/VokULlSxZUqtWrVJmZqaqV6+u77//Xhs2bFDr1q3l6+ur77//XufOncvT+m+FI3YAAAAACozJZNKqVavUokUL9enTR9WqVVP37t11/Phx+fn53XH5sLAwlSpVSomJiXryySctXuvcubOmTp2qSZMmqVatWvr4448VGxur0NBQc5/PP/9c165dU6NGjTRkyBCNHz/eYgxHR0eNGjVKdevWVYsWLWRvb6/58+fnevvq1q2rLVu26Ndff1Xz5s3VoEEDvf766ypfvrwkydvbW4sXL1ZYWJiCg4P10Ucf6auvvlKtWrXk6emprVu3ql27dqpWrZpeffVVvffee2rbtm2u138rJqMg5tb8F0hNTZWXl5dSUlLk6elp7XIAAACQS8X1e9zVq1d19OhR833cgJvl5TPCETsAAAAAsHEEOwAAAACwcQQ7AAAAALBxBDsAAAAAsHEEOwAAAACwcQQ7AAAAALBxBDsAAAAAsHEEOwAAAACwcQQ7AAAAALBxBDsAAAAAVmUymbR06VJrl2HTCHYAAAAAYONKWLsAAAAAAJZifztVpOvrU7V8ka4PBY9gl0e1x6yVnZOrtcvAv8Cx8O3WLgHIs0PRS61dwr/GqnpBhb6OqN4xhb4OZLfDqfNdj3HCf9vdF1JICBD3hkWLFmncuHE6fPiwXF1d1aBBAy1btkwHDx7UK6+8on379ikjI0P169fX5MmT1bBhQ/Oyv/32m/r166cffvhBlStX1tSpUy3GPnbsmAIDA/XNN99o2rRp+v7771W1alV99NFHCgkJMffbvn27Ro0apT179qhMmTLq0qWLYmJi5ObmJkmaMWOGJk+erJMnT8rLy0vNmzfXokWLblt/1rK2iFMxAQAAAOTa6dOn1aNHD/Xt21eHDh3S5s2b1bVrVxmGoQsXLqhXr17avn27du3apapVq6pdu3a6cOGCJCkzM1Ndu3aVo6Ojvv/+e3300Ud66aWXclzP6NGjNWLECMXHx6tatWrq0aOHrl27Jkk6cuSI2rRpo8jISB04cEALFizQ9u3bFR0dLUnas2ePBg8erDfeeEOJiYlas2aNWrRoccf6bRlH7AAAAADk2unTp3Xt2jV17dpVlSpVkiTVqVNHkhQWFmbR95NPPpG3t7e2bNmiDh066LvvvtMvv/yitWvXqnz5G0dv33rrLbVt2zbbekaMGKH27dtLksaNG6datWrp8OHDqlGjhmJiYtSzZ08NGTJEklS1alV98MEHevjhhzVz5kydOHFCbm5u6tChgzw8PFSpUiU1aNDgjvXbMo7YAQAAAMi1evXqqVWrVqpTp44ef/xxffrpp/r7778lSWfOnNEzzzyjqlWrysvLS56enrp48aJOnDghSTp06JD8/f3NoU6SxemV/1S3bl3zv8uVKydJOnv2rCRp//79mj17ttzd3c2PiIgIZWZm6ujRo3rkkUdUqVIlVa5cWU899ZTmzp2ry5cv37F+W0awAwAAAJBr9vb2Wr9+vVavXq2aNWtq2rRpql69uo4ePapevXopPj5eU6dO1c6dOxUfH6/SpUsrPT09z+txcHAw/9tkMkm6cSqnJF28eFHPPvus4uPjzY/9+/frt99+U1BQkDw8PPTjjz/qq6++Urly5fT666+rXr16Sk5Ovm39toxgBwAAACBPTCaTmjVrpnHjxmnfvn1ydHTUkiVLtGPHDg0ePFjt2rVTrVq15OTkpD///NO8XHBwsE6ePKnTp0+b23bt2pXn9Tds2FAHDx5UlSpVsj0cHR0lSSVKlFB4eLgmTpyoAwcO6NixY9q4ceNt67dlXGMHAAAAINe+//57bdiwQa1bt5avr6++//57nTt3TsHBwapataq+/PJL3X///UpNTdXIkSPl4uJiXjY8PFzVqlVTr1699O677yo1NVWjR4/Ocw0vvfSSmjZtqujoaPXv319ubm46ePCg1q9frw8//FArVqzQ77//rhYtWqhkyZJatWqVMjMzVb169dvWb8sIdgAAAAByzdPTU1u3btWUKVOUmpqqSpUq6b333lPbtm1VtmxZDRgwQA0bNpS/v7/eeustjRgxwrysnZ2dlixZon79+qlJkyYKCAjQBx98oDZt2uSphrp162rLli0aPXq0mjdvLsMwFBQUpCeeeEKS5O3trcWLF2vs2LG6evWqqlatqq+++kq1atXSoUOHblm/LTMZtj6vZxFJTU2Vl5eX/Id8zX3sUCS4jx1sEfexKzrcx+7exX3sCl7W97iUlBR5enoW+fpv5erVqzp69KgCAwPl7Oxs7XJQDOXlM8I1dgAAAABg4wh2AAAAAGDjCHYAAAAAYOMIdgAAAABg4wh2AAAAAGDjrBrsYmJi1LhxY3l4eMjX11edO3dWYmKiRZ+rV68qKipKpUuXlru7uyIjI3XmzBnz6/v371ePHj3k7+8vFxcXBQcHa+rUqRZjnD59Wk8++aSqVasmOzs7DRkypCg2DwAAALgjJqnHreTls2HVYLdlyxZFRUVp165dWr9+vTIyMtS6dWtdunTJ3Gfo0KFavny5Fi5cqC1btujUqVPq2rWr+fW9e/fK19dXc+bM0c8//6zRo0dr1KhR+vDDD8190tLS5OPjo1dffVX16tUr0m0EAAAAcuLg4CBJunz5spUrQXGVnp4uSbK3t79jX6veoHzNmjUWz2fPni1fX1/t3btXLVq0UEpKimbNmqV58+YpLCxMkhQbG6vg4GDt2rVLTZs2Vd++fS3GqFy5suLi4rR48WJFR0dLkgICAsxH8T7//PMi2DIAAADg9uzt7eXt7a2zZ89KklxdXWUymaxcFYqLzMxMnTt3Tq6uripR4s6xzarB7mYpKSmSpFKlSkm6cTQuIyND4eHh5j41atRQxYoVFRcXp6ZNm95ynKwx8istLU1paWnm56mpqXc1HgAAAHCzsmXLSpI53AH/ZGdnp4oVK+Yq8BebYJeZmakhQ4aoWbNmql27tiQpKSlJjo6O8vb2tujr5+enpKSkHMfZuXOnFixYoJUrV95VPTExMRo3btxdjQEAAADcjslkUrly5eTr66uMjAxrl4NixtHRUXZ2ubt6rtgEu6ioKCUkJGj79u35HiMhIUGdOnXSmDFj1Lp167uqZ9SoURo2bJj5eWpqqvz9/e9qTAAAACAn9vb2ubqOCriVYhHsoqOjtWLFCm3dulUVKlQwt5ctW1bp6elKTk62OGp35swZ82HrLAcPHlSrVq00YMAAvfrqq3ddk5OTk5ycnO56HAAAAAAobFadFdMwDEVHR2vJkiXauHGjAgMDLV5v1KiRHBwctGHDBnNbYmKiTpw4oZCQEHPbzz//rJYtW6pXr16aMGFCkdUPAAAAAMWBVY/YRUVFad68eVq2bJk8PDzM1815eXnJxcVFXl5e6tevn4YNG6ZSpUrJ09NTgwYNUkhIiHnilISEBIWFhSkiIkLDhg0zj2Fvby8fHx/zuuLj4yVJFy9e1Llz5xQfHy9HR0fVrFmzaDcaAAAAAAqYVYPdzJkzJUmhoaEW7bGxserdu7ckafLkybKzs1NkZKTS0tIUERGhGTNmmPsuWrRI586d05w5czRnzhxze6VKlXTs2DHz8wYNGpj/vXfvXs2bNy9bHwAAAACwRVYNdrm5k7qzs7OmT5+u6dOn5/j62LFjNXbs2AJZFwAAAADYIqteYwcAAAAAuHsEOwAAAACwcQQ7AAAAALBxBDsAAAAAsHEEOwAAAACwcQQ7AAAAALBxBDsAAAAAsHEEOwAAAACwcQQ7AAAAALBxBDsAAAAAsHEEOwAAAACwcSbDMAxrF2ELUlNT5eXlpZSUFHl6elq7HAAAAOQS3+Pwb8AROwAAAACwcQQ7AAAAALBxBDsAAAAAsHEEOwAAAACwcQQ7AAAAALBxBDsAAAAAsHEEOwAAAACwcQQ7AAAAALBxBDsAAAAAsHEEOwAAAACwcQQ7AAAAALBxBDsAAAAAsHEEOwAAAACwcQQ7AAAAALBxBDsAAAAAsHEEOwAAAACwcQQ7AAAAALBxBRLsUlNTtXTpUh06dKgghgMAAAAA5EG+gl23bt304YcfSpKuXLmi+++/X926dVPdunX1zTffFGiBAAAAAIDby1ew27p1q5o3by5JWrJkiQzDUHJysj744AONHz++QAsEAAAAANxevoJdSkqKSpUqJUlas2aNIiMj5erqqvbt2+u3334r0AIBAAAAALeXr2Dn7++vuLg4Xbp0SWvWrFHr1q0lSX///becnZ0LtEAAAAAAwO2VyM9CQ4YMUc+ePeXu7q5KlSopNDRU0o1TNOvUqVOQ9QEAAAAA7iBfwe75559XkyZNdPLkST3yyCOys7tx4K9y5cpcYwcAAAAARcxkGIZh7SJsQWpqqry8vJSSkiJPT09rlwMAAIBc4nsc/g3ydcTu+vXrmj17tjZs2KCzZ88qMzPT4vWNGzcWSHEAAAAAgDvLV7B74YUXNHv2bLVv3161a9eWyWQq6LoAAAAAALmUr2A3f/58ff3112rXrl1B1wMAAAAAyKN83e7A0dFRVapUKehaAAAAAAD5kK9gN3z4cE2dOlXMuwIAAAAA1pevUzG3b9+uTZs2afXq1apVq5YcHBwsXl+8eHGBFAcAAAAAuLN8BTtvb2916dKloGsBAAAAAORDvoJdbGxsQdcBAAAAAMinfAW7LOfOnVNiYqIkqXr16vLx8SmQogAAAAAAuZevyVMuXbqkvn37qly5cmrRooVatGih8uXLq1+/frp8+XJB1wgAAAAAuI18Bbthw4Zpy5YtWr58uZKTk5WcnKxly5Zpy5YtGj58eEHXCAAAAAC4DZORj3sWlClTRosWLVJoaKhF+6ZNm9StWzedO3euoOorNlJTU+Xl5SX/IV/LzsnV2uXAxh0L327tEoBi5VD0UmuXcM9aVS+oyNcZ1TumyNd5L9jh1LlAxjnhv61AxiksfaqWL/J1Zn2PS0lJkaenZ5GvHygK+Tpid/nyZfn5+WVr9/X15VRMAAAAAChi+Qp2ISEhGjNmjK5evWpuu3LlisaNG6eQkJACKw4AAAAAcGf5mhVz6tSpioiIUIUKFVSvXj1J0v79++Xs7Ky1a9cWaIEAAAAAgNvLV7CrXbu2fvvtN82dO1e//PKLJKlHjx7q2bOnXFxcCrRAAAAAAMDt5fs+dq6urnrmmWcKshYAAAAAQD7kOth9++23atu2rRwcHPTtt9/etu+jjz5614UBAAAAAHIn18Guc+fOSkpKkq+vrzp37nzLfiaTSdevXy+I2gAAAAAAuZDrYJeZmZnjvwEAAAAA1pWv2x3897//VVpaWrb29PR0/fe//73rogAAAAAAuZevYNenTx+lpKRka79w4YL69Olz10UBAAAAAHIvX8HOMAyZTKZs7X/88Ye8vLzuuigAAAAAQO7l6XYHDRo0kMlkkslkUqtWrVSixP8tfv36dR09elRt2rQp8CIBAAAAALeWpyN2nTt3VqdOnWQYhiIiItSpUyfzo3v37vr44481Z86cXI8XExOjxo0by8PDwzzbZmJiokWfq1evKioqSqVLl5a7u7siIyN15swZ8+v79+9Xjx495O/vLxcXFwUHB2vq1KnZ1rV582Y1bNhQTk5OqlKlimbPnp2XTQcAAACAYitPR+zGjBmj69evKyAgQK1bt1a5cuXuauVbtmxRVFSUGjdurGvXrumVV15R69atdfDgQbm5uUmShg4dqpUrV2rhwoXy8vJSdHS0unbtqh07dkiS9u7dK19fX82ZM0f+/v7auXOnBgwYIHt7e0VHR0uSjh49qvbt22vgwIGaO3euNmzYoP79+6tcuXKKiIi4q20AAAAAAGszGYZh5HUhZ2dnHTp0SIGBgQVazLlz5+Tr66stW7aoRYsWSklJkY+Pj+bNm6fHHntMkvTLL78oODhYcXFxatq0aY7jREVF6dChQ9q4caMk6aWXXtLKlSuVkJBg7tO9e3clJydrzZo1uaotNTVVXl5e8h/yteycXO9yS/Fvdyx8u7VLAIqVQ9FLrV3CPWtVvaAiX2dU75giX+e9YIdT5wIZ54T/tgIZp7D0qVq+yNeZ9T0uJSVFnp6eRb5+oCjka/KU2rVr6/fffy/oWswzbZYqVUrSjaNxGRkZCg8PN/epUaOGKlasqLi4uNuOkzWGJMXFxVmMIUkRERG3HQMAAAAAbEWeTsXMMn78eI0YMUJvvvmmGjVqZD5tMkt+/hKSmZmpIUOGqFmzZqpdu7YkKSkpSY6OjvL29rbo6+fnp6SkpBzH2blzpxYsWKCVK1ea25KSkuTn55dtjNTUVF25ckUuLi7ZxklLS7O4V19qamqetwkAAAAAikK+gl27du0kSY8++qjFbQ+yboNw/fr1PI8ZFRWlhIQEbd+e/1PUEhIS1KlTJ40ZM0atW7fO9zjSjYldxo0bd1djAAAAAEBRyFew27RpU4EWER0drRUrVmjr1q2qUKGCub1s2bJKT09XcnKyxVG7M2fOqGzZshZjHDx4UK1atdKAAQP06quvWrxWtmxZi5k0s8bw9PTM8WidJI0aNUrDhg0zP09NTZW/v39+NxEAAAAACk2+gt3DDz9cICs3DEODBg3SkiVLtHnz5myTsTRq1EgODg7asGGDIiMjJUmJiYk6ceKEQkJCzP1+/vlnhYWFqVevXpowYUK29YSEhGjVqlUWbevXr7cY42ZOTk5ycnK6m80DAAAAgCKRr2AnScnJyZo1a5YOHTokSapVq5b69u0rLy+vXI8RFRWlefPmadmyZfLw8DBfN+fl5SUXFxd5eXmpX79+GjZsmEqVKiVPT08NGjRIISEh5hkxExISFBYWpoiICA0bNsw8hr29vXx8fCRJAwcO1IcffqgXX3xRffv21caNG/X1119bXIcHAAAAALYqX7Ni7tmzR0FBQZo8ebLOnz+v8+fP6/3331dQUJB+/PHHXI8zc+ZMpaSkKDQ0VOXKlTM/FixYYO4zefJkdejQQZGRkWrRooXKli2rxYsXm19ftGiRzp07pzlz5liM0bhxY3OfwMBArVy5UuvXr1e9evX03nvv6bPPPuMedgAAAADuCfm6j13z5s1VpUoVffrppypR4sZBv2vXrql///76/ffftXXr1gIv1Nq4jx0KEvexAyxxH7vCw33sbAf3sSs83McO/wb5OhVzz549FqFOkkqUKKEXX3xR999/f4EVBwAAAAC4s3ydiunp6akTJ05kaz958qQ8PDzuuigAAAAAQO7lK9g98cQT6tevnxYsWKCTJ0/q5MmTmj9/vvr3768ePXoUdI0AAAAAgNvI16mYkyZNkslk0tNPP61r165JkhwcHPTcc8/p7bffLtACAQAAAAC3l69g5+joqKlTpyomJkZHjhyRJAUFBcnVlUlFAAAAAKCo5fs+dpLk6uoqb29v878BAAAAAEUvX9fYXbt2Ta+99pq8vLwUEBCggIAAeXl56dVXX1VGRkZB1wgAAAAAuI18HbEbNGiQFi9erIkTJyokJESSFBcXp7Fjx+qvv/7SzJkzC7RIAAAAAMCt5SvYzZs3T/Pnz1fbtm3NbXXr1pW/v7969OhBsAMAAACAIpSvUzGdnJwUEBCQrT0wMFCOjo53WxMAAAAAIA/yFeyio6P15ptvKi0tzdyWlpamCRMmKDo6usCKAwAAAADcWb5Oxdy3b582bNigChUqqF69epKk/fv3Kz09Xa1atVLXrl3NfRcvXlwwlQIAAAAAcmQyDMPI60J9+vTJdd/Y2Ni8Dl8spaamysvLSykpKfL09LR2OQAAAMglvsfh3yBfR+zulbAGAAAAAPeCu7pB+blz55SYmChJql69unx8fAqkKAAAAABA7uVr8pRLly6pb9++KleunFq0aKEWLVqofPny6tevny5fvlzQNQIAAAAAbiNfwW7YsGHasmWLli9fruTkZCUnJ2vZsmXasmWLhg8fXtA1AgAAAABuI1+Tp5QpU0aLFi1SaGioRfumTZvUrVs3nTt3rqDqKza46BYAAMA28T0O/wb5OmJ3+fJl+fn5ZWv39fXlVEwAAAAAKGL5CnYhISEaM2aMrl69am67cuWKxo0bp5CQkAIrDgAAAABwZ/maFXPKlClq06ZNthuUOzs7a+3atQVaIAAAAADg9vJ1jZ1043TMuXPn6pdffpEkBQcHq2fPnnJxcSnQAosLzs0GAACwTXyPw79Bno/YZWRkqEaNGlqxYoWeeeaZwqgJAAAAAJAHeb7GzsHBweLaOgAAAACAdeVr8pSoqCi98847unbtWkHXAwAAAADIo3xNnrJ7925t2LBB69atU506deTm5mbx+uLFiwukOAAAAADAneUr2Hl7eysyMrKgawEAAAAA5EOegl1mZqbeffdd/frrr0pPT1dYWJjGjh17z86ECQAAAAC2IE/X2E2YMEGvvPKK3N3ddd999+mDDz5QVFRUYdUGAAAAAMiFPAW7//73v5oxY4bWrl2rpUuXavny5Zo7d64yMzMLqz4AAAAAwB3kKdidOHFC7dq1Mz8PDw+XyWTSqVOnCrwwAAAAAEDu5CnYXbt2Tc7OzhZtDg4OysjIKNCiAAAAAAC5l6fJUwzDUO/eveXk5GRuu3r1qgYOHGhxywNudwAAAAAARSdPwa5Xr17Z2v7zn/8UWDEAAAAAgLzLU7CLjY0trDoAAAAAAPmUp2vsAAAAAADFD8EOAAAAAGwcwQ4AAAAAbBzBDgAAAABsHMEOAAAAAGwcwQ4AAAAAbBzBDgAAAABsHMEOAAAAAGwcwQ4AAAAAbBzBDgAAAABsHMEOAAAAAGwcwQ4AAAAAbBzBDgAAAABsXAlrF2Brao9ZKzsnV2uXAWRzLHy7tUsACtWh6KXWLuGes6peUJGuL6p3TJGuz5p2OHUutLFP+G8rtLGLQp+q5a1dAnBP4ogdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOKsGu5iYGDVu3FgeHh7y9fVV586dlZiYaNHn6tWrioqKUunSpeXu7q7IyEidOXPGos/gwYPVqFEjOTk5qX79+jmu6+uvv1b9+vXl6uqqSpUq6d133y2szQIAAACAImXVYLdlyxZFRUVp165dWr9+vTIyMtS6dWtdunTJ3Gfo0KFavny5Fi5cqC1btujUqVPq2rVrtrH69u2rJ554Isf1rF69Wj179tTAgQOVkJCgGTNmaPLkyfrwww8LbdsAAAAAoKiUsObK16xZY/F89uzZ8vX11d69e9WiRQulpKRo1qxZmjdvnsLCwiRJsbGxCg4O1q5du9S0aVNJ0gcffCBJOnfunA4cOJBtPV9++aU6d+6sgQMHSpIqV66sUaNG6Z133lFUVJRMJlNhbiYAAAAAFKpidY1dSkqKJKlUqVKSpL179yojI0Ph4eHmPjVq1FDFihUVFxeX63HT0tLk7Oxs0ebi4qI//vhDx48fL4DKAQAAAMB6ik2wy8zM1JAhQ9SsWTPVrl1bkpSUlCRHR0d5e3tb9PXz81NSUlKux46IiNDixYu1YcMGZWZm6tdff9V7770nSTp9+nSOy6SlpSk1NdXiAQAAAADFUbEJdlFRUUpISND8+fMLfOxnnnlG0dHR6tChgxwdHdW0aVN1795dkmRnl/NbEBMTIy8vL/PD39+/wOsCAAAAgIJQLIJddHS0VqxYoU2bNqlChQrm9rJlyyo9PV3JyckW/c+cOaOyZcvmenyTyaR33nlHFy9e1PHjx5WUlKQmTZpIunG9XU5GjRqllJQU8+PkyZN53zAAAAAAKAJWDXaGYSg6OlpLlizRxo0bFRgYaPF6o0aN5ODgoA0bNpjbEhMTdeLECYWEhOR5ffb29rrvvvvk6Oior776SiEhIfLx8cmxr5OTkzw9PS0eAAAAAFAcWXVWzKioKM2bN0/Lli2Th4eH+bo5Ly8vubi4yMvLS/369dOwYcNUqlQpeXp6atCgQQoJCTHPiClJhw8f1sWLF5WUlKQrV64oPj5eklSzZk05Ojrqzz//1KJFixQaGqqrV68qNjbWfPsEAAAAALB1Vg12M2fOlCSFhoZatMfGxqp3796SpMmTJ8vOzk6RkZFKS0tTRESEZsyYYdG/f//+FiGtQYMGkqSjR48qICBAkvTFF19oxIgRMgxDISEh2rx5s/l0TAAAAACwZVYNdoZh3LGPs7Ozpk+frunTp9+yz+bNm287RpkyZfJ0ewQAAAAAsCXFYvIUAAAAAED+EewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxpkMwzCsXYQtSE1NlZeXl1JSUuTp6WntcgAAAJBLfI/DvwFH7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGlbB2Abam9pi1snNytXYZQIE6Fr7d2iXgHnYoeqm1S7gnrKoXVCjjRvWOKZRx71U7nDrf1fIn/LcVTCE2rE/V8tYuAbgnccQOAAAAAGwcwQ4AAAAAbBzBDgAAAABsHMEOAAAAAGwcwQ4AAAAAbBzBDgAAAABsHMEOAAAAAGwcwQ4AAAAAbBzBDgAAAABsHMEOAAAAAGwcwQ4AAAAAbBzBDgAAAABsnFWDXUxMjBo3biwPDw/5+vqqc+fOSkxMtOhz9epVRUVFqXTp0nJ3d1dkZKTOnDlj0Wfw4MFq1KiRnJycVL9+/RzXtXbtWjVt2lQeHh7y8fFRZGSkjh07VkhbBgAAAABFx6rBbsuWLYqKitKuXbu0fv16ZWRkqHXr1rp06ZK5z9ChQ7V8+XItXLhQW7Zs0alTp9S1a9dsY/Xt21dPPPFEjus5evSoOnXqpLCwMMXHx2vt2rX6888/cxwHAAAAAGxNCWuufM2aNRbPZ8+eLV9fX+3du1ctWrRQSkqKZs2apXnz5iksLEySFBsbq+DgYO3atUtNmzaVJH3wwQeSpHPnzunAgQPZ1rN3715dv35d48ePl53djSw7YsQIderUSRkZGXJwcCjMzQQAAACAQlWsrrFLSUmRJJUqVUrSjUCWkZGh8PBwc58aNWqoYsWKiouLy/W4jRo1kp2dnWJjY3X9+nWlpKToyy+/VHh4OKEOAAAAgM0rNsEuMzNTQ4YMUbNmzVS7dm1JUlJSkhwdHeXt7W3R18/PT0lJSbkeOzAwUOvWrdMrr7wiJycneXt7648//tDXX399y2XS0tKUmppq8QAAAACA4qjYBLuoqCglJCRo/vz5BT52UlKSnnnmGfXq1Uu7d+/Wli1b5OjoqMcee0yGYeS4TExMjLy8vMwPf3//Aq8LAAAAAApCsQh20dHRWrFihTZt2qQKFSqY28uWLav09HQlJydb9D9z5ozKli2b6/GnT58uLy8vTZw4UQ0aNFCLFi00Z84cbdiwQd9//32Oy4waNUopKSnmx8mTJ/O1bQAAAABQ2Kwa7AzDUHR0tJYsWaKNGzcqMDDQ4vVGjRrJwcFBGzZsMLclJibqxIkTCgkJyfV6Ll++bJ40JYu9vb2kG6eA5sTJyUmenp4WDwAAAAAojqw6K2ZUVJTmzZunZcuWycPDw3zdnJeXl1xcXOTl5aV+/fpp2LBhKlWqlDw9PTVo0CCFhISYZ8SUpMOHD+vixYtKSkrSlStXFB8fL0mqWbOmHB0d1b59e02ePFlvvPGGevTooQsXLuiVV15RpUqV1KBBA2tsOgAAAAAUGKsGu5kzZ0qSQkNDLdpjY2PVu3dvSdLkyZNlZ2enyMhIpaWlKSIiQjNmzLDo379/f23ZssX8PCusHT16VAEBAQoLC9O8efM0ceJETZw4Ua6urgoJCdGaNWvk4uJSeBsIAAAAAEXAqsHuVhOX/JOzs7OmT5+u6dOn37LP5s2b7zhO9+7d1b1797yUBwAAAAA2oVhMngIAAAAAyD+CHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4k2EYhrWLsAWpqany8vJSSkqKPD09rV0OAAAAconvcfg34IgdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2DiCHQAAAADYOIIdAAAAANg4gh0AAAAA2LgS1i7A1tQes1Z2Tq7WLgM26Fj4dmuXABQLh6KXWruEYmdVvaAiX2dU75giX2dxscOpc4GMc8J/W4GM82/Tp2p5a5cA3JM4YgcAAAAANo5gBwAAAAA2jmAHAAAAADaOYAcAAAAANo5gBwAAAAA2jmAHAAAAADaOYAcAAAAANo5gBwAAAAA2jmAHAAAAADaOYAcAAAAANo5gBwAAAAA2jmAHAAAAADbOqsEuJiZGjRs3loeHh3x9fdW5c2clJiZa9Ll69aqioqJUunRpubu7KzIyUmfOnLHoM3jwYDVq1EhOTk6qX79+tvWMHTtWJpMp28PNza0wNw8AAAAAioRVg92WLVsUFRWlXbt2af369crIyFDr1q116dIlc5+hQ4dq+fLlWrhwobZs2aJTp06pa9eu2cbq27evnnjiiRzXM2LECJ0+fdriUbNmTT3++OOFtm0AAAAAUFRKWHPla9assXg+e/Zs+fr6au/evWrRooVSUlI0a9YszZs3T2FhYZKk2NhYBQcHa9euXWratKkk6YMPPpAknTt3TgcOHMi2Hnd3d7m7u5uf79+/XwcPHtRHH31UWJsGAAAAAEWmWF1jl5KSIkkqVaqUJGnv3r3KyMhQeHi4uU+NGjVUsWJFxcXF5Xs9n332mapVq6bmzZvfXcEAAAAAUAxY9YjdP2VmZmrIkCFq1qyZateuLUlKSkqSo6OjvL29Lfr6+fkpKSkpX+u5evWq5s6dq5dffvm2/dLS0pSWlmZ+npqamq/1AQAAAEBhKzZH7KKiopSQkKD58+cX6nqWLFmiCxcuqFevXrftFxMTIy8vL/PD39+/UOsCAAAAgPwqFsEuOjpaK1as0KZNm1ShQgVze9myZZWenq7k5GSL/mfOnFHZsmXzta7PPvtMHTp0kJ+f3237jRo1SikpKebHyZMn87U+AAAAAChsVg12hmEoOjpaS5Ys0caNGxUYGGjxeqNGjeTg4KANGzaY2xITE3XixAmFhITkeX1Hjx7Vpk2b1K9fvzv2dXJykqenp8UDAAAAAIojq15jFxUVpXnz5mnZsmXy8PAwXzfn5eUlFxcXeXl5qV+/fho2bJhKlSolT09PDRo0SCEhIeYZMSXp8OHDunjxopKSknTlyhXFx8dLkmrWrClHR0dzv88//1zlypVT27Zti3Q7AQAAAKAwWTXYzZw5U5IUGhpq0R4bG6vevXtLkiZPniw7OztFRkYqLS1NERERmjFjhkX//v37a8uWLebnDRo0kHTjCF1AQICkG5OzzJ49W71795a9vX3hbBAAAAAAWIFVg51hGHfs4+zsrOnTp2v69Om37LN58+Y7jmNnZ8d1cgAAAADuScVi8hQAAAAAQP4R7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGmQzDMKxdhC1ITU2Vl5eXUlJS5Onpae1yAAAAkEt8j8O/AUfsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGEewAAAAAwMYR7AAAAADAxhHsAAAAAMDGlbB2AbbCMAxJUmpqqpUrAQAAQF5kfX/L+j4H3IsIdrn0119/SZL8/f2tXAkAAADy48KFC/Ly8rJ2GUChINjlUqlSpSRJJ06c4BfCPSY1NVX+/v46efKkPD09rV0OChD79t7G/r13sW/vXdbat4Zh6MKFCypfvnyRrRMoagS7XLKzu3E5opeXF//J3KM8PT3Zt/co9u29jf1772Lf3russW/5wzzudUyeAgAAAAA2jmAHAAAAADaOYJdLTk5OGjNmjJycnKxdCgoY+/bexb69t7F/713s23sX+xYoPCaDeV8BAAAAwKZxxA4AAAAAbBzBDgAAAABsHMEOAAAAAGwcwe7/mz59ugICAuTs7KwHHnhAP/zww237L1y4UDVq1JCzs7Pq1KmjVatWFVGlyI+87N9PP/1UzZs3V8mSJVWyZEmFh4ff8fMA68nrz26W+fPny2QyqXPnzoVbIPItr/s2OTlZUVFRKleunJycnFStWjV+Nxdjed2/U6ZMUfXq1eXi4iJ/f38NHTpUV69eLaJqkVtbt25Vx44dVb58eZlMJi1duvSOy2zevFkNGzaUk5OTqlSpotmzZxd6ncC9iGAnacGCBRo2bJjGjBmjH3/8UfXq1VNERITOnj2bY/+dO3eqR48e6tevn/bt26fOnTurc+fOSkhIKOLKkRt53b+bN29Wjx49tGnTJsXFxcnf31+tW7fW//73vyKuHHeS132b5dixYxoxYoSaN29eRJUir/K6b9PT0/XII4/o2LFjWrRokRITE/Xpp5/qvvvuK+LKkRt53b/z5s3Tyy+/rDFjxujQoUOaNWuWFixYoFdeeaWIK8edXLp0SfXq1dP06dNz1f/o0aNq3769WrZsqfj4eA0ZMkT9+/fX2rVrC7lS4B5kwGjSpIkRFRVlfn79+nWjfPnyRkxMTI79u3XrZrRv396i7YEHHjCeffbZQq0T+ZPX/Xuza9euGR4eHsYXX3xRWCUin/Kzb69du2Y8+OCDxmeffWb06tXL6NSpUxFUirzK676dOXOmUblyZSM9Pb2oSsRdyOv+jYqKMsLCwizahg0bZjRr1qxQ68TdkWQsWbLktn1efPFFo1atWhZtTzzxhBEREVGIlQH3pn/9Ebv09HTt3btX4eHh5jY7OzuFh4crLi4ux2Xi4uIs+ktSRETELfvDevKzf292+fJlZWRkqFSpUoVVJvIhv/v2jTfekK+vr/r161cUZSIf8rNvv/32W4WEhCgqKkp+fn6qXbu23nrrLV2/fr2oykYu5Wf/Pvjgg9q7d6/5dM3ff/9dq1atUrt27YqkZhQevlMBBaeEtQuwtj///FPXr1+Xn5+fRbufn59++eWXHJdJSkrKsX9SUlKh1Yn8yc/+vdlLL72k8uXLZ/uPB9aVn327fft2zZo1S/Hx8UVQIfIrP/v2999/18aNG9WzZ0+tWrVKhw8f1vPPP6+MjAyNGTOmKMpGLuVn/z755JP6888/9dBDD8kwDF27dk0DBw7kVMx7wK2+U6WmpurKlStycXGxUmWA7fnXH7EDbuftt9/W/PnztWTJEjk7O1u7HNyFCxcu6KmnntKnn36qMmXKWLscFLDMzEz5+vrqk08+UaNGjfTEE09o9OjR+uijj6xdGgrA5s2b9dZbb2nGjBn68ccftXjxYq1cuVJvvvmmtUsDgGLjX3/ErkyZMrK3t9eZM2cs2s+cOaOyZcvmuEzZsmXz1B/Wk5/9m2XSpEl6++239d1336lu3bqFWSbyIa/79siRIzp27Jg6duxobsvMzJQklShRQomJiQoKCircopEr+fm5LVeunBwcHGRvb29uCw4OVlJSktLT0+Xo6FioNSP38rN/X3vtNT311FPq37+/JKlOnTq6dOmSBgwYoNGjR8vOjr9T26pbfafy9PTkaB2QR//634SOjo5q1KiRNmzYYG7LzMzUhg0bFBISkuMyISEhFv0laf369bfsD+vJz/6VpIkTJ+rNN9/UmjVrdP/99xdFqcijvO7bGjVq6KefflJ8fLz58eijj5pnYvP39y/K8nEb+fm5bdasmQ4fPmwO65L066+/qly5coS6YiY/+/fy5cvZwltWiDcMo/CKRaHjOxVQgKw9e0txMH/+fMPJycmYPXu2cfDgQWPAgAGGt7e3kZSUZBiGYTz11FPGyy+/bO6/Y8cOo0SJEsakSZOMQ4cOGWPGjDEcHByMn376yVqbgNvI6/59++23DUdHR2PRokXG6dOnzY8LFy5YaxNwC3ndtzdjVsziK6/79sSJE4aHh4cRHR1tJCYmGitWrDB8fX2N8ePHW2sTcBt53b9jxowxPDw8jK+++sr4/fffjXXr1hlBQUFGt27drLUJuIULFy4Y+/btM/bt22dIMt5//31j3759xvHjxw3DMIyXX37ZeOqpp8z9f//9d8PV1dUYOXKkcejQIWP69OmGvb29sWbNGmttAmCzCHb/37Rp04yKFSsajo6ORpMmTYxdu3aZX3v44YeNXr16WfT/+uuvjWrVqhmOjo5GrVq1jJUrVxZxxciLvOzfSpUqGZKyPcaMGVP0heOO8vqz+08Eu+Itr/t2586dxgMPPGA4OTkZlStXNiZMmGBcu3atiKtGbuVl/2ZkZBhjx441goKCDGdnZ8Pf3994/vnnjb///rvoC8dtbdq0Kcf/Q7P2Z69evYyHH3442zL169c3HB0djcqVKxuxsbFFXjdwLzAZBucwAAAAAIAt+9dfYwcAAAAAto5gBwAAAAA2jmAHAAAAADaOYAcAAAAANo5gBwAAAAA2jmAHAAAAADaOYAcAAAAANo5gBwAAAAA2jmAHAAAAADaOYAcA9xDDMBQeHq6IiIhsr82YMUPe3t76448/rFAZAAAoTAQ7ALiHmEwmxcbG6vvvv9fHH39sbj969KhefPFFTZs2TRUqVCjQdWZkZBToeAAAIO8IdgBwj/H399fUqVM1YsQIHT16VIZhqF+/fmrdurUaNGigtm3byt3dXX5+fnrqqaf0559/mpdds2aNHnroIXl7e6t06f/Xzv27pBPHcRx/3eEgFJFIQUJ0NUThFSSt0daUIG0uQRA0NPRjaWtxigiacw50uD/AxQZrCsGoBinhiKYoIRACyWz48pUiiPjyLfnk8wE33Yfj/VkOntyHC2t+fl6VSqV13/d9WZalbDar2dlZBYNBHR4etmObAADgDavZbDbbPQQA4P9LJBJ6fHzUwsKCUqmULi8vFY1Gtby8rMXFRT09PWlra0vPz8/K5/OSJM/zZFmWJicnVavVtL29Ld/3VSqVZNu2fN/X8PCwHMfR3t6epqamFAwGNTAw0ObdAgDQ2Qg7APil7u7uFI1GVa1W5XmeLi4uVCgUlMvlWmtub281ODiocrms0dHRD8+4v79XX1+fzs/P5bpuK+z29/e1trb2k9sBAACf4CgmAPxS/f39WllZ0fj4uBKJhM7OznR0dKTu7u7WNTY2Jkmt45ZXV1dKJpMaGRlRT0+PHMeRJN3c3Lx79vT09I/uBQAAfC7Q7gEAAN8nEAgoEPjzqq/VaorH49rZ2fmw7u9Ryng8rqGhIaXTaUUiEb28vMh1XdXr9Xfru7q6vn94AADwZYQdAHSIWCwmz/PkOE4r9t56eHhQuVxWOp3WzMyMJOn4+PinxwQAAP+Ao5gA0CFWV1dVrVaVTCZ1enqqSqWiXC6npaUlNRoNhUIhhcNhHRwc6Pr6Wvl8Xpubm+0eGwAAfAFhBwAdIhKJ6OTkRI1GQ3Nzc5qYmND6+rp6e3tl27Zs21Ymk1GxWJTrutrY2NDu7m67xwYAAF/AXzEBAAAAwHB8sQMAAAAAwxF2AAAAAGA4wg4AAAAADEfYAQAAAIDhCDsAAAAAMBxhBwAAAACGI+wAAAAAwHCEHQAAAAAYjrADAAAAAMMRdgAAAABgOMIOAAAAAAxH2AEAAACA4V4BpsuLPITH3GgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate proportions of emotions for each year\n",
    "proportion_emotions = count_emotions.div(count_emotions.sum(axis=1), axis=0)\n",
    "\n",
    "# Create a horizontal stacked bar plot for emotion proportions\n",
    "proportion_emotions.plot(\n",
    "    kind='barh',  # Horizontal bar chart\n",
    "    stacked=True,  # Stacked to show proportions\n",
    "    figsize=(9, 6),  # Figure size\n",
    "    colormap='tab20'  # Color map for differentiation\n",
    ")\n",
    "\n",
    "# Add title and axis labels\n",
    "plt.title(\"2017 - 2023 Proportions of 7 Emotions in Social Media Texts\")\n",
    "plt.xlabel(\"Proportions\")  # X-axis shows proportions\n",
    "plt.ylabel(\"Year\")  # Y-axis represents the years\n",
    "\n",
    "# Add legend for the emotions\n",
    "plt.legend(title=\"Emotions\", bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "# Adjust layout to ensure all elements fit properly\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of UEFA Champions League winners from 2017 to 2023\n",
    "champions = ['Real Madrid', 'Real Madrid', 'Liverpool', 'Bayern Munich', 'Chelsea', 'Real Madrid', 'Manchester City']\n",
    "\n",
    "# Initialize a DataFrame to store emotion proportions for champion teams\n",
    "champions_emotions = pd.DataFrame(index=years, columns=emotions)\n",
    "\n",
    "# Sum up emotion counts across all years for all teams\n",
    "sum_teams_emotions = (teams_emotions[2017] + teams_emotions[2018] + teams_emotions[2019] +\n",
    "                      teams_emotions[2020] + teams_emotions[2021] + teams_emotions[2022] +\n",
    "                      teams_emotions[2023])\n",
    "\n",
    "# Calculate the proportion of each emotion across all years for all teams\n",
    "proportion_teams_emotions = sum_teams_emotions.div(sum_teams_emotions.sum(axis=1), axis=0)\n",
    "\n",
    "# Initialize a dictionary to store yearly emotion proportions\n",
    "year_proportion = {}\n",
    "\n",
    "# Calculate emotion proportion differences for champions\n",
    "for i in range(7):\n",
    "    # Calculate the proportion of emotions for all teams in the current year\n",
    "    year_proportion[years[i]] = teams_emotions[years[i]].div(teams_emotions[years[i]].sum(axis=1), axis=0)\n",
    "\n",
    "    # Compute the difference between the champion's yearly emotion proportions and the overall proportion\n",
    "    champions_emotions.iloc[i] = year_proportion[years[i]].loc[champions[i]] - proportion_teams_emotions.loc[champions[i]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a line plot for proportions change\n",
    "champions_emotions.plot(\n",
    "    kind='line', \n",
    "    figsize=(9, 6), \n",
    "    colormap='tab20', \n",
    "    linewidth=2.5\n",
    ")\n",
    "\n",
    "# Add chart details\n",
    "plt.title(\"2017 - 2023 Proportions Change of 7 Emotions for Championship Team\")\n",
    "plt.ylabel(\"Proportions Change (%)\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.legend(title=\"Emotions\", bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "# Set Y-axis limits and format ticks as percentages\n",
    "plt.ylim(-0.08, 0.08)  # Set Y-axis range (-8% to +8%)\n",
    "plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f\"{x*100:.0f}%\"))\n",
    "\n",
    "# Add a horizontal dashed line at y=0\n",
    "plt.axhline(y=0, color='black', linestyle='--', linewidth=1)\n",
    "\n",
    "# Apply tight layout and show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the proportions of each emotion for different teams as a stacked bar chart\n",
    "ax = proportion_teams_emotions.plot(\n",
    "    kind='bar',  # Create a vertical bar chart\n",
    "    stacked=True,  # Stack bars to represent proportions\n",
    "    figsize=(9, 6),  # Set figure size\n",
    "    colormap='tab20',  # Apply color mapping for distinct emotions\n",
    "    width=0.8  # Adjust bar width\n",
    ")\n",
    "\n",
    "# Annotate each bar with percentage values\n",
    "for i in range(len(proportion_teams_emotions)):  # Loop through each team (bar)\n",
    "    bottom = 0  # Initialize the starting position for stacking\n",
    "    for col in proportion_teams_emotions.columns:  # Loop through each emotion\n",
    "        value = proportion_teams_emotions.iloc[i][col]\n",
    "        if value > 0:  # Only display non-zero values\n",
    "            ax.text(\n",
    "                i, bottom + value / 2, f\"{value * 100:.2f}%\",  # Text in the middle of each section\n",
    "                ha='center', va='center', fontsize=6, color='black'\n",
    "            )\n",
    "            bottom += value  # Update the base position for the next segment\n",
    "\n",
    "# Add chart details\n",
    "plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels 45 degrees, align to the right\n",
    "plt.title(\"2017 - 2023 Proportions of Each Emotion for Different Teams\")  # Add title\n",
    "plt.ylabel(\"Proportions\")  # Label for the y-axis\n",
    "plt.xlabel(\"Teams\")  # Label for the x-axis\n",
    "plt.legend(title=\"Emotions\", bbox_to_anchor=(1.05, 1), loc='upper left')  # Position legend outside the plot\n",
    "plt.tight_layout()  # Adjust layout to prevent overlap\n",
    "\n",
    "# Format y-axis as percentages\n",
    "plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f\"{x*100:.0f}%\"))\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  }
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