Projet_BigData.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ed401360",
   "metadata": {},
   "source": [
    "# Projet - Python Pour Le BigData\n",
    "\n",
    "## Introduction\n",
    "\n",
    "A l’heure actuelle, les cryptomonnaies suscitent un vif intérêt de la part des investisseurs, des entreprises et du grand public, par les multiples avantages et nouvelles fonctionnalités qu’elles apportent.\n",
    "\n",
    "Parmi celles-ci, le Bitcoin, créé en 2009 par Satoshi Nakamoto, se présente comme leur porte-étendard, étant la plus populaire et la plus utilisée à ce jour.\n",
    "\n",
    "Ainsi, il peut être intéressant de prédire l’évolution du Bitcoin au cours du temps, pour capitaliser et profiter des avantages octroyés par la monnaie, ou encore prévenir les risques liés à son utilisation.\n",
    "\n",
    "Pour cela, nous aurons besoin de modules et de librairies essentiels :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "75508a49",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import datetime as dt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pylab as plt\n",
    "from matplotlib import dates as mdates\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30cdc07e",
   "metadata": {},
   "source": [
    "## I. Courbes prédictives\n",
    "\n",
    "Nous possédons un ensemble de données entourant l’évolution du Bitcoin entre 2015 et 2017, dont son cours au travers de cette période."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "021c7ac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>PriceUSD</th>\n",
       "      <th>HashRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>315.942732</td>\n",
       "      <td>3.333450e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>285.647310</td>\n",
       "      <td>3.272842e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>263.334575</td>\n",
       "      <td>3.474869e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>275.003852</td>\n",
       "      <td>3.151626e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>287.549521</td>\n",
       "      <td>3.192031e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>2017-06-26</td>\n",
       "      <td>2426.365051</td>\n",
       "      <td>5.023770e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907</th>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>2530.345536</td>\n",
       "      <td>4.634604e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>2017-06-28</td>\n",
       "      <td>2562.792264</td>\n",
       "      <td>5.554450e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>2017-06-29</td>\n",
       "      <td>2540.443032</td>\n",
       "      <td>4.245439e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>2452.712064</td>\n",
       "      <td>5.448314e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>911 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date     PriceUSD      HashRate\n",
       "0   2015-01-02   315.942732  3.333450e+05\n",
       "1   2015-01-03   285.647310  3.272842e+05\n",
       "2   2015-01-04   263.334575  3.474869e+05\n",
       "3   2015-01-05   275.003852  3.151626e+05\n",
       "4   2015-01-06   287.549521  3.192031e+05\n",
       "..         ...          ...           ...\n",
       "906 2017-06-26  2426.365051  5.023770e+06\n",
       "907 2017-06-27  2530.345536  4.634604e+06\n",
       "908 2017-06-28  2562.792264  5.554450e+06\n",
       "909 2017-06-29  2540.443032  4.245439e+06\n",
       "910 2017-06-30  2452.712064  5.448314e+06\n",
       "\n",
       "[911 rows x 3 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning and creating dataframe\n",
    "df = pd.read_csv(\"timeseries/external.csv\", sep=\",\", index_col=\"date\")\n",
    "df = df.drop(\"Unnamed: 0\", axis=1)\n",
    "df.index.names = [\"Date\"]\n",
    "df = df.reset_index()\n",
    "df[\"Date\"] = pd.to_datetime(df[\"Date\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c0bc4e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a plot\n",
    "plt.plot(df[\"Date\"], df[\"PriceUSD\"], label='price_usd')\n",
    "plt.legend()\n",
    "plt.title(\"Bitcoin price evolution throughout time\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"PriceUSD\")\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9339cc05",
   "metadata": {},
   "source": [
    "Pour obtenir une équation de courbe prédictive de l’évolution du cours du Bitcoin à travers le temps, nous serions tentés d’effectuer une régression linéaire sur notre jeu de données.\n",
    "\n",
    "Cette méthode permet de rechercher la droite permettant d'expliquer le comportement d'une variable statistique y, ici le cours du Bitcoin, comme étant une fonction affine d'une autre variable statistique x, ici le temps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ae14e4c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting Date data to ordinal values\n",
    "df[\"Date\"] = pd.to_datetime(df[\"Date\"]).map(dt.datetime.toordinal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "96c2d3f1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² = 0.63\n"
     ]
    }
   ],
   "source": [
    "# Creating a Linear regression model from our data\n",
    "fit = np.polyfit(df[\"Date\"], df[\"PriceUSD\"], 1)\n",
    "fit1d = np.poly1d(fit)\n",
    "\n",
    "# Recovering both coeffs from function\n",
    "a = round(fit1d[1], 2)\n",
    "b = round(fit1d[0], 2)\n",
    "\n",
    "# Creating a plot\n",
    "plt.plot(df[\"Date\"], df[\"PriceUSD\"], 'o', label='Original data')\n",
    "plt.plot(df[\"Date\"], fit1d(df[\"Date\"]), label='Fitted line')\n",
    "plt.text(df['Date'].min(), df['PriceUSD'].max() * 0.8, f'y = {a}x + {b}', fontsize=12)\n",
    "plt.legend()\n",
    "plt.title(\"Bitcoin price evolution throughout time, with fitted line\")\n",
    "plt.xlabel(\"Time (ordinal)\")\n",
    "plt.ylabel(\"PriceUSD\")\n",
    "plt.show()\n",
    "\n",
    "# Calculating determination coefficient R²\n",
    "mean_price_usd = np.mean(df[\"PriceUSD\"])\n",
    "total_square_sum = np.sum((df[\"PriceUSD\"] - mean_price_usd) ** 2)\n",
    "residual_square_sum = np.sum((df[\"PriceUSD\"] - fit1d(df[\"Date\"])) ** 2)\n",
    "r_2 = 1 - (residual_square_sum / total_square_sum)\n",
    "print(\"R² =\", round(r_2, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293678ef",
   "metadata": {},
   "source": [
    "Le coefficient de détermination R² permet d'expliquer la quantité de variation prédite par notre régression.\n",
    "Plus R² se rapproche de 1, plus la courbe de régression explique la variance totale des données et plus le modèle est précis.\n",
    "R² ici est moyennement haut (0.7 > R² > 0.5), notre modèle prédit ainsi convenablement la tendance d'évolution des données. \n",
    "\n",
    "Nous constatons cependant que la prédiction ne colle pas à l'évolution réelle du cours.\n",
    "Une piste serait que nos données ne semblent pas évoluées de manière linéaire, l’évolution semblant être exponentielle.\n",
    "\n",
    "Nous serions alors tentés d’effectuer une régression exponentielle pour matcher davantage l'évolution de notre jeu de donnée."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9651e1c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² = 0.83\n"
     ]
    }
   ],
   "source": [
    "# Creating an Exponential regression model from our data\n",
    "fit = np.polyfit(df[\"Date\"], np.log(df[\"PriceUSD\"]), 1, w=np.sqrt(df[\"PriceUSD\"]))\n",
    "fit1d = np.poly1d(fit)\n",
    "\n",
    "# Recovering main coeffs from function\n",
    "b = round(fit1d[0], 2)\n",
    "\n",
    "# Creating a plot\n",
    "plt.plot(df[\"Date\"], df[\"PriceUSD\"], 'o', label='Original data')\n",
    "plt.plot(df[\"Date\"], np.exp(fit1d(df[\"Date\"])), label='Fitted curve')\n",
    "plt.text(df['Date'].min(), df['PriceUSD'].max() * 0.8, f'log(y) = (28.15e-4)x + {b}', fontsize=12)\n",
    "plt.legend()\n",
    "plt.title(\"Bitcoin price evolution throughout time, with fitted curve\")\n",
    "plt.xlabel(\"Time (ordinal)\")\n",
    "plt.ylabel(\"PriceUSD\")\n",
    "plt.show()\n",
    "\n",
    "# Calculating determination coefficient R²\n",
    "mean_price_usd = np.mean(df[\"PriceUSD\"])\n",
    "total_square_sum = np.sum((df[\"PriceUSD\"] - mean_price_usd) ** 2)\n",
    "residual_square_sum = np.sum((df[\"PriceUSD\"] - np.exp(fit1d(df[\"Date\"]))) ** 2)\n",
    "r_2 = 1 - (residual_square_sum / total_square_sum)\n",
    "print(\"R² =\", round(r_2, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27eaf046",
   "metadata": {},
   "source": [
    "La courbe exponentielle match en effet beaucoup mieux l’évolution réelle du cours du Bitcoin (R² > 0.7).\n",
    "\n",
    "La correspondance n’est cependant pas parfaite, notre jeu de donnée semblant croitre beaucoup plus rapidement que notre courbe prédictive.\n",
    "\n",
    "Une régression polynomiale de notre jeu de donnée pourrait être une piste de sortie, nous permettant de trouver une courbe matchant au mieux les variations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d61906ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² = 0.9\n"
     ]
    }
   ],
   "source": [
    "# Creating a Polynomial regression model from our data\n",
    "fit = np.polyfit(df[\"Date\"], df[\"PriceUSD\"], 3)\n",
    "fit1d = np.poly1d(fit)\n",
    "\n",
    "# Recovering main coeffs from function\n",
    "b = round(fit1d[2], 2)\n",
    "c = round(fit1d[1], 2)\n",
    "d = round(fit1d[0], 2)\n",
    "\n",
    "# Creating a plot\n",
    "plt.plot(df[\"Date\"], df[\"PriceUSD\"], 'o', label='Original data')\n",
    "plt.plot(df[\"Date\"], fit1d(df[\"Date\"]), label='Fitted curve')\n",
    "plt.text(df['Date'].min(), df['PriceUSD'].max() * 0.8, f'y = (9.58e-6)x^3 + {b}x^2 + {c}x + {b}', fontsize=9)\n",
    "plt.legend()\n",
    "plt.title(\"Bitcoin price evolution throughout time, with fitted curve\")\n",
    "plt.xlabel(\"Time (ordinal)\")\n",
    "plt.ylabel(\"PriceUSD\")\n",
    "plt.show()\n",
    "\n",
    "# Calculating determination coefficient R²\n",
    "mean_price_usd = np.mean(df[\"PriceUSD\"])\n",
    "total_square_sum = np.sum((df[\"PriceUSD\"] - mean_price_usd) ** 2)\n",
    "residual_square_sum = np.sum((df[\"PriceUSD\"] - fit1d(df[\"Date\"])) ** 2)\n",
    "r_2 = 1 - (residual_square_sum / total_square_sum)\n",
    "print(\"R² =\", round(r_2, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a462459",
   "metadata": {},
   "source": [
    "La courbe polynomiale match bien l'évolution réelle du cours du Bitcoin (R² > 0.7), croissant plus rapidement que la courbe exponentielle, correspondant mieux aux variations de notre jeu de données.\n",
    "\n",
    "Nous prédisons finalement que le cours du Bitcoin pourrait continuer de croitre drastiquement dans le future.\n",
    "\n",
    "Le pouvoir prédictif de notre courbe est cependant limité. \n",
    "\n",
    "Premièrement, elle ne croit toujours pas aussi rapidement que l'évolution réelle des données.\n",
    "\n",
    "Secondement, notre modèle n'inclut pas l'influence de paramètres pouvant modifier la tendance d'évolution du cours du Bitcoin.\n",
    "\n",
    "Finalement, nous manquons de données. ne traitant qu'une courte echelle de temps (2015 - 2017) par à rapport à l'évolution du cours d'une monnaie, nous ne pouvons avoir un avis davantage robuste sur la tendance réelle de l'évolution cours du Bitcoin."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6265f39",
   "metadata": {},
   "source": [
    "## II. Corrélations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "873c16a6",
   "metadata": {},
   "source": [
    "Importons un ensemble de paramètres dans notre précédent tableau pouvant influencer le cours du Bitcoin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d1fb544a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price_usd</th>\n",
       "      <th>hash_rate</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>total_fee</th>\n",
       "      <th>total_received_satoshi</th>\n",
       "      <th>total_sent_satoshi</th>\n",
       "      <th>mean_fee_satoshi</th>\n",
       "      <th>mean_feeUSD</th>\n",
       "      <th>...</th>\n",
       "      <th>nb_transactions</th>\n",
       "      <th>nb_payments</th>\n",
       "      <th>mean_nb_inputs</th>\n",
       "      <th>mean_nb_outputs</th>\n",
       "      <th>nb_mining</th>\n",
       "      <th>total_mining_satoshi</th>\n",
       "      <th>newly_created_coins</th>\n",
       "      <th>self_spent_satoshi</th>\n",
       "      <th>total_received</th>\n",
       "      <th>volume_without_self</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>315.942732</td>\n",
       "      <td>3.333450e+05</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1159934218</td>\n",
       "      <td>73370568521080</td>\n",
       "      <td>72955568521080</td>\n",
       "      <td>14978.682808</td>\n",
       "      <td>4.732406e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>77439</td>\n",
       "      <td>257633</td>\n",
       "      <td>2.715557</td>\n",
       "      <td>3.326915</td>\n",
       "      <td>166</td>\n",
       "      <td>416159934218</td>\n",
       "      <td>415000000000</td>\n",
       "      <td>4316724676318</td>\n",
       "      <td>73370568521080</td>\n",
       "      <td>69053843844762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>285.647310</td>\n",
       "      <td>3.272842e+05</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1196476043</td>\n",
       "      <td>82635189454578</td>\n",
       "      <td>82220189454578</td>\n",
       "      <td>14304.385766</td>\n",
       "      <td>4.086009e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>83644</td>\n",
       "      <td>295618</td>\n",
       "      <td>2.781885</td>\n",
       "      <td>3.534240</td>\n",
       "      <td>166</td>\n",
       "      <td>416196476043</td>\n",
       "      <td>415000000000</td>\n",
       "      <td>5584584356557</td>\n",
       "      <td>82635189454578</td>\n",
       "      <td>77050605098021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>263.334575</td>\n",
       "      <td>3.474869e+05</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2159195328</td>\n",
       "      <td>115159442449532</td>\n",
       "      <td>114726942449532</td>\n",
       "      <td>25138.199015</td>\n",
       "      <td>6.619757e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>85893</td>\n",
       "      <td>280288</td>\n",
       "      <td>2.770133</td>\n",
       "      <td>3.263223</td>\n",
       "      <td>173</td>\n",
       "      <td>434659195328</td>\n",
       "      <td>432500000000</td>\n",
       "      <td>6554587956125</td>\n",
       "      <td>115159442449532</td>\n",
       "      <td>108604854493407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>275.003852</td>\n",
       "      <td>3.151626e+05</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1608443040</td>\n",
       "      <td>112439228285641</td>\n",
       "      <td>112051728285641</td>\n",
       "      <td>16955.609622</td>\n",
       "      <td>4.662858e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>94862</td>\n",
       "      <td>286552</td>\n",
       "      <td>2.954471</td>\n",
       "      <td>3.020725</td>\n",
       "      <td>155</td>\n",
       "      <td>389108443040</td>\n",
       "      <td>387500000000</td>\n",
       "      <td>5063695492444</td>\n",
       "      <td>112439228285641</td>\n",
       "      <td>107375532793197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>287.549521</td>\n",
       "      <td>3.192031e+05</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1296373032</td>\n",
       "      <td>118260758863552</td>\n",
       "      <td>117868258863552</td>\n",
       "      <td>14667.176159</td>\n",
       "      <td>4.217540e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>88386</td>\n",
       "      <td>252571</td>\n",
       "      <td>2.869595</td>\n",
       "      <td>2.857591</td>\n",
       "      <td>157</td>\n",
       "      <td>393796373032</td>\n",
       "      <td>392500000000</td>\n",
       "      <td>4026430543611</td>\n",
       "      <td>118260758863552</td>\n",
       "      <td>114234328319941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26</th>\n",
       "      <td>2426.365051</td>\n",
       "      <td>5.023770e+06</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>35993152232</td>\n",
       "      <td>375012739382430</td>\n",
       "      <td>374842739382430</td>\n",
       "      <td>145080.443192</td>\n",
       "      <td>3.520181e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>248091</td>\n",
       "      <td>626222</td>\n",
       "      <td>2.565329</td>\n",
       "      <td>2.524163</td>\n",
       "      <td>136</td>\n",
       "      <td>205993152232</td>\n",
       "      <td>170000000000</td>\n",
       "      <td>14151200557043</td>\n",
       "      <td>375012739382430</td>\n",
       "      <td>360861538825387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-27</th>\n",
       "      <td>2530.345536</td>\n",
       "      <td>4.634604e+06</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "      <td>38384859820</td>\n",
       "      <td>287624055555912</td>\n",
       "      <td>287452805555912</td>\n",
       "      <td>142346.452989</td>\n",
       "      <td>3.601857e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>269658</td>\n",
       "      <td>747924</td>\n",
       "      <td>2.314862</td>\n",
       "      <td>2.773602</td>\n",
       "      <td>137</td>\n",
       "      <td>209634859820</td>\n",
       "      <td>171250000000</td>\n",
       "      <td>19821969138631</td>\n",
       "      <td>287624055555912</td>\n",
       "      <td>267802086417281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-28</th>\n",
       "      <td>2562.792264</td>\n",
       "      <td>5.554450e+06</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>38663204682</td>\n",
       "      <td>282904720130478</td>\n",
       "      <td>282717220130478</td>\n",
       "      <td>140744.451983</td>\n",
       "      <td>3.606988e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>274705</td>\n",
       "      <td>686018</td>\n",
       "      <td>2.471178</td>\n",
       "      <td>2.497290</td>\n",
       "      <td>150</td>\n",
       "      <td>226163204682</td>\n",
       "      <td>187500000000</td>\n",
       "      <td>10958737629631</td>\n",
       "      <td>282904720130478</td>\n",
       "      <td>271945982500847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-29</th>\n",
       "      <td>2540.443032</td>\n",
       "      <td>4.245439e+06</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>33153365173</td>\n",
       "      <td>317698686929569</td>\n",
       "      <td>317543686929569</td>\n",
       "      <td>144453.442667</td>\n",
       "      <td>3.669758e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>229509</td>\n",
       "      <td>570680</td>\n",
       "      <td>2.451838</td>\n",
       "      <td>2.486526</td>\n",
       "      <td>124</td>\n",
       "      <td>188153365173</td>\n",
       "      <td>155000000000</td>\n",
       "      <td>16909883109542</td>\n",
       "      <td>317698686929569</td>\n",
       "      <td>300788803820027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>2452.712064</td>\n",
       "      <td>5.448314e+06</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>36690726272</td>\n",
       "      <td>333927062356198</td>\n",
       "      <td>333740812356198</td>\n",
       "      <td>134084.908792</td>\n",
       "      <td>3.288717e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>273638</td>\n",
       "      <td>709817</td>\n",
       "      <td>2.498776</td>\n",
       "      <td>2.594000</td>\n",
       "      <td>149</td>\n",
       "      <td>222940726272</td>\n",
       "      <td>186250000000</td>\n",
       "      <td>19474802932200</td>\n",
       "      <td>333927062356198</td>\n",
       "      <td>314452259423998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>911 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              price_usd     hash_rate  year  month  day    total_fee  \\\n",
       "date                                                                   \n",
       "2015-01-02   315.942732  3.333450e+05  2015      1    2   1159934218   \n",
       "2015-01-03   285.647310  3.272842e+05  2015      1    3   1196476043   \n",
       "2015-01-04   263.334575  3.474869e+05  2015      1    4   2159195328   \n",
       "2015-01-05   275.003852  3.151626e+05  2015      1    5   1608443040   \n",
       "2015-01-06   287.549521  3.192031e+05  2015      1    6   1296373032   \n",
       "...                 ...           ...   ...    ...  ...          ...   \n",
       "2017-06-26  2426.365051  5.023770e+06  2017      6   26  35993152232   \n",
       "2017-06-27  2530.345536  4.634604e+06  2017      6   27  38384859820   \n",
       "2017-06-28  2562.792264  5.554450e+06  2017      6   28  38663204682   \n",
       "2017-06-29  2540.443032  4.245439e+06  2017      6   29  33153365173   \n",
       "2017-06-30  2452.712064  5.448314e+06  2017      6   30  36690726272   \n",
       "\n",
       "            total_received_satoshi  total_sent_satoshi  mean_fee_satoshi  \\\n",
       "date                                                                       \n",
       "2015-01-02          73370568521080      72955568521080      14978.682808   \n",
       "2015-01-03          82635189454578      82220189454578      14304.385766   \n",
       "2015-01-04         115159442449532     114726942449532      25138.199015   \n",
       "2015-01-05         112439228285641     112051728285641      16955.609622   \n",
       "2015-01-06         118260758863552     117868258863552      14667.176159   \n",
       "...                            ...                 ...               ...   \n",
       "2017-06-26         375012739382430     374842739382430     145080.443192   \n",
       "2017-06-27         287624055555912     287452805555912     142346.452989   \n",
       "2017-06-28         282904720130478     282717220130478     140744.451983   \n",
       "2017-06-29         317698686929569     317543686929569     144453.442667   \n",
       "2017-06-30         333927062356198     333740812356198     134084.908792   \n",
       "\n",
       "             mean_feeUSD  ...  nb_transactions  nb_payments  mean_nb_inputs  \\\n",
       "date                      ...                                                 \n",
       "2015-01-02  4.732406e+06  ...            77439       257633        2.715557   \n",
       "2015-01-03  4.086009e+06  ...            83644       295618        2.781885   \n",
       "2015-01-04  6.619757e+06  ...            85893       280288        2.770133   \n",
       "2015-01-05  4.662858e+06  ...            94862       286552        2.954471   \n",
       "2015-01-06  4.217540e+06  ...            88386       252571        2.869595   \n",
       "...                  ...  ...              ...          ...             ...   \n",
       "2017-06-26  3.520181e+08  ...           248091       626222        2.565329   \n",
       "2017-06-27  3.601857e+08  ...           269658       747924        2.314862   \n",
       "2017-06-28  3.606988e+08  ...           274705       686018        2.471178   \n",
       "2017-06-29  3.669758e+08  ...           229509       570680        2.451838   \n",
       "2017-06-30  3.288717e+08  ...           273638       709817        2.498776   \n",
       "\n",
       "            mean_nb_outputs  nb_mining  total_mining_satoshi  \\\n",
       "date                                                           \n",
       "2015-01-02         3.326915        166          416159934218   \n",
       "2015-01-03         3.534240        166          416196476043   \n",
       "2015-01-04         3.263223        173          434659195328   \n",
       "2015-01-05         3.020725        155          389108443040   \n",
       "2015-01-06         2.857591        157          393796373032   \n",
       "...                     ...        ...                   ...   \n",
       "2017-06-26         2.524163        136          205993152232   \n",
       "2017-06-27         2.773602        137          209634859820   \n",
       "2017-06-28         2.497290        150          226163204682   \n",
       "2017-06-29         2.486526        124          188153365173   \n",
       "2017-06-30         2.594000        149          222940726272   \n",
       "\n",
       "            newly_created_coins  self_spent_satoshi   total_received  \\\n",
       "date                                                                   \n",
       "2015-01-02         415000000000       4316724676318   73370568521080   \n",
       "2015-01-03         415000000000       5584584356557   82635189454578   \n",
       "2015-01-04         432500000000       6554587956125  115159442449532   \n",
       "2015-01-05         387500000000       5063695492444  112439228285641   \n",
       "2015-01-06         392500000000       4026430543611  118260758863552   \n",
       "...                         ...                 ...              ...   \n",
       "2017-06-26         170000000000      14151200557043  375012739382430   \n",
       "2017-06-27         171250000000      19821969138631  287624055555912   \n",
       "2017-06-28         187500000000      10958737629631  282904720130478   \n",
       "2017-06-29         155000000000      16909883109542  317698686929569   \n",
       "2017-06-30         186250000000      19474802932200  333927062356198   \n",
       "\n",
       "            volume_without_self  \n",
       "date                             \n",
       "2015-01-02       69053843844762  \n",
       "2015-01-03       77050605098021  \n",
       "2015-01-04      108604854493407  \n",
       "2015-01-05      107375532793197  \n",
       "2015-01-06      114234328319941  \n",
       "...                         ...  \n",
       "2017-06-26      360861538825387  \n",
       "2017-06-27      267802086417281  \n",
       "2017-06-28      271945982500847  \n",
       "2017-06-29      300788803820027  \n",
       "2017-06-30      314452259423998  \n",
       "\n",
       "[911 rows x 21 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning et merging dataframes\n",
    "df = df.set_index(\"Date\")\n",
    "df.index = df.index.map(dt.datetime.fromordinal)\n",
    "df2 = pd.read_csv(\"timeseries/global.csv\", sep=\",\", index_col=\"Unnamed: 0\")\n",
    "df2.index.names = [\"Date\"]\n",
    "df2.index = pd.to_datetime(df2.index)\n",
    "df = df.merge(df2, how=\"inner\", on=\"Date\")\n",
    "df.rename(columns = {'PriceUSD':'price_usd', 'HashRate':'hash_rate'}, inplace = True)\n",
    "df.index.names = [\"date\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b60f40d",
   "metadata": {},
   "source": [
    "Construisons d'abord la table de corrélation entre chaque paramètre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "43df5bc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price_usd</th>\n",
       "      <th>hash_rate</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>total_fee</th>\n",
       "      <th>total_received_satoshi</th>\n",
       "      <th>total_sent_satoshi</th>\n",
       "      <th>mean_fee_satoshi</th>\n",
       "      <th>mean_feeUSD</th>\n",
       "      <th>...</th>\n",
       "      <th>nb_transactions</th>\n",
       "      <th>nb_payments</th>\n",
       "      <th>mean_nb_inputs</th>\n",
       "      <th>mean_nb_outputs</th>\n",
       "      <th>nb_mining</th>\n",
       "      <th>total_mining_satoshi</th>\n",
       "      <th>newly_created_coins</th>\n",
       "      <th>self_spent_satoshi</th>\n",
       "      <th>total_received</th>\n",
       "      <th>volume_without_self</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price_usd</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.937447</td>\n",
       "      <td>0.787660</td>\n",
       "      <td>-0.056973</td>\n",
       "      <td>0.021653</td>\n",
       "      <td>0.951675</td>\n",
       "      <td>0.097569</td>\n",
       "      <td>0.097788</td>\n",
       "      <td>0.951570</td>\n",
       "      <td>0.937520</td>\n",
       "      <td>...</td>\n",
       "      <td>0.700906</td>\n",
       "      <td>0.525985</td>\n",
       "      <td>-0.384144</td>\n",
       "      <td>-0.238592</td>\n",
       "      <td>0.065426</td>\n",
       "      <td>-0.569811</td>\n",
       "      <td>-0.639300</td>\n",
       "      <td>0.236655</td>\n",
       "      <td>0.097569</td>\n",
       "      <td>0.089751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hash_rate</th>\n",
       "      <td>0.937447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.914395</td>\n",
       "      <td>-0.106731</td>\n",
       "      <td>0.025523</td>\n",
       "      <td>0.909859</td>\n",
       "      <td>0.112789</td>\n",
       "      <td>0.113037</td>\n",
       "      <td>0.884613</td>\n",
       "      <td>0.794962</td>\n",
       "      <td>...</td>\n",
       "      <td>0.828416</td>\n",
       "      <td>0.608560</td>\n",
       "      <td>-0.476140</td>\n",
       "      <td>-0.299943</td>\n",
       "      <td>0.148014</td>\n",
       "      <td>-0.663786</td>\n",
       "      <td>-0.723154</td>\n",
       "      <td>0.319184</td>\n",
       "      <td>0.112789</td>\n",
       "      <td>0.102167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0.787660</td>\n",
       "      <td>0.914395</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.285986</td>\n",
       "      <td>-0.005827</td>\n",
       "      <td>0.749550</td>\n",
       "      <td>0.181758</td>\n",
       "      <td>0.182001</td>\n",
       "      <td>0.711172</td>\n",
       "      <td>0.582998</td>\n",
       "      <td>...</td>\n",
       "      <td>0.869806</td>\n",
       "      <td>0.627481</td>\n",
       "      <td>-0.552526</td>\n",
       "      <td>-0.342411</td>\n",
       "      <td>0.086513</td>\n",
       "      <td>-0.664620</td>\n",
       "      <td>-0.706534</td>\n",
       "      <td>0.345867</td>\n",
       "      <td>0.181758</td>\n",
       "      <td>0.170497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>-0.056973</td>\n",
       "      <td>-0.106731</td>\n",
       "      <td>-0.285986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007689</td>\n",
       "      <td>-0.097611</td>\n",
       "      <td>-0.087468</td>\n",
       "      <td>-0.087428</td>\n",
       "      <td>-0.095197</td>\n",
       "      <td>-0.070545</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046060</td>\n",
       "      <td>0.047068</td>\n",
       "      <td>0.015556</td>\n",
       "      <td>-0.010861</td>\n",
       "      <td>0.020750</td>\n",
       "      <td>-0.136662</td>\n",
       "      <td>-0.117949</td>\n",
       "      <td>-0.153500</td>\n",
       "      <td>-0.087468</td>\n",
       "      <td>-0.082498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>0.021653</td>\n",
       "      <td>0.025523</td>\n",
       "      <td>-0.005827</td>\n",
       "      <td>0.007689</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.027023</td>\n",
       "      <td>0.034609</td>\n",
       "      <td>0.034626</td>\n",
       "      <td>0.037035</td>\n",
       "      <td>0.024519</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000371</td>\n",
       "      <td>-0.041488</td>\n",
       "      <td>-0.017612</td>\n",
       "      <td>-0.051263</td>\n",
       "      <td>-0.090921</td>\n",
       "      <td>-0.049790</td>\n",
       "      <td>-0.049768</td>\n",
       "      <td>-0.013102</td>\n",
       "      <td>0.034609</td>\n",
       "      <td>0.035208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_fee</th>\n",
       "      <td>0.951675</td>\n",
       "      <td>0.909859</td>\n",
       "      <td>0.749550</td>\n",
       "      <td>-0.097611</td>\n",
       "      <td>0.027023</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.107786</td>\n",
       "      <td>0.107987</td>\n",
       "      <td>0.981124</td>\n",
       "      <td>0.927938</td>\n",
       "      <td>...</td>\n",
       "      <td>0.691433</td>\n",
       "      <td>0.540462</td>\n",
       "      <td>-0.368566</td>\n",
       "      <td>-0.199885</td>\n",
       "      <td>0.088749</td>\n",
       "      <td>-0.506483</td>\n",
       "      <td>-0.584979</td>\n",
       "      <td>0.256746</td>\n",
       "      <td>0.107786</td>\n",
       "      <td>0.099313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_received_satoshi</th>\n",
       "      <td>0.097569</td>\n",
       "      <td>0.112789</td>\n",
       "      <td>0.181758</td>\n",
       "      <td>-0.087468</td>\n",
       "      <td>0.034609</td>\n",
       "      <td>0.107786</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.086294</td>\n",
       "      <td>0.074591</td>\n",
       "      <td>...</td>\n",
       "      <td>0.226881</td>\n",
       "      <td>0.174043</td>\n",
       "      <td>-0.146362</td>\n",
       "      <td>-0.101107</td>\n",
       "      <td>0.104866</td>\n",
       "      <td>0.036941</td>\n",
       "      <td>0.023045</td>\n",
       "      <td>0.137045</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_sent_satoshi</th>\n",
       "      <td>0.097788</td>\n",
       "      <td>0.113037</td>\n",
       "      <td>0.182001</td>\n",
       "      <td>-0.087428</td>\n",
       "      <td>0.034626</td>\n",
       "      <td>0.107987</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.086487</td>\n",
       "      <td>0.074744</td>\n",
       "      <td>...</td>\n",
       "      <td>0.227111</td>\n",
       "      <td>0.174196</td>\n",
       "      <td>-0.146533</td>\n",
       "      <td>-0.101203</td>\n",
       "      <td>0.104775</td>\n",
       "      <td>0.036601</td>\n",
       "      <td>0.022703</td>\n",
       "      <td>0.137090</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_fee_satoshi</th>\n",
       "      <td>0.951570</td>\n",
       "      <td>0.884613</td>\n",
       "      <td>0.711172</td>\n",
       "      <td>-0.095197</td>\n",
       "      <td>0.037035</td>\n",
       "      <td>0.981124</td>\n",
       "      <td>0.086294</td>\n",
       "      <td>0.086487</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.953039</td>\n",
       "      <td>...</td>\n",
       "      <td>0.602853</td>\n",
       "      <td>0.483522</td>\n",
       "      <td>-0.291459</td>\n",
       "      <td>-0.158865</td>\n",
       "      <td>0.044525</td>\n",
       "      <td>-0.487153</td>\n",
       "      <td>-0.564747</td>\n",
       "      <td>0.234160</td>\n",
       "      <td>0.086294</td>\n",
       "      <td>0.078516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_feeUSD</th>\n",
       "      <td>0.937520</td>\n",
       "      <td>0.794962</td>\n",
       "      <td>0.582998</td>\n",
       "      <td>-0.070545</td>\n",
       "      <td>0.024519</td>\n",
       "      <td>0.927938</td>\n",
       "      <td>0.074591</td>\n",
       "      <td>0.074744</td>\n",
       "      <td>0.953039</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.477831</td>\n",
       "      <td>0.378173</td>\n",
       "      <td>-0.210316</td>\n",
       "      <td>-0.136339</td>\n",
       "      <td>0.044489</td>\n",
       "      <td>-0.367922</td>\n",
       "      <td>-0.446822</td>\n",
       "      <td>0.155399</td>\n",
       "      <td>0.074591</td>\n",
       "      <td>0.069501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_fee_for100</th>\n",
       "      <td>0.218620</td>\n",
       "      <td>0.184963</td>\n",
       "      <td>0.107912</td>\n",
       "      <td>0.062479</td>\n",
       "      <td>-0.015174</td>\n",
       "      <td>0.242341</td>\n",
       "      <td>-0.021573</td>\n",
       "      <td>-0.021539</td>\n",
       "      <td>0.247203</td>\n",
       "      <td>0.245922</td>\n",
       "      <td>...</td>\n",
       "      <td>0.159894</td>\n",
       "      <td>0.216485</td>\n",
       "      <td>0.068303</td>\n",
       "      <td>0.042150</td>\n",
       "      <td>0.007897</td>\n",
       "      <td>-0.078548</td>\n",
       "      <td>-0.100195</td>\n",
       "      <td>0.053289</td>\n",
       "      <td>-0.021573</td>\n",
       "      <td>-0.023515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nb_transactions</th>\n",
       "      <td>0.700906</td>\n",
       "      <td>0.828416</td>\n",
       "      <td>0.869806</td>\n",
       "      <td>0.046060</td>\n",
       "      <td>-0.000371</td>\n",
       "      <td>0.691433</td>\n",
       "      <td>0.226881</td>\n",
       "      <td>0.227111</td>\n",
       "      <td>0.602853</td>\n",
       "      <td>0.477831</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.754425</td>\n",
       "      <td>-0.646055</td>\n",
       "      <td>-0.353345</td>\n",
       "      <td>0.173817</td>\n",
       "      <td>-0.630704</td>\n",
       "      <td>-0.668322</td>\n",
       "      <td>0.329620</td>\n",
       "      <td>0.226881</td>\n",
       "      <td>0.216372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nb_payments</th>\n",
       "      <td>0.525985</td>\n",
       "      <td>0.608560</td>\n",
       "      <td>0.627481</td>\n",
       "      <td>0.047068</td>\n",
       "      <td>-0.041488</td>\n",
       "      <td>0.540462</td>\n",
       "      <td>0.174043</td>\n",
       "      <td>0.174196</td>\n",
       "      <td>0.483522</td>\n",
       "      <td>0.378173</td>\n",
       "      <td>...</td>\n",
       "      <td>0.754425</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.480603</td>\n",
       "      <td>0.333082</td>\n",
       "      <td>0.142599</td>\n",
       "      <td>-0.408969</td>\n",
       "      <td>-0.443362</td>\n",
       "      <td>0.278910</td>\n",
       "      <td>0.174043</td>\n",
       "      <td>0.165076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_nb_inputs</th>\n",
       "      <td>-0.384144</td>\n",
       "      <td>-0.476140</td>\n",
       "      <td>-0.552526</td>\n",
       "      <td>0.015556</td>\n",
       "      <td>-0.017612</td>\n",
       "      <td>-0.368566</td>\n",
       "      <td>-0.146362</td>\n",
       "      <td>-0.146533</td>\n",
       "      <td>-0.291459</td>\n",
       "      <td>-0.210316</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.646055</td>\n",
       "      <td>-0.480603</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.237808</td>\n",
       "      <td>0.040082</td>\n",
       "      <td>0.485798</td>\n",
       "      <td>0.496967</td>\n",
       "      <td>-0.144665</td>\n",
       "      <td>-0.146362</td>\n",
       "      <td>-0.141945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_nb_outputs</th>\n",
       "      <td>-0.238592</td>\n",
       "      <td>-0.299943</td>\n",
       "      <td>-0.342411</td>\n",
       "      <td>-0.010861</td>\n",
       "      <td>-0.051263</td>\n",
       "      <td>-0.199885</td>\n",
       "      <td>-0.101107</td>\n",
       "      <td>-0.101203</td>\n",
       "      <td>-0.158865</td>\n",
       "      <td>-0.136339</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.353345</td>\n",
       "      <td>0.333082</td>\n",
       "      <td>0.237808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059515</td>\n",
       "      <td>0.275205</td>\n",
       "      <td>0.280565</td>\n",
       "      <td>-0.083650</td>\n",
       "      <td>-0.101107</td>\n",
       "      <td>-0.098621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nb_mining</th>\n",
       "      <td>0.065426</td>\n",
       "      <td>0.148014</td>\n",
       "      <td>0.086513</td>\n",
       "      <td>0.020750</td>\n",
       "      <td>-0.090921</td>\n",
       "      <td>0.088749</td>\n",
       "      <td>0.104866</td>\n",
       "      <td>0.104775</td>\n",
       "      <td>0.044525</td>\n",
       "      <td>0.044489</td>\n",
       "      <td>...</td>\n",
       "      <td>0.173817</td>\n",
       "      <td>0.142599</td>\n",
       "      <td>0.040082</td>\n",
       "      <td>-0.059515</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.295313</td>\n",
       "      <td>0.268142</td>\n",
       "      <td>0.075973</td>\n",
       "      <td>0.104866</td>\n",
       "      <td>0.102663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_mining_satoshi</th>\n",
       "      <td>-0.569811</td>\n",
       "      <td>-0.663786</td>\n",
       "      <td>-0.664620</td>\n",
       "      <td>-0.136662</td>\n",
       "      <td>-0.049790</td>\n",
       "      <td>-0.506483</td>\n",
       "      <td>0.036941</td>\n",
       "      <td>0.036601</td>\n",
       "      <td>-0.487153</td>\n",
       "      <td>-0.367922</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.630704</td>\n",
       "      <td>-0.408969</td>\n",
       "      <td>0.485798</td>\n",
       "      <td>0.275205</td>\n",
       "      <td>0.295313</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995608</td>\n",
       "      <td>-0.106488</td>\n",
       "      <td>0.036941</td>\n",
       "      <td>0.040796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newly_created_coins</th>\n",
       "      <td>-0.639300</td>\n",
       "      <td>-0.723154</td>\n",
       "      <td>-0.706534</td>\n",
       "      <td>-0.117949</td>\n",
       "      <td>-0.049768</td>\n",
       "      <td>-0.584979</td>\n",
       "      <td>0.023045</td>\n",
       "      <td>0.022703</td>\n",
       "      <td>-0.564747</td>\n",
       "      <td>-0.446822</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.668322</td>\n",
       "      <td>-0.443362</td>\n",
       "      <td>0.496967</td>\n",
       "      <td>0.280565</td>\n",
       "      <td>0.268142</td>\n",
       "      <td>0.995608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.128039</td>\n",
       "      <td>0.023045</td>\n",
       "      <td>0.027591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>self_spent_satoshi</th>\n",
       "      <td>0.236655</td>\n",
       "      <td>0.319184</td>\n",
       "      <td>0.345867</td>\n",
       "      <td>-0.153500</td>\n",
       "      <td>-0.013102</td>\n",
       "      <td>0.256746</td>\n",
       "      <td>0.137045</td>\n",
       "      <td>0.137090</td>\n",
       "      <td>0.234160</td>\n",
       "      <td>0.155399</td>\n",
       "      <td>...</td>\n",
       "      <td>0.329620</td>\n",
       "      <td>0.278910</td>\n",
       "      <td>-0.144665</td>\n",
       "      <td>-0.083650</td>\n",
       "      <td>0.075973</td>\n",
       "      <td>-0.106488</td>\n",
       "      <td>-0.128039</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.137045</td>\n",
       "      <td>0.102864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_received</th>\n",
       "      <td>0.097569</td>\n",
       "      <td>0.112789</td>\n",
       "      <td>0.181758</td>\n",
       "      <td>-0.087468</td>\n",
       "      <td>0.034609</td>\n",
       "      <td>0.107786</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.086294</td>\n",
       "      <td>0.074591</td>\n",
       "      <td>...</td>\n",
       "      <td>0.226881</td>\n",
       "      <td>0.174043</td>\n",
       "      <td>-0.146362</td>\n",
       "      <td>-0.101107</td>\n",
       "      <td>0.104866</td>\n",
       "      <td>0.036941</td>\n",
       "      <td>0.023045</td>\n",
       "      <td>0.137045</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume_without_self</th>\n",
       "      <td>0.089751</td>\n",
       "      <td>0.102167</td>\n",
       "      <td>0.170497</td>\n",
       "      <td>-0.082498</td>\n",
       "      <td>0.035208</td>\n",
       "      <td>0.099313</td>\n",
       "      <td>0.999407</td>\n",
       "      <td>0.999406</td>\n",
       "      <td>0.078516</td>\n",
       "      <td>0.069501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.216372</td>\n",
       "      <td>0.165076</td>\n",
       "      <td>-0.141945</td>\n",
       "      <td>-0.098621</td>\n",
       "      <td>0.102663</td>\n",
       "      <td>0.040796</td>\n",
       "      <td>0.027591</td>\n",
       "      <td>0.102864</td>\n",
       "      <td>0.999407</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        price_usd  hash_rate      year     month       day  \\\n",
       "price_usd                1.000000   0.937447  0.787660 -0.056973  0.021653   \n",
       "hash_rate                0.937447   1.000000  0.914395 -0.106731  0.025523   \n",
       "year                     0.787660   0.914395  1.000000 -0.285986 -0.005827   \n",
       "month                   -0.056973  -0.106731 -0.285986  1.000000  0.007689   \n",
       "day                      0.021653   0.025523 -0.005827  0.007689  1.000000   \n",
       "total_fee                0.951675   0.909859  0.749550 -0.097611  0.027023   \n",
       "total_received_satoshi   0.097569   0.112789  0.181758 -0.087468  0.034609   \n",
       "total_sent_satoshi       0.097788   0.113037  0.182001 -0.087428  0.034626   \n",
       "mean_fee_satoshi         0.951570   0.884613  0.711172 -0.095197  0.037035   \n",
       "mean_feeUSD              0.937520   0.794962  0.582998 -0.070545  0.024519   \n",
       "mean_fee_for100          0.218620   0.184963  0.107912  0.062479 -0.015174   \n",
       "nb_transactions          0.700906   0.828416  0.869806  0.046060 -0.000371   \n",
       "nb_payments              0.525985   0.608560  0.627481  0.047068 -0.041488   \n",
       "mean_nb_inputs          -0.384144  -0.476140 -0.552526  0.015556 -0.017612   \n",
       "mean_nb_outputs         -0.238592  -0.299943 -0.342411 -0.010861 -0.051263   \n",
       "nb_mining                0.065426   0.148014  0.086513  0.020750 -0.090921   \n",
       "total_mining_satoshi    -0.569811  -0.663786 -0.664620 -0.136662 -0.049790   \n",
       "newly_created_coins     -0.639300  -0.723154 -0.706534 -0.117949 -0.049768   \n",
       "self_spent_satoshi       0.236655   0.319184  0.345867 -0.153500 -0.013102   \n",
       "total_received           0.097569   0.112789  0.181758 -0.087468  0.034609   \n",
       "volume_without_self      0.089751   0.102167  0.170497 -0.082498  0.035208   \n",
       "\n",
       "                        total_fee  total_received_satoshi  total_sent_satoshi  \\\n",
       "price_usd                0.951675                0.097569            0.097788   \n",
       "hash_rate                0.909859                0.112789            0.113037   \n",
       "year                     0.749550                0.181758            0.182001   \n",
       "month                   -0.097611               -0.087468           -0.087428   \n",
       "day                      0.027023                0.034609            0.034626   \n",
       "total_fee                1.000000                0.107786            0.107987   \n",
       "total_received_satoshi   0.107786                1.000000            1.000000   \n",
       "total_sent_satoshi       0.107987                1.000000            1.000000   \n",
       "mean_fee_satoshi         0.981124                0.086294            0.086487   \n",
       "mean_feeUSD              0.927938                0.074591            0.074744   \n",
       "mean_fee_for100          0.242341               -0.021573           -0.021539   \n",
       "nb_transactions          0.691433                0.226881            0.227111   \n",
       "nb_payments              0.540462                0.174043            0.174196   \n",
       "mean_nb_inputs          -0.368566               -0.146362           -0.146533   \n",
       "mean_nb_outputs         -0.199885               -0.101107           -0.101203   \n",
       "nb_mining                0.088749                0.104866            0.104775   \n",
       "total_mining_satoshi    -0.506483                0.036941            0.036601   \n",
       "newly_created_coins     -0.584979                0.023045            0.022703   \n",
       "self_spent_satoshi       0.256746                0.137045            0.137090   \n",
       "total_received           0.107786                1.000000            1.000000   \n",
       "volume_without_self      0.099313                0.999407            0.999406   \n",
       "\n",
       "                        mean_fee_satoshi  mean_feeUSD  ...  nb_transactions  \\\n",
       "price_usd                       0.951570     0.937520  ...         0.700906   \n",
       "hash_rate                       0.884613     0.794962  ...         0.828416   \n",
       "year                            0.711172     0.582998  ...         0.869806   \n",
       "month                          -0.095197    -0.070545  ...         0.046060   \n",
       "day                             0.037035     0.024519  ...        -0.000371   \n",
       "total_fee                       0.981124     0.927938  ...         0.691433   \n",
       "total_received_satoshi          0.086294     0.074591  ...         0.226881   \n",
       "total_sent_satoshi              0.086487     0.074744  ...         0.227111   \n",
       "mean_fee_satoshi                1.000000     0.953039  ...         0.602853   \n",
       "mean_feeUSD                     0.953039     1.000000  ...         0.477831   \n",
       "mean_fee_for100                 0.247203     0.245922  ...         0.159894   \n",
       "nb_transactions                 0.602853     0.477831  ...         1.000000   \n",
       "nb_payments                     0.483522     0.378173  ...         0.754425   \n",
       "mean_nb_inputs                 -0.291459    -0.210316  ...        -0.646055   \n",
       "mean_nb_outputs                -0.158865    -0.136339  ...        -0.353345   \n",
       "nb_mining                       0.044525     0.044489  ...         0.173817   \n",
       "total_mining_satoshi           -0.487153    -0.367922  ...        -0.630704   \n",
       "newly_created_coins            -0.564747    -0.446822  ...        -0.668322   \n",
       "self_spent_satoshi              0.234160     0.155399  ...         0.329620   \n",
       "total_received                  0.086294     0.074591  ...         0.226881   \n",
       "volume_without_self             0.078516     0.069501  ...         0.216372   \n",
       "\n",
       "                        nb_payments  mean_nb_inputs  mean_nb_outputs  \\\n",
       "price_usd                  0.525985       -0.384144        -0.238592   \n",
       "hash_rate                  0.608560       -0.476140        -0.299943   \n",
       "year                       0.627481       -0.552526        -0.342411   \n",
       "month                      0.047068        0.015556        -0.010861   \n",
       "day                       -0.041488       -0.017612        -0.051263   \n",
       "total_fee                  0.540462       -0.368566        -0.199885   \n",
       "total_received_satoshi     0.174043       -0.146362        -0.101107   \n",
       "total_sent_satoshi         0.174196       -0.146533        -0.101203   \n",
       "mean_fee_satoshi           0.483522       -0.291459        -0.158865   \n",
       "mean_feeUSD                0.378173       -0.210316        -0.136339   \n",
       "mean_fee_for100            0.216485        0.068303         0.042150   \n",
       "nb_transactions            0.754425       -0.646055        -0.353345   \n",
       "nb_payments                1.000000       -0.480603         0.333082   \n",
       "mean_nb_inputs            -0.480603        1.000000         0.237808   \n",
       "mean_nb_outputs            0.333082        0.237808         1.000000   \n",
       "nb_mining                  0.142599        0.040082        -0.059515   \n",
       "total_mining_satoshi      -0.408969        0.485798         0.275205   \n",
       "newly_created_coins       -0.443362        0.496967         0.280565   \n",
       "self_spent_satoshi         0.278910       -0.144665        -0.083650   \n",
       "total_received             0.174043       -0.146362        -0.101107   \n",
       "volume_without_self        0.165076       -0.141945        -0.098621   \n",
       "\n",
       "                        nb_mining  total_mining_satoshi  newly_created_coins  \\\n",
       "price_usd                0.065426             -0.569811            -0.639300   \n",
       "hash_rate                0.148014             -0.663786            -0.723154   \n",
       "year                     0.086513             -0.664620            -0.706534   \n",
       "month                    0.020750             -0.136662            -0.117949   \n",
       "day                     -0.090921             -0.049790            -0.049768   \n",
       "total_fee                0.088749             -0.506483            -0.584979   \n",
       "total_received_satoshi   0.104866              0.036941             0.023045   \n",
       "total_sent_satoshi       0.104775              0.036601             0.022703   \n",
       "mean_fee_satoshi         0.044525             -0.487153            -0.564747   \n",
       "mean_feeUSD              0.044489             -0.367922            -0.446822   \n",
       "mean_fee_for100          0.007897             -0.078548            -0.100195   \n",
       "nb_transactions          0.173817             -0.630704            -0.668322   \n",
       "nb_payments              0.142599             -0.408969            -0.443362   \n",
       "mean_nb_inputs           0.040082              0.485798             0.496967   \n",
       "mean_nb_outputs         -0.059515              0.275205             0.280565   \n",
       "nb_mining                1.000000              0.295313             0.268142   \n",
       "total_mining_satoshi     0.295313              1.000000             0.995608   \n",
       "newly_created_coins      0.268142              0.995608             1.000000   \n",
       "self_spent_satoshi       0.075973             -0.106488            -0.128039   \n",
       "total_received           0.104866              0.036941             0.023045   \n",
       "volume_without_self      0.102663              0.040796             0.027591   \n",
       "\n",
       "                        self_spent_satoshi  total_received  \\\n",
       "price_usd                         0.236655        0.097569   \n",
       "hash_rate                         0.319184        0.112789   \n",
       "year                              0.345867        0.181758   \n",
       "month                            -0.153500       -0.087468   \n",
       "day                              -0.013102        0.034609   \n",
       "total_fee                         0.256746        0.107786   \n",
       "total_received_satoshi            0.137045        1.000000   \n",
       "total_sent_satoshi                0.137090        1.000000   \n",
       "mean_fee_satoshi                  0.234160        0.086294   \n",
       "mean_feeUSD                       0.155399        0.074591   \n",
       "mean_fee_for100                   0.053289       -0.021573   \n",
       "nb_transactions                   0.329620        0.226881   \n",
       "nb_payments                       0.278910        0.174043   \n",
       "mean_nb_inputs                   -0.144665       -0.146362   \n",
       "mean_nb_outputs                  -0.083650       -0.101107   \n",
       "nb_mining                         0.075973        0.104866   \n",
       "total_mining_satoshi             -0.106488        0.036941   \n",
       "newly_created_coins              -0.128039        0.023045   \n",
       "self_spent_satoshi                1.000000        0.137045   \n",
       "total_received                    0.137045        1.000000   \n",
       "volume_without_self               0.102864        0.999407   \n",
       "\n",
       "                        volume_without_self  \n",
       "price_usd                          0.089751  \n",
       "hash_rate                          0.102167  \n",
       "year                               0.170497  \n",
       "month                             -0.082498  \n",
       "day                                0.035208  \n",
       "total_fee                          0.099313  \n",
       "total_received_satoshi             0.999407  \n",
       "total_sent_satoshi                 0.999406  \n",
       "mean_fee_satoshi                   0.078516  \n",
       "mean_feeUSD                        0.069501  \n",
       "mean_fee_for100                   -0.023515  \n",
       "nb_transactions                    0.216372  \n",
       "nb_payments                        0.165076  \n",
       "mean_nb_inputs                    -0.141945  \n",
       "mean_nb_outputs                   -0.098621  \n",
       "nb_mining                          0.102663  \n",
       "total_mining_satoshi               0.040796  \n",
       "newly_created_coins                0.027591  \n",
       "self_spent_satoshi                 0.102864  \n",
       "total_received                     0.999407  \n",
       "volume_without_self                1.000000  \n",
       "\n",
       "[21 rows x 21 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating the correlation table between all parameters\n",
    "df_corr = df.corr()\n",
    "df_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c425733b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "colorscale": [
          [
           0,
           "rgb(103,0,31)"
          ],
          [
           0.1,
           "rgb(178,24,43)"
          ],
          [
           0.2,
           "rgb(214,96,77)"
          ],
          [
           0.3,
           "rgb(244,165,130)"
          ],
          [
           0.4,
           "rgb(253,219,199)"
          ],
          [
           0.5,
           "rgb(247,247,247)"
          ],
          [
           0.6,
           "rgb(209,229,240)"
          ],
          [
           0.7,
           "rgb(146,197,222)"
          ],
          [
           0.8,
           "rgb(67,147,195)"
          ],
          [
           0.9,
           "rgb(33,102,172)"
          ],
          [
           1,
           "rgb(5,48,97)"
          ]
         ],
         "type": "heatmap",
         "x": [
          "price_usd",
          "hash_rate",
          "year",
          "month",
          "day",
          "total_fee",
          "total_received_satoshi",
          "total_sent_satoshi",
          "mean_fee_satoshi",
          "mean_feeUSD",
          "mean_fee_for100",
          "nb_transactions",
          "nb_payments",
          "mean_nb_inputs",
          "mean_nb_outputs",
          "nb_mining",
          "total_mining_satoshi",
          "newly_created_coins",
          "self_spent_satoshi",
          "total_received",
          "volume_without_self"
         ],
         "y": [
          "price_usd",
          "hash_rate",
          "year",
          "month",
          "day",
          "total_fee",
          "total_received_satoshi",
          "total_sent_satoshi",
          "mean_fee_satoshi",
          "mean_feeUSD",
          "mean_fee_for100",
          "nb_transactions",
          "nb_payments",
          "mean_nb_inputs",
          "mean_nb_outputs",
          "nb_mining",
          "total_mining_satoshi",
          "newly_created_coins",
          "self_spent_satoshi",
          "total_received",
          "volume_without_self"
         ],
         "z": [
          [
           1,
           0.9374466899233475,
           0.7876602721474986,
           -0.05697339125185453,
           0.021652930451028174,
           0.9516748677947203,
           0.09756850965979509,
           0.09778767437689803,
           0.9515701688144628,
           0.93752004819651,
           0.21862019509203542,
           0.7009060681375394,
           0.5259850058477299,
           -0.38414361260697455,
           -0.23859188165153677,
           0.06542562794526804,
           -0.5698110553445587,
           -0.6392996477389672,
           0.23665473554166952,
           0.09756850965979509,
           0.08975105038260991
          ],
          [
           0.9374466899233475,
           1,
           0.9143946915187308,
           -0.10673123144500615,
           0.025522838814358857,
           0.9098586948977526,
           0.11278914447531957,
           0.11303707501231798,
           0.8846125770096076,
           0.7949623825815928,
           0.1849631835544514,
           0.8284159597998971,
           0.6085600963748781,
           -0.4761399757913977,
           -0.2999432581220687,
           0.14801447855134822,
           -0.6637855621161506,
           -0.7231538104321024,
           0.3191838329804173,
           0.11278914447531957,
           0.10216707208734212
          ],
          [
           0.7876602721474986,
           0.9143946915187308,
           1,
           -0.285986184730963,
           -0.0058267149539248915,
           0.7495498294258266,
           0.18175834780175285,
           0.1820011395809051,
           0.7111719425130081,
           0.5829975713935457,
           0.10791214033994762,
           0.869806008061003,
           0.6274813285857429,
           -0.5525262647452889,
           -0.3424105952326695,
           0.08651321238869787,
           -0.664620482129707,
           -0.7065342085512735,
           0.3458668978661132,
           0.18175834780175285,
           0.17049657329925033
          ],
          [
           -0.05697339125185453,
           -0.10673123144500615,
           -0.285986184730963,
           1,
           0.007689175203048241,
           -0.09761075579644608,
           -0.08746776962692061,
           -0.0874281585866228,
           -0.09519696842201285,
           -0.07054464192406962,
           0.062478809234152735,
           0.0460598847999665,
           0.047067597680220834,
           0.01555584459421733,
           -0.010860625247913298,
           0.020749700136005408,
           -0.13666166569308053,
           -0.11794882121354344,
           -0.15349980982497546,
           -0.08746776962692061,
           -0.08249802216343897
          ],
          [
           0.021652930451028174,
           0.025522838814358857,
           -0.0058267149539248915,
           0.007689175203048241,
           1,
           0.02702332129657352,
           0.03460870872493781,
           0.03462598111411348,
           0.03703478750796335,
           0.024518715139787782,
           -0.015173821359927729,
           -0.0003713184131170282,
           -0.04148764003293538,
           -0.017611772527458137,
           -0.05126265933443407,
           -0.0909207074714737,
           -0.04979013728960237,
           -0.049767502689098,
           -0.013101579493269386,
           0.03460870872493781,
           0.0352083361893551
          ],
          [
           0.9516748677947203,
           0.9098586948977526,
           0.7495498294258266,
           -0.09761075579644608,
           0.02702332129657352,
           1,
           0.10778597808745076,
           0.10798666506741945,
           0.9811238690766946,
           0.9279376312666002,
           0.24234105017585328,
           0.6914329270255365,
           0.5404620429454489,
           -0.3685657651289262,
           -0.1998849090001862,
           0.08874872810628683,
           -0.5064827144436223,
           -0.584978534814252,
           0.2567458163798541,
           0.10778597808745076,
           0.0993129122764331
          ],
          [
           0.09756850965979509,
           0.11278914447531957,
           0.18175834780175285,
           -0.08746776962692061,
           0.03460870872493781,
           0.10778597808745076,
           1,
           0.9999999416763468,
           0.08629380788477653,
           0.07459067346139386,
           -0.021573064752847853,
           0.22688123333484947,
           0.17404322207890283,
           -0.1463621076372879,
           -0.10110685000220916,
           0.1048658216138282,
           0.03694084384834224,
           0.023044791968545125,
           0.13704511897483015,
           1,
           0.9994073018791768
          ],
          [
           0.09778767437689803,
           0.11303707501231798,
           0.1820011395809051,
           -0.0874281585866228,
           0.03462598111411348,
           0.10798666506741945,
           0.9999999416763468,
           1,
           0.0864874153915437,
           0.07474390298295773,
           -0.02153900403790209,
           0.22711132330461584,
           0.1741960467988241,
           -0.14653302877624477,
           -0.10120348881542776,
           0.10477503645916834,
           0.03660100540450463,
           0.022703344649324848,
           0.13708993166796546,
           0.9999999416763469,
           0.9994056859647017
          ],
          [
           0.9515701688144628,
           0.8846125770096076,
           0.7111719425130081,
           -0.09519696842201285,
           0.03703478750796335,
           0.9811238690766946,
           0.08629380788477653,
           0.0864874153915437,
           1,
           0.9530394735274934,
           0.24720305500522397,
           0.6028526356365366,
           0.4835215341690023,
           -0.2914593936659742,
           -0.15886488145477534,
           0.04452517023611578,
           -0.4871531317004933,
           -0.564747357261683,
           0.2341602463972546,
           0.08629380788477654,
           0.07851602314285547
          ],
          [
           0.93752004819651,
           0.7949623825815928,
           0.5829975713935457,
           -0.07054464192406962,
           0.024518715139787782,
           0.9279376312666002,
           0.07459067346139386,
           0.07474390298295773,
           0.9530394735274934,
           1,
           0.245922410035795,
           0.4778307763329015,
           0.37817287832540525,
           -0.21031592955492312,
           -0.13633934875370185,
           0.04448910490383908,
           -0.36792221714471474,
           -0.4468220033479351,
           0.15539941693623974,
           0.07459067346139386,
           0.06950121334500553
          ],
          [
           0.21862019509203542,
           0.1849631835544514,
           0.10791214033994762,
           0.062478809234152735,
           -0.015173821359927729,
           0.24234105017585328,
           -0.021573064752847853,
           -0.02153900403790209,
           0.24720305500522397,
           0.245922410035795,
           1,
           0.1598943433527826,
           0.21648534759844312,
           0.06830262399579866,
           0.0421498625020709,
           0.007896687421947794,
           -0.07854778634446004,
           -0.1001948193464608,
           0.05328906096668144,
           -0.021573064752847846,
           -0.023514944808997844
          ],
          [
           0.7009060681375394,
           0.8284159597998971,
           0.869806008061003,
           0.0460598847999665,
           -0.0003713184131170282,
           0.6914329270255365,
           0.22688123333484947,
           0.22711132330461584,
           0.6028526356365366,
           0.4778307763329015,
           0.1598943433527826,
           1,
           0.7544250627073407,
           -0.6460551329378177,
           -0.3533446632132049,
           0.1738172513682461,
           -0.6307038603853691,
           -0.668321764140683,
           0.3296202502382761,
           0.22688123333484939,
           0.2163722284359449
          ],
          [
           0.5259850058477299,
           0.6085600963748781,
           0.6274813285857429,
           0.047067597680220834,
           -0.04148764003293538,
           0.5404620429454489,
           0.17404322207890283,
           0.1741960467988241,
           0.4835215341690023,
           0.37817287832540525,
           0.21648534759844312,
           0.7544250627073407,
           1,
           -0.4806034339903984,
           0.3330815994862357,
           0.14259870127079588,
           -0.4089685167066963,
           -0.44336219741838334,
           0.2789096891621621,
           0.17404322207890283,
           0.16507619809948124
          ],
          [
           -0.38414361260697455,
           -0.4761399757913977,
           -0.5525262647452889,
           0.01555584459421733,
           -0.017611772527458137,
           -0.3685657651289262,
           -0.1463621076372879,
           -0.14653302877624477,
           -0.2914593936659742,
           -0.21031592955492312,
           0.06830262399579866,
           -0.6460551329378177,
           -0.4806034339903984,
           1,
           0.23780800217624395,
           0.0400823283596915,
           0.4857981429609298,
           0.49696659373788893,
           -0.1446653982069597,
           -0.146362107637288,
           -0.14194496316228908
          ],
          [
           -0.23859188165153677,
           -0.2999432581220687,
           -0.3424105952326695,
           -0.010860625247913298,
           -0.05126265933443407,
           -0.1998849090001862,
           -0.10110685000220916,
           -0.10120348881542776,
           -0.15886488145477534,
           -0.13633934875370185,
           0.0421498625020709,
           -0.3533446632132049,
           0.3330815994862357,
           0.23780800217624395,
           1,
           -0.059514930674614226,
           0.2752053132408332,
           0.28056509163788307,
           -0.08365009851554416,
           -0.1011068500022092,
           -0.09862142028040102
          ],
          [
           0.06542562794526804,
           0.14801447855134822,
           0.08651321238869787,
           0.020749700136005408,
           -0.0909207074714737,
           0.08874872810628683,
           0.1048658216138282,
           0.10477503645916834,
           0.04452517023611578,
           0.04448910490383908,
           0.007896687421947794,
           0.1738172513682461,
           0.14259870127079588,
           0.0400823283596915,
           -0.059514930674614226,
           1,
           0.29531349540004176,
           0.2681418963433082,
           0.07597305342077436,
           0.10486582161382825,
           0.10266286216637792
          ],
          [
           -0.5698110553445587,
           -0.6637855621161506,
           -0.664620482129707,
           -0.13666166569308053,
           -0.04979013728960237,
           -0.5064827144436223,
           0.03694084384834224,
           0.03660100540450463,
           -0.4871531317004933,
           -0.36792221714471474,
           -0.07854778634446004,
           -0.6307038603853691,
           -0.4089685167066963,
           0.4857981429609298,
           0.2752053132408332,
           0.29531349540004176,
           1,
           0.9956084144572299,
           -0.106487511161423,
           0.036940843848342224,
           0.0407955793175624
          ],
          [
           -0.6392996477389672,
           -0.7231538104321024,
           -0.7065342085512735,
           -0.11794882121354344,
           -0.049767502689098,
           -0.584978534814252,
           0.023044791968545125,
           0.022703344649324848,
           -0.564747357261683,
           -0.4468220033479351,
           -0.1001948193464608,
           -0.668321764140683,
           -0.44336219741838334,
           0.49696659373788893,
           0.28056509163788307,
           0.2681418963433082,
           0.9956084144572299,
           1,
           -0.12803939754002427,
           0.023044791968545114,
           0.02759056094412718
          ],
          [
           0.23665473554166952,
           0.3191838329804173,
           0.3458668978661132,
           -0.15349980982497546,
           -0.013101579493269386,
           0.2567458163798541,
           0.13704511897483015,
           0.13708993166796546,
           0.2341602463972546,
           0.15539941693623974,
           0.05328906096668144,
           0.3296202502382761,
           0.2789096891621621,
           -0.1446653982069597,
           -0.08365009851554416,
           0.07597305342077436,
           -0.106487511161423,
           -0.12803939754002427,
           1,
           0.13704511897483018,
           0.10286421239747652
          ],
          [
           0.09756850965979509,
           0.11278914447531957,
           0.18175834780175285,
           -0.08746776962692061,
           0.03460870872493781,
           0.10778597808745076,
           1,
           0.9999999416763469,
           0.08629380788477654,
           0.07459067346139386,
           -0.021573064752847846,
           0.22688123333484939,
           0.17404322207890283,
           -0.146362107637288,
           -0.1011068500022092,
           0.10486582161382825,
           0.036940843848342224,
           0.023044791968545114,
           0.13704511897483018,
           1,
           0.9994073018791768
          ],
          [
           0.08975105038260991,
           0.10216707208734212,
           0.17049657329925033,
           -0.08249802216343897,
           0.0352083361893551,
           0.0993129122764331,
           0.9994073018791768,
           0.9994056859647017,
           0.07851602314285547,
           0.06950121334500553,
           -0.023514944808997844,
           0.2163722284359449,
           0.16507619809948124,
           -0.14194496316228908,
           -0.09862142028040102,
           0.10266286216637792,
           0.0407955793175624,
           0.02759056094412718,
           0.10286421239747652,
           0.9994073018791768,
           1
          ]
         ]
        }
       ],
       "layout": {
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Correlation Matrix Heatmap"
        },
        "xaxis": {
         "title": {
          "text": "Variables"
         }
        },
        "yaxis": {
         "title": {
          "text": "Variables"
         }
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"01d30c11-b990-49ab-aae9-0a187a842055\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"01d30c11-b990-49ab-aae9-0a187a842055\")) {                    Plotly.newPlot(                        \"01d30c11-b990-49ab-aae9-0a187a842055\",                        [{\"colorscale\":[[0.0,\"rgb(103,0,31)\"],[0.1,\"rgb(178,24,43)\"],[0.2,\"rgb(214,96,77)\"],[0.3,\"rgb(244,165,130)\"],[0.4,\"rgb(253,219,199)\"],[0.5,\"rgb(247,247,247)\"],[0.6,\"rgb(209,229,240)\"],[0.7,\"rgb(146,197,222)\"],[0.8,\"rgb(67,147,195)\"],[0.9,\"rgb(33,102,172)\"],[1.0,\"rgb(5,48,97)\"]],\"x\":[\"price_usd\",\"hash_rate\",\"year\",\"month\",\"day\",\"total_fee\",\"total_received_satoshi\",\"total_sent_satoshi\",\"mean_fee_satoshi\",\"mean_feeUSD\",\"mean_fee_for100\",\"nb_transactions\",\"nb_payments\",\"mean_nb_inputs\",\"mean_nb_outputs\",\"nb_mining\",\"total_mining_satoshi\",\"newly_created_coins\",\"self_spent_satoshi\",\"total_received\",\"volume_without_self\"],\"y\":[\"price_usd\",\"hash_rate\",\"year\",\"month\",\"day\",\"total_fee\",\"total_received_satoshi\",\"total_sent_satoshi\",\"mean_fee_satoshi\",\"mean_feeUSD\",\"mean_fee_for100\",\"nb_transactions\",\"nb_payments\",\"mean_nb_inputs\",\"mean_nb_outputs\",\"nb_mining\",\"total_mining_satoshi\",\"newly_created_coins\",\"self_spent_satoshi\",\"total_received\",\"volume_without_self\"],\"z\":[[1.0,0.9374466899233475,0.7876602721474986,-0.05697339125185453,0.021652930451028174,0.9516748677947203,0.09756850965979509,0.09778767437689803,0.9515701688144628,0.93752004819651,0.21862019509203542,0.7009060681375394,0.5259850058477299,-0.38414361260697455,-0.23859188165153677,0.06542562794526804,-0.5698110553445587,-0.6392996477389672,0.23665473554166952,0.09756850965979509,0.08975105038260991],[0.9374466899233475,1.0,0.9143946915187308,-0.10673123144500615,0.025522838814358857,0.9098586948977526,0.11278914447531957,0.11303707501231798,0.8846125770096076,0.7949623825815928,0.1849631835544514,0.8284159597998971,0.6085600963748781,-0.4761399757913977,-0.2999432581220687,0.14801447855134822,-0.6637855621161506,-0.7231538104321024,0.3191838329804173,0.11278914447531957,0.10216707208734212],[0.7876602721474986,0.9143946915187308,1.0,-0.285986184730963,-0.0058267149539248915,0.7495498294258266,0.18175834780175285,0.1820011395809051,0.7111719425130081,0.5829975713935457,0.10791214033994762,0.869806008061003,0.6274813285857429,-0.5525262647452889,-0.3424105952326695,0.08651321238869787,-0.664620482129707,-0.7065342085512735,0.3458668978661132,0.18175834780175285,0.17049657329925033],[-0.05697339125185453,-0.10673123144500615,-0.285986184730963,1.0,0.007689175203048241,-0.09761075579644608,-0.08746776962692061,-0.0874281585866228,-0.09519696842201285,-0.07054464192406962,0.062478809234152735,0.0460598847999665,0.047067597680220834,0.01555584459421733,-0.010860625247913298,0.020749700136005408,-0.13666166569308053,-0.11794882121354344,-0.15349980982497546,-0.08746776962692061,-0.08249802216343897],[0.021652930451028174,0.025522838814358857,-0.0058267149539248915,0.007689175203048241,1.0,0.02702332129657352,0.03460870872493781,0.03462598111411348,0.03703478750796335,0.024518715139787782,-0.015173821359927729,-0.0003713184131170282,-0.04148764003293538,-0.017611772527458137,-0.05126265933443407,-0.0909207074714737,-0.04979013728960237,-0.049767502689098,-0.013101579493269386,0.03460870872493781,0.0352083361893551],[0.9516748677947203,0.9098586948977526,0.7495498294258266,-0.09761075579644608,0.02702332129657352,1.0,0.10778597808745076,0.10798666506741945,0.9811238690766946,0.9279376312666002,0.24234105017585328,0.6914329270255365,0.5404620429454489,-0.3685657651289262,-0.1998849090001862,0.08874872810628683,-0.5064827144436223,-0.584978534814252,0.2567458163798541,0.10778597808745076,0.0993129122764331],[0.09756850965979509,0.11278914447531957,0.18175834780175285,-0.08746776962692061,0.03460870872493781,0.10778597808745076,1.0,0.9999999416763468,0.08629380788477653,0.07459067346139386,-0.021573064752847853,0.22688123333484947,0.17404322207890283,-0.1463621076372879,-0.10110685000220916,0.1048658216138282,0.03694084384834224,0.023044791968545125,0.13704511897483015,1.0,0.9994073018791768],[0.09778767437689803,0.11303707501231798,0.1820011395809051,-0.0874281585866228,0.03462598111411348,0.10798666506741945,0.9999999416763468,1.0,0.0864874153915437,0.07474390298295773,-0.02153900403790209,0.22711132330461584,0.1741960467988241,-0.14653302877624477,-0.10120348881542776,0.10477503645916834,0.03660100540450463,0.022703344649324848,0.13708993166796546,0.9999999416763469,0.9994056859647017],[0.9515701688144628,0.8846125770096076,0.7111719425130081,-0.09519696842201285,0.03703478750796335,0.9811238690766946,0.08629380788477653,0.0864874153915437,1.0,0.9530394735274934,0.24720305500522397,0.6028526356365366,0.4835215341690023,-0.2914593936659742,-0.15886488145477534,0.04452517023611578,-0.4871531317004933,-0.564747357261683,0.2341602463972546,0.08629380788477654,0.07851602314285547],[0.93752004819651,0.7949623825815928,0.5829975713935457,-0.07054464192406962,0.024518715139787782,0.9279376312666002,0.07459067346139386,0.07474390298295773,0.9530394735274934,1.0,0.245922410035795,0.4778307763329015,0.37817287832540525,-0.21031592955492312,-0.13633934875370185,0.04448910490383908,-0.36792221714471474,-0.4468220033479351,0.15539941693623974,0.07459067346139386,0.06950121334500553],[0.21862019509203542,0.1849631835544514,0.10791214033994762,0.062478809234152735,-0.015173821359927729,0.24234105017585328,-0.021573064752847853,-0.02153900403790209,0.24720305500522397,0.245922410035795,1.0,0.1598943433527826,0.21648534759844312,0.06830262399579866,0.0421498625020709,0.007896687421947794,-0.07854778634446004,-0.1001948193464608,0.05328906096668144,-0.021573064752847846,-0.023514944808997844],[0.7009060681375394,0.8284159597998971,0.869806008061003,0.0460598847999665,-0.0003713184131170282,0.6914329270255365,0.22688123333484947,0.22711132330461584,0.6028526356365366,0.4778307763329015,0.1598943433527826,1.0,0.7544250627073407,-0.6460551329378177,-0.3533446632132049,0.1738172513682461,-0.6307038603853691,-0.668321764140683,0.3296202502382761,0.22688123333484939,0.2163722284359449],[0.5259850058477299,0.6085600963748781,0.6274813285857429,0.047067597680220834,-0.04148764003293538,0.5404620429454489,0.17404322207890283,0.1741960467988241,0.4835215341690023,0.37817287832540525,0.21648534759844312,0.7544250627073407,1.0,-0.4806034339903984,0.3330815994862357,0.14259870127079588,-0.4089685167066963,-0.44336219741838334,0.2789096891621621,0.17404322207890283,0.16507619809948124],[-0.38414361260697455,-0.4761399757913977,-0.5525262647452889,0.01555584459421733,-0.017611772527458137,-0.3685657651289262,-0.1463621076372879,-0.14653302877624477,-0.2914593936659742,-0.21031592955492312,0.06830262399579866,-0.6460551329378177,-0.4806034339903984,1.0,0.23780800217624395,0.0400823283596915,0.4857981429609298,0.49696659373788893,-0.1446653982069597,-0.146362107637288,-0.14194496316228908],[-0.23859188165153677,-0.2999432581220687,-0.3424105952326695,-0.010860625247913298,-0.05126265933443407,-0.1998849090001862,-0.10110685000220916,-0.10120348881542776,-0.15886488145477534,-0.13633934875370185,0.0421498625020709,-0.3533446632132049,0.3330815994862357,0.23780800217624395,1.0,-0.059514930674614226,0.2752053132408332,0.28056509163788307,-0.08365009851554416,-0.1011068500022092,-0.09862142028040102],[0.06542562794526804,0.14801447855134822,0.08651321238869787,0.020749700136005408,-0.0909207074714737,0.08874872810628683,0.1048658216138282,0.10477503645916834,0.04452517023611578,0.04448910490383908,0.007896687421947794,0.1738172513682461,0.14259870127079588,0.0400823283596915,-0.059514930674614226,1.0,0.29531349540004176,0.2681418963433082,0.07597305342077436,0.10486582161382825,0.10266286216637792],[-0.5698110553445587,-0.6637855621161506,-0.664620482129707,-0.13666166569308053,-0.04979013728960237,-0.5064827144436223,0.03694084384834224,0.03660100540450463,-0.4871531317004933,-0.36792221714471474,-0.07854778634446004,-0.6307038603853691,-0.4089685167066963,0.4857981429609298,0.2752053132408332,0.29531349540004176,1.0,0.9956084144572299,-0.106487511161423,0.036940843848342224,0.0407955793175624],[-0.6392996477389672,-0.7231538104321024,-0.7065342085512735,-0.11794882121354344,-0.049767502689098,-0.584978534814252,0.023044791968545125,0.022703344649324848,-0.564747357261683,-0.4468220033479351,-0.1001948193464608,-0.668321764140683,-0.44336219741838334,0.49696659373788893,0.28056509163788307,0.2681418963433082,0.9956084144572299,1.0,-0.12803939754002427,0.023044791968545114,0.02759056094412718],[0.23665473554166952,0.3191838329804173,0.3458668978661132,-0.15349980982497546,-0.013101579493269386,0.2567458163798541,0.13704511897483015,0.13708993166796546,0.2341602463972546,0.15539941693623974,0.05328906096668144,0.3296202502382761,0.2789096891621621,-0.1446653982069597,-0.08365009851554416,0.07597305342077436,-0.106487511161423,-0.12803939754002427,1.0,0.13704511897483018,0.10286421239747652],[0.09756850965979509,0.11278914447531957,0.18175834780175285,-0.08746776962692061,0.03460870872493781,0.10778597808745076,1.0,0.9999999416763469,0.08629380788477654,0.07459067346139386,-0.021573064752847846,0.22688123333484939,0.17404322207890283,-0.146362107637288,-0.1011068500022092,0.10486582161382825,0.036940843848342224,0.023044791968545114,0.13704511897483018,1.0,0.9994073018791768],[0.08975105038260991,0.10216707208734212,0.17049657329925033,-0.08249802216343897,0.0352083361893551,0.0993129122764331,0.9994073018791768,0.9994056859647017,0.07851602314285547,0.06950121334500553,-0.023514944808997844,0.2163722284359449,0.16507619809948124,-0.14194496316228908,-0.09862142028040102,0.10266286216637792,0.0407955793175624,0.02759056094412718,0.10286421239747652,0.9994073018791768,1.0]],\"type\":\"heatmap\"}],                        {\"title\":{\"text\":\"Correlation Matrix Heatmap\"},\"xaxis\":{\"title\":{\"text\":\"Variables\"}},\"yaxis\":{\"title\":{\"text\":\"Variables\"}},\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('01d30c11-b990-49ab-aae9-0a187a842055');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Printing the heatmap correlation\n",
    "heatmap = go.Heatmap(\n",
    "    z=df_corr.values,\n",
    "    x=df_corr.columns,\n",
    "    y=df_corr.columns,\n",
    "    colorscale='RdBu',\n",
    ")\n",
    "\n",
    "layout = go.Layout(\n",
    "    title='Correlation Matrix Heatmap',\n",
    "    xaxis=dict(title='Variables'),\n",
    "    yaxis=dict(title='Variables'),\n",
    ")\n",
    "\n",
    "figure = go.Figure(data=[heatmap], layout=layout)\n",
    "\n",
    "figure.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8821450",
   "metadata": {},
   "source": [
    "Construisons ensuite la table de corrélation entre le cours du Bitcoin et les autres paramètres et gardons les corrélations significatives (> 0.1 ou < -0.1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8a1cd17c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_fee</th>\n",
       "      <td>0.951675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_fee_satoshi</th>\n",
       "      <td>0.951570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_feeUSD</th>\n",
       "      <td>0.937520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hash_rate</th>\n",
       "      <td>0.937447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0.787660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nb_transactions</th>\n",
       "      <td>0.700906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nb_payments</th>\n",
       "      <td>0.525985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>self_spent_satoshi</th>\n",
       "      <td>0.236655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_fee_for100</th>\n",
       "      <td>0.218620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_nb_outputs</th>\n",
       "      <td>-0.238592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_nb_inputs</th>\n",
       "      <td>-0.384144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_mining_satoshi</th>\n",
       "      <td>-0.569811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newly_created_coins</th>\n",
       "      <td>-0.639300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      price_usd\n",
       "total_fee              0.951675\n",
       "mean_fee_satoshi       0.951570\n",
       "mean_feeUSD            0.937520\n",
       "hash_rate              0.937447\n",
       "year                   0.787660\n",
       "nb_transactions        0.700906\n",
       "nb_payments            0.525985\n",
       "self_spent_satoshi     0.236655\n",
       "mean_fee_for100        0.218620\n",
       "mean_nb_outputs       -0.238592\n",
       "mean_nb_inputs        -0.384144\n",
       "total_mining_satoshi  -0.569811\n",
       "newly_created_coins   -0.639300"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating and cleaning the correlation table between all parameters\n",
    "df_corr = pd.DataFrame(df_corr[\"price_usd\"].sort_values(ascending=False))\n",
    "df_corr = df_corr[np.absolute(df_corr) > 0.1].dropna()\n",
    "df_corr = df_corr.drop(\"price_usd\")\n",
    "df_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "883d2e3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Printing the heatmap correlation\n",
    "sns.heatmap(df_corr, cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8f738d0",
   "metadata": {},
   "source": [
    "Nous remarquons tout d'abord que le cours du Bitcoin est positivement fortement corrélé (> 0.7) à une mesure de temps, l'année. Plus le temps avance, plus le cours du Bitcoin augmente. Cela confirme bien le comportement de nos courbes prédictives, obtenues à partir de regression sur le temps, montrant que le cours du Bitcoin croit de plus en plus rapidement au fur et à mesure du temps.\n",
    "\n",
    "Nous remarquons ensuite que le cours du Bitcoin est positivement fortement corrélé (>= 0.7) à la majorité des différentes mesures de frais de mineurs (total & mean fee), mise à part la moyenne des montants des transactions payés en frais (mean_fee_for100), tout de même corrélée positivement (~ 0.22). Plus le cours du Bitcoin augmente, plus les mesures de frais de mineurs augmentent. \n",
    "\n",
    "Cela peut suggérer que l'augmentation du cours du Bitcoin a un fort impact positif sur les frais de mineurs. Cela peut aussi suggérer que l'augmentation des frais de mineurs a un fort impact positif sur le cours du Bitcoin.\n",
    "\n",
    "En effet, les frais de mineurs sont de petites quantités de Bitcoin payées par les utilisateurs de la monnaie aux mineurs, soient aux acteurs validant les transactions effectuées sur le réseau monétaire, pour les inciter à traiter leur transaction. Leur travail est essentiel pour garantir la sécurité et la fiabilité du réseau Bitcoin. \n",
    "\n",
    "Ainsi, nous pouvons suggérer que plus la monnaie gagne en valeur, plus les mineurs sont fortement recompensés pour leur travail, plus les transactions sont importantes et traitées efficacement et rapidement, plus le réseau monétaire fait gage de qualité, plus la monnaie est attractive et gagne en valeur (ex: par l'augmentation des investissements), etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5a10bb93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a plot\n",
    "plt.plot(df.index, df[\"mean_feeUSD\"], label='mean_fee_usd')\n",
    "plt.legend()\n",
    "plt.title(\"Mean Fee (USD) evolution throughout time\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Mean Fee (USD)\")\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c9e58f3",
   "metadata": {},
   "source": [
    "Nous remarquons aussi le nombre de transactions est positivement fortement corrélé (>= 0.7) au cours du Bitcoin, de même que pour les transactions mesurés en nombre de payements, positivement moyennement corrélé (~ 0.5). Plus le cours du Bitcoin augmente, plus le nombre de transactions augmente.\n",
    "\n",
    "Cela peut suggérer que l'augmentation du cours du Bitcoin a un impact positif sur le nombre de transactions. Cela peut aussi suggérer que l'augmentation du nombre de transactions a un impact positif sur le cours du Bitcoin.\n",
    "\n",
    "En effet, nous pouvons suggérer que plus le monnaie gagne en valeur, plus elle est attractive, plus le nombre de transactions traitées augmente, plus elle gagne en valeur (ex: par l'augmentation des investissements), etc.\n",
    "\n",
    "L'interprétation corrobore celle proposée pour l'augmentation des frais de mineurs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "06a69652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAG3CAYAAABc5eoKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADV/0lEQVR4nOydd3gT9R/H32m6d0tb2rJaCpQ9BEGQqYVSkClTlA2i8kMERFDZCoiIoCJDVkFkKw72KnvvDS2FMjqg0JbuNrnfH0eS710us+lI+3k9T57evu+lyd07nynjOI4DQRAEQRBEGcKmuAdAEARBEARR1JAAIgiCIAiizEECiCAIgiCIMgcJIIIgCIIgyhwkgAiCIAiCKHOQACIIgiAIosxBAoggCIIgiDIHCSCCIAiCIMocJIAIgiAIgihzkAAiLE5UVBRkMhm2bt1a3EMxisTERPTq1QvlypWDTCbDwoULi3tIVsP06dMhk8mKexglDplMhunTp1v0mGvWrIFMJsP9+/ctetyCoPr/P3v2rLiHYhaq9/TcuXPFPZQCERQUhMGDBxf3MKwOEkBWiuqL6+joiMePH2utb9u2LerWrVsMI7M+PvvsM+zZsweTJ0/GunXr0LFjR8ntMjMzMX36dERFRRXtAIuZsnrdxcXs2bOxffv24h6GgJI4JmvnyZMnmD59Oi5dumTU9idOnMD06dORkpJSqOMqS5AAsnJycnIwd+7c4h6GVXPw4EF069YNEyZMwPvvv4+aNWtKbpeZmYkZM2aUOSGg77q//vprZGVlFf2gSjG6xMYHH3yArKwsVKlSpcSMiTCfJ0+eYMaMGSYJoBkzZkgKoNu3b+O3336z7ADLACSArJyGDRvit99+w5MnT4p7KEVORkaGRY6TlJQET09PixyLxVLjK8nY2trC0dGxuIdRJpDL5XB0dCw1LkelUons7OziHkapwMHBAXZ2dsU9DKuDBJCV8+WXX0KhUBi0At2/fx8ymQxr1qzRWieOV1D59e/cuYP3338fHh4e8PX1xZQpU8BxHB4+fIhu3brB3d0d/v7++OGHHyTPqVAo8OWXX8Lf3x8uLi7o2rUrHj58qLXd6dOn0bFjR3h4eMDZ2Rlt2rTB8ePHBduoxnTjxg2899578PLyQsuWLfVe871799C7d294e3vD2dkZb7zxBnbs2KFer3IjchyHxYsXQyaT6Xy43L9/H76+vgCAGTNmqLdVvW+DBw+Gq6srYmJi0KlTJ7i5uWHAgAEAgKNHj6J3796oXLkyHBwcUKlSJXz22WdalhPVMR4/fozu3bvD1dUVvr6+mDBhAhQKhWDbjRs3onHjxnBzc4O7uzvq1auHRYsWqdc/f/4cEyZMQL169eDq6gp3d3dERETg8uXLWteWnZ2N6dOno0aNGnB0dERAQAB69uyJmJgYg9ctFQOUn5+PWbNmISQkBA4ODggKCsKXX36JnJwcwXZBQUF45513cOzYMTRt2hSOjo6oWrUq1q5dK9guLy8PM2bMQPXq1eHo6Ihy5cqhZcuW2Ldvn+T/iiUlJQVjx45FpUqV4ODggGrVquG7776DUqlUH9vb2xtDhgzR2jctLQ2Ojo6YMGGCellSUhKGDRuG8uXLw9HREQ0aNEBkZKTBcQwePBhBQUFay8Xvn0wmQ0ZGBiIjI9XvtSq2Q1cM0K+//oo6derAwcEBgYGB+OSTT7SsBCqX+I0bN9CuXTs4OzujQoUKmDdvnsGx6xuTipSUFAwePBienp7w8PDAkCFDkJmZqXWc0aNHY/369erx7t69GwBw8eJFREREwN3dHa6urnj77bdx6tQpve+VCqn3RalUYvr06QgMDISzszPatWuHGzdu6IyVycnJwbhx4+Dr6wsXFxf06NEDT58+1drOmPda1znatm2Ltm3bAuDjJF9//XUAwJAhQ9Tvq9T9WXXtn3/+OQAgODhYvb3qmsXnVL0nx44dw5gxY+Dr6wtPT098+OGHyM3NRUpKCgYOHAgvLy94eXlh4sSJ4DhOcE6lUomFCxeiTp06cHR0RPny5fHhhx/ixYsXkmO0RmyLewBEwQgODsbAgQPx22+/YdKkSQgMDLTYsfv27YtatWph7ty52LFjB7755ht4e3tj2bJleOutt/Ddd99h/fr1mDBhAl5//XW0bt1asP+3334LmUyGL774AklJSVi4cCHCwsJw6dIlODk5AeDdTxEREWjcuDGmTZsGGxsbrF69Gm+99RaOHj2Kpk2bCo7Zu3dvVK9eHbNnz9b6wrIkJiaiRYsWyMzMxJgxY1CuXDlERkaia9eu2Lp1K3r06IHWrVtj3bp1+OCDD9C+fXsMHDhQ5/F8fX2xZMkSfPTRR+jRowd69uwJAKhfv756m/z8fISHh6Nly5aYP38+nJ2dAQBbtmxBZmYmPvroI5QrVw5nzpzBzz//jEePHmHLli2C8ygUCoSHh6NZs2aYP38+9u/fjx9++AEhISH46KOPAAD79u1D//798fbbb+O7774DANy8eRPHjx/Hp59+CoAXf9u3b0fv3r0RHByMxMRELFu2DG3atMGNGzfUnxOFQoF33nkHBw4cQL9+/fDpp5/i5cuX2LdvH65du4awsDCD1y1m+PDhiIyMRK9evTB+/HicPn0ac+bMwc2bN/HXX38Jto2OjkavXr0wbNgwDBo0CKtWrcLgwYPRuHFj1KlTBwB/858zZw6GDx+Opk2bIi0tDefOncOFCxfQvn17nePIzMxEmzZt8PjxY3z44YeoXLkyTpw4gcmTJyM+Ph4LFy6EnZ0devTogT///BPLli2Dvb29ev/t27cjJycH/fr1AwBkZWWhbdu2iI6OxujRoxEcHIwtW7Zg8ODBSElJUb/3BWHdunXq6xw5ciQAICQkROf206dPx4wZMxAWFoaPPvoIt2/fxpIlS3D27FkcP35cYBV48eIFOnbsiJ49e6JPnz7YunUrvvjiC9SrVw8REREFGlOfPn0QHByMOXPm4MKFC1ixYgX8/PzUn08VBw8exObNmzF69Gj4+PggKCgI169fR6tWreDu7o6JEyfCzs4Oy5YtQ9u2bXH48GE0a9bM5Pdx8uTJmDdvHrp06YLw8HBcvnwZ4eHhOi1O//vf/+Dl5YVp06bh/v37WLhwIUaPHo1NmzaZ9V4bolatWpg5cyamTp2KkSNHolWrVgCAFi1aSG7fs2dP3LlzBxs2bMCPP/4IHx8fAFD/ONHF//73P/j7+2PGjBk4deoUli9fDk9PT5w4cQKVK1fG7NmzsXPnTnz//feoW7eu4B744YcfYs2aNRgyZAjGjBmD2NhY/PLLL7h48aLJ11ti4QirZPXq1RwA7uzZs1xMTAxna2vLjRkzRr2+TZs2XJ06ddTzsbGxHABu9erVWscCwE2bNk09P23aNA4AN3LkSPWy/Px8rmLFipxMJuPmzp2rXv7ixQvOycmJGzRokHrZoUOHOABchQoVuLS0NPXyzZs3cwC4RYsWcRzHcUqlkqtevToXHh7OKZVK9XaZmZlccHAw1759e60x9e/f36j3Z+zYsRwA7ujRo+plL1++5IKDg7mgoCBOoVAIrv+TTz4xeMynT59qvVcqBg0axAHgJk2apLUuMzNTa9mcOXM4mUzGPXjwQOsYM2fOFGzbqFEjrnHjxur5Tz/9lHN3d+fy8/N1jjU7O1twjRzHfwYcHBwEx1+1ahUHgFuwYIHWMVT/E33Xrfq/qLh06RIHgBs+fLhguwkTJnAAuIMHD6qXValShQPAHTlyRL0sKSmJc3Bw4MaPH69e1qBBA65z5846r1UXs2bN4lxcXLg7d+4Ilk+aNImTy+VcXFwcx3Ect2fPHg4A9++//wq269SpE1e1alX1/MKFCzkA3O+//65elpubyzVv3pxzdXUVfNbF79egQYO4KlWqaI1R/P5xHMe5uLgIvk8qVN/52NhYjuP498re3p7r0KGD4H/9yy+/cAC4VatWqZe1adOGA8CtXbtWvSwnJ4fz9/fn3n33Xa1zidE1JtX4hw4dKljeo0cPrly5coJlADgbGxvu+vXrguXdu3fn7O3tuZiYGPWyJ0+ecG5ublzr1q21ziVG/L4kJCRwtra2XPfu3QXbTZ8+nQMguA7VvmFhYYJ70GeffcbJ5XIuJSWF4zjT3usqVapIvldt2rTh2rRpo54/e/asznuyFN9//73gOlnE51Rdl/je2rx5c04mk3GjRo1SL1Pd29mxHT16lAPArV+/XnCe3bt3Sy63VsgFVgqoWrUqPvjgAyxfvhzx8fEWO+7w4cPV03K5HE2aNAHHcRg2bJh6uaenJ0JDQ3Hv3j2t/QcOHAg3Nzf1fK9evRAQEICdO3cCAC5duoS7d+/ivffeQ3JyMp49e4Znz54hIyMDb7/9No4cOaJ2VagYNWqUUWPfuXMnmjZtKnCTubq6YuTIkbh//z5u3Lhh3JtgIiorDYvK2gXwcUHPnj1DixYtwHEcLl68qLW9+BpbtWoleH89PT2RkZGh1wXk4OAAGxv+661QKJCcnAxXV1eEhobiwoUL6u22bdsGHx8f/O9//9M6hjmxJqr/7bhx4wTLx48fDwACFyQA1K5dW/3rF+B/0Yo/T56enrh+/Tru3r1r0li2bNmCVq1awcvLS/3ZevbsGcLCwqBQKHDkyBEAwFtvvQUfHx/Br/0XL15g37596Nu3r+Da/P390b9/f/UyOzs7jBkzBunp6Th8+LBJ4yso+/fvR25uLsaOHav+XwPAiBEj4O7urvVeu7q64v3331fP29vbo2nTppLfXVOR+swmJycjLS1NsLxNmzaoXbu2el6hUGDv3r3o3r07qlatql4eEBCA9957D8eOHdM6hiEOHDiA/Px8fPzxx4LlUp9xFSNHjhR83lu1agWFQoEHDx4AMP29LikMGzZMcF3NmjXTuoer7u3s52DLli3w8PBA+/btBd+dxo0bw9XVFYcOHSrS6ygsSAAZ4MiRI+jSpQsCAwMhk8nMyoTgOA7z589HjRo14ODggAoVKuDbb7+16Di//vpr5OfnWzQjrHLlyoJ5Dw8PODo6qs2v7HIpv3D16tUF8zKZDNWqVVP7rVUPtEGDBsHX11fwWrFiBXJycpCamio4RnBwsFFjf/DgAUJDQ7WW16pVS73e0tja2qJixYpay+Pi4jB48GB4e3ur43ratGkDAFrX5+joqGXW9vLyEry/H3/8MWrUqIGIiAhUrFgRQ4cOVcdSqFAqlfjxxx9RvXp1ODg4wMfHB76+vrhy5YrgnDExMQgNDYWtrWW84Q8ePICNjQ2qVasmWO7v7w9PT0+t9138GQO0r3fmzJlISUlBjRo1UK9ePXz++ee4cuWKwbHcvXsXu3fv1vpshYWFAeDjeQD+//buu+/i77//Vscp/fnnn8jLyxMIoAcPHqB69eqCByBQuJ8pfajOJ/6c29vbo2rVqlrjqVixopaoFb/X5iL+P3p5eQGA1rHF39+nT58iMzNT53dVqVRKxg3qQ3Xd4s+gt7e3elymjt/U97qkIHUPB4BKlSppLWf/V3fv3kVqair8/Py0vj/p6enq7461QzFABsjIyECDBg0wdOhQdfyDqXz66afYu3cv5s+fj3r16uH58+d4/vy5RcdZtWpVvP/++1i+fDkmTZqktV7Xr3lxcC2LXC43ahkAvfE4ulBZd77//ns0bNhQchtXV1fBPGtNKWmwVhcVCoUC7du3x/Pnz/HFF1+gZs2acHFxwePHjzF48GAtC5eu95fFz88Ply5dwp49e7Br1y7s2rULq1evxsCBA9UBubNnz8aUKVMwdOhQzJo1C97e3rCxscHYsWO1zlkYGGs9Mubz1Lp1a8TExODvv//G3r17sWLFCvz4449YunSpwEopRqlUon379pg4caLk+ho1aqin+/Xrh2XLlmHXrl3o3r07Nm/ejJo1a6JBgwZGXYchzPn+WRpLfnfNPXZBvr+F+R5a8r3RN05jvt+WRNf5pJaz16pUKuHn54f169dL7m8o9shaIAFkgIiICL0Bgjk5Ofjqq6+wYcMGpKSkoG7duvjuu+/U0f43b97EkiVLcO3aNfWvB2OtGKby9ddf4/fff9cKPAQ0v2jEGQuF+ctF7LLgOA7R0dHqAFpVIKW7u7v6V7mlqFKlCm7fvq21/NatW+r1pmKOS+jq1au4c+cOIiMjBQGGxmQw6cPe3h5dunRBly5doFQq8fHHH2PZsmWYMmUKqlWrhq1bt6Jdu3ZYuXKlYL+UlBSBBS8kJASnT59GXl6ezqBGU667SpUqUCqVuHv3rtoyAvBB6SkpKWbXsFFlag0ZMgTp6elo3bo1pk+frlcAhYSEID093ajPVuvWrREQEIBNmzahZcuWOHjwIL766iuta7ty5QqUSqVA6BrzmfLy8pKs3yL1/TP2/Vad7/bt2wL3UW5uLmJjYy36nSqs1HtfX184Ozvr/K7a2NiorRXsPYwtWyF+D1XvS3R0tOBem5ycbLa1y5T3Wt//mt3X1Pe0KMsfhISEYP/+/XjzzTdL9I/OgkIusAIyevRonDx5Ehs3bsSVK1fQu3dvdOzYUf3w//fff1G1alX8999/CA4ORlBQEIYPH25xCxDAf2jff/99LFu2DAkJCYJ17u7u8PHxUcc9qPj1118tPg4Va9euxcuXL9XzW7duRXx8vFpQNm7cGCEhIZg/fz7S09O19pdKQzWWTp064cyZMzh58qR6WUZGBpYvX46goCBBHIKxqLK6TKnEqvqlxf664jhOkLJuKsnJyYJ5GxsbtahUuXDkcrnWr9ctW7ZoVQ1/99138ezZM/zyyy9a51Htb8p1d+rUCQC02oksWLAAANC5c2eDxxAjvl5XV1dUq1ZNK61eTJ8+fXDy5Ens2bNHa11KSgry8/PV8zY2NujVqxf+/fdfrFu3Dvn5+QL3F8BfW0JCgiBWKD8/Hz///DNcXV3Vbk0pQkJCkJqaKnDdxcfHa2XFAYCLi4tR73VYWBjs7e3x008/Cf7XK1euRGpqqlnvtS6MHZOpyOVydOjQAX///bcgjT0xMRF//PEHWrZsCXd3dwCaH0zsPUyVns/y9ttvw9bWFkuWLBEsl/qMG4sp73VISAhOnTqF3Nxc9bL//vtPy5Xn4uICwPj7ianbF4Q+ffpAoVBg1qxZWuvy8/NLTTVqsgAVgLi4OKxevRpxcXHqtOIJEyZg9+7dWL16NWbPno179+7hwYMH2LJlC9auXQuFQoHPPvsMvXr1wsGDBy0+pq+++grr1q3D7du31WnEKoYPH465c+di+PDhaNKkCY4cOYI7d+5YfAwqvL290bJlSwwZMgSJiYlYuHAhqlWrhhEjRgDgHzorVqxAREQE6tSpgyFDhqBChQp4/PgxDh06BHd3d/z7779mnXvSpEnYsGEDIiIiMGbMGHh7eyMyMhKxsbHYtm2blqvKGJycnFC7dm1s2rQJNWrUgLe3N+rWrau35UjNmjUREhKCCRMm4PHjx3B3d8e2bdsKFHehEtBvvfUWKlasiAcPHuDnn39Gw4YN1VaXd955BzNnzsSQIUPQokULXL16FevXrxf8AgX4QPW1a9di3LhxOHPmDFq1aoWMjAzs378fH3/8Mbp162bSdTdo0ACDBg3C8uXLkZKSgjZt2uDMmTOIjIxE9+7d0a5dO5Ovt3bt2mjbti0aN24Mb29vnDt3Dlu3bsXo0aP17vf555/jn3/+wTvvvKNOrc/IyMDVq1exdetW3L9/X2AN69u3L37++WdMmzYN9erVE1iwAD5QdtmyZRg8eDDOnz+PoKAgbN26FcePH8fChQsFAf9i+vXrhy+++AI9evTAmDFjkJmZiSVLlqBGjRqCoHSA/2Gwf/9+LFiwAIGBgQgODpZMBff19cXkyZMxY8YMdOzYEV27dsXt27fx66+/4vXXXxcEPBcUY8dkDt988w327duHli1b4uOPP4atrS2WLVuGnJwcQZ2iDh06oHLlyhg2bBg+//xzyOVyrFq1Cr6+voiLi1NvV758eXz66af44Ycf0LVrV3Ts2BGXL1/Grl274OPjY5YlxZT3evjw4di6dSs6duyIPn36ICYmBr///rtW6YCQkBB4enpi6dKlcHNzg4uLC5o1a6bTQ9C4cWMA/D2+X79+sLOzQ5cuXdTCyJK0adMGH374IebMmYNLly6hQ4cOsLOzw927d7FlyxYsWrQIvXr1svh5i5wizzuzYgBwf/31l3r+v//+4wBwLi4ugpetrS3Xp08fjuM4bsSIERwA7vbt2+r9zp8/zwHgbt26ZfZY2DR4Map0ajYNnuP4dOxhw4ZxHh4enJubG9enTx8uKSlJZxr806dPtY7r4uKidT5xyr0qDX7Dhg3c5MmTOT8/P87JyYnr3LmzIO1bxcWLF7mePXty5cqV4xwcHLgqVapwffr04Q4cOGBwTPqIiYnhevXqxXl6enKOjo5c06ZNuf/++09rOxiZBs9xHHfixAmucePGnL29veB90/XecBzH3bhxgwsLC+NcXV05Hx8fbsSIEdzly5e1UmB1HUOc/rt161auQ4cOnJ+fH2dvb89VrlyZ+/DDD7n4+Hj1NtnZ2dz48eO5gIAAzsnJiXvzzTe5kydPaqXichz/ufjqq6+44OBgzs7OjvP39+d69eolSEvWdd1Sqcl5eXncjBkz1MerVKkSN3nyZC47O1uwXZUqVSTT28Vj/Oabb7imTZtynp6enJOTE1ezZk3u22+/5XJzcyXfb5aXL19ykydP5qpVq8bZ29tzPj4+XIsWLbj58+dr7a9UKrlKlSpxALhvvvlG8niJiYnckCFDOB8fH87e3p6rV6+eUaUlOI7j9u7dy9WtW5ezt7fnQkNDud9//13y/bt16xbXunVrzsnJSZC2LU73VvHLL79wNWvW5Ozs7Ljy5ctzH330EffixQvBNuLvqApd6flidI1J1/dSaqz6vmcXLlzgwsPDOVdXV87Z2Zlr164dd+LECa3tzp8/zzVr1kz9uV+wYIHkufLz87kpU6Zw/v7+nJOTE/fWW29xN2/e5MqVKydIAdd1H1Xdww4dOiRYbsx7zXEc98MPP3AVKlTgHBwcuDfffJM7d+6c5Hfv77//5mrXrs3Z2toalRI/a9YsrkKFCpyNjY3gmnWlwYuvy9R7+/Lly7nGjRtzTk5OnJubG1evXj1u4sSJ3JMnT/SO01qQcZwFIuDKCDKZDH/99Re6d+8OANi0aRMGDBiA69evawWVubq6wt/fH9OmTcPs2bORl5enXpeVlQVnZ2fs3btXbyE3giAIwjKkpKTAy8sL33zzjVZ8F1E2IRdYAWjUqBEUCgWSkpIEtUxY3nzzTeTn5yMmJkZtAlW5nYqjqSFBEERpJysrSyt4VxWXpkpQIQiyABkgPT0d0dHRAHjBs2DBArRr1w7e3t6oXLky3n//fRw/fhw//PADGjVqhKdPn+LAgQOoX78+OnfuDKVSiddffx2urq5YuHAhlEolPvnkE7i7u2Pv3r3FfHUEQRCljzVr1mDNmjXo1KkTXF1dcezYMWzYsAEdOnSQDIonyiYkgAwQFRUlGbg5aNAgrFmzBnl5efjmm2+wdu1aPH78GD4+PnjjjTcwY8YM1KtXDwDw5MkT/O9//8PevXvh4uKCiIgI/PDDD/D29i7qyyEIgij1XLhwARMnTsSlS5eQlpaG8uXL491338U333yjVVuMKLuQACIIgiAIosxBdYAIgiAIgihzkAAiCIIgCKLMQVlgEiiVSjx58gRubm5FWn6cIAiCIAjz4TgOL1++RGBgoMGCtySAJHjy5IlWt1yCIAiCIKyDhw8fomLFinq3IQEkgaqk/cOHD9V9aAiCIAiCKNmkpaWhUqVKelvTqCABJIHK7eXu7k4CiCAIgiCsDGPCVygImiAIgiCIMgcJIIIgCIIgyhwkgAiCIAiCKHNQDBBBEARhFkqlErm5ucU9DKIMYWdnB7lcbpFjkQAiCIIgTCY3NxexsbFQKpXFPRSijOHp6Ql/f/8C1+kjAUQQBEGYBMdxiI+Ph1wuR6VKlQwWnCMIS8BxHDIzM5GUlAQACAgIKNDxSAARBEEQJpGfn4/MzEwEBgbC2dm5uIdDlCGcnJwAAElJSfDz8yuQO4xkO0EQBGESCoUCAGBvb1/MIyHKIirRnZeXV6DjkAAiCIIgzIJ6JRLFgaU+dySACIIgCIIoc5AAIgiCIAjwloXt27cX9zBKNIMHD0b37t2LexgWgQQQQViSvz4C/vusuEdBEEQRc//+fchkMly6dKm4h2IRdF3PokWLsGbNmmIZk6UhAUQQliL1EXD5D+DcKiA3s7hHQxBECcTaC0d6eHjA09OzuIdhEUgAEYSlUOZrpjkqDkcQJY22bdtizJgxmDhxIry9veHv74/p06cLtomPj0dERAScnJxQtWpVbN261ahjBwcHAwAaNWoEmUyGtm3bAtC4jL799lsEBgYiNDQUALBu3To0adIEbm5u8Pf3x3vvvaeubwMAUVFRkMlkOHDgAJo0aQJnZ2e0aNECt2/fVm9z+fJltGvXDm5ubnB3d0fjxo1x7tw5AEBycjL69++PChUqwNnZGfXq1cOGDRsEY1YqlZg3bx6qVasGBwcHVK5cGd9++61R16MiJycHY8aMgZ+fHxwdHdGyZUucPXvWYtdRmJAAIgiCIAoEx3HIzM0vlhfHcSaNNTIyEi4uLjh9+jTmzZuHmTNnYt++fer1U6ZMwbvvvovLly9jwIAB6NevH27evGnwuGfOnAEA7N+/H/Hx8fjzzz/V6w4cOIDbt29j3759+O+//wDwKdyzZs3C5cuXsX37dty/fx+DBw/WOu5XX32FH374AefOnYOtrS2GDh2qXjdgwABUrFgRZ8+exfnz5zFp0iTY2dkBALKzs9G4cWPs2LED165dw8iRI/HBBx+oxwkAkydPxty5czFlyhTcuHEDf/zxB8qXL2/welgmTpyIbdu2ITIyEhcuXEC1atUQHh6O58+fW+Q6ChMZZ+qnpwyQlpYGDw8PpKamwt3dvbiHQ1gLL+4Dixrw05MfAw6uxTocgigssrOzERsbi+DgYDg6OiIzNx+1p+4plrHcmBkOZ3vjavq2bdsWCoUCR48eVS9r2rQp3nrrLcydOxcymQyjRo3CkiVL1OvfeOMNvPbaa/j111/1Hvv+/fsIDg7GxYsX0bBhQ/XywYMHY/fu3YiLi9NbN+ncuXN4/fXX8fLlS7i6uiIqKgrt2rXD/v378fbbbwMAdu7cic6dOyMrKwuOjo5wd3fHzz//jEGDBhl1/e+88w5q1qyJ+fPn4+XLl/D19cUvv/yC4cOHm3Q9KSkp2L59OzIyMuDl5YU1a9bgvffeA8ALu6CgIIwdOxaff/55oVyH+PPHYsrzmyxABGExqCYKQZR06tevL5gPCAgQuJ6aN28uWN+8eXOjLED6qFevnpb4OX/+PLp06YLKlSvDzc0Nbdq0AQDExcXpHK+q9YNqvOPGjcPw4cMRFhaGuXPnIiYmRr2tQqHArFmzUK9ePXh7e8PV1RV79uxRH//mzZvIyclRixJziImJQV5eHt588031Mjs7OzRt2lTrPTP3OgoTaoVBEARBFAgnOzluzAwvtnObgti1IpPJCr2hq4uLi2A+IyMD4eHhCA8Px/r16+Hr64u4uDiEh4drBUmz41UVAFSNd/r06XjvvfewY8cO7Nq1C9OmTcPGjRvRo0cPfP/991i0aBEWLlyIevXqwcXFBWPHjlUfX9VSoqgw9zoKE7IAEQRBEAVCJpPB2d62WF6WrkZ96tQprflatWoZ3E9l4VG1CdHHrVu3kJycjLlz56JVq1aoWbOmwAplCjVq1MBnn32GvXv3omfPnli9ejUA4Pjx4+jWrRvef/99NGjQAFWrVsWdO3fU+1WvXh1OTk44cOCA2dcTEhICe3t7HD9+XL0sLy8PZ8+eRe3atS1yHYUJCSCCIAiCeMWWLVuwatUq3LlzB9OmTcOZM2cwevRog/v5+fnByckJu3fvRmJiIlJTU3VuW7lyZdjb2+Pnn3/GvXv38M8//2DWrFkmjTMrKwujR49GVFQUHjx4gOPHj+Ps2bNqsVa9enXs27cPJ06cwM2bN/Hhhx8iMTFRvb+joyO++OILTJw4EWvXrkVMTAxOnTqFlStXGn09Li4u+Oijj/D5559j9+7duHHjBkaMGIHMzEwMGzbMItdRmJAAIgiCIIhXzJgxAxs3bkT9+vWxdu1abNiwwShrhq2tLX766ScsW7YMgYGB6Natm85tfX19sWbNGmzZsgW1a9fG3LlzMX/+fJPGKZfLkZycjIEDB6JGjRro06cPIiIiMGPGDADA119/jddeew3h4eFo27Yt/P39tSo4T5kyBePHj8fUqVNRq1Yt9O3bV22JMvZ65s6di3fffRcffPABXnvtNURHR2PPnj3w8vKyyHUUJsWaBXbkyBF8//33OH/+POLj4/HXX3/pLbE9ePBgREZGai2vXbs2rl+/DoD3JYrfuNDQUNy6dcvocVEWGGEWLx4Ai14F+lEWGFGK0ZeFQxCFTanIAsvIyECDBg2wePFio7ZftGgR4uPj1a+HDx/C29sbvXv3FmxXp04dwXbHjh0rjOETpZG8LCDjmQUORNUlCIIgSjLFmgUWERGBiIgIo7f38PCAh4eHen779u148eIFhgwZItjO1tYW/v7+FhsnUYb4oSaQnQKMvwO4lS/u0RAEUUKYPXs2Zs+eLbmuVatW2LVrVxGPiCgoVp0Gv3LlSoSFhaFKlSqC5Xfv3kVgYCAcHR3RvHlzzJkzB5UrV9Z5nJycHOTk5Kjn09LSCm3MRAknO4X/G3cSqNPd/ONQfVGCKFWMGjUKffr0kVxX1CnlhGWwWgH05MkT7Nq1C3/88YdgebNmzbBmzRqEhoYiPj4eM2bMQKtWrXDt2jW4ublJHmvOnDlFEnBFWBE2ptUWIQiidOPt7Q1vb+/iHgZhQaw2CywyMhKenp5aQdMRERHo3bs36tevj/DwcOzcuRMpKSnYvHmzzmNNnjwZqamp6tfDhw8LefREiUdW0K8GWYAIgiBKMlZpAeI4DqtWrcIHH3ygt7cKAHh6eqJGjRqIjo7WuY2DgwMcHBwsPUzCmpGZYQFiC7KRC4wgCKJEY5UWoMOHDyM6OtqoQkvp6emIiYlR9x4hCKMgFxhBEESpplgFUHp6Oi5duoRLly4BAGJjY3Hp0iV1s7bJkydj4MCBWvutXLkSzZo1Q926dbXWTZgwAYcPH8b9+/dx4sQJ9OjRA3K5HP379y/UayFKGQUur08WIIIgiJJMsbrAzp07h3bt2qnnx40bBwAYNGgQ1qxZg/j4eK3OuKmpqdi2bRsWLVokecxHjx6hf//+SE5Ohq+vL1q2bIlTp07B19e38C6EKB2wbitzXGC6jkUQBEGUOIpVALVt2xb6ClGvWbNGa5mHhwcyMzN17rNx40ZLDI0oiyiZpn8FDoImCMLakMlkBjsSEKUHussThAqOBBBBEIQuBg8eXKrEId3lCUIFawEyKwiassAIgiCsBRJABKGCLEAEUapp27YtxowZg4kTJ8Lb2xv+/v6YPn26YJv4+HhERETAyckJVatWxdatW4069v379yGTybBx40a0aNECjo6OqFu3Lg4fPqzeRqFQYNiwYQgODoaTkxNCQ0MF8axHjhyBnZ0dEhISBMceO3YsWrVqBYAPDfH09MR///2H0NBQODs7o1evXsjMzERkZCSCgoLg5eWFMWPGQKHQ3NNycnIwYcIEVKhQAS4uLmjWrBmioqLU61XH3bNnD2rVqgVXV1d07NgR8fHxAPhG45GRkfj7778hk8kgk8kQFRWF3NxcjB49GgEBAXB0dESVKlUwZ84co96z4obu8gShQhADVNA0eLIAEWUIjgNyM4rnZaK1NTIyEi4uLjh9+jTmzZuHmTNnYt++fer1U6ZMwbvvvovLly9jwIAB6NevH27evGn08T///HOMHz8eFy9eRPPmzdGlSxckJycDAJRKJSpWrIgtW7bgxo0bmDp1Kr788kt1od7WrVujatWqWLdunfp4eXl5WL9+PYYOHapelpmZiZ9++gkbN27E7t27ERUVhR49emDnzp3YuXMn1q1bh2XLlgnE2+jRo3Hy5Els3LgRV65cQe/evdGxY0fcvXtXcNz58+dj3bp1OHLkCOLi4jBhwgQAfIZ1nz591KIoPj4eLVq0wE8//YR//vkHmzdvxu3bt7F+/XoEBQWZ9D8pLqyyECJBFAoCF1gBfxuQC4woS+RlArMDi+fcXz4B7F2M3rx+/fqYNm0aAKB69er45ZdfcODAAbRv3x4A0Lt3bwwfPhwAMGvWLOzbtw8///wzfv31V6OOP3r0aLz77rsAgCVLlmD37t1YuXIlJk6cCDs7O0HbpeDgYJw8eRKbN29W9xkbNmwYVq9ejc8//xwA8O+//yI7O1vQhywvLw9LlixBSEgIAKBXr15Yt24dEhMT4erqitq1a6Ndu3Y4dOgQ+vbti7i4OKxevRpxcXEIDOT/TxMmTMDu3buxevVqdZPXvLw8LF26VH3c0aNHY+bMmQAAV1dXODk5IScnR9BsPC4uDtWrV0fLli0hk8m0enOWZMgCRBAqWBcYCloHiCCIkkj9+vUF8wEBAUhKSlLPN2/eXLC+efPmJlmA2P1tbW3RpEkTwf6LFy9G48aN4evrC1dXVyxfvlxQ7mXw4MGIjo7GqVOnAPCuqT59+sDFRSPynJ2d1SIFAMqXL4+goCC4uroKlqmu6+rVq1AoFKhRowZcXV3Vr8OHDyMmJkbnccXvjRSDBw/GpUuXEBoaijFjxmDv3r1Gv1fFDVmACEIFawEqMGQBIsoQds68Jaa4zm3K5nZ2gnmZTAalUmnJEelk48aNmDBhAn744Qc0b94cbm5u+P7773H69Gn1Nn5+fujSpQtWr16N4OBg7Nq1SxCrA0hfg77rSk9Ph1wux/nz5yGXC937rGiSOoa+UjUA8NprryE2Nha7du3C/v370adPH4SFhRkdO1WckAAiCBUCC5A5AobZh1xgRFlCJjPJDVWSOXXqlKADwalTp9CoUSOT9m/dujUAID8/H+fPn8fo0aMBAMePH0eLFi3w8ccfq7dnLTAqhg8fjv79+6NixYoICQnBm2++ae7lAAAaNWoEhUKBpKQkdTC1Odjb2wsCq1W4u7ujb9++6Nu3L3r16oWOHTvi+fPn8Pb2LsiwCx0SQAShwqIWIIIgrJEtW7agSZMmaNmyJdavX48zZ85g5cqVRu+/ePFiVK9eHbVq1cKPP/6IFy9eqAOYq1evjrVr12LPnj0IDg7GunXrcPbsWQQHBwuOER4eDnd3d3zzzTfqGJyCUKNGDQwYMAADBw7EDz/8gEaNGuHp06c4cOAA6tevj86dOxt1nKCgIOzZswe3b99GuXLl4OHhgZ9//hkBAQFo1KgRbGxssGXLFvj7+8PT07PA4y5sKAaIIFSwFiCzDEDsTmQBIghrZMaMGdi4cSPq16+PtWvXYsOGDahdu7bR+8+dOxdz585FgwYNcOzYMfzzzz/w8fEBAHz44Yfo2bMn+vbti2bNmiE5OVlgDVJhY2ODwYMHQ6FQSPbDNIfVq1dj4MCBGD9+PEJDQ9G9e3ecPXsWlStXNvoYI0aMQGhoKJo0aQJfX18cP34cbm5umDdvHpo0aYLXX38d9+/fx86dO2FT0ESSIkDGGXLwlUHS0tLg4eGB1NRUuLu7F/dwiKLiWTTwS2N+evgBoGIT0/Z/8QBY9CrAcvxtwM1f//YEYaVkZ2cjNjYWwcHBcHR0LO7hlAju37+P4OBgXLx4EQ0bNizw8YYNG4anT5/in3/+KfjgShn6Pn+mPL/JBUYQKriCusAoBoggiIKRmpqKq1ev4o8//iDxU8iUfBsVQRQVynzNdIEFDAkggihNzJ49W5BCzr4iIiIsdp5u3bqhQ4cOGDVqlLo2EVE4kAWIIFQUNAiarD4EUWoZNWqUoBghi5OTEypUqGAwZdwYxCnvROFBAoggVBQ4DZ7dncQQQZQmvL29S3xaN2Ea5AIjCBVsMTSzBAxlgREEQVgLJIAIQkWBg6AJomxBScREcWCpzx0JIIJQoSygC4yjLDCibKBqp5Cbm1vMIyHKIpmZmQC0W3eYCsUAEYQKQSFEygIjCF3Y2trC2dkZT58+hZ2dnVUUvSOsH47jkJmZiaSkJHh6emr1NTMVEkAEoYJaYRCEUchkMgQEBCA2NhYPHjwo7uEQZQxPT0/4+xe80CwJIIJQwdYBoiwwgtCLvb09qlevTm4wokixs7MrsOVHBQkgglDBFTALjHqBEWUMGxsbaoVBWC3kuCUIFeQCIwiCKDOQACIIFQUuhEhZYARBENYCCSCCUGFRCxAJIIIgiJIMCSCCUFHQNHiy+hAEQVgNJIAIQkVBCyGykBgiCIIo0ZAAIggVBXaBkeghCIKwFkgAEYQKcoERBEGUGUgAEYQKcoERBEGUGUgAEYSKAneDJ9FDEARhLRSrADpy5Ai6dOmCwMBAyGQybN++Xe/2UVFRkMlkWq+EhATBdosXL0ZQUBAcHR3RrFkznDlzphCvgig1KKkZKkEQRFmhWAVQRkYGGjRogMWLF5u03+3btxEfH69++fn5qddt2rQJ48aNw7Rp03DhwgU0aNAA4eHhSEpKsvTwidIG2wrDrP2pECJBEIS1UKy9wCIiIhAREWHyfn5+fvD09JRct2DBAowYMQJDhgwBACxduhQ7duzAqlWrMGnSpIIMlyjtWDIGiCAIgijRWGUMUMOGDREQEID27dvj+PHj6uW5ubk4f/48wsLC1MtsbGwQFhaGkydPFsdQCWuioFlgoGaoBEEQ1oJVCaCAgAAsXboU27Ztw7Zt21CpUiW0bdsWFy5cAAA8e/YMCoUC5cuXF+xXvnx5rTghlpycHKSlpQleRBlEma+ZzkkDjnwPPL9n3rHIBUYQBFGiKVYXmKmEhoYiNDRUPd+iRQvExMTgxx9/xLp168w+7pw5czBjxgxLDJGwZlgX2I4JQOYz4PhPwOSHxu1PoocgCMJqsCoLkBRNmzZFdHQ0AMDHxwdyuRyJiYmCbRITE+Hv76/zGJMnT0Zqaqr69fChkQ88onTBusAyn/F/c8y1BpIYIgiCKMlYvQC6dOkSAgICAAD29vZo3LgxDhw4oF6vVCpx4MABNG/eXOcxHBwc4O7uLngRZRBlAbPAQFlgBEEQ1kKxusDS09PV1hsAiI2NxaVLl+Dt7Y3KlStj8uTJePz4MdauXQsAWLhwIYKDg1GnTh1kZ2djxYoVOHjwIPbu3as+xrhx4zBo0CA0adIETZs2xcKFC5GRkaHOCiMInRS4ECJBEARhLRSrADp37hzatWunnh83bhwAYNCgQVizZg3i4+MRFxenXp+bm4vx48fj8ePHcHZ2Rv369bF//37BMfr27YunT59i6tSpSEhIQMOGDbF7926twGiC0KKgzVA5ygIjCIKwFmQcR7Z6MWlpafDw8EBqaiq5w8oS+6cDx37UXj491bj9468Ay1rx06OOA/51LTY0giAIwjCmPL+tPgaIICxGQS1AZPUhCIKwGkgAEaWbvCzgeaxx2xZYALGQGCIIgijJkAAiSjdL3gR+agg8Pq9/u5SHwCnTetJpQb3ACIIgrAYSQETp5nkM//f6X/q32zGu8MdCEARBlBhIABEEAGQ8s8BBKAuMIAjCWiABRJQRZPpX28iLZhgEQRBEiYAEEFE2OPETcGeP7vU2FiiJRTFABEEQVgMJIKLs8Ecf3essIYAEkAAiCIIoyZAAIgjAQi4wEj0EQRDWAgkgggAsbwEiFxhBEESJhgQQQQAWigHSOUMQBEGUMEgAEQRQCDFABEEQREmGBBBBAJaPASIDEEEQRImGBBBBAIDM0nWASAERBEGUZEgAEQSg2wUmM+ErQoHPBEEQVgMJIIIAdAsgc2ODSAwRBEGUaEgAEQSgOwbIJAFEvcAIgiCsBRJABAHocYFRjzCCIIjSCAkgggD0uMBMEEDUC4wgCMJqIAFEEIDlY4DIBUYQBFGiIQFEEIBuS49MZsJBSPQQBEFYCySACALQLYCcfcw7HrnACIIgSjQkgAgCAGzspJe7mCCASPQQBEFYDSSACAKwUCsMFhJDBEEQJRmTBdDDhw/x6NEj9fyZM2cwduxYLF++3KIDI4giRVews0lWHcoCIwiCsBZMFkDvvfceDh06BABISEhA+/btcebMGXz11VeYOXOmxQdIEEWCzpYXJGQIgiBKIyYLoGvXrqFp06YAgM2bN6Nu3bo4ceIE1q9fjzVr1lh6fARRNOjK9jLFksNRJWiCIAhrwWQBlJeXBwcHBwDA/v370bVrVwBAzZo1ER8fb9nREURRoVPomClkyAVGEARRojFZANWpUwdLly7F0aNHsW/fPnTs2BEA8OTJE5QrV87iAySIokGHYDE3BoggCIIo0ZgsgL777jssW7YMbdu2Rf/+/dGgQQMAwD///KN2jRFEicVkywy5wAiCIEojJtf5b9u2LZ49e4a0tDR4eXmpl48cORLOzs4WHRxBWByOk4730ekBIxcYQRBEacSsOkByuVwgfgAgKCgIfn5+Jh3nyJEj6NKlCwIDAyGTybB9+3a92//5559o3749fH194e7ujubNm2PPnj2CbaZPnw6ZTCZ41axZ06RxEaUYTqFrhYnLC7otQRAEUZyYLIASExPxwQcfIDAwELa2tpDL5YKXKWRkZKBBgwZYvHixUdsfOXIE7du3x86dO3H+/Hm0a9cOXbp0wcWLFwXb1alTB/Hx8erXsWPHTBoXUYpR6hBAuiw2ZltySAwRBEGUZEx2gQ0ePBhxcXGYMmUKAgICIDOpWaSQiIgIREREGL39woULBfOzZ8/G33//jX///ReNGjVSL7e1tYW/v7/Z4yJKMTotQJY4NhVCJAiCsBZMFkDHjh3D0aNH0bBhw0IYjmkolUq8fPkS3t7eguV3795FYGAgHB0d0bx5c8yZMweVK1cuplESxYaUCOGUujY2cTlBEARhzZgsgCpVqgSuhPy6nT9/PtLT09GnTx/1smbNmmHNmjUIDQ1FfHw8ZsyYgVatWuHatWtwc3OTPE5OTg5ycnLU82lpaYU+dqIIkBI7heoCoywwgiAIa8HkGKCFCxdi0qRJuH//fiEMx3j++OMPzJgxA5s3bxYEX0dERKB3796oX78+wsPDsXPnTqSkpGDz5s06jzVnzhx4eHioX5UqVSqKSyAKm+K0AJWQHwkEQRCENCZbgPr27YvMzEyEhITA2dkZdnZ2gvXPnz+32OB0sXHjRgwfPhxbtmxBWFiY3m09PT1Ro0YNREdH69xm8uTJGDdunHo+LS2NRFBpQErs6BRAuo5hbh0ggiAIoiRjsgASByIXNRs2bMDQoUOxceNGdO7c2eD26enpiImJwQcffKBzGwcHB3V7D6I0ISFITHWBme3KIjFEEARRkjFZAA0aNMhiJ09PTxdYZmJjY3Hp0iV4e3ujcuXKmDx5Mh4/foy1a9cC4N1egwYNwqJFi9CsWTMkJCQAAJycnODh4QEAmDBhArp06YIqVargyZMnmDZtGuRyOfr372+xcRNWgqQFyMQ6QObGAJH+IQiCKNGYLIAAQKFQYPv27bh58yYAvu5O165dTa4DdO7cObRr1049r3JDDRo0CGvWrEF8fDzi4uLU65cvX478/Hx88skn+OSTT9TLVdsDwKNHj9C/f38kJyfD19cXLVu2xKlTp+Dr62vOpRLWjJR4KTILEEEQBFGSMVkARUdHo1OnTnj8+DFCQ0MB8EHElSpVwo4dOxASEmL0sdq2bas3o0wlalRERUUZPObGjRuNPj9RyrFIDJAp21IWGEEQhLVgchbYmDFjEBISgocPH+LChQu4cOEC4uLiEBwcjDFjxhTGGAnCPCzhAiMhQxAEUSox2QJ0+PBhnDp1SlB8sFy5cpg7dy7efPNNiw6OIAqGlAtMhwXI0nWAKCOMIAiiRGOyBcjBwQEvX77UWp6eng57e3uLDIogLIJFLEBmn9zCxyMIgiAsickC6J133sHIkSNx+vRpcBwHjuNw6tQpjBo1Cl27di2MMRKEeZhUCFHnQQplU4IgCKJ4MVkA/fTTTwgJCUHz5s3h6OgIR0dHvPnmm6hWrRoWLVpUGGMkCPOwRBaYua4scoERBEGUaEyOAfL09MTff/+Nu3fv4tatWwCAWrVqoVq1ahYfHEEUDCkLUGEGQVMWGEEQhLVgVh0gAKhevTqqV69uybEQhGUp8maoBEEQhLVglAAaN24cZs2aBRcXF0HPLCkWLFhgkYERRIGRjAEyVdCY2QuMhBNBEESJxigBdPHiReTl5amnCcIqKPJWGEYcjyAIgigRGCWADh06JDlNECUaS7jAzI4BIgiCIEoyJmeBDR06VLIOUEZGBoYOHWqRQRGEZbBAGjxlgREEQZRKTBZAkZGRyMrK0lqelZWl7tpOECUCk1xgOg9iwqaUBUYQBGEtGJ0FlpaWpi58+PLlSzg6OqrXKRQK7Ny5E35+foUySIIwi+KsA0QQBEGUaIwWQJ6enpDJZJDJZKhRo4bWeplMhhkzZlh0cARRIIq8FQZlgREEQVgLRgugQ4cOgeM4vPXWW9i2bZugGaq9vT2qVKmCwMDAQhkkQZhHEafBW2Q/giAIoigwWgC1adMGABAbG4vKlStDJpMV2qAIwiIUtQuMrD4EQRBWg8lB0AcPHsTWrVu1lm/ZsgWRkZEWGRRBWATJQoiF2QrDwLkJgiCIEoPJAmjOnDnw8fHRWu7n54fZs2dbZFAEYREs0grDpBOasjFBEARRjJgsgOLi4hAcHKy1vEqVKoiLi7PIoAjCIkgGQZtYB4higAiCIEolJgsgPz8/XLlyRWv55cuXUa5cOYsMiiAsgwVcYObGAJELjCAIokRjsgDq378/xowZg0OHDkGhUEChUODgwYP49NNP0a9fv8IYI0GYh6QLTIcFyCKtMAiCIAhrwegsMBWzZs3C/fv38fbbb8PWlt9dqVRi4MCBFANElCwkg6B1ucAsUQiRKkETBEFYCyYLIHt7e2zatAmzZs3C5cuX4eTkhHr16qFKlSqFMT6CMJ+iboUh2I0EEEEQREnGZAGkokaNGpIVoQmi5EB1gAiCIAhpzBJAjx49wj///IO4uDjk5uYK1i1YsMAiAyOIAlPkrTAIgiAIa8FkAXTgwAF07doVVatWxa1bt1C3bl3cv38fHMfhtddeK4wxEoR5mBIDZJEgaMoCIwiCsBZMzgKbPHkyJkyYgKtXr8LR0RHbtm3Dw4cP0aZNG/Tu3bswxkgQ5mFKKwxTjkEQBEFYPSYLoJs3b2LgwIEAAFtbW2RlZcHV1RUzZ87Ed999Z/EBEoTZmFQI0QIWII6ywAiCIKwFkwWQi4uLOu4nICAAMTEx6nXPnj2z3MgIosAUcRA0ucAIgiCsBpNjgN544w0cO3YMtWrVQqdOnTB+/HhcvXoVf/75J954443CGCNBmEdRW4AIgiAIq8FkAbRgwQKkp6cDAGbMmIH09HRs2rQJ1atXpwwwomRhUjd4E45h1LYknAiCIEoyJgugqlWrqqddXFywdOlSiw6IICyGJbrBUyFEgiCIUonJMUAPHz7Eo0eP1PNnzpzB2LFjsXz5cpNPfuTIEXTp0gWBgYGQyWTYvn27wX2ioqLw2muvwcHBAdWqVcOaNWu0tlm8eDGCgoLg6OiIZs2a4cyZMyaPjSgNFHEzVLL6EARBWA0mC6D33nsPhw4dAgAkJCQgLCwMZ86cwVdffYWZM2eadKyMjAw0aNAAixcvNmr72NhYdO7cGe3atcOlS5cwduxYDB8+HHv27FFvs2nTJowbNw7Tpk3DhQsX0KBBA4SHhyMpKcmksRGlAMkYIF1CR+dBzD25mfsRBEEQRYHJAujatWto2rQpAGDz5s2oV68eTpw4gfXr10taY/QRERGBb775Bj169DBq+6VLlyI4OBg//PADatWqhdGjR6NXr1748ccf1dssWLAAI0aMwJAhQ1C7dm0sXboUzs7OWLVqlUljI0oBprjAdB7DlG1J9BAEQVgLJgugvLw8ODg4AAD279+Prl27AgBq1qyJ+Ph4y45OxMmTJxEWFiZYFh4ejpMnTwIAcnNzcf78ecE2NjY2CAsLU28jRU5ODtLS0gQvohRgUhC0hcULiSGCIIgSjckCqE6dOli6dCmOHj2Kffv2oWPHjgCAJ0+eoFy5chYfIEtCQgLKly8vWFa+fHmkpaUhKysLz549g0KhkNwmISFB53HnzJkDDw8P9atSpUqFMn6iiDElDd7SQdDkAiMIgijRmCyAvvvuOyxbtgxt27ZF//790aBBAwDAP//8o3aNWRuTJ09Gamqq+vXw4cPiHhJhEUxphWGJIGiCIAjCWjA5Db5t27Z49uwZ0tLS4OXlpV4+cuRIODs7W3RwYvz9/ZGYmChYlpiYCHd3dzg5OUEul0Mul0tu4+/vr/O4Dg4OarceUYqwRB0gc1thkHAiCIIo0ZhsAQIAuVwuED8AEBQUBD8/P4sMShfNmzfHgQMHBMv27duH5s2bAwDs7e3RuHFjwTZKpRIHDhxQb0OUISSboZroAjNbyJAAIgiCKMmYLIASExPxwQcfIDAwELa2tmqri+plCunp6bh06RIuXboEgE9zv3TpEuLi4gDwrilV41UAGDVqFO7du4eJEyfi1q1b+PXXX7F582Z89tln6m3GjRuH3377DZGRkbh58yY++ugjZGRkYMiQIaZeKmHtFHkrDBI9BEEQ1oLJLrDBgwcjLi4OU6ZMQUBAAGQymdknP3fuHNq1a6eeHzduHABg0KBBWLNmDeLj49ViCACCg4OxY8cOfPbZZ1i0aBEqVqyIFStWIDw8XL1N37598fTpU0ydOhUJCQlo2LAhdu/erRUYTZQFirgVhiX2IwiCIIoEkwXQsWPHcPToUTRs2LDAJ2/bti04PQ8KqbpCbdu2xcWLF/Ued/To0Rg9enRBh0dYO0XdCoN6gREEQVgNJrvAKlWqpFe0EESJQUrsFGorDIIgCMJaMFkALVy4EJMmTcL9+/cLYTgEYUGKvA4QZYERBEFYCya7wPr27YvMzEyEhITA2dkZdnZ2gvXPnz+32OAIokBIusB0BUHrOgZlgREEQZRGTBZACxcuLIRhEEQhIGkBMrUVhrkxQARBEERJxmQBNGjQoMIYB0FYHosEQZt7bhJDBEEQJRmTBRBLdnY2cnNzBcvc3d0LNCCCsBiSQdAm1gEySchQFhhBEIS1YHIQdEZGBkaPHg0/Pz+4uLjAy8tL8CKIEoNJLjAV4rpWJGQIgigjPDgJHJmvp2di6cJkATRx4kQcPHgQS5YsgYODA1asWIEZM2YgMDAQa9euLYwxEoR5SIkdQy4wmY30cqPOR1lgBEFYKQ9OAKs7AgdnAVc2FfdoigSTXWD//vsv1q5di7Zt22LIkCFo1aoVqlWrhipVqmD9+vUYMGBAYYyTIEzHnFYYMpnI6ENChiCIMsDqCM3009vFN44ixGQL0PPnz1G1alUAfLyPKu29ZcuWOHLkiGVHRxAFwZw6QGILkGknLMC+BEEQJQRlfnGPoEgw+W5ftWpVxMbGAgBq1qyJzZs3A+AtQ56enhYdHEEUCCl3l0HftkRvO3PcWeQCI4iyR8pDYHVn4OZ/xT0S48hKka6NVkbuXyYLoCFDhuDy5csAgEmTJmHx4sVwdHTEZ599hs8//9ziAyQIs5H6EhuqAyTV3NfYm0EZuWkQBKGDHeOBB8eATVYQCpJ0C/iuCrChr/Y6U5tGWykmxwB99tln6umwsDDcunUL58+fR7Vq1VC/fn2LDo4gCoTUl9gsF5g5wobEEEGUOTKeFvcIjOfcKv7v3b3a6ygLTJu8vDy8/fbbuHv3rnpZlSpV0LNnTxI/RMnDlEKIagriAqMsMIIo21jR9569P8YeFa1TAGdXABveA/Ky+WVpT4A/+gJ3JASTlWKSALKzs8OVK1cKaywEYVlMKoT4CikXmDXd1AiCKD6s6ocPM9bId4SrlArenXd7B3Dpd37Z8Z+AO7uBP3oX3RALGZNjgN5//32sXLmyMMZCEJalqLPAOKoETRBlGyv63uv7Mciuy1Q1OGeuTZFXKEMqakyOAcrPz8eqVauwf/9+NG7cGC4uLoL1CxYssNjgCKJASAogQ01PKQuMIIgygL57FHvvVKXEe1TULEu6CQRYf9iLyQLo2rVreO211wAAd+7csfiACMJiSGYyGOj5VSAXGIkegijTWNMPH30WoNx0zbQqlICtDZQczQsgjtNxz7QOTBZAhw4dKoxxEITlkUyDNyMGyKybmhXdCAmCsBDW9L3XM9asFM208pW7K59pfJ4cA/wzBri8Eej0PdB4UKGMsLAxOeBh6NChePnypdbyjIwMDB061CKDIgiLIBkEbYYLzNibGvUCI4iyjTV97fX9GMxOYabT+L8KRgA9jwEuRAKKHOCc9cYEmyyAIiMjkZWVpbU8KyuLmqESJQtLBUGTmCEIorSh77aWncpMp/B/WQH0TFMKB7kZlhxVkWK0CywtLQ0cx4HjOLx8+RKOjo7qdQqFAjt37oSfn1+hDJIgzMKUGCDoEUBmxQCRaCKIsocVfe/1WYBYF1h2KhB3CjizXLMsgSmHY8V9w4wWQJ6enpDJZJDJZKhRo4bWeplMhhkzZlh0cARRINgveMWmwKMzhq05looBIqsRQRDFiSIfsJHrCVLWc49iXWCKPGBVuOjYjDUoN9PcERY7RgugQ4cOgeM4vPXWW9i2bRu8vb3V6+zt7VGlShUEBgYWyiAJwixUAqj5aCC4DV/Ay5ALzFIxQARBlD1Kyj0gOxX4pSkQ3BroubxgP+wMVc/PTtFkg1lZVpjRAqhNmzYAgNjYWFSuXBkyK7pIooyi+uLKbBjXliEXGGWBEQRhLga+96mPgIPfAs0+BAIbFt4wrv8FpCcAVzcD0fuBIbsAv5rCbQxlxKpQiooeNnxfUx0a4K1BeZnAnyOB57HAyCjA1r5Awy8qTA6CrlKlCokfwjpQCRcbucawU6jNUCkLjCDKNIa+93+OBC7/ASxvo70uLwu4vdsyLiUbO8101nPg3zF8XM8vrwMHZqoGa9yxctKF81IFEJ/eAm79ByRdB57dNmfExUIB6v4TRAmHYyxAKgVk8DtfAHFPoocgCH0k3dS9bsd4YENfXqwUFLmdcD7jKZ+u/uwOcPQHfpmxFiA2Hkh1bLmDcNlvb2mmrSgmiAQQUXpRfcHJBUYQhCkoFUDGMzN2NGAF1tdr8NJ6/u/VLWacV4SNKLolM5kPimYx9r6W9UI4L7cHnLx0b8+m0JdwjBJA//zzD/LySkfzM6IMoY4BYjIhDLrAqBs8QVgdMQeBX5sDj85Z5nj/fgp8HwLc3iWsgGwIVlTs+VJ7fUGaLZuE6J6VnSq8tyXdAq7/adyh8rOF83J7oOVY3duLLUYlGKP+Gz169EBKSgoAQC6XIykpqTDHRBCWQcoCZPBXD6XBE4TVsa4HkHQD+L2nZY53cR3/d0M/YHWECTsy3/tTv/JWl+gDmmrK5sTPchzwR1/gr4+M30dStDHn/rWZ6eNQIbcDmo4EBu8APCppr2drCJVwjBJAvr6+OHXqFACA4zgKgiasA1UMkA0bA6TL763PAmTs+agQIkEUiPxcPhBYJRhMpTDcL48LYFU6+Qsvytb35ueNsQDJ5ML55Bjgzm4+eDovm3+J3VliFDkSx7XQc1tuzyeWBLUEbB2115c2F9ioUaPQrVs3yOVyyGQy+Pv7Qy6XS77MYfHixQgKCoKjoyOaNWuGM2fO6Ny2bdu26oKM7Ktz587qbQYPHqy1vmPHjmaNjbBiBBYgdRqYjm2pFQZBFDtRs/lA4D/6FN8YcrR7XZqNypL0kDcgaN1fnkUDideFyzgFH1T88OyreeZHW3oisLAusOJtfv7ufuDJJe3z5ksIoIOzTB6+JHImxV2q2r4VucCMqgM0ffp09OvXD9HR0ejatStWr14NT09Piwxg06ZNGDduHJYuXYpmzZph4cKFCA8Px+3btyVba/z555/IzdWY95KTk9GgQQP07t1bsF3Hjh2xevVq9byDgyhqnSj9qEWN3AgXGKXBE0Sxc/FVIHDcyeIbQ1q8+fuKv/fiYGTWDaXIB35pzE+PF6WOPz4PrO4ITE0GDs/VLL8XxWd0ZTzl+3Gtf5dfPv2V1eXGP8DxRUBgI/OvwRBshplUkcSsF0BGMm+Fci/ZxZGNLoRYs2ZN1KxZE9OmTUPv3r3h7OxskQEsWLAAI0aMwJAhQwAAS5cuxY4dO7Bq1SpMmjRJa3u2AjUAbNy4Ec7OzloCyMHBAf7+/hYZI2GlKKXS4A2lflIWGEEUG5YOr0h5CNzYDjQeDDi4GbdPhkSMq1LBu30MIvrei91Z7A+sF/c10z+ESpwzH3iZAFzbplmWmayZTriqmU59BHhUBDZ/wM8XxG1nCLbG0NtTgW3DhOszngLfV+WnJ8UBjh6FN5YCYnJI+rRp0+Ds7IynT5/i2LFjOHbsGJ4+fWrWyXNzc3H+/HmEhYVpBmRjg7CwMJw8adwvgJUrV6Jfv35wcXERLI+KioKfnx9CQ0Px0UcfITk5WccRiFKLWS4waoVBEKWGdT2AvV8DOyYYv49CIoDYWLeYlgVILICY+8tTPTWBVIgzsNKeaKZv/quZ/rGOadlqBSHruWa6Xi/As4pw/csEzfTJxUUzJjMxWQBlZmZi6NChCAwMROvWrdG6dWsEBgZi2LBhyMw0rQDSs2fPoFAoUL58ecHy8uXLIyEhQcdeGs6cOYNr165h+PDhguUdO3bE2rVrceDAAXz33Xc4fPgwIiIioFBI9zTJyclBWlqa4EWUAtRB0KwLrIgsQKSFCMIMLGQBur0bWPwGkHyXn7+21fh9pdw65sYFiV1grAXo2V3D+4uDnV/EaqbFaew7x5s2NnOpJMog8xVZr1IfaqYPfwekiyxq20YAf40qnLGZiMkC6LPPPsPhw4fxzz//ICUlBSkpKfj7779x+PBhjB9fRP+AV6xcuRL16tVD06ZNBcv79euHrl27ol69eujevTv+++8/nD17FlFRUZLHmTNnDjw8PNSvSpUkUvsI64O1AKldYGYEQZsTA0QKiCBMx1IusA19hRYWpYGsKRapbY0WQKLv/ZMLwnn2+ow5ptgCxLrNxFxYa/h4BaHtZGDCXcBVFJsrF/X9EhdOfHAc2NCfD9bOfM73J7u8gY8TKmZMFkDbtm3DypUrERERAXd3d7i7u6NTp0747bffsHWrCSobgI+PD+RyORITEwXLExMTDcbvZGRkYOPGjRg2bJje7QCgatWq8PHxQXR0tOT6yZMnIzU1Vf16+PCh5HaElWFSM9RXUIkHgigbcBxwdAGfScUiJYCWNAf2TxfuqyL2CB94zHH6rcUxB4Hn9zTzYnEjxUuRJ+R5rPR2RYFzOW3xAwC2BhKMtgwGbu/kM9tYC3x+lkWHZw5mucDELisA8PPzM9kFZm9vj8aNG+PAgQPqZUqlEgcOHEDz5s317rtlyxbk5OTg/fffN3ieR48eITk5GQEBAZLrHRwc1GJO9SJKAYIYINUyA1lgBXGBsdtRPBBBmIEFfoAojexxFXsYODBDk0ml3l+HtejYj/zfPV8BP9TUuHYiuwD7pr5ySen53q/rIZzPM0IAXN0snJdKOy8qdP04FPcF0wWnABRMR4k8IwRgIWOyAGrevDmmTZuG7GzN4LOysjBjxgyDokWKcePG4bfffkNkZCRu3ryJjz76CBkZGeqssIEDB2Ly5Mla+61cuRLdu3dHuXLlBMvT09Px+eef49SpU7h//z4OHDiAbt26oVq1aggPDzd5fIQVw3aDLxIXWEH3KcPkZgCJN4p7FERxY06rCHGcjDGWFUDY64tt4CkVA6SC4/jihukJmqaiKh6cNO2Hj1StHjFXNhl/PEsS2gnoLLo+XcLS1l56uRRseYPcdN3bFRFGp8GrWLRoEcLDw1GxYkU0aNAAAHD58mU4Ojpiz549Jg+gb9++ePr0KaZOnYqEhAQ0bNgQu3fvVluZ4uLiYGMj/FLcvn0bx44dw969e7WOJ5fLceXKFURGRiIlJQWBgYHo0KEDZs2aRbWAyhqcKS4wSzRDJdFjNr+9zcdsfLAdCGlX3KMxn5hD/EOr4xz9DSMJacxxQZ9YJJzPy+TTz8XWEkU+IGceeey5UuIAv5r8tL54oRwmQealqF5Q2mMTBVDxu4B00n8DH6+zg4nr1ZVAIlUNWhdbh2imczPMG5sFMVkA1a1bF3fv3sX69etx69YtAED//v0xYMAAODk5mTWI0aNHY/To0ZLrpAKXQ0NDwen4oDk5OZklxIhSiFQavKEsMEs1QyUXmGmoAlavbS2ZAojjeDeHd1WgyRDd263rzv919AQi5urejjCN9CTg7Eqg0fuApyhJ5cTPwvl/P5V2FeVlAHKmJg3bsiHlgXECiC2SeGuncF3qI0BpQtPw4nYBVW0H3Dukvbz9TP6v2BpXu5v0cQRB0DIYfb88/B1g8yVQ+Q3jti8ETBZAAODs7IwRI0ZYeiwEUXBOLgbORwKD/xN1gzdQCZqzcAwQWYPMQ1w4rqTw6Cxw4id+Wp8AUpFuuIyHAI7jq//61AAcy3IMog4L0LbhfMzOje3AJ6eF65y8hZlHt/6TPkZuhrAoHyuAnt0FaoTzgcqxR3QP7yVTh0eZBzy5KDyeMW4tFYaywFx8+aKChQVb0ZnlzU/5v2wK/2sDAXfpGFoBbv7aljFdxB4GKrxmfQKIIEose77k/0bN0ZEGb6gZqqVigAiz0GodUELINDFl197F8DYsN//lq/gGNAQ+PGzavqUJXR6w2FfvydNbr+aPAM/uAL41NZlVMhv9Fl6Vy+XpbeD6duDOLs26xOtAwjXtQGUxmc+F87eZYyhyhUG+hsgx0DTUO6RwBZCh7xq73tFT93ZsZlu5asYLIEA7hb6IKaF3G4IoIPk5okKIBagEbU4MELnAzKOkCiA2MJbjDMeq2BvZdkHF1S383/hLpu1X6jAiBigvC1jbTSh2bGwBr2BN4UMpVAJocTNo3QcSr/EvFgcPbZGS+Rywc+HdaQDw4IRmXX6OdBd2XejreF8tjBcdqoosTt7CCswO7sJ4JHMwRQDpC05nXYZ2JrbIstFhhSoizAi5JwgrgRU1BpuhqrBQDBBhPOz/xJh+SxfWAn9+qF1wrTBhH7a6YkTYmA4HV9OOX4L7JVmEsyuBLUMMu32MCYJOT9K29DiXA+wNPHzVQbcS3+eMZ9pFBmuEA68LuwwgM1nYKiOdqWGnyJVuo6ELfQKmRkf+mlS4+GimZXLhvLlIucDYzyH7XdT3fwmbDgQ0APr+DtiZGAcsL94fPCX05xZBFBCOY1xgbBq8IRcYxQAVOWzasjECaMd4/kHz/B4wfF/hjYuFDarNz5F+eLCCzFBxODFOnprp3AzTXWglnR3j+L+O7kCXRXo2NEIAZT7TXubkzVtm9JGbwbu5pFDkAC8eCJfZ2GpnOGUmCwOdWdeoqWnd+gS8nbNQ0Dn7ALjDT1dpYbpLVgrWwlOjI9B8NOBXS7OMvRfqswD51QI+fBU3xboEjcFaXWDnzp3DzZt85katWrXQpEkTiw2KIAoOZ1olaEvXASIXmPGw6bDG1IFR/cp+dIZ/iBRFujnrAtP1K599oIlr0xiCve60eMCnmmn7WwsxB/WvN8YCJI7DAXhriSHRmfUc+KO39Lr8XCDpunCZjVz7mOnCrgUWESJS2DsLxZezt2a67rvA+TUFPwfrfnLyAoJb6d7W2PpMpd0F9ujRI7Rq1QpNmzbFp59+ik8//RRNmzZFy5Yt8ejRo8IYI0GYh0lp8PoEkNEnLMC+ZRj2l7MxwsGBMdM/PGP58UjBur10Bbo+ZLKTFLm8WyX6gHFimC3Ed2QesOl9YFlr46oFl3TYAnoGs/yMEEAZEhYgZ2/DLjA2Y0uFw6uMu9yXQPwV4TopC5C4NYUuGg4wbjtd2LkIz+0VpHFP1ettuoVRChs58MYn/LnafKF/W2OzM012gVmZABo+fDjy8vJw8+ZNPH/+HM+fP8fNmzehVCq1urITRLHCBkGrXWCGdrJUN3gjy/ETQguQMcXh2EDTJ5csPhxJWCGiK9D1zm5mm1xgWRvg957AVSN6JLLvwZVNfFZY/GXgxt/mjbck8TxGMy3l4lQq+KyuHCNdSFIuMOdyhl1gaY+1lwlcjaLvuY2tttAwRgDJ5ED19oa304eUBeijE8D423x8mT7XkVewceeQ2wEdZwOTHgDeBvYxtkClqa5baxNAhw8fxpIlSxAaGqpeFhoaip9//hlHjuipn0AQRYkgBoixAJmVBWakmKEYIG2ubAEenpVep8gDdkwArjD9jqTqqGQk8zVyAP49ZmOGxJk7hQV7Tl0WIPYBrswH0l5ZxK//pf/Yd/YAl/+QXqevLYM1EH8F+IUJj5B67078zPfT2ti/YC4wQxagFIkm1/pcNpIWICNSvOX2xvfH8q8nPQY7Z8COObedC+BRka+zozqHFBVf12zD8tYU7WUq95MxIsRoF5iJFiBrc4FVqlQJeXnaH2KFQoHAwECLDIogCg4nKoRYABeYOdYcsgABjy8Afw4HVoZJr7+4Djj7G3B8oWaZVB+nJc35TtJxp7UfoEVVTl9gAdIRA8RahthtDKVG/9FH9zpjgsJLMkk3hfN5Eg2zz/zG/409AqNcYNkp2stcy+uvVQPw1Z7F6LNYSMUAqQKgbR11t4CwtTe+P9aHR4FB/wJ+dYBeqzTL7ZyExxcLC10uMGW+dHp76wnAByIhbspny1gLECvmVO5FfVibBej777/H//73P5w7d0697Ny5c/j0008xf/58iw6OIMyG40xLg9fn5jJazLB1gEgA4UWs/vVSv8ilLECqwNMb27XFhClpxwWBFWa6qv0KrETMuEypDiymQDFperj5HxB7tHCOzSIuGZDzErh/XFiFmX3fpB604vcvJY7/W5VpmeJWXhgoLIVU1pVYALGiw8ZWd8kDuT1gr6PUgdxBaAHSZ+WQyYCKTYCPT/C1f9jjs2MRj1OXBUhfDF3IW0DbL5ljmCA+jI0BYsfp4mt4+2IWQEZlgXl5eUHGfDAzMjLQrFkz2Nryu+fn58PW1hZDhw5F9+7dC2WgBGEyJlWCfkVBXGCCfcgFZvCmKfVwYR+GHCd8+GWl8Nk6LJYSQEd/4B84zT+RXi+wAOlwgbFjY7cpyBj19aUyl7R4YNOrIN1pKeY1IDUW8fjzs4E1nYDy9YCPjr1axgoc0Vg4TrtgoCrw3Z3xOLiWN1xjSAq5HS9QVJYdFz8g9ZXAsrEVCjW2warcDnBwk45HktsLLTT2LtJWKzHs90VuJ7IAidxk+ixA+qxarCXJlKKjRluAmOO7+Arjv+xdtUsFWEMa/MKFCwt5GARhaTjTKkFbwgXGlTILUOJ13o0YUN+8/dkbrLgTNyAd3xK9n19uIwf+Gc3HEKm4/AdvymcpiHVFRXYacOBVA8g6PYQPVhVsBpEuQWOOBciQUJZyGRUUtgBfdqqwBpGl0SXgEq/ysVF1egiteuIHbWQX4NE54TLV+NnyB67lpbPDDKEKdM5VCSAfoQBiRZWzt6Y1hdyeF0BS2NoLH+z2rsYJIHsXIKgV/565VxS2mNBygelwv732Af+91XkORkiZEn9jdAwQawESFWt09NAWQMVc+d2osw8aNKiwx0EQlkUrCLoAzVDNCUS1dgGkyAeWtOCnJz00r0Ene3PLzwLkogeGrofj7V1ArXeAi79rrxN3rzal95IuWIHy8DT/UGZJiwfuM+4iXTE9CjMsQIYEnLlp8PqC+lkykwtZAL367tg6aWf4bRkMeFQSvT/MeHMzhe+7GNbl5epn2OUqhY2dUKywbhsbOdBoKHB6OVDvXT4GTS2A7HTHuMgdhBYaY6uCy2R8PJBqWp8LTJeVp9koYN9UzXyr8UCFxpp51pJkTAyQqgVH9Q6GtwW0LUAsDu4ARJl41hYDRBDWgSgIuiDNUMtiDBD7sEp9FatjqluPvcHmSQQ36xJA+jJtxMexhAuMPcbd/drrVY041dvrcoHpyBSTCuwGgMsbgQ399I/NHAHEccDqTsDK9sIaPOrxMKLLHKuJKeNQWbBqvSPdH02ftULKvcTi4gv0XAH0juQFgaE0eCnEqe5iAeRZGZh4D+jyk7A1hdxBt7DRsgAx4+r6C1BBT9FgmUwjWvW5wKSyrTwq82Nmx/n2VKBmZ+njGCM+xl4B/ndBWCFaH6yFSfBe2kpn6RWzC8xoAWRjYwO5XK73pYoJIogSgSlp8CoK1AxV4tzWijj25tYOYF5V4K4JrSdYgaMSVOdWA782B1IfCdtLsOhzF4j7JyXflU6LNgVWAF36HTi7Qrg+7pRwXvXe5OcAK8OBbSP4afY9Y11X4rglFX99qG3REmOOCyw3HYg7ATw6q3HncByQ9oSfZq/XkMhgiT0K/D1aGBeji7xsYFVHYP80ft7GVtpqIRbB7PdvaUv955A7APV7A3W68/PlQgyPS+sYtiILEOO2UX3v7Rz5cbEWJ30usNwMUQwQI5RsHYF3VwC1ugLDDVTFZkWOWPBIpc6/MYr/W7m5nmOa6AJzcDPtfbXTEQStK2uumNPgjVYsf/2lu5bFyZMn8dNPP0Ep9WuDIIoDThwDpHKB6fiMqjWOheoAlSYBlPEU2PLKDb6+FzDdiAcgIHzQ5mXx8RT/jeXnL/6unbHi4sufK+MZcHie9DFv7dBetjoC+OS09nJjET+Ed4wH7kUBYTP4m/+zO8L1qutKugk8PMW/Qt4Svmds7Ijqc8hxwKX1QMWmfE0XMTIb7c/Nxd/5ZpMmXQ8jLO9F8VWJT/zExzl1Wwx4V9WsN6WVQ+Q7/F97FyDiO/3bRu/j3xcVugSQ+HqTbmimxUJL3AFdnGru4sP3pIo9Cuz9SrNc6n1lx6XLAiS29LExR6ogaCmSo4VZYKyosnXgiw72XSe9L4s4kJqFnW89kXdRqVxdVZrzKfXs/1kFa7UqjPgbgQuMbeBqIx24bQ1ZYADQrVs3rWW3b9/GpEmT8O+//2LAgAGYOXOmRQdHEOYjSoNXu8AKMQi6oPuUJFi3zTUjKhlLwT5A8rKEjSij5gjTmAGgXDWNADq9VPqYUoUPn94yb3zqcUpYaG7+CyTdAv53Dkh+lcni5MWnUqsEEXt9SdeFFi32Qa0SJFc2AX/ryDIDgE8vAwvrCZdlPAUSbwDlaxt/Payg+/dT/jpOL+Hn/x4tfPhmPOPdZDYmREMkXDW8TVaKcF6XC8SUGC7vYL46tgqpYoMBDYD0p8Jl1cKAu3ulj2ljKzwOK3LEnwuBC0yPAAKE4ow9pq7gZUmYH2NaFiBm3sEVqPS6cH3dd6UPyQq8wujEzgoz9v1S5kv/v6wxBujJkycYMWIE6tWrh/z8fFy6dAmRkZGoUqWKpcdHEOYjFQQt5QLLy9I8WAuUBl+KLEBsC4ab/5p3DPZBfO+QtmtL7P5RmdozRA8wKXTVYDGFF/f5StTP7kqvT77LV3dOf9X+wPdVHMTh74ATvwgbYyaJRBhrAVKJSbZVhhjfWny8iRrmc2hqw02xqFCJH4B/AG96XzP/Mh5Y3BRY0Z5v2ZGXDeyfDlzeJDwG+8NBKnbr+CLgl6aaFHWx687GVvp/lmdCIUuvIAjeF13FBlkx12QY0O1X4fq6vZht7YTHYasvawkgxgXm6Ckd0wQAoZ2FD3tVDy/AtB5e7PnE8U3svCnfBVYAFUaVcdbFxoo9pUL62q3FBQYAqampmD17Nn7++Wc0bNgQBw4cQKtWejrIEkRxwemoBK1ax85HzWF2tFQdICsWQIo84MAMyxxHxf7phrf3edVex5h2A1I1RUxl4wBe+J79Tfc2GUma87kyDw/WxQLwLh8WVgCpxIC+OjVi64hfLY07SFeslBSxR3nhpgtxJta9KM32j87wAbqPX6WdN+ir2Y4du5QAOrqAF7iRXYGvE6TTnaVcYKY0s3X05K0KqrglXe0m2Ho6Ed9pWxk6fKOxatrIhcdh44HEQpK1aLiVF1qA6r4LXN8O+NcFei4THoetUG2KBcjBDRh5mB+/2FqjL9tKH6wYMyY131TY62Pvsco86Wu3FhfYvHnz8N1338Hf3x8bNmyQdIkRRMlBlAYPPQKIrYgr6QIz8gEkaAVmxYUQCyosVJiaoeX/yv0jjrmRwsFNY5kxF2P6iKmysOycdP/il4J1ISrz+UBosSWnajuNFUz1ueu7nhcgLcYA84KFYzB4zhxNnI4u2KJ/gLYoU4kfgHeNRc0G/OsDvjU1y6Uyx1QPU5XAEndWl+sQQLpcU1IENOADu1UCSJc1hc0+lIpzEe/HWoAEAkj0+XXx00y7BQgFUPUOwDsL+WViKzJbZsDUopOBDaWXC7KtfKS3kYI9v9hNaQlsbICqbYHUx0BAQ81yTmndMUCTJk2Ck5MTqlWrhsjISERGRkpu9+eff1pscARhNlpB0KwAUkKn99dilaCt2AJkqCu3Is+4G5epVYzL19UfsMpibG2V4z/x2U8d5wj/t8Z0aAc0afe2ToBvqP5t9ZHzUlhMERA+GGt1efX3Hf4F8EXx7h8FjswH/hsHhH8jjO1Q1bpSPfCN6VSuFFk19Fnb7uwCjnzPTw/do1me9kRTrFKKYwv5tiUsNrbmpamz1H1X6JrVlULNWoDE32dbR6H1RKkQWYCYz7XYAlS+jmba0VOYGu7gprtWFiv8LNW7jn0vnU0QQCyWqKElxQfbhZ9LFdacBTZw4EBBOwyCKPFIpsEDWnFA7LqyHgP0+DzfeFQfWSlCd5AuTL3BOnvz2VGqXk/6cDLQ9wngBcK+V12wXxsoDCT+a5RxY8p6lWJv5/gqBsVM1ksEpdo68Q0q404Db0gER6se1CqrzNahfKaPRyX+vd34Hv9efXSCt2IY4zo0BbYScXqSZlqZx8c/uQdKi2FV6juLLheYMVRrD7QYzQtG1/Ka5bosQKrK5e4VtNeJ07GV+cLj6LMAiSuEV3gNqPkO38S1oigImcXRk4/xen6P7/tlCVjLlikWIAAInw2cWwW8+allxiJGJpNugyMVs2UtFqA1a9YU4jAIwtJwmiJwMrnQtaXlnmIFkMQXtzRbgJ7d5VtBVHyVQrt7suF9sp4bJ4DE1gYV9m5ArkQ8jI2czwQzRgAZc9O/yGQ7iR9mbgGaGjn6UFUitnUEaoQDDh5Ajig9W6rKsRix9QfgH7whb/EvKaSK3S1qwBe8e/lEY2F7fA6o0kJT58dSpDGCStzVXdXIdvEbQF1R5WwpzBVAnX8AXh+umXdl3FC6LED2LsCXT6StC7aOErEpOgSQf13hvjIZ0O4rPtan4Xv8st6RADjpB3mXRXzmY8hbwKhjvFvUWMulKbAxRsbQ/BPdPe8KE0kLkAkd6QsBqgRNlE4ErTCYNHhAW5ywN0SpG5mx8TzWWAfolybAirc0D09jrlWqq7Z4/a2dfCsDKaRijFTCk3Uz6INNLdbFP/9jZkTX5VnJuPNEvypWZ+fEPygn3uN/9bNUbQP0+4OfrvImJAPppZASOIL1EmnjAC/cWPeiStxZ2gKUHK2ZZuvzAEDCFWBBLV4Mnl8j2lHi+m3k5gkg8UPTO5hZpyejyt5FaHEIbs3/bTpcuJ04PVtux4uVsOl8WwkxbSbyndtVGVpyW91WjMaDgc7z+fuL3Nay4iewEf85bPE/00oYFCdS/y9dn/Eigko3E6UXVQyQIRcYe8OWCposzRYgFc/u8CZ+YwJu9VVezs0A1nThm13qaqAY1FK7x5PqRli+rvb2UujqwwTwFiSxJUt1Xecj+TT7uJPGnSfxVc0b1YNYbgv0W89ntR378dUyO77dwJhLfKbQj3W1rURSGEqJNiSQVNyL4sWmpS1AbDaZOKaH7TclxkauHf9lY2eeu0Oc6cUGY+vKApOi7+98xpm49pRSoR0EXb6OJiC/pGJjw38OrQlWzEbMA+r1LnYLEAkgovSiEiE2hlxgDFIuMKPrZVihBUiFql2DMe6nzR8AUyVq02SnAfOrazKgxO/BwH94l0PCVW0BpHoIlatu3Hj1FaH7+xM+LoMlL4uPV/l3jO79ylUTWj1Y9LUiULlNVNYJJwk3mYqI74Fdn/PTtmZagMSohJilYWOAxOhr0SEV/C52gTX6QOii1IU4boQVQHYmpJQ7egDV22svV+TpToMnLAsrgNz8hXWOigkrsZ0RhImIu8Eb6wKT+kViLa0wHp4BTi8zz2WX9QK49IdxlgtlvnRz04endTf+BPh6JdXCpN1XqoeQl0Qx1U7zgdBOwmXijBtWpIrjVQBNKw4xlZoxx/QQrmOza8SuGHZe/NDUF5PBXp+lLEDGos9qVrmFZc8lxsZWeH59Y2ERW3lcfIB3fuQtCPpEsLEo80UWoOINyi11qGp7OXgIvyclRGiSACJKKeJCiDbCdQIKwwVWDHWAVrYHdk0Ebu8ybnv2uvZPB7Z/JL2dWyBfCDCIKXoqVUTNUJao6oHfoD9QuxtfN0WF6sHDFpsD+CDTpiOEwa+A9sNvYX2NwJH6f+kSQB5MLJC4oFzvNZppfa0IxA9N9hpcRONmz2cwBsjCAshTT6V+UzOJjIG11tjIhcJXV8q4GKnMoSZDgWYfFmxsKrRigErGg7nU0H8DX3l7yE7Rj4aSITRJABGlB7Ho0JUGr9cCVBABVEJcYMYUEgSEVpOXeuJHRp/hg397rdYs+yFU+/3W1fVchUoA2ToAfdYCTYZor2P/F11+0nT6fmuK8AFu6wgM3qmZT3vEB14D0uIzLxOIOaC9nA2GFrcUYLOA2EangEgAiR6abPxIaEehsHLz10zripFSn6OAAaLiNHAPibRwqXMFNDSuzIAhwmdrpuV2QpeHsRYgk3pnmYEyv0RaJkoN5UKAXiv57xJr8Szm+j8qSAARpQctF9SreRs5tCpB68Jag6DZB/TF342zQBlT4bpcdd7aYuugnfouzuYSd+8Woy9olV03eCff4VqVagzwFopPmUaY9q7aVXLzs/n+XlkSQdp5WcB/n2kvZy0y4l+lrCtLXMVZnwusUlPhdqwAYt1shgLOjRUJOvcXWcnEgqgFkyXHxu1wSmHNG6mu4gaRCd8/G1uhBchY95Upgc7moMw3nAVKWAZ935liggQQUXpgH+isdUPQDBXQLoTIrGNjgFQB0dYQA8S2J0i+q91oVApdwd32bsC4m3yhtPe36d5fXErfkADSF/PCujqC3gTe+kr7YSSTAe//CbT7mnfHiW+i/44BdoyXPr6usbFWJbGLjH0wijPfBEHQonGKLVWs8GA/X4YEkE8N/esN4ewjdMexFqDa3fieWCrY7vUcJ7RUsaLNaKsUJ2zXYGMrtCoZ+wDU1fDUUojLLpSQB3OpRFfLkWKkRAigxYsXIygoCI6OjmjWrBnOnNHdIG/NmjWQyWSCl6Oj0EzKcRymTp2KgIAAODk5ISwsDHfv6mkQSJQOWNHBiiGZTLsZqgAdLjAbEwWQrrEUBeIO6qmPDe+jywKU+5K3ALSfKR2UrEIcB8Q+RKXQJ4CM/aVf7W2gzed8GrCUtS56v/R+uho/srEo+uroiAs32un5NctaOmwd+R5WUugLGAeErTekrlUKQTdue6AOU6TQvaJmWvx+Z7MCSCEUPey0vrYLfrWF83YiAcRmgRnbJqWwXCUfHuH7rYVNF1mASsaDuVQisACVjAT0YhdAmzZtwrhx4zBt2jRcuHABDRo0QHh4OJKSknTu4+7ujvj4ePXrwYMHgvXz5s3DTz/9hKVLl+L06dNwcXFBeHg4srMN3HCskWd3gai5hn99lzY4TvsXNCs6BBYgE9Lg2QeNqRag4owBEjeoNKZQ4KE5BTsnWxAxPwc49K3+7fU9XMxxPZjSmkdX8UZWlKVJCKDOP/BigQ3YBoQp7OLrYmNdOAXQ8jM+i637En7Z6yP4rBi2wrEUju6ah0ZwG/3bqmCDxW3shGUFWLeW2LLCilelQnh9rAByEQWpsw+1iq8LrV+s4BH/CDE2hd3UfnLGEtAA6DDr1bUZyAIlLIOuitvFSLELoAULFmDEiBEYMmQIateujaVLl8LZ2RmrVq3SuY9MJoO/v7/6Vb68pj8Mx3FYuHAhvv76a3Tr1g3169fH2rVr8eTJE2zfvr0IrqiI+bU5EDXHuBYGpYldE4E5FYHE65plAgsQM21uELRKNFmDBUgco2IMp5cU7JysC+x5rOHt9QmWwr4h6up8zT7AX8bzKfcA0HMF//f14cDkR0CIqICeviww1vKRl8VXAO6/QRPT1Hk+MDFGf1CyinE3gf9dML4wX8MBzLkzhaLHszKzTvRjUGwBstMhgAIbCff7mC0oyQmFNyuAVH3h2s8CanQEanbRexlo9D7vpjO2MnhBoB6XRQP7XaMgaCA3Nxfnz59HWFiYepmNjQ3CwsJw8qTuSq3p6emoUqUKKlWqhG7duuH6dc1DMDY2FgkJCYJjenh4oFmzZjqPmZOTg7S0NMHLalD1W4o7VbzjKEpSHgJnlvO/DmMOapbrsgBp/arTZwGSa09bQwyQ2P2kyJHeToUl0vQznmoebGz8zGuDTD+WoZo4piCVXZX2SDPNBiXbOvDFCQGg5298yv0X94H6vZltJMSZB+NOElsi2Qeqru7fxlq8nL35TBp2zB9sF27DtuZgBUp2qtAixHafz3hlYX/ra/7vO0wxRRc/3QLILRDwr6+ZZwOk3QKF52AfeCp335tjgPc26Y/tmXAX6LaYzxQsEnFCAqhIoDR4Ic+ePYNCoRBYcACgfPnySEhIkNwnNDQUq1atwt9//43ff/8dSqUSLVq0wKNH/A1OtZ8px5wzZw48PDzUr0qVjOwTRBQPaUx8C/ulEgggxnSueiCqLTp6usFbLAaoiOsAiQWQOCX9zG/A9k80DWINxZ8Yw45xwMoOQE46sPLVD47ydYFaXU0/ltiyUBCk3H+JTC+r9//UTMvtgWYjgUlxGtFjjPuQrZsjVXhRhTGtRYyB7YElrg/EZlux34fcdL7pqwo27kfV3b3157yFq0YH4L0tQOXmQLdfhFYsVgDZ2mt3NO8dyVtrWowWBnyz3yupEgntvuKD2TvOFS4X13wqbAyVIyAsQwksN2B1//nmzZtj4MCBaNiwIdq0aYM///wTvr6+WLZsmdnHnDx5MlJTU9Wvhw8fWnDERUUxFN4rLtjO3scW8kXwXjzQ7wLjJ7TXiWFvhpaOAXrxADi6oHDitcQZTGIL0M4JwKXfNbVwLCGAAODJBWAO89Czc9KO7/CsAjR4D5KMjOIfhGxKdkERF1MENAHf7hWFVgvV/1hcBdoYVDE8TUfq3iagvu51psDG8oitZez7zQqXnJd8EHvLz/iyAux26UyMpSolvUYHYOhu3uLEbssKLLkD0PZL3p3WfDS/rE533lrj4Aa8PZXPXusgigeTski2mQgM/g94Q0cBzqKCXGBFAyt6jA3qL2SKdRQ+Pj6Qy+VITEwULE9MTIS/v7+OvYTY2dmhUaNGiI7me/io9ktMTERAgObXT2JiIho2bCh5DAcHBzg4FHK9CcJyqNwugMa1sfdroMsizXJOFAQN8Dc6DtBbCZpdpxJDRvcCYw8jIYBWdeQLDibdAN5dYfox9ZFtwAKkQlW7R6qVhQpx2wlTUOYLRWSfdUCtLrofMoGNCmb9CWql3VfMyZu3hKhEno2txiJo68DH5LQYwwduuwktxSbRaT4vBsSBwQDw0Uk+I02fODIF1gLEfv4BoUWIFS4qURw2XbOsfl/gyibeWqMP9mEltgC5+gJjr0rv5+YPjD6r/9glDXPEL2E6glCEkvGDvVgtQPb29mjcuDEOHNBUaFUqlThw4ACaN29u1DEUCgWuXr2qFjvBwcHw9/cXHDMtLQ2nT582+phWSXG0XigupDJD8rP1uMBePXx1BTXrSpG3dAyQqtry3X3GHc8UxBYg1sLDFklU/fLSZQHq8C3Qw3xrKvJzhcfWJ34sQc/lQLX2QJsvNMucvYHg1vy0g4cwM0l1/R1mAZ3mFezcMpm0+AGA8rX5eBdLxTbJ7fi6TNXCgMDXhCJTkPouEXfD0vVnYNg+4M2x+s/HNgVmCzKa6rpoNZ7vB/XaQAMbFqMVpn4/XvR3/K74xlAWcPLmSyX41tJfTqEIKXY71Lhx4zBo0CA0adIETZs2xcKFC5GRkYEhQ/gy+QMHDkSFChUwZw6fsjtz5ky88cYbqFatGlJSUvD999/jwYMHGD6cN0fLZDKMHTsW33zzDapXr47g4GBMmTIFgYGB6N69e3FdJmFJxL+AAWHzU4DpA8ZmgMk02wrQcfNVu8DMEJf6RFNhpPbqCoLOeSnM0DIkgOr30d+nKaABEH9Z93pFLlDlTT5QNqBB4bsX3AOB97fyHeYPv3qAOXnzVo9jP/IP3o2M+y09UfIwVkH7mZppmY3mM1ahsWa5of5htg7CStW6YH+tsxXAK71heF+Wt6fyL4Pns9UkdBQ1tvZ8lh5RuNjYAKOOaaZLAMUugPr27YunT59i6tSpSEhIQMOGDbF79251EHNcXBxsmDfrxYsXGDFiBBISEuDl5YXGjRvjxIkTqF1bU4Rr4sSJyMjIwMiRI5GSkoKWLVti9+7dWgUTSxdlyQIkJYCUQtGhEkmCmB7VtJHvlY0Oi5FOjMwCkxJwBUXlAnPx5bOzVC6w9X2AuBPa59YlgAxV+n1vM+9CqdMTWFhXe70ih3/Ijjqqva4wYcft7M0/tDu+6kXlXA54HvNqfAb6lVkL7Oe6Wns+8Dw/G3D1B5qNAk4v5esNmX18RrgGvga0mcSn4vtUM/+Y+ihOAUQUHSWszlKxCyAAGD16NEaPlvZJR0VFCeZ//PFH/Pjjj5LbqpDJZJg5cyZmzpypdzvCSlFIWFA4hcgCpBJAbGsLHUHQgnk2BqiQKkEXxo1eZQFy9uEF0JF5fFAqK34AjfDRFQNkSAC5+fOuGIB3L+WIAroLQ9wZA1tzhi1ECABdfwJ+fWW5yMssujEVJoL2LTZA33Wa+Q7f8BWgWcuQyccXfW/aFXKdsRISFEuULUqGHYooOGUpBkjqV7wxFiBdLjA2yJk9RmHVAeKUvBspxULZhv+NAxKv8dNssOy+KdrbqtKydVmATDFNS9Vy6VjA6tLmwgo3cVd3v1pFO5aiQF/qttwOqPxGwWqtFPUv9RJmGSDKBiSACOtD0gXGGe8C07IA6cjyUluALJQFxrKstbQLyRjyc4H904H7x3hr2LmVmnXifkxa+76KDbJEGjwbcNtyHF/Aju09VZQIAoHLQEZnYdeuYVPfiwKyABHFAH3qSg1lyQJkTBD0q21sJFxgYnQFJZtcCJG1ABXi/+PiOj7A99iPmqBCFfYGXFi7PudTv3PSCz4OQaq0e9EXsBOMhbmV2RoIBC4NFLZgqNkZqNcbqNDE8LaWgAQQUQzQp660IBUXU1qREiziGCDV+8GKHpkuFxhzPHadJXuBFVQQcRzfVsHBFUhl2jo8YmquDPwbSLhm+FibDaUkGwlrASpJoqNcVcPbWDv9/gA29NOuomwpbOSWr1Wl93z0KCKKHnKBlRZePgEeninuUViG9Kf6LRSSFiClUGSo0sDZYE5dlaBZAVTxdc20JWKA7u4DpnsAMzyl9zFGuJ5bxe8/pwLfdoEN8r27n//bajxQtW3Run/YGCBju3sXJh9s54thSgX/NubLaqB6eJEOqdAIehP44gHQaIDhba2BTq/6sbUaX7zjIMoUJLtLE39/Yn1VWMVkPgfmV+MDWb98LL2NZBC0OAbo1TaCGCCVNUhHEHT4bL6Mv3p7C9QBWt9L/3b52YDcVf82/32mmT4yH/AN1czf3sH/VT3080UtB2q+A9z6z/B4zYHtLWVbAgSQuGM7S8c5/PqqbYtsOIVOCamlYhFqduL7sVFVZqIIIQFUmnh2hy98p+rtY408vsD/zdVjAZJ0gSmlLTWCGCAdLi2VAApoIDTFm+wCM6MbvKl1aa5tlV6uEm6uTGuHqS/4h+T0Qnqo2JYwAaQPOye+YSdRciHxQxQxpegnBAEAOPhNcY+gYAgCmXWICEkXmEJadBiVBv9KUNnYSgdNW6oXmBSWakrqUYn/W6c7EDYDGH1OYyF462vLnEOMNQkggiAIESSAShuPzhX3CAoGKxx0iQNjKkGrMKYStEoAyeTS6cXmxAA9v8e/DCF2WZmDW4AmBkduB7QcC/gw3cNbf85XCjaEvYmWQ3kJiwEiCIIwARJAJZm4U8Dj86btY+2/xNmaO7oEkGQlaE6HADKiErTKwmMjly7IZk4WGAD80dfw/8MSAsiYQn/tZwCVRc2A2RieNz4GRhw07byCLDAr/9wRBFHmIAFUUslKAVaFA7+9ZZoLxtqLwLExMVICiOOAU4slluuyALG1f4xwgcksKICe3TGc3qtgBJAiH9g6FDjzm2nnafCe4W28qwJDdwMelTXL2Pem4xzAt4b2fvoQuMCs/HNHEESZgwRQSSXruWZaLAQ4Dvh7NHB0gfZ+1vpL/GUikPUCyGV6NbHXnXgd2D8DuLZNev/8HGD3l9rLpYKgM57y1plbO/l5lQixdRAKFpVQMlcAAcKsMinyc/hYp5O/Av9+yl/fzgnaY9AHmxVmiKavGmRW1ZMxZSxs/62SVAeIIAjCCCgLrDh5mQCcXAw0GSrs4QQILRH5OcKHzaNzfDVgKUrqL/Ezv/FCotmH2uuyU4EfXgmFTvM1y9mGnUta6D9+8l3+JUYqDX7fNCDxKnBnNzA9VdM5XW5XMBeYlFh5ckH/Pvk5wIU1wB5Rs0mOA078BJSvo71P0w+BM8s08w4G0uhZ3vgYCKjPp83PN9HiI8a5nGa6pH7uCIIgdEACqDjZMoTv1n3jb2DsFeE69qGramCpIl80z1ISLUDZaRqrRv2+gJOncD0bLJzNdBe3RIaUTMIC9OK+cBuV203uULAgaHPIz+ELHYq5vRPYN1W4rFx1PvC49QS+COGJn/nlpgQvy22ZWjg6WoMYCyuA7MgCRBDWzJE7T3H07lNM7FgTdvKy4RwiAVScxJ3g/6Y80F7H1roRCwGpOBUVUh26ixtWwEnV8LFhulanJ2mm7x0CKrxWsHNLpcGzcUZKpSarzNZBFDNkogssJ033uq6/AEEtgZ8aCpenPZJuX3F6mXDexhb4H5Phx1qbWOugKejqjWYsbMNMsgARhFUzcBXfSWDr+Uc493V7xD3PxL2n6Xi7VnkDe1ovZUPmWSNsrRuxAJJy06goTGuFubDjl6zizIw5PVEzfWAm3/+qIAhcYK/+smn07HjkOsSjse9p4nXd6wIaaLs5gVcxTRKus9jDwnk7UZNTNjDebOtLAQUQ63qjGCCCsDouPUxBSqbwnvwiMw+bzz1Eu/lRGBZ5DqfuJQMAMnPzMfPfG7j+JFXqUFYJCaCSilKPAJJy06i3NbGycGFy8Xfg9HKhBUjKrcWKENYCBAAX1pp+Xmcf5niMoJKq7PzguGa6IAJIkQ8k3dC9XpeFJPaI4WMD2q5NtlyAuZacglqABEHQZAEiCGviRPQzdF98HB1+1L4H3XiisWZfepgCjuOw7cJjrDoeiy//MqLhspVAAqgkcWgOcPBbfpq1AOWJRIOUG0mF2MKSm6G7onJhosjne5Pt+hx4HqNZLiXQ2Fo46QnCdVKZbgbPzbx3mc+YFRIP/N97aqZNEUCPzgHzQoC13YAb//AlC/TFLOk6toqgVvrXi6085lSn1qKAAiigAf/Xwb3gYoogSgHpOfmYt/uWQECUVHZf5++1SS+1a5GlZGnuobY2Moz+4yKmbOeFz+WHKUhKy8aFuBfYcSW+aAZbSJAAKilkpQCH5wJH5vENQfW5wKRaQajXiawp3wUDf/Sx3Djzsvmg3dRH+rdjA7XTnjDje/VlO/MbbyFilwFAmugLlZEkLZre/xNoPlr63LnpQJU3tZfre0jb2Gk3l9SXBv/3J7y4uhcFbP4AeGygAreh4PTARvrX24syvTgLCKCCahZHD+Dze8A4PZYvgrBiMnPzwZnQDHnurpv4NSoGnX46CgDIzlPgzwuPkPTScELHw+eZmLL9Gu4/K6Db30gUSs117buRKFgX91xTjoTjgB1Xhffl/TeT0PPXE/jkjwu49th6XWIkgEoKyYyVJDdDvwtMqhWEirTHwPW/eAvMtT95cRG9z7wx5aQD+6cDTy5plkXN5ruTr5VoLJmXzZ8zLV5otRJkduXy6f87J/AiIj9HKHCkMtwyk4WB0v71gWpvA43elx43pwD6/g7U6w30jtQs1+c6ZC00Dq+aMlZqxv89v4Yfs+oaI7sAT2/pPpYU+lxEtboArcbp359tbQEAHhVNO78kFrDauJSz7ua7BKGDuORM1J66B+M2XzZ6n3P3XwjmFx+KxrjNlzHgt9MG9x2y5izWnXqAkeuKpp2RkhF2I9YKzxmXrBFhqVnazxuV9QgAbsSXfGuXLkgAlRRWvKWZzk3XbwGScn/UiOD/PrkIbBkMnPwZsGcCZ/VZjVQcXwSsDOfT1lXzx34ElrcB5ofyVpsrW/h1ydH837xsjcC5EAlsHQL8WJsvaqgig3FDrQwD7jKCLDtVaAGSIj1BI/o+PAoM2cVP+9UCxl7V7iJtYws4ewPvruCbg6rR88Bns+cm3AY+jwFc/TTLfu/F/z2/Wn/cTpWWmunKTO0iXS4wR09erDl56T4mAJSvK5x/4xPgtYHAe1v076cPclsRhE5Wn4gFAPx18bHR++TmC63FKhfR3aR0ye1XHL2Hnw7w9cuiX21zJ1F6W0uyYO9tbDjzUOf6F5ma50VCmrb16sidp+rp9Gw+JGPT2Tgs2HvbJItZcUMCqCSS81IY55OXzWdEnfyVnxeLGTsXoEE/4bLzkcJeT6wIkeLKFr7uzMNTvAUp5yVw8x/N+vRXVpucl8L9VkfwBfUu/aGp58Mp+TYQ6n2F5lWcXqqZznoh3Q/LzlnjNvqPsY741BBmH3lWFgY9A7q7n+uzVLDvlZ0T4OIjtBglXuVdfw9OCPer1l4z3XgI0Jkp5Fi1jWZabAFy8QNGHgY+FGV76SJYFCNk7wx0/Rmo0cG4/QnCCuA4Djfj05CnKJq4xfScfIEriEXXcn3kiARQfKpu19fthJf4ZsdNLNh3Bx/9Luz5OPnPK4XqCvvpYLTR27LuMCkep2RBqeTwxbar+OlgNK5bQfyTChJAJZGcNKHISboBHP2BrxbM1q1RYWOrbWHIeg7kMV8gsQgR8+dwzbS9C7Cyg7SbJ5cRQAnX+ErH+dnA9o+EaeApcZrp638Kj5HIZBFkpUgLoKG7NZYvdTVlmbQribV0tRgDvDlWexsA8KutmXbx411PKqQsNOLA4/8+E4pCAHjzU830W1OEIsu3pmZa3BNMJgMCGwJeQdJjZem3Aaj8huHtTKVCE/4v614kiGJk9fH7iFh0FONNcDsBQJ5CiQtxL7SE08PnmVh8KBovs7Ut4M/Sc/DG7AMYvPqM1jqFksPakxL12QzACqDtFx8jK09jrVcqOUQnvcTg1Wdw+l4y9t/U3JN3XRMmf2w48xADVmjcZk9SsoyKI9JH0stsrDoWiyuPUkza76FIANUOcBfM77oaL7ASPZUIqi6pkAAqieS8FAYzZ6dopnNfandDt3PSfoBnpwpr6GwbBjy9DewYDxxbyKem/zWKFzqPJdo16EvpVrFUFGh8/6hmWtzaQRdZL6RdYA5uQOPBwmUyG2m3jR2Tjv3aQN2uHbathGt5PntJhVQBSak2FCw2trxlJnw20HEuHw/DWo0qvAa0mQREzJMYkx73ExvAXfMdoGYn/eMwl+6/As0+AkYdK5zjE4SJ/BrFx0L+c/mJgS2FLI2KQc9fT2BYpDCWZVjkWXy/5za+3q6dun08+hnSc/Jx9C7/l+XvS0K3V++lJxB54r7kuQ/dTsKi/XehVHLIzdcInrGbLgm2G7DiNMIWHEHU7afou/wUnmfoL1nyOIWPh5z57w20mHsQTb89AKWSw72n6YhP1dMN4BXXn6SqRVPssww0/fYAZv53A11/OW5gTyGsFcteboMxb1cTrH+Smo2jdzUusYIKtaKEKkGXRHLShZYOcUCx2ALk4iv9AH/I/LJJjgY2DtD0y7p/TBMcvba7cD9WcBU296KAS+u1lzu4A29N5V1Mmwbwy3RlPrGtNcTZUiwVX9dMuwcKrTVyCctSQEPp44w6DpxdAbT4Hz/f/BNmJSNs5A5AOx1CUF/8DetDL8zClq5+QMTcwjs+QZiIjZlhaZcepgDgY1Ny8hVwsOWLxariaf6+9ASL+gkzLXPyNN+tyw9T8GY1jSv9VoLQ1X/2/gucvf8CA5tXgUz03R2y+iwAwN3JVssFxnLyVUFBFVLBxWKS0rKx6nisev524ktELOJ/aMbO6YTMXAXs5DawtxXaMqKT0tH5p2OQyYDYOZ2x3YQ4Jl2E+Lrg9+HN4O/uiAAPR4EwYt1ej1OsRwCRBai40BcolvNSdy2b7DTtGCCXctIunLt7hfNss1A2liVHlMbIuq8Km9NLpFtIOLjx11XrHc21eVaRPgZbkE9fY1C2rUatd4QCSEpAOrgCb08FGn0ANOjPL/OoBPjXBbosBMqFaO/DChapYwa+GoM4ZovF2Vv6eARRyrExMzCfFR7GCAtAGNx74GYSFEoOW849xPUnqTrjf1TBzIduJeH7PbeQw1h8/r38RK8AEpNmxDijRcHTe5jsq8S0HNSZtgc1vt6FT/64AI7jsPhQNHZfS8D5B88BaB4zloipKu/uiAAPJ8hkMuwe2xpzetZDVR/+3sumwj9JMWydKimQBaioycvmLTiZybq3yXkpjGtJYaL1c9KkLUByiTgOfYX58vQE2L0w3fdtcVgLWO81wMnFfNCv5LZMjR3WHSbGRg4M/IePKWo4ADjGFFlM1OHyazWe/5udCrhXAJoM0T9u9n8jZVV6fxtvfavRUXtd70jg1BLenXbrP36ZFWVUEERB0aV/nqXnYP2pOPR5vSICPJzAcRzinmeisrczZDIZXjDtHNKy8nA29gW+2SH8TiuUHF5m5+FFZh6CyjkLBFDU7SQ428vxy6FoVPV1QS1RnIuKE9HPcO7+C3z511UAgJOdpi1Rgp6AZynSJOKSxLy3Qpg+f/6BJrt26WFN6ZQdV+IxslVVfL/nNgBgYHPNj8XcfKVWdpox2MgAVgeWd9fcZz2c7NC/aWUci36Ge88ycI2xAJEAInSzIgxIjeMfcrqImg288bFmnm2Wmp2qHQPk7KPdK0qADJL9pnTBdkt39BDW8QH4VO8HOuJGvkoAbv4L/DnC+POpqNiULwgotqzU7My/dMEKIHExQzFV22iys9gWHYZS8R09gLen6N8GELrgpKxyzt5A7a7S+9bpLkrbh6baMkGUAXRZgL7YegUHbiVhz/UE7Py0FVYcjcW3O29iVrc6+KB5kCCe5r3fTktWN776OBXvrziN9Jx8dK4XgGfpmm2eZ+Ziwxne8n3vaQayc6Xd7SdiknEhTiNC9lzXBDJn5ulw0esgJdM4SxXL8WiNN2CNKCYpOUNzPWwAd0pmrlaMkz5GtQmBh5Md/D0c8NkmTTB6BU/tfn/lXPh7HCuwnqRkgeM4TPvnOrJyFfju3fqwMde3WciQC6woyc/h06mzU/msKTGfMDE7p37VTLMp8dlp2q0wHNwAN3/d563U1LRxxl/i/9btBZRjCvC98QkwMgoYsgNo+6X2fjUi+IDs+kZWnm7zhdBKkpsOdJoHNPvQtPGa2wz0dTNEmiFcfICevwF91gHyAvy++PAo/x4bKpBIEKUIXb9fDtziewSqiu59u/MmAGDK39fBcZwgHkVK/ADArmvxaiGw42o8Tsc+V69LycxDMiOinuiw5sQ8TcezdM12bIp4Zo5pAkgcZySO45FCX2a+rtib55m5JmVmTYqoiY/ahqBRJWFtMid77Sbc+RIDepKajcl/XsXakw+w5fwjxDwt/LpG5kICqCjJfK5/vW8oUEuHdUCF2AVm7wZUb88X1NNF9fbCebcA/edQUesdYfp1x9malg3+9TTLa77Du3be/c2446poPhoYc1EzzxZPNAX3CmbuF8C31ACE6ewFpX4f3VYeYwmoD7T9ogCd3gnC+jAmBkhcaO/H/Xd1bCnk1D0D918Rbg62aFTZU7AsPjUbcsaawcYb5RYwzkZ85S2r+Uhup4spEpluAPDtjps6RaE+HO2Egqemv3YdtYi62j+8c/OV2HhWE7ZRFIUdzYUEUFGiL+5HhTj1W0xWisYF9togYFIcb+HRd+Oo31c471NDM930Q6DBe8CQ3XwdGxWeVYDQTkDbyUCrCdqp0qwAKl8HqBamu9AgW28HAIbtA0YcBBzdAY8KgHdVfnnVdrqvQR9NhgK1uwHdFpu+b7W3+arPYTPMOzdR5HAch0O3k5BqhguhpPI8Ixcj157DflFPptJCVq4CD59nGqwSLBZAadl5SM3Mg51cs1xsnVFVUjbE5VeZYixVyjnrvHVW8HJCeTdhD7/MXIVZBRKNQRxAXc1P4053stO2vhjL0bvPcFWiX1eXBoHo0iBQ537sOV0dbPFWTT+tbVpV90WVcvrCL4C1J+8jaNIOBE3agf+uPEF8ahZ6LTmB2TtvqoO1i4sSIYAWL16MoKAgODo6olmzZjhzRrswlYrffvsNrVq1gpeXF7y8vBAWFqa1/eDBgyGTyQSvjh0lgk6LGmMEkKHCeOkJmvR1uUQDT4CP0akWppn3qAT038hPVw8HPCtp1r0+HOixBKjSXBhHVKMjH4js4MrHvrCCB+B7UalaUNTpCS1UliLvEL7VQ8T3/HzYDF6wVWis2XbwDqDDN0DHOfqvXRd2jkCftbp7gxnCxYfaQlgRv596gCGrz6Lv8pNFVjG4sFm0/w723kjEcFFPptLApYcpqDV1N1rNO4Tv99zWEhCpWXn4fs8tRCe9FHwN8xRKdPvlOMJ+PIw8hWafP89rN2J2dbDFe80qmzw2VwdbuDpIu6od7eTw95BuYmwnl1nklnFwfBvJ5Z3rB8DXTRMeEOKnSe6QyQAfVx2tdUzAyc4GC/o0wHfv1sOifg0BCC06DnaaZ8vI1lW10v9VeDprxiK2mAEQuBpH/3ERzeccxLkHL7D8yD3su5FUwKsoGMUugDZt2oRx48Zh2rRpuHDhAho0aIDw8HAkJUm/MVFRUejfvz8OHTqEkydPolKlSujQoQMePxbWOejYsSPi4+PVrw0bNhTF5ejHGAHkUUnTjFOKsyuAh68yA8QVfHut4mvXdP0JGLCVjyHpv4n/xoRGAJ9e5rdhA3U9mZuGHfNl96ulf5wyGTDiEP/yq6m9vu96oNkoYMCrXlXNRgLjbkm7mtwD+Zo6bD0fgtDBquP3AfAxFNZUdl8fj60oc8ZUBq3S/ED9NSoGDWbsxZIoTQbT1L+vYfGhGLz322mBBejY3WeIfZahFb/yw747EBPo6ahlrTEGVwdbuOkQQPa2NuogXwBwZASBt4s9vJyNEyFezrorrVf1dcWvA16DjQz4urPmnvtJ22pwYWJuqvpo7tktq/nA14xrBYDqjFXJ0U4OO7kN+r5eGd0aVsClqe2x+D1NqRAHJiZJXwxzDhP8HeyjJwtXAg+n4q1CX+wCaMGCBRgxYgSGDBmC2rVrY+nSpXB2dsaqVaskt1+/fj0+/vhjNGzYEDVr1sSKFSugVCpx4MABwXYODg7w9/dXv7y8DDSbLAqkBJDKUtNmEv/X1h5o2N+444nT4eu+y/eWKhfCC5S2XwChjOXLK0i7Tg4reljztMqCo49yIcLaOiweFYCI74QZXe4BZdrSkpqZh93X4gW1QwjTSWTSl1My9VfTtRTZeQoMWHEK0/6WjrMoKC+zNYkNmbnGZ+yYy+JD0Vh3qnDLXSzafxcdFx7RqsuTnpOP73Zr2uyoGoYmvcwRPGiP3tXfv5BtyVD+VXE+U3F1sIWro0YABTHuHAdbG4FbqlV1X/W0h5Od0VaYcq4aS06TKtrPoU71AnBjZkd8wKSul3d3gDMjzGoHusPL2Q5uDrb4PDwU7o66EyzketRKVV+NQBHH+Hg62wuytViLjy7rD8B/N1RU9NK8f256xqiiTAug3NxcnD9/HmFhGneNjY0NwsLCcPLkSaOOkZmZiby8PHh7ewuWR0VFwc/PD6Ghofjoo4+QnGyE9aWwkQrybfcVHwjcdpJmWeXmRh4vxbxxNB3Jp2iLY4MqvuoNFdSK71NFWJQha85g1O8XsEDiFyxhHFm5CmQyKcrGFr0rCPGpWej001Ecj05G5MkHRne7jk5Kx9LDMQabWp6MSRa4CeKeZ2LB3tvov/yU4OFSEOKSM/HoBZ+x9DglC9/vuY0p268hKS0b/115gtP3LH9//HH/HUGmk63Egzk3XynIJGItQNdEcSvO9nJBLFCQj+ZhG+DhCD93ibpb4N1V3owlp0VIOfW0q6PQBcbG3TjYytGrcUXYy23QvWEgQnw167yc7REokRYO8MHC+8e1Vs/7MgJoeKtgdK7PJ6GwLi5HOzkcbOVY1K8hFvZtiHKuDoJx+bk54OD4tjj55duoX9FTp3CoW8EdNcrrbvpc1VdoATIWfcHp7PtQnvkfiMchZQlzdyreSjzFevZnz55BoVCgfPnyguXly5fHrVsSjTgl+OKLLxAYGCgQUR07dkTPnj0RHByMmJgYfPnll4iIiMDJkychl2v/03NycpCTozGzpqUVklm9djfAKxiIOQhc/oNf5uSpCQJWYYz1BTA/a6pcCPB5NJ9BxhLQABh7TX9KPWE2F+JSAADbzj/G5AgDLkZCwIuMXCRn5GjdtNOyC99a8tmmS7j3VCNi0rLy4fHqZr7meCwWHbiL9cPfQK0ANyw9fA91K7ijVXVffL39Kk7de471px/g6MS3dB5/7m7hva7jQk1PvX03EvUGqhpDVq4CXX45htSsPFye1gHPGJfS6hP31e6o4S2D8b+3qquvTczzjFz0XnoCrWv4YloXYZ+8Z+k5uJuYjjeqekMmk2k1H63q6wKO43tSqeA4DsMizwq2u8esvyQKWi7nag+lUuMurFJOY82oUs4Fbo7S4+5Q2x8xT9PVtYJC/d1wIoYXfC4OtoL92Ie5g60NgnxccG5KGFztbbH1gib2qJK3s8AlxuLmaItyLhohULeCu7oNhpezPWZ2rYPK3s7o06SS1r7dGmoyWp0ZF5iHkx28GBHnrkMAKZTQ6dIDgIpemuvTNX4p9LnAZveoh6+3X8OoNiHIYKyXIb4u6sKNtjYylHN1wAtR4kJxW4CsuhDi3LlzsXHjRkRFRcHRUWP+7NdP02agXr16qF+/PkJCQhAVFYW3335b6zhz5szBjBlFkAXkG8q/anYCYg7wLieplHSvKnz2lTIfOPGz7orO5gogQBPALMZT+0tJWBqq7gzwD8AdV+NR098N1fx0/2oFgO6/HseD5Ex1sKYKY9oJFBRx+vTT9By4O9lCJpNh+r98teFRv5/HpIiaatfOvdmd1Ps9fK6J77n+JBUVPZ0FIuNpmo7vN8x38U3Ychm7ryXAwdYGH7erpraUbTv/SPAQVLmfAGDFsVgoOf6h1KyqN96oWg6n7iWjvLsjgn1csPbkfcQ8zUDM0wxMfae22mNuYyPD+M2XcfjOU4xoFYwvO9XCiqOxgvEEejhpFeO7lfBSy83FFtQTp5V7ONnBTm6jFkBVfVzg6WyHlMw89G5SER5Odqjo5QQnOzkevshEvoLD6LeqYUiLYIzZeFFtjQplLBNiFxhb7VgVA+P+SiDV8te43Cp6Oan7janGohJvbo52gpo5dSto7rUuDrYo5+qALzpKxE2KcGGEjFjwsMKhb5NK2HSOTztXKjnB9YjxY2KHjMksqx3gjhvxaYioq7t0SpAP3yMMEFaqZq1pro62guuRuo7ioFgFkI+PD+RyORIThamfiYmJ8PfXb4WYP38+5s6di/3796N+/fp6t61atSp8fHwQHR0tKYAmT56MceM0BefS0tJQqVIhCgF7F03RQ111XlQusSPfa5Y1G8U3NY3ez88XZdNSwmJYS3eLWwlpUCg51AnUE5RfAPZcT8DoPy7C0c4Gt2ZFYN7uW/jn8hOsGfK6QBA9SM7Ag2TeffPPJWGX8GWHY9C9UQXJKrWm8M/lJ/h8y2Usfu81hNUWWqS9XewFlYbDFhyGn5sD6lfUvC9xzzNxgbn5301Kh73cRv0Qz8pV4Ojdpxi57jyaVPHCNz3qYuTa86gV4IbEVxaZzvUDBIKEv3b+unPzlUYVygOAl9l52PoqUyo9B5j1n6YlxKrjsXj0QiPI2EJ+qvUqJnYMxbzdfGuF0e2qCQK1rzxKRbfFfFfxIW8G4fAdvhv4b0dj0biKNxaJUtM9nOxgKxeaEVRNPfXRtUGgujP8i4w81GPEhI+rA/7+5E3YyGTqB/vB8W1hI+PFk62NpkmoPyNsqpfXPJhd7IVB0KxbSvx+1wzQfCZlkAnq4jQJ8lILoOGtggUBxPZyG0zsGIqYpAxB3JIhXOwZASSybrHzrBtJwXECN5QYNmXdmM/T9k/eRFp2HnxcdR+ThXX3sS5DG5kMrg7agqu4BVCxxgDZ29ujcePGggBmVUBz8+a642DmzZuHWbNmYffu3WjSpInB8zx69AjJyckICJBWsQ4ODnB3dxe8Ch0nT9OzniK+47O7qrTk59l2GQRhQfIUSnRceBSdfzqGxLRs3El8if9tuIiTMQWLFVl/+gGCJu1Am+8PYfEh3vWSnafEw+eZ+DUqBo9eZOGrv/hA46cvc3DpYQrO3tcIi1hRPE1adj76LTcuXlAfYzZcRE6+EsPXnkPSS6FFRir+IellDvbfFGaqrjimEQ9n7j9HvlJjwUhIy8bCVwX7zj14gd5LTyLueSb2XE9Up4U3qKgtNO8nZ+CT9RdQc8ourD15X+815CmUWH08FsuP3NO5DSt+DKESPwDwy6Fo/MV0FGezuFYfF47rs02XtI7l5mgLTzMednUraO7Fj1OyBHE+ro62qFLOBZW8hQ91W7kNnO1tBQ/4MWHV4elshyZVvBDKWHKepGRpxdqosJMLH492chv0fK0CnOzkeLdxBbQN9cWSAa9h20fN8VWn2uhUzx9rhryOFiE+gqBhBcfh47bV8EOfBia1hHBhBIM4VsaDmWfdwvUreMDfXfNjgM0sc7aXC0SJMT277G1tjBY/AODjpnHTsenx2XkKgaBTIRZ2RU2xu8DGjRuHQYMGoUmTJmjatCkWLlyIjIwMDBnCN50cOHAgKlSogDlz+Box3333HaZOnYo//vgDQUFBSEjgu+O6urrC1dUV6enpmDFjBt599134+/sjJiYGEydORLVq1RAeHl5s12kxZDLgg7+Ap7e0a/MQVkFJNgCl5+Rj67mHaFVDk/EyZfs1BHo64d/LT/Dv5Se4Mr2DWTeujJx8tbhRWTZUtJp3SD198WEKktNz0P7Hw0jJzBMErd6TCChmXUymkJqVhw1n4vB6kDAzZ8HeO0jJzIOXix1mdK2L5696LDWq7ImLr+K4DHEqJlnQtuBJSpbA2vJSFLtkIwPeZCr/vvtaRWy78Ah3k9LV79W6kw8wsHmQYL+X2XnYfS0BzYLLYdKfV9SxLYXN2fu6C9hlSQRuuzvZCawixhLo6YSmwd44E/scb1T1RiUmy0hX/R4pKng64fCEdnC0t4GDrRwtq/ngWPQzvNMgQOC2YV1ESglT7bx362N2j3pq0RFRT/Oj+tcBjQXbhtXyw+VHqWgbql1A0BhsGQGmZQFixKSjnRw7xrTElnOP8Onb1ZGSlYcf999BNT9XDG9VFd/suPnq2hwgt+HrF3EcCsWy62xvi/LuDkh6mSOwkGXmKrT+X8E+LsVuASp2AdS3b188ffoUU6dORUJCAho2bIjdu3erA6Pj4uJgwxT7W7JkCXJzc9GrVy/BcaZNm4bp06dDLpfjypUriIyMREpKCgIDA9GhQwfMmjULDg7GK9kSw9tTgQMzgc5M53Jbe75VAmGVGJtFVBzM33Mba07cF8QH7BVVJ95/IxE9X6to1PFUfZo+WHkaMU+1xYsUuflKNP5mv3pe6qFezsVe0LtJfE6FkhM8QKSYu+uWugEmC1vG39fNEUqO/91R3c9VLYCc7eXqbDRxujQA7L2RIJjfcu6h3oaUtjY2ghiT14O8sO3CI4FQfJaeg7TsPDx6noXzD54jOikdkSd1p7IHejgiT8mp6+jYyWWCgoIsPq4O6uagTnZygYjxdXPQqsWj671nGdwiSN2w083BVmCpqFvBHdceG0428Xd3xJIBryHyxH30eb0SopM0bRVMEUAABHFXKwc3wd3EdNQJdMeteE2mGmvBkBJAtnIb2BqZPPXbwCbIV3JaliRjCfRwxNs1/eBkL9eKn2GFg5ujLeoEeqBOV17QeLnY4+D4NoLMNwBoVJkX+kcntsPFuBSE1ymcZJdDE9oiL5/TGjM7v6hfQ3SqF1DsTVKLXQABwOjRozF69GjJdVFRUYL5+/fv6z2Wk5MT9uzZY6GRlQBajgMa9OeLBRJEIbPvldiR+hWvYtGBu+jRqILe2iAqfjoQjR/3C9P+61XwkCzNbwrNQ8rhP1G8DMCLnwErTiPmaTrWDWuGoHIusJFpfk3HPsvAgn130LdJJey+pr2/9vh5t5WHk53AOtCosieOR/PCrIKnE3LylYIYGbHQ2C6KXQKA1jV8ceRV7EyuQgkbGxlWD3kdN56koU+TSpj2z3WBsHqRmYem3+5Hdp5x1a+dHWxhayNTi5cGFT1xjrF29GhUQe3WCvF1UQugan6uuJ34Uh2Q7O/uqD6Gva2NIFCZpUkVLzxOyVI3Jg1hgmDdnewE6e6NKnnpFEBHPm+H1t/zFsHK5ZxRztUB4zqEal+fRHNOY3GwlauDk9mPMWtpURr3NutEJpMJ0vbN2X/l4Ncl17EWIKm0dzbdfdXgJth45iGmvlMbAF+rh63XY2mc7W0BiRJJbF0gL2d7s4WhJSn+ERD6kclI/BSAxylZiLqdVKJaJhSW/eeXg3fR89fjei0NhmAzhAA+Y6Z3Y6G150FyJlKM6MOVk6/QEj8AUCfQXeev9xrlhYU6+71eCRU8nf7f3pnHRVWvf/wzM8Cw7zuIoCCgIKgI4q5p5JKR5lY3zTXNvJBpP7XUW7c003Ipu1lmZVp5tSztmmnmvu+KooiiaKIsCsgOw/P7Y5zDOQwgpszMkef9evly5syZ4XMOh+/5zPf7LLA0V2Jgm6oUYXGGiZjzN+9i/6Uc3MovxdR1p7Rp2x/sQPG92Zolf6Rg06kb+MeXh/RScuuqYutoZQ4XUeE7cTCru70ajqLZhadE36zrKgbnYadGdIC0flmPYHdM6hEIpVJRY52Z+5kfcTC4jdpMMgvQ2tdR9HPc0Mq76hiaSyoEKyXxOuLMqLbVWh2IfyeeDpaSGBpxTJOdpZlQZNDSXIk+4VXn6M2+oZIaQX4u1tj6WlesnxArMZ2644tr5YEnQtz1Zjj+LuKJHvEslcaEZ2otROahpialYnqGeODzEVGSNHpD0StUu5ITHeAsuTZsagiINgZsgJjHmuf+sx8vfXUEQW/+JgncfBxZuDUFx9Nzse7otfvvXAu6WQAdecXlktgUHbfu1p66raO2b/g+jlZ6RgsAPnm+DaZW+6b/Zr9Q7H6jB46+1RtdWlTpsLM0x9xnq2LgPtyq7TGly0YCtJlKp67n4UZeCZJv5iOvuBz/OyOd9ekeXBXr5G6nlmTQRPtXmRMHawtJRd+WIvPgYW+JZ0VGQJxpI85aAoC4VlUZZu72aiwd1gZRTZ2wcHCE3vkQmydxj6XqGW/iQOGIJlU/z1atkpgE8Wv2VuYSE+cnCiQu05CkzYOnQ9VxeztYwVtUcVmcMedkbVHrzERpRSV6BLvj2zHRODD9CYSIlvuaudnozQa08LBDlL/UHALaWZHlL0bhy5fa12sG8mGobKCmp4+CYE87eDtYIjrAWRJsbGp8ODgCs/q3xLLn20qMtHUNAdHGgA0Q89iSW1QmTMcDwPwt5x86i+lR0BBfLAtFsz7VA2zrS3GZRi8tOrugFP6imRHdTMfNvNoN0Oe7LyHozc345l78R3X8XW0k6caAtkps/9beCBXdlBUKbZyHSqmArdpMEgBrpzZDrCg4+uM/U/HOprOSVHQx6TlFWLHnst7SlLi9QXG5RmIEeoi6XztYmcNaNDsQ26zKjGkqCaM6BeCNp4Lx9aj28BGZu+ZutpJlkGfbVM2mudio4elgifUTO+K5dvoxVeLrRFw07ylRw0pLc6VklkRcc8ZcJc3gEc8AqRQKhPs6wNHaHJ72lhggKrZYXlEpiTHxcqg6Hnsrc8lsUbBo9qGikiQZWeLZlKbO1lAqFegS5AYnGwtJVWAbtZleirwhaR+gb7SAmmOATAVLcxV2TuuBH8Z1MLaUOnGwNseYzgFws1PDXWSAHjR+q6EwDRUM0wDUlLGTmlUguXEag4YIgr52p8q4PEh7iMz8EuSXlMPeyhzR72nLUbjbabM4AO1NTdwfKdRTW9X2clYhSituomeIO8xVSuSXlGPcN0fhZqcWYnN09Vuq0yvUA1vOSoOE4+/NoIhnhoikPYjEcQtWFiq9GJC6AoIT154Umkt62lvi5r3igz6OVYPy9TvFiG1WdW2Il3scrMwlMzLimSKVUgGVUoFXugcCALYnVwWNu9upJbEvvULrnxEkvgE3FRkLsekoKa+UzNaI05yvZBfinQFhKCnXoGsLN8nvMb+kHHaW5tjzRg+Yq5QSs1Kmqay1OKCjtbnkufhxsIct+rX2xsn0XAyJ0hq63xK6IDkjX+9vTqFQYMmwSCRn3EVMgLNR40Eimzji+3Ed0MRZOrNWS7y4yVDfulCmgngJzOoh4rceJWyAmMeW6rMZAPRK9BuKClEMUkOMq2miDKvqKeZ1Eb9sH25Um82Z0K05bt0twfJdl/Fy12ZwtLbAihFRUCqB35Nu4cDlHLxzr7je0KgmeK13C3SYt72mjxcI9rDDjL4h8HWyhpWFShJsOrZzAKbGaZe+FAoFfJ2scP1OsV6Mh3gALSytqHMQ1aVOiym8Fwf0So/mmP3LWQDaRpUqpQKaSoK7nRr/6NAUe1Oz4edsjXBRDEtxWQWiA5wxPNoPge62UCgU+GhIBJbvuox/PhEk+TkRTRyFx2pzJZ5urS3m1yvUA2b3iuJtSbqJQffJpBP7ZPHMSvUmnM42VedSPHOTdbcUfi7WeH9QVcbolyOjMHdzsqC5pvYRLTxsJTVbxEUEHa3MJfFftmozrJ8Qi10pWXg+pikszJTYnNBFeD3Uy14yqyfmmUgfPBOpfVxTnzBDUtOXIrFhZB4esVk2dvq7DjZAzGOLrvnj2M4BSMkswO6ULNyqY+mmIfgrtxg/n/hLEiPSEBwQNbO8lFVQx55VaCpJz/wAQOcgVwS42uDJlp5CtWNdrEd+cYVQdh8A1h69VqsRGd+1GboEuSLYw06vPYC4sNtb97JTdPz35VjM/uUsno6QFi4Vp8y29LaXLEkFuttKUqTDvB30DBCgvWGLl4mcbSyw4ZWOWLg1BdOfCkFLb3sce6sXCNJCeFdyiqBQKDBvYFXc0cC2vjWWA3C1VWNGnxB8dzgd/Vt7Y0SsP8Z0DhCMwCvdA4XZoroY2yUAU/57Cr1CPSSdzqvPlojNkYOVOZY93xaJa09g7kD9OmFPhHrgiVAPve0AsOGVjlh9MB3/91QwVolm01p42EKpACoJCPa0h6eDlZDeDgBR/s41xus8CAPb+uKzXZckgdnG4vtxHfBbUgYmdm9ubCmPFRZmShyf1RtEf780wKOGDRDz2KKreuvrZIUmztZaA5Rfep93PVrGfXMU5zLyhfRyAA0yBXRI1K/qSk4hiss0EsOxbEcqzlzPw8fPtxEGn/M39YOU/9HBTwhebdfUSe/1PuGeSFwr3XashribFh62mNm39oavI2P98c3+K0KWiBhvRyusGFlzhffd03rg6u1CSTwLoE3BFhugIe19cSz9Dp6N9EZGvnY2CwCcbMwlwbmutmo0d7PFqtHRwjZx/aBxXQLwxZ40vNarRa3HUhMvd2uOl7tV3UDFs0L15dk2Pgh0t0ULDzuYqZQY1r4Jkm/e1cscE8cx2VuZo19rLzzZyuOBbzJt/JyEWjHiWCh3e0vsn/4EsgtKEebjACLCvIHhegHeD0NiryCEetmhcw0B94YmtrmL0ZfJH1ceVebeo4INEPPYoiv17u1oJbQbyCowrAE6l6E1GeLO1iUVtdfYeVD2XMzCR9tScOFWVTE3ImBXSiYABWKbuUBtrsSC37VtDbYnZ+KpME9sT76FMd8c1fu8d+Prri6urqEKnK6mT3M3G6HYYW0F93R4O1rh+KzesKxvVbl7+LlYw6+GpQlx7AsAhHja45dJnQAAOy9kVhkgawvYqs2wYkQUyjSV952Kn94nFCNi/SWzLIZCoVBIjJ54KctMqRBiiwJcbfBih6YoLK0QMrQe9hv2gEhv/Hj8umCCPR0s4XnvsxUKBYZH+z3U51fH0lwl6YTOMIaADRDzWJGeU4Tj6XfwTKQ38u9lQzlaWwgxOMaKARJTriGUVmhqNBMPwslruXjxy8OSbVFNnXD06h1MWH0cgDZlWlygb8LqYzgxq7ee+VEq7m9+7seCwREY+Ol+ANLicrXxKFJh/V2scSWnCE+FeeK9zck17tNBFNisy16r3vC0NlRKhVHMz/0Y1NYXa49eE2rt/Ds+7JF+vqW5Cmtfrr0fI8M8DrABYh4rBn22H1l3S1GmqRQKAmqzhbRm434ZUv+3/jSSbuThx4kdJZkxj4JmrjZCL6vC0voboEXbUvD94XSsfTkW+1Kz8b/TGfh3fBi+rSHrKdzXQVLt968aGh7GLd6tty31vb71Lku/cHAEpq47BTtLM0nKvZutGrP6t8T8387jvYc0U/Xl1392QW5RGXydrGutUiz+PeY+QIacKTP76ZYI93XAk63qZ+QYhtHHNCKRGOYRoSvZ/8b600JtHFu1mZB1lF9ce42cvKJyrD16DWdv5CPpb7ZqOHcjHymi5Shxqrang6XQY6ugjlo9RCSkyldoKrFk+0Vk3i3F25vOYtYvSThwOQfjVx3FyWtaoyNeVxdXhe0mamgqJvOu/jLgg/Tkea6dLy7N7YvnY6TLIK62aozpHICkt+MMFkNhqzYT0uPF2UrVWTM2BnaWZvhoiH7BQTliozbDPzo01auUzDBM/WEDxDy26JpV2qjNhKyj4nKNMEtQWUmSmjybTlfVrdFUUq09j2oju6AUfZfuQdzi3Sgq0xoccYzJzfwSob7KH8m3BKN07kY+4pftQ9S727A/NRsBMzYjYMZmLPj9PE5dzxXev/NClpAafTm7UIi3+WJEFF6I8cOasTEIFlXY7drCDXbVGhDq6NbCrV7LVLWhUirgJiqyZ29pJgRdG6s+yZJhkXCwMsd7z+ovB3UKdMWZf8Whf2tuK8MwjBY2QIzJceBSDs7dkGYolVZocOXe8hER4YfD6XrZRzrTUR1btZmk8ujdknIkZ+Sj1ZzfheDgfanZeOvnJGGfT3akImzO7/hyb1q9desyvYi0RRiJSNKXq3+4l6DjnV/PIX7ZPlzJLsSwzw/g5LVcZBeU4fkVh4T9l+24hG/26y9zVa+Z0sTJCu89G45Oga6SXloWZkq81V+bifVsGx+08q7K2olt7vLQgbLias66Oj7GpI2fE07O7o0XYpoaWwrDMDKAY4AYk+LM9TwM/+IgAO0MxqTuzRHTzAXv/3YeX+27ghdi/BDu44DpP50BAJyc3RtT151C50BX9AzRj4dQKrTtAhQKbTuFgtIKXL1dJATrfrrzEkbE+uOrfVck79tzMRsA8NPx6xjTOaBWvZWVBIJ2RuR3UXXjF0RGBgCmxQVjbJcA7LhQ1auqqEyD7gt31nk+aqqmvPuNHvjHl4dw+d4MkLgXkDiwOMLXAa19HRHobocWHrYwU1YZHjdbNcyVCpTV+dPrRmyA2vrpp8wbg4buD8UwzOMDGyATIb+kHESmUyET0GbMTPruOF7s0FRoVdDQbDjxl/B4d0oWjl25jZ3TeggGZc2hdMn+ke9sAwD8kZyJ8Gq1YQDt8pfupmhvqTVAQ5cfkOyT9FderX1/dFVvyyoqoakkmKsUGP/tMRSUVmBaXDBeWXMcWXdLEexhJ0lFr87oTgFQm6lq7YIc7e+M7IJSIUhajJ3aDH3CPfHfo9cBaGNdfBytBANUfclp17TuuJpTJKRQi+v5WKiUKNNUIjrAGcGedjheQ7uQ+iKu5lxTajrDMIwpwwbIBCirqET/pXuhqSRsf73bA2cfEVGDfPNd8PsFHLt6B8eu3kF8Gx9k5pdg0+kM+LtYo3OQq5DFNH/LeaRlFWLxsEhYmquw+UwGEn84iVlPt4SfszUSfjiBN/uGYrCooWNtJGdIl74KyzT1XobStWdo6mIttIOwEC3z+Dpb40ZeiV6NmhPX7uDQvUrK1hYqIXYI0GZRJfxwAr+cvAFXWzVGdfLHn+czAQCDP6syUnWZH3OVApbmWh226poNrpu9Gm526hoNUNdgN0Q1dRYMkFKpkLQqqE5TFxs0dbGp8bVdb3TH7cIyNHG2xpJhbfDu/85hfNdmtX5WXbTwsENzNxt4OlhKzBDDMIwcYANkZDLyinErv1ToW5X0V55QVr60QoNT1/IQ1dSp1iydb/ZfwdzNyfhuXIcaK/cCwJErt7Ht3C1M6d0CluYqZN0thYOVud7MQWFpBW7ll6DZvaJyV3OqbsZp2YUY/fURpIlu0J0CXbBoSCT+s/MSACBk1hbEtfLA72e1sTCzfk5CRBNH5BaVY9r608gqKMXV7CIolQq81S8UKqUCBy/nIMTTHp4Oljh29Y6kpYOOz3Zd0tvWJ8wTOy5koqS8KlD51L1ig2E+DoIBEqc9D41qIrRHGBnbFPklFdhw4i8s26H9fG8HSzRzs8XeVO3yl42FCoVlGvxyUrsMlV1QKsQM1Zd/9gxE9xD3qlkoUQuInyd1QvyyfQAAtUopaQTasbkL9us61xMwqJ0vbuWXIOZeTZvY5i56DUXrg5eDldDdu4mzNZa/WHPF5fpgYabEtte6PVQwNcMwjLFgA2QkbheWIfq9P1BRSVCJzM3Ja7mwtjBDqJcdlv2ZiqV/pmJSj+aYFhcief+J9Dv4ev8V4eY86D/70bWFG96LD4OLrQXe3JCEPRez8EJMUyzZfhGAtiWEjYUZpq4/hdhmLlg9Jgbrj1/HplM3cLuwDHdLKpB+uwib/9kFdpZmkurFPWqIVdmXmoOuC3ZItunMj45Tos/4YEuVefj+cDo6B7oKZmNEbFNJ/6GaaO3rgNPXtenpoV72yLpbKql5o8PH0Qp+ztZIv10kMRXPtvFBalYBLt66i9d6t8COC5mSJbenwryQdKMq/T3SzxH7UvUNGQA0c7MRlqDeeaaV0FyzOoPa+UpmYzoHuuKn49qfKW4lUFSmgYMolueLEVFoNed3ANru3CqlApNFTTdfiPFDSbkGnYzcOuBB0ucZhmFMCTZARqCykrB0+0WhlL2uTQMAvPs/bTXbST2aCzMTy3Zc0jNAy3dd1psB2J2ShS4f7ECol72wlKQzPwDw/m/noTZTggjYfykHcYt342KmfuPMvkv31PtYxDMwD4rO/ACo0fw4WJmjoLRCOD+jOwVgyfaLSMsuRJS/EzLyigUDtGJEFMau0lY3JiLMeboltiTdlCzvKJUK/N9TVefx6dbeeG3tKeG5j5MVDqVVGZ6ops6CAeoT5onfkrTn281OjWHtm2Du5vMAgIgaYo90eFSrTfNMpA8u3LyLpi42UCkVmPtsOBb/kYLXn2yBUlHavY3aDIPa+uLH49drbMpoplJKek0xDMMwDwanwRuYykrCs5/uk3RTrgmd+dHx25kM3MwrwSd/XsTx9DuSYnvVqR5Ho6OoTIM7RVVLQjWZn+p0CaqaYXC1VWP5i+1q3fddUTn+FztIU5F7hboLj+0ta/fdf0zpKjx2sbFAU1Ebgpbe9tj6WlccmvkEOjZ3hbdD1exOr5YeQkHAjoGueCLUAwsGRyBI1PiyOmYqJb4bFyM897S3FJoxuthYIMq/aklR3M09t6gMvVt6Cs/FnbpdbS0wb2A4Alxt8Fa/UL14LpVSgRl9Q4Uigs/H+OHwm70Q5GGHMB8HfDcuBrun9QAAfPBcaxye+YTJZFgxDMM8TrABMjD7L+Xg1HX9KsMz+4bUsHcVE9ccR4d527FwawoGfrq/xmDZ6sx9tqodQVwdJfMTewXV2tm5f2svocLu0Pa+aCPqar1iRBQ6BWpjUrq1cMNz7XwR4mkHbwdLvU7g4tosP73SSXhcfQWlmastFg6OgKutGh8OiYC7fVWqdRMna5irlMKsyohYf4T52OOVezMkWxK64KtR7dEj2B31Jdq/qrN2gKsNEnoFYXb/lvh5UiehMzYAST+ocg0hwNUG0+KCkdgrCO72llg1OhpNXayxdFgbDI/2w46p3TG2y4MHF3ds7ipkVKmUCrjXUd2YYRiG+fvwEpgBKSnX4P9+PC08T+wVBCJtCvyoTgH4dOcl5BaV44UYP5y6noukv2qeyRHTJchVqFnTPdgNXYPchGyo59r54rvDV5H0Vz5e6hiAfq298c/vTyA6wBlLhkUidt6fAAB/FxvMGxiOgZ/uh7u9Gp8831YIzg3zccCKkVG4lFWAp1t7Q6lUIMDVBn/dKUYbP0d0aB6Fb/ZfwYAIb1iaq/DLq52gUihgplKid0sPbDt3CzEBzuge7IaXuzaD2kyJQHdboXP4oLa+8Ha0wpLtFzG9TwiUSgWea+eLQW19oFAoMLF7IA5ePowgd1uh0rAOB2tz/Dq5i/Dc3d7ygQ2DmUqJTa92xrU7RWjpra2iPFpU9+edZ1ohI68EIZ52+HZMNMZ+cxTzBmqN5aQegcJ+XVu4Yde9mRuGYRjG9FEQ1VIApRGTn58PBwcH5OXlwd7e/v5vqCffHUrHzA1n4GqrxsZXO8Hb0Ury+o3cYmw6dQOD2vnC0lyFn45fRxNna6w5mI4/krXBxdH+zmjpbY8Dl3LQLdgNM/uG4n+nM/CfXalYMqwNmjpb49OdlxDqZY/eLT1QWqFBWnYhQu61SDh65TaaOFvDw94Sm89kYMf5TPw7PgyW5irkFZfDxkIFM5USl7MKcDmrsMau2XdLylFUptGLb6lOXnE5vtl/BcOj/SRF8wAgM78Eqw+lY3Qnf1hbmOFi5l209LKvMZ0/6a88OFqbCz2fjElDlRxgGIZhHp4HuX+zAaqBhjJAX+5Nw6JtKZjSu4VkluF+VFYS9l/KweakDCQ8EXRf48EwDMMwjRE2QA9JQxkgQDsrYqFS6i3nMAzDMAzzcDzI/ZtjgAyMKbW6YBiGYZjGCmeBMQzDMAzT6GADxDAMwzBMo4MNEMMwDMMwjQ42QAzDMAzDNDrYADEMwzAM0+hgA8QwDMMwTKPDJAzQsmXL4O/vD0tLS8TExODw4cN17r9u3TqEhITA0tIS4eHh2Lx5s+R1IsLs2bPh5eUFKysr9OrVCxcvXqzl0xiGYRiGaWwY3QCtXbsWU6ZMwZw5c3D8+HFEREQgLi4OmZmZNe6/f/9+DB8+HGPGjMGJEycQHx+P+Ph4JCUlCft88MEHWLp0KT777DMcOnQINjY2iIuLQ0lJiaEOi2EYhmEYE8bolaBjYmLQvn17fPLJJwCAyspKNGnSBJMnT8b06dP19h86dCgKCwvx66+/Cts6dOiAyMhIfPbZZyAieHt74/XXX8fUqVMBAHl5efDw8MDXX3+NYcOG3VdTQ1aCZhiGYRimYXiQ+7dRZ4DKyspw7Ngx9OrVS9imVCrRq1cvHDhwoMb3HDhwQLI/AMTFxQn7p6Wl4ebNm5J9HBwcEBMTU+tnlpaWIj8/X/KPYRiGYZjHF6MaoOzsbGg0Gnh4SDuOe3h44ObNmzW+5+bNm3Xur/v/QT5z3rx5cHBwEP41adLkbx0PwzAMwzDygHuBAZgxYwamTJkiPM/Ly4Ofnx/PBDEMwzCMjNDdt+sT3WNUA+Tq6gqVSoVbt25Jtt+6dQuenp41vsfT07PO/XX/37p1C15eXpJ9IiMja/xMtVoNtVotPNedQJ4JYhiGYRj5cffuXTg4ONS5j1ENkIWFBdq1a4ft27cjPj4egDYIevv27Xj11VdrfE9sbCy2b9+OxMREYdu2bdsQGxsLAAgICICnpye2b98uGJ78/HwcOnQIEydOrJcub29vXLt2DXZ2dlAoFH/7+KqTn5+PJk2a4Nq1a7IJrmbNhkGOmgF56mbNhkOOulmzYWgozUSEu3fvwtvb+777Gn0JbMqUKRg5ciSioqIQHR2NxYsXo7CwEKNGjQIAjBgxAj4+Ppg3bx4AICEhAd26dcOHH36Ifv364YcffsDRo0fx+eefAwAUCgUSExPx7rvvIigoCAEBAZg1axa8vb0Fk3U/lEolfH19G+R4AcDe3l42F6kO1mwY5KgZkKdu1mw45KibNRuGhtB8v5kfHUY3QEOHDkVWVhZmz56NmzdvIjIyElu2bBGCmNPT06FUVsVqd+zYEd999x3eeustzJw5E0FBQfj5558RFhYm7PPGG2+gsLAQ48ePR25uLjp37owtW7bA0tLS4MfHMAzDMIzpYfQ6QI0JOdYXYs2GQY6aAXnqZs2GQ466WbNhMAXNRq8E3ZhQq9WYM2eOJODa1GHNhkGOmgF56mbNhkOOulmzYTAFzTwDxDAMwzBMo4NngBiGYRiGaXSwAWIYhmEYptHBBohhGIZhmEYHGyCGYRiGYRodbIAY2ZGVlVWvPi8MIyeuXbsGjUZjbBkM88gw9bGaDdAjoqKiAoC2lYccuH79OtasWYM7d+4YW0q9uXLlCvr27YsJEyZAoVDI5lxnZmYiLS0NhYWFAOrXpM/YlJaWGlvC3+L69etYvHgxLl++DEAe5zotLQ1PP/00hg8fjry8PFlozsjIwOnTp5GdnW1sKQ+E3MZpgMfqBoWYh+af//wnPfnkk8aWUW/S09PJ1dWVFAoFbd261dhy7ktlZSWNHz+ezMzMyMfHh9zc3KikpMTYsurFq6++Sk5OThQWFkahoaG0b98+KisrM7asOklMTKSOHTvSzZs3jS3lgcjOzqaIiAiysLCg5cuXU0VFhbEl1UllZSW9/PLLpFKpKDQ0lNRqNeXk5Bhb1n1JSEggOzs7CgsLIw8PD1q/fj3l5+cbW9Z9kds4TcRjdUPDM0APQXJyMvr164dffvkF27Ztw5o1awCY/rcLCwsLdOrUCX5+fvjwww+RmZlpbEm18uGHH8LR0REnT57EkSNH8Nlnn8HNzQ1JSUnGlnZf5s2bh7179+Lnn3/G0qVLERwcjDFjxmDt2rXGllYjly5dQnx8PLZs2YIDBw7g66+/NrakB8La2hqOjo4IDQ3FunXrcObMGWNLqpUFCxYI1/Xhw4fxww8/wN/fH/v27TO2tDpZuXIlduzYgU2bNuGHH37AM888g1mzZmHJkiXGllYrch2nAR6rGxo2QA9BcnIyvLy88NVXXyEhIQFTp05FeXm5pHeZKXLixAkAwO7du7F161Zs3rxZWPIgE5p+LywsxLZt27B48WIcOnQIkZGR8PPzQ0pKiqDTlAexbdu2oUOHDujatSt69OiBH3/8Ea1atcI333yDY8eOGVueHtevX4e3tzdWrFiBBQsWYO7cuUhNTTW2rHpz/vx52NraYsOGDTh//jw2btyI3NxcAKZ1XQPAvn37sGjRIhw8eBBt27aFra0tbty4IVzPpnpdb9q0CQEBAejWrRtatWqF5cuXo3///li3bh3+/PNPY8urEbmO0wCP1Q2OUeefZIZGo5E8z87OpnPnzhERUVpaGnl7e9P06dNr3NcYiDWIH+/fv58GDRpEREQvvPAChYaGUnFxMd29e9fgGqtT/bxVVlZKXsvJyaGQkBB6//33DS3tgcjJyaHIyEhaunQpEZGwHPPnn39S586dKTEx0ZjyiIiovLxc8jw3N5dSU1OJSHveQ0JCaOTIkUZQVjfVdeuukcuXL1P37t2JiGjatGkUERFBZ8+eNYnrujbNRFXXRtu2bSkhIcGQsuqFTmtRURENHDiQpkyZInn91KlT1L9/f3rmmWeMoE4fuY3TRDxWGwvTt8AmwjvvvIMxY8bg3//+N3JycgAALi4uCA0NBQA0adIEM2bMwEcffSR0sCcjOvTqesXfdo4cOYLi4mIAwOrVq3HlyhX06tULISEhRp2Cr+kcKxQKITNGdwzW1tYoKCgwms7qfP/99zh37pzwnIjg7OwMPz8/bNy4EYD2OACgR48eiIqKwpkzZ3D27Fmj6AWA2bNnY8iQIZg8eTKSk5NRUVEBBwcHNG/eXND7wQcfYPXq1di9e7fRdFanum6NRiOc20OHDgnfMj/44AOUlZVhxIgRsLe3x5YtW0xOs+66VqlUKCoqgq+vL+7cuWMSAegrV67Etm3bAGivBSKClZUVXFxcsHPnTknwc+vWrdG7d29kZGRgx44dxpIMQH7jNMBjtVExrv8yfdLT06lt27YUHh5OkyZNIk9PT4qKiqJ169YRkdT1ZmVlUVRUFMXHxxtL7n31EhG988479PHHHxMR0caNG8nOzo5UKhUtWLDAJDXrzrHuG8eAAQOob9++kteMwa5duygsLIwUCgXNnDmTioqKiKjqG/2OHTtIoVDQrl27iKhqFuDo0aNka2tLR44cMbjmzMxM6tSpE4WHh9O//vUvatGiBUVERNBHH31ERPrns0+fPtS5c2cqLi42uFYx99NNRLR69Wrhm/2GDRvI3t6e1Go1TZ061SQ168617v/x48dThw4dJNsMzd69e6lt27akUCho9OjRdOPGDSKqunbT0tJIpVLRqlWrJO9LSUmhJk2a0IYNGwwtmYjkN04T8VhtCrABug9ff/01RUZGUm5uLhERFRQU0IABA6hz58508uRJIpJOb2/atEly0/v999/pwoULJqH32LFjRKTN8unRowd17dqVnJycaPHixeTv709jx44V3mdI6nOOxRk977zzDkVGRlJWVpbBteq4du0ajR49mhITE2nmzJnk5OREBw4ckOxTWFhIzzzzDLVp00YwR0Ta47O1tZUMdIZi48aNFBoaSunp6UREVFJSQomJiRQQEED79u0jIun1nJSURObm5rRq1SoqKyujTZs20d69e01K9549e4iIaNasWRQeHk5dunQhJycnWrRoEXXt2pWGDh1KKSkpJqVZd64rKiqEG8Pq1avJ09OTrl+/bnCtRER37tyhyZMn0/jx42nu3LnUrFkz+u6774TXdTe1CRMmUEBAgN645uzsTJ9++qlBNeuQ2zh9P808VhsGXgK7D1euXIG5uTlsbGwAADY2Nnj99dehVqsxf/58AICZmZkwjfrEE09g6NChGDlyJDp06ID4+HghENPYehcuXAgAcHJywpkzZ9CiRQscOXIECQkJ+Pjjj/Hll1/i0KFDBtNaH826c6xSqYRzbGdnh+LiYmg0GqNNX9vb26Nv374YM2YM3nvvPbi5uWHJkiWS37W1tTXmz5+PtLQ0zJgxA+fPnwcA/Prrr2jevDk6depkcN2ZmZkoKCiAh4cHAECtVmPChAkICwvD1KlTAWivZx2tWrXCq6++itdffx3t27fH4MGDUVRUZFK6p02bBgAIDg5GTk4OgoODcfToUSQmJuLtt9/GunXrsHPnToMHYdbnXKtUKmF/MzMzWFtbGy3Tx9raGvHx8ZgwYQJmzJiBwMBAfP/997hw4QKAqmXcxYsXo7y8HHPmzMHevXsBAJs3b4aPjw+6d+9uFO1yG6fvp5nHagNhTPclB6ZPn06xsbH0119/SbYvXLiQIiMjadu2bURU9e3o4sWL1Lt3b1IoFDR27FiD18eoS294eDgdPHiQbt26RWfPntWbkvz444+NUrOhvudY9w0uOTmZFAoFnTp1yuBaa2Pbtm2kUCjo559/1lva+OWXX4SaKQMGDCALCwuaOXOmUQIwP/30U4qKiqLDhw9Ltv/000/k5+dHa9euJaKq6zk1NZUGDhxICoWCxo8fb7R6L3Xp9vX1pY0bN1JpaSlduHBB77r+9ttvjXJd1/dc667r7OxsUigU9Oeffxpca03s37+fvL29afHixVRaWkpEJNSw2rlzJ/Xp04esra0pLi6OLCws6LXXXjNaULHcxun7aeax2jCwAaoF3R+K7hdYfW375MmTFBMTI4lwP3/+PLVv355atWpFSUlJhpRbL73R0dE0f/58vfcaq2Dc3znHREQHDhyg8ePHU0ZGhkmsK+uOo0+fPhQTE0PXrl3T2yclJYVWrVpFc+bMMcpgoDtPV69eJWdnZ1q8eLGkIOPVq1dpwIABNH78eGHfGzduUO/evSk4ONjg1/OD6H766afp5Zdf1rsWjHUz/jvnmojoypUrlJCQQOnp6Ua/rnXnbuzYsRQTE6O3vEukzXTcuHEjLV68mM6cOWNoiUQkv3GaiMdqU6JRG6AzZ87Q+vXra7yoxOvFgwcPpjZt2uitY8bExNDkyZOF5/n5+cI6qKnrNdTF+Cg1G/KGVl/d4udXr14lpVJJS5cuFbTWZIYaipSUFFqwYAGdP39e7zWx5kmTJlHTpk3pxIkTkn0GDhxIw4YNE56XlJTQxYsXG0yvjkep21DX9aPUbKjrur6axc9v3LhBAQEBNH36dMrLyyMiMsg1IebixYvUpUsXIfBa/Ds2xXH6UWs21DX9KDWbSnmB+9EoY4DKysowZswYtG7dGidOnJCkHerS+MzMzFBWVobU1FQsXLgQ58+fx6JFi5CXlwdA21NGrVbDyclJeK+dnR0iIiJkoVe3nt9QNIRmQxQuq6/uiooKJCcnC881Gg38/PyQmJiIRYsWYe3atYiLi8OMGTNQUlLSoJo1Gg0mTZqE8PBwJCcnIysrS3hNF/diZmaGkpISnDhxAkuWLIFGo8Enn3yCq1evSj7L0dFReKxWqxEYGCgr3Q19XTeE5oa+ruuruby8XEit1l3TXl5eePnll7Fp0yasWLECvXv3xujRo4W+dg2JroxBSEgI9u7dK5SNEKdbm9I43VCaDTFWP2rNcigyCaDxxQAtXbqUbG1tqWPHjnV+C1iyZAlZW1sL05Cff/45BQYGUlxcHP3yyy/02muvkZeXl976fmPXK1fNf0f3woULhaUN3bel9PR0UigUpFAoqF+/fgbp7fTBBx9Qp06d6ODBg5Lt4m9wS5YsITs7OyElfP369RQdHU1hYWG0YsUKSkhIIFdXV/rjjz8aXK+cdT/ummfMmCFkLIqvaTMzM1IoFBQfH2+QjJ558+aRtbU1de3aldLS0mjAgAFCUU7xrKwpjSGs2XBj9aOiURmgvLw8cnJyop49ewrbkpOTKTU1VQiCq6yspHHjxpG7uzt9++23kqm8TZs2Ud++fSk2NpaioqL0BpTGrleumv+u7upT02vWrCEzMzNq37693pJHQ1BZWUkFBQUUGxtLX3zxBRFpA1eXL19Oe/bsEarFTps2jZycnGj16tWSc33q1Cl64YUXKC4ujmJjY2uM82DdjUtz9Wt63bp1pFAoqH379nT8+PEG10xEtGLFCmrdujX997//Fba9/fbb1Lx5c+F5SUkJTZgwwWTGENZsuLH6UdIoDJD4j3rlypXk7OxMW7dupcGDB1Pz5s0pMDCQoqOj6auvviIibZCcbr2bSH89s6G7ZMtNr1w1PwrdOjQaDf3444+0fPlyg+jWkZKSQh4eHnTt2jWaMmUKeXl5UceOHcnd3Z169uxJhYWFlJWVJclyqX6Tq+l4Gho56m4smsUcOXLEYNe0bgzIycnRO28LFy6ksLAwSauWlJQUo48hrNlwY3VD8FgboEOHDhGR9BdWWVlJ0dHRpFAoaMyYMbRr1y763//+R2PGjCEvLy/avn07ERkn2l5ueuWqmUieumvSXFRURK1ataKRI0fSoEGD6Ny5c5STk0NnzpwhR0dHev31142efSFH3azZcOh01/R3pdO2Z88eUiqVRisSWR3W/HjwWBqgDRs2kLe3Nzk7O1NaWhoRSX/pR48epenTp1N2drawLS0tjZ599lmhbLchkZteInlqJpKn7ro03759m8aOHUt2dnY0cOBA0mg0wg1w5cqV5ODgIKlCzbpZsylork13bdlDqamp1LRpU2Em1liw5scLmYRq1581a9Zg7ty56Nq1K1q2bIn3338fgLTiatu2bfHmm2/CxcVF2Obv7w8bGxsQkUEyHOSqV66a5ar7fpqdnJzQs2dPWFhYQKPRSJo7tmzZEhYWFkK2GutmzaaguS7dtWUPWVpawsLCQmgMagxY8+PHY2OAdOl6gYGBeOKJJzB//nwMGDAAO3fuxM6dOyX7KBQK2NraSt5fXFyMjIwMtGrVSijzzXrlr1muuuujuaysDAAwYMAAvPjii9i4cSP++OMP4ea3d+9eREZGIjIy0iCa5aqbNRtGc3116/bRQUTw8fGBh4cHDh48CAAGbWvCmh9jjDTz9MhISUnRW8PWFW1KSkqSdKMl0g9KzM3NpfT0dBo9ejSFhoYKTehYr7w1E8lT94Nq1i11XL58mUaMGEE2NjY0cOBAGj58ODk7OwsBrA0d5yFH3azZdK+P6vtWVlZSQkICdezYkQoKChpUqw7WbBjNxkS2Bmjt2rXk7+9PwcHBFB0dTV9++aXwWvXMnpYtW9LKlSuJSLr2+dtvv9HEiRPJxcWFunfv3qAVTuWmV66a5ar772quXsH3s88+o2nTptGoUaNqrPjLulmzHK6PmmJUJkyYQBMnThT6krFmeWs2BWRpgLZu3Ur+/v60bNky2rJlC02ZMoXMzc3p888/FwL6dH/4169fpzFjxlD79u2Fuhe6X+yVK1do2bJlDV6QTG565apZrrofVrO4z5QhkaNu1iw/3bpZLEMcB2s23vViDGRlgHRO9u2336Z27dpJflGvvPIKRUVF0U8//aT3vl9//ZWioqKERpT9+vWj9PR01vuYaJar7keluX///rI814bUzZr5+mDNxtdsasjKAOkYOnQoDRkyhIiq3Ort27epc+fONHLkSMrIyCCiKkdbWFhIr7zyCikUCjIzM6O4uDgqKSlhvY+ZZrnqlqNmuepmzaybNRtfs6lg0gZo69atNHnyZFq0aJFQxIlI24fEzs5Ob8ru888/pxYtWtDOnTuFfQsKCmjRokWkUqmoe/fudPr0adYrc81y1S1HzXLVzZr5+mDNxtds6pikAbpx4wb179+f3N3d6YUXXqDw8HBycHAQfukXLlwgHx8fmjVrFhGRJFjL09OTFi1aJDw/e/YsxcTE0KpVq1ivzDXLVbccNctVN2vm64M1G1+zXDA5A1RYWEgjR46koUOH0uXLl4Xt0dHR9NJLLxERUX5+Pr377rtkZWUlrF3q1kO7detGY8eOZb2PmWa56pajZrnqZs2smzUbX7OcMLlCiNbW1lCr1XjppZcQEBCAiooKAEDfvn2RnJwMIoKdnR2ef/55tG3bFkOGDMHVq1ehUCiQnp6OzMxMxMfHs97HTLNcdctRs1x1s2bWzZqNr1lWGMl41Yk4ml1Xp+D555+ncePGSfa7fv06BQYGkr+/Pz333HPk7e1NPXv2NHh3WrnpJZKnZiJ56pajZiJ56mbNhkOOulkzI0ZBdK8xjInTuXNnjBs3DiNHjhTKcyuVSqSmpuLYsWM4dOgQIiIiMHLkSCMr1SI3vYA8NQPy1C1HzYA8dbNmwyFH3ay5EWNsB1YfLl26RB4eHnT06FFhmylXqZSbXiJ5aiaSp245aiaSp27WbDjkqJs1N25MLgZIDN2bnNq7dy9sbW3Rrl07AMDbb7+NhIQEZGZmGlOeHnLTC8hTMyBP3XLUDMhTN2s2HHLUzZoZADAztoC6UCgUAIDDhw9j0KBB2LZtG8aPH4+ioiJ8++23cHd3N7JCKXLTC8hTMyBP3XLUDMhTN2s2HHLUzZoZAKa/BFZcXEyBgYGkUChIrVbT+++/b2xJdSI3vUTy1EwkT91y1EwkT92s2XDIUTdrZmQRBN27d28EBQXho48+gqWlpbHl3Be56QXkqRmQp245agbkqZs1Gw456mbNjRtZGCCNRgOVSmVsGfVGbnoBeWoG5KlbjpoBeepmzYZDjrpZc+NGFgaIYRiGYRjmUWLSWWAMwzAMwzANARsghmEYhmEaHWyAGIZhGIZpdLABYhiGYRim0cEGiGEYhmGYRgcbIIZhGIZhGh1sgBiGeex46aWXEB8fb2wZDMOYMCbdC4xhGKY6up5ItTFnzhwsWbIEXOKMYZi6YAPEMIysyMjIEB6vXbsWs2fPxoULF4Rttra2sLW1NYY0hmFkBC+BMQwjKzw9PYV/Dg4OUCgUkm22trZ6S2Ddu3fH5MmTkZiYCCcnJ3h4eOCLL75AYWEhRo0aBTs7OwQGBuK3336T/KykpCT06dMHtra28PDwwIsvvojs7GwDHzHDMA0BGyCGYRoF33zzDVxdXXH48GFMnjwZEydOxODBg9GxY0ccP34cTz75JF588UUUFRUBAHJzc9GzZ0+0adMGR48exZYtW3Dr1i0MGTLEyEfCMMyjgA0QwzCNgoiICLz11lsICgrCjBkzYGlpCVdXV4wbNw5BQUGYPXs2cnJycPr0aQDAJ598gjZt2mDu3LkICQlBmzZtsHLlSuzYsQMpKSlGPhqGYR4WjgFiGKZR0Lp1a+GxSqWCi4sLwsPDhW0eHh4AgMzMTADAqVOnsGPHjhrjiS5duoQWLVo0sGKGYRoSNkAMwzQKzM3NJc8VCoVkmy67rLKyEgBQUFCAp59+GvPnz9f7LC8vrwZUyjCMIWADxDAMUwNt27bFjz/+CH9/f5iZ8VDJMI8bHAPEMAxTA5MmTcLt27cxfPhwHDlyBJcuXcLvv/+OUaNGQaPRGFsewzAPCRsghmGYGvD29sa+ffug0Wjw5JNPIjw8HImJiXB0dIRSyUMnw8gdBXG5VIZhGIZhGhn8NYZhGIZhmEYHGyCGYRiGYRodbIAYhmEYhml0sAFiGIZhGKbRwQaIYRiGYZhGBxsghmEYhmEaHWyAGIZhGIZpdLABYhiGYRim0cEGiGEYhmGYRgcbIIZhGIZhGh1sgBiGYRiGaXSwAWIYhmEYptHx/zDZQK1CK270AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a plot\n",
    "plt.plot(df.index, df[\"nb_transactions\"], label='nb_transactions')\n",
    "plt.plot(df.index, df[\"nb_payments\"], label='nb_payments')\n",
    "plt.legend()\n",
    "plt.title(\"Number of transactions evolution throughout time\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Nb of transactions\")\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f81bbfcd",
   "metadata": {},
   "source": [
    "Nous remarquons également que le taux de hachage est positivement fortement corrélée (> 0.7) au cours du Bitcoin. Plus le cours du Bitcoin augmente, plus le taux de hachage augmente.\n",
    "\n",
    "Cela peut suggérer que l'augmentation du cours du Bitcoin a un impact positif sur le taux de hachage. Cela peut aussi suggérer que l'augmentation du taux de hachage a un impact positif sur le cours du Bitcoin.\n",
    "\n",
    "En effet, le taux de hachage est entre autre une mesure de la puissance de calcul de traitement des transactions.\n",
    "\n",
    "Ainsi, nous pouvons suggérer que plus le taux de hachage augmente, plus la compétition entre les mineurs pour traiter et intégrer les transactions au réseau monétaire augmente, plus le réseau fait gage de qualité, plus la monaie est attractive et gagne en valeur.\n",
    "\n",
    "L'interprétation corrobore celles proposées pour les corrélations précedemment étudiées."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b60822a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between hash_rate and nb_transactions = 0.83 \n",
      "=> Strong correlation (> 0.7)\n"
     ]
    }
   ],
   "source": [
    "# Creating a plot\n",
    "plt.plot(df.index, df[\"hash_rate\"], label='hash_rate')\n",
    "plt.legend()\n",
    "plt.title(\"Hash rate evolution throughout time\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Hash rate\")\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.show()\n",
    "\n",
    "# Correlation between hash rate and nb of transactions\n",
    "print(\"Correlation between hash_rate and nb_transactions =\", round(df[\"hash_rate\"].corr(df[\"nb_transactions\"]), 2), \"\\n=> Strong correlation (> 0.7)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db5129a6",
   "metadata": {},
   "source": [
    "Nous remarquons finalement que le total des transactions de change (self_spent_satoshi) est positivement faiblement corrélée (~ 0.2) au cours du Bitcoin. Plus le cours du Bitcoin augmente, plus le total des transactions de change augmente.\n",
    "\n",
    "Cela peut suggérer que l'augmentation du cours du Bitcoin a un impact faible positif sur le total des transactions de change. Cela peut aussi suggérer que l'augmentation du total des transactions de change a un impact faible positif sur le cours du Bitcoin.\n",
    "\n",
    "En effet, les transactions de change sont des montants que les acteurs se renvoient à eux-mêmes au moment d'une transaction, à cause du fait que l'argent reçu par un acteur lors d'une transaction ne peut pas être dépensé partiellement mais doit l'être dans sa totalité.\n",
    "Ainsi, l'usage courant est de payer le montant demandé et de s'envoyer le reste.\n",
    "\n",
    "Ainsi, le total des transactions de change, soit le total des sommes que les acteurs se renvoient à eux même, peut être une approximation du nombre de transactions, pouvant impacté positivement le cours du Bitcoin.\n",
    "Approximation néanmoins pauvre due à la forte variabilité des sommes renvoyées par rapport au montant réellement payé (ex: Si l'acteur A possede 1000 BTC et doit payer 1 BTC, il se renvoie 999 à lui-même).\n",
    "\n",
    "L'interprétation corrobore celles proposées pour les corrélations précedemment étudiées."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d0e558e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between self_spent_satoshi and nb_transactions = 0.33 \n",
      "=> Medium correlation (< 0.7 and > 0.3)\n"
     ]
    }
   ],
   "source": [
    "# Creating a plot\n",
    "plt.plot(df.index, df[\"self_spent_satoshi\"], label='self_spent_satoshi')\n",
    "plt.legend()\n",
    "plt.title(\"Self spent satoshi evolution throughout time\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Self spent rate\")\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.show()\n",
    "\n",
    "# Correlation between self spent satoshi and nb of transactions\n",
    "print(\"Correlation between self_spent_satoshi and nb_transactions =\", round(df[\"self_spent_satoshi\"].corr(df[\"nb_transactions\"]), 2), \"\\n=> Medium correlation (< 0.7 and > 0.3)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5533f126",
   "metadata": {},
   "source": [
    "Cependant, nous remarquons que les nombres moyens d'entrées et de sorties par transaction sont négativement moyennement corrélées au cours du Bitcoin (> -0.7 et < -0.3). Plus le cours du Bitcoin augmente, plus les nombres moyens d'entrées et de sorties par transaction diminuent.\n",
    "\n",
    "Cela peut suggérer que l'augmentation du cours du Bitcoin a un impact modéré négatif sur les nombres d'entrées et de sorties par transaction. Cela peut aussi suggérer que l'augmentation des nombres d'entrées et de sorties par transaction a un impact modéré négatif sur le cours du Bitcoin.\n",
    "\n",
    "En effet, les transactions avec de nombreuses entrées et sorties peuvent être plus complexes et donc plus risquées à traiter pour les mineurs. Leur temps de traitement étant plus long, il diminue le nombre de transactions traités sur le réseau, paramètre pourtant positivement corrélé au cours du Bitcoin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c850b229",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a plot\n",
    "plt.plot(df.index, df[\"mean_nb_inputs\"], label='mean_nb_inputs')\n",
    "plt.plot(df.index, df[\"mean_nb_outputs\"], label='mean_nb_outputs')\n",
    "plt.legend()\n",
    "plt.title(\"Mean number of inputs/outputs evolution throughout time\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Mean number of inputs/outputs\")\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f5c537a",
   "metadata": {},
   "source": [
    "Nous remarquons enfin que les mesures de Bitcoins nouvellement créés sont négativement moyennement corrélées au cours du Bitcoin (> -0.7 et < -0.3). Plus le cours du Bitcoin augmente, plus les les mesures de Bitcoins nouvellement créés diminuent.\n",
    "\n",
    "Cela peut suggérer que l'augmentation du cours du Bitcoin a un impact modéré négatif sur les mesures de Bitcoins nouvellement créés. Cela peut aussi suggérer que l'augmentation des mesures de Bitcoins nouvellement créés a un impact modéré négatif sur le cours du Bitcoin.\n",
    "\n",
    "En effet, le protocole du Bitcoin prévoit une réduction de moitié de la récompense des mineurs tous les quatre ans environ. Cela signifie que la quantité de nouveaux bitcoins créés diminue progressivement avec le temps, ce qui peut entraîner une réduction de l'offre et une pression à la hausse sur le prix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "529cb507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a plot\n",
    "plt.plot(df.index, df[\"newly_created_coins\"], label='newly_created_coins')\n",
    "plt.legend()\n",
    "plt.title(\"Newly created coins evolution throughout time\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Newly created coins\")\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf79b63",
   "metadata": {},
   "source": [
    "Certains paramètres sont donc liés au cours du Bitcoin et peuvent l'influencer.\n",
    "\n",
    "Les paramètres pouvant l'influencer positivement d'après notre analyse seraient les différentes mesures de frais de mineurs, les nombres de transactions, le taux de hachage et le total des transactions de change.\n",
    "\n",
    "Les paramètres pouvant l'influencer négativement d'après notre analyse seraient les nombres moyens d'entrées et de sorties par transaction, et les mesures de Bitcoins nouvellement créés.\n",
    "\n",
    "Notre analyse corrélative est cependant limitée. \n",
    "\n",
    "Premièrement, nous ne controlons pas nos corrélations par des facteurs de confusion. \n",
    "\n",
    "Secondement, nos corrélations sont par essences des liens réciproques, n'étant pas des liens de causalité, nos interprétations ne restent que suggestives.\n",
    "\n",
    "Finalement, nous manquons de données. Ne traitant qu'une courte echelle de temps (2015 - 2017) par à rapport à l'évolution du cours d'une monnaie, nous ne pouvons avoir un avis davantage robuste sur le lien entre les différents paramètres."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ed7978c",
   "metadata": {},
   "source": [
    "## III. Volatilité"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97322cb8",
   "metadata": {},
   "source": [
    "Nous avons précédemment tenté de déterminer la tendance d'évolution du cours du Bitcoin ainsi que les différents paramètres pouvant l'influencer.\n",
    "\n",
    "Cependant, une des limitations était que nous manquions de données, ne traitant qu'une croute échelle de temps (2015 - 2017). Cela peut fausser notre analyse, la valeur des liens ou des courbes prédictives pouvant drastiquement changer en augmentant la durée d'étude.\n",
    "\n",
    "Pour confirmer notre inquiétude, nous pouvons mesurer la volatilité de nos données, soit la mesure des fluctuations ; plus la volatilité est importante, plus nos données fluctuent et changent au cours du temps.\n",
    "\n",
    "Pour calculer la volatilité du cours du Bitcoin, nous pouvons calculer les variations quotidiennes du prix en pourcentage, puis calculer l'écart type de ces variations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d58ea9ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BTC volatility: 3.36%\n"
     ]
    }
   ],
   "source": [
    "# Calculating the standard deviation of the daily percentage changes\n",
    "pct_change = df['price_usd'].pct_change()\n",
    "volatility = pct_change.std()\n",
    "print(\"BTC volatility: {:.2f}%\".format(volatility * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cf4cb0d",
   "metadata": {},
   "source": [
    "La volatilité du Bitcoin est d'environ 3% par jour. Pour nous faire une idée de son importance, comparons-la à celle de l'Or sur les 20 dernières années, connue pour être moins volatile que les crypto-monnaies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "019f7763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>289.5</td>\n",
       "      <td>289.50</td>\n",
       "      <td>280.00</td>\n",
       "      <td>283.7</td>\n",
       "      <td>21621</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>283.7</td>\n",
       "      <td>285.00</td>\n",
       "      <td>281.00</td>\n",
       "      <td>282.1</td>\n",
       "      <td>25448</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-06</td>\n",
       "      <td>281.6</td>\n",
       "      <td>282.80</td>\n",
       "      <td>280.20</td>\n",
       "      <td>282.4</td>\n",
       "      <td>19055</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-07</td>\n",
       "      <td>282.5</td>\n",
       "      <td>284.50</td>\n",
       "      <td>282.00</td>\n",
       "      <td>282.9</td>\n",
       "      <td>11266</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-10</td>\n",
       "      <td>282.4</td>\n",
       "      <td>283.90</td>\n",
       "      <td>281.80</td>\n",
       "      <td>282.7</td>\n",
       "      <td>30603</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5698</th>\n",
       "      <td>2022-08-29</td>\n",
       "      <td>1748.4</td>\n",
       "      <td>1757.90</td>\n",
       "      <td>1731.40</td>\n",
       "      <td>1749.7</td>\n",
       "      <td>156220</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5699</th>\n",
       "      <td>2022-08-30</td>\n",
       "      <td>1749.8</td>\n",
       "      <td>1752.80</td>\n",
       "      <td>1732.90</td>\n",
       "      <td>1736.3</td>\n",
       "      <td>130664</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5700</th>\n",
       "      <td>2022-08-31</td>\n",
       "      <td>1735.5</td>\n",
       "      <td>1738.00</td>\n",
       "      <td>1720.60</td>\n",
       "      <td>1726.2</td>\n",
       "      <td>176731</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5701</th>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>1723.0</td>\n",
       "      <td>1723.00</td>\n",
       "      <td>1699.10</td>\n",
       "      <td>1709.3</td>\n",
       "      <td>198618</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5702</th>\n",
       "      <td>2022-09-02</td>\n",
       "      <td>1707.9</td>\n",
       "      <td>1729.45</td>\n",
       "      <td>1707.05</td>\n",
       "      <td>1723.0</td>\n",
       "      <td>0</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5703 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date    Open     High      Low   Close  Volume Currency\n",
       "0    2000-01-04   289.5   289.50   280.00   283.7   21621      USD\n",
       "1    2000-01-05   283.7   285.00   281.00   282.1   25448      USD\n",
       "2    2000-01-06   281.6   282.80   280.20   282.4   19055      USD\n",
       "3    2000-01-07   282.5   284.50   282.00   282.9   11266      USD\n",
       "4    2000-01-10   282.4   283.90   281.80   282.7   30603      USD\n",
       "...         ...     ...      ...      ...     ...     ...      ...\n",
       "5698 2022-08-29  1748.4  1757.90  1731.40  1749.7  156220      USD\n",
       "5699 2022-08-30  1749.8  1752.80  1732.90  1736.3  130664      USD\n",
       "5700 2022-08-31  1735.5  1738.00  1720.60  1726.2  176731      USD\n",
       "5701 2022-09-01  1723.0  1723.00  1699.10  1709.3  198618      USD\n",
       "5702 2022-09-02  1707.9  1729.45  1707.05  1723.0       0      USD\n",
       "\n",
       "[5703 rows x 7 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the gold price data\n",
    "gold_df = pd.read_csv('timeseries/gold.csv', parse_dates=['Date'])\n",
    "gold_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "50d9feec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gold volatility: 1.10%\n"
     ]
    }
   ],
   "source": [
    "# Calculating the standard deviation of the daily percentage changes\n",
    "daily_returns = gold_df['Close'].pct_change()\n",
    "gold_volatility = daily_returns.std()\n",
    "print(\"Gold volatility: {:.2f}%\".format(gold_volatility * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1a682dc",
   "metadata": {},
   "source": [
    "La volatilité du Bitcoins entre 2015 et 2017 est ainsi plus élevée que pour l'Or sur les 20 dernières années, indiquant que le cours du Bitcoins subit de fortes fluctuations pouvant impacter la valeur des liens et des courbes prédictives, confirmant notre inquiétude."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96939fb1",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a38041af",
   "metadata": {},
   "source": [
    "En conclusion, l'étude des courbes prédictives tend à montrer que l'évolution du cours du Bitcoin serait exponentielle, augmentant de plus en plus vite au fur et à mesure du temps.\n",
    "\n",
    "L'étude corrélative tend quant à elle à montrer que l'évolution du cours du Bitcoin serait lié et possiblement influencé par plusieurs paramètres ; notamment positivement par les différentes mesures de frais de mineurs, les nombres de transactions, le taux de hachage, et le total des transactions de change, ou négativement par les nombres moyens d'entrées et de sorties par transaction, ou les mesures de Bitcoins nouvellement créés.\n",
    "\n",
    "L'étude de volatilité montre néanmoins que le cours du Bitcoin fluctue beaucoup au travers du temps, limitant les interprétations que l'on peut effectuer sur l'analyse de nos données.\n",
    "\n",
    "S'ajoute à cela d'autres limitations, comme la correspondance imparfaite de nos courbes prédictives, le manque de controle de nos corrélations par des facteurs de confusion, le fait que nos corrélations ne soient pas des liens de causalité, ou le fait que nous manquions de données.\n",
    "\n",
    "Enfin, notre étude permet tout de même de nous faire une idée sur l'évolution future du cours du Bitcoin ; continuant d'augmenter plus ou moins rapidement au fur et à mesure du temps, en connaissant des phases de flutuations importantes."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}