crypto_clustering2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Part_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Symbol</th>\n",
       "      <th>Volume</th>\n",
       "      <th>MarketCap</th>\n",
       "      <th>TotalSupply</th>\n",
       "      <th>ProofType</th>\n",
       "      <th>Network</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LEO</td>\n",
       "      <td>1.823284e+06</td>\n",
       "      <td>3586504072</td>\n",
       "      <td>985,239,504</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Ethereum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UNI</td>\n",
       "      <td>9.918768e+07</td>\n",
       "      <td>3992451215</td>\n",
       "      <td>1,000,000,000</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Ethereum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WBTC</td>\n",
       "      <td>1.415410e+08</td>\n",
       "      <td>4237575489</td>\n",
       "      <td>163,106</td>\n",
       "      <td>PoW</td>\n",
       "      <td>Ethereum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AVAX</td>\n",
       "      <td>2.197065e+08</td>\n",
       "      <td>4901630273</td>\n",
       "      <td>431,804,080</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Avalanche</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAI</td>\n",
       "      <td>2.177113e+08</td>\n",
       "      <td>5513660644</td>\n",
       "      <td>5,347,888,596</td>\n",
       "      <td>stablecoin</td>\n",
       "      <td>Ethereum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SHIB</td>\n",
       "      <td>2.479944e+08</td>\n",
       "      <td>5606251419</td>\n",
       "      <td>589,589,040,239,380</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Ethereum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LTC</td>\n",
       "      <td>5.913019e+08</td>\n",
       "      <td>5729295460</td>\n",
       "      <td>84,000,000</td>\n",
       "      <td>PoW</td>\n",
       "      <td>Litecoin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TRX</td>\n",
       "      <td>2.719028e+08</td>\n",
       "      <td>6018922345</td>\n",
       "      <td>88,996,805,683</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Tron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DOT</td>\n",
       "      <td>2.035592e+08</td>\n",
       "      <td>6659549868</td>\n",
       "      <td>1,364,182,225</td>\n",
       "      <td>PoW</td>\n",
       "      <td>Polkadot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MATIC</td>\n",
       "      <td>4.232990e+08</td>\n",
       "      <td>8053184610</td>\n",
       "      <td>10,000,000,000</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Polygon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SOL</td>\n",
       "      <td>6.338963e+08</td>\n",
       "      <td>8349992848</td>\n",
       "      <td>559,509,636</td>\n",
       "      <td>PoH</td>\n",
       "      <td>Solana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>6.311493e+08</td>\n",
       "      <td>10342393982</td>\n",
       "      <td>141,339,436,384</td>\n",
       "      <td>PoW</td>\n",
       "      <td>Dogecoin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ADA</td>\n",
       "      <td>3.647996e+08</td>\n",
       "      <td>12109137813</td>\n",
       "      <td>36,244,287,753</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Cardano</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BUSD</td>\n",
       "      <td>5.084396e+09</td>\n",
       "      <td>12132993575</td>\n",
       "      <td>2,213,836,281</td>\n",
       "      <td>stablecoin</td>\n",
       "      <td>Binance Smart Chain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>XRP</td>\n",
       "      <td>1.349225e+09</td>\n",
       "      <td>23581660945</td>\n",
       "      <td>99,988,380,689</td>\n",
       "      <td>RPCA</td>\n",
       "      <td>XRP Ledger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>USDC</td>\n",
       "      <td>3.665726e+09</td>\n",
       "      <td>37317414284</td>\n",
       "      <td>25,540,319,537</td>\n",
       "      <td>stablecoin</td>\n",
       "      <td>Ethereum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>BNB</td>\n",
       "      <td>6.807966e+08</td>\n",
       "      <td>44599661514</td>\n",
       "      <td>153,845,639</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Binance Smart Chain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>USDT</td>\n",
       "      <td>3.314948e+10</td>\n",
       "      <td>74588184814</td>\n",
       "      <td>866,300,000.00</td>\n",
       "      <td>stablecoin</td>\n",
       "      <td>Ethereum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ETH</td>\n",
       "      <td>8.843689e+09</td>\n",
       "      <td>196082390477</td>\n",
       "      <td>120,249,203</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Ethereum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BTC</td>\n",
       "      <td>2.296764e+10</td>\n",
       "      <td>459083734664</td>\n",
       "      <td>19,508,418</td>\n",
       "      <td>PoW</td>\n",
       "      <td>Bitcoin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Symbol        Volume     MarketCap          TotalSupply   ProofType  \\\n",
       "0     LEO  1.823284e+06    3586504072          985,239,504         PoS   \n",
       "1     UNI  9.918768e+07    3992451215        1,000,000,000         PoS   \n",
       "2    WBTC  1.415410e+08    4237575489              163,106         PoW   \n",
       "3    AVAX  2.197065e+08    4901630273          431,804,080         PoS   \n",
       "4     DAI  2.177113e+08    5513660644        5,347,888,596  stablecoin   \n",
       "5    SHIB  2.479944e+08    5606251419  589,589,040,239,380         PoS   \n",
       "6     LTC  5.913019e+08    5729295460           84,000,000         PoW   \n",
       "7     TRX  2.719028e+08    6018922345       88,996,805,683         PoS   \n",
       "8     DOT  2.035592e+08    6659549868        1,364,182,225         PoW   \n",
       "9   MATIC  4.232990e+08    8053184610       10,000,000,000         PoS   \n",
       "10    SOL  6.338963e+08    8349992848          559,509,636         PoH   \n",
       "11   DOGE  6.311493e+08   10342393982      141,339,436,384         PoW   \n",
       "12    ADA  3.647996e+08   12109137813       36,244,287,753         PoS   \n",
       "13   BUSD  5.084396e+09   12132993575        2,213,836,281  stablecoin   \n",
       "14    XRP  1.349225e+09   23581660945       99,988,380,689        RPCA   \n",
       "15   USDC  3.665726e+09   37317414284       25,540,319,537  stablecoin   \n",
       "16    BNB  6.807966e+08   44599661514          153,845,639         PoS   \n",
       "17   USDT  3.314948e+10   74588184814       866,300,000.00  stablecoin   \n",
       "18    ETH  8.843689e+09  196082390477          120,249,203         PoS   \n",
       "19    BTC  2.296764e+10  459083734664           19,508,418         PoW   \n",
       "\n",
       "                Network  \n",
       "0              Ethereum  \n",
       "1              Ethereum  \n",
       "2              Ethereum  \n",
       "3             Avalanche  \n",
       "4              Ethereum  \n",
       "5              Ethereum  \n",
       "6              Litecoin  \n",
       "7                  Tron  \n",
       "8              Polkadot  \n",
       "9               Polygon  \n",
       "10               Solana  \n",
       "11             Dogecoin  \n",
       "12              Cardano  \n",
       "13  Binance Smart Chain  \n",
       "14           XRP Ledger  \n",
       "15             Ethereum  \n",
       "16  Binance Smart Chain  \n",
       "17             Ethereum  \n",
       "18             Ethereum  \n",
       "19              Bitcoin  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ایمپورت دیتا\n",
    "data = pd.read_csv('coins_data.xlsx - Sheet1.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.47164572, -0.41411995],\n",
       "       [-0.4601297 , -0.41021383],\n",
       "       [-0.45512025, -0.40785519],\n",
       "       [-0.44587503, -0.4014655 ],\n",
       "       [-0.44611101, -0.3955764 ],\n",
       "       [-0.4425292 , -0.39468547],\n",
       "       [-0.40192363, -0.39350151],\n",
       "       [-0.43970138, -0.39071465],\n",
       "       [-0.44778489, -0.38455039],\n",
       "       [-0.4217946 , -0.37114051],\n",
       "       [-0.39688566, -0.36828455],\n",
       "       [-0.39721057, -0.34911321],\n",
       "       [-0.42871377, -0.33211319],\n",
       "       [ 0.12950864, -0.33188364],\n",
       "       [-0.31227828, -0.22172191],\n",
       "       [-0.03828817, -0.0895533 ],\n",
       "       [-0.39133841, -0.01948183],\n",
       "       [ 3.44897798,  0.26907471],\n",
       "       [ 0.57414873,  1.43812015],\n",
       "       [ 2.2446949 ,  3.96878013]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "#انتخاب ستون های مورد نظر برای خوشه بندی\n",
    "subset_data = data[['Volume', 'MarketCap']]\n",
    "subset_data = StandardScaler().fit_transform(subset_data)\n",
    "subset_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "subset_data = pd.DataFrame(subset_data, columns=['Volume', 'MarketCap'])\n",
    "#رسم اسکترپلات برای مارکت کپ و حجم معاملاتی\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(subset_data['Volume'], subset_data['MarketCap'], alpha=0.5)\n",
    "plt.xlabel('Standardized Volume')\n",
    "plt.ylabel('Standardized MarketCap')\n",
    "plt.title('Scatter Plot of Standardized Data')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.cluster import hierarchy as shc\n",
    "#خوشه بندی با متد واریانس\n",
    "linkage_matrix = shc.linkage(subset_data, method='ward')\n",
    "#رسم دندوگرام برای دو فیچر\n",
    "shc.dendrogram(linkage_matrix, orientation='top', labels=data['Symbol'].values, distance_sort='descending')\n",
    "plt.title('Dendrogram')\n",
    "plt.xlabel('Cryptocurrencies')\n",
    "plt.ylabel('Distance')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import fcluster\n",
    "#ایجاد و نمایش خوشه ها\n",
    "clusters = fcluster(linkage_matrix, t=2, criterion='maxclust')\n",
    "data_copy = data.copy()\n",
    "data_copy['Cluster'] = clusters\n",
    "plt.figure(figsize=(10, 6))\n",
    "for i in range(1, 3):\n",
    "    cluster = data_copy[data_copy['Cluster'] == i]\n",
    "    #نمایش خوشه ها بر اساس فاصله محاسبه شده طبق مارکت کپ و حجم معاملاتی\n",
    "    plt.scatter(cluster['Volume'], cluster['MarketCap'], label=f'Cluster {i}')\n",
    "    #نمایش نام کوین ها\n",
    "    for index, row in cluster.iterrows():\n",
    "        plt.annotate(row['Symbol'], (row['Volume'], row['MarketCap']))\n",
    "\n",
    "plt.xlabel('Volume')\n",
    "plt.ylabel('MarketCap')\n",
    "plt.title('Clustering Results')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "در این قسمت همانطور که در دندوگرام مشاهده میشود بیت کوین و تتر بدلیل حجم بالای هم مارکت کپ و هم حجم معاملاتی به هم شبیه ترند و در یک دسته قرار گرفته اند"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Part_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EssVEhYiIiGSLiQoRERHJFhMVIiIiki0mKkRERCRbTFSIiIhItsxNHQARkZwJIZCQkmbqMEwmPjk10//nRjYWZpAkydRh5DpMVIiIsiCEQOfFJxFx/7WpQ5EFrykHTB2CSXl55MOm72ozWTEynvohIspCQkoakxRSOnf/da7uXTMV9qgQEWng3JimsLU0M3UYZALxyWnwmrLf1GHkWkxUiIg0YGtpBltLHjKJjI2nfoiIiEi2mKgQERGRbDFRISIiItliokJERESyxUSFiIiIZIuJChEREckWExUiIiKSLSYqREREJFsmT1QePXqEnj17In/+/LCxsUHFihVx7tw5U4dFREREMmDSaRZfv36NunXrolGjRti9ezdcXFxw69Yt5MuXz5RhERERkUyYNFGZPn063N3dsXLlSuU2T09PE0ZEREREcmLSUz87duyAl5cXunTpAldXV1StWhXLli3LsnxSUhJiYmJUbkRERJRzmTRRuXv3LhYtWoRSpUphz549+P777zFkyBCsXr060/LBwcFwdHRU3tzd3Y0cMRERERmTSROV9PR0VKtWDVOnTkXVqlUxYMAA9O/fH4sXL860fGBgIKKjo5W3qKgoI0dMRERExmTSRKVQoUIoV66cyrayZcviwYMHmZa3srKCg4ODyo2IiIhyLpMmKnXr1sXNmzdVtv3777/w8PAwUUREREQkJyZNVIYNG4ZTp05h6tSpuH37NkJDQ7F06VIMHjzYlGERERGRTJg0UalRowa2bt2KdevWoUKFCpg8eTLmzJkDPz8/U4ZFREREMmHSeVQAoHXr1mjdurWpwyAiIiIZMvkU+kRERERZYaJCREREssVEhYiIiGSLiQoRERHJFhMVIiIiki0mKkRERCRbTFSIiIhItpioEBERkWwxUSEiIiLZYqJCREREssVEhYiIiGSLiQoRERHJFhMVIiIiki0mKkRERCRbTFSIiIhItpioEBERkWwxUSEiIiLZYqJCREREssVEhYiIiGSLiQoRERHJFhMVIiIiki0mKkRERCRbTFSIiIhItpioEBERkWwxUSEiIiLZYqJCREREssVEhYiIiGSLiQoRERHJFhMVIiIiki0mKkRERCRbTFSIiIhItpioEBERkWwxUSEiIiLZYqJCREREsmXSRGXChAmQJEnlVqZMGVOGRERERDJibuoAypcvj/379yvvm5ubPCQiIiKSCZNnBebm5ihYsKCpwyAiIiIZMvkYlVu3bqFw4cIoXrw4/Pz88ODBgyzLJiUlISYmRuVGREREOZdJE5VatWph1apVCAsLw6JFi3Dv3j189dVXePv2bablg4OD4ejoqLy5u7sbOWIiIiIyJpMmKj4+PujSpQsqVaoEb29v7Nq1C2/evMHGjRszLR8YGIjo6GjlLSoqysgRExERkTGZfIzK+/LmzYvSpUvj9u3bmT5uZWUFKysrI0dFREREpmLyMSrvi42NxZ07d1CoUCFTh0JEREQyYNJEJSAgAIcPH0ZkZCROnDiBDh06wMzMDL6+vqYMi4iIiGTCpKd+Hj58CF9fX7x8+RIuLi6oV68eTp06BRcXF1OGRURERDJh0kRl/fr1pmyeiIiIZE5WY1SIiIiI3sdEhYiIiGSLiQoRERHJFhMVIiIiki0mKkRERCRbTFSIiIhItrKdqCQmJuojDiIiIiI1OiUq6enpmDx5Mtzc3GBnZ4e7d+8CAMaOHYvly5frNUAiIiLKvXRKVKZMmYJVq1bhl19+gaWlpXJ7hQoV8Pvvv+stOCIiIsrddEpU1qxZg6VLl8LPzw9mZmbK7ZUrV8aNGzf0FhwRERHlbjolKo8ePULJkiXVtqenpyMlJSXbQREREREBOiYq5cqVw9GjR9W2b968GVWrVs12UERERESAjosSjhs3Dr1798ajR4+Qnp6OLVu24ObNm1izZg3+/vtvfcdIREREuZROPSrt2rXDX3/9hf379yNPnjwYN24crl+/jr/++gvNmjXTd4xERESUS+nUowIAX331Ffbt26fPWIiIiIhU6NSjcvbsWZw+fVpt++nTp3Hu3LlsB0VEREQE6JioDB48GFFRUWrbHz16hMGDB2c7KCIiIiJAx0Tl2rVrqFatmtr2qlWr4tq1a9kOioiIiAjQMVGxsrLC06dP1bY/fvwY5uY6D3shIiIiUqFTVtG8eXMEBgZi+/btcHR0BAC8efMGP//8M6/6ISIikhEhBERCgkZl05PT/u//8QlITzX7SGlVko0NJEnSOr5P0SlRmTFjBurXrw8PDw/lBG8XL15EgQIF8Mcff+g1QCIiItKNEAL3e/gh4cIFjconmlkCbaYCAG7VrQfrtGSN27KpVg0eIWv1nqzolKi4ubnh8uXLCAkJwaVLl2BjY4O+ffvC19cXFhYWeg2QiIiIdCMSEjROUgDAOi0Zu7cF6NRWwvnzEAkJkGxtdXp+VnQeUJInTx4MGDBAn7EQERGRgZQ6fgwKGxu915uekIBbdevpvd4MOicqt27dwqFDh/Ds2TOkp6erPDZu3LhsB0ZERET6o7CxgULPvR3GoFOismzZMnz//fdwdnZGwYIFVc5HSZLERIWIiIj0QqdEZcqUKQgKCsLIkSP1HQ8RERGRkk7zqLx+/RpdunTRdyxEREREKnRKVLp06YK9e/fqOxYiIiIiFTqd+ilZsiTGjh2LU6dOoWLFimqXJA8ZMkQvwREREVHuplOisnTpUtjZ2eHw4cM4fPiwymOSJDFRISIiIr3QKVG5d++evuMgIiIiUqPTGBUiIiIiY9B5wreHDx9ix44dePDgAZKTVdcCmDVrVrYDIyIiItIpUTlw4ADatm2L4sWL48aNG6hQoQIiIyMhhEC1atX0HSMRERHlUjqd+gkMDERAQACuXLkCa2tr/Pnnn4iKikKDBg10nl9l2rRpkCQJQ4cO1en5RERElPPolKhcv34dvXr1AgCYm5sjISEBdnZ2mDRpEqZPn651fWfPnsWSJUtQqVIlXcIhIiKiHEqnRCVPnjzKcSmFChXCnTt3lI+9ePFCq7piY2Ph5+eHZcuWIV++fLqEQ0RERDmUTonKl19+iWPHjgEAWrZsiREjRiAoKAj9+vXDl19+qVVdgwcPRqtWrdC0adNPlk1KSkJMTIzKjYiIiHIunQbTzpo1C7GxsQCAiRMnIjY2Fhs2bECpUqW0uuJn/fr1OH/+PM6ePatR+eDgYEycOFGXkImIiOgzpFOiUrx4ceX/8+TJg8WLF2tdR1RUFP73v/9h3759sLa21ug5gYGBGD58uPJ+TEwM3N3dtW6biIiIPg86nfopXrw4Xr58qbb9zZs3KknMx0RERODZs2eoVq0azM3NYW5ujsOHD2PevHkwNzdHWlqa2nOsrKzg4OCgciMiIqKcS6celcjIyEwTiaSkJDx69EijOpo0aYIrV66obOvbty/KlCmDkSNHwszMTJfQiIiIKAfRKlHZsWOH8v979uyBo6Oj8n5aWhoOHDiAYsWKaVSXvb09KlSooLItT548yJ8/v9p2IiIiyp20SlTat28P4N0Kyb1791Z5zMLCAsWKFcPMmTP1FhwRERHlblolKunp6QAAT09PnD17Fs7OznoNJjw8XK/1ERER0edNpzEq9+7dU9v25s0b5M2bN7vxEBERESnpdNXP9OnTsWHDBuX9Ll26wMnJCW5ubrh06ZLegiMiIqLcTadEZfHixcr5S/bt24f9+/cjLCwMPj4++PHHH/UaIBEREeVeOp36efLkiTJR+fvvv9G1a1c0b94cxYoVQ61atfQaIBEREeVeOvWo5MuXD1FRUQCAsLAw5To9QohM51chIiIi0oVOPSodO3ZEjx49UKpUKbx8+RI+Pj4AgAsXLqBkyZJ6DZCIiIhyL50SldmzZ6NYsWKIiorCL7/8Ajs7OwDA48ePMWjQIL0GSERERLmXTomKhYUFAgIC1LYPGzYs2wERERERZdA4UdmxYwd8fHxgYWGhMpV+Ztq2bZvtwIiIiIg0TlTat2+PJ0+ewNXVVTmVfmYkSeKAWiIiItILjROVjOnzP/w/ERERkaFoPUYlPT0dq1atwpYtWxAZGQlJklC8eHF06tQJX3/9NSRJMkScRERElAtpNY+KEAJt27bFt99+i0ePHqFixYooX748IiMj0adPH3To0MFQcRIREVEupFWPyqpVq3DkyBEcOHAAjRo1Unns4MGDaN++PdasWYNevXrpNUgiIiLKnbTqUVm3bh1+/vlntSQFABo3boxRo0YhJCREb8ERERFR7qZVonL58mW0aNEiy8d9fHy4ejIRERHpjVaJyqtXr1CgQIEsHy9QoABev36d7aCIiIiIAC0TlbS0NJibZz2sxczMDKmpqdkOioiIiAjQcjCtEAJ9+vSBlZVVpo8nJSXpJSgiIiIiQMtEpXfv3p8swyt+iIiISF+0SlRWrlxpqDiIiIiI1Gg1RoWIiIjImJioEBERkWwxUSEiIiLZYqJCREREssVEhYiIiGSLiQoRERHJFhMVIiIiki0mKkRERCRbTFSIiIhItpioEBERkWwxUSEiIiLZYqJCREREssVEhYiIiGTLpInKokWLUKlSJTg4OMDBwQG1a9fG7t27TRkSERERyYhJE5UiRYpg2rRpiIiIwLlz59C4cWO0a9cOV69eNWVYREREJBPmpmy8TZs2KveDgoKwaNEinDp1CuXLlzdRVERERCQXJk1U3peWloZNmzYhLi4OtWvXzrRMUlISkpKSlPdjYmKMFR4RERGZgMkH0165cgV2dnawsrLCd999h61bt6JcuXKZlg0ODoajo6Py5u7ubuRoiYiIyJhMnqh88cUXuHjxIk6fPo3vv/8evXv3xrVr1zItGxgYiOjoaOUtKirKyNESERGRMZn81I+lpSVKliwJAKhevTrOnj2LuXPnYsmSJWplraysYGVlZewQiYiIyERM3qPyofT0dJVxKERERJR7mbRHJTAwED4+PihatCjevn2L0NBQhIeHY8+ePaYMi4iIiGTCpInKs2fP0KtXLzx+/BiOjo6oVKkS9uzZg2bNmpkyLCIiIpIJkyYqy5cvN2XzRKRPQgAp8aaOQr+S0977fzwAM5OFoncWtoAkmToKok8y+WBaIsoBhABWeANRp00diX4JKwAr3/3/15KAlIPGz7l/CfQLY7JCssdEhYiyLyU+5yUpAGylJERa9zB1GIYRderd+2aZx9SREH0UExUi0q+A24ClramjoKwkxwMzSpo6CiKNMVEhIv2ytOWvdCLSG9nNo0JERESUgYkKERERyRYTFSIiIpItJipEREQkWxxMSyRXn9MEasnxmf//c8CJz4hkjYkKkRx9zhOofW6XvnLiMyJZ46kfIjnKoROoyVLGxGdEJEvsUSGSO06gZhic+Izos8BEhUjuOIEaEeViPPVDREREssVEhYiIiGSLiQoRERHJFhMVIiIiki0mKkRERCRbTFSIiIhItpioEBERkWwxUSEiIiLZYqJCREREssVEhYiIiGSLiQoRERHJFhMVIiIiki0mKkRERCRbTFSIiIhItpioEBERkWwxUSEiIiLZYqJCREREssVEhYiIiGSLiQoRERHJFhMVIiIiki0mKkRERCRbJk1UgoODUaNGDdjb28PV1RXt27fHzZs3TRkSERERyYhJE5XDhw9j8ODBOHXqFPbt24eUlBQ0b94ccXFxpgyLiIiIZMLclI2HhYWp3F+1ahVcXV0RERGB+vXrmygqIiIikguTJiofio6OBgA4OTll+nhSUhKSkpKU92NiYowSFxEREZmGbAbTpqenY+jQoahbty4qVKiQaZng4GA4Ojoqb+7u7kaOkoiIiIxJNonK4MGD8c8//2D9+vVZlgkMDER0dLTyFhUVZcQIiYiIyNhkcerH398ff//9N44cOYIiRYpkWc7KygpWVlZGjIyIiIhMyaSJihACP/zwA7Zu3Yrw8HB4enqaMhwiIiKSGZMmKoMHD0ZoaCi2b98Oe3t7PHnyBADg6OgIGxsbU4ZGREREMmDSMSqLFi1CdHQ0GjZsiEKFCilvGzZsMGVYREREJBMmP/VDRERElBXZXPVDRERE9CEmKkRERCRbTFSIiIhItpioEBERkWwxUSEiIiLZYqJCREREssVEhYiIiGSLiQoRERHJFhMVIiIiki0mKkRERCRbTFSIiIhItpioEBERkWwxUSEiIiLZYqJCREREssVEhYiIiGSLiQoRERHJFhMVIiIiki0mKkRERCRbTFSIiIhItpioEBERkWwxUSEiIiLZMjd1AESfFSGAlHjDt5Mcn/n/DcnCFpAk47RFRKQhJipEmhICWOENRJ02brszShqnHfcvgX5hTFaISFZ46odIUynxxk9SjCnqlHF6i4iItMAeFSJdBNwGLG1NHYV+JMcbr9eGyAiEEEhITdBbffEpae/9PwGQzPRWNwDYmNtAYk9mlpioEOnC0hawzGPqKIjoA0II9NrdCxefX9RfnekWACYDABpubABJkaK3ugGgqmtVrG6xmslKFpioEBFRjpGQmqDXJAUAJEUK7MuO0mud77vw7AISUhNga5FDemn1jIkKERHlSOFdw2FjbmPqMLKUkJqAhhsbmjoM2WOiQkREOZKNuQ17KXIAXvVDREREssVEhYiIiGSLiQoRERHJFhMVIiIiki2TJipHjhxBmzZtULhwYUiShG3btpkyHCIiIpIZkyYqcXFxqFy5MhYsWGDKMIiIiEimTHp5so+PD3x8fEwZAhEREckYx6gQERGRbH1WE74lJSUhKSlJeT8mJsaE0RAREZGhfVY9KsHBwXB0dFTe3N3dTR0SERERGdBnlagEBgYiOjpaeYuKijJ1SERERGRAn9WpHysrK1hZWZk6DCIiIjISkyYqsbGxuH37tvL+vXv3cPHiRTg5OaFo0aImjIyIiIjkwKSJyrlz59CoUSPl/eHDhwMAevfujVWrVpkoKiIiIpILkyYqDRs2hBDClCEQERGRjH1Wg2mJiIgod2GiQkRERLLFRIWIiIhki4kKERERyRYTFSIiIpItJipEREQkW0xUiIiISLaYqBAREZFsMVEhIiIi2fqsFiUkIqKcSQiBhNSEbNfzfh36qM/G3AaSJGW7HtIdExUiIjIpIQR67e6Fi88v6rXehhsbZruOqq5VsbrFaiYrJsRTP0REZFIJqQl6T1L05cKzC3rpmSHdsUeFiIhkI7xrOGzMbUwdBhJSE/TSI0PZx0SFiIhkw8bcBrYWtqYOg2SEiQp9voQAUuKN115yfOb/NwYLW4DnyIkoF2KiQp8nIYAV3kDUadO0P6Okcdtz/xLoF8ZkhYhyHQ6mpc9TSrzpkhRTiDpl3N4jIiKZYI8Kff4CbgOWOfScdnK88XtviIhkhIkKff4sbQHLPKaOgoiIDICnfoiIiEi2mKgQERGRbDFRISIiItliokJERESyxcG0RESkd9qshpydFY+5unHOx0SFiIj0KjurIWu7vg5XN875eOqHiIj0ypirIXN145yPPSpERGQwhloNmasb5x5MVIiIyGC4GjJlF0/9EBERkWwxUSEiIiLZYqJCREREssVEhYiIiGSLiQoRERHJFq/6Id0IAaTEm6795PjM/28KFrYAJ5vSP0N/xoz5GeJnhEhnskhUFixYgF9//RVPnjxB5cqV8dtvv6FmzZqmDuvzYIqEQQjgj3bAw3PGbTcrM0qatn33L4F+Yfwi0ichgBXeQNRp47Rn6M8QPyNEOjN5orJhwwYMHz4cixcvRq1atTBnzhx4e3vj5s2bcHV1NXV4+qXvpEJuCUNuFXXq3ftqmcfUkcibNp//5HjjJSnGEHUKiHsBWGo4nwh7YIiUTJ6ozJo1C/3790ffvn0BAIsXL8bOnTuxYsUKjBo1Sv8N6pIsaNstbGGjfpBhUqE/Qy5rfsAHMn8/9CE53vS9OZ8LY/aQuNUAem3T/3suBLCmHfBIx79hbT4r7IEhUjJpopKcnIyIiAgEBgYqtykUCjRt2hQnT57Uf4PG7k7OCQJua5cUaCo7B/15lbQrz4O+6aUYsYfk0dl377W+e7iS43RPUrTFXjoiJZMmKi9evEBaWhoKFCigsr1AgQK4ceOGWvmkpCQkJSUp70dHRwMAYmJiNGswOQ64fUr3gOXmh4uApf7X0EByAvBblXf/T0wF0tMM0EYccPes/uvNzO2TwMsnhvniShLv/h8TA1ga6HXKaW0Y43NriP3ICfsAGOX9jk+JR1pC2v9vIgapFqmfXRs5YR8AID0+HrFp/9eGIlUebWR8bwshPt2AMKFHjx4JAOLEiRMq23/88UdRs2ZNtfLjx48XAHjjjTfeeOONtxxwi4qK+mSuYNIeFWdnZ5iZmeHp06cq258+fYqCBQuqlQ8MDMTw4cOV99PT0/Hq1Svkz58fErv1iYiIPgtCCLx9+xaFCxf+ZFmTJiqWlpaoXr06Dhw4gPbt2wN4l3wcOHAA/v7+auWtrKxgZWWlsi1v3rxGiJSIiIj0ydHRUaNyJr/qZ/jw4ejduze8vLxQs2ZNzJkzB3FxccqrgIiIiCj3Mnmi0q1bNzx//hzjxo3DkydPUKVKFYSFhakNsCUiIqLcRxJCkyG3RERERMbHRQmJiIhItpioEBERkWwxUSEiIiLZYqJCREREssVEhYiIiGQrxyUqly9f1risEAJz5841YDSU27x+/drUIRAR5Sg5LlHx8vLCyJEjkZCQ8NFyZ8+ehZeXl8qU/HKxcOFCjcs+fPgQHTp0MGA09ClJSUnYtGkT2rdvj0KFCpk6HL3YtWsXBgwYYOowTObBgwefPIZkR3R0NFq0aIGpU6d+tFxQUBB8fHwQGxtrsFjIOBITE7FhwwZMmzYNy5Ytw+PHj00d0udDL6sLysjAgQOFQqEQnp6eYufOnWqPR0dHi++//16Ym5uLIkWKiD///FPvMcTHx4tnz57p/HxJksSXX34pLl++nGWZtLQ0MWPGDGFvby/s7e11bksIIU6ePCnOnj370TJnz54Vp06dylY7xnD69GkxduxY0blzZ9G8eXPRqVMnMXbsWHH69Gm9tpOeni727dsn+vTpIxwdHYUkScLV1VX07dtXr+28L7ufK21MmTJFKBQKg7YRHx8v7t+/r/Xz/vvvP/HFF1+IMWPGfLTc6NGjRZkyZcTTp0+1bkOhUIiQkBCtn6epKVOmCHt7+0++n0+fPhUODg5i2rRpBoslu3LS8cNQnj59KkqUKCEUCoWQJElIkiTy5Mkj9u3bp/e2DPl+eHp6iu3bt+sams5yXKIihBAnTpwQFSpUEAqFQnTu3Fk8evRICCFESEiIKFiwoDA3NxfDhw8Xb9++1bmNS5cuiTFjxogJEyaIa9euCSHefUC8vLyEQqEQCoVCuLm5iWXLlmld97p160ShQoWEhYWF+PHHH0VcXJza/lWqVElIkiTatWsnHjx4oPN+HDx4UCgUCrF27dqPllu7dq1QKBTi6NGjWtVvZ2enTKY0uTk4OOi0H8+ePRMtW7ZUORC8f1MoFKJly5bZ/qI/d+6cGDZsmChUqJCy3h49eogTJ06I9PT0bNUthGE/V9rQNVGxsbER69evV96PiYkRPj4+4tKlS2plMz5T2ho5cqRwcXH55N9vTEyMcHV1/WRCkxlJkgyaqFSvXl30799fo7IDBw4UNWrU0KmdmTNnanWbNWuWVvUb+vjxvidPnogxY8aImjVrivz58wtLS0uRP39+UatWLTF+/HidEtIMERERWt+04e/vL8zMzMSIESPEzp07xdy5c4Wzs7MoXry4zjFnxtDvh6H/LrKSIxMVIYRISUkRwcHBwtbWVjg6OoratWsLhUIhvvzyS3Hx4sVs1X3q1ClhZWWl/BK0tbUVe/fuFXZ2dqJQoUKiffv2onXr1sLJyUkoFAqdem3evHmj7B3y8PAQf/31l3j16pXo37+/MDMzEx4eHmLHjh3Z2g8hhOjatavGB8FatWoJX19frerv3bu36NOnj1Y3bcXFxYmKFSsKMzMz0a9fPxEeHi5ev34tUlNTxevXr8Xhw4dFv379hJmZmahUqZKIj4/Xqv47d+6ISZMmiS+++EIoFArh7u4uAgICxMaNG4UkSXrrlTPG50pTuiYqHx7IXrx4ISRJEgcOHFArq2uiUqFCBTFkyBCNyg4dOlRUqlRJ6zYMfUDOkyePWLJkiUZllyxZIuzs7HRqJ6ukPbPtGY9pw9DHjwwHDx4UTk5Oyr+LKlWqiHr16okqVaoIW1tbIUmScHZ21jkRyth3TW66vE6lS5dW621dv369UCgU4saNGzrFnBlDvx+mSlRMvtaPoZibmyMgIAB37tzB8uXLcerUKVSsWBH79u2DnZ1dtuqeMmUKChUqhJ07d6JAgQLo378/unfvjgoVKmD//v3IkycPAODVq1eoXbs2Zs2ahY4dO2rVhqOjIxYvXozevXvju+++Q7t27WBra4vk5GSMGDEC48ePh62tbbb2AwCOHTuW6UrVmWnfvj3mz5+vVf2rVq3SISrtzJw5E9euXcP27dvRqlUrlcfy5s2L+vXro379+ujYsSPatWuHWbNmYfTo0RrVXbt2bZw5cwbOzs7o3Lkzfv/9d9SrVw8AcOfOHb3uhzE+VznBnTt3MHToUI3Kli9fHkuXLtWpHUmSdHqeJoSWK5ekp6fr1M69e/dU7r969QrVq1dHSEgI6tSpo1Od7zP08QMAXrx4ga5du8LCwgKhoaHo3LkzzM3/76srNTUVmzZtwtChQ9GpUydcu3YN+fPn16qNlStXah2XNh48eICRI0eqbKtXrx6EEHj69Cm++OILvbRjjPfDFHJsonL06FEMHDgQN2/ehK+vL5ydnbFgwQJUqFABv/32G9q0aaNz3REREfjhhx9Qrlw5AMDYsWOxbds2fP/998ovEwBwcnJCv379EBQUpHNbLi4uyJ8/P4QQiIuLQ5kyZeDr66uXJAV4dxDQdABowYIF8fz5c720q0+bNm2Cn5+fWpLyoVatWsHPzw8bNmzQOFE5ffo0PD09MWvWLLRq1UrlAKlvxvxcfc7MzMyQnJysUdmUlBQoFLpdMzB06FCNPyeSJGmVuBYtWhQREREalY2IiEDRokU1rvt9Hh4eKvczfqQVKFBA7TFdGOP4sWzZMsTExODcuXOoWLGi2uPm5ubw9fVFuXLlUKNGDfz+++9qScGn9O7dW+u4tJGUlARra2uVbRn3U1NT9daOMd6PpUuXYv/+/RqVlSQJy5cv17qND+W4ROX169cICAjAqlWrULx4cYSFhaFZs2YAgK+//hoDBgxA+/bt0a5dO/z2229wc3PTuo2nT5/C3d1deb9IkSIAkGldbm5uiIuL07qN1NRUBAcHIzg4GLa2tliyZAmcnZ0xZMgQ1KxZEz/88AMmTZqk8gWmCwcHBzx58kSjsk+ePIGDg4NW9b969UrrmJycnLQqf+fOHQwZMkSjsl999RU2b96scd3z589HaGgoOnToACcnJ3Tq1Andu3dHw4YNtYpRE4b+XLVt21bjsrdv39aqbmMqUaIEjh07hu+///6TZY8fP44SJUro1I6bm5tOxwdNtGrVCosWLUJAQABKlSqVZblbt25h7dq1Gu2rKRj6+AEAe/fuRZs2bTJNUt5XuXJltG3bFmFhYVonKppKSUnBzp07ERISgk2bNmn13MjISJw/f155Pzo6GsC79zhv3rxq5atVq6Z1fMZ4P06fPq1xkq2vRCXHjVFxcXERlpaWYvTo0SIxMVHt8bS0NDFr1izl4M3Zs2dr3Yahz8MfPnxYlC1bVkiSJPz8/FQGib19+1b88MMPwszMTLi7u4tt27ZpHf/7fHx8RPXq1TUqW716ddGiRQut6tfm3K9CoRBmZmZa70PevHnF3LlzNSo7b948kTdvXq3buHv3rpg8ebLyfSlUqJDo0aOHUCgUYsuWLVrXlxlDf648PDxEsWLFtLrpsg+hoaFq+3Dw4EG97IMQQkyYMEFYWFiIEydOfLTcyZMnhYWFhZgwYYLWbRj6XPzTp0+Fs7OzKFSokFi/fr1ISUlReTwlJUWsX79euLm5CWdnZ/HkyRO9tPuxz5QuDH38EEKIAgUKiJkzZ2pUdtasWaJAgQJat/Ep4eHhon///spxMtqOGcrqOJjZdl3GwGQwxvGcY1T0oEyZMliyZAnKli2b6eMKhQLDhg1Dly5d4O/vjxEjRmh8vvt9cXFxyt6CjH/fvn2r1oOgy/wHDRs2RIkSJbB37140bdpU5TE7OzvMmzcPvXr1wsCBA9GxY0e0adMG27Zt07odAOjfvz86deqEgIAA/Prrr5melxdC4KeffsKFCxe06o0AgHHjxn3yXL8QAjt27MDFixe1qjtDpUqVsGXLFo16Vf78889P/jLLjKenJ8aMGYMxY8YgIiICISEh2LBhA4QQGDRoEHbv3o22bduiadOmal282jDk5yoyMlLnuLTxzTffYODAgSrbWrduDTMzM5VtunZ5Dx8+HKtXr0bz5s0xZswY9OzZU6Xn49GjR1i7di2CgoJQpEgRDBs2TKd2DMnV1RW7du1Chw4d0KNHD9jY2KB06dKwt7fH27dv8e+//yIhIQEFCxZUjlmSI0MfPwDgzZs3cHFx0ahs/vz58ebNG63byMzly5cREhKCdevW4dGjRyhYsCA6d+6Mtm3bokmTJlrVZegxMBmM8X6YhNFTI5nRpUfiY1mwPrLjMWPGZNob9KH3e4eyo0+fPkKSJFG+fHkRFBQktm/fLg4ePCi2b98upkyZIsqXLy8kSRK9e/fOVjuZ2b59u6hWrZqQJEmULFlSrFq1Sus61qxZIyRJEiNHjhRpaWmZlklPTxejRo0SCoVCrF69OrthCyHevf779u0TvXv3Fg4ODsq5EXRl6M+VMWh7hZcuV3kJ8a6Hq3LlysrXIV++fKJo0aIiX758ytenUqVK4vbt2zrVb+hfjvfv3xfx8fHizZs3Ytq0aaJu3brCyclJmJubCycnJ1GnTh0RHBwsXr9+rdd2P9bDpStDHz+0eS907aXLcP/+fREcHKyc3qJAgQKie/fuBr/KTp8M+X6YqkdFEkLL4ec5zL179+Dp6anVcyZOnKh1O+PHj9f6OZp69OhRts+lz5gxA9OmTcOrV69UsnAhBPLly4eRI0fixx9/1NuVENu3b8ekSZNw8eJFlChRAmPGjIGfn5/ar25NdevWDZs2bUKZMmXQo0cPVKpUSfnr9PLlywgNDcWNGzfQuXNnbNy4US/78L7ExERs374doaGh2L59u051GPNz9d9//yEsLAzXr19HTEwM7O3tUa5cObRo0QKFCxfWqU5jOXLkCMqWLQsnJyds3rwZO3bswI0bNxATEwMHBweUKVMGbdq0Ubs6RBsKhQJr165Fjx499Bz9O2ZmZvjjjz8MVn+GSpUqqdxPS0vD9evX4enpmen4NkmScOnSJa3bMeTxQ6FQYOjQocqr7T7myJEj+O2335CWlqZVG0uWLEFISAiOHz8OR0dHdOzYEd27d0fjxo1x9+5dlC5dGps3bzbaVXZHjhxB/fr1dX6+od4PQ/9dZCXXJiqXL1/GtGnTsHnzZo2vIJCbJ0+eYP369QgJCcHZs2ezXV9iYiKOHTum/PLKOOjXq1cPNjY2eogY2LZtmzJBKVmypLLrXtcrMzKkp6dj+vTpmDFjBl6/fq32x5k3b14EBARg1KhR2WorNjYWkZGRePv2Lezt7bM84MtVQkICAgIC8PvvvyM1NVXtMlkLCwt8++23mDFjht7ec30zxpf8/fv34eLiorer6z5krAN+w4YNtf4yOnTokE5tGer4oe3fqyRJWicqCoUCnp6emDlzJlq1agULCwvlY3fu3EGpUqWMkqjs2LED06dPx6lTp7TeBwDo168fBg4ciFq1ahnk/Vi9ejUaNGiAYsWK6fR8XeXIROXq1atYtGgR7ty5g3z58qFLly7K9XDOnz+PMWPGYM+ePbCwsECPHj2wYsUKE0esudjYWPz5558ICQnBoUOHkJaWhuLFi8v6Kg1ANUEpVaqUsgcluwnKhzL+OK9du6ZMJsqWLZvtg2VYWBiCgoJw6tQplTktzMzMUKdOHYwePVp5dZlcpaamonnz5ggPD0ejRo3Qq1cvVK5cWdnzdOnSJaxZswaHDh1Co0aNsHfvXp16uDIOKRlfkCkpKfjrr7/UyhUsWFCnuTyM+asuOjoaS5Yswd9//63W+9SmTRsMGDAAjo6OWtdrql+mn6PDhw9r/ZwGDRpoVb5mzZo4d+4cChQogC5dusDX1xe1a9cGoL9EZd++fZg7d67K91LG+Klt27ZhzJgxuH79OvLnzw9/f3+MGzdO6zaM+bm6ffs29u3bhzt37iiPtSVLlkSzZs10vtIuS0Y/2WRgJ0+eFDY2NmqzLc6ZM0fMmDFDmJmZibx584qRI0eK//77T6c2DD3d8odSUlLE9u3bRdeuXYWtra1QKBSicuXKYurUqeKff/7JVt2GtmXLFlGlShWhUCjEF198If74448sx5HI1axZs4RCoRAWFhaiadOmYujQoWLMmDFi6NChokmTJsLCwkKYmZmJefPmZasdQ3+u5s2bJyRJEgsXLvxouUWLFglJksRvv/2m9T7cvXtXWFlZiXHjxim3ZYyL+HBGVGtra3H37l2t2zDWefKTJ08KNzc3ZayVK1cW9erVE5UrVxbW1tZCkiRRpEgRcfLkSa3r/vDqKEPp27ev0dbYuXXrlli4cKEYMWKEGDBggBgxYoRYtGiRzuOEjO3WrVti3LhxolSpUkKSJOHh4SFGjhwpNm/enO0ZqHfu3Kn8/Lu4uAhzc3OhUCjEqFGjxJAhQ5Rj9BYtWiQSEhJ0bscYfxtv374V3bt3F2ZmZpnObmxmZib8/PxEbGys3trMcYmKj4+PyJcvnwgLCxNxcXHin3/+ETVq1BAODg7KtXPevHmTrTYMPd1yhiNHjoiBAweK/PnzC0mShJeXlwgICNDrtO2GXosnY/+rVasmfvnlF72vNSKEENOnT1euiyOEEKmpqeL06dOZrgVz8uRJrRYOvHbtmrCwsBB16tTJck2l+/fvizp16ghLS0tx/fp1rePPYOhLuWvUqCHat2+vUdl27doJLy8vrffhp59+EoUKFRJJSUnKbRmJyqxZs0R4eLgIDw8XBw8eFC4uLuKnn37Sug1jfMk/ePBA5M2bVxQsWFCsWbNGZX+EECIpKUmsWbNGFCxYUOTLl0/r9bYyFrH09PTU6KbrmjA59YvrfQ8ePBCnT58WL1++1Fudp06dEv7+/sLV1VX5d+nr6yuOHz+u05peDRo0EEWKFFEeH968eSNatGghrKyshLW1tViwYIFITU3NdtyGfr/T09NFo0aNhCRJwtvbW6xdu1ZcvHhR3L59W1y8eFGsXbtWNG/eXEiSJJo2baq3dnPcqZ/8+fNj0KBBmDx5snLbsWPHUL9+fQwfPhwzZszIdhurV6/W+jnazHwYGBiIdevW4cGDByhbtix8fX3RvXt3lCxZUu/nS/v06aP1OWxtLrUz1vnl97s7X758CVdXV+zbtw+NGzdWKRsSEoJevXpp3MbQoUOxZs0aZXdtVl69eoWSJUuib9++mDlzplbxZ9Dkc5WQkIClS5fi4sWLWr9W9vb2mDFjhtqlw5lZvHgxfvzxR7x9+1bj+gGgatWqqFevHn777TfltpcvX8LFxQX79+9XeT+GDRuG8PBwXLhwQas2FAqFVp9ZSZK0vhT6u+++w8aNGxEREfHRwfb37t1D9erV0a1bNyxatEjj+hUKBapUqaLVIPjMTp9p0o4hTwUIIdCkSROEh4ejefPm+Prrr1GhQgXY2dkhNjYW//zzD9asWYN9+/ahSZMm2Ldvn9ZtnD59Gnv27MGgQYPg7Oys3P7ff//B19cXx44dA/BuX//3v//p5RifIS0tDXv27EFISAh27NiB+Ph45M+fH61atdLqOOjo6IiRI0fi559/Vm6LiIhAjRo1MHHiRIwdO1Yv8SoUCgwcOFB52koTvXr10rjsli1b0LlzZ0ybNg0//fRTluWmTZuG0aNH488//0T79u01rj8rOW4elTdv3qB06dIq2zLuf/ilpStDT7c8ffp0eHp64tChQ1qfa9WWodfi+XCtEWPRV/597NgxdOnS5aNJCvBuNt0uXbrodD49w8c+V0lJSViyZAmmT5+Ox48fo0GDBpgwYYJW9UuSpNWaMbpcoXH79m189913KtsUCgUcHR1VBigC7/4udZ21smnTpmp/5/q0Z88efPvtt5+8ItDT0xPffvut1rOUAkBAQMBnP0Zl69atCA8Pz/KLq3LlyvDz81N+cW3btk3rL66FCxfi9OnTamM2evXqhaNHj6JBgwbw8vLC/v37MXv2bJQvXx59+/bNzm4pmZmZoWXLlmjZsiXi4+OxZcsWhISEIDQ0VKtE5e3bt2pLFmTcr1Gjhl5izbB06VIsWbJEo7KSJGmVqKxbtw6VK1f+aJICAKNGjcL69esRGhrKRCUzQgi1AYAZ97MzEZcxde7cGX///Te8vb3RvHlz+Pr6om3btga5uqRx48YYPXq01hMYaUof64mY0r1799CvXz+NylauXFnvEyglJSVh8eLF+OWXX/D48WM0bNgQ69at0+nSxTJlyiAsLEyj6djDwsJQpkwZrdtITU1VuyQ4X758eP36tVpZS0tLpKSkaN0G8C6p0+RLPjY2FjExMVrX//jx4ywnjfxQ2bJl8fjxY63bMJajR49q1aMkty+uU6dOoWXLlirbbt68iYMHD6Jly5b4+++/AbwbtF2zZk0sX75c50QlKSkJa9euxd69e1UGiZYqVQre3t7o0aMHevbsqdMaOR8m/hn3LS0tdYo1K5MmTUKLFi30WmeG8+fPw8/PT6Oybdu2RUhIiF7azXGJCgDs2rVLZb2D+Ph4SJKETZs2qc1+KkmS1jNXbtmyReuYtDlNs3HjRrx9+xabN29GaGgovv76a1hbW6N169aoU6eOXld1DQ8Px7fffqu3+j70sV4sSZJgbW0NDw8PtGzZEq1btzZYHLqKiYnR+KoOBwcHnb4UM5OUlIRFixbh119/xePHj9GoUSOdE5QMPXv2xLBhw7Bo0aKPJiuLFy/GX3/9hdmzZ2vdRqFChXDjxg2Nyt64cQMFCxbUug1tzJ07F+PHj9f61E/evHk1Tj4eP36s05U/xmLIX9jG+OJ6/Pix2urCO3fuhCRJKr13FhYW8PX1xdSpU7VuAwCuXLmCdu3a4f79+xBCwNHREXZ2dnj27BnOnz+PjRs3IigoCDt27NA4iX3fmjVrcOrUKeX9xMRESJKE+fPnq80sLkkS5s6dq9N+eHp6onr16jo991OePXum8Y9PDw8PPHv2TC/t5shEJTQ0FKGhoWrbM/tj1SVR6dy5MyRJUjm9kJE8ZHbKQZdxF/b29ujbty/69u2Lx48fK/cpY7KyhQsXIjExES1btsx0QSu5ePbs2UcTq/j4eOzbtw9LliyBt7c3tm/frnaKwJTS0tI0Tgy1PbWSmcTERGUPypMnT9CoUSOsX78eX331VbbqBYBBgwZhy5Yt8Pf3x9atW/H111+rXJ58+fJl/PHHH9i/fz/q1auHQYMGad1GgwYNEBISgokTJ350DpK4uDiEhITA29s7O7ukEV1OA9auXRurVq3C8OHDP9oTm5iYiFWrVqFu3brZCdGgDPkL2xhfXBYWFmqJ5vHjxwFA7XV3dXVFYmKi1m3Exsaibdu2ePbsGYKCgvD111+rLcuwZs0aTJkyBW3atMGlS5e07uHeu3cv9u7dq7Y9s+VPspOoGFJcXJzG0zxYW1sjPj5eL+3muETFGGMiPpwQ6c2bN+jQoQNmzJhhkEy2UKFCGDFiBEaMGIEbN27gjz/+wLp169CzZ0+Ym5ujbt26Ok/SZGj//PPPJ8skJCRgyZIlGD58OH755ReMHj1a63be70X7WA+apqt+vu/DX0JZ+ffff7Wu+32zZ8/Gr7/+iqdPn6Jx48bYtGmTXr8Azc3NsWvXLgwbNgwrVqzAgQMHVB7POG36zTffYPbs2TrN6jp8+HCsXbsWrVu3RkhISKZLzj9+/Bg9e/bEixcvZLkOD/Bu/Ej9+vXh4+OD1atXo2jRomploqKi0KdPH9y5c0frtVzu3bun8fo12WXIX9jG+OIqVaoUDh48CH9/fwDvjhfh4eGoVq2a2tixJ0+e6LQu0sqVK/HgwQMcOHAg05XR3dzcEBgYiFq1aqFZs2ZYtWoVBg8erHH92f0BIyf67NHXuM2cdtVPxvTaxjoIAFlf1aCr+fPno3v37ioj3DNz7NgxhISEYPPmzTqdMwXeDXTs2LGj2lTbWZEkSW8j1D/Uo0cPXLp0CVevXtXqeYa8skiXCel0PShlXM1SpUoVjRKU7PzqevToEXbv3q02MZ6Pjw+KFCmiU50Zli1bhkGDBkGhUKBhw4ZqV4EcPnwYqampmD9/vtrAW30LCgrCuHHjdJrl87fffsOwYcOgUCjw1VdfqfU+HTlyBOnp6ZgxY4ZOC5sC7yYTMzc3V/ZKJCUl4ffff8eRI0cQGxuLKlWqwN/fP9OETxOGvupHoVAgJCQEvr6+nyyr7RV3GdasWYM+ffpg4MCBqFOnDjZt2oSdO3di/vz5aqcwmzVrBgsLC+zatUurNlq0aAFJkrB7926NygLvxnFpateuXWrjbLISFxeHMWPG6HTqtW/fvvjuu+9Qq1YttcdSU1Nx69YtxMbGomzZsrCzs9O6foVCAWtra41+xKSmpiIpKUmnvz01ervQWSYUCoXRF03S99LpkiQJCwsL0apVKxEaGiri4uI+Wv7DJeK1bUubmyEXwps/f76wsbHR+nmRkZEa365cuSKOHz9ugOjfefXqlc7PNeV7cf36dTFp0iTx/fffi7lz54ro6Ohs1Xf8+HHRvHlzYWFhoRKzhYWFaNasmTh69KieIv+4KVOmZOt1OnbsmPD29s50P5o3by6OHDmiU72vXr0StWrVUs6J06hRI/Hq1StRt25dtfe5QIECOk2MJ4Th59WQJEnY2NhoNAeTjY2NTu9Fenq6GDx4sMqEgZktqHft2jWhUCh0mqiwcOHCYtq0aRqVnT59uihcuLBW9UuSJLp27frJSUY3b94sihQpIszNzbWq/307d+4UPXv2FH369FF+J23dulW4ubkpP2/W1tZi9OjRWtdtrEVHP5TjTv2IHNBBtGfPHoSGhmLbtm3YtWsX8uTJg/bt28PPzw/NmzdX+5Wv68JrGRYvXoxOnTplqw59iI+P12lftLmyKCgoSKfBlR+TlJSEHTt2ICQkBHv27EFCQoJO9Ri6e3j+/PmYN28eTpw4odJb9/fff6Nz585ISUlR/v3MmzcPp06d+mSvXlbq1KmjfC1u376tnHq+ZMmS2V4/5/z58xqX/e+//7LVVt26dREWFoaEhATcunVLZarw7OxHcHAwzp8/jxEjRqBAgQKYPXs22rVrh2vXrmHz5s1o0qQJUlNTsWPHDgwaNAjjxo3DH3/8oXU73t7eGv993Lt3D0ePHtVqMK2hp2oAoBxwOm7cONy7dw8eHh6ZDsJ2cnLCmTNn1AbeauLVq1caD+wuUKAAXr16pVX9U6dOxeTJk7F3714EBQWpjf+KjIzE4MGDERYWhqpVq2Lr1q1a1Z8hLCwMrVu3hoWFBWxsbLB27VqsWLEC33zzDcqVK4cuXbogNTUVe/bsQXBwMDw8PNC/f3+N69f2FKfe6CXdkRFjTa/9Pn33qGRITEwUmzdvFh07dlT+GnF1dRU//PCD3qbFNsXrlZn09HRRr149UadOHYO2k91f2BnS09PFvn37RJ8+fYSjo6NyplFtZr3VRkpKirh27Zo4c+ZMpjPuaqJZs2aiRYsWavW6uroKW1tbsWrVKvHPP/+I6dOnC3NzczF06FB9hK53xpoZWoh3f4O///676Nq1q6hevbooXbq0qF69uujevbtYuXKl2oy1mipVqpQYNGiQ8v6OHTuEJEkiODhYreyQIUNEwYIFdWrnw7/vly9fChsbGxEeHq5Wdu3atQbtMZUzbY6Dur5Od+/eFS1atBCSJIlatWqJixcvipSUFBEUFCRsbW2Fo6OjmDdvXraWGGnUqJGoWLGieP36tRBCiIEDBwobGxvRsmVLlRl1U1JSRPXq1UW1atV0bsuYclyPCmCawT6GaNfKygqdOnVCp06dVC5XXrRoERYsWABPT0/4+fmhR48eOv2KMIZP/fJISEjAzZs3sWjRIpw4cQJr1641UmS6iYiIQEhICNavX48nT55AkiR0794d/v7++PLLL7P9Gdi1axfWrVsHc3NzfP3112jcuDG2bdsGf39/5eWylpaWGDFiBKZMmaJV3deuXVP79XTo0CE8f/4cP//8s/LXcfny5XHp0iXs2rVLp/PkgOEW8wOM96vOkJerRkVFqQxwrVatGoB3c/F8qEqVKli4cGH2dub/E0IgMTFRP+MGjOTBgwdZPpYxxYGzs3O2//YiIyM16q3T9YINT09P7N69G+vXr8fw4cNRo0YNFC5cGFFRUejcuTPmzJmj81ikDFevXsXIkSOVV4IOGTIES5cuRc+ePVVeH3Nzc/j5+WHMmDFa1R8fH48XL16gYMGCavO/rFixAiEhIXj8+DHKlCmDwMBA/U1mZ+pMSd8MvV6KEEK0adNG5ZaRJdeuXVvtsTZt2oi2bdvqdR+fPHki5s6dK2rUqKHzPmTQ5pfE8+fPtT7/q+n7YWVlJYKCgnTZBa3o0qNy584dMWnSJPHFF18IhUIh3N3dRUBAgNi4caNe113avXu3kCRJWFpaCkdHR2Fubi7WrFkjLCwsROXKlcXQoUOFv7+/KFWqlFAoFGLp0qVa1W9tbS2WL1+usm3kyJFCoVCIc+fOqWxfsGCBsLa21mk/DLmYn7G8fftWFCtWTNja2org4GDx8OFDlccfPnwopk6dKmxtbUWJEiW0Xsfmw7+7j/XKZqenw9DtxMXFifv372fas7R8+XLRuHFjUbZsWdGhQwdx5swZ7XdAaHYMsbOzE506dRI3btwwWBv66qV79eqV6Nq1q3K8TaVKlcSjR490ru/D/Vi7dq3y/vPnz/X6fo8cOVI4Ojqq9epOnjxZ+V3k7OwsJEkStra24uLFi7rtyAdyZI+KoafXvnz5slr27uHhgcePH2c6SZS+e1oePXqEBw8e4L///oMQIlszG44fP/6jV/zEx8dj27ZtCAkJwf79+5Gamqq8TFAT48aN++j+Z0z41qRJE6NeqaWp2rVr48yZM3B2dkbnzp3x+++/o169egDeXbGhT7/88gsqVKiAI0eOIG/evPjuu+8wcOBANGvWDH///bfydUxNTcWXX36JxYsXa3V+uUCBAioTIQLvZi21tbVV+yVvaWmp0+cqKioKPj4+sLa2xurVq9GtWzeVepKTk7Fhwwb89NNPaNmyJS5dugR3d3et2zE0Q1+uCmR+XDBVb7CuJk2ahMWLF+Phw4cq7/OUKVMwfvx4SJKEfPny4caNG9izZw9OnDiRaa/Rx/zyyy+fnIvpxo0b+Pvvv3Hw4EGcOnVK6+O/sXrp/vjjDwQEBCA6OhqBgYFwdnbG+PHjUbZsWUyZMgX+/v7Z/gy8/3x9f54OHTqE1q1bq1wxFBMTgylTpsDNzQ2HDx+Gp6cnzpw5A29vb0ybNg3r1q3LfsN6SXdkRC5jLvTt1q1bYuLEiaJMmTLKzL5BgwZi2bJlyvOR+pKWliZ27dol/Pz8hJ2dnVAoFKJ06dIiICBAHD58WK9t6UNERITGt0GDBmn1K0KSJFG8eHGxbds2taurbt++rdceFVdXVzFz5kzl/atXr2a5UvCsWbOEra2tVvV36tRJlC5dWsTExAghhPjnn3+Eubm56NSpk1rZESNGiHLlymm5B+/OiefLl++TV6ncvXtX5MuXT3z33Xdat2EM3t7eauN5PlbW29tbq/olSRLVqlXTqFe2WrVqsu1RqVmzpvDz81PZFh0dLaysrIS7u7vyc3D69GmRN29e0b17dx32QjP3798Xzs7OavHIwa1bt0STJk2EJEmiXr164urVq8rHHjx4INq2bSskSRJeXl4iIiJC53YkSRJBQUHK493BgweFJEli8eLFasfCjF4Qbbi6uorp06erbFu3bp2QJEnMnz9fZfuwYcOEm5ubzvvyvhzZo2JKN27cwKZNm5Tn6fr06QMHBwed6nry5IlyfYyIiAgIIVChQgUEBwfD19dX779ET506hZCQEGzcuBEvXryAh4cH4uPjsXTpUnzzzTd6bUufvLy8NP7lIITQ6lfG/PnzERoaig4dOsDJyQmdOnVC9+7dM/2VnV3Pnz9XmazK1dUVADKdwEqXGTjHjx+PGjVqoFSpUihfvjwiIiIgSRICAwPVym7dulWnOYGMsZifMVy5cgVDhgzRqGzjxo21ns+maNGiePXqlcoYro/1ymY24Zym3h97ER0dDQC4deuW2ozWuoy9iIyMVLticNeuXUhOTsbIkSOVn4OaNWuib9++ypm1DaFo0aLo37+/6a5M+YiKFSvC1tYWS5cuVVuyxN3dHdu3b8fWrVsxZMgQ1KpVC4MHD8acOXN0amvs2LFqc11lNsu0tsdC4N3iivnz51fZduTIEUiSpDbLdLly5XSe3+tDTFR0kNVlnn/99Re6dOmC5ORk5TZdLvNcsWIFQkNDcfjwYaSlpaFIkSIICAhAz549UbFiRbXyr1+//uTqvlm5efOmcjXQu3fvokSJEujfvz98fX1hZWWF0qVL61y3sRjywDRo0CAMGjQI9+7dU75Oy5YtQ8GCBdGoUSNIkqTX7lVDdttWrFgRBw8eRFBQEO7evYsvv/wSAQEBarOWhoeHw9bWFl26dNG6jZyymJ+hL1eNjIzUISrd5MQvrqx4enpq/V4YQ+fOnTFr1qyPnt7u0KEDmjVrhtGjR2PhwoU6JSqGTtI8PDzU1vIKDw9HgQIFULJkSZXtycnJOv9I/xATFR3s2LEDJUqUUEk+UlNT8e2338LMzAwrV66El5cXdu7cidGjRyMoKEirqye+/fZbODo6ok+fPujZsyfq16+vdgB5f+6OsLAwnda3AN4dPAoWLAhfX19069ZNZZS2vsdgGIox5nLw9PTEmDFjMGbMGOWVPxs2bIAQAoMGDcLu3bvRtm1bNG3aNFurdBvy1y/wbn6TnTt3frRMw4YNceXKFZ3qzymL+SUlJWm85pS5ubnKjxNN1KtXD1999RXq1auHOnXqGOzHQE794spKZGQknJycDNqGLjSdA8fOzg5z587V+Zhm6GNh8+bNsWLFCnTu3Bm1atXCmjVrcOPGjUwXOY2IiECxYsX007BeTiDlMm5ubmLChAkq2/bu3SskSVKb7a9Hjx6idOnSWtW/ZcuWTEfRG2LuDisrK2FjYyM6d+4s/vzzT5GYmKh8TN9jMHKatLQ0sW/fPtG7d2/h4OAgJEkSefLk0bm+zK48yOpqBEPPEqyr9u3bi1KlSomEhISPlktISBClSpUSHTp0MFJk2vnwXP/Hbrqc63d3d1e+h2ZmZqJ8+fJi4MCB4o8//hD37t0zzE4ZwJAhQ4STk5NyXqfVq1cLSZJU5ojJ0K9fP+Hl5WWwWB48eCCcnZ2Fr6+vwdrI7Z4/fy6KFi0qFAqFcrZmV1dX8eTJE5VycXFxIl++fGLkyJF6aTfHrfVjDDY2NliwYAH69eun3DZq1Cj8+uuvOHPmjEpX+sKFCzFixAidZysFDDt3R3R0NDZt2oS1a9fi6NGjsLOzQ7t27dCjRw8UK1YM5cqVw+bNm9GxY0ed28gNEhMTsX37doSGhmL79u061bF69Wqtn2OM3iRtHD9+HPXr10f9+vU/uZhfeHg4jhw5IsuVhzPWXdKE+P+nTLSdm+Thw4c4duwYjh07hhMnTuDKlSvK1boLFy6MunXrol69eqhXrx4qV64syyuCXrx4gerVq+Phw4cwMzNDamoqXFxccPnyZZWxVfHx8ShSpAgGDBiAadOmadXGrFmzPvp4xlxMf/31FwDg9OnTBr3qM7d7/fo1fv/9d9y9exceHh7o16+fcjxdhjNnzuCPP/7Ad999h/Lly2e7TSYqOihWrBgGDBiAn3/+Wbmtbt26uHz5Ml6/fq0yDfzvv/+OESNGKLvxNXX37l2EhIQgJCQEt27dgpubG7p164aaNWuiW7duBkkeoqKiEBoaitDQUFy5cgV2dnaIi4vD5MmTERAQkK3LoCn3MMZifoZmiqQxNjYWJ06cwPHjx3H8+HGcPn1audqwg4MDXr9+na36DcXQX1yaLAxqa2uL5s2bY+rUqShTpoxW9ZP8MVHRQefOnXHlyhWcO3cO9vb2uHr1KqpUqYJ27dph8+bNKmUDAgKwe/durVYE/nDuDl9fX5W5O0qVKmXwXo7Lly9j7dq1WL9+PR4+fAg7Ozs0a9YMbdu2ld2veJKf48ePY/LkyTh48KDKukrm5uZo1KgRxowZg6+++sqEEcrb48ePcejQISxYsAAnT57Uqccmp7h///5HH7e2toaLi4tOK52Tdj52JWDGLMEeHh5o2bIlWrdurbd2majo4MqVK6hRowby5s2rvMwzPj4eJ0+eVLuCokSJEmjcuDGWLVumcf0KhQKenp6YNWsWWrVqpdJDY6xE5X3h4eFYu3YttmzZgujo6Fx7wCTt6Xsxv5zqn3/+wbFjx5S9Kffv34eVlRWqVq2KOnXqoG7dumjfvr2pw1Rjqi+uDKmpqbh16xZiY2NRtmxZlYnISP8qVKjwycn3oqKikJaWBm9vb2zfvl3jQekfpZeRLrnQ8ePHRcuWLUWZMmWEt7e32Ldvn1qZQ4cOiQoVKog9e/ZoVfeCBQuUy73nz59fDBgwQBw8eFCkp6ebdIBrUlKS2LJli9Hbpc+PoRbzyynCw8NFUFCQ8PHxEfny5ROSJImCBQuKjh07ihkzZogTJ058Fq9R+fLlRYUKFbK8FS9eXFhYWAiFQiF8fHxEcnKyTu3s3LlT9OzZU/Tp00c5Wd3WrVuFm5ubcoC5tbW12sUMZHzx8fFi9uzZQpIkMWXKFL3UyR4VGXt/7o4bN24o5+5Yv349Nm/ejA4dOmS7jbZt22pVXpIknQeLUu6Q1WJ+sbGxiI6OhiRJKF68uE6L+eUUCoUCFhYW6NKlC3x8fFC7dm0UL17c1GEZREJCApYsWYLhw4dj8uTJGD16tFbPDwsLQ8uWLWFhYQEbGxvExcVhxYoV+Oabb1CuXDk0atQIqamp2LNnD+7cuaP10hJkGD169MClS5e0GvaQFSYqn4n35+54/PgxChQogDZt2mR77g6FQgFra2sULFgQmnwUJEnC3bt3dWqLcr7Y2FhUrFgRz549w9ixY/H111/Dzc1N+fijR4+wZs0aTJkyBYUKFcKlS5eQJ08eE0ZsGpUrV8bVq1eVs03XqVNHOafKp2b1/Vzp+sXVuHFjvHjxQmUNrDVr1qBRo0aZroElhEBERIQhdoG0sGDBAvz444/KAeHZopd+GTIafc/dUaRIESFJkqhRo4aYPXu2ePz4sR6jpdxm3rx5QqFQiEOHDn203IEDB4RCoVBbHyQ3iYmJEXv27BHjxo0TTZo0Ua6rlXEKaObMmeLkyZM6ny6Rm/nz5wsbGxutn2foNbDIMH755Rdhb2+vl7o4TPozo1Ao0LRpU6xatQpPnz7FunXr0KRJE53ri4qKwqFDh1C1alVMnjwZ7u7uaNq0KVauXIm3b9/qMXLKDXbu3InmzZt/ci2kxo0bo1mzZsq5L3Ije3t7NG/eHBMnTsT+/fvx5s0bnDlzBoGBgbCwsMDs2bNRt25dODo6on79+qYON9vi4+NVLgzQlKHXwCL9E0Jgx44dmS75ogsmKp8xa2trdOvWLdtjRho0aIAlS5bgyZMn2Lx5M/Lnzw9/f3+4urqiY8eO2Lx5M5KSkvQUNeVkV65c0XjBxsaNG+s8VX9OZGZmhurVq2PIkCEYPXo0AgMD8eWXXyIxMRHHjx83dXjZkt0vLkOugUWay1hIM6vbo0ePcPDgQXTt2hUnTpyAv7+/XtrlWj+kZGFhgXbt2qFdu3aIjY3Fli1bsHjxYnTr1g0TJkxQW9iM6EOGXswvJ0pKSsLp06eVs9SeOnVKOUGklZWVck0gOfrU+5cxa+yiRYtw4sQJrF27Vqd2DL0GFmnG2dlZo0TRwsICkydPhq+vr17aZaJCapKSkrBnzx5s374dFy5cgLW1tf4Wl6IczdCL+eUU27dvVyYmFy5cQEpKCoQQyJ8/vzIxqVevHry8vPQzD4WBGOuLy5ArQJPmxo0b99HXN2PenCZNmnx0pWhtMVEhAEB6ejr27duHdevWYdu2bYiPj0fTpk2xbNkydOjQIVdemUG6ef/X78fk5l+/GVMLeHp6olu3bsrE5HO7XNsYX1yGXgGaNDdhwgSTtMvLk3O5EydOIDQ0FJs2bcLLly/x5ZdfokePHujatSucnZ1NHR59ZoyxmF9OsGnTJtSrVw+FChUydShEssdEJZdTKBSwsbFBy5Yt4evrq9EpnmrVqhk+MPos5YQVoIlIXpio5HLvL+T1qV/CufkXMBERmQbHqORyPP9LRERyxh4VIiIiki1O+EZERESyxUSFiIiIZIuJChEREckWExUiomwoVqwY5syZY+owiHIsJipEn7EnT57ghx9+QPHixWFlZQV3d3e0adMGBw4cMGi7kZGRkCQJFy9eNGg7n4OzZ89iwIABpg6DKMfi5clEn6nIyEjUrVsXefPmxa+//oqKFSsiJSUFe/bsweDBg3Hjxg2156SkpMh67RhdJCcnw9LSUmWbEAJpaWkwNzf8IU6fa5oQkTr2qBB9pgYNGgRJknDmzBl06tQJpUuXRvny5TF8+HCcOnUKwLtJ/BYtWoS2bdsiT548mDJlCkqWLIkZM2ao1HXx4kVIkoTbt2+rPM/Hxwc2NjYoXrw4Nm/erCzv6ekJAKhatSokSULDhg0BvFszatKkSShSpAisrKxQpUoVhIWFqbT18OFD+Pr6wsnJCXny5IGXlxdOnz4NAOjTpw/at2+vUn7o0KHK+gGgYcOG8Pf3x9ChQ+Hs7Axvb2+Eh4dDkiTs3r0b1atXh5WVFY4dO4b09HQEBwfD09MTNjY2qFy5ssp+ZDzvwIED8PLygq2tLerUqYObN2+qxPDXX3+hRo0asLa2hrOzs3KtHkD91M+bN2/w7bffwsXFBQ4ODmjcuDEuXbqkfPzSpUto1KgR7O3t4eDggOrVq+PcuXNZvs9EuR0TFaLP0KtXrxAWFobBgwdnumBk3rx5lf+fMGECOnTogCtXruCbb75Bv3791Cb6W7lyJerXr4+SJUsqt40dOxadOnXCpUuX4Ofnh+7du+P69esAgDNnzgAA9u/fj8ePH2PLli0AgLlz52LmzJmYMWMGLl++DG9vb7Rt2xa3bt0CAMTGxqJBgwZ49OgRduzYgUuXLuGnn35Cenq6Vvu/evVqWFpa4vjx41i8eLFy+6hRozBt2jRcv34dlSpVQnBwMNasWYPFixfj6tWrGDZsGHr27InDhw+r1Dd69GjMnDkT586dg7m5Ofr166d8bOfOnejQoQNatmyJCxcu4MCBA6hZs2aWsXXp0gXPnj3D7t27ERERgWrVqqFJkyZ49eoVAMDPzw9FihTB2bNnERERgVGjRuW4Xi4ivRJE9Nk5ffq0ACC2bNny0XIAxNChQ1W2PXr0SJiZmYnTp08LIYRITk4Wzs7OYtWqVSrP++6771SeV6tWLfH9998LIYS4d++eACAuXLigUqZw4cIiKChIZVuNGjXEoEGDhBBCLFmyRNjb24uXL19mGm/v3r1Fu3btVLb973//Ew0aNFDeb9CggahatapKmUOHDgkAYtu2bcptiYmJwtbWVpw4cUKl7DfffCN8fX1Vnrd//37l4zt37hQAREJCghBCiNq1aws/P79M4xVCCA8PDzF79mwhhBBHjx4VDg4OIjExUaVMiRIlxJIlS4QQQtjb26u81kT0cexRIfoMCS0mlPby8lK5X7hwYbRq1QorVqwA8O60RlJSErp06aJSrnbt2mr3M3pUMhMTE4P//vsPdevWVdlet25d5fMuXryIqlWrwsnJSeP4M1O9evVMt7+/r7dv30Z8fDyaNWsGOzs75W3NmjW4c+eOyvMqVaqk/H/GisbPnj1TxtykSRON4rp06RJiY2ORP39+lTbv3bunbHP48OH49ttv0bRpU0ybNk0tFiJSxcG0RJ+hUqVKQZKkTAfMfiizU0Pffvstvv76a8yePRsrV65Et27dYGtra4hQVdjY2Hz0cYVCoZaEpaSkqJXLbJ8+3B4bGwvg3akbNzc3lXJWVlYq998/9ZKxOGfG6ahPxfy+2NhYFCpUCOHh4WqPZZyOmzBhAnr06IGdO3di9+7dGD9+PNavX68y7oWI/g97VIg+Q05OTvD29saCBQsQFxen9vibN28++vyWLVsiT548WLRoEcLCwlTGZGTIGJD7/v2yZcsCgPIqm/dX0nZwcEDhwoVx/PhxlecdP34c5cqVA/Cu5+LixYvK8RofcnFxwePHj1W26XoJdLly5WBlZYUHDx6gZMmSKjd3d3eN66lUqZLGl3tXq1YNT548gbm5uVqbzs7OynKlS5fGsGHDsHfvXnTs2JGLgxJ9BBMVos/UggULkJaWhpo1a+LPP//ErVu3cP36dcybN0/ttM2HzMzM0KdPHwQGBqJUqVKZlt+0aRNWrFiBf//9F+PHj8eZM2fg7+8PAHB1dYWNjQ3CwsLw9OlTREdHAwB+/PFHTJ8+HRs2bMDNmzcxatQoXLx4Ef/73/8AAL6+vihYsCDat2+P48eP4+7du/jzzz9x8uRJAEDjxo1x7tw5rFmzBrdu3cL48ePxzz//6PT62NvbIyAgAMOGDcPq1atx584dnD9/Hr/99htWr16tcT3jx4/HunXrMH78eFy/fh1XrlzB9OnTMy3btGlT1K5dG+3bt8fevXsRGRmJEydOYPTo0Th37hwSEhLg7++P8PBw3L9/H8ePH8fZs2eVCSARZcLUg2SISHf//fefGDx4sPDw8BCWlpbCzc1NtG3bVhw6dEgI8W5Q7NatWzN97p07dwQA8csvv6g9BkAsWLBANGvWTFhZWYlixYqJDRs2qJRZtmyZcHd3FwqFQjnYNS0tTUyYMEG4ubkJCwsLUblyZbF7926V50VGRopOnToJBwcHYWtrK7y8vJQDe4UQYty4caJAgQLC0dFRDBs2TPj7+6sNpv3f//6nUmfGoNjXr1+rbE9PTxdz5swRX3zxhbCwsBAuLi7C29tbHD58OMvnXbhwQQAQ9+7dU277888/RZUqVYSlpaVwdnYWHTt2VD72/mBaIYSIiYkRP/zwgyhcuLCwsLAQ7u7uws/PTzx48EAkJSWJ7t27C3d3d2FpaSkKFy4s/P39lQN3iUidJIQWo/KIKMc4evQomjRpgqioKBQoUEDlMUmSsHXrVrU5TYiIjI2DaYlymaSkJDx//hwTJkxAly5d1JIUIiI54RgVolxm3bp18PDwwJs3b/DLL7+YOhwioo/iqR8iIiKSLfaoEBERkWwxUSEiIiLZYqJCREREssVEhYiIiGSLiQoRERHJFhMVIiIiki0mKkRERCRbTFSIiIhItpioEBERkWz9Pzkhp0lk2ZDHAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "#انکود کردن استرینگ های فیچر جدید به روش لیبل انکودینگ\n",
    "label_encoder = LabelEncoder()\n",
    "data_cp = data.copy()\n",
    "data_cp['ProoftypeEncoded'] = label_encoder.fit_transform(data['ProofType'])\n",
    "sub_data = pd.concat([subset_data, data_cp['ProoftypeEncoded']], axis=1)\n",
    "#خوشه بندی بر اساس سه فیچر با متد واریانس\n",
    "result = shc.linkage(sub_data, method='ward')\n",
    "shc.dendrogram(result, orientation='top', labels=data_cp['Symbol'].values, distance_sort='descending')\n",
    "plt.title('Dendrogram')\n",
    "plt.xlabel('Cryptocurrencies')\n",
    "plt.ylabel('Distance')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#نمایش کلاستر ها\n",
    "clusters = fcluster(result, t=2, criterion='maxclust')\n",
    "data_cp['Cluster'] = clusters\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "for i in range(1, 3):\n",
    "    cluster = data_cp[data_cp['Cluster'] == i]\n",
    "    #نمایس بر اسا پروف تایپ و مارکت کپ برای ویژوالایز بهتر، بدلیل سختی سه بعدی رسم کردن والیوم نمایش داده نشده\n",
    "    plt.scatter(cluster['ProoftypeEncoded'], cluster['MarketCap'], label=f'Cluster {i}')\n",
    "    for index, row in cluster.iterrows():\n",
    "        plt.annotate(row['Symbol'], (row['ProoftypeEncoded'], row['MarketCap']))\n",
    "\n",
    "plt.title('Clustering Results')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "در این قسمت با وجود تفاوت در دندوگرام باز هم نتیجه یکسان شده و ارتفاع تتر و بیت کوین ثابت مانده چون این فیچر جدید تاثیری روی کلاسترینگ ما اعمال نمیکند و برای مثال بیت کوین و تتر فارغ از پروف تایپشان که متفاوت هم هست باز هم در یک دسته قرار میگیرند."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
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   "source": [
    "\n",
    "Dimensions = {\n",
    "    'Volume': cluster['Volume'],\n",
    "    'MarketCap': cluster['MarketCap'],\n",
    "    'Prooftype': cluster['ProofType']\n",
    "}\n",
    "\n",
    "Dimensions = pd.DataFrame(Dimensions)\n",
    "# رسم نمودار سه بعدی\n",
    "fig = px.scatter_3d(Dimensions, x='Volume', y='MarketCap', z='Prooftype')\n",
    "fig.update_layout(width=900, height=800)\n",
    "\n",
    "# نمایش نمودار\n",
    "fig.show()\n"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Part_3"
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  },
  {
   "cell_type": "code",
   "execution_count": 38,
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urq6mbhIRERGZEZMGmfj4eNy+fVt5/969e7h06RJcXFxQqFAhdO7cGRcuXMCff/6JtLQ0xMTEAABcXFxgbW1tqmYTERGRmTBpkDl//jwaN26svD9ixAgAQJ8+fTBp0iTs3LkTAFC5cmWV4w4fPoxGjRoZq5lERERkpkwaZBo1agQhRJaPZ/cYERERkSyuWiIiIiLKDIMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREcmWSYPM0aNH0aZNG3h4eECSJGzfvl3lcSEEJkyYgEKFCsHOzg7NmjXDrVu3TNNYIiIiMjsmDTIJCQmoVKkSlixZkunjs2bNwsKFC7F8+XKcOXMGefLkga+vL5KSkozcUiIiIjJHlqas3M/PD35+fpk+JoTA/PnzMW7cOLRr1w4AsHbtWhQoUADbt29H9+7djdlUIiIiMkNmO0bm3r17iImJQbNmzZTbnJ2dUatWLZw6dSrL45KTkxEXF6dyIyIiotzJbINMTEwMAKBAgQIq2wsUKKB8LDMhISFwdnZW3ry8vAzaTiIiIjIdsw0yuhozZgxiY2OVt+joaFM3iYiIiAzEbINMwYIFAQBPnjxR2f7kyRPlY5mxsbGBk5OTyo2IiIhyJ7MNMj4+PihYsCAOHjyo3BYXF4czZ86gTp06JmwZERERmQuTXrUUHx+P27dvK+/fu3cPly5dgouLC4oUKYKgoCBMmzYNJUuWhI+PD8aPHw8PDw+0b9/edI0mIiIis2HSIHP+/Hk0btxYeX/EiBEAgD59+mD16tX47rvvkJCQgIEDB+LVq1eoX78+wsPDYWtra6omExERkRkxaZBp1KgRhBBZPi5JEqZMmYIpU6YYsVVEREQkF2Y7RoaIiIjoYxhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLZMetUS6ZkQQEqiqVtB73ub9t7/EwFYmKwplAkre0CSTN0KIsoBBpncQghglS8QfcbULaH3CRsAv777/48lACnZpM2hD3jVBvqHM8wQyRiDTG6RksgQY4bspWRE2fYwdTMoK9Gn3713rPOYuiVEpCMGmdwo+DZgbW/qVhCZr7eJwOwSpm4FEekBg0xuZG3Pb5hERPRJ4FVLREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbOQ4ySUlJ+mgHERERkdZ0CjLp6emYOnUqPD094eDggLt37wIAxo8fj19++UWvDSQiIiLKik5BZtq0aVi9ejVmzZoFa2tr5fby5cvj559/1lvjiIiIiLKjU5BZu3YtfvrpJwQEBMDCwkK5vVKlSrhx44beGkdERESUHZ2CzKNHj1CiRAm17enp6UhJSclxo4iIiIg0oVOQKVu2LI4dO6a2fcuWLahSpUqOG0VERESkCUtdDpowYQL69OmDR48eIT09HVu3bsXNmzexdu1a/Pnnn/puIxEREVGmdOqRadeuHf744w8cOHAAefLkwYQJE3D9+nX88ccfaN68ub7bSERERJQpnXpkAODzzz/H/v379dkWIiIiIq3o1CNz7tw5nDlzRm37mTNncP78+Rw3KkNaWhrGjx8PHx8f2NnZoXjx4pg6dSqEEHqrg4iIiORLpyAzZMgQREdHq21/9OgRhgwZkuNGZZg5cyaWLVuGxYsX4/r165g5cyZmzZqFRYsW6a0OIiIiki+dTi1du3YNVatWVdtepUoVXLt2LceNynDy5Em0a9cOrVq1AgAULVoU69evx9mzZ/VWBxEREcmXTj0yNjY2ePLkidr2x48fw9JS52E3aurWrYuDBw/in3/+AQBcvnwZx48fh5+fX5bHJCcnIy4uTuVGREREuZNOQaZFixYYM2YMYmNjldtevXqF77//Xq9XLY0ePRrdu3dH6dKlYWVlhSpVqiAoKAgBAQFZHhMSEgJnZ2flzcvLS2/tISIiIvOiU/fJ7Nmz0aBBA3h7eysnwLt06RIKFCiA3377TW+N27RpE0JDQxEWFoZy5crh0qVLCAoKgoeHB/r06ZPpMWPGjMGIESOU9+Pi4hhmiIiIcimdgoynpyeuXLmC0NBQXL58GXZ2dujXrx/8/f1hZWWlt8Z9++23yl4ZAKhQoQLu37+PkJCQLIOMjY0NbGxs9NYGIiIiMl86D2jJkycPBg4cqM+2qElMTIRCoXr2y8LCAunp6Qatl4iIiORB5yBz69YtHD58GE+fPlULFhMmTMhxwwCgTZs2mD59OooUKYJy5crh4sWLmDt3Lvr376+X8omIiEjedAoyK1euxDfffAM3NzcULFgQkiQpH5MkSW9BZtGiRRg/fjwGDx6Mp0+fwsPDA4MGDdJb+URERCRvOgWZadOmYfr06Rg1apS+26PC0dER8+fPx/z58w1aDxEREcmTTkHm5cuX6NKli77bIl9CACmJpm3D28TM/28qVvbAez11REREhqBTkOnSpQv27duHr7/+Wt/tkR8hgFW+QLT62lMmM7uEqVsAeNUG+oczzBARkUHpFGRKlCiB8ePH4/Tp06hQoYLaJdfDhg3TS+NkISXRvEKMuYg+/e5nY53H1C0hIqJcTKcg89NPP8HBwQFHjhzBkSNHVB6TJOnTCjLvC74NWNubuhWm9TbRPHqEiIjok6BTkLl3756+25E7WNuzB4KIiMiIdFpriYiIiMgc6Dwh3sOHD7Fz5048ePAAb9++VXls7ty5OW4YERER0cfoFGQOHjyItm3bolixYrhx4wbKly+PqKgoCCFQtWpVfbeRiIiIKFM6nVoaM2YMgoODcfXqVdja2uL3339HdHQ0GjZsyPlliIiIyGh0CjLXr19H7969AQCWlpZ48+YNHBwcMGXKFMycOVOvDSQiIiLKik5BJk+ePMpxMYUKFcKdO3eUjz179kw/LSMiIiL6CJ3GyNSuXRvHjx9HmTJl0LJlS4wcORJXr17F1q1bUbt2bX23kYiIiChTOgWZuXPnIj4+HgAwefJkxMfHY+PGjShZsiSvWCIiIiKj0SnIFCtWTPn/PHnyYPny5XprEBEREZGmdBojU6xYMTx//lxt+6tXr1RCDhEREZEh6RRkoqKikJaWprY9OTkZjx49ynGjiIiIiDSh1amlnTt3Kv+/d+9eODs7K++npaXh4MGDKFq0qN4aR0RERJQdrYJM+/btAbxb4bpPnz4qj1lZWaFo0aKYM2eO3hpHRERElB2tgkx6ejoAwMfHB+fOnYObm5tBGkVERESkCZ2uWrp3757atlevXiFv3rw5bQ8RERGRxnQa7Dtz5kxs3LhReb9Lly5wcXGBp6cnLl++rLfGEREREWVHpyCzfPlyeHl5AQD279+PAwcOIDw8HH5+fvj222/12kAiIiKirOh0aikmJkYZZP7880907doVLVq0QNGiRVGrVi29NpCIiIgoKzr1yOTLlw/R0dEAgPDwcDRr1gwAIITIdH4ZIiIiIkPQqUemY8eO6NGjB0qWLInnz5/Dz88PAHDx4kWUKFFCrw0kIiIiyopOQWbevHkoWrQooqOjMWvWLDg4OAAAHj9+jMGDB+u1gURERERZ0SnIWFlZITg4WG378OHDc9wgIiIiIk1pHGR27twJPz8/WFlZqSxVkJm2bdvmuGFEREREH6NxkGnfvj1iYmLg7u6uXKogM5IkccAvERERGYXGQSZjeYIP/09ERERkKlqPkUlPT8fq1auxdetWREVFQZIkFCtWDJ06dUKvXr0gSZIh2klERESkRqt5ZIQQaNu2Lb766is8evQIFSpUQLly5RAVFYW+ffuiQ4cOhmonERERkRqtemRWr16No0eP4uDBg2jcuLHKY4cOHUL79u2xdu1a9O7dW6+NJCIiIsqMVj0y69evx/fff68WYgCgSZMmGD16NEJDQ/XWOCIiIqLsaBVkrly5gi+++CLLx/38/Lj6NRERERmNVkHmxYsXKFCgQJaPFyhQAC9fvsxxo4iIiIg0oVWQSUtLg6Vl1sNqLCwskJqamuNGEREREWlCq8G+Qgj07dsXNjY2mT6enJysl0YRERERaUKrHpk+ffrA3d0dzs7Omd7c3d31fsXSo0eP0LNnT7i6usLOzg4VKlTA+fPn9VoHERERyZNWPTK//vqrodqRqZcvX6JevXpo3Lgx9uzZg/z58+PWrVvIly+fUdtBRERE5kmn1a+NZebMmfDy8lIJUD4+PiZsEREREZkTrU4tGdvOnTtRvXp1dOnSBe7u7qhSpQpWrlyZ7THJycmIi4tTuREREVHuZNZB5u7du1i2bBlKliyJvXv34ptvvsGwYcOwZs2aLI8JCQlRGbfj5eVlxBYTERGRMZl1kElPT0fVqlXxww8/oEqVKhg4cCAGDBiA5cuXZ3nMmDFjEBsbq7xFR0cbscVERERkTGYdZAoVKoSyZcuqbCtTpgwePHiQ5TE2NjZwcnJSuREREVHuZNZBpl69erh586bKtn/++Qfe3t4mahERERGZE7MOMsOHD8fp06fxww8/4Pbt2wgLC8NPP/2EIUOGmLppREREZAbMOsjUqFED27Ztw/r161G+fHlMnToV8+fPR0BAgKmbRkRERGbArOeRAYDWrVujdevWpm4GERERmSGz7pEhIiIiyg6DDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJltnP7Gt0QgApiZrv/zYx8/9/jJU9IEma709ERERqGGTeJwSwyheIPqPb8bNLaL6vV22gfzjDDBERUQ7w1NL7UhJ1DzHaij6tXc8PERERqWGPTFaCbwPW9vov922idj03RERElCUGmaxY2wPWeUzdCiIiIsoGTy0RERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFscWZfInOk7SrspB1dV60n3VjZc4FcMhgGGSJzk9NV2Ek7XPvM8LxqA/3DGWbIIHhqicjcGHMVdiJjiD7NHkYyGPbIEJkzQ63CTmQMbxPZ40UGxyBDZM64CjsRUbZ4aomIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZEtWQWbGjBmQJAlBQUGmbgoRERGZAdkEmXPnzmHFihWoWLGiqZtCREREZkIWQSY+Ph4BAQFYuXIl8uXLZ+rmEBERkZmQRZAZMmQIWrVqhWbNmn103+TkZMTFxanciIiIKHeyNHUDPmbDhg24cOECzp07p9H+ISEhmDx5soFbRURERObArHtkoqOj8b///Q+hoaGwtbXV6JgxY8YgNjZWeYuOjjZwK4mIiMhUzLpHJjIyEk+fPkXVqlWV29LS0nD06FEsXrwYycnJsLCwUDnGxsYGNjY2xm4qERERmYBZB5mmTZvi6tWrKtv69euH0qVLY9SoUWohhoiIiD4tZh1kHB0dUb58eZVtefLkgaurq9p2IiKiT4kQAm9S3+i1zMSUtPf+/waQ9NthYGdpB0mS9FqmWQcZIiIiUieEQO89vXHpv0v6LTfdCsBUAECjTQ0hKVL0Wn4V9ypY88UavYYZ2QWZiIgIUzeBiIjIpN6kvtF7iAEASZECxzKj9V5uhotPL+JN6hvYW9nrrUzZBRkiIiL6PxFdI2BnaWfqZmTrTeobNNrUyCBlM8gQERHJmJ2lnV57OOSGQYbeEQJIScx5OW8TM/9/TljZA3oeHJYj+vpZZcUQP8PMmNvPlYhIBwwy9O4P8ypfIPqMfsudXUI/5XjVBvqHm8cfXUP9rLKir59hZszp50pEpCOzntmXjCQl0Xh/mHURfdqwPSDaMPeflTbM6edKRKQj9siQquDbgLWZnGt9m2jYHomcMqeflTbM/edKRKQFBhlSZW0PWOcxdSvkgT8rIiKT46klIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki2ufk1ElJsJAaQkmqbut4mZ/9+YrOwBSTJN3WQUDDJERLmVEMAqXyD6jKlbAswuYZp6vWoD/cMZZnIxnloiIsqtUhLNI8SYUvRp0/VIkVGwR4aI6FMQfBuwtjd1K4znbaLpeoHIqBhkiIg+Bdb2gHUeU7eCSO94aomIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLl18TEeWUKZcByI45LBHwMVxCgHKIQYaIKCfMaRmA7Jjr5HBcQoByiKeWiIhygssA5AyXEKAcYo8MEZG+fGrLAOQElxAgPWGQISLSFy4DQGR0PLVEREREssUgQ0RERLLFIENERESyZdZBJiQkBDVq1ICjoyPc3d3Rvn173Lx509TNIiIiIjNh1kHmyJEjGDJkCE6fPo39+/cjJSUFLVq0QEJCgqmbRkRERGbArK9aCg8PV7m/evVquLu7IzIyEg0aNDBRq4iIiMhcmHWQ+VBsbCwAwMXFJct9kpOTkZycrLwfFxdn8HYRERGRaZj1qaX3paenIygoCPXq1UP58uWz3C8kJATOzs7Km5eXlxFbSURERMYkmyAzZMgQ/PXXX9iwYUO2+40ZMwaxsbHKW3R0tJFaSERERMYmi1NLgYGB+PPPP3H06FEULlw4231tbGxgY2NjpJYRERFpRwiBN6lvclTG+8fntKwMdpZ2kGS4eKdZBxkhBIYOHYpt27YhIiICPj4+pm4SERGRzoQQ6L2nNy79d0lvZTba1Egv5VRxr4I1X6yRXZgx6yAzZMgQhIWFYceOHXB0dERMTAwAwNnZGXZ2diZuHRERkXbepL7Ra4jRp4tPL+JN6hvYW8lr4VOzDjLLli0DADRq1Ehl+6+//oq+ffsav0FEZF6EAFISTduGt4mZ/99UrOwBmX2j/lRFdI2AnaXpv5S/SX2jt14dUzDrICOEMHUTiMhcCQGs8gWiz5i6Jf9ndglTtwDwqg30D2eYkQE7SzvZ9X6YI9lctUREpCIl0bxCjLmIPm36XioiIzLrHhkiIo0E3wasP/Fvtm8TzaNHiMjIGGSISP6s7QHrPKZuBRGZAE8tERERkWwxyBAREZFs8dQSERGRjrSdpVfXGXnlOuuuMTDIEBER6SCns/RqM3eLXGfdNQaeWiIiItKBMWfpzZh1l9SxR4aIiCiHDDVLr9xn3TUGBhkiIqIc4iy9psNTS0RERCRbDDJEREQkWwwyREREJFsMMkRERCRbHOybWwmh+Qq4bxMz/78mrOwBzmtAREQmwiCTGwkBrPIFos9of6y2q+d61Qb6hzPMEBGRSTDI5EYpibqFGF1En35Xn1xXHtam5wrQvfeKPVeaMUZPIn8XRLkKg0xuF3wbsDbA3AZvE7XvvTE3Oem5ArR7/uy5+jhj9STyd0GUqzDI5HbW9vLtLTE09lyZF2P9Pvi7IMpVGGSIAPZcmRtD/D74uyDKlRhkiAD2XJkb/j6ISEOcR4aIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki/PIEOUGXO2ciD5RDDJEcsfVzonoE8ZTS0RyZ4o1o4iIzAR7ZIhyE64ZRUSfGAYZotyEaxQR0SeGp5aIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLZkEWSWLFmCokWLwtbWFrVq1cLZs2dN3SQiIiIyA2YfZDZu3IgRI0Zg4sSJuHDhAipVqgRfX188ffrU1E0jIiIiEzP7eWTmzp2LAQMGoF+/fgCA5cuXY9euXVi1ahVGjx5t4tYREX1Am3Wv3qf1uld2qktF5GQNrSzr4NpaZP7MOsi8ffsWkZGRGDNmjHKbQqFAs2bNcOrUKRO2jIgoEzlZ90qf9DULM9fWIhkw6yDz7NkzpKWloUCBAirbCxQogBs3bmR6THJyMpKTk5X3Y2NjAQBxcXEfr/BtApAs8P8PAKzTdGs465BHHbnhObAO8yk/o47bp/VfrqncPgU8jzHMbNG54PedmJKItDdp/7+KOKRapbIOPdaR8XdbCJH9jsKMPXr0SAAQJ0+eVNn+7bffipo1a2Z6zMSJEwUA3njjjTfeeOMtF9yio6OzzQpm3SPj5uYGCwsLPHnyRGX7kydPULBgwUyPGTNmDEaMGKG8n56ejhcvXsDV1RUSu0eJiIhkQQiB169fw8PDI9v9zDrIWFtbo1q1ajh48CDat28P4F0wOXjwIAIDAzM9xsbGBjY2Nirb8ubNa+CWEhERkb45Ozt/dB+zDjIAMGLECPTp0wfVq1dHzZo1MX/+fCQkJCivYiIiIqJPl9kHmW7duuG///7DhAkTEBMTg8qVKyM8PFxtADARERF9eiQhPjYcmIiIiMg8mf3MvkRERERZYZAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIis/Dy5UtTNyHXunLlisb7CiGwYMECA7ZGvxhkKEtLly7VeN+HDx+iQ4cOBmxNzjx48ABv3rwxdTPICGJjY/HFF1/ghx9+yHa/6dOnw8/PD/Hx8UZqGWUmOTkZmzdvRvv27VGoUCFTNyfHdu/ejYEDB5q6GWqqV6+OUaNGffRz8Ny5c6hevbrKUj/aSEpKwsaNGzFjxgysXLkSjx8/1qkcrehldUeZOnXqlDh37ly2+5w7d06cPn1ap/J9fHzEjh07dDpWXxITE8XTp091OlaSJFG7dm1x5cqVLPdJS0sTs2fPFo6OjsLR0VHXZhqcQqEQoaGhBq3j33//FZ999pkYN25ctvuNHTtWlC5dWjx58kTrOgz9mjU3iYmJ4v79+1odM23aNOHo6PjR1/2TJ0+Ek5OTmDFjRk6aaDRnzpwR48ePF507dxYtWrQQnTp1EuPHjxdnzpwxWJ05+fzITnp6uti/f7/o27evcHZ2FpIkCXd3d9GvXz+912Vs06ZNEwqFQufjDfUeHzRokFAoFMLHx0fs2rVL7fHY2FjxzTffCEtLS1G4cGHx+++/a1W+EO/eU8WLFxcKhUJIkiQkSRJ58uQR+/fv17osbXyyQebQoUNCoVCIdevWZbvfunXrhEKhEMeOHdO6DkmSDP7HUwghLl++LMaNGycmTZokrl27JoR492aoXr26UCgUQqFQCE9PT7Fy5Uqtyl2/fr0oVKiQsLKyEt9++61ISEhQefzkyZOiYsWKQpIk0a5dO/HgwYMcPY+YmBgxbtw4UbNmTeHq6iqsra2Fq6urqFWrlpg4caJOf/gzGON3MWrUKJE/f37x+vXrbPeLi4sT7u7uHw08HzLGazYyMlLrm7bs7OzEhg0blPfj4uKEn5+fuHz5cpbPRRvVqlUTAwYM0GjfQYMGiRo1amhVfgYHBwdlgNfk5uTkpFM9T58+FS1btlT54/D+TaFQiJYtW+ocOAz1+ZGZ8+fPi+HDh4tChQop296jRw9x8uRJkZ6ernO5c+bM0eo2d+7cHD+XrOQkyBj6PX7y5ElRvnx5oVAoROfOncWjR4+EEEKEhoaKggULCktLSzFixIiPfoZlJTAwUFhYWIiRI0eKXbt2iQULFgg3NzdRrFgxncrT1CcbZLp27arxB1itWrWEv7+/1nUY44/n6dOnhY2NjfJDzd7eXuzbt084ODiIQoUKifbt24vWrVsLFxcXoVAotE7Zr169UiZ5b29v8ccff4gXL16IAQMGCAsLC+Ht7S127tyZ4+dx6NAh4eLionwOlStXFvXr1xeVK1cW9vb2QpIk4ebmptMfZyGM87soX768GDZsmEb7BgUFiYoVK2pVvrFesxl/vD52y9hXlzre/108e/ZMSJIkDh48qLavLkEmT548YsWKFRrtu2LFCuHg4KBV+Rn69Okj+vbtq9VNWwkJCaJChQrCwsJC9O/fX0RERIiXL1+K1NRU8fLlS3HkyBHRv39/YWFhISpWrCgSExO1Kt/Qnx9CCHHnzh0xZcoU8dlnnwmFQiG8vLxEcHCw2LRpk5AkSacyP5RVwMtsu66vW03lJMgY4z2ekpIiQkJChL29vXB2dhZ16tQRCoVC1K5dW1y6dEnr8t5XqlQptV61DRs2CIVCIW7cuJGjsrNj9mstGcrx48ezXEH7Q+3bt8fixYsN3CLdTJs2DYUKFcKuXbtQoEABDBgwAN27d0f58uVx4MAB5MmTBwDw4sUL1KlTB3PnzkXHjh01Lt/Z2RnLly9Hnz598PXXX6Ndu3awt7fH27dvMXLkSEycOBH29vY5eg7Pnj1D165dYWVlhbCwMHTu3BmWlv/30kxNTcXmzZsRFBSETp064dq1a3B1ddW6HkmSctTOj7lz5w6CgoI02rdcuXL46aeftCrfGK/ZX3/9VetjzI3QctWV9PR0nepZvXq1TsdpY86cObh27Rp27NiBVq1aqTyWN29eNGjQAA0aNEDHjh3Rrl07zJ07F2PHjtW4fEN/ftSpUwdnz56Fm5sbOnfujJ9//hn169cH8O79oi/37t1Tuf/ixQtUq1YNoaGhqFu3rt7qMTRjvMctLS0RHByMO3fu4JdffsHp06dRoUIF7N+/Hw4ODlqX974HDx5g1KhRKtvq168PIQSePHmCzz77LEflZ+WTDTLPnj3TeGBZwYIF8d9//+lUz08//YQDBw5otK8kSfjll1+0Kj8yMhJDhw5F2bJlAQDjx4/H9u3b8c033yg/hADAxcUF/fv3x/Tp07UqP0P+/Pnh6uoKIQQSEhJQunRp+Pv75zjEAMDKlSsRFxeH8+fPo0KFCmqPW1pawt/fH2XLlkWNGjXw888/q71ZNBEUFKTxh7wkSVp/0FpYWODt27ca7ZuSkgKFQrux9sZ4zfbp00frY8xNkSJFEBkZqdG+kZGRKFKkiIFbpLvNmzcjICBALcR8qFWrVggICMDGjRu1CjKG/vw4c+YMfHx8MHfuXLRq1UrlC4o+eXt7q9zP+INcoEABtcfMmTHe48eOHcOgQYNw8+ZN+Pv7w83NDUuWLEH58uWxaNEitGnTRusyMyQnJ8PW1lZlW8b91NRUncv9mE82yDg5OSEmJkajfWNiYuDk5KRTPWfOnNH4Q1WXIPPkyRN4eXkp7xcuXBgA4Onpqbavp6cnEhIStCo/NTUVISEhCAkJgb29PVasWAE3NzcMGzYMNWvWxNChQzFlyhSVDz1t7du3D23atMk0xLyvUqVKaNu2LcLDw3UKMp6enpn+XPSlePHiOH78OL755puP7nvixAkUL15cq/KN9ZrVREpKCnbt2oXQ0FBs3rzZYPXoolWrVli2bBmCg4NRsmTJLPe7desW1q1bp9HvKzMvXrzQ+hgXFxet9r9z5w6GDRum0b6ff/45tmzZolX5hv78WLx4McLCwtChQwe4uLigU6dO6N69Oxo1aqRVOabWtm1bjfe9ffu2zvUY8j3+8uVLBAcHY/Xq1ShWrBjCw8PRvHlzAECvXr0wcOBAtG/fHu3atcOiRYt0/qyMiorChQsXlPdjY2MBvHu/5c2bV23/qlWr6lSPCoOdtDJzfn5+olq1ahrtW61aNfHFF19oXYcxxmUYcrzBkSNHRJkyZYQkSSIgIEBlsO3r16/F0KFDhYWFhfDy8hLbt2/X+TkUKFBAzJkzR6N9586dKwoUKKB1Hcb4XUyaNElYWVmJkydPZrvfqVOnhJWVlZg0aZJW5RvjNfsxERERYsCAAcrxTLqML5EkSYSFhSnvZ7xmDx06pLavLmNknjx5Itzc3EShQoXEhg0bREpKisrjKSkpYsOGDcLT01O4ubmJmJgYrZ9DxvPQdDyRQqEQFhYWWteRN29esWDBAo32XbhwocibN6/Wz8GQ45Uy3L17V0ydOlX5eVKoUCHRo0cPoVAoxNatW3UqMzvZPQ9deHt7i6JFi2p104Uh3+P58+cX1tbWYuzYsSIpKUnt8bS0NDF37lzl4PR58+ZpXHaGrN4TmW3X51ilT7ZHZsCAAejUqROCg4Px448/Zjp+QgiB7777DhcvXtT6m44xJSQkKL8dZvz7+vVrtW+M2s6X0ahRIxQvXhz79u1Ds2bNVB5zcHDAwoUL0bt3bwwaNAgdO3ZEmzZtsH37dq3b/+rVK+TPn1+jfV1dXfHq1Sut6zCGESNGYM2aNWjRogXGjRuHnj17qnyrefToEdatW4fp06ejcOHCGD58uFblm+o1e+XKFYSGhmL9+vV49OgRChYsiM6dO6Nt27Zo2rSpTmV++eWXGDRokMq21q1bw8LCQmWbLt3R7u7u2L17Nzp06IAePXrAzs4OpUqVgqOjI16/fo1//vkHb968QcGCBZVjQ3QxYcKEj467EkJg586duHTpkk51VKxYEVu3btWoV+b333//aK9mZgz1+fE+Hx8fjBs3DuPGjUNkZCRCQ0OxceNGCCEwePBg7NmzB23btkWzZs3UTk2Yg6ioKKPUY8j3eOnSpbFixQqUKVMm08cVCgWGDx+OLl26IDAwECNHjtR4zF8Gk42x00sckqm+ffsKSZJEuXLlxPTp08WOHTvEoUOHxI4dO8S0adNEuXLlhCRJok+fPjqVb6weGU3Sry4JeNy4cZkm9w+9n+R1fQ6a/px0/VZorEvh7969KypVqqT8WefLl08UKVJE5MuXT/k7qFixorh9+7ZO5Rv6NZvh/v37IiQkRHmpZoECBUT37t11vnLlw+dgyKt97t+/LxITE8WrV6/EjBkzRL169YSLi4uwtLQULi4uom7duiIkJES8fPkyR8/jY3bs2CGqVq0qJEkSJUqUEKtXr9a6jLVr1wpJksSoUaNEWlpapvukp6eL0aNHC4VCIdasWaNV+Yb8/PiYtLQ0sX//ftGnTx/h5OSknHNEH7Lr5TN3xnqPf0xOetmNTRJCyyH+uczs2bMxY8YMvHjxQiX9CiGQL18+jBo1Ct9++61OV7woFAqsW7cOPXr00GeTVUyePFnrYyZOnGiAlrzrcdDlvKpCoUBQUJDyaobsHD16FIsWLUJaWprWdRj6d3H06FGUKVMGLi4u2LJlC3bu3IkbN24gLi4OTk5OKF26NNq0aaN2VZa2DPmaXbFiBUJDQ3HixAk4OzujY8eO6N69O5o0aYK7d++iVKlS2LJli1ZXrhibhYUFfvvtN4P+rrOzY8cOTJkyBZcuXULx4sUxbtw4BAQEqPU2aapbt27YvHkzSpcujR49eqBixYrK3qUrV64gLCwMN27cQOfOnbFp0yatyjaXz4+kpCTs2LEDYWFh2LFjh9bHV6xYUeV+Wloarl+/Dh8fn0zH70mShMuXL+vU1n///Rfh4eG4fv064uLi4OjoiLJly+KLL76Ah4eHTmV+yJDvcU3du3cPPj4+BisfePeZ2aBBgxyX88kGmf79+2PQoEGoVasWkpKScPz4ceULM+OPTv369WFnZ6dzHWvWrEHDhg1RtGhR/TXcDMXExGDDhg0IDQ3FuXPntD5e26t3JEnSOsjcv38f+fPn18tVVlkx5h9QQ71mFQoFfHx8MGfOHLRq1QpWVlbKx+7cuYOSJUuafZAxRmjNzPbt25UBpkSJEsrTi9q+vj+Unp6OmTNnYvbs2Xj58qXaH7a8efMiODgYo0ePznFdhhYfH4+oqCi8fv0ajo6OWQYNbTVq1EjrP+qHDx/Wav83b94gODgYP//8M1JTU9Uu87eyssJXX32F2bNn5+g9mMFQ7/GPuXLlCmbMmIEtW7ZofBWmtnbu3ImZM2fi9OnTWn+WZ+aTDTLG/rC7ffs29u/fjzt37ijfxCVKlEDz5s21vnrFHMTHx+P3339HaGgoDh8+jLS0NBQrVkynEftHjhzR+piGDRtqfQzwbgT9ihUr8Oeff6p9o2rTpg0GDhwIZ2dnnco21R9QfapZsybOnz+PAgUKoEuXLvD390edOnUA6DfIZHzsZPzxSUlJwR9//KG2X8GCBbWeB8TYv4f3A0zJkiWVPTD6DhUZf9iuXbum/AwpU6aMwf+w6UN4eDimT5+O06dPq8zbY2Fhgbp162Ls2LHKK2jMUWpqKlq0aIGIiAg0btwYvXv3RqVKlZQ9Y5cvX8batWtx+PBhNG7cGPv27dO5B86Q/v77byxbtgx37txBvnz50KVLF+UaeRcuXMC4ceOwd+9eWFlZoUePHli1apXWdezfvx8LFixQqSNjPOD27dsxbtw4XL9+Ha6urggMDMSECRNy/sRMc0bL9Iw1ZuL169eie/fuwsLCItMZJi0sLERAQICIj4/XqXxjTCmfISUlRezYsUN07dpV2NvbC4VCISpVqiR++OEH8ddff+lcrrGcOnVKeHp6CkmShK2trahUqZKoX7++qFSpkrC1tRWSJInChQuLU6dO6VS+sV5TQghx69YtsXTpUjFy5EgxcOBAMXLkSLFs2TKdx958WPaECRNEyZIlhSRJwtvbW4waNUps2bJFLzOx3r17V9jY2IgJEyYot2WMafhwNlZbW1tx9+5drcr/8KooQ9m6dauoXLmyUCgU4rPPPhO//fZbluNYzJUxPj/mzp0rFAqFsLKyEs2aNRNBQUFi3LhxIigoSDRt2lRYWVkJCwsLsXDhQp2fR79+/Qy6vtjChQuFJEli6dKl2e63bNkyIUmSWLRokcHaoqtTp04JOzs7tRmO58+fL2bPni0sLCxE3rx5xahRo8S///6rUx27du1Svofz588vLC0thUKhEKNHjxbDhg1TjhdbtmyZePPmjd6eG4OMAaWnp4vGjRsLSZKEr6+vWLdunbh06ZK4ffu2uHTpkli3bp1o0aKFkCRJNGvWTKc6jHEJ6NGjR8WgQYOEq6urkCRJVK9eXQQHB+ttevGPefDggThz5ox4/vx5jsrImzevKFiwoFi7dq1ITk5WeTw5OVmsXbtWFCxYUOTLl0+ndaOM8QfU0MH4Q6dPnxaBgYHC3d1d+Vrz9/cXJ06c0HltnO+++04UKlRI5XeQEWTmzp0rIiIiREREhDh06JDInz+/+O6777QqX/r/CxD6+PhodNN1HZiMn0fVqlXFrFmzDLK+z8yZM5XrHwkhRGpqqjhz5kyma+GcOnVK60UXDb0kxbVr14SVlZWoW7dulu+p+/fvi7p16wpra2tx/fp1rcp//3kY8vO8Ro0aon379hrt265dO1G9enWd6jHk+l1+fn4iX758Ijw8XCQkJIi//vpL1KhRQzg5OSnX03v16pVO7c7QsGFDUbhwYeXv8dWrV+KLL74QNjY2wtbWVixZskSkpqbmqI7MfNKnlgYNGqTsNtdE7969tapj69at6Ny5M2bMmIHvvvsuy/1mzJiBsWPH4vfff0f79u21qmPNmjUf3efNmzf46aefcOnSJa3Gl4wZMwbr16/HgwcPUKZMGfj7+6N79+4oUaKEXk8znDlzBnv37sXgwYPh5uam3P7vv//C398fx48fB/Dud/a///0Ps2fP1rqOr7/+Gps2bUJkZGS2A9ju3buHatWqoVu3bli2bJlWdSgUCq3O00uSpNXlxUIING3aFBEREWjRogV69eqF8uXLw8HBAfHx8fjrr7+wdu1a7N+/H02bNsX+/fu1an920tLSsHfvXoSGhmLnzp1ITEyEq6srWrVqpfUll1WqVEH9+vWxaNEi5bbnz58jf/78OHDgAJo0aaLcPnz4cERERODixYsal69QKFC5cmWtBp5ndlpLk3q0ocvYrg9Pkz1//hzu7u7Yv3+/ys8JAEJDQ9G7d2+t6tDk8+ND2sz+HBQUhLVr1ypPM2TlxYsXKFGiBPr164c5c+Zo3SZDn050dHTE7Nmz1aYMyMzy5cvx7bff4vXr11rX07dvX63H+mj6/nN1dcXgwYMxdepU5bbjx4+jQYMGGDFihE6fqx9ydnbGqFGj8P333yu3RUZGokaNGpg8eTLGjx+f4zoy88nOIwO8Wz5gxYoVGu0rSZLWQWb9+vWoVKlStiEGAEaPHo0NGzYgLCxM6yCT3YdKcnIyVqxYgZkzZ+Lx48do2LAhJk2apHHZM2fOhI+PDw4fPqzzmBRNLF26FGfOnFE7V9q7d28cO3YMDRs2RPXq1XHgwAHMmzcP5cqVQ79+/bSqY+/evfjqq68+Ogrfx8cHX331lc6z1TZr1gylSpXS6diP2bZtGyIiIrIMxpUqVUJAQIAyGG/fvl3r11NWLCws0LJlS7Rs2RKJiYnYunUrQkNDERYWpnWQuX37Nr7++muVbQqFAs7OziqDiwGgVKlSWs92DQDBwcEGHyPz4fo+xqLP756GXpLi+PHj6NKlS7YhBng343GXLl10Gi9nDJIkabUml65XExly/a5Xr16pfTZl3P8wFOvq9evXaktCZNyvUaOGXurIzCcdZKZMmYIvvvjCYOVfuHABAQEBGu3btm1bhIaG6qXe5ORkLF++HLNmzcLjx4/RqFEjrF+/XuvL3Dp37ow///wTvr6+aNGiBfz9/dG2bVu9XGXwvtOnT6Nly5Yq227evIlDhw6hZcuW+PPPPwG8GxBas2ZN/PLLL1oHmcePH2c5EdSHypQpg8ePH2tVfoY+ffpo9Ac0Pj4ecXFxWpVtjGCcITk5GevWrcO+fftUBqiXLFkSvr6+6NGjB3r27KnTWi+pqalql5/ny5cPL1++VNvX2toaKSkpOj0HQ5PTGj6mcu/ePfTv31+jfStVqpSjSRyPHTumVQ+nNl9MS5cujfDwcI2WswgPD0fp0qU1Lvt9TZo0wdixY3WeaDI7Qgi1AcgZ9/U5CeGHIS7jvrW1td7q+NAnHWR8fHxQrVo1g5X/9OlTjT/svL298fTp0xzVl5ycjGXLluHHH3/E48eP0bhxY50CTIZNmzbh9evX2LJlC8LCwtCrVy/Y2tqidevWqFu3rt7mMHj8+LHaqqi7du2CJEkq39ytrKzg7++PH374Qes68ubNq3E4efz4sc5XLmlqwYIFmDhxolYfvMYKxlevXkW7du1w//59CCHg7OwMBwcHPH36FBcuXMCmTZswffp07Ny5U+Nw+L5ChQrhxo0bGu1748YNFCxYUOs6jCG7b7GSJMHW1hbe3t5o2bIlWrdubcSWaW7r1q1aH6PNqeS4uDiN30tOTk5ah/v3GbKHvWfPnhg+fDiWLVuWbZhZvnw5/vjjD8ybN0/jst8XERGBr776SqdjNbF7926VtZwSExMhSRI2b96sNvu0JElazz4OAGvXrsXp06eV95OSkiBJEhYvXqw287skSViwYIHWdXzokw4yhpaQkKDxZZG2trZITEzUqZ6kpCRlD0xMTAwaN26MDRs24PPPP9epvPc5OjqiX79+6NevHx4/foywsDCEhYUpJ95aunQpkpKS0LJly0wXBNOElZWV2h/0EydOAADq1aunst3d3R1JSUla11GnTh2sXr0aI0aMyPbbR1JSElavXq1WryFoe4rAGME4Pj4ebdu2xdOnTzF9+nT06tVLbZmFtWvXYtq0aWjTpg0uX76sdQ9dw4YNERoaismTJ2c7r09CQgJCQ0Ph6+ur9fMwhqdPn2Yb5hMTE7F//36sWLECvr6+2LFjh9qpM1Pr3LkzJElSeS1mPKfMXp/ajvNJS0vT+AuPtqdvPmTIHvbBgwdj69atCAwMxLZt29CrVy+Vy6+vXLmC3377DQcOHED9+vUxePBgg7QjpzI+vz+UWQDUNcjs27cP+/btU9ue2fI1DDIyYciZFwFg3rx5+PHHH/HkyRM0adIEmzdvNtgf4UKFCmHkyJEYOXIkbty4gd9++w3r169Hz549YWlpiXr16mk9yRQAlCxZEocOHUJgYCCAd4OTIyIiULVqVbVz6zExMTqtjRMcHIwGDRrAz88Pa9asQZEiRdT2iY6ORt++fXHnzh3TrRmSDWME419//RUPHjzAwYMHM12h2NPTE2PGjEGtWrXQvHlzrF69GkOGDNGqjhEjRmDdunVo3bo1QkNDUahQIbV9Hj9+jJ49e+LZs2daf5jeu3dP47W7cuKvv/766D5v3rzBihUrMGLECMyaNQtjx47Vup73v0Vn9w06MjJS67I/fL++evUKHTp0wOzZs/XWW/3hN/Ss/PPPPzmqx5A97JaWlti9ezeGDx+OVatW4eDBgyqPZ5y2+fLLLzFv3rwczdxtKMYY05WTIJoTn+xVS/369cPXX3+NWrVqqT2WmpqKW7duIT4+HmXKlIGDg4NOdSgUCtja2mr0ok5NTUVycrJOVzVIkoTKlStrFGC0ScCLFy9G9+7dVa4kyszx48cRGhqKLVu26DRmYu3atejbty8GDRqEunXrYvPmzdi1axcWL16s1o3bvHlzWFlZYffu3VrXs2jRIgwfPhwKhQKff/652jeqo0ePIj09HbNnz9Z6sTRtTZ8+HRMmTNDq961QKBAaGgp/f/+P7qvLFSwA8MUXX0CSJOzZs0ejfYF3YwK0tXLlSgwePBgKhQKNGjVSu/rqyJEjSE1NxeLFi9UGBmvqzp07sLS0VPZiJScn4+eff8bRo0cRHx+PypUrIzAwMNMgpW89evTA5cuX8ffff2t1nDGujHpfVleP6UqXSQF1+WNozEkQHz16hD179qhNTOjn54fChQvnqGyFQoGOHTuqLbmQFUmSNL4SKGMJFUOG/N27d6uNd8xKQkICxo0bp/NpOBV6v6BbRnbt2iV69uwp+vbtq1zufdu2bcLT01M5d4Ktra0YO3asTuUbenE8IUSmc4lkd9NmHghJkoSVlZVo1aqVCAsLEwkJCdnun5KSonX7hXg3386QIUNUJkPLbEG0a9euCYVCkaPJpo4fPy58fX2FlZWVys/FyspKtGjRQhw9elTnsrUxbdo0refkkCRJ2NnZaTS/hJ2dnU4L/Hl4eIgZM2ZotO/MmTOFh4eH1nVkOHHihGjRokWmv4vmzZuLY8eO6VTuixcvRK1atZTv4caNG4sXL16IevXqqb0fChQooPWEe7pYvHixsLOz0/q4qKgojW9Xr14VJ06cyFE7M+bzyfg8NLYXL17odJwxJ6P80PXr18WUKVPEN998IxYsWCBiY2N1LsuQn+cKhcIoixh37dr1oxPqbdmyRRQuXFhYWlrqpV7z6/8ykvDwcLRu3RpWVlaws7PDunXrsGrVKnz55ZcoW7YsunTpgtTUVOzduxchISHw9vbGgAEDtKrDGKcnDNmVt3fvXoSFhWH79u3YvXs38uTJg/bt2yMgIAAtWrRQ+7ala3dqxkCwCRMm4N69e/D29s50gKeLiwvOnj2rNjBYG/Xq1UN4eDjevHmDW7duqSwXkdN1mC5cuKDxvv/++6/W5Rv6Ulng3Xwemg6uLVCgAF68eKFzXXXr1sXevXvx5s0b3L59W7lcRE5/FyEhIbhw4QJGjhyJAgUKYN68eWjXrh2uXbuGLVu2oGnTpkhNTcXOnTsxePBgTJgwAb/99pvO9WkiMTFRp/eHNldGTZ8+XesB5OYgOTkZO3fuRGhoqPL1oC1fX1+Nf1b37t3DsWPHtBrsu3jxYixcuBAnT55U6aH+888/0blzZ6SkpCjHFC1cuBCnT5/+aE92VpYvX45OnTrpdGx2hBFOvvzwww+YOnUq9u3bh+nTp6uNFYqKisKQIUMQHh6OKlWqYNu2bfqpWC9xSIYaN24sKlSoIF6+fCmEEGLQoEHCzs5OtGzZUmXG0pSUFFGtWjVRtWpVE7VUdykpKeLatWvi7Nmzmc4EqqmkpCSxZcsW0bFjR+U3fXd3dzF06FCDTgtuCElJSeLnn38WXbt2FdWqVROlSpUS1apVE927dxe//vqr2oy/2jD0LKnGoM0323Xr1pnlcyhZsqQYPHiw8v7OnTuFJEkiJCREbd9hw4aJggULGrQ96enpon79+qJu3boGrUeXXr4PGatHJj09Xezfv1/07dtXODs7K2dj1nZm4gwfvm6fP38u7OzsREREhNq+urxumzdvLr744guVbSkpKcLd3V3Y29uL1atXi7/++kvMnDlTWFpaiqCgIL08D30yVq/V3bt3xRdffCEkSRK1atUSly5dEikpKWL69OnC3t5eODs7i4ULF+p1OY9Ptkfm77//xqhRo5RX2gwbNgw//fQTevbsqTJA19LSEgEBARg3bpzWdSQmJuLZs2coWLCg2jX0q1atQmhoKB4/fozSpUtjzJgxOk8YtHv3bqxfvx6Wlpbo1asXmjRpgu3btyMwMFB5ybG1tTVGjhyJadOmaV2+jY0NOnXqhE6dOqlcjr1s2TIsWbIEPj4+CAgIQI8ePXTqLXnw4EGWj2Vcxurm5pbjgdOGvqzYHAcI6yIqKkqj3qWcDh401AKe0dHRKoM+q1atCuDdPCUfqly5MpYuXapT+z/WG/XmzRvcvHkTy5Ytw8mTJ7Fu3Tqd6jEFQ12kEBkZidDQUGzYsAExMTGQJAndu3dHYGAgateurbd6hRBISkrSy8rKAHDt2jW1HvnDhw/jv//+w/fff6/sLS1XrhwuX76M3bt362fsh54Z+uIT4N2g6z179mDDhg0YMWIEatSoAQ8PD0RHR6Nz586YP3++/sel6S0SyYwkSWLdunXK+//991+W30R0/eY5atQo4ezsrNYbMnXqVOW6R25ubkKSJGFvby8uXbqkdR179uwRkiQJa2tr4ezsLCwtLcXatWuFlZWVqFSpkggKChKBgYGiZMmSQqFQiJ9++knrOrISExMjFixYIGrUqKHzOk5CaNaT4eDgIDp16iRu3LihUx2vX78WRYsWFfb29iIkJEQ8fPhQ5fGHDx+KH374Qdjb24vixYvrba0ifUpISBD379/PtNfol19+EU2aNBFlypQRHTp0EGfPntWpDmP1KhlyAc8Pv3lm18uQk14lTX9WNjY2Yvr06TrVoQ1demTatGmjcsv4Jl2nTh21x9q0aSPatm2rdbvu3LkjpkyZIj777DOhUCiEl5eXCA4OFps2bdLbem2G/p3b2tqKX375RWXbqFGjhEKhEOfPn1fZvmTJEmFra6tV+Rm06TX577//tBovaIx1+d734sUL0bVrV+V4nooVK4pHjx7lqMysfLI9MoBqOjVEUj18+DBat26tctVTXFwcpk2bBk9PTxw5cgQ+Pj44e/YsfH19MWPGDKxfv16rOmbNmoXy5cvj6NGjyJs3L77++msMGjQIzZs3x59//ql8XqmpqahduzaWL1+u9VifrDx69AgPHjzAv//+CyGEzjM3zpo166Pzcdy4cQN//vknDh06hNOnT2u9DIAxLis2tClTpmD58uV4+PChys962rRpmDhxIiRJQr58+XDjxg3s3bsXJ0+ezLQXIjvG6FWKjo6Gn58fbG1tsWbNGnTr1k3l+bx9+xYbN27Ed999h5YtW+Ly5cvw8vLSqo7MXk/6fo9PmDAh2zIzJsRr2rSpUS4H18WVK1fUnoO3tzceP36c6QSS2v4M69Spg7Nnz8LNzQ2dO3fGzz//jPr16wN4d1WZXBQoUEBlIjng3UzC9vb2au8xa2trnT8LJ06cmO0VS4mJidi+fTtCQ0Nx4MABpKamKqet0IQhl1B532+//Ybg4GDExsZizJgxcHNzw8SJE1GmTBlMmzYNgYGB+n0/GiQeyYAkSWL69OnKpekPHTokJEkSy5cvV1u2PqMHRVvu7u5i5syZKtvWr18vJEkSixcvVtk+fPhw4enpqVMdc+bMUd7/+++/s1yFee7cucLe3l7rOt5369YtMXnyZFG6dGllcm/YsKFYuXKlcryRody/f1+4ubmJgIAArY/19fVVO8ed3b6+vr5a12FoNWvWVHvusbGxwsbGRnh5eSmvvjlz5ozImzev6N69uyma+VGDBg0S+fLl++jVQnfv3hX58uUTX3/9tVblS5IkqlatqlEvQ9WqVc1ynE+GDz+LsrsNHjzY7J6LJEmiWLFiYvv27WpXNd6+fVs2PTKdOnUSpUqVEnFxcUIIIf766y9haWkpOnXqpLbvyJEjRdmyZbV8BllLS0sTu3fvFgEBAcLBwUEoFApRqlQpERwcLI4cOaJxOcYYI3Pr1i3RtGlTIUmSqF+/vvj777+Vjz148EC0bdtWSJIkqlevLiIjI/VW7yfdIzN+/Hi1a/Azm5FRCKFTenz9+jVcXV1Vth09ehSSJKnNVlq2bFmd5mD577//VCaIc3d3B4BMJ43TdVbcmJgY5do9kZGREEKgfPnyCAkJgb+/v9bflnVVpEgRDBgwQKdeg6tXr2LYsGEa7dukSRO9zDapb1FRUWpXM+zevRtv377FqFGjlAti1qxZE/369VPOvmxuDL2AZ5EiRfDixQuVMSzZ9TJkNjmiuahevbrGnz26fk5l5caNG9i8ebNyHF/fvn3h5OSkVRmLFy9GWFgYOnToABcXF3Tq1Andu3fPtFc0p94f2xUbGwsAuHXrltqM47qM7Zo4cSJq1KiBkiVLoly5coiMjIQkSRgzZozavtu2bdPLHDynT59GaGgoNm3ahGfPnsHb2xuJiYn46aef8OWXX+a4fEOoUKEC7O3t8dNPP6ktteDl5YUdO3Zg27ZtGDZsGGrVqoUhQ4Zg/vz5Oa73kw0yxuhC9/b2VltTJiIiAgUKFECJEiVUtr99+1brD4kMhjpFtmrVKoSFheHIkSNIS0tD4cKFERwcjJ49e6JChQpq+798+fKjq9zmlI+Pj06X/BrzsmJDMUYwNgZDL+AZFRWlQ6vMk6E/p7K6rPiPP/5Aly5d8PbtW+U2XS4rHjx4MAYPHox79+4pV0tfuXIlChYsiMaNG0OSJL19Zhnyi2mFChVw6NAhTJ8+HXfv3kXt2rURHBysNpNwREQE7O3t0aVLF+2fAN4tlpvxc7p79y6KFy+OAQMGwN/fHzY2NihVqpTBP2NzonPnzpg7d262p1I7dOiA5s2bY+zYsVi6dCmDTE4YY06OFi1aYNWqVejcuTNq1aqFtWvX4saNG5kuOhYZGYmiRYvqVI+hvol89dVXcHZ2Rt++fdGzZ080aNBA7QPg/TkgwsPDderx0UZUVBRcXFy0Pi45OVnjdW4sLS1VPsDNhbGCsaEZegHP+vXr4/PPP0f9+vVRt25ds/7g/xhDf07t3LkTxYsXVwknqamp+Oqrr2BhYYFff/0V1atXx65duzB27FhMnz5dp6txfHx8MG7cOIwbN0555dLGjRshhMDgwYOxZ88etG3bFs2aNdNpJWZjfDGtW7cudu3ale0+jRo1wtWrV3Wuo2zZsihYsCD8/f3RrVs3lStZ5TCmSNP5mBwcHLBgwQL9vb71dpKK1Pz333+iSJEiQqFQKGcvdXd3FzExMSr7JSQkiHz58olRo0ZpXUdmI9GzGp2u7VUmW7duzfQKGX3PAaGpBw8eCDc3N+Hv76/1sR+OicrupuuYKEMbNmyYcHFxUc7ds2bNGiFJksqcKRn69+8vqlevbuwmaqR9+/aiZMmS4s2bN9nu9+bNG1GyZEnRoUMHrcr38vJSvtYtLCxEuXLlxKBBg8Rvv/0m7t27l4OW5z6enp5i0qRJKtv27dsnJElSm9G8R48eolSpUnqrOy0tTezfv1/06dNHODk5CUmSRJ48efRWvhzZ2NgIOzs70blzZ/H777+LpKQk5WP6HFOU23yyay0Zy8uXL/Hzzz/j7t278Pb2Rv/+/ZXjWDKcPXsWv/32G77++muUK1dOq/LXrFmjdZt0TcGGmgNi7ty52T6eMR/HH3/8AQA4c+aM1iPvM9ak0oT4/13P+pqDQl+ePXuGatWq4eHDh7CwsEBqairy58+PK1euqIyJSkxMROHChTFw4EDMmDHDhC3O3IkTJ9CgQQM0aNDgowt4RkRE4OjRo1ovhPrw4UMcP34cx48fx8mTJ3H16lXlSsweHh6oV68e6tevj/r166NSpUpGmV/DHNnZ2WHJkiXo37+/ctvo0aPx448/4uzZsyqnTpYuXYqRI0fqNPPuxyQlJWHHjh0ICwvDjh079F6+XMTGxmLz5s1Yt24djh07BgcHB7Rr1w49evRA0aJFUbZsWWzZsgUdO3Y0dVPNCoMMZevu3bsIDQ1FaGgobt26BU9PT3Tr1g01a9ZEt27d9PKm0mRhOXt7e7Ro0QI//PADSpcurXUdxgx8hmToYGwsxl7AMz4+HidPnsSJEydw4sQJnDlzRrk6uJOTE16+fJnjOuSoaNGiGDhwIL7//nvltnr16uHKlSt4+fKlyrIKP//8M0aOHKk8fU2GFR0djbCwMISFheHq1atwcHBAQkICpk6diuDgYJ0v8c6NGGQMKLuR6xkz1np7e6Nly5Zo3bq1EVummQ/ngPD391eZA6JkyZJ6CTL379/P9nFbW1vkz59fp5V0yXydOHECU6dOxaFDh1TWB7K0tETjxo0xbtw4fP7553qv9/Hjxzh8+DCWLFmCU6dOmWXvm7F07twZV69exfnz5+Ho6Ii///4blStXRrt27bBlyxaVfYODg7Fnzx6tV/CmnLty5QrWrVuHDRs24OHDh3BwcEDz5s3Rtm1bs/zCZWwMMgZUvnz5j070Fh0djbS0NPj6+mLHjh0aD0g1BoVCAR8fH8ydOxetWrVS+XamzyCTndTUVNy6dQvx8fEoU6aMyuSCnxq5B+OsGGIBz/f99ddfOH78uLI35v79+7CxsUGVKlVQt25d1KtXD+3bt9dbfXJy9epV1KhRA3nz5lVeVpyYmIhTp06pXZFTvHhxNGnSBCtXrjRRawl4N8B/3bp12Lp1K2JjYz/ZEK7CZKNzSAghRGJiopg3b56QJElMmzbN1M1RsWTJElGvXj0hSZJwdXUVAwcOFIcOHRLp6el6H3i2a9cu0bNnT9G3b1/lJFbbtm0Tnp6eysHKtra2agMQPyXlypUT5cuXz/JWrFgxYWVlJRQKhfDz8xNv3741dZOzZagFPCMiIsT06dOFn5+fyJcvn5AkSRQsWFB07NhRzJ49W5w8eTJHi4PmNidOnBAtW7YUpUuXFr6+vmL//v1q+xw+fFiUL19e7N271wQtpMwkJyeLrVu3mroZZoE9MmaiR48euHz5sll2274/B8SNGzeUc0Bs2LABW7ZsQYcOHXJUfnh4OFq2bAkrKyvY2dkhISEBq1atwpdffomyZcuicePGSE1Nxd69e3Hnzh29LrOQ27x58wYrVqzAiBEjMHXqVIwdO9bUTcpUVgt4xsfHIzY2FpIkoVixYjot4KlQKGBlZYUuXbrAz88PderUQbFixQz0TIj0p23btlrtL0nSJz04OgODjJlYsmQJvv32W+UARHP1/hwQjx8/RoECBdCmTZsczQHRpEkTPHv2TGW9qLVr16Jx48aZrhclhEBkZKS+n1quYs7BOD4+HhUqVMDTp08xfvx49OrVC56ensrHHz16hLVr12LatGkoVKgQLl++jDx58mhcfqVKlfD3338rZ6CuW7euck6Zj80mTGRKCoUCtra2KFiwIDT50yxJEu7evWuElpk503UG0ftmzZolHB0dTd0MjelzDghjrxf1KVi8eLGws7MzdTMytXDhQqFQKMThw4ez3e/gwYNCoVCorUumibi4OLF3714xYcIE0bRpU+UaNRmnmObMmSNOnTpl9qff6NNSuHBhIUmSqFGjhpg3b554/PixqZskC7wMxAwIIbBz585Mp/03VwqFAs2aNcPq1avx5MkTrF+/Hk2bNtWpLGOsF/WpSUxMVBmcbU527dqFFi1afHS9nSZNmqB58+bK+YO04ejoiBYtWmDy5Mk4cOAAXr16hbNnz2LMmDGwsrLCvHnzUK9ePTg7O6NBgwY6PhMi/YqOjsbhw4dRpUoVTJ06FV5eXmjWrBl+/fVXvH792tTNM1sMMgaUsXBdVrdHjx7h0KFD6Nq1K06ePKnVcuzmxNbWFt26dcvRuVpDrRf1KTL3YHz16lWNFw1s0qRJjqZ8z2BhYYFq1aph2LBhGDt2LMaMGYPatWsjKSkJJ06cyHH5RPrSsGFDrFixAjExMdiyZQtcXV0RGBgId3d3dOzYEVu2bEFycrKpm2lWzPMrWy7h5uam0R9lKysrTJ06Ff7+/kZolXky5Mq1ucXHFrLMmAF52bJlOHnyJNatW2eklmnHmAt4Jicn48yZM8pZfk+fPq18fdnY2CjXZCIyN1ZWVmjXrh3atWuH+Ph4bN26FcuXL0e3bt0wadIktQUyP2UMMgY0YcKEbINMxrwfTZs2zXa10E+BIVeuzS1ySzA29AKeO3bsUAaXixcvIiUlBUIIuLq6KoNL/fr1Ub16dbOat4koM8nJydi7dy927NiBixcvwtbWVucFhnMrBhkDmjRpkqmbIAvGWLk2N8hNwfj9Hrjs6NIDlzEdgI+PD7p166YMLtpexk1kKunp6di/fz/Wr1+P7du3IzExEc2aNcPKlSvRoUMHra7i+xTw8msiMipDL+C5efNm1K9fH4UKFdK1iUQmcfLkSYSFhWHz5s14/vw5ateujR49eqBr165wc3MzdfPMFoMMERlVblnAk0jfFAoF7Ozs0LJlS/j7+2t0Cqlq1aqGb5iZY5AhIiIyA+8vjPuxXktdeitzK46RISIiMgMcL6gb9sgQERGRbHFCPCIiIpItBhkiIiKSLQYZIiIiki0GGSIiAypatCjmz59v6mYQ5VoMMkS5WExMDIYOHYpixYrBxsYGXl5eaNOmDQ4ePGjQeqOioiBJEi5dumTQeuTg3LlzGDhwoKmbQZRr8fJrolwqKioK9erVQ968efHjjz+iQoUKSElJwd69ezFkyBDcuHFD7ZiUlJRct/7Q27dvYW1trbJNCIG0tDRYWhr+I9Dcl4sgkjv2yBDlUoMHD4YkSTh79iw6deqEUqVKoVy5chgxYgROnz4N4N2kW8uWLUPbtm2RJ08eTJs2DSVKlMDs2bNVyrp06RIkScLt27dVjvPz84OdnR2KFSuGLVu2KPf38fEBAFSpUgWSJKFRo0YA3q0hM2XKFBQuXBg2NjaoXLkywsPDVep6+PAh/P394eLigjx58qB69eo4c+YMAKBv375o3769yv5BQUHK8gGgUaNGCAwMRFBQENzc3ODr64uIiAhIkoQ9e/agWrVqsLGxwfHjx5Geno6QkBD4+PjAzs4OlSpVUnkeGccdPHgQ1atXh729PerWrYubN2+qtOGPP/5AjRo1YGtrCzc3N+V6T4D6qaVXr17hq6++Qv78+eHk5IQmTZrg8uXLyscvX76Mxo0bw9HREU5OTqhWrRrOnz+f5e+Z6FPHIEOUC7148QLh4eEYMmRIpgvM5c2bV/n/SZMmoUOHDrh69Sq+/PJL9O/fX21irl9//RUNGjRAiRIllNvGjx+PTp064fLlywgICED37t1x/fp1AMDZs2cBAAcOHMDjx4+xdetWAMCCBQswZ84czJ49G1euXIGvry/atm2LW7duAQDi4+PRsGFDPHr0CDt37sTly5fx3XffIT09Xavnv2bNGlhbW+PEiRNYvny5cvvo0aMxY8YMXL9+HRUrVkRISAjWrl2L5cuX4++//8bw4cPRs2dPHDlyRKW8sWPHYs6cOTh//jwsLS3Rv39/5WO7du1Chw4d0LJlS1y8eBEHDx5EzZo1s2xbly5d8PTpU+zZsweRkZGoWrUqmjZtihcvXgAAAgICULhwYZw7dw6RkZEYPXp0ruslI9IrQUS5zpkzZwQAsXXr1mz3AyCCgoJUtj169EhYWFiIM2fOCCGEePv2rXBzcxOrV69WOe7rr79WOa5WrVrim2++EUIIce/ePQFAXLx4UWUfDw8PMX36dJVtNWrUEIMHDxZCCLFixQrh6Ogonj9/nml7+/TpI9q1a6ey7X//+59o2LCh8n7Dhg1FlSpVVPY5fPiwACC2b9+u3JaUlCTs7e3FyZMnVfb98ssvhb+/v8pxBw4cUD6+a9cuAUC8efNGCCFEnTp1REBAQKbtFUIIb29vMW/ePCGEEMeOHRNOTk4iKSlJZZ/ixYuLFStWCCGEcHR0VPlZE1H22CNDlAsJLSbsrl69usp9Dw8PtGrVCqtWrQLw7rRJcnIyunTporJfnTp11O5n9MhkJi4uDv/++y/q1aunsr1evXrK4y5duoQqVarAxcVF4/Znplq1apluf/+53r59G4mJiWjevDkcHByUt7Vr1+LOnTsqx1WsWFH5/4xVtZ8+fapsc9OmTTVq1+XLlxEfHw9XV1eVOu/du6esc8SIEfjqq6/QrFkzzJgxQ60tRKSKg32JcqGSJUtCkqRMB/R+KLNTT1999RV69eqFefPm4ddff0W3bt1gb29viKaqsLOzy/ZxhUKhFtJSUlLU9svsOX24PT4+HsC7U0Oenp4q+9nY2Kjcf//UTsZifhmnuz7W5vfFx8ejUKFCiIiIUHss43TfpEmT0KNHD+zatQt79uzBxIkTsWHDBpVxN0T0f9gjQ5QLubi4wNfXF0uWLEFCQoLa469evcr2+JYtWyJPnjxYtmwZwsPDVcaEZMgYMPz+/TJlygCA8iqh91fmdXJygoeHB06cOKFy3IkTJ1C2bFkA73o+Ll26pBwv8qH8+fPj8ePHKtt0vcS7bNmysLGxwYMHD1CiRAmVm5eXl8blVKxYUePL2atWrYqYmBhYWlqq1enm5qbcr1SpUhg+fDj27duHjh07cjFBomwwyBDlUkuWLEFaWhpq1qyJ33//Hbdu3cL169excOFCtdNCH7KwsEDfvn0xZswYlCxZMtP9N2/ejFWrVuGff/7BxIkTcfbsWQQGBgIA3N3dYWdnh/DwcDx58gSxsbEAgG+//RYzZ87Exo0bcfPmTYwePRqXLl3C//73PwCAv78/ChYsiPbt2+PEiRO4e/cufv/9d5w6dQoA0KRJE5w/fx5r167FrVu3MHHiRPz11186/XwcHR0RHByM4cOHY82aNbhz5w4uXLiARYsWYc2aNRqXM3HiRKxfvx4TJ07E9evXcfXqVcycOTPTfZs1a4Y6deqgffv22LdvH6KionDy5EmMHTsW58+fx5s3bxAYGIiIiAjcv38fJ06cwLlz55QBkYgyYepBOkRkOP/++68YMmSI8Pb2FtbW1sLT01O0bdtWHD58WAjxbtDutm3bMj32zp07AoCYNWuW2mMAxJIlS0Tz5s2FjY2NKFq0qNi4caPKPitXrhReXl5CoVAoB+OmpaWJSZMmCU9PT2FlZSUqVaok9uzZo3JcVFSU6NSpk3BychL29vaievXqyoHHQggxYcIEUaBAAeHs7CyGDx8uAgMD1Qb7/u9//1MpM2PQ7suXL1W2p6eni/nz54vPPvtMWFlZifz58wtfX19x5MiRLI+7ePGiACDu3bun3Pb777+LypUrC2tra+Hm5iY6duyofOz9wb5CCBEXFyeGDh0qPDw8hJWVlfDy8hIBAQHiwYMHIjk5WXTv3l14eXkJa2tr4eHhIQIDA5UDi4lInSSEFqMCieiTcezYMTRt2hTR0dEoUKCAymOSJGHbtm1qc7oQERkbB/sSkYrk5GT8999/mDRpErp06aIWYoiIzAnHyBCRivXr18Pb2xuvXr3CrFmzTN0cIqJs8dQSERERyRZ7ZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLb+H1VdMJRToc5uAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "#اضافه کردن فیچر جدید و انکود کردن آن با لیبل انکودینگ\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "data_cpy = data.copy()\n",
    "data_cpy['ProoftypeEncoded'] = label_encoder.fit_transform(data['ProofType'])\n",
    "data_cpy['NetworkEncoded'] = label_encoder.fit_transform(data['Network'])\n",
    "sub_data = pd.concat([subset_data, data_cpy['ProoftypeEncoded'], data_cpy['NetworkEncoded']], axis=1)\n",
    "#خوشه بندی چهار فیچر بر اساس متد واریانس\n",
    "result = shc.linkage(sub_data, method='ward')\n",
    "shc.dendrogram(result, orientation='top', labels=data_cpy['Symbol'].values, distance_sort='descending')\n",
    "plt.title('Dendrogram')\n",
    "plt.xlabel('Cryptocurrencies')\n",
    "plt.ylabel('Distance')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#نمایش کلاستر های نهایی\n",
    "clusters = fcluster(result, t=2, criterion='maxclust')\n",
    "data_cpy['Cluster'] = clusters\n",
    "plt.figure(figsize=(12, 8))\n",
    "for i in range(1, 3):\n",
    "    cluster = data_cpy[data_cpy['Cluster'] == i]\n",
    "    plt.scatter(cluster['NetworkEncoded'], cluster['MarketCap'])\n",
    "    for index, row in cluster.iterrows():\n",
    "        plt.annotate(row['Symbol'], (row['NetworkEncoded'], row['MarketCap']))\n",
    "\n",
    "plt.ylabel('NetworkEncoded')\n",
    "plt.xlabel('MarketCap')\n",
    "plt.title('Clustering Results')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "در این قسمت اما با اضافه کردن فیچر جدید، ارتفاع بیت کوین و تتر کمتر شده که به منزله کاهش شباهت آن ها با در نظر گرفتن فیچر جدید است."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Volume=%{x}<br>MarketCap=%{y}<br>Network=%{z}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "",
         "scene": "scene",
         "showlegend": false,
         "type": "scatter3d",
         "x": [
          1823284.25,
          99187680.79,
          141541023.4,
          219706474,
          217711327.7,
          247994398.3,
          591301857.2,
          203559209.8,
          631149332.6,
          364799566,
          5084396129,
          3665726400,
          680796581.6,
          33149475161,
          8843689497,
          22967636872
         ],
         "y": [
          3586504072,
          3992451215,
          4237575489,
          4901630273,
          5513660644,
          5606251419,
          5729295460,
          6659549868,
          10342393982,
          12109137813,
          12132993575,
          37317414284,
          44599661514,
          74588184814,
          196082390477,
          459083734664
         ],
         "z": [
          "Ethereum",
          "Ethereum",
          "Ethereum",
          "Avalanche",
          "Ethereum",
          "Ethereum",
          "Litecoin",
          "Polkadot",
          "Dogecoin",
          "Cardano",
          "Binance Smart Chain",
          "Ethereum",
          "Binance Smart Chain",
          "Ethereum",
          "Ethereum",
          "Bitcoin"
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       ],
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        "height": 800,
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         "yaxis": {
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        "width": 1000
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     },
     "metadata": {},
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   ],
   "source": [
    "Dimensions = {\n",
    "    'Volume': cluster['Volume'],\n",
    "    'MarketCap': cluster['MarketCap'],\n",
    "    'Network': cluster['Network']\n",
    "}\n",
    "\n",
    "Dimensions = pd.DataFrame(Dimensions)\n",
    "# رسم نمودار سه بعدی\n",
    "fig = px.scatter_3d(Dimensions, x='Volume', y='MarketCap', z='Network')\n",
    "fig.update_layout(width=1000, height=800)\n",
    "\n",
    "# نمایش نمودار\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extra Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature_1: TotalSupply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     9.852395e+08\n",
       "1     1.000000e+09\n",
       "2     1.631060e+05\n",
       "3     4.318041e+08\n",
       "4     5.347889e+09\n",
       "5     5.895890e+14\n",
       "6     8.400000e+07\n",
       "7     8.899681e+10\n",
       "8     1.364182e+09\n",
       "9     1.000000e+10\n",
       "10    5.595096e+08\n",
       "11    1.413394e+11\n",
       "12    3.624429e+10\n",
       "13    2.213836e+09\n",
       "14    9.998838e+10\n",
       "15    2.554032e+10\n",
       "16    1.538456e+08\n",
       "17    8.663000e+08\n",
       "18    1.202492e+08\n",
       "19    1.950842e+07\n",
       "Name: TotalSupply, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_commas_and_convert_to_float(value):\n",
    "    if value == '866,300,000.00':\n",
    "        value = '866,300,000'\n",
    "    value = ''.join(value.split(','))  # Remove commas\n",
    "    return float(value)\n",
    "\n",
    "# Apply the function to the specified column\n",
    "data['TotalSupply'] = data['TotalSupply'].apply(remove_commas_and_convert_to_float)\n",
    "data['TotalSupply'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset_data = data[['Volume', 'MarketCap', 'TotalSupply']]\n",
    "subset_data = StandardScaler().fit_transform(subset_data)\n",
    "result = shc.linkage(subset_data, method='ward')\n",
    "shc.dendrogram(result, orientation='top', labels=data['Symbol'].values, distance_sort='descending')\n",
    "plt.title('Dendrogram')\n",
    "plt.xlabel('Cryptocurrencies')\n",
    "plt.ylabel('Distance')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters = fcluster(result, t=2, criterion='maxclust')\n",
    "data_copy['Cluster'] = clusters.copy()\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "for i in range(1, 3):\n",
    "    cluster = data_copy[data_copy['Cluster'] == i]\n",
    "    plt.scatter(cluster['Volume'], cluster['MarketCap'], label=f'Cluster {i}')\n",
    "    for index, row in cluster.iterrows():\n",
    "        plt.annotate(row['Symbol'], (row['Volume'], row['MarketCap']))\n",
    "\n",
    "plt.xlabel('Volume')\n",
    "plt.ylabel('MarketCap')\n",
    "plt.title('Clustering Results')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Volume=%{x}<br>MarketCap=%{y}<br>TotalSupply=%{z}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "",
         "scene": "scene",
         "showlegend": false,
         "type": "scatter3d",
         "x": [
          1823284.25,
          99187680.79,
          141541023.4,
          219706474,
          217711327.7,
          247994398.3,
          591301857.2,
          271902785.1,
          203559209.8,
          423298996.1,
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          631149332.6,
          364799566,
          5084396129,
          1349225371,
          3665726400,
          680796581.6,
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         ],
         "y": [
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         ],
         "z": [
          "985,239,504",
          "1,000,000,000",
          "163,106",
          "431,804,080",
          "5,347,888,596",
          "589,589,040,239,380",
          "84,000,000",
          "88,996,805,683",
          "1,364,182,225",
          "10,000,000,000",
          "559,509,636",
          "141,339,436,384",
          "36,244,287,753",
          "2,213,836,281",
          "99,988,380,689",
          "25,540,319,537",
          "153,845,639",
          "120,249,203"
         ]
        }
       ],
       "layout": {
        "height": 800,
        "legend": {
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "scene": {
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "xaxis": {
          "title": {
           "text": "Volume"
          }
         },
         "yaxis": {
          "title": {
           "text": "MarketCap"
          }
         },
         "zaxis": {
          "title": {
           "text": "TotalSupply"
          }
         }
        },
        "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"
             ],
             [
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              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
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              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
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             ],
             [
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             ],
             [
              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"
             ],
             [
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             ],
             [
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             ],
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             ],
             [
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             ],
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             ],
             [
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              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
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            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
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             ],
             [
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              "#9c179e"
             ],
             [
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              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
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   "source": [
    "\n",
    "Dimensions = {\n",
    "    'Volume': cluster['Volume'],\n",
    "    'MarketCap': cluster['MarketCap'],\n",
    "    'TotalSupply': cluster['TotalSupply']\n",
    "}\n",
    "\n",
    "Dimensions = pd.DataFrame(Dimensions)\n",
    "# رسم نمودار سه بعدی\n",
    "fig = px.scatter_3d(Dimensions, x='Volume', y='MarketCap', z='TotalSupply')\n",
    "fig.update_layout(width=1000, height=800)\n",
    "\n",
    "# نمایش نمودار\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Volume</th>\n",
       "      <th>MarketCap</th>\n",
       "      <th>TotalSupply</th>\n",
       "      <th>ProofType</th>\n",
       "      <th>Network</th>\n",
       "      <th>MarketDominance</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LEO</td>\n",
       "      <td>1.823284e+06</td>\n",
       "      <td>3586504072</td>\n",
       "      <td>9.852395e+08</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>0.384617</td>\n",
       "      <td>929022784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UNI</td>\n",
       "      <td>9.918768e+07</td>\n",
       "      <td>3992451215</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>0.428151</td>\n",
       "      <td>106912512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WBTC</td>\n",
       "      <td>1.415410e+08</td>\n",
       "      <td>4237575489</td>\n",
       "      <td>1.631060e+05</td>\n",
       "      <td>PoW</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>0.454438</td>\n",
       "      <td>163106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AVAX</td>\n",
       "      <td>2.197065e+08</td>\n",
       "      <td>4901630273</td>\n",
       "      <td>4.318041e+08</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Avalanche</td>\n",
       "      <td>0.525652</td>\n",
       "      <td>354834080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAI</td>\n",
       "      <td>2.177113e+08</td>\n",
       "      <td>5513660644</td>\n",
       "      <td>5.347889e+09</td>\n",
       "      <td>stablecoin</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>0.591286</td>\n",
       "      <td>5347888640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SHIB</td>\n",
       "      <td>2.479944e+08</td>\n",
       "      <td>5606251419</td>\n",
       "      <td>5.895890e+14</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>0.601215</td>\n",
       "      <td>589346889531392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LTC</td>\n",
       "      <td>5.913019e+08</td>\n",
       "      <td>5729295460</td>\n",
       "      <td>8.400000e+07</td>\n",
       "      <td>PoW</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>0.614410</td>\n",
       "      <td>73779672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TRX</td>\n",
       "      <td>2.719028e+08</td>\n",
       "      <td>6018922345</td>\n",
       "      <td>8.899681e+10</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Tron</td>\n",
       "      <td>0.645470</td>\n",
       "      <td>88900517888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DOT</td>\n",
       "      <td>2.035592e+08</td>\n",
       "      <td>6659549868</td>\n",
       "      <td>1.364182e+09</td>\n",
       "      <td>PoW</td>\n",
       "      <td>Polkadot</td>\n",
       "      <td>0.714171</td>\n",
       "      <td>1265961344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MATIC</td>\n",
       "      <td>4.232990e+08</td>\n",
       "      <td>8053184610</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Polygon</td>\n",
       "      <td>0.863625</td>\n",
       "      <td>9299803136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SOL</td>\n",
       "      <td>6.338963e+08</td>\n",
       "      <td>8349992848</td>\n",
       "      <td>5.595096e+08</td>\n",
       "      <td>PoH</td>\n",
       "      <td>Solana</td>\n",
       "      <td>0.895454</td>\n",
       "      <td>416599712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>6.311493e+08</td>\n",
       "      <td>10342393982</td>\n",
       "      <td>1.413394e+11</td>\n",
       "      <td>PoW</td>\n",
       "      <td>Dogecoin</td>\n",
       "      <td>1.109120</td>\n",
       "      <td>141479018496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ADA</td>\n",
       "      <td>3.647996e+08</td>\n",
       "      <td>12109137813</td>\n",
       "      <td>3.624429e+10</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>1.298586</td>\n",
       "      <td>35218583552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BUSD</td>\n",
       "      <td>5.084396e+09</td>\n",
       "      <td>12132993575</td>\n",
       "      <td>2.213836e+09</td>\n",
       "      <td>stablecoin</td>\n",
       "      <td>Binance Smart Chain</td>\n",
       "      <td>1.301144</td>\n",
       "      <td>2102166528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>XRP</td>\n",
       "      <td>1.349225e+09</td>\n",
       "      <td>23581660945</td>\n",
       "      <td>9.998838e+10</td>\n",
       "      <td>RPCA</td>\n",
       "      <td>XRP Ledger</td>\n",
       "      <td>2.528901</td>\n",
       "      <td>53441028096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>USDC</td>\n",
       "      <td>3.665726e+09</td>\n",
       "      <td>37317414284</td>\n",
       "      <td>2.554032e+10</td>\n",
       "      <td>stablecoin</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>4.001925</td>\n",
       "      <td>25392713728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>BNB</td>\n",
       "      <td>6.807966e+08</td>\n",
       "      <td>44599661514</td>\n",
       "      <td>1.538456e+08</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Binance Smart Chain</td>\n",
       "      <td>4.782874</td>\n",
       "      <td>151705408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>USDT</td>\n",
       "      <td>3.314948e+10</td>\n",
       "      <td>74588184814</td>\n",
       "      <td>8.663000e+08</td>\n",
       "      <td>stablecoin</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>7.998848</td>\n",
       "      <td>83814883328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ETH</td>\n",
       "      <td>8.843689e+09</td>\n",
       "      <td>196082390477</td>\n",
       "      <td>1.202492e+08</td>\n",
       "      <td>PoS</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>21.027905</td>\n",
       "      <td>120263584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BTC</td>\n",
       "      <td>2.296764e+10</td>\n",
       "      <td>459083734664</td>\n",
       "      <td>1.950842e+07</td>\n",
       "      <td>PoW</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>49.232208</td>\n",
       "      <td>19518400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Symbol        Volume     MarketCap   TotalSupply   ProofType  \\\n",
       "0     LEO  1.823284e+06    3586504072  9.852395e+08         PoS   \n",
       "1     UNI  9.918768e+07    3992451215  1.000000e+09         PoS   \n",
       "2    WBTC  1.415410e+08    4237575489  1.631060e+05         PoW   \n",
       "3    AVAX  2.197065e+08    4901630273  4.318041e+08         PoS   \n",
       "4     DAI  2.177113e+08    5513660644  5.347889e+09  stablecoin   \n",
       "5    SHIB  2.479944e+08    5606251419  5.895890e+14         PoS   \n",
       "6     LTC  5.913019e+08    5729295460  8.400000e+07         PoW   \n",
       "7     TRX  2.719028e+08    6018922345  8.899681e+10         PoS   \n",
       "8     DOT  2.035592e+08    6659549868  1.364182e+09         PoW   \n",
       "9   MATIC  4.232990e+08    8053184610  1.000000e+10         PoS   \n",
       "10    SOL  6.338963e+08    8349992848  5.595096e+08         PoH   \n",
       "11   DOGE  6.311493e+08   10342393982  1.413394e+11         PoW   \n",
       "12    ADA  3.647996e+08   12109137813  3.624429e+10         PoS   \n",
       "13   BUSD  5.084396e+09   12132993575  2.213836e+09  stablecoin   \n",
       "14    XRP  1.349225e+09   23581660945  9.998838e+10        RPCA   \n",
       "15   USDC  3.665726e+09   37317414284  2.554032e+10  stablecoin   \n",
       "16    BNB  6.807966e+08   44599661514  1.538456e+08         PoS   \n",
       "17   USDT  3.314948e+10   74588184814  8.663000e+08  stablecoin   \n",
       "18    ETH  8.843689e+09  196082390477  1.202492e+08         PoS   \n",
       "19    BTC  2.296764e+10  459083734664  1.950842e+07         PoW   \n",
       "\n",
       "                Network  MarketDominance   CirculatingSup  \n",
       "0              Ethereum         0.384617        929022784  \n",
       "1              Ethereum         0.428151        106912512  \n",
       "2              Ethereum         0.454438           163106  \n",
       "3             Avalanche         0.525652        354834080  \n",
       "4              Ethereum         0.591286       5347888640  \n",
       "5              Ethereum         0.601215  589346889531392  \n",
       "6              Litecoin         0.614410         73779672  \n",
       "7                  Tron         0.645470      88900517888  \n",
       "8              Polkadot         0.714171       1265961344  \n",
       "9               Polygon         0.863625       9299803136  \n",
       "10               Solana         0.895454        416599712  \n",
       "11             Dogecoin         1.109120     141479018496  \n",
       "12              Cardano         1.298586      35218583552  \n",
       "13  Binance Smart Chain         1.301144       2102166528  \n",
       "14           XRP Ledger         2.528901      53441028096  \n",
       "15             Ethereum         4.001925      25392713728  \n",
       "16  Binance Smart Chain         4.782874        151705408  \n",
       "17             Ethereum         7.998848      83814883328  \n",
       "18             Ethereum        21.027905        120263584  \n",
       "19              Bitcoin        49.232208         19518400  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature_2: MarketDominance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Shape of passed values is (20, 3), indices imply (20, 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32md:\\quera\\tamrin\\faz2\\Q2-3.ipynb Cell 27\u001b[0m line \u001b[0;36m3\n\u001b[0;32m     <a href='vscode-notebook-cell:/d%3A/quera/tamrin/faz2/Q2-3.ipynb#X31sZmlsZQ%3D%3D?line=27'>28</a>\u001b[0m data_copy[\u001b[39m'\u001b[39m\u001b[39mProoftypeEncoded\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m label_encoder\u001b[39m.\u001b[39mfit_transform(data[\u001b[39m'\u001b[39m\u001b[39mProofType\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[0;32m     <a href='vscode-notebook-cell:/d%3A/quera/tamrin/faz2/Q2-3.ipynb#X31sZmlsZQ%3D%3D?line=29'>30</a>\u001b[0m \u001b[39m# Convert subset_data to a DataFrame if it's not already\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/d%3A/quera/tamrin/faz2/Q2-3.ipynb#X31sZmlsZQ%3D%3D?line=30'>31</a>\u001b[0m subset_data \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mDataFrame(subset_data, columns\u001b[39m=\u001b[39;49m[\u001b[39m'\u001b[39;49m\u001b[39mVolume\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mMarketCap\u001b[39;49m\u001b[39m'\u001b[39;49m])\n\u001b[0;32m     <a href='vscode-notebook-cell:/d%3A/quera/tamrin/faz2/Q2-3.ipynb#X31sZmlsZQ%3D%3D?line=32'>33</a>\u001b[0m \u001b[39m# Concatenate data\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/d%3A/quera/tamrin/faz2/Q2-3.ipynb#X31sZmlsZQ%3D%3D?line=33'>34</a>\u001b[0m sub_data \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mconcat([subset_data, data_copy[[\u001b[39m'\u001b[39m\u001b[39mMarketDominance\u001b[39m\u001b[39m'\u001b[39m]]], axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\frame.py:758\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    747\u001b[0m         mgr \u001b[39m=\u001b[39m dict_to_mgr(\n\u001b[0;32m    748\u001b[0m             \u001b[39m# error: Item \"ndarray\" of \"Union[ndarray, Series, Index]\" has no\u001b[39;00m\n\u001b[0;32m    749\u001b[0m             \u001b[39m# attribute \"name\"\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    755\u001b[0m             copy\u001b[39m=\u001b[39m_copy,\n\u001b[0;32m    756\u001b[0m         )\n\u001b[0;32m    757\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 758\u001b[0m         mgr \u001b[39m=\u001b[39m ndarray_to_mgr(\n\u001b[0;32m    759\u001b[0m             data,\n\u001b[0;32m    760\u001b[0m             index,\n\u001b[0;32m    761\u001b[0m             columns,\n\u001b[0;32m    762\u001b[0m             dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[0;32m    763\u001b[0m             copy\u001b[39m=\u001b[39;49mcopy,\n\u001b[0;32m    764\u001b[0m             typ\u001b[39m=\u001b[39;49mmanager,\n\u001b[0;32m    765\u001b[0m         )\n\u001b[0;32m    767\u001b[0m \u001b[39m# For data is list-like, or Iterable (will consume into list)\u001b[39;00m\n\u001b[0;32m    768\u001b[0m \u001b[39melif\u001b[39;00m is_list_like(data):\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\internals\\construction.py:337\u001b[0m, in \u001b[0;36mndarray_to_mgr\u001b[1;34m(values, index, columns, dtype, copy, typ)\u001b[0m\n\u001b[0;32m    332\u001b[0m \u001b[39m# _prep_ndarraylike ensures that values.ndim == 2 at this point\u001b[39;00m\n\u001b[0;32m    333\u001b[0m index, columns \u001b[39m=\u001b[39m _get_axes(\n\u001b[0;32m    334\u001b[0m     values\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m], values\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m], index\u001b[39m=\u001b[39mindex, columns\u001b[39m=\u001b[39mcolumns\n\u001b[0;32m    335\u001b[0m )\n\u001b[1;32m--> 337\u001b[0m _check_values_indices_shape_match(values, index, columns)\n\u001b[0;32m    339\u001b[0m \u001b[39mif\u001b[39;00m typ \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39marray\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m    340\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39missubclass\u001b[39m(values\u001b[39m.\u001b[39mdtype\u001b[39m.\u001b[39mtype, \u001b[39mstr\u001b[39m):\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\internals\\construction.py:408\u001b[0m, in \u001b[0;36m_check_values_indices_shape_match\u001b[1;34m(values, index, columns)\u001b[0m\n\u001b[0;32m    406\u001b[0m passed \u001b[39m=\u001b[39m values\u001b[39m.\u001b[39mshape\n\u001b[0;32m    407\u001b[0m implied \u001b[39m=\u001b[39m (\u001b[39mlen\u001b[39m(index), \u001b[39mlen\u001b[39m(columns))\n\u001b[1;32m--> 408\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mShape of passed values is \u001b[39m\u001b[39m{\u001b[39;00mpassed\u001b[39m}\u001b[39;00m\u001b[39m, indices imply \u001b[39m\u001b[39m{\u001b[39;00mimplied\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
      "\u001b[1;31mValueError\u001b[0m: Shape of passed values is (20, 3), indices imply (20, 2)"
     ]
    }
   ],
   "source": [
    "# from sklearn.preprocessing import LabelEncoder\n",
    "# total_market_cap = sum(data[\"MarketCap\"])\n",
    "# data['MarketDominance']=(data[\"MarketCap\"] / total_market_cap) * 100\n",
    "# label_encoder = LabelEncoder()\n",
    "\n",
    "# data_copy = data.copy()\n",
    "# data_copy['ProoftypeEncoded'] = label_encoder.fit_transform(data['ProofType'])\n",
    "# sub_data = pd.concat([subset_data, data_copy['MarketDominance']], axis=1)\n",
    "\n",
    "# result = shc.linkage(sub_data, method='ward')\n",
    "# shc.dendrogram(result, orientation='top', labels=data_copy['Symbol'].values, distance_sort='descending')\n",
    "# plt.title('Dendrogram')\n",
    "# plt.xlabel('Cryptocurrencies')\n",
    "# plt.ylabel('Distance')\n",
    "# plt.xticks(rotation=90)\n",
    "# plt.show()\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Assuming subset_data is already a DataFrame\n",
    "# Assuming data is already a DataFrame\n",
    "\n",
    "total_market_cap = sum(data[\"MarketCap\"])\n",
    "data['MarketDominance'] = (data[\"MarketCap\"] / total_market_cap) * 100\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "data_copy = data.copy()\n",
    "data_copy['ProoftypeEncoded'] = label_encoder.fit_transform(data['ProofType'])\n",
    "\n",
    "# Convert subset_data to a DataFrame if it's not already\n",
    "subset_data = pd.DataFrame(subset_data, columns=['Volume', 'MarketCap'])\n",
    "\n",
    "# Concatenate data\n",
    "sub_data = pd.concat([subset_data, data_copy[['MarketDominance']]], axis=1)\n",
    "\n",
    "result = shc.linkage(sub_data, method='ward')\n",
    "shc.dendrogram(result, orientation='top', labels=data_copy['Symbol'].values, distance_sort='descending')\n",
    "plt.title('Dendrogram')\n",
    "plt.xlabel('Cryptocurrencies')\n",
    "plt.ylabel('Distance')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters = fcluster(result, t=2, criterion='maxclust')\n",
    "data_copy['Cluster'] = clusters.copy()\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "for i in range(1, 3):\n",
    "    cluster = data_copy[data_copy['Cluster'] == i]\n",
    "    plt.scatter(cluster['Volume'], cluster['MarketCap'], label=f'Cluster {i}')\n",
    "    for index, row in cluster.iterrows():\n",
    "        plt.annotate(row['Symbol'], (row['Volume'], row['MarketCap']))\n",
    "\n",
    "plt.xlabel('Volume')\n",
    "plt.ylabel('MarketCap')\n",
    "plt.title('Clustering Results')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Volume=%{x}<br>MarketCap=%{y}<br>MarketDominance=%{z}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "",
         "scene": "scene",
         "showlegend": false,
         "type": "scatter3d",
         "x": [
          1823284.25,
          99187680.79,
          141541023.4,
          219706474,
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          591301857.2,
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         "y": [
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         "z": [
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        }
       ],
       "layout": {
        "height": 800,
        "legend": {
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "scene": {
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "xaxis": {
          "title": {
           "text": "Volume"
          }
         },
         "yaxis": {
          "title": {
           "text": "MarketCap"
          }
         },
         "zaxis": {
          "title": {
           "text": "MarketDominance"
          }
         }
        },
        "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": [
             [
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             ],
             [
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              "#46039f"
             ],
             [
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             ],
             [
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             ],
             [
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             [
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             ],
             [
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              "#fdca26"
             ],
             [
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              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
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             [
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             [
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             ],
             [
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            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
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             ],
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             [
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             [
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             [
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   "source": [
    "\n",
    "Dimensions = {\n",
    "    'Volume': cluster['Volume'],\n",
    "    'MarketCap': cluster['MarketCap'],\n",
    "    'MarketDominance': cluster['MarketDominance']\n",
    "}\n",
    "\n",
    "Dimensions = pd.DataFrame(Dimensions)\n",
    "# رسم نمودار سه بعدی\n",
    "fig = px.scatter_3d(Dimensions, x='Volume', y='MarketCap', z='MarketDominance')\n",
    "fig.update_layout(width=1000, height=800)\n",
    "\n",
    "# نمایش نمودار\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature_3: CirculatingSup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LEO-USD: Circulating Supply = 929022784\n",
      "UNI-USD: Circulating Supply = 106912512\n",
      "WBTC-USD: Circulating Supply = 163106\n",
      "AVAX-USD: Circulating Supply = 354834080\n",
      "DAI-USD: Circulating Supply = 5347888640\n",
      "SHIB-USD: Circulating Supply = 589346889531392\n",
      "LTC-USD: Circulating Supply = 73779672\n",
      "TRX-USD: Circulating Supply = 88900517888\n",
      "DOT-USD: Circulating Supply = 1265961344\n",
      "MATIC-USD: Circulating Supply = 9299803136\n",
      "SOL-USD: Circulating Supply = 416599712\n",
      "DOGE-USD: Circulating Supply = 141479018496\n",
      "ADA-USD: Circulating Supply = 35218583552\n",
      "BUSD-USD: Circulating Supply = 2102166528\n",
      "XRP-USD: Circulating Supply = 53441028096\n",
      "USDC-USD: Circulating Supply = 25392713728\n",
      "BNB-USD: Circulating Supply = 151705408\n",
      "USDT-USD: Circulating Supply = 83814883328\n",
      "ETH-USD: Circulating Supply = 120263584\n",
      "BTC-USD: Circulating Supply = 19518400\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "\n",
    "### First get a list of our coins from main dataset\n",
    "### And add '-USD' suffix so that yfinance is happy :)\n",
    "crypto_coins = [f'{s}-USD' for s in data['Symbol']]\n",
    "\n",
    "### Create an empty dictionary to store the circulating supply for each coin\n",
    "circulating_supply = {}\n",
    "\n",
    "### Fetch data for each coin and extract circulating supply\n",
    "### Using yfinance module\n",
    "for coin in crypto_coins:\n",
    "    ticker = yf.Ticker(coin)\n",
    "    ### Get all info here\n",
    "    info = ticker.info  \n",
    "    ### Select only circulating_supply  \n",
    "    circulating_supply[coin] = info.get('circulatingSupply')\n",
    "\n",
    "### Print the circulating supply for each coin for confirmation\n",
    "for coin, supply in circulating_supply.items():\n",
    "    print(f'{coin}: Circulating Supply = {supply}')\n",
    "\n",
    "### Add our new feature (circulating supply) to main(default) dataframe (Q2_data.csv)\n",
    "data['CirculatingSup'] = circulating_supply.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset_data = data[['Volume', 'MarketCap', 'CirculatingSup']]\n",
    "subset_data = StandardScaler().fit_transform(subset_data)\n",
    "result = shc.linkage(subset_data, method='ward')\n",
    "shc.dendrogram(result, orientation='top', labels=data['Symbol'].values, distance_sort='descending')\n",
    "plt.title('Dendrogram')\n",
    "plt.xlabel('Cryptocurrencies')\n",
    "plt.ylabel('Distance')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters = fcluster(result, t=2, criterion='maxclust')\n",
    "data_copy = data.copy()\n",
    "data_copy['Cluster'] = clusters.copy()\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "for i in range(1, 3):\n",
    "    cluster = data_copy[data_copy['Cluster'] == i]\n",
    "    plt.scatter(cluster['Volume'], cluster['MarketCap'], label=f'Cluster {i}')\n",
    "    for index, row in cluster.iterrows():\n",
    "        plt.annotate(row['Symbol'], (row['Volume'], row['MarketCap']))\n",
    "\n",
    "plt.xlabel('Volume')\n",
    "plt.ylabel('MarketCap')\n",
    "plt.title('Clustering Results')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Volume=%{x}<br>MarketCap=%{y}<br>CirculatingSup=%{z}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "",
         "scene": "scene",
         "showlegend": false,
         "type": "scatter3d",
         "x": [
          1823284.25,
          99187680.79,
          141541023.4,
          219706474,
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         "y": [
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         ],
         "z": [
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         ]
        }
       ],
       "layout": {
        "height": 800,
        "legend": {
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "scene": {
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "xaxis": {
          "title": {
           "text": "Volume"
          }
         },
         "yaxis": {
          "title": {
           "text": "MarketCap"
          }
         },
         "zaxis": {
          "title": {
           "text": "CirculatingSup"
          }
         }
        },
        "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": [
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             ],
             [
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             ],
             [
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             ],
             [
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              "#9c179e"
             ],
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              "#bd3786"
             ],
             [
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              "#d8576b"
             ],
             [
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             ],
             [
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             [
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              "#fdca26"
             ],
             [
              1,
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             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
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              "#0d0887"
             ],
             [
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             [
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             [
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             ],
             [
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              "#fdca26"
             ],
             [
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            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
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              "#0d0887"
             ],
             [
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             ],
             [
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             ],
             [
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              "#9c179e"
             ],
             [
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             ],
             [
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              "#d8576b"
             ],
             [
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              "#ed7953"
             ],
             [
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              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
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            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
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   "source": [
    "Dimensions = {\n",
    "    'Volume': cluster['Volume'],\n",
    "    'MarketCap': cluster['MarketCap'],\n",
    "    'CirculatingSup': cluster['CirculatingSup']\n",
    "}\n",
    "\n",
    "Dimensions = pd.DataFrame(Dimensions)\n",
    "# رسم نمودار سه بعدی\n",
    "fig = px.scatter_3d(Dimensions, x='Volume', y='MarketCap', z='CirculatingSup')\n",
    "fig.update_layout(width=1000, height=800)\n",
    "\n",
    "# نمایش نمودار\n",
    "fig.show()\n"
   ]
  }
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