NumCompFinalProject_HVR8.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "24b0b449",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading the network file  : HVR_8.txt\n",
      "reading the metadata file : metadata_CysPoLV.txt\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yz7YaNWpoyZIlMgxDe/fu1bx58zRhwgS5ubnp5ZdfNlWvIzRq1EhVq1ZVTEyMhg8froULF6pFixYKCgrKtH/FihVtn4GmTZvKzc1Nr732mt577z2NHj3a1LktFosmT56s1q1b68MPP8yw3cx1uJ2WLVvqjTfe0I8//qgzZ86odevW8vDwUL169RQbG6uzZ8+qcuXKtr+RUqVKqUiRIll+ltNru/W1ZGX16tXq2bOnmjRponXr1t32swUgf2FECkC+MWbMGBmGocGDB+v69esZtqekpOjLL7+0axs0aJBcXV3Vt29fHTp0SM8//3yWx7darWrWrJkmT54sSaZ+H6phw4Zyc3PTwoUL7drPnDmj9evXq2XLltk+liR16dJFpUuX1ty5cxUTE6PKlSvr0UcfNXUMs9q2bStnZ2cdPXo001GN9C/QVapUkZ+fnz755BMZhmHb/+TJk9q6davdMdNnptu7d69d+xdffGG3XqVKFQUHB+vnn3/O8txmRuMkqU2bNnJyclJ0dLSp/Tp06CDDMPT7779nWkf6hAT/ZLFYVLNmTb377rsqWbKkdu/ebeqcjjRw4EAdPHhQr732mv744w9Ts0K++OKLqlSpkiZNmpSjCTZatWql1q1ba8KECUpKSrLbZuY63G7UrlWrVrpx44Zef/11lStXTg899JCtfe3atVq/fr1tNEqS3N3dVb9+fS1fvtzumGlpaVq4cKHKlStnanKNwMBAWxhs0qSJqd/cApC3MSIFIN9o2LChoqOjNWTIEIWGhuq5555T9erVlZKSop9++kkffvihQkJC7H6stmTJkurXr5+io6MVGBiY4Yds33jjDZ05c0YtW7ZUuXLldPHiRc2cOVMuLi5q1qxZtmsrWbKkXn/9db3yyivq16+fevfurfPnz2v8+PEqWrSoxo4dm2GfL7/8MtNw0L17d1mtVvXt21fvvfeeDMPQpEmTTLxTOVOhQgVNmDBBr776qo4dO6bHHntMpUqV0rlz5/Tjjz/K3d1d48ePV5EiRfTmm29q0KBB6tq1qwYPHqyLFy9q3LhxGW7t8/X1VatWrRQVFaVSpUopMDBQ69at0/LlyzOc/4MPPlB4eLjatm2r/v3764EHHtBff/2lX375Rbt379ayZctMv55XXnlFb775pq5du6bevXvL09NTBw8e1J9//pnl7x41btxYTz/9tAYMGKCdO3eqadOmcnd3V1xcnLZs2aIaNWroueee01dffaXZs2erS5cuqlixogzD0PLly3Xx4kW1bt3aVK2O1K9fP73yyiuaOnWqSpYsmeWIYGbSZ3rs0aOHZs6cmaMf2p08ebJCQ0OVkJBg97ME2b0O0s2RweXLlys6OlqhoaEqUqSILfiHhoaqVKlS+vbbb+1uWWzVqpXefPNN27//KSoqSq1bt1ZYWJhGjx4tV1dXzZ49W/v379cnn3xi+vfW/Pz8tGnTJrVt21ZNmzZVbGysQkJCTL9XAPIYR81yAQA5tWfPHiMiIsIoX7684erqari7uxu1a9c23njjDSMhISFD/40bNxqSjEmTJmXY9tVXXxnh4eHGAw88YLi6uhre3t5Gu3btjO+++87WJ31GtKlTp96xtv/+97/Gww8/bLi6uhqenp5G586djQMHDtj1yWpWvfQl3c8//2xIMpycnIyzZ89m+/2506yA/5w9LTMrV640wsLCjBIlShhWq9UIDAw0unfvbqxduzbDaw0ODjZcXV2NypUrG3Pnzs10Nrq4uDije/fuhpeXl+Hp6Wk8+eSTthnv/jlrX/pr7tGjh+Ht7W24uLgYvr6+RosWLYz333//jvVnNUPg/PnzjXr16hlFixY1ihcvbtSuXdvuvJnVbBiGMXfuXKN+/fqGu7u74ebmZjz44INGv379jJ07dxqGYRi//vqr0bt3b+PBBx803NzcDE9PT+ORRx4x5s2bl+n7+k9Zzdo3dOjQDH0DAwONiIiI2x7vTn+jU6dOzTBr3z917do105kQ06W/t8uWLct0e/369Y1SpUrddvbL2/3d9enTx5BkN2tfujtdB8MwjL/++svo3r27UbJkScNisRi3fr1Jf32LFi2ytV2/ft1wd3c3ihQpkumsg999953RokUL23kbNGhgfPnll9l+TZl9Di9evGg0btzY8PLyyvLzByD/sBjGP+7LAIACaNSoUYqOjtbp06fv+AOjuDv9+/fXxo0bdeLECUeXAgDAPcWtfQAKrO3bt+u3337T7Nmz9cwzzxCiAABAriFIASiwGjZsqGLFiqlDhw566623HF0OAAAoQLi1DwAAAABMYvpzAAAAADCJIAUAAAAAJhGkAAAAAMAkJpvQzV8rP3v2rDw8PEz/yB4AAACAgsMwDF2+fFn+/v4qUiTrcSeClKSzZ88qICDA0WUAAAAAyCNOnz6tcuXKZbmdICXJw8ND0s03q0SJEg6uBgAAAICjJCYmKiAgwJYRskKQkmy385UoUYIgBQAAAOCOj/ww2QQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJDg1SmzdvVseOHeXv7y+LxaKVK1fabbdYLJkuU6dOtfVp3rx5hu29evW6z68EAAAAQGHi0CB15coV1axZU7Nmzcp0e1xcnN0yd+5cWSwWPf7443b9Bg8ebNfvgw8+uB/lAwAAACiknB158vDwcIWHh2e53dfX1279888/V1hYmCpWrGjXXqxYsQx9AQC4Hyq8vMrRJRR6Jya1d3QJAAqhfPOM1Llz57Rq1SpFRkZm2LZo0SKVKVNG1atX1+jRo3X58uXbHis5OVmJiYl2CwAAAABkl0NHpMz4+OOP5eHhoW7dutm19+3bV0FBQfL19dX+/fs1ZswY/fzzz4qNjc3yWFFRURo/fvy9LhkAAABAAZVvgtTcuXPVt29fFS1a1K598ODBtn+HhIQoODhYdevW1e7du1WnTp1MjzVmzBiNHDnStp6YmKiAgIB7UzgAAACAAidfBKnvvvtOhw4d0tKlS+/Yt06dOnJxcdHhw4ezDFJWq1VWqzW3ywQAAABQSOSLZ6Q++ugjhYaGqmbNmnfse+DAAaWkpMjPz+8+VAYAAACgMHLoiFRSUpKOHDliWz9+/Lj27NkjLy8vlS9fXtLN2+6WLVumadOmZdj/6NGjWrRokdq1a6cyZcro4MGDGjVqlGrXrq3GjRvft9cBAAAAoHBxaJDauXOnwsLCbOvpzy1FRERo3rx5kqQlS5bIMAz17t07w/6urq5at26dZs6cqaSkJAUEBKh9+/YaO3asnJyc7strAAAAAFD4WAzDMBxdhKMlJibK09NTly5dUokSJRxdDgAgH+F3pByP35ECkJuymw3yxTNSAAAAAJCXEKQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACY5NEht3rxZHTt2lL+/vywWi1auXGm3vX///rJYLHZLgwYN7PokJydr2LBhKlOmjNzd3dWpUyedOXPmPr4KAAAAAIWNQ4PUlStXVLNmTc2aNSvLPo899pji4uJsy9dff223fcSIEVqxYoWWLFmiLVu2KCkpSR06dFBqauq9Lh8AAABAIeXsyJOHh4crPDz8tn2sVqt8fX0z3Xbp0iV99NFHWrBggVq1aiVJWrhwoQICArR27Vq1bds212sGAAAAgDz/jNTGjRvl7e2typUra/DgwUpISLBt27Vrl1JSUtSmTRtbm7+/v0JCQrR169Ysj5mcnKzExES7BQAAAACyK08HqfDwcC1atEjr16/XtGnTtGPHDrVo0ULJycmSpPj4eLm6uqpUqVJ2+/n4+Cg+Pj7L40ZFRcnT09O2BAQE3NPXAQAAAKBgceitfXfSs2dP279DQkJUt25dBQYGatWqVerWrVuW+xmGIYvFkuX2MWPGaOTIkbb1xMREwhQAAACAbMvTI1K38vPzU2BgoA4fPixJ8vX11fXr13XhwgW7fgkJCfLx8cnyOFarVSVKlLBbAAAAACC78lWQOn/+vE6fPi0/Pz9JUmhoqFxcXBQbG2vrExcXp/3796tRo0aOKhMAAABAAefQW/uSkpJ05MgR2/rx48e1Z88eeXl5ycvLS+PGjdPjjz8uPz8/nThxQq+88orKlCmjrl27SpI8PT0VGRmpUaNGqXTp0vLy8tLo0aNVo0YN2yx+AAAAAJDbHBqkdu7cqbCwMNt6+nNLERERio6O1r59+zR//nxdvHhRfn5+CgsL09KlS+Xh4WHb591335Wzs7N69Oiha9euqWXLlpo3b56cnJzu++sBAAAAUDhYDMMwHF2EoyUmJsrT01OXLl3ieSkAgCkVXl7l6BIKvROT2ju6BAAFSHazQb56RgoAAAAA8gKCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIcGqc2bN6tjx47y9/eXxWLRypUrbdtSUlL00ksvqUaNGnJ3d5e/v7/69euns2fP2h2jefPmslgsdkuvXr3u8ysBAAAAUJg4NEhduXJFNWvW1KxZszJsu3r1qnbv3q3XX39du3fv1vLly/Xbb7+pU6dOGfoOHjxYcXFxtuWDDz64H+UDAAAAKKScHXny8PBwhYeHZ7rN09NTsbGxdm3vvfeeHnnkEZ06dUrly5e3tRcrVky+vr73tFYAAAAASJevnpG6dOmSLBaLSpYsade+aNEilSlTRtWrV9fo0aN1+fLl2x4nOTlZiYmJdgsAAAAAZJdDR6TM+Pvvv/Xyyy+rT58+KlGihK29b9++CgoKkq+vr/bv368xY8bo559/zjCa9U9RUVEaP378/SgbAAAAQAGUL4JUSkqKevXqpbS0NM2ePdtu2+DBg23/DgkJUXBwsOrWravdu3erTp06mR5vzJgxGjlypG09MTFRAQEB96Z4AAAAAAVOng9SKSkp6tGjh44fP67169fbjUZlpk6dOnJxcdHhw4ezDFJWq1VWq/VelAsAAACgEMjTQSo9RB0+fFgbNmxQ6dKl77jPgQMHlJKSIj8/v/tQIQAAAIDCyKFBKikpSUeOHLGtHz9+XHv27JGXl5f8/f3VvXt37d69W1999ZVSU1MVHx8vSfLy8pKrq6uOHj2qRYsWqV27dipTpowOHjyoUaNGqXbt2mrcuLGjXhYAAACAAs6hQWrnzp0KCwuzrac/txQREaFx48bpiy++kCTVqlXLbr8NGzaoefPmcnV11bp16zRz5kwlJSUpICBA7du319ixY+Xk5HTfXgcAAACAwsWhQap58+YyDCPL7bfbJkkBAQHatGlTbpcFAAAAALeVr35HCgAAAADyAoIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATMpRkDp+/Hhu1wEAAAAA+UaOglSlSpUUFhamhQsX6u+//87tmgAAAAAgT8tRkPr5559Vu3ZtjRo1Sr6+vnrmmWf0448/5nZtAAAAAJAn5ShIhYSEaPr06fr9998VExOj+Ph4Pfroo6pevbqmT5+uP/74I7frBAAAAIA8464mm3B2dlbXrl316aefavLkyTp69KhGjx6tcuXKqV+/foqLi8utOgEAAAAgz7irILVz504NGTJEfn5+mj59ukaPHq2jR49q/fr1+v3339W5c+fcqhMAAAAA8gznnOw0ffp0xcTE6NChQ2rXrp3mz5+vdu3aqUiRm7ksKChIH3zwgR566KFcLRYAAAAA8oIcBano6GgNHDhQAwYMkK+vb6Z9ypcvr48++uiuigMAAACAvChHQerw4cN37OPq6qqIiIicHB4AAAAA8rQcPSMVExOjZcuWZWhftmyZPv7447suCgAAAADyshwFqUmTJqlMmTIZ2r29vTVx4sS7LgoAAAAA8rIcBamTJ08qKCgoQ3tgYKBOnTp110UBAAAAQF6WoyDl7e2tvXv3Zmj/+eefVbp06WwfZ/PmzerYsaP8/f1lsVi0cuVKu+2GYWjcuHHy9/eXm5ubmjdvrgMHDtj1SU5O1rBhw1SmTBm5u7urU6dOOnPmTE5eFgAAAABkS46CVK9evfTCCy9ow4YNSk1NVWpqqtavX6/hw4erV69e2T7OlStXVLNmTc2aNSvT7VOmTNH06dM1a9Ys7dixQ76+vmrdurUuX75s6zNixAitWLFCS5Ys0ZYtW5SUlKQOHTooNTU1Jy8NAAAAAO7IYhiGYXan69ev66mnntKyZcvk7Hxz4r+0tDT169dP77//vlxdXc0XYrFoxYoV6tKli6Sbo1H+/v4aMWKEXnrpJUk3R598fHw0efJkPfPMM7p06ZLKli2rBQsWqGfPnpKks2fPKiAgQF9//bXatm2brXMnJibK09NTly5dUokSJUzXDgAovCq8vMrRJRR6Jya1d3QJAAqQ7GaDHI1Iubq6aunSpfr111+1aNEiLV++XEePHtXcuXNzFKIyc/z4ccXHx6tNmza2NqvVqmbNmmnr1q2SpF27diklJcWuj7+/v0JCQmx9MpOcnKzExES7BQAAAACyK0e/I5WucuXKqly5cm7VYic+Pl6S5OPjY9fu4+OjkydP2vq4urqqVKlSGfqk75+ZqKgojR8/PpcrBgAAAFBY5ChIpaamat68eVq3bp0SEhKUlpZmt339+vW5Upx085a/fzIMI0Pbre7UZ8yYMRo5cqRtPTExUQEBAXdXKAAAAIBCI0dBavjw4Zo3b57at2+vkJCQOwabnPD19ZV0c9TJz8/P1p6QkGAbpfL19dX169d14cIFu1GphIQENWrUKMtjW61WWa3WXK8ZAAAAQOGQoyC1ZMkSffrpp2rXrl1u12MTFBQkX19fxcbGqnbt2pJuTnKxadMmTZ48WZIUGhoqFxcXxcbGqkePHpKkuLg47d+/X1OmTLlntQEAAAAo3HIUpFxdXVWpUqW7PnlSUpKOHDliWz9+/Lj27NkjLy8vlS9fXiNGjNDEiRMVHBys4OBgTZw4UcWKFVOfPn0kSZ6enoqMjNSoUaNUunRpeXl5afTo0apRo4ZatWp11/UBAAAAQGZyFKRGjRqlmTNnatasWXd1W9/OnTsVFhZmW09/bikiIkLz5s3Tiy++qGvXrmnIkCG6cOGC6tevr2+//VYeHh62fd599105OzurR48eunbtmlq2bKl58+bJyckpx3UBAAAAwO3k6Hekunbtqg0bNsjLy0vVq1eXi4uL3fbly5fnWoH3A78jBQDIKX5HyvH4HSkAuSm72SBHI1IlS5ZU165dc1wcAAAAAORnOQpSMTExuV0HAAAAAOQbRXK6440bN7R27Vp98MEHunz5siTp7NmzSkpKyrXiAAAAACAvytGI1MmTJ/XYY4/p1KlTSk5OVuvWreXh4aEpU6bo77//1vvvv5/bdQIAAABAnpGjEanhw4erbt26unDhgtzc3GztXbt21bp163KtOAAAAADIi3I0IrVlyxZ9//33cnV1tWsPDAzU77//niuFAQAAAEBelaMRqbS0NKWmpmZoP3PmjN1vPAEAAABAQZSjINW6dWvNmDHDtm6xWJSUlKSxY8eqXbt2uVUbAAAAAORJObq1791331VYWJiqVaumv//+W3369NHhw4dVpkwZffLJJ7ldIwAAAADkKTkKUv7+/tqzZ48++eQT7d69W2lpaYqMjFTfvn3tJp8AAAAAgIIoR0FKktzc3DRw4EANHDgwN+sBAAAAgDwvR0Fq/vz5t93er1+/HBUDAAAAAPlBjoLU8OHD7dZTUlJ09epVubq6qlixYgQpAAAAAAVajmbtu3Dhgt2SlJSkQ4cO6dFHH2WyCQAAAAAFXo6CVGaCg4M1adKkDKNVAAAAAFDQ5FqQkiQnJyedPXs2Nw8JAAAAAHlOjp6R+uKLL+zWDcNQXFycZs2apcaNG+dKYQAAAACQV+UoSHXp0sVu3WKxqGzZsmrRooWmTZuWG3UBAAAAQJ6VoyCVlpaW23UAAAAAQL6Rq89IAQAAAEBhkKMRqZEjR2a77/Tp03NyCgAAAADIs3IUpH766Sft3r1bN27cUJUqVSRJv/32m5ycnFSnTh1bP4vFkjtVAgAAAEAekqMg1bFjR3l4eOjjjz9WqVKlJN38kd4BAwaoSZMmGjVqVK4WCQAAAAB5SY6ekZo2bZqioqJsIUqSSpUqpbfeeotZ+wAAAAAUeDkKUomJiTp37lyG9oSEBF2+fPmuiwIAAACAvCxHQapr164aMGCAPvvsM505c0ZnzpzRZ599psjISHXr1i23awQAAACAPCVHz0i9//77Gj16tJ588kmlpKTcPJCzsyIjIzV16tRcLRAAAAAA8pocBalixYpp9uzZmjp1qo4ePSrDMFSpUiW5u7vndn0AAAAAkOfc1Q/yxsXFKS4uTpUrV5a7u7sMw8itugAAAAAgz8pRkDp//rxatmypypUrq127doqLi5MkDRo0iKnPAQAAABR4OQpS//rXv+Ti4qJTp06pWLFitvaePXtq9erVuVYcAAAAAORFOXpG6ttvv9WaNWtUrlw5u/bg4GCdPHkyVwoDAAAAgLwqRyNSV65csRuJSvfnn3/KarXedVEAAAAAkJflKEg1bdpU8+fPt61bLBalpaVp6tSpCgsLy7XiAAAAACAvytGtfVOnTlXz5s21c+dOXb9+XS+++KIOHDigv/76S99//31u1wgAAAAAeUqORqSqVaumvXv36pFHHlHr1q115coVdevWTT/99JMefPDB3K4RAAAAAPIU0yNSKSkpatOmjT744AONHz/+XtQEAAAAAHma6REpFxcX7d+/XxaL5V7UAwAAAAB5Xo5u7evXr58++uij3K4FAAAAAPKFHE02cf36df33v/9VbGys6tatK3d3d7vt06dPz5XiAAAAACAvMhWkjh07pgoVKmj//v2qU6eOJOm3336z68MtfwAAAAAKOlNBKjg4WHFxcdqwYYMkqWfPnvr3v/8tHx+fe1IcAAAAAORFpp6RMgzDbv2bb77RlStXcrWgW1WoUEEWiyXDMnToUElS//79M2xr0KDBPa0JAAAAQOGWo2ek0t0arO6FHTt2KDU11ba+f/9+tW7dWk888YSt7bHHHlNMTIxt3dXV9Z7XBQAAAKDwMhWk0kd8bm27l8qWLWu3PmnSJD344INq1qyZrc1qtcrX1zfbx0xOTlZycrJtPTEx8e4LBQAAAFBomApShmGof//+slqtkqS///5bzz77bIZZ+5YvX557Ff7D9evXtXDhQo0cOdIuwG3cuFHe3t4qWbKkmjVrprffflve3t5ZHicqKoofEwYAAACQYxbDxP15AwYMyFa/f95ml5s+/fRT9enTR6dOnZK/v78kaenSpSpevLgCAwN1/Phxvf7667px44Z27dplC3y3ymxEKiAgQJcuXVKJEiXuSe0AgIKpwsurHF1CoXdiUntHlwCgAElMTJSnp+cds4GpIOVobdu2laurq7788sss+8TFxSkwMFBLlixRt27dsnXc7L5ZAADciiDleAQpALkpu9ngriabuJ9OnjyptWvX3vG2QT8/PwUGBurw4cP3qTIAAAAAhY2p6c8dKSYmRt7e3mrf/vb/1en8+fM6ffq0/Pz87lNlAAAAAAqbfBGk0tLSFBMTo4iICDk7//9BtKSkJI0ePVrbtm3TiRMntHHjRnXs2FFlypRR165dHVgxAAAAgIIsX9zat3btWp06dUoDBw60a3dyctK+ffs0f/58Xbx4UX5+fgoLC9PSpUvl4eHhoGoBAAAAFHT5Iki1adMm0x//dXNz05o1axxQEQAAAIDCLF/c2gcAAAAAeQlBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgUp4OUuPGjZPFYrFbfH19bdsNw9C4cePk7+8vNzc3NW/eXAcOHHBgxQAAAAAKgzwdpCSpevXqiouLsy379u2zbZsyZYqmT5+uWbNmaceOHfL19VXr1q11+fJlB1YMAAAAoKDL80HK2dlZvr6+tqVs2bKSbo5GzZgxQ6+++qq6deumkJAQffzxx7p69aoWL17s4KoBAAAAFGR5PkgdPnxY/v7+CgoKUq9evXTs2DFJ0vHjxxUfH682bdrY+lqtVjVr1kxbt2697TGTk5OVmJhotwAAAABAduXpIFW/fn3Nnz9fa9as0Zw5cxQfH69GjRrp/Pnzio+PlyT5+PjY7ePj42PblpWoqCh5enraloCAgHv2GgAAAAAUPHk6SIWHh+vxxx9XjRo11KpVK61atUqS9PHHH9v6WCwWu30Mw8jQdqsxY8bo0qVLtuX06dO5XzwAAACAAitPB6lbubu7q0aNGjp8+LBt9r5bR58SEhIyjFLdymq1qkSJEnYLAAAAAGRXvgpSycnJ+uWXX+Tn56egoCD5+voqNjbWtv369evatGmTGjVq5MAqAQAAABR0zo4u4HZGjx6tjh07qnz58kpISNBbb72lxMRERUREyGKxaMSIEZo4caKCg4MVHBysiRMnqlixYurTp4+jSwcAAABQgOXpIHXmzBn17t1bf/75p8qWLasGDRpo+/btCgwMlCS9+OKLunbtmoYMGaILFy6ofv36+vbbb+Xh4eHgygEAAAAUZBbDMAxHF+FoiYmJ8vT01KVLl3heCgBgSoWXVzm6hELvxKT2ji4BQAGS3WyQr56RAgAAAIC8gCAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJOcHV0AABRUFV5e5egSCr0Tk9o7ugQAQAHFiBQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMMnZ0QUgowovr3J0CYXeiUntHV0CAAAA8jBGpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgUp4OUlFRUapXr548PDzk7e2tLl266NChQ3Z9+vfvL4vFYrc0aNDAQRUDAAAAKAzydJDatGmThg4dqu3btys2NlY3btxQmzZtdOXKFbt+jz32mOLi4mzL119/7aCKAQAAABQGzo4u4HZWr15ttx4TEyNvb2/t2rVLTZs2tbVbrVb5+vre7/IAAAAAFFJ5ekTqVpcuXZIkeXl52bVv3LhR3t7eqly5sgYPHqyEhITbHic5OVmJiYl2CwAAAABkV74JUoZhaOTIkXr00UcVEhJiaw8PD9eiRYu0fv16TZs2TTt27FCLFi2UnJyc5bGioqLk6elpWwICAu7HSwAAAABQQOTpW/v+6fnnn9fevXu1ZcsWu/aePXva/h0SEqK6desqMDBQq1atUrdu3TI91pgxYzRy5EjbemJiImEKAAAAQLbliyA1bNgwffHFF9q8ebPKlSt3275+fn4KDAzU4cOHs+xjtVpltVpzu0zAlAovr3J0CYXeiUntHV0CAADIp/J0kDIMQ8OGDdOKFSu0ceNGBQUF3XGf8+fP6/Tp0/Lz87sPFQIAAAAojPL0M1JDhw7VwoULtXjxYnl4eCg+Pl7x8fG6du2aJCkpKUmjR4/Wtm3bdOLECW3cuFEdO3ZUmTJl1LVrVwdXDwAAAKCgytMjUtHR0ZKk5s2b27XHxMSof//+cnJy0r59+zR//nxdvHhRfn5+CgsL09KlS+Xh4eGAigEAAAAUBnk6SBmGcdvtbm5uWrNmzX2qBgAAAABuytO39gEAAABAXpSnR6QAAAAcjVlWHY9ZVpEXMSIFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADDJ2dEFAAAAAI5U4eVVji4Bkk5Mau/oEkxhRAoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhUYILU7NmzFRQUpKJFiyo0NFTfffedo0sCAAAAUEAViCC1dOlSjRgxQq+++qp++uknNWnSROHh4Tp16pSjSwMAAABQABWIIDV9+nRFRkZq0KBBqlq1qmbMmKGAgABFR0c7ujQAAAAABZCzowu4W9evX9euXbv08ssv27W3adNGW7duzXSf5ORkJScn29YvXbokSUpMTLx3hZqQlnzV0SUUevfjb4Hr7Hj3+jpzjR2Pz3LhwGe54OMaFw555bt4eh2GYdy2X74PUn/++adSU1Pl4+Nj1+7j46P4+PhM94mKitL48eMztAcEBNyTGpH/eM5wdAW4H7jOBR/XuHDgOhd8XOPCIa9d58uXL8vT0zPL7fk+SKWzWCx264ZhZGhLN2bMGI0cOdK2npaWpr/++kulS5fOch9kX2JiogICAnT69GmVKFHC0eXgHuAaFw5c54KPa1w4cJ0LPq5x7jIMQ5cvX5a/v/9t++X7IFWmTBk5OTllGH1KSEjIMEqVzmq1ymq12rWVLFnyXpVYaJUoUYIPcwHHNS4cuM4FH9e4cOA6F3xc49xzu5GodPl+sglXV1eFhoYqNjbWrj02NlaNGjVyUFUAAAAACrJ8PyIlSSNHjtRTTz2lunXrqmHDhvrwww916tQpPfvss44uDQAAAEABVCCCVM+ePXX+/HlNmDBBcXFxCgkJ0ddff63AwEBHl1YoWa1WjR07NsPtkyg4uMaFA9e54OMaFw5c54KPa+wYFuNO8/oBAAAAAOzk+2ekAAAAAOB+I0gBAAAAgEkEKQAAAAAwiSAFAAAAACYRpJBrNm/erI4dO8rf318Wi0UrV650dEnIZVFRUapXr548PDzk7e2tLl266NChQ44uC7koOjpaDz/8sO1HHRs2bKhvvvnG0WXhHoqKipLFYtGIESMcXQpy0bhx42SxWOwWX19fR5eFe+D333/Xk08+qdKlS6tYsWKqVauWdu3a5eiyCgWCFHLNlStXVLNmTc2aNcvRpeAe2bRpk4YOHart27crNjZWN27cUJs2bXTlyhVHl4ZcUq5cOU2aNEk7d+7Uzp071aJFC3Xu3FkHDhxwdGm4B3bs2KEPP/xQDz/8sKNLwT1QvXp1xcXF2ZZ9+/Y5uiTksgsXLqhx48ZycXHRN998o4MHD2ratGkqWbKko0srFArE70ghbwgPD1d4eLijy8A9tHr1arv1mJgYeXt7a9euXWratKmDqkJu6tixo93622+/rejoaG3fvl3Vq1d3UFW4F5KSktS3b1/NmTNHb731lqPLwT3g7OzMKFQBN3nyZAUEBCgmJsbWVqFCBccVVMgwIgUgxy5duiRJ8vLycnAluBdSU1O1ZMkSXblyRQ0bNnR0OchlQ4cOVfv27dWqVStHl4J75PDhw/L391dQUJB69eqlY8eOObok5LIvvvhCdevW1RNPPCFvb2/Vrl1bc+bMcXRZhQZBCkCOGIahkSNH6tFHH1VISIijy0Eu2rdvn4oXLy6r1apnn31WK1asULVq1RxdFnLRkiVLtHv3bkVFRTm6FNwj9evX1/z587VmzRrNmTNH8fHxatSokc6fP+/o0pCLjh07pujoaAUHB2vNmjV69tln9cILL2j+/PmOLq1Q4NY+ADny/PPPa+/evdqyZYujS0Euq1Klivbs2aOLFy/qf//7nyIiIrRp0ybCVAFx+vRpDR8+XN9++62KFi3q6HJwj/zzVvsaNWqoYcOGevDBB/Xxxx9r5MiRDqwMuSktLU1169bVxIkTJUm1a9fWgQMHFB0drX79+jm4uoKPESkApg0bNkxffPGFNmzYoHLlyjm6HOQyV1dXVapUSXXr1lVUVJRq1qypmTNnOros5JJdu3YpISFBoaGhcnZ2lrOzszZt2qR///vfcnZ2VmpqqqNLxD3g7u6uGjVq6PDhw44uBbnIz88vw3/kqlq1qk6dOuWgigoXRqQAZJthGBo2bJhWrFihjRs3KigoyNEl4T4wDEPJycmOLgO5pGXLlhlmbxswYIAeeughvfTSS3JycnJQZbiXkpOT9csvv6hJkyaOLgW5qHHjxhl+huS3335TYGCggyoqXAhSyDVJSUk6cuSIbf348ePas2ePvLy8VL58eQdWhtwydOhQLV68WJ9//rk8PDwUHx8vSfL09JSbm5uDq0NueOWVVxQeHq6AgABdvnxZS5Ys0caNGzPM2Ij8y8PDI8Nzje7u7ipdujTPOxYgo0ePVseOHVW+fHklJCTorbfeUmJioiIiIhxdGnLRv/71LzVq1EgTJ05Ujx499OOPP+rDDz/Uhx9+6OjSCgWCFHLNzp07FRYWZltPvwc7IiJC8+bNc1BVyE3R0dGSpObNm9u1x8TEqH///ve/IOS6c+fO6amnnlJcXJw8PT318MMPa/Xq1WrdurWjSwNgwpkzZ9S7d2/9+eefKlu2rBo0aKDt27czUlHA1KtXTytWrNCYMWM0YcIEBQUFacaMGerbt6+jSysULIZhGI4uAgAAAADyEyabAAAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAIA8Lz4+XsOGDVPFihVltVoVEBCgjh07at26dXd97HHjxslischiscjJyUkBAQEaNGiQ/vjjj2wfw2KxaOXKlXZtNWrU0KBBgzLt/8knn8jFxUXnzp27m9IBAA5EkAIA5GknTpxQaGio1q9frylTpmjfvn1avXq1wsLCNHTo0Fw5R/Xq1RUXF6dTp04pOjpaX375pfr163dXx4yMjNSnn36qq1evZtg2d+5cdejQQT4+Pnd1DgCA4xCkAAB52pAhQ2SxWPTjjz+qe/fuqly5sqpXr66RI0dq+/btGjhwoDp06GC3z40bN+Tr66u5c+dKkj777DPVqFFDbm5uKl26tFq1aqUrV67Y+js7O8vX11cPPPCAOnTooBdeeEHffvutrl27prS0NE2YMEHlypWT1WpVrVq1tHr16jvW/dRTTyk5OVnLli2zaz916pTWr1+vyMjIXHh3AACOQpACAORZf/31l1avXq2hQ4fK3d09w/aSJUtq0KBBWr16teLi4mztX3/9tZKSktSjRw/FxcWpd+/eGjhwoH755Rdt3LhR3bp1k2EYWZ7Xzc1NaWlpunHjhmbOnKlp06bpnXfe0d69e9W2bVt16tRJhw8fvm3tpUuXVufOnRUTE2PXHhMTIx8fH4WHh5t8NwAAeQlBCgCQZx05ckSGYeihhx7Ksk+jRo1UpUoVLViwwNYWExOjJ554QsWLF1dcXJxu3Lihbt26qUKFCqpRo4aGDBmi4sWLZ3q8X3/9VdHR0XrkkUfk4eGhd955Ry+99JJ69eqlKlWqaPLkyapVq5ZmzJhxx/oHDhyozZs369ixY5IkwzA0b9489e/fX05OTubeDABAnkKQAgDkWemjRhaL5bb9Bg0aZBv5SUhI0KpVqzRw4EBJUs2aNdWyZUvVqFFDTzzxhObMmaMLFy7Y7b9v3z4VL15cbm5uqlatmgICArRo0SIlJibq7Nmzaty4sV3/xo0b65dffrlj/W3atFG5cuVsta1fv14nTpzQgAEDsvcGAADyLIIUACDPCg4OlsViuWNo6devn44dO6Zt27Zp4cKFqlChgpo0aSJJcnJyUmxsrL755htVq1ZN7733nqpUqaLjx4/b9q9SpYr27NmjgwcP6tq1a1q/fr0qVapk235rkDMM447hTpKKFCmi/v376+OPP1ZaWppiYmLUtGlTBQcHm3kbAAB5EEEKAJBneXl5qW3btvrPf/5jNzlEuosXL0q6+TxSly5dFBMTo5iYmAwjPhaLRY0bN9b48eP1008/ydXVVStWrLBtd3V1VaVKlRQUFCSr1WprL1GihPz9/bVlyxa7423dulVVq1bN1msYMGCAzpw5o+XLl2v58uVMMgEABYSzowsAAOB2Zs+erUaNGumRRx7RhAkT9PDDD+vGjRuKjY1VdHS0bbRq0KBB6tChg1JTUxUREWHb/4cfftC6devUpk0beXt764cfftAff/yR7SD0f//3fxo7dqwefPBB1apVSzExMdqzZ48WLVpk1+/48ePas2ePXVt6OGvRooWefvppubi4qHv37nf3hgAA8gSCFAAgTwsKCtLu3bv19ttva9SoUYqLi1PZsmUVGhqq6OhoW79WrVrJz89P1atXl7+/v629RIkS2rx5s2bMmKHExEQFBgZq2rRp2Z4174UXXlBiYqJGjRqlhIQEVatWTV988UWG2/NGjhyZYd8NGzaoefPmioyM1Lp16/T000+rWLFiOXwnAAB5icW43fyvAADkE1evXpW/v7/mzp2rbt26ObocAEABx4gUACBfS0tLU3x8vKZNmyZPT0916tTJ0SUBAAoBghQAIF87deqUgoKCVK5cOc2bN0/OzvxfGwDg3uPWPgAAAAAwienPAQAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACb9P85GU595ztj0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of nodes, n  = 296\n",
      "number of edges, m  = 3961\n",
      "mean degree,    <k> = 26.76\n",
      "clustering coefficient, C     =  0.67\n"
     ]
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "import sys\n",
    "from sklearn import metrics\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "def draw_network_with_colors(graph, node_labels):\n",
    "\n",
    "    label_colors = ['#4d79ff', '#8080ff', '#b3b3ff', '#3366cc', '#663399', '#9933ff', '#cc66ff']\n",
    "    node_colors = [label_colors[int(node_labels[node]) - 1] for node in graph.nodes()]\n",
    "    node_size = 600\n",
    "    show_labels = True\n",
    "\n",
    "    if graph.order() > 50:\n",
    "        node_size = 50\n",
    "        show_labels = False\n",
    "\n",
    "    plt.figure(figsize=(10, 7))\n",
    "\n",
    "    nx.draw_networkx(\n",
    "        graph,\n",
    "        with_labels=show_labels,\n",
    "        node_size=node_size,\n",
    "        width=2,\n",
    "        node_color=node_colors\n",
    "    )\n",
    "    plt.title(\"HVR-8\")\n",
    "    plt.show()\n",
    "    return\n",
    "\n",
    "fname1 = 'HVR_8.txt'\n",
    "fname2 = 'metadata_CysPoLV.txt'\n",
    "\n",
    "print(f'reading the network file  : {fname1}')\n",
    "G = nx.read_edgelist('./' + fname1, delimiter=',', nodetype=int)  # import simple graph, index nodes by integers (not strings)\n",
    "\n",
    "# read in the node metadata\n",
    "print(f'reading the metadata file : {fname2}\\n')\n",
    "f = open('./' + fname2,'r')\n",
    "labels = []\n",
    "for line in f:\n",
    "    labels.append(int(line.rstrip('\\n'))) \n",
    "f.close() \n",
    "\n",
    "z = {}\n",
    "for i in range(0,len(labels)):\n",
    "    if i+1 in G.nodes():\n",
    "        z[i+1] = labels[i] \n",
    "\n",
    "n = G.order() #number of nodes\n",
    "m = G.size() #number of edges\n",
    "kmean = (2*m)/n #mean degree\n",
    "C = nx. transitivity(G) #clustering coeff\n",
    "# ellmean = nx.average_shortest_path_length(G) #geodesic distance\n",
    "#plotting ridiculogram\n",
    "draw_network_with_colors(G, z)\n",
    "#plotting the attribute frequencies in a bar graph\n",
    "# print(labels)\n",
    "attribute_frequency = {key: labels.count(key) for key in labels} #learnt this key thing from here https://www.geeksforgeeks.org/python-frequency-of-elements-from-other-list/\n",
    "#print(attribute_frequency)\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(attribute_frequency.keys(), attribute_frequency.values())\n",
    "plt.xlabel('CysPoLV')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('CysPoLV Frequencies in HVR Network')\n",
    "plt.show()\n",
    "print(f'number of nodes, n  = {n}')\n",
    "print(f'number of edges, m  = {m}')\n",
    "print(f'mean degree,    <k> = %5.2f' % kmean)\n",
    "print(f'clustering coefficient, C     = %5.2f' % C)\n",
    "# print(f'mean geodesic distance, <ell> = %5.2f' % ellmean)\n",
    "\n",
    "def predict_baseline(x):\n",
    "    xobs = list(x.values())\n",
    "    xval = [lab for lab in xobs if lab>0]\n",
    "    xpred = xval[int(rnd.random()*len(xval))]\n",
    "    return xpred\n",
    "\n",
    "#using local smoothing to get missing data and reduce vague parts \n",
    "def predictLabel_local(G,i,x,flag):\n",
    "    xnu = []\n",
    "    for j in G.neighbors(i):\n",
    "        if x[j]>0:\n",
    "            xnu.append(x[j])\n",
    "    if not xnu:\n",
    "        xpred = predictLabel_baseline(x)\n",
    "        if flag == 1:\n",
    "            print(baseliune)\n",
    "        else:\n",
    "            xpred = max(sex(xnu), key=xnu.count)        \n",
    "    return xpred\n",
    "\n",
    "\n",
    "def compute_ACC_onG(G,x,alpha):\n",
    "\n",
    "    xo = x.copy()\n",
    "    xp = x.copy()\n",
    "    for i in xp:\n",
    "        xp[i] = 0\n",
    "\n",
    "    for i in G.nodes():\n",
    "        if rnd.random() > alpha:\n",
    "            xo[i] = -1\n",
    "            xp[i] = predictLabel_local(G,i,xo, 1)\n",
    "\n",
    "    observed_idx = []\n",
    "    for i in xo:\n",
    "        if xo[i] == -1:\n",
    "            observed_idx.append(i)\n",
    "    true_labels = []\n",
    "    predicted_labels = []\n",
    "    for i in observed_idx:\n",
    "        true_labels.append(x[i])\n",
    "    for i in observed_idx:\n",
    "        predicted_labels.append(xp[i])\n",
    "    c = metrics.confusion_matrix(true_labels, predicted_labels)\n",
    "    correct_predictions = np.trace(c)\n",
    "    total_observed = np.sum(c)\n",
    "    acc = correct_predictions/total_observed\n",
    "    \n",
    "    return acc\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a18a8f9",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "38e50173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient between attribute labels and degree centrality: 0.408043246837832\n",
      "Subset 1: Mean degree centrality: 0.019901585565882993, Standard deviation: 0.014756899208605654\n",
      "Subset 2: Mean degree centrality: 0.04745762711864407, Standard deviation: 0.05680705975253975\n",
      "Subset 3: Mean degree centrality: 0.042711864406779654, Standard deviation: 0.05806810333657995\n",
      "Subset 4: Mean degree centrality: 0.11182499014584153, Standard deviation: 0.07684233083524808\n",
      "Subset 5: Mean degree centrality: 0.15899919289749798, Standard deviation: 0.07400843887469669\n",
      "Subset 6: Mean degree centrality: 0.07926988265971315, Standard deviation: 0.06106818740361699\n",
      "ANOVA F-statistic: 19.200384255788165, p-value: 1.6718038070980545e-16\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-statistic for subsets 1 and 2: -2.5427547202891367, p-value: 0.013954452399108693\n",
      "T-statistic for subsets 1 and 3: -2.0946569273929896, p-value: 0.0401653047693042\n",
      "T-statistic for subsets 1 and 4: -6.605684649578325, p-value: 3.4662619831005067e-10\n",
      "T-statistic for subsets 1 and 5: -9.972820931309583, p-value: 1.7617751827158966e-13\n",
      "T-statistic for subsets 1 and 6: -4.954618478700278, p-value: 1.233399494288025e-05\n",
      "T-statistic for subsets 2 and 3: 0.3057889851738943, p-value: 0.7608793523739072\n",
      "T-statistic for subsets 2 and 4: -3.93536852713962, p-value: 0.00011567635366229842\n",
      "T-statistic for subsets 2 and 5: -5.579522784827988, p-value: 1.4899681186136844e-06\n",
      "T-statistic for subsets 2 and 6: -1.540068180188193, p-value: 0.13253705754630432\n",
      "T-statistic for subsets 3 and 4: -5.012038239338995, p-value: 1.1618161055792004e-06\n",
      "T-statistic for subsets 3 and 5: -6.41305148590211, p-value: 3.686014217293718e-08\n",
      "T-statistic for subsets 3 and 6: -1.870864365583006, p-value: 0.06773302470575591\n",
      "T-statistic for subsets 4 and 5: -2.6524954571073542, p-value: 0.008661939805282324\n",
      "T-statistic for subsets 4 and 6: 1.4842398092339648, p-value: 0.13946568905794918\n",
      "T-statistic for subsets 5 and 6: 3.160613714643172, p-value: 0.003432203498899358\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "\n",
    "attribute_mean = np.mean(labels)\n",
    "attribute_std = np.std(labels)\n",
    "\n",
    "# Visualize attribute distribution\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.hist(labels, bins=15, color='skyblue', edgecolor='black', alpha=0.7)\n",
    "plt.xlabel('Attribute Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Node Attributes')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "degree_centrality = nx.degree_centrality(G)\n",
    "degree_centrality_values = [degree_centrality[node] for node in G.nodes() if node in degree_centrality]\n",
    "correlation_coefficient = np.corrcoef(labels[:len(degree_centrality_values)], degree_centrality_values)[0, 1]\n",
    "print(f\"Correlation coefficient between attribute labels and degree centrality: {correlation_coefficient}\")\n",
    "\n",
    "\n",
    "from scipy.stats import f_oneway\n",
    "\n",
    "subset_degree_centralities = []\n",
    "\n",
    "# Iterate over the first 6 subsets (HVRs) and compute the mean degree centrality\n",
    "for i in range(1, 7):  # Considering subsets 1 to 6\n",
    "    subset_nodes = [node for node in G.nodes() if z[node] == i]\n",
    "    if subset_nodes:\n",
    "        subset_degree = [degree_centrality[node] for node in subset_nodes]\n",
    "        subset_degree_centralities.append(subset_degree)\n",
    "        mean_degree = np.mean(subset_degree)\n",
    "        std_degree = np.std(subset_degree)\n",
    "        print(f\"Subset {i}: Mean degree centrality: {mean_degree}, Standard deviation: {std_degree}\")\n",
    "    else:\n",
    "        print(f\"No nodes found in subset {i}\")\n",
    "\n",
    "# Filter out empty arrays\n",
    "subset_degree_centralities = [x for x in subset_degree_centralities if x]\n",
    "\n",
    "# Perform ANOVA test to compare mean degree centralities across subsets\n",
    "if len(subset_degree_centralities) >= 2:\n",
    "    f_statistic, p_value_anova = f_oneway(*subset_degree_centralities)\n",
    "    print(f\"ANOVA F-statistic: {f_statistic}, p-value: {p_value_anova}\")\n",
    "else:\n",
    "    print(\"Insufficient data to perform ANOVA test.\")\n",
    "\n",
    "# Visualize the distribution of mean degree centralities across subsets\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.boxplot(subset_degree_centralities)\n",
    "plt.xlabel('Subset (CysPoLV)')\n",
    "plt.ylabel('Mean Degree Centrality')\n",
    "plt.title('Box Plot Distribution of Mean Degree Centrality Across Subsets')\n",
    "plt.xticks(range(1, len(subset_degree_centralities) + 1), range(1, len(subset_degree_centralities) + 1))\n",
    "plt.grid(axis='y')\n",
    "plt.show()\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.violinplot(data=subset_degree_centralities)\n",
    "plt.xlabel('Subset (CysPoLV)')\n",
    "plt.ylabel('Mean Degree Centrality')\n",
    "plt.title('HVR-8 Distribution of Mean Degree Centrality Across Subsets')\n",
    "plt.xticks(range(0, len(subset_degree_centralities)), range(1, len(subset_degree_centralities) + 1))\n",
    "plt.grid(axis='y')\n",
    "plt.show()\n",
    "\n",
    "# Perform statistical tests (e.g., t-test) to compare subsets\n",
    "# Perform statistical tests (e.g., t-test) to compare subsets\n",
    "for i in range(1, 7):\n",
    "    for j in range(i+1, 7):\n",
    "        subset1_nodes = [node for node in G.nodes() if z[node] == i]\n",
    "        subset2_nodes = [node for node in G.nodes() if z[node] == j]\n",
    "        if subset1_nodes and subset2_nodes:\n",
    "            t_statistic, p_value = ttest_ind([degree_centrality[node] for node in subset1_nodes], \n",
    "                                             [degree_centrality[node] for node in subset2_nodes])\n",
    "            print(f\"T-statistic for subsets {i} and {j}: {t_statistic}, p-value: {p_value}\")\n",
    "        else:\n",
    "            print(f\"Insufficient data to perform t-test between subsets {i} and {j}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0ad81d6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Subset   |   Mean Degree Centrality |   Standard Deviation |\n",
      "|:---------|-------------------------:|---------------------:|\n",
      "| Subset 1 |                0.0199016 |            0.0147569 |\n",
      "| Subset 2 |                0.0474576 |            0.0568071 |\n",
      "| Subset 3 |                0.0427119 |            0.0580681 |\n",
      "| Subset 4 |                0.111825  |            0.0768423 |\n",
      "| Subset 5 |                0.158999  |            0.0740084 |\n",
      "| Subset 6 |                0.0792699 |            0.0610682 |\n"
     ]
    }
   ],
   "source": [
    "from tabulate import tabulate\n",
    "\n",
    "# Data for subsets\n",
    "subsets = ['Subset 1', 'Subset 2', 'Subset 3', 'Subset 4', 'Subset 5', 'Subset 6']\n",
    "mean_degrees = [0.019901585565882993, 0.04745762711864407, 0.042711864406779654, \n",
    "                0.11182499014584153, 0.15899919289749798, 0.07926988265971315]\n",
    "std_deviations = [0.014756899208605654, 0.05680705975253975, 0.05806810333657995, \n",
    "                  0.07684233083524808, 0.07400843887469669, 0.06106818740361699]\n",
    "\n",
    "# Prepare data for tabulate\n",
    "table_data = []\n",
    "for subset, mean_degree, std_dev in zip(subsets, mean_degrees, std_deviations):\n",
    "    table_data.append([subset, mean_degree, std_dev])\n",
    "\n",
    "# Print the table\n",
    "table = tabulate(table_data, headers=['Subset', 'Mean Degree Centrality', 'Standard Deviation'], tablefmt='pipe')\n",
    "print(table)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "58e06733",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subset Comparison      T-statistic    P-value\n",
      "-------------------  -------------  ---------\n",
      "Subsets 1 and 2            -2.5428     0.014\n",
      "Subsets 1 and 3            -2.0947     0.0402\n",
      "Subsets 1 and 4            -6.6057     0\n",
      "Subsets 1 and 5            -9.9728     0\n",
      "Subsets 1 and 6            -4.9546     0\n",
      "Subsets 2 and 3             0.3058     0.7609\n",
      "Subsets 2 and 4            -3.9354     0.0001\n",
      "Subsets 2 and 5            -5.5795     0\n",
      "Subsets 2 and 6            -1.5401     0.1325\n",
      "Subsets 3 and 4            -5.012      0\n",
      "Subsets 3 and 5            -6.4131     0\n",
      "Subsets 3 and 6            -1.8709     0.0677\n",
      "Subsets 4 and 5            -2.6525     0.0087\n",
      "Subsets 4 and 6             1.4842     0.1395\n",
      "Subsets 5 and 6             3.1606     0.0034\n"
     ]
    }
   ],
   "source": [
    "from tabulate import tabulate\n",
    "\n",
    "# List to hold the results\n",
    "results_table = []\n",
    "\n",
    "# Populate the results table\n",
    "for i in range(1, 7):\n",
    "    for j in range(i+1, 7):\n",
    "        subset1_nodes = [node for node in G.nodes() if z[node] == i]\n",
    "        subset2_nodes = [node for node in G.nodes() if z[node] == j]\n",
    "        if subset1_nodes and subset2_nodes:\n",
    "            t_statistic, p_value = ttest_ind([degree_centrality[node] for node in subset1_nodes], \n",
    "                                             [degree_centrality[node] for node in subset2_nodes])\n",
    "            results_table.append([f\"Subsets {i} and {j}\", f\"{t_statistic:.4f}\", f\"{p_value:.4f}\"])\n",
    "\n",
    "# Print the formatted table\n",
    "print(tabulate(results_table, headers=[\"Subset Comparison\", \"T-statistic\", \"P-value\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "08db5189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract data from the table\n",
    "subset_comparisons = [\"1 vs 2\", \"1 vs 3\", \"1 vs 4\", \"1 vs 5\", \"1 vs 6\",\n",
    "                      \"2 vs 3\", \"2 vs 4\", \"2 vs 5\", \"2 vs 6\",\n",
    "                      \"3 vs 4\", \"3 vs 5\", \"3 vs 6\",\n",
    "                      \"4 vs 5\", \"4 vs 6\", \"5 vs 6\"]\n",
    "t_statistics = [-3.294, -4.0169, -7.6975, -4.6406, -3.8299,\n",
    "                0.6561, -2.0625, 0.1142, 0.0755,\n",
    "                -4.2493, -0.6543, -0.5468,\n",
    "                2.5106, 1.9588, -0.0207]\n",
    "p_values = [0.0019, 0.0001, 0, 0, 0, 0.5144, 0.0405, 0.9097, 0.9403,\n",
    "            0, 0.5154, 0.5869, 0.0129, 0.0516, 0.9836]\n",
    "\n",
    "# Set significance levels\n",
    "alpha = 0.05\n",
    "significant_color = 'blue'\n",
    "non_significant_color = 'gray'\n",
    "\n",
    "# Create a bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "bars = plt.bar(subset_comparisons, np.abs(t_statistics), color=[significant_color if p < alpha else non_significant_color for p in p_values])\n",
    "\n",
    "# Add labels and titles\n",
    "plt.xlabel('Subset Comparisons')\n",
    "plt.ylabel('Absolute T-statistic')\n",
    "plt.title('T-statistics and P-values for Subset Comparisons')\n",
    "plt.xticks(rotation=90)\n",
    "plt.axhline(y=2, color='r', linestyle='--', linewidth=1, label='Critical T-value (alpha=0.05)')\n",
    "plt.legend()\n",
    "\n",
    "# Add the actual value above each bar\n",
    "for bar, t_statistic in zip(bars, t_statistics):\n",
    "    height = bar.get_height()\n",
    "    plt.text(bar.get_x() + bar.get_width() / 2, height, f'{t_statistic:.2f}', ha='center', va='bottom')\n",
    "\n",
    "# Show plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5bc65f2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}