Codigo Tesis/d_7. Análisis de propiedades de los nodos muestreados y de los nei de los muestrreados.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "34b119aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\56961\n"
     ]
    }
   ],
   "source": [
    "cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "53fab797",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\56961\\Desktop\\portafolio\n"
     ]
    }
   ],
   "source": [
    "cd Desktop\\portafolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94c56abb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\56961\\AppData\\Local\\Temp\\ipykernel_14364\\3674724974.py:1: DeprecationWarning: `magic(...)` is deprecated since IPython 0.13 (warning added in 8.1), use run_line_magic(magic_name, parameter_s).\n",
      "  get_ipython().magic('reset -sf')\n"
     ]
    }
   ],
   "source": [
    "get_ipython().magic('reset -sf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3acc7729",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cargar librerias\n",
    "\n",
    "# definir librerias\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import networkx as nx\n",
    "import pickle \n",
    "from collections import Counter\n",
    "from datetime import datetime\n",
    "#import graphviz\n",
    "\n",
    "from librerias.algoritmos_frecuentista  import *\n",
    "from datetime import datetime\n",
    "#from librerias.compute_c_ab_sample_7_8_cython  import compute_c_ab_sample_7_8_cython"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5649fb4",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "# Lista de estimadores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "caf08d01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 PIMPLE\n",
      "2 EMV\n",
      "3 GNSUM\n",
      "4 RDS_I\n",
      "5 RDS_II\n"
     ]
    }
   ],
   "source": [
    "L_name_est=[\"PIMPLE\",\"EMV\",\"GNSUM\",\"RDS_I\",\"RDS_II\"]\n",
    "for i,j in zip(range(0,16),L_name_est):\n",
    "    print(i+1,j)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3863622",
   "metadata": {},
   "source": [
    "# Todas las configuraciones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c47af4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# input comple\n",
    "# input comple\n",
    "L_metodo_infec=[\"1\",\"2\",\"3\",\"4\",\"DP\",\"IP\"]\n",
    "L_porc_nodos_infec=[0.01,0.1,0.5, 0.9]\n",
    "\n",
    "L_tipo_sample_p_gral=[\"5\",\"6\",\"7\",\"8\"]\n",
    "L_porc_nodos_muestra=[0.01,0.1,0.5,0.9]\n",
    "L_name_est=[\"PIMPLE\",\"EMV\",\"GNSUM\",\"RDS_I\",\"RDS_II\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877b9f1a",
   "metadata": {},
   "source": [
    "# Cargar grafo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "321758b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "name_grafo=\"datos grafos reales/pickle/grafo_twitch_gamers\"\n",
    "file = open(name_grafo, 'rb')\n",
    "# dump information to that filea\n",
    "G= pickle.load(file)\n",
    "#close the file\n",
    "file.close()\n",
    "\n",
    "# Propiedades del grafo\n",
    "set_nodos=set(G.nodes())\n",
    "N=len(set_nodos)\n",
    "dict_nei_G={x_i:set(G.neighbors(x_i))  for x_i in list(G.nodes())}    \n",
    "dic_grados_G=dict(G.degree( list(G.nodes()) ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b6ad7ff",
   "metadata": {},
   "source": [
    "# Calculo de distribucion de grados de N(V(G)), V(G), y H o una muestra aleatoria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a198a739",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist_degree_pob_N_V(nodos_G,dict_nei_G,dic_grados_G):\n",
    "\n",
    "    dist_Nei_pob=[]\n",
    "    for v in nodos_G:\n",
    "        nei_v=dict_nei_G[v]\n",
    "        dist_Nei_v=[]\n",
    "        for u in nei_v:\n",
    "            d_u=dic_grados_G[u]\n",
    "            dist_Nei_v.append(d_u)\n",
    "        dist_Nei_pob.extend(dist_Nei_v)\n",
    "\n",
    "    dist_degree_pob=[]\n",
    "    for v in nodos_G:\n",
    "        d_v=dic_grados_G[v]\n",
    "        dist_degree_pob.append(d_v)\n",
    "    return dist_Nei_pob,dist_degree_pob\n",
    "\n",
    "def compute_dist_degree_set_H(nodos_H,dic_grados_G):\n",
    "    dist_degree_H=[]\n",
    "    for v in nodos_H:\n",
    "        d_v=dic_grados_G[v]\n",
    "        dist_degree_H.append(d_v)\n",
    "    return dist_degree_H\n",
    "\n",
    "dist_Nei_pob,dist_degree_pob=dist_degree_pob_N_V(list(set_nodos),dict_nei_G,dic_grados_G) # dist poblacional\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1d33581",
   "metadata": {},
   "source": [
    "Ejemplo  de cargar la muestra i del grafo de twitch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12381d58",
   "metadata": {},
   "outputs": [],
   "source": [
    "i=10\n",
    "name=\"datos_muestra/muestras_grafo_twitch/id_\"+str(i)+\"_subset_nodos\"\n",
    "\n",
    "file_1 = open(name, 'rb')\n",
    "dic_lista_infec_y_sample= pickle.load(file_1)\n",
    "file_1.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1e989af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "57fde6d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def grafica_dist_grado_H_vs_pob(dist_degree_pob,dist_Nei_pob,dic_grados_G,dist_degree_set_H):\n",
    "    size_figure=(12,15)\n",
    "    fig, axes = plt.subplots(2,2, figsize=size_figure, sharex=False, sharey=False)    \n",
    "    # set grado de todos los sample con los parametros\n",
    "    num_bins=75\n",
    "\n",
    "    for num_row in range(0,2):\n",
    "        for num_col in range(0,2):\n",
    "            if num_row==0:\n",
    "                log_bool=True\n",
    "                y_label_=\"Densidad(log)\"\n",
    "            else:\n",
    "                log_bool=False\n",
    "                y_label_=\"Densidad\"\n",
    "            if num_col==0:\n",
    "                ref_pob=dist_degree_pob\n",
    "                label_pob_ref=\"dist grado pob\"\n",
    "                x_label_=\"V(G)\"\n",
    "            else:\n",
    "                ref_pob=dist_Nei_pob\n",
    "                label_pob_ref=\"dist grado Nei pob\"    \n",
    "                x_label_=\"N(V(G))\"    \n",
    "                \n",
    "            axes[num_row,num_col].hist(dist_degree_set_H, bins=num_bins, density=True, edgecolor='none', alpha=0.3, fill=True,color=\"blue\",label=\"dist_degree_set_H\", log=log_bool)        \n",
    "            axes[num_row,num_col].hist(ref_pob, bins=num_bins, density=True, edgecolor='black', alpha=1, fill=False,label=label_pob_ref, log=log_bool) \n",
    "\n",
    "            axes[num_row,num_col].set_xlabel(x_label_,fontsize=10)\n",
    "            axes[num_row,num_col].set_ylabel(y_label_,fontsize=10)\n",
    "            axes[num_row,num_col].set_title(\"...\")\n",
    "            axes[num_row,num_col].legend(loc='upper right',fontsize=10)\n",
    "            axes[num_row,num_col].grid(True)\n",
    "    # Ajustar los espacios entre subplots\n",
    "    plt.tight_layout()          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9ecfd2aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dic_conjuntos_H['views<=520']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf48cceb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "10bcb387",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 1200x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grafica_dist_grado_H_vs_pob(dist_degree_pob,dist_Nei_pob,dic_grados_G,lista_degree_sample_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d171c3af",
   "metadata": {},
   "source": [
    "# caso muestreo 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c95d7989",
   "metadata": {},
   "outputs": [],
   "source": [
    "keys_sample=\"tipo_muestreo5porc_nodos0.1\"\n",
    "\n",
    "#def all_degree_sample(tipo_sample,porc_sample,dic_grados_G):\n",
    "lista_degree_nei_sample_all=[]\n",
    "\n",
    "for i in range(0,10):\n",
    "    # cargar el sample i\n",
    "    #i=10\n",
    "    name=\"datos_muestra/muestras_grafo_twitch/id_\"+str(i)+\"_subset_nodos\"\n",
    "\n",
    "    file_1 = open(name, 'rb')\n",
    "    dic_lista_infec_y_sample= pickle.load(file_1)\n",
    "    file_1.close()\n",
    "    muestra=dic_lista_infec_y_sample[keys_sample]\n",
    "    \n",
    "    nei_muestra=[]\n",
    "    for xi in muestra:\n",
    "        nei_muestra.extend(list(G.neighbors(xi)))\n",
    "\n",
    "    sample_degree=list(G.degree( nei_muestra))\n",
    "    for i in range(len(sample_degree)):\n",
    "        lista_degree_nei_sample_all.append(sample_degree[i][1])\n",
    "\n",
    "\n",
    "#return lista_degree_sample_all\n",
    "#sample_degree\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "cf11dcec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grafica_dist_grado_H_vs_pob(dist_degree_pob,dist_Nei_pob,dic_grados_G,lista_degree_nei_sample_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b62cd22d",
   "metadata": {},
   "source": [
    "# caso muestreo 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3fcb80d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "# Crear una lista de elementos\n",
    "secuencia = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "\n",
    "# Tomar una muestra de tamaño 5 sin repeticiones\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "keys_sample=\"tipo_muestreo6porc_nodos0.1\"\n",
    "\n",
    "#def all_degree_sample(tipo_sample,porc_sample,dic_grados_G):\n",
    "lista_degree_nei_sample_all=[]\n",
    "\n",
    "# for i in range(0,1): se demora 4 min\n",
    "for i in range(0,8):  # Con 10 sample sobrepasa la memoria ram, # 5 samples son el limite # se demora mas de 13 min\n",
    "    # cargar el sample i\n",
    "    #i=10\n",
    "    name=\"datos_muestra/muestras_grafo_twitch/id_\"+str(i)+\"_subset_nodos\"\n",
    "\n",
    "    file_1 = open(name, 'rb')\n",
    "    dic_lista_infec_y_sample= pickle.load(file_1)\n",
    "    file_1.close()\n",
    "    muestra=dic_lista_infec_y_sample[keys_sample]\n",
    "    \n",
    "    nei_muestra=[]\n",
    "    for xi in muestra:\n",
    "        sample_nei=list(G.neighbors(xi))\n",
    "        muestra_parcial = random.sample(sample_nei, int(len(sample_nei)*0.1))\n",
    "        nei_muestra.extend(muestra_parcial)\n",
    "\n",
    "    sample_degree=list(G.degree( nei_muestra))\n",
    "    for i in range(len(sample_degree)):\n",
    "        lista_degree_nei_sample_all.append(sample_degree[i][1])\n",
    "\n",
    "\n",
    "#return lista_degree_sample_all\n",
    "#sample_degree\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5f404a83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kTrZRlAIAOK1WrVrJzc1N6enpatasmSTpl19+0bfffqveZdyOxtPTUwMHDtTAgQM1btw4tW3bVl9//bW6dOmiOnXqqKioqNzzt2vXTunp6VZt//vf/6wed+nSRQcOHLCZLBQXF6t169b64YcflJ2dbXnH78svv7zm3L6+vmrcuLG+++47PfbYYzb7dOnSRWvWrJGPj4+8vLzKG5aVzp07q3PnzoqLi1NYWJjeffdd3XnnnVeNq4Sbm9t1HU8AAFC5anruJElt2rTRDz/8oBMnTljyG3KnqkNRCgDgtOrVq6eRI0dq0qRJuuWWW+Tj46MXXnhBLi62b06bmJiooqIihYaGymw26+2335anp6eaN28u6dJdT7Zs2aJHHnlE7u7uatSo0VXnf/rpp9WzZ0+99NJLGjRokD7++GOr088lKT4+Xvfff7+aNWumhx9+WC4uLtq9e7f27NmjWbNmqW/fvmrVqpWGDRumefPm6ddff9XUqVMl6Zp3sZk5c6aefvpp1a9fX5GRkcrPz9f27dv1yy+/KDY2Vo899pjmz5+vQYMGadasWbrtttt09OhRffDBB5o8eXKZ14+QpCNHjuiNN97QAw88oMaNG+vAgQM6ePCghg4des24XnzxRcvxTElJUc+ePeXu7q4GDRpcNR4AAFC5anru9OKLL6pfv35q1aqVnnrqKb300kvKzc0ld6pCtl85AAA4ifnz5+t3v/udBg4cqPDwcPXq1Utdu3a12dfb21tvvvmmevbsqU6dOumTTz7Rv//9b91yyy2SpFmzZun7779Xq1atynWK9p133qk333xTixcvVnBwsDZu3GhJikpEREToP//5jzZu3Kg77rhDd955p15++WVLMufq6qoPPvhA586d0x133KFRo0bphRdekHTtU7lHjRqlZcuWacWKFQoKClLv3r2VmJiogIAASZLZbNaWLVvUrFkzPfTQQ2rXrp1GjhypCxcuXPPdP7PZrP379+v3v/+9br/9do0ZM0bjxo3Tn/70p3LFJUkLFixQcnKymjZtqs6dO1/zeAIAgMpXm3Kn0NBQcqcqZjIMw6jqRTirnJwc1a9fX2fPnq3wqX1lKSws1Pr163XfffdV2md1qytiJ3Zni12q+vgvXLigI0eOKCAg4IY+014RxcXFysnJkZeXV5nv0tVWZcX+xRdfqFevXjp06JDl7i21kT2f+6u9hivz7zUqprKek6r+XVqVnDl2ybnjJ/aqib0qc6cS5FCXY3eW3EmqfvkTH98DAKCG+/DDD+Xl5aXAwEAdOnRI48ePV8+ePWt9UgUAAFARH374oVxcXBQcHKzvvvuO3KkKOVc5FAAAB7r33ntVr149m19//vOf7TbPr7/+arlw6PDhw3XHHXfoo48+stv4ZSkrtnr16umzzz6z+3wdOnSwmsPLy0u33XabvLy8bN7lBgAA1CyOzJ0mTZqk9u3bO1XudGX+VF1yJ86UAgCgkixbtkznz5+3ua1hw4Z2m2fo0KEaPny43cYrr4yMjDK3NWnSxO7zrV+/XoWFhZbHxcXFOnfunOrVqyd/f3+7zwcAABzLkblTVFSUwz+6WNW5k3Q5f6ouZ4VRlAIAoJJURnJRnVztlsSV4coLeUrOfS0MAABqI3In+/pt7iRdzp9uvvlmh66lLGRwAAAAAAAAcDiKUgAAAAAAAHA4Pr5Xy+3evbvURxoaNWqkZs2aVdGKAAAAAAAAKErVWj/++KMk6a677ip1oTiz2ax9+/ZRmAIAALDhv/+VTCbrtoEDq2YtAADUZhSlaqmff/5ZkvTmm2+qXbt2lvZ9+/bp8ccf16lTpyhKAQAAAACAKsM1pWq5Nm3aqEuXLpavKwtUAIDS+vTpowkTJlget2jRQosWLaqy9djDb2OqKsOHD1dUVFRVLwMAANgRuVPl+v7772UymZSRkVGp88yYMUMhISGVOoctnCkFAKg0mZmZOnXqVKXOUVxcrNzcXNWtW1c+Pj52Pwv0yy+/VN26dcvVt0WLFpowYUK1SWIAAEDN4ojcqURxcbHc3d3VoUMHu45L7nRJixYtdPToUaWlpenOO++0tE+YMEEZGRlKTU0t1zhNmzbV8ePH1ahRo0paadWiKAUAqBSZmZlq166d8vLyHDZnZVwz79Zbb7XbWDeisLBQbm5uVb0MAABQSaoqd/rmm2/UokULu41J7nSZh4eHnnvuOX366acVHsPV1VV+fn52XFX1wsf3AACV4tSpU8rLy9Pbb7+tHTt2VNrXl19+qdTUVK1atUp5eXnX9e5ibm6uhg4dqnr16snf318LFiwo1efKU9ANw9CMGTPUrFkzubu7q3Hjxnr66aclXTrN++jRo5o4caJMJpNMv71K8hX279+vXr16ycPDQ+3bt9cnn3wik8mkpKQkSZdP016zZo169+4tDw8PvfPOO/r55581ZMgQNWnSRGazWcHBwfrHP/5x3TH98ssvGjp0qBo0aCCz2ax7771XBw8evOqxMplMev3113XvvffK09NTLVu2LDX3119/rbvvvluenp665ZZbNGbMGJ07d67UWDNnztStt94qLy8vPfnkkyooKLjq3AAAOANH5U4lX86YOwUFBem999677pgqkjtJ0pgxY/S///1P69evv2q/ZcuWqV27dvLw8FDbtm312muvWbaV5+N7LVq00OzZszVkyBDVrVtXTZo00dKlS636ZGZmatCgQfLy8lKzZs00ePBgZWdnlxrrb3/7m5o2bSqz2aw//OEPOnv27DXjvBGcKQUAqFTt2rVTly5dKm384uJi5eTklPs08StNmjRJn376qT766CP5+PhoypQp2rlzZ5mfp//nP/+pl19+WatXr1aHDh2UlZWl3bt3S5I++OADBQcHa8yYMRo9enSZcxYVFSkqKkrNmjVTenq6fv31Vz3zzDM2+z7//PNasGCBOnfuLA8PD124cEFdu3bVc889Jy8vL/3nP//Rk08+qY4dO1pOCy9PTMOHD9fBgwf1r3/9S15eXnruued03333ae/evVd9R3HatGn6y1/+osWLF+vvf/+7HnnkEX399ddq166dcnNzFRERobCwMH355Zc6ceKERo0apZiYGCUmJlrGSElJkYeHh1JTU/X9999rxIgRuuWWWzRnzpwy5wUAwJlUdu5Uori4+Lr3qem507p16/THP/5RAQEBatu2bbljqmjuFBAQoCeffFJxcXGKjIyUi0vp84LeeecdxcfHa8mSJercubN27dql0aNHq27duho2bFiZY//W/PnzNWXKFM2cOVMff/yxxo8fr9tvv139+vVTcXGxBg0apHr16mnz5s06e/asnn/+eQ0ePNjqY4SHDh3S+++/r3//+9/KycnRyJEj9dRTT+mdd94p9zquF0UpAIBTOnfunN566y29/fbbuueeeyRJK1eu1G233VbmPpmZmfLz81N4eLjc3NzUrFkzde/eXZLUsGFDubq66uabb77qKdbJyck6fPiwUlNTLf3mzJmjfv36leo7YcIEPfTQQ1Ztzz77rOXnmJgYrVu3TmvXrtWdd95ZrphKEqovvvhCPXr0kHQpGWratKmSkpIUHR1d5tqjo6M1atQoSdLs2bOVnJysV199Va+99preffddXbhwQatWrbIUCJcsWaKBAwfqr3/9q3x9fSVJderU0fLly2U2m9WhQwfNmjVLkyZN0uzZs20magAAoHqoDbnT//3f/+njjz/W2rVrNW3atErPnSRp6tSpWrFihd555x398Y9/LLV9+vTpWrBggWXdAQEB2rt3r/72t79dV1GqZ8+eev755yVJt99+u7744gu9/PLL6tevn1JSUvT111/ryJEjatKkiXJycpSYmKigoCB9+eWXuuOOOyTJkss1adJEkvTqq69qwIABWrBgQaV9hJDsDwDglA4fPqyCggKFhoZa2ho2bKg2bdqUuU90dLTOnz+vli1bavTo0frwww918eLF65r3wIEDatq0qdUf9pLk7Le6detm9bioqEizZ89WUFCQGjZsKC8vL23atEmZmZnljmnfvn266aabrPrccsstatOmjfbt23fVtYeFhZV6XLLPvn37FBwcbHXGWs+ePVVcXKwDBw5Y2oKDg2U2m63GOHfunH744Yerzg0AAKpWbcid6tWrp48//thhuZN06Rpbzz77rOLj40tdsiA3N1eHDx/WyJEjVa9ePcvXiy++qMOHD19z7CtdK09r2rSpmjZtatnevn17eXt7W8XQrFkzS0GqZIzf5nL2RlEKAIByatq0qQ4cOKDXXntNnp6eeuqpp3TXXXepsLCwUub77UcS58+fr8WLF+u5557T5s2btXPnTt19991ckwkAAFRL1S13ysjIUEREhMNzp9jYWJ0/f97qWlGSLNfefPPNN5WRkWH52rNnj/73v/85dI1VhaIUAMAptWrVSm5ubkpPT7e0/fLLL/r222+vup+np6cGDhyoV155RampqUpLS9PXX38t6dJH04qKiq66f5s2bfTDDz9YXVjyyy+/LNeav/jiCw0aNEiPP/64goOD1bJlS6t30coTU7t27XTx4kWrPj///LMOHDig9u3bX3X+3yZH//vf/9SuXTvLuLt371Zubq7Vel1cXKzebdy9e7fOnz9vNUa9evWs3rkDAADVT23Jna5cb2XnTiXq1aunadOmac6cOfr1118t7b6+vmrcuLG+++47tW7d2uorICCgXGOXuFae9sMPP1idmb53716dOXPGKobMzEz99NNPVmP8NpezN4pSAACnVK9ePY0cOVKTJk3Spk2btGfPHg0fPvyq1zVKTEzUW2+9pT179ui7777T22+/LU9PTzVv3lzSpTufbNmyRceOHSvzTjb9+vVTq1atNGzYMH311Vf64osvNHXqVEm66l1nJCkwMFDJycnaunWr9u3bpyeffFInTpy4rpgCAwM1aNAgjR49Wp9//rl2796txx9/XE2aNNGgQYOuOv/atWu1fPlyffvtt5o+fbq2bdummJgYSdJjjz0mDw8PDRs2THv27NHmzZv1f//3f/rjH/9ouZ6UJBUUFGjkyJHau3ev1q9fr+nTpysmJobrSQEAUM3VhtzpT3/6k1Vxq7JzpyuNGTNG9evX17vvvmvVPnPmTM2dO1evvPKKvv32W3399ddasWKFFi5cWO6xpUsFuHnz5unbb7/V0qVLtXbtWo0fP16SFB4erqCgID322GPauXOnduzYoeHDh6t3795WH3ksyeV2796tzz77TE8//bT+8Ic/VNr1pCQudA4AqGTl+az9jSguLlZubq7l2gDXY/78+Tp37pwGDhyom2++Wc8888xVb3vr7e2tv/zlL4qNjVVRUZGCgoL073//W7fccoskadasWfrTn/6kVq1aKT8/X4ZhlBrD1dVVSUlJGjVqlO644w61bNlS8+fP18CBA+Xh4XHV9U6dOlXfffedIiIiZDabNXr0aA0YMEB5eXnXFdOKFSs0fvx43X///SooKNBdd92l9evXX/XuMdKlpGn16tV66qmn5O/vr/fee8/y7prZbLbc6eWOO+6Q2WzW73//+1IJ1T333KPAwEDdddddys/P15AhQzRjxoyrzgsAgDOp7NzpRuap6bnTmDFjFBUVpTNnzlxXTBXNna7k5uam2bNn69FHH7VqHzVqlMxms+bPn69Jkyapbt26CgoK0oQJE8o9tiQ988wz2r59u2bOnCkvLy8tXLhQERERki4V7z766CP93//9n/r06SMXFxdFRERoyZIlVmO0bt1aDz30kO677z6dPn1a999/f6mPHNqbybD1rMMhcnJyVL9+fZ09e1ZeXl52HXv79u06duyYmjRpYlX53Llzp7p27aodO3Y45DajVaGwsFDr16/Xfffdd12/JGoDYnfO2KWqj//ChQs6cuSIAgICLMlBZmam2rVrZ1UwqWxms1n79u1Ts2bNHDanPXzxxRfq1auXDh06pFatWpV7v+LiYuXk5MjLy6vSzzQymUz68MMPFRUVVanzXA97xm/rNVyiMv9eo2Iq6zkp+V1qMt0nk8n6d+nAgXabplqq6r8jVc2Z4yf2qom9OuVO33zzjVq0aOGwOe2horlTCUfmUI7QokULTZgwoVyFrOqWP3GmFACgUjRr1kz79u0r81Rseyk5U6pu3bry8fGpEQWpDz/8UPXq1VNgYKAOHTqk8ePHq2fPnhVKqgAAQO3gqNypRHFxsdzd3cmdUKUoSgEAKk2zZs0qPdGpie90/frrr3ruueeUmZmpRo0aKTw8XAsWLKjqZQEAgCrmiNypREkOVROQO9VeFKUAAHCwoUOHaujQoVW9jOvCp/0BAEBVqYm5kyN9//33Vb2ECqsZbykDAAAAAACgVqEoBQAAAAAAAIejKAUAsBs+4oWaitcuAKAq8PcHNZk9Xr8UpQAAN6zkNsqOvIUxYE8lr11nux06AKBqkDuhNrBH/lQtLnS+dOlSzZ8/X1lZWQoODtarr76q7t27l9l/7dq1mjZtmr7//nsFBgbqr3/9q+677z7LdsMwNH36dL355ps6c+aMevbsqddff12BgYGSLl0EbPbs2dq0aZOysrLUuHFjPf7443rhhRdUp04dyzhfffWVxo0bpy+//FK33nqr/u///k+TJ0++rrUAgDNwdXWVt7e3Tpw4IUkym80ymUwOmbu4uFgFBQW6cOFCjbn7nr04c+ySfeI3DEN5eXk6ceKEvL295erqaudVAgBQWlXmTiWcOY8g9uqTP1V5UWrNmjWKjY1VQkKCQkNDtWjRIkVEROjAgQPy8fEp1X/r1q0aMmSI5s6dq/vvv1/vvvuuoqKitHPnTnXs2FGSNG/ePL3yyitauXKlAgICNG3aNEVERGjv3r3y8PDQ/v37VVxcrL/97W9q3bq19uzZo9GjRys3N1cvvfSSJCknJ0f9+/dXeHi4EhIS9PXXX+uJJ56Qt7e3xowZU+61AICz8PPzkyRLcuUohmHo/Pnz8vT0dHgyV9WcOXbJvvF7e3tbXsMAADhCVeVOJZw5jyD26pM/VXlRauHChRo9erRGjBghSUpISNC6deu0fPlyPf/886X6L168WJGRkZo0aZIkafbs2UpOTtaSJUuUkJAgwzC0aNEiTZ06VYMGDZIkrVq1Sr6+vkpKStIjjzyiyMhIRUZGWsZs2bKlDhw4oNdff91SlHrnnXdUUFCg5cuXq06dOurQoYMyMjK0cOFCS1HqWmsBAGdiMpnk7+8vHx8fFRYWOmzewsJCbdmyRXfddZfTffTKmWOX7Be/m5sbZ0gBAByuqnKnEs6cRxB79cmfqrQoVVBQoB07diguLs7S5uLiovDwcKWlpdncJy0tTbGxsVZtERERSkpKkiQdOXJEWVlZCg8Pt2yvX7++QkNDlZaWpkceecTmuGfPnlXDhg2t5rnrrrusPs4XERGhv/71r/rll1/UoEGDa67lt/Lz85Wfn295nJOTI+nSi8Lev4SKi4st368cu7i4WJ6enqXaa5OSuGprfFdD7M4Zu1T94nfkf/CLi4t18eJFubq6Ol1hwZljl+wXf3FxseXvpi3V5d8VAKB2qqq/466urrp48aI8PDycrjBD7NUn9iotSp06dUpFRUXy9fW1avf19dX+/ftt7pOVlWWzf1ZWlmV7SVtZfX7r0KFDevXVVy1nSZWMExAQUGqMkm0NGjS45lp+a+7cuZo5c2ap9o0bN8psNtvc50YdP35cx48ft2p77733dOzYMR07dqxS5qwukpOTq3oJVYbYnZczx0/szquy4+citAAAAJWjyj++V9WOHTumyMhIRUdHa/To0ZU6V1xcnNWZVTk5OWratKn69+8vLy8vu861a9cuHT9+XP7+/urcubOlfffu3brrrru0ZcsWBQcH23XO6qKwsFDJycnq169ftaj8OhKxO2fsknPHT+zOGbvkuPhLzmwGAACAfVVpUapRo0ZydXVVdna2VXt2dnaZF8vy8/O7av+S79nZ2fL397fqExISYrXfTz/9pL59+6pHjx564403yjXPlXNcay2/5e7uLnd391Ltbm5udk+mS66i7+LiYjW2i4uLzp8/X6q9NqqM41pTELtzxi45d/zE7pyxS5UfvzMfWwAAgMpUpfc+rFOnjrp27aqUlBRLW3FxsVJSUhQWFmZzn7CwMKv+0qXT9kv6BwQEyM/Pz6pPTk6O0tPTrcY8duyY+vTpo65du2rFihWlboUYFhamLVu2WF1HIjk5WW3atFGDBg3KtRYAAAAAAADYVqVFKUmKjY3Vm2++qZUrV2rfvn0aO3ascnNzLXfjGzp0qNWF0MePH68NGzZowYIF2r9/v2bMmKHt27crJiZG0qU7GEyYMEEvvvii/vWvf+nrr7/W0KFD1bhxY0VFRUm6XJBq1qyZXnrpJZ08eVJZWVlW14J69NFHVadOHY0cOVLffPON1qxZo8WLF1t9/O5aawEAAAAAAIBtVX5NqcGDB+vkyZOKj49XVlaWQkJCtGHDBssFxDMzM63OYurRo4feffddTZ06VVOmTFFgYKCSkpLUsWNHS5/JkycrNzdXY8aM0ZkzZ9SrVy9t2LBBHh4eki6dzXTo0CEdOnRIt912m9V6DMOQdOmOfRs3btS4cePUtWtXNWrUSPHx8RozZsx1rQUAAAAAAAClVXlRSpJiYmLKPLsoNTW1VFt0dLSio6PLHM9kMmnWrFmaNWuWze3Dhw/X8OHDr7muTp066bPPPrtqn2utBQAAAAAAAKVV+cf3AAAAAAAA4HwoSgEAAAAAAMDhKEoBAAAAAADA4ShKAQAAAAAAwOEoSgEAAAAAAMDhKEoBAAAAAADA4ShKAQAAAAAAwOEoSgEAAAAAAMDhKEoBAAAAAADA4ShKAQAAAAAAwOEoSgEAAAAAAMDhKEoBAAAAAADA4ShKAQAAAAAAwOEoSgEAAAAAAMDhKEoBAAAAAADA4ShKAQAAAAAAwOEoSgEAAAAAAMDhbqrqBaByffGFdPz45ceHD1fdWgAAAAAAAEpwphQAAAAAAAAcjqIUAAAAAAAAHI6iFAAAAAAAAByOohQAAEANtHTpUrVo0UIeHh4KDQ3Vtm3brtp/7dq1atu2rTw8PBQUFKT169dbbTcMQ/Hx8fL395enp6fCw8N18OBBqz5z5sxRjx49ZDab5e3tXWqOxMREmUwmm18nTpyQJKWmptrcnpWVdWMHBAAA1DgUpQAAAGqYNWvWKDY2VtOnT9fOnTsVHBysiIgIS+Hnt7Zu3aohQ4Zo5MiR2rVrl6KiohQVFaU9e/ZY+sybN0+vvPKKEhISlJ6errp16yoiIkIXLlyw9CkoKFB0dLTGjh1rc57Bgwfr+PHjVl8RERHq3bu3fHx8rPoeOHDAqt9vtwMAgNqPohQAAEANs3DhQo0ePVojRoxQ+/btlZCQILPZrOXLl9vsv3jxYkVGRmrSpElq166dZs+erS5dumjJkiWSLp0ltWjRIk2dOlWDBg1Sp06dtGrVKv30009KSkqyjDNz5kxNnDhRQUFBNufx9PSUn5+f5cvV1VWbNm3SyJEjS/X18fGx6uviQloKAICzuamqFwAAAIDyKygo0I4dOxQXF2dpc3FxUXh4uNLS0mzuk5aWptjYWKu2iIgIS8HpyJEjysrKUnh4uGV7/fr1FRoaqrS0ND3yyCMVWuuqVatkNpv18MMPl9oWEhKi/Px8dezYUTNmzFDPnj3LHCc/P1/5+fmWxzk5OZKkwsJCFRYWVmhttpSMZRilx7TjNNVSSez2PJ41iTPHT+zOGbvk3PETe+XHXt7xKUoBAADUIKdOnVJRUZF8fX2t2n19fbV//36b+2RlZdnsX3Idp5LvV+tTEW+99ZYeffRReXp6Wtr8/f2VkJCgbt26KT8/X8uWLVOfPn2Unp6uLl262Bxn7ty5mjlzZqn2jRs3ymw2V3h9ZUuWYVi3/OYSXLVWcnJyVS+hSjlz/MTuvJw5fmKvPHl5eeXqR1EKAAAAdpeWlqZ9+/bp73//u1V7mzZt1KZNG8vjHj1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      "text/plain": [
       "<Figure size 1200x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#len(lista_degree_nei_sample_all)\n",
    "# 22109585\n",
    "# 17681925\n",
    "grafica_dist_grado_H_vs_pob(dist_degree_pob,dist_Nei_pob,dic_grados_G,lista_degree_nei_sample_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "011fa47b",
   "metadata": {},
   "source": [
    "# caso muestreo 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9a3cc4d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "keys_sample=\"tipo_muestreo7porc_nodos0.1\"\n",
    "\n",
    "#def all_degree_sample(tipo_sample,porc_sample,dic_grados_G):\n",
    "lista_degree_nei_sample_all=[]\n",
    "\n",
    "# for i in range(0,1): se demora 4 min\n",
    "for i in range(0,1):  # Con 10 sample sobrepasa la memoria ram, # 5 samples son el limite # se demora mas de 13 min\n",
    "    # cargar el sample i\n",
    "    #i=10\n",
    "    name=\"datos_muestra/muestras_grafo_twitch/id_\"+str(i)+\"_subset_nodos\"\n",
    "\n",
    "    file_1 = open(name, 'rb')\n",
    "    dic_lista_infec_y_sample= pickle.load(file_1)\n",
    "    file_1.close()\n",
    "    muestra=dic_lista_infec_y_sample[keys_sample]\n",
    "    \n",
    "    nei_muestra=[]\n",
    "    for xi in muestra:\n",
    "        nei_muestra.extend(list(G.neighbors(xi)))\n",
    "\n",
    "    sample_degree=list(G.degree( nei_muestra))\n",
    "    for i in range(len(sample_degree)):\n",
    "        lista_degree_nei_sample_all.append(sample_degree[i][1])\n",
    "\n",
    "\n",
    "#return lista_degree_sample_all\n",
    "#sample_degree\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9bdd66d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grafica_dist_grado_H_vs_pob(dist_degree_pob,dist_Nei_pob,dic_grados_G,lista_degree_nei_sample_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48b3fd06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "edb4cf4a",
   "metadata": {},
   "source": [
    "# caso muestreo 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b34737ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "keys_sample=\"tipo_muestreo8porc_nodos0.1\"\n",
    "\n",
    "#def all_degree_sample(tipo_sample,porc_sample,dic_grados_G):\n",
    "lista_degree_nei_sample_all=[]\n",
    "\n",
    "# for i in range(0,1): se demora 4 min\n",
    "for i in range(0,1):  # Con 10 sample sobrepasa la memoria ram, # 5 samples son el limite # se demora mas de 13 min\n",
    "    # cargar el sample i\n",
    "    #i=10\n",
    "    name=\"datos_muestra/muestras_grafo_twitch/id_\"+str(i)+\"_subset_nodos\"\n",
    "\n",
    "    file_1 = open(name, 'rb')\n",
    "    dic_lista_infec_y_sample= pickle.load(file_1)\n",
    "    file_1.close()\n",
    "    muestra=dic_lista_infec_y_sample[keys_sample]\n",
    "    \n",
    "    nei_muestra=[]\n",
    "    for xi in muestra:\n",
    "        nei_muestra.extend(list(G.neighbors(xi)))\n",
    "\n",
    "    sample_degree=list(G.degree( nei_muestra))\n",
    "    for i in range(len(sample_degree)):\n",
    "        lista_degree_nei_sample_all.append(sample_degree[i][1])\n",
    "\n",
    "\n",
    "#return lista_degree_sample_all\n",
    "#sample_degree\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d4b5c218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grafica_dist_grado_H_vs_pob(dist_degree_pob,dist_Nei_pob,dic_grados_G,lista_degree_nei_sample_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cbcf56f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94aceeec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e12ea1a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce15ab9d",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}