analisis_paquetes.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análisis de paquetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import collections\n",
    "import matplotlib.pyplot as plt\n",
    "import import_ipynb\n",
    "import utils as utils"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graficar entropia a medida que llegan paquetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(utils.get_entropy())\n",
    "plt.title(\"Entropía de la fuente a medida que llegan paquetes\")\n",
    "plt.xlabel(\"Cantidad de paquetes\")\n",
    "plt.ylabel(\"Entropia\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Análisis de la fuente final: \n",
    " - Cantidad de Broadcast vs Unicast "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percent unicast 0.86106\n",
      "percent broadcast 0.13894\n"
     ]
    }
   ],
   "source": [
    "unicast_broadcast_symbol, protocol, percent_unicast, percent_broadcast , symbol_proba= utils.show_source()\n",
    "print(\"percent unicast\", percent_unicast)\n",
    "print(\"percent broadcast\", percent_broadcast)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Porcentaje de cada protocolo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "porcentaje por protocolo \n",
      " {' 2048': 0.7458, ' 2054': 0.11191000000000001, ' 34525': 0.14229}\n"
     ]
    }
   ],
   "source": [
    "percent_per_protocol = {p : 0 for p in np.unique(protocol)}\n",
    "\n",
    "\n",
    "for s, proba in symbol_proba.items():\n",
    "    percent_per_protocol[s[0]] += proba\n",
    "\n",
    "print(\"porcentaje por protocolo \\n\", percent_per_protocol)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Gráfico de la fuente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{' 2048': 0.70729, ' 2054': 0.01148, ' 34525': 0.14229}\n",
      "{' 2048': 0.03851, ' 2054': 0.10043, ' 34525': 0.0}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "protocols_unique = np.unique(protocol)\n",
    "unicast = {}\n",
    "broadcast = {}\n",
    "\n",
    "for p in protocols_unique:\n",
    "    if((p, 'UNICAST') in list(symbol_proba.keys())):\n",
    "        unicast[p] = symbol_proba[(p, 'UNICAST')] \n",
    "    else:\n",
    "        if(not (p, 'BROADCAST') in symbol_proba.keys()):\n",
    "            unicast[p] = 0\n",
    "    if((p, 'BROADCAST') in list(symbol_proba.keys())):\n",
    "        broadcast[p] = symbol_proba[(p, 'BROADCAST')] \n",
    "    else:\n",
    "        if(not (p, 'UNICAST') in symbol_proba.keys()):\n",
    "            broadcast[p] = 0\n",
    "\n",
    "p = list(symbol_proba.keys())[4]\n",
    "broadcast[' 34525'] = 0.0000000\n",
    "\n",
    "print(unicast)\n",
    "print(broadcast)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.bar(unicast.keys(), list(unicast.values()), label = \"unicast\")\n",
    "ax.bar(broadcast.keys(), list(broadcast.values()), bottom=list(unicast.values()), label = \"broadcast\")\n",
    "plt.xlabel(\"Protocolo\")\n",
    "ax.set_title(\"Símbolos de la fuente\")\n",
    "ax.legend()\n",
    "ax.set_yticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_52293/650508972.py:25: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(protocols_unique)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f88891a6af0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unicast = {}\n",
    "broadcast = {}\n",
    "\n",
    "for p in protocols_unique:\n",
    "    if((p, 'UNICAST') in list(symbol_proba.keys())):\n",
    "        unicast[p] = symbol_proba[(p, 'UNICAST')] \n",
    "    else:\n",
    "        unicast[p] = 0\n",
    "    if((p, 'BROADCAST') in list(symbol_proba.keys())):\n",
    "        broadcast[p] = symbol_proba[(p, 'BROADCAST')] \n",
    "    else:\n",
    "        \n",
    "        broadcast[p] = 0\n",
    "\n",
    "x_protocols = np.arange(len(protocols_unique))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "width = 0.27\n",
    "ax.bar(x_protocols, list(unicast.values()),width = width, label = \"unicast\", color = \"darkorange\")\n",
    "ax.bar(x_protocols + width, list(broadcast.values()), width = width,label = \"broadcast\", color = \"purple\")\n",
    "\n",
    "plt.xlabel(\"Protocolo\")\n",
    "plt.ylabel(\"Probabilidad del símbolo\")\n",
    "ax.set_title(\"Probabilidad de los símbolos de la fuente\")\n",
    "ax.set_xticklabels(protocols_unique)\n",
    "ax.set_xticks(x_protocols+width)\n",
    "ax.legend()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Información de los símbolos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f888b5dfc70>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0uqSFkhb6iS0zs/IppQmlPzABGALsDPSWdELxchExIyJqIqKmqqqq7ZWamdkHlNKE8gVgRUTURcS7wCzggPKUZWZmzSklwJ8H9pfUS9m3iB4CLCtPWWZm1pxS2sAfBm4BFgNP5NuaUaa6zMysGSX1RhgR5wHnlakWMzNrBT+JaWaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZomq6OwCrOuq3nhjZ5fQZdV2dgG2VfAVuJlZohzgZmaJcoCbmSXKAW5mligHuJlZokoKcEn9JN0iabmkZZI+U67CzMxsy0q9jfA/gbsiYqKkbYBeZajJzMxaoM0BLqkv8FngywAR8Q7wTnnKMjOz5pTShDIEqAN+LelRSb+S1Lt4IUmnS1ooaWFdXV0JuzMzs0KlBHgFMAr4RUSMBN4EphQvFBEzIqImImqqqqpK2J2ZmRUqJcBXAisj4uF8/BayQDczsw7Q5gCPiNXAC5L2yCcdAiwtS1VmZtasUu9C+QZwQ34HynPAyaWXZGZmLVFSgEfEY0BNeUoxM7PW8JOYZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZokrtjbDjTO3b2RV0XVPXd3YFZtYGvgI3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0tUyQEuqYekRyXNLUdBZmbWMuW4Av8WsKwM2zEzs1YoKcAlDQLGAb8qTzlmZtZSpV6BXwJ8H3ivqQUknS5poaSFdXV1Je7OzMzqtTnAJY0HXomIRVtaLiJmRERNRNRUVVW1dXdmZlaklCvwMcBRkmqB3wCfl3R9WaoyM7NmtTnAI+LfI2JQRFQDk4B7I+KEslVmZmZb5PvAzcwSVVGOjUTEPGBeObZlZmYt4ytwM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS1RZvhPTzNJXPeX2zi6hy6qdNq5dtusrcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRLU5wCUNlvQnSUslPSXpW+UszMzMtqyU7mQ3AWdHxGJJfYBFkv4YEUvLVJuZmW1Bm6/AI+KliFicD28AlgEDy1WYmZltWVnawCVVAyOBhxuZd7qkhZIW1tXVlWN3ZmZGGQJc0nbArcBZEfF68fyImBERNRFRU1VVVeruzMwsV1KAS+pJFt43RMSs8pRkZmYtUcpdKAKuBJZFxEXlK8nMzFqilCvwMcCJwOclPZa/jixTXWZm1ow230YYEQ8AKmMtZmbWCn4S08wsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEVXR2AS1VvfHGzi6hy6rt7ALMrE18BW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJKinAJR0u6WlJz0iaUq6izMyseW0OcEk9gMuBI4DhwHGShperMDMz27JSrsBHA89ExHMR8Q7wG2BCecoyM7PmlPIo/UDghYLxlcCnixeSdDpwej76hqSnS9hnSnYA1nR2ES2hn3Z2BVuFZM4X+JzlkjlnZThfuzY2sd37QomIGcCM9t7P1kbSwoio6ew6rGV8vtLjc1ZaE8qLwOCC8UH5NDMz6wClBPgjwO6ShkjaBpgEzClPWWZm1pw2N6FExCZJXwd+D/QAroqIp8pWWfq6XbNR4ny+0tPtz5kiorNrMDOzNvCTmGZmiXKAm5klygHeQpJ6Sbpd0nJJT0maVjBvW0m/zbsUeFhSddG6u0h6Q9J3C6Z9O9/Ok5JmSqrswMPpFtpyziRVS/qbpMfy1383st05kp7swEPp8iTdJenx/Dz9d/6kd+H8syWFpB3y8bGS1hecp3Pz6YMl/UnS0nxb3yrYxlRJLxasc2THHmX5OcBbZ3pEDANGAmMkHZFPPxVYFxGfBC4Gim/bvwi4s35E0kDgm0BNRIwg+xB4UnsX30215Zw9GxH75q+vFm5M0jHAGx1ReDfzLxHx98AIoAr45/oZkgYDhwHPF61zf8F5+lE+bRNwdkQMB/YHzizq4uPignXuaLej6SAO8BaKiLci4k/58DvAYrJ73yHrQuCafPgW4BBJApB0NLACKL5DpwL4qKQKoBewql0PoBtq6zlriqTtgO8AP26firuviHg9H6wAtgEK7664GPh+0bSmtvNSRCzOhzcAy8ieGu+SHOBtIKkf8A/APfmkhm4FImITsB4YkP/ATwbOL1w/Il4EppNdUbwErI+IP3RI8d1US89ZPm+IpEclzZd0UMFmLgB+DrzVIUV3M5J+D7wCbCD7pYqkCcCLEfF4I6t8Jm92uVPSXo1sr5rsL6+HCyZ/XdISSVdJ6l/2g+hgDvBWyq+YZwKXRsRzzSw+lexPtg/8yZ3/x5kADAF2BnpLOqEdyjVafc5eAnaJiJFkV9s3SvqYpH2B3SLitvattvuKiC8COwHbAp+X1Av4AXBuI4svBnbNm10uA2YXzswvnm4Fziq4uv8FsBuwL9l5/nn5j6JjtXtfKF3QDOAvEXFJwbT6bgVW5mHRF1hL1rnXREk/A/oB70naCLwMrIiIOgBJs4ADgOs76iC6mRafs8gejHgbICIWSXoWGAp8CqiRVEv2c/NxSfMiYmyHHUU3EBEbJf2O7AJnNdlFzuN569YgYLGk0RGxumCdOyRdIWmHiFgjqSdZeN8QEbMKlnu5fljS/wBzO+ao2o+vwFtB0o/JftDPKpo1BzgpH54I3BuZgyKiOiKqgUuAn0TEf5E1neyf3yUh4BCytjors9aeM0lV9XdASPo7YHfguYj4RUTsnJ/LA4H/c3iXh6TtJO2UD1cA44DlEfFERHy84GdoJTAqIlZL+kTB50yjybJsbT7tSmBZRFxUtJ+dCkb/EUj+TiJfgbeQpEHAD4HlZFcBAP8VEb8i+w9znaRngFdp5o6SiHhY0i1kfwZuAh7FjwWXXRvP2WeBH0l6F3gP+GpEvNrhxXcvvYE5krYlC+I/AR+6fbPIROAMSZuAvwGT8l/ABwInAk9Ieixf9gf5HSc/y5vCAqgFvlLuA+lofpTezCxRbkIxM0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9y6LUk/aIdtTlVBr5Nm7ckBbl1acbekRcoe4GYdyQFuycr77l4u6QZJyyTdkj/dWivpp5IWA/8s6ThJTyjre/2n+brTyHqDfEzSDfm07+TLPCnprIL9/GveAdLjkq4r2Pe9+fR7JO3SCW+BdXN+EtNStwdwakT8r6SrgK/l09dGxChJOwN/BvYD1gF/kHR0REyR9PWI2BdA0n7AyWT91wh4WNJ84B3gHOCAvJ+N7fPtXwZcExHXSDoFuBQ4uiMO2Kyer8AtdS9ExP/mw9eT9VMC8Nv8308B8yKiLu829gayx+WLHQjcFhFv5r1HzgIOAj4P3BwRawAKHqv/DHBjPnxdwX7NOowD3FJX3BdE/fibHV2IWUdzgFvqdpH0mXz4S8ADRfMXAAdL2iH/QPM4YH4+792861GA+4Gj8zb03mS91d0P3EvWjj4AoKAJ5UHe7wDr+HxZsw7lALfUPU32vYfLgP5knfY3iIiXgClkPdw9DiyKiN/ls2cASyTdkH8N19Vkgf8w8KuIeDQingIuBOZLepzs+00BvgGcLGkJWe93DV+ea9ZR3BuhJSv/yqy5+RdDm3U7vgI3M0uUr8DNzBLlK3Azs0Q5wM3MEuUANzNLlAPczCxRDnAzs0T9P+Ed6/zEZbG6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unicastRes, broadcastRes, sumRes, info_symbol = utils.informationForSymbol()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(unicastRes.keys(), list(unicastRes.values()), label = \"unicast\")\n",
    "ax.bar(broadcastRes.keys(), list(broadcastRes.values()), bottom=list(unicastRes.values()), label = \"broadcast\")\n",
    "plt.xlabel(\"protocol\")\n",
    "ax.set_title(\"Informacion de cada símbolo\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_52293/4055560564.py:28: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(protocols_unique)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unicast_info = {}\n",
    "broadcast_info = {}\n",
    "\n",
    "for p in protocols_unique:\n",
    "    \n",
    "    if((p, \"'UNICAST'\") in list(info_symbol.keys())):\n",
    "        unicast_info[p] =info_symbol[(p, \"'UNICAST'\")] \n",
    "    else:\n",
    "        unicast_info[p] = 0\n",
    "    if((p, \"'BROADCAST'\") in list(info_symbol.keys())):\n",
    "        broadcast_info[p] =info_symbol[(p, \"'BROADCAST'\")] \n",
    "    else:\n",
    "        \n",
    "        broadcast_info[p] = 0\n",
    "\n",
    "x_protocols = np.arange(len(protocols_unique))\n",
    "width = 0.27\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "\n",
    "ax.set_title(\"Información de cada símbolo\")\n",
    "ax.bar(x_protocols, unicast_info.values(), width = width,  color = \"forestgreen\")\n",
    "ax.bar(x_protocols + width, broadcast_info.values(), width = width,color = \"tomato\")\n",
    "plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right')\n",
    "ax.legend((\"unicast\", \"broadcast\"))\n",
    "ax.set_ylabel(\"Información del símbolo\")\n",
    "ax.set_xticklabels(protocols_unique)\n",
    "ax.set_xticks(x_protocols+width)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}