graficoMotor.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "source": [
    "---\n",
    "title: Tabela Decisao\n",
    "show-code: false\n",
    "params:\n",
    "    sexo:\n",
    "        label: Sexo\n",
    "        input: select\n",
    "        value: 'M'\n",
    "        choices: ['M','F']\n",
    "        multi: False\n",
    "    faixaDeCep:\n",
    "        label: CEP\n",
    "        input: select\n",
    "        value: '0101'\n",
    "        choices: ['0101','0202']\n",
    "        multi: False\n",
    "    dominioTeste:\n",
    "        label: Dominio Teste\n",
    "        input: select\n",
    "        value: 'S'\n",
    "        choices: ['S','N']\n",
    "        multi: False\n",
    "---"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "outputs": [],
   "source": [
    "sexo = \"F\"\n",
    "faixaDeCep = \"0101\"\n",
    "dominioTeste = \"S\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Grafico Comum"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "url = 'http://dev.abaccusapi.com.br:8080/app/motor/contexto/1513'\n",
    "# url = 'http://localhost:8080/app/motor/contexto/1513'\n",
    "headers = {'Accept': 'application/json', 'Content-type': 'application/json'}\n",
    "\n",
    "r = requests.post(url, json={'sexo': sexo, 'faixaDeCep': faixaDeCep, 'dominioTeste': dominioTeste}, headers=headers)\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "faixaDeCep: cep\n",
      "V1: 1.0\n",
      "V2: 2.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "cal = []\n",
    "\n",
    "for i in r.json()['objeto']['tabelas']:\n",
    "    if i['codigoRetorno'] == 5192:\n",
    "        for c in i['resultados']:\n",
    "            cal.append(c['saida'])\n",
    "\n",
    "print(\"faixaDeCep: \" + cal[0])\n",
    "print(\"V1: \" + cal[1])\n",
    "print(\"V2: \" + cal[2])\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "url = 'http://dev.abaccusapi.com.br:8080/asset/regra/condicao/5192'\n",
    "\n",
    "headers = {'Accept': 'application/json', 'Content-type': 'application/json'}\n",
    "\n",
    "r = requests.get(url, headers=headers)\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   CEP V1  V2\n",
      "0  101  1   5\n",
      "1  101  2   6\n",
      "2  202  1   7\n",
      "3  202  2   8\n",
      "4  101  1   9\n",
      "5  101  2  10\n",
      "6  202  1  11\n",
      "7  202  2  12\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from decimal import Decimal\n",
    "\n",
    "cal = []\n",
    "count = 0\n",
    "exp = []\n",
    "cep = ''\n",
    "\n",
    "for i in r.json():\n",
    "    if i['condicional'] == 'ATRIBUIR':\n",
    "        for f in i['expressoes']:\n",
    "            if f['operador'] == 'atribuir':\n",
    "\n",
    "                exp.append(Decimal(f['constante']))\n",
    "                count = count + 1\n",
    "\n",
    "    if count == 3:\n",
    "        cal.append(exp)\n",
    "        count = 0\n",
    "        cep = ''\n",
    "        exp = []\n",
    "\n",
    "cal = pd.DataFrame.from_records(cal,columns=['CEP', 'V1', 'V2'])\n",
    "\n",
    "print(cal)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x1000 with 1 Axes>",
      "image/png": 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jjjgiDzzwQJJk5cqV+dSnPpVRo0ald+/e2XfffXPeeeelqampzfOfeuqpnHTSSdl1113Tr1+/fPazn82rr77auv2WW27Jcccdl7333jt1dXUZM2ZM5mxujwcAgNdYvHjr2xct6pg53oouEUqvvPJKJkyYkEqlkmuuuWarj502bVqamppab0uXLu2gKTvWd7/73VxwwQW55JJL8vDDD2f06NEZO3ZsnnvuuSxbtizLli3LFVdckcceeywzZszI7Nmzc/bZZ7c+v6WlJSeddFJefvnl3H///bn++uszY8aMXHzxxa2Puffee3PcccflRz/6UR566KEce+yxGT9+fB555JFq/MoAAHQRw4Ztffvw4R0zx1tRU6lUKtUeIklqampy66235uSTT26zvimSnnjiidx9993Zc8892/W6zc3Nqa+vT1NT0+uORL300ktZsmRJhgwZkl69er2l+Rcu3FjOw4d3zGHEI444Iu9973tz1VVXJUk2bNiQwYMH51Of+lQuvPDC1z3+pptuykc/+tGsXbs23bt3z5133pkPfehDWbZsWfr3758kufbaa/O5z30uf/7zn9OjR4/N/tyDDjoop512Wpugeq3t+TcFAKDrGjdu42eSWlr+Z622NmloqN5nlLbWBqVOfURpUyQ1NjZm7ty57Y6kjlCNq3m8/PLLeeihh9LQ0NC61q1btzQ0NGT+/Pmbfc6mnaF7940fS5s/f34OPvjg1khKkrFjx6a5uTm//e1vN/saGzZsyOrVq9O3b9/t+NsAALAzmjVrYxS9VkPDxvWuoKoXc1izZk0WveYExSVLlmTBggXp27dvBg4cmL/927/Nww8/nDvuuCMtLS1Zvnx5kqRv375bPOLR0apxNY/nn38+LS0tbSInSfr3758//OEPm338F7/4xXziE59oXVu+fPlmn79p2+ZcccUVWbNmTSZMmPBWfwUAAHZye+yx8f1wY+PGzyR11JlX20tVQ+nBBx/Mscce23r/ggsuSJJMmTIll156aX7wgx8kSQ455JA2z/vJT36SD3zgAx015hZ1hat5NDc356STTsqBBx6YSy+99E2/zsyZM3PZZZfl9ttvT79+/bbfgAAA7NRGjKj+e+I3o6qh9IEPfCBb+4hUJ/n41BZty9U8dsROsddee6W2tjbPPvtsm/Vnn302AwYMaL2/evXqjBs3Ln369Mmtt96aXXbZpXXbgAEDWq+S99rnb9r2Wt/5znfysY99LDfddFOb0/0AAGBn1ak/o9TZVetqHj169Mihhx6aefPmta5t2LAh8+bNy5gxY5JsPJJ0/PHHp0ePHvnBD37wugsrjBkzJo8++miee+651rW77rordXV1OfDAA1vXZs2albPOOiuzZs3KSSedtGN+IQAA6GS61BfOdjYjRyZjx275ah478hDjBRdckClTpuSwww7L4Ycfnn/913/N2rVrc9ZZZ7VG0rp163LDDTekubm59Yt3995779TW1ub444/PgQcemL/7u7/LV7/61Sxfvjz/9E//lHPOOSc9e/ZMsvF0uylTpuTf/u3fcsQRR7R+dql3796pr6/fcb8cAABUmSNKb1G1ruZx2mmn5YorrsjFF1+cQw45JAsWLMjs2bPTv3//PPzww/nlL3+ZRx99NMOHD8/AgQNbb5u+V6q2tjZ33HFHamtrM2bMmHz0ox/NGWeckS984QutP+Mb3/hGXn311ZxzzjltXuPTn/70jv3lAACgyjrN9yjtKB31PUpd9Woe25vvUQIAoLNqz/coOfVuO+mqV/MAAABez6l3AAAABaEEAABQEEoAAAAFoZTO/8W2XYm/JQAAO4O3dSjtsssuSZJ169ZVeZKdx6a/5aa/LQAAdEVv66ve1dbWZvfdd89zzz2XJNl1111TU1NT5am6pkqlknXr1uW5557L7rvvntra2mqPBAAAb9rbOpSSZMCAAUnSGku8Nbvvvnvr3xQAALqqt30o1dTUZODAgenXr19eeeWVao/Tpe2yyy6OJAEAsFN424fSJrW1td7kAwAASd7mF3MAAADYHKEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAACFqobSvffem/Hjx2fQoEGpqanJbbfd1mb7LbfckuOPPz577rlnampqsmDBgqrMub0sXJjceWfS2FjtSQAAgK2paiitXbs2o0ePztVXX73F7UcffXS+8pWvdPBk29fKlcm4ccmoUcmJJyYjR268/8IL1Z4MAADYnO7V/OEnnHBCTjjhhC1u/7u/+7skyZNPPtlBE+0YkyYlc+e2XZs7N5k4MZk9uzozAQAAW1bVUNoR1q9fn/Xr17feb25uruI0G0+3mzPn9estLRvXGxuTESM6fi4AAGDLdrqLOUyfPj319fWtt8GDB1d1nsWLt7590aKOmQMAANh2O10oTZs2LU1NTa23pUuXVnWeYcO2vn348I6ZAwAA2HY73al3PXv2TM+ePas9RquRI5OxYzd+Jqml5X/Wa2uThgan3QEAQGe00x1R6oxmzdoYRa/V0LBxHQAA6HyqekRpzZo1WfSaD+ksWbIkCxYsSN++fbPvvvtm5cqVeeqpp7Js2bIkyeOPP54kGTBgQAYMGFCVmd+MPfbYeHW7xsaNn0kaPtyRJAAA6MxqKpVKpVo//Kc//WmOPfbY161PmTIlM2bMyIwZM3LWWWe9bvsll1ySSy+9dJt+RnNzc+rr69PU1JS6urq3OjIAANBFtacNqhpKHUEoAQAASfvawGeUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApVDaV7770348ePz6BBg1JTU5PbbrutzfZKpZKLL744AwcOTO/evdPQ0JDGxsbqDAsAALTbwhULc2fjnWlc0bXex1c1lNauXZvRo0fn6quv3uz2r371q7nyyitz7bXX5pe//GV22223jB07Ni+99FIHTwoAALTHyhdXZtwN4zLqqlE5ceaJGXnVyIy7YVxeePGFao+2TWoqlUql2kMkSU1NTW699dacfPLJSTYeTRo0aFD+4R/+IZ/5zGeSJE1NTenfv39mzJiR008/fZtet7m5OfX19WlqakpdXd2OGh8AAHiNcTeMy9wn5qal0tK6VltTm4ahDZn90dlVmak9bdBpP6O0ZMmSLF++PA0NDa1r9fX1OeKIIzJ//vwtPm/9+vVpbm5ucwMAADrOwhULM2fxnDaRlCQtlZbMWTynS5yG12lDafny5UmS/v37t1nv379/67bNmT59eurr61tvgwcP3qFzAgAAbS1euXir2xetXNRBk7x5nTaU3qxp06alqamp9bZ06dJqjwQAAG8rw/oO2+r24X2Hd9Akb16nDaUBAwYkSZ599tk2688++2zrts3p2bNn6urq2twAAICOM3LPkRk7bGxqa2rbrNfW1GbssLEZseeIKk227TptKA0ZMiQDBgzIvHnzWteam5vzy1/+MmPGjKniZAAAwBuZdcqsNAxtaLPWMLQhs06ZVaWJ2qd7NX/4mjVrsmjR/5yfuGTJkixYsCB9+/bNvvvum/PPPz+XX355RowYkSFDhuSiiy7KoEGDWq+MBwAAdE579N4jsz86O40rGrNo5aIM7zu8SxxJ2qSqofTggw/m2GOPbb1/wQUXJEmmTJmSGTNm5B//8R+zdu3afOITn8iqVaty9NFHZ/bs2enVq1e1RgYAANphxJ4julQgbdJpvkdpR/E9SgAAQLKTfI8SAABAtQglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACh0+lBavXp1zj///Oy3337p3bt3jjzyyPzqV7+q9lhvysIVC3Nn451pXNFY7VEAAICt6F7tAd7Ixz72sTz22GP59re/nUGDBuWGG25IQ0NDfve73+Wd73xntcfbJitfXJlJN0/KnMVzWtfGDhubWafMyh6996jiZAAAwObUVCqVSrWH2JIXX3wxffr0ye23356TTjqpdf3QQw/NCSeckMsvv/wNX6O5uTn19fVpampKXV3djhx3i8bdMC5zn5iblkpL61ptTW0ahjZk9kdnV2UmAAB4u2lPG3TqI0qvvvpqWlpa0qtXrzbrvXv3zs9+9rPNPmf9+vVZv3596/3m5uYdOuMbWbhiYZsjSZu0VFoyZ/GcNK5ozIg9R1RhMgAAYEs69WeU+vTpkzFjxuSLX/xili1blpaWltxwww2ZP39+nnnmmc0+Z/r06amvr2+9DR48uIOnbmvxysVb3b5o5aIOmgQAANhWnTqUkuTb3/52KpVK3vnOd6Znz5658sorM3HixHTrtvnRp02blqamptbb0qVLO3jitob1HbbV7cP7Du+gSQAAgG3V6UNp2LBhueeee7JmzZosXbo0DzzwQF555ZUMHTp0s4/v2bNn6urq2tyqaeSeIzN22NjU1tS2Wa+tqc3YYWOddgcAAJ1Qpw+lTXbbbbcMHDgwL7zwQubMmZOPfOQj1R5pm806ZVYahja0WWsY2pBZp8yq0kQAAMDWdOqr3iXJnDlzUqlUMmrUqCxatCif/exn06tXr9x3333ZZZdd3vD5neGqd5s0rmjMopWLMrzvcEeSAACgg+00V71LkqampkybNi1PP/10+vbtm1NOOSVf+tKXtimSOpsRe44QSAAA0AV0+iNKb1VnOqIEAABUT3vaoMt8RgkAAKCjCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAotCuU/t//+39paGjIhAkTMm/evDbbnn/++QwdOnS7DgcAAFAN2xxKV155ZT772c/mgAMOSM+ePXPiiSdm+vTprdtbWlryxz/+cYcMCQAA0JG6b+sD//3f/z3/8R//kUmTJiVJ/v7v/z4nn3xyXnzxxXzhC1/YYQMCAAB0tG0OpSVLluTII49svX/kkUfm7rvvTkNDQ1555ZWcf/75O2I+AACADrfNobTXXntl6dKl2X///VvX3v3ud+fuu+/OX//1X2fZsmU7Yj4AAIAOt82fUTr66KNzyy23vG79wAMPzLx583LnnXdu18EAAACqZZuPKE2bNi0PPfTQZrcddNBBufvuu3PzzTdvt8EAAACqZZtDafTo0Xnve9+bV155Jaeffnr69OnTZvu73/3uvPvd797uAwIAAHS0bT717p577sm73/3u/MM//EMGDhyYKVOm5L777tuRswEAAFTFNofS+973vnzzm9/MM888k69//et58sknc8wxx2TkyJH5yle+kuXLl+/IOQEAADrMNofSJrvttlvOOuus3HPPPVm4cGFOPfXUXH311dl3333z4Q9/eEfMCAAA0KFqKpVK5a28wNq1a3PjjTdm2rRpWbVqVVpaWrbXbNtFc3Nz6uvr09TUlLq6umqPAwAAVEl72mCbL+ZQuvfee/Otb30rN998c7p165YJEybk7LPPfrMvBwAA0Gm0K5SWLVuWGTNmZMaMGVm0aFGOPPLIXHnllZkwYUJ22223HTUjAABAh9rmUDrhhBMyd+7c7LXXXjnjjDMyderUjBo1akfOBgAAUBXbHEq77LJLvv/97+dDH/pQamtrd+RMAAAAVbXNofSDH/xgR84BAADQabT78uAAAAA7O6EEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAAWhBAAAUBBKAAAABaEEAABQEEoAAAAFoQQAAFAQSgAAAIVOHUotLS256KKLMmTIkPTu3TvDhg3LF7/4xVQqlWqPBgAAbIOFC5M770waG6s9Sft0r/YAW/OVr3wl11xzTa6//vocdNBBefDBB3PWWWelvr4+5513XrXHAwAAtmDlymTSpGTOnP9ZGzs2mTUr2WOP6s21rTp1KN1///35yEc+kpNOOilJsv/++2fWrFl54IEHqjwZAACwNZMmJXPntl2bOzeZODGZPbs6M7VHpz717sgjj8y8efOycOHCJMmvf/3r/OxnP8sJJ5ywxeesX78+zc3NbW4AAEDHWbhw45Gklpa26y0tG9e7wml4nfqI0oUXXpjm5uYccMABqa2tTUtLS770pS9l8uTJW3zO9OnTc9lll3XglAAAwGstXrz17YsWJSNGdMwsb1anPqL0ve99LzfeeGNmzpyZhx9+ONdff32uuOKKXH/99Vt8zrRp09LU1NR6W7p0aQdODAAADBu29e3Dh3fMHG9FTaUTX0Ju8ODBufDCC3POOee0rl1++eW54YYb8oc//GGbXqO5uTn19fVpampKXV3djhoVAAB4jXHjNn4m6bWn39XWJg0N1fuMUnvaoFMfUVq3bl26dWs7Ym1tbTZs2FCliQAAgG0xa9bGKHqthoaN611Bp/6M0vjx4/OlL30p++67bw466KA88sgj+drXvpapU6dWezQAAGAr9thj45GjxsaNn0kaPrzzfy7ptTr1qXerV6/ORRddlFtvvTXPPfdcBg0alIkTJ+biiy9Ojx49tuk1nHoHAAAk7WuDTh1K24NQAgAAkp3oM0oAAADVIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoNDpQ2n//fdPTU3N627nnHNOtUdrt4ULkzvvTBobqz0JAACwNd2rPcAb+dWvfpWWlpbW+4899liOO+64nHrqqVWcqn1WrkwmTUrmzPmftbFjk1mzkj32qN5cAADA5nX6I0p77713BgwY0Hq74447MmzYsBxzzDHVHm2bTZqUzJ3bdm3u3GTixOrMAwAAbF2nD6XXevnll3PDDTdk6tSpqamp2exj1q9fn+bm5ja3alq4cOORpNccFEuy8f6cOU7DAwCAzqhLhdJtt92WVatW5cwzz9ziY6ZPn576+vrW2+DBgztuwM1YvHjr2xct6pg5AACAbVdTqVQq1R5iW40dOzY9evTIf//3f2/xMevXr8/69etb7zc3N2fw4MFpampKXV1dR4zZxsKFyahRW98+YkTHzQMAAG9Xzc3Nqa+v36Y26PQXc9jkj3/8Y+bOnZtbbrllq4/r2bNnevbs2UFTvbGRIzdeuGHu3Lan39XWJg0NIgkAADqjLnPq3XXXXZd+/frlpJNOqvYo7TZr1sYoeq2Gho3rAABA59Mljiht2LAh1113XaZMmZLu3bvEyG3ssUcye/bGCzcsWpQMH+5IEgAAdGZdojrmzp2bp556KlOnTq32KG/JiBECCQAAuoIuEUrHH398utA1JwAAgC6uy3xGCQAAoKMIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAoCCUAAICCUAIAACgIJQAAgIJQAgAAKAglAACAglACAAAodPpQ+tOf/pSPfvSj2XPPPdO7d+8cfPDBefDBB6s9FgAAsA0WrliYOxvvTOOKxmqP0i7dqz3A1rzwwgs56qijcuyxx+bOO+/M3nvvncbGxuyxxx7VHg0AANiKlS+uzKSbJ2XO4jmta2OHjc2sU2Zlj96d//18TaVSqVR7iC258MIL8/Of/zz33Xffm36N5ubm1NfXp6mpKXV1ddtxOgAAYEvG3TAuc5+Ym5ZKS+tabU1tGoY2ZPZHZ1dlpva0Qac+9e4HP/hBDjvssJx66qnp169f/uIv/iL/8R//sdXnrF+/Ps3NzW1uAABAx1m4YmHmLJ7TJpKSpKXSkjmL53SJ0/A6dSg98cQTueaaazJixIjMmTMnf//3f5/zzjsv119//RafM3369NTX17feBg8e3IETAwAAi1cu3ur2RSsXddAkb16nPvWuR48eOeyww3L//fe3rp133nn51a9+lfnz52/2OevXr8/69etb7zc3N2fw4MFOvQMAgA6ycMXCjLpq1Ja3n7swI/Yc0YETbbTTnHo3cODAHHjggW3W3vWud+Wpp57a4nN69uyZurq6NjcAAKDjjNxzZMYOG5vamto267U1tRk7bGxVIqm9OnUoHXXUUXn88cfbrC1cuDD77bdflSYCAAC2xaxTZqVhaEObtYahDZl1yqwqTdQ+nfry4P/7f//vHHnkkfnyl7+cCRMm5IEHHsg3vvGNfOMb36j2aAAAwFbs0XuPzP7o7DSuaMyilYsyvO/wLnEkaZNO/RmlJLnjjjsybdq0NDY2ZsiQIbngggvy8Y9/fJuf7/LgAABA0r426PSh9FYJJQAAINmJLuYAAABQDUIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAAqdPpQuvfTS1NTUtLkdcMAB1R7rTVm4YmHubLwzjSsaqz0KAACwFd2rPcC2OOiggzJ37tzW+927d4mxW618cWUm3TwpcxbPaV0bO2xsZp0yK3v03qOKkwEAAJvT6Y8oJRvDaMCAAa23vfbaq9ojtcukmydl7hNz26zNfWJuJt48sUoTAQAAW9MlQqmxsTGDBg3K0KFDM3ny5Dz11FNbfOz69evT3Nzc5lZNC1cszJzFc9JSaWmz3lJpyZzFc5yGBwAAnVCnD6UjjjgiM2bMyOzZs3PNNddkyZIled/73pfVq1dv9vHTp09PfX19623w4MEdPHFbi1cu3ur2RSsXddAkAADAtqqpVCqVag/RHqtWrcp+++2Xr33tazn77LNft339+vVZv3596/3m5uYMHjw4TU1Nqaur68hRk2w8ojTqqlFb3n7uwozYc0QHTgQAAG9Pzc3Nqa+v36Y26PRHlEq77757Ro4cmUWLNn8kpmfPnqmrq2tzq6aRe47M2GFjU1tT22a9tqY2Y4eNFUkAANAJdblQWrNmTRYvXpyBAwdWe5RtNuuUWWkY2tBmrWFoQ2adMqtKEwEAAFvT6a+z/ZnPfCbjx4/Pfvvtl2XLluWSSy5JbW1tJk7sOleM26P3Hpn90dlpXNGYRSsXZXjf4Y4kAQBAJ9bpQ+npp5/OxIkTs2LFiuy99945+uij84tf/CJ77713tUdrtxF7jhBIAADQBXT6UPrOd75T7REAAIC3mS73GSUAAIAdTSgBAAAUhBIAAEBBKAEAABSEEgAAQEEoAQAAFIQSAABAQSgBAAAUhBIAAEBBKAEAABSEEgAAQEEoAQAAFIQSAABAQSgBAAAUhBIAAEBBKAEAABSEEgAAQEEoAQAAFIQSAABAQSgBAAAUhBIAAEBBKAEAABSEEgAAQEEoAQAAFIQSAABAQSgBAAAUhBIAAEChe7UH2NEqlUqSpLm5ucqTAAAA1bSpCTY1wtbs9KG0evXqJMngwYOrPAkAANAZrF69OvX19Vt9TE1lW3KqC9uwYUOWLVuWPn36pKampqqzNDc3Z/DgwVm6dGnq6uqqOgtdg32G9rLP0F72GdrLPkN7daZ9plKpZPXq1Rk0aFC6ddv6p5B2+iNK3bp1yz777FPtMdqoq6ur+k5C12Kfob3sM7SXfYb2ss/QXp1ln3mjI0mbuJgDAABAQSgBAAAUhFIH6tmzZy655JL07Nmz2qPQRdhnaC/7DO1ln6G97DO0V1fdZ3b6izkAAAC0lyNKAAAABaEEAABQEEoAAAAFoQQAAFAQStvRvffem/Hjx2fQoEGpqanJbbfd9obP+elPf5q//Mu/TM+ePTN8+PDMmDFjh89J59HefeaWW27Jcccdl7333jt1dXUZM2ZM5syZ0zHD0im8mX9nNvn5z3+e7t2755BDDtlh89G5vJn9Zf369fk//+f/ZL/99kvPnj2z//7751vf+taOH5ZO4c3sMzfeeGNGjx6dXXfdNQMHDszUqVOzYsWKHT8sncL06dPz3ve+N3369Em/fv1y8skn5/HHH3/D591000054IAD0qtXrxx88MH50Y9+1AHTto9Q2o7Wrl2b0aNH5+qrr96mxy9ZsiQnnXRSjj322CxYsCDnn39+Pvaxj3nj+zbS3n3m3nvvzXHHHZcf/ehHeeihh3Lsscdm/PjxeeSRR3bwpHQW7d1nNlm1alXOOOOMfPCDH9xBk9EZvZn9ZcKECZk3b16++c1v5vHHH8+sWbMyatSoHTglnUl795mf//znOeOMM3L22Wfnt7/9bW666aY88MAD+fjHP76DJ6WzuOeee3LOOefkF7/4Re6666688sorOf7447N27dotPuf+++/PxIkTc/bZZ+eRRx7JySefnJNPPjmPPfZYB07+xlwefAepqanJrbfempNPPnmLj/nc5z6XH/7wh212itNPPz2rVq3K7NmzO2BKOpNt2Wc256CDDsppp52Wiy++eMcMRqfVnn3m9NNPz4gRI1JbW5vbbrstCxYs2OHz0blsy/4ye/bsnH766XniiSfSt2/fjhuOTmlb9pkrrrgi11xzTRYvXty69vWvfz1f+cpX8vTTT3fAlHQ2f/7zn9OvX7/cc889ef/737/Zx5x22mlZu3Zt7rjjjta1v/qrv8ohhxySa6+9tqNGfUOOKFXR/Pnz09DQ0GZt7NixmT9/fpUmoqvZsGFDVq9e7Q0NW3XdddfliSeeyCWXXFLtUejkfvCDH+Swww7LV7/61bzzne/MyJEj85nPfCYvvvhitUejkxozZkyWLl2aH/3oR6lUKnn22Wfz/e9/PyeeeGK1R6NKmpqakmSr7026ynvg7tUe4O1s+fLl6d+/f5u1/v37p7m5OS+++GJ69+5dpcnoKq644oqsWbMmEyZMqPYodFKNjY258MILc99996V7d//ks3VPPPFEfvazn6VXr1659dZb8/zzz+d//a//lRUrVuS6666r9nh0QkcddVRuvPHGnHbaaXnppZfy6quvZvz48e0+PZidw4YNG3L++efnqKOOyrvf/e4tPm5L74GXL1++o0dsF0eUoIuaOXNmLrvssnzve99Lv379qj0OnVBLS0smTZqUyy67LCNHjqz2OHQBGzZsSE1NTW688cYcfvjhOfHEE/O1r30t119/vaNKbNbvfve7fPrTn87FF1+chx56KLNnz86TTz6ZT37yk9UejSo455xz8thjj+U73/lOtUfZLvznxSoaMGBAnn322TZrzz77bOrq6hxNYqu+853v5GMf+1huuumm1x26hk1Wr16dBx98MI888kjOPffcJBvfCFcqlXTv3j0//vGP89d//ddVnpLOZODAgXnnO9+Z+vr61rV3vetdqVQqefrppzNixIgqTkdnNH369Bx11FH57Gc/myR5z3vek9122y3ve9/7cvnll2fgwIFVnpCOcu655+aOO+7Ivffem3322Werj93Se+ABAwbsyBHbzRGlKhozZkzmzZvXZu2uu+7KmDFjqjQRXcGsWbNy1llnZdasWTnppJOqPQ6dWF1dXR599NEsWLCg9fbJT34yo0aNyoIFC3LEEUdUe0Q6maOOOirLli3LmjVrWtcWLlyYbt26veEbH96e1q1bl27d2r6drK2tTZK4XtjbQ6VSybnnnptbb701d999d4YMGfKGz+kq74EdUdqO1qxZk0WLFrXeX7JkSRYsWJC+fftm3333zbRp0/KnP/0p//Vf/5Uk+eQnP5mrrroq//iP/5ipU6fm7rvvzve+97388Ic/rNavQAdr7z4zc+bMTJkyJf/2b/+WI444ovVc3t69e7f5L8DsvNqzz3Tr1u1154j369cvvXr12uq54+w82vtvzKRJk/LFL34xZ511Vi677LI8//zz+exnP5upU6c60+Ftor37zPjx4/Pxj38811xzTcaOHZtnnnkm559/fg4//PAMGjSoWr8GHeicc87JzJkzc/vtt6dPnz6t703q6+tb/90444wz8s53vjPTp09Pknz605/OMccck3/+53/OSSedlO985zt58MEH841vfKNqv8dmVdhufvKTn1SSvO42ZcqUSqVSqUyZMqVyzDHHvO45hxxySKVHjx6VoUOHVq677roOn5vqae8+c8wxx2z18ez83sy/M691ySWXVEaPHt0hs1J9b2Z/+f3vf19paGio9O7du7LPPvtULrjggsq6des6fniq4s3sM1deeWXlwAMPrPTu3bsycODAyuTJkytPP/10xw9PVWxuf0nS5j3tMccc87r3Kt/73vcqI0eOrPTo0aNy0EEHVX74wx927ODbwPcoAQAAFHxGCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQA6PLGjx+fcePGbXbbfffdl5qamvzmN7/Jeeedl0MPPTQ9e/bMIYcc0rFDAtClCCUAuryzzz47d911V55++unXbbvuuuty2GGH5T3veU+SZOrUqTnttNM6ekQAuhihBECX96EPfSh77713ZsyY0WZ9zZo1uemmm3L22WcnSa688sqcc845GTp0aBWmBKArEUoAdHndu3fPGWeckRkzZqRSqbSu33TTTWlpacnEiROrOB0AXZFQAmCnMHXq1CxevDj33HNP69p1112XU045JfX19VWcDICuSCgBsFM44IADcuSRR+Zb3/pWkmTRokW57777Wk+7A4D2EEoA7DTOPvvs3HzzzVm9enWuu+66DBs2LMccc0y1xwKgCxJKAOw0JkyYkG7dumXmzJn5r//6r0ydOjU1NTXVHguALqh7tQcAgO3lHe94R0477bRMmzYtzc3NOfPMM9tsX7RoUdasWZPly5fnxRdfzIIFC5IkBx54YHr06NHxAwPQadVUXnt5IADo4ubPn58jjzwyJ554Yn74wx+22faBD3ygzcUeNlmyZEn233//DpoQgK5AKAEAABR8RgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACgIJQAAAAKQgkAAKAglAAAAApCCQAAoCCUAAAACkIJAACg8P8BTFzEF+7LE64AAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = cal[cal.CEP==101].plot(kind='scatter',x='V1',y='V2',color='green', label='0101')\n",
    "cal[cal.CEP==202].plot(kind='scatter',x='V1',y='V2',color='blue', label='0202',ax=fig)\n",
    "\n",
    "fig.set_xlabel(\"V1\")\n",
    "fig.set_ylabel(\"V2\")\n",
    "fig.set_title(\"Valores\")\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(10,10)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x1000 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cal.CEP.hist(edgecolor='black', linewidth=1.2)\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(10,10)\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}