{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducción a Python para ciencias e ingenierías (notebook 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ing. Martín Gaitán\n",
    "\n",
    "Twitter: `@tin_nqn_`\n",
    "\n",
    "** Registrá tu asistencia a esta clase **\n",
    "### http://goo.gl/forms/olbkNwX700\n",
    "\n",
    "**Links útiles**\n",
    "\n",
    "Repositorio del curso:\n",
    "\n",
    "### http://bit.ly/cursopy\n",
    "\n",
    "Python \"temporal\" online:\n",
    "\n",
    "### http://try.jupyter.org\n",
    "\n",
    "- Descarga de [Python \"Anaconda\"](http://continuum.io/downloads#py34)\n",
    "- Resumen de [sintaxis markdown](https://github.com/jupyter/strata-sv-2015-tutorial/blob/master/resources/Working%20With%20Markdown%20Cells.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Matplotlib, un gráfico vale más que mil palabras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python es un lenguaje muy completo pero, aunque es muy grande, su librería estándar no es infinita. Por suerte hay [miles y miles de bibliotecas extras](https://pypi.python.org) para complementar casi cualquier aspecto en el que queramos aplicar Python. En algunos ámbitos, con soluciones muy destacadas.\n",
    "\n",
    "Para hacer gráficos la opción canónica es Matplotlib http://matplotlib.org/ . Ya viene instalado con la versión completa de Anaconda.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot   # generalmente importado \"as plt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-5.0,\n",
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       " -1.1,\n",
       " -1.0,\n",
       " -0.9,\n",
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       " -0.5,\n",
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       " 1.7000000000000002,\n",
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       " 2.0,\n",
       " 2.1,\n",
       " 2.2,\n",
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       " 2.5,\n",
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       " 2.7,\n",
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       " 3.0,\n",
       " 3.1,\n",
       " 3.2,\n",
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       " 3.4000000000000004,\n",
       " 3.5,\n",
       " 3.6,\n",
       " 3.7,\n",
       " 3.8000000000000003,\n",
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       " 4.1000000000000005,\n",
       " 4.2,\n",
       " 4.3,\n",
       " 4.4,\n",
       " 4.5,\n",
       " 4.6000000000000005,\n",
       " 4.7,\n",
       " 4.800000000000001,\n",
       " 4.9,\n",
       " 5.0]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [0.1*i for i in range(-50, 51)]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[25.0,\n",
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       " 12.25,\n",
       " 11.560000000000002,\n",
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       " 10.240000000000002,\n",
       " 9.610000000000001,\n",
       " 9.0,\n",
       " 8.410000000000002,\n",
       " 7.840000000000002,\n",
       " 7.290000000000001,\n",
       " 6.760000000000001,\n",
       " 6.25,\n",
       " 5.760000000000002,\n",
       " 5.290000000000001,\n",
       " 4.840000000000001,\n",
       " 4.41,\n",
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       " 3.24,\n",
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       " 2.25,\n",
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       " 0.25,\n",
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       " 0.04000000000000001,\n",
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       " 0.16000000000000003,\n",
       " 0.25,\n",
       " 0.3600000000000001,\n",
       " 0.4900000000000001,\n",
       " 0.6400000000000001,\n",
       " 0.81,\n",
       " 1.0,\n",
       " 1.2100000000000002,\n",
       " 1.4400000000000004,\n",
       " 1.6900000000000002,\n",
       " 1.9600000000000004,\n",
       " 2.25,\n",
       " 2.5600000000000005,\n",
       " 2.8900000000000006,\n",
       " 3.24,\n",
       " 3.6100000000000003,\n",
       " 4.0,\n",
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       " 11.560000000000002,\n",
       " 12.25,\n",
       " 12.96,\n",
       " 13.690000000000001,\n",
       " 14.440000000000001,\n",
       " 15.210000000000003,\n",
       " 16.0,\n",
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       " 17.64,\n",
       " 18.49,\n",
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       " 20.25,\n",
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       " 22.090000000000003,\n",
       " 23.040000000000006,\n",
       " 24.010000000000005,\n",
       " 25.0]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = [x_i**2 for x_i in x]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa2d5b495f8>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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fxGWXwXvv6W+iKHWV5JCmTf2inDFjQicRyQ29e/sFNyraZacWdxaNGQM9e8Lrr2tBjuS3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d5e3de48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los gráficos emergentes son buenos porque tiene la barra de herramientas (interactividad) y podemos guardarlos en excelente calidad a golpe de mouse. Pero en los notebooks podemos poner los gráficos directamente incrustados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TCj4Ery5nZ2dHZPsvv5xK9+6Qnx+gdm3/5afL/l7Oy0tl7lx45pkAgYD78Xhh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d5b8b8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.plot(x,y)\n",
    "pyplot.title('Pará bola!')\n",
    "pyplot.scatter([0, 2], [15, 25])\n",
    "pyplot.annotate(s='un punto', xy=(0, 15), xytext=(0.3, 15.2))\n",
    "pyplot.annotate(s='otro un punto', xy=(2, 25), xytext=(2.3, 25.2))\n",
    "pyplot.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib sabe hacer muchísimos tipos de gráficos!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Component,Mol fraction\r\n",
      "n2,0.46\r\n",
      "co2,3.36\r\n",
      "c1,62.36\r\n",
      "c2,8.9\r\n",
      "c3,5.31\r\n",
      "c4,3.01\r\n",
      "c5,1.5\r\n",
      "c6,1.05\r\n",
      "c7,2.0\r\n",
      "c7+,12.049\r\n"
     ]
    }
   ],
   "source": [
    "!cat data/near_critical_oil.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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P2VXMppWFNbxJSfvf7vP5uPvu/5NHHx2q33zzuTz00K3A6cDoA57YDfxPCP4O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\npNAVEcmRQldEJEf/ArkAFcSlB2OFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d5b2f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "with open('data/near_critical_oil.csv') as csv_file:\n",
    "    reader = csv.reader(csv_file)\n",
    "    critical_oil = [line for line in reader]   #o list(reader)\n",
    "\n",
    "components = [c for (c, f) in critical_oil[1:]]\n",
    "fraction = [float(f) for (c, f) in critical_oil[1:]]\n",
    "# el ; evita el output de la celda\n",
    "pyplot.pie(fraction, labels=components, shadow=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "campana = [random.gauss(0, 0.5) for i in range(1000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2f068e668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.hist(campana, bins=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### La \"papa\" de matplotlib\n",
    "\n",
    "   **Este es el algoritmo más importante para graficar con matplotlib**\n",
    "\n",
    "\n",
    "1. Ir a http://matplotlib.org/gallery\n",
    "2. Elegir el gráfico de ejemplo que más se parezca a lo que queremos lograr\n",
    "3. Copiar el código del ejemplo y adaptarlo a nuestros datos y gustos\n",
    "\n",
    "![](files/img/lo_importante.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ScQKA2qXJDQAmwQ50aDEhhLgbjcZFS5cutcoqUalUOLd9K8Z4Wra8RymF0XT/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\new2hMK/JRFM+IShYw+BYJ3s//BZHdQTXjL6WIYOT8FP74bB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pKCgo8L/YyNjV6dWrF/fd\ndx/rN77HySNpjJ04oNXPFORWsHtrLmpHX1Ys/EWrCXBOpZ3i+bUv44jXseBXd7V4obudLvqPHUbq\nu1u4efwsxo+t8elwuVy8/vHHyKlT0DVQWgV79jBn2DAiI9u/xNm/d3T9cP9iYmJiWlSOdURERHDT\n3FvZ8Mm7LLhvMip140vxqilDcbYQnR0QqGHxquns+vIQX/z7a1T+i4gd2R+Xw0nOF/u5acE41G1c\nTi7JL8dZomb0ytHNnlNdXU1ISPtHoEFBQVwzYQLXTJhQU3/p9GmOHtrDF8lH6R/oZFi0BofTTb+k\n0XgjwXplZSWVlZVqvGx8BR8rEymljIiIOJicnDxz5cqVHpdfUlLC1q1bWb16tcdlQ41j19RJs3j7\nk72MvsbdrNdiVYWJ3d9mUZWnY9519zLqqlEtejg6HA6+2PIl32ccYfwvVxDWK6bVkZufVsPJT7Yx\nb+A4Zky5ENb/5TffcDpQS8K4C09pm8GA4tgx5v/4oXZ+4zpks8qkPYweNYYzGens33KCyYsa38gt\nTXfqEEIwbeF4ksYm1PqkFOEWMLxXBP2HtP3hlLrtHPOmLW02i31eXh4pKSmsWLGizTKbIjAwkLHj\nxjF23DisVivp6emkHd7H15s2ctcjt12W7Ob4/vvv0el0JyoqKrye1cvn3qcVFRXvf/LJJ55bFmlA\ndHS0V1MSQM3qSkzoME4fv3SJ22K2s+OrdDa+nsOwuGX8/Ce/Z8zoMS0qkrKyMv74yovsseXW+440\np0icdjtbXv4vbpeL1I+/Y2JYAgvnXl9//tmzZ9lwPJU+Cxc2klGQksKiMWM79KQFyMotanaa0x6E\nECxdtAxLhpLMtOZz9nz0ypeXrP6YDJb6PtT7pADuY1nMXDC2zX3IP18E5QGMG9v8lKh3796XrUgu\nRqPRMGrUKFbeeR/3/fxpJkyc6FH5daxdu9ZWVVX1XutnXj4+VybApk2bNvk5HF7JldTmeW5HOXPm\nDOOumsLhnWX1F7fD4eLArvN88MppQuV0fv7Qc0yfNhN1GzKO+/v7U2GoIm5sYiPfEYDP//ga7gYj\nApWfHzPuXsbxz3cynEhuWXJhzm80Gnl140bCFy/Gr4HtwlJZieZ0OrOmN79q0SrSMyMTqPm+q2+6\nk6Obzje5LAxww53X1SdYqmPLezsbKZg6n5R7f7H0Ege15nC73Rzdeo4bZi/zae3jyZMn4+/v+Vgf\nt9vNgQMH3FLKzzwuvAl8rkyklAUajSZz1y7vrVx5qUwIUJOMacyYMUTqkjh1LI9Tx/J4/5XjWPOH\n8vDdz3DDgqXtSsEXFBTEI6vvIXPDLszVhsZt/eouFEpl/ZKqlJLzKcfoU6HgjuW31t8QUkreWb8e\nw8gRhPVrHNtSuHMnyyZObNY42hb6947ymDKBmif//ElL2bX+aJMjnoBATb2SrPvuy+6fh1Z3afEu\nTTsC8E4fyiDWfwDDhzW/fLxly5Y2y+tqHDhwAClluZTSa3lfG+JzZQJgNpvf+/TTTz2f27+Wv/3t\nb94SDdQM1+fPWsrOz8rIPBDGHTc9wW0r7iQiomNZvQYmDGT5xDmc+OBbnPYLP4uydk7/2bNrcTqd\nnEs5SnCGgXtXrWk06tmZksI+q4U+F40+jMXFBGVlM3VS0x6nbUY2v5rTUaZNmU6cahBHdzaf08Vu\ns/OPX73jETd2s9FC+vZCblxwc7PTSCml14t1vfLKK16T/d577zltNtuHXmvgIrqEMnE4HBs/+ugj\nu7dKlT755JNekduQLVu28OCdT/LA3T/DEzlapk2eytTYJFI/+f6SG/f6X9zJe4/9BQ7m8tDt9zSK\nIC4vL+fdrVsJnzLlEttMyY4d3DJ9+mUPqbPyij06MgEuuNsfNFKYXdLkOUeT07j5x/M94kaw78sT\nzBgzt8WVJyFEI38fb+CNhYc6PvnkE7vNZtvgtQYuoksoEyC1urraWRfn0R1ZtGgRffr08Zi/jBCC\nmxYtIckdxvHPdmAzXzA4lmcXMK7fEB6pjbdpSHh4OPcvWYJt0yZyUlLqbSzVuXlElZYx4eqO1ehp\njOdsJg0JCgri1sU/Yt+GNKyWS6em18weRa8BNTe/lLJJl/y2kHEiG1GiY9bM2c2e4y0b3sV0ZGm+\nLWRmZlJZWekCvJN2sAm6hDKRUkqlUvnxZ5995hXXeoCMjIwWo2Evl7i4OI8ntFEqlaxZsZp+VWo+\n+uWLmKsNlGblU/zFQX5y691ERERgs9k4efJk/WeEEIwfN44/PvAgY/ILOP/6G1RkZlK6fRsrrr3W\nI0W8+/eK9Pg0p44hiUOYnHQde2rd7TNO5nBy/6UuEhaTlS/e+q4JCS1jrDaR+lU2q268o9nfQkrJ\nn//853bLbg8Oh8OrtrzPP/9cqtXqzd6Ox2lIl1AmACaT6ZP333/faynlXC4X58+f95b4es6ePetR\neWq1mntX/Ygbxs/gyEffkPnRTh66+Y56F3W1Wk1T1RFDQ0N54Pbb+dWcOag+/4L4ykpGj27eKatd\nSLdXRiZ1zJt9PX6V4Zw+lIFWp2HouEud+7S6AJY/tKBdcl0uF7vWH2X+pCWXRGI3RAjB008/3e5+\nt4edO3dy4sQJr8l/4403THq9/iOvNdAEwlt2ivYihPD38/Orys3N1URHe75+a2fx8ccfc8MNN3ik\napzJZMJgMBAbG4vdbufdj99nwpjxLa4+uN2XOs+5XC6qq6sJD7/8mjHJycm88cLjPPXC2wwe3LHy\nHK2h1+sxGAy8+r+XmLZmBOHRLYdFVJXpUamVrcbh7NuSSmBpPGtuu9srdWm6CtXV1URFRdkdDkeE\nN3O+XkyXGZlIKW0BAQHb//e///m6K5fF8uXLPVZ+Mjk5uf6i9/Pz485Vd7SoSABeeOEFzObGSZuU\nSqVHFMkFvGMzqePzzz8nMDCQZXNuJaU2u31LCIXgyM5TLZ5zLjUL42nBimWrmlUkdrv9kvwt3ZFN\nmzah0+n2daYigS6kTACqq6v/uW7dOkPrZ3act956y+MZsprCE23Mnz+/0WpDW56mv/rVr+p9SLz1\nPfvHh3vcZmIwXPi3r169mtDQUMaMHsuw6PEc2NrydCAkPIhpNzRvWC7OLePUlgLuXHlfi/41breb\nmTNntrvv7WXHjh2tn3QZ/P3vfzdUVlb+06uNNEGXUibAltzcXMuBAwe81sDs2bM7RZl89tlnHDly\npN2fM5lMHqv7c+DAATZv3uwRWY3x7MikpKSETz/99JLjde72xrMKsk7nNfHJS0k7lNHIk7aqTM/u\nD09x+9J7Wg1A1Gg07U7H0F6sVmubPKE7SmpqKidOnJDARq810gxdSplIKV0Wi+Wl3/72t56vclVL\nnz59PL7q0hRLly7tkMEzJyenPgfJ5XLNNdc0ik3Kz780d2tHyMovu+yRybFjx6isrARqYqjuuOOO\nJs/TaDTccfNdHPkio1l3+4bED4imKKcMqFm52f7uEW6edRuJg5vPFVJUVNSkEdsbaDQar6RmrOPl\nl1+2ud3uf0opO2dtuwFdSpkAuFyu17dt2ybKy71TMQ1qhv96vd5r8uvoiJEvKSmpTSH8HWHfvn14\n5ne9/JGJwWBoc5hB7969mTtxCbs2HGtViYWEBzFoZD9MBgvfv3OQBRNuYuyYcS1+JiMjg7i4uDb3\nvatiMBj44IMPpN1uf9UX7Xc5ZSKlLPP39//irbfe8prPCcC6deu8Kb4ep9PJCy+80OI5JpOJDz/0\nvtfzsmXL6l38rVYrf/rTnzokp39cWLuVyTfffEPD+KupU6e2K3/H9CnTiVUkcCy59RKqxmoT3729\nH3V1KCOGtV7kbcqUKe2Kn+oozz33nFflv/POO1KtVm+XUnpmCNpOuszScEOEEBPj4uK+zcvLC+yM\nKYm3MZvNLRr+TCYTJpMJXy6J5+fns3nzZu677z6gxhgphLhkdJWcnMx37/6B+Xc+w8QGYfNSSmw2\nW/1K1uHDh8nNzWXJkiUe66Ner+fF155n3C0DiO3bdEmIqjI92989woIJN5E0ZBgqlarZ+JoTJ04w\nYsQIj/WvNWw2m1eig6Hm94+NjbWWlJQslFJ+75VGWqGr3qn7jEZjiXeMh51PaxG6gYGBPlUkcCFz\nXB1ZWVm8++679ftnzpyhbtk+K7+crKysRsuop0+f5uuvv67fHzt2rEcVCdQktF55wx3sXX+ySXf7\n4twytr19hJtmrGbKpKmEh4c3q0jsdnun2Unq8JYiAdi1axdms7kc2Oa1RlqhSyoTKaU0GAx//Mc/\n/uHVdXIpZaMbxtukpKSwc+dOoGb689e//rXT2m4vCQkJ3H777fX7iYmJ3HZbbTYwKenVqxerVq2q\nfz8pKYmlS5d6vV9Dhwxl0tDr2PNFaqNVuXPHs9n3wRnWLHmwSRvJiy++2Mh93c/Pr1P6C7Bnj9dz\nOfPSSy+ZzGbzX6UPpxpdUpnU8v6uXbsU2dnZXmtACNFoqO5tpkyZwtW1gXYqlYqHH36409r2JP3j\nQ73qtNYa8+csQF0eSvrh87hcLvZvTSX7ez0P3/HzZldtHnzwQfz9/Tl+/PglTn3eprr68sqhtEZh\nYSFffvml0u12/8erDbVCl1UmUkqTQqH4z5///Gev5TkBWixt4A3qbApSykuKj3cfJG6X98qvtoZK\npWL1zWs4uSWPja98j7Yknkfu/XmLq2B1v3Vubq7HPJTbyvz5870q/y9/+YtTpVKtl1JWebWhVuiy\nygTAbDY//+abb8rCQs+X4byYjIxOSUbFs88+i9Pp5L333vN4UGBnkVVYhauTQvSbIyoqiluuX838\nsctZc9vdbc4cN3/+fJ599lkv966GzkhjUF5ezrp165wmk+n3Xm+sFbq0MpFS5iiVynXedGKrIyUl\npVM8Y59++umaujG33dbpoyJPoRACt9t305w6xo4Zx8xpM1v159myZUt9dUSFQsFvfvMbr/fNZrPx\n0ksveb2dP/zhD3YhxPtSSp8/mbrk0nBDhBARAQEB2ampqYHd9eYzm834+fk1mz8jIyODhISEbhHJ\nmpycTPL7v2f0/B+zYPEyX3enTWRlZTXpVex2uzGbzeh0batF3NXIyckhMTHRarPZEqSU3h++t0KX\nHpkASCnLXS7XX++++26vj05q2/O4zA8++KBFI1xRUVGn5FrxFEqFApfTdzaTtuByuer/l82FJxiN\nxm4dJfzkk09ahBCvdAVFAt1AmQDY7fYXDhw4YDt8+LDX21q7di3FxcUelXnXXXe1mFx6ypQprVb3\n60pk5lfidndtZbJ27dr62J/mCA4ObuRb4wmysrLYtGmTR2U2xalTp9iwYYPLarV61622HXQLZSKl\nNDkcjqd++tOfei/vYi0PPPCAR2JjTCYTOTk57f7cV199xaFDXi8Le1kolAKX07cG2NZ46KGH2pXD\npaCgwCNLuOHh4cydO/ey5bTGT37yE5PT6fyDr1dwGtItlAmA0+lcd/jwYYO365h4ym6xd+/eDnk8\nXn/99Ywc2Xo8iS8Z0jeyy01zpJS8+uqrHY5m1mg07N2797L7ERwc7JWawQ3Zu3cvu3fvdjgcjk7P\nWdIS3UaZSCntJpPpsfvvv9/SGUbjiooKXn/99Q5/ftasWR0e4dRdjDk5ORw7dqzDffAWCoXoctMc\nIQTLli3rcHqJ8PBw5s2b1+H2N27c2ClLwVJKHn30UaPNZvuFlNLi9QbbQbdRJrV8WFFRkbdhg/dL\ngYSHh7e7ponJZMKTlQl79erlU0/T5sjIq+gSIxOLxdLo9/ZU6oY9e/a0e8ozaNAgryY9quPrr78m\nLS2tUkrpU2/XpuhWykRK6TYYDA89/PDDZqPR++kt27tkWFhYSGJi80l42otSqWTs2AtFuI8cOdIp\nvjCtoVB0DZtJaWkpffr08bjcIUOG0F5Hyc6IPrZarfzoRz+yGgyGR6SUvtfmF9GtlAmAlPIbo9G4\nedWqVV51s2/Ie++9R1lZWavnDRo0yKvRvyUlJVitnbJC3iLDBkT7zGnt0KFD9Qme+vbtS7+Lail7\ngvDwcIYOHdrqeZWVlaSkpHi8/eZ46qmn7Far9XspZaenZGwL3U6ZABiNxvu//fZb0/bt2zulvcWL\nFzc7SjGZTHRWRv158+bVx5ikp6dz6lTLGdm9hUIIn01z7HY7oaEtl77wJC35CJWWljJ8eMvVAjzF\n3r17Wbt2rdVgMNzZKQ12gG6pTKSUlRaL5Y5bb72106Y7zQWHqVSqyzLcdZQBAwZ4rapea5zJLcPl\n6pxpzvnz59m48cKDeNKkSSiVyk5pG2oUeHO2kMTExE5RbFarlZtuuslsNpvvlVI2XYi5C9AtlQmA\nlHKT0WjcdP/993uvxuKlbV4Sb+Hv7++1erEt4efn12ie/vbbb5OZmdkpbXs7NqehXSg6OppFixZ5\nra3WCAsLaxREKKVspNw6gyeeeMJuMBh2SCk7tUJfe+m2ygTAaDQ+sGHDBktnTXeEEKxZswa3283z\nzz/fKW22lTVr1tS7jUspvWqsHTEwxmvTHKfTyXPPPVffd51O55H6yJ7gL3/5C2azuVMDNPfu3ctr\nr71mNRgMazqt0Q7SrZWJlLLSarWuXrVqVadMd6DmSaVQKHjssce6xMpKQ+oc7qSU5Obm1vfP5XJ5\ndEqkVHjWZvL6669TVFQE1Ewbn3766S4Z9PjTn/6UwMDATssba7VaWbFihamrT2/q6NbKBEBKudlo\nNG567LHHvL7M0fCG7MppFxUKBYsXL6534CovL+e1116rf/9yFUtaVullTXO2b9/O8ePH6/fvuOMO\nYmNjL6tP3kRKyd/+9rdGtpPOsFc99NBD9srKyuSuPr2pR0rZ7TcgTKPRVH377bfSm/zxj3+UNput\nft/tdnu1PW+RlpYmP/jgg/r9kpISWVRU1KbP7ty5U/7+3mvl337/sza3980338hdu3bV75eWlna7\n3666urr+tdPplH/4wx+82t6ePXukVqutBqJlF7jH2rJ1+XwmbUUIsTAqKuqjEydOaDs707vdbsdk\nMhEWFtap7XqK7OxsiouLueaaawDYvXs3BoOhfpUqLS0Nh8PBVVddRXJyMqc2/5Wz1cG88GpNMu59\n+/ZhMBiYPXs2UFNL1+Vycd111wE1T/HuWLLE6XT6xF5TWVnJyJEjzfn5+WuklB93egc6SPf7DzeD\nlHKzyWT654IFC0x2u+f82UwmU6Os5k1htVo7JezcW/Tr169ekQBMnjy50XJ3TEwMUVEX6tRo/FWo\nVRcunbFjx9YrDoAZM2Y02u+OigRqDK6thTM4nc5GRdcvF6fTyZQpU2yVlZVvdSdFAleQMgEwm81P\npqen7122bJnNUyOuTz/9tNWLJTg4uFFZiCuN8PDwRuUzC8sMBOkuJMNWq9XdVmG0xJNPPtmqT4vR\naOSTTz7xWJs/+9nPbHl5eQfMZvNPPSa0k7iirgAppdtoNN64ffv2gldeecUjjhCrV69ulx+J0Wjk\nnXfe8UTTXZaa2JyuF4DoCbZu3dquVbrQ0FDuvNMzTqlvvvmm+6233iozGAyLZReMvWmNK0qZAEgp\nDSaTafbjjz9u+v77jlVJNJlMHc5Wr9PpmDNnToc+210YkxjXJRJKexopJUFBQR1els7Ozu5wgqUd\nO3bw0EMPWU0m02wpZcsp4rooV5wyAZBSnjebzUtvuOEGW0eUwuHDhwkKCupw+42mBJ1QpqOzUSgE\nSOkzd35PU2djE0IwadKkDssJCgrqUJa83Nxcli5darZarcullK1XZu+iXJHKBEBKuc3hcPxyzpw5\npvYayKZNm+aR6F8pJZs2bbpibro6jp4pQqmgS+ZaaS/l5eW88cYbHpEVHh7eyPDcFsxmM/PmzTNZ\nrdY/SCm/9EhHfMQVq0wAHA7HK6WlpeuXL19ubu2GNplMdHRa1BxCCO69994r0jhZk22t+yvJiIgI\nHnzwQY/L3blzZ6tTHiklc+bMseXm5n5ttVr/7PFOdDJX3lXeACmlNBqN96akpJy+9dZbWwxzLS8v\n93o4+TvvvNNplQO9yejE2G49MklJSfH4g+NiRowY0WoOnGeeecaZmpqaYTQaV8srwOHrinFaawkh\nRJRWqz309NNPxz3xxBNdI2qsm5KcnIw99Q2O5Cm4/8mXL8u25CscDkenpFhsiZdfftn11FNPlZpM\npnFSygKfdsZDXNEjkzqklKVms3nSc889V/rSSy/VP07NZjNvvfWWT/qUkZHRaUmVPM3RM0UoROfE\np3iKtWvXYrHU5F/ubEXy3//+F71eX7//zDPPuJ544olKk8k08UpRJAA/mKe0lDJfCDHpySefPGQw\nGMJ++9vfKvz8/Fi8eLFP+jNw4MBmK811B7rbNGfFihX1Weo6m0WLFtUn1/rPf/4jn3/++WqLxTJJ\nSpntkw55iR/EyKQOKWW21Wqd8Pzzz1e+8cYbbpVK1WKlPW/T0Lvy5ZdfpqKiwmd9aQ81NhPRpZVJ\neno6DasY+DJuKiwsDD8/P95//3354x//uNpisUyVUp7zWYe8xA9mZFKHlDJDCDHlkUceSXE4HGEP\nPPBAl1CojzzyiK+70C4UQnZpZdKvXz+PVgq4XF5++WX5+OOPG6xW63QpZZqv++MNusSN1NlIKdPN\nZvOExx57rOrZZ5/tEneEEKLe8zIzM5O3337btx1qgRo/k663NPzcc8/VB2VqNJouk2Dp3//+t+uJ\nJ56otFqtE6WUx1v/RPfkBzcyqaN2hDL2+eef36tUKiO70irPgAEDGDBgQP1+WloaAwYMaDaptS/o\nCjYTu92O0Wisryn81FNP+bQ/TfHiiy+6fvOb31RYLJbJV+LUpiE/yJFJHVLKbJPJNP65554rfPTR\nRx1d7UnbkKysLF93oZ7RibFdYjVn27ZtVFZ2zTAWKSVPPfWU8+mnn64wm83XXOmKBLqZMhFC9BFC\nbBNCnBRCnBBCPFJ7/K9CiDQhxDEhxAYhREjt8f5CCIsQ4kjt9n8NZN0ghDgG/M5kMo1/4403Tl9/\n/fWdlku2PSQlJTUqCvWnP/3Jp8W4coqqKavUd/rI5Ny5c7z77rv1+/PmzWPgwIGd2oe2YDabufnm\nmy3//Oc/0y0Wy2igSAixTwhxVAhxSgjxJwAhxPLaa9klhKgv3diW61YIsc4HX61lfJ3qrT0bEAuM\nrn2tA9KBJGAOoKg9/jzwfO3r/sDxZmR9QI0yfQYYDvjrdLp3Bw0aZDx//rzsLlRWVsqvvvqq09pL\nTU2Vv/7Zg/I/r/1T5ufne7Utp9Mpd+zYUb/vcrm82p4nyMnJkQkJCaagoKBPgAB54XrT1v5VAXuB\nqcBQIBHYBoxtcG6br9umzvHV1q1GJlLKIinl0drXRiANiJdSfiOlrBtz7wN6t0GcAvAHtIBdSmkz\nGo235+TkPDVy5Ejr1q1bvfEVPE5wcHAjf5WioiKvuuyPHDmS+Ytv4Y57HyY+Pt7j8s+fP4+zQeb7\nhkbUrh7jtGfPHq666ipzXl7ecwaDYbmU0lL3npTSXPvSD1ACFVLK01LKM+1sptF165GOewpfa7OO\nbtRo72xAd9HxL4BVDc4xAkeA7cDUBufNBg4Cf25C9qyAgAD9v/71r67/KLyIiooKuXfv3vr9zMxM\nmZOT48Metcz58+elXq+v3//oo4+k1Wr1YY86xhtvvOHSarUGYIFs+npVAEcBA/CXi95ramTS7uvW\n15vPO9ChTtdMcQ4CSy86/hSwvsG+HxBW+3oskAMEtbGNgTqdLnPNmjWWhhnpuxsFBQVhxnBBAAAJ\ne0lEQVTy+PHj9fs7d+5stN/ZpKSkyKysrPr9rVu3yqqqKp/153JxOBxyzZo11oCAgAIgSbZ+XYVQ\nM82Z2eDYxcqkw9etLzefd6DdHQY1sAX46UXH1wApgKaFzzb6p7WhrWCdTvf9qFGjTCUlJfJKwGq1\nyoqKivr9zZs3NxrJ7Nu3Tza0GRmNRulwOBrJ2LZtW/1rh8PRqPzHoUOHZGZmZv3+xx9/3Eh5nT9/\nXlosFk98FZ9TUVEhp02bZtLpdLvqbv62bMBvgF/INl6X7b1ufbX5vAPt6iwI4B3gpYuOzwdOApEX\nHY8ElLWvE4A8ILSdbSq0Wu1fYmJiTMnJyfJKp7CwUJaVldXvb926VR47dqx+f+PGjfLf//53/f6m\nTZsaKYvMzExZWVnZOZ31Id9//72MiYkxBwYGvgKoZMvXUGTddQcEADuBWQ3e3waMkx68bn2x+bwD\n7epsjQXcXTv3PFK7XQ+cpcZ+Unfs/2rPvwk4UXvsELDwMtpeEhAQULV8+XKbyWRq4vLq4YeAxWKR\nP//5z20BAQF64BbZtmtnJHC49rpNBX5Ze/xGIBewAEXAV9LD121nbj+IfCaeQggRERQUtC44OHju\nBx98EDh16lRfd6mHTmT37t3ceuutpqqqqmS9Xr9GSlns6z51Jbr2WlsXQ0pZrtfrl+Xn56+eO3du\n1a233mo3m82tf/AKY/v27b7uQqditVr5xS9+YZ81a5YxJyfnbr1ev6BHkVxKjzLpAFLKjRaLZdDm\nzZu/GjJkiHnXrl2+7lIPXmL//v0kJSWZ1q1b973Vah0kpfxQ9gznm6RnmnOZCCGWarXat2655ZaA\nf/3rX/5ardbXXerBA1itVp566in7v/71L7vNZrsH+KhHibRMjzLxAEKICJ1O92ZQUNDs1157Tbtw\n4cIuE/7eQ/uQUvLNN99w//33myoqKnpsI+3B1xbgK2kDFgUFBWWNHj3a8P3338srlYZ+JlcS+/bt\nk8OHDzcHBgYWAMuofdj2bG3bemwmHkRKuclgMAw8duzYTxYuXFgxd+5c04kTJ3zdrR5aIT09nRtu\nuMF87bXXVqalpf3UZDL1lVJukFL2DNvbQc80x0sIITQqleohtVr9/6677jr1K6+8ounOCaSvRPLy\n8njyySctH330EW63+1mHw/F3eSEgr4d20qNMvIwQIkSj0TwOPHLPPfcof/vb3/pHRUX5uls/aCoq\nKnjmmWfsr732mksI8arZbH5WdtNi4V0KX8+zfigbEKPT6dZptVrz6tWrnQUFBbK70l1tJsXFxfL3\nv/+9MyAgwKLT6d4BeskucG1cKVuPzaSTkFIWGwyGe81m84iNGze+l5CQYFmyZIlpx44ddcqmBy8g\npWTHjh3Mnj3b0rdvX+sLL7zwkcViGW0wGO6QUub7un9XEj3THB8hhAhRKBR3aLXaX4eEhIQ+/vjj\n2jvuuEMEBwf7umtXBEajkf/973/yhRdeMBYWFhotFssLbrf7bSll9yhO1A3pUSY+RtQ4pMwMCQn5\npd1uv3bevHnid7/7nf+oUaN83bVuSVpaGv/85z9tb775pvD399+h1+v/CnwnL2Ti68FL9CiTLoQQ\nIl6tVj+gVqsfHjp0qOqxxx4LWrRoESEhIb7uWiO2b9/OzJkzfd2NegwGA//5z3948803jenp6S63\n2/2q1Wr9Pyllrq/79kOiR5l0QYQQauCG0NDQR41G48Tx48dbV65cGbR48WLRsJ6Or+gKyiQnJ4fP\nPvuMDz/8UH/w4EH/gICAI1VVVX8HPpVSdq3cqD8QepRJF0cIEQjMCQ4OXmG32xeGhYUpf/SjH/nd\neOONqvHjx3f5JMueQkrJ4cOH2bhxo+uDDz4w5+bmKv39/b/W6/XvAVullAZf9/GHTo8y6UYIIZTA\nNRqNZpmfn98Kt9sdNWPGDO6//37NtGnTCA0N9XUXPYper2fXrl2sX7/eun79erfL5dK7XK4PLBbL\nBmCPlNLZqpAeOo0eZdKNEUIMFEIsDgsLW2U0GkfqdDrX6NGj3fPmzQscP368GDt2rFcUjDemOXq9\nnsOHD3Po0CFSUlKMe/fupaSkxD84OPhkdXX1e263+3MpZbpHG+3Bo/QokyuE2lHLEGBcYGDgJH9/\n/2l6vT4xLCzMMXHiRDllypTAMWPGiKuvvpqwsLDLautylUl1dTVHjhxh8+bNnD171njgwAFKSkr8\nAgMDsx0Oxzaz2bybmnSFp3tGH92HHmVyBdNQwWi12slqtXqmyWRKUCqVIiIiwqrVahWDBw92JyYm\nanr37q2Oioqib9++xMXFER8fT1BQULtSKRgMBgoKCigsLKSgoIC0tDSqq6sdubm51ry8PHdGRoaf\nyWRSut1ugoODz1mt1t1mszmFmrIlPYqjm9OjTH5g1Pq1BAPxQFztFh8QENDX398/UQgR63K5oi0W\nS7jb7Vaq1WqXUql0K5VKKaUUfn5+ToVCgcvlEjabTVnrSq1wOBwKQAYEBFT4+fkVSynzLRZLtsVi\nyQQKgMLarQColj0X3hVHjzLpoVmEEFpqCkKpqKlXpKrdBOAAnLWbo3Yz9yiJHy49yqSHHnrwCD8M\nJ4UeeujB6/Qokx566MEj9CiTHnrowSP0KJMeeujBI/Qokx566MEj9CiTHgAQQvQRQmwTQpwUQpwQ\nQjxSe/xDIcSR2i1TCHGkwWeeEEKcFUKcFkLMbXD8BiHEMSHEOl98lx58g8rXHeihy+AAfialPCqE\n0AGHhBDfSClX1J0ghHgBqKp9PQxYAQwDegHfCiEG1/qZ3AaMAX4nhBgupTzZ2V+mh86nZ2TSAwBS\nyiIp5dHa10YgjRovWaDec/YW4P3aQ0uA96WUDillFnAOmFD7ngLwB7RAT26RHwg9yqSHSxBC9Kdm\nZLGvweFpQLGUMqN2Px7Ia/B+HjUjFIDXgOT/384do0QQRFEUvR/EVWhg4AbEzNQNmCm4AhNjd+EG\nzERBFMTIwBVMaNAGxroLeQbTM5QmJjXSMPdkXVUNFT3qd/Eb+EryvtLNajIsc/TDWOLcA+fjCWXh\nBLj54/UAJHkB9lezQ02VYaKl8XeRD8B1ksdmfAM4Avaa5R/AdvO8NY5pTVnmCFh+E7kChiSXv6YP\ngbckn83YE3BcVZtVtQPsArP/2a2myJOJFg6AU+C1uf69SPLM/Nbmtl2cZKiqO2Bg3jl8ZsfwerNr\nWFIXljmSujBMJHVhmEjqwjCR1IVhIqkLw0RSF4aJpC6+AYbbu4AHp0m9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d5180ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "N = 20\n",
    "theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)\n",
    "radii = 10 * np.random.rand(N)\n",
    "width = np.pi / 4 * np.random.rand(N)\n",
    "\n",
    "ax = plt.subplot(111, projection='polar')\n",
    "bars = ax.bar(theta, radii, width=width, bottom=0.0)\n",
    "\n",
    "# Use custom colors and opacity\n",
    "for r, bar in zip(radii, bars):\n",
    "    bar.set_facecolor(plt.cm.jet(r / 10.))\n",
    "    bar.set_alpha(0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejercicios\n",
    "\n",
    "1. Dada la función para el cálculo de raices de una ecuación de segundo grado [implementada](https://gist.githubusercontent.com/mgaitan/cb0ad9778453607acb49/raw/8d85d2184a4b46b48440cf5b5d95062801a08cce/baskara.py) en clases anteriores, crear una función que dados los coeficientes grafique la parábola y denote las raices con puntos rojos y el valor de X en cada una. \n",
    "\n",
    "<!-- https://gist.githubusercontent.com/mgaitan/f0ceedbc1890038cf27f/raw/48fe60095644647d8b5b084b60547b4da0751201/plot_parabola.py -->\n",
    "\n",
    "\n",
    "2. Basado en el [ejemplo de grafico de torta](http://matplotlib.org/examples/pie_and_polar_charts/pie_demo_features.html) de la galería, adaptar el ejemplo que grafica \"near_critical_oil.csv\" para que no se vea \"ovoide\" y la porción correspondiente a \"C2\" quede separada. Agregar un título al gráfico\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o2rUrxx13HBAK+pQpUxg3bhwTJkxg+PDhzJ8/P064MtzhscegbVto1gxuv93Y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\n8pQKuIhInlIBFxHJU/8fABDcyeICNy4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d50bac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load https://gist.githubusercontent.com/mgaitan/f0ceedbc1890038cf27f/raw/48fe60095644647d8b5b084b60547b4da0751201/plot_parabola.py\n",
    "def baskara(a, b=0, c=0):\n",
    "    \"\"\"dados los coeficientes, encuentra los valores de x tal que ax^2 + bx + c = 0\"\"\"\n",
    "\n",
    "    discriminante = (b**2 - 4*a*c)**0.5\n",
    "    x1 = (-b + discriminante)/(2*a)\n",
    "    x2 = (-b - discriminante)/(2*a)\n",
    "    return (x1, x2)\n",
    "\n",
    "\n",
    "def plot_parabola(a, b=0, c=0):\n",
    "    X = [.1 * i for i in range(-50, 51)]\n",
    "    Y = [(a*x**2 + b*x + c)  for x in X]\n",
    "    pyplot.plot(X,Y)\n",
    "    pyplot.plot(x, [0 for x in X], linestyle='dashed')\n",
    "    pyplot.title('un titulo')\n",
    "    x1, x2 = baskara(a, b, c)\n",
    "    if not isinstance(x1, complex):\n",
    "        pyplot.scatter([x1, x2], [0, 0], color='red')\n",
    "        pyplot.annotate(s=round(x1, 2), xy=(x1, 0), xytext=(x1+0.3, .2))\n",
    "        pyplot.annotate(s=round(x2, 2), xy=(x2, 0), xytext=(x2 + .3, .2))\n",
    "        \n",
    "    \n",
    "plot_parabola(1.2, b=0.4, c=-1.4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kSOjadd92v98v69atk/vuu08//XShdO68G7hKa6dlHwre72FpOsxq483QFVhL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\niH4DJf4cj4WMUhgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d5092390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "with open('data/near_critical_oil.csv') as csv_file:\n",
    "    reader = csv.reader(csv_file)\n",
    "    critical_oil = [line for line in reader]   #o list(reader)\n",
    "\n",
    "components = [c for (c, f) in critical_oil[1:]]\n",
    "fraction = [float(f) for (c, f) in critical_oil[1:]]\n",
    "explode = [0.2 if c == 'c2' else 0 for c in components]\n",
    "# el ; evita el output de la celda\n",
    "\n",
    "pyplot.pie(fraction, explode=explode, labels=components, shadow=True)\n",
    "pyplot.axis('equal');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antes de seguir con Matplotlib debemos aprender el corazón del Python Cientifico: **Numpy**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy, todo es un array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El paquete **numpy** es usado en casi todos los cálculos numéricos usando Python. Es un paquete que provee a Python de estructuras de datos vectoriales, matriciales y de rango mayor, de alto rendimiento. Está implementado en C y Fortran, de modo que cuando los cálculos son vectorizados (formulados con vectores y matrices), el rendimiento es muy bueno."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El pilar de numpy (y toda la computación científica basada en Python) es el tipo de datos `ndarray`, o sea arreglos de datos multidimensionales.\n",
    "\n",
    "¿Otra secuencia más? ¿pero que tenían de malo las listas?\n",
    "\n",
    "Las listas son geniales paro guardar **cualquier tipo de objeto**, pero esa flexibilidad las vuelve ineficientes cuando lo que queremos es almacenar datos homogéneos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 1.08 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit [0.1*i for i in range(10000)]    # %timeit es otra magia de ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 13.32 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "10000 loops, best of 3: 38.2 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit np.arange(0, 1000, .1)    # arange es igual a range, pero soporta paso de tipo flotante y devuelve un array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loops, best of 3: 991 ms per loop\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<TimeitResult : 1 loops, best of 3: 991 ms per loop>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%timeit -o\n",
    "X = range(10000000)\n",
    "Y = range(10000000)\n",
    "Z = [(x + y) for x,y in zip(X,Y)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 156 ms per loop\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<TimeitResult : 10 loops, best of 3: 156 ms per loop>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%timeit -o\n",
    "X = np.arange(10000000)\n",
    "Y = np.arange(10000000)\n",
    "Z = X + Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.370105952818687"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "__.best / _.best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Existen varias formas para inicializar nuevos arrays de numpy, por ejemplo desde"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Listas o tuplas\n",
    "- Usando funciones dedicadas a generar arreglos numpy, como `arange`, `linspace`,`ones`, `zeros` etc.\n",
    "- Leyendo datos desde archivos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = np.array([1,2,3,4])\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  2.],\n",
       "       [ 3.,  4.]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# una matriz: el argumento de la función array function es una lista anidada de Python\n",
    "M = np.array([[1, 2],\n",
    "              [3, 4.0]])\n",
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(numpy.ndarray, numpy.ndarray)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(v), type(M)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dimensiones, tamaño, tipo, forma\n",
    "\n",
    "Los ndarrays tienen distintos atributos. Por ejemplo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.ndim, M.ndim    # cantidad de dimensiones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4,), (2, 2))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.shape, M.shape  # tupla de \"forma\". len(v.shape) == v.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 4)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.size, M.size   # cantidad de elementos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3],\n",
       "       [2, 4]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.T   # transpuesta!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A diferencia de las listas, los *arrays* tambien **tienen un tipo homogéneo**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dtype('int64'), dtype('float64'))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.dtype, M.dtype    #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se puede definir explicitamente el tipo de datos del array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "a float is required",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-36-737d9df13f43>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'f'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcomplex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: a float is required"
     ]
    }
   ],
   "source": [
    "np.array([[1, 2], [3, 4]], dtype=complex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una gran ventaja del atributo `shape` es que podemos cambiarlo. Es decir, reacomodar la distrución de los elementos (por supuesto, sin perderlos en el camino)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.arange(0, 12)\n",
    "A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.shape = 3, 4\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El método `reshape` es otra manera de definir la forma de un array, generando uno nuevo array (a diferencia de `A.shape` que simplemente es otra vista del mismo array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.arange(12).reshape((3,4))\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vistas\n",
    "\n",
    "Esto es porque numpy en general no mueve los elementos de la memoria y en cambio usa **vistas** para mostrar los elementos de distinta forma. Es importante entender esto porque incluso los slicings son vistas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "b = a[::2]  # todo de 2 en 2\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12,  1,  2,  3,  4,  5,  6,  7,  8,  9])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[0] = 12\n",
    "a  # chan!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En cambio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.arange(10)\n",
    "d = c[::2].copy()\n",
    "d[0] = 12\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una forma de saber si un array es \"base\" o hereda los datos de otro array (es una vista), es verificar el atributo `base`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.base is a and a.base is None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Otras funciones constructuras de arrays\n",
    "\n",
    "\n",
    "Además de `arange` hay otras funciones que devuelven arrays. Por ejemplo `linspace`, que a diferencia de `arange` no se da el tamaño del paso, sino la cantidad de puntos que queremos en el rango"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.06346652,  0.12693304,  0.19039955,  0.25386607,\n",
       "        0.31733259,  0.38079911,  0.44426563,  0.50773215,  0.57119866,\n",
       "        0.63466518,  0.6981317 ,  0.76159822,  0.82506474,  0.88853126,\n",
       "        0.95199777,  1.01546429,  1.07893081,  1.14239733,  1.20586385,\n",
       "        1.26933037,  1.33279688,  1.3962634 ,  1.45972992,  1.52319644,\n",
       "        1.58666296,  1.65012947,  1.71359599,  1.77706251,  1.84052903,\n",
       "        1.90399555,  1.96746207,  2.03092858,  2.0943951 ,  2.15786162,\n",
       "        2.22132814,  2.28479466,  2.34826118,  2.41172769,  2.47519421,\n",
       "        2.53866073,  2.60212725,  2.66559377,  2.72906028,  2.7925268 ,\n",
       "        2.85599332,  2.91945984,  2.98292636,  3.04639288,  3.10985939,\n",
       "        3.17332591,  3.23679243,  3.30025895,  3.36372547,  3.42719199,\n",
       "        3.4906585 ,  3.55412502,  3.61759154,  3.68105806,  3.74452458,\n",
       "        3.8079911 ,  3.87145761,  3.93492413,  3.99839065,  4.06185717,\n",
       "        4.12532369,  4.1887902 ,  4.25225672,  4.31572324,  4.37918976,\n",
       "        4.44265628,  4.5061228 ,  4.56958931,  4.63305583,  4.69652235,\n",
       "        4.75998887,  4.82345539,  4.88692191,  4.95038842,  5.01385494,\n",
       "        5.07732146,  5.14078798,  5.2042545 ,  5.26772102,  5.33118753,\n",
       "        5.39465405,  5.45812057,  5.52158709,  5.58505361,  5.64852012,\n",
       "        5.71198664,  5.77545316,  5.83891968,  5.9023862 ,  5.96585272,\n",
       "        6.02931923,  6.09278575,  6.15625227,  6.21971879,  6.28318531])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 2 * np.pi, 100)      # por defecto, incluye el limite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_.size   # en cualquier consola, python guarda el ultimo output en la variable _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matriz_de_ceros = np.zeros((4,6))\n",
    "matriz_de_ceros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero numpy no sólo nos brinda los arrays. Los conceptos claves que aporta son *vectorización* y *broadcasting*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorización y funciones universales\n",
    "\n",
    "La **vectorización** define que las operaciones aritméticas entre arrays de igual forma se realizan implicitamente **elemento a elemento**, y por lo tanto hay una **ausencia de iteraciones explícitas y de indización**. \n",
    "La vectorización tiene muchas ventajas:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* El código vectorizado es más conciso y fácil de leer.\n",
    "* Menos líneas de código habitualmente implican menos errores.\n",
    "* El código se parece más a la notación matemática estándar (por lo que es más fácil,\n",
    "por lo general, corregir código asociado a construcciones matemáticas\n",
    "* La vectorización redunda en un código más \"pythónico\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3. ,  0. ,  3.4])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([3, 4.3, 1])\n",
    "b = np.array([-1, 0, 3.4])\n",
    "c = a * b\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para dar soporte a la vectorización, numpy reimplementa funciones matemáticas como \"funciones universales\", que son aquellas que funcionan tanto para escalares como para arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "only length-1 arrays can be converted to Python scalars",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-58-b6b58fec0881>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: only length-1 arrays can be converted to Python scalars"
     ]
    }
   ],
   "source": [
    "import math\n",
    "math.sin(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.14112001, -0.91616594,  0.84147098])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sin(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y funciona para simples escalares\n",
    "np.sin(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "¡Basta de bucles for todos lados!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Broadcasting\n",
    "\n",
    "\n",
    "El **broadcasting** (*difusión*) es el otro concepto importante. Describe el **comportamiento de las operaciones con arrays de distinta forma**. Con ciertas restricciones, se trata de que el array de menores dimensiones se \"difunde\" al más grande, siempre que tengan formas compatibles\n",
    "\n",
    "En Numpy todas las operaciones adoptan por defecto un comportamiento de este tipo (no sólo las operaciones\n",
    "aritméticas sino las lógicas, las funcionales y las de nivel de bits)\n",
    "\n",
    "La forma más obvia de observar el broadcasting es cuando se opera un array con un escalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  4.,  6.])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1., 2., 3.])\n",
    "b = 2.\n",
    "a * b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "podemos interpretar que el escalar  `b` es un array adimensional que \"se estira\" para ser compatible con las dimensiones de `a`\n",
    "\n",
    "![](files/img/image001.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regla general del broadcasting\n",
    "\n",
    "       Dos arrays son compatibles para operar via *broadcasting* si sus dimensiones \n",
    "       (de atrás hacia adelante) son iguales o alguna es 1. \n",
    "\n",
    "\n",
    "En otras palabras:\n",
    "\n",
    "      Debe cumplirse que el `shape` de uno sea \"sufijo\" del `shape` del otro array (1 es comodin) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4, 3), (3,))"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[ 0.0, 0.0, 0.0],\n",
    "              [10.0, 10.0, 10.0],\n",
    "              [20.0, 20.0, 20.0],\n",
    "              [30.0, 30.0, 30.0]])\n",
    "b = np.array([1.0, 2.0, 3.0])\n",
    "\n",
    "a.shape, b.shape   # son compatibles para broadcasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.,   2.,   3.],\n",
       "       [ 11.,  12.,  13.],\n",
       "       [ 21.,  22.,  23.],\n",
       "       [ 31.,  32.,  33.]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](files/img/image002.gif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (4,) (3,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-65-24b35ac83fd9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m#fails\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (4,) (3,) "
     ]
    }
   ],
   "source": [
    "a = np.array([0.0, 10.0, 20.0, 30.0])\n",
    "b = np.array([1.0, 2.0, 3.0])\n",
    "\n",
    "#fails\n",
    "a + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero podemos elevar la dimensionalidad de `a` (agregar una dimensión sin cambiar los elementos) para poder operar entre los dos arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   1.,   2.],\n",
       "       [ 10.,  11.,  12.],\n",
       "       [ 20.,  21.,  22.],\n",
       "       [ 30.,  31.,  32.]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a =  a.reshape((4,1))   # equivalente a  a[:,np.newaxis]\n",
    "b = np.array([0.0, 1.0, 2.0])\n",
    "a + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](files/img/image004.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejercicio\n",
    "\n",
    "1. Crear una array de 10x10 donde cada fila va del 0 al 9 (tip: implementar via broadcasting y también ver la función `np.tile`)\n",
    "2. Dado un vector `[0 20 32 100 123]` que indica las distancias entre los pueblos sobre una ruta, encontrar la \"matriz de distancias\" (que da la distancia absoluta entre cualquier par de pueblos)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((10, 10)) * np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "       [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "       [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "       [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "       [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "       [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "       [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "       [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "       [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "       [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tile(np.arange(10), 10).reshape((10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0, 35, 40, 60, 70],\n",
       "       [35,  0,  5, 25, 35],\n",
       "       [40,  5,  0, 20, 30],\n",
       "       [60, 25, 20,  0, 10],\n",
       "       [70, 35, 30, 10,  0]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ruta = np.array([0, 35, 40, 60, 70])\n",
    "\n",
    "np.abs(ruta - ruta.reshape(ruta.size,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing extendido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El funcionamiento básico del indexado y el slicing funciona con `ndarrays` igual que con cualquier secuencia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ruido = np.random.random(1000)   # 1000 numeros aleatorios entre [0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ruido[0] == ruido[-1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ruido[999] == ruido[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.43760886,  0.86255503,  0.97694348,  0.95594497])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ruido[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ruido[0:10] = np.zeros((10,))  # claro que los arrays son mutables!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "         0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "         0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "         0.00000000e+00,   5.71360247e-01,   7.76374424e-01,\n",
       "         5.24283827e-01,   7.44727118e-01,   9.60821247e-01,\n",
       "         4.66316117e-01,   4.41502108e-01,   3.30996599e-04,\n",
       "         7.72040951e-01,   1.25332263e-02,   1.71393983e-01,\n",
       "         8.17819996e-01,   3.36185628e-01,   4.91193819e-03,\n",
       "         6.57807215e-01,   9.90611420e-01,   3.26871910e-01,\n",
       "         1.90268850e-01,   1.79921248e-01,   5.07157415e-01,\n",
       "         4.76731333e-02,   9.30680735e-01,   8.29399948e-01,\n",
       "         9.49006824e-01,   3.07500757e-01,   8.86812990e-01,\n",
       "         9.96494933e-01,   8.86081584e-01,   7.93895311e-01,\n",
       "         2.82447909e-01,   9.20229450e-01,   2.34296454e-01,\n",
       "         2.94541549e-01,   1.14515140e-01,   5.39715188e-01,\n",
       "         2.15519565e-01,   9.19664714e-01,   9.07329270e-01,\n",
       "         9.90562538e-01,   4.89832474e-01,   1.45673289e-01,\n",
       "         7.97621964e-01,   8.76627798e-01,   4.33040716e-01,\n",
       "         9.72108734e-01,   5.63180506e-01,   7.22339145e-02,\n",
       "         2.98084224e-01,   2.83617639e-01,   6.16905431e-02,\n",
       "         1.58750128e-01,   7.46605213e-01,   6.29434062e-01,\n",
       "         5.74034117e-01,   5.85940124e-01,   3.84601138e-01,\n",
       "         1.64994806e-01,   4.35881130e-01,   9.81488274e-01,\n",
       "         2.26478767e-01,   9.48275280e-01,   9.03878818e-02,\n",
       "         8.96752755e-01,   9.26416095e-01,   1.47970702e-01,\n",
       "         6.66725222e-01,   7.79832826e-01,   1.34974026e-01,\n",
       "         1.90621408e-03,   8.60727212e-01,   7.91636943e-01,\n",
       "         9.40641759e-01,   2.84071052e-01,   6.03138092e-01,\n",
       "         7.96821743e-01,   3.57610340e-01,   9.20415179e-02,\n",
       "         3.32350016e-01,   1.58317667e-01,   6.11319431e-01,\n",
       "         5.92338952e-01,   3.32454465e-01,   3.79716343e-01,\n",
       "         2.36860676e-01,   5.40631673e-01,   3.02765976e-01,\n",
       "         6.20953674e-01,   6.94021542e-01,   1.04025370e-01,\n",
       "         5.26215204e-01,   3.96538616e-01,   2.28653025e-01,\n",
       "         1.27556918e-01,   2.78148985e-01,   4.91609580e-01,\n",
       "         9.90678241e-01,   3.73473486e-01,   9.74886448e-01,\n",
       "         8.93953083e-02,   9.77793464e-01,   5.70968534e-01,\n",
       "         1.79802995e-01,   9.91302382e-01,   2.49035050e-02,\n",
       "         6.16641324e-01,   7.19949473e-01,   8.76332539e-01,\n",
       "         8.32800308e-02,   9.23121899e-01,   1.89792071e-01,\n",
       "         1.07511920e-01,   9.51026778e-01,   7.03787393e-01,\n",
       "         5.32369626e-01,   6.79152075e-01,   9.03726329e-01,\n",
       "         6.79613275e-01,   4.04080196e-01,   1.36383474e-01,\n",
       "         2.74137206e-01,   2.75532806e-01,   3.71039370e-01,\n",
       "         1.08904423e-01,   9.64097168e-02,   2.28284963e-01,\n",
       "         1.95189818e-01,   8.19252298e-01,   4.14808854e-01,\n",
       "         3.20756538e-01,   7.74288902e-01,   1.14969659e-01,\n",
       "         4.13060449e-02,   7.05372500e-02,   6.94087914e-01,\n",
       "         7.92541167e-01,   6.91854246e-01,   7.14225310e-01,\n",
       "         9.81155898e-01,   8.48668650e-01,   7.22318594e-01,\n",
       "         4.96099431e-01,   5.73182241e-01,   8.77290616e-01,\n",
       "         2.88677596e-02,   7.20689325e-01,   3.21457647e-01,\n",
       "         3.44272792e-01,   2.45888546e-01,   4.46705462e-01,\n",
       "         6.58860724e-01,   8.09363280e-01,   3.98607283e-01,\n",
       "         6.64704933e-01,   2.79742478e-01,   3.51310962e-01,\n",
       "         8.39449323e-01,   3.77785044e-01,   4.84786919e-01,\n",
       "         9.84745542e-01,   9.02590580e-01,   9.72400438e-01,\n",
       "         4.74753799e-01,   4.68429955e-01,   1.60925716e-01,\n",
       "         1.47981126e-01,   1.16505497e-01,   2.13242410e-01,\n",
       "         7.53233419e-01,   6.98475963e-01,   4.13596092e-03,\n",
       "         6.05538047e-01,   6.23588308e-01,   2.44554574e-01,\n",
       "         5.61443227e-01,   7.15471498e-01,   6.56853238e-01,\n",
       "         8.71094868e-01,   9.28336638e-01,   7.86366323e-01,\n",
       "         9.62055270e-01,   6.53693387e-01,   6.77138093e-01,\n",
       "         4.17253511e-01,   7.39426898e-01,   5.07180861e-01,\n",
       "         3.01714499e-01,   7.04179744e-01,   7.25650093e-01,\n",
       "         6.18280712e-01,   6.23184241e-01,   8.49436335e-01,\n",
       "         6.23006857e-01,   1.98750161e-01,   6.78470264e-01,\n",
       "         7.14202485e-01,   3.32525816e-01,   9.25157614e-01,\n",
       "         6.13759413e-02,   9.71668640e-01,   2.03363884e-01,\n",
       "         7.88018235e-01,   6.41555105e-01,   9.95321966e-01,\n",
       "         2.12346210e-01,   1.40833755e-01,   8.14381161e-01,\n",
       "         2.12148627e-02,   4.52234104e-02,   7.22371284e-01,\n",
       "         7.24315001e-01,   5.28686736e-01,   3.06927479e-01,\n",
       "         2.80055600e-01,   7.01040990e-01,   7.14135378e-01,\n",
       "         4.03802126e-01,   5.68509690e-01,   3.19264866e-01,\n",
       "         7.26163597e-01,   7.55783680e-02,   3.55339949e-01,\n",
       "         6.71896315e-02,   5.70396603e-01,   6.83757550e-01,\n",
       "         3.55548387e-01,   8.91550710e-01,   8.20079276e-01,\n",
       "         6.54298051e-01,   8.84088361e-01,   5.09505403e-02,\n",
       "         5.93742139e-01,   5.31025951e-01,   9.95116357e-01,\n",
       "         6.09850015e-01,   4.33593896e-01,   7.63397840e-01,\n",
       "         1.24619000e-01,   7.72288124e-01,   2.40957376e-01,\n",
       "         4.94877155e-01,   5.33848202e-01,   8.25789780e-01,\n",
       "         4.32960617e-01,   3.35214197e-01,   9.36023461e-01,\n",
       "         6.14352823e-01,   6.75124430e-01,   8.43796537e-01,\n",
       "         1.47365912e-01,   3.32783728e-01,   5.15973282e-01,\n",
       "         2.00637781e-01,   3.93961174e-01,   4.47698323e-01,\n",
       "         4.51317371e-01,   9.65964766e-01,   1.18203353e-01,\n",
       "         7.10298130e-01,   9.35435705e-01,   7.11567682e-01,\n",
       "         4.30014343e-01,   9.81398983e-01,   9.17723237e-01,\n",
       "         2.71380298e-01,   3.98299552e-01,   4.27068412e-01,\n",
       "         6.28527424e-01,   4.36229261e-01,   7.21666677e-01,\n",
       "         3.82017786e-01,   8.85426286e-01,   8.85613982e-01,\n",
       "         4.98058148e-01,   3.78013021e-01,   9.82012478e-01,\n",
       "         3.00621850e-01,   9.13661218e-01,   3.86115502e-01,\n",
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       "         3.18266559e-01,   7.03676797e-01,   7.96312273e-01,\n",
       "         3.06886799e-01,   1.34497042e-01,   9.01398111e-01,\n",
       "         1.19532481e-01,   4.50512939e-01,   8.76218908e-02,\n",
       "         2.32563801e-01,   4.97757950e-01,   3.87508834e-01,\n",
       "         6.85585133e-01,   3.60052088e-01,   2.97964316e-01,\n",
       "         5.11398447e-01,   3.97381331e-01,   2.41122601e-01,\n",
       "         7.85736514e-01,   2.98644460e-01,   1.09999419e-01,\n",
       "         6.11660339e-01,   2.45290853e-01,   3.31141044e-01,\n",
       "         6.46371577e-01,   6.35793109e-01,   2.88566199e-01,\n",
       "         9.67339183e-01,   9.59780384e-01,   1.20716476e-01,\n",
       "         8.64211105e-01,   3.24303293e-01,   2.46165529e-01,\n",
       "         8.71455572e-01,   2.52885998e-01,   2.37445949e-01,\n",
       "         5.28515886e-01,   5.48988502e-01,   3.62408315e-01,\n",
       "         3.25267517e-01,   2.98209820e-01,   5.30907136e-01,\n",
       "         2.78877782e-01,   9.05197087e-01,   9.31142159e-01,\n",
       "         4.81078257e-01,   4.69470228e-01,   3.82106163e-01,\n",
       "         3.60003918e-01,   8.91401504e-01,   1.07546923e-01,\n",
       "         7.50539595e-01,   9.62796550e-01,   5.82395841e-01,\n",
       "         9.38058809e-01,   6.39901503e-01,   8.32138266e-01,\n",
       "         5.56707702e-01,   5.66113758e-01,   5.57316386e-01,\n",
       "         3.47402908e-01,   9.62289850e-02,   9.22123223e-01,\n",
       "         6.41952081e-01])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ruido"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero veamos algo más. Supongamos que tenemos una matriz de 3x3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = np.arange(0, 9).reshape(3,3)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[0]       # primer indice: filas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero la sintaxis se extiende de una manera eficiente y compacta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 66.75 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "1000000 loops, best of 3: 226 ns per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit m[1][1]      # buuuuh!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "%timeit m[1,1]    # yeaaaa!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 6])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[:,0]    # quiero la primer columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[0:2, 0:2]   # la submatriz superior izquierda de 2x2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se acuerdan que en el slicing común había un tercer parametro opcional que era el paso? Funciona acá también"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 2],\n",
       "       [6, 8]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[::2, ::2]    # esquinas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [10, 11, 12, 13, 14, 15],\n",
       "       [20, 21, 22, 23, 24, 25],\n",
       "       [30, 31, 32, 33, 34, 35],\n",
       "       [40, 41, 42, 43, 44, 45],\n",
       "       [50, 51, 52, 53, 54, 55]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(60).reshape(6, 10)[:,:6]\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2, 12, 22, 32, 42, 52])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a[:,2:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como resumen (corregir ejemplo rojo!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://www.tp.umu.se/~nylen/pylect/_images/numpy_indexing.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejercicios\n",
    "\n",
    "1. Dado el array del gráfico (`np.arange(60).reshape(6, 10)[:,:6]`):\n",
    "   \n",
    " 1.1 Obtener la segunda columna exceptuando el valor de la primera y la última fila\n",
    " \n",
    " 1.2 Obtener toda la submatriz central de 4x4 \n",
    " \n",
    " 1.3 \"Rodear\" el array con ceros (obteniendo una matriz de 8x8)\n",
    " \n",
    " 1.4 Reconfigurar la forma para que cada \"fila\" resultante se convierta en una matriz de 2x3\n",
    "  \n",
    "2. Crear la estructura de datos de un [Cubo de Rubik](https://es.wikipedia.org/wiki/Cubo_de_Rubik) en su estado inicial: 6x3x3 donde cada una de las 6 \"caras\" *i* tiene todos sus elementos con valor *i*\n",
    "\n",
    "<!-- solucion\n",
    "https://gist.githubusercontent.com/mgaitan/1c90f89c49927d329cb6/raw/6d1f1534e8aa692b64e92c4db193500c5a3a2c16/rubik.py\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0.]],\n",
       "\n",
       "       [[ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.]],\n",
       "\n",
       "       [[ 2.,  2.,  2.],\n",
       "        [ 2.,  2.,  2.],\n",
       "        [ 2.,  2.,  2.]],\n",
       "\n",
       "       [[ 3.,  3.,  3.],\n",
       "        [ 3.,  3.,  3.],\n",
       "        [ 3.,  3.,  3.]],\n",
       "\n",
       "       [[ 4.,  4.,  4.],\n",
       "        [ 4.,  4.,  4.],\n",
       "        [ 4.,  4.,  4.]],\n",
       "\n",
       "       [[ 5.,  5.,  5.],\n",
       "        [ 5.,  5.,  5.],\n",
       "        [ 5.,  5.,  5.]]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %load https://gist.githubusercontent.com/mgaitan/1c90f89c49927d329cb6/raw/6d1f1534e8aa692b64e92c4db193500c5a3a2c16/rubik.py\n",
    "np.zeros([6,3,3]) + np.arange(6)[:,np.newaxis, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0]],\n",
       "\n",
       "       [[1]],\n",
       "\n",
       "       [[2]],\n",
       "\n",
       "       [[3]],\n",
       "\n",
       "       [[4]],\n",
       "\n",
       "       [[5]]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " np.arange(6)[:, np.newaxis,  np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   1.,   2.,   3.,   4.,   5.,   0.],\n",
       "       [  0.,  10.,  11.,  12.,  13.,  14.,  15.,   0.],\n",
       "       [  0.,  20.,  21.,  22.,  23.,  24.,  25.,   0.],\n",
       "       [  0.,  30.,  31.,  32.,  33.,  34.,  35.,   0.],\n",
       "       [  0.,  40.,  41.,  42.,  43.,  44.,  45.,   0.],\n",
       "       [  0.,  50.,  51.,  52.,  53.,  54.,  55.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.zeros((8,8))\n",
    "c[1:-1,1:-1] = a\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.arange(60).reshape(6, 10)[:,:6]\n",
    "\n",
    "a[0::2,0::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.diag(1+np.arange(4),k=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  (tip: investigar la función `diag()` y `rot90()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aleatorio = np.random.normal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aleatorio = np.random.normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.zeros((10,10)) + np.arange(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funciones de reducciones/agregación\n",
    "\n",
    "numpy tiene muchas funciones y/o métodos de \"reducción\", que sumarizan información del array. Por ejemplo: sumatoria, media, maximo y minimo, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1, 2, 3, 4])\n",
    "np.sum(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las funciones más importantes también se implementan como métodos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuando tenemos un array de más de una dimensión podemos aplicar la función por ejes, a traves del parámetro `axis`\n",
    "\n",
    "![](files/img/reductions.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([[1, 1], [2, 2]])\n",
    "x.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [1, 2]])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funciones de transformación"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función `diag` poner un array 1D en diagonal (convirtiendolo en 2D) o bien extraer una diagonal de un array 2D dado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0],\n",
       "       [0, 2, 0, 0, 0],\n",
       "       [0, 0, 3, 0, 0],\n",
       "       [0, 0, 0, 4, 0],\n",
       "       [0, 0, 0, 0, 4]])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag([1,2,3,4, 4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos decirle qué diagonal con un offset entero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0],\n",
       "       [1, 0, 0, 0],\n",
       "       [0, 2, 0, 0],\n",
       "       [0, 0, 3, 0]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag([1,2,3], k=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  7, 14, 21, 28])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag(np.arange(30).reshape(5,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`rot90` permite rotar un array multidimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  7, 11],\n",
       "       [ 2,  6, 10],\n",
       "       [ 1,  5,  9],\n",
       "       [ 0,  4,  8]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.rot90(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11, 10,  9,  8],\n",
       "       [ 7,  6,  5,  4],\n",
       "       [ 3,  2,  1,  0]])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.rot90(A, k=2)  # rotamos 180º"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ejercicio \n",
    "\n",
    "\n",
    "1. Crear un array 2D de 6x5 de la siguiente forma\n",
    "\n",
    "\n",
    "    array([[0, 0, 0, 0, 5],\n",
    "           [0, 0, 0, 4, 0],\n",
    "           [0, 0, 3, 0, 0],\n",
    "           [0, 2, 0, 0, 0],\n",
    "           [1, 0, 0, 0, 0],\n",
    "           [0, 0, 0, 0, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 5],\n",
       "       [0, 0, 0, 4, 0],\n",
       "       [0, 0, 3, 0, 0],\n",
       "       [0, 2, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.rot90(np.diag(np.arange(1,6), k=1))[:,:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Meshgrids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una funcion importante es `meshgrid`, que permite crear una \"rejilla\" para definir dominios de más de una variable a partir de arrays unidemensionales. \n",
    "\n",
    "\n",
    "Por ejemplo, si quisieramos definir un dominio de $\\R3$ (x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.] [ 2.   2.5  3.   3.5  4.   4.5  5. ]\n",
      "(7, 11) (7, 11)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[array([[  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.],\n",
       "        [  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.],\n",
       "        [  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.],\n",
       "        [  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.],\n",
       "        [  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.],\n",
       "        [  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.],\n",
       "        [  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.]]),\n",
       " array([[ 2. ,  2. ,  2. ,  2. ,  2. ,  2. ,  2. ,  2. ,  2. ,  2. ,  2. ],\n",
       "        [ 2.5,  2.5,  2.5,  2.5,  2.5,  2.5,  2.5,  2.5,  2.5,  2.5,  2.5],\n",
       "        [ 3. ,  3. ,  3. ,  3. ,  3. ,  3. ,  3. ,  3. ,  3. ,  3. ,  3. ],\n",
       "        [ 3.5,  3.5,  3.5,  3.5,  3.5,  3.5,  3.5,  3.5,  3.5,  3.5,  3.5],\n",
       "        [ 4. ,  4. ,  4. ,  4. ,  4. ,  4. ,  4. ,  4. ,  4. ,  4. ,  4. ],\n",
       "        [ 4.5,  4.5,  4.5,  4.5,  4.5,  4.5,  4.5,  4.5,  4.5,  4.5,  4.5],\n",
       "        [ 5. ,  5. ,  5. ,  5. ,  5. ,  5. ,  5. ,  5. ,  5. ,  5. ,  5. ]])]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 11)\n",
    "y = np.linspace(2, 5, 7)    \n",
    "print(x, y)\n",
    "grid = Xm, Ym = np.meshgrid(x, y)\n",
    "print(Xm.shape, Ym.shape)\n",
    "grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/meshgrid2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entonces podemos aplicar una función para este dominio, por ejemplo:  $f(x,y) = (xy)^2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0.  ,     4.  ,    16.  ,    36.  ,    64.  ,   100.  ,\n",
       "          144.  ,   196.  ,   256.  ,   324.  ,   400.  ],\n",
       "       [    0.  ,     6.25,    25.  ,    56.25,   100.  ,   156.25,\n",
       "          225.  ,   306.25,   400.  ,   506.25,   625.  ],\n",
       "       [    0.  ,     9.  ,    36.  ,    81.  ,   144.  ,   225.  ,\n",
       "          324.  ,   441.  ,   576.  ,   729.  ,   900.  ],\n",
       "       [    0.  ,    12.25,    49.  ,   110.25,   196.  ,   306.25,\n",
       "          441.  ,   600.25,   784.  ,   992.25,  1225.  ],\n",
       "       [    0.  ,    16.  ,    64.  ,   144.  ,   256.  ,   400.  ,\n",
       "          576.  ,   784.  ,  1024.  ,  1296.  ,  1600.  ],\n",
       "       [    0.  ,    20.25,    81.  ,   182.25,   324.  ,   506.25,\n",
       "          729.  ,   992.25,  1296.  ,  1640.25,  2025.  ],\n",
       "       [    0.  ,    25.  ,   100.  ,   225.  ,   400.  ,   625.  ,\n",
       "          900.  ,  1225.  ,  1600.  ,  2025.  ,  2500.  ]])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = (Xm * Ym) ** 2\n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y graficamos, por ejemplo, las curvas de nivel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ypAHgGeAYSa9L+mPgTuDD6VS4AeASgIgYpNK+GAQeAZZGRKSPWgrcDmwBtkbEo6Md1y0L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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d49e52e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.contourf(x, y, z)\n",
    "pyplot.colorbar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ejercicio\n",
    "\n",
    "1. Basado en el ejemplo de [wireframe 3D](http://matplotlib.org/examples/mplot3d/wire3d_demo.html), graficar $z = xe^{-(x^2 + y^2)}$ con $x, y \\in [-2, 2]$ con una resolución de 100 puntos en cada eje\n",
    "\n",
    "<!-- solución https://gist.githubusercontent.com/mgaitan/56a24b6e737677e5a964/raw/f3eddb9c4af6204939107b0159ced4df699c9967/wireframe.py -->\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = y = np.linspace(2, -2, 100)\n",
    "xx,yy = np.meshgrid(x, y)\n",
    "z = xx*np.exp(-(xx**2 + yy**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'module' object has no attribute '_string_to_bool'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-147-ac345543e602>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m111\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprojection\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'3d'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_wireframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrstride\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcstride\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.4/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36madd_subplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    962\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    963\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 964\u001b[1;33m             \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    965\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    966\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.4/dist-packages/matplotlib/axes/_subplots.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[0;32m     76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m         \u001b[1;31m# _axes_class is set in the subplot_class_factory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 78\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axes_class\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigbox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python3/dist-packages/mpl_toolkits/mplot3d/axes3d.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, rect, *args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m         Axes.__init__(self, fig, rect,\n\u001b[0;32m     90\u001b[0m                       \u001b[0mframeon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 91\u001b[1;33m                       *args, **kwargs)\n\u001b[0m\u001b[0;32m     92\u001b[0m         \u001b[1;31m# Disable drawing of axes by base class\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m         \u001b[0mAxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_axis_off\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.4/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, rect, axisbg, frameon, sharex, sharey, label, xscale, yscale, **kwargs)\u001b[0m\n\u001b[0;32m    435\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'axes.hold'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    436\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_connected\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m  \u001b[1;31m# a dict from events to (id, func)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 437\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcla\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    438\u001b[0m         \u001b[1;31m# funcs used to format x and y - fall back on major formatters\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    439\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfmt_xdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python3/dist-packages/mpl_toolkits/mplot3d/axes3d.py\u001b[0m in \u001b[0;36mcla\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1043\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_zmargin\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1044\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1045\u001b[1;33m         \u001b[0mAxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcla\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1046\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1047\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'axes3d.grid'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.4/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mcla\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    906\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontainers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    907\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 908\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gridOn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwhich\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'axes.grid.which'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    909\u001b[0m         props = font_manager.FontProperties(size=rcParams['axes.titlesize'],\n\u001b[0;32m    910\u001b[0m                                         weight=rcParams['axes.titleweight'])\n",
      "\u001b[1;32m/usr/lib/python3/dist-packages/mpl_toolkits/mplot3d/axes3d.py\u001b[0m in \u001b[0;36mgrid\u001b[1;34m(self, b, **kwargs)\u001b[0m\n\u001b[0;32m   1254\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1255\u001b[0m             \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1256\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_grid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_string_to_bool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1257\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1258\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mticklabel_format\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'module' object has no attribute '_string_to_bool'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d4869320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import axes3d\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot_wireframe(xx, yy, z, rstride=10, cstride=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fancy indexing  y máscaras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy permite indizar a través de una secuencia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 27,  2, 27, 17,  7,  3,  5, 15,  7])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.random_integers(0, 30, 10)   # 10 enteros aleatorios entre [0, 30]\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esto se conoce como *fancy indexing*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27,  2, 17, 27])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[[1, 2, 4, 1]]    # selecciona el elemento 1, 2, el 4 y  de nuevo el 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por otro lado, podemos indexar elementos a traves de un array (o cualquier secuencia array-like) de **tipo booleano**. Esto es, hacer una **máscara**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21,  2, 27,  3])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[np.array([True, False, True, True, False, False, True, False, False])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero por otro lado sabemos que via broadcasting podemos obtener un array booleano "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False,  True,  True, False, False, False, False, False], dtype=bool)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a > 15    # qué elementos de a son mayores a 10 ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por lo tanto, podemos \"filtrar\" los valores que cumplan determinada condición en un array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 27, 27, 17, 15])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a > 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Más aun, podemos combinar condiciones a través de operadores lógicos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 17, 15])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[(a > 10) & (a < 25)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Este tipo de slicing especial **crea copias**, no vistas. Usar cuando lo amerite.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Si en vez de los valores que cumplen una condición, queremos las posiciones, podemos usar la función `where`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.where(a > 15)     # devuelve las posiciones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Además, la función where funciona como estructura ternaria a nivel arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 27,  0, 27, 17,  0,  0,  0, 15,  0])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = 0\n",
    "# para cada i-elemento a[i] si True, si no b[i] (o constante)\n",
    "np.where(a > 10, a, b)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ejemplos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://scipy-lectures.github.io/_images/numpy_fancy_indexing.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lectura desde texto y archivos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como numpy se especializa en manejar números, tiene muchas funciones para crear arrays a partir de información numérica a partir de texto o archivos (como los CSV, por ejemplo)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1. ,  2.3,  3. ,  4.1],\n",
       "       [-3.1,  2. ,  5. ,  4.5]])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_desde_str = np.fromstring(\"\"\"1.0 2.3   3.0 4.1\n",
    "-3.1 2  5.0 4.5\"\"\", sep=\" \", dtype=float)\n",
    "a_desde_str.shape = (2, 4)\n",
    "a_desde_str"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para cargar desde un archivo existe la función `loadtxt`. Por ejemplo tenemos el archivo `data/critical.dat` que es el resultado del cálculo de una linea crítica global para un sistema químico binario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T(K)     P(bar)   d(mol/L)   x(1)\r\n",
      " CRI\r\n",
      " 304.2100   73.8300    8.7568  0.000000 0.10000E+01\r\n",
      " 304.2056   73.8318    8.7570  0.000050 0.99995E+00   3   1\r\n",
      " 304.2011   73.8340    8.7572  0.000117 0.99988E+00   2   1\r\n",
      " 304.1920   73.8384    8.7577  0.000250 0.99975E+00   2   1\r\n",
      " 304.1740   73.8472    8.7586  0.000517 0.99948E+00   2   1\r\n",
      " 304.1379   73.8649    8.7604  0.001050 0.99895E+00   2   1\r\n",
      " 304.0657   73.9002    8.7640  0.002117 0.99788E+00   2   1\r\n",
      " 303.9211   73.9706    8.7711  0.004250 0.99575E+00   2   1\r\n"
     ]
    }
   ],
   "source": [
    "!head data/critical.dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.loadtxt?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos que el patrón es en columnas separadas por espacios en blanco y las dos primeras filas son headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(211, 4)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cri_data = np.loadtxt('data/critical.dat', skiprows=2, usecols=[0, 1, 2, 3])\n",
    "cri_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por defecto, devuelve una matriz 2D `numero_lineas` x `columnas`, o sea, la fila 0 es la primer linea de números"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 304.21  ,  304.2056,  304.2011,  304.192 ,  304.174 ,  304.1379,\n",
       "        304.0657,  303.9211,  303.6309,  303.2894,  302.9461,  302.6013,\n",
       "        302.2547,  301.9064,  301.5565,  301.2048,  300.8514,  300.4962,\n",
       "        300.1394,  299.7807,  299.4203,  299.0581,  298.6941,  298.3283,\n",
       "        297.9606,  297.5911,  297.2198,  296.8466,  296.4716,  296.0946,\n",
       "        295.7158,  295.335 ,  294.9524,  294.5677,  294.1812,  293.7927,\n",
       "        293.4022,  293.0097,  292.6152,  292.2187,  291.8201,  291.4195,\n",
       "        291.0169,  290.6122,  290.2054,  289.7965,  289.3855,  288.9724,\n",
       "        288.5572,  288.1398,  287.7202,  287.2984,  286.8745,  286.4484,\n",
       "        286.02  ,  285.5894,  285.1566,  284.7215,  284.2841,  283.8444,\n",
       "        283.4025,  282.9582,  282.5116,  282.0627,  281.6114,  281.1577,\n",
       "        280.7017,  280.2432,  279.7824,  279.3192,  278.8535,  278.3854,\n",
       "        277.9148,  277.4417,  276.9662,  276.4882,  276.0077,  275.5246,\n",
       "        275.0391,  274.551 ,  274.0603,  273.5671,  273.0714,  272.573 ,\n",
       "        272.0721,  271.5686,  271.0625,  270.5537,  270.0424,  269.5284,\n",
       "        269.0117,  268.4925,  267.9705,  267.446 ,  266.9187,  266.3888,\n",
       "        265.8562,  265.321 ,  264.7831,  264.2425,  263.6992,  263.1532,\n",
       "        262.6045,  262.0532,  261.4991,  260.9424,  260.383 ,  259.821 ,\n",
       "        259.2562,  258.6888,  258.1187,  257.546 ,  256.9706,  256.3925,\n",
       "        255.8118,  255.2285,  254.6426,  254.0541,  253.4629,  252.8692,\n",
       "        252.2729,  251.6741,  251.0727,  250.4688,  249.8625,  249.2536,\n",
       "        248.6422,  248.0284,  247.4122,  246.7935,  246.1725,  245.5491,\n",
       "        244.9234,  244.2953,  243.665 ,  243.0323,  242.3974,  241.7603,\n",
       "        241.121 ,  240.4795,  239.8358,  239.19  ,  238.542 ,  237.892 ,\n",
       "        237.2398,  236.5856,  235.9294,  235.2711,  234.6108,  233.9484,\n",
       "        233.2841,  232.6178,  231.9494,  231.2791,  230.6067,  229.9324,\n",
       "        229.256 ,  228.5776,  227.8971,  227.2145,  226.5299,  225.843 ,\n",
       "        225.154 ,  224.4628,  223.7692,  223.0734,  222.3751,  221.6743,\n",
       "        220.971 ,  220.265 ,  219.5564,  218.8448,  218.1304,  217.4129,\n",
       "        216.6923,  215.9683,  215.2409,  214.51  ,  213.7753,  213.0367,\n",
       "        212.2941,  211.5473,  210.7961,  210.0403,  209.2797,  208.5141,\n",
       "        207.7433,  206.967 ,  206.1851,  205.3974,  204.6034,  203.8032,\n",
       "        202.9963,  202.1825,  201.3615,  200.5331,  199.697 ,  198.8528,\n",
       "        198.0004,  197.1393,  196.2693,  195.3901,  194.5014,  193.6027,\n",
       "        192.6939,  191.7744,  190.8441,  190.7051,  190.6356,  190.6008,\n",
       "        190.564 ])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cri_data[:, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si directamente queremos los vectores (las columnas), podemos pedir que \"desempaque\" las columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t, p, d, x = np.loadtxt('data/critical.dat', skiprows=2, usecols=[0, 1, 2, 3], unpack=True)   # o loadtxt().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos graficar algo sencillo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Mf/UVpY88Am+8QdlZZ0HnzsnKl4D5iuny8nIARo8enczrECTtCtwP7A4sA+4GJgBbAfPN\nbIik84Eib1R2qbFqVbie4OKL4a9/hf7903NFcVo9+ST84Q9w6KEwdCi0aRN3osRKbKOymU0D7gEm\nAW9Hi28HrgK6SZpJOFq4Kl8Z4pJZeqeR569GeTl06xbquV99FQYOzEth4K9/JYcfHnpXhXDm1ksv\nNez+K0n765+LvFZ2mtlQM9vRzHY2s15m9p2ZLTCzA82sk5kdZGYL85nBuQZx773h4qqDDw79Dm27\nbdyJmpY2beC228IZSccfH47Ovvsu7lSNjndd4VxNliwJV9ROnBguLNtll7gTuXnz4JRT4PPP4YEH\n4Gc/iztRYiS2ysi51Js6FX7xizD+8MSJXhgkRbt2oV2hd2/Ya69wppdrEF4g5EHa6yCbfH6z0HDc\nrRtcdFEYk2DddRskWzaa/OufDSkcuZWVwVVXQd++obuQBpD21z8XXiA4l+nbb+Hkk2HEiHD1bM+e\ncSdyNdlppzDG9IIFsO++YTwJV2/ehuBchY8/ht/8JtRJjxxZ0KMClyMzuOaacFrqPfeExv8myNsQ\nnGsIL74YBq//3e9CQ6UXBukihc7xHn44HOENGeL9IdWDFwh5kPY6yCaXf8SIUBDcd1/4UYl5gPgm\n9/o3pK5dQxXSQw+FM5FWrKjzLtL++ufCCwTXdH3/fRi567rrwoVmBxwQdyLXELbYAsaPh4ULw4kB\nX34Zd6LU8DYE1zQtXRoajL/+GsaObZxjGjd1q1bBhRfCI4+EoTu33z7uRHnnbQjO1dVnn4VeNdu2\nDcNZemHQODVrFk5JHTQo9Jw6fnzciRLPC4Q8SHsdZKPO/8EHsPfe4WyiO++EVq0Klitbjfr1j0Pv\n3qF96Jhj4PHHa109cfkLqEXcAZwrmIkT4YgjwgAsJ58cdxpXSN26wb//Db/+dej64vTT406USN6G\n4JqGceNCm8GoUaFQcE3Thx+GaxROOilchR7zGWUNLdc2BC8QXOP38MPQr19oXPzlL+NO4+I2bx50\n7x6qDm+4oVGNcOeNygmU9jrIRpV/5MgwbsG4cakpDBrV659E7dqFCxEnTw5VR6tW/ejuxOfPIy8Q\nXON1881w2WWhAzTvqdRlatMmnGE2c2ZodF65Mu5EieBVRq5xuvZauPFGeP556Ngx7jQuqb75Bo46\nKpx6fN990LJl3Ily4m0IzlU2eHA4pfT556FDh7jTuKRbtgx69AjjXowZA2utFXeievM2hARKex1k\nqvNfeillt94axt1NaWGQ6tefFOZfe+1wtXrz5tCjB2XjxsWdKDZ5LRAkbStpSsZtkaQBki6WNDtj\n+SH5zOGaiCuvhAcfDH0Tbb553GlcmrRqFTrEa94cLr+8ybYpFKzKSFIz4FOgC3AKsNjMrqlhfa8y\nctm75powAPtLL3lh4Opv+XI48kjYeGMYPToUECmSpiqjA4EPzewTQNHNudzddFO4vfCCFwYuN2ut\nFaqPPv0UzjhjjVNSG7tCFgjHAw9G0wb0kzRN0ihJRQXMkXepq0OtJFX5b78dhg0LhcGWWwIpy18F\nzx+vsgkTQu+oM2bAgAFNaqCdglQZSWpFqC7awcy+kLQp8EV092XAZmbWp9I21qtXL4qLiwEoKiqi\npKSE0tJSYPWHLonzmV+IJORptPnHjaN09GgoK6Ns9uz05a9m3vMnJP+SJZRecgkccABlhxySmHyZ\n8xXT5eXlAIwePTr5p51KOhL4g5mt0XgsqRh4wsx2rrTc2xBc9Z54Ak47LRwZ7LBD3GlcYzV/frjC\n/dRTw2BKCZdrG0Khejv9Hauri5C0mZnNiWaPBqYXKIdrDMaPhz594MknvTBw+bXRRuGK5n33hU03\nhRNPjDtRXuW9DUHSuoQG5bEZi4dIelvSNKArMDDfOQop83AujRKdf+rU0K/9Aw9Aly5VrpLo/Fnw\n/PFaI/+WW4aus889F55+OpZMhZL3IwQzWwpsXGnZ7/P9uK4R+s9/4NBD4ZZb4MAD407jmpIddoDH\nHgunpD7xBOyxR9yJ8sK7rnDpMGcO7LMPnH8+9O0bdxrXVD31VKiuLCuD7baLO80a0nQdgnP1s2QJ\nHH546JXSCwMXp8MOC+M0d+8Oc+fGnabBeYGQB42uDjVOK1fC8cdDSQn87W9ZbZKo/PXg+eNVa/7e\nveGKK6B160LEKSgfU9kllxn07w8rVsCIEY1uuEOXYj17xp0gL7wNwSXXsGFw773wyiuwwQZxp3Eu\n8dJyHYJzdTNmTBjv9vXXvTBwrkC8DSEPGn0dar698gr06xcuPNtiizpvHnv+HHn+eKU9fy68QHDJ\nMnNmGL3q3nth113jTuNck+JtCC45vvoK9twzXBF62mlxp3EudXxMZdc4rFwZzvHebju4/vq40ziX\nSn5hWgKlvQ4ylvx/+UsYjGT48Jx35a9/vDx/evlZRi5+d94Z+od5801o4R9J5+LiVUYuXq++Ckcf\nDS+/nMi+YZxLE68ycun18cdw7LFhMHMvDJyLnRcIeZD2OsiC5F+6NHQlfPbZoaOwBuSvf7w8f3p5\ngeAKzwxOPhl22QXOOSfuNM65iLchuMK7+urQNcX48bD22nGnca7R8L6MXLq8+GIoECZM8MLAuYSp\nscpI0jlZ3E4vVNi0SHsdZN7yz54dug2+7z7o0CE/j4G//nHz/OlVWxvCucB6NdzWj9ZZg6RtJU3J\nuC2S1F9SW0njJM2U9KykooZ7Oi6xli8PfRT17+/jITuXUDW2IUgaZmZ/rnEH2a3TDPgU6AL0A740\ns6GSzgM2NLPzq9jG2xAakzPPhM8+g7FjfaAb5/Ik730ZRT/mPczs4Xo/iHQQ8Dcz+6Wk94GuZjZP\nUnugzMzWOAndC4RG5J574PLLYeJEaNMm7jTONVp5vzDNzFYB59X3ASLHAw9G0+3MbF40PQ9ol+O+\nEyftdZANmn/q1HBq6dixBSsM/PWPl+dPr2zPMhon6VxgDLC0YqGZLahtQ0mtgF9TRaFiZiap2sOA\n3r17U1xcDEBRURElJSWUlpYCq980n0/w/OLFlP7pT3DDDZR9+SWUlSUrn8/7fMrnK6bLy8tpCFld\nhyCpHFhjRTPrmMW2RwJ/MLNDovn3gVIzmytpM+BFrzJqhMxCH0UdOoShMJ1zeVeQ6xDMrLi+DwD8\njtXVRQD/BHoBQ6K/j+ewb5dU118Pn34KD9e76ck5V2BZd10haSdJx0n6fcUti23WBQ4ExmYsvgro\nJmkm8KtovlHJPJxLo5zzT5wIV14ZrkZu1apBMtVFk3/9Y+b50yurIwRJFwNdgR2Bp4DuwCvAPTVt\nZ2ZLgY0rLVtAKCRcY7RwIfz2tzBiBGy9ddxpnHN1kG0bwjvArsBkM9tVUjvgfjPL2w+7tyGkkBkc\ncwxssYW3GzgXg0L1ZfStmX0vaaWkNsDnwJb1fVDXSN10Uxjj4MEHa1/XOZc42bYhTJS0IXAHMAmY\nAryWt1Qpl/Y6yHrlnzQJLrsstBustVaDZ6qLJvn6J4jnT69szzL6YzQ5QtLTwAZm9nb+YrlUWbQo\ntBvccgtss03caZxz9ZRtG4KA3wD7Eq5HGG9mj+U1mLchpINZKAw22QRuvjnuNM41aYVqQ7gF2IZw\nPYGA0yV1yzhycE3VqFEwc2bor8g5l2rZtiHsDxxiZneZ2Z3AoYRrCFwV0l4HmXX+99+HCy4IjcgJ\nGuymybz+CeX50yvbAuFDIHNEkw7RMtdULV8Ov/sdXHEFbL993Gmccw2gtvEQnogmNyCMZTCB0IbQ\nBZhoZl3zFszbEJLtnHNg1ix49FEf38C5hMh3G8Lw6K8R2g4y+a91U/XMM6GPoqlTvTBwrhGpscrI\nzMqi20sZ0z8sK1TItEl7HWSN+T//HE4+OTQib7RRwTLVRaN+/VPA86dXjQWCpCdr20E267hGwiwU\nBr17w/77x53GOdfAamtDWAS8XMs+dspmXIS68jaEBLrhBrj/fnjlFWjZMu40zrlK8t2GcGQW+1he\n3wd3KfL226Frijfe8MLAuUYq2zaEmm6vFypsWqS9DnKN/MuWQc+ecPXVqeiaotG9/inj+dMr6wFy\nXBM2aBBstx38vtYxkZxzKZZVX0Zx8DaEhCgrgxNOgGnTYOONa13dORefXNsQ6jKEZmtJ29b3gVwK\nLVoUzigaOdILA+eagKwKBElHEMZAeCaa7yzpn/kMlmZpr4P8IX///tC9e7ilSKN5/VPK86dXtr2d\nXgzsAbwIYGZTJGU1YK6kImAkYTxmA04BDgFOBb6IVrvAzJ7OPrbLu0cfhddeC1cjO+eahGzHQ3jT\nzPaQNMXMOkfL3jazXbLYdjTwkpndKakFsC7wJ2CxmV1Tw3behhCXOXOgc2d4/HHYc8+40zjnslSo\n8RDelXQC0ELSz4D+ZDGEZjT+8i/NrBeAma0EFoXxdtboG8klgRn06QN9+3ph4FwTk22j8lmEKp/l\nhEFyvib8l1+bjsAXku6SNFnSHZJaR/f1kzRN0qioWqnRSHUd5G23Ufbf/8Lf/hZ3knpL9euP549b\n2vPnotYqo6iaZ5yZ1bnzGkm7Aa8De5vZREnXEQqTG4Evo9UuAzYzsz6VtrVevXpRXFwMQFFRESUl\nJZSWlgKr37Qkzmd+oJKQJ+v52bMpHTiQsuHDoUOH+PPUcz61r7/nT8R8mvJXTJeXlwMwevTonKqM\nsm1DeB44xswW1mnnUnvg9Yq+jiTtC5xvZodnrFMMPGFmO1fa1tsQCmnlSth333DNQb9+cadxztVD\nodoQlgLTJY2LpgHMzPrXtJGZzZX0iaROZjYTOJDQHtHezOZGqx0NTK9PeNeArroK1l8fzjwz7iTO\nuZhk24YwFvgboefTtzJu2egH3C9pGrALMBgYKuntaFlXYGCdUidc5uFcKkyeHHoyvesuaNYsffkr\n8fzx8vzpldURgpndXd8HMLNpwO6VFnunOEmxfHm4Gnn4cNhii7jTOOdilG0bwqwqFpuZZXVxWn14\nG0KBDBoE06eHaw58OEznUq1QbQiZ/+GvDfQAkjl+osvexIlwxx2h4zovDJxr8rJqQzCzLzNus83s\nOuCwPGdLrVTUQS5bFqqKrrsO2rf/0V2pyF8Dzx8vz59eWR0hSPoFoR8iCIXIbkDzfIVyBXDxxbD9\n9nD88XEncc4lRLZtCGWsLhBWAuXA1Wb2Qd6CeRtC/rzxBhx1VBgWc9NN407jnGsgBWlDMLPS+j6A\nS5hvv4VeveDGG70wcM79SLbjIQyQtIGCUVG/RAfnO1xaJboOctCg0JPpscdWu0qi82fB88fL86dX\nthem9TGzr4GDgLaE6wiuylsqlx+vvAIPPAA33RR3EudcAmXbhjDdzHaWdANQZmZjM8dGyEswb0No\nWEuXQkkJDBsW2g+cc41Orm0I2RYIdwObA1sDuxLOMHrRzH5R3wfO4jG9QGhIAwbAggVw771xJ3HO\n5UmuBUK2VUanABcAu5nZUqAlcHJ9H7SxS1wd5EsvhSExr78+q9UTl7+OPH+8PH96ZVsg7AV8YGYL\nJZ0EDAIW5S+WazBLl8Ipp8Ctt0LbtnGncc4lWNZtCISeSncB7gZGAseZWde8BfMqo4YxcCB88QXc\nd1/cSZxzeVaovoxWmplJOgq42cxGSupT61YuXq+9Bg89BO+8E3cS51wKZFtltFjShcCJwJOSmhPa\nEVwVElEHuWxZqCq68UbYqG79ECYifw48f7w8f3plWyD8FlgGnBKNdPYTYFjeUrncXXop7LQT9OgR\ndxLnXEpk1YYAP4x9/FMze05Sa6BFdLFafoJ5G0L9vfUWHHpo6Na6Uk+mzrnGqyCnnUrqC/wduC1a\ntAXwWH0f1OXRihWhqmj4cC8MnHN1km2V0ZnAvsDXAGY2E/Ce0aoRax3kVVfBllvCCSfUexdpr0P1\n/PHy/OmV7VlGy81suaJRtSS1YHV32NWSVEQ4RXXHaP2Tgf8AY4CtCN1oH2dmC+uc3K1p+vTQiDxl\nio+A5pyrs2yvQxgGLCR0ancW8Edghpn9tZbtRgMvmdmdUSGyLvBX4EszGyrpPGBDMzu/im29DaEu\nVq6EvfaCvn3htNPiTuOci0Gh+jJqBpxK6O0U4BlgZE2/2JLaAFPMbOtKy98HuprZPEntCZ3lbVfF\n9l4g1MUZkzjJAAASLklEQVSwYfDMMzBunB8dONdE5b1ROfrPfoaZ3W5mPaLbHVn8WncEvpB0VzR+\nwh2S1gXamdm8aJ15QLv6hk+qgtdBfvABDBkCd9zRIIVB2utQPX+8PH961dqGYGYrJX0gaSsz+6iO\n+/45cJaZTZR0HfCjqqHo6udqC5bevXtTXFwMQFFRESUlJZSWlgKr37QmP7/fftCnD2U9e8JHH1Ha\nsWOy8vm8z/t83uYrpsvLy2kI2VYZjQc6AxOApdFiM7MjatimPfC6mXWM5vcl9Ji6NbC/mc2VtBmh\nG22vMqqvG2+EMWPg5ZehWbYnjTnnGqNC9WU0qOLxMpbV+Gsd/eB/IqlTdJrqgcC70a0XMCT6+3jd\nIrsfzJoFl1wS+izywsA5l6Maf0UkrSNpIHAcsB3wqpmVRbeXsth/P+B+SdMIPaVeQRh6s5ukmcCv\naIRDcWYezuWNWTib6C9/gU6dGnTXBcmfR54/Xp4/vWo7QhgNrADGA4cCOwADst25mU0Ddq/irgOz\n3YerxqhRsGgRnH123Emcc41EjW0IFWMpR9MtgIn5HEe50mN7G0J1Zs+Gzp3hhRdg553jTuOcS4h8\nn3a6smLCzFbWtKIrEDM44wzo188LA+dcg6qtQNhF0uKKG7BzxnzeejpNu7zWQT7wAHz8MZy/xsXd\nDSbtdaieP16eP71qbEMws+aFCuKy8Pnnoc3gqaegVau40zjnGpmsx0MoNG9DqMLxx8NWW4Wrkp1z\nrpJCXYfg4vaPf8DkyXDXXXEncc41Un41Ux40eB3kwoVw5pkwciSss07D7rsKaa9D9fzx8vzp5QVC\nGpx7LhxxBOy3X9xJnHONmLchJN3zz4chMadPhw02iDuNcy7BCjKmsovJ0qWhe4oRI7wwcM7lnRcI\nedBgdZCDBsG++0L37g2zvyylvQ7V88fL86eXn2WUVK+/Dg89BO+8E3cS51wT4W0ISbR8eeir6JJL\n4Nhj407jnEsJb0NojC6/HLbdFnr0iDuJc64J8QIhD3Kqg5w2DW67DW65pUHGR66PtNehev54ef70\n8gIhSVauDKeYXnUVbLZZ3Gmcc02MtyEkyZAh8Nxz8OyzsR0dOOfSK9c2BC8QkmLmTNh7b5g4ETp2\njDuNcy6FvFE5gepcB7lqFfTpA//3f4koDNJeh+r54+X50yvvBYKkcklvS5oiaUK07GJJs6NlUyQd\nku8ciTZiRCgUzjwz7iTOuSYs71VGkmYBvzCzBRnLLgIWm9k1NWzXNKqMPvoIdtsNXn4Ztt8+7jTO\nuRRLS5VRVQG91bRifOSBA70wcM7FrhAFggHPSZok6bSM5f0kTZM0SlJRAXIUTNZ1kPfeC3Pnwp//\nnNc8dZX2OlTPHy/Pn16F6MtoHzObI2kTYJyk94FbgUuj+y8DhgN9Km/Yu3dviouLASgqKqKkpITS\n0lJg9ZuW2vmxY2HAAEqffx5atow/j8/7vM+nbr5iury8nIZQ0NNOo7aDJWY2PGNZMfCEme1cad3G\n3YZw7LHw05/C4MFxJ3HONRKJbkOQ1FrS+tH0usBBwHRJ7TNWOxqYns8ciTN2LLz9Nlx0UdxJnHPu\nB/luQ2gHjJc0FXgTeNLMngWGRqeiTgO6AgPznKOgMg/n1vDVV3DWWTBqFKy9dsEy1UWN+VPA88fL\n86dXXtsQzGwWUFLF8t/n83ET7eyz4ZhjwsA3zjmXIN51RSE9+yz07RvGR15//bjTOOcamUS3IbgM\nS5aEwuC227wwcM4lkhcIeVBlHeSFF0JpKRx8cKHj1Fna61A9f7w8f3r5mMqF8Oqr8MgjPj6ycy7R\nvA0h35Ytg5ISuOKK0JjsnHN54uMhJN2FF4axDh55JO4kzrlGzhuVE+iHOsgpU2DkSLjppljz1FXa\n61A9f7w8f3p5gZAv330XxkceNgzat699feeci5lXGeXLlVeGMQ7+/W8fH9k5VxDehpBE778frkR+\n6y3Yaqu40zjnmghvQ0ia77+nrEcPuOSS1BYGaa9D9fzx8vzp5QVCQ7vlllBF9Ic/xJ3EOefqxKuM\nGlJ5eRgf+dVXYdtt407jnGtivMooKcxCX0XnnuuFgXMulbxAaCh33w3z58O556a+DtLzx8vzxyvt\n+XPhfRk1hDlz4LzzQvfWLfwldc6lk7ch5Mos9FG0ww5w+eVxp3HONWG5tiH4v7O5evRReO89eOCB\nuJM451xO8tqGIKk8Gjt5iqQJ0bK2ksZJminpWUlF+cyQV/PnQ//+a4yPnPY6SM8fL88fr7Tnz0W+\nG5UNKDWzzmbWJVp2PjDOzDoBz0fz6TRwIBx7LOy9d9xJnHMuZ3ltQ5A0C9jNzOZnLHsf6Gpm8yS1\nB8rMbLsqtk12G8K//w1//GMYH3m99eJO45xzib8OwYDnJE2SdFq0rJ2ZzYum5wHt8pyh4X39NZxx\nBtx+uxcGzrlGI98Fwj5m1hnoDpwp6ZeZd0aHAAk+DKjGBRfAgQdCt25V3p32OkjPHy/PH6+0589F\nXs8yMrM50d8vJD0GdAHmSWpvZnMlbQZ8Xt32vXv3pri4GICioiJKSkooLS0FVr9pBZ9v1gwef5yy\nESOgrCz+PD7v8z7fZOcrpsvLy2kIeWtDkNQaaG5miyWtCzwLXAIcCMw3syGSzgeKzGyNhuVEtiF8\n+y3suisMHQpHHRV3Guec+5HEjocgqSPwWDTbArjfzAZLags8DHQAyoHjzGxhFdsnr0A477zQgd2Y\nMXEncc65NSS2UdnMZplZSXTbycwGR8sXmNmBZtbJzA6qqjBIpEmTQn9FN9xQ66qZh3Np5Pnj5fnj\nlfb8ufDO7bKxYgX06QPDh0O79J0U5Zxz2fC+jLJx+eXw2mvw1FM+PrJzLrES24aQq8QUCDNmQNeu\nMHkybLll3Gmcc65aiW1DaBS+/z5UFV16aZ0Kg7TXQXr+eHn+eKU9fy68QKjJdddBq1Zw+ulxJ3HO\nubzzKqPqvP8+7LsvTJgAW28dXw7nnMuSVxnlw8qV0KtXqCrywsA510R4gVCV4cNDp3VnnFGvzdNe\nB+n54+X545X2/LnwEdMqe/dduPpqmDgRmnl56ZxrOrwNIdN338Fee0HfvuHmnHMp4m0IDWnoUGjb\nFk47rfZ1nXOukfECocLbb4fTTEeNyvlq5LTXQXr+eHn+eKU9fy68QIBQVdSrFwwZ4lcjO+eaLG9D\nALjkEnjzTe+ryDmXat6XUa6mTIGDDw5/f/KT/D+ec87liTcq52LFilBVNHx4gxYGaa+D9Pzx8vzx\nSnv+XDTtAuGyy6BjRzjxxLiTOOdc7JpuldGkSXDYYTBtGrRvn7/Hcc65AvEqo/p65hm49lovDJxz\nLpL3AkFSc0lTJD0RzV8saXa0bIqkQ/KdoUp//Sv07JmXXae9DtLzx8vzxyvt+XNRiCOEAcAMoKL+\nx4BrzKxzdHu6ABkKaurUqXFHyInnj5fnj1fa8+cirwWCpC2AQ4GRQEW9ljKmG6WFCxfGHSEnnj9e\nnj9eac+fi3wfIVwL/BlYlbHMgH6SpkkaJakozxmcc85lIW8FgqTDgc/NbAo/PiK4FegIlABzgOH5\nyhCX8vLyuCPkxPPHy/PHK+35c5G3004lXQmcBKwE1gY2AB41s99nrFMMPGFmO1exfTLPh3XOuQRL\nfNcVkroC55rZryVtZmZzouUDgd3NLD+n+zjnnMtaoUZME6vPMhoqaddofhZweoEyOOecq0Fir1R2\nzjlXWLFcqSzpTknzJE3PWNZF0oToYrWJknbPuO8CSf+R9L6kg+LInEnSlpJelPSupHck9Y+Wt5U0\nTtJMSc9mnkGVlOdQQ/Zhkt6Lzv4aK6lN0rJHWarMn3H/OZJWSWqbsSwV+SX1i96DdyQNyVie+Pxp\n+f5KWlvSm5KmSpohaXC0PPHf3ShLdfkb5vtrZgW/Ab8EOgPTM5aVAQdH092BF6PpHYCpQEugGPgQ\naBZH7oys7YGSaHo94ANge2Ao8Jdo+XnAVUl7DjVk71aRCbgqidlryh/Nbwk8TaiKbJum/MD+wDig\nZXTfJinLn6bvb+vobwvgDWDfNHx3a8nfIN/fWI4QzGw88FWlxXOAilKtCPg0mj4SeNDMvjOzcsIT\n6lKInNUxs7lmNjWaXgK8B/wEOAIYHa02Gjgqmk7Mc6gm++ZmNs7MKq4XeRPYIppOTHaoPn909zXA\nXyptkob8PwHOAAab2XfRfV9Em6Qlf5q+v99Ek62A5oTfosR/dytUkX9BQ31/k9S53fnAcEkfA8OA\nC6LlmwOzM9abTfgAJkJ06mxnwpvQzszmRXfNA9pF04l8DpWyZzoF+Fc0ncjs8OP8ko4EZpvZ25VW\nS0V+oBOwn6Q3JJVJ2i1aLQ353yBF319JzSRNJXxHXzSzd0nRd7eK/DMqrVLv72+SCoRRQH8z6wAM\nBO6sYd1EtIRLWg94FBhgZosz77NwvFZTzlifQ5T9EUL2JRnL/wqsMLMHatg89tc/Mz/hSvgLgYsy\nV6lh80Tljz47LYANzWxPwtX9D9ewedLyLyFF318zW2VmJYT/oveTtH+l+xP93a0if2nFfbl+f5NU\nIHQxs8ei6UdYfVjzKaFuuMIWrD4cjY2kloTC4F4zezxaPE9S++j+zYDPo+WJeg4Z2e/LyI6k3oS+\np07IWD1R2aHK/NsQ6kenSZpFyPiWpHakIz+E/9zGApjZRGCVpI1JT/5UfX8BzGwR8BTwC1Ly3c2U\nkX83aKDvb4wNI8X8uFF5MtA1mj4AmFipUaQVocuL/xKdLhtjdgH3ANdWWj4UOC+aPp81G3Zifw41\nZD8EeBfYuNLyxGSvKX+ldapqVE50fsL1OJdE052Aj1OWPxXfX2BjoCiaXgd4Ocqb+O9uLfkb5Psb\n15N6EPgMWAF8ApxMKOXejMK/DnTOWP9CQmPI+0RnMsR5I7Tqr4qyToluhwBtgeeAmcCzFW9ckp5D\nNdm7A/8BPspYdkvSsteUv9I6/yMqEFKS/xDCWSD3AtOBt4DSFOXvnpbvL7AzofCaCrwN/Dlanvjv\nbi35G+T76xemOeecA5LVhuCccy5GXiA455wDvEBwzjkX8QLBOecc4AWCc865iBcIzjnnAC8QXAJJ\n2ijqRnmKpDmSZkfTkyUValCnrEjqKmmvPO27WNK3kiZnLMvsZuRQSR9I6iBpoKSPJN2YjyyuaUjU\nl8s5ADObT+g0DUkXAYvN7Jq48khqbmbfV3P3/sBiwsVY2e6vhZmtzHL1D83s5xnzFu3jAOB64CAz\n+xi4VtICom4MnKsPP0JwaSBJv4h6AZ0k6emMfmfKJF0TDcrynqTdJT0WDXRyWbROcTQ4yH3RoCJ/\nl7ROdF9N+71W0kRggKTDo55IJ0cDqWwa9fZ5OjAwWr6vpLslHZMRfEn0t1TSeEn/AN6JeqwcpjCo\nzDRJfevwYuwH3A4cZmazMu/K4TV2zgsElwoCbgB6mNluwF3AFdF9Biw3s92BW4F/EMYW2AnoLWnD\naL1OwM1mtgPwNfDHqPrpRuCYavbb0sx2j45OXjGzPaP/1scQBlMpB0YA15jZz83sFdbsSTJzvjOh\nR9DtgFOBhWbWhdAR3GlRAVObtYHHgCPNbGYNj+VcnXmVkUuDtQg/8OMkQRgU5LOM+/8Z/X0HeMei\nfu0l/Y/Q0+PXwCdmVlGtcx/QnzC62o7Ac9Xsd0zG9JaSHiaMGNaK0F9ShWz/M59gZh9F0wcBO0vq\nEc1vAPwUKK9lHyuAVwkFyp+yfFznsuIFgksDAe+a2d7V3L88+rsqY7pivuIznvnfs6L52va7NGP6\nRuBqM3tSUlfg4mq2WUl05C2pGaHwqGp/AGeZ2bhq9lOdVcBxwAuSLjCzwXXc3rlqeZWRS4PlwCaS\n9oTQH7+kHeq4jw4V2wM9gfGE8YBr2m/mf/4bsProoXfG8sXA+hnz5YT+9SEMy9iymjzPsLraCkmd\nJLXO5omY2TLgMOAESadks41z2fACwaXB90APYEg0dOAUoKpTPWsa6eoD4ExJMwhj/95qYfzimvab\nua+Lgb9LmgR8kXHfE8DR0Wmx+wB3AF2j/e0JLKlmfyOBGcBkSdMJ7R/ZHLEbgJl9Reg2e5Ckw7PY\nzrlaeffXrtGLGmufMLOdY45SJ3XNHY2Y9Qsz65fHWK4R8yME11Sk8T+flUCbzAvTqiNpIGGkr0V5\nT+UaLT9CcM45B/gRgnPOuYgXCM455wAvEJxzzkW8QHDOOQd4geCccy7iBYJzzjkA/h9wHcYKWaqO\nWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d4838710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "pyplot.plot(, p, 'r')   # el tercer parámetro es el formato\n",
    "pyplot.title('Critical pressure vs temperature')\n",
    "pyplot.grid()\n",
    "pyplot.xlabel('Temperature [K]')\n",
    "pyplot.ylabel('Pressure [bar]')\n",
    "# el punto y coma evita el output\n",
    "pyplot.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Otras herramientas de numpy\n",
    "\n",
    "ya mencionamos el subpaquete `numpy.random` que tiene funciones análogas a `random` de la biblioteca estándar, pero que construyen arrays \n",
    "\n",
    "\n",
    "Otras funciones y utilidades incorporadas en numpy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.13600982+0.39307569j,  0.13600982-0.39307569j])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## tanto trabajo con nuestra función \"baskara\" y ya estaba hecho!\n",
    "np.roots([2, 2, 1j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# encima funciona para grado n\n",
    "np.roots([1j, -4+0.4j, 18, -np.pi, 0])  # polinomio de grado 5!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De manera contraria, la función `poly` devuelve los coeficientes de un polinomio dadas sus raíces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.+0.j,  0.-1.j, -4.+0.j,  0.+4.j])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.poly([-2, 2, 1j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.00000000e+00+0.j ,   1.00000000e+00+0.j ,   6.93254368e-09+0.5j])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.poly(np.array([-1.13600982+0.39307569j,  0.13600982-0.39307569j]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También podemos encontrar la inversa de una matriz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2. ,  1. ],\n",
       "       [ 1.5, -0.5]])"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[1,2],[3,4]])\n",
    "invA = np.linalg.inv(A)\n",
    "invA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y calcular el producto punto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.00000000e+00,   1.11022302e-16],\n",
       "       [  0.00000000e+00,   1.00000000e+00]])"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(A,invA)   # equivalente  A @ invA  en py3.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "que son las operaciones para resolver un sistema de ecuaciones lineales $Ax = b$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.13333333, -1.06666667])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[1, 2], [0.5, -2]])\n",
    "b = np.array([4, 5.2])\n",
    "\n",
    "x = np.linalg.solve(A, b)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejercicios\n",
    "\n",
    "- Graficar el polinomio con raices en -4.,  2. y  -1. entre [-5, 3]\n",
    "\n",
    "<!-- solucion\n",
    "https://gist.githubusercontent.com/mgaitan/921135ff67bc92a28e8a/raw/15909542eeb5e68f07a185a50a4bbeaf875432eb/poly.py\n",
    "-->\n",
    "\n",
    "\n",
    "- Resuelva el siguiente sistema de ecuaciones\n",
    "\n",
    "  $$\\begin{array} - -x + z = -2\\\\ 2x - y + z = 1 \\\\ -3x + 2y -2z = -1 \\end{array}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplolib orientado a objeto\n",
    "\n",
    "\n",
    "Hasta ahora hemos usado `pyplot`, el módulo de `matptlotlib` que emula la forma de uso (la API) de Matlab. En este modo cada función que invocamos **afecta un estado interno** en el que podemos ir \"acumulando\" cambios (por ejemplo, el título, la etiqueta de los ejes, etc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este tipo de funcionamiento es práctico y fácil, pero limitado, porque es totalmente \"lineal\" (procedural) . Para mejorar las prestaciones y la posibilidad de \"tocar\" todo lo que queramos, tenemos que usar el **modo \"orientado a objetos\"**. Este modo es un poco más verborrágico pero también más explícito y potente. En general se usa una mezcla entre la \"practicidad\" de `pyplot` (modo procedural) y la orientación a objetos. Veamos un ejemplo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'matplotlib.figure.Figure'>\n",
      "<class 'matplotlib.axes._subplots.AxesSubplot'>\n",
      "<class 'list'>\n",
      "<class 'matplotlib.text.Text'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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OxNxvwIIJhw+z6x47tpjLeaFduY1s6sQT+QvVk/zv2WMnM9+yhXM105ilq4vF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d4846198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "t = x = np.arange(0, 10, 0.1)\n",
    "y = np.random.randn(t.size)\n",
    "\n",
    "fig = plt.figure()      # con una funcion de pyplot creamos un objeto tipo Figure.\n",
    "print(type(fig))\n",
    "ax = fig.add_subplot(1, 1, 1)  # en la posicion 1-1 creamos un Subplot en nuestro objeto fig (que tendrá solo un plot)\n",
    "print(type(ax))\n",
    "lines = ax.plot(t, y)      # en ese objeto plot graficamos x vs y\n",
    "print(type(lines))\n",
    "t = ax.set_title('random numbers')    # y al mismo plot le ponemos un titulo\n",
    "print(type(t))\n",
    "plt.show()    # por ultimo mostramos el grafico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supongamos que queremos graficar muchas curvas en el mismo Subplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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za5xzf27jpmuAOc65/c659cBa4MJiz5MEu3fvDvoSQkO18KkWvljV4pRT4Lvf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\npkFeRCTGNMiLiMTY/wfAkXRnn3Vu9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d4833438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()      # con una funcion de pyplot creamos un objeto tipo Figure.\n",
    "ax = fig.add_subplot(1, 1, 1)  # en la posicion 1-1 creamos un Subplot en nuestro objeto fig (que tendrá solo un plot)\n",
    "\n",
    "\n",
    "ax.plot(x, x + 1, c='red', label=r'$y = x + 1$' )        # label usando LaTex\n",
    "ax.plot(x, .5*x - 2, c='green', label=r'$y = 0.5x - 2$' )\n",
    "ax.plot(x, -x, c='black', label=r'$y = -x$' )\n",
    "ax.grid()\n",
    "ax.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero también podemos tener una figura con múltiples subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ItKguSsVRAHQDhmX+6EUqX/WkAxDJJe5+k5nNI4ofHgMWApOAq4hChw2Ad1Ob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\nYdXIqzYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d5a904a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 2*np.pi, 100)\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(2, 1, 1)    # el plot 1 en una figura de 2x1\n",
    "ax1.set_title('Seno')\n",
    "ax1.plot(x, np.sin(x), c='red')\n",
    "ax2 = fig.add_subplot(2, 1, 2)   # el plot 2 en una figura de 2x1\n",
    "ax2.plot(x, np.cos(x), c='blue')\n",
    "ax2.set_title('Coseno')\n",
    "fig.tight_layout()  # ajusta el espaciado entre los subplots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aunque ya la mostramos, todavia tenemos control sobre los objetos. Por ejemplo, podemos guardar la figura en un archivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig.savefig('senos.png', format='png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!eog senos.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Otra manera de crear figuras con múltiples subplots es usar la función `subplots`. Por supuesto, cada gráfico puede ser de un tipo distinto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sj6c+EWkgInlA+SClaLDj3t1vAW4JLyKJwq6Du/j9jt8zc1xRzGEcmkxqiLTH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\n/wO17ppPDUq23AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d44e3b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "xx = np.linspace(-0.75, 1., 100)\n",
    "n = np.arange(0,6)\n",
    "\n",
    "# devuelve la figura y la lista de subplots\n",
    "fig, axes = plt.subplots(1, 5, figsize=(17,3))\n",
    "\n",
    "# scatter grafica puntos pero no los une\n",
    "axes[0].scatter(xx, xx + 0.25, c='red', marker='^')\n",
    "\n",
    "axes[1].step(n, n**2, 'g', lw=2)\n",
    "\n",
    "axes[2].bar(n, n**2, align=\"center\", width=0.5, alpha=0.5)\n",
    "\n",
    "axes[3].fill_between(x, x**2, x**3, color=\"green\", alpha=0.5);\n",
    "\n",
    "# ax = fig.add_subplot(1, 5, 5, projection='3d')\n",
    "# t = np.linspace(0, 2 *np.pi, 100)\n",
    "# ax.plot(t, t, t, color='blue', lw=3)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráficos 3D\n",
    "\n",
    "Hacer gráficos en 3D no es el fin principal de matplotlib y por eso está en un toolkit aparte, pero es muy fácil. Primero hace falta importar la clase para el tipo Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d.axes3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eso \"parcha\" la clase para que los subplot acepte la proyección 3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'module' object has no attribute '_string_to_bool'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-179-e6691d232c9a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# `ax` is a 3D-aware axis instance, because of the projection='3d' keyword argument to add_subplot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprojection\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'3d'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mtheta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m4\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.4/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36madd_subplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    962\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    963\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 964\u001b[1;33m             \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    965\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    966\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.4/dist-packages/matplotlib/axes/_subplots.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[0;32m     76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m         \u001b[1;31m# _axes_class is set in the subplot_class_factory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 78\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axes_class\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigbox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python3/dist-packages/mpl_toolkits/mplot3d/axes3d.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, rect, *args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m         Axes.__init__(self, fig, rect,\n\u001b[0;32m     90\u001b[0m                       \u001b[0mframeon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 91\u001b[1;33m                       *args, **kwargs)\n\u001b[0m\u001b[0;32m     92\u001b[0m         \u001b[1;31m# Disable drawing of axes by base class\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m         \u001b[0mAxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_axis_off\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.4/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, rect, axisbg, frameon, sharex, sharey, label, xscale, yscale, **kwargs)\u001b[0m\n\u001b[0;32m    435\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'axes.hold'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    436\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_connected\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m  \u001b[1;31m# a dict from events to (id, func)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 437\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcla\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    438\u001b[0m         \u001b[1;31m# funcs used to format x and y - fall back on major formatters\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    439\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfmt_xdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python3/dist-packages/mpl_toolkits/mplot3d/axes3d.py\u001b[0m in \u001b[0;36mcla\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1043\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_zmargin\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1044\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1045\u001b[1;33m         \u001b[0mAxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcla\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1046\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1047\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'axes3d.grid'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.4/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mcla\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    906\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontainers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    907\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 908\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gridOn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwhich\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'axes.grid.which'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    909\u001b[0m         props = font_manager.FontProperties(size=rcParams['axes.titlesize'],\n\u001b[0;32m    910\u001b[0m                                         weight=rcParams['axes.titleweight'])\n",
      "\u001b[1;32m/usr/lib/python3/dist-packages/mpl_toolkits/mplot3d/axes3d.py\u001b[0m in \u001b[0;36mgrid\u001b[1;34m(self, b, **kwargs)\u001b[0m\n\u001b[0;32m   1254\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1255\u001b[0m             \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1256\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_grid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_string_to_bool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1257\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1258\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mticklabel_format\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'module' object has no attribute '_string_to_bool'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d45b40b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "\n",
    "# `ax` is a 3D-aware axis instance, because of the projection='3d' keyword argument to add_subplot\n",
    "ax = fig.add_subplot(1, 1, 1, projection='3d')\n",
    "\n",
    "theta = np.linspace(-4 * np.pi, 4 * np.pi, 1000)\n",
    "z = np.linspace(-2, 2, 1000)\n",
    "r = z**2 + 1\n",
    "x = r * np.sin(theta)\n",
    "y = r * np.cos(theta)\n",
    "ax.plot(x,y,z, lw=1.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¡Matplotlib es genial! Es libre y gratis y brinda resultados excepcionales. Seguro lo usarás para tu próximo paper/poster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Para terminar... descontracturemos\n",
    "\n",
    "Hay un comic online que a los nerds nos gusta mucho: [XKCD](http://xkcd.com)\n",
    "\n",
    "\n",
    "![](http://imgs.xkcd.com/comics/compiling.png)\n",
    "\n",
    "![](http://www.attendly.com/wp-content/uploads/2012/09/computer_problems722.png)\n",
    "\n",
    "![](http://imgs.xkcd.com/comics/sandwich.png)\n",
    "\n",
    "\n",
    "Nos gusta tanto, que Python le rinde homenaje con un huevo de pascua\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import antigravity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Muchas veces, la tira tiene gráficos de este estilo\n",
    "\n",
    "![](http://imgs.xkcd.com/comics/self_description.png)\n",
    "\n",
    "![](https://imgs.xkcd.com/comics/fiction_rule_of_thumb.png)\n",
    "\n",
    "![](https://imgs.xkcd.com/comics/i_dont_own_a_tv.png)\n",
    "\n",
    "Si intentáramos hacer nuestra propia versión, arruinaríamos el chiste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o3qVJdmp/X5Pk4iQXtulpSXZOckkr3y/Jj1rZd5K8e6Lu1UmePZr3223esvZ4RWvXXP2f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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d483c630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.bar([-0.125, 1.0-0.125], [0, 100], 0.25)\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.set_xticks([0, 1])\n",
    "ax.set_xlim([-0.5, 1.5])\n",
    "ax.set_ylim([0, 110])\n",
    "ax.set_xticklabels(['CONFIRMED BY\\nEXPERIMENT', 'REFUTED BY\\nEXPERIMENT'])\n",
    "plt.yticks([])\n",
    "plt.title(\"CLAIMS OF SUPERNATURAL POWERS\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Pero qué tal usar el modo XKCD de Matplotlib?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+Zuz0EwqFcqT/qwMmE6+z2FjSpKvT6TjpMiZSkUgEEkkuJN0atGhRmO0gRCdp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\nfGiNifPb/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d43aa080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "with plt.xkcd():\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(1, 1, 1)\n",
    "    ax.bar([-0.125, 1.0-0.125], [0, 100], 0.25)\n",
    "    ax.spines['right'].set_color('none')\n",
    "    ax.spines['top'].set_color('none')\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    ax.set_xticks([0, 1])\n",
    "    ax.set_xlim([-0.5, 1.5])\n",
    "    ax.set_ylim([0, 110])\n",
    "    ax.set_xticklabels(['CONFIRMED BY\\nEXPERIMENT', 'REFUTED BY\\nEXPERIMENT'])\n",
    "    plt.yticks([])\n",
    "    plt.title(\"CLAIMS OF SUPERNATURAL POWERS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JwS92AWqeJF3EWLs8IpGE8LiTHL43gsjEi8jEutyM3oFMosvtF3s4/3gRis/o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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2d4409160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.xkcd():\n",
    "    labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
    "    sizes = [15, 30, 45, 10]\n",
    "    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']\n",
    "    explode = (0, 0.1, 0, 0) # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "    plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
    "            autopct='%1.f%%', shadow=True, startangle=60)\n",
    "    # Set aspect ratio to be equal so that pie is drawn as a circle.\n",
    "    plt.axis('equal');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cualquier gráfico se puede \"xkcdear\" ;-). http://matplotlib.org/xkcd/"
   ]
  }
 ],
 "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": "3.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}