{
 "metadata": {
  "name": ""
 },
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Parte 3 - Visualizaci\u00f3n con matplotlib"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "\u00bfQu\u00e9 es matplotlib?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Est\u00e1ndar *de facto* para visualizaci\u00f3n en Python\n",
      "* Pretende ser similar a las funciones de visualizaci\u00f3n de MATLAB\n",
      "* Diferentes formas de usarla: interfaz `pyplot` y orientada a objetos"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lo primero que vamos a hacer es activar el modo *inline* - de esta manera las figuras aparecer\u00e1n autom\u00e1ticamente incrustadas en el notebook."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Importamos los paquetes necesarios:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La biblioteca matplotlib es gigantesca y es dif\u00edcil hacerse una idea global de todas sus posibilidades en una primera toma de contacto. Es recomendable tener a mano la documentaci\u00f3n y la galer\u00eda:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML('<iframe src=\"http://matplotlib.org/gallery.html#pylab_examples\" width=\"800\" height=\"600\"></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=\"http://matplotlib.org/gallery.html#pylab_examples\" width=\"800\" height=\"600\"></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<IPython.core.display.HTML at 0x7f77923f65d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Si hacemos clic en cualquiera de las im\u00e1genes, accedemos al c\u00f3digo fuente que la ha generado:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML('<iframe src=\"http://matplotlib.org/examples/pylab_examples/annotation_demo.html\" width=\"800\" height=\"600\"></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=\"http://matplotlib.org/examples/pylab_examples/annotation_demo.html\" width=\"800\" height=\"600\"></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<IPython.core.display.HTML at 0x7f77923cc250>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Interfaz pyplot"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Funci\u00f3n `plot`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La interfaz `pyplot` proporciona una serie de funciones que operan sobre un *estado global* - es decir, nosotros no especificamos sobre qu\u00e9 gr\u00e1fica o ejes estamos actuando. Es una forma r\u00e1pida y c\u00f3moda de crear gr\u00e1ficas pero perdemos parte del control."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "El paquete `pyplot` se suele importar bajo el alias `plt`, de modo que todas las funciones se acceden a trav\u00e9s de `plt.<funcion>`. La funci\u00f3n m\u00e1s b\u00e1sica es la funci\u00f3n `plot`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot([0, 0.1, 0.2, 0.5,0.6], [1, -1, 0, 3, -1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f77925ac450>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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GxM6nUgF48flZTca5AEuAyCCVCoCsIeNcgCXgMJX3XVXOBvTNp2IBeOn52UXGuYDpzxje\nvn07GhoaMHz4cGzevDnmOXv27EFjYyP8fj+WLl2KvLw8s5clco2KBUDWkPFzh02vBO677z6sXbt2\nwK+Hw2GcPn0a27Ztw+LFi7Fr1y6zl5SayvuuKmcDrudTuQC88PycINtcwHQJTJo0CWlpaQN+vb6+\nHnPmzAEA5Ofn4/Lly4hGo2YvS+Q4lQuArCPbXMD2mUAkEkF2dnbPcXZ2NiKRiN2XFZbK+64qZzt3\n8QoWrNurdAGo/PwA5/LJNhcwPRPQQ9O0hM4PhUI9S7fuB6fKcVNTk1D3w+PBjy9d6cCe97/BlFsz\nMH3MNdTV1Ql1fzwW77h7LhD5+rij1zfCpyX6N3QMbW1tqKysjDkYfuONNzBlyhQUFRUBAMrKylBe\nXo7MzMyYP6u2thZT7pgK/xBH+okoLm4BkRGVb4UwekQGFj40zbFrhsNhFBcXJ/x9tm8HFRYW4vDh\nwwCAlpYWpKWlDVgA3d499F923xbRoFgAZJRMcwHTJbB161asX78eX3/9NZYsWYL3338fwWAQwWAQ\nABAIBJCTk4Nly5bhzTffxKJFiwb9mTX/2Yir1zrM3pqQVN53VSlbrAJQKV8szGcdmeYCpvdcysrK\nBj1Hz1/8vU0cPwLvHvovPHr/HUZvi8gwrgDILJleLyDkK4YX/2iGsqsBlX8XW4Vs8QpAhXzxMJ+1\nZHm9gJAlMDnvlp7VAJFTuAIgK8kyFxCyBAB1VwMq77vKnE1PAcicTw/ms5YscwFhS4CrAXIKVwBk\nB1k+X0DYEgDUXA2ovO8qY7ZECkDGfIlgPuvJMBcQugS4GiA7cQVAdpNhLiB0CQDqrQZU3neVKZuR\nApApnxHMZz0Z5gLClwBXA2Q1rgDIKTLMBYQvAUCt1YDK+64yZDNTADLkM4P57CH6XECKEuBqgKzA\nFQC5QfS5gBQlAKizGlB531XkbFYUgMj5rMB89hB9LiBNCXA1QEZxBUBuEn0uIE0JAGqsBlTedxUx\nm5UFIGI+KzGffUSeC0hVAlwNUCK4AiBRiDwXkKoEAPlXAyrvu4qUzY4CECmfHZjPPiLPBaQrAa4G\naDBcAZBoRJ4LSFcCgNyrAZX3XUXIZmcBiJDPTsxnL1HnAlKWAFcDFAtXACQyUecCUpYAIO9qQOV9\nVzezOVEAKj87gPnsJupcwPRnDDc2NqKmpgZdXV2YO3cu5s2b1+frzc3N2LRpE0aOHAkAmD17NubP\nn2/2sn1WA/wsYm/jCoBkIOrnDpsqga6uLuzevRvr169HVlYW1qxZg8LCQuTm5vY5b/LkyVi9erWp\nG41l8Y9m4LktB/HIvRPhH2K6zxzh9r6kndzI5mQBqPzsAOZzQvdcQKQSMLUd1NrailGjRiEnJwcp\nKSkoKipCfX19v/M0TTNzmQFxNuBtXAGQbEScC5gqgUgkguzs7J7jrKwsRCKRPuf4fD60tLRg1apV\nqKiowMmTJ81csh/ZZgNu70vayclsbhSAys8OYD4niDgXsH0wnJeXhx07dqCqqgqlpaWoqqoa9Ht6\nP6xQKBT3OPLVceQMT+pZDQx2vtvHTU1NQt2PjMfvBQ/1FMD0MddQV1cn1P3xmMcDHTf/7a/ISPX1\nvF7A6p9vhE8zsVfT0tKCd955B+vWrQMAvPvuu/D5fP2Gw70988wzqKysRHp6esyv19bWIhAIJHQf\nf//iGzy35SD2b35UmtkAGcMtIJJd5VshjB6RgYUPTbP054bDYRQXFyf8faZWAhMmTMCpU6fQ1taG\njo4OHDlyBIWFhX3OiUajPTOB1tZWABiwAIzibMAbWACkAtHmAqZKIDk5GU899RQ2btyIFStW4K67\n7kJubi6CwSCCwSAA4OOPP8bKlSuxatUq1NTUYPny5Zbc+I1kmQ2YXbqJzM5sIhSAys8OYD6niDYX\nML13UlBQgIKCgj5/VlJS0vPPpaWlKC0tNXuZQfF1A+oSoQCIrCLa6wWkfcVwLDKsBkT4XWW72JFN\npAJQ+dkBzOckkd5HSKkS4GxALSIVAJGVRJoLKFUCgPirAVH2Je1gZTYRC0DlZwcwn5NEmgsoVwJc\nDchPxAIgspJIny+gXAkAYq8GRNqXtJoV2UQuAJWfHcB8ThNlLqBkCXA1ICeRC4DIaqLMBZQsAUDc\n1YBI+5JWM5NNhgJQ+dkBzOc0UeYCypYAVwPykKEAiKwmylxA2RIAxFwNiLYvaSUj2WQqAJWfHcB8\nbhBhLqB0CXA1IDaZCoDIDiLMBZQuAUC81YBo+5JWSiSbjAWg8rMDmM8NIswFlC8BrgbEI2MBENlB\nhLmA8iUAiLUaEHFf0ip6sslcACo/O4D53OL2XMATJcDVgBhkLgAiu7g9F/BECQDirAZE3Je0Srxs\nKhSAys8OYD63uD0X8EwJcDXgHhUKgMgubs8FPFMCgBirAVH3Ja0QK5tKBaDyswOYz01uzgU8VQJc\nDThLpQIgspObcwFPlQDg/mpA1H1JK/TOpmIBqPzsAOZzk5tzAdOfMdzY2Iiamhp0dXVh7ty5mDdv\nXr9z9uzZg8bGRvj9fixduhR5eXlmL2sYP4vYfioWAJGd3PzcYVMrga6uLuzevRtr167Fli1bUFdX\nh5MnT/Y5JxwO4/Tp09i2bRsWL16MXbt2mbphK7i5GhB5X9Ksu+++W+kCUPnZAcznNrfmAqZKoLW1\nFaNGjUJOTg5SUlJQVFSE+vr6PufU19djzpw5AID8/HxcvnwZ0WjUzGVN42zAHioXAJHd3JoLmCqB\nSCSC7OzsnuOsrCxEIpG452RnZ/c7xw1urQZE3pc049zFK1iwbq/SBaDqs+vGfO5yay7gyGBY07SE\nzu/9sEKhkC3H3auBzXv+ryPX69bU1OTo9Zw4fi94CL945Q+YcmsGpo+5hrq6OqHuj8c8luH45uFD\nkZHqw9v7aw3/PCN8WqJ/Q/fS0tKCd955B+vWrQMAvPvuu/D5fH2Gw2+88QamTJmCoqIiAEBZWRnK\ny8uRmZkZ82fW1tYiEAgYvaWE/P2Lb/DcloPYv/lR+IeYnpF7EreAiKxT+VYIo0dkYOFD0xL+3nA4\njOLi4oS/z9RKYMKECTh16hTa2trQ0dGBI0eOoLCwsM85hYWFOHz4MIDrpZGWljZgATiNswFzWABE\n1nJjLmCqBJKTk/HUU09h48aNWLFiBe666y7k5uYiGAwiGAwCAAKBAHJycrBs2TK8+eabWLRokSU3\nbhWnZwNml26iiFUAqmQbCPPJTYZ8bswFTO+BFBQUoKCgoM+flZSU9DkW7S/+3vi6gcRxBUBkDzde\nL+C5VwzH4uRqQPTfVR5MvAKQPdtgmE9usuRz+vUCLAFwNqAXVwBE9nN6LsAS+G9OrQZk2JeMRU8B\nyJpNL+aTmyz5nJ4LsAT+G1cDA+MKgMg5Tn++AEugFydWA7LsS3ZLpABky5Yo5pObTPmcnAuwBHrh\naqAvrgCI3OHkXIAlcAO7VwOy7EsaKQBZshnFfHKTKZ+TcwGWwA24GuAKgMhtTs4FWAIx2LkaEH1f\n0kwBiJ7NLOaTm2z5nJoLsARi8OpqgCsAInE4NRdgCQzArtWAqPuSVhSAqNmswnxyky2fU3MBlsAA\nvLQa4AqASDxOzQVYAnHYsRoQbV/SygIQLZvVmE9uMuZzYi7AEohD9dUAVwBEYnNiLsASGITVqwFR\n9iXtKABRstmF+eQmYz4n5gIsgUGouBrgCoBIDk7MBVgCOli5GnB7X9LOAnA7m92YT26y5rN7LsAS\n0EGV1QBXAETysXsuwBLQyarVgFv7kk4UgIx7rolgPrnJms/uuYDhzxi+dOkSXn31VZw5cwa33HIL\nVqxYgbS0tH7nPfPMMxg6dCiSkpKQnJyMiooKUzfsFpk/i5grACJ52f25w4ZLYN++fZg6dSp++MMf\nYt++fdi3bx9+9rOfxTy3vLwc6enphm9SFIt/NAPPbTmIR+6dCP8QY//TOb0v6WQByLrnqhfzyU3m\nfN1zATtKwPB2UH19PebMmQMAuPfee/HJJ58MeK6maUYvIxTZZgNcARCpwc65gOESOH/+PDIzMwEA\nN910E86fPx/zPJ/Phw0bNuCFF17An//8Z6OXE4bZ2YBT+5JuFICse656MZ/cZM5n51wg7p7Ghg0b\nEI1G+/35Y4891uc43l8wGzZswM0334wLFy5gw4YNGDt2LCZNmhT3pkKhUM/SrfvBiXIc+eo4coYn\n9cwGEv3+pqYm2+/30pUO7Hn/G9wzfRymj7mGuro6Yf734zGPeWzseFRWOt7eX4txOUMHPN8In2Zw\nr6asrAzl5eXIzMzEuXPn8PLLL2Pr1q1xv+edd95BamoqHn744QHPqa2tRSAQMHJLjvn7F9/guS0H\nsX/zo4ZnA3bhFhCRmirfCmH0iAwsfGhazK+Hw2EUFxcn/HMNbwcVFhbi0KFDAIAPPvgAM2fO7HfO\n1atXceXKFQBAe3s7/va3v2HcuHFGLykMUWcDLAAiddk1FzBcAvPmzUNTUxOWL1+OTz/9FPPmzQMA\nRCKRnl8DjUajeOmll7Bq1SqsW7cOgUAA06bFbjHZGJ0N2LUvKUIByLznqgfzyU32fHbNBQzvZaSn\np2P9+vX9/jwrKwtr1qwBAIwcORJVVVXG705gIr1uQIQCICJ72fV6Ab5i2AQjqwGrf1dZpAKQ+few\n9WA+uamQz473EWIJmOD2bECkAiAi+9kxF2AJmJToasCqfUkRC0D2PdfBMJ/cVMhnx1yAJWCSG6sB\nEQuAiOxnx+cLsAQskMhqwOy+pMgFoMKeazzMJzdV8lk9F2AJWMCp1YDIBUBEzrB6LsASsIje1YDR\nfUkZCkCFPdd4mE9uquSzei7AErCInasBGQqAiJxh9VyAJWAhPauBRPclZSoAVfZcB8J8clMpn5Vz\nAZaAhaxeDchUAETkHCvnAiwBiw22GtC7LyljAaiy5zoQ5pObSvmsnAuwBCxmxWpAxgIgIudYORdg\nCdgg3mpgsH1JmQtApT3XWJhPbqrls2ouwBKwgdHVgMwFQETOsmouwBKwyUCrgYH2JVUoAJX2XGNh\nPrmpls+quQBLwCaJrAZUKAAicpZVcwGWgI1irQZu3JdUqQBU23O9EfPJTcV8VswFWAI2Gmw1oFIB\nEJHzrJgLGC6Bjz76CM899xx++tOf4vPPPx/wvMbGRpSVleGXv/wl9u3bZ/Ry0rpxNdC9L6liAai2\n53oj5pObivmsmAsYLoFx48Zh5cqVmDx58oDndHV1Yffu3Vi7di22bNmCuro6nDx50uglpRRrNaBi\nARCR86yYCxgugbFjx2LMmDFxz2ltbcWoUaOQk5ODlJQUFBUVob6+3uglpdV7NTBl2gxlC0DFPdfe\nmE9uquYzOxewdSYQiUSQnZ3dc5yVlYVIJGLnJYXUvRr43X82KlsAROQOs3OBlHhf3LBhA6LRaL8/\nf+yxx1BYWGj4ol60+Ecz8L/W/wdKCkYoWwChUEjZ/9oCmE92quYLTByNl3d9AMwdaej745bA+vXr\nDf3QbllZWTh79ru9qrNnzyIrKyvu92RmZiIcDpu6rqh2PTcTANDQ0ODyndhj2LBhyj47gPlkp3K+\nrf97GjIzMw19b9wSMGvChAk4deoU2trakJWVhSNHjmD58uVxv2fGjBl23hIREfXi0zRNM/KNR48e\nxe9+9ztcuHABw4YNQ15eHtauXYtIJILXX38da9asAXD9v3pramrQ1dWFuXPn4pFHHrE0ABERGWe4\nBIiISH58xTARkYexBIiIPMzWwfBAGhsb+8wJ5s2b1++cPXv2oLGxEX6/H0uXLkVeXp4Ld2rMYPm+\n+uorbN++HV9++SUeffRRPPzwwy7dqTGD5fvwww9x4MABaJqGoUOH4umnn8att97q0t0mbrB8n3zy\nCd5++234fD74fD4sWLAAd9xxh0t3mzg9//4B11/s+eKLL2LFihWYPXu2w3dpzGDZmpubsWnTJowc\nef3XKWfPno358+e7cauG6Hl2zc3NeOutt9DZ2YmMjAyUl5fH/6Gawzo7O7Vnn31WO336tPbtt99q\nK1eu1P75z3/2Oeevf/2r9sorr2iapmktLS3a2rVrnb5Nw/TkO3/+vNba2qr9/ve/1w4cOODSnRqj\nJ9/x48eSveSAAAADuElEQVS1y5cva5qmaQ0NDco9vytXrvT884kTJ7Rnn33W6ds0TE++7vPKy8u1\niooK7aOPPnLhThOnJ9unn36q/epXv3LpDs3Rk+/SpUvaihUrtDNnzmiadv3vmsE4vh2k560k6uvr\nMWfOHABAfn4+Ll++HPNFayLSk2/48OGYMGECkpOTXbpL4/Tku/322zFs2DAAwG233dbntSKi05Mv\nNTW155/b29sxfPhwp2/TML1v5fLHP/4Rd955p5LZNEl/F0ZPvlAohNmzZ/e8U4Oe5+d4Ceh5K4kb\nz8nOzpbm7SZUf6uMRPO9//77KCgocOLWLKE339GjR7FixQq88sor+PnPf+7kLZqi99+/+vp63H//\n/QAgzavb9WTz+XxoaWnBqlWrUFFRIdUbWurJ969//QuXLl3Cyy+/jBdeeAGHDx8e9Oe6MhPQQ9a2\npu98+umn+Mtf/oINGza4fSuWmzVrFmbNmoVjx47ht7/9LX7zm9+4fUuWqampweOPPw6fzwdN05T6\ndzEvLw87duyA3+9HQ0MDqqqqlHp2nZ2d+OKLL/DSSy/h6tWrePHFF5Gfn4/Ro0cP+D2Ol4Cet5Iw\n8nYTopD53vXQm+/EiRN4/fXXsW7dOqSnpzt5i6Yk+vwmTZqErq4uXLx4ERkZGU7coil68n3++efY\nunUrAODixYtobGxESkqK8O8Xpifb0KFDe/65oKAAu3btwqVLl6T4/6iefNnZ2cjIyMCQIUMwZMgQ\nTJo0CSdOnIhbAo5vB/V+K4mOjg4cOXKk3/+5CgsLe5YxLS0tSEtLM/y+GE7Tk6+bjP+FpSffmTNn\n8Otf/xrLli3DqFGjXLpTY/TkO3XqVM+z6/5AJRkKANCX77XXXkN1dTWqq6tx55134umnnxa+AAB9\n2aLRaM+za21tBQApCgDQl2/mzJk4fvw4urq6cPXqVXz22WfIzc2N+3NdecVwrLeSCAaDAICSkhIA\nwO7du9HY2IjU1FQsWbIE3/ve95y+TcMGyxeNRrFmzRr8+9//RlJSElJTU/Hqq6/2GTiKbLB8O3fu\nxNGjRzFixAgAQHJyMioqKty85YQMlm///v04fPgwkpOTkZqaioULF+K2225z+a710/PvX7ft27dj\nxowZ0vyK6GDZ3nvvPQSDQSQlJcHv92PhwoW4/fbbXb5r/fQ8uwMHDuDQoUPw+XwoLi7Ggw8+GPdn\n8m0jiIg8jK8YJiLyMJYAEZGHsQSIiDyMJUBE5GEsASIiD2MJEBF5GEuAiMjDWAJERB72/wFGXEmJ\nA8JgMgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f77ac21e410>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La funci\u00f3n `plot` recibe una sola lista (si queremos especificar los valores *y*) o dos listas (si especificamos *x* e *y*). Naturalmente si especificamos dos listas ambas tienen que tener la misma longitud."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La tarea m\u00e1s habitual a la hora de trabajar con matplotlib es representar una funci\u00f3n. Lo que tendremos que hacer es definir un dominio y evaluarla en dicho dominio. Por ejemplo:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$$f(x) = e^{-x^2}$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def f(x):\n",
      "    return np.exp(-x ** 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Definimos el dominio con la funci\u00f3n `np.linspace`, que crea un vector de puntos equiespaciados:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(-1, 5, num=30)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Y representamos la funci\u00f3n:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(x, f(x), label=\"Funci\u00f3n f(x)\")\n",
      "plt.xlabel(\"Eje $x$\")\n",
      "plt.ylabel(\"$f(x)$\")\n",
      "plt.legend()\n",
      "plt.title(\"Funci\u00f3n $f(x)$\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.text.Text at 0x7f779236b210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jg1qLhFot+uczCSG/MbkiIcZWk1j6ogNc+sHGygKXC2736HNiya83WM4NoPxM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\nEoQQQnSiIkEIIUQnKhKEEEJ0+v++1Pc94avCewAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f77925359d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notamos varias cosas:\n",
      "\n",
      "* Con diversas llamadas a funciones dentro de `plt.` se actualiza el gr\u00e1fico *actual*. Esa es la forma de trabajar con la interfaz pyplot.\n",
      "* Podemos a\u00f1adir etiquetas, y escribir $\\LaTeX$ en ellas. Tan solo hay que encerrarlo entre signos de d\u00f3lar $$."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Personalizaci\u00f3n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La funci\u00f3n `plot` acepta una serie de argumentos para personalizar el aspecto de la funci\u00f3n. Con una letra podemos especificar el color, y con un s\u00edmbolo el tipo de l\u00ednea."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(x, f(x), 'ro')\n",
      "plt.plot(x, 1 - f(x), 'g--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f7792343290>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uoAd2CMSzdz+L8hp11khXuo93+OJhVNRUIKpflKL7bS2Z+5Qy5wYwPyMw5Dz0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\ndCIiSfw/J+dTqozxJUsAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7792115f90>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Esto en realidad son c\u00f3digos abreviados, que se corresponden con argumentos de la funci\u00f3n `plot`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(x, f(x), color='red', linestyle='', marker='o')\n",
      "plt.plot(x, 1 - f(x), c='g', ls='--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f77919f3890>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uoAd2CMSzdz+L8hp11khXuo93+OJhVNRUIKpflKL7bS2Z+5Qy5wYwPyMw5Dz0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\ndCIiSfw/J+dTqozxJUsAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f77919fc510>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La lista de posibles argumentos y abreviaturas est\u00e1 disponible en la documentaci\u00f3n de la funci\u00f3n `plot` http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Otro tipo de gr\u00e1ficas"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La funci\u00f3n `scatter` muestra una nube de puntos, con posibilidad de variar tambi\u00e9n el tama\u00f1o y el color."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N = 100\n",
      "x = np.random.randn(N)\n",
      "y = np.random.randn(N)\n",
      "\n",
      "plt.scatter(x, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "<matplotlib.collections.PathCollection at 0x7f7791f05750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xi9X+9u/fT3R0RgyGXwAZGs0XzJpVgKpVK3D06DFy5cpOkSJFkr2qSq0OHTow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\nn6HOCQhCAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f77921723d0>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Con `s` y `c` podemos modificar el tama\u00f1o y el color respectivamente. Para el color, a cada valor num\u00e9rico se le asigna un color a trav\u00e9s de un *mapa de colores*; ese mapa se puede cambiar con el argumento `cmap`. Esa correspondencia se puede visualizar llamando a la funci\u00f3n `colorbar`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = 50 + 50 * np.random.randn(N)\n",
      "c = np.random.randn(N)\n",
      "\n",
      "plt.scatter(x, y, s=s, c=c, cmap=plt.cm.Blues)\n",
      "plt.colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "<matplotlib.colorbar.Colorbar at 0x7f77911ab590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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iz8m8CnrNsyeP0uwrX64cL25nXSD02a0LlC6bPlfGwUNe2DfvyIMr/6JQKGjS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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f779173c190>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(x, y, s=s, c=c, cmap=plt.cm.Oranges)\n",
      "plt.colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "<matplotlib.colorbar.Colorbar at 0x7f7791107d90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2BeChvbvoVr0IJ24Hg1LJjFZlUpaRS6US5nRwpfmxWAadi6H+8SSat25L0aJF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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f77914620d0>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "matplotlib trae por defecto muchos mapas de colores. En las SciPy Lecture Notes dan una lista de todos ellos:\n",
      "\n",
      "![matplotlib colormaps](/files/plot_colormaps.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La funci\u00f3n `contour` se utiliza para visualizar las curvas de nivel de funciones de dos variables y est\u00e1 muy ligada a la funci\u00f3n `np.meshgrid`. Veamos un ejemplo:\n",
      "\n",
      "$$f(x) = \\cos{x} + \\sin^2{y}$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def f(x, y):\n",
      "    return np.cos(x) + np.sin(y) ** 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(-2, 2)\n",
      "y = np.linspace(-2, 2)\n",
      "xx, yy = np.meshgrid(x, y)\n",
      "\n",
      "plt.contour(xx, yy, f(xx, yy))\n",
      "plt.colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "<matplotlib.colorbar.Colorbar at 0x7f7790f79410>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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G5Hwlvt2s4cHZZibd6eLGS/wyKp3ZRJTzDgaDDB06lLFjx5KWlsao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\nESJEiHABEgneESJEiHAB8n9eiLdopdmofQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f779154ded0>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La funci\u00f3n `contourf` es casi id\u00e9ntica pero rellena el espacio entre niveles. Podemos especificar manualmente estos niveles usando el cuarto argumento:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zz = f(xx, yy)\n",
      "plt.contourf(xx, yy, zz, np.linspace(-0.5, 2.0))\n",
      "plt.colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "<matplotlib.colorbar.Colorbar at 0x7f77906da910>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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oPIjZ8vxlJaQQSR4w0+Zp37O+R2xE9N7AT7JPogt2CZ/8rpAbX5fwAbmJW/p3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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7790ae4ad0>"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Para guardar las gr\u00e1ficas en archivos aparte podemos usar la funci\u00f3n `plt.savefig`. matplotlib usar\u00e1 el tipo de archivo adecuado seg\u00fan la extensi\u00f3n que especifiquemos. Veremos esto con m\u00e1s detalle cuando hablemos de la interfaz orientada a objetos."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Interfaz orientada a objetos"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "array([-1.        , -0.79310345, -0.5862069 , -0.37931034, -0.17241379,\n",
        "        0.03448276,  0.24137931,  0.44827586,  0.65517241,  0.86206897,\n",
        "        1.06896552,  1.27586207,  1.48275862,  1.68965517,  1.89655172,\n",
        "        2.10344828,  2.31034483,  2.51724138,  2.72413793,  2.93103448,\n",
        "        3.13793103,  3.34482759,  3.55172414,  3.75862069,  3.96551724,\n",
        "        4.17241379,  4.37931034,  4.5862069 ,  4.79310345,  5.        ])"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "array([  3.67879441e-01,   5.33117686e-01,   7.09185234e-01,\n",
        "         8.65994832e-01,   9.70710971e-01,   9.98811646e-01,\n",
        "         9.43400884e-01,   8.17952307e-01,   6.50996579e-01,\n",
        "         4.75607235e-01,   3.18960733e-01,   1.96356377e-01,\n",
        "         1.10961373e-01,   5.75595853e-02,   2.74083260e-02,\n",
        "         1.19802640e-02,   4.80694649e-03,   1.77048075e-03,\n",
        "         5.98593712e-04,   1.85777072e-04,   5.29262471e-05,\n",
        "         1.38410522e-05,   3.32266179e-06,   7.32187295e-07,\n",
        "         1.48107572e-07,   2.75011817e-08,   4.68753445e-09,\n",
        "         7.33426522e-10,   1.05338655e-10,   1.38879439e-11])"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = plt.subplots()\n",
      "\n",
      "axes.plot(x, f(x), 'ro', label=\"Funci\u00f3n F(x)\")\n",
      "axes.set_xlim(-2, 4)\n",
      "axes.set_ylim(-1, 2)\n",
      "\n",
      "fig.savefig(\"grafica1.png\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAECCAYAAAD9z2x7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4BH/D8ihlOQVz2HtAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f70c7cb5250>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = plt.subplots(1, 2, sharey=True)\n",
      "\n",
      "axes[0].plot(x, f(x), color=\"blue\")\n",
      "axes[0].set_xlabel(\"Eje x\")\n",
      "\n",
      "axes[1].plot(x, -f(x), 'r')\n",
      "axes[1].set_xlabel(\"Eje x de la gr\u00e1fica derecha\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "<matplotlib.text.Text at 0x7f70c765f750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WBufvn3NB+KPLgs9T346ORftc3bpBKCyEcOmS1/vy9hya09LuOh2Tck59Mdyl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\nima4M+JteQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f70c7974ed0>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.random.randn(100)\n",
      "y = np.random.randn(100)\n",
      "\n",
      "s = 200 * np.random.randn(100)\n",
      "c = np.random.randn(100)\n",
      "\n",
      "plt.scatter(x, y, s, c, cmap=plt.cm.BrBG_r)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "<matplotlib.collections.PathCollection at 0x7f70c7338b50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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37+jdszuBr55QImcctjaS/9Xxv7/2kIDCTsm7sNf06dmVJk2bs+DPRZinsBRA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\nOqVWCNxiAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f70c7464710>"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$$g(x, y) = \\cos(x) + \\sin(y)^2$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(-2, 2)\n",
      "y = np.linspace(-2, 2)\n",
      "\n",
      "xx, yy = np.meshgrid(x, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def g(x, y):\n",
      "    return np.cos(x) + np.sin(y) ** 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zz = g(xx, yy)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = plt.subplots()\n",
      "\n",
      "axes.contourf(xx, yy, zz, np.linspace(-1, 1), cmap=plt.cm.autumn)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "<matplotlib.contour.QuadContourSet at 0x7f70c6dc52d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GBsL/dwmUHcDBgwdx22234Xvf+x4OHjyIgwcP4oc//GFg223btmHy5MlyA+h4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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f70c6fa6c10>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* `fig, axes = plt.subplots`\n",
      "* Diferencias de conceptos"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ejemplo con datos reales"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!cat temperaturas.csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "STATION,DATE,TMAX,TMIN\r\n",
        "GHCND:USW00094728,20130101,44,-33\r\n",
        "GHCND:USW00094728,20130102,6,-56\r\n",
        "GHCND:USW00094728,20130103,0,-44\r\n",
        "GHCND:USW00094728,20130104,28,-11\r\n",
        "GHCND:USW00094728,20130105,56,0\r\n",
        "GHCND:USW00094728,20130106,78,11\r\n",
        "GHCND:USW00094728,20130107,72,28\r\n",
        "GHCND:USW00094728,20130108,89,17\r\n",
        "GHCND:USW00094728,20130109,94,39\r\n",
        "GHCND:USW00094728,20130110,83,44\r\n",
        "GHCND:USW00094728,20130111,78,28\r\n",
        "GHCND:USW00094728,20130112,83,56\r\n",
        "GHCND:USW00094728,20130113,100,61\r\n",
        "GHCND:USW00094728,20130114,133,33\r\n",
        "GHCND:USW00094728,20130115,33,17\r\n",
        "GHCND:USW00094728,20130116,28,0\r\n",
        "GHCND:USW00094728,20130117,61,17\r\n",
        "GHCND:USW00094728,20130118,17,-39\r\n",
        "GHCND:USW00094728,20130119,106,-11\r\n",
        "GHCND:USW00094728,20130120,117,-11\r\n",
        "GHCND:USW00094728,20130121,0,-33\r\n",
        "GHCND:USW00094728,20130122,-28,-106\r\n",
        "GHCND:USW00094728,20130123,-67,-117\r\n",
        "GHCND:USW00094728,20130124,-56,-111\r\n",
        "GHCND:USW00094728,20130125,-44,-106\r\n",
        "GHCND:USW00094728,20130126,-28,-94\r\n",
        "GHCND:USW00094728,20130127,11,-72\r\n",
        "GHCND:USW00094728,20130128,22,-17\r\n",
        "GHCND:USW00094728,20130129,94,22\r\n",
        "GHCND:USW00094728,20130130,150,39\r\n",
        "GHCND:USW00094728,20130131,161,-11\r\n",
        "GHCND:USW00094728,20130201,-6,-44\r\n",
        "GHCND:USW00094728,20130202,-17,-72\r\n",
        "GHCND:USW00094728,20130203,-11,-44\r\n",
        "GHCND:USW00094728,20130204,-11,-50\r\n",
        "GHCND:USW00094728,20130205,0,-22\r\n",
        "GHCND:USW00094728,20130206,39,-17\r\n",
        "GHCND:USW00094728,20130207,0,-33\r\n",
        "GHCND:USW00094728,20130208,11,-28\r\n",
        "GHCND:USW00094728,20130209,0,-61\r\n",
        "GHCND:USW00094728,20130210,22,-78\r\n",
        "GHCND:USW00094728,20130211,72,11\r\n",
        "GHCND:USW00094728,20130212,67,22\r\n",
        "GHCND:USW00094728,20130213,67,6\r\n",
        "GHCND:USW00094728,20130214,78,6\r\n",
        "GHCND:USW00094728,20130215,128,28\r\n",
        "GHCND:USW00094728,20130216,50,-6\r\n",
        "GHCND:USW00094728,20130217,0,-78\r\n",
        "GHCND:USW00094728,20130218,17,-83\r\n",
        "GHCND:USW00094728,20130219,94,6\r\n",
        "GHCND:USW00094728,20130220,33,-39\r\n",
        "GHCND:USW00094728,20130221,11,-44\r\n",
        "GHCND:USW00094728,20130222,33,-39\r\n",
        "GHCND:USW00094728,20130223,56,22\r\n",
        "GHCND:USW00094728,20130224,83,11\r\n",
        "GHCND:USW00094728,20130225,72,0\r\n",
        "GHCND:USW00094728,20130226,67,17\r\n",
        "GHCND:USW00094728,20130227,83,22\r\n",
        "GHCND:USW00094728,20130228,106,39\r\n",
        "GHCND:USW00094728,20130301,72,22\r\n",
        "GHCND:USW00094728,20130302,44,-6\r\n",
        "GHCND:USW00094728,20130303,44,-17\r\n",
        "GHCND:USW00094728,20130304,56,-28\r\n",
        "GHCND:USW00094728,20130305,94,-6\r\n",
        "GHCND:USW00094728,20130306,56,33\r\n",
        "GHCND:USW00094728,20130307,33,6\r\n",
        "GHCND:USW00094728,20130308,56,-6\r\n",
        "GHCND:USW00094728,20130309,128,17\r\n",
        "GHCND:USW00094728,20130310,100,22\r\n",
        "GHCND:USW00094728,20130311,122,44\r\n",
        "GHCND:USW00094728,20130312,139,61\r\n",
        "GHCND:USW00094728,20130313,111,33\r\n",
        "GHCND:USW00094728,20130314,50,-17\r\n",
        "GHCND:USW00094728,20130315,83,-11\r\n",
        "GHCND:USW00094728,20130316,50,0\r\n",
        "GHCND:USW00094728,20130317,33,-17\r\n",
        "GHCND:USW00094728,20130318,17,-22\r\n",
        "GHCND:USW00094728,20130319,61,6\r\n",
        "GHCND:USW00094728,20130320,72,0\r\n",
        "GHCND:USW00094728,20130321,44,-11\r\n",
        "GHCND:USW00094728,20130322,50,-22\r\n",
        "GHCND:USW00094728,20130323,78,0\r\n",
        "GHCND:USW00094728,20130324,83,6\r\n",
        "GHCND:USW00094728,20130325,44,22\r\n",
        "GHCND:USW00094728,20130326,117,28\r\n",
        "GHCND:USW00094728,20130327,117,22\r\n",
        "GHCND:USW00094728,20130328,106,28\r\n",
        "GHCND:USW00094728,20130329,128,44\r\n",
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        "GHCND:USW00094728,20131231,0,-60\r\n",
        "GHCND:USW00094728,20140101,6,-43\r\n"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datos = np.loadtxt(\"temperaturas.csv\", usecols=(1, 2, 3), skiprows=1, delimiter=',')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = plt.subplots()\n",
      "\n",
      "x = np.arange(len(datos[:, 1]))\n",
      "\n",
      "temp_media = (datos[:, 1] + datos[:, 2]) / 2\n",
      "\n",
      "axes.plot(x, datos[:, 1], 'r')\n",
      "axes.plot(x, datos[:, 2], 'b')\n",
      "axes.plot(x, temp_media, 'k')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f70c6b449d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ICImE5imQz/PEgQOsWrVKresv8RSEdJqkyYTdbgfA7XYTUhQMhStpALGrS03I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\nE5/PZPHiD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UbqLCzbt2/n2muvPaPnnrFRePfdd3nuueeIx+O43W7q6upYtWoVAOvWrWPD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\nfyAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f70c7550bd0>"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Referencias"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Documentaci\u00f3n pyplot http://matplotlib.org/api/pyplot_api.html\n",
      "* Tutorial de matplotlib en espa\u00f1ol http://pybonacci.wordpress.com/tag/tutorial-matplotlib-pyplot/\n",
      "* Referencia r\u00e1pida de matplotlib http://scipy-lectures.github.io/intro/matplotlib/matplotlib.html#quick-references"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}