nb01-empezando-python-cientifico.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comenzando con python para computo cientifico: el entorno notebook, numpy y matplotlib\n",
    "En este notebook, empezaremos por explorar las herramientas que contiene el ecosistema de python cientifico (SciPy)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "8feae209-830c-4f75-be7f-8220417846f2"
    }
   },
   "source": [
    "## El Ipython notebook\n",
    "Este mismo documento es un ipython notebook, es un documento computacionalmente interactivo que puede ser modificado y ejecutado ahora con la herramienta jupyter. Aqui incluimos codigo en python, codigo markdown, html, imagenes y multimedia.\n",
    "\n",
    "Cada celda tiene propiedades que le permiten mostrar los elementos anteriores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "4b942b3f-f83c-47b1-a3f1-6be7231ef4fe"
    }
   },
   "source": [
    "## Numpy desde arriba\n",
    "Empezemos por visualizar algunos datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "925a95fe-d944-4b8f-98a3-5b1a88146e92"
    }
   },
   "outputs": [],
   "source": [
    "# importamos la funcionalidad numpy usando la convencion.\n",
    "import numpy as np \n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "c8b26eab-e26b-44ea-9c76-8cbe12f7ec0d"
    }
   },
   "outputs": [],
   "source": [
    "x = np.arange( -2*np.pi , 2*np.pi , 0.1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "1e695bca-d65c-4362-8365-5480cf678b15"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-6.28318531, -6.18318531, -6.08318531, -5.98318531, -5.88318531,\n",
       "       -5.78318531, -5.68318531, -5.58318531, -5.48318531, -5.38318531,\n",
       "       -5.28318531, -5.18318531, -5.08318531, -4.98318531, -4.88318531,\n",
       "       -4.78318531, -4.68318531, -4.58318531, -4.48318531, -4.38318531,\n",
       "       -4.28318531, -4.18318531, -4.08318531, -3.98318531, -3.88318531,\n",
       "       -3.78318531, -3.68318531, -3.58318531, -3.48318531, -3.38318531,\n",
       "       -3.28318531, -3.18318531, -3.08318531, -2.98318531, -2.88318531,\n",
       "       -2.78318531, -2.68318531, -2.58318531, -2.48318531, -2.38318531,\n",
       "       -2.28318531, -2.18318531, -2.08318531, -1.98318531, -1.88318531,\n",
       "       -1.78318531, -1.68318531, -1.58318531, -1.48318531, -1.38318531,\n",
       "       -1.28318531, -1.18318531, -1.08318531, -0.98318531, -0.88318531,\n",
       "       -0.78318531, -0.68318531, -0.58318531, -0.48318531, -0.38318531,\n",
       "       -0.28318531, -0.18318531, -0.08318531,  0.01681469,  0.11681469,\n",
       "        0.21681469,  0.31681469,  0.41681469,  0.51681469,  0.61681469,\n",
       "        0.71681469,  0.81681469,  0.91681469,  1.01681469,  1.11681469,\n",
       "        1.21681469,  1.31681469,  1.41681469,  1.51681469,  1.61681469,\n",
       "        1.71681469,  1.81681469,  1.91681469,  2.01681469,  2.11681469,\n",
       "        2.21681469,  2.31681469,  2.41681469,  2.51681469,  2.61681469,\n",
       "        2.71681469,  2.81681469,  2.91681469,  3.01681469,  3.11681469,\n",
       "        3.21681469,  3.31681469,  3.41681469,  3.51681469,  3.61681469,\n",
       "        3.71681469,  3.81681469,  3.91681469,  4.01681469,  4.11681469,\n",
       "        4.21681469,  4.31681469,  4.41681469,  4.51681469,  4.61681469,\n",
       "        4.71681469,  4.81681469,  4.91681469,  5.01681469,  5.11681469,\n",
       "        5.21681469,  5.31681469,  5.41681469,  5.51681469,  5.61681469,\n",
       "        5.71681469,  5.81681469,  5.91681469,  6.01681469,  6.11681469,\n",
       "        6.21681469])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "f3b436da-3a6b-4ea2-b99d-3d13617f4f07"
    }
   },
   "source": [
    "Que tipo de objeto es: x "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "b0b5f8a1-4158-4276-9c40-fb82e4def479"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x.dot?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "0433d528-4717-49e2-880e-1105c48790db"
    }
   },
   "source": [
    "El corazon de la libreria numpy es el objeto ndarray, un arreglo de *n*-dimensiones, este objeto tiene propiedades que describen los datos y metodos, en este entorno podemos explorar que es lo que contiene, presionando **TAB** despues de escribir **x.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "3addacc0-c734-44dd-b21d-45a76bb4dc22"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "40aed528-1d6f-472d-9c59-13fa7b3ccbd3"
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    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "4bf3b57e-b097-42b3-b110-21d024a72a04"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "12bf985a-7f38-4fcc-9691-b698b61cdba6"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(126,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "6db97975-0ceb-4aab-8cd9-d1f5e995b5bb"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.1813487046306523"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "c2c0d875-6f19-4f16-9dc5-25dc487204ed"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.14159265, -2.93215314, -2.72271363, -2.51327412, -2.30383461,\n",
       "       -2.0943951 , -1.88495559, -1.67551608, -1.46607657, -1.25663706,\n",
       "       -1.04719755, -0.83775804, -0.62831853, -0.41887902, -0.20943951,\n",
       "        0.        ,  0.20943951,  0.41887902,  0.62831853,  0.83775804,\n",
       "        1.04719755,  1.25663706,  1.46607657,  1.67551608,  1.88495559,\n",
       "        2.0943951 ,  2.30383461,  2.51327412,  2.72271363,  2.93215314,\n",
       "        3.14159265])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Arreglo de numeros uniformemente espaciados.\n",
    "x = np.linspace(-1*np.pi , 1*np.pi , 31)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "2357aaa7-515c-4d8c-b3a9-52a0fa9a5865"
    }
   },
   "source": [
    "**Ejemplo:** Evaluaremos el cada elemento del arreglo x con la funcion seno. La forma funcional, o al \"estilo python\" de hacerlo es la siguiente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "6512381c-5bf3-4df5-949e-33dc2ae940d0"
    }
   },
   "outputs": [],
   "source": [
    "from math import sin \n",
    "\n",
    "sin1 = [sin(i) for i in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "d5befe13-b5f9-491f-b2f5-0583b406f28c"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-1.2246467991473532e-16,\n",
       " -0.20791169081775931,\n",
       " -0.40673664307580043,\n",
       " -0.5877852522924732,\n",
       " -0.7431448254773942,\n",
       " -0.8660254037844385,\n",
       " -0.9510565162951536,\n",
       " -0.9945218953682733,\n",
       " -0.9945218953682733,\n",
       " -0.9510565162951535,\n",
       " -0.8660254037844387,\n",
       " -0.7431448254773945,\n",
       " -0.5877852522924731,\n",
       " -0.4067366430758003,\n",
       " -0.20791169081775962,\n",
       " 0.0,\n",
       " 0.20791169081775918,\n",
       " 0.40673664307579993,\n",
       " 0.5877852522924731,\n",
       " 0.7431448254773941,\n",
       " 0.8660254037844385,\n",
       " 0.9510565162951535,\n",
       " 0.9945218953682733,\n",
       " 0.9945218953682734,\n",
       " 0.9510565162951536,\n",
       " 0.8660254037844389,\n",
       " 0.7431448254773945,\n",
       " 0.5877852522924732,\n",
       " 0.4067366430758008,\n",
       " 0.20791169081775973,\n",
       " 1.2246467991473532e-16]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sin1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "b4cd7cc2-42f6-4434-9497-351df91385d1"
    }
   },
   "source": [
    "Podemos convertir una lista a un objeto mas eficiente ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "480b73f3-1f5e-44cf-b185-6c94cd33d26c"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sin2 = np.array(sin1)\n",
    "type(sin2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "2b3dd24f-f690-4d60-9277-40fbfc57821b"
    }
   },
   "source": [
    "Lo anterior no es lo mas eficiente que podemos hacer, en su lugar el paquete numpy contiene versiones de funciones math en metodos vectorizados y optimizados para el objeto ndarray, llamados *\"ufunc\"* corto para *\"universal functions\"*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "2ac5d98b-9338-4347-9cd5-8d775b2765d2"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -1.22464680e-16,  -2.07911691e-01,  -4.06736643e-01,\n",
       "        -5.87785252e-01,  -7.43144825e-01,  -8.66025404e-01,\n",
       "        -9.51056516e-01,  -9.94521895e-01,  -9.94521895e-01,\n",
       "        -9.51056516e-01,  -8.66025404e-01,  -7.43144825e-01,\n",
       "        -5.87785252e-01,  -4.06736643e-01,  -2.07911691e-01,\n",
       "         0.00000000e+00,   2.07911691e-01,   4.06736643e-01,\n",
       "         5.87785252e-01,   7.43144825e-01,   8.66025404e-01,\n",
       "         9.51056516e-01,   9.94521895e-01,   9.94521895e-01,\n",
       "         9.51056516e-01,   8.66025404e-01,   7.43144825e-01,\n",
       "         5.87785252e-01,   4.06736643e-01,   2.07911691e-01,\n",
       "         1.22464680e-16])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sin3 = np.sin(x)\n",
    "sin3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "6f8ba47c-eca0-48a5-9512-99040c6d5cd2"
    }
   },
   "source": [
    "Que tan optimizado?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "13c57300-0fcb-4579-a4d3-afc1f2c9ab55"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 5.49 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "100000 loops, best of 3: 7.6 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit sin2 = np.array([sin(i) for i in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "096e86e8-5df7-4bd3-a388-44b18a527f7c"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 14.01 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "1000000 loops, best of 3: 1.44 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit sin3 = np.sin(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "fd51bf62-60e2-4a6e-bacd-9bc5a0f879dd"
    }
   },
   "source": [
    "**np.sin()** es una funcion universal que evalua elemento por elemento. Numpy promueve que pensemos de forma vectorizada, que hagamos operaciones sobre los arreglos como si fuera una unidad.  De esta forma dejamos que el nucleo de estas librerias se encargen de realizar la parte complicada e incluso encargarse de optimizaciones, en multiples CPUs, o GPU's."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ejercicio** Ejecutar funciones artimeticas y matematicas sobre arreglos numpy.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Que es numpy?\n",
    "Pensemos en el como el objeto que nos provee con metodos optimizados la siguiente funcionalidad\n",
    "- arreglo de datos numericos, adecuado para datos cientificos (vectores, matrices)\n",
    "- operaciones vectorizadas para sus arreglos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Por ejemplo operaciones aritmeticas sobre arreglos\n",
    "x = np.array([1.,2.,3.,4.,5.])\n",
    "y = np.array([6,7,8,9,10]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  6.,  14.,  24.,  36.,  50.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x * y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -2.9,  -5.8,  -8.7, -11.6, -14.5])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x\n",
    "x * -2.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('complex128')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = x + 2j\n",
    "z.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizando los datos: matplotlib\n",
    "Ahora que ya tenemos datos, realizamos operaciones sobre ellos y representan un posible resultado de algun experimento, lo que sigue es visualizarlos.\n",
    "El paquete estandar para hacer esto es matplotlib, que es parte del paquete de scipy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "e75f55d2-a3d7-42d0-8ae5-210894d13a79"
    }
   },
   "outputs": [],
   "source": [
    "# importando matplotlib siguiendo la convencion\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recordemos como obtuvimos datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "c7a47147-2486-41bf-ae03-30e55887beee"
    }
   },
   "outputs": [],
   "source": [
    "x = np.linspace(-2*np.pi, 2*np.pi, 100)\n",
    "y = np.sin(x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hagamos un grafico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "7ad0977b-8708-4ad5-8aeb-549af1fbabaa"
    }
   },
   "outputs": [],
   "source": [
    "myplt = plt.plot(x,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Muestrame mi grafico!\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe0b40c4550>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ToooKmDjRdxX7vPmmbujVuLHvSrJn0CDdSG3nTt+V7DNlSrYu2IIFvgnR2WdD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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe0c4f98c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Incluyelo en mi notebook\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f523f6715f8>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ew684U0FkWCSxfWO1aSgqgmnTVFkFOxg7aCxHzx/lZP1JexrwAHdKx+lF9cyA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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f523f671518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.plot(x, np.cos(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Muestrame todos los argumentos que recibe el comando plot\n",
    "plt.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f523f5ce8d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8+M03sMceNWeCfWLRIjjxRGiYyMg0R0IE6XhdvDj2itGp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\nHI6k4ZyMw+FwOJKGczIOh8PhSBr/D7iP7thhLVY1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f523f623e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x), \"--\"  , label=\"$\\sin(x)$\" )\n",
    "plt.plot(x, np.cos(x), \"ro--\", label=\"$\\cos(x)$\")  # red points and dashed lines\n",
    "plt.xlabel(\"$x$\", size=20)\n",
    "plt.ylabel(\"$\\sin(x), \\cos(x)$\", size=20)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f523f56d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Salvamos nuestro grafico asi. \n",
    "plt.savefig(\"sincos.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['seaborn-paper', 'seaborn-talk', 'dark_background', 'seaborn-dark-palette', 'ggplot', 'grayscale', 'seaborn-darkgrid', 'fivethirtyeight', 'classic', 'seaborn-ticks', 'seaborn-pastel', 'seaborn-deep', 'bmh', 'seaborn-muted', 'seaborn-whitegrid', 'seaborn-bright', 'seaborn-notebook', 'seaborn-dark', 'seaborn-poster', 'seaborn-white', 'seaborn-colorblind']\n"
     ]
    }
   ],
   "source": [
    "# Estilos de graficos disponibles\n",
    "\n",
    "print(plt.style.available)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.style.use('classic')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J+OwzqFatXNf69O23Gfj550QW2JcGfPXKK0xcssTi68TEwMyZ0LcvPPVU2eUYMkQyPr//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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe0b40953c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XKCD anyone?\n",
    "\n",
    "with plt.xkcd():\n",
    "    plt.plot(x, np.sin(x), \"--\"  , label=\"$\\sin(x)$\" )\n",
    "    plt.plot(x, np.cos(x), \"ro--\", label=\"$\\cos(x)$\")  \n",
    "    plt.xlabel(\"$x$\", size=20)\n",
    "    plt.ylabel(\"$\\sin(x), \\cos(x)$\", size=20)\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ejercicio** Que tal si hacemos un grafico de la funcion logaritmica y el exponencial en un mismo grafico mas una linea punteada, mostrando la leyenda $y=x$ para denotar la simetria.\n",
    "\n",
    "Para ajustar nuestros limites visibles del grafico podemos usar `plt.ylim([ymin, ymax])` para ajustar en el eje Y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5, 5)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nmmgU7rwT8vJg3DgFuUia0DBLqtm3D2rVcqs70+TmEiKiYRYRDbNIqGnRkIhI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\nEBaKSBMp5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f523f255cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x[x>0], np.log(x[x>0]), \"-\")\n",
    "plt.plot(x, np.exp(x), \"-\")\n",
    "plt.plot(x,x,\"--\",label=\"$y=x$\")\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "plt.axhline(y=0)\n",
    "plt.axvline(x=0)   # Lineas sobre el origen\n",
    "plt.ylim([-5,5])   # Limite visual en y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cargando datos en numpy\n",
    "Generalmente las aplicaciones cientificas no generan datos a partir de funciones, o travez de simulaciones numericas, si no de datos ya existentes.\n",
    "\n",
    "Numpy cuenta con funcionalidad para cargar y salvar datos a sus arreglos, aun mas funcionalidad esta disponible en la libreria `pandas`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.5474918 ,  0.78878503,  0.16325052,  0.40631857,  0.2774425 ,\n",
       "        0.52468145,  0.52052233,  0.61937627,  0.55938531,  0.90868494])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generemos datos aleatorios\n",
    "np.random.rand(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 28.24702562,  26.47248378,  23.85184173,  23.75468923,\n",
       "        19.94246103,  16.03053163,  18.94884992,  15.22097288,\n",
       "        16.29456242,  11.57554023,  10.89748651,  11.76946391,\n",
       "         7.63200879,   5.5685312 ,   8.53069983,   5.26402443,\n",
       "         6.05148689,   6.19798044,   3.86075681,   5.58545446,\n",
       "         1.11093367,   2.33371577,   2.86832031,   2.39863092,\n",
       "         4.33513072,   2.31218201,   2.44547957,   4.2519523 ,\n",
       "         0.8576819 ,   4.47914683,   3.96820588,   6.42379984,\n",
       "         5.61448753,   5.63050068,   5.73330696,   7.96045853,\n",
       "         8.29888478,   8.99704858,   9.27120565,  11.1755766 ,\n",
       "        10.3908207 ,  12.47633002,  17.63901878,  18.26512429,\n",
       "        16.04881155,  18.74257505,  20.99108876,  23.8788008 ,\n",
       "        23.58272932,  26.92471953])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vectores con datos , \"simulando datos cientificos\"\n",
    "x = np.linspace(-5, 5, 50)\n",
    "y = x**2 + 5*np.random.rand(x.size)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f74ec8e2390>]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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czBNH7aS2+GbYqk9gXDNZ7GN7p6RPKOvR3xERN/XZhxBH46X2hwf1N/XFPrY3SPonSZdI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\nASBh/w/1KYoYt5mjowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74ec98e470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizamos nuestros datos.\n",
    "plt.plot(x,y,'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para exportar nuestros datos, de preferencia los salvaremos en forma tabulada, creando una matriz en numpy a partir de los dos vectores x,y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ar = np.arange(12).reshape(2,6)\n",
    "ar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-6.28318531, -6.15625227, -6.02931923, -5.9023862 , -5.77545316,\n",
       "        -5.64852012, -5.52158709, -5.39465405, -5.26772102, -5.14078798,\n",
       "        -5.01385494, -4.88692191, -4.75998887, -4.63305583, -4.5061228 ,\n",
       "        -4.37918976, -4.25225672, -4.12532369, -3.99839065, -3.87145761,\n",
       "        -3.74452458, -3.61759154, -3.4906585 , -3.36372547, -3.23679243,\n",
       "        -3.10985939, -2.98292636, -2.85599332, -2.72906028, -2.60212725,\n",
       "        -2.47519421, -2.34826118, -2.22132814, -2.0943951 , -1.96746207,\n",
       "        -1.84052903, -1.71359599, -1.58666296, -1.45972992, -1.33279688,\n",
       "        -1.20586385, -1.07893081, -0.95199777, -0.82506474, -0.6981317 ,\n",
       "        -0.57119866, -0.44426563, -0.31733259, -0.19039955, -0.06346652,\n",
       "         0.06346652,  0.19039955,  0.31733259,  0.44426563,  0.57119866,\n",
       "         0.6981317 ,  0.82506474,  0.95199777,  1.07893081,  1.20586385,\n",
       "         1.33279688,  1.45972992,  1.58666296,  1.71359599,  1.84052903,\n",
       "         1.96746207,  2.0943951 ,  2.22132814,  2.34826118,  2.47519421,\n",
       "         2.60212725,  2.72906028,  2.85599332,  2.98292636,  3.10985939,\n",
       "         3.23679243,  3.36372547,  3.4906585 ,  3.61759154,  3.74452458,\n",
       "         3.87145761,  3.99839065,  4.12532369,  4.25225672,  4.37918976,\n",
       "         4.5061228 ,  4.63305583,  4.75998887,  4.88692191,  5.01385494,\n",
       "         5.14078798,  5.26772102,  5.39465405,  5.52158709,  5.64852012,\n",
       "         5.77545316,  5.9023862 ,  6.02931923,  6.15625227,  6.28318531]),\n",
       " array([  2.44929360e-16,   1.26592454e-01,   2.51147987e-01,\n",
       "          3.71662456e-01,   4.86196736e-01,   5.92907929e-01,\n",
       "          6.90079011e-01,   7.76146464e-01,   8.49725430e-01,\n",
       "          9.09631995e-01,   9.54902241e-01,   9.84807753e-01,\n",
       "          9.98867339e-01,   9.96854776e-01,   9.78802446e-01,\n",
       "          9.45000819e-01,   8.95993774e-01,   8.32569855e-01,\n",
       "          7.55749574e-01,   6.66769001e-01,   5.67059864e-01,\n",
       "          4.58226522e-01,   3.42020143e-01,   2.20310533e-01,\n",
       "          9.50560433e-02,  -3.17279335e-02,  -1.58001396e-01,\n",
       "         -2.81732557e-01,  -4.00930535e-01,  -5.13677392e-01,\n",
       "         -6.18158986e-01,  -7.12694171e-01,  -7.95761841e-01,\n",
       "         -8.66025404e-01,  -9.22354294e-01,  -9.63842159e-01,\n",
       "         -9.89821442e-01,  -9.99874128e-01,  -9.93838464e-01,\n",
       "         -9.71811568e-01,  -9.34147860e-01,  -8.81453363e-01,\n",
       "         -8.14575952e-01,  -7.34591709e-01,  -6.42787610e-01,\n",
       "         -5.40640817e-01,  -4.29794912e-01,  -3.12033446e-01,\n",
       "         -1.89251244e-01,  -6.34239197e-02,   6.34239197e-02,\n",
       "          1.89251244e-01,   3.12033446e-01,   4.29794912e-01,\n",
       "          5.40640817e-01,   6.42787610e-01,   7.34591709e-01,\n",
       "          8.14575952e-01,   8.81453363e-01,   9.34147860e-01,\n",
       "          9.71811568e-01,   9.93838464e-01,   9.99874128e-01,\n",
       "          9.89821442e-01,   9.63842159e-01,   9.22354294e-01,\n",
       "          8.66025404e-01,   7.95761841e-01,   7.12694171e-01,\n",
       "          6.18158986e-01,   5.13677392e-01,   4.00930535e-01,\n",
       "          2.81732557e-01,   1.58001396e-01,   3.17279335e-02,\n",
       "         -9.50560433e-02,  -2.20310533e-01,  -3.42020143e-01,\n",
       "         -4.58226522e-01,  -5.67059864e-01,  -6.66769001e-01,\n",
       "         -7.55749574e-01,  -8.32569855e-01,  -8.95993774e-01,\n",
       "         -9.45000819e-01,  -9.78802446e-01,  -9.96854776e-01,\n",
       "         -9.98867339e-01,  -9.84807753e-01,  -9.54902241e-01,\n",
       "         -9.09631995e-01,  -8.49725430e-01,  -7.76146464e-01,\n",
       "         -6.90079011e-01,  -5.92907929e-01,  -4.86196736e-01,\n",
       "         -3.71662456e-01,  -2.51147987e-01,  -1.26592454e-01,\n",
       "         -2.44929360e-16]))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x , y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -6.28318531e+00,   2.44929360e-16],\n",
       "       [ -6.15625227e+00,   1.26592454e-01],\n",
       "       [ -6.02931923e+00,   2.51147987e-01],\n",
       "       [ -5.90238620e+00,   3.71662456e-01],\n",
       "       [ -5.77545316e+00,   4.86196736e-01],\n",
       "       [ -5.64852012e+00,   5.92907929e-01],\n",
       "       [ -5.52158709e+00,   6.90079011e-01],\n",
       "       [ -5.39465405e+00,   7.76146464e-01],\n",
       "       [ -5.26772102e+00,   8.49725430e-01],\n",
       "       [ -5.14078798e+00,   9.09631995e-01],\n",
       "       [ -5.01385494e+00,   9.54902241e-01],\n",
       "       [ -4.88692191e+00,   9.84807753e-01],\n",
       "       [ -4.75998887e+00,   9.98867339e-01],\n",
       "       [ -4.63305583e+00,   9.96854776e-01],\n",
       "       [ -4.50612280e+00,   9.78802446e-01],\n",
       "       [ -4.37918976e+00,   9.45000819e-01],\n",
       "       [ -4.25225672e+00,   8.95993774e-01],\n",
       "       [ -4.12532369e+00,   8.32569855e-01],\n",
       "       [ -3.99839065e+00,   7.55749574e-01],\n",
       "       [ -3.87145761e+00,   6.66769001e-01],\n",
       "       [ -3.74452458e+00,   5.67059864e-01],\n",
       "       [ -3.61759154e+00,   4.58226522e-01],\n",
       "       [ -3.49065850e+00,   3.42020143e-01],\n",
       "       [ -3.36372547e+00,   2.20310533e-01],\n",
       "       [ -3.23679243e+00,   9.50560433e-02],\n",
       "       [ -3.10985939e+00,  -3.17279335e-02],\n",
       "       [ -2.98292636e+00,  -1.58001396e-01],\n",
       "       [ -2.85599332e+00,  -2.81732557e-01],\n",
       "       [ -2.72906028e+00,  -4.00930535e-01],\n",
       "       [ -2.60212725e+00,  -5.13677392e-01],\n",
       "       [ -2.47519421e+00,  -6.18158986e-01],\n",
       "       [ -2.34826118e+00,  -7.12694171e-01],\n",
       "       [ -2.22132814e+00,  -7.95761841e-01],\n",
       "       [ -2.09439510e+00,  -8.66025404e-01],\n",
       "       [ -1.96746207e+00,  -9.22354294e-01],\n",
       "       [ -1.84052903e+00,  -9.63842159e-01],\n",
       "       [ -1.71359599e+00,  -9.89821442e-01],\n",
       "       [ -1.58666296e+00,  -9.99874128e-01],\n",
       "       [ -1.45972992e+00,  -9.93838464e-01],\n",
       "       [ -1.33279688e+00,  -9.71811568e-01],\n",
       "       [ -1.20586385e+00,  -9.34147860e-01],\n",
       "       [ -1.07893081e+00,  -8.81453363e-01],\n",
       "       [ -9.51997774e-01,  -8.14575952e-01],\n",
       "       [ -8.25064737e-01,  -7.34591709e-01],\n",
       "       [ -6.98131701e-01,  -6.42787610e-01],\n",
       "       [ -5.71198664e-01,  -5.40640817e-01],\n",
       "       [ -4.44265628e-01,  -4.29794912e-01],\n",
       "       [ -3.17332591e-01,  -3.12033446e-01],\n",
       "       [ -1.90399555e-01,  -1.89251244e-01],\n",
       "       [ -6.34665183e-02,  -6.34239197e-02],\n",
       "       [  6.34665183e-02,   6.34239197e-02],\n",
       "       [  1.90399555e-01,   1.89251244e-01],\n",
       "       [  3.17332591e-01,   3.12033446e-01],\n",
       "       [  4.44265628e-01,   4.29794912e-01],\n",
       "       [  5.71198664e-01,   5.40640817e-01],\n",
       "       [  6.98131701e-01,   6.42787610e-01],\n",
       "       [  8.25064737e-01,   7.34591709e-01],\n",
       "       [  9.51997774e-01,   8.14575952e-01],\n",
       "       [  1.07893081e+00,   8.81453363e-01],\n",
       "       [  1.20586385e+00,   9.34147860e-01],\n",
       "       [  1.33279688e+00,   9.71811568e-01],\n",
       "       [  1.45972992e+00,   9.93838464e-01],\n",
       "       [  1.58666296e+00,   9.99874128e-01],\n",
       "       [  1.71359599e+00,   9.89821442e-01],\n",
       "       [  1.84052903e+00,   9.63842159e-01],\n",
       "       [  1.96746207e+00,   9.22354294e-01],\n",
       "       [  2.09439510e+00,   8.66025404e-01],\n",
       "       [  2.22132814e+00,   7.95761841e-01],\n",
       "       [  2.34826118e+00,   7.12694171e-01],\n",
       "       [  2.47519421e+00,   6.18158986e-01],\n",
       "       [  2.60212725e+00,   5.13677392e-01],\n",
       "       [  2.72906028e+00,   4.00930535e-01],\n",
       "       [  2.85599332e+00,   2.81732557e-01],\n",
       "       [  2.98292636e+00,   1.58001396e-01],\n",
       "       [  3.10985939e+00,   3.17279335e-02],\n",
       "       [  3.23679243e+00,  -9.50560433e-02],\n",
       "       [  3.36372547e+00,  -2.20310533e-01],\n",
       "       [  3.49065850e+00,  -3.42020143e-01],\n",
       "       [  3.61759154e+00,  -4.58226522e-01],\n",
       "       [  3.74452458e+00,  -5.67059864e-01],\n",
       "       [  3.87145761e+00,  -6.66769001e-01],\n",
       "       [  3.99839065e+00,  -7.55749574e-01],\n",
       "       [  4.12532369e+00,  -8.32569855e-01],\n",
       "       [  4.25225672e+00,  -8.95993774e-01],\n",
       "       [  4.37918976e+00,  -9.45000819e-01],\n",
       "       [  4.50612280e+00,  -9.78802446e-01],\n",
       "       [  4.63305583e+00,  -9.96854776e-01],\n",
       "       [  4.75998887e+00,  -9.98867339e-01],\n",
       "       [  4.88692191e+00,  -9.84807753e-01],\n",
       "       [  5.01385494e+00,  -9.54902241e-01],\n",
       "       [  5.14078798e+00,  -9.09631995e-01],\n",
       "       [  5.26772102e+00,  -8.49725430e-01],\n",
       "       [  5.39465405e+00,  -7.76146464e-01],\n",
       "       [  5.52158709e+00,  -6.90079011e-01],\n",
       "       [  5.64852012e+00,  -5.92907929e-01],\n",
       "       [  5.77545316e+00,  -4.86196736e-01],\n",
       "       [  5.90238620e+00,  -3.71662456e-01],\n",
       "       [  6.02931923e+00,  -2.51147987e-01],\n",
       "       [  6.15625227e+00,  -1.26592454e-01],\n",
       "       [  6.28318531e+00,  -2.44929360e-16]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Arreglo z con columnas x, y\n",
    "z = np.stack((x,y), axis=1) \n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -6.28318531e+00,   2.44929360e-16],\n",
       "       [ -6.15625227e+00,   1.26592454e-01],\n",
       "       [ -6.02931923e+00,   2.51147987e-01],\n",
       "       [ -5.90238620e+00,   3.71662456e-01],\n",
       "       [ -5.77545316e+00,   4.86196736e-01],\n",
       "       [ -5.64852012e+00,   5.92907929e-01],\n",
       "       [ -5.52158709e+00,   6.90079011e-01],\n",
       "       [ -5.39465405e+00,   7.76146464e-01],\n",
       "       [ -5.26772102e+00,   8.49725430e-01],\n",
       "       [ -5.14078798e+00,   9.09631995e-01],\n",
       "       [ -5.01385494e+00,   9.54902241e-01],\n",
       "       [ -4.88692191e+00,   9.84807753e-01],\n",
       "       [ -4.75998887e+00,   9.98867339e-01],\n",
       "       [ -4.63305583e+00,   9.96854776e-01],\n",
       "       [ -4.50612280e+00,   9.78802446e-01],\n",
       "       [ -4.37918976e+00,   9.45000819e-01],\n",
       "       [ -4.25225672e+00,   8.95993774e-01],\n",
       "       [ -4.12532369e+00,   8.32569855e-01],\n",
       "       [ -3.99839065e+00,   7.55749574e-01],\n",
       "       [ -3.87145761e+00,   6.66769001e-01],\n",
       "       [ -3.74452458e+00,   5.67059864e-01],\n",
       "       [ -3.61759154e+00,   4.58226522e-01],\n",
       "       [ -3.49065850e+00,   3.42020143e-01],\n",
       "       [ -3.36372547e+00,   2.20310533e-01],\n",
       "       [ -3.23679243e+00,   9.50560433e-02],\n",
       "       [ -3.10985939e+00,  -3.17279335e-02],\n",
       "       [ -2.98292636e+00,  -1.58001396e-01],\n",
       "       [ -2.85599332e+00,  -2.81732557e-01],\n",
       "       [ -2.72906028e+00,  -4.00930535e-01],\n",
       "       [ -2.60212725e+00,  -5.13677392e-01],\n",
       "       [ -2.47519421e+00,  -6.18158986e-01],\n",
       "       [ -2.34826118e+00,  -7.12694171e-01],\n",
       "       [ -2.22132814e+00,  -7.95761841e-01],\n",
       "       [ -2.09439510e+00,  -8.66025404e-01],\n",
       "       [ -1.96746207e+00,  -9.22354294e-01],\n",
       "       [ -1.84052903e+00,  -9.63842159e-01],\n",
       "       [ -1.71359599e+00,  -9.89821442e-01],\n",
       "       [ -1.58666296e+00,  -9.99874128e-01],\n",
       "       [ -1.45972992e+00,  -9.93838464e-01],\n",
       "       [ -1.33279688e+00,  -9.71811568e-01],\n",
       "       [ -1.20586385e+00,  -9.34147860e-01],\n",
       "       [ -1.07893081e+00,  -8.81453363e-01],\n",
       "       [ -9.51997774e-01,  -8.14575952e-01],\n",
       "       [ -8.25064737e-01,  -7.34591709e-01],\n",
       "       [ -6.98131701e-01,  -6.42787610e-01],\n",
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       "       [ -1.90399555e-01,  -1.89251244e-01],\n",
       "       [ -6.34665183e-02,  -6.34239197e-02],\n",
       "       [  6.34665183e-02,   6.34239197e-02],\n",
       "       [  1.90399555e-01,   1.89251244e-01],\n",
       "       [  3.17332591e-01,   3.12033446e-01],\n",
       "       [  4.44265628e-01,   4.29794912e-01],\n",
       "       [  5.71198664e-01,   5.40640817e-01],\n",
       "       [  6.98131701e-01,   6.42787610e-01],\n",
       "       [  8.25064737e-01,   7.34591709e-01],\n",
       "       [  9.51997774e-01,   8.14575952e-01],\n",
       "       [  1.07893081e+00,   8.81453363e-01],\n",
       "       [  1.20586385e+00,   9.34147860e-01],\n",
       "       [  1.33279688e+00,   9.71811568e-01],\n",
       "       [  1.45972992e+00,   9.93838464e-01],\n",
       "       [  1.58666296e+00,   9.99874128e-01],\n",
       "       [  1.71359599e+00,   9.89821442e-01],\n",
       "       [  1.84052903e+00,   9.63842159e-01],\n",
       "       [  1.96746207e+00,   9.22354294e-01],\n",
       "       [  2.09439510e+00,   8.66025404e-01],\n",
       "       [  2.22132814e+00,   7.95761841e-01],\n",
       "       [  2.34826118e+00,   7.12694171e-01],\n",
       "       [  2.47519421e+00,   6.18158986e-01],\n",
       "       [  2.60212725e+00,   5.13677392e-01],\n",
       "       [  2.72906028e+00,   4.00930535e-01],\n",
       "       [  2.85599332e+00,   2.81732557e-01],\n",
       "       [  2.98292636e+00,   1.58001396e-01],\n",
       "       [  3.10985939e+00,   3.17279335e-02],\n",
       "       [  3.23679243e+00,  -9.50560433e-02],\n",
       "       [  3.36372547e+00,  -2.20310533e-01],\n",
       "       [  3.49065850e+00,  -3.42020143e-01],\n",
       "       [  3.61759154e+00,  -4.58226522e-01],\n",
       "       [  3.74452458e+00,  -5.67059864e-01],\n",
       "       [  3.87145761e+00,  -6.66769001e-01],\n",
       "       [  3.99839065e+00,  -7.55749574e-01],\n",
       "       [  4.12532369e+00,  -8.32569855e-01],\n",
       "       [  4.25225672e+00,  -8.95993774e-01],\n",
       "       [  4.37918976e+00,  -9.45000819e-01],\n",
       "       [  4.50612280e+00,  -9.78802446e-01],\n",
       "       [  4.63305583e+00,  -9.96854776e-01],\n",
       "       [  4.75998887e+00,  -9.98867339e-01],\n",
       "       [  4.88692191e+00,  -9.84807753e-01],\n",
       "       [  5.01385494e+00,  -9.54902241e-01],\n",
       "       [  5.14078798e+00,  -9.09631995e-01],\n",
       "       [  5.26772102e+00,  -8.49725430e-01],\n",
       "       [  5.39465405e+00,  -7.76146464e-01],\n",
       "       [  5.52158709e+00,  -6.90079011e-01],\n",
       "       [  5.64852012e+00,  -5.92907929e-01],\n",
       "       [  5.77545316e+00,  -4.86196736e-01],\n",
       "       [  5.90238620e+00,  -3.71662456e-01],\n",
       "       [  6.02931923e+00,  -2.51147987e-01],\n",
       "       [  6.15625227e+00,  -1.26592454e-01],\n",
       "       [  6.28318531e+00,  -2.44929360e-16]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Segundo metodo\n",
    "z = np.hstack((x[:,np.newaxis],y[:,np.newaxis]))\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.savetxt(\"cuadratica-ruido.txt\", z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5.000000000000000000e+00 2.824702562420766583e+01\r\n",
      "-4.795918367346938993e+00 2.647248378150305115e+01\r\n",
      "-4.591836734693877986e+00 2.385184173119323248e+01\r\n",
      "-4.387755102040816091e+00 2.375468923151409228e+01\r\n",
      "-4.183673469387755084e+00 1.994246103445604490e+01\r\n",
      "-3.979591836734694077e+00 1.603053162724983238e+01\r\n",
      "-3.775510204081632626e+00 1.894884991956936915e+01\r\n",
      "-3.571428571428571175e+00 1.522097287953479139e+01\r\n",
      "-3.367346938775510168e+00 1.629456241638368397e+01\r\n",
      "-3.163265306122449161e+00 1.157554023382974506e+01\r\n",
      "-2.959183673469387710e+00 1.089748651161886706e+01\r\n",
      "-2.755102040816326259e+00 1.176946390905610684e+01\r\n",
      "-2.551020408163265252e+00 7.632008793374686739e+00\r\n",
      "-2.346938775510204245e+00 5.568531201749807202e+00\r\n",
      "-2.142857142857142794e+00 8.530699829161520142e+00\r\n",
      "-1.938775510204081343e+00 5.264024433670237535e+00\r\n",
      "-1.734693877551020336e+00 6.051486889151954429e+00\r\n",
      "-1.530612244897959329e+00 6.197980440787007161e+00\r\n",
      "-1.326530612244897878e+00 3.860756806616196801e+00\r\n",
      "-1.122448979591836427e+00 5.585454460204204885e+00\r\n",
      "-9.183673469387754196e-01 1.110933667200340924e+00\r\n",
      "-7.142857142857144126e-01 2.333715771550480156e+00\r\n",
      "-5.102040816326525174e-01 2.868320306634464689e+00\r\n",
      "-3.061224489795915105e-01 2.398630915603418234e+00\r\n",
      "-1.020408163265305035e-01 4.335130720253237868e+00\r\n",
      "1.020408163265305035e-01 2.312182009856941889e+00\r\n",
      "3.061224489795915105e-01 2.445479571020570120e+00\r\n",
      "5.102040816326534056e-01 4.251952295242000801e+00\r\n",
      "7.142857142857144126e-01 8.576818989232950496e-01\r\n",
      "9.183673469387754196e-01 4.479146831473111767e+00\r\n",
      "1.122448979591837315e+00 3.968205881682113745e+00\r\n",
      "1.326530612244898322e+00 6.423799835472181918e+00\r\n",
      "1.530612244897959329e+00 5.614487529912880248e+00\r\n",
      "1.734693877551020336e+00 5.630500681861990486e+00\r\n",
      "1.938775510204081343e+00 5.733306962408925678e+00\r\n",
      "2.142857142857143238e+00 7.960458525412763109e+00\r\n",
      "2.346938775510204245e+00 8.298884777030480109e+00\r\n",
      "2.551020408163265252e+00 8.997048583115015319e+00\r\n",
      "2.755102040816327147e+00 9.271205646681103474e+00\r\n",
      "2.959183673469388154e+00 1.117557660184447244e+01\r\n",
      "3.163265306122449161e+00 1.039082069834641331e+01\r\n",
      "3.367346938775510168e+00 1.247633002061017393e+01\r\n",
      "3.571428571428571175e+00 1.763901878040118376e+01\r\n",
      "3.775510204081632182e+00 1.826512429422394845e+01\r\n",
      "3.979591836734694965e+00 1.604881155333788811e+01\r\n",
      "4.183673469387755972e+00 1.874257504581618505e+01\r\n",
      "4.387755102040816979e+00 2.099108875662204099e+01\r\n",
      "4.591836734693877986e+00 2.387880080401864547e+01\r\n",
      "4.795918367346938993e+00 2.358272932452515036e+01\r\n",
      "5.000000000000000000e+00 2.692471953440272969e+01\r\n"
     ]
    }
   ],
   "source": [
    "# Comando magico cat - funciona en windows? (intenta %more)\n",
    "%cat cuadratica-ruido.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.savetxt(\"cuadratica-ruido.txt\", z, fmt=\"%10.5f\", delimiter=\",\", header=\"X, Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuadratica-ruido.txt\r\n",
      "dask-dataframes-demo.ipynb\r\n",
      "\u001b[0m\u001b[01;34mdata\u001b[0m/\r\n",
      "extra-tabla-equivalencias-matlab-numpy.ipynb\r\n",
      "\u001b[01;34mimages\u001b[0m/\r\n",
      "LICENSE\r\n",
      "nb00-python-basics.ipynb\r\n",
      "nb01-empezando-python-cientifico.ipynb\r\n",
      "nb02-numpy-basics.ipynb\r\n",
      "nb03-timeseries-numpy.ipynb\r\n",
      "nb05-pandas-demo-ejercicios.ipynb\r\n",
      "nb05-pandas-demo-ejercicios-SOLUCIONES.ipynb\r\n",
      "nb06-pandas-demo-ejercicios.ipynb\r\n",
      "nb06-pandas-demo-ejercicios-SOLUCIONES.ipynb\r\n",
      "nb07-pandas-demo-ejercicios.ipynb\r\n",
      "nb07-pandas-demo-ejercicios-SOLUCIONES.ipynb\r\n",
      "README.md\r\n",
      "\u001b[01;35msincos.png\u001b[0m\r\n",
      "texto.txt\r\n"
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    "zz = np.loadtxt(\"cuadratica-ruido.txt\", delimiter=\",\")\n",
    "zz "
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   "execution_count": 39,
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   "source": [
    "**Ejercicio** Como podemos extraer ahora los elementos de cada columna de la matriz que salvamos.\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Documentando nuestro codigo en el entorno Notebook \"Literate programming\"\n",
    "Podemos agregar informacion complementaria utilizando un lenguaje ligero para hacer anotaciones llamado **markdown**.\n",
    "\n",
    "Markdown nos permite escribir en *italicas*, poner texto en **negritas**, \n",
    "- hacer listas, \n",
    "- definir titulos, \n",
    "- tablas, resaltar algun texto:.\n",
    "\n",
    "> Alguna frase\n",
    "\n",
    "Resulta util para el computo cientifico poder incluir en celdas con Markup formulas escritas con LaTeX, encasillando las ecuaciones entre `$`, y `$$` para parrafos.\n",
    "\n",
    "Por ejemplo $y=x^2$ , Una mas complicada seria:\n",
    "$$\n",
    "\\mathcal{N}(x)=Ce^{-x^2}\n",
    "$$delimiter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resumen\n",
    "Hemos revisado de manera general los componentes fundamentales del paquete SciPy\n",
    "\n",
    "- El IPython Notebook\n",
    "    - Un entorno que facilita los flujos de trabajo de computo\n",
    "    - Soporte para visualizacion interactiva\n",
    "    - Posibilidad de exportar en distintos formatos\n",
    "- numpy\n",
    "    - Manejo de vectores, matrices y arreglos de n-dimensiones\n",
    "    - Provee de funciones vectorizadas optimizadas para arreglos    \n",
    "\n",
    "- matplotlib\n",
    "    - Flexibilidad\n",
    "    - Calidad para publicaciones\n"
   ]
  },
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   "metadata": {
    "collapsed": true
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