Notebooks/Clase3a_Entrada-Salida-Algebra-Lineal.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"../static/aeropython_name_mini.png\" alt=\"AeroPython\" style=\"width: 300px;\" />"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Clase 3a: Entrada/Salida y \u00c1lgebra Lineal"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_Una vez hemos visto el manejo b\u00e1sico de arrays en Python con NumPy, es hora de pasar a operaciones m\u00e1s interesantes como son las propias del \u00c1lgebra Lineal._\n",
      "\n",
      "_Los productos escalares y las inversiones de matrices est\u00e1n por todas partes en los programas cient\u00edficos e ingenieriles, as\u00ed que vamos a estudiar c\u00f3mo se realizan en Python._"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "\u00c1lgebra lineal"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Como sabemos, las operaciones del \u00e1lgebra lineal aparecen con mucha frecuencia a la hora de resolver sistemas de ecuaciones en derivadas parciales y en general al linealizar problemas de todo tipo, y suele ser necesario resolver sistemas con un n\u00famero enorme de ecuaciones e inc\u00f3gnitas. Gracias a los arrays de NumPy podemos abordar este tipo de c\u00e1lculos en Python, ya que todas las funciones est\u00e1n escritas en C o Fortran y tenemos la opci\u00f3n de usar bibliotecas optimizadas al l\u00edmite."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "El paquete de \u00e1lgebra lineal en NumPy se llama `linalg`, as\u00ed que importando NumPy con la convenci\u00f3n habitual podemos acceder a \u00e9l escribiendo `np.linalg`. Si imprimimos la ayuda del paquete vemos que tenemos funciones para:\n",
      "\n",
      "* Funciones b\u00e1sicas (norma de un vector, inversa de una matriz, determinante, traza)\n",
      "* Resoluci\u00f3n de sistemas\n",
      "* Autovalores y autovectores\n",
      "* Descomposiciones matriciales (QR, SVD)\n",
      "* Pseudoinversas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "help(np.linalg)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Help on package numpy.linalg in numpy:\n",
        "\n",
        "NAME\n",
        "    numpy.linalg\n",
        "\n",
        "DESCRIPTION\n",
        "    Core Linear Algebra Tools\n",
        "    -------------------------\n",
        "    Linear algebra basics:\n",
        "    \n",
        "    - norm            Vector or matrix norm\n",
        "    - inv             Inverse of a square matrix\n",
        "    - solve           Solve a linear system of equations\n",
        "    - det             Determinant of a square matrix\n",
        "    - lstsq           Solve linear least-squares problem\n",
        "    - pinv            Pseudo-inverse (Moore-Penrose) calculated using a singular\n",
        "                      value decomposition\n",
        "    - matrix_power    Integer power of a square matrix\n",
        "    \n",
        "    Eigenvalues and decompositions:\n",
        "    \n",
        "    - eig             Eigenvalues and vectors of a square matrix\n",
        "    - eigh            Eigenvalues and eigenvectors of a Hermitian matrix\n",
        "    - eigvals         Eigenvalues of a square matrix\n",
        "    - eigvalsh        Eigenvalues of a Hermitian matrix\n",
        "    - qr              QR decomposition of a matrix\n",
        "    - svd             Singular value decomposition of a matrix\n",
        "    - cholesky        Cholesky decomposition of a matrix\n",
        "    \n",
        "    Tensor operations:\n",
        "    \n",
        "    - tensorsolve     Solve a linear tensor equation\n",
        "    - tensorinv       Calculate an inverse of a tensor\n",
        "    \n",
        "    Exceptions:\n",
        "    \n",
        "    - LinAlgError     Indicates a failed linear algebra operation\n",
        "\n",
        "PACKAGE CONTENTS\n",
        "    _umath_linalg\n",
        "    info\n",
        "    lapack_lite\n",
        "    linalg\n",
        "    setup\n",
        "\n",
        "DATA\n",
        "    absolute_import = _Feature((2, 5, 0, 'alpha', 1), (3, 0, 0, 'alpha', 0...\n",
        "    division = _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192...\n",
        "    print_function = _Feature((2, 6, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0)...\n",
        "\n",
        "FILE\n",
        "    /home/juanlu/.local/lib/python3.4/site-packages/numpy/linalg/__init__.py\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Recordemos que si queremos usar una funci\u00f3n de un paquete pero no queremos escribir la \"ruta\" completa cada vez, podemos usar la sintaxis `from package import func`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy.linalg import norm, det\n",
      "norm"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<function numpy.linalg.linalg.norm>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "El producto matricial usual (no el que se hace elemento a elemento, sino el del \u00e1lgebra lineal) se calcula con la misma funci\u00f3n que el producto matriz-vector y el producto escalar vector-vector: con la funci\u00f3n `dot`, que **no** est\u00e1 en el paquete `linalg` sino directamente en `numpy` y no hace falta importarlo."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.dot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<function numpy.core.multiarray.dot>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Una consideraci\u00f3n importante a tener en cuenta es que en NumPy no hace falta ser estricto a la hora de manejar vectores como si fueran matrices columna, siempre que la operaci\u00f3n sea consistente. Un vector es una matriz con una sola dimensi\u00f3n: por eso si calculamos su traspuesta no funciona."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M = np.array([\n",
      "    [1, 2],\n",
      "    [3, 4]\n",
      "])\n",
      "v = np.array([1, -1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v.T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "array([ 1, -1])"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "u = np.dot(M, v)\n",
      "u"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "array([-1, -1])"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Para hacer comparaciones entre arrays de punto flotante se pueden usar las funciones `np.allclose` y `np.isclose`. La primera comprueba si todos los elementos de los arrays son iguales dentro de una tolerancia, y la segunda compara elemento a elemento y devuelve un array de valores `True` y `False`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "u, v"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "(array([-1, -1]), array([ 1, -1]))"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.allclose(u, v)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.isclose(0.0, 1e-8, atol=1e-10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Ejercicios"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "1- Hallar el producto de estas dos matrices y su determinante:\n",
      "\n",
      "$$\\begin{pmatrix} 1 & 0 & 0 \\\\ 2 & 1 & 1 \\\\ -1 & 0 & 1 \\end{pmatrix} \\begin{pmatrix} 2 & 3 & -1 \\\\ 0 & -2 & 1 \\\\ 0 & 0 & 3 \\end{pmatrix}$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy.linalg import det"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = np.array([\n",
      "    [1, 0, 0],\n",
      "    [2, 1, 1],\n",
      "    [-1, 0, 1]\n",
      "])\n",
      "B = np.array([\n",
      "    [2, 3, -1],\n",
      "    [0, -2, 1],\n",
      "    [0, 0, 3]\n",
      "])\n",
      "print(A)\n",
      "print(B)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 1  0  0]\n",
        " [ 2  1  1]\n",
        " [-1  0  1]]\n",
        "[[ 2  3 -1]\n",
        " [ 0 -2  1]\n",
        " [ 0  0  3]]\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "C = np.dot(A, B)\n",
      "C"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "array([[ 2,  3, -1],\n",
        "       [ 4,  4,  2],\n",
        "       [-2, -3,  4]])"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "det(C)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "-12.0"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "2- Resolver el siguiente sistema:\n",
      "\n",
      "$$ \\begin{pmatrix} 2 & 0 & 0 \\\\ -1 & 1 & 0 \\\\ 3 & 2 & -1 \\end{pmatrix} \\begin{pmatrix} 1 & 1 & 1 \\\\ 0 & 1 & 2 \\\\ 0 & 0 & 1 \\end{pmatrix} \\begin{pmatrix} x \\\\ y \\\\ z \\end{pmatrix} = \\begin{pmatrix} -1 \\\\ 3 \\\\ 0 \\end{pmatrix} $$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M = np.dot(\n",
      "np.array([\n",
      "    [2, 0, 0],\n",
      "    [-1, 1, 0],\n",
      "    [3, 2, -1]\n",
      "]),\n",
      "np.array([\n",
      "    [1, 1, 1],\n",
      "    [0, 1, 2],\n",
      "    [0, 0, 1]\n",
      "]))\n",
      "M"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "array([[ 2,  2,  2],\n",
        "       [-1,  0,  1],\n",
        "       [ 3,  5,  6]])"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linalg.solve(M, np.array([-1, 3, 0]))\n",
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "array([ 0.5, -4.5,  3.5])"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.allclose(np.dot(M, x), np.array([-1, 3, 0]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "3- Hallar la inversa de la matriz $H$ y comprobar que $H H^{-1} = I$ (recuerda la funci\u00f3n `np.eye`)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = np.arange(1, 37).reshape(6,6)\n",
      "A[1, 1::2] = 0\n",
      "A[3, ::2] = 1\n",
      "A[4, :] += 30\n",
      "B = (2 ** np.arange(36)).reshape((6,6))\n",
      "H = A + B\n",
      "print(H)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[          2           4           7          12          21          38]\n",
        " [         71         128         265         512        1035        2048]\n",
        " [       4109        8206       16399       32784       65553      131090]\n",
        " [     262145      524308     1048577     2097174     4194305     8388632]\n",
        " [   16777271    33554488    67108921   134217786   268435515   536870972]\n",
        " [ 1073741855  2147483680  4294967329  8589934626 17179869219 34359738404]]\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.linalg.det(H)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "456269.59150316147"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Hinv = np.linalg.inv(H)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.isclose(np.dot(Hinv, H), np.eye(6))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "array([[False, False, False, False, False, False],\n",
        "       [False, False, False, False, False, False],\n",
        "       [False, False, False, False, False, False],\n",
        "       [False, False, False, False, False, False],\n",
        "       [False,  True, False, False,  True, False],\n",
        "       [False,  True, False,  True,  True, False]], dtype=bool)"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.set_printoptions(precision=3)\n",
      "print(np.dot(Hinv, H))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[  2.188e+00   2.375e+00   5.000e+00   9.000e+00   1.900e+01   4.000e+01]\n",
        " [  1.875e-01   1.438e+00   8.750e-01   1.750e+00   3.500e+00   6.000e+00]\n",
        " [ -5.000e-01  -5.000e-01  -5.000e-01  -2.000e+00  -6.000e+00  -1.600e+01]\n",
        " [  1.250e-01   2.500e-01   5.000e-01   1.500e+00   2.000e+00   6.000e+00]\n",
        " [  6.250e-02   0.000e+00   2.500e-01  -5.000e-01   1.000e+00   2.000e+00]\n",
        " [ -3.125e-02   0.000e+00  -1.250e-01   0.000e+00   0.000e+00   0.000e+00]]\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div class=\"alert alert-warning\">\u00a1No funciona! Y no solo eso sino que los resultados var\u00edan de un ordenador a otro.</div>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "4- Comprobar el n\u00famero de condici\u00f3n de la matriz $H$."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.linalg.cond(H)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "1.0682988820367766e+17"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div class=\"alert alert-warning\">La matriz est\u00e1 mal condicionada.</div>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Secciones de arrays"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Hasta ahora hemos visto c\u00f3mo acceder a elementos aislados del array, pero la potencia de NumPy est\u00e1 en poder acceder a secciones enteras. Para ello se usa la sintaxis `inicio:final:paso`: si alguno de estos valores no se pone toma un valor por defecto. Veamos ejemplos:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M = np.arange(36, dtype=float).reshape(4, 9)\n",
      "M"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "array([[  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.],\n",
        "       [  9.,  10.,  11.,  12.,  13.,  14.,  15.,  16.,  17.],\n",
        "       [ 18.,  19.,  20.,  21.,  22.,  23.,  24.,  25.,  26.],\n",
        "       [ 27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.]])"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# De la segunda a la tercera fila, incluida\n",
      "M[1:3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "array([[  9.,  10.,  11.,  12.,  13.,  14.,  15.,  16.,  17.],\n",
        "       [ 18.,  19.,  20.,  21.,  22.,  23.,  24.,  25.,  26.]])"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Hasta la tercera fila sin incluir y de la segunda a la quinta columnas saltando dos\n",
      "M[:2, 1:5:2]\n",
      "#M[1:2:1, 1:5:2]  # Equivalente"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "array([[  1.,   3.],\n",
        "       [ 10.,  12.]])"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Ejercicio"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pintar un tablero de ajedrez usando la funci\u00f3n `plt.matshow`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tablero = np.zeros([8, 8], dtype=int)\n",
      "\n",
      "tablero[0::2, 1::2] = 1\n",
      "tablero[1::2, 0::2] = 1\n",
      "\n",
      "tablero"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "array([[0, 1, 0, 1, 0, 1, 0, 1],\n",
        "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
        "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
        "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
        "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
        "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
        "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
        "       [1, 0, 1, 0, 1, 0, 1, 0]])"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "plt.matshow(tablero, cmap=plt.cm.gray_r)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "<matplotlib.image.AxesImage at 0x7fa7aa64a908>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAD7CAYAAABZjGkWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSIhgAwn9D4CVTnEA1ECjAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fa7b5096128>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Entrada/Salida"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Con E/S (I/O en ingl\u00e9s) entendemos leer y escribir datos archivos. Es algo que necesitaremos hacer con relativa frecuencia, y en NumPy es muy sencillo de hacer. Para el caso de la **lectura** se usa la funci\u00f3n `np.loadtxt`."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Para practicar, vamos a leer el archivo `temperaturas.csv` que contiene datos diarios de temperaturas en Nueva York entre el 1 de enero de 2013 y el 1 de enero de 2014, obtenidos gratuitamente de http://ncdc.noaa.gov/. Como los hemos descargado en formato CSV habr\u00e1 que tener algunas precauciones a la hora de leer el archivo."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head ../static/temperaturas.csv  # Esta l\u00ednea no funciona en Windows"
     ],
     "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"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datos = np.loadtxt(\"../static/temperaturas.csv\",\n",
      "                   skiprows=1,  # Saltamos una l\u00ednea\n",
      "                   usecols=(1, 2, 3),  # Solo columnas 2, 3 y 4\n",
      "                   delimiter=',')  # Separados por comas"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datos[:9]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "array([[  2.013e+07,   4.400e+01,  -3.300e+01],\n",
        "       [  2.013e+07,   6.000e+00,  -5.600e+01],\n",
        "       [  2.013e+07,   0.000e+00,  -4.400e+01],\n",
        "       [  2.013e+07,   2.800e+01,  -1.100e+01],\n",
        "       [  2.013e+07,   5.600e+01,   0.000e+00],\n",
        "       [  2.013e+07,   7.800e+01,   1.100e+01],\n",
        "       [  2.013e+07,   7.200e+01,   2.800e+01],\n",
        "       [  2.013e+07,   8.900e+01,   1.700e+01],\n",
        "       [  2.013e+07,   9.400e+01,   3.900e+01]])"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La primera columna es un entero con formato \"AAAAMMDD\" que vamos a ignorar. Las temperaturas est\u00e1n medidas en d\u00e9cimas de grado Celsius, as\u00ed que hay que pasarlas a grados Celsius. Vamos a calcular tambi\u00e9n la temperatura media."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Tmax = datos[:, 1] / 10\n",
      "Tmin = datos[:, 2] / 10\n",
      "Tavg = (Tmax + Tmin) / 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Como vamos a ignorar la columna de las fechas tenemos que crear un dominio para el eje x. Simplemente construiremos un array de enteros desde 0 hasta 365."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.arange(366)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Ejercicio"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Y ahora representamos la evoluci\u00f3n de la temperatura media (por ejemplo de color negro), indicando \"Daily summaries\" en el t\u00edtulo, \"Days\" en el eje x y \"Temperature (C)\" en el eje y, usando la interfaz orientada a objetos de matplotlib (funci\u00f3n `plt.subplots`). Podemos crear una zona rellena entre la m\u00e1xima y la m\u00ednima con la funci\u00f3n `fill_between(x, max, min)` (por ejemplo de color #4f88b1). Si los l\u00edmites del eje x no quedan como queremos podemos usar la funci\u00f3n `set_xlim(xmin, xmax)`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots()\n",
      "\n",
      "ax.plot(x, Tavg, 'k')\n",
      "ax.set_xlim(0, 366)\n",
      "ax.fill_between(x, Tmin, Tmax, facecolor='#4f88b1', edgecolor='none')\n",
      "ax.set_title(\"Res\u00famenes diarios\")\n",
      "ax.set_xlabel(\"D\u00edas\")\n",
      "ax.set_ylabel(\"Temperatura (C)\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "<matplotlib.text.Text at 0x7fa7aa5a0240>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lJUy7w+N///uc+OqnQUqqZ48Bq5fy1molEvkx0qPT1BfO8MXf+0nmyq0+30hD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\nC3wf2E6dt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       "text": [
        "<matplotlib.figure.Figure at 0x7fa7aa5bec88>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_Ya hemos aprendido a efectuar algunas operaciones \u00fatiles con NumPy e incluso hemos hecho nuestro primer ejercicio de lectura de datos. Estamos en condiciones de empezar a escribir programas m\u00e1s interesantes, pero a\u00fan queda lo mejor._\n",
      "\n",
      "* [\u00c1lgebra lineal en Python con NumPy en Pybonacci](http://pybonacci.org/2012/06/07/algebra-lineal-en-python-con-numpy-i-operaciones-basicas/)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Si te ha gustado esta clase:\n",
      "\n",
      "<a href=\"https://twitter.com/share\" class=\"twitter-share-button\" data-url=\"https://github.com/AeroPython/Curso_AeroPython\" data-text=\"Aprendiendo Python con\" data-via=\"pybonacci\" data-size=\"large\" data-hashtags=\"AeroPython\">Tweet</a>\n",
      "<script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script>\n",
      "\n",
      "---"
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "<img src=\"../static/linkedin.png\" alt=\"AeroPython\" style=\"width: 25px\" align=\"right\";/> Curso impartido por: [Juan Luis Cano](http://es.linkedin.com/in/juanluiscanor)  & [\u00c1lex S\u00e1ez](http://es.linkedin.com/pub/alejandro-saez-mollejo/55/22/473)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "En colaboraci\u00f3n: Alberto Lorenzo"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "<h4 align=\"right\">\u00a1S\u00edguenos en Twitter!"
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "<a href=\"https://twitter.com/Pybonacci\" class=\"twitter-follow-button\" data-show-count=\"false\">Follow @Pybonacci</a>\n",
      "<script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script>\n",
      "\n",
      "<a href=\"https://twitter.com/Alex__S12\" class=\"twitter-follow-button\" data-show-count=\"false\" align=\"right\";>Follow @Alex__S12</a>\n",
      "<script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script>\n",
      "\n",
      "<a href=\"https://twitter.com/newlawrence\" class=\"twitter-follow-button\" data-show-count=\"false\" align=\"right\";>Follow @newlawrence</a>\n",
      "<script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/deed.es\"><img alt=\"Licencia Creative Commons\" style=\"border-width:0\" src=\"http://i.creativecommons.org/l/by/4.0/88x31.png\" /></a><br /><span xmlns:dct=\"http://purl.org/dc/terms/\" property=\"dct:title\">Curso AeroPython</span> por <span xmlns:cc=\"http://creativecommons.org/ns#\" property=\"cc:attributionName\">Juan Luis Cano Rodriguez y Alejandro S\u00e1ez Mollejo</span> se distribuye bajo una <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/deed.es\">Licencia Creative Commons Atribuci\u00f3n 4.0 Internacional</a>."
     ]
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "<script src=\"//platform.linkedin.com/in.js\" type=\"text/javascript\"></script>\n",
      "<script type=\"IN/MemberProfile\" data-id=\"http://es.linkedin.com/in/juanluiscanor\" data-format=\"inline\" data-related=\"false\"></script>\n",
      "<script src=\"//platform.linkedin.com/in.js\" type=\"text/javascript\"></script>\n",
      "<script type=\"IN/MemberProfile\" data-id=\"http://es.linkedin.com/in/alejandrosaezm\" data-format=\"inline\" data-related=\"false\"></script>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---\n",
      "_Las siguientes celdas contienen configuraci\u00f3n del Notebook_\n",
      "\n",
      "_Para visualizar y utlizar los enlaces a Twitter el notebook debe ejecutarse como [seguro](http://ipython.org/ipython-doc/dev/notebook/security.html)_\n",
      "\n",
      "    File > Trusted Notebook"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%html\n",
      "<a href=\"https://twitter.com/Pybonacci\" class=\"twitter-follow-button\" data-show-count=\"false\">Follow @Pybonacci</a>\n",
      "<script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script>"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<a href=\"https://twitter.com/Pybonacci\" class=\"twitter-follow-button\" data-show-count=\"false\">Follow @Pybonacci</a>\n",
        "<script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x430e208>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Esta celda da el estilo al notebook\n",
      "from IPython.core.display import HTML\n",
      "css_file = '../static/styles/style.css'\n",
      "HTML(open(css_file, \"r\").read())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<link href='http://fonts.googleapis.com/css?family=Source+Sans+Pro|Josefin+Sans:400,700,400italic|Ubuntu+Condensed' rel='stylesheet' type='text/css'>\n",
        "\n",
        "El estilo se ha aplicado =)\n",
        "\n",
        "<style>\n",
        "\n",
        "\n",
        "\n",
        "#notebook_panel { /* main background */\n",
        "    background: #f7f7f7;\n",
        "}\n",
        "\n",
        "div.cell { /* set cell width */\n",
        "    width: 900px;\n",
        "}\n",
        "\n",
        "div #notebook { /* centre the content */\n",
        "    background: #fff; /* white background for content */\n",
        "    width: 950px;\n",
        "    margin: auto;\n",
        "    padding-left: 0em;\n",
        "}\n",
        "\n",
        "#notebook li { /* More space between bullet points */\n",
        "    margin-top:0.7em;\n",
        "}\n",
        "\n",
        "/* draw border around running cells */\n",
        "div.cell.border-box-sizing.code_cell.running { \n",
        "    border: 1px solid #111;\n",
        "}\n",
        "\n",
        "/* Put a solid color box around each cell and its output, visually linking them*/\n",
        "div.cell.code_cell {\n",
        "    font-family: 'Source Sans Pro', sans-serif;\n",
        "    background-color: rgb(256,256,256);\n",
        "    font-size: 110%;\n",
        "    border-radius: 0px; \n",
        "    padding: 0.5em;\n",
        "    margin-left:1em;\n",
        "    margin-top: 1em;\n",
        "}\n",
        "\n",
        "div.text_cell_render{\n",
        "    font-family: 'Josefin Sans', serif;\n",
        "    line-height: 145%;\n",
        "    font-size: 125%;\n",
        "    font-weight: 500;\n",
        "    width:750px;\n",
        "    margin-left:auto;\n",
        "    margin-right:auto;\n",
        "}\n",
        "\n",
        "\n",
        "/* Formatting for header cells */\n",
        ".text_cell_render h1, .text_cell_render h2, .text_cell_render h3,\n",
        ".text_cell_render h4, .text_cell_render h5 {\n",
        "    font-family: 'Ubuntu Condensed', sans-serif;\n",
        "}\n",
        "/*\n",
        ".text_cell_render h1 {\n",
        "    font-family: Flux, 'Ubuntu Condensed', serif;\n",
        "    font-style:regular;\n",
        "    font-weight: 400;    \n",
        "    font-size: 30pt;\n",
        "    text-align: center;\n",
        "    line-height: 100%;\n",
        "    color: #335082;\n",
        "    margin-bottom: 0.5em;\n",
        "    margin-top: 0.5em;\n",
        "    display: block;\n",
        "}\n",
        "*/\n",
        ".text_cell_render h1 {\n",
        "    font-weight: 600;\n",
        "    font-size: 35pt;\n",
        "    line-height: 100%;\n",
        "    color: #234765;\n",
        "    margin-bottom: 0.1em;\n",
        "    margin-top: 0.3em;\n",
        "    display: block;\n",
        "}\n",
        "\n",
        ".text_cell_render h2 {\n",
        "    margin-top:16px;\n",
        "    font-size: 27pt;\n",
        "    font-weight: 550;\n",
        "    margin-bottom: 0.1em;\n",
        "    margin-top: 0.3em;\n",
        "    font-style: regular;\n",
        "    color: #234765;\n",
        "}\t\n",
        "\n",
        ".text_cell_render h3 {\n",
        "    font-size: 20pt;\n",
        "    font-weight: 550\n",
        "    text-align: left;\n",
        "    margin-bottom: 0.1em;\n",
        "    margin-top: 0.3em;\n",
        "    font-style: regular;\n",
        "    color:  #376f9e;\n",
        "}\n",
        "\n",
        ".text_cell_render h4 {    /*Use this for captions*/\n",
        "    font-size: 18pt;\n",
        "    font-weight: 450\n",
        "    text-align: left;\n",
        "    margin-bottom: 0.1em;\n",
        "    margin-top: 0.3em;\n",
        "    font-style: regular;\n",
        "    color:  #376f9e;\n",
        "}\n",
        "\n",
        ".text_cell_render h5 {  /*Use this for small titles*/\n",
        "    font-size: 18pt;\n",
        "    font-weight: 550;\n",
        "    color: rgb(163,0,0);\n",
        "    font-style: italic;\n",
        "    margin-bottom: .1em;\n",
        "    margin-top: 0.8em;\n",
        "    display: block;\n",
        "    color:  #b21c0d;\n",
        "}\n",
        "\n",
        ".text_cell_render h6 { /*use this for copyright note*/\n",
        "    font-family: 'Ubuntu Condensed', sans-serif;\n",
        "    font-weight: 300;\n",
        "    font-size: 14pt;\n",
        "    line-height: 100%;\n",
        "    color: #252525;\n",
        "    text-align: right;\n",
        "    margin-bottom: 1px;\n",
        "    margin-top: 1px;\n",
        "}\n",
        "\n",
        ".CodeMirror{\n",
        "        font-family: 'Duru Sans', sans-serif;\n",
        "        font-size: 100%;\n",
        "}\n",
        "\n",
        "</style>\n",
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
        "                           extensions: [\"AMSmath.js\"],\n",
        "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
        "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
        "                }\n",
        "        });\n",
        "</script>\n"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.HTML at 0x430e358>"
       ]
      }
     ],
     "prompt_number": 1
    }
   ],
   "metadata": {}
  }
 ]
}