{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Texto y c\u00f3digo sujeto bajo Creative Commons Attribution license, CC-BY-SA. (c) Original por Lorena A. Barba, 2013, traducido por F.J. Navarro-Brull para CAChemE.org "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[@LorenaABarba](https://twitter.com/LorenaABarba)\n",
      "[@CAChemEorg](https://twitter.com/cachemeorg)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "12 pasos para Navier-Stokes\n",
      "=====\n",
      "***"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u00bfHicistes cambios en los Pasos [1](http://nbviewer.ipython.org/urls/bitbucket.org/franktoffel/cfd-python-class-es/raw/master/lecciones/01%2520-%2520Paso%25201.ipynb) y [2](http://nbviewer.ipython.org/urls/bitbucket.org/franktoffel/cfd-python-class-es/raw/master/lecciones/02%2520-%2520Paso%25202.ipynb) usando diferentes valores de par\u00e1metros? Si es as\u00ed, es probable que te encontraras con un comportamiento inesperado. \u00bfTu soluci\u00f3n nunca se descontrol\u00f3? (En mi experiencia, los estudiantes de CFD *aman* hacer que las cosas fallen).\n",
      "\n",
      "Probablemente te est\u00e9s preguntando porqu\u00e9 el cambio de los par\u00e1metros de discretizaci\u00f3n afecta a la soluci\u00f3n de una manera tan dr\u00e1stica. Este notebook complementa nuestras [lecciones interactivas de CFD](https://bitbucket.org/franktoffel/cfd-python-class-es) al discutir la condici\u00f3n CFL. Y aprender m\u00e1s observando las clases en YouTube de la profesora Barba (enlaces m\u00e1s abajo)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Convergencia y la condici\u00f3n CFL\n",
      "----\n",
      "***"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Para los primeros pasos, hemos estado usando las mismas condiciones iniciales y de contorno generales. Con los par\u00e1metros que inicialmente sugerimos, la cuadr\u00edcula cuenta con 41 puntos y el incremento de tiempo es de 0,025 segundos. Ahora, vamos a experimentar con el aumento del tama\u00f1o de nuestra cuadr\u00edcula. El siguiente c\u00f3digo es id\u00e9ntico al c\u00f3digo que se utiliz\u00f3 en el [Paso 1](http://nbviewer.ipython.org/urls/bitbucket.org/franktoffel/cfd-python-class-es/raw/master/lecciones/01%2520-%2520Paso%25201.ipynb) pero aqu\u00ed se ha incluido en una funci\u00f3n para que podamos examinar f\u00e1cilmente lo que sucede a medida que se ajusta una sola variable: **el tama\u00f1o de la cuadr\u00edcula.**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "# El comando de arriba har\u00e1 que figuras de este notebook se representen junto al texto\n",
      "\n",
      "import numpy as np                 # numpy es una libreria que realiza operaciones matriciales estilo MATLAB\n",
      "import matplotlib.pyplot as plt    # matplotlib es una librer\u00eda para dibujar gr\u00e1ficas en 2D \n",
      "\n",
      "# Listos para crear la funci\u00f3n linearconv()\n",
      "\n",
      "def linearconv(nx):\n",
      "    dx = 2./(nx-1)\n",
      "    nt = 20    # nt es el n\u00famero de intervalos de tiempo que se desea calcular\n",
      "    dt = .025  # dt es la cantidad de tiempo que cada incremento de tiempo comprende (delta t)\n",
      "    c = 1\n",
      "\n",
      "    u = np.ones(nx)      # definiendo array de numpy con nx elementos iguales a 1\n",
      "    u[.5/dx : 1/dx+1]=2  # estableciendo u = 2 entre 0.5 y 1 paras las condiciones iniciales (I.C.s)\n",
      "\n",
      "    un = np.ones(nx) #nicializar el marcador de posici\u00f3n de conjunto de 'un', para almacenar la soluci\u00f3n de tiempo instante por instante (n+1)\n",
      "\n",
      "    for n in range(nt):  # iteraci\u00f3n a trav\u00e9s del tiempo\n",
      "        un[:] = u[:] ## copia los valores existentes de 'u' en 'un'\n",
      "        for i in range(1,nx):\n",
      "            u[i] = un[i]-c*dt/dx*(un[i]-un[i-1])\n",
      "        \n",
      "    plt.plot(np.linspace(0,2,nx),u)\n",
      "        "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ahora vamos a examinar los resultados de nuestro problema de convecci\u00f3n lineal con una malla (cuadr\u00edcula) mas fina cada vez."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "linearconv(41) # convecction usando 41 puntos en la malla (mesh)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD9CAYAAACoXlzKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXUTEYxTsIiIeo2AXEfGY/wfiTyBlK1pjLwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0080f83350>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Este es el mismo resultado que en nuestro de c\u00e1lculo del Paso 1, reproducido arriba para servir como referencia."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "linearconv(61)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD9CAYAAACoXlzKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0080f18e50>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Aqu\u00ed, a\u00fan est\u00e1 presente cierta difusi\u00f3n num\u00e9rica _(numerical diffusion)_, pero es menos grave."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "linearconv(71)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD9CAYAAACoXlzKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnyHYAcBn/h+RmeoUK0CwGAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0080edf5d0>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Aqu\u00ed se representa el mismo patr\u00f3n, la onda es m\u00e1s cuadradada que en previos resultados."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "linearconv(85)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD9CAYAAABDaefJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhoJnRBoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0080f6e750>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Esto no se parece en nada a nuestra funci\u00f3n sombrero original. "
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "\u00bfQu\u00e9 ha ocurrido?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Para responder a esta pregunta, tenemos que pensar un poco acerca de lo que en realidad estamos implementando en el c\u00f3digo.\n",
      "\n",
      "En cada iteraci\u00f3n de nuestro bucle que incrementa el tiempo, se utilizan los datos existentes acerca de nuestra onda para estimar la velocidad de la onda en el intervalo de tiempo siguiente. Inicialmente, el aumento en el n\u00famero de puntos de la cuadr\u00edcula nos devolvi\u00f3 respuestas m\u00e1s precisas. Hubo menos difusi\u00f3n num\u00e9rica y la onda cuadrada se ve\u00eda mucho m\u00e1s como tal con respecto a lo que lo hizo en el primer ejemplo.\n",
      "\n",
      "Cada iteraci\u00f3n de nuestro bucle de tiempo abarca un incremento de longitud $\\Delta t$, que hemos estado definiendo como 0.025\n",
      "\n",
      "Durante esta iteraci\u00f3n, se eval\u00faa la velocidad de la onda en cada uno de los puntos $x$ que hemos creado. En la \u00faltima ocasi\u00f3n, algo ha ido claramente mal.\n",
      "\n",
      "\n",
      "Lo que ha pasado es que a lo largo del per\u00edodo de tiempo $\\Delta t$, la onda se desplaza una distancia que es mayor que `dx`. La longitud `dx` de cada celda de la malla (cuadr\u00edcula) est\u00e1 relacionado con el n\u00famero de puntos totales `nx`, por lo que la estabilidad puede asegurarse si el incremento de tiempo $\\Delta t$ se calcula con respecto al tama\u00f1o de `dx`.\n",
      "\n",
      "$$\\sigma = \\frac{u \\Delta t}{\\Delta x} \\leq \\sigma_{max}$$\n",
      "\n",
      "donde $u$ es la velocidad de la onda, $\\sigma$ se conoce como el [**n\u00famero de Courant**](http://es.wikipedia.org/wiki/N%C3%BAmero_de_Courant-Friedrich-Levy) y el valor de $\\sigma_{max}$ que garantice la estabilidad depende de la discretizaci\u00f3n utilizada.\n",
      "\n",
      "En una nueva versi\u00f3n de nuestro c\u00f3digo, vamos a utilizar el n\u00famero CFL para calcular un incremento de tiempo `dt` apropiado en funci\u00f3n del tama\u00f1o de `dx`.\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "def linearconv(nx):\n",
      "    dx = 2./(nx-1)\n",
      "    nt = 20    # nt es el n\u00famero de intervalos de tiempo que se desea calcular\n",
      "    c = 1\n",
      "    sigma = .5\n",
      "    \n",
      "    dt = sigma*dx\n",
      "\n",
      "    u = np.ones(nx) \n",
      "    u[.5/dx : 1/dx+1]=2\n",
      "\n",
      "    un = np.ones(nx)\n",
      "\n",
      "    for n in range(nt):  # teraci\u00f3n a trav\u00e9s del tiempo\n",
      "        un[:] = u[:] ## copia los valores existentes de 'u' en 'un'\n",
      "        for i in range(1,nx):\n",
      "            u[i] = un[i]-c*dt/dx*(un[i]-un[i-1])\n",
      "        \n",
      "    plt.plot(np.linspace(0,2,nx),u)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "linearconv(41)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD9CAYAAACoXlzKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXUTEYxTsIiIeo2AXEfGY/wfiTyBlK1pjLwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0080e89490>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "linearconv(61)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD9CAYAAACoXlzKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHCJJREFUeJzt3X9U1fX9B/DnRTBBMFAUDW4pgvyQX/4kazSsWcoZZGka\nZ24GHCPr5Fx/tJ12doTqa62tba5Wo62zjrOaP9bS5Y/OtrxYOsIfhL8CQUABjXlN8lfKBT7fP96h\ngcD99bn3/fnxfJzDAb3Xe19+/PDk4+vz+rw/FkVRFBARkWEEyC6AiIjUxWAnIjIYBjsRkcEw2ImI\nDIbBTkRkMAx2IiKDGTTYm5ubMXv2bEyePBkpKSn4/e9/3+/zVqxYgfj4eKSnp6OqqsonhRIRkWsC\nB3swKCgIv/3tb5GRkYGLFy9i2rRpmDNnDpKSkq49Z9u2baivr0ddXR0+/fRTLF++HBUVFT4vnIiI\n+jfoEfvYsWORkZEBAAgNDUVSUhJOnTrV6zlbtmzB0qVLAQCZmZlob29HW1ubj8olIiJnXO6xNzU1\noaqqCpmZmb1+v7W1FVar9dqvY2Ji0NLSol6FRETklkFbMT0uXryIhQsXYs2aNQgNDb3h8b6rElgs\nlhue09/vERGRc+6u/OL0iN3hcGDBggVYsmQJ5s+ff8Pj0dHRaG5uvvbrlpYWREdHD1gcP7z/WLVq\nlfQa9P5RU6Pg3nsVJCUpmD9/FQ4fVuBw+O79OjsV1NYqePddBTNmKJg1S8H+/fK3gy8+uH+q++GJ\nQYNdURQUFRUhOTkZK1eu7Pc5eXl5WLt2LQCgoqIC4eHhiIqK8qgYIl+7cAF4+mngzjuB++4DqquB\n9HRg8mQg0KX/v3pmyBBg0iTg4YeBigqgqAjIyQEeeww4e9Z370vmNGiw7969G+vWrcPOnTsxZcoU\nTJkyBdu3b0dZWRnKysoAADk5OYiNjUVcXByKi4vx2muv+aVwInetXw8kJQFtbcDhw8BTTwFBQf6v\nIyBABPvnnwNDh4qa/vhHwMODM6IbWBRPj/XdfSOLxeP/VlBvNpsN2dnZssvQlR07gGXLRLjfcUfv\nx2Rvz4MHxZH8008DjzwirQzVyN6eRuNJdjLYyfDa24HUVOCtt4B77pFdTf+qq4HvfQ84cAD41pAZ\nEYOdqD9LlwKhocAf/iC7ksE9/zywaxfw4YcAh8iohyfZybViyNA2bwY++QT45S9lV+Lcz34GnDsH\nfHP6ishjPGInw7LbgbQ00VfPypJdjWuOHgXuuguorARiY2VXQ1rAVgzRtyxeDMTEAC+/LLsS9/zq\nV8DWrcBHH4kJGjI3tmKIvrFhg5g2ef552ZW476mnAIcDeOUV2ZWQXvGInQynrU1cdLR5M9BnaSPd\nqKsDZs0C9uwRFzaRebEVQwRg+XIgLAx46SXZlXjnN78RUzLvvy+7EpKJwU6m53AA48YB+/cDt90m\nuxrvnD8vZtpPnADCw2VXQ7Kwx06m99FHQFyc/kMdAEaMAO6+m0fs5D4GOxnK+vViGsYoFi0SJ4KJ\n3MFWDBlGRwcwdqyYhomJkV2NOi5eBKKjgYYGYNQo2dWQDGzFkKn9619AcrJxQh0QSyHMmcN2DLmH\nwU6GYbQ2TI/Fi8XfjchVbMWQIVy5IqZhjhwBbrlFdjXqunxZ/N3q64HRo2VXQ/7GVgyZ1ocfiouS\njBbqABASAsybB7z3nuxKSC8Y7GQIGzaICRKjYjuG3MFWDOne11+LVkVtLWDU2+32/B1rasTkD5kH\nWzFkStu3A9OmGTfUASA4GPj+94G//112JaQHDHbSPaNOw/TFdgy5iq0Y0rVLl8QJ0+PHgchI2dX4\n1tWroh1z6JC4aInMga0YMp2tW4Hbbzd+qAPATTcBeXnApk2yKyGtY7CTrpmlDdODa8eQK9iKId3q\nWUelsREYOVJ2Nf7R0SFaT1VVYklfMj62YshUKirEzarNEuoAMHSoWMrXZpNdCWkZg510a+9eYOZM\n2VX438yZ4u9ONBAGO+nW3r3AjBmyq/C/GTMY7DQ49thJt6xW0ZKYOFF2Jf514YK4+rS9HQgKkl0N\n+Rp77GQap0+LVQ9jY2VX4n9hYcD48WKenag/DHbSpZ42jMUiuxI52GenwTDYSZfM2l/vwT47DYbB\nTrrEYGew08B48pR0R1HEEgKHD4u1U8zo6lUgIgI4cwYYPlx2NeRLPHlKptDQIO4qZNZQB8S6MSkp\n4gpUor4Y7KQ7Zm/D9GA7hgbCYCfdYbALDHYaiNNgLywsRFRUFFJTU/t93G63Y+7cucjIyEBKSgre\neusttWsk6qWyksEOiG1QWSm7CtIipydPP/74Y4SGhuJHP/oRDvVzRURJSQmuXr2KF154AXa7HQkJ\nCWhra0NgYGDvN+LJU1JBZ6c4adjcDISHy65Grq4usS2amsy1EJrZ+OTkaVZWFiIiIgZ8fNy4cTh/\n/jwA4Pz58xg1atQNoU6kls8/F8vWmj3UAWDIEGDqVGDfPtmVkNZ4ncDLli3D3XffjVtuuQUXLlzA\nhkHuAlBSUnLt6+zsbGRnZ3v79mQybMP01tOOufde2ZWQWmw2G2xersvs0hx7U1MTcnNz+23FPP/8\n87Db7fjd736H48ePY86cOaiurkZYWFjvN2IrhlTw2GNAcjKwYoXsSrRh40Zg3Tpg82bZlZCvSJlj\n37NnDx566CEAwMSJEzFhwgTU1tZ6+7JE/eJETG+cjKH+eB3siYmJ+Pe//w0AaGtrQ21tLWLNuOQe\n+dyVK6LHnpEhuxLtuO02wOEAWltlV0Ja4rTHnp+fj/LyctjtdlitVpSWlsLhcAAAiouL8cwzz6Cg\noADp6eno7u7GSy+9hJE8RU8+8NlnQEICEBwsuxLtsFiu99kfeEB2NaQVXCuGdOOVV8T6MGVlsivR\nlpIScZPr1atlV0K+wLViyNDYX+8f++zUF4OddIPB3r8ZM8Qse3e37EpIKxjspAtffSWuNp08WXYl\n2jNmDDBiBFBfL7sS0goGO+nC/v1iGoYXNfePt8qjb2Owky6wDTM49tnp2xjspAsM9sEx2OnbGOyk\nC0eOAAOsHE0Q2+bIEXHbQCIGO2mewwE0NgLx8bIr0a7ISHH+oa1NdiWkBQx20ryGBiA6Ghg2THYl\n2paYCNTUyK6CtIDBTppXUyNCiwbHYKceDHbSvNpaBrsrEhPFtiJisJPm8YjdNTxipx4MdtI8Brtr\nGOzUg6s7kqYpCjBqlGgxjB4tuxpt6+wEwsKAs2eBkBDZ1ZBauLojGc6ZM2LN8chI2ZVoX2AgMHEi\nUFcnuxKSjcFOmtbThrFYZFeiD2zHEMBgJ41jf909DHYCGOykcQx29zDYCWCwk8Yx2N3DYCeAwU4a\nx2B3T0ICcOwY76Zkdgx20qyvvwZOnwYmTJBdiX6EhQEREeJuU2ReDHbSrLo6IDaWd01yF9sxxGAn\nzWIbxjMMdmKwk2Yx2D3DYCcGO2kWg90zDHZisJNmMdg9w2AnLgJGmtTdLSY8Tp8GRoyQXY2+KIrY\nZs3NQHi47GrIW1wEjAyjpUWEEkPdfRaLmGfnTTfMi8FOmsQ2jHfYjjE3BjtpEoPdOwx2c2OwkyYx\n2L3DYDc3BjtpUk2N6BOTZxISGOxmxmAnTaqt5RG7N+LjgcZGwOGQXQnJwGAnzTl/HmhvB2JiZFei\nX8OGAdHRItzJfBjspDm1taKVEMC90yvss5vXoN86hYWFiIqKQmpq6oDPsdlsmDJlClJSUpCdna12\nfWRCPHGqDga7eQ0a7AUFBdixY8eAj7e3t+OJJ57AP//5Txw+fBibNm1SvUAyHwa7Ohjs5jVosGdl\nZSEiImLAx9955x0sWLAAMd80QyMjI9WtjkyJwa4OBrt5edXFrKurw5dffonZs2dj+vTp+Otf/6pW\nXWRiDHZ19AQ7l2gyH6/uTeNwOHDgwAH85z//weXLlzFr1izcfvvtiI+P7/f5JSUl177Ozs5mT55u\n0NkJNDSIcT3yTmSkWDfmzBlgzBjZ1ZCrbDYbbDabV6/hVbBbrVZERkYiODgYwcHBuOuuu1BdXe1S\nsBP1p7ERGDcOCA6WXYn+WSzXj9oZ7PrR96C3tLTU7dfwqhVz//3345NPPkFXVxcuX76MTz/9FMnJ\nyd68JJkcrzhVF/vs5jToEXt+fj7Ky8tht9thtVpRWloKxzeXshUXFyMxMRFz585FWloaAgICsGzZ\nMgY7eaW+Hpg0SXYVxhEfL7YpmQtvtEGa8vjjQFIS8OSTsisxhk2bgLffBv7xD9mVkKd4ow3Svfp6\nIC5OdhXGERfHI3YzYrCTptTXcyJGTXFxwPHj4laDZB4MdtKMjg6gtRW47TbZlRhHaChw883AqVOy\nKyF/YrCTZjQ2AlYrEBQkuxJjYTvGfBjspBlsw/gGJ2PMh8FOmlFXxxOnvhAXJ7YtmQeDnTSDEzG+\nwVaM+TDYSTPYivENBrv5MNhJM9iK8Y2ekUdeH2geDHbShI4OoKUFGD9ediXGM2IEMHw4cPq07ErI\nXxjspAknToibVw8dKrsSY2I7xlwY7KQJbMP4FkcezYXBTprAiRjf4sijuTDYSRM4EeNbbMWYC4Od\nNIGtGN9iK8ZcGOykCWzF+NbEiWIbc+TRHBjsJJ3DATQ3AxMmyK7EuMLDgWHDgLY22ZWQPzDYSboT\nJ8QNrG+6SXYlxsZ2jHkw2Ek6tmH8g5Mx5sFgJ+k4EeMfnIwxDwY7SceJGP9gK8Y8GOwkHVsx/sFW\njHkw2Ek6tmL8o6cVw5FH42Owk1SdnWIqhqOOvhcRIe4ne+aM7ErI1xjsJNXJk0BUlJixJt9jn90c\nGOwkFdsw/sU+uzkw2EkqTsT4F0cezYHBTlJxIsa/2IoxBwY7ScVWjH+xFWMODHaSiq0Y/+LIozkw\n2Emari6gqQmIjZVdiXmMGgUEBABnz8quhHyJwU7SNDcDY8YAwcGyKzEXtmOMj8FO0rANIwcnY4yP\nwU7ScCJGDk7GGB+DnaThRIwcbMUYH4OdpGErRg62Yoxv0GAvLCxEVFQUUlNTB32RvXv3IjAwEO+9\n956qxZGxsRUjR88RO0cejWvQYC8oKMCOHTsGfYGuri789Kc/xdy5c6FwTyEXdXYCjY0MdhkiIwGL\nBbDbZVdCvjJosGdlZSEiImLQF3jllVewcOFCjB49WtXCyNgaG8UNrDnq6H8WC5CYCNTUyK6EfMWr\nHntrays2b96M5cuXAwAsFosqRZHx1dSIcCE5GOzGFujNH165ciVefPFFWCwWKIritBVTUlJy7evs\n7GxkZ2d78/akYwx2uRjs2mWz2WCz2bx6DYviJI2bmpqQm5uLQ4cO3fBYbGzstTC32+0ICQnBn/70\nJ+Tl5d34Rt+EPxEAFBUBmZnAo4/KrsSctmwBysqArVtlV0LOeJKdXh2xNzQ0XPu6oKAAubm5/YY6\nUV81NcDSpbKrMC8esRvboMGen5+P8vJy2O12WK1WlJaWwuFwAACKi4v9UiAZj6IAn3/OVoxMEyYA\nra3AlSu8LaEROW3FqPZGbMXQN86cARISxAqDPN8uT3IysH494OQyFZLMk+zklafkdz0nThnqcrEd\nY1wMdvK7mhpxxE5yJSQw2I2KwU5+V1vL/roW8IjduBjs5HecYdeGxETxQ5aMh8FOfsdg14aEBBHs\nnGkwHgY7+dWVK0BLC+9zqgXh4UBoqBh7JGNhsJNf1deLGeqgINmVEMA+u1Ex2Mmv2IbRFga7MTHY\nya8Y7NrCYDcmBjv5FYNdWxjsxsRgJ79isGsLg92YuFYM+Y2iACNGAM3NYiKD5OvuBsLCgC++EJ9J\ne7hWDGlaa6sYr2Ooa0dAADBpEi9UMhoGO/kN2zDaxHaM8TDYyW8Y7NrEYDceBjv5DYNdmxjsxsNg\nJ79hsGsTg914GOzkNwx2bYqPB44fBzo7ZVdCamGwk19cuACcOwdYrbIrob5CQoCxY4GmJtmVkFoY\n7OQXtbVirC6Ae5wmsR1jLPw2I79gG0bbGOzGwmAnv2CwaxuD3VgY7OQXvIG1tvHG1sbCYCe/4BG7\ntvGI3VgY7ORznZ3izkmTJsmuhAYSFSX+nex22ZWQGhjs5HNNTWKcLiREdiU0EIuFR+1GwmAnn6ut\nZRtGDxITucqjUTDYyeeOHmWw60FiInDkiOwqSA0MdvK5ffuAqVNlV0HOTJ0K7N8vuwpSA4OdfG7v\nXmDmTNlVkDPTpwNVVUBXl+xKyFsMdvIpux04e5YTMXowcqSYjuEJVP1jsJNP7dsHTJvGNWL0YsYM\noLJSdhXkLX67kU9VVoqwIH2YOVO0zkjfGOzkU+yv68uMGQx2I7AoiqL45Y0sFvjprUgjFAUYN04c\ntd96q+xqyBWXLwORkWLt/Jtukl0NAZ5lJ4/YyWeam0W48+Ya+hESIk50V1fLroS84TTYCwsLERUV\nhdTU1H4ff/vtt5Geno60tDTceeedOHjwoOpFkj71tGEsFtmVkDvYjtE/p8FeUFCAHTt2DPh4bGws\ndu3ahYMHD+IXv/gFHn30UVULJP3au5cnTvWIwa5/ToM9KysLERERAz4+a9Ys3HzzzQCAzMxMtLS0\nqFcd6RqDXZ9mzuTIo94Fqvlib775JnJycgZ8vKSk5NrX2dnZyM7OVvPtSUO6u8UMO4NdfyZPBk6e\nFDcgDwuTXY352Gw22Gw2r17DpamYpqYm5Obm4tChQwM+Z+fOnXjiiSewe/fufo/wORVjLjU1QE4O\n0NAguxLyxB13AKtXAzz2kk/aVMzBgwexbNkybNmyZdC2DZkH2zD6xitQ9c3rYD958iQefPBBrFu3\nDnFxcWrURAbAYNc3XoGqb0577Pn5+SgvL4fdbofVakVpaSkcDgcAoLi4GM8++yzOnTuH5cuXAwCC\ngoJQyR/1pldZCSxYILsK8tSMGcDPfy67CvIUrzwl1XV0ABERQFsbEBoquxryRHe3WO3x2DFgzBjZ\n1ZgbrzwlTTh8GJgwgaGuZwEBnGfXMwY7qY4rOhoDg12/GOykOq7oaAycjNEvBjupjhMxxtAzGcNT\nY/rDYCdVXboEHD8OpKXJroS8FR0NBAUBJ07IroTcxWAnVR04IC5JHzpUdiWkBvbZ9YnBTqpif91Y\nuCCYPjHYSVXsrxsLj9j1icFOqmKwG8v06aK91tUluxJyB4OdVNPaCpw9CyQkyK6E1DJyJHDLLcBn\nn8muhNzBYCfVbNoE3H8/MGSI7EpITQ88AGzcKLsKcgeDnVSzYQOweLHsKkhtixeLf1vOs+sHg51U\n0dwM1NYC99wjuxJSW3o6EBgo7ohF+sBgJ1Vs3AjMn8/5dSOyWK4ftZM+MNhJFevXA4sWya6CfGXR\nIrZj9ITBTl5rbBT3Nr37btmVkK+kpADDhwMVFbIrIVcw2MlrGzcCDz4o+rBkTBbL9aN20j4GO3lt\n/XpOw5jBokXih3h3t+xKyBkGO3mlvh5oaQHuukt2JeRrycnilod79siuhJxhsJNXNmwAFi5kG8Ys\nFi8W/0MjbWOwk1c2bOA0jJksWiSuMObaMdrGYCeP1dYC//sf8J3vyK6E/GXSJGDsWODjj2VXQoNh\nsJPHNmwAHnqIa8OYDS9W0j6LovjnkgOLxQI/vRX5SUoKUFYG3Hmn7ErInxoagFmzxGqePLfie55k\nJ4/YySNHjgBffSW+wclcYmOBW28FystlV0IDYbCTR959V7RhArgHmdKiRcDbb8uuggbCVgy57dgx\n0X6pqAAmTpRdDclw5gyQmgq8/z5w++2yqzE2tmLI57q6gKVLgVWrGOpmNno08OqrYl+4fFl2NdQX\ng53c8utfA8HBwOOPy66EZFu4EJg2DXjmGdmVUF9sxZDLDh8GZs8WN6weP152NaQFX34pWjLvvAN8\n97uyqzEmtmLIZxwO8d/u1asZ6nTdyJFi5LWgALh4UXY11INH7OSSZ58F/vtfYNs2sYQr0bcVFADD\nhgGvvy67EuPxJDsZ7ORUVRVw333ic3S07GpIi776SrRk3nwTmDNHdjXGwlYMqe7SJdGCefllhjoN\n7OabgT//GSgqAux22dUQg536pShiedakJLHI15Ilsisirbv3XhHskycDb7zBFSBlchrshYWFiIqK\nQmpq6oDPWbFiBeLj45Geno6qqipVC6Qb2Ww2n77+oUPi/qUvvCCmHV57zdh9dV9vTzNZtQr4v/+z\nYe1aIDNTnJch/3Ma7AUFBdixY8eAj2/btg319fWoq6vDG2+8geXLl6taIN3IV0HU3g78+MfAPfeI\n5QL27zfHkrwMdnW1tNjw8cfAT34iZt0feQT44gvZVZmL07XZsrKy0NTUNODjW7ZswdKlSwEAmZmZ\naG9vR1tbG6KiolQrktR37hxQXQ189pn4XF0N1NQAP/whcPQoEBkpu0LSM4sF+MEPgLw84LnnxFXK\nsbFAejqQkXH98+jRsis1Jq8X3WxtbYXVar3265iYGLS0tPQb7Lm53r4bAeIGF/v29f49RREf3d3X\nP3d3iz7n5ctixvjSpesfgYFAWpr4Bps1C3jsMbEM7/Dhcv5OZExhYcBLL4lx2aNHrx9IfPCB+Pz1\n12Kf6/sxZIhYYO7bHxZL/y1BI7cJPaXKasp9R3EsA2zpDz7gv4Ba6upKvX6NPXt4Y+IepaXeb0+6\nzp3tefWquIKV1ON1sEdHR6O5ufnar1taWhDdz1wcZ9iJiPzD63HHvLw8rF27FgBQUVGB8PBw9teJ\niCRyesSen5+P8vJy2O12WK1WlJaWwuFwAACKi4uRk5ODbdu2IS4uDsOHD8df/vIXnxdNRESDUFS0\nfft2JSEhQYmLi1NefPHFfp/z5JNPKnFxcUpaWppy4MABNd/ecJxtz507dyojRoxQMjIylIyMDOW5\n556TUKU+FBQUKGPGjFFSUlIGfA73Tdc5257cN1138uRJJTs7W0lOTlYmT56srFmzpt/nubN/qhbs\nnZ2dysSJE5XGxkalo6NDSU9PV44ePdrrOVu3blXmzZunKIqiVFRUKJmZmWq9veG4sj137typ5Obm\nSqpQX3bt2qUcOHBgwCDivukeZ9uT+6brTp8+rVRVVSmKoigXLlxQJk2a5HV2qrakQGVlJeLi4jB+\n/HgEBQXh4YcfxubNm3s9Z6CZd7qRK9sT4ElpV2VlZSEiImLAx7lvusfZ9gS4b7pq7NixyMjIAACE\nhoYiKSkJp06d6vUcd/dP1YK9v3n21tZWp89paWlRqwRDcWV7WiwW7NmzB+np6cjJycHRo0f9XaZh\ncN9UF/dNzzQ1NaGqqgqZmZm9ft/d/VOVOXZg4Nn1vvr+FHf1z5mNK9tl6tSpaG5uRkhICLZv3475\n8+fj2LFjfqjOmLhvqof7pvsuXryIhQsXYs2aNQgNDb3hcXf2T9WO2PvOszc3NyMmJmbQ5ww0806u\nbc+wsDCEhIQAAObNmweHw4EveaWHR7hvqov7pnscDgcWLFiAJUuWYP78+Tc87u7+qVqwT58+HXV1\ndWhqakJHRwfWr1+PvLy8Xs/hzLvrXNmebW1t136KV1ZWQlEUjBw5Uka5usd9U13cN12nKAqKioqQ\nnJyMlStX9vscd/dP1VoxgYGBePXVV3Hfffehq6sLRUVFSEpKQllZGQDOvLvLle25adMmvP766wgM\nDERISAj+9re/Sa5au3g9hrqcbU/um67bvXs31q1bh7S0NEyZMgUAsHr1apw8eRKAZ/un326NR0RE\n/sE7KBERGQyDnYjIYBjsREQGw2AnIjIYBjsRkcEw2ImIDOb/AYNJgAwBQ6yZAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0080e88750>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "linearconv(81)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD9CAYAAACoXlzKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0080f75050>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "linearconv(101)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD9CAYAAACoXlzKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAOAxBDsAeMz/Aa0/RNZtFwQ8AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0080f183d0>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "linearconv(121)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD9CAYAAACoXlzKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0080fa6590>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Date cuenta que como el n\u00famero de puntos, `nx`, aumenta, la onda experimenta la convecci\u00f3n cada vez de forma m\u00e1s corta (menor \"tama\u00f1o\"). Si bien el n\u00famero de iteraciones de tiempo que hemos hecho avanzar la soluci\u00f3n se mantiene constante a `nt = 20`, dependiendo del valor de `nx` y los valores correspondientes de `dx` y `dt` se examina un intervalo de tiempo m\u00e1s corto en general, lo que provoca el efecto mencionado de acortamiento."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Aprende m\u00e1s\n",
      "-----\n",
      "***"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Es posible, en algunos casos, hacer un an\u00e1lisis riguroso de la estabilidad de los sistemas num\u00e9ricos. \u00c9chale un vistazo a la presentaci\u00f3n en ingl\u00e9s de la Profesora Barba sobre este tema en YouTube."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import YouTubeVideo\n",
      "YouTubeVideo('Yw1YPBupZxU')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "            <iframe\n",
        "                width=\"400\"\n",
        "                height=\"300\"\n",
        "                src=\"http://www.youtube.com/embed/Yw1YPBupZxU\"\n",
        "                frameborder=\"0\"\n",
        "                allowfullscreen\n",
        "            ></iframe>\n",
        "        "
       ],
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0x1082ad210>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "def css_styling():\n",
      "    styles = open(\"../styles/custom.css\", \"r\").read()\n",
      "    return HTML(styles)\n",
      "css_styling()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<style>\n",
        "    @font-face {\n",
        "        font-family: \"Computer Modern\";\n",
        "        src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
        "    }\n",
        "    div.cell{\n",
        "        width:800px;\n",
        "        margin-left:16% !important;\n",
        "        margin-right:auto;\n",
        "    }\n",
        "    h1 {\n",
        "        font-family: Helvetica, serif;\n",
        "    }\n",
        "    h2 {\n",
        "        font-family: Helvetica, serif;\n",
        "    }\n",
        "    h4{\n",
        "        margin-top:12px;\n",
        "        margin-bottom: 3px;\n",
        "       }\n",
        "    div.text_cell_render{\n",
        "        font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
        "        line-height: 135%;\n",
        "        font-size: 120%;\n",
        "        width:600px;\n",
        "        margin-left:auto;\n",
        "        margin-right:auto;\n",
        "    }\n",
        "    .CodeMirror{\n",
        "            font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
        "    }\n",
        "/*    .prompt{\n",
        "        display: None;\n",
        "    }*/\n",
        "    .text_cell_render h5 {\n",
        "        font-weight: 300;\n",
        "        font-size: 16pt;\n",
        "        color: #4057A1;\n",
        "        font-style: italic;\n",
        "        margin-bottom: .5em;\n",
        "        margin-top: 0.5em;\n",
        "        display: block;\n",
        "    }\n",
        "    \n",
        "    .warning{\n",
        "        color: rgb( 240, 20, 20 )\n",
        "        }  \n",
        "</style>\n",
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
        "                           extensions: [\"AMSmath.js\"]\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>"
       ],
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<IPython.core.display.HTML at 0x10bb0c490>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> (La celda de arriba establece el formato de este notebook.)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}