QuickTour.v2.ipynb

{
 "metadata": {
  "name": "QuickTour"
 },
 "nbformat": 2,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "# Fluid everyday scientific computing\n",
      "<!--====-->"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = linspace(0, 3*pi, 500)\n",
      "plot(x, sin(x**2))\n",
      "title('A simple chirp');"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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WNd1TNijL0o53HN4nA9n+o4AU6c+bNw979uxBbW0tTp48ifXr16O6unrQNY2N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\nMILw/wFa1cBzw0Gh4AAAAABJRU5ErkJggg==\n",
       "text": [
        "Figure(480x320)"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "## Errors are shown in informative ways:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = 1\n",
      "y = 4\n",
      "z = y/(1-x)\n",
      "print z"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "ename": "ZeroDivisionError",
       "evalue": "division by zero",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-10-39ebfbab3e22>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mz\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%qtconsole"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "<!--====-->\n",
      "## Document your workflows\n",
      "\n",
      "You can *italicize*, **boldface**\n",
      "\n",
      "* build\n",
      "* lists\n",
      "\n",
      "and embed code meant for illustration instead of execution in Python:\n",
      "\n",
      "    def f(x):\n",
      "        \"\"\"a docstring\"\"\"\n",
      "        return x**2\n",
      "\n",
      "or other languages:\n",
      "\n",
      "    if (i=0; i<n; i++) {\n",
      "      printf(\"hello %d\\n\", i);\n",
      "      x += 4;\n",
      "    }"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "source": [
      "Courtesy of MathJax, you can include mathematical expressions both inline: \n",
      "$e^{i\\pi} + 1 = 0$  and displayed:\n",
      "\n",
      "$$e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "<!--====-->\n",
      "## Rich displays: include anyting a browser can show\n",
      "\n",
      "### Images"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image(filename='fig/logo.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "png": 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L0amUWggcqrXO8gg2FKHG2CdW4Uem9XvBlUflu7RUaiByU3lPa92ZKN8cSav8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\nHMRRca/E5hVINNIVwI2B56z6/3ExLRI31pXNAAAAAElFTkSuQmCC\n",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.Image at 0x1dfca10>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML(\"\"\"<table> <tr>\n",
      "<th>Header 1</th>\n",
      "<th>Header 2</th></tr>\n",
      "<tr><td>row 1, cell 1</td>\n",
      "<td>row 1, cell 2</td></tr>\n",
      "<tr><td>row 2, cell 1</td>\n",
      "<td>row 2, cell 2</td></tr></table>\"\"\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table> <tr>\n",
        "<th>Header 1</th>\n",
        "<th>Header 2</th></tr>\n",
        "<tr><td>row 1, cell 1</td>\n",
        "<td>row 1, cell 2</td></tr>\n",
        "<tr><td>row 2, cell 1</td>\n",
        "<td>row 2, cell 2</td></tr></table>"
       ],
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.HTML at 0x2c62990>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas\n",
      "pandas.core.format.set_printoptions(notebook_repr_html=True)\n",
      "pandas.read_csv('data.csv')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>Date</th>\n",
        "      <th>Open</th>\n",
        "      <th>High</th>\n",
        "      <th>Low</th>\n",
        "      <th>Close</th>\n",
        "      <th>Volume</th>\n",
        "      <th>Adj Close</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> 2012-06-01</td>\n",
        "      <td> 569.16</td>\n",
        "      <td> 590.00</td>\n",
        "      <td> 548.50</td>\n",
        "      <td> 584.00</td>\n",
        "      <td> 14077000</td>\n",
        "      <td> 581.50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 2012-05-01</td>\n",
        "      <td> 584.90</td>\n",
        "      <td> 596.76</td>\n",
        "      <td> 522.18</td>\n",
        "      <td> 577.73</td>\n",
        "      <td> 18827900</td>\n",
        "      <td> 575.26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 2012-04-02</td>\n",
        "      <td> 601.83</td>\n",
        "      <td> 644.00</td>\n",
        "      <td> 555.00</td>\n",
        "      <td> 583.98</td>\n",
        "      <td> 28759100</td>\n",
        "      <td> 581.48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 2012-03-01</td>\n",
        "      <td> 548.17</td>\n",
        "      <td> 621.45</td>\n",
        "      <td> 516.22</td>\n",
        "      <td> 599.55</td>\n",
        "      <td> 26486000</td>\n",
        "      <td> 596.99</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 2012-02-01</td>\n",
        "      <td> 458.41</td>\n",
        "      <td> 547.61</td>\n",
        "      <td> 453.98</td>\n",
        "      <td> 542.44</td>\n",
        "      <td> 22001000</td>\n",
        "      <td> 540.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>5</strong></td>\n",
        "      <td> 2012-01-03</td>\n",
        "      <td> 409.40</td>\n",
        "      <td> 458.24</td>\n",
        "      <td> 409.00</td>\n",
        "      <td> 456.48</td>\n",
        "      <td> 12949100</td>\n",
        "      <td> 454.53</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "\n",
        "         Date    Open    High     Low   Close    Volume  Adj Close\n",
        "0  2012-06-01  569.16  590.00  548.50  584.00  14077000     581.50\n",
        "1  2012-05-01  584.90  596.76  522.18  577.73  18827900     575.26\n",
        "2  2012-04-02  601.83  644.00  555.00  583.98  28759100     581.48\n",
        "3  2012-03-01  548.17  621.45  516.22  599.55  26486000     596.99\n",
        "4  2012-02-01  458.41  547.61  453.98  542.44  22001000     540.12\n",
        "5  2012-01-03  409.40  458.24  409.00  456.48  12949100     454.53"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "<!--====-->\n",
      "### Video"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "video = open(\"fig/animation.m4v\", \"rb\").read()\n",
      "video_encoded = video.encode(\"base64\")\n",
      "video_tag = '<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'.format(video_encoded)\n",
      "HTML(data=video_tag)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "html": [
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        "\">"
       ],
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<IPython.core.display.HTML at 0x1dfa090>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "source": [
      "And more exotic objects can also be displayed, as long as their representation supports \n",
      "the IPython display protocol.\n",
      "\n",
      "For example, videos hosted externally on YouTube are easy to load (and writing a similar wrapper for other\n",
      "hosted content is trivial):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import YouTubeVideo\n",
      "YouTubeVideo('F4rFuIb1Ie4')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "html": [
        "\n",
        "            <iframe\n",
        "                width=\"400\"\n",
        "                height=\"300\"\n",
        "                src=\"http://www.youtube.com/embed/F4rFuIb1Ie4\"\n",
        "                frameborder=\"0\"\n",
        "                allowfullscreen\n",
        "            ></iframe>\n",
        "        "
       ],
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0x1dfc4d0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "### Embed entire websites\n",
      "<!--====-->"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=650 height=350>')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "html": [
        "<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=650 height=350>"
       ],
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<IPython.core.display.HTML at 0xa3bbd4c>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "<!--====-->\n",
      "## Load remote code\n",
      "\n",
      "Use ``%load`` with any local or remote url, such as [the always useful Matplotlib Gallery!](http://matplotlib.org/gallery.html)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "%load http://matplotlib.org/mpl_examples/api/hinton_demo.py"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "<!--====-->\n",
      "## LaTeX rendering and SymPy support\n",
      "\n",
      "Objects can have a LaTeX representation, rendered thanks to MathJax:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Math\n",
      "Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "latex": [
        "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$"
       ],
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<IPython.core.display.Math at 0x1dfce10>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Latex\n",
      "Latex(r\"\"\"\\begin{eqnarray}\n",
      "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \n",
      "\\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
      "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
      "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \n",
      "\\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
      "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
      "\\end{eqnarray}\"\"\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "latex": [
        "\\begin{eqnarray}\n",
        "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \n",
        "\\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
        "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
        "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \n",
        "\\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
        "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
        "\\end{eqnarray}"
       ],
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<IPython.core.display.Latex at 0xa3bb36c>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "<!--====-->\n",
      "## This plays very nicely with SymPy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext sympyprinting\n",
      "from __future__ import division\n",
      "from sympy import *\n",
      "x, y = symbols(\"x y\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "eq = ((x+y)**2 * (x+1)); eq"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAHQAAAAbCAYAAACtOKuoAAAABHNCSVQICAgIfAhkiAAAA+lJREFU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\n",
       "prompt_number": 2,
       "text": [
        "\n",
        "               2\n",
        "(x + 1)\u22c5(x + y) "
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expand(eq)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAQ0AAAAbCAYAAABm6to6AAAABHNCSVQICAgIfAhkiAAABP5JREFU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\n7l6NeSaTyWQymUwmk8lkMpke518QvOZokU+z1gAAAABJRU5ErkJggg==\n",
       "prompt_number": 3,
       "text": [
        "\n",
        " 3      2      2      2            2\n",
        "x  + 2\u22c5x \u22c5y + x  + x\u22c5y  + 2\u22c5x\u22c5y + y "
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(1/cos(x)).series(x, 0, 6)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAMEAAAAfCAYAAABedqnDAAAABHNCSVQICAgIfAhkiAAABkNJREFU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\nNfEf/bCaiZS/zxYAAAAASUVORK5CYII=\n",
       "prompt_number": 4,
       "text": [
        "\n",
        "     2      4        \n",
        "    x    5\u22c5x     \u239b 6\u239e\n",
        "1 + \u2500\u2500 + \u2500\u2500\u2500\u2500 + O\u239dx \u23a0\n",
        "    2     24         "
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diff(cos(x**2)**2 / (1+x), x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAL4AAAAoCAYAAABAS0DDAAAABHNCSVQICAgIfAhkiAAABjZJREFU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\nAABJRU5ErkJggg==\n",
       "prompt_number": 5,
       "text": [
        "\n",
        "                           2    \n",
        "         \u239b 2\u239e    \u239b 2\u239e       \u239b 2\u239e\n",
        "  4\u22c5x\u22c5sin\u239dx \u23a0\u22c5cos\u239dx \u23a0   cos \u239dx \u23a0\n",
        "- \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 - \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
        "         x + 1                 2\n",
        "                        (x + 1) "
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "# <!--====-->\n",
      "Use other tools seamlessly: talk to R (or Octave)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "%load_ext rmagic "
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "source": [
      "A simple dataset plotted in Python"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pylab\n",
      "X = np.array([0,1,2,3,4])\n",
      "Y = np.array([3,5,4,6,7])\n",
      "pylab.scatter(X, Y);"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "Figure(480x320)"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "## Use R to do a linear fit\n",
      "<!--====-->"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i X,Y -o XYcoef\n",
      "XYlm = lm(Y~X)\n",
      "XYcoef = coef(XYlm)\n",
      "print(summary(XYlm))\n",
      "par(mfrow=c(2,2))\n",
      "plot(XYlm)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "display_data",
       "text": [
        "\n",
        "Call:\n",
        "lm(formula = Y ~ X)\n",
        "\n",
        "Residuals:\n",
        "   1    2    3    4    5 \n",
        "-0.2  0.9 -1.0  0.1  0.2 \n",
        "\n",
        "Coefficients:\n",
        "            Estimate Std. Error t value Pr(>|t|)  \n",
        "(Intercept)   3.2000     0.6164   5.191   0.0139 *\n",
        "X             0.9000     0.2517   3.576   0.0374 *\n",
        "---\n",
        "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1 \n",
        "\n",
        "Residual standard error: 0.7958 on 3 degrees of freedom\n",
        "Multiple R-squared:  0.81,\tAdjusted R-squared: 0.7467 \n",
        "F-statistic: 12.79 on 1 and 3 DF,  p-value: 0.03739 \n",
        "\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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g0NQ+Qt5gMktHjUp+9owu8fDc+PIl+d692r17S8vKaBFPaam2trZA2lhj1NXV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      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "source": [
      "And now we have the *python* variable `XYcoef`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "XYcoef"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "[ 3.2  0.9]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "### <!--====-->\n",
      "Seamless Cython"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext cythonmagic"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def f(x):\n",
      "    return x**2-x\n",
      "\n",
      "def integrate_f(a, b, N):\n",
      "    s = 0; dx = (b-a)/N\n",
      "    for i in range(N):\n",
      "        s += f(a+i*dx)\n",
      "    return s * dx"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython\n",
      "cdef double fcy(double x) except? -2:\n",
      "    return x**2-x\n",
      "\n",
      "def integrate_fcy(double a, double b, int N):\n",
      "    cdef int i\n",
      "    cdef double s, dx\n",
      "    s = 0; dx = (b-a)/N\n",
      "    for i in range(N):\n",
      "        s += fcy(a+i*dx)\n",
      "    return s * dx"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit integrate_f(0, 1, 100)\n",
      "%timeit integrate_fcy(0, 1, 100)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10000 loops, best of 3: 29.7 us per loop\n",
        "1000000 loops, best of 3: 537 ns per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}