01_Introducing_Python.ipynb

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      "<style>\n",
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      "# Introducing Python\n",
      "# Python in HPC\n",
      "\n",
      "## Supercomputing 2012\n",
      "\n",
      "Presenters: \n",
      "\n",
      "Andy R. Terrel, PhD  \n",
      "Texas Advanced Computing Center  \n",
      "University of Texas at Austin  \n",
      "\n",
      "Travis Oliphant, PhD  \n",
      "Continuum Analytics \n",
      "\n",
      "Aron Ahmadia, PhD  \n",
      "Supercomputing Laboratory  \n",
      "King Abdullah University of Science and Technoglogy \n",
      "\n",
      "[![Creative Commons License](/files/figures/creative_commons_logo.png)](http://creativecommons.org/licenses/by/3.0/deed.en_US)  \n",
      "\n",
      "![TACC Logo](/files/figures/TACC_logo.png)\n",
      "![Continuum Logo](/files/figures/continuum.png)\n",
      "![KAUST Logo](/files/figures/kaust.png)\n",
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      "---\n",
      "\n",
      "## About This Tutorial\n",
      "\n",
      "### PyHPC Tutorial on GitHub\n",
      "\n",
      "These presentation materials are part of a continuously updated tutorial on Python for High Performance Computing.  To get the presentation materials as they were delivered today, please use the SC2012 tag on github.  Future versions of this presentation will be found at:\n",
      "\n",
      "https://github.com/pyhpc/pyhpc-tutorial\n",
      "\n",
      "Please set all permanent bookmarks to this URL.\n",
      "\n",
      "### Direct download of this SC2012 presentation\n",
      "You can download a zip or tar ball from the [github SC2012 tag](https://github.com/pyHPC/pyhpc-tutorial/tags):\n",
      "\n",
      "```\n",
      "wget --no-check-certificate https://github.com/pyhpc/pyhpc-tutorial/zipball/SC2012\n",
      "wget --no-check-certificate https://github.com/pyhpc/pyhpc-tutorial/tarball/SC2012\n",
      "```\n",
      "\n",
      "### \n",
      "2) Checkout from git\n",
      "\n",
      "```\n",
      "git clone https://github.com/pyhpc/pyhpc-tutorial.git\n",
      "```\n",
      "\n",
      "### Viewing the read-only version of this presentation on nbviewer: \n",
      "\n",
      "* http://nbviewer.ipython.org/urls/raw.github.com/pyhpc/pyhpc-tutorial/master/01_Introducing_Python.ipynb"
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      "--- \n",
      "\n",
      "## Using the Texas Advanced Computing Center IPython Notebooks\n",
      "\n",
      "You will need a modern web browser to interact with the TACC nodes, there are no other requirements.\n",
      "\n",
      "### TACC IPython Notebooks\n",
      "* http://pyhpc01.tacc.utexas.edu/\n",
      "* http://pyhpc02.tacc.utexas.edu/\n",
      "* http://pyhpc03.tacc.utexas.edu/\n",
      "* http://pyhpc04.tacc.utexas.edu/\n",
      "* http://pyhpc05.tacc.utexas.edu/\n",
      "* http://pyhpc06.tacc.utexas.edu/\n",
      "* http://pyhpc07.tacc.utexas.edu/\n",
      "* http://pyhpc08.tacc.utexas.edu/\n",
      "* http://pyhpc09.tacc.utexas.edu/\n",
      "* http://pyhpc10.tacc.utexas.edu/\n",
      "\n",
      "The TACC nodes will not be available after this workshop, please contact the presenters for more permanent hosting solutions."
     ]
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      "---\n",
      "\n",
      "## Interacting with the Tutorial Slides\n",
      "\n",
      "This tutorial is an interactive worksheet designed to encourage you to try out the lessons during the demonstration.  If you are looking at the PDF version of these slides, we encourage you to download the updated version (see previous slide) and try the interactive version.\n",
      "\n",
      "To run the interactive version of this notebook, you will need a Python 2.7 environment including: \n",
      "\n",
      "* IPython version >= 13.0\n",
      "* numpy version >= 1.6\n",
      "* scipy >= 0.10\n",
      "* matplotlib >= 1.0.0\n",
      "\n",
      "Move to the directory containing the tarball and execute:\n",
      "\n",
      "    $ ipython notebook --pylab=inline\n",
      "\n",
      "If you are installing the packages yourself, Continuum Analytics (disclaimer, Travis Oliphant is CEO of this company) provides both free community as well as professional versions of the Anaconda installer, which provides all the packages you will need for this portion of the tutorial. The installer is available from the [Anaconda download page at Continuum Analytics](https://store.continuum.io/cshop/anaconda).\n",
      "\n",
      "You are also welcome to use Enthought's Python Distribution (free for Academic users), available from the [EPD download page at Enthought](http://www.enthought.com/products/epd.php).\n",
      "\n",
      "Unfortunately, you will need to upgrade your EPD's IPython to 0.13 if you go this route, and this may be non-trivial, please see the [Enthought discussion thread here](https://mail.enthought.com/pipermail/epd-users/2012-August/000800.html)."
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      "---\n",
      "## Presentation mode\n",
      "The slideshow mode is only supported by an IPython development branch version. If you would like to write your own slideshows (the notebooks will work regardless of slideshow capabilities), you will need to install Matthias Bussonnier's slideshow_metadata branch.  Here are some sample command-line instructions.\n",
      "\n",
      "    #!/bin/bash\n",
      "    git clone carreau git://github.com/Carreau/ipython.git # Matthias' IPython fork\n",
      "    git checkout origin/slideshow_metadata                 # Select the right branch\n",
      "    python setup.py develop                                # Install the development version\n",
      "    ipython notebook                                       # Check out the slideshows!"
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      "---\n",
      "## Slideshow Setup Code"
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      "# The below command starts pylab inline mode, which simplifies syntax for many common \n",
      "# scientific computing commands.  All commands starting with % are IPython extensions.\n",
      "%pylab inline\n",
      "\n",
      "import numpy as np\n",
      "import scipy as sp\n",
      "\n",
      "# The following two lines of code load the slideshow extension and start it,\n",
      "# you only need to run these if you are writing or running your own slideshow\n",
      "\n",
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        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
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      "---\n",
      "\n",
      "Introduction to Python (This Notebook)\n",
      "------------\n",
      "\n",
      "### Objectives\n",
      "\n",
      "1. You will understand how scripting languages fit into the toolbox of\n",
      "a computational scientist.\n",
      "2. You will see why Python is a powerful choice\n",
      "3. You will get a taste of Python for actual scientific computing\n",
      "\n",
      "The first part of this introduction is adapted from *Python\n",
      "Scripting for Computational Science* by \n",
      "[Hans Petter Langtangen](http://folk.uio.no/hpl/), Simula \n",
      "and also includes motivating material from \n",
      "[Nathan Collier](http://www.mendeley.com/profiles/nathan-collier/), KAUST.\n",
      "\n",
      "<a href=\"http://www.amazon.com/Hans-Petter-Langtangen/e/B001K6A0IA\"><img src=\"/files/figures/books.png\"/> </a>\n"
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      "Scripting vs Traditional programming\n",
      "------------------------------------\n",
      "\n",
      "In traditional programming, large applications were typically\n",
      "written at a low level. Scripting, by contrast, is programming at a\n",
      "very high level with expressive, dynamic languages.\n",
      "\n",
      "> Traditional programming: Fortran, C, C++, C#, Java\n",
      ">\n",
      "> Scripting: Python, Perl, Ruby, MATLAB\n",
      "\n",
      "In the past, a major gain for scripting languages was that they could automate many \n",
      "tasks that otherwise would be performed by hand.\n",
      "\n",
      "As domain-specific scripting languages such as MATLAB and Python evolved, scientists realized\n",
      "that there were also great benefits in programming at a high level, allowing them to naturally\n",
      "express their computations in fewer lines of code, resulting in greater productivity and less \n",
      "defects.\n",
      "\n",
      "Computational scientists are now demonstrating that high level languages can also be more performant than their low-level\n",
      "counterparts, and Python is at the forefront of this.\n"
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     "source": [
      "---\n",
      "\n",
      "Python Scripting - Has this ever happened to you?\n",
      "------------------------------\n",
      "\n",
      "### Scenario 1\n",
      "You are working on data for a presentation your advisor is giving\n",
      "at a conference. At the last minute, you realize that there is a\n",
      "major bug in your code and you need to regenerate all the images\n",
      "and graphs that you have given him. You spend 30 minutes\n",
      "regenerating the data and 6 hours regenerating the graphs because\n",
      "you had them in Excel and had done them by hand.\n",
      "\n",
      "### Scenario 2\n",
      "You are working on your thesis and as you near the finish you\n",
      "review some graphs you generated months earlier and you aren't\n",
      "sure if they are now completely up to date. You spend half a day\n",
      "locating the old code that you wrote on your second laptop and\n",
      "hours more again creating and polishing the charts.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_step": false
      }
     },
     "source": [
      "---\n",
      "\n",
      "Buyer Beware\n",
      "------------\n",
      "<!--====-->\n",
      "\n",
      "Learning to automate many of these common tasks can greatly\n",
      "increase your productivity (and make your research\n",
      "reproducible). However, beware that there is no end to the number\n",
      "of different ways to do essentially the same thing. \n",
      "\n",
      "![Recall we want to script to SAVE time](/files/figures/careful.png)\n",
      "\n",
      "> Image credit [Bruno Oliveira](https://plus.google.com/102451193315916178828/posts/MGxauXypb1Y)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Why Python is useful\n",
      "-----------------------\n",
      "<!--====-->\n",
      "\n",
      "There are perhaps many reasons, here we list a few:\n",
      "\n",
      "* like other scripting languages, has an easier interface to learn\n",
      "* allow you to build your own work environment\n",
      "* scientific computing is more than crunching numbers\n",
      "* easier creation of GUIs, demos, and fancy HTML notebooks\n",
      "* create modern interfaces to old codes\n",
      "* allow you to explore, develop and test interactively\n",
      "* cleaner, shorter, easy to read, write, and maintain code"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Is Python > MATLAB?\n",
      "-------------------\n",
      "\n",
      "### Python == MATLAB\n",
      "\n",
      "* Simple and easy to use syntax (dynamic typing, array and linear algebra language)\n",
      "* Easy creation of interactive interfaces and GUIs\n",
      "* Merge simulation and visualization\n",
      "* Commercially supported (Continuum Analytics, Enthought)\n",
      "* Vibrant, enthusiastic academic communities for getting support and learning more\n",
      "* JIT support\n",
      "* Amazon EC2 support\n",
      "\n",
      "### MATLAB > Python\n",
      "\n",
      "* Several MATLAB toolboxes offer capabilities unavailable to the Python community\n",
      "\n",
      "### Python > MATLAB\n",
      "\n",
      "* Python is free \n",
      "* Object-oriented programming is more convenient in Python\n",
      "* Extensive tools within and beyond scientific computing\n",
      "    + IPython notebook relies on a Python webserver as well as\n",
      "extensive asynchronous messaging support through PyZMQ\n",
      "* Python runs on all modern Cray and IBM supercomputers (and anywhere C does)\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Some Sample Applications\n",
      "========================"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Teaching the finite element method\n",
      "----------------------------------\n",
      "\n",
      "### Stiffness matrix computation\n",
      "\n",
      "![Stiffness matrix](/files/figures/fem.png)\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Monitor program progress\n",
      "------------------------\n",
      "\n",
      "### Python parses a datafile and makes plots of current progress\n",
      "\n",
      "![Log](/files/figures/log.png)\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Problem prototypes\n",
      "------------------\n",
      "\n",
      "### Nonlinear, time dependent problem\n",
      "\n",
      "![NonLinear problem](/files/figures/dsw.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Problem prototypes\n",
      "------------------\n",
      "\n",
      "### Method for fitting surface to data\n",
      "\n",
      "![Prototypes](/files/figures/molt.png)\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "\n",
      "---\n",
      "\n",
      "Structural program\n",
      "------------------\n",
      "\n",
      "### Python manages floors and columns\n",
      "\n",
      "![Structural program](/files/figures/struct.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Auto-generation of results\n",
      "--------------------------\n",
      "\n",
      "### Python runs C code, post-processes the results, and generates a LaTeX table\n",
      "\n",
      "![Result Generation](/files/figures/dpg.png)\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "\n",
      "---\n",
      "\n",
      "# A Tour of Python for Simple Scripting\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Script 1\n",
      "--------\n",
      "    "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#!/usr/bin/env python\n",
      "import math\n",
      "r = math.pi / 2.0\n",
      "s = math.sin(r)\n",
      "print \"Hello world, sin(%f)=%f\" % (r,s)\n"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": false
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Hello world, sin(1.570796)=1.000000\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Script 2\n",
      "--------\n",
      "    "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "infile = \"data/numbers\"\n",
      "outfile = \"data/f_numbers\"\n",
      "\n",
      "f = open(infile,'r')\n",
      "g = open(outfile,'w')\n",
      "\n",
      "def func(y):\n",
      "    if y >= 0.0:\n",
      "        return y**5.0*math.exp(-y)\n",
      "    else:\n",
      "        return 0.0\n",
      "    \n",
      "for line in f:\n",
      "    line = line.split()\n",
      "    x = float(line[0])\n",
      "    y = float(line[1])\n",
      "    fy = func(y)\n",
      "    g.write(\"%g %12.5e\\n\" % (x,fy))\n",
      "    \n",
      "f.close() \n",
      "g.close()\n"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Script 3\n",
      "--------\n",
      "    "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys,os\n",
      "cmd = 'date'\n",
      "output = os.popen(cmd)\n",
      "lines = output.readlines()\n",
      "fail = output.close()\n",
      "if fail: print 'You do not have the date command'; sys.exit()\n",
      "for line in lines:\n",
      "    line = line.split()\n",
      "    print \"The current time is %s on %s %s, %s\" % (line[3],line[2],line[1],line[-1])"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The current time is 06:29:55 on 11 Nov, 2012\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Script 4\n",
      "--------\n",
      "    "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "source": [
      "---\n",
      "\n",
      "A bib-file (see data/test.bib)\n",
      "----------\n",
      "<!--====-->\n",
      "\n",
      "    @Book{Langtangen2011,\n",
      "      author = \t  {Hans Petter Langtangen},\n",
      "      title = \t  {A Primer on Scientific Programming with Python},\n",
      "      publisher = {Springer},\n",
      "      year = \t  {2011}\n",
      "    }\n",
      "    @Book{Langtangen2010,\n",
      "      author = \t  {Hans Petter Langtangen},\n",
      "      title = \t  {Python Scripting for Computational Science},\n",
      "      publisher = {Springer},\n",
      "      year = \t  {2010}\n",
      "    }\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import re\n",
      "pattern1 = \"@Book{(.*),\"\n",
      "pattern2 = \"\\s+title\\s+=\\s+{(.*)},\"\n",
      "for line in file('data/test.bib'):\n",
      "    match = re.search(pattern1,line)\n",
      "    if match: \n",
      "        print \"Found a book with the tag '%s'\" % match.group(1)\n",
      "    match = re.search(pattern2,line)\n",
      "    if match:\n",
      "        print \"The title is '%s'\" % match.group(1)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Found a book with the tag 'Langtangen2011'\n",
        "The title is 'A Primer on Scientific Programming with Python'\n",
        "Found a book with the tag 'Langtangen2010'\n",
        "The title is 'Python Scripting for Computational Science'\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "# A Tour of Python for Scientific Computing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Arrays\n",
      "-------------------\n",
      "<!--====-->\n",
      "\n",
      "**Python** has built-in:\n",
      "\n",
      "containers: lists (costless insertion and append), dictionaries (fast lookup)  \n",
      "high-level number objects: integers, floating point  \n",
      "\n",
      "**Numpy** is:\n",
      "\n",
      "extension package to Python for multi-dimensional arrays  \n",
      "closer to hardware (efficiency)  \n",
      "designed for scientific computation (convenience)  \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.array([0,1,2,3,4])\n",
      "a"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array([0, 1, 2, 3, 4])"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print a.ndim # Dimensionality\n",
      "print a.shape # Shape"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1\n",
        "(5,)\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = np.array([[0, 1, 2], [3, 4, 5]])\n",
      "print b\n",
      "print b.ndim\n",
      "print b.shape"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[0 1 2]\n",
        " [3 4 5]]\n",
        "2\n",
        "(2, 3)\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Common Arrays\n",
      "-------------------\n",
      "<!--====-->\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print np.arange(10) # Like range [0, 1, ..., 9]\n",
      "print np.arange(1,9, 2) # [1, 3, 5, 7]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0 1 2 3 4 5 6 7 8 9]\n",
        "[1 3 5 7]\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print np.linspace(0, 1, 6) # A linear space of [0,1] with 6 pts\n",
      "print np.linspace(0, 1, 6, endpoint=False) # [0,1["
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.   0.2  0.4  0.6  0.8  1. ]\n",
        "[ 0.          0.16666667  0.33333333  0.5         0.66666667  0.83333333]\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print np.ones((3,3)) # 3 X 3 2D array of 1's"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 1.  1.  1.]\n",
        " [ 1.  1.  1.]\n",
        " [ 1.  1.  1.]]\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print np.eye(3)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 1.  0.  0.]\n",
        " [ 0.  1.  0.]\n",
        " [ 0.  0.  1.]]\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print np.diag(arange(4))"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[0 0 0 0]\n",
        " [0 1 0 0]\n",
        " [0 0 2 0]\n",
        " [0 0 0 3]]\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print np.random.rand(4) # Uniform distribution\n",
      "print np.random.randn(4) # Gaussian distribution"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.83631573  0.12891168  0.75794954  0.53667915]\n",
        "[-0.54066149 -0.80747702  0.03263313  0.72331973]\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "## Other Numpy Features to be aware of\n",
      "<!--====-->\n",
      "\n",
      "* Reshaping"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arr = np.arange(1000000)\n",
      "arr2 = arr.reshape((10,100000))\n",
      "print(arr2)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[     0      1      2 ...,  99997  99998  99999]\n",
        " [100000 100001 100002 ..., 199997 199998 199999]\n",
        " [200000 200001 200002 ..., 299997 299998 299999]\n",
        " ..., \n",
        " [700000 700001 700002 ..., 799997 799998 799999]\n",
        " [800000 800001 800002 ..., 899997 899998 899999]\n",
        " [900000 900001 900002 ..., 999997 999998 999999]]\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "* Memory Views"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arr2.view?"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "* Index Slicing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.arange(0, 20, 2); y = x**2\n",
      "((y[1:] - y[:-1]) / (x[1:] - x[:-1])) # dy/dx"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "array([ 2,  6, 10, 14, 18, 22, 26, 30, 34])"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "* Fancy Indexing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "evens = arr[arr%2 == 0]\n",
      "print(evens)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[     0      2      4 ..., 999994 999996 999998]\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "LinAlg, FFTs, and Random numbers\n",
      "-------------------\n",
      "<!--====-->"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.dot(arange(10), arange(10))"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "285"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.dot(arange(9).reshape(3,3),\n",
      "    arange(9).reshape(3,3))"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "array([[ 15,  18,  21],\n",
        "       [ 42,  54,  66],\n",
        "       [ 69,  90, 111]])"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.fft?"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_step": true
      }
     },
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.linalg?"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random?"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Quick Visualization / MatPlotLib\n",
      "-------------------\n",
      "<!--====-->\n",
      "\n",
      "How quickly can we plot 10K numbers?\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_ = hist(random.randn(100000), 100)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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hmPudTiexWOyy56mpqTG3/X4/fr9/OtcjIrKgBYNBgsHgjMomFfinT59m06ZN7Nq1iyVL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\nAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "Basic Plotting\n",
      "-------------------\n",
      "<!--====-->\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_ = plot(range(100))\n",
      "_ = xlabel(\"measured\")\n",
      "_ = ylabel(\"calculated\")\n",
      "grid(True)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ZTCZD1Gnv3r0YOXIkEhMT+9wXY5Ra/Pzzz3C5XJg/fz5cLheuu+66y5ZQjFKL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\nxQmCiIh6xQmCiIh6xQmCiIh69f8s3XxPIvxpbgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = [val*.01 for val in range(100)]  # [0, .01, .02, ..., .98, .99]\n",
      "linear = [val for val in x] \n",
      "square = [val**2 for val in x]\n",
      "_ = plot(x, linear, x, square)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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43EFJBJytG+5BwQN8c+wbOP/gjNyCXCS+nogPe3/Ixk5UTUzuBmJaN4y2UIuN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\n"
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num_vals = 10\n",
      "x = [float(val)/num_vals for val in range(num_vals)]  # [0, .01, .02, ..., .98, .99]\n",
      "linear = [val for val in x] \n",
      "square = [val**2 for val in x]\n",
      "_= plot(x, linear, 'g:+', x, square, 'r--o')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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hGKEx6bl7kK3mrTyy6hHbsRR2JysoYOSpU8z0r75n7EthYYyYMkWjUEJYyZi7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\nICnuQgjhgaS4CyGEB/r/ro17hOBEh3cAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num_vals = 10\n",
      "x = [float(val)/num_vals for val in range(num_vals)]  # [0, .01, .02, ..., .98, .99]\n",
      "linear = [val for val in x] \n",
      "square = [val**2 for val in x]\n",
      "_ = plot(x, linear, 'g:+', x, square, 'r--o')\n",
      "_ = legend(('linear', 'square'), loc='upper left')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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CyM/Ptxzn5+cTGBho1WbPnj3ExsYCcP78eTZt2oSXlxcxMbI9mbC/Xnf0wr+p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\n"
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_ = plot(linear)\n",
      "_ = axes([0.2, 0.5, 0.25, 0.25])\n",
      "_ = plot(square)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "SciPy: Collection of High-Level Tools\n",
      "--------------------------------------\n",
      "\n",
      "* mathematical algorithms and convenience functions built on the Numpy,\n",
      "* organized into subpackages covering different scientific computing domains,\n",
      "* a data-processing and system-prototyping environment rivaling sytems such as MATLAB, IDL, Octave, R-Lab, and SciLab\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "SciPy: Collection of High-Level Tools\n",
      "--------------------------------------\n",
      "\n",
      "* File IO (scipy.io)\n",
      "* Special functions (scipy.special)\n",
      "* Integration (scipy.integrate)\n",
      "* Optimization (scipy.optimize)\n",
      "* Interpolation (scipy.interpolate)\n",
      "* Fourier Transforms (scipy.fftpack)\n",
      "* Signal Processing (scipy.signal)\n",
      "* Linear Algebra (scipy.linalg)\n",
      "* Sparse Eigenvalue Problems with ARPACK\n",
      "* Statistics (scipy.stats)\n",
      "* Multi-dimensional image processing (scipy.ndimage)\n",
      "* Weave (scipy.weave)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "scipy.io\n",
      "--------\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy\n",
      "from scipy import io as spio\n",
      "a = np.ones((3, 3))\n",
      "spio.savemat('file.mat', {'a': a}) # savemat expects a dictionary\n",
      "data = spio.loadmat('file.mat', struct_as_record=True)\n",
      "data['a']\n"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/opt/anaconda/lib/python2.7/site-packages/scipy/io/matlab/mio.py:266: FutureWarning: Using oned_as default value ('column') This will change to 'row' in future versions\n",
        "  oned_as=oned_as)\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "array([[ 1.,  1.,  1.],\n",
        "       [ 1.,  1.,  1.],\n",
        "       [ 1.,  1.,  1.]])"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "source": [
      "Also see:\n",
      "\n",
      "* Images\n",
      "* IDL Files\n",
      "* Matrix Market Files\n",
      "* Wav files"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "scipy.special\n",
      "--------\n",
      "\n",
      "* Bessel function, such as scipy.special.jn() (nth integer order Bessel function)\n",
      "* Elliptic function (scipy.special.ellipj() for the Jacobian elliptic function, ...)\n",
      "* Gamma function: scipy.special.gamma(), also note scipy.special.gammaln() which will give the log of Gamma to a higher numerical precision.\n",
      "* Erf, the area under a Gaussian curve: scipy.special.erf()\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.special import jn\n",
      "x = np.linspace(0, 1, 10)\n",
      "print [jn(val, 1.0) for val in x]\n"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0.76519768655796649, 0.77042135015704238, 0.75775886140714455, 0.73087640216944849, 0.69324418101264496, 0.64801456180958794, 0.59794997367362868, 0.54539073261127002, 0.49225331977915776, 0.44005058574493355]\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy import *\n",
      "from scipy.special import jn, jn_zeros\n",
      "def drumhead_height(n, k, distance, angle, t):\n",
      "    nth_zero = jn_zeros(n, k)\n",
      "    return cos(t)*cos(n*angle)*jn(n, distance*nth_zero)\n",
      "theta = r_[0:2*pi:50j]\n",
      "radius = r_[0:1:50j]\n",
      "x = array([r*cos(theta) for r in radius])\n",
      "y = array([r*sin(theta) for r in radius])\n",
      "z = array([drumhead_height(1, 1, r, theta, 0.5) for r in radius])"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "from matplotlib import cm\n",
      "fig = pylab.figure()\n",
      "ax = Axes3D(fig)\n",
      "ax.plot_surface(x, y, z, rstride=1, cstride=1, cmap=cm.jet)\n",
      "_ = ax.set_xlabel('X')\n",
      "_ = ax.set_ylabel('Y')\n",
      "_ = ax.set_zlabel('Z')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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NBM1z0mpZdtX5xpvDwlNVlZKSEpKTkxvtPevzmX6/v3JEkObSbO5FQvvOo2Vx0mo1W0M8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\n6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zdCq1nYvTKbc4dz8PQzU7GeJx1fKUez3gGlnoMxTY7RoJM3z0+uhhCSy0ey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\nkxFBJ6AYAX8QjDpV/DR3qHL4n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wWlmyqo+NHFwZ+qOLC1lG49shh+/PGMG3EK5z9xKWef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\n"
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "scipy.linalg\n",
      "--------\n",
      "\n",
      "* Matrix class (all routines still work with 2D NumPy arrays)\n",
      "* Basic Linear Algebra\n",
      "* Decompositions\n",
      "* Matrix Functions\n",
      "* Special matrices\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = sp.mat('[1 3 5; 2 5 1; 2 3 8]')\n",
      "A"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "matrix([[1, 3, 5],\n",
        "        [2, 5, 1],\n",
        "        [2, 3, 8]])"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A.I"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "matrix([[-1.48,  0.36,  0.88],\n",
        "        [ 0.56,  0.08, -0.36],\n",
        "        [ 0.16, -0.12,  0.04]])"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy import linalg\n",
      "linalg.inv(A)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "array([[-1.48,  0.36,  0.88],\n",
        "       [ 0.56,  0.08, -0.36],\n",
        "       [ 0.16, -0.12,  0.04]])"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "linalg.svd(A)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "(array([[-0.52956045,  0.04298197, -0.84718255],\n",
        "       [-0.34647857, -0.92256717,  0.16977167],\n",
        "       [-0.77428569,  0.38343496,  0.50344742]]),\n",
        " array([ 11.13385134,   4.2134737 ,   0.53291053]),\n",
        " array([[-0.24888863, -0.50691635, -0.82528193],\n",
        "       [-0.24570758, -0.79117262,  0.56006577],\n",
        "       [ 0.93684696, -0.34217203, -0.07236068]]))"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "linalg.toeplitz([1,2,3],[1,2,4,5,6])"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": true
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "array([[1, 2, 4, 5, 6],\n",
        "       [2, 1, 2, 4, 5],\n",
        "       [3, 2, 1, 2, 4]])"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "## Pattern Formation Exercise\n",
      "\n",
      "This exercise has been adapted from an example by [David Ketcheson](http://www.davidketcheson.info/), KAUST"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_step": true
      }
     },
     "source": [
      "### 5 Point Laplace Operator\n",
      "We start by constructing a dense matrix representing our stencil operator applied to a vector"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def five_pt_laplacian(m, a, b):\n",
      "    \"\"\"Construct a matrix that applies the 5-point Laplacian discretization\"\"\"\n",
      "    e=np.ones(m**2)\n",
      "    e2=([0]+[1]*(m-1))*m\n",
      "    h=(b-a)/(m+1)\n",
      "    A=np.diag(-4*e,0)+np.diag(e2[1:],-1)+np.diag(e2[1:],1)+np.diag(e[m:],m)+np.diag(e[m:],-m)\n",
      "    A/=h**2\n",
      "    return A\n",
      "\n",
      "five_pt_laplacian(8, 0.0, 1.0)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": false
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "array([[-324.,   81.,    0., ...,    0.,    0.,    0.],\n",
        "       [  81., -324.,   81., ...,    0.,    0.,    0.],\n",
        "       [   0.,   81., -324., ...,    0.,    0.,    0.],\n",
        "       ..., \n",
        "       [   0.,    0.,    0., ..., -324.,   81.,    0.],\n",
        "       [   0.,    0.,    0., ...,   81., -324.,   81.],\n",
        "       [   0.,    0.,    0., ...,    0.,   81., -324.]])"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "Alternatively we can constuct a sparse representation."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.sparse import spdiags\n",
      "\n",
      "def five_pt_laplacian_sparse(m, a, b):\n",
      "    \"\"\"Construct a sparse matrix that applies the 5-point laplacian discretization\"\"\"\n",
      "    e=np.ones(m**2)\n",
      "    e2=([1]*(m-1)+[0])*m\n",
      "    e3=([0]+[1]*(m-1))*m\n",
      "    h=(b-a)/(m+1)\n",
      "    A=spdiags([-4*e,e2,e3,e,e],[0,-1,1,-m,m],m**2,m**2)\n",
      "    A/=h**2\n",
      "    return A\n",
      "\n",
      "A = five_pt_laplacian_sparse(8, 0.0, 1.0)\n",
      "A?"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false,
       "slide_step": false
      }
     },
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true
      }
     },
     "source": [
      "---\n",
      "\n",
      "## Pattern Formation Exercise"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "from IPython.display import clear_output\n",
      "\n",
      "\"\"\"Pattern formation code\n",
      "\n",
      "    Solves the pair of PDEs:\n",
      "       u_t = D_1 \\nabla^2 u + f(u,v)\n",
      "       v_t = D_2 \\nabla^2 v + g(u,v)\n",
      "\"\"\"\n",
      "\n",
      "#import matplotlib\n",
      "#matplotlib.use('TkAgg')\n",
      "import numpy as np\n",
      "#import matplotlib.pyplot as plt\n",
      "from scipy.sparse import spdiags,linalg,eye\n",
      "from time import sleep\n",
      "\n",
      "#Parameter values\n",
      "Du=0.500; Dv=1;\n",
      "delta=0.0045; tau1=0.02; tau2=0.2; alpha=0.899; beta=-0.91; gamma=-alpha;\n",
      "#delta=0.0045; tau1=0.02; tau2=0.2; alpha=1.9; beta=-0.91; gamma=-alpha;\n",
      "#delta=0.0045; tau1=2.02; tau2=0.; alpha=2.0; beta=-0.91; gamma=-alpha;\n",
      "#delta=0.0021; tau1=3.5; tau2=0; alpha=0.899; beta=-0.91; gamma=-alpha;\n",
      "#delta=0.0045; tau1=0.02; tau2=0.2; alpha=1.9; beta=-0.85; gamma=-alpha;\n",
      "#delta=0.0001; tau1=0.02; tau2=0.2; alpha=0.899; beta=-0.91; gamma=-alpha;\n",
      "#delta=0.0005; tau1=2.02; tau2=0.; alpha=2.0; beta=-0.91; gamma=-alpha; nx=150;\n",
      "\n",
      "#Define the reaction functions\n",
      "def f(u,v):\n",
      "    return alpha*u*(1-tau1*v**2) + v*(1-tau2*u);\n",
      "\n",
      "def g(u,v):\n",
      "    return beta*v*(1+alpha*tau1/beta*u*v) + u*(gamma+tau2*v);\n",
      "\n",
      "\n",
      "def five_pt_laplacian(m,a,b):\n",
      "    \"\"\"Construct a matrix that applies the 5-point laplacian discretization\"\"\"\n",
      "    e=np.ones(m**2)\n",
      "    e2=([0]+[1]*(m-1))*m\n",
      "    h=(b-a)/(m+1)\n",
      "    A=np.diag(-4*e,0)+np.diag(e2[1:],-1)+np.diag(e2[1:],1)+np.diag(e[m:],m)+np.diag(e[m:],-m)\n",
      "    A/=h**2\n",
      "    return A\n",
      "\n",
      "def five_pt_laplacian_sparse(m,a,b):\n",
      "    \"\"\"Construct a sparse matrix that applies the 5-point laplacian discretization\"\"\"\n",
      "    e=np.ones(m**2)\n",
      "    e2=([1]*(m-1)+[0])*m\n",
      "    e3=([0]+[1]*(m-1))*m\n",
      "    h=(b-a)/(m+1)\n",
      "    A=spdiags([-4*e,e2,e3,e,e],[0,-1,1,-m,m],m**2,m**2)\n",
      "    A/=h**2\n",
      "    return A\n",
      "\n",
      "# Set up the grid\n",
      "a=-1.; b=1.\n",
      "m=100; h=(b-a)/m; \n",
      "x = np.linspace(-1,1,m)\n",
      "y = np.linspace(-1,1,m)\n",
      "Y,X = np.meshgrid(y,x)\n",
      "\n",
      "# Initial data\n",
      "u=np.random.randn(m,m)/2.;\n",
      "v=np.random.randn(m,m)/2.;\n",
      "hold(False)\n",
      "plt.pcolormesh(x,y,u)\n",
      "plt.colorbar; plt.axis('image'); \n",
      "plt.draw()\n",
      "u=u.reshape(-1)\n",
      "v=v.reshape(-1)\n",
      "\n",
      "A=five_pt_laplacian_sparse(m,-1.,1.);\n",
      "II=eye(m*m,m*m)\n",
      "\n",
      "t=0.\n",
      "dt=h/delta/5.;\n",
      "fig, ax = plt.subplots()\n",
      "plt.colorbar\n",
      "\n",
      "#Now step forward in time\n",
      "for k in range(120):\n",
      "    #Simple (1st-order) operator splitting:\n",
      "    u = linalg.spsolve(II-dt*delta*Du*A,u)\n",
      "    v = linalg.spsolve(II-dt*delta*Dv*A,v)\n",
      "\n",
      "    unew=u+dt*f(u,v);\n",
      "    v   =v+dt*g(u,v);\n",
      "    u=unew;\n",
      "    t=t+dt;\n",
      "\n",
      "    #Plot every 3rd frame\n",
      "    if k/3==float(k)/3:\n",
      "        U=u.reshape((m,m))\n",
      "        ax.pcolormesh(x,y,U)\n",
      "        #ax.colorbar\n",
      "        ax.axis('image')\n",
      "        ax.set_title(str(t))\n",
      "        #ax.draw()\n",
      "        clear_output()\n",
      "        display(fig)\n",
      "\n",
      "plt.close()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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H//EJM48tW7Zg8uTJmDBhAh544IG4+m3btmH48OGYPXs2Zs+ejXvvvTfRS1pY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\nFOzgYWFhEQp28LCwsAgFO3hYWFiEwv8Hkq4GlR6Gp+0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x334c150>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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WtUgrrStiEmIR/zAH5AiW9Ojh5cxaQFQif1FVgdwqyWRDAskd/G+9iXbCsyyy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      }
     ],
     "prompt_number": 41
    }
   ],
   "metadata": {}
  }
 ]
}