{
 "metadata": {
  "name": "SlideExamples"
 }, 
 "nbformat": 2, 
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Slide 9", 
      "mp = 0.115", 
      "vp = 35.0", 
      "mo = 0.265", 
      "vo = 0.0", 
      "v = (mp * vp + mo * vo) / (mp + mo)", 
      "print v"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "10.5921052632"
       ]
      }
     ], 
     "prompt_number": 1
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Slide 10", 
      "mp = 0.115", 
      "vp = 114.13", 
      "mo = 0.265", 
      "vo = 0.0", 
      "v = (mp * vp + mo * vo) / (mp + mo)", 
      "print v"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "34.5393421053"
       ]
      }
     ], 
     "prompt_number": 2
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Slide 11", 
      "mp = 0.115", 
      "vp = 114.13 * 0.44704  # mph to m/s", 
      "mo = 0.265", 
      "vo = 0.0", 
      "v = (mp * vp + mo * vo) / (mp + mo)", 
      "print v"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "15.4404674947"
       ]
      }
     ], 
     "prompt_number": 3
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Slide 12", 
      "mp = 0.115  # kg", 
      "vp = 114.13 * 0.44704  # mph to m/s", 
      "mo = 0.265  # kg", 
      "vo = 0.0  # m/s", 
      "v = (mp * vp + mo * vo) / (mp + mo)", 
      "print v"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "15.4404674947"
       ]
      }
     ], 
     "prompt_number": 6
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Slide 15", 
      "import quantities as pq", 
      "mp = 0.115 * pq.kg", 
      "vp = 35.0 * (pq.m / pq.s)", 
      "mo = 0.265 * pq.kg", 
      "vo = 0.0 * (pq.m / pq.s)", 
      "v = (mp * vp + mo * vo) / (mp + mo)", 
      "print v"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "10.5921052632 m/s"
       ]
      }
     ], 
     "prompt_number": 7
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Slide 16", 
      "import quantities as pq", 
      "mp = 0.115 * pq.kg", 
      "vp = 114.13 * (pq.mi / pq.h)", 
      "mo = 0.265 * pq.kg", 
      "vo = 0.0 * (pq.m / pq.s)", 
      "v = (mp * vp + mo * vo) / (mp + mo)", 
      "print v"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "34.5393421053 mi/h"
       ]
      }
     ], 
     "prompt_number": 8
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Slide 17", 
      "import quantities as pq", 
      "mp = 0.115 * pq.kg", 
      "vp = 114.13 * (pq.mi / pq.h)", 
      "mo = 0.265 * pq.kg", 
      "vo = 0.0 * (pq.m / pq.s)", 
      "v = (mp * vp + mo * vo) / (mp + mo)", 
      "print v.rescale(pq.km / pq.h)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "55.5856829811 km/h"
       ]
      }
     ], 
     "prompt_number": 9
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Slide 18", 
      "import quantities as pq", 
      "mp = 6.0 * pq.ounce", 
      "vp = 114.13 * (pq.mi / pq.h)", 
      "mo = 0.265 * pq.kg", 
      "vo = 0.0 * (pq.m / pq.s)", 
      "v = (mp * vp + mo * vo) / (mp + mo)", 
      "print v.rescale(pq.km / pq.h)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "71.8057931081 km/h"
       ]
      }
     ], 
     "prompt_number": 10
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Slide 20", 
      "import quantities as pq", 
      "mp = 6.0 * pq.ounce", 
      "vp = 114.13 * (pq.mi / pq.h)", 
      "mo = 0.265 * pq.kg", 
      "vo = 0.0", 
      "v = (mp * vp + mo * vo) / (mp + mo)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "ename": "ValueError", 
       "evalue": "Unable to convert between units of \"kg\" and \"oz*mi/h\"", 
       "output_type": "pyerr", 
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)", 
        "\u001b[0;32m/Users/Trevor/Programming/git/pyconca2012/<ipython-input-11-4df6aa16c07c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.265\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mpq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mvo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmp\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mvp\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmo\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mvo\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmp\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", 
        "\u001b[0;32m/Library/Frameworks/EPD64.framework/Versions/7.2/lib/python2.7/site-packages/quantities/quantity.pyc\u001b[0m in \u001b[0;36mg\u001b[0;34m(self, other, *args)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mQuantity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dimensionality\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dimensionality\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m             \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrescale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 
        "\u001b[0;32m/Library/Frameworks/EPD64.framework/Versions/7.2/lib/python2.7/site-packages/quantities/quantity.pyc\u001b[0m in \u001b[0;36mrescale\u001b[0;34m(self, units)\u001b[0m\n\u001b[1;32m    194\u001b[0m             raise ValueError(\n\u001b[1;32m    195\u001b[0m                 \u001b[0;34m'Unable to convert between units of \"%s\" and \"%s\"'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m                 \u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfrom_u\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dimensionality\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_u\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dimensionality\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    197\u001b[0m             )\n\u001b[1;32m    198\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mQuantity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcf\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagnitude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_u\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 
        "\u001b[0;31mValueError\u001b[0m: Unable to convert between units of \"kg\" and \"oz*mi/h\""
       ]
      }
     ], 
     "prompt_number": 11
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Slides 24-25", 
      "import quantities as pq", 
      "bags = pq.UnitQuantity('milk bags', pq.L * (4. / 3.))", 
      "person = pq.UnitQuantity('person', pq.dimensionless * 1)", 
      "need = 200 * (pq.mL / person)", 
      "need *= 250 * person", 
      "print need", 
      "print need.rescale(pq.L)", 
      "print need.rescale(bags)", 
      "import numpy as np", 
      "print np.ceil(need.rescale(bags))"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "50000.0 mL", 
        "50.0 L", 
        "37.5 milk bags", 
        "38.0 milk bags"
       ]
      }
     ], 
     "prompt_number": 15
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Slide 34", 
      "import numpy as np", 
      "import quantities as pq", 
      "", 
      "def load_spikes(filename):", 
      "    # Dummy implementation: just make a spike train", 
      "    spikes = np.random.uniform(0, 20 * pq.s, size=20 * pq.s * 10 * pq.Hz)", 
      "    spikes = np.append(spikes, np.random.normal(11.0 * pq.s, size=10 * pq.Hz * 5))", 
      "    return np.sort(spikes)", 
      "", 
      "def time_slice(spikes, tstart, tend):", 
      "    return spikes[np.logical_and(tstart <= spikes, spikes < tend)]", 
      "", 
      "def raster_plot(spikes):", 
      "    plt.scatter(spikes, np.zeros(spikes.shape), color='k', marker='|')", 
      "    plt.xlim(10.5 * pq.s, 13.0 * pq.s)", 
      "    plt.xlabel(\"Time in \" + spikes.dimensionality.string)", 
      "    plt.tight_layout()", 
      "", 
      "spikes = load_spikes('spikes.csv') * pq.s", 
      "event = 11 * pq.s", 
      "window = (-0.5, 2) * pq.s", 
      "perievent = time_slice(spikes, *(event + window))", 
      "raster_plot(perievent)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": 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      }
     ], 
     "prompt_number": 28
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Slide 35", 
      "import numpy as np", 
      "import quantities as pq", 
      "", 
      "def load_spikes(filename):", 
      "    # Dummy implementation: just make a spike train", 
      "    spikes = np.random.uniform(0, 20 * pq.s, size=20 * pq.s * 10 * pq.Hz)", 
      "    spikes = np.append(spikes, np.random.normal(11.0 * pq.s, size=10 * pq.Hz * 5))", 
      "    return np.sort(spikes)", 
      "", 
      "def time_slice(spikes, tstart, tend):", 
      "    return spikes[np.logical_and(tstart <= spikes, spikes < tend)]", 
      "", 
      "def raster_plot(spikes):", 
      "    plt.scatter(spikes, np.zeros(spikes.shape), color='k', marker='|')", 
      "    plt.xlim(10500 * pq.ms, 13000 * pq.ms)  # This also needs changing", 
      "    plt.xlabel(\"Time in \" + spikes.dimensionality.string)", 
      "    plt.tight_layout()", 
      "", 
      "spikes = (load_spikes('spikes.csv') * pq.s).rescale(pq.ms)", 
      "event = 11 * pq.s", 
      "window = (-0.5, 2) * pq.s", 
      "perievent = time_slice(spikes, *(event + window))", 
      "raster_plot(perievent)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": 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      }
     ], 
     "prompt_number": 42
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Slide 36", 
      "import numpy as np", 
      "import quantities as pq", 
      "", 
      "def load_spikes(filename):", 
      "    # Dummy implementation: just make a spike train", 
      "    spikes = np.random.uniform(0, 20 * pq.s, size=20 * pq.s * 10 * pq.Hz)", 
      "    spikes = np.append(spikes, np.random.normal(11.0 * pq.s, size=10 * pq.Hz * 5))", 
      "    return np.sort(spikes)", 
      "", 
      "def bin_spikes(spikes, binsize, tstart, tend):", 
      "    binsize.units = tstart.units", 
      "    bins = np.arange(", 
      "      tstart, tend + binsize, binsize)", 
      "    return np.histogram(spikes, bins=bins)[0]", 
      "", 
      "def binned_plot(bins):", 
      "    plt.bar(range(len(bins)), bins)", 
      "    plt.xlabel(\"Time bin\")", 
      "    plt.xlim(0, bins.shape[0])", 
      "", 
      "spikes = load_spikes('spikes.csv') * pq.s", 
      "event = 11 * pq.s", 
      "window = (-0.5, 2) * pq.s", 
      "binned = bin_spikes(spikes, 20 * pq.ms, *(event + window))", 
      "binned_plot(binned)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEKCAYAAADdBdT9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nABYivAHAQoQ3AFiI8AYACxHeAGCh/w/krqLJFisItQAAAABJRU5ErkJggg==\n"
      }
     ], 
     "prompt_number": 53
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [], 
     "language": "python", 
     "outputs": []
    }
   ]
  }
 ]
}