XKCDify plots.ipynb

{
 "metadata": {
  "name": "XKCDify plots"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "XKCD plot generator\n",
      "-------------------\n",
      "Author: Jake Vanderplas\n",
      "\n",
      "This is a script that will take any matplotlib line diagram, and convert it\n",
      "to an XKCD-style plot.  It will work for plots with line & text elements,\n",
      "including axes labels and titles (but not axes tick labels).\n",
      "\n",
      "The idea for this comes from work by Damon McDougall\n",
      "  http://www.mail-archive.com/matplotlib-users@lists.sourceforge.net/msg25499.html\n",
      "\"\"\"\n",
      "import numpy as np\n",
      "import pylab as pl\n",
      "from scipy import interpolate, signal\n",
      "import matplotlib.font_manager as fm\n",
      "\n",
      "\n",
      "# We need a special font for the code below.  It can be downloaded this way:\n",
      "import os\n",
      "import urllib2\n",
      "if not os.path.exists('Humor-Sans.ttf'):\n",
      "    fhandle = urllib2.urlopen('http://antiyawn.com/uploads/Humor-Sans.ttf')\n",
      "    open('Humor-Sans.ttf', 'wb').write(fhandle.read())\n",
      "\n",
      "    \n",
      "def xkcd_line(x, y, xlim=None, ylim=None,\n",
      "              mag=1.0, f1=30, f2=0.05, f3=15):\n",
      "    \"\"\"\n",
      "    Mimic a hand-drawn line from (x, y) data\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : array_like\n",
      "        arrays to be modified\n",
      "    xlim, ylim : data range\n",
      "        the assumed plot range for the modification.  If not specified,\n",
      "        they will be guessed from the  data\n",
      "    mag : float\n",
      "        magnitude of distortions\n",
      "    f1, f2, f3 : int, float, int\n",
      "        filtering parameters.  f1 gives the size of the window, f2 gives\n",
      "        the high-frequency cutoff, f3 gives the size of the filter\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    x, y : ndarrays\n",
      "        The modified lines\n",
      "    \"\"\"\n",
      "    x = np.asarray(x)\n",
      "    y = np.asarray(y)\n",
      "    \n",
      "    # get limits for rescaling\n",
      "    if xlim is None:\n",
      "        xlim = (x.min(), x.max())\n",
      "    if ylim is None:\n",
      "        ylim = (y.min(), y.max())\n",
      "\n",
      "    if xlim[1] == xlim[0]:\n",
      "        xlim = ylim\n",
      "        \n",
      "    if ylim[1] == ylim[0]:\n",
      "        ylim = xlim\n",
      "\n",
      "    # scale the data\n",
      "    x_scaled = (x - xlim[0]) * 1. / (xlim[1] - xlim[0])\n",
      "    y_scaled = (y - ylim[0]) * 1. / (ylim[1] - ylim[0])\n",
      "\n",
      "    # compute the total distance along the path\n",
      "    dx = x_scaled[1:] - x_scaled[:-1]\n",
      "    dy = y_scaled[1:] - y_scaled[:-1]\n",
      "    dist_tot = np.sum(np.sqrt(dx * dx + dy * dy))\n",
      "\n",
      "    # number of interpolated points is proportional to the distance\n",
      "    Nu = int(200 * dist_tot)\n",
      "    u = np.arange(-1, Nu + 1) * 1. / (Nu - 1)\n",
      "\n",
      "    # interpolate curve at sampled points\n",
      "    k = min(3, len(x) - 1)\n",
      "    res = interpolate.splprep([x_scaled, y_scaled], s=0, k=k)\n",
      "    x_int, y_int = interpolate.splev(u, res[0]) \n",
      "\n",
      "    # we'll perturb perpendicular to the drawn line\n",
      "    dx = x_int[2:] - x_int[:-2]\n",
      "    dy = y_int[2:] - y_int[:-2]\n",
      "    dist = np.sqrt(dx * dx + dy * dy)\n",
      "\n",
      "    # create a filtered perturbation\n",
      "    coeffs = mag * np.random.normal(0, 0.01, len(x_int) - 2)\n",
      "    b = signal.firwin(f1, f2 * dist_tot, window=('kaiser', f3))\n",
      "    response = signal.lfilter(b, 1, coeffs)\n",
      "\n",
      "    x_int[1:-1] += response * dy / dist\n",
      "    y_int[1:-1] += response * dx / dist\n",
      "\n",
      "    # un-scale data\n",
      "    x_int = x_int[1:-1] * (xlim[1] - xlim[0]) + xlim[0]\n",
      "    y_int = y_int[1:-1] * (ylim[1] - ylim[0]) + ylim[0]\n",
      "    \n",
      "    return x_int, y_int\n",
      "\n",
      "\n",
      "def XKCDify(ax, mag=1.0,\n",
      "            f1=50, f2=0.01, f3=15,\n",
      "            bgcolor='w',\n",
      "            xaxis_loc=None,\n",
      "            yaxis_loc=None,\n",
      "            xaxis_arrow='+',\n",
      "            yaxis_arrow='+',\n",
      "            ax_extend=0.1,\n",
      "            expand_axes=False):\n",
      "    \"\"\"Make axis look hand-drawn\n",
      "\n",
      "    This adjusts all lines, text, legends, and axes in the figure to look\n",
      "    like xkcd plots.  Other plot elements are not modified.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    ax : Axes instance\n",
      "        the axes to be modified.\n",
      "    mag : float\n",
      "        the magnitude of the distortion\n",
      "    f1, f2, f3 : int, float, int\n",
      "        filtering parameters.  f1 gives the size of the window, f2 gives\n",
      "        the high-frequency cutoff, f3 gives the size of the filter\n",
      "    xaxis_loc, yaxis_log : float\n",
      "        The locations to draw the x and y axes.  If not specified, they\n",
      "        will be drawn from the bottom left of the plot\n",
      "    xaxis_arrow, yaxis_arrow : str\n",
      "        where to draw arrows on the x/y axes.  Options are '+', '-', '+-', or ''\n",
      "    ax_extend : float\n",
      "        How far (fractionally) to extend the drawn axes beyond the original\n",
      "        axes limits\n",
      "    expand_axes : bool\n",
      "        if True, then expand axes to fill the figure (useful if there is only\n",
      "        a single axes in the figure)\n",
      "    \"\"\"\n",
      "    # Get axes aspect\n",
      "    ext = ax.get_window_extent().extents\n",
      "    aspect = (ext[3] - ext[1]) / (ext[2] - ext[0])\n",
      "\n",
      "    xlim = ax.get_xlim()\n",
      "    ylim = ax.get_ylim()\n",
      "\n",
      "    xspan = xlim[1] - xlim[0]\n",
      "    yspan = ylim[1] - xlim[0]\n",
      "\n",
      "    xax_lim = (xlim[0] - ax_extend * xspan,\n",
      "               xlim[1] + ax_extend * xspan)\n",
      "    yax_lim = (ylim[0] - ax_extend * yspan,\n",
      "               ylim[1] + ax_extend * yspan)\n",
      "\n",
      "    if xaxis_loc is None:\n",
      "        xaxis_loc = ylim[0]\n",
      "\n",
      "    if yaxis_loc is None:\n",
      "        yaxis_loc = xlim[0]\n",
      "\n",
      "    # Draw axes\n",
      "    xaxis = pl.Line2D([xax_lim[0], xax_lim[1]], [xaxis_loc, xaxis_loc],\n",
      "                      linestyle='-', color='k')\n",
      "    yaxis = pl.Line2D([yaxis_loc, yaxis_loc], [yax_lim[0], yax_lim[1]],\n",
      "                      linestyle='-', color='k')\n",
      "\n",
      "    # Label axes3, 0.5, 'hello', fontsize=14)\n",
      "    ax.text(xax_lim[1], xaxis_loc - 0.02 * yspan, ax.get_xlabel(),\n",
      "            fontsize=14, ha='right', va='top', rotation=12)\n",
      "    ax.text(yaxis_loc - 0.02 * xspan, yax_lim[1], ax.get_ylabel(),\n",
      "            fontsize=14, ha='right', va='top', rotation=78)\n",
      "    ax.set_xlabel('')\n",
      "    ax.set_ylabel('')\n",
      "\n",
      "    # Add title\n",
      "    ax.text(0.5 * (xax_lim[1] + xax_lim[0]), yax_lim[1],\n",
      "            ax.get_title(),\n",
      "            ha='center', va='bottom', fontsize=16)\n",
      "    ax.set_title('')\n",
      "\n",
      "    Nlines = len(ax.lines)\n",
      "    lines = [xaxis, yaxis] + [ax.lines.pop(0) for i in range(Nlines)]\n",
      "\n",
      "    for line in lines:\n",
      "        x, y = line.get_data()\n",
      "\n",
      "        x_int, y_int = xkcd_line(x, y, xlim, ylim,\n",
      "                                 mag, f1, f2, f3)\n",
      "\n",
      "        # create foreground and background line\n",
      "        lw = line.get_linewidth()\n",
      "        line.set_linewidth(2 * lw)\n",
      "        line.set_data(x_int, y_int)\n",
      "\n",
      "        # don't add background line for axes\n",
      "        if (line is not xaxis) and (line is not yaxis):\n",
      "            line_bg = pl.Line2D(x_int, y_int, color=bgcolor,\n",
      "                                linewidth=8 * lw)\n",
      "\n",
      "            ax.add_line(line_bg)\n",
      "        ax.add_line(line)\n",
      "\n",
      "    # Draw arrow-heads at the end of axes lines\n",
      "    arr1 = 0.03 * np.array([-1, 0, -1])\n",
      "    arr2 = 0.02 * np.array([-1, 0, 1])\n",
      "\n",
      "    arr1[::2] += np.random.normal(0, 0.005, 2)\n",
      "    arr2[::2] += np.random.normal(0, 0.005, 2)\n",
      "\n",
      "    x, y = xaxis.get_data()\n",
      "    if '+' in str(xaxis_arrow):\n",
      "        ax.plot(x[-1] + arr1 * xspan * aspect,\n",
      "                y[-1] + arr2 * yspan,\n",
      "                color='k', lw=2)\n",
      "    if '-' in str(xaxis_arrow):\n",
      "        ax.plot(x[0] - arr1 * xspan * aspect,\n",
      "                y[0] - arr2 * yspan,\n",
      "                color='k', lw=2)\n",
      "\n",
      "    x, y = yaxis.get_data()\n",
      "    if '+' in str(yaxis_arrow):\n",
      "        ax.plot(x[-1] + arr2 * xspan * aspect,\n",
      "                y[-1] + arr1 * yspan,\n",
      "                color='k', lw=2)\n",
      "    if '-' in str(yaxis_arrow):\n",
      "        ax.plot(x[0] - arr2 * xspan * aspect,\n",
      "                y[0] - arr1 * yspan,\n",
      "                color='k', lw=2)\n",
      "\n",
      "    # Change all the fonts to humor-sans.\n",
      "    prop = fm.FontProperties(fname='Humor-Sans.ttf', size=16)\n",
      "    for text in ax.texts:\n",
      "        text.set_fontproperties(prop)\n",
      "    \n",
      "    # modify legend\n",
      "    leg = ax.get_legend()\n",
      "    if leg is not None:\n",
      "        leg.set_frame_on(False)\n",
      "        \n",
      "        for child in leg.get_children():\n",
      "            if isinstance(child, pl.Line2D):\n",
      "                x, y = child.get_data()\n",
      "                child.set_data(xkcd_line(x, y, mag=10, f1=100, f2=0.001))\n",
      "                child.set_linewidth(2 * child.get_linewidth())\n",
      "            if isinstance(child, pl.Text):\n",
      "                child.set_fontproperties(prop)\n",
      "    \n",
      "    # Set the axis limits\n",
      "    ax.set_xlim(xax_lim[0] - 0.1 * xspan,\n",
      "                xax_lim[1] + 0.1 * xspan)\n",
      "    ax.set_ylim(yax_lim[0] - 0.1 * yspan,\n",
      "                yax_lim[1] + 0.1 * yspan)\n",
      "\n",
      "    # adjust the axes\n",
      "    ax.set_xticks([])\n",
      "    ax.set_yticks([])      \n",
      "\n",
      "    if expand_axes:\n",
      "        ax.figure.set_facecolor(bgcolor)\n",
      "        ax.set_axis_off()\n",
      "        ax.set_position([0, 0, 1, 1])\n",
      "    \n",
      "    return ax"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import datetime\n",
      "from pandas.io.data import DataReader"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "goog = DataReader(\"GOOG\", \"yahoo\", datetime(2000, 1, 1), datetime(2012, 1, 1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = pylab.axes()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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M5yfazZTFnXviRyWzF//fvn37VF1dnYnRMi6Zvbh586YOHTqkTZs2SbJ7y2wye3Hs2DF5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\n"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax.set_title(\"Google stock price\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.text.Text at 0x1104ebf10>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax.plot(goog)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1104e8b50>,\n",
        " <matplotlib.lines.Line2D at 0x1104ee810>,\n",
        " <matplotlib.lines.Line2D at 0x1104eef50>,\n",
        " <matplotlib.lines.Line2D at 0x1104eeed0>,\n",
        " <matplotlib.lines.Line2D at 0x1104ee3d0>,\n",
        " <matplotlib.lines.Line2D at 0x1104f24d0>]"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "XKCDify(ax)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1104e7710>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "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": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "XKCDify(ax)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "Invalid cutoff frequency: frequencies must be greater than 0 and less than nyq.",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-15-40b160d070e5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mXKCDify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m<ipython-input-1-0c240e8782e8>\u001b[0m in \u001b[0;36mXKCDify\u001b[0;34m(ax, mag, f1, f2, f3, bgcolor, xaxis_loc, yaxis_loc, xaxis_arrow, yaxis_arrow, ax_extend, expand_axes)\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m         x_int, y_int = xkcd_line(x, y, xlim, ylim,\n\u001b[0;32m--> 186\u001b[0;31m                                  mag, f1, f2, f3)\n\u001b[0m\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m         \u001b[0;31m# create foreground and background line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m<ipython-input-1-0c240e8782e8>\u001b[0m in \u001b[0;36mxkcd_line\u001b[0;34m(x, y, xlim, ylim, mag, f1, f2, f3)\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;31m# create a filtered perturbation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m     \u001b[0mcoeffs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmag\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_int\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfirwin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdist_tot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwindow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'kaiser'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     91\u001b[0m     \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoeffs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/scipy/signal/fir_filter_design.py\u001b[0m in \u001b[0;36mfirwin\u001b[0;34m(numtaps, cutoff, width, window, pass_zero, scale, nyq)\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"At least one cutoff frequency must be given.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcutoff\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mcutoff\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m         raise ValueError(\"Invalid cutoff frequency: frequencies must be \"\n\u001b[0m\u001b[1;32m    254\u001b[0m                          \"greater than 0 and less than nyq.\")\n\u001b[1;32m    255\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcutoff\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mValueError\u001b[0m: Invalid cutoff frequency: frequencies must be greater than 0 and less than nyq."
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "XKCDify(ax, xaxis_loc=0.0, yaxis_loc=1.0,\n",
      "        xaxis_arrow='+-', yaxis_arrow='+-',\n",
      "        expand_axes=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1104e7710>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "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": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "XKCDify(ax, xaxis_loc=0.0, yaxis_loc=1.0,\n",
      "        xaxis_arrow='+-', yaxis_arrow='+-',\n",
      "        expand_axes=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1104e7710>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "[]"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1104e7710>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "goog.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x110629d10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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AgH379mHBggXc6j179uzB3Llz3VZvV0FmYjYmahN5WsgtIDRBKMo5/5xAenq6YE3MAQMG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\nAs4wDMNTrIAzDMPwFCvgDMMwPPUvRz6aTWcqTJYAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "XKCDify(goog['Adj Close'].plot(), xaxis_loc=0.0, yaxis_loc=1.0,\n",
      "        xaxis_arrow='+-', yaxis_arrow='+-',\n",
      "        expand_axes=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "Invalid cutoff frequency: frequencies must be greater than 0 and less than nyq.",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-24-755e9d42e9c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m XKCDify(goog['Adj Close'].plot(), xaxis_loc=0.0, yaxis_loc=1.0,\n\u001b[1;32m      2\u001b[0m         \u001b[0mxaxis_arrow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'+-'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myaxis_arrow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'+-'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m         expand_axes=True)\n\u001b[0m",
        "\u001b[0;32m<ipython-input-1-0c240e8782e8>\u001b[0m in \u001b[0;36mXKCDify\u001b[0;34m(ax, mag, f1, f2, f3, bgcolor, xaxis_loc, yaxis_loc, xaxis_arrow, yaxis_arrow, ax_extend, expand_axes)\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m         x_int, y_int = xkcd_line(x, y, xlim, ylim,\n\u001b[0;32m--> 186\u001b[0;31m                                  mag, f1, f2, f3)\n\u001b[0m\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m         \u001b[0;31m# create foreground and background line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m<ipython-input-1-0c240e8782e8>\u001b[0m in \u001b[0;36mxkcd_line\u001b[0;34m(x, y, xlim, ylim, mag, f1, f2, f3)\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;31m# create a filtered perturbation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m     \u001b[0mcoeffs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmag\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_int\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfirwin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdist_tot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwindow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'kaiser'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     91\u001b[0m     \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoeffs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/scipy/signal/fir_filter_design.py\u001b[0m in \u001b[0;36mfirwin\u001b[0;34m(numtaps, cutoff, width, window, pass_zero, scale, nyq)\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"At least one cutoff frequency must be given.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcutoff\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mcutoff\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m         raise ValueError(\"Invalid cutoff frequency: frequencies must be \"\n\u001b[0m\u001b[1;32m    254\u001b[0m                          \"greater than 0 and less than nyq.\")\n\u001b[1;32m    255\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcutoff\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mValueError\u001b[0m: Invalid cutoff frequency: frequencies must be greater than 0 and less than nyq."
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}