{ "metadata": { "name": "XKCD_plots" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "XKCD plots in Matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook originally appeared as a blog post at [Pythonic Perambulations](http://jakevdp.github.com/blog/2012/10/07/xkcd-style-plots-in-matplotlib/) by Jake Vanderplas." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the problems I've had with typical matplotlib figures is that everything in them is so precise, so perfect. For an example of what I mean, take a look at this figure:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import Image\n", "Image('http://jakevdp.github.com/figures/xkcd_version.png')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "png": 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Azp7llPPAQLXVmIYSJYD27Tkve8MGtdVYNf/+yynyGg2wciWQHmhQBG9vrqno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\neGedOxOdOFHze27dInrpJf4egCgs7CmNUJedO3eSk5MTMcb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"prompt_number": 1, "text": [ "" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes when showing schematic plots, this is the type of figure I want to display. But drawing it by hand is a pain: I'd rather just use matplotlib. The problem is, matplotlib is a bit too precise. Attempting to duplicate this figure in matplotlib leads to something like this:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "Image('http://jakevdp.github.com/figures/mpl_version.png')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "png": 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NIUaM0OOA/soVunbY2QnRpo0QW7YobgRW4CrEzMxMXL16FUIIVK9eHeYGdJ68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\nX38NTJpUsDJAE8MtnIwTTyEyk/P330D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"prompt_number": 2, "text": [ "" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It just doesn't have the same effect. Matplotlib is great for scientific plots, but sometimes you don't want to be so precise.\n", "\n", "This subject has recently come up on the matplotlib mailing list, and started some interesting discussions.\n", "As near as I can tell, this started with a thread on a\n", "[mathematica list](http://mathematica.stackexchange.com/questions/11350/xkcd-style-graphs)\n", "which prompted a thread on the [matplotlib list](http://matplotlib.1069221.n5.nabble.com/XKCD-style-graphs-td39226.html)\n", "wondering if the same could be done in matplotlib.\n", "\n", "Damon McDougall offered a quick\n", "[solution](http://www.mail-archive.com/matplotlib-users@lists.sourceforge.net/msg25499.html)\n", "which was improved by Fernando Perez in [this notebook](http://nbviewer.ipython.org/3835181/), and\n", "within a few days there was a [matplotlib pull request](https://github.com/matplotlib/matplotlib/pull/1329) offering a very general\n", "way to create sketch-style plots in matplotlib. Only a few days from a cool idea to a\n", "working implementation: this is one of the most incredible aspects of package development on github.\n", "\n", "The pull request looks really nice, but will likely not be included in a released version of\n", "matplotlib until at least version 1.3. In the mean-time, I wanted a way to play around with\n", "these types of plots in a way that is compatible with the current release of matplotlib. To do that,\n", "I created the following code:" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "The Code: XKCDify" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "XKCDify will take a matplotlib ``Axes`` instance, and modify the plot elements in-place to make\n", "them look hand-drawn.\n", "First off, we'll need to make sure we have the Humor Sans font.\n", "It can be downloaded using the command below.\n", "\n", "Next we'll create a function ``xkcd_line`` to add jitter to lines. We want this to be very general, so\n", "we'll normalize the size of the lines, and use a low-pass filter to add correlated noise, perpendicular\n", "to the direction of the line. There are a few parameters for this filter that can be tweaked to\n", "customize the appearance of the jitter.\n", "\n", "Finally, we'll create a function which accepts a matplotlib axis, and calls ``xkcd_line`` on\n", "all lines in the axis. Additionally, we'll switch the font of all text in the axes, and add\n", "some background lines for a nice effect where lines cross. We'll also draw axes, and move the\n", "axes labels and titles to the appropriate location." ] }, { "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": 3 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Testing it Out" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's test this out with a simple plot. We'll plot two curves, add some labels,\n", "and then call ``XKCDify`` on the axis. I think the results are pretty nice!" ] }, { "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": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "np.random.seed(0)\n", "\n", "ax = pylab.axes()\n", "\n", "x = np.linspace(0, 10, 100)\n", "ax.plot(x, np.sin(x) * np.exp(-0.1 * (x - 5) ** 2), 'b', lw=1, label='damped sine')\n", "ax.plot(x, -np.cos(x) * np.exp(-0.1 * (x - 5) ** 2), 'r', lw=1, label='damped cosine')\n", "\n", "ax.set_title('check it out!')\n", "ax.set_xlabel('x label')\n", "ax.set_ylabel('y label')\n", "\n", "ax.legend(loc='lower right')\n", "\n", "ax.set_xlim(0, 10)\n", "ax.set_ylim(-1.0, 1.0)\n", "\n", "#XKCDify the axes -- this operates in-place\n", "XKCDify(ax, xaxis_loc=0.0, yaxis_loc=1.0,\n", " xaxis_arrow='+-', yaxis_arrow='+-',\n", " expand_axes=True)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 5, "text": [ "" ] }, { "output_type": "display_data", "png": 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aSE5W1pYCQHo630tER7MnecIE6485Zgy7VA0G4L33gN27rT+mwH4QwqcS5G7v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\n2ZGxWo4Tn0pwU6d6dOwIBATIedpr19b89bOzgQ0b5PEjtyq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ZU+lQGHti/PLLL2Rubk6u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\nYII=\n", "text": [ "" ] } ], "prompt_number": 5 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Duplicating an XKCD Comic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's see if we can use this to replicated an XKCD comic in matplotlib.\n", "This is a good one:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "Image('http://imgs.xkcd.com/comics/front_door.png')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "png": 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ogSZNmtDJy5YtYyP7Q4cOzTACCoStoaHh5cuXQTcnT56kUUHslF6rmoE2CQlB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"prompt_number": 6, "text": [ "" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the new ``XKCDify`` function, this is relatively easy to replicate. The results\n", "are not exactly identical, but I think it definitely gets the point across!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Some helper functions\n", "def norm(x, x0, sigma):\n", " return np.exp(-0.5 * (x - x0) ** 2 / sigma ** 2)\n", "\n", "def sigmoid(x, x0, alpha):\n", " return 1. / (1. + np.exp(- (x - x0) / alpha))\n", " \n", "# define the curves\n", "x = np.linspace(0, 1, 100)\n", "y1 = np.sqrt(norm(x, 0.7, 0.05)) + 0.2 * (1.5 - sigmoid(x, 0.8, 0.05))\n", "\n", "y2 = 0.2 * norm(x, 0.5, 0.2) + np.sqrt(norm(x, 0.6, 0.05)) + 0.1 * (1 - sigmoid(x, 0.75, 0.05))\n", "\n", "y3 = 0.05 + 1.4 * norm(x, 0.85, 0.08)\n", "y3[x > 0.85] = 0.05 + 1.4 * norm(x[x > 0.85], 0.85, 0.3)\n", "\n", "# draw the curves\n", "ax = pl.axes()\n", "ax.plot(x, y1, c='gray')\n", "ax.plot(x, y2, c='blue')\n", "ax.plot(x, y3, c='red')\n", "\n", "ax.text(0.3, -0.1, \"Yard\")\n", "ax.text(0.5, -0.1, \"Steps\")\n", "ax.text(0.7, -0.1, \"Door\")\n", "ax.text(0.9, -0.1, \"Inside\")\n", "\n", "ax.text(0.05, 1.1, \"fear that\\nthere's\\nsomething\\nbehind me\")\n", "ax.plot([0.15, 0.2], [1.0, 0.2], '-k', lw=0.5)\n", "\n", "ax.text(0.25, 0.8, \"forward\\nspeed\")\n", "ax.plot([0.32, 0.35], [0.75, 0.35], '-k', lw=0.5)\n", "\n", "ax.text(0.9, 0.4, \"embarrassment\")\n", "ax.plot([1.0, 0.8], [0.55, 1.05], '-k', lw=0.5)\n", "\n", "ax.set_title(\"Walking back to my\\nfront door at night:\")\n", "\n", "ax.set_xlim(0, 1)\n", "ax.set_ylim(0, 1.5)\n", "\n", "# modify all the axes elements in-place\n", "XKCDify(ax, expand_axes=True)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 7, "text": [ "" ] }, { "output_type": "display_data", "png": 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HOHbsGADgq6++wo4dO9CqVSvMmjULUqkUycnJ+OWXX7B7927Mnj0bP//8c6U2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\nW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"text": [ "" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pretty good for a couple hours's work!\n", "\n", "I think the possibilities here are pretty limitless: this is going to be a hugely\n", "useful and popular feature in matplotlib, especially when the sketch artist PR is mature\n", "and part of the main package. I imagine using this style of plot for schematic figures\n", "in presentations where the normal crisp matplotlib lines look a bit too \"scientific\".\n", "I'm giving a few talks at the end of the month... maybe I'll even use some of\n", "this code there.\n", "\n", "This post was written entirely in an IPython Notebook: the notebook file is available for\n", "download [here](http://jakevdp.github.com/downloads/notebooks/XKCD_plots.ipynb).\n", "For more information on blogging with notebooks in octopress, see my\n", "[previous post](http://jakevdp.github.com/blog/2012/10/04/blogging-with-ipython/)\n", "on the subject." ] } ], "metadata": {} } ] }