aflaxman/mpld3-helpdesk_bbox_inches_tight_tight_layout.ipynb

## mpld3-helpdesk_bbox_inches_tight_tight_layout.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:5a7dbfd8dba085202575bcec751655327810f25b2145ce4f879398fb1bf7aab4"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!date"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Fri Mar 21 11:20:33 PDT 2014\r\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "An exploration of the `bbox_inches='tight'` option in matplotlib's savefig:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "import IPython.display"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Use of `bbox_inches`: a way to select only a portion of an image for saving."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot([3,1,4,1])\n",
      "plt.savefig('t.png', bbox_inches=matplotlib.transforms.Bbox([[0,0],[1,1]]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x2e40f50>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "IPython.display.Image(filename='t.png', embed=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAEgAAABICAYAAABV7bNHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
       "prompt_number": 35,
       "text": [
        "<IPython.core.display.Image at 0x315c390>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Is this ever used? It does not seem useful to me, except with the `bbox_inches='tight'` option. [GitHub searching](https://github.com/search?q=bbox_inches&type=Code&ref=searchresults) corroborates this.\n",
      "\n",
      "Tight option in actions:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#without bbox_inches='tight'\n",
      "\n",
      "plt.subplot(2,2,1)\n",
      "plt.plot([3,1,4,1])\n",
      "plt.savefig('t.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAMAAAACGCAYAAACL3YV2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDe8AHFvzP/CWc/koVHacAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1b82090>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "IPython.display.Image(filename='t.png', embed=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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       "prompt_number": 37,
       "text": [
        "<IPython.core.display.Image at 0x34bfe10>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# with\n",
      "\n",
      "plt.subplot(2,2,1)\n",
      "plt.plot([3,1,4,1])\n",
      "plt.savefig('t.png', bbox_inches='tight')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f817bbdde10>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "IPython.display.Image(filename='t.png', embed=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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       "prompt_number": 39,
       "text": [
        "<IPython.core.display.Image at 0x2e319d0>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And the use-case that started all of this, a figure that over-runs the boundaries:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# without\n",
      "\n",
      "plt.figure(figsize=(3,2))\n",
      "plt.axes([0,0,2,2])\n",
      "plt.plot([3,1,4,1])\n",
      "plt.savefig('t.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Vj8dHPJ7v/uXoBpvLTSeGHxOLxTRr1ix9+eWXTi7DWLmcn5KSEn3wwQeqqanR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\nuxL/Yr3z8FQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x315c590>"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "IPython.display.Image(filename='t.png', embed=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAANgAAACQCAYAAABqK6XsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
       "prompt_number": 41,
       "text": [
        "<IPython.core.display.Image at 0x2e31c10>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# with\n",
      "plt.figure(figsize=(3,2))\n",
      "plt.axes([0,0,2,2])\n",
      "plt.plot([3,1,4,1])\n",
      "plt.savefig('t.png', bbox_inches='tight')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Vj8dHPJ7v/uXoBpvLTSeGHxOLxTRr1ix9+eWXTi7DWLmcn5KSEn3wwQeqqanR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\nuxL/Yr3z8FQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1b82090>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "IPython.display.Image(filename='t.png', embed=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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       "prompt_number": 43,
       "text": [
        "<IPython.core.display.Image at 0x2e48c50>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# How do these go in mpld3?\n",
      "\n",
      "Since `bbox_inches` is part of savefig, we could claim that it is outside the scope of mpld3.  On the other hand, including the tight option in the d3 generating code could be very useful.  Let's see what is happening currently."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import mpld3\n",
      "mpld3.enable_notebook(d3_url='//raw.githubusercontent.com/jakevdp/mpld3/master/mpld3/js/d3.v3.min.js',\n",
      "                      mpld3_url='//mpld3.github.io/js/mpld3.v0.1.js')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(3,2))\n",
      "plt.axes([0,0,2,2])\n",
      "plt.plot([3,1,4,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "[<matplotlib.lines.Line2D at 0x3980f90>]"
       ]
      },
      {
       "html": [
        "\n",
        "<style>\n",
        "\n",
        "</style>\n",
        "\n",
        "<div id=\"fig5359517163689908360340\"></div>\n",
        "<script>\n",
        "function mpld3_load_lib(url, callback){\n",
        "  var s = document.createElement('script');\n",
        "  s.src = url;\n",
        "  s.async = true;\n",
        "  s.onreadystatechange = s.onload = callback;\n",
        "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
        "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
        "}\n",
        "\n",
        "function create_fig5359517163689908360340(){\n",
        "  \n",
        "  mpld3.draw_figure(\"fig5359517163689908360340\", {\"width\": 240.0, \"axes\": [{\"xlim\": [0.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 3.0], \"ylim\": [1.0, 4.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [{\"color\": \"#0000FF\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data01\", \"id\": \"535960297104\"}], \"markers\": [], \"id\": \"535960099792\", \"ydomain\": [1.0, 4.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.0, 0.0, 2.0, 2.0]}], \"data\": {\"data01\": [[0.0, 3.0], [1.0, 1.0], [2.0, 4.0], [3.0, 1.0]]}, \"id\": \"535951716368\", \"toolbar\": [\"reset\", \"move\"], \"height\": 160.0});\n",
        "}\n",
        "\n",
        "if(typeof(mpld3) !== \"undefined\"){\n",
        "   // already loaded: just create the figure\n",
        "   create_fig5359517163689908360340();\n",
        "}else if(typeof define === \"function\" && define.amd){\n",
        "   // require.js is available: use it to load d3/mpld3\n",
        "   require.config({paths: {d3: \"//raw.githubusercontent.com/jakevdp/mpld3/master/mpld3/js/d3.v3.min\"}});\n",
        "   require([\"d3\"], function(d3){\n",
        "      window.d3 = d3;\n",
        "      mpld3_load_lib(\"//mpld3.github.io/js/mpld3.v0.1.js\", create_fig5359517163689908360340);\n",
        "    });\n",
        "}else{\n",
        "    // require.js not available: dynamically load d3 & mpld3\n",
        "    mpld3_load_lib(\"//raw.githubusercontent.com/jakevdp/mpld3/master/mpld3/js/d3.v3.min.js\", function(){\n",
        "        mpld3_load_lib(\"//mpld3.github.io/js/mpld3.v0.1.js\", create_fig5359517163689908360340);})\n",
        "}\n",
        "</script>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x3152110>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Based on some web-searching, it seems that there is a work around, `plt.tight_layout`.  Tutorial [here](http://matplotlib.org/users/tight_layout_guide.html), which I found linked from [here](http://stackoverflow.com/a/16039013).  Unfortunately, it does not work with this example."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(3,2))\n",
      "plt.axes([0,0,2,2])\n",
      "plt.plot([3,1,4,1])\n",
      "plt.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "max() arg is an empty sequence",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-47-02b4fc960294>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m/homes/abie/anaconda/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mtight_layout\u001b[1;34m(pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[0;32m   1253\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1254\u001b[0m     \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1255\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh_pad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mh_pad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw_pad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mw_pad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrect\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrect\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1256\u001b[0m     \u001b[0mdraw_if_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1257\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/homes/abie/anaconda/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mtight_layout\u001b[1;34m(self, renderer, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[0;32m   1603\u001b[0m                                          \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1604\u001b[0m                                          \u001b[0mpad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh_pad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mh_pad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw_pad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mw_pad\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1605\u001b[1;33m                                          rect=rect)\n\u001b[0m\u001b[0;32m   1606\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1607\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots_adjust\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/homes/abie/anaconda/lib/python2.7/site-packages/matplotlib/tight_layout.pyc\u001b[0m in \u001b[0;36mget_tight_layout_figure\u001b[1;34m(fig, axes_list, subplotspec_list, renderer, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[0;32m    323\u001b[0m         \u001b[0msubplots\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    324\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 325\u001b[1;33m     \u001b[0mmax_nrows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnrows_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    326\u001b[0m     \u001b[0mmax_ncols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mncols_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    327\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mValueError\u001b[0m: max() arg is an empty sequence"
       ]
      },
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       "text": [
        "<matplotlib.figure.Figure at 0x39aab90>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}