p3trus/matplotlib bug.ipynb

## matplotlib bug.ipynb
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib\n",
      "print matplotlib.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1.2.0\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as pl\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "\n",
      "fig = pl.figure()\n",
      "ax = Axes3D(fig)\n",
      "\n",
      "x = y = z = range(10)\n",
      "dz = [0., 1]*5\n",
      "ax.bar3d(x, y, z, 1, 1, dz)\n",
      "fig.savefig('test.svg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "%x format: a number is required, not numpy.float64",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-2-86103168047f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mdz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0.\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbar3d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'test.svg'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\matplotlib\\figure.pyc\u001b[0m in \u001b[0;36msavefig\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1361\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'edgecolor'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'savefig.edgecolor'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1362\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1363\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\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[0m\u001b[0;32m   1364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1365\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtransparent\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\matplotlib\\backend_bases.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[0;32m   2091\u001b[0m                 \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2092\u001b[0m                 \u001b[0mbbox_inches_restore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2093\u001b[1;33m                 **kwargs)\n\u001b[0m\u001b[0;32m   2094\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2095\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\matplotlib\\backend_bases.pyc\u001b[0m in \u001b[0;36mprint_svg\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1869\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[0mbackends\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackend_svg\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mFigureCanvasSVG\u001b[0m \u001b[1;31m# lazy import\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1870\u001b[0m         \u001b[0msvg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mswitch_backends\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigureCanvasSVG\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1871\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msvg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_svg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\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[0m\u001b[0;32m   1872\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1873\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mprint_svgz\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\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[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\matplotlib\\backends\\backend_svg.pyc\u001b[0m in \u001b[0;36mprint_svg\u001b[1;34m(self, filename, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1101\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1102\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"filename must be a path or a file-like object\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1103\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_print_svg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msvgwriter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfh_to_close\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[0m\u001b[0;32m   1104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1105\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mprint_svgz\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\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[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\matplotlib\\backends\\backend_svg.pyc\u001b[0m in \u001b[0;36m_print_svg\u001b[1;34m(self, filename, svgwriter, fh_to_close, **kwargs)\u001b[0m\n\u001b[0;32m   1137\u001b[0m                     bbox_inches_restore=_bbox_inches_restore)\n\u001b[0;32m   1138\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1139\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1140\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfinalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1141\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\matplotlib\\artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     52\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 54\u001b[1;33m         \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\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[0m\u001b[0;32m     55\u001b[0m         \u001b[0mafter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     56\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\matplotlib\\figure.pyc\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    997\u001b[0m         \u001b[0mdsu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mitemgetter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    998\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 999\u001b[1;33m             \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1000\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1001\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'figure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\mpl_toolkits\\mplot3d\\axes3d.pyc\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    212\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    213\u001b[0m         \u001b[1;31m# Then rest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 214\u001b[1;33m         \u001b[0mAxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    215\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    216\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_axis_position\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\matplotlib\\artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     52\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 54\u001b[1;33m         \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\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[0m\u001b[0;32m     55\u001b[0m         \u001b[0mafter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     56\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\matplotlib\\backends\\backend_svg.pyc\u001b[0m in \u001b[0;36m_get_style\u001b[1;34m(self, gc, rgbFace)\u001b[0m\n\u001b[0;32m    416\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    417\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrgbFace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 418\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mgenerate_css\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_style_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrgbFace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    419\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    420\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_clip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\matplotlib\\backends\\backend_svg.pyc\u001b[0m in \u001b[0;36m_get_style_dict\u001b[1;34m(self, gc, rgbFace)\u001b[0m\n\u001b[0;32m    393\u001b[0m                 \u001b[0mattrib\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34mu'fill'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34mu'none'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    394\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrgbFace\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\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;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 395\u001b[1;33m                 \u001b[0mattrib\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34mu'fill'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrgb2hex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrgbFace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    396\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    397\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_alpha\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\matplotlib\\colors.pyc\u001b[0m in \u001b[0;36mrgb2hex\u001b[1;34m(rgb)\u001b[0m\n\u001b[0;32m    220\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mrgb2hex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrgb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m     \u001b[1;34m'Given an rgb or rgba sequence of 0-1 floats, return the hex string'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[1;34m'#%02x%02x%02x'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m255\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mval\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrgb\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    223\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m \u001b[0mhexColorPattern\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mre\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\A#[a-fA-F0-9]{6}\\Z\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mTypeError\u001b[0m: %x format: a number is required, not numpy.float64"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "C:\\Python27\\lib\\site-packages\\mpl_toolkits\\mplot3d\\axes3d.py:1476: RuntimeWarning: invalid value encountered in divide\n",
        "  for n in normals])\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NZrWFoZsx1hPt6upqqM4M0ATCJya+WgqfXhjsEjxxHnDWFVHouMJVa/d1SZoX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\nafk4UgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5384030>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}