matplotlib_contours_shapely.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:e589a116cadb3d25bc111e9579db5b4fbb1c941142a7429caf6fd461064570cf"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "x = [1,2,3,4]\n",
      "y = [1,2,3,4]\n",
      "m = [[15,14,13,12],[14,12,10,8],[13,10,7,4],[12,8,4,0]]\n",
      "cs = plt.contour(x,y,m)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAa2x3bkiyAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11cd92990>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cm = plt.contourf(x,y,m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmowAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11cb37910>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from shapely.geometry import LineString\n",
      "\n",
      "def plot_line(ax, ob):\n",
      "    x, y = ob.xy\n",
      "    ax.plot(x, y, color='b')\n",
      "\n",
      "lines = []\n",
      "for i in range(len(cs.collections)):\n",
      "    p = cs.collections[i].get_paths()[0]\n",
      "    v = p.vertices\n",
      "    x = v[:,0]\n",
      "    y = v[:,1]\n",
      "    lines.append(LineString([(i[0], i[1]) for i in zip(x,y)]))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt \n",
      "\n",
      "def plot_line(ax, ob):\n",
      "    x, y = ob.xy\n",
      "    ax.plot(x, y, color='b')\n",
      "\n",
      "fig = plt.figure() \n",
      "ax = fig.gca() \n",
      "for line in lines:\n",
      "    plot_line(ax, line)\n",
      "\n",
      "    \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11ce91150>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from shapely.geometry import Polygon\n",
      "polys = []\n",
      "for i in range(len(cm.collections)):\n",
      "    p = cm.collections[i].get_paths()[0]\n",
      "    v = p.vertices\n",
      "    x = v[:,0]\n",
      "    y = v[:,1]\n",
      "    polys.append(Polygon([(i[0], i[1]) for i in zip(x,y)]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt \n",
      "from descartes import PolygonPatch\n",
      "BLUE = '#6699cc'\n",
      "fig = plt.figure() \n",
      "ax = fig.gca() \n",
      "for i in polys:\n",
      "    ax.add_patch(PolygonPatch(i, fc=BLUE, ec=BLUE, alpha=0.5, zorder=2 ))\n",
      "    \n",
      "ax.axis('scaled')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "(1.0, 4.0, 1.0, 4.0)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAQcAAAEACAYAAAC+rrMfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nymJyMAyjLCYHwzDKYnIwDKMs/w9lbguaKJ1ylgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11cfc5110>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "BLUE = '#6699cc'\n",
      "fig = plt.figure() \n",
      "ax = fig.gca() \n",
      "ax.add_patch(PolygonPatch(polys[5], fc=BLUE, ec=BLUE, alpha=0.5, zorder=2 ))\n",
      "    \n",
      "ax.axis('scaled')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "(1.0, 4.0, 1.0, 4.0)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAQcAAAEACAYAAAC+rrMfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMLOeHA5m1pPDwcx6+n9vMNEeyLaxdwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11d16c750>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "polys[5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "svg": [
        "<svg\n",
        "            preserveAspectRatio=\"xMinYMin meet\"\n",
        "            viewBox=\"0.000262820465334 0.000262820465334 4.99973717953 4.99973717953\"\n",
        "            width=\"100.0\"\n",
        "            height=\"100.0\"\n",
        "            transform=\"translate(0, 100.0),scale(1, -1)\">\n",
        "            \n",
        "            <g fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" \n",
        "            stroke-width=\"0.0999947435907\" opacity=\"0.6\">\n",
        "            <path d=\"M 4.0,1.0 L 4.0,1.0 L 4.0,1.5 L 3.0,2.0 L 3.0,2.0 L 2.0,3.0 L 2.0,3.0 L 1.5,4.0 L 1.0,4.0 L 1.0,4.0 L 1.33333333333,3.0 L 2.0,2.0 L 2.0,2.0 L 3.0,1.33333333333 L 4.0,1.0 z\" />\n",
        "            </g>\n",
        "            </svg>"
       ],
       "text": [
        "<shapely.geometry.polygon.Polygon at 0x11ce65390>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}