Radiation Model

gistfile1.txt

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Radiation Model"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Import country shapes from file"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import fiona\n",
      "import shapely.geometry\n",
      "countries = {}\n",
      "for country in fiona.open('world_borders/TM_WORLD_BORDERS_SIMPL-0.3.shp'):\n",
      "    country['centroid'] = shapely.geometry.shape({\n",
      "        'type' : \"Point\",\n",
      "        'coordinates' : [country['properties']['LON'] , country['properties']['LAT']],\n",
      "    })\n",
      "    country['properties'] = dict(country['properties'])\n",
      "    country['surface'] = shapely.geometry.shape(country['geometry']).buffer(0)\n",
      "    countries[country['properties']['ISO2']] = country\n",
      "    del country['geometry']\n",
      "    del country['properties']['ISO2']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Computes the radiating parts of the countries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def radiating_countries(source, target):\n",
      "    distance = countries[source]['centroid'].distance(countries[target]['centroid'])\n",
      "    circle = countries[source]['centroid'].buffer(distance)\n",
      "    radiating_countries = []\n",
      "    for country in countries:\n",
      "            intersection = circle.intersection(countries[country]['surface'])\n",
      "            if intersection:\n",
      "                radiating_countries.append((country, intersection))\n",
      "    return radiating_countries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 111
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Compute the effective radiating populations assuming a uniform distribution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def radiating_population(source, target):\n",
      "    radiating_population = 0\n",
      "    for country, intersection in radiating_countries(source, target):\n",
      "        fraction = intersection.area/countries[country]['surface'].area\n",
      "        logging.debug(fraction)\n",
      "        radiating_population += fraction*countries[country]['properties']['POP2005']\n",
      "    return radiating_population"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 116
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Draw a sequence of shapes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def draw_shapes(shapes):\n",
      "    from matplotlib import pyplot\n",
      "    import descartes\n",
      "    fig = pyplot.figure()\n",
      "    ax = fig.add_subplot(111)\n",
      "    for shape in shapes:\n",
      "        if shape.geom_type is 'Polygon':\n",
      "            ax.add_patch(PolygonPatch(shape))\n",
      "        else:\n",
      "            for polygon in shape:\n",
      "                ax.add_patch(PolygonPatch(polygon))\n",
      "    ax.autoscale()\n",
      "    ax.set_aspect('equal')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 124
    }
   ],
   "metadata": {}
  }
 ]
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