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@robcat
Created May 18, 2015 21:10
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Radiation Model with examples
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Radiation Model"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Function definition"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import countries from pickled dictionary"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import shapely.geometry\n",
"import pickle\n",
"countries = pickle.load(open('countries.pickle', 'rb'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 191
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute the distance between two centroids"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def centroid_distance(source, target):\n",
" return countries[source]['centroid'].distance(countries[target]['centroid'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 195
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Computes the radiating parts of the countries"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def radiating_countries(center, radius):\n",
" \"\"\"Returns the intersections with a circle centered at source and with radius to target\"\"\"\n",
" circle = center.buffer(radius) # construct the circle enlarging the center by the radius\n",
" radiating_countries = {} # initialize the set of countries in the circle\n",
" for country in countries: # cycle over all the known countries\n",
" intersection = circle.intersection(countries[country]['surface']) # intersect the country with the circle\n",
" if intersection:\n",
" radiating_countries[country] = intersection # add the intersection to the returned dictionary\n",
" return radiating_countries"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 200
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute the effective radiating populations assuming a uniform distribution"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def radiating_population(center, radius):\n",
" radiating_population = 0\n",
" for country, intersection in radiating_countries(center, radius).items():\n",
" fraction = intersection.area/countries[country]['surface'].area # compute the fraction of the intersecting area \n",
" radiating_population += fraction*countries[country]['properties']['POP2013'] # add the population proportional to the area\n",
" return radiating_population"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 185
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Draw a sequence of shapes"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def draw_shapes(shapes):\n",
" \"\"\"Plot a sequence of 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": 186
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The world"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"draw_shapes([country['surface'] for country in countries.values()])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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+MFP7aMqN9JFTHQ2G1lSU1yldN2j7Teeo1yN3TplVbb4faQwVpTYNhnI0GKpQ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\n+jo6OjrXELrS19HR0bmG+P/fdgSnfTS8LwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7feb613037b8>"
]
}
],
"prompt_number": 187
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The geographical area centered on the centroid of Italy that influences France"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"it_to_fr = radiating_countries(countries['IT']['centroid'], centroid_distance('IT', 'FR'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 209
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"draw_shapes(it_to_fr.values())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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kQ0UnygaQDVINPLIDlMFMcbgd6mjwQQDP+JhvV2FATYIfoNydu4D8C0K0ghAd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\n",
"text": [
"<matplotlib.figure.Figure at 0x7feb626745f8>"
]
}
],
"prompt_number": 210
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"List all the radiating populations"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for country, intersection in sorted(it_to_fr.items()):\n",
" fraction = intersection.area/countries[country]['surface'].area\n",
" print(\n",
" country,\n",
" str(int(fraction*100))+\"%\",\n",
" int(fraction*countries[country]['properties']['POP2013']),\n",
" sep='\\t'\n",
" )"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"AL\t100%\t2897366\n",
"AT\t100%\t8479823\n",
"BA\t100%\t3829307\n",
"BE\t33%\t3731377\n",
"BG\t21%\t1591061\n",
"CH\t100%\t8087875\n",
"CZ\t100%\t10514272\n",
"DE\t83%\t67659446\n",
"DZ\t5%\t2039652\n",
"ES\t2%\t1298577\n",
"FR\t46%\t30358444\n",
"GR\t60%\t6664727\n",
"HR\t100%\t4255700\n",
"HU\t98%\t9749701\n",
"IT\t100%\t60233948\n",
"LI\t100%\t36925\n",
"LU\t100%\t543360\n",
"LY\t2%\t158566\n",
"MC\t100%\t37831\n",
"ME\t100%\t621383\n",
"MK\t100%\t2107158\n",
"MT\t100%\t423374\n",
"NL\t1%\t324385\n",
"PL\t33%\t13033400\n",
"RO\t23%\t4619873\n",
"RS\t100%\t7164132\n",
"SI\t100%\t2059953\n",
"SK\t91%\t4967914\n",
"SM\t100%\t31448\n",
"TN\t86%\t9447856\n"
]
}
],
"prompt_number": 190
}
],
"metadata": {}
}
]
}
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