source-code/gis/make_your_own_island.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make you own island using Shapely"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the required modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "from shapely.geometry import Point, LinearRing, Polygon, LineString\n",
    "import string\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The island"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the island's border as a `LinearRing`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<shapely.geometry.polygon.LinearRing at 0x7fde338ee748>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diameter = 100\n",
    "deviation = 5\n",
    "nr_points = 60\n",
    "x_center = 0.0\n",
    "y_center = 0.0\n",
    "theta = np.linspace(0.0, 2.0*np.pi, nr_points)\n",
    "x = x_center + np.random.normal(diameter, deviation, (len(theta), ))*np.cos(theta)\n",
    "y = y_center + np.random.normal(diameter, deviation, (len(theta), ))*np.sin(theta)\n",
    "country_border = LinearRing([t  for t  in zip(x, y)])\n",
    "country_border"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reuse the code to create the lake border."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"8.03406837181739 -11.524812972390624 43.60426443400736 42.9741666419068\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,19.92454069712555)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.8720852886801472\" points=\"49.073791147883995,10.0 49.593553633871636,14.254145047733108 48.25403319199388,18.90419131108618 45.83363281901507,22.025659415337316 43.30124186437972,25.246060492416454 39.25228238651253,28.008513297379622 35.44214972958939,28.864449854163354 31.1395474352525,29.61198242608814 26.79279854125987,29.834380912701086 22.545490000385445,27.998887924752342 18.667465549912578,26.182197884958416 15.46925236216048,23.245566036759367 13.273718257281054,19.991127929537736 11.549198986727987,16.254045432963963 10.32221657953762,12.213594709929692 9.64904112863248,7.889319713018329 10.462958905588955,3.6533334747284796 12.449223606354746,-0.44050663734654805 14.806400610927698,-3.864616814209347 18.52235329279859,-5.692084173563062 22.608264097656214,-8.615977579098374 26.68010485275493,-9.909840215575535 31.079133060784716,-9.886904765064166 35.3881364730642,-8.677289662566007 39.80104945550677,-7.095390934172958 42.9202954187844,-5.187096127856661 45.48116853224923,-2.457370279582591 47.870921036485704,1.3994507543909087 48.74335998532989,5.748880841412775 50.02336004900967,9.999999999999995 49.073791147883995,10.0\" opacity=\"0.8\" /></g></svg>"
      ],
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       "<shapely.geometry.polygon.LinearRing at 0x7fde338ee978>"
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diameter = 20\n",
    "deviation = 0.5\n",
    "nr_points = 30\n",
    "x_center = 30.0\n",
    "y_center = 10.0\n",
    "theta = np.linspace(0.0, 2.0*np.pi, nr_points)\n",
    "x = x_center + np.random.normal(diameter, deviation, (len(theta), ))*np.cos(theta)\n",
    "y = y_center + np.random.normal(diameter, deviation, (len(theta), ))*np.sin(theta)\n",
    "lake_border = LinearRing([t  for t  in zip(x, y)])\n",
    "lake_border"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x7fde54563160>"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "island = Polygon(country_border, [lake_border])\n",
    "island"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to compute city names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def choose_name():\n",
    "    nr_char = random.randint(5, 10)\n",
    "    city_name = ''\n",
    "    for _ in range(nr_char):\n",
    "        city_name += random.choice(string.ascii_lowercase)\n",
    "    return city_name.capitalize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create cities, ensuring that they are withint the country borders, and not in the lake, assign name and  population to each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bounds = island.bounds\n",
    "cities = list()\n",
    "nr_cities = 10\n",
    "while len(cities) < nr_cities:\n",
    "    x_pos = np.random.uniform(bounds[0], bounds[2])\n",
    "    y_pos = np.random.uniform(bounds[1], bounds[3])\n",
    "    city_point = Point((x_pos, y_pos))\n",
    "    if island.contains(city_point):\n",
    "        city_name = choose_name()\n",
    "        city_pop = random.randint(5000, 50000)\n",
    "        cities.append({'name': city_name, 'position': city_point, 'population': city_pop})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Roads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Create roads between cities, but only when they can be straight lines, and run on land."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "roads = []\n",
    "for i, city1 in enumerate(cities):\n",
    "    for city2 in cities[i + 1:]:\n",
    "        road = LineString([city1['position'], city2['position']])\n",
    "        if island.contains(road):\n",
    "            roads.append(road)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(roads)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the island"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to plot the country, its cities and roads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_island(island, cities, roads):\n",
    "    plt.axes().set_facecolor('lightblue')\n",
    "    x_island, y_island = island.boundary[0].xy\n",
    "    plt.axes().fill(x_island, y_island, color='lightgreen')\n",
    "    for lake_border in island.boundary[1:]:\n",
    "        x_lake, y_lake = lake_border.xy\n",
    "        plt.axes().fill(x_lake, y_lake, color='lightblue', linewidth=0.7, alpha=1.0)\n",
    "    for city in cities:\n",
    "        plt.plot(*city['position'].xy, marker='o', markersize=3, color='red')\n",
    "    for road in roads:\n",
    "        plt.plot(road.xy[0], road.xy[1], color='black', linewidth=0.4)\n",
    "    plt.axes().set_aspect('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HSJfkvn+976p2eyKSI6jbVjfp8yeDFC5MgafQD/oFw88UkEj6gw9h03X5TCyaDjehuBTS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fde338ee208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_island(island, cities, roads)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}