jrjames83 · July 4, 2019 03:15
diff --git a/Generating Random Points With Constrained Minimum Distance.ipynb b/Generating Random Points With Constrained Minimum Distance.ipynb
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "201\n",
      "218\n",
      " "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112db5390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%prun\n",
    "\n",
    "import random\n",
    "from geopy.distance import distance\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "# This function does a few things, it takes a min distance threshold, but also\n",
    "# Mutates an external variable which is constantly passed into it - not always a good practice\n",
    "# But you could have written it a multitude of ways\n",
    "\n",
    "def generate_random_points(current_pairs, min_distance):\n",
    "    rlat, rlng = random.uniform(0,90), random.uniform(0,180)\n",
    "    if not current_pairs:\n",
    "        current_pairs.append((rlat, rlng))\n",
    "        return \n",
    "    else:\n",
    "        for pair in current_pairs:\n",
    "            _dist = distance((rlat, rlng), pair).km\n",
    "            if _dist < min_distance:\n",
    "                return False\n",
    "    return (rlat, rlng)\n",
    "        \n",
    "    \n",
    "    \n",
    "# Get 200 points, which are at least 100km apart from one another\n",
    "required_points = 200\n",
    "tries = 0\n",
    "points = []\n",
    "\n",
    "while required_points:\n",
    "    tries += 1\n",
    "    result = generate_random_points(points, 100)\n",
    "    if result:\n",
    "        points.append(result)\n",
    "        required_points -= 1    \n",
    "    \n",
    "print(len(points))\n",
    "print(tries)\n",
    "\n",
    "xs, ys = zip(*points) # https://stackoverflow.com/a/7558990/3182843\n",
    "plt.scatter(xs,ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check validity of point distances\n",
    "\n",
    "from itertools import combinations\n",
    "\n",
    "for x in combinations(points, 2):\n",
    "    p1, p2 = x\n",
    "    assert(distance(p1,p2).km > 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"201\n",
	"218\n",
	" "
	]
	},
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x112db5390>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"%%prun\n",
	"\n",
	"import random\n",
	"from geopy.distance import distance\n",
	"import matplotlib.pyplot as plt\n",
	"%matplotlib inline\n",
	"\n",
	"\n",
	"# This function does a few things, it takes a min distance threshold, but also\n",
	"# Mutates an external variable which is constantly passed into it - not always a good practice\n",
	"# But you could have written it a multitude of ways\n",
	"\n",
	"def generate_random_points(current_pairs, min_distance):\n",
	" rlat, rlng = random.uniform(0,90), random.uniform(0,180)\n",
	" if not current_pairs:\n",
	" current_pairs.append((rlat, rlng))\n",
	" return \n",
	" else:\n",
	" for pair in current_pairs:\n",
	" _dist = distance((rlat, rlng), pair).km\n",
	" if _dist < min_distance:\n",
	" return False\n",
	" return (rlat, rlng)\n",
	" \n",
	" \n",
	" \n",
	"# Get 200 points, which are at least 100km apart from one another\n",
	"required_points = 200\n",
	"tries = 0\n",
	"points = []\n",
	"\n",
	"while required_points:\n",
	" tries += 1\n",
	" result = generate_random_points(points, 100)\n",
	" if result:\n",
	" points.append(result)\n",
	" required_points -= 1 \n",
	" \n",
	"print(len(points))\n",
	"print(tries)\n",
	"\n",
	"xs, ys = zip(*points) # https://stackoverflow.com/a/7558990/3182843\n",
	"plt.scatter(xs,ys)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [],
	"source": [
	"# Check validity of point distances\n",
	"\n",
	"from itertools import combinations\n",
	"\n",
	"for x in combinations(points, 2):\n",
	" p1, p2 = x\n",
	" assert(distance(p1,p2).km > 100)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {},
	"outputs": [],
	"source": []
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {},
	"outputs": [],
	"source": []
	}
	],
	"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": "3.6.8"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}