mountain-trees-test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import *\n",
    "from matplotlib.pyplot import *\n",
    "from numpy import *\n",
    "from random import sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10000000000000001"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = 3.0\n",
    "sx = 15  # Physical grid size (external units, e.g. meters)\n",
    "sy = 15\n",
    "n_candidates = 30\n",
    "n = sx * sy\n",
    "cell_size_upper_bound = r / sqrt(n)\n",
    "cell_size = 10**(ceil(log10(cell_size_upper_bound)) - 1)\n",
    "cell_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 39)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = int(sx / cell_size)  # Logical grid size (internal units for Bridson's algorithm)\n",
    "s2 = int(sy / cell_size)\n",
    "sr = r / cell_size  # Logical Poisson disc radius\n",
    "\n",
    "grid = zeros((s1, s2))\n",
    "grid = grid.astype(int)\n",
    "grid.fill(-1)\n",
    "start_point = random.randint(0, grid.shape[0]), random.randint(0, grid.shape[1])\n",
    "start_point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(18, 39)}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid[start_point] = 0  # 0 is the first point index so can be hardcoded\n",
    "active_points = set([start_point])  # Start the active points set\n",
    "active_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def translate_point(point, translation):\n",
    "    print(point, translation)\n",
    "    return (point[0] + translation[0]), (point[1] + translation[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 1) (6, 7)\n(2, 0) (6, 7)\n(1, 0) (6, 7)\n(1, 1) (6, 7)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(6, 8), (8, 7), (7, 7), (7, 8)]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def generate_candidates(point, selector, n):\n",
    "    candidates = set()\n",
    "    while len(candidates) < n:\n",
    "        # generate random points inside the selector's bounding box\n",
    "        metacandidate = random.randint(0, selector.shape[0]),\\\n",
    "                        random.randint(0, selector.shape[1])\n",
    "        # accept points covered by the selector\n",
    "        if selector[metacandidate]:\n",
    "            candidates.add(metacandidate)\n",
    "    candidates = [translate_point(candidate, point) for candidate in candidates]\n",
    "    return candidates\n",
    "\n",
    "test_selector = array([[0, 1, 0],\n",
    "                  [1, 1, 0],\n",
    "                  [1, 0, 0]])\n",
    "\n",
    "generate_candidates((6, 7), test_selector, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def distance_between_points(point_1, point_2):\n",
    "    # Pythagorean theorem\n",
    "    return sqrt((point_1[0] - point_2[0])**2 + (point_1[1] - point_2[1])**2)\n",
    "\n",
    "distance_between_points((1, 1), (5, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x74ffba8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X6MH3sb2NhyTlxftjCJMQi0QiLNYGX92OK9zd0LzKX/sed0845ULhsoTFYpAIi7Uit4TH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\nBpEICyFEg/z/Kq3ShwX4n1QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x748f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_annular_selector(r_o, r_i):\n",
    "    size = int(ceil(2 * r_o + 1))\n",
    "    center = (r_o, r_o)\n",
    "    selector = zeros((size, size))  # bounding box\n",
    "    for y in range(size):\n",
    "        for x in range(size):\n",
    "            distance = distance_between_points((x, y), center)\n",
    "            if distance > r_i and distance < r_o + 1:\n",
    "                selector[y][x] = 1\n",
    "    return selector\n",
    "\n",
    "generated_selector = generate_annular_selector(16, 8)\n",
    "\n",
    "pyplot.imshow(generated_selector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_collisions(grid, candidate, collider):\n",
    "    collider_t = [translate_point(point, candidate)\n",
    "                  for point in grid_to_list(collider)]\n",
    "    for point in collider_t:\n",
    "        if grid[point]:\n",
    "            return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18, 39)\n(3, 0) (18, 39)\n(2, 8) (18, 39)\n(9, 8) (18, 39)\n(2, 1) (18, 39)\n(8, 9) (18, 39)\n(9, 4) (18, 39)\n(3, 7) (18, 39)\n(7, 11) (18, 39)\n(10, 3) (18, 39)\n(1, 2) (18, 39)\n(2, 9) (18, 39)\n(11, 5) (18, 39)\n(10, 7) (18, 39)\n(6, 10) (18, 39)\n(12, 6) (18, 39)\n(8, 10) (18, 39)\n(10, 9) (18, 39)\n(12, 8) (18, 39)\n(3, 2) (18, 39)\n(5, 11) (18, 39)\n(12, 7) (18, 39)\n(1, 4) (18, 39)\n(2, 3) (18, 39)\n(1, 9) (18, 39)\n(6, 12) (18, 39)\n(2, 11) (18, 39)\n(9, 9) (18, 39)\n(1, 8) (18, 39)\n(11, 6) (18, 39)\n(10, 2) (18, 39)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'check_collisions' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-166f26f60f44>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0msucceeded\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mcandidate\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcandidates\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcheck_collisions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcandidate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcollider\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# check if point can fit\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m             \u001b[0mactive_points\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcandidate\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# add point to active_points\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m             \u001b[0mgrid\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcandidate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactive_points\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m  \u001b[1;31m# add index to grid\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'check_collisions' is not defined"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "while active_points:\n",
    "    active_point = sample(active_points, 1)[0]\n",
    "    print(active_point)\n",
    "    annulus = generate_annular_selector(2 * r, r)\n",
    "    collider = generate_annular_selector(r, 0)\n",
    "    candidates = generate_candidates(active_point, annulus, n_candidates)\n",
    "    succeeded = False\n",
    "    for candidate in candidates:\n",
    "        if not check_collisions(grid, candidate, collider):  # check if point can fit\n",
    "            active_points.add(candidate)  # add point to active_points\n",
    "            grid[int(candidate)] = len(active_points) - 1  # add index to grid\n",
    "            succeeded = True\n",
    "    if not succeeded:\n",
    "        active_points.remove(active_point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96, 69)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}