notebooks/Voroni diagrams.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Voroni diagrams\n",
    "\n",
    "Quick demo of the `Voroni` class in `geometry`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "sys.path.insert(0, os.path.abspath(\"..\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import open_cp.geometry as geometry\n",
    "import matplotlib.patches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "points = np.asarray([[1,1], [3,2], [1,3], [2,4]])\n",
    "voroni = geometry.Voroni(points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.],\n",
       "       [ 3.,  2.],\n",
       "       [ 1.,  3.],\n",
       "       [ 2.,  4.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voroni.points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.75      ,  2.        ],\n",
       "       [ 2.16666667,  2.83333333]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voroni.vertices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-3, 7), (-5, 8)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TTnBaDm+2tja0pqYiLy9PdZSQxmJWaPOGDZjOfcukE5yWqaq+HsXz5vEyngHG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\nGsJiJiIi0pD/H0uXO9oE/+WLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc894a9c588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "ax.scatter(voroni.points[:,0], voroni.points[:,1], color=\"blue\")\n",
    "for index, (x, y) in enumerate(voroni.points):\n",
    "    ax.text(x+0.02,y+0.02,index)\n",
    "ax.scatter(voroni.vertices[:,0], voroni.vertices[:,1], marker=\"+\", color=\"black\")\n",
    "for index, (x, y) in enumerate(voroni.vertices):\n",
    "    ax.text(x+0.02,y+0.02,index)\n",
    "\n",
    "for poly in voroni.polygons(inf_dist=5):\n",
    "    p = matplotlib.patches.Polygon(poly, fc=\"red\", ec=\"black\", alpha=0.5)\n",
    "    ax.add_patch(p)\n",
    "    \n",
    "ax.set_aspect(1)\n",
    "ax.set(xlim=[-5,8], ylim=[-3,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(3.9860679774997898, -2.4721359549995796),\n",
       " (-3.25, 2.0),\n",
       " array([ 1.75,  2.  ])]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voroni.polygon_for(0, inf_dist=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[-1, 1, 0], [1, -1, 0], [-1, 0], [-1, 1], []]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voroni.regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 1], [-1, 0], [-1, 1], [-1, 0], [-1, 1]]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voroni.ridge_vertices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 1],\n",
       "       [2, 0],\n",
       "       [2, 3],\n",
       "       [1, 0],\n",
       "       [1, 3]], dtype=int32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voroni.ridge_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 0, 3])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voroni.point_region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-6, 9), (-4, 11)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5+gSrFROsVpfOQ/rXzd8fqb6+2HvgAN4+fhyz09PRp08f2WNJ4fCKVggxE0C5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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc8978a17f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = np.asarray([[1,1], [3,2], [1,3], [2,4], [4,4], [5,0]])\n",
    "voroni = geometry.Voroni(points)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "ax.scatter(voroni.points[:,0], voroni.points[:,1], color=\"blue\")\n",
    "for index, (x, y) in enumerate(voroni.points):\n",
    "    ax.text(x+0.02,y+0.02,index)\n",
    "ax.scatter(voroni.vertices[:,0], voroni.vertices[:,1], marker=\"+\", color=\"black\")\n",
    "for index, (x, y) in enumerate(voroni.vertices):\n",
    "    ax.text(x+0.02,y+0.02,index)\n",
    "\n",
    "for poly in voroni.polygons(inf_dist=5):\n",
    "    p = matplotlib.patches.Polygon(poly, fc=\"red\", ec=\"black\", alpha=0.5)\n",
    "    ax.add_patch(p)\n",
    "    \n",
    "ax.set_aspect(1)\n",
    "ax.set(xlim=[-4,11], ylim=[-6,9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-6, 7), (-6, 7)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zyX37cvnOndl+yildjwRJkjddfXWu++Y389Hf+Z2uR4Hv+ae77sq13/1uXvi7vzuKyG7G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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc8949caf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = np.asarray([[0,0], [1,0], [0,1], [1,1]])\n",
    "voroni = geometry.Voroni(points)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "ax.scatter(voroni.points[:,0], voroni.points[:,1], color=\"blue\")\n",
    "for index, (x, y) in enumerate(voroni.points):\n",
    "    ax.text(x+0.02,y+0.02,index)\n",
    "ax.scatter(voroni.vertices[:,0], voroni.vertices[:,1], marker=\"+\", color=\"black\")\n",
    "for index, (x, y) in enumerate(voroni.vertices):\n",
    "    ax.text(x+0.02,y+0.02,index)\n",
    "\n",
    "for poly in voroni.polygons(inf_dist=5):\n",
    "    p = matplotlib.patches.Polygon(poly, fc=\"red\", ec=\"black\", alpha=0.5)\n",
    "    ax.add_patch(p)\n",
    "    \n",
    "ax.set_aspect(1)\n",
    "ax.set(xlim=[-6,7], ylim=[-6,7])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Estimating the required distance to infinity\n",
    "\n",
    "Currently, if we make the `inf_dist` too small, then the \"boundary polygons\" may not be large enough to contain the points which formed them (for example)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Xs+cSzcFVWM9PkT0/hVbOyVrLmoICJqSnez2KL0T7avZcoiG4CmvzKLIXpt8a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\nB2dNC3uft3RDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc894725048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = np.asarray([[1,1], [3,2], [1,3], [2,4], [4,4], [5,0]])\n",
    "voroni = geometry.Voroni(points)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "ax.scatter(voroni.points[:,0], voroni.points[:,1], color=\"blue\")\n",
    "for index, (x, y) in enumerate(voroni.points):\n",
    "    ax.text(x+0.02,y+0.02,index)\n",
    "ax.scatter(voroni.vertices[:,0], voroni.vertices[:,1], marker=\"+\", color=\"black\")\n",
    "for index, (x, y) in enumerate(voroni.vertices):\n",
    "    ax.text(x+0.02,y+0.02,index)\n",
    "\n",
    "for i in range(6):\n",
    "    poly = voroni.polygon_for_by_distance(i, 1)\n",
    "    p = matplotlib.patches.Polygon(poly, fc=\"red\", ec=\"black\", alpha=0.5)\n",
    "    ax.add_patch(p)\n",
    "    \n",
    "ax.set_aspect(1)\n",
    "ax.set(xlim=[-4,11], ylim=[-6,9])\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}