{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pysal\n",
    "import numpy as np\n",
    "import random\n",
    "import geopandas as gp\n",
    "import acs_regionalization as ACS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read in estimates and standard errors\n",
    "ests = np.genfromtxt('example_data/99999_a225_p9_pop9999_k3_j0_est.csv', delimiter=',')\n",
    "sdes = np.genfromtxt('example_data/99999_a225_p9_pop9999_k3_j0_stdErr.csv', delimiter=',')\n",
    "# build the spatial connectivity\n",
    "shp = pysal.open('example_data/99999_225.shp')    \n",
    "w = pysal.rook_from_shapefile('example_data/99999_225.shp')\n",
    "# convert standard error to margin of error\n",
    "moes = sdes * 1.645"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAEACAYAAABYh3hbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMVR7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110718bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# take a look at the map\n",
    "shp_map = gp.read_file('example_data/99999_225.shp')\n",
    "shp_map['constant'] = 1\n",
    "p = shp_map.plot('constant', alpha=0.5, colormap='winter')\n",
    "p.axis('off')\n",
    "p.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAEACAYAAABYh3hbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnR2ZKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1106fe850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# since this is a \"toy\" example, we built the data with a \"true\" map in mind\n",
    "# these are the regions in the true map\n",
    "p = shp_map.plot('region', alpha=1, colormap='Dark2')\n",
    "p.axis('off')\n",
    "p.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# set some seeds to get the same solution each time\n",
    "start_seed = 5009\n",
    "random.seed(start_seed)\n",
    "np.random.seed(start_seed)\n",
    "# run the regionalization algorithm\n",
    "results = ACS.ACS_Regions(w=w,\n",
    "                          target_est_count=ests[:,3:],\n",
    "                          target_moe_count=moes[:,3:], \n",
    "                          target_th_all=0.15,\n",
    "                          count_est=ests[:,2],\n",
    "                          count_th_min=46000,\n",
    "                          base_solutions=100,\n",
    "                          pca=False,\n",
    "                          compactness=shp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAEACAYAAABYh3hbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBc/ftwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116ae6d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can plot the results\n",
    "# note that the shapes of the regions found by the algorithm match the\n",
    "# \"true\" regions;(the color arrangement is different simply because the\n",
    "# algorithm assigned region IDs randomly)  \n",
    "shp_map['rids'] = results.region_ids\n",
    "p = shp_map.plot('rids', alpha=1, colormap='Dark2')\n",
    "p.axis('off')\n",
    "p.plot()"
   ]
  }
 ],
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  "kernelspec": {
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