Creating Network instance from road shapefile fails

hasecbinusr_issue.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `pysal.Network`\n",
    "\n",
    "# `Creating Network instance from road shapefile fails; ValueError: list.remove(x): x not in list`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### James Gaboardi, 04/2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pysal as ps\n",
    "import networkx as nx\n",
    "import geopandas as gpd\n",
    "import matplotlib as mpl\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This network fails..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "list.remove(x): x not in list",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-c1e634c811ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mntw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNetwork\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Roads_PySAL_Network_Fail.shp\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/jgaboardi/anaconda3/envs/py2/lib/python2.7/site-packages/pysal/network/network.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, in_shp, node_sig, unique_segs)\u001b[0m\n\u001b[1;32m    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0;31m# Extract the graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextractgraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnode_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/jgaboardi/anaconda3/envs/py2/lib/python2.7/site-packages/pysal/network/network.pyc\u001b[0m in \u001b[0;36mextractgraph\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    198\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mneighbors\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m                 \u001b[0mcnode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mneighbors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m                 \u001b[0msegment_nodes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m                 \u001b[0mbridge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m                 \u001b[0mnewneighbors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_yieldneighbor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msegment_nodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbridge\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list"
     ]
    }
   ],
   "source": [
    "ntw = ps.Network(\"Roads_PySAL_Network_Fail.shp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This network does not fail..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "ntw = ps.Network(\"hasecbinusr_case.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Roads and Nodes\n",
    "g1 = nx.Graph()\n",
    "\n",
    "# Graph of Roads and Nodes\n",
    "g2 = nx.MultiGraph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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2UNDLL4c6LcQEwQYAECljY5JKjedrhvakaHyJDSHYAAAi5dIlNTa+zND4EhtHsAEARMrk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\nWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130a624d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams[\"figure.figsize\"] = 7, 10\n",
    "\n",
    "#Draw Graph of Actual Nodes and Roads\n",
    "for e in ntw.edges:\n",
    "    g1.add_edge(*e)\n",
    "nx.draw(g1, ntw.node_coords, node_size=25, node_color=\"r\", alpha=0.25,  width=3, edge_color='r')\n",
    "\n",
    "#Draw only unique edges in graph\n",
    "for e in ntw.graphedges:\n",
    "    g2.add_edge(*e)\n",
    "    # highlights cases where start and end node are the same\n",
    "    if e[0]==e[1]:\n",
    "        g2.add_node(e[0])\n",
    "    \n",
    "for node_id in g2.node:\n",
    "    g2.node[node_id] = ntw.node_coords[node_id]\n",
    "nx.draw(g2, ntw.node_coords, node_size=300, node_color=\"b\", alpha=0.1, width=5, edge_color=\"b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots with `geopandas`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126d99d10>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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45d3ZtfsQu3Yfom32dC6/fAEXL5ldV/OC5YZfE9AKrAWuAx4zs+VR+UeistPA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\n01IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126ef76d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "geodataframe = gpd.read_file(\"hasecbinusr_case.shp\")\n",
    "mpl.rcParams[\"figure.figsize\"] = 10, 10\n",
    "geodataframe.plot(linewidth=10, alpha=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12fcfc750>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZPWJmM2b26ZbHGDCzx8zsR+lj/eOW23aa2aNm9pKZ/ZmZVdLj/enn+9Lbd6zE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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126f4d150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "geodataframe = gpd.read_file(\"Roads_PySAL_Network_Fail.shp\")\n",
    "mpl.rcParams[\"figure.figsize\"] = 10, 10\n",
    "geodataframe.plot(linewidth=10, alpha=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:py2]",
   "language": "python",
   "name": "conda-env-py2-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}