{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to NetworkX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The focus of this tutorial is to provide a basic introduction to using NetworkX. NetworkX is a python package which can be used for network analysis. NetworkX package contains several standard network algorithms which we will use in our course. This tutorial is by no means exhaustive. For more information, refer to the NetworkX documentation [here](http://networkx.readthedocs.io/en/latest/index.html).\n",
    "\n",
    "## Initializing the Network\n",
    "\n",
    "The first step is to import the networkx module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we initialize an empty Graph object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The following line initializes two empty directed graph objects\n",
    "G1=nx.DiGraph()\n",
    "G2=nx.DiGraph()\n",
    "# An empty undirected graph object can be initialized using the command\n",
    "# G=nx.Graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look at the network presented in Ahuja, Magnanti, and Orlin (1993) classic book on Network Flows. We can enter the nodes in multiple ways."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G1.add_node(1)\n",
    "G1.add_node(2)\n",
    "G1.add_node(3)\n",
    "G1.add_node(4)\n",
    "G1.add_node(5)\n",
    "G1.add_node(6)\n",
    "G1.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_nodes = [1, 2, 3, 4, 5, 6]\n",
    "G2.add_nodes_from(list_nodes)\n",
    "G2.nodes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now add the arcs. As with nodes, there are multiple ways to add the arcs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 2), (1, 3), (2, 3), (2, 4), (2, 5), (3, 5), (4, 6), (5, 4), (5, 6)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G1.add_edge(1, 2, weight = 2.0)\n",
    "G1.add_edge(1,3)\n",
    "G1.edge[1][3]['weight'] = 4.0\n",
    "G1.add_edge(2, 3, weight = 1.0)\n",
    "G1.add_edge(2, 4, weight = 4.0)\n",
    "G1.add_edge(2, 5, weight = 2.0)\n",
    "G1.add_edge(3, 5)\n",
    "G1.edge[3][5]['weight'] = 3.0\n",
    "G1.add_edge(4, 6, weight = 2.0)\n",
    "G1.add_edge(5, 4, weight = 3.0)\n",
    "G1.add_edge(5, 6, weight = 2.0)\n",
    "G1.edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 2), (1, 3), (2, 3), (2, 4), (2, 5), (3, 5), (4, 6), (5, 4), (5, 6)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_arcs = [(1,2,2.0) , (1,3,4.0) , (2,3,1.0) , (2,4,4.0) , (2,5,2.0) , (3,5,3.0) , (4,6,2.0) , (5,4,3.0) , (5,6,2.0)]\n",
    "G2.add_weighted_edges_from(list_arcs)\n",
    "G2.edges()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shortest Path\n",
    "\n",
    "Let us now determine the shortest path using Djikstras algorithm. The function shown below returns a list which stores the shortest path from source to target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "sp = nx.dijkstra_path(G1,source = 1, target = 6)\n",
    "print(sp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Note that there are several other routines to determine the shortest path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 4, 6]\n"
     ]
    }
   ],
   "source": [
    "print(nx.shortest_path(G1,source = 1, target = 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now visualize the network along with the shortest path. Note that in networkx and matplotlib, the arrows in the directed arcs are represented as notches and not arrows which is annoying. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fhoEYp0FAlUTnD6hYUzwQAMPynxqogSC0NAzEMA0CqjQ6f0DFovxAkOHlKQ0E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\npVTSlp8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x79551d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First we import the matplotlib python plotting package\n",
    "import matplotlib.pyplot as plt\n",
    "# We then set the coordinates of each node\n",
    "G1.node[1]['pos'] = (0,0)\n",
    "G1.node[2]['pos'] = (2,2)\n",
    "G1.node[3]['pos'] = (2,-2)\n",
    "G1.node[4]['pos'] = (5,2)\n",
    "G1.node[5]['pos'] = (5,-2)\n",
    "G1.node[6]['pos'] = (7,0)\n",
    "# The positions of each node are stored in a dictionary\n",
    "node_pos=nx.get_node_attributes(G1,'pos')\n",
    "# The edge weights of each arcs are stored in a dictionary\n",
    "arc_weight=nx.get_edge_attributes(G1,'weight')\n",
    "# Create a list of arcs in the shortest path using the zip command and store it in red edges\n",
    "red_edges = list(zip(sp,sp[1:]))\n",
    "# If the node is in the shortest path, set it to red, else set it to white color\n",
    "node_col = ['white' if not node in sp else 'red' for node in G1.nodes()]\n",
    "# If the edge is in the shortest path set it to red, else set it to white color\n",
    "edge_col = ['black' if not edge in red_edges else 'red' for edge in G1.edges()]\n",
    "# Draw the nodes\n",
    "nx.draw_networkx(G1, node_pos,node_color= node_col, node_size=450)\n",
    "# Draw the node labels\n",
    "# nx.draw_networkx_labels(G1, node_pos,node_color= node_col)\n",
    "# Draw the edges\n",
    "nx.draw_networkx_edges(G1, node_pos,edge_color= edge_col)\n",
    "# Draw the edge labels\n",
    "nx.draw_networkx_edge_labels(G1, node_pos,edge_color= edge_col, edge_labels=arc_weight)\n",
    "# Remove the axis\n",
    "plt.axis('off')\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dont forget to clear the graph object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "G1.clear()\n",
    "G2.clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G1.edges()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Let us now visualize the Sioux Falls Network Available [here](https://github.com/bstabler/TransportationNetworks/tree/master/SiouxFalls). For the sake of simplicity, I am going to assume that the cost on each arc is going to be the integer rounded costs provided in SiouxFalls_flow.tntp and the node coordinates are provided in SiouxFalls_node.tntp. Make sure both files are in a directory called SiouxFalls in the same location where you have this notebook file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76\n",
      "24\n"
     ]
    }
   ],
   "source": [
    "G3=nx.DiGraph()\n",
    "f = open(\"SiouxFalls/SiouxFalls_flow.tntp\", \"r\")\n",
    "line = f.readline()\n",
    "line = f.readline()\n",
    "while len(line):\n",
    "    l = line.split()\n",
    "    fromnode = int(l[0])\n",
    "    to = int(l[1])\n",
    "    volume = float(l[2])\n",
    "    cost = int(float(l[3]))\n",
    "    G3.add_edge(fromnode, to, weight = cost)\n",
    "    line = f.readline()\n",
    "f.close()\n",
    "print(G3.number_of_edges())\n",
    "\n",
    "f = open(\"SiouxFalls/SiouxFalls_node.tntp\", \"r\")\n",
    "line = f.readline()\n",
    "line = f.readline()\n",
    "while len(line):\n",
    "    line = line.strip(';')\n",
    "    l = line.split()\n",
    "    node = int(l[0])\n",
    "    pos1 = float(l[1])/10000\n",
    "    pos2 = float(l[2])/10000\n",
    "    G3.add_node(node, pos=(pos1,pos2))\n",
    "    line = f.readline()\n",
    "f.close()\n",
    "print(G3.number_of_nodes())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6Uezfv3/s1KlT3HDDDePhhx8eJ06cuLohvVXNcQCXtmvXLjt79uzV1RhjjPHv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\nD1vTHEDlftG2AQ6uUN9rrRBCT+CICy6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gjwvkRDC9gZYE7h3LI3BJdwP4i8BywzYvaL5r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\nRCRQCqAiIiIiEqj/D3iPQae22u5qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7961908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,12))\n",
    "# The positions of each node are stored in a dictionary\n",
    "node_pos=nx.get_node_attributes(G3,'pos')\n",
    "# The edge weights of each arcs are stored in a dictionary\n",
    "arc_weight=nx.get_edge_attributes(G3,'weight')\n",
    "# Determine the shortest path\n",
    "sp = nx.dijkstra_path(G3,source = 1, target = 20)\n",
    "# Create a list of arcs in the shortest path using the zip command and store it in red edges\n",
    "red_edges = list(zip(sp,sp[1:]))\n",
    "# If the node is in the shortest path, set it to red, else set it to white color\n",
    "node_col = ['white' if not node in sp else 'red' for node in G3.nodes()]\n",
    "# If the edge is in the shortest path set it to red, else set it to white color\n",
    "edge_col = ['black' if not edge in red_edges else 'red' for edge in G3.edges()]\n",
    "# Draw the nodes\n",
    "nx.draw_networkx(G3, node_pos,node_color= node_col, node_size=450)\n",
    "# Draw the node labels\n",
    "# nx.draw_networkx_labels(G3, node_pos,node_color= node_col)\n",
    "# Draw the edges\n",
    "nx.draw_networkx_edges(G3, node_pos,edge_color= edge_col)\n",
    "# Draw the edge labels\n",
    "nx.draw_networkx_edge_labels(G3, node_pos,edge_color= edge_col, edge_labels=arc_weight)\n",
    "# Remove the axis\n",
    "plt.axis('off')\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "G3.clear()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "There are three main issues with the above visualization:\n",
    "\n",
    "* Using of notches instead of arrows\n",
    "* When you have a two arcs connecting two nodes in opposite directions, the arcs are overlayed\n",
    "* The costs of arcs in both directions are written one over the other which is annoying\n",
    "\n",
    "So for better network visualization especially for directed networks, I recommend looking at the GraphViz or pygraphviz package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Minimum Spanning Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us use the NetworkX package to determine the Minimum Spanning Tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 2), (2, 3), (2, 5), (3, 6), (4, 5), (5, 8), (7, 8), (8, 9)]\n"
     ]
    }
   ],
   "source": [
    "G4 = nx.Graph()\n",
    "list_edges = [(1,2,2.0) , (1,4,4.0) , (2,3,3.0) , (2,5,2.0) , (3,6,1.0) , (4,5,2.0) , (4,7,6.0) , (5,6,5.0) , (5,8,3.0) , (6,9,8.0) , (8,9,1.0) , (7,8,2.0)]\n",
    "G4.add_weighted_edges_from(list_edges)\n",
    "G4.node[1]['pos'] = (0,0)\n",
    "G4.node[2]['pos'] = (0,2)\n",
    "G4.node[3]['pos'] = (0,4)\n",
    "G4.node[4]['pos'] = (2,0)\n",
    "G4.node[5]['pos'] = (2,2)\n",
    "G4.node[6]['pos'] = (2,4)\n",
    "G4.node[7]['pos'] = (4,0)\n",
    "G4.node[8]['pos'] = (4,2)\n",
    "G4.node[9]['pos'] = (4,4)\n",
    "T = nx.minimum_spanning_tree(G4)\n",
    "print(T.edges())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A+vTpg+uuuw6TJ09Gt27d8NZbb2HZsmVITk7GvHnzfB6zfrzebZuxNbEZMBER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\n2AwQERFZHJsBIiIii2MzQEREZHH/HwYNhaSPxP1sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x879b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "node_pos=nx.get_node_attributes(G4,'pos')\n",
    "edge_weight=nx.get_edge_attributes(G4,'weight')\n",
    "red_edges = T.edges()\n",
    "node_col = ['white']\n",
    "# If the edge is in the shortest path set it to red, else set it to white color\n",
    "edge_col = ['black' if not edge in red_edges else 'red' for edge in G4.edges()]\n",
    "# Draw the nodes\n",
    "nx.draw_networkx(G4, node_pos,node_color= node_col, node_size=450)\n",
    "# Draw the node labels\n",
    "# nx.draw_networkx_labels(G4, node_pos,node_color= node_col)\n",
    "# Draw the edges\n",
    "nx.draw_networkx_edges(G4, node_pos,edge_color= edge_col)\n",
    "# Draw the edge labels\n",
    "nx.draw_networkx_edge_labels(G4, node_pos,edge_color= edge_col, edge_labels=edge_weight)\n",
    "# Remove the axis\n",
    "plt.axis('off')\n",
    "# Show the plot\n",
    "plt.show()\n",
    "G4.clear()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Minimum Cost Flow\n",
    "\n",
    "We will use NetworkX package to solve the minimum cost flow problem example provided in page 322 of Ahuja, Magnanti, and Orlin (1993)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimum cost is: 14\n",
      "The optimal flow is:\n",
      "{1: {2: 2, 3: 2}, 2: {3: 2, 4: 0}, 3: {4: 4}, 4: {}}\n"
     ]
    }
   ],
   "source": [
    "G5 =nx.DiGraph()\n",
    "G5.add_edge(1,2, weight=2, capacity=4)\n",
    "G5.add_edge(1,3, weight=2, capacity=2)\n",
    "G5.add_edge(2,3, weight=1, capacity=2)\n",
    "G5.add_edge(2,4, weight=3, capacity=3)\n",
    "G5.add_edge(3,4, weight=1, capacity=5)\n",
    "G5.node[1]['demand'] = -4\n",
    "G5.node[4]['demand'] = 4\n",
    "cost, flow = nx.capacity_scaling(G5)\n",
    "print(\"The minimum cost is:\", cost)\n",
    "print(\"The optimal flow is:\")\n",
    "print(flow)\n",
    "# The data structure of flow is not consistent with dictionary datastructure\n",
    "# needed for printing the optimal solution\n",
    "optimal_flow = {}\n",
    "for i in G5.nodes_iter():\n",
    "    for j in flow[i].keys():\n",
    "        optimal_flow[i,j] = flow[i][j]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "G5.node[1]['pos'] = (0,0)\n",
    "G5.node[2]['pos'] = (3,3)\n",
    "G5.node[3]['pos'] = (3,-3)\n",
    "G5.node[4]['pos'] = (6,0)\n",
    "node_pos = nx.get_node_attributes(G5,'pos')\n",
    "arc_cost = nx.get_edge_attributes(G5,'weight')\n",
    "arc_cap  = nx.get_edge_attributes(G5,'capacity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The edge weights correspond to optimal flow values:\n"
     ]
    },
    {
     "data": {
      "image/png": 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4HolE9qRLp7QELKVYCAMsxVpybQMkbZQ0psf7+uSYW+1LyQdQjYukYaFQ6KeS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\nBNz/Dw166xGFOXE0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x49f9630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw_networkx(G5, node_pos,node_color='white' , node_size=450)\n",
    "#nx.draw_networkx_labels(G5, node_pos,node_color='white')\n",
    "#nx.draw_networkx_edges(G5, node_pos)\n",
    "nx.draw_networkx_edge_labels(G5, node_pos,edge_labels=optimal_flow)\n",
    "# Remove the axis\n",
    "plt.axis('off')\n",
    "# Show the plot\n",
    "print(\"The edge weights correspond to optimal flow values:\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [algorithms](https://networkx.readthedocs.io/en/stable/reference/algorithms.html) section of the NetworkX reference document contains details on various other network algorithms which can be solved using the NetworkX package."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}