Week_9_Exercise.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 1965206\n",
      "Number of edges: 2766607\n",
      "Average degree:   2.8156\n"
     ]
    }
   ],
   "source": [
    "#Data from: https://snap.stanford.edu/data/roadNet-CA.txt.gz\n",
    "\n",
    "#1. Via Docker\n",
    "  #1. Download data from https://snap.stanford.edu/data/roadNet-CA.txt.gz use of tqdm to show the installation is optional.\n",
    "  #2. Unzip the file.\n",
    "\n",
    "\n",
    "\n",
    "#4. Now try getting the first 200 Node IDs from roadNet-CA.txt and once again create a Directed Graph. And again Nodes that do not have a neighbour set these as cornors.\n",
    "\n",
    "#Help if needed: https://www.python-course.eu/graphs_python.php\n",
    "import sys\n",
    "import os\n",
    "sys.path.append(os.path.abspath('../modules'))\n",
    "\n",
    "import webget\n",
    "from tqdm import tqdm\n",
    "import gzip\n",
    "import networkx as nx\n",
    "\n",
    "#tqdm(webget.download('https://snap.stanford.edu/data/roadNet-CA.txt.gz', to='./data/roadnet_data.txt.gz'))\n",
    "\n",
    "#with gzip.open('data/roadnet_data.txt.gz', 'rb') as f_in:\n",
    "    #rn_data = f_in.read()\n",
    "    #with open('data/roadnet_data.txt', 'wb') as f_out:\n",
    "        #f_out.write(rn_data)\n",
    "\n",
    "g = nx.read_edgelist('data/roadnet_data.txt')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87787225\n",
      "{'25': {}, '28': {}, '29': {}}\n",
      "['25', '28', '29', '27']\n",
      "['25', '28', '29', '27']\n",
      "[(25, 23), (25, 26), (25, 27), (28, 27), (29, 27), (29, 30), (29, 3255), (27, 25), (27, 28), (27, 29)]\n",
      "['25', '28', '29', '27', '23', '26', '30', '3255']\n",
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 1965206\n",
      "Number of edges: 2766607\n",
      "Average degree:   2.8156\n"
     ]
    }
   ],
   "source": [
    "#2. Print out the length of roadNet-CA.txt and find the Node ID '27'.\n",
    "  #1. Find the neighbours of 27 and print these out\n",
    "\n",
    "\n",
    "import pygraphviz\n",
    "import matplotlib.pyplot as plt\n",
    "from networkx.drawing.nx_agraph import graphviz_layout, write_dot\n",
    "\n",
    "with open('data/roadnet_data.txt', 'rb') as f_out:\n",
    "    print(len(f_out.read()))\n",
    "\n",
    "#g = nx.read_edgelist('data/roadnet_data.txt')\n",
    "print(g['27'])\n",
    "\n",
    "  #2. With this knowledge create a small Directed Graph of this data. For the neighbours that are not 'known',\n",
    "     #add these as cornors (without a connection further)\n",
    "        \n",
    "paths = []\n",
    "node_list = list(g.neighbors('27'))\n",
    "node_list.append('27')\n",
    "print(node_list)\n",
    "\n",
    "all_node_list = node_list[:]\n",
    "print(all_node_list)\n",
    "\n",
    "for node in node_list:\n",
    "    neighbors = g.neighbors(node)\n",
    "    for n in neighbors:\n",
    "        paths.append((int(node), int(n)))\n",
    "        if n not in all_node_list:\n",
    "            all_node_list.append(n)\n",
    "            \n",
    "print(paths)\n",
    "print(all_node_list)\n",
    "\n",
    "graph = nx.DiGraph()\n",
    "graph.add_nodes_from(all_node_list)\n",
    "graph.add_edges_from(paths)\n",
    "\n",
    "#3. Find the amount of Edges of roadNet-CA.txt and Nodes and print these out\n",
    "print(nx.info(g))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3. Visualize this data using networkx and pygraphviz\n",
    "nx.draw(graph, pos=graphviz_layout(graph), \n",
    "            node_size=30, width=.05, cmap=plt.cm.Blues, \n",
    "            with_labels=True, node_color=range(len(graph)))"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}