2.2-visualize_graphs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import transit_model as tm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import networkx as nx\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = nx.read_gml('cta.gml')\n",
    "node_coords = {}\n",
    "for n in g:\n",
    "    node_coords[n] = g.nodes[n]['pos']\n",
    "\n",
    "g, passengers = tm.initialize(n_passengers=10, node_capacity=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "transit_times, graphs = tm.update(g, passengers, max_run_steps=200, graph_period=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_graph(i):\n",
    "    print(i)\n",
    "#     [G.nodes[i]['population'] for i in G.nodes]\n",
    "    G = graphs[i]\n",
    "    nx.draw(G, node_coords, \n",
    "            ax=ax, alpha=0.4, \n",
    "            node_size=10, \n",
    "            node_color=[random.choice(['k','b','g']) for j in range(140)])\n",
    "    plt.axis('equal')\n",
    "#     plt.grid(which='both')\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.animation import FuncAnimation\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "ani = FuncAnimation(fig, update_graph, frames=len(graphs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'str'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "for i in g.nodes:\n",
    "    print(type(g.nodes[i]['allnodecolors']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pos': [-87.648088, 41.846779999999995],\n",
       " 'nodecolor': 'orange',\n",
       " 'allnodecolors': 'orange',\n",
       " 'population': 0,\n",
       " 'capacity': 0}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.nodes[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'brown' in 'brown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Passenger' object has no attribute 'current_node_color'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-514735105aad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpassengers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent_node_color\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'Passenger' object has no attribute 'current_node_color'"
     ]
    }
   ],
   "source": [
    "passengers[0].current_node_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:alyssa] *",
   "language": "python",
   "name": "conda-env-alyssa-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": "3.8.1"
  }
 },
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