Reproducing AequilibraE issue #124

Assigning from tnpm to reproduce error.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import openmatrix as omx\n",
    "from aequilibrae.matrix import AequilibraeMatrix\n",
    "from aequilibrae.paths import Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = 'D:/src/TransportationNetworks/Anaheim'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explicit logging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aequilibrae import logger\n",
    "import logging\n",
    "# We redirect the logging output to the terminal\n",
    "stdout_handler = logging.StreamHandler(sys.stdout)\n",
    "logger.addHandler(stdout_handler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "matfile = os.path.join(data_folder, 'Anaheim_trips.tntp')\n",
    "\n",
    "# Creating the matrix\n",
    "f = open(matfile, 'r')\n",
    "all_rows = f.read()\n",
    "blocks = all_rows.split('Origin')[1:]\n",
    "matrix = {}\n",
    "for k in range(len(blocks)):\n",
    "    orig = blocks[k].split('\\n')\n",
    "    dests = orig[1:]\n",
    "    orig=int(orig[0])\n",
    "\n",
    "    d = [eval('{'+a.replace(';',',').replace(' ','') +'}') for a in dests]\n",
    "    destinations = {}\n",
    "    for i in d:\n",
    "        destinations = {**destinations, **i}\n",
    "    matrix[orig] = destinations\n",
    "zones = max(matrix.keys())\n",
    "mat = np.zeros((zones, zones))\n",
    "for i in range(zones):\n",
    "    for j in range(zones):\n",
    "        mat[i, j] = matrix[i+1].get(j+1,0)\n",
    "\n",
    "# Saving the matrix to an OMX container\n",
    "omxfile = matfile.replace('tntp', 'omx')\n",
    "index = np.arange(zones) + 1\n",
    "myfile = omx.open_file(omxfile,'w')\n",
    "myfile['matrix'] = mat\n",
    "myfile.create_mapping('taz', index)\n",
    "myfile.close()\n",
    "\n",
    "# Or if you prefer an AequilibraE matrix\n",
    "aemfile = matfile.replace('tntp', 'aem')\n",
    "aem = AequilibraeMatrix()\n",
    "kwargs = {'file_name': aemfile,\n",
    "          'zones': zones,\n",
    "          'matrix_names': ['matrix']}\n",
    "\n",
    "aem.create_empty(**kwargs)\n",
    "aem.matrix['matrix'][:,:] = mat[:,:]\n",
    "aem.index[:] = index[:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "netfile = os.path.join(data_folder,'Anaheim_net.tntp')\n",
    "net = pd.read_csv(netfile, skiprows=7, sep='\\t')\n",
    "\n",
    "g = Graph()\n",
    "g.cost = net['free_flow_time'].values\n",
    "g.capacity = net['capacity'].values\n",
    "g.free_flow_time = net['free_flow_time'].values\n",
    "\n",
    "network = net[['init_node', 'term_node', 'free_flow_time', 'capacity', 'b', 'power']]\n",
    "network.columns = ['a_node', 'b_node', 'free_flow_time', 'capacity', 'b', 'power']\n",
    "network = network.assign(direction=1)\n",
    "g.network = network.to_records(index=False)\n",
    "g.network_ok = True\n",
    "g.status = 'OK'\n",
    "g.prepare_graph(index)\n",
    "g.save_to_disk(os.path.join(data_folder, 'graph.aeg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aequilibrae.paths import TrafficAssignment, TrafficClass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bfw Assignment STATS\n",
      "Iteration, RelativeGap, stepsize\n",
      "1,inf,1.0\n",
      "2,0.018135363369113977,0.25610159320627784\n",
      "3,0.005430047866020795,0.0\n",
      "4,0.005430047866020795,0.0\n",
      "5,0.005430047866020795,0.0\n",
      "6,0.005430047866020795,0.0\n",
      "7,0.005430047866020795,0.0\n",
      "8,0.005430047866020795,0.0\n",
      "9,0.005430047866020795,0.0\n",
      "10,0.005430047866020795,0.0\n",
      "11,0.005430047866020795,0.0\n",
      "12,0.005430047866020795,0.0\n",
      "13,0.005430047866020795,0.0\n",
      "14,0.005430047866020795,0.0\n",
      "15,0.005430047866020795,0.0\n",
      "16,0.0054300478660209575,0.0\n",
      "17,0.005430047866020795,0.0\n",
      "18,0.005430047866020795,0.0\n",
      "19,0.005430047866020795,0.0\n",
      "20,0.005430047866020795,0.0\n",
      "21,0.005430047866020795,0.0\n",
      "22,0.005430047866020795,0.0\n",
      "23,0.005430047866020795,0.0\n",
      "24,0.005430047866020795,0.0\n",
      "25,0.005430047866020795,0.0\n",
      "26,0.005430047866020795,0.0\n",
      "27,0.0054300478660209575,0.0\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-bb29f2922b19>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[0massig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrgap_target\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.00005\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 29\u001b[1;33m \u001b[0massig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# we then execute the assignment\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\pedro\\.virtualenvs\\basic_science-b7soidpa\\lib\\site-packages\\aequilibrae\\paths\\traffic_assignment.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    295\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    296\u001b[0m         \u001b[1;34m\"\"\"Processes assignment\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 297\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massignment\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\pedro\\.virtualenvs\\basic_science-b7soidpa\\lib\\site-packages\\aequilibrae\\paths\\linear_approximation.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    294\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mpyqt\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m                     \u001b[0maon\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massignment\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignal_handler\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 296\u001b[1;33m                 \u001b[0maon\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    297\u001b[0m                 \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_aon_results\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtotal_flows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    298\u001b[0m                 \u001b[0maon_flows\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_aon_results\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtotal_link_loads\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpce\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\pedro\\.virtualenvs\\basic_science-b7soidpa\\lib\\site-packages\\aequilibrae\\paths\\all_or_nothing.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     75\u001b[0m                     \u001b[1;31m# self.func_assig_thread(orig, all_threads)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m         \u001b[0mpool\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 77\u001b[1;33m         \u001b[0mpool\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     78\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlink_loads\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maux_res\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtemp_link_loads\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python\\Python37\\Lib\\multiprocessing\\pool.py\u001b[0m in \u001b[0;36mjoin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    554\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mCLOSE\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTERMINATE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    555\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"In unknown state\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 556\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_worker_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    557\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_task_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    558\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_result_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python\\Python37\\Lib\\threading.py\u001b[0m in \u001b[0;36mjoin\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m   1042\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1043\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1044\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_wait_for_tstate_lock\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1045\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1046\u001b[0m             \u001b[1;31m# the behavior of a negative timeout isn't documented, but\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python\\Python37\\Lib\\threading.py\u001b[0m in \u001b[0;36m_wait_for_tstate_lock\u001b[1;34m(self, block, timeout)\u001b[0m\n\u001b[0;32m   1058\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlock\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# already determined that the C code is done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1059\u001b[0m             \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_stopped\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1060\u001b[1;33m         \u001b[1;32melif\u001b[0m \u001b[0mlock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1061\u001b[0m             \u001b[0mlock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1062\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "demand = aem\n",
    "# demand.load(os.path.join(data_folder, 'Anaheim_trips.omx'))\n",
    "demand.computational_view(['matrix']) # We will only assign one user class stored as 'matrix' inside the OMX file\n",
    "\n",
    "assig = TrafficAssignment()\n",
    "\n",
    "# Creates the assignment class\n",
    "assigclass = TrafficClass(g, demand)\n",
    "\n",
    "\n",
    "# The first thing to do is to add at list of traffic classes to be assigned\n",
    "assig.set_classes([assigclass])\n",
    "\n",
    "assig.set_vdf(\"BPR\")  # This is not case-sensitive # Then we set the volume delay function\n",
    "\n",
    "assig.set_vdf_parameters({\"alpha\": \"b\", \"beta\": \"power\"}) # And its parameters\n",
    "\n",
    "assig.set_capacity_field(\"capacity\") # The capacity and free flow travel times as they exist in the graph\n",
    "assig.set_time_field(\"free_flow_time\")\n",
    "\n",
    "# And the algorithm we want to use to assign\n",
    "# ['all-or-nothing', 'msa', 'frank-wolfe', 'cfw', 'bfw']\n",
    "assig.set_algorithm('bfw')  #Biconjugate Frank-Wolfe\n",
    "\n",
    "# since I haven't checked the parameters file, let's make sure convergence criteria is good\n",
    "assig.max_iter = 50\n",
    "assig.rgap_target = 0.00005\n",
    "\n",
    "assig.execute() # we then execute the assignment"
   ]
  }
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