Parsing Networks in the TNPM format for consumption in AequilibraE

tnpm_parsing.py

import aequilibrae
import os
import sys
import numpy as np
import pandas as pd
import openmatrix as omx
from aequilibrae.matrix import AequilibraeMatrix

data_folder = 'path/to/the/anaheim/folder'

matfile = os.path.join(data_folder, 'Anaheim_trips.tntp')

# Creating the matrix
f = open(matfile, 'r')
all_rows = f.read()
blocks = all_rows.split('Origin')[1:]
matrix = {}
for k in range(len(blocks)):
    orig = blocks[k].split('\n')
    dests = orig[1:]
    orig=int(orig[0])

    d = [eval('{'+a.replace(';',',').replace(' ','') +'}') for a in dests]
    destinations = {}
    for i in d:
        destinations = {**destinations, **i}
    matrix[orig] = destinations
zones = max(matrix.keys())
mat = np.zeros((zones, zones))
for i in range(zones):
    for j in range(zones):
        mat[i, j] = matrix[i+1].get(j+1,0)

# Saving the matrix to an OMX container
omxfile = matfile.replace('tntp', 'omx')
index = np.arange(zones) + 1
myfile = omx.open_file(omxfile,'w')
myfile['matrix'] = mat
myfile.create_mapping('taz', index)
myfile.close()

# Or if you prefer an AequilibraE matrix
aemfile = matfile.replace('tntp', 'aem')
aem = AequilibraeMatrix()
kwargs = {'file_name': aemfile,
          'zones': zones,
          'matrix_names': ['matrix']}

aem.create_empty(**kwargs)
aem.matrix['matrix'][:,:] = mat[:,:]
aem.index[:] = index[:]

# Now let's parse the network
net = os.path.join(data_folder, 'Anaheim_net.tntp')
net = pd.read_csv(net, skiprows=7, sep='\t')

# If you want to create an AequilibraE matrix for computation, then it follows
from aequilibrae.paths import Graph
g = Graph()
g.cost = net['Free Flow Time (min)'].values
g.capacity = net['Capacity (veh/h)'].values
g.free_flow_time = net['Free Flow Time (min)'].values

network = net[['Tail', 'Head', 'Free Flow Time (min)', 'Capacity (veh/h)' ]]
network.columns = ['a_node', 'b_node', 'time', 'capacity']
network = network.assign(direction=1)
g.network = network.to_records(index=False)
g.network_ok = True
g.status = 'OK'
g.prepare_graph(index)

This is what AequilibraE has been designed to do, but it still has a long way to go if compared to the commercial platforms you mentioned.