Creating Delaunay Lines

In this example, we show how to create AequilibraE’s famous Delaunay Lines, but in Python.

For more on this topic, the first publication is here.

We use the Sioux Falls example once again.

import pandas as pd
from uuid import uuid4
from os.path import join
import sqlite3
from tempfile import gettempdir
import matplotlib.pyplot as plt
import shapely.wkb

from aequilibrae.utils.create_example import create_example
from aequilibrae.utils.create_delaunay_network import DelaunayAnalysis

We create an empty project on an arbitrary folder

fldr = join(gettempdir(), uuid4().hex)

project = create_example(fldr)

Get the Delaunay Lines generation class

da = DelaunayAnalysis(project)

# Let's create the triangulation based on the zones, but we could create based on the network (centroids) too
da.create_network("zones")

Now we get the matrix we want and create the Delaunay Lines

demand = project.matrices.get_matrix("demand_omx")
demand.computational_view(["matrix"])

# And we will call it 'delaunay_test'./ It will also be saved in the results_database.sqlite
da.assign_matrix(demand, "delaunay_test")

we retrieve the results

conn = sqlite3.connect(join(fldr, "results_database.sqlite"))
results = pd.read_sql("Select * from delaunay_test", conn).set_index("link_id")

Now we get the matrix we want and create the Delaunay Lines

links = pd.read_sql("Select link_id, st_asBinary(geometry) geometry from delaunay_network", project.conn)
links.geometry = links.geometry.apply(shapely.wkb.loads)
links.set_index("link_id", inplace=True)

df = links.join(results)

max_vol = df.matrix_tot.max()

for idx, lnk in df.iterrows():
    geo = lnk.geometry
    plt.plot(*geo.xy, color="blue", linewidth=4 * lnk.matrix_tot / max_vol)
plt.show()

Close the project

project.close()