7.3. Creating Delaunay Lines

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

For more on this topic, the firs publication is here: https://xl-optim.com/delaunay/

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()

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

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

demand = project.matrices.get_matrix("demand_omx")

# 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)
plot delaunay lines

Close the project


Total running time of the script: ( 0 minutes 1.407 seconds)

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