Note
Go to the end to download the full example code.
Network skimming#
In this example, we show how to perform network skimming for Coquimbo, a city in La Serena Metropolitan Area in Chile.
See also
Several functions, methods, classes and modules are used in this example:
# Imports
from uuid import uuid4
from tempfile import gettempdir
from os.path import join
from aequilibrae.utils.create_example import create_example
# We create the example project inside our temp folder
fldr = join(gettempdir(), uuid4().hex)
project = create_example(fldr, "coquimbo")
import logging
import sys
When the project opens, we can tell the logger to direct all messages to the terminal as well
logger = project.logger
stdout_handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter("%(asctime)s;%(levelname)s ; %(message)s")
stdout_handler.setFormatter(formatter)
logger.addHandler(stdout_handler)
Network Skimming#
import numpy as np
Let’s build all graphs
project.network.build_graphs()
# We get warnings that several fields in the project are filled with ``NaN``s.
# This is true, but we won't use those fields.
2025-01-15 19:54:25,030;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations
2025-01-15 19:54:25,114;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations
2025-01-15 19:54:25,218;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations
2025-01-15 19:54:25,320;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations
We grab the graph for cars
graph = project.network.graphs["c"]
# we also see what graphs are available
project.network.graphs.keys()
# let's say we want to minimize the distance
graph.set_graph("distance")
# And will skim distance while we are at it, other fields like ``free_flow_time`` or ``travel_time``
# can be added here as well
graph.set_skimming(["distance"])
# But let's say we only want a skim matrix for nodes 28-40, and 49-60 (inclusive),
# these happen to be a selection of western centroids.
graph.prepare_graph(np.array(list(range(28, 41)) + list(range(49, 91))))
2025-01-15 19:54:25,405;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations
And run the skimming
skm = graph.compute_skims()
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Building network skims directly from the graph is more straightforward, though
we could alternatively use the class NetworkSkimming to achieve the same result.
# from aequilibrae.paths import NetworkSkimming
# skm = NetworkSkimming(graph)
# skm.execute()
The result is an AequilibraEMatrix object
skims = skm.results.skims
# Which we can manipulate directly from its temp file, if we wish
skims.matrices[:3, :3, :]
array([[[ 0. ],
[4166.92919206],
[5532.32681478]],
[[3733.4499255 ],
[ 0. ],
[3311.30654014]],
[[5446.26074416],
[3596.12274848],
[ 0. ]]])
Or access each matrix, lets just look at the first 3x3
skims.distance[:3, :3]
array([[ 0. , 4166.92919206, 5532.32681478],
[3733.4499255 , 0. , 3311.30654014],
[5446.26074416, 3596.12274848, 0. ]])
We can save it to the project if we want
skm.save_to_project("base_skims")
2025-01-15 19:54:25,512;WARNING ; Matrix Record has been saved to the database
We can also retrieve this skim record to write something to its description
matrices = project.matrices
mat_record = matrices.get_record("base_skims")
mat_record.description = "minimized distance while also skimming distance for just a few nodes"
mat_record.save()
project.close()
Total running time of the script: (0 minutes 0.858 seconds)