Network skimming

In this example, we show how to perform network skimming for Coquimbo, a city in La Serena Metropolitan Area in Chile.

# 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

# We 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

from aequilibrae.paths import NetworkSkimming
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 NaNs.
# This is true, but we won't use those fields.

2023-12-16 11:42:35,027;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations
2023-12-16 11:42:35,111;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations
2023-12-16 11:42:35,210;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations
2023-12-16 11:42:35,307;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))))

2023-12-16 11:42:35,390;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 = 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.        ],
        [4567.06825964],
        [6578.19145002]],

       [[4211.59500034],
        [   0.        ],
        [3820.92231366]],

       [[6427.11674525],
        [3979.68975713],
        [   0.        ]]])

# Or access each matrix, lets just look at the first 3x3
skims.distance[:3, :3]

array([[   0.        , 4567.06825964, 6578.19145002],
       [4211.59500034,    0.        , 3820.92231366],
       [6427.11674525, 3979.68975713,    0.        ]])

# We can save it to the project if we want
skm.save_to_project("base_skims")

# 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()

2023-12-16 11:42:35,501;WARNING ; Matrix Record has been saved to the database

project.close()