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.

/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/aequilibrae/project/network/network.py:327: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  df = pd.read_sql(sql, conn).fillna(value=np.nan)
2024-08-19 06:15:29,631;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations
2024-08-19 06:15:29,716;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations
2024-08-19 06:15:29,821;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations
2024-08-19 06:15:29,919;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))))

2024-08-19 06:15:30,001;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.        ],
        [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")

2024-08-19 06:15:30,211;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()