aequilibrae.distribution.GravityCalibration

class aequilibrae.distribution.GravityCalibration(project=None, **kwargs)

Calibrate a traditional gravity model

Available deterrence function forms are: ‘EXPO’, ‘POWER’ or ‘GAMMA’.

>>> from aequilibrae.distribution import GravityCalibration

>>> project = create_example(project_path)

# We load the demand matrix
>>> demand = project.matrices.get_matrix("demand_omx")
>>> demand.computational_view()

# We load the skim matrix
>>> skim = project.matrices.get_matrix("skims")
>>> skim.computational_view(["time_final"])

>>> args = {"matrix": demand,
...         "impedance": skim,
...         "row_field": "productions",
...         "function": 'expo',
...         "nan_as_zero": True}
>>> gravity = GravityCalibration(**args)

# Solve and save outputs
>>> gravity.calibrate()
>>> gravity.model.save(os.path.join(project_path, 'dist_expo_model.mod'))

__init__(project=None, **kwargs)

Instantiates the Gravity calibration problem

Arguments:

matrix (AequilibraeMatrix): Seed/base trip matrix

impedance (AequilibraeMatrix): Impedance matrix to be used

function (str): Function name to be calibrated. “EXPO” or “POWER”

project (Project, Optional): The Project to connect to. By default, uses the currently active project

parameters (str, Optional): Convergence parameters. Defaults to those in the parameter file

nan_as_zero (bool, Optional): If Nan values should be treated as zero. Defaults to True

Results:

model (SyntheticGravityModel): Calibrated model

report (list): Iteration and convergence report

error (str): Error description

Methods

__init__([project])	Instantiates the Gravity calibration problem
calibrate()	Calibrate the model

calibrate()

Calibrate the model

Resulting model is in output class member