doe_driver.py

Design-of-Experiments Driver.

class openmdao.drivers.doe_driver.DOEDriver(generator=None, **kwargs)[source]

Bases: openmdao.core.driver.Driver

Design-of-Experiments Driver.

__init__(generator=None, **kwargs)[source]

Construct A DOEDriver.

Parameters
generatorDOEGenerator, list or None

The case generator or a list of DOE cases.

**kwargsdict of keyword arguments

Keyword arguments that will be mapped into the Driver options.

add_recorder(recorder)[source]

Add a recorder to the driver.

Parameters
recorderCaseRecorder

A recorder instance.

cleanup()

Clean up resources prior to exit.

declare_coloring(num_full_jacs=3, tol=1e-25, orders=None, perturb_size=1e-09, min_improve_pct=5.0, show_summary=True, show_sparsity=False)

Set options for total deriv coloring.

Parameters
num_full_jacsint

Number of times to repeat partial jacobian computation when computing sparsity.

tolfloat

Tolerance used to determine if an array entry is nonzero during sparsity determination.

ordersint

Number of orders above and below the tolerance to check during the tolerance sweep.

perturb_sizefloat

Size of input/output perturbation during generation of sparsity.

min_improve_pctfloat

If coloring does not improve (decrease) the number of solves more than the given percentage, coloring will not be used.

show_summarybool

If True, display summary information after generating coloring.

show_sparsitybool

If True, display sparsity with coloring info after generating coloring.

get_constraint_values(ctype='all', lintype='all', driver_scaling=True)

Return constraint values.

Parameters
ctypestring

Default is ‘all’. Optionally return just the inequality constraints with ‘ineq’ or the equality constraints with ‘eq’.

lintypestring

Default is ‘all’. Optionally return just the linear constraints with ‘linear’ or the nonlinear constraints with ‘nonlinear’.

driver_scalingbool

When True, return values that are scaled according to either the adder and scaler or the ref and ref0 values that were specified when add_design_var, add_objective, and add_constraint were called on the model. Default is True.

Returns
dict

Dictionary containing values of each constraint.

get_design_var_values(get_remote=True)

Return the design variable values.

Parameters
get_remotebool or None

If True, retrieve the value even if it is on a remote process. Note that if the variable is remote on ANY process, this function must be called on EVERY process in the Problem’s MPI communicator. If False, only retrieve the value if it is on the current process, or only the part of the value that’s on the current process for a distributed variable.

Returns
dict

Dictionary containing values of each design variable.

get_objective_values(driver_scaling=True)

Return objective values.

Parameters
driver_scalingbool

When True, return values that are scaled according to either the adder and scaler or the ref and ref0 values that were specified when add_design_var, add_objective, and add_constraint were called on the model. Default is True.

Returns
dict

Dictionary containing values of each objective.

property msginfo

Return info to prepend to messages.

Returns
str

Info to prepend to messages.

record_iteration()

Record an iteration of the current Driver.

run()[source]

Generate cases and run the model for each set of generated input values.

Returns
boolean

Failure flag; True if failed to converge, False is successful.

set_design_var(name, value, set_remote=True)

Set the value of a design variable.

Parameters
namestr

Global pathname of the design variable.

valuefloat or ndarray

Value for the design variable.

set_remotebool

If True, set the global value of the variable (value must be of the global size). If False, set the local value of the variable (value must be of the local size).

use_fixed_coloring(coloring=<object object>)

Tell the driver to use a precomputed coloring.

Parameters
coloringstr

A coloring filename. If no arg is passed, filename will be determined automatically.