predeterminedruns_driver.py

Baseclass for design-of-experiments Drivers that have pre-determined parameter sets.

class openmdao.drivers.predeterminedruns_driver.PredeterminedRunsDriver(num_par_doe=1, load_balance=False)

Bases: openmdao.core.driver.Driver

Baseclass for design-of-experiments Drivers that have pre-determined parameter sets.

Args:

num_par_doe : int, optional

The number of DOE cases to run concurrently. Defaults to 1.

load_balance : bool, Optional

If True and running under MPI, use rank 0 as master and load balance cases among all of the other ranks. Default is False. If multiprocessing is being used instead of MPI, then cases are always load balanced.

add_constraint(name, lower=None, upper=None, equals=None, linear=False, jacs=None, indices=None, adder=0.0, scaler=1.0)

Adds a constraint to this driver. For inequality constraints, lower or upper must be specified. For equality constraints, equals must be specified.

Args:

name : string

Promoted pathname of the output that will serve as the quantity to constrain.

lower : float or ndarray, optional

Constrain the quantity to be greater than or equal to this value.

upper : float or ndarray, optional

Constrain the quantity to be less than or equal to this value.

equals : float or ndarray, optional

Constrain the quantity to be equal to this value.

linear : bool, optional

Set to True if this constraint is linear with respect to all design variables so that it can be calculated once and cached.

jacs : dict of functions, optional

Dictionary of user-defined functions that return the flattened Jacobian of this constraint with repsect to the design vars of this driver, as indicated by the dictionary keys. Default is None to let OpenMDAO calculate all derivatives. Note, this is currently unsupported

indices : iter of int, optional

If a constraint is an array, these indicate which entries are of interest for derivatives.

adder : float or ndarray, optional

Value to add to the model value to get the scaled value. Adder is first in precedence.

scaler : float or ndarray, optional

value to multiply the model value to get the scaled value. Scaler is second in precedence.

add_desvar(name, lower=None, upper=None, low=None, high=None, indices=None, adder=0.0, scaler=1.0)

Adds a design variable to this driver.

Args:

name : string

Name of the design variable in the root system.

lower : float or ndarray, optional

Lower boundary for the param

upper : upper or ndarray, optional

Upper boundary for the param

indices : iter of int, optional

If a param is an array, these indicate which entries are of interest for derivatives.

adder : float or ndarray, optional

Value to add to the model value to get the scaled value. Adder is first in precedence.

scaler : float or ndarray, optional

value to multiply the model value to get the scaled value. Scaler is second in precedence.

add_objective(name, indices=None, adder=0.0, scaler=1.0)

Adds an objective to this driver.

Args:

name : string

Promoted pathname of the output that will serve as the objective.

indices : iter of int, optional

If an objective is an array, these indicate which entries are of interest for derivatives.

adder : float or ndarray, optional

Value to add to the model value to get the scaled value. Adder is first in precedence.

scaler : float or ndarray, optional

value to multiply the model value to get the scaled value. Scaler is second in precedence.

add_response(name)

Add a variable(s) whose value will be collected after the execution of each case.

Args:

name : str or iter of str

The name of the response variable, or an iterator of names.

get_all_responses()

Similar to get_responses(), but this version ensures that each process gets all of the responses.

get_req_procs()

Returns:

tuple

A tuple of the form (min_procs, max_procs), indicating the min and max processors usable by this Driver.

get_responses()

Returns an iterator over tuples of the form (responses, success, msg), where responses is a list tuples containing variable names and values, success is true if there were no errors when running the case, and msg is an error message if there were errors or an empty string if not.

run(problem)

Build a runlist and execute the Problem for each set of generated parameters.

openmdao.drivers.predeterminedruns_driver.worker(problem, response_vars, case_queue, response_queue, worker_id)

This is used to run parallel DOEs using multprocessing. It takes a case off of the case_queue, runs it, then puts responses on the response_queue.