analysis_driver.py

AnalysisDriver definition.

class openmdao.drivers.analysis_driver.AnalysisDriver(samples=None, **kwargs)

Bases: Driver

A driver for repeatedly running the model with a list of sampled data.

Samples may be provided as a Sequence of dictionaries, where each entry in the sequence is a dictionary keyed by the variable names to be set for that specific execution.

For instance, the following sequence of samples provides 3 executions, testing (x=0, y=4), (x=1, y=5), and (x=2, y=6). Units may be optionally provided.

Alternatively, samples can be provided as an instance of AnalysisGenerator, which will provide each sample in a lazily-evaluated way.

Parameters:

sampleslist, tuple, or AnalysisGenerator

If given, provides a list or tuple of samples (variable names and values to be tested), or an AnalysisGenerator which provides samples.

**kwargsdict of keyword arguments

Keyword arguments that will be mapped into the Driver options.

Attributes:

_namestr

The name used to identify this driver in recorded samples.

_problem_commMPI.Comm or None

The MPI communicator for the Problem.

_colorint or None

In MPI, the cached color is used to determine which samples to run on this proc.

_num_colorsint

The number of total MPI colors for the run.

_prev_sample_varsset

The set of variables seen in the previous iteration of the driver on this rank.

_generatorAnalysisGenerator

The internal AnalysisGenerator providing samples.

__init__(samples=None, **kwargs)

Construct an AnalysisDriver.

add_recorder(recorder)

Add a recorder to the driver.

Parameters:

recorderCaseRecorder

A recorder instance.

add_response(name, indices=None, units=None, linear=False, parallel_deriv_color=None, cache_linear_solution=False, flat_indices=None, alias=None)

Add a response variable to the model associated with this AnalysisDriver.

For AnalysisDriver, a response is an “output of interest” that we want to monitor as a result of changes made in the various samples.

The AnalysisDriver.add_response interface does not support any optimization-centric arguments associated with constraints or objectives, such as scaling.

Internally, the driver does add this as an ‘objective’ to the model for the purposes of tracking derivatives.

Parameters:

namestr

Promoted name of the response variable in the system.

indicessequence of int, optional

If variable is an array, these indicate which entries are of interest for this particular response.

unitsstr, optional

Units to convert to before applying scaling.

linearbool

Set to True if constraint is linear. Default is False.

parallel_deriv_colorstr

If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.

cache_linear_solutionbool

If True, store the linear solution vectors for this variable so they can be used to start the next linear solution with an initial guess equal to the solution from the previous linear solve.

flat_indicesbool

If True, interpret specified indices as being indices into a flat source array.

aliasstr or None

Alias for this response. Necessary when adding multiple responses on different indices of the same variable.

add_responses(responses)

Add multiple responses to be recorded by the AnalysisDriver.

Parameters:

responsesSequence or dict or str

A sequence of response names to be recorded. If more metadata needs to be specified, reponses can be provided as a dictionary whose keys are the variables to be recorded, and whose associated values are dictionaries of metadata to be passed on as keyword arguments to add_response.

check_relevance()

Check if there are constraints that don’t depend on any design vars.

This usually indicates something is wrong with the problem formulation.

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, use_scaling=False, randomize_subjacs=True, randomize_seeds=False, direct=True)

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.

use_scalingbool

If True, use driver scaling when generating the sparsity.

randomize_subjacsbool

If True, use random subjacobians corresponding to their declared sparsity patterns.

randomize_seedsbool

If True, use random seeds when computing the sparsity.

directbool

If using bidirectional coloring, use the direct method when computing the column adjacency matrix instead of the substitution method.

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

Return constraint values.

Parameters:

ctypestr

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

lintypestr

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, driver_scaling=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.

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 design variable.

get_driver_derivative_calls()

Return number of derivative evaluations made during a driver run.

Returns:

int

Number of derivative evaluations made during a driver run.

get_driver_objective_calls()

Return number of objective evaluations made during a driver run.

Returns:

int

Number of objective evaluations made during a driver run.

get_exit_status()

Return exit status of driver run.

Returns:

str

String indicating result of driver run.

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.

get_reports_dir()

Get the path to the directory where the report files should go.

If it doesn’t exist, it will be created.

Returns:

str

The path to the directory where reports should be written.

property msginfo

Return info to prepend to messages.

Returns:

str

Info to prepend to messages.

record_derivatives()

Record the current total jacobian.

record_iteration()

Record an iteration of the current Driver.

run()

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

Rank 0 will both manage the distribution of samples to the other procs and serve as a worker running the samples.

All other procs just run samples.

Returns:

bool

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

scaling_report(outfile='driver_scaling_report.html', title=None, show_browser=True, jac=True)

Generate a self-contained html file containing a detailed connection viewer.

Optionally pops up a web browser to view the file.

Parameters:

outfilestr, optional

The name of the output html file. Defaults to ‘driver_scaling_report.html’.

titlestr, optional

Sets the title of the web page.

show_browserbool, optional

If True, pop up a browser to view the generated html file. Defaults to True.

jacbool

If True, show jacobian information.

Returns:

dict

Data used to create html file.

set_design_var(name, value, set_remote=True)

Set the value of a design variable.

‘name’ can be a promoted output name or an alias.

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=<openmdao.utils.coloring.STD_COLORING_FNAME object>)

Tell the driver to use a precomputed coloring.

Parameters:

coloringstr or Coloring

A coloring filename or a Coloring object. If no arg is passed, filename will be determined automatically.