problem.py

Define the Problem class and a FakeComm class for non-MPI users.

class openmdao.core.problem.ErrorTuple(forward, reverse, forward_reverse)

Bases: tuple

__contains__(self, key, /)

Return key in self.

__getitem__(self, key, /)

Return self[key].

__init__(self, /, *args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

__iter__(self, /)

Implement iter(self).

count()

property forward

Alias for field number 0

property forward_reverse

Alias for field number 2

index()

Raises ValueError if the value is not present.

property reverse

Alias for field number 1

class openmdao.core.problem.MagnitudeTuple(forward, reverse, fd)

Bases: tuple

__contains__(self, key, /)

Return key in self.

__getitem__(self, key, /)

Return self[key].

__init__(self, /, *args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

__iter__(self, /)

Implement iter(self).

count()

property fd

Alias for field number 2

property forward

Alias for field number 0

index()

Raises ValueError if the value is not present.

property reverse

Alias for field number 1

class openmdao.core.problem.Problem(model=None, driver=None, comm=None, root=None, name=None, **options)

Bases: object

Top-level container for the systems and drivers.

Attributes

model	(<System>) Pointer to the top-level <System> object (root node in the tree).
comm	(MPI.Comm or <FakeComm>) The global communicator.
driver	(<Driver>) Slot for the driver. The default driver is Driver, which just runs the model once.
cite	(str) Listing of relevant citations that should be referenced when publishing work that uses this class.
options	(<OptionsDictionary>) Dictionary with general options for the problem.
recording_options	(<OptionsDictionary>) Dictionary with problem recording options.

__getitem__(self, name)

Get an output/input variable.

Parameters

namestr

Promoted or relative variable name in the root system’s namespace.

Returns

float or ndarray or any python object

the requested output/input variable.

__init__(self, model=None, driver=None, comm=None, root=None, name=None, **options)

Initialize attributes.

Parameters

model<System> or None

The top-level <System>. If not specified, an empty <Group> will be created.

driver<Driver> or None

The driver for the problem. If not specified, a simple “Run Once” driver will be used.

commMPI.Comm or <FakeComm> or None

The global communicator.

root<System> or None

Deprecated kwarg for model.

namestr

Problem name. Can be used to specify a Problem instance when multiple Problems exist.

**optionsnamed args

All remaining named args are converted to options.

__setitem__(self, name, value)

Set an output/input variable.

Parameters

namestr

Promoted or relative variable name in the root system’s namespace.

valuefloat or ndarray or any python object

value to set this variable to.

add_recorder(self, recorder)

Add a recorder to the problem.

Parameters

recorderCaseRecorder

A recorder instance.

check_config(self, logger=None, checks=None, out_file='openmdao_checks.out')

Perform optional error checks on a Problem.

Parameters

loggerobject

Logging object.

checkslist of str or None

List of specific checks to be performed.

out_filestr or None

If not None, output will be written to this file in addition to stdout.

check_partials(self, out_stream=<object object at 0x7f0c3bd810e0>, includes=None, excludes=None, compact_print=False, abs_err_tol=1e-06, rel_err_tol=1e-06, method='fd', step=None, form='forward', step_calc='abs', force_dense=True, show_only_incorrect=False)

Check partial derivatives comprehensively for all components in your model.

Parameters

out_streamfile-like object

Where to send human readable output. By default it goes to stdout. Set to None to suppress.

includesNone or list_like

List of glob patterns for pathnames to include in the check. Default is None, which includes all components in the model.

excludesNone or list_like

List of glob patterns for pathnames to exclude from the check. Default is None, which excludes nothing.

compact_printbool

Set to True to just print the essentials, one line per unknown-param pair.

abs_err_tolfloat

Threshold value for absolute error. Errors about this value will have a ‘*’ displayed next to them in output, making them easy to search for. Default is 1.0E-6.

rel_err_tolfloat

Threshold value for relative error. Errors about this value will have a ‘*’ displayed next to them in output, making them easy to search for. Note at times there may be a significant relative error due to a minor absolute error. Default is 1.0E-6.

methodstr

Method, ‘fd’ for finite difference or ‘cs’ for complex step. Default is ‘fd’.

stepfloat

Step size for approximation. Default is None, which means 1e-6 for ‘fd’ and 1e-40 for ‘cs’.

formstring

Form for finite difference, can be ‘forward’, ‘backward’, or ‘central’. Default ‘forward’.

step_calcstring

Step type for finite difference, can be ‘abs’ for absolute’, or ‘rel’ for relative. Default is ‘abs’.

force_densebool

If True, analytic derivatives will be coerced into arrays. Default is True.

show_only_incorrectbool, optional

Set to True if output should print only the subjacs found to be incorrect.

Returns

dict of dicts of dicts

First key:

is the component name;

Second key:

is the (output, input) tuple of strings;

Third key:

is one of [‘rel error’, ‘abs error’, ‘magnitude’, ‘J_fd’, ‘J_fwd’, ‘J_rev’];

For ‘rel error’, ‘abs error’, ‘magnitude’ the value is: A tuple containing norms for

forward - fd, adjoint - fd, forward - adjoint.

For ‘J_fd’, ‘J_fwd’, ‘J_rev’ the value is: A numpy array representing the computed

Jacobian for the three different methods of computation.

check_totals(self, of=None, wrt=None, out_stream=<object object at 0x7f0c3bd810e0>, compact_print=False, driver_scaling=False, abs_err_tol=1e-06, rel_err_tol=1e-06, method='fd', step=None, form=None, step_calc='abs')

Check total derivatives for the model vs. finite difference.

Parameters

oflist of variable name strings or None

Variables whose derivatives will be computed. Default is None, which uses the driver’s objectives and constraints.

wrtlist of variable name strings or None

Variables with respect to which the derivatives will be computed. Default is None, which uses the driver’s desvars.

out_streamfile-like object

Where to send human readable output. By default it goes to stdout. Set to None to suppress.

compact_printbool

Set to True to just print the essentials, one line per unknown-param pair.

driver_scalingbool

When True, return derivatives 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 False, which is unscaled.

abs_err_tolfloat

Threshold value for absolute error. Errors about this value will have a ‘*’ displayed next to them in output, making them easy to search for. Default is 1.0E-6.

rel_err_tolfloat

Threshold value for relative error. Errors about this value will have a ‘*’ displayed next to them in output, making them easy to search for. Note at times there may be a significant relative error due to a minor absolute error. Default is 1.0E-6.

methodstr

Method, ‘fd’ for finite difference or ‘cs’ for complex step. Default is ‘fd’

stepfloat

Step size for approximation. Default is None, which means 1e-6 for ‘fd’ and 1e-40 for ‘cs’.

formstring

Form for finite difference, can be ‘forward’, ‘backward’, or ‘central’. Default None, which defaults to ‘forward’ for FD.

step_calcstring

Step type for finite difference, can be ‘abs’ for absolute’, or ‘rel’ for relative. Default is ‘abs’.

Returns

Dict of Dicts of Tuples of Floats

First key:

is the (output, input) tuple of strings;

Second key:

is one of [‘rel error’, ‘abs error’, ‘magnitude’, ‘fdstep’];

For ‘rel error’, ‘abs error’, ‘magnitude’ the value is: A tuple containing norms for

forward - fd, adjoint - fd, forward - adjoint.

cleanup(self)

Clean up resources prior to exit.

compute_jacvec_product(self, of, wrt, mode, seed)

Given a seed and ‘of’ and ‘wrt’ variables, compute the total jacobian vector product.

Parameters

oflist of str

Variables whose derivatives will be computed.

wrtlist of str

Derivatives will be computed with respect to these variables.

modestr

Derivative direction (‘fwd’ or ‘rev’).

seeddict or list

Either a dict keyed by ‘wrt’ varnames (fwd) or ‘of’ varnames (rev), containing dresidual (fwd) or doutput (rev) values, OR a list of dresidual or doutput values that matches the corresponding ‘wrt’ (fwd) or ‘of’ (rev) varname list.

Returns

dict

The total jacobian vector product, keyed by variable name.

compute_totals(self, of=None, wrt=None, return_format='flat_dict', debug_print=False, driver_scaling=False)

Compute derivatives of desired quantities with respect to desired inputs.

Parameters

oflist of variable name strings or None

Variables whose derivatives will be computed. Default is None, which uses the driver’s objectives and constraints.

wrtlist of variable name strings or None

Variables with respect to which the derivatives will be computed. Default is None, which uses the driver’s desvars.

return_formatstring

Format to return the derivatives. Can be ‘dict’, ‘flat_dict’, or ‘array’. Default is a ‘flat_dict’, which returns them in a dictionary whose keys are tuples of form (of, wrt).

debug_printbool

Set to True to print out some debug information during linear solve.

driver_scalingbool

When True, return derivatives 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 False, which is unscaled.

Returns

derivsobject

Derivatives in form requested by ‘return_format’.

final_setup(self)

Perform final setup phase on problem in preparation for run.

This is the second phase of setup, and is done automatically at the start of run_driver and run_model. At the beginning of final_setup, we have a model hierarchy with defined variables, solvers, case_recorders, and derivative settings. During this phase, the vectors are created and populated, the drivers and solvers are initialized, and the recorders are started, and the rest of the framework is prepared for execution.

get_val(self, name, units=None, indices=None, get_remote=False)

Get an output/input variable.

Function is used if you want to specify display units.

Parameters

namestr

Promoted or relative variable name in the root system’s namespace.

unitsstr, optional

Units to convert to before return.

indicesint or list of ints or tuple of ints or int ndarray or Iterable or None, optional

Indices or slice to return.

get_remotebool

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.

Returns

float or ndarray

The requested output/input variable.

is_local(self, name)

Return True if the named variable or system is local to the current process.

Parameters

namestr

Name of a variable or system.

Returns

bool

True if the named system or variable is local to this process.

list_problem_vars(self, show_promoted_name=True, print_arrays=False, desvar_opts=[], cons_opts=[], objs_opts=[])

Print all design variables and responses (objectives and constraints).

Parameters

show_promoted_namebool

If True, then show the promoted names of the variables.

print_arraysbool, optional

When False, in the columnar display, just display norm of any ndarrays with size > 1. The norm is surrounded by vertical bars to indicate that it is a norm. When True, also display full values of the ndarray below the row. Format is affected by the values set with numpy.set_printoptions Default is False.

desvar_optslist of str

List of optional columns to be displayed in the desvars table. Allowed values are: [‘lower’, ‘upper’, ‘ref’, ‘ref0’, ‘indices’, ‘adder’, ‘scaler’, ‘parallel_deriv_color’, ‘vectorize_derivs’, ‘cache_linear_solution’]

cons_optslist of str

List of optional columns to be displayed in the cons table. Allowed values are: [‘lower’, ‘upper’, ‘equals’, ‘ref’, ‘ref0’, ‘indices’, ‘index’, ‘adder’, ‘scaler’, ‘linear’, ‘parallel_deriv_color’, ‘vectorize_derivs’, ‘cache_linear_solution’]

objs_optslist of str

List of optional columns to be displayed in the objs table. Allowed values are: [‘ref’, ‘ref0’, ‘indices’, ‘adder’, ‘scaler’, ‘parallel_deriv_color’, ‘vectorize_derivs’, ‘cache_linear_solution’]

load_case(self, case)

Pull all input and output variables from a case into the model.

Parameters

caseCase object

A Case from a CaseRecorder file.

property msginfo

Return info to prepend to messages.

Returns

str

Info to prepend to messages.

record_iteration(self, case_name)

Record the variables at the Problem level.

Parameters

case_namestr

Name used to identify this Problem case.

property root

Provide ‘root’ property for backwards compatibility.

Returns

<Group>

reference to the ‘model’ property.

run(self)

Backward compatible call for run_driver.

Returns

boolean

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

run_driver(self, case_prefix=None, reset_iter_counts=True)

Run the driver on the model.

Parameters

case_prefixstr or None

Prefix to prepend to coordinates when recording.

reset_iter_countsbool

If True and model has been run previously, reset all iteration counters.

Returns

boolean

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

run_model(self, case_prefix=None, reset_iter_counts=True)

Run the model by calling the root system’s solve_nonlinear.

Parameters

case_prefixstr or None

Prefix to prepend to coordinates when recording.

reset_iter_countsbool

If True and model has been run previously, reset all iteration counters.

run_once(self)

Backward compatible call for run_model.

set_solver_print(self, level=2, depth=1e+99, type_='all')

Control printing for solvers and subsolvers in the model.

Parameters

levelint

iprint level. Set to 2 to print residuals each iteration; set to 1 to print just the iteration totals; set to 0 to disable all printing except for failures, and set to -1 to disable all printing including failures.

depthint

How deep to recurse. For example, you can set this to 0 if you only want to print the top level linear and nonlinear solver messages. Default prints everything.

type_str

Type of solver to set: ‘LN’ for linear, ‘NL’ for nonlinear, or ‘all’ for all.

set_val(self, name, value, units=None, indices=None)

Set an output/input variable.

Function is used if you want to set a value using a different unit.

Parameters

namestr

Promoted or relative variable name in the root system’s namespace.

valuefloat or ndarray or list

Value to set this variable to.

unitsstr, optional

Units that value is defined in.

indicesint or list of ints or tuple of ints or int ndarray or Iterable or None, optional

Indices or slice to set to specified value.

setup(self, vector_class=None, check=False, logger=None, mode='auto', force_alloc_complex=False, distributed_vector_class=<class 'openmdao.vectors.petsc_vector.PETScVector'>, local_vector_class=<class 'openmdao.vectors.default_vector.DefaultVector'>, derivatives=True)

Set up the model hierarchy.

When setup is called, the model hierarchy is assembled, the processors are allocated (for MPI), and variables and connections are all assigned. This method traverses down the model hierarchy to call setup on each subsystem, and then traverses up the model hierarchy to call configure on each subsystem.

Parameters

vector_classtype

Reference to an actual <Vector> class; not an instance. This is deprecated. Use distributed_vector_class instead.

checkboolean

whether to run config check after setup is complete.

loggerobject

Object for logging config checks if check is True.

modestring

Derivatives calculation mode, ‘fwd’ for forward, and ‘rev’ for reverse (adjoint). Default is ‘auto’, which will pick ‘fwd’ or ‘rev’ based on the direction resulting in the smallest number of linear solves required to compute derivatives.

force_alloc_complexbool

Force allocation of imaginary part in nonlinear vectors. OpenMDAO can generally detect when you need to do this, but in some cases (e.g., complex step is used after a reconfiguration) you may need to set this to True.

distributed_vector_classtype

Reference to the <Vector> class or factory function used to instantiate vectors and associated transfers involved in interprocess communication.

local_vector_classtype

Reference to the <Vector> class or factory function used to instantiate vectors and associated transfers involved in intraprocess communication.

derivativesbool

If True, perform any memory allocations necessary for derivative computation.

Returns

self<Problem>

this enables the user to instantiate and setup in one line.

class openmdao.core.problem.Slicer

Bases: object

Helper class that can be used with the indices argument for Problem set_val and get_val.

__getitem__(self, val)

Pass through indices or slice.

Parameters

valint or slice object or tuples of slice objects

Indices or slice to return.

Returns

indicesint or slice object or tuples of slice objects

Indices or slice to return.

__init__(self, /, *args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.