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__(key, /)

Return key in self.

__getitem__(key, /)

Return self[key].

__iter__(/)

Implement iter(self).

count(value, /)

Return number of occurrences of value.

forward

Alias for field number 0

forward_reverse

Alias for field number 2

index(value, start=0, stop=sys.maxsize, /)

Return first index of value.

Raises ValueError if the value is not present.

reverse

Alias for field number 1

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

Bases: tuple

__contains__(key, /)

Return key in self.

__getitem__(key, /)

Return self[key].

__iter__(/)

Implement iter(self).

count(value, /)

Return number of occurrences of value.

fd

Alias for field number 2

forward

Alias for field number 0

index(value, start=0, stop=sys.maxsize, /)

Return first index of value.

Raises ValueError if the value is not present.

reverse

Alias for field number 1

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

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__(name)[source]

Get an output/input variable.

Parameters
name: str

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__(model=None, driver=None, comm=None, name=None, **options)[source]

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.

comm: MPI.Comm or <FakeComm> or None

The global communicator.

name: str

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

**options: named args

All remaining named args are converted to options.

__setitem__(name, value)[source]

Set an output/input variable.

Parameters
name: str

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

value: float or ndarray or any python object

value to set this variable to.

add_recorder(recorder)[source]

Add a recorder to the problem.

Parameters
recorder: CaseRecorder

A recorder instance.

check_config(logger=None, checks={'auto_ivc_warnings': <function _check_auto_ivc_warnings>, 'comp_has_no_outputs': <function _check_comp_has_no_outputs>, 'dup_inputs': <function _check_dup_comp_inputs>, 'missing_recorders': <function _check_missing_recorders>, 'out_of_order': <function _check_ubcs_prob>, 'solvers': <function _check_solvers>, 'system': <function _check_system_configs>}, out_file='openmdao_checks.out')[source]

Perform optional error checks on a Problem.

Parameters
logger: object

Logging object.

checks: list of str or None or the string ‘all’

Determines what config checks are run. If None, no checks are run If list of str, run those config checks If ‘all’, all the checks (‘auto_ivc_warnings’, ‘comp_has_no_outputs’, ‘cycles’, ‘dup_inputs’, ‘missing_recorders’, ‘out_of_order’, ‘promotions’, ‘solvers’, ‘system’, ‘unconnected_inputs’) are run

out_file: str or None

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

check_partials(out_stream=DEFAULT_OUT_STREAM, 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)[source]

Check partial derivatives comprehensively for all components in your model.

Parameters
out_stream: file-like object

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

includes: None or list_like

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

excludes: None or list_like

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

compact_print: bool

Set to True to just print the essentials, one line per input-output pair.

abs_err_tol: float

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_tol: float

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.

method: str

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

step: float

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

form: string

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

step_calc: string

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

force_dense: bool

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

show_only_incorrect: bool, 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(of=None, wrt=None, out_stream=DEFAULT_OUT_STREAM, 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', show_progress=False)[source]

Check total derivatives for the model vs. finite difference.

Parameters
of: list of variable name strings or None

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

wrt: list 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_stream: file-like object

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

compact_print: bool

Set to True to just print the essentials, one line per input-output pair.

driver_scaling: bool

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_tol: float

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_tol: float

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.

method: str

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

step: float

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

form: string

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

step_calc: string

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

show_progress: bool

True to show progress of check_totals

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()[source]

Clean up resources prior to exit.

compute_jacvec_product(of, wrt, mode, seed)[source]

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

Parameters
of: list of str

Variables whose derivatives will be computed.

wrt: list of str

Derivatives will be computed with respect to these variables.

mode: str

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

seed: dict 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(of=None, wrt=None, return_format='flat_dict', debug_print=False, driver_scaling=False, use_abs_names=False, get_remote=True)[source]

Compute derivatives of desired quantities with respect to desired inputs.

Parameters
of: list of variable name strings or None

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

wrt: list 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_format: string

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_print: bool

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

driver_scaling: bool

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.

use_abs_names: bool

Set to True when passing in absolute names to skip some translation steps.

get_remote: bool

If True, the default, the full distributed total jacobian will be retrieved.

Returns
derivs: object

Derivatives in form requested by ‘return_format’.

final_setup()[source]

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(name, units=None, indices=None, get_remote=False)[source]

Get an output/input variable.

Function is used if you want to specify display units.

Parameters
name: str

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

units: str, optional

Units to convert to before return.

indices: int or list of ints or tuple of ints or int ndarray or Iterable or None, optional

Indices or slice to return.

get_remote: bool 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. If None and the variable is remote or distributed, a RuntimeError will be raised.

Returns
object

The value of the requested output/input variable.

is_local(name)[source]

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

Parameters
name: str

Name of a variable or system.

Returns
bool

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

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

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

Parameters
show_promoted_name: bool

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

print_arrays: bool, 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.

driver_scaling: bool, optional

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.

desvar_opts: list 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’, ‘units’]

cons_opts: list 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’, ‘units’]

objs_opts: list of str

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

load_case(case)[source]

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

Parameters
case: Case object

A Case from a CaseRecorder file.

property msginfo

Return info to prepend to messages.

Returns
str

Info to prepend to messages.

record(case_name)[source]

Record the variables at the Problem level.

Must be called after final_setup has been called. This can either happen automatically through run_driver or run_model, or it can be called manually.

Parameters
case_name: str

Name used to identify this Problem case.

record_iteration(case_name)[source]

Record the variables at the Problem level.

Parameters
case_name: str

Name used to identify this Problem case.

run_driver(case_prefix=None, reset_iter_counts=True)[source]

Run the driver on the model.

Parameters
case_prefix: str or None

Prefix to prepend to coordinates when recording.

reset_iter_counts: bool

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(case_prefix=None, reset_iter_counts=True)[source]

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

Parameters
case_prefix: str or None

Prefix to prepend to coordinates when recording.

reset_iter_counts: bool

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

set_complex_step_mode(active)[source]

Turn on or off complex stepping mode.

Parameters
active: bool

Complex mode flag; set to True prior to commencing complex step.

set_solver_print(level=2, depth=1e+99, type_='all')[source]

Control printing for solvers and subsolvers in the model.

Parameters
level: int

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.

depth: int

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(name, value, units=None, indices=None)[source]

Set an output/input variable.

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

Parameters
name: str

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

value: float or ndarray or list

Value to set this variable to.

units: str, optional

Units that value is defined in.

indices: int 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(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)[source]

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
check: None, boolean, list of strings, or the string ‘all’

Determines what config checks, if any, are run after setup is complete. If None or False, no checks are run If True, the default checks (‘out_of_order’, ‘system’, ‘solvers’, ‘dup_inputs’, ‘missing_recorders’, ‘comp_has_no_outputs’, ‘auto_ivc_warnings’) are run If list of str, run those config checks If ‘all’, all the checks (‘auto_ivc_warnings’, ‘comp_has_no_outputs’, ‘cycles’, ‘dup_inputs’, ‘missing_recorders’, ‘out_of_order’, ‘promotions’, ‘solvers’, ‘system’, ‘unconnected_inputs’) are run

logger: object

Object for logging config checks if check is True.

mode: string

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_complex: bool

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_class: type

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

local_vector_class: type

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

derivatives: bool

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[source]

Bases: object

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

__getitem__(val)[source]

Pass through indices or slice.

Parameters
val: int or slice object or tuples of slice objects

Indices or slice to return.

Returns
indices: int or slice object or tuples of slice objects

Indices or slice to return.