multifi_meta_model.py

Define the MultiFiMetaModel class.

class openmdao.components.multifi_meta_model.MultiFiMetaModel(*args, **kwargs)[source]

Bases: openmdao.components.multifi_meta_model.MultiFiMetaModelUnStructured

Deprecated.

__init__(*args, **kwargs)[source]

Capture Initialize to throw warning.

Parameters:

*args : list

Deprecated arguments.

**kwargs : dict

Deprecated arguments.

add_constraint(name, lower=None, upper=None, equals=None, ref=None, ref0=None, adder=None, scaler=None, indices=None, linear=False, parallel_deriv_color=None, vectorize_derivs=False, simul_coloring=None, simul_map=None)

Add a constraint variable to this system.

Parameters:

name : string

Name of the response variable in the system.

lower : float or ndarray, optional

Lower boundary for the variable

upper : float or ndarray, optional

Upper boundary for the variable

equals : float or ndarray, optional

Equality constraint value for the variable

ref : float or ndarray, optional

Value of response variable that scales to 1.0 in the driver.

ref0 : float or ndarray, optional

Value of response variable that scales to 0.0 in the driver.

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.

indices : sequence of int, optional

If variable is an array, these indicate which entries are of interest for this particular response. These may be positive or negative integers.

linear : bool

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

parallel_deriv_color : string

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

vectorize_derivs : bool

If True, vectorize derivative calculations.

simul_coloring : ndarray or list of int

An array or list of integer color values. Must match the size of the constraint variable.

simul_map : dict

Mapping of this response to each design variable where simultaneous derivs will be used. Each design variable entry is another dict keyed on color, and the values in the color dict are tuples of the form (resp_idxs, color_idxs).

Notes

The response can be scaled using ref and ref0. The argument ref0 represents the physical value when the scaled value is 0. The argument ref represents the physical value when the scaled value is 1.

add_design_var(name, lower=None, upper=None, ref=None, ref0=None, indices=None, adder=None, scaler=None, parallel_deriv_color=None, vectorize_derivs=False, simul_coloring=None)

Add a design variable to this system.

Parameters:

name : string

Name of the design variable in the system.

lower : float or ndarray, optional

Lower boundary for the param

upper : upper or ndarray, optional

Upper boundary for the param

ref : float or ndarray, optional

Value of design var that scales to 1.0 in the driver.

ref0 : float or ndarray, optional

Value of design var that scales to 0.0 in the driver.

indices : iter of int, optional

If a param is an array, these indicate which entries are of interest for this particular design variable. These may be positive or negative integers.

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.

parallel_deriv_color : string

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

vectorize_derivs : bool

If True, vectorize derivative calculations.

simul_coloring : ndarray or list of int

An array or list of integer color values. Must match the size of the design variable.

Notes

The response can be scaled using ref and ref0. The argument ref0 represents the physical value when the scaled value is 0. The argument ref represents the physical value when the scaled value is 1.

add_input(name, val=1.0, shape=None, src_indices=None, flat_src_indices=None, units=None, desc='', var_set=0)

Add an input variable to the component.

Parameters:

name : str

name of the variable in this component’s namespace.

val : float or list or tuple or ndarray or Iterable

The initial value of the variable being added in user-defined units. Default is 1.0.

shape : int or tuple or list or None

Shape of this variable, only required if src_indices not provided and val is not an array. Default is None.

src_indices : int or list of ints or tuple of ints or int ndarray or Iterable or None

The global indices of the source variable to transfer data from. If val is given as an array_like object, the shapes of val and src_indices must match. A value of None implies this input depends on all entries of source. Default is None.

flat_src_indices : bool

If True, each entry of src_indices is assumed to be an index into the flattened source. Otherwise each entry must be a tuple or list of size equal to the number of dimensions of the source.

units : str or None

Units in which this input variable will be provided to the component during execution. Default is None, which means it is unitless.

desc : str

description of the variable

var_set : hashable object

For advanced users only. ID or color for this variable, relevant for reconfigurability. Default is 0.

add_objective(name, ref=None, ref0=None, index=None, adder=None, scaler=None, parallel_deriv_color=None, vectorize_derivs=False, simul_coloring=None, simul_map=None)

Add a response variable to this system.

Parameters:

name : string

Name of the response variable in the system.

ref : float or ndarray, optional

Value of response variable that scales to 1.0 in the driver.

ref0 : float or ndarray, optional

Value of response variable that scales to 0.0 in the driver.

index : int, optional

If variable is an array, this indicates which entry is of interest for this particular response. This may be a positive or negative integer.

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.

parallel_deriv_color : string

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

vectorize_derivs : bool

If True, vectorize derivative calculations.

simul_coloring : ndarray or list of int

An array or list of integer color values. Must match the size of the objective variable.

simul_map : dict

Mapping of this response to each design variable where simultaneous derivs will be used. Each design variable entry is another dict keyed on color, and the values in the color dict are tuples of the form (resp_idxs, color_idxs).

Notes

The objective can be scaled using scaler and adder, where

\[x_{scaled} = scaler(x + adder)\]

or through the use of ref/ref0, which map to scaler and adder through the equations:

\[ \begin{align}\begin{aligned}0 = scaler(ref_0 + adder)\\1 = scaler(ref + adder)\end{aligned}\end{align} \]

which results in:

\[ \begin{align}\begin{aligned}adder = -ref_0\\scaler = \frac{1}{ref + adder}\end{aligned}\end{align} \]
add_output(name, val=1.0, surrogate=None, shape=None, units=None, res_units=None, desc='', lower=None, upper=None, ref=1.0, ref0=0.0, res_ref=1.0, var_set=0)

Add an output variable to the component.

Parameters:

name : str

name of the variable in this component’s namespace.

val : float or list or tuple or ndarray

The initial value of the variable being added in user-defined units. Default is 1.0.

surrogate : SurrogateModel

Surrogate model to use.

shape : int or tuple or list or None

Shape of this variable, only required if val is not an array. Default is None.

units : str or None

Units in which the output variables will be provided to the component during execution. Default is None, which means it has no units.

res_units : str or None

Units in which the residuals of this output will be given to the user when requested. Default is None, which means it has no units.

desc : str

description of the variable.

lower : float or list or tuple or ndarray or Iterable or None

lower bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no lower bound. Default is None.

upper : float or list or tuple or ndarray or or Iterable None

upper bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no upper bound. Default is None.

ref : float

Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 1. Default is 1.

ref0 : float

Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 0. Default is 0.

res_ref : float

Scaling parameter. The value in the user-defined res_units of this output’s residual when the scaled value is 1. Default is 1.

var_set : hashable object

For advanced users only. ID or color for this variable, relevant for reconfigurability. Default is 0.

add_recorder(recorder, recurse=False)

Add a recorder to the driver.

Parameters:

recorder : <BaseRecorder>

A recorder instance.

recurse : boolean

Flag indicating if the recorder should be added to all the subsystems.

add_response(name, type_, lower=None, upper=None, equals=None, ref=None, ref0=None, indices=None, index=None, adder=None, scaler=None, linear=False, parallel_deriv_color=None, vectorize_derivs=False, simul_coloring=None, simul_map=None)

Add a response variable to this system.

The response can be scaled using ref and ref0. The argument ref0 represents the physical value when the scaled value is 0. The argument ref represents the physical value when the scaled value is 1.

Parameters:

name : string

Name of the response variable in the system.

type_ : string

The type of response. Supported values are ‘con’ and ‘obj’

lower : float or ndarray, optional

Lower boundary for the variable

upper : upper or ndarray, optional

Upper boundary for the variable

equals : equals or ndarray, optional

Equality constraint value for the variable

ref : float or ndarray, optional

Value of response variable that scales to 1.0 in the driver.

ref0 : upper or ndarray, optional

Value of response variable that scales to 0.0 in the driver.

indices : sequence of int, optional

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

index : int, optional

If variable is an array, this indicates which entry is of interest for this particular response.

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.

linear : bool

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

parallel_deriv_color : string

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

vectorize_derivs : bool

If True, vectorize derivative calculations.

simul_coloring : ndarray or list of int

An array or list of integer color values. Must match the size of the response variable.

simul_map : dict

Mapping of this response to each design variable where simultaneous derivs will be used. Each design variable entry is another dict keyed on color, and the values in the color dict are tuples of the form (resp_idxs, color_idxs).

check_config(logger)

Perform optional error checks.

Parameters:

logger : object

The object that manages logging output.

compute(inputs, outputs)

Predict outputs.

If the training flag is set, train the metamodel first.

Parameters:

inputs : Vector

unscaled, dimensional input variables read via inputs[key]

outputs : Vector

unscaled, dimensional output variables read via outputs[key]

compute_jacvec_product(inputs, d_inputs, d_outputs, mode)

Compute jac-vector product. The model is assumed to be in an unscaled state.

If mode is:

‘fwd’: d_inputs |-> d_outputs

‘rev’: d_outputs |-> d_inputs

Parameters:

inputs : Vector

unscaled, dimensional input variables read via inputs[key]

d_inputs : Vector

see inputs; product must be computed only if var_name in d_inputs

d_outputs : Vector

see outputs; product must be computed only if var_name in d_outputs

mode : str

either ‘fwd’ or ‘rev’

compute_partials(inputs, partials)

Compute sub-jacobian parts. The model is assumed to be in an unscaled state.

Parameters:

inputs : Vector

unscaled, dimensional input variables read via inputs[key]

partials : Jacobian

sub-jac components written to partials[output_name, input_name]

declare_partials(of, wrt, dependent=True, rows=None, cols=None, val=None, method='exact', **kwargs)

Declare information about this component’s subjacobians.

Parameters:

of : str or list of str

The name of the residual(s) that derivatives are being computed for. May also contain a glob pattern.

wrt : str or list of str

The name of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern.

dependent : bool(True)

If False, specifies no dependence between the output(s) and the input(s). This is only necessary in the case of a sparse global jacobian, because if ‘dependent=False’ is not specified and declare_partials is not called for a given pair, then a dense matrix of zeros will be allocated in the sparse global jacobian for that pair. In the case of a dense global jacobian it doesn’t matter because the space for a dense subjac will always be allocated for every pair.

rows : ndarray of int or None

Row indices for each nonzero entry. For sparse subjacobians only.

cols : ndarray of int or None

Column indices for each nonzero entry. For sparse subjacobians only.

val : float or ndarray of float or scipy.sparse

Value of subjacobian. If rows and cols are not None, this will contain the values found at each (row, col) location in the subjac.

method : str

The type of approximation that should be used. Valid options include: ‘fd’: Finite Difference, ‘cs’: Complex Step, ‘exact’: use the component defined analytic derivatives. Default is ‘exact’.

**kwargs : dict

Keyword arguments for controlling the behavior of the approximation.

get_constraints(recurse=True)

Get the Constraint settings from this system.

Retrieve the constraint settings for the current system as a dict, keyed by variable name.

Parameters:

recurse : bool, optional

If True, recurse through the subsystems and return the path of all constraints relative to the this system.

Returns:

dict

The constraints defined in the current system.

get_design_vars(recurse=True, get_sizes=True)

Get the DesignVariable settings from this system.

Retrieve all design variable settings from the system and, if recurse is True, all of its subsystems.

Parameters:

recurse : bool

If True, recurse through the subsystems and return the path of all design vars relative to the this system.

get_sizes : bool, optional

If True, compute the size of each response.

Returns:

dict

The design variables defined in the current system and, if recurse=True, its subsystems.

get_linear_vectors(vec_name='linear')

Return the linear inputs, outputs, and residuals vectors.

Parameters:

vec_name : str

Name of the linear right-hand-side vector. The default is ‘linear’.

Returns:

(inputs, outputs, residuals) : tuple of <Vector> instances

Yields the inputs, outputs, and residuals linear vectors for vec_name.

get_nonlinear_vectors()

Return the inputs, outputs, and residuals vectors.

Returns:

(inputs, outputs, residuals) : tuple of <Vector> instances

Yields the inputs, outputs, and residuals nonlinear vectors.

get_objectives(recurse=True)

Get the Objective settings from this system.

Retrieve all objectives settings from the system as a dict, keyed by variable name.

Parameters:

recurse : bool, optional

If True, recurse through the subsystems and return the path of all objective relative to the this system.

Returns:

dict

The objectives defined in the current system.

get_responses(recurse=True, get_sizes=True)

Get the response variable settings from this system.

Retrieve all response variable settings from the system as a dict, keyed by variable name.

Parameters:

recurse : bool, optional

If True, recurse through the subsystems and return the path of all responses relative to the this system.

get_sizes : bool, optional

If True, compute the size of each response.

Returns:

dict

The responses defined in the current system and, if recurse=True, its subsystems.

initialize()

Perform any one-time initialization run at instantiation.

is_active()

Determine if the system is active on this rank.

Returns:

bool

If running under MPI, returns True if this System has a valid communicator. Always returns True if not running under MPI.

jacobian

Get the Jacobian object assigned to this system (or None if unassigned).

jacobian_context(*args, **kwds)

Context manager that yields the Jacobian assigned to this system in this system’s context.

linear_solver

Get the linear solver for this system.

list_inputs(values=True, units=False, hierarchical=True, print_arrays=False, out_stream=<object object>)

Return and optionally log a list of input names and other optional information.

If the model is parallel, only the local variables are returned to the process. Also optionally logs the information to a user defined output stream. If the model is parallel, the rank 0 process logs information about all variables across all processes.

Parameters:

values : bool, optional

When True, display/return input values. Default is True.

units : bool, optional

When True, display/return units. Default is False.

hierarchical : bool, optional

When True, human readable output shows variables in hierarchical format.

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.

out_stream : file-like object

Where to send human readable output. Default is sys.stdout. Set to None to suppress.

Returns:

list

list of input names and other optional information about those inputs

list_outputs(explicit=True, implicit=True, values=True, residuals=False, residuals_tol=None, units=False, shape=False, bounds=False, scaling=False, hierarchical=True, print_arrays=False, out_stream=<object object>)

Return and optionally log a list of output names and other optional information.

If the model is parallel, only the local variables are returned to the process. Also optionally logs the information to a user defined output stream. If the model is parallel, the rank 0 process logs information about all variables across all processes.

Parameters:

explicit : bool, optional

include outputs from explicit components. Default is True.

implicit : bool, optional

include outputs from implicit components. Default is True.

values : bool, optional

When True, display/return output values. Default is True.

residuals : bool, optional

When True, display/return residual values. Default is False.

residuals_tol : float, optional

If set, limits the output of list_outputs to only variables where the norm of the resids array is greater than the given ‘residuals_tol’. Default is None.

units : bool, optional

When True, display/return units. Default is False.

shape : bool, optional

When True, display/return the shape of the value. Default is False.

bounds : bool, optional

When True, display/return bounds (lower and upper). Default is False.

scaling : bool, optional

When True, display/return scaling (ref, ref0, and res_ref). Default is False.

hierarchical : bool, optional

When True, human readable output shows variables in hierarchical format.

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.

out_stream : file-like

Where to send human readable output. Default is sys.stdout. Set to None to suppress.

Returns:

list

list of output names and other optional information about those outputs

ln_solver

Get the linear solver for this system.

nl_solver

Get the nonlinear solver for this system.

nonlinear_solver

Get the nonlinear solver for this system.

reconfigure()

Perform reconfiguration.

Returns:

bool

If True, reconfiguration is to be performed.

record_iteration()

Record an iteration of the current System.

resetup(setup_mode='full')

Public wrapper for _setup that reconfigures after an initial setup has been performed.

Parameters:

setup_mode : str

Must be one of ‘full’, ‘reconf’, or ‘update’.

run_apply_linear(vec_names, mode, scope_out=None, scope_in=None)

Compute jac-vec product.

This calls _apply_linear, but with the model assumed to be in an unscaled state.

Parameters:

vec_names : [str, ...]

list of names of the right-hand-side vectors.

mode : str

‘fwd’ or ‘rev’.

scope_out : set or None

Set of absolute output names in the scope of this mat-vec product. If None, all are in the scope.

scope_in : set or None

Set of absolute input names in the scope of this mat-vec product. If None, all are in the scope.

run_apply_nonlinear()

Compute residuals.

This calls _apply_nonlinear, but with the model assumed to be in an unscaled state.

run_linearize(do_nl=True, do_ln=True)

Compute jacobian / factorization.

This calls _linearize, but with the model assumed to be in an unscaled state.

Parameters:

do_nl : boolean

Flag indicating if the nonlinear solver should be linearized.

do_ln : boolean

Flag indicating if the linear solver should be linearized.

run_solve_linear(vec_names, mode)

Apply inverse jac product.

This calls _solve_linear, but with the model assumed to be in an unscaled state.

Parameters:

vec_names : [str, ...]

list of names of the right-hand-side vectors.

mode : str

‘fwd’ or ‘rev’.

Returns:

boolean

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

float

relative error.

float

absolute error.

run_solve_nonlinear()

Compute outputs.

This calls _solve_nonlinear, but with the model assumed to be in an unscaled state.

Returns:

boolean

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

float

relative error.

float

absolute error.

set_check_partial_options(wrt, method='fd', form=None, step=None, step_calc=None)

Set options that will be used for checking partial derivatives.

Parameters:

wrt : str or list of str

The name or names of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern.

method : str

Method for check: “fd” for finite difference, “cs” for complex step.

form : str

Finite difference form for check, can be “forward”, “central”, or “backward”. Leave undeclared to keep unchanged from previous or default value.

step : float

Step size for finite difference check. Leave undeclared to keep unchanged from previous or default value.

step_calc : str

Type of step calculation for check, can be “abs” for absolute (default) or “rel” for relative. Leave undeclared to keep unchanged from previous or default value.

set_initial_values()

Set all input and output variables to their declared initial values.

setup()

Declare inputs and outputs.

Available attributes:
name pathname comm metadata
system_iter(include_self=False, recurse=True, typ=None)

Yield a generator of local subsystems of this system.

Parameters:

include_self : bool

If True, include this system in the iteration.

recurse : bool

If True, iterate over the whole tree under this system.

typ : type

If not None, only yield Systems that match that are instances of the given type.

class openmdao.components.multifi_meta_model.MultiFiMetaModelUnStructured(nfi=1)[source]

Bases: openmdao.components.meta_model_unstructured.MetaModelUnStructured

Generalize MetaModel to be able to train surrogates with multi-fidelity training inputs.

For a given number of levels of fidelity nfi (given at initialization) the corresponding training input variables train:[invar]_fi[2..nfi] and train:[outvar]_fi[2..nfi] are automatically created besides the given train:[invar] and train:[outvar] variables. Note the index starts at 2, the index 1 is omitted considering the simple name var is equivalent to var_fi1 which is intended to be the data of highest fidelity.

The surrogate models are trained with a list of (m samples, n dim) ndarrays built from the various training input data. By convention, the fidelities are intended to be ordered from highest to lowest fidelity. Obviously for a given level of fidelity corresponding lists train:[var]_fi[n] have to be of the same size.

Thus given the initialization:

>>> mm = MultiFiMetaModelUnStructured(nfi=2)`
>>> mm.add_input('x1', 0.)
>>> mm.add_input('x2', 0.)
>>> mm.add_ouput('y1', 0.)
>>> mm.add_ouput('y2', 0.)

the following supplementary training input variables train:x1_fi2 and train:x2_fi2 are created together with the classic ones train:x1 and train:x2 and the output variables train:y1_fi2 and train:y2_fi2 are created as well. The embedded surrogate for y1 will be trained with a couple (X, Y).

Where X is the list [X_fi1, X_fi2] where X_fi1 is an (m1, 2) ndarray filled with the m1 samples [x1 value, x2 value], X_fi2 is an (m2, 2) ndarray filled with the m2 samples [x1_fi2 value, x2_fi2 value]

Where Y is a list [Y1_fi1, Y1_fi2] where Y1_fi1 is a (m1, 1) ndarray of y1 values and Y1_fi2 a (m2, 1) ndarray y1_fi2 values.

Note

when nfi ==1 a MultiFiMetaModelUnStructured object behaves as a MetaModelUnStructured object.

__init__(nfi=1)[source]

Initialize all attributes.

Parameters:

nfi : float

number of levels of fidelity

add_constraint(name, lower=None, upper=None, equals=None, ref=None, ref0=None, adder=None, scaler=None, indices=None, linear=False, parallel_deriv_color=None, vectorize_derivs=False, simul_coloring=None, simul_map=None)

Add a constraint variable to this system.

Parameters:

name : string

Name of the response variable in the system.

lower : float or ndarray, optional

Lower boundary for the variable

upper : float or ndarray, optional

Upper boundary for the variable

equals : float or ndarray, optional

Equality constraint value for the variable

ref : float or ndarray, optional

Value of response variable that scales to 1.0 in the driver.

ref0 : float or ndarray, optional

Value of response variable that scales to 0.0 in the driver.

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.

indices : sequence of int, optional

If variable is an array, these indicate which entries are of interest for this particular response. These may be positive or negative integers.

linear : bool

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

parallel_deriv_color : string

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

vectorize_derivs : bool

If True, vectorize derivative calculations.

simul_coloring : ndarray or list of int

An array or list of integer color values. Must match the size of the constraint variable.

simul_map : dict

Mapping of this response to each design variable where simultaneous derivs will be used. Each design variable entry is another dict keyed on color, and the values in the color dict are tuples of the form (resp_idxs, color_idxs).

Notes

The response can be scaled using ref and ref0. The argument ref0 represents the physical value when the scaled value is 0. The argument ref represents the physical value when the scaled value is 1.

add_design_var(name, lower=None, upper=None, ref=None, ref0=None, indices=None, adder=None, scaler=None, parallel_deriv_color=None, vectorize_derivs=False, simul_coloring=None)

Add a design variable to this system.

Parameters:

name : string

Name of the design variable in the system.

lower : float or ndarray, optional

Lower boundary for the param

upper : upper or ndarray, optional

Upper boundary for the param

ref : float or ndarray, optional

Value of design var that scales to 1.0 in the driver.

ref0 : float or ndarray, optional

Value of design var that scales to 0.0 in the driver.

indices : iter of int, optional

If a param is an array, these indicate which entries are of interest for this particular design variable. These may be positive or negative integers.

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.

parallel_deriv_color : string

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

vectorize_derivs : bool

If True, vectorize derivative calculations.

simul_coloring : ndarray or list of int

An array or list of integer color values. Must match the size of the design variable.

Notes

The response can be scaled using ref and ref0. The argument ref0 represents the physical value when the scaled value is 0. The argument ref represents the physical value when the scaled value is 1.

add_input(name, val=1.0, shape=None, src_indices=None, flat_src_indices=None, units=None, desc='', var_set=0)[source]

Add an input variable to the component.

Parameters:

name : str

name of the variable in this component’s namespace.

val : float or list or tuple or ndarray or Iterable

The initial value of the variable being added in user-defined units. Default is 1.0.

shape : int or tuple or list or None

Shape of this variable, only required if src_indices not provided and val is not an array. Default is None.

src_indices : int or list of ints or tuple of ints or int ndarray or Iterable or None

The global indices of the source variable to transfer data from. If val is given as an array_like object, the shapes of val and src_indices must match. A value of None implies this input depends on all entries of source. Default is None.

flat_src_indices : bool

If True, each entry of src_indices is assumed to be an index into the flattened source. Otherwise each entry must be a tuple or list of size equal to the number of dimensions of the source.

units : str or None

Units in which this input variable will be provided to the component during execution. Default is None, which means it is unitless.

desc : str

description of the variable

var_set : hashable object

For advanced users only. ID or color for this variable, relevant for reconfigurability. Default is 0.

add_objective(name, ref=None, ref0=None, index=None, adder=None, scaler=None, parallel_deriv_color=None, vectorize_derivs=False, simul_coloring=None, simul_map=None)

Add a response variable to this system.

Parameters:

name : string

Name of the response variable in the system.

ref : float or ndarray, optional

Value of response variable that scales to 1.0 in the driver.

ref0 : float or ndarray, optional

Value of response variable that scales to 0.0 in the driver.

index : int, optional

If variable is an array, this indicates which entry is of interest for this particular response. This may be a positive or negative integer.

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.

parallel_deriv_color : string

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

vectorize_derivs : bool

If True, vectorize derivative calculations.

simul_coloring : ndarray or list of int

An array or list of integer color values. Must match the size of the objective variable.

simul_map : dict

Mapping of this response to each design variable where simultaneous derivs will be used. Each design variable entry is another dict keyed on color, and the values in the color dict are tuples of the form (resp_idxs, color_idxs).

Notes

The objective can be scaled using scaler and adder, where

\[x_{scaled} = scaler(x + adder)\]

or through the use of ref/ref0, which map to scaler and adder through the equations:

\[ \begin{align}\begin{aligned}0 = scaler(ref_0 + adder)\\1 = scaler(ref + adder)\end{aligned}\end{align} \]

which results in:

\[ \begin{align}\begin{aligned}adder = -ref_0\\scaler = \frac{1}{ref + adder}\end{aligned}\end{align} \]
add_output(name, val=1.0, surrogate=None, shape=None, units=None, res_units=None, desc='', lower=None, upper=None, ref=1.0, ref0=0.0, res_ref=1.0, var_set=0)[source]

Add an output variable to the component.

Parameters:

name : str

name of the variable in this component’s namespace.

val : float or list or tuple or ndarray

The initial value of the variable being added in user-defined units. Default is 1.0.

surrogate : SurrogateModel

Surrogate model to use.

shape : int or tuple or list or None

Shape of this variable, only required if val is not an array. Default is None.

units : str or None

Units in which the output variables will be provided to the component during execution. Default is None, which means it has no units.

res_units : str or None

Units in which the residuals of this output will be given to the user when requested. Default is None, which means it has no units.

desc : str

description of the variable.

lower : float or list or tuple or ndarray or Iterable or None

lower bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no lower bound. Default is None.

upper : float or list or tuple or ndarray or or Iterable None

upper bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no upper bound. Default is None.

ref : float

Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 1. Default is 1.

ref0 : float

Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 0. Default is 0.

res_ref : float

Scaling parameter. The value in the user-defined res_units of this output’s residual when the scaled value is 1. Default is 1.

var_set : hashable object

For advanced users only. ID or color for this variable, relevant for reconfigurability. Default is 0.

add_recorder(recorder, recurse=False)

Add a recorder to the driver.

Parameters:

recorder : <BaseRecorder>

A recorder instance.

recurse : boolean

Flag indicating if the recorder should be added to all the subsystems.

add_response(name, type_, lower=None, upper=None, equals=None, ref=None, ref0=None, indices=None, index=None, adder=None, scaler=None, linear=False, parallel_deriv_color=None, vectorize_derivs=False, simul_coloring=None, simul_map=None)

Add a response variable to this system.

The response can be scaled using ref and ref0. The argument ref0 represents the physical value when the scaled value is 0. The argument ref represents the physical value when the scaled value is 1.

Parameters:

name : string

Name of the response variable in the system.

type_ : string

The type of response. Supported values are ‘con’ and ‘obj’

lower : float or ndarray, optional

Lower boundary for the variable

upper : upper or ndarray, optional

Upper boundary for the variable

equals : equals or ndarray, optional

Equality constraint value for the variable

ref : float or ndarray, optional

Value of response variable that scales to 1.0 in the driver.

ref0 : upper or ndarray, optional

Value of response variable that scales to 0.0 in the driver.

indices : sequence of int, optional

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

index : int, optional

If variable is an array, this indicates which entry is of interest for this particular response.

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.

linear : bool

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

parallel_deriv_color : string

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

vectorize_derivs : bool

If True, vectorize derivative calculations.

simul_coloring : ndarray or list of int

An array or list of integer color values. Must match the size of the response variable.

simul_map : dict

Mapping of this response to each design variable where simultaneous derivs will be used. Each design variable entry is another dict keyed on color, and the values in the color dict are tuples of the form (resp_idxs, color_idxs).

check_config(logger)

Perform optional error checks.

Parameters:

logger : object

The object that manages logging output.

compute(inputs, outputs)

Predict outputs.

If the training flag is set, train the metamodel first.

Parameters:

inputs : Vector

unscaled, dimensional input variables read via inputs[key]

outputs : Vector

unscaled, dimensional output variables read via outputs[key]

compute_jacvec_product(inputs, d_inputs, d_outputs, mode)

Compute jac-vector product. The model is assumed to be in an unscaled state.

If mode is:

‘fwd’: d_inputs |-> d_outputs

‘rev’: d_outputs |-> d_inputs

Parameters:

inputs : Vector

unscaled, dimensional input variables read via inputs[key]

d_inputs : Vector

see inputs; product must be computed only if var_name in d_inputs

d_outputs : Vector

see outputs; product must be computed only if var_name in d_outputs

mode : str

either ‘fwd’ or ‘rev’

compute_partials(inputs, partials)

Compute sub-jacobian parts. The model is assumed to be in an unscaled state.

Parameters:

inputs : Vector

unscaled, dimensional input variables read via inputs[key]

partials : Jacobian

sub-jac components written to partials[output_name, input_name]

declare_partials(of, wrt, dependent=True, rows=None, cols=None, val=None, method='exact', **kwargs)

Declare information about this component’s subjacobians.

Parameters:

of : str or list of str

The name of the residual(s) that derivatives are being computed for. May also contain a glob pattern.

wrt : str or list of str

The name of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern.

dependent : bool(True)

If False, specifies no dependence between the output(s) and the input(s). This is only necessary in the case of a sparse global jacobian, because if ‘dependent=False’ is not specified and declare_partials is not called for a given pair, then a dense matrix of zeros will be allocated in the sparse global jacobian for that pair. In the case of a dense global jacobian it doesn’t matter because the space for a dense subjac will always be allocated for every pair.

rows : ndarray of int or None

Row indices for each nonzero entry. For sparse subjacobians only.

cols : ndarray of int or None

Column indices for each nonzero entry. For sparse subjacobians only.

val : float or ndarray of float or scipy.sparse

Value of subjacobian. If rows and cols are not None, this will contain the values found at each (row, col) location in the subjac.

method : str

The type of approximation that should be used. Valid options include: ‘fd’: Finite Difference, ‘cs’: Complex Step, ‘exact’: use the component defined analytic derivatives. Default is ‘exact’.

**kwargs : dict

Keyword arguments for controlling the behavior of the approximation.

get_constraints(recurse=True)

Get the Constraint settings from this system.

Retrieve the constraint settings for the current system as a dict, keyed by variable name.

Parameters:

recurse : bool, optional

If True, recurse through the subsystems and return the path of all constraints relative to the this system.

Returns:

dict

The constraints defined in the current system.

get_design_vars(recurse=True, get_sizes=True)

Get the DesignVariable settings from this system.

Retrieve all design variable settings from the system and, if recurse is True, all of its subsystems.

Parameters:

recurse : bool

If True, recurse through the subsystems and return the path of all design vars relative to the this system.

get_sizes : bool, optional

If True, compute the size of each response.

Returns:

dict

The design variables defined in the current system and, if recurse=True, its subsystems.

get_linear_vectors(vec_name='linear')

Return the linear inputs, outputs, and residuals vectors.

Parameters:

vec_name : str

Name of the linear right-hand-side vector. The default is ‘linear’.

Returns:

(inputs, outputs, residuals) : tuple of <Vector> instances

Yields the inputs, outputs, and residuals linear vectors for vec_name.

get_nonlinear_vectors()

Return the inputs, outputs, and residuals vectors.

Returns:

(inputs, outputs, residuals) : tuple of <Vector> instances

Yields the inputs, outputs, and residuals nonlinear vectors.

get_objectives(recurse=True)

Get the Objective settings from this system.

Retrieve all objectives settings from the system as a dict, keyed by variable name.

Parameters:

recurse : bool, optional

If True, recurse through the subsystems and return the path of all objective relative to the this system.

Returns:

dict

The objectives defined in the current system.

get_responses(recurse=True, get_sizes=True)

Get the response variable settings from this system.

Retrieve all response variable settings from the system as a dict, keyed by variable name.

Parameters:

recurse : bool, optional

If True, recurse through the subsystems and return the path of all responses relative to the this system.

get_sizes : bool, optional

If True, compute the size of each response.

Returns:

dict

The responses defined in the current system and, if recurse=True, its subsystems.

initialize()

Perform any one-time initialization run at instantiation.

is_active()

Determine if the system is active on this rank.

Returns:

bool

If running under MPI, returns True if this System has a valid communicator. Always returns True if not running under MPI.

jacobian

Get the Jacobian object assigned to this system (or None if unassigned).

jacobian_context(*args, **kwds)

Context manager that yields the Jacobian assigned to this system in this system’s context.

linear_solver

Get the linear solver for this system.

list_inputs(values=True, units=False, hierarchical=True, print_arrays=False, out_stream=<object object>)

Return and optionally log a list of input names and other optional information.

If the model is parallel, only the local variables are returned to the process. Also optionally logs the information to a user defined output stream. If the model is parallel, the rank 0 process logs information about all variables across all processes.

Parameters:

values : bool, optional

When True, display/return input values. Default is True.

units : bool, optional

When True, display/return units. Default is False.

hierarchical : bool, optional

When True, human readable output shows variables in hierarchical format.

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.

out_stream : file-like object

Where to send human readable output. Default is sys.stdout. Set to None to suppress.

Returns:

list

list of input names and other optional information about those inputs

list_outputs(explicit=True, implicit=True, values=True, residuals=False, residuals_tol=None, units=False, shape=False, bounds=False, scaling=False, hierarchical=True, print_arrays=False, out_stream=<object object>)

Return and optionally log a list of output names and other optional information.

If the model is parallel, only the local variables are returned to the process. Also optionally logs the information to a user defined output stream. If the model is parallel, the rank 0 process logs information about all variables across all processes.

Parameters:

explicit : bool, optional

include outputs from explicit components. Default is True.

implicit : bool, optional

include outputs from implicit components. Default is True.

values : bool, optional

When True, display/return output values. Default is True.

residuals : bool, optional

When True, display/return residual values. Default is False.

residuals_tol : float, optional

If set, limits the output of list_outputs to only variables where the norm of the resids array is greater than the given ‘residuals_tol’. Default is None.

units : bool, optional

When True, display/return units. Default is False.

shape : bool, optional

When True, display/return the shape of the value. Default is False.

bounds : bool, optional

When True, display/return bounds (lower and upper). Default is False.

scaling : bool, optional

When True, display/return scaling (ref, ref0, and res_ref). Default is False.

hierarchical : bool, optional

When True, human readable output shows variables in hierarchical format.

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.

out_stream : file-like

Where to send human readable output. Default is sys.stdout. Set to None to suppress.

Returns:

list

list of output names and other optional information about those outputs

ln_solver

Get the linear solver for this system.

nl_solver

Get the nonlinear solver for this system.

nonlinear_solver

Get the nonlinear solver for this system.

reconfigure()

Perform reconfiguration.

Returns:

bool

If True, reconfiguration is to be performed.

record_iteration()

Record an iteration of the current System.

resetup(setup_mode='full')

Public wrapper for _setup that reconfigures after an initial setup has been performed.

Parameters:

setup_mode : str

Must be one of ‘full’, ‘reconf’, or ‘update’.

run_apply_linear(vec_names, mode, scope_out=None, scope_in=None)

Compute jac-vec product.

This calls _apply_linear, but with the model assumed to be in an unscaled state.

Parameters:

vec_names : [str, ...]

list of names of the right-hand-side vectors.

mode : str

‘fwd’ or ‘rev’.

scope_out : set or None

Set of absolute output names in the scope of this mat-vec product. If None, all are in the scope.

scope_in : set or None

Set of absolute input names in the scope of this mat-vec product. If None, all are in the scope.

run_apply_nonlinear()

Compute residuals.

This calls _apply_nonlinear, but with the model assumed to be in an unscaled state.

run_linearize(do_nl=True, do_ln=True)

Compute jacobian / factorization.

This calls _linearize, but with the model assumed to be in an unscaled state.

Parameters:

do_nl : boolean

Flag indicating if the nonlinear solver should be linearized.

do_ln : boolean

Flag indicating if the linear solver should be linearized.

run_solve_linear(vec_names, mode)

Apply inverse jac product.

This calls _solve_linear, but with the model assumed to be in an unscaled state.

Parameters:

vec_names : [str, ...]

list of names of the right-hand-side vectors.

mode : str

‘fwd’ or ‘rev’.

Returns:

boolean

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

float

relative error.

float

absolute error.

run_solve_nonlinear()

Compute outputs.

This calls _solve_nonlinear, but with the model assumed to be in an unscaled state.

Returns:

boolean

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

float

relative error.

float

absolute error.

set_check_partial_options(wrt, method='fd', form=None, step=None, step_calc=None)

Set options that will be used for checking partial derivatives.

Parameters:

wrt : str or list of str

The name or names of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern.

method : str

Method for check: “fd” for finite difference, “cs” for complex step.

form : str

Finite difference form for check, can be “forward”, “central”, or “backward”. Leave undeclared to keep unchanged from previous or default value.

step : float

Step size for finite difference check. Leave undeclared to keep unchanged from previous or default value.

step_calc : str

Type of step calculation for check, can be “abs” for absolute (default) or “rel” for relative. Leave undeclared to keep unchanged from previous or default value.

set_initial_values()

Set all input and output variables to their declared initial values.

setup()

Declare inputs and outputs.

Available attributes:
name pathname comm metadata
system_iter(include_self=False, recurse=True, typ=None)

Yield a generator of local subsystems of this system.

Parameters:

include_self : bool

If True, include this system in the iteration.

recurse : bool

If True, iterate over the whole tree under this system.

typ : type

If not None, only yield Systems that match that are instances of the given type.