group.py¶

Define the Group class.

class openmdao.core.group.Group(**kwargs)[source]¶

Bases: openmdao.core.system.System

Class used to group systems together; instantiate or inherit.

__init__(**kwargs)[source]¶

Set the solvers to nonlinear and linear block Gauss–Seidel by default.

Parameters

**kwargs: dict: dict of arguments available here and in all descendants of this Group.

abs_name_iter(iotype, local=True, cont=True, discrete=False)¶

Iterate over absolute variable names for this System.

By setting appropriate values for ‘cont’ and ‘discrete’, yielded variable names can be continuous only, discrete only, or both.

Parameters

iotype: str: Either ‘input’ or ‘output’.
local: bool: If True, include only names of local variables. Default is True.
cont: bool: If True, include names of continuous variables. Default is True.
discrete: bool: If True, include names of discrete variables. Default is False.

add_constraint(name, lower=None, upper=None, equals=None, ref=None, ref0=None, adder=None, scaler=None, units=None, indices=None, linear=False, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)¶

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 for the driver. adder is first in precedence. adder and scaler are an alterantive to using ref and ref0.
scaler: float or ndarray, optional: value to multiply the model value to get the scaled value for the driver. scaler is second in precedence. adder and scaler are an alterantive to using ref and ref0.
units: str, optional: Units to convert to before applying scaling.
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.
cache_linear_solution: bool: 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.

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. The arguments (lower, upper, equals) can not be strings or variable names.

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

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 input
upper: upper or ndarray, optional: Upper boundary for the input
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 an input is an array, these indicate which entries are of interest for this particular design variable. These may be positive or negative integers.
units: str, optional: Units to convert to before applying scaling.
adder: float or ndarray, optional: Value to add to the model value to get the scaled value for the driver. adder is first in precedence. adder and scaler are an alterantive to using ref and ref0.
scaler: float or ndarray, optional: value to multiply the model value to get the scaled value for the driver. scaler is second in precedence. adder and scaler are an alterantive to using ref and ref0.
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.
cache_linear_solution: bool: 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.

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_objective(name, ref=None, ref0=None, index=None, units=None, adder=None, scaler=None, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)¶

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.
units: str, optional: Units to convert to before applying scaling.
adder: float or ndarray, optional: Value to add to the model value to get the scaled value for the driver. adder is first in precedence. adder and scaler are an alterantive to using ref and ref0.
scaler: float or ndarray, optional: value to multiply the model value to get the scaled value for the driver. scaler is second in precedence. adder and scaler are an alterantive to using ref and ref0.
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.
cache_linear_solution: bool: 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.

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_recorder(recorder, recurse=False)¶

Add a recorder to the system.

Parameters

recorder: <CaseRecorder>: 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, units=None, adder=None, scaler=None, linear=False, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)¶

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.
units: str, optional: Units to convert to before applying scaling.
adder: float or ndarray, optional: Value to add to the model value to get the scaled value for the driver. adder is first in precedence. adder and scaler are an alterantive to using ref and ref0.
scaler: float or ndarray, optional: value to multiply the model value to get the scaled value for the driver. scaler is second in precedence. adder and scaler are an alterantive to using ref and ref0.
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.
cache_linear_solution: bool: 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.

add_subsystem(name, subsys, promotes=None, promotes_inputs=None, promotes_outputs=None, min_procs=1, max_procs=None, proc_weight=1.0)[source]¶

Add a subsystem.

Parameters

name: str: Name of the subsystem being added
subsys: <System>: An instantiated, but not-yet-set up system object.
promotes: iter of (str or tuple), optional: A list of variable names specifying which subsystem variables to ‘promote’ up to this group. If an entry is a tuple of the form (old_name, new_name), this will rename the variable in the parent group.
promotes_inputs: iter of (str or tuple), optional: A list of input variable names specifying which subsystem input variables to ‘promote’ up to this group. If an entry is a tuple of the form (old_name, new_name), this will rename the variable in the parent group.
promotes_outputs: iter of (str or tuple), optional: A list of output variable names specifying which subsystem output variables to ‘promote’ up to this group. If an entry is a tuple of the form (old_name, new_name), this will rename the variable in the parent group.
min_procs: int: Minimum number of MPI processes usable by the subsystem. Defaults to 1.
max_procs: int or None: Maximum number of MPI processes usable by the subsystem. A value of None (the default) indicates there is no maximum limit.
proc_weight: float: Weight given to the subsystem when allocating available MPI processes to all subsystems. Default is 1.0.

Returns

<System>: the subsystem that was passed in. This is returned to enable users to instantiate and add a subsystem at the same time, and get the reference back.

approx_totals(method='fd', step=None, form=None, step_calc=None)[source]¶

Approximate derivatives for a Group using the specified approximation method.

Parameters

method: str: The type of approximation that should be used. Valid options include: ‘fd’: Finite Difference, ‘cs’: Complex Step
step: float: Step size for approximation. Defaults to None, in which case, the approximation method provides its default value.
form: string: Form for finite difference, can be ‘forward’, ‘backward’, or ‘central’. Defaults to None, in which case, the approximation method provides its default value.
step_calc: string: Step type for finite difference, can be ‘abs’ for absolute’, or ‘rel’ for relative. Defaults to None, in which case, the approximation method provides its default value.

check_config(logger)¶

Perform optional error checks.

Parameters

logger: object: The object that manages logging output.

cleanup()¶: Clean up resources prior to exit.

compute_sys_graph(comps_only=False)[source]¶

Compute a dependency graph for subsystems in this group.

Variable connection information is stored in each edge of the system graph.

Parameters

comps_only: bool (False): If True, return a graph of all components within this group or any of its descendants. No sub-groups will be included. Otherwise, a graph containing only direct children (both Components and Groups) of this group will be returned.

Returns

DiGraph: A directed graph containing names of subsystems and their connections.

configure()[source]¶

Configure this group to assign children settings.

This method may optionally be overidden by your Group’s method.

You may only use this method to change settings on your children subsystems. This includes setting solvers in cases where you want to override the defaults.

You can assume that the full hierarchy below your level has been instantiated and has already called its own configure methods.

Available attributes:: name pathname comm options system hieararchy with attribute access

connect(src_name, tgt_name, src_indices=None, flat_src_indices=None)[source]¶

Connect source src_name to target tgt_name in this namespace.

Parameters

src_name: str: name of the source variable to connect
tgt_name: str or [str, … ] or (str, …): name of the target variable(s) to connect
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. The shapes of the target and src_indices must match, and form of the entries within is determined by the value of ‘flat_src_indices’.
flat_src_indices: bool: If True, each entry of src_indices is assumed to be an index into the flattened source. Otherwise it must be a tuple or list of size equal to the number of dimensions of the source.

convert2units(name, val, units)¶

Convert the given value to the specified units.

Parameters

name: str: Name of the variable.
val: float or ndarray of float: The value of the variable.
units: str: The units to convert to.

Returns

float or ndarray of float: The value converted to the specified units.

convert_from_units(name, val, units)¶

Convert the given value from the specified units to those of the named variable.

Parameters

name: str: Name of the variable.
val: float or ndarray of float: The value of the variable.
units: str: The units to convert to.

Returns

float or ndarray of float: The value converted to the specified units.

convert_units(name, val, units_from, units_to)¶

Wrap the utility convert_units and give a good error message.

Parameters

name: str: Name of the variable.
val: float or ndarray of float: The value of the variable.
units_from: str: The units to convert from.
units_to: str: The units to convert to.

Returns

float or ndarray of float: The value converted to the specified units.

declare_coloring(wrt=('*'), method='fd', form=None, step=None, per_instance=True, num_full_jacs=3, tol=1e-25, orders=None, perturb_size=1e-09, min_improve_pct=5.0, show_summary=True, show_sparsity=False)¶

Set options for deriv coloring of a set of wrt vars matching the given pattern(s).

Parameters

wrt: str or list of str: The name or names of the variables that derivatives are taken with respect to. This can contain input names, output names, or glob patterns.
method: str: Method used to compute derivative: “fd” for finite difference, “cs” for complex step.
form: str: Finite difference form, can be “forward”, “central”, or “backward”. Leave undeclared to keep unchanged from previous or default value.
step: float: Step size for finite difference. Leave undeclared to keep unchanged from previous or default value.
per_instance: bool: If True, a separate coloring will be generated for each instance of a given class. Otherwise, only one coloring for a given class will be generated and all instances of that class will use it.
num_full_jacs: int: Number of times to repeat partial jacobian computation when computing sparsity.
tol: float: Tolerance used to determine if an array entry is nonzero during sparsity determination.
orders: int: Number of orders above and below the tolerance to check during the tolerance sweep.
perturb_size: float: Size of input/output perturbation during generation of sparsity.
min_improve_pct: float: If coloring does not improve (decrease) the number of solves more than the given percentage, coloring will not be used.
show_summary: bool: If True, display summary information after generating coloring.
show_sparsity: bool: If True, display sparsity with coloring info after generating coloring.

get_approx_coloring_fname()¶

Return the full pathname to a coloring file.

Parameters

system: System: The System having its coloring saved or loaded.

Returns

str: Full pathname of the coloring file.

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, use_prom_ivc=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 design variable.
use_prom_ivc: bool: Translate auto_ivc_names to their promoted input names.

Returns

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

get_io_metadata(iotypes=('input', 'output'), metadata_keys=None, includes=None, excludes=None, tags=(), get_remote=False, rank=None, return_rel_names=True)¶

Retrieve metdata for a filtered list of variables.

Parameters

iotypes: str or iter of str: Will contain either ‘input’, ‘output’, or both. Defaults to both.
metadata_keys: iter of str or None: Names of metadata entries to be retrieved or None, meaning retrieve all available ‘allprocs’ metadata. If ‘values’ or ‘src_indices’ are required, their keys must be provided explicitly since they are not found in the ‘allprocs’ metadata and must be retrieved from local metadata located in each process.
includes: str, iter of str or None: Collection of glob patterns for pathnames of variables to include. Default is None, which includes all variables.
excludes: str, iter of str or None: Collection of glob patterns for pathnames of variables to exclude. Default is None.
tags: str or iter of strs: User defined tags that can be used to filter what gets listed. Only inputs with the given tags will be listed. Default is None, which means there will be no filtering based on tags.
get_remote: bool: If True, retrieve variables from other MPI processes as well.
rank: int or None: If None, and get_remote is True, retrieve values from all MPI process to all other MPI processes. Otherwise, if get_remote is True, retrieve values from all MPI processes only to the specified rank.
return_rel_names: bool: If True, the names returned will be relative to the scope of this System. Otherwise they will be absolute names.

Returns

dict: A dict of metadata keyed on name, where name is either absolute or relative based on the value of the return_rel_names arg, and metadata is a dict containing entries based on the value of the metadata_keys arg. Every metadata dict will always contain two entries, ‘promoted_name’ and ‘discrete’, to indicate a given variable’s promoted name and whether or not it is discrete.

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_relevant_vars(desvars, responses, mode)¶

Find all relevant vars between desvars and responses.

Both vars are assumed to be outputs (either design vars or responses).

Parameters

desvars: list of str: Names of design variables.
responses: list of str: Names of response variables.
mode: str: Direction of derivatives, either ‘fwd’ or ‘rev’.

Returns

dict: Dict of ({‘outputs’: dep_outputs, ‘inputs’: dep_inputs, dep_systems) keyed by design vars and responses.

get_responses(recurse=True, get_sizes=True, use_prom_ivc=False)¶

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.
use_prom_ivc: bool: Translate auto_ivc_names to their promoted input names.

Returns

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

get_source(name)¶

Return the source variable connected to the given named variable.

The name can be a promoted name or an absolute name. If the given variable is an input, the absolute name of the connected source will be returned. If the given variable itself is a source, its own absolute name will be returned.

Parameters

name: str: Absolute or promoted name of the variable.

Returns

str: The absolute name of the source variable.

get_val(name, units=None, indices=None, get_remote=False, rank=None, vec_name='nonlinear', kind=None, flat=False, from_src=True)¶

Get an output/input/residual 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.
rank: int or None: If not None, only gather the value to this rank.
vec_name: str: Name of the vector to use. Defaults to ‘nonlinear’.
kind: str or None: Kind of variable (‘input’, ‘output’, or ‘residual’). If None, returned value will be either an input or output.
flat: bool: If True, return the flattened version of the value.
from_src: bool: If True, retrieve value of an input variable from its connected source.

Returns

object: The value of the requested output/input variable.

guess_nonlinear(inputs, outputs, residuals, discrete_inputs=None, discrete_outputs=None)[source]¶

Provide initial guess for states.

Override this method to set the initial guess for states.

Parameters

inputs: Vector: unscaled, dimensional input variables read via inputs[key]
outputs: Vector: unscaled, dimensional output variables read via outputs[key]
residuals: Vector: unscaled, dimensional residuals written to via residuals[key]
discrete_inputs: dict or None: If not None, dict containing discrete input values.
discrete_outputs: dict or None: If not None, dict containing discrete output values.

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.

property linear_solver¶: Get the linear solver for this system.

list_inputs(values=True, prom_name=False, units=False, shape=False, global_shape=False, desc=False, hierarchical=True, print_arrays=False, tags=None, includes=None, excludes=None, all_procs=False, out_stream=DEFAULT_OUT_STREAM)¶

Write a list of input names and other optional information to a specified stream.

Parameters

values: bool, optional: When True, display/return input values. Default is True.
prom_name: bool, optional: When True, display/return the promoted name of the variable. Default is False.
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.
global_shape: bool, optional: When True, display/return the global shape of the value. Default is False.
desc: bool, optional: When True, display/return description. 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.
tags: str or list of strs: User defined tags that can be used to filter what gets listed. Only inputs with the given tags will be listed. Default is None, which means there will be no filtering based on tags.
includes: None or iter of str: Collection of glob patterns for pathnames of variables to include. Default is None, which includes all input variables.
excludes: None or iter of str: Collection of glob patterns for pathnames of variables to exclude. Default is None.
all_procs: bool, optional: When True, display output on all ranks. Default is False, which will display output only from rank 0.
out_stream: file-like object: Where to send human readable output. Default is sys.stdout. Set to None to suppress.

Returns

list of (name, metadata): List of input names and other optional information about those inputs.

list_outputs(explicit=True, implicit=True, values=True, prom_name=False, residuals=False, residuals_tol=None, units=False, shape=False, global_shape=False, bounds=False, scaling=False, desc=False, hierarchical=True, print_arrays=False, tags=None, includes=None, excludes=None, all_procs=False, list_autoivcs=False, out_stream=DEFAULT_OUT_STREAM)¶

Write a list of output names and other optional information to a specified stream.

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 output values. Default is True.
prom_name: bool, optional: When True, display the promoted name of the variable. Default is False.
residuals: bool, optional: When True, display 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 units. Default is False.
shape: bool, optional: When True, display/return the shape of the value. Default is False.
global_shape: bool, optional: When True, display/return the global 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.
desc: bool, optional: When True, display/return description. 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.
tags: str or list of strs: User defined tags that can be used to filter what gets listed. Only outputs with the given tags will be listed. Default is None, which means there will be no filtering based on tags.
includes: None or iter of str: Collection of glob patterns for pathnames of variables to include. Default is None, which includes all output variables.
excludes: None or iter of str: Collection of glob patterns for pathnames of variables to exclude. Default is None.
all_procs: bool, optional: When True, display output on all processors. Default is False.
list_autoivcs: bool: If True, include auto_ivc outputs in the listing. Defaults to False.
out_stream: file-like: Where to send human readable output. Default is sys.stdout. Set to None to suppress.

Returns

list of (name, metadata): List of output names and other optional information about those outputs.

property msginfo¶

Our instance pathname, if available, or our class name. For use in error messages.

Returns

str: Either our instance pathname or class name.

property nonlinear_solver¶: Get the nonlinear solver for this system.

promotes(subsys_name, any=None, inputs=None, outputs=None, src_indices=None, flat_src_indices=None, src_shape=None)[source]¶

Promote a variable in the model tree.

Parameters

subsys_name: str: The name of the child subsystem whose inputs/outputs are being promoted.
any: Sequence of str or tuple: A Sequence of variable names (or tuples) to be promoted, regardless of if they are inputs or outputs. This is equivalent to the items passed via the promotes= argument to add_subsystem. If given as a tuple, we use the “promote as” standard of (‘real name’, ‘promoted name’)*[]:
inputs: Sequence of str or tuple: A Sequence of input names (or tuples) to be promoted. Tuples are used for the “promote as” capability.
outputs: Sequence of str or tuple: A Sequence of output names (or tuples) to be promoted. Tuples are used for the “promote as” capability.
src_indices: int or list of ints or tuple of ints or int ndarray or Iterable or None: This argument applies only to promoted inputs. The global indices of the source variable to transfer data from. A value of None implies this input depends on all entries of source. Default is None. The shapes of the target and src_indices must match, and form of the entries within is determined by the value of ‘flat_src_indices’.
flat_src_indices: bool: This argument applies only to promoted inputs. 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.
src_shape: int or tuple: Assumed shape of any connected source or higher level promoted input.

record_iteration()¶: Record an iteration of the current System.

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(sub_do_ln=True)¶

Compute jacobian / factorization.

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

Parameters

sub_do_ln: boolean: Flag indicating if the children should call linearize on their linear solvers.

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’.

run_solve_nonlinear()¶

Compute outputs.

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

set_initial_values()¶: Set all input and output variables to their declared initial values.

set_input_defaults(name, val=UNDEFINED, units=None, src_shape=None)[source]¶

Specify metadata to be assumed when multiple inputs are promoted to the same name.

Parameters

name: str: Promoted input name.
val: object: Value to assume for the promoted input.
units: str or None: Units to assume for the promoted input.
src_shape: int or tuple: Assumed shape of any connected source or higher level promoted input.

set_order(new_order)[source]¶

Specify a new execution order for this system.

Parameters

new_order: list of str: List of system names in desired new execution order.

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

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.

setup()[source]¶

Build this group.

This method should be overidden by your Group’s method. The reason for using this method to add subsystem is to save memory and setup time when using your Group while running under MPI. This avoids the creation of systems that will not be used in the current process.

You may call ‘add_subsystem’ to add systems to this group. You may also issue connections, and set the linear and nonlinear solvers for this group level. You cannot safely change anything on children systems; use the ‘configure’ method instead.

Available attributes:: name pathname comm options

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.

use_fixed_coloring(coloring=<object object>, recurse=True)¶

Use a precomputed coloring for this System.

Parameters

coloring: str: A coloring filename. If no arg is passed, filename will be determined automatically.
recurse: bool: If True, set fixed coloring in all subsystems that declare a coloring. Ignored if a specific coloring is passed in.