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 argumentref
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 argumentref
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 argumentref
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 notyetset 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 subgroups 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=1e25, orders=None, perturb_size=1e09, 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 righthandside 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 onetime 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: filelike 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: filelike
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 jacvec 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 righthandside vectors.
 mode: str
‘fwd’ or ‘rev’.
 scope_out: set or None
Set of absolute output names in the scope of this matvec product. If None, all are in the scope.
 scope_in: set or None
Set of absolute input names in the scope of this matvec 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 righthandside 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.
