group.py

Define the Group class.

class openmdao.core.group.Group(**kwargs)

Bases: openmdao.core.system.System

Class used to group systems together; instantiate or inherit.

__init__(self, **kwargs)

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

Parameters

**kwargsdict

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

add(self, name, subsys, promotes=None)

Add a subsystem (deprecated version of <Group.add_subsystem>).

Parameters

namestr

Name of the subsystem being added

subsysSystem

An instantiated, but not-yet-set up system object.

promotesiter of str, optional

A list of variable names specifying which subsystem variables to ‘promote’ up to this group. This is for backwards compatibility with older versions of OpenMDAO.

Returns

System

The System that was passed in.

add_constraint(self, 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, cache_linear_solution=False)

Add a constraint variable to this system.

Parameters

namestring

Name of the response variable in the system.

lowerfloat or ndarray, optional

Lower boundary for the variable

upperfloat or ndarray, optional

Upper boundary for the variable

equalsfloat or ndarray, optional

Equality constraint value for the variable

reffloat or ndarray, optional

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

ref0float or ndarray, optional

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

adderfloat 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.

scalerfloat 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.

indicessequence 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.

linearbool

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

parallel_deriv_colorstring

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

vectorize_derivsbool

If True, vectorize derivative calculations.

cache_linear_solutionbool

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_design_var(self, name, lower=None, upper=None, ref=None, ref0=None, indices=None, adder=None, scaler=None, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)

Add a design variable to this system.

Parameters

namestring

Name of the design variable in the system.

lowerfloat or ndarray, optional

Lower boundary for the param

upperupper or ndarray, optional

Upper boundary for the param

reffloat or ndarray, optional

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

ref0float or ndarray, optional

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

indicesiter 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.

adderfloat 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.

scalerfloat 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_colorstring

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

vectorize_derivsbool

If True, vectorize derivative calculations.

cache_linear_solutionbool

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(self, name, ref=None, ref0=None, index=None, adder=None, scaler=None, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)

Add a response variable to this system.

Parameters

namestring

Name of the response variable in the system.

reffloat or ndarray, optional

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

ref0float or ndarray, optional

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

indexint, 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.

adderfloat 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.

scalerfloat 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_colorstring

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

vectorize_derivsbool

If True, vectorize derivative calculations.

cache_linear_solutionbool

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(self, recorder, recurse=False)

Add a recorder to the system.

Parameters

recorder<CaseRecorder>

A recorder instance.

recurseboolean

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

add_response(self, 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, 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

namestring

Name of the response variable in the system.

type_string

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

lowerfloat or ndarray, optional

Lower boundary for the variable

upperupper or ndarray, optional

Upper boundary for the variable

equalsequals or ndarray, optional

Equality constraint value for the variable

reffloat or ndarray, optional

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

ref0upper or ndarray, optional

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

indicessequence of int, optional

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

indexint, optional

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

adderfloat 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.

scalerfloat 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.

linearbool

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

parallel_deriv_colorstring

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

vectorize_derivsbool

If True, vectorize derivative calculations.

cache_linear_solutionbool

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(self, name, subsys, promotes=None, promotes_inputs=None, promotes_outputs=None, min_procs=1, max_procs=None, proc_weight=1.0)

Add a subsystem.

Parameters

namestr

Name of the subsystem being added

subsys<System>

An instantiated, but not-yet-set up system object.

promotesiter 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_inputsiter 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_outputsiter 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_procsint

Minimum number of MPI processes usable by the subsystem. Defaults to 1.

max_procsint or None

Maximum number of MPI processes usable by the subsystem. A value of None (the default) indicates there is no maximum limit.

proc_weightfloat

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(self, method='fd', step=None, form=None, step_calc=None)

Approximate derivatives for a Group using the specified approximation method.

Parameters

methodstr

The type of approximation that should be used. Valid options include: ‘fd’: Finite Difference, ‘cs’: Complex Step

stepfloat

Step size for approximation. Defaults to None, in which case, the approximation method provides its default value.

formstring

Form for finite difference, can be ‘forward’, ‘backward’, or ‘central’. Defaults to None, in which case, the approximation method provides its default value.

step_calcstring

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(self, logger)

Perform optional error checks.

Parameters

loggerobject

The object that manages logging output.

cleanup(self)

Clean up resources prior to exit.

compute_sys_graph(self, comps_only=False)

Compute a dependency graph for subsystems in this group.

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

Parameters

comps_onlybool (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(self)

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(self, src_name, tgt_name, src_indices=None, flat_src_indices=None)

Connect source src_name to target tgt_name in this namespace.

Parameters

src_namestr

name of the source variable to connect

tgt_namestr or [str, … ] or (str, …)

name of the target variable(s) to connect

src_indicesint 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_indicesbool

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.

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

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

Parameters

wrtstr 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.

methodstr

Method used to compute derivative: “fd” for finite difference, “cs” for complex step.

formstr

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

stepfloat

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

per_instancebool

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_jacsint

Number of times to repeat partial jacobian computation when computing sparsity.

tolfloat

Tolerance used to determine if an array entry is nonzero during sparsity determination.

ordersint

Number of orders above and below the tolerance to check during the tolerance sweep.

perturb_sizefloat

Size of input/output perturbation during generation of sparsity.

show_summarybool

If True, display summary information after generating coloring.

show_sparsitybool

If True, display sparsity with coloring info after generating coloring.

get_approx_coloring_fname(self)

Return the full pathname to a coloring file.

Parameters

systemSystem

The System having its coloring saved or loaded.

Returns

str

Full pathname of the coloring file.

get_constraints(self, 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

recursebool, 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(self, 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

recursebool

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

get_sizesbool, optional

If True, compute the size of each design variable.

Returns

dict

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

get_linear_vectors(self, vec_name='linear')

Return the linear inputs, outputs, and residuals vectors.

Parameters

vec_namestr

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(self)

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(self, recurse=True)

Get the Objective settings from this system.

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

Parameters

recursebool, 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(self, 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

recursebool, optional

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

get_sizesbool, optional

If True, compute the size of each response.

Returns

dict

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

guess_nonlinear(self, inputs, outputs, residuals, discrete_inputs=None, discrete_outputs=None)

Provide initial guess for states.

Override this method to set the initial guess for states.

Parameters

inputsVector

unscaled, dimensional input variables read via inputs[key]

outputsVector

unscaled, dimensional output variables read via outputs[key]

residualsVector

unscaled, dimensional residuals written to via residuals[key]

discrete_inputsdict or None

If not None, dict containing discrete input values.

discrete_outputsdict or None

If not None, dict containing discrete output values.

initialize(self)

Perform any one-time initialization run at instantiation.

is_active(self)

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(self, values=True, prom_name=False, units=False, shape=False, hierarchical=True, print_arrays=False, tags=None, out_stream=<object object at 0x7f6401d061e0>)

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

valuesbool, optional

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

prom_namebool, optional

When True, display/return the promoted name of the variable. Default is False.

unitsbool, optional

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

shapebool, optional

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

hierarchicalbool, optional

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

print_arraysbool, optional

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

tagsstr 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.

out_streamfile-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(self, explicit=True, implicit=True, values=True, prom_name=False, residuals=False, residuals_tol=None, units=False, shape=False, bounds=False, scaling=False, hierarchical=True, print_arrays=False, tags=None, out_stream=<object object at 0x7f6401d061e0>)

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

explicitbool, optional

include outputs from explicit components. Default is True.

implicitbool, optional

include outputs from implicit components. Default is True.

valuesbool, optional

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

prom_namebool, optional

When True, display/return the promoted name of the variable. Default is False.

residualsbool, optional

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

residuals_tolfloat, 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.

unitsbool, optional

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

shapebool, optional

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

boundsbool, optional

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

scalingbool, optional

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

hierarchicalbool, optional

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

print_arraysbool, optional

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

tagsstr 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.

out_streamfile-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

property ln_solver

Get the linear solver for this system.

property metadata

Get the options for this System.

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 nl_solver

Get the nonlinear solver for this system.

property nonlinear_solver

Get the nonlinear solver for this system.

reconfigure(self)

Perform reconfiguration.

Returns

bool

If True, reconfiguration is to be performed.

record_iteration(self)

Record an iteration of the current System.

resetup(self, setup_mode='full')

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

Parameters

setup_modestr

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

run_apply_linear(self, 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.

modestr

‘fwd’ or ‘rev’.

scope_outset or None

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

scope_inset 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(self)

Compute residuals.

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

run_linearize(self, sub_do_ln=True)

Compute jacobian / factorization.

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

Parameters

sub_do_lnboolean

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

run_solve_linear(self, 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.

modestr

‘fwd’ or ‘rev’.

run_solve_nonlinear(self)

Compute outputs.

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

set_initial_values(self)

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

set_order(self, new_order)

Specify a new execution order for this system.

Parameters

new_orderlist of str

List of system names in desired new execution order.

setup(self)

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(self, include_self=False, recurse=True, typ=None)

Yield a generator of local subsystems of this system.

Parameters

include_selfbool

If True, include this system in the iteration.

recursebool

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

typtype

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

use_fixed_coloring(self, coloring=<object object at 0x7f640240aad0>, recurse=True)

Use a precomputed coloring for this System.

Parameters

coloringstr

A coloring filename. If no arg is passed, filename will be determined automatically.

recursebool

If True, set fixed coloring in all subsystems that declare a coloring. Ignored if a specific coloring is passed in.