system.py¶
Define the base System class.

class
openmdao.core.system.
System
(num_par_fd=1, **kwargs)[source]¶ Bases:
object
Base class for all systems in OpenMDAO.
Never instantiated; subclassed by <Group> or <Component>. All subclasses have their attributes defined here.
 In attribute names:
 abs / abs_name : absolute, unpromoted variable name, seen from root (unique). rel / rel_name : relative, unpromoted variable name, seen from current system (unique). prom / prom_name : relative, promoted variable name, seen from current system (nonunique). idx : global variable index among variables on all procs (I/O indices separate). my_idx : index among variables in this system, on this processor (I/O indices separate). io : indicates explicitly that input and output variables are combined in the same dict.
Attributes
name (str) Name of the system, must be different from siblings. pathname (str) Global name of the system, including the path. comm (MPI.Comm or <FakeComm>) MPI communicator object. options (OptionsDictionary) options dictionary recording_options (OptionsDictionary) Recording options dictionary under_complex_step (bool) When True, this system is undergoing complex step. force_alloc_complex (bool) When True, the vectors have been allocated for checking with complex step. iter_count (int) Int that holds the number of times this system has iterated in a recording run. cite (str) Listing of relevant citations that should be referenced when publishing work that uses this class. supports_multivecs (bool) If True, this system overrides compute_multi_jacvec_product (if an ExplicitComponent), or solve_multi_linear/apply_multi_linear (if an ImplicitComponent). matrix_free (Bool) This is set to True if the component overrides the appropriate function with a userdefined matrix vector product with the Jacobian or any of its subsystems do. 
__init__
(num_par_fd=1, **kwargs)[source]¶ Initialize all attributes.
Parameters:  num_par_fd : int
If FD is active, number of concurrent FD solves.
 **kwargs : dict of keyword arguments
Keyword arguments that will be mapped into the System options.

add_constraint
(name, lower=None, upper=None, equals=None, ref=None, ref0=None, adder=None, scaler=None, indices=None, linear=False, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)[source]¶ Add a constraint variable to this system.
Parameters:  name : string
Name of the response variable in the system.
 lower : float or ndarray, optional
Lower boundary for the variable
 upper : float or ndarray, optional
Upper boundary for the variable
 equals : float or ndarray, optional
Equality constraint value for the variable
 ref : float or ndarray, optional
Value of response variable that scales to 1.0 in the driver.
 ref0 : float or ndarray, optional
Value of response variable that scales to 0.0 in the driver.
 adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder is first in precedence.
 scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler is second in precedence.
 indices : sequence of int, optional
If variable is an array, these indicate which entries are of interest for this particular response. These may be positive or negative integers.
 linear : bool
Set to True if constraint is linear. Default is False.
 parallel_deriv_color : string
If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
 vectorize_derivs : bool
If True, vectorize derivative calculations.
 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_design_var
(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)[source]¶ Add a design variable to this system.
Parameters:  name : string
Name of the design variable in the system.
 lower : float or ndarray, optional
Lower boundary for the param
 upper : upper or ndarray, optional
Upper boundary for the param
 ref : float or ndarray, optional
Value of design var that scales to 1.0 in the driver.
 ref0 : float or ndarray, optional
Value of design var that scales to 0.0 in the driver.
 indices : iter of int, optional
If a param is an array, these indicate which entries are of interest for this particular design variable. These may be positive or negative integers.
 adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder is first in precedence.
 scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler is second in precedence.
 parallel_deriv_color : string
If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
 vectorize_derivs : bool
If True, vectorize derivative calculations.
 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, adder=None, scaler=None, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)[source]¶ Add a response variable to this system.
Parameters:  name : string
Name of the response variable in the system.
 ref : float or ndarray, optional
Value of response variable that scales to 1.0 in the driver.
 ref0 : float or ndarray, optional
Value of response variable that scales to 0.0 in the driver.
 index : int, optional
If variable is an array, this indicates which entry is of interest for this particular response. This may be a positive or negative integer.
 adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder is first in precedence.
 scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler is second in precedence.
 parallel_deriv_color : string
If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
 vectorize_derivs : bool
If True, vectorize derivative calculations.
 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)[source]¶ Add a recorder to the driver.
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, adder=None, scaler=None, linear=False, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)[source]¶ 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.
 adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder is first in precedence.
 scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler is second in precedence.
 linear : bool
Set to True if constraint is linear. Default is False.
 parallel_deriv_color : string
If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
 vectorize_derivs : bool
If True, vectorize derivative calculations.
 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.

check_config
(logger)[source]¶ Perform optional error checks.
Parameters:  logger : object
The object that manages logging output.

get_constraints
(recurse=True)[source]¶ 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)[source]¶ Get the DesignVariable settings from this system.
Retrieve all design variable settings from the system and, if recurse is True, all of its subsystems.
Parameters:  recurse : bool
If True, recurse through the subsystems and return the path of all design vars relative to the this system.
 get_sizes : bool, optional
If True, compute the size of each response.
Returns:  dict
The design variables defined in the current system and, if recurse=True, its subsystems.

get_linear_vectors
(vec_name='linear')[source]¶ 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
()[source]¶ 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)[source]¶ Get the Objective settings from this system.
Retrieve all objectives settings from the system as a dict, keyed by variable name.
Parameters:  recurse : bool, optional
If True, recurse through the subsystems and return the path of all objective relative to the this system.
Returns:  dict
The objectives defined in the current system.

get_responses
(recurse=True, get_sizes=True)[source]¶ Get the response variable settings from this system.
Retrieve all response variable settings from the system as a dict, keyed by variable name.
Parameters:  recurse : bool, optional
If True, recurse through the subsystems and return the path of all responses relative to the this system.
 get_sizes : bool, optional
If True, compute the size of each response.
Returns:  dict
The responses defined in the current system and, if recurse=True, its subsystems.

is_active
()[source]¶ 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.

linear_solver
¶ Get the linear solver for this system.

list_inputs
(values=True, units=False, hierarchical=True, print_arrays=False, out_stream=<object object>)[source]¶ Return and optionally log a list of input names and other optional information.
If the model is parallel, only the local variables are returned to the process. Also optionally logs the information to a user defined output stream. If the model is parallel, the rank 0 process logs information about all variables across all processes.
Parameters:  values : bool, optional
When True, display/return input values. Default is True.
 units : bool, optional
When True, display/return units. Default is False.
 hierarchical : bool, optional
When True, human readable output shows variables in hierarchical format.
 print_arrays : bool, optional
When False, in the columnar display, just display norm of any ndarrays with size > 1. The norm is surrounded by vertical bars to indicate that it is a norm. When True, also display full values of the ndarray below the row. Format is affected by the values set with numpy.set_printoptions Default is False.
 out_stream : filelike object
Where to send human readable output. Default is sys.stdout. Set to None to suppress.
Returns:  list
list of input names and other optional information about those inputs

list_outputs
(explicit=True, implicit=True, values=True, prom_name=False, residuals=False, residuals_tol=None, units=False, shape=False, bounds=False, scaling=False, hierarchical=True, print_arrays=False, out_stream=<object object>)[source]¶ Return and optionally log a list of output names and other optional information.
If the model is parallel, only the local variables are returned to the process. Also optionally logs the information to a user defined output stream. If the model is parallel, the rank 0 process logs information about all variables across all processes.
Parameters:  explicit : bool, optional
include outputs from explicit components. Default is True.
 implicit : bool, optional
include outputs from implicit components. Default is True.
 values : bool, optional
When True, display/return output values. Default is True.
 prom_name : bool, optional
When True, display/return the promoted name of the variable. Default is False.
 residuals : bool, optional
When True, display/return residual values. Default is False.
 residuals_tol : float, optional
If set, limits the output of list_outputs to only variables where the norm of the resids array is greater than the given ‘residuals_tol’. Default is None.
 units : bool, optional
When True, display/return units. Default is False.
 shape : bool, optional
When True, display/return the shape of the value. Default is False.
 bounds : bool, optional
When True, display/return bounds (lower and upper). Default is False.
 scaling : bool, optional
When True, display/return scaling (ref, ref0, and res_ref). Default is False.
 hierarchical : bool, optional
When True, human readable output shows variables in hierarchical format.
 print_arrays : bool, optional
When False, in the columnar display, just display norm of any ndarrays with size > 1. The norm is surrounded by vertical bars to indicate that it is a norm. When True, also display full values of the ndarray below the row. Format is affected by the values set with numpy.set_printoptions Default is False.
 out_stream : filelike
Where to send human readable output. Default is sys.stdout. Set to None to suppress.
Returns:  list
list of output names and other optional information about those outputs

ln_solver
¶ Get the linear solver for this system.

metadata
¶ Get the options for this System.

nl_solver
¶ Get the nonlinear solver for this system.

nonlinear_solver
¶ Get the nonlinear solver for this system.

reconfigure
()[source]¶ Perform reconfiguration.
Returns:  bool
If True, reconfiguration is to be performed.

resetup
(setup_mode='full')[source]¶ Public wrapper for _setup that reconfigures after an initial setup has been performed.
Parameters:  setup_mode : str
Must be one of ‘full’, ‘reconf’, or ‘update’.

run_apply_linear
(vec_names, mode, scope_out=None, scope_in=None)[source]¶ 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
()[source]¶ Compute residuals.
This calls _apply_nonlinear, but with the model assumed to be in an unscaled state.

run_linearize
(sub_do_ln=True)[source]¶ 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)[source]¶ 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’.
Returns:  boolean
Failure flag; True if failed to converge, False is successful.
 float
relative error.
 float
absolute error.

run_solve_nonlinear
()[source]¶ Compute outputs.
This calls _solve_nonlinear, but with the model assumed to be in an unscaled state.
Returns:  boolean
Failure flag; True if failed to converge, False is successful.
 float
relative error.
 float
absolute error.

system_iter
(include_self=False, recurse=True, typ=None)[source]¶ 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.