fastoad.openmdao.problem module

class fastoad.openmdao.problem.FASTOADProblem(*args, **kwargs)[source]

Bases: Problem

Vanilla OpenMDAO Problem except that it can write its outputs to a file.

It also runs ValidityDomainChecker after each run_model() or run_driver() (but it does nothing if no check has been registered).

Initialize attributes.

input_file_path: File path where read_inputs() will read inputs

output_file_path: File path where write_outputs() will write outputs

additional_variables: Variables that are not part of the problem but that should be written in output file.

run_model(case_prefix=None, reset_iter_counts=True)[source]

Run the model by calling the root system’s solve_nonlinear.

Parameters:

case_prefix (str or None) – Prefix to prepend to coordinates when recording. None means keep the preexisting prefix.
reset_iter_counts (bool) – If True and model has been run previously, reset all iteration counters.

run_driver(case_prefix=None, reset_iter_counts=True)[source]

Run the driver on the model.

Parameters:

case_prefix (str or None) – Prefix to prepend to coordinates when recording. None means keep the preexisting prefix.
reset_iter_counts (bool) – If True and model has been run previously, reset all iteration counters.

Returns:

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

Return type:

bool

setup(*args, **kwargs)[source]: Set up the problem before run.

write_needed_inputs(source_file_path: str | PathLike = None, source_formatter: IVariableIOFormatter = None)[source]

Writes the input file of the problem using its unconnected inputs.

Written value of each variable will be taken:

from input_data if it contains the variable

from defined default values in component definitions

Parameters:

source_file_path – if provided, variable values will be read from it
source_formatter – the class that defines format of input file. if not provided, expected format will be the default one.

write_outputs() → DataFile | None[source]: Writes all outputs in the configured output file.

read_inputs()[source]: Reads inputs of the problem.

property analysis: ProblemAnalysis

Information about inner structure of this problem.

The collected data (internally stored) are used in several steps of the computation.

This analysis is performed once. Each subsequent usage reuses the obtained data.

To ensure the analysis is run again, use reset_analysis().

reset_analysis()[source]: Ensure a new problem analysis is done at new usage of analysis.

add_recorder(recorder)

Add a recorder to the problem.

Parameters:: recorder (CaseRecorder) – A recorder instance.

check_config(logger=None, checks={'auto_ivc_warnings': <function _check_auto_ivc_warnings>, 'comp_has_no_outputs': <function _check_comp_has_no_outputs>, 'dup_inputs': <function _check_dup_comp_inputs>, 'missing_recorders': <function _check_missing_recorders>, 'out_of_order': <function _check_ubcs_prob>, 'solvers': <function _check_solvers>, 'system': <function _check_system_configs>, 'unserializable_options': <function _check_unserializable_options>}, out_file='openmdao_checks.out')

Perform optional error checks on a Problem.

Parameters:

logger (object) – Logging object.
checks (list of str or None or the str 'all') – Determines what config checks are run. If None, no checks are run If list of str, run those config checks If ‘all’, all the checks (‘auto_ivc_warnings’, ‘comp_has_no_outputs’, ‘cycles’, ‘dup_inputs’, ‘missing_recorders’, ‘out_of_order’, ‘promotions’, ‘solvers’, ‘system’, ‘unconnected_inputs’) are run.
out_file (str or None) – If not None, output will be written to this file in addition to stdout.

check_partials(out_stream=DEFAULT_OUT_STREAM, includes=None, excludes=None, compact_print=False, abs_err_tol=1e-06, rel_err_tol=1e-06, method='fd', step=None, form='forward', step_calc='abs', minimum_step=1e-12, force_dense=True, show_only_incorrect=False)

Check partial derivatives comprehensively for all components in your model.

Parameters:

out_stream (file-like object) – Where to send human readable output. By default it goes to stdout. Set to None to suppress.
includes (None or list_like) – List of glob patterns for pathnames to include in the check. Default is None, which includes all components in the model.
excludes (None or list_like) – List of glob patterns for pathnames to exclude from the check. Default is None, which excludes nothing.
compact_print (bool) – Set to True to just print the essentials, one line per input-output pair.
abs_err_tol (float) – Threshold value for absolute error. Errors about this value will have a ‘*’ displayed next to them in output, making them easy to search for. Default is 1.0E-6.
rel_err_tol (float) – Threshold value for relative error. Errors about this value will have a ‘*’ displayed next to them in output, making them easy to search for. Note at times there may be a significant relative error due to a minor absolute error. Default is 1.0E-6.
method (str) – Method, ‘fd’ for finite difference or ‘cs’ for complex step. Default is ‘fd’.
step (None, float, or list/tuple of float) – Step size(s) for approximation. Default is None, which means 1e-6 for ‘fd’ and 1e-40 for ‘cs’.
form (str) – Form for finite difference, can be ‘forward’, ‘backward’, or ‘central’. Default ‘forward’.
step_calc (str) – Step type for computing the size of the finite difference step. It can be ‘abs’ for absolute, ‘rel_avg’ for a size relative to the absolute value of the vector input, or ‘rel_element’ for a size relative to each value in the vector input. In addition, it can be ‘rel_legacy’ for a size relative to the norm of the vector. For backwards compatibilty, it can be ‘rel’, which is now equivalent to ‘rel_avg’. Defaults to None, in which case the approximation method provides its default value.
minimum_step (float) – Minimum step size allowed when using one of the relative step_calc options.
force_dense (bool) – If True, analytic derivatives will be coerced into arrays. Default is True.
show_only_incorrect (bool, optional) – Set to True if output should print only the subjacs found to be incorrect.

Returns:

First key is the component name. Second key is the (output, input) tuple of strings. Third key is one of [‘rel error’, ‘abs error’, ‘magnitude’, ‘J_fd’, ‘J_fwd’, ‘J_rev’, ‘rank_inconsistent’]. For ‘rel error’, ‘abs error’, and ‘magnitude’ the value is a tuple containing norms for forward - fd, adjoint - fd, forward - adjoint. For ‘J_fd’, ‘J_fwd’, ‘J_rev’ the value is a numpy array representing the computed Jacobian for the three different methods of computation. The boolean ‘rank_inconsistent’ indicates if the derivative wrt a serial variable is inconsistent across MPI ranks.

Return type:

dict of dicts of dicts

check_totals(of=None, wrt=None, out_stream=DEFAULT_OUT_STREAM, compact_print=False, driver_scaling=False, abs_err_tol=1e-06, rel_err_tol=1e-06, method='fd', step=None, form=None, step_calc='abs', show_progress=False, show_only_incorrect=False, directional=False)

Check total derivatives for the model vs. finite difference.

Parameters:

of (list of variable name str or None) – Variables whose derivatives will be computed. Default is None, which uses the driver’s objectives and constraints.
wrt (list of variable name str or None) – Variables with respect to which the derivatives will be computed. Default is None, which uses the driver’s desvars.
out_stream (file-like object) – Where to send human readable output. By default it goes to stdout. Set to None to suppress.
compact_print (bool) – Set to True to just print the essentials, one line per input-output pair.
driver_scaling (bool) – When True, return derivatives that are scaled according to either the adder and scaler or the ref and ref0 values that were specified when add_design_var, add_objective, and add_constraint were called on the model. Default is False, which is unscaled.
abs_err_tol (float) – Threshold value for absolute error. Errors about this value will have a ‘*’ displayed next to them in output, making them easy to search for. Default is 1.0E-6.
rel_err_tol (float) – Threshold value for relative error. Errors about this value will have a ‘*’ displayed next to them in output, making them easy to search for. Note at times there may be a significant relative error due to a minor absolute error. Default is 1.0E-6.
method (str) – Method, ‘fd’ for finite difference or ‘cs’ for complex step. Default is ‘fd’.
step (None, float, or list/tuple of float) – Step size for approximation. Default is None, which means 1e-6 for ‘fd’ and 1e-40 for ‘cs’.
form (str) – Form for finite difference, can be ‘forward’, ‘backward’, or ‘central’. Default None, which defaults to ‘forward’ for FD.
step_calc (str) – Step type for computing the size of the finite difference step. It can be ‘abs’ for absolute, ‘rel_avg’ for a size relative to the absolute value of the vector input, or ‘rel_element’ for a size relative to each value in the vector input. In addition, it can be ‘rel_legacy’ for a size relative to the norm of the vector. For backwards compatibilty, it can be ‘rel’, which is now equivalent to ‘rel_avg’. Defaults to None, in which case the approximation method provides its default value..
show_progress (bool) – True to show progress of check_totals.
show_only_incorrect (bool, optional) – Set to True if output should print only the subjacs found to be incorrect.
directional (bool) – If True, compute a single directional derivative for each ‘of’ in rev mode or each ‘wrt’ in fwd mode.

Returns:

First key:: is the (output, input) tuple of strings;
Second key:: is one of [‘rel error’, ‘abs error’, ‘magnitude’, ‘fdstep’];
For ‘rel error’, ‘abs error’, ‘magnitude’ the value is: A tuple containing norms for: forward - fd, adjoint - fd, forward - adjoint.

Return type:

Dict of Dicts of Tuples of Floats

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

compute_jacvec_product(of, wrt, mode, seed)

Given a seed and ‘of’ and ‘wrt’ variables, compute the total jacobian vector product.

Parameters:

of (list of str) – Variables whose derivatives will be computed.
wrt (list of str) – Derivatives will be computed with respect to these variables.
mode (str) – Derivative direction (‘fwd’ or ‘rev’).
seed (dict or list) – Either a dict keyed by ‘wrt’ varnames (fwd) or ‘of’ varnames (rev), containing dresidual (fwd) or doutput (rev) values, OR a list of dresidual or doutput values that matches the corresponding ‘wrt’ (fwd) or ‘of’ (rev) varname list.

Returns:

The total jacobian vector product, keyed by variable name.

Return type:

dict

compute_totals(of=None, wrt=None, return_format='flat_dict', debug_print=False, driver_scaling=False, use_abs_names=False, get_remote=True)

Compute derivatives of desired quantities with respect to desired inputs.

Parameters:

of (list of variable name str or None) – Variables whose derivatives will be computed. Default is None, which uses the driver’s objectives and constraints.
wrt (list of variable name str or None) – Variables with respect to which the derivatives will be computed. Default is None, which uses the driver’s desvars.
return_format (str) – Format to return the derivatives. Can be ‘dict’, ‘flat_dict’, or ‘array’. Default is a ‘flat_dict’, which returns them in a dictionary whose keys are tuples of form (of, wrt).
debug_print (bool) – Set to True to print out some debug information during linear solve.
driver_scaling (bool) – When True, return derivatives that are scaled according to either the adder and scaler or the ref and ref0 values that were specified when add_design_var, add_objective, and add_constraint were called on the model. Default is False, which is unscaled.
use_abs_names (bool) – Set to True when passing in absolute names to skip some translation steps.
get_remote (bool) – If True, the default, the full distributed total jacobian will be retrieved.

Returns:

Derivatives in form requested by ‘return_format’.

Return type:

object

property driver: Get the Driver for this Problem.

final_setup()

Perform final setup phase on problem in preparation for run.

This is the second phase of setup, and is done automatically at the start of run_driver and run_model. At the beginning of final_setup, we have a model hierarchy with defined variables, solvers, case_recorders, and derivative settings. During this phase, the vectors are created and populated, the drivers and solvers are initialized, and the recorders are started, and the rest of the framework is prepared for execution.

get_reports_dir(force=False)

Get the path to the directory where the report files should go.

If it doesn’t exist, it will be created.

Parameters:: force (bool) – If True, create the reports directory if it doesn’t exist, even if this Problem does not have any active reports. This can happen when running testflo.
Returns:: The path to the directory where reports should be written.
Return type:: str

get_val(name, units=None, indices=None, get_remote=False)

Get an output/input 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.

Returns:

The value of the requested output/input variable.

Return type:

object

is_local(name)

Return True if the named variable or system is local to the current process.

Parameters:: name (str) – Name of a variable or system.
Returns:: True if the named system or variable is local to this process.
Return type:: bool

iter_count_iter(include_driver=True, include_solvers=True, include_systems=False)

Yield iteration counts for driver, solvers and/or systems.

Parameters:

include_driver (bool) – If True, include the driver in the iteration counts.
include_solvers (bool) – If True, include solvers in the iteration counts.
include_systems (bool) – If True, include systems in the iteration counts.

Yields:

str – Name of the object.
str – Name of the counter.
int – Value of the counter.

list_indep_vars(include_design_vars=True, options=None, print_arrays=False, out_stream=DEFAULT_OUT_STREAM)

Retrieve the independent variables in the Problem.

Returns a dictionary mapping the promoted names of indep_vars which the user is expected to provide to the metadata for the associated independent variable.

A output is designated as an independent variable if it is tagged with ‘openmdao:indep_var’. This includes IndepVarComp by default, and users are able to apply this tag to their own component outputs if they wish to provide components with IndepVarComp-like capability.

Parameters:

include_design_vars (bool) – If True, include design variables in the list of problem inputs. The user may provide values for these but ultimately they will be overwritten by the Driver. Default is False.
options (list of str or None) – List of optional columns to be displayed in the independent variable table. Allowed values are: [‘name’, ‘units’, ‘shape’, ‘size’, ‘desc’, ‘ref’, ‘ref0’, ‘res_ref’, ‘distributed’, ‘lower’, ‘upper’, ‘tags’, ‘shape_by_conn’, ‘copy_shape’, ‘global_size’, ‘global_shape’, ‘value’].
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.
out_stream (file-like object) – Where to send human readable output. Default is sys.stdout. Set to None to suppress.

Returns:

A dictionary mapping the promoted names of all independent variables in the model to their metadata.

Return type:

dict

list_pre_post(outfile=None)

Display the pre and post optimization components.

Parameters:: outfile (file-like or str or None) – Where to send human readable output. Default is None, which sends output to stdout.

list_problem_vars(show_promoted_name=True, print_arrays=False, driver_scaling=True, desvar_opts=[], cons_opts=[], objs_opts=[], out_stream=DEFAULT_OUT_STREAM)

Print all design variables and responses (objectives and constraints).

Parameters:

show_promoted_name (bool) – If True, then show the promoted names of the variables.
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.
driver_scaling (bool, optional) – When True, return values that are scaled according to either the adder and scaler or the ref and ref0 values that were specified when add_design_var, add_objective, and add_constraint were called on the model. Default is True.
desvar_opts (list of str) – List of optional columns to be displayed in the desvars table. Allowed values are: [‘lower’, ‘upper’, ‘ref’, ‘ref0’, ‘indices’, ‘adder’, ‘scaler’, ‘parallel_deriv_color’, ‘cache_linear_solution’, ‘units’, ‘min’, ‘max’].
cons_opts (list of str) – List of optional columns to be displayed in the cons table. Allowed values are: [‘lower’, ‘upper’, ‘equals’, ‘ref’, ‘ref0’, ‘indices’, ‘adder’, ‘scaler’, ‘linear’, ‘parallel_deriv_color’, ‘cache_linear_solution’, ‘units’, ‘min’, ‘max’].
objs_opts (list of str) – List of optional columns to be displayed in the objs table. Allowed values are: [‘ref’, ‘ref0’, ‘indices’, ‘adder’, ‘scaler’, ‘units’, ‘parallel_deriv_color’, ‘cache_linear_solution’].
out_stream (file-like object) – Where to send human readable output. Default is sys.stdout. Set to None to suppress.

Returns:

Name, size, val, and other requested parameters of design variables, constraints, and objectives.

Return type:

dict

load_case(case)

Pull all input and output variables from a case into the model.

Parameters:: case (Case object) – A Case from a CaseRecorder file.

property msginfo

Return info to prepend to messages.

Returns:: Info to prepend to messages.
Return type:: str

record(case_name)

Record the variables at the Problem level.

Must be called after final_setup has been called. This can either happen automatically through run_driver or run_model, or it can be called manually.

Parameters:: case_name (str) – Name used to identify this Problem case.

set_complex_step_mode(active)

Turn on or off complex stepping mode.

Parameters:: active (bool) – Complex mode flag; set to True prior to commencing complex step.

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.

set_val(name, val=None, units=None, indices=None)

Set an output/input variable.

Function is used if you want to set a value using a different unit.

Parameters:

name (str) – Promoted or relative variable name in the root system’s namespace.
val (object) – Value to set this variable to.
units (str, optional) – Units that value is defined in.
indices (int or list of ints or tuple of ints or int ndarray or Iterable or None, optional) – Indices or slice to set to specified value.

class fastoad.openmdao.problem.AutoUnitsDefaultGroup(**kwargs)[source]

Bases: Group

OpenMDAO group that automatically use self.set_input_defaults() to resolve declaration conflicts in variable units.

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

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

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.

Yields:

str

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, cache_linear_solution=False, flat_indices=False, alias=None)

Add a constraint variable to this system.

Parameters:

name (str) – 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 (str) – If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
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.
flat_indices (bool) – If True, interpret specified indices as being indices into a flat source array.
alias (str) – Alias for this response. Necessary when adding multiple constraints on different indices or slices of a single variable.

Notes

The response can be scaled using ref and ref0. The argument ref0 represents the physical value when the scaled value is 0. The argument ref represents the physical value when the scaled value is 1. 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, cache_linear_solution=False, flat_indices=False)

Add a design variable to this system.

Parameters:

name (str) – Promoted 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.
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.
parallel_deriv_color (str) – If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
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.
flat_indices (bool) – If True, interpret specified indices as being indices into a flat source array.

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, cache_linear_solution=False, flat_indices=False, alias=None)

Add a response variable to this system.

Parameters:

name (str) – 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 (str) – If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
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.
flat_indices (bool) – If True, interpret specified indices as being indices into a flat source array.
alias (str) – Alias for this response. Necessary when adding multiple objectives on different indices or slices of a single variable.

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 (bool) – 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, cache_linear_solution=False, flat_indices=None, alias=None)

Add a response variable to this system.

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

Parameters:

name (str) – Promoted name of the response variable in the system.
type (str) – 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 (str) – If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
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.
flat_indices (bool) – If True, interpret specified indices as being indices into a flat source array.
alias (str or None) – Alias for this response. Necessary when adding multiple responses on different indices of the same variable.

add_subsystem(name, subsys, promotes=None, promotes_inputs=None, promotes_outputs=None, min_procs=1, max_procs=None, proc_weight=1.0, proc_group=None)

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.
proc_group (str or None) – Name of a processor group such that any system with that processor group name within the same parent group will be allocated on the same mpi process(es). If this is not None, then any other systems sharing the same proc_group must have identical values of min_procs, max_procs, and proc_weight or an exception will be raised.

Returns:

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.

Return type:

all_connected_nodes(graph, start, local=False)

Yield all downstream nodes starting at the given node.

Parameters:

graph (network.DiGraph) – Graph being traversed.
start (hashable object) – Identifier of the starting node.
local (bool) – If True and a non-local node is encountered in the traversal, the traversal ends on that branch.

Yields:

str – Each node found when traversal starts at start.

approx_totals(method='fd', step=None, form=None, step_calc=None)

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 (str) – 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 (str) – Step type for computing the size of the finite difference step. It can be ‘abs’ for absolute, ‘rel_avg’ for a size relative to the absolute value of the vector input, or ‘rel_element’ for a size relative to each value in the vector input. In addition, it can be ‘rel_legacy’ for a size relative to the norm of the vector. For backwards compatibilty, it can be ‘rel’, which is now equivalent to ‘rel_avg’. 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, add_edge_info=True)

Compute a dependency graph for subsystems in this group.

Variable connection information is stored in each edge of the system graph if comps_only is True and add_edge_info is True.

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.
add_edge_info (bool (True)) – If True and comps_only is also True, store variable connection information in each edge of the system graph.

Returns:

A directed graph containing names of subsystems and their connections.

Return type:

DiGraph

connect(src_name, tgt_name, src_indices=None, flat_src_indices=None)

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:

The value converted to the specified units.

Return type:

float or ndarray of float

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:

The value converted to the specified units.

Return type:

float or ndarray of float

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:

The value converted to the specified units.

Return type:

float or ndarray of float

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_coloring_fname()

Return the full pathname to a coloring file.

Returns:: Full pathname of the coloring file.
Return type:: str

get_constraints(recurse=True, get_sizes=True, use_prom_ivc=False)

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.
get_sizes (bool, optional) – If True, compute the size of each constraint.
use_prom_ivc (bool) – Translate ivc names to their promoted input names.

Returns:

The constraints defined in the current system.

Return type:

dict

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:

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

Return type:

dict

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

Retrieve metadata 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 ‘val’ 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.
is_indep_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to an output tagged openmdao:indep_var. If False, list only inputs _not_ connected to outputs tagged openmdao:indep_var.
is_design_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to outputs that are driver design variables. If False, list only inputs _not_ connected to outputs that are driver design variables.
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:

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.

Return type:

dict

get_linear_vectors()

Return the linear inputs, outputs, and residuals vectors.

Returns:: (inputs, outputs, residuals) – Yields the linear inputs, outputs, and residuals vectors.
Return type:: tuple of <Vector> instances

get_nonlinear_vectors()

Return the inputs, outputs, and residuals vectors.

Returns:: Yields the inputs, outputs, and residuals nonlinear vectors.
Return type:: (inputs, outputs, residuals)

get_objectives(recurse=True, get_sizes=True, use_prom_ivc=False)

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.
get_sizes (bool, optional) – If True, compute the size of each objective.
use_prom_ivc (bool) – Translate ivc names to their promoted input names.

Returns:

The objectives defined in the current system.

Return type:

dict

get_promotions(inprom=None, outprom=None)

Return all promotions for the given promoted variable(s).

In other words, how and where did promotions occur to convert absolute variable names into the given promoted name(s) at the current System level.

Parameters:

inprom (str or None) – The promoted input variable name.
outprom (str or None) – The promoted output variable name.

Returns:

Dictionary keyed on system pathname containing input and/or output promotion lists for each System where promotions occurred to produce the given promoted variable(s).

Return type:

dict

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 (dict) – Dictionary of design variable metadata.
responses (dict) – Dictionary of response variable metadata.
mode (str) – Direction of derivatives, either ‘fwd’ or ‘rev’.

Returns:

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

Return type:

dict

get_reports_dir()

Get the path to the directory where the report files should go.

If it doesn’t exist, it will be created.

Returns:: The path to the directory where reports should be written.
Return type:: str

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 either absolute variable name, promoted name, or alias name, depending on the value of use_prom_ivc and whether the original key was a promoted output, promoted input, or an alias.

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 ivc names to their promoted input names.

Returns:

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

Return type:

dict

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:: The absolute name of the source variable.
Return type:: str

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:

The value of the requested output/input variable.

Return type:

object

guess_nonlinear(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:

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_explicit()

Return True if this is an explicit component.

Returns:: True if this is an explicit component.
Return type:: bool

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

list_inputs(val=True, prom_name=True, units=False, shape=False, global_shape=False, desc=False, hierarchical=True, print_arrays=False, tags=None, includes=None, excludes=None, is_indep_var=None, is_design_var=None, all_procs=False, out_stream=DEFAULT_OUT_STREAM, print_min=False, print_max=False, return_format='list')

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

Parameters:

val (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 True.
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, str, or iter of str) – Collection of glob patterns for pathnames of variables to include. Default is None, which includes all input variables.
excludes (None, str, or iter of str) – Collection of glob patterns for pathnames of variables to exclude. Default is None.
is_indep_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to an output tagged openmdao:indep_var. If False, list only inputs _not_ connected to outputs tagged openmdao:indep_var.
is_design_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to outputs that are driver design variables. If False, list only inputs _not_ connected to outputs that are driver design variables.
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.
print_min (bool) – When true, if the input value is an array, print its smallest value.
print_max (bool) – When true, if the input value is an array, print its largest value.
return_format (str) – Indicates the desired format of the return value. Can have value of ‘list’ or ‘dict’. If ‘list’, the return value is a list of (name, metadata) tuples. if ‘dict’, the return value is a dictionary mapping {name: metadata}.

Returns:

list of (name, metadata) or dict of {name – List or dict of input names and other optional information about those inputs.

Return type:

metadata}

list_outputs(explicit=True, implicit=True, val=True, prom_name=True, 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, is_indep_var=None, is_design_var=None, all_procs=False, list_autoivcs=False, out_stream=DEFAULT_OUT_STREAM, print_min=False, print_max=False, return_format='list')

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.
val (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 True.
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, str, or iter of str) – Collection of glob patterns for pathnames of variables to include. Default is None, which includes all output variables.
excludes (None, str, or iter of str) – Collection of glob patterns for pathnames of variables to exclude. Default is None.
is_indep_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only outputs tagged openmdao:indep_var. If False, list only outputs that are _not_ tagged openmdao:indep_var.
is_design_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to outputs that are driver design variables. If False, list only inputs _not_ connected to outputs that are driver design variables.
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.
print_min (bool) – When true, if the output value is an array, print its smallest value.
print_max (bool) – When true, if the output value is an array, print its largest value.
return_format (str) – Indicates the desired format of the return value. Can have value of ‘list’ or ‘dict’. If ‘list’, the return value is a list of (name, metadata) tuples. if ‘dict’, the return value is a dictionary mapping {name: metadata}.

Returns:

list of (name, metadata) or dict of {name – List or dict of output names and other optional information about those outputs.

Return type:

metadata}

load_model_options()

Load the relevant model options from Problem._metadata[‘model_options’].

This method examines each path filter and corresponding options in self._problem_meta[‘model_options’]. If this System’s pathname matches the given path filter, it will assume the value for each given option which it possesses.

property model_options

Get the model options from self._problem_meta.

The user may change the contents of model_options to impact values sent to subsystems of this Group.

Returns:: The model options metadata provided by the associated Problem object.
Return type:: dict

property msginfo

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

Returns:: Either our instance pathname or class name.
Return type:: str

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)

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(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:

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 (bool) – Flag indicating if the children should call linearize on their linear solvers.

run_solve_linear(mode)

Apply inverse jac product.

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

Parameters:: 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_constraint_options(name, ref=UNDEFINED, ref0=UNDEFINED, equals=UNDEFINED, lower=UNDEFINED, upper=UNDEFINED, adder=UNDEFINED, scaler=UNDEFINED, alias=UNDEFINED)

Set options for objectives in the model.

Can be used to set options that were set using add_constraint.

Parameters:

name (str) – 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.
equals (float or ndarray, optional) – Equality constraint value for the variable.
lower (float or ndarray, optional) – Lower boundary for the variable.
upper (float or ndarray, optional) – Upper boundary for the variable.
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.
alias (str, optional) – Alias for this response. Necessary when adding multiple constraints on different indices or slices of a single variable.

set_design_var_options(name, lower=UNDEFINED, upper=UNDEFINED, scaler=UNDEFINED, adder=UNDEFINED, ref=UNDEFINED, ref0=UNDEFINED)

Set options for design vars in the model.

Can be used to set the options outside of setting them when calling add_design_var

Parameters:

name (str) – Name of the variable in this system’s namespace.
lower (float or ndarray, optional) – Lower boundary for the input.
upper (upper or ndarray, optional) – Upper boundary for the input.
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.
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.
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.

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)

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_objective_options(name, ref=UNDEFINED, ref0=UNDEFINED, adder=UNDEFINED, scaler=UNDEFINED, alias=UNDEFINED)

Set options for objectives in the model.

Can be used to set options after they have been set by add_objective.

Parameters:

name (str) – 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.
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.
alias (str) – Alias for this response. Necessary when adding multiple objectives on different indices or slices of a single variable.

set_order(new_order)

Specify a new execution order for this system.

Parameters:: new_order (list of str) – List of system names in desired new execution order.

set_output_solver_options(name, lower=UNDEFINED, upper=UNDEFINED, ref=UNDEFINED, ref0=UNDEFINED, res_ref=UNDEFINED)

Set solver output options.

Allows the user to set output solver options after the output has been defined and metadata set using the add_ouput method.

Parameters:

name (str) – Name of the variable in this system’s namespace.
lower (float or list or tuple or ndarray or None) – Lower bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no lower bound. Default is None.
upper (float or list or tuple or ndarray or None) – Upper bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no upper bound. Default is None.
ref (float) – Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 1. Default is 1.
ref0 (float) – Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 0. Default is 0.
res_ref (float) – Scaling parameter. The value in the user-defined res_units of this output’s residual when the scaled value is 1. Default is None, which means residual scaling matches output scaling.

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.

set_val(name, val, units=None, indices=None)

Set an input or output variable.

Parameters:

name (str) – Promoted or relative variable name in the system’s namespace.
val (object) – Value to assign to this variable.
units (str, optional) – Units of the value.
indices (int or list of ints or tuple of ints or int ndarray or Iterable or None, optional) – Indices or slice to set.

setup()

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.

Yields:

type or None

property under_approx

Return True if under complex step or finite difference.

Returns:: True if under CS or FD.
Return type:: bool

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.

class fastoad.openmdao.problem.FASTOADModel(**kwargs)[source]

Bases: AutoUnitsDefaultGroup

OpenMDAO group that defines active submodels after the initialization of all its subsystems, and inherits from AutoUnitsDefaultGroup for resolving declaration conflicts in variable units.

It allows to have a submodel choice in the initialize() method of a FAST-OAD module, but to possibly override it with the definition of active_submodels (i.e. from the configuration file).

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

active_submodels: Definition of active submodels that will be applied during setup()

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

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.

Yields:

str

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, cache_linear_solution=False, flat_indices=False, alias=None)

Add a constraint variable to this system.

Parameters:

name (str) – 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 (str) – If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
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.
flat_indices (bool) – If True, interpret specified indices as being indices into a flat source array.
alias (str) – Alias for this response. Necessary when adding multiple constraints on different indices or slices of a single variable.

Notes

The response can be scaled using ref and ref0. The argument ref0 represents the physical value when the scaled value is 0. The argument ref represents the physical value when the scaled value is 1. 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, cache_linear_solution=False, flat_indices=False)

Add a design variable to this system.

Parameters:

name (str) – Promoted 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.
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.
parallel_deriv_color (str) – If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
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.
flat_indices (bool) – If True, interpret specified indices as being indices into a flat source array.

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, cache_linear_solution=False, flat_indices=False, alias=None)

Add a response variable to this system.

Parameters:

name (str) – 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 (str) – If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
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.
flat_indices (bool) – If True, interpret specified indices as being indices into a flat source array.
alias (str) – Alias for this response. Necessary when adding multiple objectives on different indices or slices of a single variable.

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 (bool) – 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, cache_linear_solution=False, flat_indices=None, alias=None)

Add a response variable to this system.

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

Parameters:

name (str) – Promoted name of the response variable in the system.
type (str) – 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 (str) – If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
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.
flat_indices (bool) – If True, interpret specified indices as being indices into a flat source array.
alias (str or None) – Alias for this response. Necessary when adding multiple responses on different indices of the same variable.

add_subsystem(name, subsys, promotes=None, promotes_inputs=None, promotes_outputs=None, min_procs=1, max_procs=None, proc_weight=1.0, proc_group=None)

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.
proc_group (str or None) – Name of a processor group such that any system with that processor group name within the same parent group will be allocated on the same mpi process(es). If this is not None, then any other systems sharing the same proc_group must have identical values of min_procs, max_procs, and proc_weight or an exception will be raised.

Returns:

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.

Return type:

all_connected_nodes(graph, start, local=False)

Yield all downstream nodes starting at the given node.

Parameters:

graph (network.DiGraph) – Graph being traversed.
start (hashable object) – Identifier of the starting node.
local (bool) – If True and a non-local node is encountered in the traversal, the traversal ends on that branch.

Yields:

str – Each node found when traversal starts at start.

approx_totals(method='fd', step=None, form=None, step_calc=None)

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 (str) – 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 (str) – Step type for computing the size of the finite difference step. It can be ‘abs’ for absolute, ‘rel_avg’ for a size relative to the absolute value of the vector input, or ‘rel_element’ for a size relative to each value in the vector input. In addition, it can be ‘rel_legacy’ for a size relative to the norm of the vector. For backwards compatibilty, it can be ‘rel’, which is now equivalent to ‘rel_avg’. 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, add_edge_info=True)

Compute a dependency graph for subsystems in this group.

Variable connection information is stored in each edge of the system graph if comps_only is True and add_edge_info is True.

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.
add_edge_info (bool (True)) – If True and comps_only is also True, store variable connection information in each edge of the system graph.

Returns:

A directed graph containing names of subsystems and their connections.

Return type:

DiGraph

configure()

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)

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:

The value converted to the specified units.

Return type:

float or ndarray of float

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:

The value converted to the specified units.

Return type:

float or ndarray of float

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:

The value converted to the specified units.

Return type:

float or ndarray of float

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_coloring_fname()

Return the full pathname to a coloring file.

Returns:: Full pathname of the coloring file.
Return type:: str

get_constraints(recurse=True, get_sizes=True, use_prom_ivc=False)

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.
get_sizes (bool, optional) – If True, compute the size of each constraint.
use_prom_ivc (bool) – Translate ivc names to their promoted input names.

Returns:

The constraints defined in the current system.

Return type:

dict

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:

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

Return type:

dict

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

Retrieve metadata 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 ‘val’ 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.
is_indep_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to an output tagged openmdao:indep_var. If False, list only inputs _not_ connected to outputs tagged openmdao:indep_var.
is_design_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to outputs that are driver design variables. If False, list only inputs _not_ connected to outputs that are driver design variables.
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:

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.

Return type:

dict

get_linear_vectors()

Return the linear inputs, outputs, and residuals vectors.

Returns:: (inputs, outputs, residuals) – Yields the linear inputs, outputs, and residuals vectors.
Return type:: tuple of <Vector> instances

get_nonlinear_vectors()

Return the inputs, outputs, and residuals vectors.

Returns:: Yields the inputs, outputs, and residuals nonlinear vectors.
Return type:: (inputs, outputs, residuals)

get_objectives(recurse=True, get_sizes=True, use_prom_ivc=False)

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.
get_sizes (bool, optional) – If True, compute the size of each objective.
use_prom_ivc (bool) – Translate ivc names to their promoted input names.

Returns:

The objectives defined in the current system.

Return type:

dict

get_promotions(inprom=None, outprom=None)

Return all promotions for the given promoted variable(s).

In other words, how and where did promotions occur to convert absolute variable names into the given promoted name(s) at the current System level.

Parameters:

inprom (str or None) – The promoted input variable name.
outprom (str or None) – The promoted output variable name.

Returns:

Dictionary keyed on system pathname containing input and/or output promotion lists for each System where promotions occurred to produce the given promoted variable(s).

Return type:

dict

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 (dict) – Dictionary of design variable metadata.
responses (dict) – Dictionary of response variable metadata.
mode (str) – Direction of derivatives, either ‘fwd’ or ‘rev’.

Returns:

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

Return type:

dict

get_reports_dir()

Get the path to the directory where the report files should go.

If it doesn’t exist, it will be created.

Returns:: The path to the directory where reports should be written.
Return type:: str

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 either absolute variable name, promoted name, or alias name, depending on the value of use_prom_ivc and whether the original key was a promoted output, promoted input, or an alias.

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 ivc names to their promoted input names.

Returns:

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

Return type:

dict

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:: The absolute name of the source variable.
Return type:: str

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:

The value of the requested output/input variable.

Return type:

object

guess_nonlinear(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:

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_explicit()

Return True if this is an explicit component.

Returns:: True if this is an explicit component.
Return type:: bool

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

list_inputs(val=True, prom_name=True, units=False, shape=False, global_shape=False, desc=False, hierarchical=True, print_arrays=False, tags=None, includes=None, excludes=None, is_indep_var=None, is_design_var=None, all_procs=False, out_stream=DEFAULT_OUT_STREAM, print_min=False, print_max=False, return_format='list')

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

Parameters:

val (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 True.
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, str, or iter of str) – Collection of glob patterns for pathnames of variables to include. Default is None, which includes all input variables.
excludes (None, str, or iter of str) – Collection of glob patterns for pathnames of variables to exclude. Default is None.
is_indep_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to an output tagged openmdao:indep_var. If False, list only inputs _not_ connected to outputs tagged openmdao:indep_var.
is_design_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to outputs that are driver design variables. If False, list only inputs _not_ connected to outputs that are driver design variables.
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.
print_min (bool) – When true, if the input value is an array, print its smallest value.
print_max (bool) – When true, if the input value is an array, print its largest value.
return_format (str) – Indicates the desired format of the return value. Can have value of ‘list’ or ‘dict’. If ‘list’, the return value is a list of (name, metadata) tuples. if ‘dict’, the return value is a dictionary mapping {name: metadata}.

Returns:

list of (name, metadata) or dict of {name – List or dict of input names and other optional information about those inputs.

Return type:

metadata}

list_outputs(explicit=True, implicit=True, val=True, prom_name=True, 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, is_indep_var=None, is_design_var=None, all_procs=False, list_autoivcs=False, out_stream=DEFAULT_OUT_STREAM, print_min=False, print_max=False, return_format='list')

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.
val (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 True.
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, str, or iter of str) – Collection of glob patterns for pathnames of variables to include. Default is None, which includes all output variables.
excludes (None, str, or iter of str) – Collection of glob patterns for pathnames of variables to exclude. Default is None.
is_indep_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only outputs tagged openmdao:indep_var. If False, list only outputs that are _not_ tagged openmdao:indep_var.
is_design_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to outputs that are driver design variables. If False, list only inputs _not_ connected to outputs that are driver design variables.
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.
print_min (bool) – When true, if the output value is an array, print its smallest value.
print_max (bool) – When true, if the output value is an array, print its largest value.
return_format (str) – Indicates the desired format of the return value. Can have value of ‘list’ or ‘dict’. If ‘list’, the return value is a list of (name, metadata) tuples. if ‘dict’, the return value is a dictionary mapping {name: metadata}.

Returns:

list of (name, metadata) or dict of {name – List or dict of output names and other optional information about those outputs.

Return type:

metadata}

load_model_options()

Load the relevant model options from Problem._metadata[‘model_options’].

This method examines each path filter and corresponding options in self._problem_meta[‘model_options’]. If this System’s pathname matches the given path filter, it will assume the value for each given option which it possesses.

property model_options

Get the model options from self._problem_meta.

The user may change the contents of model_options to impact values sent to subsystems of this Group.

Returns:: The model options metadata provided by the associated Problem object.
Return type:: dict

property msginfo

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

Returns:: Either our instance pathname or class name.
Return type:: str

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)

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(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:

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 (bool) – Flag indicating if the children should call linearize on their linear solvers.

run_solve_linear(mode)

Apply inverse jac product.

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

Parameters:: 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_constraint_options(name, ref=UNDEFINED, ref0=UNDEFINED, equals=UNDEFINED, lower=UNDEFINED, upper=UNDEFINED, adder=UNDEFINED, scaler=UNDEFINED, alias=UNDEFINED)

Set options for objectives in the model.

Can be used to set options that were set using add_constraint.

Parameters:

name (str) – 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.
equals (float or ndarray, optional) – Equality constraint value for the variable.
lower (float or ndarray, optional) – Lower boundary for the variable.
upper (float or ndarray, optional) – Upper boundary for the variable.
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.
alias (str, optional) – Alias for this response. Necessary when adding multiple constraints on different indices or slices of a single variable.

set_design_var_options(name, lower=UNDEFINED, upper=UNDEFINED, scaler=UNDEFINED, adder=UNDEFINED, ref=UNDEFINED, ref0=UNDEFINED)

Set options for design vars in the model.

Can be used to set the options outside of setting them when calling add_design_var

Parameters:

name (str) – Name of the variable in this system’s namespace.
lower (float or ndarray, optional) – Lower boundary for the input.
upper (upper or ndarray, optional) – Upper boundary for the input.
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.
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.
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.

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)

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_objective_options(name, ref=UNDEFINED, ref0=UNDEFINED, adder=UNDEFINED, scaler=UNDEFINED, alias=UNDEFINED)

Set options for objectives in the model.

Can be used to set options after they have been set by add_objective.

Parameters:

name (str) – 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.
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.
alias (str) – Alias for this response. Necessary when adding multiple objectives on different indices or slices of a single variable.

set_order(new_order)

Specify a new execution order for this system.

Parameters:: new_order (list of str) – List of system names in desired new execution order.

set_output_solver_options(name, lower=UNDEFINED, upper=UNDEFINED, ref=UNDEFINED, ref0=UNDEFINED, res_ref=UNDEFINED)

Set solver output options.

Allows the user to set output solver options after the output has been defined and metadata set using the add_ouput method.

Parameters:

name (str) – Name of the variable in this system’s namespace.
lower (float or list or tuple or ndarray or None) – Lower bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no lower bound. Default is None.
upper (float or list or tuple or ndarray or None) – Upper bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no upper bound. Default is None.
ref (float) – Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 1. Default is 1.
ref0 (float) – Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 0. Default is 0.
res_ref (float) – Scaling parameter. The value in the user-defined res_units of this output’s residual when the scaled value is 1. Default is None, which means residual scaling matches output scaling.

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.

set_val(name, val, units=None, indices=None)

Set an input or output variable.

Parameters:

name (str) – Promoted or relative variable name in the system’s namespace.
val (object) – Value to assign to this variable.
units (str, optional) – Units of the value.
indices (int or list of ints or tuple of ints or int ndarray or Iterable or None, optional) – Indices or slice to set.

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.

Yields:

type or None

property under_approx

Return True if under complex step or finite difference.

Returns:: True if under CS or FD.
Return type:: bool

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.

fastoad.openmdao.problem.get_variable_list_from_system(system: System, get_promoted_names: bool = True, promoted_only: bool = True, io_status: str = 'all') → VariableList[source]

Creates a VariableList instance containing inputs and outputs of any OpenMDAO System.

Convenience method that creates a FASTOADProblem problem with only provided system and uses from_problem().

class fastoad.openmdao.problem.ProblemAnalysis(problem: Problem)[source]

Bases: object

Class for retrieving information about the input OpenMDAO problem.

At least one setup operation is done on a copy of the problem. Two setup operations will be done if the problem has unfed dynamically shaped inputs.

problem: Problem: The analyzed problem

problem_variables: VariableList: All variables of the problem

undetermined_dynamic_input_vars: VariableList: List variables that are inputs OF THE PROBLEM and dynamically shaped.

subsystem_order: list: Order of subsystems

ivc_var_names: list: Names of variables that are output of an IndepVarComp

analyze()[source]: Gets information about inner structure of the associated problem.

fills_dynamically_shaped_inputs(problem: Problem)[source]

Adds to the problem an IndepVarComp, that provides dummy variables to fit the dynamically shaped inputs of the analyzed problem.

Adding this IVC to the problem will allow to complete the setup operation.

The input problem should be the analyzed problem or a copy of it.