gemseo / core

doe_scenario module

A scenario whose driver is a design of experiments.

Classes:

DOEScenario(disciplines, formulation, ...[, ...])

A multidisciplinary scenario to be executed by a design of experiments (DOE).

class gemseo.core.doe_scenario.DOEScenario(disciplines, formulation, objective_name, design_space, name=None, grammar_type='JSONGrammar', **formulation_options)[source]

Bases: gemseo.core.scenario.Scenario

A multidisciplinary scenario to be executed by a design of experiments (DOE).

A DOEScenario is a particular Scenario whose driver is a DOE. This DOE must be implemented in a DOELibrary.

input_grammar

The input grammar.

Type

BaseGrammar

output_grammar

The output grammar.

Type

BaseGrammar

data_processor

A tool to pre- and post-process discipline data.

Type

DataProcessor

re_exec_policy

The policy to re-execute the same discipline.

Type

str

residual_variables

The output variables mapping to their inputs, to be considered as residuals; they shall be equal to zero.

Type

Mapping[str, str]

run_solves_residuals boolean

if True, the run method shall solve the residuals.

jac

The Jacobians of the outputs wrt inputs of the form {output: {input: matrix}}.

Type

Dict[str, Dict[str, ndarray]]

exec_for_lin

Whether the last execution was due to a linearization.

Type

bool

name

The name of the discipline.

Type

str

cache

The cache containing one or several executions of the discipline according to the cache policy.

Type

AbstractCache

disciplines

The disciplines.

Type

List(MDODiscipline)

formulation

The MDO formulation.

Type

MDOFormulation

formulation_name

The name of the MDO formulation.

Type

str

optimization_result

The optimization result.

Type

OptimizationResult

post_factory

The factory for post-processors if any.

Type

Optional[PostFactory]

seed

The seed used by the random number generators for replicability.

Type

int

Parameters
  • disciplines (Sequence[MDODiscipline]) – The disciplines used to compute the objective, constraints and observables from the design variables.

  • formulation (str) – The name of the MDO formulation, also the name of a class inheriting from MDOFormulation.

  • objective_name (str | Sequence[str]) – The name of the objective. If a sequence is passed, a vector objective function is created.

  • design_space (DesignSpace) – The design space.

  • name (str | None) –

    The name to be given to this scenario. If None, use the name of the class.

    By default it is set to None.

  • grammar_type (str) –

    The type of grammar to use for IO declaration , e.g. JSON_GRAMMAR_TYPE or SIMPLE_GRAMMAR_TYPE.

    By default it is set to JSONGrammar.

  • **formulation_options (Any) – The options to be passed to the MDOFormulation.

Return type

None

Attributes:

ALGO

ALGO_OPTIONS

APPROX_MODES

AVAILABLE_MODES

COMPLEX_STEP

EVAL_JAC

FINITE_DIFFERENCES

GRAMMAR_DIRECTORY

The directory in which to search for the grammar files if not the class one.

HDF5_CACHE

JSON_GRAMMAR_TYPE

L_BOUNDS

MEMORY_FULL_CACHE

N_CPUS

N_SAMPLES

RE_EXECUTE_DONE_POLICY

RE_EXECUTE_NEVER_POLICY

SEED

SIMPLE_CACHE

SIMPLE_GRAMMAR_TYPE

STATUS_DONE

STATUS_FAILED

STATUS_PENDING

STATUS_RUNNING

STATUS_VIRTUAL

U_BOUNDS

X_0

activate_cache

Whether to cache the discipline evaluations by default.

activate_counters

Whether to activate the counters (execution time, calls and linearizations).

activate_input_data_check

Whether to check the input data respect the input grammar.

activate_output_data_check

Whether to check the output data respect the output grammar.

cache_tol

The cache input tolerance.

default_inputs

The default inputs.

design_space

The design space on which the scenario is performed.

exec_time

The cumulated execution time of the discipline.

grammar_type

The type of grammar to be used for inputs and outputs declaration.

linearization_mode

The linearization mode among MDODiscipline.AVAILABLE_MODES.

local_data

The current input and output data.

n_calls

The number of times the discipline was executed.

n_calls_linearize

The number of times the discipline was linearized.

post_factory

The factory of post-processors.

posts

The available post-processors.

status

The status of the discipline.

time_stamps

use_standardized_objective

Whether to use the standardized OptimizationProblem.objective for logging and post-processing.

Methods:

activate_time_stamps()

Activate the time stamps.

add_constraint(output_name[, ...])

Add a design constraint.

add_differentiated_inputs([inputs])

Add inputs against which to differentiate the outputs.

add_differentiated_outputs([outputs])

Add outputs to be differentiated.

add_observable(output_names[, ...])

Add an observable to the optimization problem.

add_status_observer(obs)

Add an observer for the status.

auto_get_grammar_file([is_input, name, comp_dir])

Use a naming convention to associate a grammar file to the discipline.

check_input_data(input_data[, raise_exception])

Check the input data validity.

check_jacobian([input_data, derr_approx, ...])

Check if the analytical Jacobian is correct with respect to a reference one.

check_output_data([raise_exception])

Check the output data validity.

deactivate_time_stamps()

Deactivate the time stamps.

deserialize(file_path)

Deserialize a discipline from a file.

execute([input_data])

Execute the discipline.

export_to_dataset([name, by_group, ...])

Export the database of the optimization problem to a Dataset.

get_all_inputs()

Return the local input data as a list.

get_all_outputs()

Return the local output data as a list.

get_attributes_to_serialize()

Define the names of the attributes to be serialized.

get_available_driver_names()

The available drivers.

get_data_list_from_dict(keys, data_dict)

Filter the dict from a list of keys or a single key.

get_disciplines_statuses()

Retrieve the statuses of the disciplines.

get_expected_dataflow()

Return the expected data exchange sequence.

get_expected_workflow()

Return the expected execution sequence.

get_input_data()

Return the local input data as a dictionary.

get_input_data_names()

Return the names of the input variables.

get_input_output_data_names()

Return the names of the input and output variables.

get_inputs_asarray()

Return the local output data as a large NumPy array.

get_inputs_by_name(data_names)

Return the local data associated with input variables.

get_local_data_by_name(data_names)

Return the local data of the discipline associated with variables names.

get_optim_variables_names()

A convenience function to access the optimization variables.

get_optimum()

Return the optimization results.

get_output_data()

Return the local output data as a dictionary.

get_output_data_names()

Return the names of the output variables.

get_outputs_asarray()

Return the local input data as a large NumPy array.

get_outputs_by_name(data_names)

Return the local data associated with output variables.

get_sub_disciplines()

Return the sub-disciplines if any.

is_all_inputs_existing(data_names)

Test if several variables are discipline inputs.

is_all_outputs_existing(data_names)

Test if several variables are discipline outputs.

is_input_existing(data_name)

Test if a variable is a discipline input.

is_output_existing(data_name)

Test if a variable is a discipline output.

is_scenario()

Indicate if the current object is a Scenario.

linearize([input_data, force_all, force_no_exec])

Execute the linearized version of the code.

notify_status_observers()

Notify all status observers that the status has changed.

post_process(post_name, **options)

Post-process the optimization history.

print_execution_metrics()

Print the total number of executions and cumulated runtime by discipline.

remove_status_observer(obs)

Remove an observer for the status.

reset_statuses_for_run()

Set all the statuses to MDODiscipline.STATUS_PENDING.

save_optimization_history(file_path[, ...])

Save the optimization history of the scenario to a file.

serialize(file_path)

Serialize the discipline and store it in a file.

set_cache_policy([cache_type, ...])

Set the type of cache to use and the tolerance level.

set_differentiation_method([method, step])

Set the differentiation method for the process.

set_disciplines_statuses(status)

Set the sub-disciplines statuses.

set_jacobian_approximation([...])

Set the Jacobian approximation method.

set_optimal_fd_step([outputs, inputs, ...])

Compute the optimal finite-difference step.

set_optimization_history_backup(file_path[, ...])

Set the backup file for the optimization history during the run.

store_local_data(**kwargs)

Store discipline data in local data.

xdsmize([monitor, outdir, print_statuses, ...])

Create a JSON file defining the XDSM related to the current scenario.

ALGO = 'algo'
ALGO_OPTIONS = 'algo_options'
APPROX_MODES = ['finite_differences', 'complex_step']
AVAILABLE_MODES = ('auto', 'direct', 'adjoint', 'reverse', 'finite_differences', 'complex_step')
COMPLEX_STEP = 'complex_step'
EVAL_JAC = 'eval_jac'
FINITE_DIFFERENCES = 'finite_differences'
GRAMMAR_DIRECTORY: ClassVar[str | None] = None

The directory in which to search for the grammar files if not the class one.

HDF5_CACHE = 'HDF5Cache'
JSON_GRAMMAR_TYPE = 'JSONGrammar'
L_BOUNDS = 'l_bounds'
MEMORY_FULL_CACHE = 'MemoryFullCache'
N_CPUS = 2
N_SAMPLES = 'n_samples'
RE_EXECUTE_DONE_POLICY = 'RE_EXEC_DONE'
RE_EXECUTE_NEVER_POLICY = 'RE_EXEC_NEVER'
SEED = 'seed'
SIMPLE_CACHE = 'SimpleCache'
SIMPLE_GRAMMAR_TYPE = 'SimpleGrammar'
STATUS_DONE = 'DONE'
STATUS_FAILED = 'FAILED'
STATUS_PENDING = 'PENDING'
STATUS_RUNNING = 'RUNNING'
STATUS_VIRTUAL = 'VIRTUAL'
U_BOUNDS = 'u_bounds'
X_0 = 'x_0'
activate_cache: bool = True

Whether to cache the discipline evaluations by default.

activate_counters: ClassVar[bool] = True

Whether to activate the counters (execution time, calls and linearizations).

activate_input_data_check: ClassVar[bool] = True

Whether to check the input data respect the input grammar.

activate_output_data_check: ClassVar[bool] = True

Whether to check the output data respect the output grammar.

classmethod activate_time_stamps()

Activate the time stamps.

For storing start and end times of execution and linearizations.

Return type

None

add_constraint(output_name, constraint_type='eq', constraint_name=None, value=None, positive=False, **kwargs)

Add a design constraint.

This constraint is in addition to those created by the formulation, e.g. consistency constraints in IDF.

The strategy of repartition of the constraints is defined by the formulation.

Parameters
  • output_name (str | Sequence[str]) – The names of the outputs to be used as constraints. For instance, if “g_1” is given and constraint_type=”eq”, g_1=0 will be added as constraint to the optimizer. If several names are given, a single discipline must provide all outputs.

  • constraint_type (str) –

    The type of constraint, “eq” for equality constraint and “ineq” for inequality constraint.

    By default it is set to eq.

  • constraint_name (str | None) –

    The name of the constraint to be stored. If None, the name of the constraint is generated from the output name.

    By default it is set to None.

  • value (float | None) –

    The value for which the constraint is active. If None, this value is 0.

    By default it is set to None.

  • positive (bool) –

    If True, the inequality constraint is positive.

    By default it is set to False.

Raises

ValueError – If the constraint type is neither ‘eq’ or ‘ineq’.

Return type

None

add_differentiated_inputs(inputs=None)

Add inputs against which to differentiate the outputs.

This method updates MDODiscipline._differentiated_inputs with inputs.

Parameters

inputs (Iterable[str] | None) –

The input variables against which to differentiate the outputs. If None, all the inputs of the discipline are used.

By default it is set to None.

Raises

ValueError – When the inputs wrt which differentiate the discipline are not inputs of the latter.

Return type

None

add_differentiated_outputs(outputs=None)

Add outputs to be differentiated.

This method updates MDODiscipline._differentiated_outputs with outputs.

Parameters

outputs (Iterable[str] | None) –

The output variables to be differentiated. If None, all the outputs of the discipline are used.

By default it is set to None.

Raises

ValueError – When the outputs to differentiate are not discipline outputs.

Return type

None

add_observable(output_names, observable_name=None, discipline=None)

Add an observable to the optimization problem.

The repartition strategy of the observable is defined in the formulation class. When more than one output name is provided, the observable function returns a concatenated array of the output values.

Parameters
  • output_names (Sequence[str]) – The names of the outputs to observe.

  • observable_name (Sequence[str] | None) –

    The name to be given to the observable. If None, the output name is used by default.

    By default it is set to None.

  • discipline (MDODiscipline | None) –

    The discipline used to build the observable function. If None, detect the discipline from the inner disciplines.

    By default it is set to None.

Return type

None

add_status_observer(obs)

Add an observer for the status.

Add an observer for the status to be notified when self changes of status.

Parameters

obs (Any) – The observer to add.

Return type

None

auto_get_grammar_file(is_input=True, name=None, comp_dir=None)

Use a naming convention to associate a grammar file to the discipline.

Search in the directory comp_dir for either an input grammar file named name + "_input.json" or an output grammar file named name + "_output.json".

Parameters
  • is_input (bool) –

    Whether to search for an input or output grammar file.

    By default it is set to True.

  • name (str | None) –

    The name to be searched in the file names. If None, use the name of the discipline class.

    By default it is set to None.

  • comp_dir (str | Path | None) –

    The directory in which to search the grammar file. If None, use the GRAMMAR_DIRECTORY if any, or the directory of the discipline class module.

    By default it is set to None.

Returns

The grammar file path.

Return type

str

property cache_tol: float

The cache input tolerance.

This is the tolerance for equality of the inputs in the cache. If norm(stored_input_data-input_data) <= cache_tol * norm(stored_input_data), the cached data for stored_input_data is returned when calling self.execute(input_data).

Raises

ValueError – When the discipline does not have a cache.

check_input_data(input_data, raise_exception=True)

Check the input data validity.

Parameters
  • input_data (dict[str, Any]) – The input data needed to execute the discipline according to the discipline input grammar.

  • raise_exception (bool) –

    Whether to raise on error.

    By default it is set to True.

Return type

None

check_jacobian(input_data=None, derr_approx='finite_differences', step=1e-07, threshold=1e-08, linearization_mode='auto', inputs=None, outputs=None, parallel=False, n_processes=2, use_threading=False, wait_time_between_fork=0, auto_set_step=False, plot_result=False, file_path='jacobian_errors.pdf', show=False, fig_size_x=10, fig_size_y=10, reference_jacobian_path=None, save_reference_jacobian=False, indices=None)

Check if the analytical Jacobian is correct with respect to a reference one.

If reference_jacobian_path is not None and save_reference_jacobian is True, compute the reference Jacobian with the approximation method and save it in reference_jacobian_path.

If reference_jacobian_path is not None and save_reference_jacobian is False, do not compute the reference Jacobian but read it from reference_jacobian_path.

If reference_jacobian_path is None, compute the reference Jacobian without saving it.

Parameters
  • input_data (dict[str, ndarray] | None) –

    The input data needed to execute the discipline according to the discipline input grammar. If None, use the MDODiscipline.default_inputs.

    By default it is set to None.

  • derr_approx (str) –

    The approximation method, either “complex_step” or “finite_differences”.

    By default it is set to finite_differences.

  • threshold (float) –

    The acceptance threshold for the Jacobian error.

    By default it is set to 1e-08.

  • linearization_mode (str) –

    the mode of linearization: direct, adjoint or automated switch depending on dimensions of inputs and outputs (Default value = ‘auto’)

    By default it is set to auto.

  • inputs (Iterable[str] | None) –

    The names of the inputs wrt which to differentiate the outputs.

    By default it is set to None.

  • outputs (Iterable[str] | None) –

    The names of the outputs to be differentiated.

    By default it is set to None.

  • step (float) –

    The differentiation step.

    By default it is set to 1e-07.

  • parallel (bool) –

    Whether to differentiate the discipline in parallel.

    By default it is set to False.

  • n_processes (int) –

    The maximum number of processors on which to run.

    By default it is set to 2.

  • use_threading (bool) –

    Whether to use threads instead of processes to parallelize the execution; multiprocessing will copy (serialize) all the disciplines, while threading will share all the memory This is important to note if you want to execute the same discipline multiple times, you shall use multiprocessing.

    By default it is set to False.

  • wait_time_between_fork (float) –

    The time waited between two forks of the process / thread.

    By default it is set to 0.

  • auto_set_step (bool) –

    Whether to compute the optimal step for a forward first order finite differences gradient approximation.

    By default it is set to False.

  • plot_result (bool) –

    Whether to plot the result of the validation (computed vs approximated Jacobians).

    By default it is set to False.

  • file_path (str | Path) –

    The path to the output file if plot_result is True.

    By default it is set to jacobian_errors.pdf.

  • show (bool) –

    Whether to open the figure.

    By default it is set to False.

  • fig_size_x (float) –

    The x-size of the figure in inches.

    By default it is set to 10.

  • fig_size_y (float) –

    The y-size of the figure in inches.

    By default it is set to 10.

  • reference_jacobian_path (str | Path | None) –

    The path of the reference Jacobian file.

    By default it is set to None.

  • save_reference_jacobian (bool) –

    Whether to save the reference Jacobian.

    By default it is set to False.

  • indices (Iterable[int] | None) –

    The indices of the inputs and outputs for the different sub-Jacobian matrices, formatted as {variable_name: variable_components} where variable_components can be either an integer, e.g. 2 a sequence of integers, e.g. [0, 3], a slice, e.g. slice(0,3), the ellipsis symbol () or None, which is the same as ellipsis. If a variable name is missing, consider all its components. If None, consider all the components of all the inputs and outputs.

    By default it is set to None.

Returns

Whether the analytical Jacobian is correct with respect to the reference one.

check_output_data(raise_exception=True)

Check the output data validity.

Parameters

raise_exception (bool) –

Whether to raise an exception when the data is invalid.

By default it is set to True.

Return type

None

classmethod deactivate_time_stamps()

Deactivate the time stamps.

For storing start and end times of execution and linearizations.

Return type

None

property default_inputs: dict[str, Any]

The default inputs.

Raises

TypeError – When the default inputs are not passed as a dictionary.

static deserialize(file_path)

Deserialize a discipline from a file.

Parameters

file_path (str | Path) – The path to the file containing the discipline.

Returns

The discipline instance.

Return type

MDODiscipline

property design_space: gemseo.algos.design_space.DesignSpace

The design space on which the scenario is performed.

property exec_time: float | None

The cumulated execution time of the discipline.

This property is multiprocessing safe.

Raises

RuntimeError – When the discipline counters are disabled.

execute(input_data=None)

Execute the discipline.

This method executes the discipline:

Parameters

input_data (Mapping[str, Any] | None) –

The input data needed to execute the discipline according to the discipline input grammar. If None, use the MDODiscipline.default_inputs.

By default it is set to None.

Returns

The discipline local data after execution.

Raises

RuntimeError – When residual_variables are declared but self.run_solves_residuals is False. This is not suported yet.

Return type

dict[str, Any]

export_to_dataset(name=None, by_group=True, categorize=True, opt_naming=True, export_gradients=False)[source]

Export the database of the optimization problem to a Dataset.

The variables can be classified into groups: Dataset.DESIGN_GROUP or Dataset.INPUT_GROUP for the design variables and Dataset.FUNCTION_GROUP or Dataset.OUTPUT_GROUP for the functions (objective, constraints and observables).

Parameters
  • name (str | None) –

    The name to be given to the dataset. If None, use the name of the OptimizationProblem.database.

    By default it is set to None.

  • by_group (bool) –

    Whether to store the data by group in Dataset.data, in the sense of one unique NumPy array per group. If categorize is False, there is a unique group: Dataset.PARAMETER_GROUP`. If categorize is True, the groups can be either Dataset.DESIGN_GROUP and Dataset.FUNCTION_GROUP if opt_naming is True, or Dataset.INPUT_GROUP and Dataset.OUTPUT_GROUP. If by_group is False, store the data by variable names.

    By default it is set to True.

  • categorize (bool) –

    Whether to distinguish between the different groups of variables. Otherwise, group all the variables in Dataset.PARAMETER_GROUP`.

    By default it is set to True.

  • opt_naming (bool) –

    Whether to use Dataset.DESIGN_GROUP and Dataset.FUNCTION_GROUP as groups. Otherwise, use Dataset.INPUT_GROUP and Dataset.OUTPUT_GROUP.

    By default it is set to True.

  • export_gradients (bool) –

    Whether to export the gradients of the functions (objective function, constraints and observables) if the latter are available in the database of the optimization problem.

    By default it is set to False.

Returns

A dataset built from the database of the optimization problem.

Return type

Dataset

get_all_inputs()

Return the local input data as a list.

The order is given by MDODiscipline.get_input_data_names().

Returns

The local input data.

Return type

list[Any]

get_all_outputs()

Return the local output data as a list.

The order is given by MDODiscipline.get_output_data_names().

Returns

The local output data.

Return type

list[Any]

get_attributes_to_serialize()

Define the names of the attributes to be serialized.

Shall be overloaded by disciplines

Returns

The names of the attributes to be serialized.

Return type

list[str]

get_available_driver_names()

The available drivers.

Return type

list[str]

static get_data_list_from_dict(keys, data_dict)

Filter the dict from a list of keys or a single key.

If keys is a string, then the method return the value associated to the key. If keys is a list of strings, then the method returns a generator of value corresponding to the keys which can be iterated.

Parameters
  • keys (str | Iterable) – One or several names.

  • data_dict (dict[str, Any]) – The mapping from which to get the data.

Returns

Either a data or a generator of data.

Return type

Any | Generator[Any]

get_disciplines_statuses()

Retrieve the statuses of the disciplines.

Returns

The statuses of the disciplines.

Return type

dict[str, str]

get_expected_dataflow()

Return the expected data exchange sequence.

This method is used for the XDSM representation.

The default expected data exchange sequence is an empty list.

See also

MDOFormulation.get_expected_dataflow

Returns

The data exchange arcs.

Return type

list[tuple[gemseo.core.discipline.MDODiscipline, gemseo.core.discipline.MDODiscipline, list[str]]]

get_expected_workflow()

Return the expected execution sequence.

This method is used for the XDSM representation.

The default expected execution sequence is the execution of the discipline itself.

See also

MDOFormulation.get_expected_workflow

Returns

The expected execution sequence.

Return type

gemseo.core.execution_sequence.LoopExecSequence

get_input_data()

Return the local input data as a dictionary.

Returns

The local input data.

Return type

dict[str, Any]

get_input_data_names()

Return the names of the input variables.

Returns

The names of the input variables.

Return type

list[str]

get_input_output_data_names()

Return the names of the input and output variables.

Returns

The name of the input and output variables.

Return type

list[str]

get_inputs_asarray()

Return the local output data as a large NumPy array.

The order is the one of MDODiscipline.get_all_outputs().

Returns

The local output data.

Return type

numpy.ndarray

get_inputs_by_name(data_names)

Return the local data associated with input variables.

Parameters

data_names (Iterable[str]) – The names of the input variables.

Returns

The local data for the given input variables.

Raises

ValueError – When a variable is not an input of the discipline.

Return type

list[Any]

get_local_data_by_name(data_names)

Return the local data of the discipline associated with variables names.

Parameters

data_names (Iterable[str]) – The names of the variables.

Returns

The local data associated with the variables names.

Raises

ValueError – When a name is not a discipline input name.

Return type

Generator[Any]

get_optim_variables_names()

A convenience function to access the optimization variables.

Returns

The optimization variables of the scenario.

Return type

list[str]

get_optimum()

Return the optimization results.

Returns

The optimal solution found by the scenario if executed, None otherwise.

Return type

OptimizationResult | None

get_output_data()

Return the local output data as a dictionary.

Returns

The local output data.

Return type

dict[str, Any]

get_output_data_names()

Return the names of the output variables.

Returns

The names of the output variables.

Return type

list[str]

get_outputs_asarray()

Return the local input data as a large NumPy array.

The order is the one of MDODiscipline.get_all_inputs().

Returns

The local input data.

Return type

numpy.ndarray

get_outputs_by_name(data_names)

Return the local data associated with output variables.

Parameters

data_names (Iterable[str]) – The names of the output variables.

Returns

The local data for the given output variables.

Raises

ValueError – When a variable is not an output of the discipline.

Return type

list[Any]

get_sub_disciplines()

Return the sub-disciplines if any.

Returns

The sub-disciplines.

Return type

list[gemseo.core.discipline.MDODiscipline]

property grammar_type: gemseo.core.grammars.base_grammar.BaseGrammar

The type of grammar to be used for inputs and outputs declaration.

is_all_inputs_existing(data_names)

Test if several variables are discipline inputs.

Parameters

data_names (Iterable[str]) – The names of the variables.

Returns

Whether all the variables are discipline inputs.

Return type

bool

is_all_outputs_existing(data_names)

Test if several variables are discipline outputs.

Parameters

data_names (Iterable[str]) – The names of the variables.

Returns

Whether all the variables are discipline outputs.

Return type

bool

is_input_existing(data_name)

Test if a variable is a discipline input.

Parameters

data_name (str) – The name of the variable.

Returns

Whether the variable is a discipline input.

Return type

bool

is_output_existing(data_name)

Test if a variable is a discipline output.

Parameters

data_name (str) – The name of the variable.

Returns

Whether the variable is a discipline output.

Return type

bool

static is_scenario()

Indicate if the current object is a Scenario.

Returns

True if the current object is a Scenario.

Return type

bool

property linearization_mode: str

The linearization mode among MDODiscipline.AVAILABLE_MODES.

Raises

ValueError – When the linearization mode is unknown.

linearize(input_data=None, force_all=False, force_no_exec=False)

Execute the linearized version of the code.

Parameters
  • input_data (dict[str, Any] | None) –

    The input data needed to linearize the discipline according to the discipline input grammar. If None, use the MDODiscipline.default_inputs.

    By default it is set to None.

  • force_all (bool) –

    If False, MDODiscipline._differentiated_inputs and MDODiscipline._differentiated_outputs are used to filter the differentiated variables. otherwise, all outputs are differentiated wrt all inputs.

    By default it is set to False.

  • force_no_exec (bool) –

    If True, the discipline is not re-executed, cache is loaded anyway.

    By default it is set to False.

Returns

The Jacobian of the discipline.

Return type

dict[str, dict[str, ndarray]]

property local_data: gemseo.core.discipline_data.DisciplineData

The current input and output data.

property n_calls: int | None

The number of times the discipline was executed.

This property is multiprocessing safe.

Raises

RuntimeError – When the discipline counters are disabled.

property n_calls_linearize: int | None

The number of times the discipline was linearized.

This property is multiprocessing safe.

Raises

RuntimeError – When the discipline counters are disabled.

notify_status_observers()

Notify all status observers that the status has changed.

Return type

None

property post_factory: PostFactory | None

The factory of post-processors.

post_process(post_name, **options)

Post-process the optimization history.

Parameters
  • post_name (str) – The name of the post-processor, i.e. the name of a class inheriting from OptPostProcessor.

  • options – The options for the post-processor.

  • **options (OptPostProcessorOptionType | Path) –

Return type

OptPostProcessor

property posts: list[str]

The available post-processors.

print_execution_metrics()

Print the total number of executions and cumulated runtime by discipline.

Return type

None

remove_status_observer(obs)

Remove an observer for the status.

Parameters

obs (Any) – The observer to remove.

Return type

None

reset_statuses_for_run()

Set all the statuses to MDODiscipline.STATUS_PENDING.

Raises

ValueError – When the discipline cannot be run because of its status.

Return type

None

save_optimization_history(file_path, file_format='hdf5', append=False)

Save the optimization history of the scenario to a file.

Parameters
  • file_path (str) – The path to the file to save the history.

  • file_format (str) –

    The format of the file, either “hdf5” or “ggobi”.

    By default it is set to hdf5.

  • append (bool) –

    If True, the history is appended to the file if not empty.

    By default it is set to False.

Raises

ValueError – If the file format is not correct.

Return type

None

serialize(file_path)

Serialize the discipline and store it in a file.

Parameters

file_path (str | Path) – The path to the file to store the discipline.

Return type

None

set_cache_policy(cache_type='SimpleCache', cache_tolerance=0.0, cache_hdf_file=None, cache_hdf_node_name=None, is_memory_shared=True)

Set the type of cache to use and the tolerance level.

This method defines when the output data have to be cached according to the distance between the corresponding input data and the input data already cached for which output data are also cached.

The cache can be either a SimpleCache recording the last execution or a cache storing all executions, e.g. MemoryFullCache and HDF5Cache. Caching data can be either in-memory, e.g. SimpleCache and MemoryFullCache, or on the disk, e.g. HDF5Cache.

The attribute CacheFactory.caches provides the available caches types.

Parameters
  • cache_type (str) –

    The type of cache.

    By default it is set to SimpleCache.

  • cache_tolerance (float) –

    The maximum relative norm of the difference between two input arrays to consider that two input arrays are equal.

    By default it is set to 0.0.

  • cache_hdf_file (str | Path | None) –

    The path to the HDF file to store the data; this argument is mandatory when the MDODiscipline.HDF5_CACHE policy is used.

    By default it is set to None.

  • cache_hdf_node_name (str | None) –

    The name of the HDF file node to store the discipline data. If None, MDODiscipline.name is used.

    By default it is set to None.

  • is_memory_shared (bool) –

    Whether to store the data with a shared memory dictionary, which makes the cache compatible with multiprocessing.

    By default it is set to True.

Return type

None

set_differentiation_method(method='user', step=1e-06)

Set the differentiation method for the process.

Parameters
  • method (str | None) –

    The method to use to differentiate the process, either “user”, “finite_differences”, “complex_step” or “no_derivatives”, which is equivalent to None.

    By default it is set to user.

  • step (float) –

    The finite difference step.

    By default it is set to 1e-06.

Return type

None

set_disciplines_statuses(status)

Set the sub-disciplines statuses.

To be implemented in subclasses.

Parameters

status (str) – The status.

Return type

None

set_jacobian_approximation(jac_approx_type='finite_differences', jax_approx_step=1e-07, jac_approx_n_processes=1, jac_approx_use_threading=False, jac_approx_wait_time=0)

Set the Jacobian approximation method.

Sets the linearization mode to approx_method, sets the parameters of the approximation for further use when calling MDODiscipline.linearize().

Parameters
  • jac_approx_type (str) –

    The approximation method, either “complex_step” or “finite_differences”.

    By default it is set to finite_differences.

  • jax_approx_step (float) –

    The differentiation step.

    By default it is set to 1e-07.

  • jac_approx_n_processes (int) –

    The maximum number of processors on which to run.

    By default it is set to 1.

  • jac_approx_use_threading (bool) –

    Whether to use threads instead of processes to parallelize the execution; multiprocessing will copy (serialize) all the disciplines, while threading will share all the memory This is important to note if you want to execute the same discipline multiple times, you shall use multiprocessing.

    By default it is set to False.

  • jac_approx_wait_time (float) –

    The time waited between two forks of the process / thread.

    By default it is set to 0.

Return type

None

set_optimal_fd_step(outputs=None, inputs=None, force_all=False, print_errors=False, numerical_error=2.220446049250313e-16)

Compute the optimal finite-difference step.

Compute the optimal step for a forward first order finite differences gradient approximation. Requires a first evaluation of the perturbed functions values. The optimal step is reached when the truncation error (cut in the Taylor development), and the numerical cancellation errors (round-off when doing f(x+step)-f(x)) are approximately equal.

Warning

This calls the discipline execution twice per input variables.

See also

https://en.wikipedia.org/wiki/Numerical_differentiation and “Numerical Algorithms and Digital Representation”, Knut Morken , Chapter 11, “Numerical Differentiation”

Parameters
  • inputs (Iterable[str] | None) –

    The inputs wrt which the outputs are linearized. If None, use the MDODiscipline._differentiated_inputs.

    By default it is set to None.

  • outputs (Iterable[str] | None) –

    The outputs to be linearized. If None, use the MDODiscipline._differentiated_outputs.

    By default it is set to None.

  • force_all (bool) –

    Whether to consider all the inputs and outputs of the discipline;

    By default it is set to False.

  • print_errors (bool) –

    Whether to display the estimated errors.

    By default it is set to False.

  • numerical_error (float) –

    The numerical error associated to the calculation of f. By default, this is the machine epsilon (appx 1e-16), but can be higher when the calculation of f requires a numerical resolution.

    By default it is set to 2.220446049250313e-16.

Returns

The estimated errors of truncation and cancellation error.

Raises

ValueError – When the Jacobian approximation method has not been set.

set_optimization_history_backup(file_path, each_new_iter=False, each_store=True, erase=False, pre_load=False, generate_opt_plot=False)

Set the backup file for the optimization history during the run.

Parameters
  • file_path (str) – The path to the file to save the history.

  • each_new_iter (bool) –

    If True, callback at every iteration.

    By default it is set to False.

  • each_store (bool) –

    If True, callback at every call to store() in the database.

    By default it is set to True.

  • erase (bool) –

    If True, the backup file is erased before the run.

    By default it is set to False.

  • pre_load (bool) –

    If True, the backup file is loaded before run, useful after a crash.

    By default it is set to False.

  • generate_opt_plot (bool) –

    If True, generate the optimization history view at backup.

    By default it is set to False.

Raises

ValueError – If both erase and pre_load are True.

Return type

None

property status: str

The status of the discipline.

store_local_data(**kwargs)

Store discipline data in local data.

Parameters
Return type

None

time_stamps = None
property use_standardized_objective: bool

Whether to use the standardized OptimizationProblem.objective for logging and post-processing.

xdsmize(monitor=False, outdir='.', print_statuses=False, outfilename='xdsm.html', latex_output=False, open_browser=False, html_output=True, json_output=False)

Create a JSON file defining the XDSM related to the current scenario.

Parameters
  • monitor (bool) –

    If True, update the generated file at each discipline status change.

    By default it is set to False.

  • outdir (str | None) –

    The directory where the JSON file is generated. If None, the current working directory is used.

    By default it is set to ..

  • print_statuses (bool) –

    If True, print the statuses in the console at each update.

    By default it is set to False.

  • outfilename (str) –

    The name of the file of the output. The basename is used and the extension is adapted for the HTML / JSON / PDF outputs.

    By default it is set to xdsm.html.

  • latex_output (bool) –

    If True, build TEX, TIKZ and PDF files.

    By default it is set to False.

  • open_browser (bool) –

    If True, open the web browser and display the the XDSM.

    By default it is set to False.

  • html_output (bool) –

    If True, output a self contained HTML file.

    By default it is set to True.

  • json_output (bool) –

    If True, output a JSON file for XDSMjs.

    By default it is set to False.

Return type

None