gemseo / core

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mdo_scenario module

A scenario whose driver is an optimization algorithm.

class gemseo.core.mdo_scenario.MDOScenario(disciplines, formulation, objective_name, design_space, name=None, grammar_type=GrammarType.JSON, maximize_objective=False, **formulation_options)[source]

Bases: Scenario

A multidisciplinary scenario to be executed by an optimizer.

an MDOScenario is a particular Scenario whose driver is an optimization algorithm. This algorithm must be implemented in an OptimizationLibrary.

Initialize self. See help(type(self)) for accurate signature.

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

  • formulation (str) – The class name of the MDOFormulation, e.g. "MDF", "IDF" or "BiLevel".

  • objective_name (str | Sequence[str]) – The name(s) of the discipline output(s) used as objective. If multiple names are passed, the objective will be a vector.

  • design_space (DesignSpace) – The search space including at least the design variables (some formulations requires additional variables, e.g. IDF with the coupling variables).

  • name (str | None) – The name to be given to this scenario. If None, use the name of the class.

  • grammar_type (MDODiscipline.GrammarType) –

    The grammar for the scenario and the MDO formulation.

    By default it is set to “JSONGrammar”.

  • maximize_objective (bool) –

    Whether to maximize the objective.

    By default it is set to False.

  • **formulation_options (Any) – The options of the MDOFormulation.

class ApproximationMode(value)

Bases: StrEnum

The approximation derivation modes.

CENTERED_DIFFERENCES = 'centered_differences'

The centered differences method used to approximate the Jacobians by perturbing each variable with a small real number.

COMPLEX_STEP = 'complex_step'

The complex step method used to approximate the Jacobians by perturbing each variable with a small complex number.

FINITE_DIFFERENCES = 'finite_differences'

The finite differences method used to approximate the Jacobians by perturbing each variable with a small real number.

class CacheType(value)

Bases: StrEnum

The name of the cache class.

HDF5 = 'HDF5Cache'
MEMORY_FULL = 'MemoryFullCache'
NONE = ''

No cache is used.

SIMPLE = 'SimpleCache'
class DifferentiationMethod(value)

Bases: StrEnum

The differentiation methods.

CENTERED_DIFFERENCES = 'centered_differences'
COMPLEX_STEP = 'complex_step'
FINITE_DIFFERENCES = 'finite_differences'
NO_DERIVATIVE = 'no_derivative'
USER_GRAD = 'user'
class ExecutionStatus(value)

Bases: StrEnum

The execution statuses of a discipline.

DONE = 'DONE'
FAILED = 'FAILED'
LINEARIZE = 'LINEARIZE'
PENDING = 'PENDING'
RUNNING = 'RUNNING'
VIRTUAL = 'VIRTUAL'
class GrammarType(value)

Bases: StrEnum

The name of the grammar class.

JSON = 'JSONGrammar'
PYDANTIC = 'PydanticGrammar'
SIMPLE = 'SimpleGrammar'
SIMPLER = 'SimplerGrammar'
class InitJacobianType(value)

Bases: StrEnum

The way to initialize Jacobian matrices.

DENSE = 'dense'

The Jacobian is initialized as a NumPy ndarray filled in with zeros.

EMPTY = 'empty'

The Jacobian is initialized as an empty NumPy ndarray.

SPARSE = 'sparse'

The Jacobian is initialized as a SciPy CSR array with zero elements.

class LinearizationMode(value)

Bases: StrEnum

An enumeration.

ADJOINT = 'adjoint'
AUTO = 'auto'
CENTERED_DIFFERENCES = 'centered_differences'
COMPLEX_STEP = 'complex_step'
DIRECT = 'direct'
FINITE_DIFFERENCES = 'finite_differences'
REVERSE = 'reverse'
class ReExecutionPolicy(value)

Bases: StrEnum

The re-execution policy of a discipline.

DONE = 'RE_EXEC_DONE'
NEVER = 'RE_EXEC_NEVER'
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=ConstraintType.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 (MDOFunction.ConstraintType) –

    The type of 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.

  • value (float | None) – The value for which the constraint is active. If None, this value is 0.

  • 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’ nor ‘ineq’.

Return type:

None

add_differentiated_inputs(inputs=None)

Add the inputs for differentiation.

The inputs that do not represent continuous numbers are filtered out.

Parameters:

inputs (Iterable[str] | None) – The input variables against which to differentiate the outputs. If None, all the inputs of the discipline are used.

Raises:

ValueError – When ``inputs `` are not in the input grammar.

Return type:

None

add_differentiated_outputs(outputs=None)

Add the outputs for differentiation.

The outputs that do not represent continuous numbers are filtered out.

Parameters:

outputs (Iterable[str] | None) – The output variables to be differentiated. If None, all the outputs of the discipline are used.

Raises:

ValueError – When ``outputs `` are not in the output grammar.

Return type:

None

add_namespace_to_input(name, namespace)

Add a namespace prefix to an existing input grammar element.

The updated input grammar element name will be namespace + namespaces_separator + name.

Parameters:
  • name (str) – The element name to rename.

  • namespace (str) – The name of the namespace.

Return type:

None

add_namespace_to_output(name, namespace)

Add a namespace prefix to an existing output grammar element.

The updated output grammar element name will be namespace + namespaces_separator + name.

Parameters:
  • name (str) – The element name to rename.

  • namespace (str) – The name of the namespace.

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.

  • discipline (MDODiscipline | None) – The discipline used to build the observable function. If None, detect the discipline from the inner disciplines.

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.

  • 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.

Returns:

The grammar file path.

Return type:

Path

check_input_data(input_data, raise_exception=True)

Check the input data validity.

Parameters:
  • input_data (Mapping[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=ApproximationMode.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 (Mapping[str, ndarray] | None) – The input data needed to execute the discipline according to the discipline input grammar. If None, use the MDODiscipline.default_inputs.

  • derr_approx (ApproximationMode) –

    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.

  • outputs (Iterable[str] | None) – The names of the outputs to be differentiated.

  • 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 simultaneous number of threads, if use_threading is True, or processes otherwise, used to parallelize the execution.

    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.

  • 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.

Returns:

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

Return type:

bool

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

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.

Returns:

The discipline local data after execution.

Return type:

DisciplineData

static from_pickle(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

get_all_inputs()

Return the local input data.

The order is given by MDODiscipline.get_input_data_names().

Returns:

The local input data.

Return type:

Iterator[Any]

get_all_outputs()

Return the local output data.

The order is given by MDODiscipline.get_output_data_names().

Returns:

The local output data.

Return type:

Iterator[Any]

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[str]) – 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 | Iterator[Any]

get_disciplines_in_dataflow_chain()

Return the disciplines that must be shown as blocks in the XDSM.

By default, only the discipline itself is shown. This function can be differently implemented for any type of inherited discipline.

Returns:

The disciplines shown in the XDSM chain.

Return type:

list[MDODiscipline]

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[MDODiscipline, 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:

LoopExecSequence

get_input_data(with_namespaces=True)

Return the local input data as a dictionary.

Parameters:

with_namespaces (bool) –

Whether to keep the namespace prefix of the input names, if any.

By default it is set to True.

Returns:

The local input data.

Return type:

dict[str, Any]

get_input_data_names(with_namespaces=True)

Return the names of the input variables.

Parameters:

with_namespaces (bool) –

Whether to keep the namespace prefix of the input names, if any.

By default it is set to True.

Returns:

The names of the input variables.

Return type:

list[str]

get_input_output_data_names(with_namespaces=True)

Return the names of the input and output variables.

Parameters:

with_namespaces (bool) –

Whether to keep the namespace prefix of the output names, if any.

By default it is set to True.

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:

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:

Iterator[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:

Iterator[Any]

get_optim_variable_names()

A convenience function to access the optimization variables.

Returns:

The optimization variables of the scenario.

Return type:

list[str]

get_output_data(with_namespaces=True)

Return the local output data as a dictionary.

Parameters:

with_namespaces (bool) –

Whether to keep the namespace prefix of the output names, if any.

By default it is set to True.

Returns:

The local output data.

Return type:

dict[str, Any]

get_output_data_names(with_namespaces=True)

Return the names of the output variables.

Parameters:

with_namespaces (bool) –

Whether to keep the namespace prefix of the output names, if any.

By default it is set to True.

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:

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:

Iterator[Any]

get_result(name='', **options)

Return the result of the scenario execution.

Parameters:
  • name (str) –

    The class name of the ScenarioResult. If empty, use a default one (see create_scenario_result()).

    By default it is set to “”.

  • **options (Any) – The options of the ScenarioResult.

Returns:

The result of the scenario execution.

Return type:

ScenarioResult

get_sub_disciplines(recursive=False)

Determine the sub-disciplines.

This method lists the sub-disciplines’ disciplines. It will list up to one level of disciplines contained inside another one unless the recursive argument is set to True.

Parameters:

recursive (bool) –

If True, the method will look inside any discipline that has other disciplines inside until it reaches a discipline without sub-disciplines, in this case the return value will not include any discipline that has sub-disciplines. If False, the method will list up to one level of disciplines contained inside another one, in this case the return value may include disciplines that contain sub-disciplines.

By default it is set to False.

Returns:

The sub-disciplines.

Return type:

list[MDODiscipline]

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

linearize(input_data=None, compute_all_jacobians=False, execute=True)

Compute the Jacobians of some outputs with respect to some inputs.

Parameters:
  • input_data (Mapping[str, Any] | None) – The input data for which to compute the Jacobian. If None, use the MDODiscipline.default_inputs.

  • compute_all_jacobians (bool) –

    Whether to compute the Jacobians of all the output with respect to all the inputs. Otherwise, set the input variables against which to differentiate the output ones with add_differentiated_inputs() and set these output variables to differentiate with add_differentiated_outputs().

    By default it is set to False.

  • execute (bool) –

    Whether to start by executing the discipline with the input data for which to compute the Jacobian; this allows to ensure that the discipline was executed with the right input data; it can be almost free if the corresponding output data have been stored in the cache.

    By default it is set to True.

Returns:

The Jacobian of the discipline shaped as {output_name: {input_name: jacobian_array}} where jacobian_array[i, j] is the partial derivative of output_name[i] with respect to input_name[j].

Raises:

ValueError – When either the inputs for which to differentiate the outputs or the outputs to differentiate are missing.

Return type:

Mapping[str, Mapping[str, NDArray[float]]]

notify_status_observers()

Notify all status observers that the status has changed.

Return type:

None

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 (OptPostProcessorOptionType | Path) – The options for the post-processor.

Returns:

The post-processing instance related to the optimization scenario.

Return type:

OptPostProcessor

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.ExecutionStatus.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 | Path) – The path of 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

set_cache_policy(cache_type=CacheType.SIMPLE, cache_tolerance=0.0, cache_hdf_file=None, cache_hdf_node_path=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 (CacheType) –

    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.CacheType.HDF5 policy is used.

  • cache_hdf_node_path (str | None) – The name of the HDF file node to store the discipline data, possibly passed as a path root_name/.../group_name/.../node_name. If None, MDODiscipline.name is used.

  • 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=DifferentiationMethod.USER_GRAD, step=1e-06, cast_default_inputs_to_complex=False)

Set the differentiation method for the process.

When the selected method to differentiate the process is complex_step the DesignSpace current value will be cast to complex128; additionally, if the option cast_default_inputs_to_complex is True, the default inputs of the scenario’s disciplines will be cast as well provided that they are ndarray with dtype float64.

Parameters:
  • method (DifferentiationMethod) –

    The method to use to differentiate the process.

    By default it is set to “user”.

  • step (float) –

    The finite difference step.

    By default it is set to 1e-06.

  • cast_default_inputs_to_complex (bool) –

    Whether to cast all float default inputs of the scenario’s disciplines if the selected method is "complex_step".

    By default it is set to False.

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=ApproximationMode.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 (ApproximationMode) –

    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 simultaneous number of threads, if jac_approx_use_threading is True, or processes otherwise, used to parallelize the execution.

    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_linear_relationships(outputs=(), inputs=())

Set linear relationships between discipline inputs and outputs.

Parameters:
  • outputs (Iterable[str]) –

    The discipline output(s) in a linear relation with the input(s). If empty, all discipline outputs are considered.

    By default it is set to ().

  • inputs (Iterable[str]) –

    The discipline input(s) in a linear relation with the output(s). If empty, all discipline inputs are considered.

    By default it is set to ().

Return type:

None

set_optimal_fd_step(outputs=None, inputs=None, compute_all_jacobians=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.

  • outputs (Iterable[str] | None) – The outputs to be linearized. If None, use the MDODiscipline._differentiated_outputs.

  • compute_all_jacobians (bool) –

    Whether to compute the Jacobians of all the output with respect to all the inputs. Otherwise, set the input variables against which to differentiate the output ones with add_differentiated_inputs() and set these output variables to differentiate with add_differentiated_outputs().

    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.

Return type:

ndarray

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 | Path) – The path to the file to save the history.

  • each_new_iter (bool) –

    Whether the backup file is updated at every iteration of the optimization to store the database.

    By default it is set to False.

  • each_store (bool) –

    Whether the backup file is updated at every function call to store the database.

    By default it is set to True.

  • erase (bool) –

    Whether the backup file is erased before the run.

    By default it is set to False.

  • pre_load (bool) –

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

    By default it is set to False.

  • generate_opt_plot (bool) –

    Whether to plot the optimization history view at each iteration. The plots will be generated only after the first two iterations.

    By default it is set to False.

Raises:

ValueError – If both erase and pre_load are True.

Return type:

None

store_local_data(**kwargs)

Store discipline data in local data.

Parameters:

**kwargs (Any) – The data to be stored in MDODiscipline.local_data.

Return type:

None

to_dataset(name='', categorize=True, opt_naming=True, export_gradients=False)

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) –

    The name to be given to the dataset. If empty, use the name of the OptimizationProblem.database.

    By default it is set to “”.

  • 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

to_pickle(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

xdsmize(monitor=False, directory_path='.', log_workflow_status=False, file_name='xdsm', show_html=False, save_html=True, save_json=False, save_pdf=False, pdf_build=True, pdf_cleanup=True, pdf_batchmode=True)

Create a XDSM diagram of the scenario.

Parameters:
  • monitor (bool) –

    Whether to update the generated file at each discipline status change.

    By default it is set to False.

  • log_workflow_status (bool) –

    Whether to log the evolution of the workflow’s status.

    By default it is set to False.

  • directory_path (str | Path) –

    The path of the directory to save the files. If show_html=True and output_directory_path=None, the HTML file is stored in a temporary directory.

    By default it is set to “.”.

  • file_name (str) –

    The file name without the file extension.

    By default it is set to “xdsm”.

  • show_html (bool) –

    Whether to open the web browser and display the XDSM.

    By default it is set to False.

  • save_html (bool) –

    Whether to save the XDSM as a HTML file.

    By default it is set to True.

  • save_json (bool) –

    Whether to save the XDSM as a JSON file.

    By default it is set to False.

  • save_pdf (bool) –

    Whether to save the XDSM as a PDF file.

    By default it is set to False.

  • pdf_build (bool) –

    Whether the standalone pdf of the XDSM will be built.

    By default it is set to True.

  • pdf_cleanup (bool) –

    Whether pdflatex built files will be cleaned up after build is complete.

    By default it is set to True.

  • pdf_batchmode (bool) –

    Whether pdflatex is run in batchmode.

    By default it is set to True.

Returns:

A view of the XDSM if monitor is False.

Return type:

XDSM | None

ALGO = 'algo'
ALGO_OPTIONS = 'algo_options'
GRAMMAR_DIRECTORY: ClassVar[str | None] = None

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

L_BOUNDS = 'l_bounds'
MAX_ITER = 'max_iter'
N_CPUS: Final[int] = 2

The number of available CPUs.

U_BOUNDS = 'u_bounds'
X_0 = 'x_0'
X_OPT = 'x_opt'
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.

cache: AbstractCache | None

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

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.

clear_history_before_run: bool

If True, clear history before run.

data_processor: DataProcessor

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

property default_inputs: Defaults

The default inputs.

property default_outputs: Defaults

The default outputs used when virtual_execution is True.

property design_space: DesignSpace

The design space on which the scenario is performed.

property disciplines: list[MDODiscipline]

The sub-disciplines, if any.

exec_for_lin: bool

Whether the last execution was due to a linearization.

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.

formulation: MDOFormulation

The MDO formulation.

formulation_name: str

The name of the MDO formulation.

property grammar_type: GrammarType

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

input_grammar: BaseGrammar

The input grammar.

jac: MutableMapping[str, MutableMapping[str, ndarray | csr_array | JacobianOperator]]

The Jacobians of the outputs wrt inputs.

The structure is {output: {input: matrix}}.

property linear_relationships: Mapping[str, Iterable[str]]

The linear relationships between inputs and outputs.

property linearization_mode: LinearizationMode

The linearization mode among MDODiscipline.LinearizationMode.

Raises:

ValueError – When the linearization mode is unknown.

property local_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.

name: str

The name of the discipline.

optimization_result: OptimizationResult | None

The optimization result if the scenario has been executed; otherwise None.

output_grammar: BaseGrammar

The output grammar.

property post_factory: PostFactory

The factory of post-processors.

property posts: list[str]

The available post-processors.

re_exec_policy: ReExecutionPolicy

The policy to re-execute the same discipline.

residual_variables: dict[str, str]

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

run_solves_residuals: bool

Whether the run method shall solve the residuals.

property status: ExecutionStatus

The status of the discipline.

The status aims at monitoring the process and give the user a simplified view on the state (the process state = execution or linearize or done) of the disciplines. The core part of the execution is _run, the core part of linearize is _compute_jacobian or approximate jacobian computation.

time_stamps: ClassVar[dict[str, float] | None] = None

The mapping from discipline name to their execution time.

property use_standardized_objective: bool

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

The objective is OptimizationProblem.objective.

virtual_execution: ClassVar[bool] = False

Whether to skip the _run() method during execution and return the default_outputs, whatever the inputs.

Examples using MDOScenario

Example for exterior penalty applied to the Sobieski test case.

Example for exterior penalty applied to the Sobieski test case.

Examples for constraint aggregation

Examples for constraint aggregation

Create an MDO Scenario

Create an MDO Scenario

Gantt Chart

Gantt Chart

Store observables

Store observables

A from scratch example on the Sellar problem

A from scratch example on the Sellar problem

Application: Sobieski’s Super-Sonic Business Jet (MDO)

Application: Sobieski's Super-Sonic Business Jet (MDO)

GEMSEO in 10 minutes

GEMSEO in 10 minutes

MDO formulations for a toy example in aerostructure

MDO formulations for a toy example in aerostructure

Post-processing

Post-processing

Multistart optimization

Multistart optimization

Parametric scalable MDO problem - MDF

Parametric scalable MDO problem - MDF

Scalable problem

Scalable problem

BiLevel-based DOE on the Sobieski SSBJ test case

BiLevel-based DOE on the Sobieski SSBJ test case

BiLevel-based MDO on the Sobieski SSBJ test case

BiLevel-based MDO on the Sobieski SSBJ test case

IDF-based MDO on the Sobieski SSBJ test case

IDF-based MDO on the Sobieski SSBJ test case

MDF-based MDO on the Sobieski SSBJ test case

MDF-based MDO on the Sobieski SSBJ test case

Plug a surrogate discipline in a Scenario

Plug a surrogate discipline in a Scenario

Solve a 2D L-shape topology optimization problem

Solve a 2D L-shape topology optimization problem

Solve a 2D MBB topology optimization problem

Solve a 2D MBB topology optimization problem

Solve a 2D short cantilever topology optimization problem

Solve a 2D short cantilever topology optimization problem

Basic history

Basic history

Constraints history

Constraints history

Correlations

Correlations

Gradient Sensitivity

Gradient Sensitivity

Objective and constraints history

Objective and constraints history

Optimization History View

Optimization History View

Parallel coordinates

Parallel coordinates

Pareto front

Pareto front

Pareto front on the Binh and Korn problem using a BiLevel formulation

Pareto front on the Binh and Korn problem using a BiLevel formulation

Quadratic approximations

Quadratic approximations

Radar chart

Radar chart

Robustness

Robustness

Variables influence

Variables influence