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: AbstractGrammar

output_grammar¶

The output grammar.

Type: AbstractGrammar

grammar_type¶

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

Type: str

comp_dir¶

The path to the directory of the discipline module file if any.

Type: str

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 to be considered as residuals; they shall be equal to zero.

Type: List[str]

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

local_data¶

The last input and output data.

Type: Dict[str, Any]

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.

Type: PostFactory

seed¶

The seed used by the random number generators for replicability.

Type: int

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 name of the MDO formulation, also the name of a class inheriting from MDOFormulation.
objective_name (str) – The name of the objective.
design_space (DesignSpace) – The design space.
name (Optional[str]) –
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`
`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`
`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 grammar type.
`linearization_mode`	The linearization mode among `LINEARIZE_MODE_LIST`.
`n_calls`	The number of times the discipline was executed.
`n_calls_linearize`	The number of times the discipline was linearized.
`posts`	The available post-processors.
`status`	The status of the discipline.
`time_stamps`

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 a 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`(in_file)	Deserialize a discipline from a file.
`execute`([input_data])	Execute the discipline.
`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 `PENDING`.
`save_optimization_history`(file_path[, ...])	Save the optimization history of the scenario to a file.
`serialize`(out_file)	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'¶

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'¶

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 (Union[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 (Optional[str]) –
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 (Optional[float]) –
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 _differentiated_inputs with inputs.

Parameters

inputs (Optional[Iterable[str]]) –

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 _differentiated_outputs with outputs.

Parameters

outputs (Optional[Iterable[str]]) –

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 (Optional[Sequence[str]]) –
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 (Optional[gemseo.core.discipline.MDODiscipline]) –
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 a discipline.

This method searches in a directory for either an input grammar file named name + "_input.json" or an output grammar file named``name + “_output.json”``.

Parameters

is_input (bool) –
If True, autodetect the input grammar file; otherwise, autodetect the output grammar file.

By default it is set to True.
name (Optional[str]) –
The name to be searched in the file names. If None, use the name name of the discipline.

By default it is set to None.
comp_dir (Optional[Union[str, pathlib.Path]]) –
The directory in which to search the grammar file. If None, use comp_dir.

By default it is set to None.

Returns

The grammar file path.

Return type

pathlib.Path

property cache_tol¶

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).

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) –

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, figsize_x=10, figsize_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 –
The input data needed to execute the discipline according to the discipline input grammar. If None, use the default_inputs.

By default it is set to None.
derr_approx –
The approximation method, either “complex_step” or “finite_differences”.

By default it is set to finite_differences.
threshold –
The acceptance threshold for the Jacobian error.

By default it is set to 1e-08.
linearization_mode –
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 –
The names of the inputs wrt which to differentiate the outputs.

By default it is set to None.
outputs –
The names of the outputs to be differentiated.

By default it is set to None.
step –
The differentiation step.

By default it is set to 1e-07.
parallel –
Whether to differentiate the discipline in parallel.

By default it is set to False.
n_processes –
The maximum number of processors on which to run.

By default it is set to 2.
use_threading –
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 –
The time waited between two forks of the process / thread.

By default it is set to 0.
auto_set_step –
Whether to compute the optimal step for a forward first order finite differences gradient approximation.

By default it is set to False.
plot_result –
Whether to plot the result of the validation (computed vs approximated Jacobians).

By default it is set to False.
file_path –
The path to the output file if plot_result is True.

By default it is set to jacobian_errors.pdf.
show –
Whether to open the figure.

By default it is set to False.
figsize_x –
The x-size of the figure in inches.

By default it is set to 10.
figsize_y –
The y-size of the figure in inches.

By default it is set to 10.
reference_jacobian_path –
The path of the reference Jacobian file.

By default it is set to None.
save_reference_jacobian –
Whether to save the reference Jacobian.

By default it is set to False.
indices –
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¶

The default inputs.

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

static deserialize(in_file)¶

Deserialize a discipline from a file.

Parameters: in_file (Union[str, pathlib.Path]) – The path to the file containing the discipline.
Returns: The discipline instance.
Return type: gemseo.core.discipline.MDODiscipline

property design_space¶: The design space on which the scenario is performed.

property exec_time¶: The cumulated execution time of the discipline.

Note

This property is multiprocessing safe.

execute(input_data=None)¶

Execute the discipline.

This method executes the discipline:

Adds the default inputs to the input_data if some inputs are not defined in input_data but exist in _default_inputs.
Checks whether the last execution of the discipline was called with identical inputs, ie. cached in cache; if so, directly returns self.cache.get_output_cache(inputs).
Caches the inputs.
Checks the input data against input_grammar.
If data_processor is not None, runs the preprocessor.
Updates the status to RUNNING.
Calls the _run() method, that shall be defined.
If data_processor is not None, runs the postprocessor.
Checks the output data.
Caches the outputs.
Updates the status to DONE or FAILED.
Updates summed execution time.

Parameters

input_data (Optional[Dict[str, Any]]) –

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

By default it is set to None.

Returns

The discipline local data after execution.

Return type

Dict[str, Any]

get_all_inputs()¶

Return the local input data as a list.

The order is given by 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 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.

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 (Union[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

Union[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.