doe_scenario module¶
A scenario whose driver is a design of experiments.
- 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 particularScenario
whose driver is a DOE. This DOE must be implemented in aDOELibrary
.- 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
- 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
withinputs
.- 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
withoutputs
.- 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_namespace_to_input(name, namespace)¶
Add a namespace prefix to an existing input grammar element.
The updated input grammar element name will be
namespace``+:data:`~gemseo.core.namespaces.namespace_separator`+``name
.
- 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``+:data:`~gemseo.core.namespaces.namespace_separator`+``name
.
- 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 namedname + "_input.json"
or an output grammar file namedname + "_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
- check_input_data(input_data, raise_exception=True)¶
Check the input data validity.
- 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 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
isTrue
.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}
wherevariable_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 theinputs
andoutputs
.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
- 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
- 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 inMDODiscipline.default_inputs
.Checks whether the last execution of the discipline was called with identical inputs, i.e. cached in
MDODiscipline.cache
; if so, directly returnsself.cache.get_output_cache(inputs)
.Caches the inputs.
Checks the input data against
MDODiscipline.input_grammar
.If
MDODiscipline.data_processor
is not None, runs the preprocessor.Updates the status to
MDODiscipline.STATUS_RUNNING
.Calls the
MDODiscipline._run()
method, that shall be defined.If
MDODiscipline.data_processor
is not None, runs the postprocessor.Checks the output data.
Caches the outputs.
Updates the status to
MDODiscipline.STATUS_DONE
orMDODiscipline.STATUS_FAILED
.Updates summed execution time.
- 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
- 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
orDataset.INPUT_GROUP
for the design variables andDataset.FUNCTION_GROUP
orDataset.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 theOptimizationProblem.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. Ifcategorize
isFalse
, there is a unique group:Dataset.PARAMETER_GROUP`
. Ifcategorize
isTrue
, the groups can be eitherDataset.DESIGN_GROUP
andDataset.FUNCTION_GROUP
ifopt_naming
isTrue
, orDataset.INPUT_GROUP
andDataset.OUTPUT_GROUP
. Ifby_group
isFalse
, 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
andDataset.FUNCTION_GROUP
as groups. Otherwise, useDataset.INPUT_GROUP
andDataset.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
- get_all_inputs()¶
Return the local input data as a list.
The order is given by
MDODiscipline.get_input_data_names()
.
- get_all_outputs()¶
Return the local output data as a list.
The order is given by
MDODiscipline.get_output_data_names()
.
- get_attributes_to_serialize()¶
Define the names of the attributes to be serialized.
Shall be overloaded by disciplines
- 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.
- get_disciplines_in_dataflow_chain()¶
Return the disciplines that must be shown as blocks within the XDSM representation of a chain.
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
- get_disciplines_statuses()¶
Retrieve the statuses of the disciplines.
- 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
- get_input_data(with_namespaces=True)¶
Return the local input data as a dictionary.
- get_input_data_names(with_namespaces=True)¶
Return the names of the input variables.
- get_input_output_data_names(with_namespaces=True)¶
Return the names of the input and output variables.
- Args:
- with_namespaces: Whether to keep the namespace prefix of the
output names, if any.
- 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
- 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
- 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.
- 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(with_namespaces=True)¶
Return the local output data as a dictionary.
- get_output_data_names(with_namespaces=True)¶
Return the names of the output variables.
- 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
- 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
- get_sub_disciplines()¶
Return the sub-disciplines if any.
- Returns
The sub-disciplines.
- Return type
- 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.
- 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
andMDODiscipline._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
- 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.
- Return type
- 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
- 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
andHDF5Cache
. Caching data can be either in-memory, e.g.SimpleCache
andMemoryFullCache
, 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
- 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 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_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
- 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
- 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
- ALGO = 'algo'¶
- ALGO_OPTIONS = 'algo_options'¶
- APPROX_MODES = ['finite_differences', 'complex_step']¶
- AVAILABLE_MODES = ('auto', 'direct', 'adjoint', 'reverse', 'finite_differences', 'complex_step')¶
- AVAILABLE_STATUSES = ['DONE', 'FAILED', 'PENDING', 'RUNNING', 'VIRTUAL']¶
- 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_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¶
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 callingself.execute(input_data)
.- Raises
ValueError – When the discipline does not have a cache.
- data_processor: DataProcessor¶
A tool to pre- and post-process discipline data.
- property default_inputs: dict[str, Any]¶
The default inputs.
- Raises
TypeError – When the default inputs are not passed as a dictionary.
- property design_space: gemseo.algos.design_space.DesignSpace¶
The design space on which the scenario is performed.
- disciplines: list[MDODiscipline]¶
The disciplines.
- 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.
- property grammar_type: gemseo.core.grammars.base_grammar.BaseGrammar¶
The type of grammar to be used for inputs and outputs declaration.
- input_grammar: BaseGrammar¶
The input grammar.
- jac: dict[str, dict[str, ndarray]]¶
The Jacobians of the outputs wrt inputs of the form
{output: {input: matrix}}
.
- property linearization_mode: str¶
The linearization mode among
MDODiscipline.AVAILABLE_MODES
.- Raises
ValueError – When the linearization mode is unknown.
- 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.
- optimization_result: OptimizationResult¶
The optimization result.
- output_grammar: BaseGrammar¶
The output grammar.
- property post_factory: PostFactory | None¶
The factory of post-processors.
- residual_variables: Mapping[str, str]¶
The output variables mapping to their inputs, to be considered as residuals; they shall be equal to zero.
- time_stamps = None¶
- property use_standardized_objective: bool¶
Whether to use the standardized
OptimizationProblem.objective
for logging and post-processing.