gemseo¶

GEMSEO main package.

Introduction¶

This module contains the high-level functions to easily use GEMSEO without requiring a deep knowledge of this software.

Besides, these functions shall change much less often than the internal classes, which is key for backward compatibility, which means ensuring that your current scripts using GEMSEO will be usable with the future versions of GEMSEO.

The high-level functions also facilitate the interfacing of GEMSEO with a platform or other software.

To interface a simulation software with GEMSEO, please refer to: Interfacing simulation software.

Table of contents¶

High-level functions¶

gemseo.AlgorithmFeatures¶: alias of AlgorithmFeature

class gemseo.DatasetType(value)[source]¶

Bases: StrEnum

The available datasets.

BURGER = 'BurgersDataset'¶

IRIS = 'IrisDataset'¶

ROSENBROCK = 'RosenbrockDataset'¶

gemseo.compute_doe(variables_space, algo_name, size=None, unit_sampling=False, **options)[source]¶

Compute a design of experiments (DOE) in a variables space.

Parameters:

variables_space (DesignSpace) – The variables space to be sampled.
size (int | None) – The size of the DOE. If None, the size is deduced from the options.
algo_name (str) – The DOE algorithm.
unit_sampling (bool) –
Whether to sample in the unit hypercube.

By default it is set to False.
**options (DOELibraryOptionType) – The options of the DOE algorithm.

Returns:

The design of experiments whose rows are the samples and columns the variables.

Return type:

ndarray

Examples

>>> from gemseo import compute_doe, create_design_space
>>> variables_space = create_design_space()
>>> variables_space.add_variable("x", 2, l_b=-1.0, u_b=1.0)
>>> doe = compute_doe(variables_space, algo_name="lhs", size=5)

See also

get_available_doe_algorithms get_algorithm_options_schema execute_algo

gemseo.configure(activate_discipline_counters=True, activate_function_counters=True, activate_progress_bar=True, activate_discipline_cache=True, check_input_data=True, check_output_data=True, check_desvars_bounds=True)[source]¶

Update the configuration of GEMSEO if needed.

This could be useful to speed up calculations in presence of cheap disciplines such as analytic formula and surrogate models.

Warning

This function should be called before calling anything from GEMSEO.

Parameters:

activate_discipline_counters (bool) –
Whether to activate the counters attached to the disciplines, in charge of counting their execution time, number of evaluations and number of linearizations.

By default it is set to True.
activate_function_counters (bool) –
Whether to activate the counters attached to the functions, in charge of counting their number of evaluations.

By default it is set to True.
activate_progress_bar (bool) –
Whether to activate the progress bar attached to the drivers, in charge to log the execution of the process: iteration, execution time and objective value.

By default it is set to True.
activate_discipline_cache (bool) –
Whether to activate the discipline cache.

By default it is set to True.
check_input_data (bool) –
Whether to check the input data of a discipline before execution.

By default it is set to True.
check_output_data (bool) –
Whether to check the output data of a discipline before execution.

By default it is set to True.
check_desvars_bounds (bool) –
Whether to check the membership of design variables in the bounds when evaluating the functions in OptimizationProblem.

By default it is set to True.

Return type:

None

gemseo.configure_logger(logger_name=None, level=20, date_format='%H:%M:%S', message_format='%(levelname)8s - %(asctime)s: %(message)s', filename=None, filemode='a')[source]¶

Configure GEMSEO logging.

Parameters:

logger_name (str | None) – The name of the logger to configure. If None, return the root logger.
level (str | int) –
The numerical value or name of the logging level, as defined in logging. Values can either be logging.NOTSET ("NOTSET"), logging.DEBUG ("DEBUG"), logging.INFO ("INFO"), logging.WARNING ("WARNING" or "WARN"), logging.ERROR ("ERROR"), or logging.CRITICAL ("FATAL" or "CRITICAL").

By default it is set to 20.
date_format (str) –
The logging date format.

By default it is set to “%H:%M:%S”.
message_format (str) –
The logging message format.

By default it is set to “%(levelname)8s - %(asctime)s: %(message)s”.
filename (str | Path | None) – The path to the log file, if outputs must be written in a file.
filemode (str) –
The logging output file mode, either ‘w’ (overwrite) or ‘a’ (append).

By default it is set to “a”.

Return type:

Logger

Examples

>>> import logging
>>> configure_logger(level=logging.WARNING)

gemseo.create_benchmark_dataset(dataset_type, **options)[source]¶

Instantiate a dataset.

Typically, benchmark datasets can be found in gemseo.datasets.dataset.

Parameters:

dataset_type (DatasetType) – The type of the dataset.
**options (Any) – The options for creating the dataset.

Returns:

The dataset.

Return type:

IODataset

See also

create_dataset

gemseo.create_cache(cache_type, name=None, **options)[source]¶

Return a cache.

Parameters:

cache_type (str) – The type of the cache.
name (str | None) – The name to be given to the cache. If None, use cache_type.
**options (Any) – The options of the cache.

Returns:

The cache.

Return type:

AbstractCache

Examples

>>> from gemseo import create_cache
>>> cache = create_cache('MemoryFullCache')
>>> print(cache)
+--------------------------------+
|        MemoryFullCache         |
+--------------+-----------------+
|   Property   |      Value      |
+--------------+-----------------+
|     Type     | MemoryFullCache |
|  Tolerance   |       0.0       |
| Input names  |       None      |
| Output names |       None      |
|    Length    |        0        |
+--------------+-----------------+

See also

get_available_caches gemseo.core.discipline.MDODiscipline.set_cache_policy

gemseo.create_dataset(name, data, variable_names=(), variable_names_to_n_components=None, variable_names_to_group_names=None, delimiter=',', header=True)[source]¶

Create a dataset from a NumPy array or a data file.

Parameters:

name (str) – The name to be given to the dataset.
data (ndarray | str | Path) – The data to be stored in the dataset, either a NumPy array or a file path.
variable_names (str | Iterable[str]) –
The names of the variables. If empty, use default names.

By default it is set to ().
variable_names_to_n_components (dict[str, int] | None) – The number of components of the variables. If None, assume that all the variables have a single component.
variable_names_to_group_names (dict[str, str] | None) – The groups of the variables. If None, use Dataset.DEFAULT_GROUP for all the variables.
delimiter (str) –
The field delimiter.

By default it is set to “,”.
header (bool) –
If True and data is a string, read the variables names on the first line of the file.

By default it is set to True.

Returns:

The dataset generated from the NumPy array or data file.

Raises:

ValueError – If data is neither a file nor an array.

Return type:

IODataset

See also

create_benchmark_dataset

gemseo.create_design_space()[source]¶

Create an empty design space.

Returns:: An empty design space.
Return type:: DesignSpace

Examples

>>> from gemseo import create_design_space
>>> design_space = create_design_space()
>>> design_space.add_variable('x', l_b=-1, u_b=1, value=0.)
>>> print(design_space)
Design Space:
+------+-------------+-------+-------------+-------+
| name | lower_bound | value | upper_bound | type  |
+------+-------------+-------+-------------+-------+
| x    |      -1     |   0   |      1      | float |
+------+-------------+-------+-------------+-------+

See also

read_design_space write_design_space create_design_space gemseo.algos.design_space.DesignSpace

gemseo.create_discipline(discipline_name, **options)[source]¶

Instantiate one or more disciplines.

Parameters:

discipline_name (str | Iterable[str]) – Either the name of a discipline or the names of several disciplines.
**options (Any) – The options to be passed to the disciplines constructors.

Returns:

The disciplines.

Examples

>>> from gemseo import create_discipline
>>> discipline = create_discipline('Sellar1')
>>> discipline.execute()
{'x_local': array([0.+0.j]),
 'x_shared': array([1.+0.j, 0.+0.j]),
 'y_0': array([0.89442719+0.j]),
 'y_1': array([1.+0.j])}

See also

import_discipline get_available_disciplines get_discipline_inputs_schema get_discipline_outputs_schema get_discipline_options_schema get_discipline_options_defaults

gemseo.create_mda(mda_name, disciplines, **options)[source]¶

Create a multidisciplinary analysis (MDA).

Parameters:

mda_name (str) – The name of the MDA.
disciplines (Sequence[MDODiscipline]) – The disciplines.
**options (Any) – The options of the MDA.

Returns:

The MDA.

Return type:

MDA

Examples

>>> from gemseo import create_discipline, create_mda
>>> disciplines = create_discipline(['Sellar1', 'Sellar2'])
>>> mda = create_mda('MDAGaussSeidel', disciplines)
>>> mda.execute()
{'x_local': array([0.+0.j]),
 'x_shared': array([1.+0.j, 0.+0.j]),
 'y_0': array([0.79999995+0.j]),
 'y_1': array([1.79999995+0.j])}

See also

create_mda get_available_mdas get_mda_options_schema

gemseo.create_parameter_space()[source]¶

Create an empty parameter space.

Returns:: An empty parameter space.
Return type:: ParameterSpace

gemseo.create_scalable(name, data, sizes=None, **parameters)[source]¶

Create a scalable discipline from a dataset.

Parameters:

name (str) – The name of the class of the scalable model.
data (Dataset) – The learning dataset.
sizes (Mapping[str, int]) – The sizes of the input and output variables.
**parameters (Any) – The parameters of the scalable model.

Returns:

The scalable discipline.

Return type:

ScalableDiscipline

gemseo.create_scenario(disciplines, formulation, objective_name, design_space, name=None, scenario_type='MDO', grammar_type=GrammarType.JSON, maximize_objective=False, **options)[source]¶

Initialize a scenario.

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) – 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 | str | Path) – 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.
scenario_type (str) –
The type of the scenario, e.g. "MDO" or "DOE".

By default it is set to “MDO”.
grammar_type (MDODiscipline.GrammarType) –
The type of grammar to declare the input and output variables either JSON_GRAMMAR_TYPE or SIMPLE_GRAMMAR_TYPE.

By default it is set to “JSONGrammar”.
maximize_objective (bool) –
Whether to maximize the objective.

By default it is set to False.
**options (Any) – The options of the MDOFormulation.

Return type:

Scenario

Examples

>>> from gemseo import create_discipline, create_scenario
>>> from gemseo.problems.sellar.sellar_design_space import SellarDesignSpace
>>> disciplines = create_discipline(['Sellar1', 'Sellar2', 'SellarSystem'])
>>> design_space = SellarDesignSpace()
>>> scenario = create_scenario(disciplines, 'MDF', 'obj', design_space,
'SellarMDFScenario')

See also

monitor_scenario get_available_scenario_types get_scenario_options_schema get_scenario_inputs_schema get_scenario_differentiation_modes

gemseo.create_surrogate(surrogate, data=None, transformer=mappingproxy({'inputs': <gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object>, 'outputs': <gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object>}), disc_name=None, default_inputs=None, input_names=None, output_names=None, **parameters)[source]¶

Create a surrogate discipline, either from a dataset or a regression model.

Parameters:

surrogate (str | MLRegressionAlgo) – Either the class name or the instance of the MLRegressionAlgo.
data (Dataset | None) – The learning dataset to train the regression model. If None, the regression model is supposed to be trained.
transformer (TransformerType) –
The strategies to transform the variables. The values are instances of Transformer while the keys are the names of either the variables or the groups of variables, e.g. “inputs” or “outputs” in the case of the regression algorithms. If a group is specified, the Transformer will be applied to all the variables of this group. If None, do not transform the variables. The MLRegressionAlgo.DEFAULT_TRANSFORMER uses the MinMaxScaler strategy for both input and output variables.

By default it is set to {‘inputs’: <gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object at 0x7f84c0e41a60>, ‘outputs’: <gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object at 0x7f84c0e41ac0>}.
disc_name (str | None) – The name to be given to the surrogate discipline. If None, concatenate SHORT_ALGO_NAME and data.name.
default_inputs (dict[str, ndarray] | None) – The default values of the inputs. If None, use the center of the learning input space.
input_names (Iterable[str] | None) – The names of the input variables. If None, consider all input variables mentioned in the learning dataset.
output_names (Iterable[str] | None) – The names of the output variables. If None, consider all input variables mentioned in the learning dataset.
**parameters (Any) – The parameters of the machine learning algorithm.

Return type:

SurrogateDiscipline

See also

get_available_surrogates get_surrogate_options_schema

gemseo.execute_algo(opt_problem, algo_name, algo_type='opt', **options)[source]¶

Solve an optimization problem.

Parameters:

opt_problem (OptimizationProblem) – The optimization problem to be solved.
algo_name (str) – The name of the algorithm to be used to solve optimization problem.
algo_type (str) –
The type of algorithm, either “opt” for optimization or “doe” for design of experiments.

By default it is set to “opt”.
**options (Any) – The options of the algorithm.

Return type:

OptimizationResult

Examples

>>> from gemseo import execute_algo
>>> from gemseo.problems.analytical.rosenbrock import Rosenbrock
>>> opt_problem = Rosenbrock()
>>> opt_result = execute_algo(opt_problem, 'SLSQP')
>>> opt_result
Optimization result:
|_ Design variables: [0.99999787 0.99999581]
|_ Objective function: 5.054173713127532e-12
|_ Feasible solution: True

See also

get_available_opt_algorithms get_available_doe_algorithms get_algorithm_options_schema

gemseo.execute_post(to_post_proc, post_name, **options)[source]¶

Post-process a result.

Parameters:

to_post_proc (Scenario | OptimizationProblem | str | Path) – The result to be post-processed, either a DOE scenario, a MDO scenario, an optimization problem or a path to an HDF file containing a saved optimization problem.
post_name (str) – The name of the post-processing.
**options (Any) – The post-processing options.

Returns:

The figures, to be customized if not closed.

Return type:

dict[str, Figure]

Examples

>>> from gemseo import create_discipline, create_scenario, execute_post
>>> from gemseo.problems.sellar.sellar_design_space import SellarDesignSpace
>>> disciplines = create_discipline(['Sellar1', 'Sellar2', 'SellarSystem'])
>>> design_space = SellarDesignSpace()
>>> scenario = create_scenario(disciplines, 'MDF', 'obj', design_space,
'SellarMDFScenario')
>>> scenario.execute({'algo': 'NLOPT_SLSQP', 'max_iter': 100})
>>> execute_post(scenario, 'OptHistoryView', show=False, save=True)

See also

get_available_post_processings get_post_processing_options_schema

gemseo.generate_coupling_graph(disciplines, file_path='coupling_graph.pdf', full=True)[source]¶

Generate a graph of the couplings between disciplines.

Parameters:

disciplines (Sequence[MDODiscipline]) – The disciplines from which the graph is generated.
file_path (str | Path) –
The path of the file to save the figure.

By default it is set to “coupling_graph.pdf”.
full (bool) –
If True, generate the full coupling graph. Otherwise, generate the condensed one.

By default it is set to True.

Return type:

None

Examples

>>> from gemseo import create_discipline, generate_coupling_graph
>>> disciplines = create_discipline(['Sellar1', 'Sellar2', 'SellarSystem'])
>>> generate_coupling_graph(disciplines)

See also

generate_n2_plot

gemseo.generate_n2_plot(disciplines, file_path='n2.pdf', show_data_names=True, save=True, show=False, fig_size=(15.0, 10.0), show_html=False)[source]¶

Generate a N2 plot from disciplines.

It can be static (e.g. PDF, PNG, …), dynamic (HTML) or both.

The disciplines are located on the diagonal of the N2 plot while the coupling variables are situated on the other blocks of the matrix view. A coupling variable is outputted by a discipline horizontally and enters another vertically. On the static plot, a blue diagonal block represent a self-coupled discipline, i.e. a discipline having some of its outputs as inputs.

Parameters:

disciplines (Sequence[MDODiscipline]) – The disciplines from which the N2 chart is generated.
file_path (str | Path) –
The file path to save the static N2 chart.

By default it is set to “n2.pdf”.
show_data_names (bool) –
Whether to show the names of the coupling variables between two disciplines; otherwise, circles are drawn, whose size depends on the number of coupling names.

By default it is set to True.
save (bool) –
Whether to save the static N2 chart.

By default it is set to True.
show (bool) –
Whether to show the static N2 chart.

By default it is set to False.
fig_size (FigSizeType) –
The width and height of the static N2 chart.

By default it is set to (15.0, 10.0).
show_html (bool) –
Whether to display the interactive N2 chart in a web browser.

By default it is set to False.

Return type:

None

Examples

>>> from gemseo import create_discipline, generate_n2_plot
>>> disciplines = create_discipline(['Sellar1', 'Sellar2', 'SellarSystem'])
>>> generate_n2_plot(disciplines)

See also

generate_coupling_graph

gemseo.get_algorithm_features(algorithm_name)[source]¶

Return the features of an optimization algorithm.

Parameters:: algorithm_name (str) – The name of the optimization algorithm.
Returns:: The features of the optimization algorithm.
Raises:: ValueError – When the optimization algorithm does not exist.
Return type:: AlgorithmFeature

gemseo.get_algorithm_options_schema(algorithm_name, output_json=False, pretty_print=False)[source]¶

Return the schema of the options of an algorithm.

Parameters:

algorithm_name (str) – The name of the algorithm.
output_json (bool) –
Whether to apply the JSON format to the schema.

By default it is set to False.
pretty_print (bool) –
Whether to print the schema in a tabular way.

By default it is set to False.

Returns:

The schema of the options of the algorithm.

Raises:

ValueError – When the algorithm is not available.

Return type:

str | dict[str, Any]

Examples

>>> from gemseo import get_algorithm_options_schema
>>> schema = get_algorithm_options_schema('NLOPT_SLSQP', pretty_print=True)

See also

create_scenario execute_algo get_available_opt_algorithms get_available_doe_algorithms get_algorithm_options_schema

gemseo.get_available_caches()[source]¶

Return the names of the available caches.

Returns:: The names of the available caches.
Return type:: list[str]

Examples

>>> from gemseo import get_available_caches
>>> get_available_caches()
['AbstractFullCache', 'HDF5Cache', 'MemoryFullCache', 'SimpleCache']

See also

get_available_caches gemseo.core.discipline.MDODiscipline.set_cache_policy

gemseo.get_available_disciplines()[source]¶

Return the names of the available disciplines.

Returns:: The names of the available disciplines.
Return type:: list[str]

Examples

>>> from gemseo import get_available_disciplines
>>> print(get_available_disciplines())
['RosenMF', 'SobieskiAerodynamics', 'ScalableKriging', 'DOEScenario',
'MDOScenario', 'SobieskiMission', 'SobieskiDiscipline', 'Sellar1',
'Sellar2', 'MDOChain', 'SobieskiStructure', 'AutoPyDiscipline',
'Structure', 'SobieskiPropulsion', 'Scenario', 'AnalyticDiscipline',
'MDOScenarioAdapter', 'ScalableDiscipline', 'SellarSystem', 'Aerodynamics',
'Mission', 'PropaneComb1', 'PropaneComb2', 'PropaneComb3',
'PropaneReaction', 'MDOParallelChain']

See also

create_discipline import_discipline get_available_disciplines get_discipline_inputs_schema get_discipline_outputs_schema get_discipline_options_schema get_discipline_options_defaults

gemseo.get_available_doe_algorithms()[source]¶

Return the names of the available design of experiments (DOEs) algorithms.

Returns:: The names of the available DOE algorithms.
Return type:: list[str]

Examples;

>>> from gemseo import get_available_doe_algorithms
>>> get_available_doe_algorithms()

See also

create_scenario execute_algo get_available_opt_algorithms get_algorithm_options_schema

gemseo.get_available_formulations()[source]¶

Return the names of the available formulations.

Returns:: The names of the available MDO formulations.
Return type:: list[str]

Examples

>>> from gemseo import get_available_formulations
>>> get_available_formulations()

See also

create_scenario get_formulation_options_schema get_formulation_sub_options_schema get_formulations_options_defaults get_formulations_sub_options_defaults

gemseo.get_available_mdas()[source]¶

Return the names of the available multidisciplinary analyses (MDAs).

Returns:: The names of the available MDAs.
Return type:: list[str]

Examples

>>> from gemseo import get_available_mdas
>>> get_available_mdas()

See also

create_mda get_mda_options_schema

gemseo.get_available_opt_algorithms()[source]¶

Return the names of the available optimization algorithms.

Returns:: The names of the available optimization algorithms.
Return type:: list[str]

Examples

>>> from gemseo import get_available_opt_algorithms
>>> get_available_opt_algorithms()

See also

create_scenario execute_algo get_available_doe_algorithms get_algorithm_options_schema

gemseo.get_available_post_processings()[source]¶

Return the names of the available optimization post-processings.

Returns:: The names of the available post-processings.
Return type:: list[str]

Examples

>>> from gemseo import get_available_post_processings
>>> print(get_available_post_processings())
['ScatterPlotMatrix', 'VariableInfluence', 'ConstraintsHistory',
'RadarChart', 'Robustness', 'Correlations', 'SOM', 'KMeans',
'ParallelCoordinates', 'GradientSensitivity', 'OptHistoryView',
'BasicHistory', 'ObjConstrHist', 'QuadApprox']

See also

execute_post get_post_processing_options_schema

gemseo.get_available_scenario_types()[source]¶

Return the names of the available scenario types.

Returns:: The names of the available scenario types.
Return type:: list[str]

Examples

>>> from gemseo import get_available_scenario_types
>>> get_available_scenario_types()

See also

create_scenario monitor_scenario get_scenario_options_schema get_scenario_inputs_schema get_scenario_differentiation_modes

gemseo.get_available_surrogates()[source]¶

Return the names of the available surrogate disciplines.

Returns:: The names of the available surrogate disciplines.
Return type:: list[str]

Examples

>>> from gemseo import get_available_surrogates
>>> print(get_available_surrogates())
['RBFRegressor', 'GaussianProcessRegressor', 'LinearRegressor', 'PCERegressor']

See also

create_surrogate get_surrogate_options_schema

gemseo.get_discipline_inputs_schema(discipline, output_json=False, pretty_print=False)[source]¶

Return the schema of the inputs of a discipline.

Parameters:

discipline (MDODiscipline) – The discipline.
output_json (bool) –
Whether to apply the JSON format to the schema.

By default it is set to False.
pretty_print (bool) –
Whether to print the schema in a tabular way.

By default it is set to False.

Returns:

The schema of the inputs of the discipline.

Return type:

str | dict[str, Any]

Examples

>>> from gemseo import create_discipline, get_discipline_inputs_schema
>>> discipline = create_discipline('Sellar1')
>>> schema = get_discipline_inputs_schema(discipline, pretty_print=True)

See also

create_discipline import_discipline get_available_disciplines get_discipline_outputs_schema get_discipline_options_schema get_discipline_options_defaults

gemseo.get_discipline_options_defaults(discipline_name)[source]¶

Return the default values of the options of a discipline.

Parameters:: discipline_name (str) – The name of the discipline.
Returns:: The default values of the options of the discipline.
Return type:: dict[str, Any]

Examples

>>> from gemseo import get_discipline_options_defaults
>>> get_discipline_options_defaults('Sellar1')

See also

create_discipline import_discipline get_available_disciplines get_discipline_inputs_schema get_discipline_outputs_schema get_discipline_options_schema

gemseo.get_discipline_options_schema(discipline_name, output_json=False, pretty_print=False)[source]¶

Return the schema of a discipline.

Parameters:

discipline_name (str) – The name of the formulation.
output_json (bool) –
Whether to apply the JSON format to the schema.

By default it is set to False.
pretty_print (bool) –
Whether to print the schema in a tabular way.

By default it is set to False.

Returns:

The schema of the discipline.

Return type:

str | dict[str, Any]

Examples

>>> from gemseo import get_discipline_options_schema
>>> schema = get_discipline_options_schema('Sellar1', pretty_print=True)

See also

create_discipline import_discipline get_available_disciplines get_discipline_inputs_schema get_discipline_outputs_schema get_discipline_options_defaults

gemseo.get_discipline_outputs_schema(discipline, output_json=False, pretty_print=False)[source]¶

Return the schema of the outputs of a discipline.

Parameters:

discipline (MDODiscipline) – The discipline.
output_json (bool) –
Whether to apply the JSON format to the schema.

By default it is set to False.
pretty_print (bool) –
Whether to print the schema in a tabular way.

By default it is set to False.

Returns:

The schema of the outputs of the discipline.

Return type:

str | dict[str, Any]

Examples

>>> from gemseo import get_discipline_outputs_schema, create_discipline
>>> discipline = create_discipline('Sellar1')
>>> get_discipline_outputs_schema(discipline, pretty_print=True)

See also

create_discipline import_discipline get_available_disciplines get_discipline_inputs_schema get_discipline_options_schema get_discipline_options_defaults

gemseo.get_formulation_options_schema(formulation_name, output_json=False, pretty_print=False)[source]¶

Return the schema of the options of a formulation.

Parameters:

formulation_name (str) – The name of the formulation.
output_json (bool) –
Whether to apply the JSON format to the schema.

By default it is set to False.
pretty_print (bool) –
Whether to print the schema in a tabular way.

By default it is set to False.

Returns:

The schema of the options of the formulation.

Return type:

str | dict[str, Any]

Examples

>>> from gemseo import get_formulation_options_schema
>>> schema = get_formulation_options_schema('MDF', pretty_print=True)

See also

create_scenario get_available_formulations get_formulation_sub_options_schema get_formulations_options_defaults get_formulations_sub_options_defaults

gemseo.get_formulation_sub_options_schema(formulation_name, output_json=False, pretty_print=False, **formulation_options)[source]¶

Return the schema of the sub-options of a formulation.

Parameters:

formulation_name (str) – The name of the formulation.
output_json (bool) –
Whether to apply the JSON format to the schema.

By default it is set to False.
pretty_print (bool) –
Whether to print the schema in a tabular way.

By default it is set to False.
**formulation_options (Any) – The options of the formulation required for its instantiation.

Returns:

The schema of the sub-options of the formulation, if any, None otherwise.

Return type:

str | dict[str, Any] | None

Examples

>>> from gemseo import get_formulation_sub_options_schema
>>> schema = get_formulation_sub_options_schema('MDF',
>>>                                             main_mda_name='MDAJacobi',
>>>                                             pretty_print=True)

See also

create_scenario get_available_formulations get_formulation_options_schema get_formulations_options_defaults get_formulations_sub_options_defaults

gemseo.get_formulations_options_defaults(formulation_name)[source]¶

Return the default values of the options of a formulation.

Parameters:: formulation_name (str) – The name of the formulation.
Returns:: The default values of the options of the formulation.
Return type:: dict[str, Any]

Examples

>>> from gemseo import get_formulations_options_defaults
>>> get_formulations_options_defaults('MDF')
{'main_mda_name': 'MDAChain',
 'maximize_objective': False,
 'inner_mda_name': 'MDAJacobi'}

See also

create_scenario get_available_formulations get_formulation_options_schema get_formulation_sub_options_schema get_formulations_sub_options_defaults

gemseo.get_formulations_sub_options_defaults(formulation_name, **formulation_options)[source]¶

Return the default values of the sub-options of a formulation.

Parameters:

formulation_name (str) – The name of the formulation.
**formulation_options (Any) – The options of the formulation required for its instantiation.

Returns:

The default values of the sub-options of the formulation.

Return type:

dict[str, Any]

Examples

>>> from gemseo import get_formulations_sub_options_defaults
>>> get_formulations_sub_options_defaults('MDF',
>>>                                       main_mda_name='MDAJacobi')

See also

create_scenario get_available_formulations get_formulation_options_schema get_formulation_sub_options_schema get_formulations_options_defaults

gemseo.get_mda_options_schema(mda_name, output_json=False, pretty_print=False)[source]¶

Return the schema of the options of a multidisciplinary analysis (MDA).

Parameters:

mda_name (str) – The name of the MDA.
output_json (bool) –
Whether to apply the JSON format to the schema.

By default it is set to False.
pretty_print (bool) –
Whether to print the schema in a tabular way.

By default it is set to False.

Returns:

The schema of the options of the MDA.

Return type:

str | dict[str, Any]

Examples

>>> from gemseo import get_mda_options_schema
>>> get_mda_options_schema('MDAJacobi')

See also

create_mda get_available_mdas

gemseo.get_post_processing_options_schema(post_proc_name, output_json=False, pretty_print=False)[source]¶

Return the schema of the options of a post-processing.

Parameters:

post_proc_name (str) – The name of the post-processing.
output_json (bool) –
Whether to apply the JSON format to the schema.

By default it is set to False.
pretty_print (bool) –
Whether to print the schema in a tabular way.

By default it is set to False.

Returns:

The schema of the options of the post-processing.

Return type:

str | dict[str, Any]

Examples

>>> from gemseo import get_post_processing_options_schema
>>> schema = get_post_processing_options_schema('OptHistoryView',
>>>                                             pretty_print=True)

See also

execute_post get_available_post_processings

gemseo.get_scenario_differentiation_modes()[source]¶

Return the names of the available differentiation modes of a scenario.

Returns:: The names of the available differentiation modes of a scenario.
Return type:: tuple[OptimizationProblem.DifferentiationMethod]

Examples

>>> from gemseo import get_scenario_differentiation_modes
>>> get_scenario_differentiation_modes()

See also

create_scenario monitor_scenario get_available_scenario_types get_scenario_options_schema get_scenario_inputs_schema

gemseo.get_scenario_inputs_schema(scenario, output_json=False, pretty_print=False)[source]¶

Return the schema of the inputs of a scenario.

Parameters:

scenario (Scenario) – The scenario.
output_json (bool) –
Whether to apply the JSON format to the schema.

By default it is set to False.
pretty_print (bool) –
Whether to print the schema in a tabular way.

By default it is set to False.

Returns:

The schema of the inputs of the scenario.

Return type:

str | dict[str, Any]

Examples

>>> from gemseo import create_discipline, create_scenario,
get_scenario_inputs_schema
>>> from gemseo.problems.sellar.sellar_design_space import SellarDesignSpace
>>> design_space = SellarDesignSpace()
>>> disciplines = create_discipline(['Sellar1','Sellar2','SellarSystem'])
>>> scenario = create_scenario(disciplines, 'MDF', 'obj', design_space,
'my_scenario', 'MDO')
>>> get_scenario_inputs_schema(scenario)

See also

create_scenario monitor_scenario get_available_scenario_types get_scenario_options_schema get_scenario_differentiation_modes

gemseo.get_scenario_options_schema(scenario_type, output_json=False, pretty_print=False)[source]¶

Return the schema of the options of a scenario.

Parameters:

scenario_type (str) – The type of the scenario.
output_json (bool) –
Whether to apply the JSON format to the schema.

By default it is set to False.
pretty_print (bool) –
Whether to print the schema in a tabular way.

By default it is set to False.

Returns:

The schema of the options of the scenario.

Return type:

str | dict[str, Any]

Examples

>>> from gemseo import get_scenario_options_schema
>>> get_scenario_options_schema('MDO')

See also

create_scenario monitor_scenario get_available_scenario_types get_scenario_options_schema get_scenario_inputs_schema get_scenario_differentiation_modes

gemseo.get_surrogate_options_schema(surrogate_name, output_json=False, pretty_print=False)[source]¶

Return the available options for a surrogate discipline.

Parameters:

surrogate_name (str) – The name of the surrogate discipline.
output_json (bool) –
Whether to apply the JSON format to the schema.

By default it is set to False.
pretty_print (bool) –
Whether to print the schema in a tabular way.

By default it is set to False.

Returns:

The schema of the options of the surrogate discipline.

Return type:

str | dict[str, Any]

Examples

>>> from gemseo import get_surrogate_options_schema
>>> tmp = get_surrogate_options_schema('LinRegSurrogateDiscipline',
>>>                                    pretty_print=True)

See also

create_surrogate get_available_surrogates

gemseo.import_discipline(file_path, cls=None)[source]¶

Import a discipline from a pickle file.

Parameters:

file_path (str | Path) – The path to the file containing the discipline saved with the method MDODiscipline.to_pickle().
cls (type[MDODiscipline] | None) – A class of discipline. If None, use MDODiscipline.

Returns:

The discipline.

Return type:

MDODiscipline

See also

create_discipline get_available_disciplines get_discipline_inputs_schema get_discipline_outputs_schema get_discipline_options_schema get_discipline_options_defaults

gemseo.monitor_scenario(scenario, observer)[source]¶

Add an observer to a scenario.

The observer must have an update method that handles the execution status change of an atom. update(atom) is called everytime an atom execution changes.

Parameters:

scenario (Scenario) – The scenario to monitor.
observer – The observer that handles an update of status.

Return type:

None

See also

create_scenario get_available_scenario_types get_scenario_options_schema get_scenario_inputs_schema get_scenario_differentiation_modes

gemseo.print_configuration()[source]¶

Print the current configuration.

The log message contains the successfully loaded modules and failed imports with the reason.

Examples

>>> from gemseo import print_configuration
>>> print_configuration()

Return type:: None

gemseo.read_design_space(file_path, header=None)[source]¶

Read a design space from a CSV or HDF file.

In the case of a CSV file, the following columns must be in the file: “name”, “lower_bound” and “upper_bound”. This file shall contain space-separated values (the number of spaces is not important) with a row for each variable and at least the bounds of the variable.

Parameters:

file_path (str | Path) – The path to the file.
header (Iterable[str] | None) – The names of the fields saved in the CSV file. If None, read them in the first row of the CSV file.

Returns:

The design space.

Return type:

DesignSpace

Examples

>>> from gemseo import (create_design_space, write_design_space,
>>>     read_design_space)
>>> original_design_space = create_design_space()
>>> original_design_space.add_variable('x', l_b=-1, value=0., u_b=1.)
>>> write_design_space(original_design_space, 'file.csv')
>>> design_space = read_design_space('file.csv')
>>> print(design_space)
Design Space:
+------+-------------+-------+-------------+-------+
| name | lower_bound | value | upper_bound | type  |
+------+-------------+-------+-------------+-------+
| x    |      -1     |   0   |      1      | float |
+------+-------------+-------+-------------+-------+

See also

write_design_space create_design_space

gemseo.wrap_discipline_in_job_scheduler(discipline, scheduler_name, workdir_path, **options)[source]¶

Wrap the discipline within another one to delegate its execution to a job scheduler.

The discipline is serialized to the disk, its input too, then a job file is created from a template to execute it with the provided options. The submission command is launched, it will setup the environment, deserialize the discipline and its inputs, execute it and serialize the outputs. Finally, the deserialized outputs are returned by the wrapper.

All process classes MDOScenario, or MDA, inherit from MDODiscipline so can be sent to HPCs in this way.

The job scheduler template script can be provided directly or the predefined templates file names in gemseo.wrappers.job_schedulers.template can be used. SLURM and LSF templates are provided, but one can use other job schedulers or to customize the scheduler commands according to the user needs and infrastructure requirements.

The command to submit the job can also be overloaded.

Parameters:

discipline (MDODiscipline) – The discipline to wrap in the job scheduler.
scheduler_name (str) – The name of the job scheduler (for instance LSF, SLURM, PBS).
workdir_path (Path) – The path to the workdir
**options (dict[str, Any]) – The submission options.

Raises:

OSError – if the job template does not exist.

Return type:

JobSchedulerDisciplineWrapper

Warning

This method serializes the passed discipline so it has to be serializable. All disciplines provided in GEMSEO are serializable but it is possible that custom ones are not and this will make the submission proess fail.

Examples

This example execute a DOE of 100 points on a MDA, each MDA is executed on 24 CPUS using the SLURM wrapper, on a HPC, and at most 10 points run in parallel, everytime a point of the DOE is computed, another one is submitted to the queue.

>>> from gemseo.wrappers.job_schedulers.schedulers_factory import SchedulersFactory
>>> from gemseo import create_discipline, create_scenario, create_mda
>>> from gemseo.problems.sellar.sellar_design_space import SellarDesignSpace
>>> disciplines = create_discipline(['Sellar1', 'Sellar2', 'SellarSystem'])
>>> mda = create_mda(disciplines)
>>> wrapped_mda= wrap_discipline_in_job_scheduler(mda, scheduler_name="SLURM",
>>>                                               workdir_path="workdir",
>>>                                               cpus_per_task=24)
>>> scn=create_scenario(mda, "DisciplinaryOpt", "obj", SellarDesignSpace(),
>>>                     scenario_type="DOE")
>>> scn.execute(algo="lhs", n_samples=100, algo_options={"n_processes":10})

In this variant, each discipline is wrapped independently in the job scheduler, which allows to parallelize more the process because each discipline will run on indpendent nodes, whithout being parallelized using MPI. The drawback is that each discipline execution will be queued on the HPC. A HDF5 cache is attached to the MDA, so all executions will be recorded. Each wrapped discipline can also be cached using a HDF cache.

>>> from gemseo.core.discipline import MDODiscipline
>>> disciplines = create_discipline(['Sellar1', 'Sellar2', 'SellarSystem'])
>>> wrapped_discs=[wrap_discipline_in_job_scheduler(disc,
>>>                                                 workdir_path="workdir",
>>>                                                 cpus_per_task=24,
>>>                                                 scheduler_name="SLURM"),
>>>                for disc in disciplines]
>>> scn=create_scenario(wrapped_discs, "MDF", "obj", SellarDesignSpace(),
>>>                     scenario_type="DOE")
>>> scn.formulation.mda.set_cache_policy(MDODiscipline.HDF5_CACHE,
>>>                                      cache_hdf_file="mda_cache.h5")
>>> scn.execute(algo="lhs", n_samples=100, algo_options={"n_processes":10})

gemseo.write_design_space(design_space, output_file, fields=None, header_char='', **table_options)[source]¶

Save a design space to a CSV or HDF file.

Parameters:

design_space (DesignSpace) – The design space to be saved.
output_file (str | Path) – The path to the file.
fields (Sequence[str] | None) – The fields to be exported. If None, export all fields.
header_char (str) –
The header character.

By default it is set to “”.
**table_options (Any) – The names and values of additional attributes for the PrettyTable view generated by DesignSpace.get_pretty_table().

Return type:

None

Examples

>>> from gemseo import create_design_space, write_design_space
>>> design_space = create_design_space()
>>> design_space.add_variable('x', l_b=-1, u_b=1, value=0.)
>>> write_design_space(design_space, 'file.csv')

See also

read_design_space create_design_space

gemseo¶

Introduction¶

Table of contents¶

Algorithms¶

Cache¶

Configuration¶

Coupling¶

Design space¶

Disciplines¶

Formulations¶

MDA¶

Post-processing¶

Scalable¶

Scenario¶

Surrogates¶

High-level functions¶