gemseo

api module

Introduction

Here is the Application Programming Interface (API) of GEMSEO, a set of high level functions for ease of use.

Make GEMSEO ever more accessible

The aim of this API is to provide high level functions that are sufficient to use GEMSEO in most cases, without requiring a deep knowledge of GEMSEO.

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.

Connect GEMSEO to your favorite tools

The API also facilitates the interfacing of GEMSEO with a platform or other software.

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

Extending GEMSEO

See Extend GEMSEO features.

Table of contents

Algorithms

Cache

Configuration

Coupling

Design space

Disciplines

Formulations

MDA

Post-processing

Scalable

Scenario

Surrogates

API functions

gemseo.api.AlgorithmFeatures

alias of AlgorithmFeature

gemseo.api.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.api 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)
gemseo.api.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.api.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.api.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.api 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        |
+--------------+-----------------+
gemseo.api.create_dataset(name, data, variables=None, sizes=None, groups=None, by_group=True, delimiter=',', header=True, default_name=None)[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.

  • variables (list[str] | None) – The names of the variables. If None and header is True, read the names from the first line of the file. If None and header is False, use default names based on the patterns the Dataset.DEFAULT_NAMES associated with the different groups.

  • sizes (dict[str, int] | None) – The sizes of the variables. If None, assume that all the variables have a size equal to 1.

  • groups (dict[str, str] | None) – The groups of the variables. If None, use Dataset.DEFAULT_GROUP for all the variables.

  • by_group (bool) –

    If True, store the data by group. Otherwise, store them by variables.

    By default it is set to True.

  • 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.

  • default_name (str | None) – The name of the variable to be used as a pattern when variables is None. If None, use the Dataset.DEFAULT_NAMES for this group if it exists. Otherwise, use the group name.

Returns:

The dataset generated from the NumPy array or data file.

Return type:

Dataset

See also

load_dataset

gemseo.api.create_design_space()[source]

Create an empty design space.

Returns:

An empty design space.

Return type:

DesignSpace

Examples

>>> from gemseo.api 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 |
+------+-------------+-------+-------------+-------+
gemseo.api.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.api 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])}
gemseo.api.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.api 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])}
gemseo.api.create_parameter_space()[source]

Create an empty parameter space.

Returns:

An empty parameter space.

Return type:

ParameterSpace

gemseo.api.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.api.create_scenario(disciplines, formulation, objective_name, design_space, name=None, scenario_type='MDO', grammar_type='JSONGrammar', 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 (str) –

    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.api 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')
gemseo.api.create_surrogate(surrogate, data=None, transformer=mappingproxy({'inputs': <gemseo.mlearning.transform.scaler.min_max_scaler.MinMaxScaler object>, 'outputs': <gemseo.mlearning.transform.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.transform.scaler.min_max_scaler.MinMaxScaler object at 0x7f3d3e99e160>, ‘outputs’: <gemseo.mlearning.transform.scaler.min_max_scaler.MinMaxScaler object at 0x7f3d3e99e1f0>}.

  • 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

gemseo.api.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.api 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
gemseo.api.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.api 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)
gemseo.api.export_design_space(design_space, output_file, export_hdf=False, fields=None, header_char='', **table_options)[source]

Save a design space to a text or HDF file.

Parameters:
  • design_space (DesignSpace) – The design space to be saved.

  • output_file (str | Path) – The path to the file.

  • export_hdf (bool) –

    If True, save to an HDF file. Otherwise, save to a text file.

    By default it is set to False.

  • 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.api import create_design_space, export_design_space
>>> design_space = create_design_space()
>>> design_space.add_variable('x', l_b=-1, u_b=1, value=0.)
>>> export_design_space(design_space, 'file.txt')
gemseo.api.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.api import create_discipline, generate_coupling_graph
>>> disciplines = create_discipline(['Sellar1', 'Sellar2', 'SellarSystem'])
>>> generate_coupling_graph(disciplines)

See also

generate_n2_plot

gemseo.api.generate_n2_plot(disciplines, file_path='n2.pdf', show_data_names=True, save=True, show=False, fig_size=(15.0, 10.0), open_browser=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. show_data_names: 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 “n2.pdf”.

  • 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 (tuple[float, float]) –

    The width and height of the static N2 chart.

    By default it is set to (15.0, 10.0).

  • open_browser (bool) –

    Whether to display the interactive N2 chart in a browser.

    By default it is set to False.

  • show_data_names (bool) –

    By default it is set to True.

Return type:

None

Examples

>>> from gemseo.api import create_discipline, generate_n2_plot
>>> disciplines = create_discipline(['Sellar1', 'Sellar2', 'SellarSystem'])
>>> generate_n2_plot(disciplines)
gemseo.api.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.api.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.api import get_algorithm_options_schema
>>> schema = get_algorithm_options_schema('NLOPT_SLSQP', pretty_print=True)
gemseo.api.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.api import get_available_caches
>>> get_available_caches()
['AbstractFullCache', 'HDF5Cache', 'MemoryFullCache', 'SimpleCache']
gemseo.api.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.api 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']
gemseo.api.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.api import get_available_doe_algorithms
>>> get_available_doe_algorithms()
gemseo.api.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.api import get_available_formulations
>>> get_available_formulations()
gemseo.api.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.api import get_available_mdas
>>> get_available_mdas()
gemseo.api.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.api import get_available_opt_algorithms
>>> get_available_opt_algorithms()
gemseo.api.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.api import get_available_post_processings
>>> print(get_available_post_processings())
['ScatterPlotMatrix', 'VariableInfluence', 'ConstraintsHistory',
'RadarChart', 'Robustness', 'Correlations', 'SOM', 'KMeans',
'ParallelCoordinates', 'GradientSensitivity', 'OptHistoryView',
'BasicHistory', 'ObjConstrHist', 'QuadApprox']
gemseo.api.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.api import get_available_scenario_types
>>> get_available_scenario_types()
gemseo.api.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.api import get_available_surrogates
>>> print(get_available_surrogates())
['RBFRegressor', 'GaussianProcessRegressor', 'LinearRegressor', 'PCERegressor']
gemseo.api.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.api import create_discipline, get_discipline_inputs_schema
>>> discipline = create_discipline('Sellar1')
>>> schema = get_discipline_inputs_schema(discipline, pretty_print=True)
gemseo.api.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.api import get_discipline_options_defaults
>>> get_discipline_options_defaults('Sellar1')
gemseo.api.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.api import get_discipline_options_schema
>>> schema = get_discipline_options_schema('Sellar1', pretty_print=True)
gemseo.api.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.api import get_discipline_outputs_schema, create_discipline
>>> discipline = create_discipline('Sellar1')
>>> get_discipline_outputs_schema(discipline, pretty_print=True)
gemseo.api.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.api import get_formulation_options_schema
>>> schema = get_formulation_options_schema('MDF', pretty_print=True)
gemseo.api.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.

Return type:

str | dict[str, Any]

Examples

>>> from gemseo.api import get_formulation_sub_options_schema
>>> schema = get_formulation_sub_options_schema('MDF',
>>>                                             main_mda_name='MDAJacobi',
>>>                                             pretty_print=True)
gemseo.api.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.api import get_formulations_options_defaults
>>> get_formulations_options_defaults('MDF')
{'main_mda_name': 'MDAChain',
 'maximize_objective': False,
 'inner_mda_name': 'MDAJacobi'}
gemseo.api.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.api import get_formulations_sub_options_defaults
>>> get_formulations_sub_options_defaults('MDF',
>>>                                       main_mda_name='MDAJacobi')
gemseo.api.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.api import get_mda_options_schema
>>> get_mda_options_schema('MDAJacobi')
gemseo.api.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.api import get_post_processing_options_schema
>>> schema = get_post_processing_options_schema('OptHistoryView',
>>>                                             pretty_print=True)
gemseo.api.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.

Examples

>>> from gemseo.api import get_scenario_differentiation_modes
>>> get_scenario_differentiation_modes()
gemseo.api.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.api 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)
gemseo.api.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.api import get_scenario_options_schema
>>> get_scenario_options_schema('MDO')
gemseo.api.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.api import get_surrogate_options_schema
>>> tmp = get_surrogate_options_schema('LinRegSurrogateDiscipline',
>>>                                    pretty_print=True)
gemseo.api.import_discipline(file_path, cls=None)[source]

Import a discipline from a pickle file.

Parameters:
Returns:

The discipline.

Return type:

MDODiscipline

gemseo.api.load_dataset(dataset, **options)[source]

Instantiate a dataset.

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

Parameters:
  • dataset (str) – The name of the dataset (its class name).

  • **options (Any) – The options for creating the dataset.

Returns:

The dataset.

Return type:

Dataset

See also

create_dataset

gemseo.api.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

gemseo.api.print_configuration()[source]

Print the current configuration.

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

Examples

>>> from gemseo.api import print_configuration
>>> print_configuration()
Return type:

None

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

Read a design space from a file.

Parameters:
  • file_path (str | Path) – The path to the text file; it shall contain comma-separated values with a row for each variable and at least the bounds of the variable.

  • header (str | None) – The names of the fields saved in the file. If None, read them in the file.

Returns:

The design space.

Return type:

DesignSpace

Examples

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

Examples using create_dataset

MSE example - test-train split

MSE example - test-train split

MSE example - test-train split
Parametric estimation of statistics

Parametric estimation of statistics

Parametric estimation of statistics

Examples using create_design_space

Pareto front on Binh and Korn problem using a BiLevel formulation

Pareto front on Binh and Korn problem using a BiLevel formulation

Pareto front on Binh and Korn problem using a BiLevel formulation
Diagonal design of experiments

Diagonal design of experiments

Diagonal design of experiments
Create a surrogate discipline

Create a surrogate discipline

Create a surrogate discipline
Create a DOE Scenario

Create a DOE Scenario

Create a DOE Scenario
Create an MDO Scenario

Create an MDO Scenario

Create an MDO Scenario
Multistart optimization

Multistart optimization

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

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

Application: Sobieski's Super-Sonic Business Jet (MDO)
|g| in 10 minutes

GEMSEO in 10 minutes

|g| in 10 minutes
API

API

API
GP regression

GP regression

GP regression
Linear regression

Linear regression

Linear regression
PCE regression

PCE regression

PCE regression
Polynomial regression

Polynomial regression

Polynomial regression
RBF regression

RBF regression

RBF regression
Random forest regression

Random forest regression

Random forest regression
Save and Load

Save and Load

Save and Load
Use a design of experiments from a file

Use a design of experiments from a file

Use a design of experiments from a file
Use a design of experiments from an array

Use a design of experiments from an array

Use a design of experiments from an array
Design space

Design space

Design space
Scenario

Scenario

Scenario
DesignSpace creation and manipulation

DesignSpace creation and manipulation

DesignSpace creation and manipulation

Examples using create_discipline

Basic history

Basic history

Basic history
Constraints history

Constraints history

Constraints history
Correlations

Correlations

Correlations
Gantt Chart

Gantt Chart

Gantt Chart
Gradient Sensitivity

Gradient Sensitivity

Gradient Sensitivity
Objective and constraints history

Objective and constraints history

Objective and constraints history
Optimization History View

Optimization History View

Optimization History View
Parallel coordinates

Parallel coordinates

Parallel coordinates
Pareto front

Pareto front

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

Pareto front on Binh and Korn problem using a BiLevel formulation

Pareto front on Binh and Korn problem using a BiLevel formulation
Quadratic approximations

Quadratic approximations

Quadratic approximations
Radar chart

Radar chart

Radar chart
Robustness

Robustness

Robustness
Scatter plot matrix

Scatter plot matrix

Scatter plot matrix
Self-Organizing Map

Self-Organizing Map

Self-Organizing Map
Variables influence

Variables influence

Variables influence
Diagonal design of experiments

Diagonal design of experiments

Diagonal design of experiments
Scalable diagonal discipline

Scalable diagonal discipline

Scalable diagonal discipline
Scalable problem

Scalable problem

Scalable problem
Scalable study

Scalable study

Scalable study
Create a surrogate discipline

Create a surrogate discipline

Create a surrogate discipline
Plug a surrogate discipline in a Scenario

Plug a surrogate discipline in a Scenario

Plug a surrogate discipline in a Scenario
Create a DOE Scenario

Create a DOE Scenario

Create a DOE Scenario
Create an MDO Scenario

Create an MDO Scenario

Create an MDO Scenario
Store observables

Store observables

Store observables
BiLevel-based DOE on the Sobieski SSBJ test case

BiLevel-based DOE on the Sobieski SSBJ test case

BiLevel-based DOE on the Sobieski SSBJ test case
BiLevel-based MDO on the Sobieski SSBJ test case

BiLevel-based MDO on the Sobieski SSBJ test case

BiLevel-based MDO on the Sobieski SSBJ test case
IDF-based MDO on the Sobieski SSBJ test case

IDF-based MDO on the Sobieski SSBJ test case

IDF-based MDO on the Sobieski SSBJ test case
MDF-based DOE on the Sobieski SSBJ test case

MDF-based DOE on the Sobieski SSBJ test case

MDF-based DOE on the Sobieski SSBJ test case
MDF-based MDO on the Sobieski SSBJ test case

MDF-based MDO on the Sobieski SSBJ test case

MDF-based MDO on the Sobieski SSBJ test case
Multistart optimization

Multistart optimization

Multistart optimization
Dataset from an optimization problem

Dataset from an optimization problem

Dataset from an optimization problem
Application: Sobieski's Super-Sonic Business Jet (MDO)

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

Application: Sobieski's Super-Sonic Business Jet (MDO)
MDO formulations for a toy example in aerostructure

MDO formulations for a toy example in aerostructure

MDO formulations for a toy example in aerostructure
|g| in 10 minutes

GEMSEO in 10 minutes

|g| in 10 minutes
Gauss-Seidel MDA

Gauss-Seidel MDA

Gauss-Seidel MDA
Hybrid Jacobi/Newton MDA

Hybrid Jacobi/Newton MDA

Hybrid Jacobi/Newton MDA
Jacobi MDA

Jacobi MDA

Jacobi MDA
MDAChain

MDAChain

MDAChain
Newton-Raphson MDA

Newton-Raphson MDA

Newton-Raphson MDA
Quasi-Newton MDA

Quasi-Newton MDA

Quasi-Newton MDA
API

API

API
GP regression

GP regression

GP regression
Linear regression

Linear regression

Linear regression
PCE regression

PCE regression

PCE regression
Polynomial regression

Polynomial regression

Polynomial regression
RBF regression

RBF regression

RBF regression
Random forest regression

Random forest regression

Random forest regression
Save and Load

Save and Load

Save and Load
Simple disciplinary DOE example on the Sobieski SSBJ test case

Simple disciplinary DOE example on the Sobieski SSBJ test case

Simple disciplinary DOE example on the Sobieski SSBJ test case
Use a design of experiments from a file

Use a design of experiments from a file

Use a design of experiments from a file
Use a design of experiments from an array

Use a design of experiments from an array

Use a design of experiments from an array
Discipline

Discipline

Discipline
MDA

MDA

MDA
Post-processing

Post-processing

Post-processing
Scenario

Scenario

Scenario
Create a discipline from a Python function

Create a discipline from a Python function

Create a discipline from a Python function
Create a discipline from analytical expressions

Create a discipline from analytical expressions

Create a discipline from analytical expressions
Parameter space

Parameter space

Parameter space
Parameter space

Parameter space

Parameter space
Empirical estimation of statistics

Empirical estimation of statistics

Empirical estimation of statistics

Examples using create_mda

Gauss-Seidel MDA

Gauss-Seidel MDA

Gauss-Seidel MDA
Hybrid Jacobi/Newton MDA

Hybrid Jacobi/Newton MDA

Hybrid Jacobi/Newton MDA
Jacobi MDA

Jacobi MDA

Jacobi MDA
MDAChain

MDAChain

MDAChain
Newton-Raphson MDA

Newton-Raphson MDA

Newton-Raphson MDA
Quasi-Newton MDA

Quasi-Newton MDA

Quasi-Newton MDA
MDA

MDA

MDA

Examples using create_parameter_space

PCE regression

PCE regression

PCE regression

Examples using create_scalable

Scalable diagonal discipline

Scalable diagonal discipline

Scalable diagonal discipline

Examples using create_scenario

Basic history

Basic history

Basic history
Constraints history

Constraints history

Constraints history
Correlations

Correlations

Correlations
Gantt Chart

Gantt Chart

Gantt Chart
Gradient Sensitivity

Gradient Sensitivity

Gradient Sensitivity
Objective and constraints history

Objective and constraints history

Objective and constraints history
Optimization History View

Optimization History View

Optimization History View
Parallel coordinates

Parallel coordinates

Parallel coordinates
Pareto front

Pareto front

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

Pareto front on Binh and Korn problem using a BiLevel formulation

Pareto front on Binh and Korn problem using a BiLevel formulation
Quadratic approximations

Quadratic approximations

Quadratic approximations
Radar chart

Radar chart

Radar chart
Robustness

Robustness

Robustness
Scatter plot matrix

Scatter plot matrix

Scatter plot matrix
Self-Organizing Map

Self-Organizing Map

Self-Organizing Map
Variables influence

Variables influence

Variables influence
Solve a 2D L-shape topology optimization problem

Solve a 2D L-shape topology optimization problem

Solve a 2D L-shape topology optimization problem
Solve a 2D MBB topology optimization problem

Solve a 2D MBB topology optimization problem

Solve a 2D MBB topology optimization problem
Solve a 2D short cantilever topology optimization problem

Solve a 2D short cantilever topology optimization problem

Solve a 2D short cantilever topology optimization problem
Diagonal design of experiments

Diagonal design of experiments

Diagonal design of experiments
Parametric scalable MDO problem - MDF

Parametric scalable MDO problem - MDF

Parametric scalable MDO problem - MDF
Scalable diagonal discipline

Scalable diagonal discipline

Scalable diagonal discipline
Scalable problem

Scalable problem

Scalable problem
Scalable study

Scalable study

Scalable study
Create a surrogate discipline

Create a surrogate discipline

Create a surrogate discipline
Plug a surrogate discipline in a Scenario

Plug a surrogate discipline in a Scenario

Plug a surrogate discipline in a Scenario
Create a DOE Scenario

Create a DOE Scenario

Create a DOE Scenario
Create an MDO Scenario

Create an MDO Scenario

Create an MDO Scenario
Store observables

Store observables

Store observables
BiLevel-based DOE on the Sobieski SSBJ test case

BiLevel-based DOE on the Sobieski SSBJ test case

BiLevel-based DOE on the Sobieski SSBJ test case
BiLevel-based MDO on the Sobieski SSBJ test case

BiLevel-based MDO on the Sobieski SSBJ test case

BiLevel-based MDO on the Sobieski SSBJ test case
IDF-based MDO on the Sobieski SSBJ test case

IDF-based MDO on the Sobieski SSBJ test case

IDF-based MDO on the Sobieski SSBJ test case
MDF-based DOE on the Sobieski SSBJ test case

MDF-based DOE on the Sobieski SSBJ test case

MDF-based DOE on the Sobieski SSBJ test case
MDF-based MDO on the Sobieski SSBJ test case

MDF-based MDO on the Sobieski SSBJ test case

MDF-based MDO on the Sobieski SSBJ test case
Multistart optimization

Multistart optimization

Multistart optimization
Dataset from an optimization problem

Dataset from an optimization problem

Dataset from an optimization problem
A from scratch example on the Sellar problem

A from scratch example on the Sellar problem

A from scratch example on the Sellar problem
Application: Sobieski's Super-Sonic Business Jet (MDO)

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

Application: Sobieski's Super-Sonic Business Jet (MDO)
MDO formulations for a toy example in aerostructure

MDO formulations for a toy example in aerostructure

MDO formulations for a toy example in aerostructure
|g| in 10 minutes

GEMSEO in 10 minutes

|g| in 10 minutes
API

API

API
GP regression

GP regression

GP regression
Linear regression

Linear regression

Linear regression
PCE regression

PCE regression

PCE regression
Polynomial regression

Polynomial regression

Polynomial regression
RBF regression

RBF regression

RBF regression
Random forest regression

Random forest regression

Random forest regression
Save and Load

Save and Load

Save and Load
Simple disciplinary DOE example on the Sobieski SSBJ test case

Simple disciplinary DOE example on the Sobieski SSBJ test case

Simple disciplinary DOE example on the Sobieski SSBJ test case
Use a design of experiments from a file

Use a design of experiments from a file

Use a design of experiments from a file
Use a design of experiments from an array

Use a design of experiments from an array

Use a design of experiments from an array
Post-processing

Post-processing

Post-processing
Scenario

Scenario

Scenario
Parameter space

Parameter space

Parameter space
Parameter space

Parameter space

Parameter space
Empirical estimation of statistics

Empirical estimation of statistics

Empirical estimation of statistics

Examples using create_surrogate

Create a surrogate discipline

Create a surrogate discipline

Create a surrogate discipline
Plug a surrogate discipline in a Scenario

Plug a surrogate discipline in a Scenario

Plug a surrogate discipline in a Scenario

Examples using execute_post

BiLevel-based MDO on the Sobieski SSBJ test case

BiLevel-based MDO on the Sobieski SSBJ test case

BiLevel-based MDO on the Sobieski SSBJ test case
Analytical test case # 2

Analytical test case # 2

Analytical test case # 2
Analytical test case # 3

Analytical test case # 3

Analytical test case # 3
Post-processing

Post-processing

Post-processing

Examples using export_design_space

Design space

Design space

Design space
DesignSpace import and export from disk

DesignSpace import and export from disk

DesignSpace import and export from disk

Examples using generate_coupling_graph

Discipline

Discipline

Discipline

Examples using generate_n2_plot

Parametric scalable MDO problem - MDF

Parametric scalable MDO problem - MDF

Parametric scalable MDO problem - MDF
Scalable problem of Tedford and Martins, 2010

Scalable problem of Tedford and Martins, 2010

Scalable problem of Tedford and Martins, 2010
IDF-based MDO on the Sobieski SSBJ test case

IDF-based MDO on the Sobieski SSBJ test case

IDF-based MDO on the Sobieski SSBJ test case
MDF-based DOE on the Sobieski SSBJ test case

MDF-based DOE on the Sobieski SSBJ test case

MDF-based DOE on the Sobieski SSBJ test case
MDF-based MDO on the Sobieski SSBJ test case

MDF-based MDO on the Sobieski SSBJ test case

MDF-based MDO on the Sobieski SSBJ test case
Multidisciplinary coupling graph

Multidisciplinary coupling graph

Multidisciplinary coupling graph
MDO formulations for a toy example in aerostructure

MDO formulations for a toy example in aerostructure

MDO formulations for a toy example in aerostructure
|g| in 10 minutes

GEMSEO in 10 minutes

|g| in 10 minutes
Discipline

Discipline

Discipline

Examples using get_algorithm_options_schema

MDF-based MDO on the Sobieski SSBJ test case

MDF-based MDO on the Sobieski SSBJ test case

MDF-based MDO on the Sobieski SSBJ test case
DOE algorithms

DOE algorithms

DOE algorithms
Optimization algorithms

Optimization algorithms

Optimization algorithms

Examples using get_available_disciplines

Discipline

Discipline

Discipline

Examples using get_available_doe_algorithms

Plug a surrogate discipline in a Scenario

Plug a surrogate discipline in a Scenario

Plug a surrogate discipline in a Scenario
Create a DOE Scenario

Create a DOE Scenario

Create a DOE Scenario
DOE algorithms

DOE algorithms

DOE algorithms

Examples using get_available_formulations

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

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

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

Formulation

Formulation

Examples using get_available_mdas

MDA

MDA

MDA

Examples using get_available_opt_algorithms

Create an MDO Scenario

Create an MDO Scenario

Create an MDO Scenario
Optimization algorithms

Optimization algorithms

Optimization algorithms

Examples using get_available_post_processings

Create a DOE Scenario

Create a DOE Scenario

Create a DOE Scenario
Create an MDO Scenario

Create an MDO Scenario

Create an MDO Scenario
Post-processing

Post-processing

Post-processing

Examples using get_available_scenario_types

Scenario

Scenario

Scenario

Examples using get_discipline_inputs_schema

Discipline

Discipline

Discipline

Examples using get_discipline_options_defaults

Discipline

Discipline

Discipline

Examples using get_discipline_options_schema

Discipline

Discipline

Discipline

Examples using get_discipline_outputs_schema

Discipline

Discipline

Discipline

Examples using get_formulation_options_schema

Formulation

Formulation

Formulation

Examples using get_formulation_sub_options_schema

Formulation

Formulation

Formulation

Examples using get_formulations_options_defaults

Formulation

Formulation

Formulation

Examples using get_formulations_sub_options_defaults

Formulation

Formulation

Formulation

Examples using get_mda_options_schema

MDA

MDA

MDA

Examples using get_post_processing_options_schema

Basic history

Basic history

Basic history
Constraints history

Constraints history

Constraints history
Correlations

Correlations

Correlations
Objective and constraints history

Objective and constraints history

Objective and constraints history
Optimization History View

Optimization History View

Optimization History View
Parallel coordinates

Parallel coordinates

Parallel coordinates
Pareto front

Pareto front

Pareto front
Quadratic approximations

Quadratic approximations

Quadratic approximations
Radar chart

Radar chart

Radar chart
Robustness

Robustness

Robustness
Scatter plot matrix

Scatter plot matrix

Scatter plot matrix
Self-Organizing Map

Self-Organizing Map

Self-Organizing Map
Variables influence

Variables influence

Variables influence
Post-processing

Post-processing

Post-processing

Examples using get_scenario_differentiation_modes

Scenario

Scenario

Scenario

Examples using get_scenario_inputs_schema

Scenario

Scenario

Scenario

Examples using get_scenario_options_schema

Scenario

Scenario

Scenario

Examples using load_dataset

Burgers dataset

Burgers dataset

Burgers dataset
Iris dataset

Iris dataset

Iris dataset
Plot - Andrews curves

Plot - Andrews curves

Plot - Andrews curves
Plot - Parallel coordinates

Plot - Parallel coordinates

Plot - Parallel coordinates
Plot - Scatter matrix

Plot - Scatter matrix

Plot - Scatter matrix
Plot - ZvsXY

Plot - ZvsXY

Plot - ZvsXY
Rosenbrock dataset

Rosenbrock dataset

Rosenbrock dataset
Classification API

Classification API

Classification API
K nearest neighbors classification

K nearest neighbors classification

K nearest neighbors classification
Random forest classification

Random forest classification

Random forest classification
API

API

API
Gaussian Mixtures

Gaussian Mixtures

Gaussian Mixtures
K-means

K-means

K-means
Mixture of experts with PCA on Burgers dataset

Mixture of experts with PCA on Burgers dataset

Mixture of experts with PCA on Burgers dataset
Quality measure for surrogate model comparison

Quality measure for surrogate model comparison

Quality measure for surrogate model comparison
Advanced mixture of experts

Advanced mixture of experts

Advanced mixture of experts
Mixture of experts

Mixture of experts

Mixture of experts

Examples using monitor_scenario

Scenario

Scenario

Scenario

Examples using read_design_space

Design space

Design space

Design space
DesignSpace import and export from disk

DesignSpace import and export from disk

DesignSpace import and export from disk