Disciplines

Warning

Some capabilities may require the installation of GEMSEO with all its features and some others may depend on plugins.

Note

All the features of the wrapped libraries may not be exposed through GEMSEO.

Aerodynamics

Module: gemseo.problems.mdo.aerostructure.aerostructure

AnalyticDiscipline

Module: gemseo.disciplines.analytic

Required parameters

  • expressions : Mapping[str, str | Expr]

    The outputs expressed as functions of the inputs.

Optional parameters

  • fast_evaluation : bool, optional

    Whether to apply sympy.lambdify to the expressions in order to accelerate their numerical evaluation; otherwise the expressions are evaluated with sympy.Expr.evalf.

    By default it is set to True.

  • grammar_type : MDODiscipline.GrammarType, optional

    The type of the input and output grammars.

    By default it is set to JSONGrammar.

  • name : str | None, optional

    The name of the discipline. If None, use the class name.

    By default it is set to None.

AutoPyDiscipline

Module: gemseo.disciplines.auto_py

Required parameters

  • py_func : Callable

    The Python function to compute the outputs from the inputs.

Optional parameters

  • grammar_type : MDODiscipline.GrammarType, optional

    The type of the input and output grammars.

    By default it is set to JSONGrammar.

  • name : str | None, optional

    The name of the discipline. If None, use the name of the Python function.

    By default it is set to None.

  • py_jac : Callable | None, optional

    The Python function to compute the Jacobian from the inputs; its output value must be a 2D NumPy array with rows corresponding to the outputs and columns to the inputs.

    By default it is set to None.

  • use_arrays : bool, optional

    Whether the function is expected to take arrays as inputs and give outputs as arrays.

    By default it is set to False.

BaseDiscipline

Module: gemseo.problems.mdo.scalable.parametric.disciplines.base_discipline

Required parameters

  • core_discipline_parameters : Any

    The parameters to instantiate the core discipline as CoreDiscipline(*core_discipline_parameters).

Optional parameters

  • **default_input_values : Any

    The default values of the input variables.

CalibrationScenario

Note

The plugin gemseo_calibration is required.

Module: gemseo_calibration.scenario

Required parameters

  • calibration_space : DesignSpace

    The space of the parameters to be calibrated, whose current values are consider as a prior for calibration.

  • control_outputs : CalibrationMeasure | Sequence[CalibrationMeasure]

    The names of the outputs used to calibrate the disciplines with the name of the calibration measure and the corresponding weight comprised between 0 and 1 (the weights must sum to 1). When the output is a 1D function discretized over an irregular mesh, the name of the mesh can be provided. E.g. CalibrationMeasure(output="z", measure="MSE") CalibrationMeasure(output="z", measure="MSE", weight=0.3) or CalibrationMeasure(output="z", measure="MSE", mesh="z_mesh") Lastly, CalibrationMeasure can be imported from gemseo-calibration.scenario.

  • disciplines : MDODiscipline | list[MDODiscipline]

    The disciplines whose parameters must be calibrated from the reference data.

  • input_names : str | Iterable[str]

    The names of the inputs to be considered for the calibration.

  • name : str

    A name for this calibration scenario. If empty, use the name of the class.

Optional parameters

  • formulation : str, optional

    The name of a formulation to manage the multidisciplinary coupling.

    By default it is set to MDF.

  • **formulation_options : Any

    The options of the formulation.

Calibrator

Note

The plugin gemseo_calibration is required.

Module: gemseo_calibration.calibrator

Required parameters

  • control_outputs : CalibrationMeasure | Sequence[CalibrationMeasure]

    The names of the outputs used to calibrate the disciplines with the name of the calibration measure and the corresponding weight comprised between 0 and 1 (the weights must sum to 1). When the output is a 1D function discretized over an irregular mesh, the name of the mesh can be provided. E.g. CalibrationMeasure(output="z", measure="MSE") CalibrationMeasure(output="z", measure="MSE", weight=0.3) or CalibrationMeasure(output="z", measure="MSE", mesh="z_mesh") Lastly, CalibrationMeasure can be imported from gemseo-calibration.scenario.

  • disciplines : MDODiscipline | list[MDODiscipline]

    The disciplines whose parameters must be calibrated from the reference data.

  • input_names : str | Iterable[str]

    The names of the inputs to be considered for the calibration.

  • parameter_names : str | Iterable[str]

    The names of the parameters to be calibrated.

Optional parameters

  • formulation : str, optional

    The name of a formulation to manage the multidisciplinary coupling.

    By default it is set to MDF.

  • **formulation_options : Any

    The options of the formulation.

Concatenater

Module: gemseo.disciplines.concatenater

Required parameters

  • input_variables : Sequence[str]

    The input variables to concatenate.

  • output_variable : str

    The output variable name.

Optional parameters

  • input_coefficients : dict[str, float] | None, optional

    The coefficients related to the different input variables.

    By default it is set to None.

ConstraintAggregation

Module: gemseo.disciplines.constraint_aggregation

Required parameters

  • aggregation_function : EvaluationFunction

    The aggregation function or its name, e.g. IKS, lower_bound_KS,upper_bound_KS, POS_SUM and SUM.

  • constraint_names : Sequence[str]

    The names of the constraints to aggregate, which must be discipline outputs.

Optional parameters

  • name : str | None, optional

    The name of the discipline.

    By default it is set to None.

  • **options : Any

    The options for the aggregation method.

DOEScenario

Module: gemseo.core.doe_scenario

Required parameters

  • design_space : DesignSpace

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

  • disciplines : Sequence[MDODiscipline]

    The disciplines used to compute the objective, constraints and observables from the design variables.

  • formulation : str

    The class name of the MDOFormulation, e.g. "MDF", "IDF" or "BiLevel".

  • objective_name : str | Sequence[str]

    The name(s) of the discipline output(s) used as objective. If multiple names are passed, the objective will be a vector.

Optional parameters

  • grammar_type : MDODiscipline.GrammarType, optional

    The grammar for the scenario and the MDO formulation.

    By default it is set to JSONGrammar.

  • maximize_objective : bool, optional

    Whether to maximize the objective.

    By default it is set to False.

  • name : str | None, optional

    The name to be given to this scenario. If None, use the name of the class.

    By default it is set to None.

  • **formulation_options : Any

    The options of the MDOFormulation.

DensityFilter

Module: gemseo.problems.topology_optimization.density_filter_disc

Optional parameters

  • min_member_size : float, optional

    The minimum structural member size.

    By default it is set to 1.5.

  • n_x : int, optional

    The number of elements in the x-direction.

    By default it is set to 100.

  • n_y : int, optional

    The number of elements in the y-direction.

    By default it is set to 100.

  • name : str | None, optional

    The name of the discipline. If None, use the class name.

    By default it is set to None.

DiscFromExe

Module: gemseo.wrappers.disc_from_exe

Required parameters

  • executable_command : str

    The command to run the executable. E.g. python my_script.py -i input.txt -o output.txt

  • input_filename : str | Path

    The name of the input file to be generated in the output folder. E.g. "input.txt".

  • input_template : str | Path

    The path to the input template file. The input locations in the file are marked by GEMSEO_INPUT{input_name::1.0}, where input_name is the name of the input variable, and 1.0 is its default value.

  • output_filename : str | Path

    The name of the output file to be generated in the output folder. E.g. "output.txt".

  • output_folder_basepath : str | Path

    The base path of the execution directories.

  • output_template : str | Path

    The path to the output template file. The output locations in the file are marked by GEMSEO_OUTPUT{output_name::1.0}, where output_name is the name of the output variable, and 1.0 is its default value.

Optional parameters

  • clean_after_execution : bool, optional

    Whether to clean the last created directory after execution.

    By default it is set to False.

  • folders_iter : DirectoryNamingMethod, optional

    The method to create the execution directories.

    By default it is set to NUMBERED.

  • name : str | None, optional

    The name of the discipline. If None, use the class name.

    By default it is set to None.

  • parse_out_separator : str, optional

    The separator used for the KEY_VALUE output parser.

    By default it is set to =.

  • parse_outfile_method : Parser | OutputParser, optional

    The optional method that can be provided by the user to parse the output template file. If the KEY_VALUE is used as output parser, the user may specify the separator key.

    By default it is set to TEMPLATE.

  • use_shell : bool, optional

    This argument is ignored and will be removed, the shell is not used.

    By default it is set to True.

  • write_input_file_method : InputWriter | None, optional

    The method to write the input data file. If None, use write_input_file().

    By default it is set to None.

FilteringDiscipline

Module: gemseo.wrappers.filtering_discipline

Required parameters

  • discipline : MDODiscipline

    The original discipline.

Optional parameters

  • input_names : Iterable[str] | None, optional

    The names of the inputs of interest. If None, use all the inputs.

    By default it is set to None.

  • keep_in : bool, optional

    Whether to the inputs of interest. Otherwise, remove them.

    By default it is set to True.

  • keep_out : bool, optional

    Whether to the outputs of interest. Otherwise, remove them.

    By default it is set to True.

  • output_names : Iterable[str] | None, optional

    The names of the outputs of interest. If None, use all the outputs.

    By default it is set to None.

FininiteElementAnalysis

Module: gemseo.problems.topology_optimization.fea_disc

Optional parameters

  • f_amplitude : int | Sequence[int], optional

    The force amplitude for each pair (f_node, f_direction).

    By default it is set to -1.

  • f_direction : int | Sequence[int], optional

    The force direction for each f_node, either 0 for x or 1 for y.

    By default it is set to 1.

  • f_node : int | Sequence[int], optional

    The indices of the nodes where the forces are applied.

    By default it is set to 10200.

  • fixed_dir : int | Sequence[int] | None, optional

    The clamped direction for each node, encode 0 for x and 1 for y. If None, a default value is used.

    By default it is set to None.

  • fixed_nodes : int | Sequence[int] | None, optional

    The indices of the nodes where the structure is clamped. If None, a default value is used.

    By default it is set to None.

  • n_x : int, optional

    The number of elements in the x-direction.

    By default it is set to 100.

  • n_y : int, optional

    The number of elements in the y-direction.

    By default it is set to 100.

  • name : str | None, optional

    The name of the discipline. If None, use the class name.

    By default it is set to None.

  • nu : float, optional

    The material Poisson’s ratio.

    By default it is set to 0.3.

IshigamiDiscipline

Module: gemseo.problems.uncertainty.ishigami.ishigami_discipline

JEPJavaDiscipline

Note

The plugin gemseo_java is required.

Module: gemseo_java.jep_java_discipline

JavaDiscipline

Note

The plugin gemseo_java is required.

Module: gemseo_java.jnius_java_discipline

JobSchedulerDisciplineWrapper

Module: gemseo.wrappers.job_schedulers.scheduler_wrapped_disc

Required parameters

  • discipline : MDODiscipline

    The discipline to wrap in the job scheduler.

  • setup_cmd : str

    The command used before running the executable.

  • workdir_path : Path

    The path to the workdir where the files will be generated.

Optional parameters

  • job_out_filename : str, optional

    The output job file name.

    By default it is set to batch.srun.

  • job_template_path : Path | str | None, optional

    The path to the template to be used to make a submission to the job scheduler command.

    By default it is set to None.

  • scheduler_run_command : str, optional

    The command to call the job scheduler and submit the generated script.

    By default it is set to sbatch –wait.

  • use_template : bool, optional

    whether to use template based interface to the job scheduler.

    By default it is set to True.

LSF

Module: gemseo.wrappers.job_schedulers.lsf

Required parameters

  • discipline : MDODiscipline

    The discipline to wrap in the job scheduler.

  • setup_cmd : str

    The command used before running the executable.

  • user_email : str

    The user email to send the run status.

  • workdir_path : Path

    The path to the workdir where the files will be generated.

Optional parameters

  • job_out_filename : str, optional

    The output job file name.

    By default it is set to batch.sh.

  • job_template_path : Path | str | None, optional

    The path to the template to be used to make a submission to the job scheduler command.

    By default it is set to None.

  • mem_per_cpu : str, optional

    The memory per CPU.

    By default it is set to 1G.

  • ntasks : int, optional

    The number of tasks.

    By default it is set to 1.

  • ntasks_per_node : int, optional

    The number of tasks per node.

    By default it is set to 1.

  • scheduler_run_command : str, optional

    The command to call the job scheduler and submit the generated script.

    By default it is set to bsub -K.

  • wall_time : str, optional

    The wall time.

    By default it is set to 24:00:00.

  • **options : dict[str:Any]

    The job scheduler specific options to be used in the template.

LinearCombination

Module: gemseo.disciplines.linear_combination

Required parameters

  • input_names : Iterable[str]

    The names of input variables.

  • output_name : str

    The name of the output variable.

Optional parameters

  • input_coefficients : dict[str, float] | None, optional

    The coefficients related to the input variables. If None, use 1 for all the input variables.

    By default it is set to None.

  • input_size : int | None, optional

    The size of the inputs. If None, the default inputs are initialized with size 1 arrays.

    By default it is set to None.

  • offset : float, optional

    The output value when all the input variables are equal to zero.

    By default it is set to 0.0.

LinearDiscipline

Module: gemseo.problems.mdo.scalable.linear.linear_discipline

Required parameters

  • input_names : Sequence[str]

    The input data names.

  • name : str

    The discipline name.

  • output_names : Sequence[str]

    The output data names.

Optional parameters

  • grammar_type : MDODiscipline.GrammarType, optional

    The type of grammars.

    By default it is set to JSONGrammar.

  • inputs_size : int, optional

    The size of input data vectors, each input data is of shape (inputs_size,).

    By default it is set to 1.

  • matrix_density : float, optional

    The percentage of non-zero elements when the matrix is sparse.

    By default it is set to 0.1.

  • matrix_format : MatrixFormat, optional

    The format of the Jacobian matrix.

    By default it is set to dense.

  • matrix_free_jacobian : bool, optional

    Whether the Jacobians are casted as linear operators.

    By default it is set to False.

  • outputs_size : int, optional

    The size of output data vectors, each output data is of shape (outputs_size,).

    By default it is set to 1.

MDOAdditiveChain

Module: gemseo.core.chain

Required parameters

  • disciplines : Sequence[MDODiscipline]

    The disciplines.

  • outputs_to_sum : Iterable[str]

    The names of the outputs to sum.

Optional parameters

  • grammar_type : MDODiscipline.GrammarType, optional

    The type of the input and output grammars.

    By default it is set to JSONGrammar.

  • n_processes : int | None, optional

    The maximum simultaneous number of threads, if use_threading is True, or processes otherwise, used to parallelize the execution. If None, uses the number of disciplines.

    By default it is set to None.

  • name : str | None, optional

    The name of the discipline. If None, use the class name.

    By default it is set to None.

  • use_threading : bool, optional

    Whether to use threads instead of processes to parallelize the execution; multiprocessing will copy (serialize) all the disciplines, while threading will share all the memory. This is important to note if you want to execute the same discipline multiple times, you shall use multiprocessing.

    By default it is set to True.

MDOChain

Module: gemseo.core.chain

Required parameters

  • disciplines : Sequence[MDODiscipline]

    The disciplines.

Optional parameters

  • grammar_type : MDODiscipline.GrammarType, optional

    The type of the input and output grammars.

    By default it is set to JSONGrammar.

  • name : str | None, optional

    The name of the discipline. If None, use the class name.

    By default it is set to None.

MDODiscipline

Module: gemseo.core.discipline

Optional parameters

  • auto_detect_grammar_files : bool, optional

    Whether to look for "ClassName_{input,output}.json" in the GRAMMAR_DIRECTORY if any or in the directory of the discipline class module when {input,output}_grammar_file is None.

    By default it is set to False.

  • cache_file_path : str | Path | None, optional

    The HDF file path when grammar_type is MDODiscipline.CacheType.HDF5.

    By default it is set to None.

  • cache_type : CacheType, optional

    The type of cache.

    By default it is set to SimpleCache.

  • grammar_type : GrammarType, optional

    The type of the input and output grammars.

    By default it is set to JSONGrammar.

  • input_grammar_file : str | Path | None, optional

    The input grammar file path. If None and auto_detect_grammar_files=True, look for "ClassName_input.json" in the GRAMMAR_DIRECTORY if any or in the directory of the discipline class module. If None and auto_detect_grammar_files=False, do not initialize the input grammar from a schema file.

    By default it is set to None.

  • name : str | None, optional

    The name of the discipline. If None, use the class name.

    By default it is set to None.

  • output_grammar_file : str | Path | None, optional

    The output grammar file path. If None and auto_detect_grammar_files=True, look for "ClassName_output.json" in the GRAMMAR_DIRECTORY if any or in the directory of the discipline class module. If None and auto_detect_grammar_files=False, do not initialize the output grammar from a schema file.

    By default it is set to None.

MDOObjectiveScenarioAdapter

Module: gemseo.disciplines.scenario_adapters.mdo_objective_scenario_adapter

Required parameters

  • input_names : Sequence[str]

    The inputs to overload at sub-scenario execution.

  • opt_history_file_prefix : str

    The base name for the databases to be exported. The full names of the databases are built from the provided base name suffixed by "_i.h5" where i is replaced by the execution number, i.e the number of stored databases. If empty, the databases are not exported. The databases can be exported only is keep_opt_history=True.

  • output_names : Sequence[str]

    The outputs to get from the sub-scenario execution.

  • scenario : Scenario

    The scenario to adapt.

Optional parameters

  • cache_type : MDODiscipline.CacheType, optional

    The type of cache policy.

    By default it is set to SimpleCache.

  • grammar_type : MDODiscipline.GrammarType, optional

    The type of the input and output grammars.

    By default it is set to JSONGrammar.

  • keep_opt_history : bool, optional

    Whether to keep databases copies after each execution.

    By default it is set to False.

  • name : str | None, optional

    The name of the scenario adapter. If None, use the name of the scenario adapter suffixed by "_adapter".

    By default it is set to None.

  • output_multipliers : bool, optional

    If True, the Lagrange multipliers of the scenario optimal solution are computed and added to the outputs.

    By default it is set to False.

  • reset_x0_before_opt : bool, optional

    If True, reset the initial guess before running the sub optimization.

    By default it is set to False.

  • scenario_log_level : int | None, optional

    The level of the root logger during the scenario execution. If None, do not change the level of the root logger.

    By default it is set to None.

  • set_bounds_before_opt : bool, optional

    If True, set the bounds of the design space. This is useful for trust regions.

    By default it is set to False.

  • set_x0_before_opt : bool, optional

    If True, set the initial guess of the sub-scenario. This is useful for multi-start optimization.

    By default it is set to False.

MDOParallelChain

Module: gemseo.core.chain

Required parameters

  • disciplines : Sequence[MDODiscipline]

    The disciplines.

Optional parameters

  • grammar_type : MDODiscipline.GrammarType, optional

    The type of the input and output grammars.

    By default it is set to JSONGrammar.

  • n_processes : int | None, optional

    The maximum simultaneous number of threads, if use_threading is True, or processes otherwise, used to parallelize the execution. If None, uses the number of disciplines.

    By default it is set to None.

  • name : str | None, optional

    The name of the discipline. If None, use the class name.

    By default it is set to None.

  • use_deep_copy : bool, optional

    Whether to deepcopy the discipline input data.

    By default it is set to False.

  • use_threading : bool, optional

    Whether to use threads instead of processes to parallelize the execution; multiprocessing will copy (serialize) all the disciplines, while threading will share all the memory. This is important to note if you want to execute the same discipline multiple times, you shall use multiprocessing.

    By default it is set to True.

MDOScenario

Module: gemseo.core.mdo_scenario

Required parameters

  • design_space : DesignSpace

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

  • disciplines : Sequence[MDODiscipline]

    The disciplines used to compute the objective, constraints and observables from the design variables.

  • formulation : str

    The class name of the MDOFormulation, e.g. "MDF", "IDF" or "BiLevel".

  • objective_name : str | Sequence[str]

    The name(s) of the discipline output(s) used as objective. If multiple names are passed, the objective will be a vector.

Optional parameters

  • grammar_type : MDODiscipline.GrammarType, optional

    The grammar for the scenario and the MDO formulation.

    By default it is set to JSONGrammar.

  • maximize_objective : bool, optional

    Whether to maximize the objective.

    By default it is set to False.

  • name : str | None, optional

    The name to be given to this scenario. If None, use the name of the class.

    By default it is set to None.

  • **formulation_options : Any

    The options of the MDOFormulation.

MDOScenarioAdapter

Module: gemseo.disciplines.scenario_adapters.mdo_scenario_adapter

Required parameters

  • input_names : Sequence[str]

    The inputs to overload at sub-scenario execution.

  • opt_history_file_prefix : str

    The base name for the databases to be exported. The full names of the databases are built from the provided base name suffixed by "_i.h5" where i is replaced by the execution number, i.e the number of stored databases. If empty, the databases are not exported. The databases can be exported only is keep_opt_history=True.

  • output_names : Sequence[str]

    The outputs to get from the sub-scenario execution.

  • scenario : Scenario

    The scenario to adapt.

Optional parameters

  • cache_type : MDODiscipline.CacheType, optional

    The type of cache policy.

    By default it is set to SimpleCache.

  • grammar_type : MDODiscipline.GrammarType, optional

    The type of the input and output grammars.

    By default it is set to JSONGrammar.

  • keep_opt_history : bool, optional

    Whether to keep databases copies after each execution.

    By default it is set to False.

  • name : str | None, optional

    The name of the scenario adapter. If None, use the name of the scenario adapter suffixed by "_adapter".

    By default it is set to None.

  • output_multipliers : bool, optional

    If True, the Lagrange multipliers of the scenario optimal solution are computed and added to the outputs.

    By default it is set to False.

  • reset_x0_before_opt : bool, optional

    If True, reset the initial guess before running the sub optimization.

    By default it is set to False.

  • scenario_log_level : int | None, optional

    The level of the root logger during the scenario execution. If None, do not change the level of the root logger.

    By default it is set to None.

  • set_bounds_before_opt : bool, optional

    If True, set the bounds of the design space. This is useful for trust regions.

    By default it is set to False.

  • set_x0_before_opt : bool, optional

    If True, set the initial guess of the sub-scenario. This is useful for multi-start optimization.

    By default it is set to False.

MDOWarmStartedChain

Module: gemseo.core.chain

Required parameters

  • disciplines : Sequence[MDODiscipline]

    The disciplines.

  • variable_names_to_warm_start : Sequence[str]

    The names of the variables to be warm started. These names must be outputs of the disciplines in the chain. If the list is empty, no variables are warm started.

Optional parameters

  • grammar_type : MDODiscipline.GrammarType, optional

    The type of the input and output grammars.

    By default it is set to JSONGrammar.

  • name : str | None, optional

    The name of the discipline. If None, use the class name.

    By default it is set to None.

MainDiscipline

Module: gemseo.problems.mdo.scalable.parametric.disciplines.main_discipline

Required parameters

  • t_i : NDArray[float]

    The threshold vectors \(t_1,\ldots,t_N\).

Optional parameters

  • **default_input_values : NDArray[float]

    The default values of the input variables.

MaterialModelInterpolation

Module: gemseo.problems.topology_optimization.material_model_interpolation_disc

Required parameters

  • e0 : float

    The full material Young modulus.

  • empty_elements : Sequence[int]

    The index of an empty element ids that are not part of the design space.

  • full_elements : Sequence[int]

    The index of full element ids that are not part of the design space.

  • n_x : int

    The number of elements in the x-direction.

  • n_y : int

    The number of elements in the y-direction.

  • penalty : float

    The SIMP penalty coefficient.

Optional parameters

  • contrast : float, optional

    The ratio between the full material Young’s modulus and void material Young’s modulus.

    By default it is set to 1000000000.0.

MatlabDiscipline

Note

The plugin gemseo_matlab is required.

Module: gemseo_matlab.matlab_discipline

Required parameters

  • matlab_fct : str | Path

    The path of the Matlab file or Name of the function.

Optional parameters

  • add_subfold_path : bool, optional

    Whether to add all sub-folder to matlab engine path.

    By default it is set to False.

  • auto_detect_grammar_files : bool, optional

    If no input and output grammar files are provided, auto_detect_grammar_files uses a naming convention to associate a grammar file to a discipline: searches in the “comp_dir” directory containing the discipline source file for files basenames self.name _input.json and self.name _output.json.

    By default it is set to False.

  • cache_file_path : str | None, optional

    The file to store the data, mandatory when HDF caching is used.

    By default it is set to None.

  • cache_type : MDODiscipline.CacheType, optional

    The type of cache.

    By default it is set to SimpleCache.

  • check_opt_data : bool, optional

    Whether to check input and output data of discipline.

    By default it is set to True.

  • clean_cache_each_n : int | None, optional

    Iteration interval at which matlab workspace is cleaned.

    By default it is set to None.

  • grammar_type : MDODiscipline.GrammarType, optional

    The type of the input and output grammars.

    By default it is set to JSONGrammar.

  • input_grammar_file : str | None, optional

    The file for input grammar description, if None, name + “_input.json” is used.

    By default it is set to None.

  • input_names : Sequence[str] | None, optional

    The input variables.

    By default it is set to None.

  • is_jac_returned_by_func : bool, optional

    If True, the jacobian matrices should be returned of matlab function with standard outputs. Default is False. If True, the conventional name ‘jac_dout_din’ is used as jacobian term of any output ‘out’ with respect to input ‘in’.

    By default it is set to False.

  • matlab_data_file : str | Path | None, optional

    The .mat file or path containing default values of data.

    By default it is set to None.

  • matlab_engine_name : str, optional

    The name of the singleton used for this discipline.

    By default it is set to matlab.

  • name : str | None, optional

    The name of discipline.

    By default it is set to None.

  • output_grammar_file : str | None, optional

    The file for output grammar description.

    By default it is set to None.

  • output_names : Sequence[str] | None, optional

    The output variables.

    By default it is set to None.

  • search_file : str | None, optional

    The root directory to launch the research of matlab file.

    By default it is set to None.

Mission

Module: gemseo.problems.mdo.aerostructure.aerostructure

Optional parameters

  • lift_val : float, optional

    The threshold to compute the lift constraint.

    By default it is set to 0.5.

  • r_val : float, optional

    The threshold to compute the reserve factor constraint.

    By default it is set to 0.5.

PropaneComb1

Module: gemseo.problems.mdo.propane.propane

PropaneComb2

Module: gemseo.problems.mdo.propane.propane

PropaneComb3

Module: gemseo.problems.mdo.propane.propane

PropaneReaction

Module: gemseo.problems.mdo.propane.propane

RemappingDiscipline

Module: gemseo.disciplines.remapping

Required parameters

  • discipline : MDODiscipline

    The original discipline.

  • input_mapping : NameMapping

    The input names to the original input names.

  • output_mapping : NameMapping

    The output names to the original output names.

RosenMF

Module: gemseo.problems.optimization.rosen_mf

Optional parameters

  • dimension : int, optional

    The dimension of the design space.

    By default it is set to 2.

SLURM

Module: gemseo.wrappers.job_schedulers.slurm

Required parameters

  • discipline : MDODiscipline

    The discipline to wrap in the job scheduler.

  • setup_cmd : str

    The command used before running the executable.

  • user_email : str

    The user email to send the run status.

  • workdir_path : Path

    The path to the workdir where the files will be generated.

Optional parameters

  • cpus_per_task : int, optional

    The number of CPUS per task.

    By default it is set to 1.

  • job_out_filename : str, optional

    The output job file name.

    By default it is set to batch.sh.

  • job_template_path : Path | str | None, optional

    The path to the template to be used to make a submission to the job scheduler command.

    By default it is set to None.

  • mem_per_cpu : str, optional

    The memory per CPU.

    By default it is set to 1G.

  • nodes_number : int, optional

    The number nodes.

    By default it is set to 1.

  • ntasks : int, optional

    The number of tasks.

    By default it is set to 1.

  • ntasks_per_node : int, optional

    The number of tasks per node.

    By default it is set to 1.

  • scheduler_run_command : str, optional

    The command to call the job scheduler and submit the generated script.

    By default it is set to sbatch –wait.

  • use_template : bool, optional

    whether to use template based interface to the job scheduler.

    By default it is set to True.

  • wall_time : str, optional

    The wall time.

    By default it is set to 24:00:00.

  • **options : dict[str:Any]

    The job scheduler specific options to be used in the template.

ScalableDiscipline

Module: gemseo.problems.mdo.scalable.parametric.disciplines.scalable_discipline

Required parameters

  • a_i : NDArray

    The offset vector \(a_i\).

  • C_ij : Mapping[str, NDArray[float]]

    The coefficient matrices \(\left(C_{i,j}\right)_{j=1\atop j\neq i}^N\) where \(C_{i,j}\) is used to multiply the coupling variable \(y_j\).

  • D_i0 : NDArray

    The coefficient matrix \(D_{i,0}\) to multiply the shared design variable \(x_0\).

  • D_ii : NDArray

    The coefficient matrix \(D_{i,i}\) to multiply the local design variable \(x_i\).

  • index : int

    The index \(i\) of the scalable discipline.

Optional parameters

  • **default_input_values : NDArray[float]

    The default values of the input variables.

Scenario

Module: gemseo.core.scenario

Required parameters

  • design_space : DesignSpace

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

  • disciplines : Sequence[MDODiscipline]

    The disciplines used to compute the objective, constraints and observables from the design variables.

  • formulation : str

    The class name of the MDOFormulation, e.g. "MDF", "IDF" or "BiLevel".

  • objective_name : str | Sequence[str]

    The name(s) of the discipline output(s) used as objective. If multiple names are passed, the objective will be a vector.

Optional parameters

  • grammar_type : MDODiscipline.GrammarType, optional

    The grammar for the scenario and the MDO formulation.

    By default it is set to JSONGrammar.

  • maximize_objective : bool, optional

    Whether to maximize the objective.

    By default it is set to False.

  • name : str | None, optional

    The name to be given to this scenario. If None, use the name of the class.

    By default it is set to None.

  • **formulation_options : Any

    The options of the MDOFormulation.

ScilabDiscipline

Note

The plugin gemseo_scilab is required.

Module: gemseo_scilab.scilab_discipline

Required parameters

  • function_name : str

    The name of the scilab function to generate the discipline from.

  • script_dir_path : str

    The path to the directory to scan for .sci files.

Sellar1

Module: gemseo.problems.mdo.sellar.sellar

Sellar2

Module: gemseo.problems.mdo.sellar.sellar

SellarSystem

Module: gemseo.problems.mdo.sellar.sellar

SobieskiAerodynamics

Module: gemseo.problems.mdo.sobieski.disciplines

Optional parameters

  • dtype : SobieskiBase.DataType, optional

    The data type for the NumPy arrays, either “float64” or “complex128”.

    By default it is set to float64.

SobieskiAerodynamicsSG

Module: gemseo.problems.mdo.sobieski._disciplines_sg

Optional parameters

  • dtype : SobieskiBase.DataType, optional

    The data type for the NumPy arrays, either “float64” or “complex128”.

    By default it is set to float64.

SobieskiChain

Module: gemseo.problems.mdo.sobieski.process.mdo_chain

Optional parameters

  • dtype : SobieskiBase.DataType, optional

    The NumPy type for data arrays, either “float64” or “complex128”.

    By default it is set to float64.

SobieskiDiscipline

Module: gemseo.problems.mdo.sobieski.disciplines

Optional parameters

  • dtype : SobieskiBase.DataType, optional

    The data type for the NumPy arrays, either “float64” or “complex128”.

    By default it is set to float64.

SobieskiDisciplineWithSimpleGrammar

Module: gemseo.problems.mdo.sobieski._disciplines_sg

Optional parameters

  • dtype : SobieskiBase.DataType, optional

    The data type for the NumPy arrays, either “float64” or “complex128”.

    By default it is set to float64.

SobieskiMDAGaussSeidel

Module: gemseo.problems.mdo.sobieski.process.mda_gauss_seidel

Optional parameters

  • dtype : SobieskiBase.DataType, optional

    The NumPy type for data arrays, either “float64” or “complex128”.

    By default it is set to float64.

  • **mda_options : Any

    The options of the MDA.

SobieskiMDAJacobi

Module: gemseo.problems.mdo.sobieski.process.mda_jacobi

Optional parameters

  • dtype : SobieskiBase.DataType, optional

    The NumPy type for data arrays, either “float64” or “complex128”.

    By default it is set to float64.

  • n_processes : int, optional

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

    By default it is set to 1.

  • **mda_options : Any

    The options of the MDA.

SobieskiMission

Module: gemseo.problems.mdo.sobieski.disciplines

Optional parameters

  • dtype : SobieskiBase.DataType, optional

    The data type for the NumPy arrays, either “float64” or “complex128”.

    By default it is set to float64.

  • enable_delay : bool | float, optional

    If True, wait one second before computation. If a positive number, wait the corresponding number of seconds. If False, compute directly.

    By default it is set to False.

SobieskiMissionSG

Module: gemseo.problems.mdo.sobieski._disciplines_sg

Optional parameters

  • dtype : SobieskiBase.DataType, optional

    The data type for the NumPy arrays, either “float64” or “complex128”.

    By default it is set to float64.

SobieskiPropulsion

Module: gemseo.problems.mdo.sobieski.disciplines

Optional parameters

  • dtype : SobieskiBase.DataType, optional

    The data type for the NumPy arrays, either “float64” or “complex128”.

    By default it is set to float64.

SobieskiPropulsionSG

Module: gemseo.problems.mdo.sobieski._disciplines_sg

Optional parameters

  • dtype : SobieskiBase.DataType, optional

    The data type for the NumPy arrays, either “float64” or “complex128”.

    By default it is set to float64.

SobieskiStructure

Module: gemseo.problems.mdo.sobieski.disciplines

Optional parameters

  • dtype : SobieskiBase.DataType, optional

    The data type for the NumPy arrays, either “float64” or “complex128”.

    By default it is set to float64.

SobieskiStructureSG

Module: gemseo.problems.mdo.sobieski._disciplines_sg

Optional parameters

  • dtype : SobieskiBase.DataType, optional

    The data type for the NumPy arrays, either “float64” or “complex128”.

    By default it is set to float64.

Splitter

Module: gemseo.disciplines.splitter

Required parameters

  • input_name : str

    The name of the input to split.

  • output_names_to_input_indices : dict[str, Iterable[int] | int]

    The input indices associated with the output names.

Structure

Module: gemseo.problems.mdo.aerostructure.aerostructure

SurrogateDiscipline

Module: gemseo.disciplines.surrogate

Required parameters
Optional parameters

  • data : IODataset | None, optional

    The learning dataset to train the regression model. If None, the regression model is supposed to be trained.

    By default it is set to None.

  • default_inputs : dict[str, ndarray] | None, optional

    The default values of the inputs. If None, use the center of the learning input space.

    By default it is set to None.

  • disc_name : str | None, optional

    The name to be given to the surrogate discipline. If None, concatenate SHORT_ALGO_NAME and data.name.

    By default it is set to None.

  • input_names : Iterable[str] | None, optional

    The names of the input variables. If None, consider all input variables mentioned in the learning dataset.

    By default it is set to None.

  • output_names : Iterable[str] | None, optional

    The names of the output variables. If None, consider all input variables mentioned in the learning dataset.

    By default it is set to None.

  • transformer : TransformerType, optional

    The strategies to transform the variables. The values are instances of BaseTransformer 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 BaseTransformer will be applied to all the variables of this group. If The :attr:.BaseMLRegressionAlgo.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 0x7f1dec69e040>, ‘outputs’: <gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object at 0x7f1dec69e0d0>}.

  • **parameters : MLAlgoParameterType

    The parameters of the machine learning algorithm.

TaylorDiscipline

Module: gemseo.disciplines.taylor

Required parameters

  • discipline : MDODiscipline

    The discipline to be approximated by a Taylor polynomial.

  • name : str

    The name of the discipline. If None, use the class name.

Optional parameters

  • input_data : Mapping[str, NDArray[float]], optional

    The point of expansion. If empty, use the default inputs of discipline.

    By default it is set to {}.

VolumeFraction

Module: gemseo.problems.topology_optimization.volume_fraction_disc

Optional parameters

  • empty_elements : Sequence[int] | None, optional

    The index of the empty element ids that are not part of the design space.

    By default it is set to None.

  • full_elements : Sequence[int] | None, optional

    The index of the full element ids that are not part of the design space.

    By default it is set to None.

  • n_x : int, optional

    The number of elements in the x-direction.

    By default it is set to 100.

  • n_y : int, optional

    The number of elements in the y-direction.

    By default it is set to 100.

  • name : str | None, optional

    The name of the discipline. If None, use the class name.

    By default it is set to None.

XLSDiscipline

Module: gemseo.wrappers.xls_discipline

Required parameters

  • xls_file_path : Path | str

    The path to the Excel file. If the file is a XLSM, a macro named “execute” must exist and will be called by the _run() method before retrieving the outputs.

Optional parameters

  • copy_xls_at_setstate : bool, optional

    If True, create a copy of the original Excel file for each of the pickled parallel processes. This option is required to be set to True for parallelization in Windows platforms.

    By default it is set to False.

  • macro_name : str | None, optional

    The name of the macro to be executed for a XLSM file. If None is provided, do not run a macro.

    By default it is set to execute.

  • name : str | None, optional

    The name of the discipline. If None, use the class name.

    By default it is set to None.

  • recreate_book_at_run : bool, optional

    Whether to rebuild the xls objects at each _run call.

    By default it is set to False.