gemseo / problems / sobieski

disciplines module¶

The disciplines of the Sobieski’s SSBJ use case.

class gemseo.problems.sobieski.disciplines.SobieskiAerodynamics(dtype=DataType.FLOAT)[source]

Bases: SobieskiDiscipline

Aerodynamics discipline for the Sobieski’s SSBJ use case.

Initialize self. See help(type(self)) for accurate signature.

Parameters:

dtype (SobieskiBase.DataType) –

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

By default it is set to “float64”.

classmethod create_with_physical_naming(dtype=DataType.FLOAT)[source]

Parameters:

dtype (DataType) –

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

By default it is set to “float64”.

Return type:

RemappingDiscipline

cache: AbstractCache | None: The cache containing one or several executions of the discipline according to the cache policy.

data_processor: DataProcessor: A tool to pre- and post-process discipline data.

dtype: SobieskiBase.DataType: The data type for the NumPy arrays.

exec_for_lin: bool: Whether the last execution was due to a linearization.

input_grammar: BaseGrammar: The input grammar.

jac: MutableMapping[str, MutableMapping[str, ndarray | csr_array | JacobianOperator]]

The Jacobians of the outputs wrt inputs.

The structure is {output: {input: matrix}}.

name: str: The name of the discipline.

output_grammar: BaseGrammar: The output grammar.

re_exec_policy: ReExecutionPolicy: The policy to re-execute the same discipline.

residual_variables: dict[str, str]: The output variables mapping to their inputs, to be considered as residuals; they shall be equal to zero.

run_solves_residuals: bool: Whether the run method shall solve the residuals.

sobieski_problem: SobieskiProblem: The Sobieski’s SSBJ use case defining the MDO problem, e.g. disciplines, constraints, design space and reference optimum.

class gemseo.problems.sobieski.disciplines.SobieskiDiscipline(dtype=DataType.FLOAT)[source]

Bases: MDODiscipline

Abstract base discipline for the Sobieski’s SSBJ use case.

Initialize self. See help(type(self)) for accurate signature.

Parameters:

dtype (SobieskiBase.DataType) –

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

By default it is set to “float64”.

classmethod create_with_physical_naming(dtype=DataType.FLOAT)[source]

Parameters:

dtype (DataType) –

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

By default it is set to “float64”.

Return type:

RemappingDiscipline

GRAMMAR_DIRECTORY: ClassVar[str | None] = PosixPath('/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/stable/lib/python3.9/site-packages/gemseo/problems/sobieski/grammars'): The directory in which to search for the grammar files if not the class one.

cache: AbstractCache | None: The cache containing one or several executions of the discipline according to the cache policy.

data_processor: DataProcessor: A tool to pre- and post-process discipline data.

dtype: SobieskiBase.DataType: The data type for the NumPy arrays.

exec_for_lin: bool: Whether the last execution was due to a linearization.

input_grammar: BaseGrammar: The input grammar.

jac: MutableMapping[str, MutableMapping[str, ndarray | csr_array | JacobianOperator]]

The Jacobians of the outputs wrt inputs.

The structure is {output: {input: matrix}}.

name: str: The name of the discipline.

output_grammar: BaseGrammar: The output grammar.

re_exec_policy: ReExecutionPolicy: The policy to re-execute the same discipline.

residual_variables: dict[str, str]: The output variables mapping to their inputs, to be considered as residuals; they shall be equal to zero.

run_solves_residuals: bool: Whether the run method shall solve the residuals.

sobieski_problem: SobieskiProblem: The Sobieski’s SSBJ use case defining the MDO problem, e.g. disciplines, constraints, design space and reference optimum.

class gemseo.problems.sobieski.disciplines.SobieskiMission(dtype=DataType.FLOAT, enable_delay=False)[source]

Bases: SobieskiDiscipline

Mission discipline of the Sobieski’s SSBJ use case.

Compute the range with the Breguet formula.

Initialize self. See help(type(self)) for accurate signature.

Parameters:

dtype (SobieskiBase.DataType) –
The data type for the NumPy arrays, either “float64” or “complex128”.

By default it is set to “float64”.
enable_delay (bool | float) –
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.

classmethod create_with_physical_naming(dtype=DataType.FLOAT, enable_delay=False)[source]

Parameters:

enable_delay (bool | float) –
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.
dtype (SobieskiBase.DataType) –

By default it is set to “float64”.

Return type:

RemappingDiscipline

cache: AbstractCache | None: The cache containing one or several executions of the discipline according to the cache policy.

data_processor: DataProcessor: A tool to pre- and post-process discipline data.

dtype: SobieskiBase.DataType: The data type for the NumPy arrays.

enable_delay: bool | float

If True, wait one second before computation.

If a positive number, wait the corresponding number of seconds. If False, compute directly.

exec_for_lin: bool: Whether the last execution was due to a linearization.

input_grammar: BaseGrammar: The input grammar.

jac: MutableMapping[str, MutableMapping[str, ndarray | csr_array | JacobianOperator]]

The Jacobians of the outputs wrt inputs.

The structure is {output: {input: matrix}}.

name: str: The name of the discipline.

output_grammar: BaseGrammar: The output grammar.

re_exec_policy: ReExecutionPolicy: The policy to re-execute the same discipline.

residual_variables: dict[str, str]: The output variables mapping to their inputs, to be considered as residuals; they shall be equal to zero.

run_solves_residuals: bool: Whether the run method shall solve the residuals.

sobieski_problem: SobieskiProblem: The Sobieski’s SSBJ use case defining the MDO problem, e.g. disciplines, constraints, design space and reference optimum.

class gemseo.problems.sobieski.disciplines.SobieskiPropulsion(dtype=DataType.FLOAT)[source]

Bases: SobieskiDiscipline

Propulsion discipline of the Sobieski’s SSBJ use case.

Initialize self. See help(type(self)) for accurate signature.

Parameters:

dtype (SobieskiBase.DataType) –

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

By default it is set to “float64”.

classmethod create_with_physical_naming(dtype=DataType.FLOAT)[source]

Parameters:

dtype (DataType) –

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

By default it is set to “float64”.

Return type:

RemappingDiscipline

cache: AbstractCache | None: The cache containing one or several executions of the discipline according to the cache policy.

data_processor: DataProcessor: A tool to pre- and post-process discipline data.

dtype: SobieskiBase.DataType: The data type for the NumPy arrays.

exec_for_lin: bool: Whether the last execution was due to a linearization.

input_grammar: BaseGrammar: The input grammar.

jac: MutableMapping[str, MutableMapping[str, ndarray | csr_array | JacobianOperator]]

The Jacobians of the outputs wrt inputs.

The structure is {output: {input: matrix}}.

name: str: The name of the discipline.

output_grammar: BaseGrammar: The output grammar.

re_exec_policy: ReExecutionPolicy: The policy to re-execute the same discipline.

residual_variables: dict[str, str]: The output variables mapping to their inputs, to be considered as residuals; they shall be equal to zero.

run_solves_residuals: bool: Whether the run method shall solve the residuals.

sobieski_problem: SobieskiProblem: The Sobieski’s SSBJ use case defining the MDO problem, e.g. disciplines, constraints, design space and reference optimum.

class gemseo.problems.sobieski.disciplines.SobieskiStructure(dtype=DataType.FLOAT)[source]

Bases: SobieskiDiscipline

Structure discipline of the Sobieski’s SSBJ use case.

Initialize self. See help(type(self)) for accurate signature.

Parameters:

dtype (SobieskiBase.DataType) –

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

By default it is set to “float64”.

classmethod create_with_physical_naming(dtype=DataType.FLOAT)[source]

Parameters:

dtype (DataType) –

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

By default it is set to “float64”.

Return type:

RemappingDiscipline

cache: AbstractCache | None: The cache containing one or several executions of the discipline according to the cache policy.

data_processor: DataProcessor: A tool to pre- and post-process discipline data.

dtype: SobieskiBase.DataType: The data type for the NumPy arrays.

exec_for_lin: bool: Whether the last execution was due to a linearization.

input_grammar: BaseGrammar: The input grammar.

jac: MutableMapping[str, MutableMapping[str, ndarray | csr_array | JacobianOperator]]

The Jacobians of the outputs wrt inputs.

The structure is {output: {input: matrix}}.

name: str: The name of the discipline.

output_grammar: BaseGrammar: The output grammar.

re_exec_policy: ReExecutionPolicy: The policy to re-execute the same discipline.

residual_variables: dict[str, str]: The output variables mapping to their inputs, to be considered as residuals; they shall be equal to zero.

run_solves_residuals: bool: Whether the run method shall solve the residuals.

sobieski_problem: SobieskiProblem: The Sobieski’s SSBJ use case defining the MDO problem, e.g. disciplines, constraints, design space and reference optimum.

gemseo.problems.sobieski.disciplines.create_disciplines(dtype=DataType.FLOAT)[source]

Instantiate the structure, aerodynamics, propulsion and mission disciplines.

Parameters:

dtype (DataType) –

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

By default it is set to “float64”.

Returns:

The structure, aerodynamics, propulsion and mission disciplines.

Return type: