concatenater module¶

The concatenation of several input variables into a single one.

class gemseo.disciplines.concatenater.Concatenater(input_variables, output_variable, input_coefficients=None)[source]

Bases: MDODiscipline

Concatenate input variables into a single output variable.

These input variables can be scaled before concatenation.

Examples

>>> from gemseo import create_discipline
>>> sellar_system_disc = create_discipline('SellarSystem')
>>> constraint_names = ['c1', 'c2']
>>> output_name = ['c']
>>> concatenation_disc = create_discipline(
...     'Concatenater', constraint_names, output_name
... )
>>> disciplines = [sellar_system_disc, concatenation_disc]
>>> chain = create_discipline('MDOChain', disciplines=disciplines)
>>> print(chain.execute())
>>> print(chain.linearize(compute_all_jacobians=True))

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

Parameters:

input_variables (Sequence[str]) – The input variables to concatenate.
output_variable (str) – The output variable name.
input_coefficients (dict[str, float]) – The coefficients related to the different input variables.

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.

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

input_grammar: BaseGrammar: The input grammar.

jac: dict[str, dict[str, ndarray]]

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: Mapping[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.

Examples using Concatenater¶

Examples for constraint aggregation