gemseo / disciplines

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taylor module

A discipline to create Taylor polynomials from another discipline.

class gemseo.disciplines.taylor.TaylorDiscipline(discipline, input_data=mappingproxy({}), name='')

Bases: MDODiscipline

The first-order polynomial of a discipline.

The first-order polynomial of a function \(f\) at an expansion point \(a\) is \(f(a)+\sum_{i=1}^d\frac{\partial f(a)}{\partial x_i}(x_i-a_i)\).

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

Parameters:

• discipline (MDODiscipline) – The discipline to be approximated by a Taylor polynomial.

• input_data (Mapping[str, NDArray[float]]) –

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

  By default it is set to {}.

• name (str) –

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

  By default it is set to “”.

Raises:

ValueError – If neither input_data nor discipline.default_inputs is specified.

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: 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.