gemseo.mda.mda_chain module#

An advanced MDA splitting algorithm based on graphs.

class MDAChain(disciplines, settings_model=None, **settings)[source]#

Bases: BaseMDA

A chain of MDAs.

The execution sequence is provided by the DependencyGraph.

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

Parameters:

  • disciplines (Sequence[Discipline]) -- The disciplines from which to compute the MDA.

  • settings_model (MDAChain_Settings | None) -- The MDA settings as a Pydantic model. If None, use **settings.

  • **settings (Any) -- The MDA settings. These arguments are ignored when settings_model is not None.

Settings#

The pydantic model for the settings.

alias of MDAChain_Settings

add_differentiated_inputs(input_names=())[source]#

Add the inputs with respect to which to differentiate the outputs.

The inputs that do not represent continuous numbers are filtered out.

Parameters:

input_names (Iterable[str]) --

The input variables with respect to which to differentiate the outputs. If empty, use all the inputs.

By default it is set to ().

Raises:

ValueError -- When an input name is not the name of a discipline input.

Return type:

None

add_differentiated_outputs(output_names=())[source]#

Add the outputs to be differentiated.

The outputs that do not represent continuous numbers are filtered out.

Parameters:

output_names (Iterable[str]) --

The outputs to be differentiated. If empty, use all the outputs.

By default it is set to ().

Raises:

ValueError -- When an output name is not the name of a discipline output.

Return type:

None

execute(input_data=mappingproxy({}))[source]#

Execute the discipline, i.e. compute output data from input data.

If virtual_execution is True, this method returns the default_output_data. Otherwise, it calls the _run() method performing the true execution and returns the corresponding output data. This _run() method must be implemented in subclasses.

Parameters:

input_data (StrKeyMapping) --

The input data. Complete this dictionary with the default_input_data.

By default it is set to {}.

Returns:

The input and output data.

Return type:

DisciplineData

plot_residual_history(show=False, save=True, n_iterations=None, logscale=(), filename='', fig_size=(50.0, 10.0))[source]#

Generate a plot of the residual history.

The first iteration of each new execution is marked with a red dot.

Parameters:

  • show (bool) --

    Whether to display the plot on screen.

    By default it is set to False.

  • save (bool) --

    Whether to save the plot as a PDF file.

    By default it is set to True.

  • n_iterations (int | None) -- The number of iterations on the x axis. If None, use all the iterations.

  • logscale (tuple[int, int]) --

    The limits of the y axis. If empty, do not change the limits of the y axis.

    By default it is set to ().

  • filename (Path | str) --

    The name of the file to save the figure. If empty, use "{mda.name}_residual_history.pdf".

    By default it is set to "".

  • fig_size (FigSizeType) --

    The width and height of the figure in inches, e.g. (w, h).

    By default it is set to (50.0, 10.0).

Returns:

The figure, to be customized if not closed.

Return type:

None

set_bounds(variable_names_to_bounds)[source]#
Parameters:

variable_names_to_bounds (Mapping[str, tuple[RealArray | None, RealArray | None]])

Return type:

None

inner_mdas: list[BaseMDASolver]#

The ordered MDAs.

mdo_chain: MDOChain#

The chain of MDAs.

property normed_residual: float#

The normed_residuals, computed from the sub-MDAs residuals.

property scaling: ResidualScaling#

The scaling method applied to MDA residuals for convergence monitoring.

settings: MDAChain_Settings#

The settings of the MDA