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Sensitivity plots

sensitivity_plots

Classes

SensitivityBarPlot 

SensitivityBarPlot(filename='sensitivity_bar_plot')

Bases: SensitivityPlot

Source code in src/vimseo/tools/post_tools/sensitivity_plots.py 
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def __init__(self, filename="sensitivity_bar_plot"):
    super().__init__()
    self.options = {
        "filename": filename,
        "n_variables": 10,
    }
Functions
plot 
plot(
    results,
    save=False,
    show=True,
    output_names=(),
    indices=(),
    directory_path: str | Path = "",
    **options
)

Parameters:

  • results –

    A SensitivityTool result.

  • title –

    The title of the figure to be plotted.

  • **options –

    Specific options for the plot

Returns:

  • –

    A figure object.

Source code in src/vimseo/tools/post_tools/sensitivity_plots.py 
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def plot(
    self,
    results,
    save=False,
    show=True,
    output_names=(),
    indices=(),
    directory_path: str | Path = "",
    **options,
):
    self.options.update(options)

    indices = list(results.indices.keys()) if len(indices) == 0 else indices
    output_names = (
        results.metadata.settings["output_names"]
        if len(output_names) == 0
        else output_names
    )

    data = deepcopy(results.indices)

    self.figures = defaultdict(dict)

    for output_name in output_names:
        for index in indices:
            fig = make_subplots(
                rows=1,
                cols=1,
                subplot_titles=f"Index: {index}",
            )

            input_variables = [
                k
                for k, v in sorted(
                    data[index][output_name][0].items(),
                    key=operator.itemgetter(1),
                )
            ]

            color_by_variable_name = names_to_ints(input_variables)

            x = (
                pd.DataFrame
                .from_dict(data[index][output_name])
                .T.sort_values(0, ascending=True)
                .to_dict()[0]
            )
            input_variables = list(x.keys())

            if self.options["n_variables"] is not None:
                input_variables = input_variables[0 : self.options["n_variables"]]
                color_by_variable_name = color_by_variable_name[
                    0 : self.options["n_variables"]
                ]
                x = [x[k][0] for k in input_variables]

            fig.add_trace(
                go.Bar(
                    x=x,
                    y=input_variables,
                    marker={"color": array(color_by_variable_name)},
                    orientation="h",
                ),
                row=1,
                col=1,
            )

            fig.update_layout(
                title_text=f"Sensitivity of {output_name} for index {index}"
            )
            fig.layout.title.x = 0.5
            fig.update_layout(font_size=14)
            # adjust subplot title font size.
            fig.update_annotations(font_size=12)

            if show:
                fig.show()
            if save:
                directory_path = (
                    Path.cwd() if directory_path == "" else Path(directory_path)
                )
                fig.write_html(
                    directory_path
                    / f"{self.options['filename']}_{output_name}_{index}.html"
                )
            self.figures[output_name][index] = fig

    return self.figures

SensitivityPlot 

SensitivityPlot()

Bases: ABC

Source code in src/vimseo/tools/post_tools/sensitivity_plots.py 
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def __init__(self):
    self.figures = None
Functions
plot abstractmethod 
plot(
    results, save, show, variable, indices=None, **options
)

Parameters:

  • results –

    A SensitivityTool result.

  • title –

    The title of the figure to be plotted.

  • **options –

    Specific options for the plot

Returns:

  • –

    A figure object.

Source code in src/vimseo/tools/post_tools/sensitivity_plots.py 
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@abstractmethod
def plot(self, results, save, show, variable, indices=None, **options):
    """

    Args:
        results: A SensitivityTool result.
        title: The title of the figure to be plotted.
        **options: Specific options for the plot

    Returns:
        A figure object.
    """