gemseo / post / dataset

color_evolution module

Evolution of the variables by means of a color scale.

class gemseo.post.dataset.color_evolution.ColorEvolution(dataset, variables=None, use_log=False, opacity=0.6, **options)[source]

Bases: gemseo.post.dataset.dataset_plot.DatasetPlot

Evolution of the variables by means of a color scale.

Based on the matplotlib function imshow().

Tip

Use colormap to set a matplotlib colormap, e.g. "seismic".

Parameters
  • dataset (Dataset) – The dataset containing the data to plot.

  • variables (Iterable[str] | None) –

    The variables of interest If None, use all the variables.

    By default it is set to None.

  • use_log (bool) –

    Whether to use a symmetric logarithmic scale.

    By default it is set to False.

  • opacity (float) –

    The level of opacity (0 = transparent; 1 = opaque).

    By default it is set to 0.6.

  • **options (bool | float | str | None) – The options for the matplotlib function imshow().

Raises

ValueError – If the dataset is empty.

Return type

None

execute(save=True, show=False, file_path=None, directory_path=None, file_name=None, file_format=None, properties=None, fig=None, axes=None, **plot_options)

Execute the post processing.

Parameters
  • save (bool) –

    If True, save the plot.

    By default it is set to True.

  • show (bool) –

    If True, display the plot.

    By default it is set to False.

  • file_path (str | Path | None) –

    The path of the file to save the figures. If None, create a file path from directory_path, file_name and file_format.

    By default it is set to None.

  • directory_path (str | Path | None) –

    The path of the directory to save the figures. If None, use the current working directory.

    By default it is set to None.

  • file_name (str | None) –

    The name of the file to save the figures. If None, use a default one generated by the post-processing.

    By default it is set to None.

  • file_format (str | None) –

    A file format, e.g. ‘png’, ‘pdf’, ‘svg’, … If None, use a default file extension.

    By default it is set to None.

  • properties (Mapping[str, DatasetPlotPropertyType] | None) –

    The general properties of a DatasetPlot.

    By default it is set to None.

  • fig (None | Figure) –

    The figure to plot the data. If None, create a new one.

    By default it is set to None.

  • axes (None | Axes) –

    The axes to plot the data. If None, create new ones.

    By default it is set to None.

  • **plot_options – The options of the current class inheriting from DatasetPlot.

Returns

The figures.

Raises

AttributeError – When the name of a property is not the name of an attribute.

Return type

list[Figure]

color: str | list[str]

The color(s) for the series.

If empty, use a default one.

colormap: str

The color map.

dataset: Dataset

The dataset to be plotted.

fig_size: tuple[float, float]

The figure size.

property fig_size_x: float

The x-component of figure size.

property fig_size_y: float

The y-component of figure size.

font_size: int

The font size.

property labels: Mapping[str, str]

The labels of the variables.

legend_location: str

The location of the legend.

linestyle: str | list[str]

The line style(s) for the series.

If empty, use a default one.

marker: str | list[str]

The marker(s) for the series.

If empty, use a default one.

property output_files: list[str]

The paths to the output files.

title: str

The title of the plot.

xlabel: str

The label for the x-axis.

xmax: float | None

The maximum value on the x-axis.”

If None, compute it from data.

xmin: float | None

The minimum value on the x-axis.

If None, compute it from data.

ylabel: str

The label for the y-axis.

ymax: float | None

The maximum value on the y-axis.

If None, compute it from data.

ymin: float | None

The minimum value on the y-axis.

If None, compute it from data.

zlabel: str

The label for the z-axis.

zmax: float | None

The maximum value on the z-axis.

If None, compute it from data.

zmin: float | None

The minimum value on the z-axis.

If None, compute it from data.