This plot shows the eigenvalues of the PCA problem normalized to the greatest value. The first value corresponds to the dominating parameter component (input and output) group. The bar chart plot shows the weights of the different parameter groups. By these normalized eigenvalues, it is possible to choose the PCA eigenvector for the PCA data window.

Preferences

The following preference settings are available:

For more details, see Plot Preference Settings.

Python Scripting

Create Visual

Creates the visual using data with data_id.

    wpcv = Visuals.WeightedPCValues(
        Id("Weighted principal component values"),
        data_id
    )
                            

Add to Postprocessing

Adds the visual in postprocessing to control_container, using the specified relative positioning.

    control_container.add_control (
        wpcv,
        True,
        RELATIVE_POSITIONING,
        0., 0., 1., 1./2.
    )