mvtsdatatoolkit.visualizations package

Submodules

mvtsdatatoolkit.visualizations.stat_visualizer module

class mvtsdatatoolkit.visualizations.stat_visualizer.StatVisualizer(path_to_extracted_features: str = None, extracted_features: pandas.core.frame.DataFrame = None, normalize: bool = True)

Bases: object

boxplot_extracted_features(feature_names: list, output_path: str = None)

Generates a plot of boxplots, one for each extracted feature.

Parameters

• feature_names – A list of feature-names indicating the columns of interest for this visualization.

• output_path – If given, the generated plot will be stored instead of shown. Otherwise, it will be only shown if the running environment allows it.

Returns

None

plot_correlation_heatmap(feature_names: list, output_path: str = None)

Generates a heat-map for the correlation matrix of all pairs of given features.

Note: Regardless of the range of correlations, the color-map is fixed to [-1, 1]. This is especially important to avoid mapping insignificant changes of values into significant changes of colors.

Parameters

• feature_names – A list of feature-names indicating the columns of interest for this visualization.

• output_path – If given, the generated plot will be stored instead of shown. Otherwise, it will be only shown if the running environment allows it.

Returns

None

plot_covariance_heatmap(feature_names: list, output_path: str = None)

Generates a heat-map for the covariance matrix of all pairs of given features.

Note that covariance is not a standardized statistic, and because of this, the color-map might be confusing; when the difference between the largest and smallest covariance is insignificant, the colors may imply a significant difference. To avoid this, the values mapped to the colors (as shown next to the color-map) must be carefully taken into account in the analysis of the covariance.

Parameters

• feature_names – A list of feature-names indicating the columns of interest for this visualization.

• output_path – If given, the generated plot will be stored instead of shown. Otherwise, it will be only shown if the running environment allows it.

Returns

None

plot_splom(feature_names: list, output_path: str = None)

Generates a SPLOM, or a scatter plot matrix, for all pairs of features. Note that for a large number of features this may take a while (since each cell of the matrix is a scatter plot on its own), and also the final plot may become very large.

Parameters

• feature_names – A list of feature-names indicating the columns of interest for this visualization.

• output_path – If given, the generated plot will be stored instead of shown. Otherwise, it will be only shown if the running environment allows it.

Returns

None

plot_violinplot(feature_names: list, output_path: str = None)

Generates a set of violin-plots, one for each extracted feature.

Parameters

• feature_names – A list of feature-names indicating the columns of interest for this visualization.

• output_path – If given, the generated plot will be stored instead of shown. Otherwise, it will be only shown if the running environment allows it.

Returns

None

mvtsdatatoolkit.visualizations.stat_visualizer.main()

Module contents