maui.visualizations.indices_radar_plot¶

maui.visualizations.indices_radar_plot(df, indices, agg_type, group_by=None, max_cols=3, fig_size=None, show_plot=True)[source]¶

Create a radar plot to compare indices in a DataFrame.

This function generates a radar plot to compare multiple indices from a DataFrame. It allows aggregating data based on specified aggregation types and grouping by one or two columns from the DataFrame.

Parameters:

dfpandas.DataFrame: The input DataFrame containing the data.
indiceslist: A list of column names in the DataFrame representing the indices to be compared.
agg_typestr: The type of aggregation to be applied (‘mean’, ‘median’, ‘stddev’, ‘var’, ‘max’, ‘min’).
group_bylist, optional: A list of one or two column names for grouping data (default is None).
max_colsint, optional: Maximum number of columns for subplots (default is 3).
fig_sizedict, optional: A dictionary specifying the height and width of the plot (default is None).
show_plotbool, optional: Whether to display the plot (default is True).

Returns:

plotly.graph_objs._figure.Figure: A Plotly Figure object representing the radar plot.

Raises:

AssertionError: If the arguments are not correctly specified.
Exception: If the input data or arguments are invalid.

Notes

The ‘agg_type’ argument must be one of [‘mean’, ‘median’, ‘stddev’, ‘var’, ‘max’, ‘min’].
The ‘group_by’ argument can contain one or two columns for grouping data.
‘fig_size’ should be a dictionary with ‘height’ and ‘width’ keys.

Examples

>>> from maui import samples, acoustic_indices, visualizations
>>> df = samples.get_leec_audio_sample()
>>> indices_list = ['median_amplitude_envelope', 'temporal_entropy']
>>> df = acoustic_indices.calculate_acoustic_indices(df, indices_list, parallel=False)
>>> fig = visualizations.indices_radar_plot(df, indices=['m', 'ht'],
        agg_type='mean', group_by=['environment'], max_cols=3)
# Generates a radar plot comparing 'Index1' and 'Index2' aggregated by 'Category'.