uvisbox.Modules.FunctionalBoxplot.functional_boxplot_stats

Module Contents

uvisbox.Modules.FunctionalBoxplot.functional_boxplot_stats.functional_boxplot_band_depths(data, method='fbd')

Compute band depths for a set of functional curves.

Parameters:

• curves (np.ndarray) – 2D array of shape (N, D) where N is the number of curves and D is the number of points per curve.

• method (str, optional) – Band-depth method: "fbd" for functional band depth (default) or "mfbd" for modified functional band depth.

uvisbox.Modules.FunctionalBoxplot.functional_boxplot_stats.functional_boxplot_get_band(data, percentile, method='fbd')

Compute the band envelope for a given percentile using functional band depth.

Parameters:

• data (np.ndarray) – 2D array of shape (N, D) where N is the number of curves and D is the number of points per curve.

• percentile (float) – Percentile value (0-100) for the band envelope.

• method (str, optional) – Band-depth method: "fbd" for functional band depth (default) or "mfbd" for modified functional band depth.

Returns:

• bottom_curve (np.ndarray) – 1D array representing the bottom envelope of the band.

• top_curve (np.ndarray) – 1D array representing the top envelope of the band.

Examples

>>> data = np.random.randn(100, 50)  # 100 curves, 50 points each
>>> bottom, top = functional_boxplot_get_band(data, 50, method='fbd')  # 50th percentile band

uvisbox.Modules.FunctionalBoxplot.functional_boxplot_stats.functional_boxplot_summary_statistics(data, method='fbd', boxplot_style=None)

Compute functional boxplot summary statistics without visualization.

This function computes the same statistics as functional_boxplot but returns them as numpy arrays in a dictionary instead of creating a plot.

Parameters:

• data (np.ndarray) – 2D array of shape (N, D) where N is the number of curves and D is the number of points per curve.

• method (str, optional) – Band-depth method: "fbd" for functional band depth (default) or "mfbd" for modified functional band depth.

• boxplot_style (BoxplotStyleConfig, optional) – Configuration for the boxplot including percentiles and outlier settings. If None, uses default configuration.

Returns:

stats – Contains depths, median, percentile_bands, outliers, sorted_curves, and sorted_indices. Each percentile band is a (bottom_curve, top_curve) tuple; both curves have shape (D,).

Return type:

dict

Raises:

ValueError – If data is not 2D or if method is invalid.

Examples

>>> import numpy as np
>>> from uvisbox.Modules.FunctionalBoxplot.functional_boxplot_stats import functional_boxplot_summary_statistics
>>> from uvisbox.Core.CommonInterface import BoxplotStyleConfig
>>>
>>> # Generate synthetic functional data
>>> t = np.linspace(0, 1, 100)
>>> data = np.array([np.sin(2*np.pi*t) + 0.2*np.random.randn(100) for _ in range(50)])
>>>
>>> # Basic usage with default settings
>>> stats = functional_boxplot_summary_statistics(data)
>>> print(f"Median curve shape: {stats['median'].shape}")
>>> print(f"Number of outliers: {stats['outliers'].shape[0]}")
>>> print(f"Available percentile bands: {list(stats['percentile_bands'].keys())}")
>>>
>>> # Access specific percentile band
>>> bottom_25, top_25 = stats['percentile_bands']['25_percentile_band']
>>> print(f"25th percentile band shapes: {bottom_25.shape}, {top_25.shape}")
>>>
>>> # Custom percentiles
>>> style = BoxplotStyleConfig(percentiles=[10, 50, 90], show_outliers=True)
>>> stats = functional_boxplot_summary_statistics(data, boxplot_style=style)