uvisbox.Modules.FunctionalBoxplot.functional_boxplot_stats
==========================================================

.. py:module:: uvisbox.Modules.FunctionalBoxplot.functional_boxplot_stats




Module Contents
---------------

.. py:function:: functional_boxplot_band_depths(data, method='fbd')

   Compute band depths for a set of functional curves.

   :param curves: 2D array of shape (N, D) where N is the number of curves and D is the number of points per curve.
   :type curves: np.ndarray
   :param method: Band-depth method: ``"fbd"`` for functional band depth (default) or
                  ``"mfbd"`` for modified functional band depth.
   :type method: str, optional


.. py:function:: functional_boxplot_get_band(data, percentile, method='fbd')

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

   :param data: 2D array of shape (N, D) where N is the number of curves and D is the number of points per curve.
   :type data: np.ndarray
   :param percentile: Percentile value (0-100) for the band envelope.
   :type percentile: float
   :param method: Band-depth method: ``"fbd"`` for functional band depth (default) or
                  ``"mfbd"`` for modified functional band depth.
   :type method: str, optional

   :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.

   .. rubric:: 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


.. py:function:: 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.

   :param data: 2D array of shape (N, D) where N is the number of curves and D is the number
                of points per curve.
   :type data: np.ndarray
   :param method: Band-depth method: ``"fbd"`` for functional band depth (default) or
                  ``"mfbd"`` for modified functional band depth.
   :type method: str, optional
   :param boxplot_style: Configuration for the boxplot including percentiles and outlier settings.
                         If None, uses default configuration.
   :type boxplot_style: BoxplotStyleConfig, optional

   :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,)``.
   :rtype: dict

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

   .. rubric:: 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)