uvisbox.Modules.ContourBoxplot.contour_boxplot_stats
====================================================

.. py:module:: uvisbox.Modules.ContourBoxplot.contour_boxplot_stats




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

.. py:function:: contour_boxplot_summary_statistics(ensemble_images, isovalue, boxplot_style=None, eps=0, workers=11)

   Compute summary statistics for contour boxplot from ensemble scalar fields.

   This function extracts binary contours at the given isovalue, computes their band depths,
   and generates percentile bands, median, and outlier information.

   :param ensemble_images: 3D array of shape (n_ensemble, height, width) containing ensemble scalar fields.
   :type ensemble_images: np.ndarray
   :param isovalue: Threshold value for creating binary images. Pixels with values < isovalue are set to 1 (inside),
                    values >= isovalue are set to 0 (outside).
   :type isovalue: float
   :param boxplot_style: Configuration for percentiles and outlier detection. If None, uses default configuration.
   :type boxplot_style: BoxplotStyleConfig, optional
   :param eps: Epsilon tolerance for subset checks in band depth computation. With eps=0 (default),
               exact pixel-level containment is required, which can be too strict for continuously-varying
               rasterized contours. Values like 0.01-0.05 allow approximate containment.
   :type eps: float, optional
   :param workers: Number of parallel workers for band depth computation. Default is 11.
   :type workers: int, optional

   :returns: Contains ``median``, ``percentile_bands``, ``outliers``,
             ``sorted_contours``, ``sorted_indices``, and ``depths``. The median and
             outliers are binary images. Each percentile-band tuple contains a
             percentile and a 2D image whose nonzero value is that percentile
             divided by 100.
   :rtype: dict

   .. rubric:: Examples

   >>> ensemble = np.random.randn(50, 100, 100)
   >>> stats = contour_boxplot_summary_statistics(ensemble, isovalue=0.5)
   >>> print(stats.keys())
   dict_keys(['median', 'percentile_bands', 'outliers', 'sorted_contours', 'sorted_indices', 'depths'])