uvisbox.Modules.ContourBoxplot.contour_boxplot
==============================================

.. py:module:: uvisbox.Modules.ContourBoxplot.contour_boxplot




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

.. py:function:: contour_boxplot(ensemble_images, isovalue, boxplot_style=None, ax=None, eps=0, workers=11, x_coords=None, y_coords=None)

   Create a contour boxplot visualization from an ensemble of scalar fields.

   This function processes ensemble images by extracting binary contours at a given isovalue,
   computing their band depths, and visualizing the uncertainty using band envelopes.

   :param ensemble_images: 3D array of shape (n_ensemble, y_dim, x_dim) containing the ensemble scalar fields.
   :type ensemble_images: np.ndarray
   :param isovalue: Threshold value for creating binary images. Pixels with values < isovalue are set to 1.
   :type isovalue: float
   :param boxplot_style: Configuration for the boxplot visualization including percentiles,
                         and median/outlier styling. If None, uses default configuration.
                         The percentile_colormap is used for the band sum visualization.
   :type boxplot_style: BoxplotStyleConfig, optional
   :param ax: Matplotlib Axes object to plot on. If None, a new figure and axes will be created.
   :type ax: matplotlib.axes.Axes, 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
   :param x_coords: 1D array of x-axis coordinates defining the spatial domain.
                    Length must match the image width. If None, pixel indices are used.
   :type x_coords: np.ndarray, optional
   :param y_coords: 1D array of y-axis coordinates defining the spatial domain.
                    Length must match the image height. If None, pixel indices are used.
   :type y_coords: np.ndarray, optional

   :returns: **ax** -- The Axes object with the contour boxplot visualization.
   :rtype: matplotlib.axes.Axes

   .. rubric:: Examples

   >>> # Basic usage with defaults
   >>> ensemble = np.random.randn(50, 100, 100)  # 50 ensemble members
   >>> ax = contour_boxplot(ensemble, isovalue=0.5)

   >>> # Custom styling
   >>> from uvisbox.Core.CommonInterface import BoxplotStyleConfig
   >>> style = BoxplotStyleConfig(
   ...     percentiles=[25, 50, 75],
   ...     percentile_colormap='hot',
   ...     show_median=True,
   ...     show_outliers=True
   ... )
   >>> ax = contour_boxplot(ensemble, isovalue=0.5, boxplot_style=style)