uvisbox.Modules.FunctionalBoxplot.functional_boxplot_vis
========================================================

.. py:module:: uvisbox.Modules.FunctionalBoxplot.functional_boxplot_vis




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

.. py:function:: plot_band(bottom_curve, top_curve, vmin=0, vmax=1, ax=None, color='red', alpha=1.0, scale=1.0)

   Plot a functional band envelope between bottom and top curves.

   This function creates a filled area between the curves using matplotlib's fill_between.

   :param bottom_curve: 1D array representing the bottom boundary of the band.
   :type bottom_curve: np.ndarray
   :param top_curve: 1D array representing the top boundary of the band.
   :type top_curve: np.ndarray
   :param ax: Matplotlib axes to plot on. If None, creates new figure with axes.
   :type ax: matplotlib.axes.Axes, optional
   :param color: Color for the band. Default is 'red'.
                 Can be any matplotlib color specification (named color, hex, RGB tuple, etc.).
   :type color: str or tuple, optional
   :param alpha: Transparency of the band (0=transparent, 1=opaque). Default is 1.0.
   :type alpha: float, optional
   :param scale: Scale factor for the curves. Default is 1.0.
   :type scale: float, optional

   :returns: **ax** -- The matplotlib axes object used for plotting.
   :rtype: matplotlib.axes.Axes

   :raises ValueError: If bottom_curve and top_curve have different shapes.

   .. rubric:: Examples

   >>> import numpy as np
   >>> import matplotlib.pyplot as plt
   >>> from uvisbox.Modules.FunctionalBoxplot import plot_band
   >>> from uvisbox.Modules.FunctionalBoxplot.functional_boxplot_stats import functional_boxplot_get_band
   >>>
   >>> # Generate synthetic data
   >>> data = np.random.randn(100, 50).cumsum(axis=1)
   >>>
   >>> # Get 50th percentile band
   >>> bottom, top = functional_boxplot_get_band(data, 50, method='fbd')
   >>>
   >>> # Plot the band
   >>> fig, ax = plt.subplots()
   >>> plot_band(bottom, top, ax=ax, color='blue', alpha=0.5)
   >>> plt.show()


.. py:function:: visualize_functional_boxplot(mesh_data, vmin=0, vmax=1, boxplot_style=None, ax=None)

   Visualize functional boxplot from mesh data (summary statistics).

   This function creates a matplotlib visualization of the functional boxplot using
   the summary statistics output from the stats/mesh pipeline.

   :param mesh_data: Dictionary containing summary statistics with the following keys:
                     - 'percentile_bands': dict of percentile bands
                     - 'median': median curve
                     - 'outliers': outlier curves
                     - 'sorted_curves': all curves sorted by depth
   :type mesh_data: dict
   :param boxplot_style: Configuration for the boxplot visualization including percentiles, colormap,
                         and median/outlier styling. If None, uses default configuration.
   :type boxplot_style: BoxplotStyleConfig, optional
   :param ax: Matplotlib axes to plot on. If None, creates a new figure.
   :type ax: matplotlib.axes.Axes, optional

   :returns: **ax** -- The matplotlib axes object with the plot.
   :rtype: matplotlib.axes.Axes

   .. rubric:: Examples

   >>> import numpy as np
   >>> from uvisbox.Modules.FunctionalBoxplot.functional_boxplot_stats import functional_boxplot_summary_statistics
   >>> from uvisbox.Modules.FunctionalBoxplot.functional_boxplot_mesh import functional_boxplot_mesh
   >>> from uvisbox.Modules.FunctionalBoxplot.functional_boxplot_vis import visualize_functional_boxplot
   >>> 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)])
   >>>
   >>> # Process through pipeline
   >>> stats = functional_boxplot_summary_statistics(data)
   >>> mesh_data = functional_boxplot_mesh(stats)
   >>>
   >>> # Visualize
   >>> ax = visualize_functional_boxplot(mesh_data)
   >>>
   >>> # Custom styling
   >>> style = BoxplotStyleConfig(percentiles=[25, 50, 75, 90], show_outliers=True)
   >>> ax = visualize_functional_boxplot(mesh_data, boxplot_style=style)