import numpy as np
[docs]
def contour_boxplot_mesh(summary_statistics, x_coords=None, y_coords=None):
"""
Process summary statistics to create mesh data for visualization.
This function aggregates percentile bands by overwriting values in descending percentile order,
creating a single image that shows all bands with appropriate color values.
Parameters
-----------
summary_statistics : dict
Output from ``contour_boxplot_summary_statistics``. It contains the
median image, percentile bands, and outlier images.
x_coords : np.ndarray, optional
1D array of x-axis coordinates defining the spatial domain.
Length must match the image width. If None, pixel indices are used.
y_coords : np.ndarray, optional
1D array of y-axis coordinates defining the spatial domain.
Length must match the image height. If None, pixel indices are used.
Returns
--------
dict
Contains ``percentile_bands_image``, ``median``, ``outliers``,
``extent``, ``x_coords``, and ``y_coords``. The band image stores each
pixel's percentile divided by 100; lower percentiles overwrite higher
ones. ``extent`` is ``(x_min, x_max, y_min, y_max)`` when coordinates
are supplied and ``None`` otherwise.
Notes
------
The aggregation works by:
1. Starting with highest percentile band (e.g., 90th)
2. Painting pixels with value 0.90 where band is non-zero
3. Overwriting with lower percentile bands (e.g., 75th → 0.75)
4. Continuing until lowest percentile
This creates a layered visualization where inner bands overwrite outer bands.
Examples
---------
>>> stats = contour_boxplot_summary_statistics(ensemble, isovalue=0.5)
>>> mesh_data = contour_boxplot_mesh(stats)
>>> print(mesh_data['percentile_bands_image'].shape)
(100, 100)
"""
# Get image dimensions from median
median = summary_statistics['median']
height, width = median.shape
# Validate coordinate arrays if provided
if x_coords is not None:
x_coords = np.asarray(x_coords)
if x_coords.ndim != 1 or len(x_coords) != width:
raise ValueError(f"x_coords must be 1D with length {width}, got shape {x_coords.shape}")
if y_coords is not None:
y_coords = np.asarray(y_coords)
if y_coords.ndim != 1 or len(y_coords) != height:
raise ValueError(f"y_coords must be 1D with length {height}, got shape {y_coords.shape}")
# Compute extent from coordinates
if x_coords is not None and y_coords is not None:
extent = (x_coords[0], x_coords[-1], y_coords[0], y_coords[-1])
else:
extent = None
# Initialize aggregated image with zeros
percentile_bands_image = np.zeros((height, width), dtype=np.float32)
# Get percentile bands and sort by percentile in descending order
percentile_bands = summary_statistics['percentile_bands']
sorted_bands = sorted(percentile_bands, key=lambda x: x[0], reverse=True)
# Apply bands in descending order (highest to lowest)
# Lower percentile values will overwrite higher ones
for percentile, band_image in sorted_bands:
# Where band is non-zero, overwrite with the band value
mask = band_image > 0
percentile_bands_image[mask] = band_image[mask]
return {
'percentile_bands_image': percentile_bands_image,
'median': median,
'outliers': summary_statistics['outliers'],
'extent': extent,
'x_coords': x_coords,
'y_coords': y_coords,
}