environmentaltools.spatiotemporal.indicators.multivariate_neighbourhood_synergy

environmentaltools.spatiotemporal.indicators.multivariate_neighbourhood_synergy(cube_list, size=3)[source]

Calculate neighborhood synergy between multiple variables.

Computes the synergy between multiple environmental variables within spatial neighborhoods. High synergy indicates variables that co-vary coherently in space, while low synergy suggests independent patterns.

Parameters:

cube_list (list of np.ndarray) – List of 3D arrays (time, lat, lon), one per variable.
size (int, optional) – Neighborhood size. Default is 3.

Returns:

synergy_map – 2D map of neighborhood synergy (0-1 scale, higher = more synergistic).

Return type:

np.ndarray

Notes

Synergy is computed as the inverse of the coefficient of variation across variables within each neighborhood. High synergy means variables have similar relative patterns within neighborhoods.

Examples

>>> import numpy as np
>>> cube1 = np.random.random((20, 15, 15))
>>> cube2 = np.random.random((20, 15, 15))
>>> synergy = multivariate_neighbourhood_synergy([cube1, cube2], size=3)