environmentaltools.spatiotemporal.indicators.mean_representative_value

environmentaltools.spatiotemporal.indicators.mean_representative_value(data_cube, time_window=None)

Calculate representative mean value for each spatial cell.

Computes the temporal mean for each spatial location, optionally within a specified time window. This provides a representative value that summarizes the typical condition at each location.

Parameters:

• data_cube (np.ndarray) – 3D array with shape (time, lat, lon) containing spatiotemporal data.

• time_window (tuple of int, optional) – Time indices to use (start, end). If None, uses the entire time period.

Returns:

mean_map – 2D map of representative mean values for each spatial cell.

Return type:

np.ndarray

Notes

When time_window is specified, only the data within that temporal subset is used for computing means. This is useful for analyzing seasonal patterns or specific time periods of interest.

Examples

>>> import numpy as np
>>> data_cube = np.random.normal(10, 2, (365, 30, 30))  # Daily data
>>> # Full year average
>>> mean_all = mean_representative_value(data_cube)
>>> # Summer months (June-August, assuming daily data starting Jan 1)
>>> mean_summer = mean_representative_value(data_cube, time_window=(150, 243))