Temporal timeseries of variables across space and depth: compute_average vs average_between_layers

[elisadonati23]

Assuming that I don't want to compute calculations outside of Medunda and in other languages,

If I want to get 1 daily thetao value between 0-25 meters depth for the entire adriatic sea, I currently have 2 options:

1. download broad datasets (e.g 0-250 meters) -> reducer with average_between_layes to get the average of thetao for each cell between 0-25 -> get out of medunda and do the calculations to get the average across all the cells
2. download datasets with correct depths (0-25) -> use reducer with calculate_average but double axis use seems not supported:

Args:
data (xr.Dataset): Input dataset. Must include ``depth``,
``latitude``, ``longitude``, and ``time`` coordinates as required
by the chosen axis.
axis (str): Axis along which to compute the average. One of
"depth", "space"``, or "time".

I think there is the potential to expand calculate_average to accomodate a min-depth and max-depth argument and double-axis use, incorporating the functionalities of average_between_layers and reducing the steps required to get a temporal timeseries