"Numpy-isation" of irradiance decomposition functions

Problem I generate GHI data in maps and I want to run it through irradiance decomposition models (specifically dirindex) in order to obtain DNI and DHI data. My data is in three dimensions (time, latitude, longitude) so passing it through pvlib’s built-in functions requiring pandas.DataFrame isn’t easy nor very efficient.

Solution I already adapted all necessary functions to accept numpy arrays of any dimension, just with the time dimension last (see the linked PR). Results are coherent with the pvlib’s functions output and the computation is extremely fast compared to the alternative below. However, I am looking for some advices :

• Is there another solution I did not consider ?
• Is numpy the best choice, should I be using xarray so that the time dimension could be at any position?
• Is it something that could/should be implemented throughout the whole pvlib? Is it interseting

Alternatives I’ve considered I tried looping over all the lat/lon points and calling dirindex for each of them, with multiprocessing but it took too long/ was too computationally heavy for the volume of data I was using

[adriesse]

@mikofski Beta was also better than VHS. 😃

[kanderso-nrel]

Is there another solution I did not consider ?

It is a bit clunky, but the general task of applying a function only vectorized across time to data with other dimensions (e.g. spatial) can often be achieved by reshaping the data into a very long 1-D representation, running the function, and then reshaping the result back to N-D.

There was an issue with some discussion of this a year or two ago, but I don’t recall where.

Maybe https://github.com/pvlib/pvlib-python/issues/959