Spatialization of results

Summary

pySTICS should be able to compute results for different point of interests in a vectorial way

Current behavior

Currently, pySTICS compute results based on the weather dataframe with only one value for each weather data for each day.

Desired behavior

We want pySTICS to be coupled with PASE and that for light, pySTICS takes in entry a N_days X N_interest_points matrix instead of the only daily irradiation value from the weather dataframe.

Comments

pySTICS currently work with a huge dataframe containing not only the weather data but also each of the variable that are computed in the crop model. Columns aiming at receiving the variables computed are initialized at the beginning. This makes that it is a big work to spatialize pySTICS because we need to switch to the dictionnary paradigm for variables allowing the variable to be a float or an array of float.

Than what is proposed in a first time is to:

1. Connect pySTICS with PASE.
2. Enabling to run pySTICS for different positions within a loop.

And when we have more time:

3. Really spatializing pySTICS by moving to a dictionnary paradigm as explained above.