ENH: add a python function to compute gradient maps...

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A python function that enables to compute maps of spatial gradients of a cost function (global in time, no time series of gradient maps which would require to prostproc numerical adjoint model and add a new fortran variable to store backward grad trajectory...)

Based on Truyen's scripts used for WRR paper review ?

given a structure, setup incl. time period and cost func definition,

=> call of adjoint model, with user defined parameter values (either constant ones directly imposed or params maps, for ex. from an optim...)

return maps of dJ/dtheta_i, i = 1.. Nparam_type

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Next, a function to compute DGSM (Derivative Based Sobol indices) ... cf. Sobol and Kuruchenko (2010) doi:10.1016/j.matcom.2009.01.023 ; Lamboni et al. (http://dx.doi.org/10.1016/j.matcom.2013.02.002) ; used with adjoint and another prior sampling in Chelil et al. https://doi.org/10.1016/j.jhydrol.2022.128006