Changes (by Gabriele Bozzola):

Consider a thorn like NPScalars in the canuda arrangement.

NPScalars computes the Newman-Penrose grid functions in the CCTK_ANALYSIS bin. The operation requires taking derivatives. Similarly, LeanBSSNMoL computes the constraints in the same bin (this will likely be a regression I introduced in commit 80b6b1b).

My understanding is now that CCTK_ANALYSIS is not the right place where to do this because of interpolation/prolongation/notsurewhat.

Is the fix to simply replace CCTK_ANALYSIS with MoL_PseudoEvolution?

I would like to make sure that this is the case considering also parameters like compute_every. Since these are expensive diagnostics, it is best to compute them only when they are output. Do I get the correct constraints/scalars if I compute and output them only when all the refinement levels are synced?

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Ticket URL: https://bitbucket.org/einsteintoolkit/tickets/issues/2629/mol_pseudoevolution-vs-analysis