Clean up rng #285
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Clean up rng #285
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explicit numpy.random.Generator to ops that need random number. Use rng.spaw(1) to make sure that state is advanced whether or not op actually does anything or just reuse previous result. So far only tested for generate.md.md with pool parallelism.
…ions of ASE have issues with plain 'forces' per-atom property
…in_mlip_fitting.ipynb
Fix daisy chain MLIP example GAP evaluation (turn off OMP in GAP because it locks up with pool autoparallelization).
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Replace all calls to
np.random.<some sampling function>with passing an explicitnp.random.Generator, so the user can keep better track of the random number state to make scripts reproducible. In particular, the random numbers for each op are generated from a generator that's spawned from the main one, so it's easy for things to be the same whether an op is actually done or skipped because the output file is already present.Changes to pretty much every function that uses random numbers, because they all require a generator as a function argument. Since it's bad practice for reproducible scripts,
wflwill not make it easy by defaulting to a random unknown rng state.At least one test of this reproducibility, but maybe not comprehensive enough.
closes #284
closes #286