Using hdf5 training info consistently within gwsurrogate (from Bitbucket)

[sfield17]

Each surrogate hdf5 file has a param_space group which contains information about the training region -- param names, dimensionality, and min/max value of each parameter. This information is used in different ways, and the purpose has been to allow the hdf5 file to summarize the surrogate's training region. Within the code this info is used to instantiate a param_space object. Since the tidal model's region of validity is different from the aligned spin model, the hdf5 file's values are not correct. Because the limits are explicitly set, and the model's docstring is clear, I don't think this is a problem. But I wonder if there should be a cleaner solution. Personally, I like the idea of the hdf5 file containing some model metadata.. but not sure how this would be set without creating an entirely new hdf5 file that repeats all of the info besides the model intervals. Since the tidal model is somewhat special in this respect. On option that doesn't require modifying the hdf5 file would be to have the SurrogateEvaluator base class check if the hdf5 file's limit is equal to the soft_param_lims, but allow derive model's to override the method for special cases. Any thoughts on this or other solutions?

[sfield17]

Proposed fix:

• requires model boundaries to come from soft/hard parameters... which can be enforced when old models are updated to use the new interface