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Multi-state models are not currently handled correctly as the coordinates from all states are "flattened" into a single set. Thus, superposition is incorrect since each state has a different frame of reference.
To fix this, the coordinates from each state should be separated. Currently, model coordinates, after being read from the input RMFs, are stored as a large numpy array (conform). Propose changing conform (along with masses, radii and ps_names) to a dictionary with keys corresponding to the state_indexes identified in the RMF file(s).
Output will then include central models and localization densities for each state in every cluster.
Cluster precision will need to be defined as some aggregation of the precision from each state (sum? average? weighted average?).
Not sure the code can handle multi-state models currently
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