Bug with clean_traj=False // improvement suggestions

[rafwiewiora]

1) When using clean_traj=False with multiple trajectories, you call map_to_integers on each trajectory separately:

NMpathAnalysis/nmpath/nmm.py

Line 101 in d4be6ba

newseq, m_dict = map_to_integers(seq, seq_map)

and for each trajectory you start counting new state indexes from 0:

NMpathAnalysis/nmpath/auxfunctions.py

Line 284 in d4be6ba

counter = 0

hence if the 1st trajectory doesn't contain all states, you'll map the 'new' states from further trajectories to index 0 etc. again

Extra suggestion: you return m_dict here:

NMpathAnalysis/nmpath/nmm.py

Line 101 in d4be6ba

newseq, m_dict = map_to_integers(seq, seq_map)

but use seq_map (which luckily had been modified in place by map_to_integers) eventually instead:

NMpathAnalysis/nmpath/nmm.py

Line 107 in d4be6ba

self.seq_map = seq_map

2) When using coarse_macrostates=True the original dtrajs are not copied and get modified:

NMpathAnalysis/nmpath/nmm.py

Line 65 in d4be6ba

traj[i] = stateA[0]

which is not great if you don't know about this and want to use the same dtrajs downstream...

3) You use **kwargs:

NMpathAnalysis/nmpath/nmm.py

Line 59 in d4be6ba

coarse_macrostates=False, **kwargs):

they don't actually get passed to anything, but cause trouble if you misspell the existing keyword arguments - there's no error, e.g. I kept misspelling clean_trajs instead of clean_traj and it took me a while to realize why my transition paths were coming out wrong.

4) fit using list dtrajs is 6x faster than with int64 numpy array, and 36x faster than with int32 numpy array -- it should convert arrays to lists.

[RobertArbon]

Fixed 1) with PR #2