Support mdtraj 1.10

.github/workflows/build-code.yml

@@ -68,6 +68,7 @@ jobs:
       run: |
         # conda install -yq -c ${CONDA_PREFIX}/conda-bld/ msmbuilder2022
         pip install pytest==8.0.2
+        conda install scipy=1.13.1
         conda install -yq numdifftools hmmlearn
         mkdir ../../pkgs
         cp -r msmbuilder/tests ../../pkgs

msmbuilder/decomposition/utils.py

@@ -1,7 +1,6 @@
 import hashlib
 import itertools
 import numpy as np
-from six.moves import xrange
 
 
 def iterate_tracker(maxiter, max_nc, verbose=False):
@@ -12,7 +11,7 @@ def iterate_tracker(maxiter, max_nc, verbose=False):
     last_hash_count = 0
     arr = yield
 
-    for i in xrange(maxiter):
+    for i in range(maxiter):
         arr = yield i
         if arr is not None:    
             hsh = hashlib.sha1(arr.view(np.uint8)).hexdigest()

msmbuilder/featurizer/multichain.py

@@ -9,7 +9,6 @@
 
 import mdtraj as md
 from mdtraj.utils import ensure_type
-from mdtraj.utils.six import string_types
 import numpy as np
 import itertools
 import warnings
@@ -170,14 +169,15 @@ def __init__(self, protein_chain='auto', ligand_chain='auto',
 
     def _get_contact_pairs(self, contacts):
         if self.scheme=='ca':
-            if not any(a for a in self.reference_frame.top.chain(ligand_chain).atoms
+            # possible error here with "ligand_chain" from no where, change to self.ligand_chain
+            if not any(a for a in self.reference_frame.top.chain(self.ligand_chain).atoms
                        if a.name.lower() == 'ca'):
                 raise ValueError("Bad scheme: the ligand has no alpha carbons")
 
         # this is really similar to mdtraj/contact.py, but ensures that
         # md.compute_contacts  is always seeing an array of exactly the
         # contacts we want to specify
-        if isinstance(contacts, string_types):
+        if isinstance(contacts, str):
             if contacts.lower() != 'all':
                 raise ValueError('({}) is not a valid contacts specifier'.format(contacts.lower()))
 

msmbuilder/tpt/committor.py

@@ -35,8 +35,6 @@
 from __future__ import print_function, division, absolute_import
 import numpy as np
 
-from mdtraj.utils.six.moves import xrange
-
 __all__ = ['committors', 'conditional_committors',
            '_committors', '_conditional_committors']
 
@@ -190,7 +188,7 @@ def _conditional_committors(source, sink, waypoint, tprob):
     # permute the transition matrix into cannonical form - send waypoint the the
     # last row, and source + sink to the end after that
     Bsink_indices = [source, sink, waypoint]
-    perm = np.array([i for i in xrange(n_states) if i not in Bsink_indices],
+    perm = np.array([i for i in range(n_states) if i not in Bsink_indices],
                     dtype=int)
     perm = np.concatenate([perm, Bsink_indices])
     permuted_tprob = tprob[perm, :][:, perm]

msmbuilder/tpt/hub.py

@@ -18,7 +18,6 @@
 
 from . import committors, conditional_committors
 
-from mdtraj.utils.six.moves import xrange
 import itertools
 
 __all__ = ['fraction_visited', 'hub_scores']
@@ -115,15 +114,15 @@ def hub_scores(msm, waypoints=None):
     if isinstance(waypoints, int):
         waypoints = [waypoints]
     elif waypoints is None:
-        waypoints = xrange(n_states)
+        waypoints = range(n_states)
     elif not (isinstance(waypoints, list) or
               isinstance(waypoints, np.ndarray)):
         raise ValueError("waypoints (%s) must be an int, a list, or None" %
                          str(waypoints))
 
     hub_scores = []
     for waypoint in waypoints:
-        other_states = (i for i in xrange(n_states) if i != waypoint)
+        other_states = (i for i in range(n_states) if i != waypoint)
 
         # calculate the hub score for this waypoint
         hub_score = 0.0

msmbuilder/tpt/mfpt.py

@@ -20,7 +20,6 @@
 from __future__ import print_function, division, absolute_import
 import numpy as np
 import scipy
-from mdtraj.utils.six.moves import xrange
 import copy
 from msmbuilder.msm.core import _solve_msm_eigensystem
 
@@ -214,7 +213,7 @@ def _mfpts(tprob, populations, sinks, lag_time):
 
         # mfpt[i,j] = (fund_matrix[j,j] - fund_matrix[i,j]) / populations[j]
         mfpts = fund_matrix * -1
-        for j in xrange(n_states):
+        for j in range(n_states):
             mfpts[:, j] += fund_matrix[j, j]
             mfpts[:, j] /= populations[j]
 

setup.py

@@ -211,7 +211,7 @@
       },
       install_requires=[
           'numpy',
-          'mdtraj==1.9.9',
+          'mdtraj',
           'scikit-learn',
           'pandas',
           'fastcluster',
@@ -220,7 +220,7 @@
           'tables',
           'numpydoc',
           'six',
-          'scipy',
+          'scipy<=1.13.1',
           'pyhmc @ git+https://github.com/bojunliu0818/pyhmc.git@bojunliu0818-dev',
       ],
       entry_points={'console_scripts':