Fix for issue #4655 Removing mutable data structures and function calls as default arguments in the entire codebase

package/CHANGELOG

@@ -14,11 +14,13 @@ The rules for this file:
 
 
 -------------------------------------------------------------------------------
-??/??/?? IAlibay
+??/??/?? IAlibay, tanishy7777
 
  * 2.9.0
 
 Fixes
+ * Replaced mutable defaults with None and initialized them within
+   the function to prevent shared state.
 
 Enhancements
 

package/MDAnalysis/analysis/align.py

@@ -1592,7 +1592,9 @@ def get_matching_atoms(ag1, ag2, tol_mass=0.1, strict=False, match_atoms=True):
         rsize_mismatches = np.absolute(rsize1 - rsize2)
         mismatch_mask = (rsize_mismatches > 0)
         if np.any(mismatch_mask):
-            def get_atoms_byres(g, match_mask=np.logical_not(mismatch_mask)):
+            def get_atoms_byres(g, match_mask=None):
+                if match_mask is None:
+                    match_mask = np.logical_not(mismatch_mask)
                 # not pretty... but need to do things on a per-atom basis in
                 # order to preserve original selection
                 ag = g.atoms

package/MDAnalysis/analysis/base.py

@@ -479,7 +479,7 @@
 
     def _compute(self, indexed_frames: np.ndarray,
                  verbose: bool = None,
-                 *, progressbar_kwargs={}) -> "AnalysisBase":
+                 *, progressbar_kwargs=None) -> "AnalysisBase":
         """Perform the calculation on a balanced slice of frames
         that have been setup prior to that using _setup_computation_groups()
 
@@ -500,6 +500,8 @@
 
         .. versionadded:: 2.8.0
         """
+        if progressbar_kwargs is None:
+            progressbar_kwargs = {}
         logger.info("Choosing frames to analyze")
         # if verbose unchanged, use class default
         verbose = getattr(self, "_verbose", False) if verbose is None else verbose

package/MDAnalysis/analysis/encore/clustering/cluster.py

@@ -45,7 +45,7 @@
 
 
 def cluster(ensembles,
-            method = ClusteringMethod.AffinityPropagationNative(),
+            method=None,
             select="name CA",
             distance_matrix=None,
             allow_collapsed_result=True,
@@ -154,7 +154,8 @@ def cluster(ensembles,
         [array([1, 1, 1, 1, 2]), array([1, 1, 1, 1, 1])]
 
     """
-
+    if method is None:
+        method = ClusteringMethod.AffinityPropagationNative()
     # Internally, ensembles are always transformed to a list of lists
     if ensembles is not None:
         if not hasattr(ensembles, '__iter__'):

package/MDAnalysis/analysis/encore/dimensionality_reduction/reduce_dimensionality.py

@@ -45,7 +45,7 @@
 
 
 def reduce_dimensionality(ensembles,
-                          method=StochasticProximityEmbeddingNative(),
+                          method=None,
                           select="name CA",
                           distance_matrix=None,
                           allow_collapsed_result=True,
@@ -152,7 +152,8 @@ def reduce_dimensionality(ensembles,
                              encore.StochasticProximityEmbeddingNative(dimension=2)])
 
     """
-
+    if method is None:
+        method = StochasticProximityEmbeddingNative()
     if ensembles is not None:
         if not hasattr(ensembles, '__iter__'):
             ensembles = [ensembles]

package/MDAnalysis/analysis/encore/similarity.py

@@ -954,12 +954,7 @@ def hes(ensembles,
 
 def ces(ensembles,
         select="name CA",
-        clustering_method=AffinityPropagationNative(
-            preference=-1.0,
-            max_iter=500,
-            convergence_iter=50,
-            damping=0.9,
-            add_noise=True),
+        clustering_method=None,
         distance_matrix=None,
             estimate_error=False,
         bootstrapping_samples=10,
@@ -1084,7 +1079,13 @@ def ces(ensembles,
          [0.25331629 0.        ]]
 
     """
-
+    if clustering_method is None:
+        clustering_method = AffinityPropagationNative(
+            preference=-1.0,
+            max_iter=500,
+            convergence_iter=50,
+            damping=0.9,
+            add_noise=True)
     for ensemble in ensembles:
         ensemble.transfer_to_memory()
 
@@ -1220,13 +1221,7 @@ def ces(ensembles,
 
 def dres(ensembles,
          select="name CA",
-         dimensionality_reduction_method = StochasticProximityEmbeddingNative(
-             dimension=3,
-             distance_cutoff = 1.5,
-             min_lam=0.1,
-             max_lam=2.0,
-             ncycle=100,
-             nstep=10000),
+         dimensionality_reduction_method=None,
          distance_matrix=None,
          nsamples=1000,
          estimate_error=False,
@@ -1354,7 +1349,14 @@ def dres(ensembles,
     :mod:`MDAnalysis.analysis.encore.dimensionality_reduction.reduce_dimensionality``
 
     """
-
+    if dimensionality_reduction_method is None:
+        dimensionality_reduction_method = StochasticProximityEmbeddingNative(
+             dimension=3,
+             distance_cutoff=1.5,
+             min_lam=0.1,
+             max_lam=2.0,
+             ncycle=100,
+             nstep=10000)
     for ensemble in ensembles:
         ensemble.transfer_to_memory()
 
@@ -1487,12 +1489,7 @@ def dres(ensembles,
 def ces_convergence(original_ensemble,
                     window_size,
                     select="name CA",
-                    clustering_method=AffinityPropagationNative(
-                        preference=-1.0,
-                        max_iter=500,
-                        convergence_iter=50,
-                        damping=0.9,
-                        add_noise=True),
+                    clustering_method=None,
                     ncores=1):
     """
     Use the CES to evaluate the convergence of the ensemble/trajectory.
@@ -1559,7 +1556,13 @@ def ces_convergence(original_ensemble,
          [0.        ]]
 
     """
-
+    if clustering_method is None:
+        clustering_method = AffinityPropagationNative(
+                        preference=-1.0,
+                        max_iter=500,
+                        convergence_iter=50,
+                        damping=0.9,
+                        add_noise=True)
     ensembles = prepare_ensembles_for_convergence_increasing_window(
         original_ensemble, window_size, select=select)
 
@@ -1587,15 +1590,7 @@ def ces_convergence(original_ensemble,
 def dres_convergence(original_ensemble,
                      window_size,
                      select="name CA",
-                     dimensionality_reduction_method = \
-                            StochasticProximityEmbeddingNative(
-                                dimension=3,
-                                distance_cutoff=1.5,
-                                min_lam=0.1,
-                                max_lam=2.0,
-                                ncycle=100,
-                                nstep=10000
-                            ),
+                     dimensionality_reduction_method=None,
                      nsamples=1000,
                      ncores=1):
     """
@@ -1660,7 +1655,15 @@ def dres_convergence(original_ensemble,
     much the trajectory keeps on resampling the same ares of the conformational
     space, and therefore of convergence.
     """
-
+    if dimensionality_reduction_method is None:
+        dimensionality_reduction_method = StochasticProximityEmbeddingNative(
+                                dimension=3,
+                                distance_cutoff=1.5,
+                                min_lam=0.1,
+                                max_lam=2.0,
+                                ncycle=100,
+                                nstep=10000
+                            )
     ensembles = prepare_ensembles_for_convergence_increasing_window(
         original_ensemble, window_size, select=select)
 

package/MDAnalysis/analysis/helix_analysis.py

@@ -186,7 +186,7 @@
     return np.rad2deg(to_ortho)
 
 
-def helix_analysis(positions, ref_axis=[0, 0, 1]):
+def helix_analysis(positions, ref_axis=None):
     r"""
     Calculate helix properties from atomic coordinates.
 
@@ -244,7 +244,8 @@
     # θ: local_twists
     # origin: origins
     # local_axes: perpendicular to plane of screen. Orthogonal to "bisectors"
-
+    if ref_axis is None:
+        ref_axis = (0, 0, 1)
     vectors = positions[1:] - positions[:-1]  # (n_res-1, 3)
     bisectors = vectors[:-1] - vectors[1:]  # (n_res-2, 3)
     bimags = mdamath.pnorm(bisectors)  # (n_res-2,)
@@ -387,11 +388,13 @@
         'local_screw_angles': (-2,),
     }
 
-    def __init__(self, universe, select='name CA', ref_axis=[0, 0, 1],
+    def __init__(self, universe, select='name CA', ref_axis=None,
                  verbose=False, flatten_single_helix=True,
                  split_residue_sequences=True):
         super(HELANAL, self).__init__(universe.universe.trajectory,
                                       verbose=verbose)
+        if ref_axis is None:
+            ref_axis = (0, 0, 1)
         selections = util.asiterable(select)
         atomgroups = [universe.select_atoms(s) for s in selections]
         consecutive = []

package/MDAnalysis/core/universe.py

@@ -1476,7 +1476,7 @@
     @classmethod
     def from_smiles(cls, smiles, sanitize=True, addHs=True,
                     generate_coordinates=True, numConfs=1,
-                    rdkit_kwargs={}, **kwargs):
+                    rdkit_kwargs=None, **kwargs):
         """Create a Universe from a SMILES string with rdkit
 
         Parameters
@@ -1530,6 +1530,8 @@
         .. versionadded:: 2.0.0
 
         """
+        if rdkit_kwargs is None:
+            rdkit_kwargs = {}
         try:
             from rdkit import Chem
             from rdkit.Chem import AllChem

package/MDAnalysis/topology/ITPParser.py

@@ -429,8 +429,10 @@
 
         return atom_order, new_params, molnums, self.moltypes, residx
 
-    def add_param(self, line, container, n_funct=2, funct_values=[]):
+    def add_param(self, line, container, n_funct=2, funct_values=None):
         """Add defined GROMACS directive lines, only if the funct is in ``funct_values``"""
+        if funct_values is None:
+            funct_values = []
         values = line.split()
         funct = int(values[n_funct])
         if funct in funct_values: