[fmt] lib

package/MDAnalysis/lib/NeighborSearch.py

@@ -44,8 +44,9 @@ class AtomNeighborSearch(object):
     :class:`~MDAnalysis.lib.distances.capped_distance`.
     """
 
-    def __init__(self, atom_group: AtomGroup,
-                 box: Optional[npt.ArrayLike] = None) -> None:
+    def __init__(
+        self, atom_group: AtomGroup, box: Optional[npt.ArrayLike] = None
+    ) -> None:
         """
 
         Parameters
@@ -62,10 +63,9 @@ def __init__(self, atom_group: AtomGroup,
         self._u = atom_group.universe
         self._box = box
 
-    def search(self, atoms: AtomGroup,
-               radius: float,
-               level: str = 'A'
-               ) -> Optional[Union[AtomGroup, ResidueGroup, SegmentGroup]]:
+    def search(
+        self, atoms: AtomGroup, radius: float, level: str = "A"
+    ) -> Optional[Union[AtomGroup, ResidueGroup, SegmentGroup]]:
         """
         Return all atoms/residues/segments that are within *radius* of the
         atoms in *atoms*.
@@ -102,17 +102,21 @@ def search(self, atoms: AtomGroup,
         except AttributeError:
             # For atom, take the position attribute
             position = atoms.position
-        pairs = capped_distance(position, self.atom_group.positions,
-                                radius, box=self._box, return_distances=False)
+        pairs = capped_distance(
+            position,
+            self.atom_group.positions,
+            radius,
+            box=self._box,
+            return_distances=False,
+        )
 
         if pairs.size > 0:
             unique_idx = unique_int_1d(np.asarray(pairs[:, 1], dtype=np.intp))
         return self._index2level(unique_idx, level)
 
-    def _index2level(self,
-                     indices: List[int],
-                     level: str
-                     ) -> Union[AtomGroup, ResidueGroup, SegmentGroup]:
+    def _index2level(
+        self, indices: List[int], level: str
+    ) -> Union[AtomGroup, ResidueGroup, SegmentGroup]:
         """Convert list of atom_indices in a AtomGroup to either the
         Atoms or segments/residues containing these atoms.
 
@@ -125,11 +129,13 @@ def _index2level(self,
           *radius* of *atoms*.
         """
         atomgroup = self.atom_group[indices]
-        if level == 'A':
+        if level == "A":
             return atomgroup
-        elif level == 'R':
+        elif level == "R":
             return atomgroup.residues
-        elif level == 'S':
+        elif level == "S":
             return atomgroup.segments
         else:
-            raise NotImplementedError('{0}: level not implemented'.format(level))
+            raise NotImplementedError(
+                "{0}: level not implemented".format(level)
+            )

package/MDAnalysis/lib/__init__.py

@@ -27,8 +27,17 @@
 ================================================================
 """
 
-__all__ = ['log', 'transformations', 'util', 'mdamath', 'distances',
-           'NeighborSearch', 'formats', 'pkdtree', 'nsgrid']
+__all__ = [
+    "log",
+    "transformations",
+    "util",
+    "mdamath",
+    "distances",
+    "NeighborSearch",
+    "formats",
+    "pkdtree",
+    "nsgrid",
+]
 
 from . import log
 from . import transformations
@@ -39,6 +48,8 @@
 from . import formats
 from . import pkdtree
 from . import nsgrid
-from .picklable_file_io import (FileIOPicklable,
-                                BufferIOPicklable,
-                                TextIOPicklable)
+from .picklable_file_io import (
+    FileIOPicklable,
+    BufferIOPicklable,
+    TextIOPicklable,
+)

package/MDAnalysis/lib/_distopia.py

@@ -39,13 +39,15 @@
 
     # check for compatibility: currently needs to be >=0.2.0,<0.3.0 (issue
     # #4740) No distopia.__version__ available so we have to do some probing.
-    needed_funcs = ['calc_bonds_no_box_float', 'calc_bonds_ortho_float']
+    needed_funcs = ["calc_bonds_no_box_float", "calc_bonds_ortho_float"]
     has_distopia_020 = all([hasattr(distopia, func) for func in needed_funcs])
     if not has_distopia_020:
-        warnings.warn("Install 'distopia>=0.2.0,<0.3.0' to be used with this "
-                      "release of MDAnalysis. Your installed version of "
-                      "distopia >=0.3.0 will NOT be used.",
-                      category=RuntimeWarning)
+        warnings.warn(
+            "Install 'distopia>=0.2.0,<0.3.0' to be used with this "
+            "release of MDAnalysis. Your installed version of "
+            "distopia >=0.3.0 will NOT be used.",
+            category=RuntimeWarning,
+        )
         del distopia
         HAS_DISTOPIA = False
 
@@ -59,23 +61,22 @@
 def calc_bond_distance_ortho(
     coords1, coords2: np.ndarray, box: np.ndarray, results: np.ndarray
 ) -> None:
-    distopia.calc_bonds_ortho_float(
-        coords1, coords2, box[:3], results=results
-    )
+    distopia.calc_bonds_ortho_float(coords1, coords2, box[:3], results=results)
     # upcast is currently required, change for 3.0, see #3927
 
 
 def calc_bond_distance(
     coords1: np.ndarray, coords2: np.ndarray, results: np.ndarray
 ) -> None:
-    distopia.calc_bonds_no_box_float(
-        coords1, coords2, results=results
-    )
+    distopia.calc_bonds_no_box_float(coords1, coords2, results=results)
     # upcast is currently required, change for 3.0, see #3927
 
 
 def calc_bond_distance_triclinic(
-    coords1: np.ndarray, coords2: np.ndarray, box: np.ndarray, results: np.ndarray
+    coords1: np.ndarray,
+    coords2: np.ndarray,
+    box: np.ndarray,
+    results: np.ndarray,
 ) -> None:
     # redirect to serial backend
     warnings.warn(

package/MDAnalysis/lib/correlations.py

@@ -135,12 +135,18 @@ def autocorrelation(list_of_sets, tau_max, window_step=1):
     """
 
     # check types
-    if (type(list_of_sets) != list and len(list_of_sets) != 0) or type(list_of_sets[0]) != set:
-        raise TypeError("list_of_sets must be a one-dimensional list of sets")  # pragma: no cover
+    if (type(list_of_sets) != list and len(list_of_sets) != 0) or type(
+        list_of_sets[0]
+    ) != set:
+        raise TypeError(
+            "list_of_sets must be a one-dimensional list of sets"
+        )  # pragma: no cover
 
     # Check dimensions of parameters
     if len(list_of_sets) < tau_max:
-        raise ValueError("tau_max cannot be greater than the length of list_of_sets") # pragma: no cover
+        raise ValueError(
+            "tau_max cannot be greater than the length of list_of_sets"
+        )  # pragma: no cover
 
     tau_timeseries = list(range(1, tau_max + 1))
     timeseries_data = [[] for _ in range(tau_max)]
@@ -157,7 +163,7 @@ def autocorrelation(list_of_sets, tau_max, window_step=1):
                 break
 
             # continuous: IDs that survive from t to t + tau and at every frame in between
-            Ntau = len(set.intersection(*list_of_sets[t:t + tau + 1]))
+            Ntau = len(set.intersection(*list_of_sets[t : t + tau + 1]))
             timeseries_data[tau - 1].append(Ntau / float(Nt))
 
     timeseries = [np.mean(x) for x in timeseries_data]
@@ -257,4 +263,3 @@ def correct_intermittency(list_of_sets, intermittency):
 
                 seen_frames_ago[element] = 0
     return list_of_sets
-