[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

src/mopsy/adders.py

@@ -11,9 +11,7 @@
 __license__ = "MIT"
 
 
-def append_row(
-    mat: sp.sparse.spmatrix, row: Union[sp.sparse.spmatrix, np.ndarray]
-) -> sp.sparse.spmatrix:
+def append_row(mat: sp.sparse.spmatrix, row: Union[sp.sparse.spmatrix, np.ndarray]) -> sp.sparse.spmatrix:
     """A generic append function for sparse matrices.
 
     Args:
@@ -32,9 +30,7 @@ def append_row(
     return sparse_append(mat=mat, row_or_column=row, axis=0)
 
 
-def append_col(
-    mat: sp.sparse.spmatrix, col: Union[sp.sparse.spmatrix, np.ndarray]
-) -> sp.sparse.spmatrix:
+def append_col(mat: sp.sparse.spmatrix, col: Union[sp.sparse.spmatrix, np.ndarray]) -> sp.sparse.spmatrix:
     """A generic append function for sparse matrices.
 
     Args:
@@ -89,16 +85,12 @@ def sparse_append(
     new_mat = None
     if axis == 0:
         if mat.shape[0] != row_or_column.shape[0]:
-            raise TypeError(
-                "Matrix and new row do not have the same shape along the first dimension."
-            )
+            raise TypeError("Matrix and new row do not have the same shape along the first dimension.")
 
         new_mat = sp.sparse.vstack([mat, row_or_column])
     else:
         if mat.shape[1] != row_or_column.shape[0]:
-            raise TypeError(
-                "Matrix and new row do not have the same shape along the second dimension."
-            )
+            raise TypeError("Matrix and new row do not have the same shape along the second dimension.")
 
         new_mat = sp.sparse.hstack([mat, row_or_column])
 

src/mopsy/mops.py

@@ -39,12 +39,7 @@ def groupby_indices(self, group: Sequence) -> dict:
             A dictionary with each group name as the key and the values containing
             the list of indices that map to it.
         """
-        return {
-            k: [x[0] for x in v]
-            for k, v in groupby(
-                sorted(enumerate(group), key=lambda x: x[1]), lambda x: x[1]
-            )
-        }
+        return {k: [x[0] for x in v] for k, v in groupby(sorted(enumerate(group), key=lambda x: x[1]), lambda x: x[1])}
 
     def _apply(self, func: Callable[[list], Any], axis: Union[int, bool]):
         if self.non_zero:

src/mopsy/nops.py

@@ -15,9 +15,7 @@ class Nops(Mops):
     def __init__(self, mat: np.ndarray, non_zero: bool = False) -> None:
         super().__init__(mat, non_zero=non_zero)
 
-    def iter(
-        self, group: Sequence[str] = None, axis: Union[int, bool] = 0
-    ) -> Iterator[Tuple]:
+    def iter(self, group: Sequence[str] = None, axis: Union[int, bool] = 0) -> Iterator[Tuple]:
         """Iterator over groups and an axis.
 
         Args:

src/mopsy/sops.py

@@ -58,9 +58,7 @@ def iter(self, group: list = None, axis: Union[int, bool] = 0) -> Iterator[Tuple
                 else:
                     yield (k, Sops(mat[:, v], self.non_zero))
 
-    def _apply(
-        self, func: Callable[[list], Any], axis: Union[int, bool] = 0
-    ) -> np.ndarray:
+    def _apply(self, func: Callable[[list], Any], axis: Union[int, bool] = 0) -> np.ndarray:
         mat = self.matrix.tocsc() if axis == 0 else self.matrix.tocsr()
         if self.non_zero:
             # reduction along an axis