Black reformatting

sica/_whitening.py

@@ -16,40 +16,40 @@
 
 
 def whitening(
-        X: Union[np.ndarray, spmatrix],
-        n_components: int,
-        svd_solver: str,
-        chunked: bool,
-        chunk_size: Union[int, None],
-        zero_center: bool,
-        random_state: Optional[Union[int, np.random.RandomState]] = None
+    X: Union[np.ndarray, spmatrix],
+    n_components: int,
+    svd_solver: str,
+    chunked: bool,
+    chunk_size: Union[int, None],
+    zero_center: bool,
+    random_state: Optional[Union[int, np.random.RandomState]] = None,
 ) -> Tuple[np.ndarray, np.ndarray]:
-    """ Whiten data (i.e. transform variables into a set of new uncorrelated and unit-variance variables) and reduce
+    """Whiten data (i.e. transform variables into a set of new uncorrelated and unit-variance variables) and reduce
     dimension trhough a PCA-like approach. This function handles array-like formats as well as sparse matrices.
-    
+
     Parameters
     ----------
     X : 2D ndarray or spmatrix, shape (n_samples , n_features)
-        
+
     n_components : int
         number of pricipal components to compute. If None, n_components = min(X.shape)
-        
+
     svd_solver : str {‘auto’, ‘full’, ‘arpack’, ‘randomized’ , 'lobpcg'}
         solver for the different PCA methods. Please note that some solvers may not be compatible with
         some PCA methods. See PCA, TruncatedSVD and IncrementalPCA from sklearn.decompostion or
         scipy.sparse.linalg.svds.
-        
+
     chunked : boolean
-        if True, perform an incremental PCA on segments of chunk_size. The incremental PCA automatically 
+        if True, perform an incremental PCA on segments of chunk_size. The incremental PCA automatically
         zero centers and ignores settings of random_seed and svd_solver.
-        
+
     chunk_size : int
         Number of observations to include in each chunk. Required if chunked=True was passed.
-        
+
     zero_center : boolean
         If True, compute standard PCA from covariance matrix. If False, omit zero-centering variables
         (uses TruncatedSVD), which allows to handle sparse input efficiently.
-        
+
     random_state : int, RandomState, optional
         Change to use different initial states for the optimization. The default is None.
 
@@ -106,27 +106,28 @@ def whitening(
 
 
 def _pca_with_sparse(
-        X: spmatrix,
-        npcs: int,
-        solver: Optional[str] = "arpack",
-        mu=None,
-        random_state: Optional[Union[int, np.random.RandomState]] = None) -> Tuple[np.ndarray, np.ndarray]:
-    """ Compute PCA decomposition with initial centering for sparse input.
-    
+    X: spmatrix,
+    npcs: int,
+    solver: Optional[str] = "arpack",
+    mu=None,
+    random_state: Optional[Union[int, np.random.RandomState]] = None,
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Compute PCA decomposition with initial centering for sparse input.
+
     Parameters
     ----------
     X : spmatrix, shape (n_samples, n_features)
 
     npcs : int
         number of PCA componnents.
-        
+
     solver : str, optional
         Eigenvalue solver to use. Should be ‘arpack’ or ‘lobpcg’. See scipy.sparse.linalg.svds.
         The default is 'arpack'.
-        
+
     mu : TYPE, optional
         DESCRIPTION. The default is None.
-        
+
     random_state : int, RandomState, optional
         The default is None.