pre-commit

src/hamsta/core.py

@@ -208,7 +208,7 @@ def fit(
         # apply filter
         rotated_Z = rotated_Z[S_filter]
         intercept_design = intercept_design[S_filter, :]
-        intercept_design = intercept_design[: , jnp.sum(intercept_design, axis=0)>0]
+        intercept_design = intercept_design[:, jnp.sum(intercept_design, axis=0) > 0]
 
         # group intercept into multiple var components
         # bin_idx = np.arange(S.shape[0]) // self.intercept_blksize
@@ -372,7 +372,7 @@ def _jackknife(
                 S=S[selected_index],
                 jackknife=False,
                 intercept_design=intercept_design[selected_index],
-                complete=False
+                complete=False,
             )
             pseudo_val = (
                 num_blocks * param_full

src/hamsta/io.py

@@ -10,9 +10,10 @@
 
 
 def read_bed(fname: str):
-    bedf = open(fname, 'r')
+    bedf = open(fname, "r")
     return [list(map(int, line.split("\t"))) for line in bedf.read().splitlines()]
 
+
 def read_singular_val(svdprefix, svdprefix_chr, nS):
     if svdprefix is not None:
         S_ = np.load(f"{svdprefix}.SVD.S.npy")[:nS]
@@ -244,18 +245,20 @@ def read_zarr(
 def read_nc(
     fname: str,
     ancestry: str,
-    exclude: str=None,
+    exclude: str = None,
 ) -> Tuple[jnp.ndarray, pd.DataFrame]:
 
     ds = xr.open_dataset(fname).load()
 
-    #LA_matrix = jnp.array(ds.locanc.sel(ancestry=ancestry).sum(dim="ploidy"))
+    # LA_matrix = jnp.array(ds.locanc.sel(ancestry=ancestry).sum(dim="ploidy"))
     ds_LA = ds.locanc.sel(ancestry=ancestry).sum(dim="ploidy")
     if exclude is not None:
         exclude_region = read_bed(exclude)
         extract = np.logical_and.reduce(
-            [~np.logical_and(start <= ds_LA['marker'], ds_LA['marker'] < end)
-             for _, start, end in exclude_region]
+            [
+                ~np.logical_and(start <= ds_LA["marker"], ds_LA["marker"] < end)
+                for _, start, end in exclude_region
+            ]
         )
         ds_LA = ds_LA[extract, :]
 

src/hamsta/preprocess.py

@@ -29,10 +29,10 @@ def SVD(
             number of components computed in truncated
 
     Returns:
-        ``(U, S)`` in SVD of ``X = U * S @ Vh``, where X is A/sqrt(N) with A standardized
+        ``(U, S)`` in SVD of ``X = U * S @ Vh``, where X is A/sqrt(N) with A standardized # noqa: E501
 
     """
-    
+
     if LAD is not None:
         A_std = LAD
     else:
@@ -59,7 +59,6 @@ def SVD(
     else:
         U, S, _ = randomized_svd(A_std, n_components=k, random_state=None)
 
-
     if LAD is not None:
         S = jnp.sqrt(S)
     else: