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

for more information, see https://pre-commit.ci
vferat · Feb 24, 2025 · 0b0d1fc · 0b0d1fc
1 parent 2d73418
commit 0b0d1fc
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Showing 2 changed files with 14 additions and 4 deletions.
diff --git a/pycrostates/cluster/kmeans.py b/pycrostates/cluster/kmeans.py
@@ -13,7 +13,11 @@
 
 from .._typing import Picks
 from ..utils import _gev
-from ..utils._checks import _check_n_jobs, _check_random_state, _check_type, _ensure_gfp_function
+from ..utils._checks import (
+    _check_n_jobs,
+    _check_random_state,
+    _check_type,
+)
 from ..utils._docs import copy_doc, fill_doc
 from ..utils._logs import logger
 from ._base import _BaseCluster
@@ -206,7 +210,9 @@ def fit(
                 )
                 if not converged:
                     continue
-                gev = _gev(data, maps, segmentation, ch_type=self.get_channel_types()[0])
+                gev = _gev(
+                    data, maps, segmentation, ch_type=self.get_channel_types()[0]
+                )
                 if best_gev is None or gev > best_gev:
                     best_gev, best_maps, best_segmentation = (
                         gev,

diff --git a/pycrostates/segmentation/_base.py b/pycrostates/segmentation/_base.py
@@ -160,7 +160,9 @@ def compute_parameters(self, norm_gfp: bool = True, return_dist: bool = False):
             # create a 1D view of the labels array
             labels = labels.reshape(-1)
 
-        gfp_function = _ensure_gfp_function(method='auto', ch_type=self._inst.info.get_channel_types()[0])
+        gfp_function = _ensure_gfp_function(
+            method="auto", ch_type=self._inst.info.get_channel_types()[0]
+        )
         gfp = gfp_function(data)
         if norm_gfp:
             labeled = np.argwhere(labels != -1)  # ignore unlabeled segments
@@ -177,7 +179,9 @@ def compute_parameters(self, norm_gfp: bool = True, return_dist: bool = False):
                 labeled_gfp = gfp[arg_where][:, 0]
                 # Correlation (i.e explained variance)
                 dist_corr = _correlation(
-                    labeled_tp, state, ignore_polarity=self._predict_parameters["ignore_polarity"]
+                    labeled_tp,
+                    state,
+                    ignore_polarity=self._predict_parameters["ignore_polarity"],
                 )
                 params[f"{state_name}_mean_corr"] = np.mean(dist_corr)
                 # Global Explained Variance