From b124339b073efb8c4ff9848a41584fd6fa1ca12e Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 21 Feb 2024 00:24:36 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 pycrostates/cluster/_base.py      | 2 +-
 pycrostates/metrics/silhouette.py | 2 +-
 pycrostates/segmentation/_base.py | 3 ++-
 3 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/pycrostates/cluster/_base.py b/pycrostates/cluster/_base.py
index a4ac8af0..b5593cf5 100644
--- a/pycrostates/cluster/_base.py
+++ b/pycrostates/cluster/_base.py
@@ -955,7 +955,7 @@ def _segment(
         """Create segmentation. Must operate on a copy of states."""
         corr = np.zeros((states.shape[0], data.shape[1]))
         for k in range(0, states.shape[0]):
-            corr[k] = _correlation(data, states[k] , ignore_polarity=ignore_polarity)
+            corr[k] = _correlation(data, states[k], ignore_polarity=ignore_polarity)
         labels = np.argmax(corr, axis=0)
 
         if factor != 0:
diff --git a/pycrostates/metrics/silhouette.py b/pycrostates/metrics/silhouette.py
index 570ac1a3..57c08169 100644
--- a/pycrostates/metrics/silhouette.py
+++ b/pycrostates/metrics/silhouette.py
@@ -43,7 +43,7 @@ def silhouette_score(cluster):  # higher the better
     keep = np.linalg.norm(data.T, axis=1) != 0
     data = data[:, keep]
     labels = labels[keep]
-    distances = np.corrcoef(data) #TODO: memory error ?
+    distances = np.corrcoef(data)  # TODO: memory error ?
     if ignore_polarity:
         distances = np.abs(distances)
     silhouette = sk_silhouette_score(distances, labels, metric="precomputed")
diff --git a/pycrostates/segmentation/_base.py b/pycrostates/segmentation/_base.py
index 663daa66..1da7e273 100644
--- a/pycrostates/segmentation/_base.py
+++ b/pycrostates/segmentation/_base.py
@@ -169,7 +169,8 @@ def compute_parameters(self, norm_gfp: bool = True, return_dist: bool = False):
             if len(arg_where) != 0:
                 labeled_tp = data.T[arg_where][:, 0, :].T
                 labeled_gfp = gfp[arg_where][:, 0]
-                dist_corr = _correlation(labeled_tp, state, ignore_polarity=True
+                dist_corr = _correlation(
+                    labeled_tp, state, ignore_polarity=True
                 )  # TODO: ignore_polarity
                 params[f"{state_name}_mean_corr"] = np.mean(np.abs(dist_corr))
                 dist_gev = (labeled_gfp * dist_corr) ** 2 / np.sum(gfp**2)  # TODO: gev