diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 6557e47..0ea59d3 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
       - id: check-toml
       - id: check-yaml
@@ -19,21 +19,21 @@ repos:
       - id: requirements-txt-fixer
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.3.0
+    rev: v2.4.2
     hooks:
       - id: codespell
         additional_dependencies:
           - tomli
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.7.2
+    rev: v0.15.9
     hooks:
       - id: ruff
         args: ["--fix", "--show-fixes"]
       - id: ruff-format
 
   - repo: https://github.com/abravalheri/validate-pyproject
-    rev: v0.22
+    rev: v0.25
     hooks:
       - id: validate-pyproject
 
diff --git a/aok/core/kd_utils/Kd_analysis.py b/aok/core/kd_utils/Kd_analysis.py
index 7d1504b..136d8fb 100644
--- a/aok/core/kd_utils/Kd_analysis.py
+++ b/aok/core/kd_utils/Kd_analysis.py
@@ -612,9 +612,7 @@ def _fit_kd_with_method(
         )
         return kd, e0, nf
 
-    raise ValueError(
-        f"Unknown kd_fit_method: {method!r}. " f"Choose from {KD_FIT_METHODS}"
-    )
+    raise ValueError(f"Unknown kd_fit_method: {method!r}. Choose from {KD_FIT_METHODS}")
 
 
 # another solution is to calculate kd without hist
@@ -849,7 +847,7 @@ def calculate_kd(
         else:
             expected_nf_per_bin = None
             logging.info(
-                "Hybrid: insufficient noise floor estimates (%d), " "skipping pass 2",
+                "Hybrid: insufficient noise floor estimates (%d), skipping pass 2",
                 len(valid_nf_all),
             )
 
diff --git a/aok/core/main.py b/aok/core/main.py
index 9168ef0..864c21b 100644
--- a/aok/core/main.py
+++ b/aok/core/main.py
@@ -284,9 +284,9 @@ def run_pipeline(args):
             ["relative_AT_dist", "lat_bins", "photon_height"]
         ].drop_duplicates()
         unique_photon_dataset["relative_AT_dist_center"] = (
-            unique_photon_dataset.groupby(
-                "lat_bins", observed=False
-            )["relative_AT_dist"].transform("mean")
+            unique_photon_dataset.groupby("lat_bins", observed=False)[
+                "relative_AT_dist"
+            ].transform("mean")
         )
 
         _closest_rows = []