style: apply pre-commit (ruff/ruff-format) fixes

Author: khushthecoder

malariagen_data/anoph/base.py

@@ -609,7 +609,7 @@ def _read_sample_sets_manifest(self, *, single_release: str):
             # Add an "unrestricted_use" column, set to True if terms-of-use expiry date <= today's date.
             # Vectorized operation: True if NaN, else (d <= today_date_iso)
             s = df[terms_of_use_expiry_date_column]
-            df["unrestricted_use"] = (s.isna() | (s <= today_date_iso))
+            df["unrestricted_use"] = s.isna() | (s <= today_date_iso)
             # Make the "unrestricted_use" column a nullable boolean, to allow missing data.
             df["unrestricted_use"] = df["unrestricted_use"].astype(pd.BooleanDtype())
 

malariagen_data/anoph/frq_base.py

@@ -1,4 +1,3 @@
-import re
 from textwrap import dedent
 from typing import Optional, Union, List
 
@@ -113,7 +112,7 @@ def _build_cohorts_from_sample_grouping(
         df_cohorts["period_end"] = period.map(
             lambda v: v.end_time if pd.notna(v) else pd.NaT
         )
-    
+
     # Create a label that is similar to the cohort metadata,
     # although this won't be perfect.
     # Vectorized string operations
@@ -126,7 +125,11 @@ def _build_cohorts_from_sample_grouping(
     else:
         # Non-default case: replace non-alphanumeric characters with underscores
         area_str = df_cohorts["area"].astype(str)
-        taxon_clean = df_cohorts[taxon_by].astype(str).str.replace(r"[^A-Za-z0-9]+", "_", regex=True)
+        taxon_clean = (
+            df_cohorts[taxon_by]
+            .astype(str)
+            .str.replace(r"[^A-Za-z0-9]+", "_", regex=True)
+        )
         period_str = df_cohorts["period"].astype(str)
         df_cohorts["label"] = area_str + "_" + taxon_clean + "_" + period_str
 

malariagen_data/anoph/sample_metadata.py

@@ -182,9 +182,7 @@ def _parse_general_metadata(
 
             # Derive a quarter column from month.
             # Vectorized operation: quarter = ((month - 1) // 3) + 1 if month > 0 else -1
-            df["quarter"] = np.where(
-                df["month"] > 0, ((df["month"] - 1) // 3) + 1, -1
-            )
+            df["quarter"] = np.where(df["month"] > 0, ((df["month"] - 1) // 3) + 1, -1)
 
             # Add study columns.
             study_info = self.lookup_study_info(sample_set=sample_set)