style(quickadapter): collapse short expression lines

diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
index 524ca30878b6ad8bd286ed1beef05ce03dd0a675..fc408314cc041d3b7ce0d863bd74eb9c8630f744 100644 (file)
--- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
+++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
@@ -603,8 +603,8 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
                     reason_text,
                 )
                 return compose_sample_weights(
-                base_weights, None, logger=logger, context=context
-            )
+                    base_weights, None, logger=logger, context=context
+                )
             case _:
                 assert_never(policy)
 
@@ -1927,10 +1927,9 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
                     train_known_at_lookahead = known_at_lookahead.loc[
                         train_features.index
                     ]
-                    train_known_at_position = (
-                        train_positions.to_numpy(dtype=np.int64)
-                        + train_known_at_lookahead.to_numpy(dtype=np.int64)
-                    )
+                    train_known_at_position = train_positions.to_numpy(
+                        dtype=np.int64
+                    ) + train_known_at_lookahead.to_numpy(dtype=np.int64)
                     keep_mask &= train_known_at_position < first_test_position
                 else:
                     _log_known_at_none_once(dk.pair, "train_test_split causal guard")
@@ -2354,10 +2353,9 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
             )
             if known_at_lookahead is not None:
                 train_known_at_lookahead = known_at_lookahead.iloc[train_idx]
-                train_known_at_position = (
-                    train_positions.to_numpy(dtype=np.int64)
-                    + train_known_at_lookahead.to_numpy(dtype=np.int64)
-                )
+                train_known_at_position = train_positions.to_numpy(
+                    dtype=np.int64
+                ) + train_known_at_lookahead.to_numpy(dtype=np.int64)
                 keep_mask = train_known_at_position < first_test_position
                 (
                     train_features,
@@ -2737,9 +2735,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
                 selection_method,
                 keep_fraction,
             )
-        elif (
-            threshold_method in QuickAdapterRegressorV3._SKIMAGE_THRESHOLD_METHODS_SET
-        ):
+        elif threshold_method in QuickAdapterRegressorV3._SKIMAGE_THRESHOLD_METHODS_SET:
             return QuickAdapterRegressorV3.skimage_min_max(
                 pred_label,
                 threshold_method,

diff --git a/quickadapter/user_data/strategies/Utils.py b/quickadapter/user_data/strategies/Utils.py
index 9380cd5c2269de2aa4c2aed8a70045e63408b55d..5b5d5cb524a0a2ed4d84044843f75237086a8e48 100644 (file)
--- a/quickadapter/user_data/strategies/Utils.py
+++ b/quickadapter/user_data/strategies/Utils.py
@@ -1231,13 +1231,11 @@ def sanitize_and_renormalize(
         drop_mask = np.asarray(drop_mask)
         if drop_mask.shape != arr.shape:
             raise ValueError(
-                f"{context}: drop_mask shape {drop_mask.shape} != arr "
-                f"shape {arr.shape}"
+                f"{context}: drop_mask shape {drop_mask.shape} != arr shape {arr.shape}"
             )
         if not np.issubdtype(drop_mask.dtype, np.bool_):
             raise ValueError(
-                f"{context}: drop_mask dtype {drop_mask.dtype} is not "
-                f"boolean"
+                f"{context}: drop_mask dtype {drop_mask.dtype} is not boolean"
             )
         safe = np.where(drop_mask, 0.0, safe)
     total = safe.sum()
@@ -1415,9 +1413,7 @@ def compose_sample_weights(
     n = base_weights.shape[0]
     arr = np.asarray(label_weights, dtype=float)
     if arr.shape != (n,):
-        raise ValueError(
-            f"{context}: label_weights shape {arr.shape}, expected ({n},)"
-        )
+        raise ValueError(f"{context}: label_weights shape {arr.shape}, expected ({n},)")
     drop_mask = ~np.isfinite(arr) | (arr <= 0.0)
     if drop_mask.all():
         raise LabelWeightSupportError(