fixed residues.py and scores.py

saqc/funcs/residues.py

@@ -99,10 +99,16 @@ def calculatePolynomialResidues(
         Flags values may have changed relatively to the flagger input.
 
     """
-    data, flagger = fitPolynomial(data, field, flagger, winsz, polydeg, numba=numba, eval_flags=eval_flags,
-                                  min_periods=min_periods, return_residues=True, **kwargs)
-
-    return data, flagger
+    return fitPolynomial(
+        data, field, flagger,
+        winsz=winsz,
+        polydeg=polydeg,
+        numba=numba,
+        eval_flags=eval_flags,
+        min_periods=min_periods,
+        return_residues=True,
+        **kwargs
+    )
 
 
 @register(masking='field', module="residues")
@@ -118,7 +124,14 @@ def calculateRollingResidues(
         **kwargs
 ) -> Tuple[DictOfSeries, Flagger]:
 
-    data, flagger = roll(data, field, flagger, winsz, func=func, eval_flags=eval_flags,
-                         min_periods=min_periods, center=center, return_residues=True, **kwargs)
+    return roll(
+        data, field, flagger,
+        winsz=winsz,
+        func=func,
+        eval_flags=eval_flags,
+        min_periods=min_periods,
+        center=center,
+        return_residues=True,
+        **kwargs
+    )
 
-    return data, flagger

saqc/funcs/scores.py

@@ -108,17 +108,18 @@ def assignKNNScore(
 
     References
     ----------
-
     [1] https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html
-
     """
     data = data.copy()
     fields = toSequence(fields)
+
     val_frame = data[fields]
     score_index = val_frame.index_of("shared")
     score_ser = pd.Series(np.nan, index=score_index, name=target_field)
+
     val_frame = val_frame.loc[val_frame.index_of("shared")].to_df()
     val_frame.dropna(inplace=True)
+
     if not trafo_on_partition:
         val_frame = val_frame.transform(trafo)
 
@@ -154,11 +155,11 @@ def assignKNNScore(
 
         score_ser[partition.index] = resids
 
-    score_flagger = flagger.initFlags(score_ser)
-
+    # this unconditionally overwrite a column,
+    # may we should fire a warning ? -- palmb
     if target_field in flagger.columns:
-        flagger = flagger.slice(drop=target_field)
+        flagger.drop(target_field)
+    flagger[target_field] = score_ser
 
-    flagger = flagger.merge(score_flagger)
     data[target_field] = score_ser
     return data, flagger