black

saqc/lib/ts_operators.py

@@ -276,7 +276,7 @@ def meanQC(data, max_nan_total=np.inf, max_nan_consec=np.inf):
     )
 
 
-def _interpolWrapper(x, order=2, method='time', downgrade_interpolation=False):
+def _interpolWrapper(x, order=2, method="time", downgrade_interpolation=False):
     """
     Function that automatically modifies the interpolation level or returns uninterpolated
     input data if the data configuration breaks the interpolation method at the selected degree.
@@ -300,6 +300,7 @@ def _interpolWrapper(x, order=2, method='time', downgrade_interpolation=False):
         else:
             return x
 
+
 def interpolateNANs(
     data, method, order=2, inter_limit=2, downgrade_interpolation=False
 ):
@@ -360,9 +361,7 @@ def interpolateNANs(
     if method in ["linear", "time"]:
         # in the case of linear interpolation, not much can go wrong/break so this conditional branch has efficient
         # finish by just calling pandas interpolation routine to fill the gaps remaining in the data:
-        data.interpolate(
-            method=method, inplace=True, limit_area="inside"
-        )
+        data.interpolate(method=method, inplace=True, limit_area="inside")
 
     else:
         # if the method that is interpolated with depends on not only the left and right border points of any gap,
@@ -370,9 +369,14 @@ def interpolateNANs(
         # So we use the gap_mask to group the data into chunks and perform the interpolation on every chunk seperatly
         # with the .transform method of the grouper.
         gap_mask = (~gap_mask).cumsum()[data.index]
-        data = data.groupby(by=gap_mask).transform(_interpolWrapper, **{'order':order,
-                                                                        'method':method,
-                                                                        'downgrade_inerpolation':downgrade_interpolation})
+        data = data.groupby(by=gap_mask).transform(
+            _interpolWrapper,
+            **{
+                "order": order,
+                "method": method,
+                "downgrade_inerpolation": downgrade_interpolation,
+            },
+        )
     # finally reinsert the dropped data gaps
     data = data.reindex(pre_index)
     return data

tests/lib/test_ts_operators.py

@@ -228,10 +228,8 @@ def test_rateOfChange(data, expected):
     ],
 )
 def test_interpolatNANs(limit, data, expected):
-    got = interpolateNANs(
-        pd.Series(data), inter_limit=limit, method='linear'
-    )
+    got = interpolateNANs(pd.Series(data), inter_limit=limit, method="linear")
     try:
         assert got.equals(pd.Series(expected, dtype=float))
     except:
-        print('stop')
+        print("stop")
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