black

saqc/funcs/interpolation.py

@@ -144,7 +144,7 @@ class InterpolationMixin:
         method: _SUPPORTED_METHODS,
         order: int = 2,
         limit: int | None = None,
-        extrapolate: Literal['forward', 'backward', 'both'] = None,
+        extrapolate: Literal["forward", "backward", "both"] = None,
         flag: float = UNFLAGGED,
         **kwargs,
     ) -> "SaQC":
@@ -187,7 +187,7 @@ class InterpolationMixin:
             method,
             order=order,
             gap_limit=limit,
-            extrapolate=extrapolate
+            extrapolate=extrapolate,
         )
 
         interpolated = self._data[field].isna() & inter_data.notna()

saqc/lib/ts_operators.py

@@ -275,25 +275,40 @@ def meanQC(data, max_nan_total=np.inf, max_nan_consec=np.inf):
     )
 
 
-def _interpolWrapper(x, order=1, method="time", limit_area='inside', limit_direction=None):
+def _interpolWrapper(
+    x, order=1, method="time", limit_area="inside", limit_direction=None
+):
     """
     Function that automatically modifies the interpolation level or returns uninterpolated
     input data if the data configuration breaks the interpolation method at the selected degree.
     """
 
-    min_vals_dict = {'nearest': 2, 'slinear': 2, 'quadratic': 3, 'cubic':4, 'spline':order+1, 'polynomial':order+1,
-                     'piecewise_polynomial': 2, 'pchip': 2, 'akima': 2, 'cubicspline': 2}
+    min_vals_dict = {
+        "nearest": 2,
+        "slinear": 2,
+        "quadratic": 3,
+        "cubic": 4,
+        "spline": order + 1,
+        "polynomial": order + 1,
+        "piecewise_polynomial": 2,
+        "pchip": 2,
+        "akima": 2,
+        "cubicspline": 2,
+    }
     min_vals = min_vals_dict.get(method, 0)
 
     if (x.size < 3) | (x.count() < min_vals):
         return x
     else:
-        return x.interpolate(method=method, order=order, limit_area=limit_area, limit_direction=limit_direction)
+        return x.interpolate(
+            method=method,
+            order=order,
+            limit_area=limit_area,
+            limit_direction=limit_direction,
+        )
 
 
-def interpolateNANs(
-    data, method, order=2, gap_limit=2, extrapolate=None
-):
+def interpolateNANs(data, method, order=2, gap_limit=2, extrapolate=None):
     """
     The function interpolates nan-values (and nan-grids) in timeseries data. It can
     be passed all the method keywords from the pd.Series.interpolate method and will
@@ -338,7 +353,9 @@ def interpolateNANs(
                 gap_mask = gap_mask & gap_mask.shift(-1, fill_value=True)
             else:
                 # If the gap_size is bigger we make an flip-rolling combo to backpropagate the False values
-                gap_mask = ~((~gap_mask[::-1]).rolling(gap_limit, min_periods=0).sum() > 0)[::-1]
+                gap_mask = ~(
+                    (~gap_mask[::-1]).rolling(gap_limit, min_periods=0).sum() > 0
+                )[::-1]
 
     # memorizing the index for later reindexing
     pre_index = data.index
@@ -350,7 +367,12 @@ 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=limit_area, limit_direction=extrapolate)
+        data.interpolate(
+            method=method,
+            inplace=True,
+            limit_area=limit_area,
+            limit_direction=extrapolate,
+        )
 
     else:
         # if the method that is interpolated with, depends on not only the left and right border points of any gap,
@@ -365,7 +387,7 @@ def interpolateNANs(
                 "order": order,
                 "method": method,
                 "limit_area": limit_area,
-                "limit_direction": extrapolate
+                "limit_direction": extrapolate,
             },
         )
     # finally reinsert the dropped data gaps
@@ -612,6 +634,4 @@ def linearInterpolation(data, inter_limit=2):
 
 
 def polynomialInterpolation(data, inter_limit=2, inter_order=2):
-    return interpolateNANs(
-        data, "polynomial", gap_limit=inter_limit, order=inter_order
-    )
+    return interpolateNANs(data, "polynomial", gap_limit=inter_limit, order=inter_order)