black/isort

saqc/funcs/interpolation.py

@@ -213,7 +213,7 @@ class InterpolationMixin:
         method: _SUPPORTED_METHODS,
         order: int = 2,
         limit: int | None = 2,
-        extrapolate: Literal['forward', 'backward', 'both'] = None,
+        extrapolate: Literal["forward", "backward", "both"] = None,
         **kwargs,
     ) -> "SaQC":
         """

saqc/lib/ts_operators.py

@@ -21,6 +21,7 @@ import pandas as pd
 from scipy.signal import butter, filtfilt
 from scipy.stats import iqr, median_abs_deviation
 from sklearn.neighbors import NearestNeighbors
+
 from saqc.lib.tools import getFreqDelta
 
 
@@ -351,7 +352,7 @@ def interpolateNANs(data, method, order=2, gap_limit=2, extrapolate=None):
 
             # correction for initial gap
             if isinstance(gap_limit, int):
-                gap_mask.iloc[:gap_limit]=True
+                gap_mask.iloc[:gap_limit] = True
 
             if gap_limit == 2:
                 # for the common case of gap_limit=2 (default "harmonisation"), we efficiently back propagate the False

tests/lib/test_ts_operators.py

@@ -208,7 +208,7 @@ def test_rateOfChange(data, expected):
         ),
         (
             2,
-            'backward',
+            "backward",
             [np.nan, 0, np.nan, np.nan, np.nan, 4, np.nan],
             [0, 0, np.nan, np.nan, np.nan, 4, np.nan],
         ),
@@ -226,7 +226,7 @@ def test_rateOfChange(data, expected):
         ),
         (
             3,
-            'forward',
+            "forward",
             [np.nan, 0, np.nan, np.nan, np.nan, 4, np.nan],
             [np.nan, 0, np.nan, np.nan, np.nan, 4, 4],
         ),
@@ -238,7 +238,7 @@ def test_rateOfChange(data, expected):
         ),
         (
             4,
-            'both',
+            "both",
             [np.nan, 0, np.nan, np.nan, np.nan, 4, np.nan],
             [np.nan, 0, 1, 2, 3, 4, np.nan],
         ),
@@ -251,8 +251,10 @@ def test_rateOfChange(data, expected):
     ],
 )
 def test_interpolatNANs(limit, extrapolate, data, expected):
-    got = interpolateNANs(pd.Series(data), gap_limit=limit, method="linear", extrapolate=extrapolate)
+    got = interpolateNANs(
+        pd.Series(data), gap_limit=limit, method="linear", extrapolate=extrapolate
+    )
     try:
         assert got.equals(pd.Series(expected, dtype=float))
     except AssertionError:
-        print('stop')
\ No newline at end of file
+        print("stop")