added small test for modelling_rollingMean function

saqc/funcs/data_modelling.py

@@ -182,11 +182,6 @@ def modelling_rollingMean(data, field, flagger, winsz, eval_flags=True, min_peri
     center : boolean, default True
         Wheather or not to center the window the mean is calculated of around the reference value. If False,
         the reference value is placed to the right of the window (classic rolling mean with lag.)
-    kwargs
-
-    Returns
-    -------
-
     """
     data = data.copy()
     to_fit = data[field]

test/funcs/test_data_modelling.py

@@ -9,7 +9,8 @@ from dios import dios
 from test.common import TESTFLAGGER
 
 from saqc.funcs.data_modelling import (
-    modelling_polyFit
+    modelling_polyFit,
+    modelling_rollingMean
 )
 
 TF = TESTFLAGGER[:1]
@@ -32,3 +33,11 @@ def test_modelling_polyFit_forRegular(dat, flagger):
     result5, _ = modelling_polyFit(data, 'data', flagger, 11, 2, numba=True, min_periods=9)
     assert result5['data'].iloc[10:19].isna().all()
 
+@pytest.mark.parametrize("flagger", TF)
+@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_2")])
+def test_modelling_rollingMean_forRegular(dat, flagger):
+    data, _ = dat(freq='10min', periods=30, initial_level=0, final_level=100, out_val=-100)
+    data = dios.DictOfSeries(data)
+    flagger = flagger.initFlags(data)
+    modelling_rollingMean(data, 'data', flagger, 5, eval_flags=True, min_periods=0, center=True)
+    modelling_rollingMean(data, 'data', flagger, 5, eval_flags=True, min_periods=0, center=False)