done

saqc/funcs/functions.py

@@ -858,10 +858,10 @@ def flagCrossScoring(data, field, flagger, fields, thresh, cross_stat='modZscore
 
     return data, flagger
 
-def flagDriftFromNormals(data, field, flagger, fields, segment_freq, norm_spread, norm_frac=0.5,
-                                  metric=lambda x, y: scipy.spatial.distance.pdist(np.array([x, y]),
+def flagDriftFromNorm(data, field, flagger, fields, segment_freq, norm_spread, norm_frac=0.5,
+                      metric=lambda x, y: scipy.spatial.distance.pdist(np.array([x, y]),
                                                                                     metric='cityblock')/len(x),
-                                  linkage_method='single', **kwargs):
+                      linkage_method='single', **kwargs):
     """
     The function flags value courses that significantly deviate from a group of normal value courses.
 

test/funcs/test_functions.py

@@ -227,6 +227,6 @@ def test_flagDriftFromNormal(dat, flagger):
     data['d2'] = dat(periods=200, peak_level=10, name='d2')[0]['d2']
     data['d3'] = dat(periods=200, peak_level=100, name='d3')[0]['d3']
     flagger = flagger.initFlags(data)
-    data, flagger = flagDriftFromNormals(data, 'dummy', flagger, ['d1', 'd2', 'd3'], segment_freq="200min",
-                                         norm_spread=5)
+    data, flagger = flagDriftFromNorm(data, 'dummy', flagger, ['d1', 'd2', 'd3'], segment_freq="200min",
+                                      norm_spread=5)
     assert flagger.isFlagged()['d3'].all()