X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fnew%2Futils.py;h=a4e24663b50f17b1a7ed7f9947878dba1098b9e2;hb=938a0c9cec6d2177e098653ad398372fb482c36f;hp=83f4f6249b9d78fb2a9a6055ebb629d2fd01d790;hpb=beeb2acb9ac153eaa54983bea46a76d596168965;p=csit.git

diff --git a/resources/tools/presentation/new/utils.py b/resources/tools/presentation/new/utils.py
index 83f4f6249b..a4e24663b5 100644
--- a/resources/tools/presentation/new/utils.py
+++ b/resources/tools/presentation/new/utils.py
@@ -211,19 +211,21 @@ def archive_input_data(spec):
 def classify_anomalies(data):
     """Process the data and return anomalies and trending values.
 
-    Gathers data into groups with common trend value.
-    Decorates first value in the group to be an outlier, regression,
-    normal or progression.
+    Gather data into groups with average as trend value.
+    Decorate values within groups to be normal,
+    the first value of changed average as a regression, or a progression.
 
     :param data: Full data set with unavailable samples replaced by nan.
     :type data: pandas.Series
     :returns: Classification and trend values
     :rtype: 2-tuple, list of strings and list of floats
     """
-    bare_data = [sample for _, sample in data.iteritems()
-                 if not np.isnan(sample)]
+    # Nan mean something went wrong.
+    # Use 0.0 to cause that being reported as a severe regression.
+    bare_data = [0.0 if np.isnan(sample) else sample
+                 for _, sample in data.iteritems()]
     # TODO: Put analogous iterator into jumpavg library.
-    groups = BitCountingClassifier.classify(bare_data)
+    groups = BitCountingClassifier().classify(bare_data)
     groups.reverse()  # Just to use .pop() for FIFO.
     classification = []
     avgs = []
@@ -237,7 +239,7 @@ def classify_anomalies(data):
             continue
         if values_left < 1 or active_group is None:
             values_left = 0
-            while values_left < 1:  # To ignore empty groups.
+            while values_left < 1:  # Ignore empty groups (should not happen).
                 active_group = groups.pop()
                 values_left = len(active_group.values)
             avg = active_group.metadata.avg