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-rw-r--r--csit.infra.dash/app/cdash/utils/anomalies.py69
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diff --git a/csit.infra.dash/app/cdash/utils/anomalies.py b/csit.infra.dash/app/cdash/utils/anomalies.py
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+# Copyright (c) 2023 Cisco and/or its affiliates.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at:
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Functions used by Dash applications to detect anomalies.
+"""
+
+from numpy import isnan
+
+from ..jumpavg import classify
+
+
+def classify_anomalies(data):
+ """Process the data and return anomalies and trending values.
+
+ 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: OrderedDict
+ :returns: Classification and trend values
+ :rtype: 3-tuple, list of strings, list of floats and list of floats
+ """
+ # NaN means something went wrong.
+ # Use 0.0 to cause that being reported as a severe regression.
+ bare_data = [0.0 if isnan(sample) else sample for sample in data.values()]
+ # TODO: Make BitCountingGroupList a subclass of list again?
+ group_list = classify(bare_data).group_list
+ group_list.reverse() # Just to use .pop() for FIFO.
+ classification = list()
+ avgs = list()
+ stdevs = list()
+ active_group = None
+ values_left = 0
+ avg = 0.0
+ stdv = 0.0
+ for sample in data.values():
+ if isnan(sample):
+ classification.append("outlier")
+ avgs.append(sample)
+ stdevs.append(sample)
+ continue
+ if values_left < 1 or active_group is None:
+ values_left = 0
+ while values_left < 1: # Ignore empty groups (should not happen).
+ active_group = group_list.pop()
+ values_left = len(active_group.run_list)
+ avg = active_group.stats.avg
+ stdv = active_group.stats.stdev
+ classification.append(active_group.comment)
+ avgs.append(avg)
+ stdevs.append(stdv)
+ values_left -= 1
+ continue
+ classification.append("normal")
+ avgs.append(avg)
+ stdevs.append(stdv)
+ values_left -= 1
+ return classification, avgs, stdevs