diff options
Diffstat (limited to 'resources/tools/presentation/utils.py')
| -rw-r--r-- | resources/tools/presentation/utils.py | 30 |
1 files changed, 15 insertions, 15 deletions
diff --git a/resources/tools/presentation/utils.py b/resources/tools/presentation/utils.py index 3f0d6ff084..3bd5a71e00 100644 --- a/resources/tools/presentation/utils.py +++ b/resources/tools/presentation/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2018 Cisco and/or its affiliates. +# Copyright (c) 2019 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: @@ -28,8 +28,8 @@ from shutil import move, Error from datetime import datetime from pandas import Series +from resources.libraries.python import jumpavg from errors import PresentationError -from jumpavg.BitCountingClassifier import BitCountingClassifier def mean(items): @@ -270,30 +270,30 @@ def classify_anomalies(data): :returns: Classification and trend values :rtype: 2-tuple, list of strings and list of floats """ - # Nan mean something went wrong. + # Nan means something went wrong. # Use 0.0 to cause that being reported as a severe regression. - bare_data = [0.0 if np.isnan(sample.avg) else sample - for _, sample in data.iteritems()] - # TODO: Put analogous iterator into jumpavg library. - groups = BitCountingClassifier().classify(bare_data) - groups.reverse() # Just to use .pop() for FIFO. + bare_data = [0.0 if np.isnan(sample) else sample + for sample in data.itervalues()] + # TODO: Make BitCountingGroupList a subclass of list again? + group_list = jumpavg.classify(bare_data).group_list + group_list.reverse() # Just to use .pop() for FIFO. classification = [] avgs = [] active_group = None values_left = 0 avg = 0.0 - for _, sample in data.iteritems(): - if np.isnan(sample.avg): + for sample in data.itervalues(): + if np.isnan(sample): classification.append("outlier") - avgs.append(sample.avg) + avgs.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 = groups.pop() - values_left = len(active_group.values) - avg = active_group.metadata.avg - classification.append(active_group.metadata.classification) + active_group = group_list.pop() + values_left = len(active_group.run_list) + avg = active_group.stats.avg + classification.append(active_group.comment) avgs.append(avg) values_left -= 1 continue |