diff options
| author |   Tibor Frank <tifrank@cisco.com> | 2020-05-06 14:38:29 +0200 |
|---|---|---|
| committer | Tibor Frank <tifrank@cisco.com> | 2020-05-07 05:20:40 +0000 |
| commit | 48cd54ff00049d58494834d25d3f0ac846ce4017 (patch) | |
| tree | e5a021d7c70d9bb89846579c046c1a14501c79ea /resources/tools/presentation/pal_utils.py | |
| parent | 007b6075fa425f8ff906bc80f9277e2281ab6b45 (diff) | |
Trending: NDRPDR dashboard
Change-Id: I7f4c84dd47874c484f34f389b93de635c66a77c1
Signed-off-by: Tibor Frank <tifrank@cisco.com>
Diffstat (limited to 'resources/tools/presentation/pal_utils.py')
| -rw-r--r-- | resources/tools/presentation/pal_utils.py | 10 |
1 files changed, 8 insertions, 2 deletions
diff --git a/resources/tools/presentation/pal_utils.py b/resources/tools/presentation/pal_utils.py index 98d5837989..86a6679918 100644 --- a/resources/tools/presentation/pal_utils.py +++ b/resources/tools/presentation/pal_utils.py @@ -262,7 +262,7 @@ def classify_anomalies(data): :param data: Full data set with unavailable samples replaced by nan. :type data: OrderedDict :returns: Classification and trend values - :rtype: 2-tuple, list of strings and list of floats + :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. @@ -273,13 +273,16 @@ def classify_anomalies(data): group_list.reverse() # Just to use .pop() for FIFO. classification = [] avgs = [] + stdevs = [] active_group = None values_left = 0 avg = 0.0 + stdv = 0.0 for sample in data.values(): if np.isnan(sample): classification.append(u"outlier") avgs.append(sample) + stdevs.append(sample) continue if values_left < 1 or active_group is None: values_left = 0 @@ -287,14 +290,17 @@ def classify_anomalies(data): 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(u"normal") avgs.append(avg) + stdevs.append(stdv) values_left -= 1 - return classification, avgs + return classification, avgs, stdevs def convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u","): |