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authorTibor Frank <tifrank@cisco.com>2020-05-06 14:38:29 +0200
committerTibor Frank <tifrank@cisco.com>2020-05-07 05:20:40 +0000
commit48cd54ff00049d58494834d25d3f0ac846ce4017 (patch)
treee5a021d7c70d9bb89846579c046c1a14501c79ea /resources/tools/presentation/pal_utils.py
parent007b6075fa425f8ff906bc80f9277e2281ab6b45 (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.py10
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","):