-# Copyright (c) 2019 Cisco and/or its affiliates.
+# Copyright (c) 2021 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:
logging.info(u" Archiving the input data files ...")
- extension = spec.input[u"arch-file-format"]
+ extension = spec.output[u"arch-file-format"]
data_files = list()
for ext in extension:
data_files.extend(get_files(
: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.
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
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","):