from datetime import timedelta
from utils import mean, stdev, relative_change, classify_anomalies, \
- convert_csv_to_pretty_txt
+ convert_csv_to_pretty_txt, relative_change_stdev
REGEX_NIC = re.compile(r'\d*ge\dp\d\D*\d*')
"{0} Stdev [Mpps]".format(table["reference"]["title"]),
"{0} Throughput [Mpps]".format(table["compare"]["title"]),
"{0} Stdev [Mpps]".format(table["compare"]["title"]),
- "Delta [%]"]
+ "Delta [%]", "Stdev of delta [%]"]
header_str = ",".join(header) + "\n"
except (AttributeError, KeyError) as err:
logging.error("The model is invalid, missing parameter: {0}".
if data_r:
data_r_mean = mean(data_r)
item.append(round(data_r_mean / 1000000, 2))
- item.append(round(stdev(data_r) / 1000000, 2))
+ data_r_stdev = stdev(data_r)
+ item.append(round(data_r_stdev / 1000000, 2))
else:
data_r_mean = None
+ data_r_stdev = None
item.extend([None, None])
data_c = tbl_dict[tst_name]["cmp-data"]
if data_c:
data_c_mean = mean(data_c)
item.append(round(data_c_mean / 1000000, 2))
- item.append(round(stdev(data_c) / 1000000, 2))
+ data_c_stdev = stdev(data_c)
+ item.append(round(data_c_stdev / 1000000, 2))
else:
data_c_mean = None
+ data_c_stdev = None
item.extend([None, None])
- if data_r_mean and data_c_mean is not None:
- item.append(round(relative_change(data_r_mean, data_c_mean), 2))
+ if data_r_mean and data_c_mean:
+ delta, d_stdev = relative_change_stdev(
+ data_r_mean, data_c_mean, data_r_stdev, data_c_stdev)
+ item.append(round(delta, 2))
+ item.append(round(d_stdev, 2))
tbl_lst.append(item)
# Sort the table according to the relative change