tbl_lst = list()
for tst_name in tbl_dict.keys():
item = [tbl_dict[tst_name]["name"], ]
- data_t = tbl_dict[tst_name]["ref-data"]
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ data_r = tbl_dict[tst_name]["ref-data"]
+ 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))
else:
+ data_r_mean = None
item.extend([None, None])
- data_t = tbl_dict[tst_name]["cmp-data"]
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ 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))
else:
+ data_c_mean = None
item.extend([None, None])
- if item[-4] is not None and item[-2] is not None and item[-4] != 0:
- item.append(int(relative_change(float(item[-4]), float(item[-2]))))
- if len(item) == len(header):
+ if data_r_mean and data_c_mean is not None:
+ item.append(round(relative_change(data_r_mean, data_c_mean), 2))
tbl_lst.append(item)
# Sort the table according to the relative change
# Soak test - 30min Soak Test (PLRsearch), boxes
- type: "plot"
title: "VPP Throughput: 30min Soak Test (PLRsearch) boxes"
- algorithm: "plot_soak_boxes"
+ algorithm: "plot_performance_box"
output-file-type: ".html"
output-file: "{DIR[STATIC,VPP]}/soak-test-1"
data: "plot-vpp-soak-2n-skx"
# Soak test - 30min Soak Test (PLRsearch), boxes
- type: "plot"
title: "VPP Throughput: 30min Soak Test (PLRsearch) boxes"
- algorithm: "plot_soak_boxes"
+ algorithm: "plot_performance_box"
output-file-type: ".html"
output-file: "{DIR[STATIC,VPP]}/soak-test-2"
data: "plot-vpp-soak-2n-skx"
from os import walk, makedirs, environ
from os.path import join, isdir
from shutil import move, Error
-from math import sqrt
from datetime import datetime
+from pandas import Series
from errors import PresentationError
from jumpavg.BitCountingClassifier import BitCountingClassifier
:returns: Stdev.
:rtype: float
"""
-
- avg = mean(items)
- variance = [(x - avg) ** 2 for x in items]
- stddev = sqrt(mean(variance))
- return stddev
+ return Series.std(Series(items))
def relative_change(nr1, nr2):