-# Copyright (c) 2019 Cisco and/or its affiliates.
+# Copyright (c) 2020 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:
import pandas as pd
from numpy import nan, isnan
+from yaml import load, FullLoader, YAMLError
-from pal_utils import mean, stdev, relative_change, classify_anomalies, \
+from pal_utils import mean, stdev, classify_anomalies, \
convert_csv_to_pretty_txt, relative_change_stdev
data = input_data.merge_data(data)
sort_tests = table.get(u"sort", None)
- if sort_tests and sort_tests in (u"ascending", u"descending"):
+ if sort_tests:
args = dict(
inplace=True,
- ascending=True if sort_tests == u"ascending" else False
+ ascending=(sort_tests == u"ascending")
)
data.sort_index(**args)
trow, u"td", attrib=dict(align=u"left", colspan=u"6")
)
tcol.text = u"No Data"
+
+ trow = ET.SubElement(
+ tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
+ )
+ thead = ET.SubElement(
+ trow, u"th", attrib=dict(align=u"left", colspan=u"6")
+ )
+ font = ET.SubElement(
+ thead, u"font", attrib=dict(size=u"12px", color=u"#ffffff")
+ )
+ font.text = u"."
return str(ET.tostring(tbl, encoding=u"unicode"))
tbl_hdr = (
u"Average Vector Size"
)
- for dut_name, dut_data in tst_data[u"show-run"].items():
+ for dut_data in tst_data[u"show-run"].values():
trow = ET.SubElement(
tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
)
if dut_data.get(u"threads", None) is None:
tcol.text = u"No Data"
continue
- bold = ET.SubElement(tcol, u"b")
- bold.text = dut_name
- trow = ET.SubElement(
- tbl, u"tr", attrib=dict(bgcolor=colors[u"body"][0])
- )
- tcol = ET.SubElement(
- trow, u"td", attrib=dict(align=u"left", colspan=u"6")
- )
bold = ET.SubElement(tcol, u"b")
bold.text = (
f"Host IP: {dut_data.get(u'host', '')}, "
if not html_table:
continue
try:
- file_name = f"{table[u'output-file']}_{suite[u'name']}.rst"
+ file_name = f"{table[u'output-file']}{suite[u'name']}.rst"
with open(f"{file_name}", u'w') as html_file:
logging.info(f" Writing file: {file_name}")
html_file.write(u".. raw:: html\n\n\t")
"""
logging.info(f" Generating the table {table.get(u'title', u'')} ...")
+
# Transform the data
logging.info(
f" Creating the data set for the {table.get(u'type', u'')} "
data = input_data.merge_data(data)
sort_tests = table.get(u"sort", None)
- if sort_tests and sort_tests in (u"ascending", u"descending"):
+ if sort_tests:
args = dict(
inplace=True,
- ascending=True if sort_tests == u"ascending" else False
+ ascending=(sort_tests == u"ascending")
)
data.sort_index(**args)
try:
col_data = str(data[test][column[
u"data"].split(u" ")[1]]).replace(u'"', u'""')
+ # Do not include tests with "Test Failed" in test message
+ if u"Test Failed" in col_data:
+ continue
col_data = col_data.replace(
u"No Data", u"Not Captured "
)
f"{u'-'.join(col_data_lst[half:])}"
col_data = f" |prein| {col_data} |preout| "
elif column[u"data"].split(u" ")[1] in (u"msg", ):
+ # Temporary solution: remove NDR results from message:
+ if bool(table.get(u'remove-ndr', False)):
+ try:
+ col_data = col_data.split(u" |br| ", 1)[1]
+ except IndexError:
+ pass
col_data = f" |prein| {col_data} |preout| "
elif column[u"data"].split(u" ")[1] in \
- (u"conf-history", u"show-run"):
+ (u"conf-history", u"show-run"):
col_data = col_data.replace(u" |br| ", u"", 1)
col_data = f" |prein| {col_data[:-5]} |preout| "
row_lst.append(f'"{col_data}"')
except KeyError:
row_lst.append(u'"Not captured"')
- table_lst.append(row_lst)
+ if len(row_lst) == len(table[u"columns"]):
+ table_lst.append(row_lst)
# Write the data to file
if table_lst:
- file_name = f"{table[u'output-file']}_{suite_name}.csv"
+ separator = u"" if table[u'output-file'].endswith(u"/") else u"_"
+ file_name = f"{table[u'output-file']}{separator}{suite_name}.csv"
logging.info(f" Writing file: {file_name}")
with open(file_name, u"wt") as file_handler:
file_handler.write(u",".join(header) + u"\n")
"""
try:
if include_tests == u"MRR":
- target.append(src[u"result"][u"receive-rate"])
+ target.append(
+ (
+ src[u"result"][u"receive-rate"],
+ src[u"result"][u"receive-stdev"]
+ )
+ )
elif include_tests == u"PDR":
target.append(src[u"throughput"][u"PDR"][u"LOWER"])
elif include_tests == u"NDR":
:rtype: list
"""
-
tbl_new = list()
tbl_see = list()
tbl_delta = list()
# Sort the tables:
tbl_new.sort(key=lambda rel: rel[0], reverse=False)
tbl_see.sort(key=lambda rel: rel[0], reverse=False)
- tbl_see.sort(key=lambda rel: rel[-1], reverse=False)
- tbl_delta.sort(key=lambda rel: rel[-1], reverse=True)
+ tbl_see.sort(key=lambda rel: rel[-2], reverse=False)
+ tbl_delta.sort(key=lambda rel: rel[0], reverse=False)
+ tbl_delta.sort(key=lambda rel: rel[-2], reverse=True)
# Put the tables together:
table = list()
- table.extend(tbl_new)
+ # We do not want "New in CSIT":
+ # table.extend(tbl_new)
table.extend(tbl_see)
table.extend(tbl_delta)
return table
-def _tpc_generate_html_table(header, data, output_file_name):
+def _tpc_generate_html_table(header, data, out_file_name, legend=u"",
+ footnote=u""):
"""Generate html table from input data with simple sorting possibility.
:param header: Table header.
Inner lists are rows in the table. All inner lists must be of the same
length. The length of these lists must be the same as the length of the
header.
- :param output_file_name: The name (relative or full path) where the
+ :param out_file_name: The name (relative or full path) where the
generated html table is written.
+ :param legend: The legend to display below the table.
+ :param footnote: The footnote to display below the table (and legend).
:type header: list
:type data: list of lists
- :type output_file_name: str
+ :type out_file_name: str
+ :type legend: str
+ :type footnote: str
"""
+ try:
+ idx = header.index(u"Test Case")
+ except ValueError:
+ idx = 0
+ params = {
+ u"align-hdr": ([u"left", u"center"], [u"left", u"left", u"center"]),
+ u"align-itm": ([u"left", u"right"], [u"left", u"left", u"right"]),
+ u"width": ([28, 9], [4, 24, 10])
+ }
+
df_data = pd.DataFrame(data, columns=header)
df_sorted = [df_data.sort_values(
- by=[key, header[0]], ascending=[True, True]
- if key != header[0] else [False, True]) for key in header]
+ by=[key, header[idx]], ascending=[True, True]
+ if key != header[idx] else [False, True]) for key in header]
df_sorted_rev = [df_data.sort_values(
- by=[key, header[0]], ascending=[False, True]
- if key != header[0] else [True, True]) for key in header]
+ by=[key, header[idx]], ascending=[False, True]
+ if key != header[idx] else [True, True]) for key in header]
df_sorted.extend(df_sorted_rev)
fill_color = [[u"#d4e4f7" if idx % 2 else u"#e9f1fb"
table_header = dict(
values=[f"<b>{item}</b>" for item in header],
fill_color=u"#7eade7",
- align=[u"left", u"center"]
+ align=params[u"align-hdr"][idx]
)
fig = go.Figure()
columns = [table.get(col) for col in header]
fig.add_trace(
go.Table(
- columnwidth=[30, 10],
+ columnwidth=params[u"width"][idx],
header=table_header,
cells=dict(
values=columns,
fill_color=fill_color,
- align=[u"left", u"right"]
+ align=params[u"align-itm"][idx]
)
)
)
go.layout.Updatemenu(
type=u"dropdown",
direction=u"down",
- x=0.03,
+ x=0.0,
xanchor=u"left",
y=1.045,
yanchor=u"top",
active=len(menu_items) - 1,
buttons=list(buttons)
)
- ],
- annotations=[
- go.layout.Annotation(
- text=u"<b>Sort by:</b>",
- x=0,
- xref=u"paper",
- y=1.035,
- yref=u"paper",
- align=u"left",
- showarrow=False
- )
]
)
- ploff.plot(fig, show_link=False, auto_open=False, filename=output_file_name)
+ ploff.plot(
+ fig,
+ show_link=False,
+ auto_open=False,
+ filename=f"{out_file_name}_in.html"
+ )
+
+ file_name = out_file_name.split(u"/")[-1]
+ if u"vpp" in out_file_name:
+ path = u"_tmp/src/vpp_performance_tests/comparisons/"
+ else:
+ path = u"_tmp/src/dpdk_performance_tests/comparisons/"
+ with open(f"{path}{file_name}.rst", u"wt") as rst_file:
+ rst_file.write(
+ u"\n"
+ u".. |br| raw:: html\n\n <br />\n\n\n"
+ u".. |prein| raw:: html\n\n <pre>\n\n\n"
+ u".. |preout| raw:: html\n\n </pre>\n\n"
+ )
+ rst_file.write(
+ u".. raw:: html\n\n"
+ f' <iframe frameborder="0" scrolling="no" '
+ f'width="1600" height="1000" '
+ f'src="../..{out_file_name.replace(u"_build", u"")}_in.html">'
+ f'</iframe>\n\n'
+ )
+ if legend:
+ rst_file.write(legend[1:].replace(u"\n", u" |br| "))
+ if footnote:
+ rst_file.write(footnote.replace(u"\n", u" |br| ")[1:])
def table_perf_comparison(table, input_data):
# Prepare the header of the tables
try:
- header = [u"Test case", ]
+ header = [u"Test Case", ]
+ legend = u"\nLegend:\n"
- if table[u"include-tests"] == u"MRR":
- hdr_param = u"Rec Rate"
- else:
- hdr_param = u"Thput"
+ rca_data = None
+ rca = table.get(u"rca", None)
+ if rca:
+ try:
+ with open(rca.get(u"data-file", ""), u"r") as rca_file:
+ rca_data = load(rca_file, Loader=FullLoader)
+ header.insert(0, rca.get(u"title", "RCA"))
+ legend += (
+ u"RCA: Reference to the Root Cause Analysis, see below.\n"
+ )
+ except (YAMLError, IOError) as err:
+ logging.warning(repr(err))
history = table.get(u"history", list())
for item in history:
header.extend(
[
- f"{item[u'title']} {hdr_param} [Mpps]",
- f"{item[u'title']} Stdev [Mpps]"
+ f"{item[u'title']} Avg({table[u'include-tests']})",
+ f"{item[u'title']} Stdev({table[u'include-tests']})"
]
)
+ legend += (
+ f"{item[u'title']} Avg({table[u'include-tests']}): "
+ f"Mean value of {table[u'include-tests']} [Mpps] computed from "
+ f"a series of runs of the listed tests executed against "
+ f"{item[u'title']}.\n"
+ f"{item[u'title']} Stdev({table[u'include-tests']}): "
+ f"Standard deviation value of {table[u'include-tests']} [Mpps] "
+ f"computed from a series of runs of the listed tests executed "
+ f"against {item[u'title']}.\n"
+ )
header.extend(
[
- f"{table[u'reference'][u'title']} {hdr_param} [Mpps]",
- f"{table[u'reference'][u'title']} Stdev [Mpps]",
- f"{table[u'compare'][u'title']} {hdr_param} [Mpps]",
- f"{table[u'compare'][u'title']} Stdev [Mpps]",
- u"Delta [%]"
+ f"{table[u'reference'][u'title']} "
+ f"Avg({table[u'include-tests']})",
+ f"{table[u'reference'][u'title']} "
+ f"Stdev({table[u'include-tests']})",
+ f"{table[u'compare'][u'title']} "
+ f"Avg({table[u'include-tests']})",
+ f"{table[u'compare'][u'title']} "
+ f"Stdev({table[u'include-tests']})",
+ f"Diff({table[u'reference'][u'title']},"
+ f"{table[u'compare'][u'title']})",
+ u"Stdev(Diff)"
]
)
- header_str = u",".join(header) + u"\n"
+ header_str = u";".join(header) + u"\n"
+ legend += (
+ f"{table[u'reference'][u'title']} "
+ f"Avg({table[u'include-tests']}): "
+ f"Mean value of {table[u'include-tests']} [Mpps] computed from a "
+ f"series of runs of the listed tests executed against "
+ f"{table[u'reference'][u'title']}.\n"
+ f"{table[u'reference'][u'title']} "
+ f"Stdev({table[u'include-tests']}): "
+ f"Standard deviation value of {table[u'include-tests']} [Mpps] "
+ f"computed from a series of runs of the listed tests executed "
+ f"against {table[u'reference'][u'title']}.\n"
+ f"{table[u'compare'][u'title']} "
+ f"Avg({table[u'include-tests']}): "
+ f"Mean value of {table[u'include-tests']} [Mpps] computed from a "
+ f"series of runs of the listed tests executed against "
+ f"{table[u'compare'][u'title']}.\n"
+ f"{table[u'compare'][u'title']} "
+ f"Stdev({table[u'include-tests']}): "
+ f"Standard deviation value of {table[u'include-tests']} [Mpps] "
+ f"computed from a series of runs of the listed tests executed "
+ f"against {table[u'compare'][u'title']}.\n"
+ f"Diff({table[u'reference'][u'title']},"
+ f"{table[u'compare'][u'title']}): "
+ f"Percentage change calculated for mean values.\n"
+ u"Stdev(Diff): "
+ u"Standard deviation of percentage change calculated for mean "
+ u"values.\n"
+ u"NT: Not Tested\n"
+ )
except (AttributeError, KeyError) as err:
logging.error(f"The model is invalid, missing parameter: {repr(err)}")
return
# Prepare data to the table:
tbl_dict = dict()
- # topo = ""
for job, builds in table[u"reference"][u"data"].items():
- # topo = u"2n-skx" if u"2n-skx" in job else u""
for build in builds:
for tst_name, tst_data in data[job][str(build)].items():
tst_name_mod = _tpc_modify_test_name(tst_name)
- if u"across topologies" in table[u"title"].lower():
+ if (u"across topologies" in table[u"title"].lower() or
+ (u" 3n-" in table[u"title"].lower() and
+ u" 2n-" in table[u"title"].lower())):
tst_name_mod = tst_name_mod.replace(u"2n1l-", u"")
if tbl_dict.get(tst_name_mod, None) is None:
groups = re.search(REGEX_NIC, tst_data[u"parent"])
for build in builds:
for tst_name, tst_data in rpl_data[job][str(build)].items():
tst_name_mod = _tpc_modify_test_name(tst_name)
- if u"across topologies" in table[u"title"].lower():
+ if (u"across topologies" in table[u"title"].lower() or
+ (u" 3n-" in table[u"title"].lower() and
+ u" 2n-" in table[u"title"].lower())):
tst_name_mod = tst_name_mod.replace(u"2n1l-", u"")
if tbl_dict.get(tst_name_mod, None) is None:
name = \
for build in builds:
for tst_name, tst_data in data[job][str(build)].items():
tst_name_mod = _tpc_modify_test_name(tst_name)
- if u"across topologies" in table[u"title"].lower():
+ if (u"across topologies" in table[u"title"].lower() or
+ (u" 3n-" in table[u"title"].lower() and
+ u" 2n-" in table[u"title"].lower())):
tst_name_mod = tst_name_mod.replace(u"2n1l-", u"")
if tbl_dict.get(tst_name_mod, None) is None:
groups = re.search(REGEX_NIC, tst_data[u"parent"])
for build in builds:
for tst_name, tst_data in rpl_data[job][str(build)].items():
tst_name_mod = _tpc_modify_test_name(tst_name)
- if u"across topologies" in table[u"title"].lower():
+ if (u"across topologies" in table[u"title"].lower() or
+ (u" 3n-" in table[u"title"].lower() and
+ u" 2n-" in table[u"title"].lower())):
tst_name_mod = tst_name_mod.replace(u"2n1l-", u"")
if tbl_dict.get(tst_name_mod, None) is None:
name = \
for build in builds:
for tst_name, tst_data in data[job][str(build)].items():
tst_name_mod = _tpc_modify_test_name(tst_name)
- if u"across topologies" in table[u"title"].lower():
+ if (u"across topologies" in table[u"title"].lower() or
+ (u" 3n-" in table[u"title"].lower() and
+ u" 2n-" in table[u"title"].lower())):
tst_name_mod = tst_name_mod.replace(u"2n1l-", u"")
if tbl_dict.get(tst_name_mod, None) is None:
continue
u"title"]] = list()
try:
if table[u"include-tests"] == u"MRR":
- res = tst_data[u"result"][u"receive-rate"]
+ res = (tst_data[u"result"][u"receive-rate"],
+ tst_data[u"result"][u"receive-stdev"])
elif table[u"include-tests"] == u"PDR":
res = tst_data[u"throughput"][u"PDR"][u"LOWER"]
elif table[u"include-tests"] == u"NDR":
pass
tbl_lst = list()
- footnote = False
for tst_name in tbl_dict:
item = [tbl_dict[tst_name][u"name"], ]
if history:
if tbl_dict[tst_name].get(u"history", None) is not None:
for hist_data in tbl_dict[tst_name][u"history"].values():
if hist_data:
- item.append(round(mean(hist_data) / 1000000, 2))
- item.append(round(stdev(hist_data) / 1000000, 2))
+ if table[u"include-tests"] == u"MRR":
+ item.append(round(hist_data[0][0] / 1e6, 1))
+ item.append(round(hist_data[0][1] / 1e6, 1))
+ else:
+ item.append(round(mean(hist_data) / 1e6, 1))
+ item.append(round(stdev(hist_data) / 1e6, 1))
else:
- item.extend([u"Not tested", u"Not tested"])
+ item.extend([u"NT", u"NT"])
+ else:
+ item.extend([u"NT", u"NT"])
+ data_r = tbl_dict[tst_name][u"ref-data"]
+ if data_r:
+ if table[u"include-tests"] == u"MRR":
+ data_r_mean = data_r[0][0]
+ data_r_stdev = data_r[0][1]
else:
- item.extend([u"Not tested", u"Not tested"])
- data_t = tbl_dict[tst_name][u"ref-data"]
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ data_r_mean = mean(data_r)
+ data_r_stdev = stdev(data_r)
+ item.append(round(data_r_mean / 1e6, 1))
+ item.append(round(data_r_stdev / 1e6, 1))
else:
- item.extend([u"Not tested", u"Not tested"])
- data_t = tbl_dict[tst_name][u"cmp-data"]
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ data_r_mean = None
+ data_r_stdev = None
+ item.extend([u"NT", u"NT"])
+ data_c = tbl_dict[tst_name][u"cmp-data"]
+ if data_c:
+ if table[u"include-tests"] == u"MRR":
+ data_c_mean = data_c[0][0]
+ data_c_stdev = data_c[0][1]
+ else:
+ data_c_mean = mean(data_c)
+ data_c_stdev = stdev(data_c)
+ item.append(round(data_c_mean / 1e6, 1))
+ item.append(round(data_c_stdev / 1e6, 1))
else:
- item.extend([u"Not tested", u"Not tested"])
- if item[-2] == u"Not tested":
+ data_c_mean = None
+ data_c_stdev = None
+ item.extend([u"NT", u"NT"])
+ if item[-2] == u"NT":
pass
- elif item[-4] == u"Not tested":
+ elif item[-4] == u"NT":
item.append(u"New in CSIT-2001")
- # elif topo == u"2n-skx" and u"dot1q" in tbl_dict[tst_name][u"name"]:
- # item.append(u"See footnote [1]")
- # footnote = True
- elif item[-4] != 0:
- item.append(int(relative_change(float(item[-4]), float(item[-2]))))
- if (len(item) == len(header)) and (item[-3] != u"Not tested"):
+ item.append(u"New in CSIT-2001")
+ elif data_r_mean is not None and data_c_mean is not None:
+ delta, d_stdev = relative_change_stdev(
+ data_r_mean, data_c_mean, data_r_stdev, data_c_stdev
+ )
+ try:
+ item.append(round(delta))
+ except ValueError:
+ item.append(delta)
+ try:
+ item.append(round(d_stdev))
+ except ValueError:
+ item.append(d_stdev)
+ if rca_data:
+ rca_nr = rca_data.get(item[0], u"-")
+ item.insert(0, f"[{rca_nr}]" if rca_nr != u"-" else u"-")
+ if (len(item) == len(header)) and (item[-4] != u"NT"):
tbl_lst.append(item)
tbl_lst = _tpc_sort_table(tbl_lst)
with open(csv_file, u"wt") as file_handler:
file_handler.write(header_str)
for test in tbl_lst:
- file_handler.write(u",".join([str(item) for item in test]) + u"\n")
+ file_handler.write(u";".join([str(item) for item in test]) + u"\n")
txt_file_name = f"{table[u'output-file']}.txt"
- convert_csv_to_pretty_txt(csv_file, txt_file_name)
-
- if footnote:
- with open(txt_file_name, u'a') as txt_file:
- txt_file.writelines([
- u"\nFootnotes:\n",
- u"[1] CSIT-1908 changed test methodology of dot1q tests in "
- u"2-node testbeds, dot1q encapsulation is now used on both "
- u"links of SUT.\n",
- u" Previously dot1q was used only on a single link with the "
- u"other link carrying untagged Ethernet frames. This changes "
- u"results\n",
- u" in slightly lower throughput in CSIT-1908 for these "
- u"tests. See release notes."
- ])
+ convert_csv_to_pretty_txt(csv_file, txt_file_name, delimiter=u";")
+
+ footnote = u""
+ with open(txt_file_name, u'a') as txt_file:
+ txt_file.write(legend)
+ if rca_data:
+ footnote = rca_data.get(u"footnote", u"")
+ if footnote:
+ txt_file.write(footnote)
+ txt_file.write(u":END")
# Generate html table:
- _tpc_generate_html_table(header, tbl_lst, f"{table[u'output-file']}.html")
+ _tpc_generate_html_table(
+ header,
+ tbl_lst,
+ table[u'output-file'],
+ legend=legend,
+ footnote=footnote
+ )
def table_perf_comparison_nic(table, input_data):
# Prepare the header of the tables
try:
- header = [u"Test case", ]
+ header = [u"Test Case", ]
+ legend = u"\nLegend:\n"
- if table[u"include-tests"] == u"MRR":
- hdr_param = u"Rec Rate"
- else:
- hdr_param = u"Thput"
+ rca_data = None
+ rca = table.get(u"rca", None)
+ if rca:
+ try:
+ with open(rca.get(u"data-file", ""), u"r") as rca_file:
+ rca_data = load(rca_file, Loader=FullLoader)
+ header.insert(0, rca.get(u"title", "RCA"))
+ legend += (
+ u"RCA: Reference to the Root Cause Analysis, see below.\n"
+ )
+ except (YAMLError, IOError) as err:
+ logging.warning(repr(err))
history = table.get(u"history", list())
for item in history:
header.extend(
[
- f"{item[u'title']} {hdr_param} [Mpps]",
- f"{item[u'title']} Stdev [Mpps]"
+ f"{item[u'title']} Avg({table[u'include-tests']})",
+ f"{item[u'title']} Stdev({table[u'include-tests']})"
]
)
+ legend += (
+ f"{item[u'title']} Avg({table[u'include-tests']}): "
+ f"Mean value of {table[u'include-tests']} [Mpps] computed from "
+ f"a series of runs of the listed tests executed against "
+ f"{item[u'title']}.\n"
+ f"{item[u'title']} Stdev({table[u'include-tests']}): "
+ f"Standard deviation value of {table[u'include-tests']} [Mpps] "
+ f"computed from a series of runs of the listed tests executed "
+ f"against {item[u'title']}.\n"
+ )
header.extend(
[
- f"{table[u'reference'][u'title']} {hdr_param} [Mpps]",
- f"{table[u'reference'][u'title']} Stdev [Mpps]",
- f"{table[u'compare'][u'title']} {hdr_param} [Mpps]",
- f"{table[u'compare'][u'title']} Stdev [Mpps]",
- u"Delta [%]"
+ f"{table[u'reference'][u'title']} "
+ f"Avg({table[u'include-tests']})",
+ f"{table[u'reference'][u'title']} "
+ f"Stdev({table[u'include-tests']})",
+ f"{table[u'compare'][u'title']} "
+ f"Avg({table[u'include-tests']})",
+ f"{table[u'compare'][u'title']} "
+ f"Stdev({table[u'include-tests']})",
+ f"Diff({table[u'reference'][u'title']},"
+ f"{table[u'compare'][u'title']})",
+ u"Stdev(Diff)"
]
)
- header_str = u",".join(header) + u"\n"
+ header_str = u";".join(header) + u"\n"
+ legend += (
+ f"{table[u'reference'][u'title']} "
+ f"Avg({table[u'include-tests']}): "
+ f"Mean value of {table[u'include-tests']} [Mpps] computed from a "
+ f"series of runs of the listed tests executed against "
+ f"{table[u'reference'][u'title']}.\n"
+ f"{table[u'reference'][u'title']} "
+ f"Stdev({table[u'include-tests']}): "
+ f"Standard deviation value of {table[u'include-tests']} [Mpps] "
+ f"computed from a series of runs of the listed tests executed "
+ f"against {table[u'reference'][u'title']}.\n"
+ f"{table[u'compare'][u'title']} "
+ f"Avg({table[u'include-tests']}): "
+ f"Mean value of {table[u'include-tests']} [Mpps] computed from a "
+ f"series of runs of the listed tests executed against "
+ f"{table[u'compare'][u'title']}.\n"
+ f"{table[u'compare'][u'title']} "
+ f"Stdev({table[u'include-tests']}): "
+ f"Standard deviation value of {table[u'include-tests']} [Mpps] "
+ f"computed from a series of runs of the listed tests executed "
+ f"against {table[u'compare'][u'title']}.\n"
+ f"Diff({table[u'reference'][u'title']},"
+ f"{table[u'compare'][u'title']}): "
+ f"Percentage change calculated for mean values.\n"
+ u"Stdev(Diff): "
+ u"Standard deviation of percentage change calculated for mean "
+ u"values.\n"
+ u"NT: Not Tested\n"
+ )
except (AttributeError, KeyError) as err:
logging.error(f"The model is invalid, missing parameter: {repr(err)}")
return
# Prepare data to the table:
tbl_dict = dict()
- # topo = u""
for job, builds in table[u"reference"][u"data"].items():
- # topo = u"2n-skx" if u"2n-skx" in job else u""
for build in builds:
for tst_name, tst_data in data[job][str(build)].items():
if table[u"reference"][u"nic"] not in tst_data[u"tags"]:
continue
tst_name_mod = _tpc_modify_test_name(tst_name)
- if u"across topologies" in table[u"title"].lower():
+ if (u"across topologies" in table[u"title"].lower() or
+ (u" 3n-" in table[u"title"].lower() and
+ u" 2n-" in table[u"title"].lower())):
tst_name_mod = tst_name_mod.replace(u"2n1l-", u"")
if tbl_dict.get(tst_name_mod, None) is None:
name = f"{u'-'.join(tst_data[u'name'].split(u'-')[:-1])}"
if table[u"reference"][u"nic"] not in tst_data[u"tags"]:
continue
tst_name_mod = _tpc_modify_test_name(tst_name)
- if u"across topologies" in table[u"title"].lower():
+ if (u"across topologies" in table[u"title"].lower() or
+ (u" 3n-" in table[u"title"].lower() and
+ u" 2n-" in table[u"title"].lower())):
tst_name_mod = tst_name_mod.replace(u"2n1l-", u"")
if tbl_dict.get(tst_name_mod, None) is None:
name = \
if table[u"compare"][u"nic"] not in tst_data[u"tags"]:
continue
tst_name_mod = _tpc_modify_test_name(tst_name)
- if u"across topologies" in table[u"title"].lower():
+ if (u"across topologies" in table[u"title"].lower() or
+ (u" 3n-" in table[u"title"].lower() and
+ u" 2n-" in table[u"title"].lower())):
tst_name_mod = tst_name_mod.replace(u"2n1l-", u"")
if tbl_dict.get(tst_name_mod, None) is None:
name = f"{u'-'.join(tst_data[u'name'].split(u'-')[:-1])}"
if table[u"compare"][u"nic"] not in tst_data[u"tags"]:
continue
tst_name_mod = _tpc_modify_test_name(tst_name)
- if u"across topologies" in table[u"title"].lower():
+ if (u"across topologies" in table[u"title"].lower() or
+ (u" 3n-" in table[u"title"].lower() and
+ u" 2n-" in table[u"title"].lower())):
tst_name_mod = tst_name_mod.replace(u"2n1l-", u"")
if tbl_dict.get(tst_name_mod, None) is None:
name = \
if item[u"nic"] not in tst_data[u"tags"]:
continue
tst_name_mod = _tpc_modify_test_name(tst_name)
- if u"across topologies" in table[u"title"].lower():
+ if (u"across topologies" in table[u"title"].lower() or
+ (u" 3n-" in table[u"title"].lower() and
+ u" 2n-" in table[u"title"].lower())):
tst_name_mod = tst_name_mod.replace(u"2n1l-", u"")
if tbl_dict.get(tst_name_mod, None) is None:
continue
u"title"]] = list()
try:
if table[u"include-tests"] == u"MRR":
- res = tst_data[u"result"][u"receive-rate"]
+ res = (tst_data[u"result"][u"receive-rate"],
+ tst_data[u"result"][u"receive-stdev"])
elif table[u"include-tests"] == u"PDR":
res = tst_data[u"throughput"][u"PDR"][u"LOWER"]
elif table[u"include-tests"] == u"NDR":
pass
tbl_lst = list()
- footnote = False
for tst_name in tbl_dict:
item = [tbl_dict[tst_name][u"name"], ]
if history:
if tbl_dict[tst_name].get(u"history", None) is not None:
for hist_data in tbl_dict[tst_name][u"history"].values():
if hist_data:
- item.append(round(mean(hist_data) / 1000000, 2))
- item.append(round(stdev(hist_data) / 1000000, 2))
+ if table[u"include-tests"] == u"MRR":
+ item.append(round(hist_data[0][0] / 1e6, 1))
+ item.append(round(hist_data[0][1] / 1e6, 1))
+ else:
+ item.append(round(mean(hist_data) / 1e6, 1))
+ item.append(round(stdev(hist_data) / 1e6, 1))
else:
- item.extend([u"Not tested", u"Not tested"])
+ item.extend([u"NT", u"NT"])
+ else:
+ item.extend([u"NT", u"NT"])
+ data_r = tbl_dict[tst_name][u"ref-data"]
+ if data_r:
+ if table[u"include-tests"] == u"MRR":
+ data_r_mean = data_r[0][0]
+ data_r_stdev = data_r[0][1]
else:
- item.extend([u"Not tested", u"Not tested"])
- data_t = tbl_dict[tst_name][u"ref-data"]
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ data_r_mean = mean(data_r)
+ data_r_stdev = stdev(data_r)
+ item.append(round(data_r_mean / 1e6, 1))
+ item.append(round(data_r_stdev / 1e6, 1))
else:
- item.extend([u"Not tested", u"Not tested"])
- data_t = tbl_dict[tst_name][u"cmp-data"]
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ data_r_mean = None
+ data_r_stdev = None
+ item.extend([u"NT", u"NT"])
+ data_c = tbl_dict[tst_name][u"cmp-data"]
+ if data_c:
+ if table[u"include-tests"] == u"MRR":
+ data_c_mean = data_c[0][0]
+ data_c_stdev = data_c[0][1]
+ else:
+ data_c_mean = mean(data_c)
+ data_c_stdev = stdev(data_c)
+ item.append(round(data_c_mean / 1e6, 1))
+ item.append(round(data_c_stdev / 1e6, 1))
else:
- item.extend([u"Not tested", u"Not tested"])
- if item[-2] == u"Not tested":
+ data_c_mean = None
+ data_c_stdev = None
+ item.extend([u"NT", u"NT"])
+ if item[-2] == u"NT":
pass
- elif item[-4] == u"Not tested":
+ elif item[-4] == u"NT":
+ item.append(u"New in CSIT-2001")
item.append(u"New in CSIT-2001")
- # elif topo == u"2n-skx" and u"dot1q" in tbl_dict[tst_name][u"name"]:
- # item.append(u"See footnote [1]")
- # footnote = True
- elif item[-4] != 0:
- item.append(int(relative_change(float(item[-4]), float(item[-2]))))
- if (len(item) == len(header)) and (item[-3] != u"Not tested"):
+ elif data_r_mean is not None and data_c_mean is not None:
+ delta, d_stdev = relative_change_stdev(
+ data_r_mean, data_c_mean, data_r_stdev, data_c_stdev
+ )
+ try:
+ item.append(round(delta))
+ except ValueError:
+ item.append(delta)
+ try:
+ item.append(round(d_stdev))
+ except ValueError:
+ item.append(d_stdev)
+ if rca_data:
+ rca_nr = rca_data.get(item[0], u"-")
+ item.insert(0, f"[{rca_nr}]" if rca_nr != u"-" else u"-")
+ if (len(item) == len(header)) and (item[-4] != u"NT"):
tbl_lst.append(item)
tbl_lst = _tpc_sort_table(tbl_lst)
with open(csv_file, u"wt") as file_handler:
file_handler.write(header_str)
for test in tbl_lst:
- file_handler.write(u",".join([str(item) for item in test]) + u"\n")
+ file_handler.write(u";".join([str(item) for item in test]) + u"\n")
txt_file_name = f"{table[u'output-file']}.txt"
- convert_csv_to_pretty_txt(csv_file, txt_file_name)
-
- if footnote:
- with open(txt_file_name, u'a') as txt_file:
- txt_file.writelines([
- u"\nFootnotes:\n",
- u"[1] CSIT-1908 changed test methodology of dot1q tests in "
- u"2-node testbeds, dot1q encapsulation is now used on both "
- u"links of SUT.\n",
- u" Previously dot1q was used only on a single link with the "
- u"other link carrying untagged Ethernet frames. This changes "
- u"results\n",
- u" in slightly lower throughput in CSIT-1908 for these "
- u"tests. See release notes."
- ])
+ convert_csv_to_pretty_txt(csv_file, txt_file_name, delimiter=u";")
+
+ footnote = u""
+ with open(txt_file_name, u'a') as txt_file:
+ txt_file.write(legend)
+ if rca_data:
+ footnote = rca_data.get(u"footnote", u"")
+ if footnote:
+ txt_file.write(footnote)
+ txt_file.write(u":END")
# Generate html table:
- _tpc_generate_html_table(header, tbl_lst, f"{table[u'output-file']}.html")
+ _tpc_generate_html_table(
+ header,
+ tbl_lst,
+ table[u'output-file'],
+ legend=legend,
+ footnote=footnote
+ )
def table_nics_comparison(table, input_data):
# Prepare the header of the tables
try:
- header = [u"Test case", ]
-
- if table[u"include-tests"] == u"MRR":
- hdr_param = u"Rec Rate"
- else:
- hdr_param = u"Thput"
-
- header.extend(
- [
- f"{table[u'reference'][u'title']} {hdr_param} [Mpps]",
- f"{table[u'reference'][u'title']} Stdev [Mpps]",
- f"{table[u'compare'][u'title']} {hdr_param} [Mpps]",
- f"{table[u'compare'][u'title']} Stdev [Mpps]",
- u"Delta [%]"
- ]
+ header = [
+ u"Test Case",
+ f"{table[u'reference'][u'title']} "
+ f"Avg({table[u'include-tests']})",
+ f"{table[u'reference'][u'title']} "
+ f"Stdev({table[u'include-tests']})",
+ f"{table[u'compare'][u'title']} "
+ f"Avg({table[u'include-tests']})",
+ f"{table[u'compare'][u'title']} "
+ f"Stdev({table[u'include-tests']})",
+ f"Diff({table[u'reference'][u'title']},"
+ f"{table[u'compare'][u'title']})",
+ u"Stdev(Diff)"
+ ]
+ legend = (
+ u"\nLegend:\n"
+ f"{table[u'reference'][u'title']} "
+ f"Avg({table[u'include-tests']}): "
+ f"Mean value of {table[u'include-tests']} [Mpps] computed from a "
+ f"series of runs of the listed tests executed using "
+ f"{table[u'reference'][u'title']} NIC.\n"
+ f"{table[u'reference'][u'title']} "
+ f"Stdev({table[u'include-tests']}): "
+ f"Standard deviation value of {table[u'include-tests']} [Mpps] "
+ f"computed from a series of runs of the listed tests executed "
+ f"using {table[u'reference'][u'title']} NIC.\n"
+ f"{table[u'compare'][u'title']} "
+ f"Avg({table[u'include-tests']}): "
+ f"Mean value of {table[u'include-tests']} [Mpps] computed from a "
+ f"series of runs of the listed tests executed using "
+ f"{table[u'compare'][u'title']} NIC.\n"
+ f"{table[u'compare'][u'title']} "
+ f"Stdev({table[u'include-tests']}): "
+ f"Standard deviation value of {table[u'include-tests']} [Mpps] "
+ f"computed from a series of runs of the listed tests executed "
+ f"using {table[u'compare'][u'title']} NIC.\n"
+ f"Diff({table[u'reference'][u'title']},"
+ f"{table[u'compare'][u'title']}): "
+ f"Percentage change calculated for mean values.\n"
+ u"Stdev(Diff): "
+ u"Standard deviation of percentage change calculated for mean "
+ u"values.\n"
+ u":END"
)
except (AttributeError, KeyError) as err:
u"cmp-data": list()
}
try:
- result = None
if table[u"include-tests"] == u"MRR":
- result = tst_data[u"result"][u"receive-rate"]
+ result = (tst_data[u"result"][u"receive-rate"],
+ tst_data[u"result"][u"receive-stdev"])
elif table[u"include-tests"] == u"PDR":
result = tst_data[u"throughput"][u"PDR"][u"LOWER"]
elif table[u"include-tests"] == u"NDR":
tbl_lst = list()
for tst_name in tbl_dict:
item = [tbl_dict[tst_name][u"name"], ]
- data_t = tbl_dict[tst_name][u"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][u"ref-data"]
+ if data_r:
+ if table[u"include-tests"] == u"MRR":
+ data_r_mean = data_r[0][0]
+ data_r_stdev = data_r[0][1]
+ else:
+ data_r_mean = mean(data_r)
+ data_r_stdev = stdev(data_r)
+ item.append(round(data_r_mean / 1e6, 1))
+ item.append(round(data_r_stdev / 1e6, 1))
else:
+ data_r_mean = None
+ data_r_stdev = None
item.extend([None, None])
- data_t = tbl_dict[tst_name][u"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][u"cmp-data"]
+ if data_c:
+ if table[u"include-tests"] == u"MRR":
+ data_c_mean = data_c[0][0]
+ data_c_stdev = data_c[0][1]
+ else:
+ data_c_mean = mean(data_c)
+ data_c_stdev = stdev(data_c)
+ item.append(round(data_c_mean / 1e6, 1))
+ item.append(round(data_c_stdev / 1e6, 1))
else:
+ data_c_mean = None
+ data_c_stdev = 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 is not None and data_c_mean is not None:
+ delta, d_stdev = relative_change_stdev(
+ data_r_mean, data_c_mean, data_r_stdev, data_c_stdev
+ )
+ try:
+ item.append(round(delta))
+ except ValueError:
+ item.append(delta)
+ try:
+ item.append(round(d_stdev))
+ except ValueError:
+ item.append(d_stdev)
tbl_lst.append(item)
# Sort the table according to the relative change
# Generate csv tables:
with open(f"{table[u'output-file']}.csv", u"wt") as file_handler:
- file_handler.write(u",".join(header) + u"\n")
+ file_handler.write(u";".join(header) + u"\n")
for test in tbl_lst:
- file_handler.write(u",".join([str(item) for item in test]) + u"\n")
+ file_handler.write(u";".join([str(item) for item in test]) + u"\n")
convert_csv_to_pretty_txt(f"{table[u'output-file']}.csv",
- f"{table[u'output-file']}.txt")
+ f"{table[u'output-file']}.txt",
+ delimiter=u";")
+
+ with open(f"{table[u'output-file']}.txt", u'a') as txt_file:
+ txt_file.write(legend)
# Generate html table:
- _tpc_generate_html_table(header, tbl_lst, f"{table[u'output-file']}.html")
+ _tpc_generate_html_table(
+ header,
+ tbl_lst,
+ table[u'output-file'],
+ legend=legend
+ )
def table_soak_vs_ndr(table, input_data):
# Prepare the header of the table
try:
header = [
- u"Test case",
- f"{table[u'reference'][u'title']} Thput [Mpps]",
- f"{table[u'reference'][u'title']} Stdev [Mpps]",
- f"{table[u'compare'][u'title']} Thput [Mpps]",
- f"{table[u'compare'][u'title']} Stdev [Mpps]",
- u"Delta [%]", u"Stdev of delta [%]"
+ u"Test Case",
+ f"Avg({table[u'reference'][u'title']})",
+ f"Stdev({table[u'reference'][u'title']})",
+ f"Avg({table[u'compare'][u'title']})",
+ f"Stdev{table[u'compare'][u'title']})",
+ u"Diff",
+ u"Stdev(Diff)"
]
- header_str = u",".join(header) + u"\n"
+ header_str = u";".join(header) + u"\n"
+ legend = (
+ u"\nLegend:\n"
+ f"Avg({table[u'reference'][u'title']}): "
+ f"Mean value of {table[u'reference'][u'title']} [Mpps] computed "
+ f"from a series of runs of the listed tests.\n"
+ f"Stdev({table[u'reference'][u'title']}): "
+ f"Standard deviation value of {table[u'reference'][u'title']} "
+ f"[Mpps] computed from a series of runs of the listed tests.\n"
+ f"Avg({table[u'compare'][u'title']}): "
+ f"Mean value of {table[u'compare'][u'title']} [Mpps] computed from "
+ f"a series of runs of the listed tests.\n"
+ f"Stdev({table[u'compare'][u'title']}): "
+ f"Standard deviation value of {table[u'compare'][u'title']} [Mpps] "
+ f"computed from a series of runs of the listed tests.\n"
+ f"Diff({table[u'reference'][u'title']},"
+ f"{table[u'compare'][u'title']}): "
+ f"Percentage change calculated for mean values.\n"
+ u"Stdev(Diff): "
+ u"Standard deviation of percentage change calculated for mean "
+ u"values.\n"
+ u":END"
+ )
except (AttributeError, KeyError) as err:
logging.error(f"The model is invalid, missing parameter: {repr(err)}")
return
if tst_data[u"type"] not in (u"NDRPDR", u"MRR", u"BMRR"):
continue
if table[u"include-tests"] == u"MRR":
- result = tst_data[u"result"][u"receive-rate"]
+ result = (tst_data[u"result"][u"receive-rate"],
+ tst_data[u"result"][u"receive-stdev"])
elif table[u"include-tests"] == u"PDR":
result = \
tst_data[u"throughput"][u"PDR"][u"LOWER"]
item = [tbl_dict[tst_name][u"name"], ]
data_r = tbl_dict[tst_name][u"ref-data"]
if data_r:
- data_r_mean = mean(data_r)
- item.append(round(data_r_mean / 1000000, 2))
- data_r_stdev = stdev(data_r)
- item.append(round(data_r_stdev / 1000000, 2))
+ if table[u"include-tests"] == u"MRR":
+ data_r_mean = data_r[0][0]
+ data_r_stdev = data_r[0][1]
+ else:
+ data_r_mean = mean(data_r)
+ data_r_stdev = stdev(data_r)
+ item.append(round(data_r_mean / 1e6, 1))
+ item.append(round(data_r_stdev / 1e6, 1))
else:
data_r_mean = None
data_r_stdev = None
item.extend([None, None])
data_c = tbl_dict[tst_name][u"cmp-data"]
if data_c:
- data_c_mean = mean(data_c)
- item.append(round(data_c_mean / 1000000, 2))
- data_c_stdev = stdev(data_c)
- item.append(round(data_c_stdev / 1000000, 2))
+ if table[u"include-tests"] == u"MRR":
+ data_c_mean = data_c[0][0]
+ data_c_stdev = data_c[0][1]
+ else:
+ data_c_mean = mean(data_c)
+ data_c_stdev = stdev(data_c)
+ item.append(round(data_c_mean / 1e6, 1))
+ item.append(round(data_c_stdev / 1e6, 1))
else:
data_c_mean = None
data_c_stdev = None
item.extend([None, None])
- if data_r_mean and data_c_mean:
+ if data_r_mean is not None and data_c_mean is not None:
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))
+ try:
+ item.append(round(delta))
+ except ValueError:
+ item.append(delta)
+ try:
+ item.append(round(d_stdev))
+ except ValueError:
+ item.append(d_stdev)
tbl_lst.append(item)
# Sort the table according to the relative change
with open(csv_file, u"wt") as file_handler:
file_handler.write(header_str)
for test in tbl_lst:
- file_handler.write(u",".join([str(item) for item in test]) + u"\n")
+ file_handler.write(u";".join([str(item) for item in test]) + u"\n")
- convert_csv_to_pretty_txt(csv_file, f"{table[u'output-file']}.txt")
+ convert_csv_to_pretty_txt(
+ csv_file, f"{table[u'output-file']}.txt", delimiter=u";"
+ )
+ with open(f"{table[u'output-file']}.txt", u'a') as txt_file:
+ txt_file.write(legend)
# Generate html table:
- _tpc_generate_html_table(header, tbl_lst, f"{table[u'output-file']}.html")
+ _tpc_generate_html_table(
+ header,
+ tbl_lst,
+ table[u'output-file'],
+ legend=legend
+ )
def table_perf_trending_dash(table, input_data):
continue
tbl_lst.append(
[tbl_dict[tst_name][u"name"],
- round(last_avg / 1000000, 2),
+ round(last_avg / 1e6, 2),
rel_change_last,
rel_change_long,
classification_lst[-win_size:].count(u"regression"),
Code Review / csit.git / blobdiff