# 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:
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Algorithms to generate tables.
"""
import logging
import csv
import re
from collections import OrderedDict
from xml.etree import ElementTree as ET
from datetime import datetime as dt
from datetime import timedelta
from copy import deepcopy
import plotly.graph_objects as go
import plotly.offline as ploff
import pandas as pd
from numpy import nan, isnan
from yaml import load, FullLoader, YAMLError
from pal_utils import mean, stdev, classify_anomalies, \
convert_csv_to_pretty_txt, relative_change_stdev
REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)')
def generate_tables(spec, data):
"""Generate all tables specified in the specification file.
:param spec: Specification read from the specification file.
:param data: Data to process.
:type spec: Specification
:type data: InputData
"""
generator = {
u"table_merged_details": table_merged_details,
u"table_perf_comparison": table_perf_comparison,
u"table_perf_comparison_nic": table_perf_comparison_nic,
u"table_nics_comparison": table_nics_comparison,
u"table_soak_vs_ndr": table_soak_vs_ndr,
u"table_perf_trending_dash": table_perf_trending_dash,
u"table_perf_trending_dash_html": table_perf_trending_dash_html,
u"table_last_failed_tests": table_last_failed_tests,
u"table_failed_tests": table_failed_tests,
u"table_failed_tests_html": table_failed_tests_html,
u"table_oper_data_html": table_oper_data_html,
u"table_comparison": table_comparison
}
logging.info(u"Generating the tables ...")
for table in spec.tables:
try:
generator[table[u"algorithm"]](table, data)
except NameError as err:
logging.error(
f"Probably algorithm {table[u'algorithm']} is not defined: "
f"{repr(err)}"
)
logging.info(u"Done.")
def table_oper_data_html(table, input_data):
"""Generate the table(s) with algorithm: html_table_oper_data
specified in the specification file.
:param table: Table to generate.
:param input_data: Data to process.
:type table: pandas.Series
:type input_data: InputData
"""
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'')} "
f"{table.get(u'title', u'')}."
)
data = input_data.filter_data(
table,
params=[u"name", u"parent", u"show-run", u"type"],
continue_on_error=True
)
if data.empty:
return
data = input_data.merge_data(data)
sort_tests = table.get(u"sort", None)
if sort_tests:
args = dict(
inplace=True,
ascending=(sort_tests == u"ascending")
)
data.sort_index(**args)
suites = input_data.filter_data(
table,
continue_on_error=True,
data_set=u"suites"
)
if suites.empty:
return
suites = input_data.merge_data(suites)
def _generate_html_table(tst_data):
"""Generate an HTML table with operational data for the given test.
:param tst_data: Test data to be used to generate the table.
:type tst_data: pandas.Series
:returns: HTML table with operational data.
:rtype: str
"""
colors = {
u"header": u"#7eade7",
u"empty": u"#ffffff",
u"body": (u"#e9f1fb", u"#d4e4f7")
}
tbl = ET.Element(u"table", attrib=dict(width=u"100%", border=u"0"))
trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"header"]))
thead = ET.SubElement(
trow, u"th", attrib=dict(align=u"left", colspan=u"6")
)
thead.text = tst_data[u"name"]
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")
)
thead.text = u"\t"
if tst_data.get(u"show-run", u"No Data") == u"No Data":
trow = ET.SubElement(
tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
)
tcol = ET.SubElement(
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"Name",
u"Nr of Vectors",
u"Nr of Packets",
u"Suspends",
u"Cycles per Packet",
u"Average Vector Size"
)
for dut_data in tst_data[u"show-run"].values():
trow = ET.SubElement(
tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
)
tcol = ET.SubElement(
trow, u"td", attrib=dict(align=u"left", colspan=u"6")
)
if dut_data.get(u"threads", None) is None:
tcol.text = u"No Data"
continue
bold = ET.SubElement(tcol, u"b")
bold.text = (
f"Host IP: {dut_data.get(u'host', '')}, "
f"Socket: {dut_data.get(u'socket', '')}"
)
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")
)
thead.text = u"\t"
for thread_nr, thread in dut_data[u"threads"].items():
trow = ET.SubElement(
tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
)
tcol = ET.SubElement(
trow, u"td", attrib=dict(align=u"left", colspan=u"6")
)
bold = ET.SubElement(tcol, u"b")
bold.text = u"main" if thread_nr == 0 else f"worker_{thread_nr}"
trow = ET.SubElement(
tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
)
for idx, col in enumerate(tbl_hdr):
tcol = ET.SubElement(
trow, u"td",
attrib=dict(align=u"right" if idx else u"left")
)
font = ET.SubElement(
tcol, u"font", attrib=dict(size=u"2")
)
bold = ET.SubElement(font, u"b")
bold.text = col
for row_nr, row in enumerate(thread):
trow = ET.SubElement(
tbl, u"tr",
attrib=dict(bgcolor=colors[u"body"][row_nr % 2])
)
for idx, col in enumerate(row):
tcol = ET.SubElement(
trow, u"td",
attrib=dict(align=u"right" if idx else u"left")
)
font = ET.SubElement(
tcol, u"font", attrib=dict(size=u"2")
)
if isinstance(col, float):
font.text = f"{col:.2f}"
else:
font.text = str(col)
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")
)
thead.text = u"\t"
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"))
for suite in suites.values:
html_table = str()
for test_data in data.values:
if test_data[u"parent"] not in suite[u"name"]:
continue
html_table += _generate_html_table(test_data)
if not html_table:
continue
try:
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")
html_file.write(html_table)
html_file.write(u"\n\t
\n")
except KeyError:
logging.warning(u"The output file is not defined.")
return
logging.info(u" Done.")
def table_merged_details(table, input_data):
"""Generate the table(s) with algorithm: table_merged_details
specified in the specification file.
:param table: Table to generate.
:param input_data: Data to process.
:type table: pandas.Series
:type input_data: InputData
"""
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'')} "
f"{table.get(u'title', u'')}."
)
data = input_data.filter_data(table, continue_on_error=True)
data = input_data.merge_data(data)
sort_tests = table.get(u"sort", None)
if sort_tests:
args = dict(
inplace=True,
ascending=(sort_tests == u"ascending")
)
data.sort_index(**args)
suites = input_data.filter_data(
table, continue_on_error=True, data_set=u"suites")
suites = input_data.merge_data(suites)
# Prepare the header of the tables
header = list()
for column in table[u"columns"]:
header.append(
u'"{0}"'.format(str(column[u"title"]).replace(u'"', u'""'))
)
for suite in suites.values:
# Generate data
suite_name = suite[u"name"]
table_lst = list()
for test in data.keys():
if data[test][u"parent"] not in suite_name:
continue
row_lst = list()
for column in table[u"columns"]:
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 "
)
if column[u"data"].split(u" ")[1] in (u"name", ):
if len(col_data) > 30:
col_data_lst = col_data.split(u"-")
half = int(len(col_data_lst) / 2)
col_data = f"{u'-'.join(col_data_lst[:half])}" \
f"- |br| " \
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"):
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"')
if len(row_lst) == len(table[u"columns"]):
table_lst.append(row_lst)
# Write the data to file
if table_lst:
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")
for item in table_lst:
file_handler.write(u",".join(item) + u"\n")
logging.info(u" Done.")
def _tpc_modify_test_name(test_name, ignore_nic=False):
"""Modify a test name by replacing its parts.
:param test_name: Test name to be modified.
:param ignore_nic: If True, NIC is removed from TC name.
:type test_name: str
:type ignore_nic: bool
:returns: Modified test name.
:rtype: str
"""
test_name_mod = test_name.\
replace(u"-ndrpdrdisc", u""). \
replace(u"-ndrpdr", u"").\
replace(u"-pdrdisc", u""). \
replace(u"-ndrdisc", u"").\
replace(u"-pdr", u""). \
replace(u"-ndr", u""). \
replace(u"1t1c", u"1c").\
replace(u"2t1c", u"1c"). \
replace(u"2t2c", u"2c").\
replace(u"4t2c", u"2c"). \
replace(u"4t4c", u"4c").\
replace(u"8t4c", u"4c")
if ignore_nic:
return re.sub(REGEX_NIC, u"", test_name_mod)
return test_name_mod
def _tpc_modify_displayed_test_name(test_name):
"""Modify a test name which is displayed in a table by replacing its parts.
:param test_name: Test name to be modified.
:type test_name: str
:returns: Modified test name.
:rtype: str
"""
return test_name.\
replace(u"1t1c", u"1c").\
replace(u"2t1c", u"1c"). \
replace(u"2t2c", u"2c").\
replace(u"4t2c", u"2c"). \
replace(u"4t4c", u"4c").\
replace(u"8t4c", u"4c")
def _tpc_insert_data(target, src, include_tests):
"""Insert src data to the target structure.
:param target: Target structure where the data is placed.
:param src: Source data to be placed into the target stucture.
:param include_tests: Which results will be included (MRR, NDR, PDR).
:type target: list
:type src: dict
:type include_tests: str
"""
try:
if include_tests == u"MRR":
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":
target.append(src[u"throughput"][u"NDR"][u"LOWER"])
except (KeyError, TypeError):
pass
def _tpc_sort_table(table):
"""Sort the table this way:
1. Put "New in CSIT-XXXX" at the first place.
2. Put "See footnote" at the second place.
3. Sort the rest by "Delta".
:param table: Table to sort.
:type table: list
:returns: Sorted table.
:rtype: list
"""
tbl_new = list()
tbl_see = list()
tbl_delta = list()
for item in table:
if isinstance(item[-1], str):
if u"New in CSIT" in item[-1]:
tbl_new.append(item)
elif u"See footnote" in item[-1]:
tbl_see.append(item)
else:
tbl_delta.append(item)
# 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[-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()
# 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, out_file_name, legend=u"",
footnote=u"", sort_data=True, title=u""):
"""Generate html table from input data with simple sorting possibility.
:param header: Table header.
:param data: Input data to be included in the table. It is a list of lists.
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 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).
:param sort_data: If True the data sorting is enabled.
:param title: The table (and file) title.
:type header: list
:type data: list of lists
:type out_file_name: str
:type legend: str
:type footnote: str
:type sort_data: bool
:type title: str
"""
try:
idx = header.index(u"Test Case")
except ValueError:
idx = 0
params = {
u"align-hdr": (
[u"left", u"right"],
[u"left", u"left", u"right"],
[u"left", u"left", u"left", u"right"]
),
u"align-itm": (
[u"left", u"right"],
[u"left", u"left", u"right"],
[u"left", u"left", u"left", u"right"]
),
u"width": ([28, 9], [4, 24, 10], [4, 4, 32, 10])
}
df_data = pd.DataFrame(data, columns=header)
if sort_data:
df_sorted = [df_data.sort_values(
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[idx]], ascending=[False, True]
if key != header[idx] else [True, True]) for key in header]
df_sorted.extend(df_sorted_rev)
else:
df_sorted = df_data
fill_color = [[u"#d4e4f7" if idx % 2 else u"#e9f1fb"
for idx in range(len(df_data))]]
table_header = dict(
values=[f"{item.replace(u',', u',
')}" for item in header],
fill_color=u"#7eade7",
align=params[u"align-hdr"][idx],
font=dict(
family=u"Courier New",
size=12
)
)
fig = go.Figure()
if sort_data:
for table in df_sorted:
columns = [table.get(col) for col in header]
fig.add_trace(
go.Table(
columnwidth=params[u"width"][idx],
header=table_header,
cells=dict(
values=columns,
fill_color=fill_color,
align=params[u"align-itm"][idx],
font=dict(
family=u"Courier New",
size=12
)
)
)
)
buttons = list()
menu_items = [f"{itm} (ascending)" for itm in header]
menu_items_rev = [f"{itm} (descending)" for itm in header]
menu_items.extend(menu_items_rev)
for idx, hdr in enumerate(menu_items):
visible = [False, ] * len(menu_items)
visible[idx] = True
buttons.append(
dict(
label=hdr.replace(u" [Mpps]", u""),
method=u"update",
args=[{u"visible": visible}],
)
)
fig.update_layout(
updatemenus=[
go.layout.Updatemenu(
type=u"dropdown",
direction=u"down",
x=0.0,
xanchor=u"left",
y=1.045,
yanchor=u"top",
active=len(menu_items) - 1,
buttons=list(buttons)
)
],
)
else:
fig.add_trace(
go.Table(
columnwidth=params[u"width"][idx],
header=table_header,
cells=dict(
values=[df_sorted.get(col) for col in header],
fill_color=fill_color,
align=params[u"align-itm"][idx],
font=dict(
family=u"Courier New",
size=12
)
)
)
)
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
\n\n\n"
u".. |prein| raw:: html\n\n \n\n\n"
u".. |preout| raw:: html\n\n \n\n"
)
if title:
rst_file.write(f"{title}\n")
rst_file.write(f"{u'`' * len(title)}\n\n")
rst_file.write(
u".. raw:: html\n\n"
f' \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):
"""Generate the table(s) with algorithm: table_perf_comparison
specified in the specification file.
:param table: Table to generate.
:param input_data: Data to process.
:type table: pandas.Series
:type input_data: InputData
"""
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'')} "
f"{table.get(u'title', u'')}."
)
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
try:
header = [u"Test Case", ]
legend = u"\nLegend:\n"
rca_data = None
rca = table.get(u"rca", None)
if rca:
try:
with open(rca.get(u"data-file", u""), u"r") as rca_file:
rca_data = load(rca_file, Loader=FullLoader)
header.insert(0, rca.get(u"title", u"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']} 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']} "
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"
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()
for job, builds in table[u"reference"][u"data"].items():
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() 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 = tst_data[u'name'].rsplit(u'-', 1)[0]
if u"across testbeds" in table[u"title"].lower() or \
u"across topologies" in table[u"title"].lower():
name = _tpc_modify_displayed_test_name(name)
tbl_dict[tst_name_mod] = {
u"name": name,
u"replace-ref": True,
u"replace-cmp": True,
u"ref-data": list(),
u"cmp-data": list()
}
_tpc_insert_data(target=tbl_dict[tst_name_mod][u"ref-data"],
src=tst_data,
include_tests=table[u"include-tests"])
replacement = table[u"reference"].get(u"data-replacement", None)
if replacement:
rpl_data = input_data.filter_data(
table, data=replacement, continue_on_error=True)
for job, builds in replacement.items():
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() 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 = tst_data[u'name'].rsplit(u'-', 1)[0]
if u"across testbeds" in table[u"title"].lower() or \
u"across topologies" in table[u"title"].lower():
name = _tpc_modify_displayed_test_name(name)
tbl_dict[tst_name_mod] = {
u"name": name,
u"replace-ref": False,
u"replace-cmp": True,
u"ref-data": list(),
u"cmp-data": list()
}
if tbl_dict[tst_name_mod][u"replace-ref"]:
tbl_dict[tst_name_mod][u"replace-ref"] = False
tbl_dict[tst_name_mod][u"ref-data"] = list()
_tpc_insert_data(
target=tbl_dict[tst_name_mod][u"ref-data"],
src=tst_data,
include_tests=table[u"include-tests"]
)
for job, builds in table[u"compare"][u"data"].items():
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() 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 = tst_data[u'name'].rsplit(u'-', 1)[0]
if u"across testbeds" in table[u"title"].lower() or \
u"across topologies" in table[u"title"].lower():
name = _tpc_modify_displayed_test_name(name)
tbl_dict[tst_name_mod] = {
u"name": name,
u"replace-ref": False,
u"replace-cmp": True,
u"ref-data": list(),
u"cmp-data": list()
}
_tpc_insert_data(
target=tbl_dict[tst_name_mod][u"cmp-data"],
src=tst_data,
include_tests=table[u"include-tests"]
)
replacement = table[u"compare"].get(u"data-replacement", None)
if replacement:
rpl_data = input_data.filter_data(
table, data=replacement, continue_on_error=True)
for job, builds in replacement.items():
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() 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 = tst_data[u'name'].rsplit(u'-', 1)[0]
if u"across testbeds" in table[u"title"].lower() or \
u"across topologies" in table[u"title"].lower():
name = _tpc_modify_displayed_test_name(name)
tbl_dict[tst_name_mod] = {
u"name": name,
u"replace-ref": False,
u"replace-cmp": False,
u"ref-data": list(),
u"cmp-data": list()
}
if tbl_dict[tst_name_mod][u"replace-cmp"]:
tbl_dict[tst_name_mod][u"replace-cmp"] = False
tbl_dict[tst_name_mod][u"cmp-data"] = list()
_tpc_insert_data(
target=tbl_dict[tst_name_mod][u"cmp-data"],
src=tst_data,
include_tests=table[u"include-tests"]
)
for item in history:
for job, builds in item[u"data"].items():
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() 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
if tbl_dict[tst_name_mod].get(u"history", None) is None:
tbl_dict[tst_name_mod][u"history"] = OrderedDict()
if tbl_dict[tst_name_mod][u"history"].\
get(item[u"title"], None) is None:
tbl_dict[tst_name_mod][u"history"][item[
u"title"]] = list()
try:
if table[u"include-tests"] == u"MRR":
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":
res = tst_data[u"throughput"][u"NDR"][u"LOWER"]
else:
continue
tbl_dict[tst_name_mod][u"history"][item[u"title"]].\
append(res)
except (TypeError, KeyError):
pass
tbl_lst = list()
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:
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"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:
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([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:
data_c_mean = None
data_c_stdev = None
item.extend([u"NT", u"NT"])
if item[-2] == u"NT":
pass
elif item[-4] == u"NT":
item.append(u"New in CSIT-2001")
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)
# Generate csv tables:
csv_file = f"{table[u'output-file']}.csv"
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")
txt_file_name = f"{table[u'output-file']}.txt"
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,
table[u'output-file'],
legend=legend,
footnote=footnote,
title=table.get(u"title", u"")
)
def table_perf_comparison_nic(table, input_data):
"""Generate the table(s) with algorithm: table_perf_comparison
specified in the specification file.
:param table: Table to generate.
:param input_data: Data to process.
:type table: pandas.Series
:type input_data: InputData
"""
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'')} "
f"{table.get(u'title', u'')}."
)
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
try:
header = [u"Test Case", ]
legend = u"\nLegend:\n"
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']} 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']} "
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"
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()
for job, builds in table[u"reference"][u"data"].items():
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, ignore_nic=True)
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 = tst_data[u'name'].rsplit(u'-', 1)[0]
if u"across testbeds" in table[u"title"].lower() or \
u"across topologies" in table[u"title"].lower():
name = _tpc_modify_displayed_test_name(name)
tbl_dict[tst_name_mod] = {
u"name": name,
u"replace-ref": True,
u"replace-cmp": True,
u"ref-data": list(),
u"cmp-data": list()
}
_tpc_insert_data(
target=tbl_dict[tst_name_mod][u"ref-data"],
src=tst_data,
include_tests=table[u"include-tests"]
)
replacement = table[u"reference"].get(u"data-replacement", None)
if replacement:
rpl_data = input_data.filter_data(
table, data=replacement, continue_on_error=True)
for job, builds in replacement.items():
for build in builds:
for tst_name, tst_data in rpl_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, ignore_nic=True)
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 = tst_data[u'name'].rsplit(u'-', 1)[0]
if u"across testbeds" in table[u"title"].lower() or \
u"across topologies" in table[u"title"].lower():
name = _tpc_modify_displayed_test_name(name)
tbl_dict[tst_name_mod] = {
u"name": name,
u"replace-ref": False,
u"replace-cmp": True,
u"ref-data": list(),
u"cmp-data": list()
}
if tbl_dict[tst_name_mod][u"replace-ref"]:
tbl_dict[tst_name_mod][u"replace-ref"] = False
tbl_dict[tst_name_mod][u"ref-data"] = list()
_tpc_insert_data(
target=tbl_dict[tst_name_mod][u"ref-data"],
src=tst_data,
include_tests=table[u"include-tests"]
)
for job, builds in table[u"compare"][u"data"].items():
for build in builds:
for tst_name, tst_data in data[job][str(build)].items():
if table[u"compare"][u"nic"] not in tst_data[u"tags"]:
continue
tst_name_mod = _tpc_modify_test_name(tst_name, ignore_nic=True)
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 = tst_data[u'name'].rsplit(u'-', 1)[0]
if u"across testbeds" in table[u"title"].lower() or \
u"across topologies" in table[u"title"].lower():
name = _tpc_modify_displayed_test_name(name)
tbl_dict[tst_name_mod] = {
u"name": name,
u"replace-ref": False,
u"replace-cmp": True,
u"ref-data": list(),
u"cmp-data": list()
}
_tpc_insert_data(
target=tbl_dict[tst_name_mod][u"cmp-data"],
src=tst_data,
include_tests=table[u"include-tests"]
)
replacement = table[u"compare"].get(u"data-replacement", None)
if replacement:
rpl_data = input_data.filter_data(
table, data=replacement, continue_on_error=True)
for job, builds in replacement.items():
for build in builds:
for tst_name, tst_data in rpl_data[job][str(build)].items():
if table[u"compare"][u"nic"] not in tst_data[u"tags"]:
continue
tst_name_mod = \
_tpc_modify_test_name(tst_name, ignore_nic=True)
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 = tst_data[u'name'].rsplit(u'-', 1)[0]
if u"across testbeds" in table[u"title"].lower() or \
u"across topologies" in table[u"title"].lower():
name = _tpc_modify_displayed_test_name(name)
tbl_dict[tst_name_mod] = {
u"name": name,
u"replace-ref": False,
u"replace-cmp": False,
u"ref-data": list(),
u"cmp-data": list()
}
if tbl_dict[tst_name_mod][u"replace-cmp"]:
tbl_dict[tst_name_mod][u"replace-cmp"] = False
tbl_dict[tst_name_mod][u"cmp-data"] = list()
_tpc_insert_data(
target=tbl_dict[tst_name_mod][u"cmp-data"],
src=tst_data,
include_tests=table[u"include-tests"]
)
for item in history:
for job, builds in item[u"data"].items():
for build in builds:
for tst_name, tst_data in data[job][str(build)].items():
if item[u"nic"] not in tst_data[u"tags"]:
continue
tst_name_mod = \
_tpc_modify_test_name(tst_name, ignore_nic=True)
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
if tbl_dict[tst_name_mod].get(u"history", None) is None:
tbl_dict[tst_name_mod][u"history"] = OrderedDict()
if tbl_dict[tst_name_mod][u"history"].\
get(item[u"title"], None) is None:
tbl_dict[tst_name_mod][u"history"][item[
u"title"]] = list()
try:
if table[u"include-tests"] == u"MRR":
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":
res = tst_data[u"throughput"][u"NDR"][u"LOWER"]
else:
continue
tbl_dict[tst_name_mod][u"history"][item[u"title"]].\
append(res)
except (TypeError, KeyError):
pass
tbl_lst = list()
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:
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"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:
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([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:
data_c_mean = None
data_c_stdev = None
item.extend([u"NT", u"NT"])
if item[-2] == u"NT":
pass
elif item[-4] == u"NT":
item.append(u"New in CSIT-2001")
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)
# Generate csv tables:
csv_file = f"{table[u'output-file']}.csv"
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")
txt_file_name = f"{table[u'output-file']}.txt"
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,
table[u'output-file'],
legend=legend,
footnote=footnote,
title=table.get(u"title", u"")
)
def table_nics_comparison(table, input_data):
"""Generate the table(s) with algorithm: table_nics_comparison
specified in the specification file.
:param table: Table to generate.
:param input_data: Data to process.
:type table: pandas.Series
:type input_data: InputData
"""
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'')} "
f"{table.get(u'title', u'')}."
)
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
try:
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:
logging.error(f"The model is invalid, missing parameter: {repr(err)}")
return
# Prepare data to the table:
tbl_dict = dict()
for job, builds in table[u"data"].items():
for build in builds:
for tst_name, tst_data in data[job][str(build)].items():
tst_name_mod = _tpc_modify_test_name(tst_name, ignore_nic=True)
if tbl_dict.get(tst_name_mod, None) is None:
name = tst_data[u'name'].rsplit(u'-', 1)[0]
tbl_dict[tst_name_mod] = {
u"name": name,
u"ref-data": list(),
u"cmp-data": list()
}
try:
if table[u"include-tests"] == u"MRR":
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":
result = tst_data[u"throughput"][u"NDR"][u"LOWER"]
else:
continue
if result and \
table[u"reference"][u"nic"] in tst_data[u"tags"]:
tbl_dict[tst_name_mod][u"ref-data"].append(result)
elif result and \
table[u"compare"][u"nic"] in tst_data[u"tags"]:
tbl_dict[tst_name_mod][u"cmp-data"].append(result)
except (TypeError, KeyError) as err:
logging.debug(f"No data for {tst_name}\n{repr(err)}")
# No data in output.xml for this test
tbl_lst = list()
for tst_name in tbl_dict:
item = [tbl_dict[tst_name][u"name"], ]
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_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 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
tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
# Generate csv tables:
with open(f"{table[u'output-file']}.csv", u"wt") as file_handler:
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")
convert_csv_to_pretty_txt(f"{table[u'output-file']}.csv",
f"{table[u'output-file']}.txt",
delimiter=u";")
with open(table[u'output-file'], u'a') as txt_file:
txt_file.write(legend)
# Generate html table:
_tpc_generate_html_table(
header,
tbl_lst,
table[u'output-file'],
legend=legend,
title=table.get(u"title", u"")
)
def table_soak_vs_ndr(table, input_data):
"""Generate the table(s) with algorithm: table_soak_vs_ndr
specified in the specification file.
:param table: Table to generate.
:param input_data: Data to process.
:type table: pandas.Series
:type input_data: InputData
"""
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'')} "
f"{table.get(u'title', u'')}."
)
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the table
try:
header = [
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"
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
# Create a list of available SOAK test results:
tbl_dict = dict()
for job, builds in table[u"compare"][u"data"].items():
for build in builds:
for tst_name, tst_data in data[job][str(build)].items():
if tst_data[u"type"] == u"SOAK":
tst_name_mod = tst_name.replace(u"-soak", u"")
if tbl_dict.get(tst_name_mod, None) is None:
groups = re.search(REGEX_NIC, tst_data[u"parent"])
nic = groups.group(0) if groups else u""
name = (
f"{nic}-"
f"{u'-'.join(tst_data[u'name'].split(u'-')[:-1])}"
)
tbl_dict[tst_name_mod] = {
u"name": name,
u"ref-data": list(),
u"cmp-data": list()
}
try:
tbl_dict[tst_name_mod][u"cmp-data"].append(
tst_data[u"throughput"][u"LOWER"])
except (KeyError, TypeError):
pass
tests_lst = tbl_dict.keys()
# Add corresponding NDR test results:
for job, builds in table[u"reference"][u"data"].items():
for build in builds:
for tst_name, tst_data in data[job][str(build)].items():
tst_name_mod = tst_name.replace(u"-ndrpdr", u"").\
replace(u"-mrr", u"")
if tst_name_mod not in tests_lst:
continue
try:
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"],
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":
result = \
tst_data[u"throughput"][u"NDR"][u"LOWER"]
else:
result = None
if result is not None:
tbl_dict[tst_name_mod][u"ref-data"].append(
result)
except (KeyError, TypeError):
continue
tbl_lst = list()
for tst_name in tbl_dict:
item = [tbl_dict[tst_name][u"name"], ]
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_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 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
tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
# Generate csv tables:
csv_file = f"{table[u'output-file']}.csv"
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")
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,
table[u'output-file'],
legend=legend,
title=table.get(u"title", u"")
)
def table_perf_trending_dash(table, input_data):
"""Generate the table(s) with algorithm:
table_perf_trending_dash
specified in the specification file.
:param table: Table to generate.
:param input_data: Data to process.
:type table: pandas.Series
:type input_data: InputData
"""
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'')} "
f"{table.get(u'title', u'')}."
)
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
header = [
u"Test Case",
u"Trend [Mpps]",
u"Short-Term Change [%]",
u"Long-Term Change [%]",
u"Regressions [#]",
u"Progressions [#]"
]
header_str = u",".join(header) + u"\n"
# Prepare data to the table:
tbl_dict = dict()
for job, builds in table[u"data"].items():
for build in builds:
for tst_name, tst_data in data[job][str(build)].items():
if tst_name.lower() in table.get(u"ignore-list", list()):
continue
if tbl_dict.get(tst_name, None) is None:
groups = re.search(REGEX_NIC, tst_data[u"parent"])
if not groups:
continue
nic = groups.group(0)
tbl_dict[tst_name] = {
u"name": f"{nic}-{tst_data[u'name']}",
u"data": OrderedDict()
}
try:
tbl_dict[tst_name][u"data"][str(build)] = \
tst_data[u"result"][u"receive-rate"]
except (TypeError, KeyError):
pass # No data in output.xml for this test
tbl_lst = list()
for tst_name in tbl_dict:
data_t = tbl_dict[tst_name][u"data"]
if len(data_t) < 2:
continue
classification_lst, avgs = classify_anomalies(data_t)
win_size = min(len(data_t), table[u"window"])
long_win_size = min(len(data_t), table[u"long-trend-window"])
try:
max_long_avg = max(
[x for x in avgs[-long_win_size:-win_size]
if not isnan(x)])
except ValueError:
max_long_avg = nan
last_avg = avgs[-1]
avg_week_ago = avgs[max(-win_size, -len(avgs))]
if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
rel_change_last = nan
else:
rel_change_last = round(
((last_avg - avg_week_ago) / avg_week_ago) * 1e2, 2)
if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
rel_change_long = nan
else:
rel_change_long = round(
((last_avg - max_long_avg) / max_long_avg) * 1e2, 2)
if classification_lst:
if isnan(rel_change_last) and isnan(rel_change_long):
continue
if isnan(last_avg) or isnan(rel_change_last) or \
isnan(rel_change_long):
continue
tbl_lst.append(
[tbl_dict[tst_name][u"name"],
round(last_avg / 1e6, 2),
rel_change_last,
rel_change_long,
classification_lst[-win_size:].count(u"regression"),
classification_lst[-win_size:].count(u"progression")])
tbl_lst.sort(key=lambda rel: rel[0])
tbl_sorted = list()
for nrr in range(table[u"window"], -1, -1):
tbl_reg = [item for item in tbl_lst if item[4] == nrr]
for nrp in range(table[u"window"], -1, -1):
tbl_out = [item for item in tbl_reg if item[5] == nrp]
tbl_out.sort(key=lambda rel: rel[2])
tbl_sorted.extend(tbl_out)
file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
logging.info(f" Writing file: {file_name}")
with open(file_name, u"wt") as file_handler:
file_handler.write(header_str)
for test in tbl_sorted:
file_handler.write(u",".join([str(item) for item in test]) + u'\n')
logging.info(f" Writing file: {table[u'output-file']}.txt")
convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
def _generate_url(testbed, test_name):
"""Generate URL to a trending plot from the name of the test case.
:param testbed: The testbed used for testing.
:param test_name: The name of the test case.
:type testbed: str
:type test_name: str
:returns: The URL to the plot with the trending data for the given test
case.
:rtype str
"""
if u"x520" in test_name:
nic = u"x520"
elif u"x710" in test_name:
nic = u"x710"
elif u"xl710" in test_name:
nic = u"xl710"
elif u"xxv710" in test_name:
nic = u"xxv710"
elif u"vic1227" in test_name:
nic = u"vic1227"
elif u"vic1385" in test_name:
nic = u"vic1385"
elif u"x553" in test_name:
nic = u"x553"
elif u"cx556" in test_name or u"cx556a" in test_name:
nic = u"cx556a"
else:
nic = u""
if u"64b" in test_name:
frame_size = u"64b"
elif u"78b" in test_name:
frame_size = u"78b"
elif u"imix" in test_name:
frame_size = u"imix"
elif u"9000b" in test_name:
frame_size = u"9000b"
elif u"1518b" in test_name:
frame_size = u"1518b"
elif u"114b" in test_name:
frame_size = u"114b"
else:
frame_size = u""
if u"1t1c" in test_name or \
(u"-1c-" in test_name and
testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
cores = u"1t1c"
elif u"2t2c" in test_name or \
(u"-2c-" in test_name and
testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
cores = u"2t2c"
elif u"4t4c" in test_name or \
(u"-4c-" in test_name and
testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
cores = u"4t4c"
elif u"2t1c" in test_name or \
(u"-1c-" in test_name and
testbed in (u"2n-skx", u"3n-skx", u"2n-clx")):
cores = u"2t1c"
elif u"4t2c" in test_name or \
(u"-2c-" in test_name and
testbed in (u"2n-skx", u"3n-skx", u"2n-clx")):
cores = u"4t2c"
elif u"8t4c" in test_name or \
(u"-4c-" in test_name and
testbed in (u"2n-skx", u"3n-skx", u"2n-clx")):
cores = u"8t4c"
else:
cores = u""
if u"testpmd" in test_name:
driver = u"testpmd"
elif u"l3fwd" in test_name:
driver = u"l3fwd"
elif u"avf" in test_name:
driver = u"avf"
elif u"rdma" in test_name:
driver = u"rdma"
elif u"dnv" in testbed or u"tsh" in testbed:
driver = u"ixgbe"
else:
driver = u"dpdk"
if u"acl" in test_name or \
u"macip" in test_name or \
u"nat" in test_name or \
u"policer" in test_name or \
u"cop" in test_name:
bsf = u"features"
elif u"scale" in test_name:
bsf = u"scale"
elif u"base" in test_name:
bsf = u"base"
else:
bsf = u"base"
if u"114b" in test_name and u"vhost" in test_name:
domain = u"vts"
elif u"testpmd" in test_name or u"l3fwd" in test_name:
domain = u"dpdk"
elif u"memif" in test_name:
domain = u"container_memif"
elif u"srv6" in test_name:
domain = u"srv6"
elif u"vhost" in test_name:
domain = u"vhost"
if u"vppl2xc" in test_name:
driver += u"-vpp"
else:
driver += u"-testpmd"
if u"lbvpplacp" in test_name:
bsf += u"-link-bonding"
elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
domain = u"nf_service_density_vnfc"
elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
domain = u"nf_service_density_cnfc"
elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
domain = u"nf_service_density_cnfp"
elif u"ipsec" in test_name:
domain = u"ipsec"
if u"sw" in test_name:
bsf += u"-sw"
elif u"hw" in test_name:
bsf += u"-hw"
elif u"ethip4vxlan" in test_name:
domain = u"ip4_tunnels"
elif u"ip4base" in test_name or u"ip4scale" in test_name:
domain = u"ip4"
elif u"ip6base" in test_name or u"ip6scale" in test_name:
domain = u"ip6"
elif u"l2xcbase" in test_name or \
u"l2xcscale" in test_name or \
u"l2bdbasemaclrn" in test_name or \
u"l2bdscale" in test_name or \
u"l2patch" in test_name:
domain = u"l2"
else:
domain = u""
file_name = u"-".join((domain, testbed, nic)) + u".html#"
anchor_name = u"-".join((frame_size, cores, bsf, driver))
return file_name + anchor_name
def table_perf_trending_dash_html(table, input_data):
"""Generate the table(s) with algorithm:
table_perf_trending_dash_html specified in the specification
file.
:param table: Table to generate.
:param input_data: Data to process.
:type table: dict
:type input_data: InputData
"""
_ = input_data
if not table.get(u"testbed", None):
logging.error(
f"The testbed is not defined for the table "
f"{table.get(u'title', u'')}."
)
return
logging.info(f" Generating the table {table.get(u'title', u'')} ...")
try:
with open(table[u"input-file"], u'rt') as csv_file:
csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
except KeyError:
logging.warning(u"The input file is not defined.")
return
except csv.Error as err:
logging.warning(
f"Not possible to process the file {table[u'input-file']}.\n"
f"{repr(err)}"
)
return
# Table:
dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
# Table header:
trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
for idx, item in enumerate(csv_lst[0]):
alignment = u"left" if idx == 0 else u"center"
thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
thead.text = item
# Rows:
colors = {
u"regression": (
u"#ffcccc",
u"#ff9999"
),
u"progression": (
u"#c6ecc6",
u"#9fdf9f"
),
u"normal": (
u"#e9f1fb",
u"#d4e4f7"
)
}
for r_idx, row in enumerate(csv_lst[1:]):
if int(row[4]):
color = u"regression"
elif int(row[5]):
color = u"progression"
else:
color = u"normal"
trow = ET.SubElement(
dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
)
# Columns:
for c_idx, item in enumerate(row):
tdata = ET.SubElement(
trow,
u"td",
attrib=dict(align=u"left" if c_idx == 0 else u"center")
)
# Name:
if c_idx == 0:
ref = ET.SubElement(
tdata,
u"a",
attrib=dict(
href=f"../trending/"
f"{_generate_url(table.get(u'testbed', ''), item)}"
)
)
ref.text = item
else:
tdata.text = item
try:
with open(table[u"output-file"], u'w') as html_file:
logging.info(f" Writing file: {table[u'output-file']}")
html_file.write(u".. raw:: html\n\n\t")
html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
html_file.write(u"\n\t
\n")
except KeyError:
logging.warning(u"The output file is not defined.")
return
def table_last_failed_tests(table, input_data):
"""Generate the table(s) with algorithm: table_last_failed_tests
specified in the specification file.
:param table: Table to generate.
:param input_data: Data to process.
:type table: pandas.Series
:type input_data: InputData
"""
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'')} "
f"{table.get(u'title', u'')}."
)
data = input_data.filter_data(table, continue_on_error=True)
if data is None or data.empty:
logging.warning(
f" No data for the {table.get(u'type', u'')} "
f"{table.get(u'title', u'')}."
)
return
tbl_list = list()
for job, builds in table[u"data"].items():
for build in builds:
build = str(build)
try:
version = input_data.metadata(job, build).get(u"version", u"")
except KeyError:
logging.error(f"Data for {job}: {build} is not present.")
return
tbl_list.append(build)
tbl_list.append(version)
failed_tests = list()
passed = 0
failed = 0
for tst_data in data[job][build].values:
if tst_data[u"status"] != u"FAIL":
passed += 1
continue
failed += 1
groups = re.search(REGEX_NIC, tst_data[u"parent"])
if not groups:
continue
nic = groups.group(0)
failed_tests.append(f"{nic}-{tst_data[u'name']}")
tbl_list.append(str(passed))
tbl_list.append(str(failed))
tbl_list.extend(failed_tests)
file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
logging.info(f" Writing file: {file_name}")
with open(file_name, u"wt") as file_handler:
for test in tbl_list:
file_handler.write(test + u'\n')
def table_failed_tests(table, input_data):
"""Generate the table(s) with algorithm: table_failed_tests
specified in the specification file.
:param table: Table to generate.
:param input_data: Data to process.
:type table: pandas.Series
:type input_data: InputData
"""
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'')} "
f"{table.get(u'title', u'')}."
)
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
header = [
u"Test Case",
u"Failures [#]",
u"Last Failure [Time]",
u"Last Failure [VPP-Build-Id]",
u"Last Failure [CSIT-Job-Build-Id]"
]
# Generate the data for the table according to the model in the table
# specification
now = dt.utcnow()
timeperiod = timedelta(int(table.get(u"window", 7)))
tbl_dict = dict()
for job, builds in table[u"data"].items():
for build in builds:
build = str(build)
for tst_name, tst_data in data[job][build].items():
if tst_name.lower() in table.get(u"ignore-list", list()):
continue
if tbl_dict.get(tst_name, None) is None:
groups = re.search(REGEX_NIC, tst_data[u"parent"])
if not groups:
continue
nic = groups.group(0)
tbl_dict[tst_name] = {
u"name": f"{nic}-{tst_data[u'name']}",
u"data": OrderedDict()
}
try:
generated = input_data.metadata(job, build).\
get(u"generated", u"")
if not generated:
continue
then = dt.strptime(generated, u"%Y%m%d %H:%M")
if (now - then) <= timeperiod:
tbl_dict[tst_name][u"data"][build] = (
tst_data[u"status"],
generated,
input_data.metadata(job, build).get(u"version",
u""),
build
)
except (TypeError, KeyError) as err:
logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
max_fails = 0
tbl_lst = list()
for tst_data in tbl_dict.values():
fails_nr = 0
fails_last_date = u""
fails_last_vpp = u""
fails_last_csit = u""
for val in tst_data[u"data"].values():
if val[0] == u"FAIL":
fails_nr += 1
fails_last_date = val[1]
fails_last_vpp = val[2]
fails_last_csit = val[3]
if fails_nr:
max_fails = fails_nr if fails_nr > max_fails else max_fails
tbl_lst.append(
[
tst_data[u"name"],
fails_nr,
fails_last_date,
fails_last_vpp,
f"mrr-daily-build-{fails_last_csit}"
]
)
tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
tbl_sorted = list()
for nrf in range(max_fails, -1, -1):
tbl_fails = [item for item in tbl_lst if item[1] == nrf]
tbl_sorted.extend(tbl_fails)
file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
logging.info(f" Writing file: {file_name}")
with open(file_name, u"wt") as file_handler:
file_handler.write(u",".join(header) + u"\n")
for test in tbl_sorted:
file_handler.write(u",".join([str(item) for item in test]) + u'\n')
logging.info(f" Writing file: {table[u'output-file']}.txt")
convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
def table_failed_tests_html(table, input_data):
"""Generate the table(s) with algorithm: table_failed_tests_html
specified in the specification file.
:param table: Table to generate.
:param input_data: Data to process.
:type table: pandas.Series
:type input_data: InputData
"""
_ = input_data
if not table.get(u"testbed", None):
logging.error(
f"The testbed is not defined for the table "
f"{table.get(u'title', u'')}."
)
return
logging.info(f" Generating the table {table.get(u'title', u'')} ...")
try:
with open(table[u"input-file"], u'rt') as csv_file:
csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
except KeyError:
logging.warning(u"The input file is not defined.")
return
except csv.Error as err:
logging.warning(
f"Not possible to process the file {table[u'input-file']}.\n"
f"{repr(err)}"
)
return
# Table:
failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
# Table header:
trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
for idx, item in enumerate(csv_lst[0]):
alignment = u"left" if idx == 0 else u"center"
thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
thead.text = item
# Rows:
colors = (u"#e9f1fb", u"#d4e4f7")
for r_idx, row in enumerate(csv_lst[1:]):
background = colors[r_idx % 2]
trow = ET.SubElement(
failed_tests, u"tr", attrib=dict(bgcolor=background)
)
# Columns:
for c_idx, item in enumerate(row):
tdata = ET.SubElement(
trow,
u"td",
attrib=dict(align=u"left" if c_idx == 0 else u"center")
)
# Name:
if c_idx == 0:
ref = ET.SubElement(
tdata,
u"a",
attrib=dict(
href=f"../trending/"
f"{_generate_url(table.get(u'testbed', ''), item)}"
)
)
ref.text = item
else:
tdata.text = item
try:
with open(table[u"output-file"], u'w') as html_file:
logging.info(f" Writing file: {table[u'output-file']}")
html_file.write(u".. raw:: html\n\n\t")
html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
html_file.write(u"\n\t
\n")
except KeyError:
logging.warning(u"The output file is not defined.")
return
def table_comparison(table, input_data):
"""Generate the table(s) with algorithm: table_comparison
specified in the specification file.
:param table: Table to generate.
:param input_data: Data to process.
:type table: pandas.Series
:type input_data: InputData
"""
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'')} "
f"{table.get(u'title', u'')}."
)
columns = table.get(u"columns", None)
if not columns:
logging.error(
f"No columns specified for {table.get(u'title', u'')}. Skipping."
)
return
cols = list()
for idx, col in enumerate(columns):
if col.get(u"data-set", None) is None:
logging.warning(f"No data for column {col.get(u'title', u'')}")
continue
data = input_data.filter_data(
table,
params=[u"throughput", u"result", u"name", u"parent", u"tags"],
data=col[u"data-set"],
continue_on_error=True
)
col_data = {
u"title": col.get(u"title", f"Column{idx}"),
u"data": dict()
}
for builds in data.values:
for build in builds:
for tst_name, tst_data in build.items():
tst_name_mod = \
_tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
if col_data[u"data"].get(tst_name_mod, None) is None:
name = tst_data[u'name'].rsplit(u'-', 1)[0]
if u"across testbeds" in table[u"title"].lower() or \
u"across topologies" in table[u"title"].lower():
name = _tpc_modify_displayed_test_name(name)
col_data[u"data"][tst_name_mod] = {
u"name": name,
u"replace": True,
u"data": list(),
u"mean": None,
u"stdev": None
}
_tpc_insert_data(
target=col_data[u"data"][tst_name_mod][u"data"],
src=tst_data,
include_tests=table[u"include-tests"]
)
replacement = col.get(u"data-replacement", None)
if replacement:
rpl_data = input_data.filter_data(
table,
params=[u"throughput", u"result", u"name", u"parent", u"tags"],
data=replacement,
continue_on_error=True
)
for builds in rpl_data.values:
for build in builds:
for tst_name, tst_data in build.items():
tst_name_mod = \
_tpc_modify_test_name(tst_name).\
replace(u"2n1l-", u"")
if col_data[u"data"].get(tst_name_mod, None) is None:
name = tst_data[u'name'].rsplit(u'-', 1)[0]
if u"across testbeds" in table[u"title"].lower() \
or u"across topologies" in \
table[u"title"].lower():
name = _tpc_modify_displayed_test_name(name)
col_data[u"data"][tst_name_mod] = {
u"name": name,
u"replace": False,
u"data": list(),
u"mean": None,
u"stdev": None
}
if col_data[u"data"][tst_name_mod][u"replace"]:
col_data[u"data"][tst_name_mod][u"replace"] = False
col_data[u"data"][tst_name_mod][u"data"] = list()
_tpc_insert_data(
target=col_data[u"data"][tst_name_mod][u"data"],
src=tst_data,
include_tests=table[u"include-tests"]
)
if table[u"include-tests"] in (u"NDR", u"PDR"):
for tst_name, tst_data in col_data[u"data"].items():
if tst_data[u"data"]:
tst_data[u"mean"] = mean(tst_data[u"data"])
tst_data[u"stdev"] = stdev(tst_data[u"data"])
elif table[u"include-tests"] in (u"MRR", ):
for tst_name, tst_data in col_data[u"data"].items():
if tst_data[u"data"]:
tst_data[u"mean"] = tst_data[u"data"][0]
tst_data[u"stdev"] = tst_data[u"data"][0]
cols.append(col_data)
tbl_dict = dict()
for col in cols:
for tst_name, tst_data in col[u"data"].items():
if tbl_dict.get(tst_name, None) is None:
tbl_dict[tst_name] = {
"name": tst_data[u"name"]
}
tbl_dict[tst_name][col[u"title"]] = {
u"mean": tst_data[u"mean"],
u"stdev": tst_data[u"stdev"]
}
tbl_lst = list()
for tst_data in tbl_dict.values():
row = [tst_data[u"name"], ]
for col in cols:
row.append(tst_data.get(col[u"title"], None))
tbl_lst.append(row)
comparisons = table.get(u"comparisons", None)
if comparisons and isinstance(comparisons, list):
for idx, comp in enumerate(comparisons):
try:
col_ref = int(comp[u"reference"])
col_cmp = int(comp[u"compare"])
except KeyError:
logging.warning(u"Comparison: No references defined! Skipping.")
comparisons.pop(idx)
continue
if not (0 < col_ref <= len(cols) and
0 < col_cmp <= len(cols)) or \
col_ref == col_cmp:
logging.warning(f"Wrong values of reference={col_ref} "
f"and/or compare={col_cmp}. Skipping.")
comparisons.pop(idx)
continue
tbl_cmp_lst = list()
if comparisons:
for row in tbl_lst:
new_row = deepcopy(row)
add_to_tbl = False
for comp in comparisons:
ref_itm = row[int(comp[u"reference"])]
if ref_itm is None and \
comp.get(u"reference-alt", None) is not None:
ref_itm = row[int(comp[u"reference-alt"])]
cmp_itm = row[int(comp[u"compare"])]
if ref_itm is not None and cmp_itm is not None and \
ref_itm[u"mean"] is not None and \
cmp_itm[u"mean"] is not None and \
ref_itm[u"stdev"] is not None and \
cmp_itm[u"stdev"] is not None:
delta, d_stdev = relative_change_stdev(
ref_itm[u"mean"], cmp_itm[u"mean"],
ref_itm[u"stdev"], cmp_itm[u"stdev"]
)
new_row.append(
{
u"mean": delta * 1e6,
u"stdev": d_stdev * 1e6
}
)
add_to_tbl = True
else:
new_row.append(None)
if add_to_tbl:
tbl_cmp_lst.append(new_row)
tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
rcas = list()
rca_in = table.get(u"rca", None)
if rca_in and isinstance(rca_in, list):
for idx, itm in enumerate(rca_in):
try:
with open(itm.get(u"data", u""), u"r") as rca_file:
rcas.append(
{
u"title": itm.get(u"title", f"RCA{idx}"),
u"data": load(rca_file, Loader=FullLoader)
}
)
except (YAMLError, IOError) as err:
logging.warning(
f"The RCA file {itm.get(u'data', u'')} does not exist or "
f"it is corrupted!"
)
logging.debug(repr(err))
tbl_for_csv = list()
for line in tbl_cmp_lst:
row = [line[0], ]
for idx, itm in enumerate(line[1:]):
if itm is None:
row.append(u"NT")
row.append(u"NT")
else:
row.append(round(float(itm[u'mean']) / 1e6, 3))
row.append(round(float(itm[u'stdev']) / 1e6, 3))
for rca in rcas:
rca_nr = rca[u"data"].get(row[0], u"-")
row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
tbl_for_csv.append(row)
header_csv = [u"Test Case", ]
for col in cols:
header_csv.append(f"Avg({col[u'title']})")
header_csv.append(f"Stdev({col[u'title']})")
for comp in comparisons:
header_csv.append(
f"Avg({comp.get(u'title', u'')}"
)
header_csv.append(
f"Stdev({comp.get(u'title', u'')})"
)
header_csv.extend([rca[u"title"] for rca in rcas])
legend_lst = table.get(u"legend", None)
if legend_lst is None:
legend = u""
else:
legend = u"\n" + u"\n".join(legend_lst) + u"\n"
footnote = u""
for rca in rcas:
footnote += f"\n{rca[u'title']}:\n"
footnote += rca[u"data"].get(u"footnote", u"")
csv_file = f"{table[u'output-file']}-csv.csv"
with open(csv_file, u"wt", encoding='utf-8') as file_handler:
file_handler.write(
u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
)
for test in tbl_for_csv:
file_handler.write(
u",".join([f'"{item}"' for item in test]) + u"\n"
)
if legend_lst:
for item in legend_lst:
file_handler.write(f'"{item}"\n')
if footnote:
for itm in footnote.split(u"\n"):
file_handler.write(f'"{itm}"\n')
tbl_tmp = list()
max_lens = [0, ] * len(tbl_cmp_lst[0])
for line in tbl_cmp_lst:
row = [line[0], ]
for idx, itm in enumerate(line[1:]):
if itm is None:
new_itm = u"NT"
else:
if idx < len(cols):
new_itm = (
f"{round(float(itm[u'mean']) / 1e6, 1)} "
f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
replace(u"nan", u"NaN")
)
else:
new_itm = (
f"{round(float(itm[u'mean']) / 1e6, 1):+} "
f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
replace(u"nan", u"NaN")
)
if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
row.append(new_itm)
tbl_tmp.append(row)
tbl_final = list()
for line in tbl_tmp:
row = [line[0], ]
for idx, itm in enumerate(line[1:]):
if itm in (u"NT", u"NaN"):
row.append(itm)
continue
itm_lst = itm.rsplit(u"\u00B1", 1)
itm_lst[-1] = \
f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
row.append(u"\u00B1".join(itm_lst))
for rca in rcas:
rca_nr = rca[u"data"].get(row[0], u"-")
row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
tbl_final.append(row)
header = [u"Test Case", ]
header.extend([col[u"title"] for col in cols])
header.extend([comp.get(u"title", u"") for comp in comparisons])
header.extend([rca[u"title"] for rca in rcas])
# Generate csv tables:
csv_file = f"{table[u'output-file']}.csv"
with open(csv_file, u"wt", encoding='utf-8') as file_handler:
file_handler.write(u";".join(header) + u"\n")
for test in tbl_final:
file_handler.write(u";".join([str(item) for item in test]) + u"\n")
# Generate txt table:
txt_file_name = f"{table[u'output-file']}.txt"
convert_csv_to_pretty_txt(csv_file, txt_file_name, delimiter=u";")
with open(txt_file_name, u'a', encoding='utf-8') as txt_file:
txt_file.write(legend)
if footnote:
txt_file.write(footnote)
txt_file.write(u":END")
# Generate html table:
_tpc_generate_html_table(
header,
tbl_final,
table[u'output-file'],
legend=legend,
footnote=footnote,
sort_data=False,
title=table.get(u"title", u"")
)