+++ /dev/null
-# Copyright (c) 2022 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 math
-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
-import prettytable
-
-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, relative_change
-
-
-REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)')
-REGEX_TOPO_ARCH = re.compile(r'^(\dn-.{3})')
-
-NORM_FREQ = 2.0 # [GHz]
-
-
-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 = {
- "table_merged_details": table_merged_details,
- "table_soak_vs_ndr": table_soak_vs_ndr,
- "table_perf_trending_dash": table_perf_trending_dash,
- "table_perf_trending_dash_html": table_perf_trending_dash_html,
- "table_last_failed_tests": table_last_failed_tests,
- "table_failed_tests": table_failed_tests,
- "table_failed_tests_html": table_failed_tests_html,
- "table_oper_data_html": table_oper_data_html,
- "table_comparison": table_comparison,
- "table_weekly_comparison": table_weekly_comparison,
- "table_job_spec_duration": table_job_spec_duration
- }
-
- logging.info(u"Generating the tables ...")
-
- norm_factor = dict()
- for key, val in spec.environment.get("frequency", dict()).items():
- norm_factor[key] = NORM_FREQ / val
-
- for table in spec.tables:
- try:
- if table["algorithm"] == "table_weekly_comparison":
- table["testbeds"] = spec.environment.get("testbeds", None)
- if table["algorithm"] == "table_comparison":
- table["norm_factor"] = norm_factor
- generator[table["algorithm"]](table, data)
- except NameError as err:
- logging.error(
- f"Probably algorithm {table['algorithm']} is not defined: "
- f"{repr(err)}"
- )
- logging.info("Done.")
-
-
-def table_job_spec_duration(table, input_data):
- """Generate the table(s) with algorithm: table_job_spec_duration
- 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
-
- logging.info(f" Generating the table {table.get(u'title', u'')} ...")
-
- jb_type = table.get(u"jb-type", None)
-
- tbl_lst = list()
- if jb_type == u"iterative":
- for line in table.get(u"lines", tuple()):
- tbl_itm = {
- u"name": line.get(u"job-spec", u""),
- u"data": list()
- }
- for job, builds in line.get(u"data-set", dict()).items():
- for build_nr in builds:
- try:
- minutes = input_data.metadata(
- job, str(build_nr)
- )[u"elapsedtime"] // 60000
- except (KeyError, IndexError, ValueError, AttributeError):
- continue
- tbl_itm[u"data"].append(minutes)
- tbl_itm[u"mean"] = mean(tbl_itm[u"data"])
- tbl_itm[u"stdev"] = stdev(tbl_itm[u"data"])
- tbl_lst.append(tbl_itm)
- elif jb_type == u"coverage":
- job = table.get(u"data", None)
- if not job:
- return
- for line in table.get(u"lines", tuple()):
- try:
- tbl_itm = {
- u"name": line.get(u"job-spec", u""),
- u"mean": input_data.metadata(
- list(job.keys())[0], str(line[u"build"])
- )[u"elapsedtime"] // 60000,
- u"stdev": float(u"nan")
- }
- tbl_itm[u"data"] = [tbl_itm[u"mean"], ]
- except (KeyError, IndexError, ValueError, AttributeError):
- continue
- tbl_lst.append(tbl_itm)
- else:
- logging.warning(f"Wrong type of job-spec: {jb_type}. Skipping.")
- return
-
- for line in tbl_lst:
- line[u"mean"] = \
- f"{int(line[u'mean'] // 60):02d}:{int(line[u'mean'] % 60):02d}"
- if math.isnan(line[u"stdev"]):
- line[u"stdev"] = u""
- else:
- line[u"stdev"] = \
- f"{int(line[u'stdev'] //60):02d}:{int(line[u'stdev'] % 60):02d}"
-
- if not tbl_lst:
- return
-
- rows = list()
- for itm in tbl_lst:
- rows.append([
- itm[u"name"],
- f"{len(itm[u'data'])}",
- f"{itm[u'mean']} +- {itm[u'stdev']}"
- if itm[u"stdev"] != u"" else f"{itm[u'mean']}"
- ])
-
- txt_table = prettytable.PrettyTable(
- [u"Job Specification", u"Nr of Runs", u"Duration [HH:MM]"]
- )
- for row in rows:
- txt_table.add_row(row)
- txt_table.align = u"r"
- txt_table.align[u"Job Specification"] = u"l"
-
- file_name = f"{table.get(u'output-file', u'')}.txt"
- with open(file_name, u"wt", encoding='utf-8') as txt_file:
- txt_file.write(str(txt_table))
-
-
-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"telemetry-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"telemetry-show-run", None) is None or \
- isinstance(tst_data[u"telemetry-show-run"], str):
- 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"telemetry-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"runtime", None) is None:
- tcol.text = u"No Data"
- continue
-
- runtime = dict()
- for item in dut_data[u"runtime"].get(u"data", tuple()):
- tid = int(item[u"labels"][u"thread_id"])
- if runtime.get(tid, None) is None:
- runtime[tid] = dict()
- gnode = item[u"labels"][u"graph_node"]
- if runtime[tid].get(gnode, None) is None:
- runtime[tid][gnode] = dict()
- try:
- runtime[tid][gnode][item[u"name"]] = float(item[u"value"])
- except ValueError:
- runtime[tid][gnode][item[u"name"]] = item[u"value"]
-
- threads = dict({idx: list() for idx in range(len(runtime))})
- for idx, run_data in runtime.items():
- for gnode, gdata in run_data.items():
- threads[idx].append([
- gnode,
- int(gdata[u"calls"]),
- int(gdata[u"vectors"]),
- int(gdata[u"suspends"]),
- float(gdata[u"clocks"]),
- float(gdata[u"vectors"] / gdata[u"calls"]) \
- if gdata[u"calls"] else 0.0
- ])
-
- 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 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<p><br><br></p>\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"status"] != u"PASS" or \
- 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"\n", 1)[1]
- except IndexError:
- pass
- col_data = col_data.replace(u'\n', u' |br| ').\
- replace(u'\r', u'').replace(u'"', u"'")
- col_data = f" |prein| {col_data} |preout| "
- elif column[u"data"].split(u" ")[1] in (u"conf-history", ):
- col_data = col_data.replace(u'\n', u' |br| ')
- 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"-ndrpdr", 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 structure.
- :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[u"mean"] = src[u"result"][u"receive-rate"]
- target[u"stdev"] = src[u"result"][u"receive-stdev"]
- elif include_tests == u"PDR":
- target[u"data"].append(src[u"throughput"][u"PDR"][u"LOWER"])
- elif include_tests == u"NDR":
- target[u"data"].append(src[u"throughput"][u"NDR"][u"LOWER"])
- elif u"latency" in include_tests:
- keys = include_tests.split(u"-")
- if len(keys) == 4:
- lat = src[keys[0]][keys[1]][keys[2]][keys[3]]
- target[u"data"].append(
- float(u"nan") if lat == -1 else lat * 1e6
- )
- elif include_tests == u"hoststack":
- try:
- target[u"data"].append(
- float(src[u"result"][u"bits_per_second"])
- )
- except KeyError:
- target[u"data"].append(
- (float(src[u"result"][u"client"][u"tx_data"]) * 8) /
- ((float(src[u"result"][u"client"][u"time"]) +
- float(src[u"result"][u"server"][u"time"])) / 2)
- )
- elif include_tests == u"vsap":
- try:
- target[u"data"].append(src[u"result"][u"cps"])
- except KeyError:
- target[u"data"].append(src[u"result"][u"rps"])
- except (KeyError, TypeError):
- pass
-
-
-def _tpc_generate_html_table(header, data, out_file_name, legend=u"",
- footnote=u"", sort_data=True, title=u"",
- generate_rst=True):
- """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.
- :param generate_rst: If True, wrapping rst file is generated.
- :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
- :type generate_rst: bool
- """
-
- 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": ([15, 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"<b>{item.replace(u',', u',<br>')}</b>" 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"<b>{itm}</b> (ascending)" for itm in header]
- menu_items.extend([f"<b>{itm}</b> (descending)" for itm in header])
- 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=1.002,
- yanchor=u"bottom",
- 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"
- )
-
- if not generate_rst:
- return
-
- 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/"
- logging.info(f" Writing the HTML file to {path}{file_name}.rst")
- 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"
- )
- 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' <iframe frameborder="0" scrolling="no" '
- f'width="1600" height="1200" '
- f'src="../..{out_file_name.replace(u"_build", u"")}_in.html">'
- f'</iframe>\n\n'
- )
-
- if legend:
- try:
- itm_lst = legend[1:-2].split(u"\n")
- rst_file.write(
- f"{itm_lst[0]}\n\n- " + u'\n- '.join(itm_lst[1:]) + u"\n\n"
- )
- except IndexError as err:
- logging.error(f"Legend cannot be written to html file\n{err}")
- if footnote:
- try:
- itm_lst = footnote[1:].split(u"\n")
- rst_file.write(
- f"{itm_lst[0]}\n\n- " + u'\n- '.join(itm_lst[1:]) + u"\n\n"
- )
- except IndexError as err:
- logging.error(f"Footnote cannot be written to html file\n{err}")
-
-
-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."
- )
- 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_name = f"{table[u'output-file']}.csv"
- with open(csv_file_name, 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_name, f"{table[u'output-file']}.txt", delimiter=u";"
- )
- with open(f"{table[u'output-file']}.txt", u'a') as file_handler:
- file_handler.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"Runs [#]",
- u"Long-Term Change [%]",
- u"Regressions [#]",
- u"Progressions [#]"
- ]
- header_str = u",".join(header) + u"\n"
-
- incl_tests = table.get(u"include-tests", u"MRR")
-
- # 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:
- if incl_tests == u"MRR":
- tbl_dict[tst_name][u"data"][str(build)] = \
- tst_data[u"result"][u"receive-rate"]
- elif incl_tests == u"NDR":
- tbl_dict[tst_name][u"data"][str(build)] = \
- tst_data[u"throughput"][u"NDR"][u"LOWER"]
- elif incl_tests == u"PDR":
- tbl_dict[tst_name][u"data"][str(build)] = \
- tst_data[u"throughput"][u"PDR"][u"LOWER"]
- 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
-
- try:
- classification_lst, avgs, _ = classify_anomalies(data_t)
- except ValueError as err:
- logging.info(f"{err} Skipping")
- return
-
- 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))]
-
- nr_of_last_avgs = 0;
- for x in reversed(avgs):
- if x == last_avg:
- nr_of_last_avgs += 1
- else:
- break
-
- 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),
- nr_of_last_avgs,
- rel_change_long,
- classification_lst[-win_size+1:].count(u"regression"),
- classification_lst[-win_size+1:].count(u"progression")])
-
- tbl_lst.sort(key=lambda rel: rel[0])
- tbl_lst.sort(key=lambda rel: rel[2])
- tbl_lst.sort(key=lambda rel: rel[3])
- tbl_lst.sort(key=lambda rel: rel[5], reverse=True)
- tbl_lst.sort(key=lambda rel: rel[4], reverse=True)
-
- 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_lst:
- 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"
- elif u"ena" in test_name:
- nic = u"nitro50g"
- 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", u"2n-tx2")):
- 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", u"2n-tx2")):
- 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", u"2n-tx2")):
- cores = u"4t4c"
- elif u"2t1c" in test_name or \
- (u"-1c-" in test_name and
- testbed in
- (u"2n-icx", u"3n-icx", u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2",
- u"2n-aws", u"3n-aws")):
- cores = u"2t1c"
- elif u"4t2c" in test_name or \
- (u"-2c-" in test_name and
- testbed in
- (u"2n-icx", u"3n-icx", u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2",
- u"2n-aws", u"3n-aws")):
- cores = u"4t2c"
- elif u"8t4c" in test_name or \
- (u"-4c-" in test_name and
- testbed in
- (u"2n-icx", u"3n-icx", u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2",
- u"2n-aws", u"3n-aws")):
- 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"af-xdp" in test_name or u"af_xdp" in test_name:
- driver = u"af_xdp"
- elif u"rdma" in test_name:
- driver = u"rdma"
- elif u"dnv" in testbed or u"tsh" in testbed:
- driver = u"ixgbe"
- elif u"ena" in test_name:
- driver = u"ena"
- else:
- driver = u"dpdk"
-
- if u"macip-iacl1s" in test_name:
- bsf = u"features-macip-iacl1"
- elif u"macip-iacl10s" in test_name:
- bsf = u"features-macip-iacl10"
- elif u"macip-iacl50s" in test_name:
- bsf = u"features-macip-iacl50"
- elif u"iacl1s" in test_name:
- bsf = u"features-iacl1"
- elif u"iacl10s" in test_name:
- bsf = u"features-iacl10"
- elif u"iacl50s" in test_name:
- bsf = u"features-iacl50"
- elif u"oacl1s" in test_name:
- bsf = u"features-oacl1"
- elif u"oacl10s" in test_name:
- bsf = u"features-oacl10"
- elif u"oacl50s" in test_name:
- bsf = u"features-oacl50"
- elif u"nat44det" in test_name:
- bsf = u"nat44det-bidir"
- elif u"nat44ed" in test_name and u"udir" in test_name:
- bsf = u"nat44ed-udir"
- elif u"-cps" in test_name and u"ethip4udp" in test_name:
- bsf = u"udp-cps"
- elif u"-cps" in test_name and u"ethip4tcp" in test_name:
- bsf = u"tcp-cps"
- elif u"-pps" in test_name and u"ethip4udp" in test_name:
- bsf = u"udp-pps"
- elif u"-pps" in test_name and u"ethip4tcp" in test_name:
- bsf = u"tcp-pps"
- elif u"-tput" in test_name and u"ethip4udp" in test_name:
- bsf = u"udp-tput"
- elif u"-tput" in test_name and u"ethip4tcp" in test_name:
- bsf = u"tcp-tput"
- elif u"udpsrcscale" in test_name:
- bsf = u"features-udp"
- elif u"iacl" in test_name:
- bsf = u"features"
- elif u"policer" in test_name:
- bsf = u"features"
- elif u"adl" in test_name:
- bsf = u"features"
- elif u"cop" in test_name:
- bsf = u"features"
- elif u"nat" in test_name:
- bsf = u"features"
- elif u"macip" 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"nat44" in test_name or u"-pps" in test_name or u"-cps" in test_name:
- domain = u"nat44"
- if u"nat44det" in test_name:
- domain += u"-det-bidir"
- else:
- domain += u"-ed"
- if u"udir" in test_name:
- domain += u"-unidir"
- elif u"-ethip4udp-" in test_name:
- domain += u"-udp"
- elif u"-ethip4tcp-" in test_name:
- domain += u"-tcp"
- if u"-cps" in test_name:
- domain += u"-cps"
- elif u"-pps" in test_name:
- domain += u"-pps"
- elif u"-tput" in test_name:
- domain += u"-tput"
- 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"spe" in test_name:
- bsf += u"-spe"
- elif u"ethip4vxlan" in test_name:
- domain = u"ip4_tunnels"
- elif u"ethip4udpgeneve" 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'')}. Skipping."
- )
- return
-
- test_type = table.get(u"test-type", u"MRR")
- if test_type not in (u"MRR", u"NDR", u"PDR"):
- logging.error(
- f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
- f"Skipping."
- )
- return
-
- if test_type in (u"NDR", u"PDR"):
- lnk_dir = u"../ndrpdr_trending/"
- lnk_sufix = f"-{test_type.lower()}"
- else:
- lnk_dir = u"../trending/"
- lnk_sufix = u""
-
- 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 FileNotFoundError as err:
- logging.warning(f"{err}")
- return
- 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 and table.get(u"add-links", True):
- ref = ET.SubElement(
- tdata,
- u"a",
- attrib=dict(
- href=f"{lnk_dir}"
- f"{_generate_url(table.get(u'testbed', ''), item)}"
- f"{lnk_sufix}"
- )
- )
- 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<p><br><br></p>\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"")
- duration = \
- input_data.metadata(job, build).get(u"elapsedtime", 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)
- msg = tst_data[u'msg'].replace(u"\n", u"")
- msg = re.sub(r'(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})',
- 'xxx.xxx.xxx.xxx', msg)
- msg = msg.split(u'Also teardown failed')[0]
- failed_tests.append(f"{nic}-{tst_data[u'name']}###{msg}")
- tbl_list.append(passed)
- tbl_list.append(failed)
- tbl_list.append(duration)
- 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(f"{test}\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)
-
- test_type = u"MRR"
- if u"NDRPDR" in table.get(u"filter", list()):
- test_type = u"NDRPDR"
-
- # 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"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
- f"-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'')}. Skipping."
- )
- return
-
- test_type = table.get(u"test-type", u"MRR")
- if test_type not in (u"MRR", u"NDR", u"PDR", u"NDRPDR"):
- logging.error(
- f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
- f"Skipping."
- )
- return
-
- if test_type in (u"NDRPDR", u"NDR", u"PDR"):
- lnk_dir = u"../ndrpdr_trending/"
- lnk_sufix = u"-pdr"
- else:
- lnk_dir = u"../trending/"
- lnk_sufix = u""
-
- 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 and table.get(u"add-links", True):
- ref = ET.SubElement(
- tdata,
- u"a",
- attrib=dict(
- href=f"{lnk_dir}"
- f"{_generate_url(table.get(u'testbed', ''), item)}"
- f"{lnk_sufix}"
- )
- )
- 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<p><br><br></p>\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('title', '')} ...")
-
- # Transform the data
- logging.info(
- f" Creating the data set for the {table.get('type', '')} "
- f"{table.get('title', '')}."
- )
-
- normalize = table.get('normalize', False)
-
- columns = table.get("columns", None)
- if not columns:
- logging.error(
- f"No columns specified for {table.get('title', '')}. Skipping."
- )
- return
-
- cols = list()
- for idx, col in enumerate(columns):
- if col.get("data-set", None) is None:
- logging.warning(f"No data for column {col.get('title', '')}")
- continue
- tag = col.get("tag", None)
- data = input_data.filter_data(
- table,
- params=[
- "throughput",
- "result",
- "latency",
- "name",
- "parent",
- "tags"
- ],
- data=col["data-set"],
- continue_on_error=True
- )
- col_data = {
- "title": col.get("title", f"Column{idx}"),
- "data": dict()
- }
- for builds in data.values:
- for build in builds:
- for tst_name, tst_data in build.items():
- if tag and tag not in tst_data["tags"]:
- continue
- tst_name_mod = \
- _tpc_modify_test_name(tst_name, ignore_nic=True).\
- replace("2n1l-", "")
- if col_data["data"].get(tst_name_mod, None) is None:
- name = tst_data['name'].rsplit('-', 1)[0]
- if "across testbeds" in table["title"].lower() or \
- "across topologies" in table["title"].lower():
- name = _tpc_modify_displayed_test_name(name)
- col_data["data"][tst_name_mod] = {
- "name": name,
- "replace": True,
- "data": list(),
- "mean": None,
- "stdev": None
- }
- _tpc_insert_data(
- target=col_data["data"][tst_name_mod],
- src=tst_data,
- include_tests=table["include-tests"]
- )
-
- replacement = col.get("data-replacement", None)
- if replacement:
- rpl_data = input_data.filter_data(
- table,
- params=[
- "throughput",
- "result",
- "latency",
- "name",
- "parent",
- "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():
- if tag and tag not in tst_data["tags"]:
- continue
- tst_name_mod = \
- _tpc_modify_test_name(tst_name, ignore_nic=True).\
- replace("2n1l-", "")
- if col_data["data"].get(tst_name_mod, None) is None:
- name = tst_data['name'].rsplit('-', 1)[0]
- if "across testbeds" in table["title"].lower() \
- or "across topologies" in \
- table["title"].lower():
- name = _tpc_modify_displayed_test_name(name)
- col_data["data"][tst_name_mod] = {
- "name": name,
- "replace": False,
- "data": list(),
- "mean": None,
- "stdev": None
- }
- if col_data["data"][tst_name_mod]["replace"]:
- col_data["data"][tst_name_mod]["replace"] = False
- col_data["data"][tst_name_mod]["data"] = list()
- _tpc_insert_data(
- target=col_data["data"][tst_name_mod],
- src=tst_data,
- include_tests=table["include-tests"]
- )
-
- if table["include-tests"] in ("NDR", "PDR", "hoststack", "vsap") \
- or "latency" in table["include-tests"]:
- for tst_name, tst_data in col_data["data"].items():
- if tst_data["data"]:
- tst_data["mean"] = mean(tst_data["data"])
- tst_data["stdev"] = stdev(tst_data["data"])
-
- cols.append(col_data)
-
- tbl_dict = dict()
- for col in cols:
- for tst_name, tst_data in col["data"].items():
- if tbl_dict.get(tst_name, None) is None:
- tbl_dict[tst_name] = {
- "name": tst_data["name"]
- }
- tbl_dict[tst_name][col["title"]] = {
- "mean": tst_data["mean"],
- "stdev": tst_data["stdev"]
- }
-
- if not tbl_dict:
- logging.warning(f"No data for table {table.get('title', '')}!")
- return
-
- tbl_lst = list()
- for tst_data in tbl_dict.values():
- row = [tst_data[u"name"], ]
- for col in cols:
- row_data = tst_data.get(col["title"], None)
- if normalize and row_data and row_data.get("mean", None) and \
- row_data.get("stdev", None):
- groups = re.search(REGEX_TOPO_ARCH, col["title"])
- topo_arch = groups.group(0) if groups else ""
- norm_factor = table["norm_factor"].get(topo_arch, 1.0)
- row_data_norm = {
- "mean": row_data["mean"] * norm_factor,
- "stdev": row_data["stdev"] * norm_factor
- }
- else:
- row_data_norm = row_data
- row.append(row_data_norm)
- tbl_lst.append(row)
-
- comparisons = table.get("comparisons", None)
- rcas = list()
- if comparisons and isinstance(comparisons, list):
- for idx, comp in enumerate(comparisons):
- try:
- col_ref = int(comp["reference"])
- col_cmp = int(comp["compare"])
- except KeyError:
- logging.warning("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
- rca_file_name = comp.get("rca-file", None)
- if rca_file_name:
- try:
- with open(rca_file_name, "r") as file_handler:
- rcas.append(
- {
- "title": f"RCA{idx + 1}",
- "data": load(file_handler, Loader=FullLoader)
- }
- )
- except (YAMLError, IOError) as err:
- logging.warning(
- f"The RCA file {rca_file_name} does not exist or "
- f"it is corrupted!"
- )
- logging.debug(repr(err))
- rcas.append(None)
- else:
- rcas.append(None)
- else:
- comparisons = None
-
- tbl_cmp_lst = list()
- if comparisons:
- for row in tbl_lst:
- new_row = deepcopy(row)
- for comp in comparisons:
- ref_itm = row[int(comp["reference"])]
- if ref_itm is None and \
- comp.get("reference-alt", None) is not None:
- ref_itm = row[int(comp["reference-alt"])]
- cmp_itm = row[int(comp[u"compare"])]
- if ref_itm is not None and cmp_itm is not None and \
- ref_itm["mean"] is not None and \
- cmp_itm["mean"] is not None and \
- ref_itm["stdev"] is not None and \
- cmp_itm["stdev"] is not None:
- try:
- delta, d_stdev = relative_change_stdev(
- ref_itm["mean"], cmp_itm["mean"],
- ref_itm["stdev"], cmp_itm["stdev"]
- )
- except ZeroDivisionError:
- break
- if delta is None or math.isnan(delta):
- break
- new_row.append({
- "mean": delta * 1e6,
- "stdev": d_stdev * 1e6
- })
- else:
- break
- else:
- tbl_cmp_lst.append(new_row)
-
- try:
- tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
- tbl_cmp_lst.sort(key=lambda rel: rel[-1]['mean'], reverse=True)
- except TypeError as err:
- logging.warning(f"Empty data element in table\n{tbl_cmp_lst}\n{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 or not isinstance(itm, dict) or\
- itm.get('mean', None) is None or \
- itm.get('stdev', None) is None:
- row.append("NT")
- row.append("NT")
- else:
- row.append(round(float(itm['mean']) / 1e6, 3))
- row.append(round(float(itm['stdev']) / 1e6, 3))
- for rca in rcas:
- if rca is None:
- continue
- rca_nr = rca["data"].get(row[0], "-")
- row.append(f"[{rca_nr}]" if rca_nr != "-" else "-")
- tbl_for_csv.append(row)
-
- header_csv = ["Test Case", ]
- for col in cols:
- header_csv.append(f"Avg({col['title']})")
- header_csv.append(f"Stdev({col['title']})")
- for comp in comparisons:
- header_csv.append(
- f"Avg({comp.get('title', '')})"
- )
- header_csv.append(
- f"Stdev({comp.get('title', '')})"
- )
- for rca in rcas:
- if rca:
- header_csv.append(rca["title"])
-
- legend_lst = table.get("legend", None)
- if legend_lst is None:
- legend = ""
- else:
- legend = "\n" + "\n".join(legend_lst) + "\n"
-
- footnote = ""
- if rcas and any(rcas):
- footnote += "\nRoot Cause Analysis:\n"
- for rca in rcas:
- if rca:
- footnote += f"{rca['data'].get('footnote', '')}\n"
-
- csv_file_name = f"{table['output-file']}-csv.csv"
- with open(csv_file_name, "wt", encoding='utf-8') as file_handler:
- file_handler.write(
- ",".join([f'"{itm}"' for itm in header_csv]) + "\n"
- )
- for test in tbl_for_csv:
- file_handler.write(
- ",".join([f'"{item}"' for item in test]) + "\n"
- )
- if legend_lst:
- for item in legend_lst:
- file_handler.write(f'"{item}"\n')
- if footnote:
- for itm in footnote.split("\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 or not isinstance(itm, dict) or \
- itm.get('mean', None) is None or \
- itm.get('stdev', None) is None:
- new_itm = "NT"
- else:
- if idx < len(cols):
- new_itm = (
- f"{round(float(itm['mean']) / 1e6, 2)} "
- f"\u00B1{round(float(itm['stdev']) / 1e6, 2)}".
- replace("nan", "NaN")
- )
- else:
- new_itm = (
- f"{round(float(itm['mean']) / 1e6, 2):+} "
- f"\u00B1{round(float(itm['stdev']) / 1e6, 2)}".
- replace("nan", "NaN")
- )
- if len(new_itm.rsplit(" ", 1)[-1]) > max_lens[idx]:
- max_lens[idx] = len(new_itm.rsplit(" ", 1)[-1])
- row.append(new_itm)
-
- tbl_tmp.append(row)
-
- header = ["Test Case", ]
- header.extend([col["title"] for col in cols])
- header.extend([comp.get("title", "") for comp in comparisons])
-
- tbl_final = list()
- for line in tbl_tmp:
- row = [line[0], ]
- for idx, itm in enumerate(line[1:]):
- if itm in ("NT", "NaN"):
- row.append(itm)
- continue
- itm_lst = itm.rsplit("\u00B1", 1)
- itm_lst[-1] = \
- f"{' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
- itm_str = "\u00B1".join(itm_lst)
-
- if idx >= len(cols):
- # Diffs
- rca = rcas[idx - len(cols)]
- if rca:
- # Add rcas to diffs
- rca_nr = rca["data"].get(row[0], None)
- if rca_nr:
- hdr_len = len(header[idx + 1]) - 1
- if hdr_len < 19:
- hdr_len = 19
- rca_nr = f"[{rca_nr}]"
- itm_str = (
- f"{' ' * (4 - len(rca_nr))}{rca_nr}"
- f"{' ' * (hdr_len - 4 - len(itm_str))}"
- f"{itm_str}"
- )
- row.append(itm_str)
- tbl_final.append(row)
-
- # Generate csv tables:
- csv_file_name = f"{table['output-file']}.csv"
- logging.info(f" Writing the file {csv_file_name}")
- with open(csv_file_name, "wt", encoding='utf-8') as file_handler:
- file_handler.write(";".join(header) + "\n")
- for test in tbl_final:
- file_handler.write(";".join([str(item) for item in test]) + "\n")
-
- # Generate txt table:
- txt_file_name = f"{table['output-file']}.txt"
- logging.info(f" Writing the file {txt_file_name}")
- convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=";")
-
- with open(txt_file_name, 'a', encoding='utf-8') as file_handler:
- file_handler.write(legend)
- file_handler.write(footnote)
-
- # Generate html table:
- _tpc_generate_html_table(
- header,
- tbl_final,
- table['output-file'],
- legend=legend,
- footnote=footnote,
- sort_data=False,
- title=table.get("title", "")
- )
-
-
-def table_weekly_comparison(table, in_data):
- """Generate the table(s) with algorithm: table_weekly_comparison
- specified in the specification file.
-
- :param table: Table to generate.
- :param in_data: Data to process.
- :type table: pandas.Series
- :type in_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'')}."
- )
-
- incl_tests = table.get(u"include-tests", None)
- if incl_tests not in (u"NDR", u"PDR"):
- logging.error(f"Wrong tests to include specified ({incl_tests}).")
- return
-
- nr_cols = table.get(u"nr-of-data-columns", None)
- if not nr_cols or nr_cols < 2:
- logging.error(
- f"No columns specified for {table.get(u'title', u'')}. Skipping."
- )
- return
-
- data = in_data.filter_data(
- table,
- params=[u"throughput", u"result", u"name", u"parent", u"tags"],
- continue_on_error=True
- )
-
- header = [
- [u"VPP Version", ],
- [u"Start Timestamp", ],
- [u"CSIT Build", ],
- [u"CSIT Testbed", ]
- ]
- tbl_dict = dict()
- idx = 0
- tb_tbl = table.get(u"testbeds", None)
- for job_name, job_data in data.items():
- for build_nr, build in job_data.items():
- if idx >= nr_cols:
- break
- if build.empty:
- continue
-
- tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
- if tb_ip and tb_tbl:
- testbed = tb_tbl.get(tb_ip, u"")
- else:
- testbed = u""
- header[2].insert(1, build_nr)
- header[3].insert(1, testbed)
- header[1].insert(
- 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
- )
- header[0].insert(
- 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
- )
-
- for tst_name, tst_data in build.items():
- tst_name_mod = \
- _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
- if not tbl_dict.get(tst_name_mod, None):
- tbl_dict[tst_name_mod] = dict(
- name=tst_data[u'name'].rsplit(u'-', 1)[0],
- )
- try:
- tbl_dict[tst_name_mod][-idx - 1] = \
- tst_data[u"throughput"][incl_tests][u"LOWER"]
- except (TypeError, IndexError, KeyError, ValueError):
- pass
- idx += 1
-
- if idx < nr_cols:
- logging.error(u"Not enough data to build the table! Skipping")
- return
-
- cmp_dict = dict()
- for idx, cmp in enumerate(table.get(u"comparisons", list())):
- idx_ref = cmp.get(u"reference", None)
- idx_cmp = cmp.get(u"compare", None)
- if idx_ref is None or idx_cmp is None:
- continue
- header[0].append(
- f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
- f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
- )
- header[1].append(u"")
- header[2].append(u"")
- header[3].append(u"")
- for tst_name, tst_data in tbl_dict.items():
- if not cmp_dict.get(tst_name, None):
- cmp_dict[tst_name] = list()
- ref_data = tst_data.get(idx_ref, None)
- cmp_data = tst_data.get(idx_cmp, None)
- if ref_data is None or cmp_data is None:
- cmp_dict[tst_name].append(float(u'nan'))
- else:
- cmp_dict[tst_name].append(
- relative_change(ref_data, cmp_data)
- )
-
- tbl_lst_none = list()
- tbl_lst = list()
- for tst_name, tst_data in tbl_dict.items():
- itm_lst = [tst_data[u"name"], ]
- for idx in range(nr_cols):
- item = tst_data.get(-idx - 1, None)
- if item is None:
- itm_lst.insert(1, None)
- else:
- itm_lst.insert(1, round(item / 1e6, 1))
- itm_lst.extend(
- [
- None if itm is None else round(itm, 1)
- for itm in cmp_dict[tst_name]
- ]
- )
- if str(itm_lst[-1]) == u"nan" or itm_lst[-1] is None:
- tbl_lst_none.append(itm_lst)
- else:
- tbl_lst.append(itm_lst)
-
- tbl_lst_none.sort(key=lambda rel: rel[0], reverse=False)
- tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
- tbl_lst.sort(key=lambda rel: rel[-1], reverse=False)
- tbl_lst.extend(tbl_lst_none)
-
- # Generate csv table:
- csv_file_name = f"{table[u'output-file']}.csv"
- logging.info(f" Writing the file {csv_file_name}")
- with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
- for hdr in header:
- file_handler.write(u",".join(hdr) + u"\n")
- for test in tbl_lst:
- file_handler.write(u",".join(
- [
- str(item).replace(u"None", u"-").replace(u"nan", u"-").
- replace(u"null", u"-") for item in test
- ]
- ) + u"\n")
-
- txt_file_name = f"{table[u'output-file']}.txt"
- logging.info(f" Writing the file {txt_file_name}")
- convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u",")
-
- # Reorganize header in txt table
- txt_table = list()
- with open(txt_file_name, u"rt", encoding='utf-8') as file_handler:
- for line in list(file_handler):
- txt_table.append(line)
- try:
- txt_table.insert(5, txt_table.pop(2))
- with open(txt_file_name, u"wt", encoding='utf-8') as file_handler:
- file_handler.writelines(txt_table)
- except IndexError:
- pass
-
- # Generate html table:
- hdr_html = [
- u"<br>".join(row) for row in zip(*header)
- ]
- _tpc_generate_html_table(
- hdr_html,
- tbl_lst,
- table[u'output-file'],
- sort_data=True,
- title=table.get(u"title", u""),
- generate_rst=False
- )
Code Review / csit.git / blobdiff