-# Copyright (c) 2017 Cisco and/or its affiliates.
+# Copyright (c) 2021 Cisco and/or its affiliates.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at:
import logging
import csv
-import prettytable
-import pandas as pd
+import math
+import re
-from string import replace
-from math import isnan
+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
+from json import loads
+
+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, relative_change
-from errors import PresentationError
-from utils import mean, stdev, relative_change, remove_outliers, find_outliers
+
+REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)')
def generate_tables(spec, data):
:type data: InputData
"""
- logging.info("Generating the tables ...")
+ generator = {
+ u"table_merged_details": table_merged_details,
+ 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,
+ u"table_weekly_comparison": table_weekly_comparison
+ }
+
+ logging.info(u"Generating the tables ...")
for table in spec.tables:
try:
- eval(table["algorithm"])(table, data)
- except NameError:
- logging.error("The algorithm '{0}' is not defined.".
- format(table["algorithm"]))
- logging.info("Done.")
-
-
-def table_details(table, input_data):
- """Generate the table(s) with algorithm: table_detailed_test_results
+ if table[u"algorithm"] == u"table_weekly_comparison":
+ table[u"testbeds"] = spec.environment.get(u"testbeds", None)
+ 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.
:type input_data: InputData
"""
- logging.info(" Generating the table {0} ...".
- format(table.get("title", "")))
-
+ logging.info(f" Generating the table {table.get(u'title', u'')} ...")
# Transform the data
- data = input_data.filter_data(table)
-
- # Prepare the header of the tables
- header = list()
- for column in table["columns"]:
- header.append('"{0}"'.format(str(column["title"]).replace('"', '""')))
+ 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)
- # Generate the data for the table according to the model in the table
- # specification
- job = table["data"].keys()[0]
- build = str(table["data"][job][0])
- try:
- suites = input_data.suites(job, build)
- except KeyError:
- logging.error(" No data available. The table will not be generated.")
+ 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)
- for suite_longname, suite in suites.iteritems():
- # Generate data
- suite_name = suite["name"]
- table_lst = list()
- for test in data[job][build].keys():
- if data[job][build][test]["parent"] in suite_name:
- row_lst = list()
- for column in table["columns"]:
- try:
- col_data = str(data[job][build][test][column["data"].
- split(" ")[1]]).replace('"', '""')
- if column["data"].split(" ")[1] in ("vat-history",
- "show-run"):
- col_data = replace(col_data, " |br| ", "",
- maxreplace=1)
- col_data = " |prein| {0} |preout| ".\
- format(col_data[:-5])
- row_lst.append('"{0}"'.format(col_data))
- except KeyError:
- row_lst.append("No data")
- table_lst.append(row_lst)
+ def _generate_html_table(tst_data):
+ """Generate an HTML table with operational data for the given test.
- # Write the data to file
- if table_lst:
- file_name = "{0}_{1}{2}".format(table["output-file"], suite_name,
- table["output-file-ext"])
- logging.info(" Writing file: '{}'".format(file_name))
- with open(file_name, "w") as file_handler:
- file_handler.write(",".join(header) + "\n")
- for item in table_lst:
- file_handler.write(",".join(item) + "\n")
+ :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
+ """
- logging.info(" Done.")
+ 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():
+ if gdata[u"vectors"] > 0:
+ clocks = gdata[u"clocks"] / gdata[u"vectors"]
+ elif gdata[u"calls"] > 0:
+ clocks = gdata[u"clocks"] / gdata[u"calls"]
+ elif gdata[u"suspends"] > 0:
+ clocks = gdata[u"clocks"] / gdata[u"suspends"]
+ else:
+ clocks = 0.0
+ if gdata[u"calls"] > 0:
+ vectors_call = gdata[u"vectors"] / gdata[u"calls"]
+ else:
+ vectors_call = 0.0
+ if int(gdata[u"calls"]) + int(gdata[u"vectors"]) + \
+ int(gdata[u"suspends"]):
+ threads[idx].append([
+ gnode,
+ int(gdata[u"calls"]),
+ int(gdata[u"vectors"]),
+ int(gdata[u"suspends"]),
+ clocks,
+ vectors_call
+ ])
+
+ 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):
:type input_data: InputData
"""
- logging.info(" Generating the table {0} ...".
- format(table.get("title", "")))
+ logging.info(f" Generating the table {table.get(u'title', u'')} ...")
# Transform the data
- data = input_data.filter_data(table)
+ 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)
- data.sort_index(inplace=True)
- suites = input_data.filter_data(table, data_set="suites")
+ 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["columns"]:
- header.append('"{0}"'.format(str(column["title"]).replace('"', '""')))
+ for column in table[u"columns"]:
+ header.append(
+ u'"{0}"'.format(str(column[u"title"]).replace(u'"', u'""'))
+ )
- for _, suite in suites.iteritems():
+ for suite in suites.values:
# Generate data
- suite_name = suite["name"]
+ suite_name = suite[u"name"]
table_lst = list()
for test in data.keys():
- if data[test]["parent"] in suite_name:
- row_lst = list()
- for column in table["columns"]:
- try:
- col_data = str(data[test][column["data"].
- split(" ")[1]]).replace('"', '""')
- if column["data"].split(" ")[1] in ("vat-history",
- "show-run"):
- col_data = replace(col_data, " |br| ", "",
- maxreplace=1)
- col_data = " |prein| {0} |preout| ".\
- format(col_data[:-5])
- row_lst.append('"{0}"'.format(col_data))
- except KeyError:
- row_lst.append("No data")
+ 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:
- file_name = "{0}_{1}{2}".format(table["output-file"], suite_name,
- table["output-file-ext"])
- logging.info(" Writing file: '{}'".format(file_name))
- with open(file_name, "w") as file_handler:
- file_handler.write(",".join(header) + "\n")
+ 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(",".join(item) + "\n")
+ file_handler.write(u",".join(item) + u"\n")
- logging.info(" Done.")
+ logging.info(u" Done.")
-def table_performance_improvements(table, input_data):
- """Generate the table(s) with algorithm: table_performance_improvements
- specified in the specification file.
+def _tpc_modify_test_name(test_name, ignore_nic=False):
+ """Modify a test name by replacing its parts.
- :param table: Table to generate.
- :param input_data: Data to process.
- :type table: pandas.Series
- :type input_data: InputData
+ :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
"""
-
- def _write_line_to_file(file_handler, data):
- """Write a line to the .csv file.
-
- :param file_handler: File handler for the csv file. It must be open for
- writing text.
- :param data: Item to be written to the file.
- :type file_handler: BinaryIO
- :type data: list
- """
-
- line_lst = list()
- for item in data:
- if isinstance(item["data"], str):
- # Remove -?drdisc from the end
- if item["data"].endswith("drdisc"):
- item["data"] = item["data"][:-8]
- line_lst.append(item["data"])
- elif isinstance(item["data"], float):
- line_lst.append("{:.1f}".format(item["data"]))
- elif item["data"] is None:
- line_lst.append("")
- file_handler.write(",".join(line_lst) + "\n")
-
- logging.info(" Generating the table {0} ...".
- format(table.get("title", "")))
-
- # Read the template
- file_name = table.get("template", None)
- if file_name:
- try:
- tmpl = _read_csv_template(file_name)
- except PresentationError:
- logging.error(" The template '{0}' does not exist. Skipping the "
- "table.".format(file_name))
- return None
- else:
- logging.error("The template is not defined. Skipping the table.")
- return None
-
- # Transform the data
- data = input_data.filter_data(table)
-
- # Prepare the header of the tables
- header = list()
- for column in table["columns"]:
- header.append(column["title"])
-
- # Generate the data for the table according to the model in the table
- # specification
- tbl_lst = list()
- for tmpl_item in tmpl:
- tbl_item = list()
- for column in table["columns"]:
- cmd = column["data"].split(" ")[0]
- args = column["data"].split(" ")[1:]
- if cmd == "template":
- try:
- val = float(tmpl_item[int(args[0])])
- except ValueError:
- val = tmpl_item[int(args[0])]
- tbl_item.append({"data": val})
- elif cmd == "data":
- jobs = args[0:-1]
- operation = args[-1]
- data_lst = list()
- for job in jobs:
- for build in data[job]:
- try:
- data_lst.append(float(build[tmpl_item[0]]
- ["throughput"]["value"]))
- except (KeyError, TypeError):
- # No data, ignore
- continue
- if data_lst:
- tbl_item.append({"data": (eval(operation)(data_lst)) /
- 1000000})
- else:
- tbl_item.append({"data": None})
- elif cmd == "operation":
- operation = args[0]
- try:
- nr1 = float(tbl_item[int(args[1])]["data"])
- nr2 = float(tbl_item[int(args[2])]["data"])
- if nr1 and nr2:
- tbl_item.append({"data": eval(operation)(nr1, nr2)})
- else:
- tbl_item.append({"data": None})
- except (IndexError, ValueError, TypeError):
- logging.error("No data for {0}".format(tbl_item[0]["data"]))
- tbl_item.append({"data": None})
- continue
- else:
- logging.error("Not supported command {0}. Skipping the table.".
- format(cmd))
- return None
- tbl_lst.append(tbl_item)
-
- # Sort the table according to the relative change
- tbl_lst.sort(key=lambda rel: rel[-1]["data"], reverse=True)
-
- # Create the tables and write them to the files
- file_names = [
- "{0}_ndr_top{1}".format(table["output-file"], table["output-file-ext"]),
- "{0}_pdr_top{1}".format(table["output-file"], table["output-file-ext"]),
- "{0}_ndr_low{1}".format(table["output-file"], table["output-file-ext"]),
- "{0}_pdr_low{1}".format(table["output-file"], table["output-file-ext"])
- ]
-
- for file_name in file_names:
- logging.info(" Writing the file '{0}'".format(file_name))
- with open(file_name, "w") as file_handler:
- file_handler.write(",".join(header) + "\n")
- for item in tbl_lst:
- if isinstance(item[-1]["data"], float):
- rel_change = round(item[-1]["data"], 1)
- else:
- rel_change = item[-1]["data"]
- if "ndr_top" in file_name \
- and "ndr" in item[0]["data"] \
- and rel_change >= 10.0:
- _write_line_to_file(file_handler, item)
- elif "pdr_top" in file_name \
- and "pdr" in item[0]["data"] \
- and rel_change >= 10.0:
- _write_line_to_file(file_handler, item)
- elif "ndr_low" in file_name \
- and "ndr" in item[0]["data"] \
- and rel_change < 10.0:
- _write_line_to_file(file_handler, item)
- elif "pdr_low" in file_name \
- and "pdr" in item[0]["data"] \
- and rel_change < 10.0:
- _write_line_to_file(file_handler, item)
-
- logging.info(" Done.")
-
-
-def _read_csv_template(file_name):
- """Read the template from a .csv file.
-
- :param file_name: Name / full path / relative path of the file to read.
- :type file_name: str
- :returns: Data from the template as list (lines) of lists (items on line).
- :rtype: list
- :raises: PresentationError if it is not possible to read the file.
+ 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:
- with open(file_name, 'r') as csv_file:
- tmpl_data = list()
- for line in csv_file:
- tmpl_data.append(line[:-1].split(","))
- return tmpl_data
- except IOError as err:
- raise PresentationError(str(err), level="ERROR")
+ 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
+ )
+ 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
-def table_performance_comparison(table, input_data):
- """Generate the table(s) with algorithm: table_performance_comparison
+ 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.
:type input_data: InputData
"""
- logging.info(" Generating the table {0} ...".
- format(table.get("title", "")))
+ 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
+ # Prepare the header of the table
try:
- header = ["Test case",
- "{0} Throughput [Mpps]".format(table["reference"]["title"]),
- "{0} stdev [Mpps]".format(table["reference"]["title"]),
- "{0} Throughput [Mpps]".format(table["compare"]["title"]),
- "{0} stdev [Mpps]".format(table["compare"]["title"]),
- "Change [%]"]
- header_str = ",".join(header) + "\n"
+ 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("The model is invalid, missing parameter: {0}".
- format(err))
+ logging.error(f"The model is invalid, missing parameter: {repr(err)}")
return
- # Prepare data to the table:
+ # Create a list of available SOAK test results:
tbl_dict = dict()
- for job, builds in table["reference"]["data"].items():
+ for job, builds in table[u"compare"][u"data"].items():
for build in builds:
- for tst_name, tst_data in data[job][str(build)].iteritems():
- if tbl_dict.get(tst_name, None) is None:
- name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
- "-".join(tst_data["name"].
- split("-")[1:]))
- tbl_dict[tst_name] = {"name": name,
- "ref-data": list(),
- "cmp-data": list()}
- try:
- tbl_dict[tst_name]["ref-data"].\
- append(tst_data["throughput"]["value"])
- except TypeError:
- pass # No data in output.xml for this test
-
- for job, builds in table["compare"]["data"].items():
+ 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)].iteritems():
+ 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:
- tbl_dict[tst_name]["cmp-data"].\
- append(tst_data["throughput"]["value"])
- except KeyError:
- pass
- except TypeError:
- tbl_dict.pop(tst_name, None)
+ 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.keys():
- item = [tbl_dict[tst_name]["name"], ]
- if tbl_dict[tst_name]["ref-data"]:
- data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
- table["outlier-const"])
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ 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])
- if tbl_dict[tst_name]["cmp-data"]:
- data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
- table["outlier-const"])
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ data_c = tbl_dict[tst_name][u"cmp-data"]
+ if data_c:
+ if table[u"include-tests"] == u"MRR":
+ data_c_mean = data_c[0][0]
+ data_c_stdev = data_c[0][1]
+ else:
+ data_c_mean = mean(data_c)
+ data_c_stdev = stdev(data_c)
+ item.append(round(data_c_mean / 1e6, 1))
+ item.append(round(data_c_stdev / 1e6, 1))
else:
+ data_c_mean = None
+ data_c_stdev = None
item.extend([None, None])
- if item[1] is not None and item[3] is not None:
- item.append(int(relative_change(float(item[1]), float(item[3]))))
- if len(item) == 6:
+ 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 tables:
- # All tests in csv:
- tbl_names = ["{0}-ndr-1t1c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-ndr-2t2c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-ndr-4t4c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-pdr-1t1c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-pdr-2t2c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-pdr-4t4c-full{1}".format(table["output-file"],
- table["output-file-ext"])
- ]
- for file_name in tbl_names:
- logging.info(" Writing file: '{0}'".format(file_name))
- with open(file_name, "w") as file_handler:
- file_handler.write(header_str)
- for test in tbl_lst:
- if (file_name.split("-")[-3] in test[0] and # NDR vs PDR
- file_name.split("-")[-2] in test[0]): # cores
- test[0] = "-".join(test[0].split("-")[:-1])
- file_handler.write(",".join([str(item) for item in test]) +
- "\n")
-
- # All tests in txt:
- tbl_names_txt = ["{0}-ndr-1t1c-full.txt".format(table["output-file"]),
- "{0}-ndr-2t2c-full.txt".format(table["output-file"]),
- "{0}-ndr-4t4c-full.txt".format(table["output-file"]),
- "{0}-pdr-1t1c-full.txt".format(table["output-file"]),
- "{0}-pdr-2t2c-full.txt".format(table["output-file"]),
- "{0}-pdr-4t4c-full.txt".format(table["output-file"])
- ]
-
- for i, txt_name in enumerate(tbl_names_txt):
- txt_table = None
- logging.info(" Writing file: '{0}'".format(txt_name))
- with open(tbl_names[i], 'rb') as csv_file:
- csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
- for row in csv_content:
- if txt_table is None:
- txt_table = prettytable.PrettyTable(row)
- else:
- txt_table.add_row(row)
- txt_table.align["Test case"] = "l"
- with open(txt_name, "w") as txt_file:
- txt_file.write(str(txt_table))
-
- # Selected tests in csv:
- input_file = "{0}-ndr-1t1c-full{1}".format(table["output-file"],
- table["output-file-ext"])
- with open(input_file, "r") as in_file:
- lines = list()
- for line in in_file:
- lines.append(line)
-
- output_file = "{0}-ndr-1t1c-top{1}".format(table["output-file"],
- table["output-file-ext"])
- logging.info(" Writing file: '{0}'".format(output_file))
- with open(output_file, "w") as out_file:
- out_file.write(header_str)
- for i, line in enumerate(lines[1:]):
- if i == table["nr-of-tests-shown"]:
- break
- out_file.write(line)
-
- output_file = "{0}-ndr-1t1c-bottom{1}".format(table["output-file"],
- table["output-file-ext"])
- logging.info(" Writing file: '{0}'".format(output_file))
- with open(output_file, "w") as out_file:
- out_file.write(header_str)
- for i, line in enumerate(lines[-1:0:-1]):
- if i == table["nr-of-tests-shown"]:
- break
- out_file.write(line)
-
- input_file = "{0}-pdr-1t1c-full{1}".format(table["output-file"],
- table["output-file-ext"])
- with open(input_file, "r") as in_file:
- lines = list()
- for line in in_file:
- lines.append(line)
-
- output_file = "{0}-pdr-1t1c-top{1}".format(table["output-file"],
- table["output-file-ext"])
- logging.info(" Writing file: '{0}'".format(output_file))
- with open(output_file, "w") as out_file:
- out_file.write(header_str)
- for i, line in enumerate(lines[1:]):
- if i == table["nr-of-tests-shown"]:
- break
- out_file.write(line)
-
- output_file = "{0}-pdr-1t1c-bottom{1}".format(table["output-file"],
- table["output-file-ext"])
- logging.info(" Writing file: '{0}'".format(output_file))
- with open(output_file, "w") as out_file:
- out_file.write(header_str)
- for i, line in enumerate(lines[-1:0:-1]):
- if i == table["nr-of-tests-shown"]:
- break
- out_file.write(line)
-
-
-def table_performance_comparison_mrr(table, input_data):
- """Generate the table(s) with algorithm: table_performance_comparison_mrr
+ # 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.
:type input_data: InputData
"""
- logging.info(" Generating the table {0} ...".
- format(table.get("title", "")))
+ 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 = ["Test case",
- "{0} Throughput [Mpps]".format(table["reference"]["title"]),
- "{0} stdev [Mpps]".format(table["reference"]["title"]),
- "{0} Throughput [Mpps]".format(table["compare"]["title"]),
- "{0} stdev [Mpps]".format(table["compare"]["title"]),
- "Change [%]"]
- header_str = ",".join(header) + "\n"
- except (AttributeError, KeyError) as err:
- logging.error("The model is invalid, missing parameter: {0}".
- format(err))
- return
+ 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"
+
+ incl_tests = table.get(u"include-tests", u"MRR")
# Prepare data to the table:
tbl_dict = dict()
- for job, builds in table["reference"]["data"].items():
+ for job, builds in table[u"data"].items():
for build in builds:
- for tst_name, tst_data in data[job][str(build)].iteritems():
+ 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:
- name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
- "-".join(tst_data["name"].
- split("-")[1:]))
- tbl_dict[tst_name] = {"name": name,
- "ref-data": list(),
- "cmp-data": list()}
+ 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]["ref-data"].\
- append(tst_data["result"]["throughput"])
- except TypeError:
+ 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
- for job, builds in table["compare"]["data"].items():
- for build in builds:
- for tst_name, tst_data in data[job][str(build)].iteritems():
- try:
- tbl_dict[tst_name]["cmp-data"].\
- append(tst_data["result"]["throughput"])
- except KeyError:
- pass
- except TypeError:
- tbl_dict.pop(tst_name, None)
-
tbl_lst = list()
- for tst_name in tbl_dict.keys():
- item = [tbl_dict[tst_name]["name"], ]
- if tbl_dict[tst_name]["ref-data"]:
- data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
- table["outlier-const"])
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ 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))]
+
+ if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
+ rel_change_last = nan
else:
- item.extend([None, None])
- if tbl_dict[tst_name]["cmp-data"]:
- data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
- table["outlier-const"])
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ 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:
- item.extend([None, None])
- if item[1] is not None and item[3] is not None and item[1] != 0:
- item.append(int(relative_change(float(item[1]), float(item[3]))))
- if len(item) == 6:
- tbl_lst.append(item)
+ 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+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[3])
+ tbl_lst.sort(key=lambda rel: rel[2])
- # Sort the table according to the relative change
- tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
+ 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_sorted.extend(tbl_out)
- # Generate tables:
- # All tests in csv:
- tbl_names = ["{0}-1t1c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-2t2c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-4t4c-full{1}".format(table["output-file"],
- table["output-file-ext"])
- ]
- for file_name in tbl_names:
- logging.info(" Writing file: '{0}'".format(file_name))
- with open(file_name, "w") as file_handler:
- file_handler.write(header_str)
- for test in tbl_lst:
- if file_name.split("-")[-2] in test[0]: # cores
- test[0] = "-".join(test[0].split("-")[:-1])
- file_handler.write(",".join([str(item) for item in test]) +
- "\n")
-
- # All tests in txt:
- tbl_names_txt = ["{0}-1t1c-full.txt".format(table["output-file"]),
- "{0}-2t2c-full.txt".format(table["output-file"]),
- "{0}-4t4c-full.txt".format(table["output-file"])
- ]
-
- for i, txt_name in enumerate(tbl_names_txt):
- txt_table = None
- logging.info(" Writing file: '{0}'".format(txt_name))
- with open(tbl_names[i], 'rb') as csv_file:
- csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
- for row in csv_content:
- if txt_table is None:
- txt_table = prettytable.PrettyTable(row)
- else:
- txt_table.add_row(row)
- txt_table.align["Test case"] = "l"
- with open(txt_name, "w") as txt_file:
- txt_file.write(str(txt_table))
+ 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
+ """
-def table_performance_trending_dashboard(table, input_data):
- """Generate the table(s) with algorithm: table_performance_comparison
+ 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", 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-skx", u"3n-skx", u"2n-clx", u"2n-zn2")):
+ 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", u"2n-zn2")):
+ 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", u"2n-zn2")):
+ 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"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"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.
:type input_data: InputData
"""
- logging.info(" Generating the table {0} ...".
- format(table.get("title", "")))
+ 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)
+ failed_tests.append(f"{nic}-{tst_data[u'name']}")
+ 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 = ["Test Case",
- "Throughput Trend [Mpps]",
- "Trend Compliance",
- "Top Anomaly [Mpps]",
- "Change [%]",
- "Outliers [Number]"
- ]
- header_str = ",".join(header) + "\n"
+ 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)))
- # Prepare data to the table:
tbl_dict = dict()
- for job, builds in table["data"].items():
+ for job, builds in table[u"data"].items():
for build in builds:
- for tst_name, tst_data in data[job][str(build)].iteritems():
+ 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:
- name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
- "-".join(tst_data["name"].
- split("-")[1:]))
- tbl_dict[tst_name] = {"name": name,
- "data": dict()}
+ 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]["data"][str(build)] = \
- tst_data["result"]["throughput"]
- except (TypeError, KeyError):
- pass # No data in output.xml for this test
-
+ 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_name in tbl_dict.keys():
- if len(tbl_dict[tst_name]["data"]) > 2:
-
- pd_data = pd.Series(tbl_dict[tst_name]["data"])
- win_size = pd_data.size \
- if pd_data.size < table["window"] else table["window"]
- # Test name:
- name = tbl_dict[tst_name]["name"]
-
- median = pd_data.rolling(window=win_size, min_periods=2).median()
- trimmed_data, _ = find_outliers(pd_data, outlier_const=1.5)
- stdev_t = pd_data.rolling(window=win_size, min_periods=2).std()
-
- rel_change_lst = [None, ]
- classification_lst = [None, ]
- median_lst = [None, ]
- sample_lst = [None, ]
- first = True
- for build_nr, value in pd_data.iteritems():
- if first:
- first = False
- continue
- # Relative changes list:
- if not isnan(value) \
- and not isnan(median[build_nr]) \
- and median[build_nr] != 0:
- rel_change_lst.append(round(
- relative_change(float(median[build_nr]), float(value)),
- 2))
- else:
- rel_change_lst.append(None)
-
- # Classification list:
- if isnan(trimmed_data[build_nr]) \
- or isnan(median[build_nr]) \
- or isnan(stdev_t[build_nr]) \
- or isnan(value):
- classification_lst.append("outlier")
- elif value < (median[build_nr] - 3 * stdev_t[build_nr]):
- classification_lst.append("regression")
- elif value > (median[build_nr] + 3 * stdev_t[build_nr]):
- classification_lst.append("progression")
- else:
- classification_lst.append("normal")
- sample_lst.append(value)
- median_lst.append(median[build_nr])
-
- last_idx = len(classification_lst) - 1
- first_idx = last_idx - int(table["evaluated-window"])
- if first_idx < 0:
- first_idx = 0
-
- nr_outliers = 0
- consecutive_outliers = 0
- failure = False
- for item in classification_lst[first_idx:]:
- if item == "outlier":
- nr_outliers += 1
- consecutive_outliers += 1
- if consecutive_outliers == 3:
- failure = True
- else:
- consecutive_outliers = 0
-
- if failure:
- classification = "failure"
- elif "regression" in classification_lst[first_idx:]:
- classification = "regression"
- elif "progression" in classification_lst[first_idx:]:
- classification = "progression"
- else:
- classification = "normal"
-
- if classification == "normal":
- index = len(classification_lst) - 1
- else:
- tmp_classification = "outlier" if classification == "failure" \
- else classification
- for idx in range(first_idx, len(classification_lst)):
- if classification_lst[idx] == tmp_classification:
- index = idx
- break
- for idx in range(index+1, len(classification_lst)):
- if classification_lst[idx] == tmp_classification:
- if rel_change_lst[idx] > rel_change_lst[index]:
- index = idx
-
- # if "regression" in classification_lst[first_idx:]:
- # classification = "regression"
- # elif "outlier" in classification_lst[first_idx:]:
- # classification = "outlier"
- # elif "progression" in classification_lst[first_idx:]:
- # classification = "progression"
- # elif "normal" in classification_lst[first_idx:]:
- # classification = "normal"
- # else:
- # classification = None
- #
- # nr_outliers = 0
- # consecutive_outliers = 0
- # failure = False
- # for item in classification_lst[first_idx:]:
- # if item == "outlier":
- # nr_outliers += 1
- # consecutive_outliers += 1
- # if consecutive_outliers == 3:
- # failure = True
- # else:
- # consecutive_outliers = 0
- #
- # idx = len(classification_lst) - 1
- # while idx:
- # if classification_lst[idx] == classification:
- # break
- # idx -= 1
- #
- # if failure:
- # classification = "failure"
- # elif classification == "outlier":
- # classification = "normal"
-
- trend = round(float(median_lst[-1]) / 1000000, 2) \
- if not isnan(median_lst[-1]) else ''
- sample = round(float(sample_lst[index]) / 1000000, 2) \
- if not isnan(sample_lst[index]) else ''
- rel_change = rel_change_lst[index] \
- if rel_change_lst[index] is not None else ''
- tbl_lst.append([name,
- trend,
- classification,
- '-' if classification == "normal" else sample,
- '-' if classification == "normal" else rel_change,
- nr_outliers])
-
- # Sort the table according to the classification
+ 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 classification in ("failure", "regression", "progression", "normal"):
- tbl_tmp = [item for item in tbl_lst if item[2] == classification]
- tbl_tmp.sort(key=lambda rel: rel[0])
- tbl_sorted.extend(tbl_tmp)
-
- file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
-
- logging.info(" Writing file: '{0}'".format(file_name))
- with open(file_name, "w") as file_handler:
- file_handler.write(header_str)
+ 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(",".join([str(item) for item in test]) + '\n')
-
- txt_file_name = "{0}.txt".format(table["output-file"])
- txt_table = None
- logging.info(" Writing file: '{0}'".format(txt_file_name))
- with open(file_name, 'rb') as csv_file:
- csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
- for row in csv_content:
- if txt_table is None:
- txt_table = prettytable.PrettyTable(row)
- else:
- txt_table.add_row(row)
- txt_table.align["Test case"] = "l"
- with open(txt_file_name, "w") as txt_file:
- txt_file.write(str(txt_table))
+ 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_performance_trending_dashboard_html(table, input_data):
- """Generate the table(s) with algorithm:
- table_performance_trending_dashboard_html specified in the specification
- file.
+
+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 input_data: InputData
"""
- logging.info(" Generating the table {0} ...".
- format(table.get("title", "")))
+ _ = 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["input-file"], 'rb') as csv_file:
- csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
- csv_lst = [item for item in csv_content]
+ 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("The input file is not defined.")
+ logging.warning(u"The input file is not defined.")
return
except csv.Error as err:
- logging.warning("Not possible to process the file '{0}'.\n{1}".
- format(table["input-file"], err))
+ logging.warning(
+ f"Not possible to process the file {table[u'input-file']}.\n"
+ f"{repr(err)}"
+ )
return
# Table:
- dashboard = ET.Element("table", attrib=dict(width="100%", border='0'))
+ failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
# Table header:
- tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#7eade7"))
+ trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
for idx, item in enumerate(csv_lst[0]):
- alignment = "left" if idx == 0 else "center"
- th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
- th.text = item
+ 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 = "#D4E4F7" if r_idx % 2 else "white"
- tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background))
+ background = colors[r_idx % 2]
+ trow = ET.SubElement(
+ failed_tests, u"tr", attrib=dict(bgcolor=background)
+ )
# Columns:
for c_idx, item in enumerate(row):
- alignment = "left" if c_idx == 0 else "center"
- td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
+ tdata = ET.SubElement(
+ trow,
+ u"td",
+ attrib=dict(align=u"left" if c_idx == 0 else u"center")
+ )
# Name:
- url = "../trending/"
- file_name = ""
- anchor = "#"
- feature = ""
- if c_idx == 0:
- if "memif" in item:
- file_name = "container_memif.html"
-
- elif "vhost" in item:
- if "l2xcbase" in item or "l2bdbasemaclrn" in item:
- file_name = "vm_vhost_l2.html"
- elif "ip4base" in item:
- file_name = "vm_vhost_ip4.html"
-
- elif "ipsec" in item:
- file_name = "ipsec.html"
-
- elif "ethip4lispip" in item or "ethip4vxlan" in item:
- file_name = "ip4_tunnels.html"
-
- elif "ip4base" in item or "ip4scale" in item:
- file_name = "ip4.html"
- if "iacl" in item or "snat" in item or "cop" in item:
- feature = "-features"
-
- elif "ip6base" in item or "ip6scale" in item:
- file_name = "ip6.html"
-
- elif "l2xcbase" in item or "l2xcscale" in item \
- or "l2bdbasemaclrn" in item or "l2bdscale" in item \
- or "l2dbbasemaclrn" in item or "l2dbscale" in item:
- file_name = "l2.html"
- if "iacl" in item:
- feature = "-features"
-
- if "x520" in item:
- anchor += "x520-"
- elif "x710" in item:
- anchor += "x710-"
- elif "xl710" in item:
- anchor += "xl710-"
-
- if "64b" in item:
- anchor += "64b-"
- elif "78b" in item:
- anchor += "78b"
- elif "imix" in item:
- anchor += "imix-"
- elif "9000b" in item:
- anchor += "9000b-"
- elif "1518" in item:
- anchor += "1518b-"
-
- if "1t1c" in item:
- anchor += "1t1c"
- elif "2t2c" in item:
- anchor += "2t2c"
- elif "4t4c" in item:
- anchor += "4t4c"
-
- url = url + file_name + anchor + feature
-
- ref = ET.SubElement(td, "a", attrib=dict(href=url))
+ 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(u'title', u'')} ...")
- if c_idx == 2:
- if item == "regression":
- td.set("bgcolor", "#eca1a6")
- elif item == "failure":
- td.set("bgcolor", "#d6cbd3")
- elif item == "progression":
- td.set("bgcolor", "#bdcebe")
- if c_idx > 0:
- td.text = item
+ # 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
+ tag = col.get(u"tag", None)
+ data = input_data.filter_data(
+ table,
+ params=[
+ u"throughput",
+ u"result",
+ u"latency",
+ 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():
+ if tag and tag not in tst_data[u"tags"]:
+ continue
+ tst_name_mod = \
+ _tpc_modify_test_name(tst_name, ignore_nic=True).\
+ 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],
+ 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"latency",
+ 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():
+ if tag and tag not in tst_data[u"tags"]:
+ continue
+ tst_name_mod = \
+ _tpc_modify_test_name(tst_name, ignore_nic=True).\
+ 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],
+ src=tst_data,
+ include_tests=table[u"include-tests"]
+ )
+
+ if table[u"include-tests"] in (u"NDR", u"PDR") or \
+ u"latency" in table[u"include-tests"]:
+ 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"])
+
+ 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"]
+ }
+
+ if not tbl_dict:
+ logging.warning(f"No data for table {table.get(u'title', u'')}!")
+ return
+
+ 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)
+ rcas = list()
+ 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
+ rca_file_name = comp.get(u"rca-file", None)
+ if rca_file_name:
+ try:
+ with open(rca_file_name, u"r") as file_handler:
+ rcas.append(
+ {
+ u"title": f"RCA{idx + 1}",
+ u"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[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:
+ try:
+ delta, d_stdev = relative_change_stdev(
+ ref_itm[u"mean"], cmp_itm[u"mean"],
+ ref_itm[u"stdev"], cmp_itm[u"stdev"]
+ )
+ except ZeroDivisionError:
+ break
+ if delta is None or math.isnan(delta):
+ break
+ new_row.append({
+ u"mean": delta * 1e6,
+ u"stdev": d_stdev * 1e6
+ })
+ else:
+ break
+ else:
+ tbl_cmp_lst.append(new_row)
try:
- with open(table["output-file"], 'w') as html_file:
- logging.info(" Writing file: '{0}'".
- format(table["output-file"]))
- html_file.write(".. raw:: html\n\n\t")
- html_file.write(ET.tostring(dashboard))
- html_file.write("\n\t<p><br><br></p>\n")
- except KeyError:
- logging.warning("The output file is not defined.")
+ tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
+ tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'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(u'mean', None) is None or \
+ itm.get(u'stdev', None) 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:
+ if rca is None:
+ continue
+ 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'')})"
+ )
+ for rca in rcas:
+ if rca:
+ header_csv.append(rca[u"title"])
+
+ 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""
+ if rcas and any(rcas):
+ footnote += u"\nRoot Cause Analysis:\n"
+ for rca in rcas:
+ if rca:
+ footnote += f"{rca[u'data'].get(u'footnote', u'')}\n"
+
+ csv_file_name = f"{table[u'output-file']}-csv.csv"
+ with open(csv_file_name, 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 or not isinstance(itm, dict) or \
+ itm.get(u'mean', None) is None or \
+ itm.get(u'stdev', None) 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)
+
+ header = [u"Test Case", ]
+ header.extend([col[u"title"] for col in cols])
+ header.extend([comp.get(u"title", u"") 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 (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]}"
+ itm_str = u"\u00B1".join(itm_lst)
+
+ if idx >= len(cols):
+ # Diffs
+ rca = rcas[idx - len(cols)]
+ if rca:
+ # Add rcas to diffs
+ rca_nr = rca[u"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"{u' ' * (4 - len(rca_nr))}{rca_nr}"
+ f"{u' ' * (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[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:
+ 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"
+ logging.info(f" Writing the file {txt_file_name}")
+ convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u";")
+
+ with open(txt_file_name, u'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[u'output-file'],
+ legend=legend,
+ footnote=footnote,
+ sort_data=False,
+ title=table.get(u"title", u"")
+ )
+
+
+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