# Copyright (c) 2018 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 pandas as pd
from string import replace
from collections import OrderedDict
from numpy import nan, isnan
from xml.etree import ElementTree as ET
from errors import PresentationError
from utils import mean, stdev, relative_change, classify_anomalies, \
convert_csv_to_pretty_txt
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
"""
logging.info("Generating the tables ...")
for table in spec.tables:
try:
eval(table["algorithm"])(table, data)
except NameError as err:
logging.error("Probably algorithm '{alg}' is not defined: {err}".
format(alg=table["algorithm"], err=repr(err)))
logging.info("Done.")
def table_details(table, input_data):
"""Generate the table(s) with algorithm: table_detailed_test_results
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(" Generating the table {0} ...".
format(table.get("title", "")))
# Transform the data
logging.info(" Creating the data set for the {0} '{1}'.".
format(table.get("type", ""), table.get("title", "")))
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('"', '""')))
# 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.")
return
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)
# 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")
logging.info(" 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(" Generating the table {0} ...".
format(table.get("title", "")))
# Transform the data
logging.info(" Creating the data set for the {0} '{1}'.".
format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table)
data = input_data.merge_data(data)
data.sort_index(inplace=True)
logging.info(" Creating the data set for the {0} '{1}'.".
format(table.get("type", ""), table.get("title", "")))
suites = input_data.filter_data(table, data_set="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 _, suite in suites.iteritems():
# Generate data
suite_name = suite["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")
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")
for item in table_lst:
file_handler.write(",".join(item) + "\n")
logging.info(" Done.")
def table_performance_improvements(table, input_data):
"""Generate the table(s) with algorithm: table_performance_improvements
specified in the specification file.
:param table: Table to generate.
:param input_data: Data to process.
:type table: pandas.Series
:type input_data: InputData
"""
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
logging.info(" Creating the data set for the {0} '{1}'.".
format(table.get("type", ""), table.get("title", "")))
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.
"""
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")
def table_performance_comparison(table, input_data):
"""Generate the table(s) with algorithm: table_performance_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(" Generating the table {0} ...".
format(table.get("title", "")))
# Transform the data
logging.info(" Creating the data set for the {0} '{1}'.".
format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
try:
header = ["Test case", ]
history = table.get("history", None)
if history:
for item in history:
header.extend(
["{0} Throughput [Mpps]".format(item["title"]),
"{0} Stdev [Mpps]".format(item["title"])])
header.extend(
["{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
# Prepare data to the table:
tbl_dict = dict()
for job, builds in table["reference"]["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 build in builds:
for tst_name, tst_data in data[job][str(build)].iteritems():
try:
tbl_dict[tst_name]["cmp-data"].\
append(tst_data["throughput"]["value"])
except KeyError:
pass
except TypeError:
tbl_dict.pop(tst_name, None)
if history:
for item in history:
for job, builds in item["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:
continue
if tbl_dict[tst_name].get("history", None) is None:
tbl_dict[tst_name]["history"] = OrderedDict()
if tbl_dict[tst_name]["history"].get(item["title"],
None) is None:
tbl_dict[tst_name]["history"][item["title"]] = \
list()
try:
tbl_dict[tst_name]["history"][item["title"]].\
append(tst_data["throughput"]["value"])
except (TypeError, KeyError):
pass
tbl_lst = list()
for tst_name in tbl_dict.keys():
item = [tbl_dict[tst_name]["name"], ]
if history:
if tbl_dict[tst_name].get("history", None) is not None:
for hist_data in tbl_dict[tst_name]["history"].values():
if hist_data:
item.append(round(mean(hist_data) / 1000000, 2))
item.append(round(stdev(hist_data) / 1000000, 2))
else:
item.extend([None, None])
else:
item.extend([None, None])
data_t = tbl_dict[tst_name]["ref-data"]
if data_t:
item.append(round(mean(data_t) / 1000000, 2))
item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
data_t = tbl_dict[tst_name]["cmp-data"]
if data_t:
item.append(round(mean(data_t) / 1000000, 2))
item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
if item[-4] is not None and item[-2] is not None and item[-4] != 0:
item.append(int(relative_change(float(item[-4]), float(item[-2]))))
if len(item) == len(header):
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):
logging.info(" Writing file: '{0}'".format(txt_name))
convert_csv_to_pretty_txt(tbl_names[i], txt_name)
# 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
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(" Generating the table {0} ...".
format(table.get("title", "")))
# Transform the data
logging.info(" Creating the data set for the {0} '{1}'.".
format(table.get("type", ""), table.get("title", "")))
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
# Prepare data to the table:
tbl_dict = dict()
for job, builds in table["reference"]["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["result"]["throughput"])
except TypeError:
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"], ]
data_t = tbl_dict[tst_name]["ref-data"]
if data_t:
item.append(round(mean(data_t) / 1000000, 2))
item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
data_t = tbl_dict[tst_name]["cmp-data"]
if data_t:
item.append(round(mean(data_t) / 1000000, 2))
item.append(round(stdev(data_t) / 1000000, 2))
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)
# 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}-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):
logging.info(" Writing file: '{0}'".format(txt_name))
convert_csv_to_pretty_txt(tbl_names[i], txt_name)
def table_performance_trending_dashboard(table, input_data):
"""Generate the table(s) with algorithm:
table_performance_trending_dashboard
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(" Generating the table {0} ...".
format(table.get("title", "")))
# Transform the data
logging.info(" Creating the data set for the {0} '{1}'.".
format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
header = ["Test Case",
"Trend [Mpps]",
"Short-Term Change [%]",
"Long-Term Change [%]",
"Regressions [#]",
"Progressions [#]"
]
header_str = ",".join(header) + "\n"
# Prepare data to the table:
tbl_dict = dict()
for job, builds in table["data"].items():
for build in builds:
for tst_name, tst_data in data[job][str(build)].iteritems():
if tst_name.lower() in table["ignore-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": 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
tbl_lst = list()
for tst_name in tbl_dict.keys():
if len(tbl_dict[tst_name]["data"]) < 2:
continue
data_t = pd.Series(tbl_dict[tst_name]["data"])
classification_lst, avgs = classify_anomalies(data_t)
win_size = min(data_t.size, table["window"])
long_win_size = min(data_t.size, table["long-trend-window"])
try:
max_long_avg = max(
[x for x in avgs[-long_win_size:-win_size]
if not isnan(x)])
except ValueError:
max_long_avg = nan
last_avg = avgs[-1]
avg_week_ago = avgs[max(-win_size, -len(avgs))]
if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
rel_change_last = nan
else:
rel_change_last = round(
((last_avg - avg_week_ago) / avg_week_ago) * 100, 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) * 100, 2)
if classification_lst:
if isnan(rel_change_last) and isnan(rel_change_long):
continue
tbl_lst.append(
[tbl_dict[tst_name]["name"],
'-' if isnan(last_avg) else
round(last_avg / 1000000, 2),
'-' if isnan(rel_change_last) else rel_change_last,
'-' if isnan(rel_change_long) else rel_change_long,
classification_lst[-win_size:].count("regression"),
classification_lst[-win_size:].count("progression")])
tbl_lst.sort(key=lambda rel: rel[0])
tbl_sorted = list()
for nrr in range(table["window"], -1, -1):
tbl_reg = [item for item in tbl_lst if item[4] == nrr]
for nrp in range(table["window"], -1, -1):
tbl_out = [item for item in tbl_reg if item[5] == nrp]
tbl_out.sort(key=lambda rel: rel[2])
tbl_sorted.extend(tbl_out)
file_name = "{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 test in tbl_sorted:
file_handler.write(",".join([str(item) for item in test]) + '\n')
txt_file_name = "{0}.txt".format(table["output-file"])
logging.info(" Writing file: '{0}'".format(txt_file_name))
convert_csv_to_pretty_txt(file_name, txt_file_name)
def _generate_url(base, test_name):
"""Generate URL to a trending plot from the name of the test case.
:param base: The base part of URL common to all test cases.
:param test_name: The name of the test case.
:type base: str
:type test_name: str
:returns: The URL to the plot with the trending data for the given test
case.
:rtype str
"""
url = base
file_name = ""
anchor = "#"
feature = ""
if "lbdpdk" in test_name or "lbvpp" in test_name:
file_name = "link_bonding.html"
elif "testpmd" in test_name or "l3fwd" in test_name:
file_name = "dpdk.html"
elif "memif" in test_name:
file_name = "container_memif.html"
elif "srv6" in test_name:
file_name = "srv6.html"
elif "vhost" in test_name:
if "l2xcbase" in test_name or "l2bdbasemaclrn" in test_name:
file_name = "vm_vhost_l2.html"
elif "ip4base" in test_name:
file_name = "vm_vhost_ip4.html"
elif "ipsec" in test_name:
file_name = "ipsec.html"
elif "ethip4lispip" in test_name or "ethip4vxlan" in test_name:
file_name = "ip4_tunnels.html"
elif "ip4base" in test_name or "ip4scale" in test_name:
file_name = "ip4.html"
if "iacl" in test_name or "snat" in test_name or "cop" in test_name:
feature = "-features"
elif "ip6base" in test_name or "ip6scale" in test_name:
file_name = "ip6.html"
elif "l2xcbase" in test_name or "l2xcscale" in test_name \
or "l2bdbasemaclrn" in test_name or "l2bdscale" in test_name \
or "l2dbbasemaclrn" in test_name or "l2dbscale" in test_name:
file_name = "l2.html"
if "iacl" in test_name:
feature = "-features"
if "x520" in test_name:
anchor += "x520-"
elif "x710" in test_name:
anchor += "x710-"
elif "xl710" in test_name:
anchor += "xl710-"
if "64b" in test_name:
anchor += "64b-"
elif "78b" in test_name:
anchor += "78b-"
elif "imix" in test_name:
anchor += "imix-"
elif "9000b" in test_name:
anchor += "9000b-"
elif "1518" in test_name:
anchor += "1518b-"
if "1t1c" in test_name:
anchor += "1t1c"
elif "2t2c" in test_name:
anchor += "2t2c"
elif "4t4c" in test_name:
anchor += "4t4c"
return url + file_name + anchor + feature
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.
:param table: Table to generate.
:param input_data: Data to process.
:type table: pandas.Series
:type input_data: InputData
"""
logging.info(" Generating the table {0} ...".
format(table.get("title", "")))
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]
except KeyError:
logging.warning("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))
return
# Table:
dashboard = ET.Element("table", attrib=dict(width="100%", border='0'))
# Table header:
tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#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
# Rows:
colors = {"regression": ("#ffcccc", "#ff9999"),
"progression": ("#c6ecc6", "#9fdf9f"),
"normal": ("#e9f1fb", "#d4e4f7")}
for r_idx, row in enumerate(csv_lst[1:]):
if int(row[4]):
color = "regression"
elif int(row[5]):
color = "progression"
else:
color = "normal"
background = colors[color][r_idx % 2]
tr = ET.SubElement(dashboard, "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))
# Name:
if c_idx == 0:
url = _generate_url("../trending/", item)
ref = ET.SubElement(td, "a", attrib=dict(href=url))
ref.text = item
else:
td.text = item
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
\n")
except KeyError:
logging.warning("The output file is not defined.")
return
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(" Generating the table {0} ...".
format(table.get("title", "")))
# Transform the data
logging.info(" Creating the data set for the {0} '{1}'.".
format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
header = ["Test Case",
"Fails [#]",
"Last Fail [Timestamp]",
"Last Fail [VPP Build]",
"Last Fail [CSIT Build]"]
# Generate the data for the table according to the model in the table
# specification
tbl_dict = dict()
for job, builds in table["data"].items():
for build in builds:
build = str(build)
for tst_name, tst_data in data[job][build].iteritems():
if tst_name.lower() in table["ignore-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": OrderedDict()}
try:
tbl_dict[tst_name]["data"][build] = (
tst_data["status"],
input_data.metadata(job, build).get("generated", ""),
input_data.metadata(job, build).get("version", ""),
build)
except (TypeError, KeyError):
pass # No data in output.xml for this test
tbl_lst = list()
for tst_data in tbl_dict.values():
win_size = min(len(tst_data["data"]), table["window"])
fails_nr = 0
for val in tst_data["data"].values()[-win_size:]:
if val[0] == "FAIL":
fails_nr += 1
fails_last_date = val[1]
fails_last_vpp = val[2]
fails_last_csit = val[3]
if fails_nr:
tbl_lst.append([tst_data["name"],
fails_nr,
fails_last_date,
fails_last_vpp,
"mrr-daily-build-{0}".format(fails_last_csit)])
tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
tbl_sorted = list()
for nrf in range(table["window"], -1, -1):
tbl_fails = [item for item in tbl_lst if item[1] == nrf]
tbl_sorted.extend(tbl_fails)
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(",".join(header) + "\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"])
logging.info(" Writing file: '{0}'".format(txt_file_name))
convert_csv_to_pretty_txt(file_name, txt_file_name)
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
"""
logging.info(" Generating the table {0} ...".
format(table.get("title", "")))
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]
except KeyError:
logging.warning("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))
return
# Table:
failed_tests = ET.Element("table", attrib=dict(width="100%", border='0'))
# Table header:
tr = ET.SubElement(failed_tests, "tr", attrib=dict(bgcolor="#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
# Rows:
colors = {"very-bad": ("#ffcccc", "#ff9999"),
"bad": ("#e9f1fb", "#d4e4f7")}
for r_idx, row in enumerate(csv_lst[1:]):
if int(row[1]) > 7:
color = "very-bad"
else:
color = "bad"
background = colors[color][r_idx % 2]
tr = ET.SubElement(failed_tests, "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))
# Name:
if c_idx == 0:
url = _generate_url("../trending/", item)
ref = ET.SubElement(td, "a", attrib=dict(href=url))
ref.text = item
else:
td.text = item
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(failed_tests))
html_file.write("\n\t
\n")
except KeyError:
logging.warning("The output file is not defined.")
return