-# Copyright (c) 2017 Cisco and/or its affiliates.
+# Copyright (c) 2019 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 re
from string import replace
+from collections import OrderedDict
+from numpy import nan, isnan
+from xml.etree import ElementTree as ET
+from datetime import datetime as dt
+from datetime import timedelta
-from errors import PresentationError
-from utils import mean, stdev, relative_change, remove_outliers
+from utils import mean, stdev, relative_change, classify_anomalies, \
+ convert_csv_to_pretty_txt, relative_change_stdev
+
+
+REGEX_NIC = re.compile(r'\d*ge\dp\d\D*\d*')
def generate_tables(spec, data):
for table in spec.tables:
try:
eval(table["algorithm"])(table, data)
- except NameError:
- logging.error("The algorithm '{0}' is not defined.".
- format(table["algorithm"]))
+ except NameError as err:
+ logging.error("Probably algorithm '{alg}' is not defined: {err}".
+ format(alg=table["algorithm"], err=repr(err)))
logging.info("Done.")
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
try:
col_data = str(data[job][build][test][column["data"].
split(" ")[1]]).replace('"', '""')
- if column["data"].split(" ")[1] in ("vat-history",
+ if column["data"].split(" ")[1] in ("conf-history",
"show-run"):
col_data = replace(col_data, " |br| ", "",
maxreplace=1)
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)
try:
col_data = str(data[test][column["data"].
split(" ")[1]]).replace('"', '""')
- if column["data"].split(" ")[1] in ("vat-history",
+ col_data = replace(col_data, "No Data",
+ "Not Captured ")
+ if column["data"].split(" ")[1] in ("conf-history",
"show-run"):
col_data = replace(col_data, " |br| ", "",
maxreplace=1)
format(col_data[:-5])
row_lst.append('"{0}"'.format(col_data))
except KeyError:
- row_lst.append("No data")
+ row_lst.append('"Not captured"')
table_lst.append(row_lst)
# Write the data to file
logging.info(" Done.")
-def table_performance_improvements(table, input_data):
- """Generate the table(s) with algorithm: table_performance_improvements
+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.
: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
- data = input_data.filter_data(table)
+ 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 = list()
- for column in table["columns"]:
- header.append(column["title"])
+ try:
+ header = ["Test case", ]
- # 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":
+ if table["include-tests"] == "MRR":
+ hdr_param = "Receive Rate"
+ else:
+ hdr_param = "Throughput"
+
+ history = table.get("history", None)
+ if history:
+ for item in history:
+ header.extend(
+ ["{0} {1} [Mpps]".format(item["title"], hdr_param),
+ "{0} Stdev [Mpps]".format(item["title"])])
+ header.extend(
+ ["{0} {1} [Mpps]".format(table["reference"]["title"], hdr_param),
+ "{0} Stdev [Mpps]".format(table["reference"]["title"]),
+ "{0} {1} [Mpps]".format(table["compare"]["title"], hdr_param),
+ "{0} Stdev [Mpps]".format(table["compare"]["title"]),
+ "Delta [%]"])
+ 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():
+ tst_name_mod = tst_name.replace("-ndrpdrdisc", "").\
+ replace("-ndrpdr", "").replace("-pdrdisc", "").\
+ replace("-ndrdisc", "").replace("-pdr", "").\
+ replace("-ndr", "").\
+ replace("1t1c", "1c").replace("2t1c", "1c").\
+ replace("2t2c", "2c").replace("4t2c", "2c").\
+ replace("4t4c", "4c").replace("8t4c", "4c")
+ if "across topologies" in table["title"].lower():
+ tst_name_mod = tst_name_mod.replace("2n1l-", "")
+ if tbl_dict.get(tst_name_mod, None) is None:
+ groups = re.search(REGEX_NIC, tst_data["parent"])
+ nic = groups.group(0) if groups else ""
+ name = "{0}-{1}".format(nic, "-".join(tst_data["name"].
+ split("-")[:-1]))
+ if "across testbeds" in table["title"].lower() or \
+ "across topologies" in table["title"].lower():
+ name = name.\
+ replace("1t1c", "1c").replace("2t1c", "1c").\
+ replace("2t2c", "2c").replace("4t2c", "2c").\
+ replace("4t4c", "4c").replace("8t4c", "4c")
+ tbl_dict[tst_name_mod] = {"name": name,
+ "ref-data": list(),
+ "cmp-data": list()}
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]
+ # TODO: Re-work when NDRPDRDISC tests are not used
+ if table["include-tests"] == "MRR":
+ tbl_dict[tst_name_mod]["ref-data"]. \
+ append(tst_data["result"]["receive-rate"].avg)
+ elif table["include-tests"] == "PDR":
+ if tst_data["type"] == "PDR":
+ tbl_dict[tst_name_mod]["ref-data"]. \
+ append(tst_data["throughput"]["value"])
+ elif tst_data["type"] == "NDRPDR":
+ tbl_dict[tst_name_mod]["ref-data"].append(
+ tst_data["throughput"]["PDR"]["LOWER"])
+ elif table["include-tests"] == "NDR":
+ if tst_data["type"] == "NDR":
+ tbl_dict[tst_name_mod]["ref-data"]. \
+ append(tst_data["throughput"]["value"])
+ elif tst_data["type"] == "NDRPDR":
+ tbl_dict[tst_name_mod]["ref-data"].append(
+ tst_data["throughput"]["NDR"]["LOWER"])
+ else:
+ continue
+ 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():
+ tst_name_mod = tst_name.replace("-ndrpdrdisc", ""). \
+ replace("-ndrpdr", "").replace("-pdrdisc", ""). \
+ replace("-ndrdisc", "").replace("-pdr", ""). \
+ replace("-ndr", "").\
+ replace("1t1c", "1c").replace("2t1c", "1c").\
+ replace("2t2c", "2c").replace("4t2c", "2c").\
+ replace("4t4c", "4c").replace("8t4c", "4c")
+ if "across topologies" in table["title"].lower():
+ tst_name_mod = tst_name_mod.replace("2n1l-", "")
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)})
+ # TODO: Re-work when NDRPDRDISC tests are not used
+ if table["include-tests"] == "MRR":
+ tbl_dict[tst_name_mod]["cmp-data"]. \
+ append(tst_data["result"]["receive-rate"].avg)
+ elif table["include-tests"] == "PDR":
+ if tst_data["type"] == "PDR":
+ tbl_dict[tst_name_mod]["cmp-data"]. \
+ append(tst_data["throughput"]["value"])
+ elif tst_data["type"] == "NDRPDR":
+ tbl_dict[tst_name_mod]["cmp-data"].append(
+ tst_data["throughput"]["PDR"]["LOWER"])
+ elif table["include-tests"] == "NDR":
+ if tst_data["type"] == "NDR":
+ tbl_dict[tst_name_mod]["cmp-data"]. \
+ append(tst_data["throughput"]["value"])
+ elif tst_data["type"] == "NDRPDR":
+ tbl_dict[tst_name_mod]["cmp-data"].append(
+ tst_data["throughput"]["NDR"]["LOWER"])
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
+ continue
+ except KeyError:
+ pass
+ except TypeError:
+ tbl_dict.pop(tst_name_mod, 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():
+ tst_name_mod = tst_name.replace("-ndrpdrdisc", ""). \
+ replace("-ndrpdr", "").replace("-pdrdisc", ""). \
+ replace("-ndrdisc", "").replace("-pdr", ""). \
+ replace("-ndr", "").\
+ replace("1t1c", "1c").replace("2t1c", "1c").\
+ replace("2t2c", "2c").replace("4t2c", "2c").\
+ replace("4t4c", "4c").replace("8t4c", "4c")
+ if "across topologies" in table["title"].lower():
+ tst_name_mod = tst_name_mod.replace("2n1l-", "")
+ if tbl_dict.get(tst_name_mod, None) is None:
+ continue
+ if tbl_dict[tst_name_mod].get("history", None) is None:
+ tbl_dict[tst_name_mod]["history"] = OrderedDict()
+ if tbl_dict[tst_name_mod]["history"].get(item["title"],
+ None) is None:
+ tbl_dict[tst_name_mod]["history"][item["title"]] = \
+ list()
+ try:
+ # TODO: Re-work when NDRPDRDISC tests are not used
+ if table["include-tests"] == "MRR":
+ tbl_dict[tst_name_mod]["history"][item["title"
+ ]].append(tst_data["result"]["receive-rate"].
+ avg)
+ elif table["include-tests"] == "PDR":
+ if tst_data["type"] == "PDR":
+ tbl_dict[tst_name_mod]["history"][
+ item["title"]].\
+ append(tst_data["throughput"]["value"])
+ elif tst_data["type"] == "NDRPDR":
+ tbl_dict[tst_name_mod]["history"][item[
+ "title"]].append(tst_data["throughput"][
+ "PDR"]["LOWER"])
+ elif table["include-tests"] == "NDR":
+ if tst_data["type"] == "NDR":
+ tbl_dict[tst_name_mod]["history"][
+ item["title"]].\
+ append(tst_data["throughput"]["value"])
+ elif tst_data["type"] == "NDRPDR":
+ tbl_dict[tst_name_mod]["history"][item[
+ "title"]].append(tst_data["throughput"][
+ "NDR"]["LOWER"])
+ else:
+ continue
+ 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:
- logging.error("Not supported command {0}. Skipping the table.".
- format(cmd))
- return None
- tbl_lst.append(tbl_item)
+ 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]["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)
+ tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
- logging.info(" Done.")
+ # Generate csv tables:
+ csv_file = "{0}.csv".format(table["output-file"])
+ with open(csv_file, "w") as file_handler:
+ file_handler.write(header_str)
+ for test in tbl_lst:
+ file_handler.write(",".join([str(item) for item in test]) + "\n")
+
+ convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"]))
-def _read_csv_template(file_name):
- """Read the template from a .csv file.
+def table_nics_comparison(table, input_data):
+ """Generate the table(s) with algorithm: table_nics_comparison
+ specified in the specification 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.
+ :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:
- 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")
+ header = ["Test case", ]
+ if table["include-tests"] == "MRR":
+ hdr_param = "Receive Rate"
+ else:
+ hdr_param = "Throughput"
+
+ header.extend(
+ ["{0} {1} [Mpps]".format(table["reference"]["title"], hdr_param),
+ "{0} Stdev [Mpps]".format(table["reference"]["title"]),
+ "{0} {1} [Mpps]".format(table["compare"]["title"], hdr_param),
+ "{0} Stdev [Mpps]".format(table["compare"]["title"]),
+ "Delta [%]"])
+ header_str = ",".join(header) + "\n"
+ except (AttributeError, KeyError) as err:
+ logging.error("The model is invalid, missing parameter: {0}".
+ format(err))
+ return
-def table_performance_comparison(table, input_data):
- """Generate the table(s) with algorithm: table_performance_comparison
+ # 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():
+ tst_name_mod = tst_name.replace("-ndrpdrdisc", "").\
+ replace("-ndrpdr", "").replace("-pdrdisc", "").\
+ replace("-ndrdisc", "").replace("-pdr", "").\
+ replace("-ndr", "").\
+ replace("1t1c", "1c").replace("2t1c", "1c").\
+ replace("2t2c", "2c").replace("4t2c", "2c").\
+ replace("4t4c", "4c").replace("8t4c", "4c")
+ tst_name_mod = re.sub(REGEX_NIC, "", tst_name_mod)
+ if tbl_dict.get(tst_name_mod, None) is None:
+ name = "-".join(tst_data["name"].split("-")[:-1])
+ tbl_dict[tst_name_mod] = {"name": name,
+ "ref-data": list(),
+ "cmp-data": list()}
+ try:
+ if table["include-tests"] == "MRR":
+ result = tst_data["result"]["receive-rate"].avg
+ elif table["include-tests"] == "PDR":
+ result = tst_data["throughput"]["PDR"]["LOWER"]
+ elif table["include-tests"] == "NDR":
+ result = tst_data["throughput"]["NDR"]["LOWER"]
+ else:
+ result = None
+
+ if result:
+ if table["reference"]["nic"] in tst_data["tags"]:
+ tbl_dict[tst_name_mod]["ref-data"].append(result)
+ elif table["compare"]["nic"] in tst_data["tags"]:
+ tbl_dict[tst_name_mod]["cmp-data"].append(result)
+ except (TypeError, KeyError) as err:
+ logging.debug("No data for {0}".format(tst_name))
+ logging.debug(repr(err))
+ # No data in output.xml for this test
+
+ 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[-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 csv tables:
+ csv_file = "{0}.csv".format(table["output-file"])
+ with open(csv_file, "w") as file_handler:
+ file_handler.write(header_str)
+ for test in tbl_lst:
+ file_handler.write(",".join([str(item) for item in test]) + "\n")
+
+ convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"]))
+
+
+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.
format(table.get("title", "")))
# Transform the data
- data = input_data.filter_data(table)
+ 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
+ # 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 = [
+ "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"]),
+ "Delta [%]", "Stdev of delta [%]"]
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:
+ # Create a list of available SOAK test results:
tbl_dict = dict()
- for job, builds in table["reference"]["data"].items():
+ for job, builds in table["compare"]["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
+ if tst_data["type"] == "SOAK":
+ tst_name_mod = tst_name.replace("-soak", "")
+ if tbl_dict.get(tst_name_mod, None) is None:
+ groups = re.search(REGEX_NIC, tst_data["parent"])
+ nic = groups.group(0) if groups else ""
+ name = "{0}-{1}".format(nic, "-".join(tst_data["name"].
+ split("-")[:-1]))
+ tbl_dict[tst_name_mod] = {
+ "name": name,
+ "ref-data": list(),
+ "cmp-data": list()
+ }
+ try:
+ tbl_dict[tst_name_mod]["cmp-data"].append(
+ tst_data["throughput"]["LOWER"])
+ except (KeyError, TypeError):
+ pass
+ tests_lst = tbl_dict.keys()
- for job, builds in table["compare"]["data"].items():
+ # Add corresponding NDR test results:
+ for job, builds in table["reference"]["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)
+ tst_name_mod = tst_name.replace("-ndrpdr", "").\
+ replace("-mrr", "")
+ if tst_name_mod in tests_lst:
+ try:
+ if tst_data["type"] in ("NDRPDR", "MRR", "BMRR"):
+ if table["include-tests"] == "MRR":
+ result = tst_data["result"]["receive-rate"].avg
+ elif table["include-tests"] == "PDR":
+ result = tst_data["throughput"]["PDR"]["LOWER"]
+ elif table["include-tests"] == "NDR":
+ result = tst_data["throughput"]["NDR"]["LOWER"]
+ else:
+ result = None
+ if result is not None:
+ tbl_dict[tst_name_mod]["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"]:
- item.append(round(mean(remove_outliers(
- tbl_dict[tst_name]["ref-data"], 2)) / 1000000, 2))
- item.append(round(stdev(remove_outliers(
- tbl_dict[tst_name]["ref-data"], 2)) / 1000000, 2))
+ data_r = tbl_dict[tst_name]["ref-data"]
+ if data_r:
+ data_r_mean = mean(data_r)
+ item.append(round(data_r_mean / 1000000, 2))
+ data_r_stdev = stdev(data_r)
+ item.append(round(data_r_stdev / 1000000, 2))
else:
+ data_r_mean = None
+ data_r_stdev = None
item.extend([None, None])
- if tbl_dict[tst_name]["cmp-data"]:
- item.append(round(mean(remove_outliers(
- tbl_dict[tst_name]["cmp-data"], 2)) / 1000000, 2))
- item.append(round(stdev(remove_outliers(
- tbl_dict[tst_name]["cmp-data"], 2)) / 1000000, 2))
+ data_c = tbl_dict[tst_name]["cmp-data"]
+ if data_c:
+ data_c_mean = mean(data_c)
+ item.append(round(data_c_mean / 1000000, 2))
+ data_c_stdev = stdev(data_c)
+ item.append(round(data_c_stdev / 1000000, 2))
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 and data_c_mean:
+ delta, d_stdev = relative_change_stdev(
+ data_r_mean, data_c_mean, data_r_stdev, data_c_stdev)
+ item.append(round(delta, 2))
+ item.append(round(d_stdev, 2))
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: '{}'".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: '{}'".format(txt_name))
- with open(tbl_names[i], 'rb') as csv_file:
+ # Generate csv tables:
+ csv_file = "{0}.csv".format(table["output-file"])
+ with open(csv_file, "w") as file_handler:
+ file_handler.write(header_str)
+ for test in tbl_lst:
+ file_handler.write(",".join([str(item) for item in test]) + "\n")
+
+ convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"]))
+
+
+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.get("ignore-list", list()):
+ continue
+ if tbl_dict.get(tst_name, None) is None:
+ groups = re.search(REGEX_NIC, tst_data["parent"])
+ if not groups:
+ continue
+ nic = groups.group(0)
+ tbl_dict[tst_name] = {
+ "name": "{0}-{1}".format(nic, tst_data["name"]),
+ "data": OrderedDict()}
+ try:
+ tbl_dict[tst_name]["data"][str(build)] = \
+ tst_data["result"]["receive-rate"]
+ except (TypeError, KeyError):
+ pass # No data in output.xml for this test
+
+ tbl_lst = list()
+ for tst_name in tbl_dict.keys():
+ data_t = tbl_dict[tst_name]["data"]
+ if len(data_t) < 2:
+ continue
+
+ classification_lst, avgs = classify_anomalies(data_t)
+
+ win_size = min(len(data_t), table["window"])
+ long_win_size = min(len(data_t), 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
+ if (isnan(last_avg) or
+ isnan(rel_change_last) or
+ isnan(rel_change_long)):
+ continue
+ tbl_lst.append(
+ [tbl_dict[tst_name]["name"],
+ round(last_avg / 1000000, 2),
+ rel_change_last,
+ 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, testbed, 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 testbed: The testbed used for testing.
+ :param test_name: The name of the test case.
+ :type base: str
+ :type testbed: 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 = ".html#"
+ feature = ""
+
+ if "lbdpdk" in test_name or "lbvpp" in test_name:
+ file_name = "link_bonding"
+
+ elif "114b" in test_name and "vhost" in test_name:
+ file_name = "vts"
+
+ elif "testpmd" in test_name or "l3fwd" in test_name:
+ file_name = "dpdk"
+
+ elif "memif" in test_name:
+ file_name = "container_memif"
+ feature = "-base"
+
+ elif "srv6" in test_name:
+ file_name = "srv6"
+
+ elif "vhost" in test_name:
+ if "l2xcbase" in test_name or "l2bdbasemaclrn" in test_name:
+ file_name = "vm_vhost_l2"
+ if "114b" in test_name:
+ feature = ""
+ elif "l2xcbase" in test_name and "x520" in test_name:
+ feature = "-base-l2xc"
+ elif "l2bdbasemaclrn" in test_name and "x520" in test_name:
+ feature = "-base-l2bd"
+ else:
+ feature = "-base"
+ elif "ip4base" in test_name:
+ file_name = "vm_vhost_ip4"
+ feature = "-base"
+
+ elif "ipsecbasetnlsw" in test_name:
+ file_name = "ipsecsw"
+ feature = "-base-scale"
+
+ elif "ipsec" in test_name:
+ file_name = "ipsec"
+ feature = "-base-scale"
+ if "-int-" in test_name:
+ feature = "-base-scale-int"
+ elif "tnl" in test_name:
+ feature = "-base-scale-tnl"
+
+ elif "ethip4lispip" in test_name or "ethip4vxlan" in test_name:
+ file_name = "ip4_tunnels"
+ feature = "-base"
+
+ elif "ip4base" in test_name or "ip4scale" in test_name:
+ file_name = "ip4"
+ if "xl710" in test_name:
+ feature = "-base-scale-features"
+ elif "iacl" in test_name:
+ feature = "-features-iacl"
+ elif "oacl" in test_name:
+ feature = "-features-oacl"
+ elif "snat" in test_name or "cop" in test_name:
+ feature = "-features"
+ else:
+ feature = "-base-scale"
+
+ elif "ip6base" in test_name or "ip6scale" in test_name:
+ file_name = "ip6"
+ feature = "-base-scale"
+
+ 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"
+ if "macip" in test_name:
+ feature = "-features-macip"
+ elif "iacl" in test_name:
+ feature = "-features-iacl"
+ elif "oacl" in test_name:
+ feature = "-features-oacl"
+ else:
+ feature = "-base-scale"
+
+ if "x520" in test_name:
+ nic = "x520-"
+ elif "x710" in test_name:
+ nic = "x710-"
+ elif "xl710" in test_name:
+ nic = "xl710-"
+ elif "xxv710" in test_name:
+ nic = "xxv710-"
+ elif "vic1227" in test_name:
+ nic = "vic1227-"
+ elif "vic1385" in test_name:
+ nic = "vic1385-"
+ elif "x553" in test_name:
+ nic = "x553-"
+ else:
+ nic = ""
+ anchor += nic
+
+ if "64b" in test_name:
+ framesize = "64b"
+ elif "78b" in test_name:
+ framesize = "78b"
+ elif "imix" in test_name:
+ framesize = "imix"
+ elif "9000b" in test_name:
+ framesize = "9000b"
+ elif "1518b" in test_name:
+ framesize = "1518b"
+ elif "114b" in test_name:
+ framesize = "114b"
+ else:
+ framesize = ""
+ anchor += framesize + '-'
+
+ if "1t1c" in test_name:
+ anchor += "1t1c"
+ elif "2t2c" in test_name:
+ anchor += "2t2c"
+ elif "4t4c" in test_name:
+ anchor += "4t4c"
+ elif "2t1c" in test_name:
+ anchor += "2t1c"
+ elif "4t2c" in test_name:
+ anchor += "4t2c"
+ elif "8t4c" in test_name:
+ anchor += "8t4c"
+
+ return url + file_name + '-' + testbed + '-' + nic + framesize + \
+ feature.replace("-int", "").replace("-tnl", "") + 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: dict
+ :type input_data: InputData
+ """
+
+ testbed = table.get("testbed", None)
+ if testbed is None:
+ logging.error("The testbed is not defined for the table '{0}'.".
+ format(table.get("title", "")))
+ return
+
+ 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='"')
- 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: '{}'".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: '{}'".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: '{}'".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: '{}'".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)
+ 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/", testbed, 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<p><br><br></p>\n")
+ except KeyError:
+ logging.warning("The output file is not defined.")
+ return
+
+
+def table_last_failed_tests(table, input_data):
+ """Generate the table(s) with algorithm: table_last_failed_tests
+ specified in the specification file.
+
+ :param table: Table to generate.
+ :param input_data: Data to process.
+ :type table: pandas.Series
+ :type input_data: InputData
+ """
+
+ logging.info(" 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)
+
+ if data is None or data.empty:
+ logging.warn(" No data for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
+ return
+
+ tbl_list = list()
+ for job, builds in table["data"].items():
+ for build in builds:
+ build = str(build)
+ try:
+ version = input_data.metadata(job, build).get("version", "")
+ except KeyError:
+ logging.error("Data for {job}: {build} is not present.".
+ format(job=job, build=build))
+ return
+ tbl_list.append(build)
+ tbl_list.append(version)
+ for tst_name, tst_data in data[job][build].iteritems():
+ if tst_data["status"] != "FAIL":
+ continue
+ groups = re.search(REGEX_NIC, tst_data["parent"])
+ if not groups:
+ continue
+ nic = groups.group(0)
+ tbl_list.append("{0}-{1}".format(nic, tst_data["name"]))
+
+ 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:
+ for test in tbl_list:
+ file_handler.write(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(" 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",
+ "Failures [#]",
+ "Last Failure [Time]",
+ "Last Failure [VPP-Build-Id]",
+ "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("window", 7)))
+
+ 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.get("ignore-list", list()):
+ continue
+ if tbl_dict.get(tst_name, None) is None:
+ groups = re.search(REGEX_NIC, tst_data["parent"])
+ if not groups:
+ continue
+ nic = groups.group(0)
+ tbl_dict[tst_name] = {
+ "name": "{0}-{1}".format(nic, tst_data["name"]),
+ "data": OrderedDict()}
+ try:
+ generated = input_data.metadata(job, build).\
+ get("generated", "")
+ if not generated:
+ continue
+ then = dt.strptime(generated, "%Y%m%d %H:%M")
+ if (now - then) <= timeperiod:
+ tbl_dict[tst_name]["data"][build] = (
+ tst_data["status"],
+ generated,
+ input_data.metadata(job, build).get("version", ""),
+ build)
+ except (TypeError, KeyError) as err:
+ logging.warning("tst_name: {} - err: {}".
+ format(tst_name, repr(err)))
+
+ max_fails = 0
+ tbl_lst = list()
+ for tst_data in tbl_dict.values():
+ fails_nr = 0
+ for val in tst_data["data"].values():
+ 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:
+ max_fails = fails_nr if fails_nr > max_fails else max_fails
+ 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(max_fails, -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
+ """
+
+ testbed = table.get("testbed", None)
+ if testbed is None:
+ logging.error("The testbed is not defined for the table '{0}'.".
+ format(table.get("title", "")))
+ return
+
+ 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 = ("#e9f1fb", "#d4e4f7")
+ for r_idx, row in enumerate(csv_lst[1:]):
+ background = colors[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/", testbed, 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<p><br><br></p>\n")
+ except KeyError:
+ logging.warning("The output file is not defined.")
+ return