+++ /dev/null
-# 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<p><br><br></p>\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<p><br><br></p>\n")
- except KeyError:
- logging.warning("The output file is not defined.")
- return
Code Review / csit.git / blobdiff