+++ /dev/null
-# Copyright (c) 2017 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 prettytable
-import pandas as pd
-
-from string import replace
-from math import isnan
-from xml.etree import ElementTree as ET
-
-from errors import PresentationError
-from utils import mean, stdev, relative_change, remove_outliers, split_outliers
-
-
-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:
- logging.error("The algorithm '{0}' is not defined.".
- format(table["algorithm"]))
- logging.info("Done.")
-
-
-def table_details(table, input_data):
- """Generate the table(s) with algorithm: table_detailed_test_results
- 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
- 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
- data = input_data.filter_data(table)
- data = input_data.merge_data(data)
- data.sort_index(inplace=True)
-
- 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
- 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
- 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["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)
-
- tbl_lst = list()
- for tst_name in tbl_dict.keys():
- item = [tbl_dict[tst_name]["name"], ]
- if tbl_dict[tst_name]["ref-data"]:
- data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
- outlier_const=table["outlier-const"])
- # TODO: Specify window size.
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
- else:
- item.extend([None, None])
- else:
- item.extend([None, None])
- if tbl_dict[tst_name]["cmp-data"]:
- data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
- outlier_const=table["outlier-const"])
- # TODO: Specify window size.
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
- else:
- item.extend([None, None])
- else:
- 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:
- tbl_lst.append(item)
-
- # Sort the table according to the relative change
- tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
-
- # Generate tables:
- # All tests in csv:
- tbl_names = ["{0}-ndr-1t1c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-ndr-2t2c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-ndr-4t4c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-pdr-1t1c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-pdr-2t2c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-pdr-4t4c-full{1}".format(table["output-file"],
- table["output-file-ext"])
- ]
- for file_name in tbl_names:
- logging.info(" Writing file: '{0}'".format(file_name))
- with open(file_name, "w") as file_handler:
- file_handler.write(header_str)
- for test in tbl_lst:
- if (file_name.split("-")[-3] in test[0] and # NDR vs PDR
- file_name.split("-")[-2] in test[0]): # cores
- test[0] = "-".join(test[0].split("-")[:-1])
- file_handler.write(",".join([str(item) for item in test]) +
- "\n")
-
- # All tests in txt:
- tbl_names_txt = ["{0}-ndr-1t1c-full.txt".format(table["output-file"]),
- "{0}-ndr-2t2c-full.txt".format(table["output-file"]),
- "{0}-ndr-4t4c-full.txt".format(table["output-file"]),
- "{0}-pdr-1t1c-full.txt".format(table["output-file"]),
- "{0}-pdr-2t2c-full.txt".format(table["output-file"]),
- "{0}-pdr-4t4c-full.txt".format(table["output-file"])
- ]
-
- for i, txt_name in enumerate(tbl_names_txt):
- txt_table = None
- logging.info(" Writing file: '{0}'".format(txt_name))
- with open(tbl_names[i], 'rb') as csv_file:
- csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
- for row in csv_content:
- if txt_table is None:
- txt_table = prettytable.PrettyTable(row)
- else:
- txt_table.add_row(row)
- txt_table.align["Test case"] = "l"
- with open(txt_name, "w") as txt_file:
- txt_file.write(str(txt_table))
-
- # Selected tests in csv:
- input_file = "{0}-ndr-1t1c-full{1}".format(table["output-file"],
- table["output-file-ext"])
- with open(input_file, "r") as in_file:
- lines = list()
- for line in in_file:
- lines.append(line)
-
- output_file = "{0}-ndr-1t1c-top{1}".format(table["output-file"],
- table["output-file-ext"])
- logging.info(" Writing file: '{0}'".format(output_file))
- with open(output_file, "w") as out_file:
- out_file.write(header_str)
- for i, line in enumerate(lines[1:]):
- if i == table["nr-of-tests-shown"]:
- break
- out_file.write(line)
-
- output_file = "{0}-ndr-1t1c-bottom{1}".format(table["output-file"],
- table["output-file-ext"])
- logging.info(" Writing file: '{0}'".format(output_file))
- with open(output_file, "w") as out_file:
- out_file.write(header_str)
- for i, line in enumerate(lines[-1:0:-1]):
- if i == table["nr-of-tests-shown"]:
- break
- out_file.write(line)
-
- input_file = "{0}-pdr-1t1c-full{1}".format(table["output-file"],
- table["output-file-ext"])
- with open(input_file, "r") as in_file:
- lines = list()
- for line in in_file:
- lines.append(line)
-
- output_file = "{0}-pdr-1t1c-top{1}".format(table["output-file"],
- table["output-file-ext"])
- logging.info(" Writing file: '{0}'".format(output_file))
- with open(output_file, "w") as out_file:
- out_file.write(header_str)
- for i, line in enumerate(lines[1:]):
- if i == table["nr-of-tests-shown"]:
- break
- out_file.write(line)
-
- output_file = "{0}-pdr-1t1c-bottom{1}".format(table["output-file"],
- table["output-file-ext"])
- logging.info(" Writing file: '{0}'".format(output_file))
- with open(output_file, "w") as out_file:
- out_file.write(header_str)
- for i, line in enumerate(lines[-1:0:-1]):
- if i == table["nr-of-tests-shown"]:
- break
- out_file.write(line)
-
-
-def table_performance_comparison_mrr(table, input_data):
- """Generate the table(s) with algorithm: table_performance_comparison_mrr
- 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
- 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"], ]
- if tbl_dict[tst_name]["ref-data"]:
- data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
- outlier_const=table["outlier-const"])
- # TODO: Specify window size.
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
- else:
- item.extend([None, None])
- else:
- item.extend([None, None])
- if tbl_dict[tst_name]["cmp-data"]:
- data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
- outlier_const=table["outlier-const"])
- # TODO: Specify window size.
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
- else:
- item.extend([None, None])
- 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):
- txt_table = None
- logging.info(" Writing file: '{0}'".format(txt_name))
- with open(tbl_names[i], 'rb') as csv_file:
- csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
- for row in csv_content:
- if txt_table is None:
- txt_table = prettytable.PrettyTable(row)
- else:
- txt_table.add_row(row)
- txt_table.align["Test case"] = "l"
- with open(txt_name, "w") as txt_file:
- txt_file.write(str(txt_table))
-
-
-def table_performance_trending_dashboard(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
- data = input_data.filter_data(table, continue_on_error=True)
-
- # Prepare the header of the tables
- header = ["Test Case",
- "Throughput Trend [Mpps]",
- "Long Trend Compliance",
- "Trend Compliance",
- "Top Anomaly [Mpps]",
- "Change [%]",
- "Outliers [Number]"
- ]
- 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 tbl_dict.get(tst_name, None) is None:
- name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
- "-".join(tst_data["name"].
- split("-")[1:]))
- tbl_dict[tst_name] = {"name": name,
- "data": dict()}
- 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:
-
- pd_data = pd.Series(tbl_dict[tst_name]["data"])
- win_size = min(pd_data.size, table["window"])
- # Test name:
- name = tbl_dict[tst_name]["name"]
-
- median = pd_data.rolling(window=win_size, min_periods=2).median()
- median_idx = pd_data.size - table["long-trend-window"]
- median_idx = 0 if median_idx < 0 else median_idx
- max_median = max(median.values[median_idx:])
- trimmed_data, _ = split_outliers(pd_data, outlier_const=1.5,
- window=win_size)
- stdev_t = pd_data.rolling(window=win_size, min_periods=2).std()
-
- rel_change_lst = [None, ]
- classification_lst = [None, ]
- median_lst = [None, ]
- sample_lst = [None, ]
- first = True
- for build_nr, value in pd_data.iteritems():
- if first:
- first = False
- continue
- # Relative changes list:
- if not isnan(value) \
- and not isnan(median[build_nr]) \
- and median[build_nr] != 0:
- rel_change_lst.append(round(
- relative_change(float(median[build_nr]), float(value)),
- 2))
- else:
- rel_change_lst.append(None)
-
- # Classification list:
- if isnan(trimmed_data[build_nr]) \
- or isnan(median[build_nr]) \
- or isnan(stdev_t[build_nr]) \
- or isnan(value):
- classification_lst.append("outlier")
- elif value < (median[build_nr] - 3 * stdev_t[build_nr]):
- classification_lst.append("regression")
- elif value > (median[build_nr] + 3 * stdev_t[build_nr]):
- classification_lst.append("progression")
- else:
- classification_lst.append("normal")
- sample_lst.append(value)
- median_lst.append(median[build_nr])
-
- last_idx = len(classification_lst) - 1
- first_idx = last_idx - int(table["evaluated-window"])
- if first_idx < 0:
- first_idx = 0
-
- nr_outliers = 0
- consecutive_outliers = 0
- failure = False
- for item in classification_lst[first_idx:]:
- if item == "outlier":
- nr_outliers += 1
- consecutive_outliers += 1
- if consecutive_outliers == 3:
- failure = True
- else:
- consecutive_outliers = 0
-
- if failure:
- classification = "failure"
- elif "regression" in classification_lst[first_idx:]:
- classification = "regression"
- elif "progression" in classification_lst[first_idx:]:
- classification = "progression"
- else:
- classification = "normal"
-
- if classification == "normal":
- index = len(classification_lst) - 1
- else:
- tmp_classification = "outlier" if classification == "failure" \
- else classification
- index = None
- for idx in range(first_idx, len(classification_lst)):
- if classification_lst[idx] == tmp_classification:
- if rel_change_lst[idx]:
- index = idx
- break
- if index is None:
- continue
- for idx in range(index+1, len(classification_lst)):
- if classification_lst[idx] == tmp_classification:
- if rel_change_lst[idx]:
- if (abs(rel_change_lst[idx]) >
- abs(rel_change_lst[index])):
- index = idx
-
- logging.debug("{}".format(name))
- logging.debug("sample_lst: {} - {}".
- format(len(sample_lst), sample_lst))
- logging.debug("median_lst: {} - {}".
- format(len(median_lst), median_lst))
- logging.debug("rel_change: {} - {}".
- format(len(rel_change_lst), rel_change_lst))
- logging.debug("classn_lst: {} - {}".
- format(len(classification_lst), classification_lst))
- logging.debug("index: {}".format(index))
- logging.debug("classifica: {}".format(classification))
-
- try:
- trend = round(float(median_lst[-1]) / 1000000, 2) \
- if not isnan(median_lst[-1]) else '-'
- sample = round(float(sample_lst[index]) / 1000000, 2) \
- if not isnan(sample_lst[index]) else '-'
- rel_change = rel_change_lst[index] \
- if rel_change_lst[index] is not None else '-'
- if not isnan(max_median):
- if not isnan(sample_lst[index]):
- long_trend_threshold = \
- max_median * (table["long-trend-threshold"] / 100)
- if sample_lst[index] < long_trend_threshold:
- long_trend_classification = "failure"
- else:
- long_trend_classification = 'normal'
- else:
- long_trend_classification = "failure"
- else:
- long_trend_classification = '-'
- tbl_lst.append([name,
- trend,
- long_trend_classification,
- classification,
- '-' if classification == "normal" else sample,
- '-' if classification == "normal" else
- rel_change,
- nr_outliers])
- except IndexError as err:
- logging.error("{}".format(err))
- continue
-
- # Sort the table according to the classification
- tbl_sorted = list()
- for long_trend_class in ("failure", 'normal', '-'):
- tbl_long = [item for item in tbl_lst if item[2] == long_trend_class]
- for classification in \
- ("failure", "regression", "progression", "normal"):
- tbl_tmp = [item for item in tbl_long if item[3] == classification]
- tbl_tmp.sort(key=lambda rel: rel[0])
- tbl_sorted.extend(tbl_tmp)
-
- file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
-
- logging.info(" Writing file: '{0}'".format(file_name))
- with open(file_name, "w") as file_handler:
- file_handler.write(header_str)
- for test in tbl_sorted:
- file_handler.write(",".join([str(item) for item in test]) + '\n')
-
- txt_file_name = "{0}.txt".format(table["output-file"])
- txt_table = None
- logging.info(" Writing file: '{0}'".format(txt_file_name))
- with open(file_name, 'rb') as csv_file:
- csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
- for row in csv_content:
- if txt_table is None:
- txt_table = prettytable.PrettyTable(row)
- else:
- txt_table.add_row(row)
- txt_table.align["Test case"] = "l"
- with open(txt_file_name, "w") as txt_file:
- txt_file.write(str(txt_table))
-
-
-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:
- for r_idx, row in enumerate(csv_lst[1:]):
- background = "#D4E4F7" if r_idx % 2 else "white"
- tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background))
-
- # 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:
- url = "../trending/"
- file_name = ""
- anchor = "#"
- feature = ""
- if c_idx == 0:
- if "memif" in item:
- file_name = "container_memif.html"
-
- elif "vhost" in item:
- if "l2xcbase" in item or "l2bdbasemaclrn" in item:
- file_name = "vm_vhost_l2.html"
- elif "ip4base" in item:
- file_name = "vm_vhost_ip4.html"
-
- elif "ipsec" in item:
- file_name = "ipsec.html"
-
- elif "ethip4lispip" in item or "ethip4vxlan" in item:
- file_name = "ip4_tunnels.html"
-
- elif "ip4base" in item or "ip4scale" in item:
- file_name = "ip4.html"
- if "iacl" in item or "snat" in item or "cop" in item:
- feature = "-features"
-
- elif "ip6base" in item or "ip6scale" in item:
- file_name = "ip6.html"
-
- elif "l2xcbase" in item or "l2xcscale" in item \
- or "l2bdbasemaclrn" in item or "l2bdscale" in item \
- or "l2dbbasemaclrn" in item or "l2dbscale" in item:
- file_name = "l2.html"
- if "iacl" in item:
- feature = "-features"
-
- if "x520" in item:
- anchor += "x520-"
- elif "x710" in item:
- anchor += "x710-"
- elif "xl710" in item:
- anchor += "xl710-"
-
- if "64b" in item:
- anchor += "64b-"
- elif "78b" in item:
- anchor += "78b"
- elif "imix" in item:
- anchor += "imix-"
- elif "9000b" in item:
- anchor += "9000b-"
- elif "1518" in item:
- anchor += "1518b-"
-
- if "1t1c" in item:
- anchor += "1t1c"
- elif "2t2c" in item:
- anchor += "2t2c"
- elif "4t4c" in item:
- anchor += "4t4c"
-
- url = url + file_name + anchor + feature
-
- ref = ET.SubElement(td, "a", attrib=dict(href=url))
- ref.text = item
-
- if c_idx == 3:
- if item == "regression":
- td.set("bgcolor", "#eca1a6")
- elif item == "failure":
- td.set("bgcolor", "#d6cbd3")
- elif item == "progression":
- td.set("bgcolor", "#bdcebe")
- if c_idx > 0:
- td.text = item
-
- 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
Code Review / csit.git / blobdiff