X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=ef8af1de6909e086138a455c246a9055076733f2;hb=HEAD;hp=acd3024f357f610e7f129ecca8c486584699c5e0;hpb=5ec0a299c5f9ad2f4843478db69bc6bbc697b0a2;p=csit.git
diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py
deleted file mode 100644
index acd3024f35..0000000000
--- a/resources/tools/presentation/generator_tables.py
+++ /dev/null
@@ -1,1198 +0,0 @@
-# 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:
-#
-# 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 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 utils import mean, stdev, relative_change, classify_anomalies, \
- convert_csv_to_pretty_txt
-
-
-REGEX_NIC = re.compile(r'\d*ge\dp\d\D*\d*')
-
-
-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 ("conf-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('"', '""')
- 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)
- col_data = " |prein| {0} |preout| ".\
- format(col_data[:-5])
- row_lst.append('"{0}"'.format(col_data))
- except KeyError:
- row_lst.append('"Not captured"')
- 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_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", ]
-
- 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:
- # 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:
- # 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:
- 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:
- 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 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_nics_comparison(table, input_data):
- """Generate the table(s) with algorithm: table_nics_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", ]
-
- 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
-
- # 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.
- :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 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"]),
- "Delta [%]"]
- header_str = ",".join(header) + "\n"
- except (AttributeError, KeyError) as err:
- logging.error("The model is invalid, missing parameter: {0}".
- format(err))
- return
-
- # Create a list of available SOAK test results:
- tbl_dict = dict()
- for job, builds in table["compare"]["data"].items():
- for build in builds:
- for tst_name, tst_data in data[job][str(build)].iteritems():
- 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()
-
- # 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():
- 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"], ]
- 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))
- item.append(round(stdev(data_r) / 1000000, 2))
- else:
- data_r_mean = None
- item.extend([None, None])
- 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))
- item.append(round(stdev(data_c) / 1000000, 2))
- else:
- data_c_mean = None
- item.extend([None, None])
- if data_r_mean and data_c_mean is not None:
- item.append(round(relative_change(data_r_mean, data_c_mean), 2))
- 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_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:
- 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"
-
- 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-"
- 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 + \
- 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='"')
- 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
\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["ignore-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
\n")
- except KeyError:
- logging.warning("The output file is not defined.")
- return