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Diffstat (limited to 'resources/tools/presentation_new/generator_tables.py')
| -rw-r--r-- | resources/tools/presentation_new/generator_tables.py | 910 |
1 files changed, 0 insertions, 910 deletions
diff --git a/resources/tools/presentation_new/generator_tables.py b/resources/tools/presentation_new/generator_tables.py deleted file mode 100644 index 77243744f9..0000000000 --- a/resources/tools/presentation_new/generator_tables.py +++ /dev/null @@ -1,910 +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 ("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_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: - name = "{0}-{1}".format(tst_data["parent"].split("-")[0], - "-".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_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: - feature = "-base-l2xc" - elif "l2bdbasemaclrn" in test_name: - feature = "-base-l2bd" - else: - feature = "-base" - elif "ip4base" in test_name: - file_name = "vm_vhost_ip4" - feature = "-base" - - 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-" - 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<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", - "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<p><br><br></p>\n") - except KeyError: - logging.warning("The output file is not defined.") - return |