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Diffstat (limited to 'resources/tools/presentation_new/generator_tables.py')
| -rw-r--r-- | resources/tools/presentation_new/generator_tables.py | 904 |
1 files changed, 904 insertions, 0 deletions
diff --git a/resources/tools/presentation_new/generator_tables.py b/resources/tools/presentation_new/generator_tables.py new file mode 100644 index 0000000000..7590daa8fe --- /dev/null +++ b/resources/tools/presentation_new/generator_tables.py @@ -0,0 +1,904 @@ +# Copyright (c) 2018 Cisco and/or its affiliates. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at: +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Algorithms to generate tables. +""" + + +import logging +import csv +import 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 + tbl_lst.append( + [tbl_dict[tst_name]["name"], + '-' if isnan(last_avg) else + round(last_avg / 1000000, 2), + '-' if isnan(rel_change_last) else rel_change_last, + '-' if isnan(rel_change_long) else rel_change_long, + classification_lst[-win_size:].count("regression"), + classification_lst[-win_size:].count("progression")]) + + tbl_lst.sort(key=lambda rel: rel[0]) + + tbl_sorted = list() + for nrr in range(table["window"], -1, -1): + tbl_reg = [item for item in tbl_lst if item[4] == nrr] + for nrp in range(table["window"], -1, -1): + tbl_out = [item for item in tbl_reg if item[5] == nrp] + tbl_out.sort(key=lambda rel: rel[2]) + tbl_sorted.extend(tbl_out) + + file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) + + logging.info(" Writing file: '{0}'".format(file_name)) + with open(file_name, "w") as file_handler: + file_handler.write(header_str) + for test in tbl_sorted: + file_handler.write(",".join([str(item) for item in test]) + '\n') + + txt_file_name = "{0}.txt".format(table["output-file"]) + logging.info(" Writing file: '{0}'".format(txt_file_name)) + convert_csv_to_pretty_txt(file_name, txt_file_name) + + +def _generate_url(base, 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): + pass # No data in output.xml for this test + + 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: + tbl_lst.append([tst_data["name"], + fails_nr, + fails_last_date, + fails_last_vpp, + "mrr-daily-build-{0}".format(fails_last_csit)]) + + tbl_lst.sort(key=lambda rel: rel[2], reverse=True) + tbl_sorted = list() + for nrf in range(table["window"], -1, -1): + tbl_fails = [item for item in tbl_lst if item[1] == nrf] + tbl_sorted.extend(tbl_fails) + file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) + + logging.info(" Writing file: '{0}'".format(file_name)) + with open(file_name, "w") as file_handler: + file_handler.write(",".join(header) + "\n") + for test in tbl_sorted: + file_handler.write(",".join([str(item) for item in test]) + '\n') + + txt_file_name = "{0}.txt".format(table["output-file"]) + logging.info(" Writing file: '{0}'".format(txt_file_name)) + convert_csv_to_pretty_txt(file_name, txt_file_name) + + +def table_failed_tests_html(table, input_data): + """Generate the table(s) with algorithm: table_failed_tests_html + specified in the specification file. + + :param table: Table to generate. + :param input_data: Data to process. + :type table: pandas.Series + :type input_data: InputData + """ + + 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 |