X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=ef8af1de6909e086138a455c246a9055076733f2;hb=HEAD;hp=cd61e321f1496f061ae9fb87a6d63ef5cdb3ade0;hpb=eb5271b56cb94711420a1eb7372b0f4d5f275a40;p=csit.git diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py deleted file mode 100644 index cd61e321f1..0000000000 --- a/resources/tools/presentation/generator_tables.py +++ /dev/null @@ -1,975 +0,0 @@ -# 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 - -from string import replace -from collections import OrderedDict -from numpy import nan, isnan -from xml.etree import ElementTree as ET - -from errors import PresentationError -from utils import mean, stdev, relative_change, classify_anomalies, \ - convert_csv_to_pretty_txt - - -def generate_tables(spec, data): - """Generate all tables specified in the specification file. - - :param spec: Specification read from the specification file. - :param data: Data to process. - :type spec: Specification - :type data: InputData - """ - - logging.info("Generating the tables ...") - for table in spec.tables: - try: - eval(table["algorithm"])(table, data) - except NameError as err: - logging.error("Probably algorithm '{alg}' is not defined: {err}". - format(alg=table["algorithm"], err=repr(err))) - logging.info("Done.") - - -def table_details(table, input_data): - """Generate the table(s) with algorithm: table_detailed_test_results - specified in the specification file. - - :param table: Table to generate. - :param input_data: Data to process. - :type table: pandas.Series - :type input_data: InputData - """ - - logging.info(" Generating the table {0} ...". - format(table.get("title", ""))) - - # Transform the data - logging.info(" Creating the data set for the {0} '{1}'.". - format(table.get("type", ""), table.get("title", ""))) - data = input_data.filter_data(table) - - # Prepare the header of the tables - header = list() - for column in table["columns"]: - header.append('"{0}"'.format(str(column["title"]).replace('"', '""'))) - - # Generate the data for the table according to the model in the table - # specification - job = table["data"].keys()[0] - build = str(table["data"][job][0]) - try: - suites = input_data.suites(job, build) - except KeyError: - logging.error(" No data available. The table will not be generated.") - return - - for suite_longname, suite in suites.iteritems(): - # Generate data - suite_name = suite["name"] - table_lst = list() - for test in data[job][build].keys(): - if data[job][build][test]["parent"] in suite_name: - row_lst = list() - for column in table["columns"]: - try: - col_data = str(data[job][build][test][column["data"]. - split(" ")[1]]).replace('"', '""') - if column["data"].split(" ")[1] in ("vat-history", - "show-run"): - col_data = replace(col_data, " |br| ", "", - maxreplace=1) - col_data = " |prein| {0} |preout| ".\ - format(col_data[:-5]) - row_lst.append('"{0}"'.format(col_data)) - except KeyError: - row_lst.append("No data") - table_lst.append(row_lst) - - # Write the data to file - if table_lst: - file_name = "{0}_{1}{2}".format(table["output-file"], suite_name, - table["output-file-ext"]) - logging.info(" Writing file: '{}'".format(file_name)) - with open(file_name, "w") as file_handler: - file_handler.write(",".join(header) + "\n") - for item in table_lst: - file_handler.write(",".join(item) + "\n") - - logging.info(" Done.") - - -def table_merged_details(table, input_data): - """Generate the table(s) with algorithm: table_merged_details - specified in the specification file. - - :param table: Table to generate. - :param input_data: Data to process. - :type table: pandas.Series - :type input_data: InputData - """ - - logging.info(" Generating the table {0} ...". - format(table.get("title", ""))) - - # Transform the data - logging.info(" Creating the data set for the {0} '{1}'.". - format(table.get("type", ""), table.get("title", ""))) - data = input_data.filter_data(table) - data = input_data.merge_data(data) - data.sort_index(inplace=True) - - logging.info(" Creating the data set for the {0} '{1}'.". - format(table.get("type", ""), table.get("title", ""))) - suites = input_data.filter_data(table, data_set="suites") - suites = input_data.merge_data(suites) - - # Prepare the header of the tables - header = list() - for column in table["columns"]: - header.append('"{0}"'.format(str(column["title"]).replace('"', '""'))) - - for _, suite in suites.iteritems(): - # Generate data - suite_name = suite["name"] - table_lst = list() - for test in data.keys(): - if data[test]["parent"] in suite_name: - row_lst = list() - for column in table["columns"]: - try: - col_data = str(data[test][column["data"]. - split(" ")[1]]).replace('"', '""') - if column["data"].split(" ")[1] in ("vat-history", - "show-run"): - col_data = replace(col_data, " |br| ", "", - maxreplace=1) - col_data = " |prein| {0} |preout| ".\ - format(col_data[:-5]) - row_lst.append('"{0}"'.format(col_data)) - except KeyError: - row_lst.append("No data") - table_lst.append(row_lst) - - # Write the data to file - if table_lst: - file_name = "{0}_{1}{2}".format(table["output-file"], suite_name, - table["output-file-ext"]) - logging.info(" Writing file: '{}'".format(file_name)) - with open(file_name, "w") as file_handler: - file_handler.write(",".join(header) + "\n") - for item in table_lst: - file_handler.write(",".join(item) + "\n") - - logging.info(" Done.") - - -def table_performance_improvements(table, input_data): - """Generate the table(s) with algorithm: table_performance_improvements - specified in the specification file. - - # FIXME: Not used now. - - :param table: Table to generate. - :param input_data: Data to process. - :type table: pandas.Series - :type input_data: InputData - """ - - def _write_line_to_file(file_handler, data): - """Write a line to the .csv file. - - :param file_handler: File handler for the csv file. It must be open for - writing text. - :param data: Item to be written to the file. - :type file_handler: BinaryIO - :type data: list - """ - - line_lst = list() - for item in data: - if isinstance(item["data"], str): - # Remove -?drdisc from the end - if item["data"].endswith("drdisc"): - item["data"] = item["data"][:-8] - line_lst.append(item["data"]) - elif isinstance(item["data"], float): - line_lst.append("{:.1f}".format(item["data"])) - elif item["data"] is None: - line_lst.append("") - file_handler.write(",".join(line_lst) + "\n") - - logging.info(" Generating the table {0} ...". - format(table.get("title", ""))) - - # Read the template - file_name = table.get("template", None) - if file_name: - try: - tmpl = _read_csv_template(file_name) - except PresentationError: - logging.error(" The template '{0}' does not exist. Skipping the " - "table.".format(file_name)) - return None - else: - logging.error("The template is not defined. Skipping the table.") - return None - - # Transform the data - logging.info(" Creating the data set for the {0} '{1}'.". - format(table.get("type", ""), table.get("title", ""))) - data = input_data.filter_data(table) - - # Prepare the header of the tables - header = list() - for column in table["columns"]: - header.append(column["title"]) - - # Generate the data for the table according to the model in the table - # specification - tbl_lst = list() - for tmpl_item in tmpl: - tbl_item = list() - for column in table["columns"]: - cmd = column["data"].split(" ")[0] - args = column["data"].split(" ")[1:] - if cmd == "template": - try: - val = float(tmpl_item[int(args[0])]) - except ValueError: - val = tmpl_item[int(args[0])] - tbl_item.append({"data": val}) - elif cmd == "data": - jobs = args[0:-1] - operation = args[-1] - data_lst = list() - for job in jobs: - for build in data[job]: - try: - data_lst.append(float(build[tmpl_item[0]] - ["throughput"]["value"])) - except (KeyError, TypeError): - # No data, ignore - continue - if data_lst: - tbl_item.append({"data": (eval(operation)(data_lst)) / - 1000000}) - else: - tbl_item.append({"data": None}) - elif cmd == "operation": - operation = args[0] - try: - nr1 = float(tbl_item[int(args[1])]["data"]) - nr2 = float(tbl_item[int(args[2])]["data"]) - if nr1 and nr2: - tbl_item.append({"data": eval(operation)(nr1, nr2)}) - else: - tbl_item.append({"data": None}) - except (IndexError, ValueError, TypeError): - logging.error("No data for {0}".format(tbl_item[0]["data"])) - tbl_item.append({"data": None}) - continue - else: - logging.error("Not supported command {0}. Skipping the table.". - format(cmd)) - return None - tbl_lst.append(tbl_item) - - # Sort the table according to the relative change - tbl_lst.sort(key=lambda rel: rel[-1]["data"], reverse=True) - - # Create the tables and write them to the files - file_names = [ - "{0}_ndr_top{1}".format(table["output-file"], table["output-file-ext"]), - "{0}_pdr_top{1}".format(table["output-file"], table["output-file-ext"]), - "{0}_ndr_low{1}".format(table["output-file"], table["output-file-ext"]), - "{0}_pdr_low{1}".format(table["output-file"], table["output-file-ext"]) - ] - - for file_name in file_names: - logging.info(" Writing the file '{0}'".format(file_name)) - with open(file_name, "w") as file_handler: - file_handler.write(",".join(header) + "\n") - for item in tbl_lst: - if isinstance(item[-1]["data"], float): - rel_change = round(item[-1]["data"], 1) - else: - rel_change = item[-1]["data"] - if "ndr_top" in file_name \ - and "ndr" in item[0]["data"] \ - and rel_change >= 10.0: - _write_line_to_file(file_handler, item) - elif "pdr_top" in file_name \ - and "pdr" in item[0]["data"] \ - and rel_change >= 10.0: - _write_line_to_file(file_handler, item) - elif "ndr_low" in file_name \ - and "ndr" in item[0]["data"] \ - and rel_change < 10.0: - _write_line_to_file(file_handler, item) - elif "pdr_low" in file_name \ - and "pdr" in item[0]["data"] \ - and rel_change < 10.0: - _write_line_to_file(file_handler, item) - - logging.info(" Done.") - - -def _read_csv_template(file_name): - """Read the template from a .csv file. - - # FIXME: Not used now. - - :param file_name: Name / full path / relative path of the file to read. - :type file_name: str - :returns: Data from the template as list (lines) of lists (items on line). - :rtype: list - :raises: PresentationError if it is not possible to read the file. - """ - - try: - with open(file_name, 'r') as csv_file: - tmpl_data = list() - for line in csv_file: - tmpl_data.append(line[:-1].split(",")) - return tmpl_data - except IOError as err: - raise PresentationError(str(err), level="ERROR") - - -def table_performance_comparison(table, input_data): - """Generate the table(s) with algorithm: table_performance_comparison - specified in the specification file. - - :param table: Table to generate. - :param input_data: Data to process. - :type table: pandas.Series - :type input_data: InputData - """ - - logging.info(" Generating the table {0} ...". - format(table.get("title", ""))) - - # Transform the data - logging.info(" Creating the data set for the {0} '{1}'.". - format(table.get("type", ""), table.get("title", ""))) - data = input_data.filter_data(table, continue_on_error=True) - - # Prepare the header of the tables - try: - header = ["Test case", ] - - 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"]), - "Change [%]"]) - header_str = ",".join(header) + "\n" - except (AttributeError, KeyError) as err: - logging.error("The model is invalid, missing parameter: {0}". - format(err)) - return - - # Prepare data to the table: - tbl_dict = dict() - for job, builds in table["reference"]["data"].items(): - for build in builds: - for tst_name, tst_data in data[job][str(build)].iteritems(): - tst_name_mod = tst_name.replace("-ndrpdrdisc", "").\ - replace("-ndrpdr", "").replace("-pdrdisc", "").\ - replace("-ndrdisc", "").replace("-pdr", "").\ - replace("-ndr", "") - 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])) - 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", "") - 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", "") - 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: - name = "{0}-{1}".format(tst_data["parent"].split("-")[0], - tst_data["name"]) - tbl_dict[tst_name] = {"name": 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, test_name): - """Generate URL to a trending plot from the name of the test case. - - :param base: The base part of URL common to all test cases. - :param test_name: The name of the test case. - :type base: str - :type test_name: str - :returns: The URL to the plot with the trending data for the given test - case. - :rtype str - """ - - url = base - file_name = "" - anchor = "#" - feature = "" - - if "lbdpdk" in test_name or "lbvpp" in test_name: - file_name = "link_bonding.html" - - elif "testpmd" in test_name or "l3fwd" in test_name: - file_name = "dpdk.html" - - elif "memif" in test_name: - file_name = "container_memif.html" - - elif "srv6" in test_name: - file_name = "srv6.html" - - elif "vhost" in test_name: - if "l2xcbase" in test_name or "l2bdbasemaclrn" in test_name: - file_name = "vm_vhost_l2.html" - elif "ip4base" in test_name: - file_name = "vm_vhost_ip4.html" - - elif "ipsec" in test_name: - file_name = "ipsec.html" - - elif "ethip4lispip" in test_name or "ethip4vxlan" in test_name: - file_name = "ip4_tunnels.html" - - elif "ip4base" in test_name or "ip4scale" in test_name: - file_name = "ip4.html" - if "iacl" in test_name or "snat" in test_name or "cop" in test_name: - feature = "-features" - - elif "ip6base" in test_name or "ip6scale" in test_name: - file_name = "ip6.html" - - elif "l2xcbase" in test_name or "l2xcscale" in test_name \ - or "l2bdbasemaclrn" in test_name or "l2bdscale" in test_name \ - or "l2dbbasemaclrn" in test_name or "l2dbscale" in test_name: - file_name = "l2.html" - if "iacl" in test_name: - feature = "-features" - - if "x520" in test_name: - anchor += "x520-" - elif "x710" in test_name: - anchor += "x710-" - elif "xl710" in test_name: - anchor += "xl710-" - - if "64b" in test_name: - anchor += "64b-" - elif "78b" in test_name: - anchor += "78b-" - elif "imix" in test_name: - anchor += "imix-" - elif "9000b" in test_name: - anchor += "9000b-" - elif "1518" in test_name: - anchor += "1518b-" - - if "1t1c" in test_name: - anchor += "1t1c" - elif "2t2c" in test_name: - anchor += "2t2c" - elif "4t4c" in test_name: - anchor += "4t4c" - - return url + file_name + anchor + feature - - -def table_performance_trending_dashboard_html(table, input_data): - """Generate the table(s) with algorithm: - table_performance_trending_dashboard_html specified in the specification - file. - - :param table: Table to generate. - :param input_data: Data to process. - :type table: pandas.Series - :type input_data: InputData - """ - - logging.info(" Generating the table {0} ...". - format(table.get("title", ""))) - - try: - with open(table["input-file"], 'rb') as csv_file: - csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') - csv_lst = [item for item in csv_content] - except KeyError: - logging.warning("The input file is not defined.") - return - except csv.Error as err: - logging.warning("Not possible to process the file '{0}'.\n{1}". - format(table["input-file"], err)) - return - - # Table: - dashboard = ET.Element("table", attrib=dict(width="100%", border='0')) - - # Table header: - tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#7eade7")) - for idx, item in enumerate(csv_lst[0]): - alignment = "left" if idx == 0 else "center" - th = ET.SubElement(tr, "th", attrib=dict(align=alignment)) - th.text = item - - # Rows: - colors = {"regression": ("#ffcccc", "#ff9999"), - "progression": ("#c6ecc6", "#9fdf9f"), - "normal": ("#e9f1fb", "#d4e4f7")} - for r_idx, row in enumerate(csv_lst[1:]): - if int(row[4]): - color = "regression" - elif int(row[5]): - color = "progression" - else: - color = "normal" - background = colors[color][r_idx % 2] - tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background)) - - # Columns: - for c_idx, item in enumerate(row): - alignment = "left" if c_idx == 0 else "center" - td = ET.SubElement(tr, "td", attrib=dict(align=alignment)) - # Name: - if c_idx == 0: - url = _generate_url("../trending/", item) - ref = ET.SubElement(td, "a", attrib=dict(href=url)) - ref.text = item - else: - td.text = item - try: - with open(table["output-file"], 'w') as html_file: - logging.info(" Writing file: '{0}'".format(table["output-file"])) - html_file.write(".. raw:: html\n\n\t") - html_file.write(ET.tostring(dashboard)) - html_file.write("\n\t



\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 - tbl_dict = dict() - for job, builds in table["data"].items(): - for build in builds: - build = str(build) - for tst_name, tst_data in data[job][build].iteritems(): - if tst_name.lower() in table["ignore-list"]: - continue - if tbl_dict.get(tst_name, None) is None: - name = "{0}-{1}".format(tst_data["parent"].split("-")[0], - tst_data["name"]) - tbl_dict[tst_name] = {"name": name, - "data": OrderedDict()} - try: - tbl_dict[tst_name]["data"][build] = ( - tst_data["status"], - input_data.metadata(job, build).get("generated", ""), - input_data.metadata(job, build).get("version", ""), - build) - except (TypeError, KeyError): - pass # No data in output.xml for this test - - tbl_lst = list() - for tst_data in tbl_dict.values(): - win_size = min(len(tst_data["data"]), table["window"]) - fails_nr = 0 - for val in tst_data["data"].values()[-win_size:]: - if val[0] == "FAIL": - fails_nr += 1 - fails_last_date = val[1] - fails_last_vpp = val[2] - fails_last_csit = val[3] - if fails_nr: - tbl_lst.append([tst_data["name"], - fails_nr, - fails_last_date, - fails_last_vpp, - "mrr-daily-build-{0}".format(fails_last_csit)]) - - tbl_lst.sort(key=lambda rel: rel[2], reverse=True) - tbl_sorted = list() - for nrf in range(table["window"], -1, -1): - tbl_fails = [item for item in tbl_lst if item[1] == nrf] - tbl_sorted.extend(tbl_fails) - file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) - - logging.info(" Writing file: '{0}'".format(file_name)) - with open(file_name, "w") as file_handler: - file_handler.write(",".join(header) + "\n") - for test in tbl_sorted: - file_handler.write(",".join([str(item) for item in test]) + '\n') - - txt_file_name = "{0}.txt".format(table["output-file"]) - logging.info(" Writing file: '{0}'".format(txt_file_name)) - convert_csv_to_pretty_txt(file_name, txt_file_name) - - -def table_failed_tests_html(table, input_data): - """Generate the table(s) with algorithm: table_failed_tests_html - specified in the specification file. - - :param table: Table to generate. - :param input_data: Data to process. - :type table: pandas.Series - :type input_data: InputData - """ - - logging.info(" Generating the table {0} ...". - format(table.get("title", ""))) - - try: - with open(table["input-file"], 'rb') as csv_file: - csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') - csv_lst = [item for item in csv_content] - except KeyError: - logging.warning("The input file is not defined.") - return - except csv.Error as err: - logging.warning("Not possible to process the file '{0}'.\n{1}". - format(table["input-file"], err)) - return - - # Table: - failed_tests = ET.Element("table", attrib=dict(width="100%", border='0')) - - # Table header: - tr = ET.SubElement(failed_tests, "tr", attrib=dict(bgcolor="#7eade7")) - for idx, item in enumerate(csv_lst[0]): - alignment = "left" if idx == 0 else "center" - th = ET.SubElement(tr, "th", attrib=dict(align=alignment)) - th.text = item - - # Rows: - colors = ("#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/", 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