X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=724519f2d115dba377a8fe00e79c0134a3dc293b;hp=b98f32ca14cc7b7218b2e7785ef1e58856c271f0;hb=52f64f232293130904d54a62609eaffc1b145608;hpb=a5e743b7c61c9524773279c6135e835e2b640b42 diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index b98f32ca14..724519f2d1 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -16,10 +16,16 @@ import logging +import csv +import prettytable +import pandas as pd + from string import replace +from math import isnan +from xml.etree import ElementTree as ET from errors import PresentationError -from utils import mean, stdev, relative_change +from utils import mean, stdev, relative_change, remove_outliers, split_outliers def generate_tables(spec, data): @@ -64,7 +70,6 @@ def table_details(table, input_data): # 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: @@ -108,6 +113,67 @@ def table_details(table, input_data): logging.info(" Done.") +def table_merged_details(table, input_data): + """Generate the table(s) with algorithm: table_merged_details + specified in the specification file. + + :param table: Table to generate. + :param input_data: Data to process. + :type table: pandas.Series + :type input_data: InputData + """ + + logging.info(" Generating the table {0} ...". + format(table.get("title", ""))) + + # Transform the data + data = input_data.filter_data(table) + data = input_data.merge_data(data) + data.sort_index(inplace=True) + + suites = input_data.filter_data(table, data_set="suites") + suites = input_data.merge_data(suites) + + # Prepare the header of the tables + header = list() + for column in table["columns"]: + header.append('"{0}"'.format(str(column["title"]).replace('"', '""'))) + + for _, suite in suites.iteritems(): + # Generate data + suite_name = suite["name"] + table_lst = list() + for test in data.keys(): + if data[test]["parent"] in suite_name: + row_lst = list() + for column in table["columns"]: + try: + col_data = str(data[test][column["data"]. + split(" ")[1]]).replace('"', '""') + if column["data"].split(" ")[1] in ("vat-history", + "show-run"): + col_data = replace(col_data, " |br| ", "", + maxreplace=1) + col_data = " |prein| {0} |preout| ".\ + format(col_data[:-5]) + row_lst.append('"{0}"'.format(col_data)) + except KeyError: + row_lst.append("No data") + table_lst.append(row_lst) + + # Write the data to file + if table_lst: + file_name = "{0}_{1}{2}".format(table["output-file"], suite_name, + table["output-file-ext"]) + logging.info(" Writing file: '{}'".format(file_name)) + with open(file_name, "w") as file_handler: + file_handler.write(",".join(header) + "\n") + for item in table_lst: + file_handler.write(",".join(item) + "\n") + + logging.info(" Done.") + + def table_performance_improvements(table, input_data): """Generate the table(s) with algorithm: table_performance_improvements specified in the specification file. @@ -131,9 +197,14 @@ def table_performance_improvements(table, input_data): 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} ...". @@ -175,31 +246,33 @@ def table_performance_improvements(table, input_data): val = tmpl_item[int(args[0])] tbl_item.append({"data": val}) elif cmd == "data": - job = args[0] - operation = args[1] + jobs = args[0:-1] + operation = args[-1] data_lst = list() - for build in data[job]: - try: - data_lst.append(float(build[tmpl_item[0]]["throughput"] - ["value"]) / 1000000) - except (KeyError, TypeError): - # No data, ignore - continue + 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)}) + tbl_item.append({"data": (eval(operation)(data_lst)) / + 1000000}) else: tbl_item.append({"data": None}) elif cmd == "operation": operation = args[0] try: - nr1 = tbl_item[int(args[1])]["data"] - nr2 = tbl_item[int(args[2])]["data"] + 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: - logging.error("No data for {0}".format(tbl_item[1]["data"])) + except (IndexError, ValueError, TypeError): + logging.error("No data for {0}".format(tbl_item[0]["data"])) tbl_item.append({"data": None}) continue else: @@ -224,21 +297,25 @@ def table_performance_improvements(table, input_data): 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[1]["data"] \ - and item[-1]["data"] >= 10: + 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[1]["data"] \ - and item[-1]["data"] >= 10: + 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[1]["data"] \ - and item[-1]["data"] < 10: + 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[1]["data"] \ - and item[-1]["data"] < 10: + and "pdr" in item[0]["data"] \ + and rel_change < 10.0: _write_line_to_file(file_handler, item) logging.info(" Done.") @@ -262,3 +339,677 @@ def _read_csv_template(file_name): return tmpl_data except IOError as err: raise PresentationError(str(err), level="ERROR") + + +def table_performance_comparison(table, input_data): + """Generate the table(s) with algorithm: table_performance_comparison + specified in the specification file. + + :param table: Table to generate. + :param input_data: Data to process. + :type table: pandas.Series + :type input_data: InputData + """ + + logging.info(" Generating the table {0} ...". + format(table.get("title", ""))) + + # Transform the data + data = input_data.filter_data(table, continue_on_error=True) + + # Prepare the header of the tables + try: + header = ["Test case", + "{0} Throughput [Mpps]".format(table["reference"]["title"]), + "{0} stdev [Mpps]".format(table["reference"]["title"]), + "{0} Throughput [Mpps]".format(table["compare"]["title"]), + "{0} stdev [Mpps]".format(table["compare"]["title"]), + "Change [%]"] + header_str = ",".join(header) + "\n" + except (AttributeError, KeyError) as err: + logging.error("The model is invalid, missing parameter: {0}". + format(err)) + return + + # Prepare data to the table: + tbl_dict = dict() + for job, builds in table["reference"]["data"].items(): + for build in builds: + for tst_name, tst_data in data[job][str(build)].iteritems(): + if tbl_dict.get(tst_name, None) is None: + name = "{0}-{1}".format(tst_data["parent"].split("-")[0], + "-".join(tst_data["name"]. + split("-")[1:])) + tbl_dict[tst_name] = {"name": name, + "ref-data": list(), + "cmp-data": list()} + try: + tbl_dict[tst_name]["ref-data"].\ + append(tst_data["throughput"]["value"]) + except TypeError: + pass # No data in output.xml for this test + + for job, builds in table["compare"]["data"].items(): + for build in builds: + for tst_name, tst_data in data[job][str(build)].iteritems(): + try: + tbl_dict[tst_name]["cmp-data"].\ + append(tst_data["throughput"]["value"]) + except KeyError: + pass + except TypeError: + tbl_dict.pop(tst_name, None) + + tbl_lst = list() + for tst_name in tbl_dict.keys(): + item = [tbl_dict[tst_name]["name"], ] + if tbl_dict[tst_name]["ref-data"]: + data_t = remove_outliers(tbl_dict[tst_name]["ref-data"], + outlier_const=table["outlier-const"]) + # TODO: Specify window size. + if data_t: + item.append(round(mean(data_t) / 1000000, 2)) + item.append(round(stdev(data_t) / 1000000, 2)) + else: + item.extend([None, None]) + else: + item.extend([None, None]) + if tbl_dict[tst_name]["cmp-data"]: + data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"], + outlier_const=table["outlier-const"]) + # TODO: Specify window size. + if data_t: + item.append(round(mean(data_t) / 1000000, 2)) + item.append(round(stdev(data_t) / 1000000, 2)) + else: + item.extend([None, None]) + else: + item.extend([None, None]) + if item[1] is not None and item[3] is not None: + item.append(int(relative_change(float(item[1]), float(item[3])))) + if len(item) == 6: + tbl_lst.append(item) + + # Sort the table according to the relative change + tbl_lst.sort(key=lambda rel: rel[-1], reverse=True) + + # Generate tables: + # All tests in csv: + tbl_names = ["{0}-ndr-1t1c-full{1}".format(table["output-file"], + table["output-file-ext"]), + "{0}-ndr-2t2c-full{1}".format(table["output-file"], + table["output-file-ext"]), + "{0}-ndr-4t4c-full{1}".format(table["output-file"], + table["output-file-ext"]), + "{0}-pdr-1t1c-full{1}".format(table["output-file"], + table["output-file-ext"]), + "{0}-pdr-2t2c-full{1}".format(table["output-file"], + table["output-file-ext"]), + "{0}-pdr-4t4c-full{1}".format(table["output-file"], + table["output-file-ext"]) + ] + for file_name in tbl_names: + logging.info(" Writing file: '{0}'".format(file_name)) + with open(file_name, "w") as file_handler: + file_handler.write(header_str) + for test in tbl_lst: + if (file_name.split("-")[-3] in test[0] and # NDR vs PDR + file_name.split("-")[-2] in test[0]): # cores + test[0] = "-".join(test[0].split("-")[:-1]) + file_handler.write(",".join([str(item) for item in test]) + + "\n") + + # All tests in txt: + tbl_names_txt = ["{0}-ndr-1t1c-full.txt".format(table["output-file"]), + "{0}-ndr-2t2c-full.txt".format(table["output-file"]), + "{0}-ndr-4t4c-full.txt".format(table["output-file"]), + "{0}-pdr-1t1c-full.txt".format(table["output-file"]), + "{0}-pdr-2t2c-full.txt".format(table["output-file"]), + "{0}-pdr-4t4c-full.txt".format(table["output-file"]) + ] + + for i, txt_name in enumerate(tbl_names_txt): + txt_table = None + logging.info(" Writing file: '{0}'".format(txt_name)) + with open(tbl_names[i], 'rb') as csv_file: + csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') + for row in csv_content: + if txt_table is None: + txt_table = prettytable.PrettyTable(row) + else: + txt_table.add_row(row) + txt_table.align["Test case"] = "l" + with open(txt_name, "w") as txt_file: + txt_file.write(str(txt_table)) + + # Selected tests in csv: + input_file = "{0}-ndr-1t1c-full{1}".format(table["output-file"], + table["output-file-ext"]) + with open(input_file, "r") as in_file: + lines = list() + for line in in_file: + lines.append(line) + + output_file = "{0}-ndr-1t1c-top{1}".format(table["output-file"], + table["output-file-ext"]) + logging.info(" Writing file: '{0}'".format(output_file)) + with open(output_file, "w") as out_file: + out_file.write(header_str) + for i, line in enumerate(lines[1:]): + if i == table["nr-of-tests-shown"]: + break + out_file.write(line) + + output_file = "{0}-ndr-1t1c-bottom{1}".format(table["output-file"], + table["output-file-ext"]) + logging.info(" Writing file: '{0}'".format(output_file)) + with open(output_file, "w") as out_file: + out_file.write(header_str) + for i, line in enumerate(lines[-1:0:-1]): + if i == table["nr-of-tests-shown"]: + break + out_file.write(line) + + input_file = "{0}-pdr-1t1c-full{1}".format(table["output-file"], + table["output-file-ext"]) + with open(input_file, "r") as in_file: + lines = list() + for line in in_file: + lines.append(line) + + output_file = "{0}-pdr-1t1c-top{1}".format(table["output-file"], + table["output-file-ext"]) + logging.info(" Writing file: '{0}'".format(output_file)) + with open(output_file, "w") as out_file: + out_file.write(header_str) + for i, line in enumerate(lines[1:]): + if i == table["nr-of-tests-shown"]: + break + out_file.write(line) + + output_file = "{0}-pdr-1t1c-bottom{1}".format(table["output-file"], + table["output-file-ext"]) + logging.info(" Writing file: '{0}'".format(output_file)) + with open(output_file, "w") as out_file: + out_file.write(header_str) + for i, line in enumerate(lines[-1:0:-1]): + if i == table["nr-of-tests-shown"]: + break + out_file.write(line) + + +def table_performance_comparison_mrr(table, input_data): + """Generate the table(s) with algorithm: table_performance_comparison_mrr + specified in the specification file. + + :param table: Table to generate. + :param input_data: Data to process. + :type table: pandas.Series + :type input_data: InputData + """ + + logging.info(" Generating the table {0} ...". + format(table.get("title", ""))) + + # Transform the data + data = input_data.filter_data(table, continue_on_error=True) + + # Prepare the header of the tables + try: + header = ["Test case", + "{0} Throughput [Mpps]".format(table["reference"]["title"]), + "{0} stdev [Mpps]".format(table["reference"]["title"]), + "{0} Throughput [Mpps]".format(table["compare"]["title"]), + "{0} stdev [Mpps]".format(table["compare"]["title"]), + "Change [%]"] + header_str = ",".join(header) + "\n" + except (AttributeError, KeyError) as err: + logging.error("The model is invalid, missing parameter: {0}". + format(err)) + return + + # Prepare data to the table: + tbl_dict = dict() + for job, builds in table["reference"]["data"].items(): + for build in builds: + for tst_name, tst_data in data[job][str(build)].iteritems(): + if tbl_dict.get(tst_name, None) is None: + name = "{0}-{1}".format(tst_data["parent"].split("-")[0], + "-".join(tst_data["name"]. + split("-")[1:])) + tbl_dict[tst_name] = {"name": name, + "ref-data": list(), + "cmp-data": list()} + try: + tbl_dict[tst_name]["ref-data"].\ + append(tst_data["result"]["throughput"]) + except TypeError: + pass # No data in output.xml for this test + + for job, builds in table["compare"]["data"].items(): + for build in builds: + for tst_name, tst_data in data[job][str(build)].iteritems(): + try: + tbl_dict[tst_name]["cmp-data"].\ + append(tst_data["result"]["throughput"]) + except KeyError: + pass + except TypeError: + tbl_dict.pop(tst_name, None) + + tbl_lst = list() + for tst_name in tbl_dict.keys(): + item = [tbl_dict[tst_name]["name"], ] + if tbl_dict[tst_name]["ref-data"]: + data_t = remove_outliers(tbl_dict[tst_name]["ref-data"], + outlier_const=table["outlier-const"]) + # TODO: Specify window size. + if data_t: + item.append(round(mean(data_t) / 1000000, 2)) + item.append(round(stdev(data_t) / 1000000, 2)) + else: + item.extend([None, None]) + else: + item.extend([None, None]) + if tbl_dict[tst_name]["cmp-data"]: + data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"], + outlier_const=table["outlier-const"]) + # TODO: Specify window size. + if data_t: + item.append(round(mean(data_t) / 1000000, 2)) + item.append(round(stdev(data_t) / 1000000, 2)) + else: + item.extend([None, None]) + else: + item.extend([None, None]) + if item[1] is not None and item[3] is not None and item[1] != 0: + item.append(int(relative_change(float(item[1]), float(item[3])))) + if len(item) == 6: + tbl_lst.append(item) + + # Sort the table according to the relative change + tbl_lst.sort(key=lambda rel: rel[-1], reverse=True) + + # Generate tables: + # All tests in csv: + tbl_names = ["{0}-1t1c-full{1}".format(table["output-file"], + table["output-file-ext"]), + "{0}-2t2c-full{1}".format(table["output-file"], + table["output-file-ext"]), + "{0}-4t4c-full{1}".format(table["output-file"], + table["output-file-ext"]) + ] + for file_name in tbl_names: + logging.info(" Writing file: '{0}'".format(file_name)) + with open(file_name, "w") as file_handler: + file_handler.write(header_str) + for test in tbl_lst: + if file_name.split("-")[-2] in test[0]: # cores + test[0] = "-".join(test[0].split("-")[:-1]) + file_handler.write(",".join([str(item) for item in test]) + + "\n") + + # All tests in txt: + tbl_names_txt = ["{0}-1t1c-full.txt".format(table["output-file"]), + "{0}-2t2c-full.txt".format(table["output-file"]), + "{0}-4t4c-full.txt".format(table["output-file"]) + ] + + for i, txt_name in enumerate(tbl_names_txt): + txt_table = None + logging.info(" Writing file: '{0}'".format(txt_name)) + with open(tbl_names[i], 'rb') as csv_file: + csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') + for row in csv_content: + if txt_table is None: + txt_table = prettytable.PrettyTable(row) + else: + txt_table.add_row(row) + txt_table.align["Test case"] = "l" + with open(txt_name, "w") as txt_file: + txt_file.write(str(txt_table)) + + +def table_performance_trending_dashboard(table, input_data): + """Generate the table(s) with algorithm: table_performance_comparison + specified in the specification file. + + :param table: Table to generate. + :param input_data: Data to process. + :type table: pandas.Series + :type input_data: InputData + """ + + logging.info(" Generating the table {0} ...". + format(table.get("title", ""))) + + # Transform the data + data = input_data.filter_data(table, continue_on_error=True) + + # Prepare the header of the tables + header = ["Test Case", + "Throughput Trend [Mpps]", + "Long Trend Compliance", + "Trend Compliance", + "Top Anomaly [Mpps]", + "Change [%]", + "Outliers [Number]" + ] + header_str = ",".join(header) + "\n" + + # Prepare data to the table: + tbl_dict = dict() + for job, builds in table["data"].items(): + for build in builds: + for tst_name, tst_data in data[job][str(build)].iteritems(): + if tbl_dict.get(tst_name, None) is None: + name = "{0}-{1}".format(tst_data["parent"].split("-")[0], + "-".join(tst_data["name"]. + split("-")[1:])) + tbl_dict[tst_name] = {"name": name, + "data": dict()} + try: + tbl_dict[tst_name]["data"][str(build)] = \ + tst_data["result"]["throughput"] + except (TypeError, KeyError): + pass # No data in output.xml for this test + + tbl_lst = list() + for tst_name in tbl_dict.keys(): + if len(tbl_dict[tst_name]["data"]) > 2: + + pd_data = pd.Series(tbl_dict[tst_name]["data"]) + win_size = min(pd_data.size, table["window"]) + # Test name: + name = tbl_dict[tst_name]["name"] + + median = pd_data.rolling(window=win_size, min_periods=2).median() + median_idx = pd_data.size - table["long-trend-window"] + median_idx = 0 if median_idx < 0 else median_idx + max_median = max(median.values[median_idx:]) + trimmed_data, _ = split_outliers(pd_data, outlier_const=1.5, + window=win_size) + stdev_t = pd_data.rolling(window=win_size, min_periods=2).std() + + rel_change_lst = [None, ] + classification_lst = [None, ] + median_lst = [None, ] + sample_lst = [None, ] + first = True + for build_nr, value in pd_data.iteritems(): + if first: + first = False + continue + # Relative changes list: + if not isnan(value) \ + and not isnan(median[build_nr]) \ + and median[build_nr] != 0: + rel_change_lst.append(round( + relative_change(float(median[build_nr]), float(value)), + 2)) + else: + rel_change_lst.append(None) + + # Classification list: + if isnan(trimmed_data[build_nr]) \ + or isnan(median[build_nr]) \ + or isnan(stdev_t[build_nr]) \ + or isnan(value): + classification_lst.append("outlier") + elif value < (median[build_nr] - 3 * stdev_t[build_nr]): + classification_lst.append("regression") + elif value > (median[build_nr] + 3 * stdev_t[build_nr]): + classification_lst.append("progression") + else: + classification_lst.append("normal") + sample_lst.append(value) + median_lst.append(median[build_nr]) + + last_idx = len(classification_lst) - 1 + first_idx = last_idx - int(table["evaluated-window"]) + if first_idx < 0: + first_idx = 0 + + nr_outliers = 0 + consecutive_outliers = 0 + failure = False + for item in classification_lst[first_idx:]: + if item == "outlier": + nr_outliers += 1 + consecutive_outliers += 1 + if consecutive_outliers == 3: + failure = True + else: + consecutive_outliers = 0 + + if failure: + classification = "failure" + elif "regression" in classification_lst[first_idx:]: + classification = "regression" + elif "progression" in classification_lst[first_idx:]: + classification = "progression" + else: + classification = "normal" + + if classification == "normal": + index = len(classification_lst) - 1 + else: + tmp_classification = "outlier" if classification == "failure" \ + else classification + index = None + for idx in range(first_idx, len(classification_lst)): + if classification_lst[idx] == tmp_classification: + if rel_change_lst[idx]: + index = idx + break + if index is None: + continue + for idx in range(index+1, len(classification_lst)): + if classification_lst[idx] == tmp_classification: + if rel_change_lst[idx]: + if (abs(rel_change_lst[idx]) > + abs(rel_change_lst[index])): + index = idx + + logging.info("{}".format(name)) + logging.info("sample_lst: {} - {}".format(len(sample_lst), sample_lst)) + logging.info("median_lst: {} - {}".format(len(median_lst), median_lst)) + logging.info("rel_change: {} - {}".format(len(rel_change_lst), rel_change_lst)) + logging.info("classn_lst: {} - {}".format(len(classification_lst), classification_lst)) + logging.info("index: {}".format(index)) + logging.info("classifica: {}".format(classification)) + + try: + trend = round(float(median_lst[-1]) / 1000000, 2) \ + if not isnan(median_lst[-1]) else '-' + sample = round(float(sample_lst[index]) / 1000000, 2) \ + if not isnan(sample_lst[index]) else '-' + rel_change = rel_change_lst[index] \ + if rel_change_lst[index] is not None else '-' + if not isnan(max_median): + if not isnan(sample_lst[index]): + long_trend_threshold = max_median * \ + (table["long-trend-threshold"] / 100) + if sample_lst[index] < long_trend_threshold: + long_trend_classification = "failure" + else: + long_trend_classification = '-' + else: + long_trend_classification = "failure" + else: + long_trend_classification = '-' + tbl_lst.append([name, + trend, + long_trend_classification, + classification, + '-' if classification == "normal" else sample, + '-' if classification == "normal" else rel_change, + nr_outliers]) + except IndexError as err: + logging.error("{}".format(err)) + continue + + # Sort the table according to the classification + tbl_sorted = list() + for long_trend_class in ("failure", '-'): + tbl_long = [item for item in tbl_lst if item[2] == long_trend_class] + for classification in \ + ("failure", "regression", "progression", "normal"): + tbl_tmp = [item for item in tbl_long if item[3] == classification] + tbl_tmp.sort(key=lambda rel: rel[0]) + tbl_sorted.extend(tbl_tmp) + + file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) + + logging.info(" Writing file: '{0}'".format(file_name)) + with open(file_name, "w") as file_handler: + file_handler.write(header_str) + for test in tbl_sorted: + file_handler.write(",".join([str(item) for item in test]) + '\n') + + txt_file_name = "{0}.txt".format(table["output-file"]) + txt_table = None + logging.info(" Writing file: '{0}'".format(txt_file_name)) + with open(file_name, 'rb') as csv_file: + csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') + for row in csv_content: + if txt_table is None: + txt_table = prettytable.PrettyTable(row) + else: + txt_table.add_row(row) + txt_table.align["Test case"] = "l" + with open(txt_file_name, "w") as txt_file: + txt_file.write(str(txt_table)) + + +def table_performance_trending_dashboard_html(table, input_data): + """Generate the table(s) with algorithm: + table_performance_trending_dashboard_html specified in the specification + file. + + :param table: Table to generate. + :param input_data: Data to process. + :type table: pandas.Series + :type input_data: InputData + """ + + logging.info(" Generating the table {0} ...". + format(table.get("title", ""))) + + try: + with open(table["input-file"], 'rb') as csv_file: + csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') + csv_lst = [item for item in csv_content] + except KeyError: + logging.warning("The input file is not defined.") + return + except csv.Error as err: + logging.warning("Not possible to process the file '{0}'.\n{1}". + format(table["input-file"], err)) + return + + # Table: + dashboard = ET.Element("table", attrib=dict(width="100%", border='0')) + + # Table header: + tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#7eade7")) + for idx, item in enumerate(csv_lst[0]): + alignment = "left" if idx == 0 else "center" + th = ET.SubElement(tr, "th", attrib=dict(align=alignment)) + th.text = item + + # Rows: + for r_idx, row in enumerate(csv_lst[1:]): + background = "#D4E4F7" if r_idx % 2 else "white" + tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background)) + + # Columns: + for c_idx, item in enumerate(row): + alignment = "left" if c_idx == 0 else "center" + td = ET.SubElement(tr, "td", attrib=dict(align=alignment)) + # Name: + url = "../trending/" + file_name = "" + anchor = "#" + feature = "" + if c_idx == 0: + if "memif" in item: + file_name = "container_memif.html" + + elif "vhost" in item: + if "l2xcbase" in item or "l2bdbasemaclrn" in item: + file_name = "vm_vhost_l2.html" + elif "ip4base" in item: + file_name = "vm_vhost_ip4.html" + + elif "ipsec" in item: + file_name = "ipsec.html" + + elif "ethip4lispip" in item or "ethip4vxlan" in item: + file_name = "ip4_tunnels.html" + + elif "ip4base" in item or "ip4scale" in item: + file_name = "ip4.html" + if "iacl" in item or "snat" in item or "cop" in item: + feature = "-features" + + elif "ip6base" in item or "ip6scale" in item: + file_name = "ip6.html" + + elif "l2xcbase" in item or "l2xcscale" in item \ + or "l2bdbasemaclrn" in item or "l2bdscale" in item \ + or "l2dbbasemaclrn" in item or "l2dbscale" in item: + file_name = "l2.html" + if "iacl" in item: + feature = "-features" + + if "x520" in item: + anchor += "x520-" + elif "x710" in item: + anchor += "x710-" + elif "xl710" in item: + anchor += "xl710-" + + if "64b" in item: + anchor += "64b-" + elif "78b" in item: + anchor += "78b" + elif "imix" in item: + anchor += "imix-" + elif "9000b" in item: + anchor += "9000b-" + elif "1518" in item: + anchor += "1518b-" + + if "1t1c" in item: + anchor += "1t1c" + elif "2t2c" in item: + anchor += "2t2c" + elif "4t4c" in item: + anchor += "4t4c" + + url = url + file_name + anchor + feature + + ref = ET.SubElement(td, "a", attrib=dict(href=url)) + ref.text = item + + if c_idx == 3: + if item == "regression": + td.set("bgcolor", "#eca1a6") + elif item == "failure": + td.set("bgcolor", "#d6cbd3") + elif item == "progression": + td.set("bgcolor", "#bdcebe") + if c_idx > 0: + td.text = item + + try: + with open(table["output-file"], 'w') as html_file: + logging.info(" Writing file: '{0}'". + format(table["output-file"])) + html_file.write(".. raw:: html\n\n\t") + html_file.write(ET.tostring(dashboard)) + html_file.write("\n\t



\n") + except KeyError: + logging.warning("The output file is not defined.") + return