X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=814fbe450c8473f53b5d7d5cb1f9ce3a4b599c57;hp=195380f7fbdca17e53f5eef271b6ebe7f17c6fb5;hb=4a778c818108baf64a5abe6ffd6b27a4a88a2633;hpb=d08498d06de775723a32597d109b537abf34a7e9 diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index 195380f7fb..814fbe450c 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -16,10 +16,15 @@ import logging +import csv +import prettytable +import pandas as pd + from string import replace +from math import isnan from errors import PresentationError -from utils import mean, stdev, relative_change +from utils import mean, stdev, relative_change, remove_outliers, find_outliers def generate_tables(spec, data): @@ -64,7 +69,6 @@ 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 +112,67 @@ 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 +196,14 @@ 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} ...". @@ -175,29 +245,33 @@ 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 - pass + 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[0])) + except (IndexError, ValueError, TypeError): + logging.error("No data for {0}".format(tbl_item[0]["data"])) tbl_item.append({"data": None}) continue else: @@ -222,21 +296,25 @@ 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.") @@ -260,3 +338,296 @@ 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) + + # 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"], + table["outlier-const"]) + item.append(round(mean(data_t) / 1000000, 2)) + item.append(round(stdev(data_t) / 1000000, 2)) + else: + item.extend([None, None]) + if tbl_dict[tst_name]["cmp-data"]: + data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"], + table["outlier-const"]) + item.append(round(mean(data_t) / 1000000, 2)) + item.append(round(stdev(data_t) / 1000000, 2)) + 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_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) + + # Prepare the header of the tables + header = ["Test case", + "Thput trend [Mpps]", + "Change [Mpps]", + "Change [%]", + "Anomaly"] + 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": list()} + try: + tbl_dict[tst_name]["data"]. \ + append(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 = pd_data.size \ + if pd_data.size < table["window"] else table["window"] + # Test name: + name = tbl_dict[tst_name]["name"] + # Throughput trend: + trend = list(pd_data.rolling(window=win_size, min_periods=2). + median())[-2] + # Anomaly: + t_data, _ = find_outliers(pd_data) + last = list(t_data)[-1] + t_stdev = list(t_data.rolling(window=win_size, min_periods=2). + std())[-2] + if isnan(last): + anomaly = "outlier" + elif last < (trend - 3 * t_stdev): + anomaly = "regression" + elif last > (trend + 3 * t_stdev): + anomaly = "progression" + else: + anomaly = "normal" + + if not isnan(last) and not isnan(trend) and trend != 0: + # Change: + change = round(float(last - trend) / 1000000, 2) + # Relative change: + rel_change = int(relative_change(float(trend), float(last))) + + tbl_lst.append([name, + round(float(last) / 1000000, 2), + change, + rel_change, + anomaly]) + + # Sort the table according to the relative change + tbl_lst.sort(key=lambda rel: rel[-2], reverse=True) + + 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_lst: + 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))