--- modulename: output-bmk-results, funcname: (1): --- modulename: output-bmk-results, funcname: main output-bmk-results.py(322): results_csv = sys.argv[1] output-bmk-results.py(323): variability_file = sys.argv[2] output-bmk-results.py(324): run_step_artifacts_dir = sys.argv[3] output-bmk-results.py(325): metric = sys.argv[4] output-bmk-results.py(326): mode = sys.argv[5] output-bmk-results.py(327): details = sys.argv[6] output-bmk-results.py(329): merged_df = read_results_csv(results_csv) --- modulename: output-bmk-results, funcname: read_results_csv output-bmk-results.py(312): df = pd.read_csv(results_csv) output-bmk-results.py(313): df = df.fillna(-1) output-bmk-results.py(315): for metric in get_comparable_metrics(df): --- modulename: output-bmk-results, funcname: get_comparable_metrics output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(207): & metric_utils.comparable_metrics output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(316): df["rel_" + metric] = df["rel_" + metric].astype("int") output-bmk-results.py(317): df[metric + "_x"] = df[metric + "_x"].astype("int") output-bmk-results.py(318): df[metric + "_y"] = df[metric + "_y"].astype("int") output-bmk-results.py(315): for metric in get_comparable_metrics(df): output-bmk-results.py(316): df["rel_" + metric] = df["rel_" + metric].astype("int") output-bmk-results.py(317): df[metric + "_x"] = df[metric + "_x"].astype("int") output-bmk-results.py(318): df[metric + "_y"] = df[metric + "_y"].astype("int") output-bmk-results.py(315): for metric in get_comparable_metrics(df): output-bmk-results.py(316): df["rel_" + metric] = df["rel_" + metric].astype("int") output-bmk-results.py(317): df[metric + "_x"] = df[metric + "_x"].astype("int") output-bmk-results.py(318): df[metric + "_y"] = df[metric + "_y"].astype("int") output-bmk-results.py(315): for metric in get_comparable_metrics(df): output-bmk-results.py(316): df["rel_" + metric] = df["rel_" + metric].astype("int") output-bmk-results.py(317): df[metric + "_x"] = df[metric + "_x"].astype("int") output-bmk-results.py(318): df[metric + "_y"] = df[metric + "_y"].astype("int") output-bmk-results.py(315): for metric in get_comparable_metrics(df): output-bmk-results.py(319): return df output-bmk-results.py(330): read_specific_variability_file(variability_file) --- modulename: output-bmk-results, funcname: read_specific_variability_file output-bmk-results.py(51): if not os.path.exists(bmk_specific_filename): output-bmk-results.py(53): specific_variability = pd.read_csv(bmk_specific_filename, index_col=False) output-bmk-results.py(331): output_bmk_results(merged_df, run_step_artifacts_dir, metric, mode, details) --- modulename: output-bmk-results, funcname: output_bmk_results output-bmk-results.py(278): f_regr = Outfile("{0}/results.regressions".format(run_step_artifacts), "w") --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(279): f_ebp = Outfile("{0}/extra-bisect-params".format(run_step_artifacts), "w") --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(280): f_skip = Outfile("{0}/any.skipped".format(run_step_artifacts), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(282): f_ebp.write("extra_build_params=") --- modulename: output-bmk-results, funcname: write output-bmk-results.py(36): if not self.predicate or not self.outf: output-bmk-results.py(38): self.outf.write(string) output-bmk-results.py(286): df = merged_df[merged_df["benchmark"] != "Mean"] output-bmk-results.py(289): exe_df = df[df["symbol"].str.endswith("_base.default")] output-bmk-results.py(290): sym_df = df[~df["symbol"].str.endswith("_base.default")] output-bmk-results.py(293): output_bmk_results_status(exe_df, "regression", f_regr, f_ebp, run_step_artifacts, details) --- modulename: output-bmk-results, funcname: output_bmk_results_status output-bmk-results.py(256): f_out = Outfile("{0}/status.{1}".format(run_step_artifacts, change_kind), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(258): print(results_df) benchmark ... status_y 0 400.perlbench ... success 1 401.bzip2 ... success 2 403.gcc ... success 4 410.bwaves ... success 5 416.gamess ... success 6 429.mcf ... success 7 433.milc ... success 8 434.zeusmp ... success 9 435.gromacs ... success 10 436.cactusADM ... success 11 444.namd ... success 12 445.gobmk ... success 13 447.dealII ... success 15 450.soplex ... success 16 453.povray ... success 17 454.calculix ... failed-to-build 18 454.calculix ... failed-to-build 19 456.hmmer ... success 20 458.sjeng ... success 21 459.GemsFDTD ... success 22 462.libquantum ... success 23 464.h264ref ... success 25 465.tonto ... success 27 470.lbm ... success 28 471.omnetpp ... success 30 473.astar ... success 31 482.sphinx3 ... success 32 483.xalancbmk ... failed-to-build 33 483.xalancbmk ... failed-to-build [29 rows x 20 columns] output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(275): f_out.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(33): os.remove(self.filename) output-bmk-results.py(294): output_bmk_results_status(exe_df, "improvement", None, None, run_step_artifacts, details) --- modulename: output-bmk-results, funcname: output_bmk_results_status output-bmk-results.py(256): f_out = Outfile("{0}/status.{1}".format(run_step_artifacts, change_kind), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(258): print(results_df) benchmark ... status_y 0 400.perlbench ... success 1 401.bzip2 ... success 2 403.gcc ... success 4 410.bwaves ... success 5 416.gamess ... success 6 429.mcf ... success 7 433.milc ... success 8 434.zeusmp ... success 9 435.gromacs ... success 10 436.cactusADM ... success 11 444.namd ... success 12 445.gobmk ... success 13 447.dealII ... success 15 450.soplex ... success 16 453.povray ... success 17 454.calculix ... failed-to-build 18 454.calculix ... failed-to-build 19 456.hmmer ... success 20 458.sjeng ... success 21 459.GemsFDTD ... success 22 462.libquantum ... success 23 464.h264ref ... success 25 465.tonto ... success 27 470.lbm ... success 28 471.omnetpp ... success 30 473.astar ... success 31 482.sphinx3 ... success 32 483.xalancbmk ... failed-to-build 33 483.xalancbmk ... failed-to-build [29 rows x 20 columns] output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(275): f_out.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(33): os.remove(self.filename) output-bmk-results.py(297): output_bmk_results_1(exe_df, "exe", "regression", f_regr, f_skip, f_ebp, run_step_artifacts, metric, mode, details) --- modulename: output-bmk-results, funcname: output_bmk_results_1 output-bmk-results.py(218): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(220): rel_metric = "rel_" + metric output-bmk-results.py(221): out_df = results_df[results_df[rel_metric] != -1] output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 400.perlbench,perlbench_base.default : sample=-1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 401.bzip2,bzip2_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 403.gcc,gcc_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 410.bwaves,bwaves_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 416.gamess,gamess_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 429.mcf,mcf_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 433.milc,milc_base.default : sample=-1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 434.zeusmp,zeusmp_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 435.gromacs,gromacs_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 436.cactusADM,cactusADM_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 444.namd,namd_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 445.gobmk,gobmk_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 447.dealII,dealII_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 450.soplex,soplex_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 453.povray,povray_base.default : sample=-1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 456.hmmer,hmmer_base.default : sample=1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 458.sjeng,sjeng_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 459.GemsFDTD,GemsFDTD_base.default : sample=3% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 462.libquantum,libquantum_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 464.h264ref,h264ref_base.default : sample=-1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 465.tonto,tonto_base.default : sample=-1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 470.lbm,lbm_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 471.omnetpp,omnetpp_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 473.astar,astar_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 482.sphinx3,sphinx_livepretend_base.default : sample=1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(253): f_out.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(33): os.remove(self.filename) output-bmk-results.py(298): output_bmk_results_1(exe_df, "exe", "improvement", None, f_skip, None, run_step_artifacts, metric, mode, details) --- modulename: output-bmk-results, funcname: output_bmk_results_1 output-bmk-results.py(218): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(220): rel_metric = "rel_" + metric output-bmk-results.py(221): out_df = results_df[results_df[rel_metric] != -1] output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 400.perlbench,perlbench_base.default : sample=-1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 401.bzip2,bzip2_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 403.gcc,gcc_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 410.bwaves,bwaves_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 416.gamess,gamess_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 429.mcf,mcf_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 433.milc,milc_base.default : sample=-1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 434.zeusmp,zeusmp_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 435.gromacs,gromacs_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 436.cactusADM,cactusADM_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 444.namd,namd_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 445.gobmk,gobmk_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 447.dealII,dealII_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 450.soplex,soplex_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 453.povray,povray_base.default : sample=-1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 456.hmmer,hmmer_base.default : sample=1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 458.sjeng,sjeng_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 459.GemsFDTD,GemsFDTD_base.default : sample=3% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 462.libquantum,libquantum_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 464.h264ref,h264ref_base.default : sample=-1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 465.tonto,tonto_base.default : sample=-1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 470.lbm,lbm_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 471.omnetpp,omnetpp_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 473.astar,astar_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 482.sphinx3,sphinx_livepretend_base.default : sample=1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(253): f_out.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(33): os.remove(self.filename) output-bmk-results.py(300): output_bmk_results_1(sym_df, "symbol", "regression", f_regr, f_skip, f_ebp, run_step_artifacts, metric, mode, details) --- modulename: output-bmk-results, funcname: output_bmk_results_1 output-bmk-results.py(218): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(220): rel_metric = "rel_" + metric output-bmk-results.py(221): out_df = results_df[results_df[rel_metric] != -1] output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 403.gcc,libc.so.6 : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 447.dealII,libstdc++.so.6.0.33 : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 464.h264ref,libc.so.6 : sample=2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 465.tonto,libm.so.6 : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 471.omnetpp,libc.so.6 : sample=2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(253): f_out.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(33): os.remove(self.filename) output-bmk-results.py(301): output_bmk_results_1(sym_df, "symbol", "improvement", None, f_skip, None, run_step_artifacts, metric, mode, details) --- modulename: output-bmk-results, funcname: output_bmk_results_1 output-bmk-results.py(218): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(220): rel_metric = "rel_" + metric output-bmk-results.py(221): out_df = results_df[results_df[rel_metric] != -1] output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 403.gcc,libc.so.6 : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 447.dealII,libstdc++.so.6.0.33 : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 464.h264ref,libc.so.6 : sample=2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 465.tonto,libm.so.6 : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 471.omnetpp,libc.so.6 : sample=2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(253): f_out.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(33): os.remove(self.filename) output-bmk-results.py(303): f_ebp.write("\n") --- modulename: output-bmk-results, funcname: write output-bmk-results.py(36): if not self.predicate or not self.outf: output-bmk-results.py(38): self.outf.write(string) output-bmk-results.py(305): f_skip.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(33): os.remove(self.filename) output-bmk-results.py(306): f_regr.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(33): os.remove(self.filename) output-bmk-results.py(307): f_ebp.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(332): return 0