--- 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 symbol ... status_x status_y 0 500.perlbench_r perlbench_r_base.default ... success success 1 502.gcc_r cpugcc_r_base.default ... -1 -1 2 503.bwaves_r bwaves_r_base.default ... success success 3 505.mcf_r mcf_r_base.default ... success success 4 507.cactuBSSN_r cactusBSSN_r_base.default ... -1 -1 5 508.namd_r namd_r_base.default ... success success 6 510.parest_r parest_r_base.default ... success success 7 511.povray_r povray_r_base.default ... success success 8 519.lbm_r lbm_r_base.default ... success success 9 520.omnetpp_r omnetpp_r_base.default ... success success 12 521.wrf_r wrf_r_base.default ... success success 14 523.xalancbmk_r cpuxalan_r_base.default ... -1 -1 15 525.x264_r x264_r_base.default ... success success 16 526.blender_r blender_r_base.default ... success success 17 527.cam4_r cam4_r_base.default ... success success 19 531.deepsjeng_r deepsjeng_r_base.default ... success success 20 538.imagick_r imagick_r_base.default ... success success 21 541.leela_r leela_r_base.default ... success success 22 544.nab_r nab_r_base.default ... success success 24 548.exchange2_r exchange2_r_base.default ... success success 25 549.fotonik3d_r fotonik3d_r_base.default ... success success 26 554.roms_r roms_r_base.default ... success success 27 557.xz_r xz_r_base.default ... success success [23 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(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 symbol ... status_x status_y 0 500.perlbench_r perlbench_r_base.default ... success success 1 502.gcc_r cpugcc_r_base.default ... -1 -1 2 503.bwaves_r bwaves_r_base.default ... success success 3 505.mcf_r mcf_r_base.default ... success success 4 507.cactuBSSN_r cactusBSSN_r_base.default ... -1 -1 5 508.namd_r namd_r_base.default ... success success 6 510.parest_r parest_r_base.default ... success success 7 511.povray_r povray_r_base.default ... success success 8 519.lbm_r lbm_r_base.default ... success success 9 520.omnetpp_r omnetpp_r_base.default ... success success 12 521.wrf_r wrf_r_base.default ... success success 14 523.xalancbmk_r cpuxalan_r_base.default ... -1 -1 15 525.x264_r x264_r_base.default ... success success 16 526.blender_r blender_r_base.default ... success success 17 527.cam4_r cam4_r_base.default ... success success 19 531.deepsjeng_r deepsjeng_r_base.default ... success success 20 538.imagick_r imagick_r_base.default ... success success 21 541.leela_r leela_r_base.default ... success success 22 544.nab_r nab_r_base.default ... success success 24 548.exchange2_r exchange2_r_base.default ... success success 25 549.fotonik3d_r fotonik3d_r_base.default ... success success 26 554.roms_r roms_r_base.default ... success success 27 557.xz_r xz_r_base.default ... success success [23 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(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 : 500.perlbench_r,perlbench_r_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 : 502.gcc_r,cpugcc_r_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 : 503.bwaves_r,bwaves_r_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 : 505.mcf_r,mcf_r_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 : 507.cactuBSSN_r,cactusBSSN_r_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 : 508.namd_r,namd_r_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 : 510.parest_r,parest_r_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 : 511.povray_r,povray_r_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 : 519.lbm_r,lbm_r_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 : 520.omnetpp_r,omnetpp_r_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 : 521.wrf_r,wrf_r_base.default : sample=2% (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 : 523.xalancbmk_r,cpuxalan_r_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 : 525.x264_r,x264_r_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 : 526.blender_r,blender_r_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 : 527.cam4_r,cam4_r_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 : 531.deepsjeng_r,deepsjeng_r_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 : 538.imagick_r,imagick_r_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 : 541.leela_r,leela_r_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 : 544.nab_r,nab_r_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 : 548.exchange2_r,exchange2_r_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 : 549.fotonik3d_r,fotonik3d_r_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 : 554.roms_r,roms_r_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 : 557.xz_r,xz_r_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(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 : 500.perlbench_r,perlbench_r_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 : 502.gcc_r,cpugcc_r_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 : 503.bwaves_r,bwaves_r_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 : 505.mcf_r,mcf_r_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 : 507.cactuBSSN_r,cactusBSSN_r_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 : 508.namd_r,namd_r_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 : 510.parest_r,parest_r_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 : 511.povray_r,povray_r_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 : 519.lbm_r,lbm_r_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 : 520.omnetpp_r,omnetpp_r_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 : 521.wrf_r,wrf_r_base.default : sample=2% (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 : 523.xalancbmk_r,cpuxalan_r_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 : 525.x264_r,x264_r_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 : 526.blender_r,blender_r_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 : 527.cam4_r,cam4_r_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 : 531.deepsjeng_r,deepsjeng_r_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 : 538.imagick_r,imagick_r_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 : 541.leela_r,leela_r_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 : 544.nab_r,nab_r_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 : 548.exchange2_r,exchange2_r_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 : 549.fotonik3d_r,fotonik3d_r_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 : 554.roms_r,roms_r_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 : 557.xz_r,xz_r_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(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 : 520.omnetpp_r,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 : 520.omnetpp_r,libstdc++.so.6.0.33 : sample=-3% (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 : 521.wrf_r,libm.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 : 527.cam4_r,libm.so.6 : sample=3% (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 : 544.nab_r,libm.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 : 520.omnetpp_r,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 : 520.omnetpp_r,libstdc++.so.6.0.33 : sample=-3% (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 : 521.wrf_r,libm.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 : 527.cam4_r,libm.so.6 : sample=3% (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 : 544.nab_r,libm.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