--- modulename: output-bmk-results, funcname: <module>
<string>(1):  --- modulename: output-bmk-results, funcname: main
output-bmk-results.py(287):     results_csv = sys.argv[1]
output-bmk-results.py(288):     variability_file = sys.argv[2]
output-bmk-results.py(289):     run_step_artifacts_dir = sys.argv[3]
output-bmk-results.py(290):     metric = sys.argv[4]
output-bmk-results.py(291):     mode = sys.argv[5]
output-bmk-results.py(292):     details = sys.argv[6]
output-bmk-results.py(294):     merged_df = read_results_csv(results_csv)
 --- modulename: output-bmk-results, funcname: read_results_csv
output-bmk-results.py(277):     df = pd.read_csv(results_csv)
output-bmk-results.py(278):     df = df.fillna(-1)
output-bmk-results.py(280):     for metric in get_comparable_metrics(df):
 --- modulename: output-bmk-results, funcname: get_comparable_metrics
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
 --- modulename: output-bmk-results, funcname: <genexpr>
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
 --- modulename: output-bmk-results, funcname: <genexpr>
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
 --- modulename: output-bmk-results, funcname: <genexpr>
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
 --- modulename: output-bmk-results, funcname: <genexpr>
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
 --- modulename: output-bmk-results, funcname: <genexpr>
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
 --- modulename: output-bmk-results, funcname: <genexpr>
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
output-bmk-results.py(199):            & metric_utils.comparable_metrics
output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
output-bmk-results.py(281):         df["rel_" + metric] = df["rel_" + metric].astype("int")
output-bmk-results.py(282):         df[metric + "_x"] = df[metric + "_x"].astype("int")
output-bmk-results.py(283):         df[metric + "_y"] = df[metric + "_y"].astype("int")
output-bmk-results.py(280):     for metric in get_comparable_metrics(df):
output-bmk-results.py(281):         df["rel_" + metric] = df["rel_" + metric].astype("int")
output-bmk-results.py(282):         df[metric + "_x"] = df[metric + "_x"].astype("int")
output-bmk-results.py(283):         df[metric + "_y"] = df[metric + "_y"].astype("int")
output-bmk-results.py(280):     for metric in get_comparable_metrics(df):
output-bmk-results.py(281):         df["rel_" + metric] = df["rel_" + metric].astype("int")
output-bmk-results.py(282):         df[metric + "_x"] = df[metric + "_x"].astype("int")
output-bmk-results.py(283):         df[metric + "_y"] = df[metric + "_y"].astype("int")
output-bmk-results.py(280):     for metric in get_comparable_metrics(df):
output-bmk-results.py(281):         df["rel_" + metric] = df["rel_" + metric].astype("int")
output-bmk-results.py(282):         df[metric + "_x"] = df[metric + "_x"].astype("int")
output-bmk-results.py(283):         df[metric + "_y"] = df[metric + "_y"].astype("int")
output-bmk-results.py(280):     for metric in get_comparable_metrics(df):
output-bmk-results.py(284):     return df
output-bmk-results.py(295):     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(296):     output_bmk_results(merged_df, run_step_artifacts_dir, metric, mode, details)
 --- modulename: output-bmk-results, funcname: output_bmk_results
output-bmk-results.py(248):     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(249):     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(250):     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(252):     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(256):     df = merged_df[merged_df["benchmark"] != "Mean"]
output-bmk-results.py(259):     exe_df = df[df["symbol"].str.endswith("_base.default")]
output-bmk-results.py(260):     sym_df = df[~df["symbol"].str.endswith("_base.default")]
output-bmk-results.py(262):     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(210):     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(212):     rel_metric = "rel_" + metric
output-bmk-results.py(213):     out_df = results_df[results_df[rel_metric] != -1]
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 400.perlbench,perlbench_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 401.bzip2,bzip2_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 403.gcc,gcc_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 429.mcf,mcf_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 433.milc,milc_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 444.namd,namd_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 445.gobmk,gobmk_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 447.dealII,dealII_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 450.soplex,soplex_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 453.povray,povray_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 456.hmmer,hmmer_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 458.sjeng,sjeng_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 462.libquantum,libquantum_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 464.h264ref,h264ref_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 470.lbm,lbm_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 471.omnetpp,omnetpp_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 473.astar,astar_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 482.sphinx3,sphinx_livepretend_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.regression : 483.xalancbmk,Xalan_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(245):     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(263):     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(210):     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(212):     rel_metric = "rel_" + metric
output-bmk-results.py(213):     out_df = results_df[results_df[rel_metric] != -1]
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 400.perlbench,perlbench_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 401.bzip2,bzip2_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 403.gcc,gcc_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 429.mcf,mcf_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 433.milc,milc_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 444.namd,namd_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 445.gobmk,gobmk_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 447.dealII,dealII_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 450.soplex,soplex_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 453.povray,povray_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 456.hmmer,hmmer_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 458.sjeng,sjeng_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 462.libquantum,libquantum_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 464.h264ref,h264ref_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 470.lbm,lbm_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 471.omnetpp,omnetpp_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 473.astar,astar_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 482.sphinx3,sphinx_livepretend_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking exe.improvement : 483.xalancbmk,Xalan_base.default : size=0% (threshold=1%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(245):     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(265):     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(210):     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(212):     rel_metric = "rel_" + metric
output-bmk-results.py(213):     out_df = results_df[results_df[rel_metric] != -1]
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking symbol.regression : 447.dealII,libstdc++.so.6.0.30 : size=0% (threshold=10%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking symbol.regression : 471.omnetpp,libc.so.6 : size=0% (threshold=10%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_regression
output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(176):         return (result - 100 > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(245):     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(266):     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(210):     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(212):     rel_metric = "rel_" + metric
output-bmk-results.py(213):     out_df = results_df[results_df[rel_metric] != -1]
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking symbol.improvement : 447.dealII,libstdc++.so.6.0.30 : size=0% (threshold=10%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(218):         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(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(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
DEBUG: checking symbol.improvement : 471.omnetpp,libc.so.6 : size=0% (threshold=10%)
output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
 --- modulename: output-bmk-results, funcname: is_entry_improvement
output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
output-bmk-results.py(185):         return (100 - result > threshold)
output-bmk-results.py(225):             continue
output-bmk-results.py(216):     for index, row in out_df.iterrows():
output-bmk-results.py(245):     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(268):     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(270):     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(271):     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(272):     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(297):     return 0