X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=4ffa08122d72e70c0577e237355c19fad7f664d4;hp=92f0ceed450c8d5a38611a290cf315c603116de1;hb=3c510d8dd18bd8ceb2c5d86ed058976e72ddead4;hpb=52cb667958d954d6233d0865a59d90cca82db026 diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index 92f0ceed45..4ffa08122d 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -432,17 +432,20 @@ def table_performance_comparison(table, input_data): for tst_name in tbl_dict.keys(): item = [tbl_dict[tst_name]["name"], ] if history: - for hist_data in tbl_dict[tst_name]["history"].values(): - if hist_data: - data_t = remove_outliers( - hist_data, outlier_const=table["outlier-const"]) - if data_t: - item.append(round(mean(data_t) / 1000000, 2)) - item.append(round(stdev(data_t) / 1000000, 2)) + if tbl_dict[tst_name].get("history", None) is not None: + for hist_data in tbl_dict[tst_name]["history"].values(): + if hist_data: + data_t = remove_outliers( + hist_data, outlier_const=table["outlier-const"]) + if data_t: + item.append(round(mean(data_t) / 1000000, 2)) + item.append(round(stdev(data_t) / 1000000, 2)) + else: + item.extend([None, None]) else: item.extend([None, None]) - else: - item.extend([None, None]) + else: + item.extend([None, None]) if tbl_dict[tst_name]["ref-data"]: data_t = remove_outliers(tbl_dict[tst_name]["ref-data"], outlier_const=table["outlier-const"]) @@ -465,8 +468,8 @@ def table_performance_comparison(table, input_data): item.extend([None, None]) else: item.extend([None, None]) - if item[-5] is not None and item[-3] is not None and item[-5] != 0: - item.append(int(relative_change(float(item[-5]), float(item[-3])))) + if item[-4] is not None and item[-2] is not None and item[-4] != 0: + item.append(int(relative_change(float(item[-4]), float(item[-2])))) if len(item) == len(header): tbl_lst.append(item) @@ -805,9 +808,9 @@ def table_performance_trending_dashboard(table, input_data): or isnan(stdev_t[build_nr]) \ or isnan(value): classification_lst.append("outlier") - elif value < (median_t[build_nr] - 2 * stdev_t[build_nr]): + elif value < (median_t[build_nr] - 3 * stdev_t[build_nr]): classification_lst.append("regression") - elif value > (median_t[build_nr] + 2 * stdev_t[build_nr]): + elif value > (median_t[build_nr] + 3 * stdev_t[build_nr]): classification_lst.append("progression") else: classification_lst.append("normal")