diff options
Diffstat (limited to 'resources/tools')
| -rw-r--r-- | resources/tools/presentation/generator_tables.py | 91 |
1 files changed, 63 insertions, 28 deletions
diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index 29e29d0468..4bbee51ae5 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -671,12 +671,12 @@ def table_performance_trending_dashboard(table, input_data): data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the tables - header = ["Test case", + header = ["Test Case", "Throughput Trend [Mpps]", "Trend Compliance", - "Anomaly Value [Mpps]", + "Top Anomaly [Mpps]", "Change [%]", - "#Outliers" + "Outliers [Number]" ] header_str = ",".join(header) + "\n" @@ -724,9 +724,9 @@ def table_performance_trending_dashboard(table, input_data): if not isnan(value) \ and not isnan(median[build_nr]) \ and median[build_nr] != 0: - rel_change_lst.append( - int(relative_change(float(median[build_nr]), - float(value)))) + rel_change_lst.append(round( + relative_change(float(median[build_nr]), float(value)), + 2)) else: rel_change_lst.append(None) @@ -750,17 +750,6 @@ def table_performance_trending_dashboard(table, input_data): if first_idx < 0: first_idx = 0 - if "regression" in classification_lst[first_idx:]: - classification = "regression" - elif "outlier" in classification_lst[first_idx:]: - classification = "outlier" - elif "progression" in classification_lst[first_idx:]: - classification = "progression" - elif "normal" in classification_lst[first_idx:]: - classification = "normal" - else: - classification = None - nr_outliers = 0 consecutive_outliers = 0 failure = False @@ -773,23 +762,69 @@ def table_performance_trending_dashboard(table, input_data): else: consecutive_outliers = 0 - idx = len(classification_lst) - 1 - while idx: - if classification_lst[idx] == classification: - break - idx -= 1 - if failure: classification = "failure" - elif classification == "outlier": + elif "regression" in classification_lst[first_idx:]: + classification = "regression" + elif "progression" in classification_lst[first_idx:]: + classification = "progression" + else: classification = "normal" + if classification == "normal": + index = len(classification_lst) - 1 + else: + tmp_classification = "outlier" if classification == "failure" \ + else classification + for idx in range(first_idx, len(classification_lst)): + if classification_lst[idx] == tmp_classification: + index = idx + break + for idx in range(index+1, len(classification_lst)): + if classification_lst[idx] == tmp_classification: + if relative_change[idx] > relative_change[index]: + index = idx + + # if "regression" in classification_lst[first_idx:]: + # classification = "regression" + # elif "outlier" in classification_lst[first_idx:]: + # classification = "outlier" + # elif "progression" in classification_lst[first_idx:]: + # classification = "progression" + # elif "normal" in classification_lst[first_idx:]: + # classification = "normal" + # else: + # classification = None + # + # nr_outliers = 0 + # consecutive_outliers = 0 + # failure = False + # for item in classification_lst[first_idx:]: + # if item == "outlier": + # nr_outliers += 1 + # consecutive_outliers += 1 + # if consecutive_outliers == 3: + # failure = True + # else: + # consecutive_outliers = 0 + # + # idx = len(classification_lst) - 1 + # while idx: + # if classification_lst[idx] == classification: + # break + # idx -= 1 + # + # if failure: + # classification = "failure" + # elif classification == "outlier": + # classification = "normal" + trend = round(float(median_lst[-1]) / 1000000, 2) \ if not isnan(median_lst[-1]) else '' - sample = round(float(sample_lst[idx]) / 1000000, 2) \ - if not isnan(sample_lst[idx]) else '' - rel_change = rel_change_lst[idx] \ - if rel_change_lst[idx] is not None else '' + sample = round(float(sample_lst[index]) / 1000000, 2) \ + if not isnan(sample_lst[index]) else '' + rel_change = rel_change_lst[index] \ + if rel_change_lst[index] is not None else '' tbl_lst.append([name, trend, classification, |