diff options
Diffstat (limited to 'resources/tools/presentation/generator_tables.py')
| -rw-r--r-- | resources/tools/presentation/generator_tables.py | 87 |
1 files changed, 62 insertions, 25 deletions
diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index 13c8efffdb..12cbee2dae 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -574,38 +574,75 @@ def table_performance_trending_dashboard(table, input_data): tbl_lst = list() for tst_name in tbl_dict.keys(): if len(tbl_dict[tst_name]["data"]) > 2: - pd_data = pd.Series(tbl_dict[tst_name]["data"]) + sample_lst = tbl_dict[tst_name]["data"] + pd_data = pd.Series(sample_lst) win_size = pd_data.size \ if pd_data.size < table["window"] else table["window"] # Test name: name = tbl_dict[tst_name]["name"] - # Throughput trend: - trend = list(pd_data.rolling(window=win_size, min_periods=2). - median())[-2] - # Anomaly: + + # Trend list: + trend_lst = list(pd_data.rolling(window=win_size, min_periods=2). + median()) + # Stdevs list: t_data, _ = find_outliers(pd_data) - last = list(t_data)[-1] - t_stdev = list(t_data.rolling(window=win_size, min_periods=2). - std())[-2] - if isnan(last): - classification = "outlier" - last = list(pd_data)[-1] - elif last < (trend - 3 * t_stdev): + t_data_lst = list(t_data) + stdev_lst = list(t_data.rolling(window=win_size, min_periods=2). + std()) + + rel_change_lst = [None, ] + classification_lst = [None, ] + for idx in range(1, len(trend_lst)): + # Relative changes list: + if not isnan(sample_lst[idx]) \ + and not isnan(trend_lst[idx])\ + and trend_lst[idx] != 0: + rel_change_lst.append( + int(relative_change(float(trend_lst[idx]), + float(sample_lst[idx])))) + else: + rel_change_lst.append(None) + # Classification list: + if isnan(t_data_lst[idx]) or isnan(stdev_lst[idx]): + classification_lst.append("outlier") + elif sample_lst[idx] < (trend_lst[idx] - 3*stdev_lst[idx]): + classification_lst.append("regression") + elif sample_lst[idx] > (trend_lst[idx] + 3*stdev_lst[idx]): + classification_lst.append("progression") + else: + classification_lst.append("normal") + + last_idx = len(sample_lst) - 1 + first_idx = last_idx - int(table["evaluated-window"]) + if first_idx < 0: + first_idx = 0 + + if "regression" in classification_lst[first_idx:]: classification = "regression" - elif last > (trend + 3 * t_stdev): + elif "outlier" in classification_lst[first_idx:]: + classification = "outlier" + elif "progression" in classification_lst[first_idx:]: classification = "progression" else: classification = "normal" - if not isnan(last) and not isnan(trend) and trend != 0: - # Relative change: - rel_change = int(relative_change(float(trend), float(last))) - - tbl_lst.append([name, - round(float(trend) / 1000000, 2), - round(float(last) / 1000000, 2), - rel_change, - classification]) + idx = len(classification_lst) - 1 + while idx: + if classification_lst[idx] == classification: + break + idx -= 1 + + trend = round(float(trend_lst[-2]) / 1000000, 2) \ + if not isnan(trend_lst[-2]) 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 '' + tbl_lst.append([name, + trend, + sample, + rel_change, + classification]) # Sort the table according to the classification tbl_sorted = list() @@ -684,11 +721,11 @@ def table_performance_trending_dashboard_html(table, input_data): td = ET.SubElement(tr, "td", attrib=dict(align=alignment)) if c_idx == 4: if item == "regression": - td.set("bgcolor", "#FF0000") + td.set("bgcolor", "#eca1a6") elif item == "outlier": - td.set("bgcolor", "#818181") + td.set("bgcolor", "#d6cbd3") elif item == "progression": - td.set("bgcolor", "#008000") + td.set("bgcolor", "#bdcebe") td.text = item try: |