diff options
| -rw-r--r-- | resources/tools/presentation/generator_CPTA.py | 2 | ||||
| -rw-r--r-- | resources/tools/presentation/generator_tables.py | 27 | ||||
| -rw-r--r-- | resources/tools/presentation/utils.py | 10 |
3 files changed, 17 insertions, 22 deletions
diff --git a/resources/tools/presentation/generator_CPTA.py b/resources/tools/presentation/generator_CPTA.py index 3817ea9043..567b889208 100644 --- a/resources/tools/presentation/generator_CPTA.py +++ b/resources/tools/presentation/generator_CPTA.py @@ -298,7 +298,7 @@ def _generate_trending_traces(in_data, build_info, period, moving_win_size=10, hoverinfo="none", showlegend=True, legendgroup=name, - name="{name}: outliers".format(name=name), + name="{name}-anomalies".format(name=name), marker={ "size": 15, "symbol": "circle-open", diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index 724519f2d1..0f0ed6c7a5 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -811,13 +811,17 @@ def table_performance_trending_dashboard(table, input_data): abs(rel_change_lst[index])): index = idx - logging.info("{}".format(name)) - logging.info("sample_lst: {} - {}".format(len(sample_lst), sample_lst)) - logging.info("median_lst: {} - {}".format(len(median_lst), median_lst)) - logging.info("rel_change: {} - {}".format(len(rel_change_lst), rel_change_lst)) - logging.info("classn_lst: {} - {}".format(len(classification_lst), classification_lst)) - logging.info("index: {}".format(index)) - logging.info("classifica: {}".format(classification)) + logging.debug("{}".format(name)) + logging.debug("sample_lst: {} - {}". + format(len(sample_lst), sample_lst)) + logging.debug("median_lst: {} - {}". + format(len(median_lst), median_lst)) + logging.debug("rel_change: {} - {}". + format(len(rel_change_lst), rel_change_lst)) + logging.debug("classn_lst: {} - {}". + format(len(classification_lst), classification_lst)) + logging.debug("index: {}".format(index)) + logging.debug("classifica: {}".format(classification)) try: trend = round(float(median_lst[-1]) / 1000000, 2) \ @@ -828,12 +832,12 @@ def table_performance_trending_dashboard(table, input_data): if rel_change_lst[index] is not None else '-' if not isnan(max_median): if not isnan(sample_lst[index]): - long_trend_threshold = max_median * \ - (table["long-trend-threshold"] / 100) + long_trend_threshold = \ + max_median * (table["long-trend-threshold"] / 100) if sample_lst[index] < long_trend_threshold: long_trend_classification = "failure" else: - long_trend_classification = '-' + long_trend_classification = 'normal' else: long_trend_classification = "failure" else: @@ -843,7 +847,8 @@ def table_performance_trending_dashboard(table, input_data): long_trend_classification, classification, '-' if classification == "normal" else sample, - '-' if classification == "normal" else rel_change, + '-' if classification == "normal" else + rel_change, nr_outliers]) except IndexError as err: logging.error("{}".format(err)) diff --git a/resources/tools/presentation/utils.py b/resources/tools/presentation/utils.py index a15742a21f..4277aa0334 100644 --- a/resources/tools/presentation/utils.py +++ b/resources/tools/presentation/utils.py @@ -92,16 +92,6 @@ def remove_outliers(input_list, outlier_const=1.5, window=14): result_lst.append(y) return result_lst - # input_series = pd.Series() - # for index, value in enumerate(input_list): - # item_pd = pd.Series([value, ], index=[index, ]) - # input_series.append(item_pd) - # output_series, _ = split_outliers(input_series, outlier_const=outlier_const, - # window=window) - # output_list = [y for x, y in output_series.items() if not np.isnan(y)] - # - # return output_list - def split_outliers(input_series, outlier_const=1.5, window=14): """Go through the input data and generate two pandas series: |