diff options
Diffstat (limited to 'resources/tools/presentation')
| -rw-r--r-- | resources/tools/presentation/generator_tables.py | 61 | ||||
| -rw-r--r-- | resources/tools/presentation/utils.py | 29 |
2 files changed, 58 insertions, 32 deletions
diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index a5a573ad94..724519f2d1 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -796,11 +796,14 @@ def table_performance_trending_dashboard(table, input_data): else: tmp_classification = "outlier" if classification == "failure" \ else classification + index = None for idx in range(first_idx, len(classification_lst)): if classification_lst[idx] == tmp_classification: if rel_change_lst[idx]: index = idx break + if index is None: + continue for idx in range(index+1, len(classification_lst)): if classification_lst[idx] == tmp_classification: if rel_change_lst[idx]: @@ -808,31 +811,43 @@ def table_performance_trending_dashboard(table, input_data): abs(rel_change_lst[index])): index = idx - trend = round(float(median_lst[-1]) / 1000000, 2) \ - if not isnan(median_lst[-1]) 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 '-' - if not isnan(max_median): - if not isnan(sample_lst[index]): - long_trend_threshold = max_median * \ - (table["long-trend-threshold"] / 100) - if sample_lst[index] < long_trend_threshold: - long_trend_classification = "failure" + 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)) + + try: + trend = round(float(median_lst[-1]) / 1000000, 2) \ + if not isnan(median_lst[-1]) 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 '-' + if not isnan(max_median): + if not isnan(sample_lst[index]): + 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 = '-' else: - long_trend_classification = '-' + long_trend_classification = "failure" else: - long_trend_classification = "failure" - else: - long_trend_classification = '-' - tbl_lst.append([name, - trend, - long_trend_classification, - classification, - '-' if classification == "normal" else sample, - '-' if classification == "normal" else rel_change, - nr_outliers]) + long_trend_classification = '-' + tbl_lst.append([name, + trend, + long_trend_classification, + classification, + '-' if classification == "normal" else sample, + '-' if classification == "normal" else rel_change, + nr_outliers]) + except IndexError as err: + logging.error("{}".format(err)) + continue # Sort the table according to the classification tbl_sorted = list() diff --git a/resources/tools/presentation/utils.py b/resources/tools/presentation/utils.py index 2fbf70cadc..a15742a21f 100644 --- a/resources/tools/presentation/utils.py +++ b/resources/tools/presentation/utils.py @@ -81,15 +81,26 @@ def remove_outliers(input_list, outlier_const=1.5, window=14): :rtype: list of floats """ - 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 + data = np.array(input_list) + upper_quartile = np.percentile(data, 75) + lower_quartile = np.percentile(data, 25) + iqr = (upper_quartile - lower_quartile) * outlier_const + quartile_set = (lower_quartile - iqr, upper_quartile + iqr) + result_lst = list() + for y in data.tolist(): + if quartile_set[0] <= y <= quartile_set[1]: + 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): |