diff options
Diffstat (limited to 'resources/tools/presentation/new')
| -rw-r--r-- | resources/tools/presentation/new/generator_tables.py | 5 | ||||
| -rw-r--r-- | resources/tools/presentation/new/utils.py | 12 |
2 files changed, 10 insertions, 7 deletions
diff --git a/resources/tools/presentation/new/generator_tables.py b/resources/tools/presentation/new/generator_tables.py index 6951021bb9..735fd2185f 100644 --- a/resources/tools/presentation/new/generator_tables.py +++ b/resources/tools/presentation/new/generator_tables.py @@ -788,8 +788,8 @@ def table_performance_trending_dashboard(table, input_data): round(last_avg / 1000000, 2), '-' if isnan(rel_change_last) else rel_change_last, '-' if isnan(rel_change_long) else rel_change_long, - classification_lst[-long_win_size:].count("regression"), - classification_lst[-long_win_size:].count("progression")]) + classification_lst[-win_size:].count("regression"), + classification_lst[-win_size:].count("progression")]) tbl_lst.sort(key=lambda rel: rel[0]) @@ -823,6 +823,7 @@ def table_performance_trending_dashboard(table, input_data): with open(txt_file_name, "w") as txt_file: txt_file.write(str(txt_table)) + def table_performance_trending_dashboard_html(table, input_data): """Generate the table(s) with algorithm: table_performance_trending_dashboard_html specified in the specification diff --git a/resources/tools/presentation/new/utils.py b/resources/tools/presentation/new/utils.py index 83f4f6249b..a688928cda 100644 --- a/resources/tools/presentation/new/utils.py +++ b/resources/tools/presentation/new/utils.py @@ -211,17 +211,19 @@ def archive_input_data(spec): def classify_anomalies(data): """Process the data and return anomalies and trending values. - Gathers data into groups with common trend value. - Decorates first value in the group to be an outlier, regression, - normal or progression. + Gather data into groups with average as trend value. + Decorate values within groups to be normal, + the first value of changed average as a regression, or a progression. :param data: Full data set with unavailable samples replaced by nan. :type data: pandas.Series :returns: Classification and trend values :rtype: 2-tuple, list of strings and list of floats """ - bare_data = [sample for _, sample in data.iteritems() - if not np.isnan(sample)] + # Nan mean something went wrong. + # Use 0.0 to cause that being reported as a severe regression. + bare_data = [0.0 if np.isnan(sample) else sample + for _, sample in data.iteritems()] # TODO: Put analogous iterator into jumpavg library. groups = BitCountingClassifier.classify(bare_data) groups.reverse() # Just to use .pop() for FIFO. |