diff options
| -rw-r--r-- | resources/tools/presentation/generator_CPTA.py | 10 | ||||
| -rw-r--r-- | resources/tools/presentation/generator_tables.py | 4 |
2 files changed, 7 insertions, 7 deletions
diff --git a/resources/tools/presentation/generator_CPTA.py b/resources/tools/presentation/generator_CPTA.py index d72be3d589..e27a52172b 100644 --- a/resources/tools/presentation/generator_CPTA.py +++ b/resources/tools/presentation/generator_CPTA.py @@ -176,9 +176,9 @@ def _evaluate_results(trimmed_data, window=10): or np.isnan(tmm[build_nr]) or np.isnan(tmstd[build_nr])): results.append(0.0) - elif value < (tmm[build_nr] - 2 * tmstd[build_nr]): + elif value < (tmm[build_nr] - 3 * tmstd[build_nr]): results.append(0.33) - elif value > (tmm[build_nr] + 2 * tmstd[build_nr]): + elif value > (tmm[build_nr] + 3 * tmstd[build_nr]): results.append(1.0) else: results.append(0.66) @@ -187,10 +187,10 @@ def _evaluate_results(trimmed_data, window=10): try: tmm = np.median(trimmed_data) tmstd = np.std(trimmed_data) - if trimmed_data.values[-1] < (tmm - 2 * tmstd): + if trimmed_data.values[-1] < (tmm - 3 * tmstd): results.append(0.33) - elif (tmm - 2 * tmstd) <= trimmed_data.values[-1] <= ( - tmm + 2 * tmstd): + elif (tmm - 3 * tmstd) <= trimmed_data.values[-1] <= ( + tmm + 3 * tmstd): results.append(0.66) else: results.append(1.0) diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index 46aa71ca48..4ffa08122d 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -808,9 +808,9 @@ def table_performance_trending_dashboard(table, input_data): or isnan(stdev_t[build_nr]) \ or isnan(value): classification_lst.append("outlier") - elif value < (median_t[build_nr] - 2 * stdev_t[build_nr]): + elif value < (median_t[build_nr] - 3 * stdev_t[build_nr]): classification_lst.append("regression") - elif value > (median_t[build_nr] + 2 * stdev_t[build_nr]): + elif value > (median_t[build_nr] + 3 * stdev_t[build_nr]): classification_lst.append("progression") else: classification_lst.append("normal") |