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-rw-r--r--resources/tools/presentation/generator_CPTA.py10
-rw-r--r--resources/tools/presentation/generator_tables.py4
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")