Trending: switch back to 3*stdev

[csit.git] / resources / tools / presentation / generator_tables.py

diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py
index 96930cd..4ffa081 100644 (file)
--- a/resources/tools/presentation/generator_tables.py
+++ b/resources/tools/presentation/generator_tables.py
@@ -432,8 +432,8 @@ def table_performance_comparison(table, input_data):
     for tst_name in tbl_dict.keys():
         item = [tbl_dict[tst_name]["name"], ]
         if history:
-            for hist_list in tbl_dict[tst_name]["history"].values():
-                for hist_data in hist_list:
+            if tbl_dict[tst_name].get("history", None) is not None:
+                for hist_data in tbl_dict[tst_name]["history"].values():
                     if hist_data:
                         data_t = remove_outliers(
                             hist_data, outlier_const=table["outlier-const"])
@@ -444,6 +444,8 @@ def table_performance_comparison(table, input_data):
                             item.extend([None, None])
                     else:
                         item.extend([None, None])
+            else:
+                item.extend([None, None])
         if tbl_dict[tst_name]["ref-data"]:
             data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
                                      outlier_const=table["outlier-const"])
@@ -466,8 +468,8 @@ def table_performance_comparison(table, input_data):
                 item.extend([None, None])
         else:
             item.extend([None, None])
-        if item[-5] is not None and item[-3] is not None and item[-5] != 0:
-            item.append(int(relative_change(float(item[-5]), float(item[-3]))))
+        if item[-4] is not None and item[-2] is not None and item[-4] != 0:
+            item.append(int(relative_change(float(item[-4]), float(item[-2]))))
         if len(item) == len(header):
             tbl_lst.append(item)
 
@@ -806,9 +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")