X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=cb89a8bbcfcbf080f7a06c124fb292ea4971703f;hp=c2007a1a494bed37f95dc5717affdaf202026f30;hb=a1728c4f56cc44b9acb45614c20fc0a546ee3161;hpb=6e1a15acc7bd3685d71f9d34238181c681dcd4c0

diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py
index c2007a1a49..cb89a8bbcf 100644
--- a/resources/tools/presentation/generator_tables.py
+++ b/resources/tools/presentation/generator_tables.py
@@ -722,7 +722,8 @@ def table_performance_trending_dashboard(table, input_data):
             pd_data = pd.Series(tbl_dict[tst_name]["data"])
             last_key = pd_data.keys()[-1]
             win_size = min(pd_data.size, table["window"])
-            key_14 = pd_data.keys()[-(pd_data.size - win_size)]
+            win_first_idx = pd_data.size - win_size
+            key_14 = pd_data.keys()[win_first_idx]
             long_win_size = min(pd_data.size, table["long-trend-window"])
 
             data_t, _ = split_outliers(pd_data, outlier_const=1.5,
@@ -730,9 +731,9 @@ def table_performance_trending_dashboard(table, input_data):
 
             median_t = data_t.rolling(window=win_size, min_periods=2).median()
             stdev_t = data_t.rolling(window=win_size, min_periods=2).std()
-            median_idx = pd_data.size - long_win_size
+            median_first_idx = pd_data.size - long_win_size
             try:
-                max_median = max([x for x in median_t.values[median_idx:]
+                max_median = max([x for x in median_t.values[median_first_idx:]
                                   if not isnan(x)])
             except ValueError:
                 max_median = nan
@@ -748,6 +749,14 @@ def table_performance_trending_dashboard(table, input_data):
             # Test name:
             name = tbl_dict[tst_name]["name"]
 
+            logging.info("{}".format(name))
+            logging.info("pd_data : {}".format(pd_data))
+            logging.info("data_t : {}".format(data_t))
+            logging.info("median_t : {}".format(median_t))
+            logging.info("last_median_t : {}".format(last_median_t))
+            logging.info("median_t_14 : {}".format(median_t_14))
+            logging.info("max_median : {}".format(max_median))
+
             # Classification list:
             classification_lst = list()
             for build_nr, value in pd_data.iteritems():
@@ -764,26 +773,30 @@ def table_performance_trending_dashboard(table, input_data):
                 else:
                     classification_lst.append("normal")
 
-            if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0:
+            if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0.0:
                 rel_change_last = nan
             else:
                 rel_change_last = round(
-                    (last_median_t - median_t_14) / median_t_14, 2)
+                    ((last_median_t - median_t_14) / median_t_14) * 100, 2)
 
-            if isnan(max_median) or isnan(last_median_t) or max_median == 0:
+            if isnan(max_median) or isnan(last_median_t) or max_median == 0.0:
                 rel_change_long = nan
             else:
                 rel_change_long = round(
-                    (last_median_t - max_median) / max_median, 2)
-
-            tbl_lst.append([name,
-                            '-' if isnan(last_median_t) else
-                            round(last_median_t / 1000000, 2),
-                            '-' if isnan(rel_change_last) else rel_change_last,
-                            '-' if isnan(rel_change_long) else rel_change_long,
-                            classification_lst[win_size:].count("regression"),
-                            classification_lst[win_size:].count("progression"),
-                            classification_lst[win_size:].count("outlier")])
+                    ((last_median_t - max_median) / max_median) * 100, 2)
+
+            logging.info("rel_change_last : {}".format(rel_change_last))
+            logging.info("rel_change_long : {}".format(rel_change_long))
+
+            tbl_lst.append(
+                [name,
+                 '-' if isnan(last_median_t) else
+                 round(last_median_t / 1000000, 2),
+                 '-' if isnan(rel_change_last) else rel_change_last,
+                 '-' if isnan(rel_change_long) else rel_change_long,
+                 classification_lst[win_first_idx:].count("regression"),
+                 classification_lst[win_first_idx:].count("progression"),
+                 classification_lst[win_first_idx:].count("outlier")])
 
     tbl_lst.sort(key=lambda rel: rel[0])