CSIT-1041: Trending dashboard

[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 724519f..c41c6de 100644 (file)
--- a/resources/tools/presentation/generator_tables.py
+++ b/resources/tools/presentation/generator_tables.py
@@ -726,7 +726,11 @@ def table_performance_trending_dashboard(table, input_data):
             median = pd_data.rolling(window=win_size, min_periods=2).median()
             median_idx = pd_data.size - table["long-trend-window"]
             median_idx = 0 if median_idx < 0 else median_idx
-            max_median = max(median.values[median_idx:])
+            try:
+                max_median = max([x for x in median.values[median_idx:]
+                                  if not isnan(x)])
+            except ValueError:
+                max_median = None
             trimmed_data, _ = split_outliers(pd_data, outlier_const=1.5,
                                              window=win_size)
             stdev_t = pd_data.rolling(window=win_size, min_periods=2).std()
@@ -811,13 +815,17 @@ def table_performance_trending_dashboard(table, input_data):
                                     abs(rel_change_lst[index])):
                                 index = idx
 
-            logging.info("{}".format(name))
-            logging.info("sample_lst: {} - {}".format(len(sample_lst), sample_lst))
-            logging.info("median_lst: {} - {}".format(len(median_lst), median_lst))
-            logging.info("rel_change: {} - {}".format(len(rel_change_lst), rel_change_lst))
-            logging.info("classn_lst: {} - {}".format(len(classification_lst), classification_lst))
-            logging.info("index:      {}".format(index))
-            logging.info("classifica: {}".format(classification))
+            logging.debug("{}".format(name))
+            logging.debug("sample_lst: {} - {}".
+                          format(len(sample_lst), sample_lst))
+            logging.debug("median_lst: {} - {}".
+                          format(len(median_lst), median_lst))
+            logging.debug("rel_change: {} - {}".
+                          format(len(rel_change_lst), rel_change_lst))
+            logging.debug("classn_lst: {} - {}".
+                          format(len(classification_lst), classification_lst))
+            logging.debug("index:      {}".format(index))
+            logging.debug("classifica: {}".format(classification))
 
             try:
                 trend = round(float(median_lst[-1]) / 1000000, 2) \
@@ -826,14 +834,14 @@ def table_performance_trending_dashboard(table, input_data):
                     if not isnan(sample_lst[index]) else '-'
                 rel_change = rel_change_lst[index] \
                     if rel_change_lst[index] is not None else '-'
-                if not isnan(max_median):
+                if max_median is not None:
                     if not isnan(sample_lst[index]):
-                        long_trend_threshold = max_median * \
-                                               (table["long-trend-threshold"] / 100)
+                        long_trend_threshold = \
+                            max_median * (table["long-trend-threshold"] / 100)
                         if sample_lst[index] < long_trend_threshold:
                             long_trend_classification = "failure"
                         else:
-                            long_trend_classification = '-'
+                            long_trend_classification = 'normal'
                     else:
                         long_trend_classification = "failure"
                 else:
@@ -843,7 +851,8 @@ def table_performance_trending_dashboard(table, input_data):
                                 long_trend_classification,
                                 classification,
                                 '-' if classification == "normal" else sample,
-                                '-' if classification == "normal" else rel_change,
+                                '-' if classification == "normal" else
+                                rel_change,
                                 nr_outliers])
             except IndexError as err:
                 logging.error("{}".format(err))
@@ -851,7 +860,7 @@ def table_performance_trending_dashboard(table, input_data):
 
     # Sort the table according to the classification
     tbl_sorted = list()
-    for long_trend_class in ("failure", '-'):
+    for long_trend_class in ("failure", 'normal', '-'):
         tbl_long = [item for item in tbl_lst if item[2] == long_trend_class]
         for classification in \
                 ("failure", "regression", "progression", "normal"):