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 a5a573a..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()
@@ -796,11 +800,14 @@ def table_performance_trending_dashboard(table, input_data):
             else:
                 tmp_classification = "outlier" if classification == "failure" \
                     else classification
+                index = None
                 for idx in range(first_idx, len(classification_lst)):
                     if classification_lst[idx] == tmp_classification:
                         if rel_change_lst[idx]:
                             index = idx
                             break
+                if index is None:
+                    continue
                 for idx in range(index+1, len(classification_lst)):
                     if classification_lst[idx] == tmp_classification:
                         if rel_change_lst[idx]:
@@ -808,35 +815,52 @@ def table_performance_trending_dashboard(table, input_data):
                                     abs(rel_change_lst[index])):
                                 index = idx
 
-            trend = round(float(median_lst[-1]) / 1000000, 2) \
-                if not isnan(median_lst[-1]) else '-'
-            sample = round(float(sample_lst[index]) / 1000000, 2) \
-                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 not isnan(sample_lst[index]):
-                    long_trend_threshold = max_median * \
-                                           (table["long-trend-threshold"] / 100)
-                    if sample_lst[index] < long_trend_threshold:
-                        long_trend_classification = "failure"
+            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) \
+                    if not isnan(median_lst[-1]) else '-'
+                sample = round(float(sample_lst[index]) / 1000000, 2) \
+                    if not isnan(sample_lst[index]) else '-'
+                rel_change = rel_change_lst[index] \
+                    if rel_change_lst[index] is not None else '-'
+                if max_median is not None:
+                    if not isnan(sample_lst[index]):
+                        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 = 'normal'
                     else:
-                        long_trend_classification = '-'
+                        long_trend_classification = "failure"
                 else:
-                    long_trend_classification = "failure"
-            else:
-                long_trend_classification = '-'
-            tbl_lst.append([name,
-                            trend,
-                            long_trend_classification,
-                            classification,
-                            '-' if classification == "normal" else sample,
-                            '-' if classification == "normal" else rel_change,
-                            nr_outliers])
+                    long_trend_classification = '-'
+                tbl_lst.append([name,
+                                trend,
+                                long_trend_classification,
+                                classification,
+                                '-' if classification == "normal" else sample,
+                                '-' if classification == "normal" else
+                                rel_change,
+                                nr_outliers])
+            except IndexError as err:
+                logging.error("{}".format(err))
+                continue
 
     # 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"):