CSIT-1041: Trending dashboard 40/12040/1
authorTibor Frank <tifrank@cisco.com>
Tue, 24 Apr 2018 05:28:44 +0000 (07:28 +0200)
committerTibor Frank <tifrank@cisco.com>
Tue, 24 Apr 2018 05:28:44 +0000 (07:28 +0200)
Change-Id: Ida3dfcc4a7ae21424e7f6b6db597a80bc633b9da
Signed-off-by: Tibor Frank <tifrank@cisco.com>
resources/tools/presentation/generator_CPTA.py
resources/tools/presentation/generator_tables.py
resources/tools/presentation/utils.py

index 3817ea9..567b889 100644 (file)
@@ -298,7 +298,7 @@ def _generate_trending_traces(in_data, build_info, period, moving_win_size=10,
         hoverinfo="none",
         showlegend=True,
         legendgroup=name,
-        name="{name}: outliers".format(name=name),
+        name="{name}-anomalies".format(name=name),
         marker={
             "size": 15,
             "symbol": "circle-open",
index 724519f..0f0ed6c 100644 (file)
@@ -811,13 +811,17 @@ 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) \
@@ -828,12 +832,12 @@ def table_performance_trending_dashboard(table, input_data):
                     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)
+                        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 +847,8 @@ 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))
index a15742a..4277aa0 100644 (file)
@@ -92,16 +92,6 @@ def remove_outliers(input_list, outlier_const=1.5, window=14):
             result_lst.append(y)
     return result_lst
 
-    # input_series = pd.Series()
-    # for index, value in enumerate(input_list):
-    #     item_pd = pd.Series([value, ], index=[index, ])
-    #     input_series.append(item_pd)
-    # output_series, _ = split_outliers(input_series, outlier_const=outlier_const,
-    #                                   window=window)
-    # output_list = [y for x, y in output_series.items() if not np.isnan(y)]
-    #
-    # return output_list
-
 
 def split_outliers(input_series, outlier_const=1.5, window=14):
     """Go through the input data and generate two pandas series:

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