X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fpal_utils.py;h=f546aa4d101c380dbede21bba6c1feb56ebafa48;hp=20bf9a65c492a319fbcb717f4df4513934aaebae;hb=9ab83131443469ebced0c3ac34c17a385bb9cdaf;hpb=3f2ecafbe60b3664ab7b18ecfe8e4acb46a3cd3b

diff --git a/resources/tools/presentation/pal_utils.py b/resources/tools/presentation/pal_utils.py
index 20bf9a65c4..f546aa4d10 100644
--- a/resources/tools/presentation/pal_utils.py
+++ b/resources/tools/presentation/pal_utils.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019 Cisco and/or its affiliates.
+# Copyright (c) 2021 Cisco and/or its affiliates.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at:
@@ -227,7 +227,7 @@ def archive_input_data(spec):
 
     logging.info(u"    Archiving the input data files ...")
 
-    extension = spec.input[u"arch-file-format"]
+    extension = spec.output[u"arch-file-format"]
     data_files = list()
     for ext in extension:
         data_files.extend(get_files(
@@ -262,7 +262,7 @@ def classify_anomalies(data):
     :param data: Full data set with unavailable samples replaced by nan.
     :type data: OrderedDict
     :returns: Classification and trend values
-    :rtype: 2-tuple, list of strings and list of floats
+    :rtype: 3-tuple, list of strings, list of floats and list of floats
     """
     # Nan means something went wrong.
     # Use 0.0 to cause that being reported as a severe regression.
@@ -273,13 +273,16 @@ def classify_anomalies(data):
     group_list.reverse()  # Just to use .pop() for FIFO.
     classification = []
     avgs = []
+    stdevs = []
     active_group = None
     values_left = 0
     avg = 0.0
+    stdv = 0.0
     for sample in data.values():
         if np.isnan(sample):
             classification.append(u"outlier")
             avgs.append(sample)
+            stdevs.append(sample)
             continue
         if values_left < 1 or active_group is None:
             values_left = 0
@@ -287,14 +290,17 @@ def classify_anomalies(data):
                 active_group = group_list.pop()
                 values_left = len(active_group.run_list)
             avg = active_group.stats.avg
+            stdv = active_group.stats.stdev
             classification.append(active_group.comment)
             avgs.append(avg)
+            stdevs.append(stdv)
             values_left -= 1
             continue
         classification.append(u"normal")
         avgs.append(avg)
+        stdevs.append(stdv)
         values_left -= 1
-    return classification, avgs
+    return classification, avgs, stdevs
 
 
 def convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u","):
@@ -318,12 +324,13 @@ def convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u","):
                 txt_table.add_row(
                     [str(itm.replace(u"\u00B1", u"+-")) for itm in row]
                 )
-    txt_table.align = u"r"
-    txt_table.align[u"Test Case"] = u"l"
-
     if not txt_table:
         return
 
+    txt_table.align = u"r"
+    for itm in (u"Test Case", u"Build", u"Version", u"VPP Version"):
+        txt_table.align[itm] = u"l"
+
     if txt_file_name.endswith(u".txt"):
         with open(txt_file_name, u"wt", encoding='utf-8') as txt_file:
             txt_file.write(str(txt_table))