X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_cpta.py;h=923d56bf4252d3cd7941099f8071280c1f0085e2;hb=refs%2Fchanges%2F29%2F34729%2F2;hp=5cc56fd9655d994dd95f4154808a5bd2e9388477;hpb=3b5495d0943283e96ce5fa23c0b1f31846ca0f6f;p=csit.git

diff --git a/resources/tools/presentation/generator_cpta.py b/resources/tools/presentation/generator_cpta.py
index 5cc56fd965..923d56bf42 100644
--- a/resources/tools/presentation/generator_cpta.py
+++ b/resources/tools/presentation/generator_cpta.py
@@ -13,7 +13,6 @@
 
 """Generation of Continuous Performance Trending and Analysis.
 """
-
 import re
 import logging
 import csv
@@ -21,6 +20,7 @@ import csv
 from collections import OrderedDict
 from datetime import datetime
 from copy import deepcopy
+from os import listdir
 
 import prettytable
 import plotly.offline as ploff
@@ -223,7 +223,18 @@ def _generate_trending_traces(in_data, job_name, build_info,
         if u"-cps" in name:
             hover_str = hover_str.replace(u"[Mpps]", u"[Mcps]").\
                 replace(u"throughput", u"connection rate")
-        if u"dpdk" in job_name:
+        if u"vpp" in job_name:
+            hover_str = hover_str.format(
+                date=date,
+                property=u"average" if incl_tests == u"mrr" else u"throughput",
+                value=data_y_mpps[index],
+                sut=u"vpp",
+                build=build_info[job_name][str_key][1].rsplit(u'~', 1)[0],
+                test=incl_tests,
+                period=u"daily" if incl_tests == u"mrr" else u"weekly",
+                build_nr=str_key,
+                testbed=build_info[job_name][str_key][2])
+        elif u"dpdk" in job_name:
             hover_str = hover_str.format(
                 date=date,
                 property=u"average" if incl_tests == u"mrr" else u"throughput",
@@ -234,13 +245,13 @@ def _generate_trending_traces(in_data, job_name, build_info,
                 period=u"weekly",
                 build_nr=str_key,
                 testbed=build_info[job_name][str_key][2])
-        elif u"vpp" in job_name:
+        elif u"trex" in job_name:
             hover_str = hover_str.format(
                 date=date,
                 property=u"average" if incl_tests == u"mrr" else u"throughput",
                 value=data_y_mpps[index],
-                sut=u"vpp",
-                build=build_info[job_name][str_key][1].rsplit(u'~', 1)[0],
+                sut=u"trex",
+                build=u"",
                 test=incl_tests,
                 period=u"daily" if incl_tests == u"mrr" else u"weekly",
                 build_nr=str_key,
@@ -827,22 +838,74 @@ def _generate_all_charts(spec, input_data):
 
     # Evaluate result:
     if anomaly_classifications:
+        legend_str = (f"Legend:\n[ Last trend in Mpps/Mcps | number of runs for"
+                      f" last trend | ")
         result = u"PASS"
         for job_name, job_data in anomaly_classifications.items():
+            data = []
+            tb = u"-".join(job_name.split(u"-")[-2:])
+            for file in listdir(f"{spec.cpta[u'output-file']}"):
+                if tb in file and u"performance-trending-dashboard" in \
+                        file and u"txt" in file:
+                    file_to_read = f"{spec.cpta[u'output-file']}/{file}"
+                    with open(f"{file_to_read}", u"rt") as input:
+                        data = data + input.readlines()
             file_name = \
                 f"{spec.cpta[u'output-file']}/regressions-{job_name}.txt"
             with open(file_name, u'w') as txt_file:
                 for test_name, classification in job_data.items():
                     if classification == u"regression":
-                        txt_file.write(test_name + u'\n')
+                        if u"2n" in test_name:
+                            test_name = test_name.split("-", 2)
+                            tst = test_name[2].split(".")[-1]
+                            nic = test_name[1]
+                            tst_name = f"{nic}-{tst}"
+                        else:
+                            test_name = test_name.split("-", 1)
+                            tst = test_name[1].split(".")[-1]
+                            nic = test_name[0].split(".")[-1]
+                            tst_name = f"{nic}-{tst}"
+
+                        for line in data:
+                            if tst_name in line:
+                                line = line.replace(" ", "")
+                                trend = line.split("|")[2]
+                                number = line.split("|")[3]
+                                ltc = line.split("|")[4]
+                                txt_file.write(f"{tst_name} [ {trend}M | "
+                                               f"#{number} | {ltc}% ]\n")
+
                     if classification in (u"regression", u"outlier"):
                         result = u"FAIL"
+
+            txt_file.write(f"\n{legend_str}regression in percentage ]")
+
             file_name = \
                 f"{spec.cpta[u'output-file']}/progressions-{job_name}.txt"
             with open(file_name, u'w') as txt_file:
                 for test_name, classification in job_data.items():
                     if classification == u"progression":
-                        txt_file.write(test_name + u'\n')
+                        if u"2n" in test_name:
+                            test_name = test_name.split("-", 2)
+                            tst = test_name[2].split(".")[-1]
+                            nic = test_name[1]
+                            tst_name = f"{nic}-{tst}"
+                        else:
+                            test_name = test_name.split("-", 1)
+                            tst = test_name[1].split(".")[-1]
+                            nic = test_name[0].split(".")[-1]
+                            tst_name = f"{nic}-{tst}"
+
+                        for line in data:
+                            if tst_name in line:
+                                line = line.replace(" ", "")
+                                trend = line.split("|")[2]
+                                number = line.split("|")[3]
+                                ltc = line.split("|")[4]
+                                txt_file.write(f"{tst_name} [ {trend}M | "
+                                               f"#{number} | {ltc}% ]\n")
+
+            txt_file.write(f"\n{legend_str}progression in percentage ]")
     else:
         result = u"FAIL"