changed trex_analytics to support detailed mode, generating a detailed table for test runs, also inserted machine information per setup to asciidoc. to use detailed mode run ./b build --performance-detailed at doc folder on trex-05

Signed-off-by: itraviv <itraviv@cisco.com>

1 files changed, 49 insertions, 14 deletions

diff --git a/doc/TRexDataAnalysis.py b/doc/TRexDataAnalysis.py
index fb855a16..ed674262 100755
--- a/doc/TRexDataAnalysis.py
+++ b/doc/TRexDataAnalysis.py
@@ -2,34 +2,46 @@
 import pandas as pd

 import numpy as np

 import matplotlib

+

 matplotlib.use('Agg')

 from matplotlib import pyplot as plt

 import os

+import time

 

 

-def generate_dframe_for_test(test_name, test_data):

+def generate_dframe_for_test(setup_name, test_name, test_data):

     test_results = []

+    test_dates = []

+    test_build_ids = []

     test_mins = set()

     test_maxs = set()

     for query in test_data:

-        test_results.append(float(query[3]))

-        test_mins.add(float(query[4]))

-        test_maxs.add(float(query[5]))

+        test_results.append(float(query[5]))

+        date_formatted = time.strftime("%d-%m-%Y", time.strptime(query[2], "%Y%m%d"))

+        time_of_res = date_formatted + '-' + query[3] + ':' + query[4]

+        test_dates.append(time_of_res)

+        test_build_ids.append(query[8])

+        test_mins.add(float(query[6]))

+        test_maxs.add(float(query[7]))

     df = pd.DataFrame({test_name: test_results})

+    df_detailed = pd.DataFrame({(test_name + ' Results'): test_results, (test_name + ' Date'): test_dates,

+                                "Setup": ([setup_name] * len(test_results)), "Build Id": test_build_ids})

     stats = tuple([float(df.mean()), min(test_mins), max(test_maxs)])  # stats = (avg_mpps,min,max)

-    return df, stats

+    return df, stats, df_detailed

 

 

 def generate_dframe_arr_and_stats_of_tests_per_setup(date, setup_name, setup_dict):

     dframe_arr_trend = []

     stats_arr = []

     dframe_arr_latest = []

+    dframe_arr_detailed = []

     test_names = setup_dict.keys()

     for test in test_names:

-        df, stats = generate_dframe_for_test(test, setup_dict[test])

+        df, stats, df_detailed = generate_dframe_for_test(setup_name, test, setup_dict[test])

+        dframe_arr_detailed.append(df_detailed)

         dframe_arr_trend.append(df)

         stats_arr.append(stats)

-        df_latest = float(setup_dict[test][-1][3])

+        df_latest = float(setup_dict[test][-1][6])

         dframe_arr_latest.append(df_latest)

     dframe_arr_latest = pd.DataFrame({'Date': [date] * len(dframe_arr_latest),

                                       'Setup': [setup_name],

@@ -38,7 +50,7 @@ def generate_dframe_arr_and_stats_of_tests_per_setup(date, setup_name, setup_dic
                                      index=range(1, len(dframe_arr_latest) + 1))

     stats_df = pd.DataFrame(stats_arr, index=setup_dict.keys(), columns=['Avg MPPS', 'Golden Min', 'Golden Max'])

     stats_df.index.name = 'Test Name'

-    return dframe_arr_trend, stats_df, dframe_arr_latest

+    return dframe_arr_trend, stats_df, dframe_arr_latest, dframe_arr_detailed

 

 

 def create_plot_for_dframe_arr(dframe_arr, setup_name, start_date, end_date, show='no', save_path='',

@@ -78,20 +90,43 @@ def create_bar_plot_for_latest_runs_per_setup(dframe_all_tests_latest, setup_nam
         plt.show()

 

 

-def create_all_data_per_setup(setup_dict, setup_name, start_date, end_date, show='no', save_path='', add_stats=''):

-    dframe_arr, stats_arr, dframe_latest_arr = generate_dframe_arr_and_stats_of_tests_per_setup(end_date, setup_name,

-                                                                                                setup_dict)

+def create_all_data_per_setup(setup_dict, setup_name, start_date, end_date, show='no', save_path='', add_stats='',

+                              detailed_test_stats=''):

+    dframe_arr, stats_arr, dframe_latest_arr, dframe_detailed = generate_dframe_arr_and_stats_of_tests_per_setup(

+        end_date, setup_name,

+        setup_dict)

+    if detailed_test_stats:

+        detailed_table = create_detailed_table(dframe_detailed, setup_name, save_path)

+    else:

+        detailed_table = []

     create_bar_plot_for_latest_runs_per_setup(dframe_latest_arr, setup_name, show=show, save_path=save_path)

     create_plot_for_dframe_arr(dframe_arr, setup_name, start_date, end_date, show, save_path)

     if add_stats:

         stats_arr = stats_arr.round(2)

         stats_arr.to_csv(os.path.join(save_path, setup_name + '_trend_stats.csv'))

     plt.close('all')

+    return detailed_table

+

+

+def create_detailed_table(dframe_arr_detailed, setup_name, save_path=''):

+    result = reduce(lambda x, y: pd.merge(x, y, on=('Build Id', 'Setup')), dframe_arr_detailed)

+    return result

 

 

-def create_all_data(ga_data, setup_names, start_date, end_date, save_path='', add_stats=''):

+# WARNING: if the file _all_stats.csv already exists, this script deletes it, to prevent overflowing of data

+#  since data is appended to the file

+def create_all_data(ga_data, setup_names, start_date, end_date, save_path='', add_stats='', detailed_test_stats=''):

+    total_detailed_data = []

+    if detailed_test_stats:

+        if os.path.exists(os.path.join(save_path, '_detailed_table.csv')):

+            os.remove(os.path.join(save_path, '_detailed_table.csv'))

     for setup_name in setup_names:

         if setup_name == 'trex11':

             continue

-        create_all_data_per_setup(ga_data[setup_name], setup_name, start_date, end_date, show='no', save_path=save_path,

-                                  add_stats=add_stats)

+        detailed_setup_data = create_all_data_per_setup(ga_data[setup_name], setup_name, start_date, end_date,

+                                                        show='no', save_path=save_path,

+                                                        add_stats=add_stats, detailed_test_stats=detailed_test_stats)

+        total_detailed_data.append(detailed_setup_data)

+    if detailed_test_stats:

+        total_detailed_dframe = pd.DataFrame().append(total_detailed_data)

+        total_detailed_dframe.to_csv(os.path.join(save_path, '_detailed_table.csv'))