new TRexDataAnalysis version, with OOP design

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

2 files changed, 188 insertions, 3 deletions

diff --git a/doc/AnalyticsWebReport.py b/doc/AnalyticsWebReport.py
index 1806cab9..e3f6504b 100755
--- a/doc/AnalyticsWebReport.py
+++ b/doc/AnalyticsWebReport.py
@@ -1,7 +1,7 @@
 import os

 import sys

 import AnalyticsConnect as ac

-import TRexDataAnalysis as tr

+import TRexDataAnalysisV2 as tr

 import time

 import datetime

 

@@ -20,8 +20,8 @@ def main(verbose=False, detailed_test_stats=''):
         print('Saving data to %s' % dest_path)

         if detailed_test_stats:

             print('generating detailed table for test results')

-    tr.create_all_data(ga_all_data_dict, setups, start_date, current_date, save_path=dest_path,

-                       add_stats='yes', detailed_test_stats=detailed_test_stats)

+    tr.create_all_data(ga_all_data_dict, start_date, current_date, save_path=dest_path,

+                       detailed_test_stats=detailed_test_stats)

     if verbose:

         print('Done without errors.')

 

diff --git a/doc/TRexDataAnalysisV2.py b/doc/TRexDataAnalysisV2.py
new file mode 100755
index 00000000..e7e82b20
--- /dev/null
+++ b/doc/TRexDataAnalysisV2.py
@@ -0,0 +1,185 @@
+#!/scratch/Anaconda2.4.0/bin/python

+import pandas as pd

+import numpy as np

+import matplotlib

+

+matplotlib.use('Agg')

+from matplotlib import pyplot as plt

+import os

+import time

+

+

+### TODO: insert a description of a test query

+

+class Test:

+    def __init__(self, name, setup_name):

+        self.name = name

+        self.setup_name = setup_name

+        self.stats = []  # tuple

+        self.results_df = []  # dataFrame

+        self.latest_result = []  # float

+        self.latest_result_date = ''  # string

+

+    def analyze_all_test_data(self, raw_test_data):

+        test_results = []

+        test_dates = []

+        test_build_ids = []

+        test_mins = set()

+        test_maxs = set()

+        for query in raw_test_data:

+            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]))

+        test_results_df = pd.DataFrame({self.name: test_results, (self.name + ' Date'): test_dates,

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

+        stats = tuple(

+            [float(test_results_df[self.name].mean()), min(test_mins), max(test_maxs)])  # stats = (avg_mpps,min,max)

+        self.latest_result = float(test_results_df[self.name].iloc[-1])

+        self.latest_result_date = str(test_results_df[test_results_df.columns[3]].iloc[-1])

+        self.results_df = test_results_df

+        self.stats = stats

+

+

+class Setup:

+    def __init__(self, name, start_date, end_date, raw_setup_data):

+        self.name = name

+        self.start_date = start_date  # string of date

+        self.end_date = end_date  # string of date

+        self.tests = []  # list of test objects

+        self.all_tests_data_table = pd.DataFrame()  # dataframe

+        self.setup_trend_stats = pd.DataFrame()  # dataframe

+        self.latest_test_results = pd.DataFrame()  # dataframe

+        self.raw_setup_data = raw_setup_data  # dictionary

+        self.test_names = raw_setup_data.keys()  # list of names

+

+    def analyze_all_tests(self):

+        for test_name in self.test_names:

+            t = Test(test_name, self.name)

+            t.analyze_all_test_data(self.raw_setup_data[test_name])

+            self.tests.append(t)

+

+    def analyze_latest_test_results(self):

+        test_names = []

+        test_dates = []

+        test_latest_results = []

+        for test in self.tests:

+            test_names.append(test.name)

+            test_dates.append(test.latest_result_date)

+            test_latest_results.append(test.latest_result)

+        self.latest_test_results = pd.DataFrame(

+            {'Date': test_dates, 'Test Name': test_names, 'MPPS\Core (Norm)': test_latest_results},

+            index=range(1, len(test_latest_results) + 1))

+        self.latest_test_results = self.latest_test_results[[2, 1, 0]]  # re-order columns to name|MPPS|date

+

+    def analyze_all_tests_stats(self):

+        test_names = []

+        all_test_stats = []

+        for test in self.tests:

+            test_names.append(test.name)

+            all_test_stats.append(test.stats)

+        self.setup_trend_stats = pd.DataFrame(all_test_stats, index=test_names,

+                                              columns=['Avg MPPS/Core (Norm)', 'Golden Min', 'Golden Max'])

+        self.setup_trend_stats.index.name = 'Test Name'

+

+    def analyze_all_tests_trend(self):

+        all_tests_trend_data = []

+        for test in self.tests:

+            all_tests_trend_data.append(test.results_df)

+        self.all_tests_data_table = reduce(lambda x, y: pd.merge(x, y, how='outer'), all_tests_trend_data)

+

+    def plot_trend_graph_all_tests(self, save_path='', file_name='_trend_graph.png'):

+        for test_name in self.test_names:

+            self.all_tests_data_table[test_name].plot()

+            plt.legend(fontsize='small', loc='best')

+        plt.ylabel('MPPS/Core (Norm)')

+        plt.title('Setup: ' + self.name)

+        plt.tick_params(

+            axis='x',

+            which='both',

+            bottom='off',

+            top='off',

+            labelbottom='off')

+        plt.xlabel('Time Period: ' + self.start_date + ' - ' + self.end_date)

+        if save_path:

+            plt.savefig(os.path.join(save_path, self.name + file_name))

+            if not self.setup_trend_stats.empty:

+                (self.setup_trend_stats.round(2)).to_csv(os.path.join(save_path, self.name +

+                                                                      '_trend_stats.csv'))

+            plt.close('all')

+

+    def plot_latest_test_results_bar_chart(self, save_path='', img_file_name='_latest_test_runs.png',

+                                           stats_file_name='_latest_test_runs_stats.csv'):

+        plt.figure()

+        colors_for_bars = ['b', 'g', 'r', 'c', 'm', 'y']

+        self.latest_test_results[[1]].plot(kind='bar', legend=False,

+                                           color=colors_for_bars)  # plot only mpps data, which is in column 1

+        plt.xticks(rotation='horizontal')

+        plt.xlabel('Index of Tests')

+        plt.ylabel('MPPS/Core (Norm)')

+        plt.title("Test Runs for Setup: " + self.name)

+        if save_path:

+            plt.savefig(os.path.join(save_path, self.name + img_file_name))

+            (self.latest_test_results.round(2)).to_csv(

+                os.path.join(save_path, self.name + stats_file_name))

+        plt.close('all')

+

+    def analyze_all_setup_data(self):

+        self.analyze_all_tests()

+        self.analyze_latest_test_results()

+        self.analyze_all_tests_stats()

+        self.analyze_all_tests_trend()

+

+    def plot_all(self, save_path=''):

+        self.plot_latest_test_results_bar_chart(save_path)

+        self.plot_trend_graph_all_tests(save_path)

+

+

+def latest_runs_comparison_bar_chart(setup_name1, setup_name2, setup1_latest_result, setup2_latest_result,

+                                     save_path=''

+                                     ):

+    s1_res = setup1_latest_result[[0, 1]]  # column0 is test name, column1 is MPPS\Core

+    s2_res = setup2_latest_result[[0, 1, 2]]  # column0 is test name, column1 is MPPS\Core, column2 is Date

+    s1_res.columns = ['Test Name', setup_name1]

+    s2_res.columns = ['Test Name', setup_name2, 'Date']

+    compare_dframe = pd.merge(s1_res, s2_res, on='Test Name')

+    compare_dframe.plot(kind='bar')

+    plt.legend(fontsize='small', loc='best')

+    plt.xticks(rotation='horizontal')

+    plt.xlabel('Index of Tests')

+    plt.ylabel('MPPS/Core (Norm)')

+    plt.title("Comparison between " + setup_name1 + " and " + setup_name2)

+    if save_path:

+        plt.savefig(os.path.join(save_path, "_comparison.png"))

+        compare_dframe = compare_dframe.round(2)

+        compare_dframe.to_csv(os.path.join(save_path, '_comparison_stats_table.csv'))

+

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

+

+

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

+    all_setups = {}

+    all_setups_data = []

+    setup_names = ga_data.keys()

+    for setup_name in setup_names:

+        s = Setup(setup_name, start_date, end_date, ga_data[setup_name])

+        s.analyze_all_setup_data()

+        s.plot_all(save_path)

+        all_setups_data.append(s.all_tests_data_table)

+        all_setups[setup_name] = s

+

+    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'))

+        all_setups_data_dframe = pd.DataFrame().append(all_setups_data)

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

+

+    trex07setup = all_setups['trex07']

+    trex08setup = all_setups['trex08']

+    latest_runs_comparison_bar_chart('Mellanox ConnectX-4',

+                                     'Intel XL710', trex07setup.latest_test_results,

+                                     trex08setup.latest_test_results,

+                                     save_path=save_path)