added scripts and asciidoc for automated creation of trex analytics report. the creation is done by issuing './b build --performance' command

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

1 files changed, 136 insertions, 0 deletions

diff --git a/doc/TRexDataAnalysis.py b/doc/TRexDataAnalysis.py
new file mode 100755
index 00000000..3561b0f1
--- /dev/null
+++ b/doc/TRexDataAnalysis.py
@@ -0,0 +1,136 @@
+#!/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

+

+PATH_FOR_GRAPHS = 'Z:/trex/trex-doc/images/'

+

+

+def convert_dict_to_dframe(data, categories, index=''):

+	data_input = {}

+	for category in categories:

+		data_input[category] = data[category]

+	if index:

+		df = pd.DataFrame(data_input, index=data[index])

+	else:

+		df = pd.DataFrame(data_input)

+	return df

+

+

+def plot_bar_by_category(data_frame, category, index='', graph_name='graph.png', show='', gtitle='', save_path=''):

+	if index:

+		data_frame = data_frame.sort_index(by=index)

+		print data_frame[index]

+	else:

+		print data_frame

+	data_frame = pd.DataFrame(data_frame[category], columns=category).astype(float)

+	data_frame.plot(kind='bar')

+	plt.xticks(rotation='horizontal')

+	plt.title(gtitle)

+	if save_path:

+		plt.savefig(save_path + graph_name)

+	if show:

+		plt.show()

+

+

+def generate_csv(data_frame, file_name, save_path=(os.getcwd() + "/")):

+	f = open(save_path + file_name, 'w')

+	data_frame.to_csv(f)

+	f.close()

+

+

+# category is an array of category names that will appear as metrics

+def plot_bar_by_test_name(data_frame, test_name, category, graph_name='graph.png', show='', gtitle='', save_path=''):

+	data_frame = data_frame[data_frame['Test_name'] == test_name]

+	plot_bar_by_category(data_frame, category, 'Test_name', graph_name, show, gtitle=test_name, save_path=save_path)

+

+

+def generate_dframe_for_test(test_name, test_data):

+	test_results = []

+	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]))

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

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

+	return df, stats

+

+

+def generate_dframe_arr_and_stats_of_tests_per_setup(date, setup_name, setup_dict):

+	dframe_arr_trend = []

+	stats_arr = []

+	dframe_arr_latest = []

+	test_names = setup_dict.keys()

+	for test in test_names:

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

+		dframe_arr_trend.append(df)

+		stats_arr.append(stats)

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

+		dframe_arr_latest.append(df_latest)

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

+									  'Setup': [setup_name],

+									  'Test Name': test_names,

+									  'MPPS': dframe_arr_latest},

+									 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

+

+

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

+							   file_name='trend_graph'):

+	dframe_all = pd.concat(dframe_arr, axis=1)

+	dframe_all = dframe_all.astype(float)

+	dframe_all.plot()

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

+	plt.ylabel('MPPS')

+	plt.title('Setup: ' + setup_name)

+	plt.tick_params(

+		axis='x',

+		which='both',

+		bottom='off',

+		top='off',

+		labelbottom='off')

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

+	if save_path:

+		plt.savefig(save_path + setup_name + file_name + '.png')

+	if show == 'yes':

+		plt.show()

+

+

+def create_bar_plot_for_latest_runs_per_setup(dframe_all_tests_latest, setup_name, show='no', save_path=''):

+	plt.figure()

+	dframe_all_tests_latest['MPPS'].plot(kind='bar', legend=False)

+	dframe_all_tests_latest = dframe_all_tests_latest[['Test Name', 'Setup', 'Date', 'MPPS']]

+	plt.xticks(rotation='horizontal')

+	plt.xlabel('Index of Tests')

+	plt.ylabel('MPPS')

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

+	if save_path:

+		plt.savefig(save_path + setup_name + '_latest_test_runs.png')

+		dframe_all_tests_latest = dframe_all_tests_latest.round(2)

+		dframe_all_tests_latest.to_csv(save_path + setup_name + '_latest_test_runs_stats.csv')

+	if show == 'yes':

+		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)

+	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(save_path + setup_name + '_trend_stats.csv')

+	plt.close('all')

+

+

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

+	for setup_name in setup_names:

+		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)