1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
|
#!/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
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[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, 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, 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][5])
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, dframe_arr_detailed
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(os.path.join(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()
colors_for_bars = ['b', 'g', 'r', 'c', 'm', 'y']
dframe_all_tests_latest['MPPS'].plot(kind='bar', legend=False, color = colors_for_bars)
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(os.path.join(save_path, setup_name + '_latest_test_runs.png'))
dframe_all_tests_latest = dframe_all_tests_latest.round(2)
dframe_all_tests_latest.to_csv(os.path.join(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='',
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)
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, dframe_latest_arr
def create_detailed_table(dframe_arr_detailed):
result = reduce(lambda x, y: pd.merge(x, y, on=('Build Id', 'Setup')), dframe_arr_detailed)
return result
def latest_runs_comparison_bar_chart(setup_name1, setup_name2, setup1_latest_result, setup2_latest_result, save_path='',
show='no'):
s1_res = setup1_latest_result[['Test Name', 'MPPS']]
s2_res = setup2_latest_result[['Test Name', 'MPPS','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')
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'))
if show == 'yes':
plt.show()
# 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 = []
trex07_latest = []
trex08_latest = []
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 == 'trex07':
detailed_setup_data, trex07_latest = 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)
elif setup_name == 'trex08':
detailed_setup_data, trex08_latest = 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)
else:
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)[0]
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'))
latest_runs_comparison_bar_chart('Mellanox ConnectX-4',
'Intel XL710', trex07_latest, trex08_latest,
save_path=save_path, show='no')
|
