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1 | # Copyright (C) 2017 - Francis Deslauriers <francis.deslauriers@efficios.com> |
2 | # | |
3 | # This program is free software: you can redistribute it and/or modify | |
4 | # it under the terms of the GNU General Public License as published by | |
5 | # the Free Software Foundation, either version 3 of the License, or | |
6 | # (at your option) any later version. | |
7 | # | |
8 | # This program is distributed in the hope that it will be useful, | |
9 | # but WITHOUT ANY WARRANTY; without even the implied warranty of | |
10 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | |
11 | # GNU General Public License for more details. | |
12 | # | |
13 | # You should have received a copy of the GNU General Public License | |
14 | # along with this program. If not, see <http://www.gnu.org/licenses/>. | |
15 | ||
16 | ||
17 | import os, sys | |
18 | import numpy as np | |
19 | import pandas as pd | |
20 | ||
21 | #Set Matplotlib to use the PNG non interactive backend | |
22 | import matplotlib as mpl | |
23 | mpl.use('Agg') | |
24 | ||
25 | import matplotlib.pyplot as plt | |
26 | from matplotlib.ticker import MaxNLocator | |
27 | from cycler import cycler | |
28 | ||
29 | def rename_cols(df): | |
30 | new_cols = {'baseline_1thr_pereventmean': 'basel_1thr', | |
31 | 'baseline_2thr_pereventmean': 'basel_2thr', | |
32 | 'baseline_4thr_pereventmean': 'basel_4thr', | |
33 | 'baseline_8thr_pereventmean': 'basel_8thr', | |
34 | 'baseline_16thr_pereventmean': 'basel_16thr', | |
35 | 'lttng_1thr_pereventmean': 'lttng_1thr', | |
36 | 'lttng_2thr_pereventmean': 'lttng_2thr', | |
37 | 'lttng_4thr_pereventmean': 'lttng_4thr', | |
38 | 'lttng_8thr_pereventmean': 'lttng_8thr', | |
39 | 'lttng_16thr_pereventmean': 'lttng_16thr' | |
40 | } | |
41 | df.rename(columns=new_cols, inplace=True) | |
42 | return df | |
43 | ||
44 | def create_plot(df, graph_type): | |
45 | # We split the data into two plots so it's easier to read | |
46 | lower = ['basel_1thr', 'basel_2thr', 'basel_4thr', 'lttng_1thr', 'lttng_2thr', 'lttng_4thr'] | |
47 | lower_color = ['lightcoral', 'gray', 'chartreuse', 'red', 'black', 'forestgreen'] | |
48 | upper = ['basel_8thr', 'basel_16thr', 'lttng_8thr', 'lttng_16thr'] | |
49 | upper_color = ['deepskyblue', 'orange', 'mediumblue', 'saddlebrown'] | |
50 | ||
51 | ||
52 | title='Meantime per syscalls for {} testcase'.format(graph_type) | |
53 | ||
54 | # Create a plot with 2 sub-plots | |
55 | f, arrax = plt.subplots(2, sharex=True, figsize=(12, 14)) | |
56 | ||
57 | f.suptitle(title, fontsize=18) | |
58 | ||
59 | for (ax, sub, colors) in zip(arrax, [lower, upper], [lower_color,upper_color]): | |
60 | curr_df = df[sub] | |
61 | ax.set_prop_cycle(cycler('color', colors)) | |
62 | ax.plot(curr_df, marker='o') | |
63 | ax.set_ylim(0) | |
64 | ax.grid() | |
65 | ax.set_xlabel('Jenkins Build ID') | |
66 | ax.set_ylabel('Meantime per syscall [ns]') | |
67 | ax.legend(labels=curr_df.columns.values, bbox_to_anchor=(1.2,1)) | |
68 | ax.xaxis.set_major_locator(MaxNLocator(integer=True)) | |
69 | ||
70 | plt.savefig('{}.png'.format(graph_type), bbox_inches='tight') | |
71 | ||
72 | # Writes a file that contains commit id of all configurations shown in the | |
73 | # plots | |
74 | def create_metadata_file(res_dir): | |
75 | list_ = [] | |
76 | for dirname, dirnames, res_files in os.walk('./'+res_dir): | |
77 | if len(dirnames) > 0: | |
78 | continue | |
79 | metadata = pd.read_csv(os.path.join(dirname, 'metadata.csv')) | |
80 | list_.append(metadata) | |
81 | ||
82 | df = pd.concat(list_) | |
83 | df.index=df.build_id | |
84 | df.sort_index(inplace=True) | |
85 | df.to_csv('metadata.csv', index=False) | |
86 | ||
87 | #Iterates over a result directory and creates the plots for the different | |
88 | #testcases | |
89 | def create_plots(res_dir): | |
90 | df = pd.DataFrame() | |
91 | metadata_df = pd.DataFrame() | |
92 | list_ = [] | |
93 | for dirname, dirnames, res_files in os.walk('./'+res_dir): | |
94 | if len(dirnames) > 0: | |
95 | continue | |
96 | metadata = pd.read_csv(os.path.join(dirname, 'metadata.csv')) | |
97 | ||
98 | for res in res_files: | |
99 | if res in 'metadata.csv': | |
100 | continue | |
101 | tmp = pd.read_csv(os.path.join(dirname, res)) | |
102 | #Use the build id as the index for the dataframe for filtering | |
103 | tmp.index = metadata.build_id | |
104 | #Add the testcase name to the row for later filtering | |
105 | tmp['testcase'] = res.split('.')[0] | |
106 | list_.append(tmp) | |
107 | ||
108 | df = pd.concat(list_) | |
109 | df = rename_cols(df) | |
110 | df.sort_index(inplace=True) | |
111 | ||
112 | #Go over the entire dataframe by testcase and create a plot for each type | |
113 | for testcase in df.testcase.unique(): | |
114 | df_testcase = df.loc[df['testcase'] == testcase] | |
115 | create_plot(df=df_testcase, graph_type=testcase) | |
116 | ||
117 | def main(): | |
118 | res_path = sys.argv[1] | |
119 | create_plots(os.path.join(res_path)) | |
120 | create_metadata_file(os.path.join(res_path)) | |
121 | ||
122 | if __name__ == '__main__': | |
123 | main() |