| 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_peritermean': 'basel_1thr', |
| 31 | 'baseline_2thr_peritermean': 'basel_2thr', |
| 32 | 'baseline_4thr_peritermean': 'basel_4thr', |
| 33 | 'baseline_8thr_peritermean': 'basel_8thr', |
| 34 | 'baseline_16thr_peritermean': 'basel_16thr', |
| 35 | 'lttng_1thr_peritermean': 'lttng_1thr', |
| 36 | 'lttng_2thr_peritermean': 'lttng_2thr', |
| 37 | 'lttng_4thr_peritermean': 'lttng_4thr', |
| 38 | 'lttng_8thr_peritermean': 'lttng_8thr', |
| 39 | 'lttng_16thr_peritermean': '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 [us]') |
| 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() |