scripts/lttng-baremetal-tests/generate-plots.py

   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             'baseline_1thr_periterstdev': 'basel_1thr_stdev',
  41             'baseline_2thr_periterstdev': 'basel_2thr_stdev',
  42             'baseline_4thr_periterstdev': 'basel_4thr_stdev',
  43             'baseline_8thr_periterstdev': 'basel_8thr_stdev',
  44             'baseline_16thr_periterstdev': 'basel_16thr_stdev',
  45             'lttng_1thr_periterstdev': 'lttng_1thr_stdev',
  46             'lttng_2thr_periterstdev': 'lttng_2thr_stdev',
  47             'lttng_4thr_periterstdev': 'lttng_4thr_stdev',
  48             'lttng_8thr_periterstdev': 'lttng_8thr_stdev',
  49             'lttng_16thr_periterstdev': 'lttng_16thr_stdev'
  50             }
  51     df.rename(columns=new_cols, inplace=True)
  52     return df
  53
  54 def convert_us_to_ns(df):
  55     cols = [col for col in df.columns if 'periter' in col]
  56     df[cols] = df[cols].apply(lambda x: x*1000)
  57     return df
  58
  59 def create_plot(df, graph_type):
  60     # We split the data into two plots so it's easier to read
  61     lower = ['basel_{}thr'.format(s) for s in [1,2,4]]
  62     lower += ['lttng_{}thr'.format(s) for s in [1,2,4]]
  63
  64     upper = ['basel_{}thr'.format(s) for s in [8, 16]]
  65     upper += ['lttng_{}thr'.format(s) for s in [8, 16]]
  66
  67     lower_stdev = ['{}_stdev'.format(s) for s in lower]
  68     upper_stdev = ['{}_stdev'.format(s) for s in upper]
  69
  70     lower_color = ['lightcoral', 'gray', 'chartreuse', 'red', 'black', 'forestgreen']
  71     upper_color = ['deepskyblue', 'orange', 'mediumblue', 'saddlebrown']
  72
  73     title='Meantime per syscalls for {} testcase'.format(graph_type)
  74
  75     # Create a plot with 2 sub-plots
  76     f, arrax = plt.subplots(2, sharex=True, figsize=(12, 14))
  77
  78     f.suptitle(title, fontsize=18)
  79
  80     for (ax, data_cols, stdev_cols, colors)  in zip(arrax, [lower, upper], [lower_stdev, upper_stdev], [lower_color,upper_color]):
  81         curr_df = df[data_cols]
  82
  83         # set the color cycler for this plot
  84         ax.set_prop_cycle(cycler('color', colors))
  85
  86         # Plot each line and its errorbars
  87         for (data, stdev) in  zip(data_cols, stdev_cols):
  88             ax.errorbar(x=df.index.values, y=df[data], yerr=df[stdev], marker='o')
  89
  90         ax.set_ylim(0)
  91         ax.grid()
  92         ax.set_xlabel('Jenkins Build ID')
  93         ax.set_ylabel('Meantime per syscall [us]')
  94         ax.legend(labels=curr_df.columns.values, bbox_to_anchor=(1.2,1))
  95         ax.xaxis.set_major_locator(MaxNLocator(integer=True))
  96
  97     plt.savefig('{}.png'.format(graph_type), bbox_inches='tight')
  98
  99 # Writes a file that contains commit id of all configurations shown in the
 100 # plots
 101 def create_metadata_file(res_dir):
 102     list_ = []
 103     for dirname, dirnames, res_files in os.walk('./'+res_dir):
 104         if len(dirnames) > 0:
 105             continue
 106         metadata = pd.read_csv(os.path.join(dirname, 'metadata.csv'))
 107         list_.append(metadata)
 108
 109     df = pd.concat(list_)
 110     df.index=df.build_id
 111     df.sort_index(inplace=True)
 112     df.to_csv('metadata.csv', index=False)
 113
 114 #Iterates over a result directory and creates the plots for the different
 115 #testcases
 116 def create_plots(res_dir):
 117     df = pd.DataFrame()
 118     metadata_df = pd.DataFrame()
 119     list_ = []
 120     for dirname, dirnames, res_files in os.walk('./'+res_dir):
 121         if len(dirnames) > 0:
 122             continue
 123         metadata = pd.read_csv(os.path.join(dirname, 'metadata.csv'))
 124
 125         for res in res_files:
 126             if res in 'metadata.csv':
 127                 continue
 128             tmp = pd.read_csv(os.path.join(dirname, res))
 129             #Use the build id as the index for the dataframe for filtering
 130             tmp.index = metadata.build_id
 131             #Add the testcase name to the row for later filtering
 132             tmp['testcase'] = res.split('.')[0]
 133             list_.append(tmp)
 134
 135         df = pd.concat(list_)
 136         df = convert_us_to_ns(df)
 137         df = rename_cols(df)
 138         df.sort_index(inplace=True)
 139
 140     #Go over the entire dataframe by testcase and create a plot for each type
 141     for testcase in df.testcase.unique():
 142         df_testcase  = df.loc[df['testcase'] == testcase]
 143         create_plot(df=df_testcase, graph_type=testcase)
 144
 145 def main():
 146     res_path = sys.argv[1]
 147     create_plots(os.path.join(res_path))
 148     create_metadata_file(os.path.join(res_path))
 149
 150 if __name__ == '__main__':
 151     main()