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_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()