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             }
  41     df.rename(columns=new_cols, inplace=True)
  42     return df
  43
  44 def convert_us_to_ns(df):
  45     cols = [col for col in df.columns if 'periter' in col]
  46     df[cols] = df[cols].apply(lambda x: x*1000)
  47     return df
  48
  49 def create_plot(df, graph_type):
  50     # We split the data into two plots so it's easier to read
  51     lower = ['basel_1thr', 'basel_2thr', 'basel_4thr', 'lttng_1thr', 'lttng_2thr', 'lttng_4thr']
  52     lower_color = ['lightcoral', 'gray', 'chartreuse', 'red', 'black', 'forestgreen']
  53     upper = ['basel_8thr', 'basel_16thr', 'lttng_8thr', 'lttng_16thr']
  54     upper_color = ['deepskyblue', 'orange', 'mediumblue', 'saddlebrown']
  55
  56     title='Meantime per syscalls for {} testcase'.format(graph_type)
  57
  58     # Create a plot with 2 sub-plots
  59     f, arrax = plt.subplots(2, sharex=True, figsize=(12, 14))
  60
  61     f.suptitle(title, fontsize=18)
  62
  63     for (ax, sub, colors)  in zip(arrax, [lower, upper], [lower_color,upper_color]):
  64         curr_df = df[sub]
  65         ax.set_prop_cycle(cycler('color', colors))
  66         ax.plot(curr_df, marker='o')
  67         ax.set_ylim(0)
  68         ax.grid()
  69         ax.set_xlabel('Jenkins Build ID')
  70         ax.set_ylabel('Meantime per syscall [us]')
  71         ax.legend(labels=curr_df.columns.values, bbox_to_anchor=(1.2,1))
  72         ax.xaxis.set_major_locator(MaxNLocator(integer=True))
  73
  74     plt.savefig('{}.png'.format(graph_type), bbox_inches='tight')
  75
  76 # Writes a file that contains commit id of all configurations shown in the
  77 # plots
  78 def create_metadata_file(res_dir):
  79     list_ = []
  80     for dirname, dirnames, res_files in os.walk('./'+res_dir):
  81         if len(dirnames) > 0:
  82             continue
  83         metadata = pd.read_csv(os.path.join(dirname, 'metadata.csv'))
  84         list_.append(metadata)
  85
  86     df = pd.concat(list_)
  87     df.index=df.build_id
  88     df.sort_index(inplace=True)
  89     df.to_csv('metadata.csv', index=False)
  90
  91 #Iterates over a result directory and creates the plots for the different
  92 #testcases
  93 def create_plots(res_dir):
  94     df = pd.DataFrame()
  95     metadata_df = pd.DataFrame()
  96     list_ = []
  97     for dirname, dirnames, res_files in os.walk('./'+res_dir):
  98         if len(dirnames) > 0:
  99             continue
 100         metadata = pd.read_csv(os.path.join(dirname, 'metadata.csv'))
 101
 102         for res in res_files:
 103             if res in 'metadata.csv':
 104                 continue
 105             tmp = pd.read_csv(os.path.join(dirname, res))
 106             #Use the build id as the index for the dataframe for filtering
 107             tmp.index = metadata.build_id
 108             #Add the testcase name to the row for later filtering
 109             tmp['testcase'] = res.split('.')[0]
 110             list_.append(tmp)
 111
 112         df = pd.concat(list_)
 113         df = convert_us_to_ns(df)
 114         df = rename_cols(df)
 115         df.sort_index(inplace=True)
 116
 117     #Go over the entire dataframe by testcase and create a plot for each type
 118     for testcase in df.testcase.unique():
 119         df_testcase  = df.loc[df['testcase'] == testcase]
 120         create_plot(df=df_testcase, graph_type=testcase)
 121
 122 def main():
 123     res_path = sys.argv[1]
 124     create_plots(os.path.join(res_path))
 125     create_metadata_file(os.path.join(res_path))
 126
 127 if __name__ == '__main__':
 128     main()