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#!/usr/bin/env python
from __future__ import print_function
import config.config as conf
import copy
import os
import parse.ft as ft
import parse.sched as st
import re
import shutil as sh
from collections import namedtuple
from common import load_params
from optparse import OptionParser
from parse.point import ExpPoint
from parse.tuple_table import ColMap,TupleTable
def parse_args():
parser = OptionParser("usage: %prog [options] [data_dir]...")
parser.add_option('-o', '--out-dir', dest='out_dir',
help='directory for data output', default='parse-data')
parser.add_option('-c', '--clean', action='store_true', default=False,
dest='clean', help='do not output single-point csvs')
parser.add_option('-s', '--scale-against', dest='scale_against',
metavar='PARAM=VALUE', default="",
help='calculate task scaling factors against these configs')
parser.add_option('-f', '--force', action='store_true', default=False,
dest='force', help='overwrite existing data')
parser.add_option('-v', '--verbose', action='store_true', default=False,
dest='verbose', help='print out data points')
return parser.parse_args()
ExpData = namedtuple('ExpData', ['name', 'params', 'data_files'])
DataFiles = namedtuple('DataFiles', ['ft','st'])
def get_exp_params(data_dir, col_map):
param_file = "%s/%s" % (data_dir, conf.DEFAULTS['params_file'])
if not os.path.isfile:
raise Exception("No param file '%s' exists!" % param_file)
# Keep only params that uniquely identify the experiment
params = load_params(param_file)
for ignored in conf.PARAMS.itervalues():
if ignored in params:
params.pop(ignored)
# Track all changed params
for key in params.keys():
col_map.try_add(key)
return params
def gen_exp_data(exp_dirs, base_conf, col_map, force):
plain_exps = []
scaling_bases = []
for data_dir in exp_dirs:
if not os.path.isdir(data_dir):
raise IOError("Invalid experiment '%s'" % os.path.abspath(data_dir))
tmp_dir = data_dir + "/tmp"
if not os.path.exists(tmp_dir):
os.mkdir(tmp_dir)
# Read and translate exp output files
params = get_exp_params(data_dir, col_map)
st_output = st.get_st_output(data_dir, tmp_dir, force)
ft_output = ft.get_ft_output(data_dir, tmp_dir, force)
# Create experiment named after the data dir
exp_data = ExpData(data_dir, params, DataFiles(ft_output, st_output))
if base_conf and base_conf.viewitems() & params.viewitems():
if not st_output:
raise Exception("Scaling base '%s' useless without sched data!"
% data_dir)
params.pop(base_conf.keys()[0])
scaling_bases += [exp_data]
else:
plain_exps += [exp_data]
return (plain_exps, scaling_bases)
def main():
opts, args = parse_args()
args = args or [os.getcwd()]
# Configuration key for task systems used to calculate task
# execution scaling factors
base_conf = dict(re.findall("(.*)=(.*)", opts.scale_against))
col_map = ColMap()
(plain_exps, scaling_bases) = gen_exp_data(args, base_conf, col_map, opts.force)
if base_conf and base_conf.keys()[0] not in col_map:
raise IOError("Base column '%s' not present in any parameters!" %
base_conf.keys()[0])
base_table = TupleTable(col_map)
result_table = TupleTable(col_map)
# Used to find matching scaling_base for each experiment
for base in scaling_bases:
base_table.add_exp(base.params, base)
for exp in plain_exps:
result = ExpPoint(exp.name)
if exp.data_files.ft:
# Write overheads into result
ft.extract_ft_data(exp.data_files.ft, result, conf.BASE_EVENTS)
if exp.data_files.st:
base = None
if base_conf:
# Try to find a scaling base
base_params = copy.deepcopy(exp.params)
base_params.pop(base_conf.keys()[0])
base = base_table.get_exps(base_params)[0]
# Write deadline misses / tardiness into result
st.extract_sched_data(exp.data_files.st, result,
base.data_files.st if base else None)
result_table.add_exp(exp.params, result)
if opts.verbose:
print(result)
if opts.force and os.path.exists(opts.out_dir):
sh.rmtree(opts.out_dir)
# Remove un-plottable values
if opts.clean:
result_table.reduce()
result_table.write_result(opts.out_dir)
if __name__ == '__main__':
main()
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