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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
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=os.getcwd())
parser.add_option('-s', '--scale-against', dest='scale_against',
metavar='PARAM=VALUE', default="",
help='calculate task scaling factors against these configs')
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):
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)
ft_output = ft.get_ft_output(data_dir, tmp_dir)
# 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)
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:
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]
if base:
# Write scaling factor (vs base) into result
st.extract_scaling_data(exp.data_files.st,
base.data_files.st,
result)
# Write deadline misses / tardiness into result
st.extract_sched_data(exp.data_files.st, result)
result_table.add_exp(exp.params, result)
print(result)
result_table.write_result(opts.out_dir)
if __name__ == '__main__':
main()
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