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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
import sys
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():
# TODO: convert data-dir to proper option, clean 'dest' options
parser = OptionParser("usage: %prog [options] [data_dir]...")
parser.add_option('-o', '--out', dest='out',
help='file or directory for data output', default='parse-data')
# TODO: this means nothing, also remove dests
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('-i', '--ignore', metavar='[PARAM...]', default="",
help='ignore changing parameter values')
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')
parser.add_option('-m', '--write-map', action='store_true', default=False,
dest='write_map',
help='Output map of values instead of csv tree')
return parser.parse_args()
ExpData = namedtuple('ExpData', ['name', 'params', 'data_files', 'is_base'])
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():
# Always include cycles or overhead parsing fails
if ignored in params and ignored != conf.PARAMS['cycles']:
params.pop(ignored)
# Track all changed params
for key, value in params.iteritems():
col_map.try_add(key, value)
if conf.PARAMS['cycles'] not in params:
params[conf.PARAMS['cycles']] = conf.DEFAULTS['cycles']
return params
def gen_exp_data(exp_dirs, base_conf, col_map, force):
plain_exps = []
scaling_bases = []
sys.stderr.write("Generating data...\n")
for i, data_dir in enumerate(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)
cycles = int(params[conf.PARAMS['cycles']])
st_output = st.get_st_output(data_dir, tmp_dir, force)
ft_output = ft.get_ft_output(data_dir, cycles, tmp_dir, force)
if base_conf and base_conf.viewitems() & params.viewitems():
if not st_output:
raise Exception("Scaling base '%s' useless without sched data!"
% data_dir)
is_base = True
base_params = copy.deepcopy(params)
base_params.pop(base_conf.keys()[0])
base_exp = ExpData(data_dir, base_params,
DataFiles(ft_output, st_output), True)
scaling_bases += [base_exp]
else:
is_base = False
# Create experiment named after the data dir
exp_data = ExpData(data_dir, params,
DataFiles(ft_output, st_output), is_base)
plain_exps += [exp_data]
sys.stderr.write('\r {0:.2%}'.format(float(i)/len(exp_dirs)))
sys.stderr.write('\n')
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_map = copy.deepcopy(col_map)
if opts.ignore:
for param in opts.ignore.split(","):
col_map.try_remove(param)
base_table = TupleTable(base_map) # For tracking 'base' experiments
result_table = TupleTable(col_map) # For generating output
# Used to find matching scaling_base for each experiment
for base in scaling_bases:
base_table.add_exp(base.params, base)
sys.stderr.write("Parsing data...\n")
for i,exp in enumerate(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 and not exp.is_base:
# 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)
else:
sys.stderr.write('\r {0:.2%}'.format(float(i)/len(plain_exps)))
sys.stderr.write('\n')
if opts.force and os.path.exists(opts.out):
sh.rmtree(opts.out)
result_table.reduce()
sys.stderr.write("Writing result...\n")
if opts.write_map:
# Write summarized results into map
result_table.write_map(opts.out)
else:
# Write out csv directories for all variable params
result_table.write_csvs(opts.out)
if __name__ == '__main__':
main()
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