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#!/usr/bin/env python
import sys
from plot import decode
from os.path import splitext, basename
from glob import glob
from collections import defaultdict
import optparse
from util import load_csv_file, write_csv_file
o = optparse.make_option
opts = [
# o('-r', '--result-type', action='store', dest='',
# type='choice', choices=['hard', 'soft', 'tardiness', 'rel-tardiness'],
# help='what data should be emitted?'),
]
defaults = {
}
options = None
#G-EDF/testpoint_ucap=13.75_wss=1792_dist=exp-10-10-100_deadlines=implicit_host=ludwig_scheduler=G-EDF.csv
IDX_WSS = 4
IDX_UCAP = 5
IDX_HARD_IDLE = 6
IDX_HARD_LOAD = 7
IDX_SOFT_IDLE = 8
IDX_SOFT_LOAD = 17
IDX_TARD_MAX_IDLE = 9
IDX_TARD_AVG_IDLE = 10
IDX_TARD_STD_IDLE = 12
IDX_TARD_MAX_LOAD = 18
IDX_TARD_AVG_LOAD = 19
IDX_TARD_STD_LOAD = 21
IDX_RTARD_MAX_IDLE = 13
IDX_RTARD_AVG_IDLE = 14
IDX_RTARD_STD_IDLE = 16
IDX_RTARD_MAX_LOAD = 22
IDX_RTARD_AVG_LOAD = 23
IDX_RTARD_STD_LOAD = 25
def key(fname):
name, ext = splitext(basename(fname))
conf = decode(name)
sched = conf['scheduler']
if 'quanta' in conf and conf['quanta'] == 'staggered':
sched = 'S-' + sched
d = 'dist=%s' % conf['dist']
dl = 'deadlines=%s' % conf['deadlines']
h = 'host=%s' % conf['host']
return ('_'.join([d, dl, h]), sched, fname)
def get_row(i, key_col, arrays, cols):
row = []
data = arrays[0]
keys = [data[i,x] for x in key_col]
for idx, data in enumerate(arrays):
# make sure we are not combining apples and oranges...
d_keys = [data[i,x] for x in key_col]
if not d_keys == keys:
print 'Bad: missing data sched=%d row=%d %s!=%s' % (idx + 1, i+1, d_keys, keys)
assert False
row += [data[i,x] for x in cols]
return keys + row
def get_table(arrays, key_col, data_col):
data = arrays[0]
return [get_row(i, key_col, arrays, data_col) for i in xrange(len(data))]
def write_sched(name, components, hard=True):
header = ['WSS', 'ucap']
arrays = []
for (sched, data) in components:
# print sched
header += ["%s (load)" % sched , "%s (idle)" % sched]
arrays.append(data)
if hard:
data_col = [IDX_HARD_LOAD, IDX_HARD_IDLE]
else:
data_col = [IDX_SOFT_LOAD, IDX_SOFT_IDLE]
result = get_table(arrays, [IDX_WSS, IDX_UCAP], data_col)
# sort by WSS, then by ucap
result.sort(key=lambda row: (row[0], row[1]))
fname = '%s_%s.csv' % ('hard' if hard else 'soft',
name)
write_csv_file(fname, result, header=header, width=20)
def write_tardiness(name, components, relative=True):
header = ['WSS', 'ucap']
arrays = []
for (sched, data) in components:
header += ["%s (max, load)" % sched,
"%s (avg, load)" % sched,
"%s (std, load)" % sched,
"%s (max, idle)" % sched,
"%s (avg, idle)" % sched,
"%s (std, idle)" % sched]
arrays.append(data)
if relative:
data_col = [IDX_RTARD_MAX_LOAD, IDX_RTARD_AVG_LOAD, IDX_RTARD_STD_LOAD,
IDX_RTARD_MAX_IDLE, IDX_RTARD_MAX_IDLE, IDX_RTARD_STD_IDLE]
else:
data_col = [IDX_TARD_MAX_LOAD, IDX_TARD_AVG_LOAD, IDX_TARD_STD_LOAD,
IDX_TARD_MAX_IDLE, IDX_TARD_MAX_IDLE, IDX_TARD_STD_IDLE]
result = get_table(arrays, [IDX_WSS, IDX_UCAP], data_col)
# sort by WSS, then by ucap
result.sort(key=lambda row: (row[0], row[1]))
fname = '%s_%s.csv' % ('rel-tard' if relative else 'abs-tard',
name)
write_csv_file(fname, result, header=header, width=35)
def assemble_results(dir):
files = glob(dir + '/*.csv')
parts = defaultdict(list)
print 'Organizing %d files...' % len(files)
for f in files:
k, sched, fname = key(f)
parts[k].append((sched, fname))
for k in parts:
# sort by scheduler name
parts[k].sort()
for i, k in enumerate(parts):
comment = 1
print '[%d/%d] Processing %s' % (i+ 1, len(parts), k)
print 'Loading files.'
components = [(sched, load_csv_file(fname)) for (sched, fname) in parts[k]]
print 'Generating output.'
write_sched(k, components, hard=True)
write_sched(k, components, hard=False)
write_tardiness(k, components, relative=True)
write_tardiness(k, components, relative=False)
if __name__ == '__main__':
parser = optparse.OptionParser(option_list=opts)
parser.set_defaults(**defaults)
(options, dirs) = parser.parse_args()
for d in dirs:
assemble_results(d)
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