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import config.config as conf
import os
import re
import numpy as np
import subprocess
from collections import namedtuple
from point import Measurement
Task = namedtuple('Task', ['pid', 'period'])
def get_st_output(data_dir, out_dir):
bin_files = conf.FILES['sched_data'].format(".*")
bins = [f for f in os.listdir(data_dir) if re.match(bin_files, f)]
output_file = "%s/out-st" % out_dir
if os.path.isfile(output_file):
return output_file
if len(bins) != 0:
cmd_arr = [conf.BINS['st_show']]
cmd_arr.extend(bins)
with open(output_file, "w") as f:
subprocess.call(cmd_arr, cwd=data_dir, stdout=f)
else:
return None
return output_file
def get_tasks(data):
reg = r"PARAM.*?(\d+).*?cost:\s+[\d\.]+ms.*?period.*?([\d.]+)"
return [Task(x[0], x[1]) for x in re.findall(reg, data)]
def extract_tardy_vals(data, exp_point):
ratios = []
tards = []
for t in get_tasks(data):
reg = r"TARDY.*?" + t.pid + "/(\d+).*?Tot.*?([\d.]+).*?ms.*([\d.]+).*?ms.*?([\d.]+)"
matches = re.findall(reg, data)
if len(matches) != 0:
jobs = float(matches[0][0])
total_tard = float(matches[0][1])
# max_tard = float(matches[0][2])
misses = float(matches[0][3])
rel_tard = (total_tard / jobs) / float(t.period)
if misses != 0:
miss_ratio = (misses / jobs)
else:
miss_ratio = 0
ratios.append(miss_ratio)
tards.append(rel_tard)
for (array, name) in ((tards, "rel-tard"), (ratios, "miss-ratio")):
exp_point[name] = Measurement().from_array(array)
def extract_variance(data, exp_point):
varz = []
for t in get_tasks(data):
reg = r"COMPLETION.*?" + t.pid + r".*?([\d\.]+)ms"
matches = re.findall(reg, data)
if len(matches) == 0:
return 0
job_times = np.array(filter(lambda x: float(x) != 0, matches), dtype=np.float)
# Coefficient of variation
cv = job_times.std() / job_times.mean()
# Correction, assuming normal distributions
corrected = (1 + 1/(4 * len(job_times))) * cv
varz.append(corrected)
exp_point['var'] = Measurement().from_array(varz)
def get_sched_data(data_file, result):
with open(data_file, 'r') as f:
data = f.read()
# if conf != BASE:
# (our_values, their_values) = extract_exec_vals(our_data, their_data)
# conf_result = get_stats(our_values, their_values)
# for key in conf_result.keys():
# result[key][conf] = conf_result[key]
extract_tardy_vals(data, result)
extract_variance(data, result)
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