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"""
TODO: make regexes indexable by name
"""
import config.config as conf
import os
import re
import numpy as np
import subprocess
from collections import namedtuple,defaultdict
from point import Measurement,Type
TaskConfig = namedtuple('TaskConfig', ['cpu','wcet','period'])
Task = namedtuple('Task', ['pid', 'config'])
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.]+)ms.*?part.*?(\d+)"
ret = []
for match in re.findall(reg, data):
t = Task(match[0], TaskConfig(match[3],match[1],match[2]))
ret += [t]
return ret
def get_task_exits(data):
reg = r"TASK_EXIT *?(\d+)/.*?Avg.*?(\d+).*?Max.*?(\d+)"
ret = []
for match in re.findall(reg, data):
m = Measurement(match[0], {Type.Max : match[2], Type.Avg : match[1]})
ret += [m]
return ret
def extract_tardy_vals(data, exp_point):
ratios = []
avg_tards = []
max_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])
print("total tard: %s" % total_tard)
avg_tard = (total_tard / jobs) / float(t.config.period)
max_tard = float(matches[0][2]) / float(t.config.period)
print("avg tard: %s" % avg_tard)
misses = float(matches[0][3])
if misses != 0:
miss_ratio = (misses / jobs)
print("misses is %d, jobs is %d" % (misses, jobs))
else:
miss_ratio = 0
ratios += [miss_ratio]
avg_tards += [avg_tard]
max_tards += [max_tard]
exp_point["avg-rel-tard"] = Measurement().from_array(avg_tards)
exp_point["max-rel-tard"] = Measurement().from_array(max_tards)
exp_point["miss-ratio"] = Measurement().from_array(ratios)
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['exec-var'] = Measurement().from_array(varz)
def extract_sched_data(data_file, result):
with open(data_file, 'r') as f:
data = f.read()
extract_tardy_vals(data, result)
extract_variance(data, result)
def config_exit_stats(file):
with open(file, 'r') as f:
data = f.read()
tasks = get_tasks(data)
# Dictionary of task exit measurements by pid
exits = get_task_exits(data)
exit_dict = dict((e.id, e) for e in exits)
# Dictionary where keys are configurations, values are list
# of tasks with those configuratino
config_dict = defaultdict(lambda: [])
for t in tasks:
config_dict[t.config] += [t]
for config in config_dict:
task_list = sorted(config_dict[config])
# Replace tasks with corresponding exit stats
exit_list = [exit_dict[t.pid] for t in task_list]
config_dict[config] = exit_list
return config_dict
saved_stats = {}
def get_base_stats(base_file):
if base_file in saved_stats:
return saved_stats[base_file]
result = config_exit_stats(base_file)
saved_stats[base_file] = result
return result
def extract_scaling_data(data_file, base_file, result):
# Generate trees of tasks with matching configurations
data_stats = config_exit_stats(data_file)
base_stats = get_base_stats(base_file)
# Scaling factors are calculated by matching groups of tasks with the same
# config, then comparing task-to-task exec times in order of PID within
# each group
max_scales = []
avg_scales = []
for config in data_stats:
if len(data_stats[config]) != len(base_stats[config]):
# Quit, we are missing a record and can't guarantee
# a task-to-task comparison
continue
for data_stat, base_stat in zip(data_stats[config],base_stats[config]):
# How much larger is their exec stat than ours?
avg_scale = float(base_stat[Type.Avg]) / float(base_stat[Type.Avg])
max_scale = float(base_stat[Type.Max]) / float(base_stat[Type.Max])
avg_scales += [avg_scale]
max_scales += [max_scale]
result['max-scale'] = Measurement().from_array(max_scales)
result['avg-scale'] = Measurement().from_array(avg_scales)
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