我将如何使用linux下的python(在单核Raspberry Pi上运行)实现毫秒精度的数组处理.
我正在尝试解析MIDI文件中的信息,该文件已被预处理到一个数组,其中每毫秒我检查数组是否在当前时间戳处有条目并触发某些功能(如果有).
目前我正在使用time.time()并使用繁忙的等待(如here结束).这会占用所有cpu,因此我选择了更好的解决方案.
# iterate through all milliseconds
for current_ms in xrange(0,last+1):
start = time()
# check if events are to be processed
try:
events = allEvents[current_ms]
# iterate over all events for this millisecond
for event in events:
# check if event contains note information
if 'note' in event:
# check if mapping to pin exists
if event['note'] in mapping:
pin = mapping[event['note']]
# check if event contains on/off information
if 'mode' in event:
if event['mode'] == 0:
pin_off(pin)
elif event['mode'] == 1:
pin_on(pin)
else:
debug("unknown mode in event:"+event)
else:
debug("no mapping for note:" + event['note'])
except:
pass
end = time()
# fill the rest of the millisecond
while (end-start) < (1.0/(1000.0)):
end = time()
last是最后一个事件的毫秒(从预处理中得知)
这不是关于time() vs clock()更多关于sleep vs busy wait的问题.
由于too low accuracy of sleep(),我无法真正睡在“毫秒剩余”循环中.如果我要使用ctypes,我将如何正确地进行操作?
是否有一些Timer库可以每毫秒可靠地调用一次回调?
我目前的实现是在GitHub.使用这种方法我在drum_sample上得到大约4或5ms的偏差,总共3.7s(有模拟,因此没有附加真正的硬件).在30.7s样本上,偏差约为32ms(因此它至少不是线性的!).
我已尝试使用以下代码使用time.sleep()和nanosleep() via ctypes
import time
import timeit
import ctypes
libc = ctypes.CDLL('libc.so.6')
class Timespec(ctypes.Structure):
""" timespec struct for nanosleep,see:
http://linux.die.net/man/2/nanosleep """
_fields_ = [('tv_sec',ctypes.c_long),('tv_nsec',ctypes.c_long)]
libc.nanosleep.argtypes = [ctypes.POINTER(Timespec),ctypes.POINTER(Timespec)]
nanosleep_req = Timespec()
nanosleep_rem = Timespec()
def nsleep(us):
#print('nsleep: {0:.9f}'.format(us))
""" Delay microseconds with libc nanosleep() using ctypes. """
if (us >= 1000000):
sec = us/1000000
us %= 1000000
else: sec = 0
nanosleep_req.tv_sec = sec
nanosleep_req.tv_nsec = int(us * 1000)
libc.nanosleep(nanosleep_req,nanosleep_rem)
LOOPS = 10000
def do_sleep(min_sleep):
#print('try: {0:.9f}'.format(min_sleep))
total = 0.0
for i in xrange(0,LOOPS):
start = timeit.default_timer()
nsleep(min_sleep*1000*1000)
#time.sleep(min_sleep)
end = timeit.default_timer()
total += end - start
return (total / LOOPS)
iterations = 5
iteration = 1
min_sleep = 0.001
result = None
while True:
result = do_sleep(min_sleep)
#print('res: {0:.9f}'.format(result))
if result > 1.5 * min_sleep:
if iteration > iterations:
break
else:
min_sleep = result
iteration += 1
else:
min_sleep /= 2.0
print('FIN: {0:.9f}'.format(result))
我的i5的结果是
FIN: 0.000165443
而在RPi上它是
FIN: 0.000578617
这表明睡眠时间约为0.1或0.5毫秒,给定的抖动(倾向于睡得更久)最多可以帮助我减少负荷.
import sched
import time
def f(t0):
print 'Time elapsed since t0:',time.time() - t0
s = sched.scheduler(time.time,time.sleep)
for i in range(10):
s.enterabs(t0 + 10 + i,f,(t0,))
s.run()
结果:
Time elapsed since t0: 10.0058200359
Time elapsed since t0: 11.0022959709
Time elapsed since t0: 12.0017120838
Time elapsed since t0: 13.0022599697
Time elapsed since t0: 14.0022521019
Time elapsed since t0: 15.0015859604
Time elapsed since t0: 16.0023040771
Time elapsed since t0: 17.0023028851
Time elapsed since t0: 18.0023078918
Time elapsed since t0: 19.002286911
除了大约2毫秒的恒定偏移(你可以校准)之外,抖动似乎在1或2毫秒的数量级上(由time.time本身报告).不确定这是否足以满足您的应用需求.
如果您在此期间需要做一些有用的工作,您应该研究多线程或多处理.
注意:在RPi上运行的标准Linux发行版不是硬实时操作系统. Python也可以显示非确定性时序,例如什么时候开始垃圾收集.因此,您的代码可能在大多数情况下以低抖动运行,但您可能偶尔会遇到“hickups”,这会有一些延迟.