datetime.now（）的Python分辨率

Question

from datetime import datetime
import time
for i in range(1000):
    curr_time  = datetime.now()
    print(curr_time)
    time.sleep(0.0001)

我正在测试datetime.now()的分辨率。 由于它假定以微秒为单位输出，因此我希望每张纸都会有所不同。

但是，我总是得到类似的东西。

...
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
...

为什么会这样呢？ 有什么方法可以使精确的时间戳降低到微秒？ 实际上，我不需要微秒，但获得0.1ms分辨率会很好。

===更新====

我将其与使用time.perf_counter（）进行比较，并从datetime导入datetime，timedelta导入时间添加到起始datetime

datetime0 = datetime.now()
t0 = time.perf_counter()
for i in range(1000):
    print('datetime.now(): ', datetime.now())
    print('time.perf_counter(): ', datetime0 + timedelta(0, time.perf_counter()-t0))
    print('\n')

    time.sleep(0.000001)

我不确定它到底有多精确，但是分辨率至少更高。...这似乎无关紧要，因为我的计算机甚至无法以如此高的速度打印。 就我而言，我只需要不同的时间戳来区分不同的条目，这对我来说已经足够了。

...
datetime.now():  2015-07-10 23:24:18.010377
time.perf_counter():  2015-07-10 23:24:18.010352


datetime.now():  2015-07-10 23:24:18.010377
time.perf_counter():  2015-07-10 23:24:18.010545


datetime.now():  2015-07-10 23:24:18.010377
time.perf_counter():  2015-07-10 23:24:18.010745


datetime.now():  2015-07-10 23:24:18.011377
time.perf_counter():  2015-07-10 23:24:18.010961


datetime.now():  2015-07-10 23:24:18.011377
time.perf_counter():  2015-07-10 23:24:18.011155


datetime.now():  2015-07-10 23:24:18.011377
time.perf_counter():  2015-07-10 23:24:18.011369


datetime.now():  2015-07-10 23:24:18.011377
time.perf_counter():  2015-07-10 23:24:18.011596


datetime.now():  2015-07-10 23:24:18.012379
time.perf_counter():  2015-07-10 23:24:18.011829


datetime.now():  2015-07-10 23:24:18.012379
time.perf_counter():  2015-07-10 23:24:18.012026


datetime.now():  2015-07-10 23:24:18.012379
time.perf_counter():  2015-07-10 23:24:18.012232


datetime.now():  2015-07-10 23:24:18.012379
time.perf_counter():  2015-07-10 23:24:18.012424


datetime.now():  2015-07-10 23:24:18.012379
time.perf_counter():  2015-07-10 23:24:18.012619


datetime.now():  2015-07-10 23:24:18.013380
time.perf_counter():  2015-07-10 23:24:18.012844


datetime.now():  2015-07-10 23:24:18.013380
time.perf_counter():  2015-07-10 23:24:18.013044


datetime.now():  2015-07-10 23:24:18.013380
time.perf_counter():  2015-07-10 23:24:18.013242


datetime.now():  2015-07-10 23:24:18.013380
time.perf_counter():  2015-07-10 23:24:18.013437


datetime.now():  2015-07-10 23:24:18.013380
time.perf_counter():  2015-07-10 23:24:18.013638


datetime.now():  2015-07-10 23:24:18.014379
time.perf_counter():  2015-07-10 23:24:18.013903


datetime.now():  2015-07-10 23:24:18.014379
time.perf_counter():  2015-07-10 23:24:18.014125


datetime.now():  2015-07-10 23:24:18.014379
time.perf_counter():  2015-07-10 23:24:18.014328


datetime.now():  2015-07-10 23:24:18.014379
time.perf_counter():  2015-07-10 23:24:18.014526


datetime.now():  2015-07-10 23:24:18.014379
time.perf_counter():  2015-07-10 23:24:18.014721


datetime.now():  2015-07-10 23:24:18.015381
time.perf_counter():  2015-07-10 23:24:18.014919

...

Answer 1

这可能是系统上time.sleep的限制，而不是datetime.now() ...或两者都有。 您正在运行什么操作系统以及什么版本和Python发行版？

您的系统可能不提供time.sleep文档中提到的“亚秒精度”：

sleep(...)
    sleep(seconds)

    Delay execution for a given number of seconds.  The argument may be
    a floating point number for subsecond precision.

在具有CPython 2.7的amd64上的Linux 3.x上 ，我得到的结果非常接近您想要的0.0001时间步长：

2015-07-10 19:58:24.353711
2015-07-10 19:58:24.353879
2015-07-10 19:58:24.354052
2015-07-10 19:58:24.354227
2015-07-10 19:58:24.354401
2015-07-10 19:58:24.354577
2015-07-10 19:58:24.354757
2015-07-10 19:58:24.354938