在使用“两种不同的方法方法”的同一个 win10 OS 系统上,似乎有一个近似的 “500 ns” 时差。如果您关心纳秒精度,请查看下面的代码。
代码的修改是基于用户cod3monk3y和Kevin S的代码。
操作系统:python 3.7.3 (default, date, time) [MSC v.1915 64 bit (AMD64)]
def measure1(mean):
for i in range(1, my_range+1):
x = time.time()
td = x- samples1[i-1][2]
if i-1 == 0:
td = 0
td = f'{td:.6f}'
samples1.append((i, td, x))
mean += float(td)
print (mean)
sys.stdout.flush()
time.sleep(0.001)
mean = mean/my_range
return mean
def measure2(nr):
t0 = time.time()
t1 = t0
while t1 == t0:
t1 = time.time()
td = t1-t0
td = f'{td:.6f}'
return (nr, td, t1, t0)
samples1 = [(0, 0, 0)]
my_range = 10
mean1 = 0.0
mean2 = 0.0
mean1 = measure1(mean1)
for i in samples1: print (i)
print ('...\n\n')
samples2 = [measure2(i) for i in range(11)]
for s in samples2:
#print(f'time delta: {s:.4f} seconds')
mean2 += float(s[1])
print (s)
mean2 = mean2/my_range
print ('\nMean1 : ' f'{mean1:.6f}')
print ('Mean2 : ' f'{mean2:.6f}')
measure1 结果:
nr, td, t0
(0, 0, 0)
(1, '0.000000', 1562929696.617988)
(2, '0.002000', 1562929696.6199884)
(3, '0.001001', 1562929696.620989)
(4, '0.001001', 1562929696.62199)
(5, '0.001001', 1562929696.6229906)
(6, '0.001001', 1562929696.6239917)
(7, '0.001001', 1562929696.6249924)
(8, '0.001000', 1562929696.6259928)
(9, '0.001001', 1562929696.6269937)
(10, '0.001001', 1562929696.6279945)
...
measure2 结果:
nr, td , t1, t0
(0, '0.000500', 1562929696.6294951, 1562929696.6289947)
(1, '0.000501', 1562929696.6299958, 1562929696.6294951)
(2, '0.000500', 1562929696.6304958, 1562929696.6299958)
(3, '0.000500', 1562929696.6309962, 1562929696.6304958)
(4, '0.000500', 1562929696.6314962, 1562929696.6309962)
(5, '0.000500', 1562929696.6319966, 1562929696.6314962)
(6, '0.000500', 1562929696.632497, 1562929696.6319966)
(7, '0.000500', 1562929696.6329975, 1562929696.632497)
(8, '0.000500', 1562929696.633498, 1562929696.6329975)
(9, '0.000500', 1562929696.6339984, 1562929696.633498)
(10, '0.000500', 1562929696.6344984, 1562929696.6339984)
最终结果:
Mean1 : 0.001001 #(测量1函数)
Mean2 : 0.000550 # (measure2 函数)