I've been building an error logging app recently and was after a way of accurately timestamping the incoming data. When I say accurately I mean each timestamp should be accurate relative to each other (no need to sync to an atomic clock or anything like that).
I've been using datetime.now() as a first stab, but this isn't perfect:
>>> for i in range(0,1000):
... datetime.datetime.now()
...
datetime.datetime(2008, 10, 1, 13, 17, 27, 562000)
datetime.datetime(2008, 10, 1, 13, 17, 27, 562000)
datetime.datetime(2008, 10, 1, 13, 17, 27, 562000)
datetime.datetime(2008, 10, 1, 13, 17, 27, 562000)
datetime.datetime(2008, 10, 1, 13, 17, 27, 578000)
datetime.datetime(2008, 10, 1, 13, 17, 27, 578000)
datetime.datetime(2008, 10, 1, 13, 17, 27, 578000)
datetime.datetime(2008, 10, 1, 13, 17, 27, 578000)
datetime.datetime(2008, 10, 1, 13, 17, 27, 578000)
datetime.datetime(2008, 10, 1, 13, 17, 27, 609000)
datetime.datetime(2008, 10, 1, 13, 17, 27, 609000)
datetime.datetime(2008, 10, 1, 13, 17, 27, 609000)
etc.
The changes between clocks for the first second of samples looks like this:
uSecs difference
562000
578000 16000
609000 31000
625000 16000
640000 15000
656000 16000
687000 31000
703000 16000
718000 15000
750000 32000
765000 15000
781000 16000
796000 15000
828000 32000
843000 15000
859000 16000
890000 31000
906000 16000
921000 15000
937000 16000
968000 31000
984000 16000
So it looks like the timer data is only updated every ~15-32ms on my machine. The problem comes when we come to analyse the data because sorting by something other than the timestamp and then sorting by timestamp again can leave the data in the wrong order (chronologically). It would be nice to have the time stamps accurate to the point that any call to the time stamp generator gives a unique timestamp.
I had been considering some methods involving using a time.clock() call added to a s