On Feb 9, 10:10 am, Grant Edwards <inva...@invalid.invalid> wrote: > Is there another way to measure small periods of elapsed time > (say in the 1-10ms range)? > On Feb 9, 10:10 am, Grant Edwards <inva...@invalid.invalid> wrote: > Is there another way to measure small periods of elapsed time > (say in the 1-10ms range)? >
I made repeated calls to time.clock() in a generator expression, which is as fast a loop I can think of in Python. Then I computed the successive time deltas to see if any granularities jumped out. Here are the results: >>> import time >>> from itertools import groupby >>> >>> # get about 1000 different values of time.clock() >>> ts = set(time.clock() for i in range(1000)) >>> >>> # sort in ascending order >>> ts = sorted(ts) >>> >>> # compute diffs between adjacent time values >>> diffs = [j-i for i,j in zip(ts[:-1],ts[1:])] >>> >>> # sort and group >>> diffs.sort() >>> diffgroups = groupby(diffs) >>> >>> # print the distribution of time differences in microseconds >>> for i in diffgroups: print "%3d %12.6f" % (len(list(i[1])), i[0]*1e6) ... 25 2.234921 28 2.234921 242 2.514286 506 2.514286 45 2.793651 116 2.793651 1 3.073016 8 3.073016 6 3.352381 4 3.631746 3 3.911112 1 3.911112 5 4.190477 2 4.469842 1 6.146033 1 8.660319 1 9.777779 1 10.895239 1 11.174605 1 24.304765 1 41.904767 There seems to be a step size of about .28 microseconds. So I would guess time.clock() has enough resolution. But also beware of the overhead of the calls to clock() - using timeit, I find that each call takes about 2 microseconds (consistent with the smallest time difference in the above data set). -- Paul -- http://mail.python.org/mailman/listinfo/python-list
