我希望優化一些由兩個巢狀迴圈組成的
python**.我對numpy並不那麼熟悉,但據我所知,它應該能夠幫助我提高這項任務的效率.下面是我編寫的測試**,它反映了實際**中發生的情況.目前使用numpy範圍和迭代器比通常的python更慢.我究竟做錯了什麼？這個問題的最佳解決方案是什麼？
謝謝你的幫助！
import numpy
import time
# setup a problem analagous to that in the real code
npoints_per_plane = 1000
nplanes = 64
naxis = 1000
npoints3d = naxis + npoints_per_plane * nplanes
npoints = naxis + npoints_per_plane
specres = 1000
sol = dict()
sol["ems"] = numpy.zeros(npoints3d)
sol["abs"] = numpy.zeros(npoints3d)
# this would normally be non-random input data
data = dict()
data["ems"] = numpy.zeros((npoints,specres))
data["abs"] = numpy.zeros((npoints,specres))
for ip in range(npoints):
data["ems"][ip,:] = numpy.random.random(specres)[:]
data["abs"][ip,:] = numpy.random.random(specres)[:]
ems_mod = numpy.random.random(1)[0]
abs_mod = numpy.random.random(1)[0]
ispec = numpy.random.randint(specres)
# this the code i want to optimize
t0 = time.time()
# usual python range and iterator
for ip in range(npoints_per_plane):
jp = naxis + ip
for ipl in range(nplanes):
ip3d = jp + npoints_per_plane * ipl
sol["ems"][ip3d] = data["ems"][jp,ispec] * ems_mod
sol["abs"][ip3d] = data["abs"][jp,ispec] * abs_mod
t1 = time.time()
# numpy ranges and iterator
ip_vals = numpy.arange(npoints_per_plane)
ipl_vals = numpy.arange(nplanes)
for ip in numpy.nditer(ip_vals):
jp = naxis + ip
for ipl in numpy.nditer(ipl_vals):
ip3d = jp + npoints_per_plane * ipl
sol["ems"][ip3d] = data["ems"][jp,ispec] * ems_mod
sol["abs"][ip3d] = data["abs"][jp,ispec] * abs_mod
t2 = time.time()
print "plain python: %0.3f seconds" % ( t1 - t0 )
print "numpy: %0.3f seconds" % ( t2 - t1 )
附加說明：
我弄清楚如何快速做內部迴圈,但不是外迴圈：
# numpy vectorization
for ip in xrange(npoints_per_plane):
jp = naxis + ip
sol["ems"][jp:jp+npoints_per_plane*nplanes:npoints_per_plane] = data["ems"][jp,ispec] * ems_mod
sol["abs"][jp:jp+npoints_per_plane*nplanes:npoints_per_plane] = data["abs"][jp,ispec] * abs_mod
joe的解決方案顯示了如何一起做兩個,謝謝！
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