On 20/11/2016 20:46, DFS wrote:
import sys, time, numpy as np
loops=int(sys.argv[1])
x=np.array([1,2,3])
y=np.array([4,5,6])
start=time.clock()
for i in range(loops):
np.cross(x,y)
print "Numpy, %s loops: %.2g seconds" %(loops,time.clock()-start)
x=[1,2,3]
y=[4,5,6]
z=[0,0,0]
start=time.clock()
for i in range(loops):
z[0]=x[1]*y[2]-x[2]*y[1];
z[1]=x[2]*y[0]-x[0]*y[2];
z[2]=x[0]*y[1]-x[1]*y[0];
print "Calc, %s loops: %.2g seconds" %(loops,time.clock()-start)
I don't know why numpy is slow, but I can confirm similar results.
In fact if the workings are put into a function, then the difference is
even more marked, with the normal calculation being 50% faster
Maybe numpy has extra overheads, and the arrays being operated on are
very small, but even so, 30 times slower than CPython? (2.5 to 0.083
seconds.)
--
Bartc
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