[Numpy-discussion] numpy speed question

[Dave Hirschfeld]

Jean-Luc Menut <jeanluc.menut <at> free.fr> writes:
>
> I have a little question about the speed of numpy vs IDL 7.0.
>  
> Here the IDL result:
> % Compiled module: $MAIN$.
>         2.9999837
> 
> The python code:
> from numpy import *
> from time import time
> time1 = time()
> for j in range(10000):
>      for i in range(1000):
>          a=cos(2*pi*i/100.)
> time2 = time()
> print time2-time1
> 
> result:
> In [2]: run python_test_speed.py
> 24.1809999943
> 

Whilst you've imported everything from numpy you're not really using numpy -
you're still using a slow Python (double) loop. The power of numpy comes from
vectorising your code - i.e. applying functions to arrays of data.

The example below demonstrates an 80 fold increase in speed by vectorising the
calculation:

def method1():
    a = empty([1000, 10000])
    for j in range(10000):
         for i in range(1000):
             a[i,j] = cos(2*pi*i/100.)
    return a
#

def method2():
    ij = np.repeat((2*pi*np.arange(1000)/100.)[:,None], 10000, axis=1)
    return np.cos(ij)
#


In [46]: timeit method1()
1 loops, best of 3: 47.9 s per loop

In [47]: timeit method2()
1 loops, best of 3: 589 ms per loop

In [48]: allclose(method1(), method2())
Out[48]: True