benchmark np basic math

np_bench.py

import timeit
import matplotlib.pylab as plt

pow2s = arange(10,22,0.25)
t_times1 = np.zeros(len(pow2s))
t_sum = np.zeros(len(pow2s))
t_cumsum = np.zeros(len(pow2s))
for ii, pow2 in enumerate(pow2s):
    setup = "import numpy as np\na = np.random.rand({})".format(2**pow2)
    t_times1[ii] = min(timeit.Timer('a*1.0', setup=setup).repeat(3, 100))/100    
    t_sum[ii] = min(timeit.Timer('np.sum(a)', setup=setup).repeat(3, 100))/100
    t_cumsum[ii] = min(timeit.Timer('np.cumsum(a)', setup=setup).repeat(3, 100))/100    

plt.clf()    
plt.plot(2**pow2s, t_times1, 'k')
plt.plot(2**pow2s, t_sum, 'b')
plt.plot(2**pow2s, t_cumsum, 'r')
plt.xlabel('len(a)')
plt.ylabel('time in s')
plt.gca().set_yscale('log')
plt.gca().set_xscale('log')
plt.legend(['times1','sum', 'cumsum'], loc=2)