testing python linear algebra with numpy and scipy.py

import numpy as np
import scipy
import time
import matplotlib.pyplot as plt

print np.__file__


ax = plt.subplot(111)

plt.ion()
plt.show()

ax.set_xscale('log')
ax.set_yscale('log')

ax.set_xlabel('N (size of NxN matrix)')
ax.set_ylabel('Time for dot product (sec)')
ax.set_title('Comparison of scipy to numpy')

sizes = []
nps = []
sps = []

for size in np.logspace(2.3,3.7,15):
    
    x = np.random.random((size,size))
    y = np.random.random((size,size))
    
    sizes.append(size)

    np_start = time.time()
    np.dot(x,y)
    np_time = time.time() - np_start

    sp_start = time.time()
    scipy.dot(x,y)
    sp_time = time.time() - sp_start
    
    nps.append(np_time)
    sps.append(sp_time)
    

    if 'line1' not in locals():
        line1, = plt.plot(size, np_time, color='r', marker='o', ms=6, mec='none', label='numpy (Apple Accelerate)')
        line2, = plt.plot(size, sp_time, color='b', marker='o', ms=6, mec='none', label='scipy (Fortran?)')
        ax.legend(fontsize=10, frameon=False, loc=2)
    
    else:
        line1.set_data(sizes, nps)
        line2.set_data(sizes, sps)
        
    print '%ix%i - Numpy: %.1f ms, scipy: %.1f ms'%(size, size, np_time*1000, sp_time*1000)
    
    ax.relim()
    ax.autoscale_view(True,True,True)
    
    plt.draw()
    

plt.ioff()
plt.show()