import numpy as np

 print "<== print version ==>"

 print np.version.version

 print "<== 1-dimensional array ==>"

 print np.array([1, 2, 3, 4, 5])

 print "<== 2-dimentional array ==>"

 print np.array([[1,2],[3,4]])

 print "<== int32,int16,etc. ==>"

 print np.array((1, 2, 3, 4), dtype = np.float64)

 print "<== get a 3*5 array ==>"

 print np.arange(15).reshape(3, 5)

 print "<== generate 4 data from 1 to 5 ==>"

 print np.linspace(1, 5, 4)

 print "<== like what in matlab ==>"

 print np.zeros((2, 5))#

 print '\n'

 print np.ones((2,5))

 print '\n'

 print np.eye(3)

 a = np.eye(4)

 print "<== sum ==>"

 a.sum()

 a.sum(axis=0)

 print "<== min and max ==>"

 a.min()

 a.max()

 np.sin(a)

 np.floor(a)

 np.exp(a)

 np.dot(a, a)

 a = np.ones((2,2))

 b = np.eye(2)

 print "<== visit array ==>"

 print a[0, 0]

 print "<== merge: shallow copy: learn from v and h ==>"

 print np.vstack((a,b))

 print np.hstack((a,b))

 print "<== deep copy ==>"

 c = a.copy()

 print "<== transpose ==>"

 print c.transpose()

 print "<== trace ==>"

 print c.trace()

 print "<== more matrix operations in linalg ==>"

 import numpy.linalg as nplg

 print nplg.eig(a)