#coding=utf-8
import matlab.engine

if __name__ == '__main__':
    eng = matlab.engine.start_matlab('MATLAB_R2015a')
    a = eng.sqrt(4.0)
    print type(a),a
    eng.quit()
    pass

import matlab.engine
A = matlab.int8([1,2,3,4,5])
print type(A),A.size,A

#输出：
<class 'matlab.mlarray.int8'> (1, 5) [[1,2,3,4,5]]

import matlab.engine
A = matlab.double([[1,2,3,4,5], [6,7,8,9,10]])
print(A)

import matlab.engine
A = matlab.int8([1,2,3,4,5])
print(A[0])

#输出：
[1,2,3,4,5]

import matlab.engine
A = matlab.int8([1,2,3,4,5])
print(A[0][1:4])

#输出：
[2,3,4]

A = matlab.double([[1,2,3,4],[5,6,7,8]]);
A[0] = [10,20,30,40]
print(A)

#输出：
[[10.0,20.0,30.0,40.0],[5.0,6.0,7.0,8.0]]

A = matlab.int8([1,2,3,4,5,6,7,8]);
A[0][2:4] = [30,40]
A[0][6:8] = [70,80]
print(A)
#输出：
[[1,2,30,40,5,6,70,80]]

Note:   Slicing MATLAB arrays behaves differently from slicing a Python list. Slicing a MATLAB array returns a view instead of a shallow copy.
Given a MATLAB array and a Python list with the same values, assigning a slice results in different results as shown by the following code.
A = matlab.int32([[1,2],[3,4],[5,6]])
L = [[1,2],[3,4],[5,6]]
A[0] = A[0][::-1]
L[0] = L[0][::-1]
print(A)
[[2,2],[3,4],[5,6]]
print(L)
[[2, 1], [3, 4], [5, 6]]

import matlab.engine
A = matlab.int8([1,2,3,4,5,6,7,8,9])
A.reshape((3,3))
print(A)
[[1,4,7],[2,5,8],[3,6,9]]

#coding=utf-8
import matlab.engine
from numpy import *

if __name__ == '__main__':
    eng = matlab.engine.start_matlab('MATLAB_R2015a')
    A = matlab.double([[1,2],[5,6]])
    print type(A),A.size,A
    print eng.eig(A)
    eng.quit()
    pass