"Converting" Numpy arrays to Matlab and vice versa

Sure, just use scipy.io.savemat

As an example:

import numpy as np
import scipy.io

x = np.linspace(0, 2 * np.pi, 100)
y = np.cos(x)

scipy.io.savemat('test.mat', dict(x=x, y=y))

Similarly, there's scipy.io.loadmat.

You then load this in matlab with load test.

Alteratively, as @JAB suggested, you could just save things to an ascii tab delimited file (e.g. numpy.savetxt). However, you'll be limited to 2 dimensions if you go this route. On the other hand, ascii is the universial exchange format. Pretty much anything will handle a delimited text file.

A simple solution, without passing data by file or external libs.

Numpy has a method to transform ndarrays to list and matlab data types can be defined from lists. So, when can transform like:

np_a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
mat_a = matlab.double(np_a.tolist())

From matlab to python requires more attention. There is no built-in function to convert the type directly to lists. But we can access the raw data, which isn't shaped, but plain. So, we use reshape (to format correctly) and transpose (because of the different way MATLAB and numpy store data). That's really important to stress: Test it in your project, mainly if you are using matrices with more than 2 dimensions. It works for MATLAB 2015a and 2 dims.

np_a = np.array(mat_a._data.tolist())
np_a = np_a.reshape(mat_a.size).transpose()