Question

I am looking for a way to pass NumPy arrays to Matlab.

I’ve managed to do this by storing the array into an image using scipy.misc.imsave and then loading it using imread, but this of course causes the matrix to contain values between 0 and 256 instead of the ‘real’ values.

Taking the product of this matrix divided by 256, and the maximum value in the original NumPy array gives me the correct matrix, but I feel that this is a bit tedious.

is there a simpler way?

Answer 1

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.