How to efficiently convert Matlab engine arrays to numpy ndarray?

Question

I am currently working on a project where I need do some steps of processing with legacy Matlab code (using the Matlab engine) and the rest in Python (numpy).

I noti

Answer 1

Moments after posting the question I found the solution.

For one-dimensional arrays, access only the _data property of the Matlab array.

import timeit
print 'From list'
print timeit.timeit('np.array(x)', setup=setup_range, number=1000)
print 'From matlab'
print timeit.timeit('np.array(x)', setup=setup_matlab, number=1000)
print 'From matlab_data'
print timeit.timeit('np.array(x._data)', setup=setup_matlab, number=1000)

prints

From list
0.0719847538787
From matlab
7.12802865169
From matlab_data
0.118476275533

For multi-dimensional arrays you need to reshape the array afterwards. In the case of two-dimensional arrays this means calling

np.array(x._data).reshape(x.size[::-1]).T

Answer 2

Tim's answer is great for 2D arrays, but a way to adapt it to N dimensional arrays is to use the order parameter of np.reshape() :

np_x = np.array(x._data).reshape(x.size, order='F')