# 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 noticed that converting the results from Matlab’s `matlab.mlarray.double`

to numpy’s `numpy.ndarray`

seems horribly slow.

Here is some example code for creating an ndarray with 1000 elements from another ndarray, a list and an mlarray:

```
import timeit
setup_range = ("import numpy as npn"
"x = range(1000)")
setup_arange = ("import numpy as npn"
"x = np.arange(1000)")
setup_matlab = ("import numpy as npn"
"import matlab.enginen"
"eng = matlab.engine.start_matlab()n"
"x = eng.linspace(0., 1000.-1., 1000.)")
print 'From other array'
print timeit.timeit('np.array(x)', setup=setup_arange, number=1000)
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)
```

Which takes the following times:

```
From other array
0.00150722111994
From list
0.0705359556928
From matlab
7.0873282467
```

The conversion takes about 100 times as long as a conversion from list.

Is there any way to speed up the conversion?

## Answers:

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
```

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')`