提高将 numpy 数组转换为 MATLAB double 的性能

Question

Calling MATLAB from Python is bound to give some performance reduction that I could avoid by rewriting (a lot of) code in Python. However, this isn't a realistic option for me, but it annoys me that a huge loss of efficiency lies in the simple conversion from a numpy array to a MATLAB double.

I'm talking about the following conversion from data1 to data1m, where

data1 = np.random.uniform(low = 0.0, high = 30000.0, size = (1000000,))
data1m = matlab.double(list(data1))

Here matlab.double comes from Mathworks own MATLAB package / engine. The second line of code takes 20 s on my system, which just seems like too much for a conversion that doesn't really do anything other than making the numbers 'edible' for MATLAB.

So basically I'm looking for a trick opposite to the one given here that works for converting MATLAB output back to Python.

Answer 1

Passing numpy arrays efficiently

Take a look at the file mlarray_sequence.py in the folder PYTHONPATH\\Lib\\site-packages\\matlab\\_internal . There you will find the construction of the MATLAB array object. The performance problem comes from copying data with loops within the generic_flattening function.

To avoid this behavior we will edit the file a bit. This fix should work on complex and non-complex datatypes.

1. Make a backup of the original file in case something goes wrong.

2. Add import numpy as np to the other imports at the beginning of the file

3. In line 38 you should find:

    init_dims = _get_size(initializer)

   replace this with:

    try: init_dims=initializer.shape except: init_dims = _get_size(initializer)

4. In line 48 you should find:

    if is_complex: complex_array = flat(self, initializer, init_dims, typecode) self._real = complex_array['real'] self._imag = complex_array['imag'] else: self._data = flat(self, initializer, init_dims, typecode)

   Replace this with:

    if is_complex: try: self._real = array.array(typecode,np.ravel(initializer, order='F').real) self._imag = array.array(typecode,np.ravel(initializer, order='F').imag) except: complex_array = flat(self, initializer,init_dims, typecode) self._real = complex_array['real'] self._imag = complex_array['imag'] else: try: self._data = array.array(typecode,np.ravel(initializer, order='F')) except: self._data = flat(self, initializer, init_dims, typecode)

Now you can pass a numpy array directly to the MATLAB array creation method.

data1 = np.random.uniform(low = 0.0, high = 30000.0, size = (1000000,))
#faster
data1m = matlab.double(data1)
#or slower method
data1m = matlab.double(data1.tolist())

data2 = np.random.uniform(low = 0.0, high = 30000.0, size = (1000000,)).astype(np.complex128)
#faster
data1m = matlab.double(data2,is_complex=True)
#or slower method
data1m = matlab.double(data2.tolist(),is_complex=True)

The performance in MATLAB array creation increases by a factor of 15 and the interface is easier to use now.

Answer 2

While awaiting better suggestions, I'll post the best trick I've come up with so far. It comes down to saving the file with `scipy.io.savemat´ and then loading this file in MATLAB.

This is not the prettiest hack and it requires some care to ensure different processes relying on the same script don't end up writing and loading each other's .mat files, but the performance gain is worth it for me.

As a test case I wrote two simple, almost identical MATLAB functions that require 2 numpy arrays (I tested with length 1000000) and one int as input.

function d = test(x, y, fs_signal)
d = sum((x + y))./double(fs_signal);

function d = test2(path)
load(path)
d = sum((x + y))./double(fs_signal);

The function test requires conversion, while test2 requires saving.

Testing test : Converting the two numpy arrays takes cirka 40 s on my system. The total time to prepare for and run test comes down to 170 s

Testing test2 : Saving the arrays and int takes cirka 0.35 s on my system. Suprisingly, loading the .mat file in MATLAB is extremely efficient (or more suprisingly, it is extremely ineffcient at dealing with its doubles)... The total time to prepare for and run test2 comes down to 0.38 s

That's a performance gain of almost 450x...

Answer 3

My situation was a bit different (python script called from matlab) but for me converting the ndarray into an array.array massively speed up the process. Basically it is very similar to Alexandre Chabot solution but without the need to alter any files:

#untested i.e. only deducted from my "matlab calls python" situation
import numpy
import array

data1 = numpy.random.uniform(low = 0.0, high = 30000.0, size = (1000000,))
ar = array.array('d',data1.flatten('F').tolist())
p = matlab.double(ar)
C = matlab.reshape(p,data1.shape) #this part I am definitely not sure about if it will work like that

At least if done from Matlab the combination of "array.array" and "double" is relative fast. Tested with Matlab 2016b + python 3.5.4 64bit.