如何有效地将Matlab引擎数组转换为numpy ndarray? [英] How to efficiently convert Matlab engine arrays to numpy ndarray?

问题描述

我目前正在一个项目中，需要执行一些步骤以处理旧版Matlab代码(使用Matlab引擎)，其余部分则使用Python(numpy)进行处理.

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).

我注意到，将结果从Matlab的matlab.mlarray.double转换为numpy的numpy.ndarray似乎非常缓慢.

I noticed that converting the results from Matlab's matlab.mlarray.double to numpy's numpy.ndarray seems horribly slow.

下面是一些示例代码，用于从另一个ndarray，列表和mlarray创建具有1000个元素的ndarray:

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 np\n"
               "x = range(1000)")
setup_arange = ("import numpy as np\n"
                "x = np.arange(1000)")
setup_matlab = ("import numpy as np\n"
                "import matlab.engine\n"
                "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)

需要以下时间:

From other array
0.00150722111994
From list
0.0705359556928
From matlab
7.0873282467

转换所需时间约为列表转换的100倍.

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

有什么方法可以加快转换速度吗?

Is there any way to speed up the conversion?

推荐答案

发布问题后的片刻，我找到了解决方法.

Moments after posting the question I found the solution.

对于一维数组，仅访问Matlab数组的_data属性.

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)

打印

From list
0.0719847538787
From matlab
7.12802865169
From matlab_data
0.118476275533

对于多维数组，您需要随后重新调整数组的形状.对于二维数组，这意味着调用

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

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