Конвертер matlab в python

6 ways to convert MatLab code to Python

MatLab is used by many students for theses and other projects. MatLab is prominent because of the tools made available by MathWorks. However, MatLab is not free in the commercial setting. MatLab becomes very expensive with just a few tools (i.e., $15,000/license/yr). Here, we outline 6 ways to convert MatLab to Python. The first option is by manual conversion. If that is not appealing to you, skip to options 2-6.

What is Difference Between Matlab and Python Code?

Python is one of the easiest high-level programming languages ever created which is as easy as reading and writing English. It was first released in the 1990s by Guido van Rossum and now is been contributed by hundreds of developers worldwide. (pythonpool.com). Whereas, Matlab is closed source software managed by Mathworks. And most importantly, Python is free, unlike Matlab.

There are thousands of modules within Python that can be helped to access any kind of API you need in your project. Moreover, you can edit the original source code of modules in Python.

Whereas, in Matlab, this is nearly impossible. Also, the support for python by developers all around the world is increasing at an exponential rate.

According to StatisticsTimes, python is the most searched language in the PYPL index. Also, considering the usage efficiency and support available, it’s already concluded that Python is far more flexible than Matlab.

Option 1 – Manual Conversion – Free service

There are a few options for converting code. The most reliable is probably by hand. To convert by hand requires a through understanding of both python and MatLab. As a hobby, I enjoy converting MatLab scripts to Python. If you send an email to admin@bishopkingdom.com, I will see if I have the bandwidth to do so. Right now, I am accepting approximately 20% of the scripts that are sent to me. The shorter the script, the more likely I am to help out. Right now, I convert about 500 lines of code per week. Please refer to the following for further details.

Option 2 – Octave

MatLab’s syntax can be interpreted via free software, Octave/Gnu. So, here we interface Matlab’s code with Octave, and Octave then interfaces with Python via the oct2py module in order for this to all work.

First off, I’d like to start off by saying that Octave is essentially a free version of MatLab but you are stripping away all of the tools. For example, you won’t have cftool, simbio, mupad, etc. However, there are a wide range of fantastic libraries available to use within Octave. Octave is open source. A full list can be found here.

Before you delve into this ensure you have downloaded and installed Octave (Gnu) onto your computer.

Oct2Py allows you to “seamlessly call M-files and Octave functions from Python”. It manages the Octave session for you, sharing data behind the scenes using MAT files.

In order to connect python to Octave, we will then install and enact the oct2py module. I recommend using Jupyter for this which you can access via the GUI of Anaconda. You will want an Octave-kernel for Jupyter. You can do this directly from any python interface, however, as long as octave-cli.exe’s directory is added to your PATH (more information about this below). You can install the octave-kernel needed for Jupyter easily using:

pipenv install octave-kernel

You could also install this via conda:

conda install octave_kernel

This will enable you to use matlab code in python directly.

To obtain the oct2py python library, in the python environment type in:

or if you are using Anaconda, use:

conda install -c conda-forge oct2py

You can double check that Python and Octave can connect and see each other using the following command in the terminal:

python -m octave_kernel.check

If successful, the terminal should report the following (or something similar given versions may be different):

Octave kernel v0.32.0
Metakernel v0.24.4
Python v3.7.3 (default, Apr 24 2019, 15:29:51) [MSC v.1915 64 bit (AMD64)]Python path: C:\Users\cbishop\AppData\Local\Continuum\anaconda3\python.exe

Connecting to Octave…
Octave connection established
Octave Kernel v0.32.0 running GNU Octave v5.2.0
Graphics toolkit: fltk
Available toolkits: [1,1] = fltk
[1,2] = gnuplot

Documentation for the oct2py library can be found here.

Of note, be sure to also add the directory where octave-cli.exe is to your system’s PATH variable. For more information on this, here is a great link.

%matplotlib inline from oct2py import octave from oct2py import Oct2Py import numpy as np 

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Simple matlab2python converter

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ebranlard/matlab2python

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README.md

A python script to convert matlab files or lines of matlab code to python. This project is in alpha phase. This implementation relies heavily on the project SMOP by Victor Leikehman. The current implementation wraps around SMOP, with the following differences:

• It attempts to produce code that does not rely on libsmop , but only on usual python modules such as numpy .
• It uses typical shortcuts such as np instead of numpy .
• It attemps to reindex arrays and loops, starting from 0 instead of 1.
• It doesn’t use the external classes matlabarray and cellarray from libsmop
• Basic support for Matlab classes is added. The properties declared in the body of the class are initialized in the constructor.
• As a consequenc of all the above, the resulting code is «less safe» but maybe slightly closer to what a user would write.

This implementation is made straightforward, since it basically use another backend script than the one used by SMOP, here called smop\backend_m2py.py . Some function replacements were added directly there. Additional support for classes, import modules and other fine-tuning replacements (or hacks. ) are done in the file matlabparser\parser.py .

The code is written in python, you can access it as follows:

git clone https://github.com/ebranlard/matlab2python cd matlab2python # install dependencies python -m pip install --user -r requirements.txt # make the packages available from any directory python -m pip install -e . #Optional: run tests pytest #Optional: try it directly python matlab2python.py tests/files/fSpectrum.m -o fSpectrum.py

The main script at the root of the repository is executable and has a couple of command line flags (some of them taken directly from SMOP). To convert the file file.m to file.py , simply type:

python path/to/matlab2python.py file.m -o file.py

where path/to is the path to the script matlab2python.py

The python package can also be used directly to perform conversion of files or lines of code.

import matlabparser as mpars # --- Convert a matlab file pylines = mpars.matlab2python('path/to/file.m', output='file.py') # --- Convert matlab lines (string or list of strings) mlines="""# a comment x = linspace(0,1,100); y = cos(x) + x**2; """ pylines = mpars.matlablines2python(mlines, output='stdout')

If you need a script that performs the obvious conversions from matlab to python, matlab2python will hopefully work for you. These conversions are for instance:

• syntax ( def , if , for , __init__ , no more end )
• indentation
• parenthesis to brackets
• simple builtin functions replacements ( fprintf , disp , error , fopen )
• simple numpy replacements like zeros(3,4) to np.zeros((3,4)) , or cosd(x) , to np.cosd(np.pi/180 x)
• other misc functions like strcmp , strrep , reshape replaced by their python ,
• etc

As mentioned above, SMOP does a great job to produce safe code. Yet, neither SMOP nor matlab2python will generate code that is production-ready (it might in some cases). Most of the time, the user will have to go through the code and perform adjustements and some rewritting. In fact, matlab2python will likely be slightly worse than SMOP in producing a code that works out of the box. But at the end, the code produced by matlab2python should require less refactoring and help the user in its conversion. As mentioned by the author of SMOP, it is difficult not to hide the matlab flavor from the code that is generated and it’s also difficult to fully convert the code without introducing wrapped classes such that matlabarray . The implemenation of matlab2python attempts to do that, at the price of less safety.

I’ve written this wrapper script for my own needs. I was ready to convert manually a bunch of matlab scripts, but I thought I could have a script to automate some of the simple conversions and formatting. I started a quick and dirty implementation before discovering SMOP . At the end, I merged my quick and dirty implemenation with the more powerful parsing framework used by SMOP. Hopefully this can be useful to someone else! If so, feel free to contribute.

Any contributions to this project are welcome! If you find this project useful, you can also buy me a coffee (donate a small amount) with the link below:

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