Matlab code to python

Matlab code to python DEFAULT

Enter a single MatLab function or operator to convert to a Python function


*We would like to continually improve this functionality. Please leave suggestions in the comment box. We will happily revise. Thank you.

Link to other coding and programming information

Links to skip below:

  • Skip to videos below

  • Manual conversion

  • Octave

  • LibreMat

  • Skip to MathWorks info

  • OMPC

  • SMOP

  • Option 1 &#; Manual Conversion

    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. However, you are probably looking for other options if you are searching for methods to do so other than manual conversion. Below, it is assumed that MatLab syntax is well understood, but python less so.

    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:

    pip install octave-kernel

    or

    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:

    pip install oct2py

    pipenv install oct2py

    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 v
    Metakernel v
    Python v (default, Apr 24 , ) [MSC v 64 bit (AMD64)]
    Python path: C:\Users\cbishop\AppData\Local\Continuum\anaconda3\python.exe

    Connecting to Octave&#;
    Octave connection established
    Octave Kernel v running GNU Octave v
    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.

    In [1]:

    %matplotlib inline fromoct2pyimportoctavefromoct2pyimportOct2Pyimportnumpyasnp

     

    Instantiate the Oct2Py object as oc

    In [3]:

     

     

     

     

    First, lets remind ourselves of matlab sytnax of matrix/array creation:

    In [4]:

    y=oc.eval("[1 2 3];")y

    Out[4]:

     

     

    Another example of using this is the following:

     

    >>> oc=oct2py.Oct2Py()>>> x=oc.zeros(3,3)>>> print(x,x.dtype)[[ 0. 0. 0.] [ 0. 0. 0.] [ 0. 0. 0.]] float64

     

     

    Not bad. The difference in slicing arrays and matrices is a tough transition, but this makes it much easier than learning the fundamentals

    In [5]:

    y=oc.eval("[1 2 3;4 5 6];")y

    Out[5]:

    array([[ 1., 2., 3.], [ 4., 5., 6.]])

     

    We can see it creates a matlab matrix and converts it exactly how we would need it in numpy

     

    Lets try something a bit more complex with analaogous syntax. Here is a matrix define natively in python

    In [7]:

    numpyarr=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]],dtype=float)numpyarr

    Out[7]:

    array([[ 1., 2., 3., 4.], [ 5., 6., 7., 8.], [ 9., , , ], [ , , , ]])

     

    and here is the same matrix defined in matlab/octave syntax

    In [8]:

    matlabarr=oc.eval("[1 2 3 4; 5 6 7 8; 9 10 11 12; 13 14 15 16];")matlabarr

    Out[8]:

    array([[ 1., 2., 3., 4.], [ 5., 6., 7., 8.], [ 9., , , ], [ , , , ]])

     

    clearly, they are the same!

    In [9]:

    Out[9]:

    array([[ True, True, True, True], [ True, True, True, True], [ True, True, True, True], [ True, True, True, True]], dtype=bool)

     

    Plotting with oct2py

    Just using oct2py, we can plot using gnuplot, I haven&#;t been able to find a way to plot them inline, but they still look good in the gnuplot window:

    In [14]:

    x=np.arange(-2*np.pi,2*np.pi,)y=np.sin(x)oc.plot(x,y,'-o',linewidth=2)

     

    We can see that is uses wxpython, which is another gui package. I am not % sure when that package was installed, but I am guess it was installed along with Octave

    Although this doesn&#;t convert the code to python, it executes it very well. Here is a MATLAB code snippet for LU Decomposition. We can create a python string with the contents of this and evaluate it as octave code

     

    In [15]:

    x='''% function [L,U] = LUCrout(A)% Decomposes matrix A into a Lower matrix L and Upper matrix U% A = LUA = [1,5,3,5; 2,5,6,7; 9,0,3,4; 9,4,7,6]A[R,C] = size(A);for i = 1:R L(i,1) = A(i,1); U(i,i) = 1;endfor j = 2:R U(1,j) = A(1,j)/L(1,1);endfor i = 2:R for j = 2:i L(i,j) = A(i,j) - L(i,1:j-1)*U(1:j-1,j); end for j = i+1:R U(i,j) = (A(i,j) - L(i,1:i-1)*U(1:i-1,j))/L(i,i); endendLU'''

     

    A = 1 5 3 5 2 5 6 7 9 0 3 4 9 4 7 6 A = 1 5 3 5 2 5 6 7 9 0 3 4 9 4 7 6 L = e+ * U =

     

    If you are using an ipython notebook, we can take advantage of the magic functions and execute Octave/MATLAB code directly in the cells. To activate this functionality, we need to install Octave first, then install oct2py.

    In [17]:

     

    For single line octave code, we can use this syntax

    In [18]:

    x=%octave [1 2 3 4; 5 6 7 8; 9 10 11 12; 13 14 15 16]; x

    Out[18]:

    array([[ 1., 2., 3., 4.], [ 5., 6., 7., 8.], [ 9., , , ], [ , , , ]])

     

    This is very similar to the example earlier, except that is it running the code rather than evaluating a string. We can see that it creates a numpy array just like before

     

    If we want multi-line MATLAB/Octave sytax, we can use this syntax

    In [20]:

    %%octavex=[;;;];xtypeinfo(x)

     

    x = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ans = matrix

     

    in that cell, x is still a matlab matrix, but when we show &#;x&#; in the next cell, it is now a numpy array

    Out[21]:

    array([[ 1., 2., 3., 4.], [ 5., 6., 7., 8.], [ 9., , , ], [ , , , ]])

     

    Lets try our LU function by loading the file, and replacing adding the octave magic function. execute

    and then add

    to the very top and delete

    In [22]:

    %%octave% function [L,U] = LUCrout(A)% Decomposes matrix A into a Lower matrix L and Upper matrix U% A = LUA=[1,5,3,5;2,5,6,7;9,0,3,4;9,4,7,6]A[R,C]=size(A);fori=1:RL(i,1)=A(i,1);U(i,i)=1;endforj=2:RU(1,j)=A(1,j)/L(1,1);endfori=2:Rforj=2:iL(i,j)=A(i,j)-L(i,1:j-1)*U(1:j-1,j);endforj=i+1:RU(i,j)=(A(i,j)-L(i,1:i-1)*U(1:i-1,j))/L(i,i);endendLU

     

    A = 1 5 3 5 2 5 6 7 9 0 3 4 9 4 7 6 A = 1 5 3 5 2 5 6 7 9 0 3 4 9 4 7 6 L = e+ * U =

     

    Excellent. This appraoch keeps the code more native than having the copy and paste the code and create a string to be evalauted. And just for fun we can plot inline also!!!

    In [23]:

    %%octavep=[];x=;polyout(p,'x')plot(x,polyval(p,x));

     

    12*x^4 - *x^3 - 8*x^2 - *x^1 + 8

    In [24]:

    %%octavex=;y=exp(x);plot(x,y)xlabel('x')ylabel('y')title('testing MATLAB/OCTAVE in python!')

    Option 3 &#; SMOP

    SMOP (Small Matlab and Octave to Python Converter) or here

    The smop package seems impressive (SMOP development), and works well for an actual conversion of MATLAB code. Lets see how it works.

    To convert a MATLAB file named LU.m, follow the sytanx here In []:

    !python smop/smop/main.py LU.m

    the resulting file is &#;a.py&#;, which is the python equivalent. Lets have a peek and see how we did. Load the file directly in ipython by

    In []:

    # %load a.py # Autogenerated with SMOP version # smop/smop/main.py LU.m from __future__ import division try: from runtime import * except ImportError: from smop.runtime import * A=[[1,5,3,5],[2,5,6,7],[9,0,3,4],[9,4,7,6]] R,C=size(A,nargout=2) for i in arange_(1,R).reshape(-1): L[i,1]=A(i,1) U[i,i]=1 for j in arange_(2,R).reshape(-1): U[1,j]=A(1,j) / L(1,1) for i in arange_(2,R).reshape(-1): for j in arange_(2,i).reshape(-1): L[i,j]=A(i,j) - L(i,arange_(1,j - 1)) * U(arange_(1,j - 1),j) for j in arange_(i + 1,R).reshape(-1): U[i,j]=(A(i,j) - L(i,arange_(1,i - 1)) * U(arange_(1,i - 1),j)) / L(i,i) ImportError Traceback (most recent call last) in () 7 from runtime import * 8 except ImportError: > 9 from smop.runtime import * 10 11 A=[[1,5,3,5],[2,5,6,7],[9,0,3,4],[9,4,7,6]] ImportError: No module named smop.runtime

    The above code needs a bit of revamping&#;

    Option 4 &#; LibreMat

    LibreMate

    LiberMate: translate from Matlab to Python and SciPy (Requires Python 2, last update 4 years ago).  LINK

    I didn&#;t install LibreMat because of the dependent packages, so I&#;ll leave a review to someone else

    Option 5 &#; OMPC

    • There&#;s OMPC, &#;Open-source Matlab-to-Python Compiler&#;, mentioned by @IoannisFilippidis in his answer for conversion. The website for information regarding downloading and installing OMPC is here.
    • An online OMPC platform is: http://ompclib.appspot.com/m2py but do note, as they indicated on their website, that they save all that is entered. They state: &#;The files you are uploading will be saved in our database. Please do not upload anything you wouldn&#;t like us to see.&#;

    The examples are descriptive and explain many user cases. So when you are ready to convert, check out the live web converter which is great for processing snippets. However, when I tried to convert the LU function above, I get this, which creates a wierd A array and an undeclared vector mslice. There may be an explanation of this in the documentation, but it isn&#;t obvious I didn&#;t look very much

    Because the code above resulted in something not fantastic, let&#;s just brush that aside and move on!

    MathWorks

    There is a nice app in MatLab, that is owned by MathWorks, which can begin from the command line using &#;libraryCompiler&#;. You can then generate a python package and build the python application: LINK

    It is possible to also call matlab files directly from python using information found here.

    Summary

    I also prefer the oct2py package. I can run my native MATLAB code in a python environment, and surely there will be support for other languages in the ipython notebook (bash, R, Julia, etc.)

    Now that we have some options for running MATLAB/Octave code in python, the conversion process should be easier.

    <h2>Side Note &#; Converting MatLab to Fortran</h2>

    On a different note, though I&#;m not a  fan at all, For people who might find it useful there is:

    In addition, here is a link regarding using converting or using MatLab code in R.

    <h2>These are other fantastic links and information that you may find useful.</h2>

    • : communicate from Python by sending data to the MATLAB workspace, operating on them with scripts and pulling back the resulting data.
    • Python-Matlab wormholes: both directions of interaction supported.
    • Python-Matlab bridge: use Matlab from within Python, offers matlab_magic for iPython, to execute normal matlab code from within ipython.
    • PyMat: Control Matlab session from Python.
    • : continuation of the seemingly abandoned PyMat.
    • , mlabwrap-purepy: make Matlab look like Python library (based on PyMat).
    • : run GNU Octave commands from within Python. This can call *.m files within python (https://pypi.python.org/pypi/oct2py). It does require GNU Octave, however (https://www.gnu.org/software/octave/). Octave:  Octave has 99% MATLAB compatibility supposedly. It is possible to release the source code and test: www.gnu.org/software/octave 
    • : Embeds the Python Interpreter in Matlab, also on File Exchange.
    • : Access MATLAB in various ways: create variables, access .mat files, direct interface to MATLAB engine (requires MATLAB be installed).
    • MatPy: Python package for numerical linear algebra and plotting with a MatLab-like interface.
    • Many other comparisons exist for python and MatLab, like here on pyzo&#;s website. Pyzo is a free and open-source computing environment based on Python. If you&#;re used to e.g. Matlab, Pyzo can be considered a free alternative. Pyzo is a Python IDE that works with any Python interpreter installed on your system, including Conda environments. The IDE is aimed at interactivity and simplicity, and consists of an editor, a shell, and a set of tools to help the programmer in various ways. Get Pyzo going on your machine using the quickstart, or check the code on Github.

    By the way, it might be helpful to look here for other migration tips:

    Videos of potential interest

    Multiple Sources for this Content<

    Stack Overflow, SourceNeal Gordon 

    Like this:

    LikeLoading

    Related

    Sours: https://www.pharmacoengineering.com//11/29/converting-matlab-code-to-python/

    MATLAB codeto python code conversion

    # !pip install imutils

    import pandas as pd

    import numpy as np

    # import io

    import imutils

    import matplotlib.pyplot as plt

    import cv2

    import math as mt

    import math

    import skimage.io as io

    from skimage import io, color, img_as_ubyte

    from skimage.feature import greycomatrix, greycoprops

    from sklearn.metrics.cluster import entropy

    function out = Knn( img)

    I = img;

    I=WaterShed_Fun(I);

    oneuser=ones(8);

    %I=rgb2gray(I);

    %GLs > Gray level ()

    %pixelCounts > no of pixel for image ;

    [pixelCounts GLs] = imhist(I);

    % Get the number of pixels in the histogram.

    numberOfPixels = sum(pixelCounts);

    % Get the mean gray lavel.

    meanGL = sum(GLs .* pixelCounts) / numberOfPixels;

    % Get the variance, which is the second central moment.

    varianceGL = sum((GLs - meanGL) .^ 2 .* pixelCounts) / (numberOfPixels-1);

    % Get the standard deviation.

    sd = sqrt(varianceGL);

    % Get the skew.

    skewGL = sum((GLs - meanGL) .^ 3 .* pixelCounts) / ((numberOfPixels - 1) * sd^3);

    % Get the kurtosis.

    kurtosisGL = sum((GLs - meanGL) .^ 4 .* pixelCounts) / ((numberOfPixels - 1) * sd^4);

    %//////////////mean/////////

    oneuser(1,1)=meanGL;

    %/////////////standrad derivian ////////

    oneuser(1,2)=sd;

    %////////////Entropy //////////////// calc randomness (-sum(p.*log2(p)))

    oneuser(1,3)=entropy(I);

    %////////////Skewness &kourites //////////////////////////( measure of symmetry)

    oneuser(1,4)=skewGL;

    oneuser(1,5)=kurtosisGL;

    %////////////correlation///////////////

    %oneuser(1,3)=corr(I);

    %////////////Contrast//////////////////returns a measure of the intensity contrast

    % between a pixel and its neighbour over the whole image.

    kernel = [-1, -1, -1, -1, 8, -1, -1, -1]/8;

    diffImage = conv2(double(I), kernel, 'same');

    oneuser(1,6) = mean2(diffImage);

    %////////////Variance/////////////////////////

    oneuser(1,7)=varianceGL;

    %////////////Smoothness//////////////////////

    oneuser(1,8)=/(1+oneuser(1,7));

    k=1;

    [m n] = size(I);

    qmean = oneuser(1,1);

    qstanddev = oneuser(1,2);

    qEntropy = oneuser(1,3);

    qskewGL = oneuser(1,4);

    qkurtosisGL = oneuser(1,5);

    qContrast = oneuser(1,6);

    qVariance = oneuser(1,7);

    qSmoothness = oneuser(1,8);

    query = [qmean qstanddev qEntropy qskewGL qkurtosisGL qContrast qVariance qSmoothness];

    Mean = xlsread('C:\Users\Muhammad\Documents\MATLAB\tr.xls','sheet1', 'a2:a');

    Standrad_Dev = xlsread('C:\Users\Muhammad\Documents\MATLAB\tr.xls','sheet1', 'b2:b');

    Entropy = xlsread('C:\Users\Muhammad\Documents\MATLAB\tr.xls','sheet1', 'c2:c');

    Skewness = xlsread('C:\Users\Muhammad\Documents\MATLAB\tr.xls','sheet1', 'd2:d');

    Kourites = xlsread('C:\Users\Muhammad\Documents\MATLAB\tr.xls','sheet1', 'e2:e');

    Contrast = xlsread('C:\Users\Muhammad\Documents\MATLAB\tr.xls','sheet1', 'f2:f');

    Variance = xlsread('C:\Users\Muhammad\Documents\MATLAB\tr.xls','sheet1', 'g2:g');

    Smoothness = xlsread('C:\Users\Muhammad\Documents\MATLAB\tr.xls','sheet1', 'h2:h');

    target = xlsread('C:\Users\Muhammad\Documents\MATLAB\tr.xls','sheet1', 'i2:i');

    %Start(0) Calculating Euclidean Distance between the query points and

    %previous data.

    z1 = (query(1) - Mean ).^2;

    z2 = (query(2) - Standrad_Dev ).^2;

    z3 = (query(3) - Entropy).^2;

    z4 = (query(4) - Skewness).^2;

    z5 = (query(5) - Kourites).^2;

    z6 = (query(6) - Contrast).^2;

    z7 = (query(7) - Variance).^2;

    z8 = (query(8) - Smoothness).^2;

    euclideanDistance = sqrt(z1 + z2 + z3 + z4 + z5 + z6 + z7 + z8); %Euclidean distance in square units

    %End (0)

    distance = [euclideanDistance target]; % Appending the output(precipitation) vector to distance.

    sortedDistance = sortrows(distance); % sort the distance vector based on the proximity to search query.

    target = mode(sortedDistance(1:k,2)); % Find the most frequently occuring classvalue( here, precipitation)

    if(target == 1)

    out='CANCER';

    display(out);

    elseif (target==0)

    out='Ulcer';

    display(out);

    elseif (target==2)

    out='Normal';

    display(out);

    img="heba.png"

    result=main_test(img)

    print("result",result)

    end

    end

    Sours: https://www.mathworks.com/matlabcentral/answers/matlab-code-to-python-code-conversion
    1. 1987 olds 442 specs
    2. 1994 kx 80
    3. Lcd monitor white screen
    4. 2010 nissan maxima modified
    5. On semiconductor salaries

    How to convert the MATLAB code into the Python?

    clc() clear(mstring('all')) close(mstring('all')) # Generating the bit pattern with each bit 20 samples long b = round(rand(1, 30)) pattern = mcat([]) for k in mslice[]: if b(1, k) == 0: sig = -ones(1, 20) else: sig = ones(1, 20) end

    pattern = mcat([pattern, sig])

    end

    subplot(4, 1, 1)

    plot(pattern)

    axis(mcat([-1, - , ]))

    title(mstring('Original Bit Sequence'))

    # Generating the pseudo random bit pattern for spreading

    d = round(rand(1, ))

    pn_seq = mcat([])

    carrier = mcat([])

    t = mcat([mslice[ * pi / * pi]])# Creating 5 samples for one cosine

    for k in mslice[]:

    if d(1, k) == 0:

    sig = -ones(1, 5)

    else:

    sig = ones(1, 5)

    end

    c = cos(t)

    carrier = mcat([carrier, c])

    pn_seq = mcat([pn_seq, sig])

    end # Spreading of sequence spreaded_sig = patternelmul pn_seq subplot(4, 1, 2) plot(spreaded_sig) axis(mcat([-1, - , ])) title(mstring('Spreaded signal')) # BPSK Modulation of the spreaded signal bpsk_sig = spreaded_sigelmul carrier# Modulating the signal subplot(4, 1, 3) plot(bpsk_sig) axis(mcat([-1, - , ])) title(mstring('BPSK Modulated Signal')) #Plotting the FFT of DSSS signal y = abs(fft(xcorr(bpsk_sig))) subplot(4, 1, 4) plot(y / max(y)) xlabel(mstring('Frequency')) ylabel(mstring('PSD')) #Demodulation and Despreading of Received Signal figure() rxsig = bpsk_sigelmul carrier demod_sig = mcat([]) for i in mslice[]: if rxsig(i) >= 0: rxs = 1 else: rxs = -1 end demod_sig = mcat([demod_sig, rxs]) end subplot(3, 1, 1) plot(demod_sig) axis(mcat([-1, - , ])) title(mstring('Demodulated Signal')) despread_sig = demod_sigelmul pn_seq subplot(3, 1, 2) plot(despread_sig) axis(mcat([-1, - , ])) title(mstring('Despreaded data')) #Power Spectrum of Despreaded data z = + * despread_sig y = abs(fft(xcorr(z))) subplot(3, 1, 3) plot(y / max(y)) axis(mcat([0, , 0, ])) xlabel(mstring('Frequency')) ylabel(mstring('PSD'))

    Sours: https://www.mathworks.com/matlabcentral/answers/how-to-convert-the-matlab-code-into-the-python
    Using MATLAB with Python

    Matlab is used by many students for their thesis projects and many other tasks. Especially in the field of computing and mathematics, Matlab is considered to be a prominent programming language among all. Moreover, as it supports Object-Oriented Programming and Data Visualization codes, you can easily use it to your advantage. But as the world changes, new programming languages come into the picture. To convert Matlab to python, we have two options, either do it manually or take the help of some tool.

    To convert Matlab to python, a tool named SMOP (Small Matlab and Octave to Python Compiler) is used. This tool is capable of understanding basic Matlab code and then parsing it to python. Although there are always limitations to every tool, this tool works best for small-level codes.

    In this post, we&#;ll go through all the available ways to convert Matlab code into python code without any difficulty.

    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 s by Guido van Rossum and now is been contributed by hundreds of developers worldwide.

    Whereas, Matlab is closed source software managed by Mathworks. And most importantly, Python is free, unlike Matlab.

    We can get 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 next to 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 index1. Also, considering the usage efficiency and support available, it&#;s already concluded that Python is way more flexible than Matlab.

    Why Conversion to Python is Helps Us?

    One main reason to convert Matlab to python is the code simplicity. Python codes are easy to understand which can easily be interpreted even by a new person. Being open-source, it&#;s always easy to tweak around existing modules and create the best helping module for you. In Matlab, it might not be possible.

    Even the original Google algorithm2 was written in Python in the initial stages :D. Isn&#;t it easier to use open source?

    Ways to Convert Matlab To Python

    There are many ways to convert Matlab to python. But as python receives more updates, it may or may not be compatible with your current version of Python. It totally depends on the code you&#;re trying to convert.

    1. SMOP (Small Matlab and Octave to Python compiler)

    convert matlab to python using SMOP

    As discussed earlier, SMOP is currently the best available tool to parse and convert Matlab to Python. SMOP also mentions in their documentation that it even surpasses Octave in interpreting the Matlab codes!

    Installing SMOP &#;

    git clone https://github.com/victorlei/smop cd smop python setup.py install --user

    You need python installed on your computer before running this command. It&#;ll use the python package installer to install SMOP. If you&#;re on Linux and do not want to go through this hassle, use this command &#;

    Usage:

    This command will convert the file.m Matlab file and then save it to a.py file. If you want to change the output file, use the -o flag in the terminal. Also, it supports the -d flag which can be used to ignore functions by regex from the file.

    2. matlab2python

    matlab2python is a GitHub project created by ebranlard and has a recent commit before 11 months. This project heavily depends on SMOP but has simpler usage capabilities than it. In SMOP, the translation is dependent on libsmop but matlab2python uses numpy to translate array-related declarations.

    Installing matlab2python &#;

    git clone https://github.com/ebranlard/matlab2python cd matlab2python pip install -r requirements.txt

    Using matlab2python to convert Matlab to python &#;

    python matlab2python.py file.m -o file.py

    Even after translating with SMOP or matlab2python, you would need to look at the code manually and check for any possible memory errors or memory leaks.

    3. OMPC (Open-Source Matlab-To-Python Compiler)

    OMPC works easily and effectively with medium-level Matlab codes. It can easily translate and convert Matlab to python by using a python interpreter. There are two ways to use the OMPC, either by using an online converter or by OMPC locally. We&#;ll have a look at both these methods &#;

    Online Translator &#;

    Update: Online translator has been shut down by the developers due to some server issues. I&#;ll update the post, as soon as it&#;s live again.

    Using OMPC locally &#;

    First, you need to clone the zip or tar.gz file and then extract it. Use the following command for this &#;

    wget https://www.bitbucket.org/juricap/ompc/get/tip.bz2 tar xvfj tip.bz2 cd ompc-2f62b3a16cd5

    Then in the same directory open the python shell and you can import ompc directly.

    4. Mat2py

    Mat2py is an 8 old Github project which uses python to parse Matlab code. It has a proper structure of lexer, parser, and tokens in the source files but as it has not been updated for the past 8 years, we won&#;t recommend it using over SMOP.

    Usage &#;

    python mat2py.py file.m file.py

    Here file.m will be your Matlab file and file.py will be your translated python file.

    We suggest you only convert basic Matlab code using this method.

    5. Libermate

    Libermate is another 8-year-old GitHub repo that parses the Matlab code manually and then translates it to python.

    Note: The project has been declared as Deprecated and the developer has already suggested using SMOP instead.

    You can find the Matlab lexer in the repository which can help you to create your own Matlab lexer. But remember that it&#;ll still be inferior to SMOP.

    Installing Dependencies &#;

    First, save the tar.gz file for pyclibs from here. Then run the following commands &#;

    tar -zxvf pyclipstar.gz cd pyclibs sudo python setup.py build sudo python setup.py install

    Usage &#;

    git clone https://github.com/awesomebytes/libermate cd libermate python libermate.py file.m

    The above command will parse the file.m Matlab file and then create the file.ast with contains the abstract syntax tree of the file and save the translated file to file.py.

    Some Other Ways to Create Interface Between Matlab and Python

    Apart from translating there are many ways to create an interface between Matlab and python. Thanks to the wonderful community we have a large number of open source projects on this.

    pymatlab

    pymatlab is a module available in PyPi which can be used to communicate with Matlab code. This module is capable of running functions on certain data and retrieves the results from the function. To install the module, run (pip3 for python3) in your terminal.

    Usage &#;

    import pymatlab session = pymatlab.session_factory() from numpy.random import randn a = randn(20,10,30) session.putvalue('A',a) session.run('B=3*A') b = session.getvalue('B')

    The above code initializes a Matlab session and then multiples the array by 3 in Matlab way. Then we can use the getvalue() function to get the value of any variable.

    python-matlab-wormholes

    Unlike pymatlab, python-matlab-wormholes is capable of communicating both sides (python to Matlab and Matlab to python). To install &#;

    Fetch the repository from code.google.com repository and extract the tar.gz file. Then run the command to install the module.

    To use Matlab in python &#;

    from Wormhole import Wormhole W = Wormhole() import numpy as np W.put("x",np.arange(5)) W.execute("y = x.^2") W.get("y") array([ 0., 1., 4., 9., ])

    To use python in Matlab &#;

    W = Wormhole W.put("x",) W.execute("y = x**2") W.get("y")

    Convert python Ndarray to Matlab Matrix

    When using numpy interfaces while interacting with python from Matlab, you can run into an ndarray conversion error. While returning an NxN array from the python function, you&#;ll notice that it is returned as 1xN many times. This error can be frustrating but fortunately, there has been some good discussion among the community regarding this.

    The workaround goes as follows &#;

    data = double(py.array.array('d',py.numpy.nditer(x)));

    This will ensure that your data variable is initialized as a matrix of double again.

    Convert matlab code system identification toolbox example to python

    Matlab has a great tool called System Identification Toolbox which helps you in linear and nonlinear dynamic systems. The examples of this toolbox in Matlab cannot be easily converted to python code as there is no official compatible module in Python to help this.

    Fortunately, a researcher named Wilson Rocha Lacerda Junior along with his fellow researchers, published a research paper on &#;SysIdentPy: A Python package for System Identification using NARMAX models&#;. SysIdentPy is an open-source package in Python which has similar functionalities to System Identification Toolbox.

    Use to install the package in Python.

    How to convert latitude and longitude to local tangent plane python matlab

    Geodetic is a widely used module in Matlab to work with angles, coordinates, geometry, and other geology operations. Many softwares require working with latitude and longitude. So, while converting such software Matlab code to python, you&#;ll run into major problems because there are different packages to handle geology in python.

    Among all the options, pymap3d is the best and optimum alternative for the geodetic of Matlab. Use to install the module and start using it right away.

    The following example demonstrates how you can use latitude, longitude, and altitude to get local EU coordinates in Python &#;

    import pymap3d as pm origin = (, , ) #height/altitude is in meters interest = (, , ) eu = pm.geodetic2enu(*origin, *interest) print(eu)

    Output &#;

    FAQs

    Can I convert MATLAB code to Python?

    Yes, you can convert basic Matlab codes to python by using tools like SMOP, matlab2python, and OMPC. For high-level Matlab codes with multiple modules, you have to manually rewrite the codes based on alternative packages in Python.

    Can you run MATLAB code in Python?

    You can directly run Matlab code in Python by using the Matlab engine.

    References

    1. StatisticsTimes: Python is the most searched language in PYPL index.
    2. Google Algorithm: Google in initial stages.
    3. SMOP: Small Matlab and Octave to Python compiler.
    4. matlab2python: A python script to convert matlab files or lines of matlab code to python.
    5. OMPC: An Open-Source MATLAB®-to-Python® Compiler.
    6. SysIdentPy: SysIdentPy is a Python module for System Identification using NARMAX models built on top of numpy and is distributed under the 3-Clause BSD license.
    Sours: https://www.pythonpool.com/convert-matlab-to-python/

    To matlab python code

    A tool to convert MATLAB code to Python [closed]

    There are several tools for converting Matlab to Python code.

    The only one that's seen recent activity (last commit from June ) is Small Matlab toPython compiler (also developed here: [email protected]).

    Other options include:

    • LiberMate: translate from Matlab to Python and SciPy (Requires Python 2, last update 4 years ago).
    • OMPC: Matlab to Python (a bit outdated).
    • Mat2py: Matlab to Python (Requires Python 2).

    Also, for those interested in an interface between the two languages and not conversion:

    • : communicate from Python by sending data to the MATLAB workspace, operating on them with scripts and pulling back the resulting data.
    • Python-Matlab wormholes: both directions of interaction supported.
    • Python-Matlab bridge: use Matlab from within Python, offers matlab_magic for iPython, to execute normal matlab code from within ipython.
    • PyMat: Control Matlab session from Python.
    • : continuation of the seemingly abandoned PyMat.
    • , mlabwrap-purepy: make Matlab look like Python library (based on PyMat).
    • (repository): run GNU Octave commands from within Python.
    • : Embeds the Python Interpreter in Matlab, also on File Exchange.
    • : Access MATLAB in various ways: create variables, access .mat files, direct interface to MATLAB engine (requires MATLAB be installed).
    • MatPy: Python package for numerical linear algebra and plotting with a MatLab-like interface.

    Btw might be helpful to look here for other migration tips:

    On a different note, though I'm not a fan at all, for people who might find it useful there is:

    Sours: https://stackoverflow.com/questions//a-tool-to-convert-matlab-code-to-python
    conversion of matlab code to python code (EXAMPLE)

    The kit itself is simple in appearance, Anya herself would not have seen. but her chest lay in it very well. - Will you allow me.

    Now discussing:

    The rest were seen for the first time. The wife wore a gray silver dress with spaghetti straps (slightly above the knee), standard (for her) smoky stockings with elastic bands and beautiful. Dark gray stilettos. There was absolutely no sign of adventure.

    There was no familiar sparkle in the wife's eyes - a harbinger of such.



    1344 1345 1346 1347 1348