Mex.jl

Embedding Julia in the MATLAB process

Mex.jl embeds Julia into the MATLAB process using MATLAB's C++ Mex interface. This allows Julia functions to be called from MATLAB. This also allows (embedded) Julia to call MATLAB functions.

Prerequisites

Mex.jl requires MATLAB and Julia along with a C++ compiler configured to work with MATLAB's mex command, the last is required for building the mexjulia MEX function. You can check that a compiler is properly configured by executing:

>> mex -setup C++

from the MATLAB command prompt.

NOTE: This project currently only supports Julia 1.5. Support for Julia 1.6 and greater will be possible when the changes resulting from JuliaLang/julia#42411 are incorporated into the latest stable release of Julia.

Installation

First ensure that the MATLAB.jl Julia package can be properly installed.

Then enter the package manager by typing ] and then run the following:

pkg> add Mex

The build process will:

1. use julia to determine build options,
2. build the mexjulia MEX function from source,
3. add the mexjulia directory to your MATLAB path.

By default, Mex.jl uses the MATLAB installation with the greatest version number. To specify that a specific MATLAB installation should be used, set the environment variable MATLAB_ROOT.

Quick start

Use jl.eval to parse and evaluate MATLAB strings as Julia expressions:

>> jl.eval('2+2')

ans =

  int64

   4

You can evaluate multiple expressions in a single call:

>> [s, c] = jl.eval('sin(pi/3), cos(pi/3)')

s =

    0.8660


c =

    0.5000

Note that Julia's STDOUT and STDERR are not redirected to the MATLAB console. But if MATLAB is launched from the terminal they will appear there.

>> jl.eval('println("Hello, world!")');
>> jl.eval('@warn("Oh, no!")');

One can avoid the parentheses and string quotes using jleval (a simple wrapper around jl.eval) and MATLAB's command syntax:

>> jleval 1 + 1

ans =

  int64

   2

>> jleval println("Hello, world!")
Hello, world!

Use jl.call to call a Julia function specified by its name as a string:

>> jl.call('factorial', int64(10))

ans =

     3628800

Load new Julia code by calling jl.include:

>> jl.include('my_own_julia_code.jl')

Exercise more control over how data is marshaled between MATLAB and Julia by defining a Julia function with a "MEX-like" signature and invoking it with jl.mex:

>> jleval import MATLAB
>> jleval double_it(args::Vector{MATLAB.MxArray}) = [2*MATLAB.jvalue(arg) for arg in args]
>> a = rand(5,5)

a =

    0.6443    0.9390    0.2077    0.1948    0.3111
    0.3786    0.8759    0.3012    0.2259    0.9234
    0.8116    0.5502    0.4709    0.1707    0.4302
    0.5328    0.6225    0.2305    0.2277    0.1848
    0.3507    0.5870    0.8443    0.4357    0.9049

>> jl.mex('double_it', a)

ans =

    1.2886    1.8780    0.4155    0.3895    0.6222
    0.7572    1.7519    0.6025    0.4518    1.8468
    1.6232    1.1003    0.9418    0.3414    0.8604
    1.0657    1.2450    0.4610    0.4553    0.3696
    0.7015    1.1741    1.6886    0.8714    1.8098

The first argument to jl.mex is the name of the function to be invoked. All remaining arguments are treated as function arguments.

jl.mex expects the functions on which it is invoked to accept a single argument of type Vector{MATLAB.MxArray} and to return an iterable collection of values on which MATLAB.mxarray may be successfully invoked (e.g., a value of type Vector{MATLAB.MxArray}).

Additional Examples

Additional usage examples may be found in the examples folder.

Performance

To learn how to reduce the overhead associated with this package, see performance.m in the example folder.

Credits

The starting point for the development of this package was the mexjulia project, which was designed to embed early versions of Julia into the MATLAB process.