Mex.jl: embedding Julia in the MATLAB process.

NOTES:

• This project is for Julia > 1.0. For Julia 0.6 (and potentially below), see the mexjulia project, which was the starting point for this projects development.
• I created this project mainly for my own purposes and am making my changes available as a courtesy to others seeking to embed Julia in MATLAB. Therefore, I am not necessarily committed to maintaining it, but I will do my best to respond to posted issues.

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.

Installation

First ensure that the MATLAB.jl Julia package is properly installed. This package makes use of the MxArrays type defined in that package and also uses it for this packages initial configuration step.

Then simply add this package in Julia using the following command:

]add https://github.com/byuflowlab/Mex.jl

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.

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

jl.call marshals MATLAB data to/from Julia making certain default choices for doing so.

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.mexn(1, '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.mexn is the number of return values to expect. The second is the name of the function to be invoked. All remaining arguments are treated as function arguments. jl.mexn 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}).

If only the first value is returned the jl.mex function may be used:

>> 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

Known Issues

• On Windows, if a julia is on the path it must be the one against which mexjulia is built.