MATLAB.jl

The MATLAB.jl package provides an interface for using MATLAB™ from the Julia language. You cannot use MATLAB.jl without having purchased and installed a copy of MATLAB™ from MathWorks. This package is available free of charge and in no way replaces or alters any functionality of MathWorks's MATLAB product.

Julia is a technical computing language, which relies on LLVM to achieve efficiency comparable to C. As a young language, many useful functions are still lacking (e.g. 3D plot). This package allows users to call MATLAB functions from within Julia, thus making it easier to use the sheer amount of toolboxes available in MATLAB.

Overview

Generally, this package is comprised of two aspects:

• Creating and manipulating mxArrays (the data structure that MATLAB used to represent arrays and other kinds of data)

• Communicating with MATLAB engine sessions

Installation

The procedure to setup this package consists of three steps.

Linux

1. Make sure matlab is in executable path.

2. Make sure csh is installed. (Note: MATLAB for Linux relies on csh to open an engine session.)

   To install csh in Debian/Ubuntu/Linux Mint, you may type in the following command in terminal:

   sudo apt-get install csh

3. Clone this package from the GitHub repo to your Julia package directory, as

   cd <your/julia/package/path>
   git clone https://github.com/lindahua/MATLAB.jl.git MATLAB

Mac OS X

1. Ensure that MATLAB is installed in /Applications. By default, MATLAB.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_HOME. For example, if you are using MATLAB R2012b, you may add the following command to .profile:

   export MATLAB_HOME=/Applications/MATLAB_R2012b.app

2. Clone this package from the GitHub repo to your Julia package directory, as

   cd <your/julia/package/path>
   git clone https://github.com/lindahua/MATLAB.jl.git MATLAB

MxArray class

An instance of MxArray encapsulates a MATLAB variable. This package provides a series of functions to manipulate such instances.

Create MATLAB variables in Julia

One can use the function mxarray to create MATLAB variables (of type MxArray), as follows

mxarray(Float64, n)   # creates an n-by-1 MATLAB zero array of double valued type
mxarray(Int32, m, n)  # creates an m-by-n MATLAB zero array of int32 valued type 
mxarray(Bool, m, n)   # creates a MATLAB logical array of size m-by-n

mxarray(Float64, (n1, n2, n3))  # creates a MATLAB array of size n1-by-n2-by-n3

mxcellarray(m, n)        # creates a MATLAB cell array
mxstruct("a", "b", "c")  # creates a MATLAB struct with given fields

You may also convert a Julia variable to MATLAB variable

a = rand(m, n)

x = mxarray(a)     # converts a to a MATLAB array
x = mxarray(1.2)   # converts a scalar 1.2 to a MATLAB variable

a = sprand(m, n, 0.1)
x = mxarray(a)     # converts a sparse matrix to a MATLAB sparse matrix

x = mxarray("abc") # converts a string to a MATLAB char array

x = mxarray(["a", 1, 2.3])  # converts a Julia array to a MATLAB cell array

x = mxarray({"a"=>1, "b"=>"string", "c"=>[1,2,3]}) # converts a Julia dictionary to a MATLAB struct

The function mxarray can also converts a compound type to a Julia struct:

type S
	x::Float64
	y::Vector{Int32}
	z::Bool
end

s = S(1.2, Int32[1, 2], false)

x = mxarray(s)   # creates a MATLAB struct with three fields: x, y, z
xc = mxarray([s, s])  # creates a MATLAB cell array, each cell is a struct.
xs = mxstructarray([s, s])  # creates a MATLAB array of structs

Note: For safety, the conversation between MATLAB and Julia variables uses deep copy.

When you finish using a MATLAB variable, you may call delete to free the memory. But this is optional, it will be deleted when reclaimed by the garbage collector.

delete(x)

Note: if you put a MATLAB variable x to MATLAB engine session, then the MATLAB engine will take over the management of its life cylce, and you don't have to delete it explicitly.

Access MATLAB variables

You may access attributes and data of a MATLAB variable through the functions provided by this package.

 # suppose x is of type MxArray
nrows(x)    # returns number of rows in x
ncols(x)    # returns number of columns in x 
nelems(x)   # returns number of elements in x
ndims(x)    # returns number of dimensions in x
size(x)     # returns the size of x as a tuple
size(x, d)  # returns the size of x along a specific dimension

eltype(x)   # returns element type of x (in Julia Type)
elsize(x)   # return number of bytes per element

data_ptr(x)   # returns pointer to data (in Ptr{T}), where T is eltype(x)

You may also make tests on a MATLAB variable.

is_double(x)   # returns whether x is a double array
is_sparse(x)   # returns whether x is sparse
is_complex(x)  # returns whether x is complex
is_cell(x)     # returns whether x is a cell array
is_struct(x)   # returns whether x is a struct
is_empty(x)    # returns whether x is empty

...            # there are many more there

Convert MATLAB variables to Julia

a = jarray(x)   # converts x to a Julia array
a = jvector(x)  # converts x to a Julia vector (1D array) when x is a vector
a = jscalar(x)  # converts x to a Julia scalar
a = jmatrix(x)  # converts x to a Julia matrix
a = jstring(x)  # converts x to a Julia string
a = jdict(x)    # converts a MATLAB struct to a Julia dictionary (using fieldnames as keys)

a = jvariable(x)  # converts x to a Julia variable in default manner

Read/Write MAT Files

This package provides functions to manipulate MATLAB's mat files:

mf = MatFile(filename, mode)    # opens a MAT file using a specific mode, and returns a handle
mf = MatFile(filename)          # opens a MAT file for reading, equivalent to MatFile(filename, "r")
close(mf)                       # closes a MAT file.

get_mvariable(mf, name)   # gets a variable and returns an mxArray
get_variable(mf, name)    # gets a variable, but converts it to a Julia variable
                          # using `jvariable`

put_variable(mf, name, v)   # puts a variable v to the MAT file
                            # v can be either an MxArray instance or normal variable
                            # If v is not an MxArray, it will be converted using `mxarray`

put_variables(mf; name1=v1, name2=v2, ...)  # put multiple variables using keyword arguments

variable_names(mf)   # get a vector of all variable names in a MAT file

There are also convenient functions that can get/put all variables in one call:

read_matfile(filename)    # returns a dictionary that maps each variable name
                          # to an MxArray instance

write_matfile(filename; name1=v1, name2=v2, ...)  # writes all variables given in the
                                                  # keyword argument list to a MAT file

Both read_matfile and write_matfile will close the MAT file handle before returning.

Examples:

immutable S
	x::Float64
	y::Bool
	z::Vector{Float64}
end

write_matfile("test.mat"; 
	a = Int32[1 2 3; 4 5 6], 
	b = [1.2, 3.4, 5.6, 7.8], 
	c = {[0., 1.], [1., 2.], [1., 2., 3.]}, 
	d = {"name"=>"MATLAB", "score"=>100.}, 
	s = "abcde"
	ss = [S(1.0, true, [1., 2.]), S(2.0, false, [3., 4.])] )

This example will create a MAT file called test.mat, which contains six MATLAB variables:

• a: a 2-by-3 int32 array
• b: a 4-by-1 double array
• c: a 3-by-1 cell array, each cell contains a double vector
• d: a struct with two fields: name and score
• s: a string (i.e. char array)
• ss: an array of structs with two elements, and three fields: x, y, and z.

Use MATLAB Engine

Basic Use

To evaluate expressions in MATLAB, one may open a MATLAB engine session and communicate with it.

Below is a simple example that illustrates how one can use MATLAB from within Julia:

using MATLAB

restart_default_msession()   # Open a default MATLAB session

x = linspace(-10., 10., 500)

@mput x                  # put x to MATLAB's workspace
@matlab plot(x, sin(x))  # evaluate a MATLAB function

close_default_msession()    # close the default session (optional)

You can put multiple variable and evaluate multiple statement by calling @mput and @matlab once:

x = linspace(-10., 10., 500)
y = linspace(2., 3., 500)

@mput x y
@matlab begin
    u = x + y
	v = x - y
end
@mget u v

Note: There can be multiple (reasonable) ways to convert a MATLAB variable to Julia array. For example, MATLAB represents a scalar using a 1-by-1 matrix. Here we have two choice in terms of converting such a matrix back to Julia: (1) convert to a scalar number, or (2) convert to a matrix of size 1-by-1.

Here, get_mvariable returns an instance of MxArray, and the user can make his own choice by calling jarray, jvector, or jscalar to convert it to a Julia variable.

Caveats of @matlab

Note that some MATLAB expressions are not valid Julia expressions. This package provides some ways to work around this in the @matlab macro:

 # MATLAB uses single-quote for strings, while Julia uses double-quote. 
@matlab sprintf("%d", 10)   # ==> MATLAB: sprintf('%d', 10)

 # MATLAB does not allow [x, y] on the left hand side
x = linspace(-5., 5. 100)
y = x
@mput x y
@matlab begin
    (xx, yy) = meshgrid(x, y)  # ==> MATLAB: [xx, yy] = meshgrid(x, y)
	mesh(xx, yy, xx.^2 + yy.^2)
end

While we try to cover most MATLAB statements, some valid MATLAB statements remain unsupported by @matlab. For this case, one may always call the eval_string function, as follows

eval_string("[u, v] = myfun(x, y);")

mxcall

You may also directly call a MATLAB function on Julia variables

x = [-10.:0.1:10.]
y = [-10.:0.1:10.]
xx, yy = mxcall(:meshgrid, 2, x, y)

Note: Since MATLAB functions behavior depends on the number of outputs, you have to specify the number of output arguments in mxcall as the second argument.

mxcall puts the input arguments to the MATLAB workspace (using mangled names), evaluates the function call in MATLAB, and retrievs the variable from the MATLAB session. This function is mainly provided for convenience. However, you should keep in mind that it may incur considerable overhead due to the communication between MATLAB and Julia domain.

Advanced use of MATLAB Engines

This package provides a series of functions for users to control the communication with MATLAB sessions.

Here is an example:

s1 = MSession()    # creates a MATLAB session
s2 = MSession(0)   # creates a MATLAB session without recording output

x = rand(3, 4)
put_variable(s1, :x, x)  # put x to session s1

y = rand(2, 3)
put_variable(s2, :y, y)  # put y to session s2

eval_string(s1, "r = sin(x)")  # evaluate sin(x) in session s1
eval_string(s2, "r = sin(y)")  # evaluate sin(y) in session s2

r1_mx = get_mvariable(s1, :r)  # get r from s1
r2_mx = get_mvariable(s2, :r)  # get r from s2

r1 = jarray(r1_mx)
r2 = jarray(r2_mx)

...  # do other stuff on r1 and r2

close(s1)  # close session s1
close(s2)  # close session s2