8 - Interfacing Julia with other languages

Julia can natively call C and Fortran libraries and, through packages, C++, R (1,2) and Python. This allows Julia to use the huge number of libraries of these more established languages.



#ifndef _MYLIB_H_
#define _MYLIB_H_
extern float iplustwo (float i);
extern float getTen ();


iplustwo (float i){
return i+2;

Compiled with:

  • gcc -o mylib.o -c mylib.c

  • gcc -shared -o libmylib.so mylib.o -lm -fPIC

Use in julia with:

i = 2
const mylib = joinpath(@__DIR__, "libmylib.so")
j = ccall((:iplustwo, mylib), Float32, (Float32,), i)


Use Python in Julia

We show here an example with Python. The following code converts an ODS spreadsheet in a Julia DataFrame, using the Python ezodf module (of course this have to be already be available in the local installation of python):

using PyCall
using DataFrames
const ez = pyimport("ezodf") # Equiv. of Python `import ezodf as ez`
destDoc = ez.newdoc(doctype="ods", filename="anOdsSheet.ods")
sheet = ez.Sheet("Sheet1", size=(10, 10))
dcell1 = get(sheet,(2,3)) # Equiv. of Python `dcell1 = sheet[(2,3)]`. This is cell "D3" !
get(sheet,"A9").set_value(10.5) # Equiv. of Python `sheet['A9'].set_value(10.5)`
destDoc.backup = false

The first thing, is to declare we are using PyCall and to @pyimport the python module we want to work with. We can then directly call its functions with the usual Python syntax module.function().

Type conversions are automatically performed for numeric, boolean, string, IO stream, date/period, and function types, along with tuples, arrays/lists, and dictionaries of these types.

Other types are instead converted to the generic PyObject type, as it is the case for the destDoc object returned by the module function. You can then access its attributes and methods with myPyObject.attibute and myPyObject.method() respectively.

Use Julia in Python

The other way around, embedding Julia code in a Python script or terminal, is equally of interest, as it allows in many cases to benefit of substantial performance gains for Python programmers, and it may be easier than embedding C or C++ code.

We show here how to achieve it using the PyJulia Python package, a Python interface to Julia, with the warning that, at time of writing, it is not as polished, simple and stable solution as PyCall, the Julia interface to Python.


Before installing PyJulia, be sure that the PyCall module is installed in Julia and that it is using the same Python version as the one from which you want to embed the Julia code (eventually, run ENV["PYTHON"]="/path/to/python"; using Pkg; Pkg.build("PyCall"); from Julia to change its underlying Python interpreter).

At the moment only the pip package manager is supported in Python to install the PyJulia package (conda support should come soon). Please notice that the name of the package in pip is julia, not PyJulia:

$ python3 -m pip install --user julia
>>> import julia
>>> julia.install()

If we have multiple Julia versions, we can specify the one to use in Python passing julia="/path/to/julia/binary/executable" (e.g. julia = "/home/myUser/lib/julia-1.1.0/bin/julia") to the install() function.


To obtain an interface to Julia just run:

>>> import julia;
>>> jl = julia.Julia(compiled_modules=False)

The compiled_module=False in the Julia constructor is a workaround to the common situation when the Python interpreter is statically linked to libpython, but it will slow down interactive experience, as it will disable Julia packages pre-compilation, and every time we will use a module for the first time, this will need to be compiled first. Other, more efficient but also more complicate, workarounds are given in the package documentation, under the Troubleshooting section.

We can now access Julia in multiple ways.

We may want for example define all our functions in a Julia script and "include" it. Let's assume juliaScript.jl is made of the following Julia code:

function helloWorld()
println("Hello World!")
function sumMyArgs(i, j)
return i+j
function getNElement(n)
a = [0,1,2,3,4,5,6,7,8,9]
return a[n]

We can access its functions in Python with:

>>>> jl.include("juliaScript.jl")
>>>> jl.helloWorld() # Prints `Hello World!``
>>>> a = jl.sumMyArgs([1,2,3],[4,5,6]) # Returns `array([5, 7, 9], dtype=int64)``
>>>> b = jl.getNElement(1) # Returns `0`, the "first" element for Julia

As in calling Python from Julia, also here we can pass to the functions and retrieve complex data types without warring too much about the conversion. Note that now we get the Julia way on indexing (1-based).

We can otherwise embed Julia code directly in Python using the Julia eval() function:

>>> jl.eval("""
... function funnyProd(is, js)
... prod = 0
... for i in 1:is
... for j in 1:js
... prod += 1
... end
... end
... return prod
... end
... """)

We can then call the above function in Python as jl.funnyProd(2,3).

What if, instead, we want to run the function in broadcasted mode, i.e. applying the function for each elements of a given array ? In Julia we could use the dot notation, e.g. funnyProd.([2,3],[4,5]) (this would apply the funnyProd() function first to the (2,4) arguments and then to the (3,5) ones and collecting the two results in the array [8,15]). The problem is that this would not be valid Python syntax. + In cases like this one, when we can't simply call a Julia function using Python syntax, we can still rely to the same Julia eval function we used to define the Python function to also call it: jl.eval("funnyProd.([2,3],[4,5])")

Finally, we can access any module available in Julia with from julia import ModuleName, and in particular we can set and access global Julia variables using the Main module.


Use Julia in R

To embed Julia code within a R workflow, we can use the R package JuliaCall.


Install the Julia binaries for your OS from JuliaLang. Then, in R:

> install.packages("JuliaCall")

That's all.


> library(JuliaCall)
> julia_setup()

Note that, differently than PyJulia, the "setup" function need to be called every time we start a new R section, not just when we install the JuliaCall package. If we don't have julia in the path of our system, or if we have multiple versions and we want to specify the one to work with, we can pass the JULIA_HOME = "/path/to/julia/binary/executable/directory" (e.g. JULIA_HOME = "/home/myUser/lib/julia-1.1.0/bin") parameter to the julia_setup call.

JuliaCall depends for some things (like object conversion between Julia and R) from the Julia RCall package. If we don't already have it installed in Julia, it will try to install it automatically.

As expected, also JuliaCall offers multiple ways to access Julia in R.

Let's assume we have all our Julia functions in a file. We are going to reuse the juliaScript.jl script we used in PyJulia:

function helloWorld()
println("Hello World!")
function sumMyArgs(i, j)
return i+j
function getNElement(n)
a = [0,1,2,3,4,5,6,7,8,9]
return a[n]

we can access its functions in R with:

> julia_source("juliaScript.jl") # Include the file
> julia_eval("helloWorld()") # Prints `Hello World!` and returns NULL
> a <- julia_call("sumMyArgs",c(1,2,3),c(4,5,6)) # Returns `[1] 5 7 9`
> as.integer(1) %>J% getNElement -> b # Returns `0`, the "first" element for both Julia and R

Concerning the last example, it highlights the usage of the pipe operator that is very common in R. The %>J% syntax is a special "version" of it, provided by JuliaCall, allowing to mix Julia functions in a left-to-right data transformation workflow.

We can otherwise embed Julia code directly in R using the julia_eval() function:

> funnyProdR <- julia_eval('
+ function funnyProd(is, js)
+ prod = 0
+ for i in 1:is
+ for j in 1:js
+ prod += 1
+ end
+ end
+ return prod
+ end
+ ')

We can then call the above function in R either as funnyProdR(2,3), julia_eval("funnyProd(2,3)") or julia_call("funnyProd",2,3).

While other "convenience" functions are provided by the package, using julia_eval and julia_call should suffix to accomplish any task we may need in Julia.

While an updated, expanded and revised version of this chapter is available in "Chapter 7 - Interfacing Julia with Other Languages" of Antonello Lobianco (2019), "Julia Quick Syntax Reference", Apress, this tutorial remains in active development.