# The Python module JuliaCall

## Installation

It's as simple as

pip install juliacall

Developers may wish to clone the repo (https://github.com/cjdoris/PythonCall.jl) directly and pip install the module in editable mode. You should add "dev":true, "path":"../.." to python/juliacall/juliapkg.json to ensure you use the development version of PythonCall in conjunction with JuliaCall.

## Getting started

For interactive or scripting use, the simplest way to get started is:

from juliacall import Main as jl

This loads a single variable jl which represents the Main module in Julia, from which all of Julia's functionality is available:

jl.println("Hello from Julia!")
# Hello from Julia!
x = jl.rand(range(10), 3, 5)
x._jl_display()
# 3×5 Matrix{Int64}:
#  8  1  7  0  6
#  9  2  1  4  0
#  1  8  5  4  0
import numpy
numpy.sum(x, axis=0)
# array([18, 11, 13,  8,  6], dtype=int64)

In this example:

• We called the jl.println function to print a message.
• We called the jl.rand function to generate an array of random integers. Note that the first argument is range(10) which is converted to 0:9 in Julia.
• We called its special _jl_display() to show it using Julia's display mechanism.
• We called the numpy.sum function to sum each column of x. This automatically converted x to a NumPy array. (We could have done jl.sum(x, dims=1) too.)

If you are writing a package which uses Julia, then to avoid polluting the global Main namespace you instead should start with:

import juliacall
jl = juliacall.newmodule("SomeName")

Julia modules have a special method seval which will evaluate a given piece of code given as a string in the module. This is most frequently used to import modules:

from array import array
jl.seval("using Statistics")
x = array('i', [1, 2, 3])
jl.mean(x)
# 2.0
y = array('i', [2,4,8])
jl.cor(x, y)
# 0.9819805060619657

• The main functionality of this package is in AnyValue objects, which represent Julia objects, documented here.
• If you need to install Julia packages, read here.
• When you call a Julia function, such as jl.rand(...) in the above example, its arguments are converted to Julia according to this table and its return value is converted to Python according to this table.

## Managing Julia dependencies

JuliaCall manages its Julia dependencies using JuliaPkg.

A Julia environment is also created, activated and populated with any required packages. If you are in a virtual or Conda environment, the environment is put there. Otherwise a global environment is used at ~/.julia/environments/pyjuliapkg.

If your project requires any Julia packages, or a particular version of Julia itself, then create a file called juliapkg.json in your package. For example: Here is an example:

{
"julia": "1.5",
"packages": {
"Example": {
"uuid": "7876af07-990d-54b4-ab0e-23690620f79a",
"version": "0.5, 0.6"
}
}
}

Alternatively you can use add, rm, etc. from JuliaPkg to edit this file.

See JuliaPkg for more details.

## Configuration

Some features of the Julia process, such as the optimization level or number of threads, may be configured in two ways:

• As an -X argument to Python, such as -X juliacall-optlevel=3; or
• As an environment variable, such as PYTHON_JULIACALL_OPTLEVEL=3.
-X optionEnvironment VariableDescription
-X juliacall-home=<dir>PYTHON_JULIACALL_BINDIR=<dir>The directory containing the julia executable.
-X juliacall-check-bounds=<yes|no|auto>PYTHON_JULIACALL_CHECK_BOUNDS=<yes|no|auto>Enable or disable bounds checking.
-X juliacall-compile=<yes|no|all|min>PYTHON_JULIACALL_COMPILE=<yes|no|all|min>Enable or disable JIT compilation.
-X juliacall-compiled-modules=<yes|no>PYTHON_JULIACALL_COMPILED_MODULES=<yes|no>Enable or disable incrementally compiling modules.
-X juliacall-depwarn=<yes|no|error>PYTHON_JULIACALL_DEPWARN=<yes|no|error>Enable or disable deprecation warnings.
-X juliacall-inline=<yes|no>PYTHON_JULIACALL_INLINE=<yes|no>Enable or disable inlining.
-X juliacall-min-optlevel=<0|1|2|3>PYTHON_JULIACALL_MIN_OPTLEVEL=<0|1|2|3>Optimization level.
-X juliacall-optimize=<0|1|2|3>PYTHON_JULIACALL_OPTIMIZE=<0|1|2|3>Minimum optimization level.
-X juliacall-procs=<N|auto>PYTHON_JULIACALL_PROCS=<N|auto>Launch N local worker process.
-X juliacall-sysimage=<file>PYTHON_JULIACALL_SYSIMAGE=<file>Use the given system image.
-X juliacall-threads=<N|auto>PYTHON_JULIACALL_THREADS=<N|auto>Launch N threads.
-X juliacall-warn-overwrite=<yes|no>PYTHON_JULIACALL_WARN_OVERWRITE=<yes|no>Enable or disable method overwrite warnings.