Interoperability Between Julia and Python

There are currently two ways to call Julia from Python and vice versa, which are described below. When converting from Julia to Python, there are two most important differences:

1. Julia is column-major, while Python is row-major.
2. Julia adopts by default 1-based indexing, while Python adopts 0-based indexing.

JuliaCall

Vlasiator.jl can be called from Python via JuliaCall. JuliaCall will link to the first Julia version in the system path. If Vlasiator.jl has been installed, we can use it directly; otherwise we need to state it in the juliacalldeps.json file.

from juliacall import Main as jl
jl.seval("using Vlasiator")
file = "bulk.1d.vlsv"
meta = jl.load(file)

Matplotlib can then be used to visualize the data.

from matplotlib import pyplot as plt
import numpy as np
rho = jl.readvariable(meta, "proton/vg_rho")
x = np.arange(meta.coordmin[0], meta.coordmax[0], meta.dcoord[0])
plt.plot(x, rho)
plt.show()

PyJulia

Vlasiator.jl can also be called from Python with the aid of PyJulia. Following the installation steps described in the manual^[1], and then inside Python REPL:

# Handling initialization issue for Conda
from julia.api import Julia
jl = Julia(compiled_modules=False)

from julia import Vlasiator
file = "bulk1.0001000.vlsv"
meta = Vlasiator.load(file)
var = "proton/vg_rho"
data = Vlasiator.readvariable(meta, var)

To run a Julia script in Python,

# Handling initialization issue for Conda
from julia.api import Julia
jl = Julia(compiled_modules=False)
jl.eval('include("examples/demo_2dplot_pyplot.jl")')
import matplotlib.pyplot as plt
plt.show()