Utilize the concepts of EnergyModelsLanguageInterfaces

Call external functions

Call Python functions

To evaluate a python function (with input argument input) called my_func which is available in the my_python_package, you can do the following

output = EMLI.call_python_function("my_python_package", "my_func"; input)

conda create --name testenv python=3.10
conda activate testenv
conda install -c conda-forge poetry
cd path_to_your_python_project
poetry install

using Pkg
Pkg.add("PyCall")
ENV["PYTHON"] = joinpath(homedir(), "AppData", "Local", "miniconda3", "envs", "testenv", "python.exe")
Pkg.build("PyCall")

Call C/C++ functions

There is no generic way of implementing a function for compiling, loading and running C/C++ code as it is for python while maintaining generality in the number of input/output arguments (and their types). Instead, one can create specialized function to obtain this using the @ccall-macro as outlined in doubling.jl. Such a function can be made in your Julia framework, or its coding lines can be directly incorporated where you want to call your C/C++ function. For the doubling example, one is able to call the C++ function with

using EnergyModelsLanguageInterfaces
EMLI_path = pkgdir(EnergyModelsLanguageInterfaces)
libpath = joinpath(EMLI_path, "test", "doubling_module", "libdoubling.so")
filepath = joinpath(EMLI_path, "test", "doubling_module", "doubling.cpp")
cpp_function_name = "doubling"
input_cpp::Vector{Cdouble} = [1.4, 2.0, 1.2]
include(joinpath(EMLI_path, "test", "doubling_module", "doubling.jl"))
demand_profile = doubling(libpath, cpp_function_name, input_cpp; filepath)

Calling C++ functions that cannot be wrapped in a C environment requires more manual work. One must use CxxWrap. This package builds on libcxxwrap which must be installed first. Start by cloning this repository

git clone https://github.com/JuliaInterop/libcxxwrap-julia.git
git checkout v0.13.4

In VS Code open a new Julia session and navigate to the folder containing the libcxxwrap-julia-repository cloned above. Check out

] develop libcxxwrap_julia_jll

Next, import the package and call the dev_jll function:

import libcxxwrap_julia_jll
libcxxwrap_julia_jll.dev_jll()

In a terminal compile the libcxxwrap with

cd /home/user/.julia/dev/libcxxwrap_julia_jll/override
rm -rf *
cmake -DJulia_PREFIX="C:/Users/user/.julia/juliaup/julia-1.11.3+0.x64.w64.mingw32" "C:/Users/user/kode/iDesignRES/libcxxwrap-julia"
cmake --build . --config Release

where the JULIA_PREFIX above must be updated to the executable of your julia installation and the location of the libcxxwrap-julia-repository cloned above.

In Julia you can now activate your project and add CxxWrap through

using CxxWrap
CxxWrap.prefix_path()

which should return "/home/user/.julia/dev/libcxxwrap_julia_jll/override".

[3eaa8342-bff7-56a5-9981-c04077f7cee7]
libcxxwrap_julia = "C:/Users/user/.julia/dev/libcxxwrap_julia_jll/override"

An example is given by the trippling_module example.