This page is hidden, for a reason: it is just a list of tricks that I use but that I do not expect to be useful for the general public.

Installing Julia

Installing Julia is easy. You just need to download the following file from the official repository and untar it. After this,

tar zxvf julia-1.9.3-linux-x86_64.tar.gz

Add Julia to your Path. Open your bashrc

nano ~/.bashrc

Add to the end the Absolute path to Julia, which will look like

export PATH="$PATH:/path/to/julia-1.9.2/bin"

Now source your bashrc

source ~/.bashrc

You can now start Julia from terminal, using simply


Installing Julia kernel on jupyter

Jupyter notebooks are really useful. In order to be able to use them at their best, we must be able to add kernels with our own specifications.

  1. Create a Julia Environment

The first step requires the creation of a Julia environment. Let us assume that you are in a test_environment folder. You just have to create a new environment. create_env Now you can add all the packages you want!

  1. Add Jupyter Kernel

The second step requires to use IJulia to add your Julia kernel to Jupyter.

using IJulia
installkernel("Julia_env nt8", env=Dict("JULIA_NUM_THREADS"=>"8"))
#this will create a kernel using 8 threads

add_kernel More info can be found on the IJulia Documentation.

Julia and VSCode

VSCode is a great IDE for Julia, the one I am currently using. What you need, in order to freely add virtual envs to the stuff you are doing, is to add the correct path to your Julia installation, in the Julia extension settings set_vscode


When you want to show benchmark in your website (or in your package documentation), it is useful to perform them locally on your laptop, save the benchmark result and show them (after daclaring which hardware was employed!).

For instance, the benchmark presents on the Effort.jl page, have been produced with the following code

suite = BenchmarkGroup()

suite["Effort"] = BenchmarkGroup(["tag1"])

suite["Effort"]["Monopole"] = @benchmarkable Effort.get_Pℓ($input_test, $bs, $f, $Mono_Emu)
suite["Effort"]["AP_GK"] = @benchmarkable Effort.apply_AP_check($k_test, $Mono_Effort, $Quad_Effort, $Hexa_Effort,  $q_par, $q_perp)
suite["Effort"]["AP_GL"] = @benchmarkable Effort.apply_AP($k_test, $Mono_Effort, $Quad_Effort, $Hexa_Effort,  $q_par, $q_perp)


results = run(suite, verbose = true)"effort_benchmark.json", results)

In order to see the results, just load the benchmark file and use

benchmark = BenchmarkTools.load("effort_benchmark.json")


Distributed computing

Julia Envs & Distributed computing

If you want to use a Julia environment with distributed computing, it is not enough to start the julia REPL or run a script using the --project flag: if you add a process, this will use the main julia environment. Remember, when creating processes, that you should pay attention to the env you are using...with the flag exeflags="--project=$(Base.active_project())"

Create Artifact

Largely based on this. This is what we are doing to create the ACTPol lite likelihood.

using Artifacts, ArtifactUtils, ACTPolLite

tempdir = mktempdir()

npzwrite(joinpath(tempdir,"win_func_d.npy"), win_func_d)
npzwrite(joinpath(tempdir,"win_func_w.npy"), win_func_w)
npzwrite(joinpath(tempdir,"cov_ACT.npy"), cov_ACT)
npzwrite(joinpath(tempdir,"data.npy"), data)

gist = upload_to_gist(artifact_id)

add_artifact!("Artifacts.toml", "DR4_data", gist)

In the package, add a function __init__() such as

function __init__()

    global win_func_d = npzread(joinpath(artifact"DR4_data", "win_func_d.npy"))
    global win_func_w = npzread(joinpath(artifact"DR4_data", "win_func_w.npy"))
    global cov_ACT = npzread(joinpath(artifact"DR4_data", "cov_ACT.npy"))
    global data = npzread(joinpath(artifact"DR4_data", "data.npy"))