Installation Instructions

Installing CalibrateEmulateSample.jl

CalibrateEmulateSample (CES) depends on a small Python toolchain (currently scikit-learn and scipy, used by the SKLPy Gaussian-process backend). These are provisioned automatically through CondaPkg.jl and accessed from Julia via PythonCall.jl. You do not need a system Python install, and there is no build step to run.

Verified python package versions!

We have verified that these configurations work:

For Python 3.11: scipy = 1.14.1, scikit-learn = 1.5.1 [current default]
For Python 3.11 - 3.12: scipy = 1.14.1, scikit-learn = 1.5.1.
For Python 3.8 - 3.11: scipy = 1.8.1, scikit-learn = 1.1.1.

Please create an issue if you have had success with more up-to-date versions, and we can update this page!

julia --project
> ]
> add CalibrateEmulateSample

One may instead clone the project into a new local repository (using the SSH or https link from github), to easily access the CES codebase (e.g. to run our example suite).

The first time CES is used, CondaPkg creates an isolated Conda environment inside the Julia depot that matches the repo's CondaPkg.toml. No further setup (no PYTHON="", no Conda.add, no Pkg.build) is required:

> julia --project -e 'using Pkg; Pkg.instantiate()'

Changing the python / scipy / scikit-learn versions

Versions are pinned declaratively in CondaPkg.toml at the repository root:

channels = ["conda-forge"]

[deps]
python = "=3.11"
scipy = "=1.14.1"
scikit-learn = "=1.5.1"

To change a version, edit the relevant pin and re-resolve the environment:

> julia --project -e 'using CondaPkg; CondaPkg.resolve()'
> julia --project -e 'using CondaPkg; CondaPkg.status()'   # confirm what is installed

There is no SKLEARN_JL_VERSION environment variable and no Pkg.build step in this ecosystem — CondaPkg.toml is the single source of truth, and PythonCall always uses the CondaPkg-managed Python, so the old PyCall "could not be imported" / Python-path-mismatch class of error does not occur.

See the CondaPkg.jl and PythonCall.jl documentation for more on configuring the local Julia / Conda / Python environment.

To test that the package is working:

> julia --project -e 'using Pkg; Pkg.test()'

Building the documentation locally

You need to first build the top-level project before building the documentation:

cd CalibrateEmulateSample.jl
julia --project -e 'using Pkg; Pkg.instantiate()'

Then you can build the project documentation under the docs/ sub-project:

julia --project=docs/ -e 'using Pkg; Pkg.instantiate()'
julia --project=docs/ docs/make.jl

The locally rendered HTML documentation can be viewed at docs/build/index.html. Occasional figures may only be viewable in the online documentation due to the fancy-url package.