Installation#

cosmicjellyfish supports Python >=3.10,<3.13.

Install from PyPI#

pip install cosmicjellyfish

camb and classy (the CLASS Python wrapper) are required dependencies, so this already gives you both solvers. The symbolic backend ("code": "symbolic") does not run a Boltzmann solver at runtime and is used by the A verified first forecast. It relies on the symbolic_pofk package, which is not published on PyPI and therefore cannot be installed automatically with cosmicjellyfish (PyPI does not allow packages to depend on Git URLs). Install it once by hand:

pip install git+https://github.com/DeaglanBartlett/symbolic_pofk.git

Without it, "code": "symbolic" raises an ImportError with this same hint; the camb and class backends are unaffected.

Install from source#

Clone the repository and install it with uv (recommended; CI uses it) or pip:

git clone https://github.com/santiagocasas/cosmicjellyfish.git
cd cosmicjellyfish
uv sync --extra dev        # or: pip install -e ".[dev]"

Leave out --extra dev (or [dev]) for a lighter install without the test and linting tools. With uv, symbolic_pofk is installed automatically from its upstream Git repository (via a default dependency group); with pip, add it by hand as shown above.

Install with mamba or conda#

The repository ships an environment.yml that takes the compiled scientific packages (CAMB, NumPy, SciPy, h5py, …) from conda-forge and installs the rest, including symbolic_pofk and cosmicjellyfish itself (editable), with pip:

git clone https://github.com/santiagocasas/cosmicjellyfish.git
cd cosmicjellyfish
mamba env create -f environment.yml    # or: conda env create -f environment.yml
mamba activate cosmicjellyfish

classy, chainconsumer and colossus are not on conda-forge, so they come from PyPI inside that same step. classy is compiled from source and its build calls gcc, g++ and make by name, so the environment also installs the conda-forge gcc, gxx and make packages (Linux and macOS), so no system compilers are needed. On macOS the compiler still needs an SDK: install the Xcode Command Line Tools (xcode-select --install) or point SDKROOT at an SDK before creating the environment. To add optional extras afterward, use pip install -e ".[jaxcosmo]" (or emulators, docs, dev) in the activated environment. The versions in environment.yml mirror pyproject.toml; ipython is the one exception, since conda-forge stops at 8.37 and pip moves it to the pinned 8.39.

Optional extras#

Extra	Adds
`emulators`	`euclidemu2`, for the EuclidEmulator2 nonlinear boost with CLASS
`jaxcosmo`	JAX, `jax-cosmo` and compatibility dependencies for the optional differentiable backend
`docs`	Sphinx and the theme needed to build this documentation
`dev`	Linters, type checker, `pytest` and coverage, packaging tools

pip install "cosmicjellyfish[emulators]"   # from PyPI
uv sync --extra emulators                # from a source checkout
uv sync --extra docs --extra dev         # several extras

For the optional differentiable backend, use uv sync --extra jaxcosmo from a checkout containing that implementation. See JAXcosmo and automatic differentiation for the supported models, derivative providers and notebook setup.

Survey data#

The survey specifications and the Euclid luminosity-ratio file (used for intrinsic alignment) ship with the package, so no extra download is needed for the Euclid examples.

Check the installation#

python -c "import cosmicjellyfish; print('cosmicjellyfish import OK')"

Then run the A verified first forecast. From a source checkout with the dev extra, uv run pytest runs the test suite.