Installation

aspire targets Python 3.10+ and relies on numpy, matplotlib, array-api-compat and h5py for core functionality. Optional extras provide tighter integration with popular samplers and flow backends.

Basic setup

Install the library from PyPI (note the published name):

$ python -m pip install aspire-inference

The installed distribution exposes the aspire import namespace. By default, this doesn’t include any optional dependencies beyond the core ones listed above. We recommend installing with at least one backend for normalizing flows, e.g. torch (PyTorch + zuko) or jax (JAX + flowjax). and optionally the minipcn SMC kernel.

Optional extras

Additional features can be enabled by installing the relevant extras:

Extra	Purpose
scipy	Access to SciPy utilities used by certain transforms.
jax	JAX + flowjax backend for training normalizing flows.
torch	PyTorch + zuko backend (default) for normalizing flows and flow matching.
minipcn	Enables the MiniPCN SMC kernel.
emcee	Enables the emcee ensemble sampler integration.
blackjax	Enables the BlackJAX SMC kernel.
test	Installs pytest and coverage helpers for local testing.

Install extras via:

$ python -m pip install "aspire-inference[torch,minipcn]"

From source

Clone the repository and install in editable mode:

$ git clone https://github.com/mj-will/aspire.git
$ cd aspire
 # (optional) create/activate a virtual environment
$ python -m pip install -e ".[torch,minipcn]"

After installation, run the unit test suite to confirm everything is wired up:

$ python -m pytest

Building the docs locally requires sphinx and (optionally) the sphinx-. These are installed automatically when you run python -m pip install -r docs/requirements.txt if such a file exists, or you can install sphinx manually before invoking make html inside the docs directory.