jaxstar
Fast stellar isochrone fitting with HMC/NUTS, powered by JAX and NumPyro. The method is described in Masuda (2022).
Installation
jaxstar requires Python 3.10 or later. Install the released package from PyPI:
python -m pip install jaxstar
For development and testing from a source checkout:
python -m pip install -e ".[test]"
MIST grid data
MistFit() and MistGridIso() use the MIST v1.2 synthetic photometry grid.
On first use, jaxstar downloads the source CMD archive, generates the NumPy
grid, and stores it in the platform-specific user cache. Later environments
and reinstalls reuse that cached grid automatically.
To use a grid at a specific path:
from jaxstar.mistfit import MistFit
fit = MistFit(path="/path/to/mistgrid_iso.npz")
Alternatively, set the JAXSTAR_MISTGRID_PATH environment variable:
export JAXSTAR_MISTGRID_PATH=/path/to/mistgrid_iso.npz
The grid path is resolved in this order:
- explicit
path=...argument; JAXSTAR_MISTGRID_PATH;- the versioned user cache.
If the resolved default cache file is missing, it is generated transparently. The source CMDs come from the MIST synthetic photometry grids.
Optional correction utilities
The core package dependencies are installed automatically. Functions in
jaxstar.utils.correction additionally use:
- dustmaps for Bayestar extinction corrections;
- gaiadr3-zeropoint for Gaia EDR3 parallax zero-point corrections.
Install them when those utilities are needed:
python -m pip install dustmaps gaiadr3-zeropoint
Testing
The default test suite is fast, deterministic, and does not download the full MIST archive:
python -m pytest -q
To run the optional historical full-grid regression test:
JAXSTAR_MISTGRID_PATH=/path/to/mistgrid_iso.npz \
python -m pytest -m full_grid
Examples
Start with the isochrone fitting example, or browse the examples directory.
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