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A fast elliptical accretion disk modeling engine built on JAX and NumPyro.

feadme implements the elliptical accretion disk model described in Eracleous et al. (1995) and fits it to double-peaked emission line profiles via Bayesian inference.

Features

  • Fast: JAX-accelerated disk integration with JIT compilation and GPU support.
  • Bayesian: NUTS-based posterior sampling via NumPyro with full uncertainty quantification.
  • Flexible: JSON-driven model templates supporting multiple disk and line components with shared parameters.
  • Robust initialization: SVI, Pathfinder, MAP, AutoDelta-style MAP, and JAX-LSQ initializers for difficult multimodal line-profile fits.

Installation

pip install feadme         # CPU
pip install feadme[gpu]    # GPU (CUDA 12)

From source with uv:

git clone https://github.com/nmearl/feadme.git
cd feadme
uv sync --group dev

From source without uv:

git clone https://github.com/nmearl/feadme.git
cd feadme
pip install -e ".[dev]"

Quickstart

feadme run \
  --template-path my_template.json \
  --data-path my_data.csv \
  --output-path results \
  --init-method jax-lsq \
  --integrator mixed

See the documentation for the full CLI reference, data format, and template specification.

Contributing

Bug reports, feature requests, and pull requests are welcome on the GitHub repository.

Metadata

Release files for feadme 0.2.1

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