unite — Unified liNe Integration Turbo Engine

PyPI Python GPLv3 license Tests codecov Documentation DOI

unite is a Python package for fast, Bayesian inference of emission lines from astronomical spectra. It is built on JAX, NumPyro, and Astropy, and supports fitting multiple spectra simultaneously with shared kinematics, calibration tokens, and flexible priors.

Originally designed for JWST/NIRSpec but extensible to any spectrograph.

What it does

  • Two pixel-integration modes: analytic (exact CDF-based, default) and numerical LSF convolution (n_super uniform fine-grid points per pixel + banded wavelength-varying Gaussian convolution, correctly computes LSF ⊗ [F · exp(-τ · φ_intrinsic)] for absorption lines)
  • Simultaneous multi-spectrum fitting across gratings and instruments with shared kinematic parameters (redshift, FWHM)
  • Multiple line profiles: Gaussian, Cauchy, Pseudo-Voigt, Laplace, SEMG, Gauss-Hermite, Split-Normal, Skew-Normal, Skew-Voigt, Box-Gauss, Gaussian-Split-Laplace (asymmetric EMG)
  • Emission and absorption lines: flux-parametrized additive profiles and tau-parametrized multiplicative transmission exp(-tau * phi), with per-component depth ordering (zorder) so each absorber selectively attenuates only the sources behind it
  • Flexible continuum models: Linear, Polynomial, Chebyshev, Legendre, Bernstein, B-Spline, Power-Law, DLA Power-Law (UV power law with damped Lyman-alpha absorption and Lyman break), Blackbody, Modified Blackbody, Attenuated Blackbody, Template (user-supplied file) — auto-generated from line configurations
  • Calibration tokens (flux scale, resolution scale, pixel offset) with free or fixed priors, shared across spectra
  • YAML serialization for reproducible, human-editable configurations
  • User-controlled sampler — ModelBuilder returns (model_fn, model_args) for use with any NumPyro backend (NUTS, SVI, nested sampling, ...)
  • Instrument support for JWST/NIRSpec (all gratings + PRISM), SDSS, and any custom spectrograph via generic dispersers

Installation

pip install unite

Or with Pixi:

pixi add unite --pypi

Quick Start

import jax
import astropy.units as u
from numpyro import infer

from unite import line, model, prior
from unite.continuum import ContinuumConfiguration, Linear
from unite.instrument import nirspec
from unite.results import make_parameter_table, make_spectra_tables
from unite.spectrum import Spectra, from_DJA

# 1. Configure lines with shared kinematics
z    = line.Redshift('z', prior=prior.Uniform(-0.005, 0.005))
fwhm = line.FWHM('narrow', prior=prior.Uniform(100, 1000))

lc = line.LineConfiguration()
lc.add_line(
    'H_alpha',
    6563.0 * u.AA,
    redshift=z,
    fwhm_gauss=fwhm,
    flux=line.Flux(prior=prior.Uniform(0, 10))
)
lc.add_line(
    'NII_6585',
    6585.0 * u.AA,
    redshift=z,
    fwhm_gauss=fwhm,
    flux=line.Flux(prior=prior.Uniform(0, 10))
)
# Tau-parametrized absorption line: transmission = exp(-tau * phi)
lc.add_line(
    'HI_abs',
    6563.0 * u.AA,
    redshift=z,
    fwhm_gauss=line.FWHM('abs', prior=prior.Uniform(50, 500)),
    tau=line.Tau(prior=prior.Uniform(0, 5))
)

cc = ContinuumConfiguration.from_lines(lc.centers, width=15_000*u.km/u.s, form=Linear())

# 2. Load spectra (NIRSpec example; any instrument works)
g395m = nirspec.G395M()
spec = from_DJA('dja-spectrum.fits', disperser=g395m)

spectra = Spectra([spec], redshift=5.28)
filtered_lines, filtered_cont = spectra.prepare(lc, cc)
spectra.compute_scales(filtered_lines, filtered_cont, error_scale=True)

# 3. Build and run with any NumPyro sampler
# integration_mode='analytic' (default) uses exact CDF integration;
# integration_mode='convolution' convolves intrinsic model with LSF on a fine grid
#   (n_super uniform points per pixel) — most accurate for absorption lines
builder = model.ModelBuilder(filtered_lines, filtered_cont, spectra)
model_fn, model_args = builder.build(integration_mode='analytic')

mcmc = infer.MCMC(infer.NUTS(model_fn), num_warmup=500, num_samples=1000)
mcmc.run(jax.random.PRNGKey(0), model_args)

# 4. Extract results
# Get summary statistics at specific percentiles
samples = mcmc.get_samples()
param_table = make_parameter_table(samples, model_args, percentiles=[0.16, 0.5, 0.84])
spectra_tables = make_spectra_tables(samples, model_args, percentiles=[0.16, 0.5, 0.84])

Contributing

Bug reports, feature requests, and pull requests are welcome on GitHub. If you find a bug or have an idea for an improvement, please open an issue — even a brief description is helpful.

Documentation

Full documentation, tutorials, and API reference at unite.readthedocs.io.

Citing

If you use unite in your research, please cite the appropriate software version on Zenodo. If you use the built in NIRSpec LSF data, please also cite the appropriate LSF source (de Graaff et al. 2024 for point, Jakobsen et al. 2022 for uniform).

See CITATION.md for BibTeX entries and full details.

License

GPL v3 or later. See LICENSE for details.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (3.4.6)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for TheSkyentist from gravatar.com TheSkyentist

Credits

Author: Raphael Erik Hviding

License

GNU General Public License v2 or later (GPLv2+)

Requires

Python >=3.12

Provides Extra

bench bench-ci build dev docs test test-lowest

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