unite — Unified liNe Integration Turbo Engine

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.

Release files for unite 3.4.8

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for unite 3.4.8
File	Size	Uploaded
unite-3.4.8.tar.gz	1.4 MB	Sep 25, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for unite 3.4.8
File	Interpreter	ABI	Platform	Reset
unite-3.4.8-py3-none-any.whl 244.6 kB Sep 25, 2026	Python 3	none	any	Details

Total release size: 1.7 MB

Release files / unite-3.4.8.tar.gz

Download URL	unite-3.4.8.tar.gz
Size	1.4 MB
Tags	Source
SHA-256 checksum How to use checksums	`abb7bdb0ed937e689458da80b00627049fd4e73a159ef71ded71c392bbfd60ee`
BLAKE2b-256 checksum How to use checksums	`203c55fe20fe3d89a0d2db9a7265a816905e1dbbec344ee4b32458710a115c33`
Upload date	Sep 25, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/TheSkyentist/unite
Publishing commit: github.com/TheSkyentist/unite/tree/8a50a9ada9a3df4386e089510ce6f16d64c65134

Workflow

Publishing configuration: github.com/TheSkyentist/unite/blob/8a50a9ada9a3df4386e089510ce6f16d64c65134/.github/workflows/publish.yml
Publishing logs: github.com/TheSkyentist/unite/actions/runs/36138842793/attempts/1

Artifact

Subject: unite-3.4.8.tar.gz
SHA-256 checksum: abb7bdb0ed937e689458da80b00627049fd4e73a159ef71ded71c392bbfd60ee Verifying attestations

Release files / unite-3.4.8-py3-none-any.whl

Download URL	unite-3.4.8-py3-none-any.whl
Size	244.6 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`7fd9d7156a7c204ee5e5a2a746137fcb22c62a3cf556239f2c666823b4b2caa3`
BLAKE2b-256 checksum How to use checksums	`6372c1944b1ccd32d0e3f82a73c853e9fbb04e7dc189cbdb5c792d095743a8af`
Upload date	Sep 25, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/TheSkyentist/unite
Publishing commit: github.com/TheSkyentist/unite/tree/8a50a9ada9a3df4386e089510ce6f16d64c65134

Workflow

Publishing configuration: github.com/TheSkyentist/unite/blob/8a50a9ada9a3df4386e089510ce6f16d64c65134/.github/workflows/publish.yml
Publishing logs: github.com/TheSkyentist/unite/actions/runs/36138842793/attempts/1

Artifact

Subject: unite-3.4.8-py3-none-any.whl
SHA-256 checksum: 7fd9d7156a7c204ee5e5a2a746137fcb22c62a3cf556239f2c666823b4b2caa3 Verifying attestations