pyemili 1.2.0

Spectral lines identifier

PyEMILI (Tu, Fang et al. 2025; Tu, Fang et al. 2026) is a Python-based tool for automated spectral-line identification in astronomical spectra. It is designed primarily for emission-line objects, including planetary nebulae (PNe), H II regions, Herbig-Haro (HH) objects, and related sources, while also providing functionality for absorption-line identification and one-dimensional spectrum processing.

PyEMILI follows the general idea of EMILI, but implements the workflow in Python with updated atomic-data handling, automated ranking of candidate IDs, multiplet checks, and optional iterative refinement of ionization and velocity corrections. In comparison with manual line identifications in the literature, PyEMILI reaches approximately 90% agreement for tested emission-line spectra.

Key Features

• Automated emission-line identification: identify observed emission lines and rank possible candidate IDs.

• Automated absorption-line identification: support line identification in stellar spectra.

• Automatic 1D spectrum processing:

  • Continuum fitting: fit the continuum of a one-dimensional spectrum.

  • Line detection: detect spectral lines and provide an interactive interface for manual inspection, addition, and revision.

• Recombination-line fitting: estimate electron temperature, electron density, and ionic abundance from recombination lines.

Note

PyEMILI uses the Atomic Line List v3.00b4 developed by Peter A. M. van Hoof. The current v1.2 atomic transition database covers wavelengths from 1000 Angstrom to 30 micron, expanded from the v1.1 database that covered wavelengths up to 2 micron. Molecular transitions are not included in the current version.

Requirements

PyEMILI requires Python 3 and the following Python packages:

• numpy

• scipy

• matplotlib

• astropy

• pandas

• numba

• tqdm

Package Compatibility

Please make sure that the installed versions of numba and numpy are mutually compatible. If you encounter an error such as ImportError: Numba needs NumPy 1.** or less, install a compatible pair of numpy and numba versions in a clean environment.

Installation

PyEMILI is under active development. For the most up-to-date version, we recommend installing from the GitHub repository.

Release v1.2 includes the expanded atomic transition database out to 30 micron. It can be obtained from the GitHub release or by cloning the repository and installing locally.

Install from GitHub

git clone https://github.com/LuShenJ/PyEMILI.git
cd PyEMILI
pip install .

Install from PyPI

A PyPI version is also available:

pip install pyemili

If you plan to use the newest development version, please use the GitHub installation method above.

Usage

Detailed usage instructions and examples are available in the manual directory. Example data and results are provided in the test directory, including IC 418, Hf 2-2, and J0608.

Basic Example

A basic PyEMILI run requires a line list containing at least the observed wavelengths and fluxes of unidentified lines. If you need to generate such a line list automatically from a one-dimensional spectrum, see Line_finding.

import numpy as np
from pyemili.Lines import Line_list

# Load an observed line list.
# The first column is wavelength in Angstrom, and the second column is flux.
hf22 = np.loadtxt("Hf2-2_linelist.txt", skiprows=1)

hf22_output = Line_list(
    wavelength=hf22[:, 0],
    wavelength_error=10,  # uniform 1-sigma wavelength uncertainty in km/s
    flux=hf22[:, 1],
)

# Run the line-identification process and write outputs with prefix "Hf2-2".
# Here we use the built-in nebular abundance table.
hf22_output.identify("Hf2-2", abun_type="nebula")

Input Format

The main input parameters of pyemili.Lines.Line_list are:

• wavelength: observed wavelengths in Angstrom.

• wavelength_error: wavelength uncertainty. This can be provided as a single velocity uncertainty in km/s, a one-dimensional array of wavelength uncertainties in Angstrom, or a two-dimensional array giving asymmetric blue/red uncertainties for each line.

• flux: observed line fluxes. Fluxes are commonly normalized to H beta = 1.

• ral_vel: optional radial-velocity correction in km/s.

• flux_error: optional flux uncertainties.

• snr: optional signal-to-noise ratios, used in the output table.

• fwhm: optional full width at half maximum values, used in the output table.

Important Identification Parameters

The main parameters of Line_list.identify() include:

• filename: prefix of the generated output files.

• icf: ionization correction factors for the five energy bins. If not provided, PyEMILI uses default values or estimates them iteratively.

• v_cor: velocity corrections for the five energy bins. If not provided, PyEMILI uses default values or estimates them iteratively.

• sigma: search window in units of the wavelength uncertainty. The default value is 5.

• Ne: electron density in cm^-3. The default value is 10000.

• Te: electron temperature in K. The default value is 10000.

• I: instrumental resolution in km/s used for multiplet checks. The default value is 10.

• abun_type: abundance table. Built-in options are "solar" and "nebula". A custom abundance-file path can also be supplied.

• deplete: optional depletion or enhancement of selected ions, for example "Fe 10 1 3".

• col_cor: dilution factor for collisionally excited lines. The default value is 0.1.

• iteration: if True, PyEMILI performs a first-pass identification and then re-identifies lines with updated icf and v_cor.

• erc_list: if True, PyEMILI writes an additional recombination-line fitting list.

Output Files

After running pyemili.Lines.Line_list.identify(), PyEMILI generates two main output files:

• <filename>.out: complete candidate IDs for each input observed line.

• <filename>.dat: a short output table containing primarily the best-ranked candidate IDs.

If erc_list=True is used, PyEMILI also writes:

• <filename>_erc.dat: a recombination-line fitting list.

More information about the output format can be found in the manual introduction.

Citation

If you use PyEMILI in your research, please cite:

• Tu, Fang et al. 2025

• Tu, Fang et al. 2026

Please also cite the original atomic data sources and relevant references when appropriate.

Troubleshooting and Contact

If you encounter installation problems, usage issues, or questions about line-identification accuracy, please open an issue on the GitHub repository or contact the developers.

Contact:

• Email: [e-mail]