line racer documentation

line racer documentation#

Welcome to the line racer documentation. line racer is a Python package designed to compute high-resolution opacities from molecular (and in the future atomic) line lists.

Key features#

  • Combines two line profile calculation methods to achieve both high accuracy and speed

    1. A direct calculation method using the Humlíček algorithm (Humlíček, 1982) and a speedup for calculating the line wings based on Mollière et al. (2015) to calculate the lines with the most intensity.

    2. A sampling technique of the line profiles based on Min (2017) for an ultra fast calculation of the lower intensity lines.

  • Fast installation via pip and easy setup to test the calculation or to compute opacities on your own machine.

  • Highly parallelized line opacity calculations using MPI via mpi4py to efficiently calculate very large line lists on clusters (also across nodes).

  • Opacities are returned as a function of wavelength, pressure, and temperature. The output can also be returned in pRT format, used by the petitRADTRANS code (Mollière et al., 2019, Blain et al., 2024).

  • Support for ExoMol and HITRAN/HITEMP line lists. ExoAtom, VALD and Kurucz support to be implemented soon.

  • Flexible line cutoff and sub-Lorentzian treatment for the wings of the line profiles.

To get started with some examples on how to run line racer, see our line racer tutorial. Before that, make sure you download the line lists and other required data correctly as described in the downloading line list tutorial. If you want to run line racer on a cluster, have a look at the cluster calculation tutorial.

If you are interested in how the line calculations are performed in detail, check out the physical and computational background of the line profile calculation in line racer.

License and how to cite#

line racer is available under the MIT License

Please cite Hägele & Mollière (2025) when making use of line racer in your research. In addition to the short JOSS paper, a more detailed explanation of the background and methods, as well as comparison to other codes can be found here in the master’s thesis of David Hägele.

This documentation webpage contains an installation guide, a general tutorial, a tutorial to run on clusters , a explanation of the physical background, community guidelines for contributions, and an API documentation.

Developers#

  • David Hägele

Contributors#

  • Paul Mollière

Content:

Code documentation

Contents