SSAPy - Space Situational Awareness for Python

SSAPy is a flexible, physics-based orbital modeling and analysis tool for orbits spanning from low-Earth orbit into the cislunar regime.

SSAPy retains the core coordinate, observer-geometry, and propagation routines. For higher-level utilities, convenience workflows, plotting tools, workflow wrappers around coordinate conversions, Lambertian magnitude / brightness calculations, and related extensions, see the companion project SSAPy-Toolkit.

SSAPy includes:

• Ability to define satellite parameters (area, mass, radiation and drag coefficients, etc.)
• Support for multiple orbit representations and input types, including TLE-based initialization and Keplerian, equinoctial, and Kozai mean Keplerian elements
• Fully customizable analytic force propagation models, including:
  • Earth gravity models (WGS84, EGM84, EGM96, EGM2008)
  • Lunar gravity models (point source and harmonic)
  • Radiation pressure (Earth and solar)
  • Forces for planets out to Neptune
  • Atmospheric drag models
  • Maneuvering with user-defined burn profiles
• Multiple integrators, including SGP4, Runge-Kutta (4, 8, and 7/8), SciPy, Keplerian, and Taylor series methods
• User-definable timesteps and orbit information retrieval times, allowing queries for quantities of interest such as state vectors, TLEs, Keplerian elements, periapsis, apoapsis, specific angular momentum, and more
• Ground- and space-based observer models
• Lighting and visibility condition analysis
• Multiple-hypothesis tracking (MHT) UCT linker
• Vectorized computations using array broadcasting for efficient execution and easy deployment on HPC systems
• Short-arc probabilistic orbit determination methods
• Conjunction probability estimation
• Built-in uncertainty quantification
• Monte Carlo sampling, particle-based uncertainty representations, and track linking/model selection
• Support for multiple coordinate frames and coordinate transformations, including GCRF, IERS, GCRS Cartesian, TEME Cartesian, RA/Dec, NTW, zenith/azimuth, apparent positions, and orthogonal tangent plane coordinates

SSAPy provides the core propagation and modeling engine. Many higher-level, analysis-ready capabilities built on top of it live in the companion project SSAPy-Toolkit (sometimes abbreviated SSATK), including:

• Higher-level utilities and convenience workflows that wrap common SSAPy tasks
• Plotting tools for orbit and analysis visualization
• Workflow-level wrappers around SSAPy's coordinate and observer-geometry routines
• Lambertian magnitude / brightness calculations
• Additional related extensions

If your work centers on plotting, dashboards, convenience utilities, or higher-level workflows, SSAPy-Toolkit is often the best place to start — and the natural home for contributions of that kind.

For installation details, see the Installing SSAPy section of the documentation.

If you are looking for higher-level utilities or plotting-oriented workflows, you may also want to install or explore SSAPy-Toolkit.

• Python (3.10+)

The following Python packages are installed automatically when you install SSAPy:

• numpy
• scipy
• astropy
• pyerfa
• emcee
• lmfit
• sgp4
• ipython_genutils
• jplephem
• ipyvolume
• tqdm

The documentation is hosted at:

https://software.llnl.gov/SSAPy/

The API documentation may also be explored interactively:

python3

import ssapy
help(ssapy)

Contributing to SSAPy is straightforward. Please open a pull request targeting the main branch of the SSAPy repository.

For work that primarily concerns plotting, dashboards, convenience utilities, or higher-level workflows, please also consider whether the contribution belongs in the companion repository SSAPy-Toolkit.

Your PR must pass SSAPy's required CI checks. For local testing guidance, documentation builds, and Git workflow tips, see the Contribution Guide.

SSAPy's main branch contains the latest development work.

For stable installations, we recommend installing the published llnl-ssapy package from PyPI or using one of SSAPy's versioned source tags.

Please note that SSAPy has a Code of Conduct. By participating in the SSAPy community, you agree to abide by its rules.

SSAPy was developed with support from Lawrence Livermore National Laboratory's (LLNL) Laboratory Directed Research and Development (LDRD) Program under projects 19-SI-004 and 22-ERD-054, by the following individuals. For software citation order and metadata, use CITATION.cff.

• Joshua E. Meyers (SLAC National Accelerator Laboratory) - Former Lead Developer
• Travis Yeager (LLNL) - Current Lead Developer
• Michael Schneider (LLNL) - Creator, Former Lead Developer
• Edward Schlafly (STScI) - Former Lead Developer
• Julia Ebert (Fleet Robotics)
• Denvir Higgins (LLNL)
• Jason Bernstein (LLNL)
• Daniel Merl (LLNL)
• Imène Goumiri (LLNL)
• Robert Armstrong (LLNL)
• Noah Lifset (UT Austin)
• Alexx Perloff (LLNL)
• Peter McGill (LLNL)
• Nathan Golovich (LLNL)
• Kerianne Pruett (LLNL)
• Caleb Miller (LLNL)
• William A. Dawson (LLNL)

Many thanks go to SSAPy's additional contributors.

If you use SSAPy in your research, please cite the software using the repository metadata in CITATION.cff. On GitHub, use the "Cite this repository" button to copy the citation in APA or BibTeX format.

The related JOSS paper may also be cited separately:

• Meyers, J. E., Schneider, M. D., Ebert, J. T., Schlafly, E. F., Yeager, T., Perloff, A., Merl, D., Lifset, N., Bernstein, J., Dawson, W. A., Golovich, N., Higgins, D., McGill, P., Miller, C., & Pruett, K. (2025). SSAPy - Space Situational Awareness for Python. Journal of Open Source Software, 10(111), 8147. doi:10.21105/joss.08147

BibTeX:

@article{Meyers2025,
  doi       = {10.21105/joss.08147},
  url       = {https://doi.org/10.21105/joss.08147},
  year      = {2025},
  publisher = {The Open Journal},
  volume    = {10},
  number    = {111},
  pages     = {8147},
  author    = {Meyers, Joshua E. and Schneider, Michael D. and Ebert, Julia T. and Schlafly, Edward F. and Yeager, Travis and Perloff, Alexx and Merl, Daniel and Lifset, Noah and Bernstein, Jason and Dawson, William A. and Golovich, Nathan and Higgins, Denvir and McGill, Peter and Miller, Caleb and Pruett, Kerianne},
  title     = {SSAPy - Space Situational Awareness for Python},
  journal   = {Journal of Open Source Software}
}

You may also cite the following publications (click here for BibTeX entries):

• Yeager, T., Pruett, K., & Schneider, M. (2022). Unaided Dynamical Orbit Stability in the Cislunar Regime. Poster presentation, Cislunar Security Conference, USA.
• Yeager, T., Pruett, K., & Schneider, M. (2023). Long-term N-body Stability in Cislunar Space. Poster presentation, Advanced Maui Optical and Space Surveillance (AMOS) Technologies Conference, USA.
• Yeager, T., Pruett, K., & Schneider, M. (2023, September). Long-term N-body Stability in Cislunar Space. In S. Ryan (Ed.), Proceedings of the Advanced Maui Optical and Space Surveillance (AMOS) Technologies Conference (p. 208). Retrieved from https://amostech.com/TechnicalPapers/2023/Poster/Yeager.pdf

SSAPy is distributed under the terms of the MIT license. All new contributions must be made under the MIT license.

See the LICENSE and NOTICE files for details.

SPDX-License-Identifier: MIT

LLNL-CODE-862420

The structure and organization of this repository's documentation were inspired by the excellent design and layout of the Coffea project.