gmat-run 0.1.1

Run GMAT mission scripts from Python and get results as pandas DataFrames.

gmat-run

Run GMAT mission scripts from Python and get results as pandas DataFrames.

What this is

A thin, Pythonic wrapper around NASA GMAT's own gmatpy runtime. You bring a working .script; gmat-run loads it, lets you override fields from Python, runs the mission headlessly, and returns ReportFile / ephemeris / ContactLocator output as pandas DataFrames.

What this is not

• Not a way to build GMAT missions from scratch in Python — see gmatpyplus for that.
• Not a .script text generator — see pygmat.
• Not a parallel sweep runner — that's a future astro-tools project (gmat-sweep) built on top.

Requirements

• Python 3.10, 3.11, or 3.12.
• A local GMAT install. gmat-run does not ship GMAT binaries — install GMAT separately from gmat.gsfc.nasa.gov.

Supported GMAT versions

GMAT release	Status	CI
R2026a	Primary development target	Exercised on every PR (Ubuntu + Windows)
R2025a	Expected to work	Not exercised in CI for v0.1
R2024a	Expected to work	Not exercised in CI for v0.1
R2023a	Expected to work	Not exercised in CI for v0.1
R2022a	Expected to work	Not exercised in CI for v0.1

A wider CI matrix is planned for a follow-up release; report any version-specific breakage as an issue and we'll add a CI cell for it.

Installation

pip install gmat-run

Quick start

Load a script, override a field, run the mission, and read the resulting ReportFile as a pandas DataFrame:

from gmat_run import Mission

mission = Mission.load("flyby.script")
mission["Sat.SMA"] = 7000
result = mission.run()
result.reports["ReportFile1"].plot(x="UTCGregorian", y="Sat.Earth.Altitude")

Mission.load discovers a local GMAT install (honouring the GMAT_ROOT environment variable or a gmat_root= argument), bootstraps gmatpy, and parses the script into the live GMAT object graph. Subscript access reads and writes fields against that graph with type coercion. mission.run() executes the mission sequence headlessly, captures GMAT's log, and returns a Results whose reports mapping parses each ReportFile to a DataFrame on first access — with UTCGregorian and *ModJulian epoch columns promoted to datetime64[ns].

Documentation

Full docs at https://astro-tools.github.io/gmat-run/, including a getting-started guide, GMAT install instructions, and the API reference.

Development

To work on gmat-run itself:

git clone https://github.com/astro-tools/gmat-run.git
cd gmat-run
uv sync --all-groups

See CONTRIBUTING.md for the full branch / PR / test workflow.

Licence

MIT. See LICENSE.