Orbit fitting at LSST scale
To install layup, all its dependencies, as well as the ephemeris and reference data, you need about 3.2G of free space.
Before installing layup, it's a great idea to create a virtual environment with either conda or venv.
You can download the source code with:
git clone --recursive https://github.com/Smithsonian/layup.git
If you cloned the repository without --recursive flag, you can run
git submodule update --init
to download the required submodules, assist, eigen, and rebound.
Next, enter the layup directory and run
pip install -e .
to create an editable install of layup. If you're doing development work, you can install with
pip install -e ".[dev]"
to install all of the development packages as well.
Note that to get the new submodules added in an existing copy of the repo you want to run
git submodule update --init
And in subsequent clones of the repo you want to run
git clone --recursive https://github.com/Smithsonian/layup.git
Once layup is installed, download the ephemeris and reference data it needs
(SPICE planetary kernels, the small-body kernel, MPC observatory codes, and the
astrometry debiasing tables). This is a one-time download of a few hundred MB:
layup bootstrap
layup bundles a demo dataset. Copy it into your working directory and print
the matching example command with:
layup demo prepare orbitfit
layup demo howto orbitfit
prepare writes holman_data_working.csv — 4135 astrometric observations of
asteroid (3666) Holman, in ADES CSV form — to the current directory, and howto
prints the ready-to-run command. Fit it with:
layup orbitfit holman_data_working.csv ADES_csv -o my_orbit
This writes the best-fit barycentric Cartesian orbit and its covariance to
my_orbit.csv. Supported input formats are MPC80col, ADES_csv, ADES_psv,
ADES_xml, and ADES_hdf5.
Convert the result to another orbit representation (Cometary, Keplerian, …):
layup convert my_orbit.csv KEP -o my_orbit_kep
Predict future on-sky positions, with uncertainties, for an observatory:
layup predict my_orbit.csv --days 30 --station X05 -o my_predictions
Every verb takes --help for its full set of options (engine choice, IOD
method, non-gravitational parameters, parallel workers, …):
layup orbitfit --help
The same load → fit → convert → predict workflow is available directly from
Python. See the worked-example notebook
docs/notebooks/orbit_fitting_api.ipynb
and the full documentation at layup.readthedocs.io.