Metadata-Version: 2.1
Name: grss
Version: 2.8.2
Summary: GRSS: Gauss-Radau Small-body Simulator
Author: Rahil Makadia
Author-email: makadia2@illinois.edu
Maintainer: Rahil Makadia
Maintainer-email: makadia2@illinois.edu
Project-URL: Homepage, https://github.com/rahil-makadia/grss
Project-URL: Documentation, https://rahil-makadia.github.io/grss/
Project-URL: Source Code, https://github.com/rahil-makadia/grss
Project-URL: Bug Reports, https://github.com/rahil-makadia/grss/issues
Keywords: astronomy,asteroids,comets,orbit determination,orbit propagator
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Astronomy
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: astropy>=5.0
Requires-Dist: astroquery>=0.4.0
Requires-Dist: basemap>=1.4.0
Requires-Dist: healpy>=1.16.0
Requires-Dist: joblib>=1.3.0
Requires-Dist: matplotlib>=3.3.0
Requires-Dist: numba>=0.55.0
Requires-Dist: numpy>=1.22.0
Requires-Dist: pandas>=1.5.0
Requires-Dist: pybind11[global]>=2.10.0
Requires-Dist: requests>=2.22.0
Requires-Dist: spiceypy>=5.0.0

# grss

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**GRSS** (pronounced "grass"), the *Gauss-Radau Small-body Simulator* is a Python package with a C++ binding for propagating and fitting the orbits of small bodies in the solar system, such as asteroids and comets.

## Getting Started

There are currently two different ways to install the GRSS library.

* Using the [Python Package Index (PyPI)](https://pypi.org/project/grss/)
* Using the source code from the [GitHub repository](https://www.github.com/rahil-makadia/grss)

### Install via PyPI

The GRSS library is available on PyPI and can be installed using the following command:

``` console
    pip install grss
```

NOTE: The GRSS library is currently not pip-installable on Intel-based Macs. To use the library on an Intel-based Mac, please install the library using the source code from the GitHub repository (see below for instructions).

### Install via source code

The source code for the GRSS library is available on GitHub and can be downloaded using the following command:

```console
    git clone https://www.github.com/rahil-makadia/grss
```

Once the source code has been downloaded, the library can be installed using the following command:

```console
    cd grss
    source initialize.sh
    python3 -m pip install .
```

## Usage

Once the GRSS library has been installed, it can be imported into a Python script using the following command:

``` python
   import grss
```

The first time the library is imported, it will download some data files such as NAIF SPICE kernels and the data needed to debias optical astrometry. This should should take a few minutes, and if the download was completed, the following message will be printed for each file:

```console
   YYYY-MM-DD HH:MM:SS URL:url-of-downloaded-file [filesize] -> path/to/downloaded/file [1]
```

Once these files are available to the library, you are ready to use GRSS to its full potential!

Check out the [examples](https://rahil-makadia.github.io/grss/examples.html) on the [GRSS website](https://rahil-makadia.github.io/grss/) to get started.

## Acknowledgements

GRSS Development Team:

* Rahil Makadia
* Steven R. Chesley
* Siegfried Eggl
* Davide Farnocchia

The GRSS library was developed by Rahil Makadia as part of his PhD dissertation at the University of Illinois at Urbana-Champaign. This work was supported by a NASA Space Technologies Graduate Research Opportunities (NSTGRO) Fellowship, Grant #80NSSC22K1173. The author would like to thank his advisor, Dr. Siegfried Eggl as well as his collaborators, Dr. Steven R. Chesley, and Dr. Davide Farnocchia for their guidance and support.
