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https://github.com/sefffal/octofitterpy
Python wrapper of Octofitter.jl: fast and flexible orbit fitting
https://github.com/sefffal/octofitterpy
Last synced: 2 months ago
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Python wrapper of Octofitter.jl: fast and flexible orbit fitting
- Host: GitHub
- URL: https://github.com/sefffal/octofitterpy
- Owner: sefffal
- License: mit
- Created: 2024-03-06T17:34:45.000Z (10 months ago)
- Default Branch: master
- Last Pushed: 2024-10-10T20:49:37.000Z (3 months ago)
- Last Synced: 2024-10-13T18:10:02.365Z (3 months ago)
- Language: Python
- Homepage:
- Size: 8.87 MB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.md
Awesome Lists containing this project
README
# octofitterpy
`octofitterpy` is a python package for performing Bayesian inference
against a wide variety of exoplanet / binary star data. It uses the [Octofitter.jl](https://sefffal.github.io/Octofitter.jl/)
julia package under the hood (just like eg numpy uses C).
`octofitterpy` can access almost all functionality of Octofitter.jl. Currently a subset of this functionality including relative astrometry fitting, absolute astrometry fitting, and various plotting functions have been wrapped with convenient python functions. Remaining functionality can be accessed via the `octofitterpy.Octofitter` submodule.
The `examples` directory and [demo notebook](https://github.com/sefffal/octofitterpy/blob/master/examples/demo.ipynb) provide an introduction to using octofitter in Python.
Extensive documentation and tutorials are available [here](https://sefffal.github.io/Octofitter.jl/) for the Julia version, and for the most part are directly translatable to Python.
## Installation
In python 3.8 to 3.11 based environment, run:
```bash
pip install -U octofitterpy
```
## Example
```python
import octofitterpy as octo
# See demo.ipynb for more details
astrom_like = octo.PlanetRelAstromLikelihood(
epoch = [50000,50120],
sep = [505.7,600.1],
pa = [0.0,0.4,],
σ_sep = [10,10],
σ_pa = [0.01,0.01],
cor= [0,0.2]
)
planet_b = octo.Planet(
name="b",
basis="Visual{KepOrbit}",
priors=
"""
a ~ LogUniform(0.1, 500)
e ~ Uniform(0.0, 0.99)
i ~ Sine()
ω ~ UniformCircular()
Ω ~ UniformCircular()
θ ~ UniformCircular()
tp = θ_at_epoch_to_tperi(system,b,50000) # use MJD epoch of your data here!!
""",
likelihoods=[astrom_like]
)
sys = octo.System(
name="HIP100123",
priors =
"""
M ~ truncated(Normal(1.2, 0.1), lower=0)
plx ~ truncated(Normal(50.0, 0.02), lower=0)
""",
likelihoods=[],
companions=[planet_b]
)
model = octo.LogDensityModel(sys) # Compile model
chain = octo.octofit(model) # Sample model
octo.octoplot(model,chain) # Plot orbits
octo.octocorner(model,chain,small=True) # Make corner plot
octo.savechain("table.fits", chain)
```
## Read the paper
In addition to these documentation and tutorial pages, you can read the paper published in the [Astronomical Journal](https://dx.doi.org/10.3847/1538-3881/acf5cc) (open-access).
## Attribution
* If you use Octofitter in your work, please cite [Thompson et al](https://dx.doi.org/10.3847/1538-3881/acf5cc):
```bibtex
@article{Thompson_2023,
doi = {10.3847/1538-3881/acf5cc},
url = {https://dx.doi.org/10.3847/1538-3881/acf5cc},
year = {2023},
month = {sep},
publisher = {The American Astronomical Society},
volume = {166},
number = {4},
pages = {164},
author = {William Thompson and Jensen Lawrence and Dori Blakely and Christian Marois and Jason Wang and Mosé Giordano and Timothy Brandt and Doug Johnstone and Jean-Baptiste Ruffio and S. Mark Ammons and Katie A. Crotts and Clarissa R. Do Ó and Eileen C. Gonzales and Malena Rice},
title = {Octofitter: Fast, Flexible, and Accurate Orbit Modeling to Detect Exoplanets},
journal = {The Astronomical Journal},
}
```
* If you use the pairplot functionality, please cite:
```
@misc{Thompson2023,
author = {William Thompson},
title = {{PairPlots.jl} Beautiful and flexible visualizations of high dimensional data},
year = {2023},
howpublished = {\url{https://sefffal.github.io/PairPlots.jl/dev}},
}
```
* The python wrapper octofitterpy is based on the excellent [PySR by Miles Cranmer](https://github.com/MilesCranmer/PySR).
* See the documentation for a list of additional papers to consider citing.
## Ready?
Start by following this [tutorial](https://github.com/sefffal/octofitterpy/blob/master/examples/demo.ipynb).