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starry_process

starry_process: Interpretable Gaussian processes for stellar light curves - Published in JOSS (2021)

https://github.com/rodluger/starry_process

Science Score: 93.0%

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    CITATION.cff file
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    DOI references
    Found 4 DOI reference(s) in README and JOSS metadata
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    Links to: joss.theoj.org
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    JOSS paper metadata
    Published in Journal of Open Source Software

Keywords

gaussian-processes lightcurves stars timeseries-analysis

Keywords from Contributors

exoplanet astronomy astrophysics bayesian-inference mcmc gravitational-lenses

Scientific Fields

Mathematics Computer Science - 88% confidence
Last synced: 4 months ago · JSON representation

Repository

interpretable gaussian processes for stellar light curves

Basic Info
  • Host: GitHub
  • Owner: rodluger
  • License: mit
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 130 MB
Statistics
  • Stars: 17
  • Watchers: 3
  • Forks: 6
  • Open Issues: 5
  • Releases: 5
Topics
gaussian-processes lightcurves stars timeseries-analysis
Created over 5 years ago · Last pushed over 1 year ago
Metadata Files
Readme Contributing License

README.md


Documentation Status DOI badge

Interpretable Gaussian processes for stellar light curves using starry.

A Gaussian Process for Stellar Variability

The starry_process code implements an interpretable Gaussian process (GP) for modeling stellar light curves. Whether your goal is to marginalize over the stellar variability signal (if you think of it as noise) or to understand the surface features that generated it (if you think of it as data), this code is for you. The GP implemented here works just like any other GP you might already use in your analysis, except that its hyperparameters are physically interpretable. These are (among others) the radius of the spots, the mean and variance of the latitude distribution, the spot contrast, and the number of spots. Users can also specify things like the rotational period of the star, the limb darkening parameters, and the inclination (or marginalize over the inclination if it is not known).

The code is written in Python and relies on the Theano package, so a little familiarity with that is recommended. Check out the crash course here. If you would like to report an issue or contribute to the project, please check out CONTRIBUTING.md.

Installation

The quickest way is via pip:

bash pip install starry-process

Note that the starry_process package requires Python 3.6 or later.

Quickstart

Import the main interface:

python from starry_process import StarryProcess

Draw samples from a Gaussian process with small mid-latitude spots:

```python import numpy as np import matplotlib.pyplot as plt

Instantiate the GP

sp = StarryProcess( r=10, # spot radius in degrees mu=30, # central spot latitude in degrees sigma=5, # latitude std. dev. in degrees c=0.1, # fractional spot contrast n=10 # number of spots )

Draw & visualize a spherical harmonic sample

y = sp.sample_ylm().eval() sp.visualize(y)

Compute & plot the flux at some inclination

t = np.linspace(0, 4, 1000) flux = sp.flux(y, t, i=60).eval()[0] plt.plot(t, flux) ```

Same as above, but for high-latitude spots:

python sp = StarryProcess(r=10, mu=0, sigma=10, c=0.1, n=10)

Large equatorial spots:

python sp = StarryProcess(r=30, mu=0, sigma=10, c=0.1, n=10)

Small, approximately isotropic spots:

python sp = StarryProcess(r=10, mu=0, sigma=40, c=0.1, n=10)

For more information check out the full Quickstart tutorial and the complete documentation.

References & Attribution

The code is described in this JOSS paper. It is the backbone of the Mapping Stellar Surfaces paper series, including:

If you make use of this code in your research, please cite

@article{Luger2021a, author = {{Luger}, Rodrigo and {Foreman-Mackey}, Daniel and {Hedges}, Christina and {Hogg}, David W.}, title = {{Mapping stellar surfaces I: Degeneracies in the rotational light curve problem}}, journal = {arXiv e-prints}, keywords = {Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics}, year = 2021, month = jan, eid = {arXiv:2102.00007}, pages = {arXiv:2102.00007}, archiveprefix = {arXiv}, eprint = {2102.00007}, primaryclass = {astro-ph.SR}, adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210200007L}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} }

@article{Luger2021b, author = {{Luger}, Rodrigo and {Foreman-Mackey}, Daniel and {Hedges}, Christina}, title = {{Mapping stellar surfaces II: An interpretable Gaussian process model for light curves}}, journal = {arXiv e-prints}, keywords = {Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics}, year = 2021, month = feb, eid = {arXiv:2102.01697}, pages = {arXiv:2102.01697}, archiveprefix = {arXiv}, eprint = {2102.01697}, primaryclass = {astro-ph.SR}, adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210201697L}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} }

@article{Luger2021c, author = {{Luger}, Rodrigo and {Foreman-Mackey}, Daniel and {Hedges}, Christina}, title = {{starry\_process: Interpretable Gaussian processes for stellar light curves}}, journal = {arXiv e-prints}, keywords = {Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics}, year = 2021, month = feb, eid = {arXiv:2102.01774}, pages = {arXiv:2102.01774}, archiveprefix = {arXiv}, eprint = {2102.01774}, primaryclass = {astro-ph.SR}, adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210201774L}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} }

Owner

  • Name: Rodrigo Luger
  • Login: rodluger
  • Kind: user
  • Location: New York, NY
  • Company: Flatiron Institute

Astronomer at the Center for Computational Astrophysics

JOSS Publication

starry_process: Interpretable Gaussian processes for stellar light curves
Published
July 12, 2021
DOI
Volume 6, Issue 63, Page 3071
Authors
Rodrigo Luger ORCID
Center for Computational Astrophysics, Flatiron Institute, New York, NY, Virtual Planetary Laboratory, University of Washington, Seattle, WA
Daniel Foreman-Mackey ORCID
Center for Computational Astrophysics, Flatiron Institute, New York, NY
Christina Hedges ORCID
Bay Area Environmental Research Institute, P.O. Box 25, Moffett Field, CA 94035, USA, NASA Ames Research Center, Moffett Field, CA
Editor
Arfon Smith ORCID
Tags
astronomy
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GitHub Events

Total
  • Watch event: 1
  • Fork event: 1
Last Year
  • Watch event: 1
  • Fork event: 1

Committers

Last synced: 5 months ago

All Time
  • Total Commits: 512
  • Total Committers: 4
  • Avg Commits per committer: 128.0
  • Development Distribution Score (DDS): 0.063
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Rodrigo Luger r****r@g****m 480
Dan F-M f****y@g****m 30
Meistermagier d****m@g****m 1
Arfon Smith a****n 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 19
  • Total pull requests: 12
  • Average time to close issues: 6 days
  • Average time to close pull requests: 2 days
  • Total issue authors: 9
  • Total pull request authors: 5
  • Average comments per issue: 1.79
  • Average comments per pull request: 1.58
  • Merged pull requests: 11
  • Bot issues: 0
  • Bot pull requests: 1
Past Year
  • Issues: 1
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 1
View more stats
Top Authors
Issue Authors
  • rodluger (7)
  • j-faria (2)
  • christinahedges (2)
  • nespinoza (2)
  • dfm (2)
  • javiserna (1)
  • enitree (1)
  • RomanAkhmetshyn (1)
  • Meistermagier (1)
Pull Request Authors
  • dfm (5)
  • rodluger (4)
  • arfon (1)
  • Meistermagier (1)
  • dependabot[bot] (1)
Top Labels
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dependencies (1) github_actions (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 33 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 6
  • Total maintainers: 1
pypi.org: starry-process

interpretable gaussian processes for stellar light curves

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 33 Last month
Rankings
Dependent packages count: 10.1%
Forks count: 13.3%
Stargazers count: 15.6%
Average: 17.3%
Dependent repos count: 21.5%
Downloads: 25.7%
Maintainers (1)
rodluger
Last synced: 4 months ago

Dependencies

setup.py pypi
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  • openblas 0.3.6.*
  • pandoc >=2.10
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  • python 3.7.3.*
  • scipy >=1.5.2
  • setuptools >=51.3.3
  • sympy >=1.7.1
  • tectonic >=0.1.12
starry_process/app/docker/Dockerfile docker
  • continuumio/miniconda3 latest build
pyproject.toml pypi