<p align="center"><img src="docs/source/gyrointerp_logo.png" alt="gyrointerp" width="70%"/></p>
[<img src="https://readthedocs.org/projects/gyro-interp/badge/?version=latest">](https://gyro-interp.readthedocs.io/en/latest/index.html) <img src="https://github.com/lgbouma/gyro-interp/workflows/Tests/badge.svg">
## Purpose
Observations have shown that stars with the same age and mass can have a wide
range of rotation periods. The purpose of this code is to compute the
posterior probability for a star's age given its observed rotation period and
effective temperature. This is achieved by marginalizing over all possible
ages that might explain the observed stellar properties with a parametric
model that has been fitted to not only the mean rotation periods, but also the
intrinsic scatter in observed open cluster rotation sequences.
## Documentation
The documentation is hosted at
[gyro-interp.readthedocs.io](https://gyro-interp.readthedocs.io/en/latest/index.html).
A minimal example to get you started is below.
The method is described in detail in [Bouma, Palumbo & Hillenbrand (2023)](https://ui.adsabs.harvard.edu/abs/2023ApJ...947L...3B/abstract).
## Install
Preferred installation method is through PyPI:
```shell
pip install gyrointerp
```
## Minimal Examples
Given a star's rotation period, effective temperature, and uncertainties, what
is the gyrochronological age posterior over a grid spanning 0 to 2.6 Gyr?
```python
import numpy as np
from gyrointerp import gyro_age_posterior
# units: days
Prot, Prot_err = 11, 0.2
# units: kelvin
Teff, Teff_err = 4500, 100
# uniformly spaced grid between 0 and 2600 megayears
age_grid = np.linspace(0, 2600, 500)
# calculate the age posterior - takes ~30 seconds
age_posterior = gyro_age_posterior(
Prot, Teff, Prot_err=Prot_err, Teff_err=Teff_err, age_grid=age_grid
)
# calculate dictionary of summary statistics
from gyrointerp import get_summary_statistics
result = get_summary_statistics(age_grid, age_posterior)
print(f"Age = {result['median']} +{result['+1sigma']} -{result['-1sigma']} Myr.")
```
If you were interested in a slower-rotating star that might be closer to 4 Gyr
old, which is the oldest age out to which `gyro-interp` is calibrated, you
could modify the following lines:
```python
age_grid = np.linspace(0, 5000, 500)
age_posterior = gyro_age_posterior(
Prot, Teff, Prot_err=Prot_err, Teff_err=Teff_err, age_grid=age_grid,
verbose=False, bounds_error='4gyrextrap'
)
```
⚠️ Please do not use this code to try to infer ages of stars older than 4 Gyr.
This is gyrochronology by interpolation. The extrapolation model invoked by
using `bounds_error='4gyrextrap'` has no physical content beyond 4 Gyr.
[The documentation](https://gyro-interp.readthedocs.io/en/latest/index.html)
contains more extensive examples, as well as discussion of important caveats.
## Attribution
If you use the code in your work, please reference
```
@ARTICLE{2023ApJ...947L...3B,
author = {{Bouma}, Luke G. and {Palumbo}, Elsa K. and {Hillenbrand}, Lynne A.},
title = "{The Empirical Limits of Gyrochronology}",
journal = {\apjl},
keywords = {Stellar ages, Stellar rotation, Field stars, Bayesian statistics, 1581, 1629, 2103, 1900, Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
year = 2023,
month = apr,
volume = {947},
number = {1},
eid = {L3},
pages = {L3},
doi = {10.3847/2041-8213/acc589},
archivePrefix = {arXiv},
eprint = {2303.08830},
primaryClass = {astro-ph.SR},
adsurl = {https://ui.adsabs.harvard.edu/abs/2023ApJ...947L...3B},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```
If your result is particularly dependent on the rotation data from any
one cluster, we also encourage you to refer to the relevant study:
* α Per: [Boyle & Bouma (2023)](https://ui.adsabs.harvard.edu/abs/2022arXiv221109822B/abstract)
* Pleiades: [Rebull et al. (2016)](https://ui.adsabs.harvard.edu/abs/2016AJ....152..113R/abstract)
* Blanco-1: [Gillen et al. (2020)](https://ui.adsabs.harvard.edu/abs/2020MNRAS.492.1008G/abstract)
* Psc-Eri: [Curtis et al. (2019a)](https://ui.adsabs.harvard.edu/abs/2019AJ....158...77C/abstract)
* NGC-3532: [Fritzewski et al. (2022)](https://ui.adsabs.harvard.edu/abs/2021A%26A...652A..60F/abstract)
* Group-X: [Messina et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022A%26A...657L...3M/abstract)
* Praesepe: [Rampalli et al. (2021)](https://ui.adsabs.harvard.edu/abs/2021ApJ...921..167R/abstract)
* NGC-6811: [Curtis et al. (2019b)](https://ui.adsabs.harvard.edu/abs/2019ApJ...879...49C/abstract)
* NGC-6819: [Meibom et al. (2015)](https://ui.adsabs.harvard.edu/abs/2015Natur.517..589M/abstract)
* Ruprecht-147 [Curtis et al. (2020)](https://ui.adsabs.harvard.edu/abs/2020ApJ...904..140C/abstract)
* M67: [Barnes et al. (2016)](https://ui.adsabs.harvard.edu/abs/2016ApJ...823...16B/abstract), [Dungee et al (2022)](https://ui.adsabs.harvard.edu/abs/2022ApJ...938..118D/abstract), and [Gruner et al (2023)](https://ui.adsabs.harvard.edu/abs/2023A%26A...672A.159G/abstract).
Raw data
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"description": "<p align=\"center\"><img src=\"docs/source/gyrointerp_logo.png\" alt=\"gyrointerp\" width=\"70%\"/></p>\n\n[<img src=\"https://readthedocs.org/projects/gyro-interp/badge/?version=latest\">](https://gyro-interp.readthedocs.io/en/latest/index.html) <img src=\"https://github.com/lgbouma/gyro-interp/workflows/Tests/badge.svg\">\n\n## Purpose\nObservations have shown that stars with the same age and mass can have a wide\nrange of rotation periods. The purpose of this code is to compute the\nposterior probability for a star's age given its observed rotation period and\neffective temperature. This is achieved by marginalizing over all possible\nages that might explain the observed stellar properties with a parametric\nmodel that has been fitted to not only the mean rotation periods, but also the\nintrinsic scatter in observed open cluster rotation sequences.\n\n## Documentation\n\nThe documentation is hosted at\n[gyro-interp.readthedocs.io](https://gyro-interp.readthedocs.io/en/latest/index.html).\nA minimal example to get you started is below.\nThe method is described in detail in [Bouma, Palumbo & Hillenbrand (2023)](https://ui.adsabs.harvard.edu/abs/2023ApJ...947L...3B/abstract).\n\n\n## Install\nPreferred installation method is through PyPI:\n```shell\npip install gyrointerp\n```\n\n## Minimal Examples\nGiven a star's rotation period, effective temperature, and uncertainties, what\nis the gyrochronological age posterior over a grid spanning 0 to 2.6 Gyr?\n\n```python\n import numpy as np\n from gyrointerp import gyro_age_posterior\n\n # units: days\n Prot, Prot_err = 11, 0.2\n\n # units: kelvin\n Teff, Teff_err = 4500, 100\n\n # uniformly spaced grid between 0 and 2600 megayears\n age_grid = np.linspace(0, 2600, 500)\n\n # calculate the age posterior - takes ~30 seconds\n age_posterior = gyro_age_posterior(\n Prot, Teff, Prot_err=Prot_err, Teff_err=Teff_err, age_grid=age_grid\n )\n\n # calculate dictionary of summary statistics\n from gyrointerp import get_summary_statistics\n result = get_summary_statistics(age_grid, age_posterior)\n\n print(f\"Age = {result['median']} +{result['+1sigma']} -{result['-1sigma']} Myr.\")\n```\n\nIf you were interested in a slower-rotating star that might be closer to 4 Gyr\nold, which is the oldest age out to which `gyro-interp` is calibrated, you\ncould modify the following lines:\n```python\n age_grid = np.linspace(0, 5000, 500)\n\n age_posterior = gyro_age_posterior(\n Prot, Teff, Prot_err=Prot_err, Teff_err=Teff_err, age_grid=age_grid, \n verbose=False, bounds_error='4gyrextrap'\n )\n```\n\n\u26a0\ufe0f Please do not use this code to try to infer ages of stars older than 4 Gyr.\nThis is gyrochronology by interpolation. The extrapolation model invoked by\nusing `bounds_error='4gyrextrap'` has no physical content beyond 4 Gyr.\n\n[The documentation](https://gyro-interp.readthedocs.io/en/latest/index.html)\ncontains more extensive examples, as well as discussion of important caveats.\n\n\n## Attribution\n\nIf you use the code in your work, please reference\n```\n@ARTICLE{2023ApJ...947L...3B,\n author = {{Bouma}, Luke G. and {Palumbo}, Elsa K. and {Hillenbrand}, Lynne A.},\n title = \"{The Empirical Limits of Gyrochronology}\",\n journal = {\\apjl},\n keywords = {Stellar ages, Stellar rotation, Field stars, Bayesian statistics, 1581, 1629, 2103, 1900, Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},\n year = 2023,\n month = apr,\n volume = {947},\n number = {1},\n eid = {L3},\n pages = {L3},\n doi = {10.3847/2041-8213/acc589},\narchivePrefix = {arXiv},\n eprint = {2303.08830},\n primaryClass = {astro-ph.SR},\n adsurl = {https://ui.adsabs.harvard.edu/abs/2023ApJ...947L...3B},\n adsnote = {Provided by the SAO/NASA Astrophysics Data System}\n}\n```\n\nIf your result is particularly dependent on the rotation data from any\none cluster, we also encourage you to refer to the relevant study:\n\n* \u03b1 Per: [Boyle & Bouma (2023)](https://ui.adsabs.harvard.edu/abs/2022arXiv221109822B/abstract)\n* Pleiades: [Rebull et al. (2016)](https://ui.adsabs.harvard.edu/abs/2016AJ....152..113R/abstract)\n* Blanco-1: [Gillen et al. (2020)](https://ui.adsabs.harvard.edu/abs/2020MNRAS.492.1008G/abstract)\n* Psc-Eri: [Curtis et al. (2019a)](https://ui.adsabs.harvard.edu/abs/2019AJ....158...77C/abstract)\n* NGC-3532: [Fritzewski et al. (2022)](https://ui.adsabs.harvard.edu/abs/2021A%26A...652A..60F/abstract)\n* Group-X: [Messina et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022A%26A...657L...3M/abstract)\n* Praesepe: [Rampalli et al. (2021)](https://ui.adsabs.harvard.edu/abs/2021ApJ...921..167R/abstract)\n* NGC-6811: [Curtis et al. (2019b)](https://ui.adsabs.harvard.edu/abs/2019ApJ...879...49C/abstract)\n* NGC-6819: [Meibom et al. (2015)](https://ui.adsabs.harvard.edu/abs/2015Natur.517..589M/abstract)\n* Ruprecht-147 [Curtis et al. (2020)](https://ui.adsabs.harvard.edu/abs/2020ApJ...904..140C/abstract)\n* M67: [Barnes et al. 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