This package computes age posteriors for field stars from measurements of R'HK calcium emission and/or B-V color and lithium equivalent width absorption (Li EW). For calcium emission our method is calibrated to stars with B-V between 0.45 and 0.9 (~ F6-K2) and log(R'HK) between -3.7 and -5. For lithium we have calibrated BAFFLES to stars with B-V between 0.35 and 1.9 (~F2-M5) and Li EW between 3.2 and 1500 mA. See the paper Stanford-Moore et al. 2020.
Download the zipped file from GitHub or Zenodo.
Or
git clone --depth=1 https://github.com/adamstanfordmoore/BAFFLES.git
Currently there are still some large files in the git history, so clone with depth 1 to avoid all 256MB of history.
Python 3.7
astropy (if not already included in Python environment)
- Adam Stanford-Moore
- Eric Nielsen
- Rob De Rosa
- Bruce Macintosh
- Ian Czekala
Quick usage from the command line. Let's find an age for the sun using B-V of 0.65 and logR'HK = -4.908 (Mamajek & Hillenbrand 2008).
python baffles.py -bmv 0.65 -rhk -4.906 -plot
To save this probability density function in a csv file as 1000 lines of age,probability with optional filename (_calcium.csv will be appended to name):
python baffles.py -bmv 0.65 -rhk -4.906 -plot -s -filename suns_age
Now lets find the age of HR 2562 using B-V=.45 ± .02, log(R'HK) = -4.55 (Gray 2006), and lithium EW of 21 ± 5 (Mesa el al 2018). "-ul" would denote an upper limit. "-s" will save a csv file of the posterior. "-plot" will show a plot of the posterior. -maxAge 10000 will constrain the prior on age to be uniform out to 10 Gyr. "-li_err" allows input of uncertainty on Li EW, and "-bv_err" uncertainty on B-V. The following command will determine the age using calcium and lithium separately and then find the combined posterior product.
python baffles.py -bmv 0.45 -bmv_err .02 -rhk -4.55 -li 21 -li_err 5 -plot
Type python baffles.py -help into the command line to learn more.
To directly import baffles and use the module in a python script see "usage_example.py"
baffles.py : main file that computes age posteriors. It makes and stores grids of mean indicator as functions of age and B-V
usage_example.py : example script using the baffles package
refresh.py : updates any stored fits or grids (like mean R'HK as a function of age). Run python refresh.py whenever constants or data are changed.
ca_constants.py : constants related to calcium
li_constants.py : constants related to lithium
fitting.py : various fitting functions used to compute grids in baffles.py, inlcuding for mean R'HK as a function of age and mean LiEW as a function of age and B-V
plotting.py : plotting functions to display posteriors, data, and fits
probability.py : assortment of statistical functions like finding gaussian PDF/CDF
readData.py : reads in lithium and calcium data from data directory and returns data and indicator vs B-V fits
utils.py : extra helper functions
paper_plots_li.py : more advanced plotting examples for lithium used to create the plots in stanford_moore et al 2019.
paper_plots_ca.py : more advanced plotting for calcium
data/ : directory contains files with calcium/lithium data and .p pickle files with saved arrays of indicator vs B-V
grids/ : directory contains saved grids of mean/sigma indicator values as functions of age/B-V. Refresh these grids with refresh.py