~$ pip install isochrones 
~$ ipython
In [1]: from isochrones.dartmouth import Dartmouth_Isochrone
In [2]: from isochrones import StarModel
In [3]: dar = Dartmouth_Isochrone()
In [4]: model = StarModel(dar, X=(12, 0.05), Y=(11, 0.05), Z=(10, 0.05))
In [5]: model.fit()

from python terminal/script:

X = 12, 0.05
Y = 11, 0.05
Z = 10, 0.05

mystar/star.ini

$ starfit mystar

From command line:

X = 12, 0.05
Y = 11, 0.05
Z = 10, 0.05

Teff = 5300, 80 
feh = -0.1, 0.1
logg = 4.6, 0.15

mystar/star.ini

$ starfit mystar 

Also:

X = 12, 0.05
Y = 11, 0.05
Z = 10, 0.05

delta_X = 1.2, 0.05

Teff = 5300, 80 
feh = -0.1, 0.1
logg = 4.6, 0.15

mystar/star.ini

$ starfit mystar --binary

Or:

• In principle can use arbitrary stellar model grids
• 3-d linear interpolation in (M, log(age), [Fe/H])
• Any observable property can be called at any (in-bounds) values of (M, log(age), [Fe/H]); thus any property provided by stellar grids can be used as observed data for conditioning model

• Uses MultiNest to fit by default;  will use emcee if MultiNest not installed.

• Uses local [Fe/H] prior based on SDSS

Demo

To do

• Update other built-in model grids (e.g., Padova)
• Try out cubic interpolation?
• Refactor data back-end to more easily accommodate denser grids?

I have a transit signal.  

What is the probability it's an astrophysical false positive?

~$ pip install vespa

name = myplanet
ra = 296.003693
dec = 44.277561
rprs = 0.012585031528
period = 384.845905
photfile = myphotometry.csv

[constraints]
maxrad = 1.26
secthresh = 0.000120176767205

g = 13.8735234, 0.05
r = 13.3370844, 0.05
i = 13.1663488, 0.05
z = 13.1029807, 0.05
J = 12.263, 0.02
H = 11.919, 0.02
K = 11.852, 0.02
Kepler = 13.426

myplanet/star.ini

myplanet/fpp.ini

~$ calcfpp myplanet

example results