%pylab inline
from isochrones import dartmouth
import isochrones.starmodel as sm
import numpy as np

dar = dartmouth.Dartmouth_Isochrone()

mass,age,feh = (1,9.7,0.0)
dar(mass,age,feh)

ages = np.linspace(9,9.8,200)
radii1 = dar.radius(1,ages,0.0)
radii2 = dar.radius(1,ages,0.2)
radii3 = dar.radius(1,ages,-0.2)
plot(ages,radii1,label='feh=0.0')
plot(ages,radii2,label='feh=0.2')
plot(ages,radii3,label='feh=-0.2')
xlabel('log(age)'); ylabel('radius')
legend(loc='upper left')

ev = dar.evtrack(1.0,dage=0.01)
plot((ev['Teff']),ev['logL'],'.')
xlabel('Teff')
ylabel('logL');

mod = sm.StarModel(dar,Teff=(5770,50),logg=(4.44,0.10),feh=(0.0,0.1))

mod.maxlike()

mod.fit_mcmc()
mod.plot_samples('radius')
mod.plot_samples('mass')
mod.plot_samples('age')

reload(dartmouth.iso); reload(dartmouth); reload(sm)

mags = {'H': (8.4459999999999997,0.08),
 'J': (8.7710000000000008,0.08),
 'K': (8.3699999999999992,0.08),
 'g':(10.33,0.08),
 'r':(9.83,0.08)}
specprops = dict(Teff=(5626,64),logg=(4.47,0.07),feh=(-.16,0.1))

#smod = sm.StarModel(dar,**mags)
smod = sm.StarModel(dar,**specprops)
#smod.add_props(**specprops)

pars = smod.maxlike()

smod.fit_mcmc()
smod.plot_samples('radius')
smod.plot_samples('mass')
#smod.plot_samples('feh')
#smod.plot_samples('distance')
#smod.plot_samples('AV')

mod = StarModel(dar,Teff=(5626,64),logg=(4.47,0.07),feh=(-.16,0.1))

mod.fit_mcmc()
mod.plot_samples('radius')
mod.plot_samples('mass')

reload(dartmouth.iso); reload(dartmouth); reload(sm)


smod.properties

sm.salpeter_prior(1)

from keputils import koiutils as ku
ku.KICmags(1)

reload(sm)
smod = sm.StarModel(dar,
                    Teff=(5783,64),logg=(4.36,0.07),feh=(-0.06,0.10))

smod.maxlike(100)

smod.fit_mcmc()
smod.plot_samples('radius')
smod.plot_samples('mass')

from simpledist import distributions as dists

mdist = dists.Hist_Distribution(smod.prop_samples('mass',return_values=False),
                                bins=50,name='mass')
rdist = dists.Hist_Distribution(smod.prop_samples('radius',return_values=False),
                                bins=50,name='radius')

mdist.save_hdf??

mdist.plot()
rdist.plot()

smod.plot_samples('Teff')
smod.plot_samples('AV')

from keputils import kicutils as kicu

smod.sampler.__dict__

smod.sampler.naccepted/300.

smod.sampler.chain.shape

import numpy.random as rand

wokinds = np.where(smod.sampler.naccepted/smod.sampler.iterations > 0.15)[0]
print len(wokinds)
inds = rand.randint(len(wokinds),size=200)
hist(smod.sampler.chain[wokinds[inds],:,:].mean(axis=1)[:,0])

smod.fit_mcmc??

dists.Hist_Distribution?