In [1]:

    

%pylab inline
from obliquity.distributions import Cosi_Distribution
from simpledist.distributions import Box_Distribution, Gaussian_Distribution

import logging

rootLogger = logging.getLogger()
rootLogger.setLevel(logging.WARNING)


    

    




Populating the interactive namespace from numpy and matplotlib

In [5]:

    

vsini = Box_Distribution(0,2)
#vsini = Gaussian_Distribution(3,0.5)
cosi_dist = Cosi_Distribution((1.3,0.1),(15,0.3),vsini, veq_simple=True)
cosi_dist.summary_plot()


    

In [25]:

    

from obliquity.kappa_inference import like_cosi
print like_cosi(.4,vsini,cosi_dist.veq_dist,vgrid=None)


    

    




0.0434713717688

In [8]:

    

cs = np.linspace(0,1,100)
plot(cs,cosi_dist(cs))


    

    
Out[8]:





[<matplotlib.lines.Line2D at 0x7f54e03e7690>]






    

In [26]:

    

Ls = cs*0
for i,c in enumerate(cs):
    Ls[i] = like_cosi(c,vsini,cosi_dist.veq_dist)


    

In [27]:

    

plot(cs,Ls)


    

    
Out[27]:





[<matplotlib.lines.Line2D at 0x7f38b65a4550>]






    

In [3]:

    

from obliquity.kappa_inference import cosi_posterior
cs, post = cosi_posterior(vsini,cosi_dist.veq_dist)
plot(cs,post)


    

    




DEBUG:root:vgrid: 1000 pts, 0.0 to 3.32375341624






    
Out[3]:





[<matplotlib.lines.Line2D at 0x7f18f94d2c50>]






    

In [ ]: