In [1]:

    

from peerless.catalogs import TargetCatalog, EBCatalog
targets = TargetCatalog().df

bad = targets.mass.isnull()
targets.loc[bad, 'mass'] = targets.loc[bad, 'radius']


    

In [2]:

    

%run ../peerless/plot_setup.py


    

In [10]:

    

import numpy as np
from exosyspop.populations import PlanetPopulation, REARTH, RSUN

import astropy.constants as const
RJUP = const.R_jup.cgs

class SimplePlanetPopulation(PlanetPopulation):
    """ Simple planet population for testing purposes
    
    Every star has exactly one planet.
    All periods at a single period. 
    All radii at a single radius.
    All circular orbits.
    """
    single_period = 1*365.25
    single_R = 1*RJUP/RSUN
    
    param_names = ()
    default_params = {}
    
    def _sample_ecc(self, N):
        return np.zeros(N)
    
    def _sample_period(self, N):
        return np.ones(N)*self.single_period
    
    def _sample_Np(self, N):
        return np.ones(N)
        
    def _sample_Rp(self, N):
        return np.ones(N)*self.single_R


    

In [45]:

    

# Use an idealized population with all dutycycle=1, dataspan=1yr
targets_ideal = targets.copy()
targets_ideal.dutycycle = 1.#/3
targets_ideal.dataspan = 365.#/2

plpop = SimplePlanetPopulation(targets_ideal)
plpop._train_trap(N=1000);


    

    




Planet: Depth trained: R2=0.997533383133
Planet: Duration trained: R2=0.992470176005
Planet: Slope trained: R2=0.997980697933

In [46]:

    

# This is the expected number of detections: number of stars * average transit probability
p_ecl = (1./plpop.aR) * (1+plpop.radius_B/plpop.radius_A)  # R*/a corrected for grazing eclipses
n_exp = len(targets) * p_ecl.mean()
n_exp


    

    
Out[46]:





181.25221272971726

In [47]:

    

n_obs = [len(plpop.observe(new_orbits=True, regr_trap=True).query('n_pri > 0')) for i in range(100)]


    

In [48]:

    

%matplotlib inline
import matplotlib.pyplot as plt

plt.hist(n_obs, histtype='step', lw=3);
plt.axvline(n_exp, color='r', ls=':', lw=3)


    

    
Out[48]:





<matplotlib.lines.Line2D at 0x126823d90>






    

In [49]:

    

np.mean(n_obs)


    

    
Out[49]:





182.09999999999999

In [2]:

    

from peerless.catalogs import TargetCatalog, EBCatalog


    

In [4]:

    

ebs = EBCatalog()


    

In [7]:

    

ebs.df.columns


    

    
Out[7]:





Index([u'#KIC', u'period', u'period_err', u'bjd0', u'bjd0_err', u'morph',
       u'GLon', u'GLat', u'kmag', u'Teff', u'SC', u'ETV', u'Unnamed: 12'],
      dtype='object')

In [9]:

    

len(ebs.df.query('period > 1000'))


    

    
Out[9]:





10

In [ ]: