notebook.community

Edit and run



In [1]:

    
import pandas as pd
from exosyspop.populations import TRILEGAL_BGBinaryPopulation

targets = pd.read_hdf('targets.h5')
bgstars = pd.read_hdf('bgstars.h5').query('m_ini > 0.11')

# Sanitize dtypes of targets DataFrame
for c in targets.columns:
    if targets[c].dtype == object:
        targets.loc[:,c] = targets.loc[:,c].astype(str)

pop = TRILEGAL_BGBinaryPopulation(targets, bgstars)

import logging
rootLogger = logging.getLogger()









    



WARNING:root:Deprecation Warning: 'triangle' has been renamed to 'corner'. This shim should continue to work but you should use 'import corner' in new code. https://github.com/dfm/corner.py
WARNING:root:PyMultiNest not available; only emcee fits will be possible.
WARNING:root:progressbar not imported



In [2]:

    
pop = TRILEGAL_BGBinaryPopulation(targets.sample(1000), bgstars.sample(1000), fB=1)



In [3]:

    
rB = pop.radius_B









    



BGEB: dmag regressor trained, R2=0.911817563285
BGEB: qR regressor trained, R2=0.76634227607



In [4]:

    
rootLogger.setLevel(logging.INFO)
pop._train_trap(N=200);
rootLogger.setLevel(logging.DEBUG)









    



BGEB: Depth trained: R2=0.997155648551
BGEB: Duration trained: R2=0.992380949106
BGEB: Slope trained: R2=0.989042134265



In [5]:

    
obs = pop.observe(regr_trap=True)



In [6]:

    
pop.save('test', overwrite=True)



In [7]:

    
import cPickle as pickle
pickle.dump(pop, open('test.pkl', 'wb'))



In [8]:

    
pop = pickle.load(open('test.pkl', 'rb'))



In [9]:

    
print(pop.params['fB'])
pop._generate_binaries()
pop.params['fB']









    



DEBUG:root:Setting params {}.
DEBUG:root:old params = None
DEBUG:root:Generating binary companions for 863 stars...






    



1






    Out[9]:





1



In [10]:

    
pop.radius_B[:10]









    Out[10]:





array([   0.15716966,    0.22931597,    0.14225803,  119.05026062,
          0.44244812,    0.23416311,    0.31517727,    0.55514619,
          0.49646648,    0.39489863])



In [11]:

    
rB[:10]









    Out[11]:





array([   0.14651767,    0.20932919,    0.22894365,  115.90336165,
          0.49478899,    0.18239524,    0.28059119,    0.31287778,
          0.67566143,    0.53647009])



In [2]:

    
%matplotlib inline
rootLogger.setLevel(logging.INFO)
pop._train_pipelines(n_jobs=8, plot=True)
pop._train_trap(N=1000, plot=True);









    



BGEB: dmag regressor trained, R2=0.975228863924
BGEB: qR regressor trained, R2=0.908807218686
BGEB: Depth trained: R2=0.999520898496
BGEB: Duration trained: R2=0.997345061377
BGEB: Slope trained: R2=0.992131678434



In [4]:

    
from copy import deepcopy

pop2 = deepcopy(pop)



In [5]:

    
pop._generate_binaries()
pop2._generate_binaries(use_ic=True)



In [8]:

    
import matplotlib.pyplot as plt
import numpy as np

ok = np.isfinite(pop.stars.radius_B)
plt.plot(pop.stars.mass_B[ok], np.log10(pop.stars.radius_B[ok]), 'o', ms=1, mew=1)
ok = np.isfinite(pop2.stars.radius_B)
plt.figure()
plt.plot(pop2.stars.mass_B[ok], np.log10(pop2.stars.radius_B[ok]), 'o', ms=1, mew=1)









    Out[8]:





[<matplotlib.lines.Line2D at 0x7f947150c150>]



In [7]:

    
pop.stars.query('radius_B == -100')[['mass_A', 'radius_A', 'mass_B', 'radius_B']]



In [3]:

    
obs_pop = pop.get_N_observed(N=5000, new_orbits=True, query='T14_pri < 2 or T14_sec < 2')



In [4]:

    
obs_pop.trap_corner(range=[(0,0.8), (-5,0), (2,10)]);









    



WARNING:root:Too few points to create valid contours



In [5]:

    
pop.save('bgpop', overwrite=True)



In [6]:

    
pop = TRILEGAL_BGBinaryPopulation.load('bgpop')



In [7]:

    
obs = pop.observe(regr_trap=True)



In [9]:

    
rootLogger.setLevel(logging.INFO)
%timeit pop.observe(new=True, use_ic=True, regr_trap=True)









    



1 loops, best of 3: 805 ms per loop



In [10]:

    
%matplotlib inline

import matplotlib.pyplot as plt
obs = pop.observe(new=True)
plt.hist(obs.b_target.values, bins=20);
plt.xlabel('Galactic Latitude')
plt.ylabel('N');



In [ ]:

	mass_A	radius_A	mass_B	radius_B
1536	0.15215	0.195592	0.115682	-100
150565	0.11577	0.130621	0.099075	-100
2268	2.21863	4.200070	1.958928	-100
3434	4.15192	0.022123	3.452282	-100
113358	0.17211	0.181810	0.099116	-100
140075	0.12470	0.137609	0.099162	-100
62600	0.82567	0.780939	0.099180	-100
171448	0.11797	0.129300	0.099207	-100
169170	0.83517	0.020049	0.830359	-100
172439	0.83641	0.017556	0.332649	-100
3551	3.98248	0.022532	3.576042	-100
3955	0.14114	0.166740	0.140213	-100
2184	0.12455	0.157538	0.099513	-100
158566	0.11051	0.119926	0.099419	-100
1039	0.12009	0.175577	0.100736	-100
6967	0.11181	0.137548	0.099067	-100
6128	3.68339	0.023623	3.126509	-100
109522	0.23575	0.230909	0.099234	-100
3843	0.15814	0.181859	0.156931	-100
170614	0.26133	0.250789	0.099150	-100
147178	0.21801	0.217998	0.099020	-100
119448	0.13956	0.152614	0.099226	-100
147536	0.12959	0.143054	0.099227	-100
151455	0.14816	0.162772	0.099148	-100
84123	0.15898	0.176151	0.099127	-100
219	0.12598	0.259935	0.104221	-100
132619	0.13826	0.149473	0.099154	-100
126606	0.19261	0.198863	0.099047	-100
45699	4.11491	0.021973	3.885189	-100
608	0.12694	0.201147	0.100592	-100
129789	0.13220	0.146666	0.099148	-100
142616	0.12865	0.141553	0.099129	-100
599	2.04829	1.821419	0.356555	-100
1170	0.13981	0.197462	0.099813	-100
167332	0.17557	0.179862	0.099505	-100
2274	0.46156	0.402558	0.460876	-100
3576	0.16183	0.185885	0.159139	-100
4221	3.59878	0.024004	3.552550	-100
68711	4.09112	0.021910	3.902132	-100
494	0.17000	0.219249	0.100284	-100
136838	0.18372	0.191114	0.099028	-100
112112	0.19476	0.199510	0.099127	-100
172633	0.82399	1.856415	0.100625	-100
9984	3.94065	0.021727	3.634498	-100
88609	0.21807	0.227778	0.099051	-100
48706	0.30744	0.292479	0.251239	-100
83673	0.23823	0.237527	0.099028	-100
148288	0.14134	0.155005	0.099226	-100
36071	4.09106	0.021709	3.221704	-100
151230	0.21114	0.213059	0.099168	-100
117062	0.16740	0.178070	0.099077	-100
11353	1.93466	0.737932	1.683871	-100
122081	0.13934	0.152669	0.099013	-100
107711	0.11892	0.131021	0.099172	-100