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Edit and run



In [1]:

    
%matplotlib inline

import matplotlib.pyplot as plt
import numpy as np

from transitfit import LightCurve, KeplerLightCurve, TransitModel



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lc = KeplerLightCurve(1422)



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model = TransitModel(lc)



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params = lc.default_params  #super-simple guesses for parameters
fig = model.plot_planets(params); #not bad!



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model.fit_multinest(n_live_points = 400, )



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model.lnpost(params)









    Out[9]:





27.462662915940644



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%timeit f = model(params) #evaluates ln(posterior)









    



10 loops, best of 3: 74.4 ms per loop



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#fit = model.fit_leastsq(params, options={'disp':True})
#fig = model.plot_planets(lc.default_params, color='b'); 
#fig = model.plot_planets(model._bestfit, color='r', fig=fig);

Now, let's do a test emcee run (this many iterations takes about 3 minutes):



In [8]:

    
import time
start = time.time()
model.fit_emcee(nburn=5, niter=10); 
stop = time.time()
print 'emcee fit took {:.1f} minutes.'.format((stop-start)/60)









    



emcee fit took 3.7 minutes.



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model.save_hdf('test_model.h5')



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model = TransitModel.load_hdf('test_model.h5')



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model.samples.head()



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model.samples.std()









    Out[12]:





dilution    0.076139
flux_zp     0.000125
q1          0.139104
q2          0.138689
rho         1.459678
period_1    0.000116
epoch_1     0.001723
b_1         0.263112
rprs_1      0.000539
ecc_1       0.036142
omega_1     1.605390
period_2    0.000158
epoch_2     0.001222
b_2         0.264650
rprs_2      0.000480
ecc_2       0.037392
omega_2     1.577728
dtype: float64



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fig1 = model.triangle(i=0); # for first planet



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fig2 = model.triangle(i=1)



In [ ]:

	dilution	flux_zp	q1	q2	rho	period_1	epoch_1	b_1	rprs_1	ecc_1	omega_1	period_2	epoch_2	b_2	rprs_2	ecc_2	omega_2
0	0.066485	0.999970	0.487172	0.460964	4.173160	5.841694	135.922530	0.391313	0.037694	0.028174	4.826371	19.850268	133.650436	0.444249	0.038722	0.022583	4.246579
1	0.066485	0.999970	0.487172	0.460964	4.173160	5.841694	135.922530	0.391313	0.037694	0.028174	4.826371	19.850268	133.650436	0.444249	0.038722	0.022583	4.246579
2	0.068837	0.999969	0.495473	0.459917	4.169193	5.841691	135.922526	0.383592	0.037730	0.029681	4.868027	19.850270	133.650448	0.427951	0.038732	0.024850	4.167208
3	0.075099	1.000005	0.552630	0.497388	3.944421	5.841639	135.922778	0.443566	0.037987	0.045240	5.602568	19.850295	133.650077	0.594797	0.038696	0.027705	4.317960
4	0.075099	1.000005	0.552630	0.497388	3.944421	5.841639	135.922778	0.443566	0.037987	0.045240	5.602568	19.850295	133.650077	0.594797	0.038696	0.027705	4.317960