In [16]:

    

%matplotlib inline


    

In [1]:

    

import analyzeSN


    

In [2]:

    

import sncosmo


    

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model = sncosmo.Model(source='salt2')


    

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data = sncosmo.load_example_data()


    

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data.meta


    

    
Out[5]:





OrderedDict([('x1', 0.5),
             ('c', 0.2),
             ('z', 0.5),
             ('x0', 1.20482820761e-05),
             ('t0', 55100.0)])

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model.set(z=0.5)


    

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res = sncosmo.fit_lc(data, model, vparam_names=['t0', 'x0', 'x1', 'c'], )


    

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reschar = analyzeSN.ResChar.fromSNCosmoRes(res)


    

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varmu = reschar.mu_variance_linear(alpha=0.14, beta=3.14)


    

    




          mB        x1         c
mB  0.001239  0.006771  0.000706
x1  0.006771  0.116810  0.001399
c   0.000706  0.001399  0.000820 [ 1.    0.14 -3.14]

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print(varmu**0.5)


    

    




0.08859394617261082

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# If we use mcmc to do this:


    

In [10]:

    

res_mcmc = sncosmo.mcmc_lc(data, model, vparam_names=['t0', 'x0', 'x1', 'c'], )


    

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reschar_mcmc = reschar.fromSNCosmoRes(res_mcmc)


    

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# Note that mu might need additional constants


    

In [23]:

    

reschar_mcmc.salt_samples(alpha=0.1, beta=-3.14).mu.std()


    

    
Out[23]:





0.0866330665243906

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