In [1]:

    

from threeML import *

import matplotlib.pyplot as plt

%matplotlib inline


    

    




WARNING UserWarning: /home/user/.python-eggs is writable by group/others and vulnerable to attack when used with get_resource_filename. Consider a more secure location (set with .set_extraction_path or the PYTHON_EGG_CACHE environment variable).


WARNING NaimaNotAvailable: The naima package is not available. Models that depend on it will not be available







    




Configuration read from /home/user/.threeML/threeML_config.yml
Plotter is MatPlotlib






    




WARNING CannotImportPlugin: Could not import plugin /usr/local/lib/python2.7/dist-packages/threeML-0.2.0-py2.7.egg/threeML/plugins/SherpaLike.py. Do you have the relative instrument software installed and configured?

In [2]:

    

triggerName = 'bn090217206'
ra = 204.9
dec = -8.4

#Data are in the current directory

datadir = os.path.abspath('.')

#Create an instance of the GBM plugin for each detector
#Data files
obsSpectrum = os.path.join( datadir, "bn090217206_n6_srcspectra.pha{1}" )
bakSpectrum = os.path.join( datadir, "bn090217206_n6_bkgspectra.bak{1}" )
rspFile     = os.path.join( datadir, "bn090217206_n6_weightedrsp.rsp{1}" )

#Plugin instance
NaI6 = FermiGBMLike( "NaI6", obsSpectrum, bakSpectrum, rspFile )

#Choose energies to use (in this case, I exclude the energy
#range from 30 to 40 keV to avoid the k-edge, as well as anything above
#950 keV, where the calibration is uncertain)
NaI6.setActiveMeasurements( "10.0-30.0", "40.0-950.0" )


    

    




Now using 117 channels out of 128

In [3]:

    

#This declares which data we want to use. In our case, all that we have already created.

data_list = DataList( NaI6 )


    

In [4]:

    

powerlaw = powerlaw()


    

In [5]:

    

GRB = PointSource( triggerName, ra, dec, spectral_shape=powerlaw )


    

In [6]:

    

model = Model( GRB )


    

In [7]:

    

jl = JointLikelihood( model, data_list )

res = jl.fit()


    

    




Best fit values:







    






Name
Value
Unit
bn090217206.spectrum.main.powerlaw.K
2.45 +/- 0.25
1 / (cm2 keV s)
bn090217206.spectrum.main.powerlaw.index
-1.176 +/- 0.019








    




NOTE: errors on parameters are approximate. Use get_errors().

Nuisance parameters:







    






Name
Value
Unit
InterCalib_of_NaI6
1.0








    




Correlation matrix:







    






1.00
-0.99
-0.99
1.00







    




Minimum of -logLikelihood is: 575.567759251

Contributions to the -logLikelihood at the minimum:







    






Detector
-LogL
NaI6
575.57

In [8]:

    

res = jl.get_errors()


    

    






Name
Value
Unit
bn090217206.spectrum.main.powerlaw.K
2.45 -0.13 +0.20
1 / (cm2 keV s)
bn090217206.spectrum.main.powerlaw.index
-1.176 -0.015 +0.010

In [10]:

    

res = jl.get_contours(powerlaw.index,-1.3,-1.1,20)


    

    




 [*********************100%***********************]  20 of 20 completed in 0.2 s





    

In [15]:

    

res = jl.get_contours(powerlaw.index,-1.25,-1.1,50,powerlaw.K,1.9,3.2,50)


    

    




 [*********************100%***********************]  2500 of 2500 completed in 3.0 s





    

In [16]:

    

bayes = BayesianAnalysis(model, data_list)


    

In [17]:

    

powerlaw.index.prior = uniform_prior(lower_bound=-5.0, upper_bound=5.0)
powerlaw.K.prior = log_uniform_prior(lower_bound=1.0, upper_bound=10)


    

In [18]:

    

samples = bayes.sample(n_walkers=20,burn_in=100, n_samples=1000)


    

    




Running burn-in of 100 samples...

 [*********************100%***********************]  100 of 100 completed in 1.6 s

Sampling...

 [*********************100%***********************]  1000 of 1000 completed in 14.7 s
Mean acceptance fraction: 0.7198

In [20]:

    

fig = bayes.corner_plot()


    

In [ ]: