model = EBL_transmitance.transmission(data) * IC.flux(
data, distance=1.0 * u.kpc) + SYN.flux(data, distance=1.0 * u.kpc)
# The first array returned will be compared to the observed spectrum for
# fitting. All subsequent objects will be stored in the sampler metadata
# blobs.
return model, IC.compute_We(Eemin=1 * u.TeV)
if __name__ == "__main__":
# Some random values for a "beautiful double peak structure
p0 = np.array((31.0, 1.0, 0.35, 1.5, 2.3, 0.06))
labels = [
"log10(norm)",
"log10(Energy_Break)",
"index1",
"index2",
"B",
"redshift",
]
# Run interactive fitter, to show the very high energy absorption
imf = naima.InteractiveModelFitter(
ElectronEblAbsorbedSynIC,
p0,
labels=labels,
e_range=[1e-09 * u.GeV, 1e05 * u.GeV],
)
def fitter(self, p0, labels, datatable):
'''
This is the actual fitting function.
Parameters:
------------
p0: list
free parameters; 1st guess (compact using InteractiveModelFitter)
labels: list
names of the free parameters
data_table: astropy Table
list of data tables for fitting
Results of the fit (an astropy table with best param estimate and
uncertainties & the sed fit) are stored inside 'results_ssc_fit'
'''
print("Executing the fit...")
# An interactive window helps to adjust the starting point of sampling
# before calling run_sampler.
imf = naima.InteractiveModelFitter(self.model_func,
p0,
sed=True,
e_range=[1e-3 * u.eV, 1e15 * u.eV],
e_npoints=self.e_npoints,
labels=labels)
p0 = imf.pars
nwalkers = 100
nparams = len(p0)
if nparams > nwalkers:
raise RandomWalkError("The number of walkers should be atleast"
"greater than the number of parameters!!")
# Numbers for nwalkers, nburn, nrun are only preliminary here
# to achieve fast computation.
sampler, pos = naima.run_sampler(data_table=datatable,
p0=p0,
labels=labels,
model=self.model_func,
prior=self.prior_func,
nwalkers=nwalkers,
nburn=50,
nrun=100,
threads=12,
prefit=False,
data_sed=True,
interactive=False)
naima.save_results_table('./results_ssc_fit/data_fit_table', sampler)
fit_sed = naima.plot_fit(sampler,
n_samples=50,
e_range=[1e-3 * u.eV, 1e15 * u.eV],
e_npoints=self.e_npoints)
fit_sed.savefig("./results_ssc_fit/likelihoodfitresult_sed.png")
labels = sampler.labels
for index, name in enumerate(labels):
chain = naima.plot_chain(sampler, p=index)
chain.savefig(
"./results_ssc_fit/plot_chain/{}_chain.png".format(name))
corner_plt = naima.plot_corner(sampler, show_ML=True)
corner_plt.savefig("./results_ssc_fit/corner_plots.png")