results.write(f, format='ascii.fixed_width_two_line')
alabaster_width = 660
alabaster_dpi = 100 * alabaster_width / 800
print('Plotting chains...')
f = naima.plot_chain(sampler, 1)
f.savefig('RXJ1713_IC_chain_index.png', dpi=alabaster_dpi)
f = naima.plot_chain(sampler, 2)
f.savefig('RXJ1713_IC_chain_cutoff.png', dpi=alabaster_dpi)
e_range = [100 * u.GeV, 500 * u.TeV]
# with samples
print('Plotting samples...')
f = naima.plot_fit(sampler, 0, ML_info=False)
f.axes[0].set_ylim(1e-13, 2e-10)
f.tight_layout()
f.subplots_adjust(hspace=0)
f.savefig('RXJ1713_IC_model_samples.png', dpi=alabaster_dpi)
print('Plotting samples with e_range...')
f = naima.plot_fit(
sampler, 0, e_range=e_range, ML_info=False, n_samples=500)
f.axes[0].set_ylim(1e-13, 2e-10)
f.tight_layout()
f.subplots_adjust(hspace=0)
f.savefig('RXJ1713_IC_model_samples_erange.png', dpi=alabaster_dpi)
# with confs
print('Plotting confs...')
f = naima.plot_fit(sampler, 0, ML_info=False, confs=[3, 1],
# In[51]:
#Plot the fit results.
#Try confs=None, or try e_range=None
f = naima.plot_fit(sampler,
modelidx=0,
label=None,
sed=True,
last_step=False,
n_samples=100,
confs=[3, 2, 1],
ML_info=True,
figure=None,
plotdata=True,
plotresiduals=True,
e_unit=None,
e_range=(300 * u.GeV, 200 * u.TeV),
e_npoints=200,
threads=3,
xlabel=None,
ylabel=None,
ulim_opts={},
errorbar_opts={})
f.savefig("RXJ1713_SED.png")
#corner plot shows 1-D and 2-D projections of posterior probability.
#Useful to check correlations between parameters, see if the sampling was run
#with enough statistics etc.
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")
results.write(f, format='ascii.fixed_width_two_line')
alabaster_width = 660
alabaster_dpi = 100 * alabaster_width / 800
print('Plotting chains...')
f = naima.plot_chain(sampler, 1)
f.savefig('RXJ1713_IC_chain_index.png', dpi=alabaster_dpi)
f = naima.plot_chain(sampler, 2)
f.savefig('RXJ1713_IC_chain_cutoff.png', dpi=alabaster_dpi)
e_range = [100 * u.GeV, 500 * u.TeV]
# with samples
print('Plotting samples...')
f = naima.plot_fit(sampler, 0, ML_info=False)
f.axes[0].set_ylim(1e-13, 2e-10)
f.tight_layout()
f.subplots_adjust(hspace=0)
f.savefig('RXJ1713_IC_model_samples.png', dpi=alabaster_dpi)
print('Plotting samples with e_range...')
f = naima.plot_fit(sampler,
0,
e_range=e_range,
ML_info=False,
n_samples=500)
f.axes[0].set_ylim(1e-13, 2e-10)
f.tight_layout()
f.subplots_adjust(hspace=0)
f.savefig('RXJ1713_IC_model_samples_erange.png', dpi=alabaster_dpi)
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")
out_root = "RXJ1713_IC_minimal"
naima.save_run(out_root, sampler)
## Save diagnostic plots and results table
naima.save_diagnostic_plots(out_root, sampler, sed=True)
naima.save_results_table(out_root, sampler)
# In[51]:
#Plot the fit results.
#Try confs=None, or try e_range=None
f = naima.plot_fit(sampler, modelidx=0, label=None, sed=True, last_step=False,
n_samples=100, confs=[3,2,1], ML_info=True,
figure=None, plotdata=True, plotresiduals=True,
e_unit=None, e_range=(300*u.GeV,200*u.TeV), e_npoints=200, threads=3,
xlabel=None, ylabel=None, ulim_opts={}, errorbar_opts={})
f.savefig("RXJ1713_SED.png" )
#corner plot shows 1-D and 2-D projections of posterior probability.
#Useful to check correlations between parameters, see if the sampling was run
#with enough statistics etc.
#Chain plots show the 1-D projection of the posterior distribution for a given parameter
#as well as the evolution of the parameter during the sampling.