return logprob
if __name__ == "__main__":
## Set initial parameters and labels
p0 = np.array((1e30, 3.0, np.log10(30)))
labels = ["norm", "index", "log10(cutoff)"]
## Run sampler
sampler, pos = naima.run_sampler(
data_table=data,
p0=p0,
labels=labels,
model=ElectronIC,
prior=lnprior,
nwalkers=32,
nburn=100,
nrun=20,
threads=4,
prefit=True,
)
## Save run results to HDF5 file (can be read later with naima.read_run)
naima.save_run("RXJ1713_IC_run.hdf5", sampler)
## Diagnostic plots with labels for the metadata blobs
naima.save_diagnostic_plots(
"RXJ1713_IC",
sampler,
sed=True,
last_step=False,
## Set initial parameters and labels
# Estimate initial magnetic field and get value in uG
B0 = 2 * naima.estimate_B(soft_xray, vhe).to("uG").value
p0 = np.array((33, 2.5, np.log10(48.0), B0))
labels = ["log10(norm)", "index", "log10(cutoff)", "B"]
## Run sampler
sampler, pos = naima.run_sampler(
data_table=[soft_xray, vhe],
p0=p0,
labels=labels,
model=ElectronSynIC,
prior=lnprior,
nwalkers=32,
nburn=100,
nrun=20,
threads=4,
prefit=True,
interactive=False,
)
## Save run results to HDF5 file (can be read later with naima.read_run)
naima.save_run("RXJ1713_SynIC", sampler)
## Diagnostic plots
naima.save_diagnostic_plots(
"RXJ1713_SynIC",
sampler,
sed=True,
#several output files will be created.
## Set initial parameters and labels
#parameters here:
#electron normalization in eV**-1, electron index, electron cutoff energy in TeV.
p0 = np.array((1e30, 3.0, np.log10(30)))
labels = ["norm", "index", "log10(cutoff)"]
## Run sampler
sampler, pos = naima.run_sampler(
data_table=data,
p0=p0,
labels=labels,
model=ElectronIC,
prior=lnprior,
nwalkers=32,
nburn=100,
nrun=20,
threads=4,
prefit=True,
interactive=False,
)
#Try running with more walkers or increasing the number of runs.
## Save run results
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)
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")
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")
def main(input_path, output_path, n_job, n_sample, n_burn, n_walker):
if not os.path.exists(output_path):
os.makedirs(output_path)
# but kai?? why you doing global things?
# because i have to. for the sake of speed. And for queen and country of course.
# see https://emcee.readthedocs.io/en/latest/tutorials/parallel/#parallel
global f
global B
global data
global log_ampl
global alpha
global beta
global log_e_min
global log_e_max
labels = [
"log_main_amplitude", "alpha", "beta", 'log10(E_max)', 'log10(E_min)',
'B'
]
p0 = np.array([48, 3, 0.05, 15.6, 11, 100])
t = Table.read(input_path)
energy = t['energy'].data.ravel() * u.TeV
log_energy = np.log10(energy.to_value(u.TeV))
log_ampl = t['log_ampl'].data.ravel()
alpha = t['alpha'].data.ravel()
beta = t['beta'].data.ravel()
log_e_max = t['log_e_max'].data.ravel()
log_e_min = t['log_e_min'].data.ravel()
B = t['B'].data.ravel()
print('log ampl', log_ampl)
print('alpha', alpha)
print('beta', beta)
print('e_max', log_e_max)
print('e_min', log_e_min)
print('B', B)
data = np.log10(t['data'].data.squeeze())
f = RegularGridInterpolator(
(log_energy, log_ampl, alpha, beta, log_e_max, log_e_min, B),
data,
bounds_error=False,
fill_value=None)
data = read_crab_mwl_data(e_min=40 * u.keV)
sampler, pos = naima.run_sampler(
data_table=data,
p0=p0,
labels=labels,
model=lut_model,
prior=lut_prior,
nwalkers=n_walker,
nburn=n_burn,
nrun=n_sample,
threads=n_job,
prefit=True,
)
mpl.use('Agg')
mpl.rcParams['text.usetex'] = False
mpl.rcParams['backend'] = 'agg'
naima.save_run(f"{output_path}/crab_chain.h5", sampler, clobber=True)
naima.save_results_table(f"{output_path}/crab_naima_fit", sampler)
naima.save_diagnostic_plots(f"{output_path}/crab_naima", sampler)
