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,
blob_labels=["Spectrum", "$W_e$($E_e>1$ TeV)"],
)
naima.save_results_table("RXJ1713_SynIC", sampler)
## 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,
blob_labels=[
"Spectrum",
"Electron energy distribution",
"$W_e (E_e>1\, \mathrm{TeV})$",
],
)
naima.save_results_table("RXJ1713_IC", sampler)
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)
# 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,
data = ascii.read('../../examples/RXJ1713_HESS_2007.dat')
# Run sampler
sampler, pos = naima.run_sampler(
data_table=data,
p0=p0,
labels=labels,
model=ElectronIC,
prior=lnprior,
nwalkers=128,
nburn=100,
nrun=100,
threads=4,
prefit=True,
interactive=True)
# Save sampler
naima.save_run('RXJ1713_IC_sampler.hdf5', sampler)
# Diagnostic plots
naima.save_results_table('RXJ1713_IC', sampler)
from astropy.io import ascii
results = ascii.read('RXJ1713_IC_results.ecsv')
results.remove_row(-1) # remove blob2
for col in ['median', 'unc_lo', 'unc_hi']:
results[col].format = '.3g'
with open('RXJ1713_IC_results_table.txt', 'w') as f:
info = []
for key in ['n_walkers', 'n_run', 'p0', 'ML_pars', 'MaxLogLikelihood']:
info.append('{0:<18}: {1}\n'.format(key, str(results.meta[key])))
f.writelines(info)
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,
interactive=False,
)
## 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=False)
naima.save_results_table(out_root, sampler)
# Read data
data = ascii.read('../../examples/RXJ1713_HESS_2007.dat')
# Run sampler
sampler, pos = naima.run_sampler(data_table=data,
p0=p0,
labels=labels,
model=ElectronIC,
prior=lnprior,
nwalkers=128,
nburn=100,
nrun=100,
threads=4,
prefit=True,
interactive=True)
# Save sampler
naima.save_run('RXJ1713_IC_sampler.hdf5', sampler)
# Diagnostic plots
naima.save_results_table('RXJ1713_IC', sampler)
from astropy.io import ascii
results = ascii.read('RXJ1713_IC_results.ecsv')
results.remove_row(-1) # remove blob2
for col in ['median', 'unc_lo', 'unc_hi']:
results[col].format = '.3g'
with open('RXJ1713_IC_results_table.txt', 'w') as f:
info = []
for key in ['n_walkers', 'n_run', 'p0', 'ML_pars', 'MaxLogLikelihood']:
info.append('{0:<18}: {1}\n'.format(key, str(results.meta[key])))
f.writelines(info)
## 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,
blob_labels=['Spectrum', '$W_e$($E_e>1$ TeV)'])
naima.save_results_table('RXJ1713_SynIC', sampler)
## 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,
blob_labels=[
"Spectrum",
"Electron energy distribution",
"$W_e (E_e>1\, \mathrm{TeV})$",
],
)
naima.save_results_table("RXJ1713_IC", sampler)
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,
blob_labels=["Spectrum", "$W_e$($E_e>1$ TeV)"],
)
naima.save_results_table("RXJ1713_SynIC", sampler)
## 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,
blob_labels=['Spectrum', '$W_e$($E_e>1$ TeV)'])
naima.save_results_table('RXJ1713_SynIC', sampler)
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)