def extract_spectra_iact(dataset):
"""Extract 1d spectra for IACT dataset"""
log.info(f"Extracting 1d spectra for {dataset.name} dataset")
datastore = DataStore.from_dir(f"data/{dataset.name}")
observations = datastore.get_observations(dataset.obs_ids)
on_region = CircleSkyRegion(center=config.source_pos,
radius=dataset.on_radius)
exclusion_mask = config.get_exclusion_mask()
bkg_estimate = ReflectedRegionsBackgroundEstimator(
observations=observations,
on_region=on_region,
exclusion_mask=exclusion_mask)
bkg_estimate.run()
extract = SpectrumExtraction(
observations=observations,
bkg_estimate=bkg_estimate.result,
e_true=config.energy_bins,
e_reco=config.energy_bins,
containment_correction=dataset.containment_correction,
)
extract.run()
path = f"results/spectra/{dataset.name}/"
log.info(f"Writing to {path}")
if dataset.name == "fact":
# For FACT the IRFs are the same for all observations
# So we only store a stacked spectrum and response
# plus we add a LO_THRESHOLD keyword was missing
obs = extract.spectrum_observations.stack()
obs.lo_threshold = 0.4 * u.TeV
# we are writing a single observation, as for Fermi
obs.write(path, use_sherpa=True, overwrite=True)
else:
extract.write(path, ogipdir="", use_sherpa=True, overwrite=True)
def extract_spectra_IACT(dataset):
"""Extract 1d spectra for IACT dataset"""
log.info(f"Extracting 1d spectra for {dataset.name} dataset")
# Dataset class has already the method to obtain the gammapy DataStore object
datastore = dataset.get_DataStore()
obs_list = datastore.obs_list(dataset.obs_ids)
on_region = CircleSkyRegion(center=dataset.source_pos,
radius=dataset.on_radius)
exclusion_mask = config.get_exclusion_mask()
bkg_estimate = ReflectedRegionsBackgroundEstimator(
obs_list=obs_list, on_region=on_region, exclusion_mask=exclusion_mask)
bkg_estimate.run()
extract = SpectrumExtraction(
obs_list=obs_list,
bkg_estimate=bkg_estimate.result,
containment_correction=dataset.containment_correction,
)
extract.run()
path = f"{config.repo_path}/results/spectra/{dataset.name}/"
log.info(f"Writing to {path}")
if dataset.name == "fact":
# For FACT the IRFs are the same for all observations
# So we only store a stacked spectrum and response
# plus we add a LO_THRESHOLD keyword was missing
obs = extract.observations.stack()
obs.lo_threshold = 0.4 * u.TeV
# we are writing a single observation, as for Fermi
obs.write(path, use_sherpa=True, overwrite=True)
else:
extract.write(path, ogipdir="", use_sherpa=True, overwrite=True)
obs_summary.plot_excess_vs_livetime(ax=ax1)
ax2=fig.add_subplot(122)
obs_summary.plot_significance_vs_livetime(ax=ax2)
plt.plot()
plt.show()
e_reco = EnergyBounds.equal_log_spacing(0.1, 40, 40, unit='TeV')
e_true = EnergyBounds.equal_log_spacing(0.05, 100., 200, unit='TeV')
ANALYSIS_DIR = 'crab_analysis'
extraction = SpectrumExtraction(obs_list=crablist, bkg_estimate=background_estimator.result, containment_correction=False,)
extraction.run()
extraction.compute_energy_threshold(method_lo='area_max', area_percent_lo=10.0)
print(extraction.observations[0])
extraction.run(obs_list=obs_list, bkg_estimate=background_estimator.result, outdir=ANALYSIS_DIR)
extraction.run(obs_list=crablist, bkg_estimate=background_estimator.result, outdir=ANALYSIS_DIR)
extraction.write(obs_list=crablist, bkg_estimate=background_estimator.result, outdir=ANALYSIS_DIR)
extraction.write(crablist)
extraction.write(crablist, bkg_estimate=background_estimator.result, outdir=ANALYSIS_DIR)
extraction.write(crablist, background_estimator.result, outdir=ANALYSIS_DIR)
get_ipython().magic(u'pinfo extraction.write')
extraction.observations[0].peek()
model = PowerLaw(
index=2 * u.Unit(''),
amplitude=2e-11 * u.Unit('cm-2 s-1 TeV-1'),
reference=1 * u.TeV,
)
joint_fit = SpectrumFit(obs_list=extraction.observations, model=model)
joint_fit.fit()
joint_fit.est_errors()
#fit.run(outdir = ANALYSIS_DIR)