def data_prep():
data_store = DataStore.from_dir("$GAMMAPY_DATA/hess-dl3-dr1/")
OBS_ID = 23523
obs_ids = OBS_ID * np.ones(N_OBS)
observations = data_store.get_observations(obs_ids)
target_position = SkyCoord(ra=83.63308, dec=22.01450, unit="deg")
e_reco = MapAxis.from_bounds(0.1, 40, nbin=40, interp="log",
unit="TeV").edges
e_true = MapAxis.from_bounds(0.05, 100, nbin=200, interp="log",
unit="TeV").edges
on_region_radius = Angle("0.11 deg")
on_region = CircleSkyRegion(center=target_position,
radius=on_region_radius)
dataset_maker = SpectrumDatasetMaker(containment_correction=True,
selection=["counts", "aeff", "edisp"])
empty = SpectrumDatasetOnOff.create(region=on_region,
e_reco=e_reco,
e_true=e_true)
bkg_maker = ReflectedRegionsBackgroundMaker()
safe_mask_masker = SafeMaskMaker(methods=["aeff-max"], aeff_percent=10)
spectral_model = PowerLawSpectralModel(index=2.6,
amplitude=2.0e-11 *
u.Unit("1 / (cm2 s TeV)"),
reference=1 * u.TeV)
spectral_model.index.frozen = False
model = spectral_model.copy()
model.name = "crab"
datasets_1d = []
for observation in observations:
dataset = dataset_maker.run(dataset=empty.copy(),
observation=observation)
dataset_on_off = bkg_maker.run(dataset, observation)
dataset_on_off = safe_mask_masker.run(dataset_on_off, observation)
datasets_1d.append(dataset_on_off)
for dataset in datasets_1d:
model = spectral_model.copy()
model.name = "crab"
dataset.model = model
return datasets_1d
# In[ ]:
model = PowerLawSpectralModel(index=2,
amplitude=2e-11 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.TeV)
datasets_joint = extraction.spectrum_observations
for dataset in datasets_joint:
dataset.model = model
fit_joint = Fit(datasets_joint)
result_joint = fit_joint.run()
# we make a copy here to compare it later
model_best_joint = model.copy()
model_best_joint.parameters.covariance = result_joint.parameters.covariance
# In[ ]:
print(result_joint)
# In[ ]:
plt.figure(figsize=(8, 6))
ax_spectrum, ax_residual = datasets_joint[0].plot_fit()
ax_spectrum.set_ylim(0, 25)
# ## Compute Flux Points
#
# To round up our analysis we can compute flux points by fitting the norm of the global model in energy bands. We'll use a fixed energy binning for now:
# we need to set the times manually for now
for dataset, time_interval in zip(datasets_1d, time_intervals):
dataset.counts.meta = dict()
dataset.counts.meta["t_start"] = time_interval[0]
dataset.counts.meta["t_stop"] = time_interval[1]
# ## Light Curve estimation for 1D spectra
#
# Now that we've reduced the 1D data we assign again the model to the datasets
# In[ ]:
for dataset in datasets_1d:
# Copy the source model
model = spectral_model.copy()
model.name = "crab"
dataset.model = model
# We can now call the LightCurveEstimator in a perfectly identical manner.
# In[ ]:
lc_maker_1d = LightCurveEstimator(datasets_1d, source="crab", reoptimize=False)
# In[ ]:
get_ipython().run_cell_magic(
'time', '',
'lc_1d = lc_maker_1d.run(e_ref=1 * u.TeV, e_min=1.0 * u.TeV, e_max=10.0 * u.TeV)'
)
