def models(backgrounds):
spatial_model = GaussianSpatialModel(
lon_0="3 deg", lat_0="4 deg", sigma="3 deg", frame="galactic"
)
spectral_model = PowerLawSpectralModel(
index=2, amplitude="1e-11 cm-2 s-1 TeV-1", reference="1 TeV"
)
model1 = SkyModel(
spatial_model=spatial_model, spectral_model=spectral_model, name="source-1",
)
model2 = model1.copy(name="source-2")
model2.datasets_names = ["dataset-1"]
model3 = model1.copy(name="source-3")
model3.datasets_names = "dataset-2"
model3.spatial_model = PointSpatialModel(frame="galactic")
model3.parameters.freeze_all()
models = Models([model1, model2, model3] + backgrounds)
return models
def test_with_skymodel(phase_curve, light_curve):
sky_model = SkyModel(spectral_model=PowerLawSpectralModel())
out = sky_model.to_dict()
assert "temporal" not in out
sky_model = SkyModel(spectral_model=PowerLawSpectralModel(),
temporal_model=phase_curve)
sky_model2 = sky_model.copy()
assert sky_model2.temporal_model.tag == "PhaseCurveTemplateTemporalModel"
assert sky_model2.temporal_model.parameters.names == [
"time_0",
"phase_0",
"f0",
"f1",
"f2",
]
sky_model = SkyModel(spectral_model=PowerLawSpectralModel(),
temporal_model=light_curve)
assert sky_model.temporal_model.tag == "LightCurveTemplateTemporalModel"
out = sky_model.to_dict()
assert "temporal" in out
)
# In[ ]:
# You can see there are 3 datasets now
print(analysis_joint.datasets)
# In[ ]:
# You can access each one by its name, eg:
print(analysis_joint.datasets["obs_110380"])
# In[ ]:
# Add the model on each of the datasets
model_joint = model.copy()
for dataset in analysis_joint.datasets:
dataset.models = model_joint
dataset.background_model.norm.value = 1.1
# In[ ]:
get_ipython().run_cell_magic(
'time', '',
'fit_joint = Fit(analysis_joint.datasets)\nresult_joint = fit_joint.run()')
# In[ ]:
print(result)
# In[ ]:
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)
time_intervals = [(obs.tstart, obs.tstop) for obs in observations]
target_position = SkyCoord(ra=83.63308, dec=22.01450, unit="deg")
emin, emax = [0.7, 10] * u.TeV
energy_axis = MapAxis.from_bounds(emin.value,
emax.value,
10,
unit="TeV",
name="energy",
interp="log")
geom = WcsGeom.create(
skydir=target_position,
binsz=0.02,
width=(2, 2),
coordsys="CEL",
proj="CAR",
axes=[energy_axis],
)
energy_axis_true = MapAxis.from_bounds(0.1,
20,
20,
unit="TeV",
name="energy",
interp="log")
offset_max = 2 * u.deg
datasets = []
maker = MapDatasetMaker(offset_max=offset_max)
safe_mask_maker = SafeMaskMaker(methods=["offset-max"],
offset_max=offset_max)
for time_interval in time_intervals:
observations = observations.select_time(time_interval)
# Proceed with further analysis only if there are observations
# in the selected time window
if len(observations) == 0:
log.warning(f"No observations in time interval: {time_interval}")
continue
stacked = MapDataset.create(geom=geom,
energy_axis_true=energy_axis_true)
for obs in observations:
dataset = maker.run(stacked, obs)
dataset = safe_mask_maker.run(dataset, obs)
stacked.stack(dataset)
stacked.edisp = stacked.edisp.get_energy_dispersion(
position=target_position, e_reco=energy_axis.edges)
stacked.psf = stacked.psf.get_psf_kernel(position=target_position,
geom=stacked.exposure.geom,
max_radius="0.3 deg")
datasets.append(stacked)
spatial_model = PointSpatialModel(lon_0=target_position.ra,
lat_0=target_position.dec,
frame="icrs")
spatial_model.lon_0.frozen = True
spatial_model.lat_0.frozen = True
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
sky_model = SkyModel(spatial_model=spatial_model,
spectral_model=spectral_model,
name="")
for dataset in datasets:
model = sky_model.copy(name="crab")
dataset.model = model
return datasets
# In[ ]:
get_ipython().run_cell_magic(
'time', '',
'\ndatasets = []\n\nfor time_interval in time_intervals:\n # get filtered observation lists in time interval\n obs = crab_obs.select_time(time_interval)\n # Proceed with further analysis only if there are observations\n # in the selected time window\n if len(obs) == 0:\n log.warning(\n "No observations found in time interval:"\n "{t_min} - {t_max}".format(\n t_min=time_interval[0], t_max=time_interval[1]\n )\n )\n continue\n dataset = make_map_dataset(obs, target_position, geom, geom_true)\n dataset.counts.meta["t_start"] = time_interval[0]\n dataset.counts.meta["t_stop"] = time_interval[1]\n datasets.append(dataset)'
)
# ## Light Curve estimation: the 3D case
#
# Now that we have created the datasets we assign them the model to be fitted:
# In[ ]:
for dataset in datasets:
# Copy the source model
model = sky_model.copy(name="crab")
dataset.model = model
# We can now create the light curve estimator by passing it the list of datasets.
# We can optionally ask for parameters reoptimization during fit, e.g. to fit background normalization in each time bin.
# In[ ]:
lc_maker = LightCurveEstimator(datasets, source="crab", reoptimize=True)
# We now run the estimator once we pass it the energy interval on which to compute the integral flux of the source.
# In[ ]:
get_ipython().run_cell_magic(
'time', '',
# In[ ]:
spectral_model = PowerLawSpectralModel(
index=2, amplitude=2e-11 * u.Unit("cm-2 s-1 TeV-1"), reference=1 * u.TeV
)
model = SkyModel(spectral_model=spectral_model)
for dataset in datasets:
dataset.models = model
fit_joint = Fit(datasets)
result_joint = fit_joint.run()
# we make a copy here to compare it later
model_best_joint = model.copy()
model_best_joint.spectral_model.parameters.covariance = (
result_joint.parameters.covariance
)
# In[ ]:
print(result_joint)
# In[ ]:
plt.figure(figsize=(8, 6))
