def run(self, datasets):
"""Estimate flux for a given energy range.
Parameters
----------
datasets : list of `~gammapy.datasets.SpectrumDataset`
Spectrum datasets.
Returns
-------
result : dict
Dict with results for the flux point.
"""
datasets = Datasets(datasets)
datasets_sliced = datasets.slice_by_energy(energy_min=self.energy_min,
energy_max=self.energy_max)
# TODO: simplify model book-keeping!!
models = Models()
for model in datasets.models:
if "sky-model" in model.tag:
models.append(model)
elif "fov-bkg" in model.tag:
bkg_model = model.copy(dataset_name=model.datasets_names[0] +
"-sliced")
bkg_model.reset_to_default()
models.append(bkg_model)
if len(datasets) > 0:
# TODO: this relies on the energy binning of the first dataset
energy_axis = datasets_sliced[0].counts.geom.axes["energy"]
energy_min, energy_max = energy_axis.edges.min(
), energy_axis.edges.max()
else:
energy_min, energy_max = self.energy_min, self.energy_max
any_contribution = np.any(
[dataset.mask.data.any() for dataset in datasets_sliced])
model = self.get_scale_model(models)
with np.errstate(invalid="ignore", divide="ignore"):
result = self.get_reference_flux_values(model.model, energy_min,
energy_max)
if len(datasets) == 0 or not any_contribution:
result.update(self.nan_result)
else:
models[self.source].spectral_model = model
datasets_sliced.models = models
result.update(
self._parameter_estimator.run(datasets_sliced, model.norm))
result["sqrt_ts"] = self.get_sqrt_ts(result["ts"], result["norm"])
return result
def compute_npreds(datasets, n_iter, n_src):
models = Models()
positions = np.random.uniform(-4., 4., (n_src, 2))
for pos in positions:
pos = u.Quantity(pos, "deg")
model = SkyModel(spectral_model=PowerLawSpectralModel(),
spatial_model=GaussianSpatialModel(lon_0=pos[0],
lat_0=pos[1],
sigma="0.5 deg"))
models.append(model)
for i in range(n_iter):
datasets.models = models
tmp = datasets[0].npred()
def make_datasets_example():
# Define which data to use and print some information
energy_axis = MapAxis.from_edges(np.logspace(-1.0, 1.0, 4),
unit="TeV",
name="energy",
interp="log")
geom0 = WcsGeom.create(
skydir=(0, 0),
binsz=0.1,
width=(2, 2),
frame="galactic",
proj="CAR",
axes=[energy_axis],
)
geom1 = WcsGeom.create(
skydir=(1, 0),
binsz=0.1,
width=(2, 2),
frame="galactic",
proj="CAR",
axes=[energy_axis],
)
geoms = [geom0, geom1]
sources_coords = [(0, 0), (0.9, 0.1)]
names = ["gc", "g09"]
models = Models()
for idx, (lon, lat) in enumerate(sources_coords):
spatial_model = PointSpatialModel(lon_0=lon * u.deg,
lat_0=lat * u.deg,
frame="galactic")
spectral_model = ExpCutoffPowerLawSpectralModel(
index=2 * u.Unit(""),
amplitude=3e-12 * u.Unit("cm-2 s-1 TeV-1"),
reference=1.0 * u.TeV,
lambda_=0.1 / u.TeV,
)
model_ecpl = SkyModel(spatial_model=spatial_model,
spectral_model=spectral_model,
name=names[idx])
models.append(model_ecpl)
models["gc"].spectral_model.reference = models[
"g09"].spectral_model.reference
obs_ids = [110380, 111140, 111159]
data_store = DataStore.from_dir("$GAMMAPY_DATA/cta-1dc/index/gps/")
diffuse_spatial = TemplateSpatialModel.read(
"$GAMMAPY_DATA/fermi-3fhl-gc/gll_iem_v06_gc.fits.gz")
diffuse_model = SkyModel(PowerLawSpectralModel(), diffuse_spatial)
maker = MapDatasetMaker()
datasets = Datasets()
observations = data_store.get_observations(obs_ids)
for idx, geom in enumerate(geoms):
stacked = MapDataset.create(geom=geom, name=names[idx])
for obs in observations:
dataset = maker.run(stacked, obs)
stacked.stack(dataset)
bkg = stacked.models.pop(0)
stacked.models = [models[idx], diffuse_model, bkg]
datasets.append(stacked)
datasets.write(
"$GAMMAPY_DATA/tests/models",
prefix="gc_example",
overwrite=True,
write_covariance=False,
)
