def from_stack(cls, maps, axis, meta=None):
"""Create flux points by stacking list of flux points.
The first `FluxPoints` object in the list is taken as a reference to infer
column names and units for the stacked object.
Parameters
----------
maps : list of `FluxMaps`
List of maps to stack.
axis : `MapAxis`
New axis to create
Returns
-------
flux_maps : `FluxMaps`
Stacked flux maps along axis.
"""
reference = maps[0]
data = {}
for quantity in reference.available_quantities:
data[quantity] = Map.from_stack([_[quantity] for _ in maps],
axis=axis)
if meta is None:
meta = reference.meta.copy()
gti = GTI.from_stack([_.gti for _ in maps])
return cls(data=data,
reference_model=reference.reference_model,
meta=meta,
gti=gti)
def run(self, dataset):
"""Run adaptive smoothing on input MapDataset.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset` or `~gammapy.datasets.MapDatasetOnOff`
the input dataset (with one bin in energy at most)
Returns
-------
images : dict of `~gammapy.maps.WcsNDMap`
Smoothed images; keys are:
* 'counts'
* 'background'
* 'flux' (optional)
* 'scales'
* 'sqrt_ts'.
"""
energy_axis = self._get_energy_axis(dataset)
results = []
for energy_min, energy_max in progress_bar(
energy_axis.iter_by_edges, desc="Energy bins"
):
dataset_sliced = dataset.slice_by_energy(
energy_min=energy_min, energy_max=energy_max, name=dataset.name
)
dataset_sliced.models = dataset.models
result = self.estimate_maps(dataset_sliced)
results.append(result)
maps = Maps()
for name in results[0].keys():
maps[name] = Map.from_stack(
maps=[_[name] for _ in results], axis_name="energy"
)
return maps
def run(self, dataset):
"""
Run TS map estimation.
Requires a MapDataset with counts, exposure and background_model
properly set to run.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Input MapDataset.
Returns
-------
maps : dict
Dictionary containing result maps. Keys are:
* ts : delta TS map
* sqrt_ts : sqrt(delta TS), or significance map
* flux : flux map
* flux_err : symmetric error map
* flux_ul : upper limit map
"""
dataset_models = dataset.models
pad_width = self.estimate_pad_width(dataset=dataset)
dataset = dataset.pad(pad_width, name=dataset.name)
dataset = dataset.downsample(self.downsampling_factor, name=dataset.name)
energy_axis = self._get_energy_axis(dataset=dataset)
results = []
for energy_min, energy_max in progress_bar(
energy_axis.iter_by_edges, desc="Energy bins"
):
sliced_dataset = dataset.slice_by_energy(
energy_min=energy_min, energy_max=energy_max, name=dataset.name
)
if self.sum_over_energy_groups:
sliced_dataset = sliced_dataset.to_image(name=dataset.name)
sliced_dataset.models = dataset_models
result = self.estimate_flux_map(sliced_dataset)
results.append(result)
maps = Maps()
for name in self.selection_all:
m = Map.from_stack(maps=[_[name] for _ in results], axis_name="energy")
order = 0 if name == "niter" else 1
m = m.upsample(
factor=self.downsampling_factor,
preserve_counts=False,
order=order
)
maps[name] = m.crop(crop_width=pad_width)
meta = {"n_sigma": self.n_sigma, "n_sigma_ul": self.n_sigma_ul}
return FluxMaps(
data=maps,
reference_model=self.model,
gti=dataset.gti,
meta=meta
)
