def make_cubes(self, dataset):
"""Make acceptance, off acceptance, off counts cubes
Parameters
----------
dataset : `~gammapy.cube.MapDataset`
Input map dataset.
Returns
-------
cubes : dict of `~gammapy.maps.WcsNDMap`
Dictionary containing ``counts_off``, ``acceptance`` and ``acceptance_off`` cubes.
"""
counts = dataset.counts
background = dataset.background_model.map
kernels = self.kernels(counts)
if self.exclusion_mask is not None:
# reproject exclusion mask
coords = counts.geom.get_coord()
data = self.exclusion_mask.get_by_coord(coords)
exclusion = Map.from_geom(geom=counts.geom, data=data)
else:
data = np.ones(counts.geom.data_shape, dtype=bool)
exclusion = Map.from_geom(geom=counts.geom, data=data)
cubes = {}
cubes["counts_off"] = scale_cube(
(counts.data * exclusion.data)[0, Ellipsis], kernels
)
cubes["acceptance_off"] = scale_cube(
(background.data * exclusion.data)[0, Ellipsis], kernels
)
scale = background.geom.pixel_scales[0].to("deg")
theta = self.theta * scale
tophat = Tophat2DKernel(theta.value)
tophat.normalize("peak")
acceptance = background.convolve(tophat.array)
acceptance_data = acceptance.data[0, Ellipsis]
cubes["acceptance"] = np.repeat(
acceptance_data[Ellipsis, np.newaxis], len(kernels), axis=2
)
return cubes
def make_cubes(self, dataset):
"""Make acceptance, off acceptance, off counts cubes
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Input map dataset.
Returns
-------
cubes : dict of `~gammapy.maps.WcsNDMap`
Dictionary containing ``counts_off``, ``acceptance`` and ``acceptance_off`` cubes.
"""
counts = dataset.counts
background = dataset.npred_background()
kernels = self.kernels(counts)
if self.exclusion_mask:
exclusion = self.exclusion_mask.interp_to_geom(geom=counts.geom)
else:
exclusion = Map.from_geom(geom=counts.geom, data=True, dtype=bool)
cubes = {}
cubes["counts_off"] = scale_cube(
(counts.data * exclusion.data)[0, Ellipsis], kernels
)
cubes["acceptance_off"] = scale_cube(
(background.data * exclusion.data)[0, Ellipsis], kernels
)
scale = background.geom.pixel_scales[0].to("deg")
theta = self.theta * scale
tophat = Tophat2DKernel(theta.value)
tophat.normalize("peak")
acceptance = background.convolve(tophat.array)
acceptance_data = acceptance.data[0, Ellipsis]
cubes["acceptance"] = np.repeat(
acceptance_data[Ellipsis, np.newaxis], len(kernels), axis=2
)
return cubes
def estimate_maps(self, counts, background, exposure=None):
"""
Run adaptive smoothing on input Maps.
Parameters
----------
counts : `~gammapy.maps.Map`
counts map
background : `~gammapy.maps.Map`
estimated background counts map
exposure : `~gammapy.maps.Map`
exposure map. If set, it will produce a flux smoothed map.
Returns
-------
images : dict of `~gammapy.maps.WcsNDMap`
Smoothed images; keys are:
* 'counts'
* 'background'
* 'flux' (optional)
* 'scales'
* 'significance'.
"""
pixel_scale = counts.geom.pixel_scales.mean()
kernels = self.kernels(pixel_scale)
cubes = {}
cubes["counts"] = scale_cube(counts.data, kernels)
if background is not None:
cubes["background"] = scale_cube(background.data, kernels)
else:
# TODO: Estimate background with asmooth method
raise ValueError("Background estimation required.")
if exposure is not None:
flux = (counts - background) / exposure
cubes["flux"] = scale_cube(flux.data, kernels)
cubes["significance"] = self._significance_cube(cubes,
method=self.method)
smoothed = self._reduce_cubes(cubes, kernels)
result = {}
for key in ["counts", "background", "scale", "significance"]:
data = smoothed[key]
# set remaining pixels with significance < threshold to mean value
if key in ["counts", "background"]:
mask = np.isnan(data)
data[mask] = np.mean(locals()[key].data[mask])
result[key] = WcsNDMap(counts.geom, data, unit=counts.unit)
else:
result[key] = WcsNDMap(counts.geom, data, unit="deg")
if exposure is not None:
data = smoothed["flux"]
mask = np.isnan(data)
data[mask] = np.mean(flux.data[mask])
result["flux"] = WcsNDMap(counts.geom, data, unit=flux.unit)
return result
def estimate_maps(self, dataset):
"""Run adaptive smoothing on input Maps.
Parameters
----------
dataset : `MapDataset`
Dataset
Returns
-------
images : dict of `~gammapy.maps.WcsNDMap`
Smoothed images; keys are:
* 'counts'
* 'background'
* 'flux' (optional)
* 'scales'
* 'sqrt_ts'.
"""
dataset_image = dataset.to_image(name=dataset.name)
dataset_image.models = dataset.models
# extract 2d arrays
counts = dataset_image.counts.data[0].astype(float)
background = dataset_image.npred_background().data[0]
if isinstance(dataset_image, MapDatasetOnOff):
background = dataset_image.background.data[0]
if dataset_image.exposure is not None:
exposure = estimate_exposure_reco_energy(dataset_image,
self.spectrum)
else:
exposure = None
pixel_scale = dataset_image.counts.geom.pixel_scales.mean()
kernels = self.get_kernels(pixel_scale)
cubes = {}
cubes["counts"] = scale_cube(counts, kernels)
cubes["background"] = scale_cube(background, kernels)
if exposure is not None:
flux = (dataset_image.counts - background) / exposure
cubes["flux"] = scale_cube(flux.data[0], kernels)
cubes["sqrt_ts"] = self._sqrt_ts_cube(cubes, method=self.method)
smoothed = self._reduce_cubes(cubes, kernels)
result = {}
geom = dataset_image.counts.geom
for name, data in smoothed.items():
# set remaining pixels with sqrt_ts < threshold to mean value
if name in ["counts", "background"]:
mask = np.isnan(data)
data[mask] = np.mean(locals()[name][mask])
result[name] = WcsNDMap(geom, data, unit="")
else:
unit = "deg" if name == "scale" else ""
result[name] = WcsNDMap(geom, data, unit=unit)
if exposure is not None:
data = smoothed["flux"]
mask = np.isnan(data)
data[mask] = np.mean(flux.data[0][mask])
result["flux"] = WcsNDMap(geom, data, unit=flux.unit)
return result