Source code for gammapy.makers.background.ring

# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Ring background estimation."""
import itertools
import numpy as np
from astropy.convolution import Ring2DKernel, Tophat2DKernel
from astropy.coordinates import Angle
from gammapy.maps import Map
from gammapy.utils.array import scale_cube
from ..core import Maker

__all__ = ["AdaptiveRingBackgroundMaker", "RingBackgroundMaker"]


[docs]class AdaptiveRingBackgroundMaker(Maker): """Adaptive ring background algorithm. This algorithm extends the `RingBackgroundMaker` method by adapting the size of the ring to achieve a minimum on / off exposure ratio (alpha) in regions where the area to estimate the background from is limited. Parameters ---------- r_in : `~astropy.units.Quantity` Inner radius of the ring. r_out_max : `~astropy.units.Quantity` Maximal outer radius of the ring. width : `~astropy.units.Quantity` Width of the ring. stepsize : `~astropy.units.Quantity` Stepsize used for increasing the radius. threshold_alpha : float Threshold on alpha above which the adaptive ring takes action. theta : `~astropy.units.Quantity` Integration radius used for alpha computation. method : {'fixed_width', 'fixed_r_in'} Adaptive ring method. exclusion_mask : `~gammapy.maps.WcsNDMap` Exclusion mask See Also -------- RingBackgroundMaker """ tag = "AdaptiveRingBackgroundMaker" def __init__( self, r_in, r_out_max, width, stepsize="0.02 deg", threshold_alpha=0.1, theta="0.22 deg", method="fixed_width", exclusion_mask=None, ): if method not in ["fixed_width", "fixed_r_in"]: raise ValueError("Not a valid adaptive ring method.") self.r_in = Angle(r_in) self.r_out_max = Angle(r_out_max) self.width = Angle(width) self.stepsize = Angle(stepsize) self.threshold_alpha = threshold_alpha self.theta = Angle(theta) self.method = method self.exclusion_mask = exclusion_mask
[docs] def kernels(self, image): """Ring kernels according to the specified method. Parameters ---------- image : `~gammapy.maps.WcsNDMap` Map specifying the WCS information. Returns ------- kernels : list List of `~astropy.convolution.Ring2DKernel` """ scale = image.geom.pixel_scales[0] r_in = (self.r_in / scale).to_value("") r_out_max = (self.r_out_max / scale).to_value("") width = (self.width / scale).to_value("") stepsize = (self.stepsize / scale).to_value("") if self.method == "fixed_width": r_ins = np.arange(r_in, (r_out_max - width), stepsize) widths = [width] elif self.method == "fixed_r_in": widths = np.arange(width, (r_out_max - r_in), stepsize) r_ins = [r_in] else: raise ValueError(f"Invalid method: {self.method!r}") kernels = [] for r_in, width in itertools.product(r_ins, widths): kernel = Ring2DKernel(r_in, width) kernel.normalize("peak") kernels.append(kernel) return kernels
@staticmethod def _alpha_approx_cube(cubes): acceptance = cubes["acceptance"] acceptance_off = cubes["acceptance_off"] with np.errstate(divide="ignore", invalid="ignore"): alpha_approx = np.where( acceptance_off > 0, acceptance / acceptance_off, np.inf ) return alpha_approx def _reduce_cubes(self, cubes, dataset): """Compute off and off acceptance map. Calculated by reducing the cubes. The data is iterated along the third axis (i.e. increasing ring sizes), the value with the first approximate alpha < threshold is taken. """ threshold = self.threshold_alpha alpha_approx_cube = self._alpha_approx_cube(cubes) counts_off_cube = cubes["counts_off"] acceptance_off_cube = cubes["acceptance_off"] acceptance_cube = cubes["acceptance"] shape = alpha_approx_cube.shape[:2] counts_off = np.tile(np.nan, shape) acceptance_off = np.tile(np.nan, shape) acceptance = np.tile(np.nan, shape) for idx in np.arange(alpha_approx_cube.shape[-1]): mask = (alpha_approx_cube[:, :, idx] <= threshold) & np.isnan(counts_off) counts_off[mask] = counts_off_cube[:, :, idx][mask] acceptance_off[mask] = acceptance_off_cube[:, :, idx][mask] acceptance[mask] = acceptance_cube[:, :, idx][mask] counts = dataset.counts acceptance = counts.copy(data=acceptance[np.newaxis, Ellipsis]) acceptance_off = counts.copy(data=acceptance_off[np.newaxis, Ellipsis]) counts_off = counts.copy(data=counts_off[np.newaxis, Ellipsis]) return acceptance, acceptance_off, counts_off
[docs] 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
[docs] def run(self, dataset, observation=None): """Run adaptive ring background maker Parameters ---------- dataset : `~gammapy.datasets.MapDataset` Input map dataset. Returns ------- dataset_on_off : `~gammapy.datasets.MapDatasetOnOff` On off dataset. """ from gammapy.datasets import MapDatasetOnOff cubes = self.make_cubes(dataset) acceptance, acceptance_off, counts_off = self._reduce_cubes(cubes, dataset) mask_safe = dataset.mask_safe.copy() not_has_off_acceptance = acceptance_off.data <= 0 mask_safe.data[not_has_off_acceptance] = 0 dataset_on_off = MapDatasetOnOff.from_map_dataset( dataset=dataset, counts_off=counts_off, acceptance=acceptance, acceptance_off=acceptance_off, name=dataset.name, ) dataset_on_off.mask_safe = mask_safe return dataset_on_off
[docs]class RingBackgroundMaker(Maker): """Perform a local renormalisation of the existing background template, using a ring kernel. Expected signal regions should be removed by passing an exclusion mask Parameters ---------- r_in : `~astropy.units.Quantity` Inner ring radius width : `~astropy.units.Quantity` Ring width exclusion_mask : `~gammapy.maps.WcsNDMap` Exclusion mask Examples -------- For a usage example, see :doc:`/tutorials/analysis-2d/ring_background` tutorial. See Also -------- AdaptiveRingBackgroundEstimator """ tag = "RingBackgroundMaker" def __init__(self, r_in, width, exclusion_mask=None): self.r_in = Angle(r_in) self.width = Angle(width) self.exclusion_mask = exclusion_mask
[docs] def kernel(self, image): """Ring kernel. Parameters ---------- image : `~gammapy.maps.WcsNDMap` Input Map Returns ------- ring : `~astropy.convolution.Ring2DKernel` Ring kernel. """ scale = image.geom.pixel_scales[0].to("deg") r_in = self.r_in.to("deg") / scale width = self.width.to("deg") / scale ring = Ring2DKernel(r_in.value, width.value) ring.normalize("peak") return ring
[docs] def make_maps_off(self, dataset): """Make off maps Parameters ---------- dataset : `~gammapy.datasets.MapDataset` Input map dataset. Returns ------- maps_off : dict of `~gammapy.maps.WcsNDMap` Dictionary containing `counts_off` and `acceptance_off` maps. """ counts = dataset.counts background = dataset.npred_background() 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) maps_off = {} ring = self.kernel(counts) counts_excluded = counts * exclusion maps_off["counts_off"] = counts_excluded.convolve(ring.array) background_excluded = background * exclusion maps_off["acceptance_off"] = background_excluded.convolve(ring.array) return maps_off
[docs] def run(self, dataset, observation=None): """Run ring background maker Parameters ---------- dataset : `~gammapy.datasets.MapDataset` Input map dataset. Returns ------- dataset_on_off : `~gammapy.datasets.MapDatasetOnOff` On off dataset. """ from gammapy.datasets import MapDatasetOnOff maps_off = self.make_maps_off(dataset) maps_off["acceptance"] = dataset.npred_background() mask_safe = dataset.mask_safe.copy() not_has_off_acceptance = maps_off["acceptance_off"].data <= 0 mask_safe.data[not_has_off_acceptance] = 0 dataset_on_off = MapDatasetOnOff.from_map_dataset( dataset=dataset, name=dataset.name, **maps_off ) dataset_on_off.mask_safe = mask_safe return dataset_on_off
def __str__(self): return ( "RingBackground parameters: \n" f"r_in : {self.parameters['r_in']}\n" f"width: {self.parameters['width']}\n" f"Exclusion mask: {self.exclusion_mask}" )