Source code for gammapy.background.ring

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

__all__ = ["AdaptiveRingBackgroundEstimator", "RingBackgroundEstimator"]


[docs]class AdaptiveRingBackgroundEstimator: """Adaptive ring background algorithm. This algorithm extends the `RingBackgroundEstimator` 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. Examples -------- Example using `AdaptiveRingBackgroundEstimator`:: from gammapy.maps import Map from gammapy.background import AdaptiveRingBackgroundEstimator filename = '$GAMMAPY_DATA/tests/unbundled/poisson_stats_image/input_all.fits.gz' images = { 'counts': Map.read(filename, hdu='counts'), 'exposure_on': Map.read(filename, hdu='exposure'), 'exclusion': Map.read(filename, hdu='exclusion'), } adaptive_ring_bkg = AdaptiveRingBackgroundEstimator( r_in='0.22 deg', r_out_max='0.8 deg', width='0.1 deg', ) results = adaptive_ring_bkg.run(images) results['background'].plot() See Also -------- RingBackgroundEstimator, gammapy.detect.KernelBackgroundEstimator """ def __init__( self, r_in, r_out_max, width, stepsize="0.02 deg", threshold_alpha=0.1, theta="0.22 deg", method="fixed_width", ): stepsize = Angle(stepsize) theta = Angle(theta) if method not in ["fixed_width", "fixed_r_in"]: raise ValueError("Not a valid adaptive ring method.") self._parameters = { "r_in": Angle(r_in), "r_out_max": Angle(r_out_max), "width": Angle(width), "stepsize": Angle(stepsize), "threshold_alpha": threshold_alpha, "theta": Angle(theta), "method": method, } @property def parameters(self): """Parameter dict.""" return self._parameters
[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` """ p = self.parameters scale = image.geom.pixel_scales[0] r_in = (p["r_in"] / scale).to_value("") r_out_max = (p["r_out_max"] / scale).to_value("") width = (p["width"] / scale).to_value("") stepsize = (p["stepsize"] / scale).to_value("") if p["method"] == "fixed_width": r_ins = np.arange(r_in, (r_out_max - width), stepsize) widths = [width] elif p["method"] == "fixed_r_in": widths = np.arange(width, (r_out_max - r_in), stepsize) r_ins = [r_in] else: raise ValueError("Invalid method: {}".format(p["method"])) kernels = [] for r_in, width in product(r_ins, widths): kernel = Ring2DKernel(r_in, width) kernel.normalize("peak") kernels.append(kernel) return kernels
@staticmethod def _alpha_approx_cube(cubes): """Compute alpha as on_exposure / off_exposure. Where off_exposure < 0, alpha is set to infinity. """ exposure_on = cubes["exposure_on"] exposure_off = cubes["exposure_off"] alpha_approx = np.where(exposure_off > 0, exposure_on / exposure_off, np.inf) return alpha_approx @staticmethod def _exposure_off_cube(exposure_on, exclusion, kernels): """Compute off exposure cube. The on exposure is convolved with different ring kernels and stacking the data along the third dimension. """ exposure = exposure_on.data exclusion = exclusion.data return scale_cube(exposure * exclusion, kernels) def _exposure_on_cube(self, exposure_on, kernels): """Compute on exposure cube. Calculated by convolving the on exposure with a tophat of radius theta, and stacking all images along the third dimension. """ scale = exposure_on.geom.pixel_scales[0].to("deg") theta = self.parameters["theta"] * scale tophat = Tophat2DKernel(theta.value) tophat.normalize("peak") exposure_on = exposure_on.convolve(tophat.array) exposure_on_cube = np.repeat( exposure_on.data[:, :, np.newaxis], len(kernels), axis=2 ) return exposure_on_cube @staticmethod def _off_cube(counts, exclusion, kernels): """Compute off cube. Calculated by convolving the raw counts with different ring kernels and stacking the data along the third dimension. """ return scale_cube(counts.data * exclusion.data, kernels) def _reduce_cubes(self, cubes): """Compute off and off exposure map. Calulated 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._parameters["threshold_alpha"] alpha_approx_cube = cubes["alpha_approx"] off_cube = cubes["off"] exposure_off_cube = cubes["exposure_off"] shape = alpha_approx_cube.shape[:2] off = np.tile(np.nan, shape) exposure_off = np.tile(np.nan, shape) for idx in np.arange(alpha_approx_cube.shape[-1]): mask = (alpha_approx_cube[:, :, idx] <= threshold) & np.isnan(off) off[mask] = off_cube[:, :, idx][mask] exposure_off[mask] = exposure_off_cube[:, :, idx][mask] return exposure_off, off
[docs] def run(self, images): """Run adaptive ring background algorithm. Parameters ---------- images : dict of `~gammapy.maps.WcsNDMap` Input sky maps. Returns ------- result : dict of `~gammapy.maps.WcsNDMap` Result sky maps. """ required = ["counts", "background", "exclusion"] counts, exposure_on, exclusion = [images[_] for _ in required] if not counts.geom.is_image: raise ValueError("Only 2D maps are supported") kernels = self.kernels(counts) cubes = { "exposure_on": self._exposure_on_cube(exposure_on, kernels), "exposure_off": self._exposure_off_cube(exposure_on, exclusion, kernels), "off": self._off_cube(counts, exclusion, kernels), } cubes["alpha_approx"] = self._alpha_approx_cube(cubes) exposure_off, off = self._reduce_cubes(cubes) alpha = exposure_on.data / exposure_off # set data outside fov to zero not_has_exposure = ~(exposure_on.data > 0) for data in [alpha, off, exposure_off]: data[not_has_exposure] = 0 background = alpha * off return { "exposure_off": counts.copy(data=exposure_off), "off": counts.copy(data=off), "alpha": counts.copy(data=alpha), "background_ring": counts.copy(data=background), }
[docs]class RingBackgroundEstimator: """Ring background method for cartesian coordinates. - Step 1: apply exclusion mask - Step 2: ring-correlate Parameters ---------- r_in : `~astropy.units.Quantity` Inner ring radius width : `~astropy.units.Quantity` Ring width. use_fft_convolution : bool Use FFT convolution? Examples -------- Example using `RingBackgroundEstimator`:: from gammapy.maps import Map from gammapy.background import RingBackgroundEstimator filename = '$GAMMAPY_DATA/tests/unbundled/poisson_stats_image/input_all.fits.gz' images = { 'counts': Map.read(filename, hdu='counts'), 'exposure_on': Map.read(filename, hdu='exposure'), 'exclusion': Map.read(filename, hdu='exclusion'), } ring_bkg = RingBackgroundEstimator(r_in='0.35 deg', width='0.3 deg') results = ring_bkg.run(images) results['background'].plot() See Also -------- gammapy.detect.KernelBackgroundEstimator, AdaptiveRingBackgroundEstimator """ def __init__(self, r_in, width): self._parameters = {"r_in": Angle(r_in), "width": Angle(width)} @property def parameters(self): """dict of parameters""" return self._parameters
[docs] def kernel(self, image): """Ring kernel. Parameters ---------- image : `~gammapy.maps.WcsNDMap` Input Map Returns ------- ring : `~astropy.convolution.Ring2DKernel` Ring kernel. """ p = self.parameters scale = image.geom.pixel_scales[0].to("deg") r_in = p["r_in"].to("deg") / scale width = p["width"].to("deg") / scale ring = Ring2DKernel(r_in.value, width.value) ring.normalize("peak") return ring
[docs] def run(self, images): """Run ring background algorithm. Required Maps: {required} Parameters ---------- images : dict of `~gammapy.maps.WcsNDMap` Input sky maps. Returns ------- result : dict of `~gammapy.maps.WcsNDMap` Result sky maps """ required = ["counts", "background", "exclusion"] counts, background, exclusion = [images[_] for _ in required] result = {} ring = self.kernel(counts) counts_excluded = counts * exclusion result["off"] = counts_excluded.convolve(ring.array) background_excluded = background * exclusion result["exposure_off"] = background_excluded.convolve(ring.array) with np.errstate(divide="ignore", invalid="ignore"): # set pixels, where ring is too small to NaN not_has_off_exposure = result["exposure_off"].data <= 0 result["exposure_off"].data[not_has_off_exposure] = np.nan not_has_exposure = background.data <= 0 result["off"].data[not_has_exposure] = 0 result["exposure_off"].data[not_has_exposure] = 0 result["alpha"] = background / result["exposure_off"] result["alpha"].data[not_has_exposure] = 0 result["background_ring"] = result["alpha"] * result["off"] return result
def __str__(self): s = "RingBackground parameters: \n" s += "r_in : {}\n".format(self.parameters["r_in"]) s += "width: {}\n".format(self.parameters["width"]) return s