Source code for ligo.skymap.postprocess.util

# Copyright (C) 2013-2020  Leo Singer
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <>.
"""Postprocessing utilities for HEALPix sky maps."""

import astropy_healpix as ah
from astropy.coordinates import (CartesianRepresentation, SkyCoord,
from astropy import units as u
import healpy as hp
import numpy as np

__all__ = ('find_greedy_credible_levels', 'smooth_ud_grade', 'posterior_mean',

[docs] def find_greedy_credible_levels(p, ranking=None): """Find the greedy credible levels of a (possibly multi-dimensional) array. Parameters ---------- p : np.ndarray The input array, typically a HEALPix image. ranking : np.ndarray, optional The array to rank in order to determine the greedy order. The default is `p` itself. Returns ------- cls : np.ndarray An array with the same shape as `p`, with values ranging from `0` to `p.sum()`, representing the greedy credible level to which each entry in the array belongs. """ p = np.asarray(p) pflat = p.ravel() if ranking is None: ranking = pflat else: ranking = np.ravel(ranking) i = np.flipud(np.argsort(ranking)) cs = np.cumsum(pflat[i]) cls = np.empty_like(pflat) cls[i] = cs return cls.reshape(p.shape)
[docs] def smooth_ud_grade(m, nside, nest=False): """Resample a sky map to a new resolution using bilinear interpolation. Parameters ---------- m : np.ndarray The input HEALPix array. nest : bool, default=False Indicates whether the input sky map is in nested rather than ring-indexed HEALPix coordinates (default: ring). Returns ------- new_m : np.ndarray The resampled HEALPix array. The sum of `m` is approximately preserved. """ npix = ah.nside_to_npix(nside) theta, phi = hp.pix2ang(nside, np.arange(npix), nest=nest) new_m = hp.get_interp_val(m, theta, phi, nest=nest) return new_m * len(m) / len(new_m)
def posterior_mean(prob, nest=False): npix = len(prob) nside = ah.npix_to_nside(npix) xyz = hp.pix2vec(nside, np.arange(npix), nest=nest) mean_xyz = np.average(xyz, axis=1, weights=prob) pos = SkyCoord(*mean_xyz, representation_type=CartesianRepresentation) pos.representation_type = UnitSphericalRepresentation return pos def posterior_max(prob, nest=False): npix = len(prob) nside = ah.npix_to_nside(npix) i = np.argmax(prob) return SkyCoord( *hp.pix2ang(nside, i, nest=nest, lonlat=True), unit=u.deg)