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GBM-data-tools/data/localization.py

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gbm data tools 1.1.1
2022-07-15 15:36:07 +08:00
# localization.py: HEALPix and associated localization classes
#
# Authors: William Cleveland (USRA),
# Adam Goldstein (USRA) and
# Daniel Kocevski (NASA)
#
# Portions of the code are Copyright 2020 William Cleveland and
# Adam Goldstein, Universities Space Research Association
# All rights reserved.
#
# Written for the Fermi Gamma-ray Burst Monitor (Fermi-GBM)
#
# 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
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# 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 <https://www.gnu.org/licenses/>.
#
import os, re
from copy import deepcopy
import astropy.io.fits as fits
import numpy as np
from scipy.stats import chi2, norm
from warnings import warn
import warnings
import healpy as hp
from collections import OrderedDict
from matplotlib.pyplot import contour as Contour
from matplotlib.patches import Polygon
from ..plot.lal_post_subs import make_circle_poly
from ..coords import get_sun_loc, geocenter_in_radec, spacecraft_to_radec
from ..coords import latitude_from_geocentric_coords_complex, haversine
from ..coords import latitude_from_geocentric_coords_simple
from ..detectors import Detector
from .data import DataFile
from .headers import healpix_primary, healpix_image
class HealPix(DataFile):
"""Base class for HEALPix localization files.
Attributes:
centroid (float, float): The RA and Dec of the highest probability pixel
datatype (str): The datatype of the file
detector (str): The GBM detector the file is associated with
directory (str): The directory the file is located in
filename (str): The filename
full_path (str): The full path+filename
headers (dict): The headers for each extension
id (str): The GBM file ID
is_gbm_file (bool): True if the file is a valid GBM standard file,
False if it is not.
is_trigger (bool): True if the file is a GBM trigger file, False if not
npix (int): Number of pixels in the HEALPix map
nside (int): The HEALPix resolution
pixel_area (float): The area of each pixel in square degrees
trigtime (float): The time corresponding to the localization
"""
def __init__(self):
self._headers = OrderedDict()
self._prob = np.array([], dtype=float)
self._sig = np.array([], dtype=float)
super().__init__()
@property
def headers(self):
return self._headers
@property
def trigtime(self):
try:
return self._headers['PRIMARY']['TRIGTIME']
except:
return None
@property
def npix(self):
return len(self._prob)
@property
def nside(self):
return hp.npix2nside(self.npix)
@property
def pixel_area(self):
return 4.0 * 180.0 ** 2 / (np.pi * self.npix)
@property
def centroid(self):
pix = np.argmax(self._prob)
theta, phi = hp.pix2ang(self.nside, pix)
return (self._phi_to_ra(phi), self._theta_to_dec(theta))
@classmethod
def from_data(cls, prob_arr, sig_arr, trigtime=None):
"""Create a HealPix object from healpix arrays
Args:
prob_arr (np.array): The HEALPix array containing the probability/pixel
sig_arr (np.array): The HEALPix array containing the signficance
trigtime (float, optional): The time corresponding to the localization
Returns:
:class:`HealPix`: The HEALPix localization
"""
obj = cls()
obj._prob = obj._assert_prob(prob_arr)
obj._sig = obj._assert_sig(sig_arr)
# set file properties
if trigtime is None:
trigtime = 0.0
obj.set_properties(trigtime=trigtime, datatype='healpix',
extension='fit')
return obj
@classmethod
def from_annulus(cls, center_ra, center_dec, radius, sigma, nside=None,
**kwargs):
"""Create a HealPix object of a Gaussian-width annulus
Args:
center_ra (float): The RA of the center of the annulus
center_dec (float): The Dec of the center of the annulus
radius (float): The radius of the annulus, in degrees, measured to
the center of the of the annulus
sigma (float): The Gaussian standard deviation width of the annulus,
in degrees
nside (int, optional): The nside of the HEALPix to make. By default,
the nside is automatically determined by the
`sigma` width. Set this argument to
override the default.
**kwargs: Options to pass to :meth:`from_data`
Return:
:class:`HealPix`: The HEALPix annulus
"""
# Automatically calculate appropriate nside by taking the closest nside
# with an average resolution that matches 0.2*sigma
if nside is None:
nsides = 2**np.arange(15)
pix_res = hp.nside2resol(nsides, True)/60.0
idx = np.abs(pix_res-sigma/5.0).argmin()
nside = nsides[idx]
# get everything in the right units
center_phi = cls._ra_to_phi(center_ra)
center_theta = cls._dec_to_theta(center_dec)
radius_rad = np.deg2rad(radius)
sigma_rad = np.deg2rad(sigma)
# number of points in the circle based on the approximate arclength
# and resolution
res = hp.nside2resol(nside)
# calculate normal distribution about annulus radius with sigma width
x = np.linspace(0.0, np.pi, int(10.0*np.pi/res))
pdf = norm.pdf(x, loc=radius_rad, scale=sigma_rad)
# cycle through annuli of radii from 0 to 180 degree with the
# appropriate amplitude and fill the probability map
probmap = np.zeros(hp.nside2npix(nside))
for i in range(x.size):
# no need to waste time on pixels that will have ~0 probability...
if pdf[i]/pdf.max() < 1e-10:
continue
# approximate arclength determines number of points in each annulus
arclength = 2.0*np.pi*x[i]
numpts = int(np.ceil(arclength/res))*10
circ = make_circle_poly(x[i], center_theta, center_phi, numpts)
theta = np.pi / 2.0 - circ[:, 1]
phi = circ[:, 0]
# convert to pixel indixes and fill the map
idx = hp.ang2pix(nside, theta, phi)
probmap[idx] = pdf[i]
mask = (probmap[idx] > 0.0)
probmap[idx[~mask]] = pdf[i]
probmap[idx[mask]] = (probmap[idx[mask]] + pdf[i])/2.0
probmap /= probmap.sum()
# signficance map
sigmap = 1.0 - find_greedy_credible_levels(probmap)
obj = cls.from_data(probmap, sigmap, **kwargs)
return obj
@classmethod
def from_gaussian(cls, center_ra, center_dec, sigma, nside=None, **kwargs):
"""Create a HealPix object of a Gaussian
Args:
center_ra (float): The RA of the center of the Gaussian
center_dec (float): The Dec of the center of the Gaussian
sigma (float): The Gaussian standard deviation, in degrees
nside (int, optional): The nside of the HEALPix to make. By default,
the nside is automatically determined by the
`sigma` of the Gaussian. Set this argument
to override the default.
**kwargs: Options to pass to :meth:`from_data`
Returns:
:class:`HealPix`: The HEALPix Gaussian
"""
# Automatically calculate appropriate nside by taking the closest nside
# with an average resolution that matches 0.2*sigma
if nside is None:
nsides = 2**np.arange(15)
pix_res = hp.nside2resol(nsides, True)/60.0
idx = np.abs(pix_res-sigma/10.0).argmin()
nside = nsides[idx]
# get everything in the right units
center_phi = cls._ra_to_phi(center_ra)
center_theta = cls._dec_to_theta(center_dec)
sigma_rad = np.deg2rad(sigma)
# point probability
npix = hp.nside2npix(nside)
probmap = np.zeros(npix)
probmap[hp.ang2pix(nside, center_theta, center_phi)] = 1.0
# then smooth out using appropriate gaussian kernel
probmap = hp.smoothing(probmap, sigma=sigma_rad, verbose=False)
# significance map
sigmap = 1.0 - find_greedy_credible_levels(probmap)
obj = cls.from_data(probmap, sigmap, **kwargs)
return obj
@classmethod
def from_vertices(cls, ra_pts, dec_pts, nside=64, **kwargs):
"""Create a HealPix object from a list of RA, Dec vertices.
The probability within the vertices will be distributed uniformly and
zero probability outside the vertices.
Args:
ra_pts (np.array): The array of RA coordinates
dec_pts (np.array): The array of Dec coordinates
nside (int, optional): The nside of the HEALPix to make. Default is 64.
**kwargs: Options to pass to :meth:`from_data`
Returns:
:class:`HealPix`: The HEALPix object
"""
poly = Polygon(np.vstack((ra_pts, dec_pts)).T, closed=True)
npix = hp.nside2npix(nside)
theta, phi = hp.pix2ang(nside, np.arange(npix))
ra = cls._phi_to_ra(phi)
dec = cls._theta_to_dec(theta)
mask = poly.contains_points(np.vstack((ra, dec)).T)
probmap = np.zeros(npix)
probmap[mask] = 1.0
probmap /= probmap.sum()
# significance map
sigmap = 1.0 - find_greedy_credible_levels(probmap)
obj = cls.from_data(probmap, sigmap, **kwargs)
return obj
@classmethod
def multiply(cls, healpix1, healpix2, primary=1, output_nside=128):
"""Multiply two HealPix maps and return a new HealPix object
Args:
healpix1 (:class:`HealPix`): One of the HEALPix maps to multiply
healpix2 (:class:`HealPix`): The other HEALPix map to multiply
primary (int, optional): If 1, use the first map header information,
or if 2, use the second map header
information. Default is 1.
output_nside (int, optional): The nside of the multiplied map.
Default is 128.
Returns
:class:`HealPix`: The multiplied map
"""
# if different resolutions, upgrade the lower res, then multiply
if healpix1.nside > healpix2.nside:
prob = healpix1._prob * hp.ud_grade(healpix2._prob,
nside_out=healpix1.nside)
elif healpix1.nside < healpix2.nside:
prob = healpix2._prob * hp.ud_grade(healpix1._prob,
nside_out=healpix2.nside)
else:
prob = healpix1._prob * healpix2._prob
# output resolution and normalize
prob = hp.ud_grade(prob, output_nside)
prob = prob / np.sum(prob)
sig = 1.0 - find_greedy_credible_levels(prob)
# copy header info
if primary == 1:
headers = healpix1.headers
trigtime = healpix1.trigtime
else:
headers = healpix2.headers
trigtime = healpix2.trigtime
if 'HEALPIX' in headers:
headers['HEALPIX']['NSIDE'] = output_nside
obj = cls.from_data(prob, sig, trigtime=trigtime)
obj._headers = headers
return obj
def probability(self, ra, dec, per_pixel=False):
"""Calculate the localization probability at a given point. This
function interpolates the map at the requested point rather than
providing the vale at the nearest pixel center.
Args:
ra (float): The RA
dec (float): The Dec
per_pixel (bool, optional):
If True, return probability per pixel, otherwise return
probability per square degree. Default is False.
Returns:
float: The localization probability
"""
phi = self._ra_to_phi(ra)
theta = self._dec_to_theta(dec)
prob = hp.get_interp_val(self._prob, theta, phi)
if not per_pixel:
prob /= self.pixel_area
return prob
def confidence(self, ra, dec):
"""Calculate the localization confidence level for a given point.
This function interpolates the map at the requested point rather than
providing the value at the nearest pixel center.
Args:
ra (float): The RA
dec (float): The Dec
Returns:
float: The localization confidence level
"""
phi = self._ra_to_phi(ra)
theta = self._dec_to_theta(dec)
return 1.0 - hp.get_interp_val(self._sig, theta, phi)
def area(self, clevel):
"""Calculate the sky area contained within a given confidence region
Args:
clevel (float): The localization confidence level (valid range 0-1)
Returns:
float: The area contained in square degrees
"""
numpix = np.sum((1.0 - self._sig) <= clevel)
return numpix * self.pixel_area
def prob_array(self, numpts_ra=360, numpts_dec=180, sqdegrees=True,
sig=False):
"""Return the localization probability mapped to a grid on the sky
Args:
numpts_ra (int, optional): The number of grid points along the RA
axis. Default is 360.
numpts_dec (int, optional): The number of grid points along the Dec
axis. Default is 180.
sqdegrees (bool, optional):
If True, the prob_array is in units of probability per square
degrees, otherwise in units of probability per pixel.
Default is True
sig (bool, optional): Set True to retun the significance map on a
grid instead of the probability. Default is False.
Returns:
3-tuple containing:
- *np.array*: The probability (or significance) array with shape \
(``numpts_dec``, ``numpts_ra``)
- *np.array*: The RA grid points
- *np.array*: The Dec grid points
"""
grid_pix, phi, theta = self._mesh_grid(numpts_ra, numpts_dec)
if sig:
sqdegrees = False
prob_arr = self._sig[grid_pix]
else:
prob_arr = self._prob[grid_pix]
if sqdegrees:
prob_arr /= self.pixel_area
return (prob_arr, self._phi_to_ra(phi), self._theta_to_dec(theta))
def confidence_region_path(self, clevel, numpts_ra=360, numpts_dec=180):
"""Return the bounding path for a given confidence region
Args:
clevel (float): The localization confidence level (valid range 0-1)
numpts_ra (int, optional): The number of grid points along the RA
axis. Default is 360.
numpts_dec (int, optional): The number of grid points along the Dec
axis. Default is 180.
Returns:
[(np.array, np.array), ...]: A list of RA, Dec points, where each \
item in the list is a continuous closed path.
"""
# create the grid and integrated probability array
grid_pix, phi, theta = self._mesh_grid(numpts_ra, numpts_dec)
sig_arr = 1.0 - self._sig[grid_pix]
ra = self._phi_to_ra(phi)
dec = self._theta_to_dec(theta)
# use matplotlib contour to produce a path object
contour = Contour(ra, dec, sig_arr, [clevel])
# get the contour path, which is made up of segments
paths = contour.collections[0].get_paths()
# extract all the vertices
pts = [path.vertices for path in paths]
# unfortunately matplotlib will plot this, so we need to remove
for c in contour.collections:
c.remove()
return pts
def source_probability(self, ra, dec, prior=0.5):
r"""The probability that the HealPix localization is associated with
a known point location. This is calculated against the null hypothesis
that the HealPix localization originates from an unassociated random
source that has equal probability of origination anywhere in the sky:
:math:`P(A | \mathcal{I}) =
\frac{P(\mathcal{I} | A) \ P(A)}
{P(\mathcal{I} | A) \ P(A) + P(\mathcal{I} | \neg A) \ P(\neg A)}`
where
* :math:`P(\mathcal{I} | A)` is the probability of the localization at
the point source once
* :math:`P(\mathcal{I} | \neg A)` is the probability per pixel assuming
a uniform distribution on the sky (i.e. the probability the
localization is associated with a random point on the sky)
* :math:`P(A)` is the prior probability that the localization is
associated with the point source
Args:
ra (float): The RA of the known source location
dec (float): The Dec of the known source location
prior (float, optional): The prior probability that the localization
is associated with the source.
Default is 0.5
Returns:
float: The probability that the HealPix localization is spatially \
associated with the point source
"""
if (prior < 0.0) or (prior > 1.0):
raise ValueError('Prior probability must be within 0-1, inclusive')
# convert uniform prob/sr to prob/pixel
u = 1.0 / (4.0 * np.pi)
u *= hp.nside2resol(self.nside) ** 2
# the pixel probability of the skymap at the location of the point source
p = self.probability(ra, dec, per_pixel=True)
# null hypothesis is that they are not associated, therefore the sky map
# is result of some source that has uniform probability on the sky
prob = (p*prior) / ((p*prior) + (u*(1.0-prior)))
return prob
def region_probability(self, healpix, prior=0.5):
r"""The probability that the HealPix localization is associated with
another HealPix map. This is calculated against the null hypothesis
that the two HealPix maps are unassociated:
:math:`P(A | \mathcal{I}) =
\frac{P(\mathcal{I} | A) \ P(A)}
{P(\mathcal{I} | A) \ P(A) + P(\mathcal{I} | \neg A) \ P(\neg A)}`
where
* :math:`P(\mathcal{I} | A)` is the integral over the overlap of the two
maps once the Earth occultation has been removed for *this* map.
* :math:`P(\mathcal{I} | \neg A)` is the integral over the overlap of
*this* map with a uniform distribution on the sky (i.e. the probability
the localization is associated with a random point on the sky)
* :math:`P(A)` is the prior probability that *this* localization is
associated with the *other* HEALPix map.
Args:
healpix (:class:`HealPix`): The healpix map for which to calculate
the spatial association
prior (float, optional): The prior probability that the localization
is associated with the source.
Default is 0.5
Returns:
float: The probability that this HealPix localization is
associated with the input HealPix map
"""
if (prior < 0.0) or (prior > 1.0):
raise ValueError('Prior probability must be within 0-1, inclusive')
# convert uniform prob/sr to prob/pixel
u = 1.0 / (4.0 * np.pi)
# ensure maps are the same resolution
probmap1 = self._prob
probmap2 = healpix._prob
if self.nside > healpix.nside:
probmap2 = hp.ud_grade(probmap2, nside_out=self.nside)
probmap2 = self._assert_prob(probmap2)
u *= hp.nside2resol(self.nside) ** 2
elif self.nside < healpix.nside:
probmap1 = hp.ud_grade(probmap1, nside_out=healpix.nside)
probmap1 = self._assert_prob(probmap1)
u *= hp.nside2resol(healpix.nside) ** 2
else:
u *= hp.nside2resol(self.nside) ** 2
# alternative hypothesis: they are related
alt_hyp = np.sum(probmap1 * probmap2)
# null hypothesis: one of the maps is from an unassociated source
# (uniform spatial probability)
null_hyp = np.sum(probmap1 * u)
# since we have an exhaustive and complete list of possibilities, we can
# easily calculate the probability
prob = (alt_hyp*prior) / ((alt_hyp*prior) + (null_hyp*(1.0-prior)))
return prob
def convolve(self, model, *args):
"""Convolve the map with a model kernel. The model can be a Gaussian
kernel or any mixture of Gaussian kernels. Uses `healpy.smoothing
<https://healpy.readthedocs.io/en/latest/generated/healpy.sphtfunc.smoothing.html>`_.
An example of a model kernel with a 50%/50% mixture of two Gaussians,
one with a 1-deg width, and the other with a 3-deg width::
def gauss_mix_example():
sigma1 = np.deg2rad(1.0)
sigma2 = np.deg2rad(3.0)
frac1 = 0.50
return ([sigma1, sigma2], [frac1])
Args:
model (<function>): The function representing the model kernel
*args: Arguments to be passed to the model kernel function
Returns:
:class:`HealPix`: A new HealPix object that is a result of the \
convolution with the model kernel
"""
# evaluate model
sigmas, fracs = model(*args)
# determine number of gaussians, and ensure that they match the
# number of fractional weights
num_sigmas = len(sigmas)
if len(fracs) != num_sigmas:
if len(fracs) + 1 != num_sigmas:
raise ValueError(
'Number of mixture fraction parameters is incorrect')
fracs.append(1.0 - np.sum(fracs))
# for each gaussian, apply the smoothing at the prescribed weight
new_prob = np.zeros(self._prob.shape)
for i in range(num_sigmas):
new_prob += fracs[i] * hp.smoothing(self._prob, sigma=sigmas[i],
verbose=False)
# make the object
new_sig = 1.0 - find_greedy_credible_levels(new_prob)
new_obj = deepcopy(self)
new_obj._prob = new_obj._assert_prob(new_prob)
new_obj._sig = new_obj._assert_sig(new_sig)
return new_obj
@staticmethod
def _ra_to_phi(ra):
return np.deg2rad(ra)
@staticmethod
def _phi_to_ra(phi):
return np.rad2deg(phi)
@staticmethod
def _dec_to_theta(dec):
return np.deg2rad(90.0 - dec)
@staticmethod
def _theta_to_dec(theta):
return np.rad2deg(np.pi / 2.0 - theta)
def _ang_to_pix(self, ra, dec):
# convert RA/Dec to healpixels
theta = self._dec_to_theta(dec)
phi = self._ra_to_phi(ra)
pix = hp.ang2pix(self.nside, theta, phi)
return pix
def _mesh_grid(self, num_phi, num_theta):
# create the mesh grid in phi and theta
theta = np.linspace(np.pi, 0.0, num_theta)
phi = np.linspace(0.0, 2 * np.pi, num_phi)
phi_grid, theta_grid = np.meshgrid(phi, theta)
grid_pix = hp.ang2pix(self.nside, theta_grid, phi_grid)
return (grid_pix, phi, theta)
def _assert_prob(self, prob):
# ensure that the pixels have valid probability:
# each pixel must be > 0 and sum == 1.
prob[prob < 0.0] = 0.0
prob /= prob.sum()
return prob
def _assert_sig(self, sig):
# ensure that the pixels have valid significance:
# each pixel must have significance [0, 1]
if sig is not None:
sig[sig < 0.0] = 0.0
sig[sig > 1.0] = 1.0
return sig
class GbmHealPix(HealPix):
"""Class for GBM HEALPix localization files.
Attributes:
<detector_name>_pointing (float, float):
The RA, Dec of the detector pointing (e.g. ``GbmHealPix.n0_pointing``)
centroid (float, float): The RA and Dec of the highest probability pixel
datatype (str): The datatype of the file
detector (str): The GBM detector the file is associated with
directory (str): The directory the file is located in
filename (str): The filename
full_path (str): The full path+filename
geo_location (float, float): The geocenter RA, Dec at trigtime
geo_probability (float): The amount of localization probability on the Earth
geo_radius (float): The apparent Earth radius as observed by Fermi
headers (dict): The headers for each extension
id (str): The GBM file ID
is_gbm_file (bool): True if the file is a valid GBM standard file,
False if it is not.
is_trigger (bool): True if the file is a GBM trigger file, False if not
npix (int): Number of pixels in the HEALPix map
nside (int): The HEALPix resolution
quaternion (np.array): The spacecraft attitude quaternion
pixel_area (float): The area of each pixel in square degrees
scpos (np.array): The spacecraft position in Earth inertial coordinates
sun_location (float, float): The Sun RA, Dec at trigtime
trigtime (float): The time corresponding to the localization
"""
def __init__(self):
super().__init__()
@property
def sun_location(self):
try:
return (self._headers['HEALPIX']['SUN_RA'],
self._headers['HEALPIX']['SUN_DEC'])
except:
return None
@property
def geo_location(self):
try:
return (self._headers['HEALPIX']['GEO_RA'],
self._headers['HEALPIX']['GEO_DEC'])
except:
return None
@property
def geo_radius(self):
# if the radius isn't known, use the average 67.5 deg radius
try:
return self._headers['HEALPIX']['GEO_RAD']
except:
return 67.5
@property
def scpos(self):
if 'COMMENT' not in self.headers['HEALPIX']:
return None
scpos = [c for c in self.headers['HEALPIX']['COMMENT'] if 'SCPOS' in c]
if len(scpos) != 1:
return None
else:
scpos = scpos[0].split('[')[1].split(']')[0]
scpos = np.array([float(el) for el in scpos.split()])
return scpos
@property
def quaternion(self):
if 'COMMENT' not in self.headers['HEALPIX']:
return None
quat = [c for c in self.headers['HEALPIX']['COMMENT'] if 'QUAT' in c]
if len(quat) != 1:
return None
else:
quat = quat[0].split('[')[1].split(']')[0]
quat = np.array([float(el) for el in quat.split()])
return quat
@property
def geo_probability(self):
if self.geo_location is None:
return None
prob_mask, geo_mask = self._earth_mask()
return np.sum(self._prob[prob_mask][geo_mask])
@classmethod
def open(cls, filename):
"""Open a GBM HEALPix FITS file and return the GbmHealPix object
Args:
filename (str): The filename of the FITS file
Returns:
:class:`GbmHealPix`: The GBM HEALPix localization
"""
warnings.filterwarnings("ignore", category=UserWarning)
obj = cls()
obj._file_properties(filename)
# open FITS file
with fits.open(filename, mmap=False) as hdulist:
for hdu in hdulist:
obj._headers.update({hdu.name: hdu.header})
# the healpix arrays
prob, sig = hp.read_map(filename, field=(0, 1), memmap=False,
verbose=False)
obj._prob = obj._assert_prob(prob)
obj._sig = obj._assert_sig(sig)
# set the detector pointing attributes
try:
obj._set_det_attr()
except:
pass
return obj
@classmethod
def from_data(cls, prob_arr, sig_arr, tcat=None, trigtime=None,
quaternion=None, scpos=None):
"""Create a HealPix object from healpix arrays and optional metadata
Args:
prob_arr (np.array): The HEALPix array containing the probability/pixel
sig_arr (np.array): The HEALPix array containing the signficance
tcat (:class:`.Tcat`, optional): The associated Tcat to fill out
the primary header info
trigtime (float, optional): The time corresponding to the localization
quaternion (np.array, optional):
The associated spacecraft quaternion used to determine the
detector pointings in equatorial coordinates
scpos (np.array, optional):
The associated spacecraft position in Earth inertial coordinates
used to determine the geocenter location in equatorial coordinates
Returns:
:class:`GbmHealPix`: The HEALPix localization
"""
obj = cls()
obj._prob = obj._assert_prob(prob_arr)
obj._sig = obj._assert_sig(sig_arr)
if tcat is not None:
trigtime = tcat.trigtime
if trigtime is None:
trigtime = 0.0
comments = []
# if we have a trigtime, calculate sun position
sun_key = []
if trigtime is not None:
sun_loc = get_sun_loc(trigtime)
sun_key = [('SUN_RA', sun_loc[0], 'RA of Sun'),
('SUN_DEC', sun_loc[1], 'Dec of Sun')]
# if we have a scpos, calculate geocenter position, radius
geo_key = []
if scpos is not None:
comments.append(('COMMENT', 'SCPOS: ' + np.array2string(scpos)))
geo = geocenter_in_radec(scpos)
try:
_, alt = latitude_from_geocentric_coords_complex(scpos)
except:
warn('Using simple spheroidal Earth approximation')
_, alt = latitude_from_geocentric_coords_simple(scpos)
r = 6371.0 * 1000.0
geo_radius = np.rad2deg(np.arcsin(r / (r + alt)))
geo_key = [
('GEO_RA', float(geo[0]), 'RA of Geocenter relative to Fermi'),
('GEO_DEC', float(geo[1]),
'Dec of Geocenter relative to Fermi'),
('GEO_RAD', geo_radius, 'Radius of the Earth')]
# if we have a quaternion, calculate detector pointings
det_keys = []
if quaternion is not None:
comments.append(
('COMMENT', 'QUAT: ' + np.array2string(quaternion)))
keys = []
for det in Detector:
detname = det.short_name
ra, dec = spacecraft_to_radec(det.azimuth, det.zenith,
quaternion)
ra_key = (detname + '_RA', float(ra),
'RA pointing for detector ' + detname)
dec_key = (detname + '_DEC', float(dec),
'Dec pointing for detector ' + detname)
keys.append([ra_key, dec_key])
det_keys = [key for det in keys for key in det]
# put the additional keys together, and create the headers
keys = sun_key
keys.extend(geo_key)
keys.extend(det_keys)
keys.extend(comments)
prihdr = healpix_primary(tcat=tcat, trigtime=trigtime)
obj._headers['PRIMARY'] = prihdr
obj._headers['HEALPIX'] = healpix_image(nside=obj.nside,
extra_keys=keys,
object=prihdr['OBJECT'])
# set the detector pointing attributes
try:
obj._set_det_attr()
except:
pass
# set file properties
obj.set_properties(trigtime=obj.trigtime, datatype='healpix',
extension='fit')
return obj
@classmethod
def from_chi2grid(cls, chi2grid, nside=128, tcat=None):
"""Create a GbmHealPix object from a chi2grid object
Args:
chi2grid (class:`Chi2Grid`): The chi2grid object containing the
chi-squared/log-likelihood info
nside (int, optional): The nside resolution to use. Default is 128
tcat (:class:`.Tcat`, optional): The associated Tcat to fill out
the primary header info
Returns:
:class:`GbmHealPix`: The GBM HEALPix localization
"""
# fill up a low-resolution healpix map with significance
lores_nside = 64
lores_npix = hp.nside2npix(lores_nside)
lores_array = np.zeros((lores_npix))
theta = cls._dec_to_theta(chi2grid.dec)
phi = cls._ra_to_phi(chi2grid.ra)
idx = hp.ang2pix(lores_nside, theta, phi)
lores_array[idx] = chi2grid.significance
# upscale to high-resolution
hires_nside = nside
hires_npix = hp.nside2npix(hires_nside)
theta, phi = hp.pix2ang(hires_nside, np.arange(hires_npix))
sig_array = hp.get_interp_val(lores_array, theta, phi)
sig_array[sig_array < 0.0] = 0.0
# convert chisq map to probability map
loglike = -chi2grid.chisq / 2.0
probs = np.exp(loglike - np.max(loglike))
lores_array = np.zeros(lores_npix)
lores_array[idx] = probs
prob_array = hp.get_interp_val(lores_array, theta, phi)
prob_array[prob_array < 0.0] = 0.0
prob_array /= np.sum(prob_array)
obj = cls.from_data(prob_array, sig_array, tcat=tcat,
trigtime=chi2grid.trigtime, scpos=chi2grid.scpos,
quaternion=chi2grid.quaternion)
return obj
@classmethod
def multiply(cls, healpix1, healpix2, primary=1, output_nside=128):
"""Multiply two GbmHealPix maps and return a new GbmHealPix object
Note:
Either `healpix1` *or* healpix2 can be a non-GbmHealPix object,
however at least one of them must be a GbmHealPix object **and**
the `primary` argument must be set to the appropriate GbmHealPix
object otherwise a TypeError will be raised.
Args:
healpix1 (:class:`HealPix` or :class:`GbmHealPix`):
One of the HEALPix maps to multiply
healpix2 (:class:`HealPix` or :class:`GbmHealPix`):
The other HEALPix map to multiply
primary (int, optional): If 1, use the first map header information,
or if 2, use the second map header
information. Default is 1.
output_nside (int, optional): The nside of the multiplied map.
Default is 128.
Returns
:class:`GbmHealPix`: The multiplied map
"""
if primary == 1:
if not isinstance(healpix1, cls):
raise TypeError('Primary HealPix (healpix1) is not of class {}. '
'Perhaps try setting healpix2 as the primary'.format(cls.__name__))
else:
if not isinstance(healpix2, cls):
raise TypeError('Primary HealPix (healpix2) is not of class {}. '
'Perhaps try setting healpix1 as the primary'.format(cls.__name__))
obj = super().multiply(healpix1, healpix2, primary=primary,
output_nside=output_nside)
obj._set_det_attr()
return obj
def write(self, directory, filename=None):
"""Write the GbmHealPix object to a FITS file
Args:
directory (str): The directory to write to
filename (str, optional): The filename of the FITS file
"""
if filename is None:
filename = self.filename
self.headers['PRIMARY']['FILENAME'] = filename
out_file = os.path.join(directory, filename)
# get arrays in proper order, and write the healpix data to disk
prob_arr = hp.reorder(self._prob, r2n=True)
sig_arr = hp.reorder(self._sig, r2n=True)
columns = ['PROBABILITY', 'SIGNIFICANCE']
hp.write_map(out_file, (prob_arr, sig_arr), nest=True, coord='C',
overwrite=True, \
column_names=columns,
extra_header=self.headers['HEALPIX'].cards)
# healpy doesn't allow direct input into the primary header on writing,
# so we have to open the written file, add the primary header, rename
# the tables in the HEALPIX extension and write a new file
hdulist = fits.open(out_file)
hdulist[0].header.extend(self.headers['PRIMARY'])
hdulist[1].name = 'HEALPIX'
hdulist[1].header['TTYPE1'] = (
'PROBABILITY', 'Differential probability per pixel')
hdulist[1].header['TTYPE2'] = (
'SIGNIFICANCE', 'Integrated probability')
hdulist.writeto(out_file, clobber=True, checksum=True)
@classmethod
def remove_earth(cls, healpix):
"""Return a new GbmHealPix with the probability on the Earth masked out.
The remaining probability on the sky is renormalized.
Note:
The :attr:`geo_location` attribute must be available to use this function
Args:
healpix (:class:`GbmHealPix`): The map for which the Earth will be
removed
Returns:
:class:`GbmHealPix`: GBM HEALPix localization
"""
if healpix.geo_location is None:
raise ValueError('Location of geocenter is not known')
# get the non-zero probability and earth masks
prob_mask, geo_mask = healpix._earth_mask()
# zero out the probabilities behind the earth
new_prob = np.copy(healpix._prob)
temp = new_prob[prob_mask]
temp[geo_mask] = 0.0
new_prob[prob_mask] = temp
# renormalize
new_prob /= np.sum(new_prob)
# have to redo the significance
new_sig = 1.0 - find_greedy_credible_levels(new_prob)
# return a new object
obj = cls()
obj._prob = obj._assert_prob(new_prob)
obj._sig = obj._assert_sig(new_sig)
obj._headers = healpix.headers
# set the detector pointing attributes
try:
obj._set_det_attr()
except:
pass
# set file properties
obj.set_properties(trigtime=obj.trigtime, datatype='healpix',
extension='fit')
return obj
def source_probability(self, ra, dec, prior=0.5):
r"""The probability that the GbmHealPix localization is associated with
a known point location. This is calculated against the null hypothesis
that the localization originates from an unassociated random source
that has equal probability of origination anywhere in the sky:
:math:`P(A | \mathcal{I}) =
\frac{P(\mathcal{I} | A) \ P(A)}
{P(\mathcal{I} | A) \ P(A) + P(\mathcal{I} | \neg A) \ P(\neg A)}`
where
* :math:`P(\mathcal{I} | A)` is the probability of the localization at
the point source once the Earth occultation has been removed
* :math:`P(\mathcal{I} | \neg A)` is the probability per pixel assuming
a uniform distribution on the sky (i.e. the probability the
localization is associated with a random point on the sky)
* :math:`P(A)` is the prior probability that the localization is
associated with the point source
Note:
If the point source is behind the Earth, then it is assumed that
GBM could not observe it, therefore the probability will be zero.
Args:
ra (float): The RA of the known source location
dec (float): The Dec of the known source location
prior (float, optional): The prior probability that the localization
is associated with the source.
Default is 0.5
Returns:
float: The probability that the localization is spatially
associated with the point source
"""
if (prior < 0.0) or (prior > 1.0):
raise ValueError('Prior probability must be within 0-1, inclusive')
# convert uniform prob/sr to prob/pixel
u = 1.0 / (4.0 * np.pi)
u *= hp.nside2resol(self.nside) ** 2
# the pixel probability of the skymap at the location of the point source
p = type(self).remove_earth(self).probability(ra, dec, per_pixel=True)
# if we know the location of the earth and it's behind the earth,
# then we obviously couldn't have seen it
if self.geo_location is not None:
ang = haversine(*self.geo_location, ra, dec)
if ang < self.geo_radius:
p = 0.0
# null hypothesis is that they are not associated, therefore the sky map
# is result of some source that has uniform probability on the sky
prob = (p*prior) / ((p*prior) + (u*(1.0-prior)))
return prob
def region_probability(self, healpix, prior=0.5):
r"""The probability that the localization is associated with
the localization region from another map. This is calculated
against the null hypothesis that the two maps represent
unassociated sources:
:math:`P(A | \mathcal{I}) =
\frac{P(\mathcal{I} | A) \ P(A)}
{P(\mathcal{I} | A) \ P(A) + P(\mathcal{I} | \neg A) \ P(\neg A)}`
where
* :math:`P(\mathcal{I} | A)` is the integral over the overlap of the two
maps once the Earth occultation has been removed for *this* map.
* :math:`P(\mathcal{I} | \neg A)` is the integral over the overlap of
*this* map with a uniform distribution on the sky (i.e. the probability
the localization is associated with a random point on the sky)
* :math:`P(A)` is the prior probability that *this* localization is
associated with the *other* HEALPix map.
Note:
The localization region of *this* map overlapping the Earth will be
removed and the remaining unocculted region is used for the
calculation. The *other* map is assumed to have no exclusionary
region.
Args:
healpix (:class:`HealPix`): The healpix map for which to calculate
the spatial association
prior (float, optional): The prior probability that the localization
is associated with the source.
Default is 0.5
Returns:
float: The probability that the two HEALPix maps are associated.
"""
if (prior < 0.0) or (prior > 1.0):
raise ValueError('Prior probability must be within 0-1, inclusive')
# convert uniform prob/sr to prob/pixel
u = 1.0 / (4.0 * np.pi)
# get the non-zero probability and earth masks
prob_mask, geo_mask = self._earth_mask()
probmap1 = np.copy(self._prob)
temp = probmap1[prob_mask]
temp[geo_mask] = 0.0
probmap1[prob_mask] = temp
probmap1 /= np.sum(probmap1)
# ensure maps are the same resolution and convert uniform prob/sr to
# prob/pixel
probmap2 = np.copy(healpix._prob)
if self.nside > healpix.nside:
probmap2 = hp.ud_grade(probmap2, nside_out=self.nside)
probmap2 = self._assert_prob(probmap2)
u *= hp.nside2resol(self.nside) ** 2
elif self.nside < healpix.nside:
probmap1 = hp.ud_grade(probmap1, nside_out=healpix.nside)
probmap1 = self._assert_prob(probmap1)
u *= hp.nside2resol(healpix.nside) ** 2
else:
u *= hp.nside2resol(self.nside) ** 2
# alternative hypothesis: they are related
alt_hyp = np.sum(probmap1 * probmap2)
# null hypothesis: one of the maps is from an unassociated source
# (uniform spatial probability)
null_hyp = np.sum(probmap1 * u)
# since we have an exhaustive and complete list of possibilities, we can
# easily calculate the probability
prob = (alt_hyp * prior) / ((alt_hyp*prior) + (null_hyp*(1.0-prior)))
return prob
def observable_fraction(self, healpix):
"""The observable fraction of a healpix probability region on the sky.
Non-observable regions are ones that are behind the Earth.
Args:
healpix (:class:`HealPix`): The healpix region for which to
calculate the observable fraction.
Returns:
float: The fraction of the map (based on probability) that is observable.
"""
# speed things up a bit by only considering pixels with non-zero prob
prob_mask = (healpix._prob > 0.0)
# get ra, dec coords for pixels and calculate angle from geocenter
theta, phi = hp.pix2ang(healpix.nside, np.arange(healpix.npix))
ra = self._phi_to_ra(phi)[prob_mask]
dec = self._theta_to_dec(theta)[prob_mask]
# the mask of everything with prob > 0.0 and is visible
ang = haversine(*self.geo_location, ra, dec)
geo_mask = (ang > self.geo_radius)
# sum it up and divide by total prob (should be 1, but good to be sure)
temp = np.copy(healpix._prob)
temp = temp[prob_mask]
frac = np.sum(temp[geo_mask]) / np.sum(healpix._prob)
return frac
def _set_det_attr(self):
# set the detector pointing attributes
keys = list(self.headers['HEALPIX'].keys())
regex = re.compile('N._RA|B._RA')
dets = [key.split('_')[0] for key in keys if re.match(regex, key)]
for det in dets:
setattr(self, det.lower() + '_pointing',
(self.headers['HEALPIX'][det + '_RA'],
self.headers['HEALPIX'][det + '_DEC']))
def _earth_mask(self):
# speed things up a bit by only considering pixels with non-zero prob
mask = (self._prob > 0.0)
# get ra, dec coords for pixels and calculate angle from geocenter
theta, phi = hp.pix2ang(self.nside, np.arange(self.npix))
ra = self._phi_to_ra(phi)[mask]
dec = self._theta_to_dec(theta)[mask]
ang = haversine(*self.geo_location, ra, dec)
geo_radius = self.geo_radius
# the mask of the non-zero probability pixels that are behind the earth
geo_mask = (ang <= geo_radius)
return mask, geo_mask
class Chi2Grid():
"""Class for the Chi2Grid localization files/objects
Attributes:
azimuth (np.array): The spacecraft azimuth grid points
chisq (np.array): The chi-squared value at each grid point
dec (np.array): The Dec grid points
numpts (int): Number of sky points in the Chi2Grid
quaternion (np.array): The spacecraft attitude quaternion
ra (np.array): The RA grid points
scpos (np.array): The spacecraft position in Earth inertial coordinates
significance (np.array): The significance value at each point
trigtime (float): The trigger time
zenith (np.array): The spacecraft zenith grid points
"""
def __init__(self):
self._az = np.array([])
self._zen = np.array([])
self._ra = np.array([])
self._dec = np.array([])
self._chisq = np.array([])
self._quaternion = None
self._scpos = None
self._trigtime = None
@property
def quaternion(self):
return self._quaternion
@quaternion.setter
def quaternion(self, val):
if len(val) != 4:
raise ValueError('quaternion must be a 4-element array')
self._quaternion = np.asarray(val)
@property
def scpos(self):
return self._scpos
@scpos.setter
def scpos(self, val):
if len(val) != 3:
raise ValueError('scpos must be a 3-element array')
self._scpos = np.asarray(val)
@property
def trigtime(self):
return self._trigtime
@trigtime.setter
def trigtime(self, val):
try:
val = float(val)
except:
raise ValueError('trigtime must be a float')
self._trigtime = val
@property
def numpts(self):
return self._az.size
@property
def azimuth(self):
return self._az
@property
def zenith(self):
return self._zen
@property
def ra(self):
return self._ra
@property
def dec(self):
return self._dec
@property
def chisq(self):
return self._chisq
@property
def significance(self):
min_chisq = np.min(self.chisq)
return 1.0 - chi2.cdf(self.chisq - min_chisq, 2)
@classmethod
def open(cls, filename):
"""Read a chi2grid file and create a Chi2Grid object
Args:
filename (str): The filename of the chi2grid file
Returns:
:class:`Chi2Grid`: The Chi2Grid object
"""
with open(filename, 'r') as f:
txt = list(f)
obj = cls()
numpts = int(txt[0].strip())
txt = txt[1:]
obj._az = np.empty(numpts)
obj._zen = np.empty(numpts)
obj._ra = np.empty(numpts)
obj._dec = np.empty(numpts)
obj._chisq = np.empty(numpts)
for i in range(numpts):
line = txt[i].split()
obj._az[i] = float(line[0].strip())
obj._zen[i] = float(line[1].strip())
obj._chisq[i] = float(line[2].strip())
obj._ra[i] = float(line[4].strip())
obj._dec[i] = float(line[5].strip())
return obj
@classmethod
def from_data(cls, az, zen, ra, dec, chisq):
"""Create a Chi2Grid object from arrays
Args:
az (np.array): The azimuth grid points
zen (np.array): The zenith grid points
ra (np.array): The RA grid points
dec (np.array): The Dec grid points
chisq (np.array): The chi-squared values at each grid point
Returns:
:class:`Chi2Grid`: The Chi2Grid object
"""
obj = cls()
obj._az = az
obj._zen = zen
obj._ra = ra
obj._dec = dec
obj._chisq = chisq
return obj
def find_greedy_credible_levels(p):
"""Calculate the credible values of a probability array using a greedy
algorithm.
Args:
p (np.array): The probability array
Returns:
np.array: The credible values
"""
p = np.asarray(p)
pflat = p.ravel()
i = np.argsort(pflat)[::-1]
cs = np.cumsum(pflat[i])
cls = np.empty_like(pflat)
cls[i] = cs
return cls.reshape(p.shape)
# Systematic Model definitions using healpy.smoothing
# --------------------------------------------------------
def GBUTS_Model_O3():
"""The localization systematic model for the targeted search during O3:
a 2.7 deg Gaussian.
References:
arXiv:1903.12597
"""
sigma = np.deg2rad(2.7)
return ([sigma], [1.0])
def HitL_Model(az):
"""The localization systematic model for the human-in-the loop localization:
A mixture of a 4.17 deg Gaussian (91.8% weight) and a 15.3 deg Gaussian
for a centroid between azimuth 292.5 - 67.5 or azimuth 112.5 - 247.5,
otherwise a mixture of a 2.31 deg Gaussian (88.4% weight) and a
13.2 deg Gaussian.
References:
arXiv:1411.2685
Args:
az (float): The localization centroid in spacecraft azimuth
"""
if (az > 292.5) or (az <= 67.5) or ((az > 112.5) and (az < 247.5)):
sigma1 = np.deg2rad(4.17)
sigma2 = np.deg2rad(15.3)
frac1 = 0.918
else:
sigma1 = np.deg2rad(2.31)
sigma2 = np.deg2rad(13.2)
frac1 = 0.884
return ([sigma1, sigma2], [frac1])
def GA_Model():
"""The localization systematic model for the Ground-Automated localization:
A mixture of a 3.72 deg Gaussian (80.4% weight) and a 13.7 deg Gaussian.
References:
arXiv:1411.2685
"""
sigma1 = np.deg2rad(3.72)
sigma2 = np.deg2rad(13.7)
frac1 = 0.804
return ([sigma1, sigma2], [frac1])
def RoboBA_Function(grb_type):
"""The localization systematic model for the RoboBA localization:
A mixture of a 1.86 deg Gaussian (57.9% weight) and a 4.14 deg Gaussian
for a "long" GRB, and a mixture of a 2.55 deg Gaussian (39.0% weight) and a
4.43 deg Gaussian for a "short" GRB.
References:
arXiv:1909.03006)
Args:
grb_type (str): The type of GRB, either 'long' or 'short'
"""
if grb_type == 'long':
sigma1 = np.deg2rad(1.86)
sigma2 = np.deg2rad(4.14)
frac1 = 0.579
elif grb_type == 'short':
sigma1 = np.deg2rad(2.55)
sigma2 = np.deg2rad(4.43)
frac1 = 0.39
else:
raise ValueError("grb_type must either be 'long' or 'short'")
return ([sigma1, sigma2], [frac1])
def Untargeted_Search_Model():
"""The localization systematic model for the Untargeted Search:
A 5.53 deg Gaussian
"""
sigma = np.deg2rad(5.53)
return ([sigma], [1.0])
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