Source code for pycbc.distributions.sky_location

# Copyright (C) 2016  Collin Capano
# 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
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# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
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"""This modules provides classes for evaluating sky distributions in
right ascension and declination.

import logging
import numpy

from scipy.spatial.transform import Rotation

from pycbc.distributions import angular
from pycbc import VARARGS_DELIM
from import FieldArray

logger = logging.getLogger('pycbc.distributions.sky_location')

[docs]class UniformSky(angular.UniformSolidAngle): """A distribution that is uniform on the sky. This is the same as UniformSolidAngle, except that the polar angle varies from pi/2 (the north pole) to -pi/2 (the south pole) instead of 0 to pi. Also, the default names are "dec" (declination) for the polar angle and "ra" (right ascension) for the azimuthal angle, instead of "theta" and "phi". """ name = 'uniform_sky' _polardistcls = angular.CosAngle _default_polar_angle = 'dec' _default_azimuthal_angle = 'ra'
[docs]class FisherSky(): """A distribution that returns a random angle drawn from an approximate `Von_Mises-Fisher distribution`_. Assumes that the Fisher concentration parameter is large, so that we can draw the samples from a simple rotationally-invariant distribution centered at the North Pole (which factors as a uniform distribution for the right ascension, and a Rayleigh distribution for the declination, as described in `Fabrycky and Winn 2009 ApJ 696 1230`) and then rotate the samples to be centered around the specified mean position. As in UniformSky, the declination varies from π/2 to -π/2 and the right ascension varies from 0 to 2π. .. _Von_Mises-Fisher distribution: .. _Fabrycky and Winn 2009 ApJ 696 1230: .. _Briggs et al 1999 ApJS 122 503: Parameters ---------- mean_ra: float RA of the center of the distribution. mean_dec: float Declination of the center of the distribution. sigma: float Spread of the distribution. For the precise interpretation, see Eq 8 of `Briggs et al 1999 ApJS 122 503`_. This should be smaller than about 20 deg for the approximation to be valid. angle_unit: str Unit for the angle parameters: either "deg" or "rad". """ name = 'fisher_sky' _params = ['ra', 'dec'] def __init__(self, **params): if params['angle_unit'] not in ['deg', 'rad']: raise ValueError("Only deg or rad is allowed as angle unit") mean_ra = params['mean_ra'] mean_dec = params['mean_dec'] sigma = params['sigma'] if params['angle_unit'] == 'deg': mean_ra = numpy.deg2rad(mean_ra) mean_dec = numpy.deg2rad(mean_dec) sigma = numpy.deg2rad(sigma) if mean_ra < 0 or mean_ra > 2 * numpy.pi: raise ValueError( f'The mean RA must be between 0 and 2π, {mean_ra} rad given' ) if mean_dec < -numpy.pi/2 or mean_dec > numpy.pi/2: raise ValueError( 'The mean declination must be between ' f'-π/2 and π/2, {mean_dec} rad given' ) if sigma <= 0 or sigma > 2 * numpy.pi: raise ValueError( 'Sigma must be positive and smaller than 2π ' '(preferably much smaller)' ) if sigma > 0.35: logger.warning( 'Warning: sigma = %s rad is probably too large for the ' 'Fisher approximation to be valid', sigma ) self.rayleigh_scale = 0.66 * sigma # Prepare a rotation that puts the North Pole at the mean position self.rotation = Rotation.from_euler( 'yz', [numpy.pi / 2 - mean_dec, mean_ra] ) @property def params(self): return self._params
[docs] @classmethod def from_config(cls, cp, section, variable_args): tag = variable_args variable_args = variable_args.split(VARARGS_DELIM) if set(variable_args) != set(cls._params): raise ValueError("Not all parameters used by this distribution " "included in tag portion of section name") mean_ra = float(cp.get_opt_tag(section, 'mean_ra', tag)) mean_dec = float(cp.get_opt_tag(section, 'mean_dec', tag)) sigma = float(cp.get_opt_tag(section, 'sigma', tag)) angle_unit = cp.get_opt_tag(section, 'angle_unit', tag) return cls( mean_ra=mean_ra, mean_dec=mean_dec, sigma=sigma, angle_unit=angle_unit )
[docs] def rvs(self, size): # Draw samples from a distribution centered on the North pole np_ra = numpy.random.uniform( low=0, high=(2*numpy.pi), size=size ) np_dec = numpy.random.rayleigh( scale=self.rayleigh_scale, size=size ) # Convert the samples to intermediate cartesian representation np_cart = numpy.empty(shape=(size, 3)) np_cart[:, 0] = numpy.cos(np_ra) * numpy.sin(np_dec) np_cart[:, 1] = numpy.sin(np_ra) * numpy.sin(np_dec) np_cart[:, 2] = numpy.cos(np_dec) # Rotate the samples according to our pre-built rotation rot_cart = self.rotation.apply(np_cart) # Convert the samples back to spherical coordinates. # Some unpleasant conditional operations are needed # to get the correct angle convention. rot_radec = FieldArray( size, dtype=[ ('ra', '<f8'), ('dec', '<f8') ] ) rot_radec['ra'] = numpy.arctan2(rot_cart[:, 1], rot_cart[:, 0]) neg_mask = rot_radec['ra'] < 0 rot_radec['ra'][neg_mask] += 2 * numpy.pi rot_radec['dec'] = numpy.arcsin(rot_cart[:, 2]) return rot_radec
__all__ = ['UniformSky', 'FisherSky']