Commit 8fcdaab7 authored by Tito Dal Canton's avatar Tito Dal Canton

Add tool to combine sky maps from different observatories

parent 48280d5f
......@@ -5,7 +5,8 @@ Changelog
0.0.8 (unreleased)
==================
- No changes yet.
- Add ``ligo-skymap-combine``, a tool to combine sky localizations from
different observations into a joint skymap.
0.0.7 (2018-04-27)
==================
......
......@@ -92,6 +92,7 @@ Postprocessing
.. toctree::
:maxdepth: 1
ligo/skymap/tool/ligo_skymap_combine
ligo/skymap/tool/ligo_skymap_from_samples
ligo/skymap/tool/ligo_skymap_plot_stats
ligo/skymap/tool/ligo_skymap_stats
......
Joint Sky Localization (`ligo-skymap-combine`)
==============================================
.. argparse::
:module: ligo.skymap.tool.ligo_skymap_combine
:func: parser
......@@ -207,6 +207,8 @@ Check that the moments scale as expected when we vary sigma.
Check some more arbitrary values using numerical quadrature:
>>> import scipy.integrate
>>> from ligo.skymap.core import conditional_pdf
>>> sigma = 1.0
>>> for mu in np.linspace(-10, 10):
... mean, std, norm = parameters_to_moments(mu, sigma)
......
#
# Copyright (C) 2018 Tito Dal Canton, Eric Burns, 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
# 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 <http://www.gnu.org/licenses/>.
#
"""Combine different sky localizations of a common event observed by different
instruments in order to form a more constrained localization.
If one of the input maps contains distance information (for instance from
BAYESTAR or LALInference) then the marginal distance posterior in the output
map is updated according to the restriction in sky location imposed by the other
input map(s). Only one input map can currently have distance information."""
from argparse import FileType
from . import ArgumentParser
def parser():
parser = ArgumentParser(description=__doc__)
parser.add_argument('input', metavar='INPUT.fits[.gz]', type=FileType('rb'),
nargs='+', help='Input sky localizations')
# FIXME the output option has type str because astropy.io.fits.writeto()
# only honors the .gz extension when given a file name string (as of 3.0.1)
parser.add_argument('output', metavar='OUTPUT.fits[.gz]', type=str,
help='Output combined sky localization')
parser.add_argument('--origin', type=str,
help='Optional tag describing the organization'
' responsible for the combined output')
return parser
def main(args=None):
args = parser().parse_args(args)
import numpy as np
import healpy as hp
from astropy.io import fits
from astropy.time import Time
from ..distance import parameters_to_marginal_moments
from ..io import read_sky_map, write_sky_map
input_skymaps = []
dist_mu = dist_sigma = dist_norm = None
for input_file in args.input:
with fits.open(input_file) as hdus:
header = hdus[0].header.copy()
header.extend(hdus[1].header)
nonstd_name = hdus[1].columns.names[0] != 'PROB'
if nonstd_name:
# FIXME special case for Fermi/GBM localizations which use a
# different name for the first field of HDU 1
# (PROBABILITY vs PROB)
has_distance = False
data = (hp.read_map(hdus, nest=True),)
meta = {'instruments': set([header['INSTRUME']])}
else:
has_distance = 'DISTMU' in hdus[1].columns.names
data, meta = read_sky_map(hdus, nest=True,
distances=has_distance)
nside = hp.npix2nside(len(data[0]))
input_skymaps.append((nside, data[0], meta, header))
if has_distance:
if dist_mu is not None:
raise RuntimeError('only one input localization can have'
' distance information')
dist_mu = data[1]
dist_sigma = data[2]
dist_norm = data[3]
max_nside = max(x[0] for x in input_skymaps)
# upsample sky posteriors to maximum resolution and combine them
combined_prob = None
for nside, prob, _, _ in input_skymaps:
if nside < max_nside:
prob = hp.ud_grade(prob, max_nside, order_in='NESTED',
order_out='NESTED')
if combined_prob is None:
combined_prob = np.ones_like(prob)
combined_prob *= prob
# normalize joint posterior
norm = combined_prob.sum()
if norm == 0:
raise RuntimeError('input sky localizations are disjoint')
combined_prob /= norm
out_kwargs = {'gps_creation_time': Time.now().gps,
'nest': True}
if args.origin is not None:
out_kwargs['origin'] = args.origin
# average the various input event times
input_gps = [x[2]['gps_time'] for x in input_skymaps if 'gps_time' in x[2]]
if input_gps:
out_kwargs['gps_time'] = np.mean(input_gps)
# combine instrument tags
out_instruments = set()
for x in input_skymaps:
if 'instruments' in x[2]:
out_instruments.update(x[2]['instruments'])
out_kwargs['instruments'] = ','.join(out_instruments)
# update marginal distance posterior, if available
if dist_mu is not None:
if hp.npix2nside(len(dist_mu)) < max_nside:
dist_mu = hp.ud_grade(dist_mu, max_nside, order_in='NESTED',
order_out='NESTED')
dist_sigma = hp.ud_grade(dist_sigma, max_nside, order_in='NESTED',
order_out='NESTED')
dist_norm = hp.ud_grade(dist_norm, max_nside, order_in='NESTED',
order_out='NESTED')
distmean, diststd = parameters_to_marginal_moments(combined_prob,
dist_mu,
dist_sigma)
out_data = (combined_prob, dist_mu, dist_sigma, dist_norm)
out_kwargs['distmean'] = distmean
out_kwargs['diststd'] = diststd
else:
out_data = combined_prob
# save input headers in output history
out_kwargs['HISTORY'] = []
for i, x in enumerate(input_skymaps):
out_kwargs['HISTORY'].append('')
out_kwargs['HISTORY'].append('Headers of HDUs 0 and 1 of input file {:d}:'.format(i))
out_kwargs['HISTORY'].append('')
out_kwargs['HISTORY'] += ['{} = {}'.format(k, v) for k, v in x[3].items()]
write_sky_map(args.output, out_data, **out_kwargs)
......@@ -3,7 +3,12 @@ import subprocess
import pytest
import numpy as np
import healpy as hp
from . import run_entry_point
from ...io import read_sky_map, write_sky_map
from ...distance import parameters_to_moments
@pytest.fixture
......@@ -77,3 +82,60 @@ def test_stats(inj_coinc_sqlite, localize_coincs, tmpdir):
filename = str(tmpdir / 'bayestar.out')
run_entry_point('ligo-skymap-stats', '-o', filename,
inj_coinc_sqlite, os.path.join(localize_coincs, '*.fits'))
def test_combine(tmpdir):
fn1 = str(tmpdir / 'skymap1.fits.gz')
fn2 = str(tmpdir / 'skymap2.fits.gz')
fn3 = str(tmpdir / 'joint_skymap.fits.gz')
# generate a hemisphere of constant probability
nside1 = 32
npix1 = hp.nside2npix(nside1)
m1 = np.zeros(npix1)
disc_idx = hp.query_disc(nside1, (1, 0, 0), np.pi/2)
m1[disc_idx] = 1
m1 /= m1.sum()
hp.write_map(fn1, m1, column_names=['PROBABILITY'],
extra_header=[('INSTRUME', 'X1')])
# generate another hemisphere of constant probability
# but with higher resolution and rotated 90 degrees
nside2 = 64
npix2 = hp.nside2npix(nside2)
m2 = np.zeros(npix2)
disc_idx = hp.query_disc(nside2, (0, 1, 0), np.pi/2)
m2[disc_idx] = 1
m2 /= m2.sum()
hp.write_map(fn2, m2, column_names=['PROBABILITY'],
extra_header=[('INSTRUME', 'Y1')])
run_entry_point('ligo-skymap-combine', fn1, fn2, fn3)
m3 = hp.read_map(fn3, nest=True)
npix3 = len(m3)
nside3 = hp.npix2nside(npix3)
pix_area3 = hp.nside2pixarea(nside3)
# resolution must match the highest original resolution
assert npix3 == npix2
# probability must be normalized to 1
assert m3.sum() == pytest.approx(1)
# support must be ¼ of the sphere
tolerance = 10 * hp.nside2pixarea(nside1)
assert sum(m3 > 0) * pix_area3 == pytest.approx(np.pi, abs=tolerance)
# generate a BAYESTAR-like map with mock distance information
d_mu = np.zeros_like(m1)
d_sigma = np.ones_like(m1)
d_norm = np.ones_like(m1)
write_sky_map(fn1, np.vstack((m1, d_mu, d_sigma, d_norm)).T)
run_entry_point('ligo-skymap-combine', fn1, fn2, fn3)
m3, meta3 = read_sky_map(fn3, nest=True, distances=True)
# check that marginal distance moments match what was simulated
mean, std, _ = parameters_to_moments(d_mu[0], d_sigma[0])
assert meta3['distmean'] == pytest.approx(mean)
assert meta3['diststd'] == pytest.approx(std)
......@@ -79,6 +79,7 @@ bayestar-localize-coincs = ligo.skymap.tool.bayestar_localize_coincs:main
bayestar-localize-lvalert = ligo.skymap.tool.bayestar_localize_lvalert:main
bayestar-realize-coincs = ligo.skymap.tool.bayestar_realize_coincs:main
bayestar-sample-model-psd = ligo.skymap.tool.bayestar_sample_model_psd:main
ligo-skymap-combine = ligo.skymap.tool.ligo_skymap_combine:main
ligo-skymap-contour = ligo.skymap.tool.ligo_skymap_contour:main
ligo-skymap-from-samples = ligo.skymap.tool.ligo_skymap_from_samples:main
ligo-skymap-plot = ligo.skymap.tool.ligo_skymap_plot:main
......
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