# perform sky localization
log.info("starting sky localization")
sky_map = rasterize(gracedb_sky_map(
coinc_file, psd_file, opts.waveform, opts.f_low,
opts.min_distance, opts.max_distance, opts.prior_distance_power,
nside=opts.nside, f_high_truncate=opts.f_high_truncate,
method=opts.method, chain_dump=chain_dump,
enable_snr_series=opts.enable_snr_series))
sky_map.meta['objid'] = str(graceid)
sky_map.meta['url'] = 'https://gracedb.ligo.org/events/{0}'.format(graceid)
log.info("sky localization complete")
# upload FITS file
with command.TemporaryDirectory() as fitsdir:
fitspath = os.path.join(fitsdir, opts.output)
fits.write_sky_map(fitspath, sky_map, nest=True)
log.debug('wrote FITS file: %s', opts.output)
if opts.dry_run:
command.rename(fitspath, os.path.join('.', opts.output))
else:
gracedb.writeLog(
graceid, "BAYESTAR rapid sky localization ready",
filename=fitspath, tagname=("sky_loc", "lvem"))
log.debug('uploaded FITS file')
except:
# Produce log message for any otherwise uncaught exception
log.exception("sky localization failed")
if opts.dry_run:
# Then re-raise the exception if we are in dry-run mode
raise
order, ipix = moc.uniq2nest(p['UNIQ'])
nside = hp.order2nside(order.astype(int))
max_order = order.max().astype(int)
max_nside = hp.order2nside(max_order)
theta = 0.5 * np.pi - ranking_samples['dec']
phi = ranking_samples['ra']
ranking_samples['ipix'] = hp.ang2pix(max_nside, theta, phi, nest=True)
ranking_samples.sort('ipix')
result = np.transpose(
[pixstats(ranking_samples, max_nside, n, i) for n, i in zip(nside, ipix)])
# Add distance info if necessary.
if 'dist' in ranking_samples.colnames:
p['PROBDENSITY'], distmean, diststd = result
p['DISTMU'], p['DISTSIGMA'], p['DISTNORM'] = \
distance.moments_to_parameters(distmean, diststd)
# Add marginal distance moments
p.meta['distmean'] = np.mean(samples['dist'])
p.meta['diststd'] = np.std(samples['dist'])
else:
p['PROBDENSITY'] = result
# Write output to FITS file.
fits.write_sky_map(opts.output,
p,
nest=True,
creator=parser.prog,
objid=opts.objid,
gps_time=samples['time'].mean())
opts.waveform,
opts.f_low,
opts.min_distance,
opts.max_distance,
opts.prior_distance_power,
opts.cosmology,
psds=psds,
method=method,
nside=opts.nside,
chain_dump=chain_dump,
phase_convention=opts.phase_convention,
snr_series=snrs,
enable_snr_series=opts.enable_snr_series)
sky_map.meta['objid'] = coinc_event_id
except (ArithmeticError, ValueError):
log.exception("%s:method '%s':sky localization failed",
coinc_event_id, method)
count_sky_maps_failed += 1
if not opts.keep_going:
raise
else:
log.info("%s:method '%s':saving sky map", coinc_event_id, method)
filename = '%d.%s.fits' % (int_coinc_event_id, method)
fits.write_sky_map(os.path.join(opts.output, filename),
sky_map,
nest=True)
if count_sky_maps_failed > 0:
raise RuntimeError("{0} sky map{1} did not converge".format(
count_sky_maps_failed, 's' if count_sky_maps_failed > 1 else ''))
opts.waveform,
opts.f_low,
opts.min_distance,
opts.max_distance,
opts.prior_distance_power,
psds=psds,
method=method,
nside=opts.nside,
chain_dump=chain_dump,
phase_convention=opts.phase_convention,
snr_series=snrs,
enable_snr_series=opts.enable_snr_series))
sky_map.meta['objid'] = str(coinc.coinc_event_id)
except (ArithmeticError, ValueError):
log.exception("%s:method '%s':sky localization failed",
coinc.coinc_event_id, method)
count_sky_maps_failed += 1
if not opts.keep_going:
raise
else:
log.info("%s:method '%s':saving sky map", coinc.coinc_event_id,
method)
fits.write_sky_map('%s.%s.fits.gz' %
(int(coinc.coinc_event_id), method),
sky_map,
nest=True)
if count_sky_maps_failed > 0:
raise RuntimeError("{0} sky map{1} did not converge".format(
count_sky_maps_failed, 's' if count_sky_maps_failed > 1 else ''))
opts.min_distance, opts.max_distance, opts.prior_distance_power,
phase_convention=opts.phase_convention, nside=opts.nside,
f_high_truncate=opts.f_high_truncate,
method=opts.method, chain_dump=chain_dump)
prob, distmu, distsigma, _ = sky_map
distmean, diststd = distance.parameters_to_marginal_moments(
prob, distmu, distsigma)
log.info("sky localization complete")
# upload FITS file
fitsdir = tempfile.mkdtemp()
try:
fitspath = os.path.join(fitsdir, opts.output)
fits.write_sky_map(fitspath, sky_map, gps_time=float(epoch),
creator=parser.prog, objid=str(graceid),
url='https://gracedb.ligo.org/events/{0}'.format(graceid),
runtime=elapsed_time, instruments=instruments,
distmean=distmean, diststd=diststd,
origin='LIGO/Virgo', nest=True)
if not opts.dry_run:
gracedb.writeLog(graceid, "INFO:BAYESTAR:uploaded sky map",
filename=fitspath, tagname=("sky_loc", "lvem"))
else:
command.rename(fitspath, os.path.join('.', opts.output))
finally:
shutil.rmtree(fitsdir)
except:
# Produce log message for any otherwise uncaught exception
log.exception("sky localization failed")
# Then re-raise the exception
raise
chain_dump = '%s.chain.npy' % int(coinc.coinc_event_id)
else:
chain_dump = None
try:
sky_map, epoch, elapsed_time = ligolw_sky_map(
sngl_inspirals, opts.waveform, opts.f_low, opts.min_distance,
opts.max_distance, opts.prior_distance_power, psds=psds,
method=method, nside=opts.nside, chain_dump=chain_dump,
phase_convention=opts.phase_convention, snr_series=snrs,
enable_snr_series=opts.enable_snr_series)
prob, distmu, distsigma, _ = sky_map
distmean, diststd = distance.parameters_to_marginal_moments(
prob, distmu, distsigma)
except (ArithmeticError, ValueError):
log.exception("%s:method '%s':sky localization failed", coinc.coinc_event_id, method)
count_sky_maps_failed += 1
if not opts.keep_going:
raise
else:
log.info("%s:method '%s':saving sky map", coinc.coinc_event_id, method)
fits.write_sky_map('%s.%s.fits.gz' % (int(coinc.coinc_event_id), method),
sky_map, objid=str(coinc.coinc_event_id), gps_time=float(epoch),
creator=parser.prog, runtime=elapsed_time,
distmean=distmean, diststd=diststd,
instruments=instruments, nest=True)
if count_sky_maps_failed > 0:
raise RuntimeError("{0} sky map{1} did not converge".format(
count_sky_maps_failed, 's' if count_sky_maps_failed > 1 else ''))
# Evaluate per-pixel density.
p = derasterize(Table([p], names=['PROB']))
order, ipix = moc.uniq2nest(p['UNIQ'])
nside = hp.order2nside(order.astype(int))
max_order = order.max().astype(int)
max_nside = hp.order2nside(max_order)
theta = 0.5*np.pi - ranking_samples['dec']
phi = ranking_samples['ra']
ranking_samples['ipix'] = hp.ang2pix(max_nside, theta, phi, nest=True)
ranking_samples.sort('ipix')
result = np.transpose(
[pixstats(ranking_samples, max_nside, n, i) for n, i in zip(nside, ipix)])
# Add distance info if necessary.
if 'dist' in ranking_samples.colnames:
p['PROBDENSITY'], distmean, diststd = result
p['DISTMU'], p['DISTSIGMA'], p['DISTNORM'] = \
distance.moments_to_parameters(distmean, diststd)
# Add marginal distance moments
p.meta['distmean'] = np.mean(samples['dist'])
p.meta['diststd'] = np.std(samples['dist'])
else:
p['PROBDENSITY'] = result
# Write output to FITS file.
fits.write_sky_map(
opts.output, p, nest=True,
creator=parser.prog, objid=opts.objid, gps_time=samples['time'].mean())
log.info("starting sky localization")
sky_map = rasterize(localize(
event, opts.waveform, opts.f_low, opts.min_distance,
opts.max_distance, opts.prior_distance_power, opts.cosmology,
method=opts.method, nside=opts.nside,
chain_dump=chain_dump, enable_snr_series=opts.enable_snr_series,
f_high_truncate=opts.f_high_truncate))
sky_map.meta['objid'] = str(graceid)
sky_map.meta['url'] = 'https://gracedb.ligo.org/events/{0}'.format(
graceid)
log.info("sky localization complete")
# upload FITS file
with command.TemporaryDirectory() as fitsdir:
fitspath = os.path.join(fitsdir, opts.output)
fits.write_sky_map(fitspath, sky_map, nest=True)
log.debug('wrote FITS file: %s', opts.output)
if opts.dry_run:
command.rename(fitspath, os.path.join('.', opts.output))
else:
gracedb.writeLog(
graceid, "BAYESTAR rapid sky localization ready",
filename=fitspath, tagname=tags)
log.debug('uploaded FITS file')
except KeyboardInterrupt:
# Produce log message and then exit if we receive SIGINT (ctrl-C).
log.exception("sky localization failed")
raise
except:
# Produce log message for any otherwise uncaught exception.
# Unless we are in dry-run mode, keep going.
enable_snr_series=opts.enable_snr_series)
prob, distmu, distsigma, _ = sky_map
distmean, diststd = distance.parameters_to_marginal_moments(
prob, distmu, distsigma)
log.info("sky localization complete")
# upload FITS file
with command.TemporaryDirectory() as fitsdir:
fitspath = os.path.join(fitsdir, opts.output)
fits.write_sky_map(
fitspath,
sky_map,
gps_time=float(epoch),
creator=parser.prog,
objid=str(graceid),
url='https://gracedb.ligo.org/events/{0}'.format(graceid),
runtime=elapsed_time,
instruments=instruments,
distmean=distmean,
diststd=diststd,
origin='LIGO/Virgo',
nest=True)
log.debug('wrote FITS file: %s', opts.output)
if opts.dry_run:
command.rename(fitspath, os.path.join('.', opts.output))
else:
gracedb.writeLog(graceid,
"BAYESTAR rapid sky localization ready",
filename=fitspath,
tagname=("sky_loc", "lvem"))
log.debug('uploaded FITS file')
if opts.chain_dump:
chain_dump = '%s.chain.npy' % int_coinc_event_id
else:
chain_dump = None
try:
sky_map = localize(
event, opts.waveform, opts.f_low, opts.min_distance,
opts.max_distance, opts.prior_distance_power, opts.cosmology,
method=method, nside=opts.nside, chain_dump=chain_dump,
enable_snr_series=opts.enable_snr_series,
f_high_truncate=opts.f_high_truncate)
sky_map.meta['objid'] = coinc_event_id
except (ArithmeticError, ValueError):
log.exception(
"%s:method '%s':sky localization failed",
coinc_event_id, method)
count_sky_maps_failed += 1
if not opts.keep_going:
raise
else:
log.info(
"%s:method '%s':saving sky map",
coinc_event_id, method)
filename = '%d.%s.fits' % (int_coinc_event_id, method)
fits.write_sky_map(
os.path.join(opts.output, filename), sky_map, nest=True)
if count_sky_maps_failed > 0:
raise RuntimeError("{0} sky map{1} did not converge".format(
count_sky_maps_failed, 's' if count_sky_maps_failed > 1 else ''))
prob = np.mean([
hp.ud_grade(prob, nside, order_in='NESTED', order_out='NESTED', power=-2)
for prob in probs
],
axis=0)
# Describe the processing of this file
history = ['Arithmetic mean of the following {} sky maps:'.format(len(probs))]
history += [' ' + file.name for file in opts.input]
# Create metadata dictionary for FITS header
meta = dict(creator=parser.prog, nest=True, history=history)
# Copy some header values from the first sky map
copy_keys = ('objid', 'url', 'instruments', 'gps_time')
meta.update({key: metas[0][key] for key in copy_keys if key in metas[0]})
# Determine output filename
if opts.output is not None:
output = opts.output
else:
output = '_plus_'.join(
os.path.split(file.name)[1].partition('.fits')[0]
for file in opts.input) + '.fits.gz'
# Write output filename to stdout
print(output)
# Write output file
fits.write_sky_map(output, prob, **meta)
