for sngl_inspiral in sngl_inspirals)
# Loop over sky localization methods
for method in opts.method:
log.info("%s:method '%s':computing sky map", coinc.coinc_event_id, method)
if opts.chain_dump:
chain_dump = '%s.chain.npy' % int(coinc.coinc_event_id)
else:
chain_dump = None
try:
sky_map, epoch, elapsed_time = ligolw_sky_map.ligolw_sky_map(
sngl_inspirals, approximant, amplitude_order, phase_order, f_low,
opts.min_distance, opts.max_distance, opts.prior_distance_power,
psds=psds, method=method, nside=opts.nside, chain_dump=chain_dump)
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.get_prog_name(), runtime=elapsed_time,
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 ''))
fits_nest = True
if not args.enable_distance_map:
hpmap = skypost.as_healpix(args.nside, nest=fits_nest, fast=not(args.slowsmoothskymaps))
else:
print('Constructing 3D clustered posterior.')
try:
skypost3d = sac.Clustered3DKDEPosterior(np.column_stack((data['ra'], data['dec'], data['dist'])))
except:
print("ERROR, cannot use skypost3d with LIB output. Exiting..\n")
import sys
sys.exit(1)
print('Producing distance map')
hpmap = skypost3d.as_healpix(args.nside, nest=fits_nest)
names=data.dtype.names
if 'time' in names:
gps_time=data['time'].mean()
elif 'time_mean' in names:
gps_time=data['time_mean'].mean()
elif 'time_maxl' in names:
gps_time=data['time_maxl'].mean()
else:
print("Cannot find time, time_mean, or time maxl variable in posterior. Not saving sky_pos obj.\n")
exit(0)
fits.write_sky_map(os.path.join(args.outdir, args.fitsoutname),
hpmap, creator=parser.get_prog_name(),
objid=args.objid, gps_time=gps_time,
nest=fits_nest)
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
# perform sky localization
log.info("starting sky localization")
sky_map, epoch, elapsed_time, instruments = gracedb_sky_map(
coinc_file, psd_file, "TaylorF2threePointFivePN", 10)
log.info("sky localization complete")
# upload FITS file
fitsdir = tempfile.mkdtemp()
try:
fitspath = os.path.join(fitsdir, "skymap.fits.gz")
fits.write_sky_map(
fitspath,
sky_map,
gps_time=float(epoch),
creator=parser.get_prog_name(),
objid=str(graceid),
url='https://gracedb.ligo.org/events/{0}'.format(graceid),
runtime=elapsed_time,
instruments=instruments,
origin='LIGO/Virgo',
nest=True)
gracedb.writeLog(graceid,
"INFO:BAYESTAR:uploaded sky map",
filename=fitspath,
tagname="sky_loc")
finally:
shutil.rmtree(fitsdir)
except:
# Produce log message for any otherwise uncaught exception
log.exception("sky localization failed")
# Then re-raise the exception
def make_skyview(directory='.',
mdc=None,
NSIDE=128,
ra=None,
dec=None,
results=None,
npost=5000):
# -- Close any open figures
plt.close('all')
# ----------------
# Read S6 MDC log file
# -----------------
if mdc is None:
print "No MDC log provided."
# -- Save PWD for future reference
topdir = os.getcwd()
# -- Enter requested directory
os.chdir(directory)
print "Entered", os.getcwd()
jobname = glob.glob('*bayeswave.run')[0]
# ---------------------------------------
# Extract information from bayeswave.run
# Note: We may only need the trigger time from this
# ---------------------------------------
bayeswave = open(jobname, 'r')
ifoNames = []
for line in bayeswave:
spl = line.split()
if len(spl) < 1: continue
# Determine names of IFOs and MDC scale factor
if spl[0] == 'Command' and spl[1] == 'line:':
for index, splElement in enumerate(spl):
if splElement == '--ifo':
ifoNames.append(spl[index + 1])
del spl[-1]
# Determine number of IFOs
if ' '.join(spl) == 'number of detectors':
spl = line.split()
ifoNum = spl[3]
continue
# Determine gps trigger time
if ' '.join(spl) == 'trigger time (s)':
spl = line.split()
gps = spl[3]
break
bayeswave.close()
# --------------------------------
# Create skymap summary statistics
# --------------------------------
# -- Get run name
params = BwbParams()
jobname = params.jobname
# -- Input skymap data
# num, L, ralist, sin_dec, psi, e, dA, dphi, dt = np.loadtxt(filename, unpack=True)
print "Extracting RA/DEC samples"
filename = './chains/' + jobname + 'signal_params.dat.0'
data = np.loadtxt(filename, unpack=True, usecols=(0, 1, 2))
ralist = data[1]
sin_dec = data[2]
print "Total samples are {0}".format(ralist.size)
# -- Remove burn in samples
burnin = ralist.size / 4
ralist = ralist[burnin:]
sin_dec = sin_dec[burnin:]
print "After removing burn-in samples are {0}".format(ralist.size)
declist = np.arcsin(sin_dec)
thetalist = np.pi / 2.0 - declist
radec = np.column_stack((ralist, declist))
if radec.shape[0] > npost:
radec = np.random.permutation(radec)[:npost, :]
kde = sky.ClusteredSkyKDEPosterior(radec)
# -- Get the sidereal time
print "Finding sidereal time"
print "Using GPS {0}".format(gps)
trigtime = float(gps)
lalgps = LIGOTimeGPS(trigtime)
sidtime = XLALGreenwichSiderealTime(lalgps, 0)
sidtime = sidtime % (np.pi * 2)
# -- Get the injection location
print "GOT MDC?"
print mdc
if mdc is None:
injtheta = 0
injphi = 0
injdec = 0
injra = 0
else:
injtheta, injphi = mdc.get_theta_phi(trigtime)
injra = injphi + sidtime
injdec = np.pi / 2 - injtheta
print "GOT INJECTION PARAMTERS"
print injtheta, injra
# -- Special handling for injections drawn from prior
if params.ra is not None:
injdec = params.dec
injra = params.ra
injtheta = np.pi / 2 - injdec
mdc = True
# -- Make plots directory, if needed
plotsDir = './plots'
if not os.path.exists(plotsDir):
os.makedirs(plotsDir)
# Add markers (e.g., for injections or external triggers).
if (ra is not None and dec is not None):
# Convert the right ascension to either a reference angle from -pi to pi
# or a wrapped angle from 0 to 2 pi, depending on the version of Matplotlib.
#for rarad, decrad in [np.deg2rad([ra, dec])]:
(rarad, decrad) = np.deg2rad([ra, dec])
if geo:
dlon = -sidtime # + or -?
#rarad = lalinference.plot.reference_angle(rarad + dlon)
rarad = rarad + dlon
while rarad > np.pi:
rarad = rarad - 2 * np.pi
while rarad < -np.pi:
rarad = rarad + 2 * np.pi
else:
#rarad = lalinference.plot.wrapped_angle(rarad)
while rarad > 2 * np.pi:
rarad = rarad - 2 * np.pi
while rarad < 0:
rarad = rarad + 2 * np.pi
# Shift the declination to match hp.projscatter conventions
decrad = np.pi * 0.5 - decrad
# -- Plot the skymap and injection location
skymap = kde.as_healpix(NSIDE, nest=True)
#fig = plt.figure(frameon=False)
fig = plt.figure(figsize=(8, 6), frameon=False)
ax = plt.subplot(111, projection='astro mollweide')
ax.cla()
ax.grid()
lp.healpix_heatmap(skymap,
nest=True,
vmin=0.0,
vmax=np.max(skymap),
cmap=plt.get_cmap('cylon'))
if (ra is not None and dec is not None):
plt.plot(rarad, dec, 'kx', ms=30, mew=1)
if mdc is not None:
plt.plot(injra, injdec, 'kx', ms=30, mew=1)
plt.savefig(plotsDir + '/skymap.png')
plt.close()
lf.write_sky_map('skymap_{0}.fits'.format(gps),
skymap,
nest=True,
gps_time=trigtime)
# -- Calculate the 50 and 90% credible intervals
sq_deg = (180.0 / np.pi)**2
print "Calculating sky area ..."
(area50, area90) = kde.sky_area([0.5, 0.9]) * sq_deg
print "Got area50 of {0}".format(area50)
# -- Get the found contour and searched areas
if mdc is not None:
print "Calculating p value of injection"
injcontour = kde.p_values(np.array([[injra, injdec]]))[0]
print "Got contour of {0}".format(injcontour)
print "Calculating searched area..."
searcharea = (kde.searched_area(np.array([[injra, injdec]])) *
sq_deg)[0]
print "Got searched area of {0}".format(searcharea)
else:
injcontour = 0
searcharea = 0
pixarea = hp.nside2pixarea(NSIDE, degrees=True)
print("here")
if results is not None:
for name in ['injcontour', 'area50', 'area90', 'searcharea']:
if name not in results: results[name] = []
results[name].append(eval(name))
# -- Make an output file with key statistics
outfile = open('skystats.txt', 'w')
outfile.write("# area50 area90 searcharea injcontour \n")
outfile.write("{0} {1} {2} {3}".format(area50, area90, searcharea,
injcontour))
outfile.close()
if opts.chain_dump:
chain_dump = '%s.chain.npy' % int(coinc.coinc_event_id)
else:
chain_dump = None
try:
sky_map, epoch, elapsed_time = ligolw_sky_map.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)
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 ''))
def make_skyview(directory='.', mdc=None, NSIDE=128, ra=None, dec=None, results=None, npost=5000):
# -- Close any open figures
plt.close('all')
# ----------------
# Read S6 MDC log file
# -----------------
if mdc is None:
print "No MDC log provided."
# -- Save PWD for future reference
topdir = os.getcwd()
# -- Enter requested directory
os.chdir(directory)
print "Entered", os.getcwd()
jobname = glob.glob('*bayeswave.run')[0]
# ---------------------------------------
# Extract information from bayeswave.run
# Note: We may only need the trigger time from this
# ---------------------------------------
bayeswave = open(jobname, 'r')
ifoNames = []
for line in bayeswave:
spl = line.split()
if len(spl) < 1: continue
# Determine names of IFOs and MDC scale factor
if spl[0] == 'Command' and spl[1] == 'line:':
for index, splElement in enumerate(spl):
if splElement == '--ifo':
ifoNames.append(spl[index+1])
del spl[-1]
# Determine number of IFOs
if ' '.join(spl) == 'number of detectors':
spl = line.split()
ifoNum = spl[3]
continue
# Determine gps trigger time
if ' '.join(spl) == 'trigger time (s)':
spl = line.split()
gps = spl[3]
break
bayeswave.close()
# --------------------------------
# Create skymap summary statistics
# --------------------------------
# -- Get run name
params = BwbParams()
jobname = params.jobname
# -- Input skymap data
# num, L, ralist, sin_dec, psi, e, dA, dphi, dt = np.loadtxt(filename, unpack=True)
print "Extracting RA/DEC samples"
filename = './chains/' + jobname + 'signal_params.dat.0'
data = np.loadtxt(filename, unpack=True,usecols=(0,1,2))
ralist = data[1]
sin_dec = data[2]
print "Total samples are {0}".format(ralist.size)
# -- Remove burn in samples
burnin = ralist.size/4
ralist = ralist[burnin:]
sin_dec = sin_dec[burnin:]
print "After removing burn-in samples are {0}".format(ralist.size)
declist = np.arcsin(sin_dec)
thetalist = np.pi/2.0 - declist
radec = np.column_stack((ralist, declist))
if radec.shape[0] > npost:
radec = np.random.permutation(radec)[:npost,:]
kde = sky.ClusteredSkyKDEPosterior(radec)
# -- Get the sidereal time
print "Finding sidereal time"
print "Using GPS {0}".format(gps)
trigtime = float(gps)
lalgps = LIGOTimeGPS(trigtime)
sidtime = XLALGreenwichSiderealTime(lalgps, 0)
sidtime = sidtime % (np.pi*2)
# -- Get the injection location
print "GOT MDC?"
print mdc
if mdc is None:
injtheta = 0
injphi = 0
injdec = 0
injra = 0
else:
injtheta, injphi = mdc.get_theta_phi(trigtime)
injra = injphi + sidtime
injdec = np.pi/2 - injtheta
print "GOT INJECTION PARAMTERS"
print injtheta, injra
# -- Special handling for injections drawn from prior
if params.ra is not None:
injdec = params.dec
injra = params.ra
injtheta = np.pi/2 - injdec
mdc = True
# -- Make plots directory, if needed
plotsDir = './plots'
if not os.path.exists(plotsDir):
os.makedirs(plotsDir)
# Add markers (e.g., for injections or external triggers).
if (ra is not None and dec is not None):
# Convert the right ascension to either a reference angle from -pi to pi
# or a wrapped angle from 0 to 2 pi, depending on the version of Matplotlib.
#for rarad, decrad in [np.deg2rad([ra, dec])]:
(rarad, decrad) = np.deg2rad([ra, dec])
if geo:
dlon = -sidtime # + or -?
#rarad = lalinference.plot.reference_angle(rarad + dlon)
rarad = rarad + dlon
while rarad > np.pi:
rarad = rarad - 2*np.pi
while rarad < -np.pi:
rarad = rarad + 2*np.pi
else:
#rarad = lalinference.plot.wrapped_angle(rarad)
while rarad > 2*np.pi:
rarad = rarad - 2*np.pi
while rarad < 0:
rarad = rarad + 2*np.pi
# Shift the declination to match hp.projscatter conventions
decrad = np.pi*0.5 - decrad
# -- Plot the skymap and injection location
skymap = kde.as_healpix(NSIDE, nest=True)
#fig = plt.figure(frameon=False)
fig = plt.figure(figsize=(8,6), frameon=False)
ax = plt.subplot(111, projection='astro mollweide')
ax.cla()
ax.grid()
lp.healpix_heatmap(skymap, nest=True, vmin=0.0, vmax=np.max(skymap), cmap=plt.get_cmap('cylon'))
if (ra is not None and dec is not None):
plt.plot(rarad, dec, 'kx', ms=30, mew=1)
if mdc is not None:
plt.plot(injra, injdec, 'kx', ms=30, mew=1)
plt.savefig(plotsDir+'/skymap.png')
plt.close()
lf.write_sky_map('skymap_{0}.fits'.format(gps), skymap, nest=True, gps_time=trigtime)
# -- Calculate the 50 and 90% credible intervals
sq_deg = (180.0/np.pi)**2
print "Calculating sky area ..."
(area50, area90) = kde.sky_area( [0.5, 0.9] )*sq_deg
print "Got area50 of {0}".format(area50)
# -- Get the found contour and searched areas
if mdc is not None:
print "Calculating p value of injection"
injcontour = kde.p_values( np.array([[injra, injdec]]) )[0]
print "Got contour of {0}".format(injcontour)
print "Calculating searched area..."
searcharea = (kde.searched_area( np.array([[injra, injdec]]) )*sq_deg)[0]
print "Got searched area of {0}".format(searcharea)
else:
injcontour = 0
searcharea = 0
pixarea = hp.nside2pixarea(NSIDE, degrees=True)
print("here")
if results is not None:
for name in ['injcontour', 'area50', 'area90', 'searcharea']:
if name not in results: results[name] = []
results[name].append(eval(name))
# -- Make an output file with key statistics
outfile = open('skystats.txt', 'w')
outfile.write("# area50 area90 searcharea injcontour \n")
outfile.write("{0} {1} {2} {3}".format(area50, area90, searcharea, injcontour))
outfile.close()