def gen_likelihood_control(coinc_params_distributions,
seglists,
name=u"lalapps_burca_tailor",
comment=u""):
xmldoc = ligolw.Document()
node = xmldoc.appendChild(ligolw.LIGO_LW())
process = ligolw_process.register_to_xmldoc(xmldoc,
program=process_program_name,
paramdict={},
version=__version__,
cvs_repository="lscsoft",
cvs_entry_time=__date__,
comment=comment)
coinc_params_distributions.process_id = process.process_id
ligolw_search_summary.append_search_summary(xmldoc,
process,
ifos=seglists.keys(),
inseg=seglists.extent_all(),
outseg=seglists.extent_all())
node.appendChild(coinc_params_distributions.to_xml(name))
ligolw_process.set_process_end_time(process)
return xmldoc
def ligolw_bucut(xmldoc,
options,
burst_test_func,
veto_segments=segments.segmentlistdict(),
del_non_coincs=False,
del_skipped_injections=False,
program=None,
verbose=False):
contents = DocContents(xmldoc, program)
process = append_process(xmldoc, options)
apply_filters(contents,
burst_test_func,
veto_segments,
del_non_coincs=del_non_coincs,
del_skipped_injections=del_skipped_injections,
verbose=verbose)
ligolw_process.set_process_end_time(process)
seg = contents.outsegs.extent_all()
ligolw_search_summary.append_search_summary(xmldoc,
process,
inseg=seg,
outseg=seg,
nevents=len(
contents.snglbursttable))
return xmldoc
def gen_likelihood_control(coinc_params_distributions, seglists, name = u"lalapps_burca_tailor", comment = u""):
xmldoc = ligolw.Document()
node = xmldoc.appendChild(ligolw.LIGO_LW())
process = ligolw_process.register_to_xmldoc(xmldoc, program = process_program_name, paramdict = {}, version = __version__, cvs_repository = "lscsoft", cvs_entry_time = __date__, comment = comment)
coinc_params_distributions.process_id = process.process_id
ligolw_search_summary.append_search_summary(xmldoc, process, ifos = seglists.keys(), inseg = seglists.extent_all(), outseg = seglists.extent_all())
node.appendChild(coinc_params_distributions.to_xml(name))
ligolw_process.set_process_end_time(process)
return xmldoc
def ligolw_bucut(xmldoc, options, burst_test_func, veto_segments = segments.segmentlistdict(), del_non_coincs = False, del_skipped_injections = False, program = None, verbose = False): contents = DocContents(xmldoc, program) process = append_process(xmldoc, options) apply_filters(contents, burst_test_func, veto_segments, del_non_coincs = del_non_coincs, del_skipped_injections = del_skipped_injections, verbose = verbose) ligolw_process.set_process_end_time(process) seg = contents.outsegs.extent_all() ligolw_search_summary.append_search_summary(xmldoc, process, inseg = seg, outseg = seg, nevents = len(contents.snglbursttable)) return xmldoc
def ligolw_bucut(xmldoc,
burst_test_func,
veto_segments=segments.segmentlistdict(),
del_non_coincs=False,
del_skipped_injections=False,
program=None,
comment=None,
verbose=False):
process = ligolw_process.register_to_xmldoc(xmldoc,
process_program_name,
paramdict,
version=__version__,
cvs_repository=u"lscsoft",
cvs_entry_time=__date__,
comment=comment)
contents = DocContents(xmldoc, program)
apply_filters(contents,
burst_test_func,
veto_segments,
del_non_coincs=del_non_coincs,
del_skipped_injections=del_skipped_injections,
verbose=verbose)
seg = contents.outsegs.extent_all()
ligolw_search_summary.append_search_summary(xmldoc,
process,
inseg=seg,
outseg=seg,
nevents=len(
contents.snglbursttable))
ligolw_process.set_process_end_time(process)
return xmldoc
vetoes = ligolw_segments.segmenttable_get_by_name(xmldoc, options.vetoes_name).coalesce()
#
# Run coincidence algorithm.
#
thinca.ligolw_thinca(
xmldoc,
process_id = process.process_id,
delta_t = options.threshold,
ntuple_comparefunc = ntuple_comparefunc,
seglists = None, # FIXME
veto_segments = vetoes,
min_instruments = options.min_instruments,
verbose = options.verbose
)
#
# Close out the process table.
#
ligolw_process.set_process_end_time(process)
#
# Write back to disk, and clean up.
#
ligolw_utils.write_filename(xmldoc, filename, verbose = options.verbose, gz = (filename or "stdout").endswith(".gz"))
xmldoc.unlink()
lsctables.reset_next_ids(lsctables.TableByName.values())
# How many slides will go into this file?
#
N = int(round(float(len(time_slides)) / len(filenames)))
#
# Put them in.
#
for offsetvect in time_slides[:N]:
timeslidetable.append_offsetvector(offsetvect, process)
del time_slides[:N]
#
# Finish off the document.
#
ligolw_process.set_process_end_time(process)
#
# Write.
#
filename = filenames.pop(0)
ligolw_utils.write_filename(xmldoc,
filename,
verbose=options.verbose,
gz=(filename or "stdout").endswith(".gz"))
assert not time_slides
def gen_psd_xmldoc(self):
xmldoc = lal.series.make_psd_xmldoc(self.psds)
process = ligolw_process.register_to_xmldoc(xmldoc, "snax", {})
ligolw_process.set_process_end_time(process)
return xmldoc
def main(args=None):
from ligo.lw import lsctables
from ligo.lw.utils import process as ligolw_process
from ligo.lw import utils as ligolw_utils
from ligo.lw import ligolw
import lal.series
from scipy import stats
p = parser()
args = p.parse_args(args)
xmldoc = ligolw.Document()
xmlroot = xmldoc.appendChild(ligolw.LIGO_LW())
process = register_to_xmldoc(xmldoc, p, args)
cosmo = cosmology.default_cosmology.get_cosmology_from_string(
args.cosmology)
ns_mass_min = 1.0
ns_mass_max = 2.0
bh_mass_min = 5.0
bh_mass_max = 50.0
ns_astro_spin_min = -0.05
ns_astro_spin_max = +0.05
ns_astro_mass_dist = stats.norm(1.33, 0.09)
ns_astro_spin_dist = stats.uniform(ns_astro_spin_min,
ns_astro_spin_max - ns_astro_spin_min)
ns_broad_spin_min = -0.4
ns_broad_spin_max = +0.4
ns_broad_mass_dist = stats.uniform(ns_mass_min, ns_mass_max - ns_mass_min)
ns_broad_spin_dist = stats.uniform(ns_broad_spin_min,
ns_broad_spin_max - ns_broad_spin_min)
bh_astro_spin_min = -0.99
bh_astro_spin_max = +0.99
bh_astro_mass_dist = stats.pareto(b=1.3)
bh_astro_spin_dist = stats.uniform(bh_astro_spin_min,
bh_astro_spin_max - bh_astro_spin_min)
bh_broad_spin_min = -0.99
bh_broad_spin_max = +0.99
bh_broad_mass_dist = stats.reciprocal(bh_mass_min, bh_mass_max)
bh_broad_spin_dist = stats.uniform(bh_broad_spin_min,
bh_broad_spin_max - bh_broad_spin_min)
if args.distribution.startswith('bns_'):
m1_min = m2_min = ns_mass_min
m1_max = m2_max = ns_mass_max
if args.distribution.endswith('_astro'):
x1_min = x2_min = ns_astro_spin_min
x1_max = x2_max = ns_astro_spin_max
m1_dist = m2_dist = ns_astro_mass_dist
x1_dist = x2_dist = ns_astro_spin_dist
elif args.distribution.endswith('_broad'):
x1_min = x2_min = ns_broad_spin_min
x1_max = x2_max = ns_broad_spin_max
m1_dist = m2_dist = ns_broad_mass_dist
x1_dist = x2_dist = ns_broad_spin_dist
else: # pragma: no cover
assert_not_reached()
elif args.distribution.startswith('nsbh_'):
m1_min = bh_mass_min
m1_max = bh_mass_max
m2_min = ns_mass_min
m2_max = ns_mass_max
if args.distribution.endswith('_astro'):
x1_min = bh_astro_spin_min
x1_max = bh_astro_spin_max
x2_min = ns_astro_spin_min
x2_max = ns_astro_spin_max
m1_dist = bh_astro_mass_dist
m2_dist = ns_astro_mass_dist
x1_dist = bh_astro_spin_dist
x2_dist = ns_astro_spin_dist
elif args.distribution.endswith('_broad'):
x1_min = bh_broad_spin_min
x1_max = bh_broad_spin_max
x2_min = ns_broad_spin_min
x2_max = ns_broad_spin_max
m1_dist = bh_broad_mass_dist
m2_dist = ns_broad_mass_dist
x1_dist = bh_broad_spin_dist
x2_dist = ns_broad_spin_dist
else: # pragma: no cover
assert_not_reached()
elif args.distribution.startswith('bbh_'):
m1_min = m2_min = bh_mass_min
m1_max = m2_max = bh_mass_max
if args.distribution.endswith('_astro'):
x1_min = x2_min = bh_astro_spin_min
x1_max = x2_max = bh_astro_spin_max
m1_dist = m2_dist = bh_astro_mass_dist
x1_dist = x2_dist = bh_astro_spin_dist
elif args.distribution.endswith('_broad'):
x1_min = x2_min = bh_broad_spin_min
x1_max = x2_max = bh_broad_spin_max
m1_dist = m2_dist = bh_broad_mass_dist
x1_dist = x2_dist = bh_broad_spin_dist
else: # pragma: no cover
assert_not_reached()
else: # pragma: no cover
assert_not_reached()
dists = (m1_dist, m2_dist, x1_dist, x2_dist)
# Read PSDs
psds = list(
lal.series.read_psd_xmldoc(
ligolw_utils.load_fileobj(
args.reference_psd,
contenthandler=lal.series.PSDContentHandler)).values())
# Construct mass1, mass2, spin1z, spin2z grid.
m1 = np.geomspace(m1_min, m1_max, 10)
m2 = np.geomspace(m2_min, m2_max, 10)
x1 = np.linspace(x1_min, x1_max, 10)
x2 = np.linspace(x2_min, x2_max, 10)
params = m1, m2, x1, x2
# Calculate the maximum distance on the grid.
max_z = get_max_z(cosmo,
psds,
args.waveform,
args.f_low,
args.min_snr,
m1,
m2,
x1,
x2,
jobs=args.jobs)
max_distance = sensitive_distance(cosmo, max_z).to_value(units.Mpc)
# Find piecewise constant approximate upper bound on distance.
max_distance = cell_max(max_distance)
# Calculate V * T in each grid cell
cdfs = [dist.cdf(param) for param, dist in zip(params, dists)]
cdf_los = [cdf[:-1] for cdf in cdfs]
cdfs = [np.diff(cdf) for cdf in cdfs]
probs = np.prod(np.meshgrid(*cdfs, indexing='ij'), axis=0)
probs /= probs.sum()
probs *= 4 / 3 * np.pi * max_distance**3
volume = probs.sum()
probs /= volume
probs = probs.ravel()
volumetric_rate = args.nsamples / volume * units.year**-1 * units.Mpc**-3
# Draw random grid cells
dist = stats.rv_discrete(values=(np.arange(len(probs)), probs))
indices = np.unravel_index(dist.rvs(size=args.nsamples),
max_distance.shape)
# Draw random intrinsic params from each cell
cols = {}
cols['mass1'], cols['mass2'], cols['spin1z'], cols['spin2z'] = [
dist.ppf(stats.uniform(cdf_lo[i], cdf[i]).rvs(size=args.nsamples))
for i, dist, cdf_lo, cdf in zip(indices, dists, cdf_los, cdfs)
]
# Draw random extrinsic parameters
cols['distance'] = stats.powerlaw(
a=3, scale=max_distance[indices]).rvs(size=args.nsamples)
cols['longitude'] = stats.uniform(0, 2 * np.pi).rvs(size=args.nsamples)
cols['latitude'] = np.arcsin(stats.uniform(-1, 2).rvs(size=args.nsamples))
cols['inclination'] = np.arccos(
stats.uniform(-1, 2).rvs(size=args.nsamples))
cols['polarization'] = stats.uniform(0, 2 * np.pi).rvs(size=args.nsamples)
cols['coa_phase'] = stats.uniform(-np.pi,
2 * np.pi).rvs(size=args.nsamples)
cols['time_geocent'] = stats.uniform(1e9, units.year.to(
units.second)).rvs(size=args.nsamples)
# Convert from sensitive distance to redshift and comoving distance.
# FIXME: Replace this brute-force lookup table with a solver.
z = np.linspace(0, max_z.max(), 10000)
ds = sensitive_distance(cosmo, z).to_value(units.Mpc)
dc = cosmo.comoving_distance(z).to_value(units.Mpc)
z_for_ds = interp1d(ds, z, kind='cubic', assume_sorted=True)
dc_for_ds = interp1d(ds, dc, kind='cubic', assume_sorted=True)
zp1 = 1 + z_for_ds(cols['distance'])
cols['distance'] = dc_for_ds(cols['distance'])
# Apply redshift factor to convert from comoving distance and source frame
# masses to luminosity distance and observer frame masses.
for key in ['distance', 'mass1', 'mass2']:
cols[key] *= zp1
# Populate sim_inspiral table
sims = xmlroot.appendChild(lsctables.New(lsctables.SimInspiralTable))
for row in zip(*cols.values()):
sims.appendRow(**dict(dict.fromkeys(sims.validcolumns, None),
process_id=process.process_id,
simulation_id=sims.get_next_id(),
waveform=args.waveform,
f_lower=args.f_low,
**dict(zip(cols.keys(), row))))
# Record process end time.
process.comment = str(volumetric_rate)
ligolw_process.set_process_end_time(process)
# Write output file.
write_fileobj(xmldoc, args.output)
