def get_segment_definer_comments(xml_file, include_version=True):
"""Returns a dict with the comment column as the value for each segment"""
from pycbc.io.ligolw import LIGOLWContentHandler as h
# read segment definer table
xmldoc = ligolw_utils.load_fileobj(xml_file,
compress='auto',
contenthandler=h)
seg_def_table = lsctables.SegmentDefTable.get_table(xmldoc)
# put comment column into a dict
comment_dict = {}
for seg_def in seg_def_table:
if include_version:
full_channel_name = ':'.join(
[str(seg_def.ifos),
str(seg_def.name),
str(seg_def.version)])
else:
full_channel_name = ':'.join(
[str(seg_def.ifos), str(seg_def.name)])
comment_dict[full_channel_name] = seg_def.comment
return comment_dict
def psd_from_event(gid,
outdir=".",
save=False,
filename="psd.xml.gz",
verbose=False):
"""Get the psd.xml.gz given a gracedb event id.
Args:
gid (str): The gracedb event id.
outdir (str, default="."): The output directory.
filename (str, default="psd.xml.gz"): The output filename.
save (bool, default=False): True if want to save the file, False otherwise.
verbose (bool, default=False): Be verbose.
Returns:
A dictionary of complex LAL Series key by instrument.
"""
gracedb_client = gracedb.GraceDb()
psd_fileobj = lvalert_helper.get_filename(gracedb_client, gid, filename)
xmldoc = ligolw_utils.load_fileobj(
psd_fileobj, contenthandler=lal.series.PSDContentHandler)
if save:
if verbose:
sys.stderr.write("saving psd file to %s ...\n" %
os.path.join(outdir, filename))
ligolw_utils.write_filename(xmldoc,
filename,
gz=filename.endswith("gz"))
return lal.series.read_psd_xmldoc(xmldoc)
def from_xml(filename, length, delta_f, low_freq_cutoff, ifo_string=None,
root_name='psd'):
"""Read an ASCII file containing one-sided ASD or PSD data and generate
a frequency series with the corresponding PSD. The ASD or PSD data is
interpolated in order to match the desired resolution of the
generated frequency series.
Parameters
----------
filename : string
Path to a two-column ASCII file. The first column must contain
the frequency (positive frequencies only) and the second column
must contain the amplitude density OR power spectral density.
length : int
Length of the frequency series in samples.
delta_f : float
Frequency resolution of the frequency series in Herz.
low_freq_cutoff : float
Frequencies below this value are set to zero.
ifo_string : string
Use the PSD in the file's PSD dictionary with this ifo string.
If not given and only one PSD present in the file return that, if not
given and multiple (or zero) PSDs present an exception will be raised.
root_name : string (default='psd')
If given use this as the root name for the PSD XML file. If this means
nothing to you, then it is probably safe to ignore this option.
Returns
-------
psd : FrequencySeries
The generated frequency series.
"""
import lal.series
from ligo.lw import utils as ligolw_utils
with open(filename, 'rb') as fp:
ct_handler = lal.series.PSDContentHandler
xml_doc = ligolw_utils.load_fileobj(fp, compress='auto',
contenthandler=ct_handler)
psd_dict = lal.series.read_psd_xmldoc(xml_doc, root_name=root_name)
if ifo_string is not None:
psd_freq_series = psd_dict[ifo_string]
elif len(psd_dict.keys()) == 1:
psd_freq_series = psd_dict[tuple(psd_dict.keys())[0]]
else:
err_msg = "No ifo string given and input XML file contains not "
err_msg += "exactly one PSD. Specify which PSD you want to use."
raise ValueError(err_msg)
noise_data = psd_freq_series.data.data[:]
freq_data = numpy.arange(len(noise_data)) * psd_freq_series.deltaF
return from_numpy_arrays(freq_data, noise_data, length, delta_f,
low_freq_cutoff)
def test_pick_coinc():
coinc = pick_coinc()
xmldoc = utils.load_fileobj(io.BytesIO(coinc),
contenthandler=ContentHandler)
coinc_inspiral_table = lsctables.CoincInspiralTable.get_table(xmldoc)
assert len(coinc_inspiral_table) == 1
coinc_inspiral, = coinc_inspiral_table
assert coinc_inspiral.end <= mock_now()
def _jitter_snr(coinc_bytes):
coinc_xml = io.BytesIO(coinc_bytes)
xmldoc = utils.load_fileobj(coinc_xml, contenthandler=ContentHandler)
coinc_inspiral_table = lsctables.CoincInspiralTable.get_table(xmldoc)
# Add a tiny amount of jitter in SNR so that uploads have random
# preferred event precedence.
for row in coinc_inspiral_table:
row.snr += random.gauss(0, 1e-9)
coinc_xml = io.BytesIO()
utils.write_fileobj(xmldoc, coinc_xml)
return coinc_xml.getvalue()
def main():
import os
import sys
import json
import numpy as np
import pandas as pd
from ligo.lw import utils as ligolw_utils
from ligo.lw.lsctables import SimInspiralTable
from ligo.lw.lsctables import SnglInspiralTable
from ligo.skymap.io.events.ligolw import ContentHandler
p = parser()
args = p.parse_args()
if args.inj:
file_obj = open(args.inj, 'rb')
elif args.coinc:
file_obj = open(args.coinc, 'rb')
else:
print('Must supply either injection xml file or coinc xml file')
sys.exit(0)
xmldoc = ligolw_utils.load_fileobj(file_obj, contenthandler=ContentHandler)
if args.inj:
inspiral_table = SimInspiralTable.get_table(xmldoc)
else:
inspiral_table = SnglInspiralTable.get_table(xmldoc)
df = pd.DataFrame()
json_dict = {}
for column in args.cols:
values_obj = inspiral_table.getColumnByName(column)
values = values_obj.asarray()
df[column] = values
json_dict[column] = values.tolist()
df.index += 1
print(df)
if args.inj:
jsonfilename = args.inj.split(".")[0] + ".json"
elif args.coinc:
jsonfilename = args.coinc.split(".")[0] + ".json"
if args.out:
jsonfilename = args.out
with open(jsonfilename, 'w') as f:
json.dump(json_dict, f, indent=2, sort_keys=True)
def open_xmldoc(fobj, **kwargs):
"""Try and open an existing LIGO_LW-format file, or create a new Document
Parameters
----------
fobj : `str`, `file`
file path or open file object to read
**kwargs
other keyword arguments to pass to
:func:`~ligo.lw.utils.load_filename`, or
:func:`~ligo.lw.utils.load_fileobj` as appropriate
Returns
--------
xmldoc : :class:`~ligo.lw.ligolw.Document`
either the `Document` as parsed from an existing file, or a new, empty
`Document`
"""
from ligo.lw.ligolw import (Document, LIGOLWContentHandler)
from ligo.lw.lsctables import use_in
from ligo.lw.utils import (load_filename, load_fileobj)
use_in(kwargs.setdefault('contenthandler', LIGOLWContentHandler))
try: # try and load existing file
if isinstance(fobj, str):
return load_filename(fobj, **kwargs)
if isinstance(fobj, FILE_LIKE):
return load_fileobj(fobj, **kwargs)[0]
except (OSError, IOError): # or just create a new Document
return Document()
except LigolwElementError as exc:
if LIGO_LW_COMPAT_ERROR.search(str(exc)):
try:
return open_xmldoc(fobj, ilwdchar_compat=True, **kwargs)
except Exception: # for any reason, raise original
pass
raise
def open_xmldoc(fobj, **kwargs):
"""Try and open an existing LIGO_LW-format file, or create a new Document
Parameters
----------
fobj : `str`, `file`
file path or open file object to read
**kwargs
other keyword arguments to pass to
:func:`~ligo.lw.utils.load_filename`, or
:func:`~ligo.lw.utils.load_fileobj` as appropriate
Returns
--------
xmldoc : :class:`~ligo.lw.ligolw.Document`
either the `Document` as parsed from an existing file, or a new, empty
`Document`
"""
from ligo.lw.ligolw import (Document, LIGOLWContentHandler)
from ligo.lw.lsctables import use_in
from ligo.lw.utils import (load_filename, load_fileobj)
use_in(kwargs.setdefault('contenthandler', LIGOLWContentHandler))
try: # try and load existing file
if isinstance(fobj, string_types):
return load_filename(fobj, **kwargs)
if isinstance(fobj, FILE_LIKE):
return load_fileobj(fobj, **kwargs)[0]
except (OSError, IOError): # or just create a new Document
return Document()
except LigolwElementError as exc:
if LIGO_LW_COMPAT_ERROR.search(str(exc)):
try:
return open_xmldoc(fobj, ilwdchar_compat=True, **kwargs)
except Exception: # for any reason, raise original
pass
raise
def _read_xml(f, fallbackpath=None):
if f is None:
doc = filename = None
elif isinstance(f, Element):
doc = f
filename = ''
elif isinstance(f, str):
try:
doc = load_filename(f, contenthandler=ContentHandler)
except IOError as e:
if e.errno == errno.ENOENT and fallbackpath and \
not os.path.isabs(f):
f = os.path.join(fallbackpath, f)
doc = load_filename(f, contenthandler=ContentHandler)
else:
raise
filename = f
else:
doc = load_fileobj(f, contenthandler=ContentHandler)
try:
filename = f.name
except AttributeError:
filename = ''
return doc, filename
def main(args=None):
from ligo.lw import lsctables
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)
gwcosmo = GWCosmo(
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 = gwcosmo.get_max_z(psds,
args.waveform,
args.f_low,
args.min_snr,
m1,
m2,
x1,
x2,
jobs=args.jobs)
if args.max_distance is not None:
new_max_z = cosmology.z_at_value(gwcosmo.cosmo.luminosity_distance,
args.max_distance * units.Mpc)
max_z[max_z > new_max_z] = new_max_z
max_distance = gwcosmo.sensitive_distance(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)
]
# Swap binary components as needed to ensure that mass1 >= mass2.
# Note that the .copy() is important.
# See https://github.com/numpy/numpy/issues/14428
swap = cols['mass1'] < cols['mass2']
cols['mass1'][swap], cols['mass2'][swap] = \
cols['mass2'][swap].copy(), cols['mass1'][swap].copy()
cols['spin1z'][swap], cols['spin2z'][swap] = \
cols['spin2z'][swap].copy(), cols['spin1z'][swap].copy()
# 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 = gwcosmo.sensitive_distance(z).to_value(units.Mpc)
dc = gwcosmo.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)
process.set_end_time_now()
# Write output file.
write_fileobj(xmldoc, args.output)
parser.add_argument('-c',
'--cols',
nargs='+',
help='columns of parameters to be read',
required=True)
args = parser.parse_args()
if args.inj:
file_obj = open(args.inj, 'rb')
elif args.coinc:
file_obj = open(args.coinc, 'rb')
else:
print('Must supply either injection xml file or coinc xml file')
sys.exit(0)
xmldoc = ligolw_utils.load_fileobj(file_obj, contenthandler=ContentHandler)
if args.inj:
inspiral_table = SimInspiralTable.get_table(xmldoc)
else:
inspiral_table = SnglInspiralTable.get_table(xmldoc)
df = pd.DataFrame()
json_dict = {}
for column in args.cols:
values = inspiral_table.get_column(column)
df[column] = values
json_dict[column] = values.tolist()
df.index += 1
print(df)
if args.inj:
def pick_coinc():
"""Pick a coincidence from the "First Two Years" paper."""
with resources.open_binary(data_first2years, 'gstlal.xml.gz') as f:
xmldoc = utils.load_fileobj(f, contenthandler=ContentHandler)
root, = xmldoc.childNodes
# Remove unneeded tables
for name in (
'filter', # lsctables.FilterTable removed from ligo.lw
lsctables.SegmentTable.tableName,
lsctables.SegmentDefTable.tableName,
lsctables.SimInspiralTable.tableName,
lsctables.SummValueTable.tableName,
lsctables.SearchSummVarsTable.tableName):
root.removeChild(ligo.lw.table.get_table(xmldoc, name))
coinc_inspiral_table = table = lsctables.CoincInspiralTable.get_table(
xmldoc)
# Determine event with most recent sideral time
gps_time_now = lal.GPSTimeNow()
gmsts = np.asarray([lal.GreenwichMeanSiderealTime(_.end) for _ in table])
gmst_now = lal.GreenwichMeanSiderealTime(gps_time_now)
div, rem = divmod(gmst_now - gmsts, 2 * np.pi)
i = np.argmin(rem)
new_gmst = div[i] * 2 * np.pi + gmsts[i]
old_time = table[i].end
new_time = lal.LIGOTimeGPS()
result = lal.GreenwichMeanSiderealTimeToGPS(new_gmst, new_time)
result.disown()
del result
delta_t = new_time - old_time
target_coinc_event_id = int(table[i].coinc_event_id)
# Remove unneeded rows
table[:] = [
row for row in table
if int(row.coinc_event_id) == target_coinc_event_id
]
target_end_time = table[0].end
coinc_table = table = lsctables.CoincTable.get_table(xmldoc)
table[:] = [
row for row in table
if int(row.coinc_event_id) == target_coinc_event_id
]
table = lsctables.CoincMapTable.get_table(xmldoc)
table[:] = [
row for row in table
if int(row.coinc_event_id) == target_coinc_event_id
]
target_sngl_inspirals = frozenset(row.event_id for row in table)
sngl_inspiral_table = table = lsctables.SnglInspiralTable.get_table(xmldoc)
table[:] = [row for row in table if row.event_id in target_sngl_inspirals]
table = lsctables.ProcessTable.get_table(xmldoc)
table[:] = [row for row in table if row.program == 'gstlal_inspiral']
target_process_ids = frozenset(row.process_id for row in table)
table = lsctables.SearchSummaryTable.get_table(xmldoc)
table[:] = [
row for row in table if target_end_time in row.out_segment
and row.process_id in target_process_ids
]
target_process_ids = frozenset(row.process_id for row in table)
table = lsctables.ProcessTable.get_table(xmldoc)
table[:] = [row for row in table if row.process_id in target_process_ids]
table = lsctables.ProcessParamsTable.get_table(xmldoc)
table[:] = [row for row in table if row.process_id in target_process_ids]
# Shift event times
for row in coinc_inspiral_table:
row.end += delta_t
for row in sngl_inspiral_table:
row.end += delta_t
row.end_time_gmst = lal.GreenwichMeanSiderealTime(row.end)
# The old version of gstlal used to produce the "First Two Years" data set
# stored likelihood in the coinc_event.likelihood column, but newer
# versions store the *natural log* of the likelihood here. The p_astro
# calculation requires this to be log likelihood.
for row in coinc_table:
row.likelihood = np.log(row.likelihood)
# Gstlal stores the template's SVD bank index in the Gamma1 column.
# Fill this in so that we can calculate p_astro
# (see :mod:`gwcelery.tasks.p_astro_gstlal`).
for row in sngl_inspiral_table:
row.Gamma1 = 16
coinc_xml = io.BytesIO()
utils.write_fileobj(xmldoc, coinc_xml)
return coinc_xml.getvalue()
def localize(coinc_psd,
graceid,
filename='bayestar.fits.gz',
disabled_detectors=None):
"""Generate a rapid sky localization using
:mod:`BAYESTAR <ligo.skymap.bayestar>`.
Parameters
----------
coinc_psd : tuple
Tuple consisting of the byte contents of the input event's
``coinc.xml`` and ``psd.xml.gz`` files.
graceid : str
The GraceDB ID, used for FITS metadata and recording log messages
to GraceDB.
filename : str, optional
The name of the FITS file.
disabled_detectors : list, optional
List of detectors to disable.
Returns
-------
bytes
The byte contents of the finished FITS file.
Notes
-----
This task is adapted from the command-line tool
:doc:`bayestar-localize-lvalert
<ligo.skymap:tool/bayestar_localize_lvalert>`.
It should execute in a special queue for computationally intensive,
multithreaded, OpenMP tasks.
"""
# Determine the base URL for event pages.
scheme, netloc, *_ = urllib.parse.urlparse(gracedb.client.url)
base_url = urllib.parse.urlunparse((scheme, netloc, 'events', '', '', ''))
try:
# A little bit of Cylon humor
log.info('by your command...')
# Combine coinc.xml and psd.xml.gz into one XML document
doc = None
for filecontents in coinc_psd:
doc = load_fileobj(io.BytesIO(filecontents),
xmldoc=doc,
contenthandler=events.ligolw.ContentHandler)
merge_ligolws(doc)
# Parse event
event_source = events.ligolw.open(doc, psd_file=doc, coinc_def=None)
if disabled_detectors:
event_source = events.detector_disabled.open(
event_source, disabled_detectors)
event, = event_source.values()
# Run BAYESTAR
log.info('starting sky localization')
# FIXME: the low frequency cutoff should not be hardcoded.
# It should be provided in the coinc.xml file.
skymap = _bayestar.localize(event, f_low=15.0)
skymap.meta['objid'] = str(graceid)
skymap.meta['url'] = '{}/{}'.format(base_url, graceid)
log.info('sky localization complete')
with io.BytesIO() as f:
fits.write_sky_map(f, skymap, moc=True)
return f.getvalue()
except events.DetectorDisabledError:
raise Ignore()
def setup_roq(cp):
"""
Generates cp objects with the different ROQs applied
"""
use_roq = False
if cp.has_option('paths', 'roq_b_matrix_directory') or cp.has_option(
'paths', 'computeroqweights'):
if not cp.has_option('analysis', 'roq'):
print(
"Warning: If you are attempting to enable ROQ by specifying roq_b_matrix_directory or computeroqweights,\n\
please use analysis.roq in your config file in future. Enabling ROQ."
)
cp.set('analysis', 'roq', True)
if not cp.getboolean('analysis', 'roq'):
yield cp
return
path = cp.get('paths', 'roq_b_matrix_directory')
if not os.path.isdir(path):
print("The ROQ directory %s does not seem to exist\n" % path)
sys.exit(1)
use_roq = True
roq_paths = os.listdir(path)
roq_params = {}
roq_force_flow = None
if cp.has_option('lalinference', 'roq_force_flow'):
roq_force_flow = cp.getfloat('lalinference', 'roq_force_flow')
print("WARNING: Forcing the f_low to ", str(roq_force_flow), "Hz")
print(
"WARNING: Overwriting user choice of flow, srate, seglen, and (mc_min, mc_max and q-min) or (mass1_min, mass1_max, mass2_min, mass2_max)"
)
def key(item): # to order the ROQ bases
return float(item[1]['seglen'])
coinc_xml_obj = None
row = None
# Get file object of coinc.xml
if opts.gid is not None:
from ligo.gracedb.rest import GraceDb
gid = opts.gid
cwd = os.getcwd()
if cp.has_option('analysis', 'service-url'):
client = GraceDb(cp.get('analysis', 'service-url'))
else:
client = GraceDb()
coinc_xml_obj = ligolw_utils.load_fileobj(
client.files(gid, "coinc.xml"),
contenthandler=lsctables.use_in(ligolw.LIGOLWContentHandler))[0]
elif cp.has_option('input', 'coinc-xml'):
coinc_xml_obj = ligolw_utils.load_fileobj(
open(cp.get('input', 'coinc-xml'), "rb"),
contenthandler=lsctables.use_in(ligolw.LIGOLWContentHandler))[0]
# Get sim_inspiral from injection file
if cp.has_option('input', 'injection-file'):
print(
"Only 0-th event in the XML table will be considered while running with ROQ\n"
)
row = lsctables.SimInspiralTable.get_table(
ligolw_utils.load_filename(cp.get('input', 'injection-file'),
contenthandler=lsctables.use_in(
ligolw.LIGOLWContentHandler)))[0]
roq_bounds = pipe_utils.Query_ROQ_Bounds_Type(path, roq_paths)
if roq_bounds == 'chirp_mass_q':
print('ROQ has bounds in chirp mass and mass-ratio')
mc_priors, trigger_mchirp = pipe_utils.get_roq_mchirp_priors(
path,
roq_paths,
roq_params,
key,
coinc_xml_obj=coinc_xml_obj,
sim_inspiral=row)
elif roq_bounds == 'component_mass':
print('ROQ has bounds in component masses')
# get component mass bounds, then compute the chirp mass that can be safely covered
# further below we pass along the component mass bounds to the sampler, not the tighter chirp-mass, q bounds
m1_priors, m2_priors, trigger_mchirp = pipe_utils.get_roq_component_mass_priors(
path,
roq_paths,
roq_params,
key,
coinc_xml_obj=coinc_xml_obj,
sim_inspiral=row)
mc_priors = {}
for (roq, m1_prior), (roq2, m2_prior) in zip(m1_priors.items(),
m2_priors.items()):
mc_priors[roq] = sorted([
pipe_utils.mchirp_from_components(m1_prior[1], m2_prior[0]),
pipe_utils.mchirp_from_components(m1_prior[0], m2_prior[1])
])
if cp.has_option('lalinference', 'trigger_mchirp'):
trigger_mchirp = float(cp.get('lalinference', 'trigger_mchirp'))
roq_mass_freq_scale_factor = pipe_utils.get_roq_mass_freq_scale_factor(
mc_priors, trigger_mchirp, roq_force_flow)
if roq_mass_freq_scale_factor != 1.:
print(
'WARNING: Rescaling ROQ basis, please ensure it is allowed with the model used.'
)
# If the true chirp mass is unknown, add variations over the mass bins
if opts.gid is not None or (opts.injections is not None or cp.has_option(
'input', 'injection-file')) or cp.has_option(
'lalinference', 'trigger_mchirp') or cp.has_option(
'input', 'coinc-xml'):
for mc_prior in mc_priors:
mc_priors[mc_prior] = np.array(mc_priors[mc_prior])
# find mass bin containing the trigger
candidate_roq_paths = \
[roq for roq in roq_paths
if mc_priors[roq][0]*roq_mass_freq_scale_factor
<= trigger_mchirp <= mc_priors[roq][1]*roq_mass_freq_scale_factor]
if candidate_roq_paths:
margins = np.array([
min(
mc_priors[roq][1] * roq_mass_freq_scale_factor -
trigger_mchirp, trigger_mchirp -
mc_priors[roq][0] * roq_mass_freq_scale_factor)
for roq in candidate_roq_paths
])
best_path = candidate_roq_paths[np.argmax(margins)]
roq_paths = [best_path]
print(
'Prior in Mchirp will be [{}, {}] to contain the trigger Mchirp {}.'
.format(mc_priors[best_path][0] * roq_mass_freq_scale_factor,
mc_priors[best_path][1] * roq_mass_freq_scale_factor,
trigger_mchirp))
else:
print("No roq mass bins containing the trigger")
raise
else:
for mc_prior in mc_priors:
mc_priors[mc_prior] = np.array(
mc_priors[mc_prior]) * roq_mass_freq_scale_factor
# write the master configparser
cur_basedir = cp.get('paths', 'basedir')
masterpath = os.path.join(cur_basedir, 'config.ini')
with open(masterpath, 'w') as cpfile:
cp.write(cpfile)
for roq in roq_paths:
this_cp = configparser.ConfigParser()
this_cp.optionxform = str
this_cp.read(masterpath)
basedir = this_cp.get('paths', 'basedir')
for dirs in 'basedir', 'daglogdir', 'webdir':
val = this_cp.get('paths', dirs)
newval = os.path.join(val, roq)
mkdirs(newval)
this_cp.set('paths', dirs, newval)
this_cp.set(
'paths', 'roq_b_matrix_directory',
os.path.join(cp.get('paths', 'roq_b_matrix_directory'), roq))
flow = roq_params[roq]['flow'] / roq_mass_freq_scale_factor
srate = 2. * roq_params[roq]['fhigh'] / roq_mass_freq_scale_factor
#if srate > 8192:
# srate = 8192
seglen = roq_params[roq]['seglen'] * roq_mass_freq_scale_factor
# params.dat uses the convention q>1 so our q_min is the inverse of their qmax
this_cp.set('engine', 'srate', str(srate))
this_cp.set('engine', 'seglen', str(seglen))
if this_cp.has_option('lalinference', 'flow'):
tmp = this_cp.get('lalinference', 'flow')
tmp = eval(tmp)
ifos = tmp.keys()
else:
tmp = {}
ifos = eval(this_cp.get('analysis', 'ifos'))
for i in ifos:
tmp[i] = flow
this_cp.set('lalinference', 'flow', str(tmp))
if roq_bounds == 'chirp_mass_q':
mc_min = mc_priors[roq][0] * roq_mass_freq_scale_factor
mc_max = mc_priors[roq][1] * roq_mass_freq_scale_factor
# params.dat uses the convention q>1 so our q_min is the inverse of their qmax
q_min = 1. / float(roq_params[roq]['qmax'])
this_cp.set('engine', 'chirpmass-min', str(mc_min))
this_cp.set('engine', 'chirpmass-max', str(mc_max))
this_cp.set('engine', 'q-min', str(q_min))
this_cp.set(
'engine', 'comp-min',
str(
max(
roq_params[roq]['compmin'] *
roq_mass_freq_scale_factor,
mc_min * pow(1 + q_min, 1. / 5.) *
pow(q_min, 2. / 5.))))
this_cp.set(
'engine', 'comp-max',
str(mc_max * pow(1 + q_min, 1. / 5.) * pow(q_min, -3. / 5.)))
elif roq_bounds == 'component_mass':
m1_min = m1_priors[roq][0]
m1_max = m1_priors[roq][1]
m2_min = m2_priors[roq][0]
m2_max = m2_priors[roq][1]
this_cp.set('engine', 'mass1-min', str(m1_min))
this_cp.set('engine', 'mass1-max', str(m1_max))
this_cp.set('engine', 'mass2-min', str(m2_min))
this_cp.set('engine', 'mass2-max', str(m2_max))
yield this_cp
return
def main(args=None):
p = parser()
opts = p.parse_args(args)
# LIGO-LW XML imports.
from ligo.lw import ligolw
from ligo.lw.param import Param
from ligo.lw.utils.search_summary import append_search_summary
from ligo.lw import utils as ligolw_utils
from ligo.lw.lsctables import (
New, CoincDefTable, CoincID, CoincInspiralTable, CoincMapTable,
CoincTable, ProcessParamsTable, ProcessTable, SimInspiralTable,
SnglInspiralTable, TimeSlideTable)
# glue, LAL and pylal imports.
from ligo import segments
import lal
import lal.series
import lalsimulation
from lalinspiral.inspinjfind import InspiralSCExactCoincDef
from lalinspiral.thinca import InspiralCoincDef
from tqdm import tqdm
# BAYESTAR imports.
from ..io.events.ligolw import ContentHandler
from ..bayestar import filter
from ..util.progress import progress_map
# Read PSDs.
xmldoc = ligolw_utils.load_fileobj(
opts.reference_psd, contenthandler=lal.series.PSDContentHandler)
psds = lal.series.read_psd_xmldoc(xmldoc, root_name=None)
psds = {
key: filter.InterpolatedPSD(filter.abscissa(psd), psd.data.data)
for key, psd in psds.items() if psd is not None}
psds = [psds[ifo] for ifo in opts.detector]
# Extract simulation table from injection file.
inj_xmldoc = ligolw_utils.load_fileobj(
opts.input, contenthandler=ContentHandler)
orig_sim_inspiral_table = SimInspiralTable.get_table(inj_xmldoc)
# Prune injections that are outside distance limits.
orig_sim_inspiral_table[:] = [
row for row in orig_sim_inspiral_table
if opts.min_distance <= row.distance <= opts.max_distance]
# Open output file.
xmldoc = ligolw.Document()
xmlroot = xmldoc.appendChild(ligolw.LIGO_LW())
# Create tables. Process and ProcessParams tables are copied from the
# injection file.
coinc_def_table = xmlroot.appendChild(New(CoincDefTable))
coinc_inspiral_table = xmlroot.appendChild(New(CoincInspiralTable))
coinc_map_table = xmlroot.appendChild(New(CoincMapTable))
coinc_table = xmlroot.appendChild(New(CoincTable))
xmlroot.appendChild(ProcessParamsTable.get_table(inj_xmldoc))
xmlroot.appendChild(ProcessTable.get_table(inj_xmldoc))
sim_inspiral_table = xmlroot.appendChild(New(SimInspiralTable))
sngl_inspiral_table = xmlroot.appendChild(New(SnglInspiralTable))
time_slide_table = xmlroot.appendChild(New(TimeSlideTable))
# Write process metadata to output file.
process = register_to_xmldoc(
xmldoc, p, opts, instruments=opts.detector,
comment="Simulated coincidences")
# Add search summary to output file.
all_time = segments.segment([lal.LIGOTimeGPS(0), lal.LIGOTimeGPS(2e9)])
append_search_summary(xmldoc, process, inseg=all_time, outseg=all_time)
# Create a time slide entry. Needed for coinc_event rows.
time_slide_id = time_slide_table.get_time_slide_id(
{ifo: 0 for ifo in opts.detector}, create_new=process)
# Populate CoincDef table.
inspiral_coinc_def = copy.copy(InspiralCoincDef)
inspiral_coinc_def.coinc_def_id = coinc_def_table.get_next_id()
coinc_def_table.append(inspiral_coinc_def)
found_coinc_def = copy.copy(InspiralSCExactCoincDef)
found_coinc_def.coinc_def_id = coinc_def_table.get_next_id()
coinc_def_table.append(found_coinc_def)
# Precompute values that are common to all simulations.
detectors = [lalsimulation.DetectorPrefixToLALDetector(ifo)
for ifo in opts.detector]
responses = [det.response for det in detectors]
locations = [det.location for det in detectors]
if opts.jobs != 1:
from .. import omp
omp.num_threads = 1 # disable OpenMP parallelism
func = functools.partial(simulate, psds=psds,
responses=responses, locations=locations,
measurement_error=opts.measurement_error,
f_low=opts.f_low, f_high=opts.f_high,
waveform=opts.waveform)
# Make sure that each thread gets a different random number state.
# We start by drawing a random integer s in the main thread, and
# then the i'th subprocess will seed itself with the integer i + s.
#
# The seed must be an unsigned 32-bit integer, so if there are n
# threads, then s must be drawn from the interval [0, 2**32 - n).
#
# Note that *we* are thread 0, so there are a total of
# n=1+len(sim_inspiral_table) threads.
seed = np.random.randint(0, 2 ** 32 - len(sim_inspiral_table) - 1)
np.random.seed(seed)
with tqdm(desc='accepted') as progress:
for sim_inspiral, simulation in zip(
orig_sim_inspiral_table,
progress_map(
func,
np.arange(len(orig_sim_inspiral_table)) + seed + 1,
orig_sim_inspiral_table, jobs=opts.jobs)):
sngl_inspirals = []
used_snr_series = []
net_snr = 0.0
count_triggers = 0
# Loop over individual detectors and create SnglInspiral entries.
for ifo, (horizon, abs_snr, arg_snr, toa, series) \
in zip(opts.detector, simulation):
if np.random.uniform() > opts.duty_cycle:
continue
elif abs_snr >= opts.snr_threshold:
# If SNR < threshold, then the injection is not found.
# Skip it.
count_triggers += 1
net_snr += np.square(abs_snr)
elif not opts.keep_subthreshold:
continue
# Create SnglInspiral entry.
used_snr_series.append(series)
sngl_inspirals.append(
sngl_inspiral_table.RowType(**dict(
dict.fromkeys(sngl_inspiral_table.validcolumns, None),
process_id=process.process_id,
ifo=ifo,
mass1=sim_inspiral.mass1,
mass2=sim_inspiral.mass2,
spin1x=sim_inspiral.spin1x,
spin1y=sim_inspiral.spin1y,
spin1z=sim_inspiral.spin1z,
spin2x=sim_inspiral.spin2x,
spin2y=sim_inspiral.spin2y,
spin2z=sim_inspiral.spin2z,
end=toa,
snr=abs_snr,
coa_phase=arg_snr,
f_final=opts.f_high,
eff_distance=horizon / abs_snr)))
net_snr = np.sqrt(net_snr)
# If too few triggers were found, then skip this event.
if count_triggers < opts.min_triggers:
continue
# If network SNR < threshold, then the injection is not found.
# Skip it.
if net_snr < opts.net_snr_threshold:
continue
# Add Coinc table entry.
coinc = coinc_table.appendRow(
coinc_event_id=coinc_table.get_next_id(),
process_id=process.process_id,
coinc_def_id=inspiral_coinc_def.coinc_def_id,
time_slide_id=time_slide_id,
insts=opts.detector,
nevents=len(opts.detector),
likelihood=None)
# Add CoincInspiral table entry.
coinc_inspiral_table.appendRow(
coinc_event_id=coinc.coinc_event_id,
instruments=[
sngl_inspiral.ifo for sngl_inspiral in sngl_inspirals],
end=lal.LIGOTimeGPS(1e-9 * np.mean([
sngl_inspiral.end.ns()
for sngl_inspiral in sngl_inspirals
if sngl_inspiral.end is not None])),
mass=sim_inspiral.mass1 + sim_inspiral.mass2,
mchirp=sim_inspiral.mchirp,
combined_far=0.0, # Not provided
false_alarm_rate=0.0, # Not provided
minimum_duration=None, # Not provided
snr=net_snr)
# Record all sngl_inspiral records and associate them with coincs.
for sngl_inspiral, series in zip(sngl_inspirals, used_snr_series):
# Give this sngl_inspiral record an id and add it to the table.
sngl_inspiral.event_id = sngl_inspiral_table.get_next_id()
sngl_inspiral_table.append(sngl_inspiral)
if opts.enable_snr_series:
elem = lal.series.build_COMPLEX8TimeSeries(series)
elem.appendChild(
Param.from_pyvalue('event_id', sngl_inspiral.event_id))
xmlroot.appendChild(elem)
# Add CoincMap entry.
coinc_map_table.appendRow(
coinc_event_id=coinc.coinc_event_id,
table_name=sngl_inspiral_table.tableName,
event_id=sngl_inspiral.event_id)
# Record injection
if not opts.preserve_ids:
sim_inspiral.simulation_id = sim_inspiral_table.get_next_id()
sim_inspiral_table.append(sim_inspiral)
progress.update()
# Record coincidence associating injections with events.
for i, sim_inspiral in enumerate(sim_inspiral_table):
coinc = coinc_table.appendRow(
coinc_event_id=coinc_table.get_next_id(),
process_id=process.process_id,
coinc_def_id=found_coinc_def.coinc_def_id,
time_slide_id=time_slide_id,
instruments=None,
nevents=None,
likelihood=None)
coinc_map_table.appendRow(
coinc_event_id=coinc.coinc_event_id,
table_name=sim_inspiral_table.tableName,
event_id=sim_inspiral.simulation_id)
coinc_map_table.appendRow(
coinc_event_id=coinc.coinc_event_id,
table_name=coinc_table.tableName,
event_id=CoincID(i))
# Record process end time.
process.set_end_time_now()
# Write output file.
write_fileobj(xmldoc, opts.output)
