# FIXME: sample rate could be a command line option; template duration and data
# duration should be determined from chirp time
sample_rate = 4096 # sample rate in Hz
template_duration = 128 # template duration in seconds
template_length = sample_rate * template_duration # template length in samples
data_duration = 512 # data duration in seconds
data_length = sample_rate * data_duration # data length in samples
# Open output file.
out_xmldoc = ligolw.Document()
out_xmldoc.appendChild(ligolw.LIGO_LW())
# Write process metadata to output file.
process = command.register_to_xmldoc(
out_xmldoc, parser, opts, ifos=opts.detector, comment="Little hope!")
# Add search summary to output file.
all_time = segments.segment([glue.lal.LIGOTimeGPS(0), glue.lal.LIGOTimeGPS(2e9)])
search_summary_table = lsctables.New(lsctables.SearchSummaryTable)
out_xmldoc.childNodes[0].appendChild(search_summary_table)
summary = ligolw_search_summary.append_search_summary(out_xmldoc, process,
inseg=all_time, outseg=all_time)
# Read template bank file.
progress.update(-1, 'reading ' + opts.template_bank.name)
xmldoc, _ = ligolw_utils.load_fileobj(
opts.template_bank, contenthandler=ligolw_bayestar.LSCTablesContentHandler)
# Determine the low frequency cutoff from the template bank file.
template_bank_f_low = ligolw_bayestar.get_template_bank_f_low(xmldoc)
from lalinference.bayestar import timing
# Other imports.
import numpy as np
progress = ProgressBar()
# Open output file.
progress.update(-1, 'setting up output document')
out_xmldoc = ligolw.Document()
out_xmldoc.appendChild(ligolw.LIGO_LW())
# Write process metadata to output file.
process = command.register_to_xmldoc(out_xmldoc,
parser,
opts,
ifos=opts.detector,
comment="Simulated coincidences")
# Add search summary to output file.
all_time = segments.segment(
[glue.lal.LIGOTimeGPS(0),
glue.lal.LIGOTimeGPS(2e9)])
search_summary_table = lsctables.New(lsctables.SearchSummaryTable)
out_xmldoc.childNodes[0].appendChild(search_summary_table)
summary = ligolw_search_summary.append_search_summary(out_xmldoc,
process,
inseg=all_time,
outseg=all_time)
# Read PSDs.
try:
f_low, = f_lows
except ValueError:
raise ValueError(
"sim_inspiral:f_lower columns are not unique, got values: "
+ ' '.join(f_lows))
else:
f_low = opts.low_frequency_cutoff
# Open output file.
out_xmldoc = ligolw.Document()
out_xmldoc.appendChild(ligolw.LIGO_LW())
# Write process metadata to output file. Masquerade as lalapps_tmpltbank and
# encode low frequency cutoff in command line arguments.
process = command.register_to_xmldoc(
out_xmldoc, parser, opts, ifos="H1", comment="Exact-match template bank")
# Record low-frequency cutoff in the SearchSummVars table.
search_summvars_table = lsctables.New(lsctables.SearchSummVarsTable)
out_xmldoc.childNodes[0].appendChild(search_summvars_table)
search_summvars = lsctables.SearchSummVars()
search_summvars.search_summvar_id = search_summvars_table.get_next_id()
search_summvars.process_id = process.process_id
search_summvars.name = "low-frequency cutoff"
search_summvars.string = None
search_summvars.value = f_low
search_summvars_table.append(search_summvars)
# Create a SnglInspiral table and initialize its row ID counter.
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
out_xmldoc.childNodes[0].appendChild(sngl_inspiral_table)
series = lal.CreateREAL8FrequencySeries(
psd_name, 0, 0, opts.df, lal.SecondUnit, n)
if '(double f) -> double' in func.__doc__:
series.data.data = vectorize_swig_psd_func(
psd_name_prefix + psd_name)(f)
else:
func(series, 0.0)
# Find indices of first and last nonzero samples.
nonzero = np.flatnonzero(series.data.data)
# FIXME: int cast seems to be needed on old versions of Numpy
first_nonzero = int(nonzero[0])
last_nonzero = int(nonzero[-1])
# Truncate
series = lal.CutREAL8FrequencySeries(
series, first_nonzero, last_nonzero - first_nonzero + 1)
series.f0 = first_nonzero * series.deltaF
series.name = psd_name
series.data.data *= scale
psds[detector] = series
xmldoc = lal.series.make_psd_xmldoc(psds)
command.register_to_xmldoc(xmldoc, parser, opts)
with glue.ligolw.utils.SignalsTrap():
glue.ligolw.utils.write_fileobj(
xmldoc, opts.output,
gz=(os.path.splitext(opts.output.name)[-1] == ".gz"))
from lalinspiral.sbank.tau0tau3 import m1m2_to_mchirp import lalsimulation # BAYESTAR imports. from lalinference.bayestar import filter # Other imports. import numpy as np import scipy.linalg # Open output file. xmldoc = ligolw.Document() xmldoc.appendChild(ligolw.LIGO_LW()) # Write process metadata to output file. process = command.register_to_xmldoc(xmldoc, parser, opts) # Create a SnglInspiral table and initialize its row ID counter. sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable) xmldoc.childNodes[0].appendChild(sngl_inspiral_table) sngl_inspiral_table.set_next_id(lsctables.SnglInspiralID(0)) f_low = 10. f_high = 2048. df = 0.1 initial_mchirp = m1m2_to_mchirp(opts.initial_mass1, opts.initial_mass2) initial_mtotal = opts.initial_mass1 + opts.initial_mass2 initial_eta = opts.initial_mass1 * opts.initial_mass2 / initial_mtotal**2 initial_chi = 0. initial_chirp_times = lalsimulation.SimInspiralTaylorF2RedSpinChirpTimesFromMchirpEtaChi(
except ValueError:
raise ValueError(
"sim_inspiral:f_lower columns are not unique, got values: " +
' '.join(f_lows))
else:
f_low = opts.low_frequency_cutoff
# Open output file.
out_xmldoc = ligolw.Document()
out_xmldoc.appendChild(ligolw.LIGO_LW())
# Write process metadata to output file. Masquerade as lalapps_tmpltbank and
# encode low frequency cutoff in command line arguments.
process = command.register_to_xmldoc(out_xmldoc,
parser,
opts,
ifos="H1",
comment="Exact-match template bank")
# Record low-frequency cutoff in the SearchSummVars table.
search_summvars_table = lsctables.New(lsctables.SearchSummVarsTable)
out_xmldoc.childNodes[0].appendChild(search_summvars_table)
search_summvars = lsctables.SearchSummVars()
search_summvars.search_summvar_id = search_summvars_table.get_next_id()
search_summvars.process_id = process.process_id
search_summvars.name = "low-frequency cutoff"
search_summvars.string = None
search_summvars.value = f_low
search_summvars_table.append(search_summvars)
# Create a SnglInspiral table and initialize its row ID counter.
# FIXME: sample rate could be a command line option; template duration and data
# duration should be determined from chirp time
sample_rate = 4096 # sample rate in Hz
template_duration = 128 # template duration in seconds
template_length = sample_rate * template_duration # template length in samples
data_duration = 512 # data duration in seconds
data_length = sample_rate * data_duration # data length in samples
# Open output file.
out_xmldoc = ligolw.Document()
out_xmldoc.appendChild(ligolw.LIGO_LW())
# Write process metadata to output file.
process = command.register_to_xmldoc(
out_xmldoc, parser, opts, ifos=opts.detector, comment="Little hope!")
# Add search summary to output file.
all_time = segments.segment([glue.lal.LIGOTimeGPS(0), glue.lal.LIGOTimeGPS(2e9)])
search_summary_table = lsctables.New(lsctables.SearchSummaryTable)
out_xmldoc.childNodes[0].appendChild(search_summary_table)
summary = ligolw_search_summary.append_search_summary(out_xmldoc, process,
inseg=all_time, outseg=all_time)
# Read template bank file.
progress.update(-1, 'reading ' + opts.template_bank.name)
xmldoc, _ = ligolw_utils.load_fileobj(
opts.template_bank, contenthandler=ligolw_bayestar.LSCTablesContentHandler)
# Determine the low frequency cutoff from the template bank file.
template_bank_f_low = ligolw_bayestar.get_template_bank_f_low(xmldoc)
from lalinference.bayestar import timing
# Other imports.
import numpy as np
progress = ProgressBar()
# Open output file.
progress.update(-1, 'setting up output document')
out_xmldoc = ligolw.Document()
out_xmldoc.appendChild(ligolw.LIGO_LW())
# Write process metadata to output file.
process = command.register_to_xmldoc(
out_xmldoc, parser, opts, ifos=opts.detector,
comment="Simulated coincidences")
# Add search summary to output file.
all_time = segments.segment(
[glue.lal.LIGOTimeGPS(0), glue.lal.LIGOTimeGPS(2e9)])
search_summary_table = lsctables.New(lsctables.SearchSummaryTable)
out_xmldoc.childNodes[0].appendChild(search_summary_table)
summary = ligolw_search_summary.append_search_summary(out_xmldoc, process,
inseg=all_time, outseg=all_time)
# Read PSDs.
progress.update(-1, 'reading ' + opts.reference_psd.name)
xmldoc, _ = ligolw_utils.load_fileobj(
opts.reference_psd, contenthandler=lal.series.PSDContentHandler)
psds = lal.series.read_psd_xmldoc(xmldoc)
