#
# check for saved work
#
if opts.checkpoint and os.path.exists(opts.output_filename + "_checkpoint.gz"):
xmldoc = utils.load_filename(opts.output_filename + "_checkpoint.gz",
contenthandler=ContentHandler)
tbl = lsctables.SnglInspiralTable.get_table(xmldoc)
[
bank.insort(t) for t in Bank.from_sngls(
tbl,
tmplt_class,
noise_model,
opts.flow,
opts.use_metric,
opts.cache_waveforms,
opts.neighborhood_size,
opts.neighborhood_param,
coarse_match_df=opts.coarse_match_df,
iterative_match_df_max=opts.iterative_match_df_max,
fhigh_max=opts.fhigh_max)
]
if opts.verbose:
print >> sys.stdout, "Found checkpoint file %s with %d precomputed templates." % (
opts.output_filename + "_checkpoint.gz", len(tbl))
print >> sys.stdout, "Resuming from checkpoint with %d total templates..." % len(
bank)
# reset rng state
rng_state = np.load(opts.output_filename + "_checkpoint.rng.npz")
psd = REAL8FrequencySeries(name="psd", f0=0., deltaF=1., data=get_PSD(1., opts.flow, 1570., noise_model))
#
# seed the bank, if applicable
#
if opts.bank_seed is None:
# seed the process with an empty bank
# the first proposal will always be accepted
bank = Bank(waveform, noise_model, opts.flow, opts.use_metric, opts.cache_waveforms, opts.neighborhood_size, opts.neighborhood_param, coarse_match_df=opts.coarse_match_df, iterative_match_df_max=opts.iterative_match_df_max, fhigh_max=opts.fhigh_max)
else:
# seed bank with input bank. we do not prune the bank
# for overcoverage, but take it as is
tmpdoc = utils.load_filename(opts.bank_seed, contenthandler=ContentHandler)
sngl_inspiral = table.get_table(tmpdoc, lsctables.SnglInspiralTable.tableName)
bank = Bank.from_sngls(sngl_inspiral, waveform, noise_model, opts.flow, opts.use_metric, opts.cache_waveforms, opts.neighborhood_size, opts.neighborhood_param, coarse_match_df=opts.coarse_match_df, iterative_match_df_max=opts.iterative_match_df_max, fhigh_max=opts.fhigh_max)
tmpdoc.unlink()
del sngl_inspiral, tmpdoc
if opts.verbose:
print>>sys.stdout,"Initialized the template bank to seed file %s with %d precomputed templates." % (opts.bank_seed, len(bank))
#
# check for saved work
#
if opts.checkpoint and os.path.exists( fout + "_checkpoint.gz" ):
xmldoc = utils.load_filename(fout + "_checkpoint.gz", contenthandler=ContentHandler)
tbl = table.get_table(xmldoc, lsctables.SnglInspiralTable.tableName)
[bank.insort(t) for t in Bank.from_sngls(tbl, waveform, noise_model, opts.flow, opts.use_metric, opts.cache_waveforms, opts.neighborhood_size, opts.neighborhood_param, coarse_match_df=opts.coarse_match_df, iterative_match_df_max=opts.iterative_match_df_max, fhigh_max=opts.fhigh_max)]
opts_dict,
version="no version",
cvs_repository="sbank",
cvs_entry_time=strftime("%Y/%m/%d %H:%M:%S"),
)
h5file = H5File("%s.h5" % usertag, "w")
# load templates
xmldoc = utils.load_filename(tmplt_file, contenthandler=ContentHandler)
ligolw_copy_process(xmldoc, fake_xmldoc)
sngls = table.get_table(xmldoc, lsctables.SnglInspiralTable.tableName)
bank = Bank.from_sngls(
sngls,
tmplt_approx,
noise_model,
flow,
use_metric=False,
cache_waveforms=opts.cache_waveforms,
nhood_size=opts.neighborhood_size,
nhood_param=opts.neighborhood_param,
)
# write bank to h5 file, but note that from_sngls() has resorted the
# bank by neighborhood_param
sngls = lsctables.SnglInspiralTable()
for s in bank._templates:
sngls.append(s.to_sngl())
h5file.create_dataset("/sngl_inspiral", data=ligolw_table_to_array(sngls), compression="gzip", compression_opts=1)
h5file.flush()
del xmldoc, sngls[:]
if verbose:
print "Loaded %d templates" % len(bank)
tmplt_format = "".join(
else:
noise_model = noise_models[opts.noise_model]
# Seed the bank
if opts.bank_seed is None:
# seed the process with an empty bank
# the first proposal will always be accepted
bank = Bank(waveform, noise_model, opts.flow, opts.use_metric, opts.cache_waveforms)
else:
# seed bank with input bank. we do not prune the bank
# for overcoverage, but take it as is
if opts.verbose:
print>>sys.stdout,"Seeding the template bank..."
tmpdoc = utils.load_filename(opts.bank_seed, contenthandler=ContentHandler)
sngl_inspiral = table.get_table(tmpdoc, lsctables.SnglInspiralTable.tableName)
bank = Bank.from_sngls(sngl_inspiral, waveform, noise_model, opts.flow, opts.use_metric, opts.cache_waveforms)
tmpdoc.unlink()
del sngl_inspiral, tmpdoc
if opts.verbose:
print>>sys.stdout,"\tinitial bank size: %d"%len(bank)
print>>sys.stdout,"Filling regions of parameter space not covered by bank seed..."
# Choose proposal function
# TODO: Implement more of these
# populate params dictionary to be passed to the generators
params = {'mass1': (opts.mass1_min, opts.mass1_max),
'mass2': (opts.mass2_min, opts.mass2_max),
'mtotal': (opts.mtotal_min, opts.mtotal_max),
'mratio': (opts.qmin, opts.qmax),
'mchirp': (opts.mchirp_min, opts.mchirp_max),
opts_dict = dict((k, v) for k, v in opts.__dict__.iteritems() if v is not False and v is not None)
#process = ligolw_process.register_to_xmldoc(fake_xmldoc, "lalapps_cbc_sbank_sim", FIXME
# opts_dict, version=git_version.tag or git_version.id, FIXME
# cvs_repository="sbank", cvs_entry_time=git_version.date) FIXME
process = ligolw_process.register_to_xmldoc(fake_xmldoc, "lalapps_cbc_sbank_sim",
opts_dict, version="no version",
cvs_repository="sbank", cvs_entry_time=strftime('%Y/%m/%d %H:%M:%S'))
h5file = H5File("%s.h5" % usertag, "w")
# load templates
xmldoc = utils.load_filename(tmplt_file, contenthandler=ContentHandler)
ligolw_copy_process(xmldoc, fake_xmldoc)
sngls = table.get_table(xmldoc, lsctables.SnglInspiralTable.tableName)
h5file.create_dataset("/sngl_inspiral", data=ligolw_table_to_array(sngls), compression='gzip', compression_opts=1)
h5file.flush()
bank = Bank.from_sngls(sngls, tmplt_approx, noise_model, flow, use_metric=False, cache_waveforms=opts.cache_waveforms)
del xmldoc, sngls[:]
if verbose:
print "Loaded %d templates" % len(bank)
tmplt_format = "".join("%s: %s " % name_format for name_format in zip(tmplt_approx.param_names, tmplt_approx.param_formats))
# pick injection parameters
xmldoc2 = utils.load_filename(inj_file, contenthandler=ContentHandler)
ligolw_copy_process(xmldoc2, fake_xmldoc)
sims = table.get_table(xmldoc2, lsctables.SimInspiralTable.tableName)
#
# sort sims by mchirp before writing back to disk Bank.from_sims()
# sorts the waveforms by mchirp so we need to also sort here to get
# the injection --> best match mapping correct later
#
fake_xmldoc,
"lalapps_cbc_sbank_sim",
opts_dict,
version="no version",
cvs_repository="sbank",
cvs_entry_time=strftime('%Y/%m/%d %H:%M:%S'))
h5file = H5File("%s.h5" % usertag, "w")
# load templates
xmldoc = utils.load_filename(tmplt_file, contenthandler=ContentHandler)
ligolw_copy_process(xmldoc, fake_xmldoc)
sngls = table.get_table(xmldoc, lsctables.SnglInspiralTable.tableName)
bank = Bank.from_sngls(sngls,
tmplt_approx,
noise_model,
flow,
use_metric=False,
cache_waveforms=opts.cache_waveforms,
nhood_size=opts.neighborhood_size,
nhood_param=opts.neighborhood_param)
# write bank to h5 file, but note that from_sngls() has resorted the
# bank by neighborhood_param
sngls = lsctables.SnglInspiralTable()
for s in bank._templates:
sngls.append(s.to_sngl())
h5file.create_dataset("/sngl_inspiral",
data=ligolw_table_to_array(sngls),
compression='gzip',
compression_opts=1)
h5file.flush()
del xmldoc, sngls[:]
if verbose:
