# 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'))
# load templates
#
# initialize the bank
#
bank = Bank(noise_model,
flow,
use_metric=False,
cache_waveforms=opts.cache_waveforms,
nhood_size=opts.neighborhood_size,
nhood_param=opts.neighborhood_param)
for file_approx in opts.template_bank:
# if no approximant specified, take from opts.template_approx
if len(file_approx.split(":")) == 1:
seed_file = file_approx
approx = opts.template_approx
else:
# if this fails, you have an input error
seed_file, approx = file_approx.split(":")
# add templates to bank
tmpdoc = utils.load_filename(seed_file, contenthandler=ContentHandler)
sngl_inspiral = lsctables.SnglInspiralTable.get_table(tmpdoc)
# so set the PSD to infinity above the max original frequency
noise_model = lambda g: np.where(g < f_max_orig, np.exp(interpolator(g)),
np.inf)
else:
noise_model = noise_models[opts.noise_model]
#
# initialize the bank
#
bank = Bank(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,
optimize_flow=opts.optimize_flow,
flow_column=opts.flow_column)
for file_approx in opts.bank_seed:
# if no approximant specified, use same approximant as the
# templates we will add
if len(file_approx.split(":")) == 1:
seed_file = file_approx
approx = opts.approximant
else:
# if this fails, you have an input error
seed_file, approx = file_approx.split(":")
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,
