if opts.verbose:
print >> sys.stdout, "Initialized the template bank to seed with %d precomputed templates." % len(
bank)
#
# 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." % (
bank.add_from_hdf(hdf_fp)
hdf_fp.close()
if opts.verbose:
print("Initialized the template bank to seed with %d precomputed templates." % len(bank))
#
# 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("Found checkpoint file %s with %d precomputed templates." % (opts.output_filename + "_checkpoint.gz", len(tbl)))
print("Resuming from checkpoint with %d total templates..." % len(bank))
# reset rng state
rng_state = np.load(opts.output_filename + "_checkpoint.rng.npz")
rng1 = rng_state["state1"]
rng2 = rng_state["state2"]
rng3 = rng_state["state3"]
rng4 = rng_state["state4"]
np.random.mtrand.set_state( ("MT19937", rng1, rng2, rng3, rng4) )
else:
if opts.output_filename.endswith(('.xml', '.xml.gz')):
# get the correct generator
proposal = proposals[opts.approximant](opts.flow, **params)
# For robust convergence, ensure that an average of kmax/len(ks) of
# the last len(ks) proposals have been rejected by SBank.
ks = deque(10*[0], maxlen=10)
k = 0 # k is nprop per iteration
nprop = 1 # count total number of proposed templates
status_format = "bank size: %d\tproposed: %d\t" + "\t".join("%s: %s" % name_format for name_format in zip(waveform.param_names, waveform.param_formats))
while (k + float(sum(ks)))/len(ks) < opts.convergence_threshold:
tmplt = waveform(*proposal.next(), bank=bank)
k += 1
nprop += 1
if not bank.covers(tmplt, opts.match_min):
bank.insort(tmplt)
if opts.verbose:
print >>sys.stdout, status_format % ((len(bank), k) + tmplt.params)
ks.append(k)
k = 0
# clear the proposal template if caching is not enabled
if not opts.cache_waveforms:
tmplt.clear()
print "total number of proposed templates: %d" % nprop
print "total number of match calculations: %d" % bank._nmatch
print "final bank size: %d" % len(bank)
bank.clear() # clear caches
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)]
if opts.verbose:
print >>sys.stdout,"Found checkpoint file %s with %d precomputed templates." % (fout + "_checkpoint.gz", len(tbl))
print >>sys.stdout, "Resuming from checkpoint with %d total templates..." % len(bank)
# reset rng state
rng_state = np.load(fout + "_checkpoint.rng.npz")
rng1 = rng_state["state1"]
rng2 = rng_state["state2"]
rng3 = rng_state["state3"]
rng4 = rng_state["state4"]
np.random.mtrand.set_state( ("MT19937", rng1, rng2, rng3, rng4) )
else:
tmpdoc.unlink()
del sngl_inspiral, tmpdoc
if opts.verbose:
print>>sys.stdout,"\tseed bank size: %d"%len(bank)
print>>sys.stdout,"Filling regions of parameter space not covered by bank seed..."
#
# 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)]
if opts.verbose:
print >>sys.stdout,"Found checkpoint file with %d precomputed templates." % len(tbl)
print >>sys.stdout, "Resuming from checkpoint with %d total templates..." % len(bank)
# reset rng state
rng_state = np.load(fout + "_checkpoint.rng.npz")
rng1 = rng_state["state1"]
rng2 = rng_state["state2"]
rng3 = rng_state["state3"]
rng4 = rng_state["state4"]
np.random.mtrand.set_state( ("MT19937", rng1, rng2, rng3, rng4) )
else:
