#
# choose noise model
#
if opts.reference_psd is not None:
if opts.reference_psd.endswith(".txt") or opts.reference_psd.endswith(
".txt.gz") or opts.reference_psd.endswith(".dat"):
# assume psd file is a two-column ASCII formatted file
data = np.loadtxt(opts.reference_psd)
f_orig, psddata = data[:, 0], data[:, 1]
elif opts.reference_psd.endswith(".xml") or opts.reference_psd.endswith(
".xml.gz"):
# assume psd file is formatted as a LIGOLW XML
psddict = read_psd(opts.reference_psd)
if opts.instrument:
psd = psddict[opts.instrument]
elif len(psddict.keys()) == 1:
psd = psddict[psddict.keys()[0]]
else:
raise ValueError(
"More than one PSD found in file %s. Specify which you want with --instrument."
% opts.reference_psd)
f_orig = psd.f0 + np.arange(len(psd.data.data)) * psd.deltaF
psddata = psd.data.data
# cut off upper frequency content as requested by user for better
# computational performance
f_max_orig = max(f_orig)
if opts.fhigh_max:
parser.add_option("--cache-waveforms", default = False, action="store_true", help="A given waveform in the template bank will be used many times throughout the bank simulation process. You can save a considerable amount of CPU by caching the waveform from the first time it is generated; however, do so only if you are sure that storing the waveforms in memory will not overload the system memory.")
parser.add_option("--neighborhood-size", metavar="N", default = 1.0, type="float", help="Specify the window size in seconds to define \"nearby\" templates used to compute the match against each injection. The neighborhood is chosen symmetric about the injection; \"nearby\" is defined using the option --neighborhood-type. The default value of 1.0 is *not a guarantee of performance*. Choosing the neighborhood too small may lead to the reporting of lower-than-actual fitting factors.")
parser.add_option("--neighborhood-param", default="tau0", choices=["tau0", "dur"], help="Choose how the neighborhood is sorted for match calculations.")
opts, args = parser.parse_args()
if opts.reference_psd and not opts.instrument:
raise ValueError, "--instrument is a required option when specifying reference PSD"
return opts, args
opts, args = parse_command_line()
# Choose noise model
if opts.reference_psd is not None:
psd = read_psd(opts.reference_psd)[opts.instrument]
f_orig = psd.f0 + np.arange(len(psd.data.data)) * psd.deltaF
interpolator = UnivariateSpline(f_orig, np.log(psd.data.data), s=0)
noise_model = lambda g: np.exp(interpolator(g))
else:
noise_model = noise_models[opts.noise_model]
usertag = opts.user_tag # use to label output
inj_file = opts.injection_file # pre-sorted by mchirp
inj_approx = waveforms[opts.injection_approx]
flow = opts.flow
verbose = opts.verbose
if opts.use_gpu_match:
from lalinspiral.sbank.overlap_cuda import compute_match, create_workspace_cache
import sbank.waveforms
import sbank.bank
edge_list = igraph.Graph.Full(numTemplates)
print "Writing .ncol of graph edges..."
# This writes an edge list!
edge_list.write("./edge_lists/edge_list_%s.ncol" % str(numTemplates), format='ncol')
# This loads the edge list from the exported file
# of the form:
# [[0,1], [0, 2]... [2,3], [3,4]]
print "Loading edge list..."
edge_array = np.loadtxt("./edge_lists/edge_list_%s.ncol" % str(numTemplates))
edge_array = edge_array[generateFrom:generateTo]
# Read in PSD and make it usable
print "Reading PSD..."
psd = read_psd('H1L1V1-REFERENCE_PSD-966386126-24805.xml.gz')['H1']
print "Preparing PSD..."
f_orig = psd.f0 + np.arange(len(psd.data)) * psd.deltaF
f_max_orig = max(f_orig)
interpolator = UnivariateSpline(f_orig, np.log(psd.data), s=0)
noise_model = lambda g: np.where(g < f_max_orig, np.exp(interpolator(g)), np.inf)
PSD = get_PSD(1. / duration, f_low, f_high, noise_model)
# Generate ASD
print "Generating ASD"
ASD = np.sqrt(PSD)
print "Creating workspace..."
# Create workspace for match calculation
workspace_cache = CreateSBankWorkspaceCache()
fout = opts.output_filename
elif opts.user_tag:
fout = "%s-SBANK_%s-%d-%d.xml.gz" % (opts.instrument, opts.user_tag, opts.gps_start_time, opts.gps_end_time-opts.gps_start_time)
else:
fout = "%s-SBANK-%d-%d.xml.gz" % (opts.instrument, opts.gps_start_time, opts.gps_end_time-opts.gps_start_time)
#
# choose waveform approximant
#
tmplt_class = waveforms[opts.approximant]
#
# choose noise model
#
if opts.reference_psd is not None:
psd = read_psd(opts.reference_psd)[opts.instrument]
f_orig = psd.f0 + np.arange(len(psd.data.data)) * psd.deltaF
f_max_orig = max(f_orig)
if opts.fhigh_max:
if opts.fhigh_max > f_max_orig:
print >> sys.stderr, "Warning: requested fhigh-max (%.3f Hz) exceeds limits of PSD (%.3f Hz). Using PSD limit instead!" \
% (opts.fhigh_max, f_max_orig)
opts.fhigh_max = float(f_max_orig)
else:
print >> sys.stderr, "Warning: fhigh-max not specified, using maximum frequency in the PSD (%.3f Hz)" \
% f_max_orig
opts.fhigh_max = float(f_max_orig)
interpolator = UnivariateSpline(f_orig, np.log(psd.data.data), s=0)
# spline extrapolation may lead to unexpected results,
#
tmplt_class = waveforms[opts.approximant]
#
# choose noise model
#
if opts.reference_psd is not None:
if opts.reference_psd.endswith(".txt") or opts.reference_psd.endswith(".txt.gz") or opts.reference_psd.endswith(".dat"):
# assume psd file is a two-column ASCII formatted file
data = np.loadtxt(opts.reference_psd)
f_orig, psddata = data[:,0], data[:,1]
elif opts.reference_psd.endswith(".xml") or opts.reference_psd.endswith(".xml.gz"):
# assume psd file is formatted as a LIGOLW XML
psddict = read_psd(opts.reference_psd)
if opts.instrument:
psd = psddict[opts.instrument]
elif len(psddict.keys()) == 1:
psd = psddict[psddict.keys()[0]]
else:
raise ValueError("More than one PSD found in file %s. Specify which you want with --instrument." % opts.reference_psd)
f_orig = psd.f0 + np.arange(len(psd.data.data)) * psd.deltaF
psddata = psd.data.data
# cut off upper frequency content as requested by user for better
# computational performance
f_max_orig = max(f_orig)
if opts.fhigh_max:
if opts.fhigh_max > f_max_orig:
print >> sys.stderr, "Warning: requested fhigh-max (%.3f Hz) exceeds limits of PSD (%.3f Hz). Using PSD limit instead!" \
