flow = waveform.fpeak - 150
fupp = waveform.fpeak + 150
# Construct the time series for these params
waveform.make_wf_timeseries(theta=ext_params.inclination, phi=ext_params.phase)
#
# Generate IFO data
#
#print >> sys.stdout, "generating detector data objects..."
det1_data = simsig.DetData(det_site="H1",
noise_curve='aLIGO',
waveform=waveform,
ext_params=ext_params,
duration=0.5,
seed=0,
epoch=0.0,
f_low=10.0)
# --------------------------------------------------------------------
#print 'broad band pre-conditioning snr: ', \
# pycbc.filter.sigma(det1_data.td_signal,det1_data.psd,1000,5000)
#print 'f2 pre-conditioning snr: ', \
# pycbc.filter.sigma(det1_data.td_signal,det1_data.psd,flow,fupp)
# --- high-pass
high_pass = 1
knee = 1000
if high_pass:
# Construct the time series for these params
waveform.reproject_waveform(theta=ext_params.inclination,
phi=ext_params.phase)
# -----------------
#
# Generate IFO data
#
ts=time.time()
print >> sys.stdout, "generating detector responses & noise..."
det1_data = simsig.DetData(det_site=cp.get('analysis','ifo1'),
noise_curve=cp.get('analysis','noise-curve'), waveform=waveform,
ext_params=ext_params, duration=datalen, seed=seed, epoch=epoch,
f_low=flow, taper=cp.getboolean('analysis','taper-inspiral'),
target_snr=target_snr)
# Compute optimal SNR for injection
det1_optSNR=pycbc.filter.sigma(det1_data.td_signal, psd=det1_data.psd,
low_frequency_cutoff=flow, high_frequency_cutoff=0.5*srate)
det1_hrss=pycbc.filter.sigma(det1_data.td_signal,
low_frequency_cutoff=flow, high_frequency_cutoff=0.5*srate)
te=time.time()
print >> sys.stdout, "Injected SNR=%.2f"%det1_optSNR
print >> sys.stdout, "...data generation took %f sec"%(te-ts)