def GetMatch(waveform0p, waveform1p, psd_file, f_low=30., freq_step=4):
flen = max(len(waveform0p), len(waveform1p))
waveform0p.resize(flen)
waveform1p.resize(flen)
# grab and use the psd file
my_psd = pycbc.psd.read.from_txt(filename=psd_file,
length=flen,
delta_f=1.0 / 4,
low_freq_cutoff=f_low,
is_asd_file=False)
m, i = match(waveform0p,
waveform1p,
psd=my_psd,
low_frequency_cutoff=f_low)
return m
def GetMatch (template0,template1, psd_file =args.psd_filename, f_low=30):
#resize two templates
tlen = max(len(template0),len(template1))
template1.resize(tlen)
template0.resize(tlen)
#grab and use the psd file
my_psd = pycbc.psd.read.from_txt(filename = psd_file,
length = tlen,
delta_f = template0.delta_f,
low_freq_cutoff = f_low,
is_asd_file = False)
#calculate match
m,i = match(template0,template1,psd=my_psd,low_frequency_cutoff=f_low)
return m
def GetMatch(template0, template1, psd_file=args.psd_filename, f_low=30):
#resize two templates
flen = max(len(template0), len(template1))
#template1=template1.copy()
template1.resize(flen)
#template0=template0.copy()
template0.resize(flen)
#grab and use the psd file
#df = 1.0/template1.duration
my_psd = pycbc.psd.read.from_txt(filename=psd_file,
length=flen,
delta_f=1.0 / 4,
low_freq_cutoff=f_low,
is_asd_file=False)
#calculate match
m, i = match(template0, template1, psd=my_psd, low_frequency_cutoff=f_low)
return m
def compare(psd, psd_path, hep, hec, distance, f_low, f_high, approx_e, EOS_e,
hm_path):
if psd_type == 'aLIGOZeroDetHighPower':
inclination = 0.0
hhm = h5py.File(hm_path, 'r')
EOS_m = hhm.attrs['EOS']
grav_m1 = hhm.attrs['grav_mass1']
grav_m2 = hhm.attrs['grav_mass2']
s1x = hhm.attrs['spin1x']
s1y = hhm.attrs['spin1y']
s1z = hhm.attrs['spin1z']
s2x = hhm.attrs['spin2x']
s2y = hhm.attrs['spin2y']
s2z = hhm.attrs['spin2z']
f_lower_hhm = hhm.attrs[
'f_lower_at_1MSUN'] #/(2.0 * lal.TWOPI * lal.MTSUN_SI*(m1+m2))
hhm_freq = hhm.attrs['hybridize_freq']
hhm_shift_time = hhm.attrs['shift_time']
approx_m = hhm.attrs['approx']
h_matchm = hhm.attrs['hybrid_match']
sim_name = hhm.attrs['sim_name']
hhm.close()
hmp, hmc = get_td_waveform(approximant='NR_hdf5',
numrel_data=hm_path,
mass1=grav_m1,
mass2=grav_m2,
spin1z=s1z,
spin1x=s1x,
spin1y=s1y,
spin2z=s2z,
spin2x=s2x,
spin2y=s2y,
delta_t=delta_t,
distance=distance,
f_lower=f_lower_hhm,
inclination=inclination)
### multiply by stupid pycbc factor
hmp = hmp * pycbc_factor
hmc = hmc * pycbc_factor
### set a constant t_len. We do this because all the numerical waveforms will be padded to this length and a new fft plan will not be needed. Basically this makes everything
### MUCH faster.
t_len = 300000
hep.resize(t_len)
hmp.resize(t_len)
#Generate the aLIGO ZDHP PSD
delta_f = 1.0 / hmp.duration
f_len = t_len / 2 + 1
psd = aLIGOZeroDetHighPower(f_len, delta_f, f_low)
#calculate L2 distance
l2_distance = hy.delta_h(hep, hmp)
#calculate snr and match values
hep_hmp_match, index = matchedfilter.match(
hep,
hmp,
psd=psd,
low_frequency_cutoff=f_low,
high_frequency_cutoff=f_high)
hmp_norm = matchedfilter.sigmasq(hmp,
psd=psd,
low_frequency_cutoff=f_low,
high_frequency_cutoff=f_high)
overlap = matchedfilter.overlap(hep,
hmp,
psd=psd,
low_frequency_cutoff=f_low,
high_frequency_cutoff=f_high)
mismatch = 1.0 - overlap
data = (l2_distance, hep_hmp_match, overlap, mismatch, hmp_norm,
h_matchm, hhm_shift_time, hhe_freq, hhm_freq, approx_e,
approx_m, EOS_e, EOS_m, sim_name)
return data
elif psd_type != 'aLIGOZeroDetHighPower':
# similar to previous section
inclination = 0.0
hhm = h5py.File(hm_path, 'r')
EOS_m = hhm.attrs['EOS']
grav_m1 = hhm.attrs['grav_mass1']
grav_m2 = hhm.attrs['grav_mass2']
s1x = hhm.attrs['spin1x']
s1y = hhm.attrs['spin1y']
s1z = hhm.attrs['spin1z']
s2x = hhm.attrs['spin2x']
s2y = hhm.attrs['spin2y']
s2z = hhm.attrs['spin2z']
f_lower_hhm = hhm.attrs[
'f_lower_at_1MSUN'] #/(2.0 * lal.TWOPI * lal.MTSUN_SI*(m1+m2))
hhm_freq = hhm.attrs['hybridize_freq']
hhm_shift_time = hhm.attrs['shift_time']
approx_m = hhm.attrs['approx']
h_matchm = hhm.attrs['hybrid_match']
sim_name = hhm.attrs['sim_name']
hhm.close()
hmp, hmc = get_td_waveform(approximant='NR_hdf5',
numrel_data=hm_path,
mass1=grav_m1,
mass2=grav_m2,
spin1z=s1z,
spin1x=s1x,
spin1y=s1y,
spin2z=s2z,
spin2x=s2x,
spin2y=s2y,
delta_t=delta_t,
distance=distance,
f_lower=f_lower_hhm,
inclination=inclination)
### multiply by stupid pycbc factor
hmp = hmp * pycbc_factor
hmc = hmc * pycbc_factor
t_len = 300000
hep.resize(t_len)
hmp.resize(t_len)
#Generate the aLIGO ZDHP PSD
delta_f = 1.0 / hmp.duration
f_len = t_len / 2 + 1
### reads in a custom psd file
psd = pycbc.psd.read.from_txt(psd_path,
f_len,
delta_f,
f_low,
is_asd_file=True)
l2_distance = hy.delta_h(hep, hmp)
hep_hmp_match, index = matchedfilter.match(
hep,
hmp,
psd=psd,
low_frequency_cutoff=f_low,
high_frequency_cutoff=f_high)
hmp_norm = matchedfilter.sigmasq(hmp,
psd=psd,
low_frequency_cutoff=f_low,
high_frequency_cutoff=f_high)
overlap = matchedfilter.overlap(hep,
hmp,
psd=psd,
low_frequency_cutoff=f_low,
high_frequency_cutoff=f_high)
mismatch = 1.0 - overlap
data = (l2_distance, hep_hmp_match, overlap, mismatch, hmp_norm,
h_matchm, hhm_shift_time, hhe_freq, hhm_freq, approx_e,
approx_m, EOS_e, EOS_m, sim_name)
return data