def check(self, triggers, data_reader, **kwds):
""" Look for a single detector trigger that passes the thresholds in
the current data.
"""
if len(triggers['snr']) == 0:
return None
i = triggers['snr'].argmax()
# This uses the pycbc live convention of chisq always meaning the
# reduced chisq.
rchisq = triggers['chisq'][i]
nsnr = newsnr(triggers['snr'][i], rchisq)
dur = triggers['template_duration'][i]
if (nsnr > self.newsnr_threshold
and rchisq < self.reduced_chisq_threshold
and dur > self.duration_threshold):
d = {key: triggers[key][i] for key in triggers}
d['stat'] = nsnr
d['ifar'] = self.fixed_ifar
return SingleForGraceDB(
self.ifo,
d,
hardware_injection=data_reader.near_hwinj(),
**kwds)
else:
return None
def check(self, triggers, data_reader, **kwds):
""" Look for a single detector trigger that passes the thresholds in
the current data.
"""
if len(triggers['snr']) == 0:
return None
i = triggers['snr'].argmax()
# This uses the pycbc live convention of chisq always meaning the
# reduced chisq.
rchisq = triggers['chisq'][i]
nsnr = newsnr(triggers['snr'][i], rchisq)
dur = triggers['template_duration'][i]
if nsnr > self.newsnr_threshold and \
rchisq < self.reduced_chisq_threshold and \
dur > self.duration_threshold:
d = {key: triggers[key][i] for key in triggers}
d['stat'] = nsnr
d['ifar'] = self.fixed_ifar
return SingleForGraceDB(self.ifo, d,
hardware_injection=data_reader.near_hwinj(),
**kwds)
else:
return None
def xis(snr, template, strain, psd_o, fc, grafica, time_max, duration, hc,
shift, time_trigger):
nbins = 16
chisq = power_chisq(template,
strain,
nbins,
psd_o,
low_frequency_cutoff=fc,
high_frequency_cutoff=hc)
chisq = chisq.crop(4, 4)
dof = nbins * 2 - 2
chisq /= dof
nsnr = newsnr(snr, chisq)
maximo = max(nsnr)
print 'maximo ', abs(maximo)
MaxInd = np.argmax(nsnr)
time = snr.sample_times[MaxInd]
if grafica == 1:
pylab.figure('Superposition')
pylab.plot(snr.sample_times, abs(nsnr), label='Re-weighted SNR')
#pylab.plot(snr.sample_times,abs(snr),label='Original SNR')
#pylab.title('Re-weighted SNR')
#pylab.ylabel('Signal-to-noise ratio')
pylab.xlabel('Time(s)')
pylab.legend()
#pylab.xlim(time_max-0.05, time_max+0.05)
pylab.grid()
pylab.show()
pylab.figure('Re-weighted SNR')
pylab.plot(snr.sample_times, abs(nsnr))
pylab.grid()
pylab.show()
return maximo, time, nsnr
def check(self, triggers, data_reader):
""" Look for a single detector trigger that passes the thresholds in
the current data.
"""
if len(triggers['snr']) == 0:
return None
i = triggers['snr'].argmax()
# This uses the pycbc live convention of chisq always meaning the
# reduced chisq.
rchisq = triggers['chisq'][i]
nsnr = newsnr(triggers['snr'][i], rchisq)
dur = triggers['template_duration'][i]
if nsnr > self.newsnr_threshold and \
rchisq < self.reduced_chisq_threshold and \
dur > self.duration_threshold:
fake_coinc = {'foreground/%s/%s' % (self.ifo, k): triggers[k][i]
for k in triggers}
fake_coinc['foreground/stat'] = nsnr
fake_coinc['foreground/ifar'] = self.fixed_ifar
fake_coinc['HWINJ'] = data_reader.near_hwinj()
return fake_coinc
return None
def newsnr(self):
return events.newsnr(self.snr, self.rchisq)
def newsnr(self):
return events.newsnr(self.snr, self.rchisq)
def values(self, stilde, template, psd, snrv, snr_norm,
bchisq, bchisq_dof, indices):
""" Calculate sine-Gaussian chisq
Parameters
----------
stilde: pycbc.types.Frequencyseries
The overwhitened strain
template: pycbc.types.Frequencyseries
The waveform template being analyzed
psd: pycbc.types.Frequencyseries
The power spectral density of the data
snrv: numpy.ndarray
The peak unnormalized complex SNR values
snr_norm: float
The normalization factor for the snr
bchisq: numpy.ndarray
The Bruce Allen power chisq values for these triggers
bchisq_dof: numpy.ndarray
The degrees of freedom of the Bruce chisq
indics: numpy.ndarray
The indices of the snr peaks.
Returns
-------
chisq: Array
Chisq values, one for each sample index
"""
if not self.do:
return None
if template.params.template_hash not in self.params:
return numpy.ones(len(snrv))
values = self.params[template.params.template_hash].split(',')
# Get the chisq bins to use as the frequency reference point
bins = self.cached_chisq_bins(template, psd)
# This is implemented slowly, so let's not call it often, OK?
chisq = numpy.ones(len(snrv))
for i, snrvi in enumerate(snrv):
#Skip if newsnr too low
snr = abs(snrvi * snr_norm)
nsnr = newsnr(snr, bchisq[i] / bchisq_dof[i])
if nsnr < self.snr_threshold:
continue
N = (len(template) - 1) * 2
dt = 1.0 / (N * template.delta_f)
kmin = int(template.f_lower / psd.delta_f)
time = float(template.epoch) + dt * indices[i]
# Shift the time of interest to be centered on 0
stilde_shift = apply_fseries_time_shift(stilde, -time)
# Only apply the sine-Gaussian in a +-50 Hz range around the
# central frequency
qwindow = 50
chisq[i] = 0
# Estimate the maximum frequency up to which the waveform has
# power by approximating power per frequency
# as constant over the last 2 chisq bins. We cannot use the final
# chisq bin edge as it does not have to be where the waveform
# terminates.
fstep = (bins[-2] - bins[-3])
fpeak = (bins[-2] + fstep) * template.delta_f
# This is 90% of the Nyquist frequency of the data
# This allows us to avoid issues near Nyquist due to resample
# Filtering
fstop = len(stilde) * stilde.delta_f * 0.9
dof = 0
# Calculate the sum of SNR^2 for the sine-Gaussians specified
for descr in values:
# Get the q and frequency offset from the descriptor
q, offset = descr.split('-')
q, offset = float(q), float(offset)
fcen = fpeak + offset
flow = max(kmin * template.delta_f, fcen - qwindow)
fhigh = fcen + qwindow
# If any sine-gaussian tile has an upper frequency near
# nyquist return 1 instead.
if fhigh > fstop:
return numpy.ones(len(snrv))
kmin = int(flow / template.delta_f)
kmax = int(fhigh / template.delta_f)
#Calculate sine-gaussian tile
gtem = sinegauss.fd_sine_gaussian(1.0, q, fcen, flow,
len(template) * template.delta_f,
template.delta_f).astype(numpy.complex64)
gsigma = sigma(gtem, psd=psd,
low_frequency_cutoff=flow,
high_frequency_cutoff=fhigh)
#Calculate the SNR of the tile
gsnr = (gtem[kmin:kmax] * stilde_shift[kmin:kmax]).sum()
gsnr *= 4.0 * gtem.delta_f / gsigma
chisq[i] += abs(gsnr)**2.0
dof += 2
if dof == 0:
chisq[i] = 1
else:
chisq[i] /= dof
return chisq
def xis(snr,template,strain,psd_o,fc,grafica,time_max,duration,hc,shift,time_trigger):
nbins = 32
start_time = strain.start_time
end_time = strain.start_time+32
chisq = power_chisq(template, strain, nbins, psd_o, low_frequency_cutoff=fc,high_frequency_cutoff=hc)
if grafica == 1:
print(start_time)
print(end_time)
print(time_max)
print(strain.delta_f)
print(template.delta_f)
print(psd_o.delta_f)
crop_left = abs(start_time - time_max)-1.98
crop_right = abs(end_time - time_max)-1.98
print(crop_left)
print(crop_right)
chisq = chisq.crop(crop_left, crop_right)
dof = 2*nbins-2
chisq /= dof
low_chisq = abs(chisq).numpy().argmin()
if grafica == 0:
chisq = chisq.crop(4,4)
dof = 2*nbins-2
chisq /= dof
low_chisq = chisq[time_trigger]
print('Lower value of Chi')
print(low_chisq)
if grafica == 1:
nsnr=newsnr(snr,chisq)
pylab.plot(chisq.sample_times,chisq)
pylab.ylabel('$chi^2_r$')
pylab.xlabel('Time(s)')
pylab.xlim(time_max-0.1, time_max+0.1)
pylab.grid()
pylab.show()
pylab.figure('Superposition',figsize=[15,5])
pylab.plot(snr.sample_times,abs(nsnr),label='Re-weighted SNR')
pylab.plot(snr.sample_times,abs(snr),label='Original SNR')
pylab.plot(chisq.sample_times,chisq,label='$Chi^2$')
#pylab.title('Re-weighted SNR')
#pylab.ylabel('Signal-to-noise ratio')
pylab.xlabel('Time(s)')
pylab.legend()
#pylab.xlim(time_max-0.05, time_max+0.05)
pylab.grid()
pylab.show()
pylab.plot(snr.sample_times,abs(snr))
pylab.title('Matched filter output')
pylab.ylabel('Signal-to-noise ratio')
pylab.xlabel('Time(s)')
pylab.xlim(time_max-0.1, time_max+0.1)
pylab.grid()
pylab.show()
return chisq, low_chisq
def values(self, stilde, template, psd, snrv, snr_norm, bchisq, bchisq_dof,
indices):
""" Calculate sine-Gaussian chisq
Parameters
----------
stilde: pycbc.types.Frequencyseries
The overwhitened strain
template: pycbc.types.Frequencyseries
The waveform template being analyzed
psd: pycbc.types.Frequencyseries
The power spectral density of the data
snrv: numpy.ndarray
The peak unnormalized complex SNR values
snr_norm: float
The normalization factor for the snr
bchisq: numpy.ndarray
The Bruce Allen power chisq values for these triggers
bchisq_dof: numpy.ndarray
The degrees of freedom of the Bruce chisq
indics: numpy.ndarray
The indices of the snr peaks.
Returns
-------
chisq: Array
Chisq values, one for each sample index
"""
if not self.do:
return None
if template.params.template_hash not in self.params:
return numpy.ones(len(snrv))
values = self.params[template.params.template_hash].split(',')
# Get the chisq bins to use as the frequency reference point
bins = self.cached_chisq_bins(template, psd)
# This is implemented slowly, so let's not call it often, OK?
chisq = numpy.ones(len(snrv))
for i, snrvi in enumerate(snrv):
#Skip if newsnr too low
snr = abs(snrvi * snr_norm)
nsnr = newsnr(snr, bchisq[i] / bchisq_dof[i])
if nsnr < self.snr_threshold:
continue
N = (len(template) - 1) * 2
dt = 1.0 / (N * template.delta_f)
kmin = int(template.f_lower / psd.delta_f)
time = float(template.epoch) + dt * indices[i]
# Shift the time of interest to be centered on 0
stilde_shift = apply_fseries_time_shift(stilde, -time)
# Only apply the sine-Gaussian in a +-50 Hz range around the
# central frequency
qwindow = 50
chisq[i] = 0
# Estimate the maximum frequency up to which the waveform has
# power by approximating power per frequency
# as constant over the last 2 chisq bins. We cannot use the final
# chisq bin edge as it does not have to be where the waveform
# terminates.
fstep = (bins[-2] - bins[-3])
fpeak = (bins[-2] + fstep) * template.delta_f
# This is 90% of the Nyquist frequency of the data
# This allows us to avoid issues near Nyquist due to resample
# Filtering
fstop = len(stilde) * stilde.delta_f * 0.9
dof = 0
# Calculate the sum of SNR^2 for the sine-Gaussians specified
for descr in values:
# Get the q and frequency offset from the descriptor
q, offset = descr.split('-')
q, offset = float(q), float(offset)
fcen = fpeak + offset
flow = max(kmin * template.delta_f, fcen - qwindow)
fhigh = fcen + qwindow
# If any sine-gaussian tile has an upper frequency near
# nyquist return 1 instead.
if fhigh > fstop:
return numpy.ones(len(snrv))
kmin = int(flow / template.delta_f)
kmax = int(fhigh / template.delta_f)
#Calculate sine-gaussian tile
gtem = sinegauss.fd_sine_gaussian(
1.0, q, fcen, flow,
len(template) * template.delta_f,
template.delta_f).astype(numpy.complex64)
gsigma = sigma(gtem,
psd=psd,
low_frequency_cutoff=flow,
high_frequency_cutoff=fhigh)
#Calculate the SNR of the tile
gsnr = (gtem[kmin:kmax] * stilde_shift[kmin:kmax]).sum()
gsnr *= 4.0 * gtem.delta_f / gsigma
chisq[i] += abs(gsnr)**2.0
dof += 2
if dof == 0:
chisq[i] = 1
else:
chisq[i] /= dof
logging.info('Found chisq %s', chisq[i])
return chisq
gs2 = t2['L1/sigmasq'][:][tid2] ** 0.5
vs1 = t1['H1/snr'][:][tid1]
vs2 = t2['L1/snr'][:][tid2]
#pd = numpy.append(pd, (t1['H1/coa_phase'][:][tid1] - t2['L1/coa_phase'][:][tid2]) % (numpy.pi * 2.0))
#td = numpy.append(td, (t1['H1/end_time'][:][tid1] - t2['L1/end_time'][:][tid2]))
rd = numpy.append(rd, ifar/ifar2)
md = numpy.append(md, (vs1**2.0 + vs2**2.0) ** 0.5)
g1 = numpy.append(g1, gs1 / vs1)
g2 = numpy.append(g2, gs2 / vs2)
v1 = numpy.append(v1, vs1)
v2 = numpy.append(v2, vs2)
pv = numpy.append(pv, stat**2.0 - newsnr(vs1, c1)**2.0 - newsnr(vs2, c2)**2.0)
stat_diff = numpy.append(stat_diff, abs(stat2 - stat))
#print pv
#print v1[0:3], v2[0:3], o1[0:3], o2[0:3]
print "SIZE", len(o1)
print "Total number found with reference IFAR >1 and comparison IFAR <1000: {0}".format(total_found)
print "Total number found (at all) in reference but missed in comparison: {0}".format(total_missed)
df = ifs / ifs2
s = df.argsort()[::-1]
#o1 = o1[s]
#o2 = o2[s]
df = df[s]
opt_snr_final = 1. / (opt_snr[injection_index_injc]**2)
dist_injc = dist_injc[injection_index_injc]
del ifar_injc
f_inj = hf_inj[ifo]
time_inj = f_inj['end_time'][:]
snr_inj = f_inj['snr'][:]
template_dur_inj = f_inj['template_duration'][:]
template_id_inj = f_inj['template_id'][:]
chisq_inj = f_inj['chisq'][:]
chisq_dof_inj = f_inj['chisq_dof'][:]
rchisq_inj = chisq_inj / (2 * chisq_dof_inj - 2)
del chisq_inj
del chisq_dof_inj
newsnr_inj = events.newsnr(snr_inj, rchisq_inj)
del rchisq_inj
time = f['end_time'][:]
if args.just_inj == 'False':
snr = f['snr'][:]
template_dur = f['template_duration'][:]
template_id = f['template_id'][:]
chisq = f['chisq'][:]
chisq_dof = f['chisq_dof'][:]
rchisq = chisq / (2 * chisq_dof - 2)
del chisq
del chisq_dof
newsnr = events.newsnr(snr, rchisq)
del rchisq
ifo = args.ifo
tag = args.user_tag
#Downloading all triggers
from pycbc import events, init_logging, pnutils
hf_all = h5py.File('H1-HDF_TRIGGER_MERGE_NSBH02_INJ-1128299417-1083600.hdf',
'r')
f_all = hf_all['H1']
time_all = f_all['end_time'][:]
snr_all = f_all['snr'][:]
chisq = f_all['chisq'][:]
chisq_dof = f_all['chisq_dof'][:]
rchisq = chisq / (2 * chisq_dof - 2)
del chisq
del chisq_dof
newsnr_all = events.newsnr(snr_all, rchisq)
del rchisq
f = h5py.File(args.stat_file, 'r')
newsnr = f['%s/maxnewsnr' % ifo][:]
maxsnr = f['%s/maxsnr' % ifo][:]
time = f['%s/time' % ifo][:]
time_inj = f['%s/time_inj' % ifo][:]
count = f['%s/count' % ifo][:]
margl = f['%s/marg_l' % ifo][:]
delT = f['%s/delT' % ifo][:]
delta_chirp = f['%s/delta_chirp' % ifo][:]
newsnr_inj = f['%s/maxnewsnr_inj' % ifo][:]
maxsnr_inj = f['%s/maxsnr_inj' % ifo][:]
count_inj = f['%s/count_inj' % ifo][:]
margl_inj = f['%s/marg_l_inj' % ifo][:]
def xis(snr, template, strain, psd, fc, grafica, time_max, duration, hc,
shift):
nbins = 32
start_time = strain.start_time
end_time = strain.start_time + 32
#crop_left = abs(start_time - time_max-5)
#crop_right = abs(end_time - time_max-5)
#strain = strain.crop(crop_left,crop_right)
#psd = psd.crop(crop_left,crop_right)
chisq = power_chisq(template,
strain,
nbins,
psd,
low_frequency_cutoff=fc,
high_frequency_cutoff=hc)
chisq = chisq.crop(4, 4)
dof = 2 * nbins - 2
chisq /= dof
low_chisq = min(chisq)
#print 'Lower value of Chi'
#print low_chisq
if grafica == 1:
nsnr = newsnr(snr, chisq)
pylab.plot(chisq.sample_times, chisq)
pylab.ylabel('$chi^2_r$')
pylab.xlabel('Time(s)')
pylab.xlim(time_max - 0.1, time_max + 0.1)
pylab.grid()
pylab.show()
pylab.figure('Superposition', figsize=[15, 5])
pylab.plot(snr.sample_times, abs(nsnr), label='Re-weighted SNR')
pylab.plot(snr.sample_times, abs(snr), label='Original SNR')
pylab.plot(chisq.sample_times, chisq, label='$Chi^2$')
#pylab.title('Re-weighted SNR')
#pylab.ylabel('Signal-to-noise ratio')
pylab.xlabel('Time(s)')
pylab.legend()
pylab.xlim(time_max - 0.05, time_max + 0.05)
pylab.grid()
pylab.show()
pylab.plot(snr.sample_times, abs(snr))
pylab.title('Matched filter output')
pylab.ylabel('Signal-to-noise ratio')
pylab.xlabel('Time(s)')
pylab.xlim(time_max - 0.1, time_max + 0.1)
pylab.grid()
pylab.show()
return chisq, low_chisq
