def test_chisq(self):
chisq_quick = {}
chisq_full = {}
chisq_ref = {}
for ifo in self.ifos:
nbins = 26
dof = nbins * 2 - 2
bins = power_chisq_bins(self.hp,
nbins,
self.psd[ifo],
low_frequency_cutoff=20.0)
chisq = power_chisq_at_points_from_precomputed \
(self.corr[ifo], self.snr_unnorm[ifo][27402:27492].data,
self.norm[ifo], bins, indices=numpy.arange(27402,27492))
chisq_quick[ifo] = chisq / dof
chisq = power_chisq(self.hp,
self.data[ifo],
nbins,
self.psd[ifo],
low_frequency_cutoff=20.0)
chisq_full[ifo] = chisq[27402:27492] / dof
max_diff = max(abs(chisq_full[ifo] - chisq_quick[ifo]))
self.assertTrue(max_diff < 1E-5)
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 test_chisq(self):
chisq_quick = {}
chisq_full = {}
chisq_ref = {}
for ifo in self.ifos:
nbins = 26
dof = nbins * 2 - 2
bins = power_chisq_bins(self.hp, nbins, self.psd[ifo],
low_frequency_cutoff=20.0)
chisq = power_chisq_at_points_from_precomputed \
(self.corr[ifo], self.snr_unnorm[ifo][27402:27492].data,
self.norm[ifo], bins, indices=numpy.arange(27402,27492))
chisq_quick[ifo] = chisq / dof
chisq = power_chisq(self.hp, self.data[ifo], nbins,
self.psd[ifo], low_frequency_cutoff=20.0)
chisq_full[ifo] = chisq[27402:27492] / dof
max_diff = max(abs(chisq_full[ifo] - chisq_quick[ifo]))
self.assertTrue(max_diff < 1E-5)
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 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