def blendTimeSeries(hp0, hc0, mm, sample, time, t_opt):
"""
[DEPRECATED - use "blend"]
Only dealing with real part, don't do hc calculations
t_opt is length-5 array describing multiples of mm
Returns length-5 array of TimeSeries (1 per blending)
"""
#{{{
from UseNRinDA import nr_waveform
nrtool = nr_waveform()
amp = TimeSeries(np.sqrt(hp0**2 + hc0**2), copy=True, delta_t=hp0.delta_t)
max_a, max_a_index = amp.abs_max_loc()
print("Waveform max = %e, located at %d" % (max_a, max_a_index))
amp_after_peak = amp
amp_after_peak[:max_a_index] = 0
mtsun = lal.MTSUN_SI
iA, vA = min(enumerate(amp_after_peak),
key=lambda x: abs(x[1] - 0.01 * max_a))
iB, vB = min(enumerate(amp_after_peak),
key=lambda x: abs(x[1] - 0.1 * max_a))
print(iA, iB)
t = [
[
t_opt[0] * mm, 500 * mm, hp0.sample_times.data[iA] / mtsun,
hp0.sample_times.data[iA] / mtsun + t_opt[3] * mm
], # Prayush's E
[
t_opt[0] * mm, t_opt[1] * mm, hp0.sample_times.data[iA] / mtsun,
hp0.sample_times.data[iA] / mtsun + t_opt[3] * mm
],
[
t_opt[0] * mm, t_opt[1] * mm, hp0.sample_times.data[iB] / mtsun,
hp0.sample_times.data[iB] / mtsun + t_opt[4] * mm
],
[
t_opt[0] * mm, t_opt[2] * mm, hp0.sample_times.data[iA] / mtsun,
hp0.sample_times.data[iA] / mtsun + t_opt[3] * mm
],
[
t_opt[0] * mm, t_opt[2] * mm, hp0.sample_times.data[iB] / mtsun,
hp0.sample_times.data[iB] / mtsun + t_opt[4] * mm
]
]
hphc = []
#hphc.append(hp0)
for i in range(len(t)):
print(t[i])
hphc.append(nrtool.blending_function_Tukey(hp0=hp0,t=t[i],\
sample_rate=sample,time_length=time))
print("No of blending windows being tested = %d" % len(hphc))
return hphc
def test_injection_presence(self):
"""Verify presence of signals at expected times"""
injections = InjectionSet(self.inj_file.name)
for det in self.detectors:
for inj in self.injections:
ts = TimeSeries(numpy.zeros(int(10 * self.sample_rate)),
delta_t=1 / self.sample_rate,
epoch=lal.LIGOTimeGPS(inj.end_time - 5),
dtype=numpy.float64)
injections.apply(ts, det.name)
max_amp, max_loc = ts.abs_max_loc()
# FIXME could test amplitude and time more precisely
self.assertTrue(max_amp > 0 and max_amp < 1e-10)
time_error = ts.sample_times.numpy()[max_loc] - inj.end_time
self.assertTrue(abs(time_error) < 2 * self.earth_time)
def test_injection_presence(self):
"""Verify presence of signals at expected times"""
injections = InjectionSet(self.inj_file.name)
for det in self.detectors:
for inj in self.injections:
ts = TimeSeries(numpy.zeros(10 * self.sample_rate),
delta_t=1/self.sample_rate,
epoch=lal.LIGOTimeGPS(inj.end_time - 5),
dtype=numpy.float64)
injections.apply(ts, det.name)
max_amp, max_loc = ts.abs_max_loc()
# FIXME could test amplitude and time more precisely
self.assertTrue(max_amp > 0 and max_amp < 1e-10)
time_error = ts.sample_times.numpy()[max_loc] - inj.end_time
self.assertTrue(abs(time_error) < 1.5 * self.earth_time)
def test_injection_absence(self):
"""Verify absence of signals outside known injection times"""
clear_times = [
self.injections[0].end_time - 86400,
self.injections[-1].end_time + 86400
]
injections = InjectionSet(self.inj_file.name)
for det in self.detectors:
for epoch in clear_times:
ts = TimeSeries(numpy.zeros(int(10 * self.sample_rate)),
delta_t=1 / self.sample_rate,
epoch=lal.LIGOTimeGPS(epoch),
dtype=numpy.float64)
injections.apply(ts, det.name)
max_amp, max_loc = ts.abs_max_loc()
self.assertEqual(max_amp, 0)
def test_injection_absence(self):
"""Verify absence of signals outside known injection times"""
clear_times = [
self.injections[0].end_time - 86400,
self.injections[-1].end_time + 86400
]
injections = InjectionSet(self.inj_file.name)
for det in self.detectors:
for epoch in clear_times:
ts = TimeSeries(numpy.zeros(10 * self.sample_rate),
delta_t=1/self.sample_rate,
epoch=lal.LIGOTimeGPS(epoch),
dtype=numpy.float64)
injections.apply(ts, det.name)
max_amp, max_loc = ts.abs_max_loc()
self.assertEqual(max_amp, 0)
def blend(hin, mm, sample, time, t_opt, WinID=-1):
# Only dealing with real part, don't do hc calculations
# t_opt is length-5 array describing multiples of mm
# Returns length-5 array of TimeSeries (1 per blending)
#{{{
hp0, hc0 = hin.rescale_to_totalmass(mm)
hp0._epoch = hc0._epoch = 0
amp = TimeSeries(np.sqrt(hp0**2 + hc0**2), copy=True, delta_t=hp0.delta_t)
max_a, max_a_index = amp.abs_max_loc()
print("\n\n In blend:\nTotal Mass = %f, len(hp0,hc0) = %d, %d = %f s" %\
(mm, len(hp0), len(hc0), hp0.sample_times[-1]-hp0.sample_times[0]))
print("Waveform max = %e, located at %d" % (max_a, max_a_index))
#amp_after_peak = amp
#amp_after_peak[:max_a_index] = 0
mtsun = lal.MTSUN_SI
amp_after_peak = amp[max_a_index:]
iA, vA = min(enumerate(amp_after_peak),
key=lambda x: abs(x[1] - 0.01 * max_a))
iA += max_a_index
#iA, vA = min(enumerate(amp_after_peak),key=lambda x:abs(x[1]-0.01*max_a))
iB, vB = min(enumerate(amp_after_peak),
key=lambda x: abs(x[1] - 0.1 * max_a))
iB += max_a_index
if iA <= max_a_index:
print("iA = %d, iB = %d, vA = %e, vB = %e" % (iA, iB, vA, vB),
file=sys.stdout)
sys.stdout.flush()
raise RuntimeError("Couldnt find amplitude threshold time iA")
# do something
#fout = open('hpdump.dat','w+')
#for i in range( len(amp) ):
# if i > max_a_index and amp[i] == 0: break
# fout.write('%e\t%e\n' % (amp.sample_times[i],amp[i]))
#fout.close()
# Find the point the hard way
target_amp = max_a * 0.01
tmp_data = amp.data
for idx in range(max_a_index, len(amp)):
if tmp_data[idx] < target_amp: break
iA = idx
print("Newfound iA = %d" % iA)
# Yet another way
amp_after_peak = amp[max_a_index:]
iA, vA = min(enumerate(amp_after_peak),
key=lambda x: abs(x[1] - 0.01 * max_a))
iA += max_a_index
print("Newfound iA another way = %d" % iA)
raise RuntimeError("Had to find amplitude threshold the hard way")
if iB <= max_a_index:
raise RuntimeError("Couldnt find amplitude threshold time iB")
# this doesn't happen yet
pass
print("NEW: iA = %d, iB = %d, vA = %e, vB = %e" % (iA, iB, vA, vB))
t = [
[
t_opt[0] * mm, 500 * mm, hp0.sample_times.data[iA] / mtsun,
hp0.sample_times.data[iA] / mtsun + t_opt[3] * mm
], # Prayush's E
[
t_opt[0] * mm, t_opt[1] * mm, hp0.sample_times.data[iA] / mtsun,
hp0.sample_times.data[iA] / mtsun + t_opt[3] * mm
],
[
t_opt[0] * mm, t_opt[1] * mm, hp0.sample_times.data[iB] / mtsun,
hp0.sample_times.data[iB] / mtsun + t_opt[4] * mm
],
[
t_opt[0] * mm, t_opt[2] * mm, hp0.sample_times.data[iA] / mtsun,
hp0.sample_times.data[iA] / mtsun + t_opt[3] * mm
],
[
t_opt[0] * mm, t_opt[2] * mm, hp0.sample_times.data[iB] / mtsun,
hp0.sample_times.data[iB] / mtsun + t_opt[4] * mm
]
]
hphc = []
hphc.append(hp0)
for i in range(len(t)):
if (WinID >= 0 and WinID < len(t)) and i != WinID: continue
print("Testing window with t = ", t[i])
hphc.append(
hin.blending_function(hp0=hp0,
t=t[i],
sample_rate=sample,
time_length=time))
print("No of blending windows being tested = %d" % (len(hphc) - 1))
return hphc
row.amp_order = 0
row.coa_phase = 0
row.bandpass = 0
row.taper = inj.taper
row.numrel_mode_min = 0
row.numrel_mode_max = 0
row.numrel_data = None
row.source = 'ANTANI'
row.process_id = 'process:process_id:0'
row.simulation_id = 'sim_inspiral:simulation_id:0'
sim_table.append(row)
inj_file = open("injection.xml","w+")
ligolw_utils.write_fileobj(xmldoc, inj_file)
injection_set = InjectionSet("injection.xml")
sample_rate = 4096 # Hz
for det in [Detector(d) for d in ['H1', 'L1', 'V1']]:
ts = TimeSeries(numpy.zeros(int(10 * sample_rate)),
delta_t=1/sample_rate,
epoch=lal.LIGOTimeGPS(end_time - 5),
dtype=numpy.float64)
injection_set.apply(ts, det.name)
max_amp, max_loc = ts.abs_max_loc()
pylab.plot(ts,label=det.name)
pylab.legend()
ts1 = TimeSeries(np.real(wf_h), delta_t=wf_dt)
ts2 = TimeSeries(np.real(mk1.h), delta_t=mk1_dt)
tlen = max(len(ts1), len(ts2))
ts1.resize(tlen)
ts2.resize(tlen)
#ma, _ = pycbc.filter.match(ts1, ts2, low_frequency_cutoff=f_lower_int)
ma, _ = pycbc.filter.match(ts1, ts2)
print("\t {}".format(ma))
print('plotting aligned waveform')
#ts1, ts2 = pycbc.waveform.utils.coalign_waveforms(ts1, ts2, low_frequency_cutoff=f_lower_int)
ts1, ts2 = pycbc.waveform.utils.coalign_waveforms(ts1, ts2)
_, mk1_idx = ts2.abs_max_loc()
mk1_shift = ts2.sample_times[mk1_idx]
figname = outdir + '/' + "{}-vs-mk1".format(sys['name']) + '.png'
fig, axes = plt.subplots(1, 2, figsize=(14, 4))
axes[0].plot(ts1.sample_times - mk1_shift, ts1, label='{}'.format(approximant))
axes[0].plot(ts2.sample_times - mk1_shift, ts2, ls='--', label='Mk1')
axes[0].legend(loc='upper left')
axes[0].set_title('match = {:.4f}'.format(ma))
axes[0].set_xlim(start_time, 100)
axes[1].plot(ts1.sample_times - mk1_shift, ts1, label='{}'.format(approximant))
axes[1].plot(ts2.sample_times - mk1_shift, ts2, ls='--', label='Mk1')
axes[1].set_xlim(-500,100)
