def get_apastron_frequencies(hp, hc):
'''
Input
-----
hp, hc: pycbc.types.timeseries
Plus (+) and Cross (x) polarizations
Output:
-------
apastron_frequencies: numpy.array
Frequency of GW emission at periastron at increasing values of time
apastron_times: numpy.array
Times at which apastron passages occur
'''
freq = frequency_from_polarizations(hp, hc)
ft, ff = get_peak_freqs(-1 * freq)
return (ft, -1 * ff)
def gen_waveform(iota, phi, numrel_data):
print "I am in gen_waveform()!"
## FIXME(Gonghan): It seems that this variable is never used.
global wavelabel
wavelabel = os.path.join(savepath,
numrel_data.split('/')[-1].replace(".h5", ""))
f = h5py.File(numrel_data, 'r')
global hp, hc
hp, hc = get_td_waveform(approximant='NR_hdf5',
numrel_data=numrel_data,
mass1=f.attrs['mass1'] * mass,
mass2=f.attrs['mass2'] * mass,
spin1z=f.attrs['spin1z'],
spin2z=f.attrs['spin2z'],
delta_t=1.0 / sample_frequency,
f_lower=30.,
inclination=iota,
coa_phase=phi,
distance=1000)
print "in gen_waveform, have got hp, hc. iota: {}, phi: {}".format(
iota, phi)
print "hp:", hp
print "hc:", hc
f.close()
# Taper waveform for smooth FFTs
hp = taper_timeseries(hp, tapermethod="TAPER_START")
hc = taper_timeseries(hc, tapermethod="TAPER_START")
global amp, foft
amp = wfutils.amplitude_from_polarizations(hp, hc)
foft = wfutils.frequency_from_polarizations(hp, hc)
# Shift time origin to merger
global sample_times
## FIXME(Gonghan): Not sure how we want to define zero time.
sample_times = amp.sample_times - amp.sample_times[np.argmax(amp)]
def _get_tf_path(self, approximant='SEOBNRv4_opt', sample_rate=4096 * 4):
"""
Get frequency track of an event based on GraceDb parameters.
Parameters
----------
approximant : str, optional
f_lower : int, optional
sample_rate : int, optional
Returns
-------
tf_path : gwpy.timeseries.TimeSeries object
Time series of frequency.
"""
if not all(
hasattr(self, x) for x in
['graceid', 'm1', 'm2', 's1z', 's2z', 'start_time', 'end_time']):
return None
hp, hc = get_td_waveform(approximant='SEOBNRv4_opt',
mass1=self.m1,
mass2=self.m2,
spin1z=self.s1z,
spin2z=self.s2z,
delta_t=1.0 / sample_rate,
f_lower=30,
frame_axis=SIM_INSPIRAL_FRAME_AXIS_ORBITAL_L)
foft = frequency_from_polarizations(hp, hc)
try:
foft = utils.trim_foft(foft)
except:
pass
tf_path = TimeSeries(foft,
t0=self.end_time + float(foft.start_time),
dt=foft.delta_t)
tf_path = tf_path.crop(self.start_time, self.end_time)
tf_path.name = self.graceid + '_path'
return tf_path
for row in snglburst_table:
if (row.snr > args.omicron_snr_thresh and
omicron_start_time < row.peak_time < omicron_end_time):
omicron_times.append(row.peak_time + row.peak_time_ns * 10**(-9))
omicron_snr.append(row.snr)
omicron_freq.append(row.peak_frequency)
# Generate inspiral waveform and calculate f(t) to plot on top of Omicron triggers
hp, hc = get_td_waveform(approximant='SEOBNRv2', mass1=m1, mass2=m2,
spin1x=0, spin1y=0, spin1z=s1z,
spin2x=0, spin2y=0, spin2z=s2z,
delta_t=(1./32768.), f_lower=30)
f = frequency_from_polarizations(hp, hc)
amp = amplitude_from_polarizations(hp, hc)
stop_idx = amp.abs_max_loc()[1]
f = f[:stop_idx]
freq = np.array(f.data)
times = np.array(f.sample_times) + cbc_end_time
logging.info('Plotting')
plt.figure(0)
cm = plt.cm.get_cmap('Reds')
plt.scatter(omicron_times,omicron_freq,c=omicron_snr,s=30,cmap=cm,linewidth=0)
plt.grid(b=True, which='both')
cbar = plt.colorbar()
omicron_freq.append(row.peak_frequency)
# Generate inspiral waveform and calculate f(t) to plot on top of Omicron triggers
hp, hc = get_td_waveform(approximant='SEOBNRv2',
mass1=m1,
mass2=m2,
spin1x=0,
spin1y=0,
spin1z=s1z,
spin2x=0,
spin2y=0,
spin2z=s2z,
delta_t=(1. / 32768.),
f_lower=30)
f = frequency_from_polarizations(hp, hc)
amp = amplitude_from_polarizations(hp, hc)
stop_idx = amp.abs_max_loc()[1]
f = f[:stop_idx]
freq = np.array(f.data)
times = np.array(f.sample_times) + cbc_end_time
logging.info('Plotting')
plt.figure(0)
cm = plt.cm.get_cmap('Reds')
plt.scatter(omicron_times,
omicron_freq,
c=omicron_snr,
def gen_waveform(numrel_data, iota, phi):
## FIXME(Gonghan): It seems that wavelabel is never used.
# global wavelabel
# wavelabel=os.PATH.join(savepath, waveformName.split('/')[-1].replace(".h5",""))
f = h5py.File(numrel_data, 'r')
# Getting the 2-2 mode polarizations first.
hp, hc = get_td_waveform(approximant='NR_hdf5',
numrel_data=numrel_data,
mass1=f.attrs['mass1'] * mass,
mass2=f.attrs['mass2'] * mass,
spin1z=f.attrs['spin1z'],
spin2z=f.attrs['spin2z'],
delta_t=1.0 / sample_frequency,
f_lower=30.,
inclination=iota,
coa_phase=phi,
distance=1000,
mode_array=[[2, 2], [2, -2]])
# Taper waveform for smooth FFTs
hp = taper_timeseries(hp, tapermethod="TAPER_START")
hc = taper_timeseries(hc, tapermethod="TAPER_START")
amp = wfutils.amplitude_from_polarizations(hp, hc)
peakAmpTime = amp.sample_times[np.argmax(amp)]
'''mode_array=[[2,2], [2,-2]]'''
# Getting the full-mode waveform.
hp, hc = get_td_waveform(approximant='NR_hdf5',
numrel_data=numrel_data,
mass1=f.attrs['mass1'] * mass,
mass2=f.attrs['mass2'] * mass,
spin1z=f.attrs['spin1z'],
spin2z=f.attrs['spin2z'],
delta_t=1.0 / sample_frequency,
f_lower=30.,
inclination=iota,
coa_phase=phi,
distance=1000)
f.close()
# Taper waveform for smooth FFTs
hp = taper_timeseries(hp, tapermethod="TAPER_START")
hc = taper_timeseries(hc, tapermethod="TAPER_START")
amp = wfutils.amplitude_from_polarizations(hp, hc)
foft = wfutils.frequency_from_polarizations(hp, hc)
# Shift time origin to merger
## FIXME(Gonghan): Not sure how we want to define zero time.
sample_times = amp.sample_times - peakAmpTime
# sample_times = amp.sample_times
# # Trim the timeseries before the CWT
# hp_red = hp[:int(sample_frequency)]
return {
"hp": hp,
"hc": hc,
"amp": amp,
"foft": foft,
"sample_times": sample_times
}