def spa_compression(htilde,
fmin,
fmax,
min_seglen=0.02,
sample_frequencies=None):
"""Returns the frequencies needed to compress the given frequency domain
waveform. This is done by estimating t(f) of the waveform using the
stationary phase approximation.
Parameters
----------
htilde : FrequencySeries
The waveform to compress.
fmin : float
The starting frequency of the compressed waveform.
fmax : float
The ending frequency of the compressed waveform.
min_seglen : float
The inverse of this gives the maximum frequency step that is used.
sample_frequencies : {None, array}
The frequencies that the waveform is evaluated at. If None, will
retrieve the frequencies from the waveform's sample_frequencies
attribute.
Returns
-------
array
The frequencies at which to evaluate the compressed waveform.
"""
if sample_frequencies is None:
sample_frequencies = htilde.sample_frequencies.numpy()
kmin = int(fmin / htilde.delta_f)
kmax = int(fmax / htilde.delta_f)
tf = abs(
utils.time_from_frequencyseries(
htilde, sample_frequencies=sample_frequencies).data[kmin:kmax])
sample_frequencies = sample_frequencies[kmin:kmax]
sample_points = []
f = fmin
while f < fmax:
f = int(f / htilde.delta_f) * htilde.delta_f
sample_points.append(f)
jj = numpy.searchsorted(sample_frequencies, f)
f += 1. / (tf[jj:].max() + min_seglen)
# add the last point
if sample_points[-1] < fmax:
sample_points.append(fmax)
return numpy.array(sample_points)
def spa_compression(htilde, fmin, fmax, min_seglen=0.02,
sample_frequencies=None):
"""Returns the frequencies needed to compress the given frequency domain
waveform. This is done by estimating t(f) of the waveform using the
stationary phase approximation.
Parameters
----------
htilde : FrequencySeries
The waveform to compress.
fmin : float
The starting frequency of the compressed waveform.
fmax : float
The ending frequency of the compressed waveform.
min_seglen : float
The inverse of this gives the maximum frequency step that is used.
sample_frequencies : {None, array}
The frequencies that the waveform is evaluated at. If None, will
retrieve the frequencies from the waveform's sample_frequencies
attribute.
Returns
-------
array
The frequencies at which to evaluate the compressed waveform.
"""
if sample_frequencies is None:
sample_frequencies = htilde.sample_frequencies.numpy()
kmin = int(fmin/htilde.delta_f)
kmax = int(fmax/htilde.delta_f)
tf = abs(utils.time_from_frequencyseries(htilde,
sample_frequencies=sample_frequencies).data[kmin:kmax])
sample_frequencies = sample_frequencies[kmin:kmax]
sample_points = []
f = fmin
while f < fmax:
f = int(f/htilde.delta_f)*htilde.delta_f
sample_points.append(f)
jj = numpy.searchsorted(sample_frequencies, f)
f += 1./(tf[jj:].max()+min_seglen)
# add the last point
if sample_points[-1] < fmax:
sample_points.append(fmax)
return numpy.array(sample_points)
def get_gate_times_hmeco(self):
"""Gets the time to apply a gate based on the current sky position.
Returns
-------
dict :
Dictionary of detector names -> (gate start, gate width)
"""
# generate the template waveform
try:
wfs = self.get_waveforms()
except NoWaveformError:
return self._nowaveform_logl()
except FailedWaveformError as e:
if self.ignore_failed_waveforms:
return self._nowaveform_logl()
raise e
# get waveform parameters
params = self.current_params
spin1 = params['spin1z']
spin2 = params['spin2z']
# gate input for ringdown analysis which consideres a start time
# and an end time
dgate = params['gate_window']
meco_f = hybrid_meco_frequency(params['mass1'], params['mass2'], spin1,
spin2)
# figure out the gate times
gatetimes = {}
for det, h in wfs.items():
invpsd = self._invpsds[det]
h.resize(len(invpsd))
ht = h.to_timeseries()
f_low = int((self._f_lower[det] + 1) / h.delta_f)
sample_freqs = h.sample_frequencies[f_low:].numpy()
f_idx = numpy.where(sample_freqs <= meco_f)[0][-1]
# find time corresponding to meco frequency
t_from_freq = time_from_frequencyseries(
h[f_low:], sample_frequencies=sample_freqs)
if t_from_freq[f_idx] > 0:
gatestartdelay = t_from_freq[f_idx] + float(t_from_freq.epoch)
else:
gatestartdelay = t_from_freq[f_idx] + ht.sample_times[-1]
gatestartdelay = min(gatestartdelay, params['t_gate_start'])
gatetimes[det] = (gatestartdelay, dgate)
return gatetimes
