def full_matched_filter_and_cluster(self, template, template_norm, stilde, window):
""" Return the complex snr and normalization.
Calculated the matched filter, threshold, and cluster.
Parameters
----------
htilde : FrequencySeries
The template waveform. Must come from the FilterBank class.
template_norm : float
The htilde, template normalization factor.
stilde : FrequencySeries
The strain data to be filtered.
window : int
The size of the cluster window in samples.
Returns
-------
snr : TimeSeries
A time series containing the complex snr.
norm : float
The normalization of the complex snr.
corrrelation: FrequencySeries
A frequency series containing the correlation vector.
idx : Array
List of indices of the triggers.
snrv : Array
The snr values at the trigger locations.
"""
snr, corr, norm = matched_filter_core(template, stilde,
low_frequency_cutoff=self.flow,
high_frequency_cutoff=self.fhigh,
h_norm=template_norm,
out=self.snr_mem,
corr_out=self.corr_mem)
idx, snrv = events.threshold(snr[stilde.analyze], self.snr_threshold / norm)
if len(idx) == 0:
return [], [], [], [], []
logging.info("%s points above threshold" % str(len(idx)))
idx, snrv = events.cluster_reduce(idx, snrv, window)
logging.info("%s clustered points" % str(len(idx)))
return snr, norm, corr, idx, snrv
def full_matched_filter_and_cluster_fc(self, segnum, template_norm,
window):
""" Return the complex snr and normalization.
Calculated the matched filter, threshold, and cluster.
Parameters
----------
segnum : int
Index into the list of segments at MatchedFilterControl construction
against which to filter.
template_norm : float
The htilde, template normalization factor.
window : int
Size of the window over which to cluster triggers, in samples
Returns
-------
snr : TimeSeries
A time series containing the complex snr.
norm : float
The normalization of the complex snr.
corrrelation: FrequencySeries
A frequency series containing the correlation vector.
idx : Array
List of indices of the triggers.
snrv : Array
The snr values at the trigger locations.
"""
norm = (4.0 * self.delta_f) / sqrt(template_norm)
self.correlators[segnum].correlate()
self.ifft.execute()
idx, snrv = events.threshold(
self.snr_mem[self.segments[segnum].analyze],
self.snr_threshold / norm)
idx, snrv = events.cluster_reduce(idx, snrv, window)
if len(idx) == 0:
return [], [], [], [], []
logging.info("%s points above threshold" % str(len(idx)))
return self.snr_mem, norm, self.corr_mem, idx, snrv
def full_matched_filter_and_cluster_fc(self, segnum, template_norm, window):
""" Return the complex snr and normalization.
Calculated the matched filter, threshold, and cluster.
Parameters
----------
segnum : int
Index into the list of segments at MatchedFilterControl construction
against which to filter.
template_norm : float
The htilde, template normalization factor.
window : int
Size of the window over which to cluster triggers, in samples
Returns
-------
snr : TimeSeries
A time series containing the complex snr.
norm : float
The normalization of the complex snr.
corrrelation: FrequencySeries
A frequency series containing the correlation vector.
idx : Array
List of indices of the triggers.
snrv : Array
The snr values at the trigger locations.
"""
norm = (4.0 * self.delta_f) / sqrt(template_norm)
self.correlators[segnum].correlate()
self.ifft.execute()
idx, snrv = events.threshold(self.snr_mem[self.segments[segnum].analyze],
self.snr_threshold / norm)
idx, snrv = events.cluster_reduce(idx, snrv, window)
if len(idx) == 0:
return [], [], [], [], []
logging.info("%s points above threshold" % str(len(idx)))
return self.snr_mem, norm, self.corr_mem, idx, snrv
def heirarchical_matched_filter_and_cluster(self, htilde, template_norm,
stilde, window):
""" Return the complex snr and normalization.
Calculated the matched filter, threshold, and cluster.
Parameters
----------
htilde : FrequencySeries
The template waveform. Must come from the FilterBank class.
template_norm : float
The htilde, template normalization factor.
stilde : FrequencySeries
The strain data to be filtered.
window : int
The size of the cluster window in samples.
Returns
-------
snr : TimeSeries
A time series containing the complex snr at the reduced sample rate.
norm : float
The normalization of the complex snr.
corrrelation: FrequencySeries
A frequency series containing the correlation vector.
idx : Array
List of indices of the triggers.
snrv : Array
The snr values at the trigger locations.
"""
from pycbc.fft.fftw_pruned import pruned_c2cifft, fft_transpose
norm = (4.0 * stilde.delta_f) / sqrt(template_norm)
correlate(htilde[self.kmin_red:self.kmax_red],
stilde[self.kmin_red:self.kmax_red],
self.corr_mem[self.kmin_red:self.kmax_red])
ifft(self.corr_mem, self.snr_mem)
if not hasattr(stilde, 'red_analyze'):
stilde.red_analyze = \
slice(stilde.analyze.start/self.downsample_factor,
stilde.analyze.stop/self.downsample_factor)
idx_red, snrv_red = events.threshold(
self.snr_mem[stilde.red_analyze],
self.snr_threshold / norm * self.upsample_threshold)
if len(idx_red) == 0:
return [], None, [], [], []
idx_red, _ = events.cluster_reduce(idx_red, snrv_red,
window / self.downsample_factor)
logging.info("%s points above threshold at reduced resolution"\
%(str(len(idx_red)),))
# The fancy upsampling is here
if self.upsample_method == 'pruned_fft':
idx = (idx_red + stilde.analyze.start/self.downsample_factor)\
* self.downsample_factor
idx = smear(idx, self.downsample_factor)
# cache transposed versions of htilde and stilde
if not hasattr(self.corr_mem_full, 'transposed'):
self.corr_mem_full.transposed = zeros(len(self.corr_mem_full),
dtype=self.dtype)
if not hasattr(htilde, 'transposed'):
htilde.transposed = zeros(len(self.corr_mem_full),
dtype=self.dtype)
htilde.transposed[self.kmin_full:self.kmax_full] = htilde[
self.kmin_full:self.kmax_full]
htilde.transposed = fft_transpose(htilde.transposed)
if not hasattr(stilde, 'transposed'):
stilde.transposed = zeros(len(self.corr_mem_full),
dtype=self.dtype)
stilde.transposed[self.kmin_full:self.kmax_full] = stilde[
self.kmin_full:self.kmax_full]
stilde.transposed = fft_transpose(stilde.transposed)
correlate(htilde.transposed, stilde.transposed,
self.corr_mem_full.transposed)
snrv = pruned_c2cifft(self.corr_mem_full.transposed,
self.inter_vec,
idx,
pretransposed=True)
idx = idx - stilde.analyze.start
idx2, snrv = events.threshold(Array(snrv, copy=False),
self.snr_threshold / norm)
if len(idx2) > 0:
correlate(htilde[self.kmax_red:self.kmax_full],
stilde[self.kmax_red:self.kmax_full],
self.corr_mem_full[self.kmax_red:self.kmax_full])
idx, snrv = events.cluster_reduce(idx[idx2], snrv, window)
else:
idx, snrv = [], []
logging.info("%s points at full rate and clustering" % len(idx))
return self.snr_mem, norm, self.corr_mem_full, idx, snrv
else:
raise ValueError("Invalid upsample method")
def full_matched_filter_and_cluster(self, hplus, hcross, hplus_norm,
hcross_norm, psd, stilde, window):
"""
Return the complex snr and normalization.
Calculated the matched filter, threshold, and cluster.
Parameters
----------
h_quantities : Various
FILL ME IN
stilde : FrequencySeries
The strain data to be filtered.
window : int
The size of the cluster window in samples.
Returns
-------
snr : TimeSeries
A time series containing the complex snr.
norm : float
The normalization of the complex snr.
correlation: FrequencySeries
A frequency series containing the correlation vector.
idx : Array
List of indices of the triggers.
snrv : Array
The snr values at the trigger locations.
"""
I_plus, Iplus_corr, Iplus_norm = matched_filter_core(
hplus,
stilde,
h_norm=hplus_norm,
low_frequency_cutoff=self.flow,
high_frequency_cutoff=self.fhigh,
out=self.snr_plus_mem,
corr_out=self.corr_plus_mem)
I_cross, Icross_corr, Icross_norm = matched_filter_core(
hcross,
stilde,
h_norm=hcross_norm,
low_frequency_cutoff=self.flow,
high_frequency_cutoff=self.fhigh,
out=self.snr_cross_mem,
corr_out=self.corr_cross_mem)
# The information on the complex side of this overlap is important
# we may want to use this in the future.
if not id(hplus) == self.cached_hplus_hcross_hplus:
self.cached_hplus_hcross_correlation = None
if not id(hcross) == self.cached_hplus_hcross_hcross:
self.cached_hplus_hcross_correlation = None
if not id(psd) == self.cached_hplus_hcross_psd:
self.cached_hplus_hcross_correlation = None
if self.cached_hplus_hcross_correlation is None:
hplus_cross_corr = overlap_cplx(hplus,
hcross,
psd=psd,
low_frequency_cutoff=self.flow,
high_frequency_cutoff=self.fhigh,
normalized=False)
hplus_cross_corr = numpy.real(hplus_cross_corr)
hplus_cross_corr = hplus_cross_corr / (hcross_norm *
hplus_norm)**0.5
self.cached_hplus_hcross_correlation = hplus_cross_corr
self.cached_hplus_hcross_hplus = id(hplus)
self.cached_hplus_hcross_hcross = id(hcross)
self.cached_hplus_hcross_psd = id(psd)
else:
hplus_cross_corr = self.cached_hplus_hcross_correlation
snr = compute_max_snr_over_sky_loc_stat(I_plus,
I_cross,
hplus_cross_corr,
thresh=self.snr_threshold,
out=self.snr_mem,
hpnorm=Iplus_norm,
hcnorm=Icross_norm,
analyse_slice=stilde.analyze)
# FIXME: This should live further down
# Convert output to pycbc TimeSeries
delta_t = 1.0 / (self.tlen * stilde.delta_f)
snr = TimeSeries(snr, epoch=stilde._epoch, delta_t=delta_t, copy=False)
idx, snrv = events.threshold_real_numpy(snr[stilde.analyze],
self.snr_threshold)
if len(idx) == 0:
return [], 0, 0, [], [], [], [], 0, 0, 0
logging.info("%s points above threshold" % str(len(idx)))
idx, snrv = events.cluster_reduce(idx, snrv, window)
logging.info("%s clustered points" % str(len(idx)))
u_vals, coa_phase = compute_u_val_for_sky_loc_stat(
I_plus.data,
I_cross.data,
hplus_cross_corr,
indices=idx + stilde.analyze.start,
hpnorm=Iplus_norm,
hcnorm=Icross_norm)
return snr, Iplus_corr, Icross_corr, idx, snrv, u_vals, coa_phase,\
hplus_cross_corr, Iplus_norm, Icross_norm
def full_matched_filter_and_cluster(self, hplus, hcross, hplus_norm,
hcross_norm, psd, stilde, window):
"""
Return the complex snr and normalization.
Calculated the matched filter, threshold, and cluster.
Parameters
----------
h_quantities : Various
FILL ME IN
stilde : FrequencySeries
The strain data to be filtered.
window : int
The size of the cluster window in samples.
Returns
-------
snr : TimeSeries
A time series containing the complex snr.
norm : float
The normalization of the complex snr.
correlation: FrequencySeries
A frequency series containing the correlation vector.
idx : Array
List of indices of the triggers.
snrv : Array
The snr values at the trigger locations.
"""
I_plus, Iplus_corr, Iplus_norm = matched_filter_core(hplus, stilde,
h_norm=hplus_norm,
low_frequency_cutoff=self.flow,
high_frequency_cutoff=self.fhigh,
out=self.snr_plus_mem,
corr_out=self.corr_plus_mem)
I_cross, Icross_corr, Icross_norm = matched_filter_core(hcross,
stilde, h_norm=hcross_norm,
low_frequency_cutoff=self.flow,
high_frequency_cutoff=self.fhigh,
out=self.snr_cross_mem,
corr_out=self.corr_cross_mem)
# The information on the complex side of this overlap is important
# we may want to use this in the future.
if not id(hplus) == self.cached_hplus_hcross_hplus:
self.cached_hplus_hcross_correlation = None
if not id(hcross) == self.cached_hplus_hcross_hcross:
self.cached_hplus_hcross_correlation = None
if not id(psd) == self.cached_hplus_hcross_psd:
self.cached_hplus_hcross_correlation = None
if self.cached_hplus_hcross_correlation is None:
hplus_cross_corr = overlap_cplx(hplus, hcross, psd=psd,
low_frequency_cutoff=self.flow,
high_frequency_cutoff=self.fhigh,
normalized=False)
hplus_cross_corr = numpy.real(hplus_cross_corr)
hplus_cross_corr = hplus_cross_corr / (hcross_norm*hplus_norm)**0.5
self.cached_hplus_hcross_correlation = hplus_cross_corr
self.cached_hplus_hcross_hplus = id(hplus)
self.cached_hplus_hcross_hcross = id(hcross)
self.cached_hplus_hcross_psd = id(psd)
else:
hplus_cross_corr = self.cached_hplus_hcross_correlation
snr = compute_max_snr_over_sky_loc_stat(I_plus,I_cross,
hplus_cross_corr,
thresh=self.snr_threshold,
out=self.snr_mem,
hpnorm=Iplus_norm,
hcnorm=Icross_norm,
analyse_slice=stilde.analyze)
# FIXME: This should live further down
# Convert output to pycbc TimeSeries
delta_t = 1.0 / (self.tlen * stilde.delta_f)
snr = TimeSeries(snr, epoch=stilde._epoch, delta_t=delta_t, copy=False)
idx, snrv = events.threshold_real_numpy(snr[stilde.analyze],
self.snr_threshold)
if len(idx) == 0:
return [], 0, 0, [], [], [], [], 0, 0, 0
logging.info("%s points above threshold" % str(len(idx)))
idx, snrv = events.cluster_reduce(idx, snrv, window)
logging.info("%s clustered points" % str(len(idx)))
u_vals, coa_phase = compute_u_val_for_sky_loc_stat(
I_plus.data, I_cross.data, hplus_cross_corr,
indices=idx+stilde.analyze.start,
hpnorm=Iplus_norm, hcnorm=Icross_norm)
return snr, Iplus_corr, Icross_corr, idx, snrv, u_vals, coa_phase,\
hplus_cross_corr, Iplus_norm, Icross_norm
def heirarchical_matched_filter_and_cluster(self, segnum, template_norm, window):
""" Return the complex snr and normalization.
Calculated the matched filter, threshold, and cluster.
Parameters
----------
segnum : int
Index into the list of segments at MatchedFilterControl construction
template_norm : float
The htilde, template normalization factor.
window : int
Size of the window over which to cluster triggers, in samples
Returns
-------
snr : TimeSeries
A time series containing the complex snr at the reduced sample rate.
norm : float
The normalization of the complex snr.
corrrelation: FrequencySeries
A frequency series containing the correlation vector.
idx : Array
List of indices of the triggers.
snrv : Array
The snr values at the trigger locations.
"""
from pycbc.fft.fftw_pruned import pruned_c2cifft, fft_transpose
htilde = self.htilde
stilde = self.segments[segnum]
norm = (4.0 * stilde.delta_f) / sqrt(template_norm)
correlate(htilde[self.kmin_red:self.kmax_red],
stilde[self.kmin_red:self.kmax_red],
self.corr_mem[self.kmin_red:self.kmax_red])
ifft(self.corr_mem, self.snr_mem)
if not hasattr(stilde, 'red_analyze'):
stilde.red_analyze = \
slice(stilde.analyze.start/self.downsample_factor,
stilde.analyze.stop/self.downsample_factor)
idx_red, snrv_red = events.threshold(self.snr_mem[stilde.red_analyze],
self.snr_threshold / norm * self.upsample_threshold)
if len(idx_red) == 0:
return [], None, [], [], []
idx_red, _ = events.cluster_reduce(idx_red, snrv_red, window / self.downsample_factor)
logging.info("%s points above threshold at reduced resolution"\
%(str(len(idx_red)),))
# The fancy upsampling is here
if self.upsample_method=='pruned_fft':
idx = (idx_red + stilde.analyze.start/self.downsample_factor)\
* self.downsample_factor
idx = smear(idx, self.downsample_factor)
# cache transposed versions of htilde and stilde
if not hasattr(self.corr_mem_full, 'transposed'):
self.corr_mem_full.transposed = zeros(len(self.corr_mem_full), dtype=self.dtype)
if not hasattr(htilde, 'transposed'):
htilde.transposed = zeros(len(self.corr_mem_full), dtype=self.dtype)
htilde.transposed[self.kmin_full:self.kmax_full] = htilde[self.kmin_full:self.kmax_full]
htilde.transposed = fft_transpose(htilde.transposed)
if not hasattr(stilde, 'transposed'):
stilde.transposed = zeros(len(self.corr_mem_full), dtype=self.dtype)
stilde.transposed[self.kmin_full:self.kmax_full] = stilde[self.kmin_full:self.kmax_full]
stilde.transposed = fft_transpose(stilde.transposed)
correlate(htilde.transposed, stilde.transposed, self.corr_mem_full.transposed)
snrv = pruned_c2cifft(self.corr_mem_full.transposed, self.inter_vec, idx, pretransposed=True)
idx = idx - stilde.analyze.start
idx2, snrv = events.threshold(Array(snrv, copy=False), self.snr_threshold / norm)
if len(idx2) > 0:
correlate(htilde[self.kmax_red:self.kmax_full],
stilde[self.kmax_red:self.kmax_full],
self.corr_mem_full[self.kmax_red:self.kmax_full])
idx, snrv = events.cluster_reduce(idx[idx2], snrv, window)
else:
idx, snrv = [], []
logging.info("%s points at full rate and clustering" % len(idx))
return self.snr_mem, norm, self.corr_mem_full, idx, snrv
else:
raise ValueError("Invalid upsample method")
