def test_chirp(self):
### use a chirp as a signal
sigt = TimeSeries(self.sig1, self.del_t)
sig_tilde = make_frequency_series(sigt)
del_f = sig_tilde.get_delta_f()
psd = FrequencySeries(self.Psd, del_f)
flow = self.low_frequency_cutoff
with _context:
hautocor, hacorfr, hnrm = matched_filter_core(self.htilde, self.htilde, psd=psd, \
low_frequency_cutoff=flow, high_frequency_cutoff=self.fmax)
snr, cor, nrm = matched_filter_core(self.htilde, sig_tilde, psd=psd, \
low_frequency_cutoff=flow, high_frequency_cutoff=self.fmax)
hacor = Array(hautocor.real(), copy=True)
indx = Array(np.array([352250, 352256, 352260]))
snr = snr * nrm
with _context:
dof, achi_list = autochisq_from_precomputed(snr, cor, hacor, stride=3, num_points=20, \
indices=indx)
obt_snr = achi_list[1, 1]
obt_ach = achi_list[1, 2]
self.assertTrue(obt_snr > 10.0 and obt_snr < 12.0)
self.assertTrue(obt_ach < 1.e-3)
self.assertTrue(achi_list[0, 2] > 20.0)
self.assertTrue(achi_list[2, 2] > 20.0)
def test_chirp(self):
### use a chirp as a signal
sigt = TimeSeries(self.sig1, self.del_t)
sig_tilde = make_frequency_series(sigt)
del_f = sig_tilde.get_delta_f()
psd = FrequencySeries(self.Psd, del_f)
flow = self.low_frequency_cutoff
with _context:
hautocor, hacorfr, hnrm = matched_filter_core(self.htilde, self.htilde, psd=psd, \
low_frequency_cutoff=flow, high_frequency_cutoff=self.fmax)
snr, cor, nrm = matched_filter_core(self.htilde, sig_tilde, psd=psd, \
low_frequency_cutoff=flow, high_frequency_cutoff=self.fmax)
hacor = Array(hautocor.real(), copy=True)
indx = Array(np.array([352250, 352256, 352260]))
snr = snr*nrm
with _context:
dof, achi_list = autochisq_from_precomputed(snr, cor, hacor, stride=3, num_points=20, \
indices=indx)
obt_snr = achi_list[1,1]
obt_ach = achi_list[1,2]
self.assertTrue(obt_snr > 10.0 and obt_snr < 12.0)
self.assertTrue(obt_ach < 1.e-3)
self.assertTrue(achi_list[0,2] > 20.0)
self.assertTrue(achi_list[2,2] > 20.0)
def segment_snrs(filters, stilde, psd, low_frequency_cutoff):
""" This functions calculates the snr of each bank veto template against
the segment
Parameters
----------
filters: list of FrequencySeries
The list of bank veto templates filters.
stilde: FrequencySeries
The current segment of data.
psd: FrequencySeries
low_frequency_cutoff: float
Returns
-------
snr (list): List of snr time series.
norm (list): List of normalizations factors for the snr time series.
"""
snrs = []
norms = []
for bank_template in filters:
# For every template compute the snr against the stilde segment
snr, _, norm = matched_filter_core(
bank_template, stilde, h_norm=bank_template.sigmasq(psd),
psd=None, low_frequency_cutoff=low_frequency_cutoff)
# SNR time series stored here
snrs.append(snr)
# Template normalization factor stored here
norms.append(norm)
return snrs, norms
def segment_snrs(filters, stilde, psd, low_frequency_cutoff):
""" This functions calculates the snr of each bank veto template against
the segment
Parameters
----------
filters: list of FrequencySeries
The list of bank veto templates filters.
stilde: FrequencySeries
The current segment of data.
psd: FrequencySeries
low_frequency_cutoff: float
Returns
-------
snr (list): List of snr time series.
norm (list): List of normalizations factors for the snr time series.
"""
snrs = []
norms = []
for i, bank_template in enumerate(filters):
# For every template compute the snr against the stilde segment
snr, corr, norm = matched_filter_core(
bank_template,
stilde,
h_norm=bank_template.sigmasq(psd),
psd=None,
low_frequency_cutoff=low_frequency_cutoff)
# SNR time series stored here
snrs.append(snr)
# Template normalization factor stored here
norms.append(norm)
return snrs, norms
def inner(vec1,
vec2,
psd=None,
low_frequency_cutoff=None,
high_frequency_cutoff=None,
v1_norm=None,
v2_norm=None):
htilde = pf.make_frequency_series(vec1)
stilde = pf.make_frequency_series(vec2)
N = (len(htilde) - 1) * 2
global _snr
_snr = None
if _snr is None or _snr.dtype != htilde.dtype or len(_snr) != N:
_snr = pt.zeros(N, dtype=pt.complex_same_precision_as(vec1))
snr, corr, snr_norm = pf.matched_filter_core(htilde,
stilde,
psd,
low_frequency_cutoff,
high_frequency_cutoff,
v1_norm,
out=_snr)
if v2_norm is None:
v2_norm = pf.sigmasq(stilde, psd, low_frequency_cutoff,
high_frequency_cutoff)
snr.data = snr.data * snr_norm / np.sqrt(v2_norm)
return snr
def test_sg(self):
### use a sin-gaussian as a signal
sigt = TimeSeries(self.sig2, self.del_t)
sig_tilde = make_frequency_series(sigt)
del_f = sig_tilde.get_delta_f()
psd = FrequencySeries(self.Psd, del_f)
flow = self.low_frequency_cutoff
with _context:
hautocor, hacorfr, hnrm = matched_filter_core(self.htilde, self.htilde, psd=psd, \
low_frequency_cutoff=flow, high_frequency_cutoff=self.fmax)
snr, cor, nrm = matched_filter_core(self.htilde, sig_tilde, psd=psd, \
low_frequency_cutoff=flow, high_frequency_cutoff=self.fmax)
hacor = Array(hautocor.real(), copy=True)
indx = Array(np.array([301440, 301450, 301460]))
snr = snr*nrm
with _context:
dof, achi_list = autochisq_from_precomputed(snr, cor, hacor, stride=3, num_points=20, \
indices=indx)
obt_snr = achi_list[1,1]
obt_ach = achi_list[1,2]
self.assertTrue(obt_snr > 12.0 and obt_snr < 15.0)
self.assertTrue(obt_ach > 6.8e3)
self.assertTrue(achi_list[0,2] > 6.8e3)
self.assertTrue(achi_list[2,2] > 6.8e3)
with _context:
dof, achi_list = autochisq(self.htilde, sig_tilde, psd, stride=3, num_points=20, \
low_frequency_cutoff=flow, high_frequency_cutoff=self.fmax, max_snr=True)
self.assertTrue(obt_snr == achi_list[0, 1])
self.assertTrue(obt_ach == achi_list[0, 2])
for i in xrange(1, len(achi_list)):
self.assertTrue(achi_list[i,2] > 2.e3)
def __init__(self,
variable_params,
data,
low_frequency_cutoff,
sample_rate=32768,
polarization_samples=None,
**kwargs):
variable_params, kwargs = self.setup_distance_marginalization(
variable_params, marginalize_phase=True, **kwargs)
super(SingleTemplate, self).__init__(variable_params, data,
low_frequency_cutoff, **kwargs)
# Generate template waveforms
df = data[self.detectors[0]].delta_f
p = self.static_params.copy()
if 'distance' in p:
_ = p.pop('distance')
if 'inclination' in p:
_ = p.pop('inclination')
hp, _ = get_fd_waveform(delta_f=df, distance=1, inclination=0, **p)
# Extend template to high sample rate
flen = int(int(sample_rate) / df) / 2 + 1
hp.resize(flen)
#polarization array to marginalize over if num_samples given
self.pflag = 0
if polarization_samples is not None:
self.polarization = numpy.linspace(0, 2 * numpy.pi,
int(polarization_samples))
self.pflag = 1
# Calculate high sample rate SNR time series
self.sh = {}
self.hh = {}
self.det = {}
for ifo in self.data:
flow = self.kmin[ifo] * df
fhigh = self.kmax[ifo] * df
# Extend data to high sample rate
self.data[ifo].resize(flen)
self.det[ifo] = Detector(ifo)
snr, _, _ = pyfilter.matched_filter_core(
hp,
self.data[ifo],
psd=self.psds[ifo],
low_frequency_cutoff=flow,
high_frequency_cutoff=fhigh)
self.sh[ifo] = 4 * df * snr
self.hh[ifo] = pyfilter.sigmasq(hp,
psd=self.psds[ifo],
low_frequency_cutoff=flow,
high_frequency_cutoff=fhigh)
self.time = None
def __init__(self,
data,
psds,
low_frequency_cutoff=None,
high_frequency_cutoff=None,
sample_rate=32768,
**kwargs):
super(SingleTemplate, self).__init__(data=data, **kwargs)
if low_frequency_cutoff is not None:
low_frequency_cutoff = float(low_frequency_cutoff)
if high_frequency_cutoff is not None:
high_frequency_cutoff = float(high_frequency_cutoff)
# Generate template waveforms
df = data[tuple(data.keys())[0]].delta_f
p = self.static_params.copy()
if 'distance' in p:
p.pop('distance')
if 'inclination' in p:
p.pop('inclination')
hp, _ = get_fd_waveform(delta_f=df, distance=1, inclination=0, **p)
if high_frequency_cutoff is None:
high_frequency_cutoff = len(data[tuple(data.keys())[0]] - 1) * df
# Extend data and template to high sample rate
flen = int(sample_rate / df) / 2 + 1
hp.resize(flen)
for ifo in data:
data[ifo].resize(flen)
# Calculate high sample rate SNR time series
self.sh = {}
self.hh = {}
self.det = {}
for ifo in data:
self.det[ifo] = Detector(ifo)
snr, _, _ = pyfilter.matched_filter_core(
hp,
data[ifo],
psd=psds[ifo],
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff)
self.sh[ifo] = 4 * df * snr
self.hh[ifo] = -0.5 * pyfilter.sigmasq(
hp,
psd=psds[ifo],
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff)
self.time = None
def test_sg(self):
### use a sin-gaussian as a signal
sigt = TimeSeries(self.sig2, self.del_t)
sig_tilde = make_frequency_series(sigt)
del_f = sig_tilde.get_delta_f()
psd = FrequencySeries(self.Psd, del_f)
flow = self.low_frequency_cutoff
with _context:
hautocor, hacorfr, hnrm = matched_filter_core(self.htilde, self.htilde, psd=psd, \
low_frequency_cutoff=flow, high_frequency_cutoff=self.fmax)
snr, cor, nrm = matched_filter_core(self.htilde, sig_tilde, psd=psd, \
low_frequency_cutoff=flow, high_frequency_cutoff=self.fmax)
hacor = Array(hautocor.real(), copy=True)
indx = Array(np.array([301440, 301450, 301460]))
snr = snr * nrm
with _context:
dof, achi_list = autochisq_from_precomputed(snr, cor, hacor, stride=3, num_points=20, \
indices=indx)
obt_snr = achi_list[1, 1]
obt_ach = achi_list[1, 2]
self.assertTrue(obt_snr > 12.0 and obt_snr < 15.0)
self.assertTrue(obt_ach > 6.8e3)
self.assertTrue(achi_list[0, 2] > 6.8e3)
self.assertTrue(achi_list[2, 2] > 6.8e3)
with _context:
dof, achi_list = autochisq(self.htilde, sig_tilde, psd, stride=3, num_points=20, \
low_frequency_cutoff=flow, high_frequency_cutoff=self.fmax, max_snr=True)
self.assertTrue(obt_snr == achi_list[0, 1])
self.assertTrue(obt_ach == achi_list[0, 2])
for i in xrange(1, len(achi_list)):
self.assertTrue(achi_list[i, 2] > 2.e3)
def power_chisq(template,
data,
num_bins,
psd,
low_frequency_cutoff=None,
high_frequency_cutoff=None,
return_bins=False):
"""Calculate the chisq timeseries
Parameters
----------
template: FrequencySeries or TimeSeries
A time or frequency series that contains the filter template.
data: FrequencySeries or TimeSeries
A time or frequency series that contains the data to filter. The length
must be commensurate with the template.
(EXPLAINME - does this mean 'the same as' or something else?)
num_bins: int
The number of frequency bins used for chisq. The number of statistical
degrees of freedom ('dof') is 2*num_bins-2.
psd: FrequencySeries
The psd of the data.
low_frequency_cutoff: {None, float}, optional
The low frequency cutoff for the filter
high_frequency_cutoff: {None, float}, optional
The high frequency cutoff for the filter
return_bins: {boolean, False}, optional
Return a list of the individual chisq bins
Returns
-------
chisq: TimeSeries
TimeSeries containing the chisq values for all times.
"""
htilde = make_frequency_series(template)
stilde = make_frequency_series(data)
bins = power_chisq_bins(htilde, num_bins, psd, low_frequency_cutoff,
high_frequency_cutoff)
corra = zeros((len(htilde) - 1) * 2, dtype=htilde.dtype)
total_snr, corr, tnorm = matched_filter_core(htilde,
stilde,
psd,
low_frequency_cutoff,
high_frequency_cutoff,
corr_out=corra)
return power_chisq_from_precomputed(corr,
total_snr,
tnorm,
bins,
return_bins=return_bins)
def setUp(self, *args):
self.context = _context
self.scheme = _scheme
self.tolerance = 1e-6
xr = numpy.random.uniform(low=-1, high=1.0, size=2**20)
xi = numpy.random.uniform(low=-1, high=1.0, size=2**20)
self.x = Array(xr + xi * 1.0j, dtype=complex64)
self.z = zeros(2**20, dtype=float32)
for i in range(0, 4):
trusted_accum(self.z, self.x)
m = Merger("GW170814")
ifos = ['H1', 'L1', 'V1']
data = {}
psd = {}
for ifo in ifos:
# Read in and condition the data and measure PSD
ts = m.strain(ifo).highpass_fir(15, 512)
data[ifo] = resample_to_delta_t(ts, 1.0 / 2048).crop(2, 2)
p = data[ifo].psd(2)
p = interpolate(p, data[ifo].delta_f)
p = inverse_spectrum_truncation(p,
int(2 * data[ifo].sample_rate),
low_frequency_cutoff=15.0)
psd[ifo] = p
hp, _ = get_fd_waveform(approximant="IMRPhenomD",
mass1=31.36,
mass2=31.36,
f_lower=20.0,
delta_f=data[ifo].delta_f)
hp.resize(len(psd[ifo]))
# For each ifo use this template to calculate the SNR time series
snr = {}
snr_unnorm = {}
norm = {}
corr = {}
for ifo in ifos:
snr_unnorm[ifo], corr[ifo], norm[ifo] = \
matched_filter_core(hp, data[ifo], psd=psd[ifo],
low_frequency_cutoff=20)
snr[ifo] = snr_unnorm[ifo] * norm[ifo]
self.snr = snr
self.snr_unnorm = snr_unnorm
self.norm = norm
self.corr = corr
self.hp = hp
self.data = data
self.psd = psd
self.ifos = ifos
def __init__(self, data, psds,
low_frequency_cutoff=None,
high_frequency_cutoff=None,
sample_rate=32768,
**kwargs):
super(SingleTemplate, self).__init__(data=data, **kwargs)
if low_frequency_cutoff is not None:
low_frequency_cutoff = float(low_frequency_cutoff)
if high_frequency_cutoff is not None:
high_frequency_cutoff = float(high_frequency_cutoff)
# Generate template waveforms
df = data[data.keys()[0]].delta_f
p = self.static_params.copy()
if 'distance' in p:
p.pop('distance')
if 'inclination' in p:
p.pop('inclination')
hp, _ = get_fd_waveform(delta_f=df, distance=1, inclination=0, **p)
if high_frequency_cutoff is None:
high_frequency_cutoff = len(data[data.keys()[0]]-1) * df
# Extend data and template to high sample rate
flen = int(sample_rate / df) / 2 + 1
hp.resize(flen)
for ifo in data:
data[ifo].resize(flen)
# Calculate high sample rate SNR time series
self.sh = {}
self.hh = {}
self.det = {}
for ifo in data:
self.det[ifo] = Detector(ifo)
snr, _, _ = pyfilter.matched_filter_core(
hp, data[ifo],
psd=psds[ifo],
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff)
self.sh[ifo] = 4 * df * snr
self.hh[ifo] = -0.5 * pyfilter.sigmasq(
hp, psd=psds[ifo],
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff)
self.time = None
def test_sg(self):
### use a sin-gaussian as a signal
sigt = TimeSeries(self.sig2, self.del_t)
sig_tilde = make_frequency_series(sigt)
del_f = sig_tilde.get_delta_f()
psd = FrequencySeries(self.Psd, del_f)
flow = self.low_frequency_cutoff
with _context:
hautocor, hacorfr, hnrm = matched_filter_core(self.htilde, self.htilde, psd=psd, \
low_frequency_cutoff=flow, high_frequency_cutoff=self.fmax)
hautocor = hautocor * float(np.real(1./hautocor[0]))
snr, cor, nrm = matched_filter_core(self.htilde, sig_tilde, psd=psd, \
low_frequency_cutoff=flow, high_frequency_cutoff=self.fmax)
hacor = Array(hautocor.real(), copy=True)
indx = np.array([301440, 301450, 301460])
snr = snr*nrm
with _context:
dof, achisq, indices= \
autochisq_from_precomputed(snr, snr, hacor, indx, stride=3,
num_points=20)
obt_snr = abs(snr[indices[1]])
obt_ach = achisq[1]
self.assertTrue(obt_snr > 12.0 and obt_snr < 15.0)
self.assertTrue(obt_ach > 6.8e3)
self.assertTrue(achisq[0] > 6.8e3)
self.assertTrue(achisq[2] > 6.8e3)
def test_sg(self):
### use a sin-gaussian as a signal
sigt = TimeSeries(self.sig2, self.del_t)
sig_tilde = make_frequency_series(sigt)
del_f = sig_tilde.get_delta_f()
psd = FrequencySeries(self.Psd, del_f)
flow = self.low_frequency_cutoff
with _context:
hautocor, hacorfr, hnrm = matched_filter_core(self.htilde, self.htilde, psd=psd, \
low_frequency_cutoff=flow, high_frequency_cutoff=self.fmax)
hautocor = hautocor * float(np.real(1./hautocor[0]))
snr, cor, nrm = matched_filter_core(self.htilde, sig_tilde, psd=psd, \
low_frequency_cutoff=flow, high_frequency_cutoff=self.fmax)
hacor = Array(hautocor.real(), copy=True)
indx = np.array([301440, 301450, 301460])
snr = snr*nrm
with _context:
dof, achisq, indices= \
autochisq_from_precomputed(snr, snr, hacor, indx, stride=3,
num_points=20)
obt_snr = abs(snr[indices[1]])
obt_ach = achisq[1]
self.assertTrue(obt_snr > 12.0 and obt_snr < 15.0)
self.assertTrue(obt_ach > 6.8e3)
self.assertTrue(achisq[0] > 6.8e3)
self.assertTrue(achisq[2] > 6.8e3)
def setUp(self,*args):
self.context = _context
self.scheme = _scheme
self.tolerance = 1e-6
xr = numpy.random.uniform(low=-1, high=1.0, size=2**20)
xi = numpy.random.uniform(low=-1, high=1.0, size=2**20)
self.x = Array(xr + xi * 1.0j, dtype=complex64)
self.z = zeros(2**20, dtype=float32)
for i in range(0, 4):
trusted_accum(self.z, self.x)
m = Merger("GW170814")
ifos = ['H1', 'L1', 'V1']
data = {}
psd = {}
for ifo in ifos:
# Read in and condition the data and measure PSD
ts = m.strain(ifo).highpass_fir(15, 512)
data[ifo] = resample_to_delta_t(ts, 1.0/2048).crop(2, 2)
p = data[ifo].psd(2)
p = interpolate(p, data[ifo].delta_f)
p = inverse_spectrum_truncation(p, 2 * data[ifo].sample_rate,
low_frequency_cutoff=15.0)
psd[ifo] = p
hp, _ = get_fd_waveform(approximant="IMRPhenomD",
mass1=31.36, mass2=31.36,
f_lower=20.0, delta_f=data[ifo].delta_f)
hp.resize(len(psd[ifo]))
# For each ifo use this template to calculate the SNR time series
snr = {}
snr_unnorm = {}
norm = {}
corr = {}
for ifo in ifos:
snr_unnorm[ifo], corr[ifo], norm[ifo] = \
matched_filter_core(hp, data[ifo], psd=psd[ifo],
low_frequency_cutoff=20)
snr[ifo] = snr_unnorm[ifo] * norm[ifo]
self.snr = snr
self.snr_unnorm = snr_unnorm
self.norm = norm
self.corr = corr
self.hp = hp
self.data = data
self.psd = psd
self.ifos = ifos
def __init__(self,
variable_params,
data,
low_frequency_cutoff,
sample_rate=32768,
**kwargs):
super(SingleTemplate, self).__init__(variable_params, data,
low_frequency_cutoff, **kwargs)
# Generate template waveforms
df = data[self.detectors[0]].delta_f
p = self.static_params.copy()
if 'distance' in p:
_ = p.pop('distance')
if 'inclination' in p:
_ = p.pop('inclination')
hp, _ = get_fd_waveform(delta_f=df, distance=1, inclination=0, **p)
# Extend template to high sample rate
flen = int(int(sample_rate) / df) / 2 + 1
hp.resize(flen)
# Calculate high sample rate SNR time series
self.sh = {}
self.hh = {}
self.det = {}
for ifo in self.data:
flow = self.kmin[ifo] * df
fhigh = self.kmax[ifo] * df
# Extend data to high sample rate
self.data[ifo].resize(flen)
self.det[ifo] = Detector(ifo)
snr, _, _ = pyfilter.matched_filter_core(
hp,
self.data[ifo],
psd=self.psds[ifo],
low_frequency_cutoff=flow,
high_frequency_cutoff=fhigh)
self.sh[ifo] = 4 * df * snr
self.hh[ifo] = -0.5 * pyfilter.sigmasq(hp,
psd=self.psds[ifo],
low_frequency_cutoff=flow,
high_frequency_cutoff=fhigh)
self.time = None
def calculate_hihjs(self, models):
""" Pre-calculate the hihj inner products on a grid
"""
self.hihj = {}
for m1, m2 in itertools.combinations(models, 2):
self.hihj[(m1, m2)] = {}
h1 = m1.waveform
h2 = m2.waveform
for ifo in self.data:
flow = self.kmin[ifo] * self.df
fhigh = self.kmax[ifo] * self.df
h1h2, _, _ = pyfilter.matched_filter_core(
h1,
h2,
psd=self.psds[ifo],
low_frequency_cutoff=flow,
high_frequency_cutoff=fhigh)
self.hihj[(m1, m2)][ifo] = 4 * self.df * h1h2
def power_chisq(template, data, num_bins, psd,
low_frequency_cutoff=None,
high_frequency_cutoff=None,
return_bins=False):
"""Calculate the chisq timeseries
Parameters
----------
template: FrequencySeries or TimeSeries
A time or frequency series that contains the filter template.
data: FrequencySeries or TimeSeries
A time or frequency series that contains the data to filter. The length
must be commensurate with the template.
(EXPLAINME - does this mean 'the same as' or something else?)
num_bins: int
The number of bins in the chisq. Note that the dof goes as 2*num_bins-2.
psd: FrequencySeries
The psd of the data.
low_frequency_cutoff: {None, float}, optional
The low frequency cutoff for the filter
high_frequency_cutoff: {None, float}, optional
The high frequency cutoff for the filter
return_bins: {boolean, False}, optional
Return a list of the individual chisq bins
Returns
-------
chisq: TimeSeries
TimeSeries containing the chisq values for all times.
"""
htilde = make_frequency_series(template)
stilde = make_frequency_series(data)
bins = power_chisq_bins(htilde, num_bins, psd, low_frequency_cutoff,
high_frequency_cutoff)
corra = zeros((len(htilde)-1)*2, dtype=htilde.dtype)
total_snr, corr, tnorm = matched_filter_core(htilde, stilde, psd,
low_frequency_cutoff, high_frequency_cutoff,
corr_out=corra)
return power_chisq_from_precomputed(corr, total_snr, tnorm, bins, return_bins=return_bins)
def create_tf_plane(fd_psd,nchans,seg_len,filter_bank,band,fs_data):
"""
Create time-frequency map
Parameters
----------
fd_psd : array
Power Spectrum Density
"""
print "|-- Create time-frequency plane for current block"
# Return the complex snr, along with its associated normalization of the template,
# matched filtered against the data
#filter.matched_filter_core(types.FrequencySeries(tmp_filter_bank,delta_f=fd_psd.delta_f),fs_data,h_norm=1,psd=fd_psd,low_frequency_cutoff=filter_bank[0].f0,high_frequency_cutoff=filter_bank[0].f0+2*band)
print "|-- Filtering all %d channels..." % nchans
# Initialise 2D zero array
tmp_filter_bank = numpy.zeros(len(fd_psd), dtype=numpy.complex128)
# Initialise 2D zero array for time-frequency map
tf_map = numpy.zeros((nchans, seg_len), dtype=numpy.complex128)
# Loop over all the channels
for i in range(nchans):
# Reset filter bank series
tmp_filter_bank *= 0.0
# Index of starting frequency
f1 = int(filter_bank[i].f0/fd_psd.delta_f)
# Index of ending frequency
f2 = int((filter_bank[i].f0 + 2*band)/fd_psd.delta_f)+1
# (FIXME: Why is there a factor of 2 here?)
tmp_filter_bank[f1:f2] = filter_bank[i].data.data * 2
# Define the template to filter the frequency series with
template = types.FrequencySeries(tmp_filter_bank, delta_f=fd_psd.delta_f, copy=False)
# Create filtered series
filtered_series = filter.matched_filter_core(template,fs_data,h_norm=None,psd=None,
low_frequency_cutoff=filter_bank[i].f0,
high_frequency_cutoff=filter_bank[i].f0+2*band)
# Include filtered series in the map
tf_map[i,:] = filtered_series[0].numpy()
return tf_map
def align_waveforms_suboptimally(hplus1,
hcross1,
hplus2,
hcross2,
psd='aLIGOZeroDetHighPower',
low_frequency_cutoff=None,
high_frequency_cutoff=None,
tsign=1,
phsign=1,
verify=True,
trim_leading=False,
trim_trailing=False,
verbose=False):
# Cast into time-series
h_plus1 = TimeSeries(hplus1,
epoch=hplus1._epoch,
delta_t=hplus1.delta_t,
dtype=hplus1.dtype)
h_cross1 = TimeSeries(hcross1,
epoch=hplus1._epoch,
delta_t=hplus1.delta_t,
dtype=hplus1.dtype)
h_plus2 = TimeSeries(hplus2,
epoch=hplus2._epoch,
delta_t=hplus2.delta_t,
dtype=hplus2.dtype)
h_cross2 = TimeSeries(hcross2,
epoch=hplus2._epoch,
delta_t=hplus2.delta_t,
dtype=hplus2.dtype)
#
# Ensure both input hplus vectors are equal in length
if len(hplus2) > len(hplus1):
h_plus1.append_zeros(len(hplus2) - len(hplus1))
h_cross1.append_zeros(len(hplus2) - len(hplus1))
elif len(hplus2) < len(hplus1):
h_plus2.append_zeros(len(hplus1) - len(hplus2))
h_cross2.append_zeros(len(hplus1) - len(hplus2))
#
htilde = make_frequency_series(h_plus1)
stilde = make_frequency_series(h_plus2)
#
if high_frequency_cutoff == None:
high_frequency_cutoff = 1. / h_plus1.delta_t / 2.
#
if psd == None:
raise IOError("Need compatible psd [or name] as input!")
elif type(psd) == str:
psd_name = psd
psd = from_string(psd_name, len(htilde), htilde.delta_f,
low_frequency_cutoff)
#
# Determine the phase and time shifts for optimal match
snr, corr, snr_norm = matched_filter_core(
htilde,
stilde,
# h_plus1, h_plus2,
psd,
low_frequency_cutoff,
high_frequency_cutoff,
None)
max_snr, max_id = snr.abs_max_loc()
if max_id != 0:
t_shift = snr.delta_t * (len(snr) - max_id)
else:
t_shift = snr.delta_t * max_id
ph_shift = np.angle(snr[max_id]) - 0.24850315030 - 0.0465881735639
#
if verbose:
print(("max_id = %d, id_shift = %d" %
(max_id, int(t_shift / snr.delta_t))))
print(("t_shift = %f,\n ph_shift = %f" % (t_shift, ph_shift)))
#
# print(OVERLAPS
if verbose:
print(("Overlap BEFORE ALIGNMENT:",
overlap_cplx(h_plus1,
h_plus2,
psd=psd,
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff,
normalized=True)))
print(("Match BEFORE ALIGNMENT:",
match(h_plus1,
h_plus2,
psd=psd,
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff)))
# Shift whichever needs to be shifted to future time.
# Shifting back in time is tricky.
if t_shift >= 0:
hp2, hc2 = shift_waveform_phase_time(h_plus2,
h_cross2,
tsign * t_shift,
phsign * ph_shift,
verbose=verbose)
else:
hp2, hc2 = shift_waveform_phase_time(h_plus2,
h_cross2,
tsign * t_shift,
phsign * ph_shift,
verbose=verbose)
#
# Ensure both input hplus vectors are equal in length
if len(h_plus1) > len(hp2):
hp2.append_zeros(len(h_plus1) - len(hp2))
elif len(h_plus1) < len(hp2):
h_plus1.append_zeros(len(hp2) - len(h_plus1))
if verbose:
htilde = make_frequency_series(h_plus1)
psd = from_string(psd_name, len(htilde), htilde.delta_f,
low_frequency_cutoff)
print(("Overlap AFTER ALIGNMENT:",
overlap_cplx(h_plus1,
hp2,
psd=psd,
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff,
normalized=True)))
print(("Match AFTER ALIGNMENT:",
match(h_plus1,
hp2,
psd=psd,
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff)))
if verify:
#
print("Verifying time alignment...")
# Determine the phase and time shifts for optimal match
snr, corr, snr_norm = matched_filter_core( # htilde, stilde,
h_plus1, hp2, psd, low_frequency_cutoff, high_frequency_cutoff,
None)
max_snr, max_id = snr.abs_max_loc()
print(("Post-Alignment Index of MAX SNR (should be 0 or 1 or %d): %d" %
(len(snr) - 1, max_id)))
print(("Length of whole SNR time-series: ", len(snr)))
if max_id != 0 and max_id != 1 and max_id != (
len(snr) - 1) and max_id != (len(snr) - 2):
# raise RuntimeError( "Warning: ALIGNMENT NOT CORRECT (see above)" )
print("Warning: ALIGNMENT NOT CORRECT (see above)")
else:
print("Alignment in time correct..")
#
print("Verifying phase alignment...")
ph_shift = np.angle(snr[max_id])
if ph_shift != 0:
print("Warning: Phasing alignment possibly incorrect.")
print(("dphi, dphi+pi, dphi-pi: ", ph_shift, ph_shift + np.pi,
ph_shift - np.pi))
print(("dphi/pi, dphi*pi: ", ph_shift / np.pi, ph_shift * np.pi))
#
#
if trim_trailing:
hp1 = trim_trailing_zeros(hp1)
hc1 = trim_trailing_zeros(hc1)
hp2 = trim_trailing_zeros(hp2)
hc2 = trim_trailing_zeros(hc2)
if trim_leading:
hp1 = trim_leading_zeros(hp1)
hc1 = trim_leading_zeros(hc1)
hp2 = trim_leading_zeros(hp2)
hc2 = trim_leading_zeros(hc2)
#
return hplus1, hcross1, hp2, hc2
def excess_power(
ts_data, # Time series from magnetic field data
band=None, # Channel bandwidth
channel_name='channel-name', # Channel name
fmin=0, # Lowest frequency of the filter bank.
fmax=None, # Highest frequency of the filter bank.
impulse=False, # Impulse response
make_plot=True, # Condition to produce plots
max_duration=None, # Maximum duration of the tile
nchans=256, # Total number of channels
psd_estimation='median-mean', # Average method
psd_segment_length=60, # Length of each segment in seconds
psd_segment_stride=30, # Separation between 2 consecutive segments in seconds
station='station-name', # Station name
tile_fap=1e-7, # Tile false alarm probability threshold in Gaussian noise.
verbose=True, # Print details
window_fraction=0, # Withening window fraction
wtype='tukey'): # Whitening type, can tukey or hann
'''
Perform excess-power search analysis on magnetic field data.
This method will produce a bunch of time-frequency plots for every
tile duration and bandwidth analysed as well as a XML file identifying
all the triggers found in the selected data within the user-defined
time range.
Parameters
----------
ts_data : TimeSeries
Time Series from magnetic field data
psd_segment_length : float
Length of each segment in seconds
psd_segment_stride : float
Separation between 2 consecutive segments in seconds
psd_estimation : string
Average method
window_fraction : float
Withening window fraction
tile_fap : float
Tile false alarm probability threshold in Gaussian noise.
nchans : int
Total number of channels
band : float
Channel bandwidth
fmin : float
Lowest frequency of the filter bank.
fmax : float
Highest frequency of the filter bank
Examples
--------
The program can be ran as an executable by using the ``excesspower`` command
line as follows::
excesspower --station "mainz01" \\
--start-time "2017-04-15-17-1" \\
--end-time "2017-04-15-18" \\
--rep "/Users/vincent/ASTRO/data/GNOME/GNOMEDrive/gnome/serverdata/" \\
--resample 512 \\
--verbose
'''
# Determine sampling rate based on extracted time series
sample_rate = ts_data.sample_rate
# Check if tile maximum frequency is not defined
if fmax is None or fmax > sample_rate / 2.:
# Set the tile maximum frequency equal to the Nyquist frequency
# (i.e. half the sampling rate)
fmax = sample_rate / 2.0
# Check whether or not tile bandwidth and channel are defined
if band is None and nchans is None:
# Exit program with error message
exit("Either bandwidth or number of channels must be specified...")
else:
# Check if tile maximum frequency larger than its minimum frequency
assert fmax >= fmin
# Define spectral band of data
data_band = fmax - fmin
# Check whether tile bandwidth or channel is defined
if band is not None:
# Define number of possible filter bands
nchans = int(data_band / band)
elif nchans is not None:
# Define filter bandwidth
band = data_band / nchans
nchans -= 1
# Check if number of channels is superior than unity
assert nchans > 1
# Print segment information
if verbose: print '|- Estimating PSD from segments of',
if verbose:
print '%.2f s, with %.2f s stride...' % (psd_segment_length,
psd_segment_stride)
# Convert time series as array of float
data = ts_data.astype(numpy.float64)
# Define segment length for PSD estimation in sample unit
seg_len = int(psd_segment_length * sample_rate)
# Define separation between consecutive segments in sample unit
seg_stride = int(psd_segment_stride * sample_rate)
# Minimum frequency of detectable signal in a segment
delta_f = 1. / psd_segment_length
# Calculate PSD length counting the zero frequency element
fd_len = fmax / delta_f + 1
# Calculate the overall PSD from individual PSD segments
if impulse:
# Produce flat data
flat_data = numpy.ones(int(fd_len)) * 2. / fd_len
# Create PSD frequency series
fd_psd = types.FrequencySeries(flat_data, 1. / psd_segment_length,
ts_data.start_time)
else:
# Create overall PSD using Welch's method
fd_psd = psd.welch(data,
avg_method=psd_estimation,
seg_len=seg_len,
seg_stride=seg_stride)
if make_plot:
# Plot the power spectral density
plot_spectrum(fd_psd)
# We need this for the SWIG functions
lal_psd = fd_psd.lal()
# Create whitening window
if verbose: print "|- Whitening window and spectral correlation..."
if wtype == 'hann': window = lal.CreateHannREAL8Window(seg_len)
elif wtype == 'tukey':
window = lal.CreateTukeyREAL8Window(seg_len, window_fraction)
else:
raise ValueError("Can't handle window type %s" % wtype)
# Create FFT plan
fft_plan = lal.CreateForwardREAL8FFTPlan(len(window.data.data), 1)
# Perform two point spectral correlation
spec_corr = lal.REAL8WindowTwoPointSpectralCorrelation(window, fft_plan)
# Determine length of individual filters
filter_length = int(2 * band / fd_psd.delta_f) + 1
# Initialise filter bank
if verbose:
print "|- Create bank of %i filters of %i Hz bandwidth..." % (
nchans, filter_length)
# Initialise array to store filter's frequency series and metadata
lal_filters = []
# Initialise array to store filter's time series
fdb = []
# Loop over the channels
for i in range(nchans):
# Define central position of the filter
freq = fmin + band / 2 + i * band
# Create excess power filter
lal_filter = lalburst.CreateExcessPowerFilter(freq, band, lal_psd,
spec_corr)
# Testing spectral correlation on filter
#print lalburst.ExcessPowerFilterInnerProduct(lal_filter, lal_filter, spec_corr, None)
# Append entire filter structure
lal_filters.append(lal_filter)
# Append filter's spectrum
fdb.append(FrequencySeries.from_lal(lal_filter))
#print fdb[0].frequencies
#print fdb[0]
if make_plot:
# Plot filter bank
plot_bank(fdb)
# Convert filter bank from frequency to time domain
if verbose:
print "|- Convert all the frequency domain to the time domain..."
tdb = []
# Loop for each filter's spectrum
for fdt in fdb:
zero_padded = numpy.zeros(int((fdt.f0 / fdt.df).value) + len(fdt))
st = int((fdt.f0 / fdt.df).value)
zero_padded[st:st + len(fdt)] = numpy.real_if_close(fdt.value)
n_freq = int(sample_rate / 2 / fdt.df.value) * 2
tdt = numpy.fft.irfft(zero_padded, n_freq) * math.sqrt(sample_rate)
tdt = numpy.roll(tdt, len(tdt) / 2)
tdt = TimeSeries(tdt,
name="",
epoch=fdt.epoch,
sample_rate=sample_rate)
tdb.append(tdt)
# Plot time series filter
plot_filters(tdb, fmin, band)
# Computer whitened inner products of input filters with themselves
#white_filter_ip = numpy.array([lalburst.ExcessPowerFilterInnerProduct(f, f, spec_corr, None) for f in lal_filters])
# Computer unwhitened inner products of input filters with themselves
#unwhite_filter_ip = numpy.array([lalburst.ExcessPowerFilterInnerProduct(f, f, spec_corr, lal_psd) for f in lal_filters])
# Computer whitened filter inner products between input adjacent filters
#white_ss_ip = numpy.array([lalburst.ExcessPowerFilterInnerProduct(f1, f2, spec_corr, None) for f1, f2 in zip(lal_filters[:-1], lal_filters[1:])])
# Computer unwhitened filter inner products between input adjacent filters
#unwhite_ss_ip = numpy.array([lalburst.ExcessPowerFilterInnerProduct(f1, f2, spec_corr, lal_psd) for f1, f2 in zip(lal_filters[:-1], lal_filters[1:])])
# Check filter's bandwidth is equal to user defined channel bandwidth
min_band = (len(lal_filters[0].data.data) - 1) * lal_filters[0].deltaF / 2
assert min_band == band
# Create an event list where all the triggers will be stored
event_list = lsctables.New(lsctables.SnglBurstTable, [
'start_time', 'start_time_ns', 'peak_time', 'peak_time_ns', 'duration',
'bandwidth', 'central_freq', 'chisq_dof', 'confidence', 'snr',
'amplitude', 'channel', 'ifo', 'process_id', 'event_id', 'search',
'stop_time', 'stop_time_ns'
])
# Create repositories to save TF and time series plots
os.system('mkdir -p segments/time-frequency')
os.system('mkdir -p segments/time-series')
# Define time edges
t_idx_min, t_idx_max = 0, seg_len
# Loop over each segment
while t_idx_max <= len(ts_data):
# Define first and last timestamps of the block
start_time = ts_data.start_time + t_idx_min / float(
ts_data.sample_rate)
end_time = ts_data.start_time + t_idx_max / float(ts_data.sample_rate)
if verbose:
print "\n|- Analyzing block %i to %i (%.2f percent)" % (
start_time, end_time, 100 * float(t_idx_max) / len(ts_data))
# Debug for impulse response
if impulse:
for i in range(t_idx_min, t_idx_max):
ts_data[i] = 1000. if i == (t_idx_max + t_idx_min) / 2 else 0.
# Model a withen time series for the block
tmp_ts_data = types.TimeSeries(ts_data[t_idx_min:t_idx_max] *
window.data.data,
delta_t=1. / ts_data.sample_rate,
epoch=start_time)
# Save time series in relevant repository
os.system('mkdir -p segments/%i-%i' % (start_time, end_time))
if make_plot:
# Plot time series
plot_ts(tmp_ts_data,
fname='segments/time-series/%i-%i.png' %
(start_time, end_time))
# Convert times series to frequency series
fs_data = tmp_ts_data.to_frequencyseries()
if verbose:
print "|- Frequency series data has variance: %s" % fs_data.data.std(
)**2
# Whitening (FIXME: Whiten the filters, not the data)
fs_data.data /= numpy.sqrt(fd_psd) / numpy.sqrt(2 * fd_psd.delta_f)
if verbose:
print "|- Whitened frequency series data has variance: %s" % fs_data.data.std(
)**2
if verbose: print "|- Create time-frequency plane for current block"
# Return the complex snr, along with its associated normalization of the template,
# matched filtered against the data
#filter.matched_filter_core(types.FrequencySeries(tmp_filter_bank,delta_f=fd_psd.delta_f),
# fs_data,h_norm=1,psd=fd_psd,low_frequency_cutoff=lal_filters[0].f0,
# high_frequency_cutoff=lal_filters[0].f0+2*band)
if verbose: print "|- Filtering all %d channels...\n" % nchans,
# Initialise 2D zero array
tmp_filter_bank = numpy.zeros(len(fd_psd), dtype=numpy.complex128)
# Initialise 2D zero array for time-frequency map
tf_map = numpy.zeros((nchans, seg_len), dtype=numpy.complex128)
# Loop over all the channels
for i in range(nchans):
# Reset filter bank series
tmp_filter_bank *= 0.0
# Index of starting frequency
f1 = int(lal_filters[i].f0 / fd_psd.delta_f)
# Index of last frequency bin
f2 = int((lal_filters[i].f0 + 2 * band) / fd_psd.delta_f) + 1
# (FIXME: Why is there a factor of 2 here?)
tmp_filter_bank[f1:f2] = lal_filters[i].data.data * 2
# Define the template to filter the frequency series with
template = types.FrequencySeries(tmp_filter_bank,
delta_f=fd_psd.delta_f,
copy=False)
# Create filtered series
filtered_series = filter.matched_filter_core(
template,
fs_data,
h_norm=None,
psd=None,
low_frequency_cutoff=lal_filters[i].f0,
high_frequency_cutoff=lal_filters[i].f0 + 2 * band)
# Include filtered series in the map
tf_map[i, :] = filtered_series[0].numpy()
if make_plot:
# Plot spectrogram
plot_spectrogram(numpy.abs(tf_map).T,
dt=tmp_ts_data.delta_t,
df=band,
ymax=ts_data.sample_rate / 2.,
t0=start_time,
t1=end_time,
fname='segments/time-frequency/%i-%i.png' %
(start_time, end_time))
plot_tiles_ts(numpy.abs(tf_map),
2,
1,
sample_rate=ts_data.sample_rate,
t0=start_time,
t1=end_time,
fname='segments/%i-%i/ts.png' %
(start_time, end_time))
#plot_tiles_tf(numpy.abs(tf_map),2,1,ymax=ts_data.sample_rate/2,
# sample_rate=ts_data.sample_rate,t0=start_time,t1=end_time,
# fname='segments/%i-%i/tf.png'%(start_time,end_time))
# Loop through powers of 2 up to number of channels
for nc_sum in range(0, int(math.log(nchans, 2)))[::-1]:
# Calculate total number of summed channels
nc_sum = 2**nc_sum
if verbose:
print "\n\t|- Contructing tiles containing %d narrow band channels" % nc_sum
# Compute full bandwidth of virtual channel
df = band * nc_sum
# Compute minimal signal's duration in virtual channel
dt = 1.0 / (2 * df)
# Compute under sampling rate
us_rate = int(round(dt / ts_data.delta_t))
if verbose:
print "\t|- Undersampling rate for this level: %f" % (
ts_data.sample_rate / us_rate)
if verbose: print "\t|- Calculating tiles..."
# Clip the boundaries to remove window corruption
clip_samples = int(psd_segment_length * window_fraction *
ts_data.sample_rate / 2)
# Undersample narrow band channel's time series
# Apply clipping condition because [0:-0] does not give the full array
tf_map_temp = tf_map[:,clip_samples:-clip_samples:us_rate] \
if clip_samples > 0 else tf_map[:,::us_rate]
# Initialise final tile time-frequency map
tiles = numpy.zeros(((nchans + 1) / nc_sum, tf_map_temp.shape[1]))
# Loop over tile index
for i in xrange(len(tiles)):
# Sum all inner narrow band channels
ts_tile = numpy.absolute(tf_map_temp[nc_sum * i:nc_sum *
(i + 1)].sum(axis=0))
# Define index of last narrow band channel for given tile
n = (i + 1) * nc_sum - 1
n = n - 1 if n == len(lal_filters) else n
# Computer withened inner products of each input filter with itself
mu_sq = nc_sum * lalburst.ExcessPowerFilterInnerProduct(
lal_filters[n], lal_filters[n], spec_corr, None)
#kmax = nc_sum-1 if n==len(lal_filters) else nc_sum-2
# Loop over the inner narrow band channels
for k in xrange(0, nc_sum - 1):
# Computer whitened filter inner products between input adjacent filters
mu_sq += 2 * lalburst.ExcessPowerFilterInnerProduct(
lal_filters[n - k], lal_filters[n - 1 - k], spec_corr,
None)
# Normalise tile's time series
tiles[i] = ts_tile.real**2 / mu_sq
if verbose: print "\t|- TF-plane is %dx%s samples" % tiles.shape
if verbose:
print "\t|- Tile energy mean %f, var %f" % (numpy.mean(tiles),
numpy.var(tiles))
# Define maximum number of degrees of freedom and check it larger or equal to 2
max_dof = 32 if max_duration == None else int(max_duration / dt)
assert max_dof >= 2
# Loop through multiple degrees of freedom
for j in [2**l for l in xrange(0, int(math.log(max_dof, 2)))]:
# Duration is fixed by the NDOF and bandwidth
duration = j * dt
if verbose: print "\n\t\t|- Summing DOF = %d ..." % (2 * j)
if verbose:
print "\t\t|- Explore signal duration of %f s..." % duration
# Construct filter
sum_filter = numpy.array([1, 0] * (j - 1) + [1])
# Calculate length of filtered time series
tlen = tiles.shape[1] - sum_filter.shape[0] + 1
# Initialise filtered time series array
dof_tiles = numpy.zeros((tiles.shape[0], tlen))
# Loop over tiles
for f in range(tiles.shape[0]):
# Sum and drop correlate tiles
dof_tiles[f] = fftconvolve(tiles[f], sum_filter, 'valid')
if verbose:
print "\t\t|- Summed tile energy mean: %f" % (
numpy.mean(dof_tiles))
if verbose:
print "\t\t|- Variance tile energy: %f" % (
numpy.var(dof_tiles))
if make_plot:
plot_spectrogram(
dof_tiles.T,
dt,
df,
ymax=ts_data.sample_rate / 2,
t0=start_time,
t1=end_time,
fname='segments/%i-%i/%02ichans_%02idof.png' %
(start_time, end_time, nc_sum, 2 * j))
plot_tiles_ts(
dof_tiles,
2 * j,
df,
sample_rate=ts_data.sample_rate / us_rate,
t0=start_time,
t1=end_time,
fname='segments/%i-%i/%02ichans_%02idof_ts.png' %
(start_time, end_time, nc_sum, 2 * j))
plot_tiles_tf(
dof_tiles,
2 * j,
df,
ymax=ts_data.sample_rate / 2,
sample_rate=ts_data.sample_rate / us_rate,
t0=start_time,
t1=end_time,
fname='segments/%i-%i/%02ichans_%02idof_tf.png' %
(start_time, end_time, nc_sum, 2 * j))
threshold = scipy.stats.chi2.isf(tile_fap, j)
if verbose:
print "\t\t|- Threshold for this level: %f" % threshold
spant, spanf = dof_tiles.shape[1] * dt, dof_tiles.shape[0] * df
if verbose:
print "\t\t|- Processing %.2fx%.2f time-frequency map." % (
spant, spanf)
# Since we clip the data, the start time needs to be adjusted accordingly
window_offset_epoch = fs_data.epoch + psd_segment_length * window_fraction / 2
window_offset_epoch = LIGOTimeGPS(float(window_offset_epoch))
for i, j in zip(*numpy.where(dof_tiles > threshold)):
event = event_list.RowType()
# The points are summed forward in time and thus a `summed point' is the
# sum of the previous N points. If this point is above threshold, it
# corresponds to a tile which spans the previous N points. However, the
# 0th point (due to the convolution specifier 'valid') is actually
# already a duration from the start time. All of this means, the +
# duration and the - duration cancels, and the tile 'start' is, by
# definition, the start of the time frequency map if j = 0
# FIXME: I think this needs a + dt/2 to center the tile properly
event.set_start(window_offset_epoch + float(j * dt))
event.set_stop(window_offset_epoch + float(j * dt) +
duration)
event.set_peak(event.get_start() + duration / 2)
event.central_freq = lal_filters[
0].f0 + band / 2 + i * df + 0.5 * df
event.duration = duration
event.bandwidth = df
event.chisq_dof = 2 * duration * df
event.snr = math.sqrt(dof_tiles[i, j] / event.chisq_dof -
1)
# FIXME: Magic number 0.62 should be determine empircally
event.confidence = -lal.LogChisqCCDF(
event.snr * 0.62, event.chisq_dof * 0.62)
event.amplitude = None
event.process_id = None
event.event_id = event_list.get_next_id()
event_list.append(event)
for event in event_list[::-1]:
if event.amplitude != None:
continue
etime_min_idx = float(event.get_start()) - float(
fs_data.epoch)
etime_min_idx = int(etime_min_idx / tmp_ts_data.delta_t)
etime_max_idx = float(event.get_start()) - float(
fs_data.epoch) + event.duration
etime_max_idx = int(etime_max_idx / tmp_ts_data.delta_t)
# (band / 2) to account for sin^2 wings from finest filters
flow_idx = int((event.central_freq - event.bandwidth / 2 -
(df / 2) - fmin) / df)
fhigh_idx = int((event.central_freq + event.bandwidth / 2 +
(df / 2) - fmin) / df)
# TODO: Check that the undersampling rate is always commensurate
# with the indexing: that is to say that
# mod(etime_min_idx, us_rate) == 0 always
z_j_b = tf_map[flow_idx:fhigh_idx,
etime_min_idx:etime_max_idx:us_rate]
# FIXME: Deal with negative hrss^2 -- e.g. remove the event
try:
event.amplitude = measure_hrss(
z_j_b, unwhite_filter_ip[flow_idx:fhigh_idx],
unwhite_ss_ip[flow_idx:fhigh_idx - 1],
white_ss_ip[flow_idx:fhigh_idx - 1],
fd_psd.delta_f, tmp_ts_data.delta_t,
len(lal_filters[0].data.data), event.chisq_dof)
except ValueError:
event.amplitude = 0
if verbose:
print "\t\t|- Total number of events: %d" % len(event_list)
t_idx_min += int(seg_len * (1 - window_fraction))
t_idx_max += int(seg_len * (1 - window_fraction))
setname = "MagneticFields"
__program__ = 'pyburst_excesspower_gnome'
start_time = LIGOTimeGPS(int(ts_data.start_time))
end_time = LIGOTimeGPS(int(ts_data.end_time))
inseg = segment(start_time, end_time)
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
ifo = channel_name.split(":")[0]
straindict = psd.insert_psd_option_group.__dict__
proc_row = register_to_xmldoc(xmldoc,
__program__,
straindict,
ifos=[ifo],
version=git_version.id,
cvs_repository=git_version.branch,
cvs_entry_time=git_version.date)
dt_stride = psd_segment_length
sample_rate = ts_data.sample_rate
# Amount to overlap successive blocks so as not to lose data
window_overlap_samples = window_fraction * sample_rate
outseg = inseg.contract(window_fraction * dt_stride / 2)
# With a given dt_stride, we cannot process the remainder of this data
remainder = math.fmod(abs(outseg), dt_stride * (1 - window_fraction))
# ...so make an accounting of it
outseg = segment(outseg[0], outseg[1] - remainder)
ss = append_search_summary(xmldoc,
proc_row,
ifos=(station, ),
inseg=inseg,
outseg=outseg)
for sb in event_list:
sb.process_id = proc_row.process_id
sb.search = proc_row.program
sb.ifo, sb.channel = station, setname
xmldoc.childNodes[0].appendChild(event_list)
ifostr = ifo if isinstance(ifo, str) else "".join(ifo)
st_rnd, end_rnd = int(math.floor(inseg[0])), int(math.ceil(inseg[1]))
dur = end_rnd - st_rnd
fname = "%s-excesspower-%d-%d.xml.gz" % (ifostr, st_rnd, dur)
utils.write_filename(xmldoc, fname, gz=fname.endswith("gz"))
plot_triggers(fname)
def excess_power2(
ts_data, # Time series from magnetic field data
psd_segment_length, # Length of each segment in seconds
psd_segment_stride, # Separation between 2 consecutive segments in seconds
psd_estimation, # Average method
window_fraction, # Withening window fraction
tile_fap, # Tile false alarm probability threshold in Gaussian noise.
station, # Station
nchans=None, # Total number of channels
band=None, # Channel bandwidth
fmin=0, # Lowest frequency of the filter bank.
fmax=None, # Highest frequency of the filter bank.
max_duration=None, # Maximum duration of the tile
wtype='tukey'): # Whitening type, can tukey or hann
"""
Perform excess-power search analysis on magnetic field data.
This method will produce a bunch of time-frequency plots for every
tile duration and bandwidth analysed as well as a XML file identifying
all the triggers found in the selected data within the user-defined
time range.
Parameters
----------
ts_data : TimeSeries
Time Series from magnetic field data
psd_segment_length : float
Length of each segment in seconds
psd_segment_stride : float
Separation between 2 consecutive segments in seconds
psd_estimation : string
Average method
window_fraction : float
Withening window fraction
tile_fap : float
Tile false alarm probability threshold in Gaussian noise.
nchans : int
Total number of channels
band : float
Channel bandwidth
fmin : float
Lowest frequency of the filter bank.
fmax : float
Highest frequency of the filter bank
"""
# Determine sampling rate based on extracted time series
sample_rate = ts_data.sample_rate
# Check if tile maximum frequency is not defined
if fmax is None or fmax > sample_rate / 2.:
# Set the tile maximum frequency equal to the Nyquist frequency
# (i.e. half the sampling rate)
fmax = sample_rate / 2.0
# Check whether or not tile bandwidth and channel are defined
if band is None and nchans is None:
# Exit program with error message
exit("Either bandwidth or number of channels must be specified...")
else:
# Check if tile maximum frequency larger than its minimum frequency
assert fmax >= fmin
# Define spectral band of data
data_band = fmax - fmin
# Check whether tile bandwidth or channel is defined
if band is not None:
# Define number of possible filter bands
nchans = int(data_band / band) - 1
elif nchans is not None:
# Define filter bandwidth
band = data_band / nchans
nchans = nchans - 1
# Check if number of channels is superior than unity
assert nchans > 1
# Print segment information
print '|- Estimating PSD from segments of time',
print '%.2f s in length, with %.2f s stride...' % (psd_segment_length,
psd_segment_stride)
# Convert time series as array of float
data = ts_data.astype(numpy.float64)
# Define segment length for PSD estimation in sample unit
seg_len = int(psd_segment_length * sample_rate)
# Define separation between consecutive segments in sample unit
seg_stride = int(psd_segment_stride * sample_rate)
# Calculate the overall PSD from individual PSD segments
fd_psd = psd.welch(data,
avg_method=psd_estimation,
seg_len=seg_len,
seg_stride=seg_stride)
# We need this for the SWIG functions...
lal_psd = fd_psd.lal()
# Plot the power spectral density
plot_spectrum(fd_psd)
# Create whitening window
print "|- Whitening window and spectral correlation..."
if wtype == 'hann':
window = lal.CreateHannREAL8Window(seg_len)
elif wtype == 'tukey':
window = lal.CreateTukeyREAL8Window(seg_len, window_fraction)
else:
raise ValueError("Can't handle window type %s" % wtype)
# Create FFT plan
fft_plan = lal.CreateForwardREAL8FFTPlan(len(window.data.data), 1)
# Perform two point spectral correlation
spec_corr = lal.REAL8WindowTwoPointSpectralCorrelation(window, fft_plan)
# Initialise filter bank
print "|- Create filter..."
filter_bank, fdb = [], []
# Loop for each channels
for i in range(nchans):
channel_flow = fmin + band / 2 + i * band
channel_width = band
# Create excess power filter
lal_filter = lalburst.CreateExcessPowerFilter(channel_flow,
channel_width, lal_psd,
spec_corr)
filter_bank.append(lal_filter)
fdb.append(Spectrum.from_lal(lal_filter))
# Calculate the minimum bandwidth
min_band = (len(filter_bank[0].data.data) - 1) * filter_bank[0].deltaF / 2
# Plot filter bank
plot_bank(fdb)
# Convert filter bank from frequency to time domain
print "|- Convert all the frequency domain to the time domain..."
tdb = []
# Loop for each filter's spectrum
for fdt in fdb:
zero_padded = numpy.zeros(int((fdt.f0 / fdt.df).value) + len(fdt))
st = int((fdt.f0 / fdt.df).value)
zero_padded[st:st + len(fdt)] = numpy.real_if_close(fdt.value)
n_freq = int(sample_rate / 2 / fdt.df.value) * 2
tdt = numpy.fft.irfft(zero_padded, n_freq) * math.sqrt(sample_rate)
tdt = numpy.roll(tdt, len(tdt) / 2)
tdt = TimeSeries(tdt,
name="",
epoch=fdt.epoch,
sample_rate=sample_rate)
tdb.append(tdt)
# Plot time series filter
plot_filters(tdb, fmin, band)
# Compute the renormalization for the base filters up to a given bandwidth.
mu_sq_dict = {}
# Loop through powers of 2 up to number of channels
for nc_sum in range(0, int(math.log(nchans, 2))):
nc_sum = 2**nc_sum - 1
print "|- Calculating renormalization for resolution level containing %d %fHz channels" % (
nc_sum + 1, min_band)
mu_sq = (nc_sum + 1) * numpy.array([
lalburst.ExcessPowerFilterInnerProduct(f, f, spec_corr, None)
for f in filter_bank
])
# Uncomment to get all possible frequency renormalizations
#for n in xrange(nc_sum, nchans): # channel position index
for n in xrange(nc_sum, nchans, nc_sum + 1): # channel position index
for k in xrange(0, nc_sum): # channel sum index
# FIXME: We've precomputed this, so use it instead
mu_sq[n] += 2 * lalburst.ExcessPowerFilterInnerProduct(
filter_bank[n - k], filter_bank[n - 1 - k], spec_corr,
None)
#print mu_sq[nc_sum::nc_sum+1]
mu_sq_dict[nc_sum] = mu_sq
# Create an event list where all the triggers will be stored
event_list = lsctables.New(lsctables.SnglBurstTable, [
'start_time', 'start_time_ns', 'peak_time', 'peak_time_ns', 'duration',
'bandwidth', 'central_freq', 'chisq_dof', 'confidence', 'snr',
'amplitude', 'channel', 'ifo', 'process_id', 'event_id', 'search',
'stop_time', 'stop_time_ns'
])
# Create repositories to save TF and time series plots
os.system('mkdir -p segments/time-frequency')
os.system('mkdir -p segments/time-series')
# Define time edges
t_idx_min, t_idx_max = 0, seg_len
while t_idx_max <= len(ts_data):
# Define starting and ending time of the segment in seconds
start_time = ts_data.start_time + t_idx_min / float(
ts_data.sample_rate)
end_time = ts_data.start_time + t_idx_max / float(ts_data.sample_rate)
print "\n|-- Analyzing block %i to %i (%.2f percent)" % (
start_time, end_time, 100 * float(t_idx_max) / len(ts_data))
# Model a withen time series for the block
tmp_ts_data = types.TimeSeries(ts_data[t_idx_min:t_idx_max] *
window.data.data,
delta_t=1. / ts_data.sample_rate,
epoch=start_time)
# Save time series in relevant repository
segfolder = 'segments/%i-%i' % (start_time, end_time)
os.system('mkdir -p ' + segfolder)
plot_ts(tmp_ts_data,
fname='segments/time-series/%i-%i.png' %
(start_time, end_time))
# Convert times series to frequency series
fs_data = tmp_ts_data.to_frequencyseries()
print "|-- Frequency series data has variance: %s" % fs_data.data.std(
)**2
# Whitening (FIXME: Whiten the filters, not the data)
fs_data.data /= numpy.sqrt(fd_psd) / numpy.sqrt(2 * fd_psd.delta_f)
print "|-- Whitened frequency series data has variance: %s" % fs_data.data.std(
)**2
print "|-- Create time-frequency plane for current block"
# Return the complex snr, along with its associated normalization of the template,
# matched filtered against the data
#filter.matched_filter_core(types.FrequencySeries(tmp_filter_bank,delta_f=fd_psd.delta_f),
# fs_data,h_norm=1,psd=fd_psd,low_frequency_cutoff=filter_bank[0].f0,
# high_frequency_cutoff=filter_bank[0].f0+2*band)
print "|-- Filtering all %d channels..." % nchans
# Initialise 2D zero array
tmp_filter_bank = numpy.zeros(len(fd_psd), dtype=numpy.complex128)
# Initialise 2D zero array for time-frequency map
tf_map = numpy.zeros((nchans, seg_len), dtype=numpy.complex128)
# Loop over all the channels
for i in range(nchans):
# Reset filter bank series
tmp_filter_bank *= 0.0
# Index of starting frequency
f1 = int(filter_bank[i].f0 / fd_psd.delta_f)
# Index of ending frequency
f2 = int((filter_bank[i].f0 + 2 * band) / fd_psd.delta_f) + 1
# (FIXME: Why is there a factor of 2 here?)
tmp_filter_bank[f1:f2] = filter_bank[i].data.data * 2
# Define the template to filter the frequency series with
template = types.FrequencySeries(tmp_filter_bank,
delta_f=fd_psd.delta_f,
copy=False)
# Create filtered series
filtered_series = filter.matched_filter_core(
template,
fs_data,
h_norm=None,
psd=None,
low_frequency_cutoff=filter_bank[i].f0,
high_frequency_cutoff=filter_bank[i].f0 + 2 * band)
# Include filtered series in the map
tf_map[i, :] = filtered_series[0].numpy()
# Plot spectrogram
plot_spectrogram(numpy.abs(tf_map).T,
tmp_ts_data.delta_t,
band,
ts_data.sample_rate,
start_time,
end_time,
fname='segments/time-frequency/%i-%i.png' %
(start_time, end_time))
# Loop through all summed channels
for nc_sum in range(0, int(math.log(nchans, 2)))[::-1]:
nc_sum = 2**nc_sum - 1
mu_sq = mu_sq_dict[nc_sum]
# Clip the boundaries to remove window corruption
clip_samples = int(psd_segment_length * window_fraction *
ts_data.sample_rate / 2)
# Constructing tile and calculate their energy
print "\n|--- Constructing tile with %d summed channels..." % (
nc_sum + 1)
# Current bandwidth of the time-frequency map tiles
df = band * (nc_sum + 1)
dt = 1.0 / (2 * df)
# How much each "step" is in the time domain -- under sampling rate
us_rate = int(round(dt / ts_data.delta_t))
print "|--- Undersampling rate for this level: %f" % (
ts_data.sample_rate / us_rate)
print "|--- Calculating tiles..."
# Making independent tiles
# because [0:-0] does not give the full array
tf_map_temp = tf_map[:,clip_samples:-clip_samples:us_rate] \
if clip_samples > 0 else tf_map[:,::us_rate]
tiles = tf_map_temp.copy()
# Here's the deal: we're going to keep only the valid output and
# it's *always* going to exist in the lowest available indices
stride = nc_sum + 1
for i in xrange(tiles.shape[0] / stride):
numpy.absolute(tiles[stride * i:stride * (i + 1)].sum(axis=0),
tiles[stride * (i + 1) - 1])
tiles = tiles[nc_sum::nc_sum + 1].real**2 / mu_sq[nc_sum::nc_sum +
1].reshape(
-1, 1)
print "|--- TF-plane is %dx%s samples" % tiles.shape
print "|--- Tile energy mean %f, var %f" % (numpy.mean(tiles),
numpy.var(tiles))
# Define maximum number of degrees of freedom and check it larger or equal to 2
max_dof = 32 if max_duration == None else 2 * max_duration * df
assert max_dof >= 2
# Loop through multiple degrees of freedom
for j in [2**l for l in xrange(0, int(math.log(max_dof, 2)))]:
# Duration is fixed by the NDOF and bandwidth
duration = j * dt
print "\n|----- Explore signal duration of %f s..." % duration
print "|----- Summing DOF = %d ..." % (2 * j)
tlen = tiles.shape[1] - 2 * j + 1 + 1
dof_tiles = numpy.zeros((tiles.shape[0], tlen))
sum_filter = numpy.array([1, 0] * (j - 1) + [1])
for f in range(tiles.shape[0]):
# Sum and drop correlate tiles
dof_tiles[f] = fftconvolve(tiles[f], sum_filter, 'valid')
print "|----- Summed tile energy mean: %f, var %f" % (
numpy.mean(dof_tiles), numpy.var(dof_tiles))
plot_spectrogram(
dof_tiles.T,
dt,
df,
ts_data.sample_rate,
start_time,
end_time,
fname='segments/%i-%i/tf_%02ichans_%02idof.png' %
(start_time, end_time, nc_sum + 1, 2 * j))
threshold = scipy.stats.chi2.isf(tile_fap, j)
print "|------ Threshold for this level: %f" % threshold
spant, spanf = dof_tiles.shape[1] * dt, dof_tiles.shape[0] * df
print "|------ Processing %.2fx%.2f time-frequency map." % (
spant, spanf)
# Since we clip the data, the start time needs to be adjusted accordingly
window_offset_epoch = fs_data.epoch + psd_segment_length * window_fraction / 2
window_offset_epoch = LIGOTimeGPS(float(window_offset_epoch))
for i, j in zip(*numpy.where(dof_tiles > threshold)):
event = event_list.RowType()
# The points are summed forward in time and thus a `summed point' is the
# sum of the previous N points. If this point is above threshold, it
# corresponds to a tile which spans the previous N points. However, the
# 0th point (due to the convolution specifier 'valid') is actually
# already a duration from the start time. All of this means, the +
# duration and the - duration cancels, and the tile 'start' is, by
# definition, the start of the time frequency map if j = 0
# FIXME: I think this needs a + dt/2 to center the tile properly
event.set_start(window_offset_epoch + float(j * dt))
event.set_stop(window_offset_epoch + float(j * dt) +
duration)
event.set_peak(event.get_start() + duration / 2)
event.central_freq = filter_bank[
0].f0 + band / 2 + i * df + 0.5 * df
event.duration = duration
event.bandwidth = df
event.chisq_dof = 2 * duration * df
event.snr = math.sqrt(dof_tiles[i, j] / event.chisq_dof -
1)
# FIXME: Magic number 0.62 should be determine empircally
event.confidence = -lal.LogChisqCCDF(
event.snr * 0.62, event.chisq_dof * 0.62)
event.amplitude = None
event.process_id = None
event.event_id = event_list.get_next_id()
event_list.append(event)
for event in event_list[::-1]:
if event.amplitude != None:
continue
etime_min_idx = float(event.get_start()) - float(
fs_data.epoch)
etime_min_idx = int(etime_min_idx / tmp_ts_data.delta_t)
etime_max_idx = float(event.get_start()) - float(
fs_data.epoch) + event.duration
etime_max_idx = int(etime_max_idx / tmp_ts_data.delta_t)
# (band / 2) to account for sin^2 wings from finest filters
flow_idx = int((event.central_freq - event.bandwidth / 2 -
(df / 2) - fmin) / df)
fhigh_idx = int((event.central_freq + event.bandwidth / 2 +
(df / 2) - fmin) / df)
# TODO: Check that the undersampling rate is always commensurate
# with the indexing: that is to say that
# mod(etime_min_idx, us_rate) == 0 always
z_j_b = tf_map[flow_idx:fhigh_idx,
etime_min_idx:etime_max_idx:us_rate]
event.amplitude = 0
print "|------ Total number of events: %d" % len(event_list)
t_idx_min += int(seg_len * (1 - window_fraction))
t_idx_max += int(seg_len * (1 - window_fraction))
setname = "MagneticFields"
__program__ = 'pyburst_excesspower'
start_time = LIGOTimeGPS(int(ts_data.start_time))
end_time = LIGOTimeGPS(int(ts_data.end_time))
inseg = segment(start_time, end_time)
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
ifo = 'H1' #channel_name.split(":")[0]
straindict = psd.insert_psd_option_group.__dict__
proc_row = register_to_xmldoc(xmldoc,
__program__,
straindict,
ifos=[ifo],
version=git_version.id,
cvs_repository=git_version.branch,
cvs_entry_time=git_version.date)
dt_stride = psd_segment_length
sample_rate = ts_data.sample_rate
# Amount to overlap successive blocks so as not to lose data
window_overlap_samples = window_fraction * sample_rate
outseg = inseg.contract(window_fraction * dt_stride / 2)
# With a given dt_stride, we cannot process the remainder of this data
remainder = math.fmod(abs(outseg), dt_stride * (1 - window_fraction))
# ...so make an accounting of it
outseg = segment(outseg[0], outseg[1] - remainder)
ss = append_search_summary(xmldoc,
proc_row,
ifos=(station, ),
inseg=inseg,
outseg=outseg)
for sb in event_list:
sb.process_id = proc_row.process_id
sb.search = proc_row.program
sb.ifo, sb.channel = station, setname
xmldoc.childNodes[0].appendChild(event_list)
fname = 'excesspower.xml.gz'
utils.write_filename(xmldoc, fname, gz=fname.endswith("gz"))
def values(self, sn, indices, template, psd, norm, stilde=None,
low_frequency_cutoff=None, high_frequency_cutoff=None):
"""
Calculate the auto-chisq at the specified indices.
Parameters
-----------
sn : Array[complex]
SNR time series of the template for which auto-chisq is being
computed. Provided unnormalized.
indices : Array[int]
List of points at which to calculate auto-chisq
template : Pycbc template object
The template for which we are calculating auto-chisq
psd : Pycbc psd object
The PSD of the data being analysed
norm : float
The normalization factor to apply to sn
stilde : Pycbc data object, needed if using reverse-template
The data being analysed. Only needed if using reverse-template,
otherwise ignored
low_frequency_cutoff : float
The lower frequency to consider in matched-filters
high_frequency_cutoff : float
The upper frequency to consider in matched-filters
"""
if self.do and (len(indices) > 0):
htilde = make_frequency_series(template)
# Check if we need to recompute the autocorrelation
key = (id(template), id(psd))
if key != self._autocor_id:
logging.info("Calculating autocorrelation")
if not self.reverse_template:
Pt, _Ptilde, P_norm = matched_filter_core(htilde,
htilde, psd=psd,
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff)
Pt = Pt * (1./ Pt[0])
self._autocor = Array(Pt, copy=True)
else:
Pt, _Ptilde, P_norm = matched_filter_core(htilde.conj(),
htilde, psd=psd,
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff)
# T-reversed template has same norm as forward template
# so we can normalize using that
# FIXME: Here sigmasq has to be cast to a float or the
# code is really slow ... why??
norm_fac = P_norm / float(((template.sigmasq(psd))**0.5))
Pt *= norm_fac
self._autocor = Array(Pt, copy=True)
self._autocor_id = key
logging.info("...Calculating autochisquare")
sn = sn*norm
if self.reverse_template:
assert(stilde is not None)
asn, acor, ahnrm = matched_filter_core(htilde.conj(), stilde,
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff,
h_norm=template.sigmasq(psd))
correlation_snr = asn * ahnrm
else:
correlation_snr = sn
achi_list = np.array([])
index_list = np.array(indices)
dof, achi_list, _ = autochisq_from_precomputed(sn, correlation_snr,
self._autocor, index_list, stride=self.stride,
num_points=self.num_points,
oneside=self.one_sided, twophase=self.two_phase,
maxvalued=self.take_maximum_value)
self.dof = dof
return achi_list
# Generate a zeros frequency series the same length as the psd
filter = FrequencySeries(numpy.zeros(len(psd)), 1, dtype=numpy.complex128)
# Generate the waveform filter with given params
filter = get_waveform_filter(filter,
approximant=params.apx,
mass1=m1,
mass2=m2,
sp1z=params.sp1z,
sp2z=params.sp2z,
polarization=params.pol,
delta_f=psd.delta_f,
f_lower=params.flow)
# Calculate the SNR Time Series
snr, _, norm = matched_filter_core(filter, conditioned, psd=psd, low_frequency_cutoff=20)
snr = snr * norm
# Trim SNR Time Series to account for edge effects
snr = snr.crop(4 + 4, 4)
snrs[i] = snr
# Get data of the peak
peak = abs(snr).numpy().argmax()
snrp = snr[peak]
time = snr.sample_times[peak]
phase = numpy.angle(snrp)
snrname = cwd + '/' + outfile[i]
def values(self, sn, indices, template, psd, norm, stilde=None,
low_frequency_cutoff=None, high_frequency_cutoff=None):
"""
Calculate the auto-chisq at the specified indices.
Parameters
-----------
sn : Array[complex]
SNR time series of the template for which auto-chisq is being
computed. Provided unnormalized.
indices : Array[int]
List of points at which to calculate auto-chisq
template : Pycbc template object
The template for which we are calculating auto-chisq
psd : Pycbc psd object
The PSD of the data being analysed
norm : float
The normalization factor to apply to sn
stilde : Pycbc data object, needed if using reverse-template
The data being analysed. Only needed if using reverse-template,
otherwise ignored
low_frequency_cutoff : float
The lower frequency to consider in matched-filters
high_frequency_cutoff : float
The upper frequency to consider in matched-filters
"""
if self.do and (len(indices) > 0):
htilde = make_frequency_series(template)
# Check if we need to recompute the autocorrelation
key = (id(template), id(psd))
if key != self._autocor_id:
logging.info("Calculating autocorrelation")
if not self.reverse_template:
Pt, _Ptilde, P_norm = matched_filter_core(htilde,
htilde, psd=psd,
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff)
Pt = Pt * (1./ Pt[0])
self._autocor = Array(Pt, copy=True)
else:
Pt, _Ptilde, P_norm = matched_filter_core(htilde,
htilde.conj(), psd=psd,
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff)
# T-reversed template has same norm as forward template
# so we can normalize using that
# FIXME: Here sigmasq has to be cast to a float or the
# code is really slow ... why??
norm_fac = P_norm / float(((template.sigmasq(psd))**0.5))
Pt *= norm_fac
self._autocor = Array(Pt, copy=True)
self._autocor_id = key
logging.info("...Calculating autochisquare")
sn = sn*norm
if self.reverse_template:
assert(stilde is not None)
asn, acor, ahnrm = matched_filter_core(htilde.conj(), stilde,
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff,
h_norm=template.sigmasq(psd))
correlation_snr = asn * ahnrm
else:
correlation_snr = sn
achi_list = np.array([])
index_list = np.array(indices)
dof, achi_list, _ = autochisq_from_precomputed(sn, correlation_snr,
self._autocor, index_list, stride=self.stride,
num_points=self.num_points,
oneside=self.one_sided, twophase=self.two_phase,
maxvalued=self.take_maximum_value)
self.dof = dof
return achi_list
def align_waveforms_optimally(hplus1,
hcross1,
hplus2,
hcross2,
psd='aLIGOZeroDetHighPower',
low_frequency_cutoff=None,
high_frequency_cutoff=None,
tsign=1,
phsign=-1,
verify=True,
phase_tolerance=1e-3,
overlap_tolerance=1e-3,
trim_leading=False,
trim_trailing=False,
verbose=False):
"""
Align waveforms such that their inner product (noise weighted) is optimal
without requiring any phase or time shift.
The appropriate time and phase shifts are determined iteratively and applied
to the second set of (hplus, hcross) vectors.
"""
#############################################################################
# First copy over data into local memory, ensure lengths of time and
# frequency domain vectors are consistent, and compute the maximized overlap
#
# 1) Cast into time-series
h_plus1 = TimeSeries(hplus1,
epoch=hplus1._epoch,
delta_t=hplus1.delta_t,
dtype=hplus1.dtype,
copy=True)
h_cross1 = TimeSeries(hcross1,
epoch=hplus1._epoch,
delta_t=hplus1.delta_t,
dtype=hplus1.dtype,
copy=True)
h_plus2 = TimeSeries(hplus2,
epoch=hplus2._epoch,
delta_t=hplus2.delta_t,
dtype=hplus2.dtype,
copy=True)
h_cross2 = TimeSeries(hcross2,
epoch=hplus2._epoch,
delta_t=hplus2.delta_t,
dtype=hplus2.dtype,
copy=True)
#
# 2) Ensure both input hplus vectors are equal in length
if len(hplus2) > len(hplus1):
h_plus1.append_zeros(len(hplus2) - len(hplus1))
h_cross1.append_zeros(len(hplus2) - len(hplus1))
elif len(hplus2) < len(hplus1):
h_plus2.append_zeros(len(hplus1) - len(hplus2))
h_cross2.append_zeros(len(hplus1) - len(hplus2))
#
# 3) Set the upper frequency cutoff to Nyquist if not set by User
if high_frequency_cutoff == None:
high_frequency_cutoff = 1. / h_plus1.delta_t / 2.
#
# 4) Compute LIGO noise psd
if psd == None:
raise IOError("Need compatible psd [or name] as input!")
elif type(psd) == str:
htilde = make_frequency_series(h_plus1)
psd_name = psd
psd = from_string(psd_name, len(htilde), htilde.delta_f,
low_frequency_cutoff)
##
# 5) Calculate Overlap (maximized) before alignment
m = match(h_plus1,
h_plus2,
psd=psd,
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff)
optimal_overlap = m[0] # FIXME
if verbose:
print(("Overlap BEFORE ALIGNMENT:",
overlap_cplx(h_plus1,
h_plus2,
psd=psd,
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff,
normalized=True)))
print(("Match BEFORE ALIGNMENT:", m))
#############################################################################
# Iterate to obtain the correct phase and time shifts, using which we
# align the two waveforms such that their unmaximized and maximized overlaps
# agree.
#
# 1) Initialize phase/time offset counters
t_shift_counter = 0
ph_shift_counter = 0
#
# 2) Initialize initial garbage values to enter the while loop
idx = 0
ph_shift = t_shift = 1e9
olap = 0 + 0j
#
# 3) Iteration begins
# >>>>>>
while np.abs(ph_shift) > phase_tolerance or \
np.abs(t_shift) > h_plus1.delta_t or \
np.abs(np.abs(olap.real) - optimal_overlap) > overlap_tolerance:
if idx == 0:
hp2, hc2 = h_plus2, h_cross2
#
# 1) Determine the phase and time shifts for optimal match
# by comparing hplus1/hcross1 with hp2/hc2 which is phase/time shifted
# in previous iteration
snr, corr, snr_norm = matched_filter_core(h_plus1, hp2, psd,
low_frequency_cutoff,
high_frequency_cutoff, None)
max_snr, max_id = snr.abs_max_loc()
if max_id != 0:
t_shift = snr.delta_t * (len(snr) - max_id)
else:
t_shift = snr.delta_t * max_id
ph_shift = np.angle(snr[max_id])
#
# 2) Add them to running time/phase offset counter
t_shift_counter += t_shift
ph_shift_counter += ph_shift
#
if verbose:
print((" >> Iteration %d\n" % (idx + 1)))
print(("max_id = %d, id_shift = %d" %
(max_id, int(t_shift / snr.delta_t))))
print(("t_shift = %f,\n ph_shift = %f" % (t_shift, ph_shift)))
#
####
# 3) Shift the second hp/hc pair (ORIGINAL) by cumulative phase/time offset
hp2, hc2 = shift_waveform_phase_time(h_plus2,
h_cross2,
tsign * t_shift_counter,
phsign * ph_shift_counter,
verbose=verbose)
#
###
# 4) As time shifting can change array lengths, equalize again, compute psd
##
if len(h_plus1) > len(hp2):
hp2.append_zeros(len(h_plus1) - len(hp2))
htilde = make_frequency_series(h_plus1)
psd = from_string(psd_name, len(htilde), htilde.delta_f,
low_frequency_cutoff)
elif len(h_plus1) < len(hp2):
h_plus1.append_zeros(len(hp2) - len(h_plus1))
htilde = make_frequency_series(h_plus1)
psd = from_string(psd_name, len(htilde), htilde.delta_f,
low_frequency_cutoff)
#
# 5) Compute UNMAXIMIZED overlap.
olap = overlap_cplx(h_plus1,
hp2,
psd=psd,
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff,
normalized=True)
if verbose:
print(("Overlap AFTER ALIGNMENT = ", olap))
print(("Optimal Overlap = ", optimal_overlap))
#
idx += 1
if verbose:
print("\n")
# >>>>>>
# 3) Iteration ended.
#############################################################################
# Verify the alignment
###
if verify:
#
print("Verifying time alignment...")
#
# 1) Determine the phase and time shifts for optimal match
snr, corr, snr_norm = matched_filter_core(h_plus1, hp2, psd,
low_frequency_cutoff,
high_frequency_cutoff, None)
max_snr, max_id = snr.abs_max_loc()
if verbose:
print(
("Post-Alignment Index of MAX SNR (should be 0 or 1 or %d): %d"
% (len(snr) - 1, max_id)))
print(("Length of whole SNR time-series: ", len(snr)))
#
# 2) Test if current time shift is within tolerance
if max_id != 0 and max_id != 1 and \
max_id != (len(snr)-1) and max_id != (len(snr)-2):
raise RuntimeError("Warning: ALIGNMENT NOT CORRECT (see above)")
else:
print("Alignment in time correct..")
#
# 3) Test if current phase shift is within tolerance
print("Verifying phase alignment...")
ph_shift = np.angle(snr[max_id])
if np.abs(ph_shift) > phase_tolerance:
if verbose:
print(("dphi, dphi+pi, dphi-pi: ", ph_shift, ph_shift + np.pi,
ph_shift - np.pi))
print(
("dphi/pi, dphi*pi: ", ph_shift / np.pi, ph_shift * np.pi))
raise RuntimeError(
"Warning: Phasing alignment possibly incorrect.")
else:
if verbose:
print(("Post-Alignmend Phase shift (should be < %.2e): %.2e" %
(phase_tolerance, np.abs(ph_shift))))
print(("Alignment in phasing correct.. (within tol %.2e)" %
phase_tolerance))
#
#############################################################################
# TRIM the output arrays and return
if trim_trailing:
hp2 = trim_trailing_zeros(hp2)
hc2 = trim_trailing_zeros(hc2)
if trim_leading:
hp2 = trim_leading_zeros(hp2)
hc2 = trim_leading_zeros(hc2)
#
return hplus1, hcross1, hp2, hc2
