def spectral_whitening(self, trace):
"""
Function that takes an input obspy trace object that has been
time-normalised, band-pass filtered and had its repsonse removed,
delimited, demeaned and detrended.
"""
# real FFT
fft = rfft(trace.data)
# frequency step
deltaf = trace.stats.sampling_rate / trace.stats.npts
# smoothing amplitude spectrum
halfwindow = int(round(self.window_freq / deltaf / 2.0))
weight = psutils.moving_avg(abs(fft), halfwindow=halfwindow)
# normalizing spectrum and back to time domain
trace.data = np.asarray(irfft(fft / weight, n=len(trace.data)))
# re bandpass to avoid low/high freq noise
trace.filter(type="bandpass",
freqmin=self.freqmin,
freqmax=self.freqmax,
corners=self.corners,
zerophase=self.zerophase)
return trace
def spectral_whitening(self, trace):
"""
Function that takes an input obspy trace object that has been
time-normalised, band-pass filtered and had its repsonse removed,
delimited, demeaned and detrended.
"""
# real FFT
fft = rfft(trace.data)
# frequency step
deltaf = trace.stats.sampling_rate / trace.stats.npts
# smoothing amplitude spectrum
halfwindow = int(round(self.window_freq / deltaf / 2.0))
weight = psutils.moving_avg(abs(fft), halfwindow=halfwindow)
# normalizing spectrum and back to time domain
trace.data = np.asarray(irfft(fft / weight, n=len(trace.data)))
# re bandpass to avoid low/high freq noise
trace.filter(type="bandpass",
freqmin=self.freqmin,
freqmax=self.freqmax,
corners=self.corners,
zerophase=self.zerophase)
return trace
def time_norm(self, trace, trcopy):
if self.onebit_norm:
# one-bit normalization
trace.data = np.sign(trace.data)
else:
# normalization of the signal by the running mean
# in the earthquake frequency band
trcopy.filter(type="bandpass",
freqmin=self.freqmin_earthquake,
freqmax=self.freqmax_earthquake,
corners=self.corners,
zerophase=self.zerophase)
# decimating trace
psutils.resample(trcopy, self.period_resample)
# Time-normalization weights from smoothed abs(data)
# Note that trace's data can be a masked array
halfwindow = int(
round(self.window_time * trcopy.stats.sampling_rate / 2))
mask = ~trcopy.data.mask if np.ma.isMA(trcopy.data) else None
tnorm_w = psutils.moving_avg(np.abs(trcopy.data),
halfwindow=halfwindow,
mask=mask)
if np.ma.isMA(trcopy.data):
# turning time-normalization weights into a masked array
s = "[warning: {}.{} trace's data is a masked array]"
print s.format(trace.stats.network, trace.stats.station),
tnorm_w = np.ma.masked_array(tnorm_w, trcopy.data.mask)
if np.any((tnorm_w == 0.0) | np.isnan(tnorm_w)):
# illegal normalizing value -> skipping trace
raise pserrors.CannotPreprocess("Zero or NaN normalization weight")
# time-normalization
trace.data /= tnorm_w
return trace
def time_norm(self, trace, trcopy):
if self.onebit_norm:
# one-bit normalization
trace.data = np.sign(trace.data)
else:
# normalization of the signal by the running mean
# in the earthquake frequency band
trcopy.filter(type="bandpass",
freqmin=self.freqmin_earthquake,
freqmax=self.freqmax_earthquake,
corners=self.corners,
zerophase=self.zerophase)
# decimating trace
psutils.resample(trcopy, self.period_resample)
# Time-normalization weights from smoothed abs(data)
# Note that trace's data can be a masked array
halfwindow = int(round(self.window_time*trcopy.stats.sampling_rate/2))
mask = ~trcopy.data.mask if np.ma.isMA(trcopy.data) else None
tnorm_w = psutils.moving_avg(np.abs(trcopy.data),
halfwindow=halfwindow,
mask=mask)
if np.ma.isMA(trcopy.data):
# turning time-normalization weights into a masked array
s = "[warning: {}.{} trace's data is a masked array]"
print s.format(trace.stats.network, trace.stats.station),
tnorm_w = np.ma.masked_array(tnorm_w, trcopy.data.mask)
if np.any((tnorm_w == 0.0) | np.isnan(tnorm_w)):
# illegal normalizing value -> skipping trace
raise pserrors.CannotPreprocess("Zero or NaN normalization weight")
# time-normalization
trace.data /= tnorm_w
return trace