def time_norm(self, trace, trcopy):
# 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 normalisation \
weight")
# time-normalization
trace.data /= tnorm_w
return trace
def time_norm(self, trace, trcopy):
# 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 normalisation \
weight")
# time-normalization
trace.data /= tnorm_w
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 = 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 = 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 plot(self, smooth_window_freq=0.0):
"""
Plots list of spectra: rows = filtering steps, columns = stations
Plotting freq x abs(Fourier coefs)
"""
# list of stations and filters in spectra (preserving order)
filterslist = []
stationlist = []
for spect in self:
assert isinstance(spect, SpectrumInfos)
if spect.filters not in filterslist:
filterslist.append(spect.filters)
if spect.station not in stationlist:
stationlist.append(spect.station)
# rows = filtering steps, columns = stations: 1 station = 1 spectrum [+ 1 trace]
nrow = len(filterslist)
ncol = len(stationlist)
# plot per pair (station, filters)
plottraces = any([spect.savedtrace for spect in self])
plotperpair = 1 if not plottraces else 2
plt.figure()
for ipair, (filters,
station) in enumerate(it.product(filterslist,
stationlist)):
assert isinstance(filters, str)
try:
# looking for spectrum of station/filters
spect = next(
spect for spect in self
if spect.station == station and spect.filters == filters)
except StopIteration:
continue
# getting freq and amplitude arrays
spectrum = spect.spectrum
assert isinstance(spectrum, FreqAmplSpectrum)
freq = spectrum.freq
ampl = np.abs(spectrum.coef)
# smoothing amplitude spectrum (except after spectral whitening)
if not 'white' in filters.lower():
halfwindow = int(
round(smooth_window_freq / (freq[1] - freq[0]) / 2.0))
ampl = psutils.moving_avg(ampl, halfwindow)
# position of current station/filters in plot
irow = ipair / ncol + 1
icol = ipair % ncol + 1
pos = (irow - 1) * ncol * plotperpair + (icol -
1) * plotperpair + 1
# plotting frequence-amplitude
plt.subplot(nrow, ncol * plotperpair, pos)
plt.plot(freq[100:], ampl[100:])
plt.xlim((0.0, 0.5))
if icol == 1:
plt.ylabel(filters)
if irow == 1:
plt.title('{station} (ampl spectrum)'.format(
station=spect.station.name))
if irow == nrow:
plt.xlabel('Frequency (Hz)')
# plotting trace
if plottraces and spect.savedtrace:
tr = spect.savedtrace
t = np.arange(0, tr.stats.npts / tr.stats.sampling_rate,
tr.stats.delta)
t /= 3600.0
plt.subplot(nrow, ncol * plotperpair, pos + 1)
plt.plot(t, tr.data)
if irow == 1:
plt.title('{station} (last day)'.format(
station=spect.station.name))
if irow == nrow:
plt.xlabel('Time (h)')
plt.show()
def plot(self, smooth_window_freq=0.0):
"""
Plots list of spectra: rows = filtering steps, columns = stations
Plotting freq x abs(Fourier coefs)
"""
# list of stations and filters in spectra (preserving order)
filterslist = []
stationlist = []
for spect in self:
assert isinstance(spect, SpectrumInfos)
if spect.filters not in filterslist:
filterslist.append(spect.filters)
if spect.station not in stationlist:
stationlist.append(spect.station)
# rows = filtering steps, columns = stations: 1 station = 1 spectrum [+ 1 trace]
nrow = len(filterslist)
ncol = len(stationlist)
# plot per pair (station, filters)
plottraces = any([spect.savedtrace for spect in self])
plotperpair = 1 if not plottraces else 2
plt.figure()
for ipair, (filters, station) in enumerate(it.product(filterslist, stationlist)):
assert isinstance(filters, str)
try:
# looking for spectrum of station/filters
spect = next(spect for spect in self
if spect.station == station and spect.filters == filters)
except StopIteration:
continue
# getting freq and amplitude arrays
spectrum = spect.spectrum
assert isinstance(spectrum, FreqAmplSpectrum)
freq = spectrum.freq
ampl = np.abs(spectrum.coef)
# smoothing amplitude spectrum (except after spectral whitening)
if not 'white' in filters.lower():
halfwindow = int(round(smooth_window_freq / (freq[1] - freq[0]) / 2.0))
ampl = psutils.moving_avg(ampl, halfwindow)
# position of current station/filters in plot
irow = ipair / ncol + 1
icol = ipair % ncol + 1
pos = (irow - 1) * ncol * plotperpair + (icol - 1) * plotperpair + 1
# plotting frequence-amplitude
plt.subplot(nrow, ncol * plotperpair, pos)
plt.plot(freq[100:], ampl[100:])
plt.xlim((0.0, 0.5))
if icol == 1:
plt.ylabel(filters)
if irow == 1:
plt.title('{station} (ampl spectrum)'.format(station=spect.station.name))
if irow == nrow:
plt.xlabel('Frequency (Hz)')
# plotting trace
if plottraces and spect.savedtrace:
tr = spect.savedtrace
t = np.arange(0, tr.stats.npts / tr.stats.sampling_rate, tr.stats.delta)
t /= 3600.0
plt.subplot(nrow, ncol * plotperpair, pos + 1)
plt.plot(t, tr.data)
if irow == 1:
plt.title('{station} (last day)'.format(station=spect.station.name))
if irow == nrow:
plt.xlabel('Time (h)')
plt.show()
