def spectralWhitening(data, sr=None, smoothi=None, freq_domain=False, apply_filter=None):
"""
Apply spectral whitening to data.
sr: sampling rate (only needed for smoothing)
smoothi: None or int
Data is divided by its smoothed (Default: None) amplitude spectrum.
"""
if freq_domain:
mask = False
spec = data
else:
mask = np.ma.getmask(data)
N = len(data)
nfft = nextpow2(N)
spec = fft(data, nfft)
#df = sr/N
spec_ampl = np.sqrt(np.abs(np.multiply(spec, np.conjugate(spec))))
if isinstance(smoothi, basestring) and isnumber(smoothi) and smoothi > 0:
smoothi = int(smoothi * N / sr)
spec /= ifftshift(smooth(fftshift(spec_ampl), smoothi))
else:
spec /= spec_ampl
if apply_filter is not None:
spec *= filterResp(*apply_filter, sr=sr, N=len(spec), whole=True)[1]
if freq_domain:
return spec
else:
ret = np.real(ifft(spec, nfft)[:N])
if USE_FFTW3:
ret = ret.copy()
return fillArray(ret, mask=mask, fill_value=0.)
def filter2(self, freqmin=None, freqmax=None, corners=2, zerophase=False):
"""
Wrapper for Trace.filter, make entry in self.stats.filter.
"""
if self.stats.is_fft:
self.data *= filterResp(freqmin, freqmax, corners=corners, zerophase=zerophase,
sr=self.stats.sampling_rate, N=self.stats.nfft,
whole=True)[1]
self.stats.filter += 'BP%4.2f,%4.2f,%d,%d' % (freqmin, freqmax, corners, zerophase)
else:
mask = np.ma.getmask(self.data)
if freqmin and freqmax:
self.filter("bandpass", freqmin=freqmin, freqmax=freqmax, corners=corners, zerophase=zerophase)
self.stats.filter += 'BP%4.2f,%4.2f,%d,%d' % (freqmin, freqmax, corners, zerophase)
elif not freqmin and freqmax:
self.filter("lowpass", freq=freqmax, corners=corners, zerophase=zerophase)
self.stats.filter += 'LP%4.2f,%d,%d' % (freqmax, corners, zerophase)
elif freqmin and not freqmax:
self.filter("highpass", freq=freqmin, corners=corners, zerophase=zerophase)
self.stats.filter += 'HP%4.2f,%d,%d' % (freqmin, corners, zerophase)
self.data = fillArray(self.data, mask=mask, fill_value=0.)
def filter2(self, freqmin=None, freqmax=None, corners=2, zerophase=False):
"""
Wrapper for Trace.filter, make entry in self.stats.filter.
"""
if self.stats.is_fft:
self.data *= filterResp(freqmin,
freqmax,
corners=corners,
zerophase=zerophase,
sr=self.stats.sampling_rate,
N=self.stats.nfft,
whole=True)[1]
self.stats.filter += 'BP%4.2f,%4.2f,%d,%d' % (freqmin, freqmax,
corners, zerophase)
else:
mask = np.ma.getmask(self.data)
if freqmin and freqmax:
self.filter("bandpass",
freqmin=freqmin,
freqmax=freqmax,
corners=corners,
zerophase=zerophase)
self.stats.filter += 'BP%4.2f,%4.2f,%d,%d' % (
freqmin, freqmax, corners, zerophase)
elif not freqmin and freqmax:
self.filter("lowpass",
freq=freqmax,
corners=corners,
zerophase=zerophase)
self.stats.filter += 'LP%4.2f,%d,%d' % (freqmax, corners,
zerophase)
elif freqmin and not freqmax:
self.filter("highpass",
freq=freqmin,
corners=corners,
zerophase=zerophase)
self.stats.filter += 'HP%4.2f,%d,%d' % (freqmin, corners,
zerophase)
self.data = fillArray(self.data, mask=mask, fill_value=0.)
def spectralWhitening(data,
sr=None,
smoothi=None,
freq_domain=False,
apply_filter=None):
"""
Apply spectral whitening to data.
sr: sampling rate (only needed for smoothing)
smoothi: None or int
Data is divided by its smoothed (Default: None) amplitude spectrum.
"""
if freq_domain:
mask = False
spec = data
else:
mask = np.ma.getmask(data)
N = len(data)
nfft = nextpow2(N)
spec = fft(data, nfft)
#df = sr/N
spec_ampl = np.sqrt(np.abs(np.multiply(spec, np.conjugate(spec))))
if isinstance(smoothi, basestring) and isnumber(smoothi) and smoothi > 0:
smoothi = int(smoothi * N / sr)
spec /= ifftshift(smooth(fftshift(spec_ampl), smoothi))
else:
spec /= spec_ampl
if apply_filter is not None:
spec *= filterResp(*apply_filter, sr=sr, N=len(spec), whole=True)[1]
if freq_domain:
return spec
else:
ret = np.real(ifft(spec, nfft)[:N])
if USE_FFTW3:
ret = ret.copy()
return fillArray(ret, mask=mask, fill_value=0.)
