def filtered_fourier(self):
"""
Filter the time-series by passing it to the Fourier domain and null
out the frequency bands outside of the range [lb,ub]
"""
freqs = tsu.get_freqs(self.sampling_rate, self.data.shape[-1])
if self.ub is None:
self.ub = freqs[-1]
power = fftpack.fft(self.data)
idx_0 = np.hstack([np.where(freqs < self.lb)[0],
np.where(freqs > self.ub)[0]])
#Make sure that you keep the DC component:
keep_dc = np.copy(power[..., 0])
power[..., idx_0] = 0
power[..., -1 * idx_0] = 0 # Take care of the negative frequencies
power[..., 0] = keep_dc # And put the DC back in when you're done:
data_out = fftpack.ifft(power)
data_out = np.real(data_out) # In order to make sure that you are not
# left with float-precision residual
# complex parts
return ts.TimeSeries(data=data_out,
sampling_rate=self.sampling_rate,
time_unit=self.time_unit)
def filtered_fourier(self):
"""
Filter the time-series by passing it to the Fourier domain and null
out the frequency bands outside of the range [lb,ub]
"""
freqs = tsu.get_freqs(self.sampling_rate, self.data.shape[-1])
if self.ub is None:
self.ub = freqs[-1]
power = fftpack.fft(self.data)
idx_0 = np.hstack(
[np.where(freqs < self.lb)[0],
np.where(freqs > self.ub)[0]])
#Make sure that you keep the DC component:
keep_dc = np.copy(power[..., 0])
power[..., idx_0] = 0
power[..., -1 * idx_0] = 0 # Take care of the negative frequencies
power[..., 0] = keep_dc # And put the DC back in when you're done:
data_out = fftpack.ifft(power)
data_out = np.real(data_out) # In order to make sure that you are not
# left with float-precision residual
# complex parts
return ts.TimeSeries(data=data_out,
sampling_rate=self.sampling_rate,
time_unit=self.time_unit)
def wlogmorlet(f0, sd, sampling_rate, ns=5, normed='area'):
"""
returns a complex log morlet wavelet in the time domain
Parameters
----------
f0 : center frequency
sd : standard deviation of frequency
sampling_rate : samplingrate
ns : window length in number of standard deviations
"""
st = 1. / (2. * np.pi * sd)
w_sz = int(ns * st * sampling_rate) # half time window size
wf = wlogmorlet_fft(f0, sd, sampling_rate=sampling_rate, nt=2 * w_sz + 1)
w = fftpack.fftshift(fftpack.ifft(wf))
if normed == 'area':
w /= w.real.sum()
elif normed == 'max':
w /= w.real.max()
elif normed == 'energy':
w /= np.sqrt((w ** 2).sum())
else:
assert 0, 'unknown norm %s' % normed
return w