def compute_wavelet_ffts(self):
samplerate = self.time_series.attrs['samplerate']
freqs = np.atleast_1d(self.freqs)
wavelets = morlet_multi(freqs=freqs, widths=5, samplerates=samplerate)
# ADD WARNING HERE FROM PHASE_MULTI
num_wavelets = len(wavelets)
# computting length of the longest wavelet
s_w = max(map(lambda wavelet: wavelet.shape[0], wavelets))
# length of the tie axis of the time series
s_d = self.time_series['time'].shape[0]
# determine the size based on the next power of 2
convolution_size = s_w + s_d - 1
convolution_size_pow2 = np.power(2, next_pow2(convolution_size))
# preallocating arrays
wavelet_fft_array = np.empty(shape=(num_wavelets, convolution_size_pow2), dtype=np.complex64)
convolution_size_array = np.empty(shape=(num_wavelets), dtype=np.int)
# computting wavelet ffts
for i, wavelet in enumerate(wavelets):
wavelet_fft_array[i] = fft(wavelet, convolution_size_pow2)
convolution_size_array[i] = wavelet.shape[0] + s_d - 1
return wavelet_fft_array, convolution_size_array, convolution_size_pow2
def compute_wavelet_ffts(self):
samplerate = self.time_series.attrs['samplerate']
freqs = np.atleast_1d(self.freqs)
wavelets = morlet_multi(freqs=freqs, widths=5, samplerates=samplerate)
# ADD WARNING HERE FROM PHASE_MULTI
num_wavelets = len(wavelets)
# computting length of the longest wavelet
s_w = max(map(lambda wavelet: wavelet.shape[0], wavelets))
# length of the tie axis of the time series
s_d = self.time_series['time'].shape[0]
# determine the size based on the next power of 2
convolution_size = s_w + s_d - 1
convolution_size_pow2 = np.power(2, next_pow2(convolution_size))
# preallocating arrays
wavelet_fft_array = np.empty(shape=(num_wavelets,
convolution_size_pow2),
dtype=np.complex64)
convolution_size_array = np.empty(shape=(num_wavelets), dtype=np.int)
# computting wavelet ffts
for i, wavelet in enumerate(wavelets):
wavelet_fft_array[i] = fft(wavelet, convolution_size_pow2)
convolution_size_array[i] = wavelet.shape[0] + s_d - 1
return wavelet_fft_array, convolution_size_array, convolution_size_pow2
def compute_wavelet_ffts(self):
# samplerate = self.time_series.attrs['samplerate']
samplerate = float(self.time_series['samplerate'])
freqs = np.atleast_1d(self.freqs)
wavelets = morlet_multi(freqs=freqs,
widths=self.width,
samplerates=samplerate)
# ADD WARNING HERE FROM PHASE_MULTI
num_wavelets = len(wavelets)
# computing length of the longest wavelet
s_w = max(map(lambda wavelet: wavelet.shape[0], wavelets))
time_series_length = self.time_series['time'].shape[0]
if s_w > self.time_series['time'].shape[0]:
raise ValueError(
'Time series length (l_ts=%s) is shorter than maximum wavelet length (l_w=%s). '
'Please use longer time series or increase lowest wavelet frequency '
% (time_series_length, s_w))
# length of the tie axis of the time series
s_d = self.time_series['time'].shape[0]
# determine the size based on the next power of 2
convolution_size = s_w + s_d - 1
convolution_size_pow2 = np.power(2, next_pow2(convolution_size))
# preallocating arrays
# wavelet_fft_array = np.empty(shape=(num_wavelets, convolution_size_pow2), dtype=np.complex64)
wavelet_fft_array = np.empty(shape=(num_wavelets,
convolution_size_pow2),
dtype=np.complex)
convolution_size_array = np.empty(shape=(num_wavelets), dtype=np.int)
# computting wavelet ffts
for i, wavelet in enumerate(wavelets):
wavelet_fft_array[i] = fft(wavelet, convolution_size_pow2)
convolution_size_array[i] = wavelet.shape[0] + s_d - 1
return wavelet_fft_array, convolution_size_array, convolution_size_pow2
def compute_wavelet_ffts(self):
# samplerate = self.time_series.attrs['samplerate']
samplerate = float(self.time_series['samplerate'])
freqs = np.atleast_1d(self.freqs)
wavelets = morlet_multi(freqs=freqs, widths=self.width, samplerates=samplerate)
# ADD WARNING HERE FROM PHASE_MULTI
num_wavelets = len(wavelets)
# computing length of the longest wavelet
s_w = max(map(lambda wavelet: wavelet.shape[0], wavelets))
time_series_length = self.time_series['time'].shape[0]
if s_w > self.time_series['time'].shape[0]:
raise ValueError(
'Time series length (l_ts=%s) is shorter than maximum wavelet length (l_w=%s). '
'Please use longer time series or increase lowest wavelet frequency ' %
(time_series_length, s_w))
# length of the tie axis of the time series
s_d = self.time_series['time'].shape[0]
# determine the size based on the next power of 2
convolution_size = s_w + s_d - 1
convolution_size_pow2 = np.power(2, next_pow2(convolution_size))
# preallocating arrays
# wavelet_fft_array = np.empty(shape=(num_wavelets, convolution_size_pow2), dtype=np.complex64)
wavelet_fft_array = np.empty(shape=(num_wavelets, convolution_size_pow2), dtype=np.complex)
convolution_size_array = np.empty(shape=(num_wavelets), dtype=np.int)
# computting wavelet ffts
for i, wavelet in enumerate(wavelets):
wavelet_fft_array[i] = fft(wavelet, convolution_size_pow2)
convolution_size_array[i] = wavelet.shape[0] + s_d - 1
return wavelet_fft_array, convolution_size_array, convolution_size_pow2
