Beispiel #1
0
    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