Function strands_from_signal_torch1

utils/general_util.py:331–367

(signal,input_type,decode_type,norm='ortho')

def strands_from_signal_torch1(signal,input_type,decode_type,norm='ortho'):

    if input_type=='fft':
        N = signal.shape[1]//2
        F_cos = signal[:,: N]
        F_sin = signal[:,N:2 * N]

        # phase = F_cos * F_A + 1j*F_sin * F_A
        phase = F_cos + 1j * F_sin
        F_A = torch.abs(phase)

        phase = torch.angle(phase)
        reconstructed_fft_result = F_A * torch.exp(1j * phase)
        reconstructed_fft_result = torch.fft.irfft(reconstructed_fft_result,dim=-2,norm=norm)
    elif input_type=='chunked_fft':

        N = signal.shape[1]//3
        reconstructed_fft_result =[]
        for i in range(3):
            sub_signal = signal[:,N*i:(i+1)*N]

            F_cos = sub_signal[:, : N//2]
            F_sin = sub_signal[:, N//2: N]

            phase = F_cos + 1j * F_sin
            F_A = torch.abs(phase)
            phase = torch.angle(phase)
            reconstructed_fft= F_A * torch.exp(1j * phase)
            reconstructed_fft = torch.fft.irfft(reconstructed_fft, dim=-2, norm=norm) #B,nr_points//3,3
            reconstructed_fft_result.append(reconstructed_fft)
        reconstructed_fft_result = torch.cat(reconstructed_fft_result,1)

    if decode_type=='dir':
        reconstructed_fft_result = torch.cumsum(reconstructed_fft_result,dim=1)


    return reconstructed_fft_result

def strands_from_signal_torch2(signal,norm='ortho'):
    N = signal.shape[1]//2