tomodrgn.fft

Fourier and Hartley transform functions for numpy arrays and pytorch tensors

Functions

fft2_center	Batch-vectorized 2-D DFT of an even-box sized image(s) as numpy array Note: numpy promotes default input dtype np.float32 to output dtype np.complex128
fft2_center_torch	Batch-vectorized 2-D DFT of an even-box sized image(s) as torch tensor Note: torch maintains default input dtype torch.float32 to output dtype torch.float32
fft3_center	Batch-vectorized 3-D DFT of even-box sized volume(s) as numpy array Note: numpy promotes default input dtype np.float32 to output dtype np.complex128
fft3_center_torch	Batch-vectorized 3-D DFT of even-box sized volume(s) as torch tensor Note: torch maintains default input dtype torch.float32 to output dtype torch.float32
ht2_center	Batch-vectorized 2-D DHT of an even-box sized image(s) as numpy array Uses relationship DHT(x) == DFT(x).real - DFT(x).imag == (DFT(x) * (1 + i)).real Note: numpy promotes default input dtype np.float32 to output dtype np.float64
ht2_center_torch	Batch-vectorized 2-D DHT of an even-box sized image(s) as torch tensor Uses relationship DHT(x) == DFT(x).real - DFT(x).imag == (DFT(x) * (1 + i)).real Note: torch maintains default input dtype torch.float32 to output dtype torch.float32
ht3_center	Batch-vectorized 3-D DHT of even-box sized volume(s) as numpy array Uses relationship DHT(x) == DFT(x).real - DFT(x).imag == (DFT(x) * (1 + i)).real Note: numpy promotes default input dtype np.float32 to output dtype np.float64
ht3_center_torch	Batch-vectorized 3-D DHT of even-box sized volume(s) as torch tensor Uses relationship DHT(x) == DFT(x).real - DFT(x).imag == (DFT(x) * (1 + i)).real Note: torch maintains default input dtype torch.float32 to output dtype torch.float32
ifft2_center	Batch-vectorized 2-D inverse DFT of an even-box sized image(s) as numpy array Note: numpy promotes default input dtype np.float32 to output dtype np.complex128
ifft2_center_torch	Batch-vectorized 2-D inverse DFT of an even-box sized image(s) as torch tensor Note: torch maintains default input dtype torch.float32 to output dtype torch.float32
ifft3_center	Batch-vectorized 3-D inverse DFT of even-sized volume(s) as numpy array Note: numpy promotes input dtype np.float32 to output dtype np.complex128
ifft3_center_torch	Batch-vectorized 3-D inverse DFT of even-sized volume(s) as torch tensor Note: torch maintains default input dtype torch.float32 to output dtype torch.float32
iht2_center	Batch-vectorized 2-D inverse DHT of an even-box sized image(s) as numpy array By definition, is equivalent to forward DHT up to normalization scale factor (of N samples, typically D*D here) Note: numpy promotes default input dtype np.float32 to output dtype np.float64
iht2_center_torch	Batch-vectorized 2-D inverse DHT of an even-box sized image(s) as torch tensor By definition, is equivalent to forward DHT up to normalization scale factor (of N samples, typically D*D here) Note: torch maintains default input dtype torch.float32 to output dtype torch.float32
iht3_center	Batch-vectorized 3-D inverse DHT of even-sized volume(s) as numpy array Note: numpy promotes input dtype np.float32 to output dtype np.complex128
iht3_center_torch	Batch-vectorized 3-D inverse DHT of even-sized volume(s) as torch tensor Note: torch maintains default input dtype torch.float32 to output dtype torch.float32
symmetrize_ht	Mirror-pads an even-box DHT image (stack) such that DC is at D//2,D//2 and highest frequency is at both D//2,0 and D//2,D