deepmr.fft.fft#
- deepmr.fft.fft(input, axes=None, norm='ortho', centered=True)[source]#
Centered Fast Fourier Transform.
Adapted from [1].
- Parameters:
input (np.ndarray | torch.Tensor) – Input signal.
axes (Iterable[int], optional) – Axes over which to compute the FFT. If not specified, apply FFT over all the axes.
norm (str, optional) – FFT normalization. The default is
ortho
.centered (bool, optional) – FFT centering. The default is
True
.
- Returns:
output – Output signal.
- Return type:
np.ndarray | torch.Tensor
Examples
>>> import torch >>> import deepmr
First, create test image:
>>> image = torch.zeros(32, 32, dtype=torch.complex64) >>> image = image[16, 16] = 1.0
We now perform a 2D FFT:
>>> kspace = deepmr.fft.fft(image)
We can visualize the data:
>>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(1, 2) >>> im = ax[0].imshow(abs(image)) >>> ax[0].set_title("Image", color="orangered", fontweight="bold") >>> ax[0].axis("off") >>> ax[0].set_alpha(0.0) >>> fig.colorbar(im, ax=ax[0], shrink=0.5) >>> ksp = ax[1].imshow(abs(kspace)) >>> ax[1].set_title("k-Space", color="orangered", fontweight="bold") >>> ax[1].axis("off") >>> ax[1].set_alpha(0.0) >>> fig.colorbar(ksp, ax=ax[1], shrink=0.5) >>> plt.show()
References
[1] mikgroup/sigpy