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LSST Data Management Base Package
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Classes | |
class | Fourier |
Functions | |
np.ndarray | centered (np.ndarray arr, Sequence[int] newshape) |
np.ndarray | fast_zero_pad (np.ndarray arr, Sequence[Sequence[int]] pad_width) |
np.ndarray | _pad (np.ndarray arr, Sequence[int] newshape, int|Sequence[int]|None axes=None, str mode="constant", float constant_values=0) |
tuple | get_fft_shape (np.ndarray|Sequence[int] im_or_shape1, np.ndarray|Sequence[int] im_or_shape2, int padding=3, int|Sequence[int]|None axes=None, bool use_max=False) |
Fourier | _kspace_operation (Fourier image1, Fourier image2, int padding, Callable op, Sequence[int] shape, int|Sequence[int] axes) |
Fourier|np.ndarray | match_kernel (np.ndarray|Fourier kernel1, np.ndarray|Fourier kernel2, int padding=3, int|Sequence[int] axes=(-2, -1), bool return_fourier=True, bool normalize=False) |
np.ndarray|Fourier | convolve (np.ndarray|Fourier image, np.ndarray|Fourier kernel, int padding=3, int|Sequence[int] axes=(-2, -1), bool return_fourier=True, bool normalize=False) |
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protected |
Combine two images in k-space using a given `operator` Parameters ---------- image1: The LHS of the equation. image2: The RHS of the equation. padding: The amount of padding to add before transforming into k-space. op: The operator used to combine the two images. This is either ``operator.mul`` for a convolution or ``operator.truediv`` for deconvolution. shape: The shape of the output image. axes: The dimension(s) of the array that will be transformed.
Definition at line 374 of file fft.py.
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protected |
Pad an array to fit into newshape Pad `arr` with zeros to fit into newshape, which uses the `np.fft.fftshift` convention of moving the center pixel of `arr` (if `arr.shape` is odd) to the center-right pixel in an even shaped `newshape`. Parameters ---------- arr: The arrray to pad. newshape: The new shape of the array. axes: The axes that are being reshaped. mode: The numpy mode used to pad the array. In other words, how to fill the new padded elements. See ``numpy.pad`` for details. constant_values: If `mode` == "constant" then this is the value to set all of the new padded elements to.
Definition at line 98 of file fft.py.
np.ndarray lsst.scarlet.lite.fft.centered | ( | np.ndarray | arr, |
Sequence[int] | newshape ) |
Return the central newshape portion of the array. Parameters ---------- arr: The array to center. newshape: The new shape of the array. Notes ----- If the array shape is odd and the target is even, the center of `arr` is shifted to the center-right pixel position. This is slightly different than the scipy implementation, which uses the center-left pixel for the array center. The reason for the difference is that we have adopted the convention of `np.fft.fftshift` in order to make sure that changing back and forth from fft standard order (0 frequency and position is in the bottom left) to 0 position in the center.
Definition at line 34 of file fft.py.
np.ndarray | Fourier lsst.scarlet.lite.fft.convolve | ( | np.ndarray | Fourier | image, |
np.ndarray | Fourier | kernel, | ||
int | padding = 3, | ||
int | Sequence[int] | axes = (-2, -1), | ||
bool | return_fourier = True, | ||
bool | normalize = False ) |
Convolve image with a kernel Parameters ---------- image: Image either as array or as `Fourier` object kernel: Convolution kernel either as array or as `Fourier` object padding: Additional padding to use when generating the FFT to suppress artifacts. axes: Axes that contain the spatial information for the PSFs. return_fourier: Whether to return `Fourier` or array normalize: Whether or not to normalize the input kernels. Returns ------- result: The convolution of the image with the kernel.
Definition at line 481 of file fft.py.
np.ndarray lsst.scarlet.lite.fft.fast_zero_pad | ( | np.ndarray | arr, |
Sequence[Sequence[int]] | pad_width ) |
Fast version of numpy.pad when `mode="constant"` Executing `numpy.pad` with zeros is ~1000 times slower because it doesn't make use of the `zeros` method for padding. Parameters --------- arr: The array to pad pad_width: Number of values padded to the edges of each axis. See numpy.pad docs for more. Returns ------- result: np.ndarray The array padded with `constant_values`
Definition at line 71 of file fft.py.
tuple lsst.scarlet.lite.fft.get_fft_shape | ( | np.ndarray | Sequence[int] | im_or_shape1, |
np.ndarray | Sequence[int] | im_or_shape2, | ||
int | padding = 3, | ||
int | Sequence[int] | None | axes = None, | ||
bool | use_max = False ) |
Return the fast fft shapes for each spatial axis Calculate the fast fft shape for each dimension in axes. Parameters ---------- im_or_shape1: The left image or shape of an image. im_or_shape2: The right image or shape of an image. padding: Any additional padding to add to the final shape. axes: The axes that are being transformed. use_max: Whether or not to use the maximum of the two shapes, or the sum of the two shapes. Returns ------- shape: Tuple of the shape to use when the two images are transformed into k-space.
Definition at line 152 of file fft.py.
Fourier | np.ndarray lsst.scarlet.lite.fft.match_kernel | ( | np.ndarray | Fourier | kernel1, |
np.ndarray | Fourier | kernel2, | ||
int | padding = 3, | ||
int | Sequence[int] | axes = (-2, -1), | ||
bool | return_fourier = True, | ||
bool | normalize = False ) |
Calculate the difference kernel to match kernel1 to kernel2 Parameters ---------- kernel1: The first kernel, either as array or as `Fourier` object kernel2: The second kernel, either as array or as `Fourier` object padding: Additional padding to use when generating the FFT to supress artifacts. axes: Axes that contain the spatial information for the kernels. return_fourier: Whether to return `Fourier` or array normalize: Whether or not to normalize the input kernels. Returns ------- result: The difference kernel to go from `kernel1` to `kernel2`.
Definition at line 433 of file fft.py.