LSST Applications 27.0.0,g0265f82a02+469cd937ee,g02d81e74bb+21ad69e7e1,g1470d8bcf6+cbe83ee85a,g2079a07aa2+e67c6346a6,g212a7c68fe+04a9158687,g2305ad1205+94392ce272,g295015adf3+81dd352a9d,g2bbee38e9b+469cd937ee,g337abbeb29+469cd937ee,g3939d97d7f+72a9f7b576,g487adcacf7+71499e7cba,g50ff169b8f+5929b3527e,g52b1c1532d+a6fc98d2e7,g591dd9f2cf+df404f777f,g5a732f18d5+be83d3ecdb,g64a986408d+21ad69e7e1,g858d7b2824+21ad69e7e1,g8a8a8dda67+a6fc98d2e7,g99cad8db69+f62e5b0af5,g9ddcbc5298+d4bad12328,ga1e77700b3+9c366c4306,ga8c6da7877+71e4819109,gb0e22166c9+25ba2f69a1,gb6a65358fc+469cd937ee,gbb8dafda3b+69d3c0e320,gc07e1c2157+a98bf949bb,gc120e1dc64+615ec43309,gc28159a63d+469cd937ee,gcf0d15dbbd+72a9f7b576,gdaeeff99f8+a38ce5ea23,ge6526c86ff+3a7c1ac5f1,ge79ae78c31+469cd937ee,gee10cc3b42+a6fc98d2e7,gf1cff7945b+21ad69e7e1,gfbcc870c63+9a11dc8c8f
LSST Data Management Base Package
|
Classes | |
class | Monotonicity |
Functions | |
np.ndarray | prox_connected (np.ndarray morph, Sequence[Sequence[int]] centers) |
tuple[int, int] | get_peak (np.ndarray image, tuple[int, int] center, int radius=1) |
tuple[np.ndarray, np.ndarray, tuple[int, int, int, int]] | prox_monotonic_mask (np.ndarray x, tuple[int, int] center, int center_radius=1, float variance=0.0, int max_iter=3) |
np.ndarray | uncentered_operator (np.ndarray x, Callable func, tuple[int, int]|None center=None, float|None fill=None, **kwargs) |
prox_sdss_symmetry (np.ndarray x) | |
np.ndarray | prox_uncentered_symmetry (np.ndarray x, tuple[int, int]|None center=None, float|None fill=None) |
tuple[int, int] lsst.scarlet.lite.operators.get_peak | ( | np.ndarray | image, |
tuple[int, int] | center, | ||
int | radius = 1 ) |
Search around a location for the maximum flux For monotonicity it is important to start at the brightest pixel in the center of the source. This may be off by a pixel or two, so we search for the correct center before applying monotonic_tree. Parameters ---------- image: The image of the source. center: The suggested center of the source. radius: The number of pixels around the `center` to search for a higher flux value. Returns ------- new_center: The true center of the source.
Definition at line 255 of file operators.py.
np.ndarray lsst.scarlet.lite.operators.prox_connected | ( | np.ndarray | morph, |
Sequence[Sequence[int]] | centers ) |
Remove all pixels not connected to the center of a source. Parameters ---------- morph: The morphology that is being constrained. centers: The `(cy, cx)` center of any sources that all pixels must be connected to. Returns ------- result: The morphology with all pixels that are not connected to a center postion set to zero.
Definition at line 11 of file operators.py.
tuple[np.ndarray, np.ndarray, tuple[int, int, int, int]] lsst.scarlet.lite.operators.prox_monotonic_mask | ( | np.ndarray | x, |
tuple[int, int] | center, | ||
int | center_radius = 1, | ||
float | variance = 0.0, | ||
int | max_iter = 3 ) |
Apply monotonicity from any path from the center Parameters ---------- x: The input image that the mask is created for. center: The location of the center of the mask. center_radius: Radius from the center pixel to search for a better center (ie. a pixel in `X` with higher flux than the pixel given by `center`). If `center_radius == 0` then the `center` pixel is assumed to be correct. variance: The average variance in the image. This is used to allow pixels to be non-monotonic up to `variance`, so setting `variance=0` will force strict monotonicity in the mask. max_iter: Maximum number of iterations to interpolate non-monotonic pixels. Returns ------- valid: Boolean array of pixels that are monotonic. model: The model with invalid pixels masked out. bounds: The bounds of the valid monotonic pixels.
Definition at line 288 of file operators.py.
lsst.scarlet.lite.operators.prox_sdss_symmetry | ( | np.ndarray | x | ) |
SDSS/HSC symmetry operator This function uses the *minimum* of the two symmetric pixels in the update. Parameters ---------- x: The array to make symmetric. Returns ------- result: The updated `x`.
Definition at line 423 of file operators.py.
np.ndarray lsst.scarlet.lite.operators.prox_uncentered_symmetry | ( | np.ndarray | x, |
tuple[int, int] | None | center = None, | ||
float | None | fill = None ) |
Symmetry with off-center peak Symmetrize X for all pixels with a symmetric partner. Parameters ---------- x: The parameter to update. center: The center pixel coordinates to apply the symmetry operator. fill: The value to fill the region that cannot be made symmetric. When `fill` is `None` then the region of `X` that is not symmetric is not constrained. Returns ------- result: The update function based on the specified parameters.
Definition at line 444 of file operators.py.
np.ndarray lsst.scarlet.lite.operators.uncentered_operator | ( | np.ndarray | x, |
Callable | func, | ||
tuple[int, int] | None | center = None, | ||
float | None | fill = None, | ||
** | kwargs ) |
Only apply the operator on a centered patch In some cases, for example symmetry, an operator might not make sense outside of a centered box. This operator only updates the portion of `X` inside the centered region. Parameters ---------- x: The parameter to update. func: The function (or operator) to apply to `x`. center: The location of the center of the sub-region to apply `func` to `x`. fill: The value to fill the region outside of centered `sub-region`, for example `0`. If `fill` is `None` then only the subregion is updated and the rest of `x` remains unchanged. Returns ------- result: `x`, with an operator applied based on the shifted center.
Definition at line 356 of file operators.py.