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 | FactorizedChi2Initialization |
class | FactorizedInitialization |
class | FactorizedWaveletInitialization |
Functions | |
tuple[np.ndarray, Box] | trim_morphology (np.ndarray morph, float bg_thresh=0, int padding=5) |
tuple[Box, np.ndarray|None] | init_monotonic_morph (np.ndarray detect, tuple[int, int] center, Box full_box, int padding=5, bool normalize=True, Monotonicity|None monotonicity=None, float thresh=0) |
np.ndarray | multifit_spectra (Observation observation, Sequence[Image] morphs, Image|None model=None) |
Variables | |
logger = logging.getLogger("scarlet.lite.initialization") | |
tuple[Box, np.ndarray | None] lsst.scarlet.lite.initialization.init_monotonic_morph | ( | np.ndarray | detect, |
tuple[int, int] | center, | ||
Box | full_box, | ||
int | padding = 5, | ||
bool | normalize = True, | ||
Monotonicity | None | monotonicity = None, | ||
float | thresh = 0 ) |
Initialize a morphology for a monotonic source Parameters ---------- detect: The 2D detection image contained in `full_box`. center: The center of the monotonic source. full_box: The bounding box of `detect`. padding: The number of pixels to grow the morphology in each direction. This can be useful if initializing a source with a kernel that is known to be narrower than the expected value of the source. normalize: Whether or not to normalize the morphology. monotonicity: When `monotonicity` is `None`, the component is initialized with only the monotonic pixels, otherwise the monotonicity operator is used to project the morphology to a monotonic solution. thresh: The threshold (fraction above the background) to use for trimming the morphology. Returns ------- bbox: The bounding box of the morphology. morph: The initialized morphology.
Definition at line 70 of file initialization.py.
np.ndarray lsst.scarlet.lite.initialization.multifit_spectra | ( | Observation | observation, |
Sequence[Image] | morphs, | ||
Image | None | model = None ) |
Fit the spectra of multiple components simultaneously Parameters ---------- observation: The class containing the observation data. morphs: The morphology of each component. model: An optional model for sources that are not factorized, and thus will not have their spectra fit. This model is subtracted from the data before fitting the other spectra. Returns ------- spectra: The spectrum for each component, in the same order as `morphs`.
Definition at line 149 of file initialization.py.
tuple[np.ndarray, Box] lsst.scarlet.lite.initialization.trim_morphology | ( | np.ndarray | morph, |
float | bg_thresh = 0, | ||
int | padding = 5 ) |
Trim the morphology up to pixels above a threshold Parameters ---------- morph: The morphology to be trimmed. bg_thresh: The morphology is trimmed to pixels above the threshold. padding: The amount to pad each side to allow the source to grow. Returns ------- morph: The trimmed morphology box: The box that contains the morphology.
Definition at line 40 of file initialization.py.
lsst.scarlet.lite.initialization.logger = logging.getLogger("scarlet.lite.initialization") |
Definition at line 37 of file initialization.py.