lsst.scarlet.lite gee10cc3b42+90ebb246c7
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Public Member Functions | |
__init__ (self, Observation observation, Image convolved, Sequence[tuple[int, int]] centers, float min_snr=50, Monotonicity|None monotonicity=None, bool use_sparse_init=True) | |
float | get_snr (self, tuple[int, int] center) |
FactorizedComponent | get_psf_component (self, tuple[int, int] center) |
FactorizedComponent|None | get_single_component (self, tuple[int, int] center, np.ndarray detect, float thresh, int padding) |
Source|None | init_source (self, tuple[int, int] center) |
Public Attributes | |
observation | |
convolved | |
centers | |
min_snr | |
monotonicity | |
use_sparse_init | |
convolved_psf | |
py | |
px | |
psf_spectrum | |
sources | |
Common variables and methods for both Factorized Component schemes Parameters ---------- observation: The observation containing the blend centers: The center of each source to initialize. min_snr: The minimum SNR required per component. So a 2-component source requires at least `2*min_snr` while sources with SNR < `min_snr` will be initialized with the PSF. 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. use_sparse_init: Use a monotonic mask to prevent initial source models from growing too large.
FactorizedComponent lsst.scarlet.lite.initialization.FactorizedInitialization.get_psf_component | ( | self, | |
tuple[int, int] | center ) |
Create a factorized component with a PSF morphology Parameters ---------- center: The center of the component. Returns ------- component: A `FactorizedComponent` with a PSF-like morphology.
FactorizedComponent | None lsst.scarlet.lite.initialization.FactorizedInitialization.get_single_component | ( | self, | |
tuple[int, int] | center, | ||
np.ndarray | detect, | ||
float | thresh, | ||
int | padding ) |
Initialize parameters for a `FactorizedComponent` Parameters ---------- center: The location of the center of the source to detect in the full image. detect: The image used for detection of the morphology. thresh: The lower cutoff threshold to use for the morphology. padding: The amount to pad the morphology to allow for extra flux in the first few iterations before resizing. Returns ------- component: A `FactorizedComponent` created from the detection image.
float lsst.scarlet.lite.initialization.FactorizedInitialization.get_snr | ( | self, | |
tuple[int, int] | center ) |
Get the SNR at the center of a component Parameters ---------- center: The location of the center of the source. Returns ------- result: The SNR at the center of the component.
Source | None lsst.scarlet.lite.initialization.FactorizedInitialization.init_source | ( | self, | |
tuple[int, int] | center ) |
Initialize a source Parameters ---------- center: The center of the source.
Reimplemented in lsst.scarlet.lite.initialization.FactorizedChi2Initialization, and lsst.scarlet.lite.initialization.FactorizedWaveletInitialization.