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
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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.
Definition at line 197 of file initialization.py.
lsst.scarlet.lite.initialization.FactorizedInitialization.__init__ | ( | self, | |
Observation | observation, | ||
Image | convolved, | ||
Sequence[tuple[int, int]] | centers, | ||
float | min_snr = 50, | ||
Monotonicity | None | monotonicity = None, | ||
bool | use_sparse_init = True ) |
Reimplemented in lsst.scarlet.lite.initialization.FactorizedChi2Initialization, and lsst.scarlet.lite.initialization.FactorizedWaveletInitialization.
Definition at line 220 of file initialization.py.
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.
Definition at line 277 of file initialization.py.
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.
Definition at line 317 of file initialization.py.
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.
Definition at line 254 of file initialization.py.
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.
Definition at line 389 of file initialization.py.
lsst.scarlet.lite.initialization.FactorizedInitialization.centers |
Definition at line 231 of file initialization.py.
lsst.scarlet.lite.initialization.FactorizedInitialization.convolved |
Definition at line 230 of file initialization.py.
lsst.scarlet.lite.initialization.FactorizedInitialization.convolved_psf |
Definition at line 241 of file initialization.py.
lsst.scarlet.lite.initialization.FactorizedInitialization.min_snr |
Definition at line 232 of file initialization.py.
lsst.scarlet.lite.initialization.FactorizedInitialization.monotonicity |
Definition at line 233 of file initialization.py.
lsst.scarlet.lite.initialization.FactorizedInitialization.observation |
Definition at line 229 of file initialization.py.
lsst.scarlet.lite.initialization.FactorizedInitialization.psf_spectrum |
Definition at line 245 of file initialization.py.
lsst.scarlet.lite.initialization.FactorizedInitialization.px |
Definition at line 244 of file initialization.py.
lsst.scarlet.lite.initialization.FactorizedInitialization.py |
Definition at line 243 of file initialization.py.
lsst.scarlet.lite.initialization.FactorizedInitialization.sources |
Definition at line 252 of file initialization.py.
lsst.scarlet.lite.initialization.FactorizedInitialization.use_sparse_init |
Definition at line 234 of file initialization.py.