lsst.scarlet.lite gae0086650b+585e252eca
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Public Member Functions | Public Attributes | List of all members
lsst.scarlet.lite.initialization.FactorizedWaveletInitialization Class Reference
Inheritance diagram for lsst.scarlet.lite.initialization.FactorizedWaveletInitialization:
lsst.scarlet.lite.initialization.FactorizedInitialization

Public Member Functions

 __init__ (self, Observation observation, Sequence[tuple[int, int]] centers, slice bulge_slice=slice(None, 2), slice disk_slice=slice(2, -1), int bulge_padding=5, int disk_padding=5, bool use_psf=True, int scales=5, np.ndarray|None wavelets=None, Monotonicity|None monotonicity=None, float min_snr=50)
 
Source|None init_source (self, tuple[int, int] center)
 
- Public Member Functions inherited from lsst.scarlet.lite.initialization.FactorizedInitialization
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)
 

Public Attributes

 detectlets
 
 bulgelets
 
 disklets
 
 bulge_grow
 
 disk_grow
 
 use_psf
 
- Public Attributes inherited from lsst.scarlet.lite.initialization.FactorizedInitialization
 observation
 
 convolved
 
 centers
 
 min_snr
 
 monotonicity
 
 use_sparse_init
 
 convolved_psf
 
 py
 
 px
 
 psf_spectrum
 
 sources
 

Detailed Description

Parameters used to initialize all sources with wavelet detections

There are a large number of parameters that are universal for all of the
sources being initialized from the same set of wavelet coefficients.
To simplify the API those parameters are all initialized by this class
and passed to `init_wavelet_source` for each source.

Parameters
----------
observation:
    The multiband observation of the blend.
centers:
    The center of each source to initialize.
bulge_slice, disk_slice:
    The slice used to select the wavelet scales used for the
    bulge/disk.
bulge_padding, disk_padding:
    The number of pixels to grow the bounding box of the bulge/disk
    to leave extra room for growth in the first few iterations.
use_psf:
    Whether or not to use the PSF for single component sources.
    If `use_psf` is `False` then only sources with low signal
    at all scales are initialized with the PSF morphology.
scales:
    Number of wavelet scales to use.
wavelets:
    The array of wavelet coefficients `(scale, y, x)`
    used for detection.
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.
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.

Constructor & Destructor Documentation

◆ __init__()

lsst.scarlet.lite.initialization.FactorizedWaveletInitialization.__init__ ( self,
Observation observation,
Sequence[tuple[int, int]] centers,
slice bulge_slice = slice(None, 2),
slice disk_slice = slice(2, -1),
int bulge_padding = 5,
int disk_padding = 5,
bool use_psf = True,
int scales = 5,
np.ndarray | None wavelets = None,
Monotonicity | None monotonicity = None,
float min_snr = 50 )

Member Function Documentation

◆ init_source()

Source | None lsst.scarlet.lite.initialization.FactorizedWaveletInitialization.init_source ( self,
tuple[int, int] center )
Initialize a source from a chi^2 detection.

Parameter
---------
center:
    The center of the source.

Reimplemented from lsst.scarlet.lite.initialization.FactorizedInitialization.


The documentation for this class was generated from the following file: