lsst.scarlet.lite gee10cc3b42+585e252eca
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Public Member Functions | Public Attributes | List of all members
lsst.scarlet.lite.initialization.FactorizedChi2Initialization Class Reference
Inheritance diagram for lsst.scarlet.lite.initialization.FactorizedChi2Initialization:
lsst.scarlet.lite.initialization.FactorizedInitialization

Public Member Functions

 __init__ (self, Observation observation, Sequence[tuple[int, int]] centers, np.ndarray|None detect=None, float min_snr=50, Monotonicity|None monotonicity=None, float disk_percentile=25, float thresh=0.5, int padding=2)
 
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

 detect
 
 disk_percentile
 
 thresh
 
 padding
 
 observation
 
- 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

Initialize all sources with chi^2 detections

There are a large number of parameters that are universal for all of the
sources being initialized from the same set of observed images.
To simplify the API those parameters are all initialized by this class
and passed to `init_main_source` for each source.
It also creates temporary objects that only need to be created once for
all of the sources in a blend.

Parameters
----------
observation:
    The observation containing the blend
centers:
    The center of each source to initialize.
detect:
    The array that contains a 2D image used for detection.
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.
disk_percentile:
    The percentage of the overall flux to attribute to the disk.
thresh:
    The threshold used to trim the morphology,
    so all pixels below `thresh * bg_rms` are set to zero.
padding:
    The amount to pad the morphology to allow for extra flux
    in the first few iterations before resizing.

Constructor & Destructor Documentation

◆ __init__()

lsst.scarlet.lite.initialization.FactorizedChi2Initialization.__init__ ( self,
Observation observation,
Sequence[tuple[int, int]] centers,
np.ndarray | None detect = None,
float min_snr = 50,
Monotonicity | None monotonicity = None,
float disk_percentile = 25,
float thresh = 0.5,
int padding = 2 )

Member Function Documentation

◆ init_source()

Source | None lsst.scarlet.lite.initialization.FactorizedChi2Initialization.init_source ( self,
tuple[int, int] center )
Initialize a source from a chi^2 detection.

Parameter
---------
center:
    The center of the source.
init:
    The initialization parameters common to all of the sources.
max_components:
    The maximum number of components in the source.

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


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