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| __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 initial_bg_thresh=0.5, float bg_thresh=0.25, float|None thresh=None, int padding=2, bool use_sparse_init=True, int max_components=2, Image|None convolved=None, bool is_symmetric=False) |
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| thresh (self) |
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| float | get_snr (self, tuple[int, int] center) |
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| FactorizedComponent | get_psf_component (self, tuple[int, int] center) |
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| FactorizedComponent|None | get_single_component (self, tuple[int, int] center, np.ndarray detect, float thresh, int padding) |
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| Source | init_source (self, tuple[int, int] center) |
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| detect = detect |
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| observation = observation |
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| convolved = convolved |
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| centers = centers |
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| min_snr = min_snr |
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| monotonicity = monotonicity |
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| use_sparse_init = use_sparse_init |
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| is_symmetric = is_symmetric |
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| convolved_psf = observation.convolve(convolved_psf, mode="real").data |
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int | py = model_psf.shape[0] // 2 |
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int | px = model_psf.shape[1] // 2 |
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| psf_spectrum = self.convolved_psf[:, self.py, self.px] |
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| max_components = max_components |
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| initial_bg_thresh = initial_bg_thresh |
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| bg_thresh = bg_thresh |
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| padding = padding |
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| disk_percentile = disk_percentile |
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| sources = sources |
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str | reason = "This class is replaced by FactorizedInitialization and will be removed after v29.0", |
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str | version = "v29.0", |
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| category = FutureWarning, |
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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.
Public Member Functions inherited from lsst.scarlet.lite.initialization.FactorizedInitialization
1.13.2