DetectionConfig

class lsst.ip.diffim.DetectionConfig

Bases: lsst.pex.config.config.Config

!Configuration for detecting sources on images for building a PSF-matching kernel

Configuration for turning detected lsst.afw.detection.FootPrints into an acceptable (unmasked, high signal-to-noise, not too large or not too small) list of lsst.ip.diffim.KernelSources that are used to build the Psf-matching kernel

Attributes Summary

badMaskPlanes	Mask planes that lead to an invalid detection.
detOnTemplate	If true run detection on the template (image to convolve); if false run detection on the science image (bool, default True)
detThreshold	Value of footprint detection threshold (float, default 10.0)
detThresholdType	Type of detection threshold (str, default 'pixel_stdev')
fpGrowKernelScaling	If config.scaleByFwhm, grow the footprint based on the final kernelSize.
fpGrowPix	Growing radius (in pixels) for each raw detection footprint.
fpNpixMax	Maximum number of pixels in an acceptable Footprint; too big and the subsequent convolutions become unwieldy (int, default 500)
fpNpixMin	Minimum number of pixels in an acceptable Footprint (int, default 5)
scaleByFwhm	Scale fpGrowPix by input Fwhm? (bool, default True)

Attributes Documentation

badMaskPlanes

Mask planes that lead to an invalid detection. Options: NO_DATA EDGE SAT BAD CR INTRP (List, default ('NO_DATA', 'EDGE', 'SAT'))

detOnTemplate

If true run detection on the template (image to convolve); if false run detection on the science image (bool, default True)

detThreshold

Value of footprint detection threshold (float, default 10.0)

detThresholdType

Type of detection threshold (str, default 'pixel_stdev')

Allowed values:

'value'

Use counts as the detection threshold type

'stdev'

Use standard deviation of image plane

'variance'

Use variance of image plane

'pixel_stdev'

Use stdev derived from variance plane

'None'

Field is optional

fpGrowKernelScaling

If config.scaleByFwhm, grow the footprint based on the final kernelSize. Each footprint will be 2*fpGrowKernelScaling*kernelSize x 2*fpGrowKernelScaling*kernelSize. With the value of 1.0, the remaining pixels in each KernelCandiate after convolution by the basis functions will be equal to the kernel size itself. (float, default 1.0)

fpGrowPix

Growing radius (in pixels) for each raw detection footprint. The smaller the faster; however the kernel sum does not converge if the stamp is too small; and the kernel is not constrained at all if the stamp is the size of the kernel. The grown stamp is 2 * fpGrowPix pixels larger in each dimension. This is overridden by fpGrowKernelScaling if scaleByFwhm (int, default 30)

fpNpixMax

Maximum number of pixels in an acceptable Footprint; too big and the subsequent convolutions become unwieldy (int, default 500)

fpNpixMin

Minimum number of pixels in an acceptable Footprint (int, default 5)

scaleByFwhm

Scale fpGrowPix by input Fwhm? (bool, default True)