lsst.pipe.tasks
21.0.0-22-gf0532904+afb8e7912b
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Public Member Functions | |
def | __init__ (self, *args, **kwargs) |
def | subtractedBackground (self, maskedImage) |
def | run (self, maskedImage) |
def | pixelBased (self, maskedImage) |
def | imageBased (self, maskedImage) |
Static Public Attributes | |
ConfigClass = ScaleVarianceConfig | |
Scale the variance in a MaskedImage The variance plane in a convolved or warped image (or a coadd derived from warped images) does not accurately reflect the noise properties of the image because variance has been lost to covariance. This Task attempts to correct for this by scaling the variance plane to match the observed variance in the image. This is not perfect (because we're not tracking the covariance) but it's simple and is often good enough.
Definition at line 48 of file scaleVariance.py.
def lsst.pipe.tasks.scaleVariance.ScaleVarianceTask.__init__ | ( | self, | |
* | args, | ||
** | kwargs | ||
) |
Definition at line 61 of file scaleVariance.py.
def lsst.pipe.tasks.scaleVariance.ScaleVarianceTask.imageBased | ( | self, | |
maskedImage | |||
) |
Determine the variance rescaling factor from image statistics We calculate average(SNR) = stdev(image)/median(variance), and the value should be unity. The variance rescaling factor is the factor that brings this value to unity. This may not work well if the pixels from which we measure the standard deviation of the image are not effectively the same pixels from which we measure the median of the variance. In that case, use an alternate method. Parameters ---------- maskedImage : `lsst.afw.image.MaskedImage` Image for which to determine the variance rescaling factor. Returns ------- factor : `float` Variance rescaling factor.
Definition at line 164 of file scaleVariance.py.
def lsst.pipe.tasks.scaleVariance.ScaleVarianceTask.pixelBased | ( | self, | |
maskedImage | |||
) |
Determine the variance rescaling factor from pixel statistics We calculate SNR = image/sqrt(variance), and the distribution for most of the background-subtracted image should have a standard deviation of unity. The variance rescaling factor is the factor that brings that distribution to have unit standard deviation. This may not work well if the image has a lot of structure in it, as the assumptions are violated. In that case, use an alternate method. Parameters ---------- maskedImage : `lsst.afw.image.MaskedImage` Image for which to determine the variance rescaling factor. Returns ------- factor : `float` Variance rescaling factor.
Definition at line 126 of file scaleVariance.py.
def lsst.pipe.tasks.scaleVariance.ScaleVarianceTask.run | ( | self, | |
maskedImage | |||
) |
Rescale the variance in a maskedImage Parameters ---------- maskedImage : `lsst.afw.image.MaskedImage` Image for which to determine the variance rescaling factor. Returns ------- factor : `float` Variance rescaling factor. Raises ------ RuntimeError If the estimated variance rescaling factor exceeds the configured limit.
Definition at line 94 of file scaleVariance.py.
def lsst.pipe.tasks.scaleVariance.ScaleVarianceTask.subtractedBackground | ( | self, | |
maskedImage | |||
) |
Context manager for subtracting the background We need to subtract the background so that the entire image (apart from objects, which should be clipped) will have the image/sqrt(variance) distributed about zero. This context manager subtracts the background, and ensures it is restored on exit. Parameters ---------- maskedImage : `lsst.afw.image.MaskedImage` Image+mask+variance to have background subtracted and restored. Returns ------- context : context manager Context manager that ensure the background is restored.
Definition at line 66 of file scaleVariance.py.
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static |
Definition at line 58 of file scaleVariance.py.