lsst.ip.diffim g85b7a35e91+e634f8099e
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Public Member Functions | |
def | __init__ (self, *args, **kwargs) |
def | computeVarianceMean (self, exposure) |
def | run (self, scienceExposure, templateExposure, subtractedExposure, psfMatchingKernel, preConvKernel=None, xcen=None, ycen=None, svar=None, tvar=None, templateMatched=True, preConvMode=False, **kwargs) |
def | computeCommonShape (self, *shapes) |
def | computeDiffimCorrection (self, kappa, svar, tvar) |
def | computeScoreCorrection (self, kappa, svar, tvar, preConvArr) |
def | calculateVariancePlane (self, vplane1, vplane2, varMean1, varMean2, c1ft, c2ft) |
def | computeCorrectedDiffimPsf (self, corrft, psfOld) |
def | computeCorrectedImage (self, corrft, imgOld) |
Static Public Member Functions | |
def | padCenterOriginArray (A, tuple newShape, useInverse=False) |
def | estimateVariancePlane (vplane1, vplane2, c1ft, c2ft) |
Public Attributes | |
statsControl | |
freqSpaceShape | |
Static Public Attributes | |
ConfigClass = DecorrelateALKernelConfig | |
Decorrelate the effect of convolution by Alard-Lupton matching kernel in image difference
Definition at line 58 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.__init__ | ( | self, | |
* | args, | ||
** | kwargs | ||
) |
Create the image decorrelation Task Parameters ---------- args : arguments to be passed to ``lsst.pipe.base.task.Task.__init__`` kwargs : keyword arguments to be passed to ``lsst.pipe.base.task.Task.__init__``
Reimplemented in lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelMapper.
Definition at line 65 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.calculateVariancePlane | ( | self, | |
vplane1, | |||
vplane2, | |||
varMean1, | |||
varMean2, | |||
c1ft, | |||
c2ft | |||
) |
Full propagation of the variance planes of the original exposures. The original variance planes of independent pixels are convolved with the image space square of the overall kernels. Parameters ---------- vplane1, vplane2 : `numpy.ndarray` of `float` Variance planes of the original (before pre-convolution or matching) exposures. varMean1, varMean2 : `float` Replacement average values for non-finite ``vplane1`` and ``vplane2`` values respectively. c1ft, c2ft : `numpy.ndarray` of `complex` The overall convolution that includes the matching and the afterburner in frequency space. The result of either ``computeScoreCorrection`` or ``computeDiffimCorrection``. Returns ------- vplaneD : `numpy.ndarray` of `float` The variance plane of the difference/score images. Notes ------ See DMTN-179 Section 5 about the variance plane calculations. Infs and NaNs are allowed and kept in the returned array.
Definition at line 506 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCommonShape | ( | self, | |
* | shapes | ||
) |
Calculate the common shape for FFT operations. Set `self.freqSpaceShape` internally. Parameters ---------- shapes : one or more `tuple` of `int` Shapes of the arrays. All must have the same dimensionality. At least one shape must be provided. Returns ------- None. Notes ----- For each dimension, gets the smallest even number greater than or equal to `N1+N2-1` where `N1` and `N2` are the two largest values. In case of only one shape given, rounds up to even each dimension value.
Definition at line 299 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCorrectedDiffimPsf | ( | self, | |
corrft, | |||
psfOld | |||
) |
Compute the (decorrelated) difference image's new PSF. Parameters ---------- corrft : `numpy.ndarray` The frequency space representation of the correction calculated by `computeCorrection`. Shape must be `self.freqSpaceShape`. psfOld : `numpy.ndarray` The psf of the difference image to be corrected. Returns ------- correctedPsf : `lsst.meas.algorithms.KernelPsf` The corrected psf, same shape as `psfOld`, sum normed to 1. Notes ----- There is no algorithmic guarantee that the corrected psf can meaningfully fit to the same size as the original one.
Definition at line 567 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCorrectedImage | ( | self, | |
corrft, | |||
imgOld | |||
) |
Compute the decorrelated difference image. Parameters ---------- corrft : `numpy.ndarray` The frequency space representation of the correction calculated by `computeCorrection`. Shape must be `self.freqSpaceShape`. imgOld : `numpy.ndarray` The difference image to be corrected. Returns ------- imgNew : `numpy.ndarray` The corrected image, same size as the input.
Definition at line 603 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeDiffimCorrection | ( | self, | |
kappa, | |||
svar, | |||
tvar | |||
) |
Compute the Lupton decorrelation post-convolution kernel for decorrelating an image difference, based on the PSF-matching kernel. Parameters ---------- kappa : `numpy.ndarray` of `float` A matching kernel 2-d numpy.array derived from Alard & Lupton PSF matching. svar : `float` > 0. Average variance of science image used for PSF matching. tvar : `float` > 0. Average variance of the template (matched) image used for PSF matching. Returns ------- corrft : `numpy.ndarray` of `float` The frequency space representation of the correction. The array is real (dtype float). Shape is `self.freqSpaceShape`. cnft, crft : `numpy.ndarray` of `complex` The overall convolution (pre-conv, PSF matching, noise correction) kernel for the science and template images, respectively for the variance plane calculations. These are intermediate results in frequency space. Notes ----- The maximum correction factor converges to `sqrt(tvar/svar)` towards high frequencies. This should be a plausible value.
Definition at line 379 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeScoreCorrection | ( | self, | |
kappa, | |||
svar, | |||
tvar, | |||
preConvArr | |||
) |
Compute the correction kernel for a score image. Parameters ---------- kappa : `numpy.ndarray` A matching kernel 2-d numpy.array derived from Alard & Lupton PSF matching. svar : `float` Average variance of science image used for PSF matching (before pre-convolution). tvar : `float` Average variance of the template (matched) image used for PSF matching. preConvArr : `numpy.ndarray` The pre-convolution kernel of the science image. It should be the PSF of the science image or an approximation of it. It must be normed to sum 1. Returns ------- corrft : `numpy.ndarray` of `float` The frequency space representation of the correction. The array is real (dtype float). Shape is `self.freqSpaceShape`. cnft, crft : `numpy.ndarray` of `complex` The overall convolution (pre-conv, PSF matching, noise correction) kernel for the science and template images, respectively for the variance plane calculations. These are intermediate results in frequency space. Notes ----- To be precise, the science image should be _correlated_ by ``preConvArray`` but this does not matter for this calculation. ``cnft``, ``crft`` contain the scaling factor as well.
Definition at line 419 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeVarianceMean | ( | self, | |
exposure | |||
) |
Definition at line 82 of file imageDecorrelation.py.
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static |
Estimate the variance planes. The estimation assumes that around each pixel the surrounding pixels' sigmas within the convolution kernel are the same. Parameters ---------- vplane1, vplane2 : `numpy.ndarray` of `float` Variance planes of the original (before pre-convolution or matching) exposures. c1ft, c2ft : `numpy.ndarray` of `complex` The overall convolution that includes the matching and the afterburner in frequency space. The result of either ``computeScoreCorrection`` or ``computeDiffimCorrection``. Returns ------- vplaneD : `numpy.ndarray` of `float` The estimated variance plane of the difference/score image as a weighted sum of the input variances. Notes ------ See DMTN-179 Section 5 about the variance plane calculations.
Definition at line 474 of file imageDecorrelation.py.
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static |
Zero pad an image where the origin is at the center and replace the origin to the corner as required by the periodic input of FFT. Implement also the inverse operation, crop the padding and re-center data. Parameters ---------- A : `numpy.ndarray` An array to copy from. newShape : `tuple` of `int` The dimensions of the resulting array. For padding, the resulting array must be larger than A in each dimension. For the inverse operation this must be the original, before padding size of the array. useInverse : bool, optional Selector of forward, add padding, operation (False) or its inverse, crop padding, operation (True). Returns ------- R : `numpy.ndarray` The padded or unpadded array with shape of `newShape` and the same dtype as A. Notes ----- For odd dimensions, the splitting is rounded to put the center pixel into the new corner origin (0,0). This is to be consistent e.g. for a dirac delta kernel that is originally located at the center pixel.
Definition at line 332 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.run | ( | self, | |
scienceExposure, | |||
templateExposure, | |||
subtractedExposure, | |||
psfMatchingKernel, | |||
preConvKernel = None , |
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xcen = None , |
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ycen = None , |
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svar = None , |
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tvar = None , |
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templateMatched = True , |
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preConvMode = False , |
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** | kwargs | ||
) |
Perform decorrelation of an image difference or of a score difference exposure. Corrects the difference or score image due to the convolution of the templateExposure with the A&L PSF matching kernel. See [DMTN-021, Equation 1](http://dmtn-021.lsst.io/#equation-1) and [DMTN-179](http://dmtn-179.lsst.io/) for details. Parameters ---------- scienceExposure : `lsst.afw.image.Exposure` The original science exposure (before pre-convolution, if ``preConvMode==True``). templateExposure : `lsst.afw.image.Exposure` The original template exposure warped, but not psf-matched, to the science exposure. subtractedExposure : `lsst.afw.image.Exposure` the subtracted exposure produced by `ip_diffim.ImagePsfMatchTask.subtractExposures()`. The `subtractedExposure` must inherit its PSF from `exposure`, see notes below. psfMatchingKernel : `lsst.afw.detection.Psf` An (optionally spatially-varying) PSF matching kernel produced by `ip_diffim.ImagePsfMatchTask.subtractExposures()`. preConvKernel : `lsst.afw.math.Kernel`, optional If not `None`, then the `scienceExposure` was pre-convolved with (the reflection of) this kernel. Must be normalized to sum to 1. Allowed only if ``templateMatched==True`` and ``preConvMode==True``. Defaults to the PSF of the science exposure at the image center. xcen : `float`, optional X-pixel coordinate to use for computing constant matching kernel to use If `None` (default), then use the center of the image. ycen : `float`, optional Y-pixel coordinate to use for computing constant matching kernel to use If `None` (default), then use the center of the image. svar : `float`, optional Image variance for science image If `None` (default) then compute the variance over the entire input science image. tvar : `float`, optional Image variance for template image If `None` (default) then compute the variance over the entire input template image. templateMatched : `bool`, optional If True, the template exposure was matched (convolved) to the science exposure. See also notes below. preConvMode : `bool`, optional If True, ``subtractedExposure`` is assumed to be a likelihood difference image and will be noise corrected as a likelihood image. **kwargs Additional keyword arguments propagated from DecorrelateALKernelSpatialTask. Returns ------- result : `lsst.pipe.base.Struct` - ``correctedExposure`` : the decorrelated diffim Notes ----- If ``preConvMode==True``, ``subtractedExposure`` is assumed to be a score image and the noise correction for likelihood images is applied. The resulting image is an optimal detection likelihood image when the templateExposure has noise. (See DMTN-179) If ``preConvKernel`` is not specified, the PSF of ``scienceExposure`` is assumed as pre-convolution kernel. The ``subtractedExposure`` is NOT updated. The returned ``correctedExposure`` has an updated but spatially fixed PSF. It is calculated as the center of image PSF corrected by the center of image matching kernel. If ``templateMatched==True``, the templateExposure was matched (convolved) to the ``scienceExposure`` by ``psfMatchingKernel`` during image differencing. Otherwise the ``scienceExposure`` was matched (convolved) by ``psfMatchingKernel``. In either case, note that the original template and science images are required, not the psf-matched version. This task discards the variance plane of ``subtractedExposure`` and re-computes it from the variance planes of ``scienceExposure`` and ``templateExposure``. The image plane of ``subtractedExposure`` must be at the photometric level set by the AL PSF matching in `ImagePsfMatchTask.subtractExposures`. The assumptions about the photometric level are controlled by the `templateMatched` option in this task. Here we currently convert a spatially-varying matching kernel into a constant kernel, just by computing it at the center of the image (tickets DM-6243, DM-6244). We are also using a constant accross-the-image measure of sigma (sqrt(variance)) to compute the decorrelation kernel. TODO DM-23857 As part of the spatially varying correction implementation consider whether returning a Struct is still necessary.
Reimplemented in lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelMapper.
Definition at line 90 of file imageDecorrelation.py.
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static |
Definition at line 62 of file imageDecorrelation.py.
lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.freqSpaceShape |
Definition at line 328 of file imageDecorrelation.py.
lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.statsControl |
Definition at line 77 of file imageDecorrelation.py.