25 from lsst.geom import Box2I, Point2I, Extent2I
31 import lsst.meas.algorithms
as measAlg
32 import lsst.pipe.base
as pipeBase
35 from .imagePsfMatch
import (ImagePsfMatchTask, ImagePsfMatchConfig,
36 subtractAlgorithmRegistry)
38 __all__ = [
"ZogyTask",
"ZogyConfig",
39 "ZogyImagePsfMatchConfig",
"ZogyImagePsfMatchTask"]
42 """Tasks for performing the "Proper image subtraction" algorithm of
43 Zackay, et al. (2016), hereafter simply referred to as 'ZOGY (2016)'.
45 `ZogyTask` contains methods to perform the basic estimation of the
46 ZOGY diffim ``D``, its updated PSF, and the variance-normalized
47 likelihood image ``S_corr``. We have implemented ZOGY using the
48 proscribed methodology, computing all convolutions in Fourier space,
49 and also variants in which the convolutions are performed in real
50 (image) space. The former is faster and results in fewer artifacts
51 when the PSFs are noisy (i.e., measured, for example, via
52 `PsfEx`). The latter is presumed to be preferred as it can account for
53 masks correctly with fewer "ringing" artifacts from edge effects or
54 saturated stars, but noisy PSFs result in their own smaller
55 artifacts. Removal of these artifacts is a subject of continuing
56 research. Currently, we "pad" the PSFs when performing the
57 subtractions in real space, which reduces, but does not entirely
58 eliminate these artifacts.
60 All methods in `ZogyTask` assume template and science images are
61 already accurately photometrically and astrometrically registered.
63 `ZogyMapper` is a wrapper which runs `ZogyTask` in the
64 `ImageMapReduce` framework, computing of ZOGY diffim's on small,
65 overlapping sub-images, thereby enabling complete ZOGY diffim's which
66 account for spatially-varying noise and PSFs across the two input
67 exposures. An example of the use of this task is in the `testZogy.py`
73 """Configuration parameters for the ZogyTask
76 templateFluxScaling = pexConfig.Field(
79 doc=
"Template flux scaling factor (Fr in ZOGY paper)"
82 scienceFluxScaling = pexConfig.Field(
85 doc=
"Science flux scaling factor (Fn in ZOGY paper)"
88 scaleByCalibration = pexConfig.Field(
91 doc=
"Compute the flux normalization scaling based on the image calibration."
92 "This overrides 'templateFluxScaling' and 'scienceFluxScaling'."
95 correctBackground = pexConfig.Field(
98 doc=
"Subtract exposure background mean to have zero expectation value."
101 ignoreMaskPlanes = pexConfig.ListField(
103 default=(
"INTRP",
"EDGE",
"DETECTED",
"SAT",
"CR",
"BAD",
"NO_DATA",
"DETECTED_NEGATIVE"),
104 doc=
"Mask planes to ignore for statistics"
106 maxPsfCentroidDist = pexConfig.Field(
109 doc=
"Maximum centroid difference allowed between the two exposure PSFs (pixels)."
111 doSpatialGrid = pexConfig.Field(
114 doc=
"Split the exposure and perform matching with the spatially varying PSF."
116 gridInnerSize = pexConfig.Field(
119 doc=
"Approximate useful inner size of the grid cells in units of the "
120 "estimated matching kernel size (doSpatialGrid=True only)."
125 """Task to perform ZOGY proper image subtraction. See module-level documentation for
129 ConfigClass = ZogyConfig
130 _DefaultName =
"imageDifferenceZogy"
132 def _computeVarianceMean(self, exposure):
133 """Compute the sigma-clipped mean of the variance image of ``exposure``.
136 exposure.getMaskedImage().getMask(),
138 var = statObj.getValue(afwMath.MEANCLIP)
143 """Zero pad an image where the origin is at the center and replace the
144 origin to the corner as required by the periodic input of FFT.
146 Implement also the inverse operation, crop the padding and re-center data.
151 An array to copy from.
152 newShape : `tuple` of `int`
153 The dimensions of the resulting array. For padding, the resulting array
154 must be larger than A in each dimension. For the inverse operation this
155 must be the original, before padding dimensions of the array.
156 useInverse : bool, optional
157 Selector of forward, add padding, operation (False)
158 or its inverse, crop padding, operation (True).
159 dtype: `numpy.dtype`, optional
160 Dtype of output array. Values must be implicitly castable to this type.
161 Use to get expected result type, e.g. single float (nympy.float32).
162 If not specified, dtype is inherited from ``A``.
167 The padded or unpadded array with shape of `newShape` and dtype of ``dtype``.
171 For odd dimensions, the splitting is rounded to
172 put the center pixel into the new corner origin (0,0). This is to be consistent
173 e.g. for a dirac delta kernel that is originally located at the center pixel.
178 ValueError : ``newShape`` dimensions must be greater than or equal to the
179 dimensions of ``A`` for the forward operation and less than or equal to
180 for the inverse operation.
187 firstHalves = [x//2
for x
in A.shape]
188 secondHalves = [x-y
for x, y
in zip(A.shape, firstHalves)]
189 for d1, d2
in zip(newShape, A.shape):
191 raise ValueError(
"Newshape dimensions must be greater or equal")
194 secondHalves = [x//2
for x
in newShape]
195 firstHalves = [x-y
for x, y
in zip(newShape, secondHalves)]
196 for d1, d2
in zip(newShape, A.shape):
198 raise ValueError(
"Newshape dimensions must be smaller or equal")
203 R = np.zeros(newShape, dtype=dtype)
204 R[-firstHalves[0]:, -firstHalves[1]:] = A[:firstHalves[0], :firstHalves[1]]
205 R[:secondHalves[0], -firstHalves[1]:] = A[-secondHalves[0]:, :firstHalves[1]]
206 R[:secondHalves[0], :secondHalves[1]] = A[-secondHalves[0]:, -secondHalves[1]:]
207 R[-firstHalves[0]:, :secondHalves[1]] = A[:firstHalves[0], -secondHalves[1]:]
211 """Initializes a sub image.
215 fullExp : `lsst.afw.image.Exposure`
216 The full exposure to cut sub image from.
217 innerBox : `lsst.geom.Box2I`
218 The useful area of the calculation up to the whole bounding box of
219 ``fullExp``. ``fullExp`` must contain this box.
220 outerBox : `lsst.geom.Box2I`
221 The overall cutting area. ``outerBox`` must be at least 1 pixel larger
222 than ``inneBox`` in all directions and may not be fully contained by
224 noiseMeanVar : `float` > 0.
225 The noise variance level to initialize variance plane and to generate
226 white noise for the non-overlapping region.
227 useNoise : `bool`, optional
228 If True, generate white noise for non-overlapping region. Otherwise,
229 zero padding will be used in the non-overlapping region.
233 result : `lsst.pipe.base.Struct`
234 - ``subImg``, ``subVarImg`` : `lsst.afw.image.ImageD`
235 The new sub image and its sub variance plane.
239 ``innerBox``, ``outerBox`` must be in the PARENT system of ``fullExp``.
241 Supports the non-grid option when ``innerBox`` equals to the
242 bounding box of ``fullExp``.
244 fullBox = fullExp.getBBox()
245 subImg = afwImage.ImageD(outerBox, 0)
246 subVarImg = afwImage.ImageD(outerBox, noiseMeanVar)
247 borderBoxes = self.
splitBordersplitBorder(innerBox, outerBox)
250 noiseSig = np.sqrt(noiseMeanVar)
251 for box
in borderBoxes:
252 if not fullBox.contains(box):
253 R = subImg[box].array
254 R[...] = self.
rngrng.normal(scale=noiseSig, size=R.shape)
256 subImg[innerBox].array[...] = fullExp.image[innerBox].array
257 subVarImg[innerBox].array[...] = fullExp.variance[innerBox].array
259 for box
in borderBoxes:
260 overlapBox = box.clippedTo(fullBox)
261 if not overlapBox.isEmpty():
262 subImg[overlapBox].array[...] = fullExp.image[overlapBox].array
263 subVarImg[overlapBox].array[...] = fullExp.variance[overlapBox].array
264 return pipeBase.Struct(image=subImg, variance=subVarImg)
268 """Estimate the image space size of the matching kernels.
270 Return ten times the larger Gaussian sigma estimate but at least
271 the largest of the original psf dimensions.
275 psf1, psf2 : `lsst.afw.detection.Psf`
276 The PSFs of the two input exposures.
281 Conservative estimate for matching kernel size in pixels.
282 This is the minimum padding around the inner region at each side.
287 sig1 = psf1.computeShape().getDeterminantRadius()
288 sig2 = psf2.computeShape().getDeterminantRadius()
289 sig = max(sig1, sig2)
290 psfBBox1 = psf1.computeBBox()
291 psfBBox2 = psf2.computeBBox()
292 return max(10 * sig, psfBBox1.getWidth(), psfBBox1.getHeight(),
293 psfBBox2.getWidth(), psfBBox2.getHeight())
297 """Split the border area around the inner box into 8 disjunct boxes.
301 innerBox : `lsst.geom.Box2I`
303 outerBox : `lsst.geom.Box2I`
304 The outer box. It must be at least 1 pixel larger in each direction than the inner box.
308 resultBoxes : `list` of 8 boxes covering the edge around innerBox
312 The border boxes do not overlap. The border is covered counter clockwise
313 starting from lower left corner.
317 ValueError : If ``outerBox`` is not larger than ``innerBox``.
319 innerBox = innerBox.dilatedBy(1)
320 if not outerBox.contains(innerBox):
321 raise ValueError(
"OuterBox must be larger by at least 1 pixel in all directions")
324 o1, o2, o3, o4 = outerBox.getCorners()
325 i1, i2, i3, i4 = innerBox.getCorners()
326 p1 =
Point2I(outerBox.minX, innerBox.minY)
327 p2 =
Point2I(innerBox.maxX, outerBox.minY)
328 p3 =
Point2I(outerBox.maxX, innerBox.maxY)
329 p4 =
Point2I(innerBox.minX, outerBox.maxY)
332 pointPairs = ((o1, i1), (i1 +
Extent2I(1, 0), p2 +
Extent2I(-1, 0)), (o2, i2),
336 return [
Box2I(x, y, invert=
True)
for (x, y)
in pointPairs]
339 def generateGrid(imageBox, minEdgeDims, innerBoxDims, minTotalDims=None, powerOfTwo=False):
340 """Generate a splitting grid for an image.
342 The inner boxes cover the input image without overlap, the edges around the inner boxes do overlap
343 and go beyond the image at the image edges.
347 imageBox : `lsst.geom.Box2I`
348 Bounding box of the exposure to split.
349 minEdgeDims : `lsst.geom.Extent2I`
350 Minimum edge width in (x,y) directions each side.
351 innerBoxDims : `lsst.geom.Extent2I`
352 Minimum requested inner box dimensions (x,y).
353 The actual dimensions can be larger due to rounding.
354 minTotalDims: `lsst.geom.Extent2I`, optional
355 If provided, minimum total outer dimensions (x,y). The edge will be increased until satisfied.
356 powerOfTwo : `bool`, optional
357 If True, the outer box dimensions should be rounded up to a power of 2
358 by increasing the border size. This is up to 8192, above this size,
359 rounding up is disabled.
363 Inner box dimensions are chosen to be as uniform as they can, remainder pixels at the edge of the
364 input will be appended to the last column/row boxes.
366 See diffimTests/tickets/DM-28928_spatial_grid notebooks for demonstration of this code.
368 This method can be used for both PARENT and LOCAL bounding boxes.
370 The outerBox dimensions are always even.
374 boxList : `list` of `lsst.pipe.base.Struct`
375 ``innerBox``, ``outerBox`` : `lsst.geom.Box2I`, inner boxes and overlapping border around them.
378 powersOf2 = np.array([16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192])
379 doubleEdgeDims = minEdgeDims * 2
380 width, height = imageBox.getDimensions()
381 nX = width // innerBoxDims.x
383 innerWidth = width // nX
387 xCorners = np.zeros(nX + 1)
388 xCorners[:-1] = np.arange(nX)*innerWidth + imageBox.minX
389 xCorners[-1] = imageBox.endX
391 nY = height // innerBoxDims.y
393 innerHeight = height // nY
397 yCorners = np.zeros(nY + 1)
398 yCorners[:-1] = np.arange(nY)*innerHeight + imageBox.minY
399 yCorners[-1] = imageBox.endY
403 for i_y
in range(nY):
404 for i_x
in range(nX):
406 Point2I(xCorners[i_x + 1] - 1, yCorners[i_y + 1] - 1))
408 paddedWidth = innerBox.width + doubleEdgeDims.x
409 if minTotalDims
is not None and paddedWidth < minTotalDims.width:
410 paddedWidth = minTotalDims.width
412 i2x = np.searchsorted(powersOf2, paddedWidth, side=
'left')
413 if i2x < len(powersOf2):
414 paddedWidth = powersOf2[i2x]
415 if paddedWidth % 2 == 1:
418 totalXedge = paddedWidth - innerBox.width
420 paddedHeight = innerBox.height + doubleEdgeDims.y
421 if minTotalDims
is not None and paddedHeight < minTotalDims.height:
422 paddedHeight = minTotalDims.height
424 i2y = np.searchsorted(powersOf2, paddedHeight, side=
'left')
425 if i2y < len(powersOf2):
426 paddedHeight = powersOf2[i2y]
427 if paddedHeight % 2 == 1:
429 totalYedge = paddedHeight - innerBox.height
430 outerBox =
Box2I(
Point2I(innerBox.minX - totalXedge//2, innerBox.minY - totalYedge//2),
431 Extent2I(paddedWidth, paddedHeight))
432 boxes.append(pipeBase.Struct(innerBox=innerBox, outerBox=outerBox))
436 """Construct a CoaddPsf based on PSFs from individual sub image solutions.
440 gridPsfs : iterable of `lsst.pipe.base.Struct`
441 Iterable of bounding boxes (``bbox``) and Psf solutions (``psf``).
445 psf : `lsst.meas.algorithms.CoaddPsf`
446 A psf constructed from the PSFs of the individual subExposures.
448 schema = afwTable.ExposureTable.makeMinimalSchema()
449 schema.addField(
"weight", type=
"D", doc=
"Coadd weight")
450 mycatalog = afwTable.ExposureCatalog(schema)
454 wcsref = self.
fullExp1fullExp1.getWcs()
455 for i, res
in enumerate(gridPsfs):
456 record = mycatalog.getTable().makeRecord()
457 record.setPsf(res.psf)
458 record.setWcs(wcsref)
459 record.setBBox(res.bbox)
460 record[
'weight'] = 1.0
462 mycatalog.append(record)
465 psf = measAlg.CoaddPsf(mycatalog, wcsref,
'weight')
469 """Prepare and forward FFT an image array.
473 imgArr : `numpy.ndarray` of `float`
474 Original array. In-place modified as `numpy.nan` and `numpy.inf` are replaced by
479 result : `lsst.pipe.base.Struct`
480 - ``imFft`` : `numpy.ndarray` of `numpy.complex`.
482 - ``filtInf``, ``filtNaN`` : `numpy.ndarray` of `bool`
486 Save location of non-finite values for restoration, and replace them
487 with image mean values. Re-center and zero pad array by `padCenterOriginArray`.
489 filtInf = np.isinf(imgArr)
490 filtNaN = np.isnan(imgArr)
491 imgArr[filtInf] = np.nan
492 imgArr[filtInf | filtNaN] = np.nanmean(imgArr)
493 self.log.debug(
"Replacing {} Inf and {} NaN values.".format(
494 np.sum(filtInf), np.sum(filtNaN)))
496 imgArr = np.fft.fft2(imgArr)
497 return pipeBase.Struct(imFft=imgArr, filtInf=filtInf, filtNaN=filtNaN)
500 """Replace non-finite pixel values in-place.
502 Save the locations of non-finite values for restoration, and replace them
503 with image mean values.
507 imgArr : `numpy.ndarray` of `float`
508 The image array. Non-finite values are replaced in-place in this array.
512 result : `lsst.pipe.base.Struct`
513 - ``filtInf``, ``filtNaN`` : `numpy.ndarray` of `bool`
514 The filter of the pixel values that were inf or nan.
516 filtInf = np.isinf(imgArr)
517 filtNaN = np.isnan(imgArr)
520 imgArr[filtInf] = np.nan
521 imgArr[filtInf | filtNaN] = np.nanmean(imgArr)
522 self.log.debugf(
"Replacing {} Inf and {} NaN values.",
523 np.sum(filtInf), np.sum(filtNaN))
524 return pipeBase.Struct(filtInf=filtInf, filtNaN=filtNaN)
527 """Inverse FFT and crop padding from image array.
531 imgArr : `numpy.ndarray` of `numpy.complex`
532 Fourier space array representing a real image.
534 origSize : `tuple` of `int`
535 Original unpadded shape tuple of the image to be cropped to.
537 filtInf, filtNan : `numpy.ndarray` of bool or int, optional
538 If specified, they are used as index arrays for ``result`` to set values to
539 `numpy.inf` and `numpy.nan` respectively at these positions.
541 dtype : `numpy.dtype`, optional
542 Dtype of result array to cast return values to implicitly. This is to
543 spare one array copy operation at reducing double precision to single.
544 If `None` result inherits dtype of `imgArr`.
548 result : `numpy.ndarray` of `dtype`
550 imgNew = np.fft.ifft2(imgArr)
552 imgNew = self.
padCenterOriginArraypadCenterOriginArray(imgNew, origSize, useInverse=
True, dtype=dtype)
553 if filtInf
is not None:
554 imgNew[filtInf] = np.inf
555 if filtNaN
is not None:
556 imgNew[filtNaN] = np.nan
561 """Computes the PSF image at the bbox center point.
563 This may be at a fractional pixel position.
567 exposure : `lsst.afw.image.Exposure`
572 psfImg : `lsst.afw.image.Image`
573 Calculated psf image.
575 pBox = exposure.getBBox()
576 cen = pBox.getCenter()
577 psf = exposure.getPsf()
578 psfImg = psf.computeKernelImage(cen)
583 """In-place subtraction of sigma-clipped mean of the image.
587 image : `lsst.afw.image.Image`
588 Image to manipulate. Its sigma clipped mean is in-place subtracted.
590 mask : `lsst.afw.image.Mask`
591 Mask to use for ignoring pixels.
593 statsControl : `lsst.afw.math.StatisticsControl`
594 Config of sigma clipped mean statistics calculation.
602 ValueError : If image mean is nan.
605 afwMath.MEANCLIP, statsControl)
606 mean = statObj.getValue(afwMath.MEANCLIP)
607 if not np.isnan(mean):
610 raise ValueError(
"Image mean is NaN.")
613 """Performs calculations that apply to the full exposures once only.
618 exposure1, exposure2 : `lsst.afw.image.Exposure`
619 The input exposures. Copies are made for internal calculations.
621 correctBackground : `bool`, optional
622 If True, subtracts sigma-clipped mean of exposures. The algorithm
623 assumes zero expectation value at background pixels.
631 Set a number of instance fields with pre-calculated values.
635 ValueError : If photometric calibrations are not available while
636 ``config.scaleByCalibration`` equals True.
641 self.
statsControlstatsControl.setAndMask(afwImage.Mask.getPlaneBitMask(
642 self.config.ignoreMaskPlanes))
644 exposure1 = exposure1.clone()
645 exposure2 = exposure2.clone()
653 if self.config.scaleByCalibration:
654 calibObj1 = exposure1.getPhotoCalib()
655 calibObj2 = exposure2.getPhotoCalib()
656 if calibObj1
is None or calibObj2
is None:
657 raise ValueError(
"Photometric calibrations are not available for both exposures.")
658 mImg1 = calibObj1.calibrateImage(exposure1.maskedImage)
659 mImg2 = calibObj2.calibrateImage(exposure2.maskedImage)
663 self.
F1F1 = self.config.templateFluxScaling
664 self.
F2F2 = self.config.scienceFluxScaling
665 mImg1 = exposure1.maskedImage
666 mImg2 = exposure2.maskedImage
669 if correctBackground:
675 self.log.debugf(
"Minimum padding border size: {} pixels", self.
borderSizeborderSize)
682 exposure1.maskedImage = mImg1
683 exposure2.maskedImage = mImg2
689 """Perform per-sub exposure preparations.
693 sig1, sig2 : `float`, optional
694 For debug purposes only, copnsider that the image
695 may already be rescaled by the photometric calibration.
696 localCutout : `lsst.pipe.base.Struct`
697 - innerBox, outerBox: `lsst.geom.Box2I` LOCAL inner and outer boxes
698 psf1, psf2 : `lsst.afw.detection.Psf`, optional
699 If specified, use given psf as the sub exposure psf. For debug purposes.
700 sig1, sig2 : `float`, optional
701 If specified, use value as the sub-exposures' background noise sigma value.
708 self.log.debugf(
"Processing LOCAL cell w/ inner box:{}, outer box:{}",
709 localCutout.innerBox, localCutout.outerBox)
712 innerBox=localCutout.innerBox.shiftedBy(
Extent2I(self.
fullExp1fullExp1.getXY0())),
713 outerBox=localCutout.outerBox.shiftedBy(
Extent2I(self.
fullExp1fullExp1.getXY0())))
715 innerBox=localCutout.innerBox.shiftedBy(
Extent2I(self.
fullExp2fullExp2.getXY0())),
716 outerBox=localCutout.outerBox.shiftedBy(
Extent2I(self.
fullExp2fullExp2.getXY0())))
729 psfBBox1 = self.
subExpPsf1subExpPsf1.getBBox()
730 psfBBox2 = self.
subExpPsf2subExpPsf2.getBBox()
731 self.
psfShape1psfShape1 = (psfBBox1.getHeight(), psfBBox1.getWidth())
732 self.
psfShape2psfShape2 = (psfBBox2.getHeight(), psfBBox2.getWidth())
747 self.
rngrng = np.random.default_rng(seed=np.array([self.
subExpVar1subExpVar1]).view(int))
748 self.
freqSpaceShapefreqSpaceShape = (localCutout.outerBox.getHeight(), localCutout.outerBox.getWidth())
774 """Square the argument in pixel space.
778 D : 2D `numpy.ndarray` of `numpy.complex`
779 Fourier transform of a real valued array.
783 R : `numpy.ndarray` of `numpy.complex`
787 ``D`` is to be inverse Fourier transformed, squared and then
788 forward Fourier transformed again, i.e. an autoconvolution in Fourier space.
789 This operation is not distributive over multiplication.
790 ``pixelSpaceSquare(A*B) != pixelSpaceSquare(A)*pixelSpaceSquare(B)``
792 R = np.real(np.fft.ifft2(D))
799 """Calculate the centroid coordinates of a 2D array.
803 A : 2D `numpy.ndarray` of `float`
804 The input array. Must not be all exact zero.
808 Calculates the centroid as if the array represented a 2D geometrical shape with
809 weights per cell, allowing for "negative" weights. If sum equals to exact (float) zero,
810 calculates centroid of absolute value array.
812 The geometrical center is defined as (0,0), independently of the array shape.
813 For an odd dimension, this is the center of the center pixel,
814 for an even dimension, this is between the two center pixels.
818 ycen, xcen : `tuple` of `float`
825 w = np.arange(A.shape[0], dtype=float) - (A.shape[0] - 1.)/2
826 ycen = np.sum(w[:, np.newaxis]*A)/s
827 w = np.arange(A.shape[1], dtype=float) - (A.shape[1] - 1.)/2
828 xcen = np.sum(w[np.newaxis, :]*A)/s
833 """Check whether two PSF array centroids' distance is within tolerance.
837 psfArr1, psfArr2 : `numpy.ndarray` of `float`
838 Input PSF arrays to check.
847 Centroid distance exceeds `config.maxPsfCentroidDist` pixels.
853 if dy*dy + dx*dx > self.config.maxPsfCentroidDist*self.config.maxPsfCentroidDist:
855 f
"PSF centroids are offset by more than {self.config.maxPsfCentroidDist:.2f} pixels.")
858 psf2, im2, varPlane2, F2, varMean2, calculateScore=True):
859 """Convolve and subtract two images in Fourier space.
861 Calculate the ZOGY proper difference image, score image and their PSFs.
862 All input and output arrays are in Fourier space.
866 psf1, psf2 : `numpy.ndarray`, (``self.freqSpaceShape``,)
867 Psf arrays. Must be already in Fourier space.
868 im1, im2 : `numpy.ndarray`, (``self.freqSpaceShape``,)
869 Image arrays. Must be already in Fourier space.
870 varPlane1, varPlane2 : `numpy.ndarray`, (``self.freqSpaceShape``,)
871 Variance plane arrays respectively. Must be already in Fourier space.
872 varMean1, varMean2 : `numpy.float` > 0.
873 Average per-pixel noise variance in im1, im2 respectively. Used as weighing
874 of input images. Must be greater than zero.
875 F1, F2 : `numpy.float` > 0.
876 Photometric scaling of the images. See eqs. (5)--(9)
877 calculateScore : `bool`, optional
878 If True (default), calculate and return the detection significance (score) image.
879 Otherwise, these return fields are `None`.
883 result : `pipe.base.Struct`
884 All arrays are in Fourier space and have shape ``self.freqSpaceShape``.
887 Photometric level of ``D`` (`float`).
889 The difference image (`numpy.ndarray` [`numpy.complex`]).
891 Variance plane of ``D`` (`numpy.ndarray` [`numpy.complex`]).
893 PSF of ``D`` (`numpy.ndarray` [`numpy.complex`]).
895 Significance (score) image (`numpy.ndarray` [`numpy.complex`] or `None`).
897 Variance plane of ``S`` ((`numpy.ndarray` [`numpy.complex`] or `None`).
899 PSF of ``S`` (`numpy.ndarray` [`numpy.complex`]).
903 All array inputs and outputs are Fourier-space images with shape of
904 `self.freqSpaceShape` in this method.
906 ``varMean1``, ``varMean2`` quantities are part of the noise model and not to be confused
907 with the variance of image frequency components or with ``varPlane1``, ``varPlane2`` that
908 are the Fourier transform of the variance planes.
910 var1F2Sq = varMean1*F2*F2
911 var2F1Sq = varMean2*F1*F1
913 psfAbsSq1 = np.real(np.conj(psf1)*psf1)
914 psfAbsSq2 = np.real(np.conj(psf2)*psf2)
915 FdDenom = np.sqrt(var1F2Sq + var2F1Sq)
918 tiny = np.finfo(psf1.dtype).tiny * 100
919 sDenom = var1F2Sq*psfAbsSq2 + var2F1Sq*psfAbsSq1
929 fltZero = sDenom < tiny
930 nZero = np.sum(fltZero)
931 self.log.debug(f
"There are {nZero} frequencies where both FFTd PSFs are close to zero.")
934 fltZero = np.nonzero(fltZero)
935 sDenom[fltZero] = tiny
936 denom = np.sqrt(sDenom)
941 c1[fltZero] = F2/FdDenom
942 c2[fltZero] = F1/FdDenom
946 Pd = FdDenom*psf1*psf2/denom
952 c1 = F1*F2*F2*np.conj(psf1)*psfAbsSq2/sDenom
953 c2 = F2*F1*F1*np.conj(psf2)*psfAbsSq1/sDenom
964 return pipeBase.Struct(D=D, Pd=Pd, varPlaneD=varPlaneD, Fd=Fd,
965 S=S, Ps=Ps, varPlaneS=varPlaneS)
969 """Calculate the mask plane of the difference image.
973 mask1, maks2 : `lsst.afw.image.Mask`
974 Mask planes of the two exposures.
979 diffmask : `lsst.afw.image.Mask`
980 Mask plane for the subtraction result.
984 TODO DM-25174 : Specification of effPsf1, effPsf2 are not yet supported.
988 if effPsf1
is not None or effPsf2
is not None:
992 raise NotImplementedError(
"Mask plane only 'convolution' operation is not yet supported")
999 """Create a non spatially varying PSF from a `numpy.ndarray`.
1004 2D array to use as the new psf image. The pixels are copied.
1008 psfNew : `lsst.meas.algorithms.KernelPsf`
1009 The constructed PSF.
1011 psfImg = afwImage.ImageD(A.astype(np.float64, copy=
True), deep=
False)
1016 """Paste sub image results back into result Exposure objects.
1020 ftDiff : `lsst.pipe.base.Struct`
1021 Result struct by `calculateFourierDiffim`.
1022 diffExp : `lsst.afw.image.Exposure`
1023 The result exposure to paste into the sub image result.
1024 Must be dimensions and dtype of ``self.fullExp1``.
1025 scoreExp : `lsst.afw.image.Exposure` or `None`
1026 The result score exposure to paste into the sub image result.
1027 Must be dimensions and dtype of ``self.fullExp1``.
1028 If `None`, the score image results are disregarded.
1036 The PSF of the score image is just to make the score image resemble a
1037 regular exposure and to study the algorithm performance.
1039 Add an entry to the ``self.gridPsfs`` list.
1041 gridPsfs : `list` of `lsst.pipe.base.Struct`
1042 - ``bbox`` : `lsst.geom.Box2I`
1043 The inner region of the grid cell.
1044 - ``Pd`` : `lsst.meas.algorithms.KernelPsf`
1045 The diffim PSF in this cell.
1046 - ``Ps`` : `lsst.meas.algorithms.KernelPsf` or `None`
1047 The score image PSF in this cell or `None` if the score
1048 image was not calculated.
1058 self.log.infof(
"Pd sum before normalization: {:.3f}", sumPd)
1063 xy0 = self.
cutBoxes1cutBoxes1.outerBox.getMin()
1067 diffExp.image[self.
cutBoxes1cutBoxes1.innerBox] = imgD[self.
cutBoxes1cutBoxes1.innerBox]
1069 dtype=self.
fullExp1fullExp1.variance.dtype)
1070 diffExp.variance[self.
cutBoxes1cutBoxes1.innerBox] = imgVarPlaneD[self.
cutBoxes1cutBoxes1.innerBox]
1076 diffExp.maskedImage[self.
cutBoxes1cutBoxes1.innerBox] /= ftDiff.Fd
1078 if ftDiff.S
is not None and scoreExp
is not None:
1086 dtype=self.
fullExp1fullExp1.variance.dtype)
1087 scoreExp.image[self.
cutBoxes1cutBoxes1.innerBox] = imgS[self.
cutBoxes1cutBoxes1.innerBox]
1088 scoreExp.variance[self.
cutBoxes1cutBoxes1.innerBox] = imgVarPlaneS[self.
cutBoxes1cutBoxes1.innerBox]
1093 self.log.infof(
"Ps sum before normalization: {:.3f}", sumPs)
1098 scoreExp.mask[self.
cutBoxes1cutBoxes1.innerBox] = diffExp.mask[self.
cutBoxes1cutBoxes1.innerBox]
1103 self.
gridPsfsgridPsfs.append(pipeBase.Struct(bbox=self.
cutBoxes1cutBoxes1.innerBox, Pd=Pd, Ps=Ps))
1106 """Perform final steps on the full difference exposure result.
1108 Set photometric calibration, psf properties of the exposures.
1112 diffExp : `lsst.afw.image.Exposure`
1113 The result difference image exposure to finalize.
1114 scoreExp : `lsst.afw.image.Exposure` or `None`
1115 The result score exposure to finalize.
1123 diffExp.setPhotoCalib(calibOne)
1129 pipeBase.Struct(bbox=x.bbox, psf=x.Pd)
for x
in self.
gridPsfsgridPsfs
1131 if scoreExp
is not None:
1134 pipeBase.Struct(bbox=x.bbox, psf=x.Ps)
for x
in self.
gridPsfsgridPsfs
1139 diffExp.setPsf(self.
gridPsfsgridPsfs[0].Pd)
1140 if scoreExp
is not None:
1141 scoreExp.setPsf(self.
gridPsfsgridPsfs[0].Ps)
1144 if scoreExp
is not None:
1145 scoreExp.setPhotoCalib(calibOne)
1153 tiny = np.finfo(scoreExp.variance.dtype).tiny * 100
1154 flt = np.logical_or(flt, scoreExp.variance.array < tiny)
1158 scoreExp.variance.array[flt] = 1
1159 scoreExp.image.array[flt] = 0
1161 def run(self, exposure1, exposure2, calculateScore=True):
1162 """Task entry point to perform the zogy subtraction
1163 of ``exposure1-exposure2``.
1167 exposure1, exposure2 : `lsst.afw.image.Exposure`
1168 Two exposures warped and matched into matching pixel dimensions.
1169 calculateScore : `bool`, optional
1170 If True (default), calculate the score image and return in ``scoreExp``.
1175 resultName : `lsst.pipe.base.Struct`
1176 - ``diffExp`` : `lsst.afw.image.Exposure`
1177 The Zogy difference exposure (``exposure1-exposure2``).
1178 - ``scoreExp`` : `lsst.afw.image.Exposure` or `None`
1179 The Zogy significance or score (S) exposure if ``calculateScore==True``.
1180 - ``ftDiff`` : `lsst.pipe.base.Struct`
1181 Lower level return struct by `calculateFourierDiffim` with added
1182 fields from the task instance. For debug purposes.
1187 ``diffExp`` and ``scoreExp`` always inherit their metadata from
1188 ``exposure1`` (e.g. dtype, bbox, wcs).
1190 The score image (``S``) is defined in the ZOGY paper as the detection
1191 statistic value at each pixel. In the ZOGY image model, the input images
1192 have uniform variance noises and thus ``S`` has uniform per pixel
1193 variance (though it is not scaled to 1). In Section 3.3 of the paper,
1194 there are "corrections" defined to the score image to correct the
1195 significance values for some deviations from the image model. The first
1196 of these corrections is the calculation of the *variance plane* of ``S``
1197 allowing for different per pixel variance values by following the
1198 overall convolution operation on the pixels of the input images. ``S``
1199 scaled (divided) by its corrected per pixel noise is referred as
1200 ``Scorr`` in the paper.
1202 In the current implementation, ``scoreExp`` contains ``S`` in its image
1203 plane and the calculated (non-uniform) variance plane of ``S`` in its
1204 variance plane. ``scoreExp`` can be used directly for source detection
1205 as a likelihood image by respecting its variance plane or can be divided
1206 by the square root of the variance plane to scale detection significance
1207 values into units of sigma. ``S`` should be interpreted as a detection
1208 likelihood directly on a per-pixel basis. The calculated PSF
1209 of ``S`` is merely an indication how much the input PSFs localize point
1212 TODO DM-23855 : Implement further correction tags to the variance of
1213 ``scoreExp``. As of DM-25174 it is not determined how important these
1214 further correction tags are.
1217 if exposure1.getDimensions() != exposure2.getDimensions():
1218 raise ValueError(
"Exposure dimensions do not match ({} != {} )".format(
1219 exposure1.getDimensions(), exposure2.getDimensions()))
1221 self.
prepareFullExposureprepareFullExposure(exposure1, exposure2, correctBackground=self.config.correctBackground)
1225 if self.config.doSpatialGrid:
1233 self.
fullExp1fullExp1.getBBox().getDimensions(), powerOfTwo=
True)
1235 diffExp = self.
fullExp1fullExp1.clone()
1237 scoreExp = self.
fullExp1fullExp1.clone()
1242 for boxPair
in gridBoxes:
1247 calculateScore=calculateScore)
1252 ftDiff.psfShape1 = self.
psfShape1psfShape1
1253 ftDiff.psfShape2 = self.
psfShape2psfShape2
1254 ftDiff.borderSize = self.
borderSizeborderSize
1255 return pipeBase.Struct(diffExp=diffExp,
1261 """Config for the ZogyImagePsfMatchTask"""
1263 zogyConfig = pexConfig.ConfigField(
1265 doc=
'ZogyTask config to use',
1270 """Task to perform Zogy PSF matching and image subtraction.
1272 This class inherits from ImagePsfMatchTask to contain the _warper
1273 subtask and related methods.
1276 ConfigClass = ZogyImagePsfMatchConfig
1279 ImagePsfMatchTask.__init__(self, *args, **kwargs)
1281 def run(self, scienceExposure, templateExposure, doWarping=True):
1282 """Register, PSF-match, and subtract two Exposures, ``scienceExposure - templateExposure``
1283 using the ZOGY algorithm.
1287 templateExposure : `lsst.afw.image.Exposure`
1288 exposure to be warped to scienceExposure.
1289 scienceExposure : `lsst.afw.image.Exposure`
1292 what to do if templateExposure's and scienceExposure's WCSs do not match:
1293 - if True then warp templateExposure to match scienceExposure
1294 - if False then raise an Exception
1298 Do the following, in order:
1299 - Warp templateExposure to match scienceExposure, if their WCSs do not already match
1300 - Compute subtracted exposure ZOGY image subtraction algorithm on the two exposures
1302 This is the new entry point of the task as of DM-25115.
1306 results : `lsst.pipe.base.Struct` containing these fields:
1307 - subtractedExposure: `lsst.afw.image.Exposure`
1308 The subtraction result.
1309 - warpedExposure: `lsst.afw.image.Exposure` or `None`
1310 templateExposure after warping to match scienceExposure
1313 if not self.
_validateWcs_validateWcs(scienceExposure, templateExposure):
1315 self.log.info(
"Warping templateExposure to scienceExposure")
1317 scienceExposure.getWcs())
1318 psfWarped = measAlg.WarpedPsf(templateExposure.getPsf(), xyTransform)
1319 templateExposure = self.
_warper_warper.warpExposure(
1320 scienceExposure.getWcs(), templateExposure, destBBox=scienceExposure.getBBox())
1321 templateExposure.setPsf(psfWarped)
1323 raise RuntimeError(
"Input images are not registered. Consider setting doWarping=True.")
1325 config = self.config.zogyConfig
1327 results = task.run(scienceExposure, templateExposure)
1328 results.warpedExposure = templateExposure
1332 raise NotImplementedError
1335 raise NotImplementedError
1338 subtractAlgorithmRegistry.register(
'zogy', ZogyImagePsfMatchTask)
def _validateWcs(self, templateExposure, scienceExposure)
def subtractMaskedImages(self, templateExposure, scienceExposure, *args)
def __init__(self, *args, **kwargs)
def run(self, scienceExposure, templateExposure, doWarping=True)
def subtractExposures(self, templateExposure, scienceExposure, *args)
def generateGrid(imageBox, minEdgeDims, innerBoxDims, minTotalDims=None, powerOfTwo=False)
def padCenterOriginArray(A, newShape, useInverse=False, dtype=None)
def subtractImageMean(image, mask, statsControl)
def calculateFourierDiffim(self, psf1, im1, varPlane1, F1, varMean1, psf2, im2, varPlane2, F2, varMean2, calculateScore=True)
def makeKernelPsfFromArray(A)
def calculateMaskPlane(mask1, mask2, effPsf1=None, effPsf2=None)
def run(self, exposure1, exposure2, calculateScore=True)
def prepareFullExposure(self, exposure1, exposure2, correctBackground=False)
def estimateMatchingKernelSize(psf1, psf2)
def removeNonFinitePixels(self, imgArr)
def splitBorder(innerBox, outerBox)
def initializeSubImage(self, fullExp, innerBox, outerBox, noiseMeanVar, useNoise=True)
def makeSpatialPsf(self, gridPsfs)
def padAndFftImage(self, imgArr)
def checkCentroids(self, psfArr1, psfArr2)
def pasteSubDiffImg(self, ftDiff, diffExp, scoreExp=None)
def finishResultExposures(self, diffExp, scoreExp=None)
def prepareSubExposure(self, localCutout, psf1=None, psf2=None, sig1=None, sig2=None)
def computePsfAtCenter(exposure)
def inverseFftAndCropImage(self, imgArr, origSize, filtInf=None, filtNaN=None, dtype=None)
def _computeVarianceMean(self, exposure)
std::shared_ptr< TransformPoint2ToPoint2 > makeWcsPairTransform(SkyWcs const &src, SkyWcs const &dst)
Statistics makeStatistics(lsst::afw::image::Image< Pixel > const &img, lsst::afw::image::Mask< image::MaskPixel > const &msk, int const flags, StatisticsControl const &sctrl=StatisticsControl())