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961

# This file is part of ip_diffim. 

# 

# Developed for the LSST Data Management System. 

# This product includes software developed by the LSST Project 

# (https://www.lsst.org). 

# See the COPYRIGHT file at the top-level directory of this distribution 

# for details of code ownership. 

# 

# This program is free software: you can redistribute it and/or modify 

# it under the terms of the GNU General Public License as published by 

# the Free Software Foundation, either version 3 of the License, or 

# (at your option) any later version. 

# 

# This program is distributed in the hope that it will be useful, 

# but WITHOUT ANY WARRANTY; without even the implied warranty of 

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

# GNU General Public License for more details. 

# 

# You should have received a copy of the GNU General Public License 

# along with this program. If not, see <https://www.gnu.org/licenses/>. 

 

import numpy as np 

 

import lsst.daf.base as dafBase 

import lsst.pex.config as pexConfig 

import lsst.afw.detection as afwDetect 

import lsst.afw.image as afwImage 

import lsst.afw.math as afwMath 

import lsst.afw.geom as afwGeom 

import lsst.afw.table as afwTable 

import lsst.pipe.base as pipeBase 

from lsst.meas.algorithms import SourceDetectionTask, SubtractBackgroundTask, WarpedPsf 

from lsst.meas.base import SingleFrameMeasurementTask 

from .makeKernelBasisList import makeKernelBasisList 

from .psfMatch import PsfMatchTask, PsfMatchConfigDF, PsfMatchConfigAL 

from . import utils as diffimUtils 

from . import diffimLib 

from . import diffimTools 

import lsst.afw.display as afwDisplay 

 

__all__ = ["ImagePsfMatchConfig", "ImagePsfMatchTask", "subtractAlgorithmRegistry"] 

 

sigma2fwhm = 2.*np.sqrt(2.*np.log(2.)) 

 

 

class ImagePsfMatchConfig(pexConfig.Config): 

"""Configuration for image-to-image Psf matching. 

""" 

kernel = pexConfig.ConfigChoiceField( 

doc="kernel type", 

typemap=dict( 

AL=PsfMatchConfigAL, 

DF=PsfMatchConfigDF 

), 

default="AL", 

) 

selectDetection = pexConfig.ConfigurableField( 

target=SourceDetectionTask, 

doc="Initial detections used to feed stars to kernel fitting", 

) 

selectMeasurement = pexConfig.ConfigurableField( 

target=SingleFrameMeasurementTask, 

doc="Initial measurements used to feed stars to kernel fitting", 

) 

 

def setDefaults(self): 

# High sigma detections only 

self.selectDetection.reEstimateBackground = False 

self.selectDetection.thresholdValue = 10.0 

 

# Minimal set of measurments for star selection 

self.selectMeasurement.algorithms.names.clear() 

self.selectMeasurement.algorithms.names = ('base_SdssCentroid', 'base_PsfFlux', 'base_PixelFlags', 

'base_SdssShape', 'base_GaussianFlux', 'base_SkyCoord') 

self.selectMeasurement.slots.modelFlux = None 

self.selectMeasurement.slots.apFlux = None 

self.selectMeasurement.slots.calibFlux = None 

 

 

class ImagePsfMatchTask(PsfMatchTask): 

"""Psf-match two MaskedImages or Exposures using the sources in the images. 

 

Parameters 

---------- 

args : 

Arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__ 

kwargs : 

Keyword arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__ 

 

Notes 

----- 

Upon initialization, the kernel configuration is defined by self.config.kernel.active. 

The task creates an lsst.afw.math.Warper from the subConfig self.config.kernel.active.warpingConfig. 

A schema for the selection and measurement of candidate lsst.ip.diffim.KernelCandidates is 

defined, and used to initize subTasks selectDetection (for candidate detection) and selectMeasurement 

(for candidate measurement). 

 

Description 

 

Build a Psf-matching kernel using two input images, either as MaskedImages (in which case they need 

to be astrometrically aligned) or Exposures (in which case astrometric alignment will happen by 

default but may be turned off). This requires a list of input Sources which may be provided 

by the calling Task; if not, the Task will perform a coarse source detection 

and selection for this purpose. Sources are vetted for signal-to-noise and masked pixels 

(in both the template and science image), and substamps around each acceptable 

source are extracted and used to create an instance of KernelCandidate. 

Each KernelCandidate is then placed within a lsst.afw.math.SpatialCellSet, which is used by an ensemble of 

lsst.afw.math.CandidateVisitor instances to build the Psf-matching kernel. These visitors include, in 

the order that they are called: BuildSingleKernelVisitor, KernelSumVisitor, BuildSpatialKernelVisitor, 

and AssessSpatialKernelVisitor. 

 

Sigma clipping of KernelCandidates is performed as follows: 

 

- BuildSingleKernelVisitor, using the substamp diffim residuals from the per-source kernel fit, 

if PsfMatchConfig.singleKernelClipping is True 

- KernelSumVisitor, using the mean and standard deviation of the kernel sum from all candidates, 

if PsfMatchConfig.kernelSumClipping is True 

- AssessSpatialKernelVisitor, using the substamp diffim ressiduals from the spatial kernel fit, 

if PsfMatchConfig.spatialKernelClipping is True 

 

The actual solving for the kernel (and differential background model) happens in 

lsst.ip.diffim.PsfMatchTask._solve. This involves a loop over the SpatialCellSet that first builds the 

per-candidate matching kernel for the requested number of KernelCandidates per cell 

(PsfMatchConfig.nStarPerCell). The quality of this initial per-candidate difference image is examined, 

using moments of the pixel residuals in the difference image normalized by the square root of the variance 

(i.e. sigma); ideally this should follow a normal (0, 1) distribution, 

but the rejection thresholds are set 

by the config (PsfMatchConfig.candidateResidualMeanMax and PsfMatchConfig.candidateResidualStdMax). 

All candidates that pass this initial build are then examined en masse to find the 

mean/stdev of the kernel sums across all candidates. 

Objects that are significantly above or below the mean, 

typically due to variability or sources that are saturated in one image but not the other, 

are also rejected.This threshold is defined by PsfMatchConfig.maxKsumSigma. 

Finally, a spatial model is built using all currently-acceptable candidates, 

and the spatial model used to derive a second set of (spatial) residuals 

which are again used to reject bad candidates, using the same thresholds as above. 

 

Invoking the Task 

 

There is no run() method for this Task. Instead there are 4 methods that 

may be used to invoke the Psf-matching. These are 

`~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchMaskedImages`, 

`~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractMaskedImages`, 

`~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchExposures`, and 

`~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractExposures`. 

 

The methods that operate on lsst.afw.image.MaskedImage require that the images already be astrometrically 

aligned, and are the same shape. The methods that operate on lsst.afw.image.Exposure allow for the 

input images to be misregistered and potentially be different sizes; by default a 

lsst.afw.math.LanczosWarpingKernel is used to perform the astrometric alignment. The methods 

that "match" images return a Psf-matched image, while the methods that "subtract" images 

return a Psf-matched and template subtracted image. 

 

See each method's returned lsst.pipe.base.Struct for more details. 

 

Debug variables 

 

The lsst.pipe.base.cmdLineTask.CmdLineTask command line task interface supports a 

flag -d/--debug to import debug.py from your PYTHONPATH. The relevant contents of debug.py 

for this Task include: 

 

.. code-block:: py 

 

import sys 

import lsstDebug 

def DebugInfo(name): 

di = lsstDebug.getInfo(name) 

if name == "lsst.ip.diffim.psfMatch": 

di.display = True # enable debug output 

di.maskTransparency = 80 # display mask transparency 

di.displayCandidates = True # show all the candidates and residuals 

di.displayKernelBasis = False # show kernel basis functions 

di.displayKernelMosaic = True # show kernel realized across the image 

di.plotKernelSpatialModel = False # show coefficients of spatial model 

di.showBadCandidates = True # show the bad candidates (red) along with good (green) 

elif name == "lsst.ip.diffim.imagePsfMatch": 

di.display = True # enable debug output 

di.maskTransparency = 30 # display mask transparency 

di.displayTemplate = True # show full (remapped) template 

di.displaySciIm = True # show science image to match to 

di.displaySpatialCells = True # show spatial cells 

di.displayDiffIm = True # show difference image 

di.showBadCandidates = True # show the bad candidates (red) along with good (green) 

elif name == "lsst.ip.diffim.diaCatalogSourceSelector": 

di.display = False # enable debug output 

di.maskTransparency = 30 # display mask transparency 

di.displayExposure = True # show exposure with candidates indicated 

di.pauseAtEnd = False # pause when done 

return di 

lsstDebug.Info = DebugInfo 

lsstDebug.frame = 1 

 

Note that if you want addional logging info, you may add to your scripts: 

 

.. code-block:: py 

 

import lsst.log.utils as logUtils 

logUtils.traceSetAt("ip.diffim", 4) 

 

Examples 

-------- 

A complete example of using ImagePsfMatchTask 

 

This code is imagePsfMatchTask.py in the examples directory, and can be run as e.g. 

 

.. code-block:: none 

 

examples/imagePsfMatchTask.py --debug 

examples/imagePsfMatchTask.py --debug --mode="matchExposures" 

examples/imagePsfMatchTask.py --debug --template /path/to/templateExp.fits 

--science /path/to/scienceExp.fits 

 

Create a subclass of ImagePsfMatchTask that allows us to either match exposures, or subtract exposures: 

 

.. code-block:: none 

 

class MyImagePsfMatchTask(ImagePsfMatchTask): 

 

def __init__(self, args, kwargs): 

ImagePsfMatchTask.__init__(self, args, kwargs) 

 

def run(self, templateExp, scienceExp, mode): 

if mode == "matchExposures": 

return self.matchExposures(templateExp, scienceExp) 

elif mode == "subtractExposures": 

return self.subtractExposures(templateExp, scienceExp) 

 

And allow the user the freedom to either run the script in default mode, 

or point to their own images on disk. 

Note that these images must be readable as an lsst.afw.image.Exposure. 

 

We have enabled some minor display debugging in this script via the --debug option. However, if you 

have an lsstDebug debug.py in your PYTHONPATH you will get additional debugging displays. The following 

block checks for this script: 

 

.. code-block:: py 

 

if args.debug: 

try: 

import debug 

# Since I am displaying 2 images here, set the starting frame number for the LSST debug LSST 

debug.lsstDebug.frame = 3 

except ImportError as e: 

print(e, file=sys.stderr) 

 

Finally, we call a run method that we define below. 

First set up a Config and modify some of the parameters. 

E.g. use an "Alard-Lupton" sum-of-Gaussian basis, 

fit for a differential background, and use low order spatial 

variation in the kernel and background: 

 

.. code-block:: py 

 

def run(args): 

# 

# Create the Config and use sum of gaussian basis 

# 

config = ImagePsfMatchTask.ConfigClass() 

config.kernel.name = "AL" 

config.kernel.active.fitForBackground = True 

config.kernel.active.spatialKernelOrder = 1 

config.kernel.active.spatialBgOrder = 0 

 

Make sure the images (if any) that were sent to the script exist on disk and are readable. If no images 

are sent, make some fake data up for the sake of this example script (have a look at the code if you want 

more details on generateFakeImages): 

 

.. code-block:: py 

 

# Run the requested method of the Task 

if args.template is not None and args.science is not None: 

if not os.path.isfile(args.template): 

raise Exception("Template image %s does not exist" % (args.template)) 

if not os.path.isfile(args.science): 

raise Exception("Science image %s does not exist" % (args.science)) 

try: 

templateExp = afwImage.ExposureF(args.template) 

except Exception as e: 

raise Exception("Cannot read template image %s" % (args.template)) 

try: 

scienceExp = afwImage.ExposureF(args.science) 

except Exception as e: 

raise Exception("Cannot read science image %s" % (args.science)) 

else: 

templateExp, scienceExp = generateFakeImages() 

config.kernel.active.sizeCellX = 128 

config.kernel.active.sizeCellY = 128 

 

Create and run the Task: 

 

.. code-block:: py 

 

# Create the Task 

psfMatchTask = MyImagePsfMatchTask(config=config) 

# Run the Task 

result = psfMatchTask.run(templateExp, scienceExp, args.mode) 

 

And finally provide some optional debugging displays: 

 

.. code-block:: py 

 

if args.debug: 

# See if the LSST debug has incremented the frame number; if not start with frame 3 

try: 

frame = debug.lsstDebug.frame + 1 

except Exception: 

frame = 3 

afwDisplay.Display(frame=frame).mtv(result.matchedExposure, 

title="Example script: Matched Template Image") 

if "subtractedExposure" in result.getDict(): 

afwDisplay.Display(frame=frame + 1).mtv(result.subtractedExposure, 

title="Example script: Subtracted Image") 

""" 

 

ConfigClass = ImagePsfMatchConfig 

 

def __init__(self, *args, **kwargs): 

"""Create the ImagePsfMatchTask. 

""" 

PsfMatchTask.__init__(self, *args, **kwargs) 

self.kConfig = self.config.kernel.active 

self._warper = afwMath.Warper.fromConfig(self.kConfig.warpingConfig) 

# the background subtraction task uses a config from an unusual location, 

# so cannot easily be constructed with makeSubtask 

self.background = SubtractBackgroundTask(config=self.kConfig.afwBackgroundConfig, name="background", 

parentTask=self) 

self.selectSchema = afwTable.SourceTable.makeMinimalSchema() 

self.selectAlgMetadata = dafBase.PropertyList() 

self.makeSubtask("selectDetection", schema=self.selectSchema) 

self.makeSubtask("selectMeasurement", schema=self.selectSchema, algMetadata=self.selectAlgMetadata) 

 

def getFwhmPix(self, psf): 

"""Return the FWHM in pixels of a Psf. 

""" 

sigPix = psf.computeShape().getDeterminantRadius() 

return sigPix*sigma2fwhm 

 

@pipeBase.timeMethod 

def matchExposures(self, templateExposure, scienceExposure, 

templateFwhmPix=None, scienceFwhmPix=None, 

candidateList=None, doWarping=True, convolveTemplate=True): 

"""Warp and PSF-match an exposure to the reference. 

 

Do the following, in order: 

 

- Warp templateExposure to match scienceExposure, 

if doWarping True and their WCSs do not already match 

- Determine a PSF matching kernel and differential background model 

that matches templateExposure to scienceExposure 

- Convolve templateExposure by PSF matching kernel 

 

Parameters 

---------- 

templateExposure : `lsst.afw.image.Exposure` 

Exposure to warp and PSF-match to the reference masked image 

scienceExposure : `lsst.afw.image.Exposure` 

Exposure whose WCS and PSF are to be matched to 

templateFwhmPix :`float` 

FWHM (in pixels) of the Psf in the template image (image to convolve) 

scienceFwhmPix : `float` 

FWHM (in pixels) of the Psf in the science image 

candidateList : `list`, optional 

a list of footprints/maskedImages for kernel candidates; 

if `None` then source detection is run. 

 

- Currently supported: list of Footprints or measAlg.PsfCandidateF 

 

doWarping : `bool` 

what to do if ``templateExposure`` and ``scienceExposure`` WCSs do not match: 

 

- if `True` then warp ``templateExposure`` to match ``scienceExposure`` 

- if `False` then raise an Exception 

 

convolveTemplate : `bool` 

Whether to convolve the template image or the science image: 

 

- if `True`, ``templateExposure`` is warped if doWarping, 

``templateExposure`` is convolved 

- if `False`, ``templateExposure`` is warped if doWarping, 

``scienceExposure`` is convolved 

 

Returns 

------- 

results : `lsst.pipe.base.Struct` 

An `lsst.pipe.base.Struct` containing these fields: 

 

- ``matchedImage`` : the PSF-matched exposure = 

Warped ``templateExposure`` convolved by psfMatchingKernel. This has: 

 

- the same parent bbox, Wcs and PhotoCalib as scienceExposure 

- the same filter as templateExposure 

- no Psf (because the PSF-matching process does not compute one) 

 

- ``psfMatchingKernel`` : the PSF matching kernel 

- ``backgroundModel`` : differential background model 

- ``kernelCellSet`` : SpatialCellSet used to solve for the PSF matching kernel 

 

Raises 

------ 

RuntimeError 

Raised if doWarping is False and ``templateExposure`` and 

``scienceExposure`` WCSs do not match 

""" 

if not self._validateWcs(templateExposure, scienceExposure): 

if doWarping: 

self.log.info("Astrometrically registering template to science image") 

templatePsf = templateExposure.getPsf() 

# Warp PSF before overwriting exposure 

xyTransform = afwGeom.makeWcsPairTransform(templateExposure.getWcs(), 

scienceExposure.getWcs()) 

psfWarped = WarpedPsf(templatePsf, xyTransform) 

templateExposure = self._warper.warpExposure(scienceExposure.getWcs(), 

templateExposure, 

destBBox=scienceExposure.getBBox()) 

templateExposure.setPsf(psfWarped) 

else: 

self.log.error("ERROR: Input images not registered") 

raise RuntimeError("Input images not registered") 

 

if templateFwhmPix is None: 

if not templateExposure.hasPsf(): 

self.log.warn("No estimate of Psf FWHM for template image") 

else: 

templateFwhmPix = self.getFwhmPix(templateExposure.getPsf()) 

self.log.info("templateFwhmPix: {}".format(templateFwhmPix)) 

 

if scienceFwhmPix is None: 

if not scienceExposure.hasPsf(): 

self.log.warn("No estimate of Psf FWHM for science image") 

else: 

scienceFwhmPix = self.getFwhmPix(scienceExposure.getPsf()) 

self.log.info("scienceFwhmPix: {}".format(scienceFwhmPix)) 

 

kernelSize = makeKernelBasisList(self.kConfig, templateFwhmPix, scienceFwhmPix)[0].getWidth() 

candidateList = self.makeCandidateList(templateExposure, scienceExposure, kernelSize, candidateList) 

 

if convolveTemplate: 

results = self.matchMaskedImages( 

templateExposure.getMaskedImage(), scienceExposure.getMaskedImage(), candidateList, 

templateFwhmPix=templateFwhmPix, scienceFwhmPix=scienceFwhmPix) 

else: 

results = self.matchMaskedImages( 

scienceExposure.getMaskedImage(), templateExposure.getMaskedImage(), candidateList, 

templateFwhmPix=scienceFwhmPix, scienceFwhmPix=templateFwhmPix) 

 

psfMatchedExposure = afwImage.makeExposure(results.matchedImage, scienceExposure.getWcs()) 

psfMatchedExposure.setFilter(templateExposure.getFilter()) 

psfMatchedExposure.setPhotoCalib(scienceExposure.getPhotoCalib()) 

results.warpedExposure = templateExposure 

results.matchedExposure = psfMatchedExposure 

return results 

 

@pipeBase.timeMethod 

def matchMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList, 

templateFwhmPix=None, scienceFwhmPix=None): 

"""PSF-match a MaskedImage (templateMaskedImage) to a reference MaskedImage (scienceMaskedImage). 

 

Do the following, in order: 

 

- Determine a PSF matching kernel and differential background model 

that matches templateMaskedImage to scienceMaskedImage 

- Convolve templateMaskedImage by the PSF matching kernel 

 

Parameters 

---------- 

templateMaskedImage : `lsst.afw.image.MaskedImage` 

masked image to PSF-match to the reference masked image; 

must be warped to match the reference masked image 

scienceMaskedImage : `lsst.afw.image.MaskedImage` 

maskedImage whose PSF is to be matched to 

templateFwhmPix : `float` 

FWHM (in pixels) of the Psf in the template image (image to convolve) 

scienceFwhmPix : `float` 

FWHM (in pixels) of the Psf in the science image 

candidateList : `list`, optional 

A list of footprints/maskedImages for kernel candidates; 

if `None` then source detection is run. 

 

- Currently supported: list of Footprints or measAlg.PsfCandidateF 

 

Returns 

------- 

result : `callable` 

An `lsst.pipe.base.Struct` containing these fields: 

 

- psfMatchedMaskedImage: the PSF-matched masked image = 

``templateMaskedImage`` convolved with psfMatchingKernel. 

This has the same xy0, dimensions and wcs as ``scienceMaskedImage``. 

- psfMatchingKernel: the PSF matching kernel 

- backgroundModel: differential background model 

- kernelCellSet: SpatialCellSet used to solve for the PSF matching kernel 

 

Raises 

------ 

RuntimeError 

Raised if input images have different dimensions 

""" 

import lsstDebug 

display = lsstDebug.Info(__name__).display 

displayTemplate = lsstDebug.Info(__name__).displayTemplate 

displaySciIm = lsstDebug.Info(__name__).displaySciIm 

displaySpatialCells = lsstDebug.Info(__name__).displaySpatialCells 

maskTransparency = lsstDebug.Info(__name__).maskTransparency 

if not maskTransparency: 

maskTransparency = 0 

if display: 

afwDisplay.setDefaultMaskTransparency(maskTransparency) 

 

if not candidateList: 

raise RuntimeError("Candidate list must be populated by makeCandidateList") 

 

if not self._validateSize(templateMaskedImage, scienceMaskedImage): 

self.log.error("ERROR: Input images different size") 

raise RuntimeError("Input images different size") 

 

if display and displayTemplate: 

disp = afwDisplay.Display(frame=lsstDebug.frame) 

disp.mtv(templateMaskedImage, title="Image to convolve") 

lsstDebug.frame += 1 

 

if display and displaySciIm: 

disp = afwDisplay.Display(frame=lsstDebug.frame) 

disp.mtv(scienceMaskedImage, title="Image to not convolve") 

lsstDebug.frame += 1 

 

kernelCellSet = self._buildCellSet(templateMaskedImage, 

scienceMaskedImage, 

candidateList) 

 

if display and displaySpatialCells: 

diffimUtils.showKernelSpatialCells(scienceMaskedImage, kernelCellSet, 

symb="o", ctype=afwDisplay.CYAN, ctypeUnused=afwDisplay.YELLOW, 

ctypeBad=afwDisplay.RED, size=4, frame=lsstDebug.frame, 

title="Image to not convolve") 

lsstDebug.frame += 1 

 

if templateFwhmPix and scienceFwhmPix: 

self.log.info("Matching Psf FWHM %.2f -> %.2f pix", templateFwhmPix, scienceFwhmPix) 

 

if self.kConfig.useBicForKernelBasis: 

tmpKernelCellSet = self._buildCellSet(templateMaskedImage, 

scienceMaskedImage, 

candidateList) 

nbe = diffimTools.NbasisEvaluator(self.kConfig, templateFwhmPix, scienceFwhmPix) 

bicDegrees = nbe(tmpKernelCellSet, self.log) 

basisList = makeKernelBasisList(self.kConfig, templateFwhmPix, scienceFwhmPix, 

alardDegGauss=bicDegrees[0], metadata=self.metadata) 

del tmpKernelCellSet 

else: 

basisList = makeKernelBasisList(self.kConfig, templateFwhmPix, scienceFwhmPix, 

metadata=self.metadata) 

 

spatialSolution, psfMatchingKernel, backgroundModel = self._solve(kernelCellSet, basisList) 

 

psfMatchedMaskedImage = afwImage.MaskedImageF(templateMaskedImage.getBBox()) 

doNormalize = False 

afwMath.convolve(psfMatchedMaskedImage, templateMaskedImage, psfMatchingKernel, doNormalize) 

return pipeBase.Struct( 

matchedImage=psfMatchedMaskedImage, 

psfMatchingKernel=psfMatchingKernel, 

backgroundModel=backgroundModel, 

kernelCellSet=kernelCellSet, 

) 

 

@pipeBase.timeMethod 

def subtractExposures(self, templateExposure, scienceExposure, 

templateFwhmPix=None, scienceFwhmPix=None, 

candidateList=None, doWarping=True, convolveTemplate=True): 

"""Register, Psf-match and subtract two Exposures. 

 

Do the following, in order: 

 

- Warp templateExposure to match scienceExposure, if their WCSs do not already match 

- Determine a PSF matching kernel and differential background model 

that matches templateExposure to scienceExposure 

- PSF-match templateExposure to scienceExposure 

- Compute subtracted exposure (see return values for equation). 

 

Parameters 

---------- 

templateExposure : `lsst.afw.image.Exposure` 

Exposure to PSF-match to scienceExposure 

scienceExposure : `lsst.afw.image.Exposure` 

Reference Exposure 

templateFwhmPix : `float` 

FWHM (in pixels) of the Psf in the template image (image to convolve) 

scienceFwhmPix : `float` 

FWHM (in pixels) of the Psf in the science image 

candidateList : `list`, optional 

A list of footprints/maskedImages for kernel candidates; 

if `None` then source detection is run. 

 

- Currently supported: list of Footprints or measAlg.PsfCandidateF 

 

doWarping : `bool` 

What to do if ``templateExposure``` and ``scienceExposure`` WCSs do 

not match: 

 

- if `True` then warp ``templateExposure`` to match ``scienceExposure`` 

- if `False` then raise an Exception 

 

convolveTemplate : `bool` 

Convolve the template image or the science image 

 

- if `True`, ``templateExposure`` is warped if doWarping, 

``templateExposure`` is convolved 

- if `False`, ``templateExposure`` is warped if doWarping, 

``scienceExposure is`` convolved 

 

Returns 

------- 

result : `lsst.pipe.base.Struct` 

An `lsst.pipe.base.Struct` containing these fields: 

 

- ``subtractedExposure`` : subtracted Exposure 

scienceExposure - (matchedImage + backgroundModel) 

- ``matchedImage`` : ``templateExposure`` after warping to match 

``templateExposure`` (if doWarping true), 

and convolving with psfMatchingKernel 

- ``psfMatchingKernel`` : PSF matching kernel 

- ``backgroundModel`` : differential background model 

- ``kernelCellSet`` : SpatialCellSet used to determine PSF matching kernel 

""" 

results = self.matchExposures( 

templateExposure=templateExposure, 

scienceExposure=scienceExposure, 

templateFwhmPix=templateFwhmPix, 

scienceFwhmPix=scienceFwhmPix, 

candidateList=candidateList, 

doWarping=doWarping, 

convolveTemplate=convolveTemplate 

) 

 

subtractedExposure = afwImage.ExposureF(scienceExposure, True) 

if convolveTemplate: 

subtractedMaskedImage = subtractedExposure.getMaskedImage() 

subtractedMaskedImage -= results.matchedExposure.getMaskedImage() 

subtractedMaskedImage -= results.backgroundModel 

else: 

subtractedExposure.setMaskedImage(results.warpedExposure.getMaskedImage()) 

subtractedMaskedImage = subtractedExposure.getMaskedImage() 

subtractedMaskedImage -= results.matchedExposure.getMaskedImage() 

subtractedMaskedImage -= results.backgroundModel 

 

# Preserve polarity of differences 

subtractedMaskedImage *= -1 

 

# Place back on native photometric scale 

subtractedMaskedImage /= results.psfMatchingKernel.computeImage( 

afwImage.ImageD(results.psfMatchingKernel.getDimensions()), False) 

 

import lsstDebug 

display = lsstDebug.Info(__name__).display 

displayDiffIm = lsstDebug.Info(__name__).displayDiffIm 

maskTransparency = lsstDebug.Info(__name__).maskTransparency 

if not maskTransparency: 

maskTransparency = 0 

if display: 

afwDisplay.setDefaultMaskTransparency(maskTransparency) 

if display and displayDiffIm: 

disp = afwDisplay.Display(frame=lsstDebug.frame) 

disp.mtv(templateExposure, title="Template") 

lsstDebug.frame += 1 

disp = afwDisplay.Display(frame=lsstDebug.frame) 

disp.mtv(results.matchedExposure, title="Matched template") 

lsstDebug.frame += 1 

disp = afwDisplay.Display(frame=lsstDebug.frame) 

disp.mtv(scienceExposure, title="Science Image") 

lsstDebug.frame += 1 

disp = afwDisplay.Display(frame=lsstDebug.frame) 

disp.mtv(subtractedExposure, title="Difference Image") 

lsstDebug.frame += 1 

 

results.subtractedExposure = subtractedExposure 

return results 

 

@pipeBase.timeMethod 

def subtractMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList, 

templateFwhmPix=None, scienceFwhmPix=None): 

"""Psf-match and subtract two MaskedImages. 

 

Do the following, in order: 

 

- PSF-match templateMaskedImage to scienceMaskedImage 

- Determine the differential background 

- Return the difference: scienceMaskedImage 

((warped templateMaskedImage convolved with psfMatchingKernel) + backgroundModel) 

 

Parameters 

---------- 

templateMaskedImage : `lsst.afw.image.MaskedImage` 

MaskedImage to PSF-match to ``scienceMaskedImage`` 

scienceMaskedImage : `lsst.afw.image.MaskedImage` 

Reference MaskedImage 

templateFwhmPix : `float` 

FWHM (in pixels) of the Psf in the template image (image to convolve) 

scienceFwhmPix : `float` 

FWHM (in pixels) of the Psf in the science image 

candidateList : `list`, optional 

A list of footprints/maskedImages for kernel candidates; 

if `None` then source detection is run. 

 

- Currently supported: list of Footprints or measAlg.PsfCandidateF 

 

Returns 

------- 

results : `lsst.pipe.base.Struct` 

An `lsst.pipe.base.Struct` containing these fields: 

 

- ``subtractedMaskedImage`` : ``scienceMaskedImage`` - (matchedImage + backgroundModel) 

- ``matchedImage`` : templateMaskedImage convolved with psfMatchingKernel 

- `psfMatchingKernel`` : PSF matching kernel 

- ``backgroundModel`` : differential background model 

- ``kernelCellSet`` : SpatialCellSet used to determine PSF matching kernel 

 

""" 

if not candidateList: 

raise RuntimeError("Candidate list must be populated by makeCandidateList") 

 

results = self.matchMaskedImages( 

templateMaskedImage=templateMaskedImage, 

scienceMaskedImage=scienceMaskedImage, 

candidateList=candidateList, 

templateFwhmPix=templateFwhmPix, 

scienceFwhmPix=scienceFwhmPix, 

) 

 

subtractedMaskedImage = afwImage.MaskedImageF(scienceMaskedImage, True) 

subtractedMaskedImage -= results.matchedImage 

subtractedMaskedImage -= results.backgroundModel 

results.subtractedMaskedImage = subtractedMaskedImage 

 

import lsstDebug 

display = lsstDebug.Info(__name__).display 

displayDiffIm = lsstDebug.Info(__name__).displayDiffIm 

maskTransparency = lsstDebug.Info(__name__).maskTransparency 

if not maskTransparency: 

maskTransparency = 0 

if display: 

afwDisplay.setDefaultMaskTransparency(maskTransparency) 

if display and displayDiffIm: 

disp = afwDisplay.Display(frame=lsstDebug.frame) 

disp.mtv(subtractedMaskedImage, title="Subtracted masked image") 

lsstDebug.frame += 1 

 

return results 

 

def getSelectSources(self, exposure, sigma=None, doSmooth=True, idFactory=None): 

"""Get sources to use for Psf-matching. 

 

This method runs detection and measurement on an exposure. 

The returned set of sources will be used as candidates for 

Psf-matching. 

 

Parameters 

---------- 

exposure : `lsst.afw.image.Exposure` 

Exposure on which to run detection/measurement 

sigma : `float` 

Detection threshold 

doSmooth : `bool` 

Whether or not to smooth the Exposure with Psf before detection 

idFactory : 

Factory for the generation of Source ids 

 

Returns 

------- 

selectSources : 

source catalog containing candidates for the Psf-matching 

""" 

if idFactory: 

table = afwTable.SourceTable.make(self.selectSchema, idFactory) 

else: 

table = afwTable.SourceTable.make(self.selectSchema) 

mi = exposure.getMaskedImage() 

 

imArr = mi.getImage().getArray() 

maskArr = mi.getMask().getArray() 

miArr = np.ma.masked_array(imArr, mask=maskArr) 

try: 

bkgd = self.background.fitBackground(mi).getImageF() 

except Exception: 

self.log.warn("Failed to get background model. Falling back to median background estimation") 

bkgd = np.ma.extras.median(miArr) 

 

# Take off background for detection 

mi -= bkgd 

try: 

table.setMetadata(self.selectAlgMetadata) 

detRet = self.selectDetection.makeSourceCatalog( 

table=table, 

exposure=exposure, 

sigma=sigma, 

doSmooth=doSmooth 

) 

selectSources = detRet.sources 

self.selectMeasurement.run(measCat=selectSources, exposure=exposure) 

finally: 

# Put back on the background in case it is needed down stream 

mi += bkgd 

del bkgd 

return selectSources 

 

def makeCandidateList(self, templateExposure, scienceExposure, kernelSize, candidateList=None): 

"""Make a list of acceptable KernelCandidates. 

 

Accept or generate a list of candidate sources for 

Psf-matching, and examine the Mask planes in both of the 

images for indications of bad pixels 

 

Parameters 

---------- 

templateExposure : `lsst.afw.image.Exposure` 

Exposure that will be convolved 

scienceExposure : `lsst.afw.image.Exposure` 

Exposure that will be matched-to 

kernelSize : `float` 

Dimensions of the Psf-matching Kernel, used to grow detection footprints 

candidateList : `list`, optional 

List of Sources to examine. Elements must be of type afw.table.Source 

or a type that wraps a Source and has a getSource() method, such as 

meas.algorithms.PsfCandidateF. 

 

Returns 

------- 

candidateList : `list` of `dict` 

A list of dicts having a "source" and "footprint" 

field for the Sources deemed to be appropriate for Psf 

matching 

""" 

if candidateList is None: 

candidateList = self.getSelectSources(scienceExposure) 

 

if len(candidateList) < 1: 

raise RuntimeError("No candidates in candidateList") 

 

listTypes = set(type(x) for x in candidateList) 

if len(listTypes) > 1: 

raise RuntimeError("Candidate list contains mixed types: %s" % [l for l in listTypes]) 

 

if not isinstance(candidateList[0], afwTable.SourceRecord): 

try: 

candidateList[0].getSource() 

except Exception as e: 

raise RuntimeError("Candidate List is of type: %s. " % (type(candidateList[0])) + 

"Can only make candidate list from list of afwTable.SourceRecords, " + 

"measAlg.PsfCandidateF or other type with a getSource() method: %s" % (e)) 

candidateList = [c.getSource() for c in candidateList] 

 

candidateList = diffimTools.sourceToFootprintList(candidateList, 

templateExposure, scienceExposure, 

kernelSize, 

self.kConfig.detectionConfig, 

self.log) 

if len(candidateList) == 0: 

raise RuntimeError("Cannot find any objects suitable for KernelCandidacy") 

 

return candidateList 

 

def _adaptCellSize(self, candidateList): 

"""NOT IMPLEMENTED YET. 

""" 

return self.kConfig.sizeCellX, self.kConfig.sizeCellY 

 

def _buildCellSet(self, templateMaskedImage, scienceMaskedImage, candidateList): 

"""Build a SpatialCellSet for use with the solve method. 

 

Parameters 

---------- 

templateMaskedImage : `lsst.afw.image.MaskedImage` 

MaskedImage to PSF-matched to scienceMaskedImage 

scienceMaskedImage : `lsst.afw.image.MaskedImage` 

Reference MaskedImage 

candidateList : `list` 

A list of footprints/maskedImages for kernel candidates; 

 

- Currently supported: list of Footprints or measAlg.PsfCandidateF 

 

Returns 

------- 

kernelCellSet : `lsst.afw.math.SpatialCellSet` 

a SpatialCellSet for use with self._solve 

""" 

if not candidateList: 

raise RuntimeError("Candidate list must be populated by makeCandidateList") 

 

sizeCellX, sizeCellY = self._adaptCellSize(candidateList) 

 

# Object to store the KernelCandidates for spatial modeling 

kernelCellSet = afwMath.SpatialCellSet(templateMaskedImage.getBBox(), 

sizeCellX, sizeCellY) 

 

policy = pexConfig.makePolicy(self.kConfig) 

# Place candidates within the spatial grid 

for cand in candidateList: 

if isinstance(cand, afwDetect.Footprint): 

bbox = cand.getBBox() 

else: 

bbox = cand['footprint'].getBBox() 

tmi = afwImage.MaskedImageF(templateMaskedImage, bbox) 

smi = afwImage.MaskedImageF(scienceMaskedImage, bbox) 

 

if not isinstance(cand, afwDetect.Footprint): 

if 'source' in cand: 

cand = cand['source'] 

xPos = cand.getCentroid()[0] 

yPos = cand.getCentroid()[1] 

cand = diffimLib.makeKernelCandidate(xPos, yPos, tmi, smi, policy) 

 

self.log.debug("Candidate %d at %f, %f", cand.getId(), cand.getXCenter(), cand.getYCenter()) 

kernelCellSet.insertCandidate(cand) 

 

return kernelCellSet 

 

def _validateSize(self, templateMaskedImage, scienceMaskedImage): 

"""Return True if two image-like objects are the same size. 

""" 

return templateMaskedImage.getDimensions() == scienceMaskedImage.getDimensions() 

 

def _validateWcs(self, templateExposure, scienceExposure): 

"""Return True if the WCS of the two Exposures have the same origin and extent. 

""" 

templateWcs = templateExposure.getWcs() 

scienceWcs = scienceExposure.getWcs() 

templateBBox = templateExposure.getBBox() 

scienceBBox = scienceExposure.getBBox() 

 

# LLC 

templateOrigin = templateWcs.pixelToSky(afwGeom.Point2D(templateBBox.getBegin())) 

scienceOrigin = scienceWcs.pixelToSky(afwGeom.Point2D(scienceBBox.getBegin())) 

 

# URC 

templateLimit = templateWcs.pixelToSky(afwGeom.Point2D(templateBBox.getEnd())) 

scienceLimit = scienceWcs.pixelToSky(afwGeom.Point2D(scienceBBox.getEnd())) 

 

self.log.info("Template Wcs : %f,%f -> %f,%f", 

templateOrigin[0], templateOrigin[1], 

templateLimit[0], templateLimit[1]) 

self.log.info("Science Wcs : %f,%f -> %f,%f", 

scienceOrigin[0], scienceOrigin[1], 

scienceLimit[0], scienceLimit[1]) 

 

templateBBox = afwGeom.Box2D(templateOrigin.getPosition(afwGeom.degrees), 

templateLimit.getPosition(afwGeom.degrees)) 

scienceBBox = afwGeom.Box2D(scienceOrigin.getPosition(afwGeom.degrees), 

scienceLimit.getPosition(afwGeom.degrees)) 

if not (templateBBox.overlaps(scienceBBox)): 

raise RuntimeError("Input images do not overlap at all") 

 

if ((templateOrigin != scienceOrigin) or 

(templateLimit != scienceLimit) or 

(templateExposure.getDimensions() != scienceExposure.getDimensions())): 

return False 

return True 

 

 

subtractAlgorithmRegistry = pexConfig.makeRegistry( 

doc="A registry of subtraction algorithms for use as a subtask in imageDifference", 

) 

 

subtractAlgorithmRegistry.register('al', ImagePsfMatchTask)