def setUp(self):
np.random.seed(1)
self.bbox = Box2I(Point2I(1000, 2000), Extent2I(200, 100))
self.filters = ["G", "R", "I"]
self.imgValue = 10
images = [ImageF(self.bbox, self.imgValue) for f in self.filters]
mImage = MultibandImage.fromImages(self.filters, images)
self.Mask = Mask[MaskPixel]
# Store the default mask planes for later use
maskPlaneDict = self.Mask().getMaskPlaneDict()
self.defaultMaskPlanes = sorted(maskPlaneDict, key=maskPlaneDict.__getitem__)
# reset so tests will be deterministic
self.Mask.clearMaskPlaneDict()
for p in ("BAD", "SAT", "INTRP", "CR", "EDGE"):
self.Mask.addMaskPlane(p)
self.maskValue = self.Mask.getPlaneBitMask("BAD")
singles = [self.Mask(self.bbox) for f in range(len(self.filters))]
for n in range(len(singles)):
singles[n].set(self.maskValue)
mMask = MultibandMask.fromMasks(self.filters, singles)
self.varValue = 1e-2
images = [ImageF(self.bbox, self.varValue) for f in self.filters]
mVariance = MultibandImage.fromImages(self.filters, images)
self.kernelSize = 51
self.psfs = [GaussianPsf(self.kernelSize, self.kernelSize, 4.0) for f in self.filters]
self.psfImage = np.array([p.computeKernelImage().array for p in self.psfs])
self.exposure = MultibandExposure(image=mImage, mask=mMask, variance=mVariance,
psfs=self.psfs, filters=self.filters)
def setUp(self):
np.random.seed(1)
self.bbox = Box2I(Point2I(1000, 2000), Extent2I(200, 100))
self.filters = ["G", "R", "I"]
self.imgValue = 10
images = [ImageF(self.bbox, self.imgValue) for f in self.filters]
mImage = MultibandImage.fromImages(self.filters, images)
self.Mask = Mask[MaskPixel]
# Store the default mask planes for later use
maskPlaneDict = self.Mask().getMaskPlaneDict()
self.defaultMaskPlanes = sorted(maskPlaneDict,
key=maskPlaneDict.__getitem__)
# reset so tests will be deterministic
self.Mask.clearMaskPlaneDict()
for p in ("BAD", "SAT", "INTRP", "CR", "EDGE"):
self.Mask.addMaskPlane(p)
self.maskValue = self.Mask.getPlaneBitMask("BAD")
singles = [self.Mask(self.bbox) for f in range(len(self.filters))]
for n in range(len(singles)):
singles[n].set(self.maskValue)
mMask = MultibandMask.fromMasks(self.filters, singles)
self.varValue = 1e-2
images = [ImageF(self.bbox, self.varValue) for f in self.filters]
mVariance = MultibandImage.fromImages(self.filters, images)
self.maskedImage = MultibandMaskedImage(self.filters,
image=mImage,
mask=mMask,
variance=mVariance)
def setUp(self):
np.random.seed(1)
self.bbox1 = Box2I(Point2I(1000, 2000), Extent2I(200, 100))
self.filters = ["G", "R", "I", "Z", "Y"]
self.value1, self.value2 = 10, 100
images = [ImageF(self.bbox1, self.value1) for f in self.filters]
self.mImage1 = MultibandImage.fromImages(self.filters, images)
self.bbox2 = Box2I(Point2I(1100, 2025), Extent2I(30, 50))
images = [ImageF(self.bbox2, self.value2) for f in self.filters]
self.mImage2 = MultibandImage.fromImages(self.filters, images)
def setUp(self):
np.random.seed(1)
self.bbox1 = Box2I(Point2I(1000, 2000), Extent2I(200, 100))
self.filters = ["G", "R", "I", "Z", "Y"]
self.value1, self.value2 = 10, 100
images = [ImageF(self.bbox1, self.value1) for f in self.filters]
self.mImage1 = MultibandImage.fromImages(self.filters, images)
self.bbox2 = Box2I(Point2I(1100, 2025), Extent2I(30, 50))
images = [ImageF(self.bbox2, self.value2) for f in self.filters]
self.mImage2 = MultibandImage.fromImages(self.filters, images)
def _testMaskedmageModification(testCase, maskedImage):
images = [
ImageF(testCase.bbox, 10 * testCase.imgValue) for f in testCase.filters
]
mImage = MultibandImage.fromImages(testCase.filters, images)
maskedImage.image.array = mImage.array
testCase.assertFloatsEqual(maskedImage.image["G"].array, mImage.array[0])
testCase.assertFloatsEqual(maskedImage["G"].image.array, mImage.array[0])
singles = [
testCase.Mask(testCase.bbox) for f in range(len(testCase.filters))
]
for n in range(len(singles)):
singles[n].set(testCase.maskValue * 2)
mMask = MultibandMask.fromMasks(testCase.filters, singles)
maskedImage.mask.array = mMask.array
testCase.assertFloatsEqual(maskedImage.mask["G"].array, mMask.array[0])
testCase.assertFloatsEqual(maskedImage["G"].mask.array, mMask.array[0])
images = [
ImageF(testCase.bbox, .1 * testCase.varValue) for f in testCase.filters
]
mVariance = MultibandImage.fromImages(testCase.filters, images)
maskedImage.variance.array = mVariance.array
testCase.assertFloatsEqual(maskedImage.variance["G"].array,
mVariance.array[0])
testCase.assertFloatsEqual(maskedImage["G"].variance.array,
mVariance.array[0])
subBox = Box2I(Point2I(1100, 2025), Extent2I(30, 50))
maskedImage.image[:, subBox].array = 12
testCase.assertFloatsEqual(maskedImage.image["G", subBox].array, 12)
testCase.assertFloatsEqual(maskedImage["G", subBox].image.array, 12)
maskedImage["R", subBox].image[:] = 15
testCase.assertFloatsEqual(maskedImage.image["R", subBox].array, 15)
testCase.assertFloatsEqual(maskedImage["R", subBox].image.array, 15)
maskedImage.mask[:, subBox].array = 64
testCase.assertFloatsEqual(maskedImage.mask["G", subBox].array, 64)
testCase.assertFloatsEqual(maskedImage["G", subBox].mask.array, 64)
maskedImage["R", subBox].mask[:] = 128
testCase.assertFloatsEqual(maskedImage.mask["R", subBox].array, 128)
testCase.assertFloatsEqual(maskedImage["R", subBox].mask.array, 128)
maskedImage.variance[:, subBox].array = 1e-6
testCase.assertFloatsEqual(maskedImage.variance["G", subBox].array, 1e-6)
testCase.assertFloatsEqual(maskedImage["G", subBox].variance.array, 1e-6)
maskedImage["R", subBox].variance[:] = 1e-7
testCase.assertFloatsEqual(maskedImage.variance["R", subBox].array, 1e-7)
testCase.assertFloatsEqual(maskedImage["R", subBox].variance.array, 1e-7)
def _testMaskedmageModification(testCase, maskedImage):
images = [ImageF(testCase.bbox, 10*testCase.imgValue) for f in testCase.filters]
mImage = MultibandImage.fromImages(testCase.filters, images)
maskedImage.image.array = mImage.array
testCase.assertFloatsEqual(maskedImage.image["G"].array, mImage.array[0])
testCase.assertFloatsEqual(maskedImage["G"].image.array, mImage.array[0])
singles = [testCase.Mask(testCase.bbox) for f in range(len(testCase.filters))]
for n in range(len(singles)):
singles[n].set(testCase.maskValue*2)
mMask = MultibandMask.fromMasks(testCase.filters, singles)
maskedImage.mask.array = mMask.array
testCase.assertFloatsEqual(maskedImage.mask["G"].array, mMask.array[0])
testCase.assertFloatsEqual(maskedImage["G"].mask.array, mMask.array[0])
images = [ImageF(testCase.bbox, .1 * testCase.varValue) for f in testCase.filters]
mVariance = MultibandImage.fromImages(testCase.filters, images)
maskedImage.variance.array = mVariance.array
testCase.assertFloatsEqual(maskedImage.variance["G"].array, mVariance.array[0])
testCase.assertFloatsEqual(maskedImage["G"].variance.array, mVariance.array[0])
subBox = Box2I(Point2I(1100, 2025), Extent2I(30, 50))
maskedImage.image[:, subBox].array = 12
testCase.assertFloatsEqual(maskedImage.image["G", subBox].array, 12)
testCase.assertFloatsEqual(maskedImage["G", subBox].image.array, 12)
maskedImage["R", subBox].image[:] = 15
testCase.assertFloatsEqual(maskedImage.image["R", subBox].array, 15)
testCase.assertFloatsEqual(maskedImage["R", subBox].image.array, 15)
maskedImage.mask[:, subBox].array = 64
testCase.assertFloatsEqual(maskedImage.mask["G", subBox].array, 64)
testCase.assertFloatsEqual(maskedImage["G", subBox].mask.array, 64)
maskedImage["R", subBox].mask[:] = 128
testCase.assertFloatsEqual(maskedImage.mask["R", subBox].array, 128)
testCase.assertFloatsEqual(maskedImage["R", subBox].mask.array, 128)
maskedImage.variance[:, subBox].array = 1e-6
testCase.assertFloatsEqual(maskedImage.variance["G", subBox].array, 1e-6)
testCase.assertFloatsEqual(maskedImage["G", subBox].variance.array, 1e-6)
maskedImage["R", subBox].variance[:] = 1e-7
testCase.assertFloatsEqual(maskedImage.variance["R", subBox].array, 1e-7)
testCase.assertFloatsEqual(maskedImage["R", subBox].variance.array, 1e-7)
def modelToHeavy(source, mExposure, blend, xy0=Point2I(), dtype=np.float32):
"""Convert a scarlet model to a `MultibandFootprint`.
Parameters
----------
source : `scarlet.Component`
The source to convert to a `HeavyFootprint`.
mExposure : `lsst.image.MultibandExposure`
The multiband exposure containing the image,
mask, and variance data.
blend : `scarlet.Blend`
The `Blend` object that contains information about
the observation, PSF, etc, used to convolve the
scarlet model to the observed seeing in each band.
xy0 : `lsst.geom.Point2I`
`(x,y)` coordinates of the lower-left pixel of the
entire blend.
dtype : `numpy.dtype`
The data type for the returned `HeavyFootprint`.
Returns
-------
mHeavy : `lsst.detection.MultibandFootprint`
The multi-band footprint containing the model for the source.
"""
# We want to convolve the model with the observed PSF,
# which means we need to grow the model box by the PSF to
# account for all of the flux after convolution.
# FYI: The `scarlet.Box` class implements the `&` operator
# to take the intersection of two boxes.
# Get the PSF size and radii to grow the box
py, px = blend.observations[0].psf.get_model().shape[1:]
dh = py // 2
dw = px // 2
shape = (source.bbox.shape[0], source.bbox.shape[1] + py,
source.bbox.shape[2] + px)
origin = (source.bbox.origin[0], source.bbox.origin[1] - dh,
source.bbox.origin[2] - dw)
# Create the larger box to fit the model + PSf
bbox = Box(shape, origin=origin)
# Only use the portion of the convolved model that fits in the image
overlap = bbox & source.frame.bbox
# Load the full multiband model in the larger box
model = source.model_to_box(overlap)
# Convolve the model with the PSF in each band
# Always use a real space convolution to limit artifacts
model = blend.observations[0].renderer.convolve(
model, convolution_type="real").astype(dtype)
# Update xy0 with the origin of the sources box
xy0 = Point2I(overlap.origin[-1] + xy0.x, overlap.origin[-2] + xy0.y)
# Create the spans for the footprint
valid = np.max(np.array(model), axis=0) != 0
valid = Mask(valid.astype(np.int32), xy0=xy0)
spans = SpanSet.fromMask(valid)
# Add the location of the source to the peak catalog
peakCat = PeakCatalog(source.detectedPeak.table)
peakCat.append(source.detectedPeak)
# Create the MultibandHeavyFootprint
foot = Footprint(spans)
foot.setPeakCatalog(peakCat)
model = MultibandImage(mExposure.filters, model, valid.getBBox())
mHeavy = MultibandFootprint.fromImages(mExposure.filters,
model,
footprint=foot)
return mHeavy