def testRemoveMaskPlane(self):
mMask = self.mMask1
# Add mask plane FOO and make sure it got added properly
mMask.addMaskPlane("FOO")
self.assertIn("FOO", mMask.getMaskPlaneDict())
self.assertIn("FOO", Mask().getMaskPlaneDict())
# Remove plane FOO, noting that removeMaskPlane removes it from the
# default, but each instance remembers the version of the mask
# dictionary that was current when it was created, so it will still
# be in the mMask dict.
mMask.removeMaskPlane("FOO")
self.assertIn("FOO", mMask.getMaskPlaneDict())
self.assertNotIn("FOO", Mask().getMaskPlaneDict())
def __init__(self, *args, **kwargs):
pipeBase.Task.__init__(self, *args, **kwargs)
self._badPixelMask = Mask.getPlaneBitMask(self.config.badMaskPlanes)
self._statsControl = afwMath.StatisticsControl()
self._statsControl.setNumSigmaClip(self.config.numSigmaClip)
self._statsControl.setNumIter(self.config.numIter)
self._statsControl.setAndMask(self._badPixelMask)
def testMaskFitsReader(self):
maskIn = Mask(self.bbox, dtype=MaskPixel)
maskIn.array[:, :] = np.random.randint(low=1, high=5, size=maskIn.array.shape)
with lsst.utils.tests.getTempFilePath(".fits") as fileName:
maskIn.writeFits(fileName)
reader = MaskFitsReader(fileName)
self.assertEqual(reader.readBBox(), self.bbox)
self.assertEqual(reader.readDType(), MaskPixel)
self.assertEqual(reader.fileName, fileName)
for args in self.args:
with self.subTest(args=args):
array = reader.readArray(*args)
mask = reader.read(*args)
subIn = maskIn.subset(*args) if args else maskIn
self.assertEqual(MaskPixel, array.dtype)
self.assertTrue(np.all(subIn.array == array))
self.assertEqual(subIn.getXY0(), reader.readXY0(*args))
self.assertImagesEqual(subIn, mask)
def getSpanSetFromImages(images, thresh=0, xy0=None):
"""Create a Footprint from a set of Images
Parameters
----------
images: `MultibandImage` or list of `Image`, array
Images to extract the footprint from
thresh: `float`
All pixels above `thresh` will be included in the footprint
xy0: `Point2I`
Location of the minimum value of the images bounding box
(if images is an array, otherwise the image bounding box is used).
Returns
-------
spans: `SpanSet`
Union of all spans in the images above the threshold
imageBBox: `Box2I`
Bounding box for the input images.
"""
# Set the threshold for each band
if not hasattr(thresh, "__len__"):
thresh = [thresh] * len(images)
# If images is a list of `afw Image` objects then
# merge the SpanSet in each band into a single Footprint
if isinstance(images, MultibandBase) or isinstance(images[0], Image):
spans = SpanSet()
for n, image in enumerate(images):
mask = image.array > thresh[n]
mask = Mask(mask.astype(np.int32), xy0=image.getBBox().getMin())
spans = spans.union(SpanSet.fromMask(mask))
imageBBox = images[0].getBBox()
else:
# Use thresh to detect the pixels above the threshold in each band
thresh = np.array(thresh)
if xy0 is None:
xy0 = Point2I(0, 0)
mask = np.any(images > thresh[:, None, None], axis=0)
mask = Mask(mask.astype(np.int32), xy0=xy0)
spans = SpanSet.fromMask(mask)
imageBBox = mask.getBBox()
return spans, imageBBox
def testRemoveAndClearMaskPlane(self):
mMask = self.mMask1
# Add mask plane FOO and test clearing it without removing plane from
# default dict
mMask.addMaskPlane("FOO")
mMask.removeAndClearMaskPlane("FOO")
self.assertNotIn("FOO", mMask.getMaskPlaneDict())
self.assertIn("FOO", Mask().getMaskPlaneDict())
# Now also remove it from default dict
mMask.addMaskPlane("FOO")
mMask.removeAndClearMaskPlane("FOO", removeFromDefault=True)
self.assertNotIn("FOO", mMask.getMaskPlaneDict())
self.assertNotIn("FOO", Mask().getMaskPlaneDict())
# Now remove and clear the EDGE mask plane and make sure all of the planes
# in the MultibandMask (i.e. the "singles") got updated accordingly
mMask.removeAndClearMaskPlane("EDGE", removeFromDefault=True)
self.assertNotIn("EDGE", mMask.getMaskPlaneDict())
self.assertNotIn("EDGE", Mask().getMaskPlaneDict())
# Assert that all mask planes were updated (i.e. having EDGE removed)
self.assertTrue(
np.all([
s.array == self.values1[n] & ~self.EDGE
for n, s in enumerate(mMask.singles)
]))
def getSpanSetFromImages(images, thresh=0, xy0=None):
"""Create a Footprint from a set of Images
Parameters
----------
images: `MultibandImage` or list of `Image`, array
Images to extract the footprint from
thresh: `float`
All pixels above `thresh` will be included in the footprint
xy0: `Point2I`
Location of the minimum value of the images bounding box
(if images is an array, otherwise the image bounding box is used).
Returns
-------
spans: `SpanSet`
Union of all spans in the images above the threshold
imageBBox: `Box2I`
Bounding box for the input images.
"""
# Set the threshold for each band
if not hasattr(thresh, "__len__"):
thresh = [thresh] * len(images)
# If images is a list of `afw Image` objects then
# merge the SpanSet in each band into a single Footprint
if isinstance(images, MultibandBase) or isinstance(images[0], Image):
spans = SpanSet()
for n, image in enumerate(images):
mask = image.array > thresh[n]
mask = Mask(mask.astype(np.int32), xy0=image.getBBox().getMin())
spans = spans.union(SpanSet.fromMask(mask))
imageBBox = images[0].getBBox()
else:
# Use thresh to detect the pixels above the threshold in each band
thresh = np.array(thresh)
if xy0 is None:
xy0 = Point2I(0, 0)
mask = np.any(images > thresh[:, None, None], axis=0)
mask = Mask(mask.astype(np.int32), xy0=xy0)
spans = SpanSet.fromMask(mask)
imageBBox = mask.getBBox()
return spans, imageBBox
def testMaskFitsReader(self):
maskIn = Mask(self.bbox, dtype=MaskPixel)
maskIn.array[:, :] = np.random.randint(low=1,
high=5,
size=maskIn.array.shape)
with lsst.utils.tests.getTempFilePath(".fits") as fileName:
maskIn.writeFits(fileName)
reader = MaskFitsReader(fileName)
self.assertEqual(reader.readBBox(), self.bbox)
self.assertEqual(reader.readDType(), MaskPixel)
self.assertEqual(reader.fileName, fileName)
for args in self.args:
with self.subTest(args=args):
array = reader.readArray(*args)
mask = reader.read(*args)
subIn = maskIn.subset(*args) if args else maskIn
self.assertEqual(MaskPixel, array.dtype)
self.assertTrue(np.all(subIn.array == array))
self.assertEqual(subIn.getXY0(), reader.readXY0(*args))
self.assertImagesEqual(subIn, mask)
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