def read_fits(cls, filename):
"""Build an instance of this class from a file.
Parameters
----------
filename : `str`
Name of the file to read.
"""
# Extract info from metadata.
global_metadata = afwFits.readMetadata(filename, hdu=0)
has_default = global_metadata.getBool("HAS_DEFAULT")
if global_metadata.getBool("HAS_REGIONS"):
focal_plane_region_names = global_metadata.getArray("REGION_NAMES")
else:
focal_plane_region_names = []
f = afwFits.Fits(filename, "r")
n_extensions = f.countHdus()
extended_psf_parts = {}
for j in range(1, n_extensions):
md = afwFits.readMetadata(filename, hdu=j)
if has_default and md["REGION"] == "DEFAULT":
if md["EXTNAME"] == "IMAGE":
default_image = afwImage.ImageF(filename, hdu=j)
elif md["EXTNAME"] == "MASK":
default_mask = afwImage.MaskX(filename, hdu=j)
continue
if md["EXTNAME"] == "IMAGE":
extended_psf_part = afwImage.ImageF(filename, hdu=j)
elif md["EXTNAME"] == "MASK":
extended_psf_part = afwImage.MaskX(filename, hdu=j)
extended_psf_parts.setdefault(
md["REGION"], {})[md["EXTNAME"].lower()] = extended_psf_part
# Handle default if present.
if has_default:
extended_psf = cls(
afwImage.MaskedImageF(default_image, default_mask))
else:
extended_psf = cls()
# Ensure we recovered an extended PSF for all focal plane regions.
if len(extended_psf_parts) != len(focal_plane_region_names):
raise ValueError(
f"Number of per-region extended PSFs read ({len(extended_psf_parts)}) does not "
"match with the number of regions recorded in the metadata "
f"({len(focal_plane_region_names)}).")
# Generate extended PSF regions mappings.
for r_name in focal_plane_region_names:
extended_psf_image = afwImage.MaskedImageF(
**extended_psf_parts[r_name])
detector_list = global_metadata.getArray(r_name)
extended_psf.add_regional_extended_psf(extended_psf_image, r_name,
detector_list)
# Instantiate ExtendedPsf.
return extended_psf
def morphToHeavy(source, peakSchema, xy0=Point2I()):
"""Convert the morphology to a `HeavyFootprint`
Parameters
----------
source : `scarlet.Component`
The scarlet source with a morphology to convert to
a `HeavyFootprint`.
peakSchema : `lsst.daf.butler.Schema`
The schema for the `PeakCatalog` of the `HeavyFootprint`.
xy0 : `tuple`
`(x,y)` coordinates of the bounding box containing the
`HeavyFootprint`.
Returns
-------
heavy : `lsst.afw.detection.HeavyFootprint`
"""
mask = afwImage.MaskX(np.array(source.morph > 0, dtype=np.int32), xy0=xy0)
ss = SpanSet.fromMask(mask)
if len(ss) == 0:
return None
tfoot = afwDet.Footprint(ss, peakSchema=peakSchema)
cy, cx = source.pixel_center
xmin, ymin = xy0
# HeavyFootprints are not defined for 64 bit floats
morph = source.morph.astype(np.float32)
peakFlux = morph[cy, cx]
tfoot.addPeak(cx + xmin, cy + ymin, peakFlux)
timg = afwImage.ImageF(morph, xy0=xy0)
timg = timg[tfoot.getBBox()]
heavy = afwDet.makeHeavyFootprint(tfoot, afwImage.MaskedImageF(timg))
return heavy
def testProperties(self):
self.assertMaskedImagesEqual(self.exposureMiOnly.maskedImage,
self.exposureMiOnly.getMaskedImage())
mi2 = afwImage.MaskedImageF(self.exposureMiOnly.getDimensions())
mi2.image.array[:] = 5.0
mi2.variance.array[:] = 3.0
mi2.mask.array[:] = 0x1
self.exposureMiOnly.maskedImage = mi2
self.assertMaskedImagesEqual(self.exposureMiOnly.maskedImage, mi2)
self.assertImagesEqual(self.exposureMiOnly.image,
self.exposureMiOnly.maskedImage.image)
image3 = afwImage.ImageF(self.exposureMiOnly.getDimensions())
image3.array[:] = 3.0
self.exposureMiOnly.image = image3
self.assertImagesEqual(self.exposureMiOnly.image, image3)
mask3 = afwImage.MaskX(self.exposureMiOnly.getDimensions())
mask3.array[:] = 0x2
self.exposureMiOnly.mask = mask3
self.assertMasksEqual(self.exposureMiOnly.mask, mask3)
var3 = afwImage.ImageF(self.exposureMiOnly.getDimensions())
var3.array[:] = 2.0
self.exposureMiOnly.variance = var3
self.assertImagesEqual(self.exposureMiOnly.variance, var3)
def _isMasked(self, footprint, mExposure):
"""Returns whether the footprint violates the mask limits"""
bbox = footprint.getBBox()
mask = np.bitwise_or.reduce(mExposure.mask[:, bbox].array, axis=0)
size = float(footprint.getArea())
for maskName, limit in self.config.maskLimits.items():
maskVal = mExposure.mask.getPlaneBitMask(maskName)
_mask = afwImage.MaskX(mask & maskVal, xy0=bbox.getMin())
unmaskedSpan = footprint.spans.intersectNot(
_mask) # spanset of unmasked pixels
if (size - unmaskedSpan.getArea()) / size > limit:
return True
return False
def setUp(self):
self.dataDir = os.path.join(os.path.split(__file__)[0], "data")
# Check the values below against what was written by comparing with
# the code in `afw/tests/data/makeTestExposure.py`
nx = ny = 10
image = afwImage.ImageF(np.arange(nx*ny, dtype='f').reshape(nx, ny))
variance = afwImage.ImageF(np.ones((nx, ny), dtype='f'))
mask = afwImage.MaskX(nx, ny)
mask.array[5, 5] = 5
self.maskedImage = afwImage.MaskedImageF(image, mask, variance)
self.v0PhotoCalib = afwImage.makePhotoCalibFromCalibZeroPoint(1e6, 2e4)
self.v1PhotoCalib = afwImage.PhotoCalib(1e6, 2e4)
def deblend(self, mExposure, sources):
"""Deblend a data cube of multiband images
Parameters
----------
mExposure : `MultibandExposure`
The exposures should be co-added images of the same
shape and region of the sky.
sources : `SourceCatalog`
The merged `SourceCatalog` that contains parent footprints
to (potentially) deblend.
Returns
-------
fluxCatalogs : dict or None
Keys are the names of the filters and the values are
`lsst.afw.table.source.source.SourceCatalog`'s.
These are the flux-conserved catalogs with heavy footprints with
the image data weighted by the multiband templates.
If `self.config.conserveFlux` is `False`, then this item will be
None
templateCatalogs : dict or None
Keys are the names of the filters and the values are
`lsst.afw.table.source.source.SourceCatalog`'s.
These are catalogs with heavy footprints that are the templates
created by the multiband templates.
If `self.config.saveTemplates` is `False`, then this item will be
None
"""
import time
filters = mExposure.filters
self.log.info("Deblending {0} sources in {1} exposure bands".format(
len(sources), len(mExposure)))
# Create the output catalogs
templateCatalogs = {}
# This must be returned but is not calculated right now, setting it to
# None to be consistent with doc string
fluxCatalogs = None
for f in filters:
_catalog = afwTable.SourceCatalog(sources.table.clone())
_catalog.extend(sources)
templateCatalogs[f] = _catalog
n0 = len(sources)
nparents = 0
for pk, src in enumerate(sources):
foot = src.getFootprint()
bbox = foot.getBBox()
logger.info("id: {0}".format(src["id"]))
peaks = foot.getPeaks()
# Since we use the first peak for the parent object, we should
# propagate its flags to the parent source.
src.assign(peaks[0], self.peakSchemaMapper)
# Block of Skipping conditions
if len(peaks) < 2 and not self.config.processSingles:
for f in filters:
templateCatalogs[f][pk].set(self.runtimeKey, 0)
continue
if self._isLargeFootprint(foot):
src.set(self.tooBigKey, True)
self._skipParent(src, mExposure.mask)
self.log.trace('Parent %i: skipping large footprint',
int(src.getId()))
continue
if self._isMasked(foot, mExposure):
src.set(self.maskedKey, True)
mask = np.bitwise_or.reduce(mExposure.mask[:, bbox].array,
axis=0)
mask = afwImage.MaskX(mask, xy0=bbox.getMin())
self._skipParent(src, mask)
self.log.trace('Parent %i: skipping masked footprint',
int(src.getId()))
continue
if len(peaks) > self.config.maxNumberOfPeaks:
src.set(self.tooManyPeaksKey, True)
msg = 'Parent {0}: Too many peaks, using the first {1} peaks'
self.log.trace(
msg.format(int(src.getId()), self.config.maxNumberOfPeaks))
nparents += 1
self.log.trace('Parent %i: deblending %i peaks', int(src.getId()),
len(peaks))
# Run the deblender
try:
t0 = time.time()
# Build the parameter lists with the same ordering
blend, skipped = deblend(mExposure, foot, self.config)
tf = time.time()
runtime = (tf - t0) * 1000
src.set(self.deblendFailedKey, False)
src.set(self.runtimeKey, runtime)
converged = checkBlendConvergence(blend,
self.config.relativeError)
src.set(self.blendConvergenceFailedFlagKey, converged)
sources = [src for src in blend.sources]
# Re-insert place holders for skipped sources
# to propagate them in the catalog so
# that the peaks stay consistent
for k in skipped:
sources.insert(k, None)
except Exception as e:
if self.config.catchFailures:
self.log.warn("Unable to deblend source %d: %s" %
(src.getId(), e))
src.set(self.deblendFailedKey, True)
src.set(self.runtimeKey, 0)
import traceback
traceback.print_exc()
continue
else:
raise
# Add the merged source as a parent in the catalog for each band
templateParents = {}
parentId = src.getId()
for f in filters:
templateParents[f] = templateCatalogs[f][pk]
templateParents[f].set(self.runtimeKey, runtime)
templateParents[f].set(self.iterKey, len(blend.loss))
# Add each source to the catalogs in each band
templateSpans = {f: afwGeom.SpanSet() for f in filters}
nchild = 0
for k, source in enumerate(sources):
# Skip any sources with no flux or that scarlet skipped because
# it could not initialize
if k in skipped:
if not self.config.propagateAllPeaks:
# We don't care
continue
# We need to preserve the peak: make sure we have enough
# info to create a minimal child src
msg = "Peak at {0} failed deblending. Using minimal default info for child."
self.log.trace(msg.format(src.getFootprint().peaks[k]))
# copy the full footprint and strip out extra peaks
foot = afwDet.Footprint(src.getFootprint())
peakList = foot.getPeaks()
peakList.clear()
peakList.append(src.peaks[k])
zeroMimg = afwImage.MaskedImageF(foot.getBBox())
heavy = afwDet.makeHeavyFootprint(foot, zeroMimg)
models = afwDet.MultibandFootprint(
mExposure.filters, [heavy] * len(mExposure.filters))
else:
src.set(self.deblendSkippedKey, False)
models = modelToHeavy(source,
filters,
xy0=bbox.getMin(),
observation=blend.observations[0])
# TODO: We should eventually write the morphology and SED to
# the catalog
# morph = source.morphToHeavy(xy0=bbox.getMin())
# sed = source.sed / source.sed.sum()
for f in filters:
if len(models[f].getPeaks()) != 1:
err = "Heavy footprint should have a single peak, got {0}"
raise ValueError(err.format(len(models[f].peaks)))
cat = templateCatalogs[f]
child = self._addChild(parentId,
cat,
models[f],
source,
converged,
xy0=bbox.getMin())
if parentId == 0:
child.setId(src.getId())
child.set(self.runtimeKey, runtime)
else:
templateSpans[f] = templateSpans[f].union(
models[f].getSpans())
nchild += 1
# Child footprints may extend beyond the full extent of their
# parent's which results in a failure of the replace-by-noise code
# to reinstate these pixels to their original values. The
# following updates the parent footprint in-place to ensure it
# contains the full union of itself and all of its
# children's footprints.
for f in filters:
templateParents[f].set(self.nChildKey, nchild)
templateParents[f].getFootprint().setSpans(templateSpans[f])
K = len(list(templateCatalogs.values())[0])
self.log.info(
'Deblended: of %i sources, %i were deblended, creating %i children, total %i sources'
% (n0, nparents, K - n0, K))
return fluxCatalogs, templateCatalogs
def deblend(self, mExposure, sources):
"""Deblend a data cube of multiband images
Parameters
----------
mExposure : `MultibandExposure`
The exposures should be co-added images of the same
shape and region of the sky.
sources : `SourceCatalog`
The merged `SourceCatalog` that contains parent footprints
to (potentially) deblend.
Returns
-------
templateCatalogs : dict or None
Keys are the names of the filters and the values are
`lsst.afw.table.source.source.SourceCatalog`'s.
These are catalogs with heavy footprints that are the templates
created by the multiband templates.
"""
import time
filters = mExposure.filters
self.log.info("Deblending {0} sources in {1} exposure bands".format(
len(sources), len(mExposure)))
# Create the output catalogs
templateCatalogs = {}
# This must be returned but is not calculated right now, setting it to
# None to be consistent with doc string
for f in filters:
_catalog = afwTable.SourceCatalog(sources.table.clone())
_catalog.extend(sources)
templateCatalogs[f] = _catalog
n0 = len(sources)
nparents = 0
for pk, src in enumerate(sources):
foot = src.getFootprint()
bbox = foot.getBBox()
peaks = foot.getPeaks()
# Since we use the first peak for the parent object, we should
# propagate its flags to the parent source.
src.assign(peaks[0], self.peakSchemaMapper)
# Block of Skipping conditions
if len(peaks) < 2 and not self.config.processSingles:
for f in filters:
templateCatalogs[f][pk].set(self.runtimeKey, 0)
continue
if self._isLargeFootprint(foot):
src.set(self.tooBigKey, True)
self._skipParent(src, mExposure.mask)
self.log.trace('Parent %i: skipping large footprint',
int(src.getId()))
continue
if self._isMasked(foot, mExposure):
src.set(self.maskedKey, True)
mask = np.bitwise_or.reduce(mExposure.mask[:, bbox].array,
axis=0)
mask = afwImage.MaskX(mask, xy0=bbox.getMin())
self._skipParent(src, mask)
self.log.trace('Parent %i: skipping masked footprint',
int(src.getId()))
continue
if self.config.maxNumberOfPeaks > 0 and len(
peaks) > self.config.maxNumberOfPeaks:
src.set(self.tooManyPeaksKey, True)
self._skipParent(src, mExposure.mask)
msg = 'Parent {0}: Too many peaks, skipping blend'
self.log.trace(msg.format(int(src.getId())))
# Unlike meas_deblender, in scarlet we skip the entire blend
# if the number of peaks exceeds max peaks, since neglecting
# to model any peaks often results in catastrophic failure
# of scarlet to generate models for the brighter sources.
continue
nparents += 1
self.log.trace('Parent %i: deblending %i peaks', int(src.getId()),
len(peaks))
# Run the deblender
blendError = None
try:
t0 = time.time()
# Build the parameter lists with the same ordering
blend, skipped = deblend(mExposure, foot, self.config)
tf = time.time()
runtime = (tf - t0) * 1000
src.set(self.deblendFailedKey, False)
src.set(self.runtimeKey, runtime)
converged = _checkBlendConvergence(blend,
self.config.relativeError)
src.set(self.blendConvergenceFailedFlagKey, converged)
sources = [src for src in blend.sources]
# Re-insert place holders for skipped sources
# to propagate them in the catalog so
# that the peaks stay consistent
for k in skipped:
sources.insert(k, None)
# Catch all errors and filter out the ones that we know about
except Exception as e:
blendError = type(e).__name__
if isinstance(e, ScarletGradientError):
src.set(self.iterKey, e.iterations)
elif not isinstance(e, IncompleteDataError):
blendError = "UnknownError"
self._skipParent(src, mExposure.mask)
if self.config.catchFailures:
# Make it easy to find UnknownErrors in the log file
self.log.warn("UnknownError")
import traceback
traceback.print_exc()
else:
raise
self.log.warn("Unable to deblend source %d: %s" %
(src.getId(), blendError))
src.set(self.deblendFailedKey, True)
src.set(self.deblendErrorKey, blendError)
self._skipParent(src, mExposure.mask)
continue
# Add the merged source as a parent in the catalog for each band
templateParents = {}
parentId = src.getId()
for f in filters:
templateParents[f] = templateCatalogs[f][pk]
templateParents[f].set(self.nPeaksKey, len(foot.peaks))
templateParents[f].set(self.runtimeKey, runtime)
templateParents[f].set(self.iterKey, len(blend.loss))
logL = blend.loss[-1] - blend.observations[0].log_norm
templateParents[f].set(self.scarletLogLKey, logL)
# Add each source to the catalogs in each band
nchild = 0
for k, source in enumerate(sources):
# Skip any sources with no flux or that scarlet skipped because
# it could not initialize
if k in skipped:
# No need to propagate anything
continue
else:
src.set(self.deblendSkippedKey, False)
models = modelToHeavy(source,
filters,
xy0=bbox.getMin(),
observation=blend.observations[0])
flux = scarlet.measure.flux(source)
for fidx, f in enumerate(filters):
if len(models[f].getPeaks()) != 1:
err = "Heavy footprint should have a single peak, got {0}"
raise ValueError(err.format(len(models[f].peaks)))
cat = templateCatalogs[f]
child = self._addChild(src,
cat,
models[f],
source,
converged,
xy0=bbox.getMin(),
flux=flux[fidx])
if parentId == 0:
child.setId(src.getId())
child.set(self.runtimeKey, runtime)
nchild += 1
# Set the number of children for each parent
for f in filters:
templateParents[f].set(self.nChildKey, nchild)
K = len(list(templateCatalogs.values())[0])
self.log.info(
'Deblended: of %i sources, %i were deblended, creating %i children, total %i sources'
% (n0, nparents, K - n0, K))
return templateCatalogs
