def find_bright_columns(imarr, threshold):
"""Find bright columns in an image array.
Parameters
----------
imarr : `numpy.ndarrawy`, (Nx, Ny)
An array representing an image to analyze.
threshold : `float`
Pixel value threshold defining a bright column.
Returns
-------
bright_cols : `list`
List of column indices corresponding to bright columns.
"""
image = afwImage.ImageF(imarr)
fp_set = FootprintSet(image, Threshold(threshold))
columns = dict([(x, []) for x in range(0, image.getWidth())])
for footprint in fp_set.getFootprints():
for span in footprint.getSpans():
y = span.getY()
for x in range(span.getX0(), span.getX1() + 1):
columns[x].append(y)
bright_cols = []
x0 = image.getX0()
y0 = image.getY0()
for x in columns:
if bad_column(columns[x], 20):
bright_cols.append(x - x0)
#
# Sort the output.
#
bright_cols.sort()
return bright_cols
def calculateThreshold(self, exposure, seed, sigma=None):
"""Calculate new threshold
This is the main functional addition to the vanilla
`SourceDetectionTask`.
We identify sky objects and perform forced PSF photometry on
them. Using those PSF flux measurements and estimated errors,
we set the threshold so that the stdev of the measurements
matches the median estimated error.
Parameters
----------
exposure : `lsst.afw.image.Exposure`
Exposure on which we're detecting sources.
seed : `int`
RNG seed to use for finding sky objects.
sigma : `float`, optional
Gaussian sigma of smoothing kernel; if not provided,
will be deduced from the exposure's PSF.
Returns
-------
result : `lsst.pipe.base.Struct`
Result struct with components:
- ``multiplicative``: multiplicative factor to be applied to the
configured detection threshold (`float`).
- ``additive``: additive factor to be applied to the background
level (`float`).
"""
# Make a catalog of sky objects
fp = self.skyObjects.run(exposure.maskedImage.mask, seed)
skyFootprints = FootprintSet(exposure.getBBox())
skyFootprints.setFootprints(fp)
table = SourceTable.make(self.skyMeasurement.schema)
catalog = SourceCatalog(table)
catalog.reserve(len(skyFootprints.getFootprints()))
skyFootprints.makeSources(catalog)
key = catalog.getCentroidKey()
for source in catalog:
peaks = source.getFootprint().getPeaks()
assert len(peaks) == 1
source.set(key, peaks[0].getF())
source.updateCoord(exposure.getWcs())
# Forced photometry on sky objects
self.skyMeasurement.run(catalog, exposure, catalog, exposure.getWcs())
# Calculate new threshold
fluxes = catalog["base_PsfFlux_instFlux"]
area = catalog["base_PsfFlux_area"]
bg = catalog["base_LocalBackground_instFlux"]
good = (~catalog["base_PsfFlux_flag"] & ~catalog["base_LocalBackground_flag"] &
np.isfinite(fluxes) & np.isfinite(area) & np.isfinite(bg))
if good.sum() < self.config.minNumSources:
self.log.warn("Insufficient good flux measurements (%d < %d) for dynamic threshold calculation",
good.sum(), self.config.minNumSources)
return Struct(multiplicative=1.0, additive=0.0)
bgMedian = np.median((fluxes/area)[good])
lq, uq = np.percentile((fluxes - bg*area)[good], [25.0, 75.0])
stdevMeas = 0.741*(uq - lq)
medianError = np.median(catalog["base_PsfFlux_instFluxErr"][good])
return Struct(multiplicative=medianError/stdevMeas, additive=bgMedian)
def calculateThreshold(self, exposure, seed, sigma=None):
"""Calculate new threshold
This is the main functional addition to the vanilla
`SourceDetectionTask`.
We identify sky objects and perform forced PSF photometry on
them. Using those PSF flux measurements and estimated errors,
we set the threshold so that the stdev of the measurements
matches the median estimated error.
Parameters
----------
exposure : `lsst.afw.image.Exposure`
Exposure on which we're detecting sources.
seed : `int`
RNG seed to use for finding sky objects.
sigma : `float`, optional
Gaussian sigma of smoothing kernel; if not provided,
will be deduced from the exposure's PSF.
Returns
-------
result : `lsst.pipe.base.Struct`
Result struct with components:
- ``multiplicative``: multiplicative factor to be applied to the
configured detection threshold (`float`).
- ``additive``: additive factor to be applied to the background
level (`float`).
"""
# Make a catalog of sky objects
fp = self.skyObjects.run(exposure.maskedImage.mask, seed)
skyFootprints = FootprintSet(exposure.getBBox())
skyFootprints.setFootprints(fp)
table = SourceTable.make(self.skyMeasurement.schema)
catalog = SourceCatalog(table)
catalog.reserve(len(skyFootprints.getFootprints()))
skyFootprints.makeSources(catalog)
key = catalog.getCentroidKey()
for source in catalog:
peaks = source.getFootprint().getPeaks()
assert len(peaks) == 1
source.set(key, peaks[0].getF())
source.updateCoord(exposure.getWcs())
# Forced photometry on sky objects
self.skyMeasurement.run(catalog, exposure, catalog, exposure.getWcs())
# Calculate new threshold
fluxes = catalog["base_PsfFlux_instFlux"]
area = catalog["base_PsfFlux_area"]
bg = catalog["base_LocalBackground_instFlux"]
good = (~catalog["base_PsfFlux_flag"] & ~catalog["base_LocalBackground_flag"] &
np.isfinite(fluxes) & np.isfinite(area) & np.isfinite(bg))
if good.sum() < self.config.minNumSources:
self.log.warn("Insufficient good flux measurements (%d < %d) for dynamic threshold calculation",
good.sum(), self.config.minNumSources)
return Struct(multiplicative=1.0, additive=0.0)
bgMedian = np.median((fluxes/area)[good])
lq, uq = np.percentile((fluxes - bg*area)[good], [25.0, 75.0])
stdevMeas = 0.741*(uq - lq)
medianError = np.median(catalog["base_PsfFlux_instFluxErr"][good])
return Struct(multiplicative=medianError/stdevMeas, additive=bgMedian)