lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask Class Reference

Inheritance diagram for lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask:

Public Member Functions
def	__init__ (self, args, kwargs)
def	calculateThreshold (self, exposure, seed, sigma=None)
def	detectFootprints (self, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None)
def	tweakBackground (self, exposure, bgLevel, bgList=None)
def	run (self, table, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None)
def	display (self, exposure, results, convolvedImage=None)
def	applyTempLocalBackground (self, exposure, middle, results)
def	clearMask (self, mask)
def	calculateKernelSize (self, sigma)
def	getPsf (self, exposure, sigma=None)
def	convolveImage (self, maskedImage, psf, doSmooth=True)
def	applyThreshold (self, middle, bbox, factor=1.0)
def	finalizeFootprints (self, mask, results, sigma, factor=1.0)
def	reEstimateBackground (self, maskedImage, backgrounds)
def	clearUnwantedResults (self, mask, results)
def	makeThreshold (self, image, thresholdParity, factor=1.0)
def	updatePeaks (self, fpSet, image, threshold)
def	tempWideBackgroundContext (self, exposure)

Static Public Member Functions
def	setEdgeBits (maskedImage, goodBBox, edgeBitmask)

Public Attributes
	skySchema
	skyMeasurement
	negativeFlagKey

Static Public Attributes
	ConfigClass = DynamicDetectionConfig
def	makeSourceCatalog = run
	An alias for run. More...

Detailed Description

Detection of sources on an image with a dynamic threshold

We first detect sources using a lower threshold than normal (see config
parameter ``prelimThresholdFactor``) in order to identify good sky regions
(configurable ``skyObjects``). Then we perform forced PSF photometry on
those sky regions. Using those PSF flux measurements and estimated errors,
we set the threshold so that the stdev of the measurements matches the
median estimated error.

Definition at line 36 of file dynamicDetection.py.

Constructor & Destructor Documentation

__init__()

def lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask.__init__	(		self,
			args,
			kwargs
	)

Constructor

Besides the usual initialisation of configurables, we also set up
the forced measurement which is deliberately not represented in
this Task's configuration parameters because we're using it as part
of the algorithm and we don't want to allow it to be modified.

Definition at line 49 of file dynamicDetection.py.

    def __init__(self, *args, **kwargs):
        """Constructor

        Besides the usual initialisation of configurables, we also set up
        the forced measurement which is deliberately not represented in
        this Task's configuration parameters because we're using it as part
        of the algorithm and we don't want to allow it to be modified.
        """
        SourceDetectionTask.__init__(self, *args, **kwargs)
        self.makeSubtask("skyObjects")

        # Set up forced measurement.
        config = ForcedMeasurementTask.ConfigClass()
        config.plugins.names = ['base_TransformedCentroid', 'base_PsfFlux', 'base_LocalBackground']
        # We'll need the "centroid" and "psfFlux" slots
        for slot in ("shape", "psfShape", "apFlux", "modelFlux", "gaussianFlux", "calibFlux"):
            setattr(config.slots, slot, None)
        config.copyColumns = {}
        self.skySchema = SourceTable.makeMinimalSchema()
        self.skyMeasurement = ForcedMeasurementTask(config=config, name="skyMeasurement", parentTask=self,
                                                    refSchema=self.skySchema)

Member Function Documentation

applyTempLocalBackground()

def lsst.meas.algorithms.detection.SourceDetectionTask.applyTempLocalBackground (   self,   exposure,   middle,   results  )

inherited

Apply a temporary local background subtraction

This temporary local background serves to suppress noise fluctuations
in the wings of bright objects.

Peaks in the footprints will be updated.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure for which to fit local background.
middle : `lsst.afw.image.MaskedImage`
    Convolved image on which detection will be performed
    (typically smaller than ``exposure`` because the
    half-kernel has been removed around the edges).
results : `lsst.pipe.base.Struct`
    Results of the 'detectFootprints' method, containing positive and
    negative footprints (which contain the peak positions that we will
    plot). This is a `Struct` with ``positive`` and ``negative``
    elements that are of type `lsst.afw.detection.FootprintSet`.

Definition at line 414 of file detection.py.

    def applyTempLocalBackground(self, exposure, middle, results):
        """Apply a temporary local background subtraction

        This temporary local background serves to suppress noise fluctuations
        in the wings of bright objects.

        Peaks in the footprints will be updated.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure for which to fit local background.
        middle : `lsst.afw.image.MaskedImage`
            Convolved image on which detection will be performed
            (typically smaller than ``exposure`` because the
            half-kernel has been removed around the edges).
        results : `lsst.pipe.base.Struct`
            Results of the 'detectFootprints' method, containing positive and
            negative footprints (which contain the peak positions that we will
            plot). This is a `Struct` with ``positive`` and ``negative``
            elements that are of type `lsst.afw.detection.FootprintSet`.
        """
        # Subtract the local background from the smoothed image. Since we
        # never use the smoothed again we don't need to worry about adding
        # it back in.
        bg = self.tempLocalBackground.fitBackground(exposure.getMaskedImage())
        bgImage = bg.getImageF()
        middle -= bgImage.Factory(bgImage, middle.getBBox())
        thresholdPos = self.makeThreshold(middle, "positive")
        thresholdNeg = self.makeThreshold(middle, "negative")
        if self.config.thresholdPolarity != "negative":
            self.updatePeaks(results.positive, middle, thresholdPos)
        if self.config.thresholdPolarity != "positive":
            self.updatePeaks(results.negative, middle, thresholdNeg)

applyThreshold()

def lsst.meas.algorithms.detection.SourceDetectionTask.applyThreshold (   self,   middle,   bbox,   factor = 1.0  )

inherited

Apply thresholds to the convolved image

Identifies ``Footprint``s, both positive and negative.

The threshold can be modified by the provided multiplication
``factor``.

Parameters
----------
middle : `lsst.afw.image.MaskedImage`
    Convolved image to threshold.
bbox : `lsst.geom.Box2I`
    Bounding box of unconvolved image.
factor : `float`
    Multiplier for the configured threshold.

Return Struct contents
----------------------
positive : `lsst.afw.detection.FootprintSet` or `None`
    Positive detection footprints, if configured.
negative : `lsst.afw.detection.FootprintSet` or `None`
    Negative detection footprints, if configured.
factor : `float`
    Multiplier for the configured threshold.

Definition at line 570 of file detection.py.

    def applyThreshold(self, middle, bbox, factor=1.0):
        """Apply thresholds to the convolved image

        Identifies ``Footprint``s, both positive and negative.

        The threshold can be modified by the provided multiplication
        ``factor``.

        Parameters
        ----------
        middle : `lsst.afw.image.MaskedImage`
            Convolved image to threshold.
        bbox : `lsst.geom.Box2I`
            Bounding box of unconvolved image.
        factor : `float`
            Multiplier for the configured threshold.

        Return Struct contents
        ----------------------
        positive : `lsst.afw.detection.FootprintSet` or `None`
            Positive detection footprints, if configured.
        negative : `lsst.afw.detection.FootprintSet` or `None`
            Negative detection footprints, if configured.
        factor : `float`
            Multiplier for the configured threshold.
        """
        results = pipeBase.Struct(positive=None, negative=None, factor=factor)
        # Detect the Footprints (peaks may be replaced if doTempLocalBackground)
        if self.config.reEstimateBackground or self.config.thresholdPolarity != "negative":
            threshold = self.makeThreshold(middle, "positive", factor=factor)
            results.positive = afwDet.FootprintSet(
                middle,
                threshold,
                "DETECTED",
                self.config.minPixels
            )
            results.positive.setRegion(bbox)
        if self.config.reEstimateBackground or self.config.thresholdPolarity != "positive":
            threshold = self.makeThreshold(middle, "negative", factor=factor)
            results.negative = afwDet.FootprintSet(
                middle,
                threshold,
                "DETECTED_NEGATIVE",
                self.config.minPixels
            )
            results.negative.setRegion(bbox)

        return results

calculateKernelSize()

def lsst.meas.algorithms.detection.SourceDetectionTask.calculateKernelSize (   self,   sigma  )

inherited

Calculate size of smoothing kernel

Uses the ``nSigmaForKernel`` configuration parameter. Note
that that is the full width of the kernel bounding box
(so a value of 7 means 3.5 sigma on either side of center).
The value will be rounded up to the nearest odd integer.

Parameters
----------
sigma : `float`
    Gaussian sigma of smoothing kernel.

Returns
-------
size : `int`
    Size of the smoothing kernel.

Definition at line 462 of file detection.py.

    def calculateKernelSize(self, sigma):
        """Calculate size of smoothing kernel

        Uses the ``nSigmaForKernel`` configuration parameter. Note
        that that is the full width of the kernel bounding box
        (so a value of 7 means 3.5 sigma on either side of center).
        The value will be rounded up to the nearest odd integer.

        Parameters
        ----------
        sigma : `float`
            Gaussian sigma of smoothing kernel.

        Returns
        -------
        size : `int`
            Size of the smoothing kernel.
        """
        return (int(sigma * self.config.nSigmaForKernel + 0.5)//2)*2 + 1  # make sure it is odd

calculateThreshold()

def lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask.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`).

Definition at line 71 of file dynamicDetection.py.

    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)

clearMask()

def lsst.meas.algorithms.detection.SourceDetectionTask.clearMask (   self,   mask  )

inherited

Clear the DETECTED and DETECTED_NEGATIVE mask planes

Removes any previous detection mask in preparation for a new
detection pass.

Parameters
----------
mask : `lsst.afw.image.Mask`
    Mask to be cleared.

Definition at line 449 of file detection.py.

    def clearMask(self, mask):
        """Clear the DETECTED and DETECTED_NEGATIVE mask planes

        Removes any previous detection mask in preparation for a new
        detection pass.

        Parameters
        ----------
        mask : `lsst.afw.image.Mask`
            Mask to be cleared.
        """
        mask &= ~(mask.getPlaneBitMask("DETECTED") | mask.getPlaneBitMask("DETECTED_NEGATIVE"))

clearUnwantedResults()

def lsst.meas.algorithms.detection.SourceDetectionTask.clearUnwantedResults (   self,   mask,   results  )

inherited

Clear unwanted results from the Struct of results

If we specifically want only positive or only negative detections,
drop the ones we don't want, and its associated mask plane.

Parameters
----------
mask : `lsst.afw.image.Mask`
    Mask image.
results : `lsst.pipe.base.Struct`
    Detection results, with ``positive`` and ``negative`` elements;
    modified.

Definition at line 705 of file detection.py.

    def clearUnwantedResults(self, mask, results):
        """Clear unwanted results from the Struct of results

        If we specifically want only positive or only negative detections,
        drop the ones we don't want, and its associated mask plane.

        Parameters
        ----------
        mask : `lsst.afw.image.Mask`
            Mask image.
        results : `lsst.pipe.base.Struct`
            Detection results, with ``positive`` and ``negative`` elements;
            modified.
        """
        if self.config.thresholdPolarity == "positive":
            if self.config.reEstimateBackground:
                mask &= ~mask.getPlaneBitMask("DETECTED_NEGATIVE")
            results.negative = None
        elif self.config.thresholdPolarity == "negative":
            if self.config.reEstimateBackground:
                mask &= ~mask.getPlaneBitMask("DETECTED")
            results.positive = None

convolveImage()

def lsst.meas.algorithms.detection.SourceDetectionTask.convolveImage (   self,   maskedImage,   psf,   doSmooth = True  )

inherited

Convolve the image with the PSF

We convolve the image with a Gaussian approximation to the PSF,
because this is separable and therefore fast. It's technically a
correlation rather than a convolution, but since we use a symmetric
Gaussian there's no difference.

The convolution can be disabled with ``doSmooth=False``. If we do
convolve, we mask the edges as ``EDGE`` and return the convolved image
with the edges removed. This is because we can't convolve the edges
because the kernel would extend off the image.

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image to convolve.
psf : `lsst.afw.detection.Psf`
    PSF to convolve with (actually with a Gaussian approximation
    to it).
doSmooth : `bool`
    Actually do the convolution? Set to False when running on
    e.g. a pre-convolved image, or a mask plane.

Return Struct contents
----------------------
middle : `lsst.afw.image.MaskedImage`
    Convolved image, without the edges.
sigma : `float`
    Gaussian sigma used for the convolution.

Definition at line 509 of file detection.py.

    def convolveImage(self, maskedImage, psf, doSmooth=True):
        """Convolve the image with the PSF

        We convolve the image with a Gaussian approximation to the PSF,
        because this is separable and therefore fast. It's technically a
        correlation rather than a convolution, but since we use a symmetric
        Gaussian there's no difference.

        The convolution can be disabled with ``doSmooth=False``. If we do
        convolve, we mask the edges as ``EDGE`` and return the convolved image
        with the edges removed. This is because we can't convolve the edges
        because the kernel would extend off the image.

        Parameters
        ----------
        maskedImage : `lsst.afw.image.MaskedImage`
            Image to convolve.
        psf : `lsst.afw.detection.Psf`
            PSF to convolve with (actually with a Gaussian approximation
            to it).
        doSmooth : `bool`
            Actually do the convolution? Set to False when running on
            e.g. a pre-convolved image, or a mask plane.

        Return Struct contents
        ----------------------
        middle : `lsst.afw.image.MaskedImage`
            Convolved image, without the edges.
        sigma : `float`
            Gaussian sigma used for the convolution.
        """
        self.metadata.set("doSmooth", doSmooth)
        sigma = psf.computeShape().getDeterminantRadius()
        self.metadata.set("sigma", sigma)

        if not doSmooth:
            middle = maskedImage.Factory(maskedImage)
            return pipeBase.Struct(middle=middle, sigma=sigma)

        # Smooth using a Gaussian (which is separable, hence fast) of width sigma
        # Make a SingleGaussian (separable) kernel with the 'sigma'
        kWidth = self.calculateKernelSize(sigma)
        self.metadata.set("smoothingKernelWidth", kWidth)
        gaussFunc = afwMath.GaussianFunction1D(sigma)
        gaussKernel = afwMath.SeparableKernel(kWidth, kWidth, gaussFunc, gaussFunc)

        convolvedImage = maskedImage.Factory(maskedImage.getBBox())

        afwMath.convolve(convolvedImage, maskedImage, gaussKernel, afwMath.ConvolutionControl())
        #
        # Only search psf-smoothed part of frame
        #
        goodBBox = gaussKernel.shrinkBBox(convolvedImage.getBBox())
        middle = convolvedImage.Factory(convolvedImage, goodBBox, afwImage.PARENT, False)
        #
        # Mark the parts of the image outside goodBBox as EDGE
        #
        self.setEdgeBits(maskedImage, goodBBox, maskedImage.getMask().getPlaneBitMask("EDGE"))

        return pipeBase.Struct(middle=middle, sigma=sigma)

detectFootprints()

def lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask.detectFootprints	(		self,
			exposure,
			doSmooth = True,
			sigma = None,
			clearMask = True,
			expId = None
	)

Detect footprints with a dynamic threshold

This varies from the vanilla ``detectFootprints`` method because we
do detection twice: one with a low threshold so that we can find
sky uncontaminated by objects, then one more with the new calculated
threshold.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to process; DETECTED{,_NEGATIVE} mask plane will be
    set in-place.
doSmooth : `bool`, optional
    If True, smooth the image before detection using a Gaussian
    of width ``sigma``.
sigma : `float`, optional
    Gaussian Sigma of PSF (pixels); used for smoothing and to grow
    detections; if `None` then measure the sigma of the PSF of the
    ``exposure``.
clearMask : `bool`, optional
    Clear both DETECTED and DETECTED_NEGATIVE planes before running
    detection.
expId : `int`, optional
    Exposure identifier, used as a seed for the random number
    generator. If absent, the seed will be the sum of the image.

Return Struct contents
----------------------
positive : `lsst.afw.detection.FootprintSet`
    Positive polarity footprints (may be `None`)
negative : `lsst.afw.detection.FootprintSet`
    Negative polarity footprints (may be `None`)
numPos : `int`
    Number of footprints in positive or 0 if detection polarity was
    negative.
numNeg : `int`
    Number of footprints in negative or 0 if detection polarity was
    positive.
background : `lsst.afw.math.BackgroundList`
    Re-estimated background.  `None` if
    ``reEstimateBackground==False``.
factor : `float`
    Multiplication factor applied to the configured detection
    threshold.
prelim : `lsst.pipe.base.Struct`
    Results from preliminary detection pass.

Definition at line 140 of file dynamicDetection.py.

    def detectFootprints(self, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None):
        """Detect footprints with a dynamic threshold

        This varies from the vanilla ``detectFootprints`` method because we
        do detection twice: one with a low threshold so that we can find
        sky uncontaminated by objects, then one more with the new calculated
        threshold.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process; DETECTED{,_NEGATIVE} mask plane will be
            set in-place.
        doSmooth : `bool`, optional
            If True, smooth the image before detection using a Gaussian
            of width ``sigma``.
        sigma : `float`, optional
            Gaussian Sigma of PSF (pixels); used for smoothing and to grow
            detections; if `None` then measure the sigma of the PSF of the
            ``exposure``.
        clearMask : `bool`, optional
            Clear both DETECTED and DETECTED_NEGATIVE planes before running
            detection.
        expId : `int`, optional
            Exposure identifier, used as a seed for the random number
            generator. If absent, the seed will be the sum of the image.

        Return Struct contents
        ----------------------
        positive : `lsst.afw.detection.FootprintSet`
            Positive polarity footprints (may be `None`)
        negative : `lsst.afw.detection.FootprintSet`
            Negative polarity footprints (may be `None`)
        numPos : `int`
            Number of footprints in positive or 0 if detection polarity was
            negative.
        numNeg : `int`
            Number of footprints in negative or 0 if detection polarity was
            positive.
        background : `lsst.afw.math.BackgroundList`
            Re-estimated background.  `None` if
            ``reEstimateBackground==False``.
        factor : `float`
            Multiplication factor applied to the configured detection
            threshold.
        prelim : `lsst.pipe.base.Struct`
            Results from preliminary detection pass.
        """
        maskedImage = exposure.maskedImage

        if clearMask:
            self.clearMask(maskedImage.mask)
        else:
            oldDetected = maskedImage.mask.array & maskedImage.mask.getPlaneBitMask(["DETECTED",
                                                                                     "DETECTED_NEGATIVE"])

        with self.tempWideBackgroundContext(exposure):
            # Could potentially smooth with a wider kernel than the PSF in order to better pick up the
            # wings of stars and galaxies, but for now sticking with the PSF as that's more simple.
            psf = self.getPsf(exposure, sigma=sigma)
            convolveResults = self.convolveImage(maskedImage, psf, doSmooth=doSmooth)
            middle = convolveResults.middle
            sigma = convolveResults.sigma
            prelim = self.applyThreshold(middle, maskedImage.getBBox(), self.config.prelimThresholdFactor)
            self.finalizeFootprints(maskedImage.mask, prelim, sigma, self.config.prelimThresholdFactor)

            # Calculate the proper threshold
            # seed needs to fit in a C++ 'int' so pybind doesn't choke on it
            seed = (expId if expId is not None else int(maskedImage.image.array.sum())) % (2**31 - 1)
            threshResults = self.calculateThreshold(exposure, seed, sigma=sigma)
            factor = threshResults.multiplicative
            self.log.info("Modifying configured detection threshold by factor %f to %f",
                          factor, factor*self.config.thresholdValue)

            # Blow away preliminary (low threshold) detection mask
            self.clearMask(maskedImage.mask)
            if not clearMask:
                maskedImage.mask.array |= oldDetected

            # Rinse and repeat thresholding with new calculated threshold
            results = self.applyThreshold(middle, maskedImage.getBBox(), factor)
            results.prelim = prelim
            results.background = lsst.afw.math.BackgroundList()
            if self.config.doTempLocalBackground:
                self.applyTempLocalBackground(exposure, middle, results)
            self.finalizeFootprints(maskedImage.mask, results, sigma, factor)

            self.clearUnwantedResults(maskedImage.mask, results)

        if self.config.reEstimateBackground:
            self.reEstimateBackground(maskedImage, results.background)

        self.display(exposure, results, middle)

        if self.config.doBackgroundTweak:
            # Re-do the background tweak after any temporary backgrounds have been restored
            #
            # But we want to keep any large-scale background (e.g., scattered light from bright stars)
            # from being selected for sky objects in the calculation, so do another detection pass without
            # either the local or wide temporary background subtraction; the DETECTED pixels will mark
            # the area to ignore.
            originalMask = maskedImage.mask.array.copy()
            try:
                self.clearMask(exposure.mask)
                convolveResults = self.convolveImage(maskedImage, psf, doSmooth=doSmooth)
                tweakDetResults = self.applyThreshold(convolveResults.middle, maskedImage.getBBox(), factor)
                self.finalizeFootprints(maskedImage.mask, tweakDetResults, sigma, factor)
                bgLevel = self.calculateThreshold(exposure, seed, sigma=sigma).additive
            finally:
                maskedImage.mask.array[:] = originalMask
            self.tweakBackground(exposure, bgLevel, results.background)

        return results

display()

def lsst.meas.algorithms.detection.SourceDetectionTask.display (   self,   exposure,   results,   convolvedImage = None  )

inherited

Display detections if so configured

Displays the ``exposure`` in frame 0, overlays the detection peaks.

Requires that ``lsstDebug`` has been set up correctly, so that
``lsstDebug.Info("lsst.meas.algorithms.detection")`` evaluates `True`.

If the ``convolvedImage`` is non-`None` and
``lsstDebug.Info("lsst.meas.algorithms.detection") > 1``, the
``convolvedImage`` will be displayed in frame 1.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to display, on which will be plotted the detections.
results : `lsst.pipe.base.Struct`
    Results of the 'detectFootprints' method, containing positive and
    negative footprints (which contain the peak positions that we will
    plot). This is a `Struct` with ``positive`` and ``negative``
    elements that are of type `lsst.afw.detection.FootprintSet`.
convolvedImage : `lsst.afw.image.Image`, optional
    Convolved image used for thresholding.

Definition at line 360 of file detection.py.

    def display(self, exposure, results, convolvedImage=None):
        """Display detections if so configured

        Displays the ``exposure`` in frame 0, overlays the detection peaks.

        Requires that ``lsstDebug`` has been set up correctly, so that
        ``lsstDebug.Info("lsst.meas.algorithms.detection")`` evaluates `True`.

        If the ``convolvedImage`` is non-`None` and
        ``lsstDebug.Info("lsst.meas.algorithms.detection") > 1``, the
        ``convolvedImage`` will be displayed in frame 1.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to display, on which will be plotted the detections.
        results : `lsst.pipe.base.Struct`
            Results of the 'detectFootprints' method, containing positive and
            negative footprints (which contain the peak positions that we will
            plot). This is a `Struct` with ``positive`` and ``negative``
            elements that are of type `lsst.afw.detection.FootprintSet`.
        convolvedImage : `lsst.afw.image.Image`, optional
            Convolved image used for thresholding.
        """
        try:
            import lsstDebug
            display = lsstDebug.Info(__name__).display
        except ImportError:
            try:
                display
            except NameError:
                display = False
        if not display:
            return

        afwDisplay.setDefaultMaskTransparency(75)

        disp0 = afwDisplay.Display(frame=0)
        disp0.mtv(exposure, title="detection")

        def plotPeaks(fps, ctype):
            if fps is None:
                return
            with disp0.Buffering():
                for fp in fps.getFootprints():
                    for pp in fp.getPeaks():
                        disp0.dot("+", pp.getFx(), pp.getFy(), ctype=ctype)
        plotPeaks(results.positive, "yellow")
        plotPeaks(results.negative, "red")

        if convolvedImage and display > 1:
            disp1 = afwDisplay.Display(frame=1)
            disp1.mtv(convolvedImage, title="PSF smoothed")

finalizeFootprints()

def lsst.meas.algorithms.detection.SourceDetectionTask.finalizeFootprints (   self,   mask,   results,   sigma,   factor = 1.0  )

inherited

Finalize the detected footprints

Grows the footprints, sets the ``DETECTED`` and ``DETECTED_NEGATIVE``
mask planes, and logs the results.

``numPos`` (number of positive footprints), ``numPosPeaks`` (number
of positive peaks), ``numNeg`` (number of negative footprints),
``numNegPeaks`` (number of negative peaks) entries are added to the
detection results.

Parameters
----------
mask : `lsst.afw.image.Mask`
    Mask image on which to flag detected pixels.
results : `lsst.pipe.base.Struct`
    Struct of detection results, including ``positive`` and
    ``negative`` entries; modified.
sigma : `float`
    Gaussian sigma of PSF.
factor : `float`
    Multiplier for the configured threshold.

Definition at line 619 of file detection.py.

    def finalizeFootprints(self, mask, results, sigma, factor=1.0):
        """Finalize the detected footprints

        Grows the footprints, sets the ``DETECTED`` and ``DETECTED_NEGATIVE``
        mask planes, and logs the results.

        ``numPos`` (number of positive footprints), ``numPosPeaks`` (number
        of positive peaks), ``numNeg`` (number of negative footprints),
        ``numNegPeaks`` (number of negative peaks) entries are added to the
        detection results.

        Parameters
        ----------
        mask : `lsst.afw.image.Mask`
            Mask image on which to flag detected pixels.
        results : `lsst.pipe.base.Struct`
            Struct of detection results, including ``positive`` and
            ``negative`` entries; modified.
        sigma : `float`
            Gaussian sigma of PSF.
        factor : `float`
            Multiplier for the configured threshold.
        """
        for polarity, maskName in (("positive", "DETECTED"), ("negative", "DETECTED_NEGATIVE")):
            fpSet = getattr(results, polarity)
            if fpSet is None:
                continue
            if self.config.nSigmaToGrow > 0:
                nGrow = int((self.config.nSigmaToGrow * sigma) + 0.5)
                self.metadata.set("nGrow", nGrow)
                if self.config.combinedGrow:
                    fpSet = afwDet.FootprintSet(fpSet, nGrow, self.config.isotropicGrow)
                else:
                    stencil = (afwGeom.Stencil.CIRCLE if self.config.isotropicGrow else
                               afwGeom.Stencil.MANHATTAN)
                    for fp in fpSet:
                        fp.dilate(nGrow, stencil)
            fpSet.setMask(mask, maskName)
            if not self.config.returnOriginalFootprints:
                setattr(results, polarity, fpSet)

        results.numPos = 0
        results.numPosPeaks = 0
        results.numNeg = 0
        results.numNegPeaks = 0
        positive = ""
        negative = ""

        if results.positive is not None:
            results.numPos = len(results.positive.getFootprints())
            results.numPosPeaks = sum(len(fp.getPeaks()) for fp in results.positive.getFootprints())
            positive = " %d positive peaks in %d footprints" % (results.numPosPeaks, results.numPos)
        if results.negative is not None:
            results.numNeg = len(results.negative.getFootprints())
            results.numNegPeaks = sum(len(fp.getPeaks()) for fp in results.negative.getFootprints())
            negative = " %d negative peaks in %d footprints" % (results.numNegPeaks, results.numNeg)

        self.log.info("Detected%s%s%s to %g %s" %
                      (positive, " and" if positive and negative else "", negative,
                       self.config.thresholdValue*self.config.includeThresholdMultiplier*factor,
                       "DN" if self.config.thresholdType == "value" else "sigma"))

getPsf()

def lsst.meas.algorithms.detection.SourceDetectionTask.getPsf (   self,   exposure,   sigma = None  )

inherited

Retrieve the PSF for an exposure

If ``sigma`` is provided, we make a ``GaussianPsf`` with that,
otherwise use the one from the ``exposure``.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure from which to retrieve the PSF.
sigma : `float`, optional
    Gaussian sigma to use if provided.

Returns
-------
psf : `lsst.afw.detection.Psf`
    PSF to use for detection.

Definition at line 482 of file detection.py.

    def getPsf(self, exposure, sigma=None):
        """Retrieve the PSF for an exposure

        If ``sigma`` is provided, we make a ``GaussianPsf`` with that,
        otherwise use the one from the ``exposure``.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure from which to retrieve the PSF.
        sigma : `float`, optional
            Gaussian sigma to use if provided.

        Returns
        -------
        psf : `lsst.afw.detection.Psf`
            PSF to use for detection.
        """
        if sigma is None:
            psf = exposure.getPsf()
            if psf is None:
                raise RuntimeError("Unable to determine PSF to use for detection: no sigma provided")
            sigma = psf.computeShape().getDeterminantRadius()
        size = self.calculateKernelSize(sigma)
        psf = afwDet.GaussianPsf(size, size, sigma)
        return psf

makeThreshold()

def lsst.meas.algorithms.detection.SourceDetectionTask.makeThreshold (   self,   image,   thresholdParity,   factor = 1.0  )

inherited

Make an afw.detection.Threshold object corresponding to the task's
configuration and the statistics of the given image.

Parameters
----------
image : `afw.image.MaskedImage`
    Image to measure noise statistics from if needed.
thresholdParity: `str`
    One of "positive" or "negative", to set the kind of fluctuations
    the Threshold will detect.
factor : `float`
    Factor by which to multiply the configured detection threshold.
    This is useful for tweaking the detection threshold slightly.

Returns
-------
threshold : `lsst.afw.detection.Threshold`
    Detection threshold.

Definition at line 797 of file detection.py.

    def makeThreshold(self, image, thresholdParity, factor=1.0):
        """Make an afw.detection.Threshold object corresponding to the task's
        configuration and the statistics of the given image.

        Parameters
        ----------
        image : `afw.image.MaskedImage`
            Image to measure noise statistics from if needed.
        thresholdParity: `str`
            One of "positive" or "negative", to set the kind of fluctuations
            the Threshold will detect.
        factor : `float`
            Factor by which to multiply the configured detection threshold.
            This is useful for tweaking the detection threshold slightly.

        Returns
        -------
        threshold : `lsst.afw.detection.Threshold`
            Detection threshold.
        """
        parity = False if thresholdParity == "negative" else True
        thresholdValue = self.config.thresholdValue
        thresholdType = self.config.thresholdType
        if self.config.thresholdType == 'stdev':
            bad = image.getMask().getPlaneBitMask(self.config.statsMask)
            sctrl = afwMath.StatisticsControl()
            sctrl.setAndMask(bad)
            stats = afwMath.makeStatistics(image, afwMath.STDEVCLIP, sctrl)
            thresholdValue *= stats.getValue(afwMath.STDEVCLIP)
            thresholdType = 'value'

        threshold = afwDet.createThreshold(thresholdValue*factor, thresholdType, parity)
        threshold.setIncludeMultiplier(self.config.includeThresholdMultiplier)
        return threshold

reEstimateBackground()

def lsst.meas.algorithms.detection.SourceDetectionTask.reEstimateBackground (   self,   maskedImage,   backgrounds  )

inherited

Estimate the background after detection

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image on which to estimate the background.
backgrounds : `lsst.afw.math.BackgroundList`
    List of backgrounds; modified.

Returns
-------
bg : `lsst.afw.math.backgroundMI`
    Empirical background model.

Definition at line 681 of file detection.py.

    def reEstimateBackground(self, maskedImage, backgrounds):
        """Estimate the background after detection

        Parameters
        ----------
        maskedImage : `lsst.afw.image.MaskedImage`
            Image on which to estimate the background.
        backgrounds : `lsst.afw.math.BackgroundList`
            List of backgrounds; modified.

        Returns
        -------
        bg : `lsst.afw.math.backgroundMI`
            Empirical background model.
        """
        bg = self.background.fitBackground(maskedImage)
        if self.config.adjustBackground:
            self.log.warn("Fiddling the background by %g", self.config.adjustBackground)
            bg += self.config.adjustBackground
        self.log.info("Resubtracting the background after object detection")
        maskedImage -= bg.getImageF()
        backgrounds.append(bg)
        return bg

run()

def lsst.meas.algorithms.detection.SourceDetectionTask.run (   self,   table,   exposure,   doSmooth = True,   sigma = None,   clearMask = True,   expId = None  )

inherited

Run source detection and create a SourceCatalog of detections.

Parameters
----------
table : `lsst.afw.table.SourceTable`
    Table object that will be used to create the SourceCatalog.
exposure : `lsst.afw.image.Exposure`
    Exposure to process; DETECTED mask plane will be set in-place.
doSmooth : `bool`
    If True, smooth the image before detection using a Gaussian of width
    ``sigma``, or the measured PSF width. Set to False when running on
    e.g. a pre-convolved image, or a mask plane.
sigma : `float`
    Sigma of PSF (pixels); used for smoothing and to grow detections;
    if None then measure the sigma of the PSF of the exposure
clearMask : `bool`
    Clear DETECTED{,_NEGATIVE} planes before running detection.
expId : `int`
    Exposure identifier; unused by this implementation, but used for
    RNG seed by subclasses.

Returns
-------
result : `lsst.pipe.base.Struct`
  ``sources``
      The detected sources (`lsst.afw.table.SourceCatalog`)
  ``fpSets``
      The result resturned by `detectFootprints`
      (`lsst.pipe.base.Struct`).

Raises
------
ValueError
    If flags.negative is needed, but isn't in table's schema.
lsst.pipe.base.TaskError
    If sigma=None, doSmooth=True and the exposure has no PSF.

Notes
-----
If you want to avoid dealing with Sources and Tables, you can use
detectFootprints() to just get the `lsst.afw.detection.FootprintSet`s.

Definition at line 297 of file detection.py.

    def run(self, table, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None):
        """Run source detection and create a SourceCatalog of detections.

        Parameters
        ----------
        table : `lsst.afw.table.SourceTable`
            Table object that will be used to create the SourceCatalog.
        exposure : `lsst.afw.image.Exposure`
            Exposure to process; DETECTED mask plane will be set in-place.
        doSmooth : `bool`
            If True, smooth the image before detection using a Gaussian of width
            ``sigma``, or the measured PSF width. Set to False when running on
            e.g. a pre-convolved image, or a mask plane.
        sigma : `float`
            Sigma of PSF (pixels); used for smoothing and to grow detections;
            if None then measure the sigma of the PSF of the exposure
        clearMask : `bool`
            Clear DETECTED{,_NEGATIVE} planes before running detection.
        expId : `int`
            Exposure identifier; unused by this implementation, but used for
            RNG seed by subclasses.

        Returns
        -------
        result : `lsst.pipe.base.Struct`
          ``sources``
              The detected sources (`lsst.afw.table.SourceCatalog`)
          ``fpSets``
              The result resturned by `detectFootprints`
              (`lsst.pipe.base.Struct`).

        Raises
        ------
        ValueError
            If flags.negative is needed, but isn't in table's schema.
        lsst.pipe.base.TaskError
            If sigma=None, doSmooth=True and the exposure has no PSF.

        Notes
        -----
        If you want to avoid dealing with Sources and Tables, you can use
        detectFootprints() to just get the `lsst.afw.detection.FootprintSet`s.
        """
        if self.negativeFlagKey is not None and self.negativeFlagKey not in table.getSchema():
            raise ValueError("Table has incorrect Schema")
        results = self.detectFootprints(exposure=exposure, doSmooth=doSmooth, sigma=sigma,
                                        clearMask=clearMask, expId=expId)
        sources = afwTable.SourceCatalog(table)
        sources.reserve(results.numPos + results.numNeg)
        if results.negative:
            results.negative.makeSources(sources)
            if self.negativeFlagKey:
                for record in sources:
                    record.set(self.negativeFlagKey, True)
        if results.positive:
            results.positive.makeSources(sources)
        results.fpSets = results.copy()  # Backward compatibility
        results.sources = sources
        return results

setEdgeBits()

def lsst.meas.algorithms.detection.SourceDetectionTask.setEdgeBits (   maskedImage,   goodBBox,   edgeBitmask  )

staticinherited

Set the edgeBitmask bits for all of maskedImage outside goodBBox

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image on which to set edge bits in the mask.
goodBBox : `lsst.geom.Box2I`
    Bounding box of good pixels, in ``LOCAL`` coordinates.
edgeBitmask : `lsst.afw.image.MaskPixel`
    Bit mask to OR with the existing mask bits in the region
    outside ``goodBBox``.

Definition at line 875 of file detection.py.

    def setEdgeBits(maskedImage, goodBBox, edgeBitmask):
        """Set the edgeBitmask bits for all of maskedImage outside goodBBox

        Parameters
        ----------
        maskedImage : `lsst.afw.image.MaskedImage`
            Image on which to set edge bits in the mask.
        goodBBox : `lsst.geom.Box2I`
            Bounding box of good pixels, in ``LOCAL`` coordinates.
        edgeBitmask : `lsst.afw.image.MaskPixel`
            Bit mask to OR with the existing mask bits in the region
            outside ``goodBBox``.
        """
        msk = maskedImage.getMask()

        mx0, my0 = maskedImage.getXY0()
        for x0, y0, w, h in ([0, 0,
                              msk.getWidth(), goodBBox.getBeginY() - my0],
                             [0, goodBBox.getEndY() - my0, msk.getWidth(),
                              maskedImage.getHeight() - (goodBBox.getEndY() - my0)],
                             [0, 0,
                              goodBBox.getBeginX() - mx0, msk.getHeight()],
                             [goodBBox.getEndX() - mx0, 0,
                              maskedImage.getWidth() - (goodBBox.getEndX() - mx0), msk.getHeight()],
                             ):
            edgeMask = msk.Factory(msk, lsst.geom.BoxI(lsst.geom.PointI(x0, y0),
                                                       lsst.geom.ExtentI(w, h)), afwImage.LOCAL)
            edgeMask |= edgeBitmask

tempWideBackgroundContext()

def lsst.meas.algorithms.detection.SourceDetectionTask.tempWideBackgroundContext (   self,   exposure  )

inherited

Context manager for removing wide (large-scale) background

Removing a wide (large-scale) background helps to suppress the
detection of large footprints that may overwhelm the deblender.
It does, however, set a limit on the maximum scale of objects.

The background that we remove will be restored upon exit from
the context manager.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure on which to remove large-scale background.

Returns
-------
context : context manager
    Context manager that will ensure the temporary wide background
    is restored.

Definition at line 905 of file detection.py.

    def tempWideBackgroundContext(self, exposure):
        """Context manager for removing wide (large-scale) background

        Removing a wide (large-scale) background helps to suppress the
        detection of large footprints that may overwhelm the deblender.
        It does, however, set a limit on the maximum scale of objects.

        The background that we remove will be restored upon exit from
        the context manager.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure on which to remove large-scale background.

        Returns
        -------
        context : context manager
            Context manager that will ensure the temporary wide background
            is restored.
        """
        doTempWideBackground = self.config.doTempWideBackground
        if doTempWideBackground:
            self.log.info("Applying temporary wide background subtraction")
            original = exposure.maskedImage.image.array[:].copy()
            self.tempWideBackground.run(exposure).background
            # Remove NO_DATA regions (e.g., edge of the field-of-view); these can cause detections after
            # subtraction because of extrapolation of the background model into areas with no constraints.
            image = exposure.maskedImage.image
            mask = exposure.maskedImage.mask
            noData = mask.array & mask.getPlaneBitMask("NO_DATA") > 0
            isGood = mask.array & mask.getPlaneBitMask(self.config.statsMask) == 0
            image.array[noData] = np.median(image.array[~noData & isGood])
        try:
            yield
        finally:
            if doTempWideBackground:
                exposure.maskedImage.image.array[:] = original

tweakBackground()

def lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask.tweakBackground	(		self,
			exposure,
			bgLevel,
			bgList = None
	)

Modify the background by a constant value

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure for which to tweak background.
bgLevel : `float`
    Background level to remove
bgList : `lsst.afw.math.BackgroundList`, optional
    List of backgrounds to append to.

Returns
-------
bg : `lsst.afw.math.BackgroundMI`
    Constant background model.

Definition at line 254 of file dynamicDetection.py.

    def tweakBackground(self, exposure, bgLevel, bgList=None):
        """Modify the background by a constant value

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure for which to tweak background.
        bgLevel : `float`
            Background level to remove
        bgList : `lsst.afw.math.BackgroundList`, optional
            List of backgrounds to append to.

        Returns
        -------
        bg : `lsst.afw.math.BackgroundMI`
            Constant background model.
        """
        self.log.info("Tweaking background by %f to match sky photometry", bgLevel)
        exposure.image -= bgLevel
        bgStats = lsst.afw.image.MaskedImageF(1, 1)
        bgStats.set(bgLevel, 0, bgLevel)
        bg = lsst.afw.math.BackgroundMI(exposure.getBBox(), bgStats)
        bgData = (bg, lsst.afw.math.Interpolate.LINEAR, lsst.afw.math.REDUCE_INTERP_ORDER,
                  lsst.afw.math.ApproximateControl.UNKNOWN, 0, 0, False)
        if bgList is not None:
            bgList.append(bgData)
        return bg

updatePeaks()

def lsst.meas.algorithms.detection.SourceDetectionTask.updatePeaks (   self,   fpSet,   image,   threshold  )

inherited

Update the Peaks in a FootprintSet by detecting new Footprints and
Peaks in an image and using the new Peaks instead of the old ones.

Parameters
----------
fpSet : `afw.detection.FootprintSet`
    Set of Footprints whose Peaks should be updated.
image : `afw.image.MaskedImage`
    Image to detect new Footprints and Peak in.
threshold : `afw.detection.Threshold`
    Threshold object for detection.

Input Footprints with fewer Peaks than self.config.nPeaksMaxSimple
are not modified, and if no new Peaks are detected in an input
Footprint, the brightest original Peak in that Footprint is kept.

Definition at line 832 of file detection.py.

    def updatePeaks(self, fpSet, image, threshold):
        """Update the Peaks in a FootprintSet by detecting new Footprints and
        Peaks in an image and using the new Peaks instead of the old ones.

        Parameters
        ----------
        fpSet : `afw.detection.FootprintSet`
            Set of Footprints whose Peaks should be updated.
        image : `afw.image.MaskedImage`
            Image to detect new Footprints and Peak in.
        threshold : `afw.detection.Threshold`
            Threshold object for detection.

        Input Footprints with fewer Peaks than self.config.nPeaksMaxSimple
        are not modified, and if no new Peaks are detected in an input
        Footprint, the brightest original Peak in that Footprint is kept.
        """
        for footprint in fpSet.getFootprints():
            oldPeaks = footprint.getPeaks()
            if len(oldPeaks) <= self.config.nPeaksMaxSimple:
                continue
            # We detect a new FootprintSet within each non-simple Footprint's
            # bbox to avoid a big O(N^2) comparison between the two sets of
            # Footprints.
            sub = image.Factory(image, footprint.getBBox())
            fpSetForPeaks = afwDet.FootprintSet(
                sub,
                threshold,
                "",  # don't set a mask plane
                self.config.minPixels
            )
            newPeaks = afwDet.PeakCatalog(oldPeaks.getTable())
            for fpForPeaks in fpSetForPeaks.getFootprints():
                for peak in fpForPeaks.getPeaks():
                    if footprint.contains(peak.getI()):
                        newPeaks.append(peak)
            if len(newPeaks) > 0:
                del oldPeaks[:]
                oldPeaks.extend(newPeaks)
            else:
                del oldPeaks[1:]

Member Data Documentation

ConfigClass

lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask.ConfigClass = DynamicDetectionConfig

static

Definition at line 46 of file dynamicDetection.py.

makeSourceCatalog

def lsst.meas.algorithms.detection.SourceDetectionTask.makeSourceCatalog = run

staticinherited

An alias for run.

Deprecated:

Remove this alias after checking for where it's used

Definition at line 358 of file detection.py.

negativeFlagKey

lsst.meas.algorithms.detection.SourceDetectionTask.negativeFlagKey

inherited

Definition at line 280 of file detection.py.

skyMeasurement

lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask.skyMeasurement

Definition at line 68 of file dynamicDetection.py.

skySchema

lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask.skySchema

Definition at line 67 of file dynamicDetection.py.

---

The documentation for this class was generated from the following file:

• /j/snowflake/release/lsstsw/stack/Linux64/meas_algorithms/18.1.0-13-gfe4edf0b+12/python/lsst/meas/algorithms/dynamicDetection.py

---

Generated on Sat Nov 16 2019 09:15:40 for LSSTApplications by   1.8.13