Functions
	getKernelCenterDisplacement (kernel, x, y, image=None)

	getPsfFwhm (psf, average=True, position=None)

float	evaluateMeanPsfFwhm (afwImage.Exposure exposure, float fwhmExposureBuffer, int fwhmExposureGrid)

afwImage.ImageD	computeAveragePsf (afwImage.Exposure exposure, float psfExposureBuffer, int psfExposureGrid)

	computeRobustStatistics (image, mask, statsCtrl, statistic=afwMath.MEANCLIP)

	computePSFNoiseEquivalentArea (psf)

	angleMean (angles)

	evaluateMaskFraction (mask, maskPlane)

	computeDifferenceImageMetrics (science, difference, stars, sky_sources=None)

	populate_sattle_visit_cache (visit_info, historical=False)

	checkMask (mask, sources, excludeMaskPlanes)

	setSourceFootprints (sources, kernelSize)

Variables
	_LOG = getLogger(__name__)

Detailed Description

Support utilities for Measuring sources

Function Documentation

◆ angleMean()

lsst.ip.diffim.utils.angleMean ( angles )

Calculate the mean of an array of angles.

Parameters
----------
angles : `ndarray`
    An array of angles, in radians

Returns
-------
`lsst.geom.Angle`
    The mean angle

Definition at line 308 of file utils.py.

◆ checkMask()

lsst.ip.diffim.utils.checkMask	(	mask,
		sources,
		excludeMaskPlanes )

Exclude sources that have masked pixels in their footprints.

Parameters
----------
mask : `lsst.afw.image.Mask`
    The image mask plane to use to reject sources
    based on the location of their centroid on the ccd.
sources : `lsst.afw.table.SourceCatalog`
    The source catalog to evaluate.
excludeMaskPlanes : `list` of `str`
    List of the names of the mask planes to exclude.

Returns
-------
good : `numpy.ndarray` of `bool`
    Array indicating whether each source in the catalog should be
    kept (True) or rejected (False) based on the value of the
    mask plane at its location.

Definition at line 462 of file utils.py.

◆ computeAveragePsf()

afwImage.ImageD lsst.ip.diffim.utils.computeAveragePsf	(	afwImage.Exposure	exposure,
		float	psfExposureBuffer,
		int	psfExposureGrid )

Get the average PSF by evaluating it on a grid within an exposure.

Parameters
----------
exposure : `~lsst.afw.image.Exposure`
    The exposure for which the average PSF is to be computed.
    The exposure must contain a `psf` attribute.
psfExposureBuffer : `float`
    Fractional buffer margin to be left out of all sides of the image
    during the construction of the grid to compute average PSF in an
    exposure.
psfExposureGrid : `int`
    Grid size to compute the average PSF in an exposure.

Returns
-------
psfImage : `~lsst.afw.image.Image`
    The average PSF across the exposure.

Raises
------
ValueError
    Raised if the PSF cannot be computed at any of the grid points.

See Also
--------
`evaluateMeanPsfFwhm`

Definition at line 203 of file utils.py.

◆ computeDifferenceImageMetrics()

lsst.ip.diffim.utils.computeDifferenceImageMetrics	(	science,
		difference,
		stars,
		sky_sources = None )

Compute quality metrics (saved to the task metadata) on the
difference image, at the locations of detected stars on the science
image. This restricts the metric to locations that should be
well-subtracted.

Parameters
----------
science : `lsst.afw.image.ExposureF`
    Science exposure that was subtracted.
difference : `lsst.afw.image.ExposureF`
    Result of subtracting template and science.
stars : `lsst.afw.table.SourceCatalog`
    Good calibration sources detected on science image; these
    footprints are what the metrics are computed on.

Returns
-------
metrics : `lsst.pipe.base.Struct`

    ``differenceFootprintRatioMean`` : `float`
        Mean of the ratio of the absolute value of the difference image
        (with the mean absolute value of the sky regions on the difference
        image removed) to the science image, computed in the footprints
        of stars detected on the science image (the sums below are of the
        pixels in each star or sky footprint):
        :math:`\mathrm{mean}_{footprints}((\sum |difference| -
        \mathrm{mean}(\sum |difference_{sky}|)) / \sum science)`
    ``differenceFootprintRatioStdev`` : `float`
        Standard Deviation across footprints of the above ratio.
    ``differenceFootprintSkyRatioMean`` : `float`
        Mean of the ratio of the absolute value of sky source regions on
        the difference image to the science image (the sum below is of the
        pixels in each sky source footprint):
        :math:`\mathrm{mean}_{footprints}(\sum |difference_{sky}| / \sum science_{sky})`
    ``differenceFootprintSkyRatioStdev`` : `float`
        Standard Deivation across footprints of the above sky ratio.

Definition at line 344 of file utils.py.

◆ computePSFNoiseEquivalentArea()

lsst.ip.diffim.utils.computePSFNoiseEquivalentArea ( psf )

Compute the noise equivalent area for an image psf

Parameters
----------
psf : `lsst.afw.detection.Psf`

Returns
-------
nea : `float`

Definition at line 292 of file utils.py.

◆ computeRobustStatistics()

lsst.ip.diffim.utils.computeRobustStatistics	(	image,
		mask,
		statsCtrl,
		statistic = afwMath.MEANCLIP )

Calculate a robust mean of the variance plane of an exposure.

Parameters
----------
image : `lsst.afw.image.Image`
    Image or variance plane of an exposure to evaluate.
mask : `lsst.afw.image.Mask`
    Mask plane to use for excluding pixels.
statsCtrl : `lsst.afw.math.StatisticsControl`
    Statistics control object for configuring the calculation.
statistic : `lsst.afw.math.Property`, optional
    The type of statistic to compute. Typical values are
    ``afwMath.MEANCLIP`` or ``afwMath.STDEVCLIP``.

Returns
-------
value : `float`
    The result of the statistic calculated from the unflagged pixels.

Definition at line 268 of file utils.py.

◆ evaluateMaskFraction()

lsst.ip.diffim.utils.evaluateMaskFraction	(		mask,
			maskPlane )

Evaluate the fraction of masked pixels in a mask plane.

Parameters
----------
mask : `lsst.afw.image.Mask`
    The mask to evaluate the fraction on
maskPlane : `str`
    The particular mask plane to evaluate the fraction

Returns
-------
value : `float`
    The calculated fraction of masked pixels

Definition at line 325 of file utils.py.

◆ evaluateMeanPsfFwhm()

float lsst.ip.diffim.utils.evaluateMeanPsfFwhm	(	afwImage.Exposure	exposure,
		float	fwhmExposureBuffer,
		int	fwhmExposureGrid )

Get the mean PSF FWHM by evaluating it on a grid within an exposure.

Parameters
----------
exposure : `~lsst.afw.image.Exposure`
    The exposure for which the mean FWHM of the PSF is to be computed.
    The exposure must contain a `psf` attribute.
fwhmExposureBuffer : `float`
    Fractional buffer margin to be left out of all sides of the image
    during the construction of the grid to compute mean PSF FWHM in an
    exposure.
fwhmExposureGrid : `int`
    Grid size to compute the mean FWHM in an exposure.

Returns
-------
meanFwhm : `float`
    The mean PSF FWHM on the exposure.

Raises
------
ValueError
    Raised if the PSF cannot be computed at any of the grid points.

See Also
--------
`getPsfFwhm`
`computeAveragePsf`

Definition at line 143 of file utils.py.

◆ getKernelCenterDisplacement()

lsst.ip.diffim.utils.getKernelCenterDisplacement	(	kernel,
		x,
		y,
		image = None )

Calculate the PSF matching kernel peak offset from the nominal
position.

Parameters
----------
kernel : `~lsst.afw.math.LinearCombinationKernel`
    The PSF matching kernel to evaluate.
x : `float`
    The x position on the detector to evaluate the kernel
y : `float`
    The y position on the detector to evaluate the kernel
image : `~lsst.afw.image.ImageD`
    The image to use as base for computing kernel pixel values

Returns
-------
kernel_sum : `float`
    The sum of the kernel on the desired location
dx : `float`
    The displacement of the kernel averaged peak, with respect to the
    center of the extraction of the kernel
dy : `float`
    The displacement of the kernel averaged peak, with respect to the
    center of the extraction of the kernel
pos_angle: `float`
    The position angle in detector coordinates of the displacement
length : `float`
    The displacement module of the kernel centroid in pixel units

Definition at line 48 of file utils.py.

◆ getPsfFwhm()

lsst.ip.diffim.utils.getPsfFwhm	(	psf,
		average = True,
		position = None )

Directly calculate the horizontal and vertical widths
of a PSF at half its maximum value.

Parameters
----------
psf : `~lsst.afw.detection.Psf`
    Point spread function (PSF) to evaluate.
average : `bool`, optional
    Set to return the average width over Y and X axes.
position : `~lsst.geom.Point2D`, optional
    The position at which to evaluate the PSF. If `None`, then the
    average position is used.

Returns
-------
psfSize : `float` | `tuple` [`float`]
    The FWHM of the PSF computed at its average position.
    Returns the widths along the Y and X axes,
    or the average of the two if `average` is set.

See Also
--------
evaluateMeanPsfFwhm

Definition at line 107 of file utils.py.

◆ populate_sattle_visit_cache()

lsst.ip.diffim.utils.populate_sattle_visit_cache	(		visit_info,
			historical = False )

Populate a cache of predicted satellite positions in the sattle service.

Parameters
----------
visit_info: `lsst.afw.table.ExposureRecord.visitInfo`
    Visit info for the science exposure being processed.
historical: `bool`
    Set to True if observations are older than the current day.

Raises
------
requests.HTTPError
    Raised if sattle call does not return success.

Definition at line 425 of file utils.py.

◆ setSourceFootprints()

lsst.ip.diffim.utils.setSourceFootprints	(		sources,
			kernelSize )

Add footprints of fixed size to a source catalog

Parameters
----------
sources : `lsst.afw.table.SourceCatalog`
    The source catalog to add footprints to.
kernelSize : `int`
    The "radius" of the footprint, i.e half the size of the bounding box.

Returns
-------
sources : `lsst.afw.table.SourceCatalog`
    The modified source catalog

Definition at line 502 of file utils.py.

Variable Documentation

◆ _LOG

lsst.ip.diffim.utils._LOG = getLogger(__name__)

protected

Definition at line 45 of file utils.py.

Functions

Variables