lsst.meas.base.tests.TestDataset Class Reference

Public Member Functions
def	makeMinimalSchema (cls)
def	__init__ (self, bbox, threshold=10.0, exposure=None, **kwds)
def	addSource (self, instFlux, centroid, shape=None)
def	addBlend (self)
def	transform (self, wcs, **kwds)
def	realize (self, noise, schema, randomSeed=1)

Static Public Member Functions
def	makePerturbedWcs (oldWcs, minScaleFactor=1.2, maxScaleFactor=1.5, minRotation=None, maxRotation=None, minRefShift=None, maxRefShift=None, minPixShift=2.0, maxPixShift=4.0, randomSeed=1)
def	makeEmptyExposure (bbox, wcs=None, crval=None, cdelt=None, psfSigma=2.0, psfDim=17, calibration=4)
def	drawGaussian (bbox, instFlux, ellipse)

Public Attributes
	keys
	threshold
	exposure
	psfShape
	schema
	catalog

Detailed Description

A simulated dataset consisuting of test image and truth catalog.

TestDataset creates an idealized image made of pure Gaussians (including a
Gaussian PSF), with simple noise and idealized Footprints/HeavyFootprints
that simulated the outputs of detection and deblending.  Multiple noise
realizations can be created from the same underlying sources, allowing
uncertainty estimates to be verified via Monte Carlo.

Parameters
----------
bbox : `lsst.geom.Box2I` or `lsst.geom.Box2D`
    Bounding box of the test image.
threshold : `float`
    Threshold absolute value used to determine footprints for
    simulated sources.  This thresholding will be applied before noise is
    actually added to images (or before the noise level is even known), so
    this will necessarily produce somewhat artificial footprints.
exposure : `lsst.afw.image.ExposureF`
    The image to which test sources should be added. Ownership should
    be considered transferred from the caller to the TestDataset.
    Must have a Gaussian PSF for truth catalog shapes to be exact.
**kwds
    Keyword arguments forwarded to makeEmptyExposure if exposure is `None`.

Notes
-----
Typical usage:

.. code-block: py

    bbox = lsst.geom.Box2I(lsst.geom.Point2I(0,0), lsst.geom.Point2I(100,
                                                                     100))
    dataset = TestDataset(bbox)
    dataset.addSource(instFlux=1E5, centroid=lsst.geom.Point2D(25, 26))
    dataset.addSource(instFlux=2E5, centroid=lsst.geom.Point2D(75, 24),
                    shape=lsst.afw.geom.Quadrupole(8, 7, 2))
    with dataset.addBlend() as family:
        family.addChild(instFlux=2E5, centroid=lsst.geom.Point2D(50, 72))
        family.addChild(instFlux=1.5E5, centroid=lsst.geom.Point2D(51, 74))
    exposure, catalog = dataset.realize(noise=100.0,
                                        schema=TestDataset.makeMinimalSchema())

Definition at line 122 of file tests.py.

Constructor & Destructor Documentation

__init__()

def lsst.meas.base.tests.TestDataset.__init__	(		self,
			bbox,
			threshold = 10.0,
			exposure = None,
		**	kwds
	)

Definition at line 381 of file tests.py.

    def __init__(self, bbox, threshold=10.0, exposure=None, **kwds):
        if exposure is None:
            exposure = self.makeEmptyExposure(bbox, **kwds)
        self.threshold = lsst.afw.detection.Threshold(threshold, lsst.afw.detection.Threshold.VALUE)
        self.exposure = exposure
        self.psfShape = self.exposure.getPsf().computeShape(bbox.getCenter())
        self.schema = self.makeMinimalSchema()
        self.catalog = lsst.afw.table.SourceCatalog(self.schema)
 

Member Function Documentation

addBlend()

def lsst.meas.base.tests.TestDataset.addBlend

(

self

)

Return a context manager which can add a blend of multiple sources.

Notes
-----
Note that nothing stops you from creating overlapping sources just using the addSource() method,
but addBlend() is necesssary to create a parent object and deblended HeavyFootprints of the type
produced by the detection and deblending pipelines.

Examples
--------
.. code-block: py
    d = TestDataset(...)
    with d.addBlend() as b:
        b.addChild(flux1, centroid1)
        b.addChild(flux2, centroid2, shape2)

Definition at line 450 of file tests.py.

    def addBlend(self):
        """Return a context manager which can add a blend of multiple sources.
 
        Notes
        -----
        Note that nothing stops you from creating overlapping sources just using the addSource() method,
        but addBlend() is necesssary to create a parent object and deblended HeavyFootprints of the type
        produced by the detection and deblending pipelines.
 
        Examples
        --------
        .. code-block: py
            d = TestDataset(...)
            with d.addBlend() as b:
                b.addChild(flux1, centroid1)
                b.addChild(flux2, centroid2, shape2)
        """
        return BlendContext(self)
 

addSource()

def lsst.meas.base.tests.TestDataset.addSource	(		self,
			instFlux,
			centroid,
			shape = None
	)

Add a source to the simulation.

Parameters
----------
instFlux : `float`
    Total instFlux of the source to be added.
centroid : `lsst.geom.Point2D`
    Position of the source to be added.
shape : `lsst.afw.geom.Quadrupole`
    Second moments of the source before PSF convolution. Note that the
    truth catalog records post-convolution moments. If `None`, a point
    source will be added.

Returns
-------
record : `lsst.afw.table.SourceRecord`
    A truth catalog record.
image : `lsst.afw.image.ImageF`
    Single-source image corresponding to the new source.

Definition at line 406 of file tests.py.

    def addSource(self, instFlux, centroid, shape=None):
        """Add a source to the simulation.
 
        Parameters
        ----------
        instFlux : `float`
            Total instFlux of the source to be added.
        centroid : `lsst.geom.Point2D`
            Position of the source to be added.
        shape : `lsst.afw.geom.Quadrupole`
            Second moments of the source before PSF convolution. Note that the
            truth catalog records post-convolution moments. If `None`, a point
            source will be added.
 
        Returns
        -------
        record : `lsst.afw.table.SourceRecord`
            A truth catalog record.
        image : `lsst.afw.image.ImageF`
            Single-source image corresponding to the new source.
        """
        # Create and set the truth catalog fields
        record = self.catalog.addNew()
        record.set(self.keys["instFlux"], instFlux)
        record.set(self.keys["centroid"], centroid)
        covariance = np.random.normal(0, 0.1, 4).reshape(2, 2)
        covariance[0, 1] = covariance[1, 0]  # CovarianceMatrixKey assumes symmetric x_y_Cov
        record.set(self.keys["centroid_sigma"], covariance.astype(np.float32))
        if shape is None:
            record.set(self.keys["isStar"], True)
            fullShape = self.psfShape
        else:
            record.set(self.keys["isStar"], False)
            fullShape = shape.convolve(self.psfShape)
        record.set(self.keys["shape"], fullShape)
        # Create an image containing just this source
        image = self.drawGaussian(self.exposure.getBBox(), instFlux,
                                  lsst.afw.geom.Ellipse(fullShape, centroid))
        # Generate a footprint for this source
        self._installFootprint(record, image)
        # Actually add the source to the full exposure
        self.exposure.getMaskedImage().getImage().getArray()[:, :] += image.getArray()
        return record, image
 

drawGaussian()

def lsst.meas.base.tests.TestDataset.drawGaussian (   bbox,   instFlux,   ellipse  )

static

Create an image of an elliptical Gaussian.

Parameters
----------
bbox : `lsst.geom.Box2I` or `lsst.geom.Box2D`
    Bounding box of image to create.
instFlux : `float`
    Total instrumental flux of the Gaussian (normalized analytically,
    not using pixel values).
ellipse : `lsst.afw.geom.Ellipse`
    Defines the centroid and shape.

Returns
-------
image : `lsst.afw.image.ImageF`
    An image of the Gaussian.

Definition at line 354 of file tests.py.

    def drawGaussian(bbox, instFlux, ellipse):
        """Create an image of an elliptical Gaussian.
 
        Parameters
        ----------
        bbox : `lsst.geom.Box2I` or `lsst.geom.Box2D`
            Bounding box of image to create.
        instFlux : `float`
            Total instrumental flux of the Gaussian (normalized analytically,
            not using pixel values).
        ellipse : `lsst.afw.geom.Ellipse`
            Defines the centroid and shape.
 
        Returns
        -------
        image : `lsst.afw.image.ImageF`
            An image of the Gaussian.
        """
        x, y = np.meshgrid(np.arange(bbox.getBeginX(), bbox.getEndX()),
                           np.arange(bbox.getBeginY(), bbox.getEndY()))
        t = ellipse.getGridTransform()
        xt = t[t.XX] * x + t[t.XY] * y + t[t.X]
        yt = t[t.YX] * x + t[t.YY] * y + t[t.Y]
        image = lsst.afw.image.ImageF(bbox)
        image.getArray()[:, :] = np.exp(-0.5*(xt**2 + yt**2))*instFlux/(2.0*ellipse.getCore().getArea())
        return image
 

makeEmptyExposure()

def lsst.meas.base.tests.TestDataset.makeEmptyExposure (   bbox,   wcs = None,   crval = None,   cdelt = None,   psfSigma = 2.0,   psfDim = 17,   calibration = 4  )

static

Create an Exposure, with a PhotoCalib, Wcs, and Psf, but no pixel values.

Parameters
----------
bbox : `lsst.geom.Box2I` or `lsst.geom.Box2D`
    Bounding box of the image in image coordinates.
wcs : `lsst.afw.geom.SkyWcs`, optional
    New WCS for the exposure (created from CRVAL and CDELT if `None`).
crval : `lsst.afw.geom.SpherePoint`, optional
    ICRS center of the TAN WCS attached to the image. If `None`, (45
    degrees, 45 degrees) is assumed.
cdelt : `lsst.geom.Angle`, optional
    Pixel scale of the image. If `None`, 0.2 arcsec is assumed.
psfSigma : `float`, optional
    Radius (sigma) of the Gaussian PSF attached to the image
psfDim : `int`, optional
    Width and height of the image's Gaussian PSF attached to the image
calibration : `float`, optional
    The spatially-constant calibration (in nJy/count) to set the
    PhotoCalib of the exposure.

Returns
-------
exposure : `lsst.age.image.ExposureF`
    An empty image.

Definition at line 310 of file tests.py.

    def makeEmptyExposure(bbox, wcs=None, crval=None, cdelt=None, psfSigma=2.0, psfDim=17, calibration=4):
        """Create an Exposure, with a PhotoCalib, Wcs, and Psf, but no pixel values.
 
        Parameters
        ----------
        bbox : `lsst.geom.Box2I` or `lsst.geom.Box2D`
            Bounding box of the image in image coordinates.
        wcs : `lsst.afw.geom.SkyWcs`, optional
            New WCS for the exposure (created from CRVAL and CDELT if `None`).
        crval : `lsst.afw.geom.SpherePoint`, optional
            ICRS center of the TAN WCS attached to the image. If `None`, (45
            degrees, 45 degrees) is assumed.
        cdelt : `lsst.geom.Angle`, optional
            Pixel scale of the image. If `None`, 0.2 arcsec is assumed.
        psfSigma : `float`, optional
            Radius (sigma) of the Gaussian PSF attached to the image
        psfDim : `int`, optional
            Width and height of the image's Gaussian PSF attached to the image
        calibration : `float`, optional
            The spatially-constant calibration (in nJy/count) to set the
            PhotoCalib of the exposure.
 
        Returns
        -------
        exposure : `lsst.age.image.ExposureF`
            An empty image.
        """
        if wcs is None:
            if crval is None:
                crval = lsst.geom.SpherePoint(45.0, 45.0, lsst.geom.degrees)
            if cdelt is None:
                cdelt = 0.2*lsst.geom.arcseconds
            crpix = lsst.geom.Box2D(bbox).getCenter()
            wcs = lsst.afw.geom.makeSkyWcs(crpix=crpix, crval=crval,
                                           cdMatrix=lsst.afw.geom.makeCdMatrix(scale=cdelt))
        exposure = lsst.afw.image.ExposureF(bbox)
        psf = lsst.afw.detection.GaussianPsf(psfDim, psfDim, psfSigma)
        photoCalib = lsst.afw.image.PhotoCalib(calibration)
        exposure.setWcs(wcs)
        exposure.setPsf(psf)
        exposure.setPhotoCalib(photoCalib)
        return exposure
 

makeMinimalSchema()

def lsst.meas.base.tests.TestDataset.makeMinimalSchema

(

cls

)

Return the minimal schema needed to hold truth catalog fields.

Notes
-----
When `TestDataset.realize` is called, the schema must include at least
these fields.  Usually it will include additional fields for
measurement algorithm outputs, allowing the same catalog to be used
for both truth values (the fields from the minimal schema) and the
measurements.

Definition at line 167 of file tests.py.

    def makeMinimalSchema(cls):
        """Return the minimal schema needed to hold truth catalog fields.
 
        Notes
        -----
        When `TestDataset.realize` is called, the schema must include at least
        these fields.  Usually it will include additional fields for
        measurement algorithm outputs, allowing the same catalog to be used
        for both truth values (the fields from the minimal schema) and the
        measurements.
        """
        if not hasattr(cls, "_schema"):
            schema = lsst.afw.table.SourceTable.makeMinimalSchema()
            cls.keys = {}
            cls.keys["parent"] = schema.find("parent").key
            cls.keys["nChild"] = schema.addField("deblend_nChild", type=np.int32)
            cls.keys["instFlux"] = schema.addField("truth_instFlux", type=np.float64,
                                                   doc="true instFlux", units="count")
            cls.keys["centroid"] = lsst.afw.table.Point2DKey.addFields(
                schema, "truth", "true simulated centroid", "pixel"
            )
            cls.keys["centroid_sigma"] = lsst.afw.table.CovarianceMatrix2fKey.addFields(
                schema, "truth", ['x', 'y'], "pixel"
            )
            cls.keys["centroid_flag"] = schema.addField("truth_flag", type="Flag",
                                                        doc="set if the object is a star")
            cls.keys["shape"] = lsst.afw.table.QuadrupoleKey.addFields(
                schema, "truth", "true shape after PSF convolution", lsst.afw.table.CoordinateType.PIXEL
            )
            cls.keys["isStar"] = schema.addField("truth_isStar", type="Flag",
                                                 doc="set if the object is a star")
            schema.getAliasMap().set("slot_Shape", "truth")
            schema.getAliasMap().set("slot_Centroid", "truth")
            schema.getAliasMap().set("slot_ModelFlux", "truth")
            cls._schema = schema
        schema = lsst.afw.table.Schema(cls._schema)
        schema.disconnectAliases()
        return schema
 

makePerturbedWcs()

def lsst.meas.base.tests.TestDataset.makePerturbedWcs (   oldWcs,   minScaleFactor = 1.2,   maxScaleFactor = 1.5,   minRotation = None,   maxRotation = None,   minRefShift = None,   maxRefShift = None,   minPixShift = 2.0,   maxPixShift = 4.0,   randomSeed = 1  )

static

Return a perturbed version of the input WCS.

Create a new undistorted TAN WCS that is similar but not identical to
another, with random scaling, rotation, and offset (in both pixel
position and reference position).

Parameters
----------
oldWcs : `lsst.afw.geom.SkyWcs`
    The input WCS.
minScaleFactor : `float`
    Minimum scale factor to apply to the input WCS.
maxScaleFactor : `float`
    Maximum scale factor to apply to the input WCS.
minRotation : `lsst.geom.Angle` or `None`
    Minimum rotation to apply to the input WCS. If `None`, defaults to
    30 degrees.
maxRotation : `lsst.geom.Angle` or `None`
    Minimum rotation to apply to the input WCS. If `None`, defaults to
    60 degrees.
minRefShift : `lsst.geom.Angle` or `None`
    Miniumum shift to apply to the input WCS reference value. If
    `None`, defaults to 0.5 arcsec.
maxRefShift : `lsst.geom.Angle` or `None`
    Miniumum shift to apply to the input WCS reference value. If
    `None`, defaults to 1.0 arcsec.
minPixShift : `float`
    Minimum shift to apply to the input WCS reference pixel.
maxPixShift : `float`
    Maximum shift to apply to the input WCS reference pixel.
randomSeed : `int`
    Random seed.

Returns
-------
newWcs : `lsst.afw.geom.SkyWcs`
    A perturbed version of the input WCS.

Notes
-----
The maximum and minimum arguments are interpreted as absolute values
for a split range that covers both positive and negative values (as
this method is used in testing, it is typically most important to
avoid perturbations near zero). Scale factors are treated somewhat
differently: the actual scale factor is chosen between
``minScaleFactor`` and ``maxScaleFactor`` OR (``1/maxScaleFactor``)
and (``1/minScaleFactor``).

The default range for rotation is 30-60 degrees, and the default range
for reference shift is 0.5-1.0 arcseconds (these cannot be safely
included directly as default values because Angle objects are
mutable).

The random number generator is primed with the seed given. If
`None`, a seed is automatically chosen.

Definition at line 207 of file tests.py.

                         minPixShift=2.0, maxPixShift=4.0, randomSeed=1):
        """Return a perturbed version of the input WCS.
 
        Create a new undistorted TAN WCS that is similar but not identical to
        another, with random scaling, rotation, and offset (in both pixel
        position and reference position).
 
        Parameters
        ----------
        oldWcs : `lsst.afw.geom.SkyWcs`
            The input WCS.
        minScaleFactor : `float`
            Minimum scale factor to apply to the input WCS.
        maxScaleFactor : `float`
            Maximum scale factor to apply to the input WCS.
        minRotation : `lsst.geom.Angle` or `None`
            Minimum rotation to apply to the input WCS. If `None`, defaults to
            30 degrees.
        maxRotation : `lsst.geom.Angle` or `None`
            Minimum rotation to apply to the input WCS. If `None`, defaults to
            60 degrees.
        minRefShift : `lsst.geom.Angle` or `None`
            Miniumum shift to apply to the input WCS reference value. If
            `None`, defaults to 0.5 arcsec.
        maxRefShift : `lsst.geom.Angle` or `None`
            Miniumum shift to apply to the input WCS reference value. If
            `None`, defaults to 1.0 arcsec.
        minPixShift : `float`
            Minimum shift to apply to the input WCS reference pixel.
        maxPixShift : `float`
            Maximum shift to apply to the input WCS reference pixel.
        randomSeed : `int`
            Random seed.
 
        Returns
        -------
        newWcs : `lsst.afw.geom.SkyWcs`
            A perturbed version of the input WCS.
 
        Notes
        -----
        The maximum and minimum arguments are interpreted as absolute values
        for a split range that covers both positive and negative values (as
        this method is used in testing, it is typically most important to
        avoid perturbations near zero). Scale factors are treated somewhat
        differently: the actual scale factor is chosen between
        ``minScaleFactor`` and ``maxScaleFactor`` OR (``1/maxScaleFactor``)
        and (``1/minScaleFactor``).
 
        The default range for rotation is 30-60 degrees, and the default range
        for reference shift is 0.5-1.0 arcseconds (these cannot be safely
        included directly as default values because Angle objects are
        mutable).
 
        The random number generator is primed with the seed given. If
        `None`, a seed is automatically chosen.
        """
        random_state = np.random.RandomState(randomSeed)
        if minRotation is None:
            minRotation = 30.0*lsst.geom.degrees
        if maxRotation is None:
            maxRotation = 60.0*lsst.geom.degrees
        if minRefShift is None:
            minRefShift = 0.5*lsst.geom.arcseconds
        if maxRefShift is None:
            maxRefShift = 1.0*lsst.geom.arcseconds
 
        def splitRandom(min1, max1, min2=None, max2=None):
            if min2 is None:
                min2 = -max1
            if max2 is None:
                max2 = -min1
            if random_state.uniform() > 0.5:
                return float(random_state.uniform(min1, max1))
            else:
                return float(random_state.uniform(min2, max2))
        # Generate random perturbations
        scaleFactor = splitRandom(minScaleFactor, maxScaleFactor, 1.0/maxScaleFactor, 1.0/minScaleFactor)
        rotation = splitRandom(minRotation.asRadians(), maxRotation.asRadians())*lsst.geom.radians
        refShiftRa = splitRandom(minRefShift.asRadians(), maxRefShift.asRadians())*lsst.geom.radians
        refShiftDec = splitRandom(minRefShift.asRadians(), maxRefShift.asRadians())*lsst.geom.radians
        pixShiftX = splitRandom(minPixShift, maxPixShift)
        pixShiftY = splitRandom(minPixShift, maxPixShift)
        # Compute new CD matrix
        oldTransform = lsst.geom.LinearTransform(oldWcs.getCdMatrix())
        rTransform = lsst.geom.LinearTransform.makeRotation(rotation)
        sTransform = lsst.geom.LinearTransform.makeScaling(scaleFactor)
        newTransform = oldTransform*rTransform*sTransform
        matrix = newTransform.getMatrix()
        # Compute new coordinate reference pixel (CRVAL)
        oldSkyOrigin = oldWcs.getSkyOrigin()
        newSkyOrigin = lsst.geom.SpherePoint(oldSkyOrigin.getRa() + refShiftRa,
                                             oldSkyOrigin.getDec() + refShiftDec)
        # Compute new pixel reference pixel (CRPIX)
        oldPixOrigin = oldWcs.getPixelOrigin()
        newPixOrigin = lsst.geom.Point2D(oldPixOrigin.getX() + pixShiftX,
                                         oldPixOrigin.getY() + pixShiftY)
        return lsst.afw.geom.makeSkyWcs(crpix=newPixOrigin, crval=newSkyOrigin, cdMatrix=matrix)
 

realize()

def lsst.meas.base.tests.TestDataset.realize	(		self,
			noise,
			schema,
			randomSeed = 1
	)

Simulate an exposure and detection catalog for this dataset.

The simulation includes noise, and the detection catalog includes
`~lsst.afw.detection.heavyFootprint.HeavyFootprint`\ s.

Parameters
----------
noise : `float`
    Standard deviation of noise to be added to the exposure.  The
    noise will be Gaussian and constant, appropriate for the
    sky-limited regime.
schema : `lsst.afw.table.Schema`
    Schema of the new catalog to be created.  Must start with
    ``self.schema`` (i.e. ``schema.contains(self.schema)`` must be
    `True`), but typically contains fields for already-configured
    measurement algorithms as well.
randomSeed : `int`, optional
    Seed for the random number generator.
    If `None`, a seed is chosen automatically.

Returns
-------
`exposure` : `lsst.afw.image.ExposureF`
    Simulated image.
`catalog` : `lsst.afw.table.SourceCatalog`
    Simulated detection catalog.

Definition at line 518 of file tests.py.

    def realize(self, noise, schema, randomSeed=1):
        r"""Simulate an exposure and detection catalog for this dataset.
 
        The simulation includes noise, and the detection catalog includes
        `~lsst.afw.detection.heavyFootprint.HeavyFootprint`\ s.
 
        Parameters
        ----------
        noise : `float`
            Standard deviation of noise to be added to the exposure.  The
            noise will be Gaussian and constant, appropriate for the
            sky-limited regime.
        schema : `lsst.afw.table.Schema`
            Schema of the new catalog to be created.  Must start with
            ``self.schema`` (i.e. ``schema.contains(self.schema)`` must be
            `True`), but typically contains fields for already-configured
            measurement algorithms as well.
        randomSeed : `int`, optional
            Seed for the random number generator.
            If `None`, a seed is chosen automatically.
 
        Returns
        -------
        `exposure` : `lsst.afw.image.ExposureF`
            Simulated image.
        `catalog` : `lsst.afw.table.SourceCatalog`
            Simulated detection catalog.
        """
        random_state = np.random.RandomState(randomSeed)
        assert schema.contains(self.schema)
        mapper = lsst.afw.table.SchemaMapper(self.schema)
        mapper.addMinimalSchema(self.schema, True)
        exposure = self.exposure.clone()
        exposure.getMaskedImage().getVariance().getArray()[:, :] = noise**2
        exposure.getMaskedImage().getImage().getArray()[:, :] \
            += random_state.randn(exposure.getHeight(), exposure.getWidth())*noise
        catalog = lsst.afw.table.SourceCatalog(schema)
        catalog.extend(self.catalog, mapper=mapper)
        # Loop over sources and generate new HeavyFootprints that divide up
        # the noisy pixels, not the ideal no-noise pixels.
        for record in catalog:
            # parent objects have non-Heavy Footprints, which don't need to be
            # updated after adding noise.
            if record.getParent() == 0:
                continue
            # get flattened arrays that correspond to the no-noise and noisy
            # parent images
            parent = catalog.find(record.getParent())
            footprint = parent.getFootprint()
            parentFluxArrayNoNoise = np.zeros(footprint.getArea(), dtype=np.float32)
            footprint.spans.flatten(parentFluxArrayNoNoise,
                                    self.exposure.getMaskedImage().getImage().getArray(),
                                    self.exposure.getXY0())
            parentFluxArrayNoisy = np.zeros(footprint.getArea(), dtype=np.float32)
            footprint.spans.flatten(parentFluxArrayNoisy,
                                    exposure.getMaskedImage().getImage().getArray(),
                                    exposure.getXY0())
            oldHeavy = record.getFootprint()
            fraction = (oldHeavy.getImageArray() / parentFluxArrayNoNoise)
            # N.B. this isn't a copy ctor - it's a copy from a vanilla
            # Footprint, so it doesn't copy the arrays we don't want to
            # change, and hence we have to do that ourselves below.
            newHeavy = lsst.afw.detection.HeavyFootprintF(oldHeavy)
            newHeavy.getImageArray()[:] = parentFluxArrayNoisy*fraction
            newHeavy.getMaskArray()[:] = oldHeavy.getMaskArray()
            newHeavy.getVarianceArray()[:] = oldHeavy.getVarianceArray()
            record.setFootprint(newHeavy)
        return exposure, catalog
 
 

transform()

def lsst.meas.base.tests.TestDataset.transform	(		self,
			wcs,
		**	kwds
	)

Copy this dataset transformed to a new WCS, with new Psf and PhotoCalib.

Parameters
----------
wcs : `lsst.afw.geom.SkyWcs`
    WCS for the new dataset.
**kwds
    Additional keyword arguments passed on to
    `TestDataset.makeEmptyExposure`.  If not specified, these revert
    to the defaults for `~TestDataset.makeEmptyExposure`, not the
    values in the current dataset.

Returns
-------
newDataset : `TestDataset`
    Transformed copy of this dataset.

Definition at line 469 of file tests.py.

    def transform(self, wcs, **kwds):
        """Copy this dataset transformed to a new WCS, with new Psf and PhotoCalib.
 
        Parameters
        ----------
        wcs : `lsst.afw.geom.SkyWcs`
            WCS for the new dataset.
        **kwds
            Additional keyword arguments passed on to
            `TestDataset.makeEmptyExposure`.  If not specified, these revert
            to the defaults for `~TestDataset.makeEmptyExposure`, not the
            values in the current dataset.
 
        Returns
        -------
        newDataset : `TestDataset`
            Transformed copy of this dataset.
        """
        bboxD = lsst.geom.Box2D()
        xyt = lsst.afw.geom.makeWcsPairTransform(self.exposure.getWcs(), wcs)
        for corner in lsst.geom.Box2D(self.exposure.getBBox()).getCorners():
            bboxD.include(xyt.applyForward(lsst.geom.Point2D(corner)))
        bboxI = lsst.geom.Box2I(bboxD)
        result = TestDataset(bbox=bboxI, wcs=wcs, **kwds)
        oldPhotoCalib = self.exposure.getPhotoCalib()
        newPhotoCalib = result.exposure.getPhotoCalib()
        oldPsfShape = self.exposure.getPsf().computeShape(bboxD.getCenter())
        for record in self.catalog:
            if record.get(self.keys["nChild"]):
                raise NotImplementedError("Transforming blended sources in TestDatasets is not supported")
            magnitude = oldPhotoCalib.instFluxToMagnitude(record.get(self.keys["instFlux"]))
            newFlux = newPhotoCalib.magnitudeToInstFlux(magnitude)
            oldCentroid = record.get(self.keys["centroid"])
            newCentroid = xyt.applyForward(oldCentroid)
            if record.get(self.keys["isStar"]):
                newDeconvolvedShape = None
            else:
                affine = lsst.afw.geom.linearizeTransform(xyt, oldCentroid)
                oldFullShape = record.get(self.keys["shape"])
                oldDeconvolvedShape = lsst.afw.geom.Quadrupole(
                    oldFullShape.getIxx() - oldPsfShape.getIxx(),
                    oldFullShape.getIyy() - oldPsfShape.getIyy(),
                    oldFullShape.getIxy() - oldPsfShape.getIxy(),
                    False
                )
                newDeconvolvedShape = oldDeconvolvedShape.transform(affine.getLinear())
            result.addSource(newFlux, newCentroid, newDeconvolvedShape)
        return result
 

Member Data Documentation

catalog

lsst.meas.base.tests.TestDataset.catalog

Definition at line 388 of file tests.py.

exposure

lsst.meas.base.tests.TestDataset.exposure

Definition at line 385 of file tests.py.

keys

lsst.meas.base.tests.TestDataset.keys

Definition at line 180 of file tests.py.

psfShape

lsst.meas.base.tests.TestDataset.psfShape

Definition at line 386 of file tests.py.

schema

lsst.meas.base.tests.TestDataset.schema

Definition at line 387 of file tests.py.

threshold

lsst.meas.base.tests.TestDataset.threshold

Definition at line 384 of file tests.py.

---

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

• /j/snowflake/release/lsstsw/stack/lsst-scipipe-0.7.0/Linux64/meas_base/22.0.1-25-gb120d7b+8b5510f75f/python/lsst/meas/base/tests.py