def testObjective(self):
eps = 1E-6
ctrl = ms.FitPsfControl()
image = lsst.afw.image.ImageD(5, 5)
nParameters = 3
nData = image.getBBox().getArea()
nTests = 10
center = geom.Point2D(2.0, 2.0)
xGrid, yGrid = numpy.meshgrid(numpy.arange(-2, 3), numpy.arange(-2, 3))
image.getArray()[:, :] = 1.0 * numpy.exp(-0.5 * (xGrid**2 + yGrid**2))
image.getArray()[:, :] += numpy.random.randn(5, 5) * 0.1
inputs = ms.ModelInputHandler(image, center,
image.getBBox(lsst.afw.image.PARENT))
obj = ms.FitPsfAlgorithm.makeObjective(ctrl, inputs)
parameters = numpy.random.rand(nTests, nParameters) * 0.5
for i in range(nTests):
f0 = numpy.zeros(nData, dtype=float)
obj.computeFunction(parameters[i, :], f0)
f1 = obj.getModel() * obj.getAmplitude() - inputs.getData()
model = ms.FitPsfModel(ctrl, obj.getAmplitude(), parameters[i, :])
self.assertClose(
model.outer[0],
ctrl.peakRatio * model.inner[0] * ctrl.radiusRatio**2)
self.assertEqual(model.radiusRatio, ctrl.radiusRatio)
image2 = lsst.afw.image.ImageD(5, 5)
multiShapeletFunc = model.asMultiShapelet(center)
multiShapeletFunc.evaluate().addToImage(image2)
f2 = (image2.getArray().ravel() - inputs.getData())
multiGaussian = model.getMultiGaussian()
builder1 = ms.GaussianModelBuilder(inputs.getX(), inputs.getY(),
multiGaussian[0].flux,
multiGaussian[0].radius)
builder2 = ms.GaussianModelBuilder(inputs.getX(), inputs.getY(),
multiGaussian[1].flux,
multiGaussian[1].radius)
builder1.update(model.ellipse)
builder2.update(model.ellipse)
f3 = builder1.getModel() + builder2.getModel() - inputs.getData()
self.assertClose(f0, f1)
self.assertClose(f0, f2)
self.assertClose(f0, f3)
d0 = numpy.zeros((nParameters, nData), dtype=float).transpose()
d1 = numpy.zeros((nParameters, nData), dtype=float).transpose()
obj.computeDerivative(parameters[i, :], f0, d0)
for j in range(nParameters):
parameters[i, j] += eps
f1a = numpy.zeros(nData, dtype=float)
obj.computeFunction(parameters[i, :], f1a)
parameters[i, j] -= 2.0 * eps
f1b = numpy.zeros(nData, dtype=float)
obj.computeFunction(parameters[i, :], f1b)
d1[:, j] = (f1a - f1b) / (2.0 * eps)
parameters[i, j] += eps
self.assertClose(d0, d1, rtol=1E-10, atol=1E-8)
def testConvolvedModel(self):
psfModel = ms.FitPsfModel(ms.FitPsfControl(), 1.0,
numpy.array([0.1, -0.05, 1.0]))
psfMultiGaussian = psfModel.getMultiGaussian()
multiGaussian = ms.MultiGaussianRegistry.lookup(self.ctrl.profile)
parameters = numpy.array([[0.0, 0.0, 0.0], [0.0, 0.0, -16],
[0.2, -0.8, 2.3]])
sImage = lsst.afw.image.ImageD(self.bbox)
builders = []
for p in psfMultiGaussian:
psfEllipse = psfModel.ellipse.clone()
psfEllipse.scale(p.radius)
builders.extend(
ms.GaussianModelBuilder(self.x, self.y, c.flux, c.radius,
psfEllipse, p.flux)
for c in multiGaussian)
psfShapelets = psfModel.asMultiShapelet()
for row in parameters:
model = ms.FitProfileModel(self.ctrl, 1.0, row)
msf = model.asMultiShapelet(self.center).convolve(psfShapelets)
z0 = sImage.getArray()
z0[:, :] = 0.0
msf.evaluate().addToImage(sImage)
z1 = numpy.zeros(z0.shape, dtype=float)
for b in builders:
b.update(ms.MultiGaussianObjective.readParameters(row))
z1 += b.getModel().reshape(*z1.shape)
self.assertClose(z0, z1)
self.assert_(numpy.isfinite(z0).all())
self.assert_(numpy.isfinite(z1).all())
def testModel1(self):
builder = ms.GaussianModelBuilder(self.x, self.y)
builder.update(self.ellipse)
mgc = ms.GaussianComponent()
shapelet = mgc.makeShapelet(ellipses.Ellipse(self.ellipse))
z0 = builder.getModel()
z1 = self.buildModel(self.ellipse)
z2 = self.evalShapelets(shapelet)
self.assertClose(z0, z1)
self.assertClose(z0, z2)
def testModel2(self):
amplitude = 3.2
radius = 2.7
mgc = ms.GaussianComponent(amplitude, radius)
shapelet = mgc.makeShapelet(ellipses.Ellipse(self.ellipse))
builder = ms.GaussianModelBuilder(self.x, self.y, amplitude, radius)
builder.update(self.ellipse)
self.ellipse.scale(radius)
z0 = builder.getModel()
z1 = amplitude * self.buildModel(self.ellipse)
z2 = self.evalShapelets(shapelet)
self.assertClose(z0, z1)
self.assertClose(z0, z2)
def testDerivative1(self):
builder = ms.GaussianModelBuilder(self.x, self.y)
a = numpy.zeros((3, builder.getSize()), dtype=float).transpose()
builder.update(self.ellipse)
builder.computeDerivative(a)
def makeEllipse(p):
return ellipses.Axes(*p)
n = self.buildNumericalDerivative(builder,
self.ellipse.getParameterVector(),
makeEllipse)
# no hard requirement for tolerances here, but I've dialed them to the max to avoid regressions
self.assertClose(a, n, rtol=1E-15, atol=1E-9)
def testDerivative3(self):
amplitude = 3.2
radius = 2.7
psfEllipse = ellipses.Quadrupole(2.3, 1.8, 0.6)
psfAmplitude = 5.3
builder = ms.GaussianModelBuilder(self.x, self.y, amplitude, radius,
psfEllipse, psfAmplitude)
a = numpy.zeros((3, builder.getSize()), dtype=float).transpose()
builder.update(self.ellipse)
builder.computeDerivative(a)
def makeEllipse(p):
return ellipses.Axes(*p)
n = self.buildNumericalDerivative(builder,
self.ellipse.getParameterVector(),
makeEllipse)
# no hard requirement for tolerances here, but I've dialed them to the max to avoid regressions
self.assertClose(a, n, rtol=1E-15, atol=1E-9)
def testModel3(self):
amplitude = 3.2
radius = 2.7
psfEllipse = ellipses.Quadrupole(2.3, 1.8, 0.6)
psfAmplitude = 5.3
mgc = ms.GaussianComponent(amplitude, radius)
shapelet = mgc.makeShapelet(ellipses.Ellipse(self.ellipse))
psf = ms.GaussianComponent(psfAmplitude, 1.0).makeShapelet(
ellipses.Ellipse(psfEllipse))
shapelet = shapelet.convolve(psf)
builder = ms.GaussianModelBuilder(self.x, self.y, amplitude, radius,
psfEllipse, psfAmplitude)
builder.update(self.ellipse)
self.ellipse.scale(radius)
ellipse = self.ellipse.convolve(psfEllipse)
self.assertClose(builder.getModel(),
amplitude * psfAmplitude * self.buildModel(ellipse))
z0 = builder.getModel()
z1 = psfAmplitude * amplitude * self.buildModel(ellipse)
z2 = self.evalShapelets(shapelet)
self.assertClose(z0, z1)
self.assertClose(z0, z2)
def testModel(self):
multiGaussian = ms.MultiGaussianRegistry.lookup(self.ctrl.profile)
self.assertClose(multiGaussian.integrate(), 1.0)
parameters = numpy.array([[0.0, 0.0, 1.0], [0.0, 0.0, -16],
[0.2, -0.8, 2.3]])
sImage = lsst.afw.image.ImageD(self.bbox)
builders = [
ms.GaussianModelBuilder(self.x, self.y, c.flux, c.radius)
for c in multiGaussian
]
for row in parameters:
model = ms.FitProfileModel(self.ctrl, 1.0, row)
msf = model.asMultiShapelet(self.center)
self.assertClose(msf.evaluate().integrate(), 1.0)
z0 = sImage.getArray()
z0[:, :] = 0.0
msf.evaluate().addToImage(sImage)
z1 = numpy.zeros(z0.shape, dtype=float)
for b in builders:
b.update(ms.MultiGaussianObjective.readParameters(row))
z1 += b.getModel().reshape(*z1.shape)
self.assertClose(z0, z1)
self.assert_(numpy.isfinite(z0).all())
self.assert_(numpy.isfinite(z1).all())