def run(self, dataRef):
image = dataRef.get("starfield_image", immediate=True)
nx = image.getWidth()/3-1
ny = image.getWidth()/3-1
# extract only the lower-left image
bbox = lsst.afw.geom.Box2I(lsst.afw.geom.Point2I(0,0), lsst.afw.geom.Extent2I(nx, ny))
image = image[bbox].convertD()
# shift by half a pixel in both directions, so it's centered on a pixel
image = lsst.afw.math.offsetImage(image, -0.5, -0.5)
assert image.getWidth() == nx
assert image.getHeight() == ny
kernel = lsst.afw.math.FixedKernel(image)
return lsst.meas.algorithms.KernelPsf(kernel)
def testObjective(self):
"""Test that model evaluation agrees with derivative evaluation in the objective object.
"""
image = self.psf.computeKernelImage()
msf = self.Algorithm.initializeResult(self.ctrl)
self.Algorithm.fitMoments(msf, self.ctrl, image)
moments = msf.evaluate().computeMoments()
r0 = moments.getCore().getDeterminantRadius()
objective = self.Algorithm.makeObjective(moments, self.ctrl, image)
image, model = self.makeImages(msf)
parameters = numpy.zeros(4, dtype=float)
parameters[0] = msf.getComponents()[0].getCoefficients()[0]
parameters[1] = msf.getComponents()[1].getCoefficients()[0]
parameters[2] = msf.getComponents()[0].getEllipse().getCore(
).getDeterminantRadius() / r0
parameters[3] = msf.getComponents()[1].getEllipse().getCore(
).getDeterminantRadius() / r0
residuals = numpy.zeros(image.getArray().size, dtype=float)
objective.computeResiduals(parameters, residuals)
return msf
self.assertFloatsAlmostEqual(
residuals.reshape(image.getHeight(), image.getWidth()),
image.getArray() - model.getArray())
step = 1E-6
derivatives = numpy.zeros((parameters.size, residuals.size),
dtype=float).transpose()
objective.differentiateResiduals(parameters, derivatives)
for i in range(parameters.size):
original = parameters[i]
r1 = numpy.zeros(residuals.size, dtype=float)
r2 = numpy.zeros(residuals.size, dtype=float)
parameters[i] = original + step
objective.computeResiduals(parameters, r1)
parameters[i] = original - step
objective.computeResiduals(parameters, r2)
parameters[i] = original
d = (r1 - r2) / (2.0 * step)
self.assertFloatsAlmostEqual(d.reshape(image.getHeight(),
image.getWidth()),
derivatives[:, i].reshape(
image.getHeight(),
image.getWidth()),
atol=1E-11)
def testObjective(self):
"""Test that model evaluation agrees with derivative evaluation in the objective object.
"""
image = self.psf.computeKernelImage()
msf = self.Algorithm.initializeResult(self.ctrl)
self.Algorithm.fitMoments(msf, self.ctrl, image)
moments = msf.evaluate().computeMoments()
r0 = moments.getCore().getDeterminantRadius()
objective = self.Algorithm.makeObjective(moments, self.ctrl, image)
image, model = self.makeImages(msf)
parameters = numpy.zeros(4, dtype=float)
parameters[0] = msf.getComponents()[0].getCoefficients()[0]
parameters[1] = msf.getComponents()[1].getCoefficients()[0]
parameters[2] = msf.getComponents()[0].getEllipse().getCore().getDeterminantRadius() / r0
parameters[3] = msf.getComponents()[1].getEllipse().getCore().getDeterminantRadius() / r0
residuals = numpy.zeros(image.getArray().size, dtype=float)
objective.computeResiduals(parameters, residuals)
self.assertFloatsAlmostEqual(
residuals.reshape(image.getHeight(), image.getWidth()),
image.getArray() - model.getArray()
)
step = 1E-6
derivatives = numpy.zeros((parameters.size, residuals.size), dtype=float).transpose()
objective.differentiateResiduals(parameters, derivatives)
for i in range(parameters.size):
original = parameters[i]
r1 = numpy.zeros(residuals.size, dtype=float)
r2 = numpy.zeros(residuals.size, dtype=float)
parameters[i] = original + step
objective.computeResiduals(parameters, r1)
parameters[i] = original - step
objective.computeResiduals(parameters, r2)
parameters[i] = original
d = (r1 - r2)/(2.0*step)
self.assertFloatsAlmostEqual(
d.reshape(image.getHeight(), image.getWidth()),
derivatives[:, i].reshape(image.getHeight(), image.getWidth()),
atol=1E-11
)
def run(self, dataRef, dataType):
image = dataRef.get(dataType + "starfield_image", immediate=True)
nx = self.config.size - 1
ny = self.config.size - 1
# extract only the lower-left image
bbox = lsst.afw.geom.Box2I(lsst.afw.geom.Point2I(0, 0),
lsst.afw.geom.Extent2I(nx, ny))
image = image[bbox].convertD()
# shift by half a pixel in both directions, so it's centered on a pixel
image = lsst.afw.math.offsetImage(image, -0.5, -0.5)
assert image.getWidth() == nx
assert image.getHeight() == ny
kernel = lsst.afw.math.FixedKernel(image)
return lsst.meas.algorithms.KernelPsf(kernel)
def testHdu(self):
mapper = MinMapper2()
butler = dafPersist.ButlerFactory(mapper=mapper).create()
# HDU INT_MIN returns primary array (skipping empty PDU)
# HDU 0 returns (header-only) PDU
# HDU 1 returns first image plane
# HDU 2 returns mask plane
# HDU 3 returns variance plane
for i in (1, 2, 3):
loc = mapper.map("other", dict(ccd=35, hdu=i))
expectedLocations = ["bar-35.fits[%d]" % (i,)]
self.assertEqual(loc.getStorage().root, ROOT)
self.assertEqual(loc.getLocations(), expectedLocations)
image = butler.get("other", ccd=35, hdu=i, immediate=True)
self.assertIsInstance(image, lsst.afw.image.ImageF)
self.assertEqual(image.getHeight(), 2024)
self.assertEqual(image.getWidth(), 2248)
self.assertEqual(image[200, 25, lsst.afw.image.LOCAL], (0.0, 20.0, 0.0)[i-1])
self.assertAlmostEqual(image[200, 26, lsst.afw.image.LOCAL], (1.20544, 0.0, 5.82185)[i-1],
places=5)