# This file is part of meas_base. # # Developed for the LSST Data Management System. # This product includes software developed by the LSST Project # (https://www.lsst.org). # See the COPYRIGHT file at the top-level directory of this distribution # for details of code ownership. # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . import math import unittest import numpy as np import lsst.geom import lsst.afw.image as afwImage import lsst.afw.detection as afwDetection import lsst.afw.math as afwMath import lsst.afw.geom as afwGeom import lsst.afw.geom.ellipses as afwEll import lsst.meas.base as measBase import lsst.utils.tests try: display except NameError: display = False else: import lsst.afw.display as afwDisplay afwDisplay.setDefaultMaskTransparency(75) def plantSources(bbox, kwid, sky, coordList, addPoissonNoise=True): """Make an exposure with stars (modelled as Gaussians). Parameters ---------- bbox : `lsst.afw.geom.Box2I` Parent bounding box of output exposure. kwid : `float` Kernel width (and height; kernel is square). sky : `float` Amount of sky background (counts) coordList : iterable of `list` Where: - ``coordList[0]`` is the X coord of the star relative to the origin - ``coordList[1]`` is the Y coord of the star relative to the origin - ``coordList[2]`` is the integrated counts in the star - ``coordlist[3]`` is the Gaussian sigma in pixels. addPoissonNoise : `bool`, optional Add Poisson noise to the output exposure? Returns ------- exposure : `lsst.afw.image.ExposureF` Resulting exposure with simulated stars. """ # make an image with sources img = afwImage.ImageD(bbox) meanSigma = 0.0 for coord in coordList: x, y, counts, sigma = coord meanSigma += sigma # make a single gaussian psf psf = afwDetection.GaussianPsf(kwid, kwid, sigma) # make an image of it and scale to the desired number of counts thisPsfImg = psf.computeImage(lsst.geom.PointD(int(x), int(y))) thisPsfImg *= counts # bbox a window in our image and add the fake star image imgSeg = img.Factory(img, thisPsfImg.getBBox()) imgSeg += thisPsfImg meanSigma /= len(coordList) img += sky # add Poisson noise if (addPoissonNoise): np.random.seed(seed=1) # make results reproducible imgArr = img.getArray() imgArr[:] = np.random.poisson(imgArr) # bundle into a maskedimage and an exposure mask = afwImage.Mask(bbox) var = img.convertFloat() img -= sky mimg = afwImage.MaskedImageF(img.convertFloat(), mask, var) exposure = afwImage.makeExposure(mimg) # insert an approximate psf psf = afwDetection.GaussianPsf(kwid, kwid, meanSigma) exposure.setPsf(psf) return exposure class SincPhotSums(lsst.utils.tests.TestCase): def setUp(self): self.nx = 64 self.ny = 64 self.kwid = 15 self.sky = 100.0 self.val = 10000.0 self.sigma = 4.0 coordList = [[self.nx/2, self.ny/2, self.val, self.sigma]] # exposure with gaussian bbox = lsst.geom.Box2I(lsst.geom.Point2I(0, 0), lsst.geom.Extent2I(self.nx, self.ny)) self.expGaussPsf = plantSources(bbox, self.kwid, self.sky, coordList, addPoissonNoise=False) # just plain sky (ie. a constant) self.mimg = afwImage.MaskedImageF(lsst.geom.ExtentI(self.nx, self.ny)) self.mimg.set(self.sky, 0x0, self.sky) self.expSky = afwImage.makeExposure(self.mimg) if display: frame = 0 disp = afwDisplay.Display(frame=frame) disp.mtv(self.expGaussPsf, title=self._testMethodName + ": expGaussPsf") def tearDown(self): del self.mimg del self.expGaussPsf del self.expSky def testEllipticalGaussian(self): """Test measuring elliptical aperture mags for an elliptical Gaussian. """ width, height = 200, 200 xcen, ycen = 0.5*width, 0.5*height # # Make the object # gal = afwImage.ImageF(lsst.geom.ExtentI(width, height)) a, b, theta = float(10), float(5), 20 instFlux = 1e4 I0 = instFlux/(2*math.pi*a*b) c, s = math.cos(math.radians(theta)), math.sin(math.radians(theta)) for y in range(height): for x in range(width): dx, dy = x - xcen, y - ycen u = c*dx + s*dy v = -s*dx + c*dy val = I0*math.exp(-0.5*((u/a)**2 + (v/b)**2)) if val < 0: val = 0 gal[x, y, afwImage.LOCAL] = val objImg = afwImage.makeExposure(afwImage.makeMaskedImage(gal)) del gal if display: frame = 1 disp = afwDisplay.Display(frame=frame) disp.mtv(objImg, title=self._testMethodName + ": Elliptical") self.assertAlmostEqual(1.0, afwMath.makeStatistics(objImg.getMaskedImage().getImage(), afwMath.SUM).getValue()/instFlux) # # Now measure some annuli # for r1, r2 in [(0., 0.45*a), (0.45*a, 1.0*a), (1.0*a, 2.0*a), (2.0*a, 3.0*a), (3.0*a, 5.0*a), (3.0*a, 10.0*a), ]: if display: # draw the inner and outer boundaries of the aperture Mxx = 1 Myy = (b/a)**2 mxx, mxy, myy = c**2*Mxx + s**2*Myy, c*s*(Mxx - Myy), s**2*Mxx + c**2*Myy for r in (r1, r2): disp.dot("@:%g,%g,%g" % (r**2*mxx, r**2*mxy, r**2*myy), xcen, ycen) center = lsst.geom.Point2D(xcen, ycen) # this tests tests a sync algorithm with an inner and outer radius # since that is no longer available from the ApertureFluxAlgorithm, # we will calculate the two and subtract. axes = afwGeom.ellipses.Axes(r2, r2*(1-b/a), math.radians(theta)) ellipse = afwGeom.Ellipse(axes, center) result2 = measBase.ApertureFluxAlgorithm.computeSincFlux(objImg.getMaskedImage(), ellipse) axes = afwGeom.ellipses.Axes(r1, r1*(1-b/a), math.radians(theta)) ellipse = afwGeom.Ellipse(axes, center) result1 = measBase.ApertureFluxAlgorithm.computeSincFlux(objImg.getMaskedImage(), ellipse) self.assertAlmostEqual(math.exp(-0.5*(r1/a)**2) - math.exp(-0.5*(r2/a)**2), (result2.instFlux-result1.instFlux)/instFlux, 4) class SincCoeffTestCase(lsst.utils.tests.TestCase): def setUp(self): self.ellipse = afwEll.Axes(10.0, 5.0, 0.12345) self.radius1 = 0.1234 self.radius2 = 4.3210 self.inner = self.radius1/self.radius2 def tearDown(self): del self.ellipse def assertCached(self, coeff1, coeff2): np.testing.assert_array_equal(coeff1.getArray(), coeff2.getArray()) # This compares the memory address and read-only attributes of the images. self.assertEqual(coeff1.array.ctypes.data, coeff2.array.ctypes.data) def assertNotCached(self, coeff1, coeff2): np.testing.assert_array_equal(coeff1.getArray(), coeff2.getArray()) # This compares the memory address and read-only attributes of the images. self.assertNotEqual(coeff1.array.ctypes.data, coeff2.array.ctypes.data) def getCoeffCircle(self, radius2): circle = afwEll.Axes(radius2, radius2, 0.0) inner = self.radius1/radius2 coeff1 = measBase.SincCoeffsF.get(circle, inner) coeff2 = measBase.SincCoeffsF.get(circle, inner) return coeff1, coeff2 def testNoCachingElliptical(self): coeff1 = measBase.SincCoeffsF.get(self.ellipse, self.inner) coeff2 = measBase.SincCoeffsF.get(self.ellipse, self.inner) self.assertNotCached(coeff1, coeff2) def testNoCachingCircular(self): coeff1, coeff2 = self.getCoeffCircle(2*self.radius2) # not self.radius2 because that may be cached self.assertNotCached(coeff1, coeff2) def testWithCaching(self): measBase.SincCoeffsF.cache(self.radius1, self.radius2) coeff1, coeff2 = self.getCoeffCircle(self.radius2) self.assertCached(coeff1, coeff2) class TestMemory(lsst.utils.tests.MemoryTestCase): pass def setup_module(module): lsst.utils.tests.init() if __name__ == "__main__": lsst.utils.tests.init() unittest.main()