Examples with AstigmatismZModel uk.ac.sussex.gdsc.smlm.function.gaussian.AstigmatismZModel used on opensource projects

Example 6 with AstigmatismZModel

use of uk.ac.sussex.gdsc.smlm.function.gaussian.AstigmatismZModel in project GDSC-SMLM by aherbert.

the class PsfModelGradient1FunctionTest method canComputeValueAndGradient.

@Test
void canComputeValueAndGradient() {
    // Use a reasonable z-depth function from the Smith, et al (2010) paper (page 377)
    final double sx = 1.08;
    final double sy = 1.01;
    final double gamma = 0.389;
    final double d = 0.531;
    final double Ax = -0.0708;
    final double Bx = -0.073;
    final double Ay = 0.164;
    final double By = 0.0417;
    final AstigmatismZModel zModel = HoltzerAstigmatismZModel.create(sx, sy, gamma, d, Ax, Bx, Ay, By);
    // Small size ensure the PSF model covers the entire image
    final int maxx = 11;
    final int maxy = 11;
    final double[] ve = new double[maxx * maxy];
    final double[] vo = new double[maxx * maxy];
    final double[][] ge = new double[maxx * maxy][];
    final double[][] go = new double[maxx * maxy][];
    final PsfModelGradient1Function psf = new PsfModelGradient1Function(new GaussianPsfModel(zModel), maxx, maxy);
    final ErfGaussian2DFunction f = new SingleAstigmatismErfGaussian2DFunction(maxx, maxy, zModel);
    f.setErfFunction(ErfFunction.COMMONS_MATH);
    final double[] a2 = new double[Gaussian2DFunction.PARAMETERS_PER_PEAK + 1];
    final DoubleDoubleBiPredicate equality = TestHelper.doublesAreClose(1e-8, 0);
    final double c = maxx * 0.5;
    for (int i = -1; i <= 1; i++) {
        final double x0 = c + i * 0.33;
        for (int j = -1; j <= 1; j++) {
            final double x1 = c + j * 0.33;
            for (int k = -1; k <= 1; k++) {
                final double x2 = k * 0.33;
                for (final double in : new double[] { 23.2, 405.67 }) {
                    // Background is constant for gradients so just use 1 value
                    final double[] a = new double[] { 2.2, in, x0, x1, x2 };
                    psf.initialise1(a);
                    psf.forEach(new Gradient1Procedure() {

                        int index = 0;

                        @Override
                        public void execute(double value, double[] dyDa) {
                            vo[index] = value;
                            go[index] = dyDa.clone();
                            index++;
                        }
                    });
                    a2[Gaussian2DFunction.BACKGROUND] = a[0];
                    a2[Gaussian2DFunction.SIGNAL] = a[1];
                    a2[Gaussian2DFunction.X_POSITION] = a[2] - 0.5;
                    a2[Gaussian2DFunction.Y_POSITION] = a[3] - 0.5;
                    a2[Gaussian2DFunction.Z_POSITION] = a[4];
                    f.initialise1(a2);
                    f.forEach(new Gradient1Procedure() {

                        int index = 0;

                        @Override
                        public void execute(double value, double[] dyDa) {
                            ve[index] = value;
                            ge[index] = dyDa.clone();
                            index++;
                        }
                    });
                    for (int ii = 0; ii < ve.length; ii++) {
                        TestAssertions.assertTest(ve[ii], vo[ii], equality);
                        TestAssertions.assertArrayTest(ge[ii], go[ii], equality);
                    }
                }
            }
        }
    }
}