Examples with SimpsonIntegrator org.apache.commons.math3.analysis.integration.SimpsonIntegrator used on opensource projects

Example 1 with SimpsonIntegrator

use of org.apache.commons.math3.analysis.integration.SimpsonIntegrator in project GDSC-SMLM by aherbert.

the class JumpDistanceAnalysisTest method canIntegrateProbabilityToCumulativeWithSinglePopulation.

// Commented out as this test always passes
//@Test
public void canIntegrateProbabilityToCumulativeWithSinglePopulation() {
    JumpDistanceAnalysis jd = new JumpDistanceAnalysis();
    jd.setMinD(0);
    jd.setMinFraction(0);
    SimpsonIntegrator si = new SimpsonIntegrator(1e-3, 1e-8, 2, SimpsonIntegrator.SIMPSON_MAX_ITERATIONS_COUNT);
    for (double d : D) {
        final double[] params = new double[] { d };
        final JumpDistanceFunction fp = jd.new JumpDistanceFunction(null, d);
        JumpDistanceCumulFunction fc = jd.new JumpDistanceCumulFunction(null, null, d);
        double x = d / 8;
        UnivariateFunction func = new UnivariateFunction() {

            public double value(double x) {
                return fp.evaluate(x, params);
            }
        };
        for (int i = 1; i < 10; i++, x *= 2) {
            double e = fc.evaluate(x, params);
            // Integrate
            double o = si.integrate(10000, func, 0, x);
            //log("Integrate d=%.1f : x=%.1f, e=%f, o=%f, iter=%d, eval=%d\n", d, x, e, o, si.getIterations(),
            //		si.getEvaluations());
            Assert.assertEquals("Failed to integrate", e, o, e * 1e-2);
        }
    }
}

Example 2 with SimpsonIntegrator

use of org.apache.commons.math3.analysis.integration.SimpsonIntegrator in project gatk by broadinstitute.

the class CNLOHCaller method calcEffectiveAlpha.

protected double calcEffectiveAlpha(final double[] effectivePhis) {
    final QAlphaUnivariateFunction qAlpha = new QAlphaUnivariateFunction(effectivePhis);
    final AQAlphaUnivariateFunction aQAlpha = new AQAlphaUnivariateFunction(effectivePhis);
    final BaseAbstractUnivariateIntegrator integrator = new SimpsonIntegrator();
    final double numerator = integrator.integrate(10, aQAlpha, MIN_E_ELPHA_INTEGRATION_RANGE, MAX_E_ELPHA_INTEGRATION_RANGE);
    final double denominator = integrator.integrate(10, qAlpha, MIN_E_ELPHA_INTEGRATION_RANGE, MAX_E_ELPHA_INTEGRATION_RANGE);
    return numerator / denominator;
}

Example 3 with SimpsonIntegrator

use of org.apache.commons.math3.analysis.integration.SimpsonIntegrator in project GDSC-SMLM by aherbert.

the class AiryPSFModel method createAiryDistribution.

private static synchronized void createAiryDistribution() {
    if (spline != null)
        return;
    final double relativeAccuracy = 1e-4;
    final double absoluteAccuracy = 1e-8;
    final int minimalIterationCount = 3;
    final int maximalIterationCount = 32;
    UnivariateIntegrator integrator = new SimpsonIntegrator(relativeAccuracy, absoluteAccuracy, minimalIterationCount, maximalIterationCount);
    UnivariateFunction f = new UnivariateFunction() {

        public double value(double x) {
            //return AiryPattern.intensity(x) * 2 * Math.PI * x / (4 * Math.PI);
            return AiryPattern.intensity(x) * 0.5 * x;
        }
    };
    // Integrate up to a set number of dark rings
    int samples = 1000;
    final double step = RINGS[SAMPLE_RINGS] / samples;
    double to = 0, from = 0;
    r = new double[samples + 1];
    sum = new double[samples + 1];
    for (int i = 1; i < sum.length; i++) {
        from = to;
        r[i] = to = step * i;
        sum[i] = integrator.integrate(2000, f, from, to) + sum[i - 1];
    }
    if (DoubleEquality.relativeError(sum[samples], POWER[SAMPLE_RINGS]) > 1e-3)
        throw new RuntimeException("Failed to create the Airy distribution");
    SplineInterpolator si = new SplineInterpolator();
    spline = si.interpolate(sum, r);
}

Example 4 with SimpsonIntegrator

use of org.apache.commons.math3.analysis.integration.SimpsonIntegrator in project GDSC-SMLM by aherbert.

the class PoissonFunctionTest method cumulativeProbabilityIsOneWithRealAbove4.

private void cumulativeProbabilityIsOneWithRealAbove4(final double gain, final double mu, int min, int max) {
    final double o = mu * gain;
    final PoissonFunction f = new PoissonFunction(1.0 / gain, true);
    double p = 0;
    SimpsonIntegrator in = new SimpsonIntegrator(3, 30);
    try {
        p = in.integrate(Integer.MAX_VALUE, new UnivariateFunction() {

            public double value(double x) {
                return f.likelihood(x, o);
            }
        }, min, max);
        System.out.printf("g=%f, mu=%f, o=%f, p=%f\n", gain, mu, o, p);
        Assert.assertEquals(String.format("g=%f, mu=%f", gain, mu), 1, p, 0.02);
    } catch (TooManyEvaluationsException e) {
        //double inc = max / 20000.0;
        //for (double x = 0; x <= max; x += inc)
        //{
        //	final double pp = f.likelihood(x, o);
        //	//System.out.printf("g=%f, mu=%f, o=%f, p=%f\n", gain, mu, o, pp);
        //	p += pp;
        //}
        //System.out.printf("g=%f, mu=%f, o=%f, p=%f\n", gain, mu, o, p);
        Assert.assertFalse(e.getMessage(), true);
    }
}

Example 5 with SimpsonIntegrator

use of org.apache.commons.math3.analysis.integration.SimpsonIntegrator in project GDSC-SMLM by aherbert.

the class PoissonCalculatorTest method cumulativeProbabilityIsOneWithRealData.

private void cumulativeProbabilityIsOneWithRealData(final double mu, double min, double max, boolean test) {
    double p = 0;
    UnivariateIntegrator in = new SimpsonIntegrator();
    p = in.integrate(20000, new UnivariateFunction() {

        public double value(double x) {
            double v;
            v = PoissonCalculator.likelihood(mu, x);
            //System.out.printf("x=%f, v=%f\n", x, v);
            return v;
        }
    }, min, max);
    System.out.printf("mu=%f, p=%f\n", mu, p);
    if (test) {
        Assert.assertEquals(String.format("mu=%f", mu), P_LIMIT, p, 0.02);
    }
}