Example usage for org.apache.commons.math3.analysis.interpolation SplineInterpolator SplineInterpolator

Introduction

In this page you can find the example usage for org.apache.commons.math3.analysis.interpolation SplineInterpolator SplineInterpolator.

Prototype

SplineInterpolator

Source Link

Usage

From source file:uk.ac.ed.bio.SynthSys.SBMLDataTools.SBMLTimeCourseDataHelperTest.java

/**
 * Tests that the correct exception is produced when passing times data that is not in 
 * ascending order./*from  www  .j a  v a  2s. com*/
 */
@Test
public void timesDataNotAscending() {
    boolean caughtException = false;

    _times = new double[] { -3.0, -2.0, -1.0, 0, -1.0 };
    _values = new double[_times.length];

    for (int i = 0; i < _times.length; ++i) {
        _values[i] = Math.sin(_times[i]);
    }

    SBMLDocument doc = new SBMLDocument(3, 1);
    Model model = doc.createModel("test_model");

    try {
        SBMLTimeCourseDataHelper.addParameter(model, "myParam", _times, _values,
                new PolynomialInterpolator(new SplineInterpolator()));
    } catch (IllegalArgumentException iae) {
        caughtException = true;
        assertEquals("Data in times parameter must be in ascending order", iae.getMessage());
    }
    if (!caughtException)
        fail("Expected IllegalArgumentException");
}

From source file:uk.ac.ed.bio.SynthSys.SBMLDataTools.SBMLTimeCourseDataHelperTest.java

/**
 * Tests that correct execution produced fitted data that closely matches the function that
 * was sampled to produce the external data.  Here the sine function is used.
 *//*  w w  w  .  ja  v  a2s.  c  o m*/
@Test
public void normalExcecution() {
    setSinData();
    SBMLDocument doc = new SBMLDocument(3, 1);
    Model model = doc.createModel("test_model");

    SBMLTimeCourseDataHelper.addParameter(model, "myParam", _times, _values,
            new PolynomialInterpolator(new SplineInterpolator()));

    // Model must now have a parameter called myParam with specific properties
    assertEquals("Model must have one parameter", 1, model.getParameterCount());
    Parameter param = model.getParameter(0);
    assertEquals("Parameter id: ", "myParam", param.getId());
    assertEquals("Parameter name: ", "myParam", param.getName());
    assertEquals("Parameter isConstant: ", false, param.isConstant());

    // Model must have an assignment rule that is associated with the parameter
    assertEquals("Model must have one rule", 1, model.getRuleCount());
    Rule rule = model.getRule(0);
    assertTrue("Rule must be assignment rule", rule instanceof AssignmentRule);
    AssignmentRule assignmentRule = (AssignmentRule) rule;
    assertEquals("Variable of assignment rule", "myParam", assignmentRule.getVariable());

    // Now we can compare data with the fitted data - for the sine function the spline
    // should fit quite well
    for (double t = _times[0]; t < _times[_times.length - 1]; t += 0.005) {
        double sin = Math.sin(t);
        double fitted = evaluateMathML(assignmentRule.getMath(), t);

        assertTrue("Fitted must be close to actual, t=" + t + " sine=" + sin + " fitted=" + fitted + " diff="
                + Math.abs(sin - fitted), Math.abs(sin - fitted) < 0.01);
    }
}