Examples with JumpDistanceAnalysis uk.ac.sussex.gdsc.smlm.fitting.JumpDistanceAnalysis used on opensource projects

Example 1 with JumpDistanceAnalysis

use of uk.ac.sussex.gdsc.smlm.fitting.JumpDistanceAnalysis in project GDSC-SMLM by aherbert.

the class TraceDiffusion method fitJumpDistance.

/**
 * Fit the jump distance histogram.
 *
 * <p>Update the plot by adding the fit line(s).
 *
 * @param jumpDistances (in um^2)
 * @param jdHistogram the jump distance histogram
 * @return The fitted coefficients and fractions
 */
private double[][] fitJumpDistance(StoredDataStatistics jumpDistances, double[][] jdHistogram) {
    final double msd = jumpDistances.getMean();
    final double meanDistance = Math.sqrt(msd) * 1e3;
    // TODO:
    // Q. Should the beta be expressed using the mean-distance or MSD?
    // Q. Should it be normalised to the frame length. If not then the beta will be invariant on
    // jump distance length
    beta = meanDistance / precision;
    // Set the minimum diffusion coefficient using the precision:
    // Note: 4D = MSD
    // 4D = precision^2
    // D = precision^2 / 4
    // D = (precision/2)^2
    // Extra 1000 factor to convert nm to um
    final double minD = MathUtils.pow2(precision / 2000.0);
    ImageJUtils.log("Jump Distance analysis : N = %d, Time = %d frames (%s seconds). " + "MSD = %s um^2/jump, Mean Distance = %s nm/jump, Precision = %s nm, " + "Beta = %s, minD = %s um^2/jump", jumpDistances.getN(), clusteringSettings.getJumpDistance(), MathUtils.rounded(clusteringSettings.getJumpDistance() * exposureTime, 4), MathUtils.rounded(msd, 4), MathUtils.rounded(meanDistance, 4), MathUtils.rounded(precision, 4), MathUtils.rounded(beta, 4), MathUtils.rounded(minD, 4));
    final Logger logger = ImageJPluginLoggerHelper.getLogger(getClass());
    if (settings.debugFitting) {
        logger.setLevel(Level.FINE);
    }
    final JumpDistanceAnalysis jd = new JumpDistanceAnalysis(logger);
    jd.setFitRestarts(clusteringSettings.getFitRestarts());
    jd.setMinFraction(settings.minFraction);
    jd.setMinDifference(settings.minDifference);
    jd.setMinN(myMinN);
    jd.setMaxN(settings.maxN);
    jd.setMinD(minD);
    jd.setSignificanceLevel(settings.significanceLevel);
    // Update the plot with the fit
    jd.setCurveLogger(this);
    // Set the calibration
    jd.setN(clusteringSettings.getJumpDistance());
    jd.setDeltaT(exposureTime);
    if (clusteringSettings.getPrecisionCorrection()) {
        jd.setError(precision, true);
    }
    jd.setMsdCorrection(clusteringSettings.getMsdCorrection());
    double[][] fit;
    if (clusteringSettings.getMle()) {
        fit = jd.fitJumpDistancesMle(jumpDistances.getValues(), jdHistogram);
    } else {
        fit = jd.fitJumpDistanceHistogram(jumpDistances.getMean(), jdHistogram);
    }
    // Get the raw fitted D and convert it to a calibrated D*
    if (fit != null) {
        fit[0] = jd.calculateApparentDiffusionCoefficient(fit[0]);
        // Check the largest D
        checkTraceSettings(fit[0][0]);
        fitValue = jd.getLastFitValue();
    }
    return fit;
}