Examples with Statistics gdsc.core.utils.Statistics used on opensource projects

Example 31 with Statistics

use of gdsc.core.utils.Statistics in project GDSC-SMLM by aherbert.

the class TraceMolecules method summarise.

private void summarise(Trace[] traces, int filtered, double dThreshold, double tThreshold) {
    IJ.showStatus("Calculating summary ...");
    // Create summary table
    createSummaryTable();
    Statistics[] stats = new Statistics[NAMES.length];
    for (int i = 0; i < stats.length; i++) {
        stats[i] = (settings.showHistograms || settings.saveTraceData) ? new StoredDataStatistics() : new Statistics();
    }
    int singles = 0;
    for (Trace trace : traces) {
        int nBlinks = trace.getNBlinks() - 1;
        stats[BLINKS].add(nBlinks);
        int[] onTimes = trace.getOnTimes();
        int[] offTimes = trace.getOffTimes();
        double tOn = 0;
        for (int t : onTimes) {
            stats[T_ON].add(t * exposureTime);
            tOn += t * exposureTime;
        }
        stats[TOTAL_T_ON].add(tOn);
        if (offTimes != null) {
            double tOff = 0;
            for (int t : offTimes) {
                stats[T_OFF].add(t * exposureTime);
                tOff += t * exposureTime;
            }
            stats[TOTAL_T_OFF].add(tOff);
        }
        final double signal = trace.getSignal() / results.getGain();
        stats[TOTAL_SIGNAL].add(signal);
        stats[SIGNAL_PER_FRAME].add(signal / trace.size());
        if (trace.size() == 1)
            singles++;
    }
    // Add to the summary table
    StringBuilder sb = new StringBuilder();
    sb.append(results.getName()).append("\t");
    sb.append(outputName.equals("Cluster") ? settings.getClusteringAlgorithm() : settings.getTraceMode()).append("\t");
    sb.append(Utils.rounded(exposureTime * 1000, 3)).append("\t");
    sb.append(Utils.rounded(dThreshold, 3)).append("\t");
    sb.append(Utils.rounded(tThreshold, 3));
    if (settings.splitPulses)
        sb.append(" *");
    sb.append("\t");
    sb.append(timeInFrames2(tThreshold)).append("\t");
    sb.append(traces.length).append("\t");
    sb.append(filtered).append("\t");
    sb.append(singles).append("\t");
    sb.append(traces.length - singles).append("\t");
    for (int i = 0; i < stats.length; i++) {
        sb.append(Utils.rounded(stats[i].getMean(), 3)).append("\t");
    }
    if (java.awt.GraphicsEnvironment.isHeadless()) {
        IJ.log(sb.toString());
        return;
    } else {
        summaryTable.append(sb.toString());
    }
    if (settings.showHistograms) {
        IJ.showStatus("Calculating histograms ...");
        int[] idList = new int[NAMES.length];
        int count = 0;
        boolean requireRetile = false;
        for (int i = 0; i < NAMES.length; i++) {
            if (displayHistograms[i]) {
                idList[count++] = Utils.showHistogram(TITLE, (StoredDataStatistics) stats[i], NAMES[i], (integerDisplay[i]) ? 1 : 0, (settings.removeOutliers || alwaysRemoveOutliers[i]) ? 2 : 0, settings.histogramBins);
                requireRetile = requireRetile || Utils.isNewWindow();
            }
        }
        if (count > 0 && requireRetile) {
            idList = Arrays.copyOf(idList, count);
            new WindowOrganiser().tileWindows(idList);
        }
    }
    if (settings.saveTraceData) {
        saveTraceData(stats);
    }
    IJ.showStatus("");
}

Example 32 with Statistics

use of gdsc.core.utils.Statistics in project GDSC-SMLM by aherbert.

the class PCPALMAnalysis method computeRadialAverage.

/**
	 * Compute the radial average correlation function (gr)
	 * 
	 * @param maxRadius
	 *            the maximum radius to process (in pixels)
	 * @param nmPerPixel
	 *            covert pixel distances to nm
	 * @param correlation
	 *            auto-correlation
	 * @return { distances[], gr[], gr_se[] }
	 */
private double[][] computeRadialAverage(int maxRadius, double nmPerPixel, FloatProcessor correlation) {
    // Perform averaging of the correlation function using integer distance bins
    log("  Computing distance vs correlation curve");
    final int centre = correlation.getHeight() / 2;
    // Count the number of pixels at each distance and sum the correlations
    Statistics[] gr = new Statistics[maxRadius + 1];
    // Cache distances
    int[] d2 = new int[maxRadius + 1];
    for (int dy = 0; dy <= maxRadius; dy++) {
        gr[dy] = new Statistics();
        d2[dy] = dy * dy;
    }
    int[][] distance = new int[maxRadius + 1][maxRadius + 1];
    for (int dy = 0; dy <= maxRadius; dy++) {
        for (int dx = dy; dx <= maxRadius; dx++) {
            distance[dy][dx] = distance[dx][dy] = (int) Math.round(Math.sqrt(d2[dx] + d2[dy]));
        }
    }
    float[] data = (float[]) correlation.getPixels();
    for (int dy = -maxRadius; dy <= maxRadius; dy++) {
        final int absY = Math.abs(dy);
        int index = (centre + dy) * correlation.getWidth() + centre - maxRadius;
        for (int dx = -maxRadius; dx <= maxRadius; dx++, index++) {
            final int d = distance[absY][Math.abs(dx)];
            if (d > maxRadius || d == 0)
                continue;
            gr[d].add(data[index]);
        }
    }
    // Create the final data: a curve showing distance (in nm) verses the average correlation
    double[] x = new double[maxRadius + 1];
    double[] y = new double[maxRadius + 1];
    double[] sd = new double[maxRadius + 1];
    for (int i = 0; i < x.length; i++) {
        x[i] = i * nmPerPixel;
        y[i] = gr[i].getMean();
        sd[i] = gr[i].getStandardError();
    }
    y[0] = correlation.getf(centre, centre);
    return new double[][] { x, y, sd };
}