/*
 * #%L
 * ImageJ software for multidimensional image processing and analysis.
 * %%
 * Copyright (C) 2009 - 2013 Board of Regents of the University of
 * Wisconsin-Madison, Broad Institute of MIT and Harvard, and Max Planck
 * Institute of Molecular Cell Biology and Genetics.
 * %%
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 * modification, are permitted provided that the following conditions are met:
 * 
 * 1. Redistributions of source code must retain the above copyright notice,
 *    this list of conditions and the following disclaimer.
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 *    this list of conditions and the following disclaimer in the documentation
 *    and/or other materials provided with the distribution.
 * 
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
 * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
 * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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package imagej.data.autoscale;

import net.imglib2.IterableInterval;
import net.imglib2.algorithm.histogram.Histogram1d;
import net.imglib2.algorithm.histogram.Real1dBinMapper;
import net.imglib2.ops.util.Tuple2;
import net.imglib2.type.numeric.RealType;

import org.scijava.plugin.Plugin;

/**
 * Computes a 95% confidence interval from the entire set of values in an
 * {@link IterableInterval}.
 * 
 * @author Barry DeZonia
 */
@Plugin(type = AutoscaleMethod.class, name = "95% CI")
public class ConfidenceIntervalAutoscaleMethod<T extends RealType<T>> extends AbstractAutoscaleMethod<T> {

	@Override
	public Tuple2<Double, Double> getRange(IterableInterval<T> interval) {
		// pass one through data
		AutoscaleService service = getContext().getService(AutoscaleService.class);
		Tuple2<Double, Double> range = service.getDefaultIntervalRange(interval);
		// pass two through data
		Real1dBinMapper<T> mapper =
			new Real1dBinMapper<T>(range.get1(), range.get2(), 1000, false);
		Histogram1d<T> histogram = new Histogram1d<T>(mapper);
		histogram.countData(interval);
		// determine bin number containing > 2.5%
		long totValues = histogram.dfd().totalValues();
		long twoPtFivePercent = (long) Math.floor(0.025 * totValues);
		long soFar = 0;
		int bottom = 0;
		while (soFar < twoPtFivePercent) {
			soFar += histogram.frequency(bottom++);
		}
		// determine bin number containing < 97.5%
		soFar = 0;
		int top = 999;
		while (soFar < twoPtFivePercent) {
			soFar += histogram.frequency(top--);
		}
		// determine approx boundaries
		T approxMin = interval.firstElement().createVariable();
		T approxMax = approxMin.createVariable();
		histogram.getLowerBound(bottom, approxMin);
		histogram.getUpperBound(top, approxMax);
		double min = approxMin.getRealDouble();
		double max = approxMax.getRealDouble();
		// return them
		return new Tuple2<Double, Double>(min, max);
	}

}

@@ -3166,24 +3166,86 @@
 tom++);%0A%09%09%7D%0A
+%09%09while (histogram.frequency(bottom) == 0) %7B%0A%09%09%09bottom++;%0A%09%09%7D%0A
 %09%09// determi
@@ -3384,24 +3384,80 @@
 top--);%0A%09%09%7D%0A
+%09%09while (histogram.frequency(top) == 0) %7B%0A%09%09%09top--;%0A%09%09%7D%0A
 %09%09// determi

Fix bug in confidence interval calcs