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* Copyright (c) 2007-2015 Broad Institute
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package org.broad.igv.tdf;
import org.apache.commons.math.stat.StatUtils;
import org.apache.log4j.Logger;
import org.broad.igv.track.WindowFunction;
import org.broad.igv.util.collections.DownsampledDoubleArrayList;
import java.util.HashSet;
import java.util.Set;
/**
* Accumulator for a single window function.
* <p/>
* Estimating percentiles -- weighted average of multiple estimates
*
* @author jrobinso
*/
public class Accumulator {
private static Logger log = Logger.getLogger(Accumulator.class);
private static int MAX_VALUE_COUNT = 100000;
boolean isFinished = false;
WindowFunction windowFunction;
float sum = 0.0f;
int basesCovered = 0;
int nPts = 0;
float value = Float.NaN;
DownsampledDoubleArrayList valueList; // List used to accumulate values for percentile calculations
// Optional -- keep some representative data and probe names for popup text
int nRepValues;
float[] repData;
String[] repProbes;
public Accumulator(WindowFunction windowFunction, int nRepValues) {
this(windowFunction);
if (nRepValues > 0) {
this.nRepValues = nRepValues;
this.repData = new float[nRepValues];
this.repProbes = new String[nRepValues];
}
}
public Accumulator(WindowFunction windowFunction) {
this.windowFunction = windowFunction;
if (PERCENTILE_WINDOW_FUNCTIONS.contains(windowFunction)) {
valueList = new DownsampledDoubleArrayList(100, MAX_VALUE_COUNT);
}
}
public boolean hasData() {
return basesCovered > 0;
}
public void add(int nBases, float v, String probe) {
if (isFinished) {
log.error("Attempt to add data to a finalized accumulator");
throw new RuntimeException("Attempt to add data to a finalized accumulator");
}
// Some older TDF files created in previous versions of igvtools from wig files were improperly coded,
// with start=end, resulting in an nBases value of zero. This is not a possible value, so threshold it
if (nBases < 1) nBases = 1;
if (!Float.isNaN(v)) {
if (repData != null && nPts < repData.length) {
repData[nPts] = v;
repProbes[nPts] = probe;
}
switch (windowFunction) {
case min:
value = Float.isNaN(value) ? v : Math.min(value, v);
break;
case max:
value = Float.isNaN(value) ? v : Math.max(value, v);
break;
case mean:
sum += nBases * v;
break;
default:
if (valueList != null) {
valueList.add(v);
}
}
nPts++;
basesCovered += nBases;
}
}
public void finish() {
if (isFinished) {
return;
}
if (windowFunction == WindowFunction.mean) {
value = Float.isNaN(sum) ? Float.NaN : sum / basesCovered;
} else if (valueList != null) {
if (valueList.size() == 0) {
value = Float.NaN;
} else if (valueList.size() == 1) {
value = (float) valueList.get(0);
} else {
double[] valueArray = valueList.toArray();
double p = this.getPercentile(windowFunction);
if (p > 0) {
value = (float) StatUtils.percentile(valueArray, p);
} else {
value = Float.NaN;
}
}
}
valueList = null;
isFinished = true;
}
public int getNpts() {
return nPts;
}
public String[] getRepProbes() {
return repProbes;
}
public float getValue() {
if (!isFinished) finish();
return value;
}
public double getPercentile(WindowFunction wf) {
switch (wf) {
case percentile2:
return 2;
case percentile10:
return 10;
case percentile90:
return 90;
case percentile98:
return 98;
case median:
return 50;
default:
return -1.0;
}
}
public float[] getRepData() {
return repData;
}
class PercentileValue {
int nPoints;
double value;
PercentileValue(int nPoints, double value) {
this.nPoints = nPoints;
this.value = value;
}
}
static Set<WindowFunction> PERCENTILE_WINDOW_FUNCTIONS = new HashSet();
static {
PERCENTILE_WINDOW_FUNCTIONS.add(WindowFunction.median);
PERCENTILE_WINDOW_FUNCTIONS.add(WindowFunction.percentile2);
PERCENTILE_WINDOW_FUNCTIONS.add(WindowFunction.percentile10);
PERCENTILE_WINDOW_FUNCTIONS.add(WindowFunction.percentile90);
PERCENTILE_WINDOW_FUNCTIONS.add(WindowFunction.percentile98);
}
}