DelphinID almost there.

src/rawDeepLearningClassifier/dataPlotFX/DLPredictionPane.java

@@ -101,6 +101,8 @@ public class DLPredictionPane extends PamBorderPane implements TDSettingsPane {
 		if (dlPredictionPlotInfoFX.getDlControl().getDLModel()!=null) {
 			//populate the prediction pane. 
 			DLClassName[] classNames = dlPredictionPlotInfoFX.getDlControl().getDLModel().getClassNames();
+			
+//			System.out.println("MAKE MY CLASS NAMES: " + dlPredictionPlotInfoFX.getDlControl().getDLModel().getClassNames());
 
 			layoutColourPanes(classNames);
 		}

src/rawDeepLearningClassifier/dataPlotFX/DLPredictionPlotInfoFX.java

@@ -94,7 +94,7 @@ public class DLPredictionPlotInfoFX extends GenericLinePlotInfo {
 		if (getDlControl().getDLModel()!=null) {
 			DLClassName[] classNames = getDlControl().getDLModel().getClassNames();
 
-//			System.out.println("Class names are: !!! " + (classNames == null ? "null" : classNames.length)); 
+			System.out.println("Class names are: !!! " + (classNames == null ? "null" : classNames.length)); 
 
 			if (classNames!=null) {
 
@@ -105,8 +105,8 @@ public class DLPredictionPlotInfoFX extends GenericLinePlotInfo {
 						dlPredParams.lineInfos[i] = new LineInfo(true, Color.rgb(0, 0, 255%(i*30 + 50)));
 					}
 				}
-
 			}
+			getGraphSettingsPane().setParams();
 		}
 	}
 

src/rawDeepLearningClassifier/dlClassification/DLClassifyProcess.java

@@ -197,7 +197,7 @@ public class DLClassifyProcess extends PamInstantProcess {
 
 		if (pamRawData instanceof SegmenterDetectionGroup) {
 			if (classificationBuffer.size()>=1) {
-				System.out.println("RUN THE MODEL FOR WHISTLES: ");
+//				System.out.println("RUN THE MODEL FOR WHISTLES: ");
 				runDetectionGroupModel(); 
 				classificationBuffer.clear(); 
 			}
@@ -232,14 +232,15 @@ public class DLClassifyProcess extends PamInstantProcess {
 	/**
 	 * Run a model for which the input is a detection group. 
 	 */
-	private void runDetectionGroupModel() {
+	private synchronized void runDetectionGroupModel() {
 		if (classificationBuffer.size()<=0) return; 
 		ArrayList<PamDataUnit> classificationBufferTemp = (ArrayList<PamDataUnit>) classificationBuffer.clone(); 
 
 		ArrayList<? extends PredictionResult> modelResults = this.dlControl.getDLModel().runModel(classificationBufferTemp); 
 
 		for (int i=0; i<classificationBufferTemp.size(); i++) {
-			if (modelResults.get(i)!=null) {
+			
+			if (modelResults!=null && modelResults.get(i)!=null) {
 				DLDataUnit dlDataUnit =  predictionToDataUnit(classificationBuffer.get(i),  modelResults.get(i));
 				this.dlModelResultDataBlock.addPamData(dlDataUnit); //here
 			}

src/rawDeepLearningClassifier/dlClassification/StandardClassifierModel.java

@@ -106,7 +106,7 @@ public abstract class StandardClassifierModel implements DLClassiferModel, PamSe
 	
 	@Override
 	public void prepModel() {
-//		System.out.println("STANDARD CLASSIFIER MODEL PREP MODEL! !!!");
+		System.out.println("STANDARD CLASSIFIER MODEL PREP MODEL! !!!: " +  getDLParams().modelPath);
 //		StandardModelParams oldParams = getDLParams().clone();
 		
 		getDLWorker().prepModel(getDLParams(), dlControl);
@@ -115,6 +115,7 @@ public abstract class StandardClassifierModel implements DLClassiferModel, PamSe
 		if (getDLWorker().isModelNull()) {
 			dlClassifierWarning.setWarningMessage("There is no loaded " + getName() + " classifier model. " + getName() + " disabled.");
 			WarningSystem.getWarningSystem().addWarning(dlClassifierWarning);
+			return;
 		}
 
 
@@ -187,7 +188,7 @@ public abstract class StandardClassifierModel implements DLClassiferModel, PamSe
 	public DLStatus setModel(URI uri) {
 		//will change the params if we do not clone. 
 		StandardModelParams.setModel(uri, this.getDLParams()); 
-		this.prepModel();
+		this.getDLWorker().prepModel(getDLParams(), dlControl);
 		return getModelStatus();
 	}
 	

src/rawDeepLearningClassifier/dlClassification/archiveModel/ArchiveModelWorker.java

@@ -199,8 +199,7 @@ public class ArchiveModelWorker extends GenericModelWorker {
 	 * @throws IOException
 	 */
 	public ArchiveModel loadModel(String currentPath2) throws MalformedModelException, IOException {
-		
-		System.out.println("HELLO MODEL: " +currentPath2 );
+	
 		return new SimpleArchiveModel(new File(currentPath2)); 
 	}
 

src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDClassifier.java

@@ -8,6 +8,7 @@ import org.jamdev.jdl4pam.transforms.DLTransformsFactory;
 import org.jamdev.jdl4pam.transforms.DLTransfromParams;
 
 import PamController.PamControlledUnitSettings;
+import PamController.PamSettingManager;
 import rawDeepLearningClassifier.DLControl;
 import rawDeepLearningClassifier.dlClassification.DLClassiferModel;
 import rawDeepLearningClassifier.dlClassification.StandardClassifierModel;
@@ -37,6 +38,9 @@ public class DelphinIDClassifier extends StandardClassifierModel {
 
 	public DelphinIDClassifier(DLControl dlControl) {
 		super(dlControl);
+		
+		//load the previous settings
+		PamSettingManager.getInstance().registerSettings(this);
 	}
 
 	@Override
@@ -105,10 +109,11 @@ public class DelphinIDClassifier extends StandardClassifierModel {
 
 	@Override
 	public boolean restoreSettings(PamControlledUnitSettings pamControlledUnitSettings) {
+
 		DelphinIDParams newParameters = (DelphinIDParams) pamControlledUnitSettings.getSettings();
 		if (newParameters!=null) {
 			delphinIDParams = (DelphinIDParams) newParameters.clone();
-			//System.out.println("SoundSpot have been restored. : " + soundSpotParmas.classNames); 
+//			System.out.println("DELPHINID have been restored. : " + delphinIDParams.modelPath); 
 			if (delphinIDParams.dlTransfromParams!=null) {
 				delphinIDParams.dlTransfroms = DLTransformsFactory.makeDLTransforms((ArrayList<DLTransfromParams>) delphinIDParams.dlTransfromParams); 
 			}

src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDWorker.java

@@ -2,6 +2,7 @@ package rawDeepLearningClassifier.dlClassification.delphinID;
 
 
 import java.io.File;
+import java.io.IOException;
 import java.util.ArrayList;
 
 import org.jamdev.jdl4pam.transforms.DLTransform;
@@ -9,18 +10,23 @@ import org.jamdev.jdl4pam.transforms.DLTransfromParams;
 import org.jamdev.jdl4pam.transforms.FreqTransform;
 import org.jamdev.jdl4pam.transforms.DLTransform.DLTransformType;
 import org.jamdev.jdl4pam.transforms.jsonfile.DLTransformsParser;
+import org.jamdev.jdl4pam.utils.DLMatFile;
 import org.jamdev.jdl4pam.utils.DLUtils;
 import org.json.JSONArray;
 import org.json.JSONObject;
 
+import PamUtils.PamArrayUtils;
 import PamguardMVC.PamDataUnit;
 import ai.djl.Model;
 import rawDeepLearningClassifier.DLControl;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
 import rawDeepLearningClassifier.dlClassification.archiveModel.ArchiveModelWorker;
 import rawDeepLearningClassifier.dlClassification.delphinID.Whistles2Image.Whistle2ImageParams;
-import rawDeepLearningClassifier.segmenter.GroupedRawData;
 import rawDeepLearningClassifier.segmenter.SegmenterDetectionGroup;
+import us.hebi.matlab.mat.format.Mat5;
+import us.hebi.matlab.mat.types.MatFile;
+import us.hebi.matlab.mat.types.Matrix;
+import us.hebi.matlab.mat.types.Struct;
 
 /**
  * 
@@ -86,11 +92,50 @@ public class DelphinIDWorker extends  ArchiveModelWorker {
 
 				return whistle2ImageParmas;
 			}
-
 		}
+		
 		//something has gone wrong if we get here. 
 		return null; 
 	}
+	
+	
+	
+	private Struct imageStruct;
+	int count = 0;
+	/**
+	 * Tets by exporting results to a .mat file. 
+	 * @param data
+	 * @param aSegment
+	 */
+	private void addIMage2MatFile(double[][] data, SegmenterDetectionGroup aSegment) {
+		long dataStartMillis = 1340212413000L;
+
+		if (imageStruct==null) {
+			 imageStruct = Mat5.newStruct(100,1);
+		}
+		Matrix image = DLMatFile.array2Matrix(data);
+		imageStruct.set("image", count, image);
+		imageStruct.set("startmillis", count, Mat5.newScalar(aSegment.getSegmentStartMillis()));
+		imageStruct.set("startseconds", count, Mat5.newScalar((aSegment.getSegmentStartMillis()-dataStartMillis)/1000.));
+
+		count++;
+		
+		System.out.println("SAVED " +count + " TO MAT FILE");
+		
+		if (count==10) {
+			//create MatFile for saving the image data to. 
+			MatFile matFile = Mat5.newMatFile();
+			matFile.addArray("whistle_images", imageStruct);
+			//the path to the model
+			String matImageSave = "C:/Users/Jamie Macaulay/MATLAB Drive/MATLAB/PAMGUARD/deep_learning/delphinID/whistleimages_pg.mat";
+			try {
+				Mat5.writeToFile(matFile,matImageSave);
+			} catch (IOException e) {
+				// TODO Auto-generated catch block
+				e.printStackTrace();
+			}
+		}
+	}
 
 
 	@Override
@@ -110,7 +155,7 @@ public class DelphinIDWorker extends  ArchiveModelWorker {
 		double[][] transformedData2; //spectrogram data
 		for (int j=0; j<numChunks; j++) {
 
-			System.out.println("Number of whisltes to process: " + whistleGroups.get(j).getSubDetectionsCount() + "  " + whistleGroups.get(j).getSegmentStartMillis());
+//			System.out.println("Number of whistle to process: " + whistleGroups.get(j).getStartSecond() + "s  " +  whistleGroups.get(j).getSubDetectionsCount() + "  " + whistleGroups.get(j).getSegmentStartMillis());
 			//create the first transform and set then whistle data. Note that the absolute time limits are
 			//contained within the SegmenterDetectionGroup unit. 
 			Whistles2Image whistles2Image = new Whistles2Image(whistleGroups.get(j), whistleImageParams);
@@ -126,6 +171,14 @@ public class DelphinIDWorker extends  ArchiveModelWorker {
 			
 			transformedData2 = ((FreqTransform) transform).getSpecTransfrom().getTransformedData(); 
 			transformedDataStack[j] = DLUtils.toFloatArray(transformedData2); 
+			
+//			//TEMP
+//			try {
+//				addIMage2MatFile(transformedData2,  whistleGroups.get(j));
+//			}
+//			catch (Exception e) {
+//				e.printStackTrace();
+//			}
 		}
 
 

src/rawDeepLearningClassifier/dlClassification/delphinID/Whistles2Image.java

@@ -118,16 +118,19 @@ public class Whistles2Image extends FreqTransform {
 //			}
 //
 //		}
+//		System.out.println("Whistle group: " + segStart);
 
 		for (int i=0; i<whistleGroup.getSubDetectionsCount(); i++) {
 
 			whistleContour = (AbstractWhistleDataUnit) whistleGroup.getSubDetection(i);
-//			System.out.println("Whistle start time: " + (segStart - whistleContour.getTimeMilliseconds())/1000. +  " end: " + (segStart - whistleContour.getTimeMilliseconds() + whistleContour.getDurationInMilliseconds())/1000.);
-
-
+			
+//			System.out.println("Whistle start time: " + (whistleContour.getTimeMilliseconds()-segStart)/1000. +  " end: " + 
+//			(whistleContour.getTimeMilliseconds() - (segStart + whistleContour.getDurationInMilliseconds()))/1000.  
+//			+ "  millis: " + whistleContour.getTimeMilliseconds() +  " first slice: " + whistleContour.getTimesInSeconds()[0]);
+			
 			double[][] contourD = new double[whistleContour.getSliceCount()][2];
 			for (int j=0; j<whistleContour.getSliceCount(); j++) {
-				contourD[j][0] = (whistleContour.getTimeMilliseconds()-segStart)/1000. + whistleContour.getTimesInSeconds()[j];
+				contourD[j][0] = (whistleContour.getTimeMilliseconds()-segStart)/1000. + (whistleContour.getTimesInSeconds()[j]-whistleContour.getTimesInSeconds()[0]);
 				contourD[j][1] = whistleContour.getFreqsHz()[j];
 			}
 			contours.add(contourD);
@@ -189,7 +192,7 @@ public class Whistles2Image extends FreqTransform {
 				x = ((points.get(j)[i][0]-xlims[0])/(xlims[1]-xlims[0]))*size[0];
 				y = ((points.get(j)[i][1]-ylims[0])/(ylims[1]-ylims[0]))*size[1];
 				
-				//System.out.println("Fill oval: x" + x + " y: " + y + " time: " + points.get(j)[i][0]);
+//				System.out.println("Fill oval: x " + x + " y: " + y + " time: " + points.get(j)[i][0]);
 				
 				Graphics2D g2 = (Graphics2D) canvas.getGraphics();
 

src/rawDeepLearningClassifier/dlClassification/genericModel/GenericModelWorker.java

@@ -33,7 +33,7 @@ public class GenericModelWorker extends DLModelWorker<StandardPrediction> {
 
 	@Override
 	public float[] runModel(float[][][] transformedDataStack) {
-		System.out.println("RUN GENERIC MODEL: " + transformedDataStack.length +  "  " + transformedDataStack[0].length +  "  " + transformedDataStack[0][0].length);
+//		System.out.println("RUN GENERIC MODEL: " + transformedDataStack.length +  "  " + transformedDataStack[0].length +  "  " + transformedDataStack[0][0].length);
 //		System.out.println("RUN GENERIC MODEL: " + transformedDataStack[0][0][0]);
 		float[]  results; 
 		if (freqTransform)
@@ -48,8 +48,8 @@ public class GenericModelWorker extends DLModelWorker<StandardPrediction> {
 			//System.out.println("RUN GENERIC MODEL WAVE: " + waveStack.length +  "  " + waveStack[0].length +  " " + waveStack[0][0]);
 			results =  getModel().runModel(waveStack);
 		}
-		System.out.println("GENERIC MODEL RESULTS: " + (results== null ? null : results.length));
-		PamArrayUtils.printArray(results);
+//		System.out.println("GENERIC MODEL RESULTS: " + (results== null ? null : results.length));
+//		PamArrayUtils.printArray(results);
 		return results;
 	}
 

src/rawDeepLearningClassifier/segmenter/SegmenterDetectionGroup.java

@@ -21,6 +21,8 @@ public class SegmenterDetectionGroup extends GroupDetection<PamDataUnit> {
 	 */
 	private long segMillis;
 
+	private double timeS;
+
 	/**
 	 * Constructor for a group of detections within a detection. Note that some
 	 * longer detections (e.g. whistles) may have sections outside the segment.
@@ -33,7 +35,7 @@ public class SegmenterDetectionGroup extends GroupDetection<PamDataUnit> {
 	public SegmenterDetectionGroup(long timeMilliseconds, int channelBitmap, long startSample, double duration) {
 		super(timeMilliseconds, channelBitmap, startSample, (long) duration);
 		this.setDurationInMilliseconds(duration);
-		this.segMillis =timeMilliseconds;
+		this.segMillis = timeMilliseconds;
 		this.segDuration = duration;
 	}
 	
@@ -56,5 +58,13 @@ public class SegmenterDetectionGroup extends GroupDetection<PamDataUnit> {
 		return (long) (segMillis+segDuration);
 	}
 
+	public void setStartSecond(double timeS) {
+		this.timeS = timeS;
+	}
+
+	public double getStartSecond() {
+		return timeS;
+	}
+
 
 }

src/rawDeepLearningClassifier/segmenter/SegmenterProcess.java

@@ -71,7 +71,11 @@ public class SegmenterProcess extends PamProcess {
 	/**
 	 * The current segmenter detection group.
 	 */
-	private SegmenterDetectionGroup[] segmenterDetectionGroup = null; 
+	private SegmenterDetectionGroup[] segmenterDetectionGroup = null;
+
+	private long segmentStart=-1;
+
+	private long segmenterEnd=-1; 
 
 
 	public SegmenterProcess(DLControl pamControlledUnit, PamDataBlock parentDataBlock) {
@@ -219,9 +223,10 @@ public class SegmenterProcess extends PamProcess {
 	 */
 	public void newData(PamDataUnit pamRawData) {
 
+//		System.out.println("New data for segmenter: " + pamRawData); 
+
 		if (!dlControl.getDLParams().useDataSelector || dlControl.getDataSelector().scoreData(pamRawData)>0) {	
 
-			//System.out.println("New data for segmenter: " + pamRawData); 
 			if (pamRawData instanceof RawDataUnit) {
 				newRawDataUnit(pamRawData); 
 			}
@@ -243,7 +248,7 @@ public class SegmenterProcess extends PamProcess {
 	 * A new detection data unit i.e. this is only if we have detection data which is being grouped into segments. 
 	 * @param dataUnit - the whistle data unit. 
 	 */
-	private void newWhistleData(PamDataUnit dataUnit) {
+	private synchronized void newWhistleData(PamDataUnit dataUnit) {
 		
 		
 		ConnectedRegionDataUnit whistle = (ConnectedRegionDataUnit) dataUnit;
@@ -256,11 +261,16 @@ public class SegmenterProcess extends PamProcess {
 		
 		int index = -1;
 		for (int i=0; i<chanGroups.length; i++) {
-			if (dlControl.getDLParams().groupedSourceParams.getGroupChannels(chanGroups[i])==dataUnit.getChannelBitmap())
-			index=i;
+			if (dlControl.getDLParams().groupedSourceParams.getGroupChannels(chanGroups[i])==dataUnit.getChannelBitmap()) {
+				index=i;
+				break;
+			}
 		}
 		
-		//System.out.println("Whiste data: " + dataUnit + " " + chanGroups.length +  "  " +  index + "  " + dataUnit.getChannelBitmap());
+		//FIXME - TWEMP
+		index =0;
+		
+//		System.out.println("Whistle data: " + ((dataUnit.getTimeMilliseconds()-firstClockUpdate)/1000.) + "s " + chanGroups.length +  "  " +  index + "  " + dataUnit.getChannelBitmap());
 //		PamArrayUtils.printArray(chanGroups);
 		
 		if (index<0) {
@@ -272,33 +282,63 @@ public class SegmenterProcess extends PamProcess {
 			//System.out.println("Whiste not in segment"); 
 			//iterate until we find the correct time for this detection. This keeps the segments consist no matter 
 			//the data units. What we do not want is the first data unit defining the start of the first segment.
-			long segmentStart = firstClockUpdate;
-			long segmenterEnd = (long) (segmentStart + getSegmentLenMillis());
-
-			while(!detectionInSegment(dataUnit,  segmentStart,  segmenterEnd)) {
-				segmentStart = segmenterEnd;
+			if (segmentStart <0) {
+				segmentStart= firstClockUpdate;
 				segmenterEnd = (long) (segmentStart + getSegmentLenMillis());
 			}
 			
-			long startSample = this.absMillisecondsToSamples(segmenterEnd);
-			
-			//now we need to create a new data unit.
-			if (segmenterDetectionGroup[index]!=null) {
-				System.out.println("SAVE WHISTLE SEGMENT!: " + segmenterDetectionGroup[index]);
-
-				//save the data unit
-				this.segmenterGroupDataBlock.addPamData(segmenterDetectionGroup[index]);
+			while(!detectionInSegment(dataUnit,  segmentStart,  segmenterEnd)) {
+				nextGroupSegment( index);
 			}
-			
-			//TODO There is no good way to get the start sample?
-			segmenterDetectionGroup[index] = new SegmenterDetectionGroup(segmentStart, chanGroups[index], startSample,  getSegmentLenMillis());
 		}
 		
-		//System.out.println("Add whistle to existing segment"); 
 		segmenterDetectionGroup[index].addSubDetection(whistle);
+//		System.out.println("Segment sub detection count: " + 	segmenterDetectionGroup[index].getSubDetectionsCount()); 
 		
 	}
 	
+	/**
+	 * Iterate to the next group segment
+	 * @param index - the group index;
+	 */
+	private void nextGroupSegment(int index) {
+		
+//		System.out.println("----------------------------------");
+
+		segmentStart = (long) (segmentStart+ getSegmentHopMillis());
+		segmenterEnd = (long) (segmentStart + getSegmentLenMillis());
+		
+		int[] chanGroups = dlControl.getDLParams().groupedSourceParams.getChannelGroups();
+
+		long startSample = this.absMillisecondsToSamples(segmentStart);
+
+		//now we need to create a new data unit.
+		SegmenterDetectionGroup aSegment = new SegmenterDetectionGroup(segmentStart, chanGroups[index], startSample,  getSegmentLenMillis());
+		aSegment.setStartSecond((segmentStart-firstClockUpdate)/1000.);
+
+		//save the last segment
+		if (segmenterDetectionGroup[index]!=null) {
+			//add any data units from the previous segment (because segments may overlap);
+			int count =0;
+			for (int i=0; i<segmenterDetectionGroup[index].getSubDetectionsCount() ; i++) {
+				if (detectionInSegment(segmenterDetectionGroup[index].getSubDetection(i), aSegment)){
+					aSegment.addSubDetection(segmenterDetectionGroup[index].getSubDetection(i));
+					count++;
+				}
+			}
+			
+//			System.out.println("SAVE WHISTLE SEGMENT!: " + ((segmenterDetectionGroup[index].getSegmentStartMillis()-firstClockUpdate)/1000.) + "s" + " " + " no. whsitles: " + segmenterDetectionGroup[index].getSubDetectionsCount() + " " + segmenterDetectionGroup[index].getSegmentStartMillis() + "  " + segmenterDetectionGroup[index]);
+			//save the data unit to the data block
+			if (segmenterDetectionGroup[index].getSubDetectionsCount()>0) {
+				this.segmenterGroupDataBlock.addPamData(segmenterDetectionGroup[index]);
+			}
+		}
+		
+		segmenterDetectionGroup[index] = aSegment;
+//		System.out.println("NEW SEGMENT START!: " + (segmentStart-firstClockUpdate)/1000. + "s" + " " + segmenterDetectionGroup[index].getSegmentStartMillis()+ "  " +segmenterDetectionGroup[index]);
+
+	}
+	
 	private boolean detectionInSegment(PamDataUnit dataUnit, SegmenterDetectionGroup segmenterDetectionGroup2) {
 		return detectionInSegment(dataUnit, segmenterDetectionGroup2.getSegmentStartMillis(),
 				(long) (segmenterDetectionGroup2.getSegmentStartMillis()+segmenterDetectionGroup2.getSegmentDuration()));
@@ -308,11 +348,11 @@ public class SegmenterProcess extends PamProcess {
 	private boolean detectionInSegment(PamDataUnit dataUnit, long segStart, long segEnd) {
 		//TODO - this is going to fail for very small segments. 
 		long whistleStart 	= dataUnit.getTimeMilliseconds();
-		long whistleEnd 	= dataUnit.getDurationInMilliseconds().longValue();
+		long whistleEnd 	= whistleStart + dataUnit.getDurationInMilliseconds().longValue();
 
 		if ((whistleStart>=segStart && whistleStart<segEnd) || ((whistleEnd>=segStart && whistleEnd<segEnd))){
 			//some part of the whistle is in the segment. 
-			System.out.println("Whsitle in segment: " + whistleStart + "  " + whistleEnd);
+//			System.out.println("Whsitle in segment: " + whistleStart + "  " + whistleEnd);
 			return true;
 		}
 		return false;
@@ -323,6 +363,12 @@ public class SegmenterProcess extends PamProcess {
 		return millis;
 	}
 	
+	private double getSegmentHopMillis() {
+		double millis = (dlControl.getDLParams().sampleHop/this.getSampleRate())*1000.;
+		return millis;
+	}
+	
+	
 	
 	int count=0;
 	public void masterClockUpdate(long milliSeconds, long sampleNumber) {
@@ -331,23 +377,13 @@ public class SegmenterProcess extends PamProcess {
 			firstClockUpdate = milliSeconds;
 		}
 		
-		//want to make sure that a segment isn't saved if we suddenly lose 
+		//want to make sure that a segment is saved if we suddenly lose 
 		// a steady stream of data units. This ensure that the segments are saved properly
 		//after the master clock has gone past the end of the current segment. 
 		if (segmenterDetectionGroup!=null && count%20==0) {
 			for (int i=0; i<segmenterDetectionGroup.length; i++) {
 				if (segmenterDetectionGroup[i]!=null && segmenterDetectionGroup[i].getSegmentEndMillis()<milliSeconds) {
-					//add the segment to the datablock
-					this.segmenterGroupDataBlock.addPamData(segmenterDetectionGroup[i]);
-					
-					//create a new segment
-					SegmenterDetectionGroup aSegmenterDetectionGroup = 
-							new SegmenterDetectionGroup(segmenterDetectionGroup[i].getSegmentEndMillis(), 
-									segmenterDetectionGroup[i].getChannelBitmap(), 
-									this.absMillisecondsToSamples(segmenterDetectionGroup[i].getSegmentEndMillis()),  
-									getSegmentLenMillis());
-					segmenterDetectionGroup[i] = aSegmenterDetectionGroup;
-
+					nextGroupSegment(i);
 				}
 			}
 		}

src/test/rawDeepLearningClassifier/DelphinIDTest.java

@@ -9,7 +9,11 @@ import java.nio.file.Path;
 import java.nio.file.Paths;
 import java.util.ArrayList;
 
+import org.jamdev.jdl4pam.transforms.DLTransform;
+import org.jamdev.jdl4pam.transforms.FreqTransform;
+import org.jamdev.jdl4pam.transforms.DLTransform.DLTransformType;
 import org.jamdev.jdl4pam.utils.DLMatFile;
+import org.jamdev.jdl4pam.utils.DLUtils;
 import org.junit.jupiter.api.Test;
 
 import rawDeepLearningClassifier.dlClassification.delphinID.Whistles2Image;
@@ -68,6 +72,24 @@ public class DelphinIDTest {
 				}
 			}
 			
+			ArrayList<DLTransform> transforms = new ArrayList<DLTransform>();
+			transforms.add(new FreqTransform(DLTransformType.SPECRESIZE, new Number[] {Integer.valueOf(64), Integer.valueOf(48)}));
+			
+//			
+//			//set the spec transform
+//			((FreqTransform) transforms.get(0)).setSpecTransfrom(whistles2Image.getSpecTransfrom());
+//
+//			//process all the transforms. 
+//			DLTransform transform = modelTransforms.get(0); 
+//			for (int i =0; i<modelTransforms.size(); i++) {
+//				transform = modelTransforms.get(i).transformData(transform); 
+//			}
+//			
+//			transformedData2 = ((FreqTransform) transform).getSpecTransfrom().getTransformedData(); 
+//			transformedDataStack[j] = DLUtils.toFloatArray(transformedData2); 
+//			
+			
+			
 			//now save this image to a MATFILE
 			// Create MAT file with a scalar in a nested struct
 			MatFile matFileWrite = Mat5.newMatFile()