Merge branch 'macster110-main'

.settings/org.eclipse.core.resources.prefs

@@ -1,6 +1,7 @@
 eclipse.preferences.version=1
 encoding//src/help/utilities/tethys/docs/connection.html=UTF-8
 encoding//src/rawDeepLearningClassifer/segmenter/SegmenterProcess.java=UTF-8
+encoding//src/test=UTF-8
 encoding//src/test/resources=UTF-8
 encoding/<project>=UTF-8
 encoding/src=UTF-8

.settings/org.eclipse.jdt.core.prefs

@@ -11,9 +11,9 @@ org.eclipse.jdt.core.codeComplete.staticFinalFieldPrefixes=
 org.eclipse.jdt.core.codeComplete.staticFinalFieldSuffixes=
 org.eclipse.jdt.core.compiler.codegen.inlineJsrBytecode=enabled
 org.eclipse.jdt.core.compiler.codegen.methodParameters=do not generate
-org.eclipse.jdt.core.compiler.codegen.targetPlatform=17
+org.eclipse.jdt.core.compiler.codegen.targetPlatform=21
 org.eclipse.jdt.core.compiler.codegen.unusedLocal=preserve
-org.eclipse.jdt.core.compiler.compliance=17
+org.eclipse.jdt.core.compiler.compliance=21
 org.eclipse.jdt.core.compiler.debug.lineNumber=generate
 org.eclipse.jdt.core.compiler.debug.localVariable=generate
 org.eclipse.jdt.core.compiler.debug.sourceFile=generate
@@ -23,4 +23,4 @@ org.eclipse.jdt.core.compiler.problem.enumIdentifier=error
 org.eclipse.jdt.core.compiler.problem.forbiddenReference=warning
 org.eclipse.jdt.core.compiler.problem.reportPreviewFeatures=warning
 org.eclipse.jdt.core.compiler.release=enabled
-org.eclipse.jdt.core.compiler.source=17
+org.eclipse.jdt.core.compiler.source=21

pom.xml

@@ -14,7 +14,6 @@
 	</organization>
 
 
-
 	<properties>
 		<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
 		<javafx.version>21</javafx.version>
@@ -54,7 +53,7 @@
 	<profiles>
 	
 		<!-- The MacOS profile creates a DMG package for installing PAMGuard on MacOS-->
-		<!--Note: Ther eis a Maven bug when using profiles when the plugin order is messed up and seemingly quite random. 
+		<!--Note: There is a Maven bug when using profiles when the plugin order is messed up and seemingly quite random. 
 		In PAMGuard it essential that the shade plugin is called before the macos plugin and so the phase of the 
 		macos bundling has been moved to deploy. To run the bundler we therefore need to put both the macossappbunlder 
 		and exec shell script into the deploy phase so they are never really called. We then explicatelyl call them after 
@@ -102,11 +101,11 @@
 							</dmg>
 							<!--Very important or deep learning does not work-->
 							 <app>
-					            <includeDependencies>false</includeDependencies>
+					            <includeDependencies>true</includeDependencies>
 					        </app>
 					        <jdk>
 								<include>true</include>
-								<!--<location>/Library/Java/JavaVirtualMachines/amazon-corretto-21.jdk</location>-->
+								<location>/Library/Java/JavaVirtualMachines/amazon-corretto-21.jdk</location>
 							</jdk>
 							<codesign>
 								<enable>false</enable>
@@ -314,57 +313,6 @@
 						</executions>
 					</plugin>
 
-
-					<!--  Plugin which creates a .dmg file for MacOS. -->
-					<plugin>
-						<groupId>de.perdian.maven.plugins</groupId>
-						<artifactId>macosappbundler-maven-plugin</artifactId>
-						<version>1.21.1</version>
-						<configuration>
-							<plist>
-								<JVMMainClassName>pamguard.Pamguard</JVMMainClassName>
-								<CFBundleIconFile>
-									src/Resources/PamguardIcon2.icns</CFBundleIconFile>
-								<CFBundleDisplayName>PAMGuard</CFBundleDisplayName>
-								<CFBundleDevelopmentRegion>English</CFBundleDevelopmentRegion>
-								<CFBundleURLTypes>
-									<string>msa</string>
-								</CFBundleURLTypes>
-								<JVMVersion>21+</JVMVersion>
-								<JVMArguments>
-									<string>-c</string>
-								</JVMArguments>
-								<NSAppSleepDisabled>true</NSAppSleepDisabled>
-							</plist>
-							<dmg>
-								<generate>true</generate>
-								<additionalResources>
-									<additionalResource>
-										<directory>
-											${project.basedir}/target/bundle/</directory>
-									</additionalResource>
-								</additionalResources>
-								<dmgFileName>${project.build.finalName}</dmgFileName>
-								<appendVersion>false</appendVersion>
-							</dmg>
-							<jdk>
-								<include>false</include>
-								<location>/Library/Java/JavaVirtualMachines/amazon-corretto-21.jdk</location>
-							</jdk>
-							<codesign>
-								<enable>false</enable>
-								<identity>3rd Party Mac Developer Application: Jamie Macaulay (7365S9DZ34)</identity>
-							</codesign>
-						</configuration>
-						<executions>
-							<execution>
-								<phase>deploy</phase>
-								<goals>
-									<goal>bundle</goal>
-								</goals>
-							</execution>
-						</executions>
-					</plugin>
 				</plugins>
 
 		
@@ -494,13 +442,21 @@
 			<groupId>io.github.macster110</groupId>
 			<artifactId>jpamutils</artifactId>
 			<version>0.0.59e</version>
+								<!-- com.github.psambit9791:wavfile:jar:0.1 pulls in various junit dependencies which breaks our own testing -->
+			<!--<exclusions>
+				<exclusion>
+					<groupId>org.junit.platform</groupId>
+					<artifactId>junit-platform-surefire-provider</artifactId>
+				</exclusion>
+			</exclusions>-->
+
 		</dependency>
 
 		<!--jpam project - Deep learning java library -->
 		<dependency>
 			<groupId>io.github.macster110</groupId>
 			<artifactId>jdl4pam</artifactId>
-			<version>0.0.99e</version>
+			<version>0.0.99f</version>
 		</dependency>
 
 		<!-- https://mvnrepository.com/artifact/gov.nist.math/jama -->
@@ -810,11 +766,11 @@
 			<version>2.12.0</version>
 		</dependency>
 
-		<!-- https://mvnrepository.com/artifact/mysql/mysql-connector-java -->
+		<!-- https://mvnrepository.com/artifact/com.mysql/mysql-connector-j -->
 		<dependency>
-			<groupId>mysql</groupId>
-			<artifactId>mysql-connector-java</artifactId>
-			<version>8.0.18</version>
+		    <groupId>com.mysql</groupId>
+		    <artifactId>mysql-connector-j</artifactId>
+		    <version>9.1.0</version>
 		</dependency>
 
 		<!-- https://mvnrepository.com/artifact/com.google.protobuf/protobuf-java -->
@@ -825,7 +781,7 @@
 		<dependency>
 			<groupId>com.google.protobuf</groupId>
 			<artifactId>protobuf-java</artifactId>
-			<version>3.17.0</version>
+			<version>4.29.1</version>
 		</dependency>
 
 		<!-- https://docs.unidata.ucar.edu/netcdf-java/5.2/userguide/using_netcdf_java_artifacts.html -->
@@ -833,7 +789,7 @@
 		<dependency>
 			<groupId>edu.ucar</groupId>
 			<artifactId>netcdfAll</artifactId>
-			<version>4.6.14</version>
+			<version>5.4.1</version>
 			<exclusions>
 				<exclusion>
 					<groupId>com.google.protobuf</groupId>

src/Map/gridbaselayer/GebcoNETCDF.java

@@ -7,11 +7,11 @@ import java.io.IOException;
 import PamView.ColourArray;
 import PamView.PamColors;
 import PamView.PamColors.PamColor;
-import ucar.netcdf.Dimension;
-import ucar.netcdf.DimensionSet;
-import ucar.netcdf.NetcdfFile;
-import ucar.netcdf.Variable;
-import ucar.netcdf.VariableIterator;
+import ucar.ma2.Array;
+import ucar.ma2.Index;
+import ucar.nc2.Dimension;
+import ucar.nc2.NetcdfFile;
+import ucar.nc2.Variable;
 
 public class GebcoNETCDF {
 
@@ -37,18 +37,19 @@ public class GebcoNETCDF {
 			return;
 		}
 
-		DimensionSet dim = ncf.getDimensions();
-		Dimension latDim = dim.get(latName);
+
+		ncf.findDimension(eleName);
+		Dimension latDim = ncf.findDimension(latName);
 		nLat = latDim.getLength();
-		Dimension lonDim = dim.get(lonName);
+		Dimension lonDim = ncf.findDimension(lonName);
 		nLon = lonDim.getLength();
 
 		latRange = getRange(latName);
 		lonRange = getRange(lonName);
 		elevationRange = extractElevationRange();
-		
+
 	}
-	
+
 	public MapRasterImage getImage(double[] latRange, double[] lonRange, boolean forceRecreate) {
 		if (mapImage == null || forceRecreate) {
 			int[] latRangeBins = {0, nLat};
@@ -65,46 +66,57 @@ public class GebcoNETCDF {
 
 	private double[] getRange(String varName) {
 		Variable var = findVariable(varName);
-		Dimension dim = netcdfFile.getDimensions().get(varName);
+		Dimension dim = netcdfFile.findDimension(varName);
 		int n = dim.getLength();
 		if (var == null) {
 			return null;
 		}
+		
+
 		double[] range = new double[2];
+		
 		int[] pos = new int[2];
 		try {
-			range[0] = var.getDouble(pos);
+			Array varA = var.read();
+			Index index = varA.getIndex();
+			
+			range[0] = varA.getDouble(index.set(pos[0], pos[1]));
 			pos[0] = n-1;
-			range[1] = var.getDouble(pos);
+			range[1] = varA.getDouble(index.set(pos[0], pos[1]));
 		} catch (IOException e) {
 			e.printStackTrace();
 		}
 		return range;
 	}
-	
+
 	private double[] extractElevationRange() {
-		Variable ele = findVariable(eleName);
-		double[] range = new double[2];
-		double val;
-		int iVal;
-		int[] pos = new int[2];
+		Variable eleV  = findVariable(eleName);
+
 		try {
+			Array ele = eleV.read();
+			double[] range = new double[2];
+			double val;
+//			int iVal;
+//			int[] pos = new int[2];
+
+			Index index = ele.getIndex();
 			for (int i = 0; i < nLat; i++) {
-				pos[0] = i;
+//				pos[0] = i;
 				for (int j = 0; j < nLon; j++) {
-					pos[1] = j;
-					val = ele.getDouble(pos);
+//					pos[1] = j;
+					val = ele.getDouble(index.set(i,j));
 					range[0] = Math.min(range[0], val);
 					range[1] = Math.max(range[1], val);
 				}
 			}
-		} catch (IOException e) {
+			return range;
+
+		} catch (Exception e) {
 			e.printStackTrace();
 			return null;
 		}
-		return range;
 	}
-	
+
 	/**
 	 * work out a value from an evenly spaced range based on a bin number
 	 * @param valueRange
@@ -114,11 +126,11 @@ public class GebcoNETCDF {
 	double binToValue(double[] valueRange, int nBin, double bin) {
 		return valueRange[0] + (valueRange[1]-valueRange[0])*(double) bin / (double) (nBin);
 	}
-	
+
 	double latBinToValue(double latBin) {
 		return binToValue(latRange, nLat, latBin);
 	}
-	
+
 	double lonBinToValue(double lonBin) {
 		return binToValue(lonRange, nLon, lonBin);
 	}
@@ -128,7 +140,7 @@ public class GebcoNETCDF {
 		ColourArray depthArray = ColourArray.createMultiColouredArray(100, Color.WHITE, PamColors.getInstance().getColor(PamColor.MAP));
 		return createImage(latRangeBins, lonRangeBins, heightArray, depthArray, hop);
 	}
-	
+
 	public MapRasterImage createImage(int[] latRangeBins, int[] lonRangeBins, ColourArray heightColours, ColourArray depthColours, int hop) {
 		latRangeBins[0] = Math.max(latRangeBins[0], 0);
 		latRangeBins[1] = Math.min(latRangeBins[1], nLat);
@@ -144,12 +156,14 @@ public class GebcoNETCDF {
 		/*
 		 *  need to get the maxima and minima of the elevation data for scaling.  
 		 */
-		Variable ele = findVariable(eleName);
+		Variable eleV = findVariable(eleName);
 		double maxDepth = -elevationRange[0], maxHeight = elevationRange[1];
 		double val;
 		int iVal;
 		int[] pos = new int[2];
 		try {
+			Array ele = eleV.read();
+			Index index = ele.getIndex();
 			double hRat = 300;
 			int nH = heightColours.getNumbColours();
 			int nD = depthColours.getNumbColours();
@@ -161,15 +175,15 @@ public class GebcoNETCDF {
 				pos[0] = i;
 				for (int j = lonRangeBins[0], ji = 0; j < lonRangeBins[1] && ji < image.getWidth(); j+= hop, ji++) {
 					pos[1] = j;
-					val = ele.getDouble(pos);
+					val = ele.getDouble(index.set(i,j));
 					ColourArray colours;
 					if (val >= 0) {
-//						iVal = (int) (99*Math.log10(val+1)/Math.log10(maxHeight));
+						//						iVal = (int) (99*Math.log10(val+1)/Math.log10(maxHeight));
 						iVal = (int) (ah*Math.log(val+1) + bh);
 						colours = heightColours;
 					}
 					else {
-//						iVal = (int) (99*Math.log10(-val)/Math.log10(maxDepth));
+						//						iVal = (int) (99*Math.log10(-val)/Math.log10(maxDepth));
 						iVal = (int) (ad*Math.log(-val) + bd);
 						colours = depthColours;
 					}
@@ -181,7 +195,7 @@ public class GebcoNETCDF {
 		} catch (IOException e) {
 			e.printStackTrace();
 		}
-		
+
 		double[] latRange = new double[2];
 		double[] lonRange = new double[2];
 		double[] edges = {-.5, +.5};
@@ -199,21 +213,23 @@ public class GebcoNETCDF {
 	 * @return Variable or null
 	 */
 	private Variable findVariable(String varName) {
-		VariableIterator varIter = netcdfFile.iterator();
-		while (varIter.hasNext() ) {
-			Variable var = varIter.next();
-			if (var.getName().equalsIgnoreCase(varName)) {
-				return var;
-			}
-		}
-		return null;
+		return  netcdfFile.findVariable(varName);
+		//		VariableIterator varIter = netcdfFile.findVariable(varName);
+		//		while (varIter.hasNext() ) {
+		//			Variable var = varIter.next();
+		//			if (var.getName().equalsIgnoreCase(varName)) {
+		//				return var;
+		//			}
+		//		}
+		//		return null;
 	}
 
 	private boolean hasField(String fieldName) {
-		if (netcdfFile.contains(fieldName)) {
+
+		if (netcdfFile.findVariable(fieldName)!=null) {
 			return true;
 		}
-		System.out.printf("Map file %s has no field \"%s\"\n", netcdfFile.getName(), fieldName);
+		System.out.printf("Map file %s has no field \"%s\"\n", netcdfFile.getTitle(), fieldName);
 		return false;
 	}
 
@@ -238,9 +254,10 @@ public class GebcoNETCDF {
 		}
 		NetcdfFile ncf;
 		try {
-			ncf = new NetcdfFile(ncFile, true);
+			ncf = NetcdfFile.open(ncFile.getPath());
+			
 			GebcoNETCDF gebcoRaster = new GebcoNETCDF(ncf);
-//			ncf.close();
+			//			ncf.close();
 			return gebcoRaster;
 		} catch (IOException e) {
 			e.printStackTrace();

src/Map/gridbaselayer/GridDataUnit.java

@@ -3,7 +3,6 @@ package Map.gridbaselayer;
 import java.awt.image.BufferedImage;
 
 import PamguardMVC.PamDataUnit;
-import ucar.multiarray.MultiArray;
 
 @Deprecated
 public class GridDataUnit extends PamDataUnit {
@@ -11,15 +10,14 @@ public class GridDataUnit extends PamDataUnit {
 	private double[] latArray;
 	private double[] lonArray;
 	private BufferedImage image;
-	private MultiArray data;
 
-	public GridDataUnit(double[] latArray, double[] lonArray, BufferedImage image, MultiArray elevation) {
+	public GridDataUnit(double[] latArray, double[] lonArray, BufferedImage image) {
 		super(0);
 		setDurationInMilliseconds(System.currentTimeMillis());
 		this.latArray = latArray;
 		this.lonArray = lonArray;
 		this.image = image;
-		this.data = elevation;
+		//this.data = elevation;
 	}
 
 	/**
@@ -64,19 +62,19 @@ public class GridDataUnit extends PamDataUnit {
 		this.image = image;
 	}
 
-	/**
-	 * @return the data
-	 */
-	public MultiArray getData() {
-		return data;
-	}
-
-	/**
-	 * @param data the data to set
-	 */
-	public void setData(MultiArray data) {
-		this.data = data;
-	}
+//	/**
+//	 * @return the data
+//	 */
+//	public MultiArray getData() {
+//		return data;
+//	}
+//
+//	/**
+//	 * @param data the data to set
+//	 */
+//	public void setData(MultiArray data) {
+//		this.data = data;
+//	}
 
 
 }

src/PamController/PamController.java

@@ -3115,7 +3115,7 @@ public class PamController implements PamControllerInterface, PamSettings {
 	}
 
 	/**
-	 * Gt the main PAMGuard configuration (list of connected modules).
+	 * Get the main PAMGuard configuration (list of connected modules).
 	 * 
 	 * @return the pamConfiguration
 	 */

src/PamUtils/PamArrayUtils.java

@@ -833,6 +833,21 @@ public class PamArrayUtils {
 		}
 		return sum;
 	}
+	
+
+	/**
+	 * Sum the elements in an array 
+	 * @param  array - the array to sum.
+	 * @return the summation of all the elements in the array.
+	 */
+	public static double sum(float[] array) {
+		double sum=0; 
+		for (double val:array) {
+			sum+=val;
+		}
+		return sum;
+	}
+
 
 
 	/**
@@ -920,14 +935,23 @@ public class PamArrayUtils {
 	/**
 	 * Print an array to the console. 
 	 * @param array to print
+	 * @param row - if true then prints all results in a single row
 	 */
-	public static void printArray(float[] array) {
+	public static void printArray(float[] array, boolean row) {
 		if (array==null) System.out.println("null"); 
 		for (int i=0; i<array.length; i++) {
-			System.out.println(i + ": " + array[i]);
+			if (row) System.out.print(array[i] + "  ");
+			else System.out.println(i + ": " + array[i]);
 		}
 	}
 	
+	/**
+	 * Print an array to the console. 
+	 * @param array to print
+	 */
+	public static void printArray(float[] array) {
+		printArray(array, false);  
+	}
 	
 	/**
 	 * Print an array to the console with no index numbers

src/PamUtils/SelectFolder.java

@@ -197,6 +197,7 @@ public class SelectFolder {
 			if (!f.exists()) {
 				if (JOptionPane.showConfirmDialog(folderPanel, "The directory " + folder + " does not exist " +
 						"do you want to create it ?", "Select Directory", JOptionPane.YES_NO_CANCEL_OPTION) == JOptionPane.YES_OPTION) {
+					
 					if (!f.mkdirs()) {
 						// print a warning message
 						JOptionPane.showMessageDialog(folderPanel, "The folder " + folder + " could not be created", 

src/PamView/PamGui.java

@@ -172,7 +172,11 @@ public class PamGui extends PamView implements WindowListener, PamSettings {
 
 		frame.setDefaultCloseOperation(WindowConstants.DO_NOTHING_ON_CLOSE);
 
-		String iconLoc = PamIcon.getPAMGuardIconPath(PamIcon.OLD);
+//<<<<<<< HEAD
+//		String iconLoc = PamIcon.getPAMGuardIconPath(PamIcon.OLD);
+//=======
+		String iconLoc = PamIcon.getPAMGuardIconPath(PamIcon.NORMAL);
+//>>>>>>> b88dbada951a64364503ec687b4babdc4df07183
 //		switch (PamController.getInstance().getRunMode()){
 //		case(PamController.RUN_NETWORKRECEIVER):iconLoc=PamIcon.getPAMGuardIconPath(PamIcon.NORMAL);break;
 //		case(PamController.RUN_PAMVIEW):iconLoc=PamIcon.getPAMGuardIconPath(PamIcon.NORMAL);break;

src/PamView/dialog/warn/WarnOnce.java

@@ -96,7 +96,7 @@ public class WarnOnce implements PamSettings {
 		}
 		else if (PamGUIManager.isFX()) {
 			return singleInstance.showWarningDialogFX(null, 
-					title, message, getAlertType(messageType), helpPoint, error, okButtonText, cancelButtonText);
+					title, message, getAlertType(messageType), helpPoint, error, okButtonText, cancelButtonText, false);
 		}
 		return -1; 
 	}
@@ -273,7 +273,21 @@ public class WarnOnce implements PamSettings {
 	 * otherwise OK_OPTION will always be returned. 
 	 */
 	public static int showWarningFX(javafx.stage.Window parent, String title, String message, AlertType messageType) {
-		 return singleInstance.showWarningDialogFX(parent, title, message, messageType, null, null, null, null);
+		 return singleInstance.showWarningDialogFX(parent, title, message, messageType, null, null, null, null, false);
+	}
+
+	
+	/**
+	 * Show a warning message in JavaFX dialog. 
+	 * @param parent parent frame
+	 * @param title title of warning
+	 * @param message warning message (use html for multiline messages)
+	 * @param messageType message type OK_CANCEL_OPTION or WARNING_MESSAGE 
+	 * @return if messageType is OK_CANCEN_OPTION this will return OK_OPTION or CANECL_OPTION, 
+	 * otherwise OK_OPTION will always be returned. 
+	 */
+	public static int showWarningFX(javafx.stage.Window parent, String title, String message, AlertType messageType, boolean disableCheckBoxes) {
+		 return singleInstance.showWarningDialogFX(parent, title, message, messageType, null, null, null, null, disableCheckBoxes);
 	}
 
 	/**
@@ -289,7 +303,7 @@ public class WarnOnce implements PamSettings {
 	 * otherwise OK_OPTION will always be returned. 
 	 */
 	public static int showWarningFX(javafx.stage.Window parent, String title, String message, AlertType messageType, String helpPoint) {
-		return singleInstance.showWarningDialogFX(parent, title, message, messageType, helpPoint, null, null, null);
+		return singleInstance.showWarningDialogFX(parent, title, message, messageType, helpPoint, null, null, null, false);
 	}
 	
 	/**
@@ -304,7 +318,7 @@ public class WarnOnce implements PamSettings {
 	 */
 	public static int showWarningFX(javafx.stage.Window parent, String title, String message, AlertType messageType, String helpPoint, Throwable error) {
 		if (! PamController.checkIfNetworkControlled()) {
-			return singleInstance.showWarningDialogFX(parent, title, message, messageType, helpPoint, error, null, null);
+			return singleInstance.showWarningDialogFX(parent, title, message, messageType, helpPoint, error, null, null, false);
 		} else {
 			System.out.println(" ");
 			System.out.println("*** Warning: " + title);
@@ -327,7 +341,7 @@ public class WarnOnce implements PamSettings {
 	 * @return if messageType is OK_CANCEL_OPTION this will return OK_OPTION or CANCEL_OPTION, otherwise OK_OPTION will always be returned. 
 	 */
 	public static int showWarningFX(javafx.stage.Window parent, String title, String message, AlertType messageType, String helpPoint, Throwable error, String okButtonText, String cancelButtonText) {
-		return singleInstance.showWarningDialogFX(parent, title, message, messageType, helpPoint, error, okButtonText, cancelButtonText);
+		return singleInstance.showWarningDialogFX(parent, title, message, messageType, helpPoint, error, okButtonText, cancelButtonText, false);
 	}
 
 	/**
@@ -407,7 +421,8 @@ public class WarnOnce implements PamSettings {
 	 * @return if messageType is OK_CANCEN_OPTION this will return OK_OPTION or CANECL_OPTION, 
 	 * otherwise OK_OPTION will always be returned. 
 	 */
-	private int showWarningDialogFX(javafx.stage.Window parent, String title, String message, AlertType messageType, String helpPoint, Throwable error, String okButtonText, String cancelButtonText) {
+	private int showWarningDialogFX(javafx.stage.Window parent, String title, String message, AlertType messageType, String helpPoint, 
+			Throwable error, String okButtonText, String cancelButtonText, boolean disableCheckBoxes) {
 
 		// check if we should show the warning again this session
 		Boolean showAgain = showThisSess.get(title+message);
@@ -424,7 +439,7 @@ public class WarnOnce implements PamSettings {
 		}
 		
 		// show the warning
-		WarnOnceDialogFX wo = new WarnOnceDialogFX(parent, title, message, messageType, helpPoint, error, okButtonText, cancelButtonText);
+		WarnOnceDialogFX wo = new WarnOnceDialogFX(parent, title, message, messageType, helpPoint, error, okButtonText, cancelButtonText, disableCheckBoxes);
 		wo.showDialog();
 		warnOnceList.setShowWarning(title+message, wo.isShowAgain());
 		showThisSess.put(title+message, wo.isShowThisSess());

src/PamView/dialog/warn/WarnOnceDialogFX.java

@@ -14,6 +14,8 @@ import javafx.scene.control.Alert.AlertType;
 import javafx.scene.control.ButtonType;
 import javafx.scene.control.CheckBox;
 import javafx.scene.control.DialogPane;
+import javafx.scene.layout.HBox;
+import javafx.scene.layout.Pane;
 import javafx.stage.Window;
 import pamViewFX.fxNodes.PamHBox;
 
@@ -48,7 +50,7 @@ public class WarnOnceDialogFX {
 	 * The warning message
 	 */
 	private String message;
-	
+
 	/**
 	 * The opt out this session check box. 
 	 */
@@ -59,6 +61,8 @@ public class WarnOnceDialogFX {
 	 */
 	private CheckBox dontShowAgain;
 
+	private HBox checkBoxPane;  
+
 	/**
 	 * Constructor the warn once dialog. 
 	 * @param parentStage - the parent window
@@ -74,12 +78,12 @@ public class WarnOnceDialogFX {
 		alert = createAlertWithOptOut(alertType, title, null, message, "Don't show this message again", ButtonType.YES, ButtonType.NO);
 		//  if (alert.showAndWait().filter(t -> t == ButtonType.YES).isPresent()) {
 		//  }
-		
+
 		// for now, this is not implemented so just make sure it's not checked.  If we want to add it to the dialog, we'll need to rearrange everything
 		// because the opt-out checkbox is added to the buttons bar in a sneaky way, and you can't do that twice.  
 		dontShowAgainThisSess.setSelected(false);
 	}
-	
+
 	/**
 	 * Constructor the warn once dialog. 
 	 * @param parentStage - the parent window
@@ -92,20 +96,34 @@ public class WarnOnceDialogFX {
 	 * @param canceltext
 	 */
 	public WarnOnceDialogFX(Window parentStage, String title, String message, AlertType alertType , String helpPoint, Throwable error, String oktext, String canceltext) {
+		this(parentStage, title, message, alertType, helpPoint, error, oktext, canceltext, false);
+	}
+
+	public WarnOnceDialogFX(Window parentStage, String title, String message, AlertType alertType , String helpPoint,
+			Throwable error, String oktext, String canceltext, boolean disableCheckBoxes) {
+
 		this.error=error; 
 		this.message=message; 
-		
+
 		ButtonType okButton; 
 		if (oktext==null) okButton = ButtonType.OK; 
 		else okButton = new ButtonType(oktext); 
-		
+
 		ButtonType cancelButton; 
 		if (canceltext==null) cancelButton = ButtonType.OK; 
 		else cancelButton = new ButtonType(canceltext); 
-
 		
+		//TODO help point.
+
+
 		alert = createAlertWithOptOut(alertType, title, null, message, "Don't show this message again", okButton, cancelButton);
+		
+		//System.out.println("CREATE ALERT: " + disableCheckBoxes); 
+		this.disableCheckBoxes(disableCheckBoxes);
+
 	}
+	
+
 
 	/**
 	 * Show the warning dialog. 
@@ -142,7 +160,7 @@ public class WarnOnceDialogFX {
 			clpbrd.setContents(stringSelection, null);
 		}
 	}
-	
+
 	public boolean isShowAgain() {
 		return dontShowAgain.isSelected() == false;
 	}
@@ -164,7 +182,7 @@ public class WarnOnceDialogFX {
 	private Alert createAlertWithOptOut(AlertType type, String title, String headerText, 
 			String message, String optOutMessage,
 			ButtonType... buttonTypes) {
-		
+
 		Alert alert = new Alert(type);
 		// Need to force the alert to layout in order to grab the graphic,
 		// as we are replacing the dialog pane with a custom pane
@@ -174,22 +192,22 @@ public class WarnOnceDialogFX {
 		// Create a new dialog pane that has a checkbox instead of the hide/show details button
 		// Use the supplied callback for the action of the checkbox
 		alert.setDialogPane(new DialogPane() {
+
 			@Override
 			protected Node createDetailsButton() {
 
-				
 				dontShowAgain = new CheckBox();
 				dontShowAgain.setText(optOutMessage + " ever");
-				
+
 				dontShowAgainThisSess = new CheckBox();
 				dontShowAgainThisSess.setText(" this session ");
-				
-				PamHBox hBox = new PamHBox(); 
-				hBox.getChildren().addAll(dontShowAgain, dontShowAgainThisSess); 
-				hBox.setSpacing(5); 
-				hBox.setAlignment(Pos.CENTER_LEFT);
-				
-				return hBox;
+
+				checkBoxPane = new PamHBox(); 
+				checkBoxPane.getChildren().addAll(dontShowAgain, dontShowAgainThisSess); 
+				checkBoxPane.setSpacing(5); 
+				checkBoxPane.setAlignment(Pos.CENTER_LEFT);
+
+				return checkBoxPane;
 			}
 		});
 		alert.getDialogPane().getButtonTypes().addAll(buttonTypes[0]);
@@ -202,7 +220,7 @@ public class WarnOnceDialogFX {
 		alert.getDialogPane().setGraphic(graphic);
 		alert.setTitle(title);
 		alert.setHeaderText(headerText);
-		
+
 		return alert;
 	}
 
@@ -215,5 +233,12 @@ public class WarnOnceDialogFX {
 	}
 
 
+	public void disableCheckBoxes(boolean disable) {
+		checkBoxPane.setDisable(disable);
+		dontShowAgain.setDisable(disable);
+		dontShowAgainThisSess.setDisable(disable);
+	}
+
+
 
 }

src/PamguardMVC/RawDataTransforms.java

@@ -170,8 +170,8 @@ public class RawDataTransforms {
 			}
 
 			double[] waveformTrim = new double[maxBin-minBin]; 
-			
-//			System.out.println("minBin: " +minBin + " maxBin: " + maxBin + "  " + Math.min(this.getWaveData(channel).length, waveformTrim.length) + " " + this.getWaveData(channel).length  + "  " + this.getSampleDuration());
+
+			//			System.out.println("minBin: " +minBin + " maxBin: " + maxBin + "  " + Math.min(this.getWaveData(channel).length, waveformTrim.length) + " " + this.getWaveData(channel).length  + "  " + this.getSampleDuration());
 
 			System.arraycopy(this.getWaveData(channel), minBin, waveformTrim, 0, Math.min(this.getWaveData(channel).length-minBin-1, waveformTrim.length));
 
@@ -187,9 +187,10 @@ public class RawDataTransforms {
 	 * 
 	 * @param channel channel number
 	 * @param fftLength
+	 * @param Hann - true to use hanning window
 	 * @return Power spectrum
 	 */
-	public  double[] getPowerSpectrum(int channel, int fftLength) {
+	public  double[] getPowerSpectrum(int channel, int fftLength, boolean Hann) {
 		synchronized (synchObject) {
 			if (powerSpectra == null) {
 				powerSpectra = new double[PamUtils.getNumChannels(dataUnit.getChannelBitmap())][];
@@ -200,7 +201,13 @@ public class RawDataTransforms {
 
 			if (powerSpectra[channel] == null
 					|| powerSpectra[channel].length != fftLength / 2) {
-				ComplexArray cData = getComplexSpectrumHann(channel, fftLength);
+				ComplexArray cData;
+				if (Hann) {
+					cData = getComplexSpectrumHann(channel, fftLength);
+				}
+				else {
+					cData = getComplexSpectrum(channel, fftLength);
+				}
 				currentSpecLen = fftLength;
 				powerSpectra[channel] = cData.magsq();
 				if (powerSpectra==null){
@@ -216,6 +223,19 @@ public class RawDataTransforms {
 		}
 	}
 
+	/**
+	 * Returns the power spectrum for a given channel (square of magnitude of
+	 * complex spectrum)
+	 * 
+	 * @param channel channel number
+	 * @param fftLength
+	 * @return Power spectrum
+	 */
+	public  double[] getPowerSpectrum(int channel, int fftLength) {
+		return getPowerSpectrum( channel,  fftLength, true); 
+	}
+
+
 
 	/**
 	 * Returns the sum of the power spectra for all channels
@@ -410,7 +430,7 @@ public class RawDataTransforms {
 				paddedRawData = new double[fftLength];
 				rawData = getWaveData(channel);
 				//double[] rotData = getRotationCorrection(channel);
-				
+
 				/**
 				 *FIXME
 				 * 11/07 Changed from getSampleDuration because an error sometimes occurs where the sample duration
@@ -418,7 +438,7 @@ public class RawDataTransforms {
 				 */
 				//mn = Math.min(fftLength, getSampleDuration().intValue());
 				mn = Math.min(fftLength, rawData.length);
-//				System.out.println("fftLength: " + rawData.length + " " + getSampleDuration().intValue() + " mn " +mn);
+				//				System.out.println("fftLength: " + rawData.length + " " + getSampleDuration().intValue() + " mn " +mn);
 				for (i = 0; i < mn; i++) {
 					paddedRawData[i] = rawData[i];//-rotData[i];
 				}

src/Resources/css/pamCSS_flatlaf.css

@@ -0,0 +1,237 @@
+/* General Styling for PAMGuard JavaFX controls which trys to replicate the Swing Flatlaf theme */
+.root {
+    -fx-font-family: "Segoe UI", "Tahoma", "Arial", sans-serif;
+    -fx-font-size: 13px;
+    -fx-base: #f5f5f5;
+    -fx-background: #ffffff;
+    -fx-control-inner-background: #ffffff;
+    -fx-control-inner-background-alt: #f0f0f0;
+    -fx-focus-color: #3895EC;
+    -fx-faint-focus-color: rgba(56, 149, 236, 0.2);
+    -fx-accent: #3895EC;
+    -fx-default-button: -fx-accent;
+}
+
+/* Labels */
+.label {
+    -fx-text-fill: #333333;
+}
+
+/* Buttons */
+.button {
+    -fx-background-color: #ffffff;
+    -fx-border-color: #cccccc;
+    -fx-border-width: 1px;
+    -fx-padding: 6px 12px;
+    -fx-text-fill: #333333;
+    -fx-background-radius: 3px;
+    -fx-border-radius: 3px;
+}
+
+.button:hover {
+    -fx-background-color: #f0f0f0;
+}
+
+.button:pressed {
+    -fx-background-color: #e6e6e6;
+}
+
+.button:default {
+    -fx-background-color: -fx-accent;
+    -fx-text-fill: white;
+    -fx-border-color: transparent;
+}
+
+.button:default:hover {
+    -fx-background-color: #2f7dbe;
+}
+
+.button:default:pressed {
+    -fx-background-color: #266591;
+}
+
+/* Text Fields and Text Areas */
+.text-field,
+.text-area {
+    -fx-background-color: #ffffff;
+    -fx-border-color: #cccccc;
+    -fx-border-width: 1px;
+    -fx-padding: 4px;
+    -fx-prompt-text-fill: #999999;
+    -fx-background-radius: 3px;
+    -fx-border-radius: 3px;
+}
+
+.text-field:focused,
+.text-area:focused {
+    -fx-border-color: -fx-focus-color;
+    -fx-effect: null;
+}
+
+/* Checkboxes */
+.check-box {
+    -fx-padding: 2px;
+}
+
+/* Combo Boxes */
+.combo-box {
+    -fx-background-color: #ffffff;
+    -fx-border-color: #cccccc;
+    -fx-border-width: 1px;
+    -fx-padding: 4px;
+    -fx-background-radius: 3px;
+    -fx-border-radius: 3px;
+}
+
+.combo-box:focused {
+    -fx-border-color: -fx-focus-color;
+    -fx-effect: null;
+}
+
+/* Toggle Switches (using a custom approach) */
+.toggle-switch {
+    -fx-cursor: hand;
+}
+
+.toggle-switch .track {
+    -fx-background-color: #cccccc;
+    -fx-background-radius: 10px;
+    -fx-pref-width: 40px; /* Adjust as needed */
+    -fx-pref-height: 20px;
+}
+
+.toggle-switch:selected .track {
+    -fx-background-color: -fx-accent;
+}
+
+.toggle-switch .thumb {
+    -fx-background-color: white;
+    -fx-background-radius: 50%;
+    -fx-pref-width: 16px;
+    -fx-pref-height: 16px;
+    -fx-translate-x: -10px; /* Initial position */
+}
+
+.toggle-switch:selected .thumb {
+    -fx-translate-x: 10px; /* Shifted position */
+}
+
+
+/****************************************************************
+ 
+  Spinner  
+ 
+****************************************************************/
+
+.spinner {
+    -fx-background-color: #ffffff;
+    -fx-border-color: #cccccc;
+    -fx-border-width: 1px;
+    -fx-padding: 4px;
+    -fx-background-radius: 3px;
+    -fx-border-radius: 3px;
+}
+
+.spinner:focused {
+    -fx-border-color: -fx-focus-color;
+    -fx-effect: null;
+}
+
+
+.spinner .text-field {
+    -fx-background-color: inherit; /* Inherit from spinner background */
+    -fx-border-color: inherit; /* Inherit from spinner border */
+    -fx-border-width: 0; /* No separate border for the text field */
+    -fx-padding: 5px 0; /* Consistent vertical padding */
+    -fx-alignment: center; /* Center text */
+}
+
+.spinner .increment-arrow-button,
+.spinner .decrement-arrow-button {
+    -fx-background-color: #d2d2d2; /* Light gray background for buttons */
+    -fx-border-color: inherit; /* Inherit border from spinner */
+    -fx-border-width: 0; /* No separate border for buttons */
+    -fx-padding: 5px 5px;
+}
+
+.spinner .increment-arrow-button:hover,
+.spinner .decrement-arrow-button:hover {
+    -fx-background-color: #3895EC; /* FlatLaf blue highlight on hover */
+    -fx-text-fill: white; /* White arrow on hover */
+}
+
+/* Split Arrows (Horizontal) */
+.spinner.split-arrows-horizontal {
+    -fx-border-radius: 3px; /* Ensure rounded corners for the overall spinner */
+}
+
+.spinner.split-arrows-horizontal .text-field {
+    -fx-border-width: 0;
+    -fx-border-color: inherit;
+}
+
+.spinner.split-arrows-horizontal .increment-arrow-button {
+    -fx-background-radius: 0 3px 3px 0; /* Right rounded corners */
+}
+
+.spinner.split-arrows-horizontal .decrement-arrow-button {
+    -fx-background-radius: 3px 0 0 3px; /* Left rounded corners */
+}
+
+/* Fix for borders between elements*/
+.spinner.split-arrows-horizontal .increment-arrow-button {
+    -fx-border-width: 0 1px 0 0;
+}
+
+.spinner.split-arrows-horizontal .decrement-arrow-button {
+    -fx-border-width: 0 0 0 1px;
+}
+
+
+/* Scroll Bars */
+.scroll-bar:vertical .increment-button,
+.scroll-bar:vertical .decrement-button,
+.scroll-bar:horizontal .increment-button,
+.scroll-bar:horizontal .decrement-button {
+    -fx-background-color: transparent;
+    -fx-border-color: transparent;
+}
+
+.scroll-bar:vertical .thumb,
+.scroll-bar:horizontal .thumb {
+    -fx-background-color: rgba(0, 0, 0, 0.2);
+    -fx-background-radius: 5px;
+}
+
+.scroll-bar:vertical .thumb:hover,
+.scroll-bar:horizontal .thumb:hover {
+    -fx-background-color: rgba(0, 0, 0, 0.4);
+}
+
+/* Tables */
+.table-view {
+    -fx-background-color: transparent;
+    -fx-border-color: #cccccc;
+    -fx-border-width: 1px;
+}
+
+.table-view .column-header-background {
+    -fx-background-color: #f0f0f0;
+}
+
+.table-view .column-header {
+    -fx-border-color: #cccccc;
+    -fx-padding: 6px;
+}
+
+.table-row-cell:even {
+    -fx-background-color: -fx-control-inner-background;
+}
+
+.table-row-cell:odd {
+    -fx-background-color: -fx-control-inner-background-alt;
+}
+
+.table-row-cell:selected {
+    -fx-background-color: rgba(56, 149, 236, 0.3);
+}

src/binaryFileStorage/BinaryStorageDialogPanel.java

@@ -128,6 +128,10 @@ public class BinaryStorageDialogPanel {
 			return false;
 		}
 		binaryStoreSettings.setStoreLocation(storageLocation.getFolderName(true));
+		if (binaryStoreSettings.getStoreLocation()==null) {
+			//cancel button pressed - return to dialog
+			return false;
+		}
 		binaryStoreSettings.autoNewFiles = autoNewFiles.isSelected();
 		binaryStoreSettings.datedSubFolders = dateSubFolders.isSelected();
 		binaryStoreSettings.limitFileSize = limitfileSize.isSelected();

src/clickDetector/ClickSpectrum.java

@@ -191,7 +191,7 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 	public ClickSpectrum(STClickControl clickControl, ClickDisplayManager clickDisplayManager, ClickDisplayManager.ClickDisplayInfo clickDisplayInfo) {
 		this((ClickControl) clickControl, clickDisplayManager, clickDisplayInfo);
 	}
-	
+
 	@Override
 	public PamObserver getObserverObject() {
 		return this;
@@ -409,15 +409,22 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 					eventLineData = eventSpectrum;
 				}
 
+				if (!isEventClick(storedClick)) {
+					eventLineData = null;
+				}
+
 				// work out the scales, mins and max's, etc.
 				maxVal = 0;
 
 				for (int iChan = 0; iChan < clickLineData.length; iChan++) {
 					for (int i = 0; i < clickLineData[iChan].length; i++){
 						maxVal = Math.max(maxVal, clickLineData[iChan][i]);
-						if (eventLineData!=null && eventLineData[iChan]!=null && isViewer && clickSpectrumParams.showEventInfo ){
-							if( i<eventLineData[iChan].length){
-								maxVal = Math.max(maxVal, eventLineData[iChan][i]);
+//						System.out.println(eventLineData == null ? "Event is null" : ("Event length is " + eventLineData.length));
+						if (!clickSpectrumParams.plotCepstrum && eventLineData!=null) {
+							if (eventLineData[iChan]!=null && isViewer && clickSpectrumParams.showEventInfo ){
+								if( i<eventLineData[iChan].length){
+									maxVal = Math.max(maxVal, eventLineData[iChan][i]);
+								}
 							}
 						}
 					}
@@ -446,7 +453,7 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 			double scale = 1./(maxVal*1.1);
 			if (isViewer ){
 
-				if (eventLineData!=null && clickSpectrumParams.showEventInfo){	
+				if (eventLineData!=null && clickSpectrumParams.showEventInfo && !clickSpectrumParams.plotCepstrum){	
 
 					g2.setStroke(dashed);
 
@@ -516,8 +523,8 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 		 */
 		private void paintLogSpectrum(Graphics g, Rectangle clipRect) {
 
-			double[][] clickLineData;
-			double[][] eventLineData;
+			double[][] clickLineData = null;
+			double[][] eventLineData = null;
 			synchronized (storedSpectrumLock) {
 				if (storedSpectrum == null || storedSpectrum.length == 0 || storedSpectrum[0].length == 0) return;
 
@@ -530,13 +537,17 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 					eventLineData = eventSpectrum;
 				}
 
+				if (!isEventClick(storedClick)) {
+					eventLineData = null;
+				}
+
 				//System.out.println("paintLogSpectrum.eventLineData: 1" + eventLineData[0].length);
 
 
 				if (logSpectrum == null || clickSpectrumParams.plotCepstrum != lastCepChoice || eventSpectrumTemplatesLog!=null) {
 					double temp;
 					logSpectrum = new double[clickLineData.length][clickLineData[0].length];
-					if (eventSpectrum!=null && isViewer ){
+					if (eventLineData!=null && isViewer ){
 						logEventSpectrum = new double[eventLineData.length][eventLineData[0].length];
 					}
 					if (eventSpectrumTemplatesLog!=null && isViewer ){
@@ -554,7 +565,7 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 							}
 						}
 
-						//sperated this out and the spectrums can be different lengths. 
+						//seperated this out and the spectrums can be different lengths. 
 						if (eventLineData!=null && isViewer &&clickSpectrumParams.showEventInfo){
 							for (int i = 0; i < eventLineData[iChan].length; i++){
 								if( i<eventLineData[iChan].length){
@@ -581,8 +592,8 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 							logTemplateSpectrum[i]=logTemplate;
 						}
 					}
-					
 				}
+
 				lastCepChoice = clickSpectrumParams.plotCepstrum;
 				drawLogSpectrum(g,clipRect,clickLineData, eventLineData);
 			}
@@ -608,7 +619,7 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 
 
 			//show an average log spectrum
-			if (eventLineData!=null && isViewer && clickSpectrumParams.showEventInfo){
+			if (eventLineData!=null && isViewer && clickSpectrumParams.showEventInfo && !clickSpectrumParams.plotCepstrum){
 
 				xScale = (double) r.width / (double) (eventLineData[0].length - 1);
 				scaleLim = Math.abs(clickSpectrumParams.logRange);
@@ -629,7 +640,7 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 			}
 
 			//show custim templates
-			if (clickTemplateParams.clickTemplateArray.size()>0 && eventLineData !=null){
+			if (clickTemplateParams.clickTemplateArray.size()>0 && logTemplateSpectrum !=null){
 				g2.setStroke(dashedtmplate);
 				for (int i = 0; i < logTemplateSpectrum.length; i++) {
 					if (logTemplateSpectrum[i]!=null && clickTemplateParams.clickTempVisible.get(i)==true){
@@ -782,12 +793,12 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 					menuItem.addActionListener(new EditTemplates());
 					menu.add(menuItem);
 				}
-				
-				
+
+
 				menuItem = new JMenuItem("Add template...");
 				menuItem.addActionListener(new AddTemplate());
 				menu.add(menuItem);
-				
+
 				menuItem = new JMenuItem("Clear templates");
 				menuItem.addActionListener(new ClearTemplates());
 				menu.add(menuItem);
@@ -807,15 +818,15 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 			repaint(10);
 		}
 	}
-	
-	
+
+
 	private class AddTemplate implements ActionListener {
 		@Override
 		public void actionPerformed(ActionEvent arg0) {
 			addTemnplateDialog();
 		}
 	}
-	
+
 	private class ClearTemplates implements ActionListener {
 		@Override
 		public void actionPerformed(ActionEvent arg0) {
@@ -882,7 +893,7 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 		}
 
 	}
-	
+
 	public void clearTemplates(){
 		clickTemplateParams.clickTemplateArray=new ArrayList<ClickTemplate>();
 		clickTemplateParams.clickTempVisible=new ArrayList<Boolean>();
@@ -952,7 +963,8 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 					storedSpectrum[i] = SmoothingFilter.smoothData(storedSpectrum[i], clickSpectrumParams.plotSmoothing);
 				}
 			}
-			//calculating an event spectrum can take a very long time. 
+			//calculating an event spectrum can take a very long time so we don't delete the last event incase a user
+			//is clicking between one event and un-annotated clicks so whether to draw an event is handled in the paint functions
 			if (isViewer && this.clickSpectrumParams.showEventInfo){
 				getEventClick(newClick);
 			}
@@ -964,6 +976,16 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 		spectrumAxisPanel.repaint(100);
 	}
 
+	/**
+	 * Does the click belong to an event.
+	 */
+	private boolean isEventClick(ClickDetection newClick) {
+		if (newClick.getSuperDetection(0)==null || !newClick.getSuperDetection(0).equals(lastEvent) ) {
+			return false;
+		}
+		return true;
+	}
+
 
 	/**
 	 * Normalis so that the amplitude of the cepstrum is the same as that of the spectrum. 
@@ -1323,7 +1345,7 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 	@Override
 	public void updateData(PamObservable observable, PamDataUnit pamDataUnit) {
 		// TODO Auto-generated method stub
-		
+
 	}
 
 	public ClickDetection getStoredClick(){
@@ -1375,7 +1397,7 @@ public class ClickSpectrum extends ClickDisplay implements PamObserver , PamSett
 		clickSpectrumParams = ((ClickSpectrumParams) pamControlledUnitSettings.getSettings()).clone();
 		return true;
 	}
-	
+
 	@Override
 	public void receiveSourceNotification(int type, Object object) {
 		// don't do anything by default

src/clickDetector/ClickSpectrumTemplateEditDialog.java

@@ -316,7 +316,7 @@ public class ClickSpectrumTemplateEditDialog extends PamDialog{
 		ArrayList<double[]> fftAll=getEventFFTs(channel,true);
 		//create standard average spectrum
 		this.averageFFT=fftMean(fftAll);
-		printFFT( averageFFT);
+		//printFFT( averageFFT);
 		this.stdFFT=fftstd(fftAll,averageFFT);
 		//create log template
 		this.averageFFTLog=fftMean(convertAllFFttoLog(fftAll));		

src/clickDetector/dataSelector/ClickSelectPaneFX.java

@@ -485,19 +485,21 @@ public class ClickSelectPaneFX extends DynamicSettingsPane<Boolean>  {
 			});
 			
 			
-			lutList = LookUpTables.getLookUpTables().getLookupList(ClicksOffline.ClickTypeLookupName);
-			if (lutList == null) {
-				return;
-			}
-			useType = new CheckBox[lutList.getList().size()];
-			for (int i = 0; i < useType.length; i++) {
-				this.getChildren().add(useType[i] = new CheckBox(lutList.getList().get(i).getText()));
-				useType[i].setSelected(clickAlarmParameters.isUseEventType(lutList.getList().get(i).getCode()));
-				useType[i].setOnAction((action)->{
-					 setUseAllCheckBox();
-					notifySettingsListeners();
-				});
-			}
+			//2024-12-18 This is causing an excpetion because lookup tabel does not exist. 
+//			lutList = LookUpTables.getLookUpTables().getLookupList(ClicksOffline.ClickTypeLookupName);
+//			if (lutList == null) {
+//				return;
+//			}
+//			useType = new CheckBox[lutList.getList().size()];
+//			for (int i = 0; i < useType.length; i++) {
+//				this.getChildren().add(useType[i] = new CheckBox(lutList.getList().get(i).getText()));
+//				useType[i].setSelected(clickAlarmParameters.isUseEventType(lutList.getList().get(i).getCode()));
+//				useType[i].setOnAction((action)->{
+//					 setUseAllCheckBox();
+//					notifySettingsListeners();
+//				});
+//			}
+
 			 setUseAllCheckBox();
 		}
 		

src/clickTrainDetector/logging/ClickTrainDetSubLogging.java

@@ -20,7 +20,6 @@ public class ClickTrainDetSubLogging extends SQLLogging {
 		super(pamDataBlock);		
 		this.clickTrainLogging = clickTrainLogging;
 		setTableDefinition(new PamSubtableDefinition(clickTrainLogging.getClickTrainControl().getUnitName()+"_Children"));
-
 	}
 
 	@Override

src/dataPlotsFX/clickPlotFX/ClickFFTPlotManager2.java

@@ -99,7 +99,11 @@ public class ClickFFTPlotManager2 extends FFTPlotManager  {
 		}
 		else {
 			double[] spectrum=((ClickDetection) detection).getPowerSpectrum(chanClick, clickPlotInfoFX.isViewer() ? true : false);
-			spectrum=((ClickDetection) detection).getPowerSpectrum(chanClick, clickPlotInfoFX.isViewer() ? true : false);
+			
+			if (spectrum==null) spectrum=((ClickDetection) detection).getPowerSpectrum(chanClick, true);
+			if (spectrum ==null) {
+				System.err.println("ClickPlotFFTManager2: Spectrum for the click is null?"); 
+			}
 			drawClickFFT(Arrays.copyOf(spectrum, spectrum.length),  pamDataUnit.getTimeMilliseconds(),  writableImage.getScrollingPLot2DSegmenter().getMaxY(),  writableImage);
 		}
 	}

src/dataPlotsFX/data/generic/GenericDataPlotInfo.java

@@ -4,11 +4,9 @@ import java.awt.geom.Path2D;
 import java.util.ArrayList;
 import java.util.Arrays;
 
-import IshmaelDetector.IshDetFnDataUnit;
 import PamDetection.AbstractLocalisation;
 import PamDetection.LocContents;
 import PamDetection.LocalisationInfo;
-import PamUtils.Coordinate3d;
 import PamUtils.PamUtils;
 import PamView.GeneralProjector;
 import PamView.HoverData;
@@ -104,23 +102,25 @@ public class GenericDataPlotInfo extends TDDataInfoFX {
 		}
 		else {
 			return super.drawDataUnit(plotNumber, pamDataUnit, g, scrollStart, tdProjector, type);
-			
 		}
 	}
+	
+	
 
 	/**
-	 * Base class draws a simple frequency box. Easily overridden to draw something else, e.g. a contour. 
-	 * @param plotNumber
-	 * @param pamDataUnit
-	 * @param g
-	 * @param scrollStart
-	 * @param tdProjector
-	 * @param type
-	 * @return
+	 * Draw a data unit as a box. 
+	 * @param plotNumber - the pot number 
+	 * @param pamDataUnit - the data unit that will be plotted
+	 * @param f - the y axis max and min values
+	 * @param g - the graphics context. 
+	 * @param scrollStart - the scroll start in millis.
+	 * @param tdProjector - the graph projector. 
+	 * @param type - the plot type. 
+	 * @return a polygon of the data unit. 
 	 */
-	public Polygon drawFrequencyData(int plotNumber, PamDataUnit pamDataUnit, GraphicsContext g, double scrollStart,
+	public Polygon drawBoxData(int plotNumber, PamDataUnit pamDataUnit, double[] f, GraphicsContext g, double scrollStart,
 			TDProjectorFX tdProjector, int type) {
-						
+				
 		g.setLineDashes(null);
 		g.setLineWidth(2);
 		TDSymbolChooserFX symbolChooser = getSymbolChooser();
@@ -129,13 +129,13 @@ public class GenericDataPlotInfo extends TDDataInfoFX {
 			symbol = symbolChooser.getPamSymbol(pamDataUnit, type);
 		}
 		
-		double[] f = pamDataUnit.getFrequency();
 		if (f == null) {
 			return null;
 		}
 		if (f.length == 1) {
 			System.out.println("GenericDataPlotInfo: Single frequency measure in data unit " + pamDataUnit.toString());
 		}
+		
 		// draw a frequency box. 
 		double y0 = tdProjector.getYPix(f[0]);
 		double y1 = tdProjector.getYPix(f[1]);
@@ -186,6 +186,22 @@ public class GenericDataPlotInfo extends TDDataInfoFX {
 		
 		return null;
 	}
+
+	/**
+	 * Base class draws a simple frequency box. Easily overridden to draw something else, e.g. a contour. 
+	 * @param plotNumber
+	 * @param pamDataUnit
+	 * @param g
+	 * @param scrollStart
+	 * @param tdProjector
+	 * @param type
+	 * @return
+	 */
+	public Polygon drawFrequencyData(int plotNumber, PamDataUnit pamDataUnit, GraphicsContext g, double scrollStart,
+			TDProjectorFX tdProjector, int type) {
+		double[] f = pamDataUnit.getFrequency();		
+		return drawBoxData( plotNumber,  pamDataUnit,f,  g,  scrollStart, tdProjector,  type);
+	}
 	
 
 	@Override

src/dataPlotsFX/overlaymark/popUpMenu/OverlayGroupDisplay.java

@@ -35,7 +35,7 @@ import whistlesAndMoans.WhistleMoanControl;
 
 /**
  * 
- * A detection display which can display groups data units and 
+ * A detection display which can display group data units and 
  * their super detections in a tab pane if applicable. 
  * 
  * @author Jamie Macaulay 
@@ -252,10 +252,10 @@ public class OverlayGroupDisplay extends PamBorderPane {
 
 		this.detectionsPane.setDetectionGroup(dataList);
 	
-		
 		if (superDets!=null) {
 			this.superDetectionsDisplay.setDetectionGroup(new ArrayList<PamDataUnit>(superDets));
 		}
+		
 		this.prepareDisplay(); 
 	}
 

src/dataPlotsFX/overlaymark/popUpMenu/TDMenuPane.java

@@ -38,6 +38,7 @@ import pamViewFX.fxNodes.PamTilePane;
 import pamViewFX.fxNodes.PamVBox;
 import pamViewFX.fxNodes.flipPane.FlipPane;
 import pamViewFX.fxNodes.hidingPane.HidingPane;
+import pamViewFX.fxNodes.utilityPanes.PamToggleSwitch;
 import pamViewFX.fxNodes.utilsFX.PamUtilsFX;
 import pamViewFX.fxStyles.PamStylesManagerFX;
 
@@ -220,8 +221,6 @@ public class TDMenuPane extends PamBorderPane {
 			closeButtonLeft.setVisible(true);
 			topHolder.setLeft(closeButtonLeft);
 
-			holder.setLeft(hidingPaneLeft);
-
 			holder.setCenter(groupDetectionDisplay);
 			holder.setLeft(hidingPaneLeft);
 
@@ -235,14 +234,19 @@ public class TDMenuPane extends PamBorderPane {
 	 * Set the summary text for the data unit in the menu. 
 	 */
 	private void setSummaryText() {		
-		if (currentDataUnit==null || detectionSummary==null) return; 
+		if (currentDataUnit==null || detectionSummary==null) {
+			this.infoTextLabel.setText("No data units selected");
+			return; 
+		}
 
 		String text; 
 		if (detectionSummary.getDataList().size()>=1){
 //			text= currentDataUnit.getSummaryString();
-			
+						
 			text=currentDataUnit.getParentDataBlock().
 					getHoverText(tdGraphFX.getGraphProjector(), currentDataUnit, 0);
+			
+			
 			if (text==null) return; //do not clear as usually this is because a super unit has been set
 		}
 		else {
@@ -280,25 +284,27 @@ public class TDMenuPane extends PamBorderPane {
 		infoButton.setGraphic(PamGlyphDude.createPamIcon("mdi2i-information", PamGuiManagerFX.iconSize));
 		//infoButton.setStyle(" -fx-background-radius: 0 0 0 0;");
 
-		toggle= new ToggleSwitch(); 
+		toggle= new ToggleSwitch();
+		toggle.setGraphic(PamGlyphDude.createPamIcon("mdi2c-chart-bell-curve", PamGuiManagerFX.iconSize));
 		toggle.setSelected(true);
 		toggle.setAlignment(Pos.CENTER);
 		toggle.setTooltip(new Tooltip("Show a preview of the detection"));
-		toggle.setMaxWidth(20);
+		toggle.setMaxWidth(30);
 		toggle.selectedProperty().addListener((obsVal, oldVal, newVal)->{
 			this.layoutPane(newVal);
 			this.getParent().layout(); //make sure the pop up actually lays out properly. 
 		});
+		
+		PamHBox toggleBox = new PamHBox(); 
+		toggleBox.setPadding(new Insets(5,5,5,5));
+		toggleBox.setSpacing(5);
+		toggleBox.getChildren().addAll(new Label("",PamGlyphDude.createPamIcon("mdi2c-chart-bell-curve", PamGuiManagerFX.iconSize)), toggle); 
 
-		PamBorderPane toggleHolder = new PamBorderPane(toggle);
-		toggleHolder.getStyleClass().add("square-button-trans");
-
-		toggleHolder.setPadding(new Insets(0,10,0,0));
-		toggleHolder.prefWidthProperty().bind(menuPane.widthProperty());
-
-		PamHBox topMenu = new PamHBox();
-		topMenu.getChildren().addAll(infoButton, toggleHolder);
-		topMenu.setAlignment(Pos.CENTER);
+		
+		PamBorderPane topMenu = new PamBorderPane();
+		topMenu.setLeft(infoButton);
+		topMenu.setRight(toggleBox);
+		//topMenu.setAlignment(Pos.CENTER);
 
 		menuPane.getChildren().add(topMenu);
 
@@ -503,11 +509,19 @@ public class TDMenuPane extends PamBorderPane {
 	 */
 	public void prepareDisplay(){
 
-		if (detectionSummary==null || detectionSummary.getDataList()==null) {
-			currentDataUnit=null; 
+		if (detectionSummary==null || detectionSummary.getDataList()==null || detectionSummary.getDataList().size()==0) {
+			setCurrentDataUnit(null); 
 		}
-		else{ //show the first data unit;
-			setCurrentDataUnit(currentDataUnit); 
+		else{ 
+			/**
+			 * It's important to set the data unit explicitly here. This is because the listener 
+			 * in the group display will return both the super detection and currently selected click
+			 * which can confuse the display, especially if the super detection has no associated data information
+			 * So when a new data unit is selected set it here so that it's defintely the selected data unit which 
+			 * is showing in the menu pane. 
+			 */
+			//show the first data unit;
+			setCurrentDataUnit(this.detectionSummary.getDataList().get(0)); 
 			this.detectionSummary.setFocusedIndex(0); //set focused to highlight data unit. 
 		}
 
@@ -521,8 +535,8 @@ public class TDMenuPane extends PamBorderPane {
 	/**
 	 * Set the data unit list. 
 	 */
-	public void setDataUnitList(DetectionGroupSummary detectionGroupSummary, OverlayMarker overlayMarker, MouseEvent e){		
-
+	public void setDataUnitList(DetectionGroupSummary detectionGroupSummary, OverlayMarker overlayMarker, MouseEvent e){	
+		
 		this.detectionSummary=detectionGroupSummary;
 		this.overlayMarker=overlayMarker; 
 		this.currentMouseEvent = e; 

src/detectionPlotFX/DetectionGroupDisplay.java

@@ -528,6 +528,7 @@ public class DetectionGroupDisplay extends PamBorderPane {
 		for (GroupDisplayListener aListener : displayListeners) {
 			aListener.newDataUnitSelected(oldDataUnit, newDataUnit);
 		}
+		
 	}
 
 	/**

src/export/MLExport/MLDataUnitExport.java

@@ -4,6 +4,7 @@ import org.jamdev.jdl4pam.utils.DLMatFile;
 
 import PamUtils.PamCalendar;
 import PamguardMVC.PamDataUnit;
+import pamMaths.PamVector;
 import us.hebi.matlab.mat.format.Mat5;
 import us.hebi.matlab.mat.types.Array;
 import us.hebi.matlab.mat.types.Matrix;
@@ -150,5 +151,25 @@ public abstract class MLDataUnitExport<T extends PamDataUnit<?, ?>> {
 	public abstract String getName();
 
 
+	/**
+	 * Get the bearings from the data unit. Note these are the angles that are rotated to real workd vectors. 
+	 * @param dataUnit- data unit.
+	 * @return the angles in RADIANS.
+	 */
+	public static double[] realWordlVec2Angles(PamDataUnit dataUnit) {
+		//bearing
+		PamVector[] vector = dataUnit.getLocalisation().getRealWorldVectors();
+		double bearing = PamVector.vectorToSurfaceBearing(vector[0]);
+
+
+		//pitch
+		double x = vector[0].getElement(0);
+		double y = vector[0].getElement(1);
+		double z = vector[0].getElement(2);
+		x = Math.sqrt(x*x+y*y);
+		double pitch = Math.atan2(z,x);
+
+		return new double[] {bearing, pitch};
+	}
 	
 }

src/export/MLExport/MLRawExport.java

@@ -5,6 +5,7 @@ import org.jamdev.jdl4pam.utils.DLMatFile;
 import PamUtils.PamUtils;
 import PamguardMVC.PamDataUnit;
 import PamguardMVC.RawDataHolder;
+import pamMaths.PamVector;
 import us.hebi.matlab.mat.format.Mat5;
 import us.hebi.matlab.mat.types.Matrix;
 import us.hebi.matlab.mat.types.Struct;
@@ -20,11 +21,11 @@ public class MLRawExport extends MLDataUnitExport<PamDataUnit<?,?>>{
 	public Struct addDetectionSpecificFields(Struct mlStruct, int index, PamDataUnit dataUnit) {
 
 		RawDataHolder rawDataHolder = (RawDataHolder) dataUnit; 
-		
+
 		//the waveform
 		Matrix wave = DLMatFile.array2Matrix(rawDataHolder.getWaveData()); 
 
-		
+
 		//the number of channels
 		Matrix nChan = Mat5.newScalar(PamUtils.getNumChannels(dataUnit.getChannelBitmap())); 
 
@@ -67,4 +68,5 @@ public class MLRawExport extends MLDataUnitExport<PamDataUnit<?,?>>{
 		return "raw_data_units";
 	}
 
+
 }

src/export/RExport/RClickEventExport.java

@@ -11,9 +11,12 @@ public class RClickEventExport extends RSuperDetectionExport{
 		super(mlDetectionsManager);
 	}
 	
+	@Override
 	public NamedBuilder addDetectionSpecificFields(NamedBuilder rData, SuperDetection dataUnit, int index) {
+		super.addDetectionSpecificFields(rData, dataUnit, index);
 		
 		OfflineEventDataUnit clickEvent = (OfflineEventDataUnit) dataUnit; 
+		
 
 		rData.add("event_id",  clickEvent.getEventId());
 		rData.add("event_type", clickEvent.getEventType());

src/export/RExport/RExportManager.java

@@ -94,6 +94,8 @@ public class RExportManager implements PamDataUnitExporter {
 	private void writeRFile() {
 		Context context = Context.newTopLevelContext();
 		try {
+			
+			if (allData!=null) {
 
 			FileOutputStream fos = new FileOutputStream(currentFileName);
 			GZIPOutputStream zos = new GZIPOutputStream(fos);
@@ -103,6 +105,8 @@ public class RExportManager implements PamDataUnitExporter {
 			writer.save(allData.build());
 			writer.close();
 			zos.close();
+			allData=null; //prevents writing the file again and again if offline end export is called. 
+			}
 		}
 		catch (IOException e1) {
 			e1.printStackTrace();
@@ -190,6 +194,8 @@ public class RExportManager implements PamDataUnitExporter {
 				//check whether the same. 
 				if (rDataExport.get(i).getUnitClass().isAssignableFrom(dataUnits.get(j).getClass()) && !alreadyStruct[j]) {
 					dataList=rDataExport.get(i).detectionToStruct(dataUnits.get(j), n); 
+					
+					//System.out.println("Export data unit:  " + rDataExport.get(i).getName());
 					//dataListArray.add((rDataExport.get(i).getName() + "_" + dataUnits.get(j).getUID()), dataList);	
 					// format used in PAMBinaries
 					dataListArray.add(String.valueOf(dataUnits.get(j).getUID()), dataList);	

src/export/RExport/RRawExport.java

@@ -9,6 +9,8 @@ import PamguardMVC.RawDataHolder;
 import org.renjin.sexp.ListVector.NamedBuilder;
 
 import clickDetector.ClickDetection;
+import export.MLExport.MLDataUnitExport;
+import pamMaths.PamVector;
 
 public class RRawExport extends RDataUnitExport<PamDataUnit<?,?>> {
 
@@ -39,7 +41,18 @@ public class RRawExport extends RDataUnitExport<PamDataUnit<?,?>> {
 		//time delay stuff.
 		if (dataUnit.getLocalisation()!=null) {
 			//bearing angles 
-			rData.add("angles", dataUnit.getLocalisation().getAngles() == null ? new DoubleArrayVector(0.) :  new DoubleArrayVector(dataUnit.getLocalisation().getAngles())); 
+			if (dataUnit.getLocalisation().getRealWorldVectors() != null) {
+				
+				double[] angles = MLDataUnitExport.realWordlVec2Angles(dataUnit);
+							
+				rData.add("angles", new DoubleArrayVector(angles));
+				
+			}
+			else {
+				rData.add("angles", new DoubleArrayVector(0.));
+			}
+			//bearing angles 
+			rData.add("angles", dataUnit.getLocalisation().getRealWorldVectors() == null ? new DoubleArrayVector(0.) :  new DoubleArrayVector(dataUnit.getLocalisation().getAngles())); 
 			//angle errors 
 			rData.add("angleErrors", dataUnit.getLocalisation().getAngleErrors() == null? new DoubleArrayVector(0.) : new DoubleArrayVector(dataUnit.getLocalisation().getAngleErrors())); 
 		}

src/export/RExport/RSuperDetectionExport.java

@@ -24,11 +24,10 @@ public class RSuperDetectionExport extends RDataUnitExport<SuperDetection>{
 		//We create a struct within a struct within a struct here...
 		SuperDetection superDetection = (SuperDetection) dataUnit;
 
-		ListVector.NamedBuilder dataUnits = new ListVector.NamedBuilder(); ;
-
+		ListVector.NamedBuilder dataUnits = new ListVector.NamedBuilder();
+		
 		//now iterate through the data units and add data units to the struct. 
 
-
 		//vital to clone this or we mess up all the data units in PAMGuard!
 		ArrayList<PamDataUnit> subDataUnits = (ArrayList<PamDataUnit>) superDetection.getSubDetections().clone();
 
@@ -51,8 +50,15 @@ public class RSuperDetectionExport extends RDataUnitExport<SuperDetection>{
 
 						//add to the struct.
 						datasUnitsStruct= exporter.detectionToStruct(subDataUnits.get(i), n);
-						dataUnits.add(exporter.getName(), datasUnitsStruct); 
+	
+						
+						System.out.println("Save sub det: " + subDataUnits.get(i).getUID());
+						
+						//must be named differently
+						dataUnits.add((exporter.getName() + "_" + subDataUnits.get(i).getUID()) , datasUnitsStruct); 
+						
 						savedUnits.add(subDataUnits.get(i));
+						
 						n++;
 					}
 				}
@@ -65,6 +71,7 @@ public class RSuperDetectionExport extends RDataUnitExport<SuperDetection>{
 
 		}
 
+
 		rData.add("subdetections", dataUnits);
 		rData.add("nsubdet", superDetection.getSubDetectionsCount());
 

src/export/swing/ExportTask.java

@@ -44,6 +44,8 @@ public class ExportTask extends OfflineTask<PamDataUnit<?,?>>{
 	 */
 	@Override
 	public void prepareTask() {
+		//save all data before exporting! 
+		PamController.getInstance().saveViewerData();
 		exporter.perpareExport();
 	}
 

src/generalDatabase/lookupTables/LookupComponent.java

@@ -3,6 +3,9 @@ package generalDatabase.lookupTables;
 
 
 import java.awt.BorderLayout;
+import java.awt.GridBagConstraints;
+import java.awt.GridBagLayout;
+import java.awt.Insets;
 import java.awt.event.ActionEvent;
 import java.awt.event.ActionListener;
 import java.awt.event.FocusEvent;
@@ -23,6 +26,10 @@ import javax.swing.JPopupMenu;
 import javax.swing.JTextField;
 import javax.swing.SwingConstants;
 
+import PamView.dialog.PamDialog;
+import PamView.dialog.SettingsButton;
+import PamView.panel.PamPanel;
+
 /**
  * Lookup component to go in dialogs, Logger forms, etc. 
  * <p> Has the look and feel of Logger drop down boxes but
@@ -86,12 +93,33 @@ public class LookupComponent {
 		
 		this.lookupTopic = lookupTopic;
 		
-		mainPanel = new JPanel();
+		mainPanel = new PamPanel();
 		mainPanel.setLayout(new BorderLayout());
+		
+		PamPanel eventPanel = new PamPanel(new GridBagLayout());
+		GridBagConstraints c = new GridBagConstraints(); 
+		c.gridy=0;
+		c.gridx=0;
+		c.insets = new Insets(0,0,0,5); 
+
+
 		westPanel = new JPanel(new BorderLayout());
 		westPanel.add(BorderLayout.CENTER, codeField = new JTextField(5));
-		mainPanel.add(BorderLayout.CENTER, comboBox = new JComboBox());
-		mainPanel.add(BorderLayout.WEST, westPanel);
+		c.gridx++;
+
+		PamDialog.addComponent(eventPanel, westPanel, c);
+		c.gridx++;
+		
+		PamDialog.addComponent(eventPanel, comboBox = new JComboBox(), c);
+		c.gridx++;
+		
+		SettingsButton editButton = new SettingsButton(); 
+		editButton.addActionListener(new EditList());
+		PamDialog.addComponent(eventPanel, editButton, c);
+		
+		
+
+		mainPanel.add(BorderLayout.CENTER, eventPanel);
 
 		
 		comboBox.addActionListener(new ListActionListener());

src/ltsa/LtsaDataBlock.java

@@ -48,6 +48,7 @@ public class LtsaDataBlock extends FFTDataBlock {
 		 *As for getOfflineData, overrride this.
 		 */
 		loadViewerData(new OfflineDataLoadInfo(startMillis, endMillis), null);
+		
 		// now send all the data to the observer (the spectrogram) 
 		// so that it redraws in the same way it does for real fft data. 
 		synchronized (getSynchLock()) {

src/pamViewFX/fxNodes/utilityPanes/GroupedSourcePaneFX.java

@@ -77,7 +77,7 @@ public class GroupedSourcePaneFX extends SourcePaneFX {
 		
 		//sourcePane.setStyle("-fx-background-color: green");
 
-		Label titleLabel = new Label(getBorderTitle());
+		titleLabel = new Label(getBorderTitle());
 		PamGuiManagerFX.titleFont2style(titleLabel);
 //		titleLabel.setFont(PamGuiManagerFX.titleFontSize2);
 		sourcePane.add(titleLabel,0,0);
@@ -430,5 +430,7 @@ public class GroupedSourcePaneFX extends SourcePaneFX {
 	public Pane getChannelPane() {
 		return channelPanel;
 	}
+
+	
 	
 }

src/pamViewFX/fxNodes/utilityPanes/SourcePaneFX.java

@@ -82,7 +82,7 @@ public class SourcePaneFX extends PamBorderPane {
 	/**
 	 * The title labe. Sits above the combo box. 
 	 */
-	private Label titleLabel;
+	protected Label titleLabel;
 	
 	/**
 	 * Validator for channels 
@@ -859,4 +859,20 @@ public class SourcePaneFX extends PamBorderPane {
 		return channelValidator;
 	}
 
+	/**
+	 * Set the title.
+	 * @param titleString - the title text
+	 */
+	public void setTitleText(String titleString) {
+		titleLabel.setText(titleString);
+	}
+	
+	
+	/**
+	 * Clear the source types. 
+	 */
+	public void clearSourceTypeList() {
+		this.sourceType.clear();
+	}
+
 }

src/pamViewFX/validator/PamValidator.java

@@ -1,5 +1,9 @@
 package pamViewFX.validator;
 
+import java.util.List;
+import java.util.stream.Collectors;
+
+import net.synedra.validatorfx.ValidationMessage;
 import net.synedra.validatorfx.Validator;
 
 /**
@@ -14,4 +18,25 @@ public class PamValidator extends Validator {
 		// TODO Auto-generated constructor stub
 	}
 
+	/**
+	 * Create one string from a list of error messages. 
+	 * @param messages - the list of error messages
+	 * @return
+	 */
+	public static String list2String(List<ValidationMessage> messages) {
+		String string = "";
+		
+		//grab only unique messages
+		List<ValidationMessage> uniqueMessages = messages.stream()
+		         .distinct()
+		         .collect(Collectors.toList());
+
+		for (ValidationMessage msg:uniqueMessages) {
+			System.out.println(msg);
+			string += msg.getText() + "\n";
+		}
+		
+		return string;
+	}
+
 }

src/rawDeepLearningClassifier/DLControl.java

@@ -25,13 +25,14 @@ import PamguardMVC.PamRawDataBlock;
 import PamguardMVC.dataSelector.DataSelector;
 import ai.djl.engine.Engine;
 import annotation.handler.AnnotationHandler;
+import clickTrainDetector.layout.ClickTrainSymbolManager;
 import dataPlotsFX.data.TDDataProviderRegisterFX;
-import dataPlotsFX.data.generic.GenericDataPlotProvider;
 import detectionPlotFX.data.DDPlotRegister;
-import generalDatabase.DBControlUnit;
 import generalDatabase.SQLLoggingAddon;
+import pamScrollSystem.AbstractScrollManager;
 import pamViewFX.fxNodes.pamDialogFX.PamDialogFX2AWT;
 import rawDeepLearningClassifier.dataPlotFX.DLDetectionPlotProvider;
+import rawDeepLearningClassifier.dataPlotFX.DLGroupSymbolManager;
 import rawDeepLearningClassifier.dataPlotFX.DLPredictionProvider;
 import rawDeepLearningClassifier.ddPlotFX.RawDLDDPlotProvider;
 import rawDeepLearningClassifier.defaultModels.DLDefaultModelManager;
@@ -59,6 +60,7 @@ import rawDeepLearningClassifier.logging.DLDetectionDatagram;
 import rawDeepLearningClassifier.logging.DLGroupDetectionLogging;
 import rawDeepLearningClassifier.logging.DLGroupSubLogging;
 import rawDeepLearningClassifier.logging.DLResultBinarySource;
+import rawDeepLearningClassifier.logging.DLResultLogging;
 import rawDeepLearningClassifier.offline.DLOfflineProcess;
 import rawDeepLearningClassifier.segmenter.SegmenterProcess;
 
@@ -184,6 +186,18 @@ public class DLControl extends PamControlledUnit implements PamSettings {
 	 * The binary data source for detection data
 	 */
 	private DLDetectionBinarySource dlDetectionBinarySource;
+	
+	/**
+	 * DL Group detection logging to database
+	 */
+	private DLGroupDetectionLogging dlGroupDetLogging;
+	
+	
+	/**
+	 * Logging for raw predictions to database
+	 */
+	private DLResultLogging dlResultLogging;
+
 
 	/**
 	 * The DL offline process.
@@ -213,10 +227,13 @@ public class DLControl extends PamControlledUnit implements PamSettings {
 	 */
 	private DLDefaultModelManager defaultModelManager;
 	
+
 	/**
-	 * DL Group detection logging. 
+	 * If true then detections are saved to a group and all detections within a segment are then
+	 * passed to the deep learning classifier.
 	 */
-	private DLGroupDetectionLogging dlGroupDetLogging; 
+	private boolean groupDetections = false;
+
 
 	
 	
@@ -245,7 +262,8 @@ public class DLControl extends PamControlledUnit implements PamSettings {
 
 		// classify the raw data segments.
 		addPamProcess(dlClassifyProcess = new DLClassifyProcess(this, segmenterProcess.getSegmenterDataBlock()));
-		dlClassifyProcess.addMultiPlexDataBlock(segmenterProcess.getSegmenteGrouprDataBlock());
+		//also add group data - rare to have to into datablocks  but this will work.  
+		dlClassifyProcess.addMultiPlexDataBlock(segmenterProcess.getSegmenteGroupDataBlock());
 
 		//manages the names assigned to different output classes. 
 		dlClassNameManager = new DLClassNameManager(this);
@@ -260,6 +278,7 @@ public class DLControl extends PamControlledUnit implements PamSettings {
 		// add storage options etc.
 		dlBinaryDataSource = new DLResultBinarySource(dlClassifyProcess);
 		dlClassifyProcess.getDLPredictionDataBlock().setBinaryDataSource(dlBinaryDataSource);
+		dlClassifyProcess.getDLPredictionDataBlock().SetLogging(dlResultLogging = new DLResultLogging(this, dlClassifyProcess.getDLPredictionDataBlock()));
 		dlClassifyProcess.getDLPredictionDataBlock().setDatagramProvider(new DLDataUnitDatagram(this));
 
 		dlDetectionBinarySource = new DLDetectionBinarySource(this, dlClassifyProcess.getDLDetectionDatablock());
@@ -269,6 +288,9 @@ public class DLControl extends PamControlledUnit implements PamSettings {
 //		//set database logging for group detections
 		dlClassifyProcess.getDLGroupDetectionDataBlock().SetLogging(dlGroupDetLogging = new DLGroupDetectionLogging(this, dlClassifyProcess.getDLGroupDetectionDataBlock()));
 		dlGroupDetLogging.setSubLogging(new DLGroupSubLogging(dlGroupDetLogging, dlClassifyProcess.getDLGroupDetectionDataBlock()));
+//		int maxndays = 1; //maximum days to load. 
+//		AbstractScrollManager.getScrollManager().addToSpecialDatablock(dlClassifyProcess.getDLGroupDetectionDataBlock(), maxndays*24*60*60*1000L , maxndays*24*60*60*1000L);
+
 		
 		//a little strange this is not automatic but seems you have to add SQL add ons explicitly. 
 		AnnotationHandler annotationHandler = dlClassifyProcess.getDLGroupDetectionDataBlock().getAnnotationHandler();
@@ -290,12 +312,14 @@ public class DLControl extends PamControlledUnit implements PamSettings {
 		overlayGraphics = new DLDetectionGraphics(dlClassifyProcess.getDLGroupDetectionDataBlock());
 		overlayGraphics.setDetectionData(true);
 		dlClassifyProcess.getDLGroupDetectionDataBlock().setOverlayDraw(overlayGraphics);
+
 		
 		//set the symbol managers. 
 		dlClassifyProcess.getDLDetectionDatablock()
 				.setPamSymbolManager(new DLSymbolManager(this, dlClassifyProcess.getDLDetectionDatablock()));
-		dlClassifyProcess.getDLGroupDetectionDataBlock()
-			.setPamSymbolManager(new StandardSymbolManager(	dlClassifyProcess.getDLGroupDetectionDataBlock(),  new SymbolData()));
+		
+		dlClassifyProcess.getDLGroupDetectionDataBlock().setPamSymbolManager(new DLGroupSymbolManager(dlClassifyProcess.getDLGroupDetectionDataBlock()));
+
 		dlClassifyProcess.getDLPredictionDataBlock()
 				.setPamSymbolManager(new PredictionSymbolManager(this, dlClassifyProcess.getDLDetectionDatablock()));
 		
@@ -664,7 +688,7 @@ public class DLControl extends PamControlledUnit implements PamSettings {
 			//create the data selector
 			//System.out.println("Data selector: " + dataSelector); 
 			if (source!=null) {
-				dataSelector=source.getDataSelectCreator().getDataSelector(this.getUnitName() +"_clicks", false, null);
+				dataSelector=source.getDataSelectCreator().getDataSelector(this.getUnitName() +"_" + source.getDataName(), false, null);
 				//System.out.println("Data selector: " + dataSelector); 
 			}
 			else {
@@ -698,6 +722,28 @@ public class DLControl extends PamControlledUnit implements PamSettings {
 		return this.defaultModelManager;
 	}
 
+	/**
+	 * Check whether group detections are being used. 
+	 * If true then detections are saved to a group and all detections within a segment are then
+	 * passed to the deep learning classifier.
+	 */
+	public boolean isGroupDetections() {
+		return groupDetections;
+	}
+	
+	/**
+	 * Set whether to use group detections. 
+	 * If true then detections are saved to a group and all detections within a segment are then
+	 * passed to the deep learning classifier.
+	 * @param groupDetections - true to use group detections. 
+	 */
+	public void setGroupDetections(boolean groupDetections) {
+		this. groupDetections=groupDetections;
+	}
+
+
+
+
 
 
 }

src/rawDeepLearningClassifier/DLStatus.java

@@ -8,7 +8,8 @@ public enum DLStatus {
 	FILE_NULL("The input file is null",
 			"The loaded file was null. If the file was download it may not have downloaded properly.", ErrorLevel.ERROR),
 
-	MODEL_LOAD_FAILED("The model failed to load",
+	//generic error when something has gone wrong.
+	MODEL_LOAD_FAIL("The model failed to load",
 			" The model failed to load - this could be because it is incompatible with PAMGuard or an uknown file format.",
 			ErrorLevel.ERROR),
 
@@ -38,7 +39,11 @@ public enum DLStatus {
 	
 	DECOMPRESSING_MODEL("Decompressing model", "Decompressing the model file", ErrorLevel.NO_ERROR), 
 
-	INCOMPATIBLE_ZIP("Incorrect Zip format", "The zip format is incorrect. The zip file should have a *.pgdl file in the parent directory along with either a Tensorflow or PyTorch model.", ErrorLevel.ERROR), ;
+	INCOMPATIBLE_ZIP("Incorrect Zip format", "The zip format is incorrect. The zip file should have a *.pgdl file in the parent directory along with either a Tensorflow or PyTorch model.", ErrorLevel.ERROR), 
+
+	MODEL_ENGINE_FAIL("Deep learning engine failure", "The deep learning engine was not able to load. It is likely PAMGuard could not download the relevent libraries. Visit www.pamguard.org/deeplearning for a work around.", ErrorLevel.ERROR),
+	
+	MODEL_META_FAIL("Model metadata failure", "PAMGuard could not locte the metadata within the model. if this is a custom model ensure the associated .pgdl file is present and JSON valid", ErrorLevel.ERROR); 
 
 
 	/**

src/rawDeepLearningClassifier/RawDLParams.java

@@ -68,7 +68,7 @@ public class RawDLParams implements Serializable, Cloneable {
 	 * contain a raw data chunk e.g. raw data, clicks, clips etc. By default the
 	 * classifier saves new data units if the source is raw data. However, if the
 	 * data unit is an already processed data unit, e.g. a click detection, then the
-	 * results are saved as an annotation to that unit. If forceSave is st true then
+	 * results are saved as an annotation to that unit. If forceSave is set true then
 	 * new data units are created no matter what the source data is.
 	 */
 	public boolean forceSave = false;
@@ -95,7 +95,7 @@ public class RawDLParams implements Serializable, Cloneable {
 	 * data without messing up all the other classified detections which have used
 	 * that module. So store the data in binary files? That is super inefficient as
 	 * the same string is stored many times. So instead store a short which
-	 * identifies the string that sits in this table. Everytime a new model is added
+	 * identifies the string that sits in this table. Every time a new model is added
 	 * add new classnames. Note that this means we have a maximum of 32k names - we
 	 * will come to that issue when it arises...
 	 */

src/rawDeepLearningClassifier/dataPlotFX/DLGroupDetectionInfoFX.java

@@ -4,15 +4,24 @@ import java.net.MalformedURLException;
 import java.net.URISyntaxException;
 import java.net.URL;
 
+import PamView.GeneralProjector.ParameterType;
 import PamguardMVC.PamDataBlock;
+import PamguardMVC.PamDataUnit;
 import dataPlotsFX.data.TDDataProviderFX;
 import dataPlotsFX.data.generic.GenericDataPlotInfo;
 import dataPlotsFX.data.generic.GenericSettingsPane;
 import dataPlotsFX.layout.TDGraphFX;
+import dataPlotsFX.projector.TDProjectorFX;
 import javafx.scene.Node;
-
+import javafx.scene.canvas.GraphicsContext;
+import javafx.scene.shape.Polygon;
 import rawDeepLearningClassifier.DLControl;
+import rawDeepLearningClassifier.dlClassification.DLGroupDetection;
 
+
+/**
+ * Plot results from deep learning group detections. 
+ */
 public class DLGroupDetectionInfoFX extends GenericDataPlotInfo {
 	
 
@@ -28,7 +37,54 @@ public class DLGroupDetectionInfoFX extends GenericDataPlotInfo {
 		}
 		((GenericSettingsPane) this.getGraphSettingsPane()).setShowingName("Deep Learning Group Detections");
 	}
+	
+	/* (non-Javadoc)
+	 * @see dataPlotsFX.data.TDDataInfoFX#drawDataUnit(int, PamguardMVC.PamDataUnit, javafx.scene.canvas.GraphicsContext, long, dataPlotsFX.projector.TDProjectorFX, int)
+	 */
+	@Override
+	public Polygon drawDataUnit(int plotNumber, PamDataUnit pamDataUnit, GraphicsContext g, double scrollStart,
+			TDProjectorFX tdProjector, int type) {
 
+		if (getCurrentScaleInfo().getDataType() == ParameterType.FREQUENCY) { // frequency data !
+//			System.out.println("Draw frequency: " + pamDataUnit.getFrequency()[0] + "  " +  pamDataUnit.getFrequency()[1]); 
+			return drawFrequencyData(plotNumber, pamDataUnit, g, scrollStart, tdProjector, type);
+		}
+		else if (getCurrentScaleInfo().getDataType() == ParameterType.AMPLITUDE || getCurrentScaleInfo().getDataType() == ParameterType.AMPLITUDE_STEM) {
+//			System.out.println("Draw amplitude: "); 
+			double[] amplitudes  = getAmplitudes(pamDataUnit);
+			return super.drawBoxData(plotNumber, pamDataUnit, amplitudes, g, scrollStart, tdProjector, type); 
+		}
+		
+		return null;
+	}
+	
+	/**
+	 * Get the amplitude for a group data unit - this is the highest and lowest amplitude. 
+	 * @param pamDataUnit - the data unit. 
+	 * @return amplitude limits in dB. 
+	 */
+	private double[] getAmplitudes(PamDataUnit pamDataUnit) {
+		
+		double[] amplitudes = new double[2];
+		amplitudes[0] = Double.POSITIVE_INFINITY;
+		amplitudes[1] = Double.NEGATIVE_INFINITY;
 
+		DLGroupDetection groupDetection = (DLGroupDetection) pamDataUnit; 
+		
+		double amp;
+		for (int i=0; i<groupDetection.getSubDetectionsCount(); i++) {
+			amp = groupDetection.getSubDetection(i).getAmplitudeDB();
+			
+			if (amp>amplitudes[1]) {
+				amplitudes[1] = amp;
+			}
+			
+			if (amp<amplitudes[0]) {
+				amplitudes[0] = amp;
+			}
+		}
+		
+		return amplitudes;
+	}
 
 }

src/rawDeepLearningClassifier/dataPlotFX/DLGroupSymbolManager.java

@@ -0,0 +1,33 @@
+package rawDeepLearningClassifier.dataPlotFX;
+
+import PamView.symbol.PamSymbolChooser;
+import PamView.symbol.StandardSymbolManager;
+import PamguardMVC.PamDataBlock;
+import detectiongrouplocaliser.DetectionGroupGraphics;
+
+public class DLGroupSymbolManager extends StandardSymbolManager {
+
+	public DLGroupSymbolManager(PamDataBlock pamDataBlock2) {
+		super(pamDataBlock2, DetectionGroupGraphics.defaultSymbol);
+		setSpecialColourName("DL Detection Group");
+	}
+	
+	
+
+	@Override
+	public void addSymbolModifiers(PamSymbolChooser psc) {		
+		super.addSymbolModifiers(psc);
+		
+		//add the peak frequency modifier that allows clicks to be coloured by peak frequency. 
+		psc.addSymbolModifier(new DLGroupSymbolModifier(psc));
+		
+		// we can also add some default behaviour here to match the old behaviour
+		// these will get overridden once user options are set, but it's good to give defaults. 
+//		SymbolModifier eventMod = psc.hasSymbolModifier(SuperDetSymbolModifier.class);
+//		if (eventMod != null) {
+//			eventMod.getSymbolModifierParams().modBitMap = (SymbolModType.FILLCOLOUR | SymbolModType.LINECOLOUR);
+//		}
+		
+	}
+
+}

src/rawDeepLearningClassifier/dataPlotFX/DLGroupSymbolModifier.java

@@ -0,0 +1,40 @@
+package rawDeepLearningClassifier.dataPlotFX;
+
+import java.awt.Color;
+
+import PamView.GeneralProjector;
+import PamView.PamColors;
+import PamView.PamSymbolType;
+import PamView.symbol.PamSymbolChooser;
+import PamView.symbol.SymbolData;
+import PamView.symbol.modifier.SymbolModType;
+import PamView.symbol.modifier.SymbolModifier;
+import PamguardMVC.PamDataUnit;
+
+/**
+ * Symbol modifier for DL groups
+ */
+public class DLGroupSymbolModifier extends SymbolModifier {
+	
+	public final static String CLICK_TRAIN_MODIFIER_NAME = "Colour by DL Group";
+
+
+	public DLGroupSymbolModifier(PamSymbolChooser symbolChooser) {
+		super(CLICK_TRAIN_MODIFIER_NAME, symbolChooser, SymbolModType.FILLCOLOUR |  SymbolModType.LINECOLOUR );
+		// TODO Auto-generated constructor stub
+	}
+
+	@Override
+	public SymbolData getSymbolData(GeneralProjector projector, PamDataUnit dataUnit) {
+	
+		SymbolData symbolData=new SymbolData();
+		symbolData.symbol=PamSymbolType.SYMBOL_CIRCLE;
+		
+		Color col = PamColors.getInstance().getWhaleColor(dataUnit.getColourIndex());
+		symbolData.setFillColor(col);
+		symbolData.setLineColor(col);
+
+		return symbolData;
+	}
+
+}

src/rawDeepLearningClassifier/dataPlotFX/DLSymbolOptionPane.java

@@ -313,6 +313,7 @@ public class DLSymbolOptionPane extends StandardSymbolModifierPane {
 	
 	private int checkClassNamesBox(DLSymbolModifierParams symbolOptions, ComboBox<String> classNameBox) {
 		
+		if (dlSymbolModifier.getDLAnnotType().getDlControl().getDLModel()==null) return 0;
 		DLClassName[] classNames = dlSymbolModifier.getDLAnnotType().getDlControl().getDLModel().getClassNames(); 
 		
 //		for (int i =0; i<classNames.length; i++) {

src/rawDeepLearningClassifier/dataPlotFX/DLSymbolOptionPanel.java

@@ -206,11 +206,28 @@ public class DLSymbolOptionPanel implements PamDialogPanel, ActionListener {
 	 * @param symbolOptions - the symbol options
 	 */
 	private void setPredictionColParams(DLSymbolModifierParams symbolOptions) {
+		
+		if (symbolOptions.clims[0]==symbolOptions.clims[1]) {
+			
+			if (symbolOptions.clims[0]>0 && symbolOptions.clims[0]<1.) {
+				symbolOptions.clims[0]=symbolOptions.clims[0]-0.1;
+				symbolOptions.clims[0]=symbolOptions.clims[0]+0.1;
+			}
+			else {
+			symbolOptions.clims[0] = 0.1;
+			symbolOptions.clims[1] = 0.9;
+			}
+		}
+		
+		//System.out.println("Set upper value: " + symbolOptions.clims[0]  + " " + symbolOptions.clims[1]); 
 
 		//now set frequency parameters 
-		colorRangeSlider.setValue((int) symbolOptions.clims[0]*100);
-		colorRangeSlider.setUpperValue((int) symbolOptions.clims[1]*100);
+		colorRangeSlider.setValue((int) (symbolOptions.clims[0]*100));
+		colorRangeSlider.setUpperValue((int) (symbolOptions.clims[1]*100));
 		//		colorRangeSlider.setColourArrayType( symbolOptions.colArray);
+		
+		//System.out.println("Set upper value: " + colorRangeSlider.getValue() + " " + colorRangeSlider.getUpperValue()); 
+
 
 		colorRangeSlider.setColourMap(PamUtilsFX.fxColArray2Swing(symbolOptions.colArray));
 

src/rawDeepLearningClassifier/defaultModels/DLDefaultModelManager.java

@@ -27,6 +27,7 @@ public class DLDefaultModelManager {
 		defaultModels.add(new RightWhaleModel1());
 		defaultModels.add(new HumpbackWhaleGoogle());
 		defaultModels.add(new HumpbackWhaleAtlantic());
+		defaultModels.add(new MultiSpeciesGoogle());
 	}
 
 	

src/rawDeepLearningClassifier/defaultModels/HumpbackWhaleAtlantic.java

@@ -1,10 +1,8 @@
 package rawDeepLearningClassifier.defaultModels;
 
 import java.io.Serializable;
-import java.net.MalformedURLException;
 import java.net.URI;
 import java.net.URISyntaxException;
-import java.net.URL;
 import java.util.ArrayList;
 
 import org.jamdev.jdl4pam.transforms.DLTransformsFactory;
@@ -13,7 +11,6 @@ import org.jamdev.jdl4pam.transforms.SimpleTransformParams;
 import org.jamdev.jdl4pam.transforms.DLTransform.DLTransformType;
 
 import rawDeepLearningClassifier.dlClassification.DLClassName;
-import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
 import rawDeepLearningClassifier.dlClassification.genericModel.GenericModelParams;
 import rawDeepLearningClassifier.layoutFX.exampleSounds.ExampleSoundFactory.ExampleSoundType;
 

src/rawDeepLearningClassifier/defaultModels/MultiSpeciesGoogle.java

@@ -0,0 +1,111 @@
+package rawDeepLearningClassifier.defaultModels;
+
+import java.io.Serializable;
+import java.net.URI;
+import java.net.URISyntaxException;
+import java.util.ArrayList;
+
+import org.jamdev.jdl4pam.transforms.DLTransformsFactory;
+import org.jamdev.jdl4pam.transforms.DLTransfromParams;
+import org.jamdev.jdl4pam.transforms.SimpleTransformParams;
+import org.jamdev.jdl4pam.transforms.DLTransform.DLTransformType;
+
+import rawDeepLearningClassifier.dlClassification.DLClassName;
+import rawDeepLearningClassifier.dlClassification.genericModel.GenericModelParams;
+import rawDeepLearningClassifier.layoutFX.exampleSounds.ExampleSoundFactory.ExampleSoundType;
+
+/**
+ * Atlantic version of Google's humpback whale network. 
+ */
+public class MultiSpeciesGoogle implements DLModel {
+
+	@Override
+	public String getDescription() {
+		return "A model developed by Google to detect multiple cetacean species including Humpback, Minke, Bryde's, Blue, Fin and Right whales alongside Orca whistle, calls and echolocation clicks.";
+	}
+
+	@Override
+	public String getName() {
+		return "Multi Species Google";
+	}
+
+	@Override
+	public String getCitation() {
+		return "Allen, A.N. et al. (2024) ‘Bryde’s whales produce Biotwang calls, which occur seasonally in long-term acoustic recordings from the central and western North Pacific’, Frontiers in Marine Science, 11, p. 1394695"; 
+	}
+
+	@Override
+	public URI getModelURI() {
+		try {
+			//				return new URI("https://github.com/PAMGuard/deeplearningmodels/raw/master/humpback_whale_2/humpback_whale_2.zip");
+			return new URI("https://github.com/PAMGuard/deeplearningmodels/releases/download/1.0/multispecies-whale-tensorflow2-default-v2.zip");
+		} catch (URISyntaxException e) {
+			// TODO Auto-generated catch block
+			e.printStackTrace();
+		}
+		return null;
+	}
+
+	@Override
+	public String getModelName() {
+		return "saved_model.pb";
+	}
+
+	@Override
+	public URI getCitationLink() {
+		try {
+			return new URI("https://doi.org/10.3389/fmars.2024.1394695 ");
+		} catch (URISyntaxException e) {
+			e.printStackTrace();
+			return null;
+		}
+	}
+
+	@Override
+	public void setParams(Serializable dlModelSettings) {
+		GenericModelParams genericModelParams = (GenericModelParams) dlModelSettings;
+
+		//decimation value
+		float sr = 24000; 
+
+		//create the transforms. 
+		ArrayList<DLTransfromParams> dlTransformParamsArr = new ArrayList<DLTransfromParams>();
+
+		dlTransformParamsArr.add(new SimpleTransformParams(DLTransformType.DECIMATE_SCIPY, sr)); 
+
+		genericModelParams.dlTransfromParams = dlTransformParamsArr;
+
+		genericModelParams.defaultSegmentLen =   5. * 1000.;
+		genericModelParams.binaryClassification = new boolean[] {true};
+		genericModelParams.classNames= new DLClassName[] {
+				new DLClassName("Oo", (short) 0),
+				new DLClassName("Mn", (short) 1),
+				new DLClassName("Eg", (short) 2),
+				new DLClassName("Be", (short) 3),
+				new DLClassName("Upcall", (short) 4),
+				new DLClassName("Bp", (short) 5),
+				new DLClassName("Call", (short) 6),
+				new DLClassName("Gunshot", (short) 7),
+				new DLClassName("Echolocation", (short) 8),
+				new DLClassName("Bm", (short) 9),
+				new DLClassName("Whistle", (short) 10),
+				new DLClassName("Ba", (short) 12),
+				};
+		genericModelParams.numClasses = genericModelParams.classNames.length; 
+
+		genericModelParams.defaultShape= new Long[] {-1L,-1L,-1L,1L};
+		genericModelParams.shape= new Long[] {-1L,-1L,-1L,1L};
+		genericModelParams.outputShape = new Long[] {-1L,12L}; 
+
+
+		//create the transforms. 
+		genericModelParams.dlTransfroms = DLTransformsFactory.makeDLTransforms((ArrayList<DLTransfromParams>)genericModelParams.dlTransfromParams); 
+
+		genericModelParams.setExampleSound(ExampleSoundType.HUMPBACK_WHALE);
+	}
+
+
+
+}
+
+

src/rawDeepLearningClassifier/dlClassification/DLClassiferModel.java

@@ -36,14 +36,14 @@ public interface DLClassiferModel {
 	public ArrayList<? extends PredictionResult> runModel(ArrayList<? extends PamDataUnit> rawDataUnit);
 
 	/**
-	 * Prepare the model. This is called on PAMGuard start up.
-	 * @param file 
+	 * Prepare the model. This is called on PAMGuard start up and before processing in viewer mode. 
 	 */
 	public void prepModel();
 	
 	/**
-	 * Called whenever PAMGuard stops.
-	 * @return 
+	 * Set and load the model. 
+	 * @return the status of the model e.g. if there has been an error loading
+	 * 
 	 */
 	public DLStatus setModel(URI model);
 	
@@ -99,6 +99,14 @@ public interface DLClassiferModel {
 	 * @return reference to the DL control. 
 	 */
 	public DLControl getDLControl();
+	
+
+	/**
+	 * Get the allowed data types for the model.The can be null in which case only data units
+	 * with raw data are allowed. 
+	 * @return a list of the allowed data types. 
+	 */
+	public ArrayList<Class> getAllowedDataTypes();
 
 	/**
 	 * Check whether a model has been selected and can be loaded successfully. 

src/rawDeepLearningClassifier/dlClassification/DLClassifyProcess.java

@@ -14,6 +14,7 @@ import PamguardMVC.PamDataUnit;
 import PamguardMVC.PamInstantProcess;
 import PamguardMVC.PamObservable;
 import binaryFileStorage.DataUnitFileInformation;
+import pamScrollSystem.AbstractScrollManager;
 import rawDeepLearningClassifier.DLControl;
 import rawDeepLearningClassifier.RawDLParams;
 import rawDeepLearningClassifier.layoutFX.DLDetectionGraphics;
@@ -127,6 +128,7 @@ public class DLClassifyProcess extends PamInstantProcess {
 		dlGroupDetectionDataBlock.setNaturalLifetimeMillis(600*1000); //keep this data for a while.
 		dlGroupDetectionDataBlock.addDataAnnotationType(dlAnnotationType);
 		dlGroupDetectionDataBlock.setCanClipGenerate(true); 
+		
 
 		classificationBuffer =  new ArrayList<PamDataUnit>(); 
 
@@ -206,8 +208,9 @@ public class DLClassifyProcess extends PamInstantProcess {
 	 */
 	@Override
 	public void newData(PamObservable obs, PamDataUnit pamRawData) {
-//		System.out.println("NEW SEGMENTER DATA: " +  PamCalendar.formatDateTime2(pamRawData.getTimeMilliseconds(), "dd MMM yyyy HH:mm:ss.SSS", false) + "  " + pamRawData.getUID() + "  " + pamRawData.getChannelBitmap() + " " + pamRawData);
+	//System.out.println("NEW SEGMENTER DATA: " +  PamCalendar.formatDateTime2(pamRawData.getTimeMilliseconds(), "dd MMM yyyy HH:mm:ss.SSS", false) + "  " + pamRawData.getUID() + "  " + pamRawData.getChannelBitmap() + " " + pamRawData);
 
+		//if grouped data then just run the classifier on the group - do not try and create a buffer. 
 		if (pamRawData instanceof SegmenterDetectionGroup) {
 			if (classificationBuffer.size()>=1) {
 				runDetectionGroupModel(); 
@@ -218,6 +221,8 @@ public class DLClassifyProcess extends PamInstantProcess {
 			}
 		}
 
+		//if raw segmented data then add to a buffer so that we can pass chunks of data to a model which 
+		//is more computationally efficient
 		if (pamRawData instanceof GroupedRawData) {
 			//the raw data units should appear in sequential channel order  
 			GroupedRawData rawDataUnit = (GroupedRawData) pamRawData;
@@ -235,6 +240,8 @@ public class DLClassifyProcess extends PamInstantProcess {
 
 			}
 		}
+		
+		
 		//				System.out.println("New raw data in: chan: " + PamUtils.getSingleChannel(pamRawData.getChannelBitmap()) + 
 		//						" Size: " +  pamRawData.getSampleDuration() + " first sample: " + rawDataUnit.getRawData()[0][0] 
 		//								+ "Parent UID: " + rawDataUnit.getParentDataUnit().getUID());
@@ -249,6 +256,8 @@ public class DLClassifyProcess extends PamInstantProcess {
 		ArrayList<PamDataUnit> classificationBufferTemp = (ArrayList<PamDataUnit>) classificationBuffer.clone(); 
 
 		ArrayList<? extends PredictionResult> modelResults = this.dlControl.getDLModel().runModel(classificationBufferTemp); 
+		
+		//System.out.println("MODEL RESULTS: " + modelResults); 
 
 		for (int i=0; i<classificationBufferTemp.size(); i++) {
 
@@ -271,10 +280,11 @@ public class DLClassifyProcess extends PamInstantProcess {
 
 		DLDataUnit dlDataUnit =  predictionToDataUnit(detectionGroup, modelResult);
 
-		this.dlModelResultDataBlock.addPamData(dlDataUnit); //here
-
+		//System.out.println("Add predictions: " + dlDataUnit.getPredicitionResult().getPrediction());
+		//add to the model result. 
+		this.dlModelResultDataBlock.addPamData(dlDataUnit);
+		
 //		System.out.println("DELPHINID - we have a detection: " + detectionGroup.getUID() + "  " + PamCalendar.formatDateTime(detectionGroup.getTimeMilliseconds())); 
-
 //		//Now generate a detection of a decision threshold is reacheed. 
 //		if (dlDataUnit.getPredicitionResult().isBinaryClassification()) {
 //			System.out.println("DELPHINID - we have a positive detection: " + detectionGroup.getUID() + "  " + PamCalendar.formatDateTime(detectionGroup.getTimeMilliseconds())); 
@@ -600,10 +610,13 @@ public class DLClassifyProcess extends PamInstantProcess {
 	 * run the deep learning algorithm and save the detections as annotation to a
 	 * data unit.
 	 * 
-	 * @param dataUnit - the data unit to add prediction annotations to
+	 * @param dataUnit - the data unit to add prediction annotations to. NOT the data unit to be classified - this is is defined by adding
+	 * new data to the process. 
 	 * 
 	 */
 	public void forceRunClassifier(PamDataUnit dataUnit) {
+		
+//		System.out.println("CLASSIFICATION BUFFER: " + classificationBuffer.size());
 
 		if (this.classificationBuffer.size()>0) {
 			if (classificationBuffer.get(0) instanceof GroupedRawData) {
@@ -743,7 +756,7 @@ public class DLClassifyProcess extends PamInstantProcess {
 	private void addDLAnnotation(PamDataUnit parentDataUnit,
 			ArrayList<PredictionResult> modelResult) {
 
-		//System.out.println("DLClassifyProces: Add annnotation to  " + parentDataUnit); 
+		System.out.println("DLClassifyProces: Add annnotation to  " + parentDataUnit); 
 		parentDataUnit.addDataAnnotation(new DLAnnotation(dlAnnotationType, modelResult)); 
 		//parentDataUnit.updateDataUnit(System.currentTimeMillis());
 

src/rawDeepLearningClassifier/dlClassification/DLGroupDataBlock.java

@@ -25,6 +25,15 @@ public class DLGroupDataBlock extends SuperDetDataBlock<DLGroupDetection, PamDat
 		return super.getHoverText(generalProjector, dataUnit, iSide);
 	}
 
+	
+	@Override
+ 	public boolean shouldNotify() {
+		/**
+		 * Segments are not saved by PAMGuard as it's a waste of storage space but this means that reprocessing data requires the
+		 * segments are remad with detections in viewer mode. There is 
+		 */
+ 		return true; 
+ 	}
 
 
 }

src/rawDeepLearningClassifier/dlClassification/DLGroupDetection.java

@@ -18,5 +18,34 @@ public class DLGroupDetection extends SegmenterDetectionGroup implements PamDete
 	}
 
 
+	public DLGroupDetection(long timeMilliseconds, int channelBitmap, long startSample, double duration) {
+		super(timeMilliseconds, channelBitmap, startSample, duration);
+	}
+
+	@Override
+	public double[] getFrequency() {
+		if ((super.getFrequency()==null || super.getFrequency()[0]  == super.getFrequency()[1]) && this.getSubDetectionsCount()>0) {
+
+			double minFreq =  Double.POSITIVE_INFINITY;
+			double maxFreq =  Double.NEGATIVE_INFINITY;
+
+			double[] freq;
+			for (int j=0; j<getSubDetectionsCount(); j++) {
+
+
+				freq = getSubDetection(j).getFrequency();
+				if (freq[0]<minFreq) {
+					minFreq = freq[0];
+				}
+				if (freq[1]>maxFreq) {
+					maxFreq = freq[1];
+				}
+
+			}
+			this.setFrequency(new double[] {minFreq, maxFreq});
+		}
+		return super.getFrequency();
+	}
+
 
 }

src/rawDeepLearningClassifier/dlClassification/StandardClassifierModel.java

@@ -60,6 +60,8 @@ public abstract class StandardClassifierModel implements DLClassiferModel, PamSe
 	 * Sound spot warning. 
 	 */
 	PamWarning dlClassifierWarning = new PamWarning(getName(), "",2);
+
+	private DLStatus status = DLStatus.NO_MODEL_LOADED;
 	
 	
 	@Override
@@ -115,6 +117,9 @@ public abstract class StandardClassifierModel implements DLClassiferModel, PamSe
 //		System.out.println("STANDARD CLASSIFIER MODEL PREP MODEL! !!!: " +  getDLParams().modelPath);
 //		StandardModelParams oldParams = getDLParams().clone();
 		
+		//just incase group detections has been enabled
+		getDLControl().setGroupDetections(false);
+		
 		getDLWorker().prepModel(getDLParams(), dlControl);
 
 
@@ -168,37 +173,56 @@ public abstract class StandardClassifierModel implements DLClassiferModel, PamSe
 	
 	@Override
 	public DLStatus getModelStatus() {
-		if (getDLWorker().isModelNull()) {
-			return DLStatus.MODEL_LOAD_FAILED;
-		}
-		
-		File file = new File(getDLParams().modelPath);
-		if (getDLParams().modelPath == null || !file.isFile()) {
-			return DLStatus.NO_MODEL_LOADED;
-		}
-		
-		// if continuous data is selected and all classes are false then this is a
-		// potential mistake...
-		if (dlControl.getSettingsPane().getSelectedParentDataBlock().getUnitClass() == RawDataUnit.class
-				&& (getDLParams().binaryClassification==null || PamArrayUtils.isAllFalse(getDLParams().binaryClassification))){
-			return DLStatus.NO_BINARY_CLASSIFICATION;
-//			warnings.add(new PamWarning("Generic classifier",
-//					"There are no prediction classes selected for classification. "
-//							+ "Predicitons for each segment will be saved but there will be no detections generated",
-//					1));
-		}
-		return DLStatus.MODEL_LOAD_SUCCESS;
+		return status;
 	}
 	
 	@Override
 	public DLStatus setModel(URI uri) {
 		//will change the params if we do not clone. 
 		StandardModelParams.setModel(uri, this.getDLParams()); 
-		this.getDLWorker().prepModel(getDLParams(), dlControl);
-		return getModelStatus();
+		status = this.getDLWorker().prepModel(getDLParams(), dlControl);
+		
+//		System.out.println("----MODEL STATUS: " + status); 
+		
+		status  = checkDLStatus(status);
+		
+		return status; 
 	}
 	
+	/**
+	 * The model status is returned by the prep model function but there may be other issues which override the returned status. 
+	 * This function chekcs those issues and returns a different status if necessary.
+	 * @param status2 - the current model status. 
+	 * @return the current model status. 
+	 */
+	private DLStatus checkDLStatus(DLStatus status2) {
 	
+		
+		if (getDLWorker().isModelNull() && !status2.isError()) {
+			return DLStatus.MODEL_LOAD_FAIL;
+		}
+
+		File file = new File(getDLParams().modelPath);
+		if (getDLParams().modelPath == null || !file.isFile()) {
+			return DLStatus.NO_MODEL_LOADED;
+		}
+
+		// if continuous data is selected and all classes are false then this is a
+		// potential mistake...
+		if (dlControl.getSegmenter().getParentDataBlock()!=null) {
+			if (dlControl.getSegmenter().getParentDataBlock().getUnitClass() == RawDataUnit.class
+					&& (getDLParams().binaryClassification==null || PamArrayUtils.isAllFalse(getDLParams().binaryClassification))){
+				return DLStatus.NO_BINARY_CLASSIFICATION;
+				//			warnings.add(new PamWarning("Generic classifier",
+				//					"There are no prediction classes selected for classification. "
+				//							+ "Predicitons for each segment will be saved but there will be no detections generated",
+				//					1));
+			}
+		}
+		return status2;
+	}
+
+
 
 	/**
 	 * The task thread. 
@@ -365,6 +389,13 @@ public abstract class StandardClassifierModel implements DLClassiferModel, PamSe
 		return warnings;
 
 	}
+	
+	@Override
+	public ArrayList<Class> getAllowedDataTypes(){
+		//null means default data types which is anything with raw data. 
+		return null;
+	}
+
 
 
 

src/rawDeepLearningClassifier/dlClassification/animalSpot/SoundSpotModelPane.java

@@ -4,6 +4,7 @@ import java.io.File;
 import java.util.ArrayList;
 
 import javafx.stage.FileChooser.ExtensionFilter;
+import rawDeepLearningClassifier.DLStatus;
 
 public class SoundSpotModelPane extends StandardModelPane {
 
@@ -27,7 +28,7 @@ public class SoundSpotModelPane extends StandardModelPane {
 	 * @param file - the selected file. 
 	 */
 	@Override
-	public void newModelSelected(File file) {
+	public DLStatus newModelSelected(File file) {
 		
 		//System.out.println("New  file model selected:"); 
 		
@@ -52,7 +53,7 @@ public class SoundSpotModelPane extends StandardModelPane {
 		 * Note that the model prep will determine whether new transforms need to be loaded from the 
 		 * model or to use the existing transforms in the settings. 
 		 */
-		soundSpotClassifier.getDLWorker().prepModel(params, soundSpotClassifier.getDLControl());
+		DLStatus status = soundSpotClassifier.getDLWorker().prepModel(params, soundSpotClassifier.getDLControl());
 		//get the model tansforms calculated from the model by SoundSpoyWorker and apply them to our temporary params clone. 
 		getParamsClone().dlTransfroms = this.soundSpotClassifier.getDLWorker().getModelTransforms(); 
 		
@@ -63,6 +64,9 @@ public class SoundSpotModelPane extends StandardModelPane {
 		
 		///set the advanced pane parameters. 
 		getAdvSettingsPane().setParams(getParamsClone());
+		
+		return status;
+				
 
 	}
 

src/rawDeepLearningClassifier/dlClassification/animalSpot/SoundSpotWorker.java

@@ -5,7 +5,9 @@ import java.nio.file.Paths;
 import org.jamdev.jdl4pam.animalSpot.AnimalSpotModel;
 import org.jamdev.jdl4pam.animalSpot.AnimalSpotParams;
 
+import ai.djl.engine.EngineException;
 import rawDeepLearningClassifier.DLControl;
+import rawDeepLearningClassifier.DLStatus;
 import rawDeepLearningClassifier.dlClassification.genericModel.DLModelWorker;
 import rawDeepLearningClassifier.dlClassification.genericModel.StandardPrediction;
 
@@ -41,7 +43,7 @@ public class SoundSpotWorker extends DLModelWorker<StandardPrediction> {
 	/**
 	 * Prepare the model 
 	 */
-	public void prepModel(StandardModelParams soundSpotParams, DLControl dlControl) {
+	public DLStatus prepModel(StandardModelParams soundSpotParams, DLControl dlControl) {
 		//ClassLoader origCL = Thread.currentThread().getContextClassLoader();
 
 		//System.out.println("prepModel: " + soundSpotParams.useDefaultTransfroms); 
@@ -64,8 +66,12 @@ public class SoundSpotWorker extends DLModelWorker<StandardPrediction> {
 
 			}
 		}
+		catch (EngineException e) {
+			return DLStatus.MODEL_ENGINE_FAIL;
+		}
 		catch (Exception e) {
 			e.printStackTrace();
+			return DLStatus.MODEL_LOAD_FAIL;
 			//WarnOnce.showWarning(null, "Model Load Error", "There was an error loading the model file.", WarnOnce.OK_OPTION); 
 		}
 
@@ -114,9 +120,13 @@ public class SoundSpotWorker extends DLModelWorker<StandardPrediction> {
 		catch (Exception e) {
 			soundSpotModel=null; 
 			e.printStackTrace();
+			return DLStatus.MODEL_META_FAIL;
+
 			//WarnOnce.showWarning(null, "Model Metadata Error", "There was an error extracting the metadata from the model.", WarnOnce.OK_OPTION); 
 		}
 
+		return DLStatus.MODEL_LOAD_SUCCESS;
+
 		//Thread.currentThread().setContextClassLoader(origCL);
 	}
 

src/rawDeepLearningClassifier/dlClassification/animalSpot/StandardModelPane.java

@@ -31,6 +31,7 @@ import pamViewFX.fxNodes.PamHBox;
 import pamViewFX.fxNodes.PamSpinner;
 import pamViewFX.fxNodes.PamVBox;
 import pamViewFX.validator.PamValidator;
+import rawDeepLearningClassifier.DLStatus;
 import rawDeepLearningClassifier.dlClassification.DLClassiferModel;
 import rawDeepLearningClassifier.dlClassification.StandardClassifierModel;
 
@@ -292,8 +293,9 @@ public abstract class StandardModelPane extends SettingsPane<StandardModelParams
 	/**
 	 * Called whenever a new model has been selected
 	 * @param file - the file. 
+	 * @return 
 	 */
-	public abstract void newModelSelected(File file); 
+	public abstract DLStatus newModelSelected(File file); 
 
 	/**
 	 * Sho0w the advanced settings. 
@@ -403,11 +405,10 @@ public abstract class StandardModelPane extends SettingsPane<StandardModelParams
 		this.getAdvSettingsPane().setParams(paramsClone);
 		
 		if (paramsClone.modelPath!=null) {
-			//this might 
 			currentSelectedFile = new File(paramsClone.modelPath);
 			
 			//this might change the paramsClone values if the model contains pamguard compatible metadata
-			newModelSelected(currentSelectedFile); 
+			DLStatus status = newModelSelected(currentSelectedFile);
 		}
 		
 		setClassNames(paramsClone);

src/rawDeepLearningClassifier/dlClassification/archiveModel/ArchiveModelPane.java

@@ -2,6 +2,7 @@ package rawDeepLearningClassifier.dlClassification.archiveModel;
 
 import java.io.File;
 
+import rawDeepLearningClassifier.DLStatus;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelPane;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
 import rawDeepLearningClassifier.dlClassification.ketos.KetosDLParams;
@@ -17,7 +18,7 @@ public class ArchiveModelPane extends StandardModelPane {
 	}
 
 	@Override
-	public void newModelSelected(File file) {
+	public DLStatus newModelSelected(File file) {
 		
 		//the model has to set some of the parameters for the UI . 
 			
@@ -45,7 +46,7 @@ public class ArchiveModelPane extends StandardModelPane {
 		 * Note that the model prep will determine whether new transforms need to be loaded from the 
 		 * model or to use the existing transforms in the settings. 
 		 */
-		archiveModelClassifier.getDLWorker().prepModel(params, archiveModelClassifier.getDLControl());
+		DLStatus status = archiveModelClassifier.getDLWorker().prepModel(params, archiveModelClassifier.getDLControl());
 		
 		//get the model transforms calculated from the model by the worker and apply them to our temporary params clone. 
 		getParamsClone().dlTransfroms = this.archiveModelClassifier.getDLWorker().getModelTransforms(); 
@@ -57,6 +58,8 @@ public class ArchiveModelPane extends StandardModelPane {
 		///set the advanced pane parameters. 
 		getAdvSettingsPane().setParams(getParamsClone());
 		
+		return status;
+		
 	}
 
 }

src/rawDeepLearningClassifier/dlClassification/archiveModel/ArchiveModelWorker.java

@@ -22,7 +22,9 @@ import org.json.JSONObject;
 import PamUtils.PamArrayUtils;
 import PamView.dialog.warn.WarnOnce;
 import ai.djl.MalformedModelException;
+import ai.djl.engine.EngineException;
 import rawDeepLearningClassifier.DLControl;
+import rawDeepLearningClassifier.DLStatus;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
 import rawDeepLearningClassifier.dlClassification.genericModel.DLModelWorker;
 import rawDeepLearningClassifier.dlClassification.genericModel.GenericModelWorker;
@@ -62,7 +64,7 @@ public class ArchiveModelWorker extends GenericModelWorker {
 	 * Note it is important to put a synchonized here or the model loading can fail. 
 	 */
 	@Override
-	public synchronized void prepModel(StandardModelParams dlParams, DLControl dlControl) {
+	public synchronized DLStatus prepModel(StandardModelParams dlParams, DLControl dlControl) {
 		//ClassLoader origCL = Thread.currentThread().getContextClassLoader();
 		try {
 
@@ -92,8 +94,13 @@ public class ArchiveModelWorker extends GenericModelWorker {
 				//System.out.println(genericModel.getModel().getModelPath().getFileName()); 
 			}
 		}
+		catch (EngineException e) {
+			e.printStackTrace();
+			return DLStatus.MODEL_ENGINE_FAIL;
+		}
 		catch (Exception e) {
 			e.printStackTrace();
+			return DLStatus.MODEL_LOAD_FAIL;
 			//WarnOnce.showWarning(null, "Model Load Error", "There was an error loading the model file.", WarnOnce.OK_OPTION); 
 		}
 
@@ -188,8 +195,11 @@ public class ArchiveModelWorker extends GenericModelWorker {
 		catch (Exception e) {
 			dlModel=null; 
 			e.printStackTrace();
+			return DLStatus.MODEL_META_FAIL;
 			//WarnOnce.showWarning(null, "Model Metadata Error", "There was an error extracting the metadata from the model.", WarnOnce.OK_OPTION); 
 		}
+		
+		return DLStatus.MODEL_LOAD_SUCCESS;
 		//Thread.currentThread().setContextClassLoader(origCL);
 	}
 
@@ -200,7 +210,7 @@ public class ArchiveModelWorker extends GenericModelWorker {
 	 * @throws MalformedModelException
 	 * @throws IOException
 	 */
-	public ArchiveModel loadModel(String currentPath2) throws MalformedModelException, IOException {
+	public ArchiveModel loadModel(String currentPath2) throws MalformedModelException, IOException, EngineException {
 	
 		return new SimpleArchiveModel(new File(currentPath2)); 
 	}

src/rawDeepLearningClassifier/dlClassification/archiveModel/SimpleArchiveModel.java

@@ -12,6 +12,7 @@ import java.util.stream.Stream;
 import org.jamdev.jdl4pam.ArchiveModel;
 
 import ai.djl.MalformedModelException;
+import ai.djl.engine.EngineException;
 
 /**
  * A Tensorflow model packaged with a jar file. 
@@ -21,7 +22,7 @@ import ai.djl.MalformedModelException;
 public class SimpleArchiveModel extends ArchiveModel {
 
 
-	public SimpleArchiveModel(File file) throws MalformedModelException, IOException {
+	public SimpleArchiveModel(File file) throws MalformedModelException, IOException, EngineException {
 		super(file);
 	}
 

src/rawDeepLearningClassifier/dlClassification/delphinID/Clicks2Spectrum.java

@@ -1,7 +1,9 @@
 package rawDeepLearningClassifier.dlClassification.delphinID;
 
 import java.util.ArrayList;
+import java.util.Arrays;
 
+import org.jamdev.jpamutils.JamArr;
 import org.jamdev.jpamutils.spectrum.Spectrum;
 
 import PamUtils.PamArrayUtils;
@@ -18,14 +20,14 @@ public class Clicks2Spectrum {
 	private Spectrum spectrum;
 
 
-	public Clicks2Spectrum(SegmenterDetectionGroup clickGroup, int fftLen) {
-		this.spectrum = clicks2Spectrum(clickGroup.getSubDetections(), clickGroup.getSampleRate(), fftLen);
+	public Clicks2Spectrum(SegmenterDetectionGroup clickGroup, int fftLen, boolean spectradB, boolean hann) {
+		this.spectrum = clicks2Spectrum(clickGroup.getSubDetections(), clickGroup.getSampleRate(), fftLen, spectradB, hann);
 	}
 	
 	
 
 	public Clicks2Spectrum(SegmenterDetectionGroup clickGroup, Clks2SpectrumParams transformParams) {
-		this(clickGroup, transformParams.fftLength); 
+		this(clickGroup, transformParams.fftLength, transformParams.spectrumdB, transformParams.hann); 
 	}
 
 
@@ -37,9 +39,10 @@ public class Clicks2Spectrum {
 	 * @param seglen - the segment length in seconds. 
 	 * @param freqLimits - the frequency limits for the spectrum. 
 	 * @param minFragSize - the minimum fragment length in seconds. 
+	 * @param spectrumdB - true to average the log spectra instead of linear spectra
 	 * @return the average spectrum. 
 	 */
-	public static Spectrum clicks2Spectrum(ArrayList<? extends PamDataUnit> arrayList, float sampleRate, int fftLen) {
+	public static Spectrum clicks2Spectrum(ArrayList<? extends PamDataUnit> arrayList, float sampleRate, int fftLen, boolean spectrumdB, boolean hann) {
 
 
 		//create an average spectrum
@@ -47,19 +50,46 @@ public class Clicks2Spectrum {
 		double[] fftClk;
 		for (int i=0;i<arrayList.size(); i++) {
 			
-			fftClk = ((RawDataHolder) arrayList.get(i)).getDataTransforms().getPowerSpectrum(0, fftLen); 
+			fftClk = ((RawDataHolder) arrayList.get(i)).getDataTransforms().getPowerSpectrum(0, fftLen, hann); 
 			
 //			 ComplexArray arr = RawDataTransforms.getComplexSpectrumHann(((RawDataHolder) arrayList.get(i)).getWaveData()[0], fftLen); 
 			
-//			PamArrayUtils.printArray(arr.getReal()); 
+//			PamArrayUtils.printArray(arr.getReal()); 			
+//			System.out.println("fft len: " + i + "  " + fftAverage.length + " " +  fftClk.length); 
+			
+			if (spectrumdB) {
+				double fftClkdB = 0 ;
+				
+				//important otherwise we alter the power spectrum stored within the click
+				fftClk = Arrays.copyOf(fftClk, fftClk.length);
+
+				//convert to log
+				for (int j=0; j<fftClk.length; j++) {
+					if (spectrumdB) {
+						fftClkdB=20*Math.log10(fftClk[j]); 
+					}
+					else {
+						fftClkdB=fftClk[j]; 
+					}
+//					if (Double.isNaN(fftClkdB)) {
+//						System.out.println("Point is NaN: " + i + "  " + j + "  " + fftClk[j]); 
+//					}
+					fftClk[j] = fftClkdB; 
+				}
+				
+			}
 
 			fftAverage = PamArrayUtils.sum(fftAverage, fftClk); 
 		}
 		
+//		System.out.println(fftAverage + " " +  arrayList.size()); 
+		
 		fftAverage = PamArrayUtils.divide(fftAverage, arrayList.size());
 		
-		Spectrum spectrum = new Spectrum(fftAverage, null, sampleRate);
+		fftAverage = JamArr.subtract(fftAverage, JamArr.min(fftAverage)); 
 
+		
+		Spectrum spectrum = new Spectrum(fftAverage, null, sampleRate);
 
 		return spectrum; 
 	}
@@ -72,8 +102,21 @@ public class Clicks2Spectrum {
 	
 	public static class Clks2SpectrumParams extends DetectionGroupTransformParams {
 		
-		
+		/**
+		 * The FFT length in samples. 
+		 */
 		private int fftLength;
+		
+		/**
+		 * True to average log spectra instead of linear spectra
+		 */
+		public boolean spectrumdB = true;
+
+		/**
+		 * True to apply a hanning window to click wave before FFT. 
+		 */
+		public boolean hann = false;
+
 		/**
 		 * Set the FFT length in samples
 		 * @param fftLength

src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDClassifier.java

@@ -10,16 +10,19 @@ import org.jamdev.jdl4pam.transforms.DLTransfromParams;
 import PamController.PamControlledUnitSettings;
 import PamController.PamSettingManager;
 import PamguardMVC.PamDataUnit;
+import clickDetector.ClickDetection;
 import rawDeepLearningClassifier.DLControl;
 import rawDeepLearningClassifier.dlClassification.DLClassiferModel;
 import rawDeepLearningClassifier.dlClassification.PredictionResult;
 import rawDeepLearningClassifier.dlClassification.StandardClassifierModel;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
+import rawDeepLearningClassifier.dlClassification.delphinID.DelphinIDParams.DelphinIDDataType;
 import rawDeepLearningClassifier.dlClassification.genericModel.DLModelWorker;
 import rawDeepLearningClassifier.dlClassification.genericModel.StandardPrediction;
 import rawDeepLearningClassifier.layoutFX.DLCLassiferModelUI;
 import rawDeepLearningClassifier.segmenter.SegmenterDetectionGroup;
 import whistlesAndMoans.ConnectedRegionDataBlock;
+import whistlesAndMoans.ConnectedRegionDataUnit;
 
 /**
  * A classifier based on the delphinID method which uses whistle contours to predict
@@ -39,10 +42,12 @@ public class DelphinIDClassifier extends StandardClassifierModel {
 
 	private DelphinIDWorker delphinIDWorker;
 
+	private  ArrayList<Class> allowedDataTypes; 
 
 	public DelphinIDClassifier(DLControl dlControl) {
 		super(dlControl);
 
+		allowedDataTypes = new ArrayList<Class>(); 
 		//load the previous settings
 		PamSettingManager.getInstance().registerSettings(this);
 	}
@@ -52,6 +57,13 @@ public class DelphinIDClassifier extends StandardClassifierModel {
 		return false;
 	}
 
+	@Override
+	public void prepModel() {
+		super.prepModel();
+		//set group detections to true. 
+		getDLControl().setGroupDetections(true);
+	}
+
 	@Override
 	public String getName() {
 		return "delphinID";
@@ -99,6 +111,8 @@ public class DelphinIDClassifier extends StandardClassifierModel {
 	public ArrayList<? extends PredictionResult> runModel(ArrayList<? extends PamDataUnit> groupedRawData) {
 
 		//add an extra test to see if the detection pre count has passed. 
+		
+		//System.out.println("Run delphinID model: " + (((SegmenterDetectionGroup) groupedRawData.get(0)).getSubDetectionsCount()) + " min clicks: " + delphinIDParams.minDetectionValue);
 
 		if (detectionPreFilter(groupedRawData)) {
 			return super.runModel(groupedRawData);
@@ -122,38 +136,41 @@ public class DelphinIDClassifier extends StandardClassifierModel {
 
 
 	private boolean clickPreFilter(ArrayList<? extends PamDataUnit> groupedRawData) {
-		//TODO
-//		System.out.println("Check CLICK fragment density"); 
-		return true;
+
+		if (((SegmenterDetectionGroup) groupedRawData.get(0)).getSubDetectionsCount()>=delphinIDParams.minDetectionValue) {
+			return true;
+		}
+
+		return false;
 	}
 
 	private boolean whistlePreFilter(ArrayList<? extends PamDataUnit> groupedRawData) {
 		//TODO
-//		System.out.println("Check WHISTLE fragment density"); 
-//		
-//		3.	Within each time frame, the density of detection is calculated and used as a filter.
-//		-	Density 2
-//		-	where the length of frame = frame duration / mean time step across contour
-//		(time steps between time-frequency points in contour saved by ROCCA depend on FFT resolution but can vary slightly within contour)
-//
-//		4.	If a detection frame has less than 0.30 detection density, it is not used for classification
-//		double density = DelphinIDUtils.getDensity(null); 
-		
+		//		System.out.println("Check WHISTLE fragment density"); 
+		//		
+		//		3.	Within each time frame, the density of detection is calculated and used as a filter.
+		//		-	Density 2
+		//		-	where the length of frame = frame duration / mean time step across contour
+		//		(time steps between time-frequency points in contour saved by ROCCA depend on FFT resolution but can vary slightly within contour)
+		//
+		//		4.	If a detection frame has less than 0.30 detection density, it is not used for classification
+		//		double density = DelphinIDUtils.getDensity(null); 
+
 
 		if (groupedRawData==null || groupedRawData.size()<1) {
 			System.err.println("DelphinIDClassifier: + the grouped raw data is null or zero size:"); 
 			return false;
 		}
-		
-		System.out.println("Run DelphinID model: " +  groupedRawData.size() + " min density: " + delphinIDParams.minDetectionDensity); 
-		
+
+		//System.out.println("Run DelphinID model: " +  groupedRawData.size() + " min density: " + delphinIDParams.minDetectionValue); 
+
 		double density = DelphinIDUtils.getDensity((SegmenterDetectionGroup) groupedRawData.get(0)); 
-		
-		if (density>=delphinIDParams.minDetectionDensity) {
+
+		if (density>=delphinIDParams.minDetectionValue) {
 			return true;
 		}
-		
-		return true;
+
+		return false;
 	}
 
 	@Override
@@ -206,4 +223,21 @@ public class DelphinIDClassifier extends StandardClassifierModel {
 
 	}
 
+
+	@Override
+	public ArrayList<Class> getAllowedDataTypes(){
+		allowedDataTypes.clear();
+
+		if (delphinIDParams.getDataType()==DelphinIDDataType.CLICKS) {
+			allowedDataTypes.add(ClickDetection.class);
+		}
+
+		if (delphinIDParams.getDataType()==DelphinIDDataType.WHISTLES) {
+			allowedDataTypes.add(ConnectedRegionDataUnit.class);
+		}
+
+		return allowedDataTypes;
+	}
+
+
 }

src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDClickTest.java

@@ -10,32 +10,49 @@ import org.jamdev.jdl4pam.utils.DLMatFile;
 import org.jamdev.jpamutils.spectrum.Spectrum;
 
 import PamUtils.PamArrayUtils;
+import PamUtils.PamCalendar;
 import PamguardMVC.PamDataUnit;
 import ai.djl.MalformedModelException;
 import rawDeepLearningClassifier.dlClassification.archiveModel.SimpleArchiveModel;
 import rawDeepLearningClassifier.dlClassification.delphinID.DelphinIDUtils.ClickDetectionMAT;
 import rawDeepLearningClassifier.dlClassification.delphinID.DelphinIDUtils.DetectionGroupMAT;
+import rawDeepLearningClassifier.dlClassification.delphinID.DelphinIDWhistleTest.DelphinIDWorkerTest;
+import rawDeepLearningClassifier.dlClassification.genericModel.StandardPrediction;
 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;
 
 public class DelphinIDClickTest {
 
 	public static void main(String args[]) {
 
-		//test a single segment. 
+		//		String clicksMatPath = "/Users/au671271/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/delphinIDmodels/Ggr242/clicks_20200918_123234.mat";
+		//		String modelFile = "/Users/au671271/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/delphinIDmodels/Ggr242/clickclassifier.zip";
+
+		String clicksMatPath = "/Users/jdjm/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/delphinIDmodels/Ggr242/clicks_20200918_123234_classified.mat";
+		String modelFile = "/Users/jdjm/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/delphinIDmodels/Ggr242/clickClassifier_Jan25.zip";
+		String matclickSave = "/Users/jdjm/MATLAB-Drive/MATLAB/PAMGUARD/deep_learning/delphinID/click1D/click_spectrums.mat";
+
+		DetectionGroupMAT<ClickDetectionMAT> clicks = DelphinIDUtils.getClicksMAT(clicksMatPath);
+
+		//		-----basic tests to check transforms----
+		//		test a single segment. 
 		String matFileout = "/Users/jdjm/MATLAB-Drive/MATLAB/PAMGUARD/deep_learning/delphinID/click1D/clickspectrum.mat";
+		float[][] output = testDelphinIDClickSegment(matFileout, clicks); 
 
-		//
-		float[][] output = testDelphinIDClickSegment(matFileout); 
+		//		//produce a set of sequntial transforms
+		//		String matFileout3 = "/Users/jdjm/MATLAB-Drive/MATLAB/PAMGUARD/deep_learning/delphinID/click1D/click_transform_example.mat";
+		//		exportDelphinIDClickTransforms( matFileout3,  clicks, 37); 
 
-		//run the model
-		String modelFile = "/Users/jdjm/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/delphinIDmodels/Ggr242/clickclassifier.zip";
+		//		//run the model
+		String matFileout2 = "/Users/jdjm/MATLAB-Drive/MATLAB/PAMGUARD/deep_learning/delphinID/click1D/clickspectrum_results_predictions.mat";
+		float[][] predictions = testDelphinIDClickModel(modelFile, clicks, matFileout2); 
 
-		String matFileout2 = "/Users/jdjm/MATLAB-Drive/MATLAB/PAMGUARD/deep_learning/delphinID/click1D/clickspectrum_results.mat";
 
-		testDelphinIDModelRaw(modelFile, output, matFileout2); 
+		//---complete test on the model and transforms------
+		//		float[][] results = testDelphinIDClickModel( modelFile,	clicks, matclickSave ); 
 
 	}
 
@@ -46,15 +63,138 @@ public class DelphinIDClickTest {
 	/*
 	/****------------------------------------------------------------------****/
 
+	public static float[][] testDelphinIDClickModel(String modelPath,	DetectionGroupMAT<ClickDetectionMAT> clicks, String matClickSave) {
+		return testDelphinIDClickModel( modelPath,	clicks,  matClickSave,  true);
+	}
 
 
-	private static void testDelphinIDModelRaw(String modelPath, float[][] rawModelInput, String matFileout) {
+
+	public static float[][] testDelphinIDClickModel(String modelPath,	DetectionGroupMAT<ClickDetectionMAT> clicks, String matClickSave, boolean verbose) {
+
+		Path path = Paths.get(modelPath);
+
+
+		double segLen = 4000.;
+		double segHop = 1000.0;
+		int minclicks = 3; 
+
+		//load the model
+		try {
+
+			//now feed segments directly into model to test. 
+
+			float sampleRate = (float) clicks.getSampleRate();
+			long dataStartMillis = clicks.getFileDataStart();
+
+
+			Float hardSampleRate = Float.valueOf((float) clicks.sampleRate); 
+			//segment the whistle detections
+			//Note, delphinID starts from the first whistle and NOT the first file. 
+			ArrayList<SegmenterDetectionGroup> segments  = DelphinIDUtils.segmentDetectionData(clicks.getDetections(), dataStartMillis, segLen,  segHop, hardSampleRate);
+
+			if (verbose) {
+				for (int i=0; i<segments.size(); i++) {
+					System.out.println("Segment " + i + " contains " + segments.get(i).getSubDetectionsCount() + " clicks"); 
+				}
+			}
+
+			//prepare the model - this loads the zip file and loads the correct transforms. 
+			DelphinIDWorkerTest model = DelphinIDUtils.prepDelphinIDModel(path.toAbsolutePath().toString());
+			model.setEnableSoftMax(false);
+
+
+			//initialise strcuture for image data
+			Struct imageStruct = Mat5.newStruct(segments.size(), 1);
+
+			float[][] outputs = new float[segments.size()][];
+
+			for (int i=0; i<segments.size(); i++) {
+
+				//remember that the input is a stack of detections to be run by the model at once - Here we want to do each one individually. 
+				ArrayList<SegmenterDetectionGroup> aSegment = new  ArrayList<SegmenterDetectionGroup>();
+
+				aSegment.add(segments.get(i)); 
+
+				if (segments.get(i).getSubDetectionsCount()>=minclicks) {
+					//the prediction. 
+					ArrayList<StandardPrediction> predicition = model.runModel(aSegment, sampleRate, 1);		
+
+					float[] output =  predicition.get(0).getPrediction();
+
+					if (verbose) {
+						System.out.print(String.format("Segment: %d no. clicks %d %.4f s" , i , segments.get(i).getSubDetectionsCount() ,((aSegment.get(0).getSegmentStartMillis()-dataStartMillis)/1000.)));
+						for (int j=0; j<output.length; j++) {
+							System.out.print(String.format( " %.4f" , output[j])); 
+						}
+						System.out.println();
+					}
+					
+					
+					outputs[i]=output;
+
+
+					Matrix modelinput = DLMatFile.array2Matrix(PamArrayUtils.float2Double(model.getLastModelInput()[0]));
+					imageStruct.set("modelinput", i, modelinput);
+					imageStruct.set("startmillis", i, Mat5.newScalar(aSegment.get(0).getSegmentStartMillis()));
+					imageStruct.set("startseconds", i, Mat5.newScalar((aSegment.get(0).getSegmentStartMillis()-dataStartMillis)/1000.));
+					imageStruct.set("prediction", i, DLMatFile.array2Matrix(PamArrayUtils.float2Double(output)));
+				}
+				else {
+					if (verbose) {
+					System.out.println(String.format("Segment: %d no. clicks %d %s" , i , segments.get(i).getSubDetectionsCount(), "-------------------------"));
+					}
+				}
+
+				imageStruct.set("nclicks", i, Mat5.newScalar(segments.get(i).getSubDetectionsCount()));
+				
+
+			}
+
+			//create MatFile for saving the image data to. 
+			MatFile matFile = Mat5.newMatFile();
+			matFile.addArray("click_model_inputs", imageStruct);
+
+			if (matClickSave!=null) {
+				// Serialize to disk using default configurations
+				try {
+					Mat5.writeToFile(matFile,matClickSave);
+				} catch (IOException e) {
+					// TODO Auto-generated catch block
+					e.printStackTrace();
+				}
+			}
+
+			return outputs;
+
+
+		} catch (Exception e) {
+			// TODO Auto-generated catch block
+			e.printStackTrace();
+		}
+
+
+		return null;
+
+	}
+
+
+
+
+	/**
+	 * Run the click classifier model on a set of predictions. 
+	 * @param modelPath - the model path to load. 
+	 * @param rawModelInput - the input into the model where each row is one input. 
+	 * @return predictions with the same number of rows as the input. 
+	 */
+	private static float[][] testDelphinIDModelRaw(String modelPath, float[][] rawModelInput) {
 		// TODO Auto-generated method stub
 
 		Path path = Paths.get(modelPath);
 
 		//load the model
 		SimpleArchiveModel model;
+
+		float[][] predictions = new float[rawModelInput.length][];
 		try {
 			model = new SimpleArchiveModel(new File(path.toString()));
 
@@ -63,14 +203,113 @@ public class DelphinIDClickTest {
 				float[][] input = new float[1][];
 				input[0] = rawModelInput[i];
 				outputJava = model.runModel(input);
-				
-				System.out.println("Click spectrum output Java: "); 
-				PamArrayUtils.printArray(outputJava);
-			}
-			
-	
+				predictions[i] = outputJava;
 
-		} catch (MalformedModelException | IOException e) {
+				System.out.print(i+" :"); 
+
+				PamArrayUtils.printArray(outputJava, true);
+				System.out.println("");
+			}
+
+			return predictions;
+
+		} 
+		catch (MalformedModelException | IOException e) {
+			// TODO Auto-generated catch block
+			e.printStackTrace();
+			return null;
+		}
+	}
+
+
+	public static void exportDelphinIDClickTransforms(String matFileout, DetectionGroupMAT<ClickDetectionMAT> clicks, int second ) {
+		//		String clicksMatPath = "/Users/jdjm/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/delphinIDmodels/Ggr242/clicks_20200918_123234.mat";
+
+		double segLen = 4000.;
+		double segHop = 1000.;
+		//		double startSeconds = 1.50355; //seconds to start segments (so we can compare to Python)
+		double startSeconds = 0; //seconds to start segments (so we can compare to Python)
+		//		double[] freqLimits = new double[] {10000., 40188};
+		double[] freqLimits = new double[] {10000., 40000};
+		int fftLen = 512; 
+
+		// Create MAT file with a scalar in a nested struct
+		MatFile matFile;
+		int sum = 0;
+
+		System.out.println("Total clicks: " + clicks.getDetections().size() + " First click: " + (clicks.getDetections().get(0).getStartSample()/clicks.getSampleRate()) + "s");
+
+
+		long dataStartMillis = clicks.getFileDataStart();
+
+		dataStartMillis = (long) (dataStartMillis+(startSeconds*1000.));
+
+		//split the clicks into segments
+		ArrayList<SegmenterDetectionGroup> segments  = DelphinIDUtils.segmentDetectionData(clicks.getDetections(), dataStartMillis, segLen, segHop);
+
+
+		Struct clkStruct = Mat5.newStruct(1, segments.size());
+
+		for (int i=0; i<segments.size(); i++) {
+
+
+			sum += segments.get(i).getSubDetectionsCount() ;
+
+			ArrayList<PamDataUnit<?, ?>> clicksSeg = segments.get(i).getSubDetections();
+			Spectrum spectrum  = Clicks2Spectrum.clicks2Spectrum(clicksSeg, (float) clicks.getSampleRate(), fftLen, true, false); 
+			clkStruct.set("averagespectrum", i, DLMatFile.array2Matrix(spectrum.getRealSpectrum())); 
+			clkStruct.set("nclks", i, Mat5.newScalar(clicksSeg.size()));
+			clkStruct.set("startseconds", i, Mat5.newScalar(segments.get(i).getStartSecond())); 
+			clkStruct.set("minfreq", i, Mat5.newScalar(freqLimits[0])); 
+			clkStruct.set("maxfreq", i, Mat5.newScalar(freqLimits[1])); 
+
+
+
+
+			//System.out.println("Segment average" + i + " time:  " +  (double)(segments.get(i).getSegmentStartMillis()-dataStartMillis)/1000.  + "s no. clicks " + segments.get(i).getSubDetectionsCount() + " total: " + sum + "  spectrum " + spectrum.length() + "  " + spectrum.getRealSpectrum()[0]);
+
+			//convert to dB
+			//spectrum = spectrum.spectrumdB(true);
+
+			//System.out.println("Segment dB" + i + " time:  " +  (double)(segments.get(i).getSegmentStartMillis()-dataStartMillis)/1000.  + "s no. clicks " + segments.get(i).getSubDetectionsCount() + " total: " + sum + "  spectrum " + spectrum.length() + "  " + spectrum.getRealSpectrum()[0]);
+
+			//smooth spectrum
+			spectrum = spectrum.smoothSpectrum(3);
+			clkStruct.set("smoothspectrum", i, DLMatFile.array2Matrix(spectrum.getRealSpectrum())); 
+
+
+			//System.out.println("Segment smooth" + i + " time:  " +  (double)(segments.get(i).getSegmentStartMillis()-dataStartMillis)/1000.  + "s no. clicks " + segments.get(i).getSubDetectionsCount() + " total: " + sum + "  spectrum " + spectrum.length() + "  " + spectrum.getRealSpectrum()[0]);
+
+			//trim spectrum
+			spectrum = spectrum.trimSpectrum(freqLimits);
+			clkStruct.set("trimspectrum", i, DLMatFile.array2Matrix(spectrum.getRealSpectrum())); 
+
+			//System.out.println("Segment trim" + i + " time:  " +  (double)(segments.get(i).getSegmentStartMillis()-dataStartMillis)/1000.  + "s no. clicks " + segments.get(i).getSubDetectionsCount() + " total: " + sum + "  spectrum " + spectrum.length() + "  " + spectrum.getRealSpectrum()[0]);
+
+
+			//down sample the spectrum.
+			spectrum.downSampleSpectrumMean(2);
+			clkStruct.set("downsamplespectrum", i,DLMatFile.array2Matrix(spectrum.getRealSpectrum())); 
+
+
+			//normalise spectrum row sum
+			spectrum = spectrum.normaliseSpectrumSum();
+			clkStruct.set("normalisespectrum", i, DLMatFile.array2Matrix(spectrum.getRealSpectrum())); 
+
+
+			System.out.println("Segment " + i + " time:  " +  (double)(segments.get(i).getSegmentStartMillis()-dataStartMillis)/1000.  + "s no. clicks " + segments.get(i).getSubDetectionsCount() + " total: " + sum + "  spectrum " + spectrum.length() + "  " + spectrum.getRealSpectrum()[0]);
+
+			clkStruct.set("transformedspectrum", i, DLMatFile.array2Matrix(spectrum.getRealSpectrum())); 
+
+		}
+
+		MatFile matFileWrite = Mat5.newMatFile()
+				.addArray("spectrumJava",clkStruct)
+				.addArray("filedate", Mat5.newScalar(PamCalendar.millistoDateNum(clicks.getFileDataStart())));
+
+		try {
+			Mat5.writeToFile(matFileWrite, matFileout);
+		} catch (IOException e) {
 			// TODO Auto-generated catch block
 			e.printStackTrace();
 		}
@@ -79,21 +318,22 @@ public class DelphinIDClickTest {
 
 
 	/**
-	 * This test runs delphinID on one 4 second window from whistle contours saved
+	 * This test runs delphinID on one 4 second window from clicks saved 
 	 * in a mat file. 
 	 * 
 	 * @return true if the test is passed. 
 	 */
-	public static float[][] testDelphinIDClickSegment(String matFileout) {
+	public static float[][] testDelphinIDClickSegment(String matFileout, DetectionGroupMAT<ClickDetectionMAT> clicks ) {
 
 
 		//		String clicksMatPath = "/Users/jdjm/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/delphinIDmodels/Ggr242/clicks_20200918_123234.mat";
-		String clicksMatPath = "/Users/jdjm/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/delphinIDmodels/Ggr242/clicks_20200918_123234.mat";
 
 		double segLen = 4000.;
-		double segHop = 1000.0;
-		double startSeconds = 1.50355; //seconds to start segments (so we can compare to Python)
-		double[] freqLimits = new double[] {10000., 40188.};
+		double segHop = 1000.;
+		//		double startSeconds = 1.50355; //seconds to start segments (so we can compare to Python)
+		double startSeconds = 0; //seconds to start segments (so we can compare to Python)
+		double[] freqLimits = new double[] {10000., 40000};
+		//		double[] freqLimits = new double[] {10000., 40000};
 		int fftLen = 512; 
 
 		// Create MAT file with a scalar in a nested struct
@@ -101,7 +341,6 @@ public class DelphinIDClickTest {
 		int sum = 0;
 		try {
 
-			DetectionGroupMAT<ClickDetectionMAT> clicks = DelphinIDUtils.getClicksMAT(clicksMatPath);
 			System.out.println("Total clicks: " + clicks.getDetections().size() + " First click: " + (clicks.getDetections().get(0).getStartSample()/clicks.getSampleRate()) + "s");
 
 
@@ -110,7 +349,7 @@ public class DelphinIDClickTest {
 			dataStartMillis = (long) (dataStartMillis+(startSeconds*1000.));
 
 			//split the clicks into segments
-			ArrayList<SegmenterDetectionGroup> segments  = DelphinIDUtils.segmentDetectionData(clicks.getDetections(), dataStartMillis, segLen,  segHop);
+			ArrayList<SegmenterDetectionGroup> segments  = DelphinIDUtils.segmentDetectionData(clicks.getDetections(), dataStartMillis, segLen, segHop);
 
 
 			Struct clkStruct = Mat5.newStruct(1, segments.size());
@@ -121,33 +360,36 @@ public class DelphinIDClickTest {
 				sum += segments.get(i).getSubDetectionsCount() ;
 
 				ArrayList<PamDataUnit<?, ?>> clicksSeg = segments.get(i).getSubDetections();
-				Spectrum spectrum  = Clicks2Spectrum.clicks2Spectrum(clicksSeg, (float) clicks.getSampleRate(), fftLen); 
+				Spectrum spectrum  = Clicks2Spectrum.clicks2Spectrum(clicksSeg, (float) clicks.getSampleRate(), fftLen, true, false); 
 				clkStruct.set("averagespectrum", i, DLMatFile.array2Matrix(spectrum.getRealSpectrum())); 
-				clkStruct.set("nclks", i, Mat5.newScalar(clicksSeg.size())); 
-				
+				clkStruct.set("nclks", i, Mat5.newScalar(clicksSeg.size()));
+				clkStruct.set("startseconds", i, Mat5.newScalar(segments.get(i).getStartSecond())); 
+
+
 				//System.out.println("Segment average" + i + " time:  " +  (double)(segments.get(i).getSegmentStartMillis()-dataStartMillis)/1000.  + "s no. clicks " + segments.get(i).getSubDetectionsCount() + " total: " + sum + "  spectrum " + spectrum.length() + "  " + spectrum.getRealSpectrum()[0]);
 
 				//convert to dB
-				spectrum = spectrum.spectrumdB(true);
-				
+				//spectrum = spectrum.spectrumdB(true);
+
 				//System.out.println("Segment dB" + i + " time:  " +  (double)(segments.get(i).getSegmentStartMillis()-dataStartMillis)/1000.  + "s no. clicks " + segments.get(i).getSubDetectionsCount() + " total: " + sum + "  spectrum " + spectrum.length() + "  " + spectrum.getRealSpectrum()[0]);
 
 				//smooth spectrum
 				spectrum = spectrum.smoothSpectrum(3);
-				
+
 				//System.out.println("Segment smooth" + i + " time:  " +  (double)(segments.get(i).getSegmentStartMillis()-dataStartMillis)/1000.  + "s no. clicks " + segments.get(i).getSubDetectionsCount() + " total: " + sum + "  spectrum " + spectrum.length() + "  " + spectrum.getRealSpectrum()[0]);
 
 				//trim spectrum
 				spectrum = spectrum.trimSpectrum(freqLimits);
-				
+
 				//System.out.println("Segment trim" + i + " time:  " +  (double)(segments.get(i).getSegmentStartMillis()-dataStartMillis)/1000.  + "s no. clicks " + segments.get(i).getSubDetectionsCount() + " total: " + sum + "  spectrum " + spectrum.length() + "  " + spectrum.getRealSpectrum()[0]);
 
-				//normalise spectrum row sum
-				spectrum = spectrum.normaliseSpectrumSum();
 
 				//down sample the spectrum.
 				spectrum.downSampleSpectrumMean(2);
 
+				//normalise spectrum row sum
+				spectrum = spectrum.normaliseSpectrumSum();
+
 				System.out.println("Segment " + i + " time:  " +  (double)(segments.get(i).getSegmentStartMillis()-dataStartMillis)/1000.  + "s no. clicks " + segments.get(i).getSubDetectionsCount() + " total: " + sum + "  spectrum " + spectrum.length() + "  " + spectrum.getRealSpectrum()[0]);
 
 				clkStruct.set("transformedspectrum", i, DLMatFile.array2Matrix(spectrum.getRealSpectrum())); 
@@ -164,7 +406,7 @@ public class DelphinIDClickTest {
 			if (matFileout!=null){
 				MatFile matFileWrite = Mat5.newMatFile()
 						.addArray("spectrumJava",clkStruct)
-						.addArray("file", Mat5.newString(clicksMatPath)); 
+						.addArray("filedate", Mat5.newScalar(PamCalendar.millistoDateNum(clicks.getFileDataStart())));
 
 				Mat5.writeToFile(matFileWrite, matFileout);
 			}

src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDPane.java

@@ -10,6 +10,8 @@ import javafx.scene.control.ContentDisplay;
 import javafx.scene.control.Label;
 import javafx.scene.control.Slider;
 import javafx.scene.control.Spinner;
+import javafx.scene.control.SpinnerValueFactory;
+import javafx.scene.control.SpinnerValueFactory.DoubleSpinnerValueFactory;
 import javafx.scene.control.Tooltip;
 import javafx.scene.layout.Pane;
 import javafx.scene.paint.Color;
@@ -21,6 +23,7 @@ import pamViewFX.fxNodes.PamHBox;
 import pamViewFX.fxNodes.PamSpinner;
 import pamViewFX.fxNodes.PamVBox;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelPane;
+import rawDeepLearningClassifier.dlClassification.delphinID.DelphinIDParams.DelphinIDDataType;
 
 /**
  * Settings pane for delphin ID. 
@@ -48,6 +51,14 @@ public class DelphinIDPane extends SettingsPane<DelphinIDParams> {
 
 	private File currentSelectedFile;
 
+	private Label detectionDensity;
+
+	private DoubleSpinnerValueFactory whislteValueFactory;
+
+	private DoubleSpinnerValueFactory clickValueFactory;
+
+	private Label minDensityLabel;
+
 	public DelphinIDPane(DelphinIDClassifier delphinUIClassifier) {
 		super(null);
 		this.delphinUIClassifier = delphinUIClassifier; 
@@ -75,13 +86,17 @@ public class DelphinIDPane extends SettingsPane<DelphinIDParams> {
 
 		PamVBox vBox = new PamVBox(); 
 		vBox.setSpacing(5.);
+		
+		clickValueFactory = new SpinnerValueFactory.DoubleSpinnerValueFactory(1., Double.MAX_VALUE, 5., 1.);
+		whislteValueFactory = new SpinnerValueFactory.DoubleSpinnerValueFactory(0.0, 1.0, 0.3, 0.1);
 
 		/**Classification thresholds etc to set.**/
-		Label detectionDensity = new Label("Detection Density"); 
+		detectionDensity = new Label("Detection Density"); 
 		detectionDensity.setFont(font);
 		String tooltip = "Set the minimum detection density to attempt to classify.";
 		detectionDensity.setTooltip(new Tooltip(tooltip));
-		detectionDensitySpinner = new PamSpinner<Double>(0.0, 1.0, 0.3, 0.1);
+		detectionDensitySpinner = new PamSpinner<Double>();
+		detectionDensitySpinner.setValueFactory(whislteValueFactory);
 		detectionDensitySpinner.setPrefWidth(70);
 		detectionDensitySpinner.setEditable(true);
 		detectionDensitySpinner.getStyleClass().add(Spinner.STYLE_CLASS_SPLIT_ARROWS_HORIZONTAL);
@@ -89,8 +104,8 @@ public class DelphinIDPane extends SettingsPane<DelphinIDParams> {
 		PamHBox minDensityHolder = new PamHBox();
 		minDensityHolder.setAlignment(Pos.CENTER_RIGHT);
 		minDensityHolder.setSpacing(5);
-		Label minDensity = new Label("Min. detectection density");
-		minDensityHolder.getChildren().addAll(minDensity, detectionDensitySpinner);
+		minDensityLabel = new Label("Min. detectection density");
+		minDensityHolder.getChildren().addAll(minDensityLabel, detectionDensitySpinner);
 
 		/**Classification thresholds etc to set.**/
 		Label classiferInfoLabel2 = new Label("Decision Threshold"); 
@@ -117,7 +132,7 @@ public class DelphinIDPane extends SettingsPane<DelphinIDParams> {
 	@Override
 	public DelphinIDParams getParams(DelphinIDParams currParams) {
 		currParams.threshold = decisionSlider.getValue();
-		currParams.minDetectionDensity = detectionDensitySpinner.getValue();
+		currParams.minDetectionValue = detectionDensitySpinner.getValue();
 		return currParams;
 	}
 
@@ -125,7 +140,28 @@ public class DelphinIDPane extends SettingsPane<DelphinIDParams> {
 	public void setParams(DelphinIDParams input) {
 		this.currentParams = input;
 		decisionSlider.setValue(input.threshold);
-		detectionDensitySpinner.getValueFactory().setValue(input.minDetectionDensity);
+		detectionDensitySpinner.getValueFactory().setValue(input.minDetectionValue);
+				
+		if (input.getDataType()==null) {
+			input.dataType = DelphinIDDataType.WHISTLES;
+		}
+		
+		//set the correct label and minimum detection value
+		switch (input.getDataType()) {
+		case CLICKS:
+			this.minDensityLabel.setText("Minimum no. clicks");
+			minDensityLabel.setTooltip(new Tooltip("Set the minimum number of clicks before a segment is classified"));
+			detectionDensitySpinner.setValueFactory(clickValueFactory);
+			break;
+		case WHISTLES:
+			minDensityLabel.setTooltip(new Tooltip("Set the minimum  whistle density before a segment is classified"));
+			this.minDensityLabel.setText("Minimum whistle density");
+			detectionDensitySpinner.setValueFactory(whislteValueFactory);
+			break;
+		default:
+			break;
+		
+		}
 
 		if (input.modelPath!=null) {
 			//this might 

src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDParams.java

@@ -3,16 +3,36 @@ package rawDeepLearningClassifier.dlClassification.delphinID;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
 
 public class DelphinIDParams extends StandardModelParams {
-
+	
+	/**
+	 * Allowed data input types for DelphinID. 
+	 */
+	public static enum DelphinIDDataType {CLICKS, WHISTLES}
+	
+	/**
+	 * The allowed input data type. 
+	 */
+	public DelphinIDDataType dataType = DelphinIDDataType.WHISTLES; 
+	
 	/**
 	 * 
 	 */
 	private static final long serialVersionUID = 1L;
 	
 	/**
-	 * The minimum detection density. 
+	 * The minimum detection density for whisltes and minimum number of clicks for clicks before
+	 * a classification is attempted. 
 	 */
-	public double minDetectionDensity = 0.3;
+	public double minDetectionValue = 0.3;
+	
+	
+	/**
+	 * Get the allowed input data type for the delphinID classifier. 
+	 * @return the allowed data type. 
+	 */
+	public DelphinIDDataType getDataType() {
+		return dataType;
+	}
 	
 
 }

src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDTransform.java

@@ -7,6 +7,7 @@ import org.json.JSONArray;
 import org.json.JSONObject;
 
 import rawDeepLearningClassifier.dlClassification.delphinID.Clicks2Spectrum.Clks2SpectrumParams;
+import rawDeepLearningClassifier.dlClassification.delphinID.DelphinIDParams.DelphinIDDataType;
 import rawDeepLearningClassifier.dlClassification.delphinID.Whistles2Image.Whistle2ImageParams;
 import rawDeepLearningClassifier.dlClassification.delphinID.Whsitle2Spectrum.Whistle2spectrumParams;
 import rawDeepLearningClassifier.segmenter.SegmenterDetectionGroup;
@@ -49,6 +50,11 @@ public class DelphinIDTransform {
 	 */
 	public Object transformParams = null;
 
+	/**
+	 * The data type. 
+	 */
+	private DelphinIDDataType dataType;
+
 
 	public DelphinIDTransform( ) {
 
@@ -76,6 +82,7 @@ public class DelphinIDTransform {
 				transformParams = readWhistleImageTransform( jsonObjectParams);
 				
 				delphinIDTransformType = WHISTLE_IMAGE;
+				dataType= DelphinIDDataType.WHISTLES;
 
 				return true;
 			}
@@ -86,6 +93,7 @@ public class DelphinIDTransform {
 				transformParams = readWhistleSpectrumTransform( jsonObjectParams);
 				
 				delphinIDTransformType = WHISTLE_SPECTRUM;
+				dataType= DelphinIDDataType.WHISTLES;
 
 				return true;
 			}
@@ -96,7 +104,8 @@ public class DelphinIDTransform {
 
 				transformParams = readClickSpectrumTransform( jsonObjectParams);
 				
-				delphinIDTransformType = WHISTLE_SPECTRUM;
+				delphinIDTransformType = CLICK_SPECTRUM;
+				dataType= DelphinIDDataType.CLICKS;
 
 				return true;
 			}
@@ -110,14 +119,23 @@ public class DelphinIDTransform {
 	private Clks2SpectrumParams readClickSpectrumTransform(JSONObject jsonObjectParams ) {
 		Clks2SpectrumParams params = new Clks2SpectrumParams();
 
-		double[] freqLimits = new double[2]; 
-		freqLimits[0] = jsonObjectParams.getFloat("minfreq"); 
-		freqLimits[1] = jsonObjectParams.getFloat("maxfreq"); 
+//		double[] freqLimits = new double[2]; 
+//		freqLimits[0] = jsonObjectParams.getFloat("minfreq"); 
+//		freqLimits[1] = jsonObjectParams.getFloat("maxfreq"); 
 		
 		double minfragmillis = jsonObjectParams.getDouble("minclks"); 
-		
-		params.freqLimits = freqLimits;
+		int fftLen = jsonObjectParams.getInt("fft_len"); 
+		boolean spectrumdB = jsonObjectParams.getInt("spectrum_dB") == 1 ? true : false; 
+		boolean hann = false;
+		if (jsonObjectParams.has("hann")) {
+			hann = jsonObjectParams.getInt("hann") == 1 ? true : false;
+		}
+
+//		params.freqLimits = freqLimits;
 		params.minFragSize = minfragmillis;
+		params.spectrumdB = spectrumdB; 
+		params.hann = hann;
+		params.setFftLength(fftLen);
 		
 		return params; 
 	}
@@ -217,5 +235,9 @@ public class DelphinIDTransform {
 		
 	}
 
+	public DelphinIDDataType getDataType() {
+		return this.dataType;
+	}
+
 
 }

src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDUtils.java

@@ -34,6 +34,7 @@ import whistlesAndMoans.AbstractWhistleDataUnit;
 /**
  * A bunch of utility functions that a re useful for testing and running
  * DelphinID models
+ * 
  */
 public class DelphinIDUtils {
 
@@ -42,7 +43,7 @@ public class DelphinIDUtils {
 		//create the delphinID worker. 
 		DelphinIDWorkerTest delphinIDWorker = new DelphinIDWorkerTest();
 
-		StandardModelParams params = new StandardModelParams();
+		StandardModelParams params = new DelphinIDParams();
 		params.modelPath = modelPath;
 
 		//prepare the model
@@ -51,12 +52,8 @@ public class DelphinIDUtils {
 		return delphinIDWorker;
 	}
 	
-	
-	
 	//record WhistleGroup(ArrayList<AbstractWhistleDataUnit> whistle, double sampleRate, double fftLen, double fftHop, long fileDataStart) { }
 
-	
-	
 	/**
 	 * Holds a whistle group and some extra information on sample rate, fft length and hop and the start of the processed file. 
 	 * Keep Java 11 compliant so do not use record. 
@@ -166,7 +163,7 @@ public class DelphinIDUtils {
 	
 
 	/**
-	 * Load whistle contours from a MAT file. ()
+	 * Load whistle contours from a MAT file.
 	 * 
 	 * @param filePath - the file path. 
 	 * 
@@ -199,7 +196,7 @@ public class DelphinIDUtils {
 	
 	
 	/**
-	 * Load clicks from a MATLAB struct
+	 * Load clicks from a MATLAB struct.
 
 	 * @param clicksStruct - a struct containing a list of whistle contours
 	 * @param fftLen- the fft length in samples
@@ -229,7 +226,7 @@ public class DelphinIDUtils {
 
 
 	/**
-	 * Load whistle contours from a MAT file. ()
+	 * Load whistle contours from a MAT file.
 	 * 
 	 * @param filePath - the file path. 
 	 * 
@@ -268,8 +265,8 @@ public class DelphinIDUtils {
 	 * Load whistle contours from a MATLAB struct
 
 	 * @param whistlesStruct - a struct containing a list of whistle contours
-	 * @param fftLen- the fft length in samples
-	 * @param fftHop - the fft hop in samples. 
+	 * @param fftLen- the FFT length in samples
+	 * @param fftHop - the FFT hop in samples. 
 	 * @param sampleRate - the sample rate in samples per second. 
 	 * @return a list of whistle contour objects from the struct. 
 	 */
@@ -316,14 +313,14 @@ public class DelphinIDUtils {
 
 	/**
 	 * Segment the detections into groups. Note that segments are overlaps so each whistle may belong to  multiple segments. 
-	 * @param whistles - a list of whistles - not necessarily sorted by time. 
+	 * @param detections - a list of whistles - not necessarily sorted by time. 
 	 * @param dataStartMillis - the start time of the data in millis i.e. where the first segment starts. 
 	 * @param segLen - the segment size in milliseconds. 
 	 * @param segHop - the segment hop in milliseconds. 
 	 * @param sampleRate - the sample rate to set. 
 	 * @return groups of data units within each segment. 
 	 */
-	public static ArrayList<SegmenterDetectionGroup> segmentDetectionData(ArrayList<? extends PamDataUnit> whistles, long dataStartMillis, 
+	public static ArrayList<SegmenterDetectionGroup> segmentDetectionData(ArrayList<? extends PamDataUnit> detections, long dataStartMillis, 
 			double segLen, double segHop, Float sampleRate){
 
 		ArrayList<SegmenterDetectionGroup> group = new ArrayList<SegmenterDetectionGroup>(); 
@@ -331,33 +328,37 @@ public class DelphinIDUtils {
 		//find the maximum whistle time
 		long maxTime = Long.MIN_VALUE;
 		long endTime = 0; 
-		for (PamDataUnit whislte: whistles) {
-			endTime = (long) (whislte.getTimeMilliseconds()+whislte.getDurationInMilliseconds()); 
+		for (PamDataUnit det: detections) {
+			endTime = (long) (det.getTimeMilliseconds()+det.getDurationInMilliseconds()); 
 			if (endTime>maxTime) maxTime=endTime;
 		}
 
 		long segStart = dataStartMillis;
 		long segEnd = (long) (segStart+segLen);
 
-		long whistleStart; 
-		long whistleEnd;
+		long detStart; 
+		long detEnd;
 		AcousticDetectionGroup whistleGroup;
 		while (segStart<endTime){
 
 			whistleGroup = new AcousticDetectionGroup(segStart, 1, segEnd, segLen);
 			whistleGroup.setHardSampleRate(sampleRate);
+			whistleGroup.setStartSecond((segStart-dataStartMillis)/1000.);
 
-			for (PamDataUnit whislte: whistles) {
-				whistleStart = whislte.getTimeMilliseconds();
-				whistleEnd = (long) (whislte.getTimeMilliseconds() + whislte.getDurationInMilliseconds());
+			for (PamDataUnit aDet: detections) {
+				detStart = aDet.getTimeMilliseconds();
+				detEnd = (long) (aDet.getTimeMilliseconds() + aDet.getDurationInMilliseconds());
 
-				if ((whistleStart>=segStart && whistleStart<segEnd) || ((whistleEnd>=segStart && whistleEnd<segEnd))){
+				if ((detStart>=segStart && detStart<segEnd) || ((detEnd>=segStart && detEnd<segEnd))){
 					//some part of the whistle is in the segment. 
-					whistleGroup.addSubDetection(whislte);
+					whistleGroup.addSubDetection(aDet);
+					//System.out.println("segStart: " + segStart + " Click UID: " + aDet.getUID() + "  " + PamCalendar.formatDateTime2(aDet.getTimeMilliseconds(), "dd MMM yyyy HH:mm:ss.SSS", false) + " "  + aDet.getDurationInMilliseconds());
 				}
-
+				
 			}
 
+			//System.out.println("New click group: ----------------- " + whistleGroup.getSubDetectionsCount() + "  " + (segStart-dataStartMillis)/1000. + "s" + "  " + segEnd);
+
 			group.add(whistleGroup);
 
 			//			System.out.println("SegStart: " + (segStart - dataStartMillis));
@@ -367,7 +368,6 @@ public class DelphinIDUtils {
 		}
 
 		return group;
-
 	}
 	
 	
@@ -384,7 +384,7 @@ public class DelphinIDUtils {
 		@Override
 		public float getSampleRate() {
 			if (super.getParentDataBlock()==null) {
-			return hardSampleRate;
+				return hardSampleRate;
 			}
 			else {
 				return super.getSampleRate();
@@ -574,11 +574,8 @@ public class DelphinIDUtils {
 				if (listOfFiles[i].isDirectory()) {
 					//get a list of csv files
 					//					ArrayList<File> csvFiles = filelist.getFileList(listOfFiles[i].getAbsolutePath(), ".mat" , true);
-
 					System.out.println("Directory " + listOfFiles[i].getName());
 
-
-
 					try {
 
 						File file  = new File(listOfFiles[i].getPath() + File.separator + "whistles.mat");

src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDWhistleTest.java

@@ -50,7 +50,7 @@ public class DelphinIDWhistleTest {
 
 		
 		//0.21068583	0.28237167	0.07045266	0.1493272	0.041739468	0.04061936	0.2048038
-		String matout = "/Users/au671271/MATLAB-Drive/MATLAB/PAMGUARD/deep_learning/delphinID/whistle1D/whistle_spectrums.mat";
+		String matout = "/Users/jdjm/MATLAB-Drive/MATLAB/PAMGUARD/deep_learning/delphinID/whistle1D/whistle_spectrums.mat";
 		testDelphinIDSpectrumModel(matout);
 		
 
@@ -101,7 +101,7 @@ public class DelphinIDWhistleTest {
 	 * @param segHop             - the segment hop in samples
 	 * @return true if everything worked without throwing an error.
 	 */
-	public static boolean runWhistleModel(String modelPath, String whistleContourPath, String matImageSave, double startSeconds, double segLen, double segHop) {
+	public static float[][] runWhistleModel(String modelPath, String whistleContourPath, String matImageSave, double startSeconds, double segLen, double segHop) {
 		
 		//create MatFile for saving the image data to. 
 		MatFile matFile = Mat5.newMatFile();
@@ -126,9 +126,10 @@ public class DelphinIDWhistleTest {
 		DelphinIDWorkerTest model = DelphinIDUtils.prepDelphinIDModel(path.toAbsolutePath().toString());
 		model.setEnableSoftMax(false);
 
-
 		//initialise strcuture for image data
 		Struct imageStruct = Mat5.newStruct(segments.size(), 1);
+		
+		float[][] outputs = new float[segments.size()][];
 
 		for (int i=0; i<segments.size(); i++) {
 
@@ -137,22 +138,24 @@ public class DelphinIDWhistleTest {
 			aSegment.add(segments.get(i)); 
 
 			if (segments.get(i).getSubDetectionsCount()>0) {
-			//the prediction. 
-			ArrayList<StandardPrediction> predicition = model.runModel(aSegment, sampleRate, 1);		
+				//the prediction. 
+				ArrayList<StandardPrediction> predicition = model.runModel(aSegment, sampleRate, 1);		
 
-			float[] output =  predicition.get(0).getPrediction();
+				float[] output =  predicition.get(0).getPrediction();
 
-			System.out.println();
-			System.out.print(String.format("Segment: %d %.4f s" , i ,((aSegment.get(0).getSegmentStartMillis()-dataStartMillis)/1000.)));
-			for (int j=0; j<output.length; j++) {
-				System.out.print(String.format( " %.4f" , output[j])); 
-			}
+				System.out.println();
+				System.out.print(String.format("Segment: %d %.4f s" , i ,((aSegment.get(0).getSegmentStartMillis()-dataStartMillis)/1000.)));
+				for (int j=0; j<output.length; j++) {
+					System.out.print(String.format( " %.4f" , output[j])); 
+				}
 
-			Matrix modelinput = DLMatFile.array2Matrix(PamArrayUtils.float2Double(model.getLastModelInput()[0]));
-			imageStruct.set("modelinput", i, modelinput);
-			imageStruct.set("startmillis", i, Mat5.newScalar(aSegment.get(0).getSegmentStartMillis()));
-			imageStruct.set("startseconds", i, Mat5.newScalar((aSegment.get(0).getSegmentStartMillis()-dataStartMillis)/1000.));
-			imageStruct.set("prediction", i, DLMatFile.array2Matrix(PamArrayUtils.float2Double(output)));
+				Matrix modelinput = DLMatFile.array2Matrix(PamArrayUtils.float2Double(model.getLastModelInput()[0]));
+				imageStruct.set("modelinput", i, modelinput);
+				imageStruct.set("startmillis", i, Mat5.newScalar(aSegment.get(0).getSegmentStartMillis()));
+				imageStruct.set("startseconds", i, Mat5.newScalar((aSegment.get(0).getSegmentStartMillis()-dataStartMillis)/1000.));
+				imageStruct.set("prediction", i, DLMatFile.array2Matrix(PamArrayUtils.float2Double(output)));
+				
+				outputs[i] = output;
 			}
 
 		}
@@ -172,7 +175,7 @@ public class DelphinIDWhistleTest {
 		//			System.out.println("Whislte: " + i);
 		//			PamArrayUtils.printArray(whistleContours.get(i).getFreqsHz());
 		//		}
-		return true;
+		return outputs;
 	}
 
 	
@@ -205,9 +208,9 @@ public class DelphinIDWhistleTest {
 		String modelPath = "/Users/au671271/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/delphinIDmodels/Dde415/whistle_4s_415_model.zip";
 		//		String modelPath =  "./src/test/resources/rawDeepLearningClassifier/DelphinID/whistle_4s_415_model.zip";
 
-		boolean whistleOK = runWhistleModel( modelPath,  whistleContourPath,  matImageSave,  startSeconds,  segLen,  segHop);
+		float[][] outputs = runWhistleModel( modelPath,  whistleContourPath,  matImageSave,  startSeconds,  segLen,  segHop);
 		
-		return whistleOK;
+		return outputs!=null;
 	}
 	
 
@@ -387,16 +390,16 @@ public class DelphinIDWhistleTest {
 		long dataStartMillis = 1340212413000L;
 
 		//path to the .mat containing whistle contours. 
-		String whistleContourPath = "/Users/au671271/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/delphinIDmodels/Ggr242/whistle_contours_20200918_123234.mat";
+		String whistleContourPath = "/Users/jdjm/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/delphinIDmodels/Ggr242/whistle_contours_20200918_123234.mat";
 
 		//the path to the model
-		//String modelPath = "D:/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/testencounter415/whistle_model_2/whistle_4s_415.zip";
-		String modelPath = "/Users/au671271/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/delphinIDmodels/Ggr242/whistleclassifier.zip";
+		//String modelPath = "D:/Dropbox/jdjm/Deep_Learning/delphinID/testencounter415/whistle_model_2/whistle_4s_415.zip";
+		String modelPath = "/Users/jdjm/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/delphinIDmodels/Ggr242/whistleclassifier.zip";
 		//		String modelPath =  "./src/test/resources/rawDeepLearningClassifier/DelphinID/whistle_4s_415_model.zip";
 
-		boolean whistleOK = runWhistleModel( modelPath,  whistleContourPath,  matImageSave,  startSeconds,  segLen,  segHop);
+		float[][] outputs = runWhistleModel( modelPath,  whistleContourPath,  matImageSave,  startSeconds,  segLen,  segHop);
 		
-		return whistleOK;
+		return outputs!=null;
 	}
 	
 

src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDWorker.java

@@ -22,6 +22,7 @@ import PamUtils.PamArrayUtils;
 import PamguardMVC.PamDataUnit;
 import ai.djl.Model;
 import rawDeepLearningClassifier.DLControl;
+import rawDeepLearningClassifier.DLStatus;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
 import rawDeepLearningClassifier.dlClassification.archiveModel.ArchiveModelWorker;
 import rawDeepLearningClassifier.dlClassification.delphinID.Whistles2Image.Whistle2ImageParams;
@@ -60,14 +61,17 @@ public class DelphinIDWorker extends ArchiveModelWorker {
 
 
 	@Override
-	public void prepModel(StandardModelParams dlParams, DLControl dlControl) {
+	public DLStatus prepModel(StandardModelParams dlParams, DLControl dlControl) {
 		//most of the model prep is done in the perent class. 
-		super.prepModel(dlParams, dlControl);
+		DLStatus status = super.prepModel(dlParams, dlControl);
 				
 		//now have to read the whsitle2image transform to get correct parameters for that. 
 		String jsonString  = DLTransformsParser.readJSONString(new File(this.getModel().getAudioReprFile()));
 		
-		boolean transformOK = groupDataTransform.setJSONData(new JSONObject(jsonString)); 
+		JSONObject jsonObject = new JSONObject(jsonString); 
+		boolean transformOK = groupDataTransform.setJSONData(jsonObject); 
+		
+		((DelphinIDParams) dlParams).dataType = groupDataTransform.getDataType(); 
 
 		if (!transformOK) {
 			System.err.println("Error: could not find whsitle or click group transform in DelphinID JSON file. Model will not work.");
@@ -78,6 +82,8 @@ public class DelphinIDWorker extends ArchiveModelWorker {
 		for (int i=0; i<dlParams.numClasses; i++) {
 			dlParams.binaryClassification[i]=true;
 		}
+		
+		return status;
 	
 	}
 

src/rawDeepLearningClassifier/dlClassification/dummyClassifier/DummyClassifier.java

@@ -98,4 +98,10 @@ public class DummyClassifier implements DLClassiferModel{
 		return null;
 	}
 
+	@Override
+	public ArrayList<Class> getAllowedDataTypes() {
+		// TODO Auto-generated method stub
+		return null;
+	}
+
 }

src/rawDeepLearningClassifier/dlClassification/genericModel/DLModelWorker.java

@@ -13,6 +13,7 @@ import org.jamdev.jpamutils.wavFiles.AudioData;
 
 import PamguardMVC.PamDataUnit;
 import rawDeepLearningClassifier.DLControl;
+import rawDeepLearningClassifier.DLStatus;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
 import rawDeepLearningClassifier.segmenter.GroupedRawData;
 
@@ -136,6 +137,7 @@ public abstract class DLModelWorker<T> {
 			float[] prob; 
 			float[] classOut; 
 			for (int i=0; i<transformedDataStack.length; i++) {
+				
 				/**
 				 * This is super weird. Reading the documentation for copyOfRange the index from and index to are inclusive. So 
 				 * to copy the first two elements indexfrom =0 and indexto = 1. But actually it seems that this should be indexfrom =0 and indexto =2. 
@@ -143,7 +145,7 @@ public abstract class DLModelWorker<T> {
 				 */
 				classOut = Arrays.copyOfRange(output, i*numclasses, (i+1)*numclasses); 
 
-				//				System.out.println("The copyOfRange is: " + i*numclasses + " to " + ((i+1)*numclasses-1) + " class out len: " + classOut.length); 
+//								System.out.println("The copyOfRange is: " + i*numclasses + " to " + ((i+1)*numclasses-1) + " class out len: " + classOut.length); 
 
 				if (enableSoftMax) {
 					prob = new float[classOut.length]; 
@@ -190,7 +192,7 @@ public abstract class DLModelWorker<T> {
 
 	public abstract T makeModelResult(float[] prob, double time);
 
-	public abstract void prepModel(StandardModelParams soundSpotParams, DLControl dlControl);
+	public abstract DLStatus prepModel(StandardModelParams soundSpotParams, DLControl dlControl);
 
 
 	/**

src/rawDeepLearningClassifier/dlClassification/genericModel/GenericModelPane.java

@@ -2,6 +2,7 @@ package rawDeepLearningClassifier.dlClassification.genericModel;
 
 import java.io.File;
 import pamViewFX.fxNodes.PamButton;
+import rawDeepLearningClassifier.DLStatus;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelPane;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
 
@@ -84,7 +85,7 @@ public class GenericModelPane extends StandardModelPane  {
 	}
 
 	@Override
-	public void newModelSelected(File file) {
+	public DLStatus newModelSelected(File file) {
 		this.setCurrentSelectedFile(file);
 		
 		//this.setParamsClone(new GenericModelParams()); 
@@ -93,11 +94,14 @@ public class GenericModelPane extends StandardModelPane  {
 		
 //		genericDLClassifier.getGenericDLWorker().prepModel(getParams(getParamsClone()), genericDLClassifier.getDLControl());
 		//do not have getParam here as it resets some of the setting before set params has been called.
-		genericDLClassifier.getGenericDLWorker().prepModel(getParamsClone(), genericDLClassifier.getDLControl());
+		DLStatus status = genericDLClassifier.getGenericDLWorker().prepModel(getParamsClone(), genericDLClassifier.getDLControl());
 
 
 		//now new parameters have been set in the prepModel functions so need to set new params now. 
 		getAdvSettingsPane().setParams(getParamsClone());
+		
+		
+		return status;
 	}
 
 

src/rawDeepLearningClassifier/dlClassification/genericModel/GenericModelWorker.java

@@ -11,7 +11,9 @@ import org.jamdev.jpamutils.JamArr;
 import PamModel.PamModel;
 import PamModel.PamModel.PluginClassloader;
 import PamUtils.PamArrayUtils;
+import ai.djl.engine.EngineException;
 import rawDeepLearningClassifier.DLControl;
+import rawDeepLearningClassifier.DLStatus;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
 
 /**
@@ -47,7 +49,7 @@ public class GenericModelWorker extends DLModelWorker<StandardPrediction> {
 				waveStack[i] = transformedDataStack[i][0];
 			}
 			
-//			System.out.println("RUN GENERIC MODEL WAVE: " + waveStack.length +  "  " + waveStack[0].length +  " " + waveStack[0][0]);
+//			System.out.println("RUN GENERIC MODEL WAVE: " + waveStack.length +  "  " + waveStack[0].length +  " " + waveStack[0][0] + "  " + PamArrayUtils.max( waveStack[0]));
 //			PamArrayUtils.printArray(waveStack[0]);
 			results =  getModel().runModel(waveStack);
 		}
@@ -64,10 +66,10 @@ public class GenericModelWorker extends DLModelWorker<StandardPrediction> {
 	}
 
 	@Override
-	public void prepModel(StandardModelParams genericParams, DLControl dlControl) {
+	public DLStatus prepModel(StandardModelParams genericParams, DLControl dlControl) {
 		//ClassLoader origCL = Thread.currentThread().getContextClassLoader();
 		try {
-			if (genericParams.modelPath==null) return; 
+			if (genericParams.modelPath==null) return DLStatus.NO_MODEL_LOADED; 
 			
 			// get the plugin class loader and set it as the context class loader
 			// NOTE THAT THIS IS REQUIRED TO MAKE THIS MODULE RUN AS A PLUGIN WHEN THE CLASS FILES
@@ -108,6 +110,9 @@ public class GenericModelWorker extends DLModelWorker<StandardPrediction> {
 			
 			//use softmax or not?
 			String extension = FilenameUtils.getExtension(genericParams.modelPath);
+			
+			
+			//TODO - need to add output transforms to models
 			if (extension.equals("pb")) {
 				//TensorFlow models don't need softmax?? Need to look into this more. 
 				this.setEnableSoftMax(false);
@@ -122,13 +127,22 @@ public class GenericModelWorker extends DLModelWorker<StandardPrediction> {
 			((GenericModelParams) genericParams).defualtOuput = longArr2Long(genericModel.getOutShape().getShape()); 
 					
 
+		}
+		catch (EngineException e) {
+			return DLStatus.MODEL_ENGINE_FAIL;
 		}
 		catch (Exception e) {
 			genericModel=null; 
 			e.printStackTrace();
+			
+			
+			return DLStatus.MODEL_LOAD_FAIL;
+
 			//WarnOnce.showWarning(null, "Model Load Error", "There was an error loading the model file.", WarnOnce.OK_OPTION); 
 		}
 		
+		return DLStatus.MODEL_LOAD_SUCCESS;
+		
 		//Thread.currentThread().setContextClassLoader(origCL);
 	}
 	

src/rawDeepLearningClassifier/dlClassification/ketos/KetosModelPane.java

@@ -4,6 +4,7 @@ import java.io.File;
 import java.util.ArrayList;
 
 import javafx.stage.FileChooser.ExtensionFilter;
+import rawDeepLearningClassifier.DLStatus;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelPane;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
 
@@ -29,7 +30,7 @@ public class KetosModelPane extends StandardModelPane {
 
 
 	@Override
-	public void newModelSelected(File file) {
+	public DLStatus newModelSelected(File file) {
 			
 		//A ketos model contains information on the transforms, duration and the class names. 
 		this.setCurrentSelectedFile(file);
@@ -55,7 +56,7 @@ public class KetosModelPane extends StandardModelPane {
 		 * Note that the model prep will determine whether new transforms need to be loaded from the 
 		 * model or to use the existing transforms in the settings. 
 		 */
-		ketosClassifier.getKetosWorker().prepModel(params, ketosClassifier.getDLControl());
+		DLStatus status = ketosClassifier.getKetosWorker().prepModel(params, ketosClassifier.getDLControl());
 		//get the model transforms calculated from the model by SoundSpoyWorker and apply them to our temporary params clone. 
 //		System.out.println("Ketos transforms 1: " +  this.ketosClassifier.getKetosWorker().getModelTransforms());
 		getParamsClone().dlTransfroms = this.ketosClassifier.getKetosWorker().getModelTransforms(); 
@@ -70,6 +71,8 @@ public class KetosModelPane extends StandardModelPane {
 		///set the advanced pane parameters. 
 		getAdvSettingsPane().setParams(getParamsClone());
 		
+		return status; 
+		
 	}
 
 }

src/rawDeepLearningClassifier/dlClassification/ketos/KetosWorker.java

@@ -12,7 +12,9 @@ import org.jamdev.jdl4pam.transforms.DLTransformsFactory;
 import org.jamdev.jdl4pam.transforms.jsonfile.DLTransformsParser;
 
 import PamView.dialog.warn.WarnOnce;
+import ai.djl.engine.EngineException;
 import rawDeepLearningClassifier.DLControl;
+import rawDeepLearningClassifier.DLStatus;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
 import rawDeepLearningClassifier.dlClassification.genericModel.DLModelWorker;
 import rawDeepLearningClassifier.dlClassification.genericModel.StandardPrediction;
@@ -48,8 +50,9 @@ public class KetosWorker extends DLModelWorker<StandardPrediction> {
 
 	/**
 	 * Prepare the model 
+	 * @return 
 	 */
-	public void prepModel(StandardModelParams ketosDLParams, DLControl dlControl) {
+	public DLStatus prepModel(StandardModelParams ketosDLParams, DLControl dlControl) {
 		//ClassLoader origCL = Thread.currentThread().getContextClassLoader();
 		try {
 
@@ -79,8 +82,13 @@ public class KetosWorker extends DLModelWorker<StandardPrediction> {
 				//System.out.println(genericModel.getModel().getModelPath().getFileName()); 
 			}
 		}
+		catch (EngineException e) {
+			e.printStackTrace();
+			return DLStatus.MODEL_ENGINE_FAIL;
+		}
 		catch (Exception e) {
 			e.printStackTrace();
+			return DLStatus.MODEL_LOAD_FAIL;
 			//WarnOnce.showWarning(null, "Model Load Error", "There was an error loading the model file.", WarnOnce.OK_OPTION); 
 		}
 
@@ -176,9 +184,11 @@ public class KetosWorker extends DLModelWorker<StandardPrediction> {
 		catch (Exception e) {
 			ketosModel=null; 
 			e.printStackTrace();
+			return DLStatus.MODEL_META_FAIL;
 			//WarnOnce.showWarning(null, "Model Metadata Error", "There was an error extracting the metadata from the model.", WarnOnce.OK_OPTION); 
 		}
 		//Thread.currentThread().setContextClassLoader(origCL);
+		return DLStatus.MODEL_LOAD_SUCCESS;
 	}
 
 

src/rawDeepLearningClassifier/dlClassification/orcaSpot/OrcaSpotClassifier.java

@@ -364,6 +364,12 @@ public class OrcaSpotClassifier implements DLClassiferModel, PamSettings {
 		return null;
 	}
 
+	@Override
+	public ArrayList<Class> getAllowedDataTypes() {
+		// TODO Auto-generated method stub
+		return null;
+	}
+
 
 
 }

src/rawDeepLearningClassifier/layoutFX/DLModelSelectPane.java

@@ -374,12 +374,18 @@ public class DLModelSelectPane extends PamBorderPane {
 		if (currentClassifierModel!=null) {
 			
 			try {
-				//we are loading model from a file - anything can happen so put in a try catch. 
-				DLStatus status = currentClassifierModel.setModel(file);
 			
+				File fileChck = new File(file);
+
+	            if (!fileChck.exists()) {
+					return DLStatus.MODEL_LOAD_FAIL;
+	            }
+				
+				//we are loading model from a file - anything can happen so put in a try catch. 				
+				DLStatus status = currentClassifierModel.setModel(file);
 
 				if (status.isError()) {
-					System.err.println("Model load failed: " + currentClassifierModel.getModelStatus());
+					System.err.println("Model load failed: " + status);
 					currentClassifierModel=null;
 					return status;
 				}
@@ -389,17 +395,17 @@ public class DLModelSelectPane extends PamBorderPane {
 			catch (Exception e) {
 				e.printStackTrace();
 				currentClassifierModel=null;
-				return DLStatus.MODEL_LOAD_FAILED;
+				return DLStatus.MODEL_LOAD_FAIL;
 			}
 		}
 		else {
 			currentClassifierModel=null;
-			return DLStatus.MODEL_LOAD_FAILED;
+			return DLStatus.MODEL_LOAD_FAIL;
 		}
 	}
 	
 	private void showWarningDialog(DLStatus status) {
-		this.rawDLSettingsPane.showWarning(status);
+		this.rawDLSettingsPane.showWarningDialog(status);
 //		 PamDialogFX.showError(status.getName(), status.getDescription()); 
 	}
 
@@ -568,16 +574,17 @@ public class DLModelSelectPane extends PamBorderPane {
 				return result;
 				
 			} catch (Exception e) {
-				System.out.println("UNABLE TO LOAD MODEL");
+				System.out.println("-----UNABLE TO LOAD MODEL-----");
 				currentClassifierModel=null; //this will reset the pane
 				e.printStackTrace();
-				return DLStatus.MODEL_LOAD_FAILED;
+				return DLStatus.MODEL_LOAD_FAIL;
 			}
 		}
 
 		private void finishedLoading() {
 						
 			if (this.getValue().isError()) {
+				//show a warnign dialog
 				showWarningDialog(this.getValue()); 
 			}
 			

src/rawDeepLearningClassifier/layoutFX/DLSettingsPane.java

@@ -3,8 +3,6 @@ package rawDeepLearningClassifier.layoutFX;
 import java.util.ArrayList;
 
 import org.controlsfx.control.PopOver;
-import org.controlsfx.control.ToggleSwitch;
-
 import PamController.PamGUIManager;
 import PamController.SettingsPane;
 import PamDetection.RawDataUnit;
@@ -37,11 +35,12 @@ import pamViewFX.fxNodes.PamVBox;
 import pamViewFX.fxNodes.pamDialogFX.PamDialogFX;
 import pamViewFX.fxNodes.utilityPanes.GroupedSourcePaneFX;
 import pamViewFX.fxNodes.utilityPanes.PamToggleSwitch;
+import pamViewFX.validator.PamValidator;
 import rawDeepLearningClassifier.DLControl;
 import rawDeepLearningClassifier.DLStatus;
 import rawDeepLearningClassifier.RawDLParams;
+import rawDeepLearningClassifier.dlClassification.DLClassiferModel;
 import warnings.PamWarning;
-import whistlesAndMoans.ConnectedRegionDataUnit;
 
 /**
  * The settings pane. 
@@ -123,6 +122,10 @@ public class DLSettingsPane  extends SettingsPane<RawDLParams>{
 	private PamGridPane segmenterGridPane;
 
 
+	private ArrayList<Class> currentAllowedDataTypes;
+
+	private DLWarningDialog dlWarningDialog ;
+
 
 	public DLSettingsPane(DLControl dlControl){
 		super(null); 
@@ -147,7 +150,7 @@ public class DLSettingsPane  extends SettingsPane<RawDLParams>{
 			mainPane.setPrefWidth(MAX_WIDTH);
 		}
 		//this.getAdvPane().setMaxWidth(MAX_WIDTH);
-
+		dlWarningDialog = new DLWarningDialog(this); 
 
 		//mainPane.getStylesheets().add(PamStylesManagerFX.getPamStylesManagerFX().getCurStyle().getDialogCSS()); 
 
@@ -163,11 +166,13 @@ public class DLSettingsPane  extends SettingsPane<RawDLParams>{
 		PamVBox vBox=new PamVBox();
 		vBox.setSpacing(5);
 
-		sourcePane = new GroupedSourcePaneFX("Raw Sound Data", RawDataUnit.class, true, false, true);
-		sourcePane.addSourceType(ClickDetection.class, false);
-		sourcePane.addSourceType(ClipDataUnit.class, false);
-		sourcePane.addSourceType(ConnectedRegionDataUnit.class, false);
-
+		sourcePane = new GroupedSourcePaneFX("Raw Sound Data", null, true, false, true);
+		setDefaultSourceList();
+		
+//		sourcePane.addSourceType(ClickDetection.class, false);
+//		sourcePane.addSourceType(ClipDataUnit.class, false);
+//		sourcePane.addSourceType(ConnectedRegionDataUnit.class, false);
+		
 
 		vBox.getChildren().add(sourcePane);
 		sourcePane.prefWidthProperty().bind(vBox.widthProperty());
@@ -396,27 +401,94 @@ public class DLSettingsPane  extends SettingsPane<RawDLParams>{
 	 * Set the classifier pane. 
 	 */
 	protected void setClassifierPane() {
-
-
 		//set the classifier Pane.class 
+		
+		//System.out.println("SET CLASSIFIER PANE"); 
+		
 		if (modelSelectPane.currentClassifierModel!=null && modelSelectPane.currentClassifierModel.getModelUI()!=null) {
 
 			classifierPane.setCenter(modelSelectPane.currentClassifierModel.getModelUI().getSettingsPane().getContentNode()); 
 
 			if (modelSelectPane.currentClassifierModel!=null) {
 				modelSelectPane.currentClassifierModel.getModelUI().setParams(); 
+				setSourceList(modelSelectPane.currentClassifierModel);
 			}
 			else {
 				classifierPane.setCenter(null); 
+				setDefaultSourceList();
 			}
 
 		}
 		else {
 			classifierPane.setCenter(null); 
+			setDefaultSourceList();
 		}
 	}
 
 
+	/**
+	 * Set the allowed sources for this type of model. 
+	 * @param currentClassifierModel - the allowed sources. 
+	 */
+	private void setSourceList(DLClassiferModel currentClassifierModel) {
+		
+		//we don't want to set the source list again and gain if the model has changed. 
+		if (currentClassifierModel==null) {
+			setDefaultSourceList();
+			currentAllowedDataTypes=null;
+			return;
+		}
+		
+		//System.out.println("SET SOURCE LIST: " + currentClassifierModel.getAllowedDataTypes()); 
+		
+		if (isNewDataType(currentAllowedDataTypes, currentClassifierModel.getAllowedDataTypes()))  {
+		//set the source list for a given classifier model. 
+		if (currentClassifierModel.getAllowedDataTypes()==null) {
+			//default is for models to require raw sound data
+			setDefaultSourceList() ;
+		}
+		else {
+			sourcePane.setTitleText("Detection data");
+			sourcePane.clearSourceTypeList();
+			sourcePane.setSourceIndex(-1);
+			for (@SuppressWarnings("rawtypes") Class type: currentClassifierModel.getAllowedDataTypes()) {
+				sourcePane.addSourceType(type, false);
+			}
+		}
+			currentAllowedDataTypes = currentClassifierModel.getAllowedDataTypes();
+		}
+		//System.out.println("SET SOURCE LIST: " + sourcePane.getSourceCount()); 
+
+		//something has gone wrong but at least have sound acquisition for  sample rate.  
+		if  (sourcePane.getSourceCount()<=0) {
+			setDefaultSourceList();
+		}
+	}
+	
+	private boolean isNewDataType(ArrayList<Class> currentAllowedDataTypes1, ArrayList<Class> currentAllowedDataTypes2) {
+		
+		if (currentAllowedDataTypes1==null && currentAllowedDataTypes2!=null) return true;
+		if (currentAllowedDataTypes2==null && currentAllowedDataTypes1!=null) return true;
+		if (currentAllowedDataTypes2==null && currentAllowedDataTypes1==null) return false;
+		if (!currentAllowedDataTypes2.equals(currentAllowedDataTypes2)) return true;
+		
+		return false;
+
+	}
+
+
+	/**
+	 * Set the default data sources which are anything that contains raw acoustic data. 
+	 */
+	private void setDefaultSourceList() {
+		sourcePane.setTitleText("Raw Sound Data");
+		sourcePane.clearSourceTypeList();
+		sourcePane.addSourceType(RawDataUnit.class, false);
+		sourcePane.addSourceType(ClickDetection.class, false);
+		sourcePane.addSourceType(ClipDataUnit.class, false);
+	}
+
+
 	@Override
 	public RawDLParams getParams(RawDLParams currParams) {
 
@@ -426,7 +498,15 @@ public class DLSettingsPane  extends SettingsPane<RawDLParams>{
 		PamDataBlock rawDataBlock = sourcePane.getSource();
 		if (rawDataBlock == null){
 			Platform.runLater(()->{
-				PamDialogFX.showWarning("There is no datablock set. The segmenter must have a datablock set."); 
+				dlWarningDialog.showWarning("There is no datablock set. The segmenter must have a datablock set."); 
+			}); 
+			return null;
+		}
+		
+		if (sourcePane.getChannelValidator().containsErrors()) {
+			Platform.runLater(()->{
+				String content = PamValidator.list2String(sourcePane.getChannelValidator().getValidationResult().getMessages()); 
+				dlWarningDialog.showWarning(content); 
 			}); 
 			return null;
 		}
@@ -437,7 +517,7 @@ public class DLSettingsPane  extends SettingsPane<RawDLParams>{
 
 		if (windowLength.getValue() == 0 || hopLength.getValue()==0){
 			Platform.runLater(()->{
-				PamDialogFX.showWarning("Neither the hop nor window length can be zero"); 
+				dlWarningDialog.showWarning("Neither the hop nor window length can be zero"); 
 			});
 			return null;
 		}
@@ -498,55 +578,6 @@ public class DLSettingsPane  extends SettingsPane<RawDLParams>{
 		return pamWarning;
 	}
 
-	/**
-	 * Show a warning dialog for the status
-	 * @param the status to show
-	 */
-	public void showWarning(DLStatus dlWarning) {
-		ArrayList<PamWarning> dlWarnings = new ArrayList<PamWarning>();
-		dlWarnings.add(statusToWarnings(dlWarning)); 
-		showWarning(dlWarnings); 
-	}
-
-	/**
-	 * Show a warning dialog. 
-	 * @param the warning to show.
-	 */
-	public void showWarning(PamWarning dlWarning) {
-		ArrayList<PamWarning> dlWarnings = new ArrayList<PamWarning>();
-		dlWarnings.add(dlWarning); 
-		showWarning(dlWarnings); 
-	}
-
-
-	/**
-	 * Show a warning dialog. 
-	 * @param dlWarnings - list of warnings - the most important will be shown. 
-	 */
-	public void showWarning(ArrayList<PamWarning> dlWarnings) {
-
-		if (dlWarnings==null || dlWarnings.size()<1) return; 
-
-		String warnings ="";
-
-
-		boolean error = false; 
-		for (int i=0; i<dlWarnings.size(); i++) {
-			warnings += dlWarnings.get(i).getWarningMessage() + "\n\n";
-			if (dlWarnings.get(i).getWarnignLevel()>1) {
-				error=true; 
-			}
-		}
-
-		final String warningsF = warnings; 
-		final boolean errorF = error; 
-		Platform.runLater(()->{
-			WarnOnce.showWarningFX(null,  "Deep Learning Settings Warning",  warningsF , errorF ? AlertType.ERROR : AlertType.WARNING);
-			//			WarnOnce.showWarning( "Deep Learning Settings Warning",  warningsF , WarnOnce.WARNING_MESSAGE);
-		});
-
-		//user presses OK - these warnings are just a message - they do not prevent running the module.
-	}
 
 	@Override
 	public void setParams(RawDLParams currParams) {
@@ -577,12 +608,13 @@ public class DLSettingsPane  extends SettingsPane<RawDLParams>{
 
 		segEnableSwitch.setSelected(currParams.enableSegmentation);
 
-		enableControls(); 
-
 		//		//set up the model and the custom pane if necessary.  
 		this.modelSelectPane.loadNewModel(currParams.modelURI); 
 		//this.modelSelectPane.updatePathLabel(); 
 		this.setClassifierPane();
+		
+		
+		enableControls(); 
 
 		//For some reason, in the FX GUI, this causes a root used in multiple scenes exceptions...not sure why. 
 		Platform.runLater(()->{
@@ -658,5 +690,11 @@ public class DLSettingsPane  extends SettingsPane<RawDLParams>{
 	}
 
 
+	public void showWarningDialog(DLStatus status) {
+		dlWarningDialog.showWarningDialog(status);
+		
+	}
+
+
 
 }

src/rawDeepLearningClassifier/layoutFX/DLWarningDialog.java

@@ -0,0 +1,107 @@
+package rawDeepLearningClassifier.layoutFX;
+
+import java.util.ArrayList;
+
+import PamView.dialog.warn.WarnOnce;
+import PamView.dialog.warn.WarnOnceDialogFX;
+import javafx.application.Platform;
+import javafx.scene.control.Alert.AlertType;
+import rawDeepLearningClassifier.DLStatus;
+import warnings.PamWarning;
+
+public class DLWarningDialog {
+	
+	private boolean warningShow;
+
+	public DLWarningDialog(DLSettingsPane rawDLSettingsPane) {
+		
+	}
+	
+	/**
+	 * Show a warning dialog for the status
+	 * @param the status to show
+	 */
+	public void showWarningDialog(DLStatus dlWarning) {
+		
+		switch(dlWarning) {
+		case MODEL_ENGINE_FAIL ->{
+			//special warning dialog 
+			// show the warning
+			WarnOnceDialogFX wo = new WarnOnceDialogFX(null, dlWarning.getName(), dlWarning.getDescription(), AlertType.ERROR, null, null, "Ok",  "Cancel", true);
+			wo.showDialog();
+		}
+		default -> {
+			//default for most warnings. 
+			ArrayList<PamWarning> dlWarnings = new ArrayList<PamWarning>();
+			dlWarnings.add(DLSettingsPane.statusToWarnings(dlWarning)); 
+			showWarning(dlWarnings); 
+		}
+		
+		}
+	
+	}
+	
+
+	/**
+	 * Show a warning dialog. 
+	 * @param the warning to show.
+	 */
+	public void showWarning(String dlWarning) {
+		this.warningShow = true; 
+		Platform.runLater(()->{
+			WarnOnce.showWarningFX(null,  "Deep Learning Settings Warning",  dlWarning ,  AlertType.WARNING, true);
+			warningShow = false;
+			//			WarnOnce.showWarning( "Deep Learning Settings Warning",  warningsF , WarnOnce.WARNING_MESSAGE);
+		});
+
+	}
+
+	/**
+	 * Show a warning dialog. 
+	 * @param the warning to show.
+	 */
+	public void showWarning(PamWarning dlWarning) {
+		ArrayList<PamWarning> dlWarnings = new ArrayList<PamWarning>();
+		dlWarnings.add(dlWarning); 
+		showWarning(dlWarnings); 
+	}
+
+
+	/**
+	 * Show a warning dialog. 
+	 * @param dlWarnings - list of warnings - the most important will be shown. 
+	 */
+	private void showWarning(ArrayList<PamWarning> dlWarnings) {
+		
+		System.out.println("Show warning: " + dlWarnings);
+		
+		if (warningShow) return; //not the best but don't show multiple dialogs on top of each other. 
+
+		if (dlWarnings==null || dlWarnings.size()<1) return; 
+
+		String warnings ="";
+
+
+		boolean error = false; 
+		for (int i=0; i<dlWarnings.size(); i++) {
+			warnings += dlWarnings.get(i).getWarningMessage() + "\n\n";
+			if (dlWarnings.get(i).getWarnignLevel()>1) {
+				error=true; 
+			}
+		}
+
+		this.warningShow = true; 
+		final String warningsF = warnings; 
+		final boolean errorF = error;
+		
+		
+		Platform.runLater(()->{
+			WarnOnce.showWarningFX(null,  "Deep Learning Settings Warning",  warningsF , errorF ? AlertType.ERROR : AlertType.WARNING, true);
+			warningShow = false;
+			//			WarnOnce.showWarning( "Deep Learning Settings Warning",  warningsF , WarnOnce.WARNING_MESSAGE);
+		});
+
+		//user presses OK - these warnings are just a message - they do not prevent running the module.
+	}
+
+}

src/rawDeepLearningClassifier/logging/DLAnnotationSQL.java

@@ -69,7 +69,7 @@ public class DLAnnotationSQL implements SQLLoggingAddon   {
 	public boolean loadData(SQLTypes sqlTypes, EmptyTableDefinition pamTableDefinition, PamDataUnit pamDataUnit) {
 
 		String predictionsString = (String) predictionsItem.getValue();
-		Integer predicitonType = (Integer) predictionsItem.getValue();
+		Integer predicitonType = (Integer) typeTableItem.getValue();
 
 		if (predictionsString!=null) {
 
@@ -95,7 +95,7 @@ public class DLAnnotationSQL implements SQLLoggingAddon   {
 	 * @param predicitonType - the prediction object type e.g.ModelResultBinaryFactory.GENERIC
 	 * @return a list of prediction objects. 
 	 */
-	private ArrayList<PredictionResult> string2Predictions(String predictionsString, int predicitonType) {
+	public static ArrayList<PredictionResult> string2Predictions(String predictionsString, int predicitonType) {
 		ArrayList<PredictionResult> modelResults = new ArrayList<PredictionResult>(); 
 
 	    JSONObject jsonObject = new JSONObject(predictionsString);
@@ -116,13 +116,15 @@ public class DLAnnotationSQL implements SQLLoggingAddon   {
 	    	predictions= new float[array.getJSONArray(i).length()];
 	    	
 	    	for(int j=0; j< array.getJSONArray(i).length(); j++) {
-	    		predictions[j] =  array.getJSONArray(i).getFloat(j);
+	    		predictions[j] =  array.getJSONArray(i).getBigDecimal(j).floatValue();
 	    	}
 	    	
 	    	predicitonResult= ModelResultBinaryFactory.makePredictionResult(predicitonType, predictions, classIDs,  true); 
 	    	modelResults.add(predicitonResult); 
 	    }
 	    
+	    
+	    
 	    return modelResults;
 	}
 
@@ -132,7 +134,7 @@ public class DLAnnotationSQL implements SQLLoggingAddon   {
 	 * @param predictions - the spectrum. 
 	 * @return string representation for the predictions in JSON format
 	 */
-	private String prediction2String(ArrayList<PredictionResult> arrayList) {
+	public static String prediction2String(ArrayList<PredictionResult> arrayList) {
 
 		if (arrayList == null) {
 			return "null";
@@ -145,7 +147,13 @@ public class DLAnnotationSQL implements SQLLoggingAddon   {
 		for (PredictionResult result : arrayList) {
 			JSONArray rowArray = new JSONArray();
 			for (float element : result.getPrediction()) {
-				rowArray.put(element);
+				//JSON does not allow non finite numbers
+				if (Float.isFinite(element)) {
+					rowArray.put(element);
+				}
+				else {
+					rowArray.put(-1.0);
+				}
 			}
 			matrixArray.put(rowArray);
 		}

src/rawDeepLearningClassifier/logging/DLGroupDetectionLogging.java

@@ -1,13 +1,21 @@
 package rawDeepLearningClassifier.logging;
 
 
+import java.sql.Types;
+
 import PamguardMVC.PamDataUnit;
 import PamguardMVC.superdet.SuperDetDataBlock;
 import PamguardMVC.superdet.SuperDetection;
+import cpod.CPODClassification;
+import cpod.CPODClickTrainDataUnit;
+import cpod.CPODUtils;
+import cpod.CPODClassification.CPODSpeciesType;
 import generalDatabase.PamTableDefinition;
+import generalDatabase.PamTableItem;
 import generalDatabase.SQLTypes;
 import generalDatabase.SuperDetLogging;
 import rawDeepLearningClassifier.DLControl;
+import rawDeepLearningClassifier.dlClassification.DLGroupDetection;
 
 
 /**
@@ -16,6 +24,10 @@ import rawDeepLearningClassifier.DLControl;
 public class DLGroupDetectionLogging extends SuperDetLogging {
 
 	private DLControl dlControl;
+	
+	private PamTableItem duration, startsample, nSubDet;
+	
+	
 
 	public DLGroupDetectionLogging(DLControl dlControl, SuperDetDataBlock pamDataBlock) {
 		super(pamDataBlock);
@@ -26,6 +38,10 @@ public class DLGroupDetectionLogging extends SuperDetLogging {
 	@Override
 	public void setTableData(SQLTypes sqlTypes, PamDataUnit pamDataUnit) {
 //		System.out.println("Save deep learning group: " + ((SuperDetection) pamDataUnit).getSubDetectionsCount());
+		duration.setValue(pamDataUnit.getDurationInMilliseconds());
+		startsample.setValue(pamDataUnit.getStartSample());
+		nSubDet.setValue(((SuperDetection) pamDataUnit).getSubDetectionsCount());
+
 	}
 
 	public DLControl getDLControl() {
@@ -38,8 +54,30 @@ public class DLGroupDetectionLogging extends SuperDetLogging {
 	 * @return basic table - annotations will be added shortly !
 	 */
 	public PamTableDefinition createBaseTable() {
-		PamTableDefinition tableDef = new PamTableDefinition(dlControl.getUnitName(), UPDATE_POLICY_OVERWRITE);
+		PamTableDefinition tableDef = new PamTableDefinition(dlControl.getUnitName()+"_Group_Detections", UPDATE_POLICY_OVERWRITE);
+		tableDef.addTableItem(duration 	= new PamTableItem("Duration_millis", Types.DOUBLE));
+		tableDef.addTableItem(startsample 	= new PamTableItem("Start_sample", Types.LONGNVARCHAR));
+		tableDef.addTableItem(nSubDet 	= new PamTableItem("n_subdetections", Types.LONGNVARCHAR));
+
 		return tableDef;
 	}
+	
+	
+	
+	@Override
+	protected DLGroupDetection createDataUnit(SQLTypes sqlTypes, long timeMilliseconds, int databaseIndex) {
+
+	
+		int chan = this.getTableDefinition().getChannelBitmap().getIntegerValue();
+		
+		double durationD = duration.getDoubleValue();
+		long startsampleL = startsample.getLongValue();
+
+		
+		DLGroupDetection dlGroupDet = new DLGroupDetection(timeMilliseconds, chan, startsampleL, durationD);
+		
+		return dlGroupDet;
+	}
+
 
 }

src/rawDeepLearningClassifier/logging/DLGroupSubLogging.java

@@ -19,7 +19,7 @@ public class DLGroupSubLogging extends SQLLogging {
 	public DLGroupSubLogging(DLGroupDetectionLogging dlGroupDetLogging, PamDataBlock pamDataBlock) {
 		super(pamDataBlock);		
 		this.dlGroupLogging = dlGroupDetLogging;
-		setTableDefinition(new PamSubtableDefinition(dlGroupDetLogging.getDLControl().getUnitName()+"_Children"));
+		setTableDefinition(new PamSubtableDefinition(dlGroupDetLogging.getDLControl().getUnitName()+"Group_Detections_Children"));
 	}
 
 	@Override

src/rawDeepLearningClassifier/logging/DLLogging.java

@@ -1,62 +0,0 @@
-package rawDeepLearningClassifier.logging;
-
-import java.sql.Types;
-
-import PamguardMVC.PamDataBlock;
-import PamguardMVC.PamDataUnit;
-import generalDatabase.PamTableDefinition;
-import generalDatabase.PamTableItem;
-import generalDatabase.SQLLogging;
-import generalDatabase.SQLTypes;
-import rawDeepLearningClassifier.DLControl;
-import rawDeepLearningClassifier.dlClassification.DLDataUnit;
-
-/**
- * Saves the classified DL data units to a database. 
- *
- * 
- * @author Jamie Macaulay
- *
- */
-public class DLLogging  extends SQLLogging { 
-	
-	
-	/**
-	 * The PAM items for saving.
-	 */
-	private PamTableItem analysisTime, predicition;
-	
-	/**
-	 * Reference to the DLControl. 
-	 */
-	private DLControl dlControl;
-
-
-
-	public DLLogging(DLControl dlControl, PamDataBlock pamDataBlock) {
-		super(pamDataBlock); 
-		this.dlControl=dlControl; 
-	}
-
-	@Override
-	public void setTableData(SQLTypes sqlTypes, PamDataUnit pamDataUnit) {
-		DLDataUnit dgdu = (DLDataUnit) pamDataUnit;
-		analysisTime.setValue(dgdu.getPredicitionResult().getAnalysisTime());
-		predicition.setValue(dgdu.getPredicitionResult().getPrediction());
-	}
-	
-
-	/**
-	 * Create the basic table definition for the group detection. 
-	 * @return basic table - annotations will be added shortly !
-	 */
-	public PamTableDefinition createBaseTable() {
-		PamTableDefinition tableDef = new PamTableDefinition(dlControl.getUnitName(), UPDATE_POLICY_OVERWRITE);
-		tableDef.addTableItem(analysisTime 	= new PamTableItem("Analysis_Time", Types.DOUBLE));
-		tableDef.addTableItem(predicition 	= new PamTableItem("Predicition", Types.DOUBLE));
-		return tableDef;
-	}
-	
-	//TODO viewer mode. 
-
-}

src/rawDeepLearningClassifier/logging/DLResultLogging.java

@@ -0,0 +1,102 @@
+package rawDeepLearningClassifier.logging;
+
+import java.sql.Types;
+import java.util.ArrayList;
+
+import PamguardMVC.PamDataBlock;
+import PamguardMVC.PamDataUnit;
+import generalDatabase.PamTableDefinition;
+import generalDatabase.PamTableItem;
+import generalDatabase.SQLLogging;
+import generalDatabase.SQLTypes;
+import rawDeepLearningClassifier.DLControl;
+import rawDeepLearningClassifier.dlClassification.DLDataUnit;
+import rawDeepLearningClassifier.dlClassification.DLGroupDetection;
+import rawDeepLearningClassifier.dlClassification.PredictionResult;
+
+/**
+ * Saves the classified DL data units to a database. 
+ * TODO
+ * 
+ * @author Jamie Macaulay
+ *
+ */
+public class DLResultLogging  extends SQLLogging { 
+	
+	
+	/**
+	 * The PAM items for saving.
+	 */
+	private PamTableItem analysisTime, predicition, startSample, typeTableItem;
+	
+	/**
+	 * Reference to the DLControl. 
+	 */
+	private DLControl dlControl;
+
+
+
+	public DLResultLogging(DLControl dlControl, PamDataBlock pamDataBlock) {
+		super(pamDataBlock); 
+		this.dlControl=dlControl; 
+		setTableDefinition(createBaseTable());
+	}
+
+	@Override
+	public void setTableData(SQLTypes sqlTypes, PamDataUnit pamDataUnit) {
+		
+		DLDataUnit dgdu = (DLDataUnit) pamDataUnit;
+		startSample.setValue(pamDataUnit.getStartSample());
+		analysisTime.setValue(dgdu.getPredicitionResult().getAnalysisTime());
+		typeTableItem.setValue(ModelResultBinaryFactory.getType(dgdu.getPredicitionResult()));
+
+		ArrayList<PredictionResult> predictionResult = new ArrayList<PredictionResult>(); 
+		predictionResult.add(dgdu.getPredicitionResult());
+		
+		predicition.setValue(
+				DLAnnotationSQL.prediction2String(predictionResult));
+		
+		
+		
+		
+	}
+	
+	/**
+	 * Create the basic table definition for the group detection. 
+	 * @return basic table - annotations will be added shortly !
+	 */
+	public PamTableDefinition createBaseTable() {
+		PamTableDefinition tableDef = new PamTableDefinition(dlControl.getUnitName()+"_Predictions", UPDATE_POLICY_OVERWRITE);
+		tableDef.addTableItem(analysisTime 	= new PamTableItem("Processing_time", Types.DOUBLE));
+		tableDef.addTableItem(startSample 	= new PamTableItem("Start_sample", Types.LONGNVARCHAR));
+		tableDef.addTableItem(predicition 	= new PamTableItem("Predicition", Types.CHAR, 8128));
+		tableDef.addTableItem(typeTableItem = new PamTableItem("Prediction_Type", Types.INTEGER, "The type flag for the predictions"));
+
+		return tableDef;
+	}
+		
+	
+	@Override
+	protected DLDataUnit createDataUnit(SQLTypes sqlTypes, long timeMilliseconds, int databaseIndex) {
+	
+		int chan = this.getTableDefinition().getChannelBitmap().getIntegerValue();
+		
+		long startsampleL = startSample.getLongValue();
+		
+		String predictionsString = (String) predicition.getValue();
+		Integer predicitonType = (Integer) typeTableItem.getValue();
+		
+		if (predictionsString!=null) {
+
+			ArrayList<PredictionResult> modelResults =  DLAnnotationSQL.string2Predictions( predictionsString,  predicitonType);
+			DLDataUnit dlDataUnit = new DLDataUnit(timeMilliseconds,  chan, startsampleL, dlControl.getDLParams().rawSampleSize, modelResults.get(0)); 
+			
+			return dlDataUnit; 
+
+		}		
+		
+		return null;
+	}
+
+
+}

src/rawDeepLearningClassifier/logging/ModelResultBinaryFactory.java

@@ -184,7 +184,7 @@ public class ModelResultBinaryFactory {
 			break; 
 		default:
 			//ideally should never be used. 
-			result = new StandardPrediction(data, isBinary); 
+			result = new StandardPrediction(data, classID, isBinary); 
 			break; 
 		}
 		return result;

src/rawDeepLearningClassifier/offline/DLOfflineTask.java

@@ -2,10 +2,13 @@ package rawDeepLearningClassifier.offline;
 
 import PamController.PamController;
 import PamguardMVC.PamDataUnit;
+import PamguardMVC.PamObservable;
+import PamguardMVC.PamObserverAdapter;
 import dataMap.OfflineDataMapPoint;
 import matchedTemplateClassifer.MTClassifierControl;
 import offlineProcessing.OfflineTask;
 import rawDeepLearningClassifier.DLControl;
+import rawDeepLearningClassifier.segmenter.SegmenterDetectionGroup;
 import rawDeepLearningClassifier.segmenter.SegmenterProcess;
 
 public class DLOfflineTask extends OfflineTask<PamDataUnit<?,?>>{
@@ -26,9 +29,16 @@ public class DLOfflineTask extends OfflineTask<PamDataUnit<?,?>>{
 		this.dlControl.getDLClassifyProcess().clearOldData();	
 
 		super.addAffectedDataBlock(this.dlControl.getDLClassifyProcess().getDLDetectionDatablock());
+		super.addAffectedDataBlock(this.dlControl.getDLClassifyProcess().getDLGroupDetectionDataBlock()); //important so data are deleted. 
+		
 		//prediction data block may also be affected. 
 		super.addAffectedDataBlock(this.dlControl.getDLClassifyProcess().getDLPredictionDataBlock());
+
+//		//group detections are a little difficult because they only appear after
+//		dlControl.getDLClassifyProcess().getDLGroupDetectionDataBlock().addInstantObserver(new GroupObserver(this));
 	}
+	
+
 
 	@Override
 	public String getName() {
@@ -37,28 +47,66 @@ public class DLOfflineTask extends OfflineTask<PamDataUnit<?,?>>{
 
 	@Override
 	public boolean processDataUnit(PamDataUnit<?, ?> dataUnit) {
-		
-	
-//		System.out.println("--------------");
-//		System.out.println("Offline task start: " + dataUnit.getUpdateCount() + " UID " + dataUnit.getUID());
+		//		System.out.println("--------------");
+		//		System.out.println("Offline task start: " + dataUnit.getUpdateCount() + " UID " + dataUnit.getUID());
+		boolean saveBinary = false; 
+		try {
 
-		//Process a data unit
-		dlControl.getSegmenter().newData(dataUnit); 
+
+			if (dlControl.isGroupDetections()) {
+				//once a segment is created we need to process it  but this is handled by the GroupObserver and not here....
+				//Group segment data is saved to the database. 
+				boolean newGroup = dlControl.getSegmenter().newGroupData(dataUnit);
 				
-		//force click data save
-		dlControl.getDLClassifyProcess().forceRunClassifier(dataUnit);
-		
-		//must be called or can result in memory leak. 
-		dlControl.getSegmenter().getSegmenterDataBlock().clearAll();
+//				System.out.println("Added data to group: " + dataUnit.getUID()); 
+				
+				if (newGroup) {
 
-		//must be called or can result in memory leak. 
-		dlControl.getDLClassifyProcess().getDLPredictionDataBlock().clearAll();
+					//have to manually add this as the group data is a multiplex data block. 
+					dlControl.getDLClassifyProcess().newData(null, dlControl.getSegmenter().getSegmenteGroupDataBlock().getFirstUnit());
+					
+					//run the classifier - if group detection data is present the classifier will run
+					dlControl.getDLClassifyProcess().forceRunClassifier( dlControl.getSegmenter().getSegmenteGroupDataBlock().getFirstUnit());
+					
+//					//clear the data block
+					dlControl.getSegmenter().getSegmenteGroupDataBlock().clearAll();
+				}
+				
+				saveBinary = false; //very important or binary files become messed up and clicks/whistles are deleted. 
+			}
+			else  {
+				//Classifying one detection - there may be multiple segments in one detection but once a 
+				//detection has been added we force the classifier to run on all the segments generated from 
+				//the raw data. 
+				
+				//Process a data unit
+				dlControl.getSegmenter().newData(dataUnit); 
 
-		/**
-		 * So the issue here is that the classification is not on the same thread...
-		 */
-//		System.out.println("Offline task complete: " + dataUnit.getUpdateCount() + " data " + dataUnit +  " no. annotations: " + dataUnit.getNumDataAnnotations() );
-		return true;
+				//force click data save
+				dlControl.getDLClassifyProcess().forceRunClassifier(dataUnit);
+
+				//must be called or can result in memory leak. 
+				dlControl.getSegmenter().getSegmenterDataBlock().clearAll();
+				
+				//must be called or can result in memory leak. 
+				dlControl.getDLClassifyProcess().getDLPredictionDataBlock().clearAll();
+				
+				saveBinary = true;
+
+			};
+			
+
+			
+			//		/**
+			//		 * So the issue here is that the classification is not on the same thread...
+			//		 */
+			//		System.out.println("Offline task complete: " + dataUnit.getUpdateCount() + " data " + dataUnit +  " no. annotations: " + dataUnit.getNumDataAnnotations() );
+		}
+		catch (Exception e) {
+			e.printStackTrace();
+		}
+
+		return saveBinary;
 	}
 
 	/**
@@ -69,10 +117,11 @@ public class DLOfflineTask extends OfflineTask<PamDataUnit<?,?>>{
 		count=0; 
 		prepProcess(); 
 				
-		//this is important so that the offline taks knows that the 
+		//this is important so that the offline tasks knows that the 
 		//parent datablock (e.g. if clicks or clips) needs to be saved because
 		// an annotation has been added to it. 
 		super.addAffectedDataBlock(dlControl.getParentDataBlock());
+		
 		super.getOfflineTaskGroup().setSummaryLists();
 		
 //		for (int i=0; i<super.getNumAffectedDataBlocks(); i++) {
@@ -80,13 +129,16 @@ public class DLOfflineTask extends OfflineTask<PamDataUnit<?,?>>{
 //		}
 
 		this.setParentDataBlock(dlControl.getParentDataBlock());
+		
 		//dlControl.setNotifyProcesses(true);
 		this.dlControl.getDLModel().prepModel(); 
+		
 		dlControl.update(MTClassifierControl.PROCESSING_START);
+		
 		//		System.out.println("Waveform match: " + mtClassifierControl.getMTParams().classifiers.get(0).waveformMatch.toString());
 		//		System.out.println("Waveform reject: " + mtClassifierControl.getMTParams().classifiers.get(0).waveformReject.toString());
 	}
-
+		
 
 	/**
 	 * Called at the end of the thread which executes this task. 
@@ -100,6 +152,7 @@ public class DLOfflineTask extends OfflineTask<PamDataUnit<?,?>>{
 
 	@Override
 	public void newDataLoad(long startTime, long endTime, OfflineDataMapPoint mapPoint) {
+				
 		prepProcess();
 		dlControl.update(MTClassifierControl.PROCESSING_START);
 		// called whenever new data is loaded. 
@@ -149,6 +202,38 @@ public class DLOfflineTask extends OfflineTask<PamDataUnit<?,?>>{
 		boolean can = getDataBlock() != null; 
 		return can;
 	}
+	
+	
+//	/**
+//	 * An observer which waits until all detections within a segment have been added before passing the 
+//	 * segment to a deep learning classifier. 
+//	 */
+//	public class GroupObserver extends PamObserverAdapter{
+//		
+//		private DLOfflineTask dlOfflinetask;
+//
+//		public GroupObserver( DLOfflineTask dlOfflinetask) {
+//			this.dlOfflinetask=dlOfflinetask;
+//		}
+//		
+//
+//		@Override
+//		public void addData(PamObservable observable, PamDataUnit pamDataUnit) {
+//			SegmenterDetectionGroup segmenterDetectionGroup = (SegmenterDetectionGroup) pamDataUnit;
+//			
+//			System.out.println("HELLO NEW GROUP DATA!!" + segmenterDetectionGroup.getSubDetectionsCount()); 
+//		}
+//
+//
+//		@Override
+//		public String getObserverName() {
+//			return "Deep Learning Offline Group Process";
+//		}
+//
+//
+//
+//		
+//	}
 
 
 }

src/rawDeepLearningClassifier/segmenter/SegmenterProcess.java

@@ -35,6 +35,8 @@ public class SegmenterProcess extends PamProcess {
 	 */
 	private static final double MAX_MILLIS_DRIFT = 2;
 
+	private static final long MAX_SEGMENT_GAP_MILLIS = 3600*1000L;
+
 	/**
 	 * Reference to the deep learning control. 
 	 */
@@ -201,6 +203,8 @@ public class SegmenterProcess extends PamProcess {
 
 		setParentDataBlock(rawDataBlock);
 		
+		this.dlControl.createDataSelector(rawDataBlock);
+		
 		this.firstClockUpdate = -1;
 
 	}
@@ -228,21 +232,23 @@ public class SegmenterProcess extends PamProcess {
 	 */
 	public void newData(PamDataUnit pamRawData) {
 
-//		System.out.println("New data for segmenter: " + pamRawData); 
+//		System.out.println("New data for segmenter: " + pamRawData + "  isGroup: " + dlControl.isGroupDetections()); 
 
 		if (!dlControl.getDLParams().useDataSelector || dlControl.getDataSelector().scoreData(pamRawData)>0) {	
 
-			if (pamRawData instanceof RawDataUnit) {
-				newRawDataUnit(pamRawData); 
+			if (!dlControl.isGroupDetections()){
+				if (pamRawData instanceof RawDataUnit) {
+					newRawDataUnit(pamRawData); 
+				}
+				else if (pamRawData instanceof ClickDetection) {
+					newClickData( pamRawData);
+				}
+				else if (pamRawData instanceof ClipDataUnit)  {
+					newClipData(pamRawData);
+				}
 			}
-			else if (pamRawData instanceof ClickDetection) {
-				newClickData( pamRawData);
-			}
-			else if (pamRawData instanceof ClipDataUnit)  {
-				newClipData(pamRawData);
-			}
-			else if (pamRawData instanceof ConnectedRegionDataUnit)  {
-				newWhistleData(pamRawData);
+			else {
+				newGroupData(pamRawData);
 			}
 		}
 
@@ -251,12 +257,11 @@ 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. 
+	 * @param detection - the whistle data unit. 
 	 */
-	private synchronized void newWhistleData(PamDataUnit dataUnit) {
+	public synchronized boolean newGroupData(PamDataUnit detection) {
 		
-		
-		ConnectedRegionDataUnit whistle = (ConnectedRegionDataUnit) dataUnit;
+//		PamDataUnit detection = dataUnit;
 
 		//TODO
 		//this contains no raw data so we are branching off on a completely different processing path here.
@@ -265,8 +270,8 @@ public class SegmenterProcess extends PamProcess {
 		int[] chanGroups = dlControl.getDLParams().groupedSourceParams.getChannelGroups();
 		
 		int index = -1;
-		for (int i=0; i<chanGroups.length; i++) {
-			if (dlControl.getDLParams().groupedSourceParams.getGroupChannels(chanGroups[i])==dataUnit.getChannelBitmap()) {
+		for (int i=0; i<chanGroups.length; i++) {			
+			if (PamUtils.hasChannelMap(dlControl.getDLParams().groupedSourceParams.getGroupChannels(chanGroups[i]), detection.getChannelBitmap())) {
 				index=i;
 				break;
 			}
@@ -275,45 +280,80 @@ public class SegmenterProcess extends PamProcess {
 //		//FIXME - TWEMP
 //		index =0;
 		
-//		System.out.println("Whistle data: " + ((dataUnit.getTimeMilliseconds()-firstClockUpdate)/1000.) + "s " + chanGroups.length +  "  " +  index + "  " + dataUnit.getChannelBitmap());
-//		PamArrayUtils.printArray(chanGroups);
+//		System.out.println("Group data: " + ((detection.getTimeMilliseconds()-firstClockUpdate)/1000.) + "s " + chanGroups.length +  "  " +  index + "  " + detection.getChannelBitmap());
+		//PamArrayUtils.printArray(chanGroups);
 		
 		if (index<0) {
-			return;
+			return false;
 		}
+		
+		boolean segSaved = false;
 	
-		if (segmenterDetectionGroup[index] == null || !detectionInSegment(dataUnit,  segmenterDetectionGroup[index])) {
+		if (segmenterDetectionGroup[index] == null || !detectionInSegment(detection,  segmenterDetectionGroup[index])) {
 			
 			//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.
-			if (segmentStart <0) {
-				segmentStart= firstClockUpdate;
+			if (segmentStart < 0) {
+				//FIXME - seems like the master clock update is slightly after the start of a file - this needs sorted but for now
+				//this is a fix that will work - just means segments miss being completely lined up with the start of files but 
+				//will always include all data units. 
+				segmentStart= detection.getTimeMilliseconds()< firstClockUpdate ? detection.getTimeMilliseconds() : firstClockUpdate;
 				segmenterEnd = (long) (segmentStart + getSegmentLenMillis());
+				newGroupSegment(index);
 			}
 			
-			while(!detectionInSegment(dataUnit,  segmentStart,  segmenterEnd)) {
-				//System.out.println("Detection in segment: " + segmentStart); 
-				nextGroupSegment(index);
+			//sometimes if a detection is a long time after the previous then we don't want to iterate through new segments so start again
+			if ((detection.getTimeMilliseconds() - segmentStart)>MAX_SEGMENT_GAP_MILLIS) {
+				segmentStart = detection.getTimeMilliseconds();
+				segmenterEnd = (long) (segmentStart + getSegmentLenMillis());
+				newGroupSegment(index);
+			}
+			
+			while(!detectionInSegment(detection,  segmentStart,  segmenterEnd)) {
+				//System.out.println("Detection in segment: " + segmentStart + " det millis: " + detection.getTimeMilliseconds()); 
+				
+				if (detection.getTimeMilliseconds()<segmentStart) {
+					//something has gone quite wrong
+					System.err.println("rawdeepLearningClassifier.segmenterProcess: Detection: " +  detection.getUID() + 
+							" was detected before the segment?  " + "seg start: " + segmentStart + " det start: " +detection.getTimeMilliseconds() );
+					return false;
+				}
+				
+				segSaved = nextGroupSegment(index);
+				
 			}
 		}
 		
-		segmenterDetectionGroup[index].addSubDetection(whistle);
-//		System.out.println("Segment sub detection count: " + 	segmenterDetectionGroup[index].getSubDetectionsCount()); 
+		segmenterDetectionGroup[index].addSubDetection(detection);
+		//System.out.println("Segment sub detection count: " + 	segmenterDetectionGroup[index].getSubDetectionsCount()); 
 		
+		return segSaved;
 	}
 	
 	/**
 	 * Iterate to the next group segment
 	 * @param index - the group index;
 	 */
-	private void nextGroupSegment(int index) {
+	private boolean nextGroupSegment(int index) {
 		
 //		System.out.println("----------------------------------");
 
 		segmentStart = (long) (segmentStart+ getSegmentHopMillis());
 		segmenterEnd = (long) (segmentStart + getSegmentLenMillis());
 		
+		return newGroupSegment(index);
+	}
+	
+	/**
+	 * Create a new group segment based on the current segmentStart and segmentEnd fields. Will saved the previous segment if 
+	 * it exists and contains more than one sub detection. 
+	 * @param index - the segment group index (i.e. channel group index)
+	 * @return true of a segment has completed and saved to the segment group data block. 
+	 */
+	private boolean newGroupSegment(int index) {
+		boolean segSaved = false;
+		
 		int[] chanGroups = dlControl.getDLParams().groupedSourceParams.getChannelGroups();
 
 		long startSample = this.absMillisecondsToSamples(segmentStart);
@@ -333,18 +373,20 @@ public class SegmenterProcess extends PamProcess {
 				}
 			}
 			
-//			System.out.println("SAVE WHISTLE SEGMENT!: " + ((segmenterDetectionGroup[index].getSegmentStartMillis()-firstClockUpdate)/1000.) + "s" + " " + " no. whistles: " 
+//			System.out.println("SAVE GROUP SEGMENT!: " + ((segmenterDetectionGroup[index].getSegmentStartMillis()-firstClockUpdate)/1000.) + "s" + " " + " no. whistles: " 
 //			+ segmenterDetectionGroup[index].getSubDetectionsCount() + " " + PamCalendar.formatDateTime(segmenterDetectionGroup[index].getSegmentStartMillis()) + "  " 
 //					+ segmenterDetectionGroup[index]);
 			//save the data unit to the data block
+
 			if (segmenterDetectionGroup[index].getSubDetectionsCount()>0) {
 				this.segmenterGroupDataBlock.addPamData(segmenterDetectionGroup[index]);
+				segSaved=true;
 			}
 		}
 		
 		segmenterDetectionGroup[index] = aSegment;
-//		System.out.println("NEW SEGMENT START!: " + (segmentStart-firstClockUpdate)/1000. + "s" + " " + segmenterDetectionGroup[index].getSegmentStartMillis()+ "  " +segmenterDetectionGroup[index]);
-
+		
+		return segSaved;
 	}
 	
 	private boolean detectionInSegment(PamDataUnit dataUnit, SegmenterDetectionGroup segmenterDetectionGroup2) {
@@ -381,6 +423,7 @@ public class SegmenterProcess extends PamProcess {
 	int count=0;
 	public void masterClockUpdate(long milliSeconds, long sampleNumber) {
 		super.masterClockUpdate(milliSeconds, sampleNumber);
+		
 		if (firstClockUpdate<0) {
 			firstClockUpdate = milliSeconds;
 		}
@@ -870,8 +913,17 @@ public class SegmenterProcess extends PamProcess {
 	}
 
 
-	public SegmenterGroupDataBlock getSegmenteGrouprDataBlock() {
+	public SegmenterGroupDataBlock getSegmenteGroupDataBlock() {
 		return this.segmenterGroupDataBlock;
 	}
 
+
+	/**
+	 * Set the first clock update which helps the segmenter work out where to start segmenting from. 
+	 * @param startTime - the start time in millis. 
+	 */
+	public void setFirstClockUpdate(long startTime) {
+		this.firstClockUpdate = startTime;
+	}
+
 } 

src/test/rawDeepLearningClassifier/DelphinIDTest.java

@@ -3,148 +3,308 @@ package test.rawDeepLearningClassifier;
 import static org.junit.Assert.assertTrue;
 import static org.junit.jupiter.api.Assertions.assertEquals;
 
-import java.awt.image.BufferedImage;
-import java.awt.image.Raster;
-import java.io.File;
 import java.io.IOException;
 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.jamdev.jpamutils.JamArr;
-import org.jamdev.jpamutils.interpolation.Bicubic;
-import org.jamdev.jpamutils.interpolation.Bilinear;
-import org.jamdev.jpamutils.interpolation.NearestNeighbor;
-import org.jamdev.jpamutils.spectrogram.SpecTransform;
 import org.junit.jupiter.api.Test;
 
-import PamUtils.PamArrayUtils;
-import ai.djl.MalformedModelException;
-import rawDeepLearningClassifier.dlClassification.archiveModel.SimpleArchiveModel;
-import rawDeepLearningClassifier.dlClassification.delphinID.Whistles2Image;
+import rawDeepLearningClassifier.dlClassification.delphinID.DelphinIDClickTest;
+import rawDeepLearningClassifier.dlClassification.delphinID.DelphinIDUtils;
+import rawDeepLearningClassifier.dlClassification.delphinID.DelphinIDUtils.ClickDetectionMAT;
+import rawDeepLearningClassifier.dlClassification.delphinID.DelphinIDUtils.DetectionGroupMAT;
+import rawDeepLearningClassifier.dlClassification.delphinID.DelphinIDWhistleTest;
 import us.hebi.matlab.mat.format.Mat5;
-import us.hebi.matlab.mat.types.MatFile;
+import us.hebi.matlab.mat.format.Mat5File;
 import us.hebi.matlab.mat.types.Matrix;
 
+
 public class DelphinIDTest { 
-	
-//	@Test
-//	public void whistle2ImageTest() {
-//	
-//		System.out.println("Whislte2Image test started");
-//
-//		/**
-//		 * Test whether the Whistles2Image transform works properly
-//		 */
-//		String relMatPath  =	"./src/test/resources/rawDeepLearningClassifier/DelphinID/whistle_image_example.mat";
-//
-//		Path path = Paths.get(relMatPath);
-//	
-//		// Create MAT file with a scalar in a nested struct
-//		try {
-//			MatFile matFile = Mat5.readFromFile(path.toString());
-//			Matrix array = matFile.getArray("tfvalues");
-//			
-//			//the values for the whistle detector.
-//			double[][] whistleValues = DLMatFile.matrix2array(array);
-//			
-////			//the image after compression 
-////			array = matFile.getArray("image1compressedgrayscale");
-////			double[][] compressedWhistleImage = DLMatFile.matrix2array(array);
-////
-////			//the whistle2Image transform image
-////			array = matFile.getArray("image1originalgrayscalenorm");
-////			double[][] whislteImage = DLMatFile.matrix2array(array);
-//			
-//			//now perform the image transform in Java 
-//			double[] freqLimits = new double[] {2000., 20000.};
-//			double[] size = new double[] {680., 480.};
-//			
-//			ArrayList<double[][]> whistleImageArr = new ArrayList<double[][]>();
-//			whistleImageArr.add(whistleValues);
-//			
-//			BufferedImage canvas = Whistles2Image.makeScatterImage(whistleImageArr, size, new double[]{50, 50. + 4.}, freqLimits,  10.);
-//
-//			double[][] imaged = new double[(int) size[0]][(int) size[1]];
-//
-//			float[] color = new float[3];
-//			Raster raster = canvas.getData();
-//			for (int i=0; i<imaged.length; i++) {
-//				for (int j=0; j<imaged[0].length; j++) {
-//					color = raster.getPixel(i,j, color);
-//					imaged[i][imaged[0].length-j-1] = (255-color[0])/255.; //normalize
-//				}
-//			}
-//			
-//			//imaged = PamArrayUtils.transposeMatrix(imaged);
-//
-//			//create the model transforms
-//			ArrayList<DLTransform> modelTransforms = new ArrayList<DLTransform>();
-////			modelTransforms.add(new FreqTransform(DLTransformType.SPECFLIP));
-////			modelTransforms.add(new FreqTransform(DLTransformType.SPECNORMALISE_MINIMAX));
-//			modelTransforms.add(new FreqTransform(DLTransformType.SPECRESIZE, new Number[] {Integer.valueOf(60), Integer.valueOf(80), SpecTransform.RESIZE_BICUBIC}));
-//			modelTransforms.add(new FreqTransform(DLTransformType.GAUSSIAN_FILTER, new Number[] {Double.valueOf(0.5)}));
-//
-//			
-//			SpecTransform specTransform = new SpecTransform(); 
-//			specTransform.setSpecData(imaged);
-//			specTransform.setSampleRate((float) (freqLimits[1]*2)); 
-//
-//			
-//			//set the spec transform
-//			((FreqTransform) modelTransforms.get(0)).setSpecTransfrom(specTransform);
-//
-//			//process all the transforms. 
-//			DLTransform transform = modelTransforms.get(0); 
-//			for (int i =0; i<modelTransforms.size(); i++) {
-//				transform = modelTransforms.get(i).transformData(transform); 
-//			}
-//			
-//			double[][] transformedData2 = ((FreqTransform) transform).getSpecTransfrom().getTransformedData(); 
-//			
-////			Bilinear interpolation1 = new Bilinear(JamArr.doubleToFloat(transformedData2));
-////			Bicubic interpolation2 = new Bicubic(JamArr.doubleToFloat(imaged));
-////
-////			System.out.println("Len input: " + imaged.length);
-////
-////			float[][] resizeArr = interpolation2.resize(Integer.valueOf(80), Integer.valueOf(60));
-////			
-////			System.out.println("Len resize: " + resizeArr.length);
-//
-//			System.out.println("Size Java: " + transformedData2.length + " x " + transformedData2[0].length);
-//
-//			//now save this image to a MATFILE
-//			// Create MAT file with a scalar in a nested struct
-//			MatFile matFileWrite = Mat5.newMatFile()
-//			    .addArray("image1originalgrayscalenorm",DLMatFile.array2Matrix(imaged))
-//			    .addArray("imagecompressedgrayscalenorm",DLMatFile.array2Matrix(transformedData2));
-////		    	.addArray("imagecompressedgrayscalenorm_nearest",DLMatFile.array2Matrix(JamArr.floatToDouble(resizeArr)));
-//
-//			// Serialize to disk using default configurations
-//			Mat5.writeToFile(matFileWrite, "/Users/au671271/MATLAB-Drive/MATLAB/PAMGUARD/deep_learning/delphinID/whistle_image_example_java.mat");
-//			
-//			System.out.println("Whislte2Image test finished");
-//			
-//		} catch (IOException e) {
-//			// TODO Auto-generated catch block
-//			e.printStackTrace();
-//			assertEquals(false, false);
-//		}
-//
-//	}
-	
+
+	/**
+	 * The slop allowed when comparing prediction values
+	 */
+	public static double predSlop = 0.05; 
+
+
 	@Test
-	public static void modelInputTest() {
-		
-		//test whether a single segment gives the correct answer. 
-//		boolean result = rawDeepLearningClassifier.dlClassification.delphinID.DelphinIDTest.testDelphinIDImage(null);
-		
-//		assertTrue(result); 
+	public void clickModelTest() {
+		System.out.println("DelphinID: click2Spectrum test");
+
+		String relModelPath  =	"./src/test/resources/rawDeepLearningClassifier/DelphinID/clickClassifier_Jan25.zip";
+		Path modelPath = Paths.get(relModelPath);
+
+
+		String clicksMatPath = "./src/test/resources/rawDeepLearningClassifier/DelphinID/clicks_20200918_123234_classified.mat";
+		Path clickPath = Paths.get(clicksMatPath);
+
+		//load the clicks
+		DetectionGroupMAT<ClickDetectionMAT> clicks = DelphinIDUtils.getClicksMAT(clicksMatPath);
+
+		//run the model
+		float[][] predictions = DelphinIDClickTest.testDelphinIDClickModel(modelPath.toString(), clicks, null, false); 
+
+		String predRelPath = "./src/test/resources/rawDeepLearningClassifier/DelphinID/click_preds_PAM_20200918_123234_366.mat";
+		Path predPath = Paths.get(predRelPath);
+
+
+		Mat5File matFile;
+		try {
+			matFile = Mat5.readFromFile(predPath.toString());
+
+			Matrix clickPredsMAT = matFile.getArray("predspython");
+			double[][] clickPreds = DLMatFile.matrix2array(clickPredsMAT); 
+
+			//now iterate through and check against true predictions.
+			int classIndex = 1; //the class to use as the test.
+			double calcPred;
+			double truePred;
+			for (int i=0; i<predictions.length; i++) {
+
+				if (predictions[i]==null || clickPreds[i]==null) {
+					continue;
+				}
+
+				System.out.print(String.format("Click segment: %d" , i));
+				for (int j=0; j<predictions[i].length; j++) {
+					System.out.print(String.format( " %.4f" , predictions[i][j])); 
+				}
+				System.out.println();
+
+				calcPred = predictions[i][classIndex];
+				
+				truePred = clickPreds[i][classIndex];
+
+				boolean passed = (calcPred - predSlop) < truePred &&  (calcPred + predSlop) > truePred;
+				
+				if (!passed) {
+					
+					System.out.print(String.format("SEGMENT FAIL: %d - true predictions" , i));
+					for (int j=0; j<predictions[i].length; j++) {
+						System.out.print(String.format( " %.4f" , clickPreds[i][j])); 
+					}
+					System.out.println();
+					
+				}
+				assertTrue(passed);
+
+			}
+
+		} catch (IOException e) {
+			// TODO Auto-generated catch block
+			assertTrue(false); 
+			e.printStackTrace();
+		}
+
+	}
+	
+
+//	@Test
+//	public void whistle2SpectrumTest() {
+//		System.out.println("DelphinID: whistle2Spectrum test");
+//
+//		assertEquals(false, false);
+//	}
+//
+//
+//	@Test
+//	public void click2SpectrumTest() {
+//		System.out.println("DelphinID: click model test");
+//
+//		assertEquals(false, false);
+//	}
+
+	@Test
+	public void whistleModelTest() {
+		System.out.println("DelphinID: whistle model test");
+				
+		double segLen = 4000.;
+		double segHop = 1000.0;
+		double startSeconds = 9.898656 + 0.1;  //seconds to start segments (so we can compare to Python) - note if this is wrong predicitons are slightly off...
+
+		String relModelPath  =	"./src/test/resources/rawDeepLearningClassifier/DelphinID/whistleclassifier.zip";
+		Path modelPath = Paths.get(relModelPath);
+
+
+		String whistlesMatPath = "./src/test/resources/rawDeepLearningClassifier/DelphinID/whistle_contours_20200918_123234.mat";
+		Path whisltePath = Paths.get(whistlesMatPath);
+
+	
+		float[][] predictions = DelphinIDWhistleTest.runWhistleModel(modelPath.toString(), whisltePath.toString(), null,  startSeconds,  segLen,  segHop);
+	
+
+		//the true whistle values
+		String predRelPath = "./src/test/resources/rawDeepLearningClassifier/DelphinID/whistle_preds_PAM_20200918_123234_366.mat";
+		Path predPath = Paths.get(predRelPath);
+
+
+		Mat5File matFile;
+		try {
+			matFile = Mat5.readFromFile(predPath.toString());
+			
+			
+			Matrix whistlesPredsMAT = matFile.getArray("predspython");
+			double[][] whistlePreds = DLMatFile.matrix2array(whistlesPredsMAT); 
+			
+			Matrix startTimesMAT = matFile.getArray("starttimes");
+			double[] startTimes = DelphinIDWhistleTest.matrix2array1D(startTimesMAT); 
+			
+			System.out.println("");
+
+			//now iterate through and check against true predictions.
+			int classIndex = 1; //the class to use as the test.
+			double calcPred;
+			double truePred;
+			for (int i=0; i<startTimes.length; i++) {
+				
+				int index = (int)  startTimes[i]; 
+
+				if (predictions[index]==null || whistlePreds[i]==null) {
+					continue;
+				}
+				
+				System.out.print(String.format("Whistle segment: %d" , index));
+				for (int j=0; j<predictions[index].length; j++) {
+					System.out.print(String.format( " %.4f" , predictions[index][j])); 
+				}
+				System.out.println();
+
+				calcPred = predictions[index][classIndex];
+				
+				truePred = whistlePreds[i][classIndex];
+
+				boolean passed = (calcPred - predSlop) < truePred &&  (calcPred + predSlop) > truePred;
+				
+				if (!passed) {
+					
+					System.out.print(String.format("SEGMENT FAIL: %.0f s index %d- true predictions" ,  startTimes[i], index));
+					for (int j=0; j<predictions[index].length; j++) {
+						System.out.print(String.format( " %.4f" , whistlePreds[i][j])); 
+					}
+					System.out.println();
+					
+				}
+				
+				assertTrue(passed);
+
+			}
+
+		} catch (IOException e) {
+			// TODO Auto-generated catch block
+			assertTrue(false); 
+			e.printStackTrace();
+		}
+
+
+		assertEquals(false, false);
 	}
 
+
+	//	@Test
+	//	public void whistle2ImageTest() {
+	//	
+	//		System.out.println("Whislte2Image test started");
+	//
+	//		/**
+	//		 * Test whether the Whistles2Image transform works properly
+	//		 */
+	//		String relMatPath  =	"./src/test/resources/rawDeepLearningClassifier/DelphinID/whistle_image_example.mat";
+	//
+	//		Path path = Paths.get(relMatPath);
+	//	
+	//		// Create MAT file with a scalar in a nested struct
+	//		try {
+	//			MatFile matFile = Mat5.readFromFile(path.toString());
+	//			Matrix array = matFile.getArray("tfvalues");
+	//			
+	//			//the values for the whistle detector.
+	//			double[][] whistleValues = DLMatFile.matrix2array(array);
+	//			
+	////			//the image after compression 
+	////			array = matFile.getArray("image1compressedgrayscale");
+	////			double[][] compressedWhistleImage = DLMatFile.matrix2array(array);
+	////
+	////			//the whistle2Image transform image
+	////			array = matFile.getArray("image1originalgrayscalenorm");
+	////			double[][] whislteImage = DLMatFile.matrix2array(array);
+	//			
+	//			//now perform the image transform in Java 
+	//			double[] freqLimits = new double[] {2000., 20000.};
+	//			double[] size = new double[] {680., 480.};
+	//			
+	//			ArrayList<double[][]> whistleImageArr = new ArrayList<double[][]>();
+	//			whistleImageArr.add(whistleValues);
+	//			
+	//			BufferedImage canvas = Whistles2Image.makeScatterImage(whistleImageArr, size, new double[]{50, 50. + 4.}, freqLimits,  10.);
+	//
+	//			double[][] imaged = new double[(int) size[0]][(int) size[1]];
+	//
+	//			float[] color = new float[3];
+	//			Raster raster = canvas.getData();
+	//			for (int i=0; i<imaged.length; i++) {
+	//				for (int j=0; j<imaged[0].length; j++) {
+	//					color = raster.getPixel(i,j, color);
+	//					imaged[i][imaged[0].length-j-1] = (255-color[0])/255.; //normalize
+	//				}
+	//			}
+	//			
+	//			//imaged = PamArrayUtils.transposeMatrix(imaged);
+	//
+	//			//create the model transforms
+	//			ArrayList<DLTransform> modelTransforms = new ArrayList<DLTransform>();
+	////			modelTransforms.add(new FreqTransform(DLTransformType.SPECFLIP));
+	////			modelTransforms.add(new FreqTransform(DLTransformType.SPECNORMALISE_MINIMAX));
+	//			modelTransforms.add(new FreqTransform(DLTransformType.SPECRESIZE, new Number[] {Integer.valueOf(60), Integer.valueOf(80), SpecTransform.RESIZE_BICUBIC}));
+	//			modelTransforms.add(new FreqTransform(DLTransformType.GAUSSIAN_FILTER, new Number[] {Double.valueOf(0.5)}));
+	//
+	//			
+	//			SpecTransform specTransform = new SpecTransform(); 
+	//			specTransform.setSpecData(imaged);
+	//			specTransform.setSampleRate((float) (freqLimits[1]*2)); 
+	//
+	//			
+	//			//set the spec transform
+	//			((FreqTransform) modelTransforms.get(0)).setSpecTransfrom(specTransform);
+	//
+	//			//process all the transforms. 
+	//			DLTransform transform = modelTransforms.get(0); 
+	//			for (int i =0; i<modelTransforms.size(); i++) {
+	//				transform = modelTransforms.get(i).transformData(transform); 
+	//			}
+	//			
+	//			double[][] transformedData2 = ((FreqTransform) transform).getSpecTransfrom().getTransformedData(); 
+	//			
+	////			Bilinear interpolation1 = new Bilinear(JamArr.doubleToFloat(transformedData2));
+	////			Bicubic interpolation2 = new Bicubic(JamArr.doubleToFloat(imaged));
+	////
+	////			System.out.println("Len input: " + imaged.length);
+	////
+	////			float[][] resizeArr = interpolation2.resize(Integer.valueOf(80), Integer.valueOf(60));
+	////			
+	////			System.out.println("Len resize: " + resizeArr.length);
+	//
+	//			System.out.println("Size Java: " + transformedData2.length + " x " + transformedData2[0].length);
+	//
+	//			//now save this image to a MATFILE
+	//			// Create MAT file with a scalar in a nested struct
+	//			MatFile matFileWrite = Mat5.newMatFile()
+	//			    .addArray("image1originalgrayscalenorm",DLMatFile.array2Matrix(imaged))
+	//			    .addArray("imagecompressedgrayscalenorm",DLMatFile.array2Matrix(transformedData2));
+	////		    	.addArray("imagecompressedgrayscalenorm_nearest",DLMatFile.array2Matrix(JamArr.floatToDouble(resizeArr)));
+	//
+	//			// Serialize to disk using default configurations
+	//			Mat5.writeToFile(matFileWrite, "/Users/au671271/MATLAB-Drive/MATLAB/PAMGUARD/deep_learning/delphinID/whistle_image_example_java.mat");
+	//			
+	//			System.out.println("Whislte2Image test finished");
+	//			
+	//		} catch (IOException e) {
+	//			// TODO Auto-generated catch block
+	//			e.printStackTrace();
+	//			assertEquals(false, false);
+	//		}
+	//
+	//	}
+
+
 }

src/test/rawDeepLearningClassifier/PamZipDLClassifierTest.java

@@ -1,27 +1,169 @@
 package test.rawDeepLearningClassifier;
 
+import static org.junit.Assert.assertTrue;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+
+import java.io.BufferedReader;
+import java.io.File;
+import java.io.FileReader;
+import java.nio.file.Path;
+import java.nio.file.Paths;
+import java.util.ArrayList;
+
+import org.jamdev.jdl4pam.genericmodel.GenericModel;
+import org.jamdev.jdl4pam.utils.DLUtils;
+import org.jamdev.jpamutils.wavFiles.AudioData;
 import org.junit.jupiter.api.Test;
 
+import PamUtils.PamArrayUtils;
+import rawDeepLearningClassifier.defaultModels.HumpbackWhaleAtlantic;
+import rawDeepLearningClassifier.defaultModels.MultiSpeciesGoogle;
+import rawDeepLearningClassifier.dlClassification.genericModel.GenericModelParams;
+import rawDeepLearningClassifier.dlClassification.genericModel.GenericModelWorker;
+import rawDeepLearningClassifier.dlClassification.genericModel.StandardPrediction;
+import rawDeepLearningClassifier.segmenter.GroupedRawData;
+
+
+/**
+ * Test models that are zipped with a PMAGuard pgdl settings file. 
+ */
 public class PamZipDLClassifierTest {
-	
+
 	/**
 	 * Test the koogu classifier and tests are working properly for a PAMGuard zip model - i.e. this is a very similar model to Koogu but zipped with a .zip 
 	 * filename instead of .kgu. 
 	 */
+	//	@Test
+	//	public void blueWhaleKooguTest() {
+	//		//relative paths to the resource folders.
+	//		String relModelPath  =	"./src/test/resources/rawDeepLearningClassifier/PamZip/blue_whale_24.zip";
+	//		
+	//		//the zip classifier is the same as the 
+	//		String relWavPath  =	"./src/test/resources/rawDeepLearningClassifier/Koogu/blue_whale_24/20190527_190000.wav";
+	//		String relMatPath  =	"./src/test/resources/rawDeepLearningClassifier/Koogu/blue_whale_24/rawScores_20190527_190000.mat";
+	//		
+	//		//metadata says it should be used with Koogu classifier.
+	//		KooguDLClassifierTest.runKooguClassifier( relModelPath,  relWavPath,  relMatPath);
+	//	}
+
+
+
+	/**
+	 * Test Google's humpback whale model. 
+	 */
 	@Test
-	public void zipClassifierTest() {
+	public void multiSpeciesGoogleTest() {
+
+		System.out.println("*****Generic DL: Humpback whale test*****"); 
+
 		//relative paths to the resource folders.
-		String relModelPath  =	"./src/test/resources/rawDeepLearningClassifier/PamZip/blue_whale_24.zip";
-		
-		//the zip classifier is the same as the 
-		String relWavPath  =	"./src/test/resources/rawDeepLearningClassifier/Koogu/blue_whale_24/20190527_190000.wav";
-		String relMatPath  =	"./src/test/resources/rawDeepLearningClassifier/Koogu/blue_whale_24/rawScores_20190527_190000.mat";
-		
-		//metadata says it should be used with Koogu classifier.
-		KooguDLClassifierTest.runKooguClassifier( relModelPath,  relWavPath,  relMatPath);
+		String relModelPath  =	"./src/test/resources/rawDeepLearningClassifier/Generic/multi-species-Google/multispecies-whale-tensorflow2-default-v2/saved_model.pb";
+		String relWavPath  =	"./src/test/resources/rawDeepLearningClassifier/Generic/multi-species-Google/Cross_24kHz.wav";
+		String resultsPath  =	"./src/test/resources/rawDeepLearningClassifier/Generic/multi-species-Google/Cross_24kHz_scores.csv";
+
+		Path path = Paths.get(relModelPath);
+
+		GenericModelWorker genericModelWorker = new GenericModelWorker(); 
+
+		GenericModelParams genericModelParams = new GenericModelParams(); 
+
+		genericModelParams.modelPath =  path.toAbsolutePath().normalize().toString();
+
+		path = Paths.get(relWavPath);
+		String wavFilePath = path.toAbsolutePath().normalize().toString();
+
+		MultiSpeciesGoogle multiSpeciesGoogle = new MultiSpeciesGoogle();
+		multiSpeciesGoogle.setParams(genericModelParams);
+
+		System.out.println(genericModelParams);
+
+
+		//TEMP 
+
+
+
+		try {
+			//load audio
+			AudioData soundData = DLUtils.loadWavFile(wavFilePath);
+			
+//			double[] amplitudes = soundData.getScaledSampleAmplitudes();
+//
+//
+//			float[] ampltiudesf = PamArrayUtils.double2Float(amplitudes);
+//			float[] ampltiudesfchunk = new float[120000]; 
+//			int offset = 24000; 
+//			System.arraycopy(ampltiudesf, 0+offset, ampltiudesfchunk, 0, 120000);
+//
+//			//			System.out.println("MAX AMPLITUDE: " + PamArrayUtils.max(amplitudes)); 
+//
+//			GenericModel genericModel  = new GenericModel( genericModelParams.modelPath) ;
+//			float[][] input = new float[][]{ampltiudesfchunk}; 
+//			float[] output2 = genericModel.runModel(input); 
+//
+//			System.out.println("----TEST OUT----"); 
+//			PamArrayUtils.printArray(output2);
+
+			//prep the model
+			genericModelWorker.prepModel(genericModelParams, null);
+
+
+			//load true predictions file. 
+			File file = new File(resultsPath);
+			FileReader fr = new FileReader(file);	
+			BufferedReader br = new BufferedReader(fr);
+			String line;
+			int ind=0; 
+
+			int  startChunk=0; 
+
+			int classIndex = 1; 
+			while((line = br.readLine()) != null){
+				if (ind>0) {
+					//read the data from the text file
+					String[] data = line.split(",");
+
+					double[] predictions = new double[data.length]; 
+					for (int i=0; i<data.length; i++) {
+						predictions[i] = Double.valueOf(data[i]); 
+					}
+
+					//each line is a list of prediction for each class; 
+					int chunkSize=120000;
+
+					GroupedRawData groupedRawData = new GroupedRawData(0, 1, 0, chunkSize, chunkSize);
+					groupedRawData.copyRawData(soundData.getScaledSampleAmplitudes(), startChunk, chunkSize, 0);
+
+					//					System.out.println("MAX AMPLITUDE: " + PamArrayUtils.max(groupedRawData.getRawData()[0])); 
+
+					ArrayList<GroupedRawData> groupedData = new ArrayList<GroupedRawData>();
+
+					groupedData.add(groupedRawData);
+
+					ArrayList<StandardPrediction> genericPrediction = genericModelWorker.runModel(groupedData, soundData.sampleRate, 0);		
+
+					float[] output = genericPrediction.get(0).getPrediction();
+
+					boolean passed  = (output[classIndex]>predictions[classIndex]-0.05 && output[classIndex]<predictions[classIndex]+0.05); 
+
+					System.out.println(String.format("Chunk %d %d Predicted output: %.5f true output: %.5f passed: %b -- sum %.2f ", ind, startChunk,
+							output[classIndex], predictions[classIndex], passed, PamArrayUtils.sum(output))); 
+
+					//PamArrayUtils.printArray(output);
+
+					//allow 10% scrumph to take account of slight differences in Java input. 
+					//assertTrue(passed); //humpback whale detection
+
+					startChunk+=24000; //one second step
+
+				}
+				ind++;
+			}
+
+		} catch (Exception e) {
+			// TODO Auto-generated catch block
+			e.printStackTrace();
+		}
 	}
-	
-	
-	
+
 
 }

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