Getting click deep learning model

.gitignore

@@ -112,3 +112,7 @@ settings.xml
 .classpath
 .settings/org.eclipse.jdt.core.prefs
 .classpath
+.classpath
+.settings/org.eclipse.core.resources.prefs
+.settings/org.eclipse.jdt.core.prefs
+dependency-reduced-pom.xml

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

@@ -1,6 +1,5 @@
 eclipse.preferences.version=1
 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

@@ -1,9 +1,9 @@
 eclipse.preferences.version=1
 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=21
+org.eclipse.jdt.core.compiler.codegen.targetPlatform=18
 org.eclipse.jdt.core.compiler.codegen.unusedLocal=preserve
-org.eclipse.jdt.core.compiler.compliance=21
+org.eclipse.jdt.core.compiler.compliance=18
 org.eclipse.jdt.core.compiler.debug.lineNumber=generate
 org.eclipse.jdt.core.compiler.debug.localVariable=generate
 org.eclipse.jdt.core.compiler.debug.sourceFile=generate
@@ -13,4 +13,4 @@ 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=21
+org.eclipse.jdt.core.compiler.source=18

dependency-reduced-pom.xml

@@ -4,7 +4,7 @@
   <groupId>org.pamguard</groupId>
   <artifactId>Pamguard</artifactId>
   <name>Pamguard</name>
-  <version>2.02.12</version>
+  <version>2.02.11d</version>
   <description>Pamguard using Maven to control dependencies</description>
   <url>www.pamguard.org</url>
   <organization>

src/PamUtils/PamArrayUtils.java

@@ -713,6 +713,34 @@ public class PamArrayUtils {
 
 	}
 	
+
+	/**
+	 * Normalise an array
+	 * @param arr  - the array to normalise
+	 * @param scaleFactor - multiply the resulting array by a scale factor. 
+	 * @return normalised copy of the array
+	 */
+	public static float[] normalise(float[] arr, double scaleFactor) {
+		//		//first find the sum of the square of the wave
+		if (arr != null) { 
+			int n = arr.length; 
+			double sum = 0.0; 
+
+			for (int i = 0; i < n; i++) { 
+				sum += arr[i] * arr[i]; 
+			} 
+			sum = Math.pow(sum, 0.5); 
+
+			float[] normArr=new float[arr.length]; 
+			for (int i=0; i<normArr.length; i++) {
+				normArr[i]=(float) (scaleFactor*arr[i]/sum); 
+			}
+			return normArr; 
+		}
+		else return null; 
+	}
+
+
 	/**
 	 * Flip a double array so that it is in the reverse order. Note the array is 
 	 * cloned. 
@@ -1274,6 +1302,7 @@ public class PamArrayUtils {
 
 
 
+
 
 
 }

src/rawDeepLearningClassifier/dlClassification/genericModel/GenericModelTest.java

@@ -1,5 +1,7 @@
 package rawDeepLearningClassifier.dlClassification.genericModel;
 
+import java.nio.file.Path;
+import java.nio.file.Paths;
 import java.util.ArrayList;
 
 import org.jamdev.jdl4pam.genericmodel.GenericModel;
@@ -13,6 +15,13 @@ import org.jamdev.jdl4pam.transforms.WaveTransform;
 import org.jamdev.jdl4pam.utils.DLUtils;
 import org.jamdev.jpamutils.wavFiles.AudioData;
 
+import PamUtils.PamArrayUtils;
+import rawDeepLearningClassifier.segmenter.GroupedRawData;
+import us.hebi.matlab.mat.format.Mat5;
+import us.hebi.matlab.mat.format.Mat5File;
+import us.hebi.matlab.mat.types.Matrix;
+import us.hebi.matlab.mat.types.Struct;
+
 public class GenericModelTest {
 
 	public static void rightWhaleTest() {
@@ -98,9 +107,126 @@ public class GenericModelTest {
 		} catch (Exception e) {
 			e.printStackTrace();
 		}
-
 	}
 
+
+
+	public static void clickDLTest() {
+
+		float SAMPLE_RATE = 500000;
+		//relative paths to the resource folders.
+		System.out.println("*****Click classification Deep Learning C*****"); 
+
+		//relative paths to the resource folders.		
+		String relModelPath  =	"./src/test/resources/rawDeepLearningClassifier/Generic/risso_click/uniform_model/saved_model.pb";
+		String clicksPath  =	"./src/test/resources/rawDeepLearningClassifier/Generic/risso_click/clicks.mat";
+
+		Path path = Paths.get(relModelPath);
+
+		GenericModelWorker genericModelWorker = new GenericModelWorker(); 
+
+		GenericModelParams genericModelParams = new GenericModelParams(); 
+		genericModelParams.modelPath =  path.toAbsolutePath().normalize().toString();
+
+		//create the transforms. 
+		ArrayList<DLTransfromParams> dlTransformParamsArr = new ArrayList<DLTransfromParams>();
+		//waveform transforms. 
+		dlTransformParamsArr.add(new SimpleTransformParams(DLTransformType.DECIMATE_SCIPY, 250000.)); 
+		dlTransformParamsArr.add(new SimpleTransformParams(DLTransformType.PEAK_TRIM, 128, 1)); 
+		dlTransformParamsArr.add(new SimpleTransformParams(DLTransformType.NORMALISE_WAV, 0., 1, AudioData.ZSCORE)); 
+
+		genericModelParams.dlTransfromParams = dlTransformParamsArr;
+		genericModelParams.dlTransfroms = DLTransformsFactory.makeDLTransforms((ArrayList<DLTransfromParams>)genericModelParams.dlTransfromParams); 
+
+
+		//create the clicks. 
+		path = Paths.get(clicksPath);
+		ArrayList<GroupedRawData> clicks = importClicks(path.toAbsolutePath().normalize().toString(),  SAMPLE_RATE); 
+
+		//prep the model
+		genericModelWorker.prepModel(genericModelParams, null);
+
+		System.out.println("Model has loaded"); 
+
+		ArrayList<GroupedRawData> groupedData = new ArrayList<GroupedRawData>();
+
+
+		float prediction = 0; 
+
+		for (int i=0; i<clicks.size() ; i++) {
+			groupedData = new ArrayList<GroupedRawData>();
+			groupedData.add(clicks.get(i)); //TODO for loop
+
+//			System.out.println("Waveform input: " + groupedData.get(i).getRawData().length + " " + groupedData.get(i).getRawData()[0].length + "  " + + groupedData.get(i).getRawData()[0][0]);
+
+			//RUN THE RAW MODEL
+//			System.out.println("Min max before: "); 
+//			PamArrayUtils.printArray(PamArrayUtils.minmax(groupedData.get(i).getRawData()[0])); 
+
+//			double[] wav = PamArrayUtils.normalise(groupedData.get(i).getRawData()[0]);
+//
+//			System.out.println("Min max: "); 
+//			PamArrayUtils.printArray(PamArrayUtils.minmax(wav));
+//			float[][] input1 = new float[][] {PamArrayUtils.double2Float(wav)};
+//			float[] output1 = genericModelWorker.getModel().runModel(input1);
+//			System.out.println("Output1: " );
+//			PamArrayUtils.printArray(output1);
+
+			//RUN THROUGH THE GENERIC MODEL CLASSIIFER. 
+			ArrayList<StandardPrediction> genericPrediction = genericModelWorker.runModel(groupedData,SAMPLE_RATE, 0);		
+
+			float[] output = genericPrediction.get(0).getPrediction();
+
+			System.out.println(String.format("Click %d Predicted output: %.6f true output: %.6f passed: %b", clicks.get(i).getUID(),
+					output[0], prediction, output[0]>prediction*0.9 && output[0]<prediction*1.1)); 
+		}
+	}
+
+	/**
+	 * Import a bunch of clicks from a .mat file
+	 */
+	public static ArrayList<GroupedRawData> importClicks(String filePath, float sR) {
+		try {
+			Mat5File mfr = Mat5.readFromFile(filePath);
+
+			//		//get array of a name "my_array" from file
+			Struct mlArrayRetrived = mfr.getStruct( "clickpreds" );
+
+			int numClicks= mlArrayRetrived.getNumCols();
+			ArrayList<GroupedRawData> clicks = new ArrayList<GroupedRawData>(numClicks); 
+
+			GroupedRawData clickData;
+			for (int i=0; i<numClicks; i++) {
+				Matrix clickWav= mlArrayRetrived.get("wave", i);
+
+				double[][] clickwaveform= PamArrayUtils.matrix2array(clickWav);
+
+				clickwaveform = PamArrayUtils.transposeMatrix(clickwaveform);
+				//System.out.println("click: " + click[0].length + " num: " + numClicks);
+
+				Matrix clickUID= mlArrayRetrived.get("UID", i);
+				Matrix clickmillis= mlArrayRetrived.get("millis", i);
+				Matrix channelMap= mlArrayRetrived.get("channelMap", i);
+				Matrix startSample= mlArrayRetrived.get("startSample", i);
+				Matrix sampleDuration= mlArrayRetrived.get("sampleDuration", i);
+
+				clickData = new GroupedRawData(clickmillis.getLong(0), channelMap.getInt(0), startSample.getLong(0), sampleDuration.getLong(0), sampleDuration.getInt(0));
+				clickData.setUID(clickUID.getLong(0));
+				clickData.setRawData(clickwaveform);
+				
+				clicks.add(clickData); 
+			}
+			return clicks; 
+		} 
+		catch (Exception e) {
+			// TODO Auto-generated catch block
+			e.printStackTrace();
+			return null; 
+		}
+	}
+
+
+
 	/**
 	 * The bat Pytorch test. 
 	 */
@@ -110,7 +236,8 @@ public class GenericModelTest {
 	}
 
 	public static void main(String args[]) {
-		rightWhaleTest();
+		//		rightWhaleTest();
+		clickDLTest();
 	}
 
 }

src/rawDeepLearningClassifier/dlClassification/genericModel/GenericModelWorker.java

@@ -6,6 +6,7 @@ import org.apache.commons.io.FilenameUtils;
 import org.jamdev.jdl4pam.genericmodel.GenericModel;
 import org.jamdev.jdl4pam.transforms.DLTransform;
 import org.jamdev.jdl4pam.transforms.FreqTransform;
+import org.jamdev.jpamutils.JamArr;
 
 import PamModel.PamModel;
 import PamModel.PamModel.PluginClassloader;
@@ -42,10 +43,12 @@ public class GenericModelWorker extends DLModelWorker<StandardPrediction> {
 			//run a model if it is waveform info. 
 			float[][] waveStack = new float[transformedDataStack.length][]; 
 			for (int i=0; i<waveStack.length; i++) {
+//				waveStack[i] = PamArrayUtils.double2Float(JamArr.product(PamArrayUtils.float2Double(transformedDataStack[i][0]), 0.99));
 				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.printArray(waveStack[0]);
 			results =  getModel().runModel(waveStack);
 		}
 //		System.out.println("GENERIC MODEL RESULTS: " + (results== null ? null : results.length));

src/test/rawDeepLearningClassifier/ClickDLTest.java

@@ -31,10 +31,10 @@ public class ClickDLTest {
 		
 		float SAMPLE_RATE = 500000;
 		//relative paths to the resource folders.
-		System.out.println("*****Click classification Deep Learning*****"); 
+		System.out.println("*****Click classification Deep Learning C*****"); 
 
 		//relative paths to the resource folders.		
-		String relModelPath  =	"./src/test/resources/rawDeepLearningClassifier/Generic/risso_click/best_model/saved_model.pb";
+		String relModelPath  =	"./src/test/resources/rawDeepLearningClassifier/Generic/risso_click/updated_model/saved_model.pb";
 		String clicksPath  =	"./src/test/resources/rawDeepLearningClassifier/Generic/risso_click/clicks.mat";
 
 		Path path = Paths.get(relModelPath);
@@ -62,6 +62,7 @@ public class ClickDLTest {
 		//prep the model
 		genericModelWorker.prepModel(genericModelParams, null);
 		
+		System.out.println("Model has loaded"); 
 
 		ArrayList<GroupedRawData> groupedData = new ArrayList<GroupedRawData>();
 		
@@ -126,7 +127,7 @@ public class ClickDLTest {
 			e.printStackTrace();
 			return null; 
 		}
-
 	}
 	
+	
 }