diff --git a/cfg/transducerers.properties b/cfg/transducerers.properties index eeb3dab..96cfb87 100644 --- a/cfg/transducerers.properties +++ b/cfg/transducerers.properties @@ -1,7 +1,7 @@ BIOCLIMATE_HSPEC=org.gcube.dataanalysis.ecoengine.transducers.BioClimateHSPECTransducer BIOCLIMATE_HCAF=org.gcube.dataanalysis.ecoengine.transducers.BioClimateHCAFTransducer BIOCLIMATE_HSPEN=org.gcube.dataanalysis.ecoengine.transducers.BioClimateHSPENTransducer -#HCAF_INTERPOLATION=org.gcube.dataanalysis.ecoengine.transducers.InterpolationTransducer +HCAF_INTERPOLATION=org.gcube.dataanalysis.ecoengine.transducers.InterpolationTransducer HCAF_FILTER=org.gcube.dataanalysis.ecoengine.transducers.simplequeryexecutors.HcafFilter HSPEN_FILTER=org.gcube.dataanalysis.ecoengine.transducers.simplequeryexecutors.HspenFilter ABSENCE_CELLS_FROM_AQUAMAPS=org.gcube.dataanalysis.ecoengine.transducers.simplequeryexecutors.MarineAbsencePointsFromAquamapsDistribution diff --git a/cfg/userperspective.properties b/cfg/userperspective.properties index f7ea9a8..f64fcc8 100644 --- a/cfg/userperspective.properties +++ b/cfg/userperspective.properties @@ -1,6 +1,6 @@ ANOMALIES_DETECTION=DBSCAN,KMEANS,XMEANS CLASSIFICATION=FEED_FORWARD_A_N_N_DISTRIBUTION -CLIMATE=BIOCLIMATE_HSPEC,BIOCLIMATE_HCAF,BIOCLIMATE_HSPEN +CLIMATE=BIOCLIMATE_HSPEC,BIOCLIMATE_HCAF,BIOCLIMATE_HSPEN,HCAF_INTERPOLATION CORRELATION_ANALYSIS=HRS DATA_CLUSTERING=DBSCAN,KMEANS,XMEANS FILTERING=HCAF_FILTER,HSPEN_FILTER diff --git a/deployDesktop.jardesc b/deployDesktop.jardesc index 2823481..2e5538a 100644 --- a/deployDesktop.jardesc +++ b/deployDesktop.jardesc @@ -1,6 +1,6 @@ - + diff --git a/src/main/java/org/gcube/contentmanagement/graphtools/abstracts/GenericStandaloneGraph.java b/src/main/java/org/gcube/contentmanagement/graphtools/abstracts/GenericStandaloneGraph.java index cbc6d7a..652a645 100644 --- a/src/main/java/org/gcube/contentmanagement/graphtools/abstracts/GenericStandaloneGraph.java +++ b/src/main/java/org/gcube/contentmanagement/graphtools/abstracts/GenericStandaloneGraph.java @@ -184,4 +184,5 @@ public abstract class GenericStandaloneGraph extends ApplicationFrame { return dataset; } + } diff --git a/src/main/java/org/gcube/dataanalysis/ecoengine/models/testing/FeedForwardNNFile.java b/src/main/java/org/gcube/dataanalysis/ecoengine/models/testing/FeedForwardNNFile.java index c03b4a1..3676ee2 100644 --- a/src/main/java/org/gcube/dataanalysis/ecoengine/models/testing/FeedForwardNNFile.java +++ b/src/main/java/org/gcube/dataanalysis/ecoengine/models/testing/FeedForwardNNFile.java @@ -1,11 +1,14 @@ package org.gcube.dataanalysis.ecoengine.models.testing; +import java.awt.Color; +import java.awt.Image; import java.io.File; import java.util.ArrayList; import java.util.Arrays; import java.util.HashMap; import java.util.List; +import org.gcube.contentmanagement.graphtools.data.conversions.ImageTools; import org.gcube.contentmanagement.lexicalmatcher.utils.AnalysisLogger; import org.gcube.dataanalysis.ecoengine.configuration.AlgorithmConfiguration; import org.gcube.dataanalysis.ecoengine.datatypes.ColumnType; @@ -25,10 +28,58 @@ import org.gcube.dataanalysis.ecoengine.models.ModelAquamapsNN; import org.gcube.dataanalysis.ecoengine.models.cores.neuralnetworks.Neural_Network; import org.gcube.dataanalysis.ecoengine.utils.DatabaseFactory; import org.gcube.dataanalysis.ecoengine.utils.DatabaseUtils; +import org.jfree.chart.JFreeChart; +import org.jfree.chart.axis.NumberAxis; +import org.jfree.chart.plot.XYPlot; +import org.jfree.chart.renderer.xy.XYSplineRenderer; +import org.jfree.data.general.Dataset; +import org.jfree.data.xy.XYDataset; +import org.jfree.data.xy.XYSeriesCollection; +import org.jfree.ui.RectangleInsets; + +public class FeedForwardNNFile extends ModelAquamapsNN { + + final float frequency = 0.3f;// 1f; + int samplingRate = 32; + float timeShift = 1f / (float) samplingRate; + + double[] generateSinSignal() { + double samples[] = new double[200]; + float time = 0; + for (int i = 0; i < samples.length; i++) { + samples[i] = (float) Math.sin(2f * Math.PI * frequency * time); + time += timeShift; + } + return samples; + } + + public static Image displaySignal(double[] signal, float timeshift) { + org.jfree.data.xy.XYSeries xyseries = new org.jfree.data.xy.XYSeries("signal"); + float time = 0; + for (int i = 0; i < signal.length; i++) { + xyseries.add(time, signal[i]); + time = time + timeshift; + } + + XYSeriesCollection collection = new XYSeriesCollection(xyseries); + + NumberAxis numberaxis = new NumberAxis("X"); + numberaxis.setAutoRangeIncludesZero(true); + NumberAxis numberaxis1 = new NumberAxis("Y"); + numberaxis1.setAutoRangeIncludesZero(true); + XYSplineRenderer xysplinerenderer = new XYSplineRenderer(); + XYPlot xyplot = new XYPlot((XYDataset) collection, numberaxis, numberaxis1, xysplinerenderer); + xyplot.setBackgroundPaint(Color.lightGray); + xyplot.setDomainGridlinePaint(Color.white); + xyplot.setRangeGridlinePaint(Color.white); + xyplot.setAxisOffset(new RectangleInsets(4D, 4D, 4D, 4D)); + JFreeChart chart = new JFreeChart("Numeric Series", JFreeChart.DEFAULT_TITLE_FONT, xyplot, true); + Image image = ImageTools.toImage(chart.createBufferedImage(640, 480)); + + return image; + + } -public class FeedForwardNNFile extends ModelAquamapsNN{ - - @Override public String getName() { return "FEED_FORWARD_ANN"; @@ -38,33 +89,40 @@ public class FeedForwardNNFile extends ModelAquamapsNN{ public String getDescription() { return "A Neural Networks to be trained on features of Real values"; } - + @Override public StatisticalType getOutput() { - HashMap map = new HashMap(); - PrimitiveType p = new PrimitiveType(File.class.getName(), new File(fileName), PrimitiveTypes.FILE, "NeuralNetwork","Trained Neural Network"); - PrimitiveType score = new PrimitiveType(String.class.getName(), ""+nn.en, PrimitiveTypes.STRING, "LearningScore","Learning Score"); + HashMap map = new HashMap(); + PrimitiveType p = new PrimitiveType(File.class.getName(), new File(fileName), PrimitiveTypes.FILE, "NeuralNetwork", "Trained Neural Network"); + PrimitiveType score = new PrimitiveType(String.class.getName(), "" + nn.en, PrimitiveTypes.STRING, "LearningScore", "Learning Score"); List template = new ArrayList(); template.add(TableTemplates.GENERIC); - OutputTable outTable = new OutputTable(template, trainingDataSet, trainingDataSet, "Output table"); + OutputTable outTable = new OutputTable(template, "Example Trained Table", trainingDataSet, "Output table"); map.put("NeuralNetwork", p); map.put("LearningScore", score); map.put("FormerInputTable", outTable); - PrimitiveType output = new PrimitiveType(HashMap.class.getName(), map, PrimitiveTypes.MAP, "ResultsMap","Results Map"); - + HashMap producedImages = new HashMap(); + producedImages.put("Random img", displaySignal(generateSinSignal(), timeShift)); + + PrimitiveType images = new PrimitiveType("Random Images", producedImages, PrimitiveTypes.IMAGES, "Charts", "A map with keys and Images"); + + map.put("Images", images); + + PrimitiveType output = new PrimitiveType(HashMap.class.getName(), map, PrimitiveTypes.MAP, "ResultsMap", "Results Map"); + return output; } - + @Override public float getStatus() { - if (status==100) + if (status == 100) return status; - else if ((nn!=null) && (nn.status>0)) - return nn.status*100f; + else if ((nn != null) && (nn.status > 0)) + return nn.status * 100f; else - return status; + return status; } - + protected static String TrainingDataSet = "TrainingDataSet"; protected String trainingDataSet; protected static String TrainingDataSetColumns = "TrainingColumns"; @@ -78,27 +136,27 @@ public class FeedForwardNNFile extends ModelAquamapsNN{ protected static String LearningThreshold = "LearningThreshold"; protected static String MaxIterations = "MaxIterations"; protected static String ModelName = "ModelName"; - protected static String UserName= "UserName"; + protected static String UserName = "UserName"; protected float learningThr; - protected int maxiter; - + protected int maxiter; + @Override public List getInputParameters() { List parameters = new ArrayList(); List templatesOccurrences = new ArrayList(); templatesOccurrences.add(TableTemplates.GENERIC); - - InputTable p1 = new InputTable(templatesOccurrences,TrainingDataSet,"a table containing real values colums for training the ANN"); + + InputTable p1 = new InputTable(templatesOccurrences, TrainingDataSet, "a table containing real values colums for training the ANN"); ColumnTypesList p2 = new ColumnTypesList(TrainingDataSet, TrainingDataSetColumns, "column names to use as features vectors", false); ColumnType p3 = new ColumnType(TrainingDataSet, TrainingDataSetTargetColumn, "the column to use as target", "probability", false); - PrimitiveTypesList p4 = new PrimitiveTypesList(Integer.class.getName(), PrimitiveTypes.NUMBER,LayersNeurons,"a list of neurons number for each inner layer",true); - PrimitiveType p5 = new PrimitiveType(String.class.getName(), null, PrimitiveTypes.STRING, Reference,"the phenomenon this ANN is trying to model - can be a generic identifier. Put 1 for not specifying","1"); - PrimitiveType p6 = new PrimitiveType(Float.class.getName(), null, PrimitiveTypes.NUMBER, LearningThreshold,"the learning threshold for this ANN","0.01"); - PrimitiveType p7 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, MaxIterations,"the maximum number of iterations in the training","100"); - PrimitiveType p11 = new PrimitiveType(String.class.getName(), null, PrimitiveTypes.STRING, ModelName,"The name of this Neural Network - insert without spaces","neuralnet_"); - ServiceType p10 = new ServiceType(ServiceParameters.USERNAME, UserName,"LDAP username"); - PrimitiveType p12 = new PrimitiveType(File.class.getName(), null, PrimitiveTypes.FILE, "PreviousNeuralNetwork","Previous Neural Network"); - + PrimitiveTypesList p4 = new PrimitiveTypesList(Integer.class.getName(), PrimitiveTypes.NUMBER, LayersNeurons, "a list of neurons number for each inner layer", true); + PrimitiveType p5 = new PrimitiveType(String.class.getName(), null, PrimitiveTypes.STRING, Reference, "the phenomenon this ANN is trying to model - can be a generic identifier. Put 1 for not specifying", "1"); + PrimitiveType p6 = new PrimitiveType(Float.class.getName(), null, PrimitiveTypes.NUMBER, LearningThreshold, "the learning threshold for this ANN", "0.01"); + PrimitiveType p7 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, MaxIterations, "the maximum number of iterations in the training", "100"); + PrimitiveType p11 = new PrimitiveType(String.class.getName(), null, PrimitiveTypes.STRING, ModelName, "The name of this Neural Network - insert without spaces", "neuralnet_"); + ServiceType p10 = new ServiceType(ServiceParameters.USERNAME, UserName, "LDAP username"); + PrimitiveType p12 = new PrimitiveType(File.class.getName(), null, PrimitiveTypes.FILE, "PreviousNeuralNetwork", "Previous Neural Network"); + parameters.add(p1); parameters.add(p2); parameters.add(p3); @@ -109,7 +167,7 @@ public class FeedForwardNNFile extends ModelAquamapsNN{ parameters.add(p11); parameters.add(p10); parameters.add(p12); - + DatabaseType.addDefaultDBPars(parameters); return parameters; } @@ -119,105 +177,103 @@ public class FeedForwardNNFile extends ModelAquamapsNN{ AnalysisLogger.setLogger(config.getConfigPath() + AlgorithmConfiguration.defaultLoggerFile); // init the database try { - connection = DatabaseUtils.initDBSession(config); + connection = DatabaseUtils.initDBSession(config); } catch (Exception e) { e.printStackTrace(); AnalysisLogger.getLogger().trace("ERROR initializing connection"); } - fileName = config.getPersistencePath()+Neural_Network.generateNNName(config.getParam(Reference), config.getParam(UserName), config.getParam(ModelName)); + fileName = config.getPersistencePath() + Neural_Network.generateNNName(config.getParam(Reference), config.getParam(UserName), config.getParam(ModelName)); trainingDataSet = config.getParam(TrainingDataSet); trainingDataSetColumns = config.getParam(TrainingDataSetColumns); trainingColumn = config.getParam(TrainingDataSetTargetColumn); - File previousfile = new File (config.getParam("PreviousNeuralNetwork")); - - AnalysisLogger.getLogger().info("Previous File is "+previousfile.getAbsolutePath()); - + File previousfile = new File(config.getParam("PreviousNeuralNetwork")); + + AnalysisLogger.getLogger().info("Previous File is " + previousfile.getAbsolutePath()); + learningThr = Float.parseFloat(config.getParam(LearningThreshold)); maxiter = Integer.parseInt(config.getParam(MaxIterations)); - + String layersNeurons$ = config.getParam(LayersNeurons); - if ((layersNeurons$!=null)&&(layersNeurons$.length()>0)) - { - String [] split = layersNeurons$.split(AlgorithmConfiguration.getListSeparator()); + if ((layersNeurons$ != null) && (layersNeurons$.length() > 0)) { + String[] split = layersNeurons$.split(AlgorithmConfiguration.getListSeparator()); layersNeurons = new int[split.length]; - boolean nullhyp=true; - for (int i = 0;i0)&&(nullhyp)) - nullhyp=false; + if ((layersNeurons[i] > 0) && (nullhyp)) + nullhyp = false; } - + if (nullhyp) - layersNeurons=null; - - + layersNeurons = null; + } dbcolumns = ""; dbcolumnsList = trainingDataSetColumns.split(AlgorithmConfiguration.getListSeparator()); - for (int i=0;i features = DatabaseFactory.executeSQLQuery(query, connection); int numbOfFeatures = features.size(); - //get reduction factor for normalizing the outputs - List maxmin = DatabaseFactory.executeSQLQuery("select max("+trainingColumn+"), min("+trainingColumn+") from "+trainingDataSet, connection); - maxfactor = Double.parseDouble(""+((Object[])maxmin.get(0))[0]); - minfactor = Double.parseDouble(""+((Object[])maxmin.get(0))[1]); - AnalysisLogger.getLogger().debug("Calculated max: "+maxfactor+" min: "+minfactor); + // get reduction factor for normalizing the outputs + List maxmin = DatabaseFactory.executeSQLQuery("select max(" + trainingColumn + "), min(" + trainingColumn + ") from " + trainingDataSet, connection); + maxfactor = Double.parseDouble("" + ((Object[]) maxmin.get(0))[0]); + minfactor = Double.parseDouble("" + ((Object[]) maxmin.get(0))[1]); + AnalysisLogger.getLogger().debug("Calculated max: " + maxfactor + " min: " + minfactor); // setup Neural Network int numberOfInputNodes = dbcolumnsList.length; int numberOfOutputNodes = 1; - - AnalysisLogger.getLogger().debug("Training the ANN with "+numbOfFeatures+" training data and "+numberOfInputNodes+" inputs"); - if (layersNeurons!=null){ + + AnalysisLogger.getLogger().debug("Training the ANN with " + numbOfFeatures + " training data and " + numberOfInputNodes + " inputs"); + if (layersNeurons != null) { int[] innerLayers = Neural_Network.setupInnerLayers(layersNeurons); nn = new Neural_Network(numberOfInputNodes, numberOfOutputNodes, innerLayers, Neural_Network.ACTIVATIONFUNCTION.SIGMOID); - } - else + } else nn = new Neural_Network(numberOfInputNodes, numberOfOutputNodes, Neural_Network.ACTIVATIONFUNCTION.SIGMOID); - - nn.maxfactor=maxfactor; - nn.minfactor=minfactor; + + nn.maxfactor = maxfactor; + nn.minfactor = minfactor; nn.setThreshold(learningThr); nn.setCycles(maxiter); - AnalysisLogger.getLogger().debug("network parameters: M: "+maxfactor+", m: "+minfactor+", lt: "+learningThr+", it: "+maxiter); - AnalysisLogger.getLogger().debug("topology: "+nn.griglia.length+"X"+nn.griglia[0].length); - + AnalysisLogger.getLogger().debug("network parameters: M: " + maxfactor + ", m: " + minfactor + ", lt: " + learningThr + ", it: " + maxiter); + AnalysisLogger.getLogger().debug("topology: " + nn.griglia.length + "X" + nn.griglia[0].length); + AnalysisLogger.getLogger().debug("Features preprocessing"); double[][] in = new double[numbOfFeatures][]; double[][] out = new double[numbOfFeatures][]; // build NN input for (int i = 0; i < numbOfFeatures; i++) { -// out[i] = new double[0]; + // out[i] = new double[0]; Object[] feats = (Object[]) features.get(i); in[i] = Neural_Network.preprocessObjects(Arrays.copyOfRange((Object[]) features.get(i), 1, feats.length)); out[i] = Neural_Network.preprocessObjects(Arrays.copyOfRange((Object[]) features.get(i), 0, 1)); - //apply reduction factor -// AnalysisLogger.getLogger().debug("Output Transformed from "+out[i][0]); - out[i][0] =nn.getCorrectValueForOutput(out[i][0]); -// AnalysisLogger.getLogger().debug("To "+out[i][0]); + // apply reduction factor + // AnalysisLogger.getLogger().debug("Output Transformed from "+out[i][0]); + out[i][0] = nn.getCorrectValueForOutput(out[i][0]); + // AnalysisLogger.getLogger().debug("To "+out[i][0]); } AnalysisLogger.getLogger().debug("Features were correctly preprocessed - Training"); // train the NN nn.train(in, out); - + AnalysisLogger.getLogger().debug("Saving Network"); save(fileName, nn); AnalysisLogger.getLogger().debug("Done"); @@ -225,9 +281,8 @@ public class FeedForwardNNFile extends ModelAquamapsNN{ e.printStackTrace(); AnalysisLogger.getLogger().error("ERROR during training"); } - + status = 100f; } - } diff --git a/src/main/java/org/gcube/dataanalysis/ecoengine/transducers/InterpolationTransducer.java b/src/main/java/org/gcube/dataanalysis/ecoengine/transducers/InterpolationTransducer.java index 20dff81..bc2b254 100644 --- a/src/main/java/org/gcube/dataanalysis/ecoengine/transducers/InterpolationTransducer.java +++ b/src/main/java/org/gcube/dataanalysis/ecoengine/transducers/InterpolationTransducer.java @@ -1,6 +1,7 @@ package org.gcube.dataanalysis.ecoengine.transducers; import java.util.ArrayList; +import java.util.HashMap; import java.util.List; import org.gcube.dataanalysis.ecoengine.configuration.AlgorithmConfiguration; @@ -88,20 +89,27 @@ public class InterpolationTransducer implements Transducerer{ @Override public StatisticalType getOutput() { + HashMap map = new HashMap(); + List template = new ArrayList(); template.add(TableTemplates.HCAF); - TablesList p = new TablesList(template, "INTEPOLATED_HCAF_TABLE_LIST", "List of HCAF tables produced by the interpolation", false); +// TablesList p = new TablesList(template, "INTEPOLATED_HCAF_TABLE_LIST", "List of HCAF tables produced by the interpolation", false); if ((producedtables!=null) &&(producedtables.length>0)){ int i=1; for (String table:producedtables){ - p.add(new OutputTable(template,table,table,"Interpolation point number "+i)); - i++; +// p.add(new OutputTable(template,table,table,"Interpolation number "+i)); + map.put("Interpolation "+i, new OutputTable(template,table,table,"Interpolation number "+i)); + i++; + } } - return p; + PrimitiveType output = new PrimitiveType(HashMap.class.getName(), map, PrimitiveTypes.MAP, "INTEPOLATED_HCAF_TABLE_LIST", "List of HCAF tables produced by the interpolation"); + + + return output; } diff --git a/target/apidocs/allclasses-frame.html b/target/apidocs/allclasses-frame.html index 74a2795..c533141 100644 --- a/target/apidocs/allclasses-frame.html +++ b/target/apidocs/allclasses-frame.html @@ -2,13 +2,13 @@ - + All Classes (ecological-engine 1.6.0-SNAPSHOT API) - + diff --git a/target/apidocs/allclasses-noframe.html b/target/apidocs/allclasses-noframe.html index fac9589..0c2b0eb 100644 --- a/target/apidocs/allclasses-noframe.html +++ b/target/apidocs/allclasses-noframe.html @@ -2,13 +2,13 @@ - + All Classes (ecological-engine 1.6.0-SNAPSHOT API) - + diff --git a/target/apidocs/constant-values.html b/target/apidocs/constant-values.html index 0463c26..991d9d0 100644 --- a/target/apidocs/constant-values.html +++ b/target/apidocs/constant-values.html @@ -2,13 +2,13 @@ - + Constant Field Values (ecological-engine 1.6.0-SNAPSHOT API) - + diff --git a/target/apidocs/deprecated-list.html b/target/apidocs/deprecated-list.html index be92db0..da10c7d 100644 --- a/target/apidocs/deprecated-list.html +++ b/target/apidocs/deprecated-list.html @@ -2,13 +2,13 @@ - + Deprecated List (ecological-engine 1.6.0-SNAPSHOT API) - + diff --git a/target/apidocs/help-doc.html b/target/apidocs/help-doc.html index 17d364f..8be1dac 100644 --- a/target/apidocs/help-doc.html +++ b/target/apidocs/help-doc.html @@ -2,13 +2,13 @@ - + API Help (ecological-engine 1.6.0-SNAPSHOT API) - + diff --git a/target/apidocs/index-all.html b/target/apidocs/index-all.html index 09c19fc..70b71ee 100644 --- a/target/apidocs/index-all.html +++ b/target/apidocs/index-all.html @@ -2,13 +2,13 @@ - + Index (ecological-engine 1.6.0-SNAPSHOT API) - + diff --git a/target/apidocs/index.html b/target/apidocs/index.html index effd5b4..cf48dfb 100644 --- a/target/apidocs/index.html +++ b/target/apidocs/index.html @@ -2,7 +2,7 @@ - + ecological-engine 1.6.0-SNAPSHOT API diff --git a/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/GenericDBExtractor.html b/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/GenericDBExtractor.html index 94715b8..65c1464 100644 --- a/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/GenericDBExtractor.html +++ b/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/GenericDBExtractor.html @@ -2,13 +2,13 @@ <!--NewPage--> <HTML> <HEAD> -<!-- Generated by javadoc (build 1.6.0_26) on Tue Dec 04 16:41:38 CET 2012 --> +<!-- Generated by javadoc (build 1.6.0_26) on Fri Dec 07 16:41:10 CET 2012 --> <META http-equiv="Content-Type" content="text/html; charset=UTF-8"> <TITLE> GenericDBExtractor (ecological-engine 1.6.0-SNAPSHOT API) - + diff --git a/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/GenericStandaloneGraph.html b/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/GenericStandaloneGraph.html index a2c9994..ba9d5dc 100644 --- a/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/GenericStandaloneGraph.html +++ b/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/GenericStandaloneGraph.html @@ -2,13 +2,13 @@ - + GenericStandaloneGraph (ecological-engine 1.6.0-SNAPSHOT API) - + diff --git a/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/SamplesTable.html b/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/SamplesTable.html index e4fbd10..c2a7b49 100644 --- a/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/SamplesTable.html +++ b/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/SamplesTable.html @@ -2,13 +2,13 @@ - + SamplesTable (ecological-engine 1.6.0-SNAPSHOT API) - + diff --git a/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/class-use/GenericDBExtractor.html b/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/class-use/GenericDBExtractor.html index a221ccf..431ebc9 100644 --- a/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/class-use/GenericDBExtractor.html +++ b/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/class-use/GenericDBExtractor.html @@ -2,13 +2,13 @@ - + Uses of Interface org.gcube.contentmanagement.graphtools.abstracts.GenericDBExtractor (ecological-engine 1.6.0-SNAPSHOT API) - + diff --git a/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/class-use/GenericStandaloneGraph.html b/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/class-use/GenericStandaloneGraph.html index 3d75d46..3e4908b 100644 --- a/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/class-use/GenericStandaloneGraph.html +++ b/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/class-use/GenericStandaloneGraph.html @@ -2,13 +2,13 @@ - + Uses of Class org.gcube.contentmanagement.graphtools.abstracts.GenericStandaloneGraph (ecological-engine 1.6.0-SNAPSHOT API) - + diff --git a/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/class-use/SamplesTable.html b/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/class-use/SamplesTable.html index 3396cdc..33b4409 100644 --- a/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/class-use/SamplesTable.html +++ b/target/apidocs/org/gcube/contentmanagement/graphtools/abstracts/class-use/SamplesTable.html @@ -2,13 +2,13 @@ - 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+TIMESERIES

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