git-svn-id: https://svn.d4science.research-infrastructures.eu/gcube/trunk/data-analysis/EcologicalEngineSmartExecutor@152921 82a268e6-3cf1-43bd-a215-b396298e98cf
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<attribute name="maven.pomderived" value="true"/>
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<attribute name="maven.pomderived" value="true"/>
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</attributes>
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</attributes>
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</classpathentry>
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</classpathentry>
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<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER/org.eclipse.jdt.internal.debug.ui.launcher.StandardVMType/JavaSE-1.7">
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<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
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<attributes>
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<attribute name="maven.pomderived" value="true"/>
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</attributes>
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</classpathentry>
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<classpathentry kind="output" path="target/classes"/>
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<classpathentry kind="output" path="target/classes"/>
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</classpath>
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</classpath>
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@ -1,32 +0,0 @@
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#### Use two appenders, one to log to console, another to log to a file
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log4j.rootCategory= R
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#### First appender writes to console
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log4j.appender.stdout=org.apache.log4j.ConsoleAppender
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log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
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log4j.appender.stdout.layout.ConversionPattern=%d{dd/MM/yyyy HH:mm:ss} %p %t %c - %m%n
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#log4j.appender.stdout.layout.ConversionPattern=%m%n
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#log4j.appender.stdout.File=Analysis.log
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#### Second appender writes to a file
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log4j.logger.AnalysisLogger=trace,stdout, R
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log4j.appender.R=org.apache.log4j.RollingFileAppender
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#log4j.appender.R=org.apache.log4j.AsyncAppender
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#log4j.appender.R.Threshold=INFO
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log4j.appender.R.File=Analysis.log
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log4j.appender.R.MaxFileSize=50000KB
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log4j.appender.R.MaxBackupIndex=2
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log4j.appender.R.layout=org.apache.log4j.PatternLayout
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log4j.appender.R.layout.ConversionPattern=%d{dd/MM/yyyy HH:mm:ss} %p %t %c - %m%n
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#log4j.appender.R.layout.ConversionPattern=%m%n
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#### Third appender writes to a file
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log4j.logger.org.hibernate=H
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#log4j.appender.H=org.apache.log4j.RollingFileAppender
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log4j.appender.H=org.apache.log4j.AsyncAppender
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#log4j.appender.H.File=HibernateLog.log
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#log4j.appender.H.MaxFileSize=1024KB
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#log4j.appender.H.MaxBackupIndex=2
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log4j.appender.H.layout=org.apache.log4j.PatternLayout
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log4j.appender.H.layout.ConversionPattern=%d{dd/MM/yyyy HH:mm:ss} %p %t %c - %m%n
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@ -1,18 +0,0 @@
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<?xml version='1.0' encoding='UTF-8'?>
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<hibernate-configuration>
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<session-factory>
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<property name="connection.driver_class">org.postgresql.Driver</property>
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<property name="connection.provider_class">org.hibernate.connection.C3P0ConnectionProvider</property>
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<property name="connection.url">jdbc:postgresql://localhost/testdb</property>
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<property name="connection.username">gcube</property>
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<property name="connection.password">d4science2</property>
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<!-- <property name="dialect">org.hibernatespatial.postgis.PostgisDialect</property>-->
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<property name="dialect">org.hibernate.dialect.PostgreSQLDialect</property>
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<property name="transaction.factory_class">org.hibernate.transaction.JDBCTransactionFactory</property>
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<property name="c3p0.timeout">0</property>
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<property name="c3p0.max_size">10</property>
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<property name="c3p0.max_statements">0</property>
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<property name="c3p0.min_size">1</property>
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<property name="current_session_context_class">thread</property>
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</session-factory>
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</hibernate-configuration>
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AQUAMAPS_SUITABLE=org.gcube.dataanalysis.ecoengine.spatialdistributions.AquamapsSuitable
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AQUAMAPS_NATIVE=org.gcube.dataanalysis.ecoengine.spatialdistributions.AquamapsNative
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AQUAMAPS_NATIVE_2050=org.gcube.dataanalysis.ecoengine.spatialdistributions.AquamapsNative2050
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AQUAMAPS_SUITABLE_2050=org.gcube.dataanalysis.ecoengine.spatialdistributions.AquamapsSuitable2050
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AQUAMAPS_NATIVE_NEURALNETWORK=org.gcube.dataanalysis.ecoengine.spatialdistributions.AquamapsNN
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AQUAMAPS_SUITABLE_NEURALNETWORK=org.gcube.dataanalysis.ecoengine.spatialdistributions.AquamapsNNSuitable
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FEED_FORWARD_A_N_N_DISTRIBUTION=org.gcube.dataanalysis.ecoengine.spatialdistributions.FeedForwardNeuralNetworkDistribution
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LWR=org.gcube.dataanalysis.executor.nodes.algorithms.LWR
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CMSY=org.gcube.dataanalysis.executor.nodes.algorithms.CMSY
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FAOMSY=org.gcube.dataanalysis.executor.nodes.algorithms.FAOMSY
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ICCAT_VPA=org.gcube.dataanalysis.executor.nodes.algorithms.ICCATVPA
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DBSCAN=org.gcube.dataanalysis.ecoengine.clustering.DBScan
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KMEANS=org.gcube.dataanalysis.ecoengine.clustering.KMeans
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XMEANS=org.gcube.dataanalysis.ecoengine.clustering.XMeansWrapper
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DISCREPANCY_ANALYSIS=org.gcube.dataanalysis.ecoengine.evaluation.DiscrepancyAnalysis
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QUALITY_ANALYSIS=org.gcube.dataanalysis.ecoengine.evaluation.DistributionQualityAnalysis
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HRS=org.gcube.dataanalysis.ecoengine.evaluation.HabitatRepresentativeness
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@ -1,6 +0,0 @@
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LOCAL_WITH_DATABASE=org.gcube.dataanalysis.ecoengine.processing.LocalSplitGenerator
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SIMPLE_LOCAL=org.gcube.dataanalysis.ecoengine.processing.LocalSimpleSplitGenerator
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D4SCIENCE=org.gcube.dataanalysis.executor.generators.D4ScienceDistributedProcessing
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#OCCURRENCES_MERGER=org.gcube.dataanalysis.executor.generators.D4ScienceDistributedProcessing
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OCCURRENCES_INTERSECTOR=org.gcube.dataanalysis.executor.generators.D4ScienceDistributedProcessing
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#OCCURRENCES_SUBTRACTION=org.gcube.dataanalysis.executor.generators.D4ScienceDistributedProcessing
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@ -1,439 +0,0 @@
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cat("Retrieving Input Parameters\n")
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inputFile<-'tacsat.csv'
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outputFile<-'tacsat_interpolated.csv'
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require(data.table)
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print(Sys.time())
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memory.size(max = TRUE)
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memory.limit(size = 4000)
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interCubicHermiteSpline <- function(spltx,spltCon,res,params,headingAdjustment){
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#Formula of Cubic Hermite Spline
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t <- seq(0,1,length.out=res)
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F00 <- 2*t^3 -3*t^2 + 1
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F10 <- t^3-2*t^2+t
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F01 <- -2*t^3+3*t^2
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F11 <- t^3-t^2
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#Making tacsat dataset ready
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spltx[spltCon[,1],"SI_HE"][which(is.na(spltx[spltCon[,1],"SI_HE"]))] <- 0
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spltx[spltCon[,2],"SI_HE"][which(is.na(spltx[spltCon[,2],"SI_HE"]))] <- 0
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#Heading at begin point in degrees
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Hx0 <- sin(spltx[spltCon[,1],"SI_HE"]/(180/pi))
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Hy0 <- cos(spltx[spltCon[,1],"SI_HE"]/(180/pi))
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#Heading at end point in degrees
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Hx1 <- sin(spltx[spltCon[,2]-headingAdjustment,"SI_HE"]/(180/pi))
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Hy1 <- cos(spltx[spltCon[,2]-headingAdjustment,"SI_HE"]/(180/pi))
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#Start and end positions
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Mx0 <- spltx[spltCon[,1],"SI_LONG"]
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Mx1 <- spltx[spltCon[,2],"SI_LONG"]
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My0 <- spltx[spltCon[,1],"SI_LATI"]
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My1 <- spltx[spltCon[,2],"SI_LATI"]
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#Corrected for longitude lattitude effect
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Hx0 <- Hx0 * params$fm * spltx[spltCon[,1],"SI_SP"] /((params$st[2]-params$st[1])/2+params$st[1])
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Hx1 <- Hx1 * params$fm * spltx[spltCon[,2],"SI_SP"] /((params$st[2]-params$st[1])/2+params$st[1])
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Hy0 <- Hy0 * params$fm * lonLatRatio(spltx[spltCon[,1],"SI_LONG"],spltx[spltCon[,1],"SI_LATI"]) * spltx[spltCon[,1],"SI_SP"]/((params$st[2]-params$st[1])/2+params$st[1])
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Hy1 <- Hy1 * params$fm * lonLatRatio(spltx[spltCon[,2],"SI_LONG"],spltx[spltCon[,2],"SI_LATI"]) * spltx[spltCon[,2],"SI_SP"]/((params$st[2]-params$st[1])/2+params$st[1])
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#Get the interpolation
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fx <- outer(F00,Mx0,"*")+outer(F10,Hx0,"*")+outer(F01,Mx1,"*")+outer(F11,Hx1,"*")
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fy <- outer(F00,My0,"*")+outer(F10,Hy0,"*")+outer(F01,My1,"*")+outer(F11,Hy1,"*")
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#Create output format
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intsx <- lapply(as.list(1:nrow(spltCon)),function(x){
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matrix(rbind(spltx$ID[spltCon[x,]],cbind(fx[,x],fy[,x])),ncol=2,
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dimnames=list(c("startendVMS",seq(1,res,1)),c("x","y")))})
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return(intsx)}
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rbindTacsat <- function(set1,set2){
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cln1 <- colnames(set1)
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cln2 <- colnames(set2)
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if(any(duplicated(cln1)==TRUE) || any(duplicated(cln2)==TRUE)) stop("Duplicate column names in datasets")
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idx1 <- which(is.na(pmatch(cln1,cln2))==TRUE)
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idx2 <- which(is.na(pmatch(cln2,cln1))==TRUE)
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if(length(idx1)>0){
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for(i in idx1) set2 <- cbind(set2,NA)
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colnames(set2) <- c(cln2,cln1[idx1])}
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if(length(idx2)>0){
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for(i in idx2) set1 <- cbind(set1,NA)
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colnames(set1) <- c(cln1,cln2[idx2])}
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cln1 <- colnames(set1)
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cln2 <- colnames(set2)
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mtch <- pmatch(cln1,cln2)
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if(any(is.na(mtch))==TRUE) stop("Cannot find nor create all matching column names")
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set3 <- rbind(set1,set2[,cln2[mtch]])
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return(set3)}
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bearing <- function(lon,lat,lonRef,latRef){
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x1 <- lon
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y1 <- lat
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x2 <- lonRef
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y2 <- latRef
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y <- sin((x2-x1)*pi/180) * cos(y2*pi/180)
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x <- cos(y1*pi/180) * sin(y2*pi/180) - sin(y1*pi/180) * cos(y2*pi/180) * cos((x2-x1)*pi/180)
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bearing <- atan2(y,x)*180/pi
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bearing <- (bearing + 360)%%360
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return(bearing)}
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`distance` <-
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function(lon,lat,lonRef,latRef){
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pd <- pi/180
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a1<- sin(((latRef-lat)*pd)/2)
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a2<- cos(lat*pd)
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a3<- cos(latRef*pd)
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a4<- sin(((lonRef-lon)*pd)/2)
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a <- a1*a1+a2*a3*a4*a4
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c <- 2*atan2(sqrt(a),sqrt(1-a));
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return(6371*c)}
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distanceInterpolation <- function(interpolation){
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res <- unlist(lapply(interpolation,function(x){
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dims <- dim(x)
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res <- distance(x[3:dims[1],1],x[3:dims[1],2],x[2:(dims[1]-1),1],x[2:(dims[1]-1),2])
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return(sum(res,na.rm=TRUE))}))
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return(res)}
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equalDistance <- function(interpolation,res=10){
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#Calculate ditance of all interpolations at the same time
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totDist <- distanceInterpolation(interpolation)
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#Get dimensions of interpolations
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lngInt <- lapply(interpolation,dim)
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#Warn if resolution of equal distance is too high compared to original resolution of interpolation
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if(min(unlist(lngInt)[seq(1,length(totDist),2)],na.rm=TRUE) < 9*res) warnings("Number of intermediate points in the interpolation might be too small for the equal distance pionts chosen")
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#Get distance steps to get equal distance
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eqStep <- totDist/(res-1)
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#Get x-y values of all interpolations
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intidx <- matrix(unlist(lapply(interpolation,function(x){return(x[1,])})),ncol=2,byrow=TRUE)
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#Do the calculation
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result <- lapply(interpolation,function(ind){
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i <- which(intidx[,1] == ind[1,1] & intidx[,2] == ind[1,2])
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idx <- apply(abs(outer(
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cumsum(distance(ind[3:lngInt[[i]][1],1],ind[3:lngInt[[i]][1],2],ind[2:(lngInt[[i]][1]-1),1],ind[2:(lngInt[[i]][1]-1),2])),
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seq(eqStep[i],totDist[i],eqStep[i]),
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"-")),
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2,which.min)+1
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idx <- c(1,idx)
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return(ind[c(1,idx+1),])})
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#Return the equal distance interpolated set in the same format as the interpolated dataset (as a list)
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return(result)}
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interStraightLine <- function(spltx,spltCon,res){
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fx <- mapply(seq,spltx[spltCon[,1],"SI_LONG"],spltx[spltCon[,2],"SI_LONG"],length.out=res)
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fy <- mapply(seq,spltx[spltCon[,1],"SI_LATI"],spltx[spltCon[,2],"SI_LATI"],length.out=res)
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#Create output format
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intsx <- lapply(as.list(1:nrow(spltCon)),function(x){
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matrix(rbind(spltx$ID[spltCon[x,]],cbind(fx[,x],fy[,x])),ncol=2,
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dimnames=list(c("startendVMS",seq(1,res,1)),c("x","y")))})
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return(intsx)}
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interpolation2Tacsat <- function(interpolation,tacsat,npoints=10,equalDist=TRUE){
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# This function takes the list of tracks output by interpolateTacsat and converts them back to tacsat format.
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# The npoints argument is the optional number of points between each 'real' position.
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tacsat <- sortTacsat(tacsat)
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if(!"HL_ID" %in% colnames(tacsat)) tacsat$HL_ID <- 1:nrow(tacsat)
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if(!"SI_DATIM" %in% colnames(tacsat)) tacsat$SI_DATIM <- as.POSIXct(paste(tacsat$SI_DATE, tacsat$SI_TIME, sep=" "), tz="GMT", format="%d/%m/%Y %H:%M")
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if(equalDist){
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interpolationEQ <- equalDistance(interpolation,npoints) #Divide points equally along interpolated track (default is 10).
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} else {
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interpolationEQ <- lapply(interpolation,function(x){idx <- round(seq(2,nrow(x),length.out=npoints)); return(x[c(1,idx),])})
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}
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res <- lapply(interpolationEQ,function(x){
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idx <- unlist(x[1,1:2]@.Data); x <- data.frame(x)
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colnames(x) <- c("SI_LONG","SI_LATI")
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cls <- which(apply(tacsat[c(idx),],2,function(y){return(length(unique(y)))})==1)
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for(i in cls){
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x <- cbind(x,rep(tacsat[idx[1],i],nrow(x)));
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colnames(x) <- c(colnames(x)[1:(ncol(x)-1)],colnames(tacsat)[i])
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}
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if(!"VE_COU" %in% colnames(x)) x$VE_COU <- rep(tacsat$VE_COU[idx[1]],nrow(x))
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if(!"VE_REF" %in% colnames(x)) x$VE_REF <- rep(tacsat$VE_REF[idx[1]],nrow(x))
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if(!"FT_REF" %in% colnames(x)) x$FT_REF <- rep(tacsat$FT_REF[idx[1]],nrow(x))
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x$SI_DATIM <- tacsat$SI_DATIM[idx[1]]
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x$SI_DATIM[-c(1:2)] <- as.POSIXct(cumsum(rep(difftime(tacsat$SI_DATIM[idx[2]],tacsat$SI_DATIM[idx[1]],units="secs")/(nrow(x)-2),nrow(x)-2))+tacsat$SI_DATIM[idx[1]],tz="GMT",format = "%d/%m/%Y %H:%M")
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x$SI_DATE <- format(x$SI_DATIM,format="%d/%m/%Y")
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timeNotation <- ifelse(length(unlist(strsplit(tacsat$SI_TIME[1],":")))>2,"secs","mins")
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if(timeNotation == "secs") x$SI_TIME <- format(x$SI_DATIM,format="%H:%M:%S")
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|
||||||
if(timeNotation == "mins") x$SI_TIME <- format(x$SI_DATIM,format="%H:%M")
|
|
||||||
x$SI_SP <- mean(c(tacsat$SI_SP[idx[1]],tacsat$SI_SP[idx[2]]),na.rm=TRUE)
|
|
||||||
x$SI_HE <- NA;
|
|
||||||
x$SI_HE[-c(1,nrow(x))] <- bearing(x$SI_LONG[2:(nrow(x)-1)],x$SI_LATI[2:(nrow(x)-1)],x$SI_LONG[3:nrow(x)],x$SI_LATI[3:nrow(x)])
|
|
||||||
x$HL_ID <- tacsat$HL_ID[idx[1]]
|
|
||||||
return(x[-c(1,2,nrow(x)),])})
|
|
||||||
|
|
||||||
#interpolationTot <- do.call(rbind,res)
|
|
||||||
interpolationTot <- res[[1]][,which(duplicated(colnames(res[[1]]))==FALSE)]
|
|
||||||
if(length(res)>1){
|
|
||||||
for(i in 2:length(res)){
|
|
||||||
if(nrow(res[[i]])>0)
|
|
||||||
interpolationTot <- rbindTacsat(interpolationTot,res[[i]][,which(duplicated(colnames(res[[i]]))==FALSE)])
|
|
||||||
}
|
|
||||||
}
|
|
||||||
#tacsatInt <- rbind(interpolationTot,tacsat[,colnames(interpolationTot)])
|
|
||||||
tacsatInt <- rbindTacsat(tacsat,interpolationTot)
|
|
||||||
tacsatInt <- sortTacsat(tacsatInt)
|
|
||||||
|
|
||||||
return(tacsatInt)
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
`sortTacsat` <-
|
|
||||||
function(dat){
|
|
||||||
require(doBy)
|
|
||||||
|
|
||||||
if(!"SI_DATIM" %in% colnames(dat)) dat$SI_DATIM <- as.POSIXct(paste(dat$SI_DATE, dat$SI_TIME, sep=" "), tz="GMT", format="%d/%m/%Y %H:%M")
|
|
||||||
|
|
||||||
#Sort the tacsat data first by ship, then by date
|
|
||||||
if("VE_REF" %in% colnames(dat)) dat <- orderBy(~VE_REF+SI_DATIM,data=dat)
|
|
||||||
if("OB_REF" %in% colnames(dat)) dat <- orderBy(~OB_REF+SI_DATIM,data=dat)
|
|
||||||
|
|
||||||
return(dat)}
|
|
||||||
|
|
||||||
`lonLatRatio` <-
|
|
||||||
function(x1,lat){
|
|
||||||
#Based on the Haversine formula
|
|
||||||
#At the position, the y-position remains the same, hence, cos(lat)*cos(lat) instead of cos(lat) * cos(y2)
|
|
||||||
a <- cos(lat*pi/180)*cos(lat*pi/180)*sin((0.1*pi/180)/2)*sin((0.1*pi/180)/2);
|
|
||||||
c <- 2*atan2(sqrt(a),sqrt(1-a));
|
|
||||||
R <- 6371;
|
|
||||||
dx1 <- R*c
|
|
||||||
|
|
||||||
return(c(dx1/11.12))}
|
|
||||||
|
|
||||||
|
|
||||||
`an` <-
|
|
||||||
function(x){return(as.numeric(x))}
|
|
||||||
|
|
||||||
|
|
||||||
`findEndTacsat` <-
|
|
||||||
function(tacsat
|
|
||||||
,startTacsat #Starting point of VMS
|
|
||||||
,interval #Specify in minutes, NULL means use all points
|
|
||||||
,margin #Specify the margin in minutes it might deviate from the interval time, in minutes
|
|
||||||
){
|
|
||||||
VMS <- tacsat
|
|
||||||
if(!"SI_DATIM" %in% colnames(VMS)) VMS$SI_DATIM <- as.POSIXct(paste(tacsat$SI_DATE, tacsat$SI_TIME, sep=" "), tz="GMT", format="%d/%m/%Y %H:%M")
|
|
||||||
|
|
||||||
startVMS <- startTacsat
|
|
||||||
clStartVMS <- startVMS #Total VMS list starting point instead of subset use
|
|
||||||
iShip <- VMS$VE_REF[startVMS]
|
|
||||||
VMS. <- subset(VMS,VE_REF==iShip)
|
|
||||||
startVMS <- which(VMS$VE_REF[startVMS] == VMS.$VE_REF & VMS$SI_DATIM[startVMS] == VMS.$SI_DATIM)
|
|
||||||
if(clStartVMS != dim(VMS)[1]){
|
|
||||||
if(VMS$VE_REF[clStartVMS] != VMS$VE_REF[clStartVMS+1]){
|
|
||||||
#End of dataset reached
|
|
||||||
endDataSet <- 1
|
|
||||||
endVMS <- NA
|
|
||||||
} else {
|
|
||||||
#Calculate the difference in time between the starting VMS point and its succeeding points
|
|
||||||
diffTime <- difftime(VMS.$SI_DATIM[(startVMS+1):dim(VMS.)[1]],VMS.$SI_DATIM[startVMS],units=c("mins"))
|
|
||||||
if(length(which(diffTime >= (interval-margin) & diffTime <= (interval+margin)))==0){
|
|
||||||
warning("No succeeding point found, no interpolation possible")
|
|
||||||
endVMS <- NA
|
|
||||||
#Check if end of dataset has been reached
|
|
||||||
ifelse(all((diffTime < (interval-margin))==TRUE),endDataSet <- 1,endDataSet <- 0)
|
|
||||||
} else {
|
|
||||||
res <- which(diffTime >= (interval-margin) & diffTime <= (interval+margin))
|
|
||||||
if(length(res)>1){
|
|
||||||
res2 <- which.min(abs(interval-an(diffTime[res])))
|
|
||||||
endVMS <- startVMS + res[res2]
|
|
||||||
endDataSet <- 0
|
|
||||||
} else {
|
|
||||||
endVMS <- startVMS + res
|
|
||||||
endDataSet <- 0
|
|
||||||
}
|
|
||||||
}
|
|
||||||
#Build-in check
|
|
||||||
if(is.na(endVMS)==FALSE){
|
|
||||||
if(!an(difftime(VMS.$SI_DATIM[endVMS],VMS.$SI_DATIM[startVMS],units=c("mins"))) %in% seq((interval-margin),(interval+margin),1)) stop("found endVMS point not within interval range")
|
|
||||||
endVMS <- clStartVMS + (endVMS - startVMS)
|
|
||||||
}
|
|
||||||
|
|
||||||
}
|
|
||||||
} else { endDataSet <- 1; endVMS <- NA}
|
|
||||||
|
|
||||||
return(c(endVMS,endDataSet))}
|
|
||||||
|
|
||||||
`interpolateTacsat` <-
|
|
||||||
function(tacsat #VMS datapoints
|
|
||||||
,interval=120 #Specify in minutes, NULL means use all points
|
|
||||||
,margin=12 #Specify the margin in minutes that the interval might deviate in a search for the next point
|
|
||||||
,res=100 #Resolution of interpolation method (default = 100)
|
|
||||||
,method="cHs" #Specify the method to be used: Straight line (SL) of cubic Hermite spline (cHs)
|
|
||||||
,params=list(fm=0.5,distscale=20,sigline=0.2,st=c(2,6)) #Specify the three parameters: fm, distscale, sigline, speedthreshold
|
|
||||||
,headingAdjustment=0
|
|
||||||
,fast=FALSE){
|
|
||||||
|
|
||||||
if(!"SI_DATIM" %in% colnames(tacsat)) tacsat$SI_DATIM <- as.POSIXct(paste(tacsat$SI_DATE, tacsat$SI_TIME, sep=" "), tz="GMT", format="%d/%m/%Y %H:%M")
|
|
||||||
|
|
||||||
#Start interpolating the data
|
|
||||||
if(!method %in% c("cHs","SL")) stop("method selected that does not exist")
|
|
||||||
|
|
||||||
#-------------------------------------------------------------------------------
|
|
||||||
#Fast method or not
|
|
||||||
#-------------------------------------------------------------------------------
|
|
||||||
if(fast){
|
|
||||||
#Interpolation only by vessel, so split tacsat up
|
|
||||||
tacsat$ID <- 1:nrow(tacsat)
|
|
||||||
splitTa <- split(tacsat,tacsat$VE_REF)
|
|
||||||
spltTaCon <- lapply(splitTa,function(spltx){
|
|
||||||
#Calculate time different between every record
|
|
||||||
dftimex <- outer(spltx$SI_DATIM,spltx$SI_DATIM,difftime,units="mins")
|
|
||||||
iStep <- 1
|
|
||||||
connect <- list()
|
|
||||||
counter <- 1
|
|
||||||
#Loop over all possible combinations and store if a connection can be made
|
|
||||||
while(iStep <= nrow(spltx)){
|
|
||||||
endp <- which(dftimex[,iStep] >= (interval - margin) & dftimex[,iStep] <= (interval + margin))
|
|
||||||
if(length(endp)>0){
|
|
||||||
if(length(endp)>1) endp <- endp[which.min(abs(interval - dftimex[endp,iStep]))][1]
|
|
||||||
connect[[counter]] <- c(iStep,endp)
|
|
||||||
counter <- counter + 1
|
|
||||||
iStep <- endp
|
|
||||||
} else { iStep <- iStep + 1}
|
|
||||||
}
|
|
||||||
#Return matrix of conenctions
|
|
||||||
return(do.call(rbind,connect))})
|
|
||||||
|
|
||||||
if(method=="cHs") returnInterpolations <- unlist(lapply(as.list(names(unlist(lapply(spltTaCon,nrow)))),function(y){
|
|
||||||
return(interCubicHermiteSpline(spltx=splitTa[[y]],spltCon=spltTaCon[[y]],res,params,headingAdjustment))}),recursive=FALSE)
|
|
||||||
if(method=="SL") returnInterpolations <- unlist(lapply(as.list(names(unlist(lapply(spltTaCon,nrow)))),function(y){
|
|
||||||
return(interStraightLine(splitTa[[y]],spltTaCon[[y]],res))}),recursive=FALSE)
|
|
||||||
|
|
||||||
} else {
|
|
||||||
|
|
||||||
|
|
||||||
#Initiate returning result object
|
|
||||||
returnInterpolations <- list()
|
|
||||||
|
|
||||||
#Start iterating over succeeding points
|
|
||||||
for(iStep in 1:(dim(tacsat)[1]-1)){
|
|
||||||
if(iStep == 1){
|
|
||||||
iSuccess <- 0
|
|
||||||
endDataSet <- 0
|
|
||||||
startVMS <- 1
|
|
||||||
ship <- tacsat$VE_REF[startVMS]
|
|
||||||
} else {
|
|
||||||
if(is.na(endVMS)==TRUE) endVMS <- startVMS + 1
|
|
||||||
startVMS <- endVMS
|
|
||||||
#-Check if the end of the dataset is reached
|
|
||||||
if(endDataSet == 1 & rev(unique(tacsat$VE_REF))[1] != ship){
|
|
||||||
startVMS <- which(tacsat$VE_REF == unique(tacsat$VE_REF)[which(unique(tacsat$VE_REF)==ship)+1])[1]
|
|
||||||
ship <- tacsat$VE_REF[startVMS]
|
|
||||||
endDataSet<- 0
|
|
||||||
}
|
|
||||||
if(endDataSet == 1 & rev(unique(tacsat$VE_REF))[1] == ship) endDataSet <- 2 #Final end of dataset
|
|
||||||
}
|
|
||||||
|
|
||||||
#if end of dataset is not reached, try to find succeeding point
|
|
||||||
if(endDataSet != 2){
|
|
||||||
result <- findEndTacsat(tacsat,startVMS,interval,margin)
|
|
||||||
endVMS <- result[1]
|
|
||||||
endDataSet <- result[2]
|
|
||||||
if(is.na(endVMS)==TRUE) int <- 0 #No interpolation possible
|
|
||||||
if(is.na(endVMS)==FALSE) int <- 1 #Interpolation possible
|
|
||||||
|
|
||||||
#Interpolate according to the Cubic Hermite Spline method
|
|
||||||
if(method == "cHs" & int == 1){
|
|
||||||
|
|
||||||
#Define the cHs formula
|
|
||||||
F00 <- numeric()
|
|
||||||
F10 <- numeric()
|
|
||||||
F01 <- numeric()
|
|
||||||
F11 <- numeric()
|
|
||||||
i <- 0
|
|
||||||
t <- seq(0,1,length.out=res)
|
|
||||||
F00 <- 2*t^3 -3*t^2 + 1
|
|
||||||
F10 <- t^3-2*t^2+t
|
|
||||||
F01 <- -2*t^3+3*t^2
|
|
||||||
F11 <- t^3-t^2
|
|
||||||
|
|
||||||
if (is.na(tacsat[startVMS,"SI_HE"])=="TRUE") tacsat[startVMS,"SI_HE"] <- 0
|
|
||||||
if (is.na(tacsat[endVMS, "SI_HE"])=="TRUE") tacsat[endVMS, "SI_HE"] <- 0
|
|
||||||
|
|
||||||
#Heading at begin point in degrees
|
|
||||||
Hx0 <- sin(tacsat[startVMS,"SI_HE"]/(180/pi))
|
|
||||||
Hy0 <- cos(tacsat[startVMS,"SI_HE"]/(180/pi))
|
|
||||||
#Heading at end point in degrees
|
|
||||||
Hx1 <- sin(tacsat[endVMS-headingAdjustment,"SI_HE"]/(180/pi))
|
|
||||||
Hy1 <- cos(tacsat[endVMS-headingAdjustment,"SI_HE"]/(180/pi))
|
|
||||||
|
|
||||||
Mx0 <- tacsat[startVMS, "SI_LONG"]
|
|
||||||
Mx1 <- tacsat[endVMS, "SI_LONG"]
|
|
||||||
My0 <- tacsat[startVMS, "SI_LATI"]
|
|
||||||
My1 <- tacsat[endVMS, "SI_LATI"]
|
|
||||||
|
|
||||||
#Corrected for longitude lattitude effect
|
|
||||||
Hx0 <- Hx0 * params$fm * tacsat[startVMS,"SI_SP"] /((params$st[2]-params$st[1])/2+params$st[1])
|
|
||||||
Hx1 <- Hx1 * params$fm * tacsat[endVMS,"SI_SP"] /((params$st[2]-params$st[1])/2+params$st[1])
|
|
||||||
Hy0 <- Hy0 * params$fm * lonLatRatio(tacsat[c(startVMS,endVMS),"SI_LONG"],tacsat[c(startVMS,endVMS),"SI_LATI"])[1] * tacsat[startVMS,"SI_SP"]/((params$st[2]-params$st[1])/2+params$st[1])
|
|
||||||
Hy1 <- Hy1 * params$fm * lonLatRatio(tacsat[c(startVMS,endVMS),"SI_LONG"],tacsat[c(startVMS,endVMS),"SI_LATI"])[2] * tacsat[endVMS,"SI_SP"]/((params$st[2]-params$st[1]) /2+params$st[1])
|
|
||||||
|
|
||||||
#Finalizing the interpolation based on cHs
|
|
||||||
fx <- numeric()
|
|
||||||
fy <- numeric()
|
|
||||||
fx <- F00*Mx0+F10*Hx0+F01*Mx1+F11*Hx1
|
|
||||||
fy <- F00*My0+F10*Hy0+F01*My1+F11*Hy1
|
|
||||||
|
|
||||||
#Add one to list of successful interpolations
|
|
||||||
iSuccess <- iSuccess + 1
|
|
||||||
returnInterpolations[[iSuccess]] <- matrix(rbind(c(startVMS,endVMS),cbind(fx,fy)),ncol=2,dimnames=list(c("startendVMS",seq(1,res,1)),c("x","y")))
|
|
||||||
}
|
|
||||||
|
|
||||||
#Interpolate according to a straight line
|
|
||||||
if(method == "SL" & int == 1){
|
|
||||||
fx <- seq(tacsat$SI_LONG[startVMS],tacsat$SI_LONG[endVMS],length.out=res)
|
|
||||||
fy <- seq(tacsat$SI_LATI[startVMS],tacsat$SI_LATI[endVMS],length.out=res)
|
|
||||||
|
|
||||||
#Add one to list of successful interpolations
|
|
||||||
iSuccess <- iSuccess + 1
|
|
||||||
returnInterpolations[[iSuccess]] <- matrix(rbind(c(startVMS,endVMS),cbind(fx,fy)),ncol=2,dimnames=list(c("startendVMS",seq(1,res,1)),c("x","y")))
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
return(returnInterpolations)}
|
|
||||||
|
|
||||||
|
|
||||||
cat("Loading Table\n")
|
|
||||||
tacsatX <-read.table(inputFile,sep=",",header=T)
|
|
||||||
cat("Adjusting Columns Types\n")
|
|
||||||
tacsatX<-transform(tacsatX, VE_COU= as.character(VE_COU), VE_REF= as.character(VE_REF), SI_LATI= as.numeric(SI_LATI), SI_LONG= as.numeric(SI_LONG), SI_DATE= as.character(SI_DATE),SI_TIME= as.character(SI_TIME),SI_SP= as.numeric(SI_SP),SI_HE= as.numeric(SI_HE))
|
|
||||||
tacsatX$SI_DATIM=NULL
|
|
||||||
cat("Sorting dataset\n")
|
|
||||||
tacsatS <- sortTacsat(tacsatX)
|
|
||||||
tacsatCut<-tacsatS
|
|
||||||
tacsatCut <- tacsatS[1:1000,]
|
|
||||||
|
|
||||||
cat("Interpolating\n")
|
|
||||||
interpolation <- interpolateTacsat(tacsatCut,interval=interval,margin=margin,res=res, method=method,params=list(fm=fm,distscale=distscale,sigline=sigline,st=st),headingAdjustment=headingAdjustment,fast=fast)
|
|
||||||
cat("Reconstructing Dataset\n")
|
|
||||||
tacsatInt <- interpolation2Tacsat(interpolation=interpolation,tacsat=tacsatCut,npoints=npoints,equalDist=equalDist)
|
|
||||||
tacsatInt <- sortTacsat(tacsatInt)
|
|
||||||
cat("Writing output file\n")
|
|
||||||
write.csv(tacsatInt, outputFile, row.names=T)
|
|
||||||
print(Sys.time())
|
|
||||||
cat("All Done.\n")
|
|
Binary file not shown.
|
@ -1 +0,0 @@
|
||||||
HSPEN_MODELER=org.gcube.dataanalysis.ecoengine.modeling.SimpleModeler
|
|
|
@ -1,4 +0,0 @@
|
||||||
HSPEN=org.gcube.dataanalysis.ecoengine.models.ModelHSPEN
|
|
||||||
AQUAMAPSNN=org.gcube.dataanalysis.ecoengine.models.ModelAquamapsNN
|
|
||||||
FEED_FORWARD_ANN=org.gcube.dataanalysis.ecoengine.models.FeedForwardNN
|
|
||||||
FEED_FORWARD_ANN_FILE=org.gcube.dataanalysis.ecoengine.models.testing.FeedForwardNNFile
|
|
|
@ -1,13 +0,0 @@
|
||||||
AQUAMAPS_SUITABLE=org.gcube.dataanalysis.executor.nodes.algorithms.AquamapsSuitableNode
|
|
||||||
AQUAMAPS_NATIVE=org.gcube.dataanalysis.executor.nodes.algorithms.AquamapsNativeNode
|
|
||||||
AQUAMAPS_NATIVE_2050=org.gcube.dataanalysis.executor.nodes.algorithms.AquamapsNative2050Node
|
|
||||||
AQUAMAPS_SUITABLE_2050=org.gcube.dataanalysis.executor.nodes.algorithms.AquamapsSuitable2050Node
|
|
||||||
OCCURRENCES_MERGER=org.gcube.dataanalysis.executor.nodes.transducers.OccurrenceMergingNode
|
|
||||||
OCCURRENCES_INTERSECTOR=org.gcube.dataanalysis.executor.nodes.transducers.OccurrenceIntersectionNode
|
|
||||||
OCCURRENCES_SUBTRACTION=org.gcube.dataanalysis.executor.nodes.transducers.OccurrenceSubtractionNode
|
|
||||||
LWR=org.gcube.dataanalysis.executor.nodes.algorithms.LWR
|
|
||||||
BIONYM=org.gcube.dataanalysis.executor.nodes.transducers.bionym.BionymFlexibleWorkflowTransducer
|
|
||||||
BIONYM_BIODIV=org.gcube.dataanalysis.executor.nodes.transducers.bionym.BionymBiodiv
|
|
||||||
CMSY=org.gcube.dataanalysis.executor.nodes.algorithms.CMSY
|
|
||||||
FAOMSY=org.gcube.dataanalysis.executor.nodes.algorithms.FAOMSY
|
|
||||||
ICCAT_VPA=org.gcube.dataanalysis.executor.nodes.algorithms.ICCATVPA
|
|
2620
cfg/operators.xml
2620
cfg/operators.xml
File diff suppressed because it is too large
Load Diff
|
@ -1,18 +0,0 @@
|
||||||
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_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
|
|
||||||
PRESENCE_CELLS_GENERATION=org.gcube.dataanalysis.ecoengine.transducers.simplequeryexecutors.MarinePresencePoints
|
|
||||||
OCCURRENCES_MERGER=org.gcube.dataanalysis.ecoengine.transducers.OccurrencePointsMerger
|
|
||||||
OCCURRENCES_INTERSECTOR=org.gcube.dataanalysis.ecoengine.transducers.OccurrencePointsIntersector
|
|
||||||
OCCURRENCES_MARINE_TERRESTRIAL=org.gcube.dataanalysis.ecoengine.transducers.OccurrencePointsInSeaOnEarth
|
|
||||||
OCCURRENCES_DUPLICATES_DELETER=org.gcube.dataanalysis.ecoengine.transducers.OccurrencePointsDuplicatesDeleter
|
|
||||||
OCCURRENCES_SUBTRACTION=org.gcube.dataanalysis.ecoengine.transducers.OccurrencePointsSubtraction
|
|
||||||
BIONYM=org.gcube.dataanalysis.executor.nodes.transducers.bionym.BionymTransducer
|
|
||||||
BIONYM_LOCAL=org.gcube.dataanalysis.executor.nodes.transducers.bionym.BionymLocalTransducer
|
|
||||||
SGVM_INTERPOLATION=org.gcube.dataanalysis.executor.rscripts.SGVMS_Interpolation
|
|
||||||
KNITR_COMPILER=org.gcube.dataanalysis.executor.rscripts.KnitrCompiler
|
|
||||||
WEB_APP_PUBLISHER= org.gcube.dataanalysis.executor.nodes.transducers.WebApplicationPublisher
|
|
|
@ -1,13 +0,0 @@
|
||||||
ANOMALIES_DETECTION=DBSCAN,KMEANS,XMEANS
|
|
||||||
CLASSIFICATION=FEED_FORWARD_A_N_N_DISTRIBUTION
|
|
||||||
CLIMATE=BIOCLIMATE_HSPEC,BIOCLIMATE_HCAF,BIOCLIMATE_HSPEN,HCAF_INTERPOLATION
|
|
||||||
CORRELATION_ANALYSIS=HRS
|
|
||||||
DATA_CLUSTERING=DBSCAN,KMEANS,XMEANS
|
|
||||||
FILTERING=HCAF_FILTER,HSPEN_FILTER
|
|
||||||
FUNCTION_SIMULATION=FEED_FORWARD_A_N_N_DISTRIBUTION
|
|
||||||
OCCURRENCES=ABSENCE_CELLS_FROM_AQUAMAPS,PRESENCE_CELLS_GENERATION,OCCURRENCES_MERGER,OCCURRENCES_INTERSECTOR,OCCURRENCES_MARINE_TERRESTRIAL,OCCURRENCES_DUPLICATES_DELETER,OCCURRENCES_SUBTRACTION
|
|
||||||
PERFORMANCES_EVALUATION=QUALITY_ANALYSIS,DISCREPANCY_ANALYSIS
|
|
||||||
SPECIES_SIMULATION=AQUAMAPS_SUITABLE,AQUAMAPS_NATIVE,AQUAMAPS_NATIVE_2050,AQUAMAPS_SUITABLE_2050,AQUAMAPS_NATIVE_NEURALNETWORK,AQUAMAPS_SUITABLE_NEURALNETWORK
|
|
||||||
TRAINING=HSPEN,AQUAMAPSNN,FEED_FORWARD_ANN
|
|
||||||
TIME_SERIES=HCAF_INTERPOLATION
|
|
||||||
VESSELS=SGVM_INTERPOLATION
|
|
|
@ -21,7 +21,7 @@ public abstract class StandardLocalInfraAlgorithm extends StandardLocalExternalA
|
||||||
if (!serviceAddress.endsWith("/"))
|
if (!serviceAddress.endsWith("/"))
|
||||||
serviceAddress = serviceAddress+"/";
|
serviceAddress = serviceAddress+"/";
|
||||||
|
|
||||||
String requestForMessage = serviceAddress + "messages/writeMessageToUsers" + "?gcube-token=" + config.getGcubeToken();
|
String requestForMessage = serviceAddress + "2/messages/writeMessageToUsers" + "?gcube-token=" + config.getGcubeToken();
|
||||||
requestForMessage = requestForMessage.replace("http://", "https://").replace(":80", ""); // remove the port (or set it to 443) otherwise you get an SSL error
|
requestForMessage = requestForMessage.replace("http://", "https://").replace(":80", ""); // remove the port (or set it to 443) otherwise you get an SSL error
|
||||||
|
|
||||||
LOGGER.debug("Emailing System->Request url is going to be " + requestForMessage);
|
LOGGER.debug("Emailing System->Request url is going to be " + requestForMessage);
|
||||||
|
|
|
@ -59,9 +59,8 @@ public class InfraRetrieval {
|
||||||
public static String findEmailingSystemAddress(String scope) throws Exception {
|
public static String findEmailingSystemAddress(String scope) throws Exception {
|
||||||
|
|
||||||
String serviceName = "SocialNetworking";
|
String serviceName = "SocialNetworking";
|
||||||
String serviceClass = "Portal";
|
|
||||||
|
|
||||||
List<String> endpoints = retrieveServiceAddress(serviceClass, serviceName,scope,"test");
|
List<String> endpoints = retrieveService(serviceName,scope);
|
||||||
|
|
||||||
|
|
||||||
/* new code - deprecated
|
/* new code - deprecated
|
||||||
|
@ -83,10 +82,10 @@ public class InfraRetrieval {
|
||||||
*/
|
*/
|
||||||
|
|
||||||
String resourceEntyName = endpoints.get(0);
|
String resourceEntyName = endpoints.get(0);
|
||||||
if (endpoints == null || endpoints.isEmpty()) throw new Exception("Cannot retrieve the GCoreEndpoint serviceName: "+serviceName +", serviceClass: " +serviceClass +", in scope: "+scope);
|
if (endpoints == null || endpoints.isEmpty()) throw new Exception("Cannot retrieve the GCoreEndpoint serviceName: "+serviceName +", in scope: "+scope);
|
||||||
|
|
||||||
if(resourceEntyName==null)
|
if(resourceEntyName==null)
|
||||||
throw new Exception("Endpoint:"+serviceName+", is null for serviceName: "+serviceName +", serviceClass: " +serviceClass +", in scope: "+scope);
|
throw new Exception("Endpoint:"+serviceName+", is null for serviceName: "+serviceName +", in scope: "+scope);
|
||||||
|
|
||||||
|
|
||||||
return resourceEntyName;
|
return resourceEntyName;
|
||||||
|
|
Loading…
Reference in New Issue