#Atver programmu bibliotēku dplR
#Open package dplR
library(dplR)

#Ielasa datu failus
#Read data files
s0<-read.table("Skrunda.txt",header=TRUE,sep="\t")
s1<-read.table("S1.txt",header=TRUE,sep="\t")
s2<-read.table("S2.txt",header=TRUE,sep="\t")


#Izveido datu vektoru no katras pārbaudāmā datu faila ailes
#Create data vectors from each column of data file
a1<-s2[,1]
names(a1)<-row.names(s2)
a2<-s2[,2]
names(a2)<-row.names(s1)
a3<-s2[,3]
names(a3)<-row.names(s2)
a4<-s2[,4]
names(a4)<-row.names(s2)
a5<-s2[,5]
names(a5)<-row.names(s2)
a6<-s2[,6]
names(a6)<-row.names(s2)
a7<-s2[,7]
names(a7)<-row.names(s2)
a8<-s2[,8]
names(a8)<-row.names(s2)


#Pārbauda datu faila aiļu savstarpējo korelāciju
#Check correlation between columns of data file
kontrole<-corr.rwl.seg(s0,seg.length=40,bin.floor=10)

#Pārbauda 1. ailes datu korelāciju ar kontroli
#Check correlation of 1. column of data file with control
corr1<-corr.series.seg(s0,a1,seg.length=40,bin.floor=10)

#Veic 1. datu ailes šķērskorelāciju
#Crosscorrelation for 1. column
ccf1<-ccf.series.rwl(s0,a1,seg.length=40,bin.floor=10,lag.max=5)