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createAllDiurnalFilesYear.R
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#!/usr/bin/env Rscript
library("ncdf4")
library(plyr)
library("PhenologyBayesModeling")
library(doParallel)
#detect cores.
#n.cores <- detectCores()
n.cores <- 6
#register the cores.
registerDoParallel(cores=n.cores)
createNDVI_GOES_diurnal <- function(lat,long,siteID,startDay,endDay,orbitVersion){
#load/calcuate GOES NDVI data
lat.rd <- lat*2*pi/360
long.rd <- long*2*pi/360
Ind2 <- getDataIndex(getABI_Index(lat.rd,long.rd,orbitVersion="OLD"),2,orbitVersion=orbitVersion)
Ind3 <- getDataIndex(getABI_Index(lat.rd,long.rd,orbitVersion="OLD"),3,orbitVersion=orbitVersion)
ACM.ind <- getDataIndex(getABI_Index(lat.rd,long.rd,orbitVersion="OLD"),"ACM",orbitVersion=orbitVersion)
i2 <- Ind2[1]
j2 <- Ind2[2]
i3 <- Ind3[1]
j3 <- Ind3[2]
NDVI.vals <- list()
days <- seq(startDay,endDay,1)
day.time.vals <- list()
for(i in 1:length(days)){
#print(days)
days[i] <- as.numeric(days[i])
if(as.numeric(days[i]) < 10){
days[i] <- paste("00",as.character(days[i]),sep="")
}
else if(as.numeric(days[i]) < 100){
days[i] <- paste("0",as.character(days[i]),sep="")
}
}
for (i in 1:length(days)){
print(days)
days[i] <- as.numeric(days[i])
if(days[i] < 10){
days[i] <- paste("00",as.character(days[i]),sep="")
}
else if(days[i] < 100){
days[i] <- paste("0",as.character(days[i]),sep="")
}
print(days[i])
days[i] <- as.character(days[i])
filestrACM <- paste("OR_ABI-L2-ACMC-M3_G16_s2017",days[i],sep="")
ACM.files <- dir(path="GOES_Data2017",pattern=filestrACM)
print(length(ACM.files))
if(!dir.exists((paste("GOES_Data2017/",dir(path="GOES_Data2017",pattern=filestrACM),sep="")))){
if(length(ACM.files>1)){
for(j in 1:length(ACM.files)){
day.time <- substr(ACM.files[j],24,34)
#print(j)
print(day.time)
day.time.vals <- c(day.time.vals,day.time)
filePath <- paste("GOES_Data2017/",ACM.files[j],sep="")
#print(filePath)
ACM.file <-nc_open(paste("GOES_Data2017/",ACM.files[j],sep=""))
#print(dim(ncvar_get(ACM.file, "BCM")))
#print(ACM.ind)
clouds <- ncvar_get(ACM.file,"BCM")[ACM.ind[1],ACM.ind[2]]
if(!is.na(clouds)){
if (clouds ==0){
filestrC03 <- paste("OR_ABI-L1b-RadC-M3C03_G16_s",day.time,sep="")
filestrC02 <- paste("OR_ABI-L1b-RadC-M3C02_G16_s",day.time,sep="")
filePathC02 <- paste("GOES_Data2017/",dir(path="GOES_Data2017",pattern=filestrC02),sep="")
filePathC03 <- paste("GOES_Data2017/",dir(path="GOES_Data2017",pattern=filestrC03),sep="")
if(nchar(filePathC02)>20 & nchar(filePathC03)>20){
R2.file <- nc_open(paste("GOES_Data2017/",dir(path="GOES_Data2017",pattern=filestrC02),sep=""))
R3.file <- nc_open(paste("GOES_Data2017/",dir(path="GOES_Data2017",pattern=filestrC03),sep=""))
R3.DQF <- ncvar_get(R3.file,"DQF")
R2.DQF <- ncvar_get(R2.file,"DQF")
if(R3.DQF[i3,j3]==0 & R2.DQF[i2,j2]==0 & R2.DQF[i2,j2]==0 & R2.DQF[(i2+1),j2]==0 & R2.DQF[i2,(j2+1)]==0 & R2.DQF[(i2+1),(j2+1)]==0){
NDVI.val <- getSpecificNDVI(Ind2,Ind3,day.time)
}
else{
NDVI.val <- NA
}
}
else{
NDVI.val <- NA
}
}
else{
NDVI.val <- NA
}
}
else{
NDVI.val <- NA
}
NDVI.vals <- c(NDVI.vals,NDVI.val)
}
}
}
}
fileName <- paste("GOES_NDVI_Diurnal",siteID,"_",startDay,"_",endDay,"_kappaDQF.csv",sep="")
output <- rbind(t(day.time.vals),NDVI.vals)
write.table(output,file=fileName,sep=",",col.names=FALSE,row.names=FALSE)
}
siteData <- read.csv("GOES_Paper_Sites.csv",header=TRUE)
siteName <- as.character(siteData[1,1])
lat <- as.numeric(siteData[1,2])
long <- as.numeric(siteData[1,3])
timeFrames <- matrix(ncol=3,nrow=3)
#timeFrames[1,] <- c(201,206,"OLD")
timeFrames[1,] <- c(207,212,"OLD")
#timeFrames[3,] <- c(251,256,"OLD")
timeFrames[2,] <- c(257,261,"OLD")
timeFrames[3,] <- c(312,317,"OLD")
#timeFrames[6,] <- c(318,321,"OLD")
#
# timeFrames[9,] <- c(342,351,"NEW")
# timeFrames[10,] <- c(352,365,"NEW") #Dec 2
# timeFrames[1,] <- c(189,200,"OLD") #July1
# timeFrames[2,] <- c(201,212,"OLD") #July2
#
# timeFrames[3,] <- c(220,231,"OLD")
# timeFrames[4,] <- c(232,243,"OLD") #Aug 2
#
# timeFrames[5,] <- c(251,261,"OLD")
# timeFrames[6,] <- c(262,273,"OLD") #Sept 2
#
# timeFrames[7,] <- c(312,321,"OLD")
# timeFrames[8,] <- c(322,334,"OLD") #Nov 2
#
# timeFrames[9,] <- c(342,351,"NEW")
# timeFrames[10,] <- c(352,365,"NEW") #Dec 2
# timeFrames <- matrix(ncol=2,nrow=5)
# timeFrames[1,] <- c(189,189) #July1
# #timeFrames[2,] <- c(201,202) #July2
#
# timeFrames[2,] <- c(220,220)
# #timeFrames[4,] <- c(232,233) #Aug 2
#
# timeFrames[3,] <- c(251,251)
# #timeFrames[6,] <- c(262,263) #Sept 2
#
# timeFrames[4,] <- c(312,312)
# #timeFrames[8,] <- c(322,323) #Nov 2
#
# timeFrames[5,] <- c(342,342)
# #timeFrames[10,] <- c(352,353) #Jan 2
output <- foreach(i = 1:3) %dopar% {
startDay <- timeFrames[i,1]
endDay <- timeFrames[i,2]
orbitVersion <- timeFrames[i,3]
print(timeFrames[i,])
createNDVI_GOES_diurnal(lat=lat, long=long, siteID=siteName,startDay=startDay,endDay=endDay,orbitVersion = orbitVersion)
print(paste(i, "done",sep=" "))
}