###  Script to compile the monthly cloud data from earth engine into a netcdf file for further processing

setwd("~/acrobates/adamw/projects/cloud")

library(raster)

library(doMC)

library(foreach)

#library(doMPI)

registerDoMC(10)

#beginCluster(4)

tempdir="tmp"

if(!file.exists(tempdir)) dir.create(tempdir)

## Load list of tiles

tiles=read.table("tile_lat_long_10d.txt",header=T)

## bin sizes

ybin=30

xbin=30

tiles=expand.grid(ulx=seq(-180,180-xbin,by=xbin),uly=seq(90,-90+ybin,by=-ybin))

tiles$h=factor(tiles$ulx,labels=paste("h",sprintf("%02d",1:length(unique(tiles$ulx))),sep=""))

tiles$v=factor(tiles$uly,labels=paste("v",sprintf("%02d",1:length(unique(tiles$uly))),sep=""))

tiles$tile=paste(tiles$h,tiles$v,sep="")

tiles$urx=tiles$ulx+xbin

tiles$ury=tiles$uly

tiles$lrx=tiles$ulx+xbin

tiles$lry=tiles$uly-ybin

tiles$llx=tiles$ulx

tiles$lly=tiles$uly-ybin

tiles$cy=(tiles$uly+tiles$lry)/2

tiles$cx=(tiles$ulx+tiles$urx)/2

tiles=tiles[,c("tile","h","v","ulx","uly","urx","ury","lrx","lry","llx","lly","cx","cy")]

jobs=expand.grid(tile=tiles$tile,year=2000:2012,month=1:12)

jobs[,c("ulx","uly","urx","ury","lrx","lry","llx","lly")]=tiles[match(jobs$tile,tiles$tile),c("ulx","uly","urx","ury","lrx","lry","llx","lly")]

## drop Janurary 2000 from list (pre-modis)

jobs=jobs[!(jobs$year==2000&jobs$month==1),]

#jobs=jobs[jobs$month==1,]

#system("~/acrobates/adamw/projects/environmental-layers/climate/procedures/ee.MOD09.py -projwin -159 20 -154.5 18.5 -year 2001 -month 6 -region test")   

#testtiles=c("h02v07","h02v06","h02v08","h03v07","h03v06","h03v08")

#todo=which(jobs$tile%in%testtiles)

#todo=todo[1:3]

#todo=1

checkcomplete=T

if(checkcomplete&exists("df")){  #if desired (and "df" exists from below) drop complete date-tiles

todo=which(!paste(jobs$tile,jobs$year,jobs$month)%in%paste(df$region,df$year,df$month))

}

writeLines(paste("Tiling options will produce",nrow(tiles),"tiles and ",nrow(jobs),"tile-months.  Current todo list is ",length(todo)))

foreach(i=todo) %dopar%{

    system(paste("python ~/acrobates/adamw/projects/environmental-layers/climate/procedures/ee.MOD09.py -projwin ",

                      jobs$ulx[i]," ",jobs$uly[i]," ",jobs$urx[i]," ",jobs$ury[i]," ",jobs$lrx[i]," ",jobs$lry[i]," ",jobs$llx[i]," ",jobs$lly[i]," ",

                      "  -year ",jobs$year[i]," -month ",jobs$month[i]," -region ",jobs$tile[i],sep=""))

     }

addstats=F

if(addstats){

    df[,c("max","min","mean","sd")]=do.call(rbind.data.frame,mclapply(1:nrow(df),function(i) as.numeric(sub("^.*[=]","",grep("STATISTICS",system(paste("gdalinfo -stats",df$path[i]),inter=T),value=T)))))

    table(df$sd==0)

}

## Loop over existing months to build composite netcdf files

foreach(date=unique(df$date)) %dopar% {

## get date

  print(date)

  ## Define output and check if it already exists

  system(paste("gdal_merge.py -o ",tffile," -init -32768  -n -32768.000 -ot Int16 ",paste(df$path[df$date==date],collapse=" ")))

  system(paste("gdal_translate -of netCDF ",tffile," ",ncfile," -ot Int16 "))

  file.remove(tffile)

## create temporary nc file with time information to append to MOD06 data

  cat(paste("

    netcdf time {

      dimensions:

        time = 1 ;

      variables:

        int time(time) ;

      time:units = \"days since 2000-01-01 00:00:00\" ;

      time:calendar = \"gregorian\";

      time:long_name = \"time of observation\"; 

    data:

      time=",as.integer(date-as.Date("2000-01-01")),";

    }"),file=paste(tempdir,"/",date,"_time.cdl",sep=""))

system(paste("ncgen -o ",tempdir,"/",date,"_time.nc ",tempdir,"/",date,"_time.cdl",sep=""))

system(paste("ncks -A ",tempdir,"/",date,"_time.nc ",ncfile,sep=""))

## add other attributes

  system(paste("ncrename -v Band1,CF ",ncfile,sep=""))

  system(paste("ncatted ",

#               " -a valid_range,CF,o,b,\"0,100\" ",

               " -a scale_factor,CF,o,f,\"0.1\" ",

               " -a long_name,CF,o,c,\"Proportion cloudy days (%)\" ",

               ncfile,sep=""))

               ## add the fillvalue attribute back (without changing the actual values)

#system(paste("ncatted -a _FillValue,CF,o,b,-32768 ",ncfile,sep=""))

if(as.numeric(system(paste("cdo -s ntime ",ncfile),intern=T))<1) {

  print(paste(ncfile," has no time, deleting"))

  print(paste(basename(ncfile)," Finished"))

}

#system(paste("ncdump -h ",list.files(tempdir,pattern="mod09.*.nc$",full=T)[10]))

system(paste("cdo -O mergetime ",paste(list.files(tempdir,pattern="mod09.*.nc$",full=T),collapse=" ")," data/mod09.nc"))

### generate the monthly mean and sd

#  Overall mean

### Long term summaries

seasconc <- function(x,return.Pc=T,return.thetat=F) {

          #################################################################################################

          ## Precipitation Concentration function

          ## This function calculates Precipitation Concentration based on Markham's (1970) technique as described in Schulze (1997)

          ## South Africa Atlas of Agrohydology and Climatology - R E Schulze, M Maharaj, S D Lynch, B J Howe, and B Melvile-Thomson

          ## Pages 37-38

          #################################################################################################

          ## x is a vector of precipitation quantities - the mean for each factor in "months" will be taken,

          ## so it does not matter if the data are daily or monthly, as long as the "months" factor correctly

          ## identifies them into 12 monthly bins, collapse indicates whether the data are already summarized as monthly means.

          #################################################################################################

          theta=seq(30,360,30)*(pi/180)                                       # set up angles for each month & convert to radians

                  if(sum(is.na(x))==12) { return(cbind(Pc=NA,thetat=NA)) ; stop}

                  if(return.Pc) {

                              rt=sqrt(sum(x * cos(theta))^2 + sum(x * sin(theta))^2)    # the magnitude of the summation

                                        Pc=as.integer(round((rt/sum(x))*100))}

                  if(return.thetat){

                              s1=sum(x*sin(theta),na.rm=T); s2=sum(x*cos(theta),na.rm=T)

                                        if(s1>=0 & s2>=0)  {thetat=abs((180/pi)*(atan(sum(x*sin(theta),na.rm=T)/sum(x*cos(theta),na.rm=T))))}

                                        if(s1>0 & s2<0)  {thetat=180-abs((180/pi)*(atan(sum(x*sin(theta),na.rm=T)/sum(x*cos(theta),na.rm=T))))}

                                        if(s1<0 & s2<0)  {thetat=180+abs((180/pi)*(atan(sum(x*sin(theta),na.rm=T)/sum(x*cos(theta),na.rm=T))))}

                                        if(s1<0 & s2>0)  {thetat=360-abs((180/pi)*(atan(sum(x*sin(theta),na.rm=T)/sum(x*cos(theta),na.rm=T))))}

                             thetat=as.integer(round(thetat))

                            }

                  if(return.thetat&return.Pc) return(c(conc=Pc,theta=thetat))

                  if(return.Pc)          return(Pc)

                  if(return.thetat)  return(thetat)

        }

## read in monthly dataset