diff --git a/code/05_ExtractEnvironment.Rmd b/code/05_ExtractEnvironment.Rmd
index 473e97d311c575f48fc4af0331d5d4aa182c57fd..b834e31efac5b54f73f88fa15d7e21e1a5d05eb9 100644
--- a/code/05_ExtractEnvironment.Rmd
+++ b/code/05_ExtractEnvironment.Rmd
@@ -125,20 +125,21 @@ for(i in 1:19){
Intersect `header.shp` with each raster of the CHELSA collection.
Performed in EVE HPC cluster using function `A98_PredictorsExtract.R`. Divided in 99 chunks.
```{r, warning=F, message=F, eval=F}
+header.shp.path <- "../_derived/header.shp.shp"
+
for(i in 1:19){
ff <- paste("/data/sPlot/users/Francesco/Ancillary_Data/CHELSA/CHELSA_bio10_",
stringr::str_pad(i, width=2, side="left", pad="0"), ".tif", sep="")
- chelsa.i <- raster(ff)
#define output paths
output.ff1 <- paste("/data/sPlot/users/Francesco/sPlot3/_derived/output_pred/CHELSA_bio10_",
stringr::str_pad(i, width=2, side="left", pad="0"), ".csv", sep="")
- output.ff2 <- paste("/data/sPlot/users/Francesco/sPlot3/_derived/output_pred/CHELSA_bio10_",
+ output.ff2 <- paste("/data/sPlot/users/Francesco/sPlot3/_derived/output_pred/CHELSA_bio10",
stringr::str_pad(i, width=2, side="left", pad="0"), "_sd.csv", sep="")
- # Run PredExtr function
- chelsa.out <- PredExtr(x.shp = header.shp, toextract = chelsa.i, myfunction = "robust.mean",
+ # Run PredExtr function - sink to file
+ PredExtr(x.shp = header.shp.path, toextract = ff, myfunction = robust.mean,
ncores = 4, typp = "raster", output.ff1)
- chelsa.sd <- PredExtr(x.shp = header.shp, toextract = chelsa.i, myfunction = "sd",
- ncores = 4, typp = "raster", output).ff2
+ PredExtr(x.shp = header.shp.path, toextract = ff, myfunction = robust.sd,
+ ncores = 4, typp = "raster", output.ff2)
}
```
Reimport and assemble output.
@@ -223,11 +224,11 @@ for(i in 1:8){
pattern = "^[^(Gene)]", full.names = T)[i], pattern="//")[[1]][2]
output.ff1 <- gsub(pattern=".tif", replacement=".csv", filename)
output.ff2 <- gsub(pattern=".tif", replacement="_sd.csv", filename)
- # Run PredExtr function
- chelsa.out <- PredExtr(x.shp = header.shp, toextract = isric.i, myfunction = "robust.mean",
+ # Run PredExtr function - sink to file
+ PredExtr(x.shp = header.shp.path, toextract = isric.i, myfunction = robust.mean,
ncores = 4, typp = "raster", output.ff1)
- chelsa.sd <- PredExtr(x.shp = header.shp, toextract = isric.i, myfunction = "sd",
- ncores = 4, typp = "raster", output).ff2
+ PredExtr(x.shp = header.shp.path, toextract = isric.i, myfunction = robust.sd,
+ ncores = 4, typp = "raster", output.ff2)
}
```
diff --git a/code/A98_PredictorsExtract.R b/code/A98_PredictorsExtract.R
index 5cb6f4b1aef0fc198d2afb2bc03b13a8d4eb46a5..b13dd91afc9806dbdf420fc3ccac4f97976d37f5 100644
--- a/code/A98_PredictorsExtract.R
+++ b/code/A98_PredictorsExtract.R
@@ -1,5 +1,14 @@
-PredExtr <- function(x.shp, myfunction=NA, output,
+# define ancillary functions
+robust.mean <- function(x){mean(x, na.rm=T)}
+robust.mode <- function(x){if(any(x!=0)) {
+ a <- x[which(x!=0)] #exclude zero (i.e. NAs)
+ return(as.numeric(names(sort(table(a), decreasing=T))[1]))} else
+ return(NA)}
+robust.sd <- function(x){sd(x, na.rm=T)}
+
+#main function
+PredExtr <- function(x.shp, myfunction=NA, output=NA,
toextract, typp=c("raster", "shp"), ncores, chunkn=1, chunk.i=NA){
print("Load Packages")
require(foreach)
@@ -12,9 +21,12 @@ PredExtr <- function(x.shp, myfunction=NA, output,
print(paste("Loading", typp, "data :", toextract))
print(paste("output will be:", output))
if(typp=="raster"){ mypredictor <- raster(toextract)} else
- mypredictor <- readOGR(toextract)
+ mypredictor <- readOGR(toextract)
+ colnames(mypredictor@data)
header.shp <-readOGR(x.shp)
crs(mypredictor) <- crs(header.shp) #should be verified beforehand!
+
+
## Divide in chunks if requested
if(chunkn>1 & !is.na(chunk.i)){
@@ -24,13 +36,9 @@ PredExtr <- function(x.shp, myfunction=NA, output,
header.shp <- header.shp[chunks[[chunk.i]],]
}
- # define ancillary functions
- robust.mean <- function(x){mean(x, rm.na=T)}
- robust.mode <- function(x){if(any(x!=0)) {
- a <- x[which(x!=0)] #exclude zero (i.e. NAs)
- return(as.numeric(names(sort(table(a), decreasing=T))[1]))} else
- return(NA) }
-
+ print("myfunction defined as")
+ print(myfunction)
+
print("go parallel")
cl <- makeForkCluster(ncores, outfile="" )
registerDoParallel(cl)
@@ -39,9 +47,9 @@ PredExtr <- function(x.shp, myfunction=NA, output,
if(typp=="raster"){
out <- foreach(i=1:length(header.shp), .packages=c('raster'), .combine=rbind) %dopar% {
tmp <- raster::extract(mypredictor, header.shp[i,],
- buffer=min(10000, max(250, header.shp@data$loc.uncert[i])), fun=myfunction)
- }
- write.csv(out, file = output)
+ buffer=min(10000, max(250, header.shp@data$lc_ncrt[i])), fun=myfunction)
+ }
+ if(!is.na(output)) {write.csv(out, file = output)} else{return(out)}
} else {
out <- sp::over(x=header.shp, y=mypredictor)
toassign <- header.shp[which(is.na(out[,1])),]
@@ -60,7 +68,7 @@ PredExtr <- function(x.shp, myfunction=NA, output,
out[which(is.na(out[,1])),] <- mypredictor@data[nearest[,"ID"],]
}
- write.csv(out, file=output)
+ if(!is.na(output)) {write.csv(out, file = output)} else{return(out)}
}
stopCluster(cl)
}
diff --git a/code/submit-A98.sh b/code/submit-A98.sh
index 25e5c6dbc776f3ff101afb5c86bc4cd872c3eb50..182138bd3a2fd41bef081f646604bf8ed828c96c 100644
--- a/code/submit-A98.sh
+++ b/code/submit-A98.sh
@@ -3,32 +3,29 @@
#$ -S /bin/bash
#$ -N PredictorExtract
-#$ -o /work/$USER/$JOB_NAME-$JOB_ID-$TASK_ID.log
+#$ -o /work/$USER/$JOB_NAME-$JOB_ID.log
#$ -j y
#$ -l h_rt=00:30:00:00
-#$ -l h_vmem=1G
+#$ -l h_vmem=4G
-#$ -pe smp 8-32
+#$ -pe smp 12-32
#$ -cwd
-#$ -t 1-99
-
-
module load R
filename="$1"
shift
-output=/data/splot/_data_splot3/output_pred/$(basename $filename .shp)-$SGE_TASK_ID.csv
+output=/data/splot/_data_splot3/output_pred/$(basename $filename .shp).csv
Rscript \
cli_A98.r \
--cores ${NSLOTS:-1} \
- --typp "shp" \
- --chunk.i $SGE_TASK_ID \
- --chunkn 99 \
+ --typp "raster" \
+ --chunk.i 1 \
+ --chunkn 1 \
/data/splot/_data_splot3/header.shp.shp \
robust.mean \
"$output" \