please rewrite this code from R to Python

countvar<-countvar+1 # clip1 <- crop(statescorrected, extent(countries[j,])) #crop to extent of polygon # clip2 <- rasterize(countries[j,], clip1, mask=TRUE) #crops to polygon edge & converts to raster # rm(clip1) # bs <- blockSize(clip2) # table_countrylandcover<-NULL # # for (k in 1:bs$n) { # v <- as.data.frame(getValues(clip2, row=bs$row[k], nrows=bs$nrows[k] )) # if(all(is.na(v[,1]))){ # v<-NULL} else{ # v<-sum(v[,1],na.rm=T) # } # table_countrylandcover<-rbind(table_countrylandcover,v) # } #rm(clip2) #rm(bs) regionselect<-Which(regions_ext==j, cells = TRUE) table_countrylandcover<-extract(statescorrected,regionselect) if(s==2 && grepl("bioh",transitionsnames[i])){ if(firstvar==1){ table_countrylandcovertogether[1,countvar]<-j table_countrylandcovertogether[1,countvar+1]<-mean(table_countrylandcover,na.rm=T) } else{table_countrylandcovertogether[1,countvar+1]<-mean(table_countrylandcover,na.rm=T)} } else{ if(firstvar==1){ table_countrylandcovertogether[1,countvar]<-j table_countrylandcovertogether[1,countvar+1]<-sum(table_countrylandcover,na.rm=T) } else{table_countrylandcovertogether[1,countvar+1]<-sum(table_countrylandcover,na.rm=T)} } } } return(table_countrylandcovertogether) rm(table_countrylandcover) rm(table_countrylandcovertogether) } stopCluster(cl) colnames(output)<-c("region",paste0(statesnames[iterstates]),paste0(transitionsnames[itertransitions]),paste0(managementnames[itermanagement])) #countriestr<-spTransform(countries,CRS ="+proj=moll +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs") #output$countrysize<-sapply(slot(countriestr, "polygons"), slot, "area")/1000000 #dir.create(paste("Y:/Jelle_simulations/Aafke_brick/results/Band",y,sep=""), showWarnings = FALSE) write.table(output,paste0(location,"/Year",years[y],".txt"),sep="\t",row.names=F) } }

#cropland: (c3ann + c3per + c4ann + c4per + c3nfx ). #forestry: (primf_harv + primn_harv)

# files<-list.files("Y:/Jelle_simulations/Aafke_brick/results/", pattern="WdProd*", full.names=TRUE) # files<-files[c(1,3,5)] # conversion<-read.table("D:/Aafkebrick/Country_to_Image.txt",sep="\t",header=T,na.strings = c('', 'NA', '')) # # for (i in 1:length(files)){ # results<-read.table(files[i],sep="\t",header=T) # resultswdprod<-results # resultswdprod[,2:6]<-results[,2:6]*results[,7] # results<-resultswdprod # conversion$country<-results$country # together<-merge(results,conversion) # summedtogether<-list() # for (j in 2:(ncol(results)-1)){ # summed<-aggregate(together[,j]~together[,ncol(together)],together,sum) # summedsize<-aggregate(together$countrysize~together[,ncol(together)],together,sum) # summed<-merge(summed,summedsize) # summed$final<-summed[,2]/summed[,3] # summed<-summed[,c(1,4)] # if(j==2){summedtogether[[j-1]]<-summed # } else{summedtogether[[j-1]]<-summed[,2]} # # } # summedtogether<-data.frame(summedtogether) # colnames(summedtogether)<-colnames(results)[1:(ncol(results)-1)] # write.table(summedtogether,paste0("Y:/Jelle_simulations/Aafke_brick/results/WdProdYear_",selectedyears[i],"_Regions.txt"),sep="\t",row.names=F) # # } #country/Image regio niveau #per cell de areas (fractie x kaart grootte cell degree) van alle landtypes #checken of cellen optellen tot totale fractie van 1 -> Ja in Sahara, maar niet in landen die omringd worden met water of waar water voorkomt (zoet zowel als zout) #naast states, ook transitions + management