tile_size <- "1000x3000"

#pred_data_month_info <- read.table(file=file.path(out_dir,paste("pred_data_month_info_",out_prefix,".txt",sep="")),sep=",")

#pred_data_day_info <- read.table(file=file.path(out_dir,paste("pred_data_day_info_",out_prefix,".txt",sep="")),sep=",")

#add column for tile size later on!!!

tb_month_s$pred_mod <- as.character(tb_month_s$pred_mod)

tb_month_s$tile_id<- as.character(tb_month_s$tile_id)

tb_s$pred_mod <- as.character(tb_s$pred_mod)

tb_s$tile_id <- as.character(tb_s$tile_id)

  tb <- merge(tb,df_tile_processed,by="tile_id")

  tb_s <- merge(tb_s,df_tile_processed,by="tile_id")

  tb_month_s<- merge(tb_month_s,df_tile_processed,by="tile_id")

  summary_metrics_v <- merge(summary_metrics_v,df_tile_processed,by="tile_id")

summary_metrics_v <- subset(summary_metrics_v,reg!="reg3b")

tb_s_all <- tb_s

tb_s_all <- subset(tb_s,reg!="reg3b")

tb_month_s_all <- tb_month_s

tb_month_s <- subset(tb_month_s,reg!="reg3b")

df_tile_processed_all <- df_tile_processed

df_tile_processed <- subset(df_tile_processed,reg!="reg3b")

#pred_data_month_info_all <- pred_data_month_info

#pred_data_month_info <- subset(pred_data_month_info,reg!="reg3b")

#pred_data_month_info_all <- pred_data_month_info

#pred_data_month_info <- subset(pred_data_month_info,reg!="reg3b")

#path_reg3 <- "/data/project/layers/commons/NEX_data/output_run8_global_analyses_10212014"

#summary_metrics_v_reg3 <- read.table(file.path(path_reg3,"summary_metrics_v2_NA_run8_global_analyses_10212014.txt"),sep=",")

#tb_diagnostic_v_NA_run8_global_analyses_10212014.txt

#tb_month_diagnostic_s_NA_run8_global_analyses_10212014.txt

#tb_diagnostic_s_NA_run8_global_analyses_10212014.txt

#pred_data_month_info_run8_global_analyses_10212014.txt

#pred_data_day_info_run8_global_analyses_10212014.txt

#df_tile_processed_run8_global_analyses_10212014.txt

  #coordinates(ac_mod) <- ac_mod[,c("lon","lat")] 

  coordinates(ac_mod) <- ac_mod[,c("lon.x","lat.x")] #solve this later

#coordinates(summary_metrics_v) <- c("lon","lat")

coordinates(summary_metrics_v) <- c("lon.y","lat.y")

#summary_metrics_v <- merge(summary_metrics_v,df_tile_processed,by="tile_id")

names(lf_mosaics_reg) <- l_reg_name

lf_mosaics_mask_reg <- vector("list",length=length(l_reg_name))

for(i in 1:length(l_reg_name)){

  #

  lf_m <- lf_mosaics_reg[[i]]

  out_dir_str <- out_dir

  reg_name <- paste(l_reg_name[i],"_",tile_size,sep="") #make this automatic

  #lapply()

  list_param_plot_daily_mosaics <- list(lf_m=lf_m,reg_name=reg_name,out_dir_str=out_dir_str,out_suffix=out_prefix,l_dates=day_to_mosaic)

  #lf_m_mask_reg4_1500x4500 <- mclapply(1:2,FUN=plot_daily_mosaics,list_param=list_param_plot_daily_mosaics,mc.preschedule=FALSE,mc.cores = 6)

  lf_mosaics_mask_reg[[i]] <- mclapply(1:length(lf_m),FUN=plot_daily_mosaics,list_param=list_param_plot_daily_mosaics,mc.preschedule=FALSE,mc.cores = 10)

}

################## WORLD MOSAICS NEEDS MAJOR CLEAN UP OF CODE HERE

##make functions!!

###Combine mosaics with modified code from Alberto

#use list from above!!

test_list <-list.files(path=out_dir,    

           pattern=paste("reg.*._CAI_TMAX_clim_month_20100101_mod1_all_mean.tif",sep=""), 

)

test_list<-lapply(1:30,FUN=function(i){lapply(1:x[[i]]},x=lf_mosaics_mask_reg)

test_list<-unlist(test_list)

mosaic_list_mean <- vector("list",length=1)

mosaic_list_mean[[1]] <- test_list 

out_rastnames <- "world_test_mosaic_20100101"

out_path <- out_dir

list_param_mosaic<-list(mosaic_list_mean,out_path,out_rastnames,file_format,NA_flag_val,out_suffix)

names(list_param_mosaic)<-c("mosaic_list","out_path","out_rastnames","file_format","NA_flag_val","out_suffix")

#mean_m_list <-mclapply(1:12, list_param=list_param_mosaic, mosaic_m_raster_list,mc.preschedule=FALSE,mc.cores = 6) #This is the end bracket from mclapply(...) statement

lf <- mosaic_m_raster_list(1,list_param_mosaic)

debug(mosaic_m_raster_list)

mosaic_list<-list_param$mosaic_list

out_path<-list_param$out_path

out_names<-list_param$out_rastnames

file_format <- list_param$file_format

NA_flag_val <- list_param$NA_flag_val

out_suffix <- list_param$out_suffix

##Now mosaic for mean: should reorder files!!

#out_rastnames_mean<-paste("_",lst_pat,"_","mean",out_suffix,sep="")

#list_date_names<-c("jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec")

#mosaic_list<-split(tmp_str1,list_date_names)

#new_list<-vector("list",length(mosaic_list))

# for (i in 1:length(list_date_names)){

#    j<-grep(list_date_names[i],mosaic_list,value=FALSE)

#    names(mosaic_list)[j]<-list_date_names[i]

#    new_list[i]<-mosaic_list[j]

#  }

#  mosaic_list_mean <-new_list #list ready for mosaicing

#  out_rastnames_mean<-paste(list_date_names,out_rastnames_mean,sep="")

  ### Now mosaic tiles...Note that function could be improved to use less memory

################## PLOTTING WORLD MOSAICS ################

lf_world_pred <- list.files(pattern="world.*2010090.*.tif$")

r_test <- raster(lf_world_pred[6])

m <- c(-Inf, -100, NA,  

         -100, 100, 1, 

         100, Inf, NA) #can change the thresholds later

rclmat <- matrix(m, ncol=3, byrow=TRUE)

rc <- reclassify(r_test, rclmat,filename=paste("rc_tmp_",i,".tif",sep=""),dataType="FLT4S",overwrite=T)

#file_name <- unlist(strsplit(basename(lf_m[i]),"_"))

#date_proc <- file_name[7] #specific tot he current naming of files

#date_proc <- l_dates[i]

date_proc<- "2010010905"

#paste(raster_name[1:7],collapse="_")

#add filename option later

extension_str <- extension(filename(r_test))

raster_name_tmp <- gsub(extension_str,"",basename(filename(r_test)))

raster_name <- file.path(out_dir_str,paste(raster_name_tmp,"_masked.tif",sep=""))

r_pred <- mask(r_test,rc,filename=raster_name,overwrite=TRUE)

res_pix <- 1200

#res_pix <- 480

col_mfrow <- 1

row_mfrow <- 1

png(filename=paste("Figure10_clim_world_mosaics_day_","_",date_proc,"_",tile_size,"_",out_suffix,".png",sep=""),

    width=col_mfrow*res_pix,height=row_mfrow*res_pix)

  plot(r_pred,main=paste("Predicted on ",date_proc , " ", tile_size,sep=""),cex.main=1.5)

dev.off()

#lf_world_mask_reg <- vector("list",length=length(lf_world_pred))

#for(i in 1:length(lf_world_pred)){

  #

#  lf_m <- lf_mosaics_reg[i]

#  out_dir_str <- out_dir

  #reg_name <- paste(l_reg_name[i],"_",tile_size,sep="") #make this automatic

  #lapply()

#  list_param_plot_daily_mosaics <- list(lf_m=lf_m,reg_name=tile_size,out_dir_str=out_dir_str,out_suffix=out_prefix,l_dates=day_to_mosaic)

  #lf_m_mask_reg4_1500x4500 <- mclapply(1:2,FUN=plot_daily_mosaics,list_param=list_param_plot_daily_mosaics,mc.preschedule=FALSE,mc.cores = 6)

  #lf_world_mask_reg[[i]] <- mclapply(1:length(lf_m),FUN=plot_daily_mosaics,list_param=list_param_plot_daily_mosaics,mc.preschedule=FALSE,mc.cores = 10)

}