Revision 5c60138a
Added by Benoit Parmentier over 9 years ago
| climate/research/oregon/interpolation/global_run_scalingup_assessment_part2.R | ||
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#Analyses, figures, tables and data are also produced in the script. |
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#AUTHOR: Benoit Parmentier |
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#CREATED ON: 03/23/2014 |
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#MODIFIED ON: 03/07/2015
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#MODIFIED ON: 03/11/2015
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#Version: 4 |
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#PROJECT: Environmental Layers project |
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#COMMENTS: analyses for run 10 global analyses,all regions 1500x4500km and other tiles |
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interpolation_method <- c("gam_CAI") #PARAM2
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#out_suffix<-"run10_global_analyses_01282015" |
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#out_suffix <- "output_run10_1000x3000_global_analyses_02102015" |
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out_suffix <- "run10_1500x4500_global_analyses_03052015" #PARAM3
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out_dir <- "/data/project/layers/commons/NEX_data/output_run10_1500x4500_global_analyses_03052015" #PARAM4
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out_suffix <- "run10_1500x4500_global_analyses_03112015" #PARAM3
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out_dir <- "/data/project/layers/commons/NEX_data/output_run10_1500x4500_global_analyses_03112015" #PARAM4
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create_out_dir_param <- FALSE #PARAM 5 |
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mosaic_plot <- FALSE #PARAM6 |
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text(coordinates(pt)[1],coordinates(pt)[2],labels=i,cex=1.3,font=2,col=c("red"))
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} |
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} |
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title(paste("Tiles 1000x3000 ", region_name,sep=""))
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title(paste("Tiles ", tile_size,region_name,sep=""))
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dev.off() |
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#plot(ac_mod1,cex=sqrt(ac_mod1$rmse),pch=1,add=T) |
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#plot(ac_mod,cex=(ac_mod$rmse^2)/10,pch=1,col="red",add=T) |
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#coordinates(ac_mod) <- ac_mod[,c("lon","lat")]
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coordinates(ac_mod) <- ac_mod[,c("lon.x","lat.x")] #solve this later
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coordinates(ac_mod) <- ac_mod[,c("lon","lat")]
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#coordinates(ac_mod) <- ac_mod[,c("lon.x","lat.x")] #solve this later
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p_shp <- layer(sp.polygons(reg_layer, lwd=1, col='black')) |
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#title("(a) Mean for 1 January")
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p <- bubble(ac_mod,"rmse",main=paste("Averrage RMSE per tile and by ",model_name[i]))
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| ... | ... | |
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sum(df_tile_processed$metrics_v)/length(df_tile_processed$metrics_v) #87.85% of tiles with info |
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#coordinates |
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#coordinates(summary_metrics_v) <- c("lon","lat")
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coordinates(summary_metrics_v) <- c("lon.y","lat.y")
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coordinates(summary_metrics_v) <- c("lon","lat")
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#coordinates(summary_metrics_v) <- c("lon.y","lat.y")
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threshold_missing_day <- c(367,365,300,200) |
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#lf_m_mask_reg6_1000x3000 <- mclapply(1:length(lf_m),FUN=plot_daily_mosaics,list_param=list_param_plot_daily_mosaics,mc.preschedule=FALSE,mc.cores = 10) |
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lf_mosaics_mask_reg <- vector("list",length=length(l_reg_name))
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for(i in 1:length(l_reg_name)){
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# |
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lf_m <- lf_mosaics_reg[[i]] |
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out_dir_str <- out_dir |
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reg_name <- paste(l_reg_name[i],"_",tile_size,sep="") #make this automatic |
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#lapply() |
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list_param_plot_daily_mosaics <- list(lf_m=lf_m,reg_name=reg_name,out_dir_str=out_dir_str,out_suffix=out_suffix,l_dates=day_to_mosaic) |
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#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) |
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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) |
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if(plot_region==TRUE){
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lf_mosaics_mask_reg <- vector("list",length=length(l_reg_name))
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for(i in 1:length(l_reg_name)){
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d |
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# |
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lf_m <- lf_mosaics_reg[[i]] |
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out_dir_str <- out_dir |
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reg_name <- paste(l_reg_name[i],"_",tile_size,sep="") #make this automatic |
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#lapply() |
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list_param_plot_daily_mosaics <- list(lf_m=lf_m,reg_name=reg_name,out_dir_str=out_dir_str,out_suffix=out_suffix,l_dates=day_to_mosaic) |
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#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) |
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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) |
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} |
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} |
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################## WORLD MOSAICS NEEDS MAJOR CLEAN UP OF CODE HERE |
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#use list from above!! |
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test_list <-list.files(path=out_dir, |
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pattern=paste("reg.*._CAI_TMAX_clim_month_20100101_mod1_all_mean.tif",sep=""),
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) |
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test_list<-lapply(1:30,FUN=function(i){lapply(1:x[[i]]},x=lf_mosaics_mask_reg)
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test_list<-unlist(test_list) |
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mosaic_list_mean <- vector("list",length=1)
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mosaic_list_mean[[1]] <- test_list |
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out_rastnames <- "world_test_mosaic_20100101" |
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out_path <- out_dir |
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list_param_mosaic<-list(mosaic_list_mean,out_path,out_rastnames,file_format,NA_flag_val,out_suffix) |
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names(list_param_mosaic)<-c("mosaic_list","out_path","out_rastnames","file_format","NA_flag_val","out_suffix")
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#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 |
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lf <- mosaic_m_raster_list(1,list_param_mosaic) |
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debug(mosaic_m_raster_list) |
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mosaic_list<-list_param$mosaic_list |
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out_path<-list_param$out_path |
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out_names<-list_param$out_rastnames |
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file_format <- list_param$file_format |
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NA_flag_val <- list_param$NA_flag_val |
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out_suffix <- list_param$out_suffix |
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# test_list <-list.files(path=file.path(out_dir,"mosaics"), |
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# pattern=paste("^world_mosaics.*.tif$",sep=""),
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# ) |
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# #world_mosaics_mod1_output1500x4500_km_20101105_run10_1500x4500_global_analyses_03112015.tif |
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# |
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# #test_list<-lapply(1:30,FUN=function(i){lapply(1:x[[i]]},x=lf_mosaics_mask_reg)
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# #test_list<-unlist(test_list) |
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# #mosaic_list_mean <- vector("list",length=1)
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# mosaic_list_mean <- test_list |
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# out_rastnames <- "world_test_mosaic_20100101" |
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# out_path <- out_dir |
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# |
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# list_param_mosaic<-list(mosaic_list_mean,out_path,out_rastnames,file_format,NA_flag_val,out_suffix) |
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# names(list_param_mosaic)<-c("mosaic_list","out_path","out_rastnames","file_format","NA_flag_val","out_suffix")
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# #mean_m_list <-mclapply(1:12, list_param=list_param_mosaic, mosaic_m_raster_list,mc.preschedule=FALSE,mc.cores = 6) #This is the end bracket from mclapply(...) statement |
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# |
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# lf <- mosaic_m_raster_list(1,list_param_mosaic) |
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# |
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# debug(mosaic_m_raster_list) |
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# mosaic_list<-list_param$mosaic_list |
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# out_path<-list_param$out_path |
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# out_names<-list_param$out_rastnames |
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# file_format <- list_param$file_format |
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# NA_flag_val <- list_param$NA_flag_val |
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# out_suffix <- list_param$out_suffix |
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##Now mosaic for mean: should reorder files!! |
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#out_rastnames_mean<-paste("_",lst_pat,"_","mean",out_suffix,sep="")
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################## PLOTTING WORLD MOSAICS ################ |
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lf_world_pred <- list.files(pattern="world.*2010090.*.tif$") |
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lf_raster_fname <- list.files(pattern="world.*2010*.*02162015.tif$",full.names=T) |
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lf_world_pred <-list.files(path=file.path(out_dir,"mosaics"), |
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pattern=paste("^world_mosaics.*.tif$",sep=""),full.names=T)
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) |
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#mosaic_list_mean <- test_list |
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#out_rastnames <- "world_test_mosaic_20100101" |
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#out_path <- out_dir |
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#lf_world_pred <- list.files(pattern="world.*2010090.*.tif$") |
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#lf_raster_fname <- list.files(pattern="world.*2010*.*02162015.tif$",full.names=T) |
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lf_raster_fname <- lf_world_pred |
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prefix_str <- "Figure10_clim_world_mosaics_day_" |
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l_dates <-day_to_mosaic |
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screenRast=TRUE |
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list_param_plot_screen_raster <- list(lf_raster_fname,screenRast,l_dates,out_dir,prefix_str,out_suffix) |
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names(list_param_plot_screen_raster) <- c("lf_raster_fname","screenRast","l_dates","out_dir_str","prefix_str","out_suffix_str")
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#debug(plot_screen_raster_val)
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undebug(plot_screen_raster_val)
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#plot_screen_raster_val(1,list_param_plot_screen_raster) |
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world_m_list <- mclapply(1:10, list_param=list_param_plot_screen_raster, plot_screen_raster_val,mc.preschedule=FALSE,mc.cores = 5) #This is the end bracket from mclapply(...) statement |
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world_m_list <- mclapply(11:30, list_param=list_param_plot_screen_raster, plot_screen_raster_val,mc.preschedule=FALSE,mc.cores = 7) #This is the end bracket from mclapply(...) statement
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#world_m_list1<- plot_screen_raster_val(1,list_param_plot_screen_raster)
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#world_m_list <- mclapply(1:10, list_param=list_param_plot_screen_raster, plot_screen_raster_val,mc.preschedule=FALSE,mc.cores = 5) #This is the end bracket from mclapply(...) statement
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world_m_list <- mclapply(1:30, list_param=list_param_plot_screen_raster, plot_screen_raster_val,mc.preschedule=FALSE,mc.cores = 7) #This is the end bracket from mclapply(...) statement |
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plot_screen_raster_val<-function(i,list_param){
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##USAGE### |
Also available in: Unified diff
1500x4500km, part 2 global assessment run10 - production of global mosaics and modifications to code