Revision 8bb64f7a
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: 02/16/2015
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#MODIFIED ON: 03/07/2015
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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, Europe, Australia, 1000x300km |
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#COMMENTS: analyses for run 10 global analyses,all regions 1500x4500km and other tiles |
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#TODO: |
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#1) Split functions and master script |
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#2) Make this is a script/function callable from the shell/bash |
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#3) Check image format for tif |
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################################################################################################# |
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### Loading R library and packages |
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col_mfrow <- 1 |
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row_mfrow <- 1 |
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png(filename=paste("Figure9_",names(r),"_map_processed_region_",region_name,"_",out_prefix,".png",sep=""),
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png(filename=paste("Figure9_",names(r),"_map_processed_region_",region_name,"_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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#plot(reg_layer) |
| ... | ... | |
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#out_dir <- "/nobackup/bparmen1/" #on NEX |
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#in_dir_shp <- "/nobackupp4/aguzman4/climateLayers/output4/subset/shapefiles/" |
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y_var_name <- "dailyTmax" |
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interpolation_method <- c("gam_CAI")
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#out_prefix<-"run10_global_analyses_01282015" |
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#out_prefix <- "output_run10_1000x3000_global_analyses_02102015" |
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out_prefix <- "run10_1000x3000_global_analyses_02162015" |
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y_var_name <- "dailyTmax" #PARAM1 |
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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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create_out_dir_param <- FALSE #PARAM 5 |
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mosaic_plot <- FALSE |
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mosaic_plot <- FALSE #PARAM6
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#if daily mosaics NULL then mosaicas all days of the year |
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day_to_mosaic <- c("20100101","20100102","20100103","20100104","20100105",
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"20100301","20100302","20100303","20100304","20100305", |
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"20100501","20100502","20100503","20100504","20100505", |
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"20100701","20100702","20100703","20100704","20100705", |
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"20100901","20100902","20100903","20100904","20100905", |
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"20101101","20101102","20101103","20101104","20101105") |
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"20101101","20101102","20101103","20101104","20101105") #PARAM7
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#CRS_locs_WGS84 <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84
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CRS_WGS84 <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84
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CRS_WGS84 <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84 #CONSTANT1
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CRS_locs_WGS84<-CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84
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proj_str<- CRS_WGS84 |
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file_format <- ".rst" |
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NA_value <- -9999 |
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proj_str<- CRS_WGS84 #PARAM 8 #check this parameter
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file_format <- ".rst" #PARAM 9
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NA_value <- -9999 #PARAM10
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NA_flag_val <- NA_value |
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out_suffix <-out_prefix |
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tile_size <- "1500x4500" #PARAM 11 |
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mulitple_region <- TRUE #PARAM 12 |
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region_name <- "world" #PARAM 13 |
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########################## START SCRIPT ############################## |
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####### PART 1: Read in data ######## |
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#out_dir <-paste(out_dir,"_",out_prefix,sep="") |
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create_out_dir_param <- FALSE |
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#out_dir <-"/data/project/layers/commons/NEX_data/output_run10_global_analyses_01282015/" |
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out_dir <- "/data/project/layers/commons/NEX_data/output_run10_1000x3000_global_analyses_02162015" |
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if(create_out_dir_param==TRUE){
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out_dir <- create_dir_fun(out_dir,out_prefix)
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out_dir <- create_dir_fun(out_dir,out_suffix)
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setwd(out_dir) |
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}else{
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setwd(out_dir) #use previoulsy defined directory |
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} |
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setwd(out_dir) |
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CRS_locs_WGS84<-CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84
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CRS_WGS84<-c("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84
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region_name <- "world" |
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tile_size <- "1000x3000" |
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###Table 1: Average accuracy metrics |
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###Table 2: daily accuracy metrics for all tiles |
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summary_metrics_v <- read.table(file=file.path(out_dir,paste("summary_metrics_v2_NA_",out_prefix,".txt",sep="")),sep=",")
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#fname <- file.path(out_dir,paste("summary_metrics_v2_NA_",out_prefix,".txt",sep=""))
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tb <- read.table(file=file.path(out_dir,paste("tb_diagnostic_v_NA","_",out_prefix,".txt",sep="")),sep=",")
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summary_metrics_v <- read.table(file=file.path(out_dir,paste("summary_metrics_v2_NA_",out_suffix,".txt",sep="")),sep=",")
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#fname <- file.path(out_dir,paste("summary_metrics_v2_NA_",out_suffix,".txt",sep=""))
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tb <- read.table(file=file.path(out_dir,paste("tb_diagnostic_v_NA","_",out_suffix,".txt",sep="")),sep=",")
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#tb_diagnostic_s_NA_run10_global_analyses_11302014.txt |
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tb_s <- read.table(file=file.path(out_dir,paste("tb_diagnostic_s_NA","_",out_prefix,".txt",sep="")),sep=",")
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tb_s <- read.table(file=file.path(out_dir,paste("tb_diagnostic_s_NA","_",out_suffix,".txt",sep="")),sep=",")
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tb_month_s <- read.table(file=file.path(out_dir,paste("tb_month_diagnostic_s_NA","_",out_prefix,".txt",sep="")),sep=",")
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#pred_data_month_info <- read.table(file=file.path(out_dir,paste("pred_data_month_info_",out_prefix,".txt",sep="")),sep=",")
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#pred_data_day_info <- read.table(file=file.path(out_dir,paste("pred_data_day_info_",out_prefix,".txt",sep="")),sep=",")
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df_tile_processed <- read.table(file=file.path(out_dir,paste("df_tile_processed_",out_prefix,".txt",sep="")),sep=",")
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########################## START SCRIPT ############################## |
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tb_month_s <- read.table(file=file.path(out_dir,paste("tb_month_diagnostic_s_NA","_",out_suffix,".txt",sep="")),sep=",")
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#pred_data_month_info <- read.table(file=file.path(out_dir,paste("pred_data_month_info_",out_suffix,".txt",sep="")),sep=",")
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#pred_data_day_info <- read.table(file=file.path(out_dir,paste("pred_data_day_info_",out_suffix,".txt",sep="")),sep=",")
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df_tile_processed <- read.table(file=file.path(out_dir,paste("df_tile_processed_",out_suffix,".txt",sep="")),sep=",")
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#add column for tile size later on!!! |
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|
| ... | ... | |
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tb_s$pred_mod <- as.character(tb_s$pred_mod) |
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tb_s$tile_id <- as.character(tb_s$tile_id) |
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mulitple_region <- TRUE |
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#multiple regions? |
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if(mulitple_region==TRUE){
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| ... | ... | |
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} |
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### |
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"/data/project/layers/commons/NEX_data/output_run8_global_analyses_10212014/tb_diagnostic_v_NA_run8_global_analyses_10212014.txt" |
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#drop 3b |
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tb_all <- tb |
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#tb <- subset(tb,reg!="reg3b") |
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summary_metrics_v_all <- summary_metrics_v |
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#summary_metrics_v <- subset(summary_metrics_v,reg!="reg3b") |
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#tb_s_all <- tb_s |
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#tb_s_all <- subset(tb_s,reg!="reg3b") |
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#tb_month_s_all <- tb_month_s |
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#tb_month_s <- subset(tb_month_s,reg!="reg3b") |
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#df_tile_processed_all <- df_tile_processed |
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#df_tile_processed <- subset(df_tile_processed,reg!="reg3b") |
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#pred_data_month_info_all <- pred_data_month_info |
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#pred_data_month_info <- subset(pred_data_month_info,reg!="reg3b") |
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#pred_data_month_info_all <- pred_data_month_info |
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#pred_data_month_info <- subset(pred_data_month_info,reg!="reg3b") |
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#path_reg3 <- "/data/project/layers/commons/NEX_data/output_run8_global_analyses_10212014" |
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#summary_metrics_v_reg3 <- read.table(file.path(path_reg3,"summary_metrics_v2_NA_run8_global_analyses_10212014.txt"),sep=",") |
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#tb_diagnostic_v_NA_run8_global_analyses_10212014.txt |
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#tb_month_diagnostic_s_NA_run8_global_analyses_10212014.txt |
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#tb_diagnostic_s_NA_run8_global_analyses_10212014.txt |
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#pred_data_month_info_run8_global_analyses_10212014.txt |
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#pred_data_day_info_run8_global_analyses_10212014.txt |
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#df_tile_processed_run8_global_analyses_10212014.txt |
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############ PART 2: PRODUCE FIGURES ################ |
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############### |
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###########################
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### Figure 1: plot location of the study area with tiles processed |
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#df_tiled_processed <- na.omit(df_tile_processed) #remove other list of folders irrelevant |
| ... | ... | |
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list_shp_reg_files <- file.path("/data/project/layers/commons/NEX_data/",out_dir,
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"shapefiles",basename(list_shp_reg_files)) |
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### Potential function starts here: |
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#function(in_dir,out_dir,list_shp_reg_files,title_str,region_name,num_cores,out_suffix,out_suffix) |
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### First get background map to display where study area is located |
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#can make this more general later on.. |
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#can make this more general later on..should have this already in a local directory on Atlas or NEX!!!! |
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if (region_name=="USA"){
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usa_map <- getData('GADM', country='USA', level=1) #Get US map
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#usa_map <- getData('GADM', country=region_name,level=1) #Get US map, this is not working right now
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| ... | ... | |
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#collect info: read in all shapfiles |
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#This is slow...make a function and use mclapply?? |
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#/data/project/layers/commons/NEX_data/output_run6_global_analyses_09162014/shapefiles |
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for(i in 1:length(list_shp_reg_files)){
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#path_to_shp <- dirname(list_shp_reg_files[[i]]) |
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path_to_shp <- file.path(out_dir,"/shapefiles") |
| ... | ... | |
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#fun <- function(i,list_shp_files) |
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#coord_names <- c("lon","lat")
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#l_ras#t <- rasterize_df_fun(test,coord_names,proj_str,out_suffix=out_prefix,out_dir=".",file_format,NA_flag_val,tolerance_val=0.000120005)
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#l_ras#t <- rasterize_df_fun(test,coord_names,proj_str,out_suffix=out_suffix,out_dir=".",file_format,NA_flag_val,tolerance_val=0.000120005)
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#remove try-error polygons...we loose three tiles because they extend beyond -180 deg |
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tmp <- shps_tiles |
| ... | ... | |
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col_mfrow <- 1 |
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row_mfrow <- 1 |
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png(filename=paste("Figure1_tile_processed_region_",region_name,"_",out_prefix,".png",sep=""),
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png(filename=paste("Figure1_tile_processed_region_",region_name,"_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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plot(reg_layer) |
| ... | ... | |
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col_mfrow <- 1 |
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row_mfrow <- 1 |
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png(filename=paste("Figure2a_boxplot_with_oultiers_by_tiles_",model_name[i],"_",out_prefix,".png",sep=""),
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png(filename=paste("Figure2a_boxplot_with_oultiers_by_tiles_",model_name[i],"_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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boxplot(rmse~tile_id,data=subset(tb,tb$pred_mod==model_name[i])) |
| ... | ... | |
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res_pix <- 480 |
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col_mfrow <- 1 |
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row_mfrow <- 1 |
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png(filename=paste("Figure2b_boxplot_scaling_by_tiles","_",model_name[i],"_",out_prefix,".png",sep=""),
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png(filename=paste("Figure2b_boxplot_scaling_by_tiles","_",model_name[i],"_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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model_name <- unique(tb$pred_mod) |
| ... | ... | |
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col_mfrow <- 1 |
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row_mfrow <- 1 |
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png(filename=paste("Figure3a_boxplot_overall_region_with_oultiers_",model_name[i],"_",out_prefix,".png",sep=""),
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png(filename=paste("Figure3a_boxplot_overall_region_with_oultiers_",model_name[i],"_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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boxplot(rmse~pred_mod,data=tb)#,names=tb$pred_mod) |
| ... | ... | |
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dev.off() |
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|
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## Figure 3b |
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png(filename=paste("Figure3b_boxplot_overall_region_scaling_",model_name[i],"_",out_prefix,".png",sep=""),
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png(filename=paste("Figure3b_boxplot_overall_region_scaling_",model_name[i],"_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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boxplot(rmse~pred_mod,data=tb,ylim=c(0,5),outline=FALSE)#,names=tb$pred_mod) |
| ... | ... | |
| 628 | 610 |
# col_mfrow <- 1 |
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# row_mfrow <- 1 |
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# |
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# png(filename=paste("Figure4_models_predicted_surfaces_",model_name[i],"_",name_method_var,"_",data_selected,"_",out_prefix,".png",sep=""),
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# png(filename=paste("Figure4_models_predicted_surfaces_",model_name[i],"_",name_method_var,"_",data_selected,"_",out_suffix,".png",sep=""),
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| 632 | 614 |
# width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 633 | 615 |
# |
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# plot(r_pred) |
| ... | ... | |
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col_mfrow <- 1 |
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row_mfrow <- 1 |
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png(filename=paste("Figure5_ac_metrics_ranked_",model_name[i],"_",out_prefix,".png",sep=""),
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png(filename=paste("Figure5_ac_metrics_ranked_",model_name[i],"_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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x<- as.character(df_ac_mod$tile_id) |
| 665 | 647 |
barplot(df_ac_mod$rmse, names.arg=x) |
| ... | ... | |
| 689 | 671 |
# col_mfrow <- 1 |
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# row_mfrow <- 1 |
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# |
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# png(filename=paste("Figure6_ac_metrics_map_centroids_tile_",model_name[i],"_",out_prefix,".png",sep=""),
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# png(filename=paste("Figure6_ac_metrics_map_centroids_tile_",model_name[i],"_",out_suffix,".png",sep=""),
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| 693 | 675 |
# width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 694 | 676 |
# |
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# plot(r_pred) |
| ... | ... | |
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col_mfrow <- 1 |
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row_mfrow <- 1 |
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png(filename=paste("Figure6_ac_metrics_map_centroids_tile_",model_name[i],"_",out_prefix,".png",sep=""),
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png(filename=paste("Figure6_ac_metrics_map_centroids_tile_",model_name[i],"_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 731 | 713 |
|
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#plot(r_pred) |
| ... | ... | |
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row_mfrow <- 1 |
| 786 | 768 |
|
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png(filename=paste("Figure7a_ac_metrics_map_centroids_tile_",model_name[j],"_","missing_day_",threshold_missing_day[i],
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"_",out_prefix,".png",sep=""),
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"_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 790 | 772 |
|
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model_name[j] |
| ... | ... | |
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|
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# |
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## Figure 7a |
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png(filename=paste("Figure7a_number_daily_predictions_per_models","_",out_prefix,".png",sep=""),
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png(filename=paste("Figure7a_number_daily_predictions_per_models","_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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|
| 821 | 803 |
xyplot(n~pred_mod | tile_id,data=subset(as.data.frame(summary_metrics_v), |
| ... | ... | |
| 844 | 826 |
histogram(test$predicted~test$tile_id) |
| 845 | 827 |
#table(tb) |
| 846 | 828 |
## Figure 7b |
| 847 |
#png(filename=paste("Figure7b_number_daily_predictions_per_models","_",out_prefix,".png",sep=""),
|
|
| 829 |
#png(filename=paste("Figure7b_number_daily_predictions_per_models","_",out_suffix,".png",sep=""),
|
|
| 848 | 830 |
# width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 849 | 831 |
|
| 850 | 832 |
#xyplot(n~month | tile_id + pred_mod,data=subset(as.data.frame(tb_month_s), |
| ... | ... | |
| 868 | 850 |
col_mfrow <- 1 |
| 869 | 851 |
row_mfrow <- 1 |
| 870 | 852 |
|
| 871 |
png(filename=paste("Figure8a_boxplot_overall_separated_by_region_with_oultiers_",model_name[i],"_",out_prefix,".png",sep=""),
|
|
| 853 |
png(filename=paste("Figure8a_boxplot_overall_separated_by_region_with_oultiers_",model_name[i],"_",out_suffix,".png",sep=""),
|
|
| 872 | 854 |
width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 873 | 855 |
|
| 874 | 856 |
p<- bwplot(rmse~pred_mod | reg, data=tb, |
| ... | ... | |
| 877 | 859 |
dev.off() |
| 878 | 860 |
|
| 879 | 861 |
## Figure 8b |
| 880 |
png(filename=paste("Figure8b_boxplot_overall_separated_by_region_scaling_",model_name[i],"_",out_prefix,".png",sep=""),
|
|
| 862 |
png(filename=paste("Figure8b_boxplot_overall_separated_by_region_scaling_",model_name[i],"_",out_suffix,".png",sep=""),
|
|
| 881 | 863 |
width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 882 | 864 |
|
| 883 | 865 |
boxplot(rmse~pred_mod,data=tb,ylim=c(0,5),outline=FALSE)#,names=tb$pred_mod) |
| ... | ... | |
| 911 | 893 |
#out_dir_str <- out_dir |
| 912 | 894 |
#reg_name <- "reg6_1000x3000" |
| 913 | 895 |
#lapply() |
| 914 |
#list_param_plot_daily_mosaics <- list(lf_m=lf_m,reg_name=reg_name,out_dir_str=out_dir_str,out_suffix=out_prefix)
|
|
| 915 |
#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)
|
|
| 896 |
#list_param_plot_daily_mosaics <- list(lf_m=lf_m,reg_name=reg_name,out_dir_str=out_dir_str,out_suffix=out_suffix)
|
|
| 897 |
#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)
|
|
| 916 | 898 |
|
| 917 | 899 |
#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) |
| 918 | 900 |
#debug(plot_daily_mosaics) |
| ... | ... | |
| 927 | 909 |
out_dir_str <- out_dir |
| 928 | 910 |
reg_name <- paste(l_reg_name[i],"_",tile_size,sep="") #make this automatic |
| 929 | 911 |
#lapply() |
| 930 |
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)
|
|
| 912 |
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)
|
|
| 931 | 913 |
#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) |
| 932 | 914 |
|
| 933 | 915 |
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) |
| ... | ... | |
| 1058 | 1040 |
# out_dir_str <- out_dir |
| 1059 | 1041 |
#reg_name <- paste(l_reg_name[i],"_",tile_size,sep="") #make this automatic |
| 1060 | 1042 |
#lapply() |
| 1061 |
# 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)
|
|
| 1043 |
# list_param_plot_daily_mosaics <- list(lf_m=lf_m,reg_name=tile_size,out_dir_str=out_dir_str,out_suffix=out_suffix,l_dates=day_to_mosaic)
|
|
| 1062 | 1044 |
#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) |
| 1063 | 1045 |
|
| 1064 | 1046 |
#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) |
Also available in: Unified diff
global assessment part2, clean up of code, 1500x4500km til size production of figures