Revision 6f37bb0b
Added by Benoit Parmentier over 10 years ago
| climate/research/oregon/interpolation/global_run_scalingup_assessment_part1.R | ||
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#### FUNCTION USED IN SCRIPT |
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function_analyses_paper1 <-"contribution_of_covariates_paper_interpolation_functions_10222013.R" #first interp paper
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function_analyses_paper1 <-"contribution_of_covariates_paper_interpolation_functions_05212014.R" #first interp paper
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function_analyses_paper2 <-"multi_timescales_paper_interpolation_functions_05052014.R" |
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load_obj <- function(f) |
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list_names_tile_id <- paste("tile",1:length(list_raster_obj_files),sep="_")
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names(list_raster_obj_files)<- list_names_tile_id |
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lf_covar_obj <- lapply(in_dir_list,FUN=function(x){list.files(path=x,pattern="covar_obj.*.RData",full.names=T)})
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lf_covar_tif <- lapply(in_dir_list,FUN=function(x){list.files(path=x,pattern="covar.*.tif",full.names=T)})
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########################## START SCRIPT ############################## |
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################################################################ |
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#First create table of tiles under analysis and their coord |
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df_tile_processed <- data.frame(tile_coord=basename(in_dir_list)) |
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df_tile_processed$tile_id <- unlist(list_names_tile_id) |
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df_tile_processed$path_NEX <- in_dir_list |
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##Quick exploration of raster object |
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robj1 <- load_obj(list_raster_obj_files[[1]]) |
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names(robj1) |
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y_var_name <- "dailyTmax" |
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interpolation_method <- c("gam_CAI")
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name_method <- paste(interpolation_method,"_",y_var_name,"_",sep="") |
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l_pattern_models <- lapply(c(".*predicted_mod1_0_1.*",".*predicted_mod2_0_1.*",".*predicted_mod3_0_1.*"),
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l_pattern_models <- lapply(c(".*predicted_mod1_0_1.*",".*predicted_mod2_0_1.*",".*predicted_mod3_0_1.*",".*predicted_mod_kr_0_1.*"),
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FUN=function(x){paste(x,dates_l,".*.tif",sep="")})
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out_prefix_s <- paste(name_method,c("predicted_mod1_0_01","predicted_mod2_0_01","predicted_mod3_0_01"),sep="")
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#gam_CAI_dailyTmax_predicted_mod_kr_0_1_20101231_30_145.0_-120.0.tif |
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out_prefix_s <- paste(name_method,c("predicted_mod1_0_01","predicted_mod2_0_01","predicted_mod3_0_01","predicted_mod_kr_0_1"),sep="")
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dates_l #list of predicted dates |
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#l_out_rastnames_var <- paste(name_method,"predicted_mod1_0_01_",dates_l,sep="") |
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l_out_rastnames_var <- lapply(out_prefix_s, |
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FUN=function(x){paste(x,"_",dates_l,sep="")})
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for (i in 1:lenth(l_pattern_models)){
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#gam_CAI_dailyTmax_predicted_mod_kr_0_1_20101231_30_145.0_-120.0.tif |
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##Get list of predicted tif across all tiles, models and dates... |
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#this takes time, use mclapply!! |
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lf_pred_tif <- vector("list",length=length(l_pattern_models)) #number of models is 3
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for (i in 1:length(l_pattern_models)){
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l_pattern_mod <- l_pattern_models[[i]] |
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list_tif_files_dates <-lapply(1:length(l_pattern_mod),FUN=list_tif_fun, |
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in_dir_list=in_dir_list,pattern_str=l_pattern_models[[i]]) |
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lf_pred_tif[[i]] <- list_tif_files_dates |
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} |
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#Now get the clim surfaces: |
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month_l <- paste("clim_month_",1:12,sep="")
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l_pattern_models <- lapply(c("_mod1_0_1.*","_mod2_0_1.*","_mod3_0_1.*","_mod_kr_0_1.*"),
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FUN=function(x){paste("*.",month_l,x,".*.tif",sep="")})
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#"CAI_TMAX_clim_month_11_mod2_0_145.0_-120.0.tif" |
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lf_clim_tif <- vector("list",length=nb_mod) #number of models is 3
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for (i in 1:length(l_pattern_models)){
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l_pattern_mod <- l_pattern_models[[i]] |
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list_tif_files_dates <- lapply(1:length(l_pattern_mod),FUN=list_tif_fun, |
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in_dir_list=in_dir_list,pattern_str=l_pattern_models[[i]]) |
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lf_clim_tif[[i]] <- list_tif_files_dates |
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} |
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#### NOW create mosaic images |
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nb_mod <- 4 |
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for (i in 1:nb_mod){
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#l_pattern_mod <- l_pattern_models[[i]] |
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#out_prefix_s <- |
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#list_tif_files_dates <- list_tif_fun(1,in_dir_list,l_pattern_mod) |
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##List of predicted tif ... |
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list_tif_files_dates <-lapply(1:length(l_pattern_mod),FUN=list_tif_fun, |
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in_dir_list=in_dir_list,pattern_str=l_pattern_mod) |
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#list_tif_files_dates <-lapply(1:length(l_pattern_mod),FUN=list_tif_fun, |
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# in_dir_list=in_dir_list,pattern_str=l_pattern_mod) |
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list_tif_files_dates <- lf_pred_tif[[i]] |
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#save(list_tif_files_dates, file=paste("list_tif_files_dates","_",out_prefix,".RData",sep=""))
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mosaic_list_var <- list_tif_files_dates |
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# l_out_rastnames_var <- paste(name_method,"predicted_mod1_0_01_",dates_l,sep="") |
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# out_rastnames_var <- l_out_rastnames_var[i] |
| ... | ... | |
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list_var_mosaiced <- mclapply(1:2,FUN=mosaic_m_raster_list,list_param=list_param_mosaic,mc.preschedule=FALSE,mc.cores = 2) |
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#list_var_mosaiced <- mclapply(1:365,FUN=mosaic_m_raster_list,list_param=list_param_mosaic,mc.preschedule=FALSE,mc.cores = 2) |
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} |
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### Now find out how many files were predicted |
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l_f_t12 <- list.files(path=in_dir_list[12],".*predicted_mod1_0_1.*") |
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l_f_bytiles<-lapply(in_dir_list,function(x){list.files(path=x,pattern=".*predicted_mod1_0_1.*")})
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l_f_bytiles<-lapply(in_dir_list,function(x){list.files(path=x,pattern=".*predicted_mod1_0_1.*")})
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l_f_bytiles<-lapply(in_dir_list,function(x){list.files(path=x,pattern=".*predicted_mod1_0_1.*")})
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########### LAST PART: COPY SOME DATA BACK TO ATLAS ##### |
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#Copy back to atlas |
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#Copy summary and mosaic back to atlas
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system("scp -p ./*.txt parmentier@atlas.nceas.ucsb.edu:/data/project/layers/commons/NEX_data/output_run2_global_analyses_05122014")
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system("scp -p ./*.txt ./*.tif parmentier@atlas.nceas.ucsb.edu:/data/project/layers/commons/NEX_data/output_run2_global_analyses_05122014")
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#Copy specific tiles info back... |
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#tile_6: Oregon (45.0_-120.0) |
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system("scp -p ./*.txt ./*.tif parmentier@atlas.nceas.ucsb.edu:/data/project/layers/commons/NEX_data/output_run2_05122014/output/45.0_-120.0")
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#tile_8: Californi-Arizona |
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system("scp -p ./*.txt ./*.tif parmentier@atlas.nceas.ucsb.edu:/data/project/layers/commons/NEX_data/output_run2_05122014/output/45.0_-120.0")
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df_tile_processed$path_NEX |
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list_raster_obj_files[6] |
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list_raster_obj_files[8] |
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Atlas_dir <- "/data/project/layers/commons/NEX_data/output_run2_05122014/output/45.0_-120.0" |
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Atlas_hostname <- "parmentier@atlas.nceas.ucsb.edu" |
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#Oregon tile |
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filenames_NEX <- list_raster_obj_files[6] #copy raster prediction object |
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cmd_str <- paste("scp -p",filenames_NEX,paste(Atlas_hostname,Atlas_dir,sep=":"), sep=" ")
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system(cmd_str) |
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#Now copy back tif for specific dates and tile (date 1 and date 244) |
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date_selected <- c("20100101","20100901")
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date_index <- c(1,244) |
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tile_nb <- 6 |
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nb_mod <- 3+1 |
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lf_cp <- vector("list",length=length(date_selected))
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for(i in 1:length(date_selected)){
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index <- date_index[i] |
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#get all predicted tmax files for all models and specific date, tile |
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lf_cp[[i]] <- unlist(lapply(1:nb_mod,FUN=function(x){lf_pred_tif[[x]][[index]][[tile_nb]]}))
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} |
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lf_clim_tiff[[]] |
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filenames_NEX <- paste(list_tif_files_dates[[1]][[6]],list_tif_files_dates[[244]][[6]],lf_covar_tif[6]) #to get first date and tile 6 prediction mod1 |
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cmd_str <- paste("scp -p",filenames_NEX,paste(Atlas_hostname,Atlas_dir,sep=":"), sep=" ")
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system(cmd_str) |
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#California-Arizona tile |
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Atlas_dir <- "/data/project/layers/commons/NEX_data/output_run2_05122014/output/35.0_-115.0" |
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filenames_NEX <- paste(list_raster_obj_files[8],lf_covar_obj[8]) #copy raster prediction object |
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cmd_str <- paste("scp -p",filenames_NEX,paste(Atlas_hostname,Atlas_dir,sep=":"), sep=" ")
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system(cmd_str) |
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#Now copy back tif for specific dates and tile (date 1 and date 244) |
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filenames_NEX <- paste(list_tif_files_dates[[1]][[8]],list_tif_files_dates[[244]][[8]],lf_covar_tif[8]) #to get first date and tile 6 prediction mod1 |
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cmd_str <- paste("scp -p",filenames_NEX,paste(Atlas_hostname,Atlas_dir,sep=":"), sep=" ")
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system(cmd_str) |
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##################### END OF SCRIPT ###################### |
Also available in: Unified diff
global scalingup assessment part 1 generating mosaic for clim and generating automatic listing for part 3