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Revision ed15fe32

Added by Benoit Parmentier almost 10 years ago

NEX part1 accuracy assessment mosaicing for 1000x3000km with python code

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climate/research/oregon/interpolation/global_run_scalingup_assessment_part1.R
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#Part 1 create summary tables and inputs for figure in part 2 and part 3.
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#AUTHOR: Benoit Parmentier
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#CREATED ON: 03/23/2014
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#MODIFIED ON: 02/10/2015
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#MODIFIED ON: 02/16/2015
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#Version: 4
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#PROJECT: Environmental Layers project
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#TO DO:
......
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#### FUNCTION USED IN SCRIPT
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function_analyses_paper1 <- "global_run_scalingup_assessment_part1_functions_02052015.R"
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function_analyses_paper1 <- "global_run_scalingup_assessment_part1_functions_02112015.R"
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script_path <- "/nobackupp8/bparmen1/env_layers_scripts" #path to script
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source(file.path(script_path,function_analyses_paper1)) #source all functions used in this script
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  ......




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in_dir1 <- "/nobackupp6/aguzman4/climateLayers/output1000x3000_km"


  		




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region_names <- c("reg1","reg2","reg3","reg4","reg5","reg6") #selected region names


  		




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region_names <- c("reg1","reg2","reg3b","reg4","reg5","reg6") #selected region names


  		




  
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#region_names <- c("reg1","reg2","reg3b","reg4","reg5","reg6") #selected region names


  		




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in_dir_list <- list.dirs(path=in_dir1,recursive=FALSE) #get the list regions processed for this run


  		




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#basename(in_dir_list)


  		




  ......




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y_var_name <- "dailyTmax"


  		




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interpolation_method <- c("gam_CAI")


  		




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out_prefix<-"run10_1000x3000_global_analyses_02102015"


  		




  
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out_prefix<-"run10_1000x3000_global_analyses_02162015"


  		




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#out_dir<-"/data/project/layers/commons/NEX_data/" #On NCEAS Atlas


  		




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#out_dir <- "/nobackup/bparmen1/" #on NEX


  		




  ......




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### Now find out how many files were predicted


  		




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# will be useful later on


  		




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#sh /nobackupp6/aguzman4/climateLayers/sharedCode/shMergeFromFile.sh list_mosaics_20100901.txt world_mosaics_1000x3000_20100901.tif


  		




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


  		




  
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  pattern_str <- paste("*.",day_to_mosaic[i],".*.tif",sep="")


  		




  
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  lf_day_to_mosaic <- list.files(path=out_dir,pattern=pattern_str,full.names=T) 


  		




  
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  #write.table(lf_day_to_mosaic,file=file.path(out_dir,paste("list_to_mosaics_",day_to_mosaic[i],".txt",sep="")))


  		




  
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  writeLines(lf_day_to_mosaic,con=file.path(out_dir,paste("list_to_mosaics_",day_to_mosaic[i],".txt",sep="")))


  		




  
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  in_file_to_mosaics <- file.path(out_dir,paste("list_to_mosaics_",day_to_mosaic[i],".txt",sep=""))        


  		




  
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  #in_dir_mosaics <- file.path(in_dir1,region_names[i])


  		




  
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  #out_dir_mosaics <- "/nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg5/mosaicsMean"


  		




  
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  #Can be changed to have mosaics in different dir..


  		




  
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  #out_dir_mosaics <- out_dir


  		




  
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  #prefix_str <- "reg4_1500x4500"


  		




  
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  #tile_size <- basename(dirname(in_dir[[i]]))


  		




  
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  tile_size <- basename(in_dir1)


  		




  
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  #prefix_str <- paste(region_names[i],"_",tile_size,sep="")


  		




  
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  mod_str <- "mod1" #use mod2 which corresponds to model with LST and elev


  		




  
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  out_mosaic_name <- paste("world_mosaics_",mod_str,"_",tile_size,"_",day_to_mosaic[i],"_",out_prefix,".tif",sep="")


  		




  
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  module_path <- "/nobackupp6/aguzman4/climateLayers/sharedCode"


  		




  
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  cmd_str <- paste("sh", file.path(module_path,"shMergeFromFile.sh"),


  		




  
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                 in_file_to_mosaics,


  		




  
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                 out_mosaic_name,


  		




  
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                 sep=" ")


  		




  
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  system(cmd_str)


  		




  
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}


  		




  
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######################################################


  		




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####### PART 3: EXAMINE STATIONS AND MODEL FITTING ###


  		




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