Revision 5c03e335
Added by Benoit Parmentier almost 10 years ago
| climate/research/oregon/interpolation/global_run_scalingup_assessment_part1.R | ||
|---|---|---|
| 5 | 5 |
#Part 1 create summary tables and inputs for figure in part 2 and part 3. |
| 6 | 6 |
#AUTHOR: Benoit Parmentier |
| 7 | 7 |
#CREATED ON: 03/23/2014 |
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#MODIFIED ON: 12/23/2014
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#Version: 3
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#MODIFIED ON: 01/28/2015
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#Version: 4
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| 10 | 10 |
#PROJECT: Environmental Layers project |
| 11 | 11 |
#TO DO: |
| 12 | 12 |
# - generate delta and clim mosaic |
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# - generate monthly inputs data_month |
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# - generate table of number of observations per tile for use in map part 2 |
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# - generate data_s and data_v inputs as giant table |
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# - generate accuracy for mosaic (part 2 and part3) |
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| 17 | 13 |
# - clean up |
| 18 | 14 |
|
| 19 | 15 |
#First source file: |
| ... | ... | |
| 468 | 464 |
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| 469 | 465 |
#reg1 (North Am), reg2(Europe),reg3(Asia), reg4 (South Am), reg5 (Africa), reg6 (Australia-Asia) |
| 470 | 466 |
#master directory containing the definition of tile size and tiles predicted |
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in_dir1 <- "/nobackupp4/aguzman4/climateLayers/output1000x3000_km/"
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in_dir1 <- "/nobackupp6/aguzman4/climateLayers/output1500x4500_km/"
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| 472 | 468 |
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region_names <- c("reg2","reg6") #selected region names
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region_names <- c("reg1") #selected region names
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| 474 | 470 |
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| 475 | 471 |
in_dir_list <- list.dirs(path=in_dir1,recursive=FALSE) #get the list regions processed for this run |
| 476 | 472 |
#basename(in_dir_list) |
| ... | ... | |
| 499 | 495 |
|
| 500 | 496 |
y_var_name <- "dailyTmax" |
| 501 | 497 |
interpolation_method <- c("gam_CAI")
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out_prefix<-"run10_global_analyses_12232014"
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out_prefix<-"run10_global_analyses_01282015"
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| 503 | 499 |
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| 504 | 500 |
#out_dir<-"/data/project/layers/commons/NEX_data/" #On NCEAS Atlas |
| 505 | 501 |
out_dir <- "/nobackup/bparmen1/" #on NEX |
| 502 |
#out_dir <- "/nobackupp8/bparmen1/" # |
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| 506 | 503 |
#out_dir <-paste(out_dir,"_",out_prefix,sep="") |
| 507 | 504 |
create_out_dir_param <- TRUE |
| 508 | 505 |
|
| ... | ... | |
| 743 | 740 |
y_var_name <- "dailyTmax" #set up in parameters of this script |
| 744 | 741 |
interpolation_method <- c("gam_CAI") #set up in parameters of the script
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| 745 | 742 |
name_method <- paste(interpolation_method,"_",y_var_name,"_",sep="") |
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#make it general using nb_mod!! |
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#could be set up at the begining? |
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mod_id <- c(1:(nb_mod-1),"_kr") |
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pred_pattern_str <- paste(".*predicted_mod",mod_id,"_0_1.*",sep="")
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#,".*predicted_mod2_0_1.*",".*predicted_mod3_0_1.*",".*predicted_mod_kr_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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l_pattern_models <- lapply(pred_pattern_str, |
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FUN=function(x){paste(x,dates_l,".*.tif",sep="")})
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#gam_CAI_dailyTmax_predicted_mod_kr_0_1_20101231_30_145.0_-120.0.tif |
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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]] #365 dates |
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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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list_tif_files_dates <-mclapply(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]],mc.preschedule=FALSE,mc.cores = 6) |
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lf_pred_tif[[i]] <- list_tif_files_dates |
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} |
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#Need to check how many dates were predicted (have tif) !!! make a table with that!! |
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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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#generate this automatically!!! |
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l_pattern_models <- lapply(c("_mod1_0_1.*","_mod2_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]] #12 dates |
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list_tif_files_dates <- mclapply(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]],mc.preschedule=FALSE,mc.cores = 6) |
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lf_clim_tif[[i]] <- list_tif_files_dates |
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} |
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#Now get delta surfaces: |
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#mod_id <- c(1:(nb_mod-1),"_kr") |
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#pred_pattern_str <- paste(".*predicted_mod",mod_id,"_0_1.*",sep="")
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#,".*predicted_mod2_0_1.*",".*predicted_mod3_0_1.*",".*predicted_mod_kr_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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#l_pattern_models <- lapply(pred_pattern_str, |
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# FUN=function(x){paste(x,dates_l,".*.tif",sep="")})
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#date_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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#l_pattern_models <- lapply(c(".*delta_dailyTmax_mod1_del_0_1.*",".*delta_dailyTmax_mod2_del_0_1.*",".*delta_dailyTmax_mod3_del_0_1.*",".*delta_dailyTmax_mod_kr_del_0_1.*"),
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# FUN=function(x){paste(x,dates_l,".*.tif",sep="")})
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l_pattern_models <- lapply(c(".*delta_dailyTmax_mod1_del_0_1.*",".*delta_dailyTmax_mod2_del_0_1.*",".*delta_dailyTmax_mod_kr_del_0_1.*"),
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FUN=function(x){paste(x,dates_l,".*.tif",sep="")})
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lf_delta_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 <- mclapply(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]],mc.preschedule=FALSE,mc.cores = 6) |
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lf_delta_tif[[i]] <- list_tif_files_dates |
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} |
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##Use python code written by Alberto Guzman |
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#### NOW create mosaic images for daily prediction |
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#system("MODULEPATH=$MODULEPATH:/nex/modules/files")
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#system("module load /nex/modules/files/pythonkits/gdal_1.10.0_python_2.7.3_nex")
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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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out_prefix_s <- paste(name_method,c("predicted_mod1_0_01","predicted_mod2_0_01","predicted_mod_kr_0_1"),sep="")
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module_path <- ""
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module_path <- "/nobackupp6/aguzman4/climateLayers/sharedCode/"
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| 823 | 750 |
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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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#l_dates <- paste(day_to_mosaic,collapse=",",sep=" ") |
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l_dates <- paste(day_to_mosaic,collapse=",") |
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## use region 2 first |
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| 828 | 754 |
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#nb_mod <- 4 #this is set up earlier |
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##Add option to specify wich dates to mosaic?? |
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day_to_mosaic <- c("20100101","20100901")
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if (!is.null(day_to_mosaic)){
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list_days <-match(day_to_mosaic,dates_l) |
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}else{
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list_days <- 1:365 #should check for year in case it has 366, add later!! |
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} |
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#make this a function later...with following param |
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#input: |
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#region_names |
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#in_dir1 |
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##out_dir , not ehta out_dir moasic s can be created in rhe future function |
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#mod_str |
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#For the time being use mean,median from python function by Alberto... |
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| 837 | 762 |
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##Use python code written by Alberto Guzman |
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for (i in 1:length(region_names)){
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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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| 839 | 771 |
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system("MODULEPATH=$MODULEPATH:/nex/modules/files")
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system("module load /nex/modules/files/pythonkits/gdal_1.10.0_python_2.7.3_nex")
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prefix_str <- paste(region_names[i],"_",tile_size,sep="") |
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| 842 | 773 |
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module_path <- "" |
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module_path <- "/nobackupp6/aguzman4/climateLayers/sharedCode/" |
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in_dir_mosaics <- "/nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg2/" |
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out_dir_mosaics <- "/nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg2/mosaics" |
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mod_str <- "mod1" #use mod2 which corresponds to model with LST and elev |
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| 847 | 775 |
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l_dates <- "20100101,20100102" |
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cmd_str <- paste("python", file.path(module_path,"mosaicUsingGdalMerge.py"),
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cmd_str <- paste("python", file.path(module_path,"mosaicUsingGdalMerge.py"),
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| 850 | 777 |
in_dir_mosaics, |
| 851 | 778 |
out_dir_mosaics, |
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prefix_str, |
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"--mods", mod_str, |
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"--date", l_dates,sep=" ") |
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system(cmd_str) |
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system(cmd_str) |
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} |
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| 854 | 785 |
|
| 855 | 786 |
### Now find out how many files were predicted |
| 856 | 787 |
# will be useful later on |
| ... | ... | |
| 871 | 802 |
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| 872 | 803 |
names(data_month) #this contains LST means (mm_1, mm_2 etc.) as well as TMax and other info |
| 873 | 804 |
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#problem with tile 12...the raster ojbect has missing sub object |
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#data_month_list <- lapply(1:length(list_raster_obj_files),x=list_raster_obj_files, |
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# FUN=function(i,x){x<-load_obj(x[[i]]);
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# extract_from_list_obj(x$validation_mod_month_obj,"data_s")}) |
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### make this part a function: |
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#create a table for every month, day and tiles... |
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# data_month_list <- lapply(1:length(list_raster_obj_files),x=list_raster_obj_files, |
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# FUN=function(i,x){x<-load_obj(x[[i]]);
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# extract_from_list_obj(x$clim_method_mod_obj,"data_month")}) |
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# |
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# names(data_month_list) <- paste("tile","_",1:length(data_month_list),sep="")
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# |
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# #names(data_month_list) <- basename(in_dir_list) #use folder id instead |
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# |
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# list_names_tile_id <- paste("tile",1:length(list_raster_obj_files),sep="_")
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# |
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# #tile_id <- lapply(1:length(data_month_list), |
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# # FUN=function(i,x){rep(names(x)[i],nrow(x[[i]]))},x=data_month_list)
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# |
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# data_month_NAM <- do.call(rbind.fill,data_month_list) #combined data_month for "NAM" North America |
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# data_month_NAM$tile_id <- unlist(tile_id) |
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# |
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# names(robj1$validation_mod_day_obj[[1]]$data_s) # daily for January with predictions |
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# dim(robj1$validation_mod_month_obj[[1]]$data_s) # daily for January with predictions |
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# |
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| 902 | 805 |
use_day=TRUE |
| 903 | 806 |
use_month=TRUE |
| 904 | 807 |
|
| ... | ... | |
| 937 | 840 |
#for i in 1:length(df_tiled_processed$tile_coord) |
| 938 | 841 |
#output_atlas_dir <- "/data/project/layers/commons/NEX_data/output_run3_global_analyses_06192014/output10Deg/reg1" |
| 939 | 842 |
#output_atlas_dir <- "/data/project/layers/commons/NEX_data/output_run5_global_analyses_08252014/output20Deg" |
| 940 |
output_atlas_dir <- "/data/project/layers/commons/NEX_data/output_run10_global_analyses_12152014"
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output_atlas_dir <- file.path("/data/project/layers/commons/NEX_data/",out_dir)
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| 941 | 844 |
#Make directories on ATLAS |
| 942 | 845 |
#for (i in 1:length(df_tile_processed$tile_coord)){
|
| 943 | 846 |
# create_dir_fun(file.path(output_atlas_dir,as.character(df_tile_processed$tile_coord[i])),out_suffix=NULL) |
| ... | ... | |
| 948 | 851 |
# create_dir_fun(file.path(output_atlas_dir,as.character(df_tile_processed$tile_coord[i]),"/shapefiles"),out_suffix=NULL) |
| 949 | 852 |
#} |
| 950 | 853 |
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| 951 |
|
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#Copy summary textfiles and mosaic back to atlas |
|
| 854 |
#Copy summary textfiles back to atlas |
|
| 953 | 855 |
|
| 954 | 856 |
Atlas_dir <- file.path("/data/project/layers/commons/NEX_data/",basename(out_dir))#,"output/subset/shapefiles")
|
| 955 | 857 |
Atlas_hostname <- "parmentier@atlas.nceas.ucsb.edu" |
| 956 |
lf_cp_f <- list.files(out_dir,full.names=T)#copy all files can filter later |
|
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lf_cp_f <- list.files(out_dir,full.names=T,pattern="*.txt")#copy all files can filter later
|
|
| 957 | 859 |
filenames_NEX <- paste(lf_cp_f,collapse=" ") #copy raster prediction object |
| 958 | 860 |
cmd_str <- paste("scp -p",filenames_NEX,paste(Atlas_hostname,Atlas_dir,sep=":"), sep=" ")
|
| 959 | 861 |
system(cmd_str) |
| ... | ... | |
| 979 | 881 |
|
| 980 | 882 |
###### COPY MOSAIC files |
| 981 | 883 |
|
| 982 |
#> system("ls -ltr /nobackupp6/aguzman4/climateLayers/output1500x4500_km/reg5")
|
|
| 983 |
#Copy all mosaics related files in one unique directory called mosaics on Atlas |
|
| 884 |
#Copy region mosaics back to atlas |
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| 984 | 885 |
|
| 985 | 886 |
Atlas_dir <- file.path("/data/project/layers/commons/NEX_data/",basename(out_dir),"mosaics")
|
| 986 | 887 |
Atlas_hostname <- "parmentier@atlas.nceas.ucsb.edu" |
| 987 |
#lf_reg4 <- list.files(path=file.path(dirname(in_dir_list[[2]]),"mosaics"),full.names=T) |
|
| 988 |
#lf_reg5 <- list.files(path=file.path(dirname(in_dir_list[[20]]),"mosaics"),full.names=T) |
|
| 989 |
lf_reg2 <- list.files(path=file.path("/nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg2","mosaics"),
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full.names=T) |
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|
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| 992 |
#lf_cp_mosaics <- c(lf_reg4,lf_reg5) |
|
| 993 |
#filenames_NEX <- paste(lf_cp_mosaics,collapse=" ") #copy raster prediction object |
|
| 994 |
#cmd_str <- paste("scp -p",filenames_NEX,paste(Atlas_hostname,Atlas_dir,sep=":"), sep=" ")
|
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| 995 |
#system(cmd_str) |
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| 996 |
|
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| 997 |
#since they have the same name, must place them in separate dir... |
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filenames_NEX <- paste(lf_reg2,collapse=" ") #copy raster prediction object |
|
| 999 |
cmd_str <- paste("scp -p",filenames_NEX,paste(Atlas_hostname,file.path(Atlas_dir,"reg2"),sep=":"), sep=" ")
|
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system(cmd_str) |
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|
|
| 1002 |
############# COPY FILES USED FOR DIFFERENCES OF IMAGES |
|
| 1003 |
#copy mosaics from other tiles of 1,500x4,500 |
|
| 1004 |
|
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| 1005 |
|
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| 1006 |
lf_reg4_1500x4500 <- list.files(path="/nobackup/bparmen1//output_run10_global_analyses_12152014/mosaics/reg4_1500x4500/",full.names=T) |
|
| 1007 |
lf_reg5_1500x4500 <- list.files(path="/nobackup/bparmen1//output_run10_global_analyses_12152014/mosaics/reg5_1500x4500/",full.names=T) |
|
| 1008 |
lf_reg4_1000x3000 <- list.files(path="/nobackup/bparmen1//output_run10_global_analyses_12152014/mosaics/reg4_1000x3000/",full.names=T) |
|
| 1009 |
lf_reg5_1000x3000 <- list.files(path="/nobackup/bparmen1//output_run10_global_analyses_12152014/mosaics/reg5_1000x3000/",full.names=T) |
|
| 1010 |
|
|
| 1011 |
Atlas_dir <- file.path("/data/project/layers/commons/NEX_data/",basename(out_dir),"mosaics")
|
|
| 1012 |
Atlas_hostname <- "parmentier@atlas.nceas.ucsb.edu" |
|
| 1013 |
#lf_reg4 <- list.files(path=file.path(dirname(in_dir_list[[2]]),"mosaics"),full.names=T) |
|
| 1014 |
|
|
| 1015 |
#copy reg5_1000x3000 |
|
| 1016 |
filenames_NEX <- paste(lf_reg5_1000x3000,collapse=" ") #copy raster prediction object |
|
| 1017 |
cmd_str <- paste("scp -p",filenames_NEX,paste(Atlas_hostname,file.path(Atlas_dir,"reg5_1000x3000"),sep=":"), sep=" ")
|
|
| 1018 |
system(cmd_str) |
|
| 1019 |
|
|
| 1020 |
#copy reg4_1000x3000 |
|
| 1021 |
filenames_NEX <- paste(lf_reg4_1000x3000,collapse=" ") #copy raster prediction object |
|
| 1022 |
cmd_str <- paste("scp -p",filenames_NEX,paste(Atlas_hostname,file.path(Atlas_dir,"reg4_1000x3000"),sep=":"), sep=" ")
|
|
| 1023 |
system(cmd_str) |
|
| 1024 |
|
|
| 1025 |
#copy reg4_1500x4500 |
|
| 1026 |
filenames_NEX <- paste(lf_reg4_1500x4500,collapse=" ") #copy raster prediction object |
|
| 1027 |
cmd_str <- paste("scp -p",filenames_NEX,paste(Atlas_hostname,file.path(Atlas_dir,"reg4_1500x4500"),sep=":"), sep=" ")
|
|
| 1028 |
system(cmd_str) |
|
| 1029 |
|
|
| 1030 |
#copy reg5_1500x4500 |
|
| 1031 |
filenames_NEX <- paste(lf_reg5_1500x4500,collapse=" ") #copy raster prediction object |
|
| 1032 |
cmd_str <- paste("scp -p",filenames_NEX,paste(Atlas_hostname,file.path(Atlas_dir,"reg5_1500x4500"),sep=":"), sep=" ")
|
|
| 888 |
lf_cp_f <- list.files(out_dir,full.names=T,pattern="*.tif")#copy all files can filter later |
|
| 889 |
filenames_NEX <- paste(lf_cp_f,collapse=" ") #copy raster prediction object |
|
| 890 |
cmd_str <- paste("scp -p",filenames_NEX,paste(Atlas_hostname,Atlas_dir,sep=":"), sep=" ")
|
|
| 1033 | 891 |
system(cmd_str) |
| 1034 | 892 |
|
| 1035 |
|
|
| 1036 | 893 |
##################### END OF SCRIPT ###################### |
| 1037 | 894 |
|
| 1038 |
|
|
| 1039 | 895 |
###Mosaic ... |
| 1040 | 896 |
#python mosaicUsingGdalMerge.py /nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg5/ /nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg5/mosaics/ |
| 1041 | 897 |
#specify which month you want to process with the '-m' option. |
| ... | ... | |
| 1046 | 902 |
#python /nobackupp6/aguzman4/climateLayers/sharedCode/mosaicUsingGdalMerge.py /nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg2/ /nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg2/mosaics/ --date 20100101,20100102,20100103,20100104 |
| 1047 | 903 |
#python /nobackupp6/aguzman4/climateLayers/sharedCode/mosaicUsingGdalMerge.py /nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg2/ /nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg2/mosaics/ --m 1" |
| 1048 | 904 |
|
| 1049 |
# cmd_str <- paste("python", module_path,"mosaicUsingGdalMerge.py",
|
|
| 1050 |
# "/nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg5/" |
|
| 1051 |
# "/nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg5/mosaics/" |
|
| 1052 |
# "--date 20100101,20100102,20100103,20100104",sep=" ") |
|
| 1053 |
# cmd_str <- paste("python", module_path,"mosaicUsingGdalMerge.py",
|
|
| 1054 |
# "/nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg5/" |
|
| 1055 |
# "/nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg5/mosaics/" |
|
| 1056 |
# "--date 20100101,20100102,20100103,20100104",sep=" ") |
|
| 1057 |
# |
|
| 1058 |
# system("MODULEPATH=$MODULEPATH:/nex/modules/files")
|
|
| 1059 |
# system("module load /nex/modules/files/pythonkits/gdal_1.10.0_python_2.7.3_nex")
|
|
| 1060 |
# |
|
| 1061 |
# module_path <- "" |
|
| 1062 |
#module_path <- "/nobackupp6/aguzman4/climateLayers/sharedCode/" |
|
| 1063 |
#in_dir_mosaics <- "/nobackupp6/aguzman4/climateLayers/output1500x4500_km/reg5/" |
|
| 1064 |
#out_dir_mosaics <- "/nobackupp6/aguzman4/climateLayers/output1500x4500_km/reg5/mosaics" |
|
| 1065 |
#l_dates <- "20100101,20100102,20100103,20100104,20100901,20100902,20100903,20100904" |
|
| 1066 |
#cmd_str <- paste("python", file.path(module_path,"mosaicUsingGdalMerge.py"),
|
|
| 1067 |
# in_dir_mosaics, |
|
| 1068 |
# out_dir_mosaics, |
|
| 1069 |
# "--date", l_dates,sep=" ") |
|
| 1070 |
#system(cmd_str) |
|
| 1071 |
|
|
| 1072 |
#module_path <- "/nobackupp6/aguzman4/climateLayers/sharedCode/" |
|
| 1073 |
#in_dir_mosaics <- "/nobackupp6/aguzman4/climateLayers/output1500x4500_km/reg4/" |
|
| 1074 |
#out_dir_mosaics <- "/nobackupp6/aguzman4/climateLayers/output1500x4500_km/reg4/mosaics" |
|
| 1075 |
#l_dates <- "20100101,20100102,20100103,20100104,20100901,20100902,20100903,20100904" |
|
| 1076 |
#cmd_str <- paste("python", file.path(module_path,"mosaicUsingGdalMerge.py"),
|
|
| 1077 |
# in_dir_mosaics, |
|
| 1078 |
# out_dir_mosaics, |
|
| 1079 |
# "--date", l_dates,sep=" ") |
|
| 1080 |
|
|
| 1081 |
system("MODULEPATH=$MODULEPATH:/nex/modules/files")
|
|
| 1082 |
system("module load /nex/modules/files/pythonkits/gdal_1.10.0_python_2.7.3_nex")
|
|
| 1083 |
|
|
| 1084 |
module_path <- "" |
|
| 1085 |
module_path <- "/nobackupp6/aguzman4/climateLayers/sharedCode/" |
|
| 1086 |
in_dir_mosaics <- "/nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg4/" |
|
| 1087 |
out_dir_mosaics <- "/nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg4/mosaicsMean" |
|
| 1088 |
out_dir_mosaics <- "/nobackup/bparmen1//output_run10_global_analyses_12152014/mosaics/reg4_1000x3000" |
|
| 1089 |
#/nobackup/bparmen1//output_run10_global_analyses_12152014 |
|
| 1090 |
prefix_str <- "reg4_1000x300" |
|
| 1091 |
|
|
| 1092 |
#l_dates <- "20100101,20100102" |
|
| 1093 |
l_dates <- "20100101,20100102,20100103,20100104,20100301,20100501,20100701,20100901,20100902,20100903,20100904" |
|
| 1094 |
cmd_str <- paste("python", file.path(module_path,"mosaicUsingGdalMerge.py"),
|
|
| 1095 |
in_dir_mosaics, |
|
| 1096 |
out_dir_mosaics, |
|
| 1097 |
prefix_str, |
|
| 1098 |
"--date", l_dates,sep=" ") |
|
| 1099 |
system(cmd_str) |
|
| 1100 |
|
|
| 1101 |
#reg5 100x3000 |
|
| 1102 |
|
|
| 1103 |
system("MODULEPATH=$MODULEPATH:/nex/modules/files")
|
|
| 1104 |
system("module load /nex/modules/files/pythonkits/gdal_1.10.0_python_2.7.3_nex")
|
|
| 1105 |
|
|
| 1106 |
module_path <- "" |
|
| 1107 |
module_path <- "/nobackupp6/aguzman4/climateLayers/sharedCode/" |
|
| 1108 |
in_dir_mosaics <- "/nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg5/" |
|
| 1109 |
#out_dir_mosaics <- "/nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg5/mosaicsMean" |
|
| 1110 |
out_dir_mosaics <- "/nobackup/bparmen1//output_run10_global_analyses_12152014/mosaics/reg5_1000x3000" |
|
| 1111 |
#/nobackup/bparmen1//output_run10_global_analyses_12152014 |
|
| 1112 |
prefix_str <- "reg5_1000x300" |
|
| 1113 |
|
|
| 1114 |
#l_dates <- "20100101,20100102" |
|
| 1115 |
l_dates <- "20100101,20100102,20100103,20100104,20100301,20100501,20100701,20100901,20100902,20100903,20100904" |
|
| 1116 |
cmd_str <- paste("python", file.path(module_path,"mosaicUsingGdalMerge.py"),
|
|
| 1117 |
in_dir_mosaics, |
|
| 1118 |
out_dir_mosaics, |
|
| 1119 |
prefix_str, |
|
| 1120 |
"--date", l_dates,sep=" ") |
|
| 1121 |
system(cmd_str) |
|
| 1122 |
|
|
| 1123 |
#reg5 1500x4500 |
|
| 1124 |
|
|
| 1125 |
#system("MODULEPATH=$MODULEPATH:/nex/modules/files")
|
|
| 1126 |
#system("module load /nex/modules/files/pythonkits/gdal_1.10.0_python_2.7.3_nex")
|
|
| 1127 |
|
|
| 1128 |
module_path <- "" |
|
| 1129 |
module_path <- "/nobackupp6/aguzman4/climateLayers/sharedCode/" |
|
| 1130 |
in_dir_mosaics <- "/nobackupp6/aguzman4/climateLayers/output1500x4500_km/reg5/" |
|
| 1131 |
#out_dir_mosaics <- "/nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg5/mosaicsMean" |
|
| 1132 |
out_dir_mosaics <- "/nobackup/bparmen1//output_run10_global_analyses_12152014/mosaics/reg5_1500x4500" |
|
| 1133 |
#/nobackup/bparmen1//output_run10_global_analyses_12152014 |
|
| 1134 |
prefix_str <- "reg5_1500x4500" |
|
| 1135 |
|
|
| 1136 |
#l_dates <- "20100101,20100102" |
|
| 1137 |
l_dates <- "20100101,20100102,20100103,20100104,20100301,20100501,20100701,20100901,20100902,20100903,20100904" |
|
| 1138 |
cmd_str <- paste("python", file.path(module_path,"mosaicUsingGdalMerge.py"),
|
|
| 1139 |
in_dir_mosaics, |
|
| 1140 |
out_dir_mosaics, |
|
| 1141 |
prefix_str, |
|
| 1142 |
"--date", l_dates,sep=" ") |
|
| 1143 |
system(cmd_str) |
|
| 1144 |
|
|
| 1145 |
##### |
|
| 1146 |
#reg4 1500x4500: NEED TO USE MOD2!!! in this specific case... |
|
| 1147 |
|
|
| 1148 |
#system("MODULEPATH=$MODULEPATH:/nex/modules/files")
|
|
| 1149 |
#system("module load /nex/modules/files/pythonkits/gdal_1.10.0_python_2.7.3_nex")
|
|
| 1150 |
|
|
| 1151 |
module_path <- "" |
|
| 1152 |
module_path <- "/nobackupp6/aguzman4/climateLayers/sharedCode/" |
|
| 1153 |
in_dir_mosaics <- "/nobackupp6/aguzman4/climateLayers/output1500x4500_km/reg4/" |
|
| 1154 |
#out_dir_mosaics <- "/nobackupp6/aguzman4/climateLayers/output1000x3000_km/reg5/mosaicsMean" |
|
| 1155 |
out_dir_mosaics <- "/nobackup/bparmen1//output_run10_global_analyses_12152014/mosaics/reg4_1500x4500" |
|
| 1156 |
#/nobackup/bparmen1//output_run10_global_analyses_12152014 |
|
| 1157 |
prefix_str <- "reg4_1500x4500" |
|
| 1158 |
mod_str <- "mod2" #use mod2 which corresponds to model with LST and elev |
|
| 1159 |
|
|
| 1160 |
#l_dates <- "20100101,20100102" |
|
| 1161 |
l_dates <- "20100101,20100102,20100103,20100104,20100301,20100501,20100701,20100901,20100902,20100903,20100904" |
|
| 1162 |
cmd_str <- paste("python", file.path(module_path,"mosaicUsingGdalMerge.py"),
|
|
| 1163 |
in_dir_mosaics, |
|
| 1164 |
out_dir_mosaics, |
|
| 1165 |
prefix_str, |
|
| 1166 |
"--mods", mod_str, |
|
| 1167 |
"--date", l_dates,sep=" ") |
|
| 1168 |
system(cmd_str) |
|
| 1169 |
|
|
| 1170 |
###copy folder in mosaics... |
|
Also available in: Unified diff
NEX global assessment run 10 1000x3000km tiles part1