Revision d094f2ca
Added by Benoit Parmentier over 8 years ago
| climate/research/oregon/interpolation/global_run_scalingup_assessment_part3.R | ||
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## Step 2: only read what is necessary at this stage... |
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list_tb <- lapply(list_tb_fname,function(x){read.table(x,stringsAsFactors=F,sep=",")})
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list_tb_updated <- mclapply(list_tb,FUN=adding_tile_nb_fun,mc.preschedule=FALSE,mc.cores = num_cores,num_cores_tmp=1) |
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adding_tile_nb_fun <- function(x,num_cores_tmp){
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x$tile_id <- factor(x$tile_id, levels=unique(mixedsort(x$tile_id)))#fix level ordering for plotting |
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#num_cores_tmp <- 1 |
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x$tile_nb <- unlist(mclapply(as.character(x$tile_id),FUN=function(y){strsplit(y,"_")[[1]][2]},mc.preschedule=FALSE,mc.cores = num_cores_tmp))
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return(x) |
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} |
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#tb$tile_id <- factor(tb$tile_id, levels=unique(mixedsort(tb$tile_id)))#fix level ordering for plotting |
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#fix level ordering for plotting |
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#tb$tile_nb <- unlist(mclapply(as.character(tb$tile_id),FUN=function(x){strsplit(x,"_")[[1]][2]},mc.preschedule=FALSE,mc.cores = num_cores))
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#unlist(mclapply(as.character(tb$tile_id[1:10]),FUN=function(x){strsplit(x,"_")[[1]][2]},mc.preschedule=FALSE,mc.cores = num_cores))
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tb <- do.call(rbind,list_tb) |
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list_tb_s <- lapply(list_tb_s_fname,function(x){read.table(x,stringsAsFactors=F,sep=",")})
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Also available in: Unified diff
adding variables to plot RMSE by tiles summary over 30 years