Revision e8db2d42
Added by Benoit Parmentier over 10 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: 05/15/2014
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#MODIFIED ON: 06/01/2014
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#Version: 3 |
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#PROJECT: Environmental Layers project |
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#PROJECT: Environmental Layers project |
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#COMMENTS: analyses for run 3 global using 2 specific tiles |
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################################################################################################# |
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### Loading R library and packages |
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#on ATLAS |
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#in_dir1 <- "/data/project/layers/commons/NEX_data/test_run1_03232014/output" #On Atlas |
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#parent output dir : contains subset of the data produced on NEX |
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in_dir1 <- "/data/project/layers/commons/NEX_data/output_run2_05122014/output/"
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in_dir1 <- "/data/project/layers/commons/NEX_data/output_run3_global_analyses_05292014/output"
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# parent output dir for the curent script analyes |
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out_dir <-"/data/project/layers/commons/NEX_data/" #On NCEAS Atlas |
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out_dir <-"/data/project/layers/commons/NEX_data/output_run3_global_analyses_05292014/" #On NCEAS Atlas |
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out_dir <-"/data/project/layers/commons/NEX_data/output_run3_global_analyses_05292014/" |
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# input dir containing shapefiles defining tiles |
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in_dir_shp <- "/data/project/layers/commons/NEX_data/output_run2_05122014/output/subset/shapefiles"
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in_dir_shp <- "/data/project/layers/commons/NEX_data/output_run3_global_analyses_05292014/output/subset/shapefiles"
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#On NEX |
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#contains all data from the run by Alberto |
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in_dir_list <- file.path(in_dir1,read.table(file.path(in_dir1,"processed.txt"))$V1) |
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y_var_name <- "dailyTmax" |
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interpolation_method <- c("gam_CAI")
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out_prefix<-"run2_global_analyses_05122014"
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out_prefix<-"run3_global_analyses_05292014"
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#out_dir <-paste(out_dir,"_",out_prefix,sep="") |
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create_out_dir_param <- TRUE
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create_out_dir_param <- FALSE
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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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###Table 1: Average accuracy metrics |
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###Table 2: daily accuracy metrics for all tiles |
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#lf_tables <- list.files(out_dir,) |
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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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tb <- read.table(file=file.path(out_dir,paste("tb_diagnostic_v_NA","_",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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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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shps_tiles <- vector("list",length(list_shp_reg_files))
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#collect info: read in all shapfiles |
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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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layer_name <- basename(list_shp_reg_files[[i]]) |
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shp1 <- readOGR(path_to_shp, layer_name) |
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shp1<-readOGR(dirname(list_shp_reg_files[[i]]),sub(".shp","",basename(list_shp_reg_files[[i]])))
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pt <- gCentroid(shp1) |
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centroids_pts[[i]] <-pt |
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date_selected <- "20100101" |
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name_method_var <- paste(interpolation_method,"_",y_var_name,"_",sep="") |
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pattern_str <- paste("mosaiced","_",name_method_var,".*.",date_selected,".*.tif",sep="")
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lf_list <- list.files(pattern=pattern_str) |
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pattern_str <- paste("mosaiced","_",name_method_var,"predicted",".*.",date_selected,".*.tif",sep="")
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lf_pred_list <- list.files(pattern=pattern_str)
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for(i in 1:length(lf_list)){
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for(i in 1:length(lf_pred_list)){
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r_pred <- raster(lf_list[i]) |
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r_pred <- raster(lf_pred_list[i])
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res_pix <- 480 |
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col_mfrow <- 1 |
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} |
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## plotting of delta and clim for later scripts... |
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#### Now combined plot... |
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#pred_s <- stack(lf_list) #problem different extent!! |
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### Figure 5: plot map of average RMSE per tile at centroids |
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#Turn summary table to a point shp |
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coordinates(summary_metrics_v) <- cbind(long,lat) |
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list_df_ac_mod <- vector("list",length=length(lf_list))
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coordinates(summary_metrics_v) <- cbind(summary_metrics_v$lon,summary_metrics_v$lat) |
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proj4string(summary_metrics_v) <- CRS_WGS84 |
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list_df_ac_mod <- vector("list",length=length(lf_pred_list))
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for (i in 1:length(lf_list)){
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ac_mod <- summary_metrics_v[summary_metrics_v$pred_mod==model_name[i],] |
Also available in: Unified diff
assessment NEX run part2: debugging