#Analyses, figures, tables and data are also produced in the script.

#AUTHOR: Benoit Parmentier 

#CREATED ON: 03/23/2014  

#Version: 4

#PROJECT: Environmental Layers project     

#COMMENTS: analyses for run 10 global analyses, Europe, Australia, 1000x300km

y_var_name <- "dailyTmax"

interpolation_method <- c("gam_CAI")

mosaic_plot <- FALSE

day_to_mosaic <- c("20100101","20100102","20100103","20100104","20100105",

#out_dir <-paste(out_dir,"_",out_prefix,sep="")

create_out_dir_param <- FALSE

if(create_out_dir_param==TRUE){

  out_dir <- create_dir_fun(out_dir,out_prefix)

  setwd(out_dir)

###Table 2: daily accuracy metrics for all tiles

summary_metrics_v <- read.table(file=file.path(out_dir,paste("summary_metrics_v2_NA_",out_prefix,".txt",sep="")),sep=",")

tb <- read.table(file=file.path(out_dir,paste("tb_diagnostic_v_NA","_",out_prefix,".txt",sep="")),sep=",")

#tb_diagnostic_s_NA_run10_global_analyses_11302014.txt

tb_s <- read.table(file=file.path(out_dir,paste("tb_diagnostic_s_NA","_",out_prefix,".txt",sep="")),sep=",")

}

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

### Figure 1: plot location of the study area with tiles processed

#collect info: read in all shapfiles

#This is slow...make a function and use mclapply??

#/data/project/layers/commons/NEX_data/output_run6_global_analyses_09162014/shapefiles

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

  #path_to_shp <- dirname(list_shp_reg_files[[i]])

  path_to_shp <- file.path(out_dir,"/shapefiles")

      pt <- gCentroid(shp1)

      centroids_pts[[i]] <- pt

  }else{

  }

  shps_tiles[[i]] <- shp1

}

#coord_names <- c("lon","lat")

#remove try-error polygons...we loose three tiles because they extend beyond -180 deg

tmp <- shps_tiles

shps_tiles <- remove_errors_list(shps_tiles) #[[!inherits(shps_tiles,"try-error")]]

#shps_tiles <- tmp

tmp_pts <- centroids_pts 

centroids_pts <- remove_errors_list(centroids_pts) #[[!inherits(shps_tiles,"try-error")]]

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

### Figure 2: boxplot of average accuracy by model and by tiles

tb$tile_id <- factor(tb$tile_id, levels=unique(tb$tile_id))

model_name <- as.character(unique(tb$pred_mod))

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

summary_metrics_v <- merge(summary_metrics_v,df_tile_processed,by="tile_id")

table(summary_metrics_v$reg)

#           pattern="CAI_TMAX_clim_month_.*_mod1_all.tif", full.names=T))

lf_mosaics_reg <- vector("list",length=length(l_reg_name))

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

}

#This part should be automated...