#Part 1 create summary tables and inputs for figure in part 2 and part 3.

#AUTHOR: Benoit Parmentier 

#CREATED ON: 03/23/2014  

#Version: 3

#PROJECT: Environmental Layers project  

#TO DO:

    pred_data_day_info$method_interp <- rep(method_interp,nrow(pred_data_day_info)) 

    pred_data_day_info$var_interp <- rep(var_interp,nrow(pred_data_day_info)) 

  }

  if(use_month==TRUE){

    pred_data_month_info$method_interp <- rep(method_interp,nrow(pred_data_month_info)) 

    pred_data_month_info$var_interp <- rep(var_interp,nrow(pred_data_month_info)) 

    pred_data_month_info$tile_id <- rep(tile_id,nrow(pred_data_month_info)) 

  }    

  if(use_month==FALSE){

#in_dir1 <- "/data/project/layers/commons/NEX_data/test_run1_03232014/output" #On Atlas

in_dir1 <- "/nobackupp4/aguzman4/climateLayers/output10Deg/reg1/" #On NEX

#use subset for now:

#in_dir_list <- file.path(in_dir1,read.table(file.path(in_dir1,"processed.txt"))$V1)

#in_dir_list <- as.list(in_dir_list[-1])

#in_dir_list <- in_dir_list[grep("bak",basename(basename(in_dir_list)),invert=TRUE)] #the first one is the in_dir1

#in_dir_shp <- in_dir_list[grep("shapefiles",basename(in_dir_list),invert=FALSE)] #select directory with shapefiles...

in_dir_shp <- "/nobackupp4/aguzman4/climateLayers/output10Deg/reg1/subset/shapefiles/"

#in_dir_list <- in_dir_list[grep("shapefiles",basename(in_dir_list),invert=TRUE)] 

#the first one is the in_dir1

# the last directory contains shapefiles 

y_var_name <- "dailyTmax"

interpolation_method <- c("gam_CAI")

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

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

##raster_prediction object : contains testing and training stations with RMSE and model object

list_raster_obj_files <- lapply(in_dir_list,FUN=function(x){list.files(path=x,pattern="^raster_prediction_obj.*.RData",full.names=T)})

basename(dirname(list_raster_obj_files[[1]]))

list_names_tile_coord <- lapply(list_raster_obj_files,FUN=function(x){basename(dirname(x))})

#### Table 1: Average accuracy metrics

#can use a maximum of 6 cores on the NEX Bridge

summary_metrics_v_list <- mclapply(list_raster_obj_files,FUN=function(x){try( x<- load_obj(x)); try(x[["summary_metrics_v"]]$avg)},mc.preschedule=FALSE,mc.cores = 6)                           

names(summary_metrics_v_list) <- list_names_tile_id

pred_pattern_str <- paste(".*predicted_mod",mod_id,"_0_1.*",sep="")

#,".*predicted_mod2_0_1.*",".*predicted_mod3_0_1.*",".*predicted_mod_kr_0_1.*")

#l_pattern_models <- lapply(c(".*predicted_mod1_0_1.*",".*predicted_mod2_0_1.*",".*predicted_mod3_0_1.*",".*predicted_mod_kr_0_1.*"),

l_pattern_models <- lapply(pred_pattern_str,

                           FUN=function(x){paste(x,dates_l,".*.tif",sep="")})

#gam_CAI_dailyTmax_predicted_mod_kr_0_1_20101231_30_145.0_-120.0.tif

lf_pred_tif <- vector("list",length=length(l_pattern_models)) #number of models is 3

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

  l_pattern_mod <- l_pattern_models[[i]] #365 dates

  lf_pred_tif[[i]] <- list_tif_files_dates

}

lf_clim_tif <- vector("list",length=nb_mod) #number of models is 3

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

  l_pattern_mod <- l_pattern_models[[i]] #12 dates

  lf_clim_tif[[i]] <- list_tif_files_dates

}

lf_delta_tif <- vector("list",length=nb_mod) #number of models is 3

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

  l_pattern_mod <- l_pattern_models[[i]]

  lf_delta_tif[[i]] <- list_tif_files_dates

}

# use_day=TRUE

# use_month=TRUE

# 

# list_param_training_testing_info <- list(list_raster_obj_files,use_month,use_day,list_names_tile_id)

# names(list_param_training_testing_info) <- c("list_raster_obj_files","use_month","use_day","list_names_tile_id")

# 

#in_dir_shp <- "/nobackupp4/aguzman4/climateLayers/output4/subset/shapefiles/"

#get shape files for the region being assessed:

pattern_str <- basename(in_dir_list)

list_shp_reg_files <- lapply(pattern_str,function(x){list_shp_global_tiles_files[grep(x,invert=FALSE,list_shp_global_tiles_files)]}) #select directory with shapefiles...

df_tile_processed$shp_files <- unlist(list_shp_reg_files)

#copy shapefiles defining regions

Atlas_dir <- file.path("/data/project/layers/commons/NEX_data/",basename(out_dir),"output/subset/shapefiles")

Atlas_hostname <- "parmentier@atlas.nceas.ucsb.edu"

filenames_NEX <- paste(lf_cp_shp,collapse=" ")  #copy raster prediction object

cmd_str <- paste("scp -p",filenames_NEX,paste(Atlas_hostname,Atlas_dir,sep=":"), sep=" ")

system(cmd_str)

cmd_str <- paste("scp -p",filenames_NEX,paste(Atlas_hostname,Atlas_dir,sep=":"), sep=" ")

system(cmd_str)

system("scp -p /nobackupp4/aguzman4/climateLayers/output4/subset/shapefiles/* parmentier@atlas.nceas.ucsb.edu:/data/project/layers/commons/NEX_data/shapefiles")