# script_path <- "/nobackupp8/bparmen1/env_layers_scripts" #path to script

# function_assessment_part1_functions <- "global_run_scalingup_assessment_part1_functions_02112015.R" #PARAM12

# function_assessment_part1a <-"global_run_scalingup_assessment_part1a_01042016.R"

# function_assessment_part2 <- "global_run_scalingup_assessment_part2_01062016.R"

# function_assessment_part2_functions <- "global_run_scalingup_assessment_part2_functions_01032016.R"

# source(file.path(script_path,function_assessment_part1_functions)) #source all functions used in this script 

# source(file.path(script_path,function_assessment_part1a)) #source all functions used in this script 

# source(file.path(script_path,function_assessment_part2)) #source all functions used in this script 

# source(file.path(script_path,function_assessment_part2_functions)) #source all functions used in this script 

# 

# ### Parameters and arguments ###

#   

# var<-"TMAX" # variable being interpolated

# if (var == "TMAX") {

#   y_var_name <- "dailyTmax"

#   y_var_month <- "TMax"

# }

# if (var == "TMIN") {

#   y_var_name <- "dailyTmin"

#   y_var_month <- "TMin"

# }

# 

# #interpolation_method<-c("gam_fusion") #other otpions to be added later

# interpolation_method<-c("gam_CAI")

# CRS_interp<-"+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs";

# #CRS_interp <-"+proj=lcc +lat_1=43 +lat_2=45.5 +lat_0=41.75 +lon_0=-120.5 +x_0=400000 +y_0=0 +ellps=GRS80 +units=m +no_defs";

# CRS_locs_WGS84<-CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84

# 

# out_region_name<-""

# list_models<-c("y_var ~ s(lat,lon,k=5) + s(elev_s,k=3) + s(LST,k=3)")

# 

# #reg1 (North Am), reg2(Europe),reg3(Asia), reg4 (South Am), reg5 (Africa), reg6 (Australia-Asia)

# #master directory containing the definition of tile size and tiles predicted

# in_dir1 <- "/nobackupp6/aguzman4/climateLayers/out/"

# #/nobackupp6/aguzman4/climateLayers/out_15x45/1982

# 

# #region_names <- c("reg23","reg4") #selected region names, #PARAM2

# region_name <- c("reg4") #run assessment by region, this is a unique region only

# #region_names <- c("reg1","reg2","reg3","reg4","reg5","reg6") #selected region names, #PARAM2

# interpolation_method <- c("gam_CAI") #PARAM4

# out_prefix <- "run_global_analyses_pred_12282015" #PARAM5

# #out_dir <- "/nobackupp8/bparmen1/" #PARAM6

# out_dir <- "/nobackupp8/bparmen1/output_run_global_analyses_pred_12282015"

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

# create_out_dir_param <- FALSE #PARAM7

# 

# #CRS_interp<-"+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs";

# #CRS_interp <-"+proj=lcc +lat_1=43 +lat_2=45.5 +lat_0=41.75 +lon_0=-120.5 +x_0=400000 +y_0=0 +ellps=GRS80 +units=m +no_defs";

# CRS_locs_WGS84<-CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84

# 

# #list_year_predicted <- 1984:2004

# list_year_predicted <- c("2014")

# #year_predicted <- list_year_predicted[1]

# 

# file_format <- ".tif" #format for mosaiced files #PARAM10

# NA_flag_val <- -9999  #No data value, #PARAM11

# num_cores <- 6 #number of cores used #PARAM13

# plotting_figures <- TRUE #running part2 of assessment to generate figures...

#   

# ##Additional parameters used in part 2, some these may be removed as code is simplified

# mosaic_plot <- FALSE #PARAM14

# day_to_mosaic <- c("19920102","19920103","19920103") #PARAM15

# multiple_region <- TRUE #PARAM16

# countries_shp <- "/nobackupp8/bparmen1/NEX_data/countries.shp" #PARAM17

# #countries_shp <-"/data/project/layers/commons/NEX_data/countries.shp" #Atlas

# plot_region <- TRUE  #PARAM18

# threshold_missing_day <- c(367,365,300,200)#PARAM19

# 

# year_predicted <- list_year_predicted[1]

# in_dir <- out_dir #PARAM 0

# #y_var_name <- "dailyTmax" #PARAM1 , already set

# #interpolation_method <- c("gam_CAI") #PARAM2, already set

# out_suffix <- out_prefix #PARAM3

# #out_dir <-  #PARAM4, already set

# create_out_dir_param <- FALSE #PARAM 5, already created and set

# #mosaic_plot <- FALSE #PARAM6

# #if daily mosaics NULL then mosaicas all days of the year

# #day_to_mosaic <- c("19920101","19920102","19920103") #PARAM7

# #CRS_locs_WGS84 already set

# proj_str <- CRS_locs_WGS84 #PARAM 8 #check this parameter

# #file_format <- ".rst" #PARAM 9, already set

# #NA_flag_val <- -9999 #PARAM 11, already set

# #multiple_region <- TRUE #PARAM 12

# #countries_shp <-"/data/project/layers/commons/NEX_data/countries.shp" #PARAM 13, copy this on NEX too

# #plot_region <- TRUE

# #num_cores <- 6 #PARAM 14, already set

# #region_name <- c("reg4") #reference region to merge if necessary, if world all the regions are together #PARAM 16

# #use previous files produced in step 1a and stored in a data.frame

# df_assessment_files_name <- "df_assessment_files_reg4_2014_run_global_analyses_pred_12282015.txt"# #PARAM 17, set in the script

# df_assessment_files <- read.table(df_assessment_files_name,stringsAsFactors=F,sep=",")

# #threshold_missing_day <- c(367,365,300,200) #PARM18

# 

# list_param_run_assessment_plotting <-list(

#     in_dir,y_var_name, interpolation_method, out_suffix,

#     out_dir, create_out_dir_param, mosaic_plot, proj_str, file_format, NA_flag_val,

#     multiple_region, countries_shp, plot_region, num_cores,

#     region_name, df_assessment_files_name, threshold_missing_day,year_predicted

#   )

# 

# names(list_param_run_assessment_plotting) <- c(

#     "in_dir","y_var_name","interpolation_method","out_suffix",

#     "out_dir","create_out_dir_param","mosaic_plot","proj_str","file_format","NA_flag_val",

#     "multiple_region","countries_shp","plot_region","num_cores",

#     "region_name","df_assessment_files_name","threshold_missing_day","year_predicted"

#   )

# 

# #function_assessment_part2 <- "global_run_scalingup_assessment_part2_01032016.R"

# #source(file.path(script_path,function_assessment_part2)) #source all functions used in this script

# 

# debug(run_assessment_plotting_prediction_fun)

# df_assessment_figures_files <-

#   run_assessment_plotting_prediction_fun(list_param_run_assessment_plotting)