#There are 21 parameters, 1 constant and 8 arguments (drawn from the parameters) for the Rscript call.

#The arguments are passed directly from Rscript:

#var <- args[1] # variable being interpolated #param 1, arg 1

#in_dir1 <- args[2] # This is the output directory containing global prediction e.g./nobackupp6/aguzman4/climateLayers/out/ param 5, arg 2

#region_name <- args[3] # region e.g. "reg4" param 6, arg 3

#out_prefix <- args[4] # this is used in creating an output directory,include region name? param 7, arg 4

#out_dir <- args[5] # output parent dir, can be home dir or other, param 8, arg 5)

#create_out_dir_param <- args[6] # if TRUE create a output from "output"+out_prefix param 9, arg 6

#list_year_predicted <- args[7] # enter as list but currently runs on the first element of the list, param 10, arg 7

#num_cores <- args[8] #number of cores used # param 13, arg 8

#max_mem <- args[9] # maximum memory, param 21

function_mosaicing_functions <- "global_run_scalingup_mosaicing_function_04112016.R" #PARAM12

function_mosaicing <-"global_run_scalingup_mosaicing_04102016.R"

#There are 21 parameters, 1 constant and 8 arguments (drawn from the parameters) for the Rscript call.

#region_name <- args[3] # region e.g. "reg4" param 6, arg 3

#out_dir <- args[5] # output parent dir, can be home dir or other, param 8, arg 5

#create_out_dir_param <- args[6] # if TRUE create a output from "output"+out_prefix param 9, arg 6

#year_predicted <- args[7] # enter as list but currently runs on the first element of the list, param 10, arg 7

#num_cores <- args[8] #number of cores used # param 13, arg 8

#max_mem <- args[9] # maximum memory, param 21

#mosaicing_method <- arg[10] #PARAM5

#metric_name <- arg[11] #"rmse" #RMSE, MAE etc. #PARAM 8

#day_to_mosaic_range <- arg[12] #c("19910101","19910103") #if null run all year

#infile_mask <- arg[12] # "/nobackupp8/bparmen1/NEX_data/regions_input_files/r_mask_reg4.tif"

#df_assessment_files_name <- arg[13] #"/nobackupp6/aguzman4/climateLayers/out/reg4/assessment/output_reg4_1991/df_assessment_files_reg4_1991_reg4_1991.txt" # data.frame with all files used in assessmnet, PARAM 21

#algorithm <- arg[14] #"python" #PARAM 28 #if R use mosaic function for R, if python use modified gdalmerge script from Alberto Guzmann

#layers_option <- arg[15] #c("var_pred") #options are:

#res_training, res_testing,ac_training, ac_testing, var_pred

#tmp_files <- arg[16] #FALSE

#mosaicing_method <- c("unweighted","use_edge_weights") #PARAM5

#metric_name <- "rmse" #RMSE, MAE etc. #PARAM 8

#day_to_mosaic_range <- c("19910101","19910103") #if null run all year

#infile_mask <- "/nobackupp8/bparmen1/NEX_data/regions_input_files/r_mask_reg4.tif"

#df_assessment_files_name <- "/nobackupp6/aguzman4/climateLayers/out/reg4/assessment/output_reg4_1991/df_assessment_files_reg4_1991_reg4_1991.txt" # data.frame with all files used in assessmnet, PARAM 21

#algorithm <- "python" #PARAM 28 #if R use mosaic function for R, if python use modified gdalmerge script from Alberto Guzmann

#layers_option <- c("var_pred") #options are:

#res_training, res_testing,ac_training, ac_testing, var_pred

#tmp_files <- FALSE

var<-"TMAX" # variable being interpolated #param 1, arg 1

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"

}

in_dir <- "/nobackupp6/aguzman4/climateLayers/out/"

in_dir <- args[2]

interpolation_method <- c("gam_CAI") #PARAM3

#var <- args[1] # variable being interpolated #param 1, arg 1

#in_dir <- args[2] # This is the output directory containing global prediction e.g./nobackupp6/aguzman4/climateLayers/out/ param 5, arg 2

#region_name <- args[3] # region e.g. "reg4" param 6, arg 3

#out_suffix <- args[4] # formely out_prefix, this is used in creating an output directory, it is suggested to use "reg4" or same as region_name

#out_dir <- args[5] # output parent dir, can be home dir or other, param 8, arg 5

#create_out_dir_param <- args[6] # if TRUE create a output from "output"+out_prefix param 9, arg 6

#year_predicted <- args[7] # enter as list but currently runs on the first element of the list, param 10, arg 7

#num_cores <- args[8] #number of cores used # param 13, arg 8

#max_mem <- args[9] # maximum memory, param 21

#mosaicing_method <- args[10] #PARAM5

#metric_name <- args[11] #"rmse" #RMSE, MAE etc. #PARAM 8

#day_to_mosaic_range <- arg[12] #c("19910101","19910103") #if null run all year

#infile_mask <- args[13] # "/nobackupp8/bparmen1/NEX_data/regions_input_files/r_mask_reg4.tif"

#df_assessment_files_name <- args[14] #"/nobackupp6/aguzman4/climateLayers/out/reg4/assessment/output_reg4_1991/df_assessment_files_reg4_1991_reg4_1991.txt" # data.frame with all files used in assessmnet, PARAM 21

#algorithm <- args[15] #"python" #PARAM 28 #if R use mosaic function for R, if python use modified gdalmerge script from Alberto Guzmann

#layers_option <- args[16] #c("var_pred") #options are:

#res_training, res_testing,ac_training, ac_testing, var_pred

#tmp_files <- args[17] #FALSE

region_name <- args[3]

region_name <- "reg4" #PARAM 4 #reg4 South America, Africa reg5,Europe reg2, North America reg1, Asia reg3

out_suffix <- args[4]

out_suffix <- region_name #PARAM 6

out_suffix_str <- region_name #PARAM 7

#out_dir <- in_dir #PARAM 11

out_dir <- args[5]

out_dir <- "/nobackupp8/bparmen1/climateLayers/out/reg4" #PARAM 11, use this location for now

create_out_dir_param <- args[6]

create_out_dir_param <- TRUE #PARAM 12

year_predicted <- args[7]

year_predicted <- 1991 #PARAM 31

num_cores <- args[8]

num_cores <- 6 #PARAM 17         

max_mem<-args[9]

mosaicing_method <- c("unweighted","use_edge_weights") #PARAM5

mosaicing_method <- args[10]

metric_name <- "rmse" #RMSE, MAE etc. #PARAM 8

#if daily mosaics NULL then mosaicas all days of the year #PARAM 13

#day_to_mosaic <- c("19910101","19910102","19910103") #,"19920104","19920105") #PARAM9, two dates note in /tiles for now on NEX

day_to_mosaic_range <- c("19910101","19910103") #if null run all year

day_to_mosaic_range <- c("19910101","19910101") #if null run all year

day_to_mosaic_range <- args[12]

###Separate folder for masks by regions, should be listed as just the dir!!... #PARAM 20

infile_mask <- "/nobackupp8/bparmen1/NEX_data/regions_input_files/r_mask_reg4.tif"

inflie_mask <- args[13]

df_assessment_files_name <- args[14]

df_assessment_files_name <- "/nobackupp6/aguzman4/climateLayers/out/reg4/assessment/output_reg4_1991/df_assessment_files_reg4_1991_reg4_1991.txt" # data.frame with all files used in assessmnet, PARAM 21

algorithm <- args[15]

algorithm <- "python" #PARAM 28 #if R use mosaic function for R, if python use modified gdalmerge script from Alberto Guzmann

layers_option <- c("var_pred") #options are:

tmp_files <- FALSE

pred_mod_name <- "mod1" #PARAM 9

var_pred <- "res_mod1" #used in residuals mapping #PARAM 10

#CRS_WGS84 <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84 #CONSTANT1

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

#proj_str<- CRS_WGS84 #PARAM 8 #check this parameter

file_format <- ".tif" #PARAM 14

NA_value <- -9999 #PARAM 15

NA_flag_val <- NA_value #PARAM 16

#region_names <- c("reg23","reg4") #selected region names, ##PARAM 18 

use_autokrige <- F #PARAM 19

proj_str <- CRS_locs_WGS84

#in_dir can be on NEX or Atlas

mosaic_python <- "/nobackupp6/aguzman4/climateLayers/sharedCode/"

python_bin <- "/nobackupp6/aguzman4/climateLayers/sharedModules2/bin"

#mosaic_python <- "/data/project/layers/commons/NEX_data/sharedCode" #PARAM 26

#python_bin <- "/usr/bin" #PARAM 27

                 mosaicing_method,out_suffix,out_suffix_str,metric_name,pred_mod_name,var_pred,

                 "mosaicing_method","out_suffix","out_suffix_str","metric_name","pred_mod_name","var_pred",