#MODIFIED ON: 01/05/2016            

# function_mosaicing <-"global_run_scalingup_mosaicing_function_12192015.R"

# 

# #in_dir_script <-"/home/parmentier/Data/IPLANT_project/env_layers_scripts" #NCEAS UCSB

# in_dir_script <- "/nobackupp8/bparmen1/env_layers_scripts" #NASA NEX

# source(file.path(in_dir_script,function_mosaicing))

# 

# load_obj <- function(f)

# {

#   env <- new.env()

#   nm <- load(f, env)[1]

#   env[[nm]]

# }

# 

# create_dir_fun <- function(out_dir,out_suffix){

#   if(!is.null(out_suffix)){

#     out_name <- paste("output_",out_suffix,sep="")

#     out_dir <- file.path(out_dir,out_name)

#   }

#   #create if does not exists

#   if(!file.exists(out_dir)){

#     dir.create(out_dir)

#   }

#   return(out_dir)

# }

# in_dir <- "/data/project/layers/commons/NEX_data/output_run10_1500x4500_global_analyses_pred_1992_12072015" #NCEAS

# #in_dir <- "/nobackupp8/bparmen1/output_run10_1500x4500_global_analyses_pred_1992_12072015" #NEX

# 

# in_dir_tiles <- file.path(in_dir,"tiles") #this is valid both for Atlas and NEX

# y_var_name <- "dailyTmax" #PARAM2

# interpolation_method <- c("gam_CAI") #PARAM3

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

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

# out_suffix <- paste(region_name,"_","run10_1500x4500_global_analyses_pred_1992_12072015",sep="") #PARAM 6

# out_suffix_str <- "run10_1500x4500_global_analyses_pred_1992_12072015" #PARAM 7

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

# pred_mod_name <- "mod1" #PARAM 9

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

# 

# out_dir <- in_dir #PARAM 11

# create_out_dir_param <- FALSE #PARAM 12

# 

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

# day_to_mosaic <- c("19920101","19920102","19920103") #,"19920104","19920105") #PARAM9, two dates note in /tiles for now on NEX

# 

# #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

#      

# num_cores <- 6 #PARAM 17                 

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

# use_autokrige <- F #PARAM 19

# 

# ###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"

# infile_mask <- "/data/project/layers/commons/NEX_data/regions_input_files/r_mask_reg4.tif"

# ## All of this is interesting so use df_assessment!!

# df_assessment_files_name <- "df_assessment_files_reg4_1984_run_global_analyses_pred_12282015.txt" # data.frame with all files used in assessmnet, PARAM 21

# 

# #in_dir can be on NEX or Atlas

# 

# #python script and gdal on NEX NASA:

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

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

# #python script and gdal on Atlas NCEAS

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

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

# 

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

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

# match_extent <- "FALSE" #PARAM 29 #try without matching!!!

# 

# #for residuals...

# list_models <- NULL #PARAM 30

# #list_models <- paste(var_pred,"~","1",sep=" ") #if null then this is the default...

# 

# list_param_run_mosaicing_prediction <- list(in_dir,y_var_name,interpolation_method,region_name,

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

#                  create_out_dir_param,day_to_mosaic,proj_str,file_format,NA_value,num_cores,

#                  region_name,use_autokrige,infile_mask,df_assessment_files_name,mosaic_python,

#                  python_bin,algorithm,match_extent,list_models)

# param_names <- c("in_dir","y_var_name","interpolation_method","region_name",

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

#                  "create_out_dir_param","day_to_mosaic","proj_str","file_format","NA_value","num_cores",

#                  "region_name","use_autokrige","infile_mask","df_assessment_files_name","mosaic_python",

#                  "python_bin","algorithm","match_extent","list_models")

# names(list_param_run_mosaicing_prediction) <- param_names