#STAGE 2: Covariates preparation for study/processing area: calculation of covariates (spect,land cover,etc.) and reprojection

#STAGE 3: Data preparation: meteorological station database query and extraction of covariates values from raster brick

#STAGE 4: Raster prediction: run interpolation method (-- gam fusion, gam CAI, ...) and perform validation 

#STAGE 7: Mosaicing of predicted surfaces and accuracy metrics (RMSE,MAE) by regions 

#AUTHOR: Benoit Parmentier                                                                        

#CREATED ON: 01/01/2016  

#PROJECT: NCEAS INPLANT: Environment and Organisms                                                                           

#First source these files:

## TODO:

# 

# 

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

#script_path <- "/home/parmentier/Data/IPLANT_project/env_layers_scripts"

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

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

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

source(file.path(script_path,function_assessment_part3)) #source all functions used in this script 

### Parameters, constants and arguments ###

#####mosaicing parameters

#Data is on ATLAS or NASA NEX

### PARAMETERS DEFINED IN THE SCRIPT

### Use the following values to run code from the shell:

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

#in_dir <- "/nobackupp6/aguzman4/climateLayers/out/" #PARAM2,arg 2

#region_name <- "reg4" #PARAM 3, arg 3 #reg4 South America, Africa reg5,Europe reg2, North America reg1, Asia reg3

#out_suffix <- "reg4" #PARAM 4, arg 4

#out_dir <- "/nobackupp8/bparmen1/climateLayers/out/reg4" #PARAM 5,arg 5 use this location for now

#create_out_dir_param <- TRUE #PARAM 6, arg 6

#year_predicted <- 1991 #PARAM 7, arg 7

#num_cores <- 6 #PARAM 8, arg 8         

#max_mem = 1e+07 #param 9, arg 9

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

#infile_mask <- "/nobackupp8/bparmen1/NEX_data/regions_input_files/r_mask_reg4.tif" #PARAM 14, arg 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 15

#algorithm <- "python" #PARAM 16 #if R use mosaic function for R, if python use modified gdalmerge script from Alberto Guzmann

#layers_option <- c("var_pred") #arg 17 ,param 17, options are:#res_training, res_testing,ac_training, ac_testing, var_pred

#tmp_files <- FALSE #arg 18, param 18

#path_assessment <- NOT USED "/nobackupp6/aguzman4/climateLayers/out/reg4/assessment/output_reg4_1991" #PARAM 14a, arg 14

### Testing several years on the bridge before running jobs on nodes with qsub

#Use the following command to run as script via the shell on the bridge 

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

out_suffix <- args[4] #PARAM 4

#out_suffix <- region_name #PARAM 4

out_dir <- args[5] #PARAM 5

#out_dir <- "/nobackupp8/bparmen1/climateLayers/out/reg4" #PARAM 5, use this location for now

create_out_dir_param <- args[6] #PARAM 6

#path_assessment <- "/nobackupp6/aguzman4/climateLayers/out/reg4/assessment/output_reg4_1991"

#path_assessment <- file.path(in_dir,region_name,"assessment",paste("output_",region_name,year_processed,sep=""))

#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 14

algorithm <- args[16] #PARAM 16

#algorithm <- "python" #PARAM 15 #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 <- args[18] #PARAM 18

#tmp_files <- FALSE

#python script and gdal on NEX NASA:

#python script and gdal on Atlas NCEAS

#python_bin <- "/usr/bin" #PARAM 30

#for residuals...

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

if (var == "TMAX") {

#browser()

#rasterOptions(maxmemory=1e+07,timer=TRUE)

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, out_dir,

                 create_out_dir_param,day_to_mosaic_range,year_predicted,proj_str,file_format,NA_value,num_cores,

                 use_autokrige,infile_mask,df_assessment_files_name,mosaic_python,

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","out_dir",

                 "create_out_dir_param","day_to_mosaic_range","year_predicted","proj_str","file_format","NA_value","num_cores",

                 "use_autokrige","infile_mask","df_assessment_files_name","mosaic_python",

names(list_param_run_mosaicing_prediction) <- param_names

#list_param_run_mosaicing_prediction

#debug(run_mosaicing_prediction_fun)