lf_diagnostic_obj <- lf_diagnostic_obj[grep("lk_min",lf_diagnostic_obj,invert=T)] #remove object that have lk_min...

## This will be part of the raster_obj function

out_prefix_str <- paste(basename(in_dir_list),out_prefix,sep="_") 

lf_raster_obj <- create_raster_prediction_obj(in_dir_list,interpolation_method, y_var_name,out_prefix_str,out_path_list=NULL)

##Quick exploration of raster object

robj1 <- load_obj(list_raster_obj_files[[2]]) #This is tile corresponding to Oregon

names(robj1)

names(robj1$method_mod_obj[[1]]) #for January 1, 2010

for (j in 1:length(list_tile_scp)){

  tile_nb <- list_tile_scp[j]

  #nb_mod <- 3+1 #set up earlier

  #tile_nb <- 1

  in_dir_tile <- basename(df_tile_processed$path_NEX[tile_nb])

#Analyses, figures, tables and data are also produced in the script.

#AUTHOR: Benoit Parmentier 

#CREATED ON: 05/21/2014  

#Version: 1

#PROJECT: Environmental Layers project                                     

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

function_analyses_paper1 <- "contribution_of_covariates_paper_interpolation_functions_05212014.R" #first interp paper

function_analyses_paper2 <- "multi_timescales_paper_interpolation_functions_05052014.R"

function_assessment_by_tile <- "results_interpolation_date_output_analyses_05212014.R"

load_obj <- function(f)

{

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

#parent output dir : contains subset of the data produced on NEX

# parent output dir for the curent script analyes

# input dir containing shapefiles defining tiles

#On NEX

#contains all data from the run by Alberto

y_var_name <- "dailyTmax"

interpolation_method <- c("gam_CAI")

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

create_out_dir_param <- FALSE

setwd(out_dir)

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

###Table 1: Average accuracy metrics

###Table 2: daily accuracy metrics for all tiles

#Now add things here...

#

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

in_dir_list <- list.files(path=in_dir1,full.names=T)

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

##Quick interactive  exploration of raster object to check possible errors

#Get the number of models predicted

### Figure 1: plot location of the study area with tiles processed

##Quick exploration of raster object

date_selected_results <- c("20100101") 

var <- "TMAX"

names(list_param_results_analyses)<-c("out_path","in_path_tile","script_path","raster_prediction_obj","interpolation_method",

                     "covar_obj","date_selected_results","var","out_prefix")

#list_param <- list_param_results_analyses

#Run modified code from stage 5...

#plots_assessment_by_date<-function(j,list_param){

#Use lapply or mclapply

#Call as function...

#Boxplots...etc...