#MODIFIED ON: 04/27/2015            

day_to_mosaic <- c("20100101","20100102","20100103","20100104","20100105",

                   "20100301","20100302","20100303","20100304","20100305",

                   "20100501","20100502","20100503","20100504","20100505",

                   "20100701","20100702","20100703","20100704","20100705",

                   "20100901","20100902","20100903","20100904","20100905",

                   "20101101","20101102","20101103","20101104","20101105")

#day_to_mosaic <- NULL #if day to mosaic is null then mosaic all dates?

#gather station data

##### SPDF of daily Station info

#load data_month for specific tiles

# data_month <- extract_from_list_obj(robj1$clim_method_mod_obj,"data_month")

# names(data_month) #this contains LST means (mm_1, mm_2 etc.) as well as TMax and other info

# 

data_day_s_list <- mclapply(list_raster_obj_files,FUN=function(x){try(x<-load_obj(x));try(x$validation_mod_obj[["data_s"]])},mc.preschedule=FALSE,mc.cores = num_cores)    

data_day_v_list <- mclapply(list_raster_obj_files,FUN=function(x){try(x<-load_obj(x));try(x$validation_mod_obj[["data_v"]])},mc.preschedule=FALSE,mc.cores = num_cores)    

data_day_s_list <- mclapply(list_raster_obj_files[1:6],FUN=function(x){try(x<-load_obj(x));try(x$validation_mod_obj[["data_s"]])},mc.preschedule=FALSE,mc.cores = num_cores)    

data_day_v_list <- mclapply(list_raster_obj_files,FUN=function(x){try(x<-load_obj(x));try(extract_list_from_list_obj(x$validation_mod_obj,"data_v"))},mc.preschedule=FALSE,mc.cores = num_cores)    

data_day_s_list <- mclapply(list_raster_obj_files,FUN=function(x){try(x<-load_obj(x));try(extract_list_from_list_obj(x$validation_mod_obj,"data_s"))},mc.preschedule=FALSE,mc.cores = num_cores)    

list_data_day_v <- try(extract_list_from_list_obj(raster_obj$validation_mod_obj,"data_v"))

list_data_day_s <- try(extract_list_from_list_obj(raster_obj$validation_mod_obj,"data_s"))

sampling_dat_day <- extract_list_from_list_obj(raster_obj$method_mod_obj,"daily_dev_sampling_dat")

#debug(pred_data_info_fun)

#list_pred_data_day_s_info <- pred_data_info_fun(1,list_data=list_data_day_s,pred_mod=pred_mod,sampling_dat_info=sampling_dat_day)

list_pred_data_day_s_info <- lapply(1:length(sampling_dat_day),FUN=pred_data_info_fun,

           list_data=list_data_day_s,pred_mod=pred_mod,sampling_dat_info=sampling_dat_day)

list_pred_data_day_v_info <- lapply(1:length(sampling_dat_day),FUN=pred_data_info_fun,

           list_data=list_data_day_v,pred_mod=pred_mod,sampling_dat_info=sampling_dat_day)

pred_data_day_s_info <- do.call(rbind,list_pred_data_day_s_info)

pred_data_day_v_info <- do.call(rbind,list_pred_data_day_v_info)

pred_data_day_s_info$training <- rep(1,nrow(pred_data_day_s_info)) 

pred_data_day_v_info$training <- rep(0,nrow(pred_data_day_v_info)) 

pred_data_day_info <-rbind(pred_data_day_v_info,pred_data_day_s_info)

# 

 names(data_month_s_list) <- list_names_tile_id

# 

# data_month_tmp <- remove_from_list_fun(data_month_s_list)$list

# #df_tile_processed$metrics_v <- remove_from_list_fun(data_month_s_list)$valid

# 

# tile_id <- lapply(1:length(data_month_tmp),

#                   FUN=function(i,x){rep(names(x)[i],nrow(x[[i]]))},x=data_month_tmp)

# data_month_NAM <- do.call(rbind.fill,data_month_list) #combined data_month for "NAM" North America

# data_month_NAM$tile_id <- unlist(tile_id)

# 

# write.table((data_month_NAM),

#             file=file.path(out_dir,paste("data_month_s_NAM","_",out_prefix,".txt",sep="")),sep=",")

# Transform this into a function that takes in a list of files!!! We can skip the region stage to reduce the number of files..