#MODIFIED ON: 10/09/2015            

function_mosaicing <-"global_run_scalingup_mosaicing_function_10052015.R"

#must be cleaned up

#tb_diagnostic_v_NA_run10_1500x4500_global_analyses_pred_1992_10052015.txt

### Start new function here

## Add numcores

## use mclapply

create_accuracy_metric_raster <- function(i, list_param){

  #This function generates weights from a point location on a raster layer.

  #Note that the weights are normatlized on 0-1 scale using max and min values.

  #Inputs:

  #lf: list of raster files

  #tb: data.frame table #fitting or validation table with all days

  #metric_name <- list_param$metric_name #RMSE, MAE etc.

  #pred_mod_name <- list_param$pred_mod_name #mod1, mod_kr etc.

  #y_var_name <- list_param$y_var_name #"dailyTmax" #PARAM2

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

  #date_processed <- list_param$days_to_process[i]

  #NA_flag_val <- list_param$NA_flag_val

  #NAflag,file_format,out_suffix etc...

  #file_format <- list_param$file_format

  #out_dir_str <- list_param$out_dir

  #out_suffix_str <- list_param$out_suffix

  #Outputs:

  #raster list of weights and product of wegihts and inuts

  #TODO: 

  # - improve efficiency

  #

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

  lf <- list_param$lf #list of files to mosaic

  tb <- list_param$tb #fitting or validation table with all days

  metric_name <- list_param$metric_name #RMSE, MAE etc.

  pred_mod_name <- list_param$pred_mod_name #mod1, mod_kr etc.

  y_var_name <- list_param$y_var_name #"dailyTmax" #PARAM2

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

  date_processed <- list_param$days_to_process[i]

  NA_flag_val <- list_param$NA_flag_val

  #NAflag,file_format,out_suffix etc...

  file_format <- list_param$file_format

  out_dir_str <- list_param$out_dir

  out_suffix_str <- list_param$out_suffix

  ####### START SCRIPT #####

  #r_in <- raster(lf[i]) #input image

  #date_processed <- day_to_mosaic[i]

  #lf_to_mosaic <-list.files(path=file.path(in_dir_tiles),    

  #         pattern=paste(".*.",date_processed,".*.tif$",sep=""),full.names=T) #choosing date 2...20100901

  lf_tmp <- gsub(file_format,"",lf)

  tx<-strsplit(as.character(lf_tmp),"_")

  #deal with the fact that we have number "1" attached to the out_suffix (centroids of tiles)

  pos_lat <- lapply(1:length(tx),function(i,x){length(x[[i]])-1},x=tx)

  pos_lon <- lapply(1:length(tx),function(i,x){length(x[[i]])},x=tx)

  lat_val <- unlist(lapply(1:length(tx),function(i,x,y){x[[i]][pos_lat[[i]]]},x=tx,y=pos_lat))

  lat <- as.character(lapply(1:length(lat_val),function(i,x){substr(x[[i]],2,nchar(x[i]))},x=lat_val)) #first number not in the coordinates

  long <- as.character(lapply(1:length(tx),function(i,x,y){x[[i]][pos_lon[[i]]]},x=tx,y=lon_lat))

  df_centroids <- data.frame(long=as.numeric(long),lat=as.numeric(lat))

  df_centroids$ID <- as.numeric(1:nrow(df_centroids))

  df_centroids$tile_coord <- paste(lat,long,sep="_")

  df_centroids$files <- lf

  df_centroids$date <- date_processed

  write.table(df_centroids,paste("df_centroids_",date_processed,"_",out_suffix,".txt",sep=""),sep=',')

  #sprintf(" %3.1f", df_centroids$lat)

  tb_date <- subset(tb,date==date_processed & pred_mod==pred_mod_name)

  tb_date$tile_coord <- as.character(tb_date$tile_coord)

  df_centroids <- merge(df_centroids,tb_date,by="tile_coord")

  #r1 <- raster(lf[i])

  #function(j,df_centroids,metric_name,date_processed,interpolation,)

  #loop over files, make this a function later, works for now

  #use mclapply  

  list_raster_name <- vector("list",length=length(lf))

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

    inFilename <- df_centroids$files[j]

    r1 <- raster(inFilename)

    r1[] <- df_centroids[[metric_name]][j] #improve this

    #set1f <- function(x){rep(NA, x)}

    #r_init <- init(r_in, fun=set1f)

    lf_tmp <- gsub(file_format,"",lf)

    extension_str <- extension(inFilename)

    raster_name_tmp <- gsub(extension_str,"",basename(inFilename))

    outFilename <- file.path(out_dir,paste(raster_name_tmp,"_",metric_name,"_",out_suffix,file_format,sep="")) #for use in function later...

    #raster_name <- file.path(out_dir_str,paste(raster_name_tmp,"_",out_suffix,file_format,sep=""))#output file

    #raster_name_tmp <- paste("r_",metric_name,"_",interpolation_method,"_",date_processed,"_",out_suffix_str,sep="")

    #raster_name <- file.path(out_dir_str,raster_name_tmp)

    writeRaster(r1, NAflag=NA_flag_val,filename=outFilename,overwrite=TRUE)  

    list_raster_name[[j]] <- outFilename

  }

  raster_created_obj <- list(list_raster_name,df_centroids)

  names(raster_created_obj) <- c("list_raster_name","df_centroids")

  return(raster_created_obj)

}

#### end of function

lf <- lf_mosaic1 #list of files to mosaic

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

pred_mod_name <- "mod1"

debug(create_accuracy_metric_raster)

lf_accuracy_raster <- unlist(raster_created_obj$list_raster_name)

lf_r_rmse <- mclapply(1:length(days_to_process),FUN=create_accuracy_metric_raster,list_param=list_param_accuracy_metric_raster,mc.preschedule=FALSE,mc.cores = 3)                           

#lf_mosaic <- lf_mosaic[1:20]

r2 <- raster(lf_mosaic2[2]) 

plot(r2)

  out_suffix_str <- paste(day_to_mosaic[i],out_suffix,sep="_")

  #undebug(mosaicFiles)

                                        python_bin=NULL,

  out_suffix_str <- paste(day_to_mosaic[i],out_suffix,sep="_")

  #undebug(mosaicFiles)

                                        python_bin=NULL,

  #mosaic_unweighted_obj <- mosaicFiles(lf_mosaic1,mosaic_method="unweighted",

  #                                      num_cores=num_cores,

  #                                      python_bin=NULL,

  #                                      df_points=NULL,NA_flag=NA_flag_val,

  #                                      file_format=file_format,out_suffix=out_suffix_str,

  #                                      out_dir=out_dir)

  #list_mosaic_obj[[i]] <- list(unweighted=mosaic_unweighted_obj,edge=mosaic_edge_obj)

  #list_mosaic_obj[[i]] <- list(prediction=mosaic_edge_obj_accuracy,accuracy=mosaic_edge_obj_accuracy)

#undebug(plot_mosaic)

num_cores <- 2

l_diff_png_files <- mclapply(1:length(lf1),FUN=plot_diff_raster,list_param= list_param_plot_diff,

                        mc.preschedule=FALSE,mc.cores = num_cores)