plot_raster_mosaic <- function(i,list_param){

  #Function to plot mosaic for poster

  #

  l_dates <- list_param$l_dates

  r_mosaiced_scaled <- list_param$r_mosaiced_scaled

  NA_flag_val <- list_param$NA_flag_val

  out_dir <- list_param$out_dir

  out_suffix <- list_param$out_suffix

  region_name <- list_param$region_name

  variable_name <- list_param$variable_name

#for (i in 1:length(nlayers(r_mosaic_scaled))){

  date_proc <- l_dates[i]

  r_pred <- subset(r_mosaic_scaled,i)

  NAvalue(r_pred)<- NA_flag_val 

  date_proc <- l_dates[i]

  date_val <- as.Date(strptime(date_proc,"%Y%m%d"))

  #month_name <- month.name(date_val)

  month_str <- format(date_val, "%b") ## Month, char, abbreviated

  year_str <- format(date_val, "%Y") ## Year with century

  day_str <- as.numeric(format(date_val, "%d")) ## numeric month

  date_str <- paste(month_str," ",day_str,", ",year_str,sep="")

  res_pix <- 1200

  #res_pix <- 480

  col_mfrow <- 1

  row_mfrow <- 1

  png_filename <-  file.path(out_dir,paste("Figure4_clim_mosaics_day_","_",date_proc,"_",region_name,"_",out_suffix,".png",sep =""))

  title_str <-  paste("Predicted ",variable_name, " on ",date_str , " ", sep = "")

  png(filename=png_filename,width = col_mfrow * res_pix,height = row_mfrow * res_pix)

  plot(r_pred,main =title_str,cex.main =1.5,col=matlab.like(255),zlim=c(-50,50),

       legend.shrink=0.8,legend.width=0.8)

       #axis.args = list(cex.axis = 1.6), #control size of legend z

       #legend.args=list(text='dNBR', side=4, line=2.5, cex=2.2))

       #legend.args=list(text='dNBR', side=4, line=2.49, cex=1.6))

  dev.off()

  return(png_filename)

}

extract_date <- function(i,x,item_no=NULL){

  y <- unlist(strsplit(x[[i]],"_"))

  if(is.null(item_no)){

    date_str <- y[length(y)-2] #count from end

  }else{

    date_str <- y[item_no]

  }

  return(date_str)

}

df_points_tmp <- df_points

df_points <- as.data.frame(t(df_points))

names(df_points) <- paste0("ID_",1:ncol(df_points))

#df_centroids <- read.table(df_centroids_fname,sep=",")

#test <- extract_date(6431,lf_mosaic_list,12) #extract item number 12 from the name of files to get the data

list_dates_produced <- unlist(mclapply(1:length(lf_mosaic_list),FUN=extract_date,x=lf_mosaic_list,item_no=12,mc.preschedule=FALSE,mc.cores = num_cores))                         

#list_dates_produced <-  mclapply(6400:6431,FUN=extract_date,x=lf_mosaic_list,item_no=12,mc.preschedule=FALSE,mc.cores = num_cores)                         

unique_date_tb <-table(df_points$date)

unique_date <- unique(df_points$date)

station_id <- 8

var_name <-paste0("ID_",station_id)

#aggregate(sdf_tmp

if(max(unique_date_tb)>1){

#  formula_str <- paste(var_name," ~ ","TRIP_START_DATE_f",sep="")

   var_pix <- aggregate(ID_8 ~ date, data = df_points, mean) #aggregate by date

}

#start_date <-input$dates[1]

#end_date <-input$dates[2]

d_z_tmp <- zoo(df_points[,station_id],df_points$date)

d_z <- window(d_z_tmp,start=start_date,end=end_date)   

#df_tmp <- subset(data_var,data_var$ID_stat==id_name)

#if(da)