scaling <- 0.01 #was scaled on 100 

df_centroids_fname <- "/data/project/layers/commons/NEX_data/climateLayers/out/reg4/mosaic/output_reg4_1999/df_centroids_19990701_reg4_1999.txt"

raster_name_lf <- c("/data/project/layers/commons/NEX_data/climateLayers/out/reg4/mosaic/int_mosaics/comp_r_m_use_edge_weights_weighted_mean_gam_CAI_dailyTmax_19990101_reg4_1999_m_gam_CAI_dailyTmax_19990101_reg4_1999.tif",

                    "/data/project/layers/commons/NEX_data/climateLayers/out/reg4/mosaic/int_mosaics/comp_r_m_use_edge_weights_weighted_mean_gam_CAI_dailyTmax_19990102_reg4_1999_m_gam_CAI_dailyTmax_19990102_reg4_1999.tif",

                    "/data/project/layers/commons/NEX_data/climateLayers/out/reg4/mosaic/int_mosaics/comp_r_m_use_edge_weights_weighted_mean_gam_CAI_dailyTmax_19990103_reg4_1999_m_gam_CAI_dailyTmax_19990103_reg4_1999.tif",

                    "/data/project/layers/commons/NEX_data/climateLayers/out/reg4/mosaic/int_mosaics/comp_r_m_use_edge_weights_weighted_mean_gam_CAI_dailyTmax_19990701_reg4_1999_m_gam_CAI_dailyTmax_19990701_reg4_1999.tif",

                    "/data/project/layers/commons/NEX_data/climateLayers/out/reg4/mosaic/int_mosaics/comp_r_m_use_edge_weights_weighted_mean_gam_CAI_dailyTmax_19990702_reg4_1999_m_gam_CAI_dailyTmax_19990702_reg4_1999.tif",

                    "/data/project/layers/commons/NEX_data/climateLayers/out/reg4/mosaic/int_mosaics/comp_r_m_use_edge_weights_weighted_mean_gam_CAI_dailyTmax_19990703_reg4_1999_m_gam_CAI_dailyTmax_19990703_reg4_1999.tif")

l_dates <- c("19990101","19990102","19990103","19990701","19990702","19990703")

#start_date <- day_start #PARAM 12 arg 12

#end_date <- day_end #PARAM 13 arg 13

#date_to_plot <- seq(as.Date(strptime(start_date,"%Y%m%d")), as.Date(strptime(end_date,"%Y%m%d")), 'day')

#l_dates <- format(date_to_plot,"%Y%m%d") #format back to the relevant date format for files

mask_pred <- TRUE

list_param_pre_process <- list(raster_name_lf,python_bin,infile_mask,scaling,mask_pred,NA_flag_val,out_suffix,out_dir) 

names(list_param_pre_process) <- c("lf","python_bin","infile_mask","scaling","mask_pred","NA_flag_val","out_suffix","out_dir") 

#debug(pre_process_raster_mosaic_fun)

#lf_mosaic_scaled <- mclapply(1:length(raster_name_lf),FUN=pre_process_raster_mosaic_fun,list_param=list_param_pre_process,mc.preschedule=FALSE,mc.cores = num_cores)                         

lf_mosaic_scaled <- mclapply(1:length(raster_name_lf),FUN=pre_process_raster_mosaic_fun,list_param=list_param_pre_process,mc.preschedule=FALSE,mc.cores = num_cores)                         

test <- pre_process_raster_mosaic_fun(2,list_param_pre_process)

pre_process_raster_mosaic_fun <- function(i,list_param){

  ## Extract parameters

  lf <- list_param$lf

  python_bin <- list_param$python_bin

  infile_mask <- list_param$infile_mask

  scaling <- list_param$scaling

  mask_pred <- list_param$mask_pred

  NA_flag_val <- list_param$NA_flag_val

  out_suffix <- list_param$out_suffix

  out_dir <- list_param$out_dir

  raster_name_in <- lf[i]

  #Step 1: match extent and resolution

  lf_files <- c(raster_name_in) #match to mask

  rast_ref <- infile_mask

  ##Maching resolution is probably only necessary for the r mosaic function

  #Modify later to take into account option R or python...

  list_param_raster_match <- list(lf_files,rast_ref,file_format,python_bin,out_suffix,out_dir)

  names(list_param_raster_match) <- c("lf_files","rast_ref","file_format","python_bin","out_suffix","out_dir_str")

  r_pred_matched <- raster_match(1,list_param_raster_match)

  raster_name_in <- c(r_pred_matched)

  #Step 2: mask

  if(mask_pred==TRUE){

    r_mask <- raster(infile_mask)

    extension_str <- extension(raster_name_in)

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

    raster_name <- file.path(out_dir,paste(raster_name_tmp,"_masked.tif",sep = ""))

    r_pred <- mask(raster(raster_name_in),r_mask,filename = raster_name,overwrite = TRUE)

  }

  NAvalue(r_pred) <- NA_flag_val

  #r_pred <- setMinMax(r_pred)

  #Step 3: remove scaling factor

  raster_name_in <- filename(r_pred)

  raster_name_tmp <- gsub(extension_str,"",basename(filename(r_pred)))

  raster_name_out <- file.path(out_dir,paste(raster_name_tmp,"_rescaled.tif",sep = ""))

  #r_pred <- overlay(r_pred, fun=function(x){x*1/scaling},filename=raster_name,overwrite=T)

  #raster_name_in <- "comp_r_m_use_edge_weights_weighted_mean_gam_CAI_dailyTmax_19990102_reg4_1999_m_gam_CAI_dailyTmax_19990102_reg4_1999_m__meeting_NASA_reg4_04292016_masked.tif"

  python_cmd <- file.path(python_bin,"gdal_calc.py")

  cmd_str3 <- paste(python_cmd, 

                     paste("-A ", raster_name_in,sep=""),

                     paste("--outfile=",raster_name_out,sep=""),

                     #paste("--type=","Int32",sep=""),

                     "--co='COMPRESS=LZW'",

                     paste("--NoDataValue=",NA_flag_val,sep=""),

                     paste("--calc='(A)*",scaling,"'",sep=""),

                     "--overwrite",sep=" ") #division by zero is problematic...

  #system(cmd_str3)

  system(cmd_str3)

  #NAvalue(r_pred) <- NA_flag_val

  #r_pred <- 

  r_pred <- setMinMax(raster(raster_name_out))

  return(raster_name_out)

#### Extract time series

#-65,-22

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

raster()