#Different options to explore mosaicing are tested. This script only contains functions.

#AUTHOR: Benoit Parmentier 

#CREATED ON: 04/14/2015  

#Version: 1

#PROJECT: Environmental Layers project     

#COMMENTS: first commit of function script to test mosaicing using 1500x4500km and other tiles

  lf_files <- list_param$lf_files

  rast_ref <- list_param$rast_ref #name of reference file

  file_format <- list_param$file_format #".tif",".rst" or others

  out_suffix <- list_param$out_suffix

  out_dir_str <- list_param$out_dir_str

  #cmd_str <- paste("/usr/bin/gdalwarp",inFilename,raster_name,sep=" ") #this may be a problem

  cmd_str <- paste(python_cmd,inFilename,raster_name,sep=" ") #this may be a problem

  #gdalwarp -t_srs '+proj=utm +zone=11 +datum=WGS84' raw_spot.tif utm11.tif

  system(cmd_str)

  ##return name of file created

  #2)mosaic_method: mosaic methods availbable:use_sine_weights,use_edge,use_linear_weights

  #3)num_cores: number of cores used in parallilization in mclapply

  #4)r_mask_raster_name: mask rference raster image

  #             here e.g. dailyTmax and date!!

  #

  #OUTPUT:

  # Object is produced with 3 components:

  #mosaic function using gdal_merge to compute a reference image to mach.

  rast_ref <- file.path(out_dir,paste("avg_",out_suffix,file_format,sep="")) #this is a the ref

  system(cmd_str)

  ## Create raster image for original predicted images with matching resolution and extent to the mosaic (reference image)

    lf_files <- unlist(list_weights)

    #debug(raster_match)

    #r_test <- raster(raster_match(1,list_param_raster_match))

    list_weights_m <- mclapply(1:length(lf_files),FUN=raster_match,list_param=list_param_raster_match,mc.preschedule=FALSE,mc.cores = num_cores)                           

    lf_files <- unlist(list_weights_prod)

    #num_cores <-11

    list_weights_prod_m <- mclapply(1:length(lf_files),FUN=raster_match,list_param=list_param_raster_match,mc.preschedule=FALSE,mc.cores = num_cores)                           

    r_weights_sum_raster_name <- file.path(out_dir,paste("r_weights_sum_m_",method_str,"_weighted_mean_",out_suffix,".tif",sep=""))

    list_args_weights$filename <- r_weights_sum_raster_name

    list_args_weights$overwrite<- TRUE

    list_args_weights$fun <- "sum" #we want the sum to compute the weighted mean

    list_args_weights$na.rm <- TRUE

    #writeRaster(r_m_weighted_mean, NAflag=NA_flag_val,filename=raster_name,overwrite=TRUE)  

    #now use the mask

  }

  if(mosaic_method=="unweighted"){

    list_args_pred_m$fun <- "mean"

    list_args_pred_m$na.rm <- TRUE

    #list_args_pred_m$filename <- 

    r_m_mean <- do.call(mosaic,list_args_pred_m) #this is unweighted mean from the predicted raster

    #r_m_mean_unweighted <- paste("r_m_mean_",out_suffix,".tif",sep="")

  }