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

#AUTHOR: Benoit Parmentier 

#CREATED ON: 04/14/2015  

#Version: 2

#PROJECT: Environmental Layers project     

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

  out_dir_str <- out_dir

  lf_r_weights <- vector("list",length=length(lf_mosaic))

    #num_cores <- 11

    #debug(create_weights_fun)

    #This is the function creating the weights by tile. Distance from the centroids needs to be change from distance to

    #the edges...can use rows and columsn to set edges to 1 and 0 for the others.

    #list_param_create_weights <- list(lf_mosaic,df_points,r_feature,method,out_dir_str) 

    #names(list_param_create_weights) <- c("lf","df_points","r_feature","method","out_dir_str") 

    #num_cores <- 11

    #weights_obj <- create_weights_fun(3,list_param=list_param_create_weights)

    #This is the function creating the weights by tile. Distance from the centroids needs to be change from distance to

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

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

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

        #undebug(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)

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

        #num_cores <-11

      #write.table(lf_day_to_mosaic,file=file.path(out_dir,paste("list_to_mosaics_",day_to_mosaic[i],".txt",sep="")))

      #filename_list_mosaics <- file.path(out_dir,paste("list_to_mosaics_",day_to_mosaic[i],".txt",sep=""))

      #writeLines(unlist(list_weights_m),con=filename_list_mosaics_weights_m) #weights files to mosaic 

      #writeLines(unlist(list_weights_prod_m),con=filename_list_mosaics_prod_weights_m) #prod weights files to mosaic

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

      #out_mosaic_name_prod_weights_m <- r_weights_sum_raster_name <- file.path(out_dir,paste("r_prod_weights_sum_m_",mosaic_method,"_weighted_mean_",out_suffix,".tif",sep=""))

      #in_file_to_mosaics <- filename_list_mosaics        

      #in_dir_mosaics <- file.path(in_dir1,region_names[i])

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

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

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

      #undebug(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)

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

      #num_cores <-11

      list_args_weights_prod <- lapply(1:length(list_args_weights_prod), FUN=function(i,x){raster(x[[i]])},x=list_args_weights_prod)

      list_args_weights_prod$fun <- "sum" #use sum while mosaicing

      list_args_weights_prod$na.rm <- TRUE #deal with NA by removal

      list_args_weights$filename <- r_weights_sum_raster_name

      list_args_weights$overwrite<- TRUE

      list_args_weights_prod$overwrite<- TRUE  #add to overwrite existing image  

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

      list_args_weights$na.rm <- TRUE

      list_args_weights_prod$filename <- r_prod_sum_raster_name

      #Mosaic files: this is where we can use Alberto Python function but modified with option for

    #r_m_weighted_mean <- r_prod_sum/r_weights_sum #this is the mosaic using weighted mean...

    if(is.null(python_bin)){

      python_bin=""

      #undebug(raster_match)

      r_m_weighted_mean_raster_name_matched <- raster_match(1,list_param_raster_match)

      mask(raster(r_m_weighted_mean_raster_name_matched),mask=raster(r_mask_raster_name),

           filename=r_m_weighted_mean_mask_raster_name,overwrite=TRUE)

      raster_name <- r_m_weighted_mean_mask_raster_name

  if(mosaic_method=="unweighted"){

    #### Fourth use original images

    #macth file to mosaic extent using the original predictions

    #list_mosaiced_files <- list.files(pattern="r_m.*._weighted_mean_.*.tif")

    #names(list_mosaiced_files) <- c("edge","linear","sine")

  }

  #Create return object

  mosaic_obj <- list(raster_name,list_weights,list_weights_prod,mosaic_method)

  names(mosaic_obj) <- c("mean_mosaic","r_weights","r_weigths_prod","method")

  return(mosaic_obj)

}