#MODIFIED ON: 06/09/2015            

  tile_no <- i

    vx_rep <- unlist((lapply(1:n_row,FUN=function(j){rep(vx[j],time=n_col)})))  

      #r_dist <- distance(r_init)

      srcfile <- file.path(out_dir,paste("feature_target_",tile_no,".tif",sep=""))

      writeRaster(r_init,filename=srcfile,overwrite=T)

      dstfile <- file.path(out_dir,paste("feature_target_edge_distance",tile_no,".tif",sep=""))

      n_values <- "1"

      cmd_str <- paste("gdal_proximity.py", srcfile, dstfile,"-values",n_values,sep=" ")

      system(cmd_str)

      r_dist<- raster(dstfile)

      r <- abs(r_dist - min_val)/ (max_val - min_val) #no need to inverse...

  } #too slow with R so used http://www.gdal.org/gdal_proximity.html

out_suffix <- paste(region_name,"_","mosaic_run10_1500x4500_global_analyses_06072015",sep="") 

### Second use distance from edge weights

method <- "use_edge"

df_points <- NULL

r_feature <- NULL

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(1,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

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

use_edge_weights_obj_list <- mclapply(1:length(lf_mosaic),FUN=create_weights_fun,list_param=list_param_create_weights,mc.preschedule=FALSE,mc.cores = num_cores)                           

list_edge_r_weights <- lapply(1:length(use_edge_weights_obj_list), FUN=function(i,x){x[[i]]$r_weights},x=use_edge_weights_obj_list)

list_edge_r_weights_prod <- lapply(1:length(use_edge_weights_obj_list), FUN=function(i,x){x[[i]]$r_weights_prod},x=use_edge_weights_obj_list)

### Third use sine weights

#### Second use use edge (dist) images

lf_files <- unlist(list_edge_r_weights)

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

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

#debug(raster_match)

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

list_edge_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_edge_r_weights_prod)

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

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

num_cores <-11

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

### third match wegihts from sine option

#### Fourth use original images

##Make this a function later

list_weights_m <- list(list_linear_weights_m,list_edge_weights_m,list_sine_weights_m)

list_weights_prod_m <- list(list_linear_weights_prod_m,list_edge_weights_prod_m,list_sine_weights_prod_m)

list_methods <- c("linear","edge","sine")

list_mosaiced_files <- vector("list",length=length(list_methods))

for(k in 1:length(list_methods)){

  list_args_weights <- list_weights_m[[k]]

  list_args_weights_prod <- list_weights_prod_m[[k]]

  method_str <- list_methods[[k]]

  #list_args_weights <- (mixedsort(list.files(pattern="r_weights_m_.*.tif")))

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

  #get the list of weights product into raster objects

  #list_args_weights_prod <- list_weights_prod_m

  #list_args_weights_prod <- (mixedsort(list.files(pattern="r_weights_prod_m_.*.tif")))

  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"

  list_args_weights_prod$na.rm <- TRUE

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

  list_args_weights$na.rm <- TRUE

   #Mosaic files

   r_weights_sum <- do.call(mosaic,list_args_weights) #weights sum image mosaiced

   r_prod_sum <- do.call(mosaic,list_args_weights_prod) #weights sum product image mosacied

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

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

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

}

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

###

#get the list of edge weights into raster objects

list_args_edge_weights <- list_linear_weights_m

#list_args_weights <- (mixedsort(list.files(pattern="r_weights_m_.*.tif")))

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

#get the list of weights product into raster objects

list_args_linear_weights_prod <- list_linear_weights_prod_m

#list_args_weights_prod <- (mixedsort(list.files(pattern="r_weights_prod_m_.*.tif")))

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