#MODIFIED ON: 11/09/2016            

#COMMIT: macthing extent to region for tile predicted   

#python_bin <- "/usr/bin" #PARAM 30, NCEAS

python_bin <- "/nobackupp6/aguzman4/climateLayers/sharedModules2/bin" #PARAM 30"

#file_format <- ".rst" #PARAM 9

  ### use rasterize

  spdf_tiles <- do.call(bind, shps_tiles) #bind all tiles together in one shapefile

  #function_product_assessment_part0_functions <- "global_product_assessment_part0_functions_11052016.R"

  #source(file.path(script_path,function_product_assessment_part0_functions)) #source all functions used in this script 

  #undebug(rasterize_tile_day)

  #r <- raster(matrix(runif(20),5,4))

  #r[r>.5] <- NA

  #g <- as(r, 'SpatialGridDataFrame')

  #p <- as(r, 'SpatialPixels')

  #p_spdf <- as(r_overlap_m,"SpatialPointsDataFrame")

  #plot(r)

  #points(p)

  ### now assign id and match extent for tiles

  lf_files <- unlist(list_predicted)

  rast_ref_name <- infile_mask

  rast_ref <- rast_ref_name

  ##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_str_tmp,out_dir_str)

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

  #undebug(raster_match)

  #r_test <- raster_match(1,list_param_raster_match)

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

  list_tiles_predicted_m <- unlist(mclapply(1:length(lf_files),

                                            FUN=raster_match,list_param=list_param_raster_match,

                                            mc.preschedule=FALSE,mc.cores = num_cores))                           

  #r_stack <- stack(list_tiles_predicted_m)

  list_mask_out_file_name <- lf_files

  mclapply(1:length(list_tiles_predicted_m),

           FUN=function(i){mask(raster(list_tiles_predicted_m[i]),r_mask,filename=list_mask_out_file_name[i])},

                                                       mc.preschedule=FALSE,mc.cores = num_cores)                         

  #r_stack_masked <- mask(r, m2) #, maskvalue=TRUE)

  ##Now loop through every day if missing then generate are raster showing map of number of prediction

  # spdf_tiles <- do.call(intersect, shps_tiles)

  # 

  # ### Now use intersect to retain actual overlap

  # 

  # for(i in 1:length(shps_tiles)){

  #   overlap_intersect <- intersect(shps_tiles[[1]],shps_tiles[[i]])

  # }

  # 

  # overlap_intersect <- lapply(1:length(shps_tiles),FUN=function(i){intersect(shps_tiles[[1]],shps_tiles[[i]])})

  # #test <- overlap_intersect <- intersect(shps_tiles[[1]],shps_tiles[[2]]))

  # 

  # names(overlap_intersect) <- basename(in_dir_reg)

  # shp_selected <- unlist(lapply(1:length(overlap_intersect),function(i){!is.null(overlap_intersect[[i]])}))

  # test_list <- overlap_intersect[shp_selected]

  # spdf_tiles_test <- do.call(bind, test_list) #combines all intersect!!

  # #ll <- ll[ ! sapply(ll, is.null) ]

  # test <- overlap_intersect[!lapply(overlap_intersect,is.null)]

  # spdf_tiles_test <- do.call(bind, test) #combines all intersect!!

  # #ll <- ll[ ! sapply(ll, is.null) ]

  # spdf_tiles <- do.call(bind, overlap_intersect[1:4]) #combines all intersect!!

  # spdf_tiles_test <- do.call(bind, test) #combines all intersect!!

  # 

  # plot(spdf_tiles_test,add=T,border="green",usePolypath = FALSE) #added usePolypath following error on brige and NEX

  # 

  # matrix_overlap%*%df_missing[1,1:26]

  # 

  # 

  # ## For each day can do overalp matrix* prediction

  # ## if prediction and overlap then 1 else 0, if no-overlap is then NA

  # ## then for each tile compute the number of excepted predictions taken into account in a tile

  # 

  # #combine polygon

  # #http://gis.stackexchange.com/questions/155328/merging-multiple-spatialpolygondataframes-into-1-spdf-in-r

  # 

  # #http://gis.stackexchange.com/questions/116388/count-overlapping-polygons-in-single-shape-file-with-r

  # 

  # #### Use the predictions directory

  # #By region

  # #For each polygon/tile find polygon overlapping with count and ID (like list w)

  # #for each polygon/tile and date find if there is a prediction using the tif (multiply number)

  # #for each date of year report data in table.

  # 

  # #go through table and hsow if there are missing data (no prediction) or report min predictions for tile set?

  #   

  # #for each polygon find you overlap!!

  # #plot number of overlap

  # #for specific each find prediction...