#MODIFIED ON: 11/04/2016            

##COMMIT: adding necessary function for assessment from other script

extract_date <- function(i,x,item_no=NULL){

  y <- unlist(strsplit(x[[i]],"_"))

  if(is.null(item_no)){

    date_str <- y[length(y)-2] #count from end

  }else{

    date_str <- y[item_no]

  }

  return(date_str)

}

finding_missing_dates <- function(date_start,date_end,list_dates){

  #this assumes daily time steps!!

  #can be improved later on

  #date_start <- "19840101"

  #date_end <- "19991231"

  date1 <- as.Date(strptime(date_start,"%Y%m%d"))

  date2 <- as.Date(strptime(date_end,"%Y%m%d"))

  dates_range <- seq.Date(date1, date2, by="1 day") #sequence of dates

  missing_dates <- setdiff(as.character(dates_range),as.character(list_dates))

  #df_dates_missing <- data.frame(date=missing_dates)

  #which(df_dates$date%in%missing_dates)

  #df_dates_missing$missing <- 1

  df_dates <- data.frame(date=as.character(dates_range),missing = 0) 

  df_dates$missing[df_dates$date %in% missing_dates] <- 1

  #a$flag[a$id %in% temp] <- 1

  missing_dates_obj <- list(missing_dates,df_dates)

  names(missing_dates_obj) <- c("missing_dates","df_dates")

  return(missing_dates_obj)

}

centroids_shp_fun <- function(i,list_shp_reg_files){

  #

  shp_filename <- list_shp_reg_files[[i]]

  layer_name <- sub(".shp","",basename(shp_filename))

  path_to_shp <- dirname(shp_filename)

  shp1 <- try(readOGR(path_to_shp, layer_name)) #use try to resolve error below

  #shp_61.0_-160.0

  #Geographical CRS given to non-conformant data: -186.331747678

  #shp1<-readOGR(dirname(list_shp_reg_files[[i]]),sub(".shp","",basename(list_shp_reg_files[[i]])))

  if (!inherits(shp1,"try-error")) {

    pt <- gCentroid(shp1)

    #centroids_pts[[i]] <- pt

  }else{

    pt <- shp1

    #centroids_pts[[i]] <- pt

  }

  #shps_tiles[[i]] <- shp1

  #centroids_pts[[i]] <- centroids

  shp_obj <- list(shp1,pt)

  names(shp_obj) <- c("spdf","centroid")

  return(shp_obj)

}

rasterize_tile_day <- function(i,list_spdf,df_missing,list_r_ref,col_name,date_val){

  #

  #

  tile_spdf <- list_spdf[[i]]

  tile_coord <- names(list_spdf)[i]

  r_ref <- list_r_ref[[i]]

  df_tmp <- subset(df_missing,date==date_val,select=tile_coord)

  #for each row (date)

  val <- df_tmp[[tile_coord]]

  if(val==1){

    val<-0 #missing then not predicted

  }else{

    val<-1

  }

  tile_spdf$predicted <- val

  tile_spdf$tile_coord <- tile_coord

  tile_spdf$overlap <- 1

  #r <- rasterize(tile_spdf,r_ref,"predicted")

  #r <- rasterize(tile_spdf,r_ref,col_name)

  #r <- raster(r_ref,crs=projection(r_ref)) #new layer without values

  r <- raster(r_ref) #new layer without values

  if(col_name=="overlap"){

    set1f <- function(x){rep(1, x)}

   	r <- init(r, fun=set1f, overwrite=TRUE)

  }

  if(col_name=="predicted"){

    set1f <- function(x){rep(val, x)}

   	r <- init(r, fun=set1f, overwrite=TRUE)

  }

  return(r)

}