R/mon.anomaly.climatology.R

In cmsafops: Tools for CM SAF NetCDF Data

#' Designed for the CM SAF R Toolbox.
#'
#' This function is a helper function (warming stripes plot, trend plot, time series plot) called by the CM SAF R Toolbox.
#' 
#'@param var Name of NetCDF variable (character).
#'@param infile Filename of input NetCDF file. This may include the directory
#'  (character).
#'@param outfile Filename of output NetCDF file. This may include the directory
#'  (character).
#' @param climatology_file Filename of input NetCDF climatology file. This may include the directory
#'  (character).
#'@param nc34 NetCDF version of output file. If \code{nc34 = 3} the output file will be
#'  in NetCDFv3 format (numeric). Default output is NetCDFv4.
#'@param overwrite logical; should existing output file be overwritten?
#'@param verbose logical; if TRUE, progress messages are shown
#'@param nc Alternatively to \code{infile} you can specify the input as an
#'  object of class `ncdf4` (as returned from \code{ncdf4::nc_open}).
#'@export
mon.anomaly.climatology <- function(var, 
                                    infile,
                                    outfile,
                                    climatology_file, 
                                    nc34 = 4, 
                                    overwrite = FALSE, 
                                    verbose = FALSE,
                                    nc = NULL)
{
  calc_time_start <- Sys.time()
  
  check_variable(var)
  check_infile(infile)
  check_outfile(outfile)
  outfile <- correct_filename(outfile)
  check_overwrite(outfile, overwrite)
  check_nc_version(nc34)
  
  ##### extract data from file #####
  file_data_clim <- read_file(climatology_file, var)
  file_data_clim$variable$prec <- "float"
  nc_clim <- nc_open(climatology_file)
  clim.data <- ncvar_get(nc_clim, file_data_clim$variable$name)
  nc_close(nc_clim)
  
  file_data <- read_file(infile, var, nc = nc)
  file_data$variable$prec <- "float"
  months_all <- get_date_time(file_data$dimension_data$t, file_data$time_info$units)$months
  years_all <- get_date_time(file_data$dimension_data$t, file_data$time_info$units)$year
  mul <- months_all * years_all
  months_unique <- sort(unique(months_all))
  
  ### calculate monthly means
  if (!is.null(nc)) nc_monmean <- nc
  else nc_monmean <- nc_open(infile)
  #monmeans <- ncvar_get(nc_monmean, file_data$variable$name)
  
  monmeans <- array(NA, dim = c(length(file_data$dimension_data$x),
                                 length(file_data$dimension_data$y),
                                 length(file_data$dimension_data$t)))
  
  for (i in seq_along(file_data$dimension_data$t)) {
    dum_dat_t <- ncvar_get(
      nc_monmean,
      file_data$variable$name,
      start = c(1, 1, i), count = c(-1, -1, 1),
      collapse_degen = FALSE
    )
    monmeans[,,i] <- dum_dat_t
  }
  
  
  monmeans_months_all <- get_date_time(ncvar_get(nc_monmean, "time"), ncatt_get(nc_monmean, "time", "units")$value)$months
  if (is.null(nc)) nc_close(nc_monmean)
  
  dates <- as.POSIXlt(get_time(file_data$time_info$units, file_data$dimension_data$t), format = "%Y-%m-%d")
  dates$mday <- rep(1, length(dates))
  dates <- as.Date(dates, format = "%Y-%m-%d")
  time_data <- unique(dates)
  unit_vec <- unlist(strsplit(file_data$time_info$units, split = " "))
  snc_index <- which(unit_vec == "since")
  time_data <- as.numeric(difftime(time_data, as.Date(unit_vec[snc_index + 1]), units = unit_vec[snc_index - 1]))
  
  testnum <- mul
  test_count <- 0
  test <- -999
  
  for (i in seq_along(mul)) {
    if (sum(testnum == mul[i]) >= 1) {
      test_count <- test_count + 1
      test <- cbind(test, mul[i])
      testnum[testnum == mul[i]] <- -999
    }
  }
  
  # Use placeholder for result so that it can be calculated later without the
  # need to have all input data in memory concurrently.
  data_placeholder <- array(
    file_data$variable$attributes$missing_value,
    dim = c(length(file_data$dimension_data$x),
            length(file_data$dimension_data$y),
            length(time_data))
  )
  
  time_bnds <- get_time_bounds_mul(
    file_data$dimension_data$t, test, test_count, mul
  )
  
  vars_data <- list(result = data_placeholder, time_bounds = time_bnds)
  
  nc_format <- get_nc_version(nc34)
  cmsaf_info <- paste0("cmsafops::mon.anomaly for variable ",
                       file_data$variable$name)
  
  ##### prepare output #####
  global_att_list <- names(file_data$global_att)
  global_att_list <- global_att_list[toupper(global_att_list) %in% toupper(GLOBAL_ATT_DEFAULT)]
  global_attributes <- file_data$global_att[global_att_list]
  
  dims <- define_dims(file_data$grid$is_regular,
                      file_data$dimension_data$x,
                      file_data$dimension_data$y,
                      time_data,
                      NB2,
                      file_data$time_info$units,
                      with_time_bnds = file_data$time_info$has_time_bnds)
  
  vars <- define_vars(file_data$variable, dims, nc_format$compression, with_time_bnds = file_data$time_info$has_time_bnds)
  
  write_output_file(
    outfile,
    nc_format$force_v4,
    vars,
    vars_data,
    file_data$variable$name,
    file_data$grid$vars, file_data$grid$vars_data,
    cmsaf_info,
    file_data$time_info$calendar,
    file_data$variable$attributes,
    global_attributes,
    with_time_bnds = file_data$time_info$has_time_bnds
  )
  
  ##### calculate and write result #####
  nc_out <- nc_open(outfile, write = TRUE)
  dummy_vec <- seq_along(months_all)
  
  for (j in seq_along(months_unique)) {
    monmean_dummy <- which(months_all == months_unique[j])
    startt <- dummy_vec[monmean_dummy]
    
    if (!is.null(nc)) nc_in <- nc
    else nc_in <- nc_open(infile)
    
    dum_dat <- array(NA, dim = c(length(file_data$dimension_data$x), length(file_data$dimension_data$y), length(startt)))
    
    for (i in seq_along(startt)) {
      dum_dat[, , i] <- ncvar_get(nc_in, file_data$variable$name, start = c(1, 1, startt[i]), count = c(-1, -1, 1), collapse_degen = FALSE)
    }
    
    if (is.null(nc)) nc_close(nc_in)
    
    if (verbose) message(paste0("apply monthly anomaly ", j,
                                " of ", length(months_unique)))
    
    # clim calc. 
    #mean_data <- rowMeans(dum_dat, dims = 2, na.rm = TRUE)
    mean_data <- clim.data[,,j]
    #mean_data <- array(mean_data, dim = c(dim(mean_data), length(mon_dummy)))
    mean_data <- array(mean_data, dim = c(dim(mean_data), length(monmean_dummy)))
    anom_data <- array(monmeans[,,monmean_dummy], dim(mean_data)) - mean_data
    anom_data[is.na(anom_data)] <- file_data$variable$attributes$missing_value
    for (i in seq_along(monmean_dummy)) {
      ncvar_put(nc_out, vars[[1]], anom_data[,,i], start = c(1, 1, monmean_dummy[i]), count = c(-1, -1, 1))
    }
  }
  
  nc_close(nc_out)
  
  calc_time_end <- Sys.time()
  if (verbose) message(get_processing_time_string(calc_time_start, calc_time_end))
  
}