R/wtr.layer.segments.R

Defines functions depth.filter wtr.layer

Documented in depth.filter wtr.layer

#' @title Exploration of lake water column layers
#' 
#' @description Extract water column parameters of a given parameter from a profile using
#' the split-and-merge algorithm.  The cline is defined as the midpoint of the
#' layer of water where the physical property change in the greatest over a
#' small difference. The exact cline depends on the specification of measure.
#' For example if temperature is specified, then we can expect cline to output
#' the thermocline.
#' 
#' 
#' @param data data supplied as a bare (unquoted) value
#' @param depth depth in metres; should be an increasing vector; supplied as a
#' bare (unquoted) value
#' @param measure parameter measured in the water column profile; supplied as a
#' bare (unquoted) value
#' @param thres error norm; defaults to 0.1
#' @param z0 initial depth in metres. Defaults to 2.5m
#' @param zmax maximum depth in metres: defaults to 150m
#' @param nseg optional parameter to define the number of segments a priori;
#' defaults to an unconstrained approach whereby the algorithm determines
#' segmentations by minimzing the error norm over each segment
#' @return a dataframes with a list column. This includes: nseg (number of
#' segments), mld (mix layer depth), cline (the midpoint of the segment
#' connecting inflection points that has the maximum slope; thermocline for
#' temperature measures) and segments calculated by the sm algorithm.
#' @references Thomson, R. and I. Fine. 2003. Estimating Mixed Layer Depth from
#' Oceanic Profile Data. Journal of Atmospheric and Oceanic Technology. 20(2),
#' 319-329.
#' @examples
#' 
#' data("latesummer")
#' df1 <- wtr.layer(depth=latesummer$depth, measure = latesummer$temper)
#' df1$mld
#' df1$segments
#' 
#' wtr.layer(data = latesummer, depth=depth, measure = temper, nseg=4)
#' 
#' 
#' @export wtr.layer
wtr.layer <-
  function(data,
           depth,
           measure,
           thres = 0.1,
           z0 = 2.5,
           zmax = 150,
           nseg = "unconstrained") {


    ## Note accounting for difference between interval (nimax=neg-1) and segments (nseg=nimax+1)
    ## NSE to account for data argument
    if (missing(data)) {
      if (length(depth) <= 30) {
        warning("Profile does not have enough readings for sm algorithm (<30): returning NA")
        return(data.frame(min_depth = NA, nseg = NA, mld = NA, cline = NA))
      }

      if (nseg == "unconstrained") {
        sam_list <- by_s_m(thres = thres, z0 = z0, zmax = zmax, z = depth, sigma = measure)
        nseg <- sam_list[["nimax"]] + 1
      } else {
        sam_list <- by_s_m3(nr = nseg - 1, z0 = z0, zmax = zmax, z = depth, sigma = measure)
        nseg <- nseg
      }
    } else {
      if (length(data[["depth"]]) <= 30) {
        warning("Profile does not have enough readings for sm algorithm (<30): returning NA")
        return(data.frame(min_depth = NA, nseg = NA, mld = NA, cline = NA))
      }

      if (nseg == "unconstrained") {
        sam_list <- eval(substitute(by_s_m(thres = thres, z0 = z0, zmax = zmax, z = depth, sigma = measure)), data)
        nseg <- sam_list[["nimax"]] + 1
      } else {
        sam_list <- eval(substitute(by_s_m3(nr = nseg - 1, z0 = z0, zmax = zmax, z = depth, sigma = measure)), data)
        nseg <- nseg
      }
    }
    layers <- data.frame(
      min_depth = z0,
      nseg = nseg,
      mld = sam_list[["by_s_m"]],
      cline = cline_calc(z_seg = sam_list[["smz"]], sigma_seg = sam_list[["sms"]])
    )
    layers[["segments"]] <- list(data.frame(
      segment_depth = sam_list[["smz"]],
      segment_measure = sam_list[["sms"]]
    ))
    return(layers)
  }


#' @title Data filter to remove soak, heave and upcast
#' 
#' @description \itemize{ \item Soak period: water profiling instruments typically require a
#' soak period where you let the instrument rest submerged at the surface.
#' While it is "soaking" it is collecting data. We don't want that data \item
#' Upcast versus downcast: typically instruments are turned on before you put
#' them in the water and turn them off once you pull them out. The data
#' consequence of that is that you collect both the "downcast" and the
#' "upcast". In some case the upcast is of interest but usually it isn't. And
#' because we would prefer increasing depth data it is better to remove an
#' upcast if it is present.  \item Heave: when lowering the instrument in rough
#' weather a boat will heave side to side. Sometimes it will heave enough that
#' you get small data groupings where the decreases a little while the boat
#' heaves then go down. The overall trend is still down but those slight
#' upticks in depth cause problems for our algorithm. }
#' 
#' 
#' @param z0 depth vector
#' @param run_length Length of run upon which to start the soak removal
#' @param index Logical: Should the function return an index value or actual
#' value?
#' @return index values of z0 of filtered data. Will return a warning if the
#' function removed more than 10% of the data
#' @export depth.filter
depth.filter <- function(z0, run_length=20, index = FALSE) {
  n_start <- length(z0)

  ## REMOVES SOAK PERIOD
  ## s are where the runs start;  tack on length(z0)+1, where the next run would start if the vector continued
  ## subsequent runs start where there is a
  s <- 1L + c(0L, which(z0[-1L] < z0[-length(z0)]), length(z0))
  ## Index of first run of numbers greater than run_length (defaults to 20)

  # if( length(s) > run_length ) {
  w <- min(which(diff(s) >= run_length))

  ## Index from first run GTE 20
  start <- s[w]
  ## TODO: fix the max depth identification
  end <- which.max(z0)

  ## Index numbers of x of non soak
  idx_soak <- start:end

  ## Depth values using soak index
  ## I'm sure there is a better way to do this.
  x1 <- z0[idx_soak]

  ## Index of heave
  idx_heave <- unique(Reduce(function(p, i) if (x1[i] > x1[p]) i else p, seq_along(x1), accumulate = TRUE))

  ## Percent data loss
  p_loss <- 100 - (length(z0[idx_heave]) / n_start) * 100

  if (p_loss > 10) {
    message(paste0("Soak, heave and bottom data filter removed ", round(p_loss, 2), "% of the data"))
  }

  idx <- idx_soak[idx_heave]

  if (index == TRUE) {
    return(idx)
  } else {
    return(z0[idx])
  }
}

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rLakeAnalyzer documentation built on March 18, 2018, 1:51 p.m.