#' @title Calculate the Average Dose and the dose rate dispersion
#'
#' @description
#' This functions calculates the Average Dose and its extrinsic dispersion,
#' estimating the standard errors by bootstrapping based on the Average
#' Dose Model by Guérin et al., 2017.
#'
#' **`sigma_m`**\cr
#'
#'The program requires the input of a known value of `sigma_m`,
#'which corresponds to the intrinsic overdispersion, as determined
#'by a dose recovery experiment. Then the dispersion in doses (`sigma_d`)
#'will be that over and above `sigma_m` (and individual uncertainties `sigma_wi`).
#'
#' @param data [Luminescence::RLum.Results-class] or [data.frame] (**required**):
#' for [data.frame]: two columns with `De` `(data[,1])` and `De error` `(values[,2])`
#'
#' @param sigma_m [numeric] (**required**):
#' the overdispersion resulting from a dose recovery
#' experiment, i.e. when all grains have  received the same dose. Indeed in such a case, any
#' overdispersion (i.e. dispersion on top of analytical uncertainties) is, by definition, an
#' unrecognised measurement uncertainty.
#'
#' @param Nb_BE [integer] (*with default*):
#' sample size used for the bootstrapping
#'
#' @param na.rm [logical] (*with default*):
#' exclude `NA` values from the data set prior to any further operation.
#'
#' @param plot [logical] (*with default*):
#' enable/disable the plot output.
#'
#' @param verbose [logical] (*with default*):
#' enable/disable output to the terminal.
#'
#' @param ... further arguments that can be passed to [graphics::hist]. As three plots
#' are returned all arguments need to be provided as [list],
#' e.g., `main = list("Plot 1", "Plot 2", "Plot 3")`.
#' Note: not all arguments of `hist` are
#' supported, but the output of `hist` is returned and can be used of own plots. \cr
#'
#' Further supported arguments: `mtext` ([character]), `rug` (`TRUE/FALSE`).
#'
#' @section Function version: 0.1.7
#'
#' @author Claire Christophe, IRAMAT-CRP2A, Université de Nantes (France)\cr
#' Anne Philippe, Université de Nantes, (France)\cr
#' Guillaume Guérin, IRAMAT-CRP2A, Université Bordeaux Montaigne, (France)\cr
#' Sebastian Kreutzer, F2.1 Geophysical Parametrisation/Regionalisation, LIAG - Institute for Applied Geophysics (Germany)
#'
#' @seealso [read.table], [graphics::hist]
#'
#' @return
#' The function produces numerical output and an optional plot. In addition,
#' an [Luminescence::RLum.Results-class] object containing the following
#' elements is returned:
#'
#' \item{$summary}{[data.frame] summary of all relevant model results.}
#' \item{$data}{[data.frame] with original input data.}
#' \item{$dstar}{[matrix] with bootstrap values.}
#' \item{$hist}{[list] object as produced by the [hist] function.}
#'
#' The `$summary` data.frame contains the following columns:
#' \tabular{lll}{
#'  **Column** \tab **Type** \tab **Description**\cr
#'  AVERAGE_DOSE \tab [numeric] \tab the obtained average dose\cr
#'  AVERAGE_DOSE.SE \tab [numeric] \tab the average dose error \cr
#'  SIGMA_D \tab [numeric]\tab sigma \cr
#'  SIGMA_D.SE \tab [numeric]\tab standard error of the sigma  \cr
#'  IC_AVERAGE_DOSE.LEVEL  \tab [character]\tab confidence level average dose\cr
#'  IC_AVERAGE_DOSE.LOWER  \tab [character]\tab lower quantile of average dose \cr
#'  IC_AVERAGE_DOSE.UPPER \tab [character]\tab upper quantile of average dose\cr
#'  IC_SIGMA_D.LEVEL \tab [integer]\tab confidence level sigma\cr
#'  IC_SIGMA_D.LOWER \tab [character]\tab lower sigma quantile\cr
#'  IC_SIGMA_D.UPPER \tab [character]\tab upper sigma quantile\cr
#'  L_MAX \tab [character]\tab maximum likelihood value \cr
#'  de \tab [numeric] \tab same as `AVERAGE_DOSE` (internal use)\cr
#'  de_err \tab [numeric] \tab same as `AVERAGE_DOSE.SE` (internal use)\cr
#' }
#'
#' The function returns two different plot panels.
#'
#' (1) An abanico plot with the dose values
#'
#' (2) A histogram panel comprising 3 histograms with the equivalent dose and the bootstrapped average
#' dose and the sigma values.
#'
#' @references
#' Guérin, G., Christophe, C., Philippe, A., Murray, A.S., Thomsen, K.J., Tribolo, C., Urbanova, P.,
#' Jain, M., Guibert, P., Mercier, N., Kreutzer, S., Lahaye, C., 2017. Absorbed dose, equivalent dose,
#' measured dose rates, and implications for OSL age estimates: Introducing the Average Dose Model.
#' Quaternary Geochronology 1-32. doi:10.1016/j.quageo.2017.04.002
#'
#' **Further reading**\cr
#'
#' Efron, B., Tibshirani, R., 1986. Bootstrap Methods for Standard Errors, Confidence Intervals,
#' and Other Measures of Statistical Accuracy. Statistical Science 1, 54-75.
#'
#' @note This function has beta status!
#'
#' @keywords datagen
#'
#' @examples
#'
#' ## load example data
#' data(ExampleData.DeValues, envir = environment())
#'
#' ## calculate Average dose (using only the first 56 values here)
#' AD <- calc_AverageDose(ExampleData.DeValues$CA1[1:56, ], sigma_m = 0.1)
#'
#' ## plot De and set Average dose as central value
#' plot_AbanicoPlot(AD, z.0 = AD$summary$de)
#'
#' @export
calc_AverageDose <- function(
  data,
  sigma_m,
  Nb_BE = 500,
  na.rm = TRUE,
  plot = TRUE,
  verbose = TRUE,
  ...
) {
  .set_function_name("calc_AverageDose")
  on.exit(.unset_function_name(), add = TRUE)

  # Define internal functions ------------------------------------------------------------------

  # function which compute mle's for data (yu,su)
  .mle <- function(yu , su, wu.start, sigma_d.start, delta.start){

    ##set start parameters, otherwise the function will try to get them
    ##from the parent environment, which is not wanted ...
    delta.temp <- 0
    sigma_d.temp <- 0

    sigma_d <- sigma_d.start
    delta <- delta.start
    wu <- wu.start

    j <- 0
    iteration_limit <- 10000

    ##loop until convergence or the iteration limit is reached
    while(j < iteration_limit) {

      ##code by Claire; in the 2nd and 3rd line delta and sigma_d are replaced by delta.temp and
      ##sigma_d.temp; otherwise the iteration and its test for convergence will not work
      delta.temp <- exp( sum(wu*(yu+(0.5*(sigma_d^2)))) / sum(wu) )
      sigma_d.temp <- sigma_d*sum( (wu^2) * (yu-log(delta.temp)+0.5*sigma_d^2)^2) / (sum( wu*(1+yu-log(delta.temp)+0.5*sigma_d^2)))
      wu <- 1/(sigma_d.temp^2 + su^2)

      ##break loop if convergence is reached ... if not update values
      if(is.infinite(delta.temp) | is.infinite(sigma_d.temp)){
        .throw_warning("Inf/NaN values produced by .mle(), NA returned")
        return(c(NA,NA))

      }else if (
        ##compare values ... if they are equal we have convergence
        all(
          c(round(c(delta, sigma_d), 4)) == c(round(c(delta.temp, sigma_d.temp), 4))
        )
      ) {
        break()

      } else{
        ##update input values
        delta <- delta.temp
        sigma_d <- sigma_d.temp
        j <- j + 1
      }
    }

    ##if no convergence was reached stop entire function; no stop as this may happen during the
    ##bootstraping procedure
    if(j == iteration_limit){
      .throw_warning("No convergence reached by .mle() after ",
                     iteration_limit, " iterations, NA returned")
      return(c(NA,NA))
    }else{
      return(c(round(c(delta, sigma_d),4)))
    }
  }

  .CredibleInterval <- function(a_chain, level = 0.95) {
    ## Aim : estimation of the shortest credible interval of the sample of parameter a
    # A level % credible interval is an interval that keeps N*(1-level) elements of the sample
    # The level % credible interval is the shortest of all those intervals.
    ## Parameters :
    # a_chain : the name of the values of the parameter a
    # level : the level of the credible interval expected
    ## Returns : the level and the endpoints

    sorted_sample <- sort(a_chain)
    N <- length(a_chain)
    OutSample <- N * (1 - level)

    I <- cbind(sorted_sample[1:(OutSample + 1)] , sorted_sample[(N - OutSample):N])

    l <-  I[, 2] - I[, 1] # length of intervals
    i <- which.min(l) # look for the shortest interval

    return(c(
      level = level,
      CredibleIntervalInf = I[i, 1],
      CredibleIntervalSup = I[i, 2]
    ))
  }

  ##////////////////////////////////////////////////////////////////////////////////////////////////
  ##HERE THE MAIN FUNCTION STARTS
  ##////////////////////////////////////////////////////////////////////////////////////////////////

  # Integrity checks ----------------------------------------------------------------------------

  .validate_class(data, c("RLum.Results", "data.frame"))
  .validate_positive_scalar(sigma_m)
  .validate_positive_scalar(Nb_BE, int = TRUE)

  # Data preparation -----------------------------------------------------------------------------

  if (inherits(data, "RLum.Results")) {
    data <- get_RLum(data)
  }

  ## check that we actually have data
  if (length(data) == 0 || nrow(data) == 0) {
    .throw_message("'data' contains no data, NULL returned")
    return(NULL)
  }

  ##problem: the entire code refers to column names the user may not provide...
  ##  >> to avoid changing the entire code, the data will shape to a format that
  ##  >> fits to the code

  ##check for number of columns
  if(ncol(data)<2){
    .throw_message("'data' contains < 2 columns, NULL returned")
    return(NULL)
  }

  ##used only the first two colums
  if(ncol(data)>2){
    data <- data[,1:2]
    .throw_warning("'data' contains > 2 columns, ",
                   "only the first 2 columns were used")
  }

  ##exclude NA values
  if (anyNA(data)) {
    data <- na.exclude(data)
    .throw_warning("NA values in 'data' detected, rows with NA values removed")
  }

  if (any(data[, 1] <= 0)) {
    data <- data[data[, 1] > 0, ]
    .throw_warning("Non-positive values in 'data' detected, rows removed")
  }

  ##check data set
  if(nrow(data) == 0){
    .throw_message("After NA removal, nothing is left from the data set, ",
                   "NULL returned")
    return(NULL)
  }

  ##data becomes to dat (thus, make the code compatible with the code by Claire and Anne)
  dat <- data

  ##preset column names, as the code refers to it
  colnames(dat) <- c("cd", "se")


  # Pre calculation -----------------------------------------------------------------------------

  ##calculate  yu = log(CD) and su = se(logCD)
  yu <- log(dat$cd)

  su <- sqrt((dat$se / dat$cd) ^ 2 + sigma_m ^ 2)

  # calculate starting values and weights
  sigma_d <- sd(dat$cd) / mean(dat$cd)

  ## sigma_d may be NA if there is only one measurement (NA values in the
  ## data have already been excluded prior to this)
  if (is.na(sigma_d))
    sigma_d <- 0

  wu <- 1 / (sigma_d ^ 2 + su ^ 2)

  delta <- mean(dat$cd)
  n <- length(yu)

  ##terminal output
  if (verbose) {
    cat("\n[calc_AverageDose()]")
    cat("\n\n>> Initialisation <<")
    cat("\nn:\t\t", n)
    cat("\ndelta:\t\t", delta)
    cat("\nsigma_m:\t", sigma_m)
    cat("\nsigma_d:\t", sigma_d)
  }


  # mle's computation
  dhat <- .mle(yu, su, wu.start = wu, sigma_d.start = sigma_d, delta.start = delta)
  delta <- dhat[1]
  sigma_d <- dhat[2]
  wu <- 1 / (sigma_d ^ 2 + su ^ 2)

  # maximum log likelihood
  llik <- sum(-log(sqrt(2 * pi / wu)) - (wu / 2) * ((yu - log(delta) + 0.5 * (sigma_d ^ 2)) ^ 2))

  ##terminal output
  if(verbose){
    cat("\n\n>> Calculation <<\n")
    cat("log likelihood:\t", round(llik, 4), "\n")
  }

  if (is.na(llik)) {
    .throw_error("Maximum likelihood estimation failed")
  }

  # standard errors obtained by bootstrap, we refer to Efron B. and Tibshirani R. (1986)
  n <- length(yu)

  ##calculate dstar
  ##set matrix for I
  I <- matrix(data = sample(x = 1:n, size = n * Nb_BE, replace = TRUE), ncol = Nb_BE)

  ##iterate over the matrix and produce dstar
  ##(this looks a little bit complicated, but is far more efficient)
  dstar <- t(vapply(
    X = 1:Nb_BE,
    FUN = function(x) {
      .mle(yu[I[, x]], su[I[, x]], sigma_d.start = sigma_d, delta.start = delta, wu.start = wu)
    },
    FUN.VALUE = numeric(2)))

  ##exclude NA values
  dstar <- na.exclude(dstar)

  ## calculate confidence intervals
  IC_delta <- .CredibleInterval(dstar[,1],0.95)
  IC_sigma_d <- .CredibleInterval(dstar[,2],0.95)
  IC <- rbind(IC_delta, IC_sigma_d)

  # standard errors
  sedelta <- sqrt((1 / (Nb_BE - 1)) * sum((dstar[, 1] - mean(dstar[, 1]))^2))
  sesigma_d <- sqrt((1 / (Nb_BE - 1)) * sum((dstar[, 2] - mean(dstar[, 2]))^2))

  ##Terminal output
  if (verbose) {
    cat("\nconfidence intervals\n")
    cat("--------------------------------------------------\n")
    print(t(IC), print.gap = 6, digits = 4)
    cat("--------------------------------------------------\n")

    cat("\n>> Results <<\n")
    cat("----------------------------------------------------------\n")
    cat(
      "Average dose:\t ",
      round(delta, 4),
      "\tse(Aver. dose):\t",
      round(sedelta, 4)
    )
    cat("\nsigma_d:\t ",
        round(sigma_d, 4),
        if (sigma_d == 0) "\t",
        "\tse(sigma_d):\t",
        round(sesigma_d, 4)
        )
    cat("\n----------------------------------------------------------\n")
  }

  ##compile final results data frame
  results_df <- data.frame(
    AVERAGE_DOSE = delta,
    AVERAGE_DOSE.SE = sedelta,
    SIGMA_D = sigma_d,
    SIGMA_D.SE = sesigma_d,
    IC_AVERAGE_DOSE.LEVEL = IC_delta[1],
    IC_AVERAGE_DOSE.LOWER = IC_delta[2],
    IC_AVERAGE_DOSE.UPPER = IC_delta[3],
    IC_SIGMA_D.LEVEL = IC_sigma_d[1],
    IC_SIGMA_D.LOWER = IC_sigma_d[2],
    IC_SIGMA_D.UPPER = IC_sigma_d[3],
    L_MAX = llik,
    de = delta,
    de_err = sedelta,
    row.names = NULL
  )

  # Plotting ------------------------------------------------------------------------------------

  ##the plotting (enable/disable) is controlled below, as with this
  ##we always get a histogram object

  ##set data list
  data_list <- list(dat$cd, dstar[,1], dstar[,2])

  ##preset plot arguments
  plot_settings <- list(
    breaks = list("FD", "FD", "FD"),
    probability = list(FALSE, TRUE, TRUE),
    main = list(
      "Observed: Equivalent dose",
      "Bootstrapping: Average Dose",
      "Bootstrapping: Sigma_d"),
    xlab = list(
      "Equivalent dose [a.u.]",
      "Average dose [a.u.]",
      "Sigma_d"),
    axes = list(TRUE, TRUE, TRUE),
    col = NULL,
    border = NULL,
    density = NULL,
    freq = NULL,
    mtext = list(
      paste("n = ", length(data_list[[1]])),
      paste("n = ", length(data_list[[2]])),
      paste("n = ", length(data_list[[3]]))),
    rug = list(TRUE, TRUE, TRUE)
  )

  ##modify this list by values the user provides

  ##expand all elements in the list
  ##problem: the user might provid only one item, then the code will break
  plot_settings.user <- lapply(list(...), rep, length = 3)

  ##modify
  plot_settings <- modifyList(plot_settings, plot_settings.user)

  ##get change par setting and reset on exit
  if(plot) {
    par.default <- .par_defaults()
    on.exit(par(par.default), add = TRUE)
    par(mfrow = c(1,3))
  }

  ##Produce plots
  ##(1) - histogram of the observed equivalent dose
  ##(2) - histogram of the bootstrapped De
  ##(3) - histogram of the bootstrapped sigma_d

  ##with lapply we get fetch also the return of hist, they user might want to use this later
  hist <- lapply(1:length(data_list), function(x){
    temp <- suppressWarnings(hist(
      x = data_list[[x]],
      breaks = if (NROW(data_list[[x]]) > 1) plot_settings$breaks[[x]] else 1,
      probability = plot_settings$probability[[x]],
      main = plot_settings$main[[x]],
      xlab = plot_settings$xlab[[x]],
      axes = plot_settings$axes[[x]],
      freq = plot_settings$freq[[x]],
      plot = plot,
      col = plot_settings$col[[x]],
      border = plot_settings$border[[x]],
      density = plot_settings$density[[x]]
    ))

    if (plot) {
      ##add rug
      if (plot_settings$rug[[x]]) {
        graphics::rug(data_list[[x]])
      }

      ##plot mtext
      mtext(side = 3,
            text = plot_settings$mtext[[x]],
            cex = par()$cex)
    }

    return(temp)
  })

  # Return --------------------------------------------------------------------------------------
  set_RLum(
    class = "RLum.Results",
    data = list(
      summary = results_df,
      data = data,
      dstar = as.data.frame(dstar),
      hist = hist
    ),
    info = list(call = sys.call())
  )
}
