R/AbsToProbs.R

Defines functions .abstoprobs AbsToProbs CST_AbsToProbs

Documented in AbsToProbs CST_AbsToProbs

#'Transform ensemble forecast into probabilities
#'
#'The Cumulative Distribution Function of a forecast is used to obtain the probabilities of each value in the ensemble. If multiple initializations (start dates) are provided, the function will create the Cumulative Distribution Function excluding the corresponding initialization. 
#'
#'@param data an 's2dv_cube' object as provided function \code{CST_Load} in package CSTools.
#'@param start an optional parameter to defined the initial date of the period to select from the data by providing a list of two elements: the initial date of the period and the initial month of the period. By default it is set to NULL and the indicator is computed using all the data provided in \code{data}.
#'@param end an optional parameter to defined the final date of the period to select from the data by providing a list of two elements: the final day of the period and the final month of the period. By default it is set to NULL and the indicator is computed using all the data provided in \code{data}.
#'@param time_dim a character string indicating the name of the temporal dimension. By default, it is set to 'ftime'. More than one dimension name matching the dimensions provided in the object \code{data$data} can be specified. This dimension is required to subset the data in a requested period.
#'@param memb_dim a character string indicating the name of the dimension in which the ensemble members are stored.
#'@param sdate_dim a character string indicating the name of the dimension in which the initialization dates are stored. 
#'@param ncores an integer indicating the number of cores to use in parallel computation.
#'
#'@return A 's2dv_cube' object containing the probabilites in the element \code{data}.
#'
#'@import multiApply
#'@importFrom stats ecdf
#'
#'@examples
#'exp <- CSTools::lonlat_prec
#'exp_probs <- CST_AbsToProbs(exp)
#'exp$data <- array(rnorm(5 * 3 * 214 * 2),
#'                    c(member = 5, sdate = 3, ftime = 214, lon = 2)) 
#'exp$Dates[[1]] <- c(seq(as.Date("01-05-2000", format = "%d-%m-%Y"), 
#'                     as.Date("30-11-2000", format = "%d-%m-%Y"), by = 'day'),
#'                 seq(as.Date("01-05-2001", format = "%d-%m-%Y"), 
#'                     as.Date("30-11-2001", format = "%d-%m-%Y"), by = 'day'),
#'                 seq(as.Date("01-05-2002", format = "%d-%m-%Y"), 
#'                     as.Date("30-11-2002", format = "%d-%m-%Y"), by = 'day'))
#'exp_probs <- CST_AbsToProbs(exp, start = list(21, 4), end = list(21, 6))
#'@export
CST_AbsToProbs <- function(data, start = NULL, end = NULL,
                           time_dim = 'ftime', memb_dim = 'member',
                           sdate_dim = 'sdate', ncores = NULL) {
  if (!inherits(data, 's2dv_cube')) {
    stop("Parameter 'data' must be of the class 's2dv_cube', ",
         "as output by CSTools::CST_Load.")
  }
  # when subsetting is needed, dimensions are also needed:
  if (!is.null(start) && !is.null(end)) {
    if (is.null(dim(data$Dates$start))) {
      if (length(data$Dates$start) != dim(data$data)[time_dim]) {
        if (length(data$Dates$start) == 
            prod(dim(data$data)[time_dim] * dim(data$data)['sdate'])) {
          dim(data$Dates$start) <- c(dim(data$data)[time_dim],
                                     dim(data$data)['sdate'])
        }
      } else {
        warning("Dimensions in 'data' element 'Dates$start' are missed and",
                "all data would be used.")
      }
    }
  }
  probs <- AbsToProbs(data$data, data$Dates[[1]], start, end,
                      time_dim = time_dim, memb_dim = memb_dim,
                      sdate_dim = sdate_dim, ncores = ncores)
  data$data <- probs
  if (!is.null(start) && !is.null(end)) {
     data$Dates <- SelectPeriodOnDates(dates = data$Dates[[1]],
                                start = start, end = end, 
                                time_dim = time_dim, ncores = ncores)
  }
  return(data)
}
#'Transform ensemble forecast into probabilities
#'
#'The Cumulative Distribution Function of a forecast is used to obtain the probabilities of each value in the ensemble. If multiple initializations (start dates) are provided, the function will create the Cumulative Distribution Function excluding the corresponding initialization. 
#'
#'@param data a multidimensional array with named dimensions.
#'@param dates a vector of dates or a multidimensional array of dates with named dimensions matching the dimensions on parameter 'data'. By default it is NULL, to select a period this parameter must be provided.
#'@param start an optional parameter to defined the initial date of the period to select from the data by providing a list of two elements: the initial date of the period and the initial month of the period. By default it is set to NULL and the indicator is computed using all the data provided in \code{data}.
#'@param end an optional parameter to defined the final date of the period to select from the data by providing a list of two elements: the final day of the period and the final month of the period. By default it is set to NULL and the indicator is computed using all the data provided in \code{data}.
#'@param time_dim a character string indicating the name of the temporal dimension. By default, it is set to 'ftime'. More than one dimension name matching the dimensions provided in the object \code{data$data} can be specified. This dimension is required to subset the data in a requested period.
#'@param memb_dim a character string indicating the name of the dimension in which the ensemble members are stored.
#'@param sdate_dim a character string indicating the name of the dimension in which the initialization dates are stored. 
#'@param ncores an integer indicating the number of cores to use in parallel computation.
#'
#'@return A multidimensional array with named dimensions.
#'
#'@import multiApply
#'@importFrom stats ecdf
#'
#'@examples
#'exp <- CSTools::lonlat_prec$data
#'exp_probs <- AbsToProbs(exp)
#'data <- array(rnorm(5 * 2 * 61 * 1),
#'                    c(member = 5, sdate = 2, ftime = 61, lon = 1)) 
#'Dates <- c(seq(as.Date("01-05-2000", format = "%d-%m-%Y"), 
#'                     as.Date("30-06-2000", format = "%d-%m-%Y"), by = 'day'),
#'                 seq(as.Date("01-05-2001", format = "%d-%m-%Y"), 
#'                     as.Date("30-06-2001", format = "%d-%m-%Y"), by = 'day'),
#'                 seq(as.Date("01-05-2002", format = "%d-%m-%Y"), 
#'                     as.Date("30-06-2002", format = "%d-%m-%Y"), by = 'day'))
#'exp_probs <- AbsToProbs(exp, start = list(21, 4), end = list(21, 6))
#'@export
AbsToProbs <- function(data, dates = NULL, start = NULL, end = NULL, time_dim = 'time',
                       memb_dim = 'member', 
                       sdate_dim = 'sdate', ncores = NULL) {
  if (is.null(data)) {
    stop("Parameter 'data' cannot be NULL.")
  }
  if (!is.numeric(data)) {
    stop("Parameter 'data' must be numeric.")
  }
  if (!is.array(data)) {
    dim(data) <- c(length(data), 1)
    names(dim(data)) <- c(memb_dim, sdate_dim)
    if (!is.null(start) && !is.null(end)) {
      if (!any(c(is.list(start), is.list(end)))) {
        stop("Parameter 'start' and 'end' must be lists indicating the ",
             "day and the month of the period start and end.")
      }
      data <- SelectPeriodOnData(data, dates, start, end, 
                                 time_dim = time_dim, ncores = ncores)
    }
  }
  probs <- Apply(list(data), target_dims = c(memb_dim, sdate_dim), fun = .abstoprobs,
                 ncores = ncores)$output1
  return(probs)
}
.abstoprobs <- function(data) {
  if (dim(data)[2] > 1 ) { # Several sdates 
    qres <- unlist(
      lapply(1:(dim(data)[1]), function(x) { # dim 1: member
              lapply(1:(dim(data)[2]), function(y) { # dim 2: sdate
                       ecdf(as.vector(data[,-y]))(data[x, y])
                       })
          }))
    dim(qres) <- c(dim(data)[2], dim(data)[1])
  } else { # One sdate
    qres <- unlist(
    lapply(1:(dim(data)[1]), function(x) { # dim 1: member
                       ecdf(as.vector(data))(data[x, 1])
                       }))
    dim(qres) <- c(dim(data)[2], dim(data)[1])
  }
  return(qres)
}

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CSIndicators documentation built on May 7, 2021, 9:07 a.m.