# ==================================================================== #
# TITLE:                                                               #
# AMR: An R Package for Working with Antimicrobial Resistance Data     #
#                                                                      #
# SOURCE CODE:                                                         #
# https://github.com/msberends/AMR                                     #
#                                                                      #
# PLEASE CITE THIS SOFTWARE AS:                                        #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
# Data. Journal of Statistical Software, 104(3), 1-31.                 #
# https://doi.org/10.18637/jss.v104.i03                                #
#                                                                      #
# Developed at the University of Groningen and the University Medical  #
# Center Groningen in The Netherlands, in collaboration with many      #
# colleagues from around the world, see our website.                   #
#                                                                      #
# This R package is free software; you can freely use and distribute   #
# it for both personal and commercial purposes under the terms of the  #
# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
# the Free Software Foundation.                                        #
# We created this package for both routine data analysis and academic  #
# research and it was publicly released in the hope that it will be    #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
#                                                                      #
# Visit our website for the full manual and a complete tutorial about  #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
# ==================================================================== #

#' Random MIC Values/Disk Zones/SIR Generation
#'
#' These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice. By providing a microorganism and antimicrobial drug, the generated results will reflect reality as much as possible.
#' @param size desired size of the returned vector. If used in a [data.frame] call or `dplyr` verb, will get the current (group) size if left blank.
#' @param mo any [character] that can be coerced to a valid microorganism code with [as.mo()]
#' @param ab any [character] that can be coerced to a valid antimicrobial drug code with [as.ab()]
#' @param prob_SIR a vector of length 3: the probabilities for "S" (1st value), "I" (2nd value) and "R" (3rd value)
#' @param ... ignored, only in place to allow future extensions
#' @details The base \R function [sample()] is used for generating values.
#'
#' Generated values are based on the EUCAST `r max(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "EUCAST")$guideline)))` guideline as implemented in the [clinical_breakpoints] data set. To create specific generated values per bug or drug, set the `mo` and/or `ab` argument.
#' @return class `mic` for [random_mic()] (see [as.mic()]) and class `disk` for [random_disk()] (see [as.disk()])
#' @name random
#' @rdname random
#' @export
#' @examples
#' random_mic(25)
#' random_disk(25)
#' random_sir(25)
#'
#' \donttest{
#' # make the random generation more realistic by setting a bug and/or drug:
#' random_mic(25, "Klebsiella pneumoniae") # range 0.0625-64
#' random_mic(25, "Klebsiella pneumoniae", "meropenem") # range 0.0625-16
#' random_mic(25, "Streptococcus pneumoniae", "meropenem") # range 0.0625-4
#'
#' random_disk(25, "Klebsiella pneumoniae") # range 8-50
#' random_disk(25, "Klebsiella pneumoniae", "ampicillin") # range 11-17
#' random_disk(25, "Streptococcus pneumoniae", "ampicillin") # range 12-27
#' }
random_mic <- function(size = NULL, mo = NULL, ab = NULL, ...) {
  meet_criteria(size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE, allow_NULL = TRUE)
  meet_criteria(mo, allow_class = "character", has_length = 1, allow_NULL = TRUE)
  meet_criteria(ab, allow_class = "character", has_length = 1, allow_NULL = TRUE)
  if (is.null(size)) {
    size <- NROW(get_current_data(arg_name = "size", call = -3))
  }
  random_exec("MIC", size = size, mo = mo, ab = ab)
}

#' @rdname random
#' @export
random_disk <- function(size = NULL, mo = NULL, ab = NULL, ...) {
  meet_criteria(size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE, allow_NULL = TRUE)
  meet_criteria(mo, allow_class = "character", has_length = 1, allow_NULL = TRUE)
  meet_criteria(ab, allow_class = "character", has_length = 1, allow_NULL = TRUE)
  if (is.null(size)) {
    size <- NROW(get_current_data(arg_name = "size", call = -3))
  }
  random_exec("DISK", size = size, mo = mo, ab = ab)
}

#' @rdname random
#' @export
random_sir <- function(size = NULL, prob_SIR = c(0.33, 0.33, 0.33), ...) {
  meet_criteria(size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE, allow_NULL = TRUE)
  meet_criteria(prob_SIR, allow_class = c("numeric", "integer"), has_length = 3)
  if (is.null(size)) {
    size <- NROW(get_current_data(arg_name = "size", call = -3))
  }
  sample(as.sir(c("S", "I", "R")), size = size, replace = TRUE, prob = prob_SIR)
}

random_exec <- function(method_type, size, mo = NULL, ab = NULL) {
  df <- AMR::clinical_breakpoints %pm>%
    pm_filter(guideline %like% "EUCAST") %pm>%
    pm_arrange(pm_desc(guideline)) %pm>%
    subset(guideline == max(guideline) &
      method == method_type &
      type == "human")
  
  if (!is.null(mo)) {
    mo_coerced <- as.mo(mo)
    mo_include <- c(
      mo_coerced,
      as.mo(mo_genus(mo_coerced)),
      as.mo(mo_family(mo_coerced)),
      as.mo(mo_order(mo_coerced))
    )
    df_new <- df %pm>%
      subset(mo %in% mo_include)
    if (nrow(df_new) > 0) {
      df <- df_new
    } else {
      warning_("in `random_", tolower(method_type), "()`: no rows found that match mo '", mo, "', ignoring argument `mo`")
    }
  }

  if (!is.null(ab)) {
    ab_coerced <- as.ab(ab)
    df_new <- df %pm>%
      subset(ab %in% ab_coerced)
    if (nrow(df_new) > 0) {
      df <- df_new
    } else {
      warning_("in `random_", tolower(method_type), "()`: no rows found that match ab '", ab, "' (", ab_name(ab_coerced, tolower = TRUE, language = NULL), "), ignoring argument `ab`")
    }
  }

  if (method_type == "MIC") {
    # set range
    mic_range <- c(0.001, 0.002, 0.005, 0.010, 0.025, 0.0625, 0.125, 0.250, 0.5, 1, 2, 4, 8, 16, 32, 64, 128, 256)

    # get highest/lowest +/- random 1 to 3 higher factors of two
    max_range <- mic_range[min(
      length(mic_range),
      which(mic_range == max(df$breakpoint_R, na.rm = TRUE)) + sample(c(1:3), 1)
    )]
    min_range <- mic_range[max(
      1,
      which(mic_range == min(df$breakpoint_S, na.rm = TRUE)) - sample(c(1:3), 1)
    )]

    mic_range_new <- mic_range[mic_range <= max_range & mic_range >= min_range]
    if (length(mic_range_new) == 0) {
      mic_range_new <- mic_range
    }
    out <- as.mic(sample(mic_range_new, size = size, replace = TRUE))
    # 50% chance that lowest will get <= and highest will get >=
    if (stats::runif(1) > 0.5) {
      out[out == min(out)] <- paste0("<=", out[out == min(out)])
    }
    if (stats::runif(1) > 0.5) {
      out[out == max(out)] <- paste0(">=", out[out == max(out)])
    }
    return(out)
  } else if (method_type == "DISK") {
    set_range <- seq(
      from = as.integer(min(df$breakpoint_R, na.rm = TRUE) / 1.25),
      to = as.integer(max(df$breakpoint_S, na.rm = TRUE) * 1.25),
      by = 1
    )
    out <- sample(set_range, size = size, replace = TRUE)
    out[out < 6] <- sample(c(6:10), length(out[out < 6]), replace = TRUE)
    out[out > 50] <- sample(c(40:50), length(out[out > 50]), replace = TRUE)
    return(as.disk(out))
  }
}