#' @include prevalence.R

#' @title Command line 

#' 

#' @param target_arm_size Number of patients required per 

#' treatment arm

#' @param target_interim Number of patients required per 

#' arm for interim analysis; defaults to `target_arm_size \ 2`

#' @param target_control Number of patients required for the

#' control arm(s)

#' @param shared_control TRUE if all treatment arms share the

#' same control arm; FALSE if each treatment arm has its own 

#' control. Defaults to TRUE.

#' @param accrual_period Recruitment period (months).

#' @param interim_period Recruitment period to interim (months).

#' @param precision For the Dirichlet model of biomarker prevalences, 

#' variability decreases as precision increases. Defaults to 10.

#' @param ctrl_ratio Ratio of patient allocation to treatment arm

#' versus control for all active arms; defaults to c(1, 1).

#' @param centres_file Name of CSV file with information about 

#' each recruitment centre; this should have columns "site", 

#' "start_month", "mean_rate", "region" and optionally "site_cap"

#' if recruitment is capped per site. Defaults to `centres.csv`.

#' @param prop_file Name of CSV file with expected biomarker prevalences

#' for each region; this should have one column "category", naming

#' the biomarkers or biomarker combinations, and one column per

#' region. Defaults to `proportions.csv`.

#' @param arms_file Name of JSON file describing which recruitment

#' arms (defined by biomarkers) recruit to which treatment arms. 

#' Defaults to `arms_json`.

#' @param data_path Folder where `centres_file`, `prop_file` and

#' `arms_file` are located. Defaults to the location of the package

#' example data in the package installation; this should be changed. 

#' @param output_path Folder where data generated during execution

#' will be stored; defaults to `../biomkrAccrual_output_data/`.

#' @param figs_path Folder where figures generated during execution

#' will be stored; defaults to the `figures` subdirectory in

#' `output_path`.

#' @param fixed_centre_starts TRUE if centres are assumed to start

#' exactly when planned; FALSE if some randomisation should be added.

#' @param fixed_site_rates TRUE if centre recruitment rates should 

#' be treated as exact; FALSE if they should be drawn from a gamma

#' distribution with a mean of the specified rate.

#' @param fixed_region_prevalences TRUE if biomarker prevalences 

#' should be considered to be identical for all sites within a 

#' region; FALSE if they should be drawn from a Dirichlet distribution

#' with a mean of the specified prevalence.

#' @param quietly Defaults to FALSE, which displays the output from

#' each run. Set to TRUE to generate data and figures without displaying

#' them.

#' @param keep_files Save data files and plots generated during the run. 

#' Defaults to TRUE.

#' 

#' @examples 

#' biomkrAccrual()

#' 

#' @import checkmate 

#' @importFrom jsonlite read_json

#' @importFrom rlang abort warn

#' @importFrom utils read.csv

#' @importFrom ggplot2 ggsave

#' 

#' @export

biomkrAccrual <- function(

  target_arm_size = 60,

  target_interim = target_arm_size / 2,

  target_control = 180,

  shared_control = TRUE,

  accrual_period = 50 / 4,

  interim_period = accrual_period / 2,

  precision = 10,

  # active : control ratio (all active the same)

  ctrl_ratio = c(1, 1),

  centres_file = "centres.csv",

  prop_file = "proportions.csv",

  arms_file = "arms.json",

  data_path = "extdata/",

  output_path = "../biomkrAccrual_output_data/",

  figs_path = paste0(output_path, "figures/"),

  fixed_centre_starts = TRUE,

  fixed_site_rates = FALSE,

  fixed_region_prevalences = FALSE,

  quietly = FALSE,

  keep_files = TRUE

) {

  checkmate::assert_logical(

    fixed_region_prevalences,

    any.missing = FALSE,

    null.ok = FALSE

  )

  if (fixed_region_prevalences && 

    checkmate::test_numeric(

      precision, 

      any.missing = FALSE, 

      null.ok = FALSE

    )

  )  {

    rlang::warn(paste("Value given for precision when", 

      "fixed_region_prevalences is TRUE: fixed_region_prevalences",

      "will take precendence."

    ))

    precision <- NULL

  }

  checkmate::assert_numeric(

    precision,

    any.missing = FALSE,

    lower = 10^-6,

    finite = TRUE,

    len = 1,

    null.ok = TRUE

  )

  if (!fixed_region_prevalences && is.null(precision)) {

    rlang::abort(paste("Either fixed_region_prevalences", 

      "must be TRUE, or a value must be given for the",

      "precision of the Dirichlet distribution."

    ))

  }

  # Verify inputs

  ## append "/" if no slash on end

  data_path <- gsub("(\\w+)$", "\\1/", data_path)

  output_path <- gsub("(\\w+)$", "\\1/", output_path)

  figs_path <- gsub("(\\w+)$", "\\1/", figs_path)

  # Make into full path so only one set of syntax needed

  if (!grepl("^/", output_path)) {

    output_path <- paste0(getwd(), "/", output_path)

  }

  ## Check for switches e.g. av_site_rate_month first

  checkmate::assert_directory_exists(system.file(

    data_path, package = "biomkrAccrual"

  ), access = "rx")

  checkmate::assert_file_exists(system.file(

    data_path, prop_file, package = "biomkrAccrual"

  ), access = "r")

  checkmate::assert_file_exists(system.file(

    data_path, arms_file, package = "biomkrAccrual"

  ), access = "r")

  checkmate::assert_file_exists(system.file(

    data_path, centres_file, package = "biomkrAccrual"

  ), access = "r")

  # Set up output directory if does not already exist

  makeifnot_dir(output_path, min_access = "rwx")

  # Set up output figures directory if does not already exist

  makeifnot_dir(figs_path, min_access = "rwx")

  # Read parameters

  prop_params_df <- utils::read.csv(system.file(

    data_path, prop_file, package = "biomkrAccrual"

  )) 

  arms_ls <- 

    jsonlite::read_json(system.file(

      data_path, arms_file, package = "biomkrAccrual"

    ), simplifyVector = TRUE)

  centres_df <- utils::read.csv(system.file(

    data_path, centres_file, package = "biomkrAccrual"

  ))

  # Add fail if read fails

  # Fail if centres_file is in wrong format

  if (isFALSE(all.equal(

    names(centres_df), 

    c("site", "start_month", "mean_rate", "region", "site_cap")

    # site_cap is optional, no cap if not present

  )) || isFALSE(all.equal(

    names(centres_df), c("site", "start_month", "mean_rate", "region")

  ))) {

    rlang::abort(paste(

      "Format error: centres.csv should have columns site,",

      "start_month, mean_rate, region, and optionally site_cap"

    ))

  }

  # Set run_time to timestamp output files

  run_time <- format(Sys.time(), "%F-%H-%M-%S")

  # Get start weeks & order centres_df by start week and site number

  centres_df <- do_clean_centres(centres_df)

  centres_df$start_week <- get_weeks(centres_df$start_month - 1) + 1

  # Make control ratio sum to 1

  if (is.null(ctrl_ratio)) {

    if (!is.null(target_control)) {

      ctrl_ratio <- c(1, target_control / target_arm_size)

    } else {

      rlang::abort(paste(

        "For shared control, either ctrl_ratio or", 

        "target_control must be specified."

      ))

    }

  }

  ctrl_ratio <- ctrl_ratio / sum(ctrl_ratio)

  # Generate target_control if needed

  if (is.null(target_control) && shared_control) {

    target_control <- target_arm_size * ctrl_ratio[2]

  } 

  # Total target recruitment

  target_recruit <- ifelse(

    shared_control, 

    target_arm_size * length(arms_ls) + target_control,

    target_arm_size * length(arms_ls) * (2 * ctrl_ratio[2])

  )

  # Complete site cap if incomplete, using recruitment target

  if (!("site_cap" %in% names(centres_df))) {

    centres_df$site_cap <- target_recruit

  } else {

    centres_df$site_cap[is.na(centres_df$site_cap)] <- target_recruit

  }

  # Create structure object

  trial_structure_instance <- 

    trial_structure(

      props_df = prop_params_df, 

      arms_ls = arms_ls, 

      centres_df = centres_df, 

      precision = precision, 

      shared_control = shared_control,

      ctrl_ratio = ctrl_ratio,

      fixed_region_prevalences = fixed_region_prevalences

    )

  # Create accrual object

  accrual_instance <- accrual(

    treatment_arm_ids = trial_structure_instance@treatment_arm_ids,

    shared_control = shared_control,

    target_arm_size = target_arm_size,

    target_control = target_control,

    target_interim = target_interim,

    accrual_period = get_weeks(accrual_period),

    interim_period = get_weeks(interim_period),

    control_ratio = ctrl_ratio,

    centres_df = centres_df

  )

  while (

    # Any arms are recruiting

    any(trial_structure_instance@treatment_arm_struct) &&

      # Any sites are recruiting

      length(accrual_instance@active_sites) > 0 &&

      # Not out of time

      accrual_instance@week <= accrual_instance@accrual_period

  ) {

    # Add a week's accrual

    obj_list <- accrue_week(

      accrual_instance, 

      trial_structure_instance,

      fixed_site_rates

    )

    accrual_instance <- obj_list[[1]]

    trial_structure_instance <- obj_list[[2]]

    # Increment pointer for the next week to accrue

    accrual_instance@week <- accrual_instance@week + as.integer(1)

  }

  # Trim accrual to actual recruitment length

  accrual_instance@accrual <- 

    accrual_instance@accrual[seq(accrual_instance@week - 1), , ]

  if (keep_files) {

    write.csv(

      accrual_instance@accrual, 

      paste0(output_path, "accrual-", run_time, ".csv"),

      row.names = FALSE

    )

  }

  # Plot outcome

  if (!quietly || keep_files) {

    p <- plot(accrual_instance)

    if (!quietly) {

      print(p)

    }

    if (keep_files) {

      ggplot2::ggsave(

        paste0(figs_path, "accrual-", run_time, ".png"),

        plot = p,

        width = 12,

        height = 8,

        dpi = 400

      )

    }

  }

  if (!quietly) {

    # Print accrual object

    print(accrual_instance)

    # Print summary of accrual object

    summary(accrual_instance)

    # Print trial structure object

    print(trial_structure_instance)

    cat("\n\nTreatment arm ids\n")

    print(trial_structure_instance@treatment_arm_ids_start)

    # Print summary of trial structure object

    cat("\n\nSite prevalences\n")

    summary(trial_structure_instance)$site_prev

  }

  # Plot trial structure object

  if (!quietly || keep_files) {

    p <- plot(trial_structure_instance)

    if (!quietly) {

      print(p)

    }

    if (keep_files) {

      ggplot2::ggsave(

        paste0(figs_path, "structure-", run_time, ".png"),

        plot = p,

        width = 12,

        height = 8,

        dpi = 400

      )

    }

  }

  # Return accrual object

  return(accrual_instance)

}