## ---- include = FALSE---------------------------------------------------------
unlink("param_files", recursive = TRUE)
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(fs))
suppressPackageStartupMessages(library(rfacts))
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

## ---- output = FALSE----------------------------------------------------------
#  library(rfacts)
#  facts_file <- get_facts_file_example("dichot.facts") # could be any FACTS file
#  # On traditional HPC clusters, this should be a shared directory
#  # instead of a temp directory:
#  tmp <- fs::dir_create(tempfile())
#  all_param_files <- file.path(tmp, "param_files")
#  
#  # Set n_weeks_files to 0 so we only read the weeks files generated by
#  # trial execution mode.
#  run_flfll(facts_file, all_param_files, n_weeks_files = 0L)

## -----------------------------------------------------------------------------
#  param_files <- get_param_dirs(all_param_files)[1]
#  basename(param_files)

## -----------------------------------------------------------------------------
#  run_once <- function(index, param_files) {
#    out <- tempfile()
#    dir_copy(param_files, out) # Requires the fs package.
#    run_engine_dichot(out, n_sims = 1L)
#    pats <- read_patients(out) # Read and aggregate all the patients files.
#    # Here, do some custom data processing on the whole pats data frame...
#    # Write the processed patient data to the original patients files.
#    overwrite_csv_files(pats)
#    run_engine_dichot(
#      out,
#      n_sims = 1L,
#      seed = index,
#      mode = "r",
#      execdata = "patients00001.csv", # Custom / modified patients files.
#      final = TRUE
#    )
#    read_weeks(out)
#  }

## -----------------------------------------------------------------------------
#  library(dplyr)
#  library(fs)
#  
#  # Ignore the facts_sim column since all weeks files were indexed 00000.
#  # For data post-processing, use the facts_id column instead.
#  lapply(seq_len(2), run_once, param_files = param_files) %>%
#    bind_rows()

## -----------------------------------------------------------------------------
#  # Configure clustermq to use our grid and your template file.
#  # If you are using a scheduler like SGE, you need to write a template file
#  # like clustermq.tmpl. To learn how, visit
#  # https://mschubert.github.io/clustermq/articles/userguide.html#configuration-1
#  options(clustermq.scheduler = "sge", clustermq.template = "clustermq.tmpl")

## -----------------------------------------------------------------------------
#  library(clustermq)
#  weeks <- Q(
#    fun = run_once,
#    iter = seq_len(1e3), # Run 1000 simulations.
#    const = list(param_files = param_files),
#    pkgs = c("fs", "rfacts"),
#    n_jobs = 1e2 # Use 100 clustermq workers.
#  ) %>%
#    bind_rows()