r4ss: R Code for Stock Synthesis

###############################################################################
### automated tests of r4ss package
###############################################################################

context("SS_Sensi_plot")

example_path <- system.file("extdata", package = "r4ss")
# create a temporary directory location to write files to.
# to view the temp_path location, you can add a browser() statement after
# creating the temp_path directory and see what temp_path is. R should write
# to the same location for the same R session (if you restart R, temp_path will)
# change.
temp_path <- file.path(tempdir(), "test_simple")
dir.create(temp_path, showWarnings = FALSE)
# remove all artifacts created from testing. (developers: simply comment out
# the line below if you want to keep artifacts for troubleshooting purposes)
on.exit(unlink(temp_path, recursive = TRUE), add = TRUE)

test_that("SS_Sensi_plot runs", {
  # read model output
  simple_small <- SS_output(file.path(example_path, "simple_small"),
    verbose = FALSE, printstats = FALSE
  )

  # create a fake summary of 19 models (all the same in this case)
  # to match the example in SS_Sensi_plot()
  zz <- list()
  for (i in 1:19) {
    zz[[i]] <- simple_small
  }

  # Use the summarize function in r4ss to get model summaries
  model.summaries <- SSsummarize(zz)

  # Define the names of each model. This will be used to label runs in the table and in the figures.
  mod.names <- c(
    "Reference",
    "M: Fix to 2009",
    "M: Fix to prior",
    "M: Fix to Hamel",
    "M: Fix to VBGF",
    "M: Fix to OR",
    "VBGF 2009",
    "VBGF Grebel",
    "OR maturity",
    "Est. h",
    "All rec devs",
    "No rec devs",
    "High bias adj.",
    "Harmonic mean",
    "Dirichlet",
    "Wts = 1",
    "No blocks",
    "First blocks in 2000",
    "Alt rec catches"
  )

  # Run the sensitivity plot function
  SS_Sensi_plot(
    model.summaries = model.summaries,
    dir = temp_path,
    current.year = 2022,
    mod.names = mod.names, # List the names of the sensitivity runs
    # likelihood.out = c(1, 1, 0),
    Sensi.RE.out = "Sensi_RE_out.DMP", # Saved file of relative errors
    CI = 0.95, # Confidence interval box based on the reference model
    TRP.in = 0.4, # Target relative abundance value
    LRP.in = 0.25, # Limit relative abundance value
    sensi_xlab = "Sensitivity scenarios", # X-axis label
    ylims.in = c(-1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1), # Y-axis label
    plot.figs = c(1, 1, 1, 1, 1, 1), # Which plots to make/save?
    sensi.type.breaks = c(6.5, 9.5, 13.5, 16.5), # vertical breaks that can separate out types of sensitivities
    anno.x = c(3.75, 8, 11.5, 15, 18), # Vertical positioning of the sensitivity types labels
    anno.y = c(1, 1, 1, 1, 1), # Horizontal positioning of the sensitivity types labels
    anno.lab = c("Natural mortality", "VBGF/Mat.", "Recruitment", "Data Wts.", "Other") # Sensitivity types labels
  )
  expect_true(file.exists(file.path(temp_path, "Sensi_logREplot_FMSY.png")))
})