test_that("pseudoabsences - normal", {
  skip_on_cran()

  sa <- sdm_area(parana, 100000, crs=6933) |>
    add_predictors(bioc) |>
    select_predictors(c("bio1", "bio12")) |>
    add_scenarios()
  oc <- occurrences_sdm(occ, crs=6933)
  suppressWarnings(oc <- join_area(oc, sa))
  i <- input_sdm(oc, sa)

  suppressWarnings(i2 <- pseudoabsences(i, method = "random", n_set = 1))
  expect_equal(n_pseudoabsences(i2), n_records(i2))
  expect_equal(n_pseudoabsences(i2$occurrences), n_records(i2$occurrences))
  expect_equal("random", pseudoabsence_method(i2))
  expect_equal("random", pseudoabsence_method(i2$occurrences))
  i2 <- pseudoabsences(i2, method = "random", n_set = 1, variables_selected = c("bio1", "bio12"))
  expect_equal("random", pseudoabsence_method(i2))
  expect_equal("random", pseudoabsence_method(i2$occurrences))
  expect_equal(n_pseudoabsences(i2), n_records(i2))
  expect_equal(n_pseudoabsences(i2$occurrences), n_records(i2$occurrences))
  suppressWarnings(i3 <- vif_predictors(i, th = 0.9))
  i3 <- pseudoabsences(i3, method = "random", variables_selected = "vif", n_set = 1)
  expect_equal("random", pseudoabsence_method(i3))
  expect_equal("random", pseudoabsence_method(i3$occurrences))
  i4 <- pca_predictors(i)
  i4 <- pseudoabsences(i4, method = "random", variables_selected = "pca", n_set = 1)
  expect_equal("random", pseudoabsence_method(i4))
  expect_equal("random", pseudoabsence_method(i4$occurrences))

  n_set=3
  i5 <- pseudoabsences(i, method = "random", n_set=n_set)
  expect_equal(n_set, pseudoabsence_data(i5)[[1]] |> length())
  expect_equal(n_set, pseudoabsence_data(i5$occurrences)[[1]] |> length())
  n_pa <- 100
  expect_warning(i6 <- pseudoabsences(i, method = "random", n_pa=n_pa, n_set=1))
  expect_true(n_pa == n_pseudoabsences(i6))
  expect_true(n_pa == n_pseudoabsences(i6$occurrences))
  suppressWarnings(oc <- occurrences_sdm(rbind(occ, salm), crs=6933) |> join_area(sa))
  i6 <- input_sdm(oc, sa)
  test <- capture_warnings(i6 <- pseudoabsences(i6, method = "random", n_pa=n_pa))
  expect_equal(length(test), 2)
  test <-  n_pseudoabsences(i6)
  expect_equal(names(test), species_names(i6))
  expect_equal(length(as.numeric(test)), length(species_names(i6)))
  expect_equal(as.numeric(test), rep(n_pa, length(species_names(i6))))
  expect_error(pseudoabsences(i, method = "random", n_pa=c(1,2,3)))

  i7 <- pseudoabsences(i, method = "mahal.dist", n_set = 1)
  expect_equal(n_pseudoabsences(i7), n_records(i7))
  expect_equal("mahal.dist", pseudoabsence_method(i7))
  expect_equal("mahal.dist", pseudoabsence_method(i7$occurrences))

  env_distance_pa <- function(env_sf, occ_sf, n_pa=n_pa) {
    df <- as.data.frame(env_sf)[, -which(names(env_sf) %in% c("cell_id", "geometry"))]
    center <- colMeans(df)
    d <- sqrt(rowSums((df - center)^2))
    p <- d / sum(d)
    sample(env_sf$cell_id, size = n_pa, prob = p)
  }

  expect_warning(i8 <- pseudoabsences(i, method = env_distance_pa, n_set = 2, n_pa=n_pa))
  expect_equal(as.numeric(n_pseudoabsences(i8)), n_pa)
  expect_equal("env_distance_pa", pseudoabsence_method(i8))
  expect_equal("env_distance_pa", pseudoabsence_method(i8$occurrences))
  expect_equal(names(i8$occurrences$pseudoabsences), c("data", "method", "n_set", "n_pa"))
  expect_equal(class(i8$occurrences$pseudoabsences$n_pa), c("table"))
  expect_equal(class(i8$occurrences$pseudoabsences$n_set), c("numeric"))
  expect_equal(class(i8$occurrences$pseudoabsences$method), c("character"))
  expect_equal(class(i8$occurrences$pseudoabsences$data), c("list"))
  expect_equal(names(i8$occurrences$pseudoabsences$data), species_names(i8))
  expect_equal(class(i8$occurrences$pseudoabsences$data[[1]]), c("list"))
  expect_equal(names(i8$occurrences$pseudoabsences$data[[1]]), NULL)
  expect_equal(length(i8$occurrences$pseudoabsences$data[[1]]), 2)
  expect_equal(names(i8$occurrences$pseudoabsences$data[[1]][[1]]), c("cell_id", "bio1", "bio12", "geometry"))
  expect_equal(class(i8$occurrences$pseudoabsences$data[[1]][[1]]), c("sf", "data.frame"))

  # erros
  i <- background(i)
  expect_error(train_sdm(i, algo = c("kknn")))

})