# testthat::context("test-fit_point_process")

# normal
pattern_random <- fit_point_process(pattern = species_b, n_random = 3,
                                    verbose = FALSE)

# cluster process
pattern_random_cluster <- fit_point_process(pattern = species_b, n_random = 3,
                                            process = "cluster", verbose = FALSE)

# no input
pattern_random_ni <- fit_point_process(pattern = species_a, n_random = 3,
                                       return_input = FALSE, verbose = FALSE)

# simple output
pattern_random_simple <- fit_point_process(pattern = species_a, n_random = 1,
                                           return_input = FALSE, simplify = TRUE,
                                           verbose = FALSE)

################################################################################

testthat::test_that("Output is a long as n_random for fit_point_process", {

  testthat::expect_length(pattern_random$randomized,
                          n = 3)

  testthat::expect_length(pattern_random_cluster$randomized,
                          n = 3)
})

testthat::test_that("Output includes randomizations and original pattern for fit_point_process", {

  testthat::expect_named(pattern_random$randomized,
                         expected = paste0("randomized_", 1:3))

  testthat::expect_equal(pattern_random$observed,
                         expected = spatstat.geom::unmark(species_b))

  testthat::expect_named(pattern_random_cluster$randomized,
                         expected = paste0("randomized_", 1:3))

  testthat::expect_equal(pattern_random_cluster$observed,
                         expected = spatstat.geom::unmark(species_b))
})

testthat::test_that("Fitted patterns have same number of points for cluster process", {

  testthat::expect_true(all(vapply(pattern_random$randomized,
                                   FUN.VALUE = logical(1),
                                   function(x) x$n == species_b$n)))

  testthat::expect_true(all(vapply(pattern_random_cluster$randomized,
                                   FUN.VALUE = logical(1),
                                   function(x) x$n == species_b$n)))
})

testthat::test_that("Input pattern can not be returned for fit_point_process", {

  testthat::expect_equal(object = pattern_random_ni$observed,
                         expected = "NA")
})

testthat::test_that("simplify works for fit_point_process", {

  testthat::expect_s3_class(pattern_random_simple, "ppp")
})

testthat::test_that("fit_point_process returns errors", {

  testthat::expect_error(fit_point_process(pattern = species_b, n_random = -10,
                                           verbose = FALSE),
                         regexp = "n_random must be >= 1.")

  testthat::expect_error(fit_point_process(pattern = species_b,
                                           n_random = 19, process = "not_valid",
                                           verbose = FALSE),
                         regexp = "Please select either 'poisson' or 'cluster'.")
})

testthat::test_that("fit_point_process returns warnings", {

  testthat::expect_warning(fit_point_process(pattern = species_a,
                                             n_random = 3, return_input = FALSE,
                                             simplify = TRUE, verbose = FALSE),
                           regexp = "'simplify = TRUE' not possible for 'n_random > 1'.")

  testthat::expect_warning(fit_point_process(pattern = species_a,
                                             n_random = 1, simplify = TRUE, verbose = FALSE),
                           regexp = "'simplify = TRUE' not possible for 'return_input = TRUE'.")
})