# This unit test script makes sure that a trivially simple slendr model gives exactly the same # result (i.e. tree sequence tables) after loading than a pure SLiM script skip_if(!is_slendr_env_present()) init_env(quiet = TRUE) # number of individuals in a populations N <- 1000 # run a slendr simulation ------------------------------------------------- pop <- population("pop", time = 1, N = N) model <- compile_model(pop, generation_time = 1, direction = "forward", simulation_length = 1000) ts1 <- msprime(model, sequence_length = 1, recombination_rate = 0, random_seed = 42) # run a pure msprime version of the same model ------------------------------- simulate_msprime_ts <- function(N) { output <- normalizePath(tempfile(), winslash = "/", mustWork = FALSE) py_cmd <- sprintf("import msprime; msprime.sim_ancestry(%d, random_seed=42, population_size=%d).dump('%s')", N, N, output) reticulate::py_run_string(py_cmd) ts_read(output) } # load tree sequences, extract tables ------------------------------------- ts2 <- simulate_msprime_ts(N) shared_cols <- c("node_id", "time_tskit", "sampled", "pop_id", "ind_id") table1 <- ts_nodes(ts1) %>% dplyr::arrange(time_tskit) %>% .[, shared_cols] %>% as.data.frame() table2 <- ts_nodes(ts2) %>% dplyr::arrange(time_tskit) %>% .[, shared_cols] %>% as.data.frame() test_that("pure msprime and slendr versions of the same model give the same node/ind table", { expect_true(all(table1 == table2, na.rm = TRUE)) }) test_that("pure msprime and slendr versions of the same model give the same phylo object", { t1 <- ts1 %>% ts_phylo(0, quiet = TRUE) t2 <- ts2 %>% ts_phylo(0, quiet = TRUE) expect_equal(t1$edge, t2$edge) expect_equal(t1$edge.length, t2$edge.length) expect_equal(t1$node.label, t2$node.label) expect_equal(t1$Nnode, t2$Nnode) }) # simplification tests (after introducing constant tracking of names of sampled individuals) test_that("simplification on pure msprime tree sequence retains the correct data", { tmp_small <- normalizePath(tempfile(), winslash = "/", mustWork = FALSE) suppressWarnings(ts_small <- ts_simplify(ts2, simplify_to = c(0, 42, 100, 256))) ts_write(ts_small, tmp_small) ts_small_loaded <- ts_read(tmp_small) expect_equal(ts_nodes(ts_small_loaded) %>% dplyr::filter(sampled) %>% nrow, 4) expect_equal(ts_nodes(ts_small), ts_nodes(ts_small_loaded)) })