context("1_initialisation") backbone <- backbone( module_info = tibble( module_id = c("A", "B"), basal = c(0.2, 0.8), burn = c(TRUE, FALSE), independence = c(0.4, 0.5) ), module_network = tibble( from = c("A", "A"), to = c("A", "B"), effect = c(-1L, 1L), strength = c(10, 1), hill = c(2, 3) ), expression_patterns = tibble( from = c("burn", "start"), to = c("start", "end"), module_progression = c("+A", "+B"), start = c(TRUE, FALSE), burn = c(TRUE, FALSE), time = c(10, 10) ) ) tf_network_params <- tf_network_default() feature_network_params <- feature_network_default() kinetics_params <- kinetics_default() gold_standard_params <- gold_standard_default() simulation_params <- simulation_default() experiment_params <- experiment_snapshot() test_that("Testing normal use case of initialisation", { model <- initialise_model( backbone = backbone, num_cells = 100, num_tfs = 10, num_targets = 50, num_hks = 40, distance_metric = "euclidean", tf_network_params = tf_network_params, feature_network_params = feature_network_params, kinetics_params = kinetics_params, gold_standard_params = gold_standard_params, simulation_params = simulation_params, experiment_params = experiment_params, verbose = FALSE, download_cache_dir = "test_dir", num_cores = 2 ) simulation_params$burn_time <- 12 simulation_params$total_time <- 24 expect_equal(model$backbone, backbone) expect_equal(model$numbers, list(num_cells = 100, num_tfs = 10, num_targets = 50, num_hks = 40, num_features = 100, num_modules = 2)) expect_equal(model$distance_metric, "euclidean") expect_equal(model$tf_network_params, tf_network_params) expect_equal(model$feature_network_params, feature_network_params) expect_equal(model$kinetics_params, kinetics_params) expect_equal(model$gold_standard_params, gold_standard_params) expect_equal(model$simulation_params, simulation_params) expect_equal(model$experiment_params, experiment_params) expect_false(model$verbose) expect_equal(model$download_cache_dir, "test_dir") expect_equal(model$num_cores, 2) })