# Evaluating the size of the outputs
test_that("r_phylo outputs a list with the same size as the inputted number of assemblages, and the correct inputted number of slices", {
  # Number of iterations
  n <- sample(2:10, 20, replace = TRUE)

  # Creating empty lists to store r_phylo outputs
  r_phylo_M1 <- list()
  r_phylo_M2 <- list()
  r_phylo_M3 <- list()
  r_phylo_M4 <- list()

  # And also lists to store phylogenies
  tree <- list()
  mat <- list()
  asb <- list()

  # Create 20 random assemblages
  for(i in 1:20){
    # Create a random phylogeny
    tree[[i]] <- ape::rcoal(20)
    # Create a random matrix
    mat[[i]] <- matrix(sample(c(1, 0), 20 * 10, replace = TRUE), ncol = 20, nrow = 10)
    colnames(mat[[i]]) <- tree[[i]]$tip.label # Name its columns according to tip names
    # Create an assemblage with its neighborhoods
    asb[[i]] <- list(mat[[i]][1:5, ], mat[[i]][6:10, ])
  }

  # Run the algorithm for different phylogentic indexes while suppressing some warnings
  # (related to tips no present within the matrix, and vice-versa)
  suppressWarnings({for(i in 1:20){
    r_phylo_M1[[i]] <- r_phylo(tree[[i]], n = n[i], mat = mat[[i]], index = "PD")
    r_phylo_M2[[i]] <- r_phylo(tree[[i]], n = n[i], mat = mat[[i]], index = "PE")
    r_phylo_M3[[i]] <- r_phylo(tree[[i]], n = n[i], asb = asb[[i]], index = "PB")
    r_phylo_M4[[i]] <- r_phylo(tree[[i]], n = n[i], mat = mat[[i]], asb = asb[[i]], index = "PB_RW")
  }})

  # Test 1
  for(i in 1:20)
  {
    expect_equal(length(r_phylo_M1[[i]]), nrow(mat[[i]]))
    expect_equal(length(r_phylo_M2[[i]]), nrow(mat[[i]]))
    expect_equal(length(r_phylo_M3[[i]]), length(asb[[i]]))
    expect_equal(length(r_phylo_M4[[i]]), length(asb[[i]]))
  }

  # test 2
  for(i in 1:20)
  {
    for(j in 1:length(r_phylo_M1[[1]])){
      expect_equal(length(r_phylo_M1[[i]][[j]]), n[i])
      expect_equal(length(r_phylo_M2[[i]][[j]]), n[i])
    }
    for(k in 1:length(r_phylo_M3[[1]])){
      expect_equal(length(r_phylo_M3[[i]][[k]]), n[i])
      expect_equal(length(r_phylo_M4[[i]][[k]]), n[i])
    }
  }
})