test_that("input variables are valid", {

    # case when the input density is not a function
    expect_error(ars(hello, 100))

    # N requires input to be numeric
    expect_error(ars(dnorm, N="test"))

    # bounds requires input to be vector
    expect_error(ars(bounds=list(1,2)))
})
message(" Testing if the inputted arguments into the ars() function are valid.")


test_that("ars ouputs the required amount of samples", {
  # standard normal dist
  expect_length(ars(dnorm, 200), 200)

  # standard normal dist using the default N = 1000
  expect_length(ars(dnorm), 1000)

  # gamma dist with shape 5, rate 7
  expect_length(ars(dgamma, 50, bounds=c(0.0, Inf), shape=5.0, rate=7.0), 50)
})
message(" Testing that N number of samples are outputted.")


expect_density_apx_1 <- function(samples) {

  # https://stackoverflow.com/a/40851780/16800940
  calculate_density <- function(samples) {
    d <- density(samples)
    # plot(d)
    xx <- d$x
    dx <- xx[2L] - xx[1L]
    yy <- d$y
    C <- sum(yy) * dx
    return(C)
  }

  density_sum <- calculate_density(samples)
  return(expect_lt(density_sum, 1.1) && expect_gt(density_sum, 0.9))
}


test_that("The density is around 1", {

  samples <- ars(dgamma, 1000, bounds=c(0,Inf), shape = 2, rate = 5)
  expect_density_apx_1(samples)

  samples <- ars(dbeta, 1000, bounds=c(0,1), shape1 = 2, shape2 = 6)
  expect_density_apx_1(samples)

  samples = ars(dweibull, 1000, bounds=c(0,20), shape = 1.5 )
  expect_density_apx_1(samples)

  })
message(" Testing that the density of our sampled distribution is ~ 1.")