# Set number of data.table threads to 2
data.table::setDTthreads(threads = 2L)
# Set number of collapse threads to 1
collapse::set_collapse(nthreads = 1L)

test_that("Normal cases", {
  x <- 1:10
  expect_equal(
    growth(x, 3.75),
    3.75 / mean(x)
  )
  expect_equal(
    growth(x, 3.75, log = TRUE),
    3.75 / mean(x)
  )
  expect_equal(
    growth(0, 0),
    1
  )
  expect_equal(
    growth(0, 1),
    Inf
  )
  expect_equal(
    growth(0, 1, inf_fill = NA),
    NA_real_
  )
  expect_equal(
    growth(0, 1, inf_fill = 99),
    99
  )
  expect_equal(
    rolling_growth(c(0, x), n = 1, lag = 1),
    c(0, x) / dplyr::lag(c(0, x))
  )
  expect_equal(rolling_growth(c(0, x),
    n = 1, lag = 1,
    inf_fill = 99
  ), {
    y <- c(0, x) / dplyr::lag(c(0, x))
    y[is.infinite(y)] <- 99
    y
  })
  expect_equal(rolling_growth(c(0, x),
    n = 1, lag = 1,
    inf_fill = NA
  ), {
    y <- c(0, x) / dplyr::lag(c(0, x))
    y[is.infinite(y)] <- NA
    y
  })
  expect_equal(
    rolling_growth(x, n = 1, lag = 0),
    rep(1, length(x))
  )
  expect_equal(
    rolling_growth(x, n = 1, lag = 1),
    x / dplyr::lag(x)
  )
  expect_equal(
    rolling_growth(x, n = 1, lag = 2),
    x / dplyr::lag(x, n = 2)
  )
  expect_equal(
    rolling_growth(x, n = 1, lag = 3),
    x / dplyr::lag(x, n = 3)
  )
  expect_equal(
    rolling_growth(x, n = 3, lag = 3, partial = FALSE),
    roll_mean(x, window = 3, partial = FALSE) /
      roll_mean(dplyr::lag(x, n = 3),
        window = 3, partial = FALSE,
        na.rm = FALSE
      )
  )
  expect_equal(
    rolling_growth(x, n = 3, lag = 2, partial = FALSE),
    roll_mean(x, window = 3, partial = FALSE, na.rm = FALSE) /
      roll_mean(dplyr::lag(x, n = 2),
        window = 3, partial = FALSE,
        na.rm = FALSE
      )
  )
})

test_that("Weights and offsets", {
  set.seed(61092)
  x <- sample(1:10^5, size = 10)
  x1 <- x[1:5]
  x2 <- x[6:10]
  w <- rnorm(10)^2
  o <- rnorm(10)^2
  o1 <- o[1:5]
  o2 <- o[6:10]
  w1 <- w[1:5]
  w2 <- w[6:10]
  sum1 <- sum(x1)
  sum2 <- sum(x2)
  wsum1 <- collapse::fsum(x1, w = w1)
  wsum2 <- collapse::fsum(x2, w = w2)
  cp <- c(rep(0, length(x1)), rep(1, length(x2)))
  # No weights/offset
  mod1 <- stats::glm(x ~ cp, family = poisson)
  sum(x2) / sum(x1)
  expect_equal(exp(stats::coef(mod1))[[2]], rolling_growth(x, n = 5)[10])

  # Weights and offset
  mod2 <- stats::glm(x ~ cp + offset(log(o)), family = poisson, weights = w)
  ((sum(x2 * w2) / sum(o2 * w2)) / (sum(x1 * w1) / sum(o1 * w1)))
  ((stats::weighted.mean(x2, w = w2) / stats::weighted.mean(o2, w2)) /
    (stats::weighted.mean(x1, w1) / stats::weighted.mean(o1, w1)))
  expect_equal(
    exp(stats::coef(mod2))[[2]],
    rolling_growth(x, n = 5, weights = w, offset = o)[10]
  )
  expect_equal(
    exp(stats::coef(mod2))[[2]],
    rolling_growth(x,
      n = 5, weights = w, offset = o,
      log = TRUE
    )[10]
  )
  expect_equal(
    exp(stats::coef(mod2))[[2]],
    rolling_growth(x,
      n = 5, weights = w, offset = o,
      log = FALSE, partial = FALSE
    )[10]
  )

  # Weights only
  mod3 <- stats::glm(x ~ cp, family = poisson, weights = w)
  ((collapse::fmean(x2, w = w2)) / (collapse::fmean(x1, w = w1)))
  expect_equal(
    exp(stats::coef(mod3))[[2]],
    rolling_growth(x, n = 5, weights = w)[10]
  )
  # Offset only
  mod4 <- stats::glm(x ~ cp + offset(log(o)), family = poisson)
  (sum(x2) / sum(o2)) / (sum(x1) / sum(o1))
  expect_equal(
    exp(stats::coef(mod4))[[2]],
    rolling_growth(x, n = 5, offset = o, na.rm = TRUE)[10]
  )

  # WITH NAs

  set.seed(61092)
  x <- sample(1:10^5, size = 100)
  x[sample(1:100, size = 30)] <- NA_integer_
  # x <- x[!is.na(x)]
  x1 <- x[1:50]
  x2 <- x[51:100]
  w <- rnorm(100)^2
  o <- rnorm(100)^2
  o1 <- o[1:50]
  o2 <- o[51:100]
  w1 <- w[1:50]
  w2 <- w[51:100]
  sum1 <- sum(x1, na.rm = TRUE)
  sum2 <- sum(x2, na.rm = TRUE)
  wsum1 <- collapse::fsum(x1, w = w1, na.rm = TRUE)
  wsum2 <- collapse::fsum(x2, w = w2, na.rm = TRUE)
  cp <- c(rep(0, length(x1)), rep(1, length(x2)))
  # No weights/offset
  mod1 <- stats::glm(x ~ cp, family = poisson)
  sum(x2) / sum(x1)
  expect_equal(exp(stats::coef(mod1))[[2]], rolling_growth(x, n = 50, na.rm = TRUE)[100])

  # # Weights and offset
  mod2 <- stats::glm(x ~ cp + offset(log(o)), family = poisson, weights = w)
  ((sum(x2 * w2, na.rm = TRUE) / sum(o2 * w2, na.rm = TRUE)) / (sum(x1 * w1, na.rm = TRUE) / sum(o1 * w1, na.rm = TRUE)))
  ((stats::weighted.mean(x2, w = w2, na.rm = TRUE) / stats::weighted.mean(o2, w2, na.rm = TRUE)) /
    (stats::weighted.mean(x1, w1, na.rm = TRUE) / stats::weighted.mean(o1, w1, na.rm = TRUE)))
  expect_equal(
    exp(stats::coef(mod2))[[2]],
    rolling_growth(x, n = 50, weights = w, offset = o, na.rm = TRUE)[100]
  )

  # Weights only
  mod3 <- stats::glm(x ~ cp, family = poisson, weights = w)
  ((collapse::fmean(x2, w = w2)) / (collapse::fmean(x1, w = w1)))
  expect_equal(
    exp(stats::coef(mod3))[[2]],
    rolling_growth(x, n = 50, weights = w, na.rm = TRUE)[100]
  )
  expect_equal(
    exp(stats::coef(mod3))[[2]],
    rolling_growth(x,
      n = 50, log = TRUE,
      weights = w, na.rm = TRUE
    )[100]
  )
  # Offset only
  mod4 <- stats::glm(x ~ cp + offset(log(o)), family = poisson)
  (sum(x2) / sum(o2)) / (sum(x1) / sum(o1))
  expect_equal(
    exp(stats::coef(mod4))[[2]],
    rolling_growth(x, n = 50, offset = o, na.rm = TRUE)[100]
  )
})