##############################################

# dat1
mod <- 1 : (1 * 3 * 4 * 5 * 6 * 7)
obs <- 1 : (1 * 1 * 4 * 5 * 6 * 7)
dim(mod) <- c(dataset = 1, member = 3, sdate = 4, ftime = 5, 
                lat = 6, lon = 7)
dim(obs) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, 
                lat = 6, lon = 7)
lon <- seq(0, 30, 5)
lat <- seq(0, 25, 5)
coords <- list(lat = lat, lon = lon)
exp <- list(data = mod, coords = coords)
obs <- list(data = obs, coords = coords)
attr(exp, 'class') <- 's2dv_cube'
attr(obs, 'class') <- 's2dv_cube'

exp1 <- list(data = array(1:20, dim = c(time = 20)))
class(exp1) <- 's2dv_cube'
obs1 <- list(data = array(1:20, dim = c(time = 20)))
class(obs1) <- 's2dv_cube'
exp1_2 <- list(data = array(1:20, dim = c(20)))
class(exp1_2) <- 's2dv_cube'
obs1_2 <- list(data = array(1:20, dim = c(20)))
class(obs1_2) <- 's2dv_cube'
exp_cor1 <- list(data = array(1:20, dim = c(20)))
class(exp_cor1) <- 's2dv_cube'

# dat2
exp2 <- exp
exp2$data[1, 2, 1, 1, 1, 1] <- NA
obs2 <- obs
obs2$data[1, 1, 2, 1, 1, 1] <- NA

# dat3
exp3 <- array(1:6, c(sdate = 3, member = 2))
obs3 <- array(3:6, c(sdate = 3, member = 1))
obs3_2 <- array(3:6, c(sdate = 3))
obs3_3 <- array(3:6, c(sdate = 3, member = 2))

# dat4
exp4 <- array(1:100, dim = c(time = 5, members = 5, lat = 2, lon = 5))
obs4 <- array(1:200, dim = c(time = 5, members = 1, lat = 2, lon = 5))
obs4_1 <- obs4
obs4_1[1,1,1,1] <- NA

# dat5
set.seed(1)
exp5 <- array(rnorm(80), dim = c(member = 2, sdate = 10, lat = 2, dataset = 2))
set.seed(2)
obs5 <- array(rnorm(60), dim = c(sdate = 10, lat = 2, dataset = 3))
set.seed(3)
exp_cor5 <- array(rnorm(20), dim = c(member = 2, sdate = 10, lat = 2))

# dat6
set.seed(1)
exp6 <- array(rnorm(20), dim = c(member = 2, sdate = 10))
exp6_1 <- array(exp6, dim = c(member = 2, sdate = 10, dataset = 1))
exp6_2 <- exp6_1
exp6_2[1] <- NA
set.seed(2)
obs6 <- array(rnorm(10), dim = c(member = 1, sdate = 10))
obs6_1 <- array(obs6, dim = c(member = 1, sdate = 10, dataset = 1))
obs6_2 <- obs6_1
obs6_2[c(1, 3)] <- NA
set.seed(3)
exp_cor6 <- array(rnorm(20), dim = c(member = 2, sdate = 10))

# dat7
exp_cor7 <- array(rnorm(400), dim = c(member = 10, sdate = 10, lat = 2, dataset = 2))

##############################################

test_that("1. Input checks", {
  # s2dv_cube
  expect_error(
    CST_BiasCorrection(exp = 1),
    paste0("Parameter 'exp' and 'obs' must be of the class 's2dv_cube'.")
  )
  expect_error(
    CST_BiasCorrection(exp = exp, obs = 1),
    paste0("Parameter 'exp' and 'obs' must be of the class 's2dv_cube'.")
  )
  expect_error(
    CST_BiasCorrection(exp = exp1),
    'argument "obs" is missing, with no default'
  )
  expect_error(
    CST_BiasCorrection(exp = exp1, obs = obs1, exp_cor = 1),
    paste0("Parameter 'exp_cor' must be of the class 's2dv_cube'.")
  )
  # exp and obs
  expect_error(
    CST_BiasCorrection(exp = exp1_2, obs = obs1),
    "Parameter 'exp' must have dimension names."
  )
  expect_error(
    CST_BiasCorrection(exp = exp1, obs = obs1_2),
    "Parameter 'obs' must have dimension names."
  )
  expect_warning(
    CST_BiasCorrection(exp = exp2, obs = obs2),
    "Parameter 'exp' contains NA values."
  )
  expect_warning(
    CST_BiasCorrection(exp = exp, obs = obs2),
    "Parameter 'obs' contains NA values."
  )
  expect_warning(
    CST_BiasCorrection(exp = exp2, obs = obs2),
    "Parameter 'obs' contains NA values",
    "Parameter 'exp' contains NA values."
    )
  # exp_cor
  expect_error(
    CST_BiasCorrection(exp = exp1, obs = obs1, exp_cor = exp_cor1, sdate_dim = 'time'),
    "Parameter 'exp_cor' must have dimension names."
  )
  # sdate_dim, memb_dim
  expect_error(
    CST_BiasCorrection(exp = exp1, obs = obs1, sdate_dim = 1),
    paste0("Parameter 'sdate_dim' must be a character string.")
  )
  expect_error(
    CST_BiasCorrection(exp = exp, obs = obs, sdate_dim = 'time'),
    paste0("Parameter 'sdate_dim' is not found in 'exp' or 'obs' dimension.")
  )
  expect_error(
    BiasCorrection(exp = array(1:20, dim = c(time = 1, member = 1)), 
                   obs = array(1:20, dim = c(time = 2, member = 1)), sdate_dim = 'time'),
    paste0("Parameter 'exp' must have dimension length of 'sdate_dim' bigger than 1.")
  )
  expect_error(
    CST_BiasCorrection(exp = exp1, obs = obs1, sdate_dim = 'time'),
    paste0("Parameter 'exp' requires 'sdate_dim' and 'memb_dim' dimensions.")
  )
  expect_error(
    BiasCorrection(exp = exp3, obs = obs3_3),
    paste0("If parameter 'obs' has dimension 'memb_dim' its length must be equal to 1.")
  )
  ## dat_dim
  expect_error(
    BiasCorrection(exp = exp3, obs = obs3, dat_dim = 1),
    paste0("Parameter 'dat_dim' must be a character string.")
  )
  expect_error(
    BiasCorrection(exp = exp3, obs = obs3, dat_dim = 'dataset'),
    paste0("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.",
           " Set it as NULL if there is no dataset dimension.")
  )
  ## exp, obs, and exp_cor (2)
  expect_error(
    BiasCorrection(exp = array(1:6, c(sdate = 3, member = 2, dataset = 2, lon = 1)), 
                   obs = array(1:6, c(sdate = 3, member = 1, dataset = 3, lon = 2)), 
                   dat_dim = 'dataset'),
    paste0("Parameter 'exp' and 'obs' must have same length of all dimensions", 
           " except 'memb_dim' and 'dat_dim'.")
  )
  expect_error(
    BiasCorrection(exp = array(1:6, c(sdate = 3, member = 2, dataset = 2, lon = 1)), 
                   obs = array(1:6, c(sdate = 3, member = 1, dataset = 3, lon = 1)),
                   exp_cor = array(1:6, c(sdate = 3, member = 1, dataset = 3, lon = 1)), 
                   dat_dim = 'dataset'),
    paste0("If parameter 'exp_cor' has dataset dimension, it must be", 
           " equal to dataset dimension of 'exp'.")
  )
  expect_error(
    BiasCorrection(exp = array(1:6, c(sdate = 3, member = 2, dataset = 2, lon = 1)), 
                   obs = array(1:6, c(sdate = 3, member = 1, dataset = 3, lon = 1)),
                   exp_cor = array(1:6, c(sdate = 3, member = 1, lon = 3)), 
                   dat_dim = 'dataset'),
    paste0("Parameter 'exp' and 'exp_cor' must have the same length of all common dimensions",
           " except 'dat_dim', 'sdate_dim' and 'memb_dim'.")
  )
  ## na.rm
  expect_warning(
    CST_BiasCorrection(exp = exp, obs = obs, na.rm = 1),
    "Paramater 'na.rm' must be a logical, it has been set to FALSE."
  )
  expect_warning(
    CST_BiasCorrection(exp = exp, obs = obs, na.rm = c(T,F)),
    "Paramter 'na.rm' has length greater than 1, and only the fist element is used."
  )
  # ncores
  expect_error(
    CST_BiasCorrection(exp = exp, obs = obs, ncores = TRUE),
    "Parameter 'ncores' must be either NULL or a positive integer."
  )
})

##############################################

test_that("2. Output checks: dat1", {
  bc <- CST_BiasCorrection(exp = exp, obs = obs)
  expect_equal(
    length(bc),
    2
  )
  expect_equal(
    dim(bc$data),
    c(dataset = 1, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 7)
  )
  expect_equal(
    bc$coords$lat,
    lat
  )
  expect_equal(
    bc$coords$lon,
    lon
  )
  expect_equal(
    as.vector(BiasCorrection(exp = exp3, obs = obs3, exp_cor = exp3)), 
    c(2.663694, 3.198216, 3.732739, 4.267261, 4.801784, 5.336306), 
    tolerance = 1e-6
  )
  expect_equal(
    as.vector(BiasCorrection(exp = exp3, obs = obs3_2, exp_cor = exp3)),
    c(2.663694, 3.198216, 3.732739, 4.267261, 4.801784, 5.336306), 
    tolerance = 1e-6
  )
  expect_equal(
    dim(BiasCorrection(exp = exp4, obs = obs4, sdate_dim = 'time', memb_dim = 'members')),
    c(time = 5, members = 5, lat = 2, lon = 5)
  )
  suppressWarnings(
    expect_equal(
      sum(is.na(BiasCorrection(exp = exp4, obs = obs4_1, sdate_dim = 'time', memb_dim = 'members', na.rm = TRUE))),
      0
    )
  )
  suppressWarnings(
    expect_equal(
      sum(is.na(BiasCorrection(exp = exp4, obs = obs4_1, sdate_dim = 'time', memb_dim = 'members', na.rm = FALSE))),
      20
    )
  )
})

##############################################

test_that("3. Output checks: dat5", {
  expect_equal(
    dim(BiasCorrection(exp5, obs5, memb_dim = 'member', dat_dim = 'dataset')),
    c(member = 2, sdate = 10, lat = 2, nexp = 2, nobs = 3)
  )
  expect_equal(
    dim(BiasCorrection(exp5, obs5, exp_cor5, memb_dim = 'member', dat_dim = 'dataset')),
    c(member = 2, sdate = 10, lat = 2, nexp = 2, nobs = 3)
  )
  expect_equal(
    as.vector(BiasCorrection(exp5, obs5, memb_dim = 'member', dat_dim = 'dataset'))[5:10],
    c(0.1466060, -0.9764600, 0.6914021, 0.9330733, 0.6567210, -0.3036642),
    tolerance = 0.0001
  )
  expect_equal(
    as.vector(BiasCorrection(exp5, obs5, exp_cor5, memb_dim = 'member', dat_dim = 'dataset'))[5:10],
    c(0.21682367, 0.03815268, 0.09778966, 1.20997987, -1.30893321, 1.37258011),
    tolerance = 0.0001
  )
  expect_equal(
    as.vector(BiasCorrection(exp5[, , , 1], obs5[, , 1], memb_dim = 'member'))[1:5],
    as.vector(BiasCorrection(exp5, obs5, memb_dim = 'member', dat_dim = 'dataset')[, , , 1, 1][1:5])
  )
  expect_equal(
    as.vector(BiasCorrection(exp5[, , , 1], obs5[, , 1], exp_cor5, memb_dim = 'member'))[1:5],
    as.vector(BiasCorrection(exp5, obs5, exp_cor5, memb_dim = 'member', dat_dim = 'dataset')[, , , 1, 1][1:5])
  )
  expect_equal(
    as.vector(BiasCorrection(exp5, obs5, exp_cor5, memb_dim = 'member', dat_dim = 'dataset', na.rm = TRUE))[1:5],
    c(-1.0318284, -0.3098404, 0.2847780, -1.2369666, 0.2168237),
    tolerance = 0.0001
  )
})

##############################################
test_that("4. Output checks: dat6", {
  expect_equal(
    dim(BiasCorrection(exp6, obs6)),
    c(member = 2, sdate = 10)
  )
  expect_equal(
    as.vector(BiasCorrection(exp6, obs6))[1:5],
    c(-0.5430181, 0.2807323, -0.9954539, 1.9298249, 0.1466060),
    tolerance = 0.0001
  )
  expect_equal(
    as.vector(BiasCorrection(exp6, obs6, exp_cor6))[1:5],
    c(-1.0318284, -0.3098404, 0.2847780, -1.2369666, 0.2168237),
    tolerance = 0.0001
  )
  expect_equal(
    dim(BiasCorrection(exp6_1, obs6_1, dat_dim = 'dataset')),
    c(member = 2, sdate = 10, nexp = 1, nobs = 1)
  )
  expect_equal(
    as.vector(BiasCorrection(exp6_1, obs6_1, dat_dim = 'dataset')),
    as.vector(BiasCorrection(exp6, obs6)),
    tolerance = 0.0001
  )
  expect_equal(
    as.vector(BiasCorrection(exp6_1, obs6_1, exp_cor6, dat_dim = 'dataset')),
    as.vector(BiasCorrection(exp6, obs6, exp_cor6)),
    tolerance = 0.0001
  )
  expect_equal(
    suppressWarnings(
      as.vector(BiasCorrection(exp6_1, obs6_2, dat_dim = 'dataset'))
    ),
    rep(as.numeric(NA), 20)
  )
  expect_equal(
    suppressWarnings(
      as.vector(BiasCorrection(exp6_1, obs6_2, dat_dim = 'dataset', na.rm = T))[5:10]
    ),
    c(0.2644706, -0.8392515, 0.6458045, 0.8511290, 0.5959483, -0.2908764),
    tolerance = 0.0001
  )
  expect_equal(
    suppressWarnings(
      as.vector(BiasCorrection(exp6_2, obs6_2, exp_cor6, dat_dim = 'dataset', na.rm = T))[5:10]
    ),
    c(0.14077312, -0.02076059, 0.03315629, 1.03867041, -1.23864029, 1.18567478),
    tolerance = 0.0001
  )
})

##############################################
test_that("6. Output checks: dat4", {
  expect_equal(
    dim(BiasCorrection(exp5, obs5, exp_cor7, dat_dim = 'dataset')),
    c(member = 10, sdate = 10, lat = 2, nexp = 2, nobs = 3)
  )
})