test_that("One-year, single core classification", {
    # create a rfor model
    rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
    # create a data cube from local files
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6",
        data_dir = data_dir,
        progress = FALSE
    )
    # classify a data cube
    probs_cube <- sits_classify(
        data = cube,
        ml_model = rfor_model,
        output_dir = tempdir(),
        version = "var1",
        progress = FALSE
    )
    # smooth the probability cube using Bayesian statistics
    var_cube <- sits_variance(probs_cube, output_dir = tempdir())
    # check is variance cube
    .check_is_variance_cube(var_cube)

    r_obj <- .raster_open_rast(var_cube$file_info[[1]]$path[[1]])

    max_lyr1 <- max(.raster_get_values(r_obj)[, 1], na.rm = TRUE)
    expect_true(max_lyr1 <= 4000)

    max_lyr3 <- max(.raster_get_values(r_obj)[, 3], na.rm = TRUE)
    expect_true(max_lyr3 <= 4000)

    p <- plot(var_cube, sample_size = 10000, labels = "Cerrado")

    expect_true(p$tm_raster$style == "cont")

    p <- plot(var_cube, sample_size = 10000, labels = "Cerrado", type = "hist")
    expect_true(all(p$data_labels %in% c(
        "Cerrado", "Forest",
        "Pasture", "Soy_Corn"
    )))
    v <- p$data$variance
    expect_true(max(v) <= 100)
    expect_true(min(v) >= 0)

    # test Recovery
    out <- capture_messages({
        expect_message(
            object = {
                sits_variance(
                    cube = probs_cube,
                    output_dir = tempdir()
                )
            },
            regexp = "Recovery"
        )
    })
    expect_true(grepl("output_dir", out[1]))

    expect_true(all(file.remove(unlist(probs_cube$file_info[[1]]$path))))
    expect_true(all(file.remove(unlist(var_cube$file_info[[1]]$path))))
})