# Test Moran's I calculation
# These tests verify the correctness of Moran's I implementation

test_that("moranI returns correct structure", {
    skip_if_not_installed("RANN")
    
    set.seed(42)
    n <- 50
    x <- rnorm(n)
    coords <- cbind(runif(n), runif(n))
    rownames(coords) <- paste0("spot_", seq_len(n))

    # Build simple KNN network
    W <- buildSpatialNetwork(coords, method = "knn", k = 5)

    result <- moranI(x, W)

    expect_type(result, "list")
    expect_named(result, c("observed", "expected", "sd"))
    expect_true(is.numeric(result$observed))
    expect_true(is.numeric(result$expected))
    expect_true(is.numeric(result$sd))
})

test_that("moranI expected value is approximately -1/(n-1)", {
    skip_if_not_installed("RANN")
    
    set.seed(42)
    n <- 100
    x <- rnorm(n)
    coords <- cbind(runif(n), runif(n))
    rownames(coords) <- paste0("spot_", seq_len(n))

    W <- buildSpatialNetwork(coords, method = "knn", k = 6)
    result <- moranI(x, W)

    expected_theoretical <- -1 / (n - 1)
    expect_equal(result$expected, expected_theoretical, tolerance = 1e-10)
})

test_that("moranI_test returns valid p-values", {
    skip_if_not_installed("RANN")
    
    set.seed(42)
    n <- 50
    x <- rnorm(n)
    coords <- cbind(runif(n), runif(n))
    rownames(coords) <- paste0("spot_", seq_len(n))

    W <- buildSpatialNetwork(coords, method = "knn", k = 5)
    result <- moranI_test(x, W)

    expect_true(result["p.value"] >= 0 && result["p.value"] <= 1)
})

test_that("moranI detects spatially clustered data", {
    skip_if_not_installed("RANN")
    
    set.seed(42)
    n <- 200

    # Create spatially structured data
    coords <- cbind(x = runif(n, 0, 100), y = runif(n, 0, 100))
    rownames(coords) <- paste0("spot_", seq_len(n))

    # Expression follows spatial gradient (strong signal)
    x_spatial <- coords[, "x"] * 2 + rnorm(n, sd = 5)

    # Random expression
    x_random <- rnorm(n, mean = 50, sd = 10)

    W <- buildSpatialNetwork(coords, method = "knn", k = 10)

    result_spatial <- moranI(x_spatial, W)
    result_random <- moranI(x_random, W)

    # Spatial data should have higher Moran's I
    expect_gt(result_spatial$observed, result_random$observed)
})

test_that("moranI handles zero variance input gracefully", {
    skip_if_not_installed("RANN")
    
    n <- 50
    x_constant <- rep(5, n)
    coords <- cbind(runif(n), runif(n))
    rownames(coords) <- paste0("spot_", seq_len(n))

    W <- buildSpatialNetwork(coords, method = "knn", k = 5)

    # Should handle gracefully without error
    result <- moranI(x_constant, W)
    
    # Result should exist (may contain NA due to zero variance)
    expect_type(result, "list")
})