Add test suite for find.turn.point with gaussian mixture data

tests/testthat/test-find.turn.point.R

@@ -0,0 +1,157 @@
+test_that("find.turn.point identifies single gaussian peak", {
+  # Create a single gaussian peak
+  x <- seq(-10, 10, length.out = 200)
+  y <- dnorm(x, mean = 0, sd = 2)
+
+  result <- find.turn.point(y)
+
+  # Should have exactly one peak
+  expect_equal(length(result$pks), 1)
+  # Should have two valleys (at the ends)
+  expect_equal(length(result$vlys), 2)
+  # Peak should be near the center
+  expect_true(result$pks[1] > 90 && result$pks[1] < 110)
+  # Valleys should be at the boundaries
+  expect_equal(result$vlys[1], 1)
+  expect_equal(result$vlys[2], length(y))
+})
+
+test_that("find.turn.point identifies two-component gaussian mixture", {
+  # Create a mixture of two gaussians
+  x <- seq(-10, 10, length.out = 400)
+  y <- 0.6 * dnorm(x, mean = -3, sd = 1) + 0.4 * dnorm(x, mean = 3, sd = 1.5)
+
+  result <- find.turn.point(y)
+
+  midpoint <- length(y) / 2
+
+  # Should have exactly two peaks
+  expect_equal(length(result$pks), 2)
+  # Should have at least one valley between peaks plus boundaries
+  expect_true(length(result$vlys) >= 3)
+  # First peak should be in the left half
+  expect_true(result$pks[1] < midpoint)
+  # Second peak should be in the right half
+  expect_true(result$pks[2] > midpoint)
+  # Peaks should be ordered
+  expect_true(result$pks[1] < result$pks[2])
+})
+
+test_that("find.turn.point identifies three-component gaussian mixture", {
+  # Create a mixture of three gaussians
+  x <- seq(-15, 15, length.out = 600)
+  y <- 0.4 * dnorm(x, mean = -7, sd = 1.2) + 
+       0.5 * dnorm(x, mean = 0, sd = 1.5) + 
+       0.3 * dnorm(x, mean = 6, sd = 1)
+
+  result <- find.turn.point(y)
+
+  # Should have exactly three peaks
+  expect_equal(length(result$pks), 3)
+  # Should have valleys including boundaries
+  expect_true(length(result$vlys) >= 4)
+  # Peaks should be ordered
+  expect_true(all(diff(result$pks) > 0))
+  # Valleys should be ordered
+  expect_true(all(diff(result$vlys) > 0))
+})
+
+test_that("find.turn.point handles constant values", {
+  # All values are the same
+  y <- rep(5, 100)
+
+  result <- find.turn.point(y)
+
+  # Should have one peak in the middle
+  expect_equal(length(result$pks), 1)
+  expect_equal(result$pks[1], 50)
+  # Should have two valleys at the ends
+  expect_equal(length(result$vlys), 2)
+  expect_equal(result$vlys[1], 1)
+  expect_equal(result$vlys[2], 100)
+})
+
+test_that("find.turn.point handles NA values", {
+  # Create data with NA values
+  x <- seq(-10, 10, length.out = 200)
+  y <- dnorm(x, mean = 0, sd = 2)
+  y[c(1, 50, 100, 150)] <- NA
+
+  result <- find.turn.point(y)
+
+  # Should still identify a peak
+  expect_equal(length(result$pks), 1)
+  # Should have valleys
+  expect_true(length(result$vlys) >= 2)
+})
+
+test_that("find.turn.point handles narrow overlapping gaussians", {
+  # Create two closely overlapping gaussians
+  x <- seq(-5, 5, length.out = 500)
+  y <- dnorm(x, mean = -0.5, sd = 0.8) + dnorm(x, mean = 0.5, sd = 0.8)
+
+  result <- find.turn.point(y)
+
+  # Depending on overlap, might detect 1 or 2 peaks
+  # At minimum should detect peaks
+  expect_true(length(result$pks) >= 1)
+  # Should have valleys
+  expect_true(length(result$vlys) >= 2)
+})
+
+test_that("find.turn.point with well-separated gaussians", {
+  # Create widely separated gaussians
+  x <- seq(-20, 20, length.out = 800)
+  y <- dnorm(x, mean = -10, sd = 1) + dnorm(x, mean = 10, sd = 1)
+
+  result <- find.turn.point(y)
+
+  # Should clearly identify two peaks
+  expect_equal(length(result$pks), 2)
+  # Should have valley in the middle and at boundaries
+  expect_true(length(result$vlys) >= 3)
+  # Middle valley should be between the peaks
+  middle_valleys <- result$vlys[result$vlys > result$pks[1] & result$vlys < result$pks[2]]
+  expect_true(length(middle_valleys) >= 1)
+})
+
+test_that("find.turn.point handles unequal amplitude gaussians", {
+  # Create gaussians with very different amplitudes
+  x <- seq(-10, 15, length.out = 500)
+  y <- 5 * dnorm(x, mean = -4, sd = 1.5) + 0.5 * dnorm(x, mean = 8, sd = 1)
+
+  result <- find.turn.point(y)
+
+  # Should detect both peaks despite different amplitudes
+  expect_equal(length(result$pks), 2)
+  # Should have valleys
+  expect_true(length(result$vlys) >= 3)
+})
+
+test_that("msExtrema identifies maxima and minima correctly", {
+  # Create a simple wave pattern
+  x <- seq(0, 4 * pi, length.out = 200)
+  y <- sin(x)
+
+  result <- msExtrema(y)
+
+  # Should have both maxima and minima
+  expect_true(sum(result$index.max) > 0)
+  expect_true(sum(result$index.min) > 0)
+  # Maxima and minima should not overlap
+  expect_equal(sum(result$index.max & result$index.min), 0)
+})
+
+test_that("find_local_maxima identifies peaks correctly", {
+  # Simple curve with clear peaks
+  y <- c(1, 2, 3, 2, 1, 2, 4, 3, 2)
+
+  result <- find_local_maxima(y, ties.method = "first")
+
+  # Should identify the two peaks (at positions 3 and 7)
+  expect_true(result[3])
+  expect_true(result[7])
+  # Other positions should not be peaks
+  expect_false(result[1])
+  expect_false(result[5])
+})