test_that("read_tga_txt parses a well-formed 3-column export", {
  tmp <- tempfile(fileext = ".txt")
  writeLines(c("Temp(C)\tTime(min)\tWeight(%)",
               "25\t0\t100.0", "50\t2\t99.8", "100\t6\t98.1"), tmp)
  out <- read_tga_txt(tmp)
  unlink(tmp)
  expect_equal(names(out), c("temperature_C", "time_min", "weight_pct"))
  expect_equal(out$temperature_C, c(25, 50, 100))
  expect_equal(nrow(out), 3)
})

test_that("read_tga_txt skips malformed lines and records them", {
  tmp <- tempfile(fileext = ".txt")
  # "header junk" is silently ignored (doesn't start with a digit, per
  # skip_pattern); "25 abc 100.0" starts with a digit so is attempted, but
  # fails to parse as 3 numeric fields, so it lands in skipped_lines.
  writeLines(c("header junk", "25 0 100.0", "25 abc 100.0", "50 2 99.8"), tmp)
  out <- read_tga_txt(tmp, col_names = c("temperature_C", "time_min", "weight_pct"))
  unlink(tmp)
  expect_equal(nrow(out), 2)
  expect_true(length(attr(out, "skipped_lines")) >= 1)
})

test_that("read_tga_txt works with a 2-column file", {
  tmp <- tempfile(fileext = ".txt")
  writeLines(c("25 100.0", "100 98.1", "500 65.0"), tmp)
  out <- read_tga_txt(tmp, col_names = c("temperature_C", "weight_pct"))
  unlink(tmp)
  expect_equal(names(out), c("temperature_C", "weight_pct"))
})

test_that("read_tga_txt errors on missing file and empty data", {
  expect_error(read_tga_txt("/no/such/file.txt"), "not found")
  tmp <- tempfile(fileext = ".txt")
  writeLines(c("not numeric at all", "still not numeric"), tmp)
  expect_error(read_tga_txt(tmp), "No parseable")
  unlink(tmp)
})

test_that("tga_normalize rescales mass to percent of initial reading", {
  tga <- data.frame(temperature_C = c(25, 100, 500), weight_mg = c(10, 9.9, 6.5))
  out <- tga_normalize(tga)
  expect_equal(out$weight_pct[1], 100)
  expect_equal(out$weight_pct[3], 65)
})

test_that("tga_normalize errors on non-positive initial mass or missing column", {
  expect_error(tga_normalize(data.frame(weight_mg = c(0, 5))), "positive")
  expect_error(tga_normalize(data.frame(x = 1)), "not found")
})

test_that("tga_dtg recovers a known single-step weight loss as one DTG peak", {
  set.seed(1)
  T <- seq(25, 800, by = 4)
  W <- 100 - 40 / (1 + exp(-(T - 350) / 15)) + rnorm(length(T), 0, 0.05)
  tga <- data.frame(temperature_C = T, weight_pct = W)
  dtg <- tga_dtg(tga)
  expect_equal(nrow(dtg), length(T))
  peak_T <- dtg$temperature_C[which.max(dtg$dtg)]
  expect_equal(peak_T, 350, tolerance = 15)
})

test_that("tga_dtg errors with too few distinct temperature points", {
  tga <- data.frame(temperature_C = c(25, 25, 26), weight_pct = c(100, 100, 99))
  expect_error(tga_dtg(tga), "at least 10")
})

test_that("find_dtg_peaks finds the single peak in a clean one-step curve", {
  set.seed(2)
  T <- seq(25, 800, by = 4)
  W <- 100 - 40 / (1 + exp(-(T - 350) / 15))
  dtg <- tga_dtg(data.frame(temperature_C = T, weight_pct = W))
  pk <- find_dtg_peaks(dtg, min_prominence = 0.05)
  expect_equal(nrow(pk), 1)
  expect_equal(pk$peak_temperature_C, 350, tolerance = 10)
})

test_that("find_dtg_peaks returns an empty data frame when nothing qualifies", {
  dtg <- data.frame(temperature_C = 1:20, dtg = rep(-0.1, 20))
  pk <- find_dtg_peaks(dtg)
  expect_equal(nrow(pk), 0)
})

test_that("assign_dtg_peaks matches known ranges and returns NA outside them", {
  out <- assign_dtg_peaks(c(80, 350, 5000))
  expect_equal(out$stage, c("Moisture", "Cellulose", NA_character_))
})

test_that("tga_stages feeds into proximate_analysis and sums correctly", {
  T <- seq(25, 800, by = 5)
  W <- ifelse(T <= 110, 100 - (5 / 110) * T,
       ifelse(T <= 650, 95 - (40 / (650 - 110)) * (T - 110), 55))
  tga <- data.frame(temperature_C = T, weight_pct = W)
  out <- tga_stages(tga)
  expect_equal(out$moisture_pct, 5, tolerance = 0.5)
  expect_equal(out$VM_pct, 40, tolerance = 0.5)
  expect_equal(out$ash_pct, 55, tolerance = 0.5)
  expect_equal(out$moisture_pct + out$VM_pct + out$ash_pct + out$fixed_carbon_pct, 100,
               tolerance = 1e-6)
})

test_that("tga_stages errors when breakpoints are out of order or out of range", {
  tga <- data.frame(temperature_C = seq(25, 500, by = 25), weight_pct = seq(100, 60, length.out = 20))
  expect_error(tga_stages(tga, moisture_end = 600, vm_end = 700), "Require min")
  expect_error(tga_stages(tga, moisture_end = 300, vm_end = 200), "Require min")
})

test_that("tga_stages warns when the curve is not normalized near 100%", {
  tga <- data.frame(temperature_C = seq(25, 800, by = 25), weight_pct = seq(40, 20, length.out = 32))
  expect_warning(tga_stages(tga), "not close to 100")
})

test_that("tga_stages_batch processes multiple samples and skips failures with a warning", {
  mk <- function(id, vm_center) {
    Tt <- seq(25, 800, by = 10)
    Ww <- 100 - 5 * (Tt > 110) - 40 / (1 + exp(-(Tt - vm_center) / 20))
    data.frame(id = id, temperature_C = Tt, weight_pct = Ww)
  }
  good <- rbind(mk("A", 300), mk("B", 380))
  bad <- data.frame(id = "C", temperature_C = c(25, 50), weight_pct = c(100, 99))
  df <- rbind(good, bad)

  expect_warning(out <- tga_stages_batch(df, "id"), "could not be processed")
  expect_equal(sort(unique(out$id)), c("A", "B"))
})

test_that("tga_stages_batch errors when required columns are missing", {
  df <- data.frame(id = "A", x = 1, y = 2)
  expect_error(tga_stages_batch(df, "id"), "not found")
})

test_that("tga_kinetics_kissinger recovers a known activation energy", {
  # Simulate Kissinger-consistent (beta, Tp) pairs for a target Ea/A, then
  # check the fit recovers them (this is testing the regression math, not
  # asserting anything about real biochar decomposition kinetics).
  R_gas <- 8.314
  Ea_true <- 150e3    # J/mol
  A_true <- 1e12      # 1/min
  beta <- c(5, 10, 15, 20, 25)
  # Solve ln(beta/Tp^2) = ln(A*R/Ea) - Ea/(R*Tp) for Tp numerically per beta
  Tp <- vapply(beta, function(b) {
    f <- function(Tp) log(b / Tp^2) - (log(A_true * R_gas / Ea_true) - Ea_true / (R_gas * Tp))
    stats::uniroot(f, c(500, 900))$root
  }, numeric(1))
  fit <- tga_kinetics_kissinger(beta, Tp - 273.15)
  expect_equal(fit$Ea_kJmol, Ea_true / 1000, tolerance = 0.5)
  expect_true(fit$R2 > 0.999)
  expect_false(is.null(fit$model))
})

test_that("tga_kinetics_kissinger errors with fewer than 3 valid points", {
  expect_error(tga_kinetics_kissinger(c(5, 10), c(320, 335)), "at least 3")
})

test_that("fit_confint works directly on a Kissinger fit", {
  beta <- c(5, 10, 15, 20)
  Tp_C <- c(320, 335, 344, 351)
  fit <- tga_kinetics_kissinger(beta, Tp_C)
  ci <- fit_confint(fit)
  expect_true(all(c("parameter", "estimate", "lower", "upper") %in% names(ci)))
  expect_equal(nrow(ci), 2)
})

test_that("plot_tga runs without error and accepts a precomputed dtg", {
  T <- seq(25, 800, by = 10)
  W <- 100 - 40 / (1 + exp(-(T - 350) / 15))
  tga <- data.frame(temperature_C = T, weight_pct = W)
  pdf(NULL)
  on.exit(dev.off())
  expect_silent(plot_tga(tga))
  dtg <- tga_dtg(tga)
  expect_silent(plot_tga(tga, dtg = dtg))
})