library(testthat)
library(agroIoT)

test_that("demo_npk_data returns correct structure", {
  df <- demo_npk_data(n_hours = 10)
  expect_s3_class(df, "data.frame")
  expect_named(df, c("timestamp", "sensor_id", "parameter", "value", "unit"))
  expect_equal(nrow(df), 60L)   # 6 params * 10 hours
  expect_true(all(c("N","P","K","pH","moisture","temperature") %in% df$parameter))
})

test_that("demo_fruit_data returns correct structure", {
  df <- demo_fruit_data(n_hours = 24)
  expect_s3_class(df, "data.frame")
  expect_true(all(c("ethylene","CO2") %in% df$parameter))
  expect_equal(nrow(df), 48L)
})

test_that("demo_water_data returns correct structure", {
  df <- demo_water_data(n_hours = 12)
  expect_s3_class(df, "data.frame")
  expect_true(all(c("DO","pH","turbidity","TDS","NO3") %in% df$parameter))
  expect_equal(nrow(df), 60L)
})

test_that("qc_flag_outliers adds qc_flag column", {
  df      <- demo_npk_data()
  df_flag <- qc_flag_outliers(df)
  expect_true("qc_flag" %in% names(df_flag))
  expect_true(all(df_flag$qc_flag %in% c("ok", "outlier", "missing")))
})

test_that("qc_flag_outliers detects injected outliers", {
  df <- demo_npk_data()
  df$value[df$parameter == "pH"][1] <- -5   # impossible pH
  df_flag <- qc_flag_outliers(df)
  expect_true(any(df_flag$qc_flag == "outlier"))
})

test_that("qc_flag_outliers action='remove' drops outlier rows", {
  df <- demo_npk_data()
  df$value[1] <- -9999
  n_before <- nrow(df)
  df_clean <- qc_flag_outliers(df, action = "remove")
  expect_lt(nrow(df_clean), n_before)
})

test_that("qc_gap_fill fills introduced NAs", {
  df <- demo_npk_data()
  df$value[c(5, 10, 15)] <- NA
  df_filled <- qc_gap_fill(df, method = "linear")
  expect_true("gap_filled" %in% names(df_filled))
  expect_gt(sum(df_filled$gap_filled), 0)
})

test_that("qc_drift_correct adds value_corrected column", {
  df <- demo_npk_data()
  df_c <- qc_drift_correct(df)
  expect_true("value_corrected" %in% names(df_c))
  expect_equal(nrow(df_c), nrow(df))
})

test_that("soil_fertility_index returns expected columns and range", {
  df  <- demo_npk_data()
  sfi <- soil_fertility_index(df)
  expect_true(all(c("sfi","fertility_class") %in% names(sfi)))
  expect_true(all(sfi$sfi >= 0 & sfi$sfi <= 100, na.rm = TRUE))
  expect_true(all(sfi$fertility_class %in%
    c("Very Low","Low","Medium","High","Very High")))
})

test_that("soil_quefts_score returns limiting factor column", {
  df <- demo_npk_data()
  qs <- soil_quefts_score(df)
  expect_true("limiting_factor" %in% names(qs))
  expect_true(all(qs$limiting_factor %in% c("N","P","K")))
})

test_that("fruit_ripening_state classifies banana stages", {
  df <- demo_fruit_data(n_hours = 120)
  rs <- fruit_ripening_state(df, fruit = "banana")
  expect_true("ripening_stage" %in% names(rs))
  expect_true(all(rs$ripening_stage %in%
    c("Pre-climacteric","Climacteric rise","Ripe","Over-ripe","Senescent")))
})

test_that("water_quality_index returns wqi in valid range", {
  df  <- demo_water_data()
  wq  <- water_quality_index(df)
  expect_true("wqi" %in% names(wq))
  expect_true(all(wq$wqi >= 0, na.rm = TRUE))
  expect_true(all(wq$wq_class %in%
    c("Excellent","Good","Poor","Very Poor","Unsuitable")))
})

test_that("recommend_crop returns top_n rows", {
  df  <- demo_npk_data()
  sfi <- soil_fertility_index(df)
  rec <- recommend_crop(sfi, top_n = 3)
  expect_lte(nrow(rec), 3L)
  expect_true("suitability_score" %in% names(rec))
})

test_that("recommend_fertilizer returns dose columns", {
  df  <- demo_npk_data()
  qs  <- soil_quefts_score(df)
  rec <- recommend_fertilizer(qs, target_yield = 5, crop = "wheat")
  expect_true(all(c("N_dose_kg_ha","P_dose_kg_ha","K_dose_kg_ha") %in% names(rec)))
  expect_true(all(rec$N_dose_kg_ha >= 0))
})

test_that("plot_sensor_ts returns a ggplot object", {
  df <- demo_npk_data()
  p  <- plot_sensor_ts(df, parameters = c("N","P"))
  expect_s3_class(p, "ggplot")
})

test_that("ndvi_health_class works correctly", {
  df <- dplyr::tibble(
    timestamp = Sys.time(), sensor_id = "s1",
    parameter = "NDVI", value = c(0.05, 0.2, 0.45, 0.65, 0.8), unit = "index"
  )
  out <- ndvi_health_class(df)
  expect_true("health_class" %in% names(out))
  expect_equal(nrow(out), 5L)
})

test_that("read_sensor_csv round-trips demo data", {
  df  <- demo_npk_data(n_hours = 5)
  tmp <- tempfile(fileext = ".csv")
  utils::write.csv(df, tmp, row.names = FALSE)
  df2 <- read_sensor_csv(tmp, sensor_id = "sensor_01")
  expect_s3_class(df2, "data.frame")
  unlink(tmp)
})