# KDE can only be calculated for projected co-ordinates, so first convert data
# to use local state plane CRS
data_sf <- sf::st_transform(head(memphis_robberies, 100), 2843)
data_sf$wt <- runif(nrow(data_sf), max = 1000)
data_df <- as.data.frame(sf::st_drop_geometry(data_sf))

# To speed up the checking process, run the function with arguments that should
# not produce any errors or warnings
result <- hotspot_kde(
  data = data_sf,
  cell_size = 1000,
  bandwidth = 10000,
  quiet = TRUE
)
result_wt <- hotspot_kde(
  data_sf,
  cell_size = 1000,
  bandwidth = 10000,
  weights = wt,
  quiet = TRUE
)



# CHECK INPUTS -----------------------------------------------------------------

# Note that common inputs are tested in `validate_inputs()` and tested in the
# corresponding test file



# CHECK OUTPUTS ----------------------------------------------------------------


## Correct outputs ----

test_that("output is an SF tibble with class hspt_k", {
  expect_s3_class(result, "sf")
  expect_s3_class(result, "tbl_df")
  expect_s3_class(result, "hspt_k")
  expect_s3_class(result_wt, "sf")
  expect_s3_class(result_wt, "tbl_df")
  expect_s3_class(result_wt, "hspt_k")
})

test_that("output object has the required column names", {
  expect_equal(names(result), c("n", "kde", "geometry"))
  expect_equal(names(result_wt), c("n", "sum", "kde", "geometry"))
})

test_that("columns in output have the required types", {
  expect_type(result$n, "double")
  expect_type(result_wt$sum, "double")
  expect_type(result$kde, "double")
  expect_true(sf::st_is(result$geometry[[1]], "POLYGON"))
})