context("lrren")

##################
# lrren testthat #
##################

# Generate testing data
## Environmental Covariates
library(envi)
library(sf)
library(spatstat.data)
library(spatstat.geom)
library(spatstat.random)
library(terra)
set.seed(1234)

# -------------- #
# Prepare inputs #
# -------------- #

# Using the `bei` and `bei.extra` data from {spatstat.data}

elev <- spatstat.data::bei.extra$elev
grad <- spatstat.data::bei.extra$grad
elev$v <- scale(elev)
grad$v <- scale(grad)
elev_raster <- terra::rast(elev)
grad_raster <- terra::rast(grad)

## Presence Locations
presence <- spatstat.data::bei
spatstat.geom::marks(presence) <- data.frame("presence" = rep(1, presence$n),
                                             "lon" = presence$x,
                                             "lat" = presence$y)
spatstat.geom::marks(presence)$elev <- elev[presence]
spatstat.geom::marks(presence)$grad <- grad[presence]

# (Pseudo-)Absence Locations
set.seed(1234) # for reproducibility
absence <- spatstat.random::rpoispp(0.008, win = elev)
spatstat.geom::marks(absence) <- data.frame("presence" = rep(0, absence$n),
                                            "lon" = absence$x,
                                            "lat" = absence$y)
spatstat.geom::marks(absence)$elev <- elev[absence]
spatstat.geom::marks(absence)$grad <- grad[absence]

# Combine
obs_locs <- spatstat.geom::superimpose(presence, absence, check = FALSE)
obs_locs <- spatstat.geom::marks(obs_locs)
obs_locs$id <- seq(1, nrow(obs_locs), 1)
obs_locs <- obs_locs[ , c(6, 2, 3, 1, 4, 5)]

# Prediction Data
predict_xy <- terra::crds(elev_raster)
predict_locs <- as.data.frame(predict_xy)
colnames(predict_locs) <- c("lon", "lat")
predict_locs$elev <- terra::extract(elev_raster, predict_xy)[ , 1]
predict_locs$grad <- terra::extract(grad_raster, predict_xy)[ , 1]

# Test custom window
custom_chull <- grDevices::chull(x = obs_locs[ , 5], y = obs_locs[ , 6])
custom_chull_pts <- obs_locs[c(custom_chull, custom_chull[1]), 5:6]
custom_poly <- sf::st_polygon(list(as.matrix(custom_chull_pts)))
custom_owin <- spatstat.geom::owin(poly = list(x = rev(sf::st_coordinates(custom_poly)[ , 1]),
                                               y = rev(sf::st_coordinates(custom_poly)[ , 2])))


test_that("lrren throws error with invalid arguments", {

  # Predict without predict_locs
  expect_error(
    lrren(obs_locs = obs_locs,
          predict = TRUE,
          predict_locs = NULL,
          conserve = TRUE,
          cv = FALSE,
          kfold = 10,
          balance = FALSE,
          parallel = FALSE,
          n_core = NULL,
          poly_buffer = NULL,
          obs_window = NULL,
          verbose = FALSE)
  )

  # Fewer than 1 fold
  expect_error(
    lrren(obs_locs = obs_locs,
          predict = FALSE,
          predict_locs = NULL,
          conserve = TRUE,
          cv = TRUE,
          kfold = 0,
          balance = FALSE,
          parallel = FALSE,
          n_core = NULL,
          poly_buffer = NULL,
          obs_window = NULL,
          verbose = FALSE)
  )

  # poly_buffer not of class 'owin'
  expect_error(
    lrren(obs_locs = obs_locs,
          predict = FALSE,
          predict_locs = NULL,
          conserve = TRUE,
          cv = FALSE,
          kfold = 10,
          balance = FALSE,
          parallel = FALSE,
          n_core = NULL,
          poly_buffer = NULL,
          obs_window = custom_poly,
          verbose = FALSE)
  )
  
  # If conserve = FALSE, predict_locs must be specified
  expect_error(
    lrren(obs_locs = obs_locs,
          predict = FALSE,
          predict_locs = NULL,
          conserve = FALSE,
          cv = FALSE,
          kfold = 10,
          balance = FALSE,
          parallel = FALSE,
          n_core = NULL,
          poly_buffer = NULL,
          obs_window = custom_poly,
          verbose = FALSE)
  )
}
)

test_that("lrren throws warning for points lying outside the specified window", {

  # Only estimates ecological niche
  ## No poly_buffer
  expect_warning(
    lrren(obs_locs = obs_locs,
          predict = FALSE,
          predict_locs = NULL,
          conserve = TRUE,
          cv = FALSE,
          kfold = 10,
          balance = FALSE,
          parallel = FALSE,
          n_core = NULL,
          poly_buffer = 0,
          obs_window = NULL,
          verbose = FALSE)
  )
}
)

test_that("lrren produces progress messages", {
  expect_message(
    lrren(obs_locs = obs_locs,
          predict = TRUE,
          predict_locs = predict_locs,
          conserve = TRUE,
          cv = TRUE,
          kfold = 10,
          balance = TRUE,
          parallel = FALSE,
          n_core = NULL,
          poly_buffer = NULL,
          obs_window = NULL,
          verbose = TRUE)
  )
}
)

test_that("lrren works", {

  # Only estimates ecological niche
  ## Conserved estimation
  expect_named(
    lrren(obs_locs = obs_locs,
          predict = FALSE,
          predict_locs = NULL,
          conserve = TRUE,
          cv = FALSE,
          kfold = 10,
          balance = FALSE,
          parallel = FALSE,
          n_core = NULL,
          poly_buffer = NULL,
          obs_window = NULL,
          verbose = FALSE)
  )

  # Only estimates ecological niche
  ## Unconserved estimation
  expect_named(
    lrren(obs_locs = obs_locs,
          predict = FALSE,
          predict_locs = predict_locs,
          conserve = FALSE,
          cv = FALSE,
          kfold = 10,
          balance = FALSE,
          parallel = FALSE,
          n_core = NULL,
          poly_buffer = NULL,
          obs_window = NULL,
          verbose = FALSE)
  )

  # Only estimates ecological niche
  ## Large poly_buffer
  expect_named(
    lrren(obs_locs = obs_locs,
          predict = FALSE,
          predict_locs = predict_locs,
          conserve = FALSE,
          cv = FALSE,
          kfold = 10,
          balance = FALSE,
          parallel = FALSE,
          n_core = NULL,
          poly_buffer = 1,
          obs_window = NULL,
          verbose = FALSE)
  )

  # Only estimates ecological niche
  ## Custom obs_window
  expect_named(
    lrren(obs_locs = obs_locs,
          predict = FALSE,
          predict_locs = NULL,
          conserve = TRUE,
          cv = FALSE,
          kfold = 10,
          balance = FALSE,
          parallel = FALSE,
          n_core = NULL,
          poly_buffer = NULL,
          obs_window = custom_owin,
          verbose = FALSE)
  )
  
  # Only estimates ecological niche
  ## p-value correction
  expect_named(
    lrren(obs_locs = obs_locs,
          predict = FALSE,
          predict_locs = predict_locs,
          conserve = FALSE,
          p_correct = "Bonferroni",
          cv = FALSE,
          kfold = 10,
          balance = FALSE,
          parallel = FALSE,
          n_core = NULL,
          poly_buffer = NULL,
          obs_window = NULL,
          verbose = FALSE)
  )

  # Estimate and cross-validation
  ## Unbalanced sampling
  ## Not parallel
  expect_named(
    lrren(obs_locs = obs_locs,
          predict = FALSE,
          predict_locs = NULL,
          conserve = TRUE,
          cv = TRUE,
          kfold = 10,
          balance = FALSE,
          parallel = FALSE,
          n_core = NULL,
          poly_buffer = NULL,
          obs_window = NULL,
          verbose = FALSE)
  )

  # Estimate and cross-validation
  ## Balanced sampling
  ## Not parallel
  expect_named(
    lrren(obs_locs = obs_locs,
          predict = FALSE,
          predict_locs = NULL,
          conserve = TRUE,
          cv = TRUE,
          kfold = 10,
          balance = TRUE,
          parallel = FALSE,
          n_core = NULL,
          poly_buffer = NULL,
          obs_window = NULL,
          verbose = FALSE)
  )

  # Estimate and predict
  expect_named(
    lrren(obs_locs = obs_locs,
          predict = TRUE,
          predict_locs = predict_locs,
          conserve = TRUE,
          cv = FALSE,
          kfold = 10,
          balance = FALSE,
          parallel = FALSE,
          n_core = NULL,
          poly_buffer = NULL,
          obs_window = NULL,
          verbose = FALSE)
  )

}
)

test_that("parallel processing with future package functions properly", {
  # Estimate and cross-validation
  ## Unbalanced sampling
  ## Parallel (n = 2 cores)
  expect_named(
    lrren(obs_locs = obs_locs,
          predict = FALSE,
          predict_locs = NULL,
          conserve = TRUE,
          cv = TRUE,
          kfold = 10,
          balance = TRUE,
          parallel = TRUE,
          n_core = 2,
          poly_buffer = NULL,
          obs_window = NULL,
          verbose = FALSE)
  )
}
)