context("Utility Functions")

test_that("brewer_ramp", {

  expect_true(length(brewer_ramp(100, plt = "Spectral")) == 100)
  expect_true(length(brewer_ramp(100, plt = "Spectral",
                                 delete.lights = FALSE)) == 100)
  expect_false(length(brewer_ramp(100, plt = "Spectral")) == 50)
  expect_false(length(brewer_ramp(100, plt = "Spectral",
                                  delete.lights = FALSE)) == 50)

  expect_true(length(brewer_ramp(100, plt = "Accent")) == 100)
  expect_true(length(brewer_ramp(100, plt = "Accent",
                                 delete.lights = FALSE)) == 100)

  expect_true(length(brewer_ramp(100, plt = "Oranges")) == 100)
  expect_true(length(brewer_ramp(100, plt = "Oranges",
                                 delete.lights = FALSE)) == 100)

  expect_true(length(brewer_ramp(100, plt = "Set1")) == 100)

  expect_error(brewer_ramp(100, plt = "Jimmy"))
  expect_error(brewer_ramp(100, plt = "Jimmy", delete.lights = FALSE))
  expect_error(brewer_ramp(-1, plt = "Spectral"))

})


test_that("color_tea", {
  nw <- network_initialize(n = 100)
  formation <- ~edges
  target.stats <- 50
  coef.diss <- dissolution_coefs(dissolution = ~offset(edges), duration = 20)
  est <- netest(nw, formation, target.stats, coef.diss, verbose = FALSE)
  param <- param.net(inf.prob = 0.3, rec.rate = 0.001)
  init <- init.net(i.num = 25, r.num = 25)
  control <- control.net(type = "SIR", nsteps = 10, nsims = 1, verbose = FALSE)
  mod <- netsim(est, param, init, control)
  nd <- get_network(mod)
  nd <- color_tea(nd, verbose = FALSE)
  expect_is(nd, "networkDynamic")
  expect_true(length(unique(
    get.vertex.attribute.active(nd, "ndtvcol", at = 1))) == 3)
})

test_that("delete_attr", {
  dat <- create_dat_object()
  dat <- append_core_attr(dat, 1, 5)
  dat <- append_attr(dat, "a", 1:5, 5)
  dat <- append_attr(dat, "b", 6:10, 5)

  dat <- delete_attr(dat, 5)
  expect_is(dat, "list")

  dat <- delete_attr(dat, 2:3)
  expect_true(length(unique(vapply(get_attr_list(dat), length, 1))) == 1)

  l2 <- dat
  l3 <- delete_attr(dat, NULL)

  expect_equal(l2, l3)
})

test_that("ssample", {

  expect_true(length(ssample(1:5, 1)) == 1)
  expect_equal(ssample(5, 1), 5)
  expect_equal(ssample(5, 2), 5)
  expect_null(ssample(5, 0))

})

test_that("check_degdist_bal", {
  expect_output(check_degdist_bal(num.g1 = 500, num.g2 = 500,
                                  deg.dist.g2 = c(0.40, 0.55, 0.03, 0.02),
                                  deg.dist.g1 = c(0.48, 0.41, 0.08, 0.03)),
                "-0.015 Rel Diff")
  expect_output(check_degdist_bal(num.g1 = 500, num.g2 = 500,
                                  deg.dist.g1 = c(0.40, 0.55, 0.04, 0.01),
                                  deg.dist.g2 = c(0.48, 0.41, 0.08, 0.03)),
                "Edges balanced")
  expect_output(check_degdist_bal(num.g1 = 500, num.g2 = 500,
                                  deg.dist.g1 = c(0.45, 0.55, 0.04, 0.01),
                                  deg.dist.g2 = c(0.48, 0.41, 0.08, 0.03)),
                "deg.dist.g1 TOTAL != 1")
  expect_output(check_degdist_bal(num.g1 = 500, num.g2 = 500,
                                  deg.dist.g1 = c(0.40, 0.55, 0.04, 0.01),
                                  deg.dist.g2 = c(0.55, 0.41, 0.08, 0.03)),
                "deg.dist.g2 TOTAL != 1")
})

test_that("edgelist_censor", {
  skip_on_cran()
  nw <- network_initialize(n = 100)
  formation <- ~edges
  target.stats <- 50
  coef.diss <- dissolution_coefs(dissolution = ~offset(edges), duration = 20)
  est <- netest(nw, formation, target.stats, coef.diss, verbose = FALSE)
  sim <- netdx(est, nsims = 1, nsteps = 100,
               keep.tedgelist = TRUE, verbose = FALSE)
  el <- as.data.frame(sim)
  expect_is(edgelist_censor(el), "matrix")
})

test_that("get_degree", {
  nw <- network_initialize(n = 500)

  set.seed(1)
  fit <- ergm(nw ~ edges, target.stats = 250, eval.loglik = FALSE)
  sim <- simulate(fit, dynamic = FALSE)

  # Slow ERGM-based method
  ergm.method <- unname(summary(sim ~ sociality(base = 0)))
  ergm.method

  # Fast tabulate method with network object
  deg.net <- get_degree(sim)
  deg.net

  # Even faster if network already transformed into an edgelist
  el <- as.edgelist(sim)
  deg.el <- get_degree(el)
  deg.el

  expect_equal(ergm.method, deg.net)
  expect_equal(ergm.method, deg.el)
})

test_that("dissolution_coefs returns error for incompatible departure rate", {
  #Dividing by zero:
  d.rate_ <- round(1 - sqrt(59 / 60), 5)
  err.msg <- paste("The competing risk of departure is too high for the given",
                   " duration of ", 60, "; specify a d.rate lower than ",
                   d.rate_, ".", sep = "")
  dissolution <-  ~offset(edges)
  expect_that(dissolution_coefs(dissolution, duration = 60, d.rate = 1 / 60),
              throws_error(err.msg))
  expect_that(dissolution_coefs(dissolution, duration = 60, d.rate = 0.01),
              throws_error(err.msg))
  dissolution <-  ~offset(edges) + offset(nodematch("group", diff = TRUE))
  duration <- c(60, 30, 80, 100, 125, 160)
  dissolution <-  ~offset(edges) + offset(nodematch("age.grp", diff = TRUE))
  err.msg <- paste("The competing risk of departure is too high for the given",
                   "edge duration of 60 in place 1.",
                   "Specify a d.rate lower than 0.00837.")
  expect_that(dissolution_coefs(dissolution,
                                duration = duration, d.rate = 1 / 60),
              throws_error(err.msg))
})


test_that("get_formula_term_attr checks", {
  nw <- network_initialize(n = 100)

  expect_null(get_formula_term_attr(~edges, nw))

  nw <- network_initialize(n = 100)
  riskg <- sample(rep(1:2, each = 50))
  race <- sample(rep(0:1, each = 50))
  nw <- set_vertex_attribute(nw, "riskg", riskg)
  nw <- set_vertex_attribute(nw, "race", race)
  expect_null(get_formula_term_attr(~edges, nw))

  expect_equal(get_formula_term_attr(~edges + nodefactor("race"), nw), "race")
  expect_equal(get_formula_term_attr(~edges + nodefactor("race") +
                                       nodematch("riskg"), nw),
               c("race", "riskg"))
  expect_equal(get_formula_term_attr(~edges + nodefactor(c("race", "riskg")) +
                                       nodematch("riskg"), nw),
               c("race", "riskg"))

})