@testset "Monotonic Basin Hopping" begin

  using Thesis: mbh

  # Initial Setup
  sc = Sc("test")
  a = rand(15000:1.:40000)
  e = rand(0.01:0.01:0.5)
  i = rand(0.01:0.01:π/6)
  T = 2π*√(a^3/μs["Earth"])
  prop_time = 0.75T
  n = 10

  # A simple orbit raising
  start = oe_to_xyz([ a, e, i, 0., 0., 0. ], μs["Earth"])
#   T_craft = hcat(repeat([0.6], n), repeat([0.], n), repeat([0.], n))
  Tx, Ty, Tz = conv_T(repeat([0.8], n), repeat([0.], n), repeat([0.], n),
                      start,
                      sc.mass,
                      sc,
                      prop_time,
                      μs["Earth"])
  nominal_path, normal_mass, final = prop(hcat(Tx, Ty, Tz), start, sc, μs["Earth"], prop_time)
  new_T = 2π*√(xyz_to_oe(final, μs["Earth"])[1]^3/μs["Earth"])

  # Find the best solution
  best, archive = mbh(start,
                      final,
                      sc,
                      μs["Earth"],
                      0.0,
                      prop_time,
                      n,
                      num_iters=5,
                      patience_level=50,
                      verbose=true)

  # Test and plot
  @test converged(best)
  transit, best_masses, calc_final = prop(tanh.(best.zero), start, sc, μs["Earth"], prop_time)
  initial_path = prop(zeros((100,3)), start, sc, μs["Earth"], T)[1]
  after_transit = prop(zeros((100,3)), calc_final, sc, μs["Earth"], new_T)[1]
  final_path = prop(zeros((100,3)), final, sc, μs["Earth"], new_T)[1]
  savefig(plot_orbits([initial_path, nominal_path, final_path],
                      labels=["initial", "nominal transit", "final"],
                      colors=["#FF4444","#44FF44","#4444FF"]),
                      "../plots/mbh_nominal.html")
  savefig(plot_orbits([initial_path, transit, after_transit, final_path],
                      labels=["initial", "transit", "after transit", "final"],
                      colors=["#FFFFFF", "#FF4444","#44FF44","#4444FF"]),
                      "../plots/mbh_best.html")
  i = 0
  best_mass = best_masses[end]
  nominal_mass = normal_mass[end]
  masses = []
  for candidate in archive
    @test converged(candidate)
    path2, cand_ms, calc_final = prop(tanh.(candidate.zero), start, sc, μs["Earth"], prop_time)
    push!(masses, cand_ms[end])
    @test norm(calc_final - final) < 1e-4
  end
  @test best_mass == maximum(masses)

  # This won't always work since the test is reduced in fidelity,
  # but hopefully will usually work:
  @test (sc.mass - best_mass) < 1.1 * (sc.mass - nominal_mass)
  @show best_mass
  @show nominal_mass

end