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Diffstat (limited to 'tests/test_habitat.py')
| -rw-r--r-- | tests/test_habitat.py | 104 |
1 files changed, 104 insertions, 0 deletions
diff --git a/tests/test_habitat.py b/tests/test_habitat.py new file mode 100644 index 0000000..26f1d95 --- /dev/null +++ b/tests/test_habitat.py @@ -0,0 +1,104 @@ +import unittest + +import numpy as np + +from tests.helpers import line_world, make_race +from worldhistory.config import CONDITIONS +from worldhistory.habitat import comfort, environment, fitness, intervals, native_optima, quality, suitability + +DEPTH = CONDITIONS.index("depth") +GRAV = CONDITIONS.index("gravity") + + +class HabitatTest(unittest.TestCase): + def test_intervals_span_to_neighbour_midpoints(self): + w = line_world(4) + lo, hi = intervals(w, np.array([0.0, 100.0, 300.0, 300.0])) + np.testing.assert_array_equal(lo, [0, 50, 200, 300]) + np.testing.assert_array_equal(hi, [50, 200, 300, 300]) + + def test_environment_depth_and_temperature(self): + w = line_world(3, ocean=[False, True, True], elevation_m=[10, -200, -1000], T_mean=[20, 15, 15], + bottom_temp_c=[0, 8, 3]) + env = environment(w) + np.testing.assert_array_equal(env.x["depth"], [0, 200, 1000]) + np.testing.assert_array_equal(env.x["temperature"], [20, 8, 3]) + np.testing.assert_array_equal(env.hi["depth"], [100, 600, 1000]) + + def test_suitability_terms_gates_realm(self): + w = line_world(4, ocean=[False, False, False, True], holdridge=[20, 23, 20, 0], T_mean=[20, 20, 0, 20]) + r = make_race(habitat={"realm": "land", + "terms": [{"p": "land", "w": 0.25}, {"p": "forest", "w": 0.5}], + "gates": [{"require": "warm:0:20", "realm": "land"}]}) + np.testing.assert_allclose(suitability(w, r), [0.75, 0.25, 0.0, 0.0]) + pen = make_race(habitat={"realm": "both", "terms": [{"p": "land", "w": 1}, {"p": "sea", "w": 1}], + "gates": [{"penalty": "sea", "factor": 0.5}]}) + np.testing.assert_allclose(suitability(w, pen), [1, 1, 1, 0.5]) + + def test_quality_normalised_to_p99(self): + s = np.r_[np.linspace(0, 1, 101), 50.0] # one outlier must not squash everyone else + q = quality(s, make_race(habitat={"qmax": 0.7}), np.ones_like(s)) + self.assertAlmostEqual(float(q[100]), 0.7, places=6) + self.assertLessEqual(float(q.max()), 0.7) + + def test_quality_all_zero(self): + q = quality(np.zeros(5), make_race(), np.ones(5)) + np.testing.assert_array_equal(q, 0) + + def test_fitness_uses_distance_to_interval_and_reach(self): + w = line_world(3, ocean=[False, True, True], elevation_m=[0, -100, -400]) + env = environment(w) + r = make_race(tolerance={"depth": {"optimum": 0, "width": 50, "lo": 0, "hi": 5000}}) + O = native_optima([r])[0] + O = np.repeat(O[:, None], 3, 1) + f = fitness(env, O, r) + self.assertAlmostEqual(float(f[0]), 1.0) # interval [0, 50] holds 0 + self.assertAlmostEqual(float(f[1]), np.exp(-0.5), places=6) # interval [50, 250]: d = 50 = 1 width + self.assertAlmostEqual(float(fitness(env, O, r, reach=1.0)[1]), np.exp(-0.125), places=6) + + def test_unlisted_condition_has_no_effect(self): + w = line_world(2, gravity_g=[1.0, 0.35]) + r = make_race() + O = np.repeat(native_optima([r])[0][:, None], 2, 1) + np.testing.assert_array_equal(fitness(environment(w), O, r), [1, 1]) + + def test_comfort_piecewise(self): + r = make_race(tolerance={"gravity": {"optimum": 1.0, "width": 0.2, "lo": 0.3, "hi": 1.3, + "comfort": [[1.0, 1.0], [0.35, 1.2]]}}) + O = np.zeros((len(CONDITIONS), 3)) + O[GRAV] = [1.0, 0.675, 0.35] + np.testing.assert_allclose(comfort(O, r), [1.0, 1.1, 1.2]) + + def test_lopsided_widths(self): + w = line_world(5, gravity_g=[1.05, 1.05, 0.75, 0.45, 0.45]) + r = make_race(tolerance={"gravity": {"optimum": 0.75, "width_lo": 0.3, "width_hi": 0.15, "lo": 0.3, "hi": 1.0}}) + O = np.repeat(native_optima([r])[0][:, None], 5, 1) + f = fitness(environment(w), O, r) + # cell 0 spans [1.05, 1.05]: 0.3 above = 2 upper widths; cell 4 spans [0.45, 0.45]: 0.3 below = 1 lower width + self.assertAlmostEqual(float(f[0]), np.exp(-2.0), places=6) + self.assertAlmostEqual(float(f[4]), np.exp(-0.5), places=6) + + def test_width_sets_both_sides(self): + r = make_race(tolerance={"depth": {"optimum": 0, "width": 50, "lo": 0, "hi": 5000}}) + t = r.tolerance["depth"] + self.assertEqual((t.width_lo, t.width_hi), (50, 50)) + + def test_rain_is_log_and_ignored_at_sea(self): + w = line_world(3, ocean=[False, False, True], P_ann=[100.0, 1000.0, 1000.0], elevation_m=[10, 10, -100]) + env = environment(w) + np.testing.assert_allclose(env.x["rain"][:2], [2.0, 3.0]) + self.assertEqual(env.lo["rain"][2], -np.inf) + self.assertEqual(env.hi["rain"][2], np.inf) + r = make_race(habitat={"realm": "both", "terms": [{"p": "land", "w": 1}]}, + tolerance={"rain": {"optimum": 2.0, "width": 0.3, "lo": 1.5, "hi": 3.5}}) + O = np.repeat(native_optima([r])[0][:, None], 3, 1) + f = fitness(env, O, r) + self.assertEqual(float(f[2]), 1.0) # no rain effect in the sea + self.assertLess(float(f[1]), float(f[0])) + + def test_default_strain_off_native(self): + # adapted away from the native optimum: livable, not good — the peak falls to 0.35 at the range edge + r = make_race(tolerance={"gravity": {"optimum": 1.0, "width": 0.2, "lo": 0.3, "hi": 1.3}}) + O = np.zeros((len(CONDITIONS), 4)) + O[GRAV] = [1.0, 0.3, 0.65, 1.3] + np.testing.assert_allclose(comfort(O, r), [1.0, 0.35, 0.35 ** 0.25, 0.35], rtol=1e-9) |