def test_get_extant_taxon_objects(zone_example_grid): se = SpeciesEvolver(zone_example_grid) introduced_taxa = [TestTaxon(), TestTaxon()] se.track_taxa(introduced_taxa) se.run_one_step(10) se.run_one_step(10) # Test no parameters. queried_taxa = se.get_extant_taxon_objects() np.testing.assert_equal(Counter(queried_taxa), Counter(se._taxon_objs)) # Test `tids` parameter. queried_taxa = se.get_extant_taxon_objects(tids=[0]) ids = [t.tid for t in queried_taxa] expected_ids = [] np.testing.assert_equal(Counter(ids), Counter(expected_ids)) queried_taxa = se.get_extant_taxon_objects(tids=[4, 5]) ids = [t.tid for t in queried_taxa] expected_ids = [4, 5] np.testing.assert_equal(Counter(ids), Counter(expected_ids)) # Test `time` parameter. queried_taxa = se.get_extant_taxon_objects(time=20) ids = [t.tid for t in queried_taxa] expected_ids = [4, 5] np.testing.assert_equal(Counter(ids), Counter(expected_ids)) queried_taxa = se.get_extant_taxon_objects(time=10) ids = [t.tid for t in queried_taxa] expected_ids = [] np.testing.assert_equal(Counter(ids), Counter(expected_ids)) queried_taxa = se.get_extant_taxon_objects(time=30) ids = [t.tid for t in queried_taxa] expected_ids = [] np.testing.assert_equal(Counter(ids), Counter(expected_ids)) # Test `ancestor` parameter. queried_taxa = se.get_extant_taxon_objects(ancestor=1) ids = [t.tid for t in queried_taxa] expected_ids = [5] np.testing.assert_equal(Counter(ids), Counter(expected_ids)) queried_taxa = se.get_extant_taxon_objects(ancestor=5) np.testing.assert_equal(queried_taxa, []) queried_taxa = se.get_extant_taxon_objects(ancestor=6) np.testing.assert_equal(queried_taxa, []) # Test multiple parameters. queried_taxa = se.get_extant_taxon_objects(ancestor=1, time=10) np.testing.assert_equal(queried_taxa, [])
def test_many_to_many(zone_example_grid): mg, z = zone_example_grid # Create two zones for time 0. z[[10, 12, 17, 19, 24, 26]] = 1 se = SpeciesEvolver(mg) sc = ZoneController(mg, zone_func) taxa = sc.populate_zones_uniformly(1) se.track_taxa(taxa) expected_df = pd.DataFrame( { "time": [0], "zones": [2], "fragmentations": [np.nan], "captures": [np.nan], "area_captured_sum": [np.nan], "area_captured_max": [np.nan], } ) pd.testing.assert_frame_equal(sc.record_data_frame, expected_df, check_like=True) np.testing.assert_equal(len(se.get_extant_taxon_objects(time=0)), 2) # Modify elevation such that two zones each overlap the original two zones. z[[17, 19]] = 0 z[[11, 25]] = 1 sc.run_one_step(1) se.run_one_step(1) np.testing.assert_equal(len(sc.zones), 2) for zone in sc.zones: np.testing.assert_equal(zone._conn_type, zn.Connection.MANY_TO_MANY) expected_df = pd.DataFrame( { "time": [0, 1], "zones": [2, 2], "fragmentations": [np.nan, 0], "captures": [np.nan, 2], "area_captured_sum": [np.nan, 24], "area_captured_max": [np.nan, 12], } ) pd.testing.assert_frame_equal(sc.record_data_frame, expected_df, check_like=True) np.testing.assert_equal(len(se.get_extant_taxon_objects()), 4)
def test_one_to_many(zone_example_grid): mg, z = zone_example_grid # Create a zone for time 0. z[[9, 10, 11, 12]] = 1 se = SpeciesEvolver(mg) sc = ZoneController(mg, zone_func) taxa = sc.populate_zones_uniformly(1) se.track_taxa(taxa) expected_df = pd.DataFrame( { "time": [0], "zones": [1], "fragmentations": [np.nan], "captures": [np.nan], "area_captured_sum": [np.nan], "area_captured_max": [np.nan], } ) pd.testing.assert_frame_equal(sc.record_data_frame, expected_df, check_like=True) np.testing.assert_equal(len(se.get_extant_taxon_objects(time=0)), 1) # Break the zone in two for time 1. z[11] = 0 sc.run_one_step(1) se.run_one_step(1) np.testing.assert_equal(len(sc.zones), 2) np.testing.assert_equal( set([z._conn_type for z in sc.zones]), set([None, zn.Connection.ONE_TO_MANY]) ) expected_df = pd.DataFrame( { "time": [0, 1], "zones": [1, 2], "fragmentations": [np.nan, 2], "captures": [np.nan, 0], "area_captured_sum": [np.nan, 0], "area_captured_max": [np.nan, 0], } ) pd.testing.assert_frame_equal(sc.record_data_frame, expected_df, check_like=True) np.testing.assert_equal(len(se.get_extant_taxon_objects()), 2)
def test_one_to_one(zone_example_grid): mg, z = zone_example_grid # Create a zone for time 0. z[[9, 10, 11, 12]] = 1 se = SpeciesEvolver(mg) sc = ZoneController(mg, zone_func) taxa = sc.populate_zones_uniformly(1) se.track_taxa(taxa) np.testing.assert_equal(len(sc.zones), 1) zone = sc.zones[0] expected_df = pd.DataFrame( { "time": [0], "zones": [1], "fragmentations": [np.nan], "captures": [np.nan], "area_captured_sum": [np.nan], "area_captured_max": [np.nan], } ) pd.testing.assert_frame_equal(sc.record_data_frame, expected_df, check_like=True) # Modify elevation, although there is still one zone in time 1. z[[11, 12]] = 0 sc.run_one_step(1) se.run_one_step(1) np.testing.assert_equal(len(sc.zones), 1) np.testing.assert_equal(zone, sc.zones[0]) np.testing.assert_equal(sc.zones[0]._conn_type, zn.Connection.ONE_TO_ONE) expected_df = pd.DataFrame( { "time": [0, 1], "zones": [1, 1], "fragmentations": [np.nan, 0], "captures": [np.nan, 0], "area_captured_sum": [np.nan, 0], "area_captured_max": [np.nan, 0], } ) pd.testing.assert_frame_equal(sc.record_data_frame, expected_df, check_like=True) np.testing.assert_equal(len(se.get_extant_taxon_objects(time=1)), 1)
def test_one_to_many_to_one(zone_example_grid): mg, z = zone_example_grid z[[9, 10, 11, 12]] = 1 se = SpeciesEvolver(mg) sc = ZoneController(mg, zone_func) taxa = sc.populate_zones_uniformly(1, time_to_allopatric_speciation=1) se.track_taxa(taxa) z[11] = 0 sc.run_one_step(1) se.run_one_step(1) z[11] = 1 sc.run_one_step(1) se.run_one_step(1) np.testing.assert_equal(len(se.get_extant_taxon_objects()), 1)