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)