def test_time_to_allopatric_speciation(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, time_to_allopatric_speciation=20)
se.track_taxa(taxa)
z[[11]] = 0
while len(se.taxa_data_frame) == 1:
sc.run_one_step(10)
se.run_one_step(10)
expected_df = pd.DataFrame(
{
"pid": [np.nan, 0],
"type": 2 * [ZoneTaxon.__name__],
"t_first": [0, 30],
"t_final": 2 * [np.nan],
},
index=[0, 1],
)
expected_df.index.name = "tid"
expected_df["pid"] = expected_df["pid"].astype("Int64")
expected_df["t_final"] = expected_df["t_final"].astype("Int64")
pd.testing.assert_frame_equal(se.taxa_data_frame, expected_df, check_like=True)
def test_allopatric_wait_time(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, allopatric_wait_time=20)
se.track_taxa(taxa)
z[[11]] = 0
while len(se.taxa_data_frame) == 1:
sc.run_one_step(10)
se.run_one_step(10)
expected_df = pd.DataFrame(
{
'appeared': [0, 30, 30],
'latest_time': [30, 30, 30],
'extant': [False, True, True]
},
index=[0, 1, 2])
pd.testing.assert_frame_equal(se.taxa_data_frame,
expected_df,
check_like=True)
def test_zone_taxon_range_mask(zone_example_grid):
mg, z = zone_example_grid
# Create a zone for time 0.
ids = [9, 10, 11, 12]
z[ids] = 1
se = SpeciesEvolver(mg)
sc = ZoneController(mg, zone_func)
taxa = sc.populate_zones_uniformly(1)
se.track_taxa(taxa)
expected_mask = np.zeros(mg.number_of_nodes, bool)
expected_mask[ids] = True
np.testing.assert_array_equal(taxa[0].range_mask, expected_mask)
# Remove extent so taxa range mask is all False.
z[ids] = 0
sc.run_one_step(1)
se.run_one_step(1)
expected_mask = np.zeros(mg.number_of_nodes, bool)
np.testing.assert_array_equal(taxa[0].range_mask, expected_mask)
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_get_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_taxon_objects()
np.testing.assert_equal(
Counter(queried_taxa), Counter(se._taxa['object'])
)
# Test `time` parameter.
queried_taxa = se.get_taxon_objects(time=0)
np.testing.assert_equal(
Counter(queried_taxa), Counter(introduced_taxa)
)
queried_taxa = se.get_taxon_objects(time=10)
ids = [s.uid for s in queried_taxa]
expected_ids = [0, 1, 2, 3]
np.testing.assert_equal(Counter(ids), Counter(expected_ids))
np.testing.assert_raises(ValueError, se.get_taxon_objects, time=5)
np.testing.assert_raises(ValueError, se.get_taxon_objects, time=11)
# Test `extant_at_latest_time` parameter.
queried_taxa = se.get_taxon_objects(extant_at_latest_time=True)
ids = [s.uid for s in queried_taxa]
expected_ids = [4, 5]
np.testing.assert_equal(Counter(ids), Counter(expected_ids))
# Test `ancestor` parameter.
queried_taxa = se.get_taxon_objects(ancestor=1)
ids = [s.uid for s in queried_taxa]
expected_ids = [3, 5]
np.testing.assert_equal(Counter(ids), Counter(expected_ids))
queried_taxa = se.get_taxon_objects(ancestor=5)
np.testing.assert_equal(queried_taxa, [])
queried_taxa = se.get_taxon_objects(ancestor=6)
np.testing.assert_equal(queried_taxa, [])
# Test multiple parameters.
queried_taxa = se.get_taxon_objects(ancestor=1, time=10)
ids = [s.uid for s in queried_taxa]
expected_ids = [3]
np.testing.assert_equal(Counter(ids), Counter(expected_ids))
def test_track_taxa_and_component_attributes(zone_example_grid):
se = SpeciesEvolver(zone_example_grid)
# Introduce multiple taxa.
taxa = [TestTaxon(), TestTaxon()]
se.track_taxa(taxa)
# Introduce a single taxon.
taxon = TestTaxon()
se.track_taxa(taxon)
# Test attributes at initial time step.
expected_df = pd.DataFrame({
'appeared': [0, 0, 0],
'latest_time': [0, 0, 0],
'extant': [True, True, True]},
index=[0, 1, 2]
)
expected_df.index.name = 'uid'
pd.testing.assert_frame_equal(
se.taxa_data_frame, expected_df, check_like=True
)
expected_df = pd.DataFrame({
'time': [0],
'taxa': [3]
})
pd.testing.assert_frame_equal(
se.record_data_frame, expected_df, check_like=True
)
# Test attributes at a later time.
se.run_one_step(10)
expected_df = pd.DataFrame({
'appeared': [0, 0, 0, 10, 10, 10],
'latest_time': [10, 10, 10, 10, 10, 10],
'extant': [False, False, False, True, True, True]},
index=[0, 1, 2, 3, 4, 5]
)
pd.testing.assert_frame_equal(
se.taxa_data_frame, expected_df, check_like=True
)
expected_df = pd.DataFrame({
'time': [0, 10],
'taxa': [3, 3]
})
pd.testing.assert_frame_equal(
se.record_data_frame, expected_df, check_like=True
)
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_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 z in sc.zones:
np.testing.assert_equal(z._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_taxon_objects(extant_at_latest_time=True)), 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_none(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)
# No zones for time 1.
z[:] = 0
sc.run_one_step(1)
se.run_one_step(1)
np.testing.assert_equal(len(sc.zones), 0)
np.testing.assert_equal(zone._conn_type, zn.Connection.ONE_TO_NONE)
expected_df = pd.DataFrame(
{
"time": [0, 1],
"zones": [1, 0],
"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(se.record_data_frame.taxa.sum(), 1)
def test_track_taxa_and_component_attributes(zone_example_grid):
se = SpeciesEvolver(zone_example_grid)
# Introduce multiple taxa.
taxa = [TestTaxon(), TestTaxon()]
se.track_taxa(taxa)
# Introduce a single taxon.
taxon = TestTaxon()
se.track_taxa(taxon)
# Test attributes at initial time step.
expected_df = pd.DataFrame(
{
"pid": 3 * [np.nan],
"type": 3 * [TestTaxon.__name__],
"t_first": [0, 0, 0],
"t_final": 3 * [np.nan],
},
index=[0, 1, 2],
)
expected_df.index.name = "tid"
expected_df["pid"] = expected_df["pid"].astype("Int64")
expected_df["t_final"] = expected_df["t_final"].astype("Int64")
pd.testing.assert_frame_equal(se.taxa_data_frame, expected_df, check_like=True)
expected_df = pd.DataFrame({"time": [0], "taxa": [3]})
pd.testing.assert_frame_equal(se.record_data_frame, expected_df, check_like=True)
# Test attributes at a later time.
se.run_one_step(10)
expected_df = pd.DataFrame(
{
"pid": 3 * [np.nan] + [0, 1, 2],
"type": 6 * [TestTaxon.__name__],
"t_first": [0, 0, 0, 10, 10, 10],
"t_final": [10, 10, 10] + 3 * [np.nan],
},
index=[0, 1, 2, 3, 4, 5],
)
expected_df.index.name = "tid"
expected_df["pid"] = expected_df["pid"].astype("Int64")
expected_df["t_final"] = expected_df["t_final"].astype("Int64")
pd.testing.assert_frame_equal(se.taxa_data_frame, expected_df, check_like=True)
expected_df = pd.DataFrame({"time": [0, 10], "taxa": [3, 3]})
pd.testing.assert_frame_equal(se.record_data_frame, expected_df, check_like=True)
def test_taxa_richness_field(zone_example_grid):
mg = zone_example_grid
se = SpeciesEvolver(mg)
expected_field = np.zeros(mg.number_of_nodes)
np.testing.assert_array_equal(mg.at_node["taxa__richness"], expected_field)
introduced_taxa = [TestTaxon(), TestTaxon()]
se.track_taxa(introduced_taxa)
se.run_one_step(10)
expected_field = np.array([0, 0, 0, 2, 2, 2, 0, 0, 0])
np.testing.assert_array_equal(mg.at_node["taxa__richness"], expected_field)
def test_many_to_one(zone_example_grid):
mg, z = zone_example_grid
# Create two zones for time 0.
z[[8, 9, 10, 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": [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[11] = 1
sc.run_one_step(1)
se.run_one_step(1)
np.testing.assert_equal(len(sc.zones), 1)
np.testing.assert_equal(sc.zones[0]._conn_type, zn.Connection.MANY_TO_ONE)
expected_df = pd.DataFrame(
{
"time": [0, 1],
"zones": [2, 1],
"fragmentations": [np.nan, 0],
"captures": [np.nan, 1],
"area_captured_sum": [np.nan, 12],
"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(time=1)), 2)
def test_none_to_one(zone_example_grid):
mg, z = zone_example_grid
# No zones exist at time 0.
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), 0)
expected_df = pd.DataFrame({
'time': [0],
'zones': [0],
'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)
# Create a zone for time 1.
z[[9, 10, 11, 12]] = 1
sc.run_one_step(1)
se.run_one_step(1)
np.testing.assert_equal(len(sc.zones), 1)
np.testing.assert_equal(sc.zones[0]._conn_type, zn.Connection.NONE_TO_ONE)
expected_df = pd.DataFrame({
'time': [0, 1],
'zones': [0, 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(se.record_data_frame.taxa.sum(), 0)
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)
def test_pseudoextinction(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, persists_post_speciation=False)
se.track_taxa(taxa)
z[11] = 0
sc.run_one_step(1)
se.run_one_step(1)
expected_df = pd.DataFrame(
{
"pid": [np.nan, 0, 0],
"type": 3 * [ZoneTaxon.__name__],
"t_first": [0, 1, 1],
"t_final": [1, np.nan, np.nan],
},
index=[0, 1, 2],
)
expected_df.index.name = "tid"
expected_df["pid"] = expected_df["pid"].astype("Int64")
expected_df["t_final"] = expected_df["t_final"].astype("Int64")
pd.testing.assert_frame_equal(se.taxa_data_frame, expected_df, check_like=True)
expected_df = pd.DataFrame(
{
"time": [0, 1],
"taxa": [1, 2],
"speciations": [np.nan, 2],
"extinctions": [np.nan, 0],
"pseudoextinctions": [np.nan, 1],
}
)
pd.testing.assert_frame_equal(se.record_data_frame, expected_df, check_like=True)