def LiStephanTwoPopulation():
id = "QC-OutOfAfrica_2L06"
populations = [
stdpopsim.Population("AFR", ""),
stdpopsim.Population("EUR", ""),
]
# Parameters for the African population are taken from the section Demographic
# History of the African Population
generation_time = 0.1 # 10 generations per year
N_A0 = 8.603e6 # modern African pop. size
N_A1 = N_A0 / 5.0 # African pop. size before expansion
# Parameters for the European population are taken from the section Demographic
# History of the European Population
N_E0 = 1.075e6 # modern European pop. size
N_E1 = 2.2e3 # European founder pop. size
# Times from from the section Demographic History of the * Population
T_A0 = 6e4 / generation_time # time of 1st expansion in African pop.
T_E_A = 15.8e3 / generation_time # European/African divergence time
T_EE = T_E_A - 340 / generation_time # Time of European pop. re-expansion
return stdpopsim.DemographicModel(
id=id,
description=id,
long_description=id,
generation_time=generation_time,
populations=populations,
# Set population sizes at T=0
# pop0 is Africa, pop1 is Europe
population_configurations=[
msprime.PopulationConfiguration(initial_size=N_A0, growth_rate=0),
msprime.PopulationConfiguration(initial_size=N_E0, growth_rate=0),
],
# Now we add the demographic events working backwards in time.
demographic_events=[
# OOA bottleneck
msprime.PopulationParametersChange(
time=T_EE, initial_size=N_E1, population_id=1
),
# E and A coalesce
msprime.MassMigration(time=T_E_A, source=1, destination=0, proportion=1.0),
# Pre-expansion Africa
msprime.PopulationParametersChange(
time=T_A0, initial_size=N_A1, population_id=0
),
],
population_id_map=[
{"AFR": 0, "EUR": 1},
{"AFR": 0, "EUR": 1},
{"AFR": 0, "EUR": 1},
{"AFR": 0, "EUR": 1},
],
mutation_rate=1.450e-9,
)
def LockePongo():
id = "QC-TwoSpecies_2L11"
populations = [
stdpopsim.Population("Bornean", ""),
stdpopsim.Population("Sumatran", ""),
]
# This is a split-migration style model, with exponential growth or
# decay allowed in each population after the split. They assumed a
# generation time of 20 years and a mutation rate of 2e-8 per bp per gen
generation_time = 20
# Parameters given in Table S21-2
Ne = 17934
s = 0.592
NB0 = s * Ne
NS0 = (1 - s) * Ne
NBF = 8805
NSF = 37661
mSB = 0.395 / 2 / Ne
mBS = 0.239 / 2 / Ne
T = 403149 / generation_time
rB = np.log(NBF / NB0) / T
rS = np.log(NSF / NS0) / T
return stdpopsim.DemographicModel(
id=id,
description=id,
long_description=id,
generation_time=generation_time,
populations=populations,
# pop 0 is Bornean, pop 1 is Sumatran
population_configurations=[
msprime.PopulationConfiguration(initial_size=NBF, growth_rate=rB),
msprime.PopulationConfiguration(initial_size=NSF, growth_rate=rS),
],
migration_matrix=[[0, mBS], [mSB, 0]],
demographic_events=[
# merge, turn off migration, change size and growth rate
msprime.MassMigration(source=1, destination=0, time=T, proportion=1),
msprime.MigrationRateChange(time=T, rate=0),
msprime.PopulationParametersChange(
time=T, initial_size=Ne, growth_rate=0, population_id=0
),
],
population_id_map=[
{"Bornean": 0, "Sumatran": 1},
{"Bornean": 0, "Sumatran": 1},
],
)
def _5pop_test_demog(N=1000):
populations = [stdpopsim.Population(f"pop{i}", f"Population {i}") for i in range(5)]
pop_config = [
msprime.PopulationConfiguration(
initial_size=N, metadata=populations[i].asdict()
)
for i in range(len(populations))
]
mig_mat = [
[0, 0, 0, 0, 0],
[0, 0, 1e-5, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
]
dem_events = [
msprime.MassMigration(time=100, source=0, destination=1, proportion=0.1),
msprime.MassMigration(time=200, source=3, destination=2),
msprime.MigrationRateChange(time=200, rate=0),
msprime.MassMigration(time=300, source=1, destination=0),
msprime.MassMigration(time=400, source=2, destination=4, proportion=0.1),
msprime.MassMigration(time=600, source=2, destination=0),
msprime.MassMigration(time=700, source=4, destination=0),
]
return stdpopsim.DemographicModel(
id="5pop_test",
description="5pop_test",
long_description="5pop_test",
populations=populations,
generation_time=1,
population_configurations=pop_config,
demographic_events=dem_events,
migration_matrix=mig_mat,
)
def HuberThreeEpoch():
id = "QC-African3Epoch_1H18"
populations = [
stdpopsim.Population(id="ATL", description="A. thalina"),
]
# Time of second epoch
T_2 = 7420
T_3 = 14534
# population sizes
N_ANC = 161744
N_2 = 24076
N_3 = 203077
return stdpopsim.DemographicModel(
id=id,
description=id,
long_description=id,
generation_time=_species.generation_time,
populations=populations,
population_configurations=[
msprime.PopulationConfiguration(
initial_size=N_3, metadata=populations[0].asdict()
),
],
demographic_events=[
msprime.PopulationParametersChange(
time=T_3, initial_size=N_2, population_id=0
),
msprime.PopulationParametersChange(
time=T_2 + T_3, initial_size=N_ANC, population_id=0
),
],
)
def HuberTwoEpoch():
id = "QC-African2Epoch_1H18"
populations = [
stdpopsim.Population(id="ATL", description="A. thalina"),
]
# Time of second epoch
T_2 = 568344
# population sizes
N_ANC = 746148
N_2 = 100218
return stdpopsim.DemographicModel(
id=id,
description=id,
long_description=id,
generation_time=_species.generation_time,
populations=populations,
population_configurations=[
msprime.PopulationConfiguration(
initial_size=N_2, metadata=populations[0].asdict()
),
],
demographic_events=[
msprime.PopulationParametersChange(
time=T_2, initial_size=N_ANC, population_id=0
),
],
)
def test_sampling_times_equal(self):
no_sample_pop = stdpopsim.Population("none", "none", sampling_time=None)
zero_sample_pop = stdpopsim.Population("zero", "zero")
nonzero_sample_pop = stdpopsim.Population("nzero", "nzero", sampling_time=10)
plist1 = [no_sample_pop] * 2 + [nonzero_sample_pop] + [zero_sample_pop] * 2
plist2 = [no_sample_pop] * 4 + [nonzero_sample_pop]
plist3 = [no_sample_pop] * 3 + [nonzero_sample_pop]
self.assertFalse(
stdpopsim.sampling_times_equal([no_sample_pop], [zero_sample_pop])
)
self.assertFalse(
stdpopsim.sampling_times_equal([nonzero_sample_pop], [zero_sample_pop])
)
self.assertFalse(stdpopsim.sampling_times_equal(plist1, plist2))
self.assertFalse(stdpopsim.sampling_times_equal(plist1, plist3))
self.assertTrue(stdpopsim.sampling_times_equal(plist3, plist3))
class _PiecewiseSize(stdpopsim.DemographicModel):
"""
A copy of stdpopsim.PiecewiseConstantSize that permits growth rates.
"""
id = "Piecewise"
description = "Piecewise size population model over multiple epochs."
citations = []
populations = [stdpopsim.Population(id="pop0", description="Population 0")]
author = None
year = None
doi = None
def __init__(self, N0, growth_rate, *args):
self.population_configurations = [
msprime.PopulationConfiguration(
initial_size=N0,
growth_rate=growth_rate,
metadata=self.populations[0].asdict())
]
self.migration_matrix = [[0]]
self.demographic_events = []
for t, initial_size, growth_rate in args:
self.demographic_events.append(
msprime.PopulationParametersChange(time=t,
initial_size=initial_size,
growth_rate=growth_rate,
population_id=0))
def _sma_1pop():
# the size during the interval times[k] to times[k+1] = sizes[k]
times = np.array([
699, 2796, 6068, 9894, 14370, 19606, 25730, 32894, 41275,
51077, 62544, 75958, 91648, 110001, 131471, 156584, 185960, 220324,
260520, 307540, 362541, 426879, 502139, 590173, 693151, 813610,
954517, 1119341, 1312147, 1537686, 1801500, 2110100])
sizes = np.array([
42252426, 42252426, 60323, 72174, 40591, 21158, 21442,
39942, 78908, 111132, 110745, 96283, 87661, 83932, 83829, 91813,
111644, 143456, 181571, 217331, 241400, 246984, 238593, 228222,
217752, 198019, 165210, 121796, 121796, 73989, 73989, 73989])
# MSMC is accurate from 40Kya-1.6Mya for A.thaliana (Durvasula et al 2017)
# set the first 7 sizes
# equal to the size at 8 (~40Kya)
sizes[:8] = sizes[8]
# set the last 2 entries equal
# to the size at 30 (~1.6Mya)
sizes[30:32] = sizes[30]
demographic_events = []
for sz, t in zip(sizes, times):
demographic_events.append(
msprime.PopulationParametersChange(
time=t, initial_size=sz, population_id=0))
populations = [
stdpopsim.Population(
id="SouthMiddleAtlas",
description="Arabidopsis Thaliana South Middle Atlas population")
]
return stdpopsim.DemographicModel(
id="SouthMiddleAtlas_1D17",
description="South Middle Atlas piecewise constant size",
long_description="""
This model comes from MSMC using two randomly sampled homozygous
individuals (Khe32 and Ifr4) from the South Middle Atlas region
from the Middle Atlas Mountains in Morocco. The model is estimated
with 32 time periods. Because estimates from the recent and ancient
past are less accurate, we set the population size in the first 7
time periods equal to the size at the 8th time period and the size
during last 2 time periods equal to the size in the 30th time
period.
""",
populations=populations,
citations=[stdpopsim.Citation(
author="Durvasula et al.",
year=2017,
doi="https://doi.org/10.1073/pnas.1616736114",
reasons={stdpopsim.CiteReason.DEM_MODEL})
],
generation_time=1,
demographic_events=demographic_events,
population_configurations=[
msprime.PopulationConfiguration(
initial_size=sizes[0], metadata=populations[0].asdict())
]
)
def _afr_2epoch():
N_A = 746148
N_0 = 100218
t_1 = 568344
populations = [
stdpopsim.Population(
id="Africa", description="Arabidopsis thaliana African population")
]
return stdpopsim.DemographicModel(
id="African2Epoch_1H18",
description="African two epoch model",
long_description="""
Model estimated from site frequency spectrum of synonymous
SNPs from African samples using Williamson et al. (2005)
methodology.
""",
populations=populations,
citations=[stdpopsim.Citation(
author="Huber et al.",
year=2018,
doi="https://doi.org/10.1038/s41467-018-05281-7",
reasons={stdpopsim.CiteReason.DEM_MODEL})
],
generation_time=1,
population_configurations=[
msprime.PopulationConfiguration(
initial_size=N_0, metadata=populations[0].asdict())
],
demographic_events=[
msprime.PopulationParametersChange(
time=t_1, initial_size=N_A, population_id=0)
]
)
def HuberTwoEpoch():
id = "QC-African2Epoch_1H18"
populations = [
stdpopsim.Population(id="SouthMiddleAtlas", description="A. thalina"),
]
# Time of second epoch
T_2 = 568344
# population sizes
N_ANC = 746148
N_2 = 100218
return stdpopsim.DemographicModel(
id=id,
description=id,
long_description=id,
generation_time=_species.generation_time,
populations=populations,
population_configurations=[
msprime.PopulationConfiguration(initial_size=N_2,
metadata=populations[0].asdict()),
],
demographic_events=[
msprime.PopulationParametersChange(time=T_2,
initial_size=N_ANC,
population_id=0),
],
population_id_map=[{
"SouthMiddleAtlas": 0
}] * 2,
# Huber et al say "7e-9" but then refer to Ossowski,
# which Durvasula reported as giving 7.1e-9
mutation_rate=7e-9,
)
class TestNoQCWarning(unittest.TestCase):
species = stdpopsim.get_species("EscCol")
model = stdpopsim.DemographicModel(
id="FakeModel",
description="FakeModel",
long_description="FakeModel",
citations=[
stdpopsim.Citation(
author="Farnsworth",
year=3000,
doi="https://doi.org/10.1000/xyz123",
reasons={stdpopsim.CiteReason.DEM_MODEL},
)
],
generation_time=10,
populations=[stdpopsim.Population("Omicronians", "Popplers, etc.")],
population_configurations=[
msprime.PopulationConfiguration(initial_size=1000)
],
)
def setUp(self):
self.species.add_demographic_model(self.model)
def tearDown(self):
self.species.demographic_models.remove(self.model)
def verify_noQC_warning(self, cmd):
with self.assertWarns(stdpopsim.QCMissingWarning):
capture_output(stdpopsim.cli.stdpopsim_main, cmd.split())
def test_noQC_warning(self):
self.verify_noQC_warning("EscCol -d FakeModel -D 10 -L 10")
def test_noQC_warning_quiet(self):
self.verify_noQC_warning("-q EscCol -d FakeModel -D 10 -L 10")
def verify_noQC_citations_not_written(self, cmd):
# Non-QCed models shouldn't be used in publications, so citations
# shouldn't be offered to the user.
with self.assertLogs() as logs:
out, err = capture_output(stdpopsim.cli.stdpopsim_main,
cmd.split())
log_output = "\n".join(logs.output)
for citation in self.model.citations:
self.assertFalse(citation.author in out)
self.assertFalse(citation.doi in out)
self.assertFalse(citation.author in err)
self.assertFalse(citation.doi in err)
self.assertFalse(citation.author in log_output)
self.assertFalse(citation.doi in log_output)
def test_noQC_citations_not_written(self):
self.verify_noQC_citations_not_written(
"EscCol -d FakeModel -D 10 -L 10")
def test_noQC_citations_not_written_verbose(self):
self.verify_noQC_citations_not_written(
"-vv EscCol -d FakeModel -D 10 -L 10")
def test_population_construction_popconfig_metadata(self):
pop0 = stdpopsim.Population(id="A", description="Pop A")
pc_meta = [msprime.PopulationConfiguration(
initial_size=1, growth_rate=0.03, metadata=pop0.asdict())]
dm = stdpopsim.DemographicModel(
id="", description="", long_description="",
generation_time=1, population_configurations=pc_meta)
self.assertEqual(dm.populations[0].asdict(), pop0.asdict())
def _sma_1pop():
# the size during the interval times[k] to times[k+1] = sizes[k]
times = np.array([
699, 2796, 6068, 9894, 14370, 19606, 25730, 32894, 41275,
51077, 62544, 75958, 91648, 110001, 131471, 156584, 185960, 220324,
260520, 307540, 362541, 426879, 502139, 590173, 693151, 813610,
954517, 1119341, 1312147, 1537686, 1801500, 2110100])
sizes = np.array([
42252426, 42252426, 60323, 72174, 40591, 21158, 21442,
39942, 78908, 111132, 110745, 96283, 87661, 83932, 83829, 91813,
111644, 143456, 181571, 217331, 241400, 246984, 238593, 228222,
217752, 198019, 165210, 121796, 121796, 73989, 73989, 73989])
# MSMC is accurate from 40Kya-1.6Mya for A.thaliana (Durvasula et al 2017)
# set the first 7 sizes
# equal to the size at 8 (~40Kya)
sizes[:8] = sizes[8]
# set the last 2 entries equal
# to the size at 30 (~1.6Mya)
sizes[30:32] = sizes[30]
demographic_events = []
for sz, t in zip(sizes, times):
demographic_events.append(
msprime.PopulationParametersChange(
time=t, initial_size=sz, population_id=0))
populations = [
stdpopsim.Population(
id="SouthMiddleAtlas",
description="Arabidopsis Thaliana South Middle Atlas population")
]
return stdpopsim.DemographicModel(
id="SouthMiddleAtlas_1D17",
description="South Middle Atlas piecewise constant size",
# TODO more detail here. We should at least explain that we're skipping
# some of the estimates from MSMC because we don't think they're accurate.
long_description="""
Model estimated from two homozygous individuals from
the South Middle Atlas using MSMC (TODO: more detail).
""",
populations=populations,
citations=[stdpopsim.Citation(
author="Durvasula et al.",
year=2017,
doi="https://doi.org/10.1073/pnas.1616736114",
reasons={stdpopsim.CiteReason.DEM_MODEL})
],
generation_time=1,
demographic_events=demographic_events,
population_configurations=[
msprime.PopulationConfiguration(
initial_size=sizes[0], metadata=populations[0].asdict())
]
)
def make_model(self):
# Create populations to test on
_pop1 = stdpopsim.Population("pop0", "Test pop. 0")
_pop2 = stdpopsim.Population("pop1", "Test pop. 1", sampling_time=10)
_pop3 = stdpopsim.Population("pop2", "Test pop. 2", sampling_time=None)
# Create an model to hold populations
base_mod = models.DemographicModel(
id="x",
description="y",
long_description="z",
populations=[_pop1, _pop2, _pop3],
population_configurations=[
msprime.PopulationConfiguration(initial_size=1),
msprime.PopulationConfiguration(initial_size=1),
msprime.PopulationConfiguration(initial_size=1),
],
)
return base_mod
class _Durvasula2017MSMC(ArabidopsisThalianaModel):
id = "fixme" # FIXME
name = "Please give me a descriptive name"
description = """
Model estimated from two homozygous individuals from the South Middle Atlas
using MSMC (TODO: more detail).
"""
populations = [
stdpopsim.Population(
name="a_thaliana", description="Arabidopsis Thaliana population")
]
citations = [stdpopsim.Citation(
author="Durvasula et al.",
year=2017,
doi="TODO") # FIXME
]
def __init__(self):
super().__init__()
# the size during the interval times[k] to times[k+1] = sizes[k]
self.times = np.array([
699, 2796, 6068, 9894, 14370, 19606, 25730, 32894, 41275,
51077, 62544, 75958, 91648, 110001, 131471, 156584, 185960, 220324,
260520, 307540, 362541, 426879, 502139, 590173, 693151, 813610,
954517, 1119341, 1312147, 1537686, 1801500, 2110100])
self.sizes = np.array([
42252426, 42252426, 60323, 72174, 40591, 21158, 21442,
39942, 78908, 111132, 110745, 96283, 87661, 83932, 83829, 91813,
111644, 143456, 181571, 217331, 241400, 246984, 238593, 228222,
217752, 198019, 165210, 121796, 121796, 73989, 73989, 73989])
# MSMC is accurate from 40Kya-1.6Mya for A.thaliana(Durvasula et al 2017)
# set the first 7 sizes
# equal to the size at 8 (~40Kya)
self.sizes[:8] = self.sizes[8]
# set the last 2 entries equal
# to the size at 30 (~1.6Mya)
self.sizes[30:32] = self.sizes[30]
# generation time is 1 year
self.generation_time = 1
self.demographic_events = []
for idx, t in enumerate(self.times):
self.demographic_events.append(
msprime.PopulationParametersChange(
time=t, initial_size=self.sizes[idx], population_id=0))
self.migration_matrix = [[0]]
self.population_configurations = [
msprime.PopulationConfiguration(initial_size=self.sizes[0])
]
def _african():
id = "Africa_1T12"
description = "African population"
long_description = """
The model is a simplification of the two population Tennesen et al.
model with the European-American population removed so that we are
modeling the African population in isolation.
"""
populations = [
stdpopsim.Population(id="AFR", description="African"),
]
citations = [_tennessen_et_al]
generation_time = 25
T_AF = 148e3 / generation_time
T_EG = 5115 / generation_time
# Growth rate
r_AF = 0.0166
# population sizes
N_A = 7310
N_AF1 = 14474
# present Ne
N_AF = N_AF1 / math.exp(-r_AF * T_EG)
return stdpopsim.DemographicModel(
id=id,
description=description,
long_description=long_description,
populations=populations,
citations=citations,
generation_time=generation_time,
population_configurations=[
msprime.PopulationConfiguration(initial_size=N_AF,
growth_rate=r_AF,
metadata=populations[0].asdict()),
],
demographic_events=[
msprime.PopulationParametersChange(time=T_EG,
growth_rate=0,
initial_size=N_AF1,
population_id=0),
msprime.PopulationParametersChange(time=T_AF,
initial_size=N_A,
population_id=0)
],
)
def _afr_3epoch():
# values from the supplementary Table 1.
# the size changed as N_A -> N_2 -> N_3.
# t_2 is time of 2nd epoch and t_3 of the third epoch
N_A = 161744
N_2 = 24076
N_3 = 203077
t_2 = 7420
t_3 = 14534
populations = [
stdpopsim.Population(
id="SouthMiddleAtlas",
description="Arabidopsis Thaliana South Middle Atlas population",
)
]
return stdpopsim.DemographicModel(
id="African3Epoch_1H18",
description="South Middle Atlas African three epoch model",
long_description="""
Model estimated from site frequency spectrum of synonymous
SNPs from African (South Middle Atlas) samples using Williamson et
al. 2005 methodology. Values come from supplementary table 1 of
Huber et al 2018. Sizes change from N_A -> N_2 -> N_3 and t_2 is
the time of the second epoch and t_3 is the time of the 3rd epoch.
""",
populations=populations,
citations=[
stdpopsim.Citation(
author="Huber et al.",
year=2018,
doi="https://doi.org/10.1038/s41467-018-05281-7",
reasons={stdpopsim.CiteReason.DEM_MODEL},
)
],
generation_time=1,
population_configurations=[
msprime.PopulationConfiguration(
initial_size=N_3, metadata=populations[0].asdict()
)
],
demographic_events=[
msprime.PopulationParametersChange(
time=t_3, initial_size=N_2, population_id=0
),
msprime.PopulationParametersChange(
time=t_2 + t_3, initial_size=N_A, population_id=0
),
],
)
class _TennessenOnePopAfrica(HomoSapiensModel):
id = "african"
name = "African population"
description = """
The model is a simplification of the two population Tennesen et al.
model with the European-American population removed so that we are
modeling the African population in isolation.
"""
populations = [
stdpopsim.Population(name="AFR", description="African"),
]
citations = [_tennessen_et_al]
def __init__(self):
super().__init__()
T_AF = 148e3 / self.generation_time
T_EG = 5115 / self.generation_time
# Growth rate
r_AF = 0.0166
# population sizes
N_A = 7310
N_AF1 = 14474
# present Ne
N_AF = N_AF1 / math.exp(-r_AF * T_EG)
self.population_configurations = [
msprime.PopulationConfiguration(
initial_size=N_AF,
growth_rate=r_AF,
metadata=self.populations[0].asdict()),
]
self.migration_matrix = [[0]]
self.demographic_events = [
msprime.PopulationParametersChange(time=T_EG,
growth_rate=0,
initial_size=N_AF1,
population_id=0),
msprime.PopulationParametersChange(time=T_AF,
initial_size=N_A,
population_id=0)
]
def SheehanSongThreeEpic():
id = "QC-African3Epoch_1S16"
populations = [stdpopsim.Population("AFR", "")]
# Model from paper https://doi.org/10.1371/journal.pcbi.1004845
# Parameters are taken from table 7 using the average stat prediction values
# as those were generally stated to be the best
N_1 = 544.2e3 # recent
N_2 = 145.3e3 # bottleneck
N_3 = 652.7e3 # ancestral
# Times taken from simulating data section based on PSMC and converted to
# number of generations from coalescent units using the baseline effective
# population size. Note that the coalescent values are calculated by
N_ref = 1e5
t_1_coal = 0.5
t_2_coal = 5
T_1 = t_1_coal * 4 * N_ref
T_2 = (t_1_coal + t_2_coal) * 4 * N_ref
return stdpopsim.DemographicModel(
id=id,
description=id,
long_description=id,
generation_time=_species.generation_time,
populations=populations,
# Set population sizes at T=0
population_configurations=[
msprime.PopulationConfiguration(initial_size=N_1, growth_rate=0),
],
# Now we add the demographic events working backwards in time.
demographic_events=[
# Bottleneck
msprime.PopulationParametersChange(time=T_1,
initial_size=N_2,
population_id=0),
# Ancestral population size
msprime.PopulationParametersChange(time=T_2,
initial_size=N_3,
population_id=0),
],
population_id_map=[{
"AFR": 0
}] * 3,
)
def test_ooa_model(self):
correct_model = stdpopsim.get_species("HomSap").get_demographic_model(
"OutOfAfrica_3G09")
ooa_docs = examples.out_of_africa()
pops = []
for pop_config in ooa_docs["population_configurations"]:
pops.append(stdpopsim.Population(id=None, description=None))
pop_config.sample_size = None
local_model = stdpopsim.DemographicModel(
id=None,
description=None,
long_description=None,
generation_time=None,
populations=pops,
population_configurations=ooa_docs["population_configurations"],
migration_matrix=ooa_docs["migration_matrix"],
demographic_events=ooa_docs["demographic_events"],
)
correct_model.verify_equal(local_model)
def _afr_2epoch():
N_A = 746148
N_0 = 100218
t_1 = 568344
populations = [
stdpopsim.Population(
id="SouthMiddleAtlas",
description="Arabidopsis Thaliana South Middle Atlas population",
)
]
return stdpopsim.DemographicModel(
id="African2Epoch_1H18",
description="South Middle Atlas African two epoch model",
long_description="""
Model estimated from site frequency spectrum of synonymous
SNPs from African South Middle Atlas samples using
Williamson et al. 2005 methodology. Values come from supplementary
table 1 of Huber et al 2018. Sizes change from N_A -> N_0 and t_1 is
time of the second epoch.
""",
populations=populations,
citations=[
stdpopsim.Citation(
author="Huber et al.",
year=2018,
doi="https://doi.org/10.1038/s41467-018-05281-7",
reasons={stdpopsim.CiteReason.DEM_MODEL},
)
],
generation_time=1,
population_configurations=[
msprime.PopulationConfiguration(
initial_size=N_0, metadata=populations[0].asdict()
)
],
demographic_events=[
msprime.PopulationParametersChange(
time=t_1, initial_size=N_A, population_id=0
)
],
)
stdpopsim.Citation(author="Comeron et al",
doi="https://doi.org/10.1371/journal.pgen.1002905",
year=2012,
reasons={stdpopsim.CiteReason.GEN_MAP})
])
_species.add_genetic_map(_gm)
###########################################################
#
# Demographic models
#
###########################################################
# population definitions that are reused.
_afr_population = stdpopsim.Population(
id="AFR", description="African D. melanogaster population")
_eur_population = stdpopsim.Population(
id="EUR", description="European D. melanogaster population")
def _afr_3epoch():
id = "African3Epoch_1S16"
description = "Three epoch African population"
long_description = """
The three epoch (modern, bottleneck, ancestral) model estimated for a
single African Drosophila Melanogaster population from Sheehan and Song (2016).
Population sizes are estimated by a
deep learning model trained on simulation data. NOTE: Due to differences in
coalescence units between PSMC (2N) and msms (4N) the number of generations were
doubled from PSMC estimates when simulating data from msms in the original
publication. We have faithfully represented the published model here.
class TestNoQCWarning:
species = stdpopsim.get_species("EscCol")
model = stdpopsim.DemographicModel(
id="FakeModel",
description="FakeModel",
long_description="FakeModel",
citations=[
stdpopsim.Citation(
author="Farnsworth",
year=3000,
doi="https://doi.org/10.1000/xyz123",
reasons={stdpopsim.CiteReason.DEM_MODEL},
)
],
generation_time=10,
populations=[stdpopsim.Population("Omicronians", "Popplers, etc.")],
population_configurations=[msprime.PopulationConfiguration(initial_size=1000)],
)
@classmethod
def setup_class(cls):
cls.species.add_demographic_model(cls.model)
@classmethod
def teardown_class(cls):
cls.species.demographic_models.remove(cls.model)
def verify_noQC_warning(self, cmd):
# setup_logging() interferes with pytest.warns().
with mock.patch("stdpopsim.cli.setup_logging", autospec=True):
with pytest.warns(stdpopsim.QCMissingWarning):
capture_output(stdpopsim.cli.stdpopsim_main, cmd.split())
def test_noQC_warning(self):
self.verify_noQC_warning("EscCol -d FakeModel -D 10 -L 10")
def test_noQC_warning_quiet(self):
self.verify_noQC_warning("-q EscCol -d FakeModel -D 10 -L 10")
def verify_noQC_citations_not_written(self, cmd, caplog):
# Non-QCed models shouldn't be used in publications, so citations
# shouldn't be offered to the user.
out, err = capture_output(stdpopsim.cli.stdpopsim_main, cmd.split())
log_output = caplog.text
for citation in self.model.citations:
assert not (citation.author in out)
assert not (citation.doi in out)
assert not (citation.author in err)
assert not (citation.doi in err)
assert not (citation.author in log_output)
assert not (citation.doi in log_output)
# The following two tests use the "caplog" pytest fixture, which captures
# the logging output. The caplog param is automatically passed to test_*()
# methods by pytest, which we pass through to verify_noQC_citations_not_written().
@pytest.mark.filterwarnings("ignore::stdpopsim.QCMissingWarning")
@pytest.mark.usefixtures("caplog")
def test_noQC_citations_not_written(self, caplog):
self.verify_noQC_citations_not_written(
"EscCol -d FakeModel -D 10 -L 10", caplog
)
@pytest.mark.filterwarnings("ignore::stdpopsim.QCMissingWarning")
@pytest.mark.usefixtures("caplog")
def test_noQC_citations_not_written_verbose(self, caplog):
self.verify_noQC_citations_not_written(
"-vv EscCol -d FakeModel -D 10 -L 10", caplog
)
def hominin_composite():
id = "HomininComposite_4G20"
description = "Four population out of Africa with Neandertal admixture"
long_description = """
A composite of demographic parameters from multiple sources
"""
# samples:
# T_Altai = 115e3
# T_Vindija = 55e3
# n_YRI = 108
# n_CEU = 99
populations = [
stdpopsim.Population(id="YRI", description="1000 Genomes YRI (Yorubans)"),
stdpopsim.Population(
id="CEU",
description=(
"1000 Genomes CEU (Utah Residents (CEPH) with Northern and "
"Western European Ancestry"
),
),
stdpopsim.Population(id="Nea", description="Neandertal lineage"),
stdpopsim.Population(
id="Anc", description="Ancestral hominins", sampling_time=None
),
]
pop = {p.id: i for i, p in enumerate(populations)}
citations = [
stdpopsim.Citation(
author="Kuhlwilm et al.",
year=2016,
doi="https://doi.org/10.1038/nature16544",
),
stdpopsim.Citation(
author="Prüfer et al.",
year=2017,
doi="https://doi.org/10.1126/science.aao1887",
),
stdpopsim.Citation(
author="Ragsdale and Gravel",
year=2019,
doi="https://doi.org/10.1371/journal.pgen.1008204",
),
]
generation_time = 29
# Kuhlwilm et al. 2016
N_YRI = 27000
N_Nea = 3400
N_Anc = 18500
# Ragsdale & Gravel 2019
N_CEU0 = 1450
r_CEU = 0.00202
T_CEU_exp = 31.9e3 / generation_time
N_CEU = N_CEU0 * math.exp(r_CEU * T_CEU_exp)
T_YRI_CEU_split = 65.7e3 / generation_time
N_ooa_bottleneck = 1080
# Prüfer et al. 2017
T_Nea_human_split = 550e3 / generation_time
T_Nea_CEU_mig = 55e3 / generation_time
m_Nea_CEU = 0.0225
pop_meta = (p.asdict() for p in populations)
population_configurations = [
msprime.PopulationConfiguration(initial_size=N_YRI, metadata=next(pop_meta)),
msprime.PopulationConfiguration(
initial_size=N_CEU, growth_rate=r_CEU, metadata=next(pop_meta)
),
msprime.PopulationConfiguration(initial_size=N_Nea, metadata=next(pop_meta)),
msprime.PopulationConfiguration(initial_size=N_Anc, metadata=next(pop_meta)),
]
demographic_events = [
# out-of-Africa bottleneck
msprime.PopulationParametersChange(
time=T_CEU_exp,
initial_size=N_ooa_bottleneck,
growth_rate=0,
population_id=pop["CEU"],
),
# Neandertal -> CEU admixture
msprime.MassMigration(
time=T_Nea_CEU_mig,
proportion=m_Nea_CEU,
source=pop["CEU"],
destination=pop["Nea"],
),
# population splits
msprime.MassMigration(
time=T_YRI_CEU_split, source=pop["CEU"], destination=pop["Anc"]
),
msprime.MassMigration(
time=T_YRI_CEU_split, source=pop["YRI"], destination=pop["Anc"]
),
msprime.MassMigration(
time=T_Nea_human_split, source=pop["Nea"], destination=pop["Anc"]
),
]
return stdpopsim.DemographicModel(
id=id,
description=description,
long_description=long_description,
populations=populations,
citations=citations,
generation_time=generation_time,
population_configurations=population_configurations,
demographic_events=demographic_events,
)
def hominin_composite_archaic_africa():
dm = hominin_composite()
id = "HomininComposite2_4G20"
description = "HomininComposite2_4G20 plus archaic lineage in Africa"
generation_time = 29
# Ragsdale & Gravel 2019
N_0 = 3600
T_arch_afr_split = 499e3 / generation_time
T_arch_afr_mig = 125e3 / generation_time
T_arch_adm_end = 18.7e3 / generation_time
m_AF_arch_af = 1.98e-5
T_YRI_CEU_split = 65.7e3 / generation_time
populations = dm.populations + [
stdpopsim.Population(
"ArchaicAFR", "Putative Archaic Africans", sampling_time=None
),
]
population_configurations = dm.population_configurations + [
msprime.PopulationConfiguration(
initial_size=N_0, metadata=populations[-1].asdict()
),
]
pop = {p.id: i for i, p in enumerate(populations)}
demographic_events = dm.demographic_events + [
# migration turned on between Yoruban and archaic African populations
msprime.MigrationRateChange(
time=T_arch_adm_end,
rate=m_AF_arch_af,
matrix_index=(pop["YRI"], pop["ArchaicAFR"]),
),
msprime.MigrationRateChange(
time=T_arch_adm_end,
rate=m_AF_arch_af,
matrix_index=(pop["ArchaicAFR"], pop["YRI"]),
),
# YRI merges into Anc: turn off migration between YRI and ArchaicAFR.
msprime.MigrationRateChange(
time=T_YRI_CEU_split,
rate=0,
matrix_index=(pop["YRI"], pop["ArchaicAFR"]),
),
msprime.MigrationRateChange(
time=T_YRI_CEU_split,
rate=0,
matrix_index=(pop["ArchaicAFR"], pop["YRI"]),
),
# migration turned on between African and archaic African populations
msprime.MigrationRateChange(
time=T_YRI_CEU_split,
rate=m_AF_arch_af,
matrix_index=(pop["Anc"], pop["ArchaicAFR"]),
),
msprime.MigrationRateChange(
time=T_YRI_CEU_split,
rate=m_AF_arch_af,
matrix_index=(pop["ArchaicAFR"], pop["Anc"]),
),
# Beginning of migration between African and archaic African populations
msprime.MigrationRateChange(
time=T_arch_afr_mig,
rate=0,
matrix_index=(pop["Anc"], pop["ArchaicAFR"]),
),
msprime.MigrationRateChange(
time=T_arch_afr_mig,
rate=0,
matrix_index=(pop["ArchaicAFR"], pop["Anc"]),
),
# Archaic African merges with moderns
msprime.MassMigration(
time=T_arch_afr_split,
source=pop["ArchaicAFR"],
destination=pop["Anc"],
proportion=1.0,
),
]
demographic_events.sort(key=lambda x: x.time)
return stdpopsim.DemographicModel(
id=id,
description=description,
long_description=dm.long_description,
populations=populations,
citations=dm.citations.copy(),
generation_time=generation_time,
population_configurations=population_configurations,
demographic_events=demographic_events,
)
def _orangutan():
id = "TwoSpecies_2L11"
description = "Two population orangutan model"
long_description = """
The two orang-utan species, Sumatran (Pongo abelii) and Bornean (Pongo
pygmaeus) inferred from the joint-site frequency spectrum with ten
individuals from each population. This model is an isolation-with-
migration model, with exponential growth or decay in each population
after the split. The Sumatran population grows in size, while the
Bornean population slightly declines.
"""
citations = [_locke2011.because(stdpopsim.CiteReason.DEM_MODEL)]
populations = [
stdpopsim.Population("Bornean", "Pongo pygmaeus (Bornean) population"),
stdpopsim.Population("Sumatran", "Pongo abelii (Sumatran) population"),
]
# Parameters from paper:
# ancestral size, before split
Na = 17934
# time of split
T_split_years = 403149
# get split time in units of generations
generation_time = _species.generation_time
T_split = T_split_years / generation_time
# proportion of ancestral pop to branch B
s = 0.592
# sizes at split
Na_B = 17934 * s
Na_S = 17934 * (1 - s)
# present sizes
N_B = 8805
N_S = 37661
# get growth rates
r_B = -1 * math.log(Na_B / N_B) / T_split
r_S = -1 * math.log(Na_S / N_S) / T_split
# migration rates
m_S_B = 0.395 / 2 / Na
m_B_S = 0.239 / 2 / Na
# Population IDs correspond to their indexes in the population
# configuration array. Therefore, we have 0=B and 1=S
# initially.
return stdpopsim.DemographicModel(
id=id,
description=description,
long_description=long_description,
citations=citations,
populations=populations,
generation_time=generation_time,
population_configurations=[
msprime.PopulationConfiguration(
initial_size=N_B,
growth_rate=r_B,
metadata=populations[0].asdict()), # NOQA
msprime.PopulationConfiguration(
initial_size=N_S,
growth_rate=r_S,
metadata=populations[1].asdict()), # NOQA
],
migration_matrix=[
[0, m_B_S], # NOQA
[m_S_B, 0], # NOQA
],
demographic_events=[
# Merge populations and turn off migration, change to size Na
msprime.MassMigration(time=T_split,
source=1,
destination=0,
proportion=1.0),
msprime.MigrationRateChange(time=T_split, rate=0),
msprime.PopulationParametersChange(time=T_split,
initial_size=Na,
growth_rate=0,
population_id=0),
],
)
class TestPopulationSampling(unittest.TestCase):
# Create populations to test on
_pop1 = stdpopsim.Population("pop0", "Test pop. 0")
_pop2 = stdpopsim.Population("pop1", "Test pop. 1", sampling_time=10)
_pop3 = stdpopsim.Population("pop2", "Test pop. 2", sampling_time=None)
# Create an empty model to hold populations
base_mod = models.DemographicModel.empty(populations=[_pop1, _pop2, _pop3])
def test_num_sampling_populations(self):
self.assertEqual(self.base_mod.num_sampling_populations, 2)
def test_get_samples(self):
test_samples = self.base_mod.get_samples(2, 1)
self.assertEqual(len(test_samples), 3)
# Check for error when prohibited sampling asked for
with self.assertRaises(ValueError):
self.base_mod.get_samples(2, 2, 1)
# Get the population corresponding to each sample
sample_populations = [i.population for i in test_samples]
# Check sample populations
self.assertEqual(sample_populations, [0, 0, 1])
# Test sampling times
sample_times = [i.time for i in test_samples]
self.assertEqual(sample_times, [0, 0, 10])
# Test that all sampling populations are specified before non-sampling populations
# in the model.populations list
def test_population_order(self):
for model in stdpopsim.all_demographic_models():
allow_sample_status = [int(p.allow_samples) for p in model.populations]
num_sampling = sum(allow_sample_status)
# All sampling populations must be at the start of the list
self.assertEqual(sum(allow_sample_status[num_sampling:]), 0)
# Test that populations are listed in the same order in model.populations and
# model.population_configurations
def test_population_config_order_equal(self):
for model in stdpopsim.all_demographic_models():
pop_ids = [pop.id for pop in model.populations]
config_ids = [
config.metadata["id"] for config in model.population_configurations
]
for p, c in zip(pop_ids, config_ids):
self.assertEqual(p, c)
# Test that we are indeed getting a valid DDB back
# admittedly a pretty bad test...
def test_demography_debugger(self):
for model in stdpopsim.all_demographic_models():
ddb = model.get_demography_debugger()
self.assertIsInstance(ddb, msprime.DemographyDebugger)
# test for equality of ddbs
def test_demography_debugger_equal(self):
for model in stdpopsim.all_demographic_models():
ddb1 = model.get_demography_debugger()
ddb2 = msprime.DemographyDebugger(
population_configurations=model.population_configurations,
migration_matrix=model.migration_matrix,
demographic_events=model.demographic_events,
)
f1 = io.StringIO()
f2 = io.StringIO()
ddb1.print_history(f1)
ddb2.print_history(f2)
self.assertEqual(f1.getvalue(), f2.getvalue())
import msprime
import numpy as np
import stdpopsim
_species = stdpopsim.get_species("AraTha")
# Some generic populations to use for qc
population_sample_0 = stdpopsim.Population(
"sampling_0", "Population that samples at time 0", 0
)
def Durvasula2017MSMC():
id = "QC-SouthMiddleAtlas_1D17"
populations = [population_sample_0]
# Both of the following are directly
# converted from MSMC output scaled by A.Thaliana
# mutation rate 7e-9 and 1 generation
# per year.
times = np.array(
[
6.990000e02,
2.796000e03,
6.068000e03,
9.894000e03,
1.437000e04,
1.960600e04,
def Durvasula2017MSMC():
id = "QC-SouthMiddleAtlas_1D17"
populations = [stdpopsim.Population("SouthMiddleAtlas", "")]
# Both of the following are directly
# converted from MSMC output scaled by A.Thaliana
# mutation rate 7e-9 and 1 generation
# per year.
times = np.array([
6.990000e02,
2.796000e03,
6.068000e03,
9.894000e03,
1.437000e04,
1.960600e04,
2.573000e04,
3.289400e04,
4.127500e04,
5.107700e04,
6.254400e04,
7.595800e04,
9.164800e04,
1.100010e05,
1.314710e05,
1.565840e05,
1.859600e05,
2.203240e05,
2.605200e05,
3.075400e05,
3.625410e05,
4.268790e05,
5.021390e05,
5.901730e05,
6.931510e05,
8.136100e05,
9.545170e05,
1.119341e06,
1.312147e06,
1.537686e06,
1.801500e06,
2.110100e06,
])
sizes = np.array([
4.2252426e07,
4.2252426e07,
6.0323000e04,
7.2174000e04,
4.0591000e04,
2.1158000e04,
2.1442000e04,
3.9942000e04,
7.8908000e04,
1.1113200e05,
1.1074500e05,
9.6283000e04,
8.7661000e04,
8.3932000e04,
8.3829000e04,
9.1813000e04,
1.1164400e05,
1.4345600e05,
1.8157100e05,
2.1733100e05,
2.4140000e05,
2.4698400e05,
2.3859300e05,
2.2822200e05,
2.1775200e05,
1.9801900e05,
1.6521000e05,
1.2179600e05,
1.2179600e05,
7.3989000e04,
7.3989000e04,
7.3989000e04,
])
# The first 8 epochs are "masked" to
# the last Ne at 40kya due to
# the limitations of MSMC to infer
# population size in this range.
#
# Similarly, the last 2 entries
# are set to equal the third last.
#
# Durvasula et al 2017 shows that
# MSMC has power in A.Thaliana
# between 40kya and 1.6Mya.
sizes[:8] = sizes[8]
sizes[30:32] = sizes[30]
demographic_events = []
population_configurations = [
msprime.PopulationConfiguration(initial_size=sizes[0])
]
for i, t in enumerate(times):
demographic_events.append(
msprime.PopulationParametersChange(time=t,
initial_size=sizes[i],
population_id=0))
return stdpopsim.DemographicModel(
id=id,
description=id,
long_description=id,
generation_time=_species.generation_time,
populations=populations,
population_configurations=population_configurations,
demographic_events=demographic_events,
population_id_map=[{
"SouthMiddleAtlas": 0
}] * 33,
mutation_rate=7.1e-9,
)
def _HolsteinFriesan_1M13():
id = "HolsteinFriesian_1M13"
description = "Piecewise size changes in Holstein-Friesian cattle."
long_description = """
The piecewise-constant population size model of Holstein-Friesian cattle
from MacLeod et al. 2013. Population sizes were estimated from inferred
runs of homozygosity, with parameter values taken from Figure 4A by visual
inspection of the plots.
"""
populations = [
stdpopsim.Population(id="Holstein-Friesian", description="Holstein-Friesian"),
]
citations = [_MacLeodEtAl.because(stdpopsim.CiteReason.DEM_MODEL)]
return stdpopsim.DemographicModel(
id=id,
description=description,
long_description=long_description,
populations=populations,
citations=citations,
generation_time=_species.generation_time,
population_configurations=[
msprime.PopulationConfiguration(
initial_size=90, growth_rate=0.0166, metadata=populations[0].asdict()
)
],
# Here 'time' should be in generation notation ie. how many
# generations ago were that Ne (effective population size)
# and growth rate.
# Growth rate is "per generation exponential growth rate":
# -alpha= [ln(initial_pop_size/next_stage_pop_size)/generation_span_in_years]
# For example: ln(90/120)/3= -0.095894024
demographic_events=[
msprime.PopulationParametersChange(
time=1,
initial_size=90,
growth_rate=-0.095894024,
population_id=0,
), # Ne 90 to 120
msprime.PopulationParametersChange(
time=4, growth_rate=-0.24465639, population_id=0
), # Ne 120 to 250
msprime.PopulationParametersChange(
time=7, growth_rate=-0.0560787, population_id=0
), # Ne 250 to 350
msprime.PopulationParametersChange(
time=13, growth_rate=-0.1749704, population_id=0
), # Ne 350 to 1000
msprime.PopulationParametersChange(
time=19, growth_rate=-0.0675775, population_id=0
), # Ne 1000 to 1500
msprime.PopulationParametersChange(
time=25, growth_rate=-0.0022129, population_id=0
), # Ne 1500 to 2000
msprime.PopulationParametersChange(
time=155, growth_rate=-0.0007438, population_id=0
), # Ne 2000 to 2500
msprime.PopulationParametersChange(
time=455, growth_rate=-0.0016824, population_id=0
), # Ne 2500 to 3500
msprime.PopulationParametersChange(
time=655, growth_rate=-0.0006301, population_id=0
), # Ne 3500 to 7000
msprime.PopulationParametersChange(
time=1755, growth_rate=-0.0005945, population_id=0
), # Ne 7000 to 10000
msprime.PopulationParametersChange(
time=2355, growth_rate=-0.0005306, population_id=0
), # Ne 10000 to 17000
msprime.PopulationParametersChange(
time=3355, growth_rate=-0.0000434, population_id=0
), # Ne 17000 to 62000
msprime.PopulationParametersChange(
time=33155, growth_rate=-0.0000, population_id=0
), # Ne 62000 (model has "coalesced")
msprime.PopulationParametersChange(
time=933155, growth_rate=-0.0, population_id=0
),
],
)
