def test_get_variables_function(self):
def check(variables):
self.assertIsInstance(variables[0], PopulationSizeVariable)
self.assertIsInstance(variables[1], PopulationSizeVariable)
self.assertIsInstance(variables[2], PopulationSizeVariable)
self.assertIsInstance(variables[3], MigrationVariable)
self.assertEqual(variables[3].units, "genetic")
self.assertIsInstance(variables[4], TimeVariable)
self.assertEqual(variables[4].units, "physical")
self.assertIsInstance(variables[5], FractionVariable)
self.assertIsInstance(variables[6], GrowthRateVariable)
self.assertEqual(variables[6].units,
"universal") # TODO it is not correct behavior
self.assertIsInstance(variables[7], SelectionVariable)
p_ids = ['nu1', 'nu2', 'n', 'm_gen', 't_phys', 'p', 'g_1', 'gamma']
lower_bound = [1e-2, 1e-2, 1e-5, 0, 0, 0, -1e-3, 0]
upper_bound = [10, 1, 3, 4, 10000, 1, 1e-3, 5]
variables = get_variables(p_ids, lower_bound, upper_bound)
check(variables)
for var, lb, ub in zip(variables, lower_bound, upper_bound):
self.assertEqual(var.domain[0], lb)
self.assertEqual(var.domain[1], ub)
variables = get_variables(p_ids, None, None)
check(variables)
for var in variables:
if var.units != "physical":
self.assertEqual(list(var.domain),
list(var.__class__.default_domain))
else:
self.assertEqual(var.name, "t_phys")
self.assertNotEqual(list(var.domain),
list(var.__class__.default_domain))
variables = get_variables(None, lower_bound, upper_bound)
for var, lb, ub in zip(variables, lower_bound, upper_bound):
self.assertIsInstance(var, ContinuousVariable)
self.assertEqual(var.domain[0], lb)
self.assertEqual(var.domain[1], ub)
self.assertRaises(AssertionError, get_variables, None, None, None)
self.assertRaises(AssertionError, get_variables, p_ids, None,
upper_bound)
self.assertRaises(AssertionError, get_variables, p_ids, lower_bound,
None)
def test_get_variables_function(self):
def check(variables):
self.assertIsInstance(variables[0], PopulationSizeVariable)
self.assertIsInstance(variables[1], PopulationSizeVariable)
self.assertIsInstance(variables[2], PopulationSizeVariable)
self.assertIsInstance(variables[3], MigrationVariable)
self.assertIsInstance(variables[4], TimeVariable)
self.assertIsInstance(variables[5], FractionVariable)
p_ids = ['nu1', 'nu2', 'n', 'm', 't', 'p']
lower_bound = [1e-2, 1e-2, 1e-5, 0, 0, 0]
upper_bound = [10, 1, 3, 4, 6, 1]
variables = get_variables(p_ids, lower_bound, upper_bound)
check(variables)
for var, lb, ub in zip(variables, lower_bound, upper_bound):
self.assertEqual(var.domain[0], lb)
self.assertEqual(var.domain[1], ub)
variables = get_variables(p_ids, None, None)
check(variables)
for var in variables:
self.assertEqual(list(var.domain),
list(var.__class__.default_domain))
variables = get_variables(None, lower_bound, upper_bound)
for var, lb, ub in zip(variables, lower_bound, upper_bound):
self.assertIsInstance(var, ContinuousVariable)
self.assertEqual(var.domain[0], lb)
self.assertEqual(var.domain[1], ub)
self.assertRaises(AssertionError, get_variables, None, None, None)
self.assertRaises(AssertionError, get_variables, p_ids,
None, upper_bound)
self.assertRaises(AssertionError, get_variables, p_ids,
lower_bound, None)
def test_code_generation(self):
# old format
warnings.filterwarnings(action='ignore',
category=UserWarning,
module='.*\.dadi_moments_common',
lineno=350)
# missed lines in vcf
warnings.filterwarnings(action='ignore',
category=UserWarning,
module='.*\.dadi_moments_common',
lineno=693)
warnings.filterwarnings(action='ignore',
category=UserWarning,
module='.*\.dadi_moments_common',
lineno=703)
# repeats in vcf file
warnings.filterwarnings(action='ignore',
category=UserWarning,
module='.*\.dadi_moments_common',
lineno=710)
# Theta0 is not set and translation of Nanc variable with theta0=1 could be wrong
warnings.filterwarnings(action='ignore',
category=UserWarning,
module='.*\.dadi_moments_common',
lineno=237)
warnings.filterwarnings(action='ignore',
category=UserWarning,
module='.*\.momi_engine',
lineno=89)
nu1 = PopulationSizeVariable('nu1')
nu2 = PopulationSizeVariable('nu2')
nu3 = PopulationSizeVariable('nu3')
t = TimeVariable('t1')
t2 = TimeVariable('t2')
m = MigrationVariable('m')
s = SelectionVariable('s')
d1 = DynamicVariable('d1')
d2 = DynamicVariable('d2')
f = FractionVariable('f')
h = FractionVariable('h')
f1 = FractionVariable('f1')
f2 = FractionVariable('f2')
fxnu1 = Multiplication(f, nu1)
tf = Multiplication(f, t)
t_copy = copy.deepcopy(t)
model1 = EpochDemographicModel()
model1.add_epoch(t, [nu1])
model1.add_split(0, [nu1, nu2])
model1.add_epoch(t, [nu2, fxnu1], [[0, m], [0, 0]], [d1, d2])
model1.add_split(1, [nu2, nu1])
model1.add_epoch(t, [nu1, nu2, nu1], None, None)
model2 = EpochDemographicModel()
model2.add_epoch(t, [nu1])
model2.add_split(0, [nu1, nu2])
model2.add_epoch(0.5, [nu2, fxnu1], [[0, m], [0, 0]], [d1, d2], [0, s])
model2.add_split(1, [nu2, nu1])
model2.add_epoch(t, [nu1, nu2, nu1], None, None, [s, 0, s])
model2.get_involved_for_split_time_vars(1)
model3 = EpochDemographicModel()
model3.add_epoch(t, [nu1])
model3.add_split(0, [nu1, nu2])
model3.add_epoch(tf, [nu2, fxnu1], [[0, m], [0, 0]], [d1, d2], [0, s],
[0.1, 0.8])
model3.add_split(1, [nu2, nu1])
model3.add_epoch(t, [nu1, nu2, nu1], None, [d1, 'Sud', 'Sud'],
[s, 0, s], [h, 0.5, 0])
# create models with ancestral size
Nanc = PopulationSizeVariable("Nanc", units="physical")
Nu2 = PopulationSizeVariable("N2", units="physical")
T3 = TimeVariable("T3", units="physical")
model4 = EpochDemographicModel(Nanc_size=Nanc)
model4.add_epoch(t, [nu1])
model4.add_split(0, [nu1, nu2])
model4.add_epoch(t, [nu2, fxnu1], [[0, m], [0, 0]], [d1, d2])
model5 = EpochDemographicModel(Nanc_size=Nanc)
model5.add_epoch(t, [nu1])
model5.add_split(0, [nu1, nu2])
model5.add_epoch(T3, [0.5, fxnu1], [[0, m], [0, 0]], ["Sud", d2])
model5.add_split(1, [nu2, nu1])
model5.add_epoch(t, [nu1, nu2, nu1], None, None)
model6 = EpochDemographicModel(Nanc_size=Nanc)
model6.add_epoch(t, [nu1])
model6.add_split(0, [nu1, nu2])
model6.add_epoch(T3, [Nu2, fxnu1], [[0, m], [0, 0]], [d1, d2])
model6.add_split(1, [nu2, nu1])
model6.add_epoch(t, [nu1, nu2, nu1], None, None)
model6.add_inbreeding([f1, f2, 0.5])
model_struct_1 = StructureDemographicModel(
initial_structure=[2, 1, 1],
final_structure=[2, 1, 1],
has_migs=True,
has_sels=False,
has_dyns=True,
sym_migs=True,
frac_split=True,
)
model_struct_2 = StructureDemographicModel(
initial_structure=[2, 1, 1],
final_structure=[2, 1, 1],
has_anc_size=True,
has_migs=True,
has_sels=False,
has_dyns=True,
sym_migs=True,
frac_split=True,
)
values = {
nu1: 2,
nu2: 0.5,
nu3: 0.5,
t_copy: 0.3,
t2: 0.5,
m: 0.1,
s: 0.1,
d1: 'Exp',
d2: 'Lin',
f: 0.5,
h: 0.3,
f1: 0.1,
f2: 0.3,
Nanc: 10000,
Nu2: 5000,
T3: 4000,
'nu1F': 1.0,
'nu2B': 0.7,
'nu2F': 1.0,
'T': 0.5,
'Tp': 0.3,
'N_1F': 20000,
'r_2': -1e-5,
'N_2F': 5000,
'T_1': 500,
'T_2': 100
}
for engine in all_available_engines():
#print("!!!", engine)
models = [model1, model2, model3, model5, model6]
if engine.id == "momentsLD":
# momentsLD has problems with model 5 and 6 because of physical values
models = models[:3]
if engine.can_evaluate:
customfile = os.path.join(
EXAMPLE_FOLDER, "MODELS",
f"demographic_model_{engine.id}_3pops.py")
spec = importlib.util.spec_from_file_location(
f"module_{engine.id}", customfile)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
sys.modules[f'module_{engine.id}'] = module
func = module.model_func
par_labels = get_par_labels_from_file(customfile)
variables = get_variables(par_labels,
None,
None,
engine=engine.id)
model7 = CustomDemographicModel(func, variables)
if engine.id == "momentsLD":
model8 = copy.deepcopy(model7)
model7.fixed_anc_size = 10000
model8.has_anc_size = True
model8.add_variable(
PopulationSizeVariable("Nanc", units="physical"))
model8.fix_variable(model8.variables[-1], 10000)
model8.unfix_variable(model8._variables[-1])
models.append(model8)
models.append(model7)
models.append(model_struct_1)
models.append(model_struct_2)
for ind, model in enumerate(models):
if engine.id == "momentsLD":
dataset = self._vcf_datasets_ld_precomputed()
else:
dataset = self._sfs_datasets()
for description, data in dataset:
msg = f"for model {ind + 1} and {description} data and " \
f"{engine.id} engine"
#print(msg)
if engine.id == "momi" and isinstance(
model, StructureDemographicModel):
if not model.has_anc_size:
continue
if engine.id == 'dadi':
options = {'pts': [4, 6, 8]}
args = (options['pts'], )
else:
options = {}
args = ()
if isinstance(model, StructureDemographicModel):
input_values = {
var.name: 0.5 + np.random.uniform(-0.2, 0.2)
if not isinstance(var, DiscreteVariable) else "Sud"
for var in model.variables
}
if model.has_anc_size:
input_values[model.Nanc_size.name] = 10000
else:
input_values = copy.copy(values)
if engine.id in ["momi", "momentsLD"]:
# there is an error in momi for that case
# data for moments was simulated with msprime, which can't work with Lin DynVar
if description == "fs without pop labels":
continue
for key in input_values:
if input_values[key] == "Lin":
input_values[key] = "Exp"
data.sequence_length = 50818468
# we read data but save only updated data_holder
engine.data = data
engine.inner_data = None
engine.model = model
Nanc = None
if engine.id not in ["dadi", "moments"]:
Nanc = 10000
new_model = copy.deepcopy(model)
if new_model.has_anc_size and isinstance(
new_model,
StructureDemographicModel) and isinstance(
new_model.Nanc_size, Variable):
input_values[new_model.Nanc_size.name] = Nanc
else:
new_model.Nanc_size = Nanc
new_model.mutation_rate = 1.25e-8
engine.model = new_model
moments.LD.Inference._varcov_inv_cache = {}
cmd = engine.generate_code(input_values,
None,
*args,
nanc=Nanc)
#print(cmd)
if engine.can_evaluate:
# read data
engine.data = data
true_ll = engine.evaluate(input_values, **options)
d = {}
exec(cmd, d)
msg += f": {true_ll} != {d['ll_model']}"
self.assertTrue(np.allclose(true_ll, d['ll_model']),
msg=msg)
if (description == "dadi snp file"
and engine.id in ["dadi", "moments"]):
engine.data_holder.population_labels = None
self.assertRaises(ValueError,
engine.generate_code,
input_values,
None,
*args,
nanc=Nanc)
if description == "fs without pop labels":
engine.data_holder.population_labels = None
engine.generate_code(input_values,
None,
*args,
nanc=Nanc)