def setUp(self):
GN = fwdpy11.GaussianNoise
self.w = fwdpy11.Multiplicative(2.0)
self.t = fwdpy11.Multiplicative(
2.0, fwdpy11.GSS(0.0, 1.0))
self.tn = fwdpy11.Multiplicative(1.0,
fwdpy11.GSS(
0.0, 1.0),
GN(mean=0.1, sd=2.0))
def setUpClass(self):
# TODO add neutral variants
self.N = 1000
self.demography = np.array([self.N] * self.N, dtype=np.uint32)
self.rho = 1.
self.theta = 100.
self.nreps = 500
self.mu = self.theta / (4 * self.N)
self.r = self.rho / (4 * self.N)
self.GSS = fwdpy11.GSS(VS=1, opt=0)
a = fwdpy11.Additive(2.0, self.GSS)
self.p = {
'nregions': [],
'sregions': [fwdpy11.GaussianS(0, 1, 1, 0.25)],
'recregions': [fwdpy11.Region(0, 1, 1)],
'rates': (0.0, 0.025, self.r),
'gvalue': a,
'prune_selected': False,
'demography': self.demography
}
self.params = fwdpy11.ModelParams(**self.p)
self.rng = fwdpy11.GSLrng(101 * 45 * 110 * 210)
self.pop = fwdpy11.DiploidPopulation(self.N, 1.0)
self.all_samples = [i for i in range(2 * self.N)]
fwdpy11.evolvets(self.rng, self.pop, self.params, 100)
self.dm = fwdpy11.data_matrix_from_tables(self.pop.tables,
self.all_samples, True, True)
self.neutral = np.array(self.dm.neutral)
self.npos = np.array(self.dm.neutral.positions)
self.selected = np.array(self.dm.selected)
self.spos = np.array(self.dm.selected.positions)
def setUp(self):
import fwdpy11
import numpy as np
self.N = 1000
self.demography = np.array([self.N] * self.N, dtype=np.uint32)
self.rho = 1.
self.theta = 100.
self.nreps = 500
self.mu = self.theta / (4 * self.N)
self.r = self.rho / (4 * self.N)
self.GSS = fwdpy11.GSS(VS=1, opt=0)
a = fwdpy11.Additive(2.0, self.GSS)
self.p = {
'nregions': [],
'sregions': [fwdpy11.GaussianS(0, 1, 1, 0.25)],
'recregions': [fwdpy11.Region(0, 1, 1)],
'rates': (0.0, 0.025, self.r),
'gvalue': a,
'prune_selected': False,
'demography': self.demography
}
self.params = fwdpy11.ModelParams(**self.p)
self.rng = fwdpy11.GSLrng(101 * 45 * 110 * 210)
self.pop = fwdpy11.DiploidPopulation(self.N, 1.0)
fwdpy11.evolvets(self.rng, self.pop, self.params, 100)
def setUp(self):
self.N = 1000
self.demography = np.array([self.N] * 100, dtype=np.uint32)
self.rho = 1.
self.theta = 100.
self.nreps = 500
self.mu = self.theta / (4 * self.N)
self.r = self.rho / (4 * self.N)
self.GSS = fwdpy11.GSS(VS=1, opt=0)
a = fwdpy11.Additive(2.0, self.GSS)
self.p = {
'nregions': [],
'sregions': [fwdpy11.GaussianS(0, 1, 1, 0.25)],
'recregions': [fwdpy11.Region(0, 1, 1)],
'rates': (0.0, 0.025, self.r),
'gvalue': a,
'prune_selected': False,
'demography': self.demography
}
self.params = fwdpy11.ModelParams(**self.p)
self.rng = fwdpy11.GSLrng(101 * 45 * 110 * 210)
self.pop = fwdpy11.DiploidPopulation(self.N, 1.0)
self.recorder = fwdpy11.RandomAncientSamples(
seed=42, samplesize=10, timepoints=[i for i in range(1, 101)])
fwdpy11.evolvets(self.rng, self.pop, self.params, 100, self.recorder)
def setUpClass(self):
class CountSamplesPerTimePoint(object):
def __init__(self):
self.sample_timepoints = []
self.sample_sizes = []
self.timepoint_seen = {}
def __call__(self, pop):
assert len(pop.tables.preserved_nodes)//2 == \
len(pop.ancient_sample_metadata)
# Get the most recent ancient samples
# and record their number. We do this
# by a "brute-force" approach
for t, n, m in pop.sample_timepoints(False):
if t not in self.timepoint_seen:
self.timepoint_seen[t] = 1
else:
self.timepoint_seen[t] += 1
if t not in self.sample_timepoints:
self.sample_timepoints.append(t)
self.sample_sizes.append(len(n) // 2)
# simplify to each time point
tables, idmap = fwdpy11.simplify_tables(pop.tables, n)
for ni in n:
assert idmap[ni] != fwdpy11.NULL_NODE
assert tables.nodes[idmap[ni]].time == t
self.N = 1000
self.demography = np.array([self.N] * 101, dtype=np.uint32)
self.rho = 1.
self.r = self.rho / (4 * self.N)
self.GSS = fwdpy11.GSS(VS=1, opt=0)
a = fwdpy11.Additive(2.0, self.GSS)
self.p = {
'nregions': [],
'sregions': [fwdpy11.GaussianS(0, 1, 1, 0.25)],
'recregions': [fwdpy11.Region(0, 1, 1)],
'rates': (0.0, 0.025, self.r),
'gvalue': a,
'prune_selected': False,
'demography': self.demography
}
self.params = fwdpy11.ModelParams(**self.p)
self.rng = fwdpy11.GSLrng(101 * 45 * 110 * 210)
self.pop = fwdpy11.DiploidPopulation(self.N, 1.0)
self.all_samples = [i for i in range(2 * self.N)]
self.ancient_sample_recorder = \
fwdpy11.RandomAncientSamples(seed=42,
samplesize=10,
timepoints=[i for i in range(1, 101)])
self.resetter = CountSamplesPerTimePoint()
fwdpy11.evolvets(self.rng,
self.pop,
self.params,
5,
recorder=self.ancient_sample_recorder,
post_simplification_recorder=self.resetter)
def testQtraitSim(self):
N = 1000
demography = np.array([N] * 10 * N, dtype=np.uint32)
rho = 1.
r = rho / (4 * N)
GSS = fwdpy11.GSS(VS=1, opt=1)
a = fwdpy11.Additive(2.0, GSS)
p = {
'nregions': [],
'sregions': [fwdpy11.GaussianS(0, 1, 1, 0.25)],
'recregions': [fwdpy11.Region(0, 1, 1)],
'rates': (0.0, 0.005, r),
'gvalue': a,
'prune_selected': False,
'demography': demography
}
params = fwdpy11.ModelParams(**p)
rng = fwdpy11.GSLrng(101 * 45 * 110 * 210)
pop = fwdpy11.DiploidPopulation(N, 1.0)
fwdpy11.evolvets(rng, pop, params, 100, track_mutation_counts=True)
mc = fwdpy11.count_mutations(pop.tables, pop.mutations,
[i for i in range(2 * pop.N)])
assert len(pop.fixations) > 0, "Test is meaningless without fixations"
fixations = np.where(mc == 2 * pop.N)[0]
self.assertEqual(len(fixations), len(pop.fixations))
# Brute-force calculation of fixations
brute_force = np.zeros(len(pop.mutations), dtype=np.int32)
for g in pop.haploid_genomes:
if g.n > 0:
for k in g.smutations:
brute_force[k] += g.n
self.assertTrue(np.array_equal(brute_force, mc))
self.assertTrue(np.array_equal(brute_force, pop.mcounts))
def testQtraitSim(self):
N = 1000
demography = np.array([N] * 10 * N, dtype=np.uint32)
rho = 1.
r = rho / (4 * N)
GSS = fwdpy11.GSS(VS=1, opt=1)
a = fwdpy11.Additive(2.0, GSS)
p = {
'nregions': [],
'sregions': [fwdpy11.GaussianS(0, 1, 1, 0.25)],
'recregions': [fwdpy11.PoissonInterval(0, 1, r)],
'rates': (0.0, 0.005, None),
'gvalue': a,
'prune_selected': False,
'demography': demography
}
params = fwdpy11.ModelParams(**p)
rng = fwdpy11.GSLrng(101 * 45 * 110 * 210)
pop = fwdpy11.DiploidPopulation(N, 1.0)
class Recorder(object):
""" Records entire pop every 100 generations """
def __call__(self, pop, recorder):
if pop.generation % 100 == 0.0:
recorder.assign(np.arange(pop.N, dtype=np.int32))
r = Recorder()
fwdpy11.evolvets(rng, pop, params, 100, r)
ancient_sample_metadata = np.array(pop.ancient_sample_metadata,
copy=False)
alive_sample_metadata = np.array(pop.diploid_metadata, copy=False)
metadata = np.hstack((ancient_sample_metadata, alive_sample_metadata))
nodes = np.array(pop.tables.nodes, copy=False)
metadata_nodes = metadata['nodes'].flatten()
metadata_node_times = nodes['time'][metadata_nodes]
metadata_record_times = nodes['time'][metadata['nodes'][:, 0]]
genetic_trait_values_from_sim = []
genetic_values_from_ts = []
for u in np.unique(metadata_node_times):
samples_at_time_u = metadata_nodes[np.where(
metadata_node_times == u)]
vi = fwdpy11.VariantIterator(pop.tables, samples_at_time_u)
sum_esizes = np.zeros(len(samples_at_time_u))
for variant in vi:
g = variant.genotypes
r = variant.records[0]
mutant = np.where(g == 1)[0]
sum_esizes[mutant] += pop.mutations[r.key].s
ind = int(len(samples_at_time_u) / 2)
temp_gvalues = np.zeros(ind)
temp_gvalues += sum_esizes[0::2]
temp_gvalues += sum_esizes[1::2]
genetic_values_from_ts.extend(temp_gvalues.tolist())
genetic_trait_values_from_sim.extend(metadata['g'][np.where(
metadata_record_times == u)[0]].tolist())
for i, j in zip(genetic_trait_values_from_sim, genetic_values_from_ts):
self.assertAlmostEqual(i, j)
def setUp(self):
self.VS = 1.0
self.opt = 0.0
self.g = fwdpy11.GSS(VS=self.VS, opt=self.opt)
import fwdpy11
print(fwdpy11.__file__)
import numpy as np
import FixedCrossoverInterval
N = 1000
pdict = {
'demography': fwdpy11.DiscreteDemography(),
'simlen': 100,
'nregions': [],
'sregions': [fwdpy11.GaussianS(0, 1, 1, 0.25)],
'recregions': [FixedCrossoverInterval.FixedCrossoverInterval(0, 1, 2)],
'rates': (0., 5e-3, None),
'gvalue': fwdpy11.Additive(2., fwdpy11.GSS(VS=1, opt=1)),
'prune_selected': False
}
params = fwdpy11.ModelParams(**pdict)
print(params.recregions)
pop = fwdpy11.DiploidPopulation(N, 1.0)
rng = fwdpy11.GSLrng(42)
fwdpy11.evolvets(rng, pop, params, 100)
def setUp(self):
self.a = fwdpy11.Additive(
2.0, fwdpy11.GSS(0.0, 1.0))
self.b = fwdpy11.Additive(
2.0, fwdpy11.GSSmo([(0, 0.0, 1.0)]))
self.pop = fwdpy11.DiploidPopulation(1000)
def setUp(self):
self.x = fwdpy11.GSS(0.0, 1.0)
def setUp(self):
self.gss = fwdpy11.GSS(0.0, 1.0)
self.gnoise = fwdpy11.GaussianNoise(
mean=0.0, sd=1.0)
self.nonoise = fwdpy11.NoNoise()
