def test_VariantIteratorBeginEnd(self):
for i in np.arange(0, self.pop.tables.genome_length, 0.1):
vi = fwdpy11.VariantIterator(self.pop.tables, self.pop.mutations,
[i for i in range(2 * self.pop.N)], i,
i + 0.1)
nm = len([
j for j in self.pop.tables.mutations
if self.pop.mutations[j.key].pos >= i
and self.pop.mutations[j.key].pos < i + 0.1
])
nseen = 0
for v in vi:
r = v.record
self.assertTrue(self.pop.mutations[r.key].pos >= i)
self.assertTrue(self.pop.mutations[r.key].pos < i + 0.1)
nseen += 1
self.assertEqual(nm, nseen)
# test bad start/stop
with self.assertRaises(ValueError):
vi = fwdpy11.VariantIterator(self.pop.tables,
self.pop.mutations,
[i for i in range(2 * self.pop.N)],
begin=0.5,
end=0.25)
def test_mcounts_from_ancient_samples(self):
vi = fwdpy11.VariantIterator(self.pop.tables,
self.pop.tables.preserved_nodes)
for v in vi:
k = v.records[0]
self.assertEqual(self.pop.mcounts_ancient_samples[k.key],
v.genotypes.sum())
def test_VariantIteratorFromPreservedSamples(self):
n = np.array(self.pop.tables.nodes)
pn = np.array(self.pop.tables.preserved_nodes)
at = n['time'][pn]
for u in np.unique(at):
n = pn[np.where(at == u)[0]]
vi = fwdpy11.VariantIterator(self.pop.tables, n)
for variant in vi:
k = variant.records[0]
self.assertNotEqual(k.node, fwdpy11.NULL_NODE)
self.assertNotEqual(k.key, np.iinfo(np.uint64).max)
def test_VariantIteratorFromPopulation(self):
dm = fwdpy11.data_matrix_from_tables(
self.pop.tables, [i for i in range(2 * self.pop.N)], False, True,
True)
sa = np.array(dm.selected)
cs = np.sum(sa, axis=1)
i = 0
vi = fwdpy11.VariantIterator(self.pop)
for v in vi:
c = self.pop.mcounts[self.pop.tables.mutations[i].key]
self.assertEqual(c, cs[i])
self.assertEqual(c, v.genotypes.sum())
i += 1
mc = np.array(self.pop.mcounts)
self.assertEqual(i, len(np.where(mc > 0)[0]))
self.assertEqual(i, len(self.pop.tables.mutations))
def test_VariantIterator(self):
"""
Test VariantIterator by asserting
that sum of genotypes equal values in
the corresponding DataMatrix and
those in pop.mcounts
"""
dm = fwdpy11.data_matrix_from_tables(
self.pop.tables, [i for i in range(2 * self.pop.N)], False, True,
True)
sa = np.array(dm.selected)
cs = np.sum(sa, axis=1)
i = 0
vi = fwdpy11.VariantIterator(self.pop.tables,
[i for i in range(2 * self.pop.N)])
for v in vi:
c = self.pop.mcounts[self.pop.tables.mutations[i].key]
self.assertEqual(c, cs[i])
self.assertEqual(c, v.genotypes.sum())
i += 1
mc = np.array(self.pop.mcounts)
self.assertEqual(i, len(np.where(mc > 0)[0]))
self.assertEqual(i, len(self.pop.tables.mutations))
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)
"""
import pickle
import lzma
import sys
import numpy as np
import seaborn as sns
import fwdpy11
with lzma.open(sys.argv[1], 'rb') as f:
pop = pickle.load(f)
genetic_trait_values_from_sim = []
genetic_values_from_ts = []
idx = 0
for n, s, m in pop.sample_timepoints():
vi = fwdpy11.VariantIterator(pop.tables, s)
sum_esizes = np.zeros(len(s))
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(s) / 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(m['g'].tolist())
# plot the time vs trait value
xyplot = sns.scatterplot(genetic_trait_values_from_sim, genetic_values_from_ts)