def choose_chrom(pool, chrmap):
# Since Alleles is a subclass of ndarray, numpy has been
# treating pool as a multidimensional array. We'll generate
# the indices ourself and get them that way. Eventually
# I'll come back and fix the isinstancing of Alleles.
qi, qw = np.random.randint(0, len(pool), 2)
q, w = pool[qi], pool[qw]
r = recombine(q, w, chrmap)
return r
def test_recombine():
a = Alleles(np.zeros(10))
b = Alleles(np.ones(10))
m = np.arange(1, 100, 10)
n = recombine(a, b, m)
assert len(n) == len(m)
assert type(n) == type(a) == type(b)
assert_raises(ValueError, recombine, None, None, None)
def gamete(self):
""" Provides a set of half-genotypes to use with method fertilize """
if not self.genotypes:
self.get_genotypes()
g = [recombine(chrom[0],
chrom[1],
self.chromosomes[i].genetic_map)
for i, chrom in enumerate(self.genotypes)]
return g
def choose_chrom(pool, chrmap):
"""
Get two random chromosomes, recombine them, return the result
"""
# Since Alleles is a subclass of ndarray, numpy has been
# treating pool as a multidimensional array. We'll generate
# the indices ourself and get them that way. Eventually
# I'll come back and fix the isinstancing of Alleles.
qi, qw = np.random.randint(0, len(pool), 2)
q, w = pool[qi], pool[qw]
r = recombine(q, w, chrmap)
return r
def gamete(self):
"""
Provides a set of half-genotypes to use with method fertilize
:returns: a collection of AlleleContainers
:rtype: list
"""
if not self.genotypes:
self.get_genotypes()
g = [
recombine(chrom[0], chrom[1], self.chromosomes[i].genetic_map)
for i, chrom in enumerate(self.genotypes)
]
return g
def gamete(self):
"""
Provides a set of half-genotypes to use with method fertilize
:returns: a collection of AlleleContainers
:rtype: list
"""
if not self.genotypes:
self.get_genotypes()
g = [recombine(chrom[0],
chrom[1],
self.chromosomes[i].genetic_map)
for i, chrom in enumerate(self.genotypes)]
return g
import numpy as np
from pydigree import ChromosomeTemplate
from pydigree.recombination import recombine
from pydigree.rand import choice as randchoice
from pydigree.rand import sample_with_replacement
nmark = 5000
poolsize = 10000
ngen = 25
c = ChromosomeTemplate()
for x in range(nmark):
c.add_genotype(np.random.random(), .1)
mapp = [.1]*nmark
print("Initializing pool")
pool = [c.linkageequilibrium_chromosome(sparse=True) for x in range(poolsize)]
for x in range(ngen):
print("Iteration {}".format(x))
recombs = [recombine(randchoice(pool),
randchoice(pool),
mapp)
for x in range(poolsize)]
pool = sample_with_replacement(recombs+pool, poolsize)
