def test_counters():
nchrom = 50
pop = pydigree.Population()
for i in range(nchrom):
c = pydigree.ChromosomeTemplate()
c.add_genotype(0.1, 0)
pop.add_chromosome(c)
assert pop.chromosomes.nchrom() == nchrom
assert pop.chromosomes.nloci() == nchrom
def test_randomloci():
nchrom = 100
nloc = 50
pop = pydigree.Population()
for i in range(nchrom):
c = pydigree.ChromosomeTemplate()
c.add_genotype(0.1, 0)
pop.add_chromosome(c)
for i in range(100):
locs = list(pop.chromosomes.select_random_loci(nloc))
locs.sort()
# all the chromosomes have 1 marker
assert all(x[1] == 0 for x in locs)
# all the chromosomes are valid indices
assert all(0 <= x[0] < nchrom for x in locs)
# No duplicates!
for i in range(1, nloc):
assert locs[i] != locs[i - 1]
import numpy as np
import pydigree as pyd
from pydigree.simulation import QuantitativeTrait
ninds = 5000
nloc = 1000
maf = 0.5
traitname = 'synthetic'
# Create population
pop = pyd.Population()
# Create chromosomes
for i in range(100):
c = pyd.ChromosomeTemplate()
c.add_genotype(maf, 0)
pop.add_chromosome(c)
# Create trait QuantitativeTrait
trait = QuantitativeTrait('synthetic',
'quantitative',
chromosomes=pop.chromosomes)
for i in range(100):
trait.add_effect((i, 0), 1 * (-1 if i % 2 else 1))
print('Locus mean genotypic value: {}'.format(
trait.effects[0].expected_genotypic_value))
print('Locus variance: {}'.format(trait.effects[0].locus_additive_variance))
print('Expected trait mean: {}'.format(trait.expected_genotypic_value))
#!/usr/bin/env python3
import pydigree
from pydigree.simulation import QuantitativeTrait
nchrom = 1
popsize = 500
ngen = 100
pop = pydigree.Population(popsize)
for x in range(nchrom):
c = pydigree.ChromosomeTemplate()
c.add_genotype(0.1, 0)
pop.add_chromosome(c)
trait = QuantitativeTrait('q', 'quantitative')
for i in range(nchrom):
trait.add_effect_liability((i, 0), 0, 1)
print(trait)
pop.initialize_pool()
for x in range(popsize):
pop.add_founder_individual()
def gen(ninds):
"Advance by one generation, with ninds in the new generation"
pop.advance_generation(ninds)
pop.get_genotypes()
pop.remove_ancestry()
return pop.allele_frequency((0, 0), 1)
def genedrop(pedigree, affs, scorer, iteration):
if iteration % (args.niter / 10) == 0:
print('Simulation %s' % iteration)
pedigree.simulate_ibd_states(inds=affs)
s = scorer(affs, (0, 0))
return s
try:
scorefunction = {'sbool': sbool, 'spairs': spairs}[args.scorefunc]
except KeyError:
print("Invalid score function {}".format(args.scorefunc))
pop = pydigree.Population(5000)
peds = pydigree.io.read_ped(args.file, pop)
c = pydigree.ChromosomeTemplate()
c.add_genotype(0.5, 0)
peds.add_chromosome(c)
nulldist = {}
naff = sum(1 for ind in peds.individuals if ind.phenotypes['affected'])
print('{} affecteds'.format(naff))
if args.remove_mif:
for ind in peds.individuals:
if ind.is_marryin_founder():
ind.phenotypes['affected'] = False
#!/usr/bin/env python3
import pydigree
import time
import numpy as np
n = 5000
k = 2 * 10**5
r = .5
gens = 15
nmark = 2000
chrom_length_cm = 200
intermark_dist_cm = (float(chrom_length_cm) / nmark) / 100
pop = pydigree.Population(n)
for rx in range(1):
c = pydigree.ChromosomeTemplate()
for x in range(nmark):
c.add_genotype(np.random.random(), intermark_dist_cm)
print(c)
pop.add_chromosome(c)
print('Initializing pool')
pop.initialize_pool()
gen_sizes = [pydigree.logistic_growth(n, r, k, t) for t in range(gens)]
print('Iterating pool')
for i, g in enumerate(gen_sizes):
#print 'Generation %d (%d chromosomes)...' % (i,pop.chrom_pool_size())
#ng = pydigree.logistic_growth(p0,r,pfinal,g)
t = time.time()
pop.iterate_pool(g)
t2 = time.time()