def checkNumOffspring(numOffspring, ops=[]):
'''Check the number of offspring for each family using
information field father_idx
'''
pop = sim.Population(size=[30],
loci=1,
infoFields=['father_idx', 'mother_idx'])
pop.evolve(initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.5, 0.5]),
],
matingScheme=sim.RandomMating(ops=[
sim.MendelianGenoTransmitter(),
sim.ParentsTagger(),
] + ops,
numOffspring=numOffspring),
gen=1)
# get the parents of each offspring
parents = [
(x, y)
for x, y in zip(pop.indInfo('mother_idx'), pop.indInfo('father_idx'))
]
# Individuals with identical parents are considered as siblings.
famSize = []
lastParent = (-1, -1)
for parent in parents:
if parent == lastParent:
famSize[-1] += 1
else:
lastParent = parent
famSize.append(1)
return famSize
def _make_pop(popsize, nloci, locus_position, id_tagger, init_geno,
recomb_rate, generations, length, init_ts):
random.seed(123)
pop = sim.Population(size=[popsize],
loci=[nloci],
lociPos=locus_position,
infoFields=['ind_id'])
# tag the first generation so we can pass it to rc
id_tagger.apply(pop)
first_gen = pop.indInfo("ind_id")
haploid_labels = [(k, p) for k in first_gen for p in (0, 1)]
node_ids = {x: j for x, j in zip(haploid_labels, init_ts.samples())}
rc = ftprime.RecombCollector(ts=init_ts,
node_ids=node_ids,
locus_position=locus_position)
recombinator = sim.Recombinator(intensity=recomb_rate,
output=rc.collect_recombs,
infoFields="ind_id")
pop.evolve(
initOps=[sim.InitSex()] + init_geno,
preOps=[
sim.PyOperator(lambda pop: rc.increment_time() or True),
# Must return true or false. True keeps whole population (?)
],
matingScheme=mating_scheme_factory(recombinator, popsize,
id_tagger),
postOps=[sim.PyEval(r"'Gen: %2d\n' % (gen, )", step=1)],
gen=generations)
return pop, rc
def simulate(model, N0, N1, G0, G1, spec, s, mu, k):
'''Evolve a sim.Population using given demographic model
and observe the evolution of its allelic spectrum.
model: type of demographic model.
N0, N1, G0, G1: parameters of demographic model.
spec: initial allelic spectrum, should be a list of allele
frequencies for each allele.
s: selection pressure.
mu: mutation rate.
k: k for the k-allele model
'''
demo_func = demo_model(model, N0, N1, G0, G1)
pop = sim.Population(size=demo_func(0), loci=1, infoFields='fitness')
pop.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=spec, loci=0)
],
matingScheme=sim.RandomMating(subPopSize=demo_func),
postOps=[
sim.KAlleleMutator(k=k, rates=mu),
sim.MaSelector(loci=0, fitness=[1, 1, 1 - s], wildtype=0),
ne(loci=[0], step=100),
sim.PyEval(r'"%d: %.2f\t%.2f\n" % (gen, 1 - alleleFreq[0][0], ne[0])',
step=100),
],
gen = G0 + G1
)
def checkSexMode(ms):
'''Check the assignment of sex to offspring'''
pop = sim.Population(size=[40])
pop.evolve(initOps=sim.InitSex(), matingScheme=ms, gen=1)
# return individual sex as a string
return ''.join(
['M' if ind.sex() == sim.MALE else 'F' for ind in pop.individuals()])
def LC_evolve(popSize, alleleFreq, diseaseModel):
'''
'''
pop = sim.Population(
size=popSize,
loci=[1] * len(alleleFreq),
infoFields=['age', 'smoking', 'age_death', 'age_LC', 'LC'])
pop.setVirtualSplitter(
sim.CombinedSplitter(splitters=[
sim.InfoSplitter(field='age',
cutoff=[20, 40],
names=['youngster', 'adult', 'senior']),
sim.SexSplitter(),
sim.InfoSplitter(field='smoking',
values=[0, 1, 2],
names=['nonSmoker', 'smoker', 'formerSmoker'])
]))
pop.evolve(
initOps=[sim.InitSex(),
sim.InitInfo(range(75), infoFields='age')] + [
sim.InitGenotype(freq=[1 - f, f], loci=i)
for i, f in enumerate(alleleFreq)
] + [
sim.PyOperator(func=diseaseModel.initialize),
],
preOps=[
sim.InfoExec('age += 1'),
# die of lung cancer or natural death
sim.DiscardIf('age > age_death')
],
matingScheme=sim.HeteroMating(
[
sim.CloneMating(weight=-1),
sim.RandomMating(ops=[
sim.MendelianGenoTransmitter(),
sim.PyOperator(func=diseaseModel.initialize)
],
subPops=[(0, 'adult')])
],
subPopSize=lambda pop: pop.popSize() + popSize / 75),
postOps=[
# update individual, currently ding nothing.
sim.PyOperator(func=diseaseModel.updateStatus),
# determine if someone has LC at his or her age
sim.InfoExec('LC = age >= age_LC'),
# get statistics about COPD and LC prevalence
sim.Stat(pop,
meanOfInfo='LC',
subPops=[(0, sim.ALL_AVAIL)],
vars=['meanOfInfo', 'meanOfInfo_sp']),
sim.PyEval(
r"'Year %d: Overall %.2f%% M: %.2f%% F: %.2f%% "
r"NS: %.1f%%, S: %.2f%%\n' % (gen, meanOfInfo['LC']*100, "
r"subPop[(0,3)]['meanOfInfo']['LC']*100,"
r"subPop[(0,4)]['meanOfInfo']['LC']*100,"
r"subPop[(0,5)]['meanOfInfo']['LC']*100,"
r"subPop[(0,6)]['meanOfInfo']['LC']*100)"),
],
gen=100)
def setUp(self):
self.pop = simu.Population(size=10, loci=1, infoFields='self_gen')
self.sim = simu.Simulator(pops=self.pop)
self.initOps = [
simu.InitSex(sex=[simu.MALE, simu.FEMALE]),
simu.InitInfo(0, infoFields=['self_gen'])
]
self.sexMode = (simu.GLOBAL_SEQUENCE_OF_SEX, simu.MALE, simu.FEMALE)
def initialize_tuson_pop(self, chromosome_lengths, info_fields,
all_genotypes_handle):
tuson_founders = sim.Population(size=105, loci=chromosome_lengths,
infoFields=info_fields)
for ind, genotype in zip(tuson_founders.individuals(),
all_genotypes_handle):
ind.setGenotype(genotype)
sim.tagID(tuson_founders, reset=True)
return tuson_founders
def get_mean_r2(Ne, S, n_loci, gens, n_subpops, initial_frequencies, m):
"""Returns the mean r2 value for each subpopulation, in list of length n_subpops"""
# make pairwise migration matrix
M = get_migration_matrix(m, n_subpops)
# initialise population
n_alleles = 2
pop = sim.Population(size=[Ne] * n_subpops,
ploidy=2,
loci=[1] * n_loci,
alleleNames=[str(i) for i in range(n_alleles)],
infoFields='migrate_to')
sim.initGenotype(pop, freq=[initial_frequencies, 1 - initial_frequencies])
#sim.initGenotype(pop, freq = [1/n_alleles for i in range(n_alleles)])
sim.initSex(pop)
print(M)
# run burn in generations
pop.evolve(initOps=[],
preOps=sim.Migrator(M, mode=sim.BY_PROBABILITY),
matingScheme=sim.RandomMating(),
gen=gens)
# take sample from each subpopulation
sample_pop = drawRandomSample(pop, sizes=[S] + [0] * (n_subpops - 1))
#sim.dump(sample_pop)
# get allele frequencies
sim.stat(sample_pop, alleleFreq=range(0, n_loci), vars=['alleleFreq_sp'])
#print(sample_pop.dvars(0).alleleFreq)
# calculate r2 values
sim.stat(sample_pop,
LD=list(itertools.combinations(list(range(n_loci)), r=2)),
vars=['R2_sp'])
#print(sample_pop.dvars(0).R2)
r2s = []
for sp in [0]: #range(n_subpops*0):
allele_freqs = sample_pop.dvars(sp).alleleFreq
seg_alleles = [
k for k in range(n_loci)
if np.abs(.5 - allele_freqs[k][0]) < .5 - 0.05
]
if len(seg_alleles) < 2: raise Exception("<2 segregating alleles")
r2_sum = 0
count = 0
for pairs in itertools.combinations(seg_alleles, r=2):
r2_sum += sample_pop.dvars(sp).R2[pairs[0]][pairs[1]]
count += 1
mean_r2 = r2_sum / count
r2s.append(mean_r2)
return r2s
def _create_single_pop(self, pop_size, nloci):
init_ops = []
init_ops.append(sp.InitSex())
pop = sp.Population(pop_size, ploidy=2, loci=[1] * nloci,
chromTypes=[sp.AUTOSOME] * nloci,
infoFields=list(self._info_fields))
pre_ops = []
post_ops = []
return pop, init_ops, pre_ops, post_ops
def get_FCs(Ne, S, n_loci, gens, n_subpops, initial_frequencies, m):
''''Runs simulations for allelic fluctuations model with n subpopulations, and returns a list of FC values (one for each subpopulation)'''
# population to evolve ((from infinite gamete pool))
popNe = sim.Population(size=[Ne] * n_subpops,
ploidy=2,
loci=[1] * n_loci,
infoFields='migrate_to')
# initial sample population (from infinite gamete pool)
popS = sim.Population(size=[S] * n_subpops, ploidy=2, loci=[1] * n_loci)
sim.initGenotype(popNe, freq=initial_frequencies)
sim.initGenotype(popS, freq=initial_frequencies)
# get initial sample allele frequencies
sim.stat(popS, alleleFreq=range(n_loci), vars=['alleleFreq_sp'])
M = get_migration_matrix(m, n_subpops)
popNe.evolve(initOps=[sim.InitSex()],
preOps=sim.Migrator(rate=M),
matingScheme=sim.RandomMating(),
gen=gens)
sample_pop = drawRandomSample(popNe, sizes=[S] * n_subpops)
sim.stat(sample_pop, alleleFreq=range(n_loci), vars=['alleleFreq_sp'])
all_FCs = []
for sp in range(n_subpops):
initial_allele_frequencies = popS.dvars(sp).alleleFreq
final_allele_frequencies = sample_pop.dvars(sp).alleleFreq
sp_count = 0
sp_FC = 0
for locus in range(n_loci):
init_pair = repair(initial_allele_frequencies[locus])
end_pair = repair(final_allele_frequencies[locus])
if init_pair[0]**2 + init_pair[1]**2 != 1:
sp_FC += fc_variant([init_pair[0], init_pair[1]],
[end_pair[0], end_pair[1]])
sp_count += 1
all_FCs.append(sp_FC / sp_count)
return all_FCs
def _create_island(self, pop_sizes, mig, nloci):
init_ops = []
init_ops.append(sp.InitSex())
pop = sp.Population(pop_sizes, ploidy=2, loci=[1] * nloci,
chromTypes=[sp.AUTOSOME] * nloci,
infoFields=list(self._info_fields))
post_ops = [sp.Migrator(
demography.migrIslandRates(mig, len(pop_sizes)))]
pre_ops = []
self._info_fields.add('migrate_to')
return pop, init_ops, pre_ops, post_ops
def allelesToMutants(pop, regions, logger=None):
'''Convert a population from allele space to mutational space, using
specified regions.
'''
pops = []
for region in regions:
loci = []
ch_name = region.split(':')[0][3:]
start, end = [int(x) for x in region.split(':')[1].split('..')]
try:
ch = pop.chromByName(ch_name)
except:
raise ValueError(
'Chromosome %s is not available in passed population.' %
ch_name)
for loc in range(pop.chromBegin(ch), pop.chromEnd(ch)):
pos = pop.locusPos(loc)
if pos >= start and pos <= end:
loci.append(loc)
# get the mutants for each individual
allAlleles = []
for ind in pop.individuals():
alleles0 = []
alleles1 = []
for loc in loci:
if ind.allele(loc, 0) != 0:
alleles0.append(int(pop.locusPos(loc)))
if ind.allele(loc, 1) != 0:
alleles1.append(int(pop.locusPos(loc)))
allAlleles.extend([alleles0, alleles1])
# maximum number of mutants
maxMutants = max([len(x) for x in allAlleles])
if logger is not None:
logger.info(
'%d loci are identified with at most %d mutants in region %s.'
% (len(loci), maxMutants, region))
# create a population
mpop = sim.Population(pop.popSize(),
loci=maxMutants,
chromNames=region)
# put in mutants
for idx, ind in enumerate(mpop.individuals()):
geno = ind.genotype(0)
for loc, mut in enumerate(allAlleles[idx * 2]):
geno[loc] = mut
geno = ind.genotype(1)
for loc, mut in enumerate(allAlleles[idx * 2 + 1]):
geno[loc] = mut
pops.append(mpop)
# merge all populations into one
for pop in pops[1:]:
pops[0].addChromFrom(pop)
return pops[0]
def simulate(demo, gen):
pop = sim.Population(size=demo(0))
pop.evolve(initOps=sim.InitSex(),
preOps=[
sim.Stat(popSize=True),
sim.PyEval(r"'%d: %d ' % (gen, popSize)"),
],
matingScheme=sim.RandomMating(subPopSize=demo),
postOps=[
sim.Stat(popSize=True),
sim.PyEval(r"'--> %d\n' % popSize"),
],
gen=gen)
def simulate():
pop = sim.Population(1000, loci=10, infoFields='age')
pop.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.5, 0.5]),
sim.InitInfo(lambda: random.randint(0, 10), infoFields='age')
],
matingScheme=sim.RandomMating(),
finalOps=sim.Stat(alleleFreq=0),
gen=100
)
return pop.dvars().alleleFreq[0][0]
def test_slice_transition(self):
init_population_sizes = self.net_model.get_initial_size()
log.info("init_pop_sizes: %s", init_population_sizes)
names = self.net_model.get_subpopulation_names()
pop = sim.Population(size=init_population_sizes,
subPopNames=names,
infoFields=self.net_model.get_info_fields())
for time in range(1, self.end_sim):
pop.dvars().gen = time
self.net_model(pop)
log.info("time: %s subpop names: %s subpop sizes: %s", time,
self.net_model.get_subpopulation_names(),
self.net_model.get_subpopulation_sizes())
def setUp(self):
# A locus with 10 sites
"Let there are 5 loci with 2 sites each"
self.allele_length = 2
self.loci = 5
self.pop = simu.Population(size=10,
loci=self.allele_length * self.loci,
infoFields='self_gen')
self.sim = simu.Simulator(pops=self.pop)
self.initOps = [
simu.InitSex(sex=[simu.MALE, simu.FEMALE]),
simu.InitInfo(0, infoFields=['self_gen'])
]
self.sexMode = (simu.GLOBAL_SEQUENCE_OF_SEX, simu.MALE, simu.FEMALE)
def get_mean_r2(Ne, S, n_loci, gens, repeats, n_subpops, initial_frequencies,
m):
M = get_migration_matrix(m, n_subpops)
pop = sim.Population(size=[Ne] * n_subpops,
ploidy=2,
loci=[1] * n_loci,
infoFields='migrate_to')
sim.initGenotype(pop, freq=initial_frequencies)
pop.evolve(
initOps=[sim.InitSex(),
sim.InitGenotype(freq=initial_frequencies)],
preOps=sim.Migrator(rate=M),
matingScheme=sim.RandomMating(),
gen=gens)
sample_pop = drawRandomSample(pop, sizes=[S] * n_subpops)
# get allele frequencies
sim.stat(sample_pop, alleleFreq=range(0, n_loci), vars=['alleleFreq_sp'])
# calculate r2 values
sim.stat(sample_pop,
LD=list(combinations(list(range(n_loci)), r=2)),
vars=['R2_sp'])
r2s = []
for sp in range(n_subpops):
allele_freqs = sample_pop.dvars(sp).alleleFreq
seg_alleles = [
k for k in range(n_loci)
if np.abs(.5 - allele_freqs[k][0]) < .5 - 0.05
]
if len(seg_alleles) < 2: raise Exception("<2 segregating alleles")
r2_sum = count = 0
for pairs in combinations(seg_alleles, r=2):
r2_sum += sample_pop.dvars(sp).R2[pairs[0]][pairs[1]]
count += 1
mean_r2 = r2_sum / count
r2s.append(mean_r2)
return r2s
def mutantsToAlleles(pop, logger):
'''Convert a population from mutational space to allele space. Monomorphic
markers are ignored.
'''
# figure out chromosomes and markers
markers = {}
for ch, region in enumerate(pop.chromNames()):
chNumber = region.split(':')[0][3:]
loci = set()
for ind in pop.individuals():
loci |= set(ind.genotype(0, ch))
loci |= set(ind.genotype(1, ch))
if chNumber in markers:
markers[chNumber] |= loci
else:
markers[chNumber] = loci
# create a population for each chromosome
pops = []
chroms = list(markers.keys())
chroms.sort()
for ch in chroms:
markers[ch] = list(markers[ch])
markers[ch].remove(0)
markers[ch].sort()
if logger:
logger.info('Chromosome %s has %d markers' %
(ch, len(markers[ch])))
apop = sim.Population(pop.popSize(),
loci=len(markers[ch]),
lociPos=markers[ch])
# get a dictionary of loci position
lociPos = {}
for idx, loc in enumerate(apop.lociPos()):
lociPos[loc] = idx
for aind, mind in zip(apop.individuals(), pop.individuals()):
for p in range(2):
for mutant in mind.genotype(p):
if mutant != 0:
aind.setAllele(1, lociPos[mutant], p)
pops.append(apop)
for pop in pops[1:]:
pops[0].addChromFrom(pop)
return pops[0]
def createSinglePop(popSize, nLoci, startLambda=99999, lbd=1.0):
initOps = [sp.InitSex(maleFreq=cfg.maleProb)]
if startLambda < 99999:
preOps = [sp.ResizeSubPops(proportions=(float(lbd), ),
begin=startLambda)]
else:
preOps = []
postOps = []
pop = sp.Population(popSize, ploidy=2, loci=[1] * nLoci,
chromTypes=[sp.AUTOSOME] * nLoci,
infoFields=["ind_id", "father_id", "mother_id",
"age", "breed", "rep_succ",
"mate", "force_skip"])
for ind in pop.individuals():
ind.breed = -1000
oExpr = ('"%s/samp/%f/%%d/%%d/smp-%d-%%d-%%d.txt" %% ' +
'(numIndivs, numLoci, gen, rep)') % (
cfg.dataDir, cfg.mutFreq, popSize)
return pop, initOps, preOps, postOps, oExpr
def _create_stepping_stone(self, pop_sizes, mig, nloci):
if len(pop_sizes) == 1:
flat_pop_sizes = pop_sizes[0]
post_ops = [sp.Migrator(
demography.migrSteppingStoneRates(mig, len(flat_pop_sizes)))]
else:
flat_pop_sizes = []
for line in pop_sizes:
flat_pop_sizes.extend(line)
post_ops = [sp.Migrator(
demography.migr2DSteppingStoneRates(mig,
len(pop_sizes),
len(pop_sizes[0])))]
init_ops = []
init_ops.append(sp.InitSex())
pop = sp.Population(flat_pop_sizes, ploidy=2, loci=[1] * nloci,
chromTypes=[sp.AUTOSOME] * nloci,
infoFields=list(self._info_fields))
pre_ops = []
self._info_fields.add('migrate_to')
return pop, init_ops, pre_ops, post_ops
def importData(filename):
'Read data from ``filename`` and create a population'
data = open(filename)
header = data.readline()
fields = header.split(',')
# columns 1, 3, 5, ..., without trailing '_1'
names = [fields[x].strip()[:-2] for x in range(1, len(fields), 2)]
popSize = 0
alleleNames = set()
for line in data.readlines():
# get all allele names
alleleNames |= set([x.strip() for x in line.split(',')[1:]])
popSize += 1
# create a population
alleleNames = list(alleleNames)
pop = sim.Population(size=popSize, loci=len(names), lociNames=names,
alleleNames=alleleNames,chromTypes=sim.CHROMOSOME_X,infoFields='fitness')
# start from beginning of the file again
data.seek(0)
# discard the first line
data.readline()
for ind, line in zip(pop.individuals(), data.readlines()):
fields = [x.strip() for x in line.split(',')]
if fields[0] == '1':
sex = sim.MALE
ploidy0 = [alleleNames.index(fields[x]) for x in range(1, len(fields), 2)]
ind.setGenotype(ploidy0, 0)
ind.setSex(sex)
else:
sex = sim.FEMALE
ploidy0 = [alleleNames.index(fields[x]) for x in range(1, len(fields), 2)]
ploidy1 = [alleleNames.index(fields[x]) for x in range(2, len(fields), 2)]
ind.setGenotype(ploidy0, 0)
ind.setGenotype(ploidy1, 1)
ind.setSex(sex)
# close the file
data.close()
return pop
import simuPOP as sim
from simuPOP.utils import migrIslandRates
p = [0.2, 0.3, 0.5]
pop = sim.Population(size=[10000] * 3, loci=1, infoFields='migrate_to')
simu = sim.Simulator(pop, rep=2)
simu.evolve(
initOps=[sim.InitSex()] +
[sim.InitGenotype(prop=[p[i], 1 - p[i]], subPops=i) for i in range(3)],
preOps=sim.Migrator(rate=migrIslandRates(0.01, 3), reps=0),
matingScheme=sim.RandomMating(),
postOps=[
sim.Stat(alleleFreq=0, structure=0, vars='alleleFreq_sp', step=50),
sim.PyEval(
"'Fst=%.3f (%s)\t' % (F_st, ', '.join(['%.2f' % "
"subPop[x]['alleleFreq'][0][0] for x in range(3)]))",
step=50),
sim.PyOutput('\n', reps=-1, step=50),
],
gen=201)
# # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # # This script is an example in the simuPOP user's guide. Please refer to # the user's guide (http://simupop.sourceforge.net/manual) for a detailed # description of this example. # import simuPOP as sim from pprint import pprint pop = sim.Population(100, loci=2) sim.initGenotype(pop, freq=[0.3, 0.7]) print(pop.vars()) # No variable now pop.dvars().myVar = 21 print(pop.vars()) sim.stat(pop, popSize=1, alleleFreq=0) # pprint prints in a less messy format pprint(pop.vars()) # print number of allele 1 at locus 0 print(pop.vars()['alleleNum'][0][1]) # use the dvars() function to access dictionary keys as attributes print(pop.dvars().alleleNum[0][1]) print(pop.dvars().alleleFreq[0])
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# This script is an example in the simuPOP user's guide. Please refer to
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
import random
def demo(pop):
return [x + random.randint(50, 100) for x in pop.subPopSizes()]
pop = sim.Population(size=[500, 1000], infoFields='migrate_to')
pop.evolve(
initOps=sim.InitSex(),
matingScheme=sim.RandomMating(subPopSize=demo),
postOps=[
sim.Stat(popSize=True),
sim.PyEval(r'"%s\n" % subPopSize')
],
gen = 3
)
counter = defaultdict(int)
def countMutants(mutants):
global counter
for line in mutants.split('\n'):
# a trailing \n will lead to an empty string
if not line:
continue
(gen, loc, ploidy, a1, a2, id) = line.split('\t')
counter[int(loc)] += 1
pop = sim.Population(
[5000] * 3,
loci=[2, 1, 1],
infoFields='ind_id',
chromTypes=[sim.AUTOSOME, sim.CHROMOSOME_X, sim.CHROMOSOME_Y])
pop.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.5, 0.5]),
sim.IdTagger(),
],
preOps=[
sim.KAlleleMutator(rates=[0.001] + [0.01] * 3,
loci=range(4),
k=100,
output=countMutants),
],
matingScheme=sim.RandomMating(
import simuOpt
simuOpt.setOptions(quiet=True, alleleType='long')
import simuPOP as sim
pop = sim.Population(size=[2500]*10, loci=1)
simu = sim.Simulator(pop, rep=2)
simu.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(genotype=20),
],
preOps=[
sim.StepwiseMutator(rates=0.0001, reps=0),
sim.KAlleleMutator(k=10000, rates=0.0001, reps=1),
],
matingScheme=sim.RandomMating(),
postOps=[
# Use vars=['alleleFreq_sp'] to calculate allele frequency for
# each subpopulation
sim.Stat(alleleFreq=0, vars=['alleleFreq_sp'], step=200),
sim.PyEval('gen', step=200, reps=0),
sim.PyEval(r"'\t%.2f' % (sum([len(subPop[x]['alleleFreq'][0]) "
"for x in range(10)])/10.)", step=200),
sim.PyOutput('\n', reps=-1, step=200)
],
gen=601
)
# but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # # This script is an example in the simuPOP user's guide. Please refer to # the user's guide (http://simupop.sourceforge.net/manual) for a detailed # description of this example. # import simuPOP as sim import random pop = sim.Population(size=[200, 200], loci=[5, 5], infoFields='age') sim.initGenotype(pop, genotype=range(10)) sim.initInfo(pop, lambda: random.randint(0, 75), infoFields='age') pop.setVirtualSplitter(sim.InfoSplitter(field='age', cutoff=[20, 60])) # remove individuals pop.removeIndividuals(indexes=range(0, 300, 10)) print(pop.subPopSizes()) # remove individuals using IDs pop.setIndInfo([1, 2, 3, 4], field='age') pop.removeIndividuals(IDs=[2, 4], idField='age') # remove indiviuals using a filter function sim.initSex(pop) pop.removeIndividuals(filter=lambda ind: ind.sex() == sim.MALE) print([pop.individual(x).sex() for x in range(8)]) # # remove subpopulation
def simulation(self):
self.pop = sim.Population(size = [500, 500], loci=[1]*20,
infoFields = ["age",'ind_id', 'father_idx', 'mother_idx', "hc", "ywc",'migrate_to'],
subPopNames = ["croatia", "slovenia"])
sim.initInfo(pop = self.pop, values = list(map(int, np.random.negative_binomial(n = 1, p = 0.25, size=500))), infoFields="age")
self.pop.setVirtualSplitter(sim.CombinedSplitter([
sim.ProductSplitter([
sim.SexSplitter(),
sim.InfoSplitter(field = "age", cutoff = [1,3,6,10])])],
vspMap = [[0,1], [2], [3], [4], [5,6,7,8], [9] ]))
# Age groups: from 0 to 1 - cubs, from 1 to 3 - prereproductive, from 3 to 6 - reproductive class, from 6 to 10 - dominant
self.pop.evolve(
initOps=[
sim.InitSex(),
# random genotype
sim.InitGenotype(freq=[0.01]*2 + [0.03]*2 + [0.23]*4),
# assign an unique ID to everyone.
sim.IdTagger(),
],
# increase the age of everyone by 1 before mating.
preOps=[sim.InfoExec('age += 1'),
sim.InfoExec("hc +=1 if 0 < hc < 3 else 0"), # Mother bear can't have cubs for two years after pregnancy
sim.Migrator(rate=[[self.cro_to_slo]],
mode=sim.BY_PROPORTION,
subPops=[(0, 0)],
toSubPops=[1]), # reproductive males migrate from Cro to Slo
sim.Migrator(rate=[[self.slo_to_cro]],
mode=sim.BY_PROPORTION,
subPops=[(1, 0)],
toSubPops=[0]),
sim.Stat(effectiveSize=sim.ALL_AVAIL, subPops=[(0,1),(0,2),(0,4), (1,1), (1,2), (1,4)], vars='Ne_demo_base'),
sim.Stat(effectiveSize=sim.ALL_AVAIL,subPops=[(0,1),(0,2),(0,4), (1,1), (1,2), (1,4)], vars='Ne_demo_base_sp')
#sim.PyEval(r'"Cro %d, Slo %d' ' % (Cro, Slo)', "Cro = pop.subPopSize(0)" "Slo = pop.subPopSize(1)",exposePop='pop'),
],
matingScheme=sim.HeteroMating([
# CloneMating will keep individual sex and all
# information fields (by default).
# The age of offspring will be zero.
sim.HomoMating(subPops=sim.ALL_AVAIL,
chooser=sim.CombinedParentsChooser(
fatherChooser=sim.PyParentsChooser(generator=self.bearFather),
motherChooser=sim.PyParentsChooser(generator=self.bearMother)
),
generator=sim.OffspringGenerator(ops=[
sim.InfoExec("age = 0"),
sim.IdTagger(),
#sim.PedigreeTagger(),
sim.ParentsTagger(),
sim.MendelianGenoTransmitter()
], numOffspring=(sim.UNIFORM_DISTRIBUTION, 1, 3))),
sim.CloneMating(subPops=[(0,0), (0,1), (0,2), (0,4), (1,0), (1,1), (1,2), (1,4)], weight=-1),
], subPopSize=popmodel.demoModel),
# number of individuals?
postOps = [
#sim.PyOperator(func=popmodel.NaturalMortality),
sim.PyOperator(func = popmodel.CalcNe, param={"me":self.me, "Ne":self.Ne}, begin=int(0.2*self.generations)),
sim.PyOperator(func = popmodel.CalcLDNe, param={"me":self.me, "x":self.x}, begin=int(0.2*self.generations)),
sim.PyOperator(func=popmodel.cullCountry,param={"slo_cull": self.slo_cull, "cro_cull": self.cro_cull}),
],
gen = self.generations
)
def censuscontrol(gen):
if gen >= 10 and gen < 13:
return [10, 20]
elif gen >= 13 and gen < 15:
return [20, 40]
elif gen == 15:
return [22, 44]
else:
return [30, 60]
pop = sim.Population(
size=[8, 16],
ploidy=2,
loci=[0, 1, 2],
infoFields=['ind_id', 'father_id', 'mother_id', 'gen_id', 'sp_id'],
lociPos=(1, 2, 3),
ancGen=gen_evolve)
pop.evolve(
initOps=[
sim.IdTagger(begin=0, end=-1),
sim.InitSex(maleProp=0.5),
sim.InitGenotype(freq=[0.2, 0.2, 0.2, 0.2, 0.2], loci=[0, 1, 2]),
sim.PedigreeTagger(output='>>simp_Pedigree.ped',
outputLoci=[0, 1, 2],
outputFields=['gen_id', 'sp_id'])
], #end of initOps
preOps=[
PyOperator(lambda pop: [
pop.setIndInfo(x, "sp_id", x) for x in range(pop.numSubPop())
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# This script is an example in the simuPOP user's guide. Please refer to
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
from simuPOP.utils import viewVars
pop = sim.Population([1000, 2000], loci=3)
sim.initGenotype(pop, freq=[0.2, 0.4, 0.4], loci=0)
sim.initGenotype(pop, freq=[0.2, 0.8], loci=2)
sim.stat(pop, genoFreq=[0, 1, 2], haploFreq=[0, 1, 2],
alleleFreq=range(3),
vars=['genoFreq', 'genoNum', 'haploFreq', 'alleleNum_sp'])
viewVars(pop.vars())
