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 createAge(pop):
ageInitOps = [
# InitInfo(lambda: random.randint(0, cfg.ages-2), infoFields='age'),
sp.IdTagger(),
# PyOperator(func=outputAge,at=[0]),
sp.PyOperator(func=setAge, at=[0]),
]
agePreOps = [
sp.InfoExec("age += 1"),
sp.InfoExec("mate = -1"),
sp.InfoExec("force_skip = 0"),
sp.PyOperator(func=outputAge),
]
mySubPops = []
for age in range(cfg.ages - 2):
mySubPops.append((0, age + 1))
mateOp = sp.HeteroMating([
sp.HomoMating(
sp.PyParentsChooser(fitnessGenerator if cfg.doNegBinom
else (litterSkipGenerator if cfg.Nb is None else
restrictedGenerator)),
sp.OffspringGenerator(numOffspring=1, ops=[
sp.MendelianGenoTransmitter(), sp.IdTagger(),
sp.PedigreeTagger()],
sexMode=(sp.PROB_OF_MALES, cfg.maleProb)), weight=1),
sp.CloneMating(subPops=mySubPops, weight=-1)],
subPopSize=calcDemo)
agePostOps = [
sp.PyOperator(func=outputMega),
sp.PyOperator(func=cull),
]
pop.setVirtualSplitter(sp.InfoSplitter(field='age',
cutoff=list(range(1, cfg.ages))))
return ageInitOps, agePreOps, mateOp, agePostOps
def splitCalcMerge(pop, param):
#pop_size = pop.popSize()
sim.InfoSplitter(field="age", cutoff=[1, 8])
sim.stat(pop=pop, popSize="subPopSize")
sim.stat(pop=pop,
effectiveSize=sim.ALL_AVAIL,
vars="Ne_LD",
subPops=[(0, 1)],
suffix="_Cro")
sim.stat(pop=pop,
effectiveSize=sim.ALL_AVAIL,
vars="Ne_LD",
subPops=[(1, 1)],
suffix="_Slo")
a = pd.DataFrame.from_dict(pop.dvars().Ne_LD_Cro, orient="index")
a["cutoff"] = a.index
a["population"] = 0
a["Size"] = pop.dvars().subPopSize[0]
a["gen"] = pop.dvars().gen
param["x"].append(a)
a = pd.DataFrame.from_dict(pop.dvars().Ne_LD_Slo, orient="index")
a["cutoff"] = a.index
a["population"] = 1
a["Size"] = pop.dvars().subPopSize[1]
a["gen"] = pop.dvars().gen
param["x"].append(a)
cro_size = pop.subPopSize(0)
slo_size = pop.subPopSize(1)
pop.mergeSubPops([0, 1])
sim.InfoSplitter(field="age", cutoff=[1, 8])
sim.stat(pop=pop,
effectiveSize=sim.ALL_AVAIL,
vars="Ne_LD",
subPops=[(0, 1)])
pop.splitSubPop(0, [cro_size, slo_size])
pop.setSubPopName('croatia', 0)
pop.setSubPopName('slovenia', 1)
a = pd.DataFrame.from_dict(pop.dvars().Ne_LD, orient="index")
a["cutoff"] = a.index
a["population"] = "all"
a["Size"] = pop.popSize()
a["gen"] = pop.dvars().gen
param["x"].append(a)
return True
def CalcdemoNe(pop):
sim.InfoSplitter(field="age", cutoff=[3, 10])
sim.stat(pop=pop,
effectiveSize=range(2),
subPops=[(0, 1), (1, 1)],
vars='Ne_demo_base_sp')
sim.stat(pop=pop,
effectiveSize=range(2),
subPops=[(0, 1)],
vars='Ne_demo_base')
return True
def sample_setter(self, pop):
"""
Chooses :param:sample_size individuals and sets their infoField
value to ``1`` indicating that they have been chosen to be part
of the sample. Meant to be used during an evolutionary process.
"Sampled" individuals are in virtual sub-pop [0, 0]
:warning: Erases current virtual sub-population split
:param pop:
:return:
"""
sampled_inds = random.sample(pop.indInfo('ind_id'), self.sample_size)
for ind in sampled_inds:
pop.indByID(ind).sample = 1
pop.setVirtualSplitter(
sim.InfoSplitter('sample',
values=[1, 0],
names=['sampled', 'not_sampled']))
return True
# 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=2000, loci=[1] * 2, infoFields=['p', 'smoking'])
pop.setVirtualSplitter(sim.InfoSplitter(field='smoking', values=[0, 1]))
# the second parameter gen can be used for varying selection pressure
def penet(geno, smoking):
# BB Bb bb
# AA 0.01 0.01 0.01
# Aa 0.01 0.03 0.03
# aa 0.01 0.03 0.05
#
# geno is (A1 A2 B1 B2)
if geno[0] + geno[1] == 1 and geno[2] + geno[3] != 0:
v = 0.03 # case of AaBb
elif geno[0] + geno[1] == 2 and geno[2] + geno[3] == 1:
v = 0.03 # case of aaBb
elif geno[0] + geno[1] == 2 and geno[2] + geno[3] == 2:
# 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
# create an age-structured population with a disease
import random
pop = sim.Population(10000, loci=10, infoFields='age')
sim.initGenotype(pop, freq=[0.3, 0.7])
sim.initInfo(pop, lambda: random.randint(0, 70), infoFields='age')
pop.setVirtualSplitter(sim.InfoSplitter(cutoff=(40, 60), field='age'))
sim.maPenetrance(pop, loci=5, penetrance=(0.1, 0.2, 0.3))
#
from simuPOP.sampling import drawCaseControlSample
sample = drawCaseControlSample(pop, cases=500, controls=500, subPops=[(0, 1)])
ageInSample = sample.indInfo('age')
print(min(ageInSample), max(ageInSample))
def runSimulation(scenario_id, sub_population_size, minMatingAge, maxMatingAge,
gen):
'''
sub_population_size A vector giving the population sizes for each sub-population. The subpopulations determine which breeding ground an individual belongs to
minMatingAge minimal mating age.
maxMatingAge maximal mating age. Individuals older than this are effectively dead
years number of years to simulate
'''
# scenario_id describes the batch of files to load
# The mitochondrial DNA will be in mtdna_<scenario_id>
# The SNP DNA will be in snp_<scenario_id>
# Read the mitochondrial haplotype frequencies. There's a bit to unpack here
# We read the lines into an array, and for each one, call split() on it to get one element per column.
# However, we do not want this - we want the transpose, where haplotype_frequencies[0] is a vector of
# all the frequencies for population 0, and haplotype_frequencies[1] is the corresponding vector for
# population 2. list(map(list, zip(*t))) will achieve this transformation for us.
# While we are at it, we also convert the strings into floats.
mitochondrial_file = "mtdna_" + scenario_id + ".txt"
with open(mitochondrial_file, "r") as fd:
haplotype_frequencies = list(
map(list,
zip(*[list(map(float, line[0:-1].split())) for line in fd])))
if len(haplotype_frequencies) != len(sub_population_size):
raise ValueError(
'The number of populations in the population size vector and the number of populations deduced from the haplotype file are different'
)
# Now read the SNP data. This builds a 2D array indexed as snp[locus][population]
snp_file = "snp_" + scenario_id + ".txt"
with open(snp_file, "r") as fd:
snp = [list(map(float, line[0:-1].split())) for line in fd]
sub_population_count = len(sub_population_size)
print()
print(sub_population_count, "subpopulations detected")
# Now we can create the population. We want to give each population a population name, starting from A
sub_population_names = list(map(chr, range(65, 65 + sub_population_count)))
# We have two chromosomes. The first is an autosome with nb_loci loci, and the second is the mitochondrial chromosome with 1 locus
pop = simuPOP.Population(
sub_population_size,
ploidy=2,
loci=[nb_loci, 1],
ancGen=2,
infoFields=[
'age', 'ind_id', 'father_id', 'mother_id', 'nitrogen', 'carbon',
'feeding_ground', 'native_breeding_ground', 'migrate_to'
],
subPopNames=sub_population_names,
chromTypes=[simuPOP.AUTOSOME, simuPOP.MITOCHONDRIAL])
sub_population_names = tuple(sub_population_names)
# Create an attribute on each individual called 'age'. Set it to a random number between 0 and maxMatingAge
# Note that size is a vector - the size of each population. We have to sum these to get the total number of individuals
individual_count = sum(sub_population_size)
# Assign a random age to each individual
pop.setIndInfo(
[random.randint(0, maxMatingAge) for x in range(individual_count)],
'age')
# Assign a random feeding ground to each individual
pop.setIndInfo([
random.randint(0, numberOfFeedingGrounds - 1)
for x in range(individual_count)
], 'feeding_ground')
# Currently we have these virtual subpopulations:
# age < minMatingAge (juvenile)
# age >= minMatingAge and age < maxMatingAge + 0.1 (age <= maxMatingAge) (mature)
# age >= maxMatingAge (dead)
#
# Ideally we would want something like this:
# 1) Immature
# 2) Receptive female (every 3 years)
# 3) Non-receptive female
# 4) Mature male
# 5) Dead
#
# Note that we use a cutoff InfoSplitter here, it is also possible to
# provide a list of values, each corresponding to a virtual subpopulation.
pop.setVirtualSplitter(
simuPOP.CombinedSplitter([
simuPOP.ProductSplitter([
simuPOP.SexSplitter(),
simuPOP.InfoSplitter('age',
cutoff=[minMatingAge, maxMatingAge + 0.1],
names=['juvenile', 'mature', 'dead'])
])
],
vspMap=[[0], [1], [2], [3], [4], [5],
[0, 1, 3, 4], [1, 4]],
names=[
'Juvenile Male', 'Mature Male',
'Dead Male', 'Juvenile Female',
'Mature Female', 'Dead Female',
'Not dead yet', 'Active'
]))
pop.evolve(
initOps=[
simuPOP.InitSex(),
simuPOP.IdTagger(),
simuPOP.PyOperator(func=init_native_breeding_grounds)
] + [
simuPOP.InitGenotype(subPops=sub_population_names[i],
freq=haplotype_frequencies[i],
loci=[nb_loci])
for i in range(0, sub_population_count)
] + [
simuPOP.InitGenotype(subPops=sub_population_names[i],
freq=[snp[n][i], 1 - snp[n][i]],
loci=[n])
for i in range(0, sub_population_count)
for n in range(0, nb_loci - 1)
],
# increase age by 1
preOps=[simuPOP.InfoExec('age += 1')],
matingScheme=simuPOP.HeteroMating(
[
# age <= maxAge, copy to the next generation (weight=-1)
# subPops is a list of tuples that will participate in mating. The tuple is a pair (subPopulation, virtualSubPopulation)
# First, we propagate (clone) all individuals in all subpopulations (and all VSPs except the ones who are now in the VSP of deceased individuals) to the next generation
simuPOP.CloneMating(
ops=[simuPOP.CloneGenoTransmitter(chroms=[0, 1])],
subPops=[
(sub_population, 6)
for sub_population in range(0, sub_population_count)
],
weight=-1),
# Then we simulate random mating only in VSP 1 (ie reproductively mature individuals) within subpopulation (breeding/winter grounds)
simuPOP.RandomMating(
ops=[
simuPOP.MitochondrialGenoTransmitter(),
simuPOP.MendelianGenoTransmitter(),
simuPOP.IdTagger(),
simuPOP.InheritTagger(mode=simuPOP.MATERNAL,
infoFields=['feeding_ground']),
simuPOP.InheritTagger(
mode=simuPOP.MATERNAL,
infoFields=['native_breeding_ground']),
simuPOP.PedigreeTagger()
],
subPops=[
(sub_population, 7)
for sub_population in range(0, sub_population_count)
],
weight=1)
],
subPopSize=configure_new_population_size),
postOps=[
# Determine the isotopic ratios in individuals
simuPOP.PyOperator(func=postop_processing),
simuPOP.Migrator(mode=simuPOP.BY_IND_INFO),
# count the individuals in each virtual subpopulation
#simuPOP.Stat(popSize=True, subPops=[(0,0), (0,1), (0,2), (1,0), (1, 1), (1, 2)]),
# print virtual subpopulation sizes (there is no individual with age > maxAge after mating)
#simuPOP.PyEval(r"'Size of age groups: %s\n' % (','.join(['%d' % x for x in subPopSize]))")
# Alternatively, calculate the Fst
# FIXME: How does this actually work? Does it work for > 2 populations? I don't really understand it yet
# ELC: it is a calculation that partitions variance among and between populations, and can be calculated as a
# global statistic or on a pairwise basis. We use it as an indication of genetic differentiation.
simuPOP.Stat(structure=range(1),
subPops=sub_population_names,
suffix='_AB',
step=10),
simuPOP.PyEval(r"'Fst=%.3f \n' % (F_st_AB)", step=10)
],
gen=years)
#simuPOP.dump(pop, width=3, loci=[], subPops=[(simuPOP.ALL_AVAIL, simuPOP.ALL_AVAIL)], max=1000, structure=False);
#return
ped = simuPOP.Pedigree(pop)
print("This is the pedigree stuff")
simuPOP.dump(pop)
# Now sample the individuals
sample = drawRandomSample(pop, sizes=[sample_count] * sub_population_count)
# Print out the allele frequency data
simuPOP.stat(sample, alleleFreq=simuPOP.ALL_AVAIL)
frequencies = sample.dvars().alleleFreq
with open('freq.txt', 'w') as freqfile:
index = 0
for locus in frequencies:
if (locus == nb_loci):
continue
if (len(frequencies[locus]) < 2):
continue
print(index, end=' ', file=freqfile)
index = index + 1
for allele in frequencies[locus]:
print(frequencies[locus][allele], end=' ', file=freqfile)
print(file=freqfile)
# We want to remove monoallelic loci. This means a position in the genotype for which all individuals have the same value in both alleles
# To implement this we will build up a list of loci that get ignored when we dump out the file. Generally speaking, if we add all the values up
# then either they will sum to 0 (if all individuals have type 0) or to the number of individuals * 2 (if all individuals have type 1)
geno_sum = [0] * (nb_loci + 1) * 2
for individual in sample.individuals():
geno_sum = list(map(add, geno_sum, individual.genotype()))
final_sum = list(
map(add, geno_sum[:(nb_loci + 1)], geno_sum[(nb_loci + 1):]))
monoallelic_loci = []
for i in range(0, nb_loci):
if final_sum[i] == 0 or final_sum[
i] == sample_count * sub_population_count * 2:
monoallelic_loci = [i] + monoallelic_loci
monoallelic_loci = sorted(monoallelic_loci, reverse=True)
nb_ignored_loci = len(monoallelic_loci)
# Generate the two files
with open('mixfile.txt', 'w') as mixfile:
with open('haploiso.txt', 'w') as haplofile:
print(sub_population_count,
nb_loci - nb_ignored_loci,
2,
1,
file=mixfile)
print("sex, haplotype, iso1, iso2, native_ground", file=haplofile)
for i in range(0, nb_loci - nb_ignored_loci):
print('Loc', i + 1, sep='_', file=mixfile)
for individual in sample.individuals():
genotype = individual.genotype()
print(
1 if individual.sex() == 1 else 0,
genotype[nb_loci],
individual.info('carbon'),
individual.info('nitrogen'),
# int(individual.info('native_breeding_ground')),
file=haplofile,
sep=' ')
print(int(individual.info('native_breeding_ground') + 1),
end=' ',
file=mixfile)
for i in range(0, nb_loci):
if i not in monoallelic_loci:
print(genotype[i] + 1,
genotype[i + nb_loci + 1] + 1,
' ',
end='',
sep='',
file=mixfile)
print(file=mixfile)
return sample
# 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=[2000, 4000], loci=[30], infoFields='x')
# assign random information fields
sim.initSex(pop)
sim.initInfo(pop, lambda: random.randint(0, 3), infoFields='x')
#
# 1, use a combined splitter
pop.setVirtualSplitter(sim.CombinedSplitter(splitters = [
sim.SexSplitter(),
sim.InfoSplitter(field='x', values=[0, 1, 2, 3])
]))
pop.numVirtualSubPop() # Number of defined VSPs
pop.subPopName([0, 0]) # Each VSP has a name
pop.subPopSize([0, 0]) # sim.MALE
pop.subPopSize([1, 4]) # individuals in sp 1 with value 2 at field x
#
# use a product splitter that defines additional VSPs by sex and info
pop.setVirtualSplitter(sim.ProductSplitter(splitters = [
sim.SexSplitter(names=['M', 'F']), # give a new set of names
sim.InfoSplitter(field='x', values=[0, 1, 2, 3])
]))
pop.numVirtualSubPop() # Number of defined VSPs
pop.subPopName([0, 0]) # Each VSP has a name
pop.subPopSize([0, 0]) # sim.MALE with value 1 in sp 0
pop.subPopSize([1, 5]) # sim.FEMALE with value 1 in sp 1
# 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
import random
pop = sim.Population(20, loci=1, infoFields='a')
pop.setVirtualSplitter(sim.InfoSplitter('a', cutoff=[3]))
sim.initGenotype(pop, freq=[0.2, 0.8])
pop.setIndInfo([random.randint(2, 5) for x in range(20)], 'a')
sim.infoEval(pop, 'a', subPops=[(0, 0)]);print(' ')
sim.infoEval(pop, 'ind.allele(0, 0)', exposeInd='ind');print(' ')
# use sim.population variables
pop.dvars().b = 5
sim.infoEval(pop, '"%d " % (a+b)');print(' ')
def demo(gen, pop):
return pop.popSize()
###############################################################
# POPULATION #
###############################################################
pop = sim.Population(args.N, loci = 0, ploidy = 2, infoFields = ['age', 'a', 'b', 'smurf', 'luck', 't0'])
pop.setVirtualSplitter(
sim.CombinedSplitter(
splitters = [
sim.ProductSplitter(
splitters = [
sim.InfoSplitter(field = 'age', cutoff = 10),
sim.InfoSplitter(field = 'smurf', values = [0, 1])
]
)
],
vspMap = [(0), (2), (1,3)],
names = ['larvae', 'adults', 'smurfs']
)
)
# This is to be able to call random from InfoExec:
exec('import random', pop.vars(), pop.vars())
exec('import math', pop.vars(), pop.vars())
pop.dvars().k = args.k
pop.dvars().model = args.model
#
# 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([500], infoFields='anc')
# Defines VSP 0, 1, 2, 3, 4 by anc.
pop.setVirtualSplitter(sim.InfoSplitter('anc', cutoff=[0.2, 0.4, 0.6, 0.8]))
#
pop.evolve(
initOps=[
sim.InitSex(),
# anc is 0 or 1
sim.InitInfo(lambda : random.randint(0, 1), infoFields='anc')
],
matingScheme=sim.RandomMating(ops=[
sim.MendelianGenoTransmitter(),
sim.InheritTagger(mode=sim.MEAN, infoFields='anc')
]),
postOps=[
sim.Stat(popSize=True, meanOfInfo='anc', varOfInfo='anc',
subPops=[(0, sim.ALL_AVAIL)]),
sim.PyEval(r"'Anc: %.2f (%.2f), #inds: %s\n' %" + \
p -= 1
# if no one is invicinity, find another pair
if len(inds) == 0:
continue
# choose another parent
p2 = inds[randint(0, len(inds) - 1)]
# return indexes of both parents
if s1 == sim.MALE:
yield p1, p2
else:
yield p2, p1
pop = sim.Population(size=2000, loci=1, infoFields='x')
# define VSPs x<1, 1<=x<2, 2<=x<3, 3<=x<4, ...
pop.setVirtualSplitter(sim.InfoSplitter(field='x', cutoff=range(1, 8)))
pop.evolve(
initOps=[
sim.InitSex(),
sim.InitInfo(lambda: uniform(0, 8), infoFields='x'),
# only individuals in the middle range has certain genotype
sim.InitGenotype(freq=[0.6, 0.4], subPops=[(0, 4)]),
],
matingScheme=VicinityMating(locationField='x',
vicinity=1,
varOfLocation=0.5),
postOps=[
sim.Stat(alleleFreq=0,
subPops=[(0, sim.ALL_AVAIL)],
vars='alleleFreq_sp'),
sim.PyEval(r"'%.3f ' % alleleFreq[0][1]",
pop.individual(idx -
18).setSex(sim.MALE if sex == '2' else sim.FEMALE)
pop.individual(idx - 18).age = int(age)
#pop.individual(idx-18).age_cat = int(age_cat)
elif line.startswith('2 '):
age_cat, sex = line.strip().split()[-2:]
pop.individual(idx -
18).setSex(sim.MALE if sex == '2' else sim.FEMALE)
# no age info
pop.individual(idx - 18).age = random.randint(0, 17)
#pop.individual(idx-18).age_cat = int(age_cat)
pop.setVirtualSplitter(
sim.CombinedSplitter([
sim.SexSplitter(),
sim.InfoSplitter(field='age', cutoff=[3, 9, 17]),
]))
# population 0, from 68 female to 68 female + 62 male
pop.resize([130, 338], propagate=True)
# set sex
for idx in range(68, 130):
pop.individual(idx, 0).setSex(sim.MALE)
# add 110-37=73 male
for idx in range(192, 192 + 73):
pop.individual(idx, 1).setSex(sim.MALE)
for idx in range(192 + 73, 338):
pop.individual(idx, 1).setSex(sim.FEMALE)
migr = sim.Migrator(
rate=[
# # 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 pop.removeSubPops(1) print(pop.subPopSizes()) # remove virtual subpopulation (people with age between 20 and 60)
# 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
N = 10000
pop = sim.Population(N, infoFields=['age', 'ind_id', 'father_id', 'mother_id'])
# we simulate age 0, 1, 2, 3
pop.setVirtualSplitter(sim.InfoSplitter(field='age', values=[0, 1, 2, 3]))
pop.evolve(
initOps=[
sim.InitSex(),
# random assign age
sim.InitInfo(lambda: random.randint(0, 3), infoFields='age'),
# random genotype
sim.InitGenotype(freq=[0.5, 0.5]),
# assign an unique ID to everyone.
sim.IdTagger(),
],
# increase the age of everyone by 1 before mating.
preOps=sim.InfoExec('age += 1'),
matingScheme=sim.HeteroMating([
# age 1, 2 will be copied
sim.CloneMating(
td.randomly_convert_fixed_sites(tuson, sites)
sim.stat(tuson, alleleFreq=sim.ALL_AVAIL)
sim.stat(tuson, genoFreq=sim.ALL_AVAIL)
minor_alleles = helpers.find_minor_alleles(tuson)
major_alleles = helpers.find_major_alleles(tuson)
minor_homozygotes_by_locus = {i: (minor_alleles[i], minor_alleles[i]) for i in
range(tuson.totNumLoci())}
minor_heterozygotes_by_locus = {
i: [(minor_alleles[i], major_alleles[i]), (major_alleles[i], minor_alleles[i])]
for i in range(tuson.totNumLoci())}
primary_subpopulations = [tuson.dvars().assigned_structure[int(indid)][0] for
indid in list(tuson.indInfo('ind_id'))]
tuson.setIndInfo(values=primary_subpopulations, field='primary')
primary_subpopulation_splitter = sim.InfoSplitter(field='primary',
values=[0.0, 1.0, 2.0, 3.0,
4.0, 5.0])
tuson.setVirtualSplitter(primary_subpopulation_splitter)
experimental_meta_population_sample_size = {
0: 105,
2: 56,
4: 55,
6: 54,
8: 56,
10: 55
}
uniform_meta_population_sample_size = {
0: 400,
2: 400,
# 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
import random
pop = sim.Population(size=[4, 6], loci=2, infoFields='x')
pop.setIndInfo([random.randint(0, 10) for x in range(10)], 'x')
pop.indInfo('x')
pop.setGenotype([0, 1, 2, 3], 0)
pop.genotype(0)
pop.setVirtualSplitter(sim.InfoSplitter(cutoff=[3], field='x'))
pop.setGenotype([0]) # clear all values
pop.setGenotype([5, 6, 7], [1, 1])
pop.indInfo('x', 1)
pop.genotype(1)
# randomly choose a male
male = males[random.randint(0, len(males) - 1)]
pod = male.pod
# randomly choose a female from different pod
while True:
female = females[randint(0, len(females) - 1)]
if female.pod != pod:
break
yield (male, female)
pop = sim.Population(5000,
loci=[1, 1],
infoFields=['pod'],
chromTypes=[sim.AUTOSOME, sim.CUSTOMIZED])
pop.setVirtualSplitter(sim.InfoSplitter('pod', values=range(5)))
pop.evolve(
initOps=[
sim.InitSex(),
# assign individuals to a random pod
sim.InitInfo(lambda: randint(0, 4), infoFields='pod'),
# only the first pod has the disease alleles
sim.InitGenotype(freq=[0.8, 0.2], subPops=[(0, 0)]),
],
matingScheme=sim.HomoMating(
sim.PyParentsChooser(podParentsChooser),
sim.OffspringGenerator(
numOffspring=1,
ops=[
sim.MendelianGenoTransmitter(),
sim.MitochondrialGenoTransmitter(),
import random
def breatCancer(geno, age):
# brca1 is recessive
if 0 in geno:
return 0
return [0, 0, 0.036, 0.18, 0.57, 0.75, 0.83][int(age / 10)]
pop = sim.Population(size=10000, loci=1, infoFields='age', lociNames='BRCA1')
sim.initGenotype(pop, freq=[0.73, 0.27])
sim.initInfo(pop, lambda: random.randint(0, 69), infoFields='age')
sim.pyPenetrance(pop, func=breatCancer, loci=0)
pop.setVirtualSplitter(
sim.CombinedSplitter([
sim.InfoSplitter(field='age', cutoff=[30, 40, 50, 60]),
sim.ProductSplitter([
sim.InfoSplitter(field='age', cutoff=[30, 40, 50, 60]),
sim.GenotypeSplitter(loci=0, alleles=[1, 1], names='carrier'),
])
]))
sim.stat(pop,
numOfAffected=True,
subPops=[(0, sim.ALL_AVAIL)],
vars=['propOfAffected', 'propOfAffected_sp'])
print('Population prevalence is %.2f%%' % (pop.dvars().propOfAffected * 100))
for x in range(pop.numVirtualSubPop()):
print('Prevalence in group %s is %.2f%%' % \
(pop.subPopName((0,x)), pop.dvars((0,x)).propOfAffected*100))
import simuOpt
simuOpt.setOptions(gui=False, alleleType='binary')
import simuPOP as sim
pop.addInfoFields(['ancestry', 'migrate_to'])
# initialize ancestry
sim.initInfo(pop, [0] * pop.subPopSize(0) + [1] * pop.subPopSize(1),
infoFields='ancestry')
# define two virtual subpopulations by ancestry value
pop.setVirtualSplitter(sim.InfoSplitter(field='ancestry', cutoff=[0.5]))
transmitters = [
sim.MendelianGenoTransmitter(),
sim.InheritTagger(mode=sim.MEAN, infoFields='ancestry')
]
pop.evolve(
initOps=sim.InitSex(),
preOps=sim.Migrator(rate=[[0., 0], [0.05, 0]]),
matingScheme=sim.HeteroMating(matingSchemes=[
sim.RandomMating(ops=transmitters),
sim.RandomMating(subPops=[(0, 0)], weight=-0.80, ops=transmitters),
sim.RandomMating(subPops=[(0, 1)], weight=-0.80, ops=transmitters)
], ),
gen=10,
)
# remove the second subpop
pop.removeSubPops(1)
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 MutationSelection(N=1000,
generations=10000,
X_loci=100,
A_loci=0,
AgingModel='two_phases',
seed=2001,
reps=1,
InitMutFreq=0.001,
aging_a1=0.003,
aging_a2=0.05,
aging_b=-0.019,
aging_k=0.1911,
MutRate=0.001,
StatsStep=100,
OutPopPrefix='z1',
PrintFreqs=False,
debug=False):
'''Creates and evolves a population to reach mutation-selection balance.'''
if debug:
sim.turnOnDebug('DBG_ALL')
else:
sim.turnOffDebug('DBG_ALL')
sim.setRNG('mt19937', seed)
pop = sim.Population(N,
loci=[X_loci, A_loci],
ploidy=2,
chromTypes=[sim.CHROMOSOME_X, sim.AUTOSOME],
infoFields=[
'age', 'a', 'b', 'smurf', 'ind_id', 'father_id',
'mother_id', 'luck', 't0', 'fitness'
])
pop.setVirtualSplitter(
sim.CombinedSplitter(
splitters=[
sim.ProductSplitter(splitters=[
sim.InfoSplitter(field='age', cutoff=9),
sim.InfoSplitter(field='smurf', values=[0, 1])
]),
sim.SexSplitter(),
sim.InfoSplitter(field='age', values=0)
],
vspMap=[(0), (2), (1, 3), (4), (5), (6)],
names=['larvae', 'adults', 'smurfs', 'males', 'females', 'zero']))
pop.dvars().k = aging_k
pop.dvars().N = N
pop.dvars().seed = seed
pop.dvars().X_loci = X_loci
pop.dvars().A_loci = A_loci
pop.dvars().AgingModel = AgingModel
exec("import random\nrandom.seed(seed)", pop.vars(), pop.vars())
exec("import math", pop.vars(), pop.vars())
simu = sim.Simulator(pop, rep=reps)
simu.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[1 - InitMutFreq, InitMutFreq]),
sim.InitInfo([0], infoFields='age'),
sim.InitInfo([aging_a1], infoFields='a'),
sim.InitInfo([aging_b], infoFields='b'),
sim.InitInfo(lambda: random.random(), infoFields='luck'),
sim.InfoExec('t0 = -ind.b / ind.a', exposeInd='ind'),
sim.InfoExec(
'smurf = 1.0 if AgingModel == "two_phases" and (ind.smurf == 1 or (ind.age > ind.t0 and ind.luck < 1.0 - math.exp(-ind.a * ind.age + ind.a * ind.t0 - ind.a / 2.0))) else 0.0',
exposeInd='ind'),
sim.IdTagger(),
sim.PyExec('XFreqChange={}'),
sim.PyExec('AFreqChange={}')
],
preOps=[
sim.InfoExec('luck = random.random()'),
sim.InfoExec(
'smurf = 1.0 if AgingModel == "two_phases" and (ind.smurf == 1 or (ind.age > ind.t0 and ind.luck < 1.0 - math.exp(-ind.a * ind.age + ind.a * ind.t0 - ind.a / 2.0))) else 0.0',
exposeInd='ind'),
sim.DiscardIf(natural_death(AgingModel)),
sim.InfoExec('age += 1'),
sim.PySelector(func=fitness_func1)
],
matingScheme=sim.HeteroMating([
sim.CloneMating(subPops=[(0, 0), (0, 1), (0, 2)], weight=-1),
sim.RandomMating(ops=[
sim.IdTagger(),
sim.PedigreeTagger(),
sim.InfoExec('smurf = 0.0'),
sexSpecificRecombinator(
rates=[0.75 / X_loci for x in range(X_loci)] +
[2.07 / A_loci for x in range(A_loci)],
maleRates=0.0),
sim.PyQuanTrait(loci=sim.ALL_AVAIL,
func=TweakAdditiveRecessive(
aging_a1, aging_a2, aging_b, X_loci),
infoFields=['a', 'b'])
],
weight=1,
subPops=[(0, 1)],
numOffspring=1)
],
subPopSize=demo),
postOps=[
sim.SNPMutator(u=MutRate, subPops=[(0, 5)]),
sim.Stat(alleleFreq=sim.ALL_AVAIL, step=StatsStep),
sim.IfElse(
'X_loci > 0',
ifOps=[
sim.PyExec(
'XFreqChange[gen] = [alleleFreq[x][1] for x in range(X_loci)]'
)
],
elseOps=[sim.PyExec('XFreqChange[gen] = []')],
step=StatsStep),
sim.IfElse(
'A_loci > 0',
ifOps=[
sim.PyExec(
'AFreqChange[gen] = [alleleFreq[a][1] for a in range(X_loci, pop.totNumLoci())]',
exposePop='pop')
],
elseOps=[sim.PyExec('AFreqChange[gen] = []')],
step=StatsStep),
sim.IfElse(
PrintFreqs,
ifOps=[
sim.PyEval(
r"str(rep) + '\t' + str(gen) + '\t' + '\t'.join(map('{0:.4f}'.format, XFreqChange[gen])) + '\t\t' + '\t'.join(map('{0:.4f}'.format, AFreqChange[gen])) + '\n'"
)
],
step=StatsStep),
sim.TerminateIf(
'sum([alleleFreq[x][0] * alleleFreq[x][1] for x in range(X_loci + A_loci)]) == 0'
)
],
gen=generations)
i = 0
for pop in simu.populations():
pop.save('{}_{}.pop'.format(OutPopPrefix, i))
i += 1
