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 simulateSingleLoci(nu0=0.005, T=100, s=0.1, N=1000):
print '.',
step = 1
pop = sim.Population(size=N, ploidy=2, loci=[1],infoFields=['fitness']);sim.initGenotype(pop, prop=[1-nu0,nu0]);simulator = sim.Simulator(pop.clone(), rep=1);
# sim.stat(pop, alleleFreq=[0]); print pop.dvars().alleleFreq[0][1]
global a;a = "0;;{}\n".format(nu0)
simulator.evolve(initOps=[sim.InitSex()],
preOps=sim.MapSelector(loci=0, fitness={(0, 0): 1, (0, 1): 1 + s * 0.5, (1, 1): 1 + s}),
matingScheme=sim.RandomMating(), postOps=[sim.Stat(alleleFreq=[0], step=step),
sim.PyEval("'%d;;' % (gen+1)", reps=0, step=step,
output=fff), sim.PyEval(
r"'{}\n'.format(map(lambda x: round(x[1],5),alleleFreq.values())[0])", step=step, output=fff)],
gen=T)
return pd.DataFrame(zip(*map(lambda x: x.split(';;'), a.strip().split('\n')))).T.set_index(0)[1].astype(float)
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 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 setUp(self):
self.fs = 0.9
self.ms = 0.5
self.pop = sim.Population(size=[4, 4],
loci=[1, 1, 1, 1],
chromTypes=[sim.AUTOSOME,
sim.CHROMOSOME_X,
sim.CHROMOSOME_Y,
sim.CUSTOMIZED],
infoFields=['fitness'])
self.pop.setVirtualSplitter(sim.SexSplitter())
sim.initSex(self.pop, sex=[sim.MALE, sim.FEMALE])
for i in range(2):
for j in range(2):
sim.initGenotype(self.pop,
loci=[0], # Autosome
prop=[0.5, 0.5],
subPops=[(i,j)])
sim.initGenotype(self.pop,
loci=[3], # Mitochondria
ploidy=0,
prop=[0.5, 0.5],
subPops=[(i,j)])
sim.initGenotype(self.pop,
loci=[1], # Chromosome X
prop=[0.5, 0.5],
subPops=[(i,1)])
sim.initGenotype(self.pop,
loci=[1], # Chromosome X
prop=[0.5, 0.5],
ploidy=0,
subPops=[(i,0)])
sim.initGenotype(self.pop,
loci=[2], # Chromosome Y
prop=[0.5, 0.5],
ploidy=1,
subPops=[(i,0)])
#
# 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())
# create 3 subpops with different sizes
pop = sim.Population(size=[3, 4, 5], ploidy=1, loci=1, infoFields='x')
sim.dump(pop)
# a diploid population
pop = sim.Population(size=[10], ploidy=2, loci=10, infoFields='x')
sim.dump(pop)
# a tetraploid population
pop = sim.Population(size=[10], ploidy=4, loci=10, infoFields='x')
sim.dump(pop)
# something with frequencies
pop = sim.Population(10, ploidy=2, loci=[5])
sim.dump(pop)
sim.initGenotype(pop, freq=[0.2, 0.3, 0.5])
sim.dump(pop)
# access stuff
pop = sim.Population(size=[2, 3],
ploidy=2,
loci=[5, 10],
lociPos=list(range(0, 5)) + list(range(0, 20, 2)),
chromNames=['Chr1', 'Chr2'],
alleleNames=['A', 'C', 'T', 'G'])
pop.ploidy()
pop.popSize()
pop.alleleName(1)
pop.numChrom()
pop.chromBegin(1)
ind = pop.individual(2) # access individual
# 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 saveCSV
pop = sim.Population(size=[10],
loci=[2, 3],
lociNames=['r11', 'r12', 'r21', 'r22', 'r23'],
alleleNames=['A', 'B'],
infoFields='age')
sim.initSex(pop)
sim.initInfo(pop, [2, 3, 4], infoFields='age')
sim.initGenotype(pop, freq=[0.4, 0.6])
sim.maPenetrance(pop, loci=0, penetrance=(0.2, 0.2, 0.4))
# no filename so output to standard output
saveCSV(pop, infoFields='age')
# change affection code and how to output genotype
saveCSV(pop,
infoFields='age',
affectionFormatter={
True: 1,
False: 2
},
genoFormatter={
(0, 0): 'AA',
(0, 1): 'AB',
(1, 0): 'AB',
(1, 1): 'BB'
# 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(' ')
###################
# run simulations #
###################
for rep in range(1, 501):
pop = sim.Population([popsize] * 3,
ploidy=2,
loci=[20],
lociPos=[
1, 1.1, 2, 3, 3.1, 4, 5, 5.1, 6, 7, 7.1, 8, 9,
9.1, 10, 11, 11.1, 12, 13, 13.1
],
infoFields=['fitness', 'migrate_to'])
# initiate genotypes in subpopulation 0 as fixed for '0' ancestry:
sim.initGenotype(pop, genotype=[0] * 20, subPops=[0])
# genotypes at subpopulation 1:
#sim.initGenotype(pop, genotype=[0]*20+[1]*20, subPops=[1]) # all F1 hybrids
sim.initGenotype(pop, genotype=[0] * 20, subPops=[1]) # or a mixture of
for ind in random.sample(range(popsize, popsize * 2),
popsize / 2): # both parental species.
pop.individual(ind).setGenotype([1] * 20)
# initiate genotypes in subpopulation 2 as fixed for '1' ancestry
sim.initGenotype(pop, genotype=[1] * 20, subPops=[2])
def printAlleleFreq(pop):
'Print allele frequencies of all loci and populations'
#
# 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
pop = sim.Population(size=2000, loci=2)
sim.initGenotype(pop, freq=[.2, .8])
sim.mapPenetrance(pop, loci=0, penetrance={(0, 0): 0, (0, 1): .2, (1, 1): .3})
sim.stat(pop, genoFreq=0, numOfAffected=1, vars='genoNum')
# number of affected individuals
pop.dvars().numOfAffected
# which should be roughly (#01 + #10) * 0.2 + #11 * 0.3
(pop.dvars().genoNum[0][(0,1)] + pop.dvars().genoNum[0][(1,0)]) * 0.2 \
+ pop.dvars().genoNum[0][(1,1)] * 0.3
def do_forward_sims(sim_data, chrom_len, diploid_Ne, batchname, repilcates,
simupop_seed):
print("start getting chromosomes positions")
chromosome_positions = get_chromosome_positions(sim_data=sim_data,
chromsome_length=chrom_len)
print("done getting chromosomes positions")
haplotypes = get_haplotypes(sim_data)
loci_per_chromsome = get_loci_per_chromosome(chromosome_positions)
n_chrom = len(loci_per_chromsome)
# set up the ancestral pop in simuPOP
initial = simuPOP.Population(
diploid_Ne, # here is the diploid number
loci=
loci_per_chromsome, # should be the number of loci on each chromosome
lociPos=list(chromosome_positions),
ploidy=2,
infoFields=['father_idx', 'mother_idx', 'ind_id'],
#alleleNames=['A', 'C', 'G', 'T'],
lociNames=[
'locus_{}'.format(x) for x in xrange(len(chromosome_positions))
])
simuPOP.initGenotype(initial,
prop=[1.0 / len(haplotypes)] * len(haplotypes),
haplotypes=list(haplotypes))
simuPOP.tagID(initial, reset=1)
initial_export = get_export_genotypes(initial, initial.popSize())
np.savetxt('./share/{}/Ne-{}_Chr-{}/Ne-{}_Chr-{}.inital.txt'.format(
batchname, diploid_Ne, n_chrom, diploid_Ne, n_chrom),
initial_export,
delimiter='\t',
fmt='%01d')
# map-ped
simuPOP.utils.export(
initial,
format='PED',
output='./share/{}/Ne-{}_Chr-{}/Ne-{}_Chr-{}.inital.ped'.format(
batchname, diploid_Ne, n_chrom, diploid_Ne, n_chrom),
gui=False,
idField='ind_id')
simuPOP.utils.export(
initial,
format='MAP',
output='./share/{}/Ne-{}_Chr-{}/Ne-{}_Chr-{}.inital.map'.format(
batchname, diploid_Ne, n_chrom, diploid_Ne, n_chrom),
gui=False)
# Doesn't yet work!!
# set the seed for simuPOP
#simuPOP.setRNG(seed=simupop_seed)
# and in Python
#random.seed(simupop_seed)
# and for numpy
#np.random.seed(simupop_seed)
print("initalizing the forward simulator")
simu = simuPOP.Simulator(
simuPOP.Population(
diploid_Ne, # here is the diploid number
loci=get_loci_per_chromosome(
chromosome_positions
), # should be the number of loci on each chromosome
lociPos=list(chromosome_positions),
ploidy=2,
infoFields=['ind_id', 'father_idx', 'mother_idx'],
#alleleNames=['A', 'C', 'G', 'T'],
lociNames=[
'locus_{}'.format(x) for x in xrange(len(chromosome_positions))
]),
rep=repilcates)
print("Start evolving {} replicates".format(repilcates))
simu.evolve(
initOps=[
simuPOP.InitSex(
sex=[simuPOP.MALE, simuPOP.FEMALE]), # alternate sex
simuPOP.InitGenotype(prop=[1.0 / len(haplotypes)] *
len(haplotypes),
haplotypes=list(haplotypes))
],
matingScheme=simuPOP.HomoMating(
chooser=simuPOP.PyParentsChooser(fixedChooser),
generator=simuPOP.OffspringGenerator(
sexMode=(simuPOP.GLOBAL_SEQUENCE_OF_SEX, simuPOP.MALE,
simuPOP.FEMALE),
ops=[
simuPOP.Recombinator(intensity=1.0 / chrom_len),
simuPOP.ParentsTagger()
]),
),
postOps=[],
gen=20)
print("Done evolving {} replicates!".format(repilcates))
# export the data
print("Exporting data!".format(repilcates))
for rep, pop in enumerate(simu.populations()):
if diploid_Ne >= 200:
pop_genotypes = get_export_genotypes(pop,
n_ind=200) # select 200 inds
else:
pop_genotypes = get_export_genotypes(
pop, n_ind=diploid_Ne) # select 200 inds
np.savetxt('./share/{}/Ne-{}_Chr-{}/Ne-{}_Chr-{}_Frep-{}.geno'.format(
batchname, diploid_Ne, n_chrom, diploid_Ne, n_chrom,
rep).format(rep),
pop_genotypes,
delimiter='\t',
fmt='%01d')
if rep % 10 == 0:
print "saved rep {}".format(rep)
return (1 / 2) * (xi_1 - yi_1)**2 / (
(xi_1 + yi_1) / 2 - xi_1 * yi_1) + (1 / 2) * (xi_2 - yi_2)**2 / (
(xi_2 + yi_2) / 2 - xi_2 * yi_2)
##########################
Ne_ests = []
FCs = []
for r in range(repeats):
print(r + 1)
popNe = sim.Population(size=[Ne], ploidy=2, loci=[1] * n_loci)
popS = sim.Population(size=[S], ploidy=2, loci=[1] * n_loci)
sim.initGenotype(popNe, freq=initial_frequencies)
sim.initGenotype(popS, freq=initial_frequencies)
sim.stat(popS, alleleFreq=range(0, n_loci), vars=['alleleFreq'])
initial_allele_freqs = popS.vars()['alleleFreq']
popNe.evolve(initOps=[sim.InitSex()],
matingScheme=sim.RandomMating(),
gen=gens)
sample_pop = drawRandomSample(popNe, sizes=S)
sim.stat(sample_pop, alleleFreq=range(0, n_loci), vars=['alleleFreq'])
meanFC_repeat = 0
countFC = 0
'''
Created on Oct 6, 2010
@author: Gaurav Singhal
'''
import simuPOP a
import simuPOP as sim
pop = sim.Population(size=1000, loci=[2])
pop.evolve(initOps = [sim.InitSex(),sim.initGenotype(pop, genotype=[1, 2, 2, 1])], matingScheme=sim.RandomMating(ops=sim.Recombinator(rates=0.01)),
postOps = [sim.stat(pop, LD=[0, 1]),sim.PyEval(r"'%.2f\n' % LD[0][1]", step=10),],gen=100)
#
# 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
pop = sim.Population(
100,
loci=[1, 1, 1],
lociNames=['A', 'X', 'Y'],
chromTypes=[sim.AUTOSOME, sim.CHROMOSOME_X, sim.CHROMOSOME_Y])
sim.initGenotype(pop, freq=[0.01, 0.05, 0.94])
sim.stat(pop, genoFreq=['A', 'X']) # both loci indexes and names can be used.
print('Available genotypes on autosome:', list(pop.dvars().genoFreq[0].keys()))
for i in range(3):
for j in range(3):
print('%d-%d: %.3f' % (i, j, pop.dvars().genoFreq[0][(i, j)]))
print('Genotype frequency on chromosome X:\n', \
'\n'.join(['%s: %.3f' % (x,y) for x,y in pop.dvars().genoFreq[1].items()]))
import simuPOP as sim
def calcNe(pop, param):
'Calculated effective number of disease alleles at specified loci (param)'
sim.stat(pop, alleleFreq=param)
ne = {}
for loc in param:
freq = pop.dvars().alleleFreq[loc]
sumFreq = 1 - pop.dvars().alleleFreq[loc][0]
if sumFreq == 0:
ne[loc] = 0
else:
ne[loc] = 1. / sum([(freq[x]/sumFreq)**2 \
for x in list(freq.keys()) if x != 0])
# save the result to the sim.Population.
pop.dvars().ne = ne
return True
pop = sim.Population(1000, loci=2)
sim.initGenotype(pop, freq=[0.9] + [0.01] * 10, loci=0)
sim.initGenotype(pop, freq=[0.9] + [0.05] + [0.005] * 10, loci=1)
calcNe(pop, param=[0, 1])
print(pop.dvars().ne)
def __init__(self, loci, *args, **kwargs):
self.loci = loci
sim.PyOperator.__init__(self, func=self.calcNe, *args, **kwargs)
#
def calcNe(self, pop):
sim.stat(pop, alleleFreq=self.loci)
ne = {}
for loc in self.loci:
freq = pop.dvars().alleleFreq[loc]
sumFreq = 1 - pop.dvars().alleleFreq[loc][0]
if sumFreq == 0:
ne[loc] = 0
else:
ne[loc] = 1. / sum([(freq[x] / sumFreq)**2
for x in list(freq.keys()) if x != 0])
# save the result to the sim.Population.
pop.dvars().ne = ne
return True
def Ne(pop, loci):
'''Function form of operator ne'''
ne(loci).apply(pop)
return pop.dvars().ne
pop = sim.Population(100, loci=[10])
sim.initGenotype(pop, freq=[.2] * 5)
print(Ne(pop, loci=[2, 4]))
# (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(10, loci=[2, 3], infoFields='Sex') sim.initSex(pop) pop.setVirtualSplitter(sim.SexSplitter()) # initialize male and females with different genotypes. sim.initGenotype(pop, genotype=[0]*5, subPops=[(0, 0)]) sim.initGenotype(pop, genotype=[1]*5, subPops=[(0, 1)]) # set Sex information field to 0 for all males, and 1 for all females pop.setIndInfo([sim.MALE], 'Sex', [0, 0]) pop.setIndInfo([sim.FEMALE], 'Sex', [0, 1]) # Print individual genotypes, followed by values at information field Sex sim.dump(pop, structure=False)
#
# 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
pop = sim.Population([1000]*2, loci=3)
sim.initGenotype(pop, freq=[0.2, 0.8], subPops=0)
sim.initGenotype(pop, freq=[0.8, 0.2], subPops=1)
sim.stat(pop, LD=[[0, 1, 0, 0], [1, 2]],
vars=['LD', 'LD_prime', 'R2', 'LD_ChiSq', 'LD_ChiSq_p', 'CramerV',
'LD_prime_sp', 'LD_ChiSq_p_sp'])
from pprint import pprint
pprint(pop.vars())
# 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 pop.removeSubPops(1)
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
pop = sim.Population(size=[2000, 3000], loci=[5, 7])
# by allele frequency
def printFreq(pop, loci):
sim.stat(pop, alleleFreq=loci)
print(', '.join(
['{:.3f}'.format(pop.dvars().alleleFreq[x][0]) for x in loci]))
sim.initGenotype(pop, freq=[.4, .6])
sim.dump(pop, max=6, structure=False)
printFreq(pop, range(5))
# by proportion
sim.initGenotype(pop, prop=[0.4, 0.6])
printFreq(pop, range(5))
# by haplotype frequency
sim.initGenotype(pop, freq=[.4, .6], haplotypes=[[1, 1, 0, 1], [0, 0, 1]])
sim.dump(pop, max=6, structure=False)
printFreq(pop, range(5))
# by haplotype proportion
sim.initGenotype(pop, prop=[0.4, 0.6], haplotypes=[[1, 1, 0], [0, 0, 1, 1]])
printFreq(pop, range(5))
# by genotype
pop = sim.Population(size=[2, 3], loci=[5, 7])
sim.initGenotype(pop, genotype=[1] * 5 + [2] * 7 + [3] * 5 + [4] * 7)
# # 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])
# 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
pop = sim.Population(size=[10, 10], loci=[20, 30], infoFields='gen',
ancGen=-1)
sim.initSex(pop)
pop.setVirtualSplitter(sim.SexSplitter())
pop1 = pop.clone()
sim.initGenotype(pop, freq=[0]*20 + [0.1]*10)
pop.setIndInfo(1, 'gen')
sim.initGenotype(pop1, freq=[0]*50 + [0.1]*10)
pop1.setIndInfo(2, 'gen')
pop.push(pop1)
sim.dump(pop, width=3, loci=[5, 6, 30], subPops=([0, 0], [1, 1]),
max=10, structure=False)
# list all male individuals in all subpopulations
sim.dump(pop, width=3, loci=[5, 6, 30], subPops=[(sim.ALL_AVAIL, 0)],
max=10, structure=False)
