def initialize_meta_population(self, pop, number_of_reps=1):
if number_of_reps == 1:
sim_meta = sim.Simulator(pop, rep=1, stealPops=False)
meta_pop = sim_meta.extract(0)
meta_pop.removeSubPops(0)
return meta_pop
else:
proto_meta = pop.clone()
proto_meta.setSubPopName('meta', 0)
proto_meta.removeSubPops(0)
sim_meta = sim.Simulator(proto_meta, rep=number_of_reps,
stealPops=False)
return sim_meta
def prepare_sim(self, params):
for view in self._views:
for info in view.info_fields:
self._info_fields.add(info)
if params['num_snps'] > 0:
pop, init_ops, pre_ops, post_ops = \
self._create_island([params['pop_size']] * params['num_pops'],
params['mig'], params['num_snps'])
loci, genome_init = self._create_snp_genome(
params['num_snps'], freq=params['snp_freq'])
gpre_ops = []
else:
pop, init_ops, pre_ops, post_ops = \
self._create_island([params['pop_size']] * params['num_pops'],
params['mig'], params['num_msats'])
loci, genome_init, gpre_ops = self._create_genome(
params['num_msats'],
start_alleles=params['num_msat_alleles'])
view_ops = []
for view in self._views:
view.pop = pop
view_ops.extend(view.view_ops)
for view in self._views:
post_ops.append(sp.PyOperator(func=_hook_view, param=view))
post_ops = view_ops + post_ops
sim = sp.Simulator(pop, 1, True)
return {'sim': sim, 'pop': pop, 'init_ops': init_ops + genome_init,
'pre_ops': pre_ops, 'post_ops': post_ops,
'mating_scheme': sp.RandomMating()}
def execute(config, pop, mating_op):
"""Configure and run simulations."""
_, init_genotype_op = cf.get_init_genotype_by_count(simu, 1)
init_info_op = cf.get_init_info(simu)
mutation_op = get_mutation_operator(m_rate=config.m,
loci=config.loci,
allele_length=config.allele_length,
nrep=1,
burnin=config.burnin)
output_op = get_output_operator(config)
simulator = simu.Simulator(pops=pop, rep=1)
if config.debug > 0:
post_op = [
simu.Stat(alleleFreq=simu.ALL_AVAIL, step=config.debug),
simu.PyEval(r"'%s\n' % alleleFreq", step=config.debug)
]
else:
post_op = []
if config.output_per > 0:
post_op.append(output_op)
simulator.evolve(initOps=[init_info_op, init_genotype_op],
preOps=mutation_op,
matingScheme=mating_op,
postOps=post_op,
finalOps=output_op,
gen=config.gens + config.burnin)
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 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 execute(config, pop, mating_op):
"""
Executes simulations with appropriate mutation model and mating scheme.
"""
init = config.initial_genotype
if init[0] == 'monomorphic':
next_idx, init_genotype_op = cf.get_init_genotype_by_count(simu, 1)
elif init[0] == 'unique':
next_idx, init_genotype_op = cf.get_init_genotype_by_count(
simu, 2 * config.N)
elif init[0] == 'count':
next_idx, init_genotype_op = cf.get_init_genotype_by_count(
simu, init[1])
elif init[0] == 'frequency':
next_idx, init_genotype_op = get_init_genotype_by_prop(init[1])
init_info_op = cf.get_init_info(simu)
mutation_op = get_mutation_operator(m_rate=config.m,
loci=config.loci,
nrep=1,
burnin=config.burnin,
new_idx=next_idx)
output_op = get_output_operator(config)
simulator = simu.Simulator(pops=pop, rep=1)
if config.debug > 0:
post_op = [
simu.Stat(alleleFreq=simu.ALL_AVAIL, step=config.debug),
simu.PyEval(r"'%s\n' % alleleFreq", step=config.debug)
]
else:
post_op = []
if config.output_per > 0:
post_op.append(output_op)
simulator.evolve(initOps=[init_info_op, init_genotype_op],
preOps=mutation_op,
matingScheme=mating_op,
postOps=post_op,
finalOps=output_op,
gen=config.gens + config.burnin)
def prepare_sim(self, params):
for view in self._views:
for info in view.info_fields:
self._info_fields.add(info)
nloci = 1 + params['neutral_loci']
pop, init_ops, pre_ops, post_ops = \
self._create_single_pop(params['pop_size'], nloci)
view_ops = []
for view in self._views:
view.pop = pop
view_ops.extend(view.view_ops)
for view in self._views:
post_ops.append(sp.PyOperator(func=_hook_view, param=view))
post_ops = view_ops + post_ops
loci, genome_init = self._create_snp_genome(
nloci, freq=params['snp_freq'])
sim = sp.Simulator(pop, 1, True)
if params['sel_type'] == 'hz_advantage':
ms = sp.MapSelector(loci=0, fitness={
(0, 0): 1 - params['sel'],
(0, 1): 1,
(1, 1): 1 - params['sel']})
elif params['sel_type'] == 'recessive':
ms = sp.MapSelector(loci=0, fitness={
(0, 0): 1 - params['sel'],
(0, 1): 1 - params['sel'],
(1, 1): 1})
else: # dominant
ms = sp.MapSelector(loci=0, fitness={
(0, 0): 1 - params['sel'],
(0, 1): 1,
(1, 1): 1})
return {'sim': sim, 'pop': pop, 'init_ops': init_ops + genome_init,
'pre_ops': pre_ops, 'post_ops': post_ops,
'mating_scheme': sp.RandomMating(
ops=[sp.MendelianGenoTransmitter(), ms])}
# 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
simu = sim.Simulator(sim.Population(size=1000, loci=2), rep=3)
simu.evolve(initOps=[sim.InitSex(),
sim.InitGenotype(genotype=[1, 2, 2, 1])],
matingScheme=sim.RandomMating(ops=sim.Recombinator(rates=0.01)),
postOps=[
sim.Stat(LD=[0, 1]),
sim.PyEval(r"'%.2f\t' % LD[0][1]", step=20, output='>>LD.txt'),
sim.PyOutput('\n', reps=-1, step=20, output='>>LD.txt'),
sim.PyEval(r"'%.2f\t' % R2[0][1]", output='R2.txt'),
sim.PyEval(r"'%.2f\t' % LD[0][1]",
step=20,
output="!'>>LD_%d.txt' % rep"),
],
gen=100)
print(open('LD.txt').read())
print(open('R2.txt').read()) # Only the last write operation succeed.
# 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
simu = sim.Simulator(sim.Population(10000, loci=[100] * 5), rep=2)
simu.evolve(initOps=[sim.InitSex(),
sim.InitGenotype(freq=[0.1, 0.9])],
matingScheme=sim.RandomMating(),
postOps=[
sim.Stat(alleleFreq=0),
sim.TicToc(step=50, reps=-1),
],
gen=101)
import simuOpt
simuOpt.setOptions(quiet=True, alleleType='long')
import simuPOP as sim
pop = sim.Population(size=1000, loci=[1])
simu = sim.Simulator(pop, 10)
simu.evolve(
initOps=[sim.InitSex(),
sim.PointMutator(loci=0, allele=1, inds=0)],
matingScheme=sim.RandomMating(),
finalOps=sim.Stat(alleleFreq=0),
gen=100)
print([x.dvars().alleleNum[0][1] for x in simu.populations()])
args.popsize, args.mutationrate)
log.info("Starting data collection at generation: %s", beginCollectingData)
totalSimulationLength = beginCollectingData + args.length
log.info("Simulation will sample %s generations after stationarity",
args.length)
data.storeSimulationData(args.popsize, args.mutationrate, sim_id,
args.samplesize, args.replications, args.numloci,
__file__, args.numloci, simconfig.MAXALLELES)
initial_distribution = utils.constructUniformAllelicDistribution(args.numloci)
log.info("Initial allelic distribution: %s", initial_distribution)
pop = sim.Population(size=args.popsize, ploidy=1, loci=args.numloci)
simu = sim.Simulator(pop, rep=args.replications)
simu.evolve(
initOps=sim.InitGenotype(freq=initial_distribution),
preOps=[
sim.PyOperator(func=utils.logGenerationCount,
param=(),
step=1000,
reps=0),
],
matingScheme=sim.RandomSelection(),
postOps=[
sim.KAlleleMutator(k=simconfig.MAXALLELES,
rates=args.mutationrate,
loci=sim.ALL_AVAIL),
sim.PyOperator(func=data.sampleNumAlleles,
'founder_population_filename': founder_filename,
'simulated_pop_size': popsize,
'number_breeding_males': males,
'number_breeding_females': females,
'number_generations': generations,
'number_of_reps': replicates,
'sample_sizes': experimental_meta_population_sample_size,
}
with open('simulation_parameters.json', 'w') as simparams:
json.dump(sim_params, simparams, indent=5)
for batch in range(92, 101):
print("Executing batch: {batch_id}.".format(batch_id=batch))
subpops_and_gens = [(1, 0), (2, 2), (3, 4), (4, 6), (5, 8), (6, 10)]
tuson_replicates = sim.Simulator(tuson, rep=replicates, stealPops=False)
tuson_meta_replicates = td.initialize_meta_population(tuson,
number_of_reps=replicates)
td.replicate_tuson_drift_simulation(tuson_replicates,
tuson_meta_replicates,
experimental_meta_population_sample_size,
recom_rates)
print("Multi-replicate drift simulation complete.")
allelefrequencies = td.insert_allele_frequencies_into_aggregate_matrix(
replicates, tuson_meta_replicates,
minor_alleles)
np.savetxt('batch_' + str(batch) + '_af.txt', allelefrequencies,
fmt='%.3f', delimiter='\t')
homofrequencies, heterofrequencies = td.insert_minor_genotype_frequencies_into_aggregate_matrix(
minor_homozygotes_by_locus,
minor_heterozygotes_by_locus,
#NOTE THAT THIS OUTCOME WILL BE DIFFERENT EVERY TIME YOU RUN THE SIMULATION
simulation_outcome = {}
#Define sampling rate:
for sampling in [36, 48, 60, 72, 84]:
#Define the population
pop = sim.Population(size=100, loci=1)
#Define the demographic trajectory
demo_function = serf.demo_dynamic(101, 100., np.log(2), 100000., 10., 30,
sampling)
#Initiate the simulation
simu = sim.Simulator(pop, rep=100)
#Evolve population
simu.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(
freq=[0.95, 0.05]), #proportion of minor to major allele
sim.PyExec(
'traj=[]') #Initiate a recipient list for frequency outputs
],
matingScheme=sim.RandomSelection(
subPopSize=demo_function), #random binary fission
postOps=[
sim.Stat(alleleFreq=0),
sim.PyExec('traj.append(alleleFreq[0][1])',
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--experiment",
help="provide name for experiment",
required=True,
type=str,
default="test")
parser.add_argument("--debug", help="turn on debugging output")
parser.add_argument("--reps",
help="Replicated populations per parameter set",
type=int,
default=1)
parser.add_argument(
"--networkfile",
help=
"Name of GML file representing the network model for this simulation",
required=True,
type=str)
parser.add_argument("--numloci",
help="Number of loci per individual",
type=int,
required=True)
parser.add_argument(
"--maxinittraits",
help="Max initial number of traits per locus for initialization",
type=int,
required=True)
parser.add_argument(
"--innovrate",
help=
"Rate at which innovations occur in population as a per-locus rate",
type=float,
default=0.001)
parser.add_argument(
"--simlength",
help=
"Time at which simulation and sampling end, defaults to 3000 generations",
type=int,
default="20")
parser.add_argument(
"--popsize",
help="Initial size of population for each community in the model",
type=int,
required=True)
parser.add_argument(
"--migrationfraction",
nargs='+',
help="Fraction of population that migrates each time step",
type=float,
required=True,
default=[])
parser.add_argument(
"--seed",
type=int,
help="Seed for random generators to ensure replicability")
parser.add_argument("--k_values",
nargs='+',
type=int,
help="list of k-values to explore [e.g., 2 4 20 24",
default=[])
parser.add_argument("--sub_pops",
nargs='+',
help="Number of sub populations",
required=True,
default=[10])
parser.add_argument("--maxalleles",
type=int,
help="Maximum number of alleles",
default=50)
parser.add_argument("--save_figs",
type=bool,
help="Save figures or not?",
default=True)
parser.add_argument("--burnintime",
type=int,
help="How long to wait before making measurements? ",
default=2000)
parser.add_argument("--rewiringprob",
type=float,
help="Probability of random rewiring",
default=0)
config = parser.parse_args()
# check the k and migration rate combinations
for kvalue in config.k_values:
if float(kvalue) * float(config.migrationfraction) >= 1.0:
print("k=%s * mig=%4f is greater than 1.0\n" %
(kvalue, config.migrationfraction))
print(
"Please adjust input values for k and/or migration rate and restart.\n "
)
sys.exit()
# setup output directories for writing
output_path = utils.setup_output(config.experiment)
# save parameters
utils.save_parameters(str(sys.argv), config, output_path)
k_run_values = config.k_values
subpop_run_values = config.sub_pops
## make sure the k values are less than # of subpops and > 1
for k in k_run_values:
for subnum in subpop_run_values:
if int(k) > int(subnum) or int(k) < 2:
print(
"k values can not be greater than the number of sub populations. k = %s subpops = %s \n"
% (k, subnum))
sys.exit()
## initialize the output dictionary
for k in k_run_values:
for sb in subpop_run_values:
output[k][sb] = {}
# set up the frequencies for the alleles in each loci. Here assuming a uniform distribution as a starting point
distribution = utils.constructUniformAllelicDistribution(
config.maxinittraits)
iteration_number = -1
for k in k_run_values:
for subnum in subpop_run_values:
iteration_number += 1
## these are lists of things that simuPop will do at different stages
init_ops = OrderedDict()
pre_ops = OrderedDict()
post_ops = OrderedDict()
# Construct a demographic model from a collection of network slices which represent a temporal network
# of changing subpopulations and interaction strengths. This object is Callable, and simply is handed
# to the mating function which applies it during the copying process
#networkmodel = NetworkModel( networkmodel="/Users/clipo/Documents/PycharmProjects/RapaNuiSim/notebooks/test_graph.gml",
networkmodel = network.NetworkModel(
networkmodel="smallworld",
simulation_id=config.experiment,
sim_length=config.simlength,
burn_in_time=config.burnintime,
initial_subpop_size=config.popsize,
migrationfraction=config.migrationfraction,
sub_pops=subnum,
connectedness=k, # if 0, then distance decay
save_figs=config.save_figs,
network_iteration=iteration_number)
num_pops = networkmodel.get_subpopulation_number()
sub_pop_size = int(config.popsize / num_pops)
# The regional network model defines both of these, in order to configure an initial population for evolution
# Construct the initial population
pops = sp.Population(
size=[sub_pop_size] * num_pops,
subPopNames=str(list(networkmodel.get_subpopulation_names())),
infoFields='migrate_to',
ploidy=1,
loci=config.numloci)
### now set up the activities
init_ops['acumulators'] = sp.PyOperator(
utils.init_acumulators,
param=['fst', 'alleleFreq', 'haploFreq'])
init_ops['Sex'] = sp.InitSex()
init_ops['Freq'] = sp.InitGenotype(loci=list(range(
config.numloci)),
freq=distribution)
post_ops['Innovate'] = sp.KAlleleMutator(k=config.maxalleles,
rates=config.innovrate,
loci=sp.ALL_AVAIL)
post_ops['mig'] = sp.Migrator(
rate=networkmodel.get_migration_matrix()) #, reps=[3])
#for i, mig in enumerate(migs):
# post_ops['mig-%d' % i] = sp.Migrator(demography.migrIslandRates(mig, num_pops), reps=[i])
post_ops['Stat-fst'] = sp.Stat(structure=sp.ALL_AVAIL)
post_ops['Stat-richness'] = sp.Stat(
alleleFreq=[0],
haploFreq=[0],
vars=['alleleFreq', 'haploFreq', 'alleleNum', 'genoNum'])
post_ops['fst_acumulation'] = sp.PyOperator(
utils.update_acumulator, param=['fst', 'F_st'])
post_ops['richness_acumulation'] = sp.PyOperator(
utils.update_richness_acumulator,
param=('alleleFreq', 'Freq of Alleles'))
post_ops['class_richness'] = sp.PyOperator(
utils.calculateAlleleAndGenotypeFrequencies,
param=(config.popsize, config.numloci))
mating_scheme = sp.RandomSelection()
#mating_scheme=sp.RandomSelection(subPopSize=sub_pop_size)
## go simuPop go! evolve your way to the future!
sim = sp.Simulator(pops, rep=config.reps)
print("now evolving... k = %s with sub_pops = %s" % (k, subnum))
sim.evolve(initOps=list(init_ops.values()),
preOps=list(pre_ops.values()),
postOps=list(post_ops.values()),
matingScheme=mating_scheme,
gen=config.simlength)
# now make a figure of the Fst results
fig = plt.figure(figsize=(16, 9))
ax = fig.add_subplot(111)
count = 0
for pop in sim.populations():
ax.plot(pop.dvars().fst, label='Replicate: %s' % count)
output[k][subnum][count] = deepcopy(pop.dvars())
count += 1
ax.legend(loc=2)
ax.set_ylabel('FST')
ax.set_xlabel('Generation')
plt.show()
sum_fig = plt.figure(figsize=(16, 9))
ax = sum_fig.add_subplot(111)
iteration = -1
for k in k_run_values:
for subnum in subpop_run_values:
iteration += 1
# only label the first one
for n in range(config.reps):
if n == 0:
ax.plot(output[k][subnum][n].fst,
color=list(
dict(mcolors.BASE_COLORS,
**mcolors.CSS4_COLORS).keys())[iteration],
label='k = %s subpops = %s' % (k, subnum))
else:
ax.plot(output[k][subnum][n].fst,
color=list(
dict(mcolors.BASE_COLORS,
**mcolors.CSS4_COLORS).keys())[iteration])
ax.legend(loc=2)
ax.set_ylabel('Fst')
ax.set_xlabel('Generations')
plt.show()
savefilename = output_path + "/sum_fig.png"
sum_fig.savefig(savefilename, bbox_inches='tight')
rich_fig = plt.figure(figsize=(16, 9))
ax = rich_fig.add_subplot(111)
iteration = -1
for k in k_run_values:
for sb in subpop_run_values:
iteration += 1
# only add a label for the first one (not all the replicates)
for n in range(config.reps):
if n == 0:
ax.plot(output[k][subnum][n].richness,
color=list(
dict(mcolors.BASE_COLORS,
**mcolors.CSS4_COLORS).keys())[iteration],
label='k = %s subpops = %s' % (k, subnum))
else:
ax.plot(output[k][subnum][n].richness,
color=list(
dict(mcolors.BASE_COLORS,
**mcolors.CSS4_COLORS).keys())[iteration])
ax.legend(loc=2)
ax.set_ylabel('Richness')
ax.set_xlabel('Generations')
plt.show()
savefilename = output_path + "/richness.png"
rich_fig.savefig(savefilename, bbox_inches='tight')
## output CI for the parameters
summary_fig = plt.figure(figsize=(16, 9))
ax = summary_fig.add_subplot(111)
iteration = -1
for k in k_run_values:
for subnum in subpop_run_values:
iteration += 1
CI_average = []
CI_min = []
CI_max = []
for t in range(len(output[k][subnum][0].fst)):
point_in_time = []
for n in range(config.reps):
list_of_points = list(output[k][subnum][n].fst)
point_in_time.append(list_of_points[t])
(ave, min,
max) = utils.mean_confidence_interval(point_in_time,
confidence=0.95)
CI_average.append(ave)
CI_min.append(min)
CI_max.append(max)
ax.plot(list(CI_average),
color=list(
dict(mcolors.BASE_COLORS,
**mcolors.CSS4_COLORS).keys())[iteration],
label='k = %s subpops = %s' % (k, subnum))
ax.plot(list(CI_min), "--", color="0.5")
ax.plot(list(CI_max), "--", color="0.5")
ax.fill_between(list(CI_average),
list(CI_max),
list(CI_min),
color="None",
linestyle="--")
ax.legend(loc=2)
ax.set_ylabel('Fst')
ax.set_xlabel('Generation')
plt.show()
savefilename = output_path + "/summary-ci.png"
summary_fig.savefig(savefilename, bbox_inches='tight')
## now the richness graph
richness_sum_fig = plt.figure(figsize=(16, 9))
ax = richness_sum_fig.add_subplot(111)
iteration = -1
for k in k_run_values:
for subnum in subpop_run_values:
iteration += 1
CI_average = []
CI_min = []
CI_max = []
for t in range(len(output[k][subnum][0].richness)):
point_in_time = []
for n in range(config.reps):
list_of_points = list(output[k][subnum][n].richness)
point_in_time.append(list_of_points[t])
(ave, min,
max) = utils.mean_confidence_interval(point_in_time,
confidence=0.95)
CI_average.append(ave)
CI_min.append(min)
CI_max.append(max)
ax.plot(list(CI_average),
color=list(
dict(mcolors.BASE_COLORS,
**mcolors.CSS4_COLORS).keys())[iteration],
label='k = %s subpops = %s' % (k, subnum))
ax.plot(list(CI_min), "--", color="0.5")
ax.plot(list(CI_max), "--", color="0.5")
ax.fill_between(list(CI_average),
list(CI_max),
list(CI_min),
color="None",
linestyle="--")
ax.legend(loc=2)
ax.set_ylabel('Richness')
ax.set_xlabel('Generation')
plt.show()
savefilename = output_path + "/richness-ci.png"
richness_sum_fig.savefig(savefilename, bbox_inches='tight')
generations = 10
heritability = 0.7
number_of_qtl = 50
number_of_replicates = 2
founders = [[2, 26], [3, 25], [4, 24], [5, 23]]
os_per_pair = 500
recombination_rates = [0.01] * 1478
prefounders = sim.loadPopulation(input_file_prefix + 'bia_prefounders.pop')
config_file_template = input_file_prefix + 'gwas_pipeline.xml'
sim.tagID(prefounders, reset=True)
alleles = np.array(
pd.read_hdf(input_file_prefix + 'parameters/alleles_at_1478_loci.hdf'))
rdm_populations = sim.Simulator(prefounders,
number_of_replicates,
stealPops=False)
rdm_magic = breed.MAGIC(rdm_populations, founders, recombination_rates)
sim.tagID(prefounders, reset=27)
rdm_magic.generate_f_one(founders, os_per_pair)
sim.stat(rdm_populations.population(0), alleleFreq=sim.ALL_AVAIL)
af = analyze.allele_data(rdm_populations.population(0), alleles,
list(range(len(alleles))))
minor_alleles = np.asarray(af.minor_allele, dtype=np.int8)
rdm_magic.recombinatorial_convergence(rdm_populations, len(founders),
os_per_pair)
study = analyze.Study(run_id)
sim.stat(rdm_populations.population(0),
def main():
start = time()
MAXALLELES = 10000
parser = argparse.ArgumentParser()
parser.add_argument("--experiment",
help="provide name for experiment",
required=True,
type=str)
parser.add_argument(
"--cores",
type=int,
help=
"Number of cores to use for simuPOP, overrides devel flag and auto calculation"
)
parser.add_argument("--debug", help="turn on debugging output")
parser.add_argument("--devel",
help="Use only half of the available CPU cores",
type=int,
default=1)
parser.add_argument("--dbhost",
help="database hostname, defaults to localhost",
default="localhost")
parser.add_argument("--dbport",
help="database port, defaults to 27017",
default="27017")
parser.add_argument("--stepsize",
help="size of sample by proportion",
type=float,
default=1.0)
parser.add_argument("--reps",
help="Replicated populations per parameter set",
type=int,
default=4)
parser.add_argument(
"--networkfile",
help=
"Name of GML file representing the network model for this simulation",
required=True,
type=str)
parser.add_argument("--numloci",
help="Number of loci per individual",
type=int,
required=True)
parser.add_argument(
"--maxinittraits",
help="Max initial number of traits per locus for initialization",
type=int,
required=True)
parser.add_argument(
"--samplefraction",
help=
"Size of samples taken to calculate all statistics, as a proportion",
type=float,
required=True)
parser.add_argument(
"--innovrate",
help=
"Rate at which innovations occur in population as a per-locus rate",
type=float,
required=True)
parser.add_argument(
"--simlength",
help=
"Time at which simulation and sampling end, defaults to 3000 generations",
type=int,
default="3000")
parser.add_argument(
"--popsize",
help="Initial size of population for each community in the model",
type=int,
required=True)
parser.add_argument(
"--migrationfraction",
help="Fraction of population that migrates each time step",
type=float,
required=True,
default=0.2)
parser.add_argument(
"--seed",
type=int,
help="Seed for random generators to ensure replicability")
(config, sim_id, script, cores) = setup(parser)
log.info("config: %s", config)
beginCollectingData = cpm.expectedIAQuasiStationarityTimeHaploid(
config.popsize, config.innovrate)
log.info("Starting data collection at generation: %s", beginCollectingData)
### NOTE ###
###
### the simuPOP module is deliberately imported here because we need to process the
### command line arguments first, to understand which version of the simuPOP module (e.g.,
### long allele representation, etc, to import, and because we need to figure out how
### many cores the machine has, etc., to set it up for parallel processing. If we import
### at the top of the file as normal, the imports happen before any code is executed,
### and we can't set those options. DO NOT move these imports out of setup and main.
import simuPOP as sim
import demography as demo
log.info("Starting simulation run %s", sim_id)
log.debug("config: %s", config)
if config.seed is None:
log.info(
"No random seed given, allowing RNGs to initialize with random seed"
)
else:
log.debug("Seeding RNGs with seed: %s", config.seed)
npr.seed(config.seed)
random.seed(config.seed)
full_command_line = " ".join(sys.argv)
# Calculate the burn in time
burn_time = rapanuisim.utils.simulation_burnin_time(
config.popsize, config.innovrate)
log.info("Minimum burn in time given popsize and theta: %s", burn_time)
initial_distribution = rapanuisim.utils.constructUniformAllelicDistribution(
config.maxinittraits)
log.info("Initial allelic distribution (for each locus): %s",
initial_distribution)
#innovation_rate = pypopgen.wf_mutation_rate_from_theta(config.popsize, config.innovrate)
innovation_rate = float(config.innovrate)
log.info("Per-locus innov rate within populations: %s", innovation_rate)
# Construct a demographic model from a collection of network slices which represent a temporal network
# of changing subpopulations and interaction strengths. This object is Callable, and simply is handed
# to the mating function which applies it during the copying process
networkmodel = demo.NetworkModel(
networkmodel=config.networkfile,
simulation_id=sim_id,
sim_length=config.simlength,
burn_in_time=burn_time,
initial_subpop_size=config.popsize,
migrationfraction=config.migrationfraction)
# The regional network model defines both of these, in order to configure an initial population for evolution
# Construct the initial population
pop = sim.Population(size=networkmodel.get_initial_size(),
subPopNames=networkmodel.get_subpopulation_names(),
infoFields=networkmodel.get_info_fields(),
ploidy=1,
loci=config.numloci)
log.info("population sizes: %s names: %s", pop.subPopSizes(),
pop.subPopNames())
initial_distribution = utils.constructUniformAllelicDistribution(
config.numloci)
log.info("Initial allelic distribution: %s", initial_distribution)
# We are going to evolve the same population over several replicates, in order to measure how stochastic variation
# effects the measured copying process.
simu = sim.Simulator(pop, rep=config.reps)
# Start the simulation and evolve the population, taking samples after the burn-in time has elapsed
simu.evolve(
initOps=sim.InitGenotype(freq=initial_distribution),
preOps=[
sim.PyOperator(func=sampling.logGenerationCount,
param=(),
step=100,
reps=0)
],
matingScheme=sim.RandomSelection(subPopSize=networkmodel),
postOps=[
sim.KAlleleMutator(k=MAXALLELES,
rates=config.innovrate,
loci=sim.ALL_AVAIL),
# sim.PyOperator(func=rapanuisim.data.sampleNumAlleles,
# param=(config.samplefraction, config.innovrate, config.popsize, sim_id, config.numloci),
# step=1, begin=beginCollectingData),
# sim.PyOperator(func=rapanuisim.data.sampleTraitCounts,
# param=(config.samplefraction, config.innovrate, config.popsize, sim_id, config.numloci),
# step=1, begin=beginCollectingData),
# sim.PyOperator(func=rapanuisim.data.censusTraitCounts,
# param=(config.innovrate, config.popsize, sim_id, config.numloci), step=1,
# begin=beginCollectingData),
# sim.PyOperator(func=rapanuisim.data.censusNumAlleles,
# param=(config.innovrate, config.popsize, sim_id, config.numloci), step=1,
# begin=beginCollectingData)
# #sim.PyOperator(func=rapanuisim.data.sampleIndividuals,
# ## param=(config.samplefraction, config.innovrate, config.popsize, sim_id, config.numloci),
# # step=1, begin=beginCollectingData),
],
gen=3000)
endtime = time()
elapsed = endtime - start
#log.info("simulation complete in %s seconds with %s cores", elapsed, cores)
log.info("simulation complete,%s,%s", cores, elapsed)
log.info("Ending simulation run at generation %s",
simu.population(0).dvars().gen)
sampled_length = int(config.simlength) - burn_time
proportion_of_individuals_saved = 0.05
overshoot_as_proportion = 0.50
individuals_per_breeding_subpop = 5
heritability = 0.7
metapop_sample_size = 100
nam = sim.loadPopulation('nam_prefounders.pop')
sim.tagID(prefounders, reset=True)
founders = [1, 5, 7, 8]
pop = prefounders.clone()
ep.arbitrary_ordering_of_founders(prefounders, pop, founders)
ep.generate_f_one(pop)
ep.generate_f_two(pop)
ep.mate_and_merge(pop)
ep.interim_random_mating(pop, generations_of_random_mating)
ep.replicates = sim.Simulator(pop, stealPops=False, rep=number_of_replicates)
ep.meta_replicates = sim.Simulator(pop, rep=number_of_replicates)
"""
# Remove individuals which are initially present in Simulator objects
"""
for meta_rep in ep.meta_replicates.populations():
meta_rep.removeIndividuals(list(range(meta_rep.popSize())))
ep.pure_selection(ep.replicates, ep.meta_replicates)
syn = helpers.Synbreed()
"""
# Allele frequencies and output
"""
# Note: In PCA I should get the same results from the minor allele frequency
# as the major allele frequency
'aggregate': {},
'selected': {},
'non-selected': {}
}
s = selection.Truncation(
sim_params['gens_selection'], sim_params['gens_random_mating'],
sim_params['main_pop_size'], sim_params['proportion_saved'],
sim_params['overshoot'], sim_params['breeding_inds_per_sp'],
sim_params['heritability'], sim_params['sample_sizes'],
sim_params['replicates'])
sim.tagID(nam, reset=True)
founders = sim_params['founders']
replicated_nam = sim.Simulator(nam, rep=2)
pop = replicated_nam.extract(0)
pop.dvars().statistics = population_statistics
meta = replicated_nam.extract(0)
#meta.removeSubPops(0)
# ### Simulated Breeding Scenario ###
# In[ ]:
s.generate_f_one(pop, recombination_rates, sim_params['founders'])
s.expand_by_selfing(pop, recombination_rates)
s.mate_and_merge(pop, recombination_rates)
s.interim_random_mating(pop, recombination_rates)
sim.stat(pop,
# (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
simu = sim.Simulator(sim.Population(100, loci=1), rep=5)
simu.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.5, 0.5])
],
matingScheme = sim.RandomMating(),
postOps=[
sim.Stat(alleleFreq=0),
sim.TerminateIf('len(alleleFreq[0]) == 1')
]
)
# In[ ]:
s = simulate.Truncation(sim_params['generations_of_selection'],
sim_params['generations_of_random_mating'],
sim_params['operating_population_size'],
sim_params['proportion_of_individuals_saved'],
sim_params['overshoot_as_proportion'],
sim_params['individuals_per_breeding_subpop'],
sim_params['heritability'],
sim_params['meta_pop_sample_sizes'],
sim_params['number_of_replicates'])
# In[ ]:
founders = sim_params['founders']
replicated_nam = sim.Simulator(nam, rep=3, stealPops=False)
pop = replicated_nam.extract(0)
# ### Run MAGIC Mating Scheme
# In[ ]:
s.generate_f_one(pop, recombination_rates, sim_params['founders'])
s.recombinatorial_convergence(pop, recombination_rates)
s.expand_by_selfing(pop, recombination_rates)
s.interim_random_mating(pop, recombination_rates)
# ## Adapting QTL and Allele Effects to Multiple Replicate Case
# In[ ]:
import simuOpt
simuOpt.setOptions(quiet=True, alleleType='long')
import simuPOP as sim
pop = sim.Population(size=1000, loci=[1])
simu = sim.Simulator(pop, 5)
simu.evolve(
initOps=[
sim.InitSex(),
sim.PyExec('introGen=[]')
],
preOps=[
sim.Stat(alleleFreq=0),
sim.IfElse('alleleFreq[0][1] == 0', ifOps=[
sim.PointMutator(loci=0, allele=1, inds=0),
sim.PyExec('introGen.append(gen)')
]),
sim.TerminateIf('alleleFreq[0][1] >= 0.05')
],
matingScheme=sim.RandomMating()
)
# number of attempts
print([len(x.dvars().introGen) for x in simu.populations()])
# age of mutant
print([x.dvars().gen - x.dvars().introGen[-1] for x in simu.populations()])
#print(geno[0], geno[1], v)
return v
def sizeChange(gen):
if gen <= 40:
v = Fixed_pop_size
#print(geno[0],v)
return v
else:
v = Fixed_pop_size2
#print(geno[0], geno[1], v)
return v
simu = sim.Simulator(pop1,rep=Replicates)
simu.evolve(
initOps=[
],
preOps=[
],
matingScheme=sim.RandomMating(subPopSize=sizeChange,numOffspring=(sim.POISSON_DISTRIBUTION, Offspring_Poisson_dist_mean),ops=sim.Recombinator(loci=[0, 1, 2, 3, 4, 5, 6, 7], rates=[0.095162582, 0.075960076, 0.094257292, 0.154646165, 0.005, 0.104165865, 0.071328306,0.5]))
,
postOps=[
sim.PySelector(loci=[4], func=sel),
sim.Stat(alleleFreq=[0,1,2,3,4,5,6,7],numOfMales=True,popSize=True),
# 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
simu = sim.Simulator(sim.Population(50, loci=[10], ploidy=1),
rep=3)
simu.evolve(gen = 5)
simu.dvars(0).gen
simu.evolve(
initOps=[sim.InitGenotype(freq=[0.5, 0.5])],
matingScheme=sim.RandomSelection(),
postOps=[
sim.Stat(alleleFreq=5),
sim.IfElse('alleleNum[5][0] == 0',
sim.PyEval(r"'Allele 0 is lost in rep %d at gen %d\n' % (rep, gen)")),
sim.IfElse('alleleNum[5][0] == 50',
sim.PyEval(r"'Allele 0 is fixed in rep %d at gen %d\n' % (rep, gen)")),
sim.TerminateIf('len(alleleNum[5]) == 1'),
],
)
[simu.dvars(x).gen for x in range(3)]
# for details. # # Copyright (C) 2004 - 2010 Bo Peng ([email protected]) # # 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 simu = sim.Simulator(sim.Population(100), rep=10) simu.evolve( initOps=[sim.InitSex(), sim.InitGenotype(freq=[0.5, 0.5])], matingScheme=sim.RandomMating(), postOps=[sim.Pause(stopOnKeyStroke=str(x), reps=x) for x in range(10)], gen=100)
# 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
simu = sim.Simulator(sim.Population(100, loci=[20]), 5)
simu.evolve(
initOps=[sim.InitSex(), sim.InitGenotype(freq=[0.2, 0.8])],
matingScheme=sim.RandomMating(),
postOps=[
sim.Stat(alleleFreq=0, step=10),
sim.PyEval('gen', step=10, reps=0),
sim.PyEval(r"'\t%.2f' % alleleFreq[0][0]", step=10, reps=(0, 2, -1)),
sim.PyOutput('\n', step=10, reps=-1)
],
gen=30,
)
post_ops['Stat-richness'] = sp.Stat(
alleleFreq=[0],
haploFreq=[0],
vars=['alleleFreq', 'haploFreq', 'alleleNum', 'genoNum'])
post_ops['fst_acumulation'] = sp.PyOperator(update_acumulator,
param=['fst', 'F_st'])
post_ops['richness_acumulation'] = sp.PyOperator(
update_richness_acumulator, param=('alleleFreq', 'Freq of Alleles'))
post_ops['class_richness'] = sp.PyOperator(
sampleAlleleAndGenotypeFrequencies, param=(pop_size, num_loci))
mating_scheme = sp.RandomSelection()
#mating_scheme=sp.RandomSelection(subPopSize=sub_pop_size)
## go simuPop go! evolve your way to the future!
sim = sp.Simulator(pops) #, rep=3)
sim.evolve(initOps=list(init_ops.values()),
preOps=list(pre_ops.values()),
postOps=list(post_ops.values()),
matingScheme=mating_scheme,
gen=num_gens)
print("now evolving... k= %s" % param_value)
# now make a figure of the Fst results
fig = plt.figure(figsize=(16, 9))
ax = fig.add_subplot(111)
for pop, mig in zip(sim.populations(), migs):
ax.plot(pop.dvars().fst, label='Migration rate %.4f' % mig)
ax.legend(loc=2)
pop = sim.Population(100, loci=5, chromNames=['chrom1'])
pop.dvars().name = 'my sim.Population'
pop.save('sample.pop')
pop1 = sim.loadPopulation('sample.pop')
# genetic drift with replication
startfreq = .2
gens = 101
steps = 1
reps = 10
popsize = 100
loci = 5
f = open('simtest_stats.txt', 'w+')
f.write('freq,rep\n' + str(startfreq) + ',' + str(reps))
f.close()
simu = sim.Simulator(sim.Population(size=popsize, loci=loci), rep=reps)
f = open('simtest_out_rep.txt', 'w+')
f.write('gen,freq,rep\n')
f.close()
simu.evolve(
initOps=[
sim.InitGenotype(
freq=[startfreq, 1 -
startfreq]), # initalize genotypes with, 2 alleles w/ freq
sim.InitSex(),
# sim.Stat(alleleFreq=0),
# sim.PyEval(r"'%d,%.2f,%d\n' % (gen, alleleFreq[0][0], rep)",
# begin=0,reps=sim.ALL_AVAIL,
# output='>>>simtest_out_rep.txt')
],
# preOps=[ # do stuff before evolve
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--experiment", help="provide name for experiment", required=True, type=str, default="test")
parser.add_argument("--debug", help="turn on debugging output")
parser.add_argument("--reps", help="Replicated populations per parameter set", type=int, default=3)
parser.add_argument("--networkfile", help="Name of GML file representing the network model for this simulation",
required=True, type=str, default="smallworld")
parser.add_argument("--numloci", help="Number of loci per individual (use with care)", type=int, required=True, default=1)
parser.add_argument("--maxinittraits", help="Max initial number of traits per locus for initialization", type=int,
required=True, default=50)
parser.add_argument("--innovrate", nargs='+', help="Rate(s) at which innovations occur in population as a per-locus rate", type=float, default=[])
parser.add_argument("--simlength", help="Time at which simulation and sampling end, defaults to 3000 generations",
type=int, default="20")
parser.add_argument("--popsize", help="Initial size of population for each community in the model", type=int, required=True)
parser.add_argument("--migrationfraction", nargs='+', help="Fraction of population that migrates each time step", type=float, required=True, default=[])
parser.add_argument("--seed", type=int, help="Seed for random generators to ensure replicability")
parser.add_argument( "--k_values", nargs='+', type=int, help="list of k-values to explore [e.g., 2 4 20 24]", default=[])
parser.add_argument("--sub_pops", nargs="+", help="Number of sub populations", required=True, default=[])
parser.add_argument("--maxalleles", type=int, help="Maximum number of alleles", default=50)
parser.add_argument("--save_figs", type=bool, help="Save figures or not?", default=False)
parser.add_argument("--burnintime", type=int, help="How long to wait before making measurements? ", default=2000)
parser.add_argument("--rewiringprob", type=float, help="Probability of random rewiring", default=0)
config = parser.parse_args()
# setup output directories for writing
output_path = utils.setup_output(config.experiment)
# check the k and migration rate combinations
check = utils.check_k_and_migration_rates(config)
if check is not True:
print("\nProblem(s):\t %s\n" % check)
print("Please adjust input values for k and/or migration rate and restart.\n ")
sys.exit()
else:
print("\nChecked on the migration and k values -- all looks good!\n")
# save parameters
utils.save_parameters(str(sys.argv), config, output_path)
# set up the frequencies for the alleles in each loci. Here assuming a uniform distribution as a starting point
distribution = utils.constructUniformAllelicDistribution(config.maxinittraits)
# prepare file for output
output_data_file_name = "%s/%s-rare-trait-output.csv" % (output_path, config.experiment)
with open(output_data_file_name, mode='w') as output_file:
output_writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
output_writer.writerow(["Iteration", "k", "NumSubPops", "Migration", "InnovationRate", "Ones_Mean",
"Ones_95%_Lower", "Ones_95%_Upper", "Twos_Mean", "Twos_95%_Lower", "Twos_95%_Upper", "Richness_Mean",
"Richness_95%_Lower", "Richness_95%_Upper","Fst_Mean","Fst_95%_Lower","Fst_95%_Upper"])
output_file.flush()
subpop_run_values = config.sub_pops
k_run_values = config.k_values
mig_run_values = config.migrationfraction
innov_run_values = config.innovrate
iteration=-1
for subpop in subpop_run_values:
if k_run_values == [0]:
k_run_values = [2, int(float(subpop) * .1), int(float(subpop) * .2),
int(float(subpop) * .5), int(float(subpop) * .8),
int(float(subpop) * .9),
int(subpop) - 1]
for k in k_run_values:
for mig in mig_run_values:
for innov in innov_run_values:
## let us know whats happening
iteration += 1
print("Now running with subpops: %s k-value: %s mig rate: %4f innov rate: %4f" % (subpop,k,mig,innov))
## these are lists of things that simuPop will do at different stages
init_ops = OrderedDict()
pre_ops = OrderedDict()
post_ops = OrderedDict()
# Construct a demographic model
#networkmodel = NetworkModel( networkmodel="/Users/clipo/Documents/PycharmProjects/RapaNuiSim/notebooks/test_graph.gml",
networkmodel = network.NetworkModel( networkmodel=config.networkfile,
simulation_id=config.experiment,
sim_length=config.simlength,
burn_in_time=config.burnintime,
initial_subpop_size=config.popsize,
migrationfraction=mig,
sub_pops=subpop,
connectedness=k, # if 0, then distance decay
save_figs=config.save_figs,
network_iteration=iteration)
num_pops = networkmodel.get_subpopulation_number()
sub_pop_size = int(config.popsize / num_pops)
# The regional network model defines both of these, in order to configure an initial population for evolution
# Construct the initial population
pops = sp.Population(size = [sub_pop_size]*num_pops,
subPopNames = str(list(networkmodel.get_subpopulation_names())),
infoFields = 'migrate_to',
ploidy=1,
loci=config.numloci )
### now set up the activities
init_ops['acumulators'] = sp.PyOperator(utils.init_acumulators, param=['fst','alleleFreq', 'haploFreq'])
init_ops['subpop_counts'] = sp.PyOperator(utils.init_count_traits_in_subpops)
init_ops['Sex'] = sp.InitSex()
init_ops['Freq'] = sp.InitGenotype(loci=list(range(config.numloci)),freq=distribution)
post_ops['Innovate'] = sp.KAlleleMutator(k=config.maxalleles, rates=innov, loci=sp.ALL_AVAIL)
post_ops['mig']=sp.Migrator(rate=networkmodel.get_migration_matrix()) #, reps=[3])
post_ops['Stat-fst'] = sp.Stat(structure=sp.ALL_AVAIL)
post_ops['Stat-richness']=sp.Stat(alleleFreq=[0], haploFreq=[0], vars=['alleleFreq','haploFreq','alleleNum', 'genoNum'])
post_ops['fst_acumulation'] = sp.PyOperator(utils.update_acumulator, param=['fst','F_st'])
post_ops['richness_acumulation'] = sp.PyOperator(utils.update_richness_acumulator, param=('alleleFreq', 'Freq of Alleles'))
post_ops['class_richness']=sp.PyOperator(utils.calculateAlleleAndGenotypeFrequencies, param=(config.popsize,config.numloci))
post_ops['count_traits_in_subpops'] = sp.PyOperator(utils.count_traits_in_subpops, param=(config.numloci,num_pops), subPops=sp.ALL_AVAIL)
mating_scheme = sp.RandomSelection()
## go simuPop go! evolve your way to the future!
sim = sp.Simulator(pops, rep=config.reps)
sim.evolve(initOps=list(init_ops.values()), preOps=list(pre_ops.values()), postOps=list(post_ops.values()),
matingScheme=mating_scheme, gen=config.simlength)
count=0
for pop in sim.populations():
output[count] = deepcopy(pop.dvars())
count+=1
ones_point_in_time = []
twos_point_in_time = []
richness_point_in_time = []
fst_point_in_time = []
for n in range(config.reps):
list_of_ones = list(output[n].ones)
list_of_twos = list(output[n].twos)
list_of_richness = list(output[n].richness)
list_of_fst = list(output[n].fst)
ones_point_in_time.append(list_of_ones[2000])
twos_point_in_time.append(list_of_twos[2000])
richness_point_in_time.append(list_of_richness[2000])
fst_point_in_time.append(list_of_fst[2000])
(ones_ave, ones_min, ones_max) = utils.mean_confidence_interval(ones_point_in_time,
confidence=0.95)
(twos_ave, twos_min, twos_max) = utils.mean_confidence_interval(twos_point_in_time,
confidence=0.95)
(richness_ave, richness_min, richness_max) = utils.mean_confidence_interval(richness_point_in_time,
confidence=0.95)
(fst_ave, fst_min, fst_max) = utils.mean_confidence_interval(fst_point_in_time,
confidence=0.95)
output_writer.writerow([iteration,k,subpop,mig,innov,ones_ave,ones_min,ones_max,
twos_ave,twos_min,twos_max,richness_ave,richness_min,richness_max,fst_ave,fst_min,fst_max])
output_file.flush()
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)
def createSim(pop, reps):
sim = sp.Simulator(pop, rep=reps)
return sim
