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()
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')