def main():
try:
opts, args = getopt.getopt(sys.argv[1:], "m:e:H:S:O:N:t:s:r:F:n:")
except getopt.GetoptError as err:
# print help information and exit:
print(err) # will print something like "option -a not recognized"
usage()
sys.exit(2)
N = 1000 # pop size
e = 0.25 # s.d. of effect sizes
S = 1 # V(S)
H = None # desired b-sense H^2
mu_del_ttl = None # Mutation rate (per gamete, per generation) to alleles affecting trait value
r = 0.5 # rec. rate b/w loci (per diploid, per gen)
Opt = 0.0 # Value of optimum after 10N gens
ofile = None
seed = 0
t = None
nsam = 100
for o, a in opts:
if o == '-m':
mu_del_ttl = float(a)
elif o == '-e':
e = float(a)
elif o == '-H':
H = float(a)
elif o == '-S':
S = float(a)
elif o == '-O':
Opt = float(a)
elif o == '-N':
N = int(a)
elif o == '-s':
seed = int(a)
elif o == '-r':
r = float(a)
elif o == '-F':
ofile = a
elif o == '-t':
t = int(a)
elif o == '-n':
nsam = int(a)
if H is None:
usage()
sys.exit(2)
if mu_del_ttl is None:
usage()
sys.exit(2)
if ofile is None:
usage()
sys.exit(2)
if t is None:
t = 0.1 * float(N)
#Constants:
NLOCI = 10
NREPS = 64
##The next 2 are the 'sizes' of each locus in scaled params. Roughly 100kb in 'humans' in terms of pi.
theta = 100.0
rho = 100.0
#Can start working now:
REP = 0
out = pd.HDFStore(ofile, "w", complevel=6, complib='zlib')
little_r_per_locus = rho / (4.0 * float(N))
mu_n_region = theta / (4.0 * float(N))
rnge = fp.GSLrng(seed)
rngs = fp.GSLrng(seed)
nlist = np.array([N] * (10 * N), dtype=np.uint32)
fitness = qtm.MlocusAdditiveTrait()
sregions = [fp.GaussianS(0, 1, 1, e, 1.0)] * NLOCI
for BATCH in range(16): #16*64=1024
x = fp.MlocusPopVec(NREPS, N, NLOCI)
sampler = fp.PopSampler(len(x), nsam, rngs)
qtm.evolve_qtraits_mloc_sample_fitness(
rnge,
x,
sampler,
fitness,
nlist,
[mu_n_region] * NLOCI,
[mu_del_ttl / float(NLOCI)] * NLOCI,
sregions,
[little_r_per_locus] * NLOCI,
[0.5] * (NLOCI - 1), #loci unlinked
sample=t,
VS=S)
samples = sampler.get()
get_summstats_parallel(samples, REP, out)
sampler = fp.PopSampler(nsam, rngs)
qtm.evolve_qtraits_mloc_sample_fitnes(
rnge,
x,
sampler,
fitness,
nlist,
[mu_n_region] * NLOCI,
[mu_del_ttl / float(NLOCI)] * NLOCI,
sregions,
[little_r_per_locus] * NLOCI,
[0.5] * (NLOCI - 1), #loci unlinked
sample=t,
VS=S,
optimum=Opt)
samples = sampler.get()
get_summstats_parallel(samples, REP, out)
REP += NREPS
out.close()
import PopstatsLocus
import fwdpy as fp
import fwdpy.qtrait_mloc as qtm
import numpy as np
import pandas as pd
import getopt
import sys, math, gc, sqlite3, os
def usage():
print("something went wrong")
valid_trait_models = ['additive', 'mult']
trait_models = {
'additive': qtm.MlocusAdditiveTrait(),
'mult': qtm.MlocusMultTrait()
}
valid_sampler_names = ['lstats', 'stats', 'ages', 'ms']
def get_sampler(samplerString, length, optimum, nsam, rng, nstub, sstub):
if samplerString == 'lstats':
return PopstatsLocus.PopstatsLocus(length)
elif samplerString == 'stats':
return fp.QtraitStatsSampler(length, optimum)
elif samplerString == 'ages':
return mlocAges.MlocusAgeSampler(length)
elif samplerString == 'ms':
if nsam is None:
def main():
try:
opts, args = getopt.getopt(sys.argv[1:],"m:e:H:S:O:N:s:r:",["traj="])
except getopt.GetoptError as err:
# print help information and exit:
print(err) # will print something like "option -a not recognized"
usage()
sys.exit(2)
N=1000 # pop size
e = 0.25 # s.d. of effect sizes
S = 1 # V(S)
H = None # desired b-sense H^2
mu_del_ttl = None # Mutation rate (per gamete, per generation) to alleles affecting trait value
r = 0.5 # rec. rate b/w loci (per diploid, per gen)
Opt = 0.0 # Value of optimum after 10N gens
trajFile=None
seed = 0
for o,a in opts:
if o == '-m':
mu_del_ttl = float(a)
elif o == '-e':
e = float(a)
elif o == '-H':
H = float(a)
elif o == '-S':
S = float(a)
elif o == '-O':
Opt = float(a)
elif o == '-N':
N=int(a)
elif o == '-s':
seed = int(a)
elif o == '-r':
r = float(a)
elif o == '--traj':
trajFile=a
if H is None:
usage()
sys.exit(2)
if mu_del_ttl is None:
usage()
sys.exit(2)
if trajFile is None:
usage()
sys.exit(2)
#Constants:
NLOCI=10
NREPS=64
##The next 2 are the 'sizes' of each locus in scaled params. Roughly 100kb in 'humans' in terms of pi.
theta=100.0
rho=100.0
#Can start working now:
REP=0
out=pd.HDFStore(trajFile,"w",complevel=6,complib='zlib')
little_r_per_locus = rho/(4.0*float(N))
mu_n_region=theta/(4.0*float(N))
rnge=fp.GSLrng(seed)
nlist=np.array([N]*(10*N),dtype=np.uint32)
fitness = qtm.MlocusAdditiveTrait()
sregions=[fp.GaussianS(0,1,1,e,1.0)]*NLOCI
for BATCH in range(16): #16*64=1024
x = fp.MlocusPopVec(NREPS,N,NLOCI)
sampler=fp.FreqSampler(len(x))
qtm.evolve_qtraits_mloc_sample_fitness(rnge,x,sampler,fitness,nlist,
[mu_n_region]*NLOCI,
[mu_del_ttl/float(NLOCI)]*NLOCI,
sregions,
[little_r_per_locus]*NLOCI,
[0.5]*(NLOCI-1),#loci unlinked
sample=1,VS=S)
traj1=sampler.get()
sampler=fp.FreqSampler(len(x))
qtm.evolve_qtraits_mloc_sample_fitness(rnge,x,sampler,fitness,nlist,
[mu_n_region]*NLOCI,
[mu_del_ttl/float(NLOCI)]*NLOCI,
sregions,
[little_r_per_locus]*NLOCI,
[0.5]*(NLOCI-1),#loci unlinked
sample=1,VS=S,optimum=Opt)
traj2=sampler.get()
m=fp.tidy_trajectories(fp.merge_trajectories(traj1,traj2))
for i in m:
temp=pd.DataFrame(i)
temp['rep']=[REP]*len(temp.index)
out.append('traj',temp)
REP+=1
out.close()
def run_batch(argtuple):
args, repid, batch = argtuple
print("seed for batch = ", args.seed)
nstub = "neutral.mu" + str(args.mu) + ".opt" + str(args.opt)
sstub = "selected.mu" + str(args.mu) + ".opt" + str(args.opt)
rnge = fp.GSLrng(args.seed)
NANC = 7310
locus_boundaries = [(float(i + i * 11), float(i + i * 11 + 11))
for i in range(args.nloci)]
nregions = [
fp.Region(j[0], j[1], args.theta / (4. * float(NANC)), coupled=True)
for i, j in zip(range(args.nloci), locus_boundaries)
]
recregions = [
fp.Region(j[0], j[1], args.rho / (4. * float(NANC)), coupled=True)
for i, j in zip(range(args.nloci), locus_boundaries)
]
sregions = [
fp.GaussianS(j[0] + 5., j[0] + 6., args.mu, args.sigmu, coupled=False)
for i, j in zip(range(args.nloci), locus_boundaries)
]
f = qtm.MlocusAdditiveTrait()
nlist = np.array(get_nlist1(), dtype=np.uint32)
pops = fp.MlocusPopVec(args.ncores, nlist[0], args.nloci)
sampler = fp.NothingSampler(len(pops))
d = datetime.datetime.now()
print("starting batch, ", batch, "at ", d.now())
qtm.evolve_qtraits_mloc_regions_sample_fitness(rnge, pops, sampler, f,
nlist[0:], nregions,
sregions, recregions,
[0.5] * (args.nloci - 1), 0,
0, 0.)
d = datetime.datetime.now()
print(d.now())
nlist = np.array(get_nlist2(), dtype=np.uint32)
qtm.evolve_qtraits_mloc_regions_sample_fitness(rnge, pops, sampler, f,
nlist[0:], nregions,
sregions, recregions,
[0.5] * (args.nloci - 1), 0,
args.opt)
d = datetime.datetime.now()
print(d.now())
if args.statfile is not None:
sched = lsp.scheduler_init(args.TBB)
for pi in pops:
#Apply the sampler 1 population at a time.
#This saves a fair bit of RAM.
neutralFile = nstub + '.rep' + str(repid) + '.gz'
selectedFile = sstub + '.rep' + str(repid) + '.gz'
BIGsampler = fp.PopSampler(1,
6000,
rnge,
False,
neutralFile,
selectedFile,
recordSamples=True,
boundaries=locus_boundaries)
fp.apply_sampler_single(pi, BIGsampler)
if args.statfile is not None:
for di in BIGsampler:
process_samples((di, args.statfile, locus_boundaries, repid))
repid += 1
pops.clear()
pops = None