def run_replicate(argtuple):
args, repid, repseed = argtuple
rng = fp11.GSLrng(repseed)
NANC = args.N
locus_boundaries = [(float(i + i * 11), float(i + i * 11 + 11))
for i in range(args.nloci)]
nregions = [[
fp11.Region(j[0], j[1], args.theta / (4. * float(NANC)), coupled=True)
] for i, j in zip(range(args.nloci), locus_boundaries)]
recregions = [[
fp11.Region(j[0], j[1], args.rho / (4. * float(NANC)), coupled=True)
] for i, j in zip(range(args.nloci), locus_boundaries)]
sregions = [[
fp11.GaussianS(j[0] + 5.,
j[0] + 6.,
args.mu,
args.sigmu,
coupled=False)
] for i, j in zip(range(args.nloci), locus_boundaries)]
interlocus_rec = fp11ml.binomial_rec(rng, [0.5] * (args.nloci - 1))
nlist = np.array([NANC] * 20 * NANC, dtype=np.uint32)
env = [(0, 0, 1), (10 * NANC, args.opt, 1)]
pdict = {
'nregions':
nregions,
'sregions':
sregions,
'recregions':
recregions,
'demography':
nlist,
'interlocus':
interlocus_rec,
'agg':
fp11ml.AggAddTrait(),
'gvalue':
fp11ml.MultiLocusGeneticValue([fp11tv.SlocusAdditiveTrait(2.0)] *
args.nloci),
'trait2w':
fp11qt.GSSmo(env),
'mutrates_s': [args.mu / float(args.nloci)] * args.nloci,
'mutrates_n': [10 * args.theta / float(4 * NANC)] * args.nloci,
'recrates': [10 * args.rho / float(4 * NANC)] * args.nloci,
'prune_selected':
False
}
params = fp11.model_params.MlocusParamsQ(**pdict)
ofilename = args.stub + '.rep' + str(repid) + '.pickle.lzma'
recorder = Pickler(repid, NANC, ofilename)
pop = fp11.MlocusPop(NANC, args.nloci, locus_boundaries)
fp11qt.evolve(rng, pop, params, recorder)
#Make sure last gen got pickled!
if recorder.last_gen_recorded != pop.generation:
with open(ofilename, "ab") as f:
pickle.dump((repid, pop), f, -1)
return ofilename
def run_replicate(argtuple):
args, repid, repseed = argtuple
rng = fp11.GSLrng(repseed)
NANC = args.N
locus_boundaries = [(float(i + i * 11), float(i + i * 11 + 11))
for i in range(args.nloci)]
nregions = [[
fp11.Region(j[0], j[1], args.theta / (4. * float(NANC)), coupled=True)
] for i, j in zip(range(args.nloci), locus_boundaries)]
recregions = [[
fp11.Region(j[0], j[1], args.rho / (4. * float(NANC)), coupled=True)
] for i, j in zip(range(args.nloci), locus_boundaries)]
# Get the variance in effect sizes
sigmu = args.sigmu # default
if args.plarge is not None:
ghat = gamma_hat(1.0, args.mu)
F = generate_gaussian_function_to_minimize(ghat, args.plarge)
sigmu = get_gaussian_sigma(F)
sregions = [[
fp11.GaussianS(j[0] + 5., j[0] + 6., args.mu, sigmu, coupled=False)
] for i, j in zip(range(args.nloci), locus_boundaries)]
interlocus_rec = fp11ml.binomial_rec(rng, [0.5] * (args.nloci - 1))
nlist = np.array([NANC] * 20 * NANC, dtype=np.uint32)
env = [(0, 0, 1), (10 * NANC, args.opt, args.vsopt)]
pdict = {
'nregions':
nregions,
'sregions':
sregions,
'recregions':
recregions,
'demography':
nlist,
'interlocus':
interlocus_rec,
'agg':
fp11ml.AggAddTrait(),
'gvalue':
fp11ml.MultiLocusGeneticValue([fp11tv.SlocusAdditiveTrait(2.0)] *
args.nloci),
'trait2w':
fp11qt.GSSmo(env),
'mutrates_s': [args.mu / float(args.nloci)] * args.nloci,
'mutrates_n': [10 * args.theta / float(4 * NANC)] * args.nloci,
'recrates': [10 * args.rho / float(4 * NANC)] * args.nloci,
'prune_selected':
False
}
params = fp11.model_params.MlocusParamsQ(**pdict)
recorder = Recorder(repid, NANC, args.nsam)
pop = fp11.MlocusPop(NANC, args.nloci, locus_boundaries)
fp11qt.evolve(rng, pop, params, recorder)
return recorder
def runsim(args):
mutrate, seed, repid = args
rng = fp11.GSLrng(seed)
t2f = fp11qt.GSSmo([(0, 0, 1), (10 * N, 1, 1)])
pop = fp11.SlocusPop(N)
p = {
'nregions': [fp11.Region(0, 11, 1.0)], # nregions must be a region
'sregions': [fp11.GaussianS(5, 6, 1, 0.25)],
'recregions': [fp11.Region(0, 11, 1)],
'mutrate_n': THETA / float(4 * N),
'mutrate_s': mutrate,
'recrate': RHO / float(4 * N),
'demography': np.array([N] * (10 * N + 100), dtype=np.uint32),
'gvalue': fp11tv.SlocusAdditiveTrait(2.0),
'trait_to_fitness': t2f,
'prune_selected': False
}
params = fp11.model_params.SlocusParamsQ(**p)
fp11qt.evolve(rng, pop, params)
samples = take_sample(rng, pop)
return repid, pop, samples
def run_replicate(argtuple):
args, repid, repseed = argtuple
rng = fp11.GSLrng(repseed)
NANC = 7310
locus_boundaries = [(float(i + i * 11), float(i + i * 11 + 11))
for i in range(args.nloci)]
nregions = [[
fp11.Region(j[0], j[1], args.theta / (4. * float(NANC)), coupled=True)
] for i, j in zip(range(args.nloci), locus_boundaries)]
recregions = [[
fp11.Region(j[0], j[1], args.rho / (4. * float(NANC)), coupled=True)
] for i, j in zip(range(args.nloci), locus_boundaries)]
sregions = [[
fp11.GaussianS(j[0] + 5.,
j[0] + 6.,
args.mu,
args.sigmu,
coupled=False)
] for i, j in zip(range(args.nloci), locus_boundaries)]
interlocus_rec = fp11ml.binomial_rec(rng, [0.5] * (args.nloci - 1))
nlist = get_nlist()
env = [(0, 0, 1), (np.argmax(nlist == 1861), args.opt, 1)]
pdict = {
'nregions':
nregions,
'sregions':
sregions,
'recregions':
recregions,
'demography':
nlist,
'interlocus':
interlocus_rec,
'agg':
fp11ml.AggAddTrait(),
'gvalue':
fp11ml.MultiLocusGeneticValue([fp11tv.SlocusAdditiveTrait(2.0)] *
args.nloci),
'trait2w':
fp11qt.GSSmo(env),
'mutrates_s': [args.mu] * args.nloci,
'mutrates_n':
[float(args.nloci) * args.theta / float(4 * NANC)] * args.nloci,
'recrates':
[float(args.nloci) * args.rho / float(4 * NANC)] * args.nloci,
}
#print(pdict['mutrates_n'])
#for i in sregions:
# for j in i:
# print(str(j))
#return
params = fp11.model_params.MlocusParamsQ(**pdict)
ofilename = args.stub + '.rep' + str(repid) + '.gz'
recorder = Pickler(repid, args.nsam, 8 * NANC, np.argmax(nlist == 1861),
ofilename)
pop = fp11.MlocusPop(NANC, args.nloci, locus_boundaries)
fp11qt.evolve(rng, pop, params, recorder)
#Make sure last gen got pickled!
if recorder.last_gen_recorded != pop.generation:
with open(ofilename, "ab") as f:
with gzip.open(ofilename, "ab") as f:
m = get_matrix(pop, args.nsam)
pickle.dump(m, f, -1)
pop.clear()
del pop
pop = None
return ofilename
def run_replicate(argtuple):
args, repid, repseed = argtuple
rng = fp11.GSLrng(repseed)
NANC = args.N
locus_boundaries = [(float(i + i * 11), float(i + i * 11 + 11))
for i in range(args.nloci)]
#nregions=[[fp11.Region(j[0],j[1],args.theta/(4.*float(NANC)),coupled=True)] for i,j in zip(range(args.nloci),locus_boundaries)]
nregions = [[]] * args.nloci
#Put the neutral variants in the selected regions
nregions[0] = [
fp11.Region(locus_boundaries[0][0] + 5.0, locus_boundaries[0][0] + 6.0,
1)
]
#Put the neutral variants in middle
nregions[int(args.nloci / 2)] = [
fp11.Region(locus_boundaries[int(args.nloci / 2)][0] + 5.,
locus_boundaries[int(args.nloci / 2)][0] + 6., 1.0)
]
#Put the neutral variants at far rigth end.
nregions[args.nloci - 1] = [
fp11.Region(locus_boundaries[args.nloci - 1][1] - 1.0,
locus_boundaries[args.nloci - 1][1], 1.0)
]
recregions = [[
fp11.Region(j[0], j[1], args.rho / (4. * float(NANC)), coupled=True)
] for i, j in zip(range(args.nloci), locus_boundaries)]
sregions = [[
fp11.GaussianS(j[0] + 5.,
j[0] + 6.,
args.mu,
args.sigmu,
coupled=False)
] for i, j in zip(range(args.nloci), locus_boundaries)]
#The middle region has no sregions:
sregions[int(args.nloci / 2)] = []
mutrates_s = [args.mu / float(args.nloci - 1)] * args.nloci
#No selected mutations in the middle locus
mutrates_s[int(args.nloci / 2)] = 0.0
mutrates_n = [0.0] * args.nloci
for i in [0, int(args.nloci / 2), args.nloci - 1]:
#The next line keeps scaling, etc., same as in other sims.
mutrates_n[i] = (10.0 / 11.0) * (args.theta / float(4 * NANC))
interlocus_rec = fp11ml.binomial_rec(rng, [0.5] * (args.nloci - 1))
nlist = np.array([NANC] * 20 * NANC, dtype=np.uint32)
env = [(0, 0, 1), (10 * NANC, args.opt, 1)]
#print(mutrates_s)
#print(mutrates_n)
#print([10*args.rho/float(4*NANC)]*args.nloci)
#for i in enumerate(nregions):
# if len(nregions[i[0]])>0:
# print("the type is",type(i[1]))
# print(i,i[1][0].b,i[1][0].e,i[1][0].w)
#for i in enumerate(sregions):
# if len(sregions[i[0]])>0:
# print(i,i[1][0].b,i[1][0].e,i[1][0].w)
#for i in enumerate(recregions):
# print(i,i[1][0].b,i[1][0].e)
#sys.exit(0)
pdict = {
'nregions':
nregions,
'sregions':
sregions,
'recregions':
recregions,
'demography':
nlist,
'interlocus':
interlocus_rec,
'agg':
fp11ml.AggAddTrait(),
'gvalue':
fp11ml.MultiLocusGeneticValue([fp11tv.SlocusAdditiveTrait(2.0)] *
args.nloci),
'trait2w':
fp11qt.GSSmo(env),
'mutrates_s':
mutrates_s,
'mutrates_n':
mutrates_n,
'recrates': [10 * args.rho / float(4 * NANC)] * args.nloci,
}
params = fp11.model_params.MlocusParamsQ(**pdict)
ofilename = args.stub + '.rep' + str(repid) + '.pickle.lzma'
recorder = Pickler(repid, NANC, ofilename)
pop = fp11.MlocusPop(NANC, args.nloci, locus_boundaries)
fp11qt.evolve(rng, pop, params, recorder)
#Make sure last gen got pickled!
if recorder.last_gen_recorded != pop.generation:
with open(ofilename, "ab") as f:
pickle.dump((repid, pop), f, -1)
return ofilename
raise Warning("output file", args.output, "exits--exiting!")
sys.exit(0)
N = 1000
#KRT: I changed theta and rho
#to 1100. Thus, the value
#for each window is 100,
#which is what you will use for discoal.
theta = 1100.0 # float, not int
rho = 1100.0
rng = fp11.GSLrng(args.seed)
for rep in range(args.nreps):
t2f = fp11qt.GSSmo([(0, 0, 1), (10 * N, 1, 1)])
pop = fp11.SlocusPop(N)
p = {
'nregions': [fp11.Region(0, 11, 1.0)], #nregions must be a region
'sregions': [fp11.GaussianS(5, 6, 1, 0.25)],
'recregions': [fp11.Region(0, 11, 1)],
'mutrate_n': theta / float(4 * N),
'mutrate_s':
args.mutrate, # Add this as you are using mutrate_n and recrate
'recrate': rho / float(4 * N),
# 'rates':(2.5e-4,1e-3,5e-3), # Do not need this if using mutrate_n and recrate
# Change to N instead of 10
'demography': np.array([N] * (10 * N + 100), dtype=np.uint32),
'gvalue': fp11tv.SlocusAdditiveTrait(2.0),
'trait_to_fitness': t2f,
# Do not remove selected fixations