#!/usr/bin/env python import moments import numpy as np from numpy import array from moments import Misc, Spectrum import sys infile = sys.argv[1] pop_ids = [sys.argv[2]] projections = [int(sys.argv[3])] outname = pop_ids[0] + "_sfs_folded" dd = Misc.make_data_dict(infile) data = Spectrum.from_data_dict(dd, pop_ids, projections, polarized=False) Spectrum.to_file(data, outname, precision=4, foldmaskinfo=False)
from moments import Misc, Spectrum, Numerics, Manips, Integration, Demographics1D, Demographics2D
import sys
infile = sys.argv[1]
pop_ids = [sys.argv[2], sys.argv[3]]
projections = [int(sys.argv[4]), int(sys.argv[5])]
#params=[float(sys.argv[6]),float(sys.argv[7]),float(sys.argv[8]),float(sys.argv[9]),float(sys.argv[10]),float(sys.argv[11])]
params = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
# mutation rate per sequenced portion of genome per generation: for A.millepora, 0.02
mu = float(sys.argv[6])
# generation time, in thousand years: 0.005 (5 years)
gtime = float(sys.argv[7])
dd = Misc.make_data_dict(infile)
# set Polarized=False below for folded AFS analysis
data = Spectrum.from_data_dict(dd, pop_ids, projections, polarized=False)
ns = data.sample_sizes
np.set_printoptions(precision=3)
#-------------------
# split into unequal pop sizes with asymmetrical migration
def sc3ei(params, ns):
# p_misid: proportion of misidentified ancestral states
nu1_1, nu2_1, nu1_2, nu2_2, nu1_3, nu2_3, T1, T2, T3, m12_1, m21_1, m12_3, m21_3 = params
sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1])
fs = moments.Spectrum(sts)
fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1])
fs.integrate([nu1_1, nu2_1], T1, m=np.array([[0, 0], [0, 0]]))
fs.integrate([nu1_2, nu2_2], T2, m=np.array([[0, m12_1], [m21_1, 0]]))
#!/usr/bin/env python import moments import numpy as np from numpy import array from moments import Misc,Spectrum import sys infile=sys.argv[1] pop_ids=[sys.argv[2],sys.argv[3]] projections=[int(sys.argv[4]),int(sys.argv[5])] outname=pop_ids[0]+pop_ids[1]+"_sfs" dd = Misc.make_data_dict(infile) data = Spectrum.from_data_dict(dd, pop_ids,projections,polarized=True) Spectrum.to_file(data,outname,precision=4,foldmaskinfo=False)