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sc_dadi_2.py
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sc_dadi_2.py
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import scipy
import numpy
import dadi
import matplotlib
import matplotlib.pyplot as pyplot
import sys
import re
import pylab
from dadi import Numerics, PhiManip, Integration, Spectrum
## get population ids, 2n sample sizes, and vcf file from the command line
p1 = sys.argv[1];
p2 = sys.argv[2];
n1 = sys.argv[3];
n2 = sys.argv[4];
vv = sys.argv[5];
#n1 = 40;
#n2 = 40;
#p1 = "poppensis_TBARN_DF";
#p2 = "poppensis_FROCK_DF";
ns = numpy.array([int(n1), int(n2)])
## read data from filtered vcf and create downsampled/projected 2d sfs
dd = dadi.Misc.make_data_dict_vcf(vv,"/uufs/chpc.utah.edu/common/home/gompert-group3/projects/timema_host_ri/dadi/ids.txt")
#dd = dadi.Misc.make_data_dict_vcf("fst_gsi_admixture_stages_speciation/fst/variants.flt.100kmreads_10kdepth_20QS.90cov.vcf","/uufs/chpc.utah.edu/common/home/gompert-group3/projects/timema_host_ri/dadi/ids.txt")
fs = dadi.Spectrum.from_data_dict(dd,[p1, p2],projections=ns, polarized=False)
## downsample to 50% of minimum
downs = ns * 0.5
dns = numpy.array([int(downs[0]), int(downs[1])])
fs = fs.project(dns)
of = "dd_50_"+p1+"_"+p2+"_sc.txt"
pngf = "dd_50_"+p1+"_"+p2+"_sc.png"
ofile = open(of,"w")
ns = fs.sample_sizes
## define secondary contact model, modified dadi website, includes exponential growth
def SC(params, ns, pts):
s, nu1, nu2, Ts, Tsc, m12, m21 = params
xx = Numerics.default_grid(pts)
phi = PhiManip.phi_1D(xx)
phi = PhiManip.phi_1D_to_2D(xx, phi)
nu1_func = lambda t : s * (nu1/s) ** (t/Ts)
nu2_func = lambda t : (1-s) * (nu2/(1-s)) ** (t/Ts)
## split with no gene flow
phi = Integration.two_pops(phi, xx, Ts, nu1_func, nu2_func, m12 = 0, m21 = 0)
## secondary contact
phi = Integration.two_pops(phi, xx, Tsc, nu1_func, nu2_func, m12 = m12, m21 = m21)
fs = Spectrum.from_phi(phi, ns, (xx, xx))
return fs
# These are the grid point settings will use for extrapolation.
# smallest a bit bigger than mean sample size, this should be close
mnss = int(downs.mean())
pts_l = [mnss, mnss+10, mnss+20]
func = SC
# Now let's optimize parameters for this model.
# The upper_bound and lower_bound lists are for use in optimization.
# Occasionally the optimizer will try wacky parameter values. We in particular
# want to exclude values with very long times, very small population sizes, or
# very high migration rates, as they will take a long time to evaluate.
# Parameters are: (s, nu1, nu2, Ts, Tsc, and m12, m21)
upper_bound = [.9, 10, 10, 10, 5, 8, 8]
lower_bound = [.1, 0.1, 0.1, 0, 0, 0, 0]
# This is our initial guess for the parameters, which is somewhat arbitrary.
p0 = [0.5, 0.8, 1.2, 0.4, 0.2, 0.5, 0.5]
# Make the extrapolating version of our demographic model function.
func_ex = dadi.Numerics.make_extrap_log_func(func)
# trying 3 rounds of optimization
ofile = open(of,"w")
print(ofile)
ofile.write("{0}".format(fs.Fst()))
ofile.write("\n")
## initial defaults
ll_opt = -999999999999
theta = 1.0
mpopt = p0
for x in range(20):
# Perturb our parameters before optimization. This does so by taking each
# parameter a up to a factor of two up or down.
p0 = dadi.Misc.perturb_params(p0, fold=1, upper_bound=upper_bound,lower_bound=lower_bound)
# Do the optimization. By default we assume that theta is a free parameter,
# since it's trivial to find given the other parameters. If you want to fix
# theta, add a multinom=False to the call.
# The maxiter argument restricts how long the optimizer will run. For real
# runs, you will want to set this value higher (at least 10), to encourage
# better convergence. You will also want to run optimization several times
# using multiple sets of intial parameters, to be confident you've actually
# found the true maximum likelihood parameters.
popt = dadi.Inference.optimize_log(p0, fs, func_ex, pts_l, lower_bound=lower_bound, upper_bound=upper_bound,verbose=1, maxiter=20)
model = func_ex(popt, dns, pts_l)
ll_model = dadi.Inference.ll_multinom(model,fs)
if(ll_model > ll_opt):
ll_opt = ll_model
mpopt = popt
theta = dadi.Inference.optimal_sfs_scaling(model,fs)
## write round 1 max
ofile.write("{0}".format(ll_opt))
ofile.write(" {0}".format(theta))
for a in range(7):
ofile.write(" {0}".format(mpopt[a]))
ofile.write("\n")
## start round 2 at max
p0 = mpopt
for x in range(10):
# Perturb our parameters before optimization. This does so by taking each
# parameter a up to a factor of two up or down.
p0 = dadi.Misc.perturb_params(p0, fold=1, upper_bound=upper_bound,lower_bound=lower_bound)
# Do the optimization. By default we assume that theta is a free parameter,
# since it's trivial to find given the other parameters. If you want to fix
# theta, add a multinom=False to the call.
# The maxiter argument restricts how long the optimizer will run. For real
# runs, you will want to set this value higher (at least 10), to encourage
# better convergence. You will also want to run optimization several times
# using multiple sets of intial parameters, to be confident you've actually
# found the true maximum likelihood parameters.
popt = dadi.Inference.optimize_log(p0, fs, func_ex, pts_l, lower_bound=lower_bound, upper_bound=upper_bound,verbose=1, maxiter=30)
model = func_ex(popt, dns, pts_l)
ll_model = dadi.Inference.ll_multinom(model,fs)
if(ll_model > ll_opt):
ll_opt = ll_model
mpopt = popt
theta = dadi.Inference.optimal_sfs_scaling(model,fs)
## write round 2 max
ofile.write("{0}".format(ll_opt))
ofile.write(" {0}".format(theta))
for a in range(7):
ofile.write(" {0}".format(mpopt[a]))
ofile.write("\n")
## start round 3 at max
p0 = mpopt
mtheta = theta
for x in range(5):
# Perturb our parameters before optimization. This does so by taking each
# parameter a up to a factor of two up or down.
p0 = dadi.Misc.perturb_params(p0, fold=1, upper_bound=upper_bound,lower_bound=lower_bound)
# Do the optimization. By default we assume that theta is a free parameter,
# since it's trivial to find given the other parameters. If you want to fix
# theta, add a multinom=False to the call.
# The maxiter argument restricts how long the optimizer will run. For real
# runs, you will want to set this value higher (at least 10), to encourage
# better convergence. You will also want to run optimization several times
# using multiple sets of intial parameters, to be confident you've actually
# found the true maximum likelihood parameters.
popt = dadi.Inference.optimize_log(p0, fs, func_ex, pts_l, lower_bound=lower_bound, upper_bound=upper_bound,verbose=1, maxiter=50)
model = func_ex(popt, dns, pts_l)
ll_model = dadi.Inference.ll_multinom(model,fs)
if(ll_model > ll_opt):
ll_opt = ll_model
mpopt = popt
mtheta = dadi.Inference.optimal_sfs_scaling(model,fs)
## write each round
theta = dadi.Inference.optimal_sfs_scaling(model,fs)
ofile.write("{0}".format(ll_model))
ofile.write(" {0}".format(theta))
for a in range(7):
ofile.write(" {0}".format(popt[a]))
ofile.write("\n")
## write round 3 max... this is the answer
ofile.write("{0}".format(ll_opt))
ofile.write(" {0}".format(mtheta))
for a in range(7):
ofile.write(" {0}".format(mpopt[a]))
ofile.write("\n")
model = func_ex(mpopt, dns, pts_l)
pylab.figure(figsize=(8,6))
dadi.Plotting.plot_2d_comp_multinom(model,fs,vmin=1,resid_range=10,show=False)
pylab.savefig(pngf, dpi=400)
ofile.close()