#!/usr/bin/env python
import dadi
import pylab
import matplotlib.pyplot as plt
import numpy as np
from numpy import array
from dadi import Misc, Spectrum, Numerics, PhiManip, Integration, Demographics1D, Demographics2D
import sys
infile = sys.argv[1]
popid = [sys.argv[2]]
proj = range(int(sys.argv[3]), int(sys.argv[4]))
dd = Misc.make_data_dict(infile)
for p in range(len(proj)):
data = Spectrum.from_data_dict(dd,
pop_ids=popid,
projections=[proj[p]],
polarized=False)
print proj[p], data.S()
from dadi import Misc, Spectrum, Numerics, PhiManip, Integration, Demographics1D, Demographics2D
import sys
infile = sys.argv[1]
pop_ids = sys.argv[2]
projections = [int(sys.argv[3])]
#infile="5kA_dadi.data"
#pop_ids=["O1"]
#projections=[32]
import os
# replace this with your appropriate dir name
# os.chdir("/Users/c-monstr/Documents/allRAD_august2015/digitifera/dadi")
dd = Misc.make_data_dict(infile)
data = Spectrum.from_data_dict(dd, pop_ids, projections, polarized=True)
ns = data.sample_sizes
pts = [65, 80, 95]
np.set_printoptions(precision=3)
#-------------------
gr = Numerics.make_extrap_log_func(Demographics1D.growth)
params = array([3, 1])
upper_bound = [100, 100]
lower_bound = [0.01, 0.01]
poptg = dadi.Inference.optimize_log(params,
data,
gr,
pts,
lower_bound=lower_bound,
