#!/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,