def loadData(): f_dir = "/home/arya/storage/LD_analysis" my_global_functions.my_mkdir(f_dir) fname="%s/sim.sel.msms"%f_dir w = 1000e3 theta = 50*w/50e3 rho = 25*w/50e3 nu0 = 0.5 s = 0.05 window=50 _=mymsms.run_msms_simulator(fname, s=s,freq=nu0, n = 200,rho=rho, theta=theta, window_size=w);[M, snp_pos, ba_ind, oc] = mymsms.read_msms_file_info(fname); M=pd.DataFrame(M,columns=(pd.Series(snp_pos)*w).round().astype(int).values).astype(int); M.L=w M.siteUnderSelection=ba_ind M.posUnderSelection=M.columns[ba_ind] M.oc=oc return M
n = 1.0 * N / (np.unique(haf).shape[0]) f = Mw.mean(0) p = myHAF.neutrality_divergence_likelihood_2(h, n, f, method=3) I = np.argsort(p)[::-1] top_kw = len(I) P += list(p[I[:top_kw]]) x += list(w_st + I[:top_kw]) w_st += w_step if Mw.shape[1] < w_size: break d = pd.DataFrame([x, P]).T D = d.groupby(0).mean().reset_index() return D f_dir = "/home/arya/storage/LD_analysis" my_global_functions.my_mkdir(f_dir) fname="%s/sim.sel.msms"%f_dir w = 1000e3 theta = 50*w/50e3 rho = 25*w/50e3 nu0 = 0.5 s = 0.5 window=50 mymsms.run_msms_simulator(fname, s=s,freq=nu0, n = 200,rho=rho, theta=theta, window_size=w);[M, snp_pos, ba_ind, oc] = mymsms.read_msms_file_info(fname);M=pd.DataFrame(M,columns=(pd.Series(snp_pos)*w).round().astype(int).values); # D=est.Estimate.LD(M).round(2) # E=D.copy(True) # D # E[E<0]=None # E # F=E.applymap(lambda x: x**200).sum(1) # F2=D.applymap(lambda x: x**200).sum(1)