from hydrogen import * from qmc_stats import * norm_gauss = 1./(2.*np.pi)**(1.5) def gaussian(r): return norm_gauss * np.exp(-np.dot(r,r)*0.5) def MonteCarlo(a,nmax): E = 0. N = 0. for istep in range(nmax): r = np.random.normal(loc=0., scale=1.0, size=(3)) w = psi(a,r) w = w*w / gaussian(r) N += w E += w * e_loc(a,r) return E/N a = 0.9 nmax = 100000 X = [MonteCarlo(a,nmax) for i in range(30)] E, deltaE = ave_error(X) print(f"E = {E} +/- {deltaE}")