x0 = np.random.random((n, ) * dim)
spacing = 1.0
n_samples, n_burn_in = 10000, 50
c_len = 500
mixing_angles = [.5 * np.pi]
angle_labels = [r'\frac{\pi}{2}']
separations = range(c_len)
opFn = lambda samples: twoPoint(samples, separation=0)
op_name = r'$\hat{O} = x^2$'
op_theory = x2_1df(mu=pot.m, n=x0.size, a=spacing, sep=0)
pacc_theory = acceptance(dtau=step_size, tau=tau, n=x0.size, m=pot.m)
acFunc = lambda t, pa, theta: hmc(pa, tau * m, 1. / tau, t)
print '> Trajectory Length: tau: {}'.format(tau)
print '> Step Size: {}'.format(step_size)
print '> Theoretical <x^2>: {}'.format(op_theory)
print '> Theoretical <P_acc>: {}'.format(pacc_theory)
if '__main__' == __name__:
routine.main(x0,
pot,
file_name,
n_samples=n_samples,
n_burn_in=n_burn_in,
spacing=spacing,
mixing_angles=mixing_angles,
angle_labels=angle_labels,
a = np.asarray(a) # the autocorrelation array counts = np.array(counts) # the counts at each separation # mask = 1-np.isnan(a) # a = a[mask] a = a * counts # grab errors print 'getting errors...' ans = uWerr(op_samples, a) _, _, _, itau, itau_diff, _, acns = ans # extract data my_w = getW(itau, itau_diff, n=n_samples) # get window length my_err = acorrnErr(acns, my_w, n_samples) # get autocorr errors my_err *= np.sqrt(n_samples) / np.sqrt(counts) # theory calclations th_x = np.linspace(min_sep, max_sep, 1000) th = np.array([hmc(p, phi, r, i) for i in th_x]) from scipy.optimize import curve_fit fn = lambda x, a, b, c: a * np.cos(b * x)**2 + c expFn = lambda x, a, b, c: a * np.exp(-b * x) + c cos_sq = curve_fit(fn, separations[:50], acns[:50]) exp_fit = curve_fit(expFn, separations[1:80], counts[1:80]) norm_cnt = expFn(0, *exp_fit[0]) / counts[1] a0 = a[1] * norm_cnt pp = Pretty_Plotter() pp._teXify() pp._updateRC()
n, dim = 100, 1
x0 = np.random.random((n, ) * dim)
spacing = 1.
n_samples, n_burn_in = 10000, 1000
c_len = 500
mixing_angles = [.5 * np.pi]
angle_labels = [r'\frac{\pi}{2}']
separations = range(c_len)
opFn = magnetisation_sq
op_name = r'$\mathscr{M}^2$'
pacc_theory = acceptance(dtau=step_size, tau=tau, n=x0.size, m=pot.m)
acFunc = lambda t, pa, theta: hmc(pa, tau * m, 1. / tau, t)
op_theory = hmc(pacc_theory, tau * m, 1. / tau, 0)
print '> Trajectory Length: tau: {}'.format(tau)
print '> Step Size: {}'.format(step_size)
print '> Theoretical <\phi_0^2>: {}'.format(op_theory)
print '> Theoretical <P_acc>: {}'.format(pacc_theory)
if '__main__' == __name__:
routine.main(x0,
pot,
file_name,
n_samples=n_samples,
n_burn_in=n_burn_in,
spacing=spacing,
mixing_angles=mixing_angles,