# Integrate 5,000,000 steps. This can be done in one go as well.
for i in range(SAMP_STEPS):
if (i + 1) % 100 == 0:
print('\rrun %i: %.0f%%' % (run + 1, (i + 1) * 100. / SAMP_STEPS),
end='',
flush=True)
system.integrator.run(SAMP_LENGTH)
print()
# Finalize the accumulators and write to disk
system.auto_update_accumulators.remove(msd)
msd.finalize()
np.savetxt(
"{}/msd_{}_{}.dat".format(outdir, vel, run),
np.column_stack(
(msd.lag_times(), msd.sample_sizes(), msd.result().reshape([-1,
3]))))
system.auto_update_accumulators.remove(vacf)
vacf.finalize()
np.savetxt(
"{}/vacf_{}_{}.dat".format(outdir, vel, run),
np.column_stack((vacf.lag_times(), vacf.sample_sizes(),
vacf.result().reshape([-1, 1]))))
system.auto_update_accumulators.remove(avacf)
avacf.finalize()
np.savetxt(
"{}/avacf_{}_{}.dat".format(outdir, vel, run),
np.column_stack((avacf.lag_times(), avacf.sample_sizes(),
avacf.result().reshape([-1, 1]))))
c_vel = Correlator(obs1=vel_obs,
tau_lin=16,
tau_max=20.,
delta_N=1,
corr_operation="scalar_product",
compress1="discard1")
system.auto_update_accumulators.add(c_pos)
system.auto_update_accumulators.add(c_vel)
system.integrator.run(1000000)
c_pos.finalize()
c_vel.finalize()
msd = np.column_stack(
(c_pos.lag_times(), c_pos.sample_sizes(), c_pos.result().reshape([-1, 3])))
vacf = np.column_stack(
(c_vel.lag_times(), c_vel.sample_sizes(), c_vel.result().reshape([-1, 1])))
np.savetxt("msd.dat", msd)
np.savetxt("vacf.dat", vacf)
# Integral of vacf via Green-Kubo
# D= 1/3 int_0^infty <v(t_0)v(t_0+t)> dt
# Integrate with trapezoidal rule
I = np.trapz(vacf[:, 2], vacf[:, 0])
ratio = 1. / 3. * I / (kT / gamma)
print("Ratio of measured and expected diffusion coefficients from Green-Kubo:",
ratio)