H.local_energy_array(psi_sorted_sampler, psi_sorted, evaluation_points), method="blocking", ), ] old = psi_sorted.parameters psi_sorted.parameters = psi.parameters psi_sorted_sampler.thermalize(10000) stats.append( compute_statistics_for_series( H.local_energy_array(psi_sorted_sampler, psi_sorted, evaluation_points), method="blocking", )) labels = [ r"$\psi_{PJ}$", r"$\psi_{DNN}$", r"$\psi_{SDNN}$", r"$\hat{\psi}_{SDNN}$" ] mpiprint(stats, pretty=True) mpiprint(statistics_to_tex(stats, labels, filename=__file__ + ".table.tex")) # mpiprint(psi.parameters) if master_rank(): plot_training( [psi_energies, psi_sorted_energies, psi_bench_energies], [psi_parameters, psi_parameters], psi_symmetries, ) plt.show()
eta = timedelta(seconds=round(t1 / 500 * evaluation_points)) mpiprint(f"Calculating final energy - ETA {eta}") labels = [ r"$\psi_{PJ}$", r"$\psi_{DNN}$", r"$\psi_{SDNN}$", r"$\hat{\psi}_{SDNN}$" ] r2_stats = [ compute_statistics_for_series(H.mean_squared_radius_array( s, evaluation_points), method="blocking") for s in samplers ] mpiprint( statistics_to_tex( r2_stats, labels, filename=__file__ + ".r2-table.tex", quantity_name="$\\langle r^2\\rangle$", )) r_stats = [ compute_statistics_for_series(H.mean_radius_array(s, evaluation_points), method="blocking") for s in samplers ] mpiprint( statistics_to_tex( r_stats, labels, filename=__file__ + ".r-table.tex", quantity_name="$\\langle r\\rangle$", )) rij_stats = [ compute_statistics_for_series(H.mean_distance_array(