def load_anydata(infile1):
''' load train or valid or train data'''
init_qp, _, _, boxsize = data_io.read_trajectory_qp(infile1)
# init_qp_train.shape = [nsamples, (q, p), 1, nparticle, DIM]
init_q = torch.squeeze(init_qp[:, 0, :, :, :], dim=1)
init_p = torch.squeeze(init_qp[:, 1, :, :, :], dim=1)
phase_space.set_q(init_q)
phase_space.set_p(init_p)
phase_space.set_boxsize(boxsize)
return init_q.shape, boxsize
boxsize = infile['boxsize']
qp_traj = infile['qp_trajectory']
init_qp = qp_traj[:, :, 0, :, :]
# init_qp.shape = [nsamples, (q, p), nparticle, DIM]
qp_strike_append = qp_traj[:, :, 1, :, :]
# qp_strike_append.shape = [nsamples, (q, p), nparticle, DIM]
_, _, npar, DIM = init_qp.shape
terms = hamiltonian_obj.get_terms()
# initial state
phase_space.set_q(init_qp[:, 0, :, :])
phase_space.set_p(init_qp[:, 1, :, :])
phase_space.set_boxsize(boxsize)
init_u = terms[0].energy(phase_space) / npar
init_e = hamiltonian_obj.total_energy(phase_space) / npar
# init_e.shape = [nsamples]
########################### thrsh ##################################
q_list = init_qp[:, 0, :, :] / boxsize
_, d = phase_space.paired_distance_reduced(q_list, npar, DIM)
d = d * boxsize
rthrsh = torch.min(d)
pothrsh = 4 * (1 / (rthrsh) ** 12 - 1 / (rthrsh) ** 6)
dhdqthrsh = 4 * ((-12) / (rthrsh) ** 13) - 4 * ((-6) / (rthrsh) ** 7)
pthrsh = math.sqrt(2 * 1.0) * math.sqrt(-1. * math.log(math.sqrt(2 * math.pi) * 1e-5))