Ejemplo n.º 1
0
    def total_energy(self, q_list, l_list):

        nsample = q_list.shape[0]
        nparticle = q_list.shape[1]
        dim = q_list.shape[2]

        dq = delta_pbc(q_list, l_list)
        idx = get_paired_distance_indices.get_indices(dq.shape)
        dr = get_paired_distance_indices.reduce(dq, idx)
        dr = dr.view([nsample, nparticle, nparticle - 1, dim])

        r = torch.sqrt(torch.sum(dr * dr, dim=-1))
        e_list = self.paired_energy(r)
        e_total = torch.sum(e_list, dim=(1, 2)) * 0.5

        return e_total
Ejemplo n.º 2
0
def paired_distance_reduced(q, npar):

    l_list = torch.zeros(q.shape)
    l_list.fill_(1)
    l_list = mydevice.load(l_list)

    dq = delta_pbc(q,l_list) # shape is [nsamples, nparticle, nparticle, DIM]

    dq_reduced_index = get_paired_distance_indices.get_indices(dq.shape)
    dq_flatten = get_paired_distance_indices.reduce(dq, dq_reduced_index)
    # dq_flatten.shape is [nsamples x nparticle x (nparticle - 1) x DIM]

    dq_reshape = dq_flatten.view(q.shape[0], npar, npar - 1, q.shape[2])
    # dq_reshape.shape is [nsamples, nparticle, (nparticle - 1), DIM]

    dd = torch.sqrt(torch.sum(dq_reshape * dq_reshape, dim=-1))
    # dd.shape is [nsamples, nparticle, (nparticle - 1 )]
    return dq_reshape, dd
Ejemplo n.º 3
0
    def prepare_input(self,q_list,p_list,l_list,tau):
        nsamples, nparticle, DIM = q_list.shape

        dq = delta_pbc(q_list, l_list)
        # shape is [nsamples, nparticle, nparticle, DIM]
        dq = torch.reshape(dq, (nsamples * nparticle * nparticle, DIM))
        # shape is [nsamples* nparticle* nparticle, DIM]

        dp = delta_state(p_list)

        # dq.shape = dp.shape = [nsamples, nparticle, nparticle, 2]
        dp = torch.reshape(dp, (nsamples * nparticle * nparticle, DIM))
        # shape is [nsamples* nparticle* nparticle, DIM]

        tau_tensor = torch.zeros([nsamples*nparticle*nparticle, 1],requires_grad=False) + 0.5*tau
        tau_tensor = mydevice.load(tau_tensor)

        #tau_tensor.fill_(tau * 0.5)  # tau_tensor take them tau/2

        x = torch.cat((dq, dp, tau_tensor), dim=-1)
        # dqdp.shape is [ nsamples*nparticle*nparticle, 5]

        return x
Ejemplo n.º 4
0
    torch.set_default_dtype(torch.float64)

    nsample = 20
    nparticle = 2
    dim = 2

    q_list = torch.rand([nsample, nparticle, dim], requires_grad=True)
    l_list = torch.rand([nsample, dim]) + nparticle * nparticle
    l_list = torch.unsqueeze(l_list, dim=1)
    l_list = torch.repeat_interleave(l_list, nparticle, dim=1)

    #print('q_list ',q_list)
    #print('l_list ',l_list)

    dq = delta_pbc(q_list, l_list)  # shape [nsample,nparticle,nparticle,dim]
    dr = torch.sqrt(torch.sum(dq * dq,
                              dim=-1))  # shape [nsample,nparticle,nparticle]

    #print('dr slow ',dr)

    e_list = []
    for s in range(nsample):
        e = 0.0
        for p1 in range(nparticle):
            for p2 in range(nparticle):
                if p1 != p2:
                    r = dr[s][p1][p2]
                    e6 = 1 / (r**6 + 1e-10)
                    e12 = 1 / (r**12 + 1e-10)
                    #print('r ',r,' add to ',4*(e12-e6))
Ejemplo n.º 5
0
 def prepare_q_input(self, pwnet_id, q_list, p_list,
                     l_list):  # p_list not used here
     dq0 = delta_pbc(q_list, l_list)
     dq1 = self.make_correct_shape(dq0)
     return dq1