#!/usr/bin/env python
# Copyright 2014-2019 The PySCF Developers. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Authors: Timothy Berkelbach <tim.berkelbach@gmail.com>
#          Qiming Sun <osirpt.sun@gmail.com>
#

'''
Non-relativistic Restricted Kohn-Sham for periodic systems with k-point sampling

See Also:
    pyscf.pbc.dft.rks.py : Non-relativistic Restricted Kohn-Sham for periodic
                           systems at a single k-point
'''


import numpy as np
from pyscf import lib
from pyscf.lib import logger
from pyscf.pbc.scf import khf
from pyscf.pbc.dft import gen_grid
from pyscf.pbc.dft import rks
from pyscf.pbc.dft import multigrid
from pyscf import __config__


def get_veff(ks, cell=None, dm=None, dm_last=0, vhf_last=0, hermi=1,
             kpts=None, kpts_band=None):
    '''Coulomb + XC functional

    .. note::
        This is a replica of pyscf.dft.rks.get_veff with kpts added.
        This function will change the ks object.

    Args:
        ks : an instance of :class:`RKS`
            XC functional are controlled by ks.xc attribute.  Attribute
            ks.grids might be initialized.
        dm : ndarray or list of ndarrays
            A density matrix or a list of density matrices

    Returns:
        Veff : (nkpts, nao, nao) or (*, nkpts, nao, nao) ndarray
        Veff = J + Vxc.
    '''
    if cell is None: cell = ks.cell
    if dm is None: dm = ks.make_rdm1()
    if kpts is None: kpts = ks.kpts
    t0 = (logger.process_clock(), logger.perf_counter())

    hybrid = ks._numint.libxc.is_hybrid_xc(ks.xc)

    if not hybrid and isinstance(ks.with_df, multigrid.MultiGridFFTDF):
        n, exc, vxc = multigrid.nr_rks(ks.with_df, ks.xc, dm, hermi,
                                       kpts, kpts_band,
                                       with_j=True, return_j=False)
        logger.debug(ks, 'nelec by numeric integration = %s', n)
        t0 = logger.timer(ks, 'vxc', *t0)
        return vxc

    # ndim = 3 : dm.shape = (nkpts, nao, nao)
    ground_state = (isinstance(dm, np.ndarray) and dm.ndim == 3 and
                    kpts_band is None)

# For UniformGrids, grids.coords does not indicate whehter grids are initialized
    if ks.grids.non0tab is None:
        ks.grids.build(with_non0tab=True)
        if (isinstance(ks.grids, gen_grid.BeckeGrids) and
            ks.small_rho_cutoff > 1e-20 and ground_state):
            ks.grids = rks.prune_small_rho_grids_(ks, cell, dm, ks.grids, kpts)
        t0 = logger.timer(ks, 'setting up grids', *t0)

    if hermi == 2:  # because rho = 0
        n, exc, vxc = 0, 0, 0
    else:
        max_memory = ks.max_memory - lib.current_memory()[0]
        n, exc, vxc = ks._numint.nr_rks(cell, ks.grids, ks.xc, dm, hermi,
                                        kpts, kpts_band, max_memory=max_memory)
        logger.debug(ks, 'nelec by numeric integration = %s', n)
        t0 = logger.timer(ks, 'vxc', *t0)

    weight = 1./len(kpts)
    if not hybrid:
        ks.with_df._j_only = False
        vj = ks.get_j(cell, dm, hermi, kpts, kpts_band)
        vxc += vj
    else:
        omega, alpha, hyb = ks._numint.rsh_and_hybrid_coeff(ks.xc, spin=cell.spin)
        if getattr(ks.with_df, '_j_only', False):  # for GDF and MDF
            logger.warn(ks, 'df.j_only cannot be used with hybrid functional')
            ks.with_df._j_only = False
        vj, vk = ks.get_jk(cell, dm, hermi, kpts, kpts_band)
        vk *= hyb
        if omega != 0:
            vklr = ks.get_k(cell, dm, hermi, kpts, kpts_band, omega=omega)
            vklr *= (alpha - hyb)
            vk += vklr
        vxc += vj - vk * .5

        if ground_state:
            exc -= np.einsum('Kij,Kji', dm, vk).real * .5 * .5 * weight

    if ground_state:
        ecoul = np.einsum('Kij,Kji', dm, vj).real * .5 * weight
    else:
        ecoul = None

    vxc = lib.tag_array(vxc, ecoul=ecoul, exc=exc, vj=None, vk=None)
    return vxc

@lib.with_doc(khf.get_rho.__doc__)
def get_rho(mf, dm=None, grids=None, kpts=None):
    if dm is None: dm = mf.make_rdm1()
    if grids is None: grids = mf.grids
    if kpts is None: kpts = mf.kpts
    if isinstance(mf.with_df, multigrid.MultiGridFFTDF):
        rho = mf.with_df.get_rho(dm, kpts)
    else:
        rho = mf._numint.get_rho(mf.cell, dm, grids, kpts, mf.max_memory)
    return rho

def energy_elec(mf, dm_kpts=None, h1e_kpts=None, vhf=None):
    if h1e_kpts is None: h1e_kpts = mf.get_hcore(mf.cell, mf.kpts)
    if dm_kpts is None: dm_kpts = mf.make_rdm1()
    if vhf is None or getattr(vhf, 'ecoul', None) is None:
        vhf = mf.get_veff(mf.cell, dm_kpts)

    weight = 1./len(h1e_kpts)
    e1 = weight * np.einsum('kij,kji', h1e_kpts, dm_kpts)
    tot_e = e1 + vhf.ecoul + vhf.exc
    mf.scf_summary['e1'] = e1.real
    mf.scf_summary['coul'] = vhf.ecoul.real
    mf.scf_summary['exc'] = vhf.exc.real
    logger.debug(mf, 'E1 = %s  Ecoul = %s  Exc = %s', e1, vhf.ecoul, vhf.exc)
    return tot_e.real, vhf.ecoul + vhf.exc

class KRKS(khf.KRHF, rks.KohnShamDFT):
    '''RKS class adapted for PBCs with k-point sampling.
    '''
    def __init__(self, cell, kpts=np.zeros((1,3)), xc='LDA,VWN',
                 exxdiv=getattr(__config__, 'pbc_scf_SCF_exxdiv', 'ewald')):
        khf.KRHF.__init__(self, cell, kpts, exxdiv=exxdiv)
        rks.KohnShamDFT.__init__(self, xc)

    def dump_flags(self, verbose=None):
        khf.KRHF.dump_flags(self, verbose)
        rks.KohnShamDFT.dump_flags(self, verbose)
        return self

    get_veff = get_veff
    energy_elec = energy_elec
    get_rho = get_rho

    density_fit = rks._patch_df_beckegrids(khf.KRHF.density_fit)
    rs_density_fit = rks._patch_df_beckegrids(khf.KRHF.rs_density_fit)
    mix_density_fit = rks._patch_df_beckegrids(khf.KRHF.mix_density_fit)

    def nuc_grad_method(self):
        from pyscf.pbc.grad import krks
        return krks.Gradients(self)


if __name__ == '__main__':
    from pyscf.pbc import gto
    cell = gto.Cell()
    cell.unit = 'A'
    cell.atom = 'C 0.,  0.,  0.; C 0.8917,  0.8917,  0.8917'
    cell.a = '''0.      1.7834  1.7834
                1.7834  0.      1.7834
                1.7834  1.7834  0.    '''

    cell.basis = 'gth-szv'
    cell.pseudo = 'gth-pade'
    cell.verbose = 7
    cell.output = '/dev/null'
    cell.build()
    mf = KRKS(cell, cell.make_kpts([2,1,1]))
    print(mf.kernel())
