def get_jk(self,
dm,
hermi=1,
kpts=None,
kpts_band=None,
with_j=True,
with_k=True,
omega=None,
exxdiv=None):
if omega is not None: # J/K for RSH functionals
return _sub_df_jk_(self, dm, hermi, kpts, kpts_band, with_j,
with_k, omega, exxdiv)
if kpts is None:
if numpy.all(self.kpts == 0):
# Gamma-point calculation by default
kpts = numpy.zeros(3)
else:
kpts = self.kpts
kpts = numpy.asarray(kpts)
if kpts.shape == (3, ):
return df_jk.get_jk(self, dm, hermi, kpts, kpts_band, with_j,
with_k, exxdiv)
vj = vk = None
if with_k:
vk = df_jk.get_k_kpts(self, dm, hermi, kpts, kpts_band, exxdiv)
if with_j:
vj = df_jk.get_j_kpts(self, dm, hermi, kpts, kpts_band)
return vj, vk
def get_jk(self,
dm,
hermi=1,
kpts=None,
kpts_band=None,
with_j=True,
with_k=True,
exxdiv='ewald'):
if kpts is None:
if numpy.all(self.kpts == 0):
# Gamma-point calculation by default
kpts = numpy.zeros(3)
else:
kpts = self.kpts
kpts = numpy.asarray(kpts)
if kpts.shape == (3, ):
return df_jk.get_jk(self, dm, hermi, kpts, kpts_band, with_j,
with_k, exxdiv)
vj = vk = None
if with_k:
vk = df_jk.get_k_kpts(self, dm, hermi, kpts, kpts_band, exxdiv)
if with_j:
vj = df_jk.get_j_kpts(self, dm, hermi, kpts, kpts_band)
return vj, vk
def get_jk(mydf, dm, hermi=1, kpt=numpy.zeros(3),
kpts_band=None, with_j=True, with_k=True, exxdiv=None):
'''JK for given k-point'''
vj1, vk1 = df_jk.get_jk(mydf, dm, hermi, kpt, kpts_band, with_j, with_k, None)
vj, vk = aft_jk.get_jk(mydf, dm, hermi, kpt, kpts_band, with_j, with_k, exxdiv)
if with_j: vj += vj1
if with_k: vk += vk1
return vj, vk
def get_jk(mydf, dm, hermi=1, kpt=numpy.zeros(3),
kpt_band=None, with_j=True, with_k=True, exxdiv=None):
'''JK for given k-point'''
if rank != 0: # to apply df_jk._ewald_exxdiv_for_G0 function once
exxdiv = None
vj, vk = df_jk.get_jk(mydf, dm, hermi, kpt, kpt_band, with_j, with_k, exxdiv)
if with_j: vj = mpi.reduce(vj)
if with_k: vk = mpi.reduce(vk)
return vj, vk
def get_jk(self,
dm,
hermi=1,
kpts=None,
kpts_band=None,
with_j=True,
with_k=True,
omega=None,
exxdiv=None):
if omega is not None: # J/K for RSH functionals
cell = self.cell
# * AFT is computationally more efficient than GDF if the Coulomb
# attenuation tends to the long-range role (i.e. small omega).
# * Note: changing to AFT integrator may cause small difference to
# the GDF integrator. If a very strict GDF result is desired,
# we can disable this trick by setting
# LONGRANGE_AFT_TURNOVER_THRESHOLD to 0.
# * The sparse mesh is not appropriate for low dimensional systems
# with infinity vacuum since the ERI may require large mesh to
# sample density in vacuum.
if (omega < LONGRANGE_AFT_TURNOVER_THRESHOLD
and cell.dimension >= 2
and cell.low_dim_ft_type != 'inf_vacuum'):
mydf = aft.AFTDF(cell, self.kpts)
mydf.ke_cutoff = aft.estimate_ke_cutoff_for_omega(cell, omega)
mydf.mesh = tools.cutoff_to_mesh(cell.lattice_vectors(),
mydf.ke_cutoff)
else:
mydf = self
return _sub_df_jk_(mydf, dm, hermi, kpts, kpts_band, with_j,
with_k, omega, exxdiv)
if kpts is None:
if numpy.all(self.kpts == 0):
# Gamma-point calculation by default
kpts = numpy.zeros(3)
else:
kpts = self.kpts
kpts = numpy.asarray(kpts)
if kpts.shape == (3, ):
return df_jk.get_jk(self, dm, hermi, kpts, kpts_band, with_j,
with_k, exxdiv)
vj = vk = None
if with_k:
vk = df_jk.get_k_kpts(self, dm, hermi, kpts, kpts_band, exxdiv)
if with_j:
vj = df_jk.get_j_kpts(self, dm, hermi, kpts, kpts_band)
return vj, vk
def get_jk(mydf,
dm,
hermi=1,
kpt=numpy.zeros(3),
kpt_band=None,
with_j=True,
with_k=True,
exxdiv=None):
'''JK for given k-point'''
if rank != 0: # to apply df_jk._ewald_exxdiv_for_G0 function once
exxdiv = None
vj, vk = df_jk.get_jk(mydf, dm, hermi, kpt, kpt_band, with_j, with_k,
exxdiv)
if with_j: vj = mpi.reduce(vj)
if with_k: vk = mpi.reduce(vk)
return vj, vk
def get_jk(mydf, dm, hermi=1, kpt=numpy.zeros(3),
kpt_band=None, with_j=True, with_k=True, exxdiv=None):
'''JK for given k-point'''
vj = vk = None
if kpt_band is not None and abs(kpt-kpt_band).sum() > 1e-9:
kpt = numpy.reshape(kpt, (1,3))
if with_k:
vk = get_k_kpts(mydf, [dm], hermi, kpt, kpt_band, exxdiv)
if with_j:
vj = get_j_kpts(mydf, [dm], hermi, kpt, kpt_band)
return vj, vk
if mydf._cderi is None or mydf.auxcell is None or not mydf.has_kpts(kpt_band):
mydf.build(kpts_band=kpt_band)
vj1, vk1 = df_jk.get_jk(mydf, dm, hermi, kpt, kpt_band, with_j, with_k, None)
vj, vk = aft_jk.get_jk(mydf, dm, hermi, kpt, kpt_band, with_j, with_k, exxdiv)
if with_j: vj += vj1
if with_k: vk += vk1
return vj, vk
def get_jk(self, dm, hermi=1, kpts=None, kpts_band=None,
with_j=True, with_k=True, exxdiv=None):
if kpts is None:
if numpy.all(self.kpts == 0):
# Gamma-point calculation by default
kpts = numpy.zeros(3)
else:
kpts = self.kpts
kpts = numpy.asarray(kpts)
if kpts.shape == (3,):
return df_jk.get_jk(self, dm, hermi, kpts, kpts_band, with_j,
with_k, exxdiv)
vj = vk = None
if with_k:
vk = df_jk.get_k_kpts(self, dm, hermi, kpts, kpts_band, exxdiv)
if with_j:
vj = df_jk.get_j_kpts(self, dm, hermi, kpts, kpts_band)
return vj, vk
