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_j_kpts(mydf,
dm_kpts,
hermi=1,
kpts=numpy.zeros((1, 3)),
kpts_band=None):
vj_kpts = aft_jk.get_j_kpts(mydf, dm_kpts, hermi, kpts, kpts_band)
vj_kpts += df_jk.get_j_kpts(mydf, dm_kpts, hermi, kpts, kpts_band)
return vj_kpts
def test_j_kpts(self):
numpy.random.seed(1)
nao = cell.nao_nr()
dm = numpy.random.random((4,nao,nao))
dm = dm + dm.transpose(0,2,1)
mydf = df.DF(cell)
mydf.kpts = numpy.random.random((4,3))
mydf.gs = numpy.asarray((5,)*3)
mydf.auxbasis = 'weigend'
vj = df_jk.get_j_kpts(mydf, dm, 1, mydf.kpts)
self.assertAlmostEqual(finger(vj[0]), (0.49071716424863876-0.11866593764325209j ), 9)
self.assertAlmostEqual(finger(vj[1]), (0.54805564025672737-0.045780766515833224j), 9)
self.assertAlmostEqual(finger(vj[2]), (0.53548588134342712-0.083269294794914239j), 9)
self.assertAlmostEqual(finger(vj[3]), (0.54778931331075764+0.007709304083260985j), 9)
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 test_j_kpts(self):
numpy.random.seed(1)
nao = cell.nao_nr()
dm = numpy.random.random((4,nao,nao))
dm = dm + dm.transpose(0,2,1)
mydf = df.DF(cell)
mydf.kpts = numpy.random.random((4,3))
mydf.gs = numpy.asarray((5,)*3)
mydf.auxbasis = 'weigend'
vj = df_jk.get_j_kpts(mydf, dm, 1, mydf.kpts)
self.assertAlmostEqual(finger(vj[0]), (0.49176565003742523-0.11891097171204273j ), 9)
self.assertAlmostEqual(finger(vj[1]), (0.54901219181783034-0.046003618294160657j), 9)
self.assertAlmostEqual(finger(vj[2]), (0.53648483057543039-0.083507647862465353j), 9)
self.assertAlmostEqual(finger(vj[3]), (0.54896892736198399+0.007695802002441080j), 9)
def test_j_kpts_high_cost(self):
numpy.random.seed(1)
nao = cell.nao_nr()
dm = numpy.random.random((4,nao,nao))
dm = dm + dm.transpose(0,2,1)
mydf = rsdf.RSDF(cell).set(auxbasis='weigend')
mydf.linear_dep_threshold = 1e-7
mydf.kpts = numpy.random.random((4,3))
mydf.auxbasis = 'weigend'
mydf.omega = 0.3
mydf.exp_to_discard = 0.3
vj = df_jk.get_j_kpts(mydf, dm, 1, mydf.kpts)
self.assertAlmostEqual(lib.fp(vj[0]), (0.4917612920404451 + -0.1189108415838486j), 7)
self.assertAlmostEqual(lib.fp(vj[1]), (0.5490079977477804 + -0.0460035459549861j), 7)
self.assertAlmostEqual(lib.fp(vj[2]), (0.5364805888399165 + -0.0835075280950256j), 7)
self.assertAlmostEqual(lib.fp(vj[3]), (0.5489645342271054 + 0.0076957400601779j), 7)
def test_j_kpts(self):
numpy.random.seed(1)
nao = cell.nao_nr()
dm = numpy.random.random((4,nao,nao))
dm = dm + dm.transpose(0,2,1)
mydf = df.DF(cell).set(auxbasis='weigend')
mydf.kpts = numpy.random.random((4,3))
mydf.mesh = numpy.asarray((11,)*3)
mydf.auxbasis = 'weigend'
mydf.mesh = cell.mesh
mydf.eta = 0.3
vj = df_jk.get_j_kpts(mydf, dm, 1, mydf.kpts)
self.assertAlmostEqual(finger(vj[0]), (0.49176180692009197-0.11891083594538684j ), 9)
self.assertAlmostEqual(finger(vj[1]), (0.54900852073326378-0.04600354345316908j ), 9)
self.assertAlmostEqual(finger(vj[2]), (0.53648110926681891-0.083507522327029265j), 9)
self.assertAlmostEqual(finger(vj[3]), (0.5489650266265671 +0.007695733246577244j), 9)
def test_j_kpts_high_cost(self):
numpy.random.seed(1)
nao = cell0.nao_nr()
dm = numpy.random.random((4,nao,nao))
dm = dm + dm.transpose(0,2,1)
mydf = df.DF(cell0).set(auxbasis='weigend')
mydf.linear_dep_threshold = 1e-7
mydf.kpts = numpy.random.random((4,3))
mydf.auxbasis = 'weigend'
mydf.eta = 0.3
mydf.exp_to_discard = mydf.eta
vj = df_jk.get_j_kpts(mydf, dm, 1, mydf.kpts)
self.assertAlmostEqual(lib.fp(vj[0]), (7.240207870630442-0.001010622364950332j) , 9)
self.assertAlmostEqual(lib.fp(vj[1]), (7.248745538469966-0.001562604522803734j) , 9)
self.assertAlmostEqual(lib.fp(vj[2]), (7.241193241602369-0.002518439407055759j) , 9)
self.assertAlmostEqual(lib.fp(vj[3]), (7.2403591406956185+0.001475803952777666j), 9)
def test_j_kpts(self):
numpy.random.seed(1)
nao = cell.nao_nr()
dm = numpy.random.random((4,nao,nao))
dm = dm + dm.transpose(0,2,1)
mydf = df.DF(cell).set(auxbasis='weigend')
mydf.linear_dep_threshold = 1e-7
mydf.kpts = numpy.random.random((4,3))
mydf.mesh = numpy.asarray((11,)*3)
mydf.auxbasis = 'weigend'
mydf.mesh = cell.mesh
mydf.eta = 0.3
mydf.exp_to_discard = mydf.eta
vj = df_jk.get_j_kpts(mydf, dm, 1, mydf.kpts)
self.assertAlmostEqual(finger(vj[0]), (0.49176180692009197-0.11891083594538684j ), 9)
self.assertAlmostEqual(finger(vj[1]), (0.54900852073326378-0.04600354345316908j ), 9)
self.assertAlmostEqual(finger(vj[2]), (0.53648110926681891-0.083507522327029265j), 9)
self.assertAlmostEqual(finger(vj[3]), (0.5489650266265671 +0.007695733246577244j), 9)
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
def test_j_kpts(self):
numpy.random.seed(1)
nao = cell.nao_nr()
dm = numpy.random.random((4, nao, nao))
dm = dm + dm.transpose(0, 2, 1)
mydf = df.DF(cell)
mydf.kpts = numpy.random.random((4, 3))
mydf.gs = numpy.asarray((5, ) * 3)
mydf.auxbasis = 'weigend'
vj = df_jk.get_j_kpts(mydf, dm, 1, mydf.kpts)
self.assertAlmostEqual(finger(vj[0]),
(0.49176639512164577 - 0.11891092353150769j), 9)
self.assertAlmostEqual(finger(vj[1]),
(0.54901304965192832 - 0.046003580779851005j),
9)
self.assertAlmostEqual(finger(vj[2]),
(0.53648566446940738 - 0.083507597783243834j),
9)
self.assertAlmostEqual(finger(vj[3]),
(0.54896968903421772 + 0.007695795208980421j),
9)
def test_j_kpts(self):
numpy.random.seed(1)
nao = cell.nao_nr()
dm = numpy.random.random((4, nao, nao))
dm = dm + dm.transpose(0, 2, 1)
mydf = df.DF(cell)
mydf.kpts = numpy.random.random((4, 3))
mydf.gs = numpy.asarray((5, ) * 3)
mydf.auxbasis = 'weigend'
vj = df_jk.get_j_kpts(mydf, dm, 1, mydf.kpts)
self.assertAlmostEqual(finger(vj[0]),
(0.49071716424863876 - 0.11866593764325209j), 9)
self.assertAlmostEqual(finger(vj[1]),
(0.54805564025672737 - 0.045780766515833224j),
9)
self.assertAlmostEqual(finger(vj[2]),
(0.53548588134342712 - 0.083269294794914239j),
9)
self.assertAlmostEqual(finger(vj[3]),
(0.54778931331075764 + 0.007709304083260985j),
9)
def test_j_kpts_high_cost(self):
numpy.random.seed(1)
nao = cell.nao_nr()
dm = numpy.random.random((4, nao, nao))
dm = dm + dm.transpose(0, 2, 1)
mydf = df.DF(cell).set(auxbasis='weigend')
mydf.linear_dep_threshold = 1e-7
mydf.kpts = numpy.random.random((4, 3))
mydf.mesh = [n, n, n]
mydf.auxbasis = 'weigend'
mydf.eta = 0.3
mydf.exp_to_discard = mydf.eta
vj = df_jk.get_j_kpts(mydf, dm, 1, mydf.kpts)
self.assertAlmostEqual(lib.fp(vj[0]),
(0.49176180692009197 - 0.11891083594538684j), 9)
self.assertAlmostEqual(lib.fp(vj[1]),
(0.54900852073326378 - 0.04600354345316908j), 9)
self.assertAlmostEqual(lib.fp(vj[2]),
(0.53648110926681891 - 0.083507522327029265j),
9)
self.assertAlmostEqual(lib.fp(vj[3]),
(0.5489650266265671 + 0.007695733246577244j), 9)
def test_j_kpts(self):
numpy.random.seed(1)
nao = cell.nao_nr()
dm = numpy.random.random((4, nao, nao))
dm = dm + dm.transpose(0, 2, 1)
mydf = df.DF(cell).set(auxbasis='weigend')
mydf.kpts = numpy.random.random((4, 3))
mydf.gs = numpy.asarray((5, ) * 3)
mydf.auxbasis = 'weigend'
mydf.gs = cell.gs
mydf.eta = 0.3
vj = df_jk.get_j_kpts(mydf, dm, 1, mydf.kpts)
self.assertAlmostEqual(finger(vj[0]),
(0.49176565003906081 - 0.11891097171192379j), 9)
self.assertAlmostEqual(finger(vj[1]),
(0.54901219181920746 - 0.046003618294119357j),
9)
self.assertAlmostEqual(finger(vj[2]),
(0.53648483057579632 - 0.083507647862342937j),
9)
self.assertAlmostEqual(finger(vj[3]),
(0.54896892736259639 + 0.0076958020023144037j),
9)
def get_j_kpts(mydf, dm_kpts, hermi=1, kpts=numpy.zeros((1,3)), kpts_band=None):
vj_kpts = aft_jk.get_j_kpts(mydf, dm_kpts, hermi, kpts, kpts_band)
vj_kpts += df_jk.get_j_kpts(mydf, dm_kpts, hermi, kpts, kpts_band)
return vj_kpts
def get_j_kpts(mydf, dm_kpts, hermi=1, kpts=numpy.zeros((1,3)), kpts_band=None):
if mydf._cderi is None or mydf.auxcell is None or not mydf.has_kpts(kpts_band):
mydf.build(kpts_band=kpts_band)
vj_kpts = aft_jk.get_j_kpts(mydf, dm_kpts, hermi, kpts, kpts_band)
vj_kpts += df_jk.get_j_kpts(mydf, dm_kpts, hermi, kpts, kpts_band)
return vj_kpts
