def test_jk(self):
mf = df_jk.density_fit(mf0, auxbasis='weigend', gs=(5,)*3)
dm = mf.get_init_guess()
vj, vk = mf.get_jk(cell, dm)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ej1, 46.698942503368144, 8)
self.assertAlmostEqual(ek1, 31.723584951501 , 8)
numpy.random.seed(12)
nao = cell.nao_nr()
dm = numpy.random.random((nao,nao))
dm = dm + dm.T
vj1, vk1 = mf.get_jk(cell, dm, hermi=0)
ej1 = numpy.einsum('ij,ji->', vj1, dm)
ek1 = numpy.einsum('ij,ji->', vk1, dm)
self.assertAlmostEqual(ej1, 242.04814846354407, 8)
self.assertAlmostEqual(ek1, 280.16142282936778, 8)
numpy.random.seed(1)
kpt = numpy.random.random(3)
mydf = df.DF(cell, [kpt])
vj, vk = mydf.get_jk(dm, 1, kpt)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ej1, 231.44815444295386+0j, 8)
self.assertAlmostEqual(ek1, 682.70050207852307+0j, 8)
def test_jk_single_kpt(self):
mf = df_jk.density_fit(mf0, auxbasis='weigend', gs=(5, ) * 3)
mf.with_df.gs = cell.gs
mf.with_df.eta = 0.3
dm = mf.get_init_guess()
vj, vk = mf.get_jk(cell, dm)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ej1, 46.69888588120217, 8)
self.assertAlmostEqual(ek1, 31.72349032270801, 8)
numpy.random.seed(12)
nao = cell.nao_nr()
dm = numpy.random.random((nao, nao))
dm = dm + dm.T
vj1, vk1 = mf.get_jk(cell, dm, hermi=0)
ej1 = numpy.einsum('ij,ji->', vj1, dm)
ek1 = numpy.einsum('ij,ji->', vk1, dm)
self.assertAlmostEqual(ej1, 242.04678235140264, 8)
self.assertAlmostEqual(ek1, 280.15934926575903, 8)
numpy.random.seed(1)
kpt = numpy.random.random(3)
mydf = df.DF(cell, [kpt]).set(auxbasis='weigend')
mydf.gs = cell.gs
mydf.eta = 0.3
vj, vk = mydf.get_jk(dm, 1, kpt, exxdiv=None)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ej1, 241.15121903857556 + 0j, 8)
self.assertAlmostEqual(ek1, 279.64649194057051 + 0j, 8)
vj, vk = mydf.get_jk(dm, 1, kpt, with_j=False, exxdiv='ewald')
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ek1, 691.64624456329909 + 0j, 6)
def test_jk(self):
mf = df_jk.density_fit(mf0, auxbasis='weigend', gs=(5, ) * 3)
dm = mf.get_init_guess()
vj, vk = mf.get_jk(cell, dm)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ej1, 46.698942503368144, 8)
self.assertAlmostEqual(ek1, 31.723584951501, 8)
numpy.random.seed(12)
nao = cell.nao_nr()
dm = numpy.random.random((nao, nao))
dm = dm + dm.T
vj1, vk1 = mf.get_jk(cell, dm, hermi=0)
ej1 = numpy.einsum('ij,ji->', vj1, dm)
ek1 = numpy.einsum('ij,ji->', vk1, dm)
self.assertAlmostEqual(ej1, 242.04814846354407, 8)
self.assertAlmostEqual(ek1, 280.16142282936778, 8)
numpy.random.seed(1)
kpt = numpy.random.random(3)
mydf = df.DF(cell, [kpt])
vj, vk = mydf.get_jk(dm, 1, kpt)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ej1, 231.44815444295386 + 0j, 8)
self.assertAlmostEqual(ek1, 682.70050207852307 + 0j, 8)
def test_jk_single_kpt(self):
mf = df_jk.density_fit(mf0, auxbasis='weigend', gs=(5, ) * 3)
mf.with_df.gs = cell.gs
mf.with_df.eta = 0.3
dm = mf.get_init_guess()
vj, vk = mf.get_jk(cell, dm)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ej1, 46.69888588854387, 8)
self.assertAlmostEqual(ek1, 31.72349032270801, 8)
numpy.random.seed(12)
nao = cell.nao_nr()
dm = numpy.random.random((nao, nao))
dm = dm + dm.T
vj1, vk1 = mf.get_jk(cell, dm, hermi=0)
ej1 = numpy.einsum('ij,ji->', vj1, dm)
ek1 = numpy.einsum('ij,ji->', vk1, dm)
self.assertAlmostEqual(ej1, 242.04675958582985, 8)
self.assertAlmostEqual(ek1, 280.15934765887238, 8)
numpy.random.seed(1)
kpt = numpy.random.random(3)
mydf = df.DF(cell, [kpt]).set(auxbasis='weigend')
mydf.gs = cell.gs
mydf.eta = 0.3
vj, vk = mydf.get_jk(dm, 1, kpt, exxdiv=None)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ej1, 241.1511965365826 + 0j, 8)
self.assertAlmostEqual(ek1, 279.64649180140344 + 0j, 8)
vj, vk = mydf.get_jk(dm, 1, kpt, with_j=False, exxdiv='ewald')
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ek1, 691.64624442413174 + 0j, 6)
def test_jk(self):
mf = df_jk.density_fit(mf0, auxbasis='weigend', gs=(5, ) * 3)
dm = mf.get_init_guess()
vj, vk = mf.get_jk(cell, dm)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ej1, 46.698885887058495, 8)
self.assertAlmostEqual(ek1, 31.723490322441389, 8)
numpy.random.seed(12)
nao = cell.nao_nr()
dm = numpy.random.random((nao, nao))
dm = dm + dm.T
vj1, vk1 = mf.get_jk(cell, dm, hermi=0)
ej1 = numpy.einsum('ij,ji->', vj1, dm)
ek1 = numpy.einsum('ij,ji->', vk1, dm)
self.assertAlmostEqual(ej1, 242.04675570748412, 8)
self.assertAlmostEqual(ek1, 280.15933405636196, 8)
numpy.random.seed(1)
kpt = numpy.random.random(3)
mydf = df.DF(cell, [kpt])
vj, vk = mydf.get_jk(dm, 1, kpt, exxdiv=None)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ej1, 241.15119240999368 + 0j, 8)
self.assertAlmostEqual(ek1, 279.64647733018984 + 0j, 8)
vj, vk = mydf.get_jk(dm, 1, kpt, with_j=False, exxdiv='ewald')
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ek1, 691.98171156880562 + 0j, 8)
def test_jk(self):
mf = df_jk.density_fit(mf0, auxbasis='weigend', gs=(5,)*3)
dm = mf.get_init_guess()
vj, vk = mf.get_jk(cell, dm)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ej1, 46.698885887058495, 8)
self.assertAlmostEqual(ek1, 31.723490322441389, 8)
numpy.random.seed(12)
nao = cell.nao_nr()
dm = numpy.random.random((nao,nao))
dm = dm + dm.T
vj1, vk1 = mf.get_jk(cell, dm, hermi=0)
ej1 = numpy.einsum('ij,ji->', vj1, dm)
ek1 = numpy.einsum('ij,ji->', vk1, dm)
self.assertAlmostEqual(ej1, 242.04675958581976, 8)
self.assertAlmostEqual(ek1, 280.15934765885805, 8)
numpy.random.seed(1)
kpt = numpy.random.random(3)
mydf = df.DF(cell, [kpt])
vj, vk = mydf.get_jk(dm, 1, kpt, exxdiv=None)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ej1, 241.15119653657837+0j, 8)
self.assertAlmostEqual(ek1, 279.64649180139776+0j, 8)
vj, vk = mydf.get_jk(dm, 1, kpt, with_j=False, exxdiv='ewald')
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ek1, 691.6462444241248+0j, 6)
def test_jk_single_kpt_high_cost(self):
mf0 = pscf.RHF(cell)
mf0.exxdiv = None
mf = df_jk.density_fit(mf0, auxbasis='weigend', mesh=(11,)*3)
mf.with_df.mesh = cell.mesh
mf.with_df.eta = 0.3
mf.with_df.exp_to_discard = 0.3
dm = mf.get_init_guess()
vj, vk = mf.get_jk(cell, dm)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
j_ref = 48.283789539266174 # rsjk result
k_ref = 32.30441176447805 # rsjk result
self.assertAlmostEqual(ej1, j_ref, 4)
self.assertAlmostEqual(ek1, k_ref, 2)
self.assertAlmostEqual(ej1, 48.283745538383684, 8)
self.assertAlmostEqual(ek1, 32.30260871417842 , 8)
numpy.random.seed(12)
nao = cell.nao_nr()
dm = numpy.random.random((nao,nao))
dm = dm + dm.T
vj1, vk1 = mf.get_jk(cell, dm, hermi=0)
ej1 = numpy.einsum('ij,ji->', vj1, dm)
ek1 = numpy.einsum('ij,ji->', vk1, dm)
self.assertAlmostEqual(ej1, 242.04678235140264, 8)
self.assertAlmostEqual(ek1, 280.15934926575903, 8)
numpy.random.seed(1)
kpt = numpy.random.random(3)
mydf = df.DF(cell, [kpt]).set(auxbasis='weigend')
mydf.linear_dep_threshold = 1e-7
mydf.mesh = cell.mesh
mydf.eta = 0.3
mydf.exp_to_discard = mydf.eta
vj, vk = mydf.get_jk(dm, 1, kpt, exxdiv=None)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ej1, 241.15121903857556+0j, 8)
self.assertAlmostEqual(ek1, 279.64649194057051+0j, 8)
vj, vk = mydf.get_jk(dm, 1, kpt, with_j=False, exxdiv='ewald')
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ek1, 691.64624456329909+0j, 6)
def test_jk_single_kpt(self):
mf = df_jk.density_fit(mf0, auxbasis='weigend', mesh=(11,)*3)
mf.with_df.mesh = cell.mesh
mf.with_df.eta = 0.3
mf.with_df.exp_to_discard = 0.3
dm = mf.get_init_guess()
vj, vk = mf.get_jk(cell, dm)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ej1, 46.69888588120217, 8)
self.assertAlmostEqual(ek1, 31.72349032270801, 8)
numpy.random.seed(12)
nao = cell.nao_nr()
dm = numpy.random.random((nao,nao))
dm = dm + dm.T
vj1, vk1 = mf.get_jk(cell, dm, hermi=0)
ej1 = numpy.einsum('ij,ji->', vj1, dm)
ek1 = numpy.einsum('ij,ji->', vk1, dm)
self.assertAlmostEqual(ej1, 242.04678235140264, 8)
self.assertAlmostEqual(ek1, 280.15934926575903, 8)
numpy.random.seed(1)
kpt = numpy.random.random(3)
mydf = df.DF(cell, [kpt]).set(auxbasis='weigend')
mydf.linear_dep_threshold = 1e-7
mydf.mesh = cell.mesh
mydf.eta = 0.3
mydf.exp_to_discard = mydf.eta
vj, vk = mydf.get_jk(dm, 1, kpt, exxdiv=None)
ej1 = numpy.einsum('ij,ji->', vj, dm)
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ej1, 241.15121903857556+0j, 8)
self.assertAlmostEqual(ek1, 279.64649194057051+0j, 8)
vj, vk = mydf.get_jk(dm, 1, kpt, with_j=False, exxdiv='ewald')
ek1 = numpy.einsum('ij,ji->', vk, dm)
self.assertAlmostEqual(ek1, 691.64624456329909+0j, 6)
def density_fit(self, auxbasis=None, with_df=None):
from pyscf.pbc.df import df_jk
return df_jk.density_fit(self, auxbasis, with_df=with_df)
def density_fit(self, auxbasis=None, with_df=None):
from pyscf.df.addons import aug_etb_for_dfbasis
from pyscf.pbc.df import df_jk
if auxbasis is None:
auxbasis = aug_etb_for_dfbasis(self.cell, beta=1.8, start_at=0)
return df_jk.density_fit(self, auxbasis, with_df)
def density_fit(self, auxbasis=None, with_df=None):
from pyscf.pbc.df import df_jk
return df_jk.density_fit(self, auxbasis, with_df=with_df)
