def test_get_eri_gamma_1(self):
odf = mdf.MDF(cell1)
odf.auxbasis = df.aug_etb(cell1, 1.8)
odf.mesh = [6] * 3
odf.eta = 0.1
eri0000 = odf.get_eri()
self.assertTrue(eri0000.dtype == numpy.double)
self.assertAlmostEqual(eri0000.real.sum(), 27.271885446069433, 6)
self.assertAlmostEqual(finger(eri0000), 1.0614085634080137, 6)
eri1111 = kmdf1.get_eri((kpts[0], kpts[0], kpts[0], kpts[0]))
self.assertTrue(eri1111.dtype == numpy.double)
self.assertAlmostEqual(eri1111.real.sum(), 27.271885446069433, 6)
self.assertAlmostEqual(eri1111.imag.sum(), 0, 7)
self.assertAlmostEqual(finger(eri0000), 1.0614085634080137, 6)
self.assertAlmostEqual(abs(eri1111 - eri0000).max(), 0, 12)
def setUpModule():
global cell, cell1, mf0, kmdf, kmdf1, kpts
L = 5.
n = 11
cell = pgto.Cell()
cell.a = numpy.diag([L,L,L])
cell.mesh = numpy.array([n,n,n])
cell.atom = '''C 3. 2. 3.
C 1. 1. 1.'''
cell.basis = 'ccpvdz'
cell.verbose = 0
cell.rcut = 17
cell.build(0,0)
mf0 = pscf.RHF(cell)
mf0.exxdiv = 'vcut_sph'
numpy.random.seed(1)
kpts = numpy.random.random((5,3))
kpts[0] = 0
kpts[3] = kpts[0]-kpts[1]+kpts[2]
kpts[4] *= 1e-5
kmdf = mdf.MDF(cell)
kmdf.linear_dep_threshold = 1e-7
kmdf.auxbasis = 'weigend'
kmdf.kpts = kpts
kmdf.mesh = (11,)*3
kmdf.eta = 0.154728892598
cell1 = pgto.Cell()
cell1.a = numpy.eye(3) * 3.
cell1.mesh = [10]*3
cell1.atom = '''C 3. 2. 3.
C 1. 1. 1.'''
cell1.basis = [[0, (3.5, 1)], [0, (1.0, 1)], [1, (0.6, 1)]]
cell1.rcut = 9.5
cell1.build(0,0)
kmdf1 = mdf.MDF(cell1)
kmdf1.linear_dep_threshold = 1e-7
kmdf1.auxbasis = df.aug_etb(cell1, 1.8)
kmdf1.kpts = kpts
kmdf1.mesh = [6]*3
kmdf1.eta = 0.1
def test_get_eri_gamma_1(self):
odf = mdf.MDF(cell1)
odf.linear_dep_threshold = 1e-7
odf.auxbasis = df.aug_etb(cell1, 1.8)
odf.mesh = [6]*3
odf.eta = 0.1
eri0000 = odf.get_eri()
self.assertTrue(eri0000.dtype == numpy.double)
self.assertAlmostEqual(eri0000.real.sum(), 27.271885446069433, 6)
self.assertAlmostEqual(finger(eri0000), 1.0614085634080137, 6)
eri1111 = kmdf1.get_eri((kpts[0],kpts[0],kpts[0],kpts[0]))
self.assertTrue(eri1111.dtype == numpy.double)
self.assertAlmostEqual(eri1111.real.sum(), 27.271885446069433, 6)
self.assertAlmostEqual(eri1111.imag.sum(), 0, 7)
self.assertAlmostEqual(finger(eri0000), 1.0614085634080137, 6)
self.assertAlmostEqual(abs(eri1111-eri0000).max(), 0, 12)
def test_cell_with_cart(self):
cell = pgto.M(
atom='Li 0 0 0; H 2 2 2',
a=(numpy.ones([3, 3]) - numpy.eye(3)) * 2,
cart=True,
basis={'H': '''
H S
0.5 1''',
'Li': '''
Li S
0.8 1
0.4 1
Li P
0.8 1
0.4 1'''})
eri0 = df.FFTDF(cell).get_eri()
eri1 = mdf.MDF(cell).set(auxbasis=df.aug_etb(cell)).get_eri()
self.assertAlmostEqual(abs(eri1-eri0).max(), 0, 5)
kmdf.kpts = kpts
kmdf.mesh = (11, ) * 3
kmdf.eta = 0.154728892598
cell1 = pgto.Cell()
cell1.a = numpy.eye(3) * 3.
cell1.mesh = [10] * 3
cell1.atom = '''C 3. 2. 3.
C 1. 1. 1.'''
cell1.basis = [[0, (3.5, 1)], [0, (1.0, 1)], [1, (0.6, 1)]]
cell1.rcut = 9.5
cell1.build(0, 0)
kmdf1 = mdf.MDF(cell1)
kmdf1.linear_dep_threshold = 1e-7
kmdf1.auxbasis = df.aug_etb(cell1, 1.8)
kmdf1.kpts = kpts
kmdf1.mesh = [6] * 3
kmdf1.eta = 0.1
def finger(a):
w = numpy.cos(numpy.arange(a.size))
return numpy.dot(a.ravel(), w)
class KnowValues(unittest.TestCase):
def test_vbar(self):
auxcell = df.df.make_modrho_basis(cell, 'ccpvdz', 1.)
vbar = mdf.MDF(cell).auxbar(auxcell)
self.assertAlmostEqual(finger(vbar), -0.00438699039629, 9)
#
# By assigning the argument auxbasis of density_fit method, the DF calculation
# can use the assigned auxiliary basis. For example, in the statement below,
# the auxiliary basis is weigend Coulomb fitting basis augmented with one set
# of polarized d functions.
#
auxbasis = {'C': ('weigend', [[2, (0.5, 1)]])}
mf = scf.RHF(cell).density_fit(auxbasis=auxbasis)
mf.kernel()
#
# df.aug_etb is a shortcut function to create even-tempered gaussian basis
# based on the orbital basis. The following example produces a dense etb
# basis spectrum.
#
auxbasis = df.aug_etb(cell, beta=1.6)
mf = scf.RHF(cell).density_fit(auxbasis=auxbasis)
mf.kernel()
#
# Another straightforward way to input even-tempered Gaussian basis (for
# better resolution of auxiliary basis) is the use of function gto.expand_etbs.
#
# Note the PBC Gaussian DF module will automatically remove diffused Gaussian
# fitting functions. It is controlled by the parameter eta. The default value is
# 0.2 which removes all Gaussians whose exponents are smaller than 0.2.
# When your input DF basis has diffused functions, you need to reduce the
# value of mf.with_df.eta to reserve the diffused functions. However,
# keeping diffused functions occasionally lead numerical noise in the GDF
# method.
#
kmdf.kpts = kpts
kmdf.mesh = (11,)*3
kmdf.eta = 0.154728892598
cell1 = pgto.Cell()
cell1.a = numpy.eye(3) * 3.
cell1.mesh = [10]*3
cell1.atom = '''C 3. 2. 3.
C 1. 1. 1.'''
cell1.basis = [[0, (3.5, 1)], [0, (1.0, 1)], [1, (0.6, 1)]]
cell1.rcut = 9.5
cell1.build(0,0)
kmdf1 = mdf.MDF(cell1)
kmdf1.linear_dep_threshold = 1e-7
kmdf1.auxbasis = df.aug_etb(cell1, 1.8)
kmdf1.kpts = kpts
kmdf1.mesh = [6]*3
kmdf1.eta = 0.1
def finger(a):
w = numpy.cos(numpy.arange(a.size))
return numpy.dot(a.ravel(), w)
class KnowValues(unittest.TestCase):
def test_vbar(self):
auxcell = df.df.make_modrho_basis(cell, 'ccpvdz', 1.)
vbar = mdf.MDF(cell).auxbar(auxcell)
self.assertAlmostEqual(finger(vbar), -0.00438699039629, 9)
# By assigning the argument auxbasis of density_fit method, the DF calculation
# can use the assigned auxiliary basis. For example, in the statement below,
# the auxiliary basis is weigend Coulomb fitting basis augmented with one set
# of polarized d functions.
#
auxbasis = {'C': ('weigend', [[2, (0.5, 1)]])}
mf = scf.RHF(cell).density_fit(auxbasis=auxbasis)
mf.kernel()
#
# df.aug_etb is a shortcut function to create even-tempered gaussian basis
# based on the orbital basis. The following example produces a dense etb
# basis spectrum.
#
auxbasis = df.aug_etb(cell, beta=1.6)
mf = scf.RHF(cell).density_fit(auxbasis=auxbasis)
mf.kernel()
#
# Another straightforward way to input even-tempered Gaussian basis (for
# better resolution of auxiliary basis) is the use of function gto.expand_etbs.
#
# Note the PBC Gaussian DF module will automatically remove diffused Gaussian
# fitting functions. It is controlled by the parameter eta. The default value is
# 0.2 which removes all Gaussians whose exponents are smaller than 0.2.
# When your input DF basis has diffused functions, you need to reduce the
# value of mf.with_df.eta to reserve the diffused functions. However,
# keeping diffused functions occasionally lead numerical noise in the GDF
# method.