def test_overlap_construct_array_contraction():
"""Test gbasis.integrals.overlap.Overlap.construct_array_contraction."""
test_one = GeneralizedContractionShell(
1, np.array([0.5, 1, 1.5]), np.array([1.0, 2.0]), np.array([0.1, 0.01])
)
test_two = GeneralizedContractionShell(
2, np.array([1.5, 2, 3]), np.array([3.0, 4.0]), np.array([0.2, 0.02])
)
answer = np.array(
[
[
_compute_multipole_moment_integrals(
np.array([0, 0, 0]),
np.array([[0, 0, 0]]),
np.array([0.5, 1, 1.5]),
np.array([angmom_comp_one]),
np.array([0.1, 0.01]),
np.array([[1], [2]]),
np.array(
[
[
(2 * 0.1 / np.pi) ** (3 / 4)
* (4 * 0.1) ** (1 / 2)
/ np.sqrt(np.prod(factorial2(2 * angmom_comp_one - 1))),
(2 * 0.01 / np.pi) ** (3 / 4)
* (4 * 0.01) ** (1 / 2)
/ np.sqrt(np.prod(factorial2(2 * angmom_comp_one - 1))),
]
]
),
np.array([1.5, 2, 3]),
np.array([angmom_comp_two]),
np.array([0.2, 0.02]),
np.array([[3], [4]]),
np.array(
[
[
(2 * 0.2 / np.pi) ** (3 / 4)
* (4 * 0.2) ** (2 / 2)
/ np.sqrt(np.prod(factorial2(2 * angmom_comp_two - 1))),
(2 * 0.02 / np.pi) ** (3 / 4)
* (4 * 0.02) ** (2 / 2)
/ np.sqrt(np.prod(factorial2(2 * angmom_comp_two - 1))),
]
]
),
)
for angmom_comp_two in test_two.angmom_components_cart
]
for angmom_comp_one in test_one.angmom_components_cart
]
)
assert np.allclose(
np.squeeze(Overlap.construct_array_contraction(test_one, test_two)), np.squeeze(answer)
)
with pytest.raises(TypeError):
Overlap.construct_array_contraction(test_one, None)
with pytest.raises(TypeError):
Overlap.construct_array_contraction(None, test_two)
def test_overlap_cartesian():
"""Test gbasis.integrals.overlap.overlap_cartesian."""
basis_dict = parse_nwchem(find_datafile("data_sto6g.nwchem"))
basis = make_contractions(basis_dict, ["Kr"], np.array([[0, 0, 0]]))
overlap_obj = Overlap(basis)
assert np.allclose(
overlap_obj.construct_array_cartesian(), overlap_integral(basis, coord_type="cartesian")
)
def test_overlap_lincomb():
"""Test gbasis.integrals.overlap.overlap_lincomb."""
basis_dict = parse_nwchem(find_datafile("data_sto6g.nwchem"))
basis = make_contractions(basis_dict, ["Kr"], np.array([[0, 0, 0]]))
overlap_obj = Overlap(basis)
transform = np.random.rand(14, 18)
assert np.allclose(
overlap_obj.construct_array_lincomb(transform, "spherical"),
overlap_integral(basis, transform=transform, coord_type="spherical"),
)
def test_overlap_cartesian_norm_anorcc():
"""Test the norm of gbasis.integrals.overlap_cartesian on the ANO-RCC basis set.
The contraction coefficients in ANO-RCC is such that the cartesian contractions are normalized.
"""
basis_dict = parse_nwchem(find_datafile("data_anorcc.nwchem"))
basis = make_contractions(basis_dict, ["Kr"], np.array([[0, 0, 0]]))
overlap_obj = Overlap(basis)
assert np.allclose(np.diag(overlap_obj.construct_array_cartesian()), 1)
def test_overlap_cartesian_norm_sto6g():
"""Test the norm of gbasis.integrals.overlap_cartesian on the STO-6G basis set.
The contraction coefficients in STO-6G is such that the Cartesian contractions are not
normalized to past 3rd decimal places.
"""
basis_dict = parse_nwchem(find_datafile("data_sto6g.nwchem"))
basis = make_contractions(basis_dict, ["Kr"], np.array([[0, 0, 0]]))
overlap_obj = Overlap(basis)
assert np.allclose(np.diag(overlap_obj.construct_array_cartesian()), 1)
def assign_norm_cont(self):
r"""Store the normalization constants of the contractions.
.. math::
\int \phi_i(\mathbf{r}) \phi_i(\mathbf{r}) d\mathbf{r} &= N\\
\frac{1}{N} \int \phi_i(\mathbf{r}) \phi_i(\mathbf{r}) d\mathbf{r} &= 1\\
\int (\frac{1}{\sqrt{N}} \phi_i(\mathbf{r}))
(\frac{1}{\sqrt{N}} \phi_i(\mathbf{r})) d\mathbf{r} &= 1\\
where :math:`\phi_i` is a contraction and :math:`N` is the norm of the contraction (without
normalization).
Notes
-----
Since the `overlap` module also depends on this (`contractions`) module, if the `overlap`
module is imported at the start of this module, there is a circular import error. Therefore,
the overlap module must be imported within this method.
"""
from gbasis.integrals.overlap import Overlap # pylint: disable=R0401
self.norm_cont = np.einsum(
"ijij->ij", Overlap.construct_array_contraction(self, self))
self.norm_cont **= -0.5