def setUp(self):
self.seq_tc = [t for t in np.arange(4 * 3**3).reshape((4, 3, 3, 3))]
self.seq_tc = TensorCollection(self.seq_tc)
self.rand_tc = TensorCollection(
[t for t in np.random.random((4, 3, 3))])
self.diff_rank = TensorCollection(
[np.ones([3] * i) for i in range(2, 5)])
self.struct = self.get_structure("Si")
ieee_file_path = os.path.join(PymatgenTest.TEST_FILES_DIR,
"ieee_conversion_data.json")
self.ieee_data = loadfn(ieee_file_path)
def test_serialization(self):
# Test base serialize-deserialize
d = self.seq_tc.as_dict()
new = TensorCollection.from_dict(d)
for t, t_new in zip(self.seq_tc, new):
self.assertArrayAlmostEqual(t, t_new)
# Suppress vsym warnings and test voigt
with warnings.catch_warnings(record=True):
vsym = self.rand_tc.voigt_symmetrized
d = vsym.as_dict(voigt=True)
new_vsym = TensorCollection.from_dict(d)
for t, t_new in zip(vsym, new_vsym):
self.assertArrayAlmostEqual(t, t_new)
def get_compliance_expansion(self):
"""
Gets a compliance tensor expansion from the elastic
tensor expansion.
"""
# TODO: this might have a general form
if not self.order <= 4:
raise ValueError("Compliance tensor expansion only "
"supported for fourth-order and lower")
ce_exp = [ElasticTensor(self[0]).compliance_tensor]
einstring = "ijpq,pqrsuv,rskl,uvmn->ijklmn"
ce_exp.append(
np.einsum(einstring, -ce_exp[-1], self[1], ce_exp[-1], ce_exp[-1]))
if self.order == 4:
# Four terms in the Fourth-Order compliance tensor
# pylint: disable=E1130
einstring_1 = "pqab,cdij,efkl,ghmn,abcdefgh"
tensors_1 = [ce_exp[0]] * 4 + [self[-1]]
temp = -np.einsum(einstring_1, *tensors_1)
einstring_2 = "pqab,abcdef,cdijmn,efkl"
einstring_3 = "pqab,abcdef,efklmn,cdij"
einstring_4 = "pqab,abcdef,cdijkl,efmn"
for es in [einstring_2, einstring_3, einstring_4]:
temp -= np.einsum(es, ce_exp[0], self[-2], ce_exp[1],
ce_exp[0])
ce_exp.append(temp)
return TensorCollection(ce_exp)
def list_based_function_check(self, attribute, coll, *args, **kwargs):
"""
This function allows for more efficient testing of list-based
functions in a "collection"-style class like TensorCollection
It ensures that the test function
"""
tc_orig = TensorCollection(coll)
tc_mod = getattr(tc_orig, attribute)
if callable(tc_mod):
tc_mod = tc_mod(*args, **kwargs)
for t_orig, t_mod in zip(tc_orig, tc_mod):
this_mod = getattr(t_orig, attribute)
if callable(this_mod):
this_mod = this_mod(*args, **kwargs)
if isinstance(this_mod, np.ndarray):
self.assertArrayAlmostEqual(this_mod, t_mod)
def test_list_based_functions(self):
# zeroed
tc = TensorCollection([1e-4 * Tensor(np.eye(3))] * 4)
for t in tc.zeroed():
self.assertArrayEqual(t, np.zeros((3, 3)))
for t in tc.zeroed(1e-5):
self.assertArrayEqual(t, 1e-4 * np.eye(3))
self.list_based_function_check("zeroed", tc)
self.list_based_function_check("zeroed", tc, tol=1e-5)
# transform
symm_op = SymmOp.from_axis_angle_and_translation([0, 0, 1], 30, False,
[0, 0, 1])
self.list_based_function_check("transform",
self.seq_tc,
symm_op=symm_op)
# symmetrized
self.list_based_function_check("symmetrized", self.seq_tc)
# rotation
a = 3.14 * 42.5 / 180
rotation = SquareTensor([[math.cos(a), 0, math.sin(a)], [0, 1, 0],
[-math.sin(a), 0,
math.cos(a)]])
self.list_based_function_check("rotate",
self.diff_rank,
matrix=rotation)
# is_symmetric
self.assertFalse(self.seq_tc.is_symmetric())
self.assertTrue(self.diff_rank.is_symmetric())
# fit_to_structure
self.list_based_function_check("fit_to_structure", self.diff_rank,
self.struct)
self.list_based_function_check("fit_to_structure", self.seq_tc,
self.struct)
# fit_to_structure
self.list_based_function_check("fit_to_structure", self.diff_rank,
self.struct)
self.list_based_function_check("fit_to_structure", self.seq_tc,
self.struct)
# voigt
self.list_based_function_check("voigt", self.diff_rank)
# is_voigt_symmetric
self.assertTrue(self.diff_rank.is_voigt_symmetric())
self.assertFalse(self.seq_tc.is_voigt_symmetric())
# Convert to ieee
for entry in self.ieee_data[:2]:
entry["xtal"]
tc = TensorCollection([entry["original_tensor"]] * 3)
struct = entry["structure"]
self.list_based_function_check("convert_to_ieee", tc, struct)
# from_voigt
tc_input = [t for t in np.random.random((3, 6, 6))]
tc = TensorCollection.from_voigt(tc_input)
for t_input, t in zip(tc_input, tc):
self.assertArrayAlmostEqual(Tensor.from_voigt(t_input), t)
class TensorCollectionTest(PymatgenTest):
def setUp(self):
self.seq_tc = [t for t in np.arange(4 * 3**3).reshape((4, 3, 3, 3))]
self.seq_tc = TensorCollection(self.seq_tc)
self.rand_tc = TensorCollection(
[t for t in np.random.random((4, 3, 3))])
self.diff_rank = TensorCollection(
[np.ones([3] * i) for i in range(2, 5)])
self.struct = self.get_structure("Si")
ieee_file_path = os.path.join(PymatgenTest.TEST_FILES_DIR,
"ieee_conversion_data.json")
self.ieee_data = loadfn(ieee_file_path)
def list_based_function_check(self, attribute, coll, *args, **kwargs):
"""
This function allows for more efficient testing of list-based
functions in a "collection"-style class like TensorCollection
It ensures that the test function
"""
tc_orig = TensorCollection(coll)
tc_mod = getattr(tc_orig, attribute)
if callable(tc_mod):
tc_mod = tc_mod(*args, **kwargs)
for t_orig, t_mod in zip(tc_orig, tc_mod):
this_mod = getattr(t_orig, attribute)
if callable(this_mod):
this_mod = this_mod(*args, **kwargs)
if isinstance(this_mod, np.ndarray):
self.assertArrayAlmostEqual(this_mod, t_mod)
def test_list_based_functions(self):
# zeroed
tc = TensorCollection([1e-4 * Tensor(np.eye(3))] * 4)
for t in tc.zeroed():
self.assertArrayEqual(t, np.zeros((3, 3)))
for t in tc.zeroed(1e-5):
self.assertArrayEqual(t, 1e-4 * np.eye(3))
self.list_based_function_check("zeroed", tc)
self.list_based_function_check("zeroed", tc, tol=1e-5)
# transform
symm_op = SymmOp.from_axis_angle_and_translation([0, 0, 1], 30, False,
[0, 0, 1])
self.list_based_function_check("transform",
self.seq_tc,
symm_op=symm_op)
# symmetrized
self.list_based_function_check("symmetrized", self.seq_tc)
# rotation
a = 3.14 * 42.5 / 180
rotation = SquareTensor([[math.cos(a), 0, math.sin(a)], [0, 1, 0],
[-math.sin(a), 0,
math.cos(a)]])
self.list_based_function_check("rotate",
self.diff_rank,
matrix=rotation)
# is_symmetric
self.assertFalse(self.seq_tc.is_symmetric())
self.assertTrue(self.diff_rank.is_symmetric())
# fit_to_structure
self.list_based_function_check("fit_to_structure", self.diff_rank,
self.struct)
self.list_based_function_check("fit_to_structure", self.seq_tc,
self.struct)
# fit_to_structure
self.list_based_function_check("fit_to_structure", self.diff_rank,
self.struct)
self.list_based_function_check("fit_to_structure", self.seq_tc,
self.struct)
# voigt
self.list_based_function_check("voigt", self.diff_rank)
# is_voigt_symmetric
self.assertTrue(self.diff_rank.is_voigt_symmetric())
self.assertFalse(self.seq_tc.is_voigt_symmetric())
# Convert to ieee
for entry in self.ieee_data[:2]:
entry["xtal"]
tc = TensorCollection([entry["original_tensor"]] * 3)
struct = entry["structure"]
self.list_based_function_check("convert_to_ieee", tc, struct)
# from_voigt
tc_input = [t for t in np.random.random((3, 6, 6))]
tc = TensorCollection.from_voigt(tc_input)
for t_input, t in zip(tc_input, tc):
self.assertArrayAlmostEqual(Tensor.from_voigt(t_input), t)
def test_serialization(self):
# Test base serialize-deserialize
d = self.seq_tc.as_dict()
new = TensorCollection.from_dict(d)
for t, t_new in zip(self.seq_tc, new):
self.assertArrayAlmostEqual(t, t_new)
# Suppress vsym warnings and test voigt
with warnings.catch_warnings(record=True):
vsym = self.rand_tc.voigt_symmetrized
d = vsym.as_dict(voigt=True)
new_vsym = TensorCollection.from_dict(d)
for t, t_new in zip(vsym, new_vsym):
self.assertArrayAlmostEqual(t, t_new)
