def test_zeroed(self):
self.assertArrayEqual(
self.low_val.zeroed(),
TensorBase([[0, 1 + 1e-5, 0], [1 + 1e-6, 0, 0], [0, 0, 1 + 1e-5]]))
self.assertArrayEqual(
self.low_val.zeroed(tol=1e-6),
TensorBase([[1e-6, 1 + 1e-5, 1e-6], [1 + 1e-6, 1e-6, 1e-6],
[0, 0, 1 + 1e-5]]))
self.assertArrayEqual(
TensorBase([[1e-6, -30, 1], [1e-7, 1, 0], [1e-8, 0, 1]]).zeroed(),
TensorBase([[0, -30, 1], [0, 1, 0], [0, 0, 1]]))
def test_convert_to_ieee(self):
for entry in self.ieee_data:
xtal = entry['xtal']
orig = TensorBase(entry['original_tensor'])
ieee = TensorBase(entry['ieee_tensor'])
struct = Structure.from_dict(entry['structure'])
diff = np.max(abs(ieee - orig.convert_to_ieee(struct)))
err_msg = "{} IEEE conversion failed with max diff {}".format(
xtal, diff)
self.assertArrayAlmostEqual(ieee, orig.convert_to_ieee(struct),
err_msg = err_msg, decimal=3)
def __new__(cls, input_array, tol=1e-3):
"""
Create an ElasticTensor object. The constructor throws an error if
the shape of the input_matrix argument is not 3x3x3x3, i. e. in true
tensor notation. Issues a warning if the input_matrix argument does
not satisfy standard symmetries. Note that the constructor uses
__new__ rather than __init__ according to the standard method of
subclassing numpy ndarrays.
Args:
input_array (3x3x3x3 array-like): the 3x3x3x3 array-like
representing the elastic tensor
tol (float): tolerance for initial symmetry test of tensor
"""
obj = TensorBase(input_array).view(cls)
if obj.shape != (3, 3, 3, 3):
raise ValueError("Default elastic tensor constructor requires "
"input to be the true 3x3x3x3 representation. "
"To construct from an elastic tensor from "
"6x6 Voigt array, use ElasticTensor.from_voigt")
if not ((obj - np.transpose(obj, (1, 0, 2, 3)) < tol).all() and
(obj - np.transpose(obj, (0, 1, 3, 2)) < tol).all() and
(obj - np.transpose(obj, (1, 0, 3, 2)) < tol).all() and
(obj - np.transpose(obj, (3, 2, 0, 1)) < tol).all()):
warnings.warn("Input elasticity tensor does "
"not satisfy standard symmetries")
return obj
def test_transform(self):
# Rank 3
tensor = TensorBase(np.arange(0, 27).reshape(3, 3, 3))
symm_op = SymmOp.from_axis_angle_and_translation([0, 0, 1], 30, False,
[0, 0, 1])
new_tensor = tensor.transform(symm_op)
self.assertArrayAlmostEqual(
new_tensor, [[[-0.871, -2.884, -1.928], [-2.152, -6.665, -4.196],
[-1.026, -2.830, -1.572]],
[[0.044, 1.531, 1.804], [4.263, 21.008, 17.928],
[5.170, 23.026, 18.722]],
[[1.679, 7.268, 5.821], [9.268, 38.321, 29.919],
[8.285, 33.651, 26.000]]], 3)
def __new__(cls, input_array, tol=1e-3):
"""
Create an PiezoTensor object. The constructor throws an error if
the shape of the input_matrix argument is not 3x3x3, i. e. in true
tensor notation. Note that the constructor uses __new__ rather than
__init__ according to the standard method of subclassing numpy
ndarrays.
Args:
input_matrix (3x3x3 array-like): the 3x6 array-like
representing the piezo tensor
"""
obj = TensorBase(input_array).view(cls)
if obj.shape != (3, 3, 3):
raise ValueError("Default piezo tensor constructor requires "
"input argument to be the true 3x3x3 "
"array. To construct from a 3x6 array, use "
"PiezoTensor.from_voigt")
if not (obj - np.transpose(obj, (0, 2, 1)) < tol).all():
warnings.warn("Input piezo tensor does "
"not satisfy standard symmetries")
return obj
def setUp(self):
self.vec = TensorBase([1., 0., 0.])
self.rand_rank2 = TensorBase(np.random.randn(3, 3))
self.rand_rank3 = TensorBase(np.random.randn(3, 3, 3))
self.rand_rank4 = TensorBase(np.random.randn(3, 3, 3, 3))
a = 3.14 * 42.5 / 180
self.non_symm = SquareTensor([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6],
[0.2, 0.5, 0.5]])
self.rotation = SquareTensor([[math.cos(a), 0,
math.sin(a)], [0, 1, 0],
[-math.sin(a), 0,
math.cos(a)]])
self.low_val = TensorBase([[1e-6, 1 + 1e-5, 1e-6],
[1 + 1e-6, 1e-6, 1e-6],
[1e-7, 1e-7, 1 + 1e-5]])
self.symm_rank2 = TensorBase([[1, 2, 3], [2, 4, 5], [3, 5, 6]])
self.symm_rank3 = TensorBase([[[1, 2, 3], [2, 4, 5], [3, 5, 6]],
[[2, 4, 5], [4, 7, 8], [5, 8, 9]],
[[3, 5, 6], [5, 8, 9], [6, 9, 10]]])
self.symm_rank4 = TensorBase([[[[1.2, 0.4, -0.92], [0.4, 0.05, 0.11],
[-0.92, 0.11, -0.02]],
[[0.4, 0.05, 0.11], [0.05, -0.47, 0.09],
[0.11, 0.09, -0.]],
[[-0.92, 0.11, -0.02], [0.11, 0.09, 0.],
[-0.02, 0., -0.3]]],
[[[0.4, 0.05, 0.11], [0.05, -0.47, 0.09],
[0.11, 0.09, 0.]],
[[0.05, -0.47,
0.09], [-0.47, 0.17, 0.62],
[0.09, 0.62, 0.3]],
[[0.11, 0.09, 0.], [0.09, 0.62, 0.3],
[0., 0.3, -0.18]]],
[[[-0.92, 0.11, -0.02], [0.11, 0.09, 0.],
[-0.02, 0, -0.3]],
[[0.11, 0.09, 0.], [0.09, 0.62, 0.3],
[0., 0.3, -0.18]],
[[-0.02, 0., -0.3], [0., 0.3, -0.18],
[-0.3, -0.18, -0.51]]]])
# Structural symmetries tested using BaNiO3 piezo/elastic tensors
self.fit_r3 = TensorBase([[[0., 0., 0.03839], [0., 0., 0.],
[0.03839, 0., 0.]],
[[0., 0., 0.], [0., 0., 0.03839],
[0., 0.03839, 0.]],
[[6.89822, 0., 0.], [0., 6.89822, 0.],
[0., 0., 27.4628]]])
self.fit_r4 = TensorBase([[[[157.9, 0., 0.], [0., 63.1, 0.],
[0., 0., 29.4]],
[[0., 47.4, 0.], [47.4, 0., 0.],
[0., 0., 0.]],
[[0., 0., 4.3], [0., 0., 0.], [4.3, 0.,
0.]]],
[[[0., 47.4, 0.], [47.4, 0., 0.],
[0., 0., 0.]],
[[63.1, 0., 0.], [0., 157.9, 0.],
[0., 0., 29.4]],
[[0., 0., 0.], [0., 0., 4.3], [0., 4.3,
0.]]],
[[[0., 0., 4.3], [0., 0., 0.], [4.3, 0.,
0.]],
[[0., 0., 0.], [0., 0., 4.3], [0., 4.3,
0.]],
[[29.4, 0., 0.], [0., 29.4, 0.],
[0., 0., 207.6]]]])
self.unfit4 = TensorBase([[[[161.26, 0., 0.], [0., 62.76, 0.],
[0., 0., 30.18]],
[[0., 47.08, 0.], [47.08, 0., 0.],
[0., 0., 0.]],
[[0., 0., 4.23], [0., 0., 0.],
[4.23, 0., 0.]]],
[[[0., 47.08, 0.], [47.08, 0., 0.],
[0., 0., 0.]],
[[62.76, 0., 0.], [0., 155.28, -0.06],
[0., -0.06, 28.53]],
[[0., 0., 0.], [0., -0.06, 4.44],
[0., 4.44, 0.]]],
[[[0., 0., 4.23], [0., 0., 0.],
[4.23, 0., 0.]],
[[0., 0., 0.], [0., -0.06, 4.44],
[0., 4.44, 0.]],
[[30.18, 0., 0.], [0., 28.53, 0.],
[0., 0., 207.57]]]])
self.structure = self.get_structure('BaNiO3')
ieee_file_path = os.path.join(test_dir, "ieee_conversion_data.json")
with open(ieee_file_path) as f:
self.ieee_data = json.load(f)
def test_convert_to_ieee(self):
for xtal in self.ieee_data.keys():
orig = TensorBase(self.ieee_data[xtal]['original_tensor'])
ieee = TensorBase(self.ieee_data[xtal]['ieee_tensor'])
struct = Structure.from_dict(self.ieee_data[xtal]['structure'])
self.assertArrayAlmostEqual(ieee, orig.convert_to_ieee(struct))
