def test_disorientation_blending(self, lattice, a, b):
o = Orientation.from_random(lattice=lattice, shape=a)
p = Orientation.from_random(lattice=lattice, shape=b)
blend = util.shapeblender(o.shape, p.shape)
for loc in np.random.randint(0, blend, (10, len(blend))):
assert o[tuple(loc[:len(o.shape)])].disorientation(p[tuple(loc[-len(p.shape):])]) \
.isclose(o.disorientation(p)[tuple(loc)])
def test_disorientation_blending(self, family, left, right):
o = Orientation.from_random(family=family, shape=left)
p = Orientation.from_random(family=family, shape=right)
blend = util.shapeblender(o.shape, p.shape)
for loc in np.random.randint(0, blend, (10, len(blend))):
# print(f'{a}/{b} @ {loc}')
# print(o[tuple(loc[:len(o.shape)])].disorientation(p[tuple(loc[-len(p.shape):])]))
# print(o.disorientation(p)[tuple(loc)])
assert o[tuple(loc[:len(o.shape)])].disorientation(p[tuple(loc[-len(p.shape):])]) \
.isclose(o.disorientation(p)[tuple(loc)])
def test_disorientation(self, lattice, N):
o = Orientation.from_random(lattice=lattice, shape=N)
p = Orientation.from_random(lattice=lattice, shape=N)
d, ops = o.disorientation(p, return_operators=True)
for n in range(N):
assert np.allclose(d[n].as_quaternion(),
o[n].equivalent[ops[n][0]]
.misorientation(p[n].equivalent[ops[n][1]])
.as_quaternion()) \
or np.allclose((~d)[n].as_quaternion(),
o[n].equivalent[ops[n][0]]
.misorientation(p[n].equivalent[ops[n][1]])
.as_quaternion())
def test_IPF_color_vectorization(self, lattice, shape, vector, proper,
in_SST):
o = Orientation.from_random(lattice=lattice, shape=shape)
for r, theO in zip(
o.IPF_color(vector, in_SST=in_SST, proper=proper).reshape(
(-1, 3)), o.flatten()):
assert np.allclose(
r, theO.IPF_color(vector, in_SST=in_SST, proper=proper))
def test_to_SST_blending(self, family, left, right):
o = Orientation.from_random(family=family, shape=left)
v = np.random.random(right + (3, ))
blend = util.shapeblender(o.shape, v.shape[:-1])
for loc in np.random.randint(0, blend, (10, len(blend))):
assert np.allclose(
o[tuple(loc[:len(o.shape)])].to_SST(v[tuple(
loc[-len(v.shape[:-1]):])]),
o.to_SST(v)[tuple(loc)])
def test_Schmid_vectorization(self, lattice):
O = Orientation.from_random(shape=4, lattice=lattice) # noqa
for mode in ['slip', 'twin']:
Ps = O.Schmid(N_slip='*') if mode == 'slip' else O.Schmid(
N_twin='*')
for i in range(4):
P = O[i].Schmid(N_slip='*') if mode == 'slip' else O[i].Schmid(
N_twin='*')
assert np.allclose(P, Ps[:, i])
def test_reduced_equivalent(self, lattice):
i = Orientation(lattice=lattice)
o = Orientation.from_random(lattice=lattice)
eq = o.equivalent
FZ = np.argmin(
abs(
eq.misorientation(i.broadcast_to(
len(eq))).as_axis_angle(pair=True)[1]))
assert o.reduced == eq[FZ]
def test_to_SST_blending(self, lattice, a, b):
o = Orientation.from_random(lattice=lattice, shape=a)
v = np.random.random(b + (3, ))
blend = util.shapeblender(o.shape, b)
for loc in np.random.randint(0, blend, (10, len(blend))):
print(f'{a}/{b} @ {loc}')
print(o[tuple(loc[:len(o.shape)])].to_SST(v[tuple(loc[-len(b):])]))
print(o.to_SST(v)[tuple(loc)])
assert np.allclose(
o[tuple(loc[:len(o.shape)])].to_SST(v[tuple(loc[-len(b):])]),
o.to_SST(v)[tuple(loc)])
def test_to_pole(self, shape, lattice, a, b, c, alpha, beta, gamma, vector,
kw, with_symmetry):
o = Orientation.from_random(shape=shape,
lattice=lattice,
a=a,
b=b,
c=c,
alpha=alpha,
beta=beta,
gamma=gamma)
assert o.to_pole(**{kw:vector,'with_symmetry':with_symmetry}).shape \
== o.shape + (o.symmetry_operations.shape if with_symmetry else ()) + vector.shape
def test_to_pole_blending(self, lattice, a, b, c, alpha, beta, gamma, left,
right):
o = Orientation.from_random(shape=left,
lattice=lattice,
a=a,
b=b,
c=c,
alpha=alpha,
beta=beta,
gamma=gamma)
v = np.random.random(right + (3, ))
blend = util.shapeblender(o.shape, v.shape[:-1])
for loc in np.random.randint(0, blend, (10, len(blend))):
assert np.allclose(
o[tuple(loc[:len(o.shape)])].to_pole(
uvw=v[tuple(loc[-len(v.shape[:-1]):])]),
o.to_pole(uvw=v)[tuple(loc)])
def test_basis_real(self):
for gamma in np.random.random(2**8) * np.pi:
basis = np.tril(np.random.random((3, 3)) + 1e-6)
basis[1, :2] = basis[1, 1] * np.array(
[np.cos(gamma), np.sin(gamma)])
basis[2, :2] = basis[2, :2] * 2 - 1
lengths = np.linalg.norm(basis, axis=-1)
cosines = np.roll(
np.einsum('ij,ij->i', basis, np.roll(basis, 1, axis=0)) /
lengths / np.roll(lengths, 1), 1)
o = Orientation.from_random(
lattice='aP',
**dict(zip(['a', 'b', 'c'], lengths)),
**dict(zip(['alpha', 'beta', 'gamma'], np.arccos(cosines))),
)
assert np.allclose(
o.to_frame(uvw=np.eye(3)),
basis), 'Lattice basis disagrees with initialization'
class TestConfig:
@pytest.mark.parametrize('flow_style', [None, True, False])
def test_load_save_str(self, tmp_path, flow_style):
config = Config()
config['A'] = 1
config['B'] = [2, 3]
config.save(tmp_path / 'config.yaml', default_flow_style=flow_style)
assert Config.load(tmp_path / 'config.yaml') == config
def test_load_save_file(self, tmp_path):
config = Config()
config['A'] = 1
config['B'] = [2, 3]
with open(tmp_path / 'config.yaml', 'w') as f:
config.save(f)
with open(tmp_path / 'config.yaml') as f:
assert Config.load(f) == config
def test_add_remove(self):
dummy = {'hello': 'world', 'foo': 'bar'}
config = Config()
config |= dummy
assert config == Config() | dummy
config = config.delete(dummy)
assert config == Config()
assert (config | dummy).delete('hello') == config | {'foo': 'bar'}
assert (config | dummy).delete(['hello', 'foo']) == config
assert (config | Config(dummy)).delete({
'hello': 1,
'foo': 2
}) == config
assert (config | Config(dummy)).delete(Config({'hello':
1})) == config | {
'foo': 'bar'
}
def test_repr(self, tmp_path):
config = Config()
config['A'] = 1
config['B'] = [2, 3]
with open(tmp_path / 'config.yaml', 'w') as f:
f.write(config.__repr__())
assert Config.load(tmp_path / 'config.yaml') == config
def test_numpy(self, tmp_path):
assert Config({
'A': np.ones(3, 'i')
}).__repr__() == Config({
'A': [1, 1, 1]
}).__repr__()
def test_abstract_is_valid(self):
with pytest.raises(NotImplementedError):
Config().is_valid
def test_abstract_is_complete(self):
with pytest.raises(NotImplementedError):
Config().is_complete
@pytest.mark.parametrize(
'data',
[Rotation.from_random(),
Orientation.from_random(lattice='cI')])
def test_rotation_orientation(self, data):
assert str(Config(a=data)) == str(Config(a=data.as_quaternion()))
def test_initialize(self):
yml = """
a:
- 1
- 2
"""
assert Config(yml) == Config('{"a":[1,2]}') == Config(
a=[1, 2]) == Config(dict(a=[1, 2]))
def test_relationship_forward_backward(self, model, lattice):
o = Orientation.from_random(lattice=lattice)
for i, r in enumerate(o.related(model)):
assert o.disorientation(r.related(model)[i]).as_axis_angle(
degrees=True, pair=True)[1] < 1.0e-5
def test_invalid_rot(self):
with pytest.raises(TypeError):
Orientation.from_random(lattice='cubic') * np.ones(3)
def test_close(self, lattice, shape):
R = Orientation.from_random(lattice=lattice, shape=shape)
assert R.isclose(R.reduced).all() and R.allclose(R.reduced)
def test_close(self, family, shape):
R = Orientation.from_random(family=family, shape=shape)
assert R.isclose(R.reduced).all() and R.allclose(R.reduced)
def test_to_SST_vectorization(self, lattice, shape, vector, proper):
o = Orientation.from_random(lattice=lattice, shape=shape)
for r, theO in zip(
o.to_SST(vector=vector, proper=proper).reshape((-1, 3)),
o.flatten()):
assert np.allclose(r, theO.to_SST(vector=vector, proper=proper))
def test_reduced_vectorization(self, lattice, shape):
o = Orientation.from_random(lattice=lattice, shape=shape)
for r, theO in zip(o.reduced.flatten(), o.flatten()):
assert r == theO.reduced
def test_repr(self, kwargs):
o = Orientation.from_random(**kwargs)
assert isinstance(o.__repr__(), str)
def test_mul_invalid(self):
with pytest.raises(TypeError):
Orientation.from_random(lattice='cF') * np.ones(3)
def test_copy(self, kwargs):
o = Orientation.from_random(**kwargs)
p = o.copy(rotation=Rotation.from_random())
assert o != p
