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Merge remote-tracking branch 'origin/development' into missing-docstring

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Sharan 2022-03-20 14:50:43 +01:00
parent 87608b6ed0 2330b8ce15
commit 9087d4305a
3 changed files with 55 additions and 59 deletions
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75
python/damask/_rotation.py
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@ -418,15 +418,15 @@ class Rotation:
def reshape(self: MyType, def reshape(self: MyType,
shape: Union[int, Tuple[int, ...]], shape: Union[int, IntSequence],
order: Literal['C','F','A'] = 'C') -> MyType: order: Literal['C','F','A'] = 'C') -> MyType:
""" """
Reshape array. Reshape array.
Parameters Parameters
---------- ----------
shape : int or tuple of ints shape : int or sequence of ints
The new shape should be compatible with the original shape. New shape, number of elements needs to match the original shape.
If an integer is supplied, then the result will be a 1-D array of that length. If an integer is supplied, then the result will be a 1-D array of that length.
order : {'C', 'F', 'A'}, optional order : {'C', 'F', 'A'}, optional
'C' flattens in row-major (C-style) order. 'C' flattens in row-major (C-style) order.
@ -446,15 +446,15 @@ class Rotation:
def broadcast_to(self: MyType, def broadcast_to(self: MyType,
shape: Union[int, Tuple[int, ...]], shape: Union[int, IntSequence],
mode: Literal['left', 'right'] = 'right') -> MyType: mode: Literal['left', 'right'] = 'right') -> MyType:
""" """
Broadcast array. Broadcast array.
Parameters Parameters
---------- ----------
shape : int or tuple of ints shape : int or sequence of ints
Shape of broadcasted array. Shape of broadcasted array, needs to be compatible with the original shape.
mode : str, optional mode : str, optional
Where to preferentially locate missing dimensions. Where to preferentially locate missing dimensions.
Either 'left' or 'right' (default). Either 'left' or 'right' (default).
@ -465,9 +465,9 @@ class Rotation:
Rotation broadcasted to given shape. Rotation broadcasted to given shape.
""" """
if isinstance(shape,(int,np.integer)): shape = (shape,) shape_ = (shape,) if isinstance(shape,(int,np.integer)) else tuple(shape)
return self.copy(np.broadcast_to(self.quaternion.reshape(util.shapeshifter(self.shape,shape,mode)+(4,)), return self.copy(np.broadcast_to(self.quaternion.reshape(util.shapeshifter(self.shape,shape_,mode)+(4,)),
shape+(4,))) shape_+(4,)))
def average(self: MyType, def average(self: MyType,
@ -979,17 +979,15 @@ class Rotation:
@staticmethod @staticmethod
def from_random(shape: Tuple[int, ...] = None, def from_random(shape: Union[int, IntSequence] = None,
rng_seed: NumpyRngSeed = None) -> 'Rotation': rng_seed: NumpyRngSeed = None) -> 'Rotation':
""" """
Initialize with random rotation. Initialize with samples from a uniform distribution.
Rotations are uniformly distributed.
Parameters Parameters
---------- ----------
shape : tuple of ints, optional shape : int or sequence of ints, optional
Shape of the sample. Defaults to None, which gives a single rotation. Shape of the returned array. Defaults to None, which gives a scalar.
rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
A seed to initialize the BitGenerator. A seed to initialize the BitGenerator.
Defaults to None, i.e. unpredictable entropy will be pulled from the OS. Defaults to None, i.e. unpredictable entropy will be pulled from the OS.
@ -1011,12 +1009,12 @@ class Rotation:
@staticmethod @staticmethod
def from_ODF(weights: np.ndarray, def from_ODF(weights: np.ndarray,
phi: np.ndarray, phi: np.ndarray,
N: int = 500, shape: Union[int, IntSequence] = None,
degrees: bool = True, degrees: bool = True,
fractions: bool = True, fractions: bool = True,
rng_seed: NumpyRngSeed = None) -> 'Rotation': rng_seed: NumpyRngSeed = None) -> 'Rotation':
""" """
Sample discrete values from a binned orientation distribution function (ODF). Initialize with samples from a binned orientation distribution function (ODF).
Parameters Parameters
---------- ----------
@ -1024,9 +1022,8 @@ class Rotation:
Texture intensity values (probability density or volume fraction) at Euler space grid points. Texture intensity values (probability density or volume fraction) at Euler space grid points.
phi : numpy.ndarray, shape (n,3) phi : numpy.ndarray, shape (n,3)
Grid coordinates in Euler space at which weights are defined. Grid coordinates in Euler space at which weights are defined.
N : integer, optional shape : int or sequence of ints, optional
Number of discrete orientations to be sampled from the given ODF. Shape of the returned array. Defaults to None, which gives a scalar.
Defaults to 500.
degrees : bool, optional degrees : bool, optional
Euler space grid coordinates are in degrees. Defaults to True. Euler space grid coordinates are in degrees. Defaults to True.
fractions : bool, optional fractions : bool, optional
@ -1036,11 +1033,6 @@ class Rotation:
A seed to initialize the BitGenerator. A seed to initialize the BitGenerator.
Defaults to None, i.e. unpredictable entropy will be pulled from the OS. Defaults to None, i.e. unpredictable entropy will be pulled from the OS.
Returns
-------
samples : damask.Rotation, shape (N)
Array of sampled rotations that approximate the input ODF.
Notes Notes
----- -----
Due to the distortion of Euler space in the vicinity of ϕ = 0, probability densities, p, defined on Due to the distortion of Euler space in the vicinity of ϕ = 0, probability densities, p, defined on
@ -1063,26 +1055,27 @@ class Rotation:
dg = 1.0 if fractions else _dg(phi,degrees) dg = 1.0 if fractions else _dg(phi,degrees)
dV_V = dg * np.maximum(0.0,weights.squeeze()) dV_V = dg * np.maximum(0.0,weights.squeeze())
return Rotation.from_Euler_angles(phi[util.hybrid_IA(dV_V,N,rng_seed)],degrees) N = 1 if shape is None else np.prod(shape)
return Rotation.from_Euler_angles(phi[util.hybrid_IA(dV_V,N,rng_seed)],degrees).reshape(() if shape is None else shape)
@staticmethod @staticmethod
def from_spherical_component(center: 'Rotation', def from_spherical_component(center: 'Rotation',
sigma: float, sigma: float,
N: int = 500, shape: Union[int, IntSequence] = None,
degrees: bool = True, degrees: bool = True,
rng_seed: NumpyRngSeed = None) -> 'Rotation': rng_seed: NumpyRngSeed = None) -> 'Rotation':
""" """
Calculate set of rotations with Gaussian distribution around center. Initialize with samples from a Gaussian distribution around a given center.
Parameters Parameters
---------- ----------
center : Rotation center : Rotation or Orientation
Central Rotation. Central rotation.
sigma : float sigma : float
Standard deviation of (Gaussian) misorientation distribution. Standard deviation of (Gaussian) misorientation distribution.
N : int, optional shape : int or sequence of ints, optional
Number of samples. Defaults to 500. Shape of the returned array. Defaults to None, which gives a scalar.
degrees : bool, optional degrees : bool, optional
sigma is given in degrees. Defaults to True. sigma is given in degrees. Defaults to True.
rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
@ -1092,24 +1085,25 @@ class Rotation:
""" """
rng = np.random.default_rng(rng_seed) rng = np.random.default_rng(rng_seed)
sigma = np.radians(sigma) if degrees else sigma sigma = np.radians(sigma) if degrees else sigma
N = 1 if shape is None else np.prod(shape)
u,Theta = (rng.random((N,2)) * 2.0 * np.array([1,np.pi]) - np.array([1.0, 0])).T u,Theta = (rng.random((N,2)) * 2.0 * np.array([1,np.pi]) - np.array([1.0, 0])).T
omega = abs(rng.normal(scale=sigma,size=N)) omega = abs(rng.normal(scale=sigma,size=N))
p = np.column_stack([np.sqrt(1-u**2)*np.cos(Theta), p = np.column_stack([np.sqrt(1-u**2)*np.cos(Theta),
np.sqrt(1-u**2)*np.sin(Theta), np.sqrt(1-u**2)*np.sin(Theta),
u, omega]) u, omega])
return Rotation.from_axis_angle(p) * center return Rotation.from_axis_angle(p).reshape(() if shape is None else shape) * center
@staticmethod @staticmethod
def from_fiber_component(alpha: IntSequence, def from_fiber_component(alpha: IntSequence,
beta: IntSequence, beta: IntSequence,
sigma: float = 0.0, sigma: float = 0.0,
N: int = 500, shape: Union[int, IntSequence] = None,
degrees: bool = True, degrees: bool = True,
rng_seed: NumpyRngSeed = None): rng_seed: NumpyRngSeed = None):
""" """
Calculate set of rotations with Gaussian distribution around direction. Initialize with samples from a Gaussian distribution around a given direction.
Parameters Parameters
---------- ----------
@ -1120,8 +1114,8 @@ class Rotation:
sigma : float, optional sigma : float, optional
Standard deviation of (Gaussian) misorientation distribution. Standard deviation of (Gaussian) misorientation distribution.
Defaults to 0. Defaults to 0.
N : int, optional shape : int or sequence of ints, optional
Number of samples. Defaults to 500. Shape of the returned array. Defaults to None, which gives a scalar.
degrees : bool, optional degrees : bool, optional
sigma, alpha, and beta are given in degrees. sigma, alpha, and beta are given in degrees.
rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
@ -1139,6 +1133,7 @@ class Rotation:
if np.isclose(ax_align[3],0.0): ax_align[:3] = np.array([1,0,0]) if np.isclose(ax_align[3],0.0): ax_align[:3] = np.array([1,0,0])
R_align = Rotation.from_axis_angle(ax_align if ax_align[3] > 0.0 else -ax_align,normalize=True) # rotate fiber axis from sample to crystal frame R_align = Rotation.from_axis_angle(ax_align if ax_align[3] > 0.0 else -ax_align,normalize=True) # rotate fiber axis from sample to crystal frame
N = 1 if shape is None else np.prod(shape)
u,Theta = (rng.random((N,2)) * 2.0 * np.array([1,np.pi]) - np.array([1.0, 0])).T u,Theta = (rng.random((N,2)) * 2.0 * np.array([1,np.pi]) - np.array([1.0, 0])).T
omega = abs(rng.normal(scale=sigma_,size=N)) omega = abs(rng.normal(scale=sigma_,size=N))
p = np.column_stack([np.sqrt(1-u**2)*np.cos(Theta), p = np.column_stack([np.sqrt(1-u**2)*np.cos(Theta),
@ -1148,9 +1143,9 @@ class Rotation:
f = np.column_stack((np.broadcast_to(d_lab,(N,3)),rng.random(N)*np.pi)) f = np.column_stack((np.broadcast_to(d_lab,(N,3)),rng.random(N)*np.pi))
f[::2,:3] *= -1 # flip half the rotation axes to negative sense f[::2,:3] *= -1 # flip half the rotation axes to negative sense
return R_align.broadcast_to(N) \ return (R_align.broadcast_to(N)
* Rotation.from_axis_angle(p,normalize=True) \ * Rotation.from_axis_angle(p,normalize=True)
* Rotation.from_axis_angle(f) * Rotation.from_axis_angle(f)).reshape(() if shape is None else shape)
#################################################################################################### ####################################################################################################

6
python/tests/test_Orientation.py
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@ -146,14 +146,14 @@ class TestOrientation:
def test_from_spherical_component(self): def test_from_spherical_component(self):
assert np.all(Orientation.from_spherical_component(center=Rotation(), assert np.all(Orientation.from_spherical_component(center=Rotation(),
sigma=0.0,N=1,family='triclinic').as_matrix() sigma=0.0,shape=1,family='triclinic').as_matrix()
== np.eye(3)) == np.eye(3))
def test_from_fiber_component(self): def test_from_fiber_component(self):
r = Rotation.from_fiber_component(alpha=np.zeros(2),beta=np.zeros(2), r = Rotation.from_fiber_component(alpha=np.zeros(2),beta=np.zeros(2),
sigma=0.0,N=1,rng_seed=0) sigma=0.0,shape=1,rng_seed=0)
assert np.all(Orientation.from_fiber_component(alpha=np.zeros(2),beta=np.zeros(2), assert np.all(Orientation.from_fiber_component(alpha=np.zeros(2),beta=np.zeros(2),
sigma=0.0,N=1,rng_seed=0,family='triclinic').quaternion sigma=0.0,shape=None,rng_seed=0,family='triclinic').quaternion
== r.quaternion) == r.quaternion)
@pytest.mark.parametrize('kwargs',[ @pytest.mark.parametrize('kwargs',[

33
python/tests/test_Rotation.py
View File

@ -6,6 +6,7 @@ from damask import Rotation
from damask import Table from damask import Table
from damask import _rotation from damask import _rotation
from damask import grid_filters from damask import grid_filters
from damask import util
n = 1000 n = 1000
atol=1.e-4 atol=1.e-4
@ -1055,12 +1056,12 @@ class TestRotation:
@pytest.mark.parametrize('sigma',[5,10,15,20]) @pytest.mark.parametrize('sigma',[5,10,15,20])
@pytest.mark.parametrize('N',[1000,10000,100000]) @pytest.mark.parametrize('shape',[1000,10000,100000,(10,100)])
def test_spherical_component(self,N,sigma): def test_spherical_component(self,sigma,shape):
p = [] p = []
for run in range(5): for run in range(5):
c = Rotation.from_random() c = Rotation.from_random()
o = Rotation.from_spherical_component(c,sigma,N) o = Rotation.from_spherical_component(c,sigma,shape)
_, angles = c.misorientation(o).as_axis_angle(pair=True,degrees=True) _, angles = c.misorientation(o).as_axis_angle(pair=True,degrees=True)
angles[::2] *= -1 # flip angle for every second to symmetrize distribution angles[::2] *= -1 # flip angle for every second to symmetrize distribution
@ -1072,8 +1073,8 @@ class TestRotation:
@pytest.mark.parametrize('sigma',[5,10,15,20]) @pytest.mark.parametrize('sigma',[5,10,15,20])
@pytest.mark.parametrize('N',[1000,10000,100000]) @pytest.mark.parametrize('shape',[1000,10000,100000,(10,100)])
def test_from_fiber_component(self,N,sigma): def test_from_fiber_component(self,sigma,shape):
p = [] p = []
for run in range(5): for run in range(5):
alpha = np.random.random()*2*np.pi,np.arccos(np.random.random()) alpha = np.random.random()*2*np.pi,np.arccos(np.random.random())
@ -1084,9 +1085,9 @@ class TestRotation:
ax = np.append(np.cross(f_in_C,f_in_S), - np.arccos(np.dot(f_in_C,f_in_S))) ax = np.append(np.cross(f_in_C,f_in_S), - np.arccos(np.dot(f_in_C,f_in_S)))
n = Rotation.from_axis_angle(ax if ax[3] > 0.0 else ax*-1.0 ,normalize=True) # rotation to align fiber axis in crystal and sample system n = Rotation.from_axis_angle(ax if ax[3] > 0.0 else ax*-1.0 ,normalize=True) # rotation to align fiber axis in crystal and sample system
o = Rotation.from_fiber_component(alpha,beta,np.radians(sigma),N,False) o = Rotation.from_fiber_component(alpha,beta,np.radians(sigma),shape,False)
angles = np.arccos(np.clip(np.dot(o@np.broadcast_to(f_in_S,(N,3)),n@f_in_S),-1,1)) angles = np.arccos(np.clip(np.dot(o@np.broadcast_to(f_in_S,tuple(util.aslist(shape))+(3,)),n@f_in_S),-1,1))
dist = np.array(angles) * (np.random.randint(0,2,N)*2-1) dist = np.array(angles) * (np.random.randint(0,2,util.aslist(shape))*2-1)
p.append(stats.normaltest(dist)[1]) p.append(stats.normaltest(dist)[1])
@ -1097,8 +1098,8 @@ class TestRotation:
@pytest.mark.parametrize('fractions',[True,False]) @pytest.mark.parametrize('fractions',[True,False])
@pytest.mark.parametrize('degrees',[True,False]) @pytest.mark.parametrize('degrees',[True,False])
@pytest.mark.parametrize('N',[2**13,2**14,2**15]) @pytest.mark.parametrize('shape',[2**13,2**14,2**15,(2**8,2**6)])
def test_ODF_cell(self,ref_path,fractions,degrees,N): def test_ODF_cell(self,ref_path,fractions,degrees,shape):
steps = np.array([144,36,36]) steps = np.array([144,36,36])
limits = np.array([360.,90.,90.]) limits = np.array([360.,90.,90.])
rng = tuple(zip(np.zeros(3),limits)) rng = tuple(zip(np.zeros(3),limits))
@ -1107,14 +1108,14 @@ class TestRotation:
Eulers = grid_filters.coordinates0_point(steps,limits) Eulers = grid_filters.coordinates0_point(steps,limits)
Eulers = np.radians(Eulers) if not degrees else Eulers Eulers = np.radians(Eulers) if not degrees else Eulers
Eulers_r = Rotation.from_ODF(weights,Eulers.reshape(-1,3,order='F'),N,degrees,fractions).as_Euler_angles(True) Eulers_r = Rotation.from_ODF(weights,Eulers.reshape(-1,3,order='F'),shape,degrees,fractions).as_Euler_angles(True)
weights_r = np.histogramdd(Eulers_r,steps,rng)[0].flatten(order='F')/N * np.sum(weights) weights_r = np.histogramdd(Eulers_r.reshape(-1,3),steps,rng)[0].flatten(order='F')/np.prod(shape) * np.sum(weights)
if fractions: assert np.sqrt(((weights_r - weights) ** 2).mean()) < 4 if fractions: assert np.sqrt(((weights_r - weights) ** 2).mean()) < 4
@pytest.mark.parametrize('degrees',[True,False]) @pytest.mark.parametrize('degrees',[True,False])
@pytest.mark.parametrize('N',[2**13,2**14,2**15]) @pytest.mark.parametrize('shape',[2**13,2**14,2**15,(2**8,2**6)])
def test_ODF_node(self,ref_path,degrees,N): def test_ODF_node(self,ref_path,degrees,shape):
steps = np.array([144,36,36]) steps = np.array([144,36,36])
limits = np.array([360.,90.,90.]) limits = np.array([360.,90.,90.])
rng = tuple(zip(-limits/steps*.5,limits-limits/steps*.5)) rng = tuple(zip(-limits/steps*.5,limits-limits/steps*.5))
@ -1125,8 +1126,8 @@ class TestRotation:
Eulers = grid_filters.coordinates0_node(steps,limits)[:-1,:-1,:-1] Eulers = grid_filters.coordinates0_node(steps,limits)[:-1,:-1,:-1]
Eulers = np.radians(Eulers) if not degrees else Eulers Eulers = np.radians(Eulers) if not degrees else Eulers
Eulers_r = Rotation.from_ODF(weights,Eulers.reshape(-1,3,order='F'),N,degrees).as_Euler_angles(True) Eulers_r = Rotation.from_ODF(weights,Eulers.reshape(-1,3,order='F'),shape,degrees).as_Euler_angles(True)
weights_r = np.histogramdd(Eulers_r,steps,rng)[0].flatten(order='F')/N * np.sum(weights) weights_r = np.histogramdd(Eulers_r.reshape(-1,3),steps,rng)[0].flatten(order='F')/np.prod(shape) * np.sum(weights)
assert np.sqrt(((weights_r - weights) ** 2).mean()) < 5 assert np.sqrt(((weights_r - weights) ** 2).mean()) < 5