numpy-compatible shape definition
can be scalar or sequence. Try to avoid to use the term 'rotation' in the documentation and also don't specify the return type because it will be used by 'damask.Orientation'.
This commit is contained in:
Martin Diehl
parent
4972c43b17
commit
52d595ff62
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@ -418,15 +418,15 @@ class Rotation:
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def reshape(self: MyType,
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shape: Union[int, Tuple[int, ...]],
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shape: Union[int, IntSequence],
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order: Literal['C','F','A'] = 'C') -> MyType:
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"""
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Reshape array.
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Parameters
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----------
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shape : int or tuple of ints
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The new shape should be compatible with the original shape.
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shape : int or sequence of ints
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New shape, number of elements needs to match the original shape.
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If an integer is supplied, then the result will be a 1-D array of that length.
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order : {'C', 'F', 'A'}, optional
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'C' flattens in row-major (C-style) order.
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@ -446,15 +446,15 @@ class Rotation:
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def broadcast_to(self: MyType,
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shape: Union[int, Tuple[int, ...]],
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shape: Union[int, IntSequence],
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mode: Literal['left', 'right'] = 'right') -> MyType:
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"""
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Broadcast array.
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Parameters
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----------
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shape : int or tuple of ints
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Shape of broadcasted array.
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shape : int or sequence of ints
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Shape of broadcasted array, needs to be compatible with the original shape.
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mode : str, optional
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Where to preferentially locate missing dimensions.
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Either 'left' or 'right' (default).
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@ -465,9 +465,9 @@ class Rotation:
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Rotation broadcasted to given shape.
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"""
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if isinstance(shape,(int,np.integer)): shape = (shape,)
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return self.copy(np.broadcast_to(self.quaternion.reshape(util.shapeshifter(self.shape,shape,mode)+(4,)),
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shape+(4,)))
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shape_ = (shape,) if isinstance(shape,(int,np.integer)) else tuple(shape)
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return self.copy(np.broadcast_to(self.quaternion.reshape(util.shapeshifter(self.shape,shape_,mode)+(4,)),
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shape_+(4,)))
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def average(self: MyType,
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@ -979,17 +979,15 @@ class Rotation:
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@staticmethod
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def from_random(shape: Tuple[int, ...] = None,
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def from_random(shape: Union[int, IntSequence] = None,
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rng_seed: NumpyRngSeed = None) -> 'Rotation':
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"""
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Initialize with random rotation.
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Rotations are uniformly distributed.
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Initialize with samples from a uniform distribution.
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Parameters
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----------
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shape : tuple of ints, optional
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Shape of the sample. Defaults to None, which gives a single rotation.
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shape : int or sequence of ints, optional
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Shape of the returned array. Defaults to None, which gives a scalar.
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rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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A seed to initialize the BitGenerator.
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Defaults to None, i.e. unpredictable entropy will be pulled from the OS.
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@ -1011,12 +1009,12 @@ class Rotation:
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@staticmethod
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def from_ODF(weights: np.ndarray,
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phi: np.ndarray,
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shape: Tuple[int, ...] = None,
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shape: Union[int, IntSequence] = None,
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degrees: bool = True,
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fractions: bool = True,
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rng_seed: NumpyRngSeed = None) -> 'Rotation':
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"""
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Sample discrete values from a binned orientation distribution function (ODF).
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Initialize with samples from a binned orientation distribution function (ODF).
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Parameters
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----------
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@ -1024,9 +1022,8 @@ class Rotation:
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Texture intensity values (probability density or volume fraction) at Euler space grid points.
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phi : numpy.ndarray, shape (n,3)
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Grid coordinates in Euler space at which weights are defined.
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shape : tuple of ints, optional
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Shape of the array of discrete rotations sampled from the given ODF.
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Defaults to None, which gives a single rotation.
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shape : int or sequence of ints, optional
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Shape of the returned array. Defaults to None, which gives a scalar.
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degrees : bool, optional
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Euler space grid coordinates are in degrees. Defaults to True.
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fractions : bool, optional
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@ -1036,11 +1033,6 @@ class Rotation:
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A seed to initialize the BitGenerator.
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Defaults to None, i.e. unpredictable entropy will be pulled from the OS.
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Returns
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-------
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samples : damask.Rotation
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Array of sampled rotations that approximate the input ODF.
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Notes
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-----
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Due to the distortion of Euler space in the vicinity of ϕ = 0, probability densities, p, defined on
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@ -1070,32 +1062,26 @@ class Rotation:
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@staticmethod
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def from_spherical_component(center: 'Rotation',
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sigma: float,
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shape: Tuple[int, ...] = None,
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shape: Union[int, IntSequence] = None,
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degrees: bool = True,
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rng_seed: NumpyRngSeed = None) -> 'Rotation':
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"""
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Calculate set of rotations with Gaussian distribution around center.
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Initialize with samples from a Gaussian distribution around a given center.
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Parameters
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----------
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center : Rotation
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Central Rotation.
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center : Rotation or Orientation
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Central rotation.
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sigma : float
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Standard deviation of (Gaussian) misorientation distribution.
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shape : tuple of ints, optional
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Shape of the array of sampled rotations.
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Defaults to None, which gives a single rotation.
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shape : int or sequence of ints, optional
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Shape of the returned array. Defaults to None, which gives a scalar.
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degrees : bool, optional
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sigma is given in degrees. Defaults to True.
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rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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A seed to initialize the BitGenerator.
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Defaults to None, i.e. unpredictable entropy will be pulled from the OS.
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Returns
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-------
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samples : damask.Rotation
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Array of rotations sampled from the spherical component.
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"""
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rng = np.random.default_rng(rng_seed)
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sigma = np.radians(sigma) if degrees else sigma
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@ -1113,11 +1099,11 @@ class Rotation:
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def from_fiber_component(alpha: IntSequence,
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beta: IntSequence,
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sigma: float = 0.0,
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shape: Tuple[int, ...] = None,
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shape: Union[int, IntSequence] = None,
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degrees: bool = True,
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rng_seed: NumpyRngSeed = None):
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"""
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Calculate set of rotations with Gaussian distribution around direction.
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Initialize with samples from a Gaussian distribution around a given direction.
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Parameters
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----------
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@ -1128,20 +1114,14 @@ class Rotation:
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sigma : float, optional
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Standard deviation of (Gaussian) misorientation distribution.
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Defaults to 0.
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shape : tuple of ints, optional
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Shape of the array of sampled rotations.
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Defaults to None, which gives a single rotation.
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shape : int or sequence of ints, optional
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Shape of the returned array. Defaults to None, which gives a scalar.
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degrees : bool, optional
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sigma, alpha, and beta are given in degrees.
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rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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A seed to initialize the BitGenerator.
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Defaults to None, i.e. unpredictable entropy will be pulled from the OS.
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Returns
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-------
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samples : damask.Rotation
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Array of rotations sampled from the fiber component.
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"""
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rng = np.random.default_rng(rng_seed)
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sigma_,alpha_,beta_ = (np.radians(coordinate) for coordinate in (sigma,alpha,beta)) if degrees else \
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