hexrd.phase_transition.texture.evaluation module

ODF Evaluation Functions

Provides common evaluation functionality for orientation distribution functions. This module serves as a foundation for more complex ODF implementations.

hexrd.phase_transition.texture.evaluation.eval_odf_batch(odf, orientations, chunk_size=10000)

Evaluate ODF on large batches of orientations with memory management.

For very large orientation datasets, this function processes orientations in chunks to avoid memory issues while maintaining vectorization benefits.

Parameters

odfODF object

ODF object with eval() method

orientationsarray_like

Large array of orientation matrices, shape (N, 3, 3). A single (3, 3) matrix is also accepted.

chunk_sizeint, optional

Number of orientations to process per chunk, default 10000

Returns

numpy.ndarray or float

ODF values, shape (N,) for an (N, 3, 3) input, or a scalar float for a single (3, 3) orientation.

Examples

>>> odf = UniformODF('oh', 'triclinic')
>>> # Large batch of orientations
>>> Rs = np.random.normal(size=(50000, 3, 3))  # Not actual rotations!
>>> # In practice, use proper rotation matrices
>>> values = eval_odf_batch(odf, Rs, chunk_size=5000)

hexrd.phase_transition.texture.evaluation.eval_random_orientations(odf, n_orientations=1000, seed=None)

Evaluate ODF at random orientations for statistical analysis.

Generates random rotation matrices and evaluates the ODF at these orientations. Useful for normalization checks and Monte Carlo integration.

Parameters

odfODF object

ODF to evaluate

n_orientationsint, optional

Number of random orientations to generate, default 1000

seedint, optional

Random seed for reproducibility

Returns

tuple of (numpy.ndarray, numpy.ndarray)

orientations : Random rotation matrices, shape (n_orientations, 3, 3) values : ODF values at these orientations, shape (n_orientations,)

Examples

>>> odf = UniformODF('oh', 'triclinic')
>>> orientations, values = eval_random_orientations(odf, n_orientations=100)
>>> print(f"Mean ODF value: {np.mean(values)}")  # Should be ~1.0 (MRD)