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# Software Name: NEML2 -- the New Engineering material Model Library, version 2
# By: Argonne National Laboratory
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"""MillerIndex — a crystallographic direction or plane (h, k, l).
Base shape ``(3,)``. Mirrors ``include/neml2/tensors/MillerIndex.h``. The
stored components are the raw Miller indices (e.g. ``(1, 1, 0)`` for the
``[110]`` direction); conversion to a Cartesian lattice vector requires the
``CrystalGeometry`` lattice vectors and lives in :mod:`functions`.
The C++ class has no methods of its own — this Python wrapper is similarly
a pure labelled container, distinguished from a raw ``Vec`` only by type so
``[Tensors] type = Python`` expressions can spell ``MillerIndex(...)`` and
the eval namespace dispatches correctly. Stored as floating-point even
though the indices are integers — this matches the C++ side, which keeps
the storage in the model's default dtype to participate in autograd-friendly
expressions downstream.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import ClassVar
import torch
from neml2.types._primitive import PrimitiveTensor
from neml2.types._pytree import register
[docs]
@dataclass(frozen=True, eq=False)
class MillerIndex(PrimitiveTensor):
"""Wraps a `torch.Tensor` of shape ``(..., 3)`` carrying Miller indices.
Inherits all arithmetic and ``zeros``/``ones``/``full``/``empty``/``fill``
factories from :class:`PrimitiveTensor`. No class-specific overrides — the
C++ analogue has the same minimal surface.
"""
data: torch.Tensor
sub_batch_ndim: int = 0
sub_batch_state: tuple = ()
sub_batch_meta: tuple = ()
k_ndim: int = 0
k_state: tuple = ()
k_pairing: tuple = ()
BASE_NDIM: ClassVar[int] = 1
BASE_SHAPE: ClassVar[tuple[int, ...]] = (3,)
register(MillerIndex)
NEML2