magnon.utils¶

Functions¶

`is_array_like`(obj, ndim)	Check if the input object is an array-like structure with a specific number of dimensions.
`all_same_shape`(*arrays)	Check whether all input arrays have the same shape.
`all_same_length`(*arrays)	Check whether all input arrays have the same length.
`ensure_vector_magnetic_moments`(atoms)	Ensure that the ASE Atoms object has vector (3D) magnetic moments for each atom.

Module Contents¶

magnon.utils.is_array_like(obj, ndim)¶

Check if the input object is an array-like structure with a specific number of dimensions.

Parameters:

obj (object) – Object to be checked.
ndim (int) – Number of dimensions expected.

Returns:

True if the object is array-like with the specified number of dimensions, False otherwise.

Return type:

bool

Raises:

Exception – If the object cannot be converted to a NumPy array.

magnon.utils.all_same_shape(*arrays)¶

Check whether all input arrays have the same shape.

Parameters:: arrays (array-like) – Variable number of arrays to compare.
Returns:: True if all arrays have the same shape, False otherwise.
Return type:: bool

magnon.utils.all_same_length(*arrays)¶

Check whether all input arrays have the same length.

Parameters:: arrays (array-like) – Variable number of arrays to compare.
Returns:: True if all arrays have the same length, False otherwise.
Return type:: bool

magnon.utils.ensure_vector_magnetic_moments(atoms)¶

Ensure that the ASE Atoms object has vector (3D) magnetic moments for each atom.

Parameters:: atoms (ase.Atoms) – ASE atoms object to be validated.
Returns:: atoms – ASE Atoms object with vector magnetic moments.
Return type:: ase.Atoms

Notes

ASE allows initial_magnetic_moments to be either scalar or vector. This function guarantees vector format, which is useful for handling non-collinear magnetism.