AtomsEnsemble#

class abtem.inelastic.phonons.AtomsEnsemble(trajectory, ensemble_mean=True, ensemble_axes_metadata=None, cell=None)[source]#

Bases: abtem.inelastic.phonons.BaseFrozenPhonons

Frozen phonons based on a molecular dynamics simulation.

Parameters

trajectory (list of ASE.Atoms, dask.core.Array, list of dask.Delayed) – Sequence of atoms representing a distribution of atomic configurations.
ensemble_mean (True, optional) – If True, the mean of the ensemble of results from a multislice simulation is calculated, otherwise, the result of every frozen phonon is returned.
ensemble_axes_metadata (list of AxesMetadata, optional) – Axis metadata for each ensemble axis. The axis metadata must be compatible with the shape of the array.
cell (Cell, optional) –

__init__(trajectory, ensemble_mean=True, ensemble_axes_metadata=None, cell=None)[source]#

Methods

`__init__`(trajectory[, ensemble_mean, ...])
`copy`()	Make a copy.
`ensemble_blocks`([chunks])	Split the ensemble into an array of smaller ensembles.
`generate_blocks`([chunks])	Generate chunks of the ensemble.
`randomize`(atoms)	Randomize the atoms.
`select_block`(index, chunks)	Select a block from the ensemble.
`standard_deviations`()	Standard deviation of the positions of each atom in each direction.

Attributes

`atomic_numbers`	The unique atomic number of the atoms.
`atoms`	Base atomic configuration used for displacements.
`axes_metadata`	List of AxisMetadata.
`base_axes_metadata`	List of AxisMetadata of the base axes.
`base_shape`	Shape of the base axes.
`cell`	The cell of the atoms.
`ensemble_axes_metadata`	List of AxisMetadata of the ensemble axes.
`ensemble_mean`	The mean of the ensemble of results from a multislice simulation is calculated.
`ensemble_shape`	Shape of the ensemble axes.
`num_configs`	Number of atomic configurations.
`shape`	Shape of the ensemble.
`trajectory`	Array of atoms representing an ensemble of atomic configurations.

property atomic_numbers: numpy.ndarray#

The unique atomic number of the atoms.

Return type: ndarray

property atoms: ase.atoms.Atoms#

Base atomic configuration used for displacements.

Return type: Atoms

property axes_metadata: abtem.core.axes.AxesMetadataList#

List of AxisMetadata.

Return type: AxesMetadataList

property base_axes_metadata: list[abtem.core.axes.AxisMetadata]#

List of AxisMetadata of the base axes.

Return type: list[AxisMetadata]

property base_shape: tuple[int, ...]#

Shape of the base axes.

Return type: tuple[int, ...]

property cell: ase.cell.Cell#

The cell of the atoms.

Return type: Cell

copy()#: Make a copy.

property ensemble_axes_metadata: list[abtem.core.axes.AxisMetadata]#

List of AxisMetadata of the ensemble axes.

Return type: list[AxisMetadata]

ensemble_blocks(chunks=None)#

Split the ensemble into an array of smaller ensembles.

Parameters: chunks (iterable of tuples) – Block sizes along each dimension.
Return type: Array

property ensemble_mean#: The mean of the ensemble of results from a multislice simulation is calculated.

property ensemble_shape: tuple[int, ...]#

Shape of the ensemble axes.

Return type: tuple[int, ...]

generate_blocks(chunks=1)#

Generate chunks of the ensemble.

Parameters: chunks (iterable of tuples) – Block sizes along each dimension.

property num_configs: int#

Number of atomic configurations.

Return type: int

randomize(atoms)[source]#

Randomize the atoms.

Parameters: atoms (Atoms) –
Return type: Atoms

select_block(index, chunks)#

Select a block from the ensemble.

Parameters

index (tuple of ints) – Index of selected block.
chunks (iterable of tuples) – Block sizes along each dimension.

property shape#: Shape of the ensemble.

standard_deviations()[source]#

Standard deviation of the positions of each atom in each direction.

Return type: ndarray

property trajectory: np.ndarray | da.core.Array#

Array of atoms representing an ensemble of atomic configurations.

Return type: np.ndarray | da.core.Array