WavesToMeasurementTransform

class abtem.transform.WavesToMeasurementTransform(distributions=())

Bases: WavesTransform[object]

__init__(distributions=())

Methods

__init__([distributions])
apply(waves[, max_batch])
copy()	Make a copy.
ensemble_blocks([chunks])	Split the ensemble into an array of smaller ensembles.
generate_blocks([chunks])	Generate chunks of the ensemble.

Attributes

axes_metadata	List of AxisMetadata.
base_axes_metadata	List of AxisMetadata of the base axes.
base_shape	Shape of the base axes.
distributions
ensemble_axes_metadata	Axes metadata describing the ensemble axes added to the waves when applying the transform.
ensemble_shape	The shape of the ensemble axes added to the waves when applying the transform.
metadata	Metadata added to the waves when applying the transform.
shape	Shape of the ensemble.

property axes_metadata: AxesMetadataList

List of AxisMetadata.

property base_axes_metadata: list[AxisMetadata]

List of AxisMetadata of the base axes.

property base_shape: tuple[int, ...]

Shape of the base axes.

copy()

Make a copy.

Return type:

Self

property ensemble_axes_metadata: list[AxisMetadata]

Axes metadata describing the ensemble axes added to the waves when applying the transform.

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_shape: tuple[int, ...]

The shape of the ensemble axes added to the waves when applying the transform.

generate_blocks(chunks=1)

Generate chunks of the ensemble.

Parameters:

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

Return type:

Generator[tuple[tuple[int, ...], tuple[slice, ...], ndarray], None, None]

property metadata: dict

Metadata added to the waves when applying the transform.

property shape: tuple[int, ...]

Shape of the ensemble.