LineScan#

class abtem.scan.LineScan(start=(0.0, 0.0), end=None, gpts=None, sampling=None, endpoint=True, fractional=False, potential=None)[source]#

Bases: abtem.scan.BaseScan

A scan along a straight line.

Parameters

start (two float or Atom, optional) – Start point of the scan [Å]. May be given as fractional coordinate if fractional=True. Default is (0., 0.).
end (two float or Atom, optional) – End point of the scan [Å]. May be given as fractional coordinate if fractional=True. Default is None, the scan end point will match the extent of the potential.
gpts (int, optional) – Number of scan positions. Default is None. Provide one of gpts or sampling.
sampling (float, optional) – Sampling rate of scan positions [1 / Å]. Provide one of gpts or sampling. If not provided the sampling will match the Nyquist sampling of the Probe in a multislice simulation.
endpoint (bool, optional) – If True, end is the last position. Otherwise, it is not included. Default is True.
fractional (bool, optional) – If True, use fractional coordinates with respect to the given potential for start and end.
potential (BasePotential or Atoms, optional) – Potential defining the grid with respect to which the fractional coordinates should be given.

__init__(start=(0.0, 0.0), end=None, gpts=None, sampling=None, endpoint=True, fractional=False, potential=None)[source]#

Methods

`__init__`([start, end, gpts, sampling, ...])
`add_margin`(margin)	Extend the line scan by adding a margin to the start and end of the line scan.
`add_to_axes`(ax[, width])	Add a visualization of a scan line to a matplotlib plot.
`apply`(waves)	Apply the transform to the given waves.
`at_position`(center[, extent, angle, gpts, ...])	Make a line scan centered at a given position.
`copy`()	Make a copy.
`ensemble_blocks`([chunks])	Split the ensemble into an array of smaller ensembles.
`generate_blocks`([chunks])	Generate chunks of the ensemble.
`get_positions`([chunks, lazy])	Get the scan positions as numpy array.
`match_probe`(probe)	Sets sampling to the Nyquist frequency.
`select_block`(index, chunks)	Select a block from the ensemble.

Attributes

`angle`	Angle of the line from start to end and the x-axis [deg.].
`axes_metadata`	List of AxisMetadata.
`base_axes_metadata`	List of AxisMetadata of the base axes.
`base_shape`	Shape of the base axes.
`direction`	Normal vector pointing from start to end.
`distributions`
`end`	End point of the scan [Å].
`endpoint`	True if the scan endpoint is the last position.
`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.
`extent`	Grid extent [Å].
`gpts`	Number of grid points.
`in_place`	The array representing the waves may be modified in-place.
`limits`	Lower left and upper right corner of the bounding box containing all positions in the scan.
`metadata`	Metadata added to the waves when applying the transform.
`num_positions`	Number of probe positions in the scan.
`sampling`	Grid sampling [Å].
`shape`	The shape the scan.
`start`	Start point of the scan [Å].

add_margin(margin)[source]#

Extend the line scan by adding a margin to the start and end of the line scan.

Parameters: margin (float or tuple of float) – The margin added to the start and end of the linescan [Å]. If float the same margin is added.

add_to_axes(ax, width=0.0, **kwargs)[source]#

Add a visualization of a scan line to a matplotlib plot.

Parameters

ax (matplotlib Axes) – The axes of the matplotlib plot the visualization should be added to.
width (float, optional) – Width of line [Å].
kwargs – Additional options for matplotlib.pyplot.plot as keyword arguments.

property angle#: Angle of the line from start to end and the x-axis [deg.].

apply(waves)#

Apply the transform to the given waves.

Parameters: array_object (ArrayObject) – The array object to transform.
Returns: transformed_array_object
Return type: ArrayObject

classmethod at_position(center, extent=1.0, angle=0.0, gpts=None, sampling=None, endpoint=True)[source]#

Make a line scan centered at a given position.

Parameters

center (two float) – Center position of the line [Å]. May be given as an Atom.
angle (float) – Angle of the line [deg.].
extent (float) – Extent of the line [Å].
gpts (int) – Number of grid points along the line.
sampling (float) – Sampling of grid points along the line [Å].
endpoint (bool) – Sets whether the ending position is included or not.

Returns

line_scan

Return type

LineScan

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, ...]

copy()#: Make a copy.

property direction#: Normal vector pointing from start to end.

property end: tuple[float, float] | None#

End point of the scan [Å].

Return type: tuple[float, float] | None

property endpoint: bool#

True if the scan endpoint is the last position. Otherwise, the endpoint is not included.

Return type: bool

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

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

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

property extent: float | None#

Grid extent [Å].

Return type: float | None

generate_blocks(chunks=1)#

Generate chunks of the ensemble.

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

get_positions(chunks=None, lazy=False)[source]#

Get the scan positions as numpy array.

Return type: ndarray

property gpts: int#

Number of grid points.

Return type: int

property in_place: bool#

The array representing the waves may be modified in-place.

Return type: bool

property limits: tuple[tuple[float, float], tuple[float, float]]#

Lower left and upper right corner of the bounding box containing all positions in the scan.

Return type: tuple[tuple[float, float], tuple[float, float]]

match_probe(probe)[source]#

Sets sampling to the Nyquist frequency. If the start and end point of the scan is not given, set them to the lower and upper left corners of the probe extent.

Parameters: probe (Probe or BaseSMatrix) – The matched probe or s-matrix.

property metadata#: Metadata added to the waves when applying the transform.

property num_positions: int#

Number of probe positions in the scan.

Return type: int

property sampling: float#

Grid sampling [Å].

Return type: float

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: tuple[int]#

The shape the scan.

Return type: tuple[int]

property start: tuple[float, float] | None#

Start point of the scan [Å].

Return type: tuple[float, float] | None