tiltlib.sample.Sample

class tiltlib.sample.Sample(oris: Orientation, phase: Phase, axes: list[Axis])[source]

Bases: SampleHolder

__init__(oris: Orientation, phase: Phase, axes: list[Axis]) → None[source]

Sample of spatially distinct orientations

Parameters:

oris (Orientation) – The orientations at each point of the sample
phase (Phase) – Crystallographic phase of the sample. Only single-phase crystals are supported
axes (list[Axis]) – Tilt axes

Methods

`TEM_frame_to_sample_frame`(v)	Convert a vector in TEM coordinates to sample coordinates
`__init__`(oris, phase, axes)	Sample of spatially distinct orientations
`angle_with`(zone_axis[, degrees])	Calculate the angle between the optical axis and the target zone axis, for all pixels in the sample.
`as_matrix`()	Returns the 3x3 rotation matrix transforming the TEM coordinate system to the sample holder coordinate system
`crop`(roi)	Crop the sample with a hyperspy ROI and return a new cropped sample
`find_tilt_angles`(zone_axis[, degrees, ...])	Calculate the tilt angle(s) necessary to align the sample with a given optical axis
`from_crystal_map`(xmap, axes)	Initialize a Sample from a CrystalMap
`mean_zone_axis`()	Calculate the mean orientation in the sample, and return the zone axis.
`plot`()	Plot IPF colormap of the orientations at the given tilt angle(s)
`plot_angle_with`(zone_axis[, resolution, ...])	Make a plot of similarity score as function of tilt angle(s).
`plot_interactive`()	Make a IPF colormap plot with a slider for the tilt angle of each tilt axis
`plot_orientations`()	IPF scatterplot of orientations
`plot_orientations_interactive`()	Interactive scatterplot of the orientations, with sliders to control the tilt axes
`reset_rotation`()	Resets the sample holder to the angles it was initialized with
`rotate`(*angles[, degrees])	Rotates the sample holder by the given angles from the current position
`rotate_to`(*angles[, degrees])	Sets the angles of all rotation axes to the given angles
`rotation_matrix`()	Returns the 3x3 rotation matrix transforming the TEM coordinate system to the sample holder coordinate system
`sample_frame_to_TEM_frame`(v)	Convert a vector in sample coordinates to TEM coordinates
`to_matrix`()	Returns the 3x3 rotation matrix transforming the TEM coordinate system to the sample holder coordinate system
`to_navigator`()	Create a IPF-z colormap as a hyperspy signal
`to_signal`()	Hyperspy signal containing the quaternion data of the orientations at each sample point

Attributes

`angles`	Current tilt angles, in radians
`orientations`	The orientations of the sample at the current tilts

TEM_frame_to_sample_frame(v: Vector3d) → Vector3d

Convert a vector in TEM coordinates to sample coordinates

Parameters:: v (Vector3d) – TEM vector
Returns:: Sample vector
Return type:: Vector3d

angle_with(zone_axis: Miller, degrees: bool = True) → ndarray[source]

Calculate the angle between the optical axis and the target zone axis, for all pixels in the sample.

Parameters:

zone_axis (Miller) – Zone axis to calculate for
degrees (bool, optional) – Whether to return output in degrees(True) or radians(False), defaults to True

Returns:

Array of angles at all points in the sample

Return type:

np.ndarray

property angles: list[float]: Current tilt angles, in radians

as_matrix() → ndarray

Returns the 3x3 rotation matrix transforming the TEM coordinate system to the sample holder coordinate system

Returns:: Rotation matrix
Return type:: np.ndarray

crop(roi: BaseROI) → Sample[source]

Crop the sample with a hyperspy ROI and return a new cropped sample

Parameters:: roi (BaseROI) – Region of interest to keep
Raises:: NotImplementedError – If an unsupported ROI is supplied
Returns:: Cropped sample
Return type:: Sample

find_tilt_angles(zone_axis: Miller, degrees: bool = True, use_mean_orientation: bool = False) → tuple[float, ...][source]

Calculate the tilt angle(s) necessary to align the sample with a given optical axis

Parameters:

zone_axis (Miller) – The zone axis to align to
degrees (bool, optional) – Whether to return degrees(True) or radians(False), defaults to True
use_mean_orientation (bool, optional) – Whether to perform optimization using the mean orientation of the sample(True) or the mean angle with the zone axis(False), defaults to False

Returns:

Tilt angles

Return type:

tuple[float, …]

classmethod from_crystal_map(xmap: CrystalMap, axes: list[Axis]) → Sample[source]

Initialize a Sample from a CrystalMap

Parameters:

xmap (CrystalMap) – Orientation map
axes (list[Axis]) – Tilt axes

mean_zone_axis() → Miller[source]

Calculate the mean orientation in the sample, and return the zone axis. This is mostly useful for single-grain or cropped samples.

Returns:: Zone axis
Return type:: Miller

property orientations: Orientation

The orientations of the sample at the current tilts

Returns:: Tilted orientations
Return type:: Orientation

plot() → Figure[source]

Plot IPF colormap of the orientations at the given tilt angle(s)

Returns:: IPFs
Return type:: plt.Figure

plot_angle_with(zone_axis: Miller, resolution: float = 1.0, use_mean_orientation: bool = False) → Figure[source]

Make a plot of similarity score as function of tilt angle(s).

Parameters:

zone_axis (Miller) – Zone axis to calculate for
resolution (float, optional) – Angular resolution of the tilt axes, in degrees, defaults to 1.0
use_mean_orientation (bool, optional) – Whether to perform optimization using the mean orientation of the sample(True) or the mean angle with the zone axis(False), defaults to False

Raises:

NotImplementedError – If more than 2 tilt axes are present, as up to 2 are supported

Returns:

Line plot or colormap, depending on the number of tilt angles

Return type:

plt.Figure

plot_interactive() → tuple[Figure, Slider][source]

Make a IPF colormap plot with a slider for the tilt angle of each tilt axis

Returns:: Figure and sliders. Returning the slider avoids their functionality being deleted when the function returns
Return type:: tuple[plt.Figure, Slider]

plot_orientations() → Figure[source]

IPF scatterplot of orientations

Returns:: IPFs
Return type:: plt.Figure

plot_orientations_interactive() → tuple[Figure, Slider][source]

Interactive scatterplot of the orientations, with sliders to control the tilt axes

Returns:: Figure and sliders. Returning the slider avoids their functionality being deleted when the function returns
Return type:: tuple[plt.Figure, Slider]

reset_rotation(): Resets the sample holder to the angles it was initialized with

rotate(*angles: float, degrees: bool = False)

Rotates the sample holder by the given angles from the current position

Parameters:

angles (float) – tilt angles, in order of tilt axes
degrees (bool, optional) – Whether angles are in degrees(True) or radians(False), defaults to False

rotate_to(*angles: float, degrees: bool = False)

Sets the angles of all rotation axes to the given angles

Parameters:

angles (float) – tilt angles, in order of tilt axes
degrees (bool, optional) – Whether angles are in degrees(True) or radians(False), defaults to False

rotation_matrix() → ndarray

Returns the 3x3 rotation matrix transforming the TEM coordinate system to the sample holder coordinate system

Returns:: Rotation matrix
Return type:: np.ndarray

sample_frame_to_TEM_frame(v: Vector3d) → Vector3d

Convert a vector in sample coordinates to TEM coordinates

Parameters:: v (Vector3d) – Sample vector
Returns:: TEM vector
Return type:: Vector3d

to_matrix() → ndarray

Returns the 3x3 rotation matrix transforming the TEM coordinate system to the sample holder coordinate system

Returns:: Rotation matrix
Return type:: np.ndarray

to_navigator() → Signal1D[source]

Create a IPF-z colormap as a hyperspy signal

Returns:: IPF-z
Return type:: Signal1D

to_signal() → Signal1D[source]

Hyperspy signal containing the quaternion data of the orientations at each sample point

Returns:: Quaternion data
Return type:: Signal1D