matflow.param_classes.orientations.Orientations

class matflow.param_classes.orientations.Orientations(data: numpy.ndarray, unit_cell_alignment: matflow.param_classes.orientations.UnitCellAlignment, representation: matflow.param_classes.orientations.OrientationRepresentation)

Bases: ParameterValue

Methods

dump_to_HDF5_group
from_JSON_like	For custom initialisation via YAML or JSON.
from_file
from_random
prepare_JSON_dump
quat_sample_random	Generate random uniformly distributed unit quaternions.
save_from_HDF5_group	Save orientation data from an HDF5 group to a persistent workflow.
save_from_JSON
to_dict

Attributes

data
unit_cell_alignment
representation

Parameters:

• data (ndarray) –

• unit_cell_alignment (UnitCellAlignment) –

• representation (OrientationRepresentation) –

data: ndarray

dump_to_HDF5_group(group)

classmethod from_JSON_like(data, ori_format)

For custom initialisation via YAML or JSON.

classmethod from_file(path, representation, unit_cell_alignment, number=None, start_index=0, delimiter=' ')

classmethod from_random(number)

prepare_JSON_dump()

Return type:

Dict

static quat_sample_random(number)

Generate random uniformly distributed unit quaternions.

Parameters:

number (int) – How many quaternions to generate.

Returns:

quats

Return type:

ndarray of shape (number, 4)

References

https://stackoverflow.com/a/44031492/5042280 http://planning.cs.uiuc.edu/node198.html

representation: OrientationRepresentation

classmethod save_from_HDF5_group(group, param_id, workflow)

Save orientation data from an HDF5 group to a persistent workflow.

We avoid loading the data into memory all at once by firstly generating an Orientations object with a small data array, and then copying from the HDF5 group directly into the newly created Zarr group.

Parameters:

param_id (int) –

classmethod save_from_JSON(data, param_id, workflow)

Parameters:

param_id (int) –

to_dict()

unit_cell_alignment: UnitCellAlignment