matflow.param_classes.orientations.Orientations

class matflow.param_classes.orientations.Orientations(data, unit_cell_alignment, representation)

Bases: ParameterValue

A description of the orientations of some data.

Parameters:

• data (numpy.ndarray[Any, numpy.dtype[numpy._typing._array_like._ScalarType_co]]) – The orientation data.

• unit_cell_alignment (matflow.param_classes.orientations.UnitCellAlignment) – The alignment of the unit cell.

• representation (matflow.param_classes.orientations.OrientationRepresentation) – The orientation representation descriptor.

Methods

dump_element_group_to_HDF5_group	Write a list (from an element group) of parameter values to an HDF5 group.
dump_to_HDF5_group	Write this parameter value to an HDF5 group.
from_JSON_like	For custom initialisation via YAML or JSON.
from_file	Load orientation data from a text file.
from_random	Generate random orientation data.
prepare_JSON_dump	Prepare this parameter value for serialisation as JSON.
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	Extract a parameter value from JSON data.
to_dict	Serialise this parameter value as a dictionary.

Attributes

data	Orientation data
unit_cell_alignment	Alignment of the unit cell.
representation	How the orientation data is represented.

data: ndarray[Any, dtype[_ScalarType_co]]

Orientation data

classmethod dump_element_group_to_HDF5_group(objs, group)

Write a list (from an element group) of parameter values to an HDF5 group.

Parameters:

• objs (list[ParameterValue]) –

• group (Group) –

dump_to_HDF5_group(group)

Write this parameter value to an HDF5 group.

classmethod from_JSON_like(data, ori_format)

For custom initialisation via YAML or JSON.

Parameters:

• data (Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]) –

• ori_format (str) –

Return type:

Self

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

Load orientation data from a text file.

Parameters:

• path (str) – Path to the file to load from.

• representation (dict) – Description of how the orientation data is arranged.

• unit_cell_alignment (UnitCellAlignment) – How the unit cell is aligned.

• number (int | None) – Number of orientations to read from the file.

• start_index (int) – The line number of the file that the orientations start at. Allows skipping headers.

• delimiter (str) – The delimiter separating values in the file. Defaults to space, but commas and tabs are also sensible (and correspond to CSV and TSV files respectively).

Return type:

Self

classmethod from_random(number)

Generate random orientation data.

Parameters:

number (int) – The number of orientations to generate.

Return type:

Self

prepare_JSON_dump()

Prepare this parameter value for serialisation as JSON.

Return type:

dict[str, Any]

static quat_sample_random(number)

Generate random uniformly distributed unit quaternions.

Parameters:

number (int) – How many quaternions to generate.

Returns:

quats

Return type:

ndarray, shape (number, 4)

References

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

representation: OrientationRepresentation

How the orientation data is represented.

classmethod save_from_HDF5_group(group, param_id, workflow)

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

Note

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.

We assume that the workflow is using a Zarr datastore. This is not checked!

Parameters:

param_id (int) –

classmethod save_from_JSON(data, param_id, workflow)

Extract a parameter value from JSON data.

Parameters:

• param_id (int | list[int]) –

• workflow (Workflow) –

to_dict()

Serialise this parameter value as a dictionary.

Return type:

dict[str, Any]

unit_cell_alignment: UnitCellAlignment

Alignment of the unit cell.