atomscale.results.rheed_image.RHEEDImageCollection#

class atomscale.results.rheed_image.RHEEDImageCollection(rheed_images: list[RHEEDImageResult], extra_data: list[dict] | None = None, sort_key: str | None = None)[source]

Bases: MSONable

Collection of RHEED images

Parameters:

  • rheed_images (list[RHEEDImageResult]) – List of RHEEDImageResult objects.

  • extra_data (list[dict] | None) – List of dictionaries containing field names and values of extra data to be included in the DataFrame object. Defaults to None.

  • sort_key (str | None) – Key used to sort the data with.

__init__(rheed_images: list[RHEEDImageResult], extra_data: list[dict] | None = None, sort_key: str | None = None)[source]

Collection of RHEED images

Parameters:

  • rheed_images (list[RHEEDImageResult]) – List of RHEEDImageResult objects.

  • extra_data (list[dict] | None) – List of dictionaries containing field names and values of extra data to be included in the DataFrame object. Defaults to None.

  • sort_key (str | None) – Key used to sort the data with.

Methods

__init__(rheed_images[, extra_data, sort_key])

Collection of RHEED images

align_fingerprints([node_df, inplace, ...])

Align a collection of RHEED fingerprints by relabeling the nodes to connect the same scattering features across RHEED patterns, based on relative position to the center feature.

as_dict()

A JSON serializable dict representation of an object.

from_dict(d)

Reconstruct an MSONable object from a dict.

get_pattern_dataframe([streamline, ...])

Featurize the RHEED image collection into a DataFrame of per-node features across all images.

load(file_path)

Load an instance from a JSON file written by save().

save(json_path[, mkdir, json_kwargs, ...])

Serialize the instance to JSON on disk, pickling fields if needed.

to_json()

Returns a json string representation of the MSONable object.

unsafe_hash()

Return a hash of the current object.

validate_monty_v1(_MSONable__input_value)

Pydantic validator with correct signature for pydantic v1.x.

validate_monty_v2(_MSONable__input_value, _)

Pydantic validator with correct signature for pydantic v2.x.

Attributes

REDIRECT

extra_data

rheed_images

sort_key

align_fingerprints(node_df: DataFrame | None = None, inplace: bool = False, search_range=0.2) → RHEEDImageCollection[source]

Align a collection of RHEED fingerprints by relabeling the nodes to connect the same scattering features across RHEED patterns, based on relative position to the center feature.

Returns:

A new collection whose RHEED fingerprints have had their nodes

relabeled so that matching scattering features share the same node ID across images.

Return type:

(RHEEDImageCollection)

Parameters:

  • node_df (DataFrame | None)

  • inplace (bool)

get_pattern_dataframe(streamline: bool = True, normalize: bool = True, symmetrize: bool = False, return_as_features: bool = True) → tuple[DataFrame, DataFrame][source]

Featurize the RHEED image collection into a DataFrame of per-node features across all images.

Parameters:

  • streamline (bool) – Whether to streamline the DataFrame object and remove null values. Defaults to True.

  • normalize (bool) – Whether to min/max normalize the feature data across all images. Defaults to True.

  • symmetrize (bool) – Whether to symmetrize the RHEED images and segmented patterns about the vertical axis before obtaining the DataFrame representation. Defaults to False.

  • return_as_features (bool) – When True, return a wide feature table (one column per feature/node); when False, return the raw per-node rows. Defaults to True.

Returns:

Pandas DataFrame of per-node features across all images.

Return type:

(DataFrame)

as_dict() → dict

A JSON serializable dict representation of an object.

Return type:

dict

classmethod from_dict(d: dict) → MSONable

Reconstruct an MSONable object from a dict.

Parameters:

d (dict) – Dict representation.

Return type:

MSONable

Returns:

MSONable class.

classmethod load(file_path: PathLike | str) → MSONable

Load an instance from a JSON file written by save().

Parameters:

file_path (PathLike | str) – The JSON file to load from.

Return type:

MSONable

Returns:

An instance of the class being reloaded.

save(json_path: PathLike | str, mkdir: bool = True, json_kwargs: dict | None = None, pickle_kwargs: dict | None = None, strict: bool = True) → None

Serialize the instance to JSON on disk, pickling fields if needed.

For a fully MSONable class, only {save_dir}/class.json is written. For a partially MSONable class, non-serializable attributes are pickled individually into the same directory, keeping the JSON portion readable.

Parameters:

  • json_path (PathLike | str) – The file to which to save the JSON object. A pickled companion file with the same stem but a different extension may also be written if the class is not entirely MSONable.

  • mkdir (bool) – If True, create the target directory (including parents).

  • json_kwargs (dict | None) – Keyword arguments forwarded to the JSON serializer.

  • pickle_kwargs (dict | None) – Keyword arguments forwarded to pickle.dump.

  • strict (bool) – If True, refuse to overwrite existing files.

Return type:

None

to_json() → str

Returns a json string representation of the MSONable object.

Return type:

str

unsafe_hash() → Any

Return a hash of the current object.

This uses a generic but low performance method of converting the object to a dictionary, flattening any nested keys, and then performing a hash on the resulting object.

Return type:

Any

classmethod validate_monty_v1(_MSONable__input_value)

Pydantic validator with correct signature for pydantic v1.x.

classmethod validate_monty_v2(_MSONable__input_value, _)

Pydantic validator with correct signature for pydantic v2.x.