atomscale.client#

Classes

Client([api_key, endpoint, mute_bars])

Atomic Data Sciences API client
class atomscale.client.Client(api_key: str | None = None, endpoint: str | None = None, mute_bars: bool = False)[source]

Bases: BaseClient

Atomic Data Sciences API client

Parameters:

  • api_key (str | None)

  • endpoint (str)

  • mute_bars (bool)

  • api_key – API key. Explicit value takes precedence; if None, falls back to AS_API_KEY environment variable.

  • endpoint – Root API endpoint. Explicit value takes precedence; if None, falls back to AS_API_ENDPOINT environment variable, defaulting to ‘https://api.atomscale.ai/’ if not set.

  • mute_bars – Whether to mute progress bars. Defaults to False.

search(keywords: str | list[str] | None = None, include_organization_data: bool = True, data_ids: str | list[str] | None = None, physical_sample_ids: str | list[str] | None = None, project_ids: str | list[str] | None = None, data_type: Literal['rheed_image', 'rheed_stationary', 'rheed_rotating', 'xps', 'photoluminescence', 'pl', 'raman', 'recipe', 'all'] = 'all', status: Literal['success', 'pending', 'error', 'running', 'stream_active', 'stream_interrupted', 'stream_finalizing', 'stream_error', 'all'] = 'all', growth_length: tuple[int | None, int | None] = (None, None), upload_datetime: tuple[datetime | None, datetime | None] = (None, None), last_accessed_datetime: tuple[datetime | None, datetime | None] = (None, None)) → DataFrame[source]

Search and obtain data catalogue entries

Parameters:

  • keywords (str | list[str] | None) – Keyword or list of keywords to search all data catalogue fields with. This searching is applied after all other explicit filters. Defaults to None.

  • include_organization_data (bool) – Whether to include catalogue entries from other users in your organization. Defaults to True.

  • data_ids (str | list[str] | None) – Data ID or list of data IDs. Defaults to None.

  • physical_sample_ids (str | list[str] | None) – Physical sample ID or list of IDs. Defaults to None.

  • project_ids (str | list[str] | None) – Project ID or list of IDs. Defaults to None.

  • data_type (Literal["rheed_image", "rheed_stationary", "rheed_rotating", "xps", "photoluminescence", "raman", "all"]) – Type of data. Defaults to “all”.

  • status (Literal["success", "pending", "error", "running", "all"]) – Analyzed status of the data. Defaults to “all”.

  • growth_length (tuple[int | None, int | None]) – Minimum and maximum values of the growth length in seconds. Defaults to (None, None) which will include all non-video data.

  • upload_datetime (tuple[datetime | None, datetime | None]) – Minimum and maximum values of the upload datetime. Defaults to (None, None).

  • last_accessed_datetime (tuple[datetime | None, datetime | None]) – Minimum and maximum values of the last accessed datetime. Defaults to (None, None).

Returns:

Pandas DataFrame containing matched entries in the data catalogue.

Return type:

(DataFrame)

get(data_ids: str | list[str]) → list[RHEEDVideoResult | RHEEDImageResult | XPSResult | PhotoluminescenceResult | RamanResult | UnknownResult][source]

Get analyzed data results

Parameters:

data_ids (str | list[str]) – Data ID or list of data IDs from the data catalogue to obtain analyzed results for.

Returns:

List of result objects

Return type:

list[atomscale.results.RHEEDVideoResult | atomscale.results.RHEEDImageResult | atomscale.results.XPSResult]

list_physical_samples() → DataFrame[source]

List physical samples available to the user.

Return type:

DataFrame

list_projects() → DataFrame[source]

List projects available to the user.

Return type:

DataFrame

get_physical_sample(physical_sample_id: str, *, include_organization_data: bool = True, align: bool | str = False) → PhysicalSampleResult[source]

Get all data for a physical sample.

Parameters:

  • physical_sample_id (str) – Identifier of the physical sample.

  • include_organization_data (bool) – Whether to include organization data. Defaults to True.

  • align (bool | str) – Whether to align timeseries data. If truthy, an aligned DataFrame is returned.

Return type:

PhysicalSampleResult

get_project(project_id: str, *, include_organization_data: bool = True, align: bool | str = False) → ProjectResult[source]

Get all data grouped by physical sample for a project.

Parameters:

  • project_id (str) – Identifier of the project.

  • include_organization_data (bool) – Whether to include organization data. Defaults to True.

  • align (bool | str) – Whether to align timeseries at the project level. Defaults to False.

Return type:

ProjectResult

upload(files: list[str | BinaryIO])[source]

Upload and process files

Parameters:

files (list[str | BinaryIO]) – List containing string paths to files, or BinaryIO objects from open.

download_videos(data_ids: str | list[str], dest_dir: str | Path | None = None, data_type: Literal['raw', 'processed'] = 'processed')[source]

Download processed RHEED videos to disk.

Parameters:

  • data_ids (str | list[str]) – One or more data IDs from the data catalogue.

  • dest_dir (str | Path | None) – Directory to write the files to. Defaults to the current working directory.

  • data_type (Literal["raw", "processed"]) – Whether to download raw or processed data.