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nominal ¤

create_asset ¤

create_asset(
    name: str,
    description: str | None = None,
    *,
    properties: Mapping[str, str] | None = None,
    labels: Sequence[str] = ()
) -> Asset

Create an asset.

create_log_set ¤

create_log_set(
    name: str,
    logs: (
        Iterable[Log]
        | Iterable[tuple[datetime | IntegralNanosecondsUTC, str]]
    ),
    timestamp_type: LogTimestampType = "absolute",
    description: str | None = None,
) -> LogSet

Create an immutable log set with the given logs.

The logs are attached during creation and cannot be modified afterwards. Logs can either be of type Log or a tuple of a timestamp and a string. Timestamp type must be either 'absolute' or 'relative'.

create_run ¤

create_run(
    name: str,
    start: datetime | str | IntegralNanosecondsUTC,
    end: datetime | str | IntegralNanosecondsUTC | None,
    description: str | None = None,
    *,
    properties: Mapping[str, str] | None = None,
    labels: Sequence[str] = (),
    attachments: Iterable[Attachment] | Iterable[str] = ()
) -> Run

Create a run in the Nominal platform.

If the run has no end (for example, if it is ongoing), use end=None.

To add a dataset to the run, use run.add_dataset().

create_run_csv ¤

create_run_csv(
    file: Path | str,
    name: str,
    timestamp_column: str,
    timestamp_type: _LiteralAbsolute | Iso8601 | Epoch,
    description: str | None = None,
) -> Run

Create a dataset from a CSV file, and create a run based on it.

This is a convenience function that combines upload_csv() and create_run().

create_streaming_connection ¤

create_streaming_connection(
    datasource_id: str,
    connection_name: str,
    datasource_description: str | None = None,
    *,
    required_tag_names: list[str] | None = None
) -> StreamingConnection

Creates a new datasource and a new connection.

datasource_id: A human readable identifier. Must be unique within an organization.

create_workbook_from_template ¤

create_workbook_from_template(
    template_rid: str,
    run_rid: str,
    *,
    title: str | None = None,
    description: str | None = None,
    is_draft: bool = False
) -> Workbook

Creates a new workbook from a template. template_rid: The template to use for the workbook. run_rid: The run to associate the workbook with.

data_review_builder ¤

data_review_builder() -> DataReviewBuilder

Create a batch of data reviews to be initiated together.

Example:¤

builder = nm.data_review_builder()
builder.add_integration("integration_rid")
builder.add_request("run_rid_1", "checklist_rid_1", "commit_1")
builder.add_request("run_rid_2", "checklist_rid_2", "commit_2")
reviews = builder.initiate()

for review in reviews:
    print(review.get_violations())

download_attachment ¤

download_attachment(rid: str, file: Path | str) -> None

Retrieve an attachment from the Nominal platform and save it to file.

get_asset ¤

get_asset(rid: str) -> Asset

Retrieve an asset by its RID.

get_attachment ¤

get_attachment(rid: str) -> Attachment

Retrieve an attachment from the Nominal platform by its RID.

get_connection ¤

get_connection(rid: str) -> Connection

Retrieve a connection from the Nominal platform by its RID.

get_data_review ¤

get_data_review(rid: str) -> DataReview

Retrieve a data review from the Nominal platform by its RID.

get_dataset ¤

get_dataset(rid: str) -> Dataset

Retrieve a dataset from the Nominal platform by its RID.

get_default_client ¤

get_default_client() -> NominalClient

Retrieve the default client to the Nominal platform.

get_log_set ¤

get_log_set(rid: str) -> LogSet

Retrieve a log set from the Nominal platform by its RID.

get_run ¤

get_run(rid: str) -> Run

Retrieve a run from the Nominal platform by its RID.

get_video ¤

get_video(rid: str) -> Video

Retrieve a video from the Nominal platform by its RID.

list_streaming_checklists ¤

list_streaming_checklists(
    asset: Asset | str | None = None,
) -> Iterable[str]

List all Streaming Checklists.

Parameters:

  • asset ¤

    (Asset | str | None, default: None ) –

    if provided, only return checklists associated with the given asset.

search_assets ¤

search_assets(
    *,
    search_text: str | None = None,
    label: str | None = None,
    labels: Sequence[str] | None = None,
    property: tuple[str, str] | None = None,
    properties: Mapping[str, str] | None = None
) -> Sequence[Asset]

Search for assets meeting the specified filters. Filters are ANDed together, e.g. (asset.label == label) AND (asset.search_text =~ field)

Parameters:

  • search_text ¤

    (str | None, default: None ) –

    case-insensitive search for any of the keywords in all string fields

  • label ¤

    (str | None, default: None ) –

    Deprecated, use labels instead.

  • labels ¤

    (Sequence[str] | None, default: None ) –

    A sequence of labels that must ALL be present on a asset to be included.

  • property ¤

    (tuple[str, str] | None, default: None ) –

    Deprecated, use properties instead.

  • properties ¤

    (Mapping[str, str] | None, default: None ) –

    A mapping of key-value pairs that must ALL be present on a asset to be included.

Returns:

  • Sequence[Asset] –

    All assets which match all of the provided conditions

search_runs ¤

search_runs(
    *,
    start: str | datetime | IntegralNanosecondsUTC | None = None,
    end: str | datetime | IntegralNanosecondsUTC | None = None,
    name_substring: str | None = None,
    label: str | None = None,
    labels: Sequence[str] | None = None,
    property: tuple[str, str] | None = None,
    properties: Mapping[str, str] | None = None
) -> Sequence[Run]

Search for runs meeting the specified filters. Filters are ANDed together, e.g. (run.label == label) AND (run.end <= end)

Parameters:

  • start ¤

    (str | datetime | IntegralNanosecondsUTC | None, default: None ) –

    Inclusive start time for filtering runs.

  • end ¤

    (str | datetime | IntegralNanosecondsUTC | None, default: None ) –

    Inclusive end time for filtering runs.

  • name_substring ¤

    (str | None, default: None ) –

    Searches for a (case-insensitive) substring in the name

  • label ¤

    (str | None, default: None ) –

    Deprecated, use labels instead.

  • labels ¤

    (Sequence[str] | None, default: None ) –

    A sequence of labels that must ALL be present on a run to be included.

  • property ¤

    (tuple[str, str] | None, default: None ) –

    Deprecated, use properties instead.

  • properties ¤

    (Mapping[str, str] | None, default: None ) –

    A mapping of key-value pairs that must ALL be present on a run to be included.

Returns:

  • Sequence[Run] –

    All runs which match all of the provided conditions

set_base_url ¤

set_base_url(base_url: str) -> None

Set the default Nominal platform base url.

For production environments: "https://api.gov.nominal.io/api". For staging environments: "https://api-staging.gov.nominal.io/api". For local development: "https://api.nominal.test".

set_token ¤

set_token(base_url: str, token: str) -> None

Set the default token to be used in association with a given base url.

Use in conjunction with set_base_url().

upload_attachment ¤

upload_attachment(
    file: Path | str, name: str, description: str | None = None
) -> Attachment

Upload an attachment to the Nominal platform.

upload_csv ¤

upload_csv(
    file: Path | str,
    name: str | None,
    timestamp_column: str,
    timestamp_type: _AnyTimestampType,
    description: str | None = None,
    channel_name_delimiter: str | None = None,
    *,
    wait_until_complete: bool = True
) -> Dataset

Create a dataset in the Nominal platform from a .csv or .csv.gz file.

If name is None, the dataset is created with the name of the file.

If wait_until_complete=True (the default), this function waits until the dataset has completed ingestion before returning. If you are uploading many datasets, set wait_until_complete=False instead and call wait_until_ingestions_complete() after uploading all datasets to allow for parallel ingestion.

upload_mcap_video ¤

upload_mcap_video(
    file: Path | str,
    topic: str,
    name: str | None = None,
    description: str | None = None,
    *,
    wait_until_complete: bool = True
) -> Video

Create a video in the Nominal platform from a topic in a mcap file.

If name is None, the video is created with the name of the file.

If wait_until_complete=True (the default), this function waits until the video has completed ingestion before returning. If you are uploading many videos, set wait_until_complete=False instead and call wait_until_ingestion_complete() after uploading all videos to allow for parallel ingestion.

upload_pandas ¤

upload_pandas(
    df: DataFrame,
    name: str,
    timestamp_column: str,
    timestamp_type: _AnyTimestampType,
    description: str | None = None,
    channel_name_delimiter: str | None = None,
    *,
    wait_until_complete: bool = True
) -> Dataset

Create a dataset in the Nominal platform from a pandas.DataFrame.

upload_polars ¤

upload_polars(
    df: DataFrame,
    name: str,
    timestamp_column: str,
    timestamp_type: _AnyTimestampType,
    description: str | None = None,
    channel_name_delimiter: str | None = None,
    *,
    wait_until_complete: bool = True
) -> Dataset

Create a dataset in the Nominal platform from a polars.DataFrame.

upload_tdms ¤

upload_tdms(
    file: Path | str,
    name: str | None = None,
    description: str | None = None,
    timestamp_column: str | None = None,
    timestamp_type: _AnyTimestampType | None = None,
    *,
    wait_until_complete: bool = True
) -> Dataset

Create a dataset in the Nominal platform from a tdms file.

upload_video ¤

upload_video(
    file: Path | str,
    name: str,
    start: datetime | str | IntegralNanosecondsUTC,
    description: str | None = None,
) -> Video

Upload a video to Nominal from a file.