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nominal ¤

checklist_builder ¤

checklist_builder(
    name: str,
    description: str = "",
    assignee_email: str | None = None,
    default_ref_name: str | None = None,
) -> ChecklistBuilder

Create a checklist builder to add checks and variables, and publish the checklist to Nominal.

If assignee_email is None, the checklist is assigned to the user executing the code.

Example:¤

builder = nm.checklist_builder("Programmatically created checklist")
builder.add_check(
    name="derivative of cycle time is too high",
    priority=2,
    expression="derivative(numericChannel(channelName = 'Cycle_Time', refName = 'manufacturing')) > 0.05",
)
checklist = builder.publish()

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,
) -> 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() and can only be used with absolute timestamps. For relative timestamps or custom formats, use upload_dataset() and create_run() separately.

The name and description are added to the run. The dataset is created with the name "Dataset for Run: {name}". The reference name for the dataset in the run is "dataset".

The run start and end times are created from the minimum and maximum timestamps in the CSV file in the timestamp column.

create_streaming_connection ¤

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

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.

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_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.

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_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.

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

If 'timestamp_column' is provided, it must be present in every group and the length of all data columns must be equal to (and aligned with) with 'timestamp_column'.

If 'timestamp_column' is None, TDMS channel properties must have both a wf_increment and wf_start_time property to be included in the dataset.

Note that both 'timestamp_column' and 'timestamp_type' must be included together, or excluded together.

Channels will be named as f"{group_name}.{channel_name}" with spaces replaced with underscores.

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_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.