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_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(
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.
Retrieve an attachment from the Nominal platform and save it to file.
get_attachment(rid: str) -> Attachment
Retrieve an attachment from the Nominal platform by its RID.
Retrieve a dataset from the Nominal platform by its RID.
get_default_client() -> NominalClient
Retrieve the default client to the Nominal platform.
Retrieve a log set from the Nominal platform by its RID.
search_runs(
*,
start: str | datetime | IntegralNanosecondsUTC | None = None,
end: str | datetime | IntegralNanosecondsUTC | None = None,
name_substring: str | None = None,
label: str | None = None,
property: tuple[str, str] | None = None
) -> list[Run]
Search for runs meeting the specified filters.
Filters are ANDed together, e.g. (run.label == label) AND (run.end <= end)
- start and end times are both inclusive
- name_substring: search for a (case-insensitive) substring in the name
- property is a key-value pair, e.g. ("name", "value")
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 the default token to be used in association with a given base url.
Use in conjunction with set_base_url().
upload_attachment(
file: Path | str, name: str, description: str | None = None
) -> Attachment
Upload an attachment to the Nominal platform.
upload_csv(
file: Path | str,
name: str | None,
timestamp_column: str,
timestamp_type: _AnyTimestampType,
description: 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_pandas(
df: DataFrame,
name: str,
timestamp_column: str,
timestamp_type: _AnyTimestampType,
description: 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(
df: DataFrame,
name: str,
timestamp_column: str,
timestamp_type: _AnyTimestampType,
description: 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(
file: Path | str,
name: str | None = None,
description: str | None = None,
*,
wait_until_complete: bool = True
) -> Dataset
Create a dataset in the Nominal platform from a tdms file.
TDMS channel properties must have both a wf_increment and wf_start_time property to be included in the dataset.
Channels will be named as f"{group_name}.{channel_name}" with spaces replaced with underscores.
If name is None, the dataset is created with the name of the file with a .csv suffix.
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.