Examples

Each micro example shows one small task: first in code, then in the Nominal app.

Title	Use case
CSV upload	Upload a CSV file of time-series data to a dataset, with each column becoming a channel
Video upload	Upload a video and line it up with your time-series data
Uploading a non-native file using an extractor	Upload a file format Nominal handles using an extractor and convert that file to time-series data
Tagging data	Tag the points you upload or stream, so one dataset can be filtered by unit or execution
Finding a dataset’s RID	Get the identifier that client code and streaming tools use to point at a dataset
Creating asset metadata	Create a metadata asset for the system you’re testing and describe it with properties and labels, allowing you to track system data over time
Giving an asset a dataset	Get or create an asset’s dataset under a ref name, or attach an existing dataset, optionally only the points with a tag
Creating run metadata	Mark a time as representing a period of interest, such as a test or a flight, as a run so you can find and compare it later
Creating event metadata	Mark a point or span in time, such as a failure or unknown state, so it can be found and reviewed
Updating labels and properties	Add labels and properties to an existing run or asset
Finding runs by label or property	Find every run with a given label or property
Executing a checklist on a run	Evaluate a published checklist’s checks against a run’s data
Using workbook templates	Save a workbook’s layout and open it on any asset or run
Filtering and splitting by tag in a workbook	Show only some tag values, or plot each one as its own series
Executing SQL	Query your time-series data and metadata with SQL
Exporting data	Download channel data for a time range to use outside Nominal
Exporting data by SQL	Export the complete result of a SQL query as a CSV file

To line up runs on one timeline in a workbook, see Line up tests on one timeline.