compute_series

nominal.experimental.compute_as_code.compute_series(client, expr, inputs, start=None, end=None, *, tags_by_reference=None, name='value', enable_gzip=True)

Compute a derived series by applying a nominal.compute expression to a set of channels.

Parameters:

• client (NominalClient)

  The NominalClient to compute with.

• expr (nominal.compute.NumericSeries | nominal.compute.CategoricalSeries)

  The compute-as-code expression to evaluate (numeric or categorical), typically authored in nominal.compute and imported from your own repository.

• inputs (Mapping[str, Channel])

  Maps each reference name used in expr to the Channel it should resolve to. Look these up from the run/dataset the caller wants to compute against.

• start (_FlexibleTimestamp | None)

  Start of the time range to compute over. Defaults to the earliest supported timestamp.

• end (_FlexibleTimestamp | None)

  End of the time range to compute over. Defaults to the latest supported timestamp.

• tags_by_reference (Mapping[str, Mapping[str, str]] | None)

  Optional tag filters, keyed by reference name (the same keys as inputs) and then by tag key. Use this when the channel bound to a reference carries more than one tagged series. For example, tags_by_reference={"velocity": {"vehicle": "car_1"}} narrows the channel bound to reference "velocity" down to the series tagged vehicle=car_1. References omitted from tags_by_reference are bound without a tag filter.

• name (str)

  Name for the returned series (also the CSV column name requested from the export service).

• enable_gzip (bool)

  If true, gzip the export from Nominal.

Returns:

A pandas.Series of the computed values, indexed by timestamp. The index name is “timestamp” and the series name is name.

Return type:

pd.Series[Any]