Results

The result objects twin operations hand back.

SampleDraws

Raw joint posterior draws from twin.sample(), columnar on the wire.

Attribute	Type	Description
n	int	Draws per sampling unit.
raw	dict	The payload as it came off the wire.

Properties

• environments (list[str])

SampleDraws.to_frame

SampleDraws.to_frame() -> pd.DataFrame

Undocumented; the signature above is the contract.

SimulationResult

A completed simulation run: raw outputs plus best-effort tabular and narrative views.

Attribute	Type	Description
run_id	str	The run this result came from.
run	dict	The run document: status, timings, and the rest.
scenario	dict | None	The scenario that ran.

Properties

• results (Any)

• environment_groups (list[dict[str, Any]]): The saved environment groups this run was scoped to, as the platform froze them.

• summary (str)

• tables (list[str])

SimulationResult.to_frame

SimulationResult.to_frame(path: str | None = None) -> pd.DataFrame

Tabularize the result payload.

Simulation families answer with different shapes, so this finds record lists in the payload.

Parameter	Type	Default	Description
path	str | None	None	Which candidate table to use, as a dotted path. With several candidates and no path, the largest wins; the error message from a bad path lists what exists.

Returns (pd.DataFrame): The chosen records as a DataFrame.

Raises

• InvalidArgumentError: No tabular records in the payload, or no records at path.

SimulationResult.link

SimulationResult.link() -> Any

This run's detail view on the platform, as a clickable URL.

Opens the parent twin's Simulate tab on exactly this run.

SimulationResult.export

SimulationResult.export(fmt: str = 'csv') -> bytes

The platform's own export of the run.

Parameter	Type	Default	Description
fmt	str	'csv'	Export format, for example csv.

Returns (bytes): The export's raw bytes.

SimulationResult.sweep

SimulationResult.sweep(metric: str | None = None) -> SweepResult

Full dose-response curve of a range intervention, one metric at a time.

Parameter	Type	Default	Description
metric	str | None	None	Which metric's curve to return. Required when the run carries several; the error names them.

Returns (SweepResult): The curve as a SweepResult: displayed in a notebook it renders the interactive curve, to_frame() is the points, and DataFrame attributes pass through.

SweepResult

One metric's dose-response curve off a sweep run.

Displayed in a notebook it mounts the interactive curve; everywhere else it behaves like its DataFrame — to_frame() returns the points, and unknown attributes (head, plot, …) delegate to it.

Attribute	Type	Description
metric	str	The metric this curve measures.

Properties

• swept_variable (str | None)

SweepResult.to_frame

SweepResult.to_frame() -> pd.DataFrame

Undocumented; the signature above is the contract.

ForecastResult

Forecast run with a tidy long-format frame: environment, series, timestamp, values.

Everything on SimulationResult applies; to_frame() additionally carries an environment column on panel runs and a variable column when several targets were forecast, so no series is ever dropped.

ForecastResult.to_frame

ForecastResult.to_frame(path: str | None = None) -> pd.DataFrame

Undocumented; the signature above is the contract.

PredictionResult

Prediction run: one row per input record, per target.

Everything on SimulationResult applies. to_frame() adds a row column carrying the 0-based position of the input record each prediction answers for, so predictions join back onto the frame they were asked about; a variable column names the target when several were predicted, and an environment column the environment on a panel twin.

PredictionResult.to_frame

PredictionResult.to_frame(path: str | None = None) -> pd.DataFrame

Undocumented; the signature above is the contract.

UpdateResult

Outcome of an incremental model update (assimilation).

Attribute	Type	Description
status	str	committed, up_to_date, or retrain_required.
rows_assimilated	int | None	Rows folded into the model, when any.
reasons	list[str]	Why assimilation was not possible, when it was not.
job	dict	The underlying job document.

Properties

• retrain_required (bool)

ScoreResult

Verdicts and per-row counterfactual changes from a batch scoring run.

Attribute	Type	Description
run_id	str	The simulation run underneath.

Properties

• digest (dict[str, Any]): Verdict counts, top drivers, and the first row summaries.

ScoreResult.to_frame

ScoreResult.to_frame(max_rows: int | None = None) -> pd.DataFrame

Every scored row with its full change list, paged transparently.

Parameter	Type	Default	Description
max_rows	int | None	None	Stop after this many rows. Fetches everything when omitted.

Returns (pd.DataFrame): One row per scored input, with its change list.

ScoreResult.link

ScoreResult.link() -> Any

This scoring run's detail view on the platform, as a clickable URL.