pypomp.panel.panel.PanelPomp.probe

PanelPomp.probe(probes: dict[str, Callable[[DataFrame], float]], key: Array, nsim: int = 100, theta: PanelParameters | dict[str, DataFrame | None] | list[dict[str, DataFrame | None]] | None = None) → DataFrame

Evaluate probe statistics on the model’s true data and simulated data for each unit.

Parameters:

• probes (dict[str, Callable[[pd.DataFrame], float]]) – A dictionary of probe functions. Each function should receive a DataFrame of observations for a single unit and return a numeric scalar. Example: {“mean”: lambda df: df[“obs”].mean()}

• key (jax.Array) – JAX random key for the simulations.

• nsim (int, optional) – Number of simulations to run per parameter set. Defaults to 100.

• theta – Parameters to simulate from.

Returns:

A long-format DataFrame with columns:

probe, value, is_real_data, replicate, sim, unit

Return type:

pd.DataFrame