simulator_wrapper.md

Simulator

::: dynestyx.simulation.auto.Simulator options: show_root_heading: false show_root_toc_entry: false

Examples

??? example "Predictive with auto-routing" ```python import dynestyx as dsx import jax.numpy as jnp import jax.random as jr import numpyro import numpyro.distributions as dist from dynestyx import DynamicalModel, GaussianStateEvolution, Simulator from numpyro.infer import Predictive

state_dim = 1
observation_dim = 1

def model(phi=None, predict_times=None):
    phi = numpyro.sample("phi", dist.Uniform(0.0, 1.0), obs=phi)
    dynamics = DynamicalModel(
        control_dim=0,
        initial_condition=dist.MultivariateNormal(
            loc=jnp.zeros(state_dim),
            covariance_matrix=jnp.eye(state_dim),
        ),
        state_evolution=GaussianStateEvolution(
            F=lambda x, u, t_now, t_next: phi * x + 0.1 * jnp.sin(x),
            cov=0.2**2 * jnp.eye(state_dim),
        ),
        observation_model=lambda x, u, t: dist.MultivariateNormal(
            x,
            0.3**2 * jnp.eye(observation_dim),
        ),
    )
    return dsx.sample("f", dynamics, predict_times=predict_times)

predict_times = jnp.arange(20.0)
with Simulator():
    prior_pred = Predictive(model, num_samples=5)(
        jr.PRNGKey(0),
        predict_times=predict_times,
    )
print("Predictive keys:", sorted(prior_pred.keys()))  # e.g. ['f_observations', 'f_states', 'f_times', 'phi', ...]
print("Predictive shapes:", {k: v.shape for k, v in prior_pred.items()})  # trajectory arrays: (num_samples, n_sim, T, dim); here num_samples=5, n_sim=1
```

!!! note Simulator only auto-routes forward generation and rollout. For explicit latent-state inference use LatentPathBuilder; for marginalized inference use Filter or Smoother.