#
# ISC License
#
# Copyright (c) 2026, Autonomous Vehicle Systems Lab, University of Colorado at Boulder
#
# Permission to use, copy, modify, and/or distribute this software for any
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# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
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"""
Shared test helpers for the stochastic-integrator equivalence tests.
Both equivalence suites (``test_stochasticIntegratorsJulia.py`` and
``test_stochasticIntegratorsPaper.py``) drive a native integrator on a small toy SDE with
a prescribed sequence of Gaussian increments and compare the resulting trajectory against
a committed reference. This module factors out the identical MuJoCo/StatefulSysModel
scaffolding they share, so the per-suite files only supply the SDE (drift ``f`` and
per-source diffusion columns ``gCols``) and the reference case dict.
"""
from __future__ import annotations
from typing import Callable, List
import numpy as np
from Basilisk.utilities import macros
from Basilisk.utilities import SimulationBaseClass
from Basilisk.utilities import pythonVariableLogger
from Basilisk.simulation import svIntegrators
from Basilisk.simulation import dynParamManager
[docs]
def buildPrescribedNoiseSim(case: dict, integratorClassName: str,
f: Callable, gCols: List[Callable]):
"""Build a MuJoCo simulation that replays a reference case's prescribed noise.
Args:
case: reference-case dict with keys dt, u0, n_states, n_steps, dW, dZ and
(optionally) m. ``m`` defaults to ``n_states`` (the diagonal-noise
convention used by the Julia suite).
integratorClassName: exact ``svIntegrators`` class name to instantiate.
f: drift f(t, x) -> length-n array.
gCols: list of m diffusion columns, each g_k(t, x) -> length-n array.
Returns:
(scSim, stateModel, integrator, stateLogger). Run to case["tf"] and read
``stateLogger.x``.
"""
from Basilisk.simulation import mujoco
from Basilisk.simulation import StatefulSysModel
dt = case["dt"]
x0 = np.array(case["u0"], dtype=float)
n = case["n_states"]
m = case.get("m", n)
class GenericStochasticStateModel(StatefulSysModel.StatefulSysModel):
def registerStates(self, registerer: StatefulSysModel.DynParamRegisterer):
self.states: List[dynParamManager.StateData] = []
for i in range(n):
self.states.append(registerer.registerState(1, 1, f"y{i + 1}"))
self.states[-1].setNumNoiseSources(m)
self.states[-1].setState([[x0[i]]])
# Every noise source is shared across all states, so source k drives every
# state through the k-th diffusion column (supports non-diagonal noise).
for k in range(m):
registerer.registerSharedNoiseSource(
[(state, k) for state in self.states]
)
def UpdateState(self, CurrentSimNanos: int):
t = macros.NANO2SEC * CurrentSimNanos
x = np.array([state.getState()[0][0] for state in self.states])
deriv = f(t, x)
for i in range(n):
self.states[i].setDerivative([[deriv[i]]])
for k in range(m):
col = gCols[k](t, x)
for i in range(n):
self.states[i].setDiffusion([[col[i]]], index=k)
scSim = SimulationBaseClass.SimBaseClass()
dynProcess = scSim.CreateNewProcess("test")
dynProcess.addTask(scSim.CreateNewTask("test", macros.sec2nano(dt)))
scene = mujoco.MJScene("<mujoco/>")
scSim.AddModelToTask("test", scene)
integrator = getattr(svIntegrators, integratorClassName)(scene)
# Replay the reference increments through a prescribed-noise generator.
prescribed = svIntegrators.PrescribedGaussianNoiseGenerator()
dWsteps = case["dW"]
dZsteps = case["dZ"]
for step in range(case["n_steps"]):
prescribed.pushStep(list(dWsteps[step]), list(dZsteps[step]))
integrator.setNoiseGenerator(prescribed)
scene.setIntegrator(integrator)
stateModel = GenericStochasticStateModel()
stateModel.ModelTag = "testModel"
scene.AddModelToDynamicsTask(stateModel)
scene.AddModelToDiffusionDynamicsTask(stateModel)
stateLogger = pythonVariableLogger.PythonVariableLogger(
{"x": lambda _: np.array([s.getState()[0][0] for s in stateModel.states])}
)
scSim.AddModelToTask("test", stateLogger)
scSim.InitializeSimulation()
return scSim, stateModel, integrator, stateLogger