def test_euler(self): euler_det = integrators.EulerDeterministic() euler_sto = integrators.EulerStochastic() self.assertEqual(euler_det.dt, dt) self.assertEqual(euler_sto.dt, dt) self.assertTrue(isinstance(euler_sto.noise, noise.Additive)) self.assertEqual(euler_sto.noise.nsig, 1.0) self.assertEqual(euler_sto.noise.ntau, 0.0)
def test_euler(self): euler_det = integrators.EulerDeterministic() euler_sto = integrators.EulerStochastic() euler_sto.noise.dt = euler_sto.dt assert euler_det.dt == dt assert euler_sto.dt == dt assert isinstance(euler_sto.noise, noise.Additive) assert euler_sto.noise.nsig == 1.0 assert euler_sto.noise.ntau == 0.0 self._test_scheme(euler_det) self._test_scheme(euler_sto)
def _run_sim(self, length, model, *mons): sim = simulator.Simulator( model=model, connectivity=connectivity.Connectivity(load_default=True), coupling=coupling.Linear(), integrator=integrators.EulerDeterministic(), monitors=mons) sim.configure() ys = [] for (t, y), in sim(simulation_length=length): ys.append(y) return sim, numpy.array(ys)
def gen_sim(a): dt = 0.1 conn = connectivity.Connectivity() conn.weights = weights conn.tract_lengths = idelays * dt conn.speed = 1.0 sim = simulator.Simulator( coupling=py_coupling.Kuramoto(a=a), connectivity=conn, model=models.Kuramoto(omega=100 * 2 * numpy.pi / 1e3), monitors=monitors.Raw(), integrator=integrators.EulerDeterministic(dt=dt)) sim.configure() sim.history[:] = 0.1 return sim