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