def test_without_proxy_coupling(self):
connectivity, coupling, integrator, monitors, sim, result, result_all = self._reference_simulation(
)
# The modify model without proxy
np.random.seed(42)
init = np.concatenate((np.random.random_sample(
(385, 1, 76, 1)), np.random.random_sample((385, 1, 76, 1))),
axis=1)
model = ReducedWongWangProxy(tau_s=np.random.rand(76))
synchronization_time = 1.0
# Initialise a Simulator -- Model, Connectivity, Integrator, and Monitors.
sim_6 = CoSimulator(
voi=np.array([0]),
synchronization_time=synchronization_time,
cosim_monitors=(CosimCoupling(coupling=coupling), ),
proxy_inds=np.asarray([0], dtype=np.int),
model=model,
connectivity=connectivity,
coupling=coupling,
integrator=integrator,
monitors=(monitors, ),
initial_conditions=init,
)
sim_6.configure()
result_2_all = sim_6.run(
)[0][1][:, 0, 0,
0] # run the first steps because the history is delayed
sim_to_sync_time = int(SIMULATION_LENGTH / synchronization_time)
sync_steps = int(synchronization_time / integrator.dt)
with pytest.raises(ValueError):
coupling_future = sim_6.loop_cosim_monitor_output(sync_steps, 1)
coupling_future = sim_6.loop_cosim_monitor_output()
for i in range(sim_to_sync_time):
result_2 = sim_6.run()[0][1][:, 0, 0, 0]
np.testing.assert_array_equal(
result[i * sync_steps:(i + 1) * sync_steps] * np.NAN, result_2)
assert np.sum(np.isnan(
sim_6.loop_cosim_monitor_output()[0][1])) == 0
def test_monitor(self):
connectivity, coupling, integrator, monitors, sim, result, result_all = self._reference_simulation(
self._simulation_length)
# New simulator with proxy
np.random.seed(42)
init = np.concatenate((np.random.random_sample(
(385, 1, 76, 1)), np.random.random_sample((385, 1, 76, 1))),
axis=1)
np.random.seed(42)
model_1 = ReducedWongWangProxy(tau_s=np.random.rand(76))
# Initialise a Simulator -- Model, Connectivity, Integrator, and Monitors.
synchronization_time = 1.0
sim_1 = CoSimulator(
voi=np.array([0]),
synchronization_time=synchronization_time,
cosim_monitors=(RawCosim(),
RawVoiCosim(variables_of_interest=np.array([0])),
RawDelayed(),
RawVoiDelayed(variables_of_interest=np.array([0])),
CosimCoupling(coupling=coupling),
CosimCoupling(coupling=coupling,
variables_of_interest=np.array([0
]))),
proxy_inds=np.asarray([0], dtype=np.int),
model=model_1,
connectivity=connectivity,
coupling=coupling,
integrator=integrator,
monitors=(monitors, ),
initial_conditions=init,
)
sim_1.configure()
sim_to_sync_time = int(self._simulation_length / synchronization_time)
sync_steps = int(synchronization_time / integrator.dt)
result_1_all = [np.empty((0, )), np.empty((sync_steps, 2, 76, 1))]
result_cosim_monitors = []
sim_1.run() # run the first steps because the history is delayed
for j in range(0, sim_to_sync_time):
# This should fail for CosimCoupling that can only return FUTURE coupling values!!!
result_cosim_monitors.append(
sim_1.loop_cosim_monitor_output(sync_steps, 0))
result_1_all_step = sim_1.run(cosim_updates=[
np.array([
result_all[0][0][(sync_steps * j) + i]
for i in range(sync_steps)
]),
np.array([
result_all[0][1][(sync_steps * j) + i][0][0]
for i in range(sync_steps)
]).reshape((sync_steps, 1, 1, 1))
])
result_1_all[0] = np.concatenate(
(result_1_all[0], result_1_all_step[0][0]))
result_1_all[1] = np.concatenate(
(result_1_all[1], result_1_all_step[0][1]))
for i in range(int(self._simulation_length / integrator.dt)):
np.testing.assert_array_equal(
result_all[0][1][i][0][1:], result_1_all[1][i + sync_steps, 0,
1:])
np.testing.assert_array_equal(
result_all[0][1][i][0][:1], result_1_all[1][i + sync_steps,
0, :1])
for i in range(sim_to_sync_time):
result_step = result_cosim_monitors[i]
# check the dimension of the monitors
np.testing.assert_array_equal(result_step[0][1].shape,
(sync_steps, 2, 76, 1))
np.testing.assert_array_equal(result_step[1][1].shape,
(sync_steps, 1, 76, 1))
np.testing.assert_array_equal(result_step[3][1].shape,
(sync_steps, 1, 76, 1))
np.testing.assert_array_equal(result_step[4][1].shape,
(sync_steps, 1, 76, 1))
np.testing.assert_array_equal(result_step[5][1].shape,
(sync_steps, 1, 76, 1))
# compare the monitors between them
np.testing.assert_array_equal(result_step[2][1], result_step[3][1])
np.testing.assert_array_equal(result_step[4][1], result_step[5][1])
np.testing.assert_array_equal(result_step[0][1][:, 0, :, :],
result_step[1][1][:, 0, :, :])
