def test_run(self):
wc = WilsonCowanNetwork(self.SC,
self.DELAYS,
exc_seed=SEED,
inh_seed=SEED)
all_results = []
for backend, noise_func in BACKENDS_TO_TEST.items():
result = wc.run(
DURATION,
DT,
noise_func(ZeroInput(DURATION, DT, wc.num_noise_variables)),
backend=backend,
)
self.assertTrue(isinstance(result, xr.Dataset))
self.assertEqual(len(result),
wc.num_state_variables / wc.num_nodes)
self.assertTrue(
all(result[result_].shape == (int(DURATION / DT), wc.num_nodes)
for result_ in result))
all_results.append(result)
# test results are the same from different backends
for state_var in all_results[0]:
corr_mat = np.corrcoef(
np.vstack([
result[state_var].values.flatten().astype(float)
for result in all_results
]))
self.assertTrue(np.greater(corr_mat, CORR_THRESHOLD).all())
def test_compare_w_neurolib_native_model(self):
"""
Compare with neurolib's native Wilson-Cowan model.
"""
wc_multi = WilsonCowanNetwork(self.SC, self.DELAYS)
multi_result = wc_multi.run(DURATION,
DT,
ZeroInput(DURATION, DT).as_array(),
backend="numba")
# run neurolib's model
wc_neurolib = WCModel(Cmat=self.SC, Dmat=self.DELAYS, seed=SEED)
wc_neurolib.params["duration"] = DURATION
wc_neurolib.params["dt"] = DT
# there is no "global coupling" parameter in MultiModel
wc_neurolib.params["K_gl"] = 1.0
# delays <-> length matrix
wc_neurolib.params["signalV"] = 1.0
wc_neurolib.params["sigma_ou"] = 0.0
# match initial state
wc_neurolib.params["exc_init"] = wc_multi.initial_state[::2][:, np.
newaxis]
wc_neurolib.params["inh_init"] = wc_multi.initial_state[1::2][:, np.
newaxis]
wc_neurolib.run()
for (var_multi, var_neurolib) in NEUROLIB_VARIABLES_TO_TEST:
for node_idx in range(len(wc_multi)):
corr_mat = np.corrcoef(
wc_neurolib[var_neurolib][node_idx, :],
multi_result[var_multi].values.T[node_idx, :])
self.assertTrue(np.greater(corr_mat, CORR_THRESHOLD).all())
def test_init(self):
wc = WilsonCowanNetwork(self.SC, self.DELAYS)
self.assertTrue(isinstance(wc, WilsonCowanNetwork))
self.assertEqual(len(wc), self.SC.shape[0])
self.assertEqual(wc.initial_state.shape[0], wc.num_state_variables)
self.assertEqual(wc.default_output, f"q_mean_{EXC}")