def test_compare_w_neurolib_native_model(self):
"""
Compare with neurolib's native Hopf model.
"""
# run this model - default is diffusive coupling
hopf_multi = HopfNetwork(self.SC, self.DELAYS, seed=SEED)
multi_result = hopf_multi.run(DURATION,
DT,
ZeroInput().as_array(DURATION, DT),
backend="numba")
# run neurolib's model
hopf_neurolib = HopfModel(Cmat=self.SC, Dmat=self.DELAYS, seed=SEED)
hopf_neurolib.params["duration"] = DURATION
hopf_neurolib.params["dt"] = DT
# there is no "global coupling" parameter in MultiModel
hopf_neurolib.params["K_gl"] = 1.0
# delays <-> length matrix
hopf_neurolib.params["signalV"] = 1.0
hopf_neurolib.params["coupling"] = "diffusive"
hopf_neurolib.params["sigma_ou"] = 0.0
hopf_neurolib.params[
"xs_init"] = hopf_multi.initial_state[::2][:, np.newaxis]
hopf_neurolib.params["ys_init"] = hopf_multi.initial_state[
1::2][:, np.newaxis]
hopf_neurolib.run()
for var in NEUROLIB_VARIABLES_TO_TEST:
corr_mat = np.corrcoef(hopf_neurolib[var],
multi_result[var].values.T)
self.assertTrue(np.greater(corr_mat, CORR_THRESHOLD).all())
