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(DURATION, DT).as_array(),
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())
def test_single_node(self):
logging.info("\t > Hopf: Testing single node ...")
start = time.time()
hopf = HopfModel()
hopf.params["duration"] = 2.0 * 1000
hopf.params["sigma_ou"] = 0.03
hopf.run()
end = time.time()
logging.info("\t > Done in {:.2f} s".format(end - start))
def test_network(self):
logging.info("\t > Hopf: Testing brain network (chunkwise integration and BOLD" " simulation) ...")
start = time.time()
ds = Dataset("gw")
hopf = HopfModel(Cmat=ds.Cmat, Dmat=ds.Dmat)
hopf.params["w"] = 1.0
hopf.params["signalV"] = 0
hopf.params["duration"] = 10 * 1000
hopf.params["sigma_ou"] = 0.14
hopf.params["K_gl"] = 0.6
hopf.run(chunkwise=True, bold=True, append_outputs=True)
end = time.time()
logging.info("\t > Done in {:.2f} s".format(end - start))
def test_compare_w_neurolib_native_model(self):
"""
Compare with neurolib's native Hopf model.
"""
# run this model
hopf_multi = self._create_node()
multi_result = hopf_multi.run(DURATION,
DT,
ZeroInput(DURATION, DT).as_array(),
backend="numba")
# run neurolib's model
hopf_neurolib = HopfModel(seed=SEED)
hopf_neurolib.params["duration"] = DURATION
hopf_neurolib.params["dt"] = DT
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())
def test_chunksize(self):
model = HopfModel()
chunksize = 100
model.run(chunksize=chunksize)
self.assertEqual(model.output.shape[1], chunksize)