def setUpClass(cls):
from neurolib.models.fhn import FHNModel
fhn = FHNModel()
fhn.run()
cls.model = fhn
from neurolib.utils.parameterSpace import ParameterSpace
pars = ParameterSpace({"x": [1, 2]})
cls.pars = pars
def setUpClass(cls):
ds = Dataset("hcp")
model = FHNModel(Cmat=ds.Cmat, Dmat=ds.Dmat)
model.params.duration = 10 * 1000 # ms
model.params.dt = 0.05
model.params.bold = True
parameters = ParameterSpace(
{
"x_ext": [
np.ones((model.params["N"], )) * a
for a in np.linspace(0, 2, 2)
],
"K_gl":
np.linspace(0, 2, 2),
"coupling": ["additive", "diffusive"],
},
kind="grid",
)
search = BoxSearch(
model=model,
parameterSpace=parameters,
filename=f"test_exploration_utils_{randomString(20)}.hdf")
search.run(chunkwise=True, bold=True)
search.loadResults()
# flatten x_ext parameter
search.dfResults.x_ext = [a[0] for a in list(search.dfResults.x_ext)]
cls.model = model
cls.search = search
cls.ds = ds
def test_fhn_brain_network_exploration(self):
ds = Dataset("hcp")
model = FHNModel(Cmat=ds.Cmat, Dmat=ds.Dmat)
model.params.duration = 10 * 1000 # ms
model.params.dt = 0.2
model.params.bold = True
parameters = ParameterSpace(
{
"x_ext": [
np.ones((model.params["N"], )) * a
for a in np.linspace(0, 2, 2)
],
"K_gl":
np.linspace(0, 2, 2),
"coupling": ["additive", "diffusive"],
},
kind="grid",
)
search = BoxSearch(model=model,
parameterSpace=parameters,
filename="test_fhn_brain_network_exploration.hdf")
search.run(chunkwise=True, bold=True)
pu.getTrajectorynamesInFile(
os.path.join(paths.HDF_DIR,
"test_fhn_brain_network_exploration.hdf"))
search.loadDfResults()
search.getRun(0, pypetShortNames=True)
search.getRun(0, pypetShortNames=False)
search.loadResults()
search.info()
def test_network(self):
logging.info("\t > FHN: Testing brain network (chunkwise integration and BOLD simulation) ...")
start = time.time()
ds = Dataset("gw")
fhn = FHNModel(Cmat=ds.Cmat, Dmat=ds.Dmat)
fhn.params["signalV"] = 4.0
fhn.params["duration"] = 10 * 1000
fhn.params["sigma_ou"] = 0.1
fhn.params["K_gl"] = 0.6
fhn.params["x_ext_mean"] = 0.72
fhn.run(chunkwise=True, bold=True, append_outputs=True)
end = time.time()
logging.info("\t > Done in {:.2f} s".format(end - start))
def test_single_node(self):
logging.info("\t > FHN: Testing single node ...")
start = time.time()
fhn = FHNModel()
fhn.params["duration"] = 2.0 * 1000
fhn.params["sigma_ou"] = 0.03
fhn.run()
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 FitzHugh-Nagumo model.
"""
# run this model
fhn_multi = self._create_node()
multi_result = fhn_multi.run(DURATION, DT, ZeroInput(DURATION, DT).as_array(), backend="numba")
# run neurolib's model
fhn = FHNModel(seed=SEED)
fhn.params["duration"] = DURATION
fhn.params["dt"] = DT
fhn.run()
for var in NEUROLIB_VARIABLES_TO_TEST:
corr_mat = np.corrcoef(fhn[var], multi_result[var].values.T)
self.assertTrue(np.greater(corr_mat, CORR_THRESHOLD).all())
def test_compare_w_neurolib_native_model(self):
"""
Compare with neurolib's native FitzHugh-Nagumo model.
"""
# run this model - default is diffusive coupling
fhn_multi = FitzHughNagumoNetwork(self.SC, self.DELAYS, seed=SEED)
multi_result = fhn_multi.run(
DURATION,
DT,
ZeroInput(fhn_multi.num_noise_variables).as_array(DURATION, DT),
backend="numba")
# run neurolib's model
fhn_neurolib = FHNModel(Cmat=self.SC, Dmat=self.DELAYS, seed=SEED)
fhn_neurolib.params["duration"] = DURATION
fhn_neurolib.params["dt"] = DT
# there is no "global coupling" parameter in MultiModel
fhn_neurolib.params["K_gl"] = 1.0
# delays <-> length matrix
fhn_neurolib.params["signalV"] = 1.0
fhn_neurolib.params["coupling"] = "diffusive"
fhn_neurolib.params["sigma_ou"] = 0.0
fhn_neurolib.params["xs_init"] = fhn_multi.initial_state[::2][:, np.
newaxis]
fhn_neurolib.params["ys_init"] = fhn_multi.initial_state[1::2][:, np.
newaxis]
fhn_neurolib.run()
for var in NEUROLIB_VARIABLES_TO_TEST:
corr_mat = np.corrcoef(fhn_neurolib[var],
multi_result[var].values.T)
self.assertTrue(np.greater(corr_mat, CORR_THRESHOLD).all())