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()
# firing rate xr
dataarray = search.xr()
self.assertTrue(isinstance(dataarray, xr.DataArray))
self.assertFalse(dataarray.attrs)
# bold xr
dataarray = search.xr(bold=True)
self.assertTrue(isinstance(dataarray, xr.DataArray))
self.assertFalse(dataarray.attrs)
search.info()
def test_multimodel_explore(self):
start = time.time()
DELAY = 13.0
fhn_net = FitzHughNagumoNetwork(np.random.rand(2, 2), np.array([[0.0, DELAY], [DELAY, 0.0]]))
model = MultiModel(fhn_net)
parameters = ParameterSpace({"*noise*sigma": [0.0, 0.05], "*epsilon*": [0.5, 0.6]}, allow_star_notation=True)
search = BoxSearch(model, parameters, filename="test_multimodel.hdf")
search.run()
search.loadResults()
dataarray = search.xr()
self.assertTrue(isinstance(dataarray, xr.DataArray))
self.assertTrue(isinstance(dataarray.attrs, dict))
self.assertListEqual(list(dataarray.attrs.keys()), list(parameters.dict().keys()))
end = time.time()
logging.info("\t > Done in {:.2f} s".format(end - start))
def test_single_node(self):
start = time.time()
model = ALNModel()
parameters = ParameterSpace({"mue_ext_mean": np.linspace(0, 3, 2), "mui_ext_mean": np.linspace(0, 3, 2)})
search = BoxSearch(model, parameters, filename="test_single_nodes.hdf")
search.run()
search.loadResults()
dataarray = search.xr()
self.assertTrue(isinstance(dataarray, xr.DataArray))
self.assertFalse(dataarray.attrs)
for i in search.dfResults.index:
search.dfResults.loc[i, "max_r"] = np.max(
search.results[i]["rates_exc"][:, -int(1000 / model.params["dt"]) :]
)
end = time.time()
logging.info("\t > Done in {:.2f} s".format(end - start))
