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 setUpClass(cls):
ds = Dataset("hcp")
model = ALNModel(Cmat=ds.Cmat, Dmat=ds.Dmat)
model.params.duration = 11 * 1000 # ms
model.params.dt = 0.2
parameters = ParameterSpace(
{
"mue_ext_mean": np.linspace(0, 3, 2),
"mui_ext_mean": np.linspace(0, 3, 2),
"b": [0.0, 10.0],
},
kind="grid",
)
search = BoxSearch(
model=model,
parameterSpace=parameters,
filename=f"test_exploration_utils_{randomString(20)}.hdf")
search.run(chunkwise=True, bold=True)
search.loadResults()
cls.model = model
cls.search = search
cls.ds = ds
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()
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))
def test_circle_exploration(self):
def explore_me(traj):
pars = search.getParametersFromTraj(traj)
# let's calculate the distance to a circle
computation_result = abs((pars["x"] ** 2 + pars["y"] ** 2) - 1)
result_dict = {"distance": computation_result}
search.saveOutputsToPypet(result_dict, traj)
parameters = ParameterSpace({"x": np.linspace(-2, 2, 2), "y": np.linspace(-2, 2, 2)})
search = BoxSearch(evalFunction=explore_me, parameterSpace=parameters, filename="test_circle_exploration.hdf")
search.run()
search.loadResults(pypetShortNames=False)
for i in search.dfResults.index:
search.dfResults.loc[i, "distance"] = search.results[i]["distance"]
search.dfResults
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):
logging.info("\t > BoxSearch: Testing ALN single node ...")
start = time.time()
aln = ALNModel()
parameters = ParameterSpace({
"mue_ext_mean": np.linspace(0, 3, 2),
"mui_ext_mean": np.linspace(0, 3, 2)
})
search = BoxSearch(aln, parameters)
search.run()
search.loadResults()
for i in search.dfResults.index:
search.dfResults.loc[i, "max_r"] = np.max(
search.results[i]["rates_exc"][:,
-int(1000 / aln.params["dt"]):])
end = time.time()
logging.info("\t > Done in {:.2f} s".format(end - start))
def test_circle_exploration(self):
def explore_me(traj):
pars = search.getParametersFromTraj(traj)
# let's calculate the distance to a circle
computation_result = abs((pars["x"] ** 2 + pars["y"] ** 2) - 1)
result_dict = {"scalar_result": computation_result, "list_result": [1, 2, 3, 4], "array_result": np.ones(3)}
search.saveToPypet(result_dict, traj)
parameters = ParameterSpace({"x": np.linspace(-2, 2, 2), "y": np.linspace(-2, 2, 2)})
search = BoxSearch(evalFunction=explore_me, parameterSpace=parameters, filename="test_circle_exploration.hdf")
search.run()
search.loadResults(pypetShortNames=False)
# call the result dataframe
search.dfResults
# test integrity of dataframe
for i in search.dfResults.index:
self.assertEqual(search.dfResults.loc[i, "scalar_result"], search.results[i]["scalar_result"])
self.assertListEqual(search.dfResults.loc[i, "list_result"], search.results[i]["list_result"])
np.testing.assert_array_equal(search.dfResults.loc[i, "array_result"], search.results[i]["array_result"])
