def test_functional_connectivity(self):
# assert log warning was issued
root_logger = logging.getLogger()
with self.assertLogs(root_logger, level="ERROR") as cm:
fcs = self.signal.functional_connectivity()
self.assertEqual(
cm.output,
[
"ERROR:root:Cannot compute functional connectivity from one timeseries."
],
)
# should be None when computing on one timeseries
self.assertEqual(None, fcs)
# now proper example with network - 3D case
ds = Dataset("gw")
aln = ALNModel(Cmat=ds.Cmat, Dmat=ds.Dmat)
# in ms, simulates for 2 minutes
aln.params["duration"] = 2 * 1000
aln.run()
network_sig = RatesSignal.from_model_output(aln)
fcs = network_sig.functional_connectivity()
self.assertTrue(isinstance(fcs, xr.DataArray))
correct_shape = (network_sig.shape[0], network_sig.shape[1],
network_sig.shape[1])
self.assertTupleEqual(fcs.shape, correct_shape)
# 2D case
fc = network_sig["rates_exc"].functional_connectivity()
self.assertTrue(isinstance(fc, xr.DataArray))
correct_shape = (network_sig.shape[1], network_sig.shape[1])
self.assertTupleEqual(fc.shape, correct_shape)
def test_load_save(self):
# create temp folder
os.makedirs(self.TEST_FOLDER)
# save
filename = os.path.join(self.TEST_FOLDER, "temp")
# do operation so we also test saving and loading of preprocessing steps
self.signal.normalize(std=True)
# test basic properties
repr(self.signal)
str(self.signal)
self.signal.shape
self.signal.dims_not_time
self.signal.coords_not_time
self.signal.start_time
self.signal.end_time
self.signal.time
self.signal.preprocessing_steps
# now save
self.signal.save(filename)
# load
loaded = RatesSignal.from_file(filename)
# remove folder
rmtree(self.TEST_FOLDER)
# compare they are equal
self.assertEqual(self.signal, loaded)
def setUpClass(cls):
np.random.seed(42)
aln = ALNModel()
# simulate 10 seconds
aln.params["duration"] = 10.0 * 1000
aln.params["sigma_ou"] = 0.1 # add some noise
aln.run()
# init RatesSignal
cls.signal = RatesSignal.from_model_output(aln)
def test_iterate(self):
# 1D case
sig = RatesSignal(xr.DataArray(np.random.rand(100), dims=["time"], coords={"time": np.arange(100)}))
for name, it in sig.iterate(return_as="signal"):
self.assertTrue(isinstance(it, RatesSignal))
self.assertTrue(isinstance(name, dict))
self.assertTupleEqual(it.shape, (sig.shape[-1],))
# multiD case
for name, it in self.signal.iterate(return_as="signal"):
self.assertTrue(isinstance(it, RatesSignal))
self.assertTrue(isinstance(name, dict))
# test it is one-dim with only time axis
self.assertTupleEqual(it.shape, (1, self.signal.shape[-1]))
for name, it in self.signal.iterate(return_as="xr"):
self.assertTrue(isinstance(it, xr.DataArray))
self.assertTrue(isinstance(name, tuple))
# test it is one-dim with only time axis
self.assertTupleEqual(it.shape, (self.signal.shape[-1], 1))
with pytest.raises(ValueError):
for name, it in self.signal.iterate(return_as="abcde"):
pass
def test_load_save(self):
# save
filename = os.path.join(self.TEST_FOLDER, "temp")
# do operation so we also test saving and loading of preprocessing steps
self.signal.normalize(std=True)
# test basic properties
repr(self.signal)
str(self.signal)
self.signal.start_time
self.signal.end_time
self.signal.preprocessing_steps
# now save
self.signal.save(filename)
# load
loaded = RatesSignal.from_file(filename)
# compare they are equal
self.assertEqual(self.signal, loaded)