def test_trial_shuffling_output_format(self):
spiketrain = \
[neo.SpikeTrain([90, 93, 97, 100, 105, 150, 180, 190] * pq.ms,
t_stop=.2 * pq.s),
neo.SpikeTrain([90, 93, 97, 100, 105, 150, 180, 190] * pq.ms,
t_stop=.2 * pq.s)]
# trial_length = 200 * pq.ms
# trial_separation = 50 * pq.ms
n_surrogates = 2
dither = 10 * pq.ms
surrogate_trains = surr.trial_shifting(spiketrain,
dither=dither,
n_surrogates=n_surrogates)
self.assertIsInstance(surrogate_trains, list)
self.assertEqual(len(surrogate_trains), n_surrogates)
self.assertIsInstance(surrogate_trains[0], list)
self.assertIsInstance(surrogate_trains[0][0], neo.SpikeTrain)
for surrogate_train in surrogate_trains[0]:
self.assertEqual(surrogate_train.units, spiketrain[0].units)
self.assertEqual(surrogate_train.t_start, spiketrain[0].t_start)
self.assertEqual(surrogate_train.t_stop, spiketrain[0].t_stop)
self.assertEqual(len(surrogate_train), len(spiketrain[0]))
assert_array_less(0., np.diff(surrogate_train)) # check ordering
def test_trial_shuffling_empty_train(self):
empty_spiketrain = [neo.SpikeTrain([] * pq.ms, t_stop=500 * pq.ms),
neo.SpikeTrain([] * pq.ms, t_stop=500 * pq.ms)]
dither = 10 * pq.ms
surrogate_train = surr.trial_shifting(
empty_spiketrain, dither=dither, n_surrogates=1)[0]
self.assertEqual(len(surrogate_train), 2)
self.assertEqual(len(surrogate_train[0]), 0)