def test_trial_shuffling_output_format_concatenated(self):
spiketrain = neo.SpikeTrain([90, 93, 97, 100, 105, 150, 180, 350] *
pq.ms,
t_stop=.5 * pq.s)
trial_length = 200 * pq.ms
trial_separation = 50 * pq.ms
n_surrogates = 2
dither = 10 * pq.ms
surrogate_trains = surr._trial_shifting_of_concatenated_spiketrain(
spiketrain,
dither=dither,
n_surrogates=n_surrogates,
trial_length=trial_length,
trial_separation=trial_separation)
self.assertIsInstance(surrogate_trains, list)
self.assertEqual(len(surrogate_trains), n_surrogates)
self.assertIsInstance(surrogate_trains[0], neo.SpikeTrain)
for surrogate_train in surrogate_trains:
self.assertEqual(surrogate_train.units, spiketrain.units)
self.assertEqual(surrogate_train.t_start, spiketrain.t_start)
self.assertEqual(surrogate_train.t_stop, spiketrain.t_stop)
self.assertEqual(len(surrogate_train), len(spiketrain))
assert_array_less(0., np.diff(surrogate_train)) # check ordering
def test_trial_shuffling_empty_train_concatenated(self):
empty_spiketrain = neo.SpikeTrain([] * pq.ms, t_stop=500 * pq.ms)
trial_length = 200 * pq.ms
trial_separation = 50 * pq.ms
dither = 10 * pq.ms
surrogate_train = surr._trial_shifting_of_concatenated_spiketrain(
empty_spiketrain, dither=dither, n_surrogates=1,
trial_length=trial_length, trial_separation=trial_separation)[0]
self.assertEqual(len(surrogate_train), 0)
