def test_inserted_spikes_equal_cost_of_one(self):
num_spikes = 3
st = neo.SpikeTrain(sp.arange(3) * pq.s, t_stop=num_spikes * pq.s)
st_empty = create_empty_spike_train()
expected = sp.array([[0.0, num_spikes], [num_spikes, 0.0]])
assert_array_almost_equal(
expected, stm.victor_purpura_dist([st, st_empty]))
def test_returns_discretization_bins(self):
start = 2.0 * pq.s
stop = 5.0 * pq.s
sampling_rate = 2.0 * pq.Hz
expected = sp.array([2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0]) * pq.s
st = create_empty_spike_train(start, stop)
_, bins = sigproc.st_convolve(
st, sigproc.GaussianKernel(), sampling_rate=sampling_rate)
assert_array_almost_equal(expected, bins)
def test_returns_nan_if_one_spike_train_is_empty(self):
empty = create_empty_spike_train()
non_empty = neo.SpikeTrain(sp.array([1.0]) * pq.s, t_stop=2.0 * pq.s)
k = sigproc.GaussianKernel()
with warnings.catch_warnings():
warnings.simplefilter('ignore')
actual = stm.schreiber_similarity((empty, non_empty), k)
self.assertTrue(sp.isnan(actual[0, 0]))
self.assertTrue(sp.isnan(actual[0, 1]))
self.assertTrue(sp.isnan(actual[1, 0]))
def test_returns_nan_if_one_spike_train_is_empty(self):
empty = create_empty_spike_train()
non_empty = neo.SpikeTrain(sp.array([1.0]) * pq.s, t_stop=2.0 * pq.s)
sampling_rate = 100 * pq.Hz
smoothing_filter = sigproc.GaussianKernel()
with warnings.catch_warnings():
warnings.simplefilter('ignore')
self.assertTrue(sp.all(sp.isnan(stm.cs_dist(
[empty, non_empty], smoothing_filter,
sampling_rate=sampling_rate))[(0, 0, 1), (0, 1, 0)]))
def test_returns_discretization_bins(self):
start = 2.0 * pq.s
stop = 5.0 * pq.s
sampling_rate = 2.0 * pq.Hz
expected = sp.array([2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0]) * pq.s
st = create_empty_spike_train(start, stop)
_, bins = sigproc.st_convolve(st,
sigproc.GaussianKernel(),
sampling_rate=sampling_rate)
assert_array_almost_equal(expected, bins)
def test_length_of_returned_array_equals_sampling_rate_times_duration(self):
start = 2.0 * pq.s
stop = 5.0 * pq.s
duration = stop - start
sampling_rate = 12 * pq.Hz
expected_length = (sampling_rate * duration).simplified
st = create_empty_spike_train(start, stop)
result, _ = sigproc.st_convolve(
st, sigproc.GaussianKernel(), sampling_rate=sampling_rate)
self.assertEqual(expected_length, result.size)
def test_returns_norm_if_one_spike_train_is_empty(self):
empty = create_empty_spike_train()
non_empty = neo.SpikeTrain(sp.array([1.0]) * pq.s, t_stop=2.0 * pq.s)
sampling_rate = 100 * pq.Hz
smoothing_filter = sigproc.GaussianKernel()
norm = stm.st_norm(
non_empty, smoothing_filter, sampling_rate=sampling_rate)
expected = sp.array([[0.0, norm], [norm, 0.0]]) * pq.Hz ** 0.5
actual = stm.norm_dist(
[empty, non_empty], smoothing_filter, sampling_rate=sampling_rate)
assert_array_almost_equal(expected, actual, decimal=3)
def test_length_of_returned_array_equals_sampling_rate_times_duration(
self):
start = 2.0 * pq.s
stop = 5.0 * pq.s
duration = stop - start
sampling_rate = 12 * pq.Hz
expected_length = (sampling_rate * duration).simplified
st = create_empty_spike_train(start, stop)
result, _ = sigproc.st_convolve(st,
sigproc.GaussianKernel(),
sampling_rate=sampling_rate)
self.assertEqual(expected_length, result.size)
def test_mode_allows_valid_convolution(self):
start = 2.0 * pq.s
stop = 5.0 * pq.s
sampling_rate = 2.0 * pq.Hz
kernel = sigproc.RectangularKernel(0.6 * pq.s)
st = create_empty_spike_train(start, stop)
expected_length = (stop - start) * sampling_rate - 2
expected_bins = sp.array([2.5, 3.0, 3.5, 4.0, 4.5]) * pq.s
binned, bins = sigproc.st_convolve(
st, kernel, mode='valid', sampling_rate=sampling_rate)
self.assertEqual(binned.size, expected_length)
assert_array_almost_equal(expected_bins, bins)
def test_returns_zero_if_any_spike_train_is_empty(self):
empty = create_empty_spike_train()
non_empty = neo.SpikeTrain(sp.array([1.0]) * pq.s, t_stop=2.0 * pq.s)
smoothing_filter = sigproc.GaussianKernel()
sampling_rate = 1 * pq.Hz
expected = sp.array([0.0]) * pq.Hz
self.assertAlmostEqual(
expected, stm.st_inner(
[empty], [empty], smoothing_filter, sampling_rate))
self.assertAlmostEqual(
expected, stm.st_inner(
[empty], [non_empty], smoothing_filter, sampling_rate))
self.assertAlmostEqual(
expected, stm.st_inner(
[non_empty], [empty], smoothing_filter, sampling_rate))
def test_mode_allows_valid_convolution(self):
start = 2.0 * pq.s
stop = 5.0 * pq.s
sampling_rate = 2.0 * pq.Hz
kernel = sigproc.RectangularKernel(0.6 * pq.s)
st = create_empty_spike_train(start, stop)
expected_length = (stop - start) * sampling_rate - 2
expected_bins = sp.array([2.5, 3.0, 3.5, 4.0, 4.5]) * pq.s
binned, bins = sigproc.st_convolve(st,
kernel,
mode='valid',
sampling_rate=sampling_rate)
self.assertEqual(binned.size, expected_length)
assert_array_almost_equal(expected_bins, bins)
def test_convolution_with_empty_spike_train_returns_array_of_zeros(self):
st = create_empty_spike_train()
result, _ = sigproc.st_convolve(st, sigproc.GaussianKernel(),
1 * pq.Hz)
self.assertTrue(sp.all(result == 0.0))
def test_convolution_with_empty_spike_train_returns_array_of_zeros(self):
st = create_empty_spike_train()
result, _ = sigproc.st_convolve(st, sigproc.GaussianKernel(), 1 * pq.Hz)
self.assertTrue(sp.all(result == 0.0))
def test_raises_exception_if_number_of_trials_differs(self):
st = create_empty_spike_train()
with self.assertRaises(ValueError):
stm.victor_purpura_multiunit_dist({0: [st], 1: [st, st]}, 1.0)
def test_returns_zero_if_spike_train_is_empty(self):
empty = create_empty_spike_train()
smoothing_filter = sigproc.GaussianKernel()
self.assertAlmostEqual(0.0, stm.st_norm(
empty, smoothing_filter, 1 * pq.Hz))
def test_spike_trains_may_be_empty(self):
empty = create_empty_spike_train()
non_empty = neo.SpikeTrain(sp.array([1.0]) * pq.s, t_stop=3.0 * pq.s)
expected = sp.array([[1.0, 0.0], [0.0, 1.0]])
actual = stm.hunter_milton_similarity([empty, non_empty])
assert_array_almost_equal(expected, actual)
