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_convolved_spike_train(self):
st = neo.SpikeTrain(sp.array([1.0, 2.0]) * pq.s, t_stop=3.0 * pq.s)
kernel = sigproc.RectangularKernel(0.3 * pq.s)
# Because of the low sampling rate the expected result is a bit off
# from the analytical result.
expected = sp.array(
[0.0, 0.0, 0.0, 1.6666666, 1.6666666, 1.6666666, 0.0,
1.6666666, 1.6666666, 1.6666666, 0.0, 0.0]) * pq.Hz
actual, _ = sigproc.st_convolve(st, kernel, sampling_rate=4 * pq.Hz)
assert_array_almost_equal(expected, actual)
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_convolved_spike_train(self):
st = neo.SpikeTrain(sp.array([1.0, 2.0]) * pq.s, t_stop=3.0 * pq.s)
kernel = sigproc.RectangularKernel(0.3 * pq.s)
# Because of the low sampling rate the expected result is a bit off
# from the analytical result.
expected = sp.array([
0.0, 0.0, 0.0, 1.6666666, 1.6666666, 1.6666666, 0.0, 1.6666666,
1.6666666, 1.6666666, 0.0, 0.0
]) * pq.Hz
actual, _ = sigproc.st_convolve(st, kernel, sampling_rate=4 * pq.Hz)
assert_array_almost_equal(expected, actual)
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_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_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))
