def test_empty_sta():
"""Test that an empty with no spikes returns an array of nans"""
np.random.seed(0)
time = np.arange(100)
spikes = np.array(())
stimulus = np.random.randn(100, )
filter_length = 5
sta, _ = flt.sta(time, stimulus, spikes, filter_length)
assert np.all(np.isnan(sta))
def test_empty_sta():
"""Test that an empty with no spikes returns an array of nans"""
np.random.seed(0)
time = np.arange(100)
spikes = np.array(())
stimulus = np.random.randn(100,)
filter_length = 5
sta, _ = flt.sta(time, stimulus, spikes, filter_length)
assert np.all(np.isnan(sta))
def test_sta():
"""Test computing a spike-triggered average."""
np.random.seed(0)
time = np.arange(100)
spikes = np.array((0, 30, 70))
stimulus = np.random.randn(100,)
filter_length = 5
sta, tax = flt.sta(time, stimulus, spikes, filter_length)
tmp = np.zeros(sta.shape)
for ix in spikes[1:]: # Should ignore first spike, comes before filter_length frames
tmp += stimulus[ix - filter_length : ix]
tmp /= len(spikes)
assert np.allclose(tmp, sta)
assert np.allclose(tax, np.arange(-filter_length + 1, 1))
def test_sta():
"""Test computing a spike-triggered average."""
np.random.seed(0)
time = np.arange(100)
spikes = np.array((0, 30, 70))
stimulus = np.random.randn(100,)
filter_length = 5
sta, tax = flt.sta(time, stimulus, spikes, filter_length)
tmp = np.zeros(sta.shape)
for ix in spikes[1:]: # Should ignore first spike, comes before filter_length frames
tmp += stimulus[ix - filter_length : ix]
tmp /= len(spikes)
assert np.allclose(tmp, sta)
assert np.allclose(tax, np.arange(-filter_length + 1, 1))
def test_sta_acausal():
"""Test computing a spike-triggered average with points before and
after the time of the spike.
"""
np.random.seed(0)
time = np.arange(100)
spikes = np.array((0, 30, 70))
stimulus = np.random.randn(100,)
nbefore, nafter = 5, 2
sta, tax = flt.sta(time, stimulus, spikes, nbefore, nafter)
tmp = np.zeros(sta.shape)
for ix in spikes[1:]: # Should ignore first spike, comes before filter_length frames
tmp += stimulus[ix - nbefore : ix + nafter]
tmp /= len(spikes)
assert np.allclose(tmp, sta)
assert np.allclose(tax, np.arange(-nbefore + 1, nafter + 1))
def test_sta_acausal():
"""Test computing a spike-triggered average with points before and
after the time of the spike.
"""
np.random.seed(0)
time = np.arange(100)
spikes = np.array((0, 30, 70))
stimulus = np.random.randn(100,)
nbefore, nafter = 5, 2
sta, tax = flt.sta(time, stimulus, spikes, nbefore, nafter)
tmp = np.zeros(sta.shape)
for ix in spikes[1:]: # Should ignore first spike, comes before filter_length frames
tmp += stimulus[ix - nbefore : ix + nafter]
tmp /= len(spikes)
assert np.allclose(tmp, sta)
assert np.allclose(tax, np.arange(-nbefore + 1, nafter + 1))