def get_autocorr(self, clust_idx):
idx = np.where(self.spikes['clusters'] == self.clust_id[clust_idx])[0]
autocorr = xcorr(self.spikes['times'][idx],
self.spikes['clusters'][idx], AUTOCORR_BIN_SIZE,
AUTOCORR_WIN_SIZE)
return autocorr[0, 0, :]
def test_xcorr_2(self):
max_cluster = 10
spike_times, spike_clusters = _random_data(max_cluster)
bin_size, winsize_bins = .001, .05
c = xcorr(spike_times,
spike_clusters,
bin_size=bin_size,
window_size=winsize_bins)
self.assertEqual(c.shape, (max_cluster, max_cluster, 51))
def acorr(spike_times, bin_size=None, window_size=None):
"""Compute the auto-correlogram of a neuron.
Parameters
----------
:param spike_times: Spike times in seconds.
:type spike_times: array-like
:param bin_size: Size of the bin, in seconds.
:type bin_size: float
:param window_size: Size of the window, in seconds.
:type window_size: float
Returns an `(winsize_samples,)` array with the auto-correlogram.
"""
xc = xcorr(spike_times, np.zeros_like(spike_times), bin_size=bin_size, window_size=window_size)
return xc[0, 0, :]
def test_xcorr_0(self):
# 0: 0, 10
# 1: 10, 20
spike_times = np.array([0, 10, 10, 20])
spike_clusters = np.array([0, 1, 0, 1])
bin_size = 1
winsize_bins = 2 * 3 + 1
c_expected = np.zeros((2, 2, 7), dtype=np.int32)
c_expected[1, 0, 3] = 1
c_expected[0, 1, 3] = 1
c = xcorr(spike_times,
spike_clusters,
bin_size=bin_size,
window_size=winsize_bins)
self.assertTrue(np.allclose(c, c_expected))
def test_xcorr_1(self):
# 0: 2, 10, 12, 30
# 1: 3, 24
# 2: 20
spike_times = np.array([2, 3, 10, 12, 20, 24, 30, 40], dtype=np.uint64)
spike_clusters = np.array([0, 1, 0, 0, 2, 1, 0, 2])
bin_size = 1
winsize_bins = 2 * 3 + 1
c_expected = np.zeros((3, 3, 7), dtype=np.int32)
c_expected[0, 1, 4] = 1
c_expected[1, 0, 2] = 1
c_expected[0, 0, 1] = 1
c_expected[0, 0, 5] = 1
c = xcorr(spike_times,
spike_clusters,
bin_size=bin_size,
window_size=winsize_bins)
self.assertTrue(np.allclose(c, c_expected))