def test_crosscorrfunc(self):
Nloc = int(0.1 * self.N)
Nloceff = int(0.1 * self.Neff)
sp = cthlp.create_correlated_spiketrains_sip(self.rate, self.T,
Nloceff, self.cc)
sp_ids, sp_srt = cthlp.sort_gdf_by_id(sp, 0, Nloc)
bins, bsp = cthlp.instantaneous_spike_count(sp_srt, self.tbin)
freq, power = ctana.powerspec(bsp, self.tbin)
freq_cross, cross = ctana.crossspec(bsp, self.tbin)
time_auto, autof = ctana.autocorrfunc(freq, power)
time_cross, crossf = ctana.crosscorrfunc(freq_cross, cross)
if len(crossf[0, 0]) % 2 == 0:
mid = int(len(crossf[0, 0]) / 2 - 1)
else:
mid = int(np.floor(len(crossf[0, 0]) / 2.))
offset = self.tbin / self.T * \
(self.rate + self.rate ** 2 * self.T * 1e-3)
for i in range(Nloceff):
# consistency check with auto-correlation function
self.assertTrue(abs(np.sum(autof[i] - crossf[i, i])) < 1e-10)
for j in range(Nloceff):
if i != j:
# c(0) = corrcoef*rate+offset
self.assertTrue(
abs(crossf[i, j][mid] -
(self.cc * self.rate + offset)) <
(self.cc * self.rate + offset) * 1e-1)
# c(0)/a(0) = corrcoef
self.assertTrue(
abs((crossf[i, j][mid] - offset) / np.sqrt(
(crossf[i, i][mid] - offset) *
(crossf[j, j][mid] - offset)) -
self.cc) < self.cc * 5e-2)
freq, power = ctana.powerspec(bsp, self.tbin, units=True)
freq_cross, cross = ctana.crossspec(bsp, self.tbin, units=True)
time, autof = ctana.autocorrfunc(freq, power)
time_cross, crossf = ctana.crosscorrfunc(freq, cross)
offset_auto = self.p * self.tbin / self.T * \
(self.rate + self.rate ** 2 * self.T * 1e-3)
offset_cross = 1. * Nloceff * \
(Nloceff - 1) / Nloc / (Nloc - 1) * self.tbin / \
self.T * (self.rate + self.rate ** 2 * self.T * 1e-3)
# c(0) ~ self.p**2*corrcoef*rate+offset
self.assertTrue(
abs(crossf[mid] -
(1. * Nloceff * (Nloceff - 1) / Nloc /
(Nloc - 1) * self.cc * self.rate + offset_cross)) <
(1. * Nloceff * (Nloceff - 1) / Nloc /
(Nloc - 1) * self.cc * self.rate + offset_cross) * 2e-1)
# c(0)/a(0) = corrcoef
self.assertTrue(
abs((crossf[mid] - offset_cross) /
(autof[mid] - offset_auto) - 1. * (Nloceff - 1.) /
(Nloc - 1.) * self.cc) < 1. * (Nloceff - 1.) /
(Nloc - 1.) * self.cc * 2e-1)
def test_corrcoef(self):
Nloc = int(0.1 * self.N)
Nloceff = int(0.1 * self.Neff)
sp = cthlp.create_correlated_spiketrains_sip(
self.rate, self.T, Nloceff, self.cc)
sp_ids, sp_srt = cthlp.sort_gdf_by_id(sp, 0, Nloc)
bins, bsp = cthlp.instantaneous_spike_count(sp_srt, self.tbin)
freq_cross, cross = ctana.crossspec(bsp, self.tbin)
time_cross, crossf = ctana.crosscorrfunc(freq_cross, cross)
corrcoef = ctana.corrcoef(time_cross, crossf)
for i in xrange(Nloc):
for j in xrange(Nloc):
if i < Nloceff and j < Nloceff:
if i == j:
self.assertTrue(abs(corrcoef[i, j] - 1.) < 1e-12)
else:
self.assertTrue(
abs(corrcoef[i, j] - self.cc) < self.cc * 1e-1)
else:
self.assertTrue(abs(corrcoef[i, j]) < 1e-12)
fn = os.path.join(load_path,
'rate_time_series_full_Parameters.json')
with open(fn, 'r') as f:
params = json.load(f)
i_min = int(500. - params['t_min'])
i_max = int(T - params['t_min'])
rates = [rate_time_series1[i_min:i_max],
rate_time_series2[i_min:i_max]]
dat = [ch.centralize(rates[0], units=True),
ch.centralize(rates[1], units=True)]
freq, crossspec = corr.crossspec(dat, 1.)
t, cross = corr.crosscorrfunc(freq, crossspec)
sigma = 2.
time_range = np.arange(-5., 5.)
kernel = 1 / (np.sqrt(2.0 * np.pi) * sigma) * np.exp(-(time_range ** 2 / (2 * sigma ** 2)))
cross_conv = np.zeros_like(cross)
cross_conv[0][0] = np.convolve(kernel, cross[0][0], mode='same')
cross_conv[0][1] = np.convolve(kernel, cross[0][1], mode='same')
cross_conv[1][0] = np.convolve(kernel, cross[1][0], mode='same')
cross_conv[1][1] = np.convolve(kernel, cross[1][1], mode='same')
cross = cross_conv
fp = '_'.join(('cross_correlation',
area1,
area2))
np.save('{}/{}.npy'.format(save_path, fp), cross)
def crosscorrf(times, a_s, tmax, tbin):
times_bin, st = hlp.bin_binary_data(times, a_s, tbin)
freq, cross = ctana.crossspec(st, tbin, units=True)
return ctana.crosscorrfunc(freq, cross)