def test_plot_raster():
np.random.seed(123456789)
max_nspikes = 20
ngroups = 8
max_cells_per_group = 4
spike_trials = list()
groups = dict()
for k in range(ngroups):
current_ntrials = len(spike_trials)
ncells = np.random.randint(1, max_cells_per_group+1)
nspikes = max_nspikes - k*2
for n in range(ncells):
spikes = np.random.rand(nspikes)
spike_trials.append(spikes)
groups['G%d' % k] = range(current_ntrials, current_ntrials+ncells)
print groups
plt.figure()
plot_raster(spike_trials, duration=1.0, ylabel='', groups=groups)
plt.show()
def test_plot_raster():
np.random.seed(123456789)
max_nspikes = 20
ngroups = 8
max_cells_per_group = 4
spike_trials = list()
groups = dict()
for k in range(ngroups):
current_ntrials = len(spike_trials)
ncells = np.random.randint(1, max_cells_per_group + 1)
nspikes = max_nspikes - k * 2
for n in range(ncells):
spikes = np.random.rand(nspikes)
spike_trials.append(spikes)
groups['G%d' % k] = range(current_ntrials, current_ntrials + ncells)
print groups
plt.figure()
plot_raster(spike_trials, duration=1.0, ylabel='', groups=groups)
plt.show()
def testFFT(self):
sr = 1000.
freqs = [35.]
dur = 0.500
nt = int(dur * sr)
t = np.arange(nt) / sr
# create a psth that has the specific frequencies
psth = np.zeros([nt])
for f in freqs:
psth += np.sin(2 * np.pi * f * t)
max_spike_rate = 0.1
psth /= psth.max()
psth += 1.
psth /= 2.0
psth *= max_spike_rate
# simulate a spike train with a variety of frequencies in it
trials = simulate_poisson(psth, dur, num_trials=10)
bin_size = 0.001
binned_trials = spike_trains_to_matrix(trials, bin_size, 0.0, dur)
mean_psth = binned_trials.mean(axis=0)
# compute the power spectrum of each spike train
psds = list()
pfreq = None
win_len = 0.090
inc = 0.010
for st in binned_trials:
pfreq, psd, ps_var, phase = power_spectrum_jn(
st, 1.0 / bin_size, win_len, inc)
nz = psd > 0
psd[nz] = 20 * np.log10(psd[nz]) + 100
psd[psd < 0] = 0
psds.append(psd)
psds = np.array(psds)
mean_psd = psds.mean(axis=0)
pfreq, mean_psd2, ps_var, phase = power_spectrum_jn(
mean_psth, 1.0 / bin_size, win_len, inc)
nz = mean_psd2 > 0
mean_psd2[nz] = 20 * np.log10(mean_psd2[nz]) + 100
mean_psd2[mean_psd2 < 0] = 0
plt.figure()
ax = plt.subplot(2, 1, 1)
plot_raster(trials,
ax=ax,
duration=dur,
bin_size=0.001,
time_offset=0.0,
ylabel='Trial #',
bgcolor=None,
spike_color='k')
ax = plt.subplot(2, 1, 2)
plt.plot(pfreq, mean_psd, 'k-', linewidth=3.0)
for psd in psds:
plt.plot(pfreq, psd, '-', linewidth=2.0, alpha=0.75)
plt.plot(pfreq, mean_psd2, 'k--', linewidth=3.0, alpha=0.60)
plt.axis('tight')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Power (dB)')
plt.xlim(0, 100.)
plt.show()
def testFFT(self):
sr = 1000.
freqs = [35.]
dur = 0.500
nt = int(dur*sr)
t = np.arange(nt) / sr
# create a psth that has the specific frequencies
psth = np.zeros([nt])
for f in freqs:
psth += np.sin(2*np.pi*f*t)
max_spike_rate = 0.1
psth /= psth.max()
psth += 1.
psth /= 2.0
psth *= max_spike_rate
# simulate a spike train with a variety of frequencies in it
trials = simulate_poisson(psth, dur, num_trials=10)
bin_size = 0.001
binned_trials = spike_trains_to_matrix(trials, bin_size, 0.0, dur)
mean_psth = binned_trials.mean(axis=0)
# compute the power spectrum of each spike train
psds = list()
pfreq = None
win_len = 0.090
inc = 0.010
for st in binned_trials:
pfreq,psd,ps_var,phase = power_spectrum_jn(st, 1.0 / bin_size, win_len, inc)
nz = psd > 0
psd[nz] = 20*np.log10(psd[nz]) + 100
psd[psd < 0] = 0
psds.append(psd)
psds = np.array(psds)
mean_psd = psds.mean(axis=0)
pfreq,mean_psd2,ps_var,phase = power_spectrum_jn(mean_psth, 1.0/bin_size, win_len, inc)
nz = mean_psd2 > 0
mean_psd2[nz] = 20*np.log10(mean_psd2[nz]) + 100
mean_psd2[mean_psd2 < 0] = 0
plt.figure()
ax = plt.subplot(2, 1, 1)
plot_raster(trials, ax=ax, duration=dur, bin_size=0.001, time_offset=0.0, ylabel='Trial #', bgcolor=None, spike_color='k')
ax = plt.subplot(2, 1, 2)
plt.plot(pfreq, mean_psd, 'k-', linewidth=3.0)
for psd in psds:
plt.plot(pfreq, psd, '-', linewidth=2.0, alpha=0.75)
plt.plot(pfreq, mean_psd2, 'k--', linewidth=3.0, alpha=0.60)
plt.axis('tight')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Power (dB)')
plt.xlim(0, 100.)
plt.show()
