82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import scipy.io as scio
|
|
import seaborn as sb
|
|
from IPython import embed
|
|
sb.set_context("paper")
|
|
|
|
|
|
def plot_sta(times, stim, dt, t_min=-0.1, t_max=.1):
|
|
count = 0
|
|
sta = np.zeros((abs(t_min) + abs(t_max))/dt)
|
|
time = np.arange(t_min, t_max, dt)
|
|
if len(stim.shape) > 1 and stim.shape[1] > 1:
|
|
stim = stim[:,1]
|
|
for i in range(len(times[0])):
|
|
times = np.squeeze(spike_times[0][i])
|
|
for t in times:
|
|
if (int((t + t_min)/dt) < 0) or ((t + t_max)/dt > len(stim)):
|
|
continue;
|
|
|
|
min_index = int(np.round((t+t_min)/dt))
|
|
max_index = int(np.round((t+t_max)/dt))
|
|
snippet = np.squeeze(stim[ min_index : max_index])
|
|
sta += snippet
|
|
count += 1
|
|
sta /= count
|
|
|
|
fig = plt.figure()
|
|
fig.set_size_inches(5, 5)
|
|
fig.subplots_adjust(left=0.15, bottom=0.125, top=0.95, right=0.95, )
|
|
fig.set_facecolor("white")
|
|
|
|
ax = fig.add_subplot(111)
|
|
ax.plot(time, sta, color="darkblue")
|
|
ax.set_xlabel("time [s]")
|
|
ax.set_ylabel("stimulus")
|
|
ax.xaxis.grid('off')
|
|
ylim = ax.get_ylim()
|
|
xlim = ax.get_xlim()
|
|
ax.plot(list(xlim), [0., 0.], zorder=1, color='darkgray', ls='--')
|
|
ax.plot([0., 0.], list(ylim), zorder=1, color='darkgray', ls='--')
|
|
ax.set_xlim(list(xlim))
|
|
ax.set_ylim(list(ylim))
|
|
fig.savefig("../lecture/images/sta.pdf")
|
|
plt.close()
|
|
return sta
|
|
|
|
|
|
def reconstruct_stimulus(spike_times, sta, stimulus, t_max=30., dt=1e-4):
|
|
s_est = np.zeros((spike_times.shape[1], len(stimulus)))
|
|
for i in range(10):
|
|
times = np.squeeze(spike_times[0][i])
|
|
indices = np.asarray((np.round(times/dt)), dtype=int)
|
|
y = np.zeros(len(stimulus))
|
|
y[indices] = 1
|
|
s_est[i, :] = np.convolve(y, sta, mode='same')
|
|
|
|
plt.plot(np.arange(0, t_max, dt), stimulus[:,1], label='stimulus', color='darkblue', lw=2.)
|
|
plt.plot(np.arange(0, t_max, dt), np.mean(s_est, axis=0), label='reconstruction', color='silver', lw=1.5)
|
|
plt.xlabel('time[s]')
|
|
plt.ylabel('stimulus')
|
|
plt.xlim([0.0, 0.25])
|
|
plt.ylim([-1., 1.])
|
|
plt.legend()
|
|
plt.plot([0.0, 0.25], [0., 0.], color="darkgray", lw=1, ls='--', zorder=1)
|
|
|
|
fig = plt.gcf()
|
|
fig.set_size_inches(7.5, 5)
|
|
fig.subplots_adjust(left=0.15, bottom=0.125, top=0.95, right=0.95, )
|
|
fig.set_facecolor("white")
|
|
fig.savefig('../lecture/images/reconstruction.pdf')
|
|
plt.close()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
punit_data = scio.loadmat('../../programming/exercises/p-unit_spike_times.mat')
|
|
punit_stim = scio.loadmat('../../programming/exercises/p-unit_stimulus.mat')
|
|
spike_times = punit_data["spike_times"]
|
|
stimulus = punit_stim["stimulus"]
|
|
sta = plot_sta(spike_times, stimulus, 5e-5, -0.05, 0.05)
|
|
reconstruct_stimulus(spike_times, sta, stimulus, 10, 5e-5)
|