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