98 lines
3.8 KiB
Python
98 lines
3.8 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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return time, 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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time = np.arange(0, t_max, dt)
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return time, np.mean(s_est, axis=0)
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def plot_results(sta_time, st_average, stim_time, s_est, stimulus, duration, dt):
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sta_ax = plt.subplot2grid((1, 3), (0, 0), rowspan=1, colspan=1)
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stim_ax = plt.subplot2grid((1, 3), (0, 1), rowspan=1, colspan=2)
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fig = plt.gcf()
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fig.set_size_inches(15, 5)
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fig.subplots_adjust(left=0.075, bottom=0.12, top=0.92, right=0.975)
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fig.set_facecolor("white")
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sta_ax.plot(sta_time * 1000, st_average, color="dodgerblue", lw=2.)
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sta_ax.set_xlabel("time [ms]", fontsize=12)
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sta_ax.set_ylabel("stimulus", fontsize=12)
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sta_ax.set_xlim([-50, 50])
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# sta_ax.xaxis.grid('off')
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sta_ax.spines["right"].set_visible(False)
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sta_ax.spines["top"].set_visible(False)
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sta_ax.yaxis.set_ticks_position('left')
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sta_ax.xaxis.set_ticks_position('bottom')
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sta_ax.spines["bottom"].set_linewidth(2.0)
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sta_ax.spines["left"].set_linewidth(2.0)
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sta_ax.tick_params(direction="out", width=2.0)
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ylim = sta_ax.get_ylim()
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xlim = sta_ax.get_xlim()
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sta_ax.plot(list(xlim), [0., 0.], zorder=1, color='darkgray', ls='--', lw=0.75)
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sta_ax.plot([0., 0.], list(ylim), zorder=1, color='darkgray', ls='--', lw=0.75)
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sta_ax.set_xlim(list(xlim))
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sta_ax.set_ylim(list(ylim))
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sta_ax.text(-0.225, 1.05, "A", transform=sta_ax.transAxes, size=14)
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stim_ax.plot(stim_time * 1000, stimulus[:,1], label='stimulus', color='dodgerblue', lw=2.)
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stim_ax.plot(stim_time * 1000, s_est, label='reconstruction', color='red', lw=2)
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stim_ax.set_xlabel('time[ms]', fontsize=12)
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stim_ax.set_xlim([0.0, 250])
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stim_ax.set_ylim([-1., 1.])
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stim_ax.legend()
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stim_ax.plot([0.0, 250], [0., 0.], color="darkgray", lw=0.75, ls='--', zorder=1)
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stim_ax.spines["right"].set_visible(False)
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stim_ax.spines["top"].set_visible(False)
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stim_ax.yaxis.set_ticks_position('left')
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stim_ax.xaxis.set_ticks_position('bottom')
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stim_ax.spines["bottom"].set_linewidth(2.0)
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stim_ax.spines["left"].set_linewidth(2.0)
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stim_ax.tick_params(direction="out", width=2.0)
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stim_ax.text(-0.075, 1.05, "B", transform=stim_ax.transAxes, size=14)
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fig.savefig("sta.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_time, sta = plot_sta(spike_times, stimulus, 5e-5, -0.05, 0.05)
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stim_time, s_est = reconstruct_stimulus(spike_times, sta, stimulus, 10, 5e-5)
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plot_results(sta_time, sta, stim_time, s_est, stimulus, 10, 5e-5)
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