167 lines
5.5 KiB
Python
167 lines
5.5 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from IPython import embed
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def set_rc():
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plt.rcParams['xtick.labelsize'] = 8
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plt.rcParams['ytick.labelsize'] = 8
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plt.rcParams['xtick.major.size'] = 5
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plt.rcParams['xtick.minor.size'] = 5
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plt.rcParams['xtick.major.width'] = 2
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plt.rcParams['xtick.minor.width'] = 2
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plt.rcParams['ytick.major.size'] = 5
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plt.rcParams['ytick.minor.size'] = 5
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plt.rcParams['ytick.major.width'] = 2
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plt.rcParams['ytick.minor.width'] = 2
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plt.rcParams['xtick.direction'] = "out"
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plt.rcParams['ytick.direction'] = "out"
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def create_spikes(isi=0.08, duration=0.5, seed=57111):
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times = np.arange(0., duration, isi)
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rng = np.random.RandomState(seed)
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times += rng.randn(len(times)) * (isi / 2.5)
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times = np.delete(times, np.nonzero(times < 0))
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times = np.delete(times, np.nonzero(times > duration))
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times = np.sort(times)
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return times
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def gaussian(sigma, dt):
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x = np.arange(-4*sigma, 4*sigma, dt)
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y = np.exp(-0.5 * (x / sigma)**2)/np.sqrt(2*np.pi)/sigma;
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return x, y
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def setup_axis(spikes_ax, rate_ax):
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spikes_ax.spines["right"].set_visible(False)
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spikes_ax.spines["top"].set_visible(False)
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spikes_ax.yaxis.set_ticks_position('left')
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spikes_ax.xaxis.set_ticks_position('bottom')
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spikes_ax.set_yticks([0, 1.0])
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spikes_ax.set_ylim([0, 1.05])
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spikes_ax.set_ylabel("spikes", fontsize=9)
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spikes_ax.text(-0.125, 1.2, "A", transform=spikes_ax.transAxes, size=10)
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spikes_ax.set_xlim([0, 0.5])
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spikes_ax.set_xticklabels(np.arange(0., 600, 100))
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rate_ax.spines["right"].set_visible(False)
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rate_ax.spines["top"].set_visible(False)
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rate_ax.yaxis.set_ticks_position('left')
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rate_ax.xaxis.set_ticks_position('bottom')
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rate_ax.set_xlabel('time[ms]', fontsize=9)
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rate_ax.set_ylabel('firing rate [Hz]', fontsize=9)
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rate_ax.text(-0.125, 1.15, "B", transform=rate_ax.transAxes, size=10)
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rate_ax.set_xlim([0, 0.5])
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rate_ax.set_xticklabels(np.arange(0., 600, 100))
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def plot_bin_method():
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dt = 1e-5
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duration = 0.5
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spike_times = create_spikes(0.018, duration)
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t = np.arange(0., duration, dt)
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bins = np.arange(0, 0.55, 0.05)
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count, _ = np.histogram(spike_times, bins)
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plt.xkcd()
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set_rc()
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fig = plt.figure()
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fig.set_size_inches(5., 2.5)
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fig.set_facecolor('white')
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spikes = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)
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rate_ax = plt.subplot2grid((7,1), (3,0), rowspan=4, colspan=1)
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setup_axis(spikes, rate_ax)
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spikes.set_ylim([0., 1.25])
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spikes.vlines(spike_times, 0., 1., color="darkblue", lw=1.25)
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spikes.vlines(np.hstack((0,bins)), 0., 1.25, color="red", lw=1.5, linestyles='--')
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for i,c in enumerate(count):
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spikes.text(bins[i] + bins[1]/2, 1.05, str(c), fontdict={'color':'red', 'size':9})
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spikes.set_xlim([0, duration])
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rate = count / 0.05
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rate_ax.step(bins, np.hstack((rate, rate[-1])), where='post')
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rate_ax.set_xlim([0., duration])
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rate_ax.set_ylim([0., 100.])
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rate_ax.set_yticks(np.arange(0,105,25))
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fig.tight_layout()
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fig.savefig("binmethod.pdf")
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plt.close()
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def plot_conv_method():
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dt = 1e-5
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duration = 0.5
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spike_times = create_spikes(0.05, duration)
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kernel_time, kernel = gaussian(0.02, dt)
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t = np.arange(0., duration, dt)
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rate = np.zeros(t.shape)
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rate[np.asarray(np.round(spike_times/dt), dtype=int)] = 1
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rate = np.convolve(rate, kernel, mode='same')
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rate = np.roll(rate, -1)
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plt.xkcd()
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set_rc()
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fig = plt.figure()
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fig.set_size_inches(5., 2.5)
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fig.set_facecolor('white')
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spikes = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)
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rate_ax = plt.subplot2grid((7,1), (3,0), rowspan=4, colspan=1)
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setup_axis(spikes, rate_ax)
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spikes.vlines(spike_times, 0., 1., color="darkblue", lw=1.5, zorder=2)
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for i in spike_times:
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spikes.plot(kernel_time + i, kernel/np.max(kernel), color="orange", lw=0.75, zorder=1)
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spikes.set_xlim([0, duration])
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rate_ax.plot(t, rate, color="darkblue", lw=1, zorder=2)
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rate_ax.fill_between(t, rate, np.zeros(len(rate)), color="red", alpha=0.5)
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rate_ax.set_xlim([0, duration])
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rate_ax.set_ylim([0, 50])
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rate_ax.set_yticks(np.arange(0,75,25))
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fig.tight_layout()
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fig.savefig("convmethod.pdf")
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def plot_isi_method():
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spike_times = create_spikes(0.055, 0.5, 1000)
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plt.xkcd()
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set_rc()
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fig = plt.figure()
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fig.set_size_inches(5., 2.5)
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fig.set_facecolor('white')
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spikes = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)
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rate = plt.subplot2grid((7,1), (3,0), rowspan=4, colspan=1)
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setup_axis(spikes, rate)
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spikes.vlines(spike_times, 0., 1., color="darkblue", lw=1.25)
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spike_times = np.hstack((0, spike_times))
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for i in range(1, len(spike_times)):
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t_start = spike_times[i-1]
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t = spike_times[i]
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spikes.annotate(s='', xy=(t_start, 0.5), xytext=(t,0.5), arrowprops=dict(arrowstyle='<->'), color='red')
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spikes.text(t_start+0.01, 0.75,
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"{0:.1f}".format((t - t_start)*1000),
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fontdict={'color':'red', 'size':7})
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i_rate = 1./np.diff(spike_times)
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rate.step(spike_times, np.hstack((i_rate, i_rate[-1])),color="darkblue", lw=1.25, where="post")
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rate.set_ylim([0, 50])
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rate.set_yticks(np.arange(0,75,25))
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fig.tight_layout()
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fig.savefig("isimethod.pdf")
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if __name__ == '__main__':
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plot_isi_method()
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plot_conv_method()
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plot_bin_method()
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