145 lines
4.9 KiB
Python
145 lines
4.9 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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from plotstyle import *
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fig_size = cm_size(figure_width, figure_height)
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def create_spikes(nspikes=11, duration=0.5, seed=1000):
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rng = np.random.RandomState(seed)
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x = np.linspace(0.0, 1.0, nspikes)
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# double gaussian rate profile:
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rate = np.exp(-0.5*((x-0.35)/0.25)**2.0)
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rate += 1.*np.exp(-0.5*((x-0.9)/0.05)**2.0)
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isis = 1.0/rate
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isis += rng.randn(len(isis))*0.2
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times = np.cumsum(isis)
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times *= 1.05*duration/times[-1]
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times += 0.01
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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.show_spines('')
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spikes_ax.set_yticks([])
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spikes_ax.set_ylim(-0.2, 1.0)
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spikes_ax.text(-0.11, 0.5, 'Spikes', transform=spikes_ax.transAxes, rotation='vertical', va='center')
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spikes_ax.set_xlim(-1, 500)
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spikes_ax.set_xticklabels([])
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#spikes_ax.set_xticklabels(np.arange(0., 600, 100))
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spikes_ax.show_spines('lb')
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rate_ax.set_xlabel('Time', 'ms')
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#rate_ax.set_ylabel('Firing rate', 'Hz')
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rate_ax.text(-0.11, 0.5, axis_label('Rate', 'Hz'), transform=rate_ax.transAxes,
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rotation='vertical', va='center')
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rate_ax.set_xlim(0, 500)
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#rate_ax.set_xticklabels(np.arange(0., 600, 100))
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rate_ax.set_ylim(0, 60)
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rate_ax.set_yticks(np.arange(0,65,20))
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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()
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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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fig = plt.figure(figsize=fig_size)
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spec = gridspec.GridSpec(nrows=2, ncols=1, height_ratios=[3, 4], hspace=0.2,
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**adjust_fs(fig, left=5.5, right=1.5, top=1.5))
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spikes_ax = fig.add_subplot(spec[0, 0])
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rate_ax = fig.add_subplot(spec[1, 0])
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setup_axis(spikes_ax, rate_ax)
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for ti in spike_times:
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ti *= 1000.0
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spikes_ax.plot([ti, ti], [0., 1.], color=colors['blue'], lw=2)
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for tb in 1000.0*bins :
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spikes_ax.plot([tb, tb], [-2.0, 0.75], '-', color="#777777", lw=1, clip_on=False)
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for i,c in enumerate(count):
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spikes_ax.text(1000.0*(bins[i]+0.5*bins[1]), 1.1, str(c), color=colors['red'],
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ha='center')
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rate = count / 0.05
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rate_ax.step(1000.0*bins, np.hstack((rate, rate[-1])), color=colors['orange'], lw=2, where='post')
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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()
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kernel_time, kernel = gaussian(0.015, 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[:-1]/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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fig = plt.figure(figsize=fig_size)
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spec = gridspec.GridSpec(nrows=2, ncols=1, height_ratios=[3, 4], hspace=0.2,
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**adjust_fs(fig, left=5.5, right=1.5, top=1.5))
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spikes_ax = fig.add_subplot(spec[0, 0])
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rate_ax = fig.add_subplot(spec[1, 0])
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setup_axis(spikes_ax, rate_ax)
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for ti in spike_times:
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ti *= 1000.0
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spikes_ax.plot([ti, ti], [0., 1.], color=colors['blue'], lw=2)
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spikes_ax.plot(1000*kernel_time + ti, kernel/np.max(kernel), color=colors['red'],
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lw=1, zorder=1)
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rate_ax.plot(1000.0*t, rate, color=colors['orange'], lw=2, zorder=2)
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rate_ax.fill_between(1000.0*t, rate, np.zeros(len(rate)), color=colors['yellow'])
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fig.savefig("convmethod.pdf")
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def plot_isi_method():
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spike_times = create_spikes()
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fig = plt.figure(figsize=fig_size)
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spec = gridspec.GridSpec(nrows=2, ncols=1, height_ratios=[3, 4], hspace=0.2,
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**adjust_fs(fig, left=5.5, right=1.5, top=1.5))
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spikes_ax = fig.add_subplot(spec[0, 0])
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rate_ax = fig.add_subplot(spec[1, 0])
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setup_axis(spikes_ax, rate_ax)
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spike_times = np.hstack((0.005, spike_times))
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for i in range(1, len(spike_times)):
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t_start = 1000*spike_times[i-1]
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t = 1000*spike_times[i]
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spikes_ax.plot([t_start, t_start], [0., 1.], color=colors['blue'], lw=2)
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spikes_ax.annotate('', xy=(t_start, 0.5), xytext=(t,0.5), arrowprops=dict(arrowstyle='<->'), color=colors['red'])
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spikes_ax.text(0.5*(t_start+t), 1.05,
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"{0:.0f}".format((t - t_start)),
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color=colors['red'], ha='center')
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#spike_times = np.hstack((0, spike_times))
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i_rate = 1./np.diff(spike_times)
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rate_ax.step(1000*spike_times, np.hstack((i_rate, i_rate[-1])),color=colors['orange'], lw=2, where="post")
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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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