52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
import numpy as np
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from scipy.stats import poisson
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import matplotlib.pyplot as plt
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from plotstyle import *
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rate = 20.0
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trials = 20
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duration = 100.0
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def hompoisson(rate, trials, duration) :
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spikes = []
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for k in range(trials) :
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times = []
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t = 0.0
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while t < duration :
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t += np.random.exponential(1/rate)
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times.append( t )
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spikes.append( times )
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return spikes
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def plot_count_hist(ax, spikes, win, pmax):
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counts = []
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for times in spikes:
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c, _ = np.histogram(times, np.arange(0.0, duration, win))
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counts.extend(c)
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cb = np.arange(0.0, 15.5, 1.0)
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h, b = np.histogram(counts, cb, density=True)
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ax.bar(b[:-1], h, bar_fac, align='center', **fsA)
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ax.plot(cb, poisson.pmf(cb, rate*win), **lsBm)
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ax.plot(cb, poisson.pmf(cb, rate*win), **psBm)
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ax.text(0.9, 0.9, 'T=%.0fms' % (1000.0*win), transform=ax.transAxes, ha='right')
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ax.set_xlim(-0.5, 10.5)
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ax.set_ylim(0.0, pmax)
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ax.set_xticks(np.arange(0.0, 11.0, 5.0))
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ax.set_xlabel('Counts k')
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ax.set_ylabel('P(k)')
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if __name__ == "__main__":
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spikes = hompoisson(rate, trials, duration)
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fig, (ax1, ax2) = plt.subplots(1, 2)
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fig.subplots_adjust(**adjust_fs(fig, top=0.5, right=1.5))
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plot_count_hist(ax1, spikes, 0.02, 0.7)
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ax1.set_yticks(np.arange(0.0, 0.7, 0.2))
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plot_count_hist(ax2, spikes, 0.2, 0.22)
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ax2.set_yticks(np.arange(0.0, 0.25, 0.1))
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plt.savefig('countexamples.pdf')
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plt.close()
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