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%matplotlib inline
import pytry
import numpy as np
import pylab
import pandas
import seaborn
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data = pytry.read('exp_f1_b')
T = float(data[0]['T'])
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times = np.zeros((len(data), int(T/0.001)))
for i, d in enumerate(data):
times[i][(d['score_times']/0.001).astype(int)] = 1000
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import scipy.ndimage
seaborn.set_style("whitegrid")
mean = np.mean(times, axis=0)
meanf = scipy.ndimage.filters.gaussian_filter1d(mean, sigma=1000)
t = np.arange(len(times[0]))*0.001
pylab.plot(t, meanf)
for row in times:
rowf = scipy.ndimage.filters.gaussian_filter1d(row, sigma=1000)
pylab.plot(t, rowf, color='#666666', alpha=0.1)
pylab.xlabel('time (s)')
pylab.ylabel('targets per second')
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