Chapter 6, exercise 2, #4. Wrote a program to output the center column of
a rule 30 CA as a stream of bytes. It is very slow though. It has to run a very
long time to produce enough data for dieharder. Committing it now but will have
to let it run overnight or something to generate a large file.
| author |
Brian Neal <bgneal@gmail.com> |
| date |
Sun, 13 Jan 2013 16:24:00 -0600 |
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65b70b4e12ad |
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1 """Chapter 5.3 exercise 3 in Allen Downey's Think Complexity book.
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2
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3 "Write a function called plot_ccdf that takes a list of values and the
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4 corresponding list of probabilities and plots the CCDF on a log-y scale.
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5
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6 To test your function, use expovariate from the random module to generate 100
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7 values from an exponential distribution. Plot the CCDF on a log-y scale and see
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8 if it falls on a straight line."
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9
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10 """
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11 import random
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12 import sys
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13
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14 import matplotlib.pyplot as pyplot
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15
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16
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17 def plot_ccdf(x_vals, y_vals, title=''):
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18 """Given a set of x-values and y-values from a continuous distribution, plot
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19 the complementary distribution (CCDF) on a log-y scale.
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20
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21 """
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22 if len(x_vals) != len(y_vals):
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23 raise ValueError
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24
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25 ys = [1.0 - y for y in y_vals]
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26
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27 pyplot.clf()
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28 pyplot.xscale('linear')
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29 pyplot.yscale('log')
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30 pyplot.title(title)
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31 pyplot.xlabel('x')
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32 pyplot.ylabel('1-y')
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33 pyplot.plot(x_vals, ys, label='1-y', color='green', linewidth=3)
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34 pyplot.legend(loc='upper right')
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35 pyplot.show()
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36
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37
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38 def main(script, lambd):
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39
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40 lambd = float(lambd)
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41
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42 x_vals = range(100)
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43 y_vals = [random.expovariate(lambd) for x in x_vals]
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44 y_vals.sort()
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45
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46 plot_ccdf(x_vals, y_vals, 'expovariate w/lambda = {}'.format(lambd))
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47
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48
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49 if __name__ == '__main__':
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50 main(*sys.argv)
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