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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5a9a6d1dbf1b |
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1 """Chapter 5.3 exercise 4 in Allen Downey's Think Complexity book.
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2
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3 "Write a version of plot_ccdf that plots the complementary CCDF on a log-log
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4 scale.
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5
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6 To test your function, use paretovariate from the random module to
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7 generate 100 values from a Pareto distribution. Plot the CCDF on a log-y scale
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8 and see if it falls on a straight line. What happens to the curve as you
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9 increase the number of values?"
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10
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11 """
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12 import random
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13 import sys
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14
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15 import matplotlib.pyplot as pyplot
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16
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17
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18 def plot_ccdf_log_log(x_vals, y_vals, title=''):
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19 """Given a set of x-values and y-values from a continuous distribution, plot
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20 the complementary distribution (CCDF) on a log-log scale.
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21
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22 """
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23 if len(x_vals) != len(y_vals):
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24 raise ValueError
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25
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26 ys = [1.0 - y for y in y_vals]
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27
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28 pyplot.clf()
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29 pyplot.xscale('log')
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30 pyplot.yscale('log')
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31 pyplot.title(title)
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32 pyplot.xlabel('x')
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33 pyplot.ylabel('1-y')
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34 pyplot.plot(x_vals, ys, label='1-y', color='green', linewidth=3)
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35 pyplot.legend(loc='upper right')
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36 pyplot.show()
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37
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38
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39 def main(script, n, alpha):
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40
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41 n = int(n)
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42 alpha = float(alpha)
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43
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44 x_vals = range(n)
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45 y_vals = [random.paretovariate(alpha) for x in x_vals]
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46 y_vals.sort(reverse=True)
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47
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48 plot_ccdf_log_log(x_vals, y_vals, 'paretovariate w/alpha = {}'.format(alpha))
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49
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50
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51 if __name__ == '__main__':
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52 main(*sys.argv)
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