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11 | 11 |
|
12 | 12 | # Add a line showing the expected distribution. |
13 | 13 | y = normpdf( bins, mu, sigma) |
14 | | -l = plt.plot(bins, y, 'k--', linewidth=1.5) |
| 14 | +plt.plot(bins, y, 'k--', linewidth=1.5) |
15 | 15 |
|
16 | 16 |
|
17 | 17 | plt.figure() |
|
27 | 27 | # Add a line showing the expected distribution. |
28 | 28 | y = normpdf( bins, mu, sigma).cumsum() |
29 | 29 | y /= y[-1] |
30 | | -l = plt.plot(bins, y, 'k--', linewidth=1.5) |
| 30 | +plt.plot(bins, y, 'k--', linewidth=1.5) |
31 | 31 |
|
32 | 32 | # Create a second data-set with a smaller standard deviation. |
33 | 33 | sigma2 = 15. |
|
38 | 38 | # Add a line showing the expected distribution. |
39 | 39 | y = normpdf( bins, mu, sigma2).cumsum() |
40 | 40 | y /= y[-1] |
41 | | -l = plt.plot(bins, y, 'r--', linewidth=1.5) |
| 41 | +plt.plot(bins, y, 'r--', linewidth=1.5) |
42 | 42 |
|
43 | 43 | # Overlay a reverted cumulative histogram. |
44 | 44 | n, bins, patches = plt.hist(x, bins=bins, normed=1, |
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