|
30 | 30 | (poisson_mag + 6) * (np.arange(ramp_length) /
|
31 | 31 | poisson_mag) ** 0.5).astype(int)
|
32 | 32 | np.clip(v1, 0, None, v1)
|
33 |
| -plt.plot(v1) |
34 |
| -plt.show(block=True) |
| 33 | +#plt.plot(v1) |
| 34 | +#plt.show(block=True) |
35 | 35 |
|
36 | 36 | volume_noise = np.random.normal(scale=0.05, size=(p, w, w))
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37 | 37 | indices_pln = (horizon[np.newaxis, ...] +
|
|
40 | 40 | indices_col = np.arange(w)[np.newaxis, np.newaxis, :]
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41 | 41 | index_vol = (indices_pln, indices_row, indices_col)
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42 | 42 | volume_noise[index_vol] += v0[:, np.newaxis, np.newaxis]
|
43 |
| -plt.imshow(volume_noise[:, 50, :]) |
44 |
| -plt.show(block=True) |
45 |
| -plt.hist(volume_noise.ravel(), bins=256) |
46 |
| -plt.show(block=True) |
| 43 | +#plt.imshow(volume_noise[:, 50, :]) |
| 44 | +#plt.show(block=True) |
| 45 | +#plt.hist(volume_noise.ravel(), bins=256) |
| 46 | +#plt.show(block=True) |
47 | 47 |
|
48 | 48 | volume_noise2 = np.random.poisson(2, size=(p, w, w)) - 2
|
49 | 49 | volume_noise2[index_vol] += v1[:, np.newaxis, np.newaxis]
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50 | 50 | np.clip(volume_noise2, 0, None, volume_noise2)
|
51 |
| -plt.imshow(volume_noise2[:, 50, :]) |
52 |
| -plt.show(block=True) |
| 51 | +#plt.imshow(volume_noise2[:, 50, :]) |
| 52 | +#plt.show(block=True) |
53 | 53 |
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54 | 54 | MM = max(abs(M), abs(m))
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55 | 55 | volume0 = volume_noise[MM:-MM, :, :]
|
|
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