discretize.utils.random_model¶
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discretize.utils.random_model(shape, seed=None, anisotropy=None, its=100, bounds=None)[source]¶ Create a random model by convolving a kernel with a uniformly distributed model.
- Parameters
- shape: tuple
shape of the model.
- seed: int
pick which model to produce, prints the seed if you don’t choose.
- anisotropy: numpy.ndarray
this is the (3 x n) blurring kernel that is used.
- its: int
number of smoothing iterations
- bounds: list
bounds on the model, len(list) == 2
- Returns
- numpy.ndarray
M, the model
Examples
import matplotlib.pyplot as plt import discretize plt.colorbar(plt.imshow(discretize.utils.random_model((50, 50), bounds=[-4, 0]))) plt.title('A very cool, yet completely random model.') plt.show()
(Source code, png, pdf)
