discretize.utils.random_model

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)

../../_images/discretize-utils-random_model-1.png

Examples using discretize.utils.random_model