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:

M, the model

Return type:

numpy.ndarray

Example

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, hires.png, pdf)

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

Examples using discretize.utils.random_model