discretize.utils.random_model(shape, seed=None, anisotropy=None, its=100, bounds=None)[source]#

Create random tensor model.

Creates a random tensor model by convolving a kernel function with a uniformly distributed model. The user specifies the number of cells along the x, (y and z) directions with the input argument shape and the function outputs a tensor model with the same shape. Afterwards, the user may use the mkvc() function to convert the tensor to a vector which can be plotting on a corresponding tensor mesh.

shape(dim) tuple of int

shape of the model.

seedint, optional

pick which model to produce, prints the seed if you don’t choose

anisotropynumpy.ndarray, optional

this is the kernel that is convolved with the model

itsint, optional

number of smoothing iterations

boundslist, optional

Lower and upper bounds on the model. Has the form [lower_bound, upper_bound].


A random generated model whose shape was specified by the input parameter shape


Here, we generate a random model for a 2D tensor mesh and plot.

>>> from discretize import TensorMesh
>>> from discretize.utils import random_model, mkvc
>>> import matplotlib as mpl
>>> import matplotlib.pyplot as plt
>>> h = [(1., 50)]
>>> vmin, vmax = 0., 1.
>>> mesh = TensorMesh([h, h])
>>> model = random_model(mesh.shape_cells, seed=4, bounds=[vmin, vmax])
>>> fig = plt.figure(figsize=(5, 4))
>>> ax = plt.subplot(111)
>>> im, = mesh.plot_image(model, grid=False, ax=ax, clim=[vmin, vmax])
>>> cbar = plt.colorbar(im)
>>> ax.set_title('Random Tensor Model')
>>> plt.show()

(Source code, png, pdf)


Galleries and Tutorials using discretize.utils.random_model#

Basic: PlotImage

Basic: PlotImage

Basic: PlotImage