def random_model(shape, seed=None, anisotropy=None, its=100, bounds=None):
"""
Create a random model by convolving a kernel with a
uniformly distributed model.
:param tuple shape: shape of the model.
:param int seed: pick which model to produce, prints the seed if you don't choose.
:param numpy.ndarray anisotropy: this is the (3 x n) blurring kernel that is used.
:param int its: number of smoothing iterations
:param list bounds: bounds on the model, len(list) == 2
:rtype: numpy.ndarray
:return: M, the model
.. plot::
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()
"""
if bounds is None:
bounds = [0, 1]
if seed is None:
seed = np.random.randint(1e3)
print('Using a seed of: ', seed)
if type(shape) in num_types:
shape = (shape, ) # make it a tuple for consistency
np.random.seed(seed)
mr = np.random.rand(*shape)
if anisotropy is None:
if len(shape) is 1:
smth = np.array([1, 10., 1], dtype=float)
elif len(shape) is 2:
smth = np.array([[1, 7, 1], [2, 10, 2], [1, 7, 1]], dtype=float)
elif len(shape) is 3:
kernal = np.array([1, 4, 1], dtype=float).reshape((1, 3))
smth = np.array(sp.kron(sp.kron(kernal, kernal.T).todense()[:], kernal).todense()).reshape((3, 3, 3))
else:
assert len(anisotropy.shape) is len(shape), 'Anisotropy must be the same shape.'
smth = np.array(anisotropy, dtype=float)
smth = smth/smth.sum() # normalize
mi = mr
for i in range(its):
mi = ndi.convolve(mi, smth)
# scale the model to live between the bounds.
mi = (mi - mi.min())/(mi.max()-mi.min()) # scaled between 0 and 1
mi = mi*(bounds[1]-bounds[0])+bounds[0]
return mi
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