`scipy.stats._covariance:Covariance.colorize`

source: /scipy/stats/_covariance.py :369

Signature

def colorize ( self , x )

Summary

Perform a colorizing transformation on data.

Extended Summary

"Colorizing" ("color" as in "colored noise", in which different frequencies may have different magnitudes) transforms a set of uncorrelated random variables into a new set of random variables with the desired covariance. When a coloring transform is applied to a sample of points distributed according to a multivariate normal distribution with identity covariance and zero mean, the covariance of the transformed sample is approximately the covariance matrix used in the coloring transform.

Parameters

x : array_like: An array of points. The last dimension must correspond with the dimensionality of the space, i.e., the number of columns in the covariance matrix.

Returns

x_ : array_like: The transformed array of points.

Examples

import numpy as np
from scipy import stats
rng = np.random.default_rng(1638083107694713882823079058616272161)
n = 3
A = rng.random(size=(n, n))
cov_array = A @ A.T  # make matrix symmetric positive definite
cholesky = np.linalg.cholesky(cov_array)
cov_object = stats.Covariance.from_cholesky(cholesky)
x = rng.multivariate_normal(np.zeros(n), np.eye(n), size=(10000))
x_ = cov_object.colorize(x)
cov_data = np.cov(x_, rowvar=False)
np.allclose(cov_data, cov_array, rtol=3e-2)

✓

Aliases

scipy.stats.Covariance.colorize