In my current project, I have a collection of three-dimensional samples such as [-0.5,-0.1,0.2]*pi, [0.8,-0.1,-0.4]*pi. These variables are circular/periodic, with their values ranging from -pi to +pi. It is my goal to calculate a 3-by-3 covariance matrix for these circular variables. Python has an in-built function to calculate circular standard deviations, which I can use to calculate the standard deviations along each dimension, then use them to create a diagonal covariance matrix (i.e., without any correlation). Ideally, however, I would like to consider correlations between the parameters as well. Is there a way to calculate correlations between circular variables, or to directly compute the covariance matrix between them? import numpy as np import scipy.stats # A collection of N circular samples samples = np.asarray( [[0.384917, 1.28862, -2.034], [0.384917, 1.28862, -2.034], [0.759245, 1.16033, -2.57942], [0.45797, 1.31103, 2.9846], [0.898047, 1.20955, -3.02987], [1.25694, 1.74957, 2.46946], [1.02173, 1.26477, 1.83757], [1.22435, 1.62939, 1.99264]]) # Calculate the circular standard deviations stds = scipy.stats.circstd(samples, high = np.pi, low = -np.pi, axis = 0) # Create a diagonal covariance matrix cov = np.identity(3) np.fill_diagonal(cov,stds**2) Continue reading...
