Source code for geomstats.visualization.spd_matrices

"""Visualization for Geometric Statistics."""
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D  # NOQA

import geomstats.backend as gs

[docs] class Ellipses: """Class used to plot points on the manifold SPD(2). Elements S of the manifold of 2D Symmetric Positive Definite matrices can be conveniently represented by ellipses. We write :math:`S = O D O^T` with :math:`O` an orthogonal matrix (rotation) and :math:`D` a diagonal matrix. The positive eigenvalues, i.e. the elements of :math:`D`, are the inverse of the length of the major and minor axes of the ellipse. The rotation matrix :math:`O` determines the orientation of the 2D ellipse in the 2D plane. Parameters ---------- k_sampling_points : int Number of points to sample on the discretized ellipses. """ def __init__(self, k_sampling_points=100): self.k_sampling_points = k_sampling_points
[docs] @staticmethod def set_ax(ax=None): """Set the axis for the Figure. Parameters ---------- ax : Axis Axis of the figure. Returns ------- ax : Axis Axis of the figure. """ if ax is None: ax = plt.subplot() plt.setp(ax, xlabel="X", ylabel="Y") return ax
[docs] def draw_points(self, points=None, ax=None, **plot_kwargs): """Draw the ellipses. Parameters ---------- ax : Axis Axis of the figure. points : array-like, shape=[..., 2, 2] Points on the SPD manifold of 2D symmetric positive definite matrices. Optional, default: None. plot_kwargs : dict Dictionary of arguments related to plotting. """ if ax is None: ax = self.set_ax() if points.ndim == 2: points = [points] for point in points: x_coords, y_coords = self.compute_coordinates(point) ax.plot(x_coords, y_coords, **plot_kwargs)
[docs] def compute_coordinates(self, point): """Compute the ellipse coordinates of a 2D SPD matrix. Parameters ---------- point : array-like, shape=[2, 2] SPD matrix. Returns ------- x_coords : array-like, shape=[k_sampling_points,] x_coords coordinates of the sampling points on the discretized ellipse. Y: array-like, shape = [k_sampling_points,] y coordinates of the sampling points on the discretized ellipse. """ eigvalues, eigvectors = gs.linalg.eigh(point) eigvalues = gs.where(eigvalues < gs.atol, gs.atol, eigvalues) [eigvalue1, eigvalue2] = eigvalues rot_sin = eigvectors[1, 0] rot_cos = eigvectors[0, 0] thetas = gs.linspace(0.0, 2 * gs.pi, self.k_sampling_points + 1) x_coords = eigvalue1 * gs.cos(thetas) * rot_cos x_coords -= rot_sin * eigvalue2 * gs.sin(thetas) y_coords = eigvalue1 * gs.cos(thetas) * rot_sin y_coords += rot_cos * eigvalue2 * gs.sin(thetas) return x_coords, y_coords