code: def test_sigma_plot():
      """Test to make sure sigma's correctly mirror the shape and orientation
      of the covariance array."""
  
      x = np.array([[1, 2]])
      P = np.array([[2, 1.2], [1.2, 2]])
      kappa = 0.1
  
      # if kappa is larger, than points shoudld be closer together
  
      sp0 = JulierSigmaPoints(n=2, kappa=kappa)
      sp1 = JulierSigmaPoints(n=2, kappa=kappa * 1000)
      sp2 = MerweScaledSigmaPoints(n=2, kappa=0, beta=2, alpha=1e-3)
      sp3 = SimplexSigmaPoints(n=2)
  
      w0, _ = sp0.weights()
      w1, _ = sp1.weights()
      w2, _ = sp2.weights()
      w3, _ = sp3.weights()
  
      Xi0 = sp0.sigma_points(x, P)
      Xi1 = sp1.sigma_points(x, P)
      Xi2 = sp2.sigma_points(x, P)
      Xi3 = sp3.sigma_points(x, P)
  
      assert max(Xi1[:, 0]) > max(Xi0[:, 0])
      assert max(Xi1[:, 1]) > max(Xi0[:, 1])
  
      if DO_PLOT:
          plt.figure()
          for i in range(Xi0.shape[0]):
              plt.scatter(
                  (Xi0[i, 0] - x[0, 0]) * w0[i] + x[0, 0],
                  (Xi0[i, 1] - x[0, 1]) * w0[i] + x[0, 1],
                  color="blue",
                  label="Julier low $\kappa$",
              )
  
          for i in range(Xi1.shape[0]):
              plt.scatter(
                  (Xi1[i, 0] - x[0, 0]) * w1[i] + x[0, 0],
                  (Xi1[i, 1] - x[0, 1]) * w1[i] + x[0, 1],
                  color="green",
                  label="Julier high $\kappa$",
              )
          # for i in range(Xi2.shape[0]):
          #     plt.scatter((Xi2[i, 0] - x[0, 0]) * w2[i] + x[0, 0],
          #                 (Xi2[i, 1] - x[0, 1]) * w2[i] + x[0, 1],
          #                 color='red')
          for i in range(Xi3.shape[0]):
              plt.scatter(
                  (Xi3[i, 0] - x[0, 0]) * w3[i] + x[0, 0],
                  (Xi3[i, 1] - x[0, 1]) * w3[i] + x[0, 1],
                  color="black",
                  label="Simplex",
              )
  
          stats.plot_covariance_ellipse([1, 2], P)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import matplotlib.pyplot
  import numpy.random
  from numpy.random import randn
  from numpy import asarray
  import numpy
  from filterpy.kalman import UnscentedKalmanFilter
  from filterpy.kalman import unscented_transform, MerweScaledSigmaPoints, JulierSigmaPoints, SimplexSigmaPoints
  from filterpy.common import Q_discrete_white_noise
  import filterpy.stats
  from math import cos, sin
  from filterpy.kalman import UnscentedKalmanFilter2
label_desc: Use image node_id 2 for calling a global function with example usage: test_sigma_plot() without return types