code: def do_plot_test():
      import matplotlib.pyplot as plt
      from numpy.random import multivariate_normal as mnormal
      from filterpy.stats import covariance_ellipse, plot_covariance
  
      p = np.array([[32, 15], [15.0, 40.0]])
  
      x, y = mnormal(mean=(0, 0), cov=p, size=5000).T
      sd = 2
      a, w, h = covariance_ellipse(p, sd)
      print(np.degrees(a), w, h)
  
      count = 0
      color = []
      for i in range(len(x)):
          if _is_inside_ellipse(x[i], y[i], 0, 0, a, w, h):
              color.append("b")
              count += 1
          else:
              color.append("r")
      plt.scatter(x, y, alpha=0.2, c=color)
      plt.axis("equal")
  
      plot_covariance(
          mean=(0.0, 0.0),
          cov=p,
          std=[1, 2, 3],
          alpha=0.3,
          facecolor="none",
      )
  
      print(count / len(x))
  
imports: from __future__ import division
  from math import exp
  import warnings
  import matplotlib.pyplot
  from mpl_toolkits.mplot3d import axes3d
  import numpy
  from numpy.linalg import inv
  import scipy
  from scipy.spatial.distance import mahalanobis
  from filterpy.stats import norm_cdf, multivariate_gaussian, logpdf, mahalanobis
  from scipy import linalg
label_desc: Use image node_id 5 for calling a global function with example usage: do_plot_test() without return types