text: """
  Guassian Sims
  ===========================
  
  Gaussian `k`-sample simulations are found in :mod:`hyppo.tools`. Here, we visualize
  what these
  simulations look like. We use these Gaussian simulations when comparing our
  algorithms against multivariate analysis of variance (MANOVA).
  """
  
  import matplotlib.pyplot as plt
  import seaborn as sns
  from hyppo.tools import gaussian_3samp
  
  # make plots look pretty
  sns.set(color_codes=True, style="white", context="talk", font_scale=2)
  PALETTE = sns.color_palette("Greys", n_colors=9)
  sns.set_palette(PALETTE[2::2])
  
  # constants
  N = 500
  CASES = [1, 2, 3, 4, 5]
  
  
  # make a function to plot the guassian simulations
  def plot_gaussian_sims():
      """Plot simulations"""
      fig, ax = plt.subplots(nrows=1, ncols=5, figsize=(28, 6))
  
      sim_titles = [
          "None Different",
          "One Different",
          "All Different",
          "One Not Gaussian",
          "None Gaussian",
      ]
  
      # plt.suptitle("Gaussian Simulations", y=0.93, va="baseline")
  
      for i, col in enumerate(ax):
          sim_title = sim_titles[i]
  
          # rotated k-sample simulation
          sims = gaussian_3samp(N, epsilon=4, weight=0.9, case=CASES[i])
  
          # plot the nose and noise-free sims
          for index in range(len(sims)):
              col.scatter(
                  sims[index][:, 0],
                  sims[index][:, 1],
                  label="Sample {}".format(index + 1),
              )
  
          # make the plot look pretty
          col.set_title("{}".format(sim_title))
          col.set_xticks([])
          col.set_yticks([])
          col.set_xlim(-5, 5)
          if CASES[i] not in [2, 4]:
              col.set_ylim(-5, 5)
          sns.despine(left=True, bottom=True, right=True)
  
      leg = plt.legend(
          bbox_to_anchor=(0.5, 0.17),
          bbox_transform=plt.gcf().transFigure,
          ncol=3,
          loc="upper center",
      )
      leg.get_frame().set_linewidth(0.0)
      for legobj in leg.legendHandles:
          legobj.set_linewidth(5.0)
      plt.subplots_adjust(hspace=0.75)
  
  
  # run the created function for the guassian simulations
  plot_gaussian_sims()
  