text: """Example plot showing the profile of the WStat statistic and its connection to excess errors."""
  import numpy as np
  import matplotlib.pyplot as plt
  from gammapy.stats import WStatCountsStatistic
  
  count_statistic = WStatCountsStatistic(n_on=13, n_off=11, alpha=0.5)
  excess = count_statistic.excess
  
  errn = count_statistic.compute_errn(1.0)
  errp = count_statistic.compute_errp(1.0)
  
  errn_2sigma = count_statistic.compute_errn(2.0)
  errp_2sigma = count_statistic.compute_errp(2.0)
  
  # We compute the WStat statistic profile
  mu_signal = np.linspace(-2.5, 26, 100)
  stat_values = count_statistic._stat_fcn(mu_signal)
  
  xmin, xmax = -2.5, 26
  ymin, ymax = 0, 15
  plt.figure(figsize=(5, 5))
  plt.plot(mu_signal, stat_values, color="k")
  plt.xlim(xmin, xmax)
  plt.ylim(ymin, ymax)
  
  plt.xlabel(r"Number of expected signal event, $\mu_{sig}$")
  plt.ylabel(r"WStat value, TS ")
  
  plt.hlines(
      count_statistic.TS_max + 1,
      xmin=excess + errn,
      xmax=excess + errp,
      linestyle="dotted",
      color="r",
      label="1 sigma (68% C.L.)",
  )
  plt.vlines(
      excess + errn,
      ymin=ymin,
      ymax=count_statistic.TS_max + 1,
      linestyle="dotted",
      color="r",
  )
  plt.vlines(
      excess + errp,
      ymin=ymin,
      ymax=count_statistic.TS_max + 1,
      linestyle="dotted",
      color="r",
  )
  
  plt.hlines(
      count_statistic.TS_max + 4,
      xmin=excess + errn_2sigma,
      xmax=excess + errp_2sigma,
      linestyle="dashed",
      color="b",
      label="2 sigma (95% C.L.)",
  )
  plt.vlines(
      excess + errn_2sigma,
      ymin=ymin,
      ymax=count_statistic.TS_max + 4,
      linestyle="dashed",
      color="b",
  )
  plt.vlines(
      excess + errp_2sigma,
      ymin=ymin,
      ymax=count_statistic.TS_max + 4,
      linestyle="dashed",
      color="b",
  )
  
  plt.legend()
  