docs: def plot_qq_exp(fignum, I, title, subplot=False):
      """
      plots data against an exponential distribution in 0=>90.
  
      Parameters
      _________
      fignum : matplotlib figure number
      I : data
      title : plot title
      subplot : boolean, if True plot as subplot with 1 row, two columns with fignum the plot number
      """
      if subplot == True:
          plt.subplot(1, 2, fignum)
      else:
          plt.figure(num=fignum)
      X, Y, dpos, dneg = [], [], 0., 0.
      rad = old_div(np.pi, 180.)
      xsum = 0
      for i in I:
          theta = (90. - i) * rad
          X.append(1. - np.cos(theta))
          xsum += X[-1]
      X.sort()
      n = float(len(X))
      kappa = old_div((n - 1.), xsum)
      for i in range(len(X)):
          p = old_div((float(i) - 0.5), n)
          Y.append(-np.log(1. - p))
          f = 1. - np.exp(-kappa * X[i])
          ds = old_div(float(i), n) - f
          if dpos < ds:
              dpos = ds
          ds = f - old_div((float(i) - 1.), n)
          if dneg < ds:
              dneg = ds
      if dneg > dpos:
          ds = dneg
      else:
          ds = dpos
      Me = (ds - (old_div(0.2, n))) * (np.sqrt(n) + 0.26 +
                                       (old_div(0.5, (np.sqrt(n)))))  # Eq. 5.15 from Fisher et al. (1987)
  
      plt.plot(Y, X, 'ro')
      bounds = plt.axis()
      plt.axis([0, bounds[1], 0., bounds[3]])
      notestr = 'N: ' + '%i' % (n)
      plt.text(.1 * bounds[1], .9 * bounds[3], notestr)
      notestr = 'Me: ' + '%7.3f' % (Me)
      plt.text(.1 * bounds[1], .8 * bounds[3], notestr)
      if Me > 1.094:
          notestr = "Not Exponential"
      else:
          notestr = "Exponential (95%)"
      plt.text(.1 * bounds[1], .7 * bounds[3], notestr)
      plt.title(title)
      plt.xlabel('Exponential Quantile')
      plt.ylabel('Data Quantile')
      return Me, 1.094