docs: def plot_qq_unf(fignum, D, title, subplot=False, degrees=True):
      """
      plots data against a uniform distribution in 0=>360.
      Parameters
      _________
      fignum : matplotlib figure number
      D : data
      title : title for plot
      subplot : if True, make this number one of two subplots
      degrees : if True, assume that these are degrees
  
      Return
      Mu : Mu statistic (Fisher et al., 1987)
      Mu_crit : critical value of Mu for uniform distribution
  
      Effect
      ______
      makes a Quantile Quantile plot of data
      """
      if subplot == True:
          plt.subplot(1, 2, fignum)
      else:
          plt.figure(num=fignum)
      X, Y, dpos, dneg = [], [], 0., 0.
      if degrees:
          D = (np.array(D)) % 360
      X = D/D.max()
      X = np.sort(X)
      n = float(len(D))
      i = np.arange(0, len(D))
      Y = (i-0.5)/n
      ds = (i/n)-X
      dpos = ds.max()
      dneg = ds.min()
      plt.plot(Y, X, 'ro')
      v = dneg + dpos  # kuiper's v
      # Mu of fisher et al. equation 5.16
      Mu = v * (np.sqrt(n) - 0.567 + (old_div(1.623, (np.sqrt(n)))))
      plt.axis([0, 1., 0., 1.])
      bounds = plt.axis()
      notestr = 'N: ' + '%i' % (n)
      plt.text(.1 * bounds[1], .9 * bounds[3], notestr)
      notestr = 'Mu: ' + '%7.3f' % (Mu)
      plt.text(.1 * bounds[1], .8 * bounds[3], notestr)
      if Mu > 1.347:
          notestr = "Non-uniform (99%)"
      elif Mu < 1.207:
          notestr = "Uniform (95%)"
      elif Mu > 1.207:
          notestr = "Uniform (99%)"
      plt.text(.1 * bounds[1], .7 * bounds[3], notestr)
      plt.text(.1 * bounds[1], .7 * bounds[3], notestr)
      plt.title(title)
      plt.xlabel('Uniform Quantile')
      plt.ylabel('Data Quantile')
      return Mu, 1.207