code: def tau_davies(Z, fmax, t, y, dy, normalization='standard', dH=1, dK=3):
      
      N = len(t)
      NH = N - dH  # DOF for null hypothesis
      NK = N - dK  # DOF for periodic hypothesis
      Dt = _weighted_var(t, dy)
      Teff = np.sqrt(4 * np.pi * Dt)
      W = fmax * Teff
      if normalization == 'psd':
          # 'psd' normalization is same as Baluev's z
          return W * np.exp(-Z) * np.sqrt(Z)
      elif normalization == 'standard':
          # 'standard' normalization is Z = 2/NH * z_1
          return (_gamma(NH) * W * (1 - Z) ** (0.5 * (NK - 1))
                  * np.sqrt(0.5 * NH * Z))
      elif normalization == 'model':
          # 'model' normalization is Z = 2/NK * z_2
          return (_gamma(NK) * W * (1 + Z) ** (-0.5 * NK)
                  * np.sqrt(0.5 * NK * Z))
      elif normalization == 'log':
          # 'log' normalization is Z = 2/NK * z_3
          return (_gamma(NK) * W * np.exp(-0.5 * Z * (NK - 0.5))
                  * np.sqrt(NK * np.sinh(0.5 * Z)))
      else:
          raise NotImplementedError("normalization={0}".format(normalization))
docs: tau factor for estimating Davies bound (Baluev 2008, Table 1)