docs: def parallel_snr_func(num, binary_args, phenomdwave, signal_type,
                        noise_interpolants, prefactor, verbose):
      """SNR calulation with PhenomDWaveforms
  
      Generate PhenomDWaveforms and calculate their SNR against sensitivity curves.
  
      Args:
          num (int): Process number. If only a single process, num=0.
          binary_args (tuple): Binary arguments for
              :meth:`gwsnrcalc.utils.waveforms.EccentricBinaries.__call__`.
          phenomdwave (obj): Initialized class of
              :class:`gwsnrcalc.utils.waveforms.PhenomDWaveforms`.
          signal_type (list of str): List with types of SNR to calculate.
              Options are `all` for full wavefrom,
              `ins` for inspiral, `mrg` for merger, and/or `rd` for ringdown.
          noise_interpolants (dict): All the noise noise interpolants generated by
              :mod:`gwsnrcalc.utils.sensitivity`.
          prefactor (float): Prefactor to multiply SNR by (not SNR^2).
          verbose (int): Notify each time ``verbose`` processes finish. If -1, then no notification.
  
      Returns:
          (dict): Dictionary with the SNR output from the calculation.
  
      """
  
      wave = phenomdwave(*binary_args)
  
      out_vals = {}
  
      for key in noise_interpolants:
          hn_vals = noise_interpolants[key](wave.freqs)
          snr_out = csnr(wave.freqs, wave.hc, hn_vals,
                         wave.fmrg, wave.fpeak, prefactor=prefactor)
  
          if len(signal_type) == 1:
              out_vals[key + '_' + signal_type[0]] = snr_out[signal_type[0]]
          else:
              for phase in signal_type:
                  out_vals[key + '_' + phase] = snr_out[phase]
      if verbose > 0 and (num+1) % verbose == 0:
          print('Process ', (num+1), 'is finished.')
  
      return out_vals