docstr: Calculates the analytical return to origin probability (RTOP)
  from the signal [1]_.
  
  References
  ----------
  .. [1] Ozarslan E. et al., "Mean apparent propagator (MAP) MRI: A novel
  diffusion imaging method for mapping tissue microstructure",
  NeuroImage, 2013.
docstr_tok: ["Calculates","the","analytical","return","to","origin","probability","(","RTOP",")","from","the","signal","[","1","]","_.","References","--","--","--","--","--","..","[","1","]","Ozarslan","E.","et","al.",",","``","Mean","apparent","propagator","(","MAP",")","MRI",":","A","novel","diffusion","imaging","method","for","mapping","tissue","microstructure","''",",","NeuroImage",",","2013","."]
code: def rtop_signal(self):
      r"""Calculates the analytical return to origin probability (RTOP)
      from the signal [1]_.
  
      References
      ----------
      .. [1] Ozarslan E. et al., "Mean apparent propagator (MAP) MRI: A novel
      diffusion imaging method for mapping tissue microstructure",
      NeuroImage, 2013.
      """
      rtop = 0
      c = self._shore_coef
  
      for n in range(int(self.radial_order / 2) + 1):
          rtop += (
              c[n]
              * (-1) ** n
              * (
                  (16 * np.pi * self.zeta**1.5 * gamma(n + 1.5))
                  / (factorial(n))
              )
              ** 0.5
          )
  
      return np.clip(rtop, 0, rtop.max())
  
imports: from warnings import warn
  from math import factorial
  import numpy
  from scipy.special import genlaguerre, gamma, hyp2f1
  from dipy.reconst.cache import Cache
  from dipy.reconst.multi_voxel import multi_voxel_fit
  from dipy.reconst.shm import real_sh_descoteaux_from_index
  from dipy.core.geometry import cart2sphere
  from dipy.utils.optpkg import optional_package
label_desc: Use image node_id 6 for calling the ShoreFit obj's underlying member method code with example usage: obj.rtop_signal() and returns: np