docstr: Calculates the return to origin probability (rtop) from the signal
  rtop equals to the sum of all signal values
  
  Parameters
  ----------
  filtering : boolean, optional
      Whether to perform Hanning filtering. Default: True
  
  Returns
  -------
  rtop : float
      the return to origin probability
docstr_tok: ["Calculates","the","return","to","origin","probability","(","rtop",")","from","the","signal","rtop","equals","to","the","sum","of","all","signal","values","Parameters","--","--","--","--","--","filtering",":","boolean",",","optional","Whether","to","perform","Hanning","filtering",".","Default",":","True","Returns","--","--","--","-","rtop",":","float","the","return","to","origin","probability"]
code: def rtop_signal(self, filtering=True):
      """Calculates the return to origin probability (rtop) from the signal
      rtop equals to the sum of all signal values
  
      Parameters
      ----------
      filtering : boolean, optional
          Whether to perform Hanning filtering. Default: True
  
      Returns
      -------
      rtop : float
          the return to origin probability
      """
  
      if filtering:
          values = self.data * self.model.filter
      else:
          values = self.data
  
      rtop = values.sum()
  
      return rtop
  
imports: import numpy
  from scipy.ndimage import map_coordinates
  from scipy.fftpack import fftn, fftshift, ifftshift
  from dipy.reconst.odf import OdfModel, OdfFit
  from dipy.reconst.cache import Cache
  from dipy.reconst.multi_voxel import multi_voxel_fit
label_desc: Use image node_id 3 for calling the DiffusionSpectrumFit obj's underlying member method code with example usage: obj.rtop_signal(filtering) and returns: rtop