docstr: Compute parcel-based posterior contrast means and positive
  contrast probabilities.
  
  Parameters
  ----------
  full_res: boolean
   If True, the output images will be at the same resolution as
   the parcel image. Otherwise, resolution will match the
   first-level images.
  
  Returns
  -------
  pmap_mu_img: nipy image
    Image of posterior contrast means for each parcel.
  pmap_prob_img: nipy image
    Corresponding image of posterior probabilities of positive
    contrast.
docstr_tok: ["Compute","parcel-based","posterior","contrast","means","and","positive","contrast","probabilities",".","Parameters","--","--","--","--","--","full_res",":","boolean","If","True",",","the","output","images","will","be","at","the","same","resolution","as","the","parcel","image",".","Otherwise",",","resolution","will","match","the","first-level","images",".","Returns","--","--","--","-","pmap_mu_img",":","nipy","image","Image","of","posterior","contrast","means","for","each","parcel",".","pmap_prob_img",":","nipy","image","Corresponding","image","of","posterior","probabilities","of","positive","contrast","."]
code: def parcel_maps(self, full_res=True):
      """
      Compute parcel-based posterior contrast means and positive
      contrast probabilities.
  
      Parameters
      ----------
      full_res: boolean
       If True, the output images will be at the same resolution as
       the parcel image. Otherwise, resolution will match the
       first-level images.
  
      Returns
      -------
      pmap_mu_img: nipy image
        Image of posterior contrast means for each parcel.
      pmap_prob_img: nipy image
        Corresponding image of posterior probabilities of positive
        contrast.
      """
      if full_res:
          parcel = self.parcel_full_res
          affine = self.affine_full_res
      else:
          parcel = self.parcel
          affine = self.affine
      pmap_prob = np.zeros(parcel.shape)
      pmap_mu = np.zeros(parcel.shape)
      for label, prob, mu in zip(
          self.parcel_values, self.parcel_prob, self.parcel_mu
      ):
          pmap_prob[parcel == label] = prob
          pmap_mu[parcel == label] = mu
  
      pmap_prob_img = make_xyz_image(pmap_prob, affine, self.reference)
      pmap_mu_img = make_xyz_image(pmap_mu, affine, self.reference)
  
      if self.res_path is not None:
          _save_image(
              pmap_prob_img, join(self.res_path, "parcel_prob.nii.gz")
          )
          _save_image(
              pmap_mu_img, join(self.res_path, "parcel_mu.nii.gz")
          )
  
      return pmap_mu_img, pmap_prob_img
  
imports: import warnings
  from os.path import join
  import numpy
  import scipy.ndimage
  import scipy.stats
  from nibabel import io_orientation
  from ...None import save_image
  from ...core.image.image_spaces import make_xyz_image, xyz_affine
  from ..kernel_smooth import fwhm2sigma
  from ..registration import resample
  from ..statistics.bayesian_mixed_effects import two_level_glm
  from ..statistics.histogram import histogram
label_desc: Use image node_id 7 for calling the ParcelAnalysis obj's underlying member method code with example usage: obj.parcel_maps(full_res) and returns: pmap_mu_img,  pmap_prob_img