code: def voxel_reproducibility(
      data,
      vardata,
      domain,
      ngroups,
      method="crfx",
      swap=False,
      verbose=0,
      **kwargs,
  ):
      """return a measure of voxel-level reproducibility of activation patterns
  
      Parameters
      ----------
      data: array of shape (nvox,nsubj)
            the input data from which everything is computed
      vardata: array of shape (nvox,nsubj)
               the corresponding variance information
               ngroups (int):
               Number of subbgroups to be drawn
      domain: referential- and domain-defining image
      ngourps: int,
               number of groups to be used in the resampling procedure
      method: string, to be chosen among 'crfx', 'cmfx', 'cffx'
              inference method under study
      verbose: bool, verbosity mode
  
      Returns
      -------
      kappa (float): the desired  reproducibility index
      """
      rmap = map_reproducibility(
          data,
          vardata,
          domain,
          ngroups,
          method,
          swap,
          verbose,
          **kwargs,
      )
  
      h = np.array([np.sum(rmap == i) for i in range(ngroups + 1)])
      hr = histo_repro(h)
      return hr
  
imports: import numpy
  from nipy.io.nibcompat import get_affine
  from nipy.labs.spatial_models.discrete_domain import grid_domain_from_binary_array
label_desc: Use image node_id 13 for calling a global function with example usage: voxel_reproducibility(data, vardata, domain, ngroups, method, swap, verbose) and returns: hr