code: def affine_initializer(fixed_image, moving_image, search_factor=20,
                          radian_fraction=0.1, use_principal_axis=False, 
                          local_search_iterations=10, mask=None, txfn=None ):
      
  
      if txfn is None:
          txfn = mktemp(suffix='.mat')
  
      veccer = [fixed_image.dimension, fixed_image, moving_image, txfn,
                  search_factor, radian_fraction, int(use_principal_axis),
                  local_search_iterations]
      if mask is not None:
          veccer.append(mask)
  
      xxx = utils._int_antsProcessArguments(veccer)
      libfn = utils.get_lib_fn('antsAffineInitializer')
      retval = libfn(xxx)
  
      if retval != 0:
          warnings.warn('ERROR: Non-zero exit status!')
      
      return txfn
docs: A multi-start optimizer for affine registration
      Searches over the sphere to find a good initialization for further
      registration refinement, if needed.  This is a arapper for the ANTs
      function antsAffineInitializer.
      
      ANTsR function: `affineInitializer`
  
      Arguments
      ---------
      fixed_image : ANTsImage
          the fixed reference image
      moving_image : ANTsImage 
          the moving image to be mapped to the fixed space
      search_factor : scalar
          degree of increments on the sphere to search
      radian_fraction : scalar
          between zero and one, defines the arc to search over
      use_principal_axis : boolean
          boolean to initialize by principal axis
      local_search_iterations : scalar
          gradient descent iterations
      mask : ANTsImage (optional)
          optional mask to restrict registration
      txfn : string (optional)
          filename for the transformation
  
      Returns
      -------
      ndarray
          transformation matrix
      
      Example
      -------
      >>> import ants
      >>> fi = ants.image_read(ants.get_ants_data('r16'))
      >>> mi = ants.image_read(ants.get_ants_data('r27'))
      >>> txfile = ants.affine_initializer( fi, mi )
      >>> tx = ants.read_transform(txfile, dimension=2)