parameters: (arr)
return_statement: return mask
docstring: Return the mask of a masked array, or full boolean array of False.
  
      Return the mask of `arr` as an ndarray if `arr` is a `MaskedArray` and
      the mask is not `nomask`, else return a full boolean array of False of
      the same shape as `arr`.
  
      Parameters
      ----------
      arr : array_like
          Input `MaskedArray` for which the mask is required.
  
      See Also
      --------
      getmask : Return the mask of a masked array, or nomask.
      getdata : Return the data of a masked array as an ndarray.
  
      Examples
      --------
      >>> import numpy.ma as ma
      >>> a = ma.masked_equal([[1,2],[3,4]], 2)
      >>> a
      masked_array(data =
       [[1 --]
       [3 4]],
            mask =
       [[False  True]
       [False False]],
            fill_value=999999)
      >>> ma.getmaskarray(a)
      array([[False,  True],
             [False, False]])
  
      Result when mask == ``nomask``
  
      >>> b = ma.masked_array([[1,2],[3,4]])
      >>> b
      masked_array(data =
       [[1 2]
       [3 4]],
            mask =
       False,
            fill_value=999999)
      >>> >ma.getmaskarray(b)
      array([[False, False],
             [False, False]])
docstring_summary: Return the mask of a masked array, or full boolean array of False.
docstring_tokens: Return the mask of a masked array or full boolean array of False .
function: def getmaskarray(arr):
      """
      Return the mask of a masked array, or full boolean array of False.
  
      Return the mask of `arr` as an ndarray if `arr` is a `MaskedArray` and
      the mask is not `nomask`, else return a full boolean array of False of
      the same shape as `arr`.
  
      Parameters
      ----------
      arr : array_like
          Input `MaskedArray` for which the mask is required.
  
      See Also
      --------
      getmask : Return the mask of a masked array, or nomask.
      getdata : Return the data of a masked array as an ndarray.
  
      Examples
      --------
      >>> import numpy.ma as ma
      >>> a = ma.masked_equal([[1,2],[3,4]], 2)
      >>> a
      masked_array(data =
       [[1 --]
       [3 4]],
            mask =
       [[False  True]
       [False False]],
            fill_value=999999)
      >>> ma.getmaskarray(a)
      array([[False,  True],
             [False, False]])
  
      Result when mask == ``nomask``
  
      >>> b = ma.masked_array([[1,2],[3,4]])
      >>> b
      masked_array(data =
       [[1 2]
       [3 4]],
            mask =
       False,
            fill_value=999999)
      >>> >ma.getmaskarray(b)
      array([[False, False],
             [False, False]])
  
      """
      mask = getmask(arr)
      if mask is nomask:
          mask = make_mask_none(np.shape(arr), getattr(arr, 'dtype', None))
      return mask
function_tokens: def getmaskarray ( arr ) : mask = getmask ( arr ) if mask is nomask : mask = make_mask_none ( np . shape ( arr ) , getattr ( arr , 'dtype' , None ) ) return mask