parameters: (input, size=None, footprint=None, output=None,
                    mode="reflect", cval=0.0, origin=0)
return_statement: return _rank_filter(input, 0, size, footprint, output, mode, cval,
                          origin, 'median')
docstring: Calculate a multidimensional median filter.
  
      Parameters
      ----------
      %(input)s
      %(size_foot)s
      %(output)s
      %(mode_multiple)s
      %(cval)s
      %(origin_multiple)s
  
      Returns
      -------
      median_filter : ndarray
          Filtered array. Has the same shape as `input`.
  
      Examples
      --------
      >>> from scipy import ndimage, misc
      >>> import matplotlib.pyplot as plt
      >>> fig = plt.figure()
      >>> plt.gray()  # show the filtered result in grayscale
      >>> ax1 = fig.add_subplot(121)  # left side
      >>> ax2 = fig.add_subplot(122)  # right side
      >>> ascent = misc.ascent()
      >>> result = ndimage.median_filter(ascent, size=20)
      >>> ax1.imshow(ascent)
      >>> ax2.imshow(result)
      >>> plt.show()
docstring_summary: Calculate a multidimensional median filter.
docstring_tokens: Calculate a multidimensional median filter .
function: def median_filter(input, size=None, footprint=None, output=None,
                    mode="reflect", cval=0.0, origin=0):
      """
      Calculate a multidimensional median filter.
  
      Parameters
      ----------
      %(input)s
      %(size_foot)s
      %(output)s
      %(mode_multiple)s
      %(cval)s
      %(origin_multiple)s
  
      Returns
      -------
      median_filter : ndarray
          Filtered array. Has the same shape as `input`.
  
      Examples
      --------
      >>> from scipy import ndimage, misc
      >>> import matplotlib.pyplot as plt
      >>> fig = plt.figure()
      >>> plt.gray()  # show the filtered result in grayscale
      >>> ax1 = fig.add_subplot(121)  # left side
      >>> ax2 = fig.add_subplot(122)  # right side
      >>> ascent = misc.ascent()
      >>> result = ndimage.median_filter(ascent, size=20)
      >>> ax1.imshow(ascent)
      >>> ax2.imshow(result)
      >>> plt.show()
      """
      return _rank_filter(input, 0, size, footprint, output, mode, cval,
                          origin, 'median')
function_tokens: def median_filter ( input , size = None , footprint = None , output = None , mode = "reflect" , cval = 0.0 , origin = 0 ) : return _rank_filter ( input , 0 , size , footprint , output , mode , cval , origin , 'median' )