parameters: (data, axis=None)
docstring: Returns the McKean-Schrader estimate of the standard error of the sample
      median along the given axis. masked values are discarded.
  
      Parameters
      ----------
      data : ndarray
          Data to trim.
      axis : {None,int}, optional
          Axis along which to perform the trimming.
          If None, the input array is first flattened.
docstring_summary: Returns the McKean-Schrader estimate of the standard error of the sample
      median along the given axis. masked values are discarded.
docstring_tokens: Returns the McKean - Schrader estimate of the standard error of the sample median along the given axis . masked values are discarded .
function: def stde_median(data, axis=None):
      """Returns the McKean-Schrader estimate of the standard error of the sample
      median along the given axis. masked values are discarded.
  
      Parameters
      ----------
      data : ndarray
          Data to trim.
      axis : {None,int}, optional
          Axis along which to perform the trimming.
          If None, the input array is first flattened.
  
      """
      def _stdemed_1D(data):
          data = np.sort(data.compressed())
          n = len(data)
          z = 2.5758293035489004
          k = int(np.round((n+1)/2. - z * np.sqrt(n/4.),0))
          return ((data[n-k] - data[k-1])/(2.*z))
  
      data = ma.array(data, copy=False, subok=True)
      if (axis is None):
          return _stdemed_1D(data)
      else:
          if data.ndim > 2:
              raise ValueError("Array 'data' must be at most two dimensional, "
                               "but got data.ndim = %d" % data.ndim)
          return ma.apply_along_axis(_stdemed_1D, axis, data)
function_tokens: def stde_median ( data , axis = None ) : def _stdemed_1D ( data ) : data = np . sort ( data . compressed ( ) ) n = len ( data ) z = 2.5758293035489004 k = int ( np . round ( ( n + 1 ) / 2. - z * np . sqrt ( n / 4. ) , 0 ) ) return ( ( data [ n - k ] - data [ k - 1 ] ) / ( 2. * z ) ) data = ma . array ( data , copy = False , subok = True ) if ( axis is None ) : return _stdemed_1D ( data ) else : if data . ndim > 2 : raise ValueError ( "Array 'data' must be at most two dimensional, " "but got data.ndim = %d" % data . ndim ) return ma . apply_along_axis ( _stdemed_1D , axis , data )