parameters: (a, limits=None, inclusive=(True, True), axis=0, ddof=1)
return_statement: return sd / np.sqrt(am.count())
docstring: Compute the trimmed standard error of the mean.
  
      This function finds the standard error of the mean for given
      values, ignoring values outside the given `limits`.
  
      Parameters
      ----------
      a : array_like
          array of values
      limits : None or (lower limit, upper limit), optional
          Values in the input array less than the lower limit or greater than the
          upper limit will be ignored. When limits is None, then all values are
          used. Either of the limit values in the tuple can also be None
          representing a half-open interval.  The default value is None.
      inclusive : (bool, bool), optional
          A tuple consisting of the (lower flag, upper flag).  These flags
          determine whether values exactly equal to the lower or upper limits
          are included.  The default value is (True, True).
      axis : int or None, optional
          Axis along which to operate. If None, compute over the
          whole array. Default is zero.
      ddof : int, optional
          Delta degrees of freedom. Default is 1.
  
      Returns
      -------
      tsem : float
  
      Notes
      -----
      For more details on `tsem`, see `stats.tsem`.
docstring_summary: Compute the trimmed standard error of the mean.
docstring_tokens: Compute the trimmed standard error of the mean .
function: def tsem(a, limits=None, inclusive=(True, True), axis=0, ddof=1):
      """
      Compute the trimmed standard error of the mean.
  
      This function finds the standard error of the mean for given
      values, ignoring values outside the given `limits`.
  
      Parameters
      ----------
      a : array_like
          array of values
      limits : None or (lower limit, upper limit), optional
          Values in the input array less than the lower limit or greater than the
          upper limit will be ignored. When limits is None, then all values are
          used. Either of the limit values in the tuple can also be None
          representing a half-open interval.  The default value is None.
      inclusive : (bool, bool), optional
          A tuple consisting of the (lower flag, upper flag).  These flags
          determine whether values exactly equal to the lower or upper limits
          are included.  The default value is (True, True).
      axis : int or None, optional
          Axis along which to operate. If None, compute over the
          whole array. Default is zero.
      ddof : int, optional
          Delta degrees of freedom. Default is 1.
  
      Returns
      -------
      tsem : float
  
      Notes
      -----
      For more details on `tsem`, see `stats.tsem`.
  
      """
      a = ma.asarray(a).ravel()
      if limits is None:
          n = float(a.count())
          return a.std(axis=axis, ddof=ddof)/ma.sqrt(n)
  
      am = trima(a.ravel(), limits, inclusive)
      sd = np.sqrt(am.var(axis=axis, ddof=ddof))
      return sd / np.sqrt(am.count())
function_tokens: def tsem ( a , limits = None , inclusive = ( True , True ) , axis = 0 , ddof = 1 ) : a = ma . asarray ( a ) . ravel ( ) if limits is None : n = float ( a . count ( ) ) return a . std ( axis = axis , ddof = ddof ) / ma . sqrt ( n ) am = trima ( a . ravel ( ) , limits , inclusive ) sd = np . sqrt ( am . var ( axis = axis , ddof = ddof ) ) return sd / np . sqrt ( am . count ( ) )