parameters: (data, limits=(0.2,0.2), inclusive=(True,True),
                      alpha=0.05, axis=None)
return_statement: return np.array((tmean - tppf*tstde, tmean+tppf*tstde))
docstring: Selected confidence interval of the trimmed mean along the given axis.
  
      Parameters
      ----------
      data : array_like
          Input data.
      limits : {None, tuple}, optional
          None or a two item tuple.
          Tuple of the percentages to cut on each side of the array, with respect
          to the number of unmasked data, as floats between 0. and 1. If ``n``
          is the number of unmasked data before trimming, then
          (``n * limits[0]``)th smallest data and (``n * limits[1]``)th
          largest data are masked.  The total number of unmasked data after
          trimming is ``n * (1. - sum(limits))``.
          The value of one limit can be set to None to indicate an open interval.
  
          Defaults to (0.2, 0.2).
      inclusive : (2,) tuple of boolean, optional
          If relative==False, tuple indicating whether values exactly equal to
          the absolute limits are allowed.
          If relative==True, tuple indicating whether the number of data being
          masked on each side should be rounded (True) or truncated (False).
  
          Defaults to (True, True).
      alpha : float, optional
          Confidence level of the intervals.
  
          Defaults to 0.05.
      axis : int, optional
          Axis along which to cut. If None, uses a flattened version of `data`.
  
          Defaults to None.
  
      Returns
      -------
      trimmed_mean_ci : (2,) ndarray
          The lower and upper confidence intervals of the trimmed data.
docstring_summary: Selected confidence interval of the trimmed mean along the given axis.
docstring_tokens: Selected confidence interval of the trimmed mean along the given axis .
function: def trimmed_mean_ci(data, limits=(0.2,0.2), inclusive=(True,True),
                      alpha=0.05, axis=None):
      """
      Selected confidence interval of the trimmed mean along the given axis.
  
      Parameters
      ----------
      data : array_like
          Input data.
      limits : {None, tuple}, optional
          None or a two item tuple.
          Tuple of the percentages to cut on each side of the array, with respect
          to the number of unmasked data, as floats between 0. and 1. If ``n``
          is the number of unmasked data before trimming, then
          (``n * limits[0]``)th smallest data and (``n * limits[1]``)th
          largest data are masked.  The total number of unmasked data after
          trimming is ``n * (1. - sum(limits))``.
          The value of one limit can be set to None to indicate an open interval.
  
          Defaults to (0.2, 0.2).
      inclusive : (2,) tuple of boolean, optional
          If relative==False, tuple indicating whether values exactly equal to
          the absolute limits are allowed.
          If relative==True, tuple indicating whether the number of data being
          masked on each side should be rounded (True) or truncated (False).
  
          Defaults to (True, True).
      alpha : float, optional
          Confidence level of the intervals.
  
          Defaults to 0.05.
      axis : int, optional
          Axis along which to cut. If None, uses a flattened version of `data`.
  
          Defaults to None.
  
      Returns
      -------
      trimmed_mean_ci : (2,) ndarray
          The lower and upper confidence intervals of the trimmed data.
  
      """
      data = ma.array(data, copy=False)
      trimmed = mstats.trimr(data, limits=limits, inclusive=inclusive, axis=axis)
      tmean = trimmed.mean(axis)
      tstde = mstats.trimmed_stde(data,limits=limits,inclusive=inclusive,axis=axis)
      df = trimmed.count(axis) - 1
      tppf = t.ppf(1-alpha/2.,df)
      return np.array((tmean - tppf*tstde, tmean+tppf*tstde))
function_tokens: def trimmed_mean_ci ( data , limits = ( 0.2 , 0.2 ) , inclusive = ( True , True ) , alpha = 0.05 , axis = None ) : data = ma . array ( data , copy = False ) trimmed = mstats . trimr ( data , limits = limits , inclusive = inclusive , axis = axis ) tmean = trimmed . mean ( axis ) tstde = mstats . trimmed_stde ( data , limits = limits , inclusive = inclusive , axis = axis ) df = trimmed . count ( axis ) - 1 tppf = t . ppf ( 1 - alpha / 2. , df ) return np . array ( ( tmean - tppf * tstde , tmean + tppf * tstde ) )