parameters: (data, alpha=0.05, axis=None)
return_statement: return result
docstring: Computes the alpha-level confidence interval for the median of the data.
  
      Uses the Hettmasperger-Sheather method.
  
      Parameters
      ----------
      data : array_like
          Input data. Masked values are discarded. The input should be 1D only,
          or `axis` should be set to None.
      alpha : float, optional
          Confidence level of the intervals.
      axis : int or None, optional
          Axis along which to compute the quantiles. If None, use a flattened
          array.
  
      Returns
      -------
      median_cihs
          Alpha level confidence interval.
docstring_summary: Computes the alpha-level confidence interval for the median of the data.
docstring_tokens: Computes the alpha - level confidence interval for the median of the data .
function: def median_cihs(data, alpha=0.05, axis=None):
      """
      Computes the alpha-level confidence interval for the median of the data.
  
      Uses the Hettmasperger-Sheather method.
  
      Parameters
      ----------
      data : array_like
          Input data. Masked values are discarded. The input should be 1D only,
          or `axis` should be set to None.
      alpha : float, optional
          Confidence level of the intervals.
      axis : int or None, optional
          Axis along which to compute the quantiles. If None, use a flattened
          array.
  
      Returns
      -------
      median_cihs
          Alpha level confidence interval.
  
      """
      def _cihs_1D(data, alpha):
          data = np.sort(data.compressed())
          n = len(data)
          alpha = min(alpha, 1-alpha)
          k = int(binom._ppf(alpha/2., n, 0.5))
          gk = binom.cdf(n-k,n,0.5) - binom.cdf(k-1,n,0.5)
          if gk < 1-alpha:
              k -= 1
              gk = binom.cdf(n-k,n,0.5) - binom.cdf(k-1,n,0.5)
          gkk = binom.cdf(n-k-1,n,0.5) - binom.cdf(k,n,0.5)
          I = (gk - 1 + alpha)/(gk - gkk)
          lambd = (n-k) * I / float(k + (n-2*k)*I)
          lims = (lambd*data[k] + (1-lambd)*data[k-1],
                  lambd*data[n-k-1] + (1-lambd)*data[n-k])
          return lims
      data = ma.array(data, copy=False)
      # Computes quantiles along axis (or globally)
      if (axis is None):
          result = _cihs_1D(data, alpha)
      else:
          if data.ndim > 2:
              raise ValueError("Array 'data' must be at most two dimensional, "
                               "but got data.ndim = %d" % data.ndim)
          result = ma.apply_along_axis(_cihs_1D, axis, data, alpha)
  
      return result
function_tokens: def median_cihs ( data , alpha = 0.05 , axis = None ) : def _cihs_1D ( data , alpha ) : data = np . sort ( data . compressed ( ) ) n = len ( data ) alpha = min ( alpha , 1 - alpha ) k = int ( binom . _ppf ( alpha / 2. , n , 0.5 ) ) gk = binom . cdf ( n - k , n , 0.5 ) - binom . cdf ( k - 1 , n , 0.5 ) if gk < 1 - alpha : k -= 1 gk = binom . cdf ( n - k , n , 0.5 ) - binom . cdf ( k - 1 , n , 0.5 ) gkk = binom . cdf ( n - k - 1 , n , 0.5 ) - binom . cdf ( k , n , 0.5 ) I = ( gk - 1 + alpha ) / ( gk - gkk ) lambd = ( n - k ) * I / float ( k + ( n - 2 * k ) * I ) lims = ( lambd * data [ k ] + ( 1 - lambd ) * data [ k - 1 ] , lambd * data [ n - k - 1 ] + ( 1 - lambd ) * data [ n - k ] ) return lims data = ma . array ( data , copy = False ) # Computes quantiles along axis (or globally) if ( axis is None ) : result = _cihs_1D ( data , alpha ) else : if data . ndim > 2 : raise ValueError ( "Array 'data' must be at most two dimensional, " "but got data.ndim = %d" % data . ndim ) result = ma . apply_along_axis ( _cihs_1D , axis , data , alpha ) return result