def _ci(arr, ci):
 from scipy import stats
 (mean, sigma) = (arr.mean(0), stats.sem(arr, 0))
 return np.asarray([stats.t.interval(ci, arr.shape[0], loc=mean_, scale=sigma_) for (mean_, sigma_) in zip(mean, sigma)]).T

def _ci ( arr , ci ) : from scipy import stats ( mean , sigma ) = ( arr . mean ( 0 ) , stats . sem ( arr , 0 ) ) return np . asarray ( [ stats . t . interval ( ci , arr . shape [ 0 ] , loc = mean_ , scale = sigma_ ) for ( mean_ , sigma_ ) in zip ( mean , sigma ) ] ) . T
calculate the ci% parametric confidence interval for arr .