code: def get_integrated_act(x, axis=0, window=50, fast=False):
      
      # Compute the autocorrelation function.
      f = get_acf(x, axis=axis, fast=fast)
  
      # Special case 1D for simplicity.
      if len(f.shape) == 1:
          return 1 + 2 * np.sum(f[1:window])
  
      # N-dimensional case.
      m = [slice(None), ] * len(f.shape)
      m[axis] = slice(1, window)
      tau = 1 + 2 * np.sum(f[tuple(m)], axis=axis)
  
      return tau
docs: Estimate the integrated autocorrelation time of a time series.
  
      See `Sokal's notes <http://www.stat.unc.edu/faculty/cji/Sokal.pdf>`_ on
      MCMC and sample estimators for autocorrelation times.
  
      :param x:
          The time series. If multidimensional, set the time axis using the
          ``axis`` keyword argument and the function will be computed for every
          other axis.
  
      :param axis: (optional)
          The time axis of ``x``. Assumed to be the first axis if not specified.
  
      :param window: (optional)
          The size of the window to use. (default: 50)
  
      :param fast: (optional)
          If ``True``, only use the largest ``2^n`` entries for efficiency.
          (default: False)