code: def epochs_joint(ts, variability=None, threshold=0.0, minlength=1.0,
                   proportion=0.75, plot=True):
      
      if ts.ndim <= 2:
          return analyses1.epochs_joint(
                  ts, variability, threshold, minlength, plot)
      else:
          return distob.vectorize(analyses1.epochs_joint)(
                  ts, variability, threshold, minlength, plot)
docs: Identify epochs within a multivariate time series where at least a 
      certain proportion of channels are "stationary", based on a previously 
      computed variability measure.
  
      (Note: This requires an IPython cluster to be started first, 
       e.g. on a workstation type 'ipcluster start')
  
      Args:
        ts  Timeseries of m variables, shape (n, m). 
        variability  (optional) Timeseries of shape (n, m),  giving a scalar 
                     measure of the variability of timeseries `ts` near each 
                     point in time. (if None, we will use variability_fp())
        threshold   The maximum variability permitted in stationary epochs.
        minlength   Shortest acceptable epoch length (in seconds)
        proportion  Require at least this fraction of channels to be "stationary"
        plot  bool  Whether to display the output
  
      Returns: (variability, joint_epochs)
        joint_epochs: list of tuples
        A list of tuples (start, end) that give the starting and ending indices 
        of time epochs that are stationary for at least `proportion` of channels.
        (epochs are inclusive of start point but not the end point)