code: def corr2(X):
      ''' computes correlations between all variable pairs in a segmented time series
  
      .. note:: this feature is expensive to compute with the current implementation, and cannot be
      used with univariate time series
      '''
      X = np.atleast_3d(X)
      N = X.shape[0]
      D = X.shape[2]
  
      if D == 1:
          return np.zeros(N, dtype=np.float)
  
      trii = np.triu_indices(D, k=1)
      DD = len(trii[0])
      r = np.zeros((N, DD))
      for i in np.arange(N):
          rmat = np.corrcoef(X[i])  # get the ith window from each signal, result will be DxD
          r[i] = rmat[trii]
      return f corr2(X):
      ''' computes correlations between all variable pairs in a segmented time series
  
      .. note:: this feature is expensive to compute with the current implementation, and cannot be
      used with univariate time series
      '''
      X = np.atleast_3d(X)
      N = X.shape[0]
      D = X.shape[2]
  
      if D == 1:
          return np.zeros(N, dtype=np.float)
  
      trii = np.triu_indices(D, k=1)
      DD = len(trii[0])
      r = np.zeros((N, DD))
      for i in np.arange(N):
          rmat = np.corrcoef(X[i])  # get the ith window from each signal, result will be DxD
          r[i] = rmat[trii]
      return r
docs: computes correlations between all variable pairs in a segmented time series
  
      .. note:: this feature is expensive to compute with the current implementation, and cannot be
      used with univariate time series