code: def covar(X, remove_mean=False, modify_data=False, weights=None, sparse_mode='auto', sparse_tol=0.0):
      
      w, s, M = moments_XX(X, remove_mean=remove_mean, weights=weights, modify_data=modify_data,
                           sparse_mode=sparse_mode, sparse_tol=sparse_tol)
      return M / float(w)
docs: Computes the covariance matrix of X
  
      Computes
  
      .. math:
          C_XX &=& X^\top X
  
      while exploiting zero or constant columns in the data matrix.
      WARNING: Directly use moments_XX if you can. This function does an additional
      constant-matrix multiplication and does not return the mean.
  
      Parameters
      ----------
      X : ndarray (T, M)
          Data matrix
      remove_mean : bool
          True: remove column mean from the data, False: don't remove mean.
      modify_data : bool
          If remove_mean=True, the mean will be removed in the data matrix X,
          without creating an independent copy. This option is faster but might
          lead to surprises because your input array is changed.
      weights : None or ndarray(T, )
          weights assigned to each trajectory point of X. If None, all data points have weight one.
          If ndarray, each data point is assigned a separate weight.
      sparse_mode : str
          one of:
              * 'dense' : always use dense mode
              * 'sparse' : always use sparse mode if possible
              * 'auto' : automatic
      sparse_tol: float
          Threshold for considering column to be zero in order to save computing
          effort when the data is sparse or almost sparse.
          If max(abs(X[:, i])) < sparse_tol, then row i (and also column i if Y
          is not given) of the covariance matrix will be set to zero. If Y is
          given and max(abs(Y[:, i])) < sparse_tol, then column i of the
          covariance matrix will be set to zero.
  
      Returns
      -------
      C_XX : ndarray (M, M)
          Covariance matrix of X
  
      See also
      --------
      moments_XX