def cleanup_equivalent(labels, shifts, errs, ws, crysts, inds, hashes):
    """
    Gather equivalent graphs (identified by their shift distributions)
    
    Inputs:     - labels        List of labels of the distributions
                - shifts        List of predicted shifts in each distribution
                - errs          List of predicted errors in each distribution
                - ws            List of weights of the distributions
                - crysts        List of crystals in each distribution
                - inds          List of the atoms in each distribution
                - hashes        List of hashes for each graph
                
    Outputs:    - new_labels    Updated list of labels of the distributions
                - new_shifts    Updated list of predicted shifts in each distribution
                - new_errs      Updated list of predicted errors in each distribution
                - new_ws        Updated list of weights of the distributions
                - new_crysts    Updated list of crystals in each distribution
                - new_inds      Updated list of the atoms in each distribution
    """
    
    # Initialize the updated lists
    new_labels = []
    new_shifts = []
    new_errs = []
    new_ws = []
    new_crysts = []
    new_inds = []
    new_hashes = []
    
    # Loop over all the distributions
    for l, sh, er, w, cr, ind, h in zip(labels, shifts, errs, ws, crysts, inds, hashes):
        
        # If the distribution is already found, modify the label
        if h in new_hashes:
            i = new_hashes.index(h)
            new_labels[i] += "/{}".format(l)
        
        # Otherwise, append the distribution to the updated list
        else:
            new_labels.append(l)
            new_shifts.append(sh)
            new_errs.append(er)
            new_ws.append(w)
            new_crysts.append(cr)
            new_inds.append(ind)
            new_hashes.append(h)
    
    return new_labels, new_shifts, new_errs, new_ws, new_crysts, new_inds, new_hashes