anchor.diff:      effective_histogram_width = num_columns * avatar_size + 2
       margin_adjustment = -(effective_histogram_width - HISTOGRAM_WIDTH) / 2 #- avatar_size / 4
   
  -    DIV null,
  +    selected_opinion = hist.selected_opinions?.length == 1 and hist.selected_opinions?[0]
  +
  +    DIV style: {position: 'relative'},
  +      if selected_opinion
  +        user = fetch(selected_opinion.user)
  +        DIV
  +          style:
  +            position: 'absolute'
  +            top: -(50 + 5 + 18 + 20*2)
  +            #height: 120
  +            backgroundColor: '#f9f9f9'
  +            padding: 20
  +            marginLeft: 157
  +
  +          DIV
  +            style:
  +              marginBottom: 5
  +              fontSize: 18
  +              textAlign: 'center'
  +            'Showing opinion for'
  +          Avatar
  +            style:
  +              height: 50
  +              width: 50
  +              display: 'inline-block'
  +            key: user
  +            user: user
  +
  +          SPAN
  +            style:
  +              display: 'inline-block'
  +              fontWeight: 600
  +              marginLeft: 15
  +              fontSize: 18
  +            user.name
  +
  +
         TABLE 
           className: 'histogram'
           onClick: @onClickHistogram
  
positive.code:       ev.stopPropagation()
  
    render : ->
      selected_segment = fetch('histogram').selected_segment
      users_to_highlight = fetch('histogram').highlighted_users
  
  
      histogram_data = fetch('histogramdata/' + @proposal.slug)
  
negative.code:         is_correct_valence = pnt.is_pro == (@props.key == 'pros')
          has_not_been_included = is_results || !_.contains(included_points, pnt.key)
          is_correct_valence && has_not_been_included
      if selected_opinion
        # Just show this one person's opinion
        points =
          _.filter points, (pnt) -> pnt.key in selected_opinion.point_inclusions
      
      else if selected_segment?
        # If there is a histogram segment selected, we'll have to filter down 
        # to the points that users in this segment think are important, and 
        # order them by resonance to those users. I'm doing this quite inefficiently.
  