 
       interval = best_mantissa * ideal_magnitude
 
-      console.log("AS.gi: mantissas = #{candidate_mantissas}")
-      console.log("          errors = #{errors}")
-      console.log("        mantissa = #{best_mantissa}")
-      console.log("        interval = #{interval}")
+#       console.log("  AS.gi: mantissas = #{candidate_mantissas}")
+#       console.log("            errors = #{errors}")
+#       console.log("          mantissa = #{best_mantissa}")
+#       console.log("         magnitude = #{ideal_magnitude}")
+#       console.log("          interval = #{interval}")
 
       return clamp(interval, @get_min_interval(), @get_max_interval())
 

      start_factor = Math.floor(data_low / interval)
      end_factor   = Math.ceil(data_high / interval)
      factors = arange(start_factor, end_factor + 1)
      return factors.map((f) -> return f * interval)
  
  # FIXME Hopefully we won't actually need this.
  class SingleIntervalScale extends AbstractScale

      best_scale_ix = scale_ixs[argmin(errors)]
      best_interval = @scales[best_scale_ix].get_interval(data_low, data_high)

#       console.log("")
#       console.log("CS.gi: intervals = #{intervals}")
#       console.log("          errors = #{errors}")
#       console.log("        interval = #{best_interval}")

      return best_interval

