code: def bound_range(vals, density, time_unit='us'):
      
      if not len(vals):
          return(np.nan, np.nan, density, False)
      low, high = vals.min(), vals.max()
      invert = False
      if len(vals) > 1 and vals[0] > vals[1]:
          invert = True
      if not density:
          with warnings.catch_warnings():
              warnings.filterwarnings('ignore', r'invalid value encountered in double_scalars')
              full_precision_density = compute_density(low, high, len(vals)-1)
              density = round(full_precision_density, sys.float_info.dig)
          if density == 0:
              density = full_precision_density
      if density == 0:
          raise ValueError('Could not determine Image density, ensure it has a non-zero range.')
      halfd = 0.5/density
      if isinstance(low, datetime_types):
          halfd = np.timedelta64(int(round(halfd)), time_unit)
      return low-halfd, high+halfd, density, invert
docs: Computes a bounding range and density from a number of samples
      assumed to be evenly spaced. Density is rounded to machine precision
      using significant digits reported by sys.float_info.dig.