code: def fuzzybreaks(scale, breaks=None, boundary=None,
                  binwidth=None, bins=30, right=True):
      
      # Bins for categorical data should take the width
      # of one level, and should show up centered over
      # their tick marks. All other parameters are ignored.
      if isinstance(scale, scale_discrete):
          breaks = scale.get_breaks()
          return -0.5 + np.arange(1, len(breaks)+2)
      else:
          if breaks is not None:
              breaks = scale.transform(breaks)
  
      if breaks is not None:
          return breaks
  
      recompute_bins = binwidth is not None
      srange = scale.limits
  
      if binwidth is None or np.isnan(binwidth):
          binwidth = (srange[1]-srange[0]) / bins
  
      if boundary is None or np.isnan(boundary):
          boundary = round_any(srange[0], binwidth, np.floor)
  
      if recompute_bins:
          bins = np.int(np.ceil((srange[1]-boundary)/binwidth))
  
      # To minimise precision errors, we do not pass the boundary and
      # binwidth into np.arange as params. The resulting breaks
      # can then be adjusted with finer(epsilon based rather than
      # some arbitrary small number) precision.
      breaks = np.arange(boundary, srange[1]+binwidth, binwidth)
      return _adjust_breaks(breaks, right)
docs: Compute fuzzy breaks
  
      For a continuous scale, fuzzybreaks "preserve" the range of
      the scale. The fuzzing is close to numerical roundoff and
      is visually imperceptible.
  
      Parameters
      ----------
      scale : scale
          Scale
      breaks : array_like
          Sequence of break points. If provided and the scale is not
          discrete, they are returned.
      boundary : float
          First break. If `None` a suitable on is computed using
          the range of the scale and the binwidth.
      binwidth : float
          Separation between the breaks
      bins : int
          Number of bins
      right : bool
          If `True` the right edges of the bins are part of the
          bin. If `False` then the left edges of the bins are part
          of the bin.
  
      Returns
      -------
      out : array_like