text: from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
  import copy as _copy
  
  
  class YBins(_BaseTraceHierarchyType):
  
      # class properties
      # --------------------
      _parent_path_str = "histogram2dcontour"
      _path_str = "histogram2dcontour.ybins"
      _valid_props = {"end", "size", "start"}
  
      # end
      # ---
      @property
      def end(self):
          """
          Sets the end value for the y axis bins. The last bin may not
          end exactly at this value, we increment the bin edge by `size`
          from `start` until we reach or exceed `end`. Defaults to the
          maximum data value. Like `start`, for dates use a date string,
          and for category data `end` is based on the category serial
          numbers.
      
          The 'end' property accepts values of any type
  
          Returns
          -------
          Any
          """
          return self["end"]
  
      @end.setter
      def end(self, val):
          self["end"] = val
  
      # size
      # ----
      @property
      def size(self):
          """
          Sets the size of each y axis bin. Default behavior: If `nbinsy`
          is 0 or omitted, we choose a nice round bin size such that the
          number of bins is about the same as the typical number of
          samples in each bin. If `nbinsy` is provided, we choose a nice
          round bin size giving no more than that many bins. For date
          data, use milliseconds or "M<n>" for months, as in
          `axis.dtick`. For category data, the number of categories to
          bin together (always defaults to 1).
      
          The 'size' property accepts values of any type
  
          Returns
          -------
          Any
          """
          return self["size"]
  
      @size.setter
      def size(self, val):
          self["size"] = val
  
      # start
      # -----
      @property
      def start(self):
          """
          Sets the starting value for the y axis bins. Defaults to the
          minimum data value, shifted down if necessary to make nice
          round values and to remove ambiguous bin edges. For example, if
          most of the data is integers we shift the bin edges 0.5 down,
          so a `size` of 5 would have a default `start` of -0.5, so it is
          clear that 0-4 are in the first bin, 5-9 in the second, but
          continuous data gets a start of 0 and bins [0,5), [5,10) etc.
          Dates behave similarly, and `start` should be a date string.
          For category data, `start` is based on the category serial
          numbers, and defaults to -0.5.
      
          The 'start' property accepts values of any type
  
          Returns
          -------
          Any
          """
          return self["start"]
  
      @start.setter
      def start(self, val):
          self["start"] = val
  
      # Self properties description
      # ---------------------------
      @property
      def _prop_descriptions(self):
          return """\
          end
              Sets the end value for the y axis bins. The last bin
              may not end exactly at this value, we increment the bin
              edge by `size` from `start` until we reach or exceed
              `end`. Defaults to the maximum data value. Like
              `start`, for dates use a date string, and for category
              data `end` is based on the category serial numbers.
          size
              Sets the size of each y axis bin. Default behavior: If
              `nbinsy` is 0 or omitted, we choose a nice round bin
              size such that the number of bins is about the same as
              the typical number of samples in each bin. If `nbinsy`
              is provided, we choose a nice round bin size giving no
              more than that many bins. For date data, use
              milliseconds or "M<n>" for months, as in `axis.dtick`.
              For category data, the number of categories to bin
              together (always defaults to 1).
          start
              Sets the starting value for the y axis bins. Defaults
              to the minimum data value, shifted down if necessary to
              make nice round values and to remove ambiguous bin
              edges. For example, if most of the data is integers we
              shift the bin edges 0.5 down, so a `size` of 5 would
              have a default `start` of -0.5, so it is clear that 0-4
              are in the first bin, 5-9 in the second, but continuous
              data gets a start of 0 and bins [0,5), [5,10) etc.
              Dates behave similarly, and `start` should be a date
              string. For category data, `start` is based on the
              category serial numbers, and defaults to -0.5.
          """
  
      def __init__(self, arg=None, end=None, size=None, start=None, **kwargs):
          """
          Construct a new YBins object
          
          Parameters
          ----------
          arg
              dict of properties compatible with this constructor or
              an instance of
              :class:`plotly.graph_objs.histogram2dcontour.YBins`
          end
              Sets the end value for the y axis bins. The last bin
              may not end exactly at this value, we increment the bin
              edge by `size` from `start` until we reach or exceed
              `end`. Defaults to the maximum data value. Like
              `start`, for dates use a date string, and for category
              data `end` is based on the category serial numbers.
          size
              Sets the size of each y axis bin. Default behavior: If
              `nbinsy` is 0 or omitted, we choose a nice round bin
              size such that the number of bins is about the same as
              the typical number of samples in each bin. If `nbinsy`
              is provided, we choose a nice round bin size giving no
              more than that many bins. For date data, use
              milliseconds or "M<n>" for months, as in `axis.dtick`.
              For category data, the number of categories to bin
              together (always defaults to 1).
          start
              Sets the starting value for the y axis bins. Defaults
              to the minimum data value, shifted down if necessary to
              make nice round values and to remove ambiguous bin
              edges. For example, if most of the data is integers we
              shift the bin edges 0.5 down, so a `size` of 5 would
              have a default `start` of -0.5, so it is clear that 0-4
              are in the first bin, 5-9 in the second, but continuous
              data gets a start of 0 and bins [0,5), [5,10) etc.
              Dates behave similarly, and `start` should be a date
              string. For category data, `start` is based on the
              category serial numbers, and defaults to -0.5.
  
          Returns
          -------
          YBins
          """
          super(YBins, self).__init__("ybins")
  
          if "_parent" in kwargs:
              self._parent = kwargs["_parent"]
              return
  
          # Validate arg
          # ------------
          if arg is None:
              arg = {}
          elif isinstance(arg, self.__class__):
              arg = arg.to_plotly_json()
          elif isinstance(arg, dict):
              arg = _copy.copy(arg)
          else:
              raise ValueError(
                  """\
  The first argument to the plotly.graph_objs.histogram2dcontour.YBins 
  constructor must be a dict or 
  an instance of :class:`plotly.graph_objs.histogram2dcontour.YBins`"""
              )
  
          # Handle skip_invalid
          # -------------------
          self._skip_invalid = kwargs.pop("skip_invalid", False)
  
          # Populate data dict with properties
          # ----------------------------------
          _v = arg.pop("end", None)
          _v = end if end is not None else _v
          if _v is not None:
              self["end"] = _v
          _v = arg.pop("size", None)
          _v = size if size is not None else _v
          if _v is not None:
              self["size"] = _v
          _v = arg.pop("start", None)
          _v = start if start is not None else _v
          if _v is not None:
              self["start"] = _v
  
          # Process unknown kwargs
          # ----------------------
          self._process_kwargs(**dict(arg, **kwargs))
  
          # Reset skip_invalid
          # ------------------
          self._skip_invalid = False
  