import warnings

from itertools import chain
from types import FunctionType

import param
import numpy as np
import bokeh
import bokeh.plotting
from bokeh.core.properties import value
from bokeh.document.events import ModelChangedEvent
from bokeh.models import (
    BinnedTicker, ColorBar, ColorMapper, CustomJS, EqHistColorMapper,
    GlyphRenderer, Legend, Renderer, Title, tools,
)
from bokeh.models.axes import CategoricalAxis, DatetimeAxis
from bokeh.models.formatters import (
    TickFormatter, MercatorTickFormatter
)
from bokeh.models.layouts import Tabs
from bokeh.models.mappers import (
    LinearColorMapper, LogColorMapper, CategoricalColorMapper
)
from bokeh.models.scales import LogScale
from bokeh.models.ranges import Range1d, DataRange1d, FactorRange
from bokeh.models.tickers import (
    Ticker, BasicTicker, FixedTicker, LogTicker, MercatorTicker
)
from bokeh.models.tools import Tool

from packaging.version import Version

from ...core import DynamicMap, CompositeOverlay, Element, Dimension, Dataset
from ...core.options import abbreviated_exception, Keywords, SkipRendering
from ...core import util
from ...element import (
    Annotation, Contours, Graph, Path, Tiles, VectorField
)
from ...streams import Buffer, RangeXY, PlotSize
from ...util.transform import dim
from ..plot import GenericElementPlot, GenericOverlayPlot
from ..util import (
    dim_axis_label, process_cmap, color_intervals, dim_range_key
)
from .plot import BokehPlot
from .styles import (
    base_properties, legend_dimensions, line_properties, mpl_to_bokeh,
    property_prefixes, rgba_tuple, text_properties, validate
)
from .tabular import TablePlot
from .util import (
    TOOL_TYPES, bokeh_version, bokeh3, bokeh32, date_to_integer,
    decode_bytes, get_tab_title, glyph_order, py2js_tickformatter,
    recursive_model_update, theme_attr_json, cds_column_replace,
    hold_policy, match_dim_specs, compute_layout_properties,
    wrap_formatter, match_ax_type, match_yaxis_type_to_range,
    prop_is_none, remove_legend, property_to_dict, dtype_fix_hook,
    get_scale, get_axis_class
)

if bokeh3:
    from bokeh.models.formatters import CustomJSTickFormatter
    from bokeh.models.layouts import TabPanel
else:
    from bokeh.models.formatters import FuncTickFormatter as CustomJSTickFormatter
    from bokeh.models.layouts import Panel as TabPanel

try:
    TOOLS_MAP = Tool._known_aliases
except Exception:
    TOOLS_MAP = TOOL_TYPES


class ElementPlot(BokehPlot, GenericElementPlot):

    active_tools = param.List(default=None, doc="""
        Allows specifying which tools are active by default. Note
        that only one tool per gesture type can be active, e.g.
        both 'pan' and 'box_zoom' are drag tools, so if both are
        listed only the last one will be active. As a default 'pan'
        and 'wheel_zoom' will be used if the tools are enabled.""")

    align = param.ObjectSelector(default='start', objects=['start', 'center', 'end'], doc="""
        Alignment (vertical or horizontal) of the plot in a layout.""")

    autorange = param.ObjectSelector(default=None, objects=['x', 'y', None], doc="""
        Whether to auto-range along either the x- or y-axis, i.e.
        when panning or zooming along the orthogonal axis it will
        ensure all the data along the selected axis remains visible.""")

    border = param.Number(default=10, doc="""
        Minimum border around plot.""")

    aspect = param.Parameter(default=None, doc="""
        The aspect ratio mode of the plot. By default, a plot may
        select its own appropriate aspect ratio but sometimes it may
        be necessary to force a square aspect ratio (e.g. to display
        the plot as an element of a grid). The modes 'auto' and
        'equal' correspond to the axis modes of the same name in
        matplotlib, a numeric value specifying the ratio between plot
        width and height may also be passed. To control the aspect
        ratio between the axis scales use the data_aspect option
        instead.""")

    backend_opts = param.Dict(default={}, doc="""
        A dictionary of custom options to apply to the plot or
        subcomponents of the plot. The keys in the dictionary mirror
        attribute access on the underlying models stored in the plot's
        handles, e.g. {'colorbar.margin': 10} will index the colorbar
        in the Plot.handles and then set the margin to 10.""")

    data_aspect = param.Number(default=None, doc="""
        Defines the aspect of the axis scaling, i.e. the ratio of
        y-unit to x-unit.""")

    width = param.Integer(default=300, allow_None=True, bounds=(0, None), doc="""
        The width of the component (in pixels). This can be either
        fixed or preferred width, depending on width sizing policy.""")

    height = param.Integer(default=300, allow_None=True, bounds=(0, None), doc="""
        The height of the component (in pixels).  This can be either
        fixed or preferred height, depending on height sizing policy.""")

    frame_width = param.Integer(default=None, allow_None=True, bounds=(0, None), doc="""
        The width of the component (in pixels). This can be either
        fixed or preferred width, depending on width sizing policy.""")

    frame_height = param.Integer(default=None, allow_None=True, bounds=(0, None), doc="""
        The height of the component (in pixels).  This can be either
        fixed or preferred height, depending on height sizing policy.""")

    min_width = param.Integer(default=None, bounds=(0, None), doc="""
        Minimal width of the component (in pixels) if width is adjustable.""")

    min_height = param.Integer(default=None, bounds=(0, None), doc="""
        Minimal height of the component (in pixels) if height is adjustable.""")

    max_width = param.Integer(default=None, bounds=(0, None), doc="""
        Minimal width of the component (in pixels) if width is adjustable.""")

    max_height = param.Integer(default=None, bounds=(0, None), doc="""
        Minimal height of the component (in pixels) if height is adjustable.""")

    margin = param.Parameter(default=None, doc="""
        Allows to create additional space around the component. May
        be specified as a two-tuple of the form (vertical, horizontal)
        or a four-tuple (top, right, bottom, left).""")

    multi_y = param.Boolean(default=False, doc="""
       Enables multiple axes (one per value dimension) in
       overlays and useful for creating twin-axis plots.

       When enabled, axis options are no longer propagated between the
       elements and the overlay container, allowing customization on a
       per-axis basis.""")

    responsive = param.ObjectSelector(default=False, objects=[False, True, 'width', 'height'])

    fontsize = param.Parameter(default={'title': '12pt'}, allow_None=True,  doc="""
       Specifies various fontsizes of the displayed text.

       Finer control is available by supplying a dictionary where any
       unmentioned keys reverts to the default sizes, e.g:

          {'ticks': '20pt', 'title': '15pt', 'ylabel': '5px', 'xlabel': '5px'}""")

    gridstyle = param.Dict(default={}, doc="""
        Allows customizing the grid style, e.g. grid_line_color defines
        the line color for both grids while xgrid_line_color exclusively
        customizes the x-axis grid lines.""")

    labelled = param.List(default=['x', 'y'], doc="""
        Whether to plot the 'x' and 'y' labels.""")

    lod = param.Dict(default={'factor': 10, 'interval': 300,
                              'threshold': 2000, 'timeout': 500}, doc="""
        Bokeh plots offer "Level of Detail" (LOD) capability to
        accommodate large (but not huge) amounts of data. The available
        options are:

          * factor    : Decimation factor to use when applying
                        decimation.
          * interval  : Interval (in ms) downsampling will be enabled
                        after an interactive event.
          * threshold : Number of samples before downsampling is enabled.
          * timeout   : Timeout (in ms) for checking whether interactive
                        tool events are still occurring.""")

    show_frame = param.Boolean(default=True, doc="""
        Whether or not to show a complete frame around the plot.""")

    shared_axes = param.Boolean(default=True, doc="""
        Whether to invert the share axes across plots
        for linked panning and zooming.""")

    default_tools = param.List(default=['save', 'pan', 'wheel_zoom',
                                        'box_zoom', 'reset'],
        doc="A list of plugin tools to use on the plot.")

    tools = param.List(default=[], doc="""
        A list of plugin tools to use on the plot.""")

    toolbar = param.ObjectSelector(default='right',
                                   objects=["above", "below",
                                            "left", "right", "disable", None],
                                   doc="""
        The toolbar location, must be one of 'above', 'below',
        'left', 'right', None.""")

    xformatter = param.ClassSelector(
        default=None, class_=(str, TickFormatter, FunctionType), doc="""
        Formatter for ticks along the x-axis.""")

    yformatter = param.ClassSelector(
        default=None, class_=(str, TickFormatter, FunctionType), doc="""
        Formatter for ticks along the x-axis.""")

    _categorical = False
    _allow_implicit_categories = True

    # Declare which styles cannot be mapped to a non-scalar dimension
    _nonvectorized_styles = []

    # Declares the default types for continuous x- and y-axes
    _x_range_type = Range1d
    _y_range_type = Range1d

    # Whether the plot supports streaming data
    _stream_data = True

    def __init__(self, element, plot=None, **params):
        self.current_ranges = None
        super().__init__(element, **params)
        self.handles = {} if plot is None else self.handles['plot']
        self.static = len(self.hmap) == 1 and len(self.keys) == len(self.hmap)
        self.callbacks, self.source_streams = self._construct_callbacks()
        self.static_source = False
        self.streaming = [s for s in self.streams if isinstance(s, Buffer)]
        self.geographic = bool(self.hmap.last.traverse(lambda x: x, Tiles))
        if self.geographic and self.projection is None:
            self.projection = 'mercator'

        # Whether axes are shared between plots
        self._shared = {'x-main-range': False, 'y-main-range': False}
        self._js_on_data_callbacks = []

        # Flag to check whether plot has been updated
        self._updated = False

    def _hover_opts(self, element):
        if self.batched:
            dims = list(self.hmap.last.kdims)
        else:
            dims = list(self.overlay_dims.keys())
        dims += element.dimensions()
        return list(util.unique_iterator(dims)), {}

    def _init_tools(self, element, callbacks=[]):
        """
        Processes the list of tools to be supplied to the plot.
        """
        tooltips, hover_opts = self._hover_opts(element)
        tooltips = [(ttp.pprint_label, '@{%s}' % util.dimension_sanitizer(ttp.name))
                    if isinstance(ttp, Dimension) else ttp for ttp in tooltips]
        if not tooltips: tooltips = None

        callbacks = callbacks+self.callbacks
        cb_tools, tool_names = [], []
        hover = False
        for cb in callbacks:
            for handle in cb.models:
                if handle and handle in TOOLS_MAP:
                    tool_names.append(handle)
                    if handle == 'hover':
                        tool = tools.HoverTool(
                            tooltips=tooltips, tags=['hv_created'],
                            **hover_opts)
                        hover = tool
                    else:
                        tool = TOOLS_MAP[handle]()
                    cb_tools.append(tool)
                    self.handles[handle] = tool

        tool_list = []
        for tool in cb_tools + self.default_tools + self.tools:
            if tool in tool_names:
                continue
            if tool in ['vline', 'hline']:
                tool = tools.HoverTool(
                    tooltips=tooltips, tags=['hv_created'], mode=tool, **hover_opts
                )
            elif bokeh32 and tool in ['wheel_zoom', 'xwheel_zoom', 'ywheel_zoom']:
                if tool.startswith('x'):
                    zoom_dims = 'width'
                elif tool.startswith('y'):
                    zoom_dims = 'height'
                else:
                    zoom_dims = 'both'
                tool = tools.WheelZoomTool(
                    zoom_together='none', dimensions=zoom_dims, tags=['hv_created']
                )
            tool_list.append(tool)

        copied_tools = []
        for tool in tool_list:
            if isinstance(tool, tools.Tool):
                properties = tool.properties_with_values(include_defaults=False)
                tool = type(tool)(**properties)
            copied_tools.append(tool)

        hover_tools = [t for t in copied_tools if isinstance(t, tools.HoverTool)]
        if 'hover' in copied_tools:
            hover = tools.HoverTool(tooltips=tooltips, tags=['hv_created'], **hover_opts)
            copied_tools[copied_tools.index('hover')] = hover
        elif any(hover_tools):
            hover = hover_tools[0]
        if hover:
            self.handles['hover'] = hover

        box_tools = [t for t in copied_tools if isinstance(t, tools.BoxSelectTool)]
        if box_tools:
            self.handles['box_select'] = box_tools[0]
        lasso_tools = [t for t in copied_tools if isinstance(t, tools.LassoSelectTool)]
        if lasso_tools:
            self.handles['lasso_select'] = lasso_tools[0]

        # Link the selection properties between tools
        if box_tools and lasso_tools:
            box_tools[0].js_link('mode', lasso_tools[0], 'mode')
            lasso_tools[0].js_link('mode', box_tools[0], 'mode')

        return copied_tools

    def _update_hover(self, element):
        tool = self.handles['hover']
        if 'hv_created' in tool.tags:
            tooltips, hover_opts = self._hover_opts(element)
            tooltips = [(ttp.pprint_label, '@{%s}' % util.dimension_sanitizer(ttp.name))
                        if isinstance(ttp, Dimension) else ttp for ttp in tooltips]
            tool.tooltips = tooltips
        else:
            plot_opts = element.opts.get('plot', 'bokeh')
            new_hover = [t for t in plot_opts.kwargs.get('tools', [])
                         if isinstance(t, tools.HoverTool)]
            if new_hover:
                tool.tooltips = new_hover[0].tooltips

    def _get_hover_data(self, data, element, dimensions=None):
        """
        Initializes hover data based on Element dimension values.
        If empty initializes with no data.
        """
        if 'hover' not in self.handles or self.static_source:
            return

        for d in (dimensions or element.dimensions()):
            dim = util.dimension_sanitizer(d.name)
            if dim not in data:
                data[dim] = element.dimension_values(d)

        for k, v in self.overlay_dims.items():
            dim = util.dimension_sanitizer(k.name)
            if dim not in data:
                data[dim] = [v] * len(next(iter(data.values())))

    def _shared_axis_range(self, plots, specs, range_type, axis_type, pos):
        """
        Given a list of other plots return the shared axis from another
        plot by matching the dimensions specs stored as tags on the
        dimensions. Returns None if there is no such axis.
        """
        dim_range = None
        categorical = range_type is FactorRange
        for plot in plots:
            if plot is None or specs is None:
                continue
            ax = 'x' if pos == 0 else 'y'
            plot_range = getattr(plot, f'{ax}_range', None)
            axes = getattr(plot, f'{ax}axis', None)
            extra_ranges = getattr(plot, f'extra_{ax}_ranges', {})

            if (
                plot_range and plot_range.tags and
                match_dim_specs(plot_range.tags[0], specs) and
                match_ax_type(axes[0], axis_type) and
                not (categorical and not isinstance(dim_range, FactorRange))
            ):
                dim_range = plot_range

            if dim_range is not None:
                break

            for extra_range in extra_ranges.values():
                if (
                    extra_range.tags and match_dim_specs(extra_range.tags[0], specs) and
                    match_yaxis_type_to_range(axes, axis_type, extra_range.name) and
                    not (categorical and not isinstance(dim_range, FactorRange))
                ):
                    dim_range = extra_range
                    break

        return dim_range

    def _axis_props(self, plots, subplots, element, ranges, pos, *, dim=None,
                    range_tags_extras=[], extra_range_name=None):

        el = element.traverse(lambda x: x, [lambda el: isinstance(el, Element) and not isinstance(el, (Annotation, Tiles))])
        el = el[0] if el else element
        if isinstance(el, Graph):
            el = el.nodes

        range_el = el if self.batched and not isinstance(self, OverlayPlot) else element

        # For y-axes check if we explicitly passed in a dimension.
        # This is used by certain plot types to create an axis from
        # a synthetic dimension and exclusively supported for y-axes.
        if pos == 1 and dim:
            dims = [dim]
            v0, v1 = util.max_range([elrange.get(dim, {'combined': (None, None)})['combined'] for elrange in ranges.values()])
            axis_label = str(dim)
            specs = ((dim.name, dim.label, dim.unit),)
        else:
            if isinstance(self, OverlayPlot):
                l, b, r, t = self.get_extents(range_el, ranges, dim)
            else:
                l, b, r, t = self.get_extents(range_el, ranges)
            if self.invert_axes:
                l, b, r, t = b, l, t, r
            v0, v1 = (l, r) if pos == 0 else (b, t)

            axis_dims = list(self._get_axis_dims(el))
            if self.invert_axes:
                axis_dims[0], axis_dims[1] = axis_dims[:2][::-1]
            dims = axis_dims[pos]
            if dims:
                if not isinstance(dims, list):
                    dims = [dims]
                specs = tuple((d.name, d.label, d.unit) for d in dims)
            else:
                specs = None

            if dim:
                axis_label = str(dim)
            else:
                xlabel, ylabel, zlabel = self._get_axis_labels(dims if dims else (None, None))
                if self.invert_axes:
                    xlabel, ylabel = ylabel, xlabel
                axis_label = ylabel if pos else xlabel
            if dims:
                dims = dims[:2][::-1]

        categorical = any(self.traverse(lambda plot: plot._categorical))
        if dims is not None and any(dim.name in ranges and 'factors' in ranges[dim.name] for dim in dims):
            categorical = True
        else:
            categorical = any(isinstance(v, (str, bytes)) for v in (v0, v1))

        range_types = (self._x_range_type, self._y_range_type)
        if self.invert_axes: range_types = range_types[::-1]
        range_type = range_types[pos]
        # If multi_x/y then grab opts from element
        axis_type = 'log' if (self.logx, self.logy)[pos] else 'auto'
        if dims:
            if len(dims) > 1 or range_type is FactorRange:
                axis_type = 'auto'
                categorical = True
            elif el.get_dimension(dims[0]):
                dim_type = el.get_dimension_type(dims[0])
                if ((dim_type is np.object_ and issubclass(type(v0), util.datetime_types)) or
                    dim_type in util.datetime_types):
                    axis_type = 'datetime'

        norm_opts = self.lookup_options(el, 'norm').options
        shared_name = extra_range_name or ('x-main-range' if pos == 0 else 'y-main-range')
        if plots and self.shared_axes and not norm_opts.get('axiswise', False):
            dim_range = self._shared_axis_range(plots, specs, range_type, axis_type, pos)
            if dim_range:
                self._shared[shared_name] = True

        if self._shared.get(shared_name):
            pass
        elif categorical:
            axis_type = 'auto'
            dim_range = FactorRange()
        elif None in [v0, v1] or any(
            True if isinstance(el, (str, bytes)+util.cftime_types)
            else not util.isfinite(el) for el in [v0, v1]
        ):
            dim_range = range_type()
        else:
            dim_range = range_type(start=v0, end=v1, name=dim.name if dim else None)

        if not dim_range.tags and specs is not None:
            dim_range.tags.append(specs)
            dim_range.tags.append(range_tags_extras)

        if extra_range_name:
            dim_range.name = extra_range_name
        return axis_type, axis_label, dim_range

    def _create_extra_axes(self, plots, subplots, element, ranges):
        if self.invert_axes:
            axpos0, axpos1 = 'below', 'above'
        else:
            axpos0, axpos1 = 'left', 'right'

        ax_specs, yaxes, dimensions = {}, {}, {}
        for el, sp in zip(element, self.subplots.values()):
            ax_dims = sp._get_axis_dims(el)[:2]
            if sp.invert_axes:
                ax_dims[::-1]
            yd = ax_dims[1]
            dimensions[yd.name] = yd
            opts = el.opts.get('plot', backend='bokeh').kwargs
            if not isinstance(yd, Dimension) or yd.name in yaxes:
                continue
            yaxes[yd.name] = {
                'position': opts.get('yaxis', axpos1 if len(yaxes) else axpos0),
                'autorange': opts.get('autorange', None),
                'logx': opts.get('logx', False),
                'logy': opts.get('logy', False),
                'invert_yaxis': opts.get('invert_yaxis', False),
                # 'xlim': opts.get('xlim', (np.nan, np.nan)), # TODO
                'ylim': opts.get('ylim', (np.nan, np.nan)),
                'label': opts.get('ylabel', dim_axis_label(yd)),
                'fontsize': {
                    'axis_label_text_font_size': sp._fontsize('ylabel').get('fontsize'),
                    'major_label_text_font_size': sp._fontsize('yticks').get('fontsize')
                }
            }

        for ydim, info in yaxes.items():
            range_tags_extras = {'invert_yaxis': info['invert_yaxis']}
            if info['autorange'] == 'y':
                range_tags_extras['autorange'] = True
                lowerlim, upperlim = info['ylim'][0], info['ylim'][1]
                if not ((lowerlim is None) or np.isnan(lowerlim)):
                    range_tags_extras['y-lowerlim'] = lowerlim
                if not ((upperlim is None) or np.isnan(upperlim)):
                    range_tags_extras['y-upperlim'] = upperlim
            else:
                range_tags_extras['autorange'] = False

            ax_props = self._axis_props(
                plots, subplots, element, ranges, pos=1, dim=dimensions[ydim],
                range_tags_extras=range_tags_extras,
                extra_range_name=ydim
            )
            log_enabled = info['logx'] if self.invert_axes else info['logy']
            ax_type = 'log' if log_enabled else ax_props[0]
            ax_specs[ydim] = (
                ax_type, info['label'], ax_props[2], info['position'], info['fontsize']
            )
        return yaxes, ax_specs

    def _init_plot(self, key, element, plots, ranges=None):
        """
        Initializes Bokeh figure to draw Element into and sets basic
        figure and axis attributes including axes types, labels,
        titles and plot height and width.
        """
        subplots = list(self.subplots.values()) if self.subplots else []

        axis_specs = {'x': {}, 'y': {}}
        axis_specs['x']['x'] = self._axis_props(plots, subplots, element, ranges, pos=0) + (self.xaxis, {})
        if self.multi_y:
            if not bokeh32:
                self.param.warning('Independent axis zooming for multi_y=True only supported for Bokeh >=3.2')
            yaxes, extra_axis_specs = self._create_extra_axes(plots, subplots, element, ranges)
            axis_specs['y'].update(extra_axis_specs)
        else:
            range_tags_extras = {'invert_yaxis': self.invert_yaxis}
            if self.autorange == 'y':
                range_tags_extras['autorange'] = True
                lowerlim, upperlim = self.ylim
                if not ((lowerlim is None) or np.isnan(lowerlim)):
                    range_tags_extras['y-lowerlim'] = lowerlim
                if not ((upperlim is None) or np.isnan(upperlim)):
                    range_tags_extras['y-upperlim'] = upperlim
            else:
                range_tags_extras['autorange'] = False
            axis_specs['y']['y'] = self._axis_props(
                plots, subplots, element, ranges, pos=1, range_tags_extras = range_tags_extras
            ) + (self.yaxis, {})

        properties, axis_props = {}, {'x': {}, 'y': {}}
        for axis, axis_spec in axis_specs.items():
            for (axis_dim, (axis_type, axis_label, axis_range, axis_position, fontsize)) in axis_spec.items():
                scale = get_scale(axis_range, axis_type)
                if f'{axis}_range' in properties:
                    properties[f'extra_{axis}_ranges'] = extra_ranges = properties.get(f'extra_{axis}_ranges', {})
                    properties[f'extra_{axis}_scales'] = extra_scales = properties.get(f'extra_{axis}_scales', {})
                    extra_ranges[axis_dim] = axis_range
                    extra_scales[axis_dim] = scale
                else:
                    properties[f'{axis}_range'] = axis_range
                    properties[f'{axis}_scale'] = scale
                    properties[f'{axis}_axis_type'] = axis_type
                    if axis_label and axis in self.labelled:
                        properties[f'{axis}_axis_label'] = axis_label
                    locs = {'left': 'left', 'right': 'right'} if axis == 'y' else {'bottom': 'below', 'top': 'above'}
                    if axis_position is None:
                        axis_props[axis]['visible'] = False
                    axis_props[axis].update(fontsize)
                    for loc, pos in locs.items():
                        if axis_position and loc in axis_position:
                            properties[f'{axis}_axis_location'] = pos

        if not self.show_frame:
            properties['outline_line_alpha'] = 0

        if self.show_title and self.adjoined is None:
            title = self._format_title(key, separator=' ')
        else:
            title = ''

        if self.toolbar != 'disable':
            tools = self._init_tools(element)
            properties['tools'] = tools
            properties['toolbar_location'] = self.toolbar
        else:
            properties['tools'] = []
            properties['toolbar_location'] = None

        if self.renderer.webgl:
            properties['output_backend'] = 'webgl'

        properties.update(**self._plot_properties(key, element))

        if bokeh3:
            figure = bokeh.plotting.figure
        else:
            figure = bokeh.plotting.Figure

        with warnings.catch_warnings():
            # Bokeh raises warnings about duplicate tools but these
            # are not really an issue
            warnings.simplefilter('ignore', UserWarning)
            fig = figure(title=title, **properties)
        fig.xaxis[0].update(**axis_props['x'])
        fig.yaxis[0].update(**axis_props['y'])

        multi_ax = 'x' if self.invert_axes else 'y'
        for axis_dim, range_obj in properties.get(f'extra_{multi_ax}_ranges', {}).items():
            axis_type, axis_label, _, axis_position, fontsize = axis_specs[multi_ax][axis_dim]
            ax_cls, ax_kwargs = get_axis_class(axis_type, range_obj, dim=1)
            ax_kwargs[f'{multi_ax}_range_name'] = axis_dim
            ax_kwargs.update(fontsize)
            if axis_position is None:
                ax_kwargs['visible'] = False
                axis_position = 'above' if multi_ax == 'x' else 'right'
            if multi_ax in self.labelled:
                ax_kwargs['axis_label'] = axis_label
            ax = ax_cls(**ax_kwargs)
            fig.add_layout(ax, axis_position)
        return fig

    def _plot_properties(self, key, element):
        """
        Returns a dictionary of plot properties.
        """
        init = 'plot' not in self.handles
        size_multiplier = self.renderer.size/100.
        options = self._traverse_options(element, 'plot', ['width', 'height'], defaults=False)

        logger = self.param if init else None
        aspect_props, dimension_props = compute_layout_properties(
            self.width, self.height, self.frame_width, self.frame_height,
            options.get('width'), options.get('height'), self.aspect, self.data_aspect,
            self.responsive, size_multiplier, logger=logger)

        if not init:
            if aspect_props['aspect_ratio'] is None:
                aspect_props['aspect_ratio'] = self.state.aspect_ratio

        plot_props = {
            'align':         self.align,
            'margin':        self.margin,
            'max_width':     self.max_width,
            'max_height':    self.max_height,
            'min_width':     self.min_width,
            'min_height':    self.min_height
        }
        plot_props.update(aspect_props)
        if not self.drawn:
            plot_props.update(dimension_props)

        if self.bgcolor:
            plot_props['background_fill_color'] = self.bgcolor
        if self.border is not None:
            for p in ['left', 'right', 'top', 'bottom']:
                plot_props['min_border_'+p] = self.border
        lod = dict(self.param["lod"].default, **self.lod) if "lod" in self.param else self.lod
        for lod_prop, v in lod.items():
            plot_props['lod_'+lod_prop] = v
        return plot_props

    def _set_active_tools(self, plot):
        "Activates the list of active tools"
        if plot is None or self.toolbar == "disable":
            return

        if self.active_tools is None:
            enabled_tools = set(self.default_tools + self.tools)
            active_tools =  {'pan', 'wheel_zoom'} & enabled_tools
        else:
            active_tools = self.active_tools

        if active_tools == []:
            # Removes Bokeh default behavior of having Pan enabled by default
            plot.toolbar.active_drag = None

        for tool in active_tools:
            if isinstance(tool, str):
                tool_type = TOOL_TYPES.get(tool, type(None))
                matching = [t for t in plot.toolbar.tools
                            if isinstance(t, tool_type)]
                if not matching:
                    self.param.warning(
                        f'Tool of type {tool!r} could not be found '
                        'and could not be activated by default.'
                    )
                    continue
                tool = matching[0]
            if isinstance(tool, tools.Drag):
                plot.toolbar.active_drag = tool
            if isinstance(tool, tools.Scroll):
                plot.toolbar.active_scroll = tool
            if isinstance(tool, tools.Tap):
                plot.toolbar.active_tap = tool
            if isinstance(tool, tools.InspectTool):
                plot.toolbar.active_inspect.append(tool)

    def _title_properties(self, key, plot, element):
        if self.show_title and self.adjoined is None:
            title = self._format_title(key, separator=' ')
        else:
            title = ''
        opts = dict(text=title)

        # this will override theme if not set to the default 12pt
        title_font = self._fontsize('title').get('fontsize')
        if title_font != '12pt':
            opts['text_font_size'] = title_font
        return opts

    def _populate_axis_handles(self, plot):
        self.handles['xaxis'] = plot.xaxis[0]
        self.handles['x_range'] = plot.x_range
        self.handles['extra_x_ranges'] = plot.extra_x_ranges
        self.handles['extra_x_scales'] = plot.extra_x_scales
        self.handles['yaxis'] = plot.yaxis[0]
        self.handles['y_range'] = plot.y_range
        self.handles['extra_y_ranges'] = plot.extra_y_ranges
        self.handles['extra_y_scales'] = plot.extra_y_scales

    def _axis_properties(self, axis, key, plot, dimension=None,
                         ax_mapping={'x': 0, 'y': 1}):
        """
        Returns a dictionary of axis properties depending
        on the specified axis.
        """
        # need to copy dictionary by calling dict() on it
        axis_props = dict(theme_attr_json(self.renderer.theme, 'Axis'))

        if ((axis == 'x' and self.xaxis in ['bottom-bare', 'top-bare', 'bare']) or
            (axis == 'y' and self.yaxis in ['left-bare', 'right-bare', 'bare'])):
            zero_pt = '0pt'
            axis_props['axis_label_text_font_size'] = zero_pt
            axis_props['major_label_text_font_size'] = zero_pt
            axis_props['major_tick_line_color'] = None
            axis_props['minor_tick_line_color'] = None
        else:
            labelsize = self._fontsize(f'{axis}label').get('fontsize')
            if labelsize:
                axis_props['axis_label_text_font_size'] = labelsize
            ticksize = self._fontsize(f'{axis}ticks', common=False).get('fontsize')
            if ticksize:
                axis_props['major_label_text_font_size'] = ticksize
            rotation = self.xrotation if axis == 'x' else self.yrotation
            if rotation:
                axis_props['major_label_orientation'] = np.radians(rotation)
            ticker = self.xticks if axis == 'x' else self.yticks
            if isinstance(ticker, np.ndarray):
                ticker = list(ticker)
            if isinstance(ticker, Ticker):
                axis_props['ticker'] = ticker
            elif isinstance(ticker, int):
                axis_props['ticker'] = BasicTicker(desired_num_ticks=ticker)
            elif isinstance(ticker, (tuple, list)):
                if all(isinstance(t, tuple) for t in ticker):
                    ticks, labels = zip(*ticker)
                    # Ensure floats which are integers are serialized as ints
                    # because in JS the lookup fails otherwise
                    ticks = [int(t) if isinstance(t, float) and t.is_integer() else t
                             for t in ticks]
                    labels = [l if isinstance(l, str) else str(l)
                              for l in labels]
                else:
                    ticks, labels = ticker, None
                if ticks and util.isdatetime(ticks[0]):
                    ticks = [util.dt_to_int(tick, 'ms') for tick in ticks]
                axis_props['ticker'] = FixedTicker(ticks=ticks)
                if labels is not None:
                    axis_props['major_label_overrides'] = dict(zip(ticks, labels))
        formatter = self.xformatter if axis == 'x' else self.yformatter
        if formatter:
            formatter = wrap_formatter(formatter, axis)
            if formatter is not None:
                axis_props['formatter'] = formatter
        elif CustomJSTickFormatter is not None and ax_mapping and isinstance(dimension, Dimension):
            formatter = None
            if dimension.value_format:
                formatter = dimension.value_format
            elif dimension.type in dimension.type_formatters:
                formatter = dimension.type_formatters[dimension.type]
            if formatter:
                msg = ('%s dimension formatter could not be '
                       'converted to tick formatter. ' % dimension.name)
                jsfunc = py2js_tickformatter(formatter, msg)
                if jsfunc:
                    formatter = CustomJSTickFormatter(code=jsfunc)
                    axis_props['formatter'] = formatter

        if axis == 'x':
            axis_obj = plot.xaxis[0]
        elif axis == 'y':
            axis_obj = plot.yaxis[0]

        if (self.geographic and isinstance(self.projection, str)
            and self.projection == 'mercator'):
            dimension = 'lon' if axis == 'x' else 'lat'
            axis_props['ticker'] = MercatorTicker(dimension=dimension)
            axis_props['formatter'] = MercatorTickFormatter(dimension=dimension)
            box_zoom = self.state.select(type=tools.BoxZoomTool)
            if box_zoom:
                box_zoom[0].match_aspect = True
            wheel_zoom = self.state.select(type=tools.WheelZoomTool)
            if wheel_zoom:
                wheel_zoom[0].zoom_on_axis = False
        elif isinstance(axis_obj, CategoricalAxis):
            for key in list(axis_props):
                if key.startswith('major_label'):
                    # set the group labels equal to major (actually minor)
                    new_key = key.replace('major_label', 'group')
                    axis_props[new_key] = axis_props[key]

            # major ticks are actually minor ticks in a categorical
            # so if user inputs minor ticks sizes, then use that;
            # else keep major (group) == minor (subgroup)
            msize = self._fontsize(f'minor_{axis}ticks',
                common=False).get('fontsize')
            if msize is not None:
                axis_props['major_label_text_font_size'] = msize

        return axis_props

    def _update_plot(self, key, plot, element=None):
        """
        Updates plot parameters on every frame
        """
        plot.update(**self._plot_properties(key, element))
        if not self.multi_y:
            self._update_labels(key, plot, element)
        self._update_title(key, plot, element)
        self._update_grid(plot)

    def _update_labels(self, key, plot, element):
        el = element.traverse(lambda x: x, [Element])
        el = el[0] if el else element
        dimensions = self._get_axis_dims(el)
        props = {axis: self._axis_properties(axis, key, plot, dim)
                 for axis, dim in zip(['x', 'y'], dimensions)}
        xlabel, ylabel, zlabel = self._get_axis_labels(dimensions)
        if self.invert_axes:
            xlabel, ylabel = ylabel, xlabel
        props['x']['axis_label'] = xlabel if 'x' in self.labelled or self.xlabel else ''
        props['y']['axis_label'] = ylabel if 'y' in self.labelled or self.ylabel else ''
        recursive_model_update(plot.xaxis[0], props.get('x', {}))
        recursive_model_update(plot.yaxis[0], props.get('y', {}))

    def _update_title(self, key, plot, element):
        if plot.title:
            plot.title.update(**self._title_properties(key, plot, element))
        else:
            plot.title = Title(**self._title_properties(key, plot, element))

    def _update_backend_opts(self):
        plot = self.handles["plot"]
        model_accessor_aliases = {
            "cbar": "colorbar",
            "p": "plot",
            "xaxes": "xaxis",
            "yaxes": "yaxis",
        }

        for opt, val in self.backend_opts.items():
            parsed_opt = self._parse_backend_opt(
                opt, plot, model_accessor_aliases)
            if parsed_opt is None:
                continue
            model, attr_accessor = parsed_opt

            # not using isinstance because some models inherit from list
            if not isinstance(model, list):
                # to reduce the need for many if/else; cast to list
                # to do the same thing for both single and multiple models
                models = [model]
            else:
                models = model

            valid_options = models[0].properties()
            if attr_accessor not in valid_options:
                kws = Keywords(values=valid_options)
                matches = sorted(kws.fuzzy_match(attr_accessor))
                self.param.warning(
                    f"Could not find {attr_accessor!r} property on {type(models[0]).__name__!r} "
                    f"model. Ensure the custom option spec {opt!r} you provided references a "
                    f"valid attribute on the specified model. "
                    f"Similar options include {matches!r}"
                )

                continue

            for m in models:
                setattr(m, attr_accessor, val)


    def _update_grid(self, plot):
        if not self.show_grid:
            plot.xgrid.grid_line_color = None
            plot.ygrid.grid_line_color = None
            return
        replace = ['bounds', 'bands', 'visible', 'level', 'ticker', 'visible']
        style_items = list(self.gridstyle.items())
        both = {k: v for k, v in style_items if k.startswith(('grid_', 'minor_grid'))}
        xgrid = {k.replace('xgrid', 'grid'): v for k, v in style_items if 'xgrid' in k}
        ygrid = {k.replace('ygrid', 'grid'): v for k, v in style_items if 'ygrid' in k}
        xopts = {k.replace('grid_', '') if any(r in k for r in replace) else k: v
                 for k, v in dict(both, **xgrid).items()}
        yopts = {k.replace('grid_', '') if any(r in k for r in replace) else k: v
                 for k, v in dict(both, **ygrid).items()}
        if plot.xaxis and 'ticker' not in xopts:
            xopts['ticker'] = plot.xaxis[0].ticker
        if plot.yaxis and 'ticker' not in yopts:
            yopts['ticker'] = plot.yaxis[0].ticker
        plot.xgrid[0].update(**xopts)
        plot.ygrid[0].update(**yopts)

    def _update_ranges(self, element, ranges):
        x_range = self.handles['x_range']
        y_range = self.handles['y_range']
        plot = self.handles['plot']

        self._update_main_ranges(element, x_range, y_range, ranges)

        # ALERT: stream handling not handled
        streaming = False
        multi_dim = 'x' if self.invert_axes else 'y'
        for axis_dim, extra_y_range in self.handles[f'extra_{multi_dim}_ranges'].items():
            _, b, _, t = self.get_extents(element, ranges, dimension=axis_dim)
            factors = self._get_dimension_factors(element, ranges, axis_dim)
            extra_scale = self.handles[f'extra_{multi_dim}_scales'][axis_dim] # Assumes scales and ranges zip
            log = isinstance(extra_scale, LogScale)
            range_update = (not (self.model_changed(extra_y_range) or self.model_changed(plot))
                            and self.framewise)
            if self.drawn and not range_update:
                continue
            self._update_range(
                extra_y_range, b, t, factors,
                self._get_tag(extra_y_range, 'invert_yaxis'),
                self._shared.get(extra_y_range.name, False), log, streaming
            )

    def _update_main_ranges(self, element, x_range, y_range, ranges):
        plot = self.handles['plot']

        l, b, r, t = None, None, None, None
        if any(isinstance(r, (Range1d, DataRange1d)) for r in [x_range, y_range]):
            if self.multi_y:
                range_dim = x_range.name if self.invert_axes else y_range.name
            else:
                range_dim = None
            try:
                l, b, r, t = self.get_extents(element, ranges, dimension=range_dim)
            except Exception:
                # Backward compatibility for e.g. GeoViews=<1.10.1 since dimension
                # is a newly added keyword argument
                l, b, r, t = self.get_extents(element, ranges)
            if self.invert_axes:
                l, b, r, t = b, l, t, r

        xfactors, yfactors = None, None
        if any(isinstance(ax_range, FactorRange) for ax_range in [x_range, y_range]):
            xfactors, yfactors = self._get_factors(element, ranges)
        framewise = self.framewise
        streaming = (self.streaming and any(stream._triggering and stream.following
                                            for stream in self.streaming))
        xupdate = ((not (self.model_changed(x_range) or self.model_changed(plot))
                    and (framewise or streaming))
                   or xfactors is not None)
        yupdate = ((not (self.model_changed(x_range) or self.model_changed(plot))
                    and (framewise or streaming))
                   or yfactors is not None)

        options = self._traverse_options(element, 'plot', ['width', 'height'], defaults=False)
        fixed_width = (self.frame_width or options.get('width'))
        fixed_height = (self.frame_height or options.get('height'))
        constrained_width = options.get('min_width') or options.get('max_width')
        constrained_height = options.get('min_height') or options.get('max_height')

        data_aspect = (self.aspect == 'equal' or self.data_aspect)
        xaxis, yaxis = self.handles['xaxis'], self.handles['yaxis']
        categorical = isinstance(xaxis, CategoricalAxis) or isinstance(yaxis, CategoricalAxis)
        datetime = isinstance(xaxis, DatetimeAxis) or isinstance(yaxis, CategoricalAxis)
        range_streams = [s for s in self.streams if isinstance(s, RangeXY)]

        if data_aspect and (categorical or datetime):
            ax_type = 'categorical' if categorical else 'datetime axes'
            self.param.warning('Cannot set data_aspect if one or both '
                               'axes are %s, the option will '
                               'be ignored.' % ax_type)
        elif data_aspect:
            plot = self.handles['plot']
            xspan = r-l if util.is_number(l) and util.is_number(r) else None
            yspan = t-b if util.is_number(b) and util.is_number(t) else None

            if self.drawn or (fixed_width and fixed_height) or (constrained_width or constrained_height):
                # After initial draw or if aspect is explicit
                # adjust range to match the plot dimension aspect
                ratio = self.data_aspect or 1
                if self.aspect == 'square':
                    frame_aspect = 1
                elif self.aspect and self.aspect != 'equal':
                    frame_aspect = self.aspect
                elif plot.frame_height and plot.frame_width:
                    frame_aspect = plot.frame_height/plot.frame_width
                else:
                    # Skip if aspect can't be determined
                    return

                if self.drawn:
                    current_l, current_r = plot.x_range.start, plot.x_range.end
                    current_b, current_t = plot.y_range.start, plot.y_range.end
                    current_xspan, current_yspan = (current_r-current_l), (current_t-current_b)
                else:
                    current_l, current_r, current_b, current_t = l, r, b, t
                    current_xspan, current_yspan = xspan, yspan

                if any(rs._triggering for rs in range_streams):
                    # If the event was triggered by a RangeXY stream
                    # event we want to get the latest range span
                    # values so we do not accidentally trigger a
                    # loop of events
                    l, r, b, t = current_l, current_r, current_b, current_t
                    xspan, yspan = current_xspan, current_yspan

                size_streams = [s for s in self.streams if isinstance(s, PlotSize)]
                if any(ss._triggering for ss in size_streams) and self._updated:
                    # Do not trigger on frame size changes, except for
                    # the initial one which can be important if width
                    # and/or height constraints have forced different
                    # aspect. After initial event we skip because size
                    # changes can trigger event loops if the tick
                    # labels change the canvas size
                    return

                desired_xspan = yspan*(ratio/frame_aspect)
                desired_yspan = xspan/(ratio/frame_aspect)
                if ((np.allclose(desired_xspan, xspan, rtol=0.05) and
                     np.allclose(desired_yspan, yspan, rtol=0.05)) or
                    not (util.isfinite(xspan) and util.isfinite(yspan))):
                    pass
                elif desired_yspan >= yspan:
                    desired_yspan = current_xspan/(ratio/frame_aspect)
                    ypad = (desired_yspan-yspan)/2.
                    b, t = b-ypad, t+ypad
                    yupdate = True
                else:
                    desired_xspan = current_yspan*(ratio/frame_aspect)
                    xpad = (desired_xspan-xspan)/2.
                    l, r = l-xpad, r+xpad
                    xupdate = True
            elif not (fixed_height and fixed_width):
                # Set initial aspect
                aspect = self.get_aspect(xspan, yspan)
                width = plot.frame_width or plot.width or 300
                height = plot.frame_height or plot.height or 300

                if not (fixed_width or fixed_height) and not self.responsive:
                    fixed_height = True

                if fixed_height:
                    plot.frame_height = height
                    plot.frame_width = int(height/aspect)
                    plot.width, plot.height = None, None
                elif fixed_width:
                    plot.frame_width = width
                    plot.frame_height = int(width*aspect)
                    plot.width, plot.height = None, None
                else:
                    plot.aspect_ratio = 1./aspect

            box_zoom = plot.select(type=tools.BoxZoomTool)
            scroll_zoom = plot.select(type=tools.WheelZoomTool)
            if box_zoom:
                box_zoom.match_aspect = True
            if scroll_zoom:
                scroll_zoom.zoom_on_axis = False
        elif any(rs._triggering for rs in range_streams):
            xupdate, yupdate = False, False

        if not self.drawn or xupdate:
            self._update_range(x_range, l, r, xfactors, self.invert_xaxis,
                               self._shared['x-main-range'], self.logx, streaming)
        if not self.drawn or yupdate:
            self._update_range(
                y_range, b, t, yfactors, self._get_tag(y_range, 'invert_yaxis'),
                self._shared['y-main-range'], self.logy, streaming
            )

    def _get_tag(self, model, tag_name):
        """Get a tag from a Bokeh model

        Args:
            model (Model): Bokeh model
            tag_name (str): Name of tag to get
        Returns:
            tag_value: Value of tag or False if not found
        """
        for tag in model.tags:
            if isinstance(tag, dict) and tag_name in tag:
                return tag[tag_name]
        return False

    def _update_range(self, axis_range, low, high, factors, invert, shared, log, streaming=False):
        if isinstance(axis_range, FactorRange):
            factors = list(decode_bytes(factors))
            if invert: factors = factors[::-1]
            axis_range.factors = factors
            return

        if not (isinstance(axis_range, (Range1d, DataRange1d)) and self.apply_ranges):
            return

        if isinstance(low, util.cftime_types):
            pass
        elif (low == high and low is not None):
            if isinstance(low, util.datetime_types):
                offset = np.timedelta64(500, 'ms')
                low, high = np.datetime64(low), np.datetime64(high)
                low -= offset
                high += offset
            else:
                offset = abs(low*0.1 if low else 0.5)
                low -= offset
                high += offset
        if shared:
            shared = (axis_range.start, axis_range.end)
            low, high = util.max_range([(low, high), shared])
        if invert: low, high = high, low
        if not isinstance(low, util.datetime_types) and log and (low is None or low <= 0):
            low = 0.01 if high > 0.01 else 10**(np.log10(high)-2)
            self.param.warning(
                "Logarithmic axis range encountered value less "
                "than or equal to zero, please supply explicit "
                "lower bound to override default of %.3f." % low)
        updates = {}
        if util.isfinite(low):
            updates['start'] = (axis_range.start, low)
            updates['reset_start'] = updates['start']
        if util.isfinite(high):
            updates['end'] = (axis_range.end, high)
            updates['reset_end'] = updates['end']
        for k, (old, new) in updates.items():
            if isinstance(new, util.cftime_types):
                new = date_to_integer(new)
            axis_range.update(**{k:new})
            if streaming and not k.startswith('reset_'):
                axis_range.trigger(k, old, new)

    def _setup_autorange(self):
        """
        Sets up a callback which will iterate over available data
        renderers and auto-range along one axis.
        """
        if not isinstance(self, OverlayPlot) and not self.apply_ranges:
            return

        if self.autorange is None:
            return
        dim = self.autorange
        if dim == 'x':
            didx = 0
            odim = 'y'
        else:
            didx = 1
            odim = 'x'
        if not self.padding:
            p0, p1 = 0, 0
        elif isinstance(self.padding, tuple):
            pad = self.padding[didx]
            if isinstance(pad, tuple):
                p0, p1 = pad
            else:
                p0, p1 = pad, pad
        else:
            p0, p1 = self.padding, self.padding

        # Clean this up in bokeh 3.0 using View.find_one API
        callback = CustomJS(code=f"""
        const cb = function() {{

          function get_padded_range(key, lowerlim, upperlim, invert) {{
            let vmin = range_limits[key][0]
            let vmax = range_limits[key][1]

            if (lowerlim !== null) {{
             vmin = lowerlim
            }}

            if (upperlim !== null) {{
             vmax = upperlim
            }}

            const span = vmax-vmin
            const lower = vmin-(span*{p0})
            const upper = vmax+(span*{p1})
            return invert ? [upper, lower] : [lower, upper]
          }}

          const ref = plot.id

          const find = (view) => {{
            let iterable = view.child_views === undefined ? []  : view.child_views
            for (const sv of iterable) {{
              if (sv.model.id == ref)
                return sv
              const obj = find(sv)
              if (obj !== null)
                return obj
            }}
            return null
          }}
          let plot_view = null;
          for (const root of plot.document.roots()) {{
            const root_view = window.Bokeh.index[root.id]
            if (root_view === undefined)
              return
            plot_view = find(root_view)
            if (plot_view != null)
              break
          }}
          if (plot_view == null)
            return

          let range_limits = {{}}
          for (const dr of plot.data_renderers) {{
            const renderer = plot_view.renderer_view(dr)
            const glyph_view = renderer.glyph_view

            let [vmin, vmax] = [Infinity, -Infinity]
            let y_range_name = renderer.model.y_range_name

            if (!renderer.glyph.model.tags.includes('no_apply_ranges')) {{
              const index = glyph_view.index.index
              for (let pos = 0; pos < index._boxes.length - 4; pos += 4) {{
                const [x0, y0, x1, y1] = index._boxes.slice(pos, pos+4)
                if ({odim}0 > plot.{odim}_range.start && {odim}1 < plot.{odim}_range.end) {{
                  vmin = Math.min(vmin, {dim}0)
                  vmax = Math.max(vmax, {dim}1)
                }}
              }}
            }}

            if (y_range_name) {{
              range_limits[y_range_name] = [vmin, vmax]
            }}
          }}

           let range_tags_extras = plot.{dim}_range.tags[1]
           if (range_tags_extras['autorange']) {{
             let lowerlim = range_tags_extras['y-lowerlim'] ?? null
             let upperlim = range_tags_extras['y-upperlim'] ?? null
             let [start, end] = get_padded_range('default', lowerlim, upperlim, range_tags_extras['invert_yaxis'])
             if ((start != end) && window.Number.isFinite(start) && window.Number.isFinite(end)) {{
               plot.{dim}_range.setv({{start, end}})
             }}
           }}

          for (let key in plot.extra_{dim}_ranges) {{
            const extra_range = plot.extra_{dim}_ranges[key]
            let range_tags_extras = extra_range.tags[1]

            let lowerlim = range_tags_extras['y-lowerlim'] ?? null
            let upperlim = range_tags_extras['y-upperlim'] ?? null

            if (range_tags_extras['autorange']) {{
             let [start, end] = get_padded_range(key, lowerlim, upperlim, range_tags_extras['invert_yaxis'])
             if ((start != end) && window.Number.isFinite(start) && window.Number.isFinite(end)) {{
              extra_range.setv({{start, end}})
              }}
            }}
          }}
        }}
        // The plot changes will not propagate to the glyph until
        // after the data change event has occurred.
        setTimeout(cb, 0);
        """, args={'plot': self.state})
        self.state.js_on_event('rangesupdate', callback)
        self._js_on_data_callbacks.append(callback)

    def _categorize_data(self, data, cols, dims):
        """
        Transforms non-string or integer types in datasource if the
        axis to be plotted on is categorical. Accepts the column data
        source data, the columns corresponding to the axes and the
        dimensions for each axis, changing the data inplace.
        """
        if self.invert_axes:
            cols = cols[::-1]
            dims = dims[:2][::-1]
        ranges = [self.handles[f'{ax}_range'] for ax in 'xy']
        for i, col in enumerate(cols):
            column = data[col]
            if (isinstance(ranges[i], FactorRange) and
                (isinstance(column, list) or column.dtype.kind not in 'SU')):
                data[col] = [dims[i].pprint_value(v) for v in column]


    def get_aspect(self, xspan, yspan):
        """
        Computes the aspect ratio of the plot
        """
        if 'plot' in self.handles and self.state.frame_width and self.state.frame_height:
            return self.state.frame_width/self.state.frame_height
        elif self.data_aspect:
            return (yspan/xspan)*self.data_aspect
        elif self.aspect == 'equal':
            return yspan/xspan
        elif self.aspect == 'square':
            return 1
        elif self.aspect is not None:
            return self.aspect
        elif self.width is not None and self.height is not None:
            return self.width/self.height
        else:
            return 1

    def _get_dimension_factors(self, element, ranges, dimension):
        if dimension.values:
            values = dimension.values
        elif 'factors' in ranges.get(dimension.name, {}):
            values = ranges[dimension.name]['factors']
        else:
            values = element.dimension_values(dimension, False)
        values = np.asarray(values)
        if not self._allow_implicit_categories:
            values = values if values.dtype.kind in 'SU' else []
        return [v if values.dtype.kind in 'SU' else dimension.pprint_value(v) for v in values]

    def _get_factors(self, element, ranges):
        """
        Get factors for categorical axes.
        """
        xdim, ydim = element.dimensions()[:2]
        xvals = self._get_dimension_factors(element, ranges, xdim)
        yvals = self._get_dimension_factors(element, ranges, ydim)
        coords = (xvals, yvals)
        if self.invert_axes: coords = coords[::-1]
        return coords

    def _process_legend(self):
        """
        Disables legends if show_legend is disabled.
        """
        for l in self.handles['plot'].legend:
            l.items[:] = []
            l.border_line_alpha = 0
            l.background_fill_alpha = 0

    def _init_glyph(self, plot, mapping, properties):
        """
        Returns a Bokeh glyph object.
        """
        mapping['tags'] = ['apply_ranges' if self.apply_ranges else 'no_apply_ranges']
        properties = mpl_to_bokeh(properties)
        plot_method = self._plot_methods.get('batched' if self.batched else 'single')
        if isinstance(plot_method, tuple):
            # Handle alternative plot method for flipped axes
            plot_method = plot_method[int(self.invert_axes)]
        if 'legend_field' in properties and 'legend_label' in properties:
            del properties['legend_label']

        if self.handles['x_range'].name in plot.extra_x_ranges:
            properties['x_range_name'] = self.handles['y_range'].name
        if self.handles['y_range'].name in plot.extra_y_ranges:
            properties['y_range_name'] = self.handles['y_range'].name

        if "name" not in properties:
            properties["name"] = properties.get("legend_label") or properties.get("legend_field")
        renderer = getattr(plot, plot_method)(**dict(properties, **mapping))
        return renderer, renderer.glyph

    def _element_transform(self, transform, element, ranges):
        return transform.apply(element, ranges=ranges, flat=True)

    def _apply_transforms(self, element, data, ranges, style, group=None):
        new_style = dict(style)
        prefix = group+'_' if group else ''
        for k, v in dict(style).items():
            if isinstance(v, str):
                if validate(k, v) == True:
                    continue
                elif v in element:
                    v = dim(element.get_dimension(v))
                elif isinstance(element, Graph) and v in element.nodes:
                    v = dim(element.nodes.get_dimension(v))
                elif any(d==v for d in self.overlay_dims):
                    v = dim(next(d for d in self.overlay_dims if d==v))

            if (not isinstance(v, dim) or (group is not None and not k.startswith(group))):
                continue
            elif (not v.applies(element) and v.dimension not in self.overlay_dims):
                new_style.pop(k)
                self.param.warning(
                    f'Specified {k} dim transform {v!r} could not be applied, '
                    'as not all dimensions could be resolved.')
                continue

            if v.dimension in self.overlay_dims:
                ds = Dataset({d.name: v for d, v in self.overlay_dims.items()},
                             list(self.overlay_dims))
                val = v.apply(ds, ranges=ranges, flat=True)[0]
            else:
                val = self._element_transform(v, element, ranges)

            if (not util.isscalar(val) and len(util.unique_array(val)) == 1 and
                (('color' not in k or validate('color', val)) or k in self._nonvectorized_styles)):
                val = val[0]

            if not util.isscalar(val):
                if k in self._nonvectorized_styles:
                    element = type(element).__name__
                    raise ValueError('Mapping a dimension to the "{style}" '
                                     'style option is not supported by the '
                                     '{element} element using the {backend} '
                                     'backend. To map the "{dim}" dimension '
                                     'to the {style} use a groupby operation '
                                     'to overlay your data along the dimension.'.format(
                                         style=k, dim=v.dimension, element=element,
                                         backend=self.renderer.backend))
                elif data and len(val) != len(next(iter(data.values()))):
                    if isinstance(element, VectorField):
                        val = np.tile(val, 3)
                    elif isinstance(element, Path) and not isinstance(element, Contours):
                        val = val[:-1]
                    else:
                        continue

            if k == 'angle':
                val = np.deg2rad(val)
            elif k.endswith('font_size'):
                if util.isscalar(val) and isinstance(val, int):
                    val = str(v)+'pt'
                elif isinstance(val, np.ndarray) and val.dtype.kind in 'ifu':
                    val = [str(int(s))+'pt' for s in val]
            if util.isscalar(val):
                key = val
            else:
                # Node marker does not handle {'field': ...}
                key = k if k == 'node_marker' else {'field': k}
                data[k] = val

            # If color is not valid colorspec add colormapper
            numeric = isinstance(val, util.arraylike_types) and val.dtype.kind in 'uifMmb'
            colormap = style.get(prefix+'cmap')
            if ('color' in k and isinstance(val, util.arraylike_types) and
                (numeric or not validate('color', val) or isinstance(colormap, dict))):
                kwargs = {}
                if val.dtype.kind not in 'ifMu':
                    range_key = dim_range_key(v)
                    if range_key in ranges and 'factors' in ranges[range_key]:
                        factors = ranges[range_key]['factors']
                    else:
                        factors = util.unique_array(val)
                    if isinstance(val, util.arraylike_types) and val.dtype.kind == 'b':
                        factors = factors.astype(str)
                    kwargs['factors'] = factors
                cmapper = self._get_colormapper(v, element, ranges,
                                                dict(style), name=k+'_color_mapper',
                                                group=group, **kwargs)
                field = k
                categorical = isinstance(cmapper, CategoricalColorMapper)

                if categorical:
                    if val.dtype.kind in 'ifMub':
                        field = k + '_str__'
                        if v.dimension in element:
                            formatter = element.get_dimension(v.dimension).pprint_value
                        else:
                            formatter = str
                        data[field] = [formatter(d) for d in val]
                    if getattr(self, 'show_legend', False):
                        legend_labels = getattr(self, 'legend_labels', False)
                        if legend_labels:
                            label_field = f'_{field}_labels'
                            data[label_field] = [legend_labels.get(v, v) for v in val]
                            new_style['legend_field'] = label_field
                        else:
                            new_style['legend_field'] = field
                key = {'field': field, 'transform': cmapper}
            new_style[k] = key

        # Process color/alpha styles and expand to fill/line style
        for style, val in list(new_style.items()):
            for s in ('alpha', 'color'):
                if prefix+s != style or style not in data or validate(s, val, True):
                    continue
                supports_fill = any(
                    o.startswith(prefix+'fill') and (prefix != 'edge_' or getattr(self, 'filled', True))
                    for o in self.style_opts)
                for pprefix in [p+'_' for p in property_prefixes]+['']:
                    fill_key = prefix+pprefix+'fill_'+s
                    fill_style = new_style.get(fill_key)

                    # Do not override custom nonselection/muted alpha
                    if ((pprefix in ('nonselection_', 'muted_') and s == 'alpha')
                        or fill_key not in self.style_opts):
                        continue

                    # Override empty and non-vectorized fill_style if not hover style
                    hover = pprefix == 'hover_'
                    if ((fill_style is None or (validate(s, fill_style, True) and not hover))
                        and supports_fill):
                        new_style[fill_key] = val

                    line_key = prefix+pprefix+'line_'+s
                    line_style = new_style.get(line_key)

                    # If glyph has fill and line style is set overriding line color
                    if supports_fill and line_style is not None:
                        continue

                    # If glyph does not support fill override non-vectorized line_color
                    if ((line_style is not None and (validate(s, line_style) and not hover)) or
                        (line_style is None and not supports_fill)):
                        new_style[line_key] = val

        return new_style


    def _glyph_properties(self, plot, element, source, ranges, style, group=None):
        properties = dict(style, source=source)
        if self.show_legend:
            if self.overlay_dims:
                legend = ', '.join([d.pprint_value(v, print_unit=True) for d, v in
                                    self.overlay_dims.items()])
            else:
                legend = element.label
            if legend and self.overlaid:
                properties['legend_label'] = legend
        return properties


    def _filter_properties(self, properties, glyph_type, allowed):
        glyph_props = dict(properties)
        for gtype in ((glyph_type, '') if glyph_type else ('',)):
            for prop in ('color', 'alpha'):
                glyph_prop = properties.get(gtype+prop)
                if glyph_prop is not None and ('line_'+prop not in glyph_props or gtype):
                    glyph_props['line_'+prop] = glyph_prop
                if glyph_prop is not None and ('fill_'+prop not in glyph_props or gtype):
                    glyph_props['fill_'+prop] = glyph_prop

            props = {k[len(gtype):]: v for k, v in glyph_props.items()
                     if k.startswith(gtype)}
            if self.batched:
                glyph_props = dict(props, **glyph_props)
            else:
                glyph_props.update(props)
        return {k: v for k, v in glyph_props.items() if k in allowed}


    def _update_glyph(self, renderer, properties, mapping, glyph, source, data):
        allowed_properties = glyph.properties()
        properties = mpl_to_bokeh(properties)
        merged = dict(properties, **mapping)
        legend_props = ('legend_field', 'legend_label')
        for lp in legend_props:
            legend = merged.pop(lp, None)
            if legend is not None:
                break
        columns = list(source.data.keys())
        glyph_updates = []
        for glyph_type in ('', 'selection_', 'nonselection_', 'hover_', 'muted_'):
            if renderer:
                glyph = getattr(renderer, glyph_type+'glyph', None)
                if glyph == 'auto':
                    base_glyph = renderer.glyph
                    props = base_glyph.properties_with_values()
                    glyph = type(base_glyph)(**{k: v for k, v in props.items()
                                                if not prop_is_none(v)})
                    setattr(renderer, glyph_type+'glyph', glyph)
            if not glyph or (not renderer and glyph_type):
                continue
            filtered = self._filter_properties(merged, glyph_type, allowed_properties)

            # Ensure that data is populated before updating glyph
            dataspecs = glyph.dataspecs()
            for spec in dataspecs:
                new_spec = property_to_dict(filtered.get(spec))
                old_spec = property_to_dict(getattr(glyph, spec))
                new_field = new_spec.get('field') if isinstance(new_spec, dict) else new_spec
                old_field = old_spec.get('field') if isinstance(old_spec, dict) else old_spec
                if (data is None) or (new_field not in data or new_field in source.data or new_field == old_field):
                    continue
                columns.append(new_field)
            glyph_updates.append((glyph, filtered))

        # If a dataspec has changed and the CDS.data will be replaced
        # the GlyphRenderer will not find the column, therefore we
        # craft an event which will make the column available.
        cds_replace = True if data is None else cds_column_replace(source, data)
        if not cds_replace:
            if not self.static_source:
                self._update_datasource(source, data)
            if hasattr(self, 'selected') and self.selected is not None:
                self._update_selected(source)
        elif self.document:
            server = self.renderer.mode == 'server'
            with hold_policy(self.document, 'collect', server=server):
                empty_data = {c: [] for c in columns}
                if bokeh3:
                    event = ModelChangedEvent(
                        document=self.document,
                        model=source,
                        attr='data',
                        new=empty_data,
                        setter='empty'
                    )
                else:
                    event = ModelChangedEvent(
                        self.document, source, 'data', source.data, empty_data, empty_data, setter='empty'
                    )
                self.document.callbacks._held_events.append(event)

        if legend is not None:
            for leg in self.state.legend:
                for item in leg.items:
                    if renderer in item.renderers:
                        if isinstance(legend, dict):
                            label = legend
                        elif lp != 'legend':
                            prop = 'value' if 'label' in lp else 'field'
                            label = {prop: legend}
                        elif isinstance(item.label, dict):
                            label = {next(iter(item.label)): legend}
                        else:
                            label = {'value': legend}
                        item.label = label

        for glyph, update in glyph_updates:
            glyph.update(**update)

        if data is not None and cds_replace and not self.static_source:
            self._update_datasource(source, data)


    def _postprocess_hover(self, renderer, source):
        """
        Attaches renderer to hover tool and processes tooltips to
        ensure datetime data is displayed correctly.
        """
        hover = self.handles.get('hover')
        if hover is None:
            return
        if not isinstance(hover.tooltips, str) and 'hv_created' in hover.tags:
            for k, values in source.data.items():
                key = '@{%s}' % k
                if ((isinstance(value, np.ndarray) and value.dtype.kind == 'M') or
                    (len(values) and isinstance(values[0], util.datetime_types))):
                    hover.tooltips = [(l, f+'{%F %T}' if f == key else f) for l, f in hover.tooltips]
                    hover.formatters[key] = "datetime"

        if hover.renderers == 'auto':
            hover.renderers = []
        if renderer not in hover.renderers:
            hover.renderers.append(renderer)

    def _init_glyphs(self, plot, element, ranges, source):
        style_element = element.last if self.batched else element

        # Get data and initialize data source
        if self.batched:
            current_id = tuple(element.traverse(lambda x: x._plot_id, [Element]))
            data, mapping, style = self.get_batched_data(element, ranges)
        else:
            style = self.style[self.cyclic_index]
            data, mapping, style = self.get_data(element, ranges, style)
            current_id = element._plot_id

        with abbreviated_exception():
            style = self._apply_transforms(element, data, ranges, style)

        if source is None:
            source = self._init_datasource(data)
        self.handles['previous_id'] = current_id
        self.handles['source'] = self.handles['cds'] = source
        self.handles['selected'] = source.selected

        properties = self._glyph_properties(plot, style_element, source, ranges, style)
        if 'legend_label' in properties and 'legend_field' in mapping:
            mapping.pop('legend_field')

        with abbreviated_exception():
            renderer, glyph = self._init_glyph(plot, mapping, properties)
        self.handles['glyph'] = glyph
        if isinstance(renderer, Renderer):
            self.handles['glyph_renderer'] = renderer

        self._postprocess_hover(renderer, source)

        # Update plot, source and glyph
        with abbreviated_exception():
            self._update_glyph(renderer, properties, mapping, glyph, source, source.data)

    def _find_axes(self, plot, element):
        """
        Looks up the axes and plot ranges given the plot and an element.
        """
        axis_dims = self._get_axis_dims(element)[:2]
        x, y = axis_dims[::-1] if self.invert_axes else axis_dims
        if isinstance(x, Dimension) and x.name in plot.extra_x_ranges:
            x_range = plot.extra_x_ranges[x.name]
            xaxes = [xaxis for xaxis in plot.xaxis if xaxis.x_range_name == x.name]
            x_axis = (xaxes if xaxes else plot.xaxis)[0]
        else:
            x_range = plot.x_range
            x_axis = plot.xaxis[0]
        if isinstance(y, Dimension) and y.name in plot.extra_y_ranges:
            y_range = plot.extra_y_ranges[y.name]
            yaxes = [yaxis for yaxis in plot.yaxis if yaxis.y_range_name == y.name]
            y_axis = (yaxes if yaxes else plot.yaxis)[0]
        else:
            y_range = plot.y_range
            y_axis = plot.yaxis[0]
        return (x_axis, y_axis), (x_range, y_range)

    def initialize_plot(self, ranges=None, plot=None, plots=None, source=None):
        """
        Initializes a new plot object with the last available frame.
        """
        # Get element key and ranges for frame
        if self.batched:
            element = [el for el in self.hmap.data.values() if el][-1]
        else:
            element = self.hmap.last
        key = util.wrap_tuple(self.hmap.last_key)
        ranges = self.compute_ranges(self.hmap, key, ranges)
        self.current_ranges = ranges
        self.current_frame = element
        self.current_key = key
        style_element = element.last if self.batched else element
        ranges = util.match_spec(style_element, ranges)
        # Initialize plot, source and glyph
        if plot is None:
            plot = self._init_plot(key, style_element, ranges=ranges, plots=plots)
            self._populate_axis_handles(plot)
        else:
            axes, plot_ranges = self._find_axes(plot, element)
            self.handles['xaxis'], self.handles['yaxis'] = axes
            self.handles['x_range'], self.handles['y_range'] = plot_ranges
        self.handles['plot'] = plot

        if self.autorange:
            self._setup_autorange()
        self._init_glyphs(plot, element, ranges, source)
        if not self.overlaid:
            self._update_plot(key, plot, style_element)
            self._update_ranges(style_element, ranges)

        for cb in self.callbacks:
            cb.initialize()

        if self.top_level:
            self.init_links()

        if not self.overlaid:
            self._set_active_tools(plot)
            self._process_legend()
            self._setup_data_callbacks(plot)
        self._execute_hooks(element)

        self.drawn = True

        return plot

    def _setup_data_callbacks(self, plot):
        if not self._js_on_data_callbacks:
            return
        for renderer in plot.select({'type': GlyphRenderer}):
            cds = renderer.data_source
            for cb in self._js_on_data_callbacks:
                if cb not in cds.js_property_callbacks.get('change:data', []):
                    cds.js_on_change('data', cb)

    def _update_glyphs(self, element, ranges, style):
        plot = self.handles['plot']
        glyph = self.handles.get('glyph')
        source = self.handles['source']
        mapping = {}

        # Cache frame object id to skip updating data if unchanged
        previous_id = self.handles.get('previous_id', None)
        if self.batched:
            current_id = tuple(element.traverse(lambda x: x._plot_id, [Element]))
        else:
            current_id = element._plot_id
        self.handles['previous_id'] = current_id
        self.static_source = (self.dynamic and (current_id == previous_id))
        if self.batched:
            data, mapping, style = self.get_batched_data(element, ranges)
        else:
            data, mapping, style = self.get_data(element, ranges, style)

        # Include old data if source static
        if self.static_source:
            for k, v in source.data.items():
                if k not in data:
                    data[k] = v
                elif not len(data[k]) and len(source.data):
                    data[k] = source.data[k]

        with abbreviated_exception():
            style = self._apply_transforms(element, data, ranges, style)

        if glyph:
            properties = self._glyph_properties(plot, element, source, ranges, style)
            renderer = self.handles.get('glyph_renderer')
            if 'visible' in style and hasattr(renderer, 'visible'):
                renderer.visible = style['visible']
            with abbreviated_exception():
                self._update_glyph(renderer, properties, mapping, glyph, source, data)
        elif not self.static_source:
            self._update_datasource(source, data)


    def _reset_ranges(self):
        """
        Resets RangeXY streams if norm option is set to framewise
        """
        # Skipping conditional to temporarily revert fix (see https://github.com/holoviz/holoviews/issues/4396)
        # This fix caused PlotSize change events to rerender
        # rasterized/datashaded with the full extents which was wrong
        if self.overlaid or True:
            return
        for el, callbacks in self.traverse(lambda x: (x.current_frame, x.callbacks)):
            if el is None:
                continue
            for callback in callbacks:
                norm = self.lookup_options(el, 'norm').options
                if norm.get('framewise'):
                    for s in callback.streams:
                        if isinstance(s, RangeXY) and not s._triggering:
                            s.reset()

    def update_frame(self, key, ranges=None, plot=None, element=None):
        """
        Updates an existing plot with data corresponding
        to the key.
        """
        self._reset_ranges()
        reused = isinstance(self.hmap, DynamicMap) and (self.overlaid or self.batched)
        self.prev_frame =  self.current_frame
        if not reused and element is None:
            element = self._get_frame(key)
        elif element is not None:
            self.current_key = key
            self.current_frame = element

        renderer = self.handles.get('glyph_renderer', None)
        glyph = self.handles.get('glyph', None)
        visible = element is not None
        if hasattr(renderer, 'visible'):
            renderer.visible = visible
        if hasattr(glyph, 'visible'):
            glyph.visible = visible

        if ((self.batched and not element) or element is None or (not self.dynamic and self.static) or
            (self.streaming and self.streaming[0].data is self.current_frame.data and not self.streaming[0]._triggering)):
            return

        if self.batched:
            style_element = element.last
            max_cycles = None
        else:
            style_element = element
            max_cycles = self.style._max_cycles
        style = self.lookup_options(style_element, 'style')
        self.style = style.max_cycles(max_cycles) if max_cycles else style

        if not self.overlaid:
            ranges = self.compute_ranges(self.hmap, key, ranges)
        else:
            self.ranges.update(ranges)
        self.param.update(**self.lookup_options(style_element, 'plot').options)
        ranges = util.match_spec(style_element, ranges)
        self.current_ranges = ranges
        plot = self.handles['plot']
        if not self.overlaid:
            self._update_ranges(style_element, ranges)
            self._update_plot(key, plot, style_element)
            self._set_active_tools(plot)
            self._setup_data_callbacks(plot)
            self._updated = True

        if 'hover' in self.handles:
            self._update_hover(element)
            if 'cds' in self.handles:
                cds = self.handles['cds']
                self._postprocess_hover(renderer, cds)

        self._update_glyphs(element, ranges, self.style[self.cyclic_index])
        self._execute_hooks(element)


    def _execute_hooks(self, element):
        dtype_fix_hook(self, element)
        super()._execute_hooks(element)
        self._update_backend_opts()


    def model_changed(self, model):
        """
        Determines if the bokeh model was just changed on the frontend.
        Useful to suppress boomeranging events, e.g. when the frontend
        just sent an update to the x_range this should not trigger an
        update on the backend.
        """
        callbacks = [cb for cbs in self.traverse(lambda x: x.callbacks)
                     for cb in cbs]
        stream_metadata = [stream._metadata for cb in callbacks
                           for stream in cb.streams if stream._metadata]
        return any(md['id'] == model.ref['id'] for models in stream_metadata
                   for md in models.values())


    @property
    def framewise(self):
        """
        Property to determine whether the current frame should have
        framewise normalization enabled. Required for bokeh plotting
        classes to determine whether to send updated ranges for each
        frame.
        """
        current_frames = [el for f in self.traverse(lambda x: x.current_frame)
                          for el in (f.traverse(lambda x: x, [Element])
                                     if f else [])]
        current_frames = util.unique_iterator(current_frames)
        return any(self.lookup_options(frame, 'norm').options.get('framewise')
                   for frame in current_frames)


class CompositeElementPlot(ElementPlot):
    """
    A CompositeElementPlot is an Element plot type that coordinates
    drawing of multiple glyphs.
    """

    # Mapping between glyph names and style groups
    _style_groups = {}

    # Defines the order in which glyphs are drawn, defined by glyph name
    _draw_order = []

    def _init_glyphs(self, plot, element, ranges, source, data=None, mapping=None, style=None):
        # Get data and initialize data source
        if None in (data, mapping):
            style = self.style[self.cyclic_index]
            data, mapping, style = self.get_data(element, ranges, style)

        keys = glyph_order(dict(data, **mapping), self._draw_order)

        source_cache = {}
        current_id = element._plot_id
        self.handles['previous_id'] = current_id
        for key in keys:
            style_group = self._style_groups.get('_'.join(key.split('_')[:-1]))
            group_style = dict(style)
            ds_data = data.get(key, {})
            with abbreviated_exception():
                group_style = self._apply_transforms(element, ds_data, ranges, group_style, style_group)
            if id(ds_data) in source_cache:
                source = source_cache[id(ds_data)]
            else:
                source = self._init_datasource(ds_data)
                source_cache[id(ds_data)] = source
            self.handles[key+'_source'] = source
            properties = self._glyph_properties(plot, element, source, ranges, group_style, style_group)
            properties = self._process_properties(key, properties, mapping.get(key, {}))

            with abbreviated_exception():
                renderer, glyph = self._init_glyph(plot, mapping.get(key, {}), properties, key)
            self.handles[key+'_glyph'] = glyph
            if isinstance(renderer, Renderer):
                self.handles[key+'_glyph_renderer'] = renderer

            self._postprocess_hover(renderer, source)

            # Update plot, source and glyph
            with abbreviated_exception():
                self._update_glyph(renderer, properties, mapping.get(key, {}), glyph,
                                   source, source.data)
        if getattr(self, 'colorbar', False):
            for k, v in list(self.handles.items()):
                if not k.endswith('color_mapper'):
                    continue
                self._draw_colorbar(plot, v, k.replace('color_mapper', ''))


    def _process_properties(self, key, properties, mapping):
        key = '_'.join(key.split('_')[:-1]) if '_' in key else key
        style_group = self._style_groups[key]
        group_props = {}
        for k, v in properties.items():
            if k in self.style_opts:
                group = k.split('_')[0]
                if group == style_group:
                    if k in mapping:
                        v = mapping[k]
                    k = '_'.join(k.split('_')[1:])
                else:
                    continue
            group_props[k] = v
        return group_props


    def _update_glyphs(self, element, ranges, style):
        plot = self.handles['plot']

        # Cache frame object id to skip updating data if unchanged
        previous_id = self.handles.get('previous_id', None)
        if self.batched:
            current_id = tuple(element.traverse(lambda x: x._plot_id, [Element]))
        else:
            current_id = element._plot_id
        self.handles['previous_id'] = current_id
        self.static_source = (self.dynamic and (current_id == previous_id))
        data, mapping, style = self.get_data(element, ranges, style)

        keys = glyph_order(dict(data, **mapping), self._draw_order)
        for key in keys:
            gdata = data.get(key)
            source = self.handles[key+'_source']
            glyph = self.handles.get(key+'_glyph')
            if glyph:
                group_style = dict(style)
                style_group = self._style_groups.get('_'.join(key.split('_')[:-1]))
                with abbreviated_exception():
                    group_style = self._apply_transforms(element, gdata, ranges, group_style, style_group)
                properties = self._glyph_properties(plot, element, source, ranges, group_style, style_group)
                properties = self._process_properties(key, properties, mapping[key])
                renderer = self.handles.get(key+'_glyph_renderer')
                with abbreviated_exception():
                    self._update_glyph(renderer, properties, mapping[key],
                                       glyph, source, gdata)
            elif not self.static_source and gdata is not None:
                self._update_datasource(source, gdata)


    def _init_glyph(self, plot, mapping, properties, key):
        """
        Returns a Bokeh glyph object.
        """
        properties = mpl_to_bokeh(properties)
        plot_method = '_'.join(key.split('_')[:-1])
        renderer = getattr(plot, plot_method)(**dict(properties, **mapping))
        return renderer, renderer.glyph



class ColorbarPlot(ElementPlot):
    """
    ColorbarPlot provides methods to create colormappers and colorbar
    models which can be added to a glyph. Additionally it provides
    parameters to control the position and other styling options of
    the colorbar. The default colorbar_position options are defined
    by the colorbar_specs, but may be overridden by the colorbar_opts.
    """

    colorbar_specs = {'right':     {'pos': 'right',
                                    'opts': {'location': (0, 0)}},
                      'left':      {'pos': 'left',
                                    'opts':{'location':(0, 0)}},
                      'bottom':    {'pos': 'below',
                                    'opts': {'location': (0, 0),
                                             'orientation':'horizontal'}},
                      'top':       {'pos': 'above',
                                    'opts': {'location':(0, 0),
                                             'orientation':'horizontal'}},
                      'top_right':   {'pos': 'center',
                                      'opts': {'location': 'top_right'}},
                      'top_left':    {'pos': 'center',
                                      'opts': {'location': 'top_left'}},
                      'bottom_left': {'pos': 'center',
                                      'opts': {'location': 'bottom_left',
                                               'orientation': 'horizontal'}},
                      'bottom_right': {'pos': 'center',
                                      'opts': {'location': 'bottom_right',
                                               'orientation': 'horizontal'}}}

    color_levels = param.ClassSelector(default=None, class_=(int, list, range), doc="""
        Number of discrete colors to use when colormapping or a set of color
        intervals defining the range of values to map each color to.""")

    cformatter = param.ClassSelector(
        default=None, class_=(str, TickFormatter, FunctionType), doc="""
        Formatter for ticks along the colorbar axis.""")

    clabel = param.String(default=None, doc="""
        An explicit override of the color bar label. If set, takes precedence
        over the title key in colorbar_opts.""")

    clim = param.Tuple(default=(np.nan, np.nan), length=2, doc="""
        User-specified colorbar axis range limits for the plot, as a tuple (low,high).
        If specified, takes precedence over data and dimension ranges.""")

    clim_percentile = param.ClassSelector(default=False, class_=(int, float, bool), doc="""
        Percentile value to compute colorscale robust to outliers. If
        True, uses 2nd and 98th percentile; otherwise uses the specified
        numerical percentile value.""")


    cnorm = param.ObjectSelector(default='linear', objects=['linear', 'log', 'eq_hist'], doc="""
        Color normalization to be applied during colormapping.""")

    colorbar = param.Boolean(default=False, doc="""
        Whether to display a colorbar.""")

    colorbar_position = param.ObjectSelector(objects=list(colorbar_specs),
                                             default="right", doc="""
        Allows selecting between a number of predefined colorbar position
        options. The predefined options may be customized in the
        colorbar_specs class attribute.""")

    colorbar_opts = param.Dict(default={}, doc="""
        Allows setting specific styling options for the colorbar overriding
        the options defined in the colorbar_specs class attribute. Includes
        location, orientation, height, width, scale_alpha, title, title_props,
        margin, padding, background_fill_color and more.""")

    clipping_colors = param.Dict(default={}, doc="""
        Dictionary to specify colors for clipped values, allows
        setting color for NaN values and for values above and below
        the min and max value. The min, max or NaN color may specify
        an RGB(A) color as a color hex string of the form #FFFFFF or
        #FFFFFFFF or a length 3 or length 4 tuple specifying values in
        the range 0-1 or a named HTML color.""")

    logz = param.Boolean(default=False, doc="""
         Whether to apply log scaling to the z-axis.""")

    rescale_discrete_levels = param.Boolean(default=True, doc="""
        If ``cnorm='eq_hist`` and there are only a few discrete values,
        then ``rescale_discrete_levels=True`` decreases the lower
        limit of the autoranged span so that the values are rendering
        towards the (more visible) top of the palette, thus
        avoiding washout of the lower values.  Has no effect if
        ``cnorm!=`eq_hist``.""")

    symmetric = param.Boolean(default=False, doc="""
        Whether to make the colormap symmetric around zero.""")

    _colorbar_defaults = dict(bar_line_color='black', label_standoff=8,
                              major_tick_line_color='black')

    _default_nan = '#8b8b8b'

    _nonvectorized_styles = base_properties + ['cmap', 'palette']

    def _draw_colorbar(self, plot, color_mapper, prefix=''):
        if CategoricalColorMapper and isinstance(color_mapper, CategoricalColorMapper):
            return
        if isinstance(color_mapper, EqHistColorMapper):
            ticker = BinnedTicker(mapper=color_mapper)
        elif isinstance(color_mapper, LogColorMapper) and color_mapper.low > 0:
            ticker = LogTicker()
        else:
            ticker = BasicTicker()
        cbar_opts = dict(self.colorbar_specs[self.colorbar_position])

        # Check if there is a colorbar in the same position
        pos = cbar_opts['pos']
        if any(isinstance(model, ColorBar) for model in getattr(plot, pos, [])):
            return

        if self.clabel:
            self.colorbar_opts.update({'title': self.clabel})

        if self.cformatter is not None:
            self.colorbar_opts.update({'formatter': wrap_formatter(self.cformatter, 'c')})

        for tk in ['cticks', 'ticks']:
            ticksize = self._fontsize(tk, common=False).get('fontsize')
            if ticksize is not None:
                self.colorbar_opts.update({'major_label_text_font_size': ticksize})
                break

        for lb in ['clabel', 'labels']:
            labelsize = self._fontsize(lb, common=False).get('fontsize')
            if labelsize is not None:
                self.colorbar_opts.update({'title_text_font_size': labelsize})
                break

        opts = dict(cbar_opts['opts'], color_mapper=color_mapper, ticker=ticker,
                    **self._colorbar_defaults)
        color_bar = ColorBar(**dict(opts, **self.colorbar_opts))

        plot.add_layout(color_bar, pos)
        self.handles[prefix+'colorbar'] = color_bar


    def _get_colormapper(self, eldim, element, ranges, style, factors=None, colors=None,
                         group=None, name='color_mapper'):
        # The initial colormapper instance is cached the first time
        # and then only updated
        if eldim is None and colors is None:
            return None
        dim_name = dim_range_key(eldim)

        # Attempt to find matching colormapper on the adjoined plot
        if self.adjoined:
            cmappers = self.adjoined.traverse(
                lambda x: (x.handles.get('color_dim'),
                           x.handles.get(name),
                           [v for v in x.handles.values()
                            if isinstance(v, ColorMapper)])
                )
            cmappers = [(cmap, mappers) for cdim, cmap, mappers in cmappers
                        if cdim == eldim]
            if cmappers:
                cmapper, mappers  = cmappers[0]
                if not cmapper:
                    if mappers and mappers[0]:
                        cmapper = mappers[0]
                    else:
                        return None
                self.handles['color_mapper'] = cmapper
                return cmapper
            else:
                return None

        ncolors = None if factors is None else len(factors)
        if eldim:
            # check if there's an actual value (not np.nan)
            if all(util.isfinite(cl) for cl in self.clim):
                low, high = self.clim
            elif dim_name in ranges:
                if self.clim_percentile and 'robust' in ranges[dim_name]:
                    low, high = ranges[dim_name]['robust']
                else:
                    low, high = ranges[dim_name]['combined']
                dlow, dhigh = ranges[dim_name]['data']
                if (util.is_int(low, int_like=True) and
                    util.is_int(high, int_like=True) and
                    util.is_int(dlow) and
                    util.is_int(dhigh)):
                    low, high = int(low), int(high)
            elif isinstance(eldim, dim):
                low, high = np.nan, np.nan
            else:
                low, high = element.range(eldim.name)
            if self.symmetric:
                sym_max = max(abs(low), high)
                low, high = -sym_max, sym_max
            low = self.clim[0] if util.isfinite(self.clim[0]) else low
            high = self.clim[1] if util.isfinite(self.clim[1]) else high
        else:
            low, high = None, None

        prefix = '' if group is None else group+'_'
        cmap = colors or style.get(prefix+'cmap', style.get('cmap', 'viridis'))
        nan_colors = {k: rgba_tuple(v) for k, v in self.clipping_colors.items()}
        if isinstance(cmap, dict):
            factors = list(cmap)
            palette = [cmap.get(f, nan_colors.get('NaN', self._default_nan)) for f in factors]
            if isinstance(eldim, dim):
                if eldim.dimension in element:
                    formatter = element.get_dimension(eldim.dimension).pprint_value
                else:
                    formatter = str
            else:
                formatter = eldim.pprint_value
            factors = [formatter(f) for f in factors]
        else:
            categorical = ncolors is not None
            if isinstance(self.color_levels, int):
                ncolors = self.color_levels
            elif isinstance(self.color_levels, list):
                ncolors = len(self.color_levels) - 1
                if isinstance(cmap, list) and len(cmap) != ncolors:
                    raise ValueError('The number of colors in the colormap '
                                     'must match the intervals defined in the '
                                     'color_levels, expected %d colors found %d.'
                                     % (ncolors, len(cmap)))
            palette = process_cmap(cmap, ncolors, categorical=categorical)
            if isinstance(self.color_levels, list):
                palette, (low, high) = color_intervals(palette, self.color_levels, clip=(low, high))
        colormapper, opts = self._get_cmapper_opts(low, high, factors, nan_colors)

        cmapper = self.handles.get(name)
        if cmapper is not None:
            if cmapper.palette != palette:
                cmapper.palette = palette
            opts = {k: opt for k, opt in opts.items()
                    if getattr(cmapper, k) != opt}
            if opts:
                cmapper.update(**opts)
        else:
            cmapper = colormapper(palette=palette, **opts)
            self.handles[name] = cmapper
            self.handles['color_dim'] = eldim
        return cmapper


    def _get_color_data(self, element, ranges, style, name='color', factors=None, colors=None,
                        int_categories=False):
        data, mapping = {}, {}
        cdim = element.get_dimension(self.color_index)
        color = style.get(name, None)
        if cdim and ((isinstance(color, str) and color in element) or isinstance(color, dim)):
            self.param.warning(
                "Cannot declare style mapping for '%s' option and "
                "declare a color_index; ignoring the color_index."
                % name)
            cdim = None
        if not cdim:
            return data, mapping

        cdata = element.dimension_values(cdim)
        field = util.dimension_sanitizer(cdim.name)
        dtypes = 'iOSU' if int_categories else 'OSU'

        if factors is None and (isinstance(cdata, list) or cdata.dtype.kind in dtypes):
            range_key = dim_range_key(cdim)
            if range_key in ranges and 'factors' in ranges[range_key]:
                factors = ranges[range_key]['factors']
            else:
                factors = util.unique_array(cdata)
        if factors is not None and int_categories and cdata.dtype.kind == 'i':
            field += '_str__'
            cdata = [str(f) for f in cdata]
            factors = [str(f) for f in factors]

        mapper = self._get_colormapper(cdim, element, ranges, style,
                                       factors, colors)
        if factors is None and isinstance(mapper, CategoricalColorMapper):
            field += '_str__'
            cdata = [cdim.pprint_value(c) for c in cdata]
            factors = True

        data[field] = cdata
        if factors is not None and self.show_legend:
            mapping['legend_field'] = field
        mapping[name] = {'field': field, 'transform': mapper}

        return data, mapping


    def _get_cmapper_opts(self, low, high, factors, colors):
        if factors is None:
            opts = {}
            if self.cnorm == 'linear':
                colormapper = LinearColorMapper
            if self.cnorm == 'log' or self.logz:
                colormapper = LogColorMapper
                if util.is_int(low) and util.is_int(high) and low == 0:
                    low = 1
                    if 'min' not in colors:
                        # Make integer 0 be transparent
                        colors['min'] = 'rgba(0, 0, 0, 0)'
                elif util.is_number(low) and low <= 0:
                    self.param.warning(
                        "Log color mapper lower bound <= 0 and will not "
                        "render correctly. Ensure you set a positive "
                        "lower bound on the color dimension or using "
                        "the `clim` option."
                    )
            elif self.cnorm == 'eq_hist':
                colormapper = EqHistColorMapper
                if bokeh_version > Version('2.4.2'):
                    opts['rescale_discrete_levels'] = self.rescale_discrete_levels
            if isinstance(low, (bool, np.bool_)): low = int(low)
            if isinstance(high, (bool, np.bool_)): high = int(high)
            # Pad zero-range to avoid breaking colorbar (as of bokeh 1.0.4)
            if low == high:
                offset = self.default_span / 2
                low -= offset
                high += offset
            if util.isfinite(low):
                opts['low'] = low
            if util.isfinite(high):
                opts['high'] = high
            color_opts = [('NaN', 'nan_color'), ('max', 'high_color'), ('min', 'low_color')]
            opts.update({opt: colors[name] for name, opt in color_opts if name in colors})
        else:
            colormapper = CategoricalColorMapper
            factors = decode_bytes(factors)
            opts = dict(factors=list(factors))
            if 'NaN' in colors:
                opts['nan_color'] = colors['NaN']
        return colormapper, opts


    def _init_glyph(self, plot, mapping, properties):
        """
        Returns a Bokeh glyph object and optionally creates a colorbar.
        """
        ret = super()._init_glyph(plot, mapping, properties)
        if self.colorbar:
            for k, v in list(self.handles.items()):
                if not k.endswith('color_mapper'):
                    continue
                self._draw_colorbar(plot, v, k.replace('color_mapper', ''))
        return ret


class LegendPlot(ElementPlot):

    legend_cols = param.Integer(default=0, bounds=(0, None), doc="""
        Number of columns for legend.""")

    legend_labels = param.Dict(default=None, doc="""
        Label overrides.""")

    legend_muted = param.Boolean(default=False, doc="""
        Controls whether the legend entries are muted by default.""")

    legend_offset = param.NumericTuple(default=(0, 0), doc="""
        If legend is placed outside the axis, this determines the
        (width, height) offset in pixels from the original position.""")

    legend_position = param.ObjectSelector(objects=["top_right",
                                                    "top_left",
                                                    "bottom_left",
                                                    "bottom_right",
                                                    'right', 'left',
                                                    'top', 'bottom'],
                                                    default="top_right",
                                                    doc="""
        Allows selecting between a number of predefined legend position
        options. The predefined options may be customized in the
        legend_specs class attribute.""")

    legend_opts = param.Dict(default={}, doc="""
        Allows setting specific styling options for the colorbar.""")

    legend_specs = {
        'right': 'right', 'left': 'left', 'top': 'above', 'bottom': 'below'
    }

    def _process_legend(self, plot=None):
        plot = plot or self.handles['plot']
        if not plot.legend:
            return
        legend = plot.legend[0]
        cmappers = [cmapper for cmapper in self.handles.values()
                   if isinstance(cmapper, CategoricalColorMapper)]
        categorical = bool(cmappers)
        if ((not categorical and not self.overlaid and len(legend.items) == 1)
            or not self.show_legend):
            legend.items[:] = []
        else:
            if bokeh3 and self.legend_cols:
                plot.legend.nrows = self.legend_cols
            else:
                plot.legend.orientation = 'horizontal' if self.legend_cols else 'vertical'

            pos = self.legend_position
            if pos in self.legend_specs:
                plot.legend[:] = []
                legend.location = self.legend_offset
                if pos in ['top', 'bottom'] and not self.legend_cols:
                    plot.legend.orientation = 'horizontal'
                plot.add_layout(legend, self.legend_specs[pos])
            else:
                legend.location = pos

            # Apply muting and misc legend opts
            for leg in plot.legend:
                leg.update(**self.legend_opts)
                for item in leg.items:
                    for r in item.renderers:
                        r.muted = self.legend_muted



class AnnotationPlot:
    """
    Mix-in plotting subclass for AnnotationPlots which do not have a legend.
    """


class OverlayPlot(GenericOverlayPlot, LegendPlot):

    tabs = param.Boolean(default=False, doc="""
        Whether to display overlaid plots in separate panes""")

    style_opts = (legend_dimensions + ['border_'+p for p in line_properties] +
                  text_properties + ['background_fill_color', 'background_fill_alpha'])

    multiple_legends = param.Boolean(default=False, doc="""
        Whether to split the legend for subplots into multiple legends.""")

    _propagate_options = ['width', 'height', 'xaxis', 'yaxis', 'labelled',
                          'bgcolor', 'fontsize', 'invert_axes', 'show_frame',
                          'show_grid', 'logx', 'logy', 'xticks', 'toolbar',
                          'yticks', 'xrotation', 'yrotation', 'lod',
                          'border', 'invert_xaxis', 'invert_yaxis', 'sizing_mode',
                          'title', 'title_format', 'legend_position', 'legend_offset',
                          'legend_cols', 'gridstyle', 'legend_muted', 'padding',
                          'xlabel', 'ylabel', 'xlim', 'ylim', 'zlim',
                          'xformatter', 'yformatter', 'active_tools',
                          'min_height', 'max_height', 'min_width', 'min_height',
                          'margin', 'aspect', 'data_aspect', 'frame_width',
                          'frame_height', 'responsive', 'fontscale']

    def __init__(self, overlay, **kwargs):
        self._multi_y_propagation = self.lookup_options(overlay, 'plot').options.get('multi_y', False)
        super().__init__(overlay, **kwargs)
        self._multi_y_propagation = False

    @property
    def _x_range_type(self):
        for v in self.subplots.values():
            if not isinstance(v._x_range_type, Range1d):
                return v._x_range_type
        return self._x_range_type

    @property
    def _y_range_type(self):
        for v in self.subplots.values():
            if not isinstance(v._y_range_type, Range1d):
                return v._y_range_type
        return self._y_range_type

    def _process_legend(self, overlay):
        plot = self.handles['plot']
        subplots = self.traverse(lambda x: x, [lambda x: x is not self])
        legend_plots = any(p is not None for p in subplots
                           if isinstance(p, LegendPlot) and
                           not isinstance(p, OverlayPlot))
        non_annotation = [p for p in subplots if not
                          isinstance(p, (AnnotationPlot, OverlayPlot))]
        if (not self.show_legend or len(plot.legend) == 0 or
            (len(non_annotation) <= 1 and not (self.dynamic or legend_plots))):
            return super()._process_legend()
        elif not plot.legend:
            return

        legend = plot.legend[0]

        options = {}
        properties = self.lookup_options(self.hmap.last, 'style')[self.cyclic_index]
        for k, v in properties.items():
            if k in line_properties and 'line' not in k:
                ksplit = k.split('_')
                k = '_'.join(ksplit[:1]+'line'+ksplit[1:])
            if k in text_properties:
                k = 'label_' + k
            if k.startswith('legend_'):
                k = k[7:]
            options[k] = v

        pos = self.legend_position
        if not bokeh3:
            options['orientation'] = 'horizontal' if self.legend_cols else 'vertical'
        if pos in ['top', 'bottom'] and not self.legend_cols:
            options['orientation'] = 'horizontal'

        if overlay is not None and overlay.kdims:
            title = ', '.join([d.label for d in overlay.kdims])
            options['title'] = title

        options.update(self._fontsize('legend', 'label_text_font_size'))
        options.update(self._fontsize('legend_title', 'title_text_font_size'))
        if bokeh3 and self.legend_cols:
            options.update({"ncols": self.legend_cols})
        legend.update(**options)

        if pos in self.legend_specs:
            pos = self.legend_specs[pos]
        else:
            legend.location = pos

        if 'legend_items' not in self.handles:
            self.handles['legend_items'] = []
        legend_items = self.handles['legend_items']
        legend_labels = {
            tuple(sorted(property_to_dict(i.label).items()))
            if isinstance(property_to_dict(i.label), dict) else i.label: i
            for i in legend_items
        }
        for item in legend.items:
            item_label = property_to_dict(item.label)
            label = tuple(sorted(item_label.items())) if isinstance(item_label, dict) else item_label
            if not label or (isinstance(item_label, dict) and not item_label.get('value', True)):
                continue

            if label in legend_labels:
                prev_item = legend_labels[label]
                prev_item.renderers[:] = list(util.unique_iterator(prev_item.renderers+item.renderers))
            else:
                legend_labels[label] = item
                legend_items.append(item)
                if item not in self.handles['legend_items']:
                    self.handles['legend_items'].append(item)

        # Ensure that each renderer is only singly referenced by a legend item
        filtered = []
        renderers = []
        for item in legend_items:
            item.renderers[:] = [r for r in item.renderers if r not in renderers]
            if (item in filtered or not item.renderers or
                not any(r.visible or 'hv_legend' in r.tags for r in item.renderers)):
                continue
            item_label = property_to_dict(item.label)
            if isinstance(item_label, dict) and 'value' in item_label and self.legend_labels:
                label = item_label['value']
                item.label = {'value': self.legend_labels.get(label, label)}
            renderers += item.renderers
            filtered.append(item)
        legend.items[:] = list(util.unique_iterator(filtered))

        if self.multiple_legends:
            remove_legend(plot, legend)
            properties = legend.properties_with_values(include_defaults=False)
            legend_group = []
            for item in legend.items:
                if not isinstance(item.label, dict) or 'value'  in item.label:
                    legend_group.append(item)
                    continue
                new_legend = Legend(**dict(properties, items=[item]))
                new_legend.location = self.legend_offset
                plot.add_layout(new_legend, pos)
            if legend_group:
                new_legend = Legend(**dict(properties, items=legend_group))
                new_legend.location = self.legend_offset
                plot.add_layout(new_legend, pos)
            legend.items[:] = []
        elif pos in ['above', 'below', 'right', 'left']:
            remove_legend(plot, legend)
            legend.location = self.legend_offset
            plot.add_layout(legend, pos)

        # Apply muting and misc legend opts
        for leg in plot.legend:
            leg.update(**self.legend_opts)
            for item in leg.items:
                for r in item.renderers:
                    r.muted = self.legend_muted or r.muted


    def _init_tools(self, element, callbacks=[]):
        """
        Processes the list of tools to be supplied to the plot.
        """
        hover_tools = {}
        init_tools, tool_types = [], []
        for key, subplot in self.subplots.items():
            el = element.get(key)
            if el is not None:
                el_tools = subplot._init_tools(el, self.callbacks)
                for tool in el_tools:
                    if isinstance(tool, str):
                        tool_type = TOOL_TYPES.get(tool)
                    else:
                        tool_type = type(tool)
                    if isinstance(tool, tools.HoverTool):
                        tooltips = tuple(tool.tooltips) if tool.tooltips else ()
                        if tooltips in hover_tools:
                            continue
                        else:
                            hover_tools[tooltips] = tool
                    elif tool_type in tool_types:
                        continue
                    else:
                        tool_types.append(tool_type)
                    init_tools.append(tool)
        self.handles['hover_tools'] = hover_tools
        return init_tools


    def _merge_tools(self, subplot):
        """
        Merges tools on the overlay with those on the subplots.
        """
        if self.batched and 'hover' in subplot.handles:
            self.handles['hover'] = subplot.handles['hover']
        elif 'hover' in subplot.handles and 'hover_tools' in self.handles:
            hover = subplot.handles['hover']
            if hover.tooltips and not isinstance(hover.tooltips, str):
                tooltips = tuple((name, spec.replace('{%F %T}', '')) for name, spec in hover.tooltips)
            else:
                tooltips = ()
            tool = self.handles['hover_tools'].get(tooltips)
            if tool:
                tool_renderers = [] if tool.renderers == 'auto' else tool.renderers
                hover_renderers = [] if hover.renderers == 'auto' else hover.renderers
                renderers = [r for r in tool_renderers + hover_renderers if r is not None]
                tool.renderers = list(util.unique_iterator(renderers))
                if 'hover' not in self.handles:
                    self.handles['hover'] = tool

    def _get_dimension_factors(self, overlay, ranges, dimension):
        factors = []
        for k, sp in self.subplots.items():
            el = overlay.data.get(k)
            if el is None or not sp.apply_ranges or not sp._has_axis_dimension(el, dimension):
                continue
            dim = el.get_dimension(dimension)
            elranges = util.match_spec(el, ranges)
            fs = sp._get_dimension_factors(el, elranges, dim)
            if len(fs):
                factors.append(fs)
        return list(util.unique_iterator(chain(*factors)))

    def _get_factors(self, overlay, ranges):
        xfactors, yfactors = [], []
        for k, sp in self.subplots.items():
            el = overlay.data.get(k)
            if el is not None:
                elranges = util.match_spec(el, ranges)
                xfs, yfs = sp._get_factors(el, elranges)
                if len(xfs):
                    xfactors.append(xfs)
                if len(yfs):
                    yfactors.append(yfs)
        xfactors = list(util.unique_iterator(chain(*xfactors)))
        yfactors = list(util.unique_iterator(chain(*yfactors)))
        return xfactors, yfactors

    def _get_axis_dims(self, element):
        subplots = list(self.subplots.values())
        if subplots:
            return subplots[0]._get_axis_dims(element)
        return super()._get_axis_dims(element)

    def initialize_plot(self, ranges=None, plot=None, plots=None):
        key = util.wrap_tuple(self.hmap.last_key)
        nonempty = [(k, el) for k, el in self.hmap.data.items() if el]
        if not nonempty:
            raise SkipRendering('All Overlays empty, cannot initialize plot.')
        dkey, element = nonempty[-1]
        ranges = self.compute_ranges(self.hmap, key, ranges)

        self.tabs = self.tabs or any(isinstance(sp, TablePlot) for sp in self.subplots.values())
        if plot is None and not self.tabs and not self.batched:
            plot = self._init_plot(key, element, ranges=ranges, plots=plots)
            self._populate_axis_handles(plot)
        self.handles['plot'] = plot

        if plot and not self.overlaid:
            self._update_plot(key, plot, element)
            self._update_ranges(element, ranges)

        panels = []
        for key, subplot in self.subplots.items():
            frame = None
            if self.tabs:
                subplot.overlaid = False
            child = subplot.initialize_plot(ranges, plot, plots)
            if isinstance(element, CompositeOverlay):
                # Ensure that all subplots are in the same state
                frame = element.get(key, None)
                subplot.current_frame = frame
                subplot.current_key = dkey
            if self.batched:
                self.handles['plot'] = child
            if self.tabs:
                title = subplot._format_title(key, dimensions=False)
                if not title:
                    title = get_tab_title(key, frame, self.hmap.last)
                panels.append(TabPanel(child=child, title=title))
            self._merge_tools(subplot)

        if self.tabs:
            self.handles['plot'] = Tabs(
                tabs=panels, width=self.width, height=self.height,
                min_width=self.min_width, min_height=self.min_height,
                max_width=self.max_width, max_height=self.max_height,
                sizing_mode='fixed'
            )
        elif not self.overlaid:
            self._process_legend(element)
            self._set_active_tools(plot)
        self.drawn = True
        self.handles['plots'] = plots

        if 'plot' in self.handles and not self.tabs:
            plot = self.handles['plot']
            self.handles['xaxis'] = plot.xaxis[0]
            self.handles['yaxis'] = plot.yaxis[0]
            self.handles['x_range'] = plot.x_range
            self.handles['y_range'] = plot.y_range

        for cb in self.callbacks:
            cb.initialize()

        if self.top_level:
            self.init_links()

        self._execute_hooks(element)

        return self.handles['plot']

    def update_frame(self, key, ranges=None, element=None):
        """
        Update the internal state of the Plot to represent the given
        key tuple (where integers represent frames). Returns this
        state.
        """
        self._reset_ranges()
        reused = isinstance(self.hmap, DynamicMap) and self.overlaid
        self.prev_frame =  self.current_frame
        if not reused and element is None:
            element = self._get_frame(key)
        elif element is not None:
            self.current_frame = element
            self.current_key = key
        items = [] if element is None else list(element.data.items())

        if isinstance(self.hmap, DynamicMap):
            range_obj = element
        else:
            range_obj = self.hmap

        if element is not None:
            ranges = self.compute_ranges(range_obj, key, ranges)

            # Update plot options
            plot_opts = self.lookup_options(element, 'plot').options
            inherited = self._traverse_options(element, 'plot',
                                               self._propagate_options,
                                               defaults=False)
            plot_opts.update(**{k: v[0] for k, v in inherited.items() if k not in plot_opts})
            self.param.update(**plot_opts)

            if not self.overlaid and not self.tabs and not self.batched:
                self._update_ranges(element, ranges)

        # Determine which stream (if any) triggered the update
        triggering = [stream for stream in self.streams if stream._triggering]
        for k, subplot in self.subplots.items():
            el = None

            # If in Dynamic mode propagate elements to subplots
            if isinstance(self.hmap, DynamicMap) and element:
                # In batched mode NdOverlay is passed to subplot directly
                if self.batched:
                    el = element
                # If not batched get the Element matching the subplot
                elif element is not None:
                    idx, spec, exact = self._match_subplot(k, subplot, items, element)
                    if idx is not None:
                        _, el = items.pop(idx)
                        if not exact:
                            self._update_subplot(subplot, spec)

                # Skip updates to subplots when its streams is not one of
                # the streams that initiated the update
                if (triggering and all(s not in triggering for s in subplot.streams) and
                    subplot not in self.dynamic_subplots):
                    continue
            subplot.update_frame(key, ranges, element=el)

        if not self.batched and isinstance(self.hmap, DynamicMap) and items:
            init_kwargs = {'plots': self.handles['plots']}
            if not self.tabs:
                init_kwargs['plot'] = self.handles['plot']
            self._create_dynamic_subplots(key, items, ranges, **init_kwargs)
            if not self.overlaid and not self.tabs:
                self._process_legend(element)

        if element and not self.overlaid and not self.tabs and not self.batched:
            plot = self.handles['plot']
            self._update_plot(key, plot, element)
            self._set_active_tools(plot)
            self._setup_data_callbacks(plot)

        self._updated = True
        self._process_legend(element)
        self._execute_hooks(element)
