Source code for holoviews.plotting.bokeh.element

import warnings
from itertools import chain
from types import FunctionType

import bokeh
import bokeh.plotting
import numpy as np
import param
from import ModelChangedEvent
from bokeh.models import (
from bokeh.models.axes import CategoricalAxis, DatetimeAxis
from bokeh.models.formatters import (
from bokeh.models.layouts import TabPanel, Tabs
from bokeh.models.mappers import (
from bokeh.models.ranges import DataRange1d, FactorRange, Range1d
from bokeh.models.scales import LogScale
from bokeh.models.tickers import (
from import Tool
from packaging.version import Version

from ...core import CompositeOverlay, Dataset, Dimension, DynamicMap, Element, util
from ...core.options import Keywords, SkipRendering, abbreviated_exception
from ...element import Annotation, Contours, Graph, Path, Tiles, VectorField
from ...streams import Buffer, PlotSize, RangeXY
from ...util.transform import dim
from ..plot import GenericElementPlot, GenericOverlayPlot
from ..util import color_intervals, dim_axis_label, dim_range_key, process_cmap
from .plot import BokehPlot
from .styles import (
from .tabular import TablePlot
from .util import (

    TOOLS_MAP = Tool._known_aliases
except Exception:

[docs]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.""") subcoordinate_y = param.ClassSelector(default=False, class_=(bool, tuple), doc=""" Enables sub-coordinate systems for this plot. Accepts also a numerical two-tuple that must be a range between 0 and 1, the plot will be rendered on this vertical range of the axis.""") subcoordinate_scale = param.Number(default=1, bounds=(0, None), inclusive_bounds=(False, True), doc=""" Scale factor for subcoordinate ranges to control the level of overlap.""") 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._subcoord_standalone_ = None 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=None): """ Processes the list of tools to be supplied to the plot. """ if callbacks is None: callbacks = [] tooltips, hover_opts = self._hover_opts(element) tooltips = [(ttp.pprint_label, '@{%s}' % util.dimension_sanitizer( 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 + 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( 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( if dim not in data: data[dim] = element.dimension_values(d) for k, v in self.overlay_dims.items(): dim = util.dimension_sanitizer( 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, and not (categorical and not isinstance(dim_range, FactorRange)) ): dim_range = extra_range break return dim_range @property def _subcoord_overlaid(self): """ Indicates when the context is a subcoordinate plot, either from within the overlay rendering or one of its subplots. Used to skip code paths when rendering an element outside of an overlay. """ if self._subcoord_standalone_ is not None: return self._subcoord_standalone_ self._subcoord_standalone_ = ( (isinstance(self, OverlayPlot) and self.subcoordinate_y) or (not isinstance(self, OverlayPlot) and self.overlaid and self.subcoordinate_y) ) return self._subcoord_standalone_ def _axis_props(self, plots, subplots, element, ranges, pos, *, dim=None, range_tags_extras=None, extra_range_name=None): if range_tags_extras is None: range_tags_extras = [] 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(, {'combined': (None, None)})['combined'] for elrange in ranges.values() ]) axis_label = str(dim) specs = ((, dim.label, dim.unit),) else: try: l, b, r, t = self.get_extents(range_el, ranges, dimension=dim) except TypeError: # Backward compatibility for e.g. GeoViews=<1.10.1 since dimension # is a newly added keyword argument in HoloViews 1.17 l, b, r, t = self.get_extents(range_el, ranges) if self.invert_axes: l, b, r, t = b, l, t, r if pos == 1 and self._subcoord_overlaid: if isinstance(self.subcoordinate_y, bool): offset = self.subcoordinate_scale / 2. # This sum() is equal to n+1, n being the number of elements contained # in the overlay with subcoordinate_y=True, as the traversal goes through # the root overlay that has subcoordinate_y=True too since it's propagated. v0, v1 = 0-offset, sum(self.traverse(lambda p: p.subcoordinate_y))-2+offset else: v0, v1 = 0, 1 else: 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.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 self.subcoordinate_y: categorical = False elif dims is not None and any( in ranges and 'factors' in ranges[] 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) and not dim: 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) and not dim: 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() elif issubclass(range_type, FactorRange): dim_range = range_type( if dim else None) else: dim_range = range_type(start=v0, end=v1, 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: = 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 = {}, {}, {} subcoordinate_axes = 0 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] opts = el.opts.get('plot', backend='bokeh').kwargs if not isinstance(yd, Dimension) or in yaxes: continue if self._subcoord_overlaid: if opts.get('subcoordinate_y') is None: continue ax_name = el.label subcoordinate_axes += 1 else: ax_name = dimensions[ax_name] = yd yaxes[ax_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') }, 'subcoordinate_y': (subcoordinate_axes - 1) if self._subcoord_overlaid else None } for ydim, info in yaxes.items(): range_tags_extras = {'invert_yaxis': info['invert_yaxis']} if info['subcoordinate_y'] is not None: range_tags_extras['subcoordinate_y'] = info['subcoordinate_y'] 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, {}) if self._subcoord_overlaid: _, extra_axis_specs = self._create_extra_axes(plots, subplots, element, ranges) axis_specs['y'].update(extra_axis_specs) 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', {}) extra_ranges[axis_dim] = axis_range if not self.subcoordinate_y: properties[f'extra_{axis}_scales'] = extra_scales = properties.get(f'extra_{axis}_scales', {}) 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)) 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']) # Do not add the extra axes to the layout if subcoordinates are used if self._subcoord_overlaid: return fig 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 + 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 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=None): """ Returns a dictionary of axis properties depending on the specified axis. """ # need to copy dictionary by calling dict() on it if ax_mapping is None: ax_mapping = {'x': 0, 'y': 1} 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)) elif self._subcoord_overlaid and axis == 'y': ticks, labels = [], [] idx = 0 for el, sp in zip(self.current_frame, self.subplots.values()): if not sp.subcoordinate_y: continue ycenter = idx if isinstance(sp.subcoordinate_y, bool) else 0.5 * sum(sp.subcoordinate_y) idx += 1 ticks.append(ycenter) labels.append(el.label) 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 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 = if box_zoom: box_zoom[0].match_aspect = True wheel_zoom = 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) if self._subcoord_overlaid: return # 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(, 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 = if self.invert_axes else else: range_dim = None try: l, b, r, t = self.get_extents(element, ranges, dimension=range_dim) except TypeError: # Backward compatibility for e.g. GeoViews=<1.10.1 since dimension # is a newly added keyword argument in HoloViews 1.17 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) and not self.subcoordinate_y) 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 = scroll_zoom = 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 self.subcoordinate_y) 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 = const find = (view) => {{ let iterable = view.child_views === undefined ? [] : view.child_views for (const sv of iterable) {{ if ( == 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[] 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]
[docs] 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(, {}): values = ranges[]['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 and not self.subcoordinate_y: properties['x_range_name'] = self.handles['x_range'].name if self.handles['y_range'].name in plot.extra_y_ranges and not self.subcoordinate_y: 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") if self._subcoord_overlaid: y_source_range = self.handles['y_range'] if isinstance(self.subcoordinate_y, bool): center = y_source_range.tags[1]['subcoordinate_y'] offset = self.subcoordinate_scale/2. ytarget_range = dict(start=center-offset, end=center+offset) else: ytarget_range = dict(start=self.subcoordinate_y[0], end=self.subcoordinate_y[1]) plot = plot.subplot( x_source=plot.x_range, x_target=plot.x_range, y_source=y_source_range, y_target=Range1d(**ytarget_range), ) 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({ 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 = 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( 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 or new_field == old_field): continue columns.append(new_field) glyph_updates.append((glyph, filtered)) # If a dataspec has changed and the 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} event = ModelChangedEvent( document=self.document, model=source, attr='data', new=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 key = '@{%s}' % k if ( (len(values) and isinstance(values[0], util.datetime_types)) or (len(values) and isinstance(values[0], np.ndarray) and values[0].dtype.kind == 'M') ): 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.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, 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 in plot.extra_x_ranges: x_range = plot.extra_x_ranges[] xaxes = [xaxis for xaxis in plot.xaxis if xaxis.x_range_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 in plot.extra_y_ranges: y_range = plot.extra_y_ranges[] yaxes = [yaxis for yaxis in plot.yaxis if yaxis.y_range_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)
[docs] 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 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 if self._subcoord_overlaid: if style_element.label in plot.extra_y_ranges: self.handles['y_range'] = plot.extra_y_ranges.pop(style_element.label) 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{'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 if k not in data: data[k] = v elif not len(data[k]) and len( data[k] =[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 # 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()
[docs] 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 and not self.streaming[0]._triggering)): return if self.batched: style_element = element.last max_cycles = None else: style_element = element max_cycles = style = self.lookup_options(style_element, '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.cyclic_index]) self._execute_hooks(element)
def _execute_hooks(self, element): dtype_fix_hook(self, element) super()._execute_hooks(element) self._update_backend_opts()
[docs] 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)
[docs]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.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, 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
[docs]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( 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( 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
[docs]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 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
[docs]class AnnotationPlot: """ Mix-in plotting subclass for AnnotationPlots which do not have a legend. """
[docs]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', 'subcoordinate_y', 'subcoordinate_scale'] 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 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 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=None): """ Processes the list of tools to be supplied to the plot. """ if callbacks is None: callbacks = [] 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 = 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 = 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)
[docs] def initialize_plot(self, ranges=None, plot=None, plots=None): if self.multi_y and self.subcoordinate_y: raise ValueError('multi_y and subcoordinate_y are not supported together.') if self.subcoordinate_y: labels = self.hmap.last.traverse(lambda x: x.label, [ lambda el: isinstance(el, Element) and el.opts.get('plot').kwargs.get('subcoordinate_y', False) ]) if any(not label for label in labels): raise ValueError( 'Every element wrapped in a subcoordinate_y overlay must have ' 'a label.' ) if len(set(labels)) == 1: raise ValueError( 'Elements wrapped in a subcoordinate_y overlay must all have ' 'a unique label.' ) key = util.wrap_tuple(self.hmap.last_key) nonempty = [(k, el) for k, el in 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']
[docs] 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( 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 and exact: _, el = items.pop(idx) # 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)