import warnings
from itertools import chain
from types import FunctionType
import bokeh
import bokeh.plotting
import numpy as np
import param
from bokeh.document.events import ModelChangedEvent
from bokeh.models import (
BinnedTicker,
ColorBar,
ColorMapper,
CustomJS,
EqHistColorMapper,
GlyphRenderer,
Legend,
Renderer,
Title,
tools,
)
from bokeh.models.axes import CategoricalAxis, DatetimeAxis
from bokeh.models.formatters import (
CustomJSTickFormatter,
MercatorTickFormatter,
TickFormatter,
)
from bokeh.models.layouts import TabPanel, Tabs
from bokeh.models.mappers import (
CategoricalColorMapper,
LinearColorMapper,
LogColorMapper,
)
from bokeh.models.ranges import DataRange1d, FactorRange, Range1d
from bokeh.models.scales import LogScale
from bokeh.models.tickers import (
BasicTicker,
FixedTicker,
LogTicker,
MercatorTicker,
Ticker,
)
from bokeh.models.tools 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 (
base_properties,
legend_dimensions,
line_properties,
mpl_to_bokeh,
property_prefixes,
rgba_tuple,
text_properties,
validate,
)
from .tabular import TablePlot
from .util import (
TOOL_TYPES,
bokeh32,
bokeh_version,
cds_column_replace,
compute_layout_properties,
date_to_integer,
decode_bytes,
dtype_fix_hook,
get_axis_class,
get_scale,
get_tab_title,
glyph_order,
hold_policy,
match_ax_type,
match_dim_specs,
match_yaxis_type_to_range,
prop_is_none,
property_to_dict,
recursive_model_update,
remove_legend,
theme_attr_json,
wrap_formatter,
)
try:
TOOLS_MAP = Tool._known_aliases
except Exception:
TOOLS_MAP = TOOL_TYPES
[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(ttp.name))
if isinstance(ttp, Dimension) else ttp for ttp in tooltips]
if not tooltips: tooltips = None
callbacks = callbacks+self.callbacks
cb_tools, tool_names = [], []
hover = False
for cb in callbacks:
for handle in cb.models:
if handle and handle in TOOLS_MAP:
tool_names.append(handle)
if handle == 'hover':
tool = tools.HoverTool(
tooltips=tooltips, tags=['hv_created'],
**hover_opts)
hover = tool
else:
tool = TOOLS_MAP[handle]()
cb_tools.append(tool)
self.handles[handle] = tool
tool_list = []
for tool in cb_tools + self.default_tools + self.tools:
if tool in tool_names:
continue
if tool in ['vline', 'hline']:
tool = tools.HoverTool(
tooltips=tooltips, tags=['hv_created'], mode=tool, **hover_opts
)
elif bokeh32 and tool in ['wheel_zoom', 'xwheel_zoom', 'ywheel_zoom']:
if tool.startswith('x'):
zoom_dims = 'width'
elif tool.startswith('y'):
zoom_dims = 'height'
else:
zoom_dims = 'both'
tool = tools.WheelZoomTool(
zoom_together='none', dimensions=zoom_dims, tags=['hv_created']
)
tool_list.append(tool)
copied_tools = []
for tool in tool_list:
if isinstance(tool, tools.Tool):
properties = tool.properties_with_values(include_defaults=False)
tool = type(tool)(**properties)
copied_tools.append(tool)
hover_tools = [t for t in copied_tools if isinstance(t, tools.HoverTool)]
if 'hover' in copied_tools:
hover = tools.HoverTool(tooltips=tooltips, tags=['hv_created'], **hover_opts)
copied_tools[copied_tools.index('hover')] = hover
elif any(hover_tools):
hover = hover_tools[0]
if hover:
self.handles['hover'] = hover
box_tools = [t for t in copied_tools if isinstance(t, tools.BoxSelectTool)]
if box_tools:
self.handles['box_select'] = box_tools[0]
lasso_tools = [t for t in copied_tools if isinstance(t, tools.LassoSelectTool)]
if lasso_tools:
self.handles['lasso_select'] = lasso_tools[0]
# Link the selection properties between tools
if box_tools and lasso_tools:
box_tools[0].js_link('mode', lasso_tools[0], 'mode')
lasso_tools[0].js_link('mode', box_tools[0], 'mode')
return copied_tools
def _update_hover(self, element):
tool = self.handles['hover']
if 'hv_created' in tool.tags:
tooltips, hover_opts = self._hover_opts(element)
tooltips = [(ttp.pprint_label, '@{%s}' % util.dimension_sanitizer(ttp.name))
if isinstance(ttp, Dimension) else ttp for ttp in tooltips]
tool.tooltips = tooltips
else:
plot_opts = element.opts.get('plot', 'bokeh')
new_hover = [t for t in plot_opts.kwargs.get('tools', [])
if isinstance(t, tools.HoverTool)]
if new_hover:
tool.tooltips = new_hover[0].tooltips
def _get_hover_data(self, data, element, dimensions=None):
"""
Initializes hover data based on Element dimension values.
If empty initializes with no data.
"""
if 'hover' not in self.handles or self.static_source:
return
for d in (dimensions or element.dimensions()):
dim = util.dimension_sanitizer(d.name)
if dim not in data:
data[dim] = element.dimension_values(d)
for k, v in self.overlay_dims.items():
dim = util.dimension_sanitizer(k.name)
if dim not in data:
data[dim] = [v] * len(next(iter(data.values())))
def _shared_axis_range(self, plots, specs, range_type, axis_type, pos):
"""
Given a list of other plots return the shared axis from another
plot by matching the dimensions specs stored as tags on the
dimensions. Returns None if there is no such axis.
"""
dim_range = None
categorical = range_type is FactorRange
for plot in plots:
if plot is None or specs is None:
continue
ax = 'x' if pos == 0 else 'y'
plot_range = getattr(plot, f'{ax}_range', None)
axes = getattr(plot, f'{ax}axis', None)
extra_ranges = getattr(plot, f'extra_{ax}_ranges', {})
if (
plot_range and plot_range.tags and
match_dim_specs(plot_range.tags[0], specs) and
match_ax_type(axes[0], axis_type) and
not (categorical and not isinstance(dim_range, FactorRange))
):
dim_range = plot_range
if dim_range is not None:
break
for extra_range in extra_ranges.values():
if (
extra_range.tags and match_dim_specs(extra_range.tags[0], specs) and
match_yaxis_type_to_range(axes, axis_type, extra_range.name) and
not (categorical and not isinstance(dim_range, FactorRange))
):
dim_range = extra_range
break
return dim_range
@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(dim.name, {'combined': (None, None)})['combined']
for elrange in ranges.values()
])
axis_label = str(dim)
specs = ((dim.name, 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.name, d.label, d.unit) for d in dims)
else:
specs = None
if dim:
axis_label = str(dim)
else:
xlabel, ylabel, zlabel = self._get_axis_labels(dims if dims else (None, None))
if self.invert_axes:
xlabel, ylabel = ylabel, xlabel
axis_label = ylabel if pos else xlabel
if dims:
dims = dims[:2][::-1]
categorical = any(self.traverse(lambda plot: plot._categorical))
if self.subcoordinate_y:
categorical = False
elif dims is not None and any(dim.name in ranges and 'factors' in ranges[dim.name] for dim in dims):
categorical = True
else:
categorical = any(isinstance(v, (str, bytes)) for v in (v0, v1))
range_types = (self._x_range_type, self._y_range_type)
if self.invert_axes: range_types = range_types[::-1]
range_type = range_types[pos]
# If multi_x/y then grab opts from element
axis_type = 'log' if (self.logx, self.logy)[pos] else 'auto'
if dims:
if len(dims) > 1 or range_type is FactorRange:
axis_type = 'auto'
categorical = True
elif el.get_dimension(dims[0]):
dim_type = el.get_dimension_type(dims[0])
if ((dim_type is np.object_ and issubclass(type(v0), util.datetime_types)) or
dim_type in util.datetime_types):
axis_type = 'datetime'
norm_opts = self.lookup_options(el, 'norm').options
shared_name = extra_range_name or ('x-main-range' if pos == 0 else 'y-main-range')
if plots and self.shared_axes and not norm_opts.get('axiswise', False) 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(name=dim.name if dim else None)
else:
dim_range = range_type(start=v0, end=v1, name=dim.name if dim else None)
if not dim_range.tags and specs is not None:
dim_range.tags.append(specs)
dim_range.tags.append(range_tags_extras)
if extra_range_name:
dim_range.name = extra_range_name
return axis_type, axis_label, dim_range
def _create_extra_axes(self, plots, subplots, element, ranges):
if self.invert_axes:
axpos0, axpos1 = 'below', 'above'
else:
axpos0, axpos1 = 'left', 'right'
ax_specs, yaxes, dimensions = {}, {}, {}
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 yd.name 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 = yd.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 + self.tools)
active_tools = {'pan', 'wheel_zoom'} & enabled_tools
else:
active_tools = self.active_tools
if active_tools == []:
# Removes Bokeh default behavior of having Pan enabled by default
plot.toolbar.active_drag = None
for tool in active_tools:
if isinstance(tool, str):
tool_type = TOOL_TYPES.get(tool, type(None))
matching = [t for t in plot.toolbar.tools
if isinstance(t, tool_type)]
if not matching:
self.param.warning(
f'Tool of type {tool!r} could not be found '
'and could not be activated by default.'
)
continue
tool = matching[0]
if isinstance(tool, tools.Drag):
plot.toolbar.active_drag = tool
if isinstance(tool, tools.Scroll):
plot.toolbar.active_scroll = tool
if isinstance(tool, tools.Tap):
plot.toolbar.active_tap = tool
if isinstance(tool, tools.InspectTool):
plot.toolbar.active_inspect.append(tool)
def _title_properties(self, key, plot, element):
if self.show_title and self.adjoined is None:
title = self._format_title(key, separator=' ')
else:
title = ''
opts = dict(text=title)
# this will override theme if not set to the default 12pt
title_font = self._fontsize('title').get('fontsize')
if title_font != '12pt':
opts['text_font_size'] = title_font
return opts
def _populate_axis_handles(self, plot):
self.handles['xaxis'] = plot.xaxis[0]
self.handles['x_range'] = plot.x_range
self.handles['extra_x_ranges'] = plot.extra_x_ranges
self.handles['extra_x_scales'] = plot.extra_x_scales
self.handles['yaxis'] = plot.yaxis[0]
self.handles['y_range'] = plot.y_range
self.handles['extra_y_ranges'] = plot.extra_y_ranges
self.handles['extra_y_scales'] = plot.extra_y_scales
def _axis_properties(self, axis, key, plot, dimension=None,
ax_mapping=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 = self.state.select(type=tools.BoxZoomTool)
if box_zoom:
box_zoom[0].match_aspect = True
wheel_zoom = self.state.select(type=tools.WheelZoomTool)
if wheel_zoom:
wheel_zoom[0].zoom_on_axis = False
elif isinstance(axis_obj, CategoricalAxis):
for key in list(axis_props):
if key.startswith('major_label'):
# set the group labels equal to major (actually minor)
new_key = key.replace('major_label', 'group')
axis_props[new_key] = axis_props[key]
# major ticks are actually minor ticks in a categorical
# so if user inputs minor ticks sizes, then use that;
# else keep major (group) == minor (subgroup)
msize = self._fontsize(f'minor_{axis}ticks',
common=False).get('fontsize')
if msize is not None:
axis_props['major_label_text_font_size'] = msize
return axis_props
def _update_plot(self, key, plot, element=None):
"""
Updates plot parameters on every frame
"""
plot.update(**self._plot_properties(key, element))
if not self.multi_y:
self._update_labels(key, plot, element)
self._update_title(key, plot, element)
self._update_grid(plot)
def _update_labels(self, key, plot, element):
el = element.traverse(lambda x: x, [Element])
el = el[0] if el else element
dimensions = self._get_axis_dims(el)
props = {axis: self._axis_properties(axis, key, plot, dim)
for axis, dim in zip(['x', 'y'], dimensions)}
xlabel, ylabel, zlabel = self._get_axis_labels(dimensions)
if self.invert_axes:
xlabel, ylabel = ylabel, xlabel
props['x']['axis_label'] = xlabel if 'x' in self.labelled or self.xlabel else ''
props['y']['axis_label'] = ylabel if 'y' in self.labelled or self.ylabel else ''
recursive_model_update(plot.xaxis[0], props.get('x', {}))
recursive_model_update(plot.yaxis[0], props.get('y', {}))
def _update_title(self, key, plot, element):
if plot.title:
plot.title.update(**self._title_properties(key, plot, element))
else:
plot.title = Title(**self._title_properties(key, plot, element))
def _update_backend_opts(self):
plot = self.handles["plot"]
model_accessor_aliases = {
"cbar": "colorbar",
"p": "plot",
"xaxes": "xaxis",
"yaxes": "yaxis",
}
for opt, val in self.backend_opts.items():
parsed_opt = self._parse_backend_opt(
opt, plot, model_accessor_aliases)
if parsed_opt is None:
continue
model, attr_accessor = parsed_opt
# not using isinstance because some models inherit from list
if not isinstance(model, list):
# to reduce the need for many if/else; cast to list
# to do the same thing for both single and multiple models
models = [model]
else:
models = model
valid_options = models[0].properties()
if attr_accessor not in valid_options:
kws = Keywords(values=valid_options)
matches = sorted(kws.fuzzy_match(attr_accessor))
self.param.warning(
f"Could not find {attr_accessor!r} property on {type(models[0]).__name__!r} "
f"model. Ensure the custom option spec {opt!r} you provided references a "
f"valid attribute on the specified model. "
f"Similar options include {matches!r}"
)
continue
for m in models:
setattr(m, attr_accessor, val)
def _update_grid(self, plot):
if not self.show_grid:
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = None
return
replace = ['bounds', 'bands', 'visible', 'level', 'ticker', 'visible']
style_items = list(self.gridstyle.items())
both = {k: v for k, v in style_items if k.startswith(('grid_', 'minor_grid'))}
xgrid = {k.replace('xgrid', 'grid'): v for k, v in style_items if 'xgrid' in k}
ygrid = {k.replace('ygrid', 'grid'): v for k, v in style_items if 'ygrid' in k}
xopts = {k.replace('grid_', '') if any(r in k for r in replace) else k: v
for k, v in dict(both, **xgrid).items()}
yopts = {k.replace('grid_', '') if any(r in k for r in replace) else k: v
for k, v in dict(both, **ygrid).items()}
if plot.xaxis and 'ticker' not in xopts:
xopts['ticker'] = plot.xaxis[0].ticker
if plot.yaxis and 'ticker' not in yopts:
yopts['ticker'] = plot.yaxis[0].ticker
plot.xgrid[0].update(**xopts)
plot.ygrid[0].update(**yopts)
def _update_ranges(self, element, ranges):
x_range = self.handles['x_range']
y_range = self.handles['y_range']
plot = self.handles['plot']
self._update_main_ranges(element, x_range, y_range, ranges)
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(extra_y_range.name, False), log, streaming
)
def _update_main_ranges(self, element, x_range, y_range, ranges):
plot = self.handles['plot']
l, b, r, t = None, None, None, None
if any(isinstance(r, (Range1d, DataRange1d)) for r in [x_range, y_range]):
if self.multi_y:
range_dim = x_range.name if self.invert_axes else y_range.name
else:
range_dim = None
try:
l, b, r, t = self.get_extents(element, ranges, dimension=range_dim)
except 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 = plot.select(type=tools.BoxZoomTool)
scroll_zoom = plot.select(type=tools.WheelZoomTool)
if box_zoom:
box_zoom.match_aspect = True
if scroll_zoom:
scroll_zoom.zoom_on_axis = False
elif any(rs._triggering for rs in range_streams):
xupdate, yupdate = False, False
if not self.drawn or xupdate:
self._update_range(x_range, l, r, xfactors, self.invert_xaxis,
self._shared['x-main-range'], self.logx, streaming)
if not (self.drawn or 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 = plot.id
const find = (view) => {{
let iterable = view.child_views === undefined ? [] : view.child_views
for (const sv of iterable) {{
if (sv.model.id == ref)
return sv
const obj = find(sv)
if (obj !== null)
return obj
}}
return null
}}
let plot_view = null;
for (const root of plot.document.roots()) {{
const root_view = window.Bokeh.index[root.id]
if (root_view === undefined)
return
plot_view = find(root_view)
if (plot_view != null)
break
}}
if (plot_view == null)
return
let range_limits = {{}}
for (const dr of plot.data_renderers) {{
const renderer = plot_view.renderer_view(dr)
const glyph_view = renderer.glyph_view
let [vmin, vmax] = [Infinity, -Infinity]
let y_range_name = renderer.model.y_range_name
if (!renderer.glyph.model.tags.includes('no_apply_ranges')) {{
const index = glyph_view.index.index
for (let pos = 0; pos < index._boxes.length - 4; pos += 4) {{
const [x0, y0, x1, y1] = index._boxes.slice(pos, pos+4)
if ({odim}0 > plot.{odim}_range.start && {odim}1 < plot.{odim}_range.end) {{
vmin = Math.min(vmin, {dim}0)
vmax = Math.max(vmax, {dim}1)
}}
}}
}}
if (y_range_name) {{
range_limits[y_range_name] = [vmin, vmax]
}}
}}
let range_tags_extras = plot.{dim}_range.tags[1]
if (range_tags_extras['autorange']) {{
let lowerlim = range_tags_extras['y-lowerlim'] ?? null
let upperlim = range_tags_extras['y-upperlim'] ?? null
let [start, end] = get_padded_range('default', lowerlim, upperlim, range_tags_extras['invert_yaxis'])
if ((start != end) && window.Number.isFinite(start) && window.Number.isFinite(end)) {{
plot.{dim}_range.setv({{start, end}})
}}
}}
for (let key in plot.extra_{dim}_ranges) {{
const extra_range = plot.extra_{dim}_ranges[key]
let range_tags_extras = extra_range.tags[1]
let lowerlim = range_tags_extras['y-lowerlim'] ?? null
let upperlim = range_tags_extras['y-upperlim'] ?? null
if (range_tags_extras['autorange']) {{
let [start, end] = get_padded_range(key, lowerlim, upperlim, range_tags_extras['invert_yaxis'])
if ((start != end) && window.Number.isFinite(start) && window.Number.isFinite(end)) {{
extra_range.setv({{start, end}})
}}
}}
}}
}}
// The plot changes will not propagate to the glyph until
// after the data change event has occurred.
setTimeout(cb, 0);
""", args={'plot': self.state})
self.state.js_on_event('rangesupdate', callback)
self._js_on_data_callbacks.append(callback)
def _categorize_data(self, data, cols, dims):
"""
Transforms non-string or integer types in datasource if the
axis to be plotted on is categorical. Accepts the column data
source data, the columns corresponding to the axes and the
dimensions for each axis, changing the data inplace.
"""
if self.invert_axes:
cols = cols[::-1]
dims = dims[:2][::-1]
ranges = [self.handles[f'{ax}_range'] for ax in 'xy']
for i, col in enumerate(cols):
column = data[col]
if (isinstance(ranges[i], FactorRange) and
(isinstance(column, list) or column.dtype.kind not in 'SU')):
data[col] = [dims[i].pprint_value(v) for v in column]
[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(dimension.name, {}):
values = ranges[dimension.name]['factors']
else:
values = element.dimension_values(dimension, False)
values = np.asarray(values)
if not self._allow_implicit_categories:
values = values if values.dtype.kind in 'SU' else []
return [v if values.dtype.kind in 'SU' else dimension.pprint_value(v) for v in values]
def _get_factors(self, element, ranges):
"""
Get factors for categorical axes.
"""
xdim, ydim = element.dimensions()[:2]
xvals = self._get_dimension_factors(element, ranges, xdim)
yvals = self._get_dimension_factors(element, ranges, ydim)
coords = (xvals, yvals)
if self.invert_axes: coords = coords[::-1]
return coords
def _process_legend(self):
"""
Disables legends if show_legend is disabled.
"""
for l in self.handles['plot'].legend:
l.items[:] = []
l.border_line_alpha = 0
l.background_fill_alpha = 0
def _init_glyph(self, plot, mapping, properties):
"""
Returns a Bokeh glyph object.
"""
mapping['tags'] = ['apply_ranges' if self.apply_ranges else 'no_apply_ranges']
properties = mpl_to_bokeh(properties)
plot_method = self._plot_methods.get('batched' if self.batched else 'single')
if isinstance(plot_method, tuple):
# Handle alternative plot method for flipped axes
plot_method = plot_method[int(self.invert_axes)]
if 'legend_field' in properties and 'legend_label' in properties:
del properties['legend_label']
if self.handles['x_range'].name in plot.extra_x_ranges 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({d.name: v for d, v in self.overlay_dims.items()},
list(self.overlay_dims))
val = v.apply(ds, ranges=ranges, flat=True)[0]
else:
val = self._element_transform(v, element, ranges)
if (not util.isscalar(val) and len(util.unique_array(val)) == 1 and
(('color' not in k or validate('color', val)) or k in self._nonvectorized_styles)):
val = val[0]
if not util.isscalar(val):
if k in self._nonvectorized_styles:
element = type(element).__name__
raise ValueError('Mapping a dimension to the "{style}" '
'style option is not supported by the '
'{element} element using the {backend} '
'backend. To map the "{dim}" dimension '
'to the {style} use a groupby operation '
'to overlay your data along the dimension.'.format(
style=k, dim=v.dimension, element=element,
backend=self.renderer.backend))
elif data and len(val) != len(next(iter(data.values()))):
if isinstance(element, VectorField):
val = np.tile(val, 3)
elif isinstance(element, Path) and not isinstance(element, Contours):
val = val[:-1]
else:
continue
if k == 'angle':
val = np.deg2rad(val)
elif k.endswith('font_size'):
if util.isscalar(val) and isinstance(val, int):
val = str(v)+'pt'
elif isinstance(val, np.ndarray) and val.dtype.kind in 'ifu':
val = [str(int(s))+'pt' for s in val]
if util.isscalar(val):
key = val
else:
# Node marker does not handle {'field': ...}
key = k if k == 'node_marker' else {'field': k}
data[k] = val
# If color is not valid colorspec add colormapper
numeric = isinstance(val, util.arraylike_types) and val.dtype.kind in 'uifMmb'
colormap = style.get(prefix+'cmap')
if ('color' in k and isinstance(val, util.arraylike_types) and
(numeric or not validate('color', val) or isinstance(colormap, dict))):
kwargs = {}
if val.dtype.kind not in 'ifMu':
range_key = dim_range_key(v)
if range_key in ranges and 'factors' in ranges[range_key]:
factors = ranges[range_key]['factors']
else:
factors = util.unique_array(val)
if isinstance(val, util.arraylike_types) and val.dtype.kind == 'b':
factors = factors.astype(str)
kwargs['factors'] = factors
cmapper = self._get_colormapper(v, element, ranges,
dict(style), name=k+'_color_mapper',
group=group, **kwargs)
field = k
categorical = isinstance(cmapper, CategoricalColorMapper)
if categorical:
if val.dtype.kind in 'ifMub':
field = k + '_str__'
if v.dimension in element:
formatter = element.get_dimension(v.dimension).pprint_value
else:
formatter = str
data[field] = [formatter(d) for d in val]
if getattr(self, 'show_legend', False):
legend_labels = getattr(self, 'legend_labels', False)
if legend_labels:
label_field = f'_{field}_labels'
data[label_field] = [legend_labels.get(v, v) for v in val]
new_style['legend_field'] = label_field
else:
new_style['legend_field'] = field
key = {'field': field, 'transform': cmapper}
new_style[k] = key
# Process color/alpha styles and expand to fill/line style
for style, val in list(new_style.items()):
for s in ('alpha', 'color'):
if prefix+s != style or style not in data or validate(s, val, True):
continue
supports_fill = any(
o.startswith(prefix+'fill') and (prefix != 'edge_' or getattr(self, 'filled', True))
for o in self.style_opts)
for pprefix in [p+'_' for p in property_prefixes]+['']:
fill_key = prefix+pprefix+'fill_'+s
fill_style = new_style.get(fill_key)
# Do not override custom nonselection/muted alpha
if ((pprefix in ('nonselection_', 'muted_') and s == 'alpha')
or fill_key not in self.style_opts):
continue
# Override empty and non-vectorized fill_style if not hover style
hover = pprefix == 'hover_'
if ((fill_style is None or (validate(s, fill_style, True) and not hover))
and supports_fill):
new_style[fill_key] = val
line_key = prefix+pprefix+'line_'+s
line_style = new_style.get(line_key)
# If glyph has fill and line style is set overriding line color
if supports_fill and line_style is not None:
continue
# If glyph does not support fill override non-vectorized line_color
if ((line_style is not None and (validate(s, line_style) and not hover)) or
(line_style is None and not supports_fill)):
new_style[line_key] = val
return new_style
def _glyph_properties(self, plot, element, source, ranges, style, group=None):
properties = dict(style, source=source)
if self.show_legend:
if self.overlay_dims:
legend = ', '.join([d.pprint_value(v, print_unit=True) for d, v in
self.overlay_dims.items()])
else:
legend = element.label
if legend and self.overlaid:
properties['legend_label'] = legend
return properties
def _filter_properties(self, properties, glyph_type, allowed):
glyph_props = dict(properties)
for gtype in ((glyph_type, '') if glyph_type else ('',)):
for prop in ('color', 'alpha'):
glyph_prop = properties.get(gtype+prop)
if glyph_prop is not None and ('line_'+prop not in glyph_props or gtype):
glyph_props['line_'+prop] = glyph_prop
if glyph_prop is not None and ('fill_'+prop not in glyph_props or gtype):
glyph_props['fill_'+prop] = glyph_prop
props = {k[len(gtype):]: v for k, v in glyph_props.items()
if k.startswith(gtype)}
if self.batched:
glyph_props = dict(props, **glyph_props)
else:
glyph_props.update(props)
return {k: v for k, v in glyph_props.items() if k in allowed}
def _update_glyph(self, renderer, properties, mapping, glyph, source, data):
allowed_properties = glyph.properties()
properties = mpl_to_bokeh(properties)
merged = dict(properties, **mapping)
legend_props = ('legend_field', 'legend_label')
for lp in legend_props:
legend = merged.pop(lp, None)
if legend is not None:
break
columns = list(source.data.keys())
glyph_updates = []
for glyph_type in ('', 'selection_', 'nonselection_', 'hover_', 'muted_'):
if renderer:
glyph = getattr(renderer, glyph_type+'glyph', None)
if glyph == 'auto':
base_glyph = renderer.glyph
props = base_glyph.properties_with_values()
glyph = type(base_glyph)(**{k: v for k, v in props.items()
if not prop_is_none(v)})
setattr(renderer, glyph_type+'glyph', glyph)
if not glyph or (not renderer and glyph_type):
continue
filtered = self._filter_properties(merged, glyph_type, allowed_properties)
# Ensure that data is populated before updating glyph
dataspecs = glyph.dataspecs()
for spec in dataspecs:
new_spec = property_to_dict(filtered.get(spec))
old_spec = property_to_dict(getattr(glyph, spec))
new_field = new_spec.get('field') if isinstance(new_spec, dict) else new_spec
old_field = old_spec.get('field') if isinstance(old_spec, dict) else old_spec
if (data is None) or (new_field not in data or new_field in source.data or new_field == old_field):
continue
columns.append(new_field)
glyph_updates.append((glyph, filtered))
# If a dataspec has changed and the CDS.data will be replaced
# the GlyphRenderer will not find the column, therefore we
# craft an event which will make the column available.
cds_replace = True if data is None else cds_column_replace(source, data)
if not cds_replace:
if not self.static_source:
self._update_datasource(source, data)
if hasattr(self, 'selected') and self.selected is not None:
self._update_selected(source)
elif self.document:
server = self.renderer.mode == 'server'
with hold_policy(self.document, 'collect', server=server):
empty_data = {c: [] for c in columns}
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 source.data.items():
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.style[self.cyclic_index]
data, mapping, style = self.get_data(element, ranges, style)
current_id = element._plot_id
with abbreviated_exception():
style = self._apply_transforms(element, data, ranges, style)
if source is None:
source = self._init_datasource(data)
self.handles['previous_id'] = current_id
self.handles['source'] = self.handles['cds'] = source
self.handles['selected'] = source.selected
properties = self._glyph_properties(plot, style_element, source, ranges, style)
if 'legend_label' in properties and 'legend_field' in mapping:
mapping.pop('legend_field')
with abbreviated_exception():
renderer, glyph = self._init_glyph(plot, mapping, properties)
self.handles['glyph'] = glyph
if isinstance(renderer, Renderer):
self.handles['glyph_renderer'] = renderer
self._postprocess_hover(renderer, source)
# Update plot, source and glyph
with abbreviated_exception():
self._update_glyph(renderer, properties, mapping, glyph, source, source.data)
def _find_axes(self, plot, element):
"""
Looks up the axes and plot ranges given the plot and an element.
"""
axis_dims = self._get_axis_dims(element)[:2]
x, y = axis_dims[::-1] if self.invert_axes else axis_dims
if isinstance(x, Dimension) and x.name in plot.extra_x_ranges:
x_range = plot.extra_x_ranges[x.name]
xaxes = [xaxis for xaxis in plot.xaxis if xaxis.x_range_name == x.name]
x_axis = (xaxes if xaxes else plot.xaxis)[0]
else:
x_range = plot.x_range
x_axis = plot.xaxis[0]
if isinstance(y, Dimension) and y.name in plot.extra_y_ranges:
y_range = plot.extra_y_ranges[y.name]
yaxes = [yaxis for yaxis in plot.yaxis if yaxis.y_range_name == y.name]
y_axis = (yaxes if yaxes else plot.yaxis)[0]
else:
y_range = plot.y_range
y_axis = plot.yaxis[0]
return (x_axis, y_axis), (x_range, y_range)
[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 self.hmap.data.values() if el][-1]
else:
element = self.hmap.last
key = util.wrap_tuple(self.hmap.last_key)
ranges = self.compute_ranges(self.hmap, key, ranges)
self.current_ranges = ranges
self.current_frame = element
self.current_key = key
style_element = element.last if self.batched else element
ranges = util.match_spec(style_element, ranges)
# Initialize plot, source and glyph
if plot is None:
plot = self._init_plot(key, style_element, ranges=ranges, plots=plots)
self._populate_axis_handles(plot)
else:
axes, plot_ranges = self._find_axes(plot, element)
self.handles['xaxis'], self.handles['yaxis'] = axes
self.handles['x_range'], self.handles['y_range'] = plot_ranges
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 plot.select({'type': GlyphRenderer}):
cds = renderer.data_source
for cb in self._js_on_data_callbacks:
if cb not in cds.js_property_callbacks.get('change:data', []):
cds.js_on_change('data', cb)
def _update_glyphs(self, element, ranges, style):
plot = self.handles['plot']
glyph = self.handles.get('glyph')
source = self.handles['source']
mapping = {}
# Cache frame object id to skip updating data if unchanged
previous_id = self.handles.get('previous_id', None)
if self.batched:
current_id = tuple(element.traverse(lambda x: x._plot_id, [Element]))
else:
current_id = element._plot_id
self.handles['previous_id'] = current_id
self.static_source = (self.dynamic and (current_id == previous_id))
if self.batched:
data, mapping, style = self.get_batched_data(element, ranges)
else:
data, mapping, style = self.get_data(element, ranges, style)
# Include old data if source static
if self.static_source:
for k, v in source.data.items():
if k not in data:
data[k] = v
elif not len(data[k]) and len(source.data):
data[k] = source.data[k]
with abbreviated_exception():
style = self._apply_transforms(element, data, ranges, style)
if glyph:
properties = self._glyph_properties(plot, element, source, ranges, style)
renderer = self.handles.get('glyph_renderer')
if 'visible' in style and hasattr(renderer, 'visible'):
renderer.visible = style['visible']
with abbreviated_exception():
self._update_glyph(renderer, properties, mapping, glyph, source, data)
elif not self.static_source:
self._update_datasource(source, data)
def _reset_ranges(self):
"""
Resets RangeXY streams if norm option is set to framewise
"""
# Skipping conditional to temporarily revert fix (see https://github.com/holoviz/holoviews/issues/4396)
# This fix caused PlotSize change events to rerender
# rasterized/datashaded with the full extents which was wrong
if self.overlaid or True:
return
for el, callbacks in self.traverse(lambda x: (x.current_frame, x.callbacks)):
if el is None:
continue
for callback in callbacks:
norm = self.lookup_options(el, 'norm').options
if norm.get('framewise'):
for s in callback.streams:
if isinstance(s, RangeXY) and not s._triggering:
s.reset()
[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 self.current_frame.data and not self.streaming[0]._triggering)):
return
if self.batched:
style_element = element.last
max_cycles = None
else:
style_element = element
max_cycles = self.style._max_cycles
style = self.lookup_options(style_element, 'style')
self.style = style.max_cycles(max_cycles) if max_cycles else style
if not self.overlaid:
ranges = self.compute_ranges(self.hmap, key, ranges)
else:
self.ranges.update(ranges)
self.param.update(**self.lookup_options(style_element, 'plot').options)
ranges = util.match_spec(style_element, ranges)
self.current_ranges = ranges
plot = self.handles['plot']
if not self.overlaid:
self._update_ranges(style_element, ranges)
self._update_plot(key, plot, style_element)
self._set_active_tools(plot)
self._setup_data_callbacks(plot)
self._updated = True
if 'hover' in self.handles:
self._update_hover(element)
if 'cds' in self.handles:
cds = self.handles['cds']
self._postprocess_hover(renderer, cds)
self._update_glyphs(element, ranges, self.style[self.cyclic_index])
self._execute_hooks(element)
def _execute_hooks(self, element):
dtype_fix_hook(self, element)
super()._execute_hooks(element)
self._update_backend_opts()
[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.style[self.cyclic_index]
data, mapping, style = self.get_data(element, ranges, style)
keys = glyph_order(dict(data, **mapping), self._draw_order)
source_cache = {}
current_id = element._plot_id
self.handles['previous_id'] = current_id
for key in keys:
style_group = self._style_groups.get('_'.join(key.split('_')[:-1]))
group_style = dict(style)
ds_data = data.get(key, {})
with abbreviated_exception():
group_style = self._apply_transforms(element, ds_data, ranges, group_style, style_group)
if id(ds_data) in source_cache:
source = source_cache[id(ds_data)]
else:
source = self._init_datasource(ds_data)
source_cache[id(ds_data)] = source
self.handles[key+'_source'] = source
properties = self._glyph_properties(plot, element, source, ranges, group_style, style_group)
properties = self._process_properties(key, properties, mapping.get(key, {}))
with abbreviated_exception():
renderer, glyph = self._init_glyph(plot, mapping.get(key, {}), properties, key)
self.handles[key+'_glyph'] = glyph
if isinstance(renderer, Renderer):
self.handles[key+'_glyph_renderer'] = renderer
self._postprocess_hover(renderer, source)
# Update plot, source and glyph
with abbreviated_exception():
self._update_glyph(renderer, properties, mapping.get(key, {}), glyph,
source, source.data)
if getattr(self, 'colorbar', False):
for k, v in list(self.handles.items()):
if not k.endswith('color_mapper'):
continue
self._draw_colorbar(plot, v, k.replace('color_mapper', ''))
def _process_properties(self, key, properties, mapping):
key = '_'.join(key.split('_')[:-1]) if '_' in key else key
style_group = self._style_groups[key]
group_props = {}
for k, v in properties.items():
if k in self.style_opts:
group = k.split('_')[0]
if group == style_group:
if k in mapping:
v = mapping[k]
k = '_'.join(k.split('_')[1:])
else:
continue
group_props[k] = v
return group_props
def _update_glyphs(self, element, ranges, style):
plot = self.handles['plot']
# Cache frame object id to skip updating data if unchanged
previous_id = self.handles.get('previous_id', None)
if self.batched:
current_id = tuple(element.traverse(lambda x: x._plot_id, [Element]))
else:
current_id = element._plot_id
self.handles['previous_id'] = current_id
self.static_source = (self.dynamic and (current_id == previous_id))
data, mapping, style = self.get_data(element, ranges, style)
keys = glyph_order(dict(data, **mapping), self._draw_order)
for key in keys:
gdata = data.get(key)
source = self.handles[key+'_source']
glyph = self.handles.get(key+'_glyph')
if glyph:
group_style = dict(style)
style_group = self._style_groups.get('_'.join(key.split('_')[:-1]))
with abbreviated_exception():
group_style = self._apply_transforms(element, gdata, ranges, group_style, style_group)
properties = self._glyph_properties(plot, element, source, ranges, group_style, style_group)
properties = self._process_properties(key, properties, mapping[key])
renderer = self.handles.get(key+'_glyph_renderer')
with abbreviated_exception():
self._update_glyph(renderer, properties, mapping[key],
glyph, source, gdata)
elif not self.static_source and gdata is not None:
self._update_datasource(source, gdata)
def _init_glyph(self, plot, mapping, properties, key):
"""
Returns a Bokeh glyph object.
"""
properties = mpl_to_bokeh(properties)
plot_method = '_'.join(key.split('_')[:-1])
renderer = getattr(plot, plot_method)(**dict(properties, **mapping))
return renderer, renderer.glyph
[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(eldim.name)
if self.symmetric:
sym_max = max(abs(low), high)
low, high = -sym_max, sym_max
low = self.clim[0] if util.isfinite(self.clim[0]) else low
high = self.clim[1] if util.isfinite(self.clim[1]) else high
else:
low, high = None, None
prefix = '' if group is None else group+'_'
cmap = colors or style.get(prefix+'cmap', style.get('cmap', 'viridis'))
nan_colors = {k: rgba_tuple(v) for k, v in self.clipping_colors.items()}
if isinstance(cmap, dict):
factors = list(cmap)
palette = [cmap.get(f, nan_colors.get('NaN', self._default_nan)) for f in factors]
if isinstance(eldim, dim):
if eldim.dimension in element:
formatter = element.get_dimension(eldim.dimension).pprint_value
else:
formatter = str
else:
formatter = eldim.pprint_value
factors = [formatter(f) for f in factors]
else:
categorical = ncolors is not None
if isinstance(self.color_levels, int):
ncolors = self.color_levels
elif isinstance(self.color_levels, list):
ncolors = len(self.color_levels) - 1
if isinstance(cmap, list) and len(cmap) != ncolors:
raise ValueError('The number of colors in the colormap '
'must match the intervals defined in the '
'color_levels, expected %d colors found %d.'
% (ncolors, len(cmap)))
palette = process_cmap(cmap, ncolors, categorical=categorical)
if isinstance(self.color_levels, list):
palette, (low, high) = color_intervals(palette, self.color_levels, clip=(low, high))
colormapper, opts = self._get_cmapper_opts(low, high, factors, nan_colors)
cmapper = self.handles.get(name)
if cmapper is not None:
if cmapper.palette != palette:
cmapper.palette = palette
opts = {k: opt for k, opt in opts.items()
if getattr(cmapper, k) != opt}
if opts:
cmapper.update(**opts)
else:
cmapper = colormapper(palette=palette, **opts)
self.handles[name] = cmapper
self.handles['color_dim'] = eldim
return cmapper
def _get_color_data(self, element, ranges, style, name='color', factors=None, colors=None,
int_categories=False):
data, mapping = {}, {}
cdim = element.get_dimension(self.color_index)
color = style.get(name, None)
if cdim and ((isinstance(color, str) and color in element) or isinstance(color, dim)):
self.param.warning(
"Cannot declare style mapping for '%s' option and "
"declare a color_index; ignoring the color_index."
% name)
cdim = None
if not cdim:
return data, mapping
cdata = element.dimension_values(cdim)
field = util.dimension_sanitizer(cdim.name)
dtypes = 'iOSU' if int_categories else 'OSU'
if factors is None and (isinstance(cdata, list) or cdata.dtype.kind in dtypes):
range_key = dim_range_key(cdim)
if range_key in ranges and 'factors' in ranges[range_key]:
factors = ranges[range_key]['factors']
else:
factors = util.unique_array(cdata)
if factors is not None and int_categories and cdata.dtype.kind == 'i':
field += '_str__'
cdata = [str(f) for f in cdata]
factors = [str(f) for f in factors]
mapper = self._get_colormapper(cdim, element, ranges, style,
factors, colors)
if factors is None and isinstance(mapper, CategoricalColorMapper):
field += '_str__'
cdata = [cdim.pprint_value(c) for c in cdata]
factors = True
data[field] = cdata
if factors is not None and self.show_legend:
mapping['legend_field'] = field
mapping[name] = {'field': field, 'transform': mapper}
return data, mapping
def _get_cmapper_opts(self, low, high, factors, colors):
if factors is None:
opts = {}
if self.cnorm == 'linear':
colormapper = LinearColorMapper
if self.cnorm == 'log' or self.logz:
colormapper = LogColorMapper
if util.is_int(low) and util.is_int(high) and low == 0:
low = 1
if 'min' not in colors:
# Make integer 0 be transparent
colors['min'] = 'rgba(0, 0, 0, 0)'
elif util.is_number(low) and low <= 0:
self.param.warning(
"Log color mapper lower bound <= 0 and will not "
"render correctly. Ensure you set a positive "
"lower bound on the color dimension or using "
"the `clim` option."
)
elif self.cnorm == 'eq_hist':
colormapper = EqHistColorMapper
if bokeh_version > Version('2.4.2'):
opts['rescale_discrete_levels'] = self.rescale_discrete_levels
if isinstance(low, (bool, np.bool_)): low = int(low)
if isinstance(high, (bool, np.bool_)): high = int(high)
# Pad zero-range to avoid breaking colorbar (as of bokeh 1.0.4)
if low == high:
offset = self.default_span / 2
low -= offset
high += offset
if util.isfinite(low):
opts['low'] = low
if util.isfinite(high):
opts['high'] = high
color_opts = [('NaN', 'nan_color'), ('max', 'high_color'), ('min', 'low_color')]
opts.update({opt: colors[name] for name, opt in color_opts if name in colors})
else:
colormapper = CategoricalColorMapper
factors = decode_bytes(factors)
opts = dict(factors=list(factors))
if 'NaN' in colors:
opts['nan_color'] = colors['NaN']
return colormapper, opts
def _init_glyph(self, plot, mapping, properties):
"""
Returns a Bokeh glyph object and optionally creates a colorbar.
"""
ret = super()._init_glyph(plot, mapping, properties)
if self.colorbar:
for k, v in list(self.handles.items()):
if not k.endswith('color_mapper'):
continue
self._draw_colorbar(plot, v, k.replace('color_mapper', ''))
return ret
[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 = overlay.data.get(k)
if el is None or not sp.apply_ranges or not sp._has_axis_dimension(el, dimension):
continue
dim = el.get_dimension(dimension)
elranges = util.match_spec(el, ranges)
fs = sp._get_dimension_factors(el, elranges, dim)
if len(fs):
factors.append(fs)
return list(util.unique_iterator(chain(*factors)))
def _get_factors(self, overlay, ranges):
xfactors, yfactors = [], []
for k, sp in self.subplots.items():
el = overlay.data.get(k)
if el is not None:
elranges = util.match_spec(el, ranges)
xfs, yfs = sp._get_factors(el, elranges)
if len(xfs):
xfactors.append(xfs)
if len(yfs):
yfactors.append(yfs)
xfactors = list(util.unique_iterator(chain(*xfactors)))
yfactors = list(util.unique_iterator(chain(*yfactors)))
return xfactors, yfactors
def _get_axis_dims(self, element):
subplots = list(self.subplots.values())
if subplots:
return subplots[0]._get_axis_dims(element)
return super()._get_axis_dims(element)
[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 self.hmap.data.items() if el]
if not nonempty:
raise SkipRendering('All Overlays empty, cannot initialize plot.')
dkey, element = nonempty[-1]
ranges = self.compute_ranges(self.hmap, key, ranges)
self.tabs = self.tabs or any(isinstance(sp, TablePlot) for sp in self.subplots.values())
if plot is None and not self.tabs and not self.batched:
plot = self._init_plot(key, element, ranges=ranges, plots=plots)
self._populate_axis_handles(plot)
self.handles['plot'] = plot
if plot and not self.overlaid:
self._update_plot(key, plot, element)
self._update_ranges(element, ranges)
panels = []
for key, subplot in self.subplots.items():
frame = None
if self.tabs:
subplot.overlaid = False
child = subplot.initialize_plot(ranges, plot, plots)
if isinstance(element, CompositeOverlay):
# Ensure that all subplots are in the same state
frame = element.get(key, None)
subplot.current_frame = frame
subplot.current_key = dkey
if self.batched:
self.handles['plot'] = child
if self.tabs:
title = subplot._format_title(key, dimensions=False)
if not title:
title = get_tab_title(key, frame, self.hmap.last)
panels.append(TabPanel(child=child, title=title))
self._merge_tools(subplot)
if self.tabs:
self.handles['plot'] = Tabs(
tabs=panels, width=self.width, height=self.height,
min_width=self.min_width, min_height=self.min_height,
max_width=self.max_width, max_height=self.max_height,
sizing_mode='fixed'
)
elif not self.overlaid:
self._process_legend(element)
self._set_active_tools(plot)
self.drawn = True
self.handles['plots'] = plots
if 'plot' in self.handles and not self.tabs:
plot = self.handles['plot']
self.handles['xaxis'] = plot.xaxis[0]
self.handles['yaxis'] = plot.yaxis[0]
self.handles['x_range'] = plot.x_range
self.handles['y_range'] = plot.y_range
for cb in self.callbacks:
cb.initialize()
if self.top_level:
self.init_links()
self._execute_hooks(element)
return self.handles['plot']
[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(element.data.items())
if isinstance(self.hmap, DynamicMap):
range_obj = element
else:
range_obj = self.hmap
if element is not None:
ranges = self.compute_ranges(range_obj, key, ranges)
# Update plot options
plot_opts = self.lookup_options(element, 'plot').options
inherited = self._traverse_options(element, 'plot',
self._propagate_options,
defaults=False)
plot_opts.update(**{k: v[0] for k, v in inherited.items() if k not in plot_opts})
self.param.update(**plot_opts)
if not self.overlaid and not self.tabs and not self.batched:
self._update_ranges(element, ranges)
# Determine which stream (if any) triggered the update
triggering = [stream for stream in self.streams if stream._triggering]
for k, subplot in self.subplots.items():
el = None
# If in Dynamic mode propagate elements to subplots
if isinstance(self.hmap, DynamicMap) and element:
# In batched mode NdOverlay is passed to subplot directly
if self.batched:
el = element
# If not batched get the Element matching the subplot
elif element is not None:
idx, spec, exact = self._match_subplot(k, subplot, items, element)
if idx is not None 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)