from __future__ import absolute_import, division, unicode_literals
import sys
import warnings
from types import FunctionType
import param
import numpy as np
import bokeh
import bokeh.plotting
from bokeh.core.properties import value
from bokeh.document.events import ModelChangedEvent
from bokeh.models import (
ColorBar, ColorMapper, Legend, Renderer, Title, tools
)
from bokeh.models.axes import CategoricalAxis, DatetimeAxis
from bokeh.models.formatters import (
FuncTickFormatter, TickFormatter, MercatorTickFormatter
)
from bokeh.models.mappers import (
LinearColorMapper, LogColorMapper, CategoricalColorMapper
)
from bokeh.models.ranges import Range1d, DataRange1d, FactorRange
from bokeh.models.tickers import (
Ticker, BasicTicker, FixedTicker, LogTicker, MercatorTicker
)
from bokeh.models.tools import Tool
from bokeh.models.widgets import Panel, Tabs
from ...core import DynamicMap, CompositeOverlay, Element, Dimension, Dataset
from ...core.options import abbreviated_exception, SkipRendering
from ...core import util
from ...element import Annotation, Graph, VectorField, Path, Contours, Tiles
from ...streams import Stream, Buffer, RangeXY, PlotSize
from ...util.transform import dim
from ..plot import GenericElementPlot, GenericOverlayPlot
from ..util import process_cmap, color_intervals, dim_range_key
from .callbacks import PlotSizeCallback
from .plot import BokehPlot
from .styles import (
base_properties, legend_dimensions, line_properties, mpl_to_bokeh,
property_prefixes, rgba_tuple, text_properties, validate
)
from .tabular import TablePlot
from .util import (
TOOL_TYPES, bokeh_version, date_to_integer, decode_bytes, get_tab_title,
glyph_order, py2js_tickformatter, recursive_model_update,
theme_attr_json, cds_column_replace, hold_policy, match_dim_specs,
compute_layout_properties, wrap_formatter, match_ax_type, remove_legend
)
if bokeh_version >= '2.0.1':
try:
TOOLS_MAP = Tool._known_aliases
except Exception:
TOOLS_MAP = TOOL_TYPES
elif bokeh_version >= '2.0.0':
from bokeh.plotting._tools import TOOLS_MAP
else:
from bokeh.plotting.helpers import _known_tools as TOOLS_MAP
[docs]class ElementPlot(BokehPlot, GenericElementPlot):
active_tools = param.List(default=[], 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.""")
align = param.ObjectSelector(default='start', objects=['start', 'center', 'end'], doc="""
Alignment (vertical or horizontal) of the plot in a layout.""")
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.""")
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).""")
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_=(util.basestring, TickFormatter, FunctionType), doc="""
Formatter for ticks along the x-axis.""")
yformatter = param.ClassSelector(
default=None, class_=(util.basestring, TickFormatter, FunctionType), doc="""
Formatter for ticks along the x-axis.""")
_categorical = False
_allow_implicit_categories = True
# Declare which styles cannot be mapped to a non-scalar dimension
_nonvectorized_styles = []
# Declares the default types for continuous x- and y-axes
_x_range_type = Range1d
_y_range_type = Range1d
# Whether the plot supports streaming data
_stream_data = True
def __init__(self, element, plot=None, **params):
self.current_ranges = None
super(ElementPlot, self).__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._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': False, 'y': False}
# Flag to check whether plot has been updated
self._updated = False
def _hover_opts(self, element):
if self.batched:
dims = list(self.hmap.last.kdims)
else:
dims = list(self.overlay_dims.keys())
dims += element.dimensions()
return list(util.unique_iterator(dims)), {}
def _init_tools(self, element, callbacks=[]):
"""
Processes the list of tools to be supplied to the plot.
"""
tooltips, hover_opts = self._hover_opts(element)
tooltips = [(ttp.pprint_label, '@{%s}' % util.dimension_sanitizer(ttp.name))
if isinstance(ttp, Dimension) else ttp for ttp in tooltips]
if not tooltips: tooltips = None
callbacks = callbacks+self.callbacks
cb_tools, tool_names = [], []
hover = False
for cb in callbacks:
for handle in cb.models+cb.extra_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 = [
t for t in cb_tools + self.default_tools + self.tools
if t not in tool_names]
tool_list = [
tools.HoverTool(tooltips=tooltips, tags=['hv_created'], mode=tl, **hover_opts)
if tl in ['vline', 'hline'] else tl for tl in tool_list
]
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 for _ in range(len(list(data.values())[0]))]
def _merge_ranges(self, plots, xspecs, yspecs, xtype, ytype):
"""
Given a list of other plots return axes that are shared
with another plot by matching the dimensions specs stored
as tags on the dimensions.
"""
plot_ranges = {}
for plot in plots:
if plot is None:
continue
if hasattr(plot, 'x_range') and plot.x_range.tags and xspecs is not None:
if match_dim_specs(plot.x_range.tags[0], xspecs) and match_ax_type(plot.xaxis, xtype):
plot_ranges['x_range'] = plot.x_range
if match_dim_specs(plot.x_range.tags[0], yspecs) and match_ax_type(plot.xaxis, ytype):
plot_ranges['y_range'] = plot.x_range
if hasattr(plot, 'y_range') and plot.y_range.tags and yspecs is not None:
if match_dim_specs(plot.y_range.tags[0], yspecs) and match_ax_type(plot.yaxis, ytype):
plot_ranges['y_range'] = plot.y_range
if match_dim_specs(plot.y_range.tags[0], xspecs) and match_ax_type(plot.yaxis, xtype):
plot_ranges['x_range'] = plot.y_range
return plot_ranges
def _get_axis_dims(self, element):
"""Returns the dimensions corresponding to each axis.
Should return a list of dimensions or list of lists of
dimensions, which will be formatted to label the axis
and to link axes.
"""
dims = element.dimensions()[:2]
if len(dims) == 1:
return dims + [None, None]
else:
return dims + [None]
def _axes_props(self, plots, subplots, element, ranges):
# Get the bottom layer and range element
el = element.traverse(lambda x: x, [lambda el: isinstance(el, Element) and not isinstance(el, (Annotation, Tiles))])
el = el[0] if el else element
dims = self._get_axis_dims(el)
xlabel, ylabel, zlabel = self._get_axis_labels(dims)
if self.invert_axes:
xlabel, ylabel = ylabel, xlabel
dims = dims[:2][::-1]
xdims, ydims = dims[:2]
if xdims:
if not isinstance(xdims, list):
xdims = [xdims]
xspecs = tuple((xd.name, xd.label, xd.unit) for xd in xdims)
else:
xspecs = None
if ydims:
if not isinstance(ydims, list):
ydims = [ydims]
yspecs = tuple((yd.name, yd.label, yd.unit) for yd in ydims)
else:
yspecs = None
# Get the Element that determines the range and get_extents
range_el = el if self.batched and not isinstance(self, OverlayPlot) else element
l, b, r, t = self.get_extents(range_el, ranges)
if self.invert_axes:
l, b, r, t = b, l, t, r
categorical = any(self.traverse(lambda x: x._categorical))
if xdims is not None and any(xdim.name in ranges and 'factors' in ranges[xdim.name] for xdim in xdims):
categorical_x = True
else:
categorical_x = any(isinstance(x, (util.basestring, bytes)) for x in (l, r))
if ydims is not None and any(ydim.name in ranges and 'factors' in ranges[ydim.name] for ydim in ydims):
categorical_y = True
else:
categorical_y = any(isinstance(y, (util.basestring, bytes)) for y in (b, t))
range_types = (self._x_range_type, self._y_range_type)
if self.invert_axes: range_types = range_types[::-1]
x_range_type, y_range_type = range_types
x_axis_type = 'log' if self.logx else 'auto'
if xdims:
if len(xdims) > 1 or x_range_type is FactorRange:
x_axis_type = 'auto'
categorical_x = True
else:
if isinstance(el, Graph):
xtype = el.nodes.get_dimension_type(xdims[0])
else:
xtype = el.get_dimension_type(xdims[0])
if ((xtype is np.object_ and issubclass(type(l), util.datetime_types)) or
xtype in util.datetime_types):
x_axis_type = 'datetime'
y_axis_type = 'log' if self.logy else 'auto'
if ydims:
if len(ydims) > 1 or y_range_type is FactorRange:
y_axis_type = 'auto'
categorical_y = True
else:
if isinstance(el, Graph):
ytype = el.nodes.get_dimension_type(ydims[0])
else:
ytype = el.get_dimension_type(ydims[0])
if ((ytype is np.object_ and issubclass(type(b), util.datetime_types))
or ytype in util.datetime_types):
y_axis_type = 'datetime'
plot_ranges = {}
# Try finding shared ranges in other plots in the same Layout
norm_opts = self.lookup_options(el, 'norm').options
if plots and self.shared_axes and not norm_opts.get('axiswise', False):
plot_ranges = self._merge_ranges(plots, xspecs, yspecs, x_axis_type, y_axis_type)
# Declare shared axes
x_range, y_range = plot_ranges.get('x_range'), plot_ranges.get('y_range')
if x_range and not (x_range_type is FactorRange and not isinstance(x_range, FactorRange)):
self._shared['x'] = True
if y_range and not (y_range_type is FactorRange and not isinstance(y_range, FactorRange)):
self._shared['y'] = True
if self._shared['x']:
pass
elif categorical or categorical_x:
x_axis_type = 'auto'
plot_ranges['x_range'] = FactorRange()
else:
plot_ranges['x_range'] = x_range_type()
if self._shared['y']:
pass
elif categorical or categorical_y:
y_axis_type = 'auto'
plot_ranges['y_range'] = FactorRange()
elif 'y_range' not in plot_ranges:
plot_ranges['y_range'] = y_range_type()
x_range, y_range = plot_ranges['x_range'], plot_ranges['y_range']
if not x_range.tags and xspecs is not None:
x_range.tags.append(xspecs)
if not y_range.tags and yspecs is not None:
y_range.tags.append(yspecs)
return (x_axis_type, y_axis_type), (xlabel, ylabel, zlabel), plot_ranges
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_types, labels, plot_ranges = self._axes_props(plots, subplots, element, ranges)
xlabel, ylabel, _ = labels
x_axis_type, y_axis_type = axis_types
properties = dict(plot_ranges)
properties['x_axis_label'] = xlabel if 'x' in self.labelled or self.xlabel else ' '
properties['y_axis_label'] = ylabel if 'y' in self.labelled or self.ylabel else ' '
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))
with warnings.catch_warnings():
# Bokeh raises warnings about duplicate tools but these
# are not really an issue
warnings.simplefilter('ignore', UserWarning)
return bokeh.plotting.Figure(x_axis_type=x_axis_type,
y_axis_type=y_axis_type, title=title,
**properties)
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
if self.dynamic and aspect_props['match_aspect']:
# Sync the plot size on dynamic plots to support accurate
# scaling of dimension ranges
plot_size = [s for s in self.streams if isinstance(s, PlotSize)]
callbacks = [c for c in self.callbacks if isinstance(c, PlotSizeCallback)]
if plot_size:
stream = plot_size[0]
elif callbacks:
stream = callbacks[0].streams[0]
else:
stream = PlotSize()
self.callbacks.append(PlotSizeCallback(self, [stream], None))
stream.add_subscriber(self._update_size)
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.defaults().get('lod', {}), **self.lod)
for lod_prop, v in lod.items():
plot_props['lod_'+lod_prop] = v
return plot_props
def _update_size(self, width, height, scale):
self.state.frame_width = width
self.state.frame_height = height
def _set_active_tools(self, plot):
"Activates the list of active tools"
for tool in self.active_tools:
if isinstance(tool, util.basestring):
tool_type = TOOL_TYPES[tool]
matching = [t for t in plot.toolbar.tools
if isinstance(t, tool_type)]
if not matching:
self.param.warning('Tool of type %r could not be found '
'and could not be activated by default.'
% tool)
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.Inspection):
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'] = value(title_font)
return opts
def _init_axes(self, plot):
if self.xaxis is None:
plot.xaxis.visible = False
elif isinstance(self.xaxis, util.basestring) and 'top' in self.xaxis:
plot.above = plot.below
plot.below = []
plot.xaxis[:] = plot.above
self.handles['xaxis'] = plot.xaxis[0]
self.handles['x_range'] = plot.x_range
if self.yaxis is None:
plot.yaxis.visible = False
elif isinstance(self.yaxis, util.basestring) and'right' in self.yaxis:
plot.right = plot.left
plot.left = []
plot.yaxis[:] = plot.right
self.handles['yaxis'] = plot.yaxis[0]
self.handles['y_range'] = plot.y_range
def _axis_properties(self, axis, key, plot, dimension=None,
ax_mapping={'x': 0, 'y': 1}):
"""
Returns a dictionary of axis properties depending
on the specified axis.
"""
# need to copy dictionary by calling dict() on it
axis_props = dict(theme_attr_json(self.renderer.theme, 'Axis'))
if ((axis == 'x' and self.xaxis in ['bottom-bare', 'top-bare', 'bare']) or
(axis == 'y' and self.yaxis in ['left-bare', 'right-bare', 'bare'])):
axis_props['axis_label_text_font_size'] = value('0pt')
axis_props['major_label_text_font_size'] = value('0pt')
axis_props['major_tick_line_color'] = None
axis_props['minor_tick_line_color'] = None
else:
labelsize = self._fontsize('%slabel' % axis).get('fontsize')
if labelsize:
axis_props['axis_label_text_font_size'] = labelsize
ticksize = self._fontsize('%sticks' % axis, common=False).get('fontsize')
if ticksize:
axis_props['major_label_text_font_size'] = value(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, 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, util.basestring) else str(l)
for l in labels]
axis_props['ticker'] = FixedTicker(ticks=ticks)
axis_props['major_label_overrides'] = dict(zip(ticks, labels))
else:
axis_props['ticker'] = FixedTicker(ticks=ticker)
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 FuncTickFormatter is not None and ax_mapping and isinstance(dimension, Dimension):
formatter = None
if dimension.value_format:
formatter = dimension.value_format
elif dimension.type in dimension.type_formatters:
formatter = dimension.type_formatters[dimension.type]
if formatter:
msg = ('%s dimension formatter could not be '
'converted to tick formatter. ' % dimension.name)
jsfunc = py2js_tickformatter(formatter, msg)
if jsfunc:
formatter = FuncTickFormatter(code=jsfunc)
axis_props['formatter'] = formatter
if axis == 'x':
axis_obj = plot.xaxis[0]
elif axis == 'y':
axis_obj = plot.yaxis[0]
if self.geographic and 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
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('minor_{0}ticks'.format(axis),
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))
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_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_') or k.startswith('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):
plot = self.handles['plot']
x_range = self.handles['x_range']
y_range = self.handles['y_range']
l, b, r, t = None, None, None, None
if any(isinstance(r, (Range1d, DataRange1d)) for r in [x_range, y_range]):
l, b, r, t = self.get_extents(element, ranges)
if self.invert_axes:
l, b, r, t = b, l, t, r
xfactors, yfactors = None, None
if any(isinstance(ax_range, FactorRange) for ax_range in [x_range, y_range]):
xfactors, yfactors = self._get_factors(element, ranges)
framewise = self.framewise
streaming = (self.streaming and any(stream._triggering and stream.following
for stream in self.streaming))
xupdate = ((not (self.model_changed(x_range) or self.model_changed(plot))
and (framewise or streaming))
or xfactors is not None)
yupdate = ((not (self.model_changed(x_range) or self.model_changed(plot))
and (framewise or streaming))
or yfactors is not None)
options = self._traverse_options(element, 'plot', ['width', 'height'], defaults=False)
fixed_width = (self.frame_width or options.get('width'))
fixed_height = (self.frame_height or options.get('height'))
constrained_width = options.get('min_width') or options.get('max_width')
constrained_height = options.get('min_height') or options.get('max_height')
data_aspect = (self.aspect == 'equal' or self.data_aspect)
xaxis, yaxis = self.handles['xaxis'], self.handles['yaxis']
categorical = isinstance(xaxis, CategoricalAxis) or isinstance(yaxis, CategoricalAxis)
datetime = isinstance(xaxis, DatetimeAxis) or isinstance(yaxis, CategoricalAxis)
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
else:
frame_aspect = plot.frame_height/plot.frame_width
range_streams = [s for s in self.streams if isinstance(s, RangeXY)]
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.plot_width or 300
height = plot.frame_height or plot.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.plot_width, plot.plot_height = None, None
elif fixed_width:
plot.frame_width = width
plot.frame_height = int(width*aspect)
plot.plot_width, plot.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
if not self.drawn or xupdate:
self._update_range(x_range, l, r, xfactors, self.invert_xaxis,
self._shared['x'], self.logx, streaming)
if not self.drawn or yupdate:
self._update_range(y_range, b, t, yfactors, self.invert_yaxis,
self._shared['y'], self.logy, streaming)
def _update_range(self, axis_range, low, high, factors, invert, shared, log, streaming=False):
if isinstance(axis_range, (Range1d, DataRange1d)) and self.apply_ranges:
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)
elif isinstance(axis_range, FactorRange):
factors = list(decode_bytes(factors))
if invert: factors = factors[::-1]
axis_range.factors = factors
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['%s_range' % ax] 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_factors(self, element, ranges):
"""
Get factors for categorical axes.
"""
xdim, ydim = element.dimensions()[:2]
if xdim.values:
xvals = xdim.values
elif 'factors' in ranges.get(xdim.name, {}):
xvals = ranges[xdim.name]['factors']
else:
xvals = element.dimension_values(0, False)
if ydim.values:
yvals = ydim.values
elif 'factors' in ranges.get(ydim.name, {}):
yvals = ranges[ydim.name]['factors']
else:
yvals = element.dimension_values(1, False)
xvals, yvals = np.asarray(xvals), np.asarray(yvals)
if not self._allow_implicit_categories:
xvals = xvals if xvals.dtype.kind in 'SU' else []
yvals = yvals if yvals.dtype.kind in 'SU' else []
coords = tuple([v if vals.dtype.kind in 'SU' else dim.pprint_value(v) for v in vals]
for dim, vals in [(xdim, xvals), (ydim, 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.
"""
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)]
renderer = getattr(plot, plot_method)(**dict(properties, **mapping))
return renderer, renderer.glyph
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, util.basestring):
if validate(k, v) == True:
continue
elif v in element or (isinstance(element, Graph) and v in element.nodes):
v = dim(v)
elif any(d==v for d in self.overlay_dims):
v = dim([d for d in self.overlay_dims if d==v][0])
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(
'Specified %s dim transform %r could not be applied, '
'as not all dimensions could be resolved.' % (k, v))
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]
elif isinstance(element, Path) and not isinstance(element, Contours):
val = np.concatenate([v.apply(el, ranges=ranges, flat=True)
for el in element.split()])
else:
val = v.apply(element, ranges=ranges, flat=True)
if (not util.isscalar(val) and len(util.unique_array(val)) == 1 and
((not 'color' 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(list(data.values())[0]):
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)
categorical = isinstance(cmapper, CategoricalColorMapper)
if categorical and val.dtype.kind in 'ifMub':
if v.dimension in element:
formatter = element.get_dimension(v.dimension).pprint_value
else:
formatter = str
field = k + '_str__'
data[k+'_str__'] = [formatter(d) for d in val]
else:
field = k
if categorical and getattr(self, 'show_legend', False):
legend_prop = 'legend_field' if bokeh_version >= '1.3.5' else 'legend'
new_style[legend_prop] = 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:
legend_prop = 'legend_label' if bokeh_version >= '1.3.5' else 'legend'
properties[legend_prop] = 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 and ('line_'+prop not in glyph_props or gtype):
glyph_props['line_'+prop] = glyph_prop
if glyph_prop 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') if bokeh_version >= '1.3.5' else ('legend',)
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 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 = filtered.get(spec)
old_spec = 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(self.document, source, 'data',
source.data, empty_data, empty_data,
setter='empty')
self.document._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 = {list(item.label)[0]: 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, util.basestring) and 'hv_created' in hover.tags:
for k, values in source.data.items():
key = '@{%s}' % k
if key in hover.formatters:
continue
if ((isinstance(value, np.ndarray) and value.dtype.kind == 'M') or
(len(values) and isinstance(values[0], util.datetime_types))):
hover.tooltips = [(l, f+'{%F %T}' if f == key else f) for l, f in hover.tooltips]
hover.formatters[key] = "datetime"
if hover.renderers == 'auto':
hover.renderers = []
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)
[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._init_axes(plot)
else:
self.handles['xaxis'] = plot.xaxis[0]
self.handles['x_range'] = plot.x_range
self.handles['yaxis'] = plot.yaxis[0]
self.handles['y_range'] = plot.y_range
self.handles['plot'] = plot
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._execute_hooks(element)
self.drawn = True
trigger = self._trigger
self._trigger = []
Stream.trigger(trigger)
return plot
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')
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
"""
if self.overlaid:
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)
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.set_param(**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._updated = True
if 'hover' in self.handles:
self._update_hover(element)
self._update_glyphs(element, ranges, self.style[self.cyclic_index])
self._execute_hooks(element)
[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[:-12])
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,) if sys.version_info.major > 2 else ())), 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.""")
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.""")
cformatter = param.ClassSelector(
default=None, class_=(util.basestring, TickFormatter, FunctionType), doc="""
Formatter for ticks along the colorbar axis.""")
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.""")
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 LogColorMapper and 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]
cmapper = cmapper if cmapper else mappers[0]
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:
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, util.basestring) 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:
legend_prop = 'legend_field' if bokeh_version >= '1.3.5' else 'legend'
mapping[legend_prop] = field
mapping[name] = {'field': field, 'transform': mapper}
return data, mapping
def _get_cmapper_opts(self, low, high, factors, colors):
if factors is None:
colormapper = LinearColorMapper
if 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 corrrectly. Ensure you set a positive "
"lower bound on the color dimension or using "
"the `clim` option."
)
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
opts = {}
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(ColorbarPlot, self)._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[:-12])
return ret
[docs]class LegendPlot(ElementPlot):
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_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_cols = param.Integer(default=False, doc="""
Whether to lay out the legend as columns.""")
legend_specs = {'right': 'right', 'left': 'left', 'top': 'above',
'bottom': 'below'}
legend_opts = param.Dict(default={}, doc="""
Allows setting specific styling options for the colorbar.""")
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:
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']:
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(object):
"""
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']
@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, OverlayPlot) or isinstance(p, AnnotationPlot))]
if (not self.show_legend or len(plot.legend) == 0 or
(len(non_annotation) <= 1 and not (self.dynamic or legend_plots))):
return super(OverlayPlot, self)._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
orientation = 'horizontal' if self.legend_cols else 'vertical'
if pos in ['top', 'bottom']:
orientation = 'horizontal'
options['orientation'] = orientation
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'))
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(i.label.items())) if isinstance(i.label, dict) else i.label: i
for i in legend_items}
for item in legend.items:
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 for r in item.renderers):
continue
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
def _init_tools(self, element, callbacks=[]):
"""
Processes the list of tools to be supplied to the plot.
"""
hover_tools = {}
init_tools, tool_types = [], []
for key, subplot in self.subplots.items():
el = element.get(key)
if el is not None:
el_tools = subplot._init_tools(el, self.callbacks)
for tool in el_tools:
if isinstance(tool, util.basestring):
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, util.basestring):
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 = tool_renderers + hover_renderers
tool.renderers = list(util.unique_iterator(renderers))
if 'hover' not in self.handles:
self.handles['hover'] = tool
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)
xfactors.append(xfs)
yfactors.append(yfs)
if xfactors:
xfactors = np.concatenate(xfactors)
if yfactors:
yfactors = np.concatenate(yfactors)
return util.unique_array(xfactors), util.unique_array(yfactors)
def _get_axis_dims(self, element):
subplots = list(self.subplots.values())
if subplots:
return subplots[0]._get_axis_dims(element)
return super(OverlayPlot, self)._get_axis_dims(element)
[docs] def initialize_plot(self, ranges=None, plot=None, plots=None):
key = util.wrap_tuple(self.hmap.last_key)
nonempty = [(k, el) for k, el in self.hmap.data.items() if el]
if not nonempty:
raise SkipRendering('All Overlays empty, cannot initialize plot.')
dkey, element = nonempty[-1]
ranges = self.compute_ranges(self.hmap, key, ranges)
self.tabs = self.tabs or any(isinstance(sp, TablePlot) for sp in self.subplots.values())
if plot is None and not self.tabs and not self.batched:
plot = self._init_plot(key, element, ranges=ranges, plots=plots)
self._init_axes(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(Panel(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
self._update_callbacks(self.handles['plot'])
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
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.set_param(**plot_opts)
if not self.overlaid and not self.tabs and not self.batched:
self._update_ranges(element, ranges)
# Determine which stream (if any) triggered the update
triggering = [stream for stream in self.streams if stream._triggering]
for k, subplot in self.subplots.items():
el = None
# If in Dynamic mode propagate elements to subplots
if isinstance(self.hmap, DynamicMap) and element:
# In batched mode NdOverlay is passed to subplot directly
if self.batched:
el = element
# If not batched get the Element matching the subplot
elif element is not None:
idx, spec, exact = self._match_subplot(k, subplot, items, element)
if idx is not None:
_, el = items.pop(idx)
if not exact:
self._update_subplot(subplot, spec)
# Skip updates to subplots when its streams is not one of
# the streams that initiated the update
if (triggering and all(s not in triggering for s in subplot.streams) and
not subplot 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._updated = True
self._process_legend(element)
self._execute_hooks(element)
