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element.py
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element.py
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from io import BytesIO
import numpy as np
import bokeh
import bokeh.plotting
from bokeh.models import Range, HoverTool, Renderer
from bokeh.models.tickers import Ticker, BasicTicker, FixedTicker
from bokeh.models.widgets import Panel, Tabs
try:
from bokeh import mpl
except ImportError:
mpl = None
import param
from ...core import (Store, HoloMap, Overlay, DynamicMap,
CompositeOverlay, Element)
from ...core.options import abbreviated_exception
from ...core import util
from ...element import RGB
from ..plot import GenericElementPlot, GenericOverlayPlot
from ..util import dynamic_update
from .callbacks import Callbacks
from .plot import BokehPlot
from .util import mpl_to_bokeh, convert_datetime, update_plot, bokeh_version
if bokeh_version >= '0.12':
from bokeh.models import FuncTickFormatter
else:
FuncTickFormatter = None
# Define shared style properties for bokeh plots
line_properties = ['line_width', 'line_color', 'line_alpha',
'line_join', 'line_cap', 'line_dash']
fill_properties = ['fill_color', 'fill_alpha']
text_properties = ['text_font', 'text_font_size', 'text_font_style', 'text_color',
'text_alpha', 'text_align', 'text_baseline']
legend_dimensions = ['label_standoff', 'label_width', 'label_height', 'glyph_width',
'glyph_height', 'legend_padding', 'legend_spacing']
class ElementPlot(BokehPlot, GenericElementPlot):
callbacks = param.ClassSelector(class_=Callbacks, doc="""
Callbacks object defining any javascript callbacks applied
to the plot.""")
bgcolor = param.Parameter(default='white', doc="""
Background color of the plot.""")
border = param.Number(default=10, doc="""
Minimum border around plot.""")
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'}""")
invert_axes = param.Boolean(default=False, doc="""
Whether to invert the x- and y-axis""")
invert_xaxis = param.Boolean(default=False, doc="""
Whether to invert the plot x-axis.""")
invert_yaxis = param.Boolean(default=False, doc="""
Whether to invert the plot y-axis.""")
lod = param.Dict(default={'factor': 10, 'interval': 300,
'threshold': 2000, 'timeout': 500}, doc="""
Bokeh plots offer "Level of Detail" (LOD) capability to
accomodate 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_grid = param.Boolean(default=True, doc="""
Whether to show a Cartesian grid on the plot.""")
show_legend = param.Boolean(default=True, doc="""
Whether to show legend for 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.""")
xaxis = param.ObjectSelector(default='bottom',
objects=['top', 'bottom', 'bare', 'top-bare',
'bottom-bare', None], doc="""
Whether and where to display the xaxis, bare options allow suppressing
all axis labels including ticks and xlabel. Valid options are 'top',
'bottom', 'bare', 'top-bare' and 'bottom-bare'.""")
logx = param.Boolean(default=False, doc="""
Whether the x-axis of the plot will be a log axis.""")
xrotation = param.Integer(default=None, bounds=(0, 360), doc="""
Rotation angle of the xticks.""")
xticks = param.Parameter(default=None, doc="""
Ticks along x-axis specified as an integer, explicit list of
tick locations or bokeh Ticker object. If set to None default
bokeh ticking behavior is applied.""")
yaxis = param.ObjectSelector(default='left',
objects=['left', 'right', 'bare', 'left-bare',
'right-bare', None], doc="""
Whether and where to display the yaxis, bare options allow suppressing
all axis labels including ticks and ylabel. Valid options are 'left',
'right', 'bare' 'left-bare' and 'right-bare'.""")
logy = param.Boolean(default=False, doc="""
Whether the y-axis of the plot will be a log axis.""")
yrotation = param.Integer(default=None, bounds=(0, 360), doc="""
Rotation angle of the yticks.""")
yticks = param.Parameter(default=None, doc="""
Ticks along y-axis specified as an integer, explicit list of
tick locations or bokeh Ticker object. If set to None
default bokeh ticking behavior is applied.""")
# The plot objects to be updated on each frame
# Any entries should be existing keys in the handles
# instance attribute.
_update_handles = ['source', 'glyph']
def __init__(self, element, plot=None, show_labels=['x', 'y'], **params):
self.show_labels = show_labels
self.current_ranges = None
super(ElementPlot, self).__init__(element, **params)
self.handles = {} if plot is None else self.handles['plot']
element_ids = self.hmap.traverse(lambda x: id(x), [Element])
self.static = len(set(element_ids)) == 1 and len(self.keys) == len(self.hmap)
def _init_tools(self, element):
"""
Processes the list of tools to be supplied to the plot.
"""
tools = self.default_tools + self.tools
if 'hover' in tools:
tooltips = [(d.pprint_label, '@'+util.dimension_sanitizer(d.name))
for d in element.dimensions()]
tools[tools.index('hover')] = HoverTool(tooltips=tooltips)
return tools
def _get_hover_data(self, data, element, empty=False):
"""
Initializes hover data based on Element dimension values.
If empty initializes with no data.
"""
if 'hover' in self.default_tools + self.tools:
for d in element.dimensions(label=True):
sanitized = util.dimension_sanitizer(d)
data[sanitized] = [] if empty else element.dimension_values(d)
def _axes_props(self, plots, subplots, element, ranges):
el = element.traverse(lambda x: x, [Element])
el = el[0] if el else element
dims = el.dimensions()
xlabel, ylabel, zlabel = self._get_axis_labels(dims)
if self.invert_axes:
xlabel, ylabel = ylabel, xlabel
plot_ranges = {}
# Try finding shared ranges in other plots in the same Layout
if plots and self.shared_axes:
for plot in plots:
if plot is None or not hasattr(plot, 'xaxis'): continue
if plot.xaxis[0].axis_label == xlabel:
plot_ranges['x_range'] = plot.x_range
if plot.xaxis[0].axis_label == ylabel:
plot_ranges['y_range'] = plot.x_range
if plot.yaxis[0].axis_label == ylabel:
plot_ranges['y_range'] = plot.y_range
if plot.yaxis[0].axis_label == xlabel:
plot_ranges['x_range'] = plot.y_range
if el.get_dimension_type(0) is np.datetime64:
x_axis_type = 'datetime'
else:
x_axis_type = 'log' if self.logx else 'auto'
if len(dims) > 1 and el.get_dimension_type(1) is np.datetime64:
y_axis_type = 'datetime'
else:
y_axis_type = 'log' if self.logy else 'auto'
if not 'x_range' in plot_ranges:
if 'x_range' in ranges:
plot_ranges['x_range'] = ranges['x_range']
else:
l, b, r, t = self.get_extents(element, ranges)
low, high = (b, t) if self.invert_axes else (l, r)
if x_axis_type == 'datetime':
low = convert_datetime(low)
high = convert_datetime(high)
elif low == high and low is not None:
offset = low*0.1 if low else 0.5
low -= offset
high += offset
if all(x is not None and np.isfinite(x) for x in (low, high)):
plot_ranges['x_range'] = [low, high]
if self.invert_xaxis:
plot_ranges['x_ranges'] = plot_ranges['x_ranges'][::-1]
if not 'y_range' in plot_ranges:
if 'y_range' in ranges:
plot_ranges['y_range'] = ranges['y_range']
else:
l, b, r, t = self.get_extents(element, ranges)
low, high = (l, r) if self.invert_axes else (b, t)
if y_axis_type == 'datetime':
low = convert_datetime(low)
high = convert_datetime(high)
elif low == high and low is not None:
offset = low*0.1 if low else 0.5
low -= offset
high += offset
if all(y is not None and np.isfinite(y) for y in (low, high)):
plot_ranges['y_range'] = [low, high]
if self.invert_yaxis:
yrange = plot_ranges['y_range']
if isinstance(yrange, Range):
plot_ranges['y_range'] = yrange.__class__(start=yrange.end,
end=yrange.start)
else:
plot_ranges['y_range'] = yrange[::-1]
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
tools = self._init_tools(element)
properties = dict(plot_ranges)
properties['x_axis_label'] = xlabel if 'x' in self.show_labels else ' '
properties['y_axis_label'] = ylabel if 'y' in self.show_labels else ' '
if self.show_title:
title = self._format_title(key, separator=' ')
else:
title = ''
properties['webgl'] = self.renderer.webgl
return bokeh.plotting.Figure(x_axis_type=x_axis_type,
y_axis_type=y_axis_type, title=title,
tools=tools, **properties)
def _plot_properties(self, key, plot, element):
"""
Returns a dictionary of plot properties.
"""
plot_props = dict(plot_height=self.height, plot_width=self.width)
if bokeh_version < '0.12':
plot_props.update(self._title_properties(key, plot, element))
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.defaults()['lod'], **self.lod)
for lod_prop, v in lod.items():
plot_props['lod_'+lod_prop] = v
return plot_props
def _title_properties(self, key, plot, element):
if self.show_title:
title = self._format_title(key, separator=' ')
else:
title = ''
if bokeh_version < '0.12':
title_font = self._fontsize('title', 'title_text_font_size')
return dict(title=title, title_text_color='black', **title_font)
else:
title_font = self._fontsize('title', 'text_font_size')
return dict(text=title, text_color='black', **title_font)
def _init_axes(self, plot):
if self.xaxis is None:
plot.xaxis.visible = False
elif self.xaxis == 'top':
plot.above = plot.below
plot.below = []
plot.xaxis[:] = plot.above
if self.yaxis is None:
plot.yaxis.visible = False
elif self.yaxis == 'right':
plot.right = plot.left
plot.left = []
plot.yaxis[:] = plot.right
def _axis_properties(self, axis, key, plot, dimension,
ax_mapping={'x': 0, 'y': 1}):
"""
Returns a dictionary of axis properties depending
on the specified axis.
"""
axis_props = {}
if ((axis == 'x' and self.xaxis in ['bottom-bare', 'top-bare']) or
(axis == 'y' and self.yaxis in ['left-bare', 'right-bare'])):
axis_props['axis_label'] = ''
axis_props['major_label_text_font_size'] = '0pt'
axis_props['major_tick_line_color'] = None
axis_props['minor_tick_line_color'] = None
else:
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):
pass
else:
axis_props['ticker'] = FixedTicker(ticks=ticker)
if FuncTickFormatter is not None and ax_mapping and 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)
try:
formatter = FuncTickFormatter.from_py_func(formatter)
except RuntimeError:
self.warning(msg+'Ensure Flexx is installed '
'("conda install -c bokeh flexx" or '
'"pip install flexx")')
except Exception as e:
error = 'Pyscript raised an error: {0}'.format(e)
error = error.replace('%', '%%')
self.warning(msg+error)
else:
axis_props['formatter'] = formatter
return axis_props
def _update_plot(self, key, plot, element=None):
"""
Updates plot parameters on every frame
"""
el = element.traverse(lambda x: x, [Element])
dimensions = el[0].dimensions() if el else el.dimensions()
plot.set(**self._plot_properties(key, plot, element))
props = {axis: self._axis_properties(axis, key, plot, dim)
for axis, dim in zip(['x', 'y'], dimensions)}
plot.xaxis[0].set(**props['x'])
plot.yaxis[0].set(**props['y'])
if bokeh_version >= '0.12' and not self.overlaid:
plot.title.set(**self._title_properties(key, plot, element))
if not self.show_grid:
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = None
def _update_ranges(self, element, ranges):
framewise = self.lookup_options(element, 'norm').options.get('framewise')
l, b, r, t = self.get_extents(element, ranges)
if not framewise and not self.dynamic:
return
plot = self.handles['plot']
if self.invert_axes:
l, b, r, t = b, l, t, r
if l == r:
offset = abs(l*0.1 if l else 0.5)
l -= offset
r += offset
if b == t:
offset = abs(b*0.1 if b else 0.5)
b -= offset
t += offset
plot.x_range.start = l
plot.x_range.end = r
plot.y_range.start = b
plot.y_range.end = t
def _process_legend(self):
"""
Disables legends if show_legend is disabled.
"""
for l in self.handles['plot'].legend:
l.legends[:] = []
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')
renderer = getattr(plot, plot_method)(**dict(properties, **mapping))
return renderer, renderer.glyph
def _glyph_properties(self, plot, element, source, ranges):
properties = self.style[self.cyclic_index]
if self.show_legend:
if self.overlay_dims:
legend = ', '.join([d.pprint_value(v) for d, v in
self.overlay_dims.items()])
else:
legend = element.label
properties['legend'] = legend
properties['source'] = source
return properties
def _update_glyph(self, glyph, properties, mapping):
allowed_properties = glyph.properties()
properties = mpl_to_bokeh(properties)
merged = dict(properties, **mapping)
glyph.set(**{k: v for k, v in merged.items()
if k in allowed_properties})
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
element = self.hmap.last
key = self.keys[-1]
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)
self.handles['plot'] = plot
# Get data and initialize data source
empty = self.callbacks and self.callbacks.downsample
if self.batched:
data, mapping = self.get_batched_data(element, ranges, empty)
else:
data, mapping = self.get_data(element, ranges, empty)
if source is None:
source = self._init_datasource(data)
self.handles['source'] = source
properties = self._glyph_properties(plot, style_element, source, ranges)
with abbreviated_exception():
renderer, glyph = self._init_glyph(plot, mapping, properties)
self.handles['glyph'] = glyph
if isinstance(renderer, Renderer):
self.handles['glyph_renderer'] = renderer
# Update plot, source and glyph
with abbreviated_exception():
self._update_glyph(glyph, properties, mapping)
if not self.overlaid:
self._update_plot(key, plot, style_element)
if self.callbacks:
self.callbacks(self)
self.callbacks.update(self)
if not self.overlaid:
self._process_legend()
self.drawn = True
return plot
def update_frame(self, key, ranges=None, plot=None, element=None, empty=False):
"""
Updates an existing plot with data corresponding
to the key.
"""
reused = isinstance(self.hmap, DynamicMap) and self.overlaid
if not reused and element is None:
element = self._get_frame(key)
else:
self.current_key = key
self.current_frame = element
style_element = element.last if self.batched else element
glyph = self.handles.get('glyph', None)
if hasattr(glyph, 'visible'):
glyph.visible = bool(element)
if not element:
return
self.style = self.lookup_options(style_element, 'style')
ranges = self.compute_ranges(self.hmap, key, ranges)
self.set_param(**self.lookup_options(style_element, 'plot').options)
ranges = util.match_spec(style_element, ranges)
self.current_ranges = ranges
plot = self.handles['plot']
source = self.handles['source']
empty = (self.callbacks and self.callbacks.downsample) or empty
if self.batched:
data, mapping = self.get_batched_data(element, ranges, empty)
else:
data, mapping = self.get_data(element, ranges, empty)
self._update_datasource(source, data)
if glyph:
properties = self._glyph_properties(plot, element, source, ranges)
with abbreviated_exception():
self._update_glyph(self.handles['glyph'], properties, mapping)
if not self.overlaid:
self._update_ranges(style_element, ranges)
self._update_plot(key, plot, style_element)
if self.callbacks:
self.callbacks.update(self)
@property
def current_handles(self):
"""
Returns a list of the plot objects to update.
"""
handles = []
if self.static and not self.dynamic:
return handles
for handle in self._update_handles:
if handle in self.handles:
handles.append(self.handles[handle])
if self.overlaid:
return handles
plot = self.state
handles.append(plot)
if bokeh_version >= '0.12':
handles.append(plot.title)
if self.current_frame:
if self.subplots:
current_frames = [(sp.current_frame if isinstance(sp.current_frame, Element)
else sp.current_frame.values()[0])
for sp in self.subplots.values()
if sp.current_frame]
framewise = any(self.lookup_options(frame, 'norm').options.get('framewise')
for frame in current_frames)
else:
opts = self.lookup_options(self.current_frame, 'norm')
framewise = opts.options.get('framewise')
if framewise or isinstance(self.hmap, DynamicMap):
handles += [plot.x_range, plot.y_range]
return handles
class BokehMPLWrapper(ElementPlot):
"""
Wraps an existing HoloViews matplotlib plot and converts
it to bokeh.
"""
def __init__(self, element, plot=None, **params):
super(ElementPlot, self).__init__(element, **params)
if isinstance(element, HoloMap):
etype = element.type
else:
etype = type(element)
plot = Store.registry['matplotlib'][etype]
params = dict({k: v.default for k, v in self.params().items()
if k in ['bgcolor']})
params = dict(params, **self.lookup_options(element, 'plot').options)
style = self.lookup_options(element, 'style')
self.mplplot = plot(element, style=style, **params)
def initialize_plot(self, ranges=None, plot=None, plots=None):
self.mplplot.initialize_plot(ranges)
plot = plot if plot else self.handles.get('plot')
new_plot = mpl.to_bokeh(self.mplplot.state)
if plot:
update_plot(plot, new_plot)
else:
plot = new_plot
self.handles['plot'] = plot
if not self.overlaid:
self._update_plot(self.keys[-1], plot, self.hmap.last)
return plot
def _update_plot(self, key, plot, element=None):
"""
Updates plot parameters on every frame
"""
plot.set(**self._plot_properties(key, plot, element))
def update_frame(self, key, ranges=None, plot=None, element=None, empty=False):
self.mplplot.update_frame(key, ranges)
reused = isinstance(self.hmap, DynamicMap) and self.overlaid
if not reused and element is None:
element = self._get_frame(key)
else:
self.current_key = key
self.current_frame = element
plot = mpl.to_bokeh(self.mplplot.state)
update_plot(self.handles['plot'], plot)
if not self.overlaid:
self._update_plot(key, self.handles['plot'], element)
class BokehMPLRawWrapper(BokehMPLWrapper):
"""
Wraps an existing HoloViews matplotlib plot, renders it as
an image and displays it as a HoloViews object.
"""
def initialize_plot(self, ranges=None, plot=None, plots=None):
element = self.hmap.last
self.mplplot.initialize_plot(ranges)
plot = self._render_plot(element, plot)
self.handles['plot'] = plot
return plot
def _render_plot(self, element, plot=None):
from .raster import RGBPlot
bytestream = BytesIO()
renderer = self.mplplot.renderer.instance(dpi=120)
renderer.save(self.mplplot, bytestream, fmt='png')
group = ('RGB' if element.group == type(element).__name__ else
element.group)
rgb = RGB.load_image(bytestream, bare=True, group=group,
label=element.label)
plot_opts = self.lookup_options(element, 'plot').options
rgbplot = RGBPlot(rgb, **plot_opts)
return rgbplot.initialize_plot(plot=plot)
def update_frame(self, key, ranges=None, element=None):
element = self.get_frame(key)
if key in self.hmap:
self.mplplot.update_frame(key, ranges)
self.handles['plot'] = self._render_plot(element)
class OverlayPlot(GenericOverlayPlot, ElementPlot):
legend_position = param.ObjectSelector(objects=["top_right",
"top_left",
"bottom_left",
"bottom_right"],
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.""")
tabs = param.Boolean(default=False, doc="""
Whether to display overlaid plots in separate panes""")
style_opts = legend_dimensions + line_properties + text_properties
_update_handles = ['source']
def _process_legend(self):
plot = self.handles['plot']
if not self.show_legend or len(plot.legend) == 0:
for l in plot.legend:
l.legends[:] = []
l.border_line_alpha = 0
l.background_fill_alpha = 0
return
options = {}
properties = self.lookup_options(self.hmap.last, 'style')[self.cyclic_index]
for k, v in properties.items():
if k in line_properties:
k = 'border_' + k
elif k in text_properties:
k = 'label_' + k
options[k] = v
legend_labels = []
if not plot.legend:
return
plot.legend[0].set(**options)
legend_fontsize = self._fontsize('legend', 'size').get('size',False)
if legend_fontsize:
plot.legend[0].label_text_font_size = legend_fontsize
plot.legend.location = self.legend_position
legends = plot.legend[0].legends
new_legends = []
for label, l in legends:
if label in legend_labels:
continue
legend_labels.append(label)
new_legends.append((label, l))
plot.legend[0].legends[:] = new_legends
def _init_tools(self, element):
"""
Processes the list of tools to be supplied to the plot.
"""
tools = []
hover = False
for key, subplot in self.subplots.items():
el = element.get(key)
if el is not None:
el_tools = subplot._init_tools(el)
el_tools = [t for t in el_tools
if not (isinstance(t, HoverTool) and hover)]
tools += el_tools
if any(isinstance(t, HoverTool) for t in el_tools):
hover = True
return list(set(tools))
def initialize_plot(self, ranges=None, plot=None, plots=None):
key = self.keys[-1]
element = self._get_frame(key)
ranges = self.compute_ranges(self.hmap, key, ranges)
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)
if plot and not self.overlaid:
self._update_plot(key, plot, element)
self.handles['plot'] = plot
panels = []
for key, subplot in self.subplots.items():
if self.tabs: subplot.overlaid = False
child = subplot.initialize_plot(ranges, plot, plots)
if self.batched:
self.handles['plot'] = child
if self.tabs:
if self.hmap.type is Overlay:
title = ' '.join(key)
else:
title = ', '.join([d.pprint_value_string(k) for d, k in
zip(self.hmap.last.kdims, key)])
panels.append(Panel(child=child, title=title))
if isinstance(element, CompositeOverlay):
frame = element.get(key, None)
subplot.current_frame = frame
if self.tabs:
self.handles['plot'] = Tabs(tabs=panels)
elif not self.overlaid:
self._process_legend()
self.drawn = True
return self.handles['plot']
def update_frame(self, key, ranges=None, element=None, empty=False):
"""
Update the internal state of the Plot to represent the given
key tuple (where integers represent frames). Returns this
state.
"""
if element is None:
element = self._get_frame(key)
else:
self.current_frame = element
self.current_key = key
if isinstance(self.hmap, DynamicMap):
range_obj = element
items = element.items()
else:
range_obj = self.hmap
items = element.items()
all_empty = empty
ranges = self.compute_ranges(range_obj, key, ranges)
for k, subplot in self.subplots.items():
empty, el = False, None
if isinstance(self.hmap, DynamicMap):
idx = dynamic_update(self, subplot, k, element, items)
empty = idx is None
if not empty:
_, el = items.pop(idx)
subplot.update_frame(key, ranges, element=el, empty=(empty or all_empty))
if isinstance(self.hmap, DynamicMap) and items:
raise Exception("Some Elements returned by the dynamic callback "
"were not initialized correctly and could not be "
"rendered.")
if not self.overlaid and not self.tabs and not self.batched:
self._update_ranges(element, ranges)
self._update_plot(key, self.handles['plot'], element)