from itertools import groupby
import warnings
import param
import numpy as np
import bokeh
import bokeh.plotting
from bokeh import palettes
from bokeh.core.properties import value
from bokeh.models import (HoverTool, Renderer, Range1d, DataRange1d, Title,
FactorRange, FuncTickFormatter, Tool, Legend)
from bokeh.models.tickers import Ticker, BasicTicker, FixedTicker, LogTicker
from bokeh.models.widgets import Panel, Tabs
from bokeh.models.mappers import LinearColorMapper
try:
from bokeh.models import ColorBar
from bokeh.models.mappers import LogColorMapper, CategoricalColorMapper
except ImportError:
LogColorMapper, ColorBar = None, None
from bokeh.plotting.helpers import _known_tools as known_tools
from ...core import Store, DynamicMap, CompositeOverlay, Element, Dimension
from ...core.options import abbreviated_exception, SkipRendering
from ...core import util
from ...streams import Stream, Buffer
from ..plot import GenericElementPlot, GenericOverlayPlot
from ..util import dynamic_update
from .plot import BokehPlot, TOOLS
from .util import (mpl_to_bokeh, get_tab_title, mplcmap_to_palette,
py2js_tickformatter, rgba_tuple, recursive_model_update)
property_prefixes = ['selection', 'nonselection', 'muted', 'hover']
# Define shared style properties for bokeh plots
line_properties = ['line_color', 'line_alpha', 'color', 'alpha', 'line_width',
'line_join', 'line_cap', 'line_dash']
line_properties += ['_'.join([prefix, prop]) for prop in line_properties[:4]
for prefix in property_prefixes]
fill_properties = ['fill_color', 'fill_alpha']
fill_properties += ['_'.join([prefix, prop]) for prop in fill_properties
for prefix in property_prefixes]
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', 'click_policy']
class ElementPlot(BokehPlot, GenericElementPlot):
bgcolor = param.Parameter(default='white', doc="""
Background color of the plot.""")
border = param.Number(default=10, doc="""
Minimum border around plot.""")
finalize_hooks = param.HookList(default=[], doc="""
Optional list of hooks called when finalizing an axis.
The hook is passed the plot object and the displayed
object, other plotting handles can be accessed via plot.handles.""")
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.""")
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.""")
show_grid = param.Boolean(default=False, 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.""")
toolbar = param.ObjectSelector(default='right',
objects=["above", "below",
"left", "right", None],
doc="""
The toolbar location, must be one of 'above', 'below',
'left', 'right', None.""")
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.""")
_categorical = False
# 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
dfstream = [s for s in self.streams if isinstance(s, Buffer)]
self.streaming = dfstream[0] if any(dfstream) else None
# Whether axes are shared between plots
self._shared = {'x': False, 'y': False}
def _construct_callbacks(self):
"""
Initializes any callbacks for streams which have defined
the plotted object as a source.
"""
if isinstance(self, OverlayPlot):
zorders = []
elif self.batched:
zorders = list(range(self.zorder, self.zorder+len(self.hmap.last)))
else:
zorders = [self.zorder]
if isinstance(self, OverlayPlot) and not self.batched:
sources = []
elif not self.static or isinstance(self.hmap, DynamicMap):
sources = [(i, o) for i, inputs in self.stream_sources.items()
for o in inputs if i in zorders]
else:
sources = [(self.zorder, self.hmap.last)]
cb_classes = set()
for _, source in sources:
streams = Stream.registry.get(id(source), [])
registry = Stream._callbacks['bokeh']
cb_classes |= {(registry[type(stream)], stream) for stream in streams
if type(stream) in registry and stream.linked}
cbs = []
sorted_cbs = sorted(cb_classes, key=lambda x: id(x[0]))
for cb, group in groupby(sorted_cbs, lambda x: x[0]):
cb_streams = [s for _, s in group]
cbs.append(cb(self, cb_streams, source))
return cbs
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 known_tools:
tool_names.append(handle)
if handle == 'hover':
tool = HoverTool(tooltips=tooltips, **hover_opts)
hover = tool
else:
tool = known_tools[handle]()
cb_tools.append(tool)
self.handles[handle] = tool
tools = [t for t in cb_tools + self.default_tools + self.tools
if t not in tool_names]
copied_tools = []
for tool in tools:
if isinstance(tool, 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, HoverTool)]
if 'hover' in copied_tools:
hover = HoverTool(tooltips=tooltips, **hover_opts)
copied_tools[copied_tools.index('hover')] = hover
elif any(hover_tools):
hover = hover_tools[0]
if hover:
self.handles['hover'] = hover
return copied_tools
def _get_hover_data(self, data, element):
"""
Initializes hover data based on Element dimension values.
If empty initializes with no data.
"""
if not any(isinstance(t, HoverTool) for t in self.state.tools) or self.static_source:
return
for d in element.dimensions():
dim = util.dimension_sanitizer(d.name)
if dim not in data:
data[dim] = element.dimension_values(d)
elif isinstance(data[dim], np.ndarray) and data[dim].dtype.kind == 'M':
data[dim+'_dt_strings'] = [d.pprint_value(v) for v in data[dim]]
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, xlabel, ylabel):
"""
Given a list of other plots return axes that are shared
with another plot by matching the axes labels
"""
plot_ranges = {}
for plot in plots:
if plot is None:
continue
if hasattr(plot, 'xaxis'):
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 hasattr(plot, 'yaxis'):
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
return plot_ranges
def _axes_props(self, plots, subplots, element, ranges):
# Get the bottom layer and range element
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
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, xlabel, ylabel)
if el.get_dimension_type(0) in util.datetime_types:
x_axis_type = 'datetime'
else:
x_axis_type = 'log' if self.logx else 'auto'
if len(dims) > 1 and el.get_dimension_type(1) in util.datetime_types:
y_axis_type = 'datetime'
else:
y_axis_type = 'log' if self.logy else 'auto'
# 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
# Declare shared axes
if 'x_range' in plot_ranges:
self._shared['x'] = True
if 'y_range' in plot_ranges:
self._shared['y'] = True
categorical = any(self.traverse(lambda x: x._categorical))
categorical_x = any(isinstance(x, util.basestring) for x in (l, r))
categorical_y = any(isinstance(y, util.basestring) 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
if categorical or categorical_x:
x_axis_type = 'auto'
plot_ranges['x_range'] = FactorRange()
elif 'x_range' not in plot_ranges:
plot_ranges['x_range'] = x_range_type()
if 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()
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 else ' '
properties['y_axis_label'] = ylabel if 'y' in self.labelled else ' '
if not self.show_frame:
properties['outline_line_alpha'] = 0
if self.show_title:
title = self._format_title(key, separator=' ')
else:
title = ''
if self.toolbar:
tools = self._init_tools(element)
properties['tools'] = tools
properties['toolbar_location'] = self.toolbar
if self.renderer.webgl:
properties['output_backend'] = 'webgl'
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, plot, element):
"""
Returns a dictionary of plot properties.
"""
size_multiplier = self.renderer.size/100.
plot_props = dict(plot_height=int(self.height*size_multiplier),
plot_width=int(self.width*size_multiplier),
sizing_mode=self.sizing_mode)
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().get('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 = ''
opts = dict(text=title, text_color='black')
title_font = self._fontsize('title').get('fontsize')
if title_font:
opts['text_font_size'] = value(title_font)
return opts
def _init_axes(self, plot):
if self.xaxis is None:
plot.xaxis.visible = False
elif '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 '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.
"""
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_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)
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)
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)
jsfunc = py2js_tickformatter(formatter, msg)
if jsfunc:
formatter = FuncTickFormatter(code=jsfunc)
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()
if not len(dimensions) >= 2:
dimensions = dimensions+[None]
plot.update(**self._plot_properties(key, plot, element))
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
props['y']['axis_label'] = ylabel
recursive_model_update(plot.xaxis[0], props.get('x', {}))
recursive_model_update(plot.yaxis[0], props.get('y', {}))
if not self.overlaid:
if plot.title:
plot.title.update(**self._title_properties(key, plot, element))
else:
plot.title = Title(**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):
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)
framewise = self.framewise
if not self.drawn or (not self.model_changed(x_range) and framewise or self.streaming) or xfactors:
self._update_range(x_range, l, r, xfactors, self.invert_xaxis, self._shared['x'], self.logx)
if not self.drawn or (not self.model_changed(y_range) and framewise or self.streaming) or yfactors:
self._update_range(y_range, b, t, yfactors, self.invert_yaxis, self._shared['y'], self.logy)
def _update_range(self, axis_range, low, high, factors, invert, shared, log):
if isinstance(axis_range, (Range1d, DataRange1d)) and self.apply_ranges:
if (low == high and low is not None):
if isinstance(low, util.datetime_types):
offset = np.timedelta64(500, 'ms')
low -= offset
high += offset
else:
offset = abs(low*0.1 if low else 0.5)
low -= offset
high += offset
if invert: low, high = high, low
if shared:
shared = (axis_range.start, axis_range.end)
low, high = util.max_range([(low, high), shared])
if log and (low is None or low <= 0):
low = 0.01 if high < 0.01 else 10**(np.log10(high)-2)
self.warning("Logarithmic axis range encountered value less than or equal to zero, "
"please supply explicit lower-bound to override default of %.3f." % low)
if low is not None and (isinstance(low, util.datetime_types)
or np.isfinite(low)):
axis_range.start = low
if high is not None and (isinstance(high, util.datetime_types)
or np.isfinite(high)):
axis_range.end = high
elif isinstance(axis_range, FactorRange):
factors = list(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]
def _get_factors(self, element):
"""
Get factors for categorical axes.
"""
xdim, ydim = element.dimensions()[:2]
xvals, yvals = [element.dimension_values(i, False)
for i in range(2)]
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 _glyph_properties(self, plot, element, source, ranges, style):
properties = dict(style, source=source)
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'] = value(legend)
return properties
def _update_glyph(self, renderer, properties, mapping, glyph):
allowed_properties = glyph.properties()
properties = mpl_to_bokeh(properties)
merged = dict(properties, **mapping)
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
glyph_props = dict(merged)
for gtype in ((glyph_type, '') if glyph_type else ('',)):
for prop in ('color', 'alpha'):
glyph_prop = merged.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)
filtered = {k: v for k, v in glyph_props.items()
if k in allowed_properties}
glyph.update(**filtered)
def _execute_hooks(self, element):
"""
Executes finalize hooks
"""
for hook in self.finalize_hooks:
try:
hook(self, element)
except Exception as e:
self.warning("Plotting hook %r could not be applied:\n\n %s" % (hook, e))
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
hover.renderers.append(renderer)
# If datetime column is in the data replace hover formatter
for k, v in source.data.items():
if k+'_dt_strings' in source.data:
tooltips = []
for name, formatter in hover.tooltips:
if formatter == '@{%s}' % k:
formatter = '@{%s_dt_strings}' % k
tooltips.append((name, formatter))
hover.tooltips = tooltips
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
if source is None:
source = self._init_datasource(data)
self.handles['previous_id'] = current_id
self.handles['source'] = source
properties = self._glyph_properties(plot, style_element, source, ranges, style)
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)
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 = 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)
else:
self.handles['xaxis'] = plot.xaxis[0]
self.handles['x_range'] = plot.x_range
self.handles['y_axis'] = 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 not self.overlaid:
self._process_legend()
self._execute_hooks(element)
self.drawn = True
return plot
def _update_glyphs(self, element, ranges):
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))
style = self.style[self.cyclic_index]
if self.batched:
data, mapping, style = self.get_batched_data(element, ranges)
else:
data, mapping, style = self.get_data(element, ranges, style)
if not self.static_source:
self._update_datasource(source, data)
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)
def update_frame(self, key, ranges=None, plot=None, element=None):
"""
Updates an existing plot with data corresponding
to the key.
"""
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 = bool(element)
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.data is self.current_frame.data
and not self.streaming._triggering)):
return
if self.batched:
style_element = element.last
max_cycles = None
else:
style_element = element
max_cycles = len(self.style._options)
style = self.lookup_options(style_element, 'style')
self.style = style.max_cycles(max_cycles) if max_cycles else 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']
if not self.overlaid:
self._update_ranges(style_element, ranges)
self._update_plot(key, plot, style_element)
self._update_glyphs(element, ranges)
self._execute_hooks(element)
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 name and style groups
_style_groups = {}
def _init_glyphs(self, plot, element, ranges, source):
# Get data and initialize data source
style = self.style[self.cyclic_index]
data, mapping, style = self.get_data(element, ranges, style)
current_id = element._plot_id
self.handles['previous_id'] = current_id
for key in dict(mapping, **data):
source = self._init_datasource(data.get(key, {}))
self.handles[key+'_source'] = source
properties = self._glyph_properties(plot, element, source, ranges, style)
properties = self._process_properties(key, properties)
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)
def _process_properties(self, key, properties):
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:
if k.split('_')[0] == style_group:
k = '_'.join(k.split('_')[1:])
else:
continue
group_props[k] = v
return group_props
def _update_glyphs(self, element, ranges):
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))
style = self.style[self.cyclic_index]
data, mapping, style = self.get_data(element, ranges, style)
for key in sorted(dict(mapping, **data)):
gdata = data[key]
source = self.handles[key+'_source']
glyph = self.handles.get(key+'_glyph')
if not self.static_source:
self._update_datasource(source, gdata)
if glyph:
properties = self._glyph_properties(plot, element, source, ranges, style)
properties = self._process_properties(key, properties)
renderer = self.handles.get(key+'_glyph_renderer')
with abbreviated_exception():
self._update_glyph(renderer, properties, mapping[key], glyph)
def _init_glyph(self, plot, mapping, properties, key):
"""
Returns a Bokeh glyph object.
"""
properties = mpl_to_bokeh(properties)
plot_method = key.split('_')[0]
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'}}}
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.""")
_colorbar_defaults = dict(bar_line_color='black', label_standoff=8,
major_tick_line_color='black')
def _draw_colorbar(self, plot, color_mapper):
if CategoricalColorMapper and isinstance(color_mapper, CategoricalColorMapper):
return
if LogColorMapper and isinstance(color_mapper, LogColorMapper):
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
opts = dict(cbar_opts['opts'], **self._colorbar_defaults)
color_bar = ColorBar(color_mapper=color_mapper, ticker=ticker,
**dict(opts, **self.colorbar_opts))
plot.add_layout(color_bar, pos)
self.handles['colorbar'] = color_bar
def _get_colormapper(self, dim, element, ranges, style, factors=None, colors=None):
# The initial colormapper instance is cached the first time
# and then only updated
if dim is None:
return None
if self.adjoined:
cmappers = self.adjoined.traverse(lambda x: (x.handles.get('color_dim'),
x.handles.get('color_mapper')))
cmappers = [cmap for cdim, cmap in cmappers if cdim == dim]
if cmappers:
cmapper = cmappers[0]
self.handles['color_mapper'] = cmapper
return cmapper
else:
return None
ncolors = None if factors is None else len(factors)
low, high = ranges.get(dim.name, element.range(dim.name))
if colors:
palette = colors
else:
cmap = style.pop('cmap', 'viridis')
if isinstance(cmap, list):
palette = cmap
else:
try:
# Process as matplotlib colormap
palette = mplcmap_to_palette(cmap, ncolors)
except ValueError:
# Process as bokeh palette
palette = getattr(palettes, cmap, None)
if isinstance(palette, dict):
if ncolors in palette:
palette = palette[ncolors]
else:
palette = sorted(palette.items())[-1][1]
nan_colors = {k: rgba_tuple(v) for k, v in self.clipping_colors.items()}
colormapper, opts = self._get_cmapper_opts(low, high, factors, nan_colors)
if 'color_mapper' in self.handles and isinstance(self.handles['color_mapper'], colormapper):
cmapper = self.handles['color_mapper']
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['color_mapper'] = cmapper
self.handles['color_dim'] = dim
return cmapper
def _get_color_data(self, element, ranges, style, name='color', factors=None, colors=None):
data, mapping = {}, {}
cdim = element.get_dimension(self.color_index)
if not cdim:
return data, mapping
cdata = element.dimension_values(cdim)
if factors is None and (isinstance(cdata, list) or cdata.dtype.kind in 'OSU'):
factors = list(np.unique(cdata))
mapper = self._get_colormapper(cdim, element, ranges, style,
factors, colors)
data[cdim.name] = cdata
if factors is not None:
mapping['legend'] = {'field': cdim.name}
mapping[name] = {'field': cdim.name,
'transform': mapper}
return data, mapping
def _get_cmapper_opts(self, low, high, factors, colors):
if factors is None:
colormapper = LogColorMapper if self.logz else LinearColorMapper
if isinstance(low, (bool, np.bool_)): low = int(low)
if isinstance(high, (bool, np.bool_)): high = int(high)
opts = {}
if np.isfinite(low):
opts['low'] = low
if np.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
opts = dict(factors=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 and 'color_mapper' in self.handles:
self._draw_colorbar(plot, self.handles['color_mapper'])
return ret
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_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'}
def _process_legend(self, plot=None):
plot = plot or self.handles['plot']
if not plot.legend:
return
legend = plot.legend[0]
cmapper = self.handles.get('color_mapper')
if cmapper:
categorical = isinstance(cmapper, CategoricalColorMapper)
else:
categorical = False
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.plot = None
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
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_format', 'legend_position', 'legend_offset',
'legend_cols']
def _process_legend(self):
plot = self.handles['plot']
if not self.show_legend or len(plot.legend) == 0:
return super(OverlayPlot, self)._process_legend()
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
if not plot.legend:
return
pos = self.legend_position
orientation = 'horizontal' if self.legend_cols else 'vertical'
if pos in ['top', 'bottom']:
orientation = 'horizontal'
legend_fontsize = self._fontsize('legend', 'size').get('size',False)
legend = plot.legend[0]
legend.update(**options)
if legend_fontsize:
legend.label_text_font_size = value(legend_fontsize)
if pos in self.legend_specs:
pos = self.legend_specs[pos]
else:
legend.location = pos
leg_opts = 'right'
legend.orientation = orientation
legend_items = []
legend_labels = {}
for item in legend.items:
label = tuple(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 += item.renderers
else:
legend_labels[label] = item
legend_items.append(item)
legend.items[:] = legend_items
if self.multiple_legends:
plot.legend.pop(plot.legend.index(legend))
legend.plot = None
legends = []
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']:
plot.legend.pop(plot.legend.index(legend))
legend.plot = None
legend.location = self.legend_offset
plot.add_layout(legend, pos)
def _init_tools(self, element, callbacks=[]):
"""
Processes the list of tools to be supplied to the plot.
"""
tools = []
hover_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 = TOOLS.get(tool)
else:
tool_type = type(tool)
if isinstance(tool, 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)
tools.append(tool)
self.handles['hover_tools'] = hover_tools
return 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']
# Datetime formatter may have been applied, remove _dt_strings
# to match on the hover tooltips, then merge tool renderers
if hover.tooltips:
tooltips = tuple((name, spec.replace('_dt_strings', ''))
for name, spec in hover.tooltips)
else:
tooltips = ()
tool = self.handles['hover_tools'].get(tooltips)
if tool:
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):
xfactors, yfactors = [], []
for k, sp in self.subplots.items():
el = overlay.data.get(k)
if el is not None:
xfs, yfs = sp._get_factors(el)
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 initialize_plot(self, ranges=None, plot=None, plots=None):
key = self.keys[-1]
nonempty = [el for el in self.hmap.data.values() if el]
if not nonempty:
raise SkipRendering('All Overlays empty, cannot initialize plot.')
element = nonempty[-1]
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)
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):
frame = element.get(key, None)
subplot.current_frame = frame
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)
elif not self.overlaid:
self._process_legend()
self.drawn = True
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()
self._execute_hooks(element)
return self.handles['plot']
def update_frame(self, key, ranges=None, element=None):
"""
Update the internal state of the Plot to represent the given
key tuple (where integers represent frames). Returns this
state.
"""
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 = element.items() if element else []
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)
if element and not self.overlaid and not self.tabs and not self.batched:
self._update_ranges(element, ranges)
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 = dynamic_update(self, subplot, k, element, items)
if idx is not None:
_, el = items.pop(idx)
subplot.update_frame(key, ranges, element=el)
if not self.batched and isinstance(self.hmap, DynamicMap) and items:
self.warning("Some Elements returned by the dynamic callback "
"were not initialized correctly and could not be "
"rendered.")
if element and not self.overlaid and not self.tabs and not self.batched:
self._update_plot(key, self.handles['plot'], element)
self._execute_hooks(element)
