The main goal of Bokeh is to provide a path to create rich interactive visualizations in the browser purely from Python. However, there will always be use-cases that go beyond the capabilities of the pre-defined core library.
For this reason, Bokeh provides different ways for users to supply custom JavaScript when necessary. This way, you can add custom or topics behaviors in response to property changes and other events in the browser.
Note
As the name implies, JavaScript callbacks are snippets of JavaScript code that are executed in the browser. If you are looking for interactive callbacks that are based solely on Python and can be run with Bokeh Server, see Python callbacks.
There are mainly three options for generating a JavaScript callback:
Use the js_link Python convenience method. This method helps you link properties of different models together. With this method, Bokeh creates the necessary JavaScript code for you automatically. See Linked behavior for details.
Use the SetValue Python object to dynamically set the property of one object depending on a specific event of another object. See SetValue callbacks for more information.
Write custom JavaScript code with the CustomJS object. See CustomJS callbacks for more information.
A JavaScript callback is triggered when certain events occur in the browser. There are two main types of JavaScript callback triggers:
Most Bokeh objects have a .js_on_change property (all widgets, for example). The callback assigned to this property will be called whenever the state of the object changes. See js_on_change callback triggers for more information.
Some widgets also have a .js_on_event property. The callback assigned to this property will be called whenever a specific event occurs in the browser.
Warning
The explicit purpose of the CustomJS Model is to embed raw JavaScript code for a browser to execute. If any part of the code is derived from untrusted user inputs, then you must take appropriate care to sanitize the user input prior to passing it to Bokeh.
Additionally, you can add entire new custom extension models by writing your own Bokeh extension.
SetValue callbacks#Use the SetValue model to dynamically set specific properties of an object when an event occurs in the browser.
The SetValue model has the following properties:
obj: The object to set the value on.
attr: The property of the object to modify.
value: The value to set for the objectâs property.
Based on these parameters, Bokeh creates the necessary JavaScript code automatically:
from bokeh.io import show
from bokeh.models import Button, SetValue
button = Button(label="Foo", button_type="primary")
callback = SetValue(obj=button, attr="label", value="Bar")
button.js_on_event("button_click", callback)
show(button)
CustomJS callbacks#
Use the CustomJS model to supply a custom snippet of JavaScript code to run in the browser when an event occurs.
from bokeh.models.callbacks import CustomJS
callback = CustomJS(args=dict(xr=plot.x_range, yr=plot.y_range, slider=slider), code="""
// imports
import {some_function, SOME_VALUE} from "https://cdn.jsdelivr.net/npm/package@version/file"
// constants, definitions and state
const MY_VALUE = 3.14
function my_function(value) {
return MY_VALUE*value
}
class MyClass {
constructor(value) {
this.value = value
}
}
let count = 0
// the callback function
export default (args, obj, data, context) => {
count += 1
console.log(`CustomJS was called ${count} times`)
const a = args.slider.value
const b = obj.value
const {xr, yr} = args
xr.start = my_function(a)
xr.end = b
}
""")
The code snippet must contain a default export, which must be a function defined either using arrow function syntax () => {} or a classical function syntax function() {}. Depending on the context, this function may be an async function, a generator function or an async generator function. Also depending on the context, this function may or may not need to return a value.
The callback function uses four positional arguments:
args
this maps to CustomJS.args property, allowing for mapping names to serializable values, typically providing access to Bokeh models from the code snippet.
obj
this refers to the model that emitted the callback (this is the model that the callback is attached to).
data
this is a mapping between names and values provided by the emitter of this callback. This depends on the caller, the event and possibly the context in which the event occurred. For example, a select tool will use data to provide selection geometry, among other things.
context
this is an additional broader context provided by bokehjs, which is a mapping between names and values, similarly to data. Currently only index is provided, which allows the user to access bokehjsâ view index.
It may be convenient to the user to use object destructuring syntax to gain immediate access to passed values, for example:
from bokeh.models.callbacks import CustomJS
callback = CustomJS(args=dict(xr=plot.x_range, yr=plot.y_range, slider=slider), code="""
export default ({xr, yr, slider}, obj, {geometry}, {index}) => {
// use xr, yr, slider, geometry and index
}
""")
Code snippet is compiled once and the callback function (the default export) can be evaluated multiple times. This way the user can robustly and efficiently import external libraries, define complex classes and data structures and maintain state between calls of the callback function. The code snippet is recompiled only when properties of CustomJS instance change.
Alternatively the user can use the legacy variant of CustomJS, where the code snippet is the body of the implicit callback function:
from bokeh.models.callbacks import CustomJS callback = CustomJS(args=dict(xr=plot.x_range), code=""" // JavaScript code goes here const a = 10 // the model that triggered the callback is cb_obj: const b = cb_obj.value // models passed as args are auto-magically available xr.start = a xr.end = b """)
Bokeh distinguishes both approaches by detecting presence or absence of import and export syntax in the code snippet.
In this approach, arguments to the callback function are implicitly defined. Names provided by CustomJS.args are immediately available as positional arguments, whereas obj, data and context are all available with cb_ prefix, i.e. cb_obj, cb_data and cb_context.
Finally the user can create CustomJS from files, which use useful when dealing with large and/or complex code snippets:
from bokeh.models.callbacks import CustomJS
callback = CustomJS.from_file("./my_module.mjs", xr=plot.x_range)
The allowed extensions are:
.mjs for the new export default () => {} variant
.js for legacy CustomJS
js_on_change callback triggers#
CustomJS and SetValue callbacks can be attached to property change events on any Bokeh model, using the js_on_change method of Bokeh models:
p = figure()
# execute a callback whenever p.x_range.start changes
p.x_range.js_on_change('start', callback)
The following example attaches a CustomJS callback to a Slider widget. Whenever the slider value updates, the callback updates the plot data with a custom formula:
from bokeh.layouts import column
from bokeh.models import ColumnDataSource, CustomJS, Slider
from bokeh.plotting import figure, show
x = [x*0.005 for x in range(0, 200)]
y = x
source = ColumnDataSource(data=dict(x=x, y=y))
plot = figure(width=400, height=400, x_range=(0, 1), y_range=(0, 1))
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
callback = CustomJS(args=dict(source=source), code="""
const f = cb_obj.value
const x = source.data.x
const y = Array.from(x, (x) => Math.pow(x, f))
source.data = { x, y }
""")
slider = Slider(start=0.1, end=4, value=1, step=.1, title="power")
slider.js_on_change('value', callback)
layout = column(slider, plot)
show(layout)
js_on_event callback triggers#
In addition to responding to property change events using js_on_change, Bokeh allows CustomJS and SetValue callbacks to be triggered by specific interaction events with the plot canvas, on button click events, on LOD (Level-of-Detail) events, and document events.
These event callbacks are defined on models using the js_on_event method, with the callback receiving the event object as a locally defined cb_obj variable:
from bokeh.models.callbacks import CustomJS
callback = CustomJS(code="""
// the event that triggered the callback is cb_obj:
// The event type determines the relevant attributes
console.log('Tap event occurred at x-position: ' + cb_obj.x)
""")
p = figure()
# execute a callback whenever the plot canvas is tapped
p.js_on_event('tap', callback)
The event can be specified as a string such as 'tap' above, or an event class import from the bokeh.events module (i.e. from bokeh.events import Tap).
The following code imports bokeh.events and registers all of the available event classes using the display_event function in order to generate the CustomJS objects. This function is used to update the Div with the event name (always accessible from the event_name attribute) as well as all the other applicable event attributes. The result is a plot that displays the corresponding event on the right when the user interacts with it:
from __future__ import annotations
import numpy as np
from bokeh import events
from bokeh.io import curdoc, show
from bokeh.layouts import column, row
from bokeh.models import Button, CustomJS, Div, TextInput
from bokeh.plotting import figure
def display_event(div: Div, attributes: list[str] = []) -> CustomJS:
"""
Function to build a suitable CustomJS to display the current event
in the div model.
"""
style = 'float: left; clear: left; font-size: 13px'
return CustomJS(args=dict(div=div), code=f"""
const attrs = {attributes};
const args = [];
for (let i = 0; i < attrs.length; i++) {{
const val = JSON.stringify(cb_obj[attrs[i]], function(key, val) {{
return val.toFixed ? Number(val.toFixed(2)) : val;
}})
args.push(attrs[i] + '=' + val)
}}
const line = "<span style={style!r}><b>" + cb_obj.event_name + "</b>(" + args.join(", ") + ")</span>\\n";
const text = div.text.concat(line);
const lines = text.split("\\n")
if (lines.length > 35)
lines.shift();
div.text = lines.join("\\n");
""")
N = 4000
x = np.random.random(size=N) * 100
y = np.random.random(size=N) * 100
radii = np.random.random(size=N) * 1.5
colors = np.array([(r, g, 150) for r, g in zip(50+2*x, 30+2*y)], dtype="uint8")
p = figure(tools="pan,wheel_zoom,zoom_in,zoom_out,reset,tap,lasso_select,box_select,box_zoom,undo,redo")
p.circle(x, y, radius=radii, fill_color=colors, fill_alpha=0.6, line_color=None)
# Add a div to display events and a button to trigger button click events
div = Div(width=1000)
button = Button(label="Button", button_type="success", width=300)
text_input = TextInput(placeholder="Input a value and press Enter ...", width=300)
layout = column(button, text_input, row(p, div))
# Register event callbacks
# Button events
button.js_on_event(events.ButtonClick, display_event(div))
# TextInput events
text_input.js_on_event(events.ValueSubmit, display_event(div, ["value"]))
# AxisClick events
p.xaxis[0].js_on_event(events.AxisClick, display_event(div, ["value"]))
p.yaxis[0].js_on_event(events.AxisClick, display_event(div, ["value"]))
# LOD events
p.js_on_event(events.LODStart, display_event(div))
p.js_on_event(events.LODEnd, display_event(div))
# Point events
point_attributes = ['x','y','sx','sy']
p.js_on_event(events.Tap, display_event(div, attributes=point_attributes))
p.js_on_event(events.DoubleTap, display_event(div, attributes=point_attributes))
p.js_on_event(events.Press, display_event(div, attributes=point_attributes))
p.js_on_event(events.PressUp, display_event(div, attributes=point_attributes))
# Mouse wheel event
p.js_on_event(events.MouseWheel, display_event(div,attributes=[*point_attributes, 'delta']))
# Mouse move, enter and leave
# p.js_on_event(events.MouseMove, display_event(div, attributes=point_attributes))
p.js_on_event(events.MouseEnter, display_event(div, attributes=point_attributes))
p.js_on_event(events.MouseLeave, display_event(div, attributes=point_attributes))
# Pan events
pan_attributes = [*point_attributes, 'delta_x', 'delta_y']
p.js_on_event(events.Pan, display_event(div, attributes=pan_attributes))
p.js_on_event(events.PanStart, display_event(div, attributes=point_attributes))
p.js_on_event(events.PanEnd, display_event(div, attributes=point_attributes))
# Pinch events
pinch_attributes = [*point_attributes, 'scale']
p.js_on_event(events.Pinch, display_event(div, attributes=pinch_attributes))
p.js_on_event(events.PinchStart, display_event(div, attributes=point_attributes))
p.js_on_event(events.PinchEnd, display_event(div, attributes=point_attributes))
# Ranges Update events
p.js_on_event(events.RangesUpdate, display_event(div, attributes=['x0','x1','y0','y1']))
# Selection events
p.js_on_event(events.SelectionGeometry, display_event(div, attributes=['geometry', 'final']))
curdoc().on_event(events.DocumentReady, display_event(div))
show(layout)
JS callbacks for document events can be registered with Document.js_on_event() method. In the case of the standalone embedding mode, one will use the current document via curdoc() to set up such callbacks. For example:
from bokeh.models import Div
from bokeh.models.callbacks import CustomJS
from bokeh.io import curdoc, show
div = Div()
# execute a callback when the document is fully rendered
callback = CustomJS(args=dict(div=div, code="""div.text = "READY!"""")
curdoc().js_on_event("document_ready", callback)
show(div)
Similarly to model-level JS events, one can also use event classes in place of event names, to register document event callbacks:
from bokeh.events import DocumentReady curdoc().js_on_event(DocumentReady, callback)Examples# CustomJS for widgets#
A common use case for property callbacks is responding to changes to widgets. The code below shows an example of CustomJS set on a slider Widget that changes the source of a plot when the slider is used.
from bokeh.layouts import column
from bokeh.models import ColumnDataSource, CustomJS, Slider
from bokeh.plotting import figure, show
x = [x*0.005 for x in range(0, 200)]
y = x
source = ColumnDataSource(data=dict(x=x, y=y))
plot = figure(width=400, height=400, x_range=(0, 1), y_range=(0, 1))
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
callback = CustomJS(args=dict(source=source), code="""
const f = cb_obj.value
const x = source.data.x
const y = Array.from(x, (x) => Math.pow(x, f))
source.data = { x, y }
""")
slider = Slider(start=0.1, end=4, value=1, step=.1, title="power")
slider.js_on_change('value', callback)
show(column(slider, plot))
CustomJS for selections#
Another common scenario is wanting to specify the same kind of callback to be executed whenever a selection changes. As a simple demonstration, the example below simply copies selected points on the first plot to the second. However, more sophisticated actions and computations are easily constructed in a similar way.
from random import random
from bokeh.layouts import row
from bokeh.models import ColumnDataSource, CustomJS
from bokeh.plotting import figure, show
x = [random() for x in range(500)]
y = [random() for y in range(500)]
s1 = ColumnDataSource(data=dict(x=x, y=y))
p1 = figure(width=400, height=400, tools="lasso_select", title="Select Here")
p1.scatter('x', 'y', source=s1, alpha=0.6)
s2 = ColumnDataSource(data=dict(x=[], y=[]))
p2 = figure(width=400, height=400, x_range=(0, 1), y_range=(0, 1),
tools="", title="Watch Here")
p2.scatter('x', 'y', source=s2, alpha=0.6)
s1.selected.js_on_change('indices', CustomJS(args=dict(s1=s1, s2=s2), code="""
const inds = cb_obj.indices
const d1 = s1.data
const x = Array.from(inds, (i) => d1.x[i])
const y = Array.from(inds, (i) => d1.y[i])
s2.data = {x, y}
"""),
)
layout = row(p1, p2)
show(layout)
Another more sophisticated example is shown below. It computes the average y value of any selected points (including multiple disjoint selections) and draws a line through that value.
from random import random
from bokeh.models import ColumnDataSource, CustomJS
from bokeh.plotting import figure, show
x = [random() for x in range(500)]
y = [random() for y in range(500)]
s = ColumnDataSource(data=dict(x=x, y=y))
p = figure(width=400, height=400, tools="lasso_select", title="Select Here")
p.scatter('x', 'y', color='navy', size=8, source=s, alpha=0.4,
selection_color="firebrick")
s2 = ColumnDataSource(data=dict(x=[0, 1], ym=[0.5, 0.5]))
p.line(x='x', y='ym', color="orange", line_width=5, alpha=0.6, source=s2)
s.selected.js_on_change('indices', CustomJS(args=dict(s=s, s2=s2), code="""
const inds = s.selected.indices
if (inds.length > 0) {
const ym = inds.reduce((a, b) => a + s.data.y[b], 0) / inds.length
s2.data = { x: s2.data.x, ym: [ym, ym] }
}
"""))
show(p)
CustomJS for ranges#
The properties of range objects may also be connected to CustomJS callbacks in order to perform topics work whenever a range changes:
import numpy as np
from bokeh.layouts import row
from bokeh.models import BoxAnnotation, CustomJS
from bokeh.plotting import figure, show
N = 4000
x = np.random.random(size=N) * 100
y = np.random.random(size=N) * 100
radii = np.random.random(size=N) * 1.5
colors = np.array([(r, g, 150) for r, g in zip(50+2*x, 30+2*y)], dtype="uint8")
box = BoxAnnotation(left=0, right=0, bottom=0, top=0,
fill_alpha=0.1, line_color='black', fill_color='black')
jscode = """
box[%r] = cb_obj.start
box[%r] = cb_obj.end
"""
p1 = figure(title='Pan and Zoom Here', x_range=(0, 100), y_range=(0, 100),
tools='box_zoom,wheel_zoom,pan,reset', width=400, height=400)
p1.circle(x, y, radius=radii, fill_color=colors, fill_alpha=0.6, line_color=None)
xcb = CustomJS(args=dict(box=box), code=jscode % ('left', 'right'))
ycb = CustomJS(args=dict(box=box), code=jscode % ('bottom', 'top'))
p1.x_range.js_on_change('start', xcb)
p1.x_range.js_on_change('end', xcb)
p1.y_range.js_on_change('start', ycb)
p1.y_range.js_on_change('end', ycb)
p2 = figure(title='See Zoom Window Here', x_range=(0, 100), y_range=(0, 100),
tools='', width=400, height=400)
p2.circle(x, y, radius=radii, fill_color=colors, fill_alpha=0.6, line_color=None)
p2.add_layout(box)
layout = row(p1, p2)
show(layout)
CustomJS for tools#
Selection tools emit events that can drive useful callbacks. Below, a callback for SelectionGeometry uses the BoxSelectTool geometry (accessed via the geometry field of the cb_data callback object), in order to update a Rect glyph.
from bokeh.events import SelectionGeometry
from bokeh.models import ColumnDataSource, CustomJS, Quad
from bokeh.plotting import figure, show
source = ColumnDataSource(data=dict(left=[], right=[], top=[], bottom=[]))
callback = CustomJS(args=dict(source=source), code="""
const geometry = cb_obj.geometry
const data = source.data
// quad is forgiving if left/right or top/bottom are swapped
source.data = {
left: data.left.concat([geometry.x0]),
right: data.right.concat([geometry.x1]),
top: data.top.concat([geometry.y0]),
bottom: data.bottom.concat([geometry.y1])
}
""")
p = figure(width=400, height=400, title="Select below to draw rectangles",
tools="box_select", x_range=(0, 1), y_range=(0, 1))
# using Quad model directly to control (non)selection glyphs more carefully
quad = Quad(left='left', right='right', top='top', bottom='bottom',
fill_alpha=0.3, fill_color='#009933')
p.add_glyph(
source,
quad,
selection_glyph=quad.clone(fill_color='blue'),
nonselection_glyph=quad.clone(fill_color='gray'),
)
p.js_on_event(SelectionGeometry, callback)
show(p)
CustomJS for topics events#
In addition to the generic mechanisms described above for adding CustomJS callbacks to Bokeh models, there are also some Bokeh models that have a .callback property specifically for executing CustomJS in response to specific events or situations.
Warning
The callbacks described below were added early to Bokeh in an ad-hoc fashion. Many of them can be accomplished with the generic mechanism described above, and as such, may be deprecated in favor of the generic mechanism in the future.
CustomJS for hover tool#The HoverTool has a callback which comes with two pieces of built-in data: the index and the geometry. The index is the indices of any points that the hover tool is over.
from bokeh.models import ColumnDataSource, CustomJS, HoverTool
from bokeh.plotting import figure, show
# define some points and a little graph between them
x = [2, 3, 5, 6, 8, 7]
y = [6, 4, 3, 8, 7, 5]
links = {
0: [1, 2],
1: [0, 3, 4],
2: [0, 5],
3: [1, 4],
4: [1, 3],
5: [2, 3, 4],
}
p = figure(width=400, height=400, tools="", toolbar_location=None, title='Hover over points')
source = ColumnDataSource(dict(x0=[], y0=[], x1=[], y1=[]))
sr = p.segment(x0='x0', y0='y0', x1='x1', y1='y1', color='olive', alpha=0.6, line_width=3, source=source )
cr = p.scatter(x, y, color='olive', size=30, alpha=0.4, hover_color='olive', hover_alpha=1.0)
# add a hover tool that sets the link data for a hovered circle
code = """
const data = {x0: [], y0: [], x1: [], y1: []}
const {indices} = cb_data.index
for (const start of indices) {
for (const end of links.get(start)) {
data.x0.push(circle.data.x[start])
data.y0.push(circle.data.y[start])
data.x1.push(circle.data.x[end])
data.y1.push(circle.data.y[end])
}
}
segment.data = data
"""
callback = CustomJS(args=dict(circle=cr.data_source, segment=sr.data_source, links=links), code=code)
p.add_tools(HoverTool(tooltips=None, callback=callback, renderers=[cr]))
show(p)
OpenURL#
Opening an URL when users click on a glyph (for instance a circle marker) is a very popular feature. Bokeh lets users enable this feature by exposing an OpenURL callback object that can be passed to a Tap tool in order to have that action called whenever the user clicks on the glyph.
The following code shows how to use the OpenURL action combined with a TapTool to open an URL whenever the user clicks on a circle.
from bokeh.models import ColumnDataSource, OpenURL, TapTool
from bokeh.plotting import figure, show
p = figure(width=400, height=400,
tools="tap", title="Click the Dots")
source = ColumnDataSource(data=dict(
x=[1, 2, 3, 4, 5],
y=[2, 5, 8, 2, 7],
color=["navy", "orange", "olive", "firebrick", "gold"],
))
p.scatter('x', 'y', color='color', size=20, source=source)
# use the "color" column of the CDS to complete the URL
# e.g. if the glyph at index 10 is selected, then @color
# will be replaced with source.data['color'][10]
url = "https://www.html-color-names.com/@color.php"
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
show(p)
Please note that OpenURL callbacks specifically and only work with TapTool, and are only invoked when a glyph is hit. That is, they do not execute on every tap. If you would like to execute a callback on every mouse tap, please see js_on_event callback triggers.
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