In order to create interactive plots and applications in the browser, Bokeh has a client-side library, BokehJS, to do all the work of drawing and rendering and event handling in a browser. The Bokeh Python library (and libraries for other languages such as R, Scala, and Julia), are primarily a means to interact with BokehJS conveniently at a high level, without needing to explicitly worry about JavaScript or web development.
However, BokehJS has its own API, and it is possible to do pure JavaScript development using BokehJS directly. Additionally, Extending Bokeh with custom models typically requires interacting with BokehJS directly as well.
Warning
The BokehJS APIs is still considered experimental, and may undergo changes in future releases.
BokehJS is made available via CDN and npm. See the BokehJS section of the Installation page for more details.
npm
The low-level models for building up plots and applications (e.g. guides and glyphs and widgets, etc.) generally match the Bokeh Python models exactly. Accordingly, the Reference is a primary resource for answering questions about BokehJS models, even though it is presented from a Python perspective.
Unlike the hierarchical organization of the Python library, all of the JavaScript models are all in one flat Bokeh module. Typically any Python ClassName is available as Bokeh.ClassName from JavaScript. The complete list of models available from JavaScript can be seen at bokehjs/src/lib/api/models.ts.
Bokeh
ClassName
Bokeh.ClassName
When creating models from JavaScript, all of the keyword arguments that would get passed to the Python object initializer are passed as a JavaScript object. Here is a comparison of how to create a Range1d model. First, in Python:
xdr = Range1d(start=-0.5, end=20.5)
and the corresponding JavaScript version:
var xdr = new Bokeh.Range1d({ start: -0.5, end: 20.5 });
This pattern holds in general. Once created, Bokeh model properties are set in exactly the same way in both languages. To set the end value to 30 on the Range1d from above, use xdr.end = 30 in either Python or JavaScript.
end
xdr.end = 30
As an example, here is an example that creates a plot with axes, grids, and a line glyph from scratch. Comparison with examples in examples/models will show that the translation from Python to JavaScript at this level is nearly one-to-one:
// create some data and a ColumnDataSource var x = Bokeh.LinAlg.linspace(-0.5, 20.5, 10); var y = x.map(function (v) { return v * 0.5 + 3.0; }); var source = new Bokeh.ColumnDataSource({ data: { x: x, y: y } }); // create some ranges for the plot var xdr = new Bokeh.Range1d({ start: -0.5, end: 20.5 }); var ydr = new Bokeh.Range1d({ start: -0.5, end: 20.5 }); // make the plot var plot = new Bokeh.Plot({ title: "BokehJS Plot", x_range: xdr, y_range: ydr, plot_width: 400, plot_height: 400, background_fill_color: "#F2F2F7" }); // add axes to the plot var xaxis = new Bokeh.LinearAxis({ axis_line_color: null }); var yaxis = new Bokeh.LinearAxis({ axis_line_color: null }); plot.add_layout(xaxis, "below"); plot.add_layout(yaxis, "left"); // add grids to the plot var xgrid = new Bokeh.Grid({ ticker: xaxis.ticker, dimension: 0 }); var ygrid = new Bokeh.Grid({ ticker: yaxis.ticker, dimension: 1 }); plot.add_layout(xgrid); plot.add_layout(ygrid); // add a Line glyph var line = new Bokeh.Line({ x: { field: "x" }, y: { field: "y" }, line_color: "#666699", line_width: 2 }); plot.add_glyph(line, source); Bokeh.Plotting.show(plot);
The code above generates the following plot:
Similar to the Python Bokeh library, BokehJS provides various higher-level interfaces for interacting with and composing the low-level model objects. These higher-level interfaces currently comprise Bokeh.Plotting and Bokeh.Charts.
Bokeh.Plotting
Bokeh.Charts
Note
As of 0.12.2 the APIs described below have been split into BokehJS API, in the bokeh-api.js file, which must be imported in addition to bokeh.js.
0.12.2
bokeh-api.js
bokeh.js
The JavaScript Bokeh.Plotting API is a port of the Python bokeh.plotting interface. Accordingly, the information in the Plotting with Basic Glyphs section of the User Guide can be a useful reference in addition to the material here.
bokeh.plotting
Here is an example that is very similar to the Python example examples/plotting/file/color_scatter.py:
var plt = Bokeh.Plotting; // set up some data var M = 100; var xx = []; var yy = []; var colors = []; var radii = []; for (var y = 0; y <= M; y += 4) { for (var x = 0; x <= M; x += 4) { xx.push(x); yy.push(y); colors.push(plt.color(50+2*x, 30+2*y, 150)); radii.push(Math.random() * 0.4 + 1.7) } } // create a data source var source = new Bokeh.ColumnDataSource({ data: { x: xx, y: yy, radius: radii, colors: colors } }); // make the plot and add some tools var tools = "pan,crosshair,wheel_zoom,box_zoom,reset,save"; var p = plt.figure({ title: "Colorful Scatter", tools: tools }); // call the circle glyph method to add some circle glyphs var circles = p.circle({ field: "x" }, { field: "y" }, { source: source, radius: radii, fill_color: colors, fill_alpha: 0.6, line_color: null }); // show the plot plt.show(p);
The JavaScript Bokeh.Charts API is a high-level interface for charting that is unique to BokehJS. Currently, there are two high-level charts supported: pie and bar.
pie
bar
Bokeh.Charts.pie
To create pie charts using Bokeh.Charts.pie, the basic usage is:
Bokeh.Charts.pie(data, { options })
Where data is a JavaScript object that has labels and values keys, and options is an object that has any of the following optional keys:
data
labels
values
options
width
number — chart width in pixels
height
number — chart height in pixels
inner_radius
number — inner radius for wedges in pixels
outer_radius
number — outer radius for wedges in pixels
start_angle
number — start angle for wedges in radians
end_angle
number — end angle for wedges in radians
center
[number, number] — (x, y) location of the pie center in pixels
(x, y)
palette
Palette | Array<Color> — a named palette, or list of colors to colormap the values
slice_labels
“labels” | “values” | “percentages” — what the tooltip should show
By default, plots created Bokeh.Charts.pie automatically add a tooltip and hover policy. Here is some example code that demonstrates the pie function, with the plot it generates shown below:
var plt = Bokeh.Plotting; var pie_data = { labels: ['Work', 'Eat', 'Commute', 'Sport', 'Watch TV', 'Sleep'], values: [8, 2, 2, 4, 0, 8], }; var p1 = Bokeh.Charts.pie(pie_data); var p2 = Bokeh.Charts.pie(pie_data, { inner_radius: 0.2, start_angle: Math.PI / 2 }); var p3 = Bokeh.Charts.pie(pie_data, { inner_radius: 0.2, start_angle: Math.PI / 6, end_angle: 5 * Math.PI / 6 }); var p4 = Bokeh.Charts.pie(pie_data, { inner_radius: 0.2, palette: "Oranges9", slice_labels: "percentages" }); // add the plot to a document and display it var doc = new Bokeh.Document(); doc.add_root(plt.gridplot( [[p1, p2], [p3, p4]], {plot_width:250, plot_height:250})); Bokeh.embed.add_document_standalone(doc, document.currentScript.parentElement);
Bokeh.Charts.bar
To create bar charts using Bokeh.Charts.bar, the basic usage is:
Bokeh.Charts.bar(data, { options })
Where data is a JavaScript array that has as elements lists that are “rows” from a data table. The first “row” should contain the column headers. Here is an example that might represent sales data from different regions for different years:
var data = [ ['Region', 'Year', 'Sales'], ['East', 2015, 23000 ], ['East', 2016, 35000 ], ['West', 2015, 16000 ], ['West', 2016, 34000 ], ['North', 2016, 12000 ], ];
Similar to pie, the options parameter is an object that has any of the following optional keys:
stacked
boolean — whether the bars should be stacked or not
orientation
“horizontal” | “vertical” — how the bars should be oriented
bar_width
number — width of each bar in pixels
axis_number_format
string — a format string to use for axis ticks
By default, plots created with Bokeh.Charts.bar automatically add a tooltip and hover policy. Here is some example code that demonstrates the bar function, with the plot it generates shown below:
var plt = Bokeh.Plotting; var bar_data = [ ['City', '2010 Population', '2000 Population'], ['NYC', 8175000, 8008000], ['LA', 3792000, 3694000], ['Chicago', 2695000, 2896000], ['Houston', 2099000, 1953000], ['Philadelphia', 1526000, 1517000], ]; var p1 = Bokeh.Charts.bar(bar_data, { axis_number_format: "0.[00]a" }); var p2 = Bokeh.Charts.bar(bar_data, { axis_number_format: "0.[00]a", stacked: true }); var p3 = Bokeh.Charts.bar(bar_data, { axis_number_format: "0.[00]a", orientation: "vertical" }); var p4 = Bokeh.Charts.bar(bar_data, { axis_number_format: "0.[00]a", orientation: "vertical", stacked: true }); plt.show(plt.gridplot([[p1, p2], [p3, p4]], {plot_width:350, plot_height:350}));
A minimal example follows, demonstrating a proper import of the libraries, and dynamic creation and modification of plots.
<!doctype html> <html lang="en"> <head> <meta charset="utf-8"> <title>Complete Example</title> <script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js" integrity="sha384-T2yuo9Oe71Cz/I4X9Ac5+gpEa5a8PpJCDlqKYO0CfAuEszu1JrXLl8YugMqYe3sM" crossorigin="anonymous"></script> <script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js" integrity="sha384-98GDGJ0kOMCUMUePhksaQ/GYgB3+NH9h996V88sh3aOiUNX3N+fLXAtry6xctSZ6" crossorigin="anonymous"></script> <script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js" integrity="sha384-89bArO+nlbP3sgakeHjCo1JYxYR5wufVgA3IbUvDY+K7w4zyxJqssu7wVnfeKCq8" crossorigin="anonymous"></script> <script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-api-2.2.3.min.js" integrity="sha384-eT3dNscgjXmsGsM0ZO+nTwoL2BpJeNQnXH8gmXJmnEjqTb0ZV7c3jJ27FFe/oh1N" crossorigin="anonymous"></script> <script> //The order of CSS and JS imports above is important. </script> <script> // create a data source to hold data var source = new Bokeh.ColumnDataSource({ data: { x: [], y: [] } }); // make a plot with some tools var plot = Bokeh.Plotting.figure({ title:'Example of Random data', tools: "pan,wheel_zoom,box_zoom,reset,save", height: 300, width: 300 }); // add a line with data from the source plot.line({ field: "x" }, { field: "y" }, { source: source, line_width: 2 }); // show the plot, appending it to the end of the current section Bokeh.Plotting.show(plot); function addPoint() { // add data --- all fields must be the same length. source.data.x.push(Math.random()) source.data.y.push(Math.random()) // notify the DataSource of "in-place" changes source.change.emit() } var addDataButton = document.createElement("Button"); addDataButton.appendChild(document.createTextNode("Add Some Data!!!")); document.currentScript.parentElement.appendChild(addDataButton); addDataButton.addEventListener("click", addPoint); addPoint(); addPoint(); </script> </head> <body> </body> </html>