Embedding Plots and Apps¶

Bokeh provides a variety of ways to embed plots and data into HTML documents.

Standalone HTML¶

Bokeh can generate standalone HTML documents using the file_html() function. This function can emit HTML from its own generic template, or a template you provide. These files contain the data for the plot inline and are completely transportable, while still providing interactive tools (pan, zoom, etc.) for your plot. Here is an example:

from bokeh.plotting import figure
from bokeh.resources import CDN
from bokeh.embed import file_html

plot = figure()
plot.circle([1,2], [3,4])

html = file_html(plot, CDN, "my plot")

The returned HTML text can be saved to a file using standard python file operations.

Note

This is a fairly low-level, explicit way to generate an HTML file. When using the bokeh.plotting interface, users will typically call the function output_file() in conjunction with show() or save() instead.

You can also provide your own template and pass in custom, or additional, template variables. See the file_html() function for more details.

Components¶

For standalone Bokeh documents (i.e. not served by a Bokeh server), it is also possible to ask Bokeh to return the individual components for a inline embedding using the components() function. This function returns a <script> that contains the data for your plot, together with an accompanying <div> tag that the plot view is loaded into. These tags can be used in HTML documents however you like:

from bokeh.plotting import figure
from bokeh.embed import components

plot = figure()
plot.circle([1,2], [3,4])

script, div = components(plot)

The returned <script> will look something like:

<script type="text/javascript">
    (function() {
  var fn = function() {
    Bokeh.safely(function() {
      var docs_json = { DOCUMENT DATA HERE };
      var render_items = [{
        "docid":"6833819f-9b5b-4904-821e-3f5eec77de9b",
        "elementid":"9574d123-9332-4b5f-96cc-6323bef37f40",
        "modelid":"7b328b27-9b14-4f7b-a5d8-0138bc7b0f59"
      }];

      Bokeh.embed.embed_items(docs_json, render_items);
    });
  };
  if (document.readyState != "loading") fn();
  else document.addEventListener("DOMContentLoaded", fn);
})();

</script>

All of the data and plot or widget objects are contained in the docs_json variable (contents omitted here for brevity). The resulting <div> will look something like:

<div class="bk-root">
    <div class="bk-plotdiv" id="9574d123-9332-4b5f-96cc-6323bef37f40"></div>
</div>

These two elements can be inserted or templated into your HTML text, and the script, when executed, will replace the div with the plot.

Using these components assumes that BokehJS has already been loaded, for instance either inline in the document text, or from CDN. To load BokehJS from CDN, add the following lines in your HTML text or template with the appropriate version replacing x.y.z:

<link
    href="https://cdn.bokeh.org/bokeh/release/bokeh-x.y.z.min.css"
    rel="stylesheet" type="text/css">
<link
    href="https://cdn.bokeh.org/bokeh/release/bokeh-widgets-x.y.z.min.css"
    rel="stylesheet" type="text/css">

<script src="https://cdn.bokeh.org/bokeh/release/bokeh-x.y.z.min.js"></script>
<script src="https://cdn.bokeh.org/bokeh/release/bokeh-widgets-x.y.z.min.js"></script>

The "-widgets" files are only necessary if your document includes Bokeh widgets.

For example, to use version 0.12.6, including widgets support:

<link
    href="https://cdn.bokeh.org/bokeh/release/bokeh-0.12.6.min.css"
    rel="stylesheet" type="text/css">
<link
    href="https://cdn.bokeh.org/bokeh/release/bokeh-widgets-0.12.6.min.css"
    rel="stylesheet" type="text/css">

<script src="https://cdn.bokeh.org/bokeh/release/bokeh-0.12.6.min.js"></script>
<script src="https://cdn.bokeh.org/bokeh/release/bokeh-widgets-0.12.6.min.js"></script>

Note

You must provide the closing </script> tag. This is required by all browsers and the page will typically not render without it.

When embedding in a page served via HTTPS, any scripts and resources must also be loaded via HTTPS or the browser will refuse to load due to an “unsafe” script. For this situation, the Bokeh CDN resources are also available via HTTPS, by replacing “http” with “https” in the above URLs.

The components() function takes either a single Bokeh Model a list/tuple of Models, or a dictionary of keys and Models. Each returns a corresponding data structure of script and div pairs.

The following illustrates how different input types correlate to outputs:

components(plot)
#=> (script, plot_div)

components((plot_1, plot_2))
#=> (script, (plot_1_div, plot_2_div))

components({"Plot 1": plot_1, "Plot 2": plot_2})
#=> (script, {"Plot 1": plot_1_div, "Plot 2": plot_2_div})

Here’s an example of how you would use the multiple plot generator:

# scatter.py

from bokeh.plotting import figure
from bokeh.models import Range1d
from bokeh.embed import components

# create some data
x1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y1 = [0, 8, 2, 4, 6, 9, 5, 6, 25, 28, 4, 7]
x2 = [2, 5, 7, 15, 18, 19, 25, 28, 9, 10, 4]
y2 = [2, 4, 6, 9, 15, 18, 0, 8, 2, 25, 28]
x3 = [0, 1, 0, 8, 2, 4, 6, 9, 7, 8, 9]
y3 = [0, 8, 4, 6, 9, 15, 18, 19, 19, 25, 28]

# select the tools we want
TOOLS="pan,wheel_zoom,box_zoom,reset,save"

# the red and blue graphs will share this data range
xr1 = Range1d(start=0, end=30)
yr1 = Range1d(start=0, end=30)

# only the green will use this data range
xr2 = Range1d(start=0, end=30)
yr2 = Range1d(start=0, end=30)

# build our figures
p1 = figure(x_range=xr1, y_range=yr1, tools=TOOLS, plot_width=300, plot_height=300)
p1.scatter(x1, y1, size=12, color="red", alpha=0.5)

p2 = figure(x_range=xr1, y_range=yr1, tools=TOOLS, plot_width=300, plot_height=300)
p2.scatter(x2, y2, size=12, color="blue", alpha=0.5)

p3 = figure(x_range=xr2, y_range=yr2, tools=TOOLS, plot_width=300, plot_height=300)
p3.scatter(x3, y3, size=12, color="green", alpha=0.5)

# plots can be a single Bokeh Model, a list/tuple, or even a dictionary
plots = {'Red': p1, 'Blue': p2, 'Green': p3}

script, div = components(plots)
print(script)
print(div)

Running python scatter.py will print out:

script type="text/javascript">
    var docs_json = { DOCUMENT DATA HERE }
    var render_items = [{
      "docid":"33961aa6-fd96-4055-886f-b2afec7ff193",
      "elementid":"e89297cf-a2dc-4edd-8993-e16f0ca6af04",
      "modelid":"4eff3fdb-80f4-4b4c-a592-f99911e14398"
    },{
      "docid":"33961aa6-fd96-4055-886f-b2afec7ff193",
      "elementid":"eeb9a417-02a1-47e3-ab82-221abe8a1644",
      "modelid":"0e5ccbaf-62af-42cc-98de-7c597d83747a"
    },{
      "docid":"33961aa6-fd96-4055-886f-b2afec7ff193",
      "elementid":"c311f123-368f-43ba-88b6-4e3ecd9aed94",
      "modelid":"57f18497-9598-4c70-a251-6072baf223ff"
    }];

    Bokeh.embed.embed_items(docs_json, render_items);
</script>

    {
        'Green': '\n<div class="bk-root">\n    <div class="bk-plotdiv" id="e89297cf-a2dc-4edd-8993-e16f0ca6af04"></div>\n</div>',
        'Blue': '\n<div class="bk-root">\n    <div class="bk-plotdiv" id="eeb9a417-02a1-47e3-ab82-221abe8a1644"></div>\n</div>',
        'Red': '\n<div class="bk-root">\n    <div class="bk-plotdiv" id="c311f123-368f-43ba-88b6-4e3ecd9aed94"></div>\n</div>'
    }

Then inserting the script and div elements into this boilerplate:

<!DOCTYPE html>
<html lang="en">
    <head>
        <meta charset="utf-8">
        <title>Bokeh Scatter Plots</title>

        <link rel="stylesheet" href="https://cdn.bokeh.org/bokeh/release/bokeh-0.12.6.min.css" type="text/css" />
        <script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-0.12.6.min.js"></script>

        <!-- COPY/PASTE SCRIPT HERE -->

    </head>
    <body>
        <!-- INSERT DIVS HERE -->
    </body>
</html>

Note that above we have not included the "-widgets" JS and CSS files, since the document does not use Bokeh widgets. If required, the CDN resources are available as HTTPS URLs as well.

You can see an example by running:

python /bokeh/examples/embed/embed_multiple.py

IPython Notebook¶

Bokeh can also generate <div> tags suitable for inline display in the Jupyter notebook using the notebook_div() function:

from bokeh.plotting import figure
from bokeh.embed import notebook_div

plot = figure()
plot.circle([1,2], [3,4])

div = notebook_div(plot)

The returned div contains the same sort of <script> and <div> that the components() function above returns.

Note

This is a fairly low-level, explicit way to generate a Jupyter notebook div. When using the bokeh.plotting interface, users will typically call the function output_notebook() in conjunction with show() instead.

Autoloading¶

Finally it is possible to ask Bokeh to return a <script> tag that will replace itself with a Bokeh plot, wherever happens to be located. The script will also check for BokehJS and load it, if necessary, so it is possible to embed a plot by placing this script tag alone in your document.

There are two cases:

server data¶

The simplest case is to use the Bokeh server to persist your plot and data. Additionally, the Bokeh server affords the opportunity of animated plots or updating plots with streaming data. The autoload_server() function returns a <script> tag that will load both your plot and data from the Bokeh server.

If you are already an app on a bokeh server and have the url for it then you may want to use autoload_server() by passing the url for the server, as well as the app_path for the application on the server. As a concrete example, you could embed the sliders app from the demo site with a command like:

from bokeh.embed import autoload_server
script = autoload_server("https://demo.bokeh.org/slider")

The resulting <script> tag that you can use to embed the plot inside your HTML document looks like:

<script
    src="https://demo.bokeh.org/slider/autoload.js?bokeh-autoload-element=aee6d395-d079-4e02-ae72-8e70e617990d&bokeh-app-path=/apps/slider&bokeh-absolute-url=https://demo.bokeh.org/slider"
    id="aee6d395-d079-4e02-ae72-8e70e617990d"
    data-bokeh-model-id=""
    data-bokeh-doc-id=""
></script>

Note

When using autoload_server the brower document title will not be set.

It’s also possible to use autoload_server to generate scripts to load apps that were created using bokeh.client and push_session. Here is some code using autoload_server() with a default session:

from bokeh.client import push_session
from bokeh.embed import autoload_server
from bokeh.plotting import figure, curdoc

# figure() function auto-adds the figure to curdoc()
plot = figure()
plot.circle([1,2], [3,4])

session = push_session(curdoc())
script = autoload_server(plot, session_id=session.id)

Note

To execute the code above, a Bokeh server must already be running.

The resulting <script> tag for this use case has more information, and will look something like this:

<script
src="http://localhost:5006/autoload.js?bokeh-autoload-element=82ae93bf-79c2-4028-af7e-1cf6b1a0ea1a&bokeh-session-id=qjPGXLj7UWx7G9LDkwEq48fMOcxQfepxW7HUYPCQNrmN"
id="82ae93bf-79c2-4028-af7e-1cf6b1a0ea1a"
data-bokeh-model-id="b08c02c4-f93c-461c-bb23-514b54dfec83"
data-bokeh-doc-id=""
></script>

You can also pass arguments to your Bokeh server by passing them in a dictionary to arguments. The following illustrates how to pass and retrieve arguments.

# An example web server route (Flask)
# This will set the 'foo' argument to 'foo_id' and pass it to the Bokeh server
@app.route('/slider/<int:foo_id>')
def slider(foo_id):
    bokeh_script = autoload_server(None, url="https://demo.bokeh.org/slider", arguments=dict(foo=foo_id))
    return render_template_string(some_html, bokeh_script=bokeh_script)

# Bokeh server
# request.arguments is a dict that maps argument names to lists of strings,
args = curdoc().session_context.request.arguments

try:
    foo = int(args.get('foo_id')[0])
except (ValueError, TypeError):
    foo = 1

For full details read the autoload_server reference here: autoload_server().

Alternatively, two other methods allow to embed content from a bokeh server in a similar fashion to autoload_server but with some subtle differences: with server_document a new session will systematically be generated and an entire document will be returned; with server_session an existing session id and model must be provided. Another difference from autoload_server is that with those two methods one may choose to not load the JS/CSS resource files by passing a resources="none" parameter.

Here is an example using server_document:

from bokeh.embed import server_document
script = server_document("https://demo.bokeh.org/slider")

And here is an example using server_session:

from bokeh.client import push_session
from bokeh.embed import server_session
from bokeh.plotting import figure, curdoc

plot = figure()
plot.circle([1,2], [3,4])

session = push_session(curdoc())
script = server_session(plot, session_id=session.id)

static data¶

If you do not need or want to use the Bokeh server, then the you can use the autoload_static() function. This function takes the plot object you want to display together with a resources specification and path to load a script from. It will return a self-contained <script> tag, together with some JavaScript code that contains the data for your plot. This code should be saved to the script path you provided. The <script> tag will load this separate script to realize your plot.

Here is how you might use autoload_static() with a simple plot:

from bokeh.resources import CDN
from bokeh.plotting import figure
from bokeh.embed import autoload_static

plot = figure()
plot.circle([1,2], [3,4])

js, tag = autoload_static(plot, CDN, "some/path")

The resulting <script> tag looks like:

<script
    src="some/path"
    id="c5339dfd-a354-4e09-bba4-466f58a574f1"
    async="true"
    data-bokeh-data="static"
    data-bokeh-modelid="7b226555-8e16-4c29-ba2a-df2d308588dc"
    data-bokeh-modeltype="Plot"
    data-bokeh-loglevel="info"
></script>

The resulting JavaScript code should be saved to a file that can be reached on the server at “some/path”, from the document that has the plot embedded.

Note

In both cases the <script> tag loads a <div> in place, so it must be placed under <head>.