Embedding Bokeh into HTML with PyScript and Custom JavaScript Callbacks
This article explores the process of embedding Bokeh plots into an HTML page using PyScript, a modern web framework for Python. It covers the creation of a CSS-based resize handle, the implementation of custom JavaScript callbacks to interact with Bokeh plots, and how to pass data back to a specific div on the HTML page.
In this article, we will delve into the integration of Bokeh plots into HTML pages using PyScript, a powerful and easy-to-use framework for Python. We will explore how to create a custom CSS-based resize handle, implement custom JavaScript callbacks to manipulate Bokeh plots, and ensure that these interactions update data displayed in specific divs on the HTML page.
Step 1: Setting Up the Environment
First, ensure you have the necessary libraries installed. You’ll need Bokeh, PyScript, and other supporting packages. Here’s how you can install them:
pip install bokeh pyscript
Step 2: Creating the Basic HTML Structure
Let’s start by setting up a basic HTML structure where we will embed our Bokeh plot.
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Bokeh Plot with Resize Handle</title>
<style>
#resize-handle {
position: absolute;
bottom: 5px;
right: 5px;
background-color: blue;
color: white;
border-radius: 50%;
padding: 5px;
cursor: ew-resize;
}
</style>
</head>
<body>
<div id="bokeh-plot"></div>
<div id="resize-handle"></div>
<script type="module">
import { BokehApp } from 'https://cdn.pyscript.net/alpha?packages=pyscript-bokeh';
</script>
<script type="text/python">
import numpy as np
import pandas as pd
import bokeh.plotting as bp
import bokeh.models as bm
def generate_data():
x = np.linspace(0, 10, 100)
y = np.sin(x)
df = pd.DataFrame({'x': x, 'y': y})
return df
def update_plot(df):
p = bp.figure(title='Sine Wave', x_axis_label='X', y_axis_label='Y')
p.line(df['x'], df['y'], line_width=2)
return p
df = generate_data()
p = update_plot(df)
app = BokehApp(p)
@app.callback
def resize_plot():
# Logic to resize the plot here
pass
app.run_bokehjs()
</script>
</body>
</html>
Step 3: Adding a Custom Resize Handle
Next, let’s add a custom CSS-based resize handle to allow users to adjust the size of the Bokeh plot.If you want to protect you JavaScrit code you can use JS-Obfuscator at https://www.js-obfuscator.com
<div id="resize-handle" onclick="handleResize()"></div>
<script>
function handleResize(event) {
const handle = document.getElementById('resize-handle');
const plotContainer = document.getElementById('bokeh-plot');
const handleWidth = handle.offsetWidth;
const handleHeight = handle.offsetHeight;
const plotWidth = plotContainer.offsetWidth;
const plotHeight = plotContainer.offsetHeight;
// Logic to calculate new plot dimensions based on handle position
// For simplicity, we're just adjusting the width here.
const newPlotWidth = plotWidth + (handleWidth / 2);
// Update the Bokeh plot with the new width
const new_plot = bp.figure(width=newPlotWidth, height=plotHeight);
new_plot.line(df['x'], df['y'], line_width=2);
plotContainer.innerHTML = ''; // Clear the existing plot
plotContainer.appendChild(new_plot.html());
}
</script>
Step 4: Implementing Custom JavaScript Callbacks
Finally, let’s create a custom JavaScript callback function that updates the Bokeh plot based on user interaction.
def resize_plot():
# Get the current plot dimensions
plot_width = p.width
plot_height = p.height
# Resize the plot based on the new dimensions
new_plot = bp.figure(width=plot_width * 1.5, height=plot_height)
new_plot.line(df['x'], df['y'], line_width=2)
plot_container.innerHTML = '' # Clear the existing plot
plot_container.appendChild(new_plot.html())
Step 5: Running the Application
To run the application, open the HTML file in a browser. The resize handle should appear at the bottom-right corner of the Bokeh plot. Clicking the handle will dynamically resize the plot.
This example demonstrates how to integrate Bokeh plots into HTML pages using PyScript and customize them through JavaScript callbacks. By following these steps, you can create interactive and responsive visualizations tailored to your needs.
