Scrolling Plots with Pyqtgraph

G-Fact 62 | Scrolling Plots with PyQtGraph in Python

In this video, we will explore how to create scrolling plots using PyQtGraph in Python. PyQtGraph is a fast, interactive graphics library that is compatible with PyQt and PySide, making it ideal for creating real-time data visualizations. This tutorial is perfect for students, professionals, or anyone interested in enhancing their Python programming skills by learning how to implement dynamic, real-time plots.

Why Use PyQtGraph for Scrolling Plots?

Using PyQtGraph for scrolling plots helps to:

• Visualize Real-Time Data: Create dynamic plots that update in real-time to reflect incoming data.
• Enhance Interactivity: Provide interactive features for zooming, panning, and adjusting plot parameters.
• Improve Performance: Leverage PyQtGraph's optimized performance for handling large datasets and fast updates.

Key Concepts

1. PyQtGraph:
   • A graphics and plotting library built on PyQt/PySide that is optimized for fast, interactive displays.
2. Real-Time Plotting:
   • The process of continuously updating a plot to reflect new data as it arrives.
3. Scrolling Plots:
   • Plots where the x-axis (time axis) scrolls to accommodate new data points, giving the appearance of a moving plot.

Methods to Create Scrolling Plots

1. Setting Up PyQtGraph:
   • Install PyQtGraph and PyQt/PySide, and set up the plotting environment.
2. Creating a Basic Plot:
   • Initialize a PyQtGraph window and create a basic plot.
3. Updating the Plot:
   • Implement a method to update the plot with new data points in real-time.
4. Implementing Scrolling:
   • Adjust the x-axis range to create a scrolling effect as new data points are added.

Practical Example

Example: Creating a Scrolling Plot with PyQtGraph

Set Up PyQtGraph Environment:

• Install the necessary libraries using pip.

Initialize PyQtGraph Window:

• Create a main window and add a plot widget.

Generate Sample Data:

• Generate or simulate real-time data points to be plotted.

Update Plot in Real-Time:

• Use a timer to periodically add new data points to the plot and update the display.

Implement Scrolling Mechanism:

• Adjust the plot’s x-axis to scroll as new data points are added, maintaining a fixed window of recent data.

Practical Applications

• Monitoring Systems:
  • Visualize real-time data from sensors, network traffic, or financial markets.
• Scientific Research:
  • Display live data from experiments or simulations.
• Performance Analysis:
  • Track and analyze performance metrics in real-time for applications and systems.