Matplotlib - Timers

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In general computer programming, timers are refers to a mechanism that allows users to schedule the execution of a specific task or code snippet at predefined intervals. Timers are useful for various applications, enabling the automation of repetitive actions, periodic updates, or the triggering of events based on time-related conditions.

Timers in Matplotlib

Matplotlib timers are powerful features that enable you to integrate periodic events into the plots. And designed to work independently of specific graphical user interface (GUI) backends.

To utilize the Matplotlib timers feature, the figure.canvas.new_timer() function works as a key component to integrate timers with various GUI event loops. While its call signature may appear unconventional, due to this understanding the call signature is crucial (you need to explicitly specify the empty sequences and dictionaries if your call back function(s) don't take arguments or keyword arguments).

Here is the syntax −

Syntax

 timer = figure.canvas.new_timer(interval=5000, callbacks=[(callback_function, [], {})]) timer.start() 

This syntax creates a timer with a 5-second interval, demonstrating the timer's integration into Matplotlib plots.

Example

Here is an example, That demonstrates a simple usage of timers in Matplotlib. It sets up a timer to print "Matplotlib Timer Event" to the console every 5 seconds. This showcases how timers can be employed for periodic tasks within plots.

 import matplotlib.pyplot as plt # Function to handle the timer event def handle_timer_event(): print('Matplotlib Timer Event') # Create a new Matplotlib figure and axis custom_fig, custom_ax = plt.subplots(figsize=(7, 4)) # Create a timer with a 5000 milliseconds interval custom_timer = custom_fig.canvas.new_timer(interval=5000, callbacks=[(handle_timer_event, [], {})]) # Start the timer custom_timer.start() plt.show() 

Output

On executing the above program you will get the following output −

 Matplotlib Timer Event Matplotlib Timer Event Matplotlib Timer Event Matplotlib Timer Event Matplotlib Timer Event Matplotlib Timer Event 

Watch the video below to observe the works of this example.

Timer for Real-Time Updates

Timers can be used to achieve real-time updates within a plot, enhancing the dynamic nature of visualizations.

Example

In the example, a timer is used to update the title of a figure with the current timestamp at intervals of 500 milliseconds. Which shows that, how timers can be used for dynamic, time-sensitive updates in visualizations.

 from datetime import datetime import matplotlib.pyplot as plt import numpy as np # Function to update the title with the current timestamp def update_title(axes): axes.set_title(datetime.now()) axes.figure.canvas.draw() # Create a Matplotlib figure and axis fig, ax = plt.subplots(figsize=(7, 4)) # Generate sample data x = np.linspace(0, 10, 100) ax.plot(x, np.sin(x)) # Create a new timer with an interval of 500 milliseconds timer = fig.canvas.new_timer(interval=500) # Add the update_title function as a callback to the timer timer.add_callback(update_title, ax) # Start the timer timer.start() plt.show() 

Output

On executing the above program you will get the following output −

Watch the video below to observe the works of this example.

Animated Brownian Walk with Timer

In a more advanced scenario, we use the advantages of timers to animate a 2D Brownian walk, creating a visually dynamic plot that produces over time.

Example

This example demonstrates the application of timers in creating animated visualizations.

 import numpy as np import matplotlib.pyplot as plt # Callback function for the timer to update line data def update_line_data(line, x_data, y_data): x_data.append(x_data[-1] + np.random.normal(0, 1)) y_data.append(y_data[-1] + np.random.normal(0, 1)) line.set_data(x_data, y_data) line.axes.relim() line.axes.autoscale_view() line.axes.figure.canvas.draw() # Initial data points x_coords, y_coords = [np.random.normal(0, 1)], [np.random.normal(0, 1)] # Create a Matplotlib figure and axis fig, ax = plt.subplots(figsize=(7, 4)) line, = ax.plot(x_coords, y_coords, color='aqua', marker='o') # Create a new timer with a 100-millisecond interval animation_timer = fig.canvas.new_timer(interval=1000, callbacks=[(update_line_data, [line, x_coords, y_coords], {})]) animation_timer.start() # Display the animated plot plt.show() 

Output

On executing the above program you will get a figure with animation −

Watch the video below to observe the works of this example.