Matplotlib.pyplot.ion() in Python

Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.

The matplotlib.pyplot.ion() is used to turn on interactive mode. To check the status of interactive mode one can run the below commands, 

plt.rcParams['interactive']

or, this command

plt.isinteractive()

Matplotlib also interacts with different backends behind the scenes. The workhorse behind rendering charts in Matplotlib is its backends. Some interactive backends dynamically update and pop up to users after every change. By default, the interactive mode is turned off.

Also Read: How to turn off Interactive Mode

Matplotlib.pyplot.ion() Syntax in Python

Syntax : matplotlib.pyplot.ion()

Parameter:

ion() function in pyplot enables interactive mode, automatically updating plots in response to events without explicit commands.

Return type : None

Matplotlib.pyplot.ion() in Python Example

There are various example for use Matplotlib in interactive mode or How to use Matplotlib in interactive mode with Matplotlib.pyplot.ion() . Here we are discussing some generally use example those are following.

• Interactive Plot with Collections
• Interactive Matplotlib Plot with Multiple Lines
• Interactive Matplotlib Plot with draw()

Interactive Plot with Collections

In this example code utilizes Matplotlib to generate an interactive plot with two random data collections, represented by red 'x' and blue '+' markers. It includes a filled green region between the collections where the second one surpasses the first, and a legend with a colored background is placed at the upper center of the plot.

import matplotlib.pyplot as plt
 
#the function to turn on interactive mode
plt.ion()
#creating randomly generate collections/data
random_array = np.arange(-4, 5)
collection_1 = random_array ** 2
collection_2 = 10 / (random_array ** 2 + 1)
figure, axes = plt.subplots()
axes.plot(random_array, collection_1,
          'rx', random_array,
          collection_2, 'b+', 
          linestyle='solid')
axes.fill_between(random_array, 
                  collection_1, 
                  collection_2,
                  where=collection_2>collection_1, 
                  interpolate=True,
                  color='green', alpha=0.3)
lgnd = axes.legend(['collection-1',
                    'collection-2'], 
                   loc='upper center', 
                   shadow=True)
lgnd.get_frame().set_facecolor('#ffb19a')

Output: 

Interactive Matplotlib Plot with Multiple Lines

In this example The code sets up an interactive Matplotlib plot, plots a line with points (1.4, 2.5), and adds a title. It retrieves the current Axes object and plots another line with points (3.1, 2.2) on the same plot

import matplotlib.pyplot as plt
 
plt.ion()
plt.plot([1.4, 2.5])
plt.title(" Sample interactive plot")
 
axes = plt.gca()
axes.plot([3.1, 2.2])

Output: 

Interactive Matplotlib Plot with draw()

In this example code initializes an interactive Matplotlib plot, creating a sine wave and plotting it in blue. It then iterates through phases, updating the y-data of the sine wave and redrawing the plot, resulting in an animated sine wave with a short pause between frames.

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10 * np.pi, 100)
y = np.sin(x)
plt.ion()
fig, ax = plt.subplots()
line1, = ax.plot(x, y, 'b-')
for phase in np.linspace(0, 10 * np.pi, 100):
    line1.set_ydata(np.sin(0.5 * x + phase))
    fig.canvas.draw()
    plt.pause(0.1)  # Add a short pause to improve animation smoothness

Output :