How to Achieve Non-Blocking Plotting with Matplotlib?

Troubleshooting Non-Blocking Plotting with Matplotlib

Matplotlib's blocking nature can hinder real-time data visualization. This article explores solutions to overcome this issue, using an example provided in the original inquiry.

The Problem: Qt4Agg Backend Issues

The original code utilized Qt4Agg as the backend, which is known to face limitations in non-blocking execution. Using show(block=False) may result in a frozen window because Qt4Agg does not support this functionality.

The Solution: Interactive Mode and Pause

To achieve non-blocking plotting with matplotlib, two key steps are crucial:

1. Enable interactive mode using plt.ion(), which allows matplotlib to respond to GUI events while your code continues to run.
2. Call plt.pause(time) (e.g., plt.pause(0.001) as in the provided solution) to give the GUI time to handle events, including the redrawing of your plot.

Updated Code

Here is the updated code incorporating these solutions:

<code class="python">import numpy as np
from matplotlib import pyplot as plt

def main():
    plt.axis([-50,50,0,10000])
    plt.ion()
    plt.show()

    x = np.arange(-50, 51)
    for pow in range(1,5):   # plot x^1, x^2, ..., x^4
        y = [Xi**pow for Xi in x]
        plt.plot(x, y)
        plt.draw()
        plt.pause(0.001)
        input("Press [enter] to continue.")

if __name__ == '__main__':
    main()</code>

This code will enable you to update the existing plot in a non-blocking fashion, providing a seamless and responsive visualization experience.

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