How to improve performance when rendering multiple subplots in Matplotlib?
Could someone explain Can someone help me understand I'm working on a project and hit a roadblock. I'm upgrading from an older version and I'm currently working on a project that requires rendering a large number of subplots using Matplotlib (version 3.4.3)... I have around 25 subplots to display, and I'm finding that the rendering time is significantly impacting the user experience. While I understand that rendering many plots can be resource-intensive, I’d like to optimize this process. I’ve tried using `plt.tight_layout()` to improve spacing, but the performance doesn’t seem to improve much. Here’s a simplified version of my code: ```python import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 10, 100) y = np.sin(x) fig, axs = plt.subplots(5, 5, figsize=(15, 10)) for i in range(5): for j in range(5): axs[i, j].plot(x, y + (i + j) * 0.1) axs[i, j].set_title(f'Subplot {i * 5 + j + 1}') plt.tight_layout() plt.show() ``` While this code correctly displays the subplots, the rendering takes a few seconds, and this is not ideal for my application. I've also considered using `plt.pause()` to update the figures but haven’t implemented that yet. Would using a different backend help speed things up, or are there other strategies, such as reducing the number of points plotted or using lower resolution data? Any suggestions on how to improve the performance would be greatly appreciated. What's the correct way to implement this? Thanks, I really appreciate it! I'm using Python 3.9 in this project. I'm working with Python in a Docker container on Windows 11.