How can the drawing speed of matplotlib be increased?

There are several methods to increase the speed of matplotlib plotting:

1. Utilize the appropriate plotting engine: By default, matplotlib uses the agg plotting engine, but you can enhance plotting speed by setting it to use another engine. For example, you can opt for the faster Cairo engine by adding the following code at the beginning of your script.
2. Use the Cairo backend in matplotlib to create plots.
3. Reducing data volume: Drawing a large number of data points can slow down the plotting speed. One option is to reduce the number of data points, or to decrease the data volume by utilizing a technique like downsampling, such as only plotting every nth data point.
4. Plot every nth point in the arrays x and y using matplotlib’s plot function.
5. Using vector graphic formats, such as SVG or PDF, can render and display graphics faster than bitmap formats like PNG or JPEG. Graphics can be saved in vector formats or displayed using them during the drawing process.
6. Save the plot as a SVG file with the name ‘plot.svg’ or as a PDF file with the name ‘plot.pdf’.
7. Utilizing parallel plotting: Speed up the plotting process by utilizing multiple threads or processes to plot in parallel. Divide the plotting tasks into smaller subtasks and execute them simultaneously with multiple threads or processes. You can use Python’s built-in threading or multiprocessing modules to achieve parallel plotting.
8. Utilize alternative drawing libraries: Apart from matplotlib, there are other drawing libraries like seaborn, plotly, which can offer faster drawing speeds. Try using these libraries to accelerate the drawing speed.

It is important to note that the drawing speed is not only determined by the drawing library itself, but also influenced by factors such as computer hardware performance and data size. Therefore, by optimizing the drawing code and using appropriate drawing settings, the speed of plotting with Matplotlib can be improved to some extent.