R provides convenient multiprocessing capabilities, and Python has similar functionality.

Recently, I wrote a script to draw many images using ggplot. Although all the places where loops are needed have already been replaced with apply, it still can’t keep up when drawing hundreds of images at once. So, I fiddled around with parallel and managed to parallelize the plotting part to speed things up.

It’s been a while since the last update. Today, I’ll record the upgraded version of my homemade VPN!

Snakemake is indeed a very useful tool for workflow development and management. However, in certain scenarios, it can also bring some issues. Coincidentally, I discovered a rather unconventional way of using it: only use Snakemake’s dependency handling and task management, while generating scripts separately.

I have been using argparse for command line parsing, but it wasn’t until recently that I discovered it can be used to create subcommands.

When installing software, I thought that the portability and feasibility of using conda were better, so I decided to try it out and see how Snakemake works…

Working with R often involves encountering various issues. Here are some collected tips.

When installing packages from GitHub in R, sometimes you may encounter a connection timeout issue with GitHub. Here’s how to solve it.

Recently, I’ve been using Pandas a lot and have compiled some commonly used functions for future reference.

To prepare for future operations using Sqlite3, I recently tried using it to operate on databases at work. I decided to use sqlite3 because it seemed convenient. However, I found that not all SQL statements are supported by sqlite3, and its free-form shell commands cannot be called directly by the Python sqlite module…