Although many researchers now prefer to write their papers using various SaaS solutions, such as Overleaf, I still promote an old-style way when everything is installed on your computer. Of course, this approach has some pros and cons. However, in this article, I am not going to discuss them and will concentrate instead on the topic of how to configure forward and inverse search. In particular, I will show how to do this for my setup with LaTeX Workshop, a VS Code extension facilitating text writing in TeX, and Okular, a PDF viewer available for Linux and Windows platforms.

In the previous article, I shared my setup for producing the graphs for research papers. However, recently when I was working on figures for a new paper, I discovered that my setup must be updated. The reason is that the new matplotlib version (since 3.6) produces a warning that the embedded seaborn styles are now deprecated. In this article, I provide the updates to the setup described in the previous article.

When you write a scientific paper, one of the most common tasks is to analyze the obtained results and design beautiful graphs explaining them. Currently, in the research community, Python’s ecosystem is the most popular for achieving these goals. It provides web-based interactive computational environments (e.g., Jupyter Notebook/Lab) to write code and describe the results, and pandas and matplotlib libraries to analyze data and produce graphs correspondingly. Unfortunately, due to the rich functionality, it is hard to start using them effectively in your everyday research activities when you initiate your path as a researcher. In this article, I would like to share some tips and tricks on how to employ the matplotlib library to produce nice graphs for research papers.